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Spatio-temporal variations of sulfur dioxide concentrations in industrial and urban area via a new statistical approach

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Abstract

Sulfur dioxide (SO2) is considered the most widespread pollutant that threatens environmental and human health. The purpose of this study is to propose a new method for evaluating the spatial variation of SO2 levels in the Metropolitan Area of Porto Alegre (MAPA). This method included use of Chi-square test to better identify the origin of SO2 sources. Additionally, results of the different methods applied allowed to analyze the temporal SO2 levels and their association with meteorological parameters. SO2 at five sampling sites (Esteio, Canoas, Charqueadas, Triunfo, and Gravataí) were measured during 2010–2015; using fluorescence SO2 automated analyzers. Results showed that Charqueadas had the highest average concentration (~ 15 μg m−3), followed by Triunfo (13 μg m−3), Esteio (6 μg m−3), Canoas (3 μg m−3), and Gravataí (2 μg m−3). Chi-square test applied to SO2, and wind direction quadrants showed significant contribution of local emission sources. Seasonal variation revealed higher SO2 levels on cold days for most of the studied sites, except for Esteio site. Day-wise variations revealed higher SO2 concentration on weekdays than weekends for Esteio and Canoas sites, indicating traffic influence especially during the rush-hours. Annual averages analysis identified an increasing trend in SO2 concentrations, implying that applied emission control systems and technological improvement of engines and fuels were not sufficient and thus points out a need for better subsidies mechanisms to pollutant control and effective emission reduction strategies that decision makers, including environmental agencies, must make priority by considering the local realities.

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Acknowledgements

The authors thank the FAPERGS and CNPq for the financial support to this work.

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Correspondence to E. C. Teixeira.

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Landim, A.A., Teixeira, E.C., Agudelo-Castañeda, D. et al. Spatio-temporal variations of sulfur dioxide concentrations in industrial and urban area via a new statistical approach. Air Qual Atmos Health 11, 801–813 (2018). https://doi.org/10.1007/s11869-018-0584-2

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  • DOI: https://doi.org/10.1007/s11869-018-0584-2

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