Interaction Patterns between Climate Action and Air Cleaning in China: A Two-Way Evaluation Based on an Ensemble Learning Approach.

China will attempt to achieve its simultaneous goals in 2060, whereby carbon neutrality will be accomplished and the PM (fine particulate matter) level is expected to remain below 10 μg/m. Identifying interaction patterns between air cleaning and climate action represents an important step to obtain cobenefits. Here, we used a random sampling strategy through the combination of chemical transport modeling and machine learning approach to capture the interaction effects from two perspectives in which the driving forces of both climate action and air cleaning measures were compared.

Identification method and application of critical load contribution areas based on river retention effect.

For the entire watershed, the critical source areas (CSAs) and the critical load contribution areas (CLCAs) are two completely different concepts. The CLCAs can reflect the impact of river retention effects on pollutant transmission. In this study, an integrated modelling approach was developed for those complex watersheds by combining two models: MECM (modified export coefficient model) and SWAT (Soil and Water Assessment Tool).