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<p>To better recognise existing high pollution and its exposure, substantial knowledge of their spatiotemporal visualisation is mandatory to identify better solutions in areas that need it most. Contrary to surface-based studies that usually target limited observations, here spatiotemporal documentation of satellite&#xa0;products, tropospheric-O<sub>3</sub>&#xa0;(both vertically and horizontally) and its tropospheric precursors (formaldehyde (HCHO) and nitrogen dioxide (NO<sub>2</sub>) is done altogether to cover the whole swath of Jharkhand (India) for the first time. Here, incorporating 518 satellite products of Aqua/AIRS-ozone (1° × 1°) along with its both high resolution (~ 1&#xa0;km ×  ~ 1&#xa0;km) precursors (TROPOMI-NO<sub>2</sub> and TROPOMI-HCHO) altogether, acquisition from the Atmospheric Infrared Sounder and the Sentinel-5P satellite respectively addressed for the three conflicting timelines; before-COVID-19 (2019)- during-COVID-19 (2020)- after-COVID-19 (2021) induced lockdown phases. Analysis depicted that throughout the interested time frame, the highest, moderate and lowest values were recorded consistently for the pollutants, Aqua/AIRS-ozone, TROPOMI-HCHO and TROPOMI-NO<sub>2</sub>, respectively, for Jharkhand (India). Also, all three pollutant levels were captured at a lower level in 2020 concerning their presence in 2019 and 2021. Further, ANOVA analysis revealed a statistically significant difference (p &lt; 0.05) in the mean value of all three pollutants above, as having p-values 7.35E-17, 4.08E-21 and 1.91E-18 for 2019, 2020 and 2021, respectively. Adopting the Environmental Systems Research Institute, Inc. (ESRI) 10-m land cover map, descriptive statistics for TROPOMI-NO<sub>2</sub>, TROPOMI-HCHO, and Aqua/AIRS- ozone were also retrieved over major land classes. This representation would provide a broad view regarding the suitable deployment of monitoring stations for the most required location.</p>

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ANOVA Analysis and Spatiotemporal Visualisation of Tropospheric O3-NO2-HCHO Using GEE and GIOVANNI Platform over Mega-mining Region, Jharkhand (India): Targeting COVID-19 Lockdown Induced Three Consecutive Conflicting Periods (2019–2020–2021)

  • 期刊论文

  • 发表日期：

    2025年8月20日

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  • Shalini Priya,
  • Jawed Iqbal

摘要

To better recognise existing high pollution and its exposure, substantial knowledge of their spatiotemporal visualisation is mandatory to identify better solutions in areas that need it most. Contrary to surface-based studies that usually target limited observations, here spatiotemporal documentation of satellite products, tropospheric-O3 (both vertically and horizontally) and its tropospheric precursors (formaldehyde (HCHO) and nitrogen dioxide (NO2) is done altogether to cover the whole swath of Jharkhand (India) for the first time. Here, incorporating 518 satellite products of Aqua/AIRS-ozone (1° × 1°) along with its both high resolution (~ 1 km ×  ~ 1 km) precursors (TROPOMI-NO2 and TROPOMI-HCHO) altogether, acquisition from the Atmospheric Infrared Sounder and the Sentinel-5P satellite respectively addressed for the three conflicting timelines; before-COVID-19 (2019)- during-COVID-19 (2020)- after-COVID-19 (2021) induced lockdown phases. Analysis depicted that throughout the interested time frame, the highest, moderate and lowest values were recorded consistently for the pollutants, Aqua/AIRS-ozone, TROPOMI-HCHO and TROPOMI-NO2, respectively, for Jharkhand (India). Also, all three pollutant levels were captured at a lower level in 2020 concerning their presence in 2019 and 2021. Further, ANOVA analysis revealed a statistically significant difference (p < 0.05) in the mean value of all three pollutants above, as having p-values 7.35E-17, 4.08E-21 and 1.91E-18 for 2019, 2020 and 2021, respectively. Adopting the Environmental Systems Research Institute, Inc. (ESRI) 10-m land cover map, descriptive statistics for TROPOMI-NO2, TROPOMI-HCHO, and Aqua/AIRS- ozone were also retrieved over major land classes. This representation would provide a broad view regarding the suitable deployment of monitoring stations for the most required location.