Supplemental Information. Magnetic properties as a proxy for predicting fine-particle-bound. heavy metals in a support vector machine approach

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1 Supplemental Information Magnetic properties as a proxy for predicting fine-particle-bound heavy metals in a support vector machine approach Huiming Li a, Jinhua Wang a, Qin'geng Wang a,b, Chunhui Tian a, Xin Qian a,b, *, Xiang'zi Leng a a State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 2123, China b 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 2144, China Corresponding author: Xin Qian Address: State Key Laboratory of Pollution Control and Resources Reuse, School of the Environment, Nanjing University, Nanjing 2123, PRC. Telephone: Fax No: address: xqian@nju.edu.cn Number of pages: 2 Number of figures: 9 Number of tables: 1 1

2 Figure S1. Sampling sites. Sampling and preparation Nanjing covers approximately 66 km 2 and had a population of over 8.2 million in 215. Located in the north subtropical monsoon climate zone, it has an annual mean temperature of 16 C and a mean annual precipitation of 116 mm. The prevailing wind normally blows from the southeast in summer and from the northwest in winter. Automobiles, electrical power production, electronics, petrochemical, and steel are its five main industries. PM 2.5 samples were collected from three areas: the Xianlin and Gulou campuses of Nanjing University and the Pukou campus of the Nanjing University of Information Science & Technology. Pukou is located in the northern industrial zone of Nanjing (industrial area, IA), Xianlin in the northern suburbs (suburban area, SA), and Gulou in the densely populated city center (city center area, CA). Each filter used in sample collection was conditioned for 48 h in a desiccator at 25 C and 4% relative humidity before and after sampling. The filters were weighed using a microbalance (Mettler-Toledo, Greifensee, Switzerland). Separate samples were collected during the day (from 7: to 19:) and night (19: to 7:) from April 22 to May 7 (spring), July 8 to July 23(summer), October 14 to October 23 (autumn) in 214, and from January 8 to January 17 (winter) in 215. To ensure that the samples were representative of the local atmosphere in calm weather, samples were not collected during rainy and windy weather. 2

3 Analyses of elemental concentrations and stable isotope ratios of Pb Inductively coupled plasma mass spectrometer (ICP-MS) analyses were optimized using Mg, Rh, In, Ba, Ce, Pb, and U prepared in 2% HNO 3 and used at a concentration of 1 µg/l. The internal standard was 115 In prepared in 2% HNO 3 and used at a concentration of 2 µg/l. The concentration of each element in each PM 2.5 sample was corrected by subtracting the mean concentration simultaneously measured in blank filters. The stable Pb isotope ratios of 27 / 26 Pb and 28 / 26 Pb in digestion solution were measured by ICP-MS (NexIONTM3X, Perkin Elmer, USA) with a detection limit of.8 µg L 1. The instrument parameters were 19 sweeps/reading, 1 reading/replicate, and a dwell time of 4 ms for 24 Pb and 25 ms for 26 Pb, 27 Pb, and 28 Pb. Prior to the analysis, Pb in solution was diluted to ~15 µg L 1 using.1 M high-purity HNO 3. The standard reference material (SRM) NIST 981, used at a concentration of 15 µg L 1, was analyzed every five samples to obtain ratio correction factors to compensate for mass discrimination. The determined 27 / 26 Pb (.9149 ±.24) and 28 / 26 Pb ( ±.29) of SRM NIST 981 were in good agreement with the certified values of.9146, and , respectively. The relative standard deviation (RSD) for the PM 2.5 samples of the 1 replicates was generally<.5% for 27 Pb/ 26 Pb and 28 Pb/ 26 Pb. 3

4 Calculation of indexes The correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement (IA) are described by Eqs. (1 4), respectively: MAE= (1) RMSE= (2) IA=1 (3) R= where, is the true target metric value for observation i, is the target metric value for observation i as predicted by the model, and n is the number of data. (4) Calculation of the enrichment factor (EF) EF values were calculated with respect to Ti 1 as: EF= C /C / B /B (5) where C /C and B /B are the concentration ratios of the target metal to the reference element Ti in the samples and in the continental crust, respectively. In this study, EF <1 indicated that the target element was not enriched. EF>1 indicated that the element derived from anthropogenic sources. 4

5 T( ), RH(%), WS(m/s) T RH WS P IA P (hpa) 98 T( ), RH(%), WS(m/s) T RH WS P CA P (hpa) 98 T( ), RH(%), WS(m/s) T RH WS P SA P (hpa) 98 Figure S2. Meteorological conditions (T=temperature, RH=relative humidity, WS=wind speed, P=pressure) during the sampling periods in industrial areas (IA), city center areas (CA), and suburban areas (SA). 5

6 As concentration in PM 2.5 (ng m -3 ) Pukou Gulou Xianlin As limit of China As limit of WHO Cd concentration in PM 2.5 (ng m -3 ) Pukou Gulou Xianlin Cd limit of China and WHO 5 Pb concentration in PM 2.5 (ng/m -3 ) Pukou Gulou Xianlin Pb limit of China and WHO Ni concentration in PM 2.5 (ng m -3 ) Pukou Gulou Xianlin Ni limit of WHO Mn concentration in PM 2.5 (ng m -3 ) Pukou Gulou Xianlin Mn limit of WHO V concentration in PM 2.5 (ng m -3 ) Pukou Gulou Xianlin V limit of WHO.4 Figure S3. Comparison of As, Cd, Pb, Ni, Mn, and V concentrations in PM 2.5 to the NAAQS (GB ) and World Health Organization (WHO) limits during the sampling periods in industrial areas (IA), city center areas (CA), and suburban areas (SA). The heavy metal concentrations in PM 2.5 and the limits imposed by the new NAAQS (GB ) and the WHO are compared in the Supplementary Material (Figure S3). The As concentrations in nearly all PM 2.5 samples were above both the NAAQS (GB ) and WHO limits (6 and 6.6 ng/m 3, respectively). The Cd concentrations were higher than the NAAQS and WHO limit (5 ng/m 3 ) only in several samples collected in the industrial areas (IA) of Pukou in winter. The Pb concentrations in all PM 2.5 samples were below the NAAQS (GB ) and WHO limit of 5 ng/m 3. No NAAQS limits have been set for Ni, Mn, or V. The Ni concentration in nearly all PM 2.5 samples collected in Pukou in spring and winter exceeded the WHO limit of 25 ng/m 3. By contrast, the Ni concentration in only a few PM 2.5 samples collected in Gulou (city center areas, CA) and Xianlin (suburban areas, SA) exceeded the WHO limit. The Mn concentrations were higher than the WHO limit (15 ng/m 3 ) in only two PM 2.5 samples, both collected in Pukou. The V concentrations were much lower than the WHO limit (1 ng/m 3 ). Note that both the NAAQS and the WHO limit for atmospheric heavy metals refers to the total 6

7 concentration in coarse PM. 2 Therefore, uncertainty remains about the adverse effects of the heavy-metal concentrations in PM 2.5 even though some of the concentrations were below the limits for coarse PM. 7

8 1 1 IA Spring Fall Summer Winter Enrichment factor Al As Cd Co Cr Cu Fe Mn Ni Pb V Zn CA Spring Fall Summer Winter Enrichment factor Al As Cd Co Cr Cu Fe Mn Ni Pb V Zn SA Spring Summer Fall Winter Enrichment factor Al As Cd Co Cr Cu Fe Mn Ni Pb V Zn Figure S4. Enrichment factors obtained in industrial areas (IA), city center areas (CA), and suburban areas (SA).1 8

9 Figure S5. Hierarchical dendrograms obtained from War s clustering method, using Euclidian distances between the elements expressed as volume-related concentrations (a) and mass-related concentrations (b) (n = 24) Hierarchical cluster analysis (HCA) was conducted on the standardized bulk concentrations using War s method and Euclidian distance as the criterion for forming clusters of elements. A dendrogram was constructed to assess the cohesiveness of the clusters formed, from which correlations among elements can readily be seen. Elements belonging to the same cluster were strongly correlated with each other and may have had the same source. The clusters formed were very similar for the elements, as expressed by both volume-related and mass-related concentrations (Figure S5). Five clusters were found for the volume-related concentrations: (1) Fe, Mn, Ti, Ni, Pb, Cr and Zn; (2) Cu; (3) Cd and Co; (4) Al and V; (5) As. There were also five similar clusters for the mass-related concentrations: (1) Fe, Mn, Ti, Pb, Zn and Ni; (2) Al and V; (3) As and Cr; (4) Cu; (5) Cd and Co. Taking the volume-related concentrations as an example, five main sources with corresponding cluster elements were identified: (1) Fe, Mn, Ti, Ni, Pb, Cr and Zn were mainly from metallurgic industry dust; (2) Cu was mainly from traffic; (3) Cd and Co mainly originated from industrial emissions; (4) Al and V were mainly from natural soil dust; (5) As was principally from coal combustion. 9

10 28 Pb/ 26 Pb Background soil in Nanjing Trunk road dust in Nanjing Vehicle exhause(lead free) Cement Coal Metallurgy IA CA SA Pb/ 26 Pb Figure S6. Scatter plot of the 28 Pb/ 26 Pb ratios vs. the 27 Pb/ 26 Pb ratios of the PM 2.5 samples from industrial areas (IA), city center areas (CA), and suburban areas (SA) from Nanjing. The different anthropogenic Pb sources include vehicle exhaust (leaded or lead-free), cement and metallurgy industries, 3 coal, 3, 4 trunk road dusts, and background soil in Nanjing. 5 1

11 1 IA 1 CA.5.5 M /M S M /M S -.5 B cr /B c = 3.44 M rs /M s =.16 B c =7.63mT -.5 B cr /B c = 3.75 M rs /M s =.14 B c = 6.94mT Field (T) 1 SA Field (T).5 M /M S -.5 B cr /B c = 3.71 M rs /M s =.16 B c =6.94mT Field (T) Figure S7. Magnetic hysteresis loops for selected PM 2.5 samples in industrial areas (IA), city center areas (CA), and suburban areas (SA). 11

12 1 SP+SD IA CA SA Mrs/Ms.1 PSD.1 MD Bcr/Bc Figure S8. Day plot for the selected PM 2.5 samples in industrial areas (IA), city center areas (CA), and suburban areas (SA) 12

13 Residuels of Fe (ng m -3 ) training set test set (a) Residuels of Pb (ng m -3 ) training set test set (b) -8 Observed Fe (ng m -3 ) -6 Observed Pb (ng m -3 ) Residuels of Fe (µg g -1 ) training set test set (c) Residuels of Pb (µg g -1 ) training set test set (d) -15 Observed Fe (µg g -1 ) -.8 Observed Pb (µg g -1 ) Figure S9. Residuals at the training and test stages of (a) Fe and (b) Pb expressed as volume concentrations by Model II, and (c) Fe and (d) Pb expressed as mass concentrations by Model IV. 13

14 Table S1. Volume-related concentrations (ng m -3 ) of metals in PM 2.5 samples collected from industrial areas (IA), city center areas (CA), and suburban areas (SA) IA CA SA Spring Summer Fall Winter Spring Summer Fall Winter Spring Summer Fall Winter Al Range Mean±SD 1752± ± ± ± ± ± ± ± ± ± ± ±84.2 As Range Mean±SD 14.8± ± ± ± ± ± ± ± ± ± ± ±3.199 Cd Range Mean±SD 2.96± ± ± ± ± ± ± ± ± ± ± ±.817 Co Range Mean±SD 1.194± ± ± ± ± ± ± ± ± ± ± ±.637 Cr Range Mean±SD 51.71± ± ± ± ± ± ± ± ± ± ± ±11.83 Cu Range Mean±SD 62.92± ± ± ± ± ± ± ± ± ± ± ±18.25 Fe Range Mean±SD 1614± ± ± ± ± ± ± ± ± ± ± ±162.9 Mn Range Mean±SD 9.78± ± ± ± ± ± ± ± ± ± ± ±2.95 Ni Range Mean±SD 35.65± ± ± ± ± ± ± ± ± ± ± ±9.143 Pb Range Mean±SD 79.89± ± ± ± ± ± ± ± ± ± ± ±14.7 Ti Range Mean±SD 129.5± ± ± ± ± ± ± ± ± ± ± ±16.5 V Range Mean±SD 4.987± ± ± ± ± ± ± ± ± ± ± ±2.68 Zn Range Mean±SD 286.± ± ± ± ± ± ± ± ± ± ± ±

15 Table S2. Mass-related concentrations (µg g -1 ) of metals in PM 2.5 samples collected in industrial areas (IA), city center areas (CA), and suburban areas (SA) IA CA SA Spring Summer Fall Winter Spring Summer Fall Winter Spring Summer Fall Winter Al Range Mean±SD 2.36± ± ± ± ± ± ± ± ± ± ± ±6.858 As Range Mean±SD.175± ± ±.5.145± ± ± ± ± ± ± ± ±.27 Cd Range Mean±SD.25±.12.34±.29.17±.1.29±.14.11±.7.11±.4.28±.8.23±.12.17±.12.21±.12.24±.12.15±.7 Co Range Mean±SD.14±.7.2±.12.12±.7.16±.6.5±.4.16±.1.13±.8.16±.7.9±.6.11±.5.12±.4.15±.8 Cr Range Mean±SD.614± ± ± ± ± ± ± ± ± ± ± ±.82 Cu Range Mean±SD.747± ± ± ± ± ±.57.56± ± ± ± ± ±.184 Fe Range Mean±SD ± ± ± ± ± ± ± ± ± ± ± ±1.968 Mn Range Mean±SD 1.75± ± ± ± ± ± ± ± ± ± ± ±.21 Ni Range Mean±SD.42± ± ± ± ±.53.11±.56.81± ± ± ± ± ±.119 Pb Range Mean±SD.977± ± ± ± ± ± ± ± ± ± ± ±.176 Ti Range Mean±SD 1.531± ± ± ± ± ± ± ± ± ± ± ±.142 V Range Mean±SD.59±.32.92±.44.52±.29.26±.19.21±.5.55±.33.27±.16.55±.23.36±.18.74±.35.2±.9.4±.26 Zn Range Mean±SD 3.472± ± ± ± ± ± ± ± ± ± ± ±

16 Table S3. Summary of the magnetic properties (mass specific) of PM 2.5 samples collected from industrial areas (IA), city center areas (CA), and suburban areas (SA) IA CA SA Spring Summer Fall Winter Spring Summer Fall Winter Spring Summer Fall Winter χlf (1-8 *m 3 /kg) Range Mean±SD 131± ± ± ± ± ± ± ± ± ± ± ±127.1 χarm (1-8 *m 3 /kg) Range Mean±SD 1534± ± ± ± ± ± ± ± ± ± ± ±296.9 SIRM (1-6 *Am 2 /kg) Range Mean±SD 99852± ± ± ± ± ± ± ± ± ± ± ±12658 HIRM (1-6 *Am 2 /kg) Range Mean±SD 3392± ± ± ± ± ± ± ± ± ± ± ±56.5 S-ratio Range Mean±SD.936±.44.92± ± ± ± ±.2.916± ± ±.24.97± ±.81.94±.26 χarm/χlf Range Mean±SD 1.543± ± ± ± ± ± ± ± ± ± ± ±.533 χarm/sirm (1-5 m/a) Range Mean±SD 16.86± ± ± ± ± ± ± ± ± ± ± ±6.512 SIRM/χLF (1 2 A/m) Range Mean±SD 947.2± ± ± ± ± ± ± ± ± ± ± ±

17 Table S4. Pearson s correlation coefficients (r) between volume-related concentrations of metals, and magnetic properties, PM 2.5, and meteorological factors (T=temperature, RH=relative humidity, P=pressure, WS=wind speed) Al As Cd Co Cr Cu Fe Mn Ni Pb Ti V Zn PM **.228 **.425 **.38 **.219 **.384 **.185 **.35 **.443 **.414 **.152 * ** T ** ** ** ** ** ** * ** ** ** RH ** ** ** ** ** ** ** ** P * WS ** ** ** χ LF **.21 ** **.415 ** *.176 **.261 **.294 ** χ ARM **.29 ** *.543 **.41 ** **.138 *.235 **.33 ** SIRM *.191 ** **.41 ** *.187 **.265 **.36 ** HIRM **.278 ** **.196 ** S-ratio ** -.17 ** * -.3 ** ** ** * χ ARM/χ LF.128 * ** ** ** χ ARM/SIRM ** * *.49 SIRM/χ LF.174 ** * **.227 **.166 *.182 **.223 **.184 **.265 ** * Correlation is significant at the.5 level (2-tailed). ** Correlation is significant at the.1 level (2-tailed). Table S5. Pearson s correlation coefficients (r) between mass-related concentrations of metals, and magnetic properties, PM 2.5, and meteorological factors (T=temperature, RH=relative humidity, P=pressure, WS=wind speed) Al As Cd Co Cr Cu Fe Mn Ni Pb Ti V Zn PM ** -.51 ** ** ** ** ** ** ** ** ** T ** ** ** *.315 **.118 RH * -.26 ** ** ** P * WS * χ LF.219 **.17 **.31 **.328 **.193 **.211 **.667 **.543 **.178 **.227 **.259 **.333 **.447 ** χ ARM.194 ** **.35 **.154 *.177 **.637 **.57 **.148 *.237 **.21 **.285 **.437 ** SIRM.216 **.155 *.299 **.39 **.194 **.24 **.697 **.539 **.185 **.242 **.267 **.322 **.448 ** HIRM.169 ** **.217 ** *.576 **.389 ** *.265 **.33 ** S-ratio ** ** ** χ ARM/χ LF χ ARM/SIRM * * * ** ** -.98 SIRM/χ LF.166 ** * **.26 **.199 **.191 **.219 **.137 *.248 ** * Correlation is significant at the.5 level (2-tailed). ** Correlation is significant at the.1 level (2-tailed). 17

18 Table S6. The correlation coefficient (R) between the observed and predicted metal concentrations as determined by multiple linear regression analysis volume-related mass-related training test train test Al As Cd Co Cr Cu Fe Mn Ni Pb Ti V Zn Table S7. Correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement (IA) for Model I R MAE RMSE IA train test train test train test train test Al As Cd Co Cr Cu Fe Mn Ni Pb Ti V Zn

19 Table S8. Correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement (IA) results for Model II R MAE RMSE IA train test train test train test train test Al As Cd Co Cr Cu Fe Mn Ni Pb Ti V Zn Table S9. Correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE) and index of agreement (IA) results for Model III R MAE RMSE IA train test train test train test train test Al As Cd Co Cr Cu Fe Mn Ni Pb Ti V Zn

20 Table S1. Correlation coefficient (R), mean absolute error (MAE), root mean squared error (RMSE), and index of agreement (IA) results for model IV R MAE RMSE IA train test train test train test train test Al As Cd Co Cr Cu Fe Mn Ni Pb Ti V Zn References (1) Taylor, S. R.; Mclennan, S. M. The geochemical evolution of the continental crust. Rev Geophys. 1995, 33 (2), (2) 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. 213, 47 (23), (3) 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. Comprehensive study of lead pollution in Shanghai by multiple techniques. Anal. Chem. 26, 78, (23), 844. (4) 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. 25, 39 (7), (5) 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. 214, C,