Air Pollution Assessment by Landsat Aerosol-Level Mapping in the Macrometropolitan Region of São Paulo, Brazil
DOI:
https://doi.org/10.26848/rbgf.v19.02.p814-830Keywords:
air pollution, atmospheric aerosols, Remote Sensing, metropolitan region of são pauloAbstract
The accelerated process of industrialization and urbanization of the Metropolitan Region of São Paulo (MRSP) and Baixada Santista (MRBS), associated with the pattern of exploitation of natural resources and territorial occupation, resulted in the degradation of the quality of life of the population, such as urban air pollution. The objective of this study was to present an observational analysis of the temporal variability of the aerosol optical depth, based on remote sensing products, for two specific dates. For this, aerosol optical depth (AOD) data from the Landsat 8 satellite was used, in order to produce maps of particulate matter concentration in the MRSP and Baixada Santista and combine them with monitoring data from the Environmental Company of the State of São Paulo (CETESB). The hypothesis explored was that there is a relationship between the concentration of particulate matter and its emission sources. Among the results, it is worth highlighting that the AOD data based on Landsat are useful for assessing urban atmospheric aerosols at ground level and mapping areas influenced by aerosol pollution. Furthermore, it is advisable to combine these data with continuous monitoring of air pollution in the MRSP and Baixada Santista.
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References
Abe, K., & Miraglia, S. (2016). Health impact assessment of air pollution in São Paulo, Brazil. International Journal of Environmental Research and Public Health, 13(7), 694. https://dx.doi.org/10.3390%2Fijerph13070694 DOI: https://doi.org/10.3390/ijerph13070694
Bilal, M., Mhawish, A., Ali, M. A., Nichol, J. E., Leeuw, G. D., Khedher, K. M., ... & Nazeer, M. (2022). Integration of surface reflectance and aerosol retrieval algorithms for multi-resolution aerosol optical depth retrievals over urban areas. Remote Sensing, 14(2), 373. https://doi.org/10.3390/rs14020373 DOI: https://doi.org/10.3390/rs14020373
Bravo, M., Son, J., Freitas, C., Gouveia, N., & Bell, M. (2016). Air pollution and mortality in São Paulo, Brazil: Effects of multiple pollutants and analysis of susceptible populations. Journal of Exposure Science and Environmental Epidemiology, 26(2), 150-161. https://doi.org/10.1038/jes.2014.90 DOI: https://doi.org/10.1038/jes.2014.90
CETESB - Companhia Ambiental Do Estado De São Paulo, (2021). Qualidade do ar no estado de São Paulo. São Paulo: CETESB, 2022. https://cetesb.sp.gov.br/ar/wp-content/uploads/sites/28/2022/10/Relatorio-de-Qualidade-do-Ar-no-Estado-de-Sao-Paulo-2021.pdf
DATAGEO - Sistema Ambiental Paulista, (2023). Estações de Monitoramento. São Paulo. https://datageo.ambiente.sp.gov.br/
Gavidia-Calderón, M., Schuch, D., Vara-Vela, A., Inoue, R., Freitas, E. D., Albuquerque, T. T. D. A., ... & Bell, M. L. (2024). Air quality modeling in the metropolitan area of São Paulo, Brazil: A review. Atmospheric Environment, 319, 120301. https://doi.org/10.1016/j.atmosenv.2023.120301 DOI: https://doi.org/10.1016/j.atmosenv.2023.120301
Gouveia, N., Corrallo, F., Leon, A., Junger, & W., Freitas, C. (2017). Poluição do ar e hospitalizações na maior metrópole brasileira. Revista de Saúde Pública, 51, 117. https://doi.org/10.11606/s1518-8787.2017051000223 DOI: https://doi.org/10.11606/S1518-8787.2017051000223
Groeneveld, D. P., Ruggles, T., & Gao, B. C. (2024). Landsat-8/9 Atmospheric Correction Reliability Using Scene Statistics. Remote Sensing, 16(12), 2216. https://doi.org/10.3390/rs16122216 DOI: https://doi.org/10.3390/rs16122216
Hamilton, D. S., Perron, M. M., Bond, T. C., Bowie, A. R., Buchholz, R. R., Guieu, C., ... & Mahowald, N. M. (2022). Earth, wind, fire, and pollution: Aerosol nutrient sources and impacts on ocean biogeochemistry. Annual Review of Marine Science, 14(1), 303-330. https://doi.org/10.1146/annurev-marine-031921-013612 DOI: https://doi.org/10.1146/annurev-marine-031921-013612
Han, L., Yan, H., Xiang, X., Liu, X., Shi, R., Wang, H., ... & Wang, H. (2021). Characteristics, evolution, and potential source regions of submicron aerosol in Beijing, China. Atmospheric Environment, 246, 118061. https://doi.org/10.1016/j.atmosenv.2020.118061 DOI: https://doi.org/10.1016/j.atmosenv.2020.118061
Hoffmann, B., Boogaard, H., de Nazelle, A., Andersen, Z. J., Abramson, M., Brauer, M., ... & Thurston, G. (2021). WHO air quality guidelines 2021–aiming for healthier air for all: a joint statement by medical, public health, scientific societies and patient representative organisations. International journal of public health, 66, 1604465. https://doi.org/10.3389/ijph.2021.1604465 DOI: https://doi.org/10.3389/ijph.2021.1604465
Jebali, A., Zare, M., Ekhtesasi, M. R., & Jafari, R. (2021). Detection of areas prone to wind erosion and air pollution using DSI and PDSI indices. Natural Hazards, 108(1), 1221-1235. https://doi.org/10.1007/s11069-021-04728-3 DOI: https://doi.org/10.1007/s11069-021-04728-3
Jin, Y., Hao, Z., Huang, H., Wang, T., Mao, Z., & Pan, D. (2022). Evaluation of LaSRC aerosol optical depth from Landsat-8 and Sentinel-2 in Guangdong-Hong Kong-Macao greater bay area, China. Atmospheric Environment, 280, 119128. https://doi.org/10.1016/j.atmosenv.2022.119128 DOI: https://doi.org/10.1016/j.atmosenv.2022.119128
Leirião, L., de Oliveira, M., Martins, T., & Miraglia, S. (2023). A multi-pollutant and meteorological analysis of cardiorespiratory mortality among the elderly in São Paulo, Brazil—an artificial neural networks approach. International Journal of Environmental Research and Public Health, 20(8), 5458. https://doi.org/10.3390/ijerph20085458 DOI: https://doi.org/10.3390/ijerph20085458
Li, J., Carlson, B. E., Yung, Y. L., Lv, D., Hansen, J., Penner, J. E., ... & Dong, Y. (2022). Scattering and absorbing aerosols in the climate system. Nature Reviews Earth & Environment, 3(6), 363-379. https://doi.org/10.1038/s43017-022-00296-7 DOI: https://doi.org/10.1038/s43017-022-00296-7
Liu, Y., Huang, J., & Huang, F. (2023). A comprehensive review on study methods of aerosol optical properties in different dimensions. IEEE Access, 11, 36763-36786. https://doi.org/10.1109/ACCESS.2023.3266333 DOI: https://doi.org/10.1109/ACCESS.2023.3266333
Mansour, H. M., Muralidharan, P., & Hayes Jr, D. (2024). Inhaled nanoparticulate systems: composition, manufacture and aerosol delivery. Journal of Aerosol Medicine and Pulmonary Drug Delivery, 37(4), 202-218. https://doi.org/10.1089/jamp.2024.29117.mk DOI: https://doi.org/10.1089/jamp.2024.29117.mk
Meo, S. A., Salih, M. A., Alkhalifah, J. M., Alsomali, A. H., & Almushawah, A. A. (2024). Environmental pollutants particulate matter (PM2. 5, PM10), Carbon Monoxide (CO), Nitrogen dioxide (NO2), Sulfur dioxide (SO2), and Ozone (O3) impact on lung functions. Journal of King Saud University-Science, 36(7), 103280. https://doi.org/10.1016/j.jksus.2024.103280 DOI: https://doi.org/10.1016/j.jksus.2024.103280
Nazeer, M., Ilori, C. O., Bilal, M., Nichol, J. E., Wu, W., Qiu, Z., & Gayene, B. K. (2021). Evaluation of atmospheric correction methods for low to high resolutions satellite remote sensing data. Atmospheric Research, 249, 105308. https://doi.org/10.1016/j.atmosres.2020.105308 DOI: https://doi.org/10.1016/j.atmosres.2020.105308
Nilson, B., Jackson, P. L., Schiller, C. L., & Parsons, M. T. (2022). Development and evaluation of correction models for a low-cost fine particulate matter monitor. Atmospheric Measurement Techniques Discussions, 2022, 1-16. https://doi.org/10.5194/amt-15-3315-2022 DOI: https://doi.org/10.5194/amt-2021-425
Putman, W. (2014). GEOS-5 Aerosols Simulation for SC NASA's Goddard Space Flight Center/Global Modeling and Assimilation Office. https://svs.gsfc.nasa.gov/3063
Qayyum, F., Tariq, S., Ul-Haq, Z., Mehmood, U., & Zeydan, Ö. (2022). Air pollution trends measured from MODIS and TROPOMI: AOD and CO over Pakistan. Journal of Atmospheric Chemistry, 79(3), 199-217. https://doi.org/10.1007/s10874-022-09436-1 DOI: https://doi.org/10.1007/s10874-022-09436-1
Qin, X., Do, T. H., Hofman, J., Bonet, E. R., La Manna, V. P., Deligiannis, N., & Philips, W. (2022). Fine-grained urban air quality mapping from sparse mobile air pollution measurements and dense traffic density. Remote Sensing, 14(11), 2613. https://doi.org/10.3390/rs14112613 DOI: https://doi.org/10.3390/rs14112613
QUALAR - Sistema De Informações Da Qualidade Do Ar. (2023). Relatórios de Valores Diários. https://qualar.cetesb.sp.gov.br
Ranjan, A. K., Patra, A. K., & Gorai, A. K. (2021). A review on estimation of particulate matter from satellite-based aerosol optical depth: Data, methods, and challenges. Asia-Pacific Journal of Atmospheric Sciences, 57, 679-699. https://doi.org/10.1007/s13143-020-00215-0 DOI: https://doi.org/10.1007/s13143-020-00215-0
Ribeiro, C. B., Rodella, F. H. C., & Hoinaski, L. (2022). Regulating light-duty vehicle emissions: an overview of US, EU, China and Brazil programs and its effect on air quality. Clean Technologies and Environmental Policy, 24(3), 851-862. https://doi.org/10.1007/s10098-021-02238-1 DOI: https://doi.org/10.1007/s10098-021-02238-1
Rodriguez-Loya, J., Lerma, M., & Gardea-Torresdey, J. L. (2023). Dynamic light scattering and its application to control nanoparticle aggregation in colloidal systems: a review. Micromachines, 15(1), 24. https://doi.org/10.3390/mi15010024 DOI: https://doi.org/10.3390/mi15010024
Rudke, A. P., Martins, J. A., Hallak, R., Martins, L. D., De Almeida, D. S., Beal, A., ... & Albuquerque, T. T. A. (2023). Evaluating TROPOMI and MODIS performance to capture the dynamic of air pollution in São Paulo state: A case study during the COVID-19 outbreak. Remote sensing of environment, 289, 113514. https://doi.org/10.1016/j.rse.2023.113514 DOI: https://doi.org/10.1016/j.rse.2023.113514
Saka, M. B., Mohd Hashim, M. H., & Shehu, S. A. (2025). Mining dust: health impacts, control measures and future directions. Environmental Engineering & Management Journal (EEMJ), 24(2). https://doi.org/10.30638/eemj.2025.032 DOI: https://doi.org/10.30638/eemj.2025.032
Shah, M., Raval, M. S., & Divakaran, S. (2025). A systematic review on deep learning for atmospheric correction of satellite images. Archives of Computational Methods in Engineering, 1-31. https://doi.org/10.1007/s11831-025-10424-3 DOI: https://doi.org/10.1007/s11831-025-10424-3
Sheng, Q., Ji, Y., Zhou, C., Zhang, H., & Zhu, Z. (2023). Spatiotemporal variation and pattern analysis of air pollution and its correlation with NDVI in Nanjing City, China: A Landsat-Based study. Forests, 14(10), 2106. https://doi.org/10.3390/f14102106 DOI: https://doi.org/10.3390/f14102106
Sifakins, N., & Deschamps, P. Y. (1992). Mapping of air pollution using SPOT satellite data, Photogrammetric Engineering and Remote Sensing, 58, 1433-1433.
Singer, J. M., André, C. D. S., André, P. A., Rocha, F. M. M., Waked, D., Vaz, A. M., ... & Barrozo, L. V. (2023). Assessing socioeconomic bias of exposure to urban air pollution: an autopsy-based study in São Paulo, Brazil. The Lancet Regional Health–Americas, 22. https://doi.org/10.1016/j.lana.2023.100500 DOI: https://doi.org/10.1016/j.lana.2023.100500
Soleimany, A., Solgi, E., Ashrafi, K., Jafari, R., & Grubliauskas, R. (2022). Temporal and spatial distribution mapping of particulate matter in southwest of Iran using remote sensing, GIS, and statistical techniques. Air Quality, Atmosphere & Health, 15(6), 1057-1078. https://doi.org/10.1007/s11869-022-01179-y DOI: https://doi.org/10.1007/s11869-022-01179-y
Sun L., Wei J., Bilal M., Tian X., Jia C., Guo Y., & Mi X. (2016). Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images. Remote Sensing. 8(1), 23. https://doi.org/10.3390/rs8010023 DOI: https://doi.org/10.3390/rs8010023
Tang, X., & Kumar, R. (2022). Promoting the Implementation of the Ambitious 2021 WHO Global Air Quality Guidelines in Asia. Bulletin of the American Meteorological Society, 103(7), E1684-E1690. https://doi.org/10.1175/BAMS-D-22-0040.1 DOI: https://doi.org/10.1175/BAMS-D-22-0040.1
Tuovinen, S., Kontkanen, J., Cai, R., & Kulmala, M. (2021). Condensation sink of atmospheric vapors: the effect of vapor properties and the resulting uncertainties. Environmental Science: Atmospheres, 1(7), 543-557. https://doi.org/10.1039/D1EA00032B DOI: https://doi.org/10.1039/D1EA00032B
USGS - United States Geological Service. (2020). Landsat 8 Collection 1 (C1) Land Surface Reflectance Code (LaSRC) - Product Guide. https://d9-wret.s3.us-west-2.amazonaws.com/assets/palladium/production/s3fs-public/atoms/files/LSDS-1368_L8_C1-LandSurfaceReflectanceCode-LASRC_ProductGuide-v3.pdf
USGS - United States Geological Survey. (2023). EarthExplorer. United States of America. https://earthexplorer.usgs.gov/
Vermote, E. F. (2015). MODIS surface reflectance user’s guide. MODIS Land Surface Reflectance Science Computing Facility, version 6. https://modis-
land.gsfc.nasa.gov/pdf/MOD09_UserGuide_v1.4.pdf
WHO World Health Organization. (2023). World health statistics 2023: monitoring health for the SDGs sustainable development goals. https://www.who.int/publications/i/item/9789240074323
Wikuats, C. F. H., Nogueira, T., Squizzato, R., de Freitas, E. D., & Andrade, M. D. F. (2023). Health risk assessment of exposure to air pollutants exceeding the new WHO air quality guidelines (AQGs) in São Paulo, Brazil. International Journal of Environmental Research and Public Health, 20(9), 5707. https://doi.org/10.3390/ijerph20095707 DOI: https://doi.org/10.3390/ijerph20095707
Xu, P. W., Tan, X. P., Cai, J. Z. & Liu, J. S. (2005). Study on Influence Factors of Urban Aerosol on Visibility and Extinction Coefficient, 2005. Environ. Pollut. Control, 27, 410–413.
Zhu, H., Martin, R. V., Van Donkelaar, A., Hammer, M. S., Li, C., Meng, J., ... & Lyapustin, A. (2024). Importance of aerosol composition and aerosol vertical profiles in global spatial variation in the relationship between PM 2.5 and aerosol optical depth. Atmospheric Chemistry and Physics, 24(20), 11565-11584. https://doi.org/10.5194/acp-24-11565-2024 DOI: https://doi.org/10.5194/acp-24-11565-2024
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