This study aims to determine the relationship between Industry 5.0 processes and ESG (environmental, social and governance) processes. For this purpose, the annual reports of Vestel and Arçelik enterprises in the white goods sector, included in the BIST Sustainable 25 Index in Turkey, were analysed. Descriptive and content analysis were used in the study. The results show a close relationship between the concepts of Industry 5.0 and ESG processes. It can also be said that this relationship is bidirectional. In other words, investing in the ESG process will contribute positively to the Industry 5.0 process, and in the Industry 5.0 process will contribute positively to the ESG process. As a result of the analysis, it was determined that both enterprises carried out studies in accordance with the components of digital transformation, environment, employees and society and resilience of businesses in the Industry 5.0 process. In addition, studies on the ESG process have shown that both companies have similar characteristics. It is also one of the results that the studies carried out within the scope of the ESG process are carried out in collaboration with technology. No terminology related to Industry 5.0 was found in the documents analysed for either Vestel or Arçelik. The study results reveal that both companies exhibit weaknesses in the "Technology for Employees and People" section, an essential component of the Industry 5.0 process. The similarity of both companies in ESG-related issues may be associated with their inclusion in the BIST Sustainability 25 index. Integrating the technologies inherited from the Industry 4.0 process into the Industry 5.0 process and the themes of the ESG concept reveals the motto "Technology for Humanity".
{"title":"The Relationship Between Industry 5.0 Process and ESG Process: A Qualitative Analysis in the Context of Türkiye's BIST Sustainability 25 Index White Good Sector","authors":"İsmail Yoşumaz, Hülya Uzun","doi":"10.35208/ert.1431800","DOIUrl":"https://doi.org/10.35208/ert.1431800","url":null,"abstract":"This study aims to determine the relationship between Industry 5.0 processes and ESG (environmental, social and governance) processes. For this purpose, the annual reports of Vestel and Arçelik enterprises in the white goods sector, included in the BIST Sustainable 25 Index in Turkey, were analysed. Descriptive and content analysis were used in the study. The results show a close relationship between the concepts of Industry 5.0 and ESG processes. It can also be said that this relationship is bidirectional. In other words, investing in the ESG process will contribute positively to the Industry 5.0 process, and in the Industry 5.0 process will contribute positively to the ESG process. As a result of the analysis, it was determined that both enterprises carried out studies in accordance with the components of digital transformation, environment, employees and society and resilience of businesses in the Industry 5.0 process. In addition, studies on the ESG process have shown that both companies have similar characteristics. It is also one of the results that the studies carried out within the scope of the ESG process are carried out in collaboration with technology. No terminology related to Industry 5.0 was found in the documents analysed for either Vestel or Arçelik. The study results reveal that both companies exhibit weaknesses in the \"Technology for Employees and People\" section, an essential component of the Industry 5.0 process. The similarity of both companies in ESG-related issues may be associated with their inclusion in the BIST Sustainability 25 index. Integrating the technologies inherited from the Industry 4.0 process into the Industry 5.0 process and the themes of the ESG concept reveals the motto \"Technology for Humanity\".","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140996461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ecological footprint, which is an indicator of environmental destruction today, seems to be one of the most remarkable research topics in recent years. In order to raise awareness about minimizing environmental problems, the ecological footprint plays an important role in determining the extent of destruction to the ecosystem. The ecological footprint is remarkable for researchers in terms of revealing the level of destruction in nature and explaining the causes of destruction for a sustainable environment. This research aims to examine the scientific studies published on the international platform about ecological footprint with the bibliometric analysis method. The research is descriptive and is structured on the scanning method. Studies published between 2010 and 2021 were searched using the key concept of "Ecological footprint" in the database. In this context, the bibliometric characteristics of 2748 publications scanned in the Web of Science database were determined. The data obtained as a result of the research were analyzed as the number of publications by years and the number of publications by countries, most productive authors and journals, authors' h-indexes, most cited authors and journals, distributions by most cited references, and some relationships between these variables. The data obtained reveal the interdisciplinary importance of the subject.
生态足迹是当今环境破坏的一个指标，似乎是近年来最引人注目的研究课题之一。为了提高人们对尽量减少环境问题的认识，生态足迹在确定生态系统的破坏程度方面发挥着重要作用。对于研究人员来说，生态足迹在揭示自然界的破坏程度和解释破坏原因以实现可持续环境方面具有重要意义。本研究旨在通过文献计量分析方法，考察国际平台上发表的有关生态足迹的科学研究。本研究为描述性研究，采用扫描法。在数据库中使用 "生态足迹 "这一关键概念对 2010 至 2021 年间发表的研究进行了检索。在此背景下，确定了在 Web of Science 数据库中扫描的 2748 篇出版物的文献计量特征。对研究获得的数据进行了分析，包括按年份分列的出版物数量、按国家分列的出版物数量、最有成果的作者和期刊、作者的 h 指数、被引用次数最多的作者和期刊、被引用次数最多的参考文献的分布情况，以及这些变量之间的一些关系。所获得的数据揭示了该学科的跨学科重要性。
{"title":"BIBLIOMETRIC PROFILE OF RESEARCH ON ECOLOGICAL FOOTPRINT","authors":"Figen Durkaya, Mustafa Kaya","doi":"10.35208/ert.1366472","DOIUrl":"https://doi.org/10.35208/ert.1366472","url":null,"abstract":"Ecological footprint, which is an indicator of environmental destruction today, seems to be one of the most remarkable research topics in recent years. In order to raise awareness about minimizing environmental problems, the ecological footprint plays an important role in determining the extent of destruction to the ecosystem. The ecological footprint is remarkable for researchers in terms of revealing the level of destruction in nature and explaining the causes of destruction for a sustainable environment. This research aims to examine the scientific studies published on the international platform about ecological footprint with the bibliometric analysis method. \u0000The research is descriptive and is structured on the scanning method. Studies published between 2010 and 2021 were searched using the key concept of \"Ecological footprint\" in the database. In this context, the bibliometric characteristics of 2748 publications scanned in the Web of Science database were determined. The data obtained as a result of the research were analyzed as the number of publications by years and the number of publications by countries, most productive authors and journals, authors' h-indexes, most cited authors and journals, distributions by most cited references, and some relationships between these variables. The data obtained reveal the interdisciplinary importance of the subject.","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141022206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In order to achieve efficient projects when producing any product, the cost, quality, and production time need to be optimized. However, this is not always easy to achieve. The budgets of the projects may be exceeded, they may not be completed in the stipulated and committed time, or the products may not be produced with sufficient quality for various reasons. It is vital to analyze the project functions/steps very closely throughout the design phase to ensure that all feasible alternatives are studied and examined, and that the most appropriate alternative is picked for the functions that can meet the project criteria. Value engineering (DM) can be defined as an organized effort to analyze product features, functions and material selections; is designed to solve problems and/or reduce costs while maintaining or improving performance and quality requirements; and performs essential functions at the required quality, reliability, and life-cycle cost. In this study, value engineering was used for the selection of trees needed in the construction of parks. A value engineering team decided that the trees should be coniferous with the prerequisite that they can remain green without shedding their summer-winter leaves and determined which criteria the coniferous trees required to be located in the park should meet. The team members conducted value engineering after determining which trees met these criteria and were subsequently purchased. As a result, the team determined the most appropriate optimum cost solution with the value engineering method to meet all the criteria among the determined alternative tree species
{"title":"Determination of Tree Type Selection in Park and Garden Construction by the Value Engineering Method: Sinanoba Beach Park Example","authors":"Şenay Atabay, H. Tekin","doi":"10.35208/ert.1419063","DOIUrl":"https://doi.org/10.35208/ert.1419063","url":null,"abstract":"In order to achieve efficient projects when producing any product, the cost, quality, and production time need to be optimized. However, this is not always easy to achieve. The budgets of the projects may be exceeded, they may not be completed in the stipulated and committed time, or the products may not be produced with sufficient quality for various reasons. It is vital to analyze the project functions/steps very closely throughout the design phase to ensure that all feasible alternatives are studied and examined, and that the most appropriate alternative is picked for the functions that can meet the project criteria. Value engineering (DM) can be defined as an organized effort to analyze product features, functions and material selections; is designed to solve problems and/or reduce costs while maintaining or improving performance and quality requirements; and performs essential functions at the required quality, reliability, and life-cycle cost. In this study, value engineering was used for the selection of trees needed in the construction of parks. A value engineering team decided that the trees should be coniferous with the prerequisite that they can remain green without shedding their summer-winter leaves and determined which criteria the coniferous trees required to be located in the park should meet. The team members conducted value engineering after determining which trees met these criteria and were subsequently purchased. As a result, the team determined the most appropriate optimum cost solution with the value engineering method to meet all the criteria among the determined alternative tree species","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141021232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mitigating precipitation extremes is a major issue due to destructive global warming and climate change. Heavy rainfall and drought have posed a threat to human life and ecology. That said, new strategies and new action plans are needed at local and global levels through needed cooperation from different stakeholders to handle the possible risks associated with precipitation extremes. Türkiye has become one of the most vulnerable countries involved in climate change due to its geographical location, rapid urbanization, and deforestation. Many forests have been destroyed to make room for agriculture and animal grazing as well as for manufacturing and construction. The impact has caused complications in landscapes. Precipitation extremes, such as heavy rainfalls and drought, are posing a big threat for many cities in Türkiye. In this line, the present study aims to explore potential benefits of rain harvesting in mitigating precipitation extremes by overviewing regulatory and legislative actions of rainwater harvesting worldwide and an interview based survey. The results of the study showed that Türkiye has several problems with infrastructure to mitigate precipitation extremes, such as shortcomings in capacity and old water management systems, unseparated water collection and sewage systems and lack of green infrastructure. In addition to urbanization, expansion in industry and tourism may cause water availability. RWH promises several benefits thanks to its cost effectiveness and contribution to water storage. The study also showed many authorities around globe try to boost RWH use by either stipulating RWH or encouraging through incentives and save a large amount of water with different projects.
{"title":"Should We Value Rain Harvesting More in Turkey for Mitigating Precipitation Extremes","authors":"H. Tekin, Şenay Atabay","doi":"10.35208/ert.1419473","DOIUrl":"https://doi.org/10.35208/ert.1419473","url":null,"abstract":"Mitigating precipitation extremes is a major issue due to destructive global warming and climate change. Heavy rainfall and drought have posed a threat to human life and ecology. That said, new strategies and new action plans are needed at local and global levels through needed cooperation from different stakeholders to handle the possible risks associated with precipitation extremes. Türkiye has become one of the most vulnerable countries involved in climate change due to its geographical location, rapid urbanization, and deforestation. Many forests have been destroyed to make room for agriculture and animal grazing as well as for manufacturing and construction. The impact has caused complications in landscapes. Precipitation extremes, such as heavy rainfalls and drought, are posing a big threat for many cities in Türkiye. In this line, the present study aims to explore potential benefits of rain harvesting in mitigating precipitation extremes by overviewing regulatory and legislative actions of rainwater harvesting worldwide and an interview based survey. The results of the study showed that Türkiye has several problems with infrastructure to mitigate precipitation extremes, such as shortcomings in capacity and old water management systems, unseparated water collection and sewage systems and lack of green infrastructure. In addition to urbanization, expansion in industry and tourism may cause water availability. RWH promises several benefits thanks to its cost effectiveness and contribution to water storage. The study also showed many authorities around globe try to boost RWH use by either stipulating RWH or encouraging through incentives and save a large amount of water with different projects.","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140663453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Since St. John's wort is used extensively in industries such as food, medicine and cosmetics, it is a type of biomass with a high waste potential. The utilization of these wastes is very important both to minimize environmental negativity and to provide an economic contribution. This study aimed to determine the potential of St. John's wort wastes and biochar forms produced from these wastes to be used as solid fuel. In this context, the combustion behavior of the biomass and biochar samples obtained were determined by thermogravimetric analysis method. In addition, combustion activation energies of these samples were calculated using Kissenger-Akahira-Sunosa and Flynn-Wall-Ozawa methods. According to the analysis, more than 90% of all samples were burned and the combustion activation energy values ranged between 70.08 and 203.86 kJ/mol. When all the results obtained are evaluated, it is understood that these biomass wastes and their biochars can be used in combustion systems as direct fuel or additive.
{"title":"Thermal Analysis of St. John's Wort Wastes and Biochars: A Study of Combustion Characteristics and Kinetics","authors":"A. Koçer","doi":"10.35208/ert.1385026","DOIUrl":"https://doi.org/10.35208/ert.1385026","url":null,"abstract":"Since St. John's wort is used extensively in industries such as food, medicine and cosmetics, it is a type of biomass with a high waste potential. The utilization of these wastes is very important both to minimize environmental negativity and to provide an economic contribution. This study aimed to determine the potential of St. John's wort wastes and biochar forms produced from these wastes to be used as solid fuel. In this context, the combustion behavior of the biomass and biochar samples obtained were determined by thermogravimetric analysis method. In addition, combustion activation energies of these samples were calculated using Kissenger-Akahira-Sunosa and Flynn-Wall-Ozawa methods. According to the analysis, more than 90% of all samples were burned and the combustion activation energy values ranged between 70.08 and 203.86 kJ/mol. When all the results obtained are evaluated, it is understood that these biomass wastes and their biochars can be used in combustion systems as direct fuel or additive.","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140672859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Saurabh Pargaien, R. Prakash, V. P. Dubey, Devendra Singh
In this article, three different indices NDVI, BNDVI and GNDVI are used for the identification of wheat, mustard and sugarcane crop of Saharanpur district’s region of Uttar Pradesh. Sentinel 2B satellite images are collected from October 02, 2018 to April 15, 2019. These images are processed using Google Earth Engine. These sentinel images are used to generate NDVI, BNDVI and GNDVI images using GEE. These three different indices images are further processed using SNAP software and particular indices values for 210 different locations are calculated. The same process is used for calculating BNDVI and GNDVI values. ARIMA, LSTM and Prophet models are used to train the time series indices values (NDVI, BNDVI and GNDVI) of wheat, mustard and sugarcane crop. these models are used to analyse MSE (mean absolute percentage error) and RMSE values by considering various parameters. Using ARIMA Model, for wheat crop GNDVI indices shows minimum RMSE 0.020, For Sugarcane crop NDVI indices shows minimum RMSE 0.053, For Mustard crop GNDVI indices shows minimum RMSE 0.024. Using LSTM model, for wheat crop NDVI indices shows minimum RMSE 0.036, For Sugarcane crop BNDVI indices shows minimum RMSE 0.054, For Mustard crop GNDVI indices shows minimum RMSE 0.026. Using Prophet model, for wheat crop GNDVI indices shows minimum RMSE 0.055, For Sugarcane crop NDVI indices shows minimum RMSE 0.088, For Mustard crop GNDVI indices using Prophet model shows minimum RMSE 0.101.
{"title":"Crop cover identification based on different vegetation indices by using machine learning algorithms","authors":"Saurabh Pargaien, R. Prakash, V. P. Dubey, Devendra Singh","doi":"10.35208/ert.1446909","DOIUrl":"https://doi.org/10.35208/ert.1446909","url":null,"abstract":"In this article, three different indices NDVI, BNDVI and GNDVI are used for the identification of wheat, mustard and sugarcane crop of Saharanpur district’s region of Uttar Pradesh. Sentinel 2B satellite images are collected from October 02, 2018 to April 15, 2019. These images are processed using Google Earth Engine. These sentinel images are used to generate NDVI, BNDVI and GNDVI images using GEE. These three different indices images are further processed using SNAP software and particular indices values for 210 different locations are calculated. The same process is used for calculating BNDVI and GNDVI values. ARIMA, LSTM and Prophet models are used to train the time series indices values (NDVI, BNDVI and GNDVI) of wheat, mustard and sugarcane crop. these models are used to analyse MSE (mean absolute percentage error) and RMSE values by considering various parameters. Using ARIMA Model, for wheat crop GNDVI indices shows minimum RMSE 0.020, For Sugarcane crop NDVI indices shows minimum RMSE 0.053, For Mustard crop GNDVI indices shows minimum RMSE 0.024. Using LSTM model, for wheat crop NDVI indices shows minimum RMSE 0.036, For Sugarcane crop BNDVI indices shows minimum RMSE 0.054, For Mustard crop GNDVI indices shows minimum RMSE 0.026. Using Prophet model, for wheat crop GNDVI indices shows minimum RMSE 0.055, For Sugarcane crop NDVI indices shows minimum RMSE 0.088, For Mustard crop GNDVI indices using Prophet model shows minimum RMSE 0.101.","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681303","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Machine-learning air pollution prediction studies are widespread worldwide. This study examines the use of machine learning to predict air pollution, its current state, and its expected growth in India. Scopus was used to search 326 documents by 984 academics published in 231 journals between 2007 and 2023. Biblioshiny and Vosviewer were used to discover and visualise prominent authors, journals, research papers, and trends on these issues. In 2018, interest in this topic began to grow at a rate of 32.1 percent every year. Atmospheric Environment (263 citations), Procedia Computer Science (251), Atmospheric Pollution Research (233) and Air Quality, Atmosphere, and Health (93 citations) are the top four sources, according to the Total Citation Index. These journals are among those leading studies on using machine learning to forecast air pollution. Jadavpur University (12 articles) and IIT Delhi (10 articles) are the most esteemed institutions. Singh Kp's 2013 "Atmospheric Environment" article tops the list with 134 citations. The Ministry of Electronics and Information Technology and the Department of Science and Technology are top Indian funding agency receive five units apiece, demonstrating their commitment to technology. The authors' keyword co-occurrence network mappings suggest that machine learning (127 occurrences), air pollution (78 occurrences), and air quality index (41) are the most frequent keywords. This study predicts air pollution using machine learning. These terms largely mirror our Scopus database searches for "machine learning," "air pollution," and "air quality," showing that these are among the most often discussed issues in machine learning research on air pollution prediction. This study helps academics, professionals, and global policymakers understand "air pollution prediction using machine learning" research and recommend key areas for further research.
{"title":"Bibliometric Analysis of Indian Research Trends in Air Quality Forecasting research using machine learning from 2007-2023 using Scopus database","authors":"A. Ansari, A. R. Quaff","doi":"10.35208/ert.1434390","DOIUrl":"https://doi.org/10.35208/ert.1434390","url":null,"abstract":"Machine-learning air pollution prediction studies are widespread worldwide. This study examines the use of machine learning to predict air pollution, its current state, and its expected growth in India. Scopus was used to search 326 documents by 984 academics published in 231 journals between 2007 and 2023. Biblioshiny and Vosviewer were used to discover and visualise prominent authors, journals, research papers, and trends on these issues. In 2018, interest in this topic began to grow at a rate of 32.1 percent every year. Atmospheric Environment (263 citations), Procedia Computer Science (251), Atmospheric Pollution Research (233) and Air Quality, Atmosphere, and Health (93 citations) are the top four sources, according to the Total Citation Index. These journals are among those leading studies on using machine learning to forecast air pollution. Jadavpur University (12 articles) and IIT Delhi (10 articles) are the most esteemed institutions. Singh Kp's 2013 \"Atmospheric Environment\" article tops the list with 134 citations. The Ministry of Electronics and Information Technology and the Department of Science and Technology are top Indian funding agency receive five units apiece, demonstrating their commitment to technology. The authors' keyword co-occurrence network mappings suggest that machine learning (127 occurrences), air pollution (78 occurrences), and air quality index (41) are the most frequent keywords. This study predicts air pollution using machine learning. These terms largely mirror our Scopus database searches for \"machine learning,\" \"air pollution,\" and \"air quality,\" showing that these are among the most often discussed issues in machine learning research on air pollution prediction. This study helps academics, professionals, and global policymakers understand \"air pollution prediction using machine learning\" research and recommend key areas for further research.","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140681083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mukesh Chaudharı, Ritu Chotalıya, Gh Ali, Ajay Pandya, P. Shrivastav
Groundwater serves as a vital water source for a significant population in the Gujarat region of India. However, substantial contamination from heavy metals, pose a serious threat to human health through various pathways, including drinking water. The rapid industrial and agricultural growth in recent years has exacerbated heavy metal pollution in the state. This study focuses on assessing the heavy metal contamination in Gujarat's groundwater using the Heavy Metal Pollution Index (HPI). The research covers the entire state, considering its diverse physical, climatic, topographical, and geographical conditions. The HPI scores obtained from individual studies highlight the extent of pollution caused by heavy metals. The overall findings underscore the severe problem of heavy metal contamination in Gujarat's groundwater and the associated health risks. Various other pollution indicators, including the Heavy Metal Evaluation Index, Degree of Contamination, Metal Index, and Water Pollution Index are discussed as tools to assess contamination levels. These indices compare concentrations of different heavy metals with established limits to determine the pollution level. The goal is to provide valuable insights for investors and policymakers in formulating strategies to manage and reduce heavy metal contamination across the state. Additionally, the review explores effective, environmentally friendly, and economically viable treatment techniques to remove heavy metals from aquatic systems, safeguarding the environment. By employing pollution indicators and remedial actions, this study aims to guide efforts in mitigating the impact of heavy metal contamination in Gujarat's groundwater.
{"title":"Assessment of heavy metal contamination in the groundwater of Gujarat, India using the heavy metal pollution index","authors":"Mukesh Chaudharı, Ritu Chotalıya, Gh Ali, Ajay Pandya, P. Shrivastav","doi":"10.35208/ert.1433696","DOIUrl":"https://doi.org/10.35208/ert.1433696","url":null,"abstract":"Groundwater serves as a vital water source for a significant population in the Gujarat region of India. However, substantial contamination from heavy metals, pose a serious threat to human health through various pathways, including drinking water. The rapid industrial and agricultural growth in recent years has exacerbated heavy metal pollution in the state. This study focuses on assessing the heavy metal contamination in Gujarat's groundwater using the Heavy Metal Pollution Index (HPI). The research covers the entire state, considering its diverse physical, climatic, topographical, and geographical conditions. The HPI scores obtained from individual studies highlight the extent of pollution caused by heavy metals. The overall findings underscore the severe problem of heavy metal contamination in Gujarat's groundwater and the associated health risks. Various other pollution indicators, including the Heavy Metal Evaluation Index, Degree of Contamination, Metal Index, and Water Pollution Index are discussed as tools to assess contamination levels. These indices compare concentrations of different heavy metals with established limits to determine the pollution level. The goal is to provide valuable insights for investors and policymakers in formulating strategies to manage and reduce heavy metal contamination across the state. Additionally, the review explores effective, environmentally friendly, and economically viable treatment techniques to remove heavy metals from aquatic systems, safeguarding the environment. By employing pollution indicators and remedial actions, this study aims to guide efforts in mitigating the impact of heavy metal contamination in Gujarat's groundwater.","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140696557","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This study examined the effect of material recycling on the relationship between the waste amount and environmental pollution in EU-15 countries for the 1995-2019 period through panel smooth regression analysis by using the material recycling rate as the threshold variable. Based on the analysis results, the material recycling rate threshold level was estimated as 11.79%. In these countries, if the material recycling rate is below the threshold level, the rise in the waste amount will increase environmental pollution. If the material recycling rate is above the threshold value, the rise in the waste amount will still increase environmental pollution, but the pollution increase rate will decrease. With the increase in the waste amount in the long term, environmental pollution can only be reduced by raising the material recycling rate. For the reduction of environmental pollution, which is one of the most prioritized issues in Europe in recent years, policy makers should take measures to increase the material recycling rate by taking the results of this study into consideration and pay attention to the implementation of these measures.
{"title":"Does the Material Recycling Rate Matter in the Effect of the Generated Waste on Environmental Pollution? Panel Smooth Transition Regression Approach","authors":"Fahriye Merdivenci, Celil Aydın, Hayrullah Altınok","doi":"10.35208/ert.1441001","DOIUrl":"https://doi.org/10.35208/ert.1441001","url":null,"abstract":"This study examined the effect of material recycling on the relationship between the waste amount and environmental pollution in EU-15 countries for the 1995-2019 period through panel smooth regression analysis by using the material recycling rate as the threshold variable. Based on the analysis results, the material recycling rate threshold level was estimated as 11.79%. In these countries, if the material recycling rate is below the threshold level, the rise in the waste amount will increase environmental pollution. If the material recycling rate is above the threshold value, the rise in the waste amount will still increase environmental pollution, but the pollution increase rate will decrease. With the increase in the waste amount in the long term, environmental pollution can only be reduced by raising the material recycling rate. For the reduction of environmental pollution, which is one of the most prioritized issues in Europe in recent years, policy makers should take measures to increase the material recycling rate by taking the results of this study into consideration and pay attention to the implementation of these measures.","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140707027","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kumaran P, Dr. S. Natarajan Sengodan, Sudesh Kumar M P, Anderson A, Prakash S
The Response Surface Methodology (RSM) optimization technique to examine the effect of load, Tomato Methyl Ester (TOME), and Ethanol injection enhanced diesel on engine performance and exhaust gas emissions with normal piston and Al2O3 coated piston. TOME biodiesel (10, 20, and 30%) and Ethanol (10, 20, and 30%) were chosen to increase BTE while minimizing BSFC, NOx, CO, smoke, and HC. The RSM technique was used to operate the engine by load (0-100%). The results revealed that engine load, TOME, and ethanol concentration all exhibited a considerable effect on the response variables. The (ANOVA) results for the established quadratic models specified that each model, furthermore, an ideal was discovered by optimizing an experiment's user-defined historical design. The present research efforts to improve the performance of a diesel engine by using a thermal barrier-coated piston that runs on biodiesel blends. Al2O3 is the chosen material for TBC due to its excellent thermal insulation properties. B20E30 has a 4% higher brake thermal efficiency than diesel, but B10E20 and B30E20 mixes have a 3.6% and 12% reduction in (BSFC). The B20 blends lowered CO and HC emissions by 6% to 8% respectively. In terms of performance and emissions, biodiesel blends performed similarly to pure diesel, and the combination was optimized through a design of experiment tool.
{"title":"Investigating the Emissions and Performance of Ethanol and Biodiesel Blends on Al2O3 Thermal Barrier Coated Piston Engine Using Response Surface Methodology Design - Multiparametric Optimization","authors":"Kumaran P, Dr. S. Natarajan Sengodan, Sudesh Kumar M P, Anderson A, Prakash S","doi":"10.35208/ert.1443393","DOIUrl":"https://doi.org/10.35208/ert.1443393","url":null,"abstract":"The Response Surface Methodology (RSM) optimization technique to examine the effect of load, Tomato Methyl Ester (TOME), and Ethanol injection enhanced diesel on engine performance and exhaust gas emissions with normal piston and Al2O3 coated piston. TOME biodiesel (10, 20, and 30%) and Ethanol (10, 20, and 30%) were chosen to increase BTE while minimizing BSFC, NOx, CO, smoke, and HC. The RSM technique was used to operate the engine by load (0-100%). The results revealed that engine load, TOME, and ethanol concentration all exhibited a considerable effect on the response variables. The (ANOVA) results for the established quadratic models specified that each model, furthermore, an ideal was discovered by optimizing an experiment's user-defined historical design. The present research efforts to improve the performance of a diesel engine by using a thermal barrier-coated piston that runs on biodiesel blends. Al2O3 is the chosen material for TBC due to its excellent thermal insulation properties. B20E30 has a 4% higher brake thermal efficiency than diesel, but B10E20 and B30E20 mixes have a 3.6% and 12% reduction in (BSFC). The B20 blends lowered CO and HC emissions by 6% to 8% respectively. In terms of performance and emissions, biodiesel blends performed similarly to pure diesel, and the combination was optimized through a design of experiment tool.","PeriodicalId":126818,"journal":{"name":"Environmental Research and Technology","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140791626","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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