环境科学最新文献

Heavy metal pollution in metal mining areas has emerged as a critical global environmental issue， posing a substantial threat to ecosystems and human health. This study， based on heavy metal data from mining soils in China collected between 2008 and 2022， uses methods such as the geo-accumulation index （I_geo）， Monte Carlo simulation （MCS）， and redundancy analysis （RDA） to quantitatively analyze the pollution characteristics， probabilistic health risks estimation， and influencing factors of heavy metals. The mean concentrations of Cd， Hg， Pb， As， Zn， Cu， Ni， and Cr in the soil reached 8.90， 13.41， 629.21， 135.45， 1 142.88， 215.08， 57.38， and 91.96 mg·kg^-1， respectively， significantly exceeding regional background levels. I_geo assessments identified Cd， Hg， and Pb as the most severe pollutants， with 42%， 26.76%， and 19.1% of the samples classified as heavily to extremely polluted. Pollution was concentrated primarily in southern mining areas， with levels decreasing gradually from the southwest to the northeast. Health risk assessments （HRA） revealed that Cd， Hg， Pb， and As posed disproportionately higher risks to children compared to adults， with both carcinogenic and non-carcinogenic risks significantly elevated. MCSs estimated the probability of children exceeding the carcinogenic risk threshold 1E-4 at 83.66%， while for adult women and men， the probabilities were 37.74% and 31.76%， respectively. RDA highlighted economic activities and soil physicochemical properties （e.g.， pH， clay content， and cation exchange capacity） as the primary drivers of heavy metal distribution. These findings provide crucial quantitative evidence on the spatial distribution and health risks of heavy metal pollution in mining areas and support the development of targeted pollution control and health protection strategies.

Studying the spatial patterns of ecosystem services and their driving factors is crucial for strengthening ecological management and promoting sustainable environmental development. This research focuses on Beijing as the study area. The InVEST model was applied to analyze the spatial correlation， trade-offs， and synergies of habitat quality， carbon storage， water yield， and soil retention from 2000 to 2020. The analysis utilized methods such as spatial autocorrelation， cold/hot spot analysis， and bivariate spatial autocorrelation analysis. Additionally， the XGBoost-SHAP model was employed to identify the key factors affecting ecosystem services. The results showed that： ① The high-value areas of habitat quality were mainly concentrated in regions with higher terrain and less interference from human activities. Carbon storage exhibited a spatial distribution trend that was high in the northwest and low in the southeast. The high-value areas of water yield were concentrated in urban areas， while the high-value areas of soil conservation were primarily distributed in the southwest and were more scattered in the north. ② Global spatial autocorrelation analysis indicated that the global Moran's I indices for the four ecosystem services all passed the significance test and demonstrated significant high-value aggregation characteristics. ③ There was a significant synergistic relationship between habitat quality， carbon storage， and soil conservation. However， there was a trade-off between water yield and these factors. ④ The XGBoost regression model showed good prediction performance on both the training set and the test set， with the predictive performance on the training set being better than that on the test set. The SHAP model analysis indicated that elevation was the key driving factor affecting the four ecosystem services. Slope significantly affected habitat quality， carbon storage， and soil conservation. Population density mainly affected habitat quality and water yield， while annual precipitation had an important influence on water yield and soil conservation. The research results can provide scientific support for optimizing the spatial patterns of ecosystem services and formulating ecological protection strategies in Beijing.

In order to explore effective pathways for reducing Cd accumulation in wheat grains from Cd-contaminated farmland， a pot experiment was conducted in calcareous soil contaminated by long-term sewage irrigation to explore the mechanism and effects of the systems of soil immobilization-foliar inhibition on Cd transport and accumulation in soil-wheat plants. The systems of T1-T9 were composed of three types of soil passivators （apricot kernel biochar， agricultural and forestry waste biochar， and agricultural and forestry waste + steel slag biochar） and three types of foliar inhibitors （EDTA-Zn， Vitality 18 with amino acid， and Si-containing foliar fertilizer）. The results showed that T1， T3， T2， T4， and T8 significantly reduced Cd concentrations in wheat grains following the decreasing trend， and T1， T2， T4， T8， T3， and T7 likewise notably alleviated Cd accumulation in the grains according to the rank of decrease， whereas T5， T6， and T9 did not significantly impact grain-Cd and Cd accumulation in grain compared with that in CK. Meanwhile， the system of apricot kernel biochar and EDTA-Zn （T1） reduced the concentration and accumulation of Cd in wheat grains maximally， with reductions of 51.06% and 51.79%， respectively. Additionally， the mechanism of T1 alleviating Cd accumulation in wheat grains was clarified. The application of apricot kernel biochar （BC1） resulted in a significantly positive correlation between the proportion of changeable Cd （F1-Cd） and the relative abundance of RB41 （belonging to Acidobacteria） and negative relationship between the F1-Cd proportion and the relative abundance of Sphingomonas （belonging to Proteobacteria） in the rhizosphere soil. Furthermore， BC1 addition dramatically decreased the relative abundance of genus RB41 by 47.01% and increased the relative abundance of genus Sphingomonas by 10.71%， which increased soil pH and reduced the proportion of F1-Cd by 14.17% and accordingly enlarged the proportions of carbonate-bound Cd and iron-manganese-bound Cd by 10.41% and 11.00%， respectively. Additionally， T1 decreased root-Cd and Cd bio-accumulation factor of roots by 52.04% and 49.99%， respectively. Foliar application of EDTA-Zn during the grain-filling stage significantly reduced the Cd translocation factor from stem-leaf to rachis （at maturity） and from glume to grain （during grain-filling stage） by 45.45% and 31.29%， respectively. Finally， T1 significantly alleviated grain-Cd and Cd accumulation in wheat grains. In summary， the better pathway for effectively reducing Cd accumulation in wheat grains consists of applying apricot kernel biochar before sowing and foliar spraying of EDTA-Zn during the grain-filling stage in moderate Cd-contaminated farmland. These findings provide an effective and technical pathway for ensuring wheat grain safety production and guaranteeing safety utilization of Cd-contaminated agricultural land in the calcareous soil of North China.

As the world's largest country regarding energy consumption and carbon emissions， analyzing China's carbon emissions and emission reduction potential is essential to the fight against global climate change. This study constructs the LEAP-China model to forecast and analyze China's carbon emissions and emission reduction potential in three dimensions： primary energy， end-use industries， and carbon emission contribution. The conclusions are as follows： ① Except for the baseline scenario， the industrial structure emission reduction， technological progress， energy structure emission reduction， and blueprint scenarios were all able to realize the goal of "peaking by 2030." ② From 2022 to 2060， carbon emissions from all industries except industry were declining. ③ The carbon emissions of various industrial sectors varied significantly according to their energy consumption， with chemicals > other industries > non-metallic mineral products industry > ferrous metal smelting and rolling processing industry > non-ferrous metal smelting and rolling processing industry > paper and paper products industry. ④ The optimization of energy structure had apparent emission reduction effects in the short term； the optimization of industrial structure was a continuous driving force for carbon emission reduction， and technological progress was a long-term driving force for carbon emission reduction. The study can provide a decision-making basis for China to realize the medium- and long-term carbon emission reduction path.

As an important ecological civilization pilot zone and free trade port in China， Hainan Province undertakes the important task of coordinated development of carbon reduction and economic development under the background of the implementation of the strategy of "carbon peak and carbon neutrality." Based on the calculation of carbon source， carbon sink， and net carbon emissions in Hainan Province from 2004 to 2023， the LMDI model and Lasso analysis were used to decompose and screen the influencing factors of carbon emissions in Hainan Province， and four Lasso-Transformer neural network models were included to predict carbon emissions in Hainan Province from 2024 to 2030. The results showed that： ① The trend of total carbon sink in Hainan Province from 2004 to 2023 was relatively stable， and the change trend of net carbon emission was basically consistent with the total carbon source. ② The main influencing factors of carbon emissions in Hainan Province were energy intensity， land carbon emission intensity， economic efficiency， land use structure， population size， and land use efficiency. ③ Through model optimization， the Lasso-PatchTST model was used to predict the carbon emission of Hainan Province from 2024 to 2030 and its influencing factors， and the carbon emission in 2030 was predicted to be 43，455，300 tons. The growth rate of land use efficiency factor was the fastest， and the growth rate of population size was the slowest. By optimizing industrial structure， improving resource utilization efficiency and strengthening ecosystem protection， it can promote the coordinated development of carbon reduction and economy in Hainan Province. The results of this study can provide a reference for decision-making of low-carbon economic development in Hainan Province.

Carbon peaking and carbon neutrality are key issues in climate change research. This study constructed a novel Five-in-One Carbon （5C） Framework to evaluate the provincial carbon peak and carbon neutrality capabilities （CPCN） in China from 2010 to 2021. Additionally， social network analysis （SNA） and quadratic assignment procedure （QAP） methods were used to reveal the structural characteristics and influencing factors of the CPCN spatial correlation network. The results indicated that： ① Provincial CPCN steadily increased over the study period. In terms of CPCN distribution， the eastern region was the strongest， followed by the central region， while the western region was weaker， and the northeastern region was the weakest. ② CPCN improvements primarily relied on enhancing resource efficiency， decarbonizing economic structure， and carbon sequestration in ecosystems. ③ The network structure of CPCN was stable with high density and efficiency， with eastern provinces occupying core positions and enhanced cross-regional cooperation. ④ The spatial correlation network of CPCN could be divided into three types of plates： net benefits， brokers， and net spillovers， characterized by limited internal connections within plates and close inter-plate correlations. ⑤ Environmental regulation， new quality productive forces， urbanization level， green innovation patents， digital economy， fiscal decentralization， geographical adjacency， and cultural dissemination significantly influenced the formation and development of the spatial correlation network of CPCN， whereas industrial structure had no significant impact.

Vegetation carbon sequestration （VCS） is a crucial indicator for assessing the carbon sink capacity of ecosystems. In energy-intensive regions， mining development and urbanization have significantly increased the complexity of vegetation dynamics， making it a research priority to quantify the relative contributions of climate change and human activities to VCS. Using data such as MOD17A3H Net Primary Productivity （NPP）， mining site data， and meteorological data， VCS was calculated using the photosynthesis equation. The impacts of climate change and human activities on VCS in energy-intensive regions were quantified using the Thornthwaite Memorial model and trend analysis. Additionally， the Density-based Spatiotemporal Aggregation Clustering （D-STAC） index and Multi-Scale Geographically Weighted Regression （MGWR） model were employed to explore the spatiotemporal evolution characteristics of VCS and the influence of various factors at the pixel scale. The results indicate that： ① From 2001 to 2022， VCS in Shanxi Province showed an overall fluctuating growth trend， with an average value （in C） of 350.11 g·（m²·a）^-1 and an annual growth rate of 3.92%. The spatial distribution exhibited a gradient pattern of "high in the southeast and low in the northwest." ② VCS improved in 92.81% of the study area， primarily influenced by both climate change and human activities， accounting for 98.97% of the area. The contribution of human activities （98.52%） was significantly higher than that of climatic factors （0.45%）. ③ D-STAC analysis revealed a significant increase in negative spatial autocorrelation of VCS in areas with high-density mining sites and urbanized regions， indicating that urbanization and industrial activities had an inhibitory effect on regional VCS. ④ Precipitation and elevation generally had a positive effect on VCS， while temperature had a negative effect. Nighttime light index， population density， and mining site density exhibited bidirectional effects on VCS. The research results provide a quantitative analysis framework for understanding VCS changes in energy-intensive regions and offer scientific support for the formulation of ecological policies.

The soil of villages around a historical smelting area in Jiangxi Province was taken as the research object， and the contents of heavy metals As， Sb， Cu， Pb， Zn， Cr， Ni， and Cd were collected and determined in 40 soil samples； the pollution degree of heavy metals was evaluated using statistical methods； and the sources of heavy metals in the soil were determined using the positive matrix factorization （PMF）， coupled with the Monte Carlo simulation health risk assessment （HRA） model to quantitatively assess the health risks of different sources to human beings. It was also coupled with the HRA model of Monte Carlo simulation to quantitatively assess the health risks of different sources to human beings and determine the priority control factors. The results showed that the average contents of heavy metals were higher than the background values， except for Cu， Zn， and Pb. The ground accumulation index （I_geo） of As， Sb， Ni， and Cd reached the medium pollution level， while 60% of the samples were in the light pollution level in the pollution load index （PLI）. The PMF source analysis study identified three soil heavy metal pollution sources， including natural sources， smelting activities， and industrial activities， contributing 50.27%， 30.21%， and 19.52%， respectively. The Monte Carlo probabilistic HRA showed that the carcinogenic risk for all populations was in the acceptable range （1E-06≤TCR<1E-04）； the non-carcinogenic risk for adults was negligible （HI<1）， and the non-carcinogenic risk for children was at a high level （HI>1）. The proportion of children with non-carcinogenic risk exceeding the control value was 48.35%. Smelting activity was the largest contributor to carcinogenic risk （69.22%） and non-carcinogenic risk （55.77%）， and smelting activity was identified as a priority source of contamination for human health risk control， with As being the main target pollutant. The results of the study can provide a scientific basis for governmental departments to formulate soil pollution control strategies.

Optimizing the spatial pattern of carbon storage is of great significance for increasing the carbon sink capacity of regional ecosystems and maintaining regional carbon balance. Taking the Jiangsu section of the Yangtze River Basin as an example， combined with InVEST and PLUS models， the carbon storage and spatial distribution pattern of the ecosystem in the study area in 2030 were predicted under three different scenarios： natural development， cultivated land protection， and ecological protection. The pattern of carbon storage in the study area was optimized using a Bayesian network model with decision optimization ability. The results showed that： ① Carbon storage in the study area showed a downward trend from 2000 to 2020， with a total decrease of 4 797.63×10⁴ t， mainly due to the conversion of cultivated land and forest land to construction land. ② In 2030， the carbon storage under the ecological protection scenario of the study area was 38 528.91×10⁴ t， showing an increasing trend， while the carbon storage under the other two scenarios showed a decreasing trend. ③ By using the Bayesian network model， key variables and key state subsets were selected， and the study area was divided into four types of optimal zones： ecological protection area， cultivated land protection area， water conservation area， and economic construction area. This study sought to clarify the temporal and spatial evolution characteristics of carbon storage in the Jiangsu section of the Yangtze River Basin， predict its future development trend， and optimize its spatial pattern， which is conducive to the sustainable development of land use in the basin and provides reference for promoting the "dual carbon" goal of the basin.