BACKGROUND: Exposure to air pollution is a major health problem worldwide. This study aimed to investigate the effect of the level of air pollutants and meteorological parameters with their related lag time on the transmission and severity of coronav...
Air pollution, particularly PM2.5, has long been a critical concern for the atmospheric environment. Accurately predicting daily PM2.5 concentrations is crucial for both environmental protection and public health. This study introduces a new hybrid m...
Aedes mosquitoes, known as vectors of mosquito-borne diseases, pose significant risks to public health and safety. Modeling the population dynamics of Aedes mosquitoes requires comprehensive approaches due to the complex interplay between biological ...
Traditional statistical prediction methods on PM often focus on a single temporal or spatial dimension, with limited consideration for regional transport interactions among adjacent cities. To address this limitation, we propose a hybrid directed gra...
Ozone pollution was widely reported along with PM reduction since 2013 in China. However, the meteorological drivers for ozone varying with different regions of China remains unknown using explainable machine learning, especially during the COVID-19 ...
Accurate forecasting of blowing snow events is vital for improving numerical models of snow processes, yet traditional predictive methods often lack interpretability. This study leverages eXtreme Gradient Boosting (XGBoost) to detect blowing snow eve...
BACKGROUND: Bangladesh is facing a formidable challenge in mitigating waterborne diseases risk exacerbated by climate change. However, a comprehensive understanding of the spatio-temporal dynamics of these diseases at the district level remains elusi...
Accurate meteorological observation data is of great importance to human production activities. Meteorological observation systems have been advancing toward automation, intelligence, and informatization. Yet, instrumental malfunctions and unstable s...
PM pollution, a major environmental and health concern, is influenced by a complex interplay of emission sources and meteorological conditions. Accurately identifying these factors and their contributions is essential for effective pollution manageme...
This study presents the first-ever application of machine learning (ML)-based meteorological normalization and Shapley additive explanations (SHAP) analysis to quantify, separate, and understand the effect of meteorology on PM over Greater Bangkok (G...