工业工程最新文献

{"title":"[Occurrence Characteristics of Microplastics and Influencing Factors in Coastal Salinized Soil].","authors":"Wei-Ming Li, Lei Xu, Li-Chang Zhang, Cai-Ling Shi, Wen-Jun Xie","doi":"10.13227/j.hjkx.202407154","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407154","url":null,"abstract":"<p><p>Microplastics are widespread in terrestrial and marine environments. As a transition zone between land and ocean, coastal soils have unique microplastic pollution characteristics. To reveal the characteristics of microplastic pollution in coastal soils, soils with different salinization levels were collected from Wudi County toward the sea. The distribution characteristics of microplastics and their relationship with soil physical and chemical properties were analyzed through density separation, oxidative digestion, and micro-Raman spectroscopy techniques. The pollutant load index method was used to assess its ecological risk. The results showed that microplastics were detected in 51 sampling points of coastal soil in Wudi County, and the abundance of microplastics ranged from 550 to 3 950 n·kg^-1. Polyethylene （PE）, polyethylene terephthalate （PET）, polypropylene （PP）, polystyrene （PS）, and polyvinyl chloride （PVC） accounted for 53.1%, 13.9%, 16.4%, 8.4%, and 8.2%, respectively. The shapes of microplastics mainly included film （accounting for 62.0%）, fiber （accounting for 13.7%）, sphere （accounting for 13.2%）, and sheet （accounting for 11.1%）. Microplastics with grain size less than 1 000 μm accounted for 85.0%. The lowest abundance of microplastics appeared in the bare land with the highest degree of salinization, and the highest abundance appeared in the non-salinized cotton soil. The abundance of microplastics was significantly correlated with soil salinization levels （<i>P</i>&lt; 0.05）. With saline level increasing, the total abundance of microplastics and the abundance of film, PE, and PET microplastics decreased significantly （<i>P</i>&lt; 0.05）. The proportion of microplastics with grain size greater than 1 000 μm decreased significantly （<i>P</i>&lt; 0.05）, but the proportion of microplastics with grain size less than 100 μm increased significantly （<i>P</i>&lt; 0.05）. This may be because of the different soil use types and different sources of microplastics in soils with varied saline levels. Soil organic carbon （SOC） was significantly positively correlated with the abundance of microplastics （<i>P</i>&lt; 0.05）. The risk load index （PLI） values of all soil samples ranged from 1.19 to 2.41, which were low risk level pollution. Among them, the PLI values of wasteland and bare land with high saline level were lower, and the PLI values of soils with low saline level were higher. The results of this study can provide an important basis for understanding the microplastic pollution and exploring the relationship between soil properties and microplastic distribution characteristics in coastal saline soils.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5325-5335"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856550","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":"[Characterization of Spatial and Temporal Divergence and Coupling of Net Agricultural Carbon Sinks in China: A Case Study from 2000 to 2022].","authors":"Xiao-Wen Dai, Yi Chen, Yan-Qiu He, Fang Wang","doi":"10.13227/j.hjkx.202407038","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407038","url":null,"abstract":"<p><p>Low-carbon agriculture is crucial for China's agricultural green transformation and the development of an ecological civilization. The net carbon sink of agriculture plays a vital role in this process. Here, we take China's 31 provinces （municipalities and autonomous regions） as the research object, select the data from 2000 to 2022, and discuss them from multiple perspectives around the three dimensions of time series, space, and coupling. Additionally, we constructed an environment-economy coupling index and refined it by phases to analyze the relationship between stages and regions. The study revealed the following： ① China's overall agricultural carbon emissions fluctuated and decreased, while the agricultural carbon sink continued to expand, showing steady growth. ② The net agricultural carbon sink was distributed among provinces, and the gap between provinces in terms of net carbon sink tended to widen. Agricultural net carbon sinks exhibited regional aggregation characteristics, forming two distinct growth areas. The traditional growth area comprised Shandong and Henan as the core and Hebei, Anhui, and Jiangsu as the neighboring radiation areas. The other emerging growth areas in Northeast China included Heilongjiang, Jilin, and Liaoning. ③ The net agricultural carbon sink demonstrated a clear positive spatial correlation. However, a tendency was observed for the spatial correlation to weaken and an increase in the spatial type of low-low form of aggregation over the years. ④ From 2000 to 2022, the coupling relationship between net agricultural carbon sinks and agricultural economic growth improved, with most provinces shifting from weak or strong decoupling to expanding negative decoupling. Six provinces, namely, Zhejiang, Fujian, Yunnan, Gansu, Xinjiang, and Inner Mongolia, have shown the most significant shifts. Overall, the net agricultural carbon sinks and agricultural economic growth are expected to be in a state of negative expansion or weak decoupling for a prolonged period in the future. While the contribution of agricultural carbon sinks to the resource reserve will be substantial, the sustainable growth of the agricultural economy will face challenges.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"4839-4849"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856573","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-temporal Patterns and Driving Factors of Net Carbon Sink in Planting Industry in the West China Development Area].","authors":"Li-Chen Tang, Jie Li, Hui Chen, Mi Chen, Xian-Gang Zeng, Zhong-Yuan Zhang","doi":"10.13227/j.hjkx.202407194","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407194","url":null,"abstract":"<p><p>On the basis of calculating the spatiotemporal pattern changes of net carbon sink in the planting industry in the West China Development Area from 2001 to 2022, the GBR model was used to reveal its key driving factors and nonlinear response mechanisms. The results showed that： ① During the inspection period, the net carbon sink of the planting industry in the West China Development Area （calculated as C） showed an upward trend, but the growth rate gradually slowed, increasing from 125.641 3 million tons in 2001 to 219.106 1 million tons in 2022. ② The high value areas of net carbon sink in the planting industry were mainly in the southwest region, and the number of provinces in the high value range of net carbon sink continued to increase, showing an expanding trend from a few clusters to large-scale clusters. The net carbon sink intensity of planting industry exhibited obvious spatial agglomeration and non-equilibrium characteristics, and the net carbon sink intensity of all provinces gradually decreased during the inspection period. ③ The industrial structure factor had an inverted U-shaped relationship with the net carbon sink of the planting industry. The agricultural production structure, agricultural disaster rate, urban-rural income gap, and urbanization rate factors had a fluctuating inhibitory effect, while other factors had a significant promoting effect. At different periods, the importance of farmland irrigation condition and agricultural mechanization level factors were prominent.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"4850-4863"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856628","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 Pattern and Driving Mechanism of PM_2.5 Population Exposure Risk in Urban Agglomerations in China].","authors":"Jun Zhang, Lei-Yu Liu, Teng-Fei Zhang, Ya-Ni Geng","doi":"10.13227/j.hjkx.202405319","DOIUrl":"https://doi.org/10.13227/j.hjkx.202405319","url":null,"abstract":"<p><p>At present, China's urban agglomerations are high-risk and high-risk clusters of PM_2.5 population exposure. Based on the remote sensing data of PM_2.5 from 2000 to 2021, this study analyzed the temporal and spatial evolution characteristics of PM_2.5 population exposure risk in urban agglomerations in China by using the population exposure risk model and spatial autocorrelation method and used seven factors such as average temperature, annual precipitation, and per capita GDP as independent variables, combined with geographic detectors and spatiotemporal geographically weighted regression models to explore the spatial differentiation mechanism of PM_2.5 population exposure risk. The results showed that： ① From 2000 to 2021, the temporal range of PM_2.5 exposure risk in urban agglomerations in China was small. ② From 2000 to 2021, the PM_2.5 population exposure risk of China's urban agglomerations changed significantly in space, and the high-risk areas of PM_2.5 population exposure were concentrated in the Beijing-Tianjin-Hebei urban agglomeration, the Yangtze River Delta urban agglomeration, and the central Shanxi urban agglomeration, and the PM_2.5 population exposure risk in China's urban agglomerations showed a marked positive correlation in space, and the spatial agglomeration characteristics were obvious. ③ The exposure risk of urban agglomerations with low population density was greatly affected by annual precipitation and annual average temperature, while urban agglomerations with high population density were greatly affected by population density and environmental regulatory factors. Industrial structure and population density factors played a positive role in enhancing the population exposure risk of PM_2.5 in urban agglomerations, energy consumption and environmental regulation factors played a negative inhibiting effect, and annual average wind speed and annual precipitation factors mainly played a positive role in enhancing and negatively inhibiting the population exposure risk of the urban agglomeration on the northern slope of the Tianshan Mountains. The results of this study provide a scientific basis for atmospheric environment management and pollution prevention and control in urban agglomerations in China.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5000-5012"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856633","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 Progress on the Extraction, Qualitative, and Quantitative Methods of Microplastics in Biological Samples].","authors":"Min Li, Fei-Ping Wang, Zi-Qi Chen, Xin-Yu Li, Hai-Long Liu, Xiao-Zhi Wang, Wan-Ying Zhang, Jian Xu","doi":"10.13227/j.hjkx.202407196","DOIUrl":"10.13227/j.hjkx.202407196","url":null,"abstract":"<p><p>Microplastics （MPs）, which usually refer to plastic fragments, particles, or fibers with a diameter or length of less than 5 mm, are contaminants of emerging concern （CECs） that have a significant impact on the ecological system. MPs have been widely detected in the soil, surface water, ocean, and atmosphere. These MPs could accumulate in organisms via absorption and/or ingestion, transfer along the food chain, and ultimately pose a threat to the health of higher trophic organisms and even human beings. Therefore, the determination of MPs types and contents in organisms is important for understanding the accumulation of MPs in organisms and their potential ecological risks. Hence, in this study, we focus on the analysis of MPs in biological samples. After researching the domestic and foreign literature, the basic principles, suitable application conditions, as well as the advantages and disadvantages of each method used in four processes of MPs analysis, including biological sample collection, MPs extraction （including the digestion of biological samples and separation of MPs from digestion solutions）, and qualitative and quantitative analysis, were discussed. On this basis, a combination of different analysis methods is proposed to improve the detection accuracy of MPs analysis in biological samples, which requires further investigation in the field of MPs analysis in biological samples. Meanwhile, more efforts should be exerted on the investigation of the standard method used in biological sample pretreatment and MPs analysis.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5303-5315"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856614","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":"[Evaluation of Ecological Quality in the Chengdu-Chongqing Economic Circle Based on Human Footprint].","authors":"Yue Chang, Deng-Feng Wei, Hui Yang, Ting-Gang Zhou","doi":"10.13227/j.hjkx.202407079","DOIUrl":"10.13227/j.hjkx.202407079","url":null,"abstract":"<p><p>As an important economic center in western China, the rapid urbanization and economic development of the Chengdu-Chongqing economic circle needs to be coordinated with ecological environmental protection. In this study, we utilized remote sensing technology and multi-source data, leveraging the Google earth engine platform to construct the human remote sensing ecological index （HRSEI）, which included indicators of greenness, wetness, dryness, heat, and the human footprint. This index was used to assess the ecological quality of the Chengdu-Chongqing economic circle from 2013 to 2022. The results showed that during this period, the intensity of human activities in the region increased significantly, exerting a profound impact on ecological quality. The spatial distribution of ecological changes exhibited a core-periphery pattern, centered on Chengdu and Chongqing, radiating along major transportation corridors. Ecological quality demonstrated complex trends amidst rapid urbanization and economic growth. Among them, the largest area of ecological degradation involved transitions from \"good\" to \"fair,\" accounting for 22.8%, followed by a change from \"excellent\" to \"good\" （7.3%） and a change from \"fair\" to \"poor\" （3.6%）. The deterioration of \"poor\" and \"very poor\" ecological quality was mainly concentrated around the core city and its transportation network, while \"good\" and \"excellent\" areas had improved their ecological quality. The ecological improvement and restoration of the \"good\" and \"excellent\" areas were closely linked to the implementation of regional environmental protection policies. Geo-detector analysis further revealed that the interactions between natural factors （such as elevation and temperature） and human activities （such as land use） had an important influence on the dynamic changes of ecological quality in the Chengdu-Chongqing economic circle. This study provides a scientific basis for future coordinated regional development and ecological environmental protection.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5169-5179"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856579","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":"[Land Use Scenario Simulation and Habitat Quality Change in Pinglu River Economic Belt Based on PLUS-InVEST Model].","authors":"Shao-Qiang Wen, Bao-Qing Hu, Wei-Wei Xie, Chun-Lian Gao","doi":"10.13227/j.hjkx.202407166","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407166","url":null,"abstract":"<p><p>Exploring the spatiotemporal characteristics of habitat quality and its influencing factors in the Pinglu Canal Economic Belt is crucial for promoting the high-quality and sustainable development of this region. Based on five periods of land use data from 2000 to 2020, the PLUS model was used to predict the land use change pattern of the Pinglu Canal Economic Zone for 2030 under three scenarios： natural development （NDS）, ecological protection （EPS）, and planning for the Pinglu Canal （PS）. The InVEST model and geodetector were then coupled to explore the spatiotemporal evolution characteristics and influencing factors of habitat quality from 2000 to 2030. The results showed that： ① The predominant land use types in the Pinglu Canal Economic Belt were forest land and arable land. Between the years 2000 and 2020, a discernible trend of continuous expansion in the area of construction land and a corresponding decline in the area of other land uses was observed. Projections for different scenarios in 2030 indicated that the land changes in the NDS scenario aligned with the historical development pattern. In contrast, the EPS scenario significantly constrained the expansion of construction land, while the PS scenario exhibited varying degrees of growth in construction land, water, and forest land. In the PS scenario, a notable increase was observed in the area of construction land, water, and forest land. In the PS scenario, the area of built-up land, water, and forest land all demonstrated varying degrees of increase. ② From 2000 to 2020, the area of high and high-grade habitat quality accounted for 57% of the total area. The area of serious and slight decrease in habitat quality was concentrated in the urban area and showed a trend of the degradation of habitat quality that was predicted to continue year on year in the NDS scenario because of the expansion of built-up land. In contrast, the EPS scenario was expected to result in a significant improvement in habitat quality, as a consequence of the protection of ecological land. By 2030, the NDS scenario will continue to degrade habitat quality due to the continuous increase of construction land, whereas the EPS scenario will greatly improve habitat quality because of the effective protection of ecological land, resulting in a positive trend of change. ③ Slope was the primary factor influencing the spatial variation of habitat quality and interacted significantly with other factors. Therefore, in the future planning and construction of the Pinglu Canal, it is essential to exercise restraint in the incremental amount of construction land, maintain ecological land with high habitat quality, and avoid over-development of areas.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5122-5133"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856546","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 of Spatial and Temporal Variations of Multi-year Vegetation Cover in Different Climatic Zones of China and Their Topographic Effects].","authors":"Fan Fu, De-Xuan Zhao, Bei-Er Zhang, Zhu-Mo Zhu, Hai-Yue Lu, Can-Can Yang, Ming-Wei Zhao","doi":"10.13227/j.hjkx.202407184","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407184","url":null,"abstract":"<p><p>Exploring the spatial and temporal dynamics of vegetation cover in different regions of China and its topographic effect is crucial for maintaining the ecological environment and preventing soil erosion. Based on the MODIS NDVI data from 2000 to 2023, the vegetation cover of China for 24 years was calculated by using the pixel binary model. The spatial and temporal trends of vegetation cover in different regions of China and the influence of topographic factors on the spatial distribution of vegetation cover were investigated by dividing the study area into seven typical climate zones. The results showed that： ① From 2000 to 2023, the vegetation cover in China showed a fluctuating upward trend, with a growth rate of 0.207%·a^-1 and a basic pattern of \"low in the northwest, high in the southeast, and spatially differentiated in the central part of the country.\" The proportion of areas with improved vegetation cover over the years was 46.7%, with a risk of continuous degradation in local areas. ② With the increase in altitude, the trend of vegetation cover changes in various climatic zones was not the same. With the increase in slope, the vegetation cover of the climatic zones showed a fluctuating upward trend, and the proportion of vegetation cover of different slope direction was relatively stable. ③ The vegetation cover in the same climatic zone had a significant difference in the response to the topographic factors, and the experiment showed that topographic factors had a significant influence on the vegetation cover. The experiment showed that the influence of terrain factors on vegetation cover was as follows： elevation &gt; slope &gt; slope direction. The study of the spatial differentiation of vegetation and the driving law of topography in typical climatic zones can provide scientific basis for the improvement of China's ecological environment.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5206-5216"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856569","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":"[Prediction of Heavy Metal Concentrations in PM_2.5 in the Agricultural Area of Yangtze River Delta Region Based on Machine Learning].","authors":"Hong-Yan Zhang, Hao Jin, Ying-Ping Mo, Hai-Ou Zhang, Chao Pan, Jian-Ling Fan","doi":"10.13227/j.hjkx.202407111","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407111","url":null,"abstract":"<p><p>Heavy metals in PM_2.5 can considerably impact air quality, human health, and the ecological environment. However, studies on heavy metals in PM_2.5 in agricultural areas are relatively limited. In this study, observational data on heavy metal concentrations in PM_2.5 in the Yangtze River Delta Region from 2000 to 2020 were collected. Three machine learning-based prediction models for heavy metal concentrations in PM_2.5 were constructed to predict and analyze the regional pollution characteristics of six heavy metal elements （Pb, Cu, As, Cd, Zn, and Ti） in PM_2.5 in agricultural areas of the Yangtze River Delta. The results showed that none of the three machine learning models, random forest （RF）, support vector machine （SVM）, or gradient boosting machine （GBM）, exhibited good prediction performance when individually predicting the concentrations of heavy metal elements in PM_2.5 （<i>R</i>² &lt; 0.66 in nearly half of the models）. However, the performance improved significantly after integrating the three models with weighted averaging （<i>R</i>² &gt; 0.66 in all models）, which achieved quantitative prediction capabilities for the concentrations of the six metal elements （RPD &gt; 1.4）. The prediction results for the concentrations of heavy metals in PM_2.5 in agricultural areas of the Yangtze River Delta revealed that the average mass concentrations （ng·m^-3） of the six heavy metal elements were in the order of Zn &gt; Pb &gt; Cu/Ti &gt; As &gt; Cd, but significant differences were observed in their spatial-temporal distributions. The concentrations of Pb, Cd, As, and Zn in PM_2.5 decreased from 2015 to 2017, while the concentrations of Cu and Ti did not show significant temporal changes. Spatially, the concentrations of Pb, Cu, and Ti in PM_2.5 were higher in the northern areas of the Yangtze River Delta Region but lower in the south. The concentrations of As and Cd were higher in the mountainous areas of northern Anhui and western Zhejiang, while Zn concentrations were relatively high across all agricultural areas. These results provide an effective method for predicting regional heavy metal concentrations in atmospheric particulate matter and offer a reference basis for understanding the characteristics of atmospheric particulate matter pollution and regional pollution reduction efforts in agricultural areas of the Yangtze River Delta.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5013-5022"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856609","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 and Prediction of Land Surface Thermal Environment in A Typical Ecological City from 1990 to 2020].","authors":"Xi-Tao Zhang, De-Cheng Zhou","doi":"10.13227/j.hjkx.202407004","DOIUrl":"https://doi.org/10.13227/j.hjkx.202407004","url":null,"abstract":"<p><p>Rapid urbanization leads to the exacerbation of the urban heat island （UHI） effect, which significantly increases the climate risk of urban heatwaves. The construction of ecological cities, which aim for harmonious development between humans and nature, can substantially mitigate the UHI effect. However, research on the long-term evolution of the land surface thermal environment in ecological urban areas is relatively scarce. Taking the typical ecological city of Suzhou, China as an example, this study analyzes the spatiotemporal evolution pattern of land use changes and land surface thermal environment effects in Suzhou from 1990 to 2020 and predicts the thermal environment for 2030 based on the PLUS model. The results showed that： ① The proportion of built-up areas increased by 3.72%, 11.66%, and 5.67% in 1990-2000, 2000-2010, and 2010-2020, respectively, with built-up land area accounting for 26.83% in 2020, predominantly from the conversion of farmland to built-up areas. ② The UHI intensity showed an increasing then decreasing trend during the study period, with nearly half of the regions experiencing a decrease in UHI levels from 2010 to 2020. ③ Spatially, areas with alleviated heat islands were mainly in the four county-level cities and Gusu District of Suzhou, and the built-up area in the 74.43% of areas with alleviated heat islands increased. ④ The urban heat island intensity of areas expected to change by 2030 is projected to increase by 42.65%, mainly concentrated in the southwest of Changshu City, the central part of Wuzhong District, and the central area of industrial parks on the urban edges and surrounding suburbs, while other areas will show a decreasing trend in heat island intensity. This study demonstrates that the construction of \"ecological cities\" in Suzhou has significantly reduced the UHI effect caused by urban expansion, providing theoretical references for further development of ecological cities.</p>","PeriodicalId":35937,"journal":{"name":"环境科学","volume":"46 8","pages":"5196-5205"},"PeriodicalIF":0.0,"publicationDate":"2025-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144856632","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}