Environmental Monitoring and Assessment最新文献

{"title":"Assessing the distribution pattern of Saussurea medusa under climate change using an optimized MaxEnt model in Qinghai-Xizang Plateau","authors":"Jing-Hua Chen,&nbsp;Rui-Tao Yu","doi":"10.1007/s10661-024-13549-3","DOIUrl":"10.1007/s10661-024-13549-3","url":null,"abstract":"<div><p><i>Saussurea medusa</i> is a rare alpine plant with significant medicinal value. To better understand the changes in its habitat in the context of climate change, this study used an optimized MaxEnt model to predict the current and future habitat of <i>S. medusa</i> under four shared socioeconomic pathways (SSPs) across three time periods (current, mid-century, and end-century) based on three climate system models. The results showed that the suitable habitat of <i>S. medusa</i> is mainly located in the southern and eastern parts of the Qinghai-Xizang Plateau (QXP), exhibiting a fragmented distribution pattern. The future suitable area of <i>S. medusa</i> is projected to decrease significantly by 42.5% to 96.7%, accompanied by a southward shift in its centroid and an upward shift in altitude. The study found that the highest temperature in the warmest month is the most important environmental factor affecting the distribution of <i>S. medusa</i>. This species is highly sensitive to climate change and requires urgent protection measures. Priority should focus on strengthening habitat protection in the southeastern Qinghai-Xizang Plateau, where some stable habitats remain outside protected areas. Expanding population monitoring, promoting ex-situ conservation, enhancing public education, and encouraging community involvement are essential. Additionally, as a medicinal plant, alternative strategies are needed to curb overharvesting of wild resources.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962994","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Factors influencing spatiotemporal variability of NO2 concentration in urban area: a GIS and remote sensing–based approach","authors":"Al Jubaer,&nbsp;Rakib Hossain,&nbsp;Afzal Ahmed,&nbsp;Md.Shakhaoat Hossain","doi":"10.1007/s10661-024-13531-z","DOIUrl":"10.1007/s10661-024-13531-z","url":null,"abstract":"<div><p>The growing global attention on urban air quality underscores the need to understand the spatiotemporal dynamics of nitrogen dioxide (NO₂) and its environmental and anthropogenic factors, particularly in cities like Dhaka (Gazipur), Bangladesh, which suffers from some of the world's worst air quality. This study analysed NO₂ concentrations in Gazipur from 2019 to 2022 using Sentinel-5P TROPOMI data on the Google Earth Engine (GEE) platform. Correlations and regression analysis were done between NO₂ levels and various environmental factors, including land surface temperature (LST), normalized difference vegetation index (NDVI), land use and land cover (LULC), population density, road density, settlement density, and industry density. The results reveal significant seasonal variations. The highest annual mean NO₂ concentration (3.1 × 10^–⁴ mol/ m²)was recorded for winter 2021, and the lowest (1.1 × 10^–4 mol/m²) was for monsoon 2022. The study demonstrates a significant positive correlation between NO₂ concentrations and LST (0.47), road density (0.55), settlement density (0.44), and industrial density (0.35) and a negative correlation with NDVI (− 0.4). Regression analysis revealed that NO₂ concentrations were positively associated with land surface temperature (LST; β = 0.02, <i>R</i>² = 0.22), road density (β = 0.002, <i>R</i>² = 0.30), settlement density (β = 0.002, <i>R</i>² = 0.19), and industrial density (β = 0.007, <i>R</i>² = 0.12), while a negative association was observed with NDVI (β = − 0.28, <i>R</i>² = 0.16). This research offers critical insights for policymakers and urban planners, advocating for enhanced green infrastructure, stringent emission controls, and sustainable urban development strategies to mitigate air pollution in Gazipur. Our methodological approach and findings contribute to the broader discourse on urban air quality management in developing countries<b>.</b></p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142962996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to: Utilising date palm fibres as a permeable reactive barrier to remove methylene blue dye from groundwater: a batch and continuous adsorption study","authors":"Qahtan Adnan Ali,&nbsp;Muna Faeq Ali,&nbsp;Sabah J. Mohammed,&nbsp;Mohanad J. M-Ridha","doi":"10.1007/s10661-024-13603-0","DOIUrl":"10.1007/s10661-024-13603-0","url":null,"abstract":"","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142963061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Suboxic waters of the eastern Arabian Sea shelter secondary chlorophyll maximum dominated by heterotrophic dinoflagellate Pronoctiluca spp. (order Noctilucales)","authors":"Chazhikulam Rajan Vishal,&nbsp;Manguesh Uttam Gauns,&nbsp;Anil Kiran Pratihary","doi":"10.1007/s10661-024-13589-9","DOIUrl":"10.1007/s10661-024-13589-9","url":null,"abstract":"<div><p>In the present study, we investigated the dinoflagellate assemblages in the upper water column (&lt; 150-m depth), focusing on the suboxic waters of the eastern Arabian Sea (EAS) along 68°E from 8°N to 21°N during the southwest monsoon 2020 (SWM–2020). Dinoflagellate abundance was higher in the upper water column (0–80-m depth, mean ± SD = 411 ± 903 cells L⁻¹) compared to deeper waters (80–150-m depth, mean ± SD = 128 ± 216 cells L⁻¹). Among 11 identified taxonomic dinoflagellate orders, Peridinales were predominant in the upper waters column (71%, mean ± SD = 285 ± 858 cells L⁻¹). Noctilucales, particularly <i>Pronoctiluca</i> spp., dominated the deeper water column (78%, mean ± SD = 99 ± 223 cells L⁻¹), especially at the southern stations (8–14°N, mean ± SD = 158 ± 270 cells L⁻¹). During SWM–2020, a strong vertical gradient in <i>Pronoctiluca</i> with increased abundance in suboxic, colder waters (&lt; 0.05 mL L⁻¹, &lt; 20 °C) coincided with the secondary chlorophyll maximum layers (12°N, SCMLs ~ 145 m, maximum 832 cells L⁻¹). To compare this observation, samples were taken at 13°N during the winter monsoon (WM-2023), when the SCML peak was prominent (0.3 µg L⁻¹) in suboxic waters. The results revealed an increased abundance of <i>Pronoctiluca</i> close to the SCML depth (~ 117 m) during WM. The canonical correspondence analysis revealed a positive correlation between SCML and <i>Pronoctiluca</i>, suggesting that <i>Pronoctiluca</i> relies on prey, i.e. low-light-adapted smaller phytoplankton. The higher abundance of <i>Pronoctiluca</i> compared to other oceanic regimes highlights the importance of assessing their crucial role in nutrient recycling and remineralisation within the suboxic environments of the EAS.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Amalur EIS: a system for calculating the environmental impacts of industrial sites from E-PRTR records","authors":"Iñaki Sasia,&nbsp;Gorka Bueno,&nbsp;Iker Etxano","doi":"10.1007/s10661-024-13565-3","DOIUrl":"10.1007/s10661-024-13565-3","url":null,"abstract":"<div><p>This article presents Amalur EIS (https://www.amalur-eis.eus/), an Environmental Information System that estimates environmental impacts using data sourced from the European Pollutant Release and Transfer Register database (E-PRTR). The system uses data on the releases into land, air and water of 31,556 European industrial facilities for the period 2007–2021. Amalur EIS calculates environmental impacts of industrial releases using 31 life cycle impact assessment methods (LCIA) and covering 78 of the 91 pollutants regulated by the PRTR Protocol. The system has been constructed using a two-layer software infrastructure: (i) a data layer supported by a relational database built in Postgres and (ii) a presentation layer built in Tableau, so it provides user-friendly access to the information. For an illustrative analysis of the tool, the EF 3.0 LCIA method recommended by the European Commission was used, including normalisation and weighting steps for a better comparison. The analysis concludes that the <i>climate change</i> impact category contributes the most (68.6%) to the total impacts, while the largest contributor from an economic activity perspective is the energy sector (59.5%). Geographically, both elements coincide in the German regions of Düsseldorf, Köln and Brandenburg, resulting in the concentration of the largest impacts at the European regional level. In fact, Germany is the country with the highest impact (20.3% of total). Beyond this analysis, Amalur EIS is poised to be a valuable tool for tracking the transition towards sustainability, particularly in Europe.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10661-024-13565-3.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effects of land management technology adoptions on land use land cover dynamics using GIS and remote sensing: the case of Goyrie watershed, southern Ethiopia","authors":"Dessalegne Chanie Haile,&nbsp;Yechale Kebede Bizuneh,&nbsp;Mulugeta Debele Bedhane,&nbsp;Abren Gelaw Mekonnen","doi":"10.1007/s10661-024-13518-w","DOIUrl":"10.1007/s10661-024-13518-w","url":null,"abstract":"<div><p>Understanding land use/land cover (LULC) changes is crucial for informing policymakers and planners on the dynamics affecting environmental and resource management. Most past studies highlighted the significance of LULC changes and their driving forces in various locations. However, comprehensive analyses that combine the impact of land management technologies (LMTs) on LULC changes using GIS and remote sensing tools have not been widely addressed. Thus, the study analyzes the effects of LMT adoptions on LULC dynamics and the Normalized Difference Vegetation Index (NDVI) in the Goyrie watershed from 1993 to 2022. It also examines household perceptions of the cause of LULC changes. Methodologically, Landsat 5 TM (1993), Landsat 5 ETM + (2008), and Landsat 8 OLI/TIRS (2022) images were employed to analyze LULC changes and NDVI. Binary logistic regression models were used to identify households’ perceptions of the causes of LULC changes. The findings revealed that the Goyrie watershed has experienced significant LULC changes since 1993. During the entire study period, the shares of grassland, shrub land, cultivated land, and settlement areas increased by 89.4%, 8.5%, 53.6%, and 1613.4% from their original sizes, respectively. Conversely, the coverage of bare land and forest land declined by 99.5% and 99.7%, with annual rates of decline of 3.29% and 3.3%, respectively. Throughout the study period, the increasing trends in grassland and shrub land, along with the decline in bare land, were attributed to LMT practices. The NDVI values of moderate and dense vegetation density decreased by 81.8% and 92.2%, respectively, from 1993 to 2022 due to the expansion of settlement areas and cultivated lands. Population pressure, expansion of settlements and agriculture, fuel extraction, LMTs, and policy issues significantly influenced the LULC changes. The study concludes that more sustainable and integrated LMT practices should be essential to managing the related risks of LULC changes.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing cropping system dynamics over three decades: remote sensing and GIS insights in Murshidabad-Jiaganj Block","authors":"Lal Mohammad,&nbsp;Jatisankar Bandyopadhyay,&nbsp;Ismail Mondal,&nbsp;Hamad Ahmed Altuwaijri,&nbsp;Sarbhanu Khatun,&nbsp;SK Ariful Hossain,&nbsp;Mukhiddin Juliev","doi":"10.1007/s10661-024-13545-7","DOIUrl":"10.1007/s10661-024-13545-7","url":null,"abstract":"<p>Agriculture is a significant contributor to the country’s economic development. We used multiple Landsat images from 1990 to 2021 in the Murshidabad-Jiaganj Block to assess changes in the agricultural system and their underlying causes. The Rabi season saw a 10.99% growth in agrarian regions from 1990 to 2000 and an 8.86% increase in 2010, yet it declined by 28.12% in 2021. During the summer, the cultivated lands diminished by 26.63%, 19.43%, and 19.64%, while in the Kharif season, they declined by 21.78%, 15.68%, and 11.99% from 1990 in the years 2000, 2010, and 2021, respectively. The agricultural area had 36.82%, 34.16%, and 19.01% increases between 1990 and 2021, respectively. Regarding direction, farmland acreage decreased in all zones except the SSE, which had a 0.95% increase. Mono-, double-, and triple-cropping systems have decreased in area, while multi-cropping systems have experienced increases of 43.51%, 4.50%, and 18.49% in 1990–2021, respectively. The multi-cropping system has a good correlation with all agroclimatic factors. The reduction of irrigated lands post-2009 significantly affected the agriculture system. The fall in agricultural employment in recent decades is attributable to migration seeking higher-paying occupations. The advancement of accurate remote sensing–based modeling is crucial for mitigating food security risks, particularly those posed by climate change, and informing policy decisions.</p>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Monitoring the trends of carbon monoxide and tropospheric formaldehyde in Edo State using Sentinel-5P and Google Earth Engine from 2018 to 2023","authors":"Alex Enuneku,&nbsp;Uwadea Gracious Aigbogho,&nbsp;Chika Floyd Amaechi,&nbsp;Oziofu Ayamezimi Ehinlaiye","doi":"10.1007/s10661-024-13547-5","DOIUrl":"10.1007/s10661-024-13547-5","url":null,"abstract":"<div><p>This research was carried out to assess the concentrations of carbon monoxide (CO) and formaldehyde (HCHO) in Edo State, Southern Nigeria, using remote sensing data. A secondary data collection method was used for the assessment, and the levels of CO and HCHO were extracted annually from Google Earth Engine using information from Sentinel-5-P satellite data (COPERNISCUS/S5P/NRTI/L3_) and processed using ArcMap, Google Earth Engine, and Microsoft Excel to determine the levels of CO and HCHO in the study area from 2018 to 2023. The geometry of the study location is highlighted, saved and run, and a raster imagery file of the study area is generated after the task has been completed with a ‘projection and extent’ in the Geographic Tagged Image File Format (.tiff) and downloaded from the Google Drive and saved into folders, imported into the ArcMap for data processing and Excel worksheet for analysis. The raster data were collected annually for each pollutant with the ‘filterDate = year-01–01; year-12–31’. Results showed that the annual mean concentrations of CO ranged from ‘4.67 × 10⁻² mol/m²’ to ‘5.34 × 10⁻² mol/m²’. The maximum concentration was found in the year 2018 and the minimum was found in the year 2023, a relatively high concentration of CO may lead to the formation of carboxyhaemoglobin which decreases the capacity of the blood to transport oxygen causing lung cancer, heart problems, respiratory conditions and damage to other organs. While the annual mean concentrations of HCHO ranged from ‘1.89 × 10⁻⁴ mol/m²’ to ‘2.18 × 10⁻⁴ mol/m²’, the maximum concentration was found in the year 2021 and the minimum was found in the year 2019, increasing concentration of HCHO may be due to biomass burning and the combustion of methane (CH₄ gas), and can cause nasopharyngeal cancer in humans. Based on the result of this study, constant monitoring of the air quality and atmospheric pollutants to ensure early detection of a decrease or increase in the concentration of atmospheric pollutants, implementation of air pollution control policies, spatial data collection, air quality modelling, hotspot identification and source distribution using the geographic information system (GIS), promotion of cleaner technologies, including the use of low-emission vehicles and renewable energy sources, public awareness and education on the impact of atmospheric pollutants and the human contributions to the increasing production of atmospheric pollutants are highly recommended.\u0000</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing the efficiency of pixel-based and object-based image classification using deep learning in an agricultural Mediterranean plain","authors":"Murat Bayazit,&nbsp;Cenk Dönmez,&nbsp;Süha Berberoglu","doi":"10.1007/s10661-024-13431-2","DOIUrl":"10.1007/s10661-024-13431-2","url":null,"abstract":"<div><p>Recent advancements in satellite technology have greatly expanded data acquisition capabilities, making satellite imagery more accessible. Despite these strides, unlocking the full potential of satellite images necessitates efficient interpretation. Image classification, a widely adopted for extracting valuable information, has seen a surge in the application of deep learning methodologies due to their effectiveness. However, the success of deep learning is contingent upon the quality of the training data. In our study, we compared the efficiency of pixel-based and object-based classifications in Sentinel-2 satellite imagery using the Deeplabv3 deep learning method. The image sharpness was enhanced through a high-pass filter, aiding in data visualization and preparation. Deeplabv3 underwent training, leading to the development of classifiers following the extraction of training samples from the enhanced image. The majority zonal statistic method was implemented to assign class values to objects in the workflow. The accuracy of pixel-based and object-based classification was 83.1% and 83.5%, respectively, with corresponding kappa values of 0.786 and 0.791. These accuracies highlighted the efficient performance of the object-based method when integrated with a deep learning classifier. These results can serve as a valuable reference for future studies, aiding in the improvement of accuracy while potentially saving time and effort. By evaluating this nuanced impact pixel and object-based classification as well as on class-specific accuracy, this research contributes to the ongoing refinement of satellite image interpretation techniques in environmental applications.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of active biomonitoring technique for the assessment of air pollution by potentially toxic elements in urban areas in the Kemerovo Region, Russia","authors":"Inga Zinicovscaia,&nbsp;Nikita Yushin,&nbsp;Alexandra Peshkova,&nbsp;Maxim Noskov,&nbsp;Vladislav Koshelev,&nbsp;Denis Nosov,&nbsp;Bogdana Maksimova,&nbsp;Anna Dyakova,&nbsp;Polina Apanasevich,&nbsp;Ekaterina Dmitrieva","doi":"10.1007/s10661-024-13439-8","DOIUrl":"10.1007/s10661-024-13439-8","url":null,"abstract":"<div><p>In Kemerovo Region (Kuzbass, Southwest Siberia), there is the largest coal basin in Russia and one of the largest in the world. Active moss biomonitoring was applied to assess the impact of potentially toxic elements on air pollution in five urban areas of the region. In each of the chosen urban regions, the moss bags were exposed in November and December of 2022 at locations with varying degrees of anthropogenic pressure. Using a direct mercury analyzer in conjunction with coupled plasma-optical emission spectrometry, the content of sixteen major and trace elements (Al, Ba, Co, Cd, Cr, Cu, Fe, Mn, Ni, P, Pb, Sr, S, V, Zn, and Hg) was ascertained. Compared to unexposed the exposed moss bags showed a higher content of potentially toxic elements. To draw attention to the relationships between the elements and connect them to potential emission sources, correlation, and principal component analyses were used. A strong positive correlation was obtained for elements emitted by coal mining and burning, the metallurgical industry, and vehicles. To evaluate the degree of environmental pollution and the element enrichment in the moss, the relative accumulation factor and contamination factor were computed. The mean values of the contamination factor ranged from 0.83 to 4.8, indicating the exposure sites show no contamination to moderate contamination status.</p></div>","PeriodicalId":544,"journal":{"name":"Environmental Monitoring and Assessment","volume":"197 2","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-01-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142941198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}