Clean-soil Air Water最新文献

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

A Deep Learning Approach to Predict Surface Soil Wetness and Its Uncertainty Analysis Over the Tel River Basin, India

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

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?

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

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

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

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

引用次数: 0

Health-Risk Assessment of Groundwater Arsenic Levels in Bhagalpur, India, and Development of a Cost-Effective Paper-Based Arsenic Testing-Kit 印度巴加尔布尔地下水砷含量的健康风险评估以及成本效益型纸质砷检测工具包的开发

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.202300291

Sourav Maity, Puja Dokania, Manav Goenka, Pritam Bajirao Patil, Angana Sarkar

{"title":"Health-Risk Assessment of Groundwater Arsenic Levels in Bhagalpur, India, and Development of a Cost-Effective Paper-Based Arsenic Testing-Kit","authors":"Sourav Maity, Puja Dokania, Manav Goenka, Pritam Bajirao Patil, Angana Sarkar","doi":"10.1002/clen.202300291","DOIUrl":"https://doi.org/10.1002/clen.202300291","url":null,"abstract":"<div>\u0000 \u0000 <p>Arsenic is considered one of the most hazardous trace metals in groundwater researched to date because of the hazardous impacts like cancer, skin irritation, and other skin-related diseases. The present study involved collecting 60 water samples from Bhagalpur district, Bihar, India, to estimate the arsenic concentration. The human health risk assessment of the samples concerning children and adults was also performed, and the maximum concentration of arsenic was found to be relatively high in some sample sites. Prolonged exposure to arsenic could be fatal to the local population. The current study also focuses on developing a low-cost paper-based arsenic detection kit. The paper-based test kit was tested for parameters like color development for different forms and concentrations of arsenic, storage conditions for the test strips, the effect of different interfering agents on color development, and optimization of the AgNO<sub>3</sub> solution. The cost analysis was carried out, and it was found that the kit would cost 0.046 USD per sample, which is 70–100 times lower than the cost of current methods.</p>\u0000 </div>","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143115891","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

Monitoring Surface Energy Flux Dynamics of Irrigated Maize Using a Large Aperture Scintillometer in a Semi-Arid Region

IF 1.5 4区环境科学与生态学

Clean-soil Air Water Pub Date : 2024-12-11 DOI: 10.1002/clen.202400057

Pragya Singh, Vinay Kumar Sehgal, Rajkumar Dhakar, Alka Rani, Deb Kumar Das, Joydeep Mukherjee, Natoo Raghunathbhai Patel, Prakash Kumar Jha, Ram Narayan Singh

{"title":"Monitoring Surface Energy Flux Dynamics of Irrigated Maize Using a Large Aperture Scintillometer in a Semi-Arid Region","authors":"Pragya Singh, Vinay Kumar Sehgal, Rajkumar Dhakar, Alka Rani, Deb Kumar Das, Joydeep Mukherjee, Natoo Raghunathbhai Patel, Prakash Kumar Jha, Ram Narayan Singh","doi":"10.1002/clen.202400057","DOIUrl":"https://doi.org/10.1002/clen.202400057","url":null,"abstract":"<div>\u0000 \u0000 <p>Water, a crucial input in agricultural production, is distributed based on geographical and topographical patterns. However, anthropogenic climate change has intensified water scarcity in semi-arid regions. This research aims to precisely estimate crop evapotranspiration (ET) and examine the diurnal and seasonal patterns of surface energy fluxes in maize (<i>Zea mays</i>) crops cultivated in a semi-arid region. The precision of our methodology is underscored by the use of a large-aperture scintillometer (LAS), which measured surface energy fluxes at 5-min intervals over two crop-growing seasons. The results, a testament to the accuracy of the LAS, indicated that during the rainy (Kharif) season of 2015–2016, the seasonal sensible heat flux (<i>H</i>) and latent heat flux (LE) values were 185.91 and 242.14 mm, respectively. In the rainy (Kharif) season of 2017–2018, these values were 151.57 mm for <i>H</i> and 373.63 mm for LE. LE values ranged from 0.40 to 6.83 MJ m<sup>−2</sup> day<sup>−1</sup> throughout the growing season. The findings, which highlight the LAS's ability to accurately estimate surface energy fluxes, provide a deeper understanding of their interactions with microclimatic factors, such as weather, soil, and crop management. These insights, with their significant implications for ecophysiological studies and improving agricultural practices in semi-arid regions, underscore the importance of our research.</p>\u0000 </div>","PeriodicalId":10306,"journal":{"name":"Clean-soil Air Water","volume":"53 1","pages":""},"PeriodicalIF":1.5,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143114207","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