Huanjing Kexue/Environmental Science最新文献

{"title":"[Pollution Characteristics and Source Apportionment of Volatile Organic Compounds in Typical Solvent-using Industrial Parks in Beijing].","authors":"Rui Liu, Zhen Yao, Xiao-Hui Hua, Xiu-Rui Guo, Hai-Lin Wang, Feng Qi","doi":"10.13227/j.hjkx.202310142","DOIUrl":"https://doi.org/10.13227/j.hjkx.202310142","url":null,"abstract":"<p><p>The BCT-7800A PLUS VOC online monitor system was employed to measure ambient volatile organic compounds （VOCs） in a typical solvent-using industrial park in Beijing. From January to June 2023, the pollution characteristics, source apportionment, and ozone formation potential（OFP）of VOCs were studied, and the results of a comparative analysis were also discussed between heating and non-heating periods. The results indicated that VOC concentrations from January to June 2023 were （104.21 ± 91.31） μg·m^-3 on average. The concentrations of TVOCs under the influence of southerly and northerly winds were （214.18 ± 202.37） μg·m^-3 and （197.56 ± 188.3） μg·m^-3, respectively. Alkanes were the species with the highest average concentration and proportion, respectively （45.53 ± 41.43） μg·m^-3. The VOC concentration during the heating period was higher than those during the non-heating period, with values of （111.57 ± 83.96） μg·m^-3 and （87.92 ± 75.03） μg·m^-3, respectively. Propane and ethane were the species with the highest average concentration during the heating period. Compared with those in the non-heating period, the average concentrations of three species （propane, ethane, and n-butane） in the top ten species increased during the heating period, with average concentrations increasing by 51.94%, 54.64%, and 26.32%, respectively. The source apportionment results based on the positive matrix factorization （PMF） model indicated that the major sources of VOCs in the park during the monitoring period were printing emission sources （4.95%）, oil and gas evaporation sources （9.52%）, fuel combustion sources （15.44%）, traffic emissions sources （18.97%）, electronic equipment manufacturing （24.59%）, and industrial painting sources （26.52%）. Therefore, industrial painting sources, electronic equipment manufacturing sources, and traffic emissions sources were the emission sources that the park should focus on controlling. Compared with those during non-heating periods； industrial painting, traffic emission, and fuel combustion sources contributed more during the heating period, with VOC concentrations increasing by 15.02%, 16.53%, and 24.98%, respectively. The average OFP of VOCs from May to June during the monitoring period was 198.51 μg·m^-3 and OVOCs, olefins, and aromatic hydrocarbons contributed the most to OFP, which were 47.41%, 22.15%, and 18.41%, respectively. The electronic equipment manufacturing source was the largest contributor to the summer OFP of the park and its contribution rate was 30.11%, which should be strengthened in the future.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509653","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 Characteristics and Influencing Factors of Industrial Carbon Emissions in the Yellow River Basin].","authors":"Xi-Lian Wang, Li-Hang Qu","doi":"10.13227/j.hjkx.202308258","DOIUrl":"https://doi.org/10.13227/j.hjkx.202308258","url":null,"abstract":"<p><p>Scientific assessment of industrial carbon emissions in the Yellow River Basin and identification of its influencing factors are of great importance for promoting green transformation, ecological protection, and high-quality development of the Yellow River Basin. Considering nine provinces in the Yellow River Basin as the research objects； using relevant data on industrial development and energy consumption in the Yellow River Basin from 2000 to 2019； and with the help of IPCC carbon emission measurement, spatial autocorrelation, and LMDI decomposition, the spatial and temporal evolution characteristics and influencing factors of carbon emissions from industries and industrial sectors in the Yellow River Basin were analyzed. Reasonable suggestions were put forward for reducing the carbon emissions from industries in the Yellow River Basin. The results showed that： ① From 2000 to 2019, industrial carbon emissions in the Yellow River Basin showed a fluctuating growth trend, with a decreasing growth rate. The spatial pattern changed from \"low in the upstream and high in the middle and downstream\" to \"high and low value distribution,\" and the spatial difference gradually expanded. ② The high carbon industry was the most important source of industrial carbon emissions in the Yellow River Basin, accounting for 96.35% of the carbon emissions between the industries with a continuous growth trend, which was a significant difference. The middle and low carbon industry carbon emissions and the total proportion was low, showing different fluctuations； nine provinces and nine industrial industries had significant spatial variability. ③ Energy structure intensity, economic scale, and population scale promoted the increase in industrial carbon emissions in the Yellow River Basin and energy consumption intensity had an inhibitory effect on the increase in carbon emissions. The economic scale effect was positive and significant, which offset the negative effect of energy consumption intensity. Spatial variability was observed in the contribution value of the influence effect of the factors affecting the carbon emissions of the industry in nine provinces.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509657","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":"[Influence of Typical Regional Land Use/Landscape Pattern on Water TN of the Upper Yellow River].","authors":"Tian-Hong Zhou, Si-Lin Su, Kai Ma, Sen Du, Hui-Juan Xin","doi":"10.13227/j.hjkx.202310025","DOIUrl":"https://doi.org/10.13227/j.hjkx.202310025","url":null,"abstract":"<p><p>This study aimed to explore the relationship between land use landscape pattern and water quality in the upstream of the Gansu water conservation, water and soil erosion, and ecological fragile areas. Based on the land use data and water quality monitoring section in 2020 in the 200 m, 500 m, 1 km, 2 km, 50 km, and 10 km riparian buffer area, the single-factor index evaluation method, random forest regression model, and BP neural network were used to quantify the response relationship between land use and landscape pattern of the upper Yellow River in Gansu province and water quality index and to carry out the basin water quality prediction based on land use landscape index data. The results showed that： ① through the single-factor index method, the major indicators of the total nitrogen （TN） in July and September, dissolved oxygen （DO）, permanganate index, ammonia nitrogen （NH₄⁺ -N）, total phosphorus （TP）, and other surface indexes met the surface water environment class Ⅲ water quality standard. ② The random forest regression model was used to analyze the influence of land use and landscape index on TN, and the difference in TN in different typical areas was obtained. The land use types with the highest influence on the TN index in water conservation areas, soil and soil erosion areas, and ecological fragile areas were cultivated land, grassland, and construction land, respectively. ③ The BP neural network was used to predict the water quality index based on different typical areas of land use landscape index. The result of water conservation areas was good, the error rate between the predicted value and the actual value was below 10%, and the prediction accuracy was high. The study showed that water quality prediction based on land use and landscape index/water quality quantitative relationship model had a good water quality prediction effect.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509648","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 and Temporal Characteristics and Driving Force Analysis of Ecological Environmental Quality in Fengfeng Mining Area with Remote Sensing Ecological Index of PM_2.5 Concentration].","authors":"Jing-Han Zhang, Wei Zhang, An-Zhou Zhao, Li-Hui Sun","doi":"10.13227/j.hjkx.202311223","DOIUrl":"https://doi.org/10.13227/j.hjkx.202311223","url":null,"abstract":"<p><p>The Fengfeng mining area is an important coal-producing area in China and crucial environmental problems have been caused by large-scale exploitation of coal mines. The spatio-temporal evolution and driving factors of the ecological environment quality in this area must be explored for promoting the transformation of coal-based cities. Based on Landsat data of the Google earth engine （GEE） platform, this study constructed a new remote sensing-based ecological index （RSEInew） for the Fengfeng mining area from 2000 to 2020. The spatial and temporal evolution of RSEInew and its driving factors were evaluated by using trend analysis and geographic detector methods. The results showed that： ① From 2000 to 2020, the RSEInew of the Fengfeng mining area presented a fluctuating increasing trend （trend = 0.002 2）, and the proportion of good and excellent ecological environmental quality showed an increasing trend, rising from 24.80% in 2000 to 65.54% in 2020. ② The change in the RSEInew grade indicated that the proportion of significant improvement （3 and 4） of ecological environment quality grade in the Fengfeng Mining area from 2000 to 2020 was 10.21%, which was mainly distributed in Hexun town and Yijing town in the northwest of the Fengfeng mining area. The proportion of significant degradation （-3 and -4） was only 1.58%, mainly scattered in Linshui town and Dashe town. ③ RSEInew values increased significantly during 2000-2020 in the area accounting for 18.29%, mainly distributed in the central and northern areas and the western fringe of the Fengfeng mining area. The significantly reduced area accounted for 9.25%, mainly concentrated in the eastern area of the Fengfeng mining area. The coefficient of variation results showed that the areas with high fluctuation of RSEInew were mainly concentrated in Pengcheng town and Linshui town in the middle and eastern Fengfeng mining area. ④ From the perspective of influencing factors, the average <i>q</i> value of land use type （X6） during 2000-2020 was 0.290, which was much higher than other factors. The <i>q</i> value of social and economic factors showed an increasing trend, indicating that the spatial distribution of ecological environment quality in this region was increasingly strongly influenced by human activities. The interaction results showed that land use change was the key factor influencing ecological environment quality in the Fengfeng mining area.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509635","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 Characterization and Driving Factors of Fine Particulate Matter and Its Chemical Components in the Huaihe River Basin].","authors":"Xiao-Yong Liu, Ji-Qiang Niu, Hang Liu, Yi-Dan Zhang, Jun Yan, Jun-Hui Yan, Fang-Cheng Su","doi":"10.13227/j.hjkx.202312141","DOIUrl":"https://doi.org/10.13227/j.hjkx.202312141","url":null,"abstract":"<p><p>According to the data sets of fine particulate matter （PM_2.5） and its components in 35 cities in the Huaihe River Basin from 2015 to 2021, the temporal and spatial distribution patterns of pollutants were analyzed. The influence of meteorological factors on PM_2.5 concentrations was examined using a random forest model. The original series of PM_2.5, sulfate （SO₄^2-）, nitrate （NO₃^-）, ammonium salt （NH₄⁺）, organic matter （OM）, and black carbon （BC） were rebuilt using KZ （Kolmogorov-Zurbenko） filtering and multiple linear regression （MLR） to quantify the effects of meteorological conditions. The results demonstrated that from 2015 to 2021, the declining rates of PM_2.5, SO₄^2-, NO₃^-, NH₄⁺, OM, and BC in the Huaihe River Basin were 4.71, 0.99, 1.05, 0.77, 1.01, and 0.19 μg·（m³·a）^-1, respectively. The high mass concentrations of PM_2.5 and its components were concentrated in the central and western regions of the HRB, whereas those in coastal and southern cities were lower. The variance contributions of the short-term, seasonal, and long-term components of PM_2.5 to the original PM_2.5 sequences in 35 cities were 51.6%, 35.9%, and 7.0%, respectively. The PM_2.5 in coastal cities were more affected by the short-term components. The meteorological conditions were unfavorable for PM_2.5 reduction in the HRB from 2015 to 2018, whereas the meteorological conditions supported the PM_2.5 decrease from 2019 to 2021. From 2015 to 2021, the contribution rates of meteorological conditions to the long-term component reductions of PM_2.5, SO₄^2-, NO₃^-, NH₄⁺, OM, and BC were 28.3%, 29.1%, 31.0%, 29.3%, 27.8%, and 28.6%, respectively. The contribution rates of meteorological conditions to the long-term PM_2.5 reduction were 43.4%, 25.6%, 25.5%, and 20.6% in the HRB cities in Anhui, Shandong, Jiangsu, and Henan Provinces, respectively. With the decrease in PM_2.5 concentration in the HRB, the sulfur oxidation rate （SOR） increased significantly, while the nitrogen oxide oxidation rate （NOR） changed little.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509638","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":"[Content and Health Risks of Microplastics and Phthalate Esters in Bottled Water].","authors":"Xiao-Ge Liang, Rui-Yao Guo, Meng-Fei Su, Xue-Jing Yang, Bo Yao, Jian-Sheng Cui","doi":"10.13227/j.hjkx.202310185","DOIUrl":"https://doi.org/10.13227/j.hjkx.202310185","url":null,"abstract":"<p><p>To study the content and health risks of microplastics （MPs） and phthalate esters （PAEs） in bottled water, a quantitative analysis of MPs was conducted by using Rose Bengal staining and stereomicroscopy. Seven PAEs were quantified by using gas chromatography-triple quadrupole tandem mass spectrometry （GC-MS/MS）. The daily intake of MPs was estimated and the carcinogenic and non-carcinogenic risks of PAEs were evaluated through a health risk assessment model. The results showed that the abundance of MPs in 21 bottled waters ranged from 48 n·L^-1 to 216 n·L^-1 （with the median abundance of 88 n·L^-1）. The majority （72.1%） of MPs were fibrous in shape, and fragments accounted for only 27.9%. The average proportion of small-sized （10-50 μm） MPs was 33.9%, and that of large-sized MPs （&gt;500 μm） was 4.3%. Most MPs were blue. The ∑(PAEs) in bottled water was 1.15-2.47 μg·L^-1 （average 1.62 μg·L^-1）. PAEs detected with high frequencies （100%） included dimethyl phthalate （DMP）, diethyl phthalate （DEP）, diisobutyl phthalate （DIBP）, di-<i>n</i>-butyl phthalate （DBP）, and di（2-ethylhexyl） phthalate （DEHP）, while the detection frequencies of butylbenzyl phthalate （BBP） and di-n-octyl phthalate （DNOP） were relatively low. The concentrations of DBP, DEHP, and DEP were all below the standard limits for drinking water in China. The ∑(PAEs) in the migration experiments was 0.61-2.04 μg·L^-1 （average 1.33 μg·L^-1）. The migration amounts of DBP and DEHP were also within the allowable range under the condition of 60℃ for 10 days. Seven PAEs were detected in both the bottles and caps, and the average content of DEHP in bottles was the highest, while DBP had the highest content in caps. The estimated intake of MPs （EDI） by drinking bottled water in different age groups of humans was 2.87 n·（kg·d）^-1 for adults, 3.87 n·（kg·d）^-1 for children, and 5.85 n·（kg·d）^-1 for infants. The carcinogenic risks of DEHP in 21 bottled water samples and the migration test were less than the maximum acceptable risk level （1×10^-6）, and the non-carcinogenic risk indices （HIs） of PAEs were all less than 1, indicating no non-carcinogenic risk to humans； however, the risk value of infants and children was higher than that of adults and should not be ignored.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509623","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 Simulation Prediction of Ecosystem Carbon Storage in the Yellow River Basin Before and After the Grain for Green Project].","authors":"Xu-Meng Duan, Mei Han, Xiang-Lun Kong, Jin-Xin Sun, Hui-Xin Zhang","doi":"10.13227/j.hjkx.202310021","DOIUrl":"https://doi.org/10.13227/j.hjkx.202310021","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Under the background of \"dual carbon\", the impact of the implementation of the Grain for Green project on the carbon storage of the ecosystem in the Yellow River Basin must be explored, which can serve as an important reference for improving the policy implementation of the new round of the Grain for Green project and improving the carbon sink capacity of the ecosystem in the Yellow River Basin. In this study, 1990, before the implementation of the project, was selected as the starting year of the research period, and 2020, after the implementation of the two rounds of the project, was selected as the end year of the research period. Based on the ecosystem type data from 1990 to 2020, the InVEST model was used to calculate the soil carbon pool, underground carbon pool, below carbon pool, dead organic matter carbon pool, and total carbon storage of ecosystems in the Yellow River Basin and the area where the project was implemented from 1990 to 2020. The results showed that： ① From 1990 to 2020, the area of forest ecosystem in the Yellow River Basin expanded by 26 610.06 km&lt;sup&gt;2&lt;/sup&gt;, and the area of farmland decreased by 46 849.06 km&lt;sup&gt;2&lt;/sup&gt; after the implementation of two rounds of the project. Spatially, the upper reaches of the Yellow River were dominated by grassland and other ecosystems； the middle reaches of the Yellow River were dominated by farmland, forest, and grassland ecosystems； and the lower reaches of the Yellow River were dominated by farmland ecosystems. ② From 1990 to 2020, the carbon storage in the project implementation area showed a fluctuating and increasing trend, and the total carbon storage reached a peak （219.47×10&lt;sup&gt;8&lt;/sup&gt; t） in 2009 and decreased to 218.59×10&lt;sup&gt;8&lt;/sup&gt; t in 2020 due to the decrease of grassland ecosystem from 2010 to 2020. Spatially, the high-value areas of carbon storage were distributed in Aba Tibetan and Qiang Autonomous Prefecture of Sichuan Province and the southern tip of Gansu Province in the upper reaches of the forest and grass accumulation and in the whole of Shanxi Province and the central and southern parts of Shaanxi Province in the middle reaches. Shangluo City in Shaanxi Province and Alxa League in Inner Mongolia Autonomous Region were prefecture-level cities with the highest and lowest average carbon density. ③ In 2035, the carbon storage loss of the natural development scenario was predicted to be 0.83×10&lt;sup&gt;8&lt;/sup&gt; t, and the other three scenarios would increase this loss. Under the moderate farmland return scenario, the Yellow River Basin ecosystem had the strongest carbon sequestration capacity, and the predicted carbon storage would increase by 2.72×10&lt;sup&gt;8&lt;/sup&gt; t compared with that in 2020, and the deep farmland return scenario was the comprehensive optimal scenario. Therefore, in the future, the Yellow River Basin could refer to the deep farmland return scenario to optimize and adjust the implementation plan of the Grain for Green project, and the predicted val","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509656","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":"[Functional Genes and Metabolic Pathways of Nitrogen Metabolism Microorganisms in Lake Sediments：A Case Study of Hongfeng Lake, Guizhou Province].","authors":"Qian Liu, Xiao Chen, Yan-Cheng Li, Yu-Han He, Jiang Li","doi":"10.13227/j.hjkx.202310134","DOIUrl":"https://doi.org/10.13227/j.hjkx.202310134","url":null,"abstract":"<p><p>The nitrogen cycle is of great importance for material circulation and energy flow in lake ecosystems. It is driven by microorganisms in lake sediments and can contribute to balancing lake ecosystems. In this study, physical and chemical properties of the sediments sampled from Hongfeng Lake in Guizhou Province were assayed and analyzed using metagenomics to reveal relevant microorganisms, functional genes, metabolic pathways, and their relationships throughout nitrogen metabolism. The results showed that bacteria were dominant, and the top three relative abundant genera were <i>Thiobacillus</i> （16.64%）, <i>Rubrivivax</i>（9.43%）, and <i>Nitrospira</i> （7.09%）. Only six pathways, including nitrogen fixation, nitrification, denitrification, assimilatory nitrate reduction, dissimilatory nitrate reduction, and complete nitrification, were detected in total, of which denitrification and dissimilatory nitrate reduction were the primary processes, but anaerobic ammonia oxidation was not detected. Bacteria and archaea participated in these six pathways, while eukaryotes only functioned in dissimilatory nitrate reduction, denitrification, and complete nitrification. Ammonia nitrogen, nitrate nitrogen, and total phosphorus, as main environmental factors affecting the distribution of functional genes for nitrogen metabolism, differentiated with each other in their respective real-world conditions. A positive correlation （95.04%） was observed between the functional genes and microorganisms, and <i>narG</i>, <i>narZ</i>, and <i>nxrA</i> possessed the highest abundance and the highest host genes. On this basis, these findings are expected to further elucidate the nitrogen cycle of typical karst lakes in Guizhou Province.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509645","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 Multi-dimensional Conflict Diagnosis among Territorial Space Functions].","authors":"Xi-Ping Zheng, Zhu-An Chen","doi":"10.13227/j.hjkx.202311069","DOIUrl":"https://doi.org/10.13227/j.hjkx.202311069","url":null,"abstract":"&lt;p&gt;&lt;p&gt;Exploring the relationship between urban, agricultural, and ecological functions is crucial for optimizing the territorial spatial patterns and achieving balanced territorial spatial development. Based on the classification system of \"urban-agricultural-ecological\" territorial spatial planning and with the help of multi-source data, including land use, population density, nighttime light, road network, precipitation, and county statistical data from 2010 to 2020, we constructed an evaluation index system of territorial space functions at the grid scale in the Poyang Lake Ecological Economic Zone. The coupling coordination levels among territorial space functions was measured using the coupling coordination degree model and spatial autocorrelation analysis. Moreover, the multi-dimensional function conflict identification model was used to diagnose the type and intensity of function conflicts. The results showed that： ① The urban function was mainly improved, accounting for 75.67%. The grids with high function evaluation index were mainly located in the south of the study area, whereas the intensity of urban function improvement in the north was higher than that in the south. Otherwise, the agricultural and ecological functions were mainly reduced, accounting for 77.44 % and 57.66%, respectively, and the majority of grids with a significant decrease were distributed in the south of Poyang Lake. ② From 2010 to 2020, the coupling coordination type was dominated by an imbalance recession, accounting for 53.87% and 49.89 %, respectively. However, the number of grids with crucial imbalance and primary coordination was reduced, and the coupling coordination types tended to change from both ends to the middle. The coupling coordination hot spots were mainly concentrated in the southern plain, whereas the cold spots were mainly distributed in the northern mountainous and hilly areas, both of which showed a reducing trend. However, in the north of Poyang Lake, a small number of hot spots, distributed along the lake area with relatively flat terrain, showed an expanding trend. ③ The intensity of four territorial space function conflicts was mainly moderate or severe. In addition to the slight positive trend of 'urban-agricultural' function conflict, the function conflicts of \"urban-ecological\", \"agricultural-ecological\", and \"urban-agricultural-ecological\" were all aggravated. The grids with aggravating conflicts were mainly distributed in the Ganjiang River and Fuhe River Basin. The major reason for the aggravation was that the urban function increased obviously, whereas the ecological and agricultural functions decreased significantly. In conclusion, in both the coupling coordination types and multi-dimensional conflicts among territorial space functions, significant spatial differences existed； thus, proposing optimization strategies according to local conditions is necessary. Additionally, the protection of ecological and agricultural spaces during","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509624","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 Ozone Pollution and Precursor Control Strategies in the Pearl River Delta During Summer and Autumn Transition Season].","authors":"Qing-Qing Yu, Wei-Qiang Yang, Cheng-Lei Pei, Xin-Ming Wang","doi":"10.13227/j.hjkx.202310051","DOIUrl":"https://doi.org/10.13227/j.hjkx.202310051","url":null,"abstract":"<p><p>To analyze the causes of ozone pollution in the Pearl River Delta （PRD） Region during the summer and autumn transition seasons, a case study was carried out in Guangzhou, which is located in the center of the PRD Region, to analyze the ozone photochemical production and destruction pathways as well as emission reduction scenarios using a box model based on comprehensive observation. The results showed that the stagnant meteorological conditions and high temperature during the observation period were suitable for the photochemical production of ozone, which led to widespread and prolonged ozone pollution. Aromatic hydrocarbons （AHs） contributed the most to the ozone formation potential （OFP）, and <i>m</i>/<i>p</i>-xylene, toluene, and <i>o</i>-xylene were the major three VOC species contributing to the OFP. Box model analysis revealed that the averaged net O₃ production rate during the polluted period was 23.2×10^-9 h^-1 and the peak reached 39.2×10^-9 h^-1. The HO₂·+NO and NO₂+·OH reaction pathways contributed the most to the local photochemical ozone production （51.2%） and destruction （47.0%）, respectively. Observed ozone concentration was primarily controlled by both the local photochemical O₃ production and the export-dominated transport. The RIR and EKMA analyses showed that O₃ formation in Guangzhou during the summer-autumn transition seasons was mainly a VOC-limited regime and AHs showed the greatest sensitivity to O₃ production. Toluene, <i>m</i>/<i>p</i>-xylene, <i>o</i>-xylene, <i>n</i>-butane, and propylene were the five key components affecting O₃ generation. The analysis of reduction scenarios showed that reducing anthropogenic VOC emissions was the most favorable way to reduce O₃ concentrations； however, if NO<i>_x</i> emission was controlled after reducing VOCs, the O₃ concentration would rebound in a short time. Our results suggested that the synergistic reduction of VOCs and NO<i>_x</i> while mainly focusing on VOCs alleviation should be implemented to continuously reduce ozone concentrations in the future.</p>","PeriodicalId":35937,"journal":{"name":"Huanjing Kexue/Environmental Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142509595","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}