Monitoring Herbicide Concentrations and Loads during a Flood Event

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Monitoring herbicide concentrations and loads during a flood event: a comparison of grab sampling with passive sampling Andrew Joseph Novic, Dominique S. O'Brien, Sarit Leat Kaserzon, Darryl W. Hawker, Stephen E. Lewis, and Jochen F. Mueller Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.6b02858 • Publication Date (Web): 14 Feb 2017 Downloaded from http://pubs.acs.org on February 15, 2017

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Environmental Science & Technology

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Title: Monitoring herbicide concentrations and loads during a flood event: a comparison of grab

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sampling with passive sampling

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Authors:

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Andrew J. Novic*a

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Dominique S. O’Brienb

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Sarit L. Kaserzona

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Darryl W. Hawkerc

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Stephen E. Lewisb

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Jochen F. Muellera

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Affiliations:

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a

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Road, Coopers Plains, QLD 4108, Australia†

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b

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Queensland 4811, Australia

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c

Queensland Alliance for Environmental Health Sciences, The University of Queensland, 39 Kessels

Catchment to Reef Research Group, TropWATER, ATSIP, DB145, James Cook University, Townsville,

Griffith School of Environment, Griffith University, 170 Kessels Road, Nathan, QLD 4111, Australia

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† Queensland Alliance for Environmental Health Sciences incorporates the former National Research

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Centre for Environmental Toxicology (Entox).

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Abstract art.

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ABSTRACT: The suitability of passive samplers (Chemcatcher) as an alternative to grab sampling in

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estimating time-weighted average (TWA) concentrations and total loads of herbicides was assessed.

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Grab sampling complemented deployments of passive samplers in a tropical waterway in

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Queensland, Australia, before, during and after a flood event. Good agreement was observed

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between the two sampling modes in estimating TWA concentrations that was independent of

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herbicide concentrations ranging over two orders of magnitude. In a flood-specific deployment,

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passive sampler TWA concentrations underestimated mean grab sampler (n=258) derived

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concentrations of atrazine, diuron, ametryn and metolachlor by an average factor of 1.29. No clear

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trends were evident in the ratios of load estimates from passive samplers relative to grab samples

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that ranged between 0.3 and 1.8 for these analytes due to the limitations of using TWA

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concentrations to derive flow-weighted loads.

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generally resulted in noticeable improvements in passive sampler load estimates. By considering the

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magnitude of the uncertainty (interquartile range and the root-mean-squared error) of load

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estimates a modelling exercise showed that passive samplers were a viable alternative to grab

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sampling since between 3 and 17 grab samples were needed before grab sampling results had less

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uncertainty.

Stratification of deployments by flow however


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INTRODUCTION

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Comprehensive monitoring networks are often established to characterize and evaluate water

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quality due to the risks that offsite transport of micropollutants (including herbicides) pose to

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receiving ecosystems.1 In such networks, the estimation of average concentration and total loads of

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micropollutants over specific periods of time are tools that help define and evaluate progress

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towards water quality targets. In micropollutant monitoring, uncertainty and error in the estimation

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of concentration or loads can arise from discharge measurement, sampling, storage/preservation of

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samples and analytical results. Of these, sample collection has often been shown to be the most

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important for a range of matrices. Not only can it be the greatest source of uncertainty, but the

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variation of this uncertainty can also be the greatest.2, 3

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To date, grab (or spot) sampling remains the predominant surface water-sampling mode for

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evaluating concentration and loads of micropollutants despite its well-documented limitations. For

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example, a common criticism of grab sampling for measuring concentration is that it only provides a

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snapshot of concentration in time.4 In the absence of continuous monitoring, varying concentrations

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and flow rates of rivers and streams over time – with flood events being scattered within low flow

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periods for example – represent a form of measurement and stochastic uncertainty that may

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preclude the obtainment of reliable, representative data from grab samples. Representative samples

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are a requirement in micropollutant monitoring.5, 6

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The relationship between flow rate and chemical concentration is complex and depends upon the

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characteristics of individual catchments (e.g. scale) as well as the nature of the rainfall event(s) (e.g.

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intensity) and micropollutants (e.g. physico-chemical properties).7 Periods of maximum flow do not

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necessarily coincide with times of maximum chemical concentration that can also vary temporally.

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Thus the chemograph (a temporal plot of concentration) of each target chemical responds differently

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to a flow event than a hydrograph (a temporal plot of flow rate) and as a result, the

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representativeness of sampled data is also different for each chemical. In addition, since the

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algorithms used to estimate loads make assumptions (knowledge uncertainty) about the relationship

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between the chemograph and hydrograph profiles that are typically violated, this causes differences

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in the magnitude of uncertainty of measured loads.

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Despite these limitations for grab samples, few suitable alternatives currently exist. Automated

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samplers, some requiring no electronics, are able to provide a high temporal resolution of

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micropollutant concentrations. However, the cost, security and maintenance associated with their

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use limit applicability, particularly in remote areas. Another alternative is passive sampling. Passive

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sampling techniques have become increasingly important tools in pollution monitoring schemes 3 ACS Paragon Plus Environment


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worldwide and are beginning to be specified in water sampling protocols.8 Advantages of these

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techniques include providing time-weighted average (TWA) concentrations, relatively low limits of

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detection and cost effectiveness as well as ease and flexibility of deployment where extended

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deployment periods are desirable.4, 9

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A number of studies have been undertaken to assess the effect of variable hydrodynamics on passive

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sampling of polar analytes.10-14 Some work has also been carried out on the effect of pulses in

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concentration.15-20 Both variable hydrodynamics and concentration pulses occur in flood events.21

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How they interact and affect the performance of passive samplers under such conditions has

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received comparatively little attention, especially for organic micropollutants.22-24

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A decade ago it was suggested that passive samplers could be used in water quality monitoring to

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assess chemical contaminant loads crossing national boundaries.25 To our knowledge, three studies

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have since been published in relation to this. One investigated the SorbiCell sampler that is based on

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fluid advection,26 and more recently, two have investigated the polar organic chemical integrative

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sampler (POCIS) that is based on passive diffusion27,

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background for these sampling modes). Results from these investigations have been promising,

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suggesting that passive samplers may be a useful alternative to grab sampling regimes in estimating

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mass loads under field conditions.

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The objective of this paper is to assess the suitability of sampling based on passive diffusion as an

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alternative to grab sampling for event monitoring and estimating mass loads of herbicides. A

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comprehensive passive and grab sampling regime was conducted before, during and after a flood

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event. The performance (including uncertainty) of both sampling modes in estimating herbicide

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concentration was compared under routine and event-monitoring scenarios. These uncertainties

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were used to evaluate passive samplers as an alternative monitoring tool to grab sampling. The

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capacity of passive samplers to estimate mass loads was also investigated.

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MATERIALS AND METHODS

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Location. Barratta Creek is a waterway in the dry-tropics area of North Queensland, Australia with a

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catchment area of 1830.7 km2. It is regularly monitored for organic micropollutants and has

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frequently shown high concentrations of pesticides.30-33 Additional information concerning its

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characteristics and observed pesticide occurrences under event conditions can be found in Davis et

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al.34, 35

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Sampling regime. Passive sampling. ChemcatcherTM passive samplers comprising styrene-

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divinylbenzene reverse-phase sulfonated (SDB-RPS) Empore DisksTM as the sorbent phase were

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covered by polyethersulfone (PES) membranes (0.45 μm pore size) and deployed in duplicate. These 4

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(see Roll and Halden29 for a theoretical

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have previously been deployed for quantifying concentrations of herbicides in routine monitoring.36,

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37

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The study period spanned between December 14, 2012 and February 18, 2013 and included a 4-day

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(100-hour) flood event beginning January 23. The flood event was due to a tropical low pressure

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system (ex-Tropical Cyclone Ita). The closest functioning rainfall gauge station to the sampling

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location (approximately 9.1 km away) recorded 233 mm of rain during the event (compared with a

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mean annual rainfall of 703 mm). The closest functioning flow gauge station (approximately 3.2 km

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downstream from the sampling location) recorded a peak flow rate of 435 m3 s-1 (< 0.2% of

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discharges have exceeded this peak flow since 1990).

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During the study period, passive samplers were deployed in a staggered consecutive and overlapping

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deployment design (Figure 1). The average deployment time was 28 days, excluding Deployments F-0

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(100-hour), F-11 (15-day), P-11A and P-11B (both 11-day). The caption of Figure 1 provides more

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details on deployment period codes.

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Figure 1. Passive and grab sampling regime. Blue line: Hydrograph over sampling period. Horizontal grey solid lines:

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quarter (Q) of the deployment period the flood event occurred. F-x = flood (F) monitoring where passive samplers were

deployment periods of passive samplers. Black dots in horizontal grey lines: manually collected grab samples. Vertical grey dotted lines: date of deployment or retrieval of samplers. Codes. B-x = before (B) flood monitoring where ’x’ is the number of days before the flood event passive samplers were removed. Q-x = routine monitoring where ‘x’ represents during which

deployed at the beginning of the flood event and ‘x’ is the number of days post-flood (e.g. F-22 is a 26 day deployment covering the flood event (4 days) plus 22 post-flood days); P-x = post (P) flood monitoring where passive samplers were



126 127

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deployed after the flood event and ‘x’ is the number of days samplers were deployed for (e.g. P-11B is an 11 day deployment).

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Grab sampling. During the study period, grab samples were manually collected. Prior to (40 days) and

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after (22 days) the event, 7 and 9 samples respectively were manually collected representing low-

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resolution sampling. During the flood event, high-resolution sampling was carried out with samples

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collected at 15-minute intervals for the first 34 hours to match the increased frequency of

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corresponding flow measurements. Due to occupational health and safety protocols for researchers

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there was no sampling conducted during two subsequent periods of approximately 12 hours

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duration (SI 1, Figure S1). For the last 15 hours of sampling, sampling frequency decreased to half-

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hourly and then hourly intervals. In total, 258 samples were collected over the 100-hour period of

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the flood event. All samples collected corresponded to the timing of the discharge recordings.

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Herbicides investigated. A suite of herbicides was detected including ten parent compounds and two

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metabolites. Additional details are provided in the supporting information (SI 2) including chemicals,

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materials and reagents, sample preparation and processing, chemical analysis and QA/QC. Of those

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herbicides detected, atrazine, diuron, ametryn and metolachlor are the focus of this study. These

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analytes represent a number of different herbicide classes and were selected such that the

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estimation of mass loads from grab sampling covered a wide range of uncertainty (from small

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uncertainty to very high uncertainty) due to their noticeably different chemograph patterns. Table 1

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provides information on their relevant physico-chemical and sampling parameters.

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Table 1. Relevant parameters of herbicides investigated in the present study. Herbicide

Class

Molar mass

Aqueous

Log

Log

Log

RS relative

solubility (mg L )

KOW

KSW

KPESW

to atrazine

2.7a

4.48b

3.25c

1.0d

c

0.868

-1

-1

(g mol ) Atrazine

Chlorotriazine

215.68 a

35 a

Diuron

Phenylurea

233.09

a

35.6

Ametryn

Methylthiotriazine

227.12 a

Metolachlor

Chloroacetanilide

283.8

a

a

200 a 530

a

2.87

a

2.63a 3.4

a

b

4.53

4.61

4.46h h

4.69

e

0.824f 2.70

c

1.5

g

147 148

a – UH Pesticide Properties Database38. b – Stephens et al.39. c – Vermeirssen et al.40. d – O’Brien et al.41. e – Stephens et

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Estimation of herbicide concentrations and loads. Passive sampling. Aqueous herbicide

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concentrations estimated from passive samplers were derived using eq 1

al.22. f – Shaw and Mueller19. g – Shaw et al.42. h – Modelled using ordinary least squares linear regression.

(1) 6 ACS Paragon Plus Environment

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=

− 1 −

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where NS is the herbicide mass accumulated on the sorbent, MS the mass of the sorbent, KSW the

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sorbent-water partition coefficient of the herbicide, t the duration of the deployment (days) and RS

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the sampling rate (L d-1). 43

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To estimate KSW values for ametryn and metolachlor, we assumed a linear relationship to exist

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between the log KSW and log KOW39 for atrazine and diuron (Table 1) and estimated the data from this.

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While a linear relationship between log KSW and log KOW has not been proven in all cases with polar

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organic chemicals and passive sampling devices, in this instance, the RS of the four analytes

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investigated in this study were sufficiently close to assume such a relationship.44 When investigating

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additional substances it should be noted that this approach may not be applicable.

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Passive flow monitors (PFMs) were co-deployed with passive samplers as a flow-dependent in situ

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calibration method to correct the RS of atrazine for the deployment-specific conditions according to

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eq 2

() = 0.011 + (0.176 − 0.011) ∙ (1 − exp(−6.63()) 163

where ( is the average flow velocity (m s-1) of water in which the passive sampler is deployed. 41 This

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velocity was calculated using eq 3

)*+, − 0.12 2.62 + 6.50/ − 13.3/ 0 + 8/ 2 ( = 0.069

(3)

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where )*+, is the mass loss rate (g day-1) of the gypsum (calcium sulfate) and / is the ionic strength

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of the water12 where )*+, is the mass loss rate (g day-1) of gypsum (calcium sulfate dihydrate) and /

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is the ionic strength of the water.13 The mass loss of gypsum is limited by transport through the water

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boundary layer (WBL) and its dissolution rate can serve as a measure of the effective phase thickness

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of the WBL. The PFM is a useful in situ calibration tool (particularly for analytes such as the herbicides

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of current interest where the WBL acts as a limiting resistance in passive sampler uptake) and has

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proven to be a robust means of determining flow velocity over a range of relevant environmental

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temperatures and ionic strengths.12

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Atrazine is the only chemical investigated where in situ calibration of RS in relation to changes in flow

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velocity has been undertaken. Therefore, to derive in situ RS data for diuron, ametryn and

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metolachlor, we used a relative RS approach. For example, Shaw et al.42 observed RS values for

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atrazine and metolachlor of 0.14 L d-1 and 0.21 L d-1 respectively affording a relative RS ratio for 7 ACS Paragon Plus Environment


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metolachlor of 1.5 (Table 1). Studies from which the relative RS data were derived used similar

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passive sampler configurations (i.e. Chemcatcher as housing, SDB-RPS as sorbent and PES

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membranes (0.2 μm pore size for atrazine, ametryn and metolachlor and 0.45 μm for diuron) as the

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diffusion layer).

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Herbicide loads from passive sampler measurements were estimated with eq 4

4*5 = 5 ∙ 67 ∙ (∆ ∙ 86400 seconds) 182

(4)

where 5 is the time-averaged water concentration derived from the passive samplers (eq 1)

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during the deployment period and 67 the mean flow rate from the defined deployment period over

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time, ∆ (days).

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Grab sampling. While flow rate data (m3 s-1) was available for all time points, there were limited

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herbicide concentration data outside of the flood event period given the low-resolution sampling

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employed at these times. For consistency between the estimation of herbicide concentration and

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load using grab samples, we assumed a linear relationship to exist between concentration data at all

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time sampling points (SI 3, Figure S2). Linear interpolation was applied to these data gaps

? =

?@A − ?BA ( − BA ) + ?BA @A − BA

(5)

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where in the absence of sampled concentration values, ? is the ith missing value (to be interpolated)

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relative to the discharge C , is the ith time for which ? needs to be estimated, BA and @A are the

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times of sampling for ?BA and ?@A which are the neighbouring measured concentrations.45 The

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average concentration (

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time period was then calculated as 4 ≈ ∑ ? C where = 900 seconds and is considered as

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effectively equal to the true load.

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Estimation of uncertainty. Due to the absence of high-resolution grab sampling during low-flow

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conditions and the variability in concentration data, the uncertainty associated with this data was

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determined to evaluate comparability of sampling modes.

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Passive samples. Uncertainty was calculated based on the variance (95% confidence interval (CI)) in

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RS data from other published studies with similar sampler configurations to ours. These studies also

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had similar flow velocities when calibrating the RS of atrazine viz. 0.14 m s-1 (modelled from eq 2),

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0.14 m s-1, 0.14 m s-1and 0.13 ± 0.01 m s-1.19, 41, 42, 46 The RS data reported from each of these studies

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were 0.108 L d-1, 0.14 L d-1, 0.17 L d-1 and 0.12 L d-1 respectively with a mean RS of 0.134 L d-1. This

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resulted in 95% CIs of ± 0.029 L d-1 for the RS data. This was applied to eq 2 to provide an estimate of

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the uncertainty of RS and hence concentration.

∑ E5 F

) for each deployment period was then determined. The load for each


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Grab samples. The 95% CI data was also used to quantify the uncertainty of the mean of herbicide

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concentrations derived from grab samples during each deployment period. For the deployment that

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covered the 100-hour duration of the flood event (F-0) n=258. For all other deployments that

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included the flood event (e.g. Q-4 and F-11), the 258 samples were represented by n=1 to avoid bias.

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RESULTS AND DISCUSSION

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Part 1: Event monitoring.

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The comparability of sampling modes is illustrated in Figure 2 and discussed below. Following, the

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accuracy of passive sampling in deployment F-0 is then discussed as high-resolution grab sampling

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was also undertaken during this period, thus providing a mean value almost equal to the true TWA

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concentration (SI 1, Figure S1 provides the chemographs of these analytes during the event).

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Figure 2. Comparisons of mean concentrations from passive and grab sampling across all deployment periods. Variances for both passive and grab sampling are expressed as 95% CIs. Results from the deployment covering the flood event (F-0) are placed at the right of each plot.



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Comparability of passive sampling and grab sampling. In general, the derived concentrations of

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herbicides from passive samplers were highly comparable to mean concentrations derived from grab

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samples that were collected over the same deployment time. The level of agreement between

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sampling modes was independent of concentration that ranged over two orders of magnitude from

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10 ng L-1 for metolachlor up to 6.3 µg L-1 for atrazine. Homoscedasticity was observed over the range

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of concentration data as shown by White’s test, H = 0.1456 (SI 4, Figure S3). This means however

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that while the factor difference remained constant as concentration increases, the absolute

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magnitude of this disagreement increases at higher concentrations. In Deployment B-2 for example,

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the factor differences for ametryn and atrazine were 1.4 and 1.3 respectively while the absolute

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magnitudes of the concentration difference were approximately 30 ng L-1 and 1400 ng L-1

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respectively.

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Comparing passive and grab samples for all analytes, all deployments had a mean difference less

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than a factor of 1.5 except for Deployments P-11B (a factor of 2.41), Q-2 (a factor of 1.92) and Q-1 (a

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factor of 1.82). In general, passive sampler derived data for ametryn and metolachlor tended to be

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below grab sample concentrations (in 7 of the 12 deployment scenarios and 10 out of 12 scenarios

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respectively) while the opposite was noted for atrazine and diuron (in 8 out of 12 and 9 out of 12

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deployment scenarios respectively). The relatively low factor difference in observed concentrations

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between sampling modes highlight the usefulness of passive samplers in detecting changes in

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concentration temporally (across sampling periods) for the same analyte and between analytes –

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even when exposed to a high-flow scenario.

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When considering the uncertainty of both sampling modes, there was a crossover in their respective

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95% CIs in 90% of all scenarios. Only in Deployments Q-1 (atrazine and diuron), P-11B (atrazine and

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ametryn) and F-0 (ametryn and metolachlor) was it observed that the 95% CIs did not overlap. Even

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though differences may still exist between sampling modes in deriving CW, the uncertainties of each

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mode help emphasise similarities between both sampling modes despite variable concentrations and

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flow.

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Flood events can result in increased concentrations of inorganic cations (e.g. NHK@ )47 and organic

247

acids (e.g. humic acid) in water.48,

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uptake on SDB-RPS sorbent (which has a mixed-mode weak cation exchange mechanism) by

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competing with herbicides for adsorption sites. In this context, further investigation on sorption

250

mechanisms would be useful; however, it is still worthwhile to note the usefulness of laboratory

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derived RS applied in this work despite possible competition effects on uptake.

49

These elevated concentrations have the potential to affect


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Membrane covered samplers in high-flow (event) deployments. When a membrane-covered

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sampler is exposed to a pulse in concentration, a lag in uptake is expected resulting in a systematic

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underestimation of estimated concentration.19, 50, 51 This is expected in environments with variable

255

concentrations as the conditions for a steady state flux into the sorbent are unlikely to be

256

established.43 In our flood-event specific deployment (F-0), an underestimation was observed for all

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investigated analytes. The passive sampler derived concentrations of atrazine, diuron, ametryn and

258

metolachlor underestimated those from grab sampling by factors of 1.08, 1.1, 1.41 and 1.52

259

respectively (µ = 1.29). Despite being in a field setting, results from this study were accompanied by

260

reduced error in comparison with similar work under laboratory (controlled) conditions.19 These

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authors observed an average underestimation across all chemicals investigated by a factor of 1.85

262

and for atrazine, diuron and ametryn in particular (metolachlor was not investigated), the

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underestimation was by a factor of approximately 2.

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The improved performance when deployed in a field setting may be explained by consideration of

265

the theory surrounding adsorption-based passive sampling. In adsorption-based samplers,

266

particularly if the configuration includes a hydrophilic membrane (i.e. PES), RS control is expected to

267

be primarily a function of the WBL for most analytes and in turn dependent on the in situ

268

hydrodynamic conditions. In the work of Shaw and Mueller,19 a constant flow-rate of 0.14 m s-1 was

269

used. In comparison, the average flow predicted by the PFM in deployment F-0 was 0.38 m s-1 and

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under such conditions, all sampled chemicals approach the plateaued maximum RS and resistance is

271

shifted to trans-membrane diffusion.41,

272

through the polymer matrix and water-filled pores.53, 54 In two experiments Vermeirssen et al.40, 46

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showed diuron to have a lag phase of approximately 2 days with a PES membrane of pore size 0.1

274

μm. With a PES membrane of pore size 0.2 μm, Shaw et al.42 observed a lag of 1 day for diuron and

275

less than 1 day for atrazine, ametryn and metolachlor. In the current work, the pore size of the PES

276

membrane was 0.45 μm and thus it would be reasonable to expect resistance to mass transfer to be

277

further reduced, therefore shortening the lag phase. This may result in faster responses to changing

278

ambient concentrations, thus providing higher accuracy (and less underestimation) of TWA CW.

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Part 2: Load estimation.

280

Diffusion-based samplers such as the Chemcatcher provide TWA concentrations while micropollutant

281

loads – the product of concentration and flow – are flow-weighted. If concentration and flow were

282

independent and identically distributed (i.i.d.) random variables, the TWA concentration would

283

provide unbiased estimates of the load using eq 4. However, environmental data (time-series or

284

spatial) are autocorrelated and therefore bias is introduced into load estimations. Positive covariance

285

between concentrations and flow will likely cause underestimations of load while negative

52

Mass transfer of an analyte in these circumstances is



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286

covariance will likely cause overestimations.55 Additionally, covariance can change between flow

287

strata (low- and high-flow periods) during the same sampling period. As a result, if a passive sampler

288

hypothetically provides an accurate estimate in TWA concentration (or representative sample), this

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will likely result in a biased estimate of the load that can increase as the magnitude of the covariance

290

increases or the variance in concentration and/or flow increases. Thus, an issue in the use of

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diffusion-based passive sampling for load estimation is that the TWA concentration is fundamentally

292

not an accurate parameter for load estimation. In this context, grab sampling has a few advantages

293

over passive sampling. The sampled data provides information on the covariance between

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concentration and flow during that sampling period and this sampled data can be separated into

295

more representative flow strata. The algorithms associated with grab sampling are more suitable for

296

estimating mass loads and/or can utilise the sampled data to correct for bias caused by

297

autocorrelation.

298

Estimating mass loads with time-weighted average (TWA) concentrations. During the 66-day study

299

period, 11% of the total load of the four herbicides was delivered pre-flood (40 days), 86% was

300

delivered during the flood (4 days) and 3% was delivered post-flood (22 days). Approximately 90% of

301

the total load was delivered during all deployments that included the flood event regardless of the

302

timing of the flood event within the deployment period (Figure 3). Accurate estimation of loads

303

during high-flow periods is therefore crucial. Based on the data presented in Figure 3, if for example,

304

the total load of the analytes during period F-0 (i.e. 338 kg) was overestimated by a factor of 1.1 (i.e.

305

372 kg), this would result in an approximately equivalent overestimation error in load during period

306

B-2 (i.e. 34 kg) by a factor of 2 (i.e. 69 kg).

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Figure 3. Ratios of loads calculated from passive sampling relative to those calculated from grab sampling during each

309 310 311 312

particular deployment period (e.g. passive sampler estimated load for atrazine was 2.9 times greater than that from grab sampling in P-11B). The percentage values given at the top of the results for each deployment represent the proportion of the load accumulated during that passive sampler deployment period (for all herbicides) against the total load during the entire study period.

313 314

Under conditions where flow is relatively constant (e.g. the dry season), smaller differences between

315

the accuracy of TWA concentrations and loads estimated by passive samplers would be expected

316

(unless variations of concentration were of a similar magnitude to changes in flow). However,

317

differences in accuracy are expected when flow is variable (e.g. the wet season). We highlight these

318

inherent limitations with three examples from Figures 2 and 3 comparing concentrations and loads

319

respectively from the different sampling modes. Firstly, in Deployment Q-1, atrazine concentrations

320

estimated by passive samplers were greater by a factor of 2.7 compared to those from grab sampling

321

while loads were underestimated by a factor of 1.15. In this same deployment however, metolachlor

322

data deviated by less than a factor of 1.2 in the estimation of both concentration (1.18) and load

323

(1.03). Finally, for diuron in Q-3 and Q-1, the loads estimated by passive samplers were both

324

approximately equal to those estimated by grab sampling. However, passive samplers

325

underestimated concentrations compared to grab samples by a factor of 1.5 in Q-3 and

326

overestimated by a factor of 2.1 in Q-1.

327

Overall, the variance in the ratio of load estimates from passive samplers relative to grab samples

328

was 0.3-1.22 for atrazine, 0.56-1.8 for diuron, 0.52-0.98 for ametryn and 0.69-1.16 for metolachlor

329

(Figure 3). Despite no clear trends being evident, our data does suggest that timing of deployment in

330

relation to the flood event and the chemograph pattern prior to, during and after the flood event

331

could contribute to this variance. Understanding its impact on passive sampler uptake and

332

elimination of analytes may help in understanding the error in the TWA concentration depending on

333

chemograph type and consequently uncertainty and also potential load estimation error.

334

Stratified passive sampling in load estimation. Bias is often corrected for using information from

335

sampled data during a particular sampling period such as the variance of residuals (e.g. predicting

336

concentration using a rating curve) or the covariance between concentration (or load) and flow. An

337

approach to correct for passive sampler bias using the covariance of concentration and flow derived

338

from grab samples during the same sampling period has previously been presented.26 An alternative

339

approach to limit bias is stratified sampling. Stratification of sampler deployments between flow

340

strata (i.e. high- and low-flow) is a useful means of reducing error caused by the bias in TWA

341

concentrations and its principles are well-grounded in load estimation literature.56,

57

Error in 13



Page 14 of 36

342

estimating loads is greatest within high-flow strata and if data can be restricted to this stratum (e.g.

343

as in deployment F-0) rather than also being applied to low flow strata (e.g. Q-3), then flow would be

344

better represented during that sampling period and total error would be reduced.

345

To illustrate the effect of this strategy, consider Deployments F-0 and P-22 that together cover the

346

same sampling duration as F-22 but are separated into high- (F-0) and low-flow (P-22) strata

347

respectively (see Figure 1). For the period of F-22 (January 23 to February 18), the calculated loads

348

from grab sampling using linear interpolation (which is almost equal to the true load) was 303 kg for

349

atrazine, 35.5 kg for diuron, 5.2 kg for ametryn and 4.4 kg for metolachlor. Comparing passive

350

sampling with these results, the estimated herbicide loads using a non-stratified (F-22) and a

351

stratified (F-0 + P-22) approach respectively were 99.3 kg (73% underestimation of the 303 kg load

352

estimate) and 269 kg (11% underestimation) for atrazine, 20.1 kg (43% underestimation) and 33.9 kg

353

(4% underestimation) for diuron, 3.6 kg (31% underestimation) and 4.4 kg (15% underestimation) for

354

ametryn, and 3.7 kg (16% underestimation) and 7.8 kg (77% overestimation) for metolachlor.

355

In each case except metolachlor, stratification improved the accuracy of passive sampler load

356

estimates. Deployment F-0 had high-resolution grab sampling during the much of 100-hour period (n

357

= 258) and from this the true mean concentration and total load of metolachlor was 185 ng L-1 and

358

4.02 kg respectively. In F-0, metolachlor concentrations with passive samplers were underestimated

359

by 35% (121 ng L-1) yet the load was overestimated by 86% (7.48 kg). Hypothetically, if the passive

360

sampler predicted the abovementioned true mean concentration, the load would have instead been

361

overestimated by 183% (11.38 kg). In contrast, using the true mean concentration to predict loads

362

for atrazine, diuron and ametryn would result in load estimates approximately equal to the true load

363

with smaller overestimations of 2%, 4% and 22% respectively. This systematic bias for overestimating

364

the load is related to the negative covariance observed between concentration (chemograph) and

365

flow (hydrograph) for all analytes in the flood. The chemograph for metolachlor was characterised by

366

baseline low-concentration levels of approximately 200 ng L-1, a peak where concentrations

367

increased 10-fold (to 2000 ng L-1) followed by a rapid decline where concentrations decreased 100-

368

fold (to around 20 ng L-1). This occurred in the rising limb of the hydrograph before peak discharge (SI

369

1, Figure S1). To reduce error, the high-flow strata would therefore need to be further stratified (i.e.

370

rising and falling limb of hydrograph). This of course raises issues on operational feasibility and such a

371

sampling program would be the equivalent of 3 grab samples. It is expected however that the

372

accuracy of load estimates would increase for analytes exhibiting the chemograph profile of

373

metolachlor.

374

Preferred sampling mode during a flood event. While the estimated load for metolachlor in F-0

375

using passive sampling is not ideal, error exists within both sampling modes and each has their own 14 ACS Paragon Plus Environment

Page 15 of 36


376

limitations. In this context, the preferred sampling mode is that which provides the least uncertainty.

377

From sampling theory, as sampling effort increases, grab samples would eventually consistently

378

provide estimates with higher accuracy and precision than passive samplers. It is therefore important

379

to understand how many grab samples are required before it has less uncertainty than passive

380

sampling.

381

We are able to provide a useful comparison on the performance of passive samplers against grab

382

samples with a simple modelling exercise (Figure 4). For passive samplers, we used the standard

383

deviation in RS data (see ‘Estimation of uncertainty’ in Materials and Methods) to derive the variance

384

in estimates of the load and then modelled 1000 scenarios assuming this variance (i.e. likelihood for

385

under- and overestimation of the estimated load) was normally distributed. For grab samples, we

386

simulated 1000 scenarios using simple random sampling to derive distributions at each sampling

387

effort from 2 to 20 samples. Results for both sampling modes are a ratio (4L /4N ) of the true

388

load that was calculated by the high-resolution grab sampling in period F-0.



Page 16 of 36

389 390 391 392 393 394

Figure 4. Comparing the variance and performance in the load estimates of the passive sampler (Deployment F-0) to a simple random sampling approach during the flood event. A) Passive sampling (shaded areas and black lines): light grey – 025% and 75-100% quartiles; dark grey – interquartile range (25-75%); black line – median. Grab sampling: red lines representing maximum; 75% quartile; median; 25% quartile and minimum of load estimates. B) Passive sampling: line. Grab sampling: open circles.

395 396

Two measures of performance of a sampling mode for micropollutant load estimates are the

397

magnitude of the interquartile range (IQR) and the root mean squared error (RMSE). The sampling

398

mode with the smaller IQR would provide more precise load estimates in at least 50% of the

399

scenarios. Similarly, the sampling mode with the lower (relative) RMSE (√P̅0 + R 0 T4S ) would be

400

preferable since this shows the predictive capacity of a model by combining the magnitude of error

401

in the residuals where P̅ is the mean error and R is the standard deviation of estimates. While both

402

measures are useful tools with which to evaluate the preferred sampling mode they each have 16 ACS Paragon Plus Environment

Page 17 of 36


403

specific weaknesses. The IQR does not consider bias and can overestimate the number of samples

404

required to achieve a nominated level of performance if the sampling mode is itself biased. The

405

RMSE on the other hand considers this bias and assumes a normal distribution. The sample number

406

requirement therefore depends on the accuracy of the true load (which is subject to some

407

uncertainty during the missed sampling periods) and if the distribution of load estimates is skewed.

408

From Figure 4, for grab samples to be preferred over passive sampling in this 100-hour event, diuron

409

would require 3 grab samples based on both IQR and RMSE considerations. In contrast, ametryn

410

would require 9 (IQR) or 6 (RMSE) samples, atrazine would require 11 (IQR) or 7 (RMSE) samples and

411

metolachlor would require 17 (IQR) or 11 (RMSE) samples. Logistically, a passive sampler deployment

412

during the flood event in the current work would be the equivalent of 2 grab samples (deployment

413

and retrieval). In general, the preferred sampling mode in regard to load estimate performance will

414

be analyte-specific and dependent on the uncertainty of each sampling mode for each analyte, which

415

will change for each sampling period.

416

Part 3: Limitations of the study and implications for use of passive samplers in event monitoring

417

and load estimation.

418

Passive flow monitors (PFMs) as an in situ calibration method. The catchment monitored had

419

relatively high flow velocities throughout the study period. Mean flow predicted by the PFM across

420

all deployments in our study was 0.26 m s-1 with a standard deviation of 0.07 m s-1. Previous studies

421

with atrazine have shown a nonlinear relationship between RS and flow velocity where RS plateaus at

422

higher values of flow velocity and rPFM.12, 52 At the flow velocities measured in the current work, any

423

increase would not result in a proportional increase in the magnitude of RS. This is reflected in RS

424

values that varied from -17% to 14% from the mean values for atrazine (0.14 L d-1), diuron (0.12 L d-1),

425

ametryn (0.13 L d-1) and metolachlor (0.21 L d-1). It is reasonable to expect though that an episodic

426

discharge event would result in an increase in RS values. Use of in situ calibration would become

427

more important due to this higher variability. In the absence of in situ calibration, an additional

428

uncertainty parameter would need to be incorporated to account for higher variance in RS (see

429

‘Uncertainty’ discussion below).

430

Relative sampling rate. Due to the lack of in situ calibration data for all target chemicals, our study

431

used a relative RS approach for passive sampling (with atrazine as the reference chemical).

432

Limitations of this include factors such as the differential sorbent affinity of analytes and competitive

433

adsorption phenomena (resulting in anisotropic exchange) contributing to variation in RS values

434

between analytes.58,

435

exposure conditions act as confounding variables on the RS of an analyte, these factors would alter

59

In the same way that differences in passive sampler configuration and



Page 18 of 36

436

the RS value of one analyte relative to another. Proportional changes in the WBL mass transfer

437

coefficient are not necessarily observed for each analyte due to changes to the diffusion path length

438

(cavity depth),60, 61 form drag (geometry and orientation of the sampler in the water column)62 and in

439

situ hydrodynamics (advection and turbulence).63 In addition, the membrane (e.g. KPESW) is

440

increasingly being recognised as an important phase for both analyte accumulation and inducing lag

441

times for mass transfer into the sorbent depending on the properties of the target analyte. Diuron,

442

for example, has been particularly problematic.40,

443

biofouling (acting as an additional diffusion barrier) and temperature may also produce analyte-

444

specific effects on uptake.63

445

The cumulative effects of these differences therefore warrant careful consideration and appropriate

446

selection of RS and relative RS values or improved methods for in situ RS correction in routine

447

monitoring. (The potential for in silico modelling has been recently published and offers a promising

448

alternative in deriving RS values).44 To illustrate the importance of appropriate RS selection, in the

449

work of Vermeirssen et al.41, the RS of diuron (0.03 L d-1) was 25% that of atrazine (0.12 L d-1). Their

450

study used the new Chemcatcher design (7 mm cavity depth), a smaller PES membrane pore size (0.1

451

µm) and a lower flow rate (0.13 ± 0.01 m s-1) while in contrast, the present work employed the

452

original Chemcatcher design (20 mm cavity depth), a larger PES membrane pore size (0.45 µm) and

453

on average, a higher flow rate as measured by the PFMs (μ = 0.26 m s-1, σ = 0.07 m s-1). Adopting the

454

relative RS data from Vermeirssen et al.’s results, estimations across deployments for diuron would

455

be greater by a factor of 3.6. Instead, we employed the same proportionality factor for the RS of

456

diuron relative to atrazine as reported by Stephens et al.23 who had the same housing and a

457

membrane pore size (0.2 µm) and flow rate (0.3 m s-1) that were more comparable to those in our

458

deployments. In the absence of in situ calibration data for three of the analytes investigated, it was

459

implicit in our study’s approach that the relative RS values remained constant across a range of flow

460

rates. Available evidence suggests that there are only small differences, though data are limited at

461

present.41, 52

462

Uncertainty. Understanding the uncertainty of derived results is an important facet in comparing the

463

utility of passive and grab sampling given the potential for collecting unrepresentative samples in

464

either sampling mode. In this current work, we propose a method for quantifying uncertainty

465

(expressed as 95% CIs) in the estimation of concentrations derived using passive samplers in the

466

absence of samplers deployed in triplicate. Poulier et al.65 provide an excellent approach to approach

467

issues related to uncertainty with samplers deployed in triplicate. Their work incorporated two

468

parameters into uncertainty when using the polar organic chemical integrative sampler (POCIS). The

469

first was variance in RS in the absence of in situ calibration and the second was random error

54, 64

Additional confounding factors such as


Page 19 of 36


470

associated with deploying multiple samplers. The former was fixed to an under- or overestimation

471

by a factor of two (-50% and 100%) based on the literature surrounding POCIS9, 66 while the latter was

472

derived from triplicate deploying samplers in triplicate to derive 95% confidence intervals. Excluding

473

the first parameter, average uncertainty in RS across all chemicals in their study was 19.5% and more

474

specifically, was 18% for atrazine, 11% for diuron and 18% for metolachlor. While noting the

475

limitations of this current study (relative RS approach) in the above discussion, we did not consider

476

the first parameter for all analytes since the use of in situ calibration in our work removed this need.

477

Variance in the atrazine RS (±0.029 L d-1) was derived from literature studies involving similar sampler

478

configurations and flow rate to those employed here. Based on this, average uncertainty in our

479

passive sampling derived CW data was 26% (for overestimation) and -17% (for underestimation).

480

In this, the following assumptions were made viz. scatter in measurements of RS is normally

481

distributed and the variance in RS is constant for each chemical, despite changes in flow rate. It is

482

recognised that the assumptions involved have limitations – particularly the constant variance of RS

483

for different chemicals and varying flow rates. For the former, the uncertainty for all chemicals was

484

derived from the variance in the atrazine RS (±0.029 L d-1). This was due to insufficient data for

485

ametryn and metolachlor. However, the uncertainty could be calculated for diuron and would have

486

been ± 0.045 L d-1. For the final assumption, in O’Brien et al.,41 the standard deviation in calibrating

487

the RS of atrazine and prometryn changed between flow rates. Nevertheless, the method proposed

488

here may be a useful way to incorporate uncertainty in the absence of replicate deployments.

489

Application and transferability of results. The results of this study are applicable beyond the passive

490

sampling device, configuration adopted and chemicals monitored in this study. A principal criticism

491

of adsorption-based passive sampling for polar micropollutants is that the approach is semi-

492

quantitative.9,

493

deployment period or concentration profile, when the uncertainties of both grab and passive

494

sampling are considered, the results derived from both sampling modes are not necessarily

495

significantly different.

496

The rationale for use of a “naked” sampler is to limit (or negate) the issue of any lag phase induced

497

by a membrane mass transfer. Such a configuration is particularly important for exposure scenarios

498

that involve concentration pulses but with the trade-off of a relatively short integrative phase and a

499

greater potential for biofouling.18, 19, 22, 39, 42, 68-70 Not all passive sampling devices have this flexibility in

500

configuration however (e.g. POCIS) and our results are encouraging for devices configured with a PES

501

membrane under conditions with high flow and pulses (or higher variations) in concentration.

67

Our results indicate however that under most scenarios and regardless of



Page 20 of 36

502

Understanding the relationship between the chemograph and hydrograph (i.e. covariance between

503

concentration and flow) is an important factor in understanding load estimation bias in passive

504

samplers. The measurement of organic micropollutants such as herbicides are less dependent on

505

flow compared to more naturally occurring solutes such as metals and nutrients which can result in

506

underestimations for the latter two and overestimations for the former (and similar organic

507

micropollutants). However, the relationship between the chemograph and hydrograph for any solute

508

is event-specific and can depend on a number of factors.71-73 This means that the load estimation bias

509

using passive samplers will not only be analyte-specific, but also specific to the particular sampling

510

period and highlights a challenge in adopting passive samplers as a tool to monitor loads.

511

Stratification of a passive sampling program is useful to reduce bias. Its use is recommended when

512

the load differs widely in size between sampling periods (populations)74 such as high-flow events (as

513

done here) or during periods of significant variation in concentration (e.g. sampling periods after

514

pesticide application). However, as we observed for metolachlor, there are some instances where

515

this can result in increased bias and more work is required to understand this occurrence.

516

There are multiple sources of uncertainty for each sampling mode that contribute performance in

517

load estimation. For passive samplers, uncertainties relate to in situ calibration, analyte-specific

518

calibration data, the differences in RS variance, configuration and type of passive sampler and the

519

challenges of extrapolating from the TWA concentration with no information on transport dynamics

520

of the analyte to correct for bias. For grab samples, uncertainty is sensitive to the estimator selected

521

(knowledge uncertainty), the number of samples collected (stochastic uncertainty) and the

522

relationship between the chemograph and hydrograph during the sampling period. Using our data

523

set covering the flood event itself, the coefficient of determination (r2) of the instantaneous load

524

with flow for diuron, ametryn, atrazine and metolachlor was 0.828, 0.525, 0.493 and 0.010

525

respectively. Our results suggest that as the magnitude of this correlation increases, the uncertainty

526

and/or the number of samples required to achieve this level of uncertainty decreases. Thus,

527

uncertainty in grab sampling may be lower the more dependent the load is on flow. In the context of

528

a preferred sampling mode, this would mean that less grab samples would be required to be the

529

preferred sampling mode as this correlation increases. Because quantification of organic

530

micropollutants is less reliant on flow (compared to metals and nutrients), it is reasonable to expect

531

that relatively more samples would be required for grab samples to be preferred over passive

532

sampling. Given that the performance of grab and passive sampling is analyte-, catchment- and

533

event-specific, the preferred sampling mode will then also be specific for each analyte during a given

534

sampling period (including this study). Further investigation is required to develop criteria a

535

posteriori, however results of this work suggest that criteria could be related to passive sampling (e.g. 20 ACS Paragon Plus Environment

Page 21 of 36


536

analyte-specific variance in RS), grab sampling (e.g. predictable r2 correlation) or a combination of

537

both.

538

Conducting grab sampling was the preferred active sampling method to obtain the highest data

539

resolution for the flood event but this entailed logistical and occupational health and safety

540

challenges prior to and during sampling that should be noted for future intensive monitoring

541

campaigns. Prior to sampling, challenges included the ability to transport personnel and equipment

542

to a remote site in immediate response to meteorological forecasts and ensuring safe access for

543

setup and sampling under flood conditions. The latter required conducting a comprehensive risk

544

assessment for the planned work in the local environment and having a sufficient number of

545

personnel to prevent exhaustion. The magnitude of this flood event caused inundation of the

546

sampling point (i.e. bridge) and decreased accessibility to midstream velocity. During this period,

547

intensive grab sampling ceased in the evenings for personal safety reasons including a lack of

548

sufficient light, an increased possibility of drowning and the presence of crocodiles. The resultant

549

missing data is a source of uncertainty to this study, though it is not expected to impact our overall

550

findings. The lower volatility in chemograph patterns following the rising limb of the hydrograph

551

suggests linear interpolation was a valid approach to address this. Nevertheless, these challenges

552

further highlight the usefulness of passive samplers in floodwaters.

553

ACKNOWLEDGEMENTS

554

The authors would like to thank Aaron Davis, Christie Gallen and Chris Paxman for assistance in

555

organising the project and Geoffrey Eaglesham and Jake O’Brien for assistance in chemical analysis of

556

pesticides. The Queensland Alliance for Environmental Health Sciences, The University of Queensland

557

gratefully acknowledges the financial support of the Queensland Department of Health. Jochen

558

Mueller is funded by an ARC Future Fellowship (FT120100546). Andrew Novic receives an Australia

559

Postgraduate Award (APA) PhD scholarship.

560

SUPPORTING INFORMATION

561

Additional information including laboratory protocols, the relationship of the chemograph with the

562

hydrograph for each chemical during the flood event and for the entire sampling period, and a plot

563

on the homoscedasticity in the derived concentrations of grab and passive sampling can be found in

564

the Supporting Information. This material is available free of charge via the Internet at

565

http://pubs.acs.org.

566

AUTHOR INFORMATION

567

Corresponding author 21 ACS Paragon Plus Environment


Page 22 of 36

568

* Phone +61 428 532 053

569

* E-mail: [email protected]

570

Notes

571

The authors declare no competing financial interests.

572

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573

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Schaefer, R. B.; Paschke, A.; Liess, M. Aquatic passive sampling of a short-term thiacloprid

Shaw, M.; Mueller, J. F. Time Integrative Passive Sampling: How Well Do Chemcatchers

O'Brien, D.; Hawker, D.; Shaw, M.; Mueller, J. F. The performance of passive flow monitors

Petersen, J.; Grant, R.; Larsen, S. E.; Blicher-Mathiesen, G. Sampling of herbicides in streams

Stephens, B. S.; Kapernick, A. P.; Eaglesham, G.; Mueller, J. F. Event monitoring of herbicides

Birch, H.; Sharma, A. K.; Vezzaro, L.; Lutzhoft, H.-C. H.; Mikkelsen, P. S. Velocity Dependent



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Espaldon, M. V. O.; Stoll, S. Assessment of Trace Metals during Episodic Events using DGT Passive

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Sampler: A Proposal for Water Management Enhancement. Water Resour. Manage. 2013, 27, (12),

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4163-4181.

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A. M.; Greenwood, R. Strategic monitoring for the European Water Framework Directive. TrAC,

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Trends Anal. Chem. 2006, 25, (7), 704-715.

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Application and Evaluation of a New Passive Sampler for Measuring Average Solute Concentrations in

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a Catchment Scale Water Quality Monitoring Study. Environ. Sci. Technol. 2010, 44, (4), 1353-1359.

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G. Estimates of pesticide concentrations and fluxes in two rivers of an extensive French multi-

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agricultural watershed: application of the passive sampling strategy. Environ. Sci. Pollut. Res. 2015,

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22, (11), 8044-8057.

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S. M.; Dawson, J. J. C.; Hough, R. L. Evaluation of spot and passive sampling for monitoring, flux

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estimation and risk assessment of pesticides within the constraints of a typical regulatory monitoring

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scheme. Sci. Total Environ. 2016, 569, 1369-1379.

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samplers employed in environmental water monitoring. Water Res. 2016, 94, 200-207.

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Reef. Environ. Pollut. 2009, 157, (8-9), 2470-2484.

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monitoring across Great Barrier Reef catchments - Paddock to Reef Integrated Monitoring, Modelling

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and Reporting Program. Mar. Pollut. Bull. 2012, 65, (4-9), 117-127.

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associated drainage systems. Agr. Ecosyst. Environ. 2013, 180, 123-135.

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Downstream of a Heavily Irrigated Cropping Area: Application of Different Monitoring Techniques. J.

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Agric. Food Chem. 2016, 64, (20), 3975-3989.

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Poulier, G.; Lissalde, S.; Charriau, A.; Buzier, R.; Cleries, K.; Delmas, F.; Mazzella, N.; Guibaud,

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Davis, A. M.; Thorburn, P. J.; Lewis, S. E.; Bainbridge, Z. T.; Attard, S. J.; Milla, R.; Brodie, J. E.

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Davis, A.; Lewis, S.; Bainbridge, Z.; Brodie, J.; Shannon, E. Pesticide residues in waterways of

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the Lower Burdekin Region: challenges in ecotoxicological interpretation of monitoring data.

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Australas. J. Ecotoxicol. 2008, 14, (2-3, Sp. Iss. SI), 89-108.

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Dynamics of herbicide transport and partitioning under event flow conditions in the lower Burdekin

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Monitoring pesticides in the Great Barrier Reef. Mar. Pollut. Bull. 2010, 60, (1), 113-122.

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herbicides on naked particle loaded membranes: Accelerated measurement and empirical estimation

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Kinetics of Polar Organic Compounds over Polyethersulfone Membranes in the Passive Samplers

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rates for SDB-RPD Empore disk using a passive flow monitor (PFM). Chemosphere 2011, 83, (9), 1290-

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sediment and nutrient loads in 10 major catchments draining to the Great Barrier Reef during 2006-

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O'Brien, D.; Bartkow, M.; Mueller, J. F. Determination of deployment specific chemical uptake

Shaw, M.; Eaglesham, G.; Mueller, J. F. Uptake and release of polar compounds in SDB-RPS

Booij, K.; Vrana, B.; Huckins, J. N. Theory, modelling and calibration of passive samplers used

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Joo, M.; Raymond, M. A. A.; McNeil, V. H.; Huggins, R.; Turner, R. D. R.; Choy, S. Estimates of



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three Chemcatcher (TM) configurations. Water Res. 2009, 43, (4), 903-914.

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dissolved organic carbon. Environmetrics 2002, 13, (7), 733-750.

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stream. J. Hydrol. Hydromech. 2013, 61, (1), 81-83.

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(R) passive sampler when used to monitor 10 polar and semi-polar pesticides in 16 Central European

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streams, and comparison with two other sampling methods. Water Res. 2008, 42, (10-11), 2707-

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2717.

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concentrations of organic contaminants. Environ. Toxicol. Chem. 2010, 29, (3), 591-596.

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Pollut. 2014, 186, 98-103.

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Manahan, S. E. Development of a passive, in situ, integrative sampler for hydrophilic organic

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Characterisation and comparison of the uptake of ionizable and polar pesticides, pharmaceuticals

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Land. 1987, 12, (1), 95-104.

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different sampling frequencies and common calculation methods. Mar. Freshwater Res. 2013, 64, (5),

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373-386.

Tanaka, K.; Kodama, M.; Sawada, T.; Tsuzuki, M.; Yamamoto, Y.; Yanagisawa, T. Flood Event

Cooper, D. M.; Watts, C. D. A comparison of river load estimation techniques: application to

Buettner, O.; Tittel, J. Uncertainties in dissolved organic carbon load estimation in a small

Schaefer, R. B.; Paschke, A.; Vrana, B.; Mueller, R.; Liess, M. Performance of the Chemcatcher

Hawker, D. W. Modeling the response of passive samplers to varying ambient fluid

Kaserzon, S. L.; Hawker, D. W.; Booij, K.; O'Brien, D. S.; Kennedy, K.; Vermeirssen, E. L. M.;

Alvarez, D. A.; Petty, J. D.; Huckins, J. N.; Jones-Lepp, T. L.; Getting, D. T.; Goddard, J. P.;

Kaserzon, S. L.; Hawker, D. W.; Kennedy, K.; Bartkow, M.; Carter, S.; Booij, K.; Mueller, J. F.

Ferguson, R. I. Accuracy and precision of methods for estimating river loads. Earth Surf. Proc.

Yaksich, S. M.; Verhoff, F. H. Sampling strategy for river pollutant transport. J. Environ. Eng.

Defew, L. H.; May, L.; Heal, K. V. Uncertainties in estimated phosphorus loads as a function of


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Bauerlein, P. S.; Mansell, J. E.; ter Laak, T. L.; de Voogt, P. Sorption Behavior of Charged and

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Governs Sorption? Environ. Sci. Technol. 2012, 46, (2), 954-961.

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Mode Ion Exchange Sorbent for the Passive Sampling of Organic Acids by Polar Organic Chemical

739

Integrative Sampler (POCIS). Environ. Sci. Technol. 2012, 46, (24), 13344-13353.

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geometry on the performance of Chemcatcher (TM) passive sampler for the monitoring of

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hydrophobic organic pollutants in water. Environ. Pollut. 2008, 153, (3), 706-710.

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Chemcatcher (R) for the passive sampling of various pollutants in aquatic environments Part B: Field

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handling and environmental applications for the monitoring of pollutants and their biological effects.

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the orientation and fluid flow on the accumulation of organotin compounds to Chemcatcher passive

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samplers. Environ. Sci.: Processes Impacts 2015, 17, (4), 813-824.

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sampler for the monitoring of priority organic pollutants in water. Environ. Pollut. 2006, 142, (2), 333-

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integrated sampling of hydrophilic herbicides in aquatic environments. Environ. Toxicol. Chem. 2007,

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Mazzella, N. Can POCIS be used in Water Framework Directive (2000/60/EC) monitoring networks? A

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study focusing on pesticides in a French agricultural watershed. Sci. Total Environ. 2014, 497, 282-

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chemical integrative samplers (POCIS) for environmental monitoring. Environ. Toxicol. Chem. 2013,

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field application of passive sampling for episodic exposure to polar organic pesticides in streams.

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Page 29 of 36


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Page 33 of 36


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