Establishing an efficient PM_2.5 prediction model and in-depth knowledge of the relationship between the predictors and PM_2.5 in the model are of great significance for preventing and controlling PM_2.5 pollution and policy formulation in the Yangtze River Delta (YRD) where there is serious air pollution. In this study, the spatial pattern of PM_2.5 concentration in the YRD during 2003–2019 was analyzed by Hot Spot Analysis. We employed five algorithms to train, verify, and test 17 years of data in the YRD, and we explored the drivers of PM_2.5 exposure. Our key results demonstrated: (1) High PM_2.5 pollution in the YRD was concentrated in the western and northwestern regions and remained stable for 17 years. Compared to 2003, PM_2.5 increased by 10–20% in the southeast, southwest, and western regions in 2019. The hot spot for percentage change of PM_2.5 was mostly located in the southwest and southeast regions in 2019, while the interannual change showed a changeable spatial distribution pattern. (2) Geographically Weighted Random Forest (GWRF) has great advantages in predicting the presence of PM_2.5 in comparison with other models. GWRF not only improves the performance of RF, but also spatializes the interpretation of variables. (3) Climate and human activities are the most important drivers of PM_2.5 concentration. Drought, temperature, and temperature difference are the most critical and potentially threatening climatic factors for the increase and expansion of PM_2.5 in the YRD. With the warming and drying trend worldwide, this finding can help policymakers better consider these factors for PM_2.5 prediction. Moreover, the effect of interference from humans on ecosystems will increase again after COVID-19, leading to a rise in PM_2.5 concentration. The strong explanatory power of comprehensive ecological indicators for the distribution of PM_2.5 will be a crucial indicator worthy of consideration by decision-making departments.

Given the increasing importance of effectively identifying synergistic changes between PM_2.5 and O₃ and comprehensively analyzing their impact on air quality management in China, we employ the Sen+Mann–Kendall (Sen+M-K) trend test in this study to examine the temporal and spatial variation trends of PM_2.5 and O₃ in the Yangtze River Delta (YRD), from 2003 to 2020. We identified the regions where these pollutants exhibited synergistic changes and established the pathways between the pollutants and their potential drivers, using geographically weighted random forest algorithms and structural equation modeling. The study results revealed as follows: (1) Overall, the PM_2.5 concentrations show a decreasing trend, while the O₃ concentrations exhibit an increasing trend, in the YRD. Analysis of the combined trends indicates that approximately 95% of the area displays opposing trends for PM_2.5 and O₃, with only about 4% in the southern region showing synergistic trends for both pollutants. (2) Drought and the average temperature are the main drivers of the changes in PM_2.5 and O₃ concentrations in areas experiencing synergistic changes. Their combined effects alleviate the aggregation of PM_2.5 and reduce the formation of VOCs, indirectly reducing the generation of pollutants. The negative effect of the average temperature on the O₃ concentration may indicate the existence of nonlinear effects and complex interaction effects between the drivers. NO_x and VOCs play important dual roles in the generation and conversion of pollutants, although their overall impact is smaller than meteorological factors. They produce significant indirect effects through their interaction with meteorological and other human factors, further affecting the concentrations of PM_2.5 and O₃. In areas without coordinated changes, the main impact of meteorological factors remains unchanged, and the relationship between the two anthropogenic emission sources and their effects on PM_2.5 and O₃ are complex, with different directions and levels involved. This study provides detailed insights into the drivers of air quality changes in the YRD and offers a scientific basis for environmental management authorities to develop more comprehensive and targeted strategies for balancing the control of PM_2.5 and O₃ pollution.

Forest fires significantly impact the global climate through carbon emissions, yet the multi-scale coupling mechanisms among meteorological factors, fire behavior, and emissions remain uncertain. Focusing on tropical Asia, this study integrated satellite-based fire behavior products, meteorological datasets, and emission factors, and employed machine learning together with structural equation modeling (SEM) to explore the mediating role of fire behavior in the meteorological regulation of carbon emissions. The results revealed significant differences among vegetation types in both carbon emission intensity and sensitivity to meteorological drivers. For example, average gas emissions (GEs) and particle emissions (PEs) in mixed forests (MF, 323.68 g/m²/year for GE and 0.73 g/m²/year for PE) were approximately 172% and 151% higher, respectively, than those in evergreen broadleaf forests (EBF, 118.92 g/m²/year for GE and 0.29 g/m²/year for PE), which exhibited the lowest emission intensity. Mixed forests and deciduous broadleaf forests exhibited stronger meteorological regulation effects, whereas evergreen broadleaf forests were comparatively stable. Temperature and vapor pressure deficit emerged as the core drivers of fire behavior and carbon emissions, exerting indirect control through fire behavior. Overall, the findings highlight fire behavior as a critical link between meteorological conditions and carbon emissions, with ecosystem-specific differences determining the responsiveness of carbon emissions to meteorological drivers. These insights provide theoretical support for improving the accuracy of wildfire emission simulations in climate models and for developing vegetation-specific fire management and climate adaptation strategies.

Water quality degradation in China’s coastal waters poses a significant ecological challenge, yet the spatially varying predictors of key pollutants remain poorly quantified at the national scale. This study employed a Geographically Weighted Random Forest (GWRF) model integrating meteorological, marine, and socioeconomic data to analyze the associations between potential influencing factors and water quality variations in chemical oxygen demand (COD), dissolved inorganic nitrogen (DIN), and dissolved inorganic phosphorus (DIP) across China’s coastal waters. Results reveal significant spatial heterogeneity and clustering patterns, with distinct “High–High” hotspots and “Low–Low” cold spots for each indicator. The GWRF model showed modest improvements over traditional global models (RF, XGBoost) under spatially structured cross-validation, with significant improvement only for COD. COD variations were jointly associated with meteorological and marine factors, whereas nutrients (DIN, DIP) showed stronger associations with marine environments. Spatialized SHAP effects indicated regional specificities: cropland proportion showed positive effects on COD in Bohai Bay but negative effects in eastern Shandong Peninsula; upper ocean heat content and coastal population density emerged as key contributing factors for DIN; sea surface salinity was closely linked to DIP distribution patterns. These findings provide a systematic quantification of factor contributions at the national scale, offering spatially explicit evidence for formulating differentiated pollution control strategies for China‘s nearshore waters.

Forest fires profoundly affect ecosystem carbon sink functions through direct carbon emissions and ecological process disturbances. However, the mechanisms by which fire carbon emissions (FCE) influence net ecosystem productivity (NEP) remain poorly understood. Here, we analyzed the nonlinear impacts of FCE on NEP using long-term remote sensing data (2000~2018) across China’s forest ecosystems, employing linear mixed-effects models and logistic generalized linear mixed-effects models. Our results revealed significant spatiotemporal heterogeneity in the FCE-NEP relationship, with a conditional coefficient of determination (R² = 0.785) substantially higher than the marginal R² (0.332), indicating that the effect of fire on carbon sinks depends on where and when it occurs, with contrasting responses between evergreen and deciduous forests and between early and late growing seasons. Different vegetation functional types exhibited contrasting responses to fire. In temperate evergreen broadleaf forests, FCE showed a significant positive correlation with NEP, consistent with the mechanism that fire promotes carbon uptake through nutrient release. In contrast, a significant negative correlation was observed in temperate deciduous broadleaf forests, reflecting their vulnerability associated with shallow root systems and high leaf turnover rates. Drought and fire intensity exerted a strong synergistic amplification effect. The three-way interaction among FCE, vegetation type, and water deficit greatly improved model fit relative to models without this interaction (ΔAIC > 2500). Among multiple fire characteristic metrics, fire radiative brightness (BRIGHTN), representing fire intensity, was the most critical factor influencing carbon sinks and driving carbon decline. This study elucidates the nonlinear, heterogeneous, and synergistic characteristics of fire impacts on forest carbon sinks. The results highlight that fire effects on carbon sinks are not uniform but depend critically on vegetation type and the compounded effects of drought and fire intensity. These findings provide a scientific basis for vegetation-specific assessments and dynamic monitoring of forest carbon sink vulnerability under changing fire and drought regimes.