Pub Date : 2024-07-09DOI: 10.1088/2515-7620/ad5b3b
Devanil Choudhury, Debashis Nath, Wen Chen
The response of the Asian Summer Monsoon (ASM) circulation to the Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) forcing scenarios is examined using the CESM1 state-of-the-art global circulation model from 2021 to 2050. The projections show that monsoon precipitation will increase over East Asia, the North Pacific Ocean, the Indian Peninsula, and the Bay of Bengal under the RCP4.5 scenario. Conversely, the South Indian Ocean, West Asia, the Middle East, and the Central Pacific Ocean exhibit a decreasing trend in precipitation. Under the RCP8.5 scenario, precipitation is projected to increase over a wider swath of the Indian Ocean and the Middle East Asia. In the RCP4.5 scenario, the low-level wind circulation is likely to strengthen over the entire northern Indian Ocean, extending to the South China Sea, thereby increasing moisture transport from the Indian Ocean to peninsular India and the South China Sea. Conversely, in the RCP8.5 scenario, easterly winds strengthen over the South Indian Ocean, leading to an increase in moisture transport from the equatorial West Pacific Ocean to the Indian Ocean. A weak (strong) cyclonic circulation in response to the east-centered (west-centered) low sea level pressure trend over the North Pacific in RCP4.5 (RCP8.5) scenario is projected to help maintaining a strong (weak) ASM circulation from the India to east Asia. Internal climate variability is also calculated, revealing that the North Pacific Ocean near the Bering Sea is likely to play a dominating role and contribute significantly to the future ASM dynamics. In both scenarios, internal variability is found to substantially contribute to changes in monsoon circulation over the Indian Ocean.
{"title":"Asian summer monsoon responses under RCP4.5 and RCP8.5 scenarios in CESM large ensemble simulations","authors":"Devanil Choudhury, Debashis Nath, Wen Chen","doi":"10.1088/2515-7620/ad5b3b","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3b","url":null,"abstract":"The response of the Asian Summer Monsoon (ASM) circulation to the Representative Concentration Pathway 4.5 and 8.5 (RCP4.5 and RCP8.5) forcing scenarios is examined using the CESM1 state-of-the-art global circulation model from 2021 to 2050. The projections show that monsoon precipitation will increase over East Asia, the North Pacific Ocean, the Indian Peninsula, and the Bay of Bengal under the RCP4.5 scenario. Conversely, the South Indian Ocean, West Asia, the Middle East, and the Central Pacific Ocean exhibit a decreasing trend in precipitation. Under the RCP8.5 scenario, precipitation is projected to increase over a wider swath of the Indian Ocean and the Middle East Asia. In the RCP4.5 scenario, the low-level wind circulation is likely to strengthen over the entire northern Indian Ocean, extending to the South China Sea, thereby increasing moisture transport from the Indian Ocean to peninsular India and the South China Sea. Conversely, in the RCP8.5 scenario, easterly winds strengthen over the South Indian Ocean, leading to an increase in moisture transport from the equatorial West Pacific Ocean to the Indian Ocean. A weak (strong) cyclonic circulation in response to the east-centered (west-centered) low sea level pressure trend over the North Pacific in RCP4.5 (RCP8.5) scenario is projected to help maintaining a strong (weak) ASM circulation from the India to east Asia. Internal climate variability is also calculated, revealing that the North Pacific Ocean near the Bering Sea is likely to play a dominating role and contribute significantly to the future ASM dynamics. In both scenarios, internal variability is found to substantially contribute to changes in monsoon circulation over the Indian Ocean.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570764","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}
Pub Date : 2024-07-09DOI: 10.1088/2515-7620/ad6125
J. Ross, Nancy Kinner, S. Saupe, Kai Ziervogel
Oil spilled into the ocean interacts with suspended matter forming aggregates that transport oil into subsurface layers and towards the bottom. We conducted a series of laboratory experiments to explore aggregation of oil with natural phytoplankton assemblages from Cook Inlet, Alaska at three times during a spring bloom. Oil and phytoplankton formed marine oil snow (MOS) that remained positively buoyant with a small fraction of MOS sinking to the bottom of our experimental bottles. Seawater treatments amended with suspended sediments formed oil-mineral aggregates (OMAs) with an oil capacity similar to MOS (~20% of aggregate area was covered with oil). OMAs accelerated oil sedimentation in our bottles relative to MOS sedimentation underlining the significance of suspended matter as ballast for sinking oil. Our results reveal potential transport mechanisms of oil in Cook Inlet which apply to other coastal systems with high productivity and sediment loads.
泄漏到海洋中的油类会与悬浮物质相互作用形成聚集体，从而将油类输送到次表层并沉入海底。我们进行了一系列实验室实验，以探索油类与阿拉斯加库克湾的天然浮游植物群在春季藻类大量繁殖期间的三次聚集情况。油类和浮游植物形成的海洋油雪（MOS）保持正浮力，只有一小部分 MOS 沉入实验瓶的底部。添加了悬浮沉积物的海水处理会形成油矿物聚集体 (OMA)，其含油量与 MOS 相似（约 20% 的聚集体面积被油覆盖）。与 MOS 的沉积作用相比，OMAs 加快了油类在瓶中的沉积，这突出了悬浮物作为油类下沉的压舱物的重要性。我们的研究结果揭示了库克湾油类的潜在迁移机制，这些机制也适用于其它具有高生产力和沉积物负荷的沿岸系统。
{"title":"Sediment ballast accelerates sinking of Alaska North Slope crude oil measured ex situ with surface water from Cook Inlet","authors":"J. Ross, Nancy Kinner, S. Saupe, Kai Ziervogel","doi":"10.1088/2515-7620/ad6125","DOIUrl":"https://doi.org/10.1088/2515-7620/ad6125","url":null,"abstract":"\u0000 Oil spilled into the ocean interacts with suspended matter forming aggregates that transport oil into subsurface layers and towards the bottom. We conducted a series of laboratory experiments to explore aggregation of oil with natural phytoplankton assemblages from Cook Inlet, Alaska at three times during a spring bloom. Oil and phytoplankton formed marine oil snow (MOS) that remained positively buoyant with a small fraction of MOS sinking to the bottom of our experimental bottles. Seawater treatments amended with suspended sediments formed oil-mineral aggregates (OMAs) with an oil capacity similar to MOS (~20% of aggregate area was covered with oil). OMAs accelerated oil sedimentation in our bottles relative to MOS sedimentation underlining the significance of suspended matter as ballast for sinking oil. Our results reveal potential transport mechanisms of oil in Cook Inlet which apply to other coastal systems with high productivity and sediment loads.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141664702","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}
Pub Date : 2024-07-08DOI: 10.1088/2515-7620/ad5ccd
Jiajin Wang, Jie Guo, Chunxue Wang, Yanmei Pang
In recent years, the Chengdu-Chongqing Economic Circle (CCEC) has experienced frequent heat events, significantly impacting labor productivity. The CCEC is an important economic growth pole in western China. Therefore, an in-depth study of the impact of heat stress on labor productivity holds great significance for climate change adaptation and enhancing economic efficiency. Based on the relationship between the wet-bulb globe temperature (WBGT) and labor productivity of different industries, the labor productivity loss caused by heat in the CCEC was estimated using the observation data of the meteorological station and the projection results of the BCC-CSM2-MR model from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results showed that the impact of heat on the labor productivity of different industries in the CCEC mainly occurs from June to August, with the largest impact on agriculture, followed by industry, and the smallest impact on service sectors. Losses from heat stress to labor productivity in agriculture, industry, and services showed a significant increasing trend from 1980 to 2020 but a decreasing trend in comprehensive labor productivity loss. From 2020–2100, labor productivity losses in different industries due to heat stress show an increasing and then decreasing trend in the low emissions scenario, productivity losses in the medium emissions scenario are characterized by an increasing and then sustained change, and labor productivity losses in the high emissions scenario show a sustained increasing trend from 2020. By the end of the 21st century, the increase in labor productivity losses across different industries under the high emission scenario is approximately 15%–23%, and the large value center shifts slightly to the west. In most areas, the losses of agricultural, industrial, service, and comprehensive labor productivity exceed 45%, 32%, 20%, and 24%, respectively.
{"title":"Impact of global warming on labor productivity in the Chengdu-Chongqing economic circle, China","authors":"Jiajin Wang, Jie Guo, Chunxue Wang, Yanmei Pang","doi":"10.1088/2515-7620/ad5ccd","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5ccd","url":null,"abstract":"In recent years, the Chengdu-Chongqing Economic Circle (CCEC) has experienced frequent heat events, significantly impacting labor productivity. The CCEC is an important economic growth pole in western China. Therefore, an in-depth study of the impact of heat stress on labor productivity holds great significance for climate change adaptation and enhancing economic efficiency. Based on the relationship between the wet-bulb globe temperature (WBGT) and labor productivity of different industries, the labor productivity loss caused by heat in the CCEC was estimated using the observation data of the meteorological station and the projection results of the BCC-CSM2-MR model from the Coupled Model Intercomparison Project Phase 6 (CMIP6). The results showed that the impact of heat on the labor productivity of different industries in the CCEC mainly occurs from June to August, with the largest impact on agriculture, followed by industry, and the smallest impact on service sectors. Losses from heat stress to labor productivity in agriculture, industry, and services showed a significant increasing trend from 1980 to 2020 but a decreasing trend in comprehensive labor productivity loss. From 2020–2100, labor productivity losses in different industries due to heat stress show an increasing and then decreasing trend in the low emissions scenario, productivity losses in the medium emissions scenario are characterized by an increasing and then sustained change, and labor productivity losses in the high emissions scenario show a sustained increasing trend from 2020. By the end of the 21st century, the increase in labor productivity losses across different industries under the high emission scenario is approximately 15%–23%, and the large value center shifts slightly to the west. In most areas, the losses of agricultural, industrial, service, and comprehensive labor productivity exceed 45%, 32%, 20%, and 24%, respectively.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570762","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}
Pub Date : 2024-07-08DOI: 10.1088/2515-7620/ad6061
Vellingiri J, Kalaivanan K, K. S, Femilda Josephin Joseph Shobana Bai
Water pollution is a significant cause of death globally, resulting in 1.8 million deaths annually due to waterborne diseases. Assessing water quality is a complex process that involves identifying contaminants in water sources and determining whether it is safe for human consumption. In this study, we utilized the Cauvery River dataset to develop a model for evaluating water quality. The aim of our research was to proficiently perform feature selection and classification tasks. We introduced a novel technique called the Aquila Optimization Support Vector Machine (AO-SVM), an advanced and effective machine learning system for predicting water quality. Here SVM is used for the classification, and the Aquila algorithm is used for optimizing SVM. The results show that the proposed method achieved a maximum accuracy rate of 96.3%, an execution time of 0.75s, a precision of 93.9 %, a recall rate of 95.1 %, and an F1-Score value of 94.7%. The suggested AO-SVM model outperformed all other existing classification models regarding classification accuracy and other parameters.
{"title":"AO-SVM: A Machine Learning Model for Predicting Water Quality in the Cauvery River","authors":"Vellingiri J, Kalaivanan K, K. S, Femilda Josephin Joseph Shobana Bai","doi":"10.1088/2515-7620/ad6061","DOIUrl":"https://doi.org/10.1088/2515-7620/ad6061","url":null,"abstract":"\u0000 Water pollution is a significant cause of death globally, resulting in 1.8 million deaths annually due to waterborne diseases. Assessing water quality is a complex process that involves identifying contaminants in water sources and determining whether it is safe for human consumption. In this study, we utilized the Cauvery River dataset to develop a model for evaluating water quality. The aim of our research was to proficiently perform feature selection and classification tasks. We introduced a novel technique called the Aquila Optimization Support Vector Machine (AO-SVM), an advanced and effective machine learning system for predicting water quality. Here SVM is used for the classification, and the Aquila algorithm is used for optimizing SVM. The results show that the proposed method achieved a maximum accuracy rate of 96.3%, an execution time of 0.75s, a precision of 93.9 %, a recall rate of 95.1 %, and an F1-Score value of 94.7%. The suggested AO-SVM model outperformed all other existing classification models regarding classification accuracy and other parameters.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141669007","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}
Pub Date : 2024-07-05DOI: 10.1088/2515-7620/ad5ad9
Jinge Zhang, Chunxiang Li, Tianbao Zhao
Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.
{"title":"Qualifying uncertainty of precipitation projections over China: mitigating uncertainty with emergent constraints","authors":"Jinge Zhang, Chunxiang Li, Tianbao Zhao","doi":"10.1088/2515-7620/ad5ad9","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5ad9","url":null,"abstract":"Predicting future mean precipitation poses significant challenges due to uncertainties among climate models, complicating water resource management. In this study, we introduce a novel methodology to mitigate uncertainty in future mean precipitation projections over China on a grid-by-grid basis. By constraining precipitation parameters of the Gamma distribution, we establish emergent constraints on parameters, revealing significant correlations between historical and future simulations. Our analysis spans the periods 2040–2069 and 2070–2099 under low-to-moderate and high emission scenarios. We observe reductions in uncertainty across most regions of China, with constrained mean precipitation indicating increases in monsoon regions and decreases in non-monsoon zones relative to raw projections. Notably, the observed 30%–40% increase in mean precipitation for the whole of China underscores the efficacy of our methodology. These observationally constrained results provide valuable insights into current precipitation projections, offering actionable information for water resource planning and climate adaptation strategies amidst future uncertainties.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141570763","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}
Emerging contaminants (ECs) pollution has attracted global attention, and a large number of ECs spread in the environment, threatening the ecological environment and human health. Gut microbiota is the most complex microbial community, and its high sensitivity to ECs exposure has been widely concerned and reported by researchers. In fact, many studies have demonstrated that the gut microbiota is closely related to host health and is a toxic target of various environmental pollutants including ECs. This review evaluates the interaction of ECs (including persistent organic pollutants, antibiotics, microplastics and environmental endocrine disruptors) with the gut microbiota, and considers the possible harm of ECs to human health, finding that the gut microbiota may be involved in the regulation of various organ damage, endocrine disorders, embryotoxicity, and cancer development and other toxic processes caused by ECs exposure through related mechanisms such as the gut-liver axis, direct effects (toxins and metabolites enter the blood after intestinal injury), and gut-brain axis. In short, we hope that more future studies will pay more attention to the relationship between ECs, gut microbiota and human health.
{"title":"Interactions between gut microbiota and emerging contaminants exposure: new and profound implications for human health","authors":"Feng Zhao, Zhaoyi Liu, Yuehua Wu, Jiao Wang, Yinyin Xia, Shuqun Cheng, Xuejun Jiang, Jun Zhang, Zhen Zou, Chengzhi Chen, Jingfu Qiu","doi":"10.1088/2515-7620/ad5f7f","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5f7f","url":null,"abstract":"\u0000 Emerging contaminants (ECs) pollution has attracted global attention, and a large number of ECs spread in the environment, threatening the ecological environment and human health. Gut microbiota is the most complex microbial community, and its high sensitivity to ECs exposure has been widely concerned and reported by researchers. In fact, many studies have demonstrated that the gut microbiota is closely related to host health and is a toxic target of various environmental pollutants including ECs. This review evaluates the interaction of ECs (including persistent organic pollutants, antibiotics, microplastics and environmental endocrine disruptors) with the gut microbiota, and considers the possible harm of ECs to human health, finding that the gut microbiota may be involved in the regulation of various organ damage, endocrine disorders, embryotoxicity, and cancer development and other toxic processes caused by ECs exposure through related mechanisms such as the gut-liver axis, direct effects (toxins and metabolites enter the blood after intestinal injury), and gut-brain axis. In short, we hope that more future studies will pay more attention to the relationship between ECs, gut microbiota and human health.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.5,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141680103","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}
Pub Date : 2024-07-03DOI: 10.1088/2515-7620/ad5b3d
Jenila Vincent M and Varalakshmi P
Extracting individual buildings from satellite images is crucial for various urban applications, including population estimation, urban planning, and other related fields. However, Extracting building footprints from remote sensing data is a challenging task because of scale differences, complex structures and different types of building. Addressing these issues, an approach that can efficiently detect buildings in images by generating a segmentation mask for each instance is proposed in this paper. This approach incorporates the Regional Convolutional Neural Network (MASK-RCNN), which combines Faster R-CNN for object mask prediction and boundary box recognition and was evaluated against other models like YOLOv5, YOLOv7 and YOLOv8 in a comparative study to assess its effectiveness. The findings of this study reveals that our proposed method achieved the highest accuracy in building extraction. Furthermore, we performed experiments on well-established datasets like WHU and INRIA, and our method consistently outperformed other existing methods, producing reliable results.
{"title":"Extraction of building footprint using MASK-RCNN for high resolution aerial imagery","authors":"Jenila Vincent M and Varalakshmi P","doi":"10.1088/2515-7620/ad5b3d","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3d","url":null,"abstract":"Extracting individual buildings from satellite images is crucial for various urban applications, including population estimation, urban planning, and other related fields. However, Extracting building footprints from remote sensing data is a challenging task because of scale differences, complex structures and different types of building. Addressing these issues, an approach that can efficiently detect buildings in images by generating a segmentation mask for each instance is proposed in this paper. This approach incorporates the Regional Convolutional Neural Network (MASK-RCNN), which combines Faster R-CNN for object mask prediction and boundary box recognition and was evaluated against other models like YOLOv5, YOLOv7 and YOLOv8 in a comparative study to assess its effectiveness. The findings of this study reveals that our proposed method achieved the highest accuracy in building extraction. Furthermore, we performed experiments on well-established datasets like WHU and INRIA, and our method consistently outperformed other existing methods, producing reliable results.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551063","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}
Pub Date : 2024-07-03DOI: 10.1088/2515-7620/ad5b3f
Guneet Sandhu, Olaf Weber, Michael O Wood, Horatiu A Rus and Jason Thistlethwaite
Extant literature reveals limited examination of risk management strategies and tools to support decision-making for sustainable water management in the private sector in Ontario, Canada. Moreover, a gap persists in understanding how water risks are prioritized and managed in the private sector. Addressing these gaps, this transdisciplinary study applied a novel normative-analytical risk governance theoretical framework to water security risks, which combines analytical risk estimation with normative priorities and insights of practitioners, to examine contextually-attuned water risk management strategies and develop a decision-support tool. Using mixed methods, the study first employed a survey to elicit practitioner priorities for seven water risk indicators and investigated water risk management approaches. Then, interviews were conducted to obtain in-depth understanding about the priorities, strategies, opportunities, and role of trust in water risk management. The study found that a combination of regulatory, voluntary, and multi-stakeholder participatory approaches is needed, contingent on the severity of water risks, sector, location, and context. Moreover, the criteria of flexibility, efficiency, strategic incentives, and economic and regulatory signals, are essential. Finally, using secondary data analysis, the study integrated interdisciplinary risk data with practitioner priorities to develop a first-of-a-kind decision-support tool for water risk management in Ontario, ‘WATR-DST’. WATR-DST is an automated tool that applies the study’s findings and assists multi-sector water-related decisions, practices, and investments by providing contextually-attuned risk information in a user-friendly format. Based on the user inputs (location, sector, and source type), it displays the severity of seven water risks, qualitative themes under public and media attention, and recommends water risk management strategies. Thus, the study contributes to knowledge in sustainability management, risk analysis, and environmental management by demonstrating the novel application of the normative-analytical framework for water risk management in the private sector. WATR-DST is a key contribution envisioned to improve multi-sector water-related decisions in Ontario.
{"title":"Developing a transdisciplinary tool for water risk management and decision-support in Ontario, Canada","authors":"Guneet Sandhu, Olaf Weber, Michael O Wood, Horatiu A Rus and Jason Thistlethwaite","doi":"10.1088/2515-7620/ad5b3f","DOIUrl":"https://doi.org/10.1088/2515-7620/ad5b3f","url":null,"abstract":"Extant literature reveals limited examination of risk management strategies and tools to support decision-making for sustainable water management in the private sector in Ontario, Canada. Moreover, a gap persists in understanding how water risks are prioritized and managed in the private sector. Addressing these gaps, this transdisciplinary study applied a novel normative-analytical risk governance theoretical framework to water security risks, which combines analytical risk estimation with normative priorities and insights of practitioners, to examine contextually-attuned water risk management strategies and develop a decision-support tool. Using mixed methods, the study first employed a survey to elicit practitioner priorities for seven water risk indicators and investigated water risk management approaches. Then, interviews were conducted to obtain in-depth understanding about the priorities, strategies, opportunities, and role of trust in water risk management. The study found that a combination of regulatory, voluntary, and multi-stakeholder participatory approaches is needed, contingent on the severity of water risks, sector, location, and context. Moreover, the criteria of flexibility, efficiency, strategic incentives, and economic and regulatory signals, are essential. Finally, using secondary data analysis, the study integrated interdisciplinary risk data with practitioner priorities to develop a first-of-a-kind decision-support tool for water risk management in Ontario, ‘WATR-DST’. WATR-DST is an automated tool that applies the study’s findings and assists multi-sector water-related decisions, practices, and investments by providing contextually-attuned risk information in a user-friendly format. Based on the user inputs (location, sector, and source type), it displays the severity of seven water risks, qualitative themes under public and media attention, and recommends water risk management strategies. Thus, the study contributes to knowledge in sustainability management, risk analysis, and environmental management by demonstrating the novel application of the normative-analytical framework for water risk management in the private sector. WATR-DST is a key contribution envisioned to improve multi-sector water-related decisions in Ontario.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551064","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}
Pub Date : 2024-07-02DOI: 10.1088/2515-7620/ad5e3d
Timothy O Ogunbode, Ayobami A. Oyelami, Victor O Oyebamiji, Oluwatobi O. Faboro, Aruna O. Adelkiya
Efficient use of water could be partly achieved with sound management strategies of the non-consumptive uses (N-CUs) of water in homes being put in place. This research evaluated the non-consumptive water use component in Iwo, Osun State, Nigeria. Data required for the investigation was generated from the administration of 325 questionnaires across the five Quarters into which the town is divided, out of which 269 were completed and retrieved. Both descriptive and inferential analysis of the data were carried out. Descriptive analysis showed that households engage absolutely in different non-consumptive uses such as bathing, clothe washing, drainage cleaning and dish washing while households’ engagement in other N-CUs were in varying proportions. The results of Factor Analysis (FA) revealed that five out of the 13 variables identified and analyzed with a minimum eigen value of 1.000 were strong explanatory variables of 73.674% when engaging in issues relating to N-CUs at household level. These are water use for the following (i) drainage cleaning (16.153%); (ii) Dish washing (15.922%); (iii) Toilet cleaning (14.547%); (iv) Auto-wash (14.238%); and Bathing (12.814%). Regression analysis (RA) of the data revealed that three variables namely clothe washing, Incidental washing and auto-washing were significant (p<0.001) in generating predictive model of N-CUs of water in homes. The combined results of FA and RA implied that the set of variables in both analysis need to be considered in any issue involving the management and control of N-CUs of water in homes for a result-oriented water use efficiency at household level.
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Wind power plays a vital role in the electricity generation of many countries, including Ethiopia. It serves as a valuable complement to hydropower during the dry season, and its affordability is crucial for the growth of industrial centers. However, accurately estimating wind energy poses significant challenges due to its random nature, severe variability, and dependence on wind speed. Numerous techniques have been employed to tackle this problem, and recent research has shown that Artificial Neural Network (ANN) models excel in prediction accuracy. This study aims to assess the effectiveness of different ANN network types in estimating the monthly average daily wind power at Adama Wind Farm I. The collected data was divided into three sets: training (70%), testing (15%), and validation (15%). Four network types, namely Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg–Marquardt (LR), were utilized with seven input parameters for prediction. The performance of these networks was evaluated using Mean Absolute Percentage Error (MAPE) and R-squared (R2). The EBP network type demonstrated exceptional performance in estimating wind power for all wind turbines in Groups GI, GII, and GIII. Additionally, all proposed network types achieved impressive accuracy levels with MAPE ranging from 0.0119 to 0.0489 and R2 values ranging from 0.982 to 0.9989. These results highlight the high predictive accuracy attained at the study site. Consequently, we can conclude that the ANN model’s network types were highly effective in predicting the monthly averaged daily wind power at Adama Wind Farm I. By leveraging the power of ANN models, this research contributes to improving wind energy estimation, thereby enabling more reliable and efficient utilization of wind resources. The findings of this study have practical implications for the wind energy industry and can guide decision-making processes regarding wind power generation and integration into the energy mix.
风能在包括埃塞俄比亚在内的许多国家的发电中发挥着至关重要的作用。在旱季，它是水力发电的重要补充，其经济性对工业中心的发展至关重要。然而，由于风能的随机性、严重的多变性和对风速的依赖性，准确估算风能面临着巨大挑战。为解决这一问题，人们采用了许多技术，最近的研究表明，人工神经网络（ANN）模型在预测准确性方面表现出色。本研究旨在评估不同类型的人工神经网络在估算 Adama 风电场 I 的月平均日风力发电量方面的有效性。收集的数据分为三组：训练（70%）、测试（15%）和验证（15%）。使用了四种网络类型，即前馈反向传播（FFBP）、级联前馈反向传播（CFBP）、误差反向传播（EBP）和 Levenberg-Marquardt （LR），并使用七个输入参数进行预测。使用平均绝对百分比误差 (MAPE) 和 R 平方 (R2) 对这些网络的性能进行了评估。EBP 网络类型在估算 GI、GII 和 GIII 组所有风力涡轮机的风功率时表现出了卓越的性能。此外，所有提议的网络类型都达到了令人印象深刻的精度水平，MAPE 在 0.0119 到 0.0489 之间，R2 值在 0.982 到 0.9989 之间。这些结果凸显了研究地点所达到的高预测精度。因此，我们可以得出结论，ANN 模型的网络类型在预测 Adama 风电场 I 的月平均日风力发电量方面非常有效。通过利用 ANN 模型的强大功能，本研究有助于改进风能估算，从而更可靠、更高效地利用风能资源。本研究的结果对风能产业具有实际意义，可指导有关风力发电和将风能纳入能源组合的决策过程。
{"title":"Unleashing the power of artificial neural networks: accurate estimation of monthly averaged daily wind power at Adama wind farm I, Ethiopia","authors":"Tegenu Argaw Woldegiyorgis, Natei Ermias Benti, Birhanu Asmerom Habtemicheal and Ashenafi Admasu Jembrie","doi":"10.1088/2515-7620/ad592f","DOIUrl":"https://doi.org/10.1088/2515-7620/ad592f","url":null,"abstract":"Wind power plays a vital role in the electricity generation of many countries, including Ethiopia. It serves as a valuable complement to hydropower during the dry season, and its affordability is crucial for the growth of industrial centers. However, accurately estimating wind energy poses significant challenges due to its random nature, severe variability, and dependence on wind speed. Numerous techniques have been employed to tackle this problem, and recent research has shown that Artificial Neural Network (ANN) models excel in prediction accuracy. This study aims to assess the effectiveness of different ANN network types in estimating the monthly average daily wind power at Adama Wind Farm I. The collected data was divided into three sets: training (70%), testing (15%), and validation (15%). Four network types, namely Feedforward Backpropagation (FFBP), Cascade Feedforward Backpropagation (CFBP), Error Backpropagation (EBP), and Levenberg–Marquardt (LR), were utilized with seven input parameters for prediction. The performance of these networks was evaluated using Mean Absolute Percentage Error (MAPE) and R-squared (R2). The EBP network type demonstrated exceptional performance in estimating wind power for all wind turbines in Groups GI, GII, and GIII. Additionally, all proposed network types achieved impressive accuracy levels with MAPE ranging from 0.0119 to 0.0489 and R2 values ranging from 0.982 to 0.9989. These results highlight the high predictive accuracy attained at the study site. Consequently, we can conclude that the ANN model’s network types were highly effective in predicting the monthly averaged daily wind power at Adama Wind Farm I. By leveraging the power of ANN models, this research contributes to improving wind energy estimation, thereby enabling more reliable and efficient utilization of wind resources. The findings of this study have practical implications for the wind energy industry and can guide decision-making processes regarding wind power generation and integration into the energy mix.","PeriodicalId":48496,"journal":{"name":"Environmental Research Communications","volume":null,"pages":null},"PeriodicalIF":2.9,"publicationDate":"2024-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141551065","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}
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