Journal of Environmental Management最新文献

Building public support for climate mitigation policies is essential in making significant progress in decarbonization and achieving the global goal of net-zero by 2050. Understanding the role of social capital and network composition in fostering climate stewardship behaviors can provide valuable insights for developing such support. We conducted an online survey (n = 12,147) to analyze how social capital and homophilous/heterophilous social network diversity (SND) are associated with climate stewardship behaviors aligned with government recommendations. Social capital was significantly and positively associated with overall climate stewardship behaviors. Furthermore, homophilous SND exhibited stronger positive associations with overall climate stewardship behaviors compared to heterophilous SND. Nevertheless, the associations of homophilous and heterophilous SND with specific domains of climate stewardship behaviors were mixed. These findings underscore the critical importance of fostering and strategically leveraging social capital among individuals who share numerous, though not all, characteristics to promote climate stewardship behaviors in alignment with government climate mitigation guidelines.

Accurate downscaling of global circulation models (GCMs) is critical for assessing the impacts of climate change and water resources management. In this research, Fourteen GCMs were evaluated through a Taylor diagram, including EC-Earth3-CC, ACCESS-CM2, AWI-ESM-1-1-LR, BCC-ESM1, CanESM5, IITM-ESM, MPI ESM1-2HR, INM-CM5-0, IPSL-CM5A2-INCA, KIOST-ESM, NorCPM1, NorESM2-MM, TaiESM1, and ACCESS-ESM1-5. IITM-ESM showed the best performance, making it the preferred model for future climate studies. To downscale the selected GCM, a novel hybrid deep learning method was employed, combining a sequence-to-sequence model with a Temporal Convolutional Network (TCN) as the encoder and a Transformer as the decoder. This approach was compared to Quantile Mapping, Random Forest, long short-term memory (LSTM), and TCN models, with optimization using the Particle Swarm Optimization (PSO) algorithm. The proposed model outperformed others, achieving an NSE of 0.907, RMSE of 2.10, BIAS of 0.63, and a relative error of 21.96%. Then, an HEC-HMS model was constructed for the Wadi Dayqah basin, utilizing data from 1992 to 2006 for calibration and data from 2007 to 2011 for validation. Precipitation and temperature were downscaled for the near (2030-2039), mid (2040-2049), and far future (2040-2049) periods. Hydrological modeling was conducted for future climate scenarios SSP126, SSP245, and SSP585, revealing notable changes. SSP126 and SSP245 project substantial declines in precipitation, especially in spring and summer, while SSP585 forecasts more extreme variability and precipitation events. Temperature increases are relatively modest under SSP126, with a 5.4% rise in June, while SSP245 shows a 19.2% increase in July, and SSP585, the most extreme, predicts a 24.6% rise in June. Maximum annual streamflow is expected to decrease significantly under SSP126 and SSP245, whereas SSP585 predicts extreme peak flows up to seven times the historical average. These results underscore adaptive water management's importance in addressing the impacts of climate change.

China's aluminium industry, contributing 50% of the global aluminium sector's GHG emissions, is undergoing technology upgrading and energy transition. Facing the dual challenges of carbon neutrality and air pollution control, it is necessary to investigate the GHG emissions and air quality related health risks from aluminium production. Here, we traced the spatiotemporal GHG and air pollutant emissions from China's aluminium industry since 2010. We found that the annual GHG emissions increased from 313 Mt CO₂ to 621 Mt CO₂ over a decade, while air pollutant emissions decreased by 42.9%-68.6%. Through regional chemical transport model and the exposure-response model, we quantified the regional health risks, finding that the mortalities fell from 52,900 to 36,500 with complex spatial heterogeneity. Through emission driving force analysis and aluminium related policy review, we demonstrated that China's air pollution control policy, aluminium capacity migration plan and energy transition plan have a mitigation effect on the emissions and health risks. Moreover, we proposed six mitigation measures and investigated the future mitigation potential through scenario analysis. We found that the critical criteria for carbon neutrality should be natural gas and hydrogen dominated alumina refining, 100% electrolysis decarbonisation, 65% recycled aluminium ratio, 80% penetration rate of inert anodes and 50 Mt CO₂ capture. As a co-benefit, the emissions of SO₂, NO_x, PM_2.5 and PM₁₀ can be reduced by up to 97.1%, 97.0%, 89.6%, and 90.5%. These findings provide new insights into carbon neutrality and air pollution mitigation for the aluminium industry.

To achieve effective removal of various inorganic nitrogen in aquatic ecosystems, while expanding the applicability of existing heterotrophic nitrifying-aerobic denitrifying (HN-AD) strains and enhancing their stress tolerance, we isolated the Pseudomonas aeruginosa WS-03 from a sewage treatment plant. The results of parameter optimization indicated that the following were the most favorable conditions for nitrogen removal: using sodium citrate as the carbon source, a C/N ratio of 9, a pH of 7, a temperature of 30 °C and an NH₄⁺-N concentrations below 300 mg/L. The maximum reduction rates of nitrogen are 8.96 mg/(L·h), 4.64 mg/(L·h) and 5.12 mg/(L·h) of NH₄⁺-N, NO₃^--N and TN, respectively. The result of genome analysis and polymerase chain reaction (PCR) amplification electrophoresis revealed the presence of genes related to nitrogen metabolism, which involves nitrification, denitrification, and assimilation pathways. It also verified that absence of key nitrification genes in strain WS-03, suggesting it operates via a unique denitrification mechanism. Notably, nitrogen assimilation has been identified as the predominant pathway for nitrogen removal by the strain. The strain demonstrated an impressive efficiency of 54.28% in reducing the concentration of NH₄⁺-N in untreated landfill leachate, highlighting its potential for application in practical wastewater treatment. This study comprehensively explored the denitrification characteristics and showed its significant role in environmental remediation.

The widespread availability of glyphosate in shallow lakes is of significant concern. Glyphosate is an organophosphorus pesticide that can affect the phosphorus cycle and microbial communities in lakes. However, the effects of glyphosate on lakes in different geographical locations remain unclear. This study not only investigated glyphosate and aminomethylphosphonic acid (AMPA) residues in sediments from rural and urban lakes, but also examined differences in the effects of these substances on lake microbial communities and phosphorus cycles. Glyphosate and AMPA were detected in 100% of sediments from the three rural and three urban lakes surveyed. Glyphosate concentrations were not significantly different among all lake sediments; however, AMPA concentrations were significantly higher in rural lake sediments than in urban lake sediments (P < 0.05). The abundance of the glpC gene, encoding an organophosphorus-degrading enzyme, and the abundance of Luteitalea sp. TBR-22, which is enriched for the glpC gene, were significantly different between rural and urban lake sediments (P < 0.05). Notably, the abundance of glpC and Luteitalea sp. TBR-22 was significantly and positively correlated with AMPA concentration (P < 0.05). In addition, the AMPA concentration was significantly and positively correlated with the O-bonded inorganic phosphate (Pi) content (P < 0.05). These results suggest that high AMPA concentrations in rural lake sediments may increase the production of O-bonded Pi in lake sediments by controlling the expression of glpC in Luteitalea sp. TBR-22, leading to higher concentrations of O-bonded Pi in the rural lake sediments than in the urban lake sediments.

Soil carbon sequestration and its monitoring is important to improve climate resilience and mitigate global warming. According to the European Environment Agency (EEA), soils in Europe are losing carbon that could hamper achieving the EU climate targets. Hence, it is necessary to explore the dynamics of soil organic carbon (SOC) storage in different ecosystems so that the EU policymakers can observe the progress towards achieving EU Green Deal objectives. The aim of this research was to quantify the ΔSOC-S in woodland and shrubland in the last decade (2009-2018) and to study the ΔSOC-S due to the land use conversion. In this regard, revisited sampling points between 2009 and 2018 from the topsoil (0-20 cm) of woodland and shrubland of the EU + UK soil database named Land Use/Land Cover Area Frame Survey (LUCAS) was used. The analysis revealed that broadleaved-woodland to coniferous- or mixed-woodland conversion in 2018, and shrubland to woodland conversion in 2015 increased SOC-S. Overall, we found a net accumulation of SOC-S in woodland (2184.08 ton ha^-1) and shrubland (302.78 ton ha^-1) soil with 7.78% increment in woodland and 12.56% in shrubland between 2009/12 and 2018. Also, in central Europe, mean annual temperature (MAT) increased and precipitation (MAP) decreased between the study periods. The relationship between precipitation and temperature showed that precipitation and SOC-S in woodland had no relationship, but with the rising temperature, SOC-S in both land types significantly decreased revealing warming can significantly affect SOC-S.