Abstract A44B-07 To Be Presented at 2023 AGU Fall Meeting

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Basu Sarkar, D.; Bhatta, S. Investigating PM_2.5 Pollution in the Kathmandu Valley Using Data Predicted by Machine Learning Models, Abstract A44B-07 to be presented at 2023 Fall Meeting, AGU, San Francisco, Calif., 11-15 Dec.

PM_2.5 is a significant contributor to air pollution and poses serious health risks because of its aerodynamic diameter of less than 2.5 micrometers. This work investigates the sources and factors contributing to PM_2.5 pollution in the Kathmandu Valley by analyzing hourly PM_2.5 data. Here we study the recently published PM_2.5 concentrations from 1980 to 2021. These data were generated by employing a machine-learning technique specifically designed for this purpose using Modern-Era Retrospective Analysis for Research and Applications (MERRA-2) and Phora Durbar data. We delve into the diurnal variations observed throughout the dataset. We also examine seasonal trends in the data, taking advantage of the decades’ worth of information available to us. It allows us to understand how population growth and migration to major cities contribute to the elevated levels of PM_2.5. Our goal here is to develop a comprehensive understanding of PM_2.5 pollution in the Kathmandu Valley as it relates to urban population growth. The findings of this research may help identify the effective measures to mitigate it. Also, this study can be part of an informed policy-making process to improve air quality and better public health outcomes.