Mercury (Hg) emissions from open biomass burning represent one of the largest Hg inputs to the atmosphere, with considerable effects on the atmospheric Hg budget. However, there is currently large uncertainty in the inventory of Hg emissions from open biomass burning in China due to limitations on the coarse resolution of burned area products, rough biomass data, and the unavailability of suitable emission factors (EFs). In this study, we developed high tempo-spatial resolution (30 m) and long time-series (2000-2019) atmospheric Hg emission inventories from open biomass burning using the Global Annual Burned Area Map (GABAM) product, high-resolution biomass map, Landsat-based tree cover datasets as well as local EFs in China. The results showed that the average annual Hg emission from open biomass burning in China amounted to 172.6 kg during 2000-2019, with a range of 63-398.5 kg. The largest Hg emissions were found in cropland (72%), followed by forest (25.9%), and grassland (2.1%). On a regional level, Northeast China (NE) and Southwest China (SW) were the two main contributors, together accounting for more than 60% of total Hg emissions. The temporal distribution of Hg emissions showed that the peaks occurred in 2003 and 2014. This is a comprehensive estimation of Hg emissions from open biomass burning in China by integrating various high-resolution remotely sensed data and nationwide localized EFs, which has important implications for understanding the role of open biomass burning in China in regional and global atmospheric Hg budget.

Straw burning is recognized as a significant contributor to deteriorating air quality, but its specific impacts, particularly on PM_2.5 concentrations, are still not fully understood or quantified. In this study, we conducted a detailed examination of the spatial and temporal patterns of straw burning in Heilongjiang Province, China—a key agricultural area—utilizing high-resolution fire-point data from the Fengyun-3 satellite. We subsequently employed random forest (RF) models alongside Shapley Additive Explanations (SHAPs) to systematically evaluate the impact of various determinants, including straw burning (as indicated by crop fire-point data), meteorological conditions, and aerosol optical depth (AOD), on PM_2.5 levels across spatial and temporal dimensions. Our findings indicated a statistically nonsignificant downward trend in the number of crop fires in Heilongjiang Province from 2015 to 2023, with hotspots mainly concentrated in the western and southern parts of the province. On a monthly scale, straw burning was primarily observed from February to April and October to November—which are critical periods in the agricultural calendar—accounting for 97% of the annual fire counts. The RF models achieved excellent performance in predicting PM_2.5 levels, with R² values of 0.997 for temporal and 0.746 for spatial predictions. The SHAP analysis revealed the number of fire points to be the key determinant of temporal PM_2.5 variations during straw-burning periods, explaining 72% of the variance. However, the significance was markedly reduced in the spatial analysis. This study leveraged machine learning and interpretable modeling techniques to provide a comprehensive understanding of the influence of straw burning on PM_2.5 levels, both temporally and spatially. The detailed analysis offers valuable insights for policymakers to formulate more targeted and effective strategies to combat air pollution.

The precise identification of pollution sources constitutes a cornerstone for effective water environment management in mountainous watersheds. This study employed principal component analysis–absolute principal component scores–multiple linear regression (PCA-APCS-MLR) receptor modeling to analyze monthly water quality indicators across the Longxi River Basin. Results revealed comparable water quality between the main stream and its tributaries, with no statistically significant differences identified. Water quality exhibited a distinct spatial pattern, with superior conditions in the upstream and downstream segments compared to the middle reaches. Water quality parameters exhibited significant seasonal variations. During the wet period, the degradation of water quality was primarily driven by diffuse agricultural sources, contributing 42.9%, followed by watershed background levels and surface runoff. In the dry season, rural domestic wastewater (39.3%) was the leading pollution source. For Permanganate index (COD_Mn) exceedance, basin background and agricultural non-point sources in the wet season were the main contributors (46.8% and 44.7%, respectively). For ammonium nitrogen (NH₃-N), wet season agricultural non-point sources (44.4%) and dry season rural domestic pollution (71.8%) were key contributors. Agricultural non-point sources were the dominant pollution source for total nitrogen (TN) in the wet season (84.2%). Effective water quality improvement in the Longxi River Basin hinges on targeted strategies—to mitigate diffuse agricultural sources through optimized fertilization, and to enhance the collection and treatment of rural domestic sewage. This study not only enhances the understanding of pollution source distribution and quantification in mountainous watersheds, but also serves as a vital reference for formulating targeted water environment management strategies.

Identifying pollution sources and quantifying their contributions are essential for water environment management in mountainous watershed. This study used PCA-APCS-MLR receptor modeling to analyze monthly water quality indicators in the Longxi River basin. Results showed no significant difference between the water quality of the main stream and tributaries. The water quality of the upstream and downstream was better than the middle reaches. Significant differences were found in wet and dry seasons. In the wet season, agricultural non-point source pollution was the main driver of water quality, contributing 42.9%, followed by watershed background levels and surface runoff. In the dry season, rural domestic wastewater (39.3%) was the leading pollution source. For CODMn exceedance, basin background and agricultural non-point sources in the wet season were the main contributors (46.8% and 44.7%, respectively). For NH3-N, agricultural non-point sources in the wet season and rural domestic pollution in the dry season were the key contributors (44.4% and 71.8%, respectively). Agricultural non-point sources were the dominant pollution source for TN in the wet season (84.2%). Effective measures for improving water quality in the Longxi River Basin may include optimizing fertilization strategies and enhancing the collection and treatment of domestic sewage to control the agricultural non-point sources and rural domestic sewage. This study deepens the distribution and quantitative analysis of water pollution sources in mountainous watersheds, and also provides an important reference for water environment management.