PM2.5 is inhalable particulate with a diameter less than 2.5 μM that easily enters the lungs and causes diseases and non-accidental death. The generation and dissipation of PM2.5 are strongly affected by a variety of environmental factors, thus the concentration of PM2.5 is presumably predictable with the observations of environmental conditions. This paper used multi-year meteorological and PM2.5 concentration data across multiple megacities in China (Beijing, Chengdu, and Shenyang) and sought for a universal predictive model. Our results showed that data-driven machine-learning model was able to not only capture PM2.5 dynamics at the city where the model was trained but also could be generalized to predict PM2.5 concentrations over other cities. Therefore, the modeling results indicated a universally existing predictive relationship between PM2.5 source-sink dynamics and the environmental drivers.

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Deforestation dramatically alters land surface properties and functions through multiple biogeophysical and biogeochemical pathways. However, a quantitative identification of how deforestation affects local energy-water-vegetation coupling is still challenging. In this study we employed information theory and transfer entropy framework to identify the overall feedback pattern of land surface water-energy-vegetation coupling, using high frequency eddy covariance measurements at forested versus deforested sites. We found that deforestation strengthened the directional influence of atmospheric demand on land surface water flux, and more importantly, deforestation broke the coupling between vegetation activities and local precipitation, which led to a less efficient ecosystem to recycle and maintain water within this system.