Abstract
Four monitoring campaigns between the years 2009 and 2018 were conducted in Córdoba City, Argentina, to detect toxic metals in PM2.5 samples. The concentrations of As, Cd, Pb, Cu, Cr, Mn, Hg, Ni, and Zn, together with several other elements, were measured. The average metal concentrations followed the order: Zn > Cr > Cu > Mn > Pb > V > Ni > As ~ Sb > Cd > Tl > Pd > Hg > Pt. From the analysis of the temporal variation in the elemental concentration of PM2.5, results show seasonal variations that reach, in general, a maximum in the coldest seasons and a minimum in the warmer seasons. These differences could be explained by the different weather conditions during each season, the influence of the El Niño/La Niña regimen, and the presence of fires on certain sampling dates. The source apportionment analysis performed for the period 2017–2018 showed the contribution to PM2.5 of combustion of heavy fuel oil and diesel-powered vehicles, pet coke, metallurgical and nonferrous industries, paint plant factory, traffic, and natural sources like the soil and road dust. This last analysis completed the assignment of sources for the 10-year period of study. Thus, the results of this work contribute to the implementation of emission reduction strategies in order to decrease the impact of PM2.5 on the environment and the human health.
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References
Achad, M., López, M. L., Palancar, G. G., & Toselli, B. M. (2013). Retrieving the relative contribution of aerosol types from single particle analysis and radiation measurements and calculations: a comparison of two independent approaches. Journal of Aerosol Science, 64, 11–23.
Achad, M., López, M. L., Ceppi, S., Palancar, G. G., Tirao, G., & Toselli, B. M. (2014). Assessment of fine and sub-micrometer aerosols at an urban environment of Argentina. Atmospheric Environment, 92, 522–532.
Achad, M., Caumo, S., Castro Vasconcellos, P., Bajano, H., Gómez, D., & Smichowski, P. (2018). Chemical markers of biomass burning: determination of levoglucosan, and potassium in size-classified atmospheric aerosols collected in Buenos Aires, Argentina by different analytical techniques. Microchemical Journal, 139, 181–187.
Aguilera Sammaritano, M., Bustos, D. G., Poblete, A. G., & Wannaz, E. D. (2018). Elemental composition of PM2.5 in the urban environment of San Juan, Argentina. Environmental Science and Pollution Research, 25, 4197–4203.
Amato, F., & Hopke, P. K. (2012). Source apportionment of the ambient PM2.5 across St. Louis using constrained positive matrix factorization. Atmospheric Environment, 46, 329–337.
Amato, F., Pandolfi, M., Escrig, A., Querol, X., Alastuey, A., Pey, J., Perez, N., & Hopke, P. K. (2009). Quantifying road dust resuspension in urban environment by multilinear engine: a comparison with PMF2. Atmospheric Environment, 43, 2770–2780.
Apte, J. S., Marshall, J. D., Cohen, A. J., & Brauer, M. (2015). Addressing global mortality from ambient PM2.5. Environmental Science & Technology, 49(13), 8057–8066.
Arkouli, M., Ulke, A. G., Endlicher, W., Baumbach, G., Schultz, E., Vogt, U., Müller, M., Dawidowski, L., Faggi, A., Wolf-Benning, U., & Scheffknecht, G. (2010). Distribution and temporal behavior of particulate matter over the urban area of Buenos Aires. Atmospheric Pollution Research, 1, 1–8.
Artaxo, P., Oyola, P., & Martinez, R. (1999). Aerosol composition and source apportionment in Santiago de Chile. Nuclear Instruments and Methods in Physics Research Section B, 150, 409–416.
Barraza, F., Lambert, F., Jorquera, H., Villalobos, A. M., & Gallardo, L. (2017). Temporal evolution of main ambient PM2.5 sources in Santiago, Chile, from 1998 to 2012. Atmospheric Chemistry and Physics, 17, 10093–10107.
Brugge, D., Durant, J. L., & Rioux, C. (2007). Near-highway pollutants in motor vehicle exhaust: a review of epidemiologic evidence of cardiac and pulmonary health risks. Environmental Health, 6(23).
Cavalli, F., Viana, M., Yttri, K. E., Genberg, J., & Putaud, J.-P. (2010). Toward a standardized thermal-optical protocol for measuring atmospheric organic and elemental carbon: the EUSAAR protocol. Atmospheric Measurement Techniques, 3, 79–89.
Celo, V., Dabek-Zlotorzynska, E., Mathieu, D., & Okonskaia, I. (2010). Validation of a simple microwave-assisted acid digestion method using microvessels for analysis of trace elements in atmospheric PM2.5 in monitoring and fingerprinting studies. Open Chemical and Biomedical Methods Journal, 3, 141–150.
Cetin, M., Sevik, H., & Isınkaralar, K. (2017). Changes in the particulate matter and CO2 concentrations based on the time and weather conditions: the case of Kastamonu. Oxidation Communications, 40(1-II), 477–485.
Cetin, M., Onac, A. K., Sevik, H., & Sen, B. (2018). Temporal and regional change of some air pollution parameters in Bursa. Air Quality, Atmosphere & Health, 12(3), 311–316.
CPTEC. (2019). Centro de Previsão de Tempo e Estudos Climáticos. Brazil.
Dos Santos, M., Dawidowski, L., Smichowski, P., Ulke, A. G., & Gómez, D. (2012). Factors controlling sea salt abundances in the urban atmosphere of a coastal South American megacity. Atmospheric Environment, 59, 483–491.
Draxler, R. R., & Rolph, G. D. (2003). HYSPLIT (hybrid single-particle Lagrangian integrated trajectory) model. NOAA Air Resources Laboratory http://www.arl.noaa.gov/ready/hysplit4.html. Accessed 15 April 2019.
EEA (2013). Environment and human health, joint EEA-JRG report no 5/2013. European Environmental Agency.
European Commission (2019). http://ec.europa.eu/environment/air/quality/standards.htm. Accessed 15 April 2019.
Fujiwara, F., Rebagliati, R. J., Dawidowski, L., Gómez, D., Polla, G., Pereyra, V., & Smichowski, P. (2011). Spatial and chemical patterns of size fractionated road dust collected in a megacitiy. Atmospheric Environment, 45, 1497–1505.
Gómez, D., Nakazawa, T., Furuta, N., & Smichowski, P. (2017). Multielemental chemical characterization of fine urban aerosols collected in Buenos Aires and Tokyo by plasma-based techniques. Microchemical Journal, 133, 346–351.
Grivas, G. G., Cheristanidis, S., Chaloulakou, A., Koutrakis, P., & Mihalopoulos, N. (2018). Elemental composition and source apportionment of fine and coarse particles at traffic and urban background locations in Athens. Aerosol and Air Quality Research, 18, 1642–1659.
Gupta, P., Christopher, S. A., Box, M. A., & Box, G. P. (2007). Multiyear satellite remote sensing of particulate matter air quality over Sydney, Australia. International Journal of Remote Sensing, 28, 4483–4498.
Herrera Murillo, J., Rojas Marin, J. F., Rodriguez Roman, S., Beita Guerrero, V. H., Solorzano Arias, D., Campos Ramos, A., Cardenas Gonzalez, B., & Gibson Baumgardner, D. (2013). Chemical characterization and source apportionment of PM10 and PM2.5 in the metropolitan area of Costa Rica, Central America. Atmospheric Pollution Research, 4, 53–63.
Huang, R.-J., Wang, Y., Cao, J., Lin, C., Duan, J., Chen, Q., Li, Y., Gu, Y., Yan, J., Xu, W., Fröhlich, R., Canonaco, F., Bozzetti, C., Ovadnevaite, J., Ceburnis, D., Canagaratna, M. R., Jayne, J., Worsnop, D. R., El Haddad, I., Prévôt, A. S. H., & O’Dowd, C. D. (2019). Primary emissions versus secondary formation of fine particulate matter in the most polluted city (Shijiazhuang) in North China. Atmospheric Chemistry and Physics, 19, 2283–2298.
Hui, G., & Xiang, L. (2015). Influences of El Nino Southern Oscillation events on haze frequency in eastern China during boreal winters. International Journal of Climatology, 35(9), 2682–2688.
Jeong, J. I., Park, R. J., & Yeh, S. (2018). Dissimilar effects of two El Niño types on PM2.5 concentrations in East Asia. Environmental Pollution, 242, 1395–1403.
Jerrett, M., Burnett, R. T., Renjun, M., Pope, C. A., Krewski, D., Newbold, K. B., Thurston, G., Shi, Y., Finkelstein, N., Calle, E. E., & Thun, M. J. (2005). Spatial analysis of air pollution and mortality in Los Angeles. Epidemiology, 16, 727–736.
Jorquera, H., & Barraza, F. (2012). Source apportionment of ambient PM2.5 in Santiago, Chile: 1999 and 2004 results. Science of the Total Environment, 435, 418–429.
Jorquera, H., & Barraza, F. (2013). Source apportionment of PM10 and PM2.5 in a desert region in northern Chile. Science of the Total Environment, 444, 327–335.
Kavouras, I. G., Koutrakis, P., Cereceda-Balic, F., & Oyola, P. (2001). Source apportionment of PM10 and PM25 in five Chilean cities using factor analysis. Journal of the Air & Waste Management Association, 51, 451–464.
Kogan, F. (2000). Satellite-observed sensitivity of world land ecosystems to El Niño/La Niña. Remote Sensing of Environment, 74, 445–462.
Kogan, F., & Guo, W. (2010). Strong 2015–2016 El Niño and implication to global ecosystems from space data. International Journal of Remote Sensing, 38, 161–178.
Koutraki, P., Sax, S. N., Sarnat, J. A., Coull, B., Demokritou, P., Oyola, P., Garcia, P., & Gramsch, E. (2005). Analysis of PM10, PM2.5, and PM2.5-10 concentrations in Santiago, Chile, from 1989 to 2001. Journal of the Air & Waste Management Association, 55, 342–351.
Lanzaco, B. L., Olcese, L. E., Querol, X., & Toselli, B. M. (2017). Analysis of PM2.5 in Córdoba, Argentina under the effects of the El Niño southern oscillation. Atmospheric Environment, 171, 49–58.
Liu, B., Wu, J., Zhang, J., Wang, L., Yang, J., Liang, D., Dai, Q., Bi, X., Feng, Y., Zhang, Y., & Zhang, Q. (2017a). Characterization and source apportionment of PM2.5 based on error estimation from EPA PMF 5.0 model at a medium city in China. Environmental Pollution, 222, 10–22.
Liu, B., Yang, J., Yuan, J., Wang, J., Dai., Q., Li, T., Bi, X., Feng, Y., Xiao, Z., Zhang, Y., & Xu, H. (2017b). Source apportionment of atmospheric pollutants based on the online data by using PMF and ME2 models at a megacity, China. Atmospheric Research, 185, 22–31.
López, M. L., Ceppi, S., Palancar, G. G., Olcese, L. E., Tirao, G., & Toselli, B. M. (2011). Elemental concentration and source identification of PM10 and PM2.5 by SR-XRF in Córdoba City, Argentina. Atmospheric Environment, 45, 5450–5457.
Manousakas, M., Papaefthymiou, H., Diapouli, E., Migliori, A., Karydas, A. G., Bogdanovic-Radovic, I., & Eleftheriadis, K. (2017). Assessment of PM2.5 sources and their corresponding level of uncertainty in a coastal urban area using EPA PMF 5.0 enhanced diagnostics. Science of the Total Environmen, 574, 155–164.
Miranda, R. M., Andrade, F. M., Dutra Ribeiro, F. N., Mendonça Francisco, K. J., & Perez-Martínez, P. J. (2018). Source apportionment of fine particulate matter by positive matrix factorization in the metropolitan area of Sao Paulo, Brazil. Journal of Cleaner Production, 202, 253–263.
NOAA (2019). Cold & warm episodes by season. National Oceanic and Atmospheric Administration. National Weather Service. National Centers for Environmental Prediction. Climate Prediction Center. College Park. Maryland.
Ogundele, L. T., Owoade, O. K., Olise, F. S., & Hopke, P. K. (2016). Source identification and apportionment of PM2.5 and PM2.5-10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models. Environmental Monitoring and Assessment, 188(574), 1–21.
Onat, B., Sahin, U. A., & Akyuz, T. (2013). Elemental characterization of PM2.5 and PM1 in dense traffic area in Istanbul, Turkey. Atmospheric Pollution Research, 4, 101–105.
Paatero, P., & Tapper, U. (1994). Positive matrix factorization: a non-negative factor model with optimal utilization of error estimates of data values. Environmetrics, 5, 111–126.
Paatero, P., Eberly, S., Brown, G., & Norris, G. A. (2014). Methods for estimating uncertainty in factor analytic solutions. Atmospheric Measurement Techniques, 7, 781–797.
Pope, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K., & Thurston, G. D. (2002). Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. Journal of the American Medical Association, 287, 1132–1141.
Querol, X., Alastuey, A., Rodriguez, S., Plana, F., Ruiz, C. R., Cots, N., Massague, G., & Puig, O. (2001). PM10 and PM2.5 source apportionment in the Barcelona Metropolitan area, Catalonia. Spain. Atmospheric Environment, 35, 6407–6419.
Raysoni, A. U., Armijo, R. X., Weigel, M. M., Echanique, P., Racines, M., Pingitore Jr., N. E., & Li, W. (2017). Evaluation of sources and patterns of elemental composition of PM2.5 at three low-income neighborhood schools and residences in Quito, Ecuador. International Journal of Environmental Research and Public Health, 14(674), 1–21.
Sevik, H., Cetin, M., Ozturk, A., Ozel, H. B., & Pinar, B. (2019). Changes in Pb, Cr and Cu concentrations in some bioindicators depending on traffic density on the basis of species and organs. Applied Ecology and Environmental Research, 17(6), 12843–12857.
Smichowski, P., Gómez, D., Frazzoli, C., & Caroli, S. (2008). Traffic-related elements in airborne particulate matter. Applied Spectroscopy Reviews, 43, 23–49.
Stein, A. F., Draxler, R. R., Rolph, G. D., Stunder, B. J. B., Cohen, M. D., & Ngan, F. (2015). NOAA’S HYSPLIT atmospheric transport and dispersion modeling system. Bulletin of the American Meteorological Society, 96, 2059–2077.
Tavera Busso, I., Vera, A., Mateos, A. C., Amarillo, A. C., & Carreras, H. (2017). Histological changes in lung tissues related with sub-chronic exposure to ambient urban levels of PM2.5 in Córdoba, Argentina. Atmospheric Environment, 167, 616–624.
US EPA (2011). Policy Assessment for the Review of the Particulate Matter National Ambient Air Quality Standards. United States Environmental Protection Agency. Office of Air Quality Planning and Standards Health and Environmental Impacts Division. Research Triangle Park, NC.
US EPA (2014). EPA positive matrix factorization (PMF) 5.0 fundamentals and user guide. United States Environmental Protection Agency. National Exposure Research Laboratory. Research Triangle Park, NC.
US OSHA (2004). Safety and Health Topics: Antimony and Compounds. United States Department of Labour, Occupational, Safety and Health Administration, Washington, DC. http://www.osha.gov/dts/chemicalsampling/data/CH_219100.html. Accessed 15 April 2019.
Weather Underground database (2019). http://www.wunderground.com. Accessed 20 April 2019.
Wie, J., & Moon, B.-K. (2017). ENSO-related PM10 variability on the Korean Peninsula. Atmospheric Environment, 167, 426–433.
Wu, R., Wen, Z., & He, Z. (2013). ENSO contribution to aerosol variations over the maritime continent and the western north pacific during 2000-10. Journal of Climate, 26(17), 6541–6560.
Acknowledgments
Pamela B. Sanguineti would like to thank CONICET for a postdoctoral fellowship.
Funding
Funding for this work was provided by PIP 1120120100004CO, CONICET; grant number 05/C275, SeCyT-UNC; PICT 2014- 0876, ANPCyT, and the Research Proposal D09B-XRF-20160044, LNLS.
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Highlights
• Size segregated particulate matter (PM) was collected in the city for a 10-year period.
• Levels of toxic metal concentrations along the period were analyzed.
• The effects of different phases of ENSO were important in the PM concentrations.
• The distribution of toxic metals in the samples was analyzed and associated to PM sources.
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Sanguineti, P.B., Lanzaco, B.L., López, M.L. et al. PM2.5 monitoring during a 10-year period: relation between elemental concentration and meteorological conditions. Environ Monit Assess 192, 313 (2020). https://doi.org/10.1007/s10661-020-08288-0
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DOI: https://doi.org/10.1007/s10661-020-08288-0