Clean-soil Air Water最新文献_第2页

Issue Information: Clean Soil Air Water. 3/2025 发行信息：清洁土壤空气水。3/2025

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2025-03-04 DOI: 10.1002/clen.70008

引用次数: 0

Issue Information: Clean Soil Air Water. 2/2025 问题信息：清洁土壤、空气和水2/2025

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2025-02-26 DOI: 10.1002/clen.70007

引用次数: 0

Enhancing Phosphorus Uptake and Mitigating Lead Stress in Maize Using the Rhizospheric Fungus Talaromyces purpureogenus PH7 利用根际真菌Talaromyces purpureogenus PH7促进玉米磷吸收和减轻铅胁迫

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2025-02-26 DOI: 10.1002/clen.70006

Iqbal Hussain, Muhammad Irshad, Anwar Hussain, Muhammad Qadir, Asif Mehmood, Muneeb urrehman, Naeem Khan

{"title":"Enhancing Phosphorus Uptake and Mitigating Lead Stress in Maize Using the Rhizospheric Fungus Talaromyces purpureogenus PH7","authors":"Iqbal Hussain, Muhammad Irshad, Anwar Hussain, Muhammad Qadir, Asif Mehmood, Muneeb urrehman, Naeem Khan","doi":"10.1002/clen.70006","DOIUrl":"https://doi.org/10.1002/clen.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>Modern agriculture faces significant environmental challenges due to toxic contaminants like lead (Pb), which infiltrate the food chain and pose severe risks to all living organisms, including humans. Bioremediation, utilizing microorganisms to mitigate contamination, offers a sustainable and cost-effective solution. In this study, a fungal strain, PH7, was isolated from the rhizosphere of <i>Parthenium hysterophorus</i> and identified as <i>Talaromyces purpureogenus</i> through genetic analysis of ITS 1 and ITS 4 rRNA regions. Preliminary screenings revealed its ability to solubilize phosphate and produce key plant growth regulators, including indole acetic acid (IAA) and salicylic acid (SA), alongside beneficial metabolites like phenolics, sugars, proteins, lipids, and flavonoids. The strain demonstrated substantial Pb tolerance, up to 800 µg/g, while enhancing the antioxidant defense system in liquid culture. Under 500 µg/g Pb stress, maize (<i>Zea mays</i> L.) exhibited a significant reduction in root length (29.2%), shoot length (30.4%), fresh weight (24.5%), and dry weight (53.5%). However, treatment with <i>T. purpureogenus</i> markedly improved these parameters, along with chlorophyll and carotenoid levels. The treated plants also showed enhanced antioxidant activity, including elevated levels of enzymatic and nonenzymatic antioxidants. These findings highlight the dual role of <i>T. purpureogenus</i> as a bioremediant and biofertilizer, capable of restoring Pb-contaminated soils and enhancing agricultural productivity and plant health.</p>\u0000 </div>","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 2","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143497111","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 Deep Learning Approach to Predict Surface Soil Wetness and Its Uncertainty Analysis Over the Tel River Basin, India 印度特尔河流域表层土壤湿度预测的深度学习方法及其不确定性分析

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2025-01-27 DOI: 10.1002/clen.70003

Sovan Sankalp, Bibhuti Bhusan Sahoo, Sushindra Kumar Gupta, Mani Bhushan, Rajib Kumar Majhi, Santosh DT

{"title":"A Deep Learning Approach to Predict Surface Soil Wetness and Its Uncertainty Analysis Over the Tel River Basin, India","authors":"Sovan Sankalp, Bibhuti Bhusan Sahoo, Sushindra Kumar Gupta, Mani Bhushan, Rajib Kumar Majhi, Santosh DT","doi":"10.1002/clen.70003","DOIUrl":"https://doi.org/10.1002/clen.70003","url":null,"abstract":"<div>\u0000 \u0000 <p>Surface soil moisture (SSM) refers to the capacity of the top layer of soil to hold moisture. It is an essential part of the budget for surface water. Soil moisture monitoring is crucial to reduce the effects of precipitation deficits and determine the best ways to manage natural ecosystems in the face of climate change. The current study collected daily SSW data from MERRA-2 for the Tel River Basin in Odisha, India, from 2001 to 2020 with a spatial resolution of 0.5° × 0.625°. To forecast SSW time series (SSWTS) one step ahead, this study examines the reliability of three deep learning (DL) models: gated recurrent unit (GRU), long short-term memory (LSTM), and simple recurrent neural network (simpleRNN). This study aims to address the following research questions: (1) How accurately can DL models predict SSWTS? (2) Which DL model—GRU, LSTM, or simpleRNN—is the most reliable for SSW forecasting? (3) How can the uncertainty in the predicted SSW be quantified and analyzed? Further, in an uncertainty investigation on SSW projected values, a Wilson score technique was employed to evaluate the uncertainty of the DL methods. GRU has outdone the other two models in forecasting monthly SSW with a 12-lookback timestep with a lower error for all the stations. The model appeared more accurate as it declined in gradient on larger sequencing samples. GRU's ability to remember significant prior knowledge, whereas discarding irrelevant data may assist in finding a novel, dependable solution for SSWTS forecasting.</p>\u0000 </div>","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143120082","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

Arsenic Removal From Acid Mine Drainage Using Acid-Tolerant Bacteria 利用耐酸细菌去除矿山酸性废水中的砷

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2025-01-27 DOI: 10.1002/clen.70000

Sohei Iwama, Chikara Takano, Kazunori Nakashima, Hideki Aoyagi, Satoru Kawasaki

引用次数: 0

Which Machine Learning Algorithm Is Best Suited for Estimating Reference Evapotranspiration in Humid Subtropical Climate? 哪种机器学习算法最适合估算湿润亚热带气候的参考蒸散量？

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2025-01-24 DOI: 10.1002/clen.202300441

Proloy Deb, Virender Kumar, Anton Urfels, Jonathan Lautze, Baldev Raj Kamboj, Jeet Ram Sharma, Sudhir Yadav

{"title":"Which Machine Learning Algorithm Is Best Suited for Estimating Reference Evapotranspiration in Humid Subtropical Climate?","authors":"Proloy Deb, Virender Kumar, Anton Urfels, Jonathan Lautze, Baldev Raj Kamboj, Jeet Ram Sharma, Sudhir Yadav","doi":"10.1002/clen.202300441","DOIUrl":"https://doi.org/10.1002/clen.202300441","url":null,"abstract":"<div>\u0000 \u0000 <p>Timely and reliable estimates of reference evapotranspiration (ET<sub>0</sub>) are imperative for robust water resources planning and management. Applying machine learning (ML) algorithms for estimating ET<sub>0</sub> has been evolving, and their applicability in different sectors is still a compelling field of research. In this study, four Gaussian process regression (GPR) algorithms—polynomial kernel (PK), polynomial universal function kernel (PUK), normalized poly kernel (NPK), and radial basis function (RBF)—were compared against widely used random forest (RF) and a simpler locally weighted linear regression (LWLR) algorithm at a humid subtropical region in India. The sensitivity analysis of the input variables was followed by application of the best combination of variables in algorithm testing and training for generating ET<sub>0</sub>. The results were then compared against the Penman–Monteith method at both daily and monthly time steps. The results indicated that ET<sub>0</sub> is least sensitive to wind speed at 2 m height. Additionally, at a daily time step, RF, followed by PUK, generated the best results during both training and testing phases. In contrast, at a monthly time step, using multiple model evaluation matrices, PUK followed by RF performed best. These results demonstrate the application of the ML algorithms is subjected to user-required time steps. Although this study focused on Northwest India, the findings are relevant to all humid subtropical regions across the world.</p>\u0000 </div>","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118866","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

Deep Learning-Based Hydrological Drought Prediction in the Wardha River Basin, India 基于深度学习的印度瓦尔达河流域水文干旱预测

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2025-01-24 DOI: 10.1002/clen.202300430

Mangala Janardhana, Ayilobeni Kikon

{"title":"Deep Learning-Based Hydrological Drought Prediction in the Wardha River Basin, India","authors":"Mangala Janardhana, Ayilobeni Kikon","doi":"10.1002/clen.202300430","DOIUrl":"https://doi.org/10.1002/clen.202300430","url":null,"abstract":"<div>\u0000 \u0000 <p>Drought is an abnormal condition characterized by dry weather which can continue for days, months, and years. Drought often has major effects on the ecosystems and agriculture of vulnerable regions leading to catastrophe on the local economies. Deep learning was employed in this study to forecast hydrological drought in the Wardha River basin in Maharashtra, Vidarbha region, India. Monthly streamflow data from 1971 to 2020 for the Wardha River serve as the basis for analysis. The study calculates the standardized streamflow index (SSI) at several timescales (3, 6, 9, 12, and 24 months). Deep learning models, specifically the long short-term memory (LSTM) model and the multilayer perceptron (MLP) model, are employed for drought prediction within the study region. The models are trained with data spanning from 1971 to 2005 and tested against data from 2006 to 2020. Predictions are made for lead time scales of 6 and 12 months by considering lagged SSI values. Drought event lead time scale forecasts will serve as an early warning strategy. The 6- and 12-month lead times of the SSI forecast could be used as a warning for anticipated drought conditions. The study assesses model efficiency by comparing the root mean square error (RMSE) and mean absolute error (MAE) between the LSTM and MLP models. The results indicate that the LSTM model performs better for higher time scales in predicting hydrological drought, whereas the MLP model demonstrates superior predictive capabilities for lower time scales of drought index.</p>\u0000 </div>","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118874","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

Developing a Citizen Science Approach to Monitor Stranded Marine Plastics on a Remote Small Island in Indonesia 发展一种公民科学方法来监测印度尼西亚偏远小岛上搁浅的海洋塑料

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2025-01-24 DOI: 10.1002/clen.70001

Radisti A. Praptiwi, Carya Maharja, Fauzan Cholifatullah, Dwi C. J. Subroto, Sainal Sainal, Peter I. Miller, Victoria V. Cheung, Tatang Mitra Setia, Nasruddin, Datu, Jito Sugardjito, Melanie C. Austen

{"title":"Developing a Citizen Science Approach to Monitor Stranded Marine Plastics on a Remote Small Island in Indonesia","authors":"Radisti A. Praptiwi, Carya Maharja, Fauzan Cholifatullah, Dwi C. J. Subroto, Sainal Sainal, Peter I. Miller, Victoria V. Cheung, Tatang Mitra Setia,  Nasruddin,  Datu, Jito Sugardjito, Melanie C. Austen","doi":"10.1002/clen.70001","DOIUrl":"https://doi.org/10.1002/clen.70001","url":null,"abstract":"<p>Marine plastics stranded on the coastlines of remote small islands threaten both the ecological integrity of local ecosystems and communities’ well-being. However, despite the growing quantities of stranded plastics in these locations, the remote nature of these sites renders monitoring and intervention efforts difficult to undertake. Within this context, we developed a citizen science approach to monitor stranded marine plastics in collaboration with villagers living on a remote small island in Indonesia. This study reports the co-development and application of an approach that can be used and maintained independently by remote coastal communities. In the monitoring stage, the participants quantified both the weight and composition of stranded marine debris on a beach located in their village for a 4-week period from late May to mid-June 2021. The results revealed that the weekly accumulation of stranded marine debris on the beach was 3.97 kg/m<sup>2</sup>, with 58% categorized as plastics. The stranded plastics sampled in this study were sorted and collected for recycling, estimated to provide a total economic value of 91,700 Indonesian Rupiahs (USD 5.84), or equivalent to 12.77% of the average monthly household income in the area. The citizen science activities indicated that the local villagers were capable of operating the designed monitoring system effectively, with the added benefits of supplementary earnings from recycling. An independently operated monitoring approach combined with collection efforts for recyclable items is important as remote islands have to manage increasing quantities of stranded marine debris despite the lack of an adequate local waste management system.</p>","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/clen.70001","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143118619","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

Issue Information: Clean Soil Air Water. 1/2025 发行信息：清洁土壤空气水1/2025

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2025-01-24 DOI: 10.1002/clen.70005

引用次数: 0

Issue Information: Clean Soil Air Water. 12/2024 问题信息：清洁土壤、空气和水。12/2024

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-12-17 DOI: 10.1002/clen.202470121

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