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Prediction Tool on Fine Particle Pollutants and Air Quality for environmental Engineering

SN Computer science 2022年第3期3卷 184页

作者： Varde, Aparna S. Pandey, Abidha Du, Xu Dept. of Computer Science Environmental Science and Management PhD Program Montclair State University Montclair NJ United States Visiting Researcher at Max Planck Institute for Informatics Saarbrücken Germany Department of Computer Science Montclair State University Montclair NJ United States Department of Earth and Environmental Science Environmental Science and Management PhD Montclair State University Montclair NJ United States

This article focuses on the research, design and implementation of a prediction tool for air quality to estimate pollutant concentrations, contributing to environmental engineering. It addresses prediction of fine particle air pollutants of diameter less than 2.5 µm (particulate matter 2.5), their concentration being substantially influenced by urban traffic. We collect worldwide multicity data from health-related public sources on which mining is performed using classical data mining/machine learning paradigms: association rules, clustering and classification. Challenges include adapting appropriate techniques based on data, and capturing subtle domain-specific aspects. The prediction tool is built using knowledge discovered by mining, leveraging health standards, catering to novice, intermediate and expert users. The prediction output is accurate, efficient, interpretable and useful as evident from our experiments. The tool is helpful for urban decision support. This work is beneficial in developing software systems such as intelligent tutors, mobile device apps and smart city tools. It contributes to smart environment, mobility and living, making a positive impact on smart cities and sustainability. In this work, we claim that classical computational paradigms in their fundamental form can be adapted to solve environmental engineering problems, with easy comprehension, as per the Occam’s razor principle that advocates simplicity. This article constitutes applied research: using computational techniques to solve domain-specific problems. Future work includes exploring models in deep learning such as CNN and Bi-LSTM, and considering different types of pollutants as well as other sources besides multicity traffic data, to conduct further studies. This would address additional challenges with enhancements. © 2022, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： Applied research Decision support environmental engineering Health informatics Machine learning Urban studies

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Linking Alternative Fuel Vehicles Adoption with Socioeconomic Status and Air Quality Index

arXiv

引用

arXiv 2023年

作者： Singh, Anuradha Yadav, Jyoti Shrestha, Sarahana Varde, Aparna S. Environmental Science and Management PhD Program Department of Computer Science Montclair State University MontclairNJ07043 United States

Understanding adoption of new technology by its consumers helps us deal with the challenges it faces in any new market. Moreover, the impact it creates on society in terms of the environment, health, and justice is significantly based on the extent of adoption. Alternative fuel vehicles (AFVs) is such an area that faces challenges in terms of consumers’ diverse social status and resistance to change. In order to achieve a cleaner transportation sector, we need to address these challenges. In this paper, we conduct a study via machine learning techniques to correlate the adoption of AFVs across various regions, their socioeconomic ranking, and their impact on the air quality index (AQI);and furthermore to predict the AQI as per the region’s AFV adoption. This is an empirical study with predictive modeling based on a regional panel data analysis where we use real US census data, air quality data, and data on the number of AFVs purchased per region. Research in this area can help to promote appropriate policies for AFV adoption in the future with due justice to different population groups. This work exemplifies a modest utilization of AI techniques to enhance social good. More specifically, it makes a considerable impact on energy, climate, transportation, and environmental sustainability. © 2023, CC BY.

关键词： Population statistics

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LONG-TERM RECAPTURE OF WESTERN PAINTED TURTLE (CHRYSEMYS PICTA BELLII): LONGEVITY IMPLICATIONS

引用

Northwestern Naturalist 2023年第1期104卷 72-76页

作者： Laura B Guderyahn Elaine M Stewart Sue G Beilke Catherine de Rivera Marc P Hayes Earth Environment and Society PhD Program Department of Environmental Science and Management Portland State University Portland OR 97207 USA (LBG) Metro – Conservation Program Portland OR 97232 USA (EMS) Independent Ecologist Tigard OR 97223 USA (SGB) Department of Environmental Science and Management Portland State University Portand OR 97207 USA (CDR) Aquatic and Herpetological Research LLC Eagle Point OR 97524 USA (MPH) .

Western Painted Turtles (Chrysemys picta bellii) are 1 of only 2 native turtles in the Pacific Northwest. In October 2021, we recaptured an adult male 21 y after it was initially marked. In 2000, it had at least 9 annuli, indicating an age of at least 30 y in 2021. This approximates only half the maximum known age for the larger C. picta species complex east of the continental divide (61 y). Further, in 2000, this adult male was 37–43 mm larger than the largest or oldest males recorded from eastern populations of C. p. bellii, which suggests that fundamental differences may exist in growth between eastern and western populations that may reflect different age-size relationships. These relationships, along with longevity data, are foundational to conservation that seeks to understand demography and survival in western populations. Study of eastern populations shows that determination of age limits may span decades, so collaboration for data-sharing over long timelines to understand the patterns in western populations will be crucial.

关键词： age Chrysemys picta bellii longevity marked recapture western North America Western Painted Turtle

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

Interactive Visualization and App Development for Precipitat...

引用

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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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...

引用

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

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

引用

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

引用

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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Multispectral Drone Data Analysis on Coastal Dunes

Multispectral Drone Data Analysis on Coastal Dunes

引用

IEEE International Conference on Big Data

作者： Britnie Gonzalez-Moodie Shane Daiek Jorge Lorenzo-Trueba Aparna S. Varde Montclair State University NJ USA Environmental Science and Management PhD Program Montclair State University NJ USA Visiting Researcher Max Planck Institute for Informatics Saarbrucken Germany

ISBN: (纸本)9781665445993

We devise a method to study coastal dune vegetation based on drone data orthomosaic mapping and machine learning algorithms to distinguish objects with spatial and color accuracy from numerous high-quality drone multispectral images. It allows accurate surveying on the density of coastal dune vegetation and potentially individual species. We thus analyze big data to develop tools for coastal resiliency and sustainability.

关键词： Machine learning algorithms Data analysis Image color analysis Sea measurements Vegetation mapping Forestry Big Data

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Mining GIS data to predict urban sprawl

arXiv

引用

arXiv 2021年

作者： Pampoore-Thampi, Anita Varde, Aparna S. Yu, Danlin Department of Computer Science Montclair State University MontclairNJ United States Department of Earth and Environmental Studies Montclair State University MontclairNJ United States Environmental Management PhD Program Montclair State University MontclairNJ United States

This paper addresses the interesting problem of processing and analyzing data in geographic information systems (GIS) to achieve a clear perspective on urban sprawl. The term "urban sprawl" refers to overgrowth and expansion of low-density areas with issues such as car dependency and segregation between residential versus commercial use. Sprawl has impacts on the environment and public health. In our work, spatiotemporal features related to real GIS data on urban sprawl such as population growth and demographics are mined to discover knowledge for decision support. We adapt data mining algorithms, Apriori for association rule mining and J4.8 for decision tree classification to geospatial analysis, deploying the ArcGIS tool for mapping. Knowledge discovered by mining this spatiotemporal data is used to implement a prototype spatial decision support system (SDSS). This SDSS predicts whether "urban sprawl" is likely to occur. Further, it estimates the values of pertinent variables to understand how the variables impact each other. The SDSS can help decision-makers identify problems and create solutions for avoiding future sprawl occurrence and conducting urban planning where sprawl already occurs, thus aiding sustainable development. This work falls in the broad realm of geospatial intelligence and sets the stage for designing a large scale SDSS to process big data in complex environments, which constitutes part of our future work. © 2021, CC BY-SA.

关键词： Association rules

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Elemental Characterization of Ambient Particulate Matter for a Globally Distributed Monitoring Network: Methodology and Implications

ACS ES&T air

引用

ACS ES&T air 2024年第4期1卷 283-293页

作者： Xuan Liu Jay R Turner Christopher R Oxford Jacob McNeill Brenna Walsh Emmie Le Roy Crystal L Weagle Emily Stone Haihui Zhu Wenyu Liu Zilin Wei Nicole P Hyslop Jason Giacomo Ann M Dillner Abdus Salam Al-Amin Hossen Zubayer Islam Ihab Abboud Clement Akoshile Omar Amador-Muñoz Nguyen Xuan Anh Araya Asfaw Rajasekhar Balasubramanian Rachel Ying-Wen Chang Craig Coburn Sagnik Dey David J Diner Jinlu Dong Tareq Farrah Paterne Gahungu Rebecca M Garland Michel Grutter de la Mora Sina Hasheminassab Juanette John Jhoon Kim Jong Sung Kim Kristy Langerman Pei-Chen Lee Puji Lestari Yang Liu Tesfaye Mamo Mathieu Martins Olga L Mayol-Bracero Mogesh Naidoo Sang Seo Park Yoav Schechner Robyn Schofield Sachchida N Tripathi Eli Windwer Ming-Tsang Wu Qiang Zhang Michael Brauer Yinon Rudich Randall V Martin Department of Energy Environmental & Chemical Engineering Washington University in St. Louis St. Louis Missouri 63130 United States. Air Quality Research Center University of California Davis Davis California 95616 United States. Department of Chemistry University of Dhaka Dhaka 1000 Bangladesh. Air Quality Research Division Environment and Climate Change Canada Toronto Ontario M3H 5T4 Canada. Department of Physics University of Ilorin Ilorin 240003 Nigeria. Instituto de Ciencias de la Atmósfera y Cambio Climático Universidad Nacional Autónoma de México Mexico City 04510 Mexico. Institute of Geophysics Vietnam Academy of Science and Technology Hanoi 11307 Vietnam. Institute of Geophysics and Space Science Addis Ababa University Addis Ababa 1176 Ethiopia. Department of Civil and Environmental Engineering National University of Singapore Singapore 117576 Singapore. Department of Physics and Atmospheric Science Dalhousie University Halifax Nova Scotia B3H 4R2 Canada. Department of Geography and Environment University of Lethbridge Lethbridge Alberta T1K 3M4 Canada. Centre for Atmospheric Sciences Indian Institute of Technology Delhi New Delhi 110016 India. Jet Propulsion Laboratory California Institute of Technology Pasadena California 91109 United States. School of Environment Tsinghua University Beijing 100084 People's Republic of China. Research Laboratories Khalifa University Abu Dhabi 127788 United Arab Emirates. Institute of Applied Statistics University of Burundi Bujumbura BP1550 Burundi. Council for Scientific and Industrial Research Pretoria 0001 South Africa. Unit for Environmental Sciences and Management North-West University Potchefstroom 2531 South Africa. Department of Geography Geo-Informatics and Meteorology University of Pretoria Pretoria 0002 South Africa. Department of Atmospheric Sciences Yonsei University Seoul 03722 Republic of Korea. Department of Community Health and Epidemiology Dalhousie University Halifax Nova Scotia B3

Global ground-level measurements of elements in ambient particulate matter (PM) can provide valuable information to understand the distribution of dust and trace elements, assess health impacts, and investigate emission sources. We use X-ray fluorescence spectroscopy to characterize the elemental composition of PM samples collected from 27 globally distributed sites in the Surface PARTiculate mAtter Network (SPARTAN) over 2019-2023. Consistent protocols are applied to collect all samples and analyze them at one central laboratory, which facilitates comparison across different sites. Multiple quality assurance measures are performed, including applying reference materials that resemble typical PM samples, acceptance testing, and routine quality control. Method detection limits and uncertainties are estimated. Concentrations of dust and trace element oxides (TEO) are determined from the elemental dataset. In addition to sites in arid regions, a moderately high mean dust concentration (6 μg/m) in PM is also found in Dhaka (Bangladesh) along with a high average TEO level (6 μg/m). High carcinogenic risk (>1 cancer case per 100000 adults) from airborne arsenic is observed in Dhaka (Bangladesh), Kanpur (India), and Hanoi (Vietnam). Industries of informal lead-acid battery and e-waste recycling as well as coal-fired brick kilns likely contribute to the elevated trace element concentrations found in Dhaka.

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