Magnetic Properties as a Proxy for Predicting Fine-Particle-Bound

Article pubs.acs.org/est

Magnetic Properties as a Proxy for Predicting Fine-Particle-Bound Heavy Metals in a Support Vector Machine Approach Huiming Li,†,‡ Jinhua Wang,† Qin’geng Wang,†,‡ Chunhui Tian,† Xin Qian,*,†,‡ and Xiang’zi Leng† †

State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 210023, China ‡ Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Jiangsu Key Laboratory of Atmospheric Environment Monitoring and Pollution Control (AEMPC), School of Environmental Sciences and Engineering, Nanjing University of Information Science and Technology, Nanjing 210044, China S Supporting Information *

ABSTRACT: The development of a reasonable statistical method of predicting the concentrations of fine-particle-bound heavy metals remains challenging. In this study, daily PM2.5 samples were collected within four different seasons from a Chinese mega-city. The annual average PM2.5 concentrations determined in industrial, city center, and suburban areas were 90, 81, and 85 μg m−3, respectively. Environmental magnetic measurements, including magnetic susceptibility, anhysteretic remanent magnetization, isothermal remanent magnetization, hysteresis loops, and thermomagnetism, indicated that the main magnetic mineral of PM2.5 is low-coercivity pseudosingle domain (PSD) magnetite. Using a support vector machine (SVM), both the volume- and mass-related concentrations of heavy metals were predicted by the PM2.5 mass concentrations and meteorological factors, with or without magnetic properties as input variables. The inclusion of magnetic variables significantly improved the prediction results for most heavy metals. Predictions based on models that included the magnetic properties of the metals Al, Fe, Mn, Ni, and Ti were promising, with R values of >0.8 in both the training and the test stages as well as relatively low errors. Our results demonstrate that the inclusion of environmental magnetism in a SVM approach aids in the effective monitoring and assessment of airborne heavy-metal contamination in cities.

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INTRODUCTION

traditional chemical methods, which are both time-consuming and expensive. Airborne heavy metal pollution (such as by Fe, Pb, Zn, and Cu) has been widely evaluated using linear models with magnetic variables, including magnetic susceptibility at low frequency (χLF) and saturation isothermal remanent magnetization (SIRM) in tree leaves,8,12 street dusts,14,15 soils,16,17 and other environmental samples. Magnetic properties of roadside plant leaves can also be used to evaluate other atmospheric pollutants, including PM,18 polycyclic aromatic hydrocarbons (PAHs),19 and NO2.20 However, biomagnetic monitoring work is limited by leaf features, distribution regions, and the growth state of plants.6,12,21 The above-mentioned environmental substrates cannot directly reflect the actual pollution state of particle-bound heavy metals in the atmosphere. In recent years, although several studies have examined the magnetic property of PM sampled using pumped-air filters and its potential

Airborne particulate matter (PM), especially fine particulate matter with an aerodynamic particle diameter of ≤2.5 μm (PM2.5), is a key air pollutant in terms of adverse effects on human health and haze formation.1 Atmospheric toxic heavy metals are omnipresent in PM and have been implicated in various diseases, but they also impose a long-term burden on biogeochemical cycling in ecosystems.2−5 Therefore, urban implementation of atmospheric metal monitoring at a high temporal and spatial resolution is a prerequisite for the development of risk mitigation strategies. Magnetic iron oxides and hydroxides comprise 10−70% of the bulk Fe content in urban PM.6 The sources of this Fe and of the other airborne metals present in these magnetic particles adsorbed to PM are similar and include the combustion of fossil fuel, metallurgical processes, vehicle emissions, abrasion and corrosion of metallic parts, and crustal minerals.7−10 Thus, in the monitoring of airborne heavy metals, the statistical relationship between magnetic variables and metal concentrations11−13 supports the use of a magnetic approach as a measurement tool that is simpler and more cost-efficient than © XXXX American Chemical Society

Received: February 9, 2017 Revised: May 18, 2017 Accepted: May 22, 2017

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DOI: 10.1021/acs.est.7b00729 Environ. Sci. Technol. XXXX, XXX, XXX−XXX

Article

Environmental Science & Technology

Elemental Analysis. Metal elements in PM2.5 were digested with a mixture of HClO4, HNO3, and HF. The As, Cd, Co, Cr, Cu, Mn, Ni, Pb, Ti, and V concentrations in the extracts were determined using inductively coupled plasma mass spectrometer (ICP-MS, Elan 9000, PerkinElmer), and the Al, Fe, and Zn concentrations by inductively coupled plasma atomic emission spectrometry (ICP-AES, Perkin Elmer, Waltham, MA, USA). Elemental concentrations were expressed as both volumerelated (normalized by the volume of air sampled through the filters) and mass-related (normalized by PM 2.5 mass) concentrations in the study. Quality control was ensured by analyzing certified reference material SRM 1648a (urban particulate matter). Recovery was within ±10% of the certified values for the studied elements. The ratios of 207Pb to 206Pb and 208 Pb to 206Pb in the total Pb extracted from the PM2.5 samples were determined using inductively coupled plasma mass spectrometry (ICP-MS). The Pb isotopic standard reference material SRM 981 NIST was used for quality control. The relative standard deviation (RSD) for ten replicates was generally summer. The low pollutant concentrations in summer were mainly due to the high temperature, abundant rainfall, and the relatively strong diffusion capacity, whereas the high concentrations in winter suggested increased emissions and unfavorable meteorological conditions including temperature inversions.3,10 As shown in Figure S2, the wind speed was higher and the relative humidity lower in spring. The relative humidity was higher in summer, whereas the wind speed was slightly lower in winter, which favored the accumulation of air pollutants. Elemental Concentrations. As shown in Table S1, the concentrations of nearly all of the studied elements except Cu were significantly higher in IA than in the other areas (p < 0.05), whereas the concentrations of As, Mn, Ni, Pb, and Zn were significantly lower in CA than in IA or SA (p < 0.05). The highest concentrations of Al, As, Co, Cu, Ni, Pb, and Zn occurred in winter. A comparison between the heavy-metal concentrations in PM2.5 and the limits imposed by the new NAAQS (GB3095-2012) and the WHO are presented in the Supporting Information and Figure S3. Based on the massrelated concentrations (μg g−1) (Table S2), the concentrations of most elements were slightly higher in IA than in the other two areas. In IA, the concentrations of most elements were higher in summer and spring, whereas in CA and SA, they were higher in summer. Enrichment factors (EF) based on the normalization of a given metal concentration with respect to a conservative reference element has been widely used to distinguish between anthropogenic influences and natural background contents.10,46 The EFs were calculated using bulk crustal concentrations, and Ti was used as the reference element.47 As shown in Figure S4, Al, Co, Fe, Mn, and V had average EF values of 100)

(3)

where ε is a prescribed parameter. The ε referred to as the tube size is defined as the approximation accuracy of the training data points. The loss function ignores errors as long as their values are less than that of ε, in other words, errors below ε are not penalized. The second term, 1/2∥ω∥2, is the regularization term and a measure of function flatness. The value of the regularized constant C determines the trade-off between the risk and the regularization term. Finally, by introducing Lagrange multipliers and exploiting the optimality constraints, the decision function takes the following form: N

f (x ) =

target

∑ (αi − αi*)K (x , xi) + b i=1

(4)

where αi and αi* are the introduced Lagrange multipliers. The generalization performance of SVM depends on the careful setting of C, ε, and the kernel type as well as the corresponding kernel parameters. Here, the Gaussian radial basis function kernel (RBF) was applied: k(xi , xj) = exp( −γ × || xi − xj ||2 )

(5)

where γ is the parameter of the kernel, and xi and xj are two independent variables. The data were randomly partitioned to two sets: 80% for the training set and 20% for the test set.36,44 Maximum and minimum data of the observed concentrations for one element were included in the training set, and a test set was used to validate the external predictions of the models. Four models were developed according to the predicted targets and input variables, listed in Table 1. The best model was selected by assigning higher correlation coefficient (R) values and lower errors to the training and test stages among the >100 successful modeling processes. Evaluation of Model Performance. The performance of the model in the training and test stages was measured using the following four indexes: correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement (IA), calculated as described in the Supporting Information. The R of the observed versus predicted output was used to measure the model’s fit. MAE and RMSE measure residual errors, both of which provide a C


Article


Figure 1. Fine particulate matter (PM2.5) concentrations in industrial areas (IA), city center areas (CA), and suburban areas (SA) during the sampling periods.

Figure 2. Scatter plots of (a) χLF vs SIRM and χARM and (b) χLF vs the χARM-to-χLF and χARM-to-SIRM ratios of PM2.5 samples.

netic grains.56 HIRM is commonly used to estimate the concentration of high-coercivity minerals (e.g., hematite and goethite).57 The relative importance of low- (e.g., magnetite and maghemeite) and high-coercivity components in the total assemblage is indicated by the S-ratio,58 with higher values corresponding to higher proportions of low-coercivity minerals.54 The ratios of χARM to χLF, χARM to SIRM, and SIRM to χLF can be used to interpret mineral magnetic grain-size variations, with increasing ratios indicating decreasing grain size. In the case of χARM/χLF, its relationship to ferrimagnetic grain size depends on whether the grain assemblages are mainly larger or smaller than the SD size.59 The ratio of χARM to SIRM peaks in the SD range and decreases with increasing grain size,60 while SIRM/χLF is influenced by a number of factors, with a higher proportion of high-coercivity minerals leading to higher values.54 During the four seasons, χLF, χARM, SIRM, and the average HIRM values were clearly higher in IA than in the other two areas (p < 0.05) (Table S3), indicating the importance contribution of industrial processes in the concentration of magnetic minerals. This is in line with previous reports that the concentrations of high-coercivity minerals such as hematite are much higher in ambient PM collected near industrial plants than in traffic-derived PM.61 Among the four seasons, the values of χLF, χARM, SIRM, and HIRM in the IA were highest in summer and lowest in winter (Table S3). In contrast, in CA, χLF, χARM, SIRM, and HIRM were lowest in spring, and χLF, SIRM, and HIRM were highest in summer. In SA, χLF, χARM, and SIRM values were also highest in summer and lowest in winter.

anomalously enriched. All metals except Cu had significantly higher annual mean EF values in IA than in the other two sites (p < 0.05). Cluster analysis48,49 showed the main sources of airborne heavy metals in Nanjing are metallurgic industry dust, traffic, industrial emissions, coal combustion, and natural soil dust (Figure S5). More details are given in the Supporting Information. Anthropogenic Pb usually exhibits distinctively higher 208Pbto-206Pb and 207Pb-to-206Pb ratios in urban environments than in natural materials.50 The scatter plots of the 207Pb-to-206Pb and 208Pb-to-206Pb ratios in PM2.5 collected from the three areas are shown in Figure S6 along with the ratios of the different anthropogenic Pb sources.51−53 The 207Pb-to-206Pb and 208Pbto-206Pb ratios in selected PM2.5 samples were 0.837−0.960 (average = 0.856) and 2.043−2.175 (average = 2.096), respectively. Generally, the 207Pb-to-206Pb and 208Pb-to-206Pb ratios for most samples were close to the ratios of metallurgic industry dust, coal combustion ash, road dust, and cement, suggesting the contribution of these sources to Pb pollution. The 207Pb-to-206Pb and 208Pb-to-206Pb ratios were lower in several IA and SA samples than in the background soil of Nanjing. The ratios in a few of the CA and SA samples were close to those of exhaust emissions, suggesting that soil dust and unleaded gasoline also contribute to Pb pollution in those areas. Magnetic Properties. Both χLF and SIRM generally reflect the concentration of magnetic, and especially ferromagnetic (e.g., magnetite) minerals,54,55 but unlike SIRM, χLF is also influenced by paramagnetic and diamagnetic minerals,54 and χARM is particularly sensitive to single-domain (SD) ferrimagD


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Figure 3. Temperature-dependence of magnetic susceptibility (χ−T) to heating (red line) and cooling (blue line) as determined for selected PM2.5 samples in industrial areas (IA), city center areas (CA), and suburban areas (SA).

As shown in Figure 2a, the SIRM and χLF values were linearly correlated (R2 = 0.9113), indicating that ferrimagnetic minerals were the dominant magnetic minerals in PM2.5 samples.62 The average S-ratio values were high and varied within a narrow range, from 0.902 to 0.970, during the four seasons in the three areas, which further indicated the dominant contribution of low-coercivity magnetite-type ferrimagnetic minerals to the PM2.5 samples. The hysteresis loops for the selected PM2.5 samples were thin, closed, and approached magnetic saturation in a field of 300 mT, with Bcr 0.05), indicating a finer magnetic grain size in IA than in the other two areas. Correlations among Magnetic Properties and Metal Concentrations. The correlation coefficients of the magnetic properties and heavy metals are presented in Tables S4 and S5. When expressed as their volume-related concentrations, nearly all of the metals (except V) correlated positively and significantly with PM2.5 concentrations. The highest correlation coefficient (r) was that of Ni (0.443) and the lowest that of Ti (0.152). In contrast, when expressed as their mass-related

concentrations, many metals correlated negatively and significantly with PM2.5. This is mainly because many heavy metals tend to adsorb on finer rather than coarser particles, which can significantly influence the mass of PM2.5.66,67 The highest correlation coefficient (r) was that of As (−0.501) and the lowest that of Al (−0.201). Wind speed correlated negatively and significantly with Cd, Pb, and Zn, whereas only Ni had a positive, significant correlation with atmospheric pressure. Both temperature and RH correlated negatively with the volume-related concentrations of many of the metals. For Cd, Co, Fe, Mn, Pb, V, Ti, and Zn, their volume-related concentrations correlated significantly and positively with χLF, SIRM, χARM, and HIRM. The highest r was that of the correlation between Fe and SIRM (0.586). For the mass-related concentrations, nearly all metals correlated positively and significantly with χLF, SIRM, χARM, and HIRM. The correlation between Fe and SIRM had the highest r (0.697). It is not surprising that SIRM had strong correlations with Fe concentrations because Fe likely formed the iron oxides responsible for the magnetic remanence of PM2.5 samples. The S-ratio correlated negatively and significantly with several metals. The volume- and mass-related concentrations of all the metals tested, except As, Cd, Cr, and Cu, correlated significantly with SIRM/χLF, whereas there were few significant correlations with χARM/χLF or χARM/SIRM. Because these ratios were indicative of magnetic grain size, SIRM/χLF rather than χARM/χLF or χARM/SIRM was selected for the next simulation. The r values are comparable to those of other studies on correlations between magnetic properties and heavy metals in tree leaves,12 TSP,22 PM10,11 and street dust,15 as well as on magnetic properties of leaves and heavy metals in deposited atmospheric dust.13 Such correlation is mainly based on the fact that heavy metals occur in close association with magnetic particles, either absorbed or through a common origin.6,61 This justifies environmental magnetism analyses as proxies for evaluating the concentrations of airborne heavy metals. MLR Results. Models using a stepwise MLR were established to predict metal concentrations with the same independent variables used in SVM models II and IV. The training R and test R values are summarized in Table S6. For volume-related concentrations, the training R values of all of the metals, except Fe, Mn, Ni, and Pb, were 0.8 for training and test stages), whereas the predictions for Cd, Co, Cu, and Cr were relatively poor, as the values of the training R were lower (0.6−0.8 for training and test stages). The IRs of As, Cr, Al, and V were better in model II than in model I, whereas the IRs of Cd, Co, Cu, Fe, Mn, Ni, Pb, Ti, and Zn were better in model IV than in model III, indicating the greater improvement afforded by the inclusion of magnetic properties in the mass-related concentrations. Although metals expressed as their volume concentrations are commonly used to measure pollution levels,68 their expression as a proportion of PM mass both reflects the variation in their sources and allows the more-accurate assessment of PM toxicity per unit mass. Information on the mass concentrations of the metals in PM is crucial for pollution control. Typical source apportionments for PM2.5 use As and Cr as the representative metals for coal combustion and steel-related operations, respectively,69,70 whereas Al and V are mainly derived from soil dust.69,70 Thus, metals from different sources and their interactions with atmospheric conditions account for the complexity of predicting airborne metal concentrations and composition. The mechanisms influencing this complex relationship remain to be elucidated in further studies. Implication for Airborne Heavy-metal Monitoring. Heavy metals emitted from anthropogenic sources are often accompanied by the emissions of ferromagnetic particles. This is a consequence of the abundant presence of Fe in natural and anthropogenic materials,71,72 which contributes equally to magnetic properties (usually χLF and SIRM) and to the total iron concentration. The inclusion of magnetic measurements to evaluate heavy-metal contamination is based on the relationship not only between magnetic properties and iron concentrations but also between iron and other heavy metals.72 In a previous study, we identified strong linear correlations between the heavy metals in Nanjing dust from industrial or traffic sources and magnetic properties.15 However, heavy metals and metallic iron particles originating from multiple sources in most cases correlate poorly with magnetic properties.15,72,73 According to

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ASSOCIATED CONTENT

S Supporting Information *

The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.7b00729. Additional information on the sampling sites and preparation, elemental analyses, calculation methods, meteorological conditions, concentration comparisons, cluster analysis, plot of ratios, magnetic hysteresis loops, a Day plot, residuals of Fe and Pb obtained by Model II and IV, volume- and mass-related concentrations, magnetic properties, Pearson’s correlation coefficients, and R values. (PDF)

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AUTHOR INFORMATION

Corresponding Author

*Phone: +86 25 89680527; fax: +86 25 89680527; e-mail: [email protected]. ORCID

Xin Qian: 0000-0003-0692-1188 Author Contributions

The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by the National Natural Science Foundation of China (grant no. 41501549), the National Key Research and Development Program of China (grant no. 2016YFC0208504), and the Open Fund by the Jiangsu Key G


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(18) Hofman, J.; Lefebvre, W.; Janssen, S.; Nackaerts, R.; Nuyts, S.; Mattheyses, L.; Samson, R. Increasing the spatial resolution of air quality assessments in urban areas: A comparison of biomagnetic monitoring and urban scale modelling. Atmos. Environ. 2014, 92, 130− 140. (19) Jordanova, D.; Petrov, P.; Hoffmann, V.; Gocht, T.; Panaiotu, C.; Tsacheva, T.; Jordanova, N. Magnetic signature of different vegetation species in polluted environment. Stud. Geophys. Geod. 2010, 54 (3), 417−442. (20) Brackx, M.; Van Wittenberghe, S.; Verhelst, J.; Scheunders, P.; Samson, R. Hyperspectral leaf reflectance of Carpinus betulus L. saplings for urban air quality estimation. Environ. Pollut. 2017, 220, 159−167. (21) Rai, P. K. Environmental magnetic studies of particulates with special reference to biomagnetic monitoring using roadside plant leaves. Atmos. Environ. 2013, 72 (2), 113−129. (22) Shu, J.; Dearing, J. A.; Morse, A. P.; Yu, L.; Yuan, N. Determining the sources of atmospheric particles in Shanghai, China, from magnetic and geochemical properties. Atmos. Environ. 2001, 35 (15), 2615−2625. (23) Castañeda-Miranda, A. G.; Böhnel, H. N.; Molina-Garza, R. S.; Chaparro, M. A. E. Magnetic evaluation of TSP-filters for air quality monitoring. Atmos. Environ. 2014, 96 (7), 163−174. (24) Muxworthy, A. R.; Matzka, J.; Petersen, N. Comparison of magnetic parameters of urban atmospheric particulate matter with pollution and meteorological data. Atmos. Environ. 2001, 35 (26), 4379−4386. (25) Muxworthy, A. R.; Matzka, J.; Davila, A. F.; Petersen, N. Magnetic signature of daily sampled urban atmospheric particles. Atmos. Environ. 2003, 37 (29), 4163−4169. (26) Sagnotti, L.; Macrì, P.; Egli, R.; Mondino, M. Magnetic properties of atmospheric particulate matter from automatic air sampler stations in Latium (Italy): Toward a definition of magnetic fingerprints for natural and anthropogenic PM 10 sources. Acta Agronómica. 2006, 111 (12), 211−217. (27) Chuang, M. T.; Zhang, Y.; Kang, D. Application of WRF/ChemMADRID for real-time air quality forecasting over the Southeastern United States. Atmos. Environ. 2011, 45 (34), 6241−6250. (28) Durão, R. M.; Mendes, M. T.; Pereira, M. J. Forecasting O3 levels in industrial area surroundings up to 24h in advance, combining classification trees and MLP models. Atmos. Pollut. Res. 2016, 7 (6), 961−970. (29) Wang, P.; Liu, Y.; Qin, Z.; Zhang, G. A novel hybrid forecasting model for PM10 and SO2 daily concentrations. Sci. Total Environ. 2015, 505, 1202−1212. (30) Koo, Y. S.; Choi, D. R.; Kwon, H. Y.; Jang, Y. K.; Han, J. S. Improvement of PM10 prediction in East Asia using inverse modeling. Atmos. Environ. 2015, 106, 318−328. (31) Sousa, S. I. V.; Martins, F. G.; Alvim-Ferraz, M. C. M.; Pereira, M. C. Multiple linear regression and artificial neural networks based on principal components to predict ozone concentrations. Environ. Model. Softw. 2007, 22 (1), 97−103. (32) Genc, D. D.; Yesilyurt, C.; Tuncel, G. Air pollution forecasting in Ankara, Turkey using air pollution index and its relation to assimilative capacity of the atmosphere. Environ. Monit. Assess. 2010, 166 (1−4), 11−27. (33) Voukantsis, D.; Karatzas, K.; Kukkonen, J.; Räsänen, T.; Karppinen, A.; Kolehmainen, M. Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki. Sci. Total Environ. 2011, 409 (7), 1266−1276. (34) Luna, A. S.; Paredes, M. L. L.; de Oliveira, G. C. G.; Corrêa, S. M. Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil. Atmos. Environ. 2014, 98, 98−104. (35) Osowski, S.; Garanty, K. Forecasting of the daily meteorological pollution using wavelets and support vector machine. Eng. Appl. Artif. Intel. 2007, 20 (6), 745−755.

Laboratory of Atmospheric Environment Monitoring and Pollution Control (KHK 16001).

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REFERENCES

(1) Liang, C. S.; Duan, F. K.; He, K. B.; Ma, Y. L. Review on recent progress in observations, source identifications and countermeasures of PM2.5. Environ. Int. 2016, 86, 150−170. (2) Lu, S.; Yao, Z.; Chen, X.; Wu, M.; Sheng, G.; Fu, J.; Paul, D. The relationship between physicochemical characterization and the potential toxicity of fine particulates (PM2.5) in Shanghai atmosphere. Atmos. Environ. 2008, 42 (31), 7205−7214. (3) Duan, J.; Tan, J. Atmospheric heavy metals and Arsenic in China: Situation, sources and control policies. Atmos. Environ. 2013, 74 (2), 93−101. (4) Cakmak, S.; Dales, R.; Kauri, L. M.; Mahmud, M.; Van Ryswyk, K.; Vanos, J.; Liu, L.; Kumarathasan, P.; Thomson, E.; Vincent, R.; et al. Metal composition of fine particulate air pollution and acute changes in cardiorespiratory physiology. Environ. Pollut. 2014, 189 (12), 208−214. (5) Bari, M. A.; Kindzierski, W. B. Eight-year (2007−2014) trends in ambient fine particulate matter (PM2.5) and its chemical components in the Capital Region of Alberta, Canada. Environ. Int. 2016, 91, 122− 132. (6) Hansard, R.; Maher, B. A.; Kinnersley, R. P. Rapid magnetic biomonitoring and differentiation of atmospheric particulate pollutants at the roadside and around two major industrial sites in the U.K. Environ. Sci. Technol. 2012, 46 (8), 4403−4410. (7) Fernández-Espinosa, A. J.; Ternerorodríguez, M. Study of traffic pollution by metals in Seville (Spain) by physical and chemical speciation methods. Anal. Bioanal. Chem. 2004, 379 (4), 684−699. (8) Davila, A. F.; Rey, D.; Mohamed, K.; Rubio, B.; Guerra, A. P. Mapping the Sources of Urban Dust in a Coastal Environment by Measuring Magnetic Parameters of Platanus hispanica Leaves. Environ. Sci. Technol. 2006, 40 (12), 3922−3928. (9) Shi, M.; Wu, H.; Zhang, S.; Li, H.; Yang, T.; Liu, W.; Liu, H. Weekly cycle of magnetic characteristics of the daily PM2.5 and PM2.5−10 in Beijing, China. Atmos. Environ. 2014, 98, 357−367. (10) Li, H.; Wang, Q.; Shao, M.; Wang, J.; Wang, C.; Sun, Y.; Qian, X.; Wu, H.; Yang, M.; Li, F. Fractionation of airborne particulatebound elements in haze-fog episode and associated health risks in a megacity of southeast China. Environ. Pollut. 2016, 208, 655−662. (11) Revuelta, M. A.; Mcintosh, G.; Pey, J.; Pérez, N.; Querol, X.; Alastuey, A. Partitioning of magnetic particles in PM10, PM2.5 and PM1 aerosols in the urban atmosphere of Barcelona (Spain). Environ. Pollut. 2014, 188 (5), 109−117. (12) Castanheiro, A.; Samson, R.; De Wael, K. Magnetic- and particle-based techniques to investigate metal deposition on urban green. Sci. Total Environ. 2016, 571, 594−602. (13) Norouzi, S.; Khademi, H.; Cano, A. F.; Acosta, J. A. Biomagnetic monitoring of heavy metals contamination in deposited atmospheric dust, a case study from Isfahan, Iran. J. Environ. Manage. 2016, 173, 55−64. (14) Wang, G.; Oldfield, F.; Xia, D.; Chen, F.; Liu, X.; Zhang, W. Magnetic properties and correlation with heavy metals in urban street dust: A case study from the city of Lanzhou, China. Atmos. Environ. 2011, 46 (1), 289−298. (15) Li, H.; Qian, X.; Wei, H.; Zhang, R.; Yang, Y.; Liu, Z.; Hu, W.; Gao, H.; Wang, Y. Magnetic properties as proxies for the evaluation of heavy metal contamination in urban street dusts of Nanjing, Southeast China. Geophys. J. Int. 2014, 199 (3), 1354−1366. (16) Karimi, R.; Ayoubi, S.; Jalalian, A.; Sheikh-Hosseini, A. R.; Afyuni, M. Relationships between magnetic susceptibility and heavy metals in urban topsoils in the arid region of Isfahan, central Iran. J. Appl. Geophys. 2011, 74 (1), 1−7. (17) Xia, D.; Wang, B.; Yu, Y.; Jia, J.; Nie, Y.; Wang, X.; Xu, S. Combination of magnetic parameters and heavy metals to discriminate soil-contamination sources in Yinchuan–a typical oasis city of Northwestern China. Sci. Total Environ. 2014, 485−486 (1), 83−92. H


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Environmental Science & Technology (36) Yeganeh, B.; Motlagh, M. S. P.; Rashidi, Y.; Kamalan, H. Prediction of CO concentrations based on a hybrid Partial Least Square and Support Vector Machine model. Atmos. Environ. 2012, 55 (3), 357−365. (37) Upadhyaya, G.; Dashore, M. N. Monitoring of Air Pollution by Using Fuzzy Logic. Int. J. Adv. Trends Comput. Sci. Eng. 2010, 2 (7), 2282−2286. (38) Carbajal-Hernández, J. J.; Sánchez-Fernández, L. P.; CarrascoOchoa, J. A.; Martínez-Trinidad, J. F. Assessment and prediction of air quality using fuzzy logic and autoregressive models. Atmos. Environ. 2012, 60 (6), 37−50. (39) Hoi, K. I.; Yuen, K. V.; Mok, K. M. Kalman filter based prediction system for wintertime PM10 concentrations in Macau. Global. Nest. J. 2008, 10 (2), 140−150. (40) Bei, N.; Li, G.; Meng, Z.; Weng, Y.; Zavala, M.; Molina, L. T. Impacts of using an ensemble Kalman filter on air quality simulations along the California−Mexico border region during Cal−Mex 2010 Field Campaign. Sci. Total Environ. 2014, 499, 141−153. (41) Konovalov, I. B.; Beekmann, M.; Meleux, F.; Dutot, A.; Foret, G. Combining deterministic and statistical approaches for PM10 forecasting in Europe. Atmos. Environ. 2009, 43 (40), 6425−6434. (42) Vapnik, V. N. The Nature of Statistical Learning Theory. IEEE T. Neural. Networ. 1995, 8 (6), 1564−1564. (43) Ortiz-García, E. G.; Salcedo-Sanz, S.; Pérez-Bellido, Á . M.; Portilla-Figueras, J. A.; Prieto, L. Prediction of Hourly O 3 Concentrations using Support Vector Regression Algorithms. Atmos. Environ. 2010, 44 (35), 4481−4488. (44) Feng, X.; Li, Q.; Zhu, Y.; Hou, J.; Jin, L.; Wang, J. Artificial neural networks forecasting of PM2.5 pollution using air mass trajectory based geographic model and wavelet transformation. Atmos. Environ. 2015, 107, 118−128. (45) Vlachogianni, A.; Kassomenos, P.; Karppinen, A.; Karakitsios, S.; Kukkonen, J. Evaluation of a multiple regression model for the forecasting of the concentrations of NOx and PM10 in Athens and Helsinki. Sci. Total Environ. 2011, 409 (8), 1559−1571. (46) Betha, R.; Behera, S. N.; Balasubramanian, R. 2013 Southeast Asian smoke haze: fractionation of particulate-bound elements and associated health risk. Environ. Sci. Technol. 2014, 48 (8), 4327−4335. (47) Taylor, S. R.; Mclennan, S. M. The geochemical evolution of the continental crust. Rev. Geophys. 1995, 33 (2), 241−265. (48) Sun, Y.; Hu, X.; Wu, J.; Lian, H.; Chen, Y. Fractionation and health risks of atmospheric particle-bound As and heavy metals in summer and winter. Sci. Total Environ. 2014, 493 (5), 487−494. (49) Yongming, H.; Peixuan, D.; Junji, C.; Posmentier, E. S. Multivariate Analysis of Heavy Metal Contamination in Urban Dusts of Xi’an, Central China. Sci. Total Environ. 2006, 355 (1−3), 176−186. (50) Wang, J.; Li, S.; Cui, X.; Li, H.; Qian, X.; Wang, C.; Sun, Y. Bioaccessibility, sources and health risk assessment of trace metals in urban park dust in Nanjing, Southeast China. Ecotoxicol. Environ. Saf. 2016, 128, 161−170. (51) Chen, J.; Tan, M.; Li, Y.; Zhang, Y.; Lu, W.; Tong, Y.; Zhang, G.; Li, Y. A lead isotope record of shanghai atmospheric lead emissions in total suspended particles during the period of phasing out of leaded gasoline. Atmos. Environ. 2005, 39 (7), 1245−1253. (52) Tan, M. G.; Zhang, G. L.; Li, X. L.; Zhang, Y. X.; Yue, W. S.; Chen, J. M.; Wang, Y. S.; Li, A. G.; Li, Y.; Zhang, Y. M.; et al. Comprehensive study of lead pollution in Shanghai by multiple techniques. Anal. Chem. 2006, 78 (23), 8044−8050. (53) Liu, E.; Yan, T.; Birch, G.; Zhu, Y. Pollution and health risk of potentially toxic metals in urban road dust in Nanjing, a mega-city of China. Sci. Total Environ. 2014, 476−477, 522−531. (54) Thompson, R.; Oldfield, F. Environmental Magnetism; Allen & Unwin: Crows Nest, Australia, 1986. (55) Liu, Q.; Roberts, A. P.; Larrasoaña, J. C.; Banerjee, S. K.; Guyodo, Y.; Tauxe, L.; Oldfield, F. Environmental magnetism: Principles and applications. Rev. Geophys. 2012, 50 (4), 197−215. (56) Heider, F.; Zitzelsberger, A.; Fabian, K. Magnetic susceptibility and remanent coercive force in grown magnetite crystals from 0.1 μm to 6 mm. Phys. Earth Planet. Inter. 1996, 93 (3), 239−256.

(57) Ge, C.; Zhang, W.; Dong, C.; Dong, Y.; Bai, X.; Liu, J.; Hien, N. T. T.; Feng, H.; Yu, L. Magnetic mineral diagenesis in the riverdominated inner shelf of the East China Sea, China. J. Geophys. Res. Solid Earth. 2015, 120 (7), 4720−4733. (58) Liu, Q.; Roberts, A. P.; Torrent, J.; Horng, C.-S.; Larrasoaña, J. C. What do the HIRM and S-ratio really measure in environmental magnetism? Geochem., Geophys., Geosyst. 2007, 8 (9), Q09011. (59) Oldfield, F. Toward the discrimination of fine-grained ferrimagnets by magnetic measurements in lake and near-shore marine sediments. J. Geophys. Res. Solid Earth. 1994, 99 (B5), 9045−9050. (60) Maher, B. A. Magnetic properties of some synthetic sub-micron magnetites. Geophys. J. Int. 2007, 94 (1), 83−96. (61) Hansard, R.; Maher, B. A.; Kinnersley, R. Biomagnetic monitoring of industry-derived particulate pollution. Environ. Pollut. 2011, 159 (6), 1673−1681. (62) Oldfield, F. Environmental magnetism  A personal perspective. Quat. Sci. Rev. 1991, 10 (1), 73−85. (63) Dunlop, D. J.; Ö zdemir, Ö . Rock magnetism: Fundamentals and Frontiers; Cambridge University Press, Cambridge, U.K., 1997. (64) Liu, X. M.; Rolph, T.; An, Z.; Hesse, P. Paleoclimatic significance of magnetic properties on the Red Clay underlying the loess and paleosols in China. Palaeogeogr., Palaeoclimatol., Palaeoecol. 2003, 199 (1), 153−166. (65) Roberts, A. P.; Chang, L.; Rowan, C. J.; Horng, C. S.; Florindo, F. Magnetic properties of sedimentary greigite (Fe3S4). Earth. Planet. Sci. Lett. 1995, 134 (3−4), 227−236. (66) Zwozdziak, A.; Gini, M. I.; Samek, L.; Rogula-Kozlowska, W.; Sowka, I.; Eleftheriadis, K. Implications of the aerosol size distribution modal structure of trace and major elements on human exposure, inhaled dose and relevance to the PM2.5 and PM10 metrics in a European Pollution Hotspot urban area. J. Aerosol Sci. 2017, 103, 38− 52. (67) Zereini, F.; Alt, F.; Messerschmidt, J.; Wiseman, C.; Feldmann, I.; von Bohlen, A.; Müller, J.; Liebl, K.; Püttmann, W. Concentration and distribution of heavy metals in urban airborne particulate matter in Frankfurt am Main, Germany. Environ. Sci. Technol. 2005, 39 (9), 2983−2989. (68) Li, H.; Qian, X.; Wang, Q. Heavy metals in atmospheric particulate matter: a comprehensive understanding is needed for monitoring and risk mitigation. Environ. Sci. Technol. 2013, 47 (23), 13210−13211. (69) Li, H.; Wang, Q. G.; Yang, M.; Li, F.; Wang, J.; Sun, Y.; Wang, C.; Wu, H.; Qian, X. Chemical characterization and source apportionment of PM2.5 aerosols in a megacity of Southeast China. Atmos. Res. 2016, 181, 288−299. (70) Tao, J.; Gao, J.; Zhang, L.; Zhang, R.; Che, H.; Zhang, Z.; Lin, Z.; Jing, J.; Cao, J.; Hsu, S. C. PM2.5 pollution in a megacity of southwest China: source apportionment and implication. Atmos. Chem. Phys. 2014, 14 (4), 8679−8699. (71) Jordanova, N. V.; Jordanova, D. V.; Veneva, L.; Yorova, K.; Petrovsky, E. Magnetic response of soils and vegetation to heavy metal pollution–a case study. Environ. Sci. Technol. 2003, 37 (19), 4417− 4424. (72) Zhu, Z.; Li, Z.; Bi, X.; Han, Z.; Yu, G. Response of magnetic properties to heavy metal pollution in dust from three industrial cities in China. J. Hazard. Mater. 2013, 246−247 (4), 189−198. (73) Zhang, C.; Qiao, Q.; Piper, J. D. A.; Huang, B. Assessment of heavy metal pollution from a Fe-smelting plant in urban river sediments using environmental magnetic and geochemical methods. Environ. Pollut. 2011, 159 (10), 3057−3070.

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Magnetic Properties as a Proxy for Predicting Fine-Particle-Bound

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