Recent Advances in Environmental Analysis - ACS Publications

REVIEW pubs.acs.org/ac

Recent Advances in Environmental Analysis Ana Ballesteros-G
omez and Soledad Rubio* Department of Analytical Chemistry, Edificio Anexo Marie Curie, Campus de Rabanales, 14071 C
ordoba, Spain

’ CONTENTS Sampling Passive Sampling Calibration Selected Applications Sample Preparation Water Samples Sorption-Based Methods Liquid-Phase Microextraction Environmental and Biological Solid Samples Separation and Detection Techniques Gas Chromatography/Mass Spectrometry Comprehensive Two-Dimensional Gas Chromatography Mass Spectrometric Detectors Portable GC Liquid Chromatography
Mass Spectrometry Advances in Chromatography Target Multiresidue Analysis Nontarget Screening of Metabolites and Transformation Products Capillary Electrophoresis-Detection Systems Chromatography-Based Preconcentration Techniques Electrophoresis-Based Preconcentration Techniques Detection Systems Integrated Chemical and Biomonitoring Strategies Extraction Cleanup and Fractionation Analytical Identification/Confirmation of Toxicants Field Methods Genetically Engineered Biosensors Nanotechnology-Based Biosensors Sensor Arrays Environmental Sensor Networks Calibration Environmetrics Time Series Data Left-Censored Data r 2011 American Chemical Society

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Spatio
Temporal Modeling Impact of Air Pollution on Public Health Classification and Pattern Recognition Author Information Biographies Acknowledgment References

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his Review focuses on the most salient developments and advances in environmental analysis reported over the period 2009
2010. References were found by computer-assisted searching the Web of Knowledge. Around twenty journals that regularly publish papers on environmental analysis or related topics were directly searched. Because Analytical Chemistry’s current policy limits the retrieval of references to a maximum of 250, only a small fraction of the literature published on the topic during the reviewed period is discussed here. For this reason, we have chosen to deal only with those techniques and strategies best illustrating the recent evolution of environmental analysis with regards to the sampling, extraction, separation/detection, and quantitation of pollutants in the environment. Also, we have placed special emphasis on field methods, the use of integrated approaches based on chemical and biomonitoring strategies, and recent advances in environmetrics. Two topics of a high interest to environmental scientists have been excluded since they are addressed in excellent periodic reviews. One is concerned with the behavior, occurrence, and fate of emerging contaminants in the environment, which were the subjects of two reviews by Richardson: one1 dealing with the specific techniques used for the analysis of emerging contaminants in water and the other2 with the use of mass spectrometry for the development of methods and the study of the presence and fate of emerging contaminants in water, air, soil, sediment, and biological samples. We encourage interested readers to read both. The other topic not discussed here is the use of atomic spectrometry for the determination of trace metals and metalloids, and their associated elemental species, in environmental samples, which are the subjects of periodic reviews by Butler et al.3,4 and Broekaert et al.5 The former two are applicationoriented reviews and include the analysis of air, water, soils, plants, and geological materials, whereas the latter focuses on

Special Issue: Fundamental and Applied Reviews in Analytical Chemistry Published: April 15, 2011 4579

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Analytical Chemistry developments in atomic techniques but includes many environmental applications. Both are strongly recommended. There have been significant advances over the last two years in the development of methods for environmental analysis, which is the primary subject of this Review. Concerning sampling, research focused on improving sample representativeness in highly heterogeneous systems, reducing sampling effort, and developing fundamental and practical aspects of passive sampling, which has become a highly active research area. Remarkable progress has been made in the last two years in calibration and the use of passive samplers. The design of new samplers or modification of existing ones has permitted the development of improved strategies to meet new challenges (e.g., in situ sampling in tissues) and the expansion of passive sampling to a broader range of pollutants. One major advance has been the use of passive sampling for global monitoring of atmospheric persistent organic pollutants (POPs), an approach that will provide a deeper knowledge of the temporal and spatial distribution of these compounds and facilitate assessing the effectiveness of the strategies used for their reduction in compliance with the objectives of the Stockholm Convention. Current trends in the handling of environmental samples seemingly include the development of alternative phases and geometries for sorptive extractions; the evaluation of new solvents (e.g., ionic liquids, supramolecular solvents), configurations, and strategies in liquid-phase microextractions; and the development of new applications for enhanced-solvent extractions. Major objectives in research in this context included improving trapping efficiency for polar organic compounds, robustness, sensitivity, and selectivity, as well as the development of faster, simpler, more environmentally friendly sample preparation procedures. Solid-phase extraction (SPE) continues to be the leading technique for the extraction of pollutants from aquatic systems, and recent developments in this field are mainly related to the use of new sorbent materials and automation. Researchers mainly focused on solid-phase microextraction (SPME) and dispersive liquid
liquid microextraction (DLLME) for the isolation/enrichment of pollutants in aquatic systems; however, special attention was given to the development of magnetic extraction and the use of ultrasound-assisted extraction. Pressurized liquid extraction (PLE) is gradually becoming the leading technique for extraction of pollutants from sediments, soils, and biological samples; likewise, SPME is increasingly being used to extract pollutants from these matrixes. Outstanding applications of SPME have been developed for biological samples; thus, space-resolved SPME has enabled the determination of the distribution of contaminants, drugs, and other bioactive compounds in biological tissues. Concerning the separation and detection of environmental organic pollutants, the combinations of gas chromatography (GC) and liquid chromatography (LC) with single-mass spectrometry (MS) and tandem-MS are the prevailing choices; also, capillary electrophoresis (CE) continues to play a secondary role in this context. Fast chromatography in the form of ultra performance liquid chromatography (UPLC), narrow-bore capillary GC columns and short, narrow-bore LC columns are increasingly being used to boost sample throughput. Method development continued for the analysis of specific target compounds over the review period; however, its comprehensive nature has led multiresidue analysis to become a preferred tool for tracing down different compound classes in the environment. The usual choice here is a combination of comprehensive two-dimensional gas chromatography

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(GC  GC) with time-of-flight (TOF) or LC/tandem MS. Highly resolved, accurate hybrid tandem MS such as quadrupole/ TOF (QTOF) and linear ion trap/orbitrap (LTQ Orbitrap) technology is increasingly being used for more reliable target analysis and screening for suspected analytes and unknowns; however, these techniques are still rarely used owing to their high cost, which makes them unaffordable to most environmental laboratories. Integrated chemical and biomonitoring strategies [i.e., toxicityidentification evaluation (TIE) and effect-directed analysis (EDA)] have been developed to unravel cause
effect relationships for reliable risk assessment of environmental pollution and are currently being actively researched. Biological assays have focused on determining specific toxic activities, through reporter gene assays, mainly, and standard overall toxicity. Sample fractionation constitutes a critical step in chemical analysis since it simplifies pollutant identification and confirmation and affords more accurate toxic assessment. Both LC
MS and GC/MS are being successfully used for identification and quantitation. Again, the use of high resolution MS offers great opportunities in this context. Studies in this area have focused mainly on endocrine disrupting chemicals (ECDs) but also on emerging contaminants and some previously unaddressed pollutants. Although the use of sensors and biosensors in field pollution control is still in its infancy, research on this topic is an active area and remarkable progress is being made in the development of genetically engineered advanced biological receptors, sensor arrays, and environmental sensor networks. Nanomaterials have been successfully incorporated into environmental sensors to improve their sensibility and selectivity as a result of the unusual physicochemical properties of these materials. The synergy between sensors and nanomaterials constitutes a global, fruitful trend to sensor development. Matrix effects continue to be among the most critical aspects in quantifying pollutants. Internal standard (IS)-based calibration, matrix-matched calibration, standard addition calibration, and second-order multivariate calibration algorithms are the most widely used calibration strategies for ensuring interference-free quantitation in environmental analysis. Environmetrics, which is concerned with the development and application of quantitative methods in the environmental sciences, has become an essential tool for understanding, predicting, and controlling the impact of pollutants on the environment. Recent advances in this area are discussed here, with special emphasis on time series and left-censored data analysis, spatio
temporal modeling, the impact of air pollution on public health, and searching for structure in data. We welcome any comments or suggestions readers may have on this Review via the author’s e-mail address. A glossary of the terms used is provided in Table 1.

’ SAMPLING Sampling continues to be the greatest contributor to uncertainty in most of environmental analyses because appropriate management of spatial and temporal variability in pollutants remains a subject of concern. Major sampling guidelines have existed for decades but have scarcely been used in practice. Because obtaining a representative sample is a prerequisite for delivery of meaningful analytical results and dealing with the variability of pollutants in the environment usually requires processing a large number of samples, this section is specially concerned with those studies which have focused on reducing environmental 4580

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Analytical Chemistry Table 1. List of Acronyms ALS APCI API APPI

alternating least-squares atmospheric pressure chemical ionization atmospheric pressure ionization atmospheric pressure photoionization

BaP BDEs

benzo(a)pyrene bromodiphenyl ethers

BFRs

brominated flame retardants

BPA

bisphenol A

BTEX

benzene, toluene, ethylbenzene, and xylenes

CE

capillary electrophoresis

CEC CID

capillary electrochromatography collision energy dissociation

CNTs

carbon nanotubes

CPE

cloud point extraction

CZE

capillary zone electrophoresis

DVB

divinyl benzene

DLLME

dispersive liquid
liquid microextraction

ECD

electron capture detector

EDA EDCs

effect-directed analysis endocrine disrupting chemicals

ESNs

environmental sensor networks

EI

electron impact ionization

EPI

enhanced product ion

ESI

electrospray ionization

FAAS

flame absorption atomic spectrometry

FASI

field-amplified sample injection

FID GAMs

flame ionization detection generalized additive models

GAPS

global atmospheric passive sampling

GC

gas chromatography

GC  GC

comprehensive two-dimensional gas chromatography

GFFs

glass-fiber filters

GPC

gel permeation chromatography

HILIC

hydrophilic interaction chromatography

HRMS HS

high resolution mass spectrometry headspace

ICP

inductively coupled plasma

IDA

information-dependent acquisition

ILs

ionic liquids

IP

identification points

IS

internal standard

IT

ion trap

ITP LC

isotachophoresis liquid chromatography

LIF

laser-induced fluorescence

LPME

liquid-phase microextraction

LVI

large volume injection

LVSS

large volume sample stacking

MCR

multivariate curve resolution

MEKC

micellar electrokinetic chromatography

MESCO MIPs

membrane-enclosed sorptive coating molecularly imprinted polymers

MNPs

magnetic nanoparticles

MRM

multiple reaction monitoring

MS MWCNTs

mass spectrometry multiwalled carbon nanotubes

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Table 1. Continued NACE

nonaqueous capillary electrophoresis

NMR

nuclear magnetic resonance

NOM

natural organic matter

NPs OCPs

nanoparticles organochlorine pesticides

PAHs

polycyclic aromatic hydrocarbons

PARAFAC

parallel factor analysis

PBDEs

polybrominated diphenyl ethers

PCBs

polychlorinated biphenyls

PCDDs

polychlorinated dibenzo-p-dioxins

PCDFs

polychlorinated dibenzofurans

PDMS PFOS

polydimethylsiloxane perfluorooctane sulfonate

PFOS-F

perfluorooctane sulfonyl fluoride

PFCs

perfluorinated compounds

PHWE

pressurized hot water extraction

PLE

pressurized liquid extraction

PLS

partial least-squares

POCIS

polar organic chemical integrative sampler

POPs PPCPs

persistent organic pollutants pharmaceuticals and personal care products

PRCs

performance reference compounds

PUF

polyurethane foam

Q

single quadrupole

QDs

quantum dots

QqQ

triple quadrupole

RBL

residual bilinearization

SBSE SDME

stir bar sorptive extraction single-drop microextraction

SDLPME

solid drop-based liquid-phase microextraction

SFE

supercritical fluid extraction

SIM

single ion monitoring

SIP

sorbent-impregnated polyurethane foam

SOMs

self-organizing maps

SPE

solid-phase extraction

SPMD SPME

semipermeable membrane device solid-phase microextraction

SR

silicone rubber

SRM

selected reaction monitoring

SVOCs

semivolatile organic compounds

SWCNTs

single-walled carbon nanotubes

TBBP-A

tetrabromobisphenol A

TIE

toxicity-identification evaluation

TOF TPs

time-of-flight transformation product

TWA

time-weighted average

UPLC

ultra performance liquid chromatography

USAEME

ultrasound-assisted emulsification microextraction

VOCs

volatile organic compounds

sampling uncertainty in the past two years and highlights the more salient advances in passive sampling, which remains a subject of active research. The right way to manage temporal variability arising from the loads of pharmaceuticals and personal care products (PPCPs) currently encountered in wastewater systems was addressed in an 4581

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Analytical Chemistry interesting study comparing various sampling modes and optimization strategies.6 Most pharmaceutical residues are flushed into the sewer system via toilets, bathing water, or wastewater from washing machines, which causes substantial short-term changes in pollutant loads. Using high-frequency grab sampling and various composite (time-, flow-, and volume-proportional) sampling modes, has shown that the traditional sampling intervals of 30 min or longer can result in the collection of nonrepresentative samples. The sampling frequency needed to maximize representativeness in composite samples is largely dictated by the number of distinct wastewater packets in the sewer. If this information can be collected for all target compounds before sampling and if the smallest number of pulses to be expected is known, then a modeling approach can be effectively used to determine the appropriate sampling frequency, ideally, in the flow-proportional mode. If this information is unavailable, a precautionary high sampling frequency ( 6 may need months to years in order to dissipate sufficiently for accurate quantification. An accurate knowledge of the relationship between Rs and Kow is therefore required to extrapolate Rs estimated from PRCs dissipation to compounds in a higher Kow range. This relationship has been investigated for a range of PCBs and PAHs in silicone rubber (SR) passive samplers.32 Concentrations in the aqueous phase were maintained by continuous equilibration with SR sheets of a large total surface area which had been spiked with PCBs and PAHs. Test sheets made of the same SR but having a much smaller surface area and spiked with PRCs (deuterated PAHs) only were used to measure the uptake rate. Measured Rs values decreased with increasing Ksw according to Rs ≈ Ksw
0.08. Modeling also confirmed that uptake of the test compounds under the experimental conditions was entirely controlled by diffusion in the water phase. A model using Rs ≈ M
0.47, where M is molecular weight, was proposed for extrapolation of Rs values estimated from the dissipation of PRCs to target compounds in a higher hydrophobicity range. Time-integrative passive sampling of polar contaminants in organic water is generally carried out using Empore disks and polar organic chemical integrative samplers (POCIS). A detailed overview of the environmental effects on Rs in these samplers alongside the compound classes they can effectively collect and their areas of application has been reported.33 One major drawback which has hindered more general acceptance of polar organic passive sampling is that the applicability of the PRC concept for in situ calibration has yet to be validated. Polar analytes accumulate in Empore disks and POCIS by adsorption rather than partitioning, so the release of PRCs and uptake of target analytes is not isotropic. Reported uses of the PCR concept with polar organic water compounds have been met with mixed success. It therefore remains uncertain whether the PRC approach can be used with polar passive sampling, and effective alternatives are lacking. One alternative in situ calibration approach which holds some promise involves loading PRCs into codeployed sampling phases from which loss is observed. Shaw et al.34 conducted a laboratory-based calibration experiment where the uptake kinetics of several key pesticides in Chemcatchers using SDB-RPS Empore disks were compared with the dissipation rates of three deuterated pesticides used as PRCs (viz., dimethylpthalate-d6, diuron-d5 and chlorpyrifos-d10, with log Kow 1.56, 2.68, and 4.7, respectively) from polydimethylsiloxane (PMDS) disks. Previously, it was checked that PRC loss from Empore disks was not linear. Recoveries of individual PRCs from PDMS disks deployed for 28 days ranged from 0% for dimethylpthalate-d6 to 87% for chlorpyrifos-d10. Both values fall 4584

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Analytical Chemistry outside the recommended range for recoveries of PRCs (20
80%). The mean recovery of diuron-d5 was 23% (RSD 21%), which falls within the recommended range. Loss of PRCs from the PDMS disks was reproducible and, more important, seemingly linear (R2 = 0.87
0.96). Quantitative evaluation of the loss of PRCs from PDMS disks in relation to uptake in the Empore disks would require Ksw values which are currently unavailable. On the other hand, disks cut from thicker PDMS than those used in this study would have been more suitable since they would have increased the sampler capacity (defined as Ksw by the sampler volume) to retain the loaded compounds. Selected Applications. There were a large number of applications of passive sampling in environmental monitoring over the period 2009
2010. The design of new samplers or modification of previous ones permitted the development of improved strategies to meet new challenges and expand passive sampling to a broader range of pollutants. Research on the use of passive samplers for screening studies, source identification, pollutant dispersal, fluxes, transport, pollutant distribution, human exposure assessment, bioavailability of contaminants, bioanalytical assessment, and ecotoxicology testing continued in the reviewed period. One major advance was the use of passive sampling for global monitoring of atmospheric POPs, an approach that will provide a deeper knowledge of the temporal and spatial distribution of these pollutants and afford the assessment of the effectiveness of the strategies used to reduce them in compliance with the objectives of the Stockholm Convention. Below are discussed some selected applications representative of advances in this context over the period 2009
2010. Equilibrium passive samplers are often used to measure freely dissolved concentrations or chemical activity in water, soil, or sediments or to mimic accumulation into organisms. However, a sound knowledge of the dynamic nature of exchange processes in organisms and passive samplers is essential for bioaccumulation studies. Valuable insight into dynamic exposure of organisms including fish, bivalves, crustaceans, insects, worms, algae, and protozoans, and passive samplers [SPME, liquid-phase microextraction (LPME), semipermeable membrane device (SPMD), polymer sheet, SPE, Chemcatcher] to hydrophobic chemicals in water containing PAHs, PCBs, polychlorinated dioxins and furans, organophosphorus compounds, and OCPs was provided by a study based on previously reported information about uptake and elimination rates and 95% equilibration time, for organisms and passive samplers, that was processed in the light of diffusion-based models.35 The results obtained suggested that the surface-to-volume ratio seemingly has a critical influence on the uptake rate of the more hydrophobic chemicals in both samplers and organisms. In addition, a very rough approximation involving the combination of a first-order kinetic model with the assumption that diffusion through the aqueous boundary layers was the rate-determining step, provided a reasonable description of the experimental kinetic data. Therefore, the model could be useful toward estimating uptake and elimination rate constants of chemicals by organisms and passive samplers. As a rule, passive samplers with a high surface-to-volume ratio (e.g., microextraction devices) or microorganisms (e.g., unicellular algae) might be suitable when an expeditious response is needed. Passive samplers and organisms with low elimination rates and larger sampling volumes are better suited to studies requiring integration of contamination over long periods. The time needed to measure the freely dissolved concentration of hydrophobic organic compounds can be considerably reduced using PCRs

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Figure 1. Schematic of a silicone membrane equilibrator. A lipid rich sample is placed in the vial (left), and a small methanol plug is sent through 6 m of silicone tubing within the vial. Three types of equilibrium are achieved with this method (inset): (1) external equilibrium between sample and silicone, (2) internal equilibrium between silicone and methanol plug, and (3) equilibrium across the silicone wall. Reprinted from ref 37. Copyright 2009 American Chemical Society.

impregnated in the sampler material before use.36 A comparable behavior of samplers and organisms can be achieved provided they exhibit a similar combination of surface-to-volume ratio, partition coefficients, and internal mass transfer coefficients. Bioaccumulation of hydrophobic compounds occurs markedly in the lipid-rich tissues of animals and humans. The chemical activities of these compounds in such tissues can be directly measured using a silicone membrane tube, Figure 1.37 The procedure uses a two-step equilibrium approach. In the first step, a PDMS microtube is placed in the sample to have compounds partition from the outside into the silicone wall. In the second, the compounds are partitioned from the PDMS wall into a solvent on the inside of the tube. In this way, aacceptor = asilicone = asample. Because measurements are made on the backside of the silicone membrane, any greasy phase on the outside of the tube will not make it into the extract. The silicone membrane equilibrator method was successfully used to determine PAHs in mussels. With 6 m long silicone tubes, sample
PDMS equilibrium was reached within 10 min for 12 PAHs. A 100 μL plug of methanol was pushed through the tube to equilibrate it with the membrane and, hence, with the sample. After being expelled from the tube, the solvent plug was fed directly into an LC instrument to quantify the PAHs. Chemical activities were then calculated by multiplying the concentrations thus obtained by the analyte-specific activity coefficients in methanol. Further research was planned to delineate the possibilities and limitations of the use of PDMS thin-film extraction for in tissue equilibrium sampling in fish species of variable lipid contents by use of PCBs as model pollutants.38 Equilibration was reached in the range of hours in lipid-rich fish; however, no equilibrium was achieved in 1 week in tissues with a low or medium (up to 2%) lipid content. The method was sensitive enough to afford the determination of the equilibrium concentrations of PCBs in lipid-rich fish from the Baltic Sea. A number of approaches have been proposed to shorten the equilibration time in the passive sampling of lean tissues. Passive samplers are increasingly being considered for the screening of pollutants in waters. Thus, a UV-transparent passive concentrator/spectrum deconvolution method was developed for screening endocrine disrupting chemicals (EDCs) in natural waters.39 The device consisted of a UV-transparent polymer that concentrated EDCs and excluded many compounds likely to 4585

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Analytical Chemistry

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Figure 2. Map of sampling sites operated under GAPS in 2005. Schematic and photograph of the PUF disk sampler. Reprinted from ref 43. Copyright 2009 American Chemical Society.

interfere with detection (particles, organic matter, ionic surfactants); also, it served as an analytical optical cell. A fullspectrum deconvolution technique was used to determine EDC concentrations from UV absorbance measurements in the polymer. Analyses of polymer samplers were completed in seconds on a low cost fiber-optic spectrophotometer, and deconvolution was also done in seconds, using consumer computing hardware. Although the method needs further development in respects such as the knowledge of the partition coefficients and spectra for the target hydrophobic compounds in PDMS or the determination of the impact of highly absorbing species such as PAHs on other less-absorbing pollutants, PDMS has the advantage that it can be used for both screening and analysis when required. The sampler deployment method used was found to impact the screening results by effect of differences in mass transfer.40 Thus, passive samplers deployed in vials with permeable membrane septa failed to detect target compounds, possibly as a result of the lack of water motion in the vials. In contrast, passive samplers deployed directly in the flow succeeded in tracking the concentrations of the target pollutants and responded to temporal changes in concentration. The results of this study underline the importance of the use of PRCs to avoid false nondetection results. The use of passive sampling for the simultaneous collection of the gaseous and particulate phases of SVOCs continues to attract interest. Modifications to a previously described passive sampler involving the use of a GFF coupled with a PUF disk were used to improve and extend its applicability. The sampler was initially designed for collecting both gaseous and particulate phase PAHs simultaneously and, although it was successfully used in a field study, the low uptake rate for particulate phase PAHs (0.007 ( 0.001 m3/d) precluded application in a nonseverely contaminated environment. The sampler was redesigned41 for reduced encapsulation, which increased sampling roughly 4 times and 2 orders of magnitude for gaseous and particulate phase PAHs, respectively, thereby enabling the monitoring of PAHs in indoor environments. The sampling efficiency for PAHs was found to depend on the molecular weight, which required developing

calibration equations containing it as an independent variable in order to predict the ambient air concentrations of gaseous and particulate PAH phases. The sampler, however, was less efficient than active samplers for collecting PAHs associated to fine particles in air and, although such bias in sampled size distribution was corrected to some extent using a fine screen-mesh wrapping, this reduced the sampling efficiency. A combination of a glass fiber filter and a PUF disk was used for the passive sampling of both vapor and particulate phase brominated flame retardants (BFRs) in car trunks and cabins.42 Significant correlation (p < 0.01) was observed between passive and active sampler derived BFR concentrations (r = 0.94 and 0.89 for vapor and particulate phase BFRs, respectively). The passive uptake rates of the BFRs ranged from 0.558 to 1.509 ng/d in the vapor phase and from 0.448 to 0.579 ng/d in the particulate phase. The sampler was used to monitor BFR concentrations in 21 cars. The average concentrations of hexabromocyclododecanes, tetrabromobisphenol A (TBBP-A), and tetradecabrominated diphenyl ethers thus obtained were 400, 3, and 2200 pg m
3, respectively, in cabins, and 400, 1, and 1600 pg m
3, respectively, in trunks. One salient application of passive sampling is the monitoring of POPs in air through the Global Atmospheric Passive Sampling (GAPS) Network, which currently comprises 55 sites (Figure 2); the network consists largely of remote background locations and a few urban and agricultural sites and has been measuring POPs worldwide on both spatial and temporal scales since 2005. The Stockholm Convention, administered by the United Nations Environment Program, targets POPs for worldwide elimination because of their links to serious health effects. Initially, a set of POPs was identified that included twelve chemicals or chemical groups (viz., “the dirty dozen”) for priority action, namely, nine organochlorine pesticides [aldrin, chlordane, dichlorodiphenyltrichloroethane (DDT), dieldrin, endrin, heptachlor, hexachlorobenzene, mirex, and toxaphene] and various industrial compounds such as PCBs, polychlorinated dibenzo-p-dioxins (PCDDs), and dibenzofurans (PCDFs). In order to measure the effectiveness of international control strategies, the global monitoring of key 4586

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Analytical Chemistry environmental media, initially air and human tissues, was recommended. The GAPS network addresses air monitoring using samplers consisting of a PUF disk housed in a stainless steel chamber (Figure 2). GAPS relies on the support of international participants who volunteer their time to deploy samplers at global sites, existing monitoring stations in many instances, and is coordinated by the Hazardous Air Pollutants Laboratory, Environment Canada, in Toronto, where all samples are prepared and received after deployment for analysis of POPs. The POP results for the first full year of operation of the GAPS network (2005) have been reported.43 Samplers were deployed over four consecutive three-month periods and samples were screened for 48 PCB congeners, 17 polybrominated diphenyl ethers (PBDEs), and 19 OCPs. Although the samplers were capable of detecting 8 of the 12 chemicals covered by the treaty, the concentrations of 3 of the 8 (mirex, endrin, and aldrin) were generally too low to be detected. The annual geometric mean (GM) concentrations in air (pg/m3) were highest for endosulfan, a pesticide that is a candidate POP (GM = 82) and PCB (GM = 26). Other chemicals regularly detected included R- and γ-hexa chlorocyclohexane, chlordanes, heptachlor, heptachlor epoxide, dieldrin, dichlorodiphenyldichloroethylene (DDE), and PBDEs. The highest concentrations of the tracked POPs were in ambient air from the middle latitudes in the Northern Hemisphere, where the world's most industrialized nations are located. Although the data revealed no seasonal patterns on a global basis, the analysis did bring to light distinct seasonal variations at some sites. These data were the first to present a coherent big picture of the atmospheric occurrence and distribution of POPs on a global scale and constitute the first information of this type available for Africa and South America. In 2009, nine additional POPs were added to the Stockholm convention, including OCPs, PBDEs, perfluorooctane sulfonate (PFOS), and perfluorooctane sulfonyl fluoride (PFOS-F). A new type of passive air sampler has been proposed [viz., the sorbentimpregnated polyurethane foam (SIP) disk sampler] which captures broader classes of compounds than PUF-based samplers.44 This sampler consists of a PUF disk impregnated with finely ground XAD-4 resin that greatly improves the sorptive capacity of the PUF disk for more volatile and polar chemicals and allows for linear-phase sampling for these compounds over several weeks. A comparative study was carried out with SIP and PUF disks at 20 sites of the GAPS network over a period of 3 months in 2009. The disks were analyzed for (PCBs), neutral perfluorinated compounds (PFCs), and ionic PFCs. SIP disks effectively captured 4
50% more of the low-molecular weight PCBs. Also, the PUF disks had limitations for timeweighted passive sampling of neutral PFCs in air. Therefore, the SIP disk appears to be a promising passive air sampler for measuring both emerging and legacy POPs on a global scale. Recently, the development of a global aquatic passive sampling (AQUA-GAPS) network to monitor POPs has been strongly encouraged.45 Some authors have proposed the codeployment of polyethylene sheets and POCIS to collect nonpolar/weakly polar and polar compounds, respectively. Also, they have recommended using a combination of kinetic and equilibrium sampling modes and deploying passive samplers at background and remote sites far away from known point sources of pollution. Further suggestions about how to implement an AQUA
GAPS network46 include the need to monitor sediment pore water and sample locations impacted by human activities in addition to remote and background sites and the use of protective shields during field deployment of passive samplers.

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’ SAMPLE PREPARATION The most salient trends regarding extraction for environmental analysis in the reviewed period have seemingly been the development of alternative phases and geometries for sorptive extraction; the assessment of new solvents (e.g., ionic liquids, supramolecular solvents), configurations, and strategies in liquidphase microextraction; and the use of energetics in the solvent extraction of both liquid and solid samples. Central objectives of research in this area included expanding the scope of efficiently extracted contaminants, to polar compounds, mainly; reducing solvent consumption; making sample preparation more expeditious, inexpensive, and environmentally friendly; and saving costs. A number of general reviews of developments in environmental sample preparation were published in the period of 2009
2010 to deal with subjects such as the foundation and uses of extraction techniques,47 the critical evaluation of sample pretreatments,48 recent developments in solventless techniques,49 green analytical chemistry in sample preparation for the determination of organic pollutants,50,51 and the role of ionic liquids in sample preparation.52
54 Some dedicated reviews concerned with the extraction of specific environmental organic pollutants including POPs,55 synthetic pyrethroids,56 halogenated VOCs,57 chlorinated VOCs,58 pesticide transformation products,59 BFRs,60 and degradation intermediates of personal care products61 were also published. Below are comments on several reviews dealing with the development of specific techniques for the extraction of organic pollutants from water, soil, sediment, and biological samples. Water Samples. Both sorption-based methods and liquidphase microextraction were extensively investigated for the extraction of organic contaminants from aqueous environmental samples in the last two years. The most salient advances and representative applications are discussed below. Sorption-Based Methods. Advances in sorptive extraction of environmental samples during the reviewed period relied on the development of new sorptive phases and extraction formats or device geometries. The goal of the search for novel sorbents varied with the format of the specific extraction technique and the characteristics of the target analytes and samples. Primary objectives included selectivity (or even specificity for certain target species), increased sorptive capacity (and also increased sensitivity and detectability as a result), and the development of extractive media with enhanced thermal, chemical, or mechanical stability in order to extend their lifetime. As stated in a recent review,62 environmental applications focused on the development of molecularly imprinted polymers (MIPs), carbon nanotubes (CNTs), and coatings for SPME fibers. Dedicated reviews concerned with the development of specific sorbents such as MIPs63 and CNTs64 for uses including environmental applications have also been reported. In addition, a variety of formats or geometries have emerged for sorptive-based extraction. Below are discussed salient advances in phase development and geometries for SPME, stir bar sorptive extraction (SBSE), and magnetic SPE. SPME has become a widely accepted technique for the extraction of organic compounds from all types of environmental samples, whether aqueous, gaseous, or solid. A review discussing recent developments in SPME for environmental analysis alongside food and biological analysis has been reported.65 The search for new coatings for SPME fibers is a highly active area given the scarcity of commercially available coatings [e.g., pure polymeric phases such as PDMS and polyacrylate, and dispersions of solid 4587

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Analytical Chemistry adsorbents such as Carboxen and divinylbenzene (DVB) in polymeric agglutinants] and their limitations (e.g., solvent instability and swelling, low operating temperature, and stripping of the coating). Sol
gel technology has been successfully used to prepare SPME fibers with various coatings, mostly using fused silica fiber as support; this has fostered the development of SPME methods for environmental samples.62 Such is the case with a new SPME fiber coating consisting of hydroxyl-terminated silicone oil (TSO-OH)-functionalized single-walled carbon nanotubes (SWNTs-TSO-OH).66 The functionalized product was used as precursor and selective stationary phase to prepare sol
gel derived poly(SWNTs-TSO-OH) SPME fiber for use in the headspace (HS)-SPME/GC-ECD determination of PBDEs in water samples. Compared with commercial SPME fibers, the new coating exhibited higher extraction efficiency for PBDEs, better thermal stability (over 340 °C), and a longer life span (over 200 times). The ensuing LODs for seven PBDEs were in the range of 0.08
0.8 ng L
1, and PBDE recoveries from reservoir water and wastewater were 74
109%. Sorbents based on modified sol
gel silica materials have been supplemented by others prepared from alkoxides of some transition metals as sorbent phases for SPME.62 Thus, sol
gel titania SPME fibers coated with PDMS were used to extract BTEX, trihalomethanes, and alcohols.67 TiO2, which possesses both better chemical stability and mechanical strength than silica, is an effective choice for circumventing the limited window of pH stability of silica-based coatings. Strategies based on principles other than those of sol
gel technology are currently being explored to develop new coatings for SPME fibers. Thus, multiwalled carbon nanotubes (MWCNTs)/fused-silica fiber was prepared by chemical bonding between the coating and fiber.68 For this purpose, MWCNTs were oxidized to create
COOH groups and silica fibers treated to form
NH2 groups. MWCNTs/SPME fibers were then formed by reaction between both groups upon heating and used to extract seven phenols from real water samples. The combination of the incompact structure resulting from the chemical bonding design with the inherent stability of MWCNTs conferred the fiber special properties such as good stability at high temperatures, in organic (polar and nonpolar) solvents, and in acids and alkalis, in addition to a wide linear range and low LODs for phenols. New SPME devices and strategies have continued to be developed with the aim of improving robustness (e.g., needle traps), sensitivity (e.g., thin-film microextraction), and trapping efficiency for polar organic compounds (e.g., membrane SPME). In a needle trap device, blunt-type hypodermic needles packed with an appropriate sorbent are used for extraction, which is followed by thermal desorption into a GC system. In order to extract analytes from an aqueous solution, the needle extraction device is connected to a glass syringe (Figure 3). Needle trap devices have been used in combination with various packing materials to extract VOCs mainly. Some reviews including their use in environmental analysis have been published.69 Research on packed sorbents has continued with the incorporation of typical materials such as CNTs into conventional SPME.70 The packed fiber version was found to be more effective than selfassembled nanotubes and also to provide enhanced capabilities for the extraction of pollutants. Thin-film microextraction can also be used to extract organic contaminants from water samples, especially with on-site sampling.69 Compared with SPME fibers and coated magnetic stir bars (twisters), a thin film usually results in shorter equilibration times and greater extraction rates, which

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Figure 3. Structure of the fiber-packed needle extraction device. Reprinted with permission from ref 69. Copyright 2009 Springer.

can be ascribed to the higher surface area-to-volume ratio of the thin film. A new strategy termed “membrane SPME” (M-SPME) has been developed for the isolation and enrichment of polar organic compounds from aqueous samples.71 The fiber consists of two phases, namely, an internal layer containing a polar acceptor phase and an external layer containing a hydrophobic phase in the role of a membrane. The total equilibrium amount of analyte extracted is the combination of that present in the two layers. In contrast to standard membrane techniques, the analyte in the membrane phase is not lost there because the nonpolar phase is part of the probe and this fraction of analyte is also transferred to the gas chromatograph injection chamber. Even mild solubility of the analytes in the material of the external layer ensures proper functioning of the system. PDMS and polyethylene glycol (PEG) were used as hydrophobic and polar phase, respectively. Comparison of this probe with commercially available polyacrylic SPME fiber for the extraction of a mixture of phenols by GC revealed the new sorptive system to be roughly 10 times more efficient than polyacrylic fiber. SBSE has become a popular analytical technique for the preconcentration of organic compounds into a PDMS-coated stir bar, which has been the subject of many environmental uses discussed in a recent review.72 Because only PDMS-coated stir bars are commercially available, which has reduced the applicability of SBSE to the extraction of nonpolar compounds, research in the reviewed period has continued to focus on phase development and analyte derivatization. However, the scope of this technique has shifted from aqueous samples to headspace sampling and multiresidue analysis. SBSE coatings with a variety of polarities have been prepared by different methods. Sol
gel technology has been used to easily introduce various groups into a PDMS network. For example, DVB and β-cyclodextrin were used to improve the extraction selectivity toward PAHs and polycyclic aromatic sulfur heterocycles (PASHs) based on π
π interactions.73 A “dumbbellshaped” stir bar was proposed to prevent the friction loss of coating during the stirring process and prolong the lifetime of stir bars as a result. The ensuing method was successfully applied to the determination of seven PAHs and PASHs in lake water. One other approach relies on monolithic materials with high enough permeability to expedite mass transfer. Various monomers have been prepared and successfully assessed for the extraction of polar compounds [e.g., vinylimidazole
DVB for aromatic amines in water74]; however, the overall extraction efficiency for strongly 4588

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Analytical Chemistry polar compounds fell short of the expectations. Selective materials such as MIPs have also been proposed, but a need still exists for polymeric materials affording a high enough sensitivity to allow the recovery of a broad group of polar organic compounds and further extend SBSE applicability.72 Multiresidue analysis using SBSE has been carried out in the multishot and sequential modes. In the multishot mode, several sample aliquots are extracted under the same or different extraction conditions, using a coated stir bar per sample; then, the stir bars are simultaneously desorbed in the thermal desorption unit. In the sequential mode, the extraction conditions of a single sample aliquot are modified in accordance with the analytes to be extracted, using one or more stir bars. A multiresidue method for screening endocrine-disrupting chemicals (EDCs) and pharmaceuticals in aqueous samples by use of SBSE in the multishot mode was reported in the reviewed period.75 In situ derivatization reactions with acetic acid anhydride, ethyl chloroformate and sodium tetraethylborate were carried out in three sample aliquots, and methanol was added to a fourth aliquot in order to restrict analyte adsorption onto glass walls. After sampling, the four stir bars were introduced together with a plug of glass wool soaked in bis(trimethylylsilyl) trifluoroacetamide to derivatize hydroxyl functionalities, heat-desorbed, and analyzed simultaneously by capillary GC/MS. The ensuing method was used to screen and quantify EDCs and pharmaceutical residues in processed waters and a hospital effluent water sample. Magnetic solid-phase extraction uses functionalized magnetite (Fe3O4) or maghemite (Fe2O3) nanoparticles as sorbent. Functionalized magnetic nanoparticles (MNPs) feature a large surface area and high extraction capacity; also, they can be rapidly isolated from matrix solutions using a magnet after adsorption of the analytes. In this way, target compounds are rapidly extracted from large-volume samples and the difficulties of solid
liquid separation and the high back-pressure encountered during passage through the SPE column are avoided. Worth special note among the coatings used for environmental extraction in the last two years are hemimicelles consisting of surfactants76,77 and ionic liquids (ILs),78 chitosan-coated octadecyl79, and MWCNs.80 Hemimicelles consist of monolayers of surfactant monomers adsorbed head-down on the oppositely charged mineral oxides while the hydrocarbon chains protrude into the solution with strong mutual lateral interactions, which leads to the formation of teepee structures. Mineral oxide
surfactant systems such as alumina
dodecyl sulfate and silica
cetyltrimethylammonium have been widely used to extract pollutants from environmental waters on the grounds of the multiple interactions they provide. The recent introduction of MNPs as mineral oxide has helped expedite extractions while maintaining adsorbent features. One example is the extraction of several sulfonamides76 with octadecyltrimethylammonium-coated MNPs as adsorbent. A concentration factor of 1000 and recoveries of 70
102% were obtained by extracting the target sulfonamides in 500 mL of hospital primary and final sewage effluent samples. Evaporation and subsequent determination with LC
UV provided detection limits around 30 μg L
1. One of the greatest drawbacks of hemimicelles arises from disruption of surfactant aggregates during analyte elution; this produces extracts containing high surfactant concentrations that may suppress ionization in MS or interfere with MS, UV, or fluorescence detection. Also, the extracts are incompatible with separation techniques such as GC or CE. MNPs coated with hemimicelles of alkyl (C10
C18) carboxylates were recently

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proposed to overcome this drawback.77 The strong chemical bonding between the surfactant and magnetite relative to the electrostatic forces involved in conventional hemimicelle-based sorbents precluded leaching of the surfactant and facilitated its reuse and the obtainment of surfactant-free extracts. Tetradecanoate hemimicelles were used to extract carcinogenic PAHs prior to analysis by LC with fluorescence detection. The LOQs thus obtained, 0.2
0.5 ng L
1, met the stringent water quality requirements established by the recently amended European Water Framework Directive 2000/60/EC and also the US EPA, for the determination of CPAHs in surface and ground waters. CPAH concentrations in the range of 0.42
0.96 ng L
1 were found in the Navallana reservoir (southern Spain). Additional advantages of this sorbent include pH-independent surfactant loads, an extended working pH range, and reusability. Ionic liquids (ILs) having long hydrocarbon chains possess surfactant properties and can thus form hemimicelles on MNPs. Yao et al.78 reported the use of 1-hexadecyl-3-methylimidazolium bromide (C16mimBr) and 1-decyl-3-methyl imidazolium bromide (C10mimBr) to coat MNPs and employed the resulting sorbents to extract PAHs from environmental waters with recoveries of 76
105%. Coating of active functional groups on MNPs was proposed to prevent their contamination with natural organic matter (NOM), which might impair their adsorption capabilities.79 The porous hydrophilic polymer chitosan was used to coat octadecyl-functionalized MNPs and the resulting adsorbent to extract perfluorinated compounds (Figure 4). Chitosan played three different roles; thus, it facilitated adsorption of PFCs at a low pH, improved the dispersibility of MNPs in the water samples, and allowed analyte molecules to pass through and enter the inner layer of the adsorbent while preventing NOM macromolecules from passing through the coating by effect of size exclusion and electrostatic repulsion. PFCs were thus successfully extracted from 500 mL of wastewater samples with concentration factors of 1000 after evaporation of the eluents, and recoveries ranged from 60 to 106%. Detection limits for PFCs with LC
electrospray ionization (ESI)-MS/MS fell in the range of 0.075
0.24 ng L
1. The use of MWCNTs for coating MNPs was recently proposed.80 Silica magnetic particles were synthesized and covalently bonded to MWCNTs
OH by sol
gel technology. The resulting adsorbent was used to extract estrogens from water samples, followed by sweeping MEKC analysis. Sonication helped accelerate the adsorption of estrogens on MWCNTs
OH particles and their subsequent desorption. The recoveries thus obtained for diethylstilbestrol, estrone, and estriol were 95.9%, 93.9%, and 52.4%, respectively. The method was applied to the analysis of tap, mineral, and river waters. Liquid-Phase Microextraction. A great variety of configurations and solvent types for the extraction of analytes from aqueous samples using a microvolume of organic solvent have been reported. Below are discussed the most salient advances in single drop microextraction (SDME) and dispersive liquid
liquid microextraction (DLLME), the most actively researched techniques for the isolation and enrichment of organic compounds in environmental samples during the reviewed period. The potential of new strategies such as the use of supramolecular solvents and ultrasound in microextractions is also examined. Current research into SDME is focusing on the use of new solvents or the chemical modification of the extracting drop with the aim of expanding the range of extractable compounds.81 Direct immersion in the aqueous sample and headspace have been the two most widely used SDME modes in this context. 4589

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Figure 4. (A) Steps of synthesis of Fe3O4-C18-chitosan MNPs and (B) application as an adsorbent for analysis. Reprinted from ref 79. Copyright 2010 American Chemical Society.

Among solvents, ILs have received increasing attention on account of their tunable properties, which can be easily adjusted by appropriate selection of cations and anions. Most research with ILs has focused on the extraction of polar compounds; however, ultra hydrophobic ILs based on tris(pentafluoroethyl)trifluorophosphate (FAP) anion paired with cations such as imidazolium, phosphonium, and pyrrolidinium have been proposed as extractants for PAHs and successfully applied in the direct immersion mode to the analysis of tap, creek, and river water with recoveries ranging from 79 to 114%.82 Until recently, the use of ILs in analytical procedures involving solvent microextraction was limited to LC, CE, and spectroscopic techniques as the final determination methods, owing to their very low

volatility. However, some recent papers have reported several methods rendering ILs compatible with GC. One uses a removable interface enabling direct introduction of the IL extract into a GC/MS system while preventing the IL from entering the column.83 Another uses a commercially available thermal desorption system to thermally desorb analytes from ILs and introduce them into the GC instrument.84 Doping the extractive drop with a derivatizing or chelating reagent introduces chemical reactivity that can be exploited for a number of potential new applications. Some interesting strategies have been used in SDME to derivatize polar compounds and inorganic ions. For example, noble metal-containing aqueous drops have been used to enrich hydride-forming elements, and acid and alkaline 4590

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Analytical Chemistry aqueous drops have enabled the extraction of ionizable inorganics such as ammonia and cyanide.85 One interesting SDME approach, termed solid drop-based liquid-phase microextraction (SDLPME), was recently developed, and the features and environmental applications were the subjects of a dedicated review.86 In this technique, a volume of a few microliters of a suitable organic solvent with a melting point near room temperature is placed on the surface of an aqueous solution that is stirred at a preset temperature for a selected time. The sample vial is then transferred into an ice bath where the organic solvent solidifies and is subsequently transferred into a suitable vial for injection into the analysis system. This approach has been used to extract PAHs, OCPs, trihalomethanes, and phthalate esters, among others, in water, using 1-undecanol as solvent. DLLME involves three components, namely, the sample, the extraction solvent (a high-density liquid immiscible with the sample), and a disperser solvent miscible with both. Upon mixing, the three components form a cloudy solution containing thousands of very tiny drops. Intimate contact of the organic extraction solvent with the sample facilitates transfer of the analytes simply by effect of solvent partitioning. The principles of DLLME and its environmental applications were recently reviewed.87 DLLME in combination with GC, LC, or CE has been successfully applied to the determination of nitroaromatics, ECDs, organophosphorus, organotin and pyrethroid pesticides, triazine and amide herbicides, fungicides, PAHs, organophosphorus flame retardants, PCBs, PBDEs, trichloromethane, chlorobenzenes, phthalate esters and pharmaceuticals, among other analytes, in waters. Classical DLLME uses organic solvents heavier than water so that, after extraction, the extractant can be sedimented by centrifugation. The use of low-density organic solvents requires additional processing steps apart from the mandatory centrifugation; such steps include refrigeration to freeze the organic solvent, its manual retrieval to let it thaw, and the use of additional materials such as surfactants (or conical-bottom test tubes). A two-step extraction technique combining DLLME and dispersive micro-SPE (D-μ-SPE) has been proposed to simplify solvent separation88 and used for the fast determination of PAHs in river water samples. The D-μ-SPE system uses hydrophobic magnetic nanoparticles to retrieve the extractant (1-octanol). The optimum operating conditions are as follows: vortex in the DLLME step for 2 min and in D-μ-SPE for 1 min, followed by desorption by sonication for 4 min with acetonitrile as solvent. Ionic liquids have been proposed as an alternative to organic solvents in DLLME, mostly in a two-phase process similar to conventional DLLME. Other approaches have also been proposed such as so-called “temperature-controlled IL-DLLME”, which exploits the temperature-dependent solubility of some ILs in water. In this approach, a volume of IL is dispersed in the sample solution and heated so that the IL is completely dissolved; then, extraction is performed in a single phase for improved mass transfer. The turbid solution obtained upon cooling is centrifuged to recover the ionic liquid. 1-Hexyl-3-methylimidazolium hexafluorophosphate has been used as extraction solvent in temperature-controlled IL-DLLME for the enrichment of pyrethroid pesticides from water.89 The use of ultrasound in solvent microextractions accelerates mass transfer between two immiscible phases; this, together with the large surface area of contact between the two phases, boosts the extraction efficiency in a very little time. A new technique known as ultrasound-assisted emulsification microextraction

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(USAEME) was developed and successfully applied to the extraction of a variety of organic compounds (e.g., synthetic musk fragrances, phthalate esters, lindane, PBDEs, PCBs, organophosphorus pesticides, trichloroanisole). In a recent study, USAEME was used for the simultaneous extraction and derivatization of parabens, triclosan, and related phenols in wastewaters prior to their determination by CG
MS/MS.90 Recoveries above 85% were obtained for all target compounds, with enrichment factors of 100
200. The liquid
liquid phase separation of surfactants induced by environmental conditions such as temperature, electrolytes, and pH has been extensively used in analytical extraction and concentration schemes. The surfactant-rich phase is a nanostructured liquid recently named “supramolecular solvent” that is generated from amphiphiles through a sequential self-assembly process occurring on the molecular and nano scales. A review covering progress in both theoretical and practical terms in the use of supramolecular solvents for analytical extractions in, mainly, the environmental field, has been published.91 Supramolecular solvents produced from micelles and vesicles of a variety of surfactants have been successfully used to extract organic pollutants in waters, soils, and sediments. A dedicated review of the use of supramolecular solvents consisting of nonionic micelles (commonly known as “cloud point extraction”, CPE) for the isolation and enrichment of POPs was recently published.92 Also, outstanding applications have been reported during the reviewed period. Thus, Hinze et al.93 proposed postextraction derivatization of the surfactant to make extracts compatible with the gas chromatographic determination of pollutants. Mixtures of PAHs, herbicides, and profens were used to confirm the feasibility and performance of this approach. The derivatization step can be used to derivatize not only the surfactant but also some nonvolatile analytes. Liu et al.94 reported the extraction and enrichment of silver NPs in environmental waters. An enrichment factor of 100 was obtained with recoveries in the range of 57
116% at 0.1
146 μg L
1 spiked levels. Ag NPs preconcentrated into the Triton X-114-rich phase were identified from transmission electron microscopy/scanning electron microscopy-energy dispersive spectrometry/UV
vis spectra and quantified, following microwave digestion, by inductively coupled plasma (ICP)MS with a detection limit of 0.006 μg L
1. Environmental and Biological Solid Samples. A number of methods involving enhanced solvent-extraction techniques [e.g., PLE, pressurized hot water extraction (PHWE), focused ultrasonic-assisted extraction, supercritical fluid extraction (SFE)] have been successfully tested as alternatives to conventional solid
liquid and Soxhlet extraction. The high extraction efficiencies thus achieved have resulted in significant reductions in solvent consumption and time for sample preparation. However, SFE has failed to gain widespread use in relation to conventional liquid-based extraction or PLE in environmental analysis. A review of recent advances in SFE95 deals with the applications of this technique to the extraction of organic pollutants (e.g., dioxins, PCBs, PAHs, OCPs, fluoroquinolones) in sediments, soils, and wastewater. Sonic probes are finding a slight resurgence in the extraction of organic pollutants in soils, sediments, and biological tissues. In addition to the input of sonic energy, the localized heating applied can possibly yield favorable extraction results. A number of examples of these applications can be found in the literature published over the reviewed period. Also, the use of sorptive methods (SPE and SPME, mainly) has enabled the development of outstanding applications. 4591

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Analytical Chemistry PLE is now a well established, extensively used extraction technique in environmental analyses for POPs such as PCBs, dioxins, furans, and PAHs. Many of these compounds are well suited for PLE since they are highly stable and allow high temperatures to be used in order to improve desorption and mass transfer. The analytes are also highly uniform in the sense that they are lipophilic and contain few or no functional groups, which simplifies the development of extraction methods. Also, many are stable in harsh chemical surroundings, which facilitates the use of strong acids to remove unwanted coextracted matrix components. In contrast to POPs, application of PLE to pharmaceuticals is far from mature even though it has received considerable attention in the last two years. Reviews discussing PLE parameters during method development for pharmaceuticals in environmental and biological matrixes96 and sewage sludge97 and applications to single analyte and multiresidue methods have been reported with the aim of providing important reflections toward future developments in PLE for pharmaceuticals. An interesting PLE-based method for the extraction of drugs of abuse in airborne particles was developed by Barcel
o et al.98 They determined up to 17 different compounds belonging to five different chemical classes using PLE followed by LCMS/MS. Absolute recoveries were above 50% for most investigated compounds. Analysis of ambient air samples collected at two urban background sites in Barcelona and Madrid (Spain) revealed the presence of cocaine, benzoylecgonine, tetrahydrocannabinol, ecstasy, amphetamine, methamphetamine, and heroin in some or all the samples. The highest mean daily levels corresponded to cocaine (850 pg m
3), followed by heroin (143 pg m
3). The fundamental principles of PHWE, the parameters affecting extraction with hot water and applications of this technique to environmental solid samples were recently reviewed.99 Applications were mainly developed for the extraction of PAHs, surfactants, BFRs, and pesticides in soil and sediments. In the 150
250 °C range, water takes on a polarity similar to that of organic solvents and exhibits increased diffusion and lower viscosity. However, most analysts are reluctant to work at these temperatures; solvent concentration (i.e., water evaporation) is more difficult with water than with organic solvents, and material issues have all left the use of hot water extractions unexplored in all but a few laboratories.47 SPME is increasingly being used for the treatment of environmental solid and biological samples. Space-resolved SPME has recently emerged as an outstanding approach to determining the distribution of contaminants, drugs, and other bioactive compounds in biological tissues.100 This technique uses miniaturized segmented fibers (C18 or PDMS fibers 1 mm long) 4
5 mm apart to allow for simultaneous sampling of two different tissues or stratified matrixes (Figure 5). After extraction, the segments are subjected to stepwise desorption protocols. The efficacy of this approach was assessed in various biological matrixes (onion bulb, fish muscle, and adipose tissues) containing stratified pharmaceutical analytes. Empirically, the results agreed well with established techniques such as microdialysis and liquid extraction, but SR-SPME was simpler to implement, exhibited higher spatial resolution, and was more cost-effective than the traditional approaches. More recently, this new approach has been used to study the tissue-specific bioconcentration of pharmaceuticals in rainbow trout.101 Specifically, the segmented fiber allowed the simultaneous determination of pharmaceutical residues in fish dorsal
epaxial muscle and adipose tissue. The ability of the SPME method to repeatedly sample the same fish circumvented

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problems arising from interanimal variation, thus improving the precision of the generated bioconcentration kinetic profiles. SPME is also well suited for extracting pollutants from sediment pore water. One asset of this technique is that it can provide LODs similar to those obtained with organic solvents using a few milliliters rather than liters of pore water. Five different SPME sorbents were assessed for their ability to yield the best detection limits for di- to octachlorobiphenyl congeners, both with GC/ECD and with GC/MS.102 SPME with 7 μm PDMS fiber in combination with GC/MS yielded the best signalto-noise and selectivity results for all 62 PCB congeners. Pore water was obtained by centrifuging wet sediment, followed by flocculation to remove colloids. SPE is increasingly being used for sample cleanup of extracts from environmental and biological solid samples. MIPs are the preferred sorbents for this purpose. For example, PAHs were quantitatively enriched in extracts from coastal sediments and atmospheric particulates in an MIP were synthesized from a PAH standard as template, methacrylic acid as functional monomer, and acetonitrile as porogen.103 The adsorption stability of the MIP was tested by consecutive contact with environmental samples, and its performance found not to vary after 10 enrichment
desorption cycles. PAH recoveries ranged from 85 to 96%.

’ SEPARATION AND DETECTION TECHNIQUES A current trend in environmental analysis is a shift toward polar organic contaminants as a result of the drastic reduction of emission of POPs in industrialized countries after adoption of legal measurements. As a result, although both LC and GC continue as the leading techniques for the separation of pollutants in the environment, LC is becoming more and more the focus of research, with detection almost exclusively based on MS, especially MS/MS, in order to fulfill requirements for unequivocal confirmation of target compounds. HRMS techniques, although still rare in environmental laboratories, are fostering the development of nontarget screenings from which a deeper knowledge of the distribution and fate of metabolites and transformation products is expected. Combination of LC-MS/MS and GC/MS/MS with biological assays is providing a powerful tool to unravel cause
effect relationships for reliable risk assessment of environmental pollution. Field measurements, although still in their infancy concerning environmental applications, are an active research area. The use of nanomaterials is fostering the development of sensor arrays and networks with attractive applications. Below, advances in separation and detection techniques used in environmental analysis, both in the lab and field, are discussed. Gas Chromatography/Mass Spectrometry. The use of GC/ MS for multiresidue analysis with a view to establishing global organic pollution patterns clearly received more attention than the analysis of target contaminants over the last two years. Worth special note among general reviews on the GC/MS technique is the biannual review of fundamental developments in gas chromatography in this journal, which includes some illustrative examples of environmental applications.104 Large-volume injection (LVI), an effective way of reducing the need for preconcentration, continues to be widely used to develop high-throughput, automated, cost-effective, user-friendly methods. LVI-GC/MS has been proposed to enhance sensitivity in multiresidue analysis. A total of 41 multiclass pollutants including PAHs, PCBs, phthalate esters, nonylphenols, bisphenol A (BPA), and 4592

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Figure 5. (A) Segmented space resolved SPME fiber and the two-step desorption process and (B,C) illustration of the application of the fiber to the in situ sampling within an onion bulb and into adipose fin and muscle tissue. Reprinted from refs 100 (panels A and B) and 101 (panel C). Copyright 2009 and 2010, respectively, American Chemical Society.

selected steroid hormones were determined in this way following straightforward cleanup by solid-phase microextraction with C18 packed syringes.105 Also, LVI-GC/MS operated in the scan and SIM modes was used for target and nontarget analysis of water

pollutants; screening was done by processing scan data with Deconvolution Reporting Software in combination with a database containing the mass spectra and locked retention times for 934 organic contaminants.106 In addition to the common on-column 4593

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Analytical Chemistry injection and temperature vaporizer injection modes, alternative LVI sample introduction methods including that based on the through oven transfer adsorption desorption interface have been used for purposes such as multiresidue analysis of pesticides.107 General advances in LVI-GC/MS (injection systems, sample treatment, and outstanding applications) in the past decade were recently discussed.108 Major trends in the use of GC stationary phases during the last two years include the use of narrow-bore capillary columns for fast GC and the development of more selective ionic liquid-based stationary phases to enhance separation. The use of narrow-bore capillary columns for fast, high-throughput GC is on the increase. A GC/(ion trap) MS/MS method using a microbore capillary column was recently proposed for the analysis of explosives.109 The use of fast GC requires higher MS/MS scan rates to ensure expeditious data acquisition. High mass scan rates and dynamic collision-induced dissociation (CID), which involves activating ions for dissociation during the mass scan without the need for a separate ion activation period, were used to achieve the duty cycle required for fast GC separation. The chromatographic run took only 2.5 min, and detection limits were quite good (0.5
5 pg). Some properties of ILs such as their low volatility, high thermal stability, selectivity toward specific chemical classes, and good wetting capabilities on the inner wall of a fused silica capillary make them highly suitable as GC stationary phases. Although these materials lack the efficiency of commonly used polysiloxanes, they can help to obtain unique selectivity. A recent review discusses the separation efficiency of GC stationary phases based on ammonium, pyridinium, and phosphonium molten salts and immidazolium, pyridinium, pyrrolidinium, and phosphonium ionic liquids, with the aim of providing guidelines for selecting the most suitable column type for each application.110 Novel IL-based GC stationary phases have been used in new synthetic procedures providing enhanced separation efficiency for various classes of organic pollutants including alkanes, esters, alcohols, and aromatic compounds.111 The introduction of molecular recognition elements such as cyclodextrins, cavitands, and metallomesogens in IL-based GC columns for resolving complex mixtures is one other emerging research trend in this field.112 Comprehensive Two-Dimensional Gas Chromatography. GC  GC coupled to mass spectrometry is an excellent choice for analyzing highly complex environmental samples. GC  GC provides three major benefits, namely, enhanced chromatographic resolution, improved analyte detectability by effect of cryofocusing in the thermal modulator, and chemical class ordering in the contour plot. Research in this field has shifted from instrumental development to application to real samples over the last two years. Software improvements have facilitated GC  GC management and enabled formerly impossible quantitative analyses with this technique. TOF-MS has been the preferred detector for this purpose on the grounds of the highly sensitive, accurate, full mass spectral information it provides and of its affording the fast acquisition rates needed to define the ultra narrow peaks typically obtained with GC  GC. A recent review discusses the most salient advances in GC  GC since 2007 and deems it an active, expanding area of research including methods for environmental samples among its latest applications.113 GC  GC has been used to facilitate the chromatographic resolution of highly similar compounds such as 4-nonylphenol isomers.114 However, the most salient trend in GC  GC/MS environmental analysis over the period 2009
2010 was the

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development of multiresidue analytical methods, for which this technique is especially suitable. Matamoros et al.115 developed an SPE-GC  GC/TOF-MS screening method for 97 priority and emerging contaminants. They proposed the strong correlation between the second dimension retention time and log Kow for the target compounds as an additional identification criterion. The ensuing method provided detection limits in the low nanogram per liter range for most analytes and was successfully applied to the analysis of river water samples. The high selectivity of GC  GC/TOF-MS has facilitated the development of a wide range of analytical methods with minimal sample preparation. Thus, several solvent extraction methods followed by a simple gel permeation chromatography (GPC) step for removal of lipids have been proposed for screening nontargeted contaminants in solid samples.116,117 Regarding data management, Hilton et al.117 employed user-written scripts to filter peaks, which were defined in terms of mass, mass abundance, and abundance ratios between masses, among other criteria. In this way, analytes in various compound classes such as phthalates, PAHs, and their heterocyclic analogs, chlorinated compounds, brominated compounds, and nitro compounds were identified against mass spectral libraries. The ensuing methodology was validated by application to a NIST reference material and allowed the previous compounds to be identified at concentrations as low as 10
20 ng/g dust. Mass Spectrometric Detectors. The ion trap (IT) mass spectrometer remains the most common analyzer for GC-based environmental analysis. Although IT MS has consolidated over the past decade, TOF MS and, to a lesser extent, triple quadrupole (QqQ) MS have risen markedly in use in recent years; the reviewed period included. IT MS provides a high throughput at a low instrumental cost, all with unit mass resolution, a broad mass range, and ppt level sensitivity. Enhanced selectivity can be obtained using tandem mass spectrometry, which has the added advantage that it provides full spectra for the product ion with no loss of sensitivity. Recent advances in the application of IT MS to environmental samples include the development of methods for the determination of PBDEs118 and the multiresidue analysis of pollutants (pesticides and drugs, mainly).119,120 Single-quadrupole detectors (Q) have continued to be used in the development of novel approaches in the period 2009
2010. Traditional GC/Q-MS with electron impact (EI) ionization, which provides highly reproducible and identifiable mass spectra, has been adapted for screening purposes. Thus, Rosenfelder et al.121 developed a nontarget GC/EI-Q-MS-SIM method for the structural investigation of low concentrated polyhalogenated compounds in environmental water samples. The low sensitivity of quadrupole systems when operated in the scan mode was offset using small mass range time windows in subsequent single ion monitoring (SIM) runs based on the fact that the masses of polyhalogenated compounds correlate well with retention times on nonpolar GC columns. The preferred use of QqQ detectors in this field continues to be the quantitation of target pollutants by selective, sensitive multiple reaction monitoring (MRM) of MS/MS spectra.122 Although tandem mass spectrometry with IT or QqQ analyzers affords unquestionably good sensitivity and selectivity for predefined target contaminants, the sensitivity in the scan mode is inadequate for untargeted screening or multianalyte determinations and the unit mass accuracy is too low for unambiguous elemental composition assignment or structural identification. High resolution mass spectrometry (HRMS), which was formerly the exclusive domain of complex, expensive Fourier 4594

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Analytical Chemistry transform-ion cyclotron resonance (FTICR) instruments, has been rendered affordable in the form of Orbitrap and TOF systems with mass accuracy as good as 1
5 ppm, which allows nominally isobaric ions to be mass-resolved. As stated above, the use of GC  GC-TOF for multiresidue analysis was a major research trend during the last two years. GC/TOF-MS has also been used in combination with GC/QqQ-MS
MS for the reliable identification of pollutants [specifically, sixteen EPA priority PAHs in animal and vegetable samples from aquaculture activities123] from highly accurate full mass spectra after quantitation at the pg/g level by MS/MS monitoring. There have also been new instrumental and software advances for HRMS during the last two years. Thus, a modified hybrid linear ion trap (LTQ)-Orbitrap coupled with a GC instrument was recently designed by researchers at the University of Wisconsin-Madison and Thermo Fisher Scientific with a view to providing high resolution (up to 100 000 at m/z 400) and subparts-per-million mass accuracy.124 A high-duty cycle innovative scan type was implemented to synchronize the acquisition rate and the time scale of GC (up to 6.5 Hz at 7500 resolution). The suitability of the instrument for such challenging applications as the determination of PCDDs and PCDFs in environmental samples and the profiling of primary metabolites in plant extracts was successfully demonstrated. The widely used EI ionization technique produces standard mass spectra; however, the extensive fragmentation it causes can be a drawback for wide-scope screening purposes and require the use of reference standards for comparison. The use of soft ionization techniques such as chemical ionization, electroncapture ionization, negative-ion chemical ionization (NICI), or field ionization, whether alone or in combination with EI, for screening purposes with GC/MS has increased steadily over the past decade, the reviewed period included. Soft ionization techniques allow one to generate spectral data typically rich in molecular or quasi-molecular ion information which is especially suitable for compound confirmation. Thus, atmospheric pressure chemical ionization (APCI), which is primarily used to interface LC to MS, was tested for the use of GC/QTOF-MS in the multiresidue pesticide analysis of about one hundred organochlorine, organophosphorus, and organonitrogen compounds.125 The addition of water as modifier was tested as a way to promote the generation of protonated molecules during ionization. Likewise, GC/(NICI)Q-MS126 and GC/(NICI)-QIT-MS-MS109 were used for the determination of pesticides and explosives, respectively. Portable GC. The development and testing of portable GC instrumentation for rapid on site analysis of pollutants is an active area of research. Dorman et al.104 have compiled the main advances in portable and microfabricated GC instruments (preconcentrators, columns, and detectors, both custom-made and commercial). Commercial portable GC/MS equipment was recently tested for detection of chemical warfare agents127 and VOCs.128 Significant new advances in the design of portable GC/MS instruments for environmental analysis have also been made over the period 2009
2010. Researchers at the University of Cordoba reported a portable system combining a multicapillary column with a miniaturized ion mobility spectrometry (IMS) detector for rapid GC-IMS separation.129 Also, IL-HSSDME and room temperature GC-IMS were for the first time used for the direct determination of trihalomethanes. Interfacing of the equipment was facilitated by the new injector unit designed to permit efficient volatilization of the analytes at room temperature and to prevent the IL from entering the system.

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Liquid Chromatography
Mass Spectrometry. LC-MS/ MS techniques, mainly QqQ and to a lesser extent ion trap (IT), are today the prevalent choices for the reliable determination of emerging polar organic compounds in the environment. The extremely high selectivity and sensitivity of MRM techniques allow trace constituents of complex mixtures to be determined. Among the possible ionization techniques, ESI is by far the most widely used as compared with APCI or the more recent atmospheric pressure photoionization (APPI). With regard to chromatographic analysis, high sample throughput is often required in environmental monitoring and different strategies are increasingly being used to short chromatographic runs such as ultra high pressures and short, narrow bore columns. Recent developments on the MS side have led to a generation of hybrid instruments such as those based on quadrupole timeof-flight (Q-TOF), Q linear traps (Q-LITs), and linear ion trap Orbitrap (LTQ-Orbitrap) with capabilities of achieving accurate mass measurements and acquiring qualitative information through full-scan spectra. These hybrid instruments allow for a more reliable target analysis with reference standards, a screening for suspect analytes without reference standards, and a screening for unknowns. An emerging trend, fostered by the high resolving power and high mass spectral accuracy of hybrid MS techniques, is shifted from parent compound analysis, mainly using multiresidue methods, toward the identification of metabolites and transformation products. An excellent overview of the state of the art and future trends of the application of LC-HRMS to the environmental analysis of polar contaminants is given in a recent review article.130 Several comprehensive articles have reported an overview on the use of LC-MS in the analysis of specific classes of organic contaminants, their metabolites, and/or their degradation products, over the period 2009
2010. The target compounds included polar pesticides,131 antibiotics,132 phytotoxins and mycotoxins,133 nitroimidazoles,134 pharmaceuticals, drugs of abuse, polar pesticides, PFCs and NPs,135 and surfactants, antibiotics, PFCs and benzotriazoles.136 A special issue of Analytical and Bioanalytical Chemistry presented an overview of the state of art of antibiotic analysis in environmental and food samples. It covered some important aspects related to current analytical methods applied to antimicrobial analysis, legislation and selected applications.137 Advances in Chromatography. With the introduction of LC column with sub-2 μm particle size, the column separation efficiencies, sensitivity, sample throughput, and peak capacity have been dramatically improved. UPLC combined with MS/MS offers unprecedented on column resolving power, sensitivity, and speed of analysis. UPLC-MS/MS is especially valuable for multiresidue analysis, and many publications involving multiclass antibiotics, phenolic compounds, nitrated and oxygenated PAHs, insecticides, etc. have been reported over the reviewed period. A nice example of the utility of UPLC-MS/MS for high-sensitivity and highthroughput analysis has been reported by Cai et al.138 The United States Environmental Protection Agency (US EPA) 16 priority pollutants PAH were analyzed by UPLC-APPI(MS/MS) with run to run cycle times of approximately 3.5 min. With conventional LC column, the injection to injection cycle times usually exceed 45 min, so analysis throughput improved by at least 10-fold. APPI was used for ionization since ESI and APCI fail to ionize PAHs efficiently. The improvement in PAH ionization resulted in lower LODs (i.e., picogram level on column) using chlorobenzene as a dopant. Dynamic linear ranges covered at least 3
4 orders of magnitude. 4595

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Analytical Chemistry Although the analytical rapidity afforded by UPLC has proven beneficial in the analysis of simple matrixes, this feature should be considered secondary to enhanced resolution in complex environmental samples, which are more prone to matrix interference over rapid analysis. An illustrative example has been reported by Rice and Hale139 in the analysis of androgens and estrogens in aqueous environmental matrixes by UPLC-MS/MS. Estrogen chromatographs appeared largely unaffected by natural matrix components when pond water samples were analyzed, and although interference caused gross distortion of the IS baseline for androgens, extended chromatography was effective in handling matrix inference. Wastewater proved a greater analytical challenge than pond water. Interferences for both estrogen and androgen were evident as coeluting peaks and skewed compound recoveries. Ion suppression of the estrogen surrogate and enhancement of the androgen were observed highlighting the need of prolonged chromatographic elution or alternative MS ionization techniques to be solved. Analysis of very polar compounds can be straightforwardly addressed with hydrophilic interaction chromatography (HILIC), a strategy scarcely explored in environmental analysis so far. HILIC uses polar (e.g., aminopropyl) columns in aqueous
organic mobile phases rich in organic solvents (usually acetonitrile, methanol, or their mixtures). Using HILIC to separate highly polar compounds greatly improves sensitivity in MS detection relative to reversed-phase conventional LC, which requires a mobile phase with a high aqueous content to separate polar solutes. An example is the simultaneous analysis of nine drugs of abuse and their metabolites in wastewater based on HILICMS/MS.140 The separation by HILIC showed good performance for all compounds, especially for the hydrophilic compounds which elute early (amphetamine-like stimulants) or show no retention (ecgonine methyl ester) in reverse phase (RP)-LC. LOQs were between 1 and 2 ng/L for all the compounds. Except for 6-monoacetylmorphine, the target compounds were detected from 1 to 819 ng/L in influent wastewater samples (n = 12) collected from 11 different wastewater treatment plants across Belgium. The presence of ecgonine methyl ester in wastewater could be demonstrated for the first time. Target Multiresidue Analysis. Multiresidue methodologies have gradually become the preferred tools for the analysis of target emerging contaminants and their metabolites. Such methodologies allow determination of a large number of compounds in a single analysis, thus reducing time and cost, and provide more comprehensive information about the presence of contaminants, which is mandatory for further study of their removal, partitioning, and ultimate fate in the environment. Selected reaction monitoring (SRM) of precursor
product ion transitions using QqQ has by far been the most frequently used tandem MS technique for multiresidue analysis by virtue of its sensitivity. However, a number of studies have shown that monitoring only one transition might result in false positive for compound identification and, thus, at least two transitions are required to fulfill the European Commission Guidelines (EU Commission Decision 2002/657/EC) for identification and quantification of organic residues and contaminants. This decision, originally established for food analysis, has been recently extended to other matrixes, including environmental samples. It proposes a system of identification points (IPs), where at least three IPs are required (four in the case of banned compounds) to confirm a positive finding; an ion (or precursor ion) contributes 1 point, and each MRM product ion contributes 1.5 points. Thus,

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acquiring at least two MRM ion transitions yields the minimum requirement of four identification points and allows the calculations of at least one ratio of the product ion. In addition, the deviation of the relative intensity of the recorded ions must not exceed a certain percentage of the reference standard, and the retention time must not deviate more than 2.5%. While LC-QqQ MS has proved adequate for the majority of multiresidue analysis, the possibility exists of reporting false positives in situations where there are130,135 (i) insufficient temporal peak resolution in the chromatographic run or too short acquisition times for the individual MS/MS transitions; (ii) nonspecific transitions (e.g., loss of water or carbon dioxide), or (iii) only a transition available (e.g., analytes with poor fragmentation or low molecular weight). HRMS (e.g., LTQ Orbitrap MS, TOF MS) offers the potential to overcome the limitations of SRM analysis. LTQ Orbitrap instruments feature a dynamic range and sensitivity close to that of many QqQ instruments whereas TOF instruments have lower dynamic ranges (i.e., 10-fold) and sensitivities (from 10- to 100fold). Identification criteria for confirmation of a screening result using HRMS have been discussed by Hogenboom et al. from their experiences with the LC-LTQ Orbitrap MS in the area of water analysis.141 They propose identification based upon: (i) LC retention time; (ii) accurate mass of the precursor ion (mass resolution = 100 000 fwhm, full-width-at-half-maximum); and (iii) nominal masses of product ions. No criteria for mass accuracy are given in 2002/657/EC, but 2 identification points per ion (or precursor ion) and 2.5 per MRM product ion are earned by HRMS. Therefore, the requirements in HRMS are less stringent than confirmation criteria for low resolution MS. The authors illustrate with practical examples that, in addition to the accurate mass of the precursor ion, specific product ions are necessary to identify a compound. These criteria have been also used by Schr€oder et al. for the detection and quantitation of anabolic, doping, and lifestyle drugs and selected metabolites in wastewater from fitness center discharges and wastewater treatment plants using LC-LTQ Orbitrap MS.142 Confirmation of all compounds under investigation was obtained by means of MS/MS operated in CID mode. Of the steroids and stimulants tested, testosterone, methyltestosterone, and boldenone or ephedrine, amphetamine, and 3,4-methylendioxy-N-methylamphetamine were observed at up to 5 μg L
1 (ephedrine). Of the β2-agonists, only salbutamol, and of the diuretics, furosemide and hydrochlorothiazide were confirmed in the extracts. Quite high concentrations of the phosphodiesterase type V inhibitors sildenafil, tadalafil, and vardenafil and their metabolites were confirmed in fitness center discharges (sildenafil: 1945 ng L
1) whereas their concentrations in municipal wastewater did not exceed 35 ng L
1. Results obtained from wastewater treatment plant effluents proved that these “dual-use-drugs”, with the exception of hydrochlorothiazide, were mostly eliminated. Another approach recently proposed to confirm positive findings is the use of hybrid instruments such as QqLIT that offer unequivocal analyte identification.143 This is due to the application of the information-dependent acquisition (IDA) function that includes three steps: (i) a survey scan, i.e., a SRM including the most abundant transitions of target analytes; (ii) a dependent scan, i.e., enhanced product ion (EPI) scans, recorded at three different collision energies, for ions with intensity above a preset threshold in the survey scan, and (iii) comparison of EPI spectra with those of MS/MS library data, based on EPI spectra at the three collision energies used. The number of identification points achieved with IDA experiments for each compound is 4596

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Analytical Chemistry seven, taking into account the SRM transition (1 IP for the precursor ion and 1.5 for the product ion) and the three EPI scans carried out (1.5 IP for each), versus the 4 IPs yielded by SRM acquisition with the QqQ configuration. The IDA experiments have been successfully used for confirmation of different organic pollutants such as sulfonamides in the Llobregat river basin144 and 74 pharmaceuticals in river and waste waters.145 Nontarget Screening of Metabolites and Transformation Products. The most straightforward, unambiguous way of identifying metabolites and transformation products (TPs) in the environment is using reference standards, which enable comparison by MS and separation techniques; however, metabolites are not always available, and the chemical synthesis of some is anything but trivial. Several approaches differing in number and order of the evaluation steps have been discussed in the literature in the last two years for screening of metabolites and TPs in the absence of reference standards. Approaches are conceptually different for suspects and unknown screening.130 Thus, compound-specific information for suspects screening is available (e.g., the molecular formula allows the exact m/z to be calculated) whereas unknown screening starts without any a priori information on the compounds to be detected. An efficient procedure has been developed by Kern et al.146 to comprehensively screen for large numbers of suspected TPs in environmental samples. The target list (1794 TPs) was generated from literature sources and a knowledge-based pathway prediction software. The screening procedure was based on six steps (Figure 6).The identification of target TPs consisted of extracting the exact mass from the chromatogram using an LTQ Orbitrap at R = 60 000, selecting peaks of sufficient intensity, checking the plausibility of the retention time predicted by the Kow, and interpreting mass spectra. The procedure was used to screen for TPs of 52 pesticides, biocides, and pharmaceuticals in seven representative surface water samples from different regions in Switzerland. Altogether, 19 TPs were identified, including both some well-known and commonly detected TPs and some rarely reported ones (e.g., biotransformation products of the pharmaceuticals venlafaxine and verapamil or of the pesticide azoxystrobin). Screening for unknown compounds is more challenging, and although there are a number of procedures, three steps are common in typical screening overflows:130 (i) an automated peak detection by exact mass filtering from the chromatographic run (e.g., Hern
andez et. al147 used the ChromaLynx XS software in a study dealing with the use of LC-QTOF MS for the nontarget screening of organic contaminants in environmental waters); (ii) an assignment of an elemental formula to the exact mass of interest (e.g. Erve et. al148 comprehensively investigated the capabilities and limitations of the elemental composition prediction from accurate mass measurements using profile MS data after peak shape calibration); and (iii) a database search of plausible structures for the determined elemental formula, e.g., Pubchem. When MS analysis alone does not provide sufficient information to allow unequivocal identification of new TPs, the most powerful complementary analytical technique is nuclear magnetic resonance (NMR). The capabilities of combining 1H NMR and LC-SPE-NMR/TOF-MS for nontargeted analysis of contaminated groundwater of a former ammunition destruction site have been assessed.149 This approach allowed identifying expected residues of explosives and their TPs, BPA, and some toxicologically relevant additives for propelling charges.

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Figure 6. Six-step funneling procedure to identify target TPs using high mass resolution, plausibility of retention time, and interpretation of MS/ MS fragments. Reprinted from ref 146. Copyright 2009 American Chemical Society.

Capillary Electrophoresis-Detection Systems. Although it plays a secondary role as a separation technique in environmental analysis, capillary electrophoresis (CE) provides major advantages over the well-established LC and GC techniques including a high separation efficiency, the use of small amounts of sample, and increasing applicability in both the traditional and the microchip format. The development of CE microchips for environmental applications has attracted much interest on account of their portability, low solvent and reagent consumption, and good performance capabilities. Capillary zone electrophoresis (CZE), remains the most widely used methodology for environmental analysis, followed by micellar electrokinetic chromatography (MECK) and capillary electrochromatography (CEC). The latest applications of CE to environmental samples are compiled in the biannual broad scope CE review published in this journal.150 The increasing number of CE applications developed for the analysis of certain groups of contaminants has led to the publication of several reviews in connection with antibiotics,151 metal speciation,152 and sulfonamides153 over the last two years. The use of nonaqueous capillary electrophoresis (NACE) and the development of new chiral selectors or monolithic columns 4597

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Analytical Chemistry to introduce new selectivities or improve the separation of trace contaminants continue to be active areas of research in environmental analysis and to expand the scope of CE in this field. A number of applications of NACE to the determination of antidepressants, dyes, phenolic acids, parabens, alkaloids, and herbicides in environmental samples have been reported during the past two years. Worth special note in this context is the approach of Su et al.154 to the CE separation of open-cage fullerenes, which remains a challenging task for LC. Although chemically modified cyclodextrins are still the most commonly used chiral selectors, novel compounds such as macrocyclic antibiotics have also been used for this purpose in environmental analysis in the last two years.155 New monolithic columns based on silica xerogel156 or silica spheres coated with C18-modified gold NPs157 for the analysis of PAHs, naphthyl methacrylate (a material facilitating hydrophobic and π interactions in aromatic contaminants),158 and the surfactant poly (11-acrylaminoundecanoic acid-ethylene dimethacrylate) for the separation of pesticides159 were developed for the CEC analysis of environmental contaminants over the period 2009
2010. The introduction of MIP160 onto monolithic matrixes for separation of trace contaminants is a growing trend for obtaining highly selective separations in CEC. CE provides excellent mass detection limits by virtue of the ability to inject very small amounts of sample. However, such limits are typically 2
3 orders of magnitude higher than in LC, which constitutes a major hindrance to the use of CE for determining trace contaminants. The development of effective approaches to increasing sensitivity in CE based on prior online or off-line concentration, or CE stacking, and improvements in the subsequent detection step constitute an area of intensive research in environmental analysis. The following sections discuss recent advances in preconcentration methods and detection systems in both the conventional and the microchip format in this context. Typical examples of the use of nanomaterials for improving sensitivity and/or selectivity, an emerging trend in the last two years as in other fields of environmental analysis, are discussed below. Chromatography-Based Preconcentration Techniques. The integration of online extraction
concentration steps in conventional and microchip-based CE has so far relied largely on SPE. Monolithic columns and custom-built microcartridges or microcapillaries for microchips have been the most widely used separation devices for CE in environmental analysis during the reviewed period. Thus, Vizioli et al.161 used a microcartridge packed with C18-derivatized silica particles 20
30 μm in size for the extraction of gadolinium and lanthanum as 2-(5-bromo-2-pyridylazo)5-diethylaminophenol complexes from water in the presence of nonionic micelles of polyethylene glycol tert-octylphenyl ether. The microcartridge was coupled online to the inlet of the separation capillary, and the chelates formed were released from the sorbent with methanol and analyzed by CZE with diode array detection. Detection limits of 20
80 pg/L (i.e., a 1000-fold improvement in sensitivity over conventional injection) were thus obtained. Rodríguez-Gonzalo et al.162 reported a procedure for the in-capillary microextraction of carbamate pesticides with a monolithic polymeric sorbent made of DVB, followed by MECK
UV. They obtained LODs of 1
16 μg/L, which were 10 000 times lower than that without SPE. Proczec et al.163 proposed an integrated microdevice for coupling on-chip SPE to separation by channel electrochromatography. To this end, they prepared an acrylate-based monolithic column containing both

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hydrophobic and charged moieties within a glass microdevice which afforded the determination of a variety of directly injected neutral, ionizable, and charged contaminants. They obtained a signal enhancement factor of 270 for a mixture of PAHs within 120 s of preconcentration and of 250 for the cation Ru(bipy)32þ with 20 s injection. Capillary electrophoresis is more difficult to integrate with LLE than with SPE, owing to the frequent incompatibility between the solvents used by the two techniques. This shortcoming can be circumvented by performing three-phase LPME to have the analytes pass from the sample through an organic phase into an acceptor phase. SDME has been the dominant concentration approach during the reviewed period, usually in combination with a stacking method for enhanced sensitivity. Some novel phases such as ILs have been tested for this purpose. Thus, Wang et al.164 used IL SDME coupled online to CE to quantify trace amounts of phenols in environmental water samples. For SDME, a 2.40 nL IL microdrop was exposed to the aqueous sample for 10 min and then directly injected into the capillary column for analysis; this provided enrichment factors of 107
257 and LODs below 0.05 μg/mL. Electrophoresis-Based Preconcentration Techniques. Stacking allows analytes to be concentrated on a boundary by an effect of a velocity change. CE methods used field-strength stacking [field-amplified sample stacking (FASS), field-amplified sample injection (FASI), large volume sample stacking (LVSS), isotachophoresis stacking (ITP), and counter-flow gradient focusing and electrocapture] or chemical stacking [dynamic pH junction and sweeping with pseudophases] mainly. Sweeping, FASS, FASI, and ITP have so far been the most commonly used for online concentration of environmental samples for CE analysis. Thus, Yang et al.165 used sodium dodecyl sulfate (SDS) micelle-based sweeping-MECK to determine 14 common nitroaromatic and nitramine explosives suggested by the EPA 8330 method in soils and reduced the LODs obtained with traditional MEKC from 1.5
2.9 μg/mL to 3.1
6.5 ng/mL. Noh et al.166 designed a microchip with gold NPs for the electrochemical detection of phenolic endocrine disruptors. Gold NPs were introduced in the micellar buffer (containing SDS) for improved stacking (both FASS and FASI) and separation performance. Separation was completed within 150 s, and detection limits for real samples spanned the range 11.1
7.1 fM. An increasing trend in environmental analysis over the last two years was the combination of several stacking method (commonly used alongside SPE) concentrations to improve sensitivity. Thus, electrokinetic supercharging (EKS), which combines FASI and subsequent ITP and is one of the most powerful online preconcentration techniques, was recently used to determine hypolipidemic drugs in environmental waters by CE
MS.167 Using EKS increased the sensitivity 1000-fold over conventional injection and provided LODs of 180 ng/L. Detection Systems. Optical methods, in general, and UV spectrophotometry, in particular, continue to be the most widely used for environmental analysis in EC. Other spectroscopic methods based on laser-induced fluorescence (LIF) and, to a lesser extent, chemiluminescence and phosphorescence, have also been used in CE in the last two years. The development of more sensitive and selective detection methods remains a priority in CE research. Chen et al.168 designed a microfluidic chip device containing immobilized quantum dots for the determination of organophosphorus pesticides by MECK
LIF. The method was based on the highly sensitive and selective fluorescence 4598

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Analytical Chemistry enhancement of water-soluble CdTe/CdS core
shell quantum dots (QDs) by organophosphorus pesticides (mevinphos, phosalone, methidathion, diazinon). The method enabled the use of a simple pretreatment in complex samples of vegetables based only on solvent extraction as an alternative to commonly used timeconsuming off-line SPE. The detection limits thus obtained ranged from 50 to 180 mg/kg, and repeatability ranged from 0.36 to 0.75% in migration times and from 2.9 to 5.7% in peak heights. Electrochemical detection (amperometry, mainly) continues to be commonly used in CE for environmental analysis by virtue of its low cost, high sensitivity, and miniaturizability. A number of CE amperometric methods using the traditional format or a microchip have been reported over the last two years for a variety of environmental contaminants such as phenolic xenoestrogens, mycotoxins, aromatic amines, mercury, methyl mercury, carbamate insecticides, and organophosphorus pesticides. Coupling CE with MS combines the excellent separation capabilities of CE and the power of MS for analyte identification and structure elucidation, thus providing an effective alternative to LC
MS for the trace analysis of contaminants. Efforts at expanding the popularity of CE
MS continue to focus on the construction of effective interfaces. Recent applications of CE
MS to environmental contaminants (e.g., azo dyes, pesticides, drug residues, nitroaromatic compounds) were recently published.169 Ruggedness problems in this context, which can restrict qualitative and quantitative applications, are usually a result of oscillations in migration times due to electroosmotic flow fluctuations and/or the lack of thermostating of the capillary section linking the CE instrument to the MS source. Variables such as the sheath liquid flow rate, nebulizing gas pressure, and the capillary outlet position must be carefully adjusted in order to obtain stable electrospray conditions and hence reproducible quantitative results. The sheath
liquid interface is still the most widely used design here on account of its ease of use and robustness; however, a sheathless or low-flow interface may be preferred for lower LODs. Special attention has been given to the development of novel electrolyte systems, both volatile and amenable to CE
MS, as well as to the use of alternative interfaces to avoid the typical restrictions of ESI regarding the spectrum of “allowed” electrolyte systems. Approaches based on a liquid junction or partial filling have frequently been used to prevent the surfactants used in MEKC and chiral selectors used in chiral CE to enter the ESI source in order to avoid compatibility problems with the MS detector. The development of miniaturized devices and novel spraying units to improve sensitivity and the combination of MS detection with coated capillaries or capillaries including sections for sample pretreatment constitute growing research trends in the CE
MS analysis of environmental contaminants. The most widely used CE
MS analyzer for environmental samples continues to be the IT or Q IT. On the other hand, the use of high performance MS detectors (TOF, Q TOF) is still limited to metabolomic and proteomic analysis but might play a prominent role in future environmental screening applications. Integrated Chemical and Biomonitoring Strategies. Integrated approaches based on chemical and biological methods are necessary for the identification and assessment of major target and unknown contaminants in complex environmental samples on the basis of adverse effects and exposure. The major goal in this active area of research is to unravel cause
effect relationships for reliable risk assessment of environmental pollution. The EU Water Framework Directive (WFD) has deemed integrated

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chemical and biomonitoring strategies indispensable tools with a view to ensuring a good ecological status in European river basins by 2015. The recent EU project MODELKEY (Models for Assessing and Forecasting the Impact of Environmental Key Pollutants on Marine and Freshwater Ecosystems and Biodiversity) relies on the development of novel methods based on combined chemical and biological strategies for integrated assessment of pollution as specified in the subproject KEYTOX (http://www.modelkey.ufz.de). The last workshop on the EU project Norman, which is established as a permanent network of reference laboratories and research centers for the monitoring and biomonitoring of emerging environmental substances (http://www.norman-network.net), gathered over 100 participants from 19 different countries and different fields of expertise and focused on existing protocols and the development of integrated chemical and biological strategies for incorporation into WFD legislation. Two powerful integrated chemical and biological strategies are already well established, namely, toxicity-identification evaluation (TIE) and effect-directed analysis (EDA). TIE originated from effluent control in a regulatory context (US EPA) whereas EDA was developed by the analytical chemistry research community to identify unknown hazardous compounds in various environmental matrixes. TIE relies on standardized protocols for waters, soils, and sediments involving in vivo testing and compounds or fractions causing acute toxicity only. In a broader perspective, EDA applies both in vitro and in vivo tests and is aimed at identifying potentially hazardous contaminants at concentrations even lower than those causing acute toxicity; therefore, it requires effective extraction and concentration procedures and coupling with sensitive, selective detection methods. Both approaches are based on a three-step procedure involving (i) toxicity characterization of the sample extract by biotesting, (ii) identification of toxic compounds with suitable analytical techniques (GC/MS, ICPMS) and (iii) confirmation of the origin of toxicity with biotests comparing the activity of individual and standard mixtures of the compounds identified with the same bioassay (Figure 7). EDA involves stepwise separation and simplification of the sample until toxic (or active) fractions have been purified sufficiently to allow in-depth chemical identification at low levels. A special issue of the journal TrAC published in two consecutive months (May and June 2009) reviewed advances in combined methods integrating analytical chemical analysis and biological effects for the assessment of pollution in environmental and food samples. Also worth special note here is a recent review by Hecker et al.170 of the state of the art and future challenges in EDA for the assessment of aquatic ecotoxicology. The analytical identification of the trace contaminants detected by EDA remains a challenging task. Studies in this context have focused on priority and well-known environmental pollutants (e.g., PAHs, pharmaceuticals, PCBs, PCDD/Fs, pesticides), as well as on emerging compounds [e.g., PFCs, substituted phenols, natural or synthetic estrogens and androgens, dinaphthofurans, 2-(2-naphthalenyl) benzothiophene, N-phenyl-2-naphthylamine]. Some contaminant classes previously not included among priority contaminants are now considered hazardous. Thus, W€olz et al.171 found the high molecular weight PAHs (more than 16 aromatic C-atoms) of primary ecotoxicological concern to be the compound class with highest activity in causing antiandrogenic-related effects in samples of particulate matter collected during a flood event in rivers in southwestern Germany; in contrast, priority 4599

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Analytical Chemistry

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Figure 7. EDA scheme.

EPA-PAHs were modest contributors to contamination in the studied rivers. The biological assays used in this context focus on the determination of specific toxic activities [particularly reporter genes (e.g., AR CALUX for androgenic and antiandrogenic activities)] or standard overall toxicity (D. magna, V. fisheri). The main focus in the last two years has remained on testing endocrine disruption effects; however, other toxic activities such as genotoxicity have also been addressed in some studies. Below are discussed major research trends in EDA during the period 2009
2010 with regard to new cleanup or fractionation methods, bioavailability and advances in analytical identification techniques, and related computer tools. Extraction. Solid environmental samples (sediments and soils, mainly) are usually subjected to conventional solvent extraction or PLE, and water samples are subjected to SPE. Special attention is always given to the sampling procedure previously used in each case. Alternative strategies for water samples such as SBSE based on PDMS-coated materials have also been developed during the last two years.172 Extracting contaminants from sediments requires taking into account their variable bioavailability for providing a hazard-based prioritization of fractions and toxicants. Using exhaustive solvent extraction steps and dosing the extracts and fractions in organic solvents to biological tests, the total amounts of sediment-bound toxicants are assumed equal irrespective of their physicochemical properties and partitioning behavior; this often results in overestimation of the hazards due to lipophilic contaminants. The development of new EDA dosing techniques based on more realistic exposure scenarios for sediment samples remains a major challenge which has been addressed with new approaches in the last two years. Thus, Bandow et al.173 proposed the use of silicone rods (SRs) in a growth inhibition test with the green alga Scenedesmus vacuolatus in order to simulate partitioning of the contaminants in the sediment
water
biota system. SRs combine fast equilibration for a broad range of compounds and a high carrying capacity in the solid phase; this results in rapid compensation for losses through adsorption, volatilization, transformation, and uptake by

test organisms and provides constant concentrations during biotests. As with PDMS stir bars, silicone/water partitioning coefficients were well correlated with log KOW, which suggests reasonably accurate simulation of partitioning processes in sediments. Despite the claim of Bandow et al. that some sediment characteristics (viz., sequestration, black carbon contents, and H-donor and acceptor properties) cannot be simulated with silicone, partitioning processes in sediments were better simulated with silicone partition-based dosing than with organic solvent dosing and a clear shift in toxicity ranking of fractions was observed when comparing the two approaches. In a later study,174 the previous authors reaffirmed that using partition-based dosing had a strong effect on the identification of pollutants and noted that polar compounds such as triclosan behaved as key toxicants, whereas PAH fractions exhibited no significant effects, which contradicted the results obtained with conventional dosing. Cleanup and Fractionation. Cleanup and, especially, fractionation, constitute critical steps in EDA since they simplify the subsequent identification and confirmation of analytes, and allow for accurate toxic assessment [e.g., the presence of androgenic agonists may be masked by that of androgenic antagonist in the same extract175]. Complex samples are usually cleaned up by SPE or GPC and fractionated by column chromatography; a combination of RP-LC and NP-LC is often used. Recently, preparative capillary GC (pCGC) was tested, and trapping parameters were optimized for the fractionation of various contaminants including PAHs, phenols, and pesticides, as an alternative to preparative LC for EDA based on the increased resolution of GC.176 The solvent-filled trap approach proved more suitable than temperature-controlled trapping for EDA as it required no individual optimization of trapping parameters. Also, dichloromethane was the most suitable solvent for a broad range of compounds. Temperature-controlled trapping was suggested to be useful as a secondary fractionation step after LC for the separation of close contaminants or isomers based on vapor pressure. With this purpose, the same authors proposed a pCGC additional fractionation step following reversed-phase LC for EDA of genotoxicants in contaminated groundwater.177 4600

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Figure 8. Extraction, stepwise cleanup, and fractionation protocol for removing coextracted lipids and natural hormones from fish tissues for testing endocrine-disrupting activity by EDA. Reprinted from ref 178. Copyright 2010 American Chemical Society.

Besides abiotic environmental compartments, the analysis of biota by EDA is an increasing trend for testing bioavailability, bioaccumulation, and potential metabolization of contaminants, and the development of new, nondestructive protocols for in vitro testing is highly desirable. Simon et al.178 proposed a cleanup method for removing coextracted lipids and natural hormones from fish tissues in order to test the endocrine-disrupting capacity of their extracts by in vitro bioassay (Figure 8). Spiked and unspiked fish tissues were cleaned with a combination of dialysis, GPC, and NP-LC. Acceptable recoveries (89 ( 8% on average) were obtained for a broad range of environmental pollutants including EDCs and genotoxic compounds. Analytical Identification/Confirmation of Toxicants. GC/ MS spectra, together with retention times, constitute a common analytical starting point for the identification of thermally stable compounds by virtue of their easy recording and comparability with standardized spectral libraries or pure standards for identification. A number of LC
MS methods have recently been developed for EDA, and confirmation of nonvolatiles can be achieved based on mass accurate TOF or Orbitrap detectors and those providing broad spectra information trough LC
MSn techniques (ion trap and hybrids QTRAP), mainly by comparison with neat standards. Thus, Schriks et al.179 recently used LC-high-resolution Orbitrap MS/MS to identify contaminants causing high levels of glucocorticogenic activity by CALUX bioassay. For compounds with nonspecific mass spectra or no available standard, additional information such as UV and IR absorption spectra or capacity factors on different LC columns can significantly enhance the evidence of confirmation.

Comparing spectrometric data with spectral library data is a common approach today; however, libraries do not contain every possible compound and may thus fail to identify unknowns. An attempt at developing a generally accessible database of chromatographic and spectrometric data for newly identified key environmental pollutants generated under agreed standard conditions is currently under way in the EU project MODELKEY. Recent developments for improving structure elucidation in EDA and studies based on database searches and structure generation including integration of classifiers have recently been reviewed.180 Field Methods. Sensors and biosensors are playing an increasingly prominent role in screening methods and early warning systems for environmental monitoring thanks to their intrinsic ability to detect target analytes online in real time in distributed systems. Also, they provide further advantages such as a low cost, fast response, and usefulness as remote systems. However, their use in pollution control is still in its infancy. In fact, only a few environmental sensors are commercially available and ready for work under real field conditions with acceptable selectivity, ruggedness, long-term stability, and ease of calibration. Two broad-scope reviews of new trends and challenges in environmental sensing181,182 were published during the period 2009
2010. There were also several general reviews dealing with recent fundamental advances in electrochemical sensors183 and optical biosensors.184 The use of biosensors constitutes a major expanding area where efforts have focused lately on enhancing the specificity inherent in biological binding. Electrochemical (potentiometric and voltammetric) and optical transducers 4601

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Analytical Chemistry (mainly based on surface plasmon resonance) remain in wider use than mass-sensitive and thermal devices by virtue of their increased sensitivity. There were four major emerging research trends in environmental sensing over the period 2009
2010, namely, (a) the use of genetically engineered advanced biological receptors; (b) that of nanotechnology-based biosensors as synergistic hybrid devices; (c) the development and use of biosensor arrays for multiresidue contamination analysis; and (d) advances in environmental sensor networks. Genetically Engineered Biosensors. The use of biological receptors to construct highly selective sensors has increased sharply over the last two years. The bioreceptors used for environmental sensing are usually enzymes, antibodies, peptides, DNA, or whole cells. Genetically engineered bioreceptors (DNA, proteins, peptides, and microorganisms, mainly) have attracted much interest and found wide use in the reviewed period. Recent advances in the use of genetically encoded biosensors have been the subject of several reviews from 2009 to early 2011.185
189 The use of aptamer-based biosensors, also called aptasensors, has grown steadily since their inception about ten years ago. Nucleic acid aptamers are oligonucleotides ligands (DNA or RNA) selected from random nucleic acid libraries by an in vitro iterative process of adsorption, recovery, and reamplification named SELEX (systemic evolution of ligands by exponential enrichment). They have been termed “chemical antibodies” on the grounds of their binding affinity for the target, which can range from small molecules to macromolecules, cells, or tissues. Aptamer-based biosensors are gaining ground against immunosensors, owing to the limitations of the antigen
antibody response to aqueous environments within limited pH, temperature, and ionic activity ranges. Also, they are small in size, costeffective, and remarkably flexible in design. Proteins and polypeptides produced by phage display have also been used as recognition elements. The phage display technique uses recombinant DNA technology to create bacteriophages with a desired polypeptide embedded in the surface of their protein shells. Polypeptide libraries are thus created and then screened and purified to select the virions carrying the desired receptor. The development of coating materials containing a high density of specific receptors with intact binding efficacy is the critical step in the development of specific, sensitive biosensors and constitutes an active area of research. One other important topic is the development of label-free transduction methods to facilitate the implementation of sensors. Label-free sensing formats (e.g., quartz crystal microbalances, surface plasmon resonance, micromechanical cantilevers) have enabled reagentless, one-step analyses. Cerruti et al.190 reported a genetically engineered biosensor fulfilling these two objectives. They immobilized a high density of peptide receptors against the explosive trinitrotoluene onto a polymeric matrix consisting of poly(ethylene-co-glycidyl methacrylate). Small-sized, chemically robust oligopeptides with a high affinity were obtained by phage display. The receptors were covalently linked trough chemical reaction of amino groups present in the receptor with epoxy groups present in the copolymer. Selective detection of trinitrotoluene vs dinitrotoluene in waters was demonstrated using a quartz crystal microbalance as a label-free sensing platform. Novel bioinspired structure-switching biosensors have arisen from the use of proteins or nucleic acids undergoing reversible binding-induced changes in conformation or oligomerization to transduce chemical information into specific biochemical outputs. As in nature, they can operate in complex environments;

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also, signal transduction is fast, reversible, and reagent-free, which enables real-time detection. Biomolecular switches are quite flexible and afford transduction via changes in fluorescence emission for optical detection, electron transfer for electrochemical detection, or biochemical (catalytic or binding) activity; also, they can be engineered into a variety of biomolecules spanning a wide range of binding specificities. They have been widely used to monitor drugs and metabolites in biological samples but comparatively less so for environmental monitoring purposes. A structure-switching sensor for detection of toxic metals including As (III), Cd, and Pb was recently developed for quantifying the degree of association/dissociation of transcriptional factors; the sensor consisted of DNA immobilized on a microplate and genetically modified green fluorescent proteins obtained from transgenic bacterial cell lysates.191 Fluorescence intensity in the wells decreased in a dose-dependent manner in response to the presence of metals and enabled their quantitation. The development of genetically engineered microorganisms (particularly bacteria) was one other active area of research over the period 2009
2010. The large population size of microorganisms and their rapid growth rate, low cost, and easy maintenance provide a cost-effective choice for pollution monitoring. Rather than targeting specificity, this type of biosensor relies on bioavailability, toxicity, and genotoxicity, which cannot be probed with molecular recognition or chemical analysis. Transgenic whole cells are more selective than earlier microbial biosensors, which used the respiratory and metabolic functions for detection, the target being either a substrate or an inhibitor of the process. Microbial biosensors are genetically engineered with specific regulated metabolic pathways providing enhanced selectivity for specific targets. Electrochemical and, especially, optical transducers, have been used for this purpose. Genetic modifications in this context usually involve including genes encoding luciferase or a green fluorescent protein for bioluminescent and fluorescent sensors, respectively. Recent advances in this field include the expansion of available reporter functions (e.g., multicolored fluorescent proteins), broadening of the detected chemical effects (e.g., nutrient availability, specificity for targets), and enhancement of the spectrum for reporter microorganisms (cyanobacteria, yeasts, and fungi). More importantly, the stage has been set for the incorporation of such cells into various whole-cell array formats that provide multicontaminant discrimination or the use of nanotechnology to improve the sensor performance in terms of sensitivity, selectivity, and miniaturization. Nanotechnology-Based Biosensors. The emerging synergy between nanotechnology and sensors has been explored and successfully exploited over the past few years. Reducing the size of a system to the nanometer scale elicits a number of unusual physicochemical phenomena such as pronounced changes in thermal and optical properties, enhanced reactivity and catalytic activity, faster electron/ion transport, negative refractivity, and novel quantum mechanical properties. These changes have been demonstrated in a number of nanomaterials used to improve sensor selectivity and sensitivity. Special attention has been given to the use of CNTs and label-free optical sensors based on gold NPs and QDs. The use of graphene, the most recent member of the multidimensional carbon nanomaterial family for biosensing, in this context has grown very rapidly; its earliest applications were reported in 2008. The properties, potential, and major advances in graphene-based electrochemical sensors were recently discussed.192 The suitability of graphene for electrochemistry 4602

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Figure 9. Nanotechnology-based biosensor made of a thymine-rich, Hg2þ-specific oligonucleotide (MSO) probe immobilized onto gold NPs. The MSO probe contains seven thymine bases at both ends and a “mute” spacer in the middle, which, in the presence of Hg2þ, forms a hairpin structure via the Hg2þ-mediated coordination of T- Hg2þ-T base pairs as shown in the figure. Reprinted from ref 196. Copyright 2009 American Chemical Society.

relies on its high 2D electrical conductivity and large surface area; in fact, graphene provides substantial advantages over carbon nanotubes including the lack of impurities affecting electrochemical performance (sensor responsiveness) and inexpensive production from graphite. Trends in this field over the period 2009
2010 focused on the development of hybrid nanotechnology-based biosensors by combining principles of materials science, molecular engineering, chemistry, and biotechnology to construct highly sensitive and selective devices. The most salient advances in biomolecule
nanoparticle hybrid electrochemical biosensors193 and nanomaterial-assisted optical aptasensors194 were recently discussed. These integrated, synergistic systems interface the recognition and catalytic properties of biomolecules with electronic, optical, magnetic, and catalytic properties of nanomaterials. Kang et al.195 recently proposed a label-free immunosensor based on a multiple hybrid CdSexTe1-x nanocrystal/modified TiO2 nanotube array structure which they used for the photoelectrochemical detection of the priority pollutant pentachlorophenol. Photoelectrodeposited nanocrystals on the inner and outer space of the nanotubes boosted the photoelectrical conversion efficiency in the visible region. The immobilization of the bioreceptor on the nanomaterial surface, which can be

accomplished through covalent or noncovalent bonding, is a key step in the process. The unique properties of nanomaterial surfaces provide facile ways for bioconjugation. In this study, pentachlorophenol antibodies were easily covalently conjugated on TiO2 nanotubes thanks to their large surface area and good biocompatibility. Since the photocurrent was highly dependent on the TiO2 surface properties, the specific interaction between pentachlorophenol and antibodies resulted in a sensitive change in photocurrent that provided a limit of detection of 1 pM. Zhu et al.196 reported a highly sensitive electrochemical sensor for field detection of Hg2þ ions in environmental waters using a thymine (T)-rich, mercury-specific oligonucleotide (MSO) probe and Au NP-based signal amplification. Au NPs are comodified with the MSO probe and a linking probe that is complementary to a capture DNA probe immobilized on gold electrodes (Figure 9). The Au NP-based sensing strategy brought about an amplification factor of more than 3 orders of magnitude with respect to direct immobilization, leading to a limit of detection of 0.5 nM (100 ppt). Nanocomposite materials made of conducting polymers and nanoparticles have also been exploited for the development of biosensors as they exhibit exceptional electrical and optical properties compared to conducting polymers or individual metal 4603

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Analytical Chemistry nanoparticles. A simple, sensitive electrochemical DNA aptasensor with a high affinity for endocrine disrupting 17β-estradiol was recently developed from a composite of poly(3,4-ethylenedioxylthiophene) doped with Au NPs.197 The nanocomposite exhibited a high conductivity and enhanced catalytic properties relative to the bare electrode. Streptavidin was covalently attached to the platform to make the immobilization of the biotinylated aptamer via streptavidin
biotin interactions. The aptasensor successfully discriminated 17β-estradiol from structurally similar endocrine disrupters and provided a detection limit of 0.02 nM. Sensor Arrays. Complex environmental data requires broad sensor arrays capable of extracting a wide variety of information for subsequent chemometric processing. This principle has been used to develop so-called “electronic noses” and “electronic tongues” for pollution control of gases and waters. Like their biological counterparts, electronic noses and tongues use an array of modestly selective sensors that collectively generate a response pattern from which a range of analytes can be identified by means of pattern recognition software. The ability to detect a wide range of analytes is closely related to the stability of the sensor response over time, the breadth of the stored response database, and the recognition capabilities of the pattern recognition algorithm used. Recent advances and applications of potentiometric sensor arrays198 and electronic tongues199 and noses200 have been discussed. Over the last two years, new technologies intended to overcome selectivity and sensitivity limitations with portable, miniaturized, low-power devices have emerged. Also, there has been a trend to use artificial intelligence and information visualization methods for data processing. Colorimetric methods and fluorescent or conductive polymers are favored for transduction. Aernecke et al.201 recently reported an electronic nose for the simultaneous detection of twenty common gas pollutants below their permissible levels after only 2
5 min of exposure. The sensor response was unaffected by changes in humidity or temperature, and the sensor exhibited a long shelf life (more than 3 months). The selectivity of the colorimetric sensor array relied on a response based on chemical interactions between the dye and analyte rather than on their effects on secondary physical properties (e.g., mass, conductivity, adsorption). Four dye classes were tested, namely, dyes containing metal ions (e.g., metalloporphyrins), which respond to Lewis basicity; pH indicators, which respond to Br€onsted acidity/ basicity; dyes with large permanent dipoles (e.g., vapochromic or solvatochromic dyes), which respond to local polarity; and metal salts taking part in redox reactions. Chemically responsive nanoporous pigments were created by immobilizing dyes in organically modified siloxanes (ormosils). These nanoporous materials improved the performance, stability, and ease of manufacturing of the arrays by effect of their high surface area, relative inertness, good stability over a wide range of pH, and optical transparency. The attractive qualities of nanostructured and/or sensor arrays incorporating biological or artificial recognition elements were recently demonstrated. Nanomaterials can be integrated into array sensing platforms in order to increase the sensitivity to the target agents. Also, they can provide different sensing mechanisms for discrimination based, for example, on semiconductor materials exhibiting a disparate redox response upon exposure to specific chemicals [e.g., a combination of metal oxide nanowires or nanoparticles and carbon nanotubes;202 see Figure 10]. Also, recognition receptors have helped to meet the primary goal in developing sensor arrays, namely, to provide a number of

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different binding affinities for a variety of target analytes. In addition, the array format helps offset cross-reactivities after the chemometric processing of data. Tan et al.203 recently designed an ion imprinted mesoporous silica-based fluorescence sensor array for discriminating metal ions. The functional monomer was made fluorescent by appending an 8-hydroxyquinoline moiety. With the covalently anchored organic fluorophore in the inorganic mesoporous silica matrix (prepared by conventional onepot co-condensation), the binding of Zn2þ and Cd2þ to the imprinting sites was directly transformed into fluorescence signals. The cross-responsive nature of the sensor array allowed not only the templates (Zn2þ and Cd2þ) but also structurally related species (Mg2þ, Ca2þ, and Al3þ) to be detected and subsequently discriminated by appropriate processing of the results. Cross-reactivity to structurally related species which are highly similar to the template in size, shape, and functionality is invariably encountered and difficult to avoid in molecular imprinting; this makes imprinted sensor arrays potentially suitable for discriminating highly similar analytes such as metal ions. Environmental Sensor Networks. Environmental sensor networks (ESNs) have emerged at the highest complex level of information in long spatial and temporal scale sensing. This promising technology has arisen from developments in sensing, wireless communications, and computing technologies. Environmental sensor networks comprise an array of spatially distributed, autonomous sensor nodes and a communications system which allows one to cooperatively pass their data through the network to a server. The variety of data collected by sensor nodes (digital and analogue, spatial and temporal, alphanumeric or image, fixed or moving) can then be viewed and analyzed within a Geographic Information System (GIS) combined with a satellite image and/or map and made globally available via the Web. Unlike remote sensing, ESNs typically refers to sensing done in close proximity to the target. Sensors are specially suited for ESNs since they can be used for real time monitoring and deployed in locations that are difficult to access; in addition, they are more economical and less demanding in terms of energy consumption. Extracting data gathered by sensor nodes in remote locations involves some unique challenges such as their miniaturization to obtain unobtrusive devices that are economical to operate over long periods and rugged enough for reliable use in harsh outdoor environments. Current ESNs platforms are mainly used to study processes in the environment via measurements of certain parameters. Thus, light levels, air temperature, humidity, soil temperature, and soil moisture are frequently monitored to assess habitat quality; pH, conductivity, dissolved oxygen, and chlorophyll sensing are used to measure water quality, and priority gases measurements (e.g., CO, NO, NO2, O3) are programmed for air quality monitoring. The use of different types of sensors, communication protocols, and data processing methods for sensing with ESNs in different environmental domains, including terrestrial vegetation, animal movement, biodiversity, and soil204 and water quality in terms of salinity and water mass balances in wetlands,205 was recently discussed. A recent review describes some examples of global environmental sensor networks currently in operation by some public institutions and major commercial sensor network systems and discusses the main platforms and challenges to the use of ESNs in ecological research.206 Recent technical advances in ESNs include hardware miniaturization, development of low-power or energy-harvesting sensors, and the use of wireless communication (e.g., 3G) 4604

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Figure 10. Hybrid nanowire/carbon nanotube gas sensor array with integrated micromachined hot plates: (A) photograph of a finished chemical sensor chip; (B) field emission scanning electron microscopy and optical microscopy images of the sensor array composed of four different sensor chips; (C) representative sensor array response as bar graphs to ethanol, hydrogen, and NO2. (D, E) PCA scores and loading plots of the chemical sensor array composed of 4 different nanostructure materials (D) and only 3 metal oxide nanowires (E) operated at different concentrations and temperatures. Reprinted with permission from ref 202. Copyright 2009 Institute of Physics.

networks. Computer sciences are playing a crucial role in the development of ESNs, and much research in this area in the last two years has focused on it. Progress in new communication architectures, algorithms, and protocols in wireless sensors networks207 and multimedia wireless networks (multimedia devices capable of retrieving video, audio, images, and scalar sensor data)208 were recently discussed. The use of nanotechnology-based sensors to develop sensitive, selective sensors of low-power operation constitutes a recent trend. Thus, Choi et al.209 designed microplatform gas sensors based on mixed SnO2 nanoparticles and multiwalled carbon nanotubes for NO2, NH3, and xylene detection. The good performance of the sensor at relatively low temperatures (220 °C) and its miniaturized format resulted in good sensitivity

and selectivity at a low power operation (below 30mW). Selfpowered nanosensors capable of harvesting energy from the environment have been proposed. Nanogenerators harvesting random mechanical energy from the environment and transforming it into electric energy were recently designed using piezoelectric zinc oxide nanowire arrays. The main advantages of the use of nanowires are that they can be triggered by tiny physical motions and that their excitation frequency can range from one hertz to several thousand; all this makes them ideal for harvesting random environmental energy, which encompasses a broad spectrum of frequencies and time-dependent amplitudes. Also, thousands of nanowires can be integrated to supply the required power. Progress made in this field was recently reviewed and includes contributions published in 4605

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Figure 11. Sensor Service Arquitecture (SensorSA) of SANY EU Project.

high impact journals such as Nature Nanotechnology, Nano Letters, and Advanced Materials.210 Although some major drawbacks hindering broader application of ESNs remain unsolved, this promising technology is expected to greatly facilitate monitoring of the natural environment and ESNs to act as effective warning systems in the near future. With this purpose, the recent EU Integrated Project Sensors Anywhere (SANY; http://www.sany-ip.eu/) deals with improving the interoperability of in situ sensors and sensor networks for environmental applications. With this aim, a generic open Sensor Service Architecture (SensorSA) is established for allowing quick and cost-efficient reuse of data and services from currently incompatible sources in future environmental risk management applications (Figure 11). Making observations from sensors available in a more readily, widespread, and interoperable fashion helps to improve our understanding of environmental processes and also supports the development of fusion, interpretation, and visualization tools that provide the base for well informed, improved decision making. Three innovative risk management applications are developed within this project, covering the areas of air pollution, marine risks, and geo hazards.

’ CALIBRATION The wide variety and high complexity of environmental matrixes and the low concentration of the target contaminants often interfere with their accurate quantitation, especially when simple high-throughput methods and low selective detectors are used. Internal standard (IS)-based calibration, matrix-matched calibration, standard addition calibration and second-order multivariate calibration are the most widely used calibration strategies for ensuring interference-free quantitation in environmental analysis. IS-based calibration is a frequent choice with MS detectors (LC
MS systems, mainly) to correct ionization matrix effects leading to suppression or enhancement of the analyte signal. Adding an IS in the first stage of sample preparation helps offset

potential losses during other stages of the analytical method (e.g., extraction, cleaning, evaporation to dryness). The importance of the use of isotope-labeled substances for the analysis of persistent organic pollutants in environmental samples was recently reviewed.211 The use of isotope-labeled IS, when commercially available, improves the results in terms of accuracy and precision, as highlighted in the second international laboratory study on perfluorinated compounds in waters and fish.212 The main drawbacks of the use of isotope-labeled IS are their high price and still limited availability; the most readily available are those for PCBs, PCDDs, PCDFs, PBDEs, PFOS, PFOA, and PAHs. New ISs for some environmental contaminants have been tested [e.g., the use of Bi3þ for quantifying the mercury species Hg2þ and CH3Hgþ in fish by ICPMS213] or synthesized [e.g., 13C ergosterol produced by Saccharomyces cerevisiae grown on 13C Dglucose and used to analyze ergosterol, a marker for monitoring environmental exposure to fungi214] during the reviewed period. Several papers dealing with common errors in the IS-based quantitation of environmental samples have been published over the last two years; the ensuing recommendations included using an amount of IS similar to that expected for the analyte and facilitating connection with the matrix in the same way as the analyte in order to allow the extraction of both with identical efficiency.215,216 Chemometric techniques for calibration are now sufficiently developed and gaining widespread acceptance for quantitation of environmental pollutants. Unlike zero- and first-order calibration, second- and higher-order data afford the accurate quantitation of analytes in the presence of unknown interfering agents, that is the so-called “second-order advantage”. One major advantage of chemometric treatments is the possibiliy to analyze complex environmental samples by direct injection or with a simple pretreatment and the use of a simple, sensitive, and lowcost detector of the UV, FL, or NIR type. A recent review217 of the uses of chemometric methods over the past decade included a detailed section dealing with multivariate calibration for the quantitation of organic environmental pollutants. Research in 4606

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Analytical Chemistry this field over the last two years has comprised the development of chemometric methods for quantifying pollutants based on spectroscopic or hyphenated chromatographic
spectroscopic techniques aided by the use of an appropriate algorithm. Some representative examples reported during the last two years follow. Chemometric approaches with detection based on a colorimetric probe for the analysis of mercury in waters218 and reflectance infrared spectroscopy for the determination of metals in sediments219 were recently developed using multivariate partial least-squares calibration. Piccirilli et al.220 proposed the use of a spectrofluorimetric optosensor coupled to a flow injection system for the simultaneous determination of two widely used fungicides by employing parallel factor analysis (PARAFAC) and unfolded and multidimensional partial leastsquares coupled to residual bilinearization (U- and M-PLS/RBL) for data processing. Second-order multivariate calibration has been applied to hyphenated LC coupled to UV(DAD) or fast scanning fluorescence to increase the selectivity of analyses using the retention time as an additional dimension. One important point to be considered here is that some chemometric methods require the data to follow the so-called “trilinearity condition”, which states that each chemical component should exhibit a unique profile (both in the spectral dimension and in the retention time dimension) in all samples. Since time retention shifts are common in LC, time-alignment techniques or the use of more flexible algorithms such as multivariate curve resolution
alternating least-squares (MCR
ALS) and parallel factor analysis 2 (PARAFAC2) are usually required. With this aim, Bortolato et al.221 used both MCR-ALS and PARAFAC2 to calibrate ten PAHs that were determined by LC coupled to fast scanning fluorescence spectroscopy. Each chromatographic determination was performed under isocratic conditions and accomplished in less than 7 min. Other recently reported approaches based on LC
UV(DAD) detection are discussed below. Thus, Martínez-Galera et al.222 successfully used MCRALS in combination with standard addition calibration and matrix background correction by the Eilers methodology to cope with overlapping peak, systematic (additive) and proportional (matrix effect) errors in the analysis of pharmaceuticals in river water samples by LC
UV(DAD). Li et al.223 proposed the alternating penalty trilinear decomposition algorithm for the analysis of triazine herbicides in soils, sediments, and wastewater by LC-UV (DAD). Higher-order multivariate calibration models constructed with techniques such as multidimensional chromatography or kinetic tests have also been proposed for environmental analyses. Some effort has focused on developing effective quadrilinear algorithms for each particular application. Thus, Maggio et al.224 processed four-way data obtained by recording the kinetic evolution of excitation
emission fluorescence matrixes for samples containing the pesticides carbaryl and 1-naphthol using U-PLS combined with residual trilinearization. The algorithm provided figures of merit similar to those of classical parallel factor analysis but was considerable simpler in its computer implementation.

’ ENVIRONMETRICS Environmetrics, which is concerned with the development and application of quantitative methods in the environmental sciences, has become an essential tool for understanding, predicting, and controlling the impact of pollutants on the environment. The need for environmetrics has arisen from the fact that

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available information on the environment often includes appreciable measurement uncertainty and large spatial and temporal variability and also from it being contained in very large, complex databases, which makes it rather difficult to extract clear, reliable, environmentally significant information. The topics commonly addressed by environmetrics include the distribution of pollutants and its associated factors, the relationship between pollution and human health, the identification and apportionment of sources, and the effectiveness of mitigating measures. These topics are usually addressed by both geostatistics and chemometrics. One major reference in this context is Environmetrics, the official journal of the International Environmetrics Society, which is available online at http://www.environmetrics.org/home.html. The papers in this section have been selected with the aim of expounding a variety of conceptual frameworks, powerful methods, and comprehensive techniques used to address a number of interesting problems on which researchers focused over the period 2009
2010, namely, time series and left-censored data analysis, spatio
temporal modeling, the impact of air pollution on public health, and searching for structure in data. Time Series Data. High-resolution time series data can now be routinely obtained as a result of major sensitivity and automation improvements in environmental monitoring methods. The alignment of events in simultaneous time series probably remains the largest, least comprehensively addressed topic in this area. A three-step method for the analysis of environmental time series in cases where nonlinear time alignment is anticipated was recently proposed.225 In the first step of the process, a simple, robust propagation procedure for cases where data are filed as event lists rather than a standard time series is used. This is followed by a correlation optimized warping procedure to warp the aligned time series. Finally, potential time series features are identified using classical differentiation. The method was illustrated by analyzing routine aircraft takeoff time records and local ambient air concentration time series for nitrogen oxides collected at Heathrow airport. Harmonic regression analysis has long been the standard for dealing with the ubiquitous periodicity in environmental time series. However, generalized additive models (GAMs) afford more flexibility in the response function since they accept parametric, semiparametric, and nonparametric regression functions of the predictor variables. A comparative study of the use of harmonic regression, GAMs with cubic regression splines, and GAMs with cyclic regression splines in simulations and water quality data from the US National Estuarine Research Reserve System (NERRS) was reported.226 While the classical harmonic regression model worked well with clean, near-sinusoidal data, GAMs proved competitive and highly promising for more complex data. In addition, GAMs are more adaptive and require less intervention. Generalized additive models have also proved useful for analyzing trends in time series. Typically, long series exhibit a nonlinear trend of arbitrary shape in time. One crucial choice here is the degree of smoothness of the trend curve, which can be selected using a number of data-driven methods. Morton et al.227 represented trend as a smoothing spline for easier extrapolation. A method based on the ability to predict a short term into the future was proposed for choosing the smoothing parameter. The choice not only addressed the purpose in hand but also performed very well and avoided the tendency to undersmooth or interpolate data typical of other data-driven methods used to select the smoothing parameter. The ensuing method was applied to 4607

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Analytical Chemistry stream salinity measurements at Eppalock on the Campaspe River in Victoria, Australia. Left-Censored Data. Monitoring data usually involves measuring a combination of quantities above and below a detection limit, the latter being known as left-censored data and for which no quantitative estimate is available. Likelihood methods remain the major statistical tools for calculating summary statistics (e.g., mean and standard deviation) and their corresponding interval estimates (e.g., prediction interval, confidence interval, and tolerance interval) from data including left-censored observations. When more than two different detection limits are reported, the data are said to be multiply left-censored. Multiple left-censoring frequently arises in environmental studies by effect of (a) the adjustment of singly censored laboratory results for physical sample size; (b) the data set being generated over a period during which the analyzing laboratory has changed its detection levels as instruments have gained accuracy; (c) laboratory protocols establishing new LODs; or (d) data being collected from different laboratories using also different LODs. Two new methods for calculating maximum likelihood estimators (MLE) of population parameters from multiply censored data have been proposed. In one,228 a bootstrap procedure based on left-censored samples with multiple censoring limits was developed and used to construct bootstrap confidence intervals for the population parameters of interest in addition to the asymptotic confidence interval. Guidelines for selecting either asymptotic or bootstrap confidence intervals for practitioners were provided. The method was applied to groundwater data from illustrative situations. In the other method,229 maximum likelihood estimating equations were derived that were readily solved using a simple iterative procedure. A new method for calculating MLEs called “New MLEs” was introduced and assessed using custom software developed in R languages. Environmental studies and monitoring programs often collect data that are spatially correlated and left-censored. In order to model spatially correlated censored observations, a previously proposed Bayesian approach to handling spatially correlated data was modified to obtain bias corrected estimates of variance and spatial correlation parameters.230 The ensuing methodology was applied to a water quality data set from the Ecosystem Health Monitoring Program (EHMP) in southeast Queensland, and the results were compared with those from variance estimates uncorrected for bias to show that the latter can lead to unreliable inferences. A simulation study revealed bias corrected estimates of variance and correlation parameters to be less biased than uncorrected estimates of these parameters and also that the credible intervals for the parameters from bias corrected analyses are wider than those from uncorrected analyses. The simulation also suggested that predictions of below-detection values are generally overestimated by both bias corrected and uncorrected analyses but also that the latter predictions are more biased. With regard to predictions of detectable concentrations, the simulations suggested that bias corrected and uncorrected analyses are equally biased and that both underestimate the true values. Spatio
Temporal Modeling. Spatio
temporal modeling has experienced great methodological advances by virtue of environmental variables being generally referenced to both location and time. A number of spatio
temporal analysis techniques including space
time kriging, Bayesian maximum entropy, Bayesian data fusion, information
theoretic analysis, Radonian space transforms, nonBayesian stochastic logic, differential geometric, and space
time diagrammatics are currently available, and useful

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insights into the most appropriate choice for a specific application are being obtained from comparative studies involving the analysis of an environmental data set compiled with different techniques.231 Spatio
temporal modeling should only be applied after the absence of spatio
temporal interactions has been checked; otherwise, the spatial and temporal dependence components can be modeled independently. A new testing technique for detecting separability in the spatio
temporal dependence structure has been reported.232 Separability means that the covariance of the process can be obtained as the tensor product of a spatial covariance and a temporal covariance. An L2-distance test statistic comprising nonparametric estimators of the logspectral density under the separability hypothesis (by marginal integration and backfitting estimators) and under the general hypothesis of spatio
temporal interaction has been proposed. One of the most active research areas in environmental spatio
temporal modeling over the period 2009
2010 was the analysis of air pollution. The goals of these studies included assessing compliance with regulations, predicting pollution levels, homogenizing data monitored on heterogeneous networks, and analyzing extremes. Meteorological factors such as air temperature and humidity or wind speed and direction were commonly considered as covariates together with some additional seasonal factors. A number of studies have also focused on the spatio
temporal modeling of water quality. The primary purposes of these studies were assessing the violation frequencies of environmental thresholds in rivers, managing groundwater quality, establishing the particle size distribution of surface waters, and identifying trends in river monitoring networks. With regard to the distribution of pollutants in soils, a summary of the major methods developed in recent years was included in a recent review of the use of chemometrics to analyze soil pollutants.233 With regard to air pollution, the identification and removal of heterogeneities between monitoring networks has received increased attention as a result of the interest in merging observations from local networks into larger national and international databases. Heterogeneity in networks typically arises from differences in the equipment used for measurement and/or postprocessing of data and can lead to discontinuities complicating the use of the compiled database for international comparisons and mapping. A method in two variants exists for identifying systematic differences between observations in two monitoring networks, either between networks partly sharing a region (typically different networks in one country) or networks separated by regional boundaries (typically between countries).234 The estimated differences can be used to calculate individual biases for each network which can then be subtracted for harmonization. The ensuing method was applied to European gamma dose rate measurements on the European Radiological Data Exchange Platform database made since May 2008. The statistics of extremes in spatio
temporal processes has become one of the most challenging research areas, owing to the increasingly demanding interest on risk assessment tools. Much recent work on the statistical analysis of extreme values has focused on methods for multivariate extremes. However, it is unclear how nonstationary data can be modeled with existing methods. A hierarchical modeling approach to nonstationary multivariate processes was developed by extending the preprocessing method to the analysis of extremes in nonstationary univariate processes.235 Bayesian inference was used and predictions relied on simulations. This methodology was used to predict marginal return levels for NO, NO2, and O3 at a single 4608

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Analytical Chemistry urban location in the UK. Linear autoregressive (AR) methods provide a simple, elegant framework for capturing temporal dependencies in extreme analyses. Chevallier et al.236 linked linear AR processes to the well-established extreme value theory (EVT) in order to construct a linear EVT distributed AR model which expanded the statistical treatment of extreme environmental recordings exhibiting temporal dependencies. The model was fitted to daily and weekly maxima of methane and daily maxima of nitrous oxide, measured in Gif-sur-Yvette (France). Impact of Air Pollution on Public Health. This topic has been addressed in many studies aimed at providing support for the development of public health policies over the period 2009
2010. Quantifying the impact of air pollution on public health is difficult because of the complicated mechanism that triggers respiratory diseases. Semiparametric GAMs have been widely used for this purpose over the past decade on the grounds that the air pollution measurements are usually connected with the health indicator in a parametric fashion while the effects of other covariates are modeled through nonparametric smooth functions. The backfitting-GAM methodology and its popular implementation in S-Plus constitute the standard approach here. Its major limitations have been discussed, and a penalized likelihood methodology, combined with cubic spline smoothers, has been proposed as an alternative to circumvent most of the flaws of GAM for environmental and epidemiological research.237 This methodology allows for a computationally efficient and complete parametric representation of a GAM, either nonparametric or semiparametric. Weather factors (particularly temperature) are important in triggering illnesses. However, similarity in the number of illnesses and weather patterns often leads to distorted severity assessment of ambient air pollutants. Using the multiple index model and separately analyzing data from winter and summer, where the weather is relatively stable, the contribution of air pollutants (as the daily average levels of sulfur dioxide, respirable suspended particulates, oxides of nitrogen, nitrogen dioxide, and ozone over the period 2000
2003) to respiratory illnesses in Hong Kong was estimated.238 When exposure to the target pollutant exhibits strong spatial variability, using a single centrally located monitor, the nearest neighbor monitor or the arithmetic or inverse-distance weighted average of several monitors can lead to substantial bias (underestimation of the effect, usually), even if the true association between exposure and outcome is small and the outcome event is rare. In this situation, it is important to obtain sensible exposure measurements within clearly defined regional units. Although there are some limitations and computational pitfalls associated with staying within a likelihood framework, having many independent replicates of a defined spatial process over time has been shown to enable the reasonably accurate estimation of spatial covariance parameters.239 Having these estimates then allows one to impute subregion exposures that can be effectively incorporated into the health outcome model. This method provides reliable estimates of the association and exhibits good confidence interval coverage for associations of typical magnitudes. The method has been illustrated using nitrogen dioxide data from Atlanta in 1999, where the increase in relative risk for cardiovascular diseases corresponding to a 20 ng/mL increase in exposure was estimated to be ca. 1.06. Generally, the spatial variability of the risk of a certain disease may be related to many environmental factors. Statistically, modeling and identifying disease clusters potentially related to the presence of multiple environmental hazards is a challenging task. Low rank thin plate spline (TPS) models within a

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semiparametric approach to focused clustering for small area health data have been proposed to examine observations of lung cancer deaths taken in Ohio between 1987 and 1988.240 Both the distance from a putative source and a general, unspecified clustering process were modeled in the same fashion and entered logadditively in mixed Poisson
Normal models. The ensuing models were evaluated under different simulated scenarios, using conditional Akaike’s weights and tests for variance components to develop a comprehensive, easily applied model selection methodology. Although particulate matter PM10 and PM2.5 are the regulated airborne parameters, one current hypothesis holds that ultrafine particles (less than 0.1 μm in diameter) are especially harmful because their small size facilitates penetration into the lungs. An interesting study has reinforced this hypothesis by showing that particles with diameters between 0.02 and 0.08 μm significantly predicted mortality for hourly recorded data over two years in Fresno, CA.241 A supervised dynamic factor model was proposed to investigate this association, and a Bayesian model was introduced to convert ambient concentrations into simulated personal exposure using the EPA’s Stochastic Human Exposure and Dose Simulator. The supervised dynamic factor model reduced the dimension of the multivariate pollution time series to a small number of temporally correlated latent time series factors and extended the usual dynamic factor model by borrowing strength across neighboring diameters, thereby reducing variability in latent factors, which were used as predictors of mortality. One of the major problems faced in investigating adverse health effects of air pollution is the lengthy records of sequential missing values in the environmental data. Missing data occurs by effect of machine failures or intentional disruption for cost saving purposes. The unique characteristics of environmental data (e.g., autocorrelation) have so far restricted the use of missing value imputation methods. A new method has been developed to incorporate prediction errors with a view to imputing missing values using mean daily average sulfur dioxide levels following a stationary time series subject to a random error.242 The method validity and efficacy were demonstrated by assessing its performance against values including no prediction error. The method exhibited increased validity and accuracy in the b coefficient in the Poisson regression model for the association with asthma hospital admissions in Sydney, Nova Scotia, Canada. The method is computationally simple and can be easily incorporated into existing statistical software. Classification and Pattern Recognition. Chemometric tools have been widely used to extract maximum relevant information from the bulk of data obtained after an environmental study. PARAFAC, principal component analysis, matrix augmented principal components, Procustes rotation, MCR-ALS, TUCKER3, PLS, and artificial neural networks are among the most widely used techniques for identifying structure in data over the last two years. Studies in this field focused on the investigation, resolution, identification, and description of pollution patterns of organics and metals distributed over a particular geographical area, time, and environmental compartment including soil, water, atmosphere, sediment, and sludge. Among clustering techniques, self-organizing maps (SOMs) and Kohonen Neural Networks have proved advantageous for extracting sample patterns in a nonhierarchical, unsupervised manner. Recently, a combination of SOMs and objective variable selection by classification and regression trees was proposed as an effective means for accelerating the optimization of SOMs and simplifying their chemical interpretation.243 One interesting 4609

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Analytical Chemistry application of SOMs involving two-way data sets (59 samples  44 parameters) examined the relationship between ecotoxicity parameters and chemical components (viz., PCBs, pesticides, PAHs, and heavy metals) in sediments from Turaka Lake in Poland.244 The SOMs obtained afforded the selection of groups of similar ecotoxicity (whether acute or chronic) and the analysis of the relationship between the other chemicals and the toxicity determining parameters (EC50 and mortality) in each group. Expanding the capabilities of SOMs to handle three-way data cubes was recently proposed for the analysis of PAHs from spilled oils using the so-called “MOLMAP approach”, the acronym for its earliest application, Molecular Map of Atom Level Properties.245 MOLMAP requires three steps to handle 3-way data arrays, namely, (a) unfolding the 3-way data cube; (b) generating MOLMAP scores with a dedicated SOM; and (c) pattern recognition or classification to unravel the main patterns in the data set. The study comprised 50 PAHs that were analyzed in samples derived from the weathering of six oil products spilled under controlled conditions for about 4 months. The behavior of each of the six oils was ascertained together with their particular temporal changes, and their weathering patterns were examined in terms of the original PAHs. The six spilled products were projected onto different regions on both the MOLMAP
SOM and a subsequent principal components analysis scatter plot developed from MOLMAP scores. This methodology also afforded further distinction between unweathered or slightly weathered samples and the most weathered ones.

’ AUTHOR INFORMATION Corresponding Author

*Fax: 34-957-218644. E-mail: [email protected].

’ BIOGRAPHIES Soledad Rubio studied Chemistry at the University of C
ordoba, where she received the B.S. degree in 1979 and the Ph.D. degree, with doctorate award, in 1982. She has developed all her career in the Department of Analytical Chemistry of this University where she has been assistant professor (1979
86), associate professor (1986
2005), and full professor (2005 to present). She started working on Supramolecular Analytical Chemistry at the beginning of the 1990s. From then on, she has been working uninterruptedly on different topics related to this field, namely, micellar catalysis, the development of new measurement principles based on molecular aggregation parameter, and the development and application in extraction processes of supramolecular solvents and sorbents (i.e., hemimicelles/admicelles). Her areas of expertise include environmental and food analysis of contaminants. Ana Ballesteros-G
omez studied Environmental Sciences at the University of C
ordoba, where she received the B.S. degree in 2005 with extraordinary end-of-studies award. She is a junior scientist at the Department of Analytical Chemistry of this University, her area of expertise being the development of new analytical methods for the analysis of trace environmental contaminants in the context of supramolecular analytical chemistry. ’ ACKNOWLEDGMENT The authors gratefully acknowledge financial support from Spanish MICINN (Project No. CTQ 2008-01068).

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’ REFERENCES (1) Richardson, S. D. Anal. Chem. 2009, 81, 4646–4677. (2) Richardson, S. D. Anal. Chem. 2010, 82, 4742–4774. (3) Butler, O. T.; Cairns, W.; Cook, J. M.; Davidson, C. M. J. Anal. At. Spectrom. 2010, 25, 103–141. (4) Butler, O. T.; Cairns, W.; Cook, J. M.; Davidson, C. M. J. Anal. At. Spectrom. 2011, 26, 250–286. (5) Brings, N. H.; Bogaerts, A.; Broekaert, A. C. Anal. Chem. 2010, 82, 4653–4681. (6) Ort, C.; Lawrence, M. G.; Reungoat, J.; Mueller, J. F. Environ. Sci. Technol. 2010, 44, 6289–6296. (7) Ort, C.; Lawrence, M. G.; Rieckermann, J.; Joss, A. Environ. Sci. Technol. 2010, 44, 6024–6035. (8) Rabiet, M.; Margoum, C.; Gouy, V.; Carluer, N.; Coquery, M. Environ. Pollut. 2010, 158, 737–748. (9) Offenthaler, I.; Jakobi, G.; Kaiser, A.; Kirchner, M.; Krauchi, N.; Niedermoser, B.; Schramm, D. W.; Sedivy, I.; Staudinger, M.; Thanner, G.; Weiss, P.; Moche, W. Environ. Pollut. 2009, 157, 3290–3297. (10) Strub, M. P.; Lepot, B.; Morin, A. Trends Anal. Chem. 2009, 28, 245–261. (11) Chen, T.; Liu, X.; Li, X.; Zhao, K.; Zhang, J.; Xu, J.; Shi, J.; Dahlgren, R. A. Environ. Pollut. 2009, 157, 1003–1010. (12) Ruiz-C
ardenas, R.; Ferreira, M. A.; Schmidt, A. Environmetrics 2010, 21, 102–112. (13) Menichini, E. Chemosphere 2009, 77, 1275–1284. (14) CEN Air quality
Standard Method for the Measurement of the Concentration of Benzo(a)pyrene in Ambient Air. European Standard EN 15549:2008; CEN: Brussels, 2008. (15) Doval-Mi~ narro, M.; Gonz
alez-Ferrad
as, E.; Barber
a-Rico, J.; Díaz-Alonso, F.; Marzal-Martínez, F. J.; Romero-Trigueros, C. Environ. Sci. Pollut. Res. 2011, 18, 436–445. (16) Kr
ol, S.; Zabiegala, B.; Namiesnik, J. Trends Anal. Chem. 2010, 29, 1092–1100. (17) Kr
ol, S.; Zabiegala, B.; Namiesnik, J. Trends Anal. Chem. 2010, 29, 1101–1112. (18) Ras, M. R.; Borrull, F.; Marc
e, R. M. Trends Anal. Chem. 2009, 28, 347–361. (19) Woolfenden, E. J. Chromatogr., A 2010, 1217, 2674–2684. (20) Woolfenden, E. J. Chromatogr., A 2010, 1217, 2685–2694. (21) Yusa, V.; Coscolla, C.; Mellouki, W.; Pastor, A.; de la Guardia, M. J. Chromatogr., A 2009, 1216, 2972–2983. (22) Gu, Z. Y.; Wang, G.; Yan, X. P. Anal. Chem. 2010, 82, 1365–1370. (23) Cabrerizo, A.; Dachs, J.; Barcel
o, D. Environ. Sci. Technol. 2009, 43, 8257–8263. (24) Thomas, R. M.; Trebs, I.; Otjes, R.; Jongejan, P. A. C.; Brink, H. T.; Phillips, G.; Kortner, M.; Meixner, F. X.; Nemitz, E. Environ. Sci. Technol. 2009, 43, 1412–1418. (25) Zabiegala, B.; Kot-Wasik, A.; Urbanowicz, M.; Mamiesnik, J. Anal. Bioanal. Chem. 2010, 396, 273–296. (26) Greenwood, R.; Mills, G. A.; Vrana, B. J. Chromatogr., A 2009, 1216, 631–639. (27) Pinxteren van, M.; Paschke, A.; Popp, P. J. Chromatogr., A 2010, 1217, 2589–2598. (28) Cape, J. N. Crit. Rev. Anal. Chem. 2009, 39, 289–310. (29) Allan, I. J.; Booij, K.; Paschke, A.; Vrana, B.; Mills, G. A.; Greenwood, R. Environ. Sci. Technol. 2009, 43, 5383–5390. (30) Semedes, F.; Geertsma, R. W.; Zande, van Der T.; Booij, K. Environ. Sci. Technol. 2009, 43, 7047–7054. (31) Moeckel, C.; Harner, T.; Nizzetto, L.; Strandberg, B.; Lindroth, A.; Jones, K. C. Environ. Sci. Technol. 2009, 43, 3227–3232. (32) Rusina, T. P.; Smedes, F.; Koblizkova, M.; Klanova, J. Environ. Sci. Technol. 2010, 44, 362–367. (33) S€oderstr€on, H.; Lindberg, R. H.; Fick, J. J. Chromatogr., A 2009, 1216, 623–630. (34) Shaw, M.; Eaglesham, G.; Mueller, J. F. Chemosphere 2009, 75, 1–7. (35) Bayen, S.; Laak, T. L. T.; Buffle, J.; Hermens, J. L. M. Environ. Sci. Technol. 2009, 43, 2206–2215. 4610

dx.doi.org/10.1021/ac200921j |Anal. Chem. 2011, 83, 4579–4613

Analytical Chemistry (36) Fernandez, L. A.; Macfarlane, J. K.; Tcaciuc, A. P.; Gschwend, P. M. Environ. Sci. Technol. 2009, 43, 1430–1436. (37) Mayer, P.; Tor€ang, L.; Glaesner, N.; J€onsson, J. A. Anal. Chem. 2009, 81, 1536–1542. (38) Jhanke, A.; Mayer, P.; Broman, D.; McLachlan, M. S. Chemosphere 2009, 77, 764–770. (39) Kibbey, T. C. G.; Chen, L.; Singhputtangkul, N.; Sabatini, D. A. Chemosphere 2009, 76, 1249–1257. (40) Kibbey, T. C. G.; Chen, L.; Sabatini, D. A.; Mills, M. A.; Nietch, C. Chemosphere 2010, 80, 908–913. (41) Tao, S.; Cao, J.; Wang, W.; Zhao, J.; Wang, W.; Wang, Z.; Cao, H.; Xing, B. Environ. Sci. Technol. 2009, 43, 4124–4129. (42) Abdallah, M.A-E.; Harrad, S. Environ. Sci. Technol. 2010, 44, 3059–3065. (43) Pozo, K.; Harner, T.; Lee, S. C.; Wania, F.; Muir, D. C. G.; Jones, K. C. Environ. Sci. Technol. 2009, 43, 796–803. (44) Genualdi, S.; Lee, S. C.; Shoeib, M.; Gawor, A.; Ahrens, L.; Harner, T. Environ. Sci. Technol. 2010, 44, 5534. (45) Lohmann, R.; Muir, D. Environ. Sci. Technol. 2010, 44, 860–864. (46) Bao, L.-J. Environ. Sci. Technol. 2010, 44, 4385. (47) Raynie, D. E. Anal. Chem. 2010, 82, 4911–4916. (48) Hy€otyl€ainen, T. H. Anal. Bioanal. Chem. 2009, 394, 743–758. (49) Nerin, C.; Salafranca, J.; Aznar, M.; Batle, R. Anal. Bioanal. Chem. 2009, 393, 809–833. (50) Tobiszewski, M.; Mechlinska, A.; Zygmunt, B.; Namiesnik, J. Trends Anal. Chem. 2009, 28, 943–951. (51) Farr
e, M.; P
erez, S.; Gonc-alves, C.; Alpendurada, M. F.; Barcel
o, D. Trends. Anal. Chem. 2010, 29, 1347–1362. (52) Aguilera-Herrador, E.; Lucena, R.; C
ardenas, S.; Valc
arcel, M. Trends Anal. Chem. 2010, 29, 602–616. (53) Poole, C. F.; Poole, S. K. J. Chromatogr., A 2010, 1217, 2268–2286. (54) Liu, R.; Liu, J.-F.; Hu, X.-L.; Jiang, G. B. Anal. Bioanal. Chem. 2009, 393, 871–883. (55) Tondeur, Y.; Hart, J. Trends Anal. Chem. 2009, 28, 1137–1147. (56) Albaseer, S. S.; Rao, R. N.; Swamy, Y. V.; Mukkanti, K. J. Chromatogr., A 2010, 1217, 5537–5554. (57) Jakubowska, N.; Zygmunt, B.; Polkowska, Z.; Zabiegala, B.; Namiesnik, J. J. Chromatogr., A 2009, 1216, 422–441. (58) Dobrzynska, El; Posniak, M.; Szewczynska, M.; Buszewski, B. Crit. Rev. Anal. Chem. 2010, 40, 41–57. (59) Martínez-Vidal, J. L.; Plaza-Bola~nos, P.; Romero-Gonz
alez, R.; Garrido-Frenich, A. J. Chromatogr., A 2009, 1216, 6767–6788. (60) Covaci, A.; Harrad, S.; Abdallah, M. A. E.; Ali, N.; Law, R. J.; Herzke, D.; Wit de, C. A. Environ. Int. 2011, 37, 532–556. (61) Matamoros, V.; Jover, E.; Bayona, J. M. Anal. Bioanal. Chem. 2009, 393, 847–860. (62) Augusto, F.; Carasek, E.; Silva, R. G. C.; Rivellino, S. R.; Batista, A. D.; Martendal, E. J. Chromatogr., A 2010, 1217, 2533–2542. (63) Kloskowski, A.; Pilarczyk, M.; Przyjazny, A.; Namie
snik, J. Crit. Rev. Anal. Chem. 2009, 39, 43–58. (64) Ravelo-P
erez, L. M.; Herrera-Herrera, A. V.; Hern
andezBorges, J.; Rodríguez-Delgado, M. A. J. Chromatogr., A 2010, 1217, 2618–2641. (65) B. Risticevic, S.; Niri, V. H.; Vuckovic, D.; Pawliszyn, J. Anal. Bioanal. Chem. 2009, 393, 781–795. (66) Weiya Zhang, W.; Sun, Y.; Wu, C.; Xing, J.; Li, J. Anal. Chem. 2009, 81, 2912–2920. (67) Segro, S. S.; Cabezas, Y.; Malik, A. J. Chromatogr., A 2009, 1216, 4329–4335. (68) Liua, H.; Li, J.; Liua, X.; Jianga, S. Talanta 2009, 78, 929– 935. (69) Saito, Y.; Ueta, I.; Ogawa, M.; Abe, A.; Yogo, K.; Shirai, S.; Jinno, K. Anal. Bioanal. Chem. 2009, 393, 861–869. (70) Sae-Kwoh, O.; Mitra, S. J. Chromatogr., A 2009, 1216, 2270– 2274. (71) Kloskowski, A.; Pilarczyk, M.; Namiesnik, J. Anal. Chem. 2009, 81, 7363–7367.

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(72) Prieto, A.; Basauri, O.; Rodil, R.; Usobiaga, A.; Fern
andez, L. A.; Etxebarria, N.; Zuloaga, O. J. Chromatogr., A 2010, 1217, 2642–2666. (73) Yu, C.; Yao, X.; Hu, B. Anal. Chim. Acta 2009, 641, 75–82. (74) Huang, X.; Qiu, N.; Yuan, D.; Lin, Q. J. Chromatogr., A 2009, 1216, 4354–4359. (75) van Hoeck, E.; Canale, F.; Cordero, C.; Compernolle, S.; Bicchi, C.; Sandra, P. Anal. Bioanal. Chem. 2009, 393, 907–919. (76) Sun, L.; Chen, L.; Sun, X.; Du, X.; Yue, Y.; He, D.; Xu, H.; Zeng, Q.; Wang, H.; Ding, L. Chemosphere 2010, 77, 1306–1312. (77) Ballesteros-G
omez, A.; Rubio, S. Anal. Chem. 2009, 81, 9012–9020. (78) Zhang, Q. L.; Yang, F.; Tang, F.; Zeng, K.; Wu, K. K.; Cai, Q. Y.; Yao, S. Z. Analyst 2010, 135, 2426–2433. (79) Zhang, X.; Niu, H.; Pan, Y.; Shi, Y.; Cai, Y. Anal. Chem. 2010, 82, 2363–2371. (80) Guan, Y.; Jiang, Ch.; Hu, Ch.; Jia, L. Talanta 2010, 83, 337–343. (81) Jeannot, M. A.; Przyjazny, A.; Kokosa, J. M. J. Chromatogr., A 2010, 1217, 2326–2336. (82) Yao, C.; Pitner, W. R.; Anderson, J. L. Anal. Chem. 2009, 81, 5054–5063. (83) Aguilera-Herrador, E.; Lucena, R.; C
ardenas, S.; Valc
arcel, M. J. Chromatogr., A 2009, 1216, 5580–5588. (84) Chisvert, A.; Roman, I. P.; Vidal, L.; Canals, A. J. Chromatogr., A 2009, 1216, 1290–1296. (85) Pena-Pereira, F.; Lavilla, I.; Bendicho, C. Spectrochim. Acta, Part B 2009, 64, 1–10. (86) Ganjali, M. R.; Sobhi, H. R.; Farahanic, H.; Norouzi, P.; Dinarvand, R.; Kashtiaray, A. J. Chromatogr., A 2010, 1217, 2337–2341. (87) Rezaee, M.; Yamini, Y.; Faraji, M. J. Chromatogr., A 2010, 1217, 2342–2357. (88) Shi, Z-G; Lee, H. K. Anal. Chem. 2010, 82, 1540–1545. (89) Liu, Y.; Zhao, E.; Zhu, W.; Gao, H.; Zhou, Z. J. Chromatogr., A 2009, 1216, 885–893. (90) Regueiro, J.; Llompart, M.; Psillakis, E.; Garcia-Monteagudo, J. C.; Garcia-Jares, C. Talanta 2009, 79, 1387–1397. (91) Ballesteros-G
omez, A.; Sicilia, M. D.; Rubio, S. Anal. Chim. Acta 2010, 677, 108–130. (92) Xie, S.; Paau, M. C.; Li, C. F.; Xiao, D.; Choi, M. M. F. J. Chromatogr., A 2010, 1217, 2306–2317. (93) Takagai, Y.; Hinze, W. L. Anal. Chem. 2009, 81, 7113–7122. (94) Liu, J.; Chao, J.; Liu, R.; Tan, Z.; Yin, Y.; Wu, Y.; Jiang, G. Anal. Chem. 2009, 81, 6496–6502. (95) Herrero, M.; Mendiola, J. A.; Cifuentes, A.; Ib
a~ nez, E. J. Chromatogr., A 2010, 1217, 2495–2511. (96) Runnqvist, H.; Bak, S. A.; Hansen, M.; Styrishave, B.; HallingSorensen, B.; Bj€ orklund, E. J. Chromatogr., A 2010, 1217, 2447–2470. (97) Nieto, A.; Borrull, F.; Pocurull, E.; Marc
e, R. M. Trends. Anal. Chem. 2010, 29, 752–764. (98) Postigo, C.; L
opez de Alda, M. J.; Viana, M.; Querol, X.; Alastuey, A.; Arti~ nano, B.; Barcel
o, D. Anal. Chem. 2009, 81, 4382–4388. (99) Teo, C. C.; Tan, S. N.; Yong, J. W. H.; Hew, C. S.; Ong, E. S. J. Chromatogr., A 2010, 1217, 2484–2494. (100) Zhang, X.; Cai, J.; Oakes, K. D.; Breton, F.; Servos, M. R.; Pawliszyn, J. Anal. Chem. 2009, 81, 7349–7356. (101) Zhang, X.; Oakes, K. D.; Cui, S.; Bragg, L.; Servos, M. R.; Pawliszyn, J. Environ. Sci. Technol. 2010, 44, 3417–3422. (102) Hawthorne, S. B.; Grabanski, C. B.; Miller, D. J. Anal. Chem. 2009, 81, 6936–6943. (103) Krupadam, R. J.; Bhagat, B.; Wate, S. R.; Bodhe, G. L.; Sellergren, B.; Anjaneyulu, Y. Environ. Sci. Technol. 2009, 43, 2871– 2877. (104) Dorman, F. L.; Whiting, J. J.; Cochran, J. W.; GardeaTorresdey, J. Anal. Chem. 2010, 82, 4775–4785. (105) Prieto, A.; Schrader, S.; Moeder, M. J. Chromatogr., A 2010, 1217, 6002–6011. (106) G
omez, M. J.; G
omez-Ramos, M. M.; Aguera, A.; Mezcua, M.; Herrera, S.; Fern
andez-Alba, A. R. J. Chromatogr., A 2009, 1216, 4071– 4082. 4611

dx.doi.org/10.1021/ac200921j |Anal. Chem. 2011, 83, 4579–4613

Analytical Chemistry (107) Toledano, R. M.; Cort
es, J. M.; Andini, J. C.; Vill
en, J.; V
azquez, A. J. Chromatogr., A 2010, 1217, 4738–4742. (108) Li, Y. T.; Whitaker, J.; McCarty, C. J. Liq. Chromatogr. Relat. Technol. 2009, 32, 1644–1671. (109) Collin, O. L.; Zimmermann, C. M.; Jackson, G. P. Int. J. Mass Spec. 2009, 279, 93–99. (110) Yao, C.; Anderson, J. L. J. Chromatogr., A 2009, 1216, 1658– 1712. (111) Sun, X.; Wu, C.; Xing, J. J. Sep. Sci. 2010, 33, 3159–3167. (112) Tran, C. D.; Mejac, I.; Rebek, J.; Hooley, R. J. Anal. Chem. 2009, 81, 1244–1254. (113) Wang, Y.; Chen, Q.; Norwood, D. L.; Mc Caffrey, J. J. Liq. Chromatogr. Relat. Technol. 2010, 33, 1082–1115. (114) Eganhouse, R. P.; Pontolillo, J.; Gaines, R. B.; Frysinger, G. S.; Gabriel, F. L. P.; Kohler, H.-P.; Giger, W.; Barber, L. B. Environ. Sci. Technol. 2009, 43, 9306–9313. (115) Matamoros, V.; Jover, E.; Bayona, J. M. Anal. Chem. 2010, 82, 699–706. (116) Hoh, E.; Lehotay, S. J.; Mastovska, K.; Ngo, H. L.; Vetter, W.; Pangallo, K. C.; Reddy, C. M. Environ. Sci. Technol. 2009, 43, 3240–3247. (117) Hilton, D. C.; Jones, R. S. J. Chromatogr., A 2010, 1217, 6851–6856. (118) Losada, S.; Parera, J.; Abalos, M.; Abad, E.; Santos, F. J.; Galceran, M. T. Anal. Chim. Acta 2010, 678, 73–81. (119) Seb€ok, A.; Vasanits-Zsigrai, A.; Helenk
ar, A.; Z
aray, Gy; Moln
ar-Perl, I. J. Chromatogr., A 2009, 1216, 2288–2301. (120) Gonz
alez-Mari~no, I.; Quintana, J. B.; Rodríguez, I.; Cela, R. J. Chromatogr., A 2010, 1217, 1748–1760. (121) Rosenfelder, N.; Van Zee, N. J.; Mueller, J. F.; Gaus, C.; Vetter, W. Anal. Chem. 2010, 82, 9835–9842. (122) Padilla-S
anchez, J. A.; Plaza-Bola~nos, P.; Romero-Gonz
alez, R.; Garrido-Frenich, A.; Martínez-Vidal, J. L. J. Chromatogr., A 2010, 1217, 5724–5731. (123) N
acher-Maestre, J.; Serrano, R.; Portol
es-Nicolau, T.; Harn
andez, F.; Benedito-Palos, L.; P
erez-S
anchez, J. Rapid Commun. Mass Spectrom. 2009, 23, 2075–2086. (124) Peterson, A. C.; McAlister, C.; Quarmby, S. T.; Griep-Raming, J.; Coon, J. J. Anal. Chem. 2010, 82, 8618–8628. (125) Portol
es, T.; Sancho, J. V.; Hern
andez, F.; Newton, A.; Hancock, P. J. Mass Spec. 2010, 45, 926–936. (126) Huskova, R.; Matisova, E.; Hrouzkova, S.; Svorc, L. J. Chromatogr., A 2009, 1216, 6326–6334. (127) Smith, P. A.; Lepage, C. J.; Lukacs, M.; Martin, N.; Shufutinsky, A.; Savage, P. B. Int. J. Mass Spectrom. 2010, 295, 113–118. (128) Fair, J. F.; Bailey, W. F.; Felty, R. A.; Gifford, A. E.; Shultes, B.; Volles, L. H. J. Environ. Sci. 2009, 21, 1005–1008. (129) Aguilera-Herrador, E.; Lucena, R.; C
ardenas, S.; Valc
arcel, M. J. Chromatogr., A 2009, 1216, 5580–5587. (130) Krauss, M.; Singer, H.; Hollender, J. Anal. Bioanal. Chem. 2010, 397, 943–951. (131) Kuster, M.; L
opez de Alda, M.; Barcel
o, D. J. Chromatogr., A 2009, 1216, 520–529. (132) Seifrtov
a, M.; Nov
akov
a, L.; Lino, C.; Pena, A.; Solich, P. Anal. Chim. Acta 2009, 649, 158–179. (133) Hoerger, C. C.; Schenzel, J.; Strobel, B. W.; Bucheli, T. D. Anal. Bioanal. Chem. 2009, 395, 1261–1289. (134) Mahugo-Santana, C.; Sosa-Ferrera, Z.; Torres-Padr
on, E.; Santana-Rodríguez, J. J. Anal. Chim. Acta 2010, 665, 113–122. (135) Petrovic, M.; Farr
e, M.; L
opez de Alda, M.; P
erez, S.; Postigo, C.; K€ock, M.; Radjenovic, J.; Gros, M.; Barcel
o, D. J. Chromatogr., A 2010, 1217, 4004–4017. (136) Giger, W. Anal. Bioanal. Chem. 2009, 393, 37–44. (137) Moreno-Bondi, M. C. Anal. Bioanal. Chem. 2009, 395, 875–876. (138) Cai, S.-S.; Syage, J. A.; Hanold, K. A.; Balogh, M. P. Anal. Chem. 2009, 81, 2123–2128. (139) Rice, S. L.; Hale, R. C. Anal. Chem. 2009, 81, 6716–6724. (140) Nuijs, A. L. N.; Tarcomnicu, I.; Bervoets, L.; Blust, R.; Jorens, P. G.; Neels, H.; Covaci, A. Anal. Bioanal. Chem. 2009, 395, 819–829.

REVIEW

(141) Hogenboom, A. C.; van Leerdam, J. A.; Voogt, P. J. Chromatogr., A 2009, 1216, 510–519. (142) Schr€oder, H. F.; Gebhardt, W.; Thevis, M. Anal. Bioanal. Chem. 2010, 398, 1207–1229. (143) Gros, M.; Petrovic, M.; Barcel
o, D. Anal. Chem. 2009, 81, 898–906. (144) García-Gal
an, M. J.; Villagrasa, M.; Díaz-Cruz, M. S.; Barcel
o, D. Anal. Bioanal. Chem. 2010, 397, 1325–1334. (145) L
opez-Serna, R.; P
erez, S.; Ginebreda, A.; Petrovic, M.; Barcel
o, D. Talanta 2010, 83, 410–424. (146) Kern, S.; Fenner, K.; Singer, H.; Schwarzenbach, R.; Hollender, J. Environ. Sci. Technol. 2009, 43, 7039–7046. (147) Pitarch, E.; Portol
es, T.; Marín, J. M.; Ib
a~ nez, M.; Albarr
an, F.; Hern
andez, F. Anal. Bioanal. Chem. 2010, 397, 2763–2776. (148) Erve, J. C. L.; Gu, M.; Wang, Y.; DeMaio, W.; Talaat, R. E. J. Am. Soc. Mass Spectrom. 2009, 20, 2058–2069. (149) Godejohann, M.; Heintz, L.; Daolio, C.; Berset, J. D.; Muff, F. Environ. Sci. Technol. 2009, 43, 7055–7061. (150) Frost, N. W.; Jing, J.; Bowser, M. T. Anal. Chem. 2010, 82, 4682–4698. (151) Castro-Puyana, M.; Crego, A. L.; Marina, M. L. Electrophoresis 2010, 31, 229–250. (152) Timerbaev, A. R. Trends Anal. Chem. 2009, 28, 416–425. (153) Hoff, R.; Kist, T. B. L. J. Sep. Sci. 2009, 32, 854–886. (154) Su, H. L.; Kao, W. C.; Lee, C. Y.; Chuang, S. C.; Hsieh, Y. Z. J. Chromatogr., A 2010, 1217, 4471–4475. (155) Prokhorova, A. F.; Shapovalova, E. N.; Shpak, A. V.; Staroverov, S. M.; Shpigun, O. A. J. Chromatogr., A 2009, 1216, 3674–3677. (156) Qu, Q.-S.; Wang, S.; Mangelings, D.; Wang, C.-Y.; Yang, G.-J.; Hu, X.-Y.; Yan, C. Electrophoresis 2009, 30, 1071–1076. (157) Qu, Q.; Peng, S.; Mangelings, D.; Hu, X.; Yan, C. Electrophoresis 2010, 31, 556–562. (158) Karenga, S.; El Rassi, Z. Electrophoresis 2010, 31, 991–1002. (159) Ding, G.; Zhang, J.; Bao, J. J. Electrophoresis 2010, 31, 1983–1990. (160) Zaidi, S. A.; Han, K. M.; Hwang, D. G.; Cheong, W. J. Electrophoresis 2010, 31, 1019–1028. (161) Vizioli, N.; Gil, R.; Martínez, L. D.; Silva, M. F. Electrophoresis 2009, 30, 2681–2687. (162) Rodríguez-Gonzalo, E.; Ruano-Miguel, L.; Carabias-Martínez, R. Electrophoresis 2009, 30, 1913–1922. (163) Proczek, G.; Augustin, V.; Descroix, S.; Hennion, M. C. Electrophoresis 2009, 30, 515–524. (164) Wang, Q.; Qiu, H.; Li, J.; Liu, X.; Jiang, S. J. Chromatogr., A 2010, 1217, 5434–543. (165) Yang, Y.-Y.; Liu, J.-T.; Lin, C.-H. Electrophoresis 2009, 30, 1084–1087. (166) Noh, H. B.; Lee, K.-H.; Lim, B. S.; Kim, S.-J.; Shim, Y.-B. Electrophoresis 2010, 31, 3053–3060. (167) Dawod, M.; Breadmore, M. C.; Guijt; Haddad, P. R. Electrophoresis 2010, 31, 1184–1193. (168) Chen, Q.; Fung, Y. Electrophoresis 2010, 31, 3107–3114. (169) Rodríguez-Robledo, V.; Smyth, W. F. Electrophoresis 2009, 30, 1647–1660. (170) Hecker, M.; Hollert, H. Environ. Sci. Pollut. Res. 2009, 16, 607–613. (171) W€ olz, J.; Brack, W.; Moehlenkamp, C.; Claus, E.; Braunbeck, Th.; Hollert, H. Sci. Total Environ. 2010, 408, 3327–3333. (172) Bicchi, C.; Schiliro, T.; Pignata, C.; Fea, E.; Cordero, C.; Canale, F.; Gilli, G. Sci. Total Environ. 2009, 407, 1842–1851. (173) Bandow, N.; Altenburger, R.; Lubcke-von Varel, U.; Paschke, A.; Streck, G.; Brack, W. Environ. Sci. Technol. 2009, 43, 3891–3896. (174) Bandow, N.; Altenburger, R.; Streck, G.; Brack, W. Environ. Sci. Technol. 2009, 43, 7343–7349. (175) Weiss, J. M.; Hamers, T.; Thomas, K. V.; van der Linden, S.; Leonards, P. E. G.; Lamoree, M. H. Anal. Bioanal. Chem. 2009, 394, 1385–1397. (176) Meinert, C.; Brack, W. Chemosphere 2010, 78, 416–422. 4612

dx.doi.org/10.1021/ac200921j |Anal. Chem. 2011, 83, 4579–4613

Analytical Chemistry (177) Meinert, C.; Schymanski, E.; K€uster, E.; K€uhne, R.; Sch€u€urmann, G.; Brack, W. Environ. Sci. Pollut. Res. 2010, 17, 885–897. (178) Simon, E.; Lamoree, M.; Hamers, T.; Weiss, J.; Balaam, J.; de Boer, J.; Leonards, P. E. G. Environ. Sci. Technol. 2010, 44, 8322–8329. (179) Chriks, M.; van Leerdam, J. A.; vander Linden, S. C.; Vanderburg, B.; van Wezel, A. P.; de Voogt, P. Environ. Sci. Technol. 2010, 44, 4766–4774. (180) Schymanski, E. L.; Bataineh, M.; Goss, K.-U.; Brack, W. Trends Anal. Chem. 2009, 28, 550–561. (181) Farr
e, M.; Kantiani, L.; P
erez, S.; Barcel
o, D. Trends Anal. Chem. 2009, 28, 170–185. (182) Lieberzeit, P. A.; Dickert, F. L. Anal. Bioanal. Chem. 2009, 393, 467–472. (183) Privett, B. J.; Shin, J. H.; Schoenfisch, M. H. Anal. Chem. 2010, 82, 4723–4741. (184) Ligler, F. S. Anal. Chem. 2009, 81, 519–526. (185) van Dorst, B.; Mehta, J.; Bekaert, K.; Rouah-Martin, E.; De Coen, W.; Dubruel, P.; Blust, R.; Robbens, J. Biosens. Bioelectron. 2010, 26, 1178–1194. (186) Zhou, J.; Battig, M. R.; Wang, Y. Anal. Bioanal. Chem. 2010, 398, 2471–2480. (187) Han, K.; Liang, Z.; Zhou, N. Sensors 2010, 10, 4541–4557. (188) van der Meer, J. R.; Belkin, S. Nat. Rev. Microbiol. 2010, 8, 511–522. (189) Su, L.; Jia, W.; Hou, C.; Lei, Y. Biosens. Bioelectron. 2011, 26, 1788–1799. (190) Cerruti, M.; Jaworski, J.; Raorane, D.; Zueger, C.; Varadajan, J.; Carraro, C.; Lee, S.-W.; Maboudian, R.; Majumdar, A. Anal. Chem. 2009, 81, 4192–4199. (191) Kawakami, Y.; Siddiki, M. S. R.; Inoue, K.; Otabayashi, H.; Yoshida, K.; Ueda, S.; Miyasaka, H.; Maeda, I. Biosens. Bioelectron. 2010, 26, 1466–1473. (192) Pumera, M.; Ambrosi, A.; Bonnani, A.; Chang, E. L. K.; Poh, H. L. Trends Anal. Chem. 2010, 29, 954–965. (193) Guo, S.; Dong, S. Trends Anal. Chem. 2009, 28, 96–109. (194) Wang, G.; Wang, Y.; Chen, L.; Choo, J. Biosens. Bioelectron. 2010, 25, 1859–1868. (195) Kang, Q.; Yang, L.; Chen, Y.; Luo, s.; Wen, L.; Cai, Q; Yao, S. Anal. Chem. 2010, 82, 9749–9754. (196) Zhu, Z.; Su, Y.; Li, J.; Zhang, J.; Song, S.; Zhao, Y.; Li, G.; Fan, C. Anal. Chem. 2009, 81, 7660–7666. (197) Olowu, R. A.; Arotiba, O.; Mailu, S. N.; Waryo, T. T.; Baker, P.; Iwuoha, E. Sensors 2010, 10, 9872–9890. (198) Bratov, A.; Abramova, N.; Ipatov, A. Anal. Chim. Acta 2010, 678, 149. (199) Riul, A; Dantas, C. A. R.; Miyakazi, C. M.; Oliveira, O. N. Analyst 2010, 135, 2481–2495. (200) Wilson, A. D.; Baietto, M. Sensors 2009, 9, 5099–5148. (201) Aernecke, M. J.; Guo, J.; Sonkusale, S.; Walt, D. R. Anal. Chem. 2009, 81, 5281–5290. (202) Chen, P. C.; Ishikawa, F. N.; Chang, H.-K.; Ryu, K.; Zhou, C. Nanotechnology 2009, 20, 125503 (8 pp). (203) Tan, J.; Wang, H.-F.; Yan, X.-P. Biosens. Bioelectron. 2009, 24, 3316–3321. (204) Zerger, A.; Viscarra Rossel, R. A.; Swain, D. L.; Wark, T.; Handcock, R. N.; Doerr, V. A. J.; Bishop-Hurley, G. J.; Doerr, E. D.; Gibbons, P. G.; Lobsey, C. Int. J. Appl. Earth Obs. 2010, 12, 303–316. (205) Quinn, N. W. T.; Ortega, R.; Rahilly, P. J. A.; Royer, C. W. Environ. Model. Softw. 2009, 25, 1045–1058. (206) Rundel, P. W.; Graham, E. A.; Allen, M. F.; Fisher, J. C.; Harmon, T. C. New Phytol. 2009, 182, 589–607. (207) van Staden, J. F.; Raluca, I.; van Staden, S.; Balasoiu, S. C. Crit. Rev. Anal. Chem. 2010, 40, 226–233. (208) Almalkawi, I. T.; Guerrero-Zapata, M.; Al-Karaki, J. N.; Morillo-Pozo, J. Sensors 2010, 10, 6662–6717. (209) Choi, K.-Y.; Park, J.-S.; Park, K.-B.; Kim, H. J.; Park, H.-D.; Kim, S.-D. Sens. Actuators, B 2010, 150, 65–72. (210) Wang, F. L. Nano Today 2010, 5, 512–514.

REVIEW

(211) Mechlinska, A.; Wolska, L.; Namiesnik, J. Trends Anal. Chem. 2010, 29, 820–831. (212) van Leeuwen, S.P.J.; Swart, C. P.; Veen, van I.; Boer, de J. J. Chromatogr., A 2009, 1216, 401–409. (213) Maldonado-Santoyo, M.; Landero-Figueroa, J. A.; Wrobel, K.; Wrobel, K. Talanta 2009, 79, 706–711. (214) Sordillo, J.; Vespa, D.; Haggerty, L.; Youngs, F.; Gold, D.; Milton, D. J. Environ. Monit. 2009, 11, 1513–1517. (215) Wolska, L.; Gdaniec-Pietryka, M.; Konieczka, P.; Namiesnik, J. Talanta 2009, 78, 730–735. (216) Mechlinska, A.; Wolska, L.; Namiesnik, J. Talanta 2010, 82, 627–630. (217) Mas, S.; Juan, A.; Tauler, R.; Olivieri, A. C.; Escandar, G. M. Talanta 2010, 80, 1052–1067. (218) P
erez-Hern
andez, J.; Albero, J.; Correig, X.; Llobet, E.; Palomares, E. Anal. Chim. Acta 2009, 653, 173–180. (219) Moros, J.; Fdez-Ortiz de Vallejuelo, S.; Gredilla, A.; de Diego, A.; Madariaga, J. M.; Garrigues, S.; de la Guardia, M. Environ. Sci. Technol. 2009, 43, 9314–9320. (220) Piccirilli, G. N.; Escandar, G. M. Analyst 2010, 135, 1299–1308. (221) Bortolato, S. A.; Arancibia, J. A.; Escandar, G. M. Anal. Chem. 2009, 81, 8074–8084. (222) Martínez-Galera, M.; Gil-García, M. D.; Culzoni, M. J.; Goicoechea, H. C. J. Chromatogr., A 2010, 1217, 2042–2049. (223) Li, Y. N.; Wu, H. L.; Qing, X. D.; Li, Q.; Li, S. F.; Fu, H. Y.; Yu, Y. J.; Yu, R. Q. Anal. Chim. Acta 2010, 678, 26–33. (224) Maggio, R. M.; Damiani, P. C.; Olivieri, A. C. Anal. Chim. Acta 2010, 677, 97–107. (225) Ropkins, D.; Carlsaw, D. C.; Goodman, P. S.; Tate, J. E. Trends Anal. Chem. 2009, 28, 373–391. (226) Autin, M. A.; Edwards, D. Environmetrics 2010, 21, 588–605. (227) Morton, R.; Kang, E. L.; Henderson, B. L. Environmetrics 2009, 20, 249–259. (228) Peng, C. Environmetrics 2010, 21, 645–658. (229) El-Makarim, A.; Aboueissa, A. Environmetrics 2009, 20, 312–330. (230) Toscas, P. J. Environmetrics 2010, 21, 632–644. (231) Pang, W.; Christakos, G.; Wang, J.-F. Environmetrics 2010, 21, 305–317. (232) Crujeiras, R. M.; Fern
andez-Casal, R.; Gonz
alez-Manteiga, W. Environmetrics 2010, 21, 382–399. (233) Mostert, M. M. R.; Ayoko, G. A.; Kokot, S. Trends Anal. Chem. 2010, 29, 430–445. (234) Skoien, J. O.; Baume, O. P.; Pebesma, E. J.; Heuvelink, G. B. M. Environmetrics 2010, 21, 66–84. (235) Eastoe, E. F. Environmetrics 2009, 20, 428–444. (236) Toulemonde, G.; Guillou, A.; Naveau, P.; Vrac, M.; Chevallier, F. Environmetrics 2010, 21, 189–207. (237) Schimek, M. G. Environmetrics 2009, 20, 699–717. (238) Shao, Q.; Wong, H.; Ip, W.-C.; Li, M. Environmetrics 2010, 21, 173–188. (239) Wannemuehler, K. A.; Lyles, R. H.; Waller, L. A.; Hoekstra, R. M.; Klein, M.; Tolbert, P. Environmetrics 2009, 20, 877–894. (240) Geraci, M.; Lawson, A. B. Environmetrics 2009, 20, 360–378. (241) Reich, B. J.; Fuentes, M.; Burke, J. Environmetrics 2009, 20, 131–146. (242) Weerasinghe, S. Environmetrics 2010, 21, 162–172. (243) G
omez-Carracedo, M. P.; Andrade, J. M.; Carrera, G. V. S. M.; Aires-de-Sousa, J.; Carlosena, A.; Prada, D. Chemom. Intell. Lab. Syst. 2010, 102, 20–34. (244) Tsakovski, S.; Kudlak, B.; Simeonov, V.; Wolska, L.; Namiesnik, J. Anal. Chim. Acta 2009, 631, 142–152. (245) Fern
andez-Varela, R.; G
omez-Carracedo, M. P.; Ballabio, D.; Andrade, J. M.; Consonni, V.; Todeschini, R. Anal. Chem. 2010, 82, 4264–4271.

4613

dx.doi.org/10.1021/ac200921j |Anal. Chem. 2011, 83, 4579–4613