环境科学最新文献

{"title":"[Decoupling Status and Driving Factors of Provincial Transport Carbon Emissions in China].","authors":"Qing-Xiu Ge, Ping-Guo Yang, Ying-Xia Pu","doi":"10.13227/j.hjkx.202402109","DOIUrl":"https://doi.org/10.13227/j.hjkx.202402109","url":null,"abstract":"<p><p>In the context of the \"dual carbon\" strategy, the transportation industry actively seeks a new development path of low-carbon transformation, which is one of the hot spots in China's carbon emission reduction. Based on the Tapio decoupling and logarithmic mean Divisia index （LMDI） models, this study analyzed the carbon emission characteristics, decoupling status, and driving factors of China's provincial transportation industry from multiple perspectives, such as overall, time period, and regional decomposition from 2012 to 2021. The results showed that China's total carbon emissions from transport sector exhibited an increasing trend with passing years overall, but growth rate showed decreasing trend. The spatial distribution pattern of carbon emissions from transportation industry was higher in the southeast and lower in northwest. During the past decade, 40.0% of the provinces achieved absolute decoupling between carbon emissions from transportation industry and economic development, and 53.3% of the provinces achieved relative decoupling. From the perspective of the transportation industry, economic growth, population size, and carbon emission coefficient promoted an increase in carbon emissions, while transportation energy intensity and industry scale inhibited the increase in carbon emissions. Therefore, during the process of realizing the \"dual carbon\" goal in China, each province should formulate differentiated reduction policies in regional carbon emissions according to local conditions, actively assume emission reduction responsibilities, increase efforts to promote the decoupling process, and promote the green and low-carbon transformation of the transportation industry.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2009-2019"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Influencing Factors Analysis and Spatial Estimation of Soil Organic Carbon Content in the Hilly Areas of the Loess Plateau].","authors":"Rong-Yan-Ting Huo, Jing Liu, Rui Zhang, Gui-Ling Lin, Rui Dai","doi":"10.13227/j.hjkx.202403260","DOIUrl":"https://doi.org/10.13227/j.hjkx.202403260","url":null,"abstract":"<p><p>Soil organic carbon （SOC） is a crucial indicator for assessing soil fertility. Understanding its spatial distribution patterns and influencing factors is essential for enhancing agricultural sustainability and securing national food security. This study focused on the Fuxian County, Yan'an City, and Shaanxi Province, selecting 22 environmental variables related to SOC formation from four types of environmental factors： topography, climate, vegetation, and soil. Three digital soil mapping methods, random forest （RF）, support vector machine （SVM）, and geographically weighted regression （GWR）, were employed to establish SOC content estimation models. The influencing factors and spatial distribution characteristics of SOC content at 0-20 cm soil depth for the entire study area, garden land, cultivated land, and forest land were analyzed. The results showed that： ① The average <i>ω</i>（SOC） across the entire region of the Fuxian County was 8.54 g·kg^-1, with garden land at 6.44 g·kg^-1, cultivated land at 7.49 g·kg^-1, and forest land at 10.22 g·kg^-1. The coefficients of variation were 36.90%, 19.24%, 29.88%, and 32.56%, respectively, all of which fall into the moderate degree of variation. ② In the entire region of the Fuxian County, topography, climate, vegetation, and soil factors all significantly affected the distribution of SOC, with notable differences in their effects on SOC. In forest land, slope （SLP）, mean annual temperature （MAT）, and bulk density （BD） had significant negative effects on SOC, while mean annual precipitation （MAP） and total nitrogen （TN） had significant positive effects on SOC. In garden land, pH, TN, and total potassium （TK） all had significant positive effects on SOC. In cultivated land, MAP had a significant negative effect on SOC, while TN had a significant positive effect. ③ Comparing the performance of different estimation models, the RF estimation model used in this study had the highest <i>R</i>², the lowest root mean square error （RMSE） and mean absolute error （MAE） values, and the smallest model prediction error. In the linear fitting between measured and estimated values, the RF model's fitting accuracy <i>R</i>² reached above 0.85, demonstrating the best estimation performance among the models. ④ Utilizing the RF model for spatial estimation of SOC content in the Fuxian County revealed a distribution pattern of lower concentrations in the east and higher in the west. The results can provide decision-making reference for the optimization and adjustment of land-use structure in hilly areas of the Loess Plateau and offer technical support for the accurate estimation of SOC.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2301-2312"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144040075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Simulation and Prediction of Spatiotemporal Variation Characteristics of Nitrogen Non-point Source Pollution in Henan Province Based on FLUS and InVEST Models].","authors":"Jin-Cai Zhang, Guang-Xing Ji, Qing-Song Li, Meng Li, Hong-Kai Gao, Wei-Qiang Chen, Yu-Long Guo","doi":"10.13227/j.hjkx.202404259","DOIUrl":"https://doi.org/10.13227/j.hjkx.202404259","url":null,"abstract":"<p><p>To elucidate the characteristics of nitrogen non-point source pollution in Henan Province under the influence of climate change, this study initially utilized the InVEST model to simulate the temporal and spatial distribution of the N non-point source pollution load in Henan Province from 2000 to 2020, subsequently coupling the FLUS model with the InVEST model, nitrogen point source pollution load, and its spatial distribution in Henan Province from 2030 to 2050 under SSP2-4.5 and SSP5-8.5 climate scenarios. The findings of the study indicated that： ① Between 2000 and 2020, the total nitrogen output and load in Henan Province initially increased before decreasing, maintaining an overall downward trend. ② In terms of spatial distribution, the nitrogen output load from 2000 to 2020 displayed a pattern of \"high in the plains, low in hilly areas,\" indicating a strong correlation between nitrogen non-point source pollution and topography. ③ Under the SSP2-4.5 scenario, the total nitrogen output and load were projected to increase annually from 2030 to 2050, with a complex overall change pattern； under the SSP5-8.5 scenario, the total nitrogen output and load were anticipated to decrease initially before increasing, with a consistent overall change pattern. Based on these results and in conjunction with the practical situation of Henan Province, it is hoped that this research can provide a theoretical foundation for the prevention and control of future non-point source pollution in the province.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2242-2249"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143986473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Characteristics and Controlling Factors of Groundwater Chemical Change in a Typical Area of Groundwater Exploitation Reduction in Hebei Province].","authors":"Cong-Li Liu, Fei Liu, Pin-Na Zhen, Xiao-Shuai Guo, Hong-Li Chai, Yan-Hui Guo","doi":"10.13227/j.hjkx.202403259","DOIUrl":"https://doi.org/10.13227/j.hjkx.202403259","url":null,"abstract":"<p><p>Due to groundwater overexploitation control in Hebei Province, the significant reduction of groundwater exploitation inevitably induces changes in regional groundwater quantity and quality. How to effectively identify these changes caused by groundwater exploitation reduction （GWER） is directly related to the safety of groundwater resources in Hebei Province. The eastern plain of Handan was selected as the study area, where groundwater restoration is remarkable. Groundwater chemical changes and controlling factors were analyzed by integrating multi-statistics, graphic method, and absolute principal component-multiple linear regression receptor model （APCS-MLR）. The results showed that the variability of groundwater chemistry in this region was mainly controlled by water-rock interaction and human activities （agricultural fertilization, GWER, and inter-basin water transfer）. Although the groundwater quality in the study area still showed the vertically distributed characteristics of \"Brackish water at the top and freshwater at the bottom,\" the GWER improved the shallow groundwater quality to some extent and resulted in the evolution from salt water to brackish water. The distributed area of salt water reduced from 872 km² to 310 km², the distributed area of brackish water increased from 4 141 km² to 4 632 km², and the distributed area of freshwater increased from 2 574 km² to 2 645 km². The main controlling factors of chemical compositions in shallow groundwater were leaching-enrichment factor based on salinity, agricultural factor, and geological factor based on alkalinity, and their contribution rates were 57%, 17%, and 16%, respectively. While the main controlling factors of chemical compositions in deep confined water were the leaching-enrichment factor based on salinity, geological factor based on alkalinity, and pollutant migration factor, and their contribution rates were 61%, 15%, and 11%, respectively. The findings deepen the understanding of changes in groundwater chemistry in GWER areas, which is of great significance to the rational development and utilization of groundwater resources in GWER areas of Hebei Province.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2193-2205"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144004580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Quantitative Identification of Impact of Climate Change and Anthropogenic Activities on the Ecological Quality of Vegetation in the Shiyang River Basin over Past 20 Years].","authors":"Li-Fang Kang, Rui-Feng Zhao, Hai-Tian Lu, Fu-Shou Liu, Lin-Qi Yang, Xiao-Tong Ren","doi":"10.13227/j.hjkx.202404129","DOIUrl":"https://doi.org/10.13227/j.hjkx.202404129","url":null,"abstract":"<p><p>Climate change and a series of anthropogenic activities have caused significant changes in vegetation. Quantitative identification of the relative contributions of climate change and anthropogenic activities to the interannual changes in vegetation ecological quality in the Shiyang River Basin is of great value for coping with future climatic challenges and implementing ecological protection measures in the Shiyang River Basin. Based on vegetation ecological quality （EQI）, combined with multi-source remote sensing data, this study utilized slope trend analysis, partial correlation analysis, and residual analysis to analyze the spatial and temporal patterns of vegetation ecological quality change and the partial correlation relationship with climate factors in the Shiyang River Basin and explored the relative contribution of climate change and anthropogenic activities to EQI trend change. The results showed that： ① EQI in the Shiyang River Basin increased steadily from 2002 to 2021, and the growth rate of EQI in the mountain area and oasis was significantly higher than that in the desert area. From southwest to northeast, the distribution pattern increased first and then decreased, and the regions with a faster increase in EQI were distributed around the oasis edge. ② Both temperature and precipitation in the Shiyang River Basin increased during the recent 20 years, and the positive effect of precipitation factor on vegetation greening was greater than that of temperature. ③ Climate change and anthropogenic activities contributed 33% and 67%, respectively, to the increase of EQI in the Shiyang River Basin in the past 20 years, and the positive impact of anthropogenic activities on vegetation ecological quality was continuously strengthened. The research results provide important reference for the formulation of vegetation ecological protection and management policies in the Shiyang River basin.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2439-2449"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144052997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Soil Quality Evaluation and Obstacle Diagnosis of Saline-Alkali Cultivated Land in the Hetao Plain].","authors":"Jun-Hua Zhang, Hua-Yu Huang, Qi-Dong Ding, Ke-Li Jia","doi":"10.13227/j.hjkx.202404226","DOIUrl":"https://doi.org/10.13227/j.hjkx.202404226","url":null,"abstract":"<p><p>Soil quality of cultivated land determines food security and the development of farmland ecosystems. In this study, 16 soil physical and chemical properties were used to determine the characteristics of soil degradation index （SDI） and resistance index （SRI） in five typical areas of the Hetao Irrigation District in the middle and upper reaches of the Yellow River. Based on the total data set （TDS） and minimum data set （MDS）, the soil quality index （SQI） was calculated using six methods of membership function and linear （<i>S</i>_L） and nonlinear scoring （<i>S</i>_NL）. The difference and correlation of SQI values in different methods and the soil quality grade in each study area were discussed, and the soil obstacles in different study areas were clarified. The results showed that： ① The SDI value of EC in Hongsibu was the lowest （-265.84）, and the maximum value was AK in Huinong （60.37）. The SRI of soil TS in Hangjinhouqi was the lowest （0.634 7）, and the SRI of Huinong silt was the highest （0.878 8）. Soil EC, SAR, TS, and ESP were more sensitive to SDI and SRI. On the whole, the soil quality of the five study areas was significantly degraded. The mean values of SRI and SDI of all indicators were significantly correlated. ② MDS included five indicators of soil TN, EC, clay, pH, and AK, which could explain 76.46% of the 16 primary indicators in the whole data set. The average SQI calculated by the six evaluation methods were as follows： SQI（MDS-<i>S</i>_L）&gt;SQI（TDS-MF）&gt;SQI（MDS-MF）&gt;SQI（TDS-<i>S</i>_L）&gt;SQI（TDS-<i>S</i>_NL）&gt;SQI（MDS-<i>S</i>_NL）. With SQI（TDS-MF） as the reference value, the SQI of each method was significantly correlated with it, and the correlation coefficient with SQI（TDS-<i>S</i>_NL） was the largest. In the scoring methods, the overall performance of <i>S</i>_NL was better than that of <i>S</i>_L, but its SQI value was small. ③ The soil quality of the whole study area was dominated by medium and low grades （accounting for 54.05% of the total area）； the soil grade of Hongsibu was the lowest （medium and low-grade soil accounted for 85.71%）. High and higher quality soil of Huinong accounted for the largest proportion （70.31%）. At present, the study areas were mainly faced with soil organic matter and nutrient limitation obstacles （especially Hongsibu）. There were also texture obstacles in Huinong, Wuyuan, and Hangjinhouqi and alkali stress in Xidatan. The research results can provide a scientific basis for the evaluation of the degradation degree and soil quality of saline-alkali farmland in the Hetao Plain and the selection of reasonable improvement measures.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2325-2336"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144013667","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Impact Factors of O₃ and PM_2.5 Pollution in Typical Cities of the Shandong Province Based on Random Forest Model].","authors":"Yu Wang, Kai Qu, Yan-Zhen Ge, Hui Liu, Chao Chen, Wen-Xin Wang, Xiao Sui, Min Wei, Xiang-Li Shi, Hou-Feng Liu","doi":"10.13227/j.hjkx.202401082","DOIUrl":"https://doi.org/10.13227/j.hjkx.202401082","url":null,"abstract":"<p><p>O₃ and PM_2.5 pollution remains a challenge for further improvement of air quality in the Shandong Province. Based on the online observations of O₃ and PM_2.5 of four typical cities of Jinan, Zibo, Tai'an, and Weihai in the Shandong Province in 2021, we analyzed the relative importance of meteorological factors, conventional pollutants, and VOCs using the random forest （RF） model. The results indicated that in terms of the impact on O₃ pollution, the most influential factor was temperature, followed by precursors such as NO<i>_x</i> and VOCs. The importance of NO<i>_x</i> in Jinan and Weihai was lower than that of VOCs, and that in Zibo and Tai'an was the opposite. The VOC species affecting O₃ in different cities were related to emission source structure. Aromatic hydrocarbons and alkenes from solvent use and traffic emissions had a high impact in Jinan. C2-C5 alkanes, alkenes, and aromatic hydrocarbons emitted from petrochemicals and chemical pharmaceuticals had the greatest impact in Zibo. Plant-derived isoprene was of great importance to Tai'an. OVOCs and halogenated hydrocarbons from industries, such as ship/fishing gear manufacturing, rubber production, and electronics manufacturing, were crucial to Weihai. In terms of PM_2.5 pollution, the more influential factors were in the order： CO, NO<i>_x</i>, SO₂, meteorological factors, and VOCs. Long-chain alkanes, OVOCs, halogenated hydrocarbons, and acetylene emitted from petroleum refining, chemical pharmaceuticals, and coal and diesel combustion were species of high importance for PM_2.5 pollution. This study provides useful reference for cities to develop targeted pollution control strategies.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2103-2114"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144022442","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Impact of Rainfall on River Water Quality and Source Identification： An Example in the Maoming Section of the Jianjiang River].","authors":"Deng-Chao Wang, Fa-Dong Li, Cao-le Li, Kun Wu, Fan Wang, Shan-Bao Liu, Zhao Li, Xiao-Shu Wei, Yi-Zhe Wang, Jing-Qiu Jiang, Qiu-Ying Zhang","doi":"10.13227/j.hjkx.202401264","DOIUrl":"https://doi.org/10.13227/j.hjkx.202401264","url":null,"abstract":"&lt;p&gt;&lt;p&gt;To investigate the effects of rainfall on river water quality in a mixed-industrial-agricultural urban area and to analyze the sources of pollution, this study focused on the Jianjiang River, the primary tributary of the western reaches of the Pearl River system. To investigate the impact of rainfall on river water quality in an urban area with mixed industrial and agricultural activities, six river monitoring sections along the Maoming segment of the Jianjiang River, along with three meteorological stations, were chosen as the research sites. Utilizing box-and-whisker plots, correlation analysis, and the absolute principal component-multiple regression model, this study examined the rainfall-water quality relationship within the Maoming section of the Jianjiang River. Additionally, it assessed the contributions of various pollutant sources to water quality in the region. The results showed that： ① The river water quality in the Jianjiang River's Maoming section was generally better in spring and winter compared to that in summer and autumn. Specifically, the Zhensheng, Jiangkoumen, and Luojiangqiao sections consistently maintained water quality that exceeded Surface Water Category III standards. However, the Shibi, Mijidu, and Tangkou sections were at a higher risk of exceeding water quality standards during summer and autumn, particularly with increasing rainfall intensity. In these seasons, dissolved oxygen （DO）, chemical oxygen demand （COD）, turbidity （WT）, and total phosphorus （TP） tended to increase with rainfall intensity, while electrical conductivity （EC）, ammonia nitrogen （AN）, and total nitrogen （TN） showed opposite trends. Notably, the pH, DO, EC, and TN in the Shibi, Mijidu, and Tangkou sections decreased with rainfall intensity, whereas COD, AN, and TP exhibited the opposite pattern. ② River water quality was influenced by seasonal variations, meteorological factors, and rainfall intensity, with rainfall having a significant impact on water temperature, ammonia nitrogen, and total phosphorus. During summer and autumn, river water quality deteriorated with increasing rainfall. Moreover, as rainfall intensity rose, the relationship between season, spatial location, water quality indicators, and meteorological factors weakened, while the coupling between meteorological factors and water quality strengthened. ③ In the absence of rain, urban pollution sources and meteorological factors were the primary contributors to river water quality, with urban pollution sources accounting for 66.25% of electrical conductivity and chemical oxygen demand and 51.94% of other parameters. The contribution of other sources was relatively low but increased with rainfall intensity. Agricultural surface sources generally showed an increasing and then decreasing trend of contribution with rainfall intensity. During heavy rainfall, the contribution of other sources to water quality ranged from 35.17%-93.46%. In conclusion, the study indicates that heavy","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2165-2178"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Identifying Seasonal Variation of Nitrate Sources in Mountainous Rivers at the Qiandao Lake Basin Based on Nitrogen and Oxygen Isotopes].","authors":"Zi-Ning Zhang, Hai Xu, Wei Jiang, Xu Zhan, Guang-Wei Zhu, Hong-Wei Sun, Yu Qiu, Yuan-Yi Wang, Ming-Jie Wu, Yu-Xing Liu, Hui-Yun Li, Meng-Yuan Zhu, Bo-Qiang Qin, Yun-Lin Zhang","doi":"10.13227/j.hjkx.202403170","DOIUrl":"https://doi.org/10.13227/j.hjkx.202403170","url":null,"abstract":"<p><p>Qiandao Lake is an important source of drinking water in the Yangtze River Delta, and its ecological environment is of great strategic significance to the surrounding areas. To identify the sources and spatial distribution characteristics of nitrate （NO₃^--N） pollution in the Qiandao Lake, we conducted, for the first time, the collection of water samples from four typical mountainous inlet river basins in the Qiandao Lake Basin, analyzed the concentrations of NO₃^--N, and resolved different sources and their contribution in each water system by combining the <i>δ</i>¹⁵N-NO₃^- and <i>δ</i>¹⁸O-NO₃^- dual stable isotope analysis in R （SIAR） model. The results showed that： ① Nitrogen concentrations in the different watersheds were relatively low, with mean total nitrogen （TN） levels ranging from 0.99 to 4.31 mg·L^-1. NO₃^--N emerged as the main nitrogen source, and conspicuous disparities were observed in NO₃^--N concentrations across the four rivers, consistently demonstrating a pattern of spring &gt; winter &gt; summer &gt; autumn, of which the NO₃^--N concentration during spring could be up to 3.2 times of that observed during autumn. ② The values of <i>δ</i>¹⁵N-NO₃^- and <i>δ</i>¹⁸O-NO₃^- in each watershed ranged from 1.52‰ to 14.29‰ and from -2.76‰ to 10.13‰, respectively. ③ All four rivers showed a greater proportion of fertilizer and soil nitrogen during spring and summer, which accounted for approximately 25% to 51% and 23% to 39%, respectively, and a greater proportion of domestic sewage during autumn and winter, which accounted for approximately 26% to 67%. The study showed that the main source of NO₃^--N pollution in the Qiandao Lake Basin was agricultural non-point source pollution, and some variabilities were also observed in NO₃^--N pollution in different land-use type areas. NO₃^--N pollution contributions remained relatively stable across the larger basin area, while exhibiting significant fluctuations in the smaller basin area. This work analyzed the main sources of NO₃^--N in the Qiandao Lake Basin, providing a basis for water quality management and pollution source control in this area.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2232-2241"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143999683","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"[Carbon Emission Accounting and Peak Carbon Prediction of China's Construction Industry from a Whole Life Cycle Perspective].","authors":"Xiang-Hong Zhou, Peng-Cheng Hu, Peng-Fei Cheng","doi":"10.13227/j.hjkx.202403174","DOIUrl":"https://doi.org/10.13227/j.hjkx.202403174","url":null,"abstract":"<p><p>Carbon emission accounting and carbon peak prediction are the prerequisites for carbon reduction in the current construction industry in China, constituting an important basis for fulfilling the responsibility of carbon reduction. To accurately depict the evolutionary trend of carbon emissions in the construction industry, the carbon emissions of the Chinese construction industry were calculated in stages, based on a full life cycle perspective. The Pearson test was used to select the factors influencing carbon emissions in the construction industry, and an extended STIRPAT model was established. The logarithmic mean Divisia index （LMDI） method was used to analyze the factors in the extended model and calculate the contribution rate of each factor influencing carbon emission. Finally, a multivariate nonlinear regression prediction model based on ASO-BP was constructed to explore the evolution of carbon emissions in the construction industry under multiple scenarios, and policy suggestions were proposed for material production, building operation, and construction. The research results showed： ① Under a small sample environment, the atom search algorithm was superior to other traditional intelligent algorithms in terms of prediction accuracy and time. ② Under multiple scenarios, the Chinese construction industry will achieve carbon peaking in 2030； however, under the current population growth scenario, the construction industry will not reach its peak until 2031, lagging behind in the carbon peaking target. ③ Population changes will lead to the postponement of carbon peaking in three stages, particularly having a considerable impact on the operational stage.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 4","pages":"2020-2034"},"PeriodicalIF":0.0,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144002031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}