This study evaluates the effects of precipitation scavenging on aerosol loading and its subsequent impact on air quality in Shanghai, one of the world’s largest and fastest-growing megacities. The study employs advanced statistical techniques and machine learning models to assess which variables influence pollution levels, providing valuable information on periodic patterns and unexpected fluctuations in air quality. The use of Random Forest (RF) models demonstrated robust capabilities in predicting pollution trends over longer time scales, underscoring the importance of feature interpretability in environmental forecasting models. In addition, the study underscores the need to integrate data-driven approaches, such as machine learning, into environmental monitoring systems to improve predictive accuracy and policy effectiveness. The findings support the argument that the use of advanced computational models and large datasets can lead to more targeted interventions and better decision-making frameworks for urban planners and policy makers.
Aerosol–cloud–precipitation interaction is currently a research hotspot that is challenging but also one of the most prominent sources of uncertainty affecting climate change. We have identified 1082 mesoscale convective systems (MCSs) over eastern China from April to September in 2016 and 2017. Overall, the occurrence frequency and MCS area increased when altitude increased, as demonstrated by the t-test at 95% confidence. More MCSs appeared and matured fully, although they moved slowly, in a selected urban agglomeration area compared to a selected rural area, owing to the urbanization impact. With an increase in the concentration of particulate matter with particle size below 10 μm (PM10) averaged by the first 3 h of MCS initiations, the cloud top brightness temperature and MCS area decreased, resulting in weakened precipitation intensity and a smaller MCS area. The t-test was passed with 90% confidence, confirming this finding. In addition, high-humidity circumstances can produce enough water vapor to support the creation of many higher and deeper MCSs.
Guest editors Kaixu Bai, Simone Lolli, and Yuanjian Yang introduce the Special Section on Integrating Remote Sensing, Machine Learning, and Data Science for Air Quality Management.
In this paper, through the Aerosol-lidar observation data located in the western suburbs of Hefei, a continuous aerosol pollution incident was observed from December 13 to 15, 2018. The range corrected signal and extinction coefficient profile continuously observed through fixed-point observation of ground-based lidar can accurately reflect the aerosol content and the characteristics of aerosol changes. The aerosol is mainly distributed below 1.5km. Through depolarization analysis of aerosol particles, the depolarization ratio is below 0.16, and it is concluded that the aerosol particles are spherical particles with a smaller particle size, and combined with the HYSPLIT model analysis, the main pollutants are transmission and industrial emissions of northern and eastern coastal cities in China.
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