Phototrophic Nitrogen Fixation, a Neglected Biogeochemical Process in Mine Tailings?

Biological nitrogen fixation (BNF) has important ecological significance in mine tailing by contributing to the initial accumulation of nitrogen. In addition to chemolithotrophic and heterotrophic BNF, light may also fuel BNF in oligotrophic mine tailings. However, knowledge regarding the occurrence and ecological significance of this biogeochemical process in mine tailings remains ambiguous. The current study observed phototrophic BNF in enrichment cultures established from three primary successional stages (i.e., original tailings, biological crusts, and pioneer plants) of tailings. Notably, phototrophic BNF in tailings may be more active at vegetation stages (i.e., biological crusts and pioneering plants) than in bare tailings. DNA-stable isotope probing identified Roseomonas species as potential aerobic anoxygenic phototrophs responsible for phototrophic BNF. Furthermore, metagenomic binning as well as genome mining revealed that Roseomonas spp. contained essential genes involved in nitrogen fixation, anoxygenic photosynthesis, and carbon fixation, suggesting their genetic potential to mediate phototrophic BNF. A causal inference framework equipped with the structural causal model suggested that the enrichment of putative phototrophic diazotrophic Roseomonas may contribute to an elevated total nitrogen content during primary succession in these mine tailings. Collectively, our findings suggest that phototrophic diazotrophs may play important roles in nutrient accumulation and hold the potential to facilitate ecological succession in tailings.

Eutrophication-driven infochemical dimethylsulfide accelerates carbon transfer in freshwater food chain.

Dimethylsulfide (DMS) is a major organic sulfide in aquatic ecosystems and an infochemical that is considered as a key predictor of changes in energy and material fluxes and stocks. It is largely unknown how DMS changes and affects the food webs and material cycles in eutrophicated freshwater. In this study, field monitoring and literature surveys were conducted to analyze the effects of eutrophication on DMS concentrations. Daphnia-zebrafish microcosms were then used to investigate the effects of DMS concentrations on carbon transfer. The results demonstrated that the concentration of DMS was increased by eutrophication related indicators (chlorophyll and phosphorus). Eutrophication driven DMS altered carbon transfer in the freshwater food chain. Low concentrations (0.1-1 nM) of DMS promoted the predation of daphnia by zebrafish compared to the 0.01 nM DMS, which further stimulated the total carbon transfer from daphnia to zebrafish and altered the dissolved organic carbon (DOC) distribution in water. High concentrations (10-100 nM) of DMS did not alter zebrafish predation on daphnia and carbon transfer. DOC excreted by zebrafish altered carbon emission potential, and DMS in water showed a unimodal relationship with the carbon emission potential, peaking at 0.40 nM DMS. Keeping the DMS in water at 1.82 nM may maintain a lower carbon emission potential. These results improved the understanding of the effects of eutrophication on DMS, demonstrated the ecological role of DMS on freshwater fish and the carbon cycle, estimated the effects of DMS on the carbon emission potential of fish, and offered new insights into the management of eutrophication.

A novel four-dimensional prediction model of soil heavy metal pollution: Geographical explanations beyond artificial intelligence "black box".

The current artificial intelligence (AI)-based prediction approaches of soil pollutants are inadequate in estimating the geospatial source-sink processes and striking a balance between the interpretability and accuracy, resulting in poor spatial extrapolation and generalization. In this study, we developed and tested a geographically interpretable four-dimensional AI prediction model for soil heavy metal (Cd) contents (4DGISHM) in Shaoguan city of China from 2016 to 2030. The 4DGISHM approach characterized spatio-temporal changes in source-sink processes of soil Cd by estimating spatio-temporal patterns and the effects of drivers and their interactions of soil Cd at local to regional scales using TreeExplainer-based SHAP and parallel ensemble AI algorithms. The results demonstrate that the prediction model achieved MSE and R2 values of 0.012 and 0.938, respectively, at a spatial resolution of 1 km. The predicted areas exceeding the risk control values for soil Cd across Shaoguan from 2022 to 2030 increased by 22.92% at the baseline scenario. By 2030, enterprise and transportation emissions (SHAP values 0.23 and 0.12 mg/kg, respectively) were the major drivers. The influence of driver interactions on soil Cd was marginal. Our approach surpasses the limitations of the AI "black box" by integrating spatio-temporal source-sink explanation and accuracy. This advancement enables geographically precise prediction and control of soil pollutants.

Development of a new framework to estimate the environmental risk of heavy metal(loid)s focusing on the spatial heterogeneity of the industrial layout.

Industrialization and urbanization have increased the risk of heavy metal(loid)s coming from a wide range of pathways and processes. Regional environmental risk assessment mainly focuses on the regional functional layout, industrial orientation, and enterprise location. These aspects may generate immense environmental risks and hazards. However, many gaps in regional environmental risk assessment remain, particularly concerning the spatial heterogeneity of environmental processes and mechanisms affected by the industrial layout. Most of the risk estimation often neglected the risk factor interaction. Here, we developed a framework to estimate the environmental risk of heavy metal(loid)s focusing on the spatial heterogeneity of the industrial layout. This framework was operationalized by performing an integrated risk detection of heavy metal(loid)s, spatial heterogeneity identification of the industrial layout, the power of risk factors and factor interaction examination, risk factor condition quantification and key risk source apportionment. Shaoguan city, one of six trial zones for China's pollution prevention and control of heavy metal(loid)s, was taken as a case study. Among all of the natural and socioeconomic factors, the running time of the industry was the most important risk factor of the Cd, As and Pb in soil and rice in all subregions. These subregions were divided based on the spatial heterogeneity of the industrial layout. The threshold of the running time of the industry for soil Cd was 11.97 years. The power of other dominant risk factors was different in different subregions, and the joint risk of the dominant risk factors was larger than the single risk of the running time of the industry. Our results suggest that the environmental risk of heavy metal(loid)s in Shaoguan could be mitigated by adjusting the industrial structure and controlling the running time of enterprises. Our study also indicates that estimating the regional environmental risk of heavy metal(loid)s focusing on the spatial heterogeneity of the industrial layout can help define specific strategies to achieve environmentally friendly industrial development.