Abstract
Among statistical tools for the study of atmospheric pollutants, trajectory regression analysis (TRA), cluster analysis (CA), and principal component analysis (PCA) can be highlighted. Therefore, this article presents a systematic review of such techniques based on (i) air mass influences on particulate matter (PM) and (ii) the study of the relationship between PM and meteorological variables. This article aims to review studies that use TRA and to review studies that adopt CA and/or PCA to identify the associations and relationship between meteorological variables and atmospheric pollutants. Papers published between 2006 and 2018 and indexed by five of the main scientific databases were considered (ScienceDirect, Web of Science, PubMed, SciELO, and Scopus databases). PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) recommendations supported this systematic review. From the resulting most relevant papers, eight studies analyzed the influence of air mass trajectories on PM using TRA and twenty-one studies searched for the relationship between meteorological variables and PM using CA and/or PCA. A combination of TRA and time series models was identified as the possibility of future works. Besides, studies that simultaneously combine the three techniques to identify both the influence of air masses on PM and its relationship with meteorological variables are a possibility of future papers, because it can lead to a better comprehension of such a phenomenon.
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Austin, E., Coull, B., Thomas, D., & Koutrakis, P. (2012). A framework for identifying distinct multipollutant profiles in air pollution data. Environment International, 45, 112–121. https://doi.org/10.1016/j.envint.2012.04.003.
Banerjee, T., Singh, S. B., & Srivastava, R. K. (2011). Development and performance evaluation of statistical models correlating air pollutants and meteorological variables at Pantnagar, India. Atmospheric Research, 99, 505–517. https://doi.org/10.1016/j.atmosres.2010.12.003.
Binaku, K., & Schmeling, M. (2017). Mutivariate statistical analyses of air pollutants and meteorology in Chicago during summers 2010–2012. Air Quality, Atmosphere and Health, 10, 1227–1236. https://doi.org/10.1007/s11869-017-0507-7.
Carmo, C., Hacon, S., Longo, K., Freitas, S., Ignotti, E., & Ponce De Leon, A. (2010). Associação entre material particulado de queimadas e doenças respiratórias na região sul da Amazônia brasileira. Revista Panamericana Salud Pública, 27, 6–10. https://doi.org/10.1590/s1020-49892010000100002.
CETESB. (2016). Companhia Ambiental do Estado de São Paulo. http://www.cetesb.sp.gov.br/ar/qualidade-do-ar/32-qualar, available at CETESB, São Paulo (Accessed 11.02.2016).
Chalbot, M. C., Lianou, M., Vei, I. C., Kotronarou, A., & Kavouras, I. (2013a). Spatial attribution of sulfate and dust aerosol sources in an urban area using receptor modeling coupled with Lagrangian trajectories. Atmospheric Pollution Research, 4, 346–353. https://doi.org/10.5094/apr.2013.039.
Chalbot, M. C., McElroy, B., & Kavouras, I. G. (2013b). Sources, trends and regional impacts of fine particulate matter in southern Mississipi valley: significance of emissions from sources in the Gulf of Mexico coast. Atmospheric Chemistry and Physics, 13, 3721–3732. https://doi.org/10.5194/acp-13-3721-2013.
De La Rosa, J. D., De La Camapa, A. M. S., Alastuey, A., Querol, X., González-Castanedo, Y., Fernandez-Camacho, R., & Stein, A. F. (2010). Using PM10 geochemical maps for defining the origin of atmospheric pollution in Andalusia (southern Spain). Atmospheric Environment, 44, 4595–4605. https://doi.org/10.1016/j.atmosenv.2010.08.009.
Dimitriou, K., & Kassomenos, P. (2014a). A study on the reconstitution of daily PM10 and PM2.5 levels in Paris with a multivariate linear regression model. Atmospheric Environment, 98, 648–654. https://doi.org/10.1016/j.atmosenv.2014.09.047.
Dimitriou, K., & Kassomenos, P. (2014b). Decomposing the profile of PM in two low polluted German cities—mapping of air mass residence time, focusing on potencial long range transport impacts. Environmental Pollution, 190, 91–110. https://doi.org/10.1016/j.envpol.2014.03.019.
Dimitriou, K., & Kassomenos, P. (2018). Quantifying daily contributions of source regions to PM concentrations in Marseille based on the trails of incoming air masses. Air Quality, Atmosphere & Health, 11, 571–580. https://doi.org/10.1007/s11869-018-0564-6.
Dominick, D., Juahir, H., Latif, M. T., Zain, S. M., & Aris, A. Z. (2012). Spatial assessment of air quality patterns in Malaysia using multivariate analysis. Atmospheric Environment, 60, 172–181. https://doi.org/10.1016/j.atmosenv.2012.06.021.
Filonchyk, M., & Yan, H. (2018). The characteristics of air pollutants during different seasons in the urban area of Lanzhou, Northwest China. Environmental Earth Science, 77, 763. https://doi.org/10.1007/s12665-018-7925-1.
Franceschi, F., Cobo, M., & Figueredo, M. (2018). Discovering relationships and forecasting PM10 and PM2.5 concentrations in Bogotá, Colombia, using artificial neural networks, principal component analysis, and k-means clustering. Atmospheric Pollution Research, 9, 912–922. https://doi.org/10.1016/j.apr.2018.02.006.
Gonçalves, F. L. T., Carvalho, L. M. V., Conde, F. C., Latorre, M. R. D. O., Saldiva, P. H. N., & Braga, A. L. F. (2005). The effects of air pollution and meteorological parameters on respiratory morbidity during summer in São Paulo City. Environment International, 31, 343–349. https://doi.org/10.1016/j.envint.2004.08.004.
Goyal, P., Chan, A., & Jaiswal, N. (2006). Statistical models for the prediction of respirable suspended particulate matter in urban cities. Atmospheric Environment, 40, 2068–2077. https://doi.org/10.1016/j.atmosenv.2005.11.041.
Guo, Y., Ma, Y., Zhang, Y., Huang, S., Wu, Y., Yu, S., Zou, F., & Cheng, J. (2017). Time series analysis of ambient air pollution effects on daily mortality. Environmental Science and Pollution Research, 24, 20261–20272. https://doi.org/10.1007/s11356-017-9502-7.
Halim, N. D. A., Latif, M. T., Ahamad, F., Dominick, D., Chung, J. X., Juneng, L., & Khan, M. F. (2018). The long-term assessment of air quality on an island in Malaysia. Heliyon, 4, e01054. https://doi.org/10.1016/j.heliyon.2018.e01054.
He, H., & Lu, W. (2012). Decomposition of pollution contributors to urban ozone levels concerning regional and local scales. Building and Environment, 49, 97–103. https://doi.org/10.1016/j.buildenv.2011.09.019.
Hinds, W. (1998). Aerosol technology (2nd ed.). Wiley.
Huang, L., Gong, S., Sharma, S., Lavoué, D., & Jia, C. Q. (2010). A trajectory analysis of atmospheric transport of black carbon aerosols to Canadian high Arctic in winter and spring (1990–2005). Atmospheric Chemistry and Physics, 10, 5065–5073. https://doi.org/10.5194/acp-10-5065-2010.
Huang, P., Zhang, J., Tang, Y., & Liu, L. (2015). Spatial and temporal distribution of PM2.5 pollution in Xi’an City, China. International Journal of Environmental Research and Public Health, 12, 6608–6625. https://doi.org/10.3390/ijerph120606608.
Jassim, M. S., Coskuner, G., Marzooq, H., AlAsfoor, A., & Taki, A. A. (2018). Spatial distribution and source apportionment of air pollution in Bahrain using multivariate analysis methods. Environment Asia, 11(2), 9–22.
Johnson, R. A., & Wichern, D. W. (Eds.). (1988). Applied multivariate statistical analysis. Upper Saddle River: Prentice-Hall, Inc..
Kassomenos, P., Vardoulakis, S., Chaloulakou, A., Paschalidou, A., Grivas, G., Borge, R., & Lumbreras, J. (2014). Study of PM10 and PM2.5 levels in three European cities: analysis of intra and inter urban variations. Atmospheric Environment, 87, 153–163. https://doi.org/10.1016/j.atmosenv.2014.01.004.
Kavouras, I. G., Lianou, M., Chalbot, M. C., Vei, I. C., Kotronarou, A., Hoek, G., Hameri, K., & Harrison, R. M. (2013). Quantitative determination of regional contributions to fine and coarse particle mass in urban receptor sites. Environmental Pollution, 176, 1–9. https://doi.org/10.1016/j.envpol.2013.01.004.
Khan, M. B., Masiol, M., Bruno, C., Pasqualetto, A., Formenton, G. M., Agostinelli, C., & Pavoni, B. (2018). Potencial sources and meteorological factors affecting PM2.5-bound polycyclic aromatic hydrocarbon levels in six main cities of northeastern Italy: an assessment of the related carcinogenic and mutagenic risks. Environmental Science and Pollution Research, 25, 31987–32000. https://doi.org/10.1007/s11356-018-2841-1.
Kwon, S., Jeong, W., Park, D., Kim, K., & Cho, K. (2015). A multivariate study for characterizing particulate matter (PM10, PM2.5, and PM1) in Seoul metropolitan subway stations, Korea. Journal of Hazardous Materials, 297, 295–303. https://doi.org/10.1016/j.jhazmat.2015.05.015.
Latif, M. T., Dominick, D., Ahamad, F., Khan, M. F., Juneng, L., Hamzah, F. M., & Naddzir, M. S. M. (2014). Long term assessment of air quality from a background station on the Malaysian Peninsula. Science of the Total Environment, 482-483, 336–348. https://doi.org/10.1016/j.scitotenv.2014.02.132.
Lu, W. Z., He, H. D., & Dong, L. Y. (2011). Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis. Building and Environment, 46, 577–583. https://doi.org/10.1016/j.buildenv.2010.09.004.
Luna, A., Paredes, M., De Oliveira, G., & Corrêa, S. (2014). Prediction of ozone concentration in tropospheric levels using artificial neural networks and support vector machine at Rio de Janeiro, Brazil. Atmospheric Environment, 98, 98–104. https://doi.org/10.1016/j.atmosenv.2014.08.060.
Ma, R., Zhang, Y., Sun, Z., Xu, D., & Li, T. (2020). Effects of ambient particulate matter on fasting blood glucose: A systematic review and meta-analysis. Environmental Pollution, 258, 113589. https://doi.org/10.1016/j.envpol.2019.113589.
Martins, E. H., Nogarotto, D. C., Mortatti, J., & Pozza, S. A. (2019). Chemical composition of rainwater in an urban area of the southeast of Brazil. Atmospheric Pollution Research, 10, 520–530. https://doi.org/10.1016/j.apr.2018.10.003.
Matyasovsky, I., Makra, L., Bálint, B., Guba, Z., & Sümeghy, Z. (2011). Multivariate analysis respiratory problems and their connection with meteorological parameters and the main biological and chemical air pollutants. Atmospheric Environment, 45, 4152–4159. https://doi.org/10.1016/j.atmosenv.2011.05.024.
Moher, D., Liberati, A., Tetzlaff, J., Altman, D., & The PRISMA group. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analysis: The PRISMA statement. PLoS Medicine. https://doi.org/10.1371/journal.pmed.1000097.
Navares, R., Díaz, J., Linares, C., & Aznarte, J. L. (2018). Comparing ARIMA and computational intelligence methods to forecast daily hospital admissions due to circulatory and respiratory causes in Madrid. Stochastic Environmental Research and Risk Assessment, 32, 2849–2859. https://doi.org/10.1007/s00477-018-1519-z.
Niu, H., He, Y., Lu, X. X., Shen, J., Du, J., Zhang, T., Pu, T., Xin, H., & Chang, L. (2014). Chemical composition of rainwater in the yulong snow mountain region, southwestern China. Atmospheric Research, 144, 195–206. https://doi.org/10.1016/j.atmosres.2014.03.010.
Pavón-Domínguez, P., Jiménez-Hornero, F., & Gutiérrez De Ravé, E. (2014). Proposal for estimating ground-level ozone concentrations at urban areas based on multivariate statistical methods. Atmospheric Environment, 90, 59–70. https://doi.org/10.1016/j.atmosenv.2014.03.032.
Querol, X., Alastuey, A., Viana, M., Rodriguez, S., Artiñano, B., Salvador, P., Garcia Dos Santos, S., Fernandez Patier, R., Ruiz, C., De La Rosa, J., Sanchez De La Campa, A., Mennedez, M., & Ji, G. (2004). Speciation and origin of PM10 and PM2.5 in Spain. Journal of Aerosol Science, 35, 1151–1172. https://doi.org/10.1016/j.jaerosci.2004.04.002.
Revuelta, M. A., Mcintosh, G., Pey, J., Pérez, N., Querol, X., & Alastuey, A. (2014). Partitioning of magnetic particles in PM10, PM2.5 and PM1 aerosols in the urban atmospheric of Barcelona (Spain). Environmental Pollution, 188, 109–117. https://doi.org/10.1016/j.envpol.2014.01.025.
Rodopoulou, S., Chalbot, M. C., Samoli, E., Dubois, D. W., San Filippo, B. D., & Kavouras, I. G. (2014). Air pollution and hospital emergency room and admissions for cardiovascular and respiratory diseases in Doña Ana County, New Mexico. Environmental Research, 129, 39–46. https://doi.org/10.1016/j.envres.2013.12.006.
Rolph, G. D. (2016). Real-time environmental applications and display sYstem (READY). College Park, MD: NOAA Air Resources Laboratory http://www.ready.noaa.gov.
Seinfeld, J.H. (1986). Atmospheric chemistry and physics of air pollution, 1st edn. Jonh Wiley & Sons.
Sharma, A., Massey, D. D., & Taneja, A. (2018). A study of horizontal distribution pattern of particulate and gaseous pollutants based on ambient monitoring near a busy highway. Urban Climate, 24, 643–656. https://doi.org/10.1016/j.uclim.2017.08.003.
Sheehan, M. C., Lam, J., Acien, A. N., & Chang, H. H. (2016). Ambient air pollution epidemiology systematic review and meta-analysis: a review of reporting and methods practice. Environment International, 92-93, 647–656. https://doi.org/10.1016/j.envint.2016.02.016.
Souza, J., Reisen, V., Santos, J., & Franco, G. (2014). Principal components and generalized linear modeling in the correlation between hospital admissions and air pollution. Revista Saúde Pública, 48(3), 451–458. https://doi.org/10.1590/s0034-8910.2014048005078.
Souza, A., Santos, D. A. S., Aristone, F., Kovač-Andrić, E., Matasović, B., Pires, J. C. M., & Ikefuti, P. V. (2017). Meteorological impact factors on the modeling of ozone concentrations using analysis of temporal series and multivariate statistic methods. HOLOS, 5, 2. https://doi.org/10.15628/holos.2017.5033.
Statheropoulos, M., Vaassiliadis, N., & Pappa, A. (1998). Principal component and canonical correlation analysis for examining air pollution and meteorological data. Atmospheric Environment, 32, 1087–1095. https://doi.org/10.1016/s1352-2310(97)00377-4.
Stein, A., Draxler, R., Rolph, G., Stunder, B., Cohen, M., & Ngan, F. (2015). NOAA's HYSPLIT atmospheric transport and dispersion modeling system. American Meteorological Society, 96, 2059–2077. https://doi.org/10.1175/bams-d-14-00110.1.
Targino, A., & Krecl, P. (2016). Local and regional contributions to black carbon aerosols in a mid-Sized City in southern Brazil. Aerosol and Air Quality Research, 16, 125–137. https://doi.org/10.4209/aaqr.2015.06.0388.
Tiwari, S., Srivastava, A., Bisht, D., Parmita, P., Srivastava, M., & Attri, S. (2013). Diurnal and seasonal variations of black carbon and PM2.5 over New Delhi, India: Influence of meteorology. Atmospheric Research, 125-126, 50–62. https://doi.org/10.1016/j.atmosres.2013.01.011.
Toh, Y., Lim, S., & Glasow, R. (2013). The influence of meteorological factors and biomass burning on surface ozone concentrations at Tanah Rata, Malaysia. Atmospheric Environment, 70, 435–446. https://doi.org/10.1016/j.atmosenv.2013.01.018.
Vardoulakis, S., & Kassomenos, P. (2008). Sources and factors affecting PM10 levels in two European cities: Implications for local air quality management. Atmospheric Environment, 42, 3949–3963. https://doi.org/10.1016/j.atmosenv.2006.12.021.
Varela, M. M. S., Costa, I. P., & González, A. L. (2017). Systematic review of the association between particulate matter exposure and autism spectrum disorders. Environmental Research, 153, 150–160. https://doi.org/10.1016/j.envres.2016.11.022.
Ventura, L. M. B., Pinto, F. O., Soares, L. M., Luna, A. S., & Gioda, A. (2018). Evaluation of air quality in a megacity using statistics tools. Meteorology and Atmospheric Physics, 130, 361–370. https://doi.org/10.1007/s00703-017-0517-x.
Weisberg, S. (2005). Applied linear regression, 3rd edn. John Wiley & Sons, Inc.
Xu, J., Dubois, D., Pitchford, M., Green, M., & Etyemezian, V. (2006). Attribution of sulfate aerosols in Federal Class I areas of the western United States based on trajectory regression analysis. Atmospheric Environment, 40, 3433–3447. https://doi.org/10.1016/j.atmosenv.2006.02.009.
Yamamoto, S. S., Phalkey, R., & Malik, A. (2014). A systematic review of air pollution as a risk factor for cardiovascular disease in South Asia: limited evidence from India and Pakistan. International Journal of Hygiene and Environmental Health, 217(2–3), 133–144. https://doi.org/10.1016/j.ijheh.2013.08.003.
Yang, D., Lu, D., Xu, J., Ye, C., Zhao, J., Tian, G., Wang, X., & Zhu, N. (2018). Predicting spatio-temporal concentrations of PM2.5 using land use and meteorological data in Yangtze River Delta, China. Stochastic Environmental Reseasch and Risk Assessment, 32, 2445–2456. https://doi.org/10.1007/s00477-017-1497-6.
Zhao, M., Li, L., Liu, Z., Chen, B., Huang, J., Cai, J., & Deng, S. (2013). Chemical composition and sources of rainwater collected at a semi-rural site in Ya'an, southwestern China. Atmospheric and Climate Sciences, 03(04), 486–496. https://doi.org/10.4236/acs.2013.34051.
Zhao, R., Chen, S., Wang, W., Huang, J., Wang, K., Liu, L., & Wei, S. (2017). The impact of short-term exposure to air pollutants on the onset of out-of hospital cardiac arrest: A systematic review and meta-analysis. International Journal of Cardiology, 226, 110–117. https://doi.org/10.1016/j.ijcard.2016.10.053.
Acknowledgments
The authors gratefully acknowledge the NOAA Air Resources Laboratory (ARL) for the provision of the HYSPLIT transport and dispersion model at the READY website (http://www.ready.noaa.gov). The authors also thank Espaço da Escrita–Pró-Reitoria de Pesquisa—UNICAMP—for the language services provided.
Funding
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.
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Nogarotto, D.C., Pozza, S.A. A review of multivariate analysis: is there a relationship between airborne particulate matter and meteorological variables?. Environ Monit Assess 192, 573 (2020). https://doi.org/10.1007/s10661-020-08538-1
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DOI: https://doi.org/10.1007/s10661-020-08538-1