Monitoring spatiotemporal changes of NO2	using TROPOMI and sentinel-5 images for Dhaka city and its surrounding areas of Bangladesh

Nobonita Shobnom; Shahadad Hossain; Rezaul Roni

doi:10.18502/japh.v8i3.13785

Nobonita Shobnom Department of Geography and Environment, Jahangirnagar University, Dhaka, Bangladesh
Shahadad Hossain Department of Geography and Environment, Jahangirnagar University, Dhaka, Bangladesh
Rezaul Roni Department of Geography and Environment, Jahangirnagar University, Dhaka, Bangladesh

DOI: https://doi.org/10.18502/japh.v8i3.13785

Keywords: Air pollution; Nitrogen dioxide (NO2); Tropospheric monitoring instrument (TROPOMI) sensor; Google earth

Abstract

Introduction: Discernable air pollution occurs in most developing countries due to rapid urbanization which can be parameterized by air, humidity, population density, temperature, contaminants, exorbitant fossil fuel consumption, and inadequate transportation. Nitrogen dioxide (NO₂), one of the most widely recognized air pollutants, has a detrimental impact on human health explicitly or implicitly and considerably influences on atmospheric composition.

Materials and methods: In this study, NO₂ intensity was analyzed from 2018 with aiming to monitor spatiotemporal changes in Dhaka and its surrounding areas with the Tropospheric Monitoring Instrument (TROPOMI) sensor data. Copernicus Sentinel-5 Precursor satellite data was used in the Google Earth Engine platform to get the result.

Results: The results revealed a strong relationship (R² =0.9478) between the NO₂ concentration and high population density and the temporal variation is higher during the pre-monsoon than throughout the post-monsoon. The reason behind is the lack of sunlight and the difficulty to break down the NO₂, which causes the removal of NO₂ from the atmosphere to proceed more slowly. In contrast, Land Use and Land Cover (LULC) are also impacted by the high concentration which is remains in the built-up area.

Conclusion: This research mainly considered that how NO₂ concentration measured from satellite images with temporal variation within a year and what factors strongly influence raising NO₂ levels. This model can be used for policy-making to take proper initiatives to reduce NO₂ concentrations. The result showed significant uses of TROPOMI with relating population density and LULC in Dhaka and its surrounding areas of Bangladesh.