In the context of serious urban air pollution and limited land resources, it is important to
understand the environmental value of ecological land. Previous studies focused mostly on …

High levels of fine particulate matter (PM 2.5) pose a severe air pollution challenge in China.
Both land use changes and anthropogenic emissions can affect PM2. 5 concentrations. Only …

In recent years, geographically weighted regression (GWR) models have been widely used
to address the spatial heterogeneity and spatial autocorrelation of PM2. 5, but these studies …

Dust emission induced by agricultural soil wind erosion is one of the main sources of
atmospheric particulate matter (PM) in dryland areas. However, most current air quality …

PM 2.5 is one of the most harmful air pollutants affecting sustainable economic and social
development in China. The analysis of influencing factors affecting PM 2.5 concentration is …

With urbanization and increasing consumption, there is a growing need to prioritize
sustainable development across various industries. Particularly, sustainable development is …

Spatiotemporal variation of PM 2.5 in 2018 and 2020 were compared to analyze the impacts
of COVID-19, the spatial heterogeneity of PM 2.5, and meteorological and socioeconomic …

Background The association between residential greenness and chronotype remains
unclear, especially among children. The current study aimed to explore the associations …

We propose an adjusted robust heteroscedastic autoregressive spatiotemporal model with a
data-driven process to predict the hourly PM\(_ {2.5}\) concentrations in Xi'an and Xianyang …

Accurate prediction of PM2. 5 concentrations can provide a solid foundation for preventing
and controlling air pollution. When the Long Short-Term Memory (LSTM) is applied to predict …