Clean-soil Air Water最新文献_第7页

Assessment of temporal and spatial variations of water quality parameters in the Zarafshan River basin 扎拉夫山河流域水质参数的时空变化评估

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-07-29 DOI: 10.1002/clen.202300454

Shobegim Shoergashova, Tie Liu, Nigora Ibrokhimova, Liliya Latisheva, Bakhtiyor Karimov

{"title":"Assessment of temporal and spatial variations of water quality parameters in the Zarafshan River basin","authors":"Shobegim Shoergashova, Tie Liu, Nigora Ibrokhimova, Liliya Latisheva, Bakhtiyor Karimov","doi":"10.1002/clen.202300454","DOIUrl":"10.1002/clen.202300454","url":null,"abstract":"River ecosystems in Central Asia face significant stress from environmental changes and pollution. This study assesses temporal and spatial variations in water quality parameters within the Zarafshan River Basin using retrospective data and field measurements. Water quality indicators, including electrical conductivity (EC), total suspended solids (TSS), ammonium nitrogen (N-NH4), nitrite nitrogen (N-NO2), nitrate nitrogen (N-NO3), temperature (T), chemical oxygen demand (COD), dissolved oxygen (DO), and discharge, were analyzed using the Pearson's correlation coefficient and ANOVA, with the Mann–Kendall (MK) test detecting trends over time. Obtained results indicate significant seasonal effects with elevated TSS during summer, increasing sediment load and changing aquatic habitats. The strong inverse correlation (–0.89) between DO and N-NH4 signifies ecological challenges particularly in low DO concentrations during summer (3.25 mg L–1). Long-term analysis identifies Navoiazot chemical factory as a major pollution hotspot. Spatial analyses based on extended sampling have revealed the Siab and Dargom canals and Samarkand City as major pollution sources of elevated N-NO2 and COD, respectively. Trends at various gauging stations (MK-test) show increasing EC (τ = 0.72) and N-NH4 (τ = 0.46) levels, with decreasing TSS, N-NO3, T, and COD levels over time. Recommendations include targeted measures to reduce pollution at the Navoiazot factory and downstream, introducing sustainable agriculture practices, increasing public awareness for environmental conservation, and improving urban wastewater treatment to meet water quality requirements for different users.","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"52 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141867813","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}

引用次数: 0

Performance evaluation of different machine learning algorithms for prediction of nitrate in groundwater in Thiruvannamalai District 用于预测 Thiruvannamalai 地区地下水中硝酸盐含量的不同机器学习算法的性能评估

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-07-17 DOI: 10.1002/clen.202400060

Christina Jacob, Uma Shankar Masilamani

{"title":"Performance evaluation of different machine learning algorithms for prediction of nitrate in groundwater in Thiruvannamalai District","authors":"Christina Jacob, Uma Shankar Masilamani","doi":"10.1002/clen.202400060","DOIUrl":"10.1002/clen.202400060","url":null,"abstract":"The prevalence of nitrates (NO3−) in groundwater due to the extensive application of fertilizers and anthropogenic sources pollutes the groundwater. Machine learning (ML) techniques are now being increasingly deployed to achieve high precision in predicting water quality. This study assesses the efficacy of nine distinct ML algorithms, namely, linear regression, polynomial regression, decision tree, random forest (RF), support vector machine, multilayer perceptron regressor, eXtreme gradient boosting (XGB), light gradient boosting (LGB), and K-nearest neighbors to predict nitrate concentration in the groundwater in Thiruvannamalai District, Tamil Nadu. Overall, 360 water samples for 1 year and 14 groundwater variables were determined to predict nitrate. Performance evaluation metrics such as root mean square error (RMSE), moving average error (MAE), and correlation coefficient (R2) were evaluated for pre-monsoon, monsoon, and post-monsoon seasons. For all three seasons, RF predicted the nitrate concentration with low values of RMSE, MAE, and higher values of R2. The results show values for RF with: RSME: 0.49, MAE: 1.30, and R2: 0.94, which has a higher prediction tailed by LGB and XGB and is true for all the seasons. The results from the study will aid the policymakers in planning the strategy for remediation.","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141742135","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}

引用次数: 0

Issue Information: Clean Soil Air Water. 7/2024 问题信息：清洁土壤、空气和水。7/2024

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-07-14 DOI: 10.1002/clen.202470071

引用次数: 0

Nitrogen removal and microbial community variation in a shallow constructed wetland with Fe–C porous filter material as substrate 以Fe-C多孔过滤材料为基质的浅层人工湿地的脱氮效果和微生物群落变化

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-07-04 DOI: 10.1002/clen.202300246

Shuyi Chu, Zhuangzhuang Cao, Zhongping Su, Jibo Xiao, Jun Li

{"title":"Nitrogen removal and microbial community variation in a shallow constructed wetland with Fe–C porous filter material as substrate","authors":"Shuyi Chu, Zhuangzhuang Cao, Zhongping Su, Jibo Xiao, Jun Li","doi":"10.1002/clen.202300246","DOIUrl":"10.1002/clen.202300246","url":null,"abstract":"This study aims to examine the nitrogen removal characteristics and microbial community variation at low hydraulic retention time (HRT) in a shallow constructed wetland (SCW) using iron–carbon (Fe–C) porous filter material (PFM) as substrate. Effects of influent nitrogen forms and chemical oxygen demand (COD)/N ratio on nitrogen removal performance at HRT of 1 day were investigated. Results showed that total nitrogen (TN) removal declined with the decrease of influent NH4+-N-to-NO3−-N ratio. When the influent NH4+-N/NO3−-N ratio was 0.1, TN removal decreased by 15.4% compared with that at ratio of 2.0. The increase of influent COD/N ratio enhanced NO3−-N reduction, and TN removal reached 74.5% at influent COD/N ratio 7.0. The microbial community was analyzed for the biofilm samples on Fe–C PFM at front (WF), middle (WM), and back (WB) of SCW. Proteobacteria, Bacteroidetes, Chloroflexi, and Firmicutes were dominant bacteria phyla. The relative abundance of genera involved in the nitrification and denitrification decreased with the influent flow. The iron autotrophic denitrifying and macromolecular organics degrading bacteria were abundant in the middle and back of SCW. Microbial nitrification and denitrification, plant uptake, and plant synergism contributed to 86.3%, 7.41%, and 19.9% of N removal, respectively. These results demonstrated that the SCW with Fe–C PFM as substrate was efficient in nitrogen removal at low HRT.","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"52 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551643","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}

引用次数: 0

Former landfills as environmental liabilities in central Chile: Evidence from a pilot study in Lepanto 智利中部作为环境负债的旧垃圾填埋场：来自莱潘托试点研究的证据

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-07-01 DOI: 10.1002/clen.202400085

Jennifer Saa, María José Valdivia, Joaquín Ruiz, Carlos A. Manzano

{"title":"Former landfills as environmental liabilities in central Chile: Evidence from a pilot study in Lepanto","authors":"Jennifer Saa, María José Valdivia, Joaquín Ruiz, Carlos A. Manzano","doi":"10.1002/clen.202400085","DOIUrl":"10.1002/clen.202400085","url":null,"abstract":"The former Lepanto landfill in the Santiago Metropolitan Region in Central Chile ceased operations in 2002 after 24 years. Currently, a biogas recovery center operates on this site. The objective of this pilot study was to evaluate the atmospheric concentrations of two polycyclic aromatic hydrocarbons (PAHs, i.e., phenanthrene and fluoranthene) at three points downwind from the former Lepanto landfill using passive air samplers and polyurethane foam disks to provide 3-month average concentrations of contaminants during winter months. The results showed concentrations ranging between 2 and 65 ng m−3 and decreasing with distance from the former landfill. Given that the distance to other potential sources was similar in all sampling sites, the evidence found suggested that the former Lepanto landfill may contribute to the local PAH inventory. Thus, we suggest that former landfills are potential candidates for long-term monitoring as they can represent a less-known environmental liability for residents. Additionally, we provide insights on how the Chilean environmental protection system can establish reclamation steps for their management after closure.","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"52 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141506098","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}

引用次数: 0

Cd(II) and Zn(II) adsorption on lignite-derived humic substances and cattle manure biochar 褐煤腐殖质和牛粪生物炭对镉(II)和锌(II)的吸附作用

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-07-01 DOI: 10.1002/clen.202400226

Yihan Zhao, M. Anne Naeth

{"title":"Cd(II) and Zn(II) adsorption on lignite-derived humic substances and cattle manure biochar","authors":"Yihan Zhao, M. Anne Naeth","doi":"10.1002/clen.202400226","DOIUrl":"10.1002/clen.202400226","url":null,"abstract":"Excessive industrial release of trace elements may pose a great risk to the environment. Conventional remediation treatments have considerable limitations, making less expensive new technologies an important research area. Batch experiments were conducted at room temperature to investigate the use of two lignite-derived humic products (nano-humus and humic powder) and a cattle manure biochar as adsorbents in the removal of cadmium and zinc from laboratory synthesized water. Nano-humus was most effective in adsorbing Cd(II), wherein 89% adsorption was rapidly achieved in 15 min. The adsorbed amount of Cd(II) and Zn(II) both increased with initial metal ion concentrations from 25 to 125 mg L−1. Despite being produced from the same sources, nano-humus and humic powder showed different properties and adsorption behaviors. The adsorption mechanism of nano-humus followed the Freundlich isotherm model and pseudo-second-order kinetic model, indicating multilayer chemisorption. Humic powder followed the pseudo-second-order kinetic model, although it had a low isotherm model fit, implying chemisorption-dominated adsorption. Cattle manure biochar followed the Freundlich isotherm and pseudo-first-order kinetics model, suggesting diffusion-dominated multilayer adsorption. Of the three adsorbents tested, nano-humus had greatest potential as an effective and inexpensive material for metal remediation.","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"52 8","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/clen.202400226","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141525469","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}

引用次数: 0

A hybrid CNN–RNN model for rainfall–runoff modeling in the Potteruvagu watershed of India 印度Potteruvagu流域降雨径流模拟的CNN-RNN混合模型

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-06-17 DOI: 10.1002/clen.202300341

Padala Raja Shekar, Aneesh Mathew, Kul Vaibhav Sharma

{"title":"A hybrid CNN–RNN model for rainfall–runoff modeling in the Potteruvagu watershed of India","authors":"Padala Raja Shekar, Aneesh Mathew, Kul Vaibhav Sharma","doi":"10.1002/clen.202300341","DOIUrl":"https://doi.org/10.1002/clen.202300341","url":null,"abstract":"Accurate rainfall-runoff analysis is essential for water resource management, with artificial intelligence (AI) increasingly used in this and other hydrological areas. The need for precise modelling has driven substantial advancements in recent decades. This study employed six AI models. These were the support vector regression model (SVR), the multilinear regression model (MLR), the extreme gradient boosting model (XGBoost), the long-short-term memory (LSTM) model, the convolutional neural network (CNN) model, and the convolutional recurrent neural network (CNN-RNN) hybrid model. It covered 1998–2006, with 1998–2004 for calibration/training and 2005–2006 for validation/testing. Five metrics were used to measure model performance: coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), mean absolute error (MAE), root-mean square error (RMSE), and RMSE-observations standard deviation ratio (RSR). The hybrid CNN-RNN model performed best in both training and testing periods (training: R2 is 0.92, NSE is 0.91, MAE is 10.37 m3s−1, RMSE is 13.13 m3s−1, and RSR is 0.30; testing: R2 is 0.95, NSE is 0.94, MAE is 12.18 m3s−1, RMSE is 15.86 m3s−1, and RSR is 0.25). These results suggest the hybrid CNN-RNN model is highly effective for rainfall-runoff analysis in the Potteruvagu watershed.","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143116118","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}

引用次数: 0

Effect of effective microorganisms on water purification and microbial community in a pond 有效微生物对池塘水质净化和微生物群落的影响

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-06-17 DOI: 10.1002/clen.202300353

Miho Mori, Shouhei Miki, Masaki Inayoshi, Masaki Yamamoto, Ryota Ozaki, Yoshikazu Sakagami, Toru Jojima

{"title":"Effect of effective microorganisms on water purification and microbial community in a pond","authors":"Miho Mori, Shouhei Miki, Masaki Inayoshi, Masaki Yamamoto, Ryota Ozaki, Yoshikazu Sakagami, Toru Jojima","doi":"10.1002/clen.202300353","DOIUrl":"https://doi.org/10.1002/clen.202300353","url":null,"abstract":"There are very few reports about water purification and its influence on indigenous microorganisms by effective microorganisms (EM) in the fields. This study investigated the effect of EM on water purification and microbial community via in vitro and field experiments. In in vitro water purification experiments, we examined values for chemical oxygen demand (COD), total phosphorus, and total nitrogen (TN). In systems in which the active EM solutions were at a concentration of less than 1:2000 of the total water sample, the values after 28 days were equal to or lower than those of the control. In systems using 1:200 and 1:100 active EM solutions, COD and TN values were significantly lower in artificial wastewater compared to control. Conversely, they were significantly higher than the control in the pond water. When the active EM solutions were added to the pond water, total microbial plate counts were higher than the control after 28 days for all concentrations of the EM active solutions. Excessive use of EM caused deterioration of water quality and increase of microbial counts. The results of field experiments for 4 years suggested that EM treatment did not purify the pond water, establish introduced EM, or affect indigenous microorganisms.","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"52 7","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141624578","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}

引用次数: 0

Optimizing drain depth and spacing to minimize nitrogen loss in southwest Punjab using DRAINMOD-NII 利用 DRAINMOD-NII 优化排水深度和间距，尽量减少旁遮普西南部的氮流失

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-06-14 DOI: 10.1002/clen.202300271

Mehraj U. Din Dar, J. P. Singh

{"title":"Optimizing drain depth and spacing to minimize nitrogen loss in southwest Punjab using DRAINMOD-NII","authors":"Mehraj U. Din Dar, J. P. Singh","doi":"10.1002/clen.202300271","DOIUrl":"10.1002/clen.202300271","url":null,"abstract":"The DRAINMOD-NII model was used to evaluate the water table behavior and nitrate movement using field data from the Thehri experimental field in Muktsar district, southwest Punjab, India. To optimize the drainage design parameters for efficient nitrogen management, a scenario analysis of water table depth, drain outflows, and nitrogen load was carried out for different drain depths and spacings. The rice–wheat cropping system was followed for 2 years, from 2018 to 2020. The conventional subsurface drainage system was installed in the study area. The recorded data consisted of daily drain outflows, water table depths, daily nitrate concentrations in the drain outflows, and relative crop yields. The DRAINMOD-NII model was calibrated and validated for 2018–2019 and 2019–2020, respectively. The model's reliability was assessed by comparing the measured and predicted values of daily drain outflows, water table depth, daily nitrate loads, and relative crop yields during each season. A better agreement was found between the observed and simulated values. Root mean square error, Nash–Sutcliffe model efficiency, R2, and percentage bias values during the calibration period ranged from 1.76 to 7.5; 0.48 to 0.88; 0.70 to 1.00; and −6.04 to 5.02, respectively, for the recorded parameters. A similar statistical evaluation was also performed during the validation period. The scenario analysis of drain depth and spacing concluded that a depth of 1.3 m and spacing of 42 m could be optimal for better crop yield and lesser nitrogen losses. Hence DRAINMOD NII model is a tool for drainage systems design.","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141338548","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}

引用次数: 0

A hydrodynamic lake model coupled with a three-dimensional dynamic visualization method 结合三维动态可视化方法的水动力湖泊模型

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-06-14 DOI: 10.1002/clen.202300128

Zhenliang Liao, Yanfeng Chi, Xianyong Gu, Guozheng Zhi, Wenchong Tian, Jiaqiang Xie, Junwei Zhou

{"title":"A hydrodynamic lake model coupled with a three-dimensional dynamic visualization method","authors":"Zhenliang Liao, Yanfeng Chi, Xianyong Gu, Guozheng Zhi, Wenchong Tian, Jiaqiang Xie, Junwei Zhou","doi":"10.1002/clen.202300128","DOIUrl":"10.1002/clen.202300128","url":null,"abstract":"The hydrodynamic lake model is an important tool for lake management and decision-making. When model results are analyzed by traditional analysis methods, the multisource heterogeneous data are not expressed systematically and intuitively, which leads to the inability to extract useful information efficiently. In order to solve the above problems, a three-dimensional dynamic visualization analysis method of the hydrodynamic lake model (3DV-HLM) is proposed by coupling the hydrodynamic lake model with the three-dimensional dynamic visualization technology. Chaohu Lake was taken as an example to verify the feasibility of the method. Thirteen working conditions were set up and the simulated water flows changing with space and time were analyzed and compared by the 3DV-HLM method. The results show that the 3DV-HLM method proposed in this study is more systematic and effective in the expression of multisource heterogeneous information than the traditional analysis methods. It is easier to discover rules and obtain useful information from huge data set by the 3DV-HLM method. Besides, the intuitive display of the model results by the 3DV-HLM method is close to the real environment, which can enhance the understanding of the hydrodynamic characteristics of the lake by the water environment managers.","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-06-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141341755","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}

引用次数: 0