环境科学最新文献

{"title":"[Analysis of Carbon Emission Impact Factors and Peak Scenario Simulation for Resource-based Cities in China Based on RF-RFECV Feature Selection and BO-CNN-BiLSTM-attention].","authors":"Yi Han, Zhen-Mei Hou","doi":"10.13227/j.hjkx.202501278","DOIUrl":"https://doi.org/10.13227/j.hjkx.202501278","url":null,"abstract":"<p><p>As China's 2030 carbon peak target approaches, carbon emission reduction efforts have become increasingly urgent and crucial. Resource-based cities, characterized by their reliance on high-carbon industries, play a pivotal role in the nation's carbon peak progress. This study focuses on 108 resource-based cities from 2000 to 2022, employing the RF-RFECV algorithm to identify key factors influencing carbon emissions in these cities and utilizing the SHAP algorithm to evaluate feature importance. Furthermore, a BO-CNN-BiLSTM-attention prediction model is constructed, combined with scenario analysis to simulate the dynamic pathways of carbon peaking in resource-based cities under low-carbon, baseline, and high-speed scenarios. The results indicate the following: ① From the perspective of influencing factors, energy consumption was the most critical driver of carbon emissions in resource-based cities, reflecting their dependence on energy-intensive industries. The GDP of the primary industry and population density had a negative impact on carbon emissions, while the other six variables exerted a positive influence. ② In terms of city types, the impact of energy consumption on regenerative cities gradually declined, the development of secondary industries varied in its influence across different city types, and urbanization levels had the most significant impact on growing resource-based cities. ③ According to the peak scenario simulations, under the baseline and high-speed scenarios, carbon emissions in resource-based cities will continue to rise before 2040, whereas under the low-carbon scenario, emissions are projected to peak by 2034. Based on these findings, resource-based cities should achieve low-carbon transformation and sustainable development by improving energy efficiency, developing renewable energy, advancing green finance, adjusting industrial structures, and establishing carbon emission trading markets.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1433-1448"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147459551","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":"[Analysis of Digital-real Economy Integration Driving Green and Low-carbon Transition in Resource-Based Cities].","authors":"Yang Chen, Wen-Ge Liu","doi":"10.13227/j.hjkx.202502011","DOIUrl":"https://doi.org/10.13227/j.hjkx.202502011","url":null,"abstract":"<p><p>As pivotal pillars of China's national energy security strategy, resource-based cities can leverage the data-sharing capabilities, real-time transmission features, and low marginal cost advantages inherent in digital-real integration to forge new pathways for overcoming the \"resource curse\" and \"transition inertia\" dilemmas. Based on panel data of China's resource-based cities from 2011 to 2022, this study constructs a multidimensional econometric framework incorporating two-way fixed effects models, mediation and moderation effect models, and threshold regression analysis to systematically deconstruct the operational impacts and mechanistic drivers of digital-real integration in propelling green and low-carbon urban transitions. The results showed that: ① Digital-real integration demonstrated statistically significant positive effects on green low-carbon transition in resource-based cities, with robustness confirmed through multiple empirical tests. ② Mechanism tests revealed that digital-real integration significantly facilitated green and low-carbon transition in resource-based cities through innovation-driven effects and environmental regulation effects, whereas industrial optimization effects demonstrated no significant driving force. Concurrently, government intervention exhibited a negative moderating effect on this transition process driven by digital-real integration. ③ Heterogeneity tests revealed significant differential effects across three dimensions: typology of resource-based cities, economic development levels, and digital technology innovation capacities. ④ Threshold effect tests confirmed that higher digital economy policy supply levels intensified the green and low-carbon transition effects of digital-real integration.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1576-1585"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147459843","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 Prediction of \"Production-Living-Ecology\" Space Conflict in the Dongting Lake Eco-economic Zone].","authors":"Jie Tan, Si-Yu Fan, De Yu, Hui-Ting Deng, Zhao-Yang Liao, Qin Liu","doi":"10.13227/j.hjkx.202502145","DOIUrl":"https://doi.org/10.13227/j.hjkx.202502145","url":null,"abstract":"&lt;p&gt;&lt;p&gt;The dissection of the spatiotemporal evolution characteristics and future development trends of the \"production-living-ecology\" space in the Great Lake Economic Zone is a fundamental basis for territorial spatial planning and governance. It can provide strategic support for the orderly utilization, renovation, and restoration of regional territorial space, thereby illuminating the direction for constructing a high-quality regional territorial spatial pattern. In this study, a multi-source indicator identification system was utilized to analyze the evolutionary characteristics of the spatial pattern of \"production-living-ecology\" space in the Dongting Lake Eco-economic Zone from 2014 to 2022. The spatial conflict comprehensive index model and the IM model were employed to measure the evolutionary characteristics of spatial conflict. Subsequently, the PLUS model was applied to forecast the potential development trends of the spatial pattern of \"production-living-ecology\" space in the Dongting Lake Eco-economic Zone by 2030 under three scenarios: natural development, economic priority, and ecological priority. The results showed that: ① The \"production-living-ecology\" space in the Dongting Lake Eco-economic Zone featured a distribution characterized by \"three parts mountain, one part water, and five parts farmland.\" Living space, with the existing urban areas as the central point, continuously expanded linearly outward. There was frequent conversion between production space and ecological space, resulting in a significant reduction of ecological space. Particularly in the northern, western, and southwestern parts of the East Dongting Lake area, as well as in the Hong Lake area, there were clear instances of production space encroaching on ecological space. ② The conflict pattern changes of \"production-living-ecology\" space in the Dongting Lake Eco-economic Zone were significant. Slight conflicts were the main type of conflicts in the region, which showed a continuous increasing trend, while the number of severe conflict types accounted for the lowest proportion of the total conflict types, showing a trend of rising first and then declining. Concurrently, the systemic tendency was always the main transformation tendency of regional conflict types, with the overall conflict level of the region aggregating towards slight conflicts, and the fragmentation of \"production-living-ecology\" space landscape exhibited an increasing trend. ③ Under the natural development scenario, economic development priority scenario, and ecological development priority scenario for the Dongting Lake Eco-economic Zone, there were significant spatial concentration changes in the distribution of various types of spatial transformations. The mutual conversion between production space and ecological space primarily occurred in the mountainous forests surrounding the region and in the low-lying river and lake areas. The increase in living space showed little variation among t","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1845-1857"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460227","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":"[Research on Influencing Factors and Forecasting of Carbon Emissions from Planting Industry in Heilongjiang Province].","authors":"Yun Teng, Tian-Lei Yan, Rui-Xiang Ge, Li Ma, Ya-Lin Wang, Hui Xu","doi":"10.13227/j.hjkx.202502021","DOIUrl":"https://doi.org/10.13227/j.hjkx.202502021","url":null,"abstract":"<p><p>Under the dual carbon goals, reducing carbon emissions in the planting industry is crucial for achieving green and low-carbon transformation in agriculture. This study focuses on the planting industry in Heilongjiang Province, utilizing the LMDI model, an extended STIRPAT model, and ridge regression to measure carbon emissions from 2002 to 2021, identify influencing factors, and predict future carbon emissions. The results indicate that: ① From 2002 to 2021, carbon emissions showed an overall fluctuating upward trend, divided into four phases: a slow growth period from 2002 to 2004, an accelerated growth period from 2004 to 2016, a fluctuating decline period from 2016 to 2019, and a stable growth period from 2019 to 2021. ② Economic level and agricultural structure promoted carbon emissions, while production efficiency and labor scale inhibited them. ③ Future carbon emissions will maintain a slow growth trend. By 2031, carbon emissions were projected to reach 10.832 million tons, an increase of 593 500 tons compared to that in 2021, with an average annual growth rate of 0.53%. Although Heilongjiang Province has made initial progress in carbon emission reduction, future challenges remain. It is recommended to further develop practical carbon reduction strategies.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1521-1531"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460315","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":"[Spatiotemporal Evolution of Carbon Storage and Multi-scenario Prediction in the Yunnan-Guizhou Plateau Based on the InVSET-Ridge Regression-PLUS Model].","authors":"Yuan Li, Yu-Ling Peng, Hao-Na Peng, Wei-Ying Cheng","doi":"10.13227/j.hjkx.202501105","DOIUrl":"https://doi.org/10.13227/j.hjkx.202501105","url":null,"abstract":"<p><p>As an important indicator for measuring the carbon sequestration capacity of ecosystems，carbon storage is of great significance for alleviating global climate change. By taking advantage of machine learning and ecosystem service models，an integrated analysis framework based on the InVEST-Ridge Regression-PLUS model was constructed to conduct a quantitative analysis of the spatio-temporal evolution characteristics and driving mechanisms of carbon storage in the Yunnan-Guizhou Plateau from 2000 to 2020，and future scenarios were designed to predict the changing trends of regional carbon storage under different land use paths. The results show that：Firstly，from 2000 to 2020，the carbon storage in the Yunnan-Guizhou Plateau generally presented a slow growth trend，and the growth rate continued to decline，showing a distribution pattern of \"higher in the south and lower in the north.\" Secondly，vegetation coverage was a crucial determining factor for carbon storage in this area，and the conversion between different land use types affected the spatial distribution of carbon storage. Thirdly，in the future scenario simulation，the carbon storage under the carbon sink enhancement scenario performed best，effectively verifying the effects of ecological projects such as the conversion of farmland to forest and grassland restoration，providing a scientific basis for the dynamic assessment and optimization of carbon storage in the Yunnan-Guizhou Plateau and similar karst areas.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1928-1940"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460430","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":"[Contamination Characteristics and Source Apportionment of Heavy Metals in Soil around the Agricultural Land in Coal Mining Concentration Areas Based on APCS-MLR and PMF Models].","authors":"Fen Hu, Qiao-Hong Chen, Lie Jiang, Lian-Wei Yang","doi":"10.13227/j.hjkx.202501218","DOIUrl":"https://doi.org/10.13227/j.hjkx.202501218","url":null,"abstract":"<p><p>In order to explore the characteristics and sources of heavy metal pollution in the surrounding soil of a coal mine concentration area in Pingxiang, Jiangxi Province, based on the test results of six heavy metal indicators (Cd, Hg, Pb, Cu, Ni, and Zn) and pH values of 127 sampling sites, the geo-accumulation index and Nemerow integrated pollution index methods were used to compare and evaluate the pollution characteristics of soil heavy metals. On the basis of correlation analysis, APCS-MLR and PMF models were used to quantitatively analyze the sources and contributions of soil heavy metal pollution. The results showed that the average content of heavy metals in six types of soils was higher than the soil environment background value in Jiangxi Province. There were five types of soil heavy metals, Cd, Hg, Cu, Ni, and Zn, with contents exceeding the risk screening value in GB 15618-2018, and some samples had Cd content exceeding the control value of GB 15618-2018. The spatial distribution characteristics of Pb, Cu, and Ni were highly similar. Cd was highly enriched in the downstream area of coal mine concentration. The spatial distribution and correlation coefficient of Hg indicated that its source may have been different from other indicators. The high-value area of Zn was significantly smaller than that of other indicators. The pollution assessment showed that the overall soil in the study area was moderately polluted or above, with Cd, Hg, and Ni as the main pollution indicators. The heavy metal pollution in the coal gangue accumulation area was more severe than in other surrounding areas. The APCS-MLR model analyzed two known sources and one unknown source, with contribution rates of 65.69% from a mixture of industrial dust, natural causes, transportation, and coal mine pollution; 8.02% from agricultural activities; and 26.29% from unknown sources. The PMF model identified five pollution sources, agricultural activity sources, industrial dust sources, coal mine pollution sources, transportation sources, and naturally occurring sources, with corresponding contribution rates of 23.61%, 16.84%, 22.74%, 25.85%, and 10.96%, respectively. The source analysis results were consistent with the actual situation in the study area, which can provide theoretical support for the prevention, control, and remediation of soil heavy metal pollution in the study area.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"2007-2019"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460311","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":"[Spatial Distribution and Source Analysis of Heavy Metals in Multiple Environmental Media Around a Waste Incineration Plant in Jiangxi, China].","authors":"Yan-Hong Zhang, Kang-Cheng Deng, Sheng-Pin Yu, Cheng-Gong Yu, Zai-Qian Ma, Xiao-Meng Li","doi":"10.13227/j.hjkx.202501083","DOIUrl":"https://doi.org/10.13227/j.hjkx.202501083","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Heavy metals surrounding waste incineration plants undergo cross-media migration and enrich in the soil-water system, resulting in a systematic deterioration of the physical and chemical properties of the soil and posing potential hazards to the ecological environment and human health. Taking the heavy metals in the soil, sediment, surface water, and groundwater around a waste incineration plant in Jiangxi Province as the research objects, descriptive statistical analysis was conducted on the contents (or concentrations) of heavy metals in multi-media. The spatial distribution characteristics of heavy metals within the soil-water system were analyzed, and the sources of heavy metals in the soil were deciphered by comprehensively using correlation analysis and the Absolute Principal Component Analysis-Multiple Linear Regression (APCS-MLR) model. The results showed that heavy metals around the waste incineration plant were mainly enriched in the soil and sediments. The content of Cd in the soil exceeded the risk screening value, and the contents of Cd and Zn exceeded the soil background values in Jiangxi Province. The average values of As, Cr, Ni, Pb, and Zn in the sediments exceeded the sediment background values in Jiangxi Province. The concentrations of heavy metals As and Pb in the groundwater exceeded the groundwater standard (Class Ⅲ). The variation coefficients of As and Cd in the soil were 53.97% and 39.84%, respectively, which belonged to a strong variation degree and exhibited obvious characteristics of point source pollution. The average content of Zn in the soil samples was higher than that in the sediment samples, while the contents of As, Cd, Cr, Cu, Ni, and Pb in the sediments were higher than those in the soil. The results of the correlation analysis between the distance from the riverbank and the cumulative concentration of heavy metals indicated that the concentration of heavy metals in the groundwater was affected by the recharge of surface water. The contents of heavy metals As, Cd, Ni, and Pb in the soil were higher in the southeast wind direction, which was obviously affected by the perennial dominant wind direction of the waste incineration plant, while Cr, Cu, and Zn were not significantly affected by the wind direction. The contents (concentrations) of heavy metals in the sediments and surface water generally showed a trend of first increasing and then decreasing along the flow direction of the surface water. The contents (or concentrations) of the other six heavy metals except Cr were higher near the sewage outlet and in the lower reaches of the river, among which the influence of As was the most obvious, which may have been related to the industrial activities of the waste incineration plant. The heavy metals in the soil mainly came from industrial sources of waste incineration, agricultural sources of pesticides and fertilizers, and natural sources of parent materials, with corresponding contribution rates of 31.14","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1995-2006"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460321","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":"[Coupling Coordination Measurement and Convergence of Provincial Carbon Emissions and New Quality Productivity in China].","authors":"Dong-Sheng Yu, Juan-Juan Yu, Xiao-Ping Li","doi":"10.13227/j.hjkx.202412148","DOIUrl":"https://doi.org/10.13227/j.hjkx.202412148","url":null,"abstract":"<p><p>The coupling and coordination relationship between provincial carbon emissions and new quality productivity in China is a key path to achieve the \"dual carbon\" goals and promote high-quality development in a coordinated manner. Based on panel data from 30 provinces in China from 2012 to 2022, a coupling coordination degree model, spatial autocorrelation analysis, and <i>β</i> convergence model were constructed to systematically measure the dynamic coordination and regional convergence characteristics of carbon emission intensity and new quality productivity. The results showed that: ① The national coupling coordination degree increased from 0.529 to 0.664, upgrading from \"barely coordinated\" to \"primary coordinated, \" with an average annual growth rate of 2.55%. ② The spatial differentiation presented a pattern of \"high in the southeast and low in the northwest, \" and the global Moran index verified a significant positive spatial correlation. The coupling coordination degree H-H agglomeration area expanded from 7 to 11 provinces, reflecting the radiation effect of the Yangtze River Delta and Pearl River Delta extending to the central and western regions, while the northwest and northeast L-L agglomeration areas are still constrained by \"high carbon lock-in\" and ecological vulnerability. ③ There were absolute <i>β</i> and conditional <i>β</i> convergences in the coupling coordination degree, and the convergence speed of underdeveloped provinces was significantly faster than that of developed provinces. The advantage of latecomers and the diffusion effect of technology drove the narrowing of regional differences. Based on this, it is recommended to strengthen the global technology sharing network, promote industrial structure transformation through differentiation, and improve cross regional ecological compensation mechanisms to promote the coordinated transition of low-carbon development and new quality productivity.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1413-1422"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460347","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":"[Driving Factors and Decoupling Evolution Path of Carbon Emissions from the Paper Industry in Guangdong Province].","authors":"Hong-Ping Wang, Jian-Zhou Yang","doi":"10.13227/j.hjkx.202502129","DOIUrl":"https://doi.org/10.13227/j.hjkx.202502129","url":null,"abstract":"<p><p>Exploring the carbon reduction pathways of the paper industry in key regions of China is of significant importance for achieving low-carbon sustainable development in the Chinese paper industry. Taking the paper industry in Guangdong Province as the research subject, this study employs the logarithmic mean Divisia index (LMDI) method to conduct continuous and phase-wise decomposition of the factors influencing carbon emissions based on its energy consumption characteristics. The Tapio decoupling model is used to analyze the decoupling status between the output of the paper industry and carbon emissions, and it is combined with the LMDI model to construct a decoupling effort model, thereby elucidating the extent of efforts made by each influencing factor towards achieving decoupling. The results indicate that during the period from 2007 to 2022, the effect of industrial output value was the main driving factor for the increase in carbon emissions in the paper industry, while the energy intensity effect served as the primary inhibitory factor. The energy structure effect and the carbon emission intensity effect of electricity were both secondary inhibitory factors. The paper industry predominantly experienced an evolutionary path from weak decoupling to strong decoupling and then to regressive decoupling. The carbon emission intensity effect of electricity and the energy structure effect primarily manifested as weak decoupling efforts. The decoupling effort index of energy intensity remained generally consistent with the changes in the overall decoupling effort index. Continuous improvement in various factors will play a positive role in promoting decoupling of carbon emissions. Therefore, it is necessary to enhance the guidance for the reform and optimization of the paper industry structure and its green transformation and upgrading, thus facilitating its transition to a technology-intensive industry and fully achieving a strong decoupling between paper industry output and carbon emissions.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1544-1553"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460408","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":"[Direct CO₂ Emission Characteristics and Fossil Source CO₂ Contributions of Municipal Wastewater Treatment Plant with a Typical AAO Process].","authors":"Yu-Yu Xu, Xue-Biao Nie, Yi-Long Wei, Si-Qi Hu, Tian-Lan Liao, Qi Yang, Bing Zhang, Tan Chen, Ting Yang","doi":"10.13227/j.hjkx.202503333","DOIUrl":"https://doi.org/10.13227/j.hjkx.202503333","url":null,"abstract":"&lt;p&gt;&lt;p&gt;In order to analyze the characteristics of fossil source CO&lt;sub&gt;2&lt;/sub&gt; emissions in the AAO process of municipal wastewater treatment, &lt;i&gt;in&lt;/i&gt;-&lt;i&gt;situ&lt;/i&gt; monitoring in a typical AAO process of a municipal wastewater treatment plant in North China was conducted in this work. The CO&lt;sub&gt;2&lt;/sub&gt; emission flux of each main process unit (selection tank, anaerobic tank, anoxic tank, aerobic tank, sludge return gallery, and secondary sedimentation tank) from September 2023 to August 2024 was obtained, and the 24 h day and night continuous change pattern and the emission contribution of fossil source CO&lt;sub&gt;2&lt;/sub&gt; was analyzed. Through 12 months of continuous monitoring, the direct CO&lt;sub&gt;2&lt;/sub&gt; emission fluxes of major process units such as selection tank, anaerobic tank, anoxic tank, aerobic tank, sludge return gallery, and secondary sedimentation tank were (30.20±2.85), (43.50±5.81), (44.41±4.69), (2 736.82±213.26), (82.68±7.21), and (11.59±1.15) g·(m&lt;sup&gt;2&lt;/sup&gt;·d)&lt;sup&gt;-1&lt;/sup&gt;, respectively. In summer, the AAO process showed \"double peaks\" of direct CO&lt;sub&gt;2&lt;/sub&gt; emission flux in 24 h, with peak periods at 06:00-09:00 [average 12 443.14 μg·(m&lt;sup&gt;2&lt;/sup&gt;·s)&lt;sup&gt;-1&lt;/sup&gt;] and 21:00-24:00 [average 12 395.38 μg·(m&lt;sup&gt;2&lt;/sup&gt;·s)&lt;sup&gt;-1&lt;/sup&gt;], both of which were 20% higher than the average value of direct CO&lt;sub&gt;2&lt;/sub&gt; emission flux in summer for 24 h. In winter, the AAO process showed a \"single peak\" of direct CO&lt;sub&gt;2&lt;/sub&gt; emission flux in 24 h, with peak periods at 09:00-12:00 [average 16 705.90 μg·(m&lt;sup&gt;2&lt;/sup&gt;·s)&lt;sup&gt;-1&lt;/sup&gt;], which was 21% higher than the average value of direct CO&lt;sub&gt;2&lt;/sub&gt; emission flux in winter for 24 h. The direct CO&lt;sub&gt;2&lt;/sub&gt; emission flux in winter [24 h average 13 811.81 μg·(m&lt;sup&gt;2&lt;/sup&gt;·s)&lt;sup&gt;-1&lt;/sup&gt;, CV=9.0%] was higher than that in summer [24 h average 10 388.41 μg·(m&lt;sup&gt;2&lt;/sup&gt;·s)&lt;sup&gt;-1&lt;/sup&gt;, CV=14.4%] but with smaller fluctuations. The monthly direct CO&lt;sub&gt;2&lt;/sub&gt; emission monitoring results showed that the average direct CO&lt;sub&gt;2&lt;/sub&gt; emission flux of the AAO process for 12 months was (1 094.86±80.97) g·(m&lt;sup&gt;2&lt;/sup&gt;·d)&lt;sup&gt;-1&lt;/sup&gt; with large-size monthly fluctuations (CV=35.6%); the peak occurred in March 2024 [1 737.74 g·(m&lt;sup&gt;2&lt;/sup&gt;·d)&lt;sup&gt;-1&lt;/sup&gt;], which was 59% higher than the average value of 12 months. The direct CO&lt;sub&gt;2&lt;/sub&gt; emission intensity of the AAO process varied significantly with the seasons. The average direct CO&lt;sub&gt;2&lt;/sub&gt; emission intensities in spring, winter, summer, and autumn were (3 546.76±616.24), (3 089.66±363.98), (2 738.55±120.38), and (2 267.45±229.33) kg·d&lt;sup&gt;-1&lt;/sup&gt;, respectively. The direct CO&lt;sub&gt;2&lt;/sub&gt; emissions of different process units were obviously different, and the aerobic tank was the main source of CO&lt;sub&gt;2&lt;/sub&gt; emissions in the AAO process, with an average annual emission flux, average annual daily emission intensity, and average annual emission factor (measured in CO&lt;sub&gt;2&lt;/sub&gt;/COD) of (2 736.82±213.26) g·(m&lt;sup&gt;2&lt;/sup&gt;·d)&lt;sup&gt;-1&lt;","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"47 3","pages":"1634-1645"},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147460438","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}