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Mississippi Burning: An Exploration of Community Noise Levels from a Rural Mississippi Community Grappling with Wood Pellet Production Manufacturing

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SSRN 2024年

作者： Walker, Erica D. Franzen Lee, Nina Nica, C. Barnes, A. Graham, Barbara Martin, K. Community Noise Lab Department of Epidemiology Brown University School of Public Health ProvidenceRI02903 United States Environmental Science PhD Program College of Science Engineering and Technology Jackson State University JacksonMS39217 United States Greater Greener Gloster Project GlosterMS39638 United States

Wood pellets are being touted as a renewable and sustainable form of energy. As a result of this growing popularity, the global wood pellet market is expected to reach $16 billion by 2026. Currently, there are approximately 100 pellet manufacturing plants in the United States, with most of the production occurring in predominantly Black and poor communities in the rural South. At the request of residents concerned about the noise pollution emanating from wood pellet production plants in their community, we carried out a community noise exposure assessment. From August 2023 to March 2024, and working closely with a local community organization we measured a variety of noise metrics in Gloster, a wood pellet impacted city in rural Mississippi, comparing these noise pollution levels to a background community with no wood pellet industrial activity. We observed statistically significant higher levels of A-weighted, C-weighted, and Z-weighted sound in Gloster compared to Mendenhall. These differences persisted across different times of the day and days of the week. When examining the decibel levels for each of the center frequencies of the 1:3 octave band, this pattern persisted. Our preliminary findings suggest that a rural community’s soundscape can be negatively impacted by unchecked wood pellet manufacturing. We argue that the sound level differences in communities impacted by wood pellet manufacturing and those that are not, combined with the strategic placement of wood pellet plants in vulnerable communities in the rural south, may also be an environmental justice issue. © 2024, The Authors. All rights reserved.

关键词： Pelletizing

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Hybrid CNN-LSTM and Domain Modeling in Climate-Energy Analysis for a Smart Environment

Hybrid CNN-LSTM and Domain Modeling in Climate-Energy Analys...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Sarahana Shrestha Brian Buckley Aparna S. Varde Daniel Cwynar Environmental Science & Mangement PhD Program Montclair State University Montclair NJ USA Clean Energy & Sustainability Analytics Center Montclair State University Montclair NJ USA School of Computing Montclair State University Montclair NJ USA

This study aims to model the effect of local climate on energy generation in power plants in the United States. It considers climate change as well as the ever-growing electricity demand, contributing to greenhouse gas emissions. We propose an integration of AI-based and domain-specific paradigms via a hybrid approach of convolutional neural networks (CNN) & long short-term memory (LSTM) coupled with environmental models. Erstwhile studies focus mainly on specific energy sources, e.g. solar/wind power. There is a need for more comprehensive examination, e.g. temperature effects across various energy generation types at the macro level of states and micro level of power plants. This forms the major focus and novelty of our study. Fathoming the factors driving temperature-energy correlations, and assessing power plant vulnerability to changing climate patterns, are both essential to promote energy infrastructure resilience and sustainability. Our study offers novel insights into the complex interplay of temperature, energy generation, sustainability and related aspects to enable enhanced macro & micro level decision-making in energy sector. It therefore makes contributions to smart environment goals for smart cities and a smart planet.

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Amplifying concerns: An exploration of community noise levels in rural communities impacted by wood pellet production

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environmental Challenges 2024年 17卷

作者： Walker, E.D. Lee, N. Franzen Nica, C. Barnes, A. Graham, B. Martin, K. Community Noise Lab Department of Epidemiology Brown University School of Public Health Providence RI United States Environmental Science PhD Program College of Science Engineering and Technology Jackson State University Jackson MS United States Greater Greener Gloster Project Gloster MS United States

Wood pellets are increasingly promoted as a renewable and sustainable energy source, driving the global wood pellet market to a projected value of $16 billion by 2026. However, the rapid expansion of this industry disproportionately affects predominantly Black and low-income communities in the rural South, where approximately 100 pellet manufacturing plants are currently located in the United States. From August 2023 to March 2024, we conducted a noise exposure assessment in Gloster, Mississippi, a community affected by pellet production, and compared it to Mendenhall, a nearby community without industrial activity. In collaboration with a local organization, we measured noise metrics and found significantly higher A-weighted, C-weighted, and Z-weighted sound levels in Gloster, along with elevated decibels at multiple center frequencies. These findings suggest that wood pellet manufacturing can severely alter the soundscape of rural communities, raising environmental justice concerns. Our findings underscore the importance of investigating the cumulative health impacts of noise and other environmental burdens on these vulnerable communities to better inform policy. © 2024

关键词： Community noise environmental justice Low-frequency noise Mississippi Rural Wood biomass Wood pellet production

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Interactive Visualization and App Development for Precipitation Data in Sub-Saharan Africa

Interactive Visualization and App Development for Precipitat...

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IOT, Electronics and Mechatronics Conference (IEMTRONICS), IEEE International

作者： Divydharshini Karthikeyan Sudha Shah Aparna S. Varde Clement Alo Department of Computer Science Environmental Management and Science PhD program Department of Earth and Environmental Science Environmental Management and Science PhD program

ISBN: (数字)9781728196152

ISBN: (纸本)9781728196169

Precipitation plays a highly significant part in the hydrological cycle and is responsible for depositing most of the freshwater on earth. It can also impact the climate ecosystem. Considerable work has been performed to understand, model and predict precipitation. However, to the best of our knowledge, hardly any interactive visualizations exist that allow end-users to obtain a better perception of local impacts; also there are no specific apps for this purpose. The two goals of our work are thus: to design a visualization tool that illustrates changes in precipitation in a given time period with analysis; and to design a mobile application (app) that displays Sub-Saharan precipitation patterns, both using big data. This work would be advantageous to environmental scientists, as well as local residents of the respective areas. It would also be beneficial to students interested in conducting climate-based research. It addresses the theme of IoT (Internet of Things) which involves providing ubiquitous access to Internet-Based information on platforms such as smartphones and other mobile devices.

关键词： Data visualization Tools Big Data Meteorology Africa Mobile applications Human computer interaction

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Ciwars: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

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SSRN 2024年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech BlacksburgVA24060 United States Fralin Life Science Institute Virginia Tech BlacksburgVA24060 United States Department of Civil and Environmental Engineering Virginia Tech BlacksburgVA24060 United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech BlacksburgVA24060 United States

The rise of antibiotic resistance (AR) poses substantial threats to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate continuous AR monitoring through longitudinal metagenomic data are lacking. To address this, we introduce Cyberinfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive ARG and taxonomic profiling, along with detection of anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and demonstrates its potential for informing effective and timely interventions to mitigate the spread and transmission of AR. CIWARS is broadly applicable to longitudinal metagenomic data from any environment and thus could be a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use when it is needed. The web server is freely available at https://***/. © 2024, The Authors. All rights reserved.

关键词： Gene encoding

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Nanoplastics impair growth and nitrogen fixation of marine nitrogen-fixing cyanobacteria

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environmental Pollution 2024年 350卷 123960页

作者： Deng, Lixia Cheung, Shunyan Liu, Jiaxing Chen, Jiawei Chen, Fengyuan Zhang, Xiaodong Liu, Hongbin Department of Ocean Science The Hong Kong University of Science and Technology China Institute of Marine Biology National Taiwan Ocean University Keelung Taiwan Center of Excellence for the Oceans National Taiwan Ocean University Keelung Taiwan Key Laboratory of Tropical Marine Bio‐resources and Ecology South China Sea Institute of Oceanology Chinese Academy of Sciences Guangzhou China SZU-HKUST Joint PhD Program in Marine Environmental Science Shenzhen University Shenzhen China Guangzhou China The Hong Kong University of Science and Technology China

Nanoplastics pollution is a growing environmental problem worldwide. Recent research has demonstrated the toxic effects of nanoplastics on various marine organisms. However, the influences of nanoplastics on marine nitrogen-fixing cyanobacteria, a critical nitrogen source in the ocean, remained unknown. Here, we report that nanoplastics exposure significantly reduced growth, photosynthetic, and nitrogen fixation rates of Crocosphaera watsonii (a major marine nitrogen-fixing cyanobacterium). Transcriptomic analysis revealed that nanoplastics might harm C. watsonii via downregulation of photosynthetic pathways and DNA damage repair genes, while genes for respiration, cell damage, nitrogen limitation, and iron (and phosphorus) scavenging were upregulated. The number and size of starch grains and electron-dense vacuoles increased significantly after nanoplastics exposure, suggesting that C. watsonii allocated more resources to storage instead of growth under stress. We propose that nanoplastics can damage the cell (e.g., DNA, cell membrane, and membrane-bound transporters), inhibit nitrogen and carbon fixation, and hence lead to nutrient limitation and impaired growth. Our findings suggest the possibility that nanoplastics pollution could reduce the new nitrogen input and hence affect the productivity in the ocean. The impact of nanoplastics on marine nitrogen fixation and productivity should be considered when predicting the ecosystem response and biogeochemical cycling in the changing ocean. © 2024 Elsevier Ltd

关键词： Nitrogen fixation

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Analyzing Land Use Change and Climate Data to Forecast Energy Demand for a Smart Environment

Analyzing Land Use Change and Climate Data to Forecast Energ...

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International Renewable and Sustainable Energy Conference (IRSEC)

作者： Archana Prasad Aparna S. Varde Raga Gottimukkala Clement Alo Pankaj Lal Montclair State University Montclair NJ USA Environmental Science and Management PhD Program Montclair State University Montclair NJ USA

ISBN: (纸本)9781665413206

Energy is essential for the sustainable development of nations. Increasing population growth, along with expected increases in duration and intensity of extreme weather, can increase energy demands. There is the potential for further interruption if companies do not appropriately account for an increase in demand, especially with the state and federal agencies implementing a transition to clean energy production by the end of the decade. In order to assess energy demand with changing variables, we conduct energy demand analysis in a moderate emissions scenario in the residential sector that consumes the most energy of all energy sectors. We assess changes in energy demand by comparing results from the data mining / machine learning techniques of Support Vector Machines (SVM) and Artificial Neural Network (ANN). Our results are helpful in contributing to sustainable energy goals in line with smart environment initiatives via greenness and energy efficiency.

关键词： Support vector machines Renewable energy sources Sociology Green products Artificial neural networks Production Machine learning

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CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

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Journal of Molecular Biology 2025年第15期437卷

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

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Determinant of importance-performance and willingness to participate in Komodo adaptive conservation programs

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Journal for Nature Conservation 2024年 81卷

作者： Sianipar, Imelda Masni Juniaty Lee, Chun-Hung Wang, Hurng-Jyuhn Kim, Doo-Chul Suryawan, I Wayan Koko PhD Student in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of International Relations Faculty of Social Science and Political Science Universitas Kristen Indonesia Department of Natural Resources and Environmental Studies College of Environmental Studies and Oceanography National Dong Hwa University Program in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of Rural Environment Management Graduate School of Environmental and Life Science Okayama University Ph.D Program in Asia-Pacific Regional Studies College of Humanities and Social Sciences National Dong Hwa University Department of Environmental Engineering Faculty of Infrastructure Planning Universitas Pertamina Jalan Sinabung II Terusan Simprug

This study delves into the evaluation of the Komodo dragons Adaptive Conservation program (KACP) at Komodo National Park in Indonesia through Importance-Performance Analysis (IPA). It aims to understand how the programs components align with community priorities and assess its effectiveness. Utilizing a mixed-methods approach, this research analyzes community perceptions of KACP's critical elements, spanning eco-tourism initiatives to environmental protection strategies. The community is categorized into local advocates, eco-tourism enthusiasts, and eco-community harmonizers, identifying how each group perceives and interacts with various facets of KACP. The findings reveal significant differences in perceptions of importance and performance among these groups, providing insights into areas of strength and those needing improvement. Additionally, the study includes an analysis of demographic influences on these perceptions through Logit and Probit models, aiming to understand the impacts across different community segments. The results highlight the necessity for an adaptive and responsive design in conservation efforts, considering community expectations and ecological impacts. Despite offering comprehensive insights, the study acknowledges limitations in the demographic scope and suggests future research directions, such as longitudinal studies and an expanded analysis of variables. These findings offer critical guidance for policymakers and conservation practitioners to enhance conservation programs’ effectiveness and community alignment, emphasizing the need to adapt to the challenges and changes introduced by KACP. © 2024 Elsevier GmbH

关键词： Adaptive capacity Community perception Conservation strategy evaluation Importance-performance analysis Protected areas

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An Ordinance-Tweet Mining App to Disseminate Urban Policy Knowledge for Smart Governance 19th

An Ordinance-Tweet Mining App to Disseminate Urban Policy Kn...

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19th IFIP WG 6.11 Conference on e-Business, e-Services, and e-Society, I3E 2020

作者： Varghese, Christina Varde, Aparna S. Du, Xu Department of Computer Science Montclair State University MontclairNJ United States Department of Earth and Environmental Science Montclair State University MontclairNJ United States Environmental Science and Management PhD Program Montclair State University MontclairNJ United States

ISBN: (纸本)9783030450014

This paper focuses on how populations by the use of technology, more specifically an app, can comprehend the enactment of ordinances (local laws) in an urban area along with their public reactions expressed as tweets. Furthermore, they can understand how well the area is developing and enhancing as a Smart City. The main goal of this research is to develop an Ordinance-Tweet Mining App that disseminates the results of analyzing ordinances and tweets about them, especially related to Smart City Characteristics such as Smart Environment, Smart Mobility etc. This app would be beneficial to various users such as environmental scientists, policy makers, city committees as well as the common public in becoming more aware of legislative bodies, and possibly contributing in different aspects to make the urban area improve as a Smart City. This work fits the realm of Smart Governance due to transparency via public involvement. © 2020, IFIP International Federation for Information Processing.

关键词： Sentiment analysis

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