详情

原文传递 Reconstructing PM_2.5 Data Record for the Kathmandu Valley Using a Machine Learning Model.

题名：	Reconstructing PM_2.5 Data Record for the Kathmandu Valley Using a Machine Learning Model.
作者：	Bhatta, S; Yang, Y.
摘要：	This paper presents a method for reconstructing the historical hourly concentrations of particulate matter 2.5 (PM 2.5) over the Kathmandu Valley from 1980 to the present. The method uses a machine learning model that is trained using PM 2.5 readings from US Embassy (Phora Durbar) as a ground truth, and the meteorological data from Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA2) as input. The Extreme Gradient Boosting (XGBoost) model acquires a credible 10-fold crossvalidation (CV) score of ~83.4%, an r2-score of ~84%, a Root Mean Square Error (RMSE) of ~15.82 µg/m 3, and a Mean Absolute Error (MAE) of ~10.27 µ g/m 3. Further demonstrating the model's applicability to years other than those for which truth values are unavailable, the multiple cross-test with an unseen data set offered r2-scores for 2018, 2019, and 2020 ranging from 56% to 67%. The model-predicted data agrees with true values and indicates that MERRA2 underestimates PM 2.5 over the region. It strongly agrees with ground-based evidence showing substantially higher mass concentrations in the dry pre- and post-monsoon seasons than in the monsoon months. It also shows a strong anti-correlation between PM 2.5 concentration and humidity. The results also demonstrate that none of the years fulfilled the annual mean air quality index (AQI) standards set by the World Health Organization (WHO).
总页数：	18 pages