Heavy metal pollution in cultivated land poses a serious threat to environmental health and farmers’ livelihoods. As the direct user of cultivated land, understanding farmers’ adaptive behavior to heavy metal pollution, and its influencing factors, can provide insight and information relevant for decision-making, so as to better manage the hazards and risks of heavy metal pollution. We proposed a conceptual framework of “farmers’ characteristics-perceptions-adaptive behaviors”. Factor analysis and mediation effect analysis were used to explore the influence of characteristics and perceptions on adaptive behaviors. The data of 278 farmers in a typical mining area in Daye, China, show that local farmers perceive the hazards of heavy metal pollution, but their adaptive behaviors are hindered to a certain extent. The results of the mediation effect analysis show that perceptions of health impact, self-efficacy, and adaptive cost play a partial mediating role in the impact of characteristics on adaptive behaviors. In addition, the influence of the “factor of dependence on farmland” and the “factor of obstacles to action” on adaptive behavior have no significant relationship with perception levels. By comparing the influencing factors, we found that although farmers’ perceptions have mediating effects between characteristics and adaptive behaviors, characteristics still play a decisive role in adaptive behaviors.

The frequent occurrence of floods in urban areas caused by climate change challenges urban resilience. This research aims to construct an ecological security pattern (ESP) that is adaptive to floods to enhance urban resilience in the hope that it will help cities cope with floods better. In this research, the main urban area of Wuhan (WUH) represents the study area. The lakes were selected as the ecological sources and the Soil Conservation Service-Curve Number (SCS-CN) model was used to calculate the runoff volume corresponding to each land type and, based on this, assign resistance values to the land types; as such, the land type surface is referred to as the runoff resistance surface, and the runoff resistance surface is then modified by ecosystem service capabilities. The Minimum Cumulative Resistance (MCR) model was used to extract the connecting corridors between the sources. This research plan includes 18 ecological sources, 10 key ecological corridors, and 22 potential ecological corridors, with a total length of about 344.21 km. Finally, it provides a two-axis and three-core urban ecological resilience optimization strategy for decision makers and a new approach for controlling floods in urban areas from the perspective of ecological resilience.

Heavy metal(loid)s (HMs) in soils near mining sites often cause serious environmental and health issues. Accurately assessing soil HM risks and identifying priority pollutants are crucial for improving risk control efficiency with limited management costs and resources. Traditional deterministic assessments may yield biased results due to the imprecision and ambiguity of environmental data and assessment processes. To compensate for the deficiencies of deterministic assessment, a comprehensive probabilistic-fuzzy model was developed based on fuzzy theory, probability methods, the soil contamination risk (SCR) index, and a human health risk (HR) assessment framework. According to this model, the soil HM risk status in a typical mining area in China was evaluated. The results indicated that Cd and Cu significantly violated the relevant environmental guidelines and were considered priority metals for environmental risk (ER). Notably, Cd’s hazard predominantly manifested in a solid potential ecological risk (PER), whereas Cu’s environmental impact primarily manifested as a soil contamination risk (SCR). From the perspective of HR, soil HMs already pose a considerable threat to human health, with children facing greater HRs than adults. As was identified as a priority element for HRs, with carcinogenic and non-carcinogenic risks reaching unacceptable levels. Regarding general risk (GR), Cd and Cu ranked in the first gradient and As in the second gradient. Overall, the accumulation of soil HMs—especially Cd, Cu, and As—in the study area has posed a significant threat to the ecosystem and human health. The risks of other HMs (Pb, Zn, Cr, and Ni) are relatively low, but the superimposed risks of multiple HMs should not be ignored. The probabilistic–fuzzy model reduces the uncertainty of risk assessment, and the model integrates the environmental and health risks of HMs, providing more comprehensive risk information. The assessment results can serve as a reference for managers to develop targeted control strategies.

Top-of-atmosphere (TOA) outgoing longwave radiation (OLR), a key component of the Earth’s energy budget, serves as a diagnostic of the Earth’s climate system response to incoming solar radiation. However, existing products are typically estimated using the traditional two-step method, which may bring extra uncertainties. This paper presents a direct machine learning method to estimate TOA OLR by directly linking Himawari-8/Advanced Himawari Imager (AHI) TOA radiances with TOA OLR determined by Clouds and the Earth’s Radiant Energy System (CERES) and other information, such as the viewing geometry. Models are built separately under clear- and cloudy-sky conditions using a gradient-boosting regression tree. Independent test results show that the root mean square errors (RMSEs) of the clear-sky and cloudy-sky models for estimating instantaneous values are 7.46 W/m² (3.0%) and 11.61 W/m² (5.8%), respectively. Daily results are obtained by averaging all the instantaneous results in one day. Intercomparisons of the daily results with CERES TOA OLR data show that the RMSE of the estimated AHI OLR is ~6 W/m² (3%). The developed high-resolution AHI TOA OLR dataset will be beneficial in analyzing the regional energy budget.