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44th Asian Conference on Remote Sensing, ACRS 2023

作者： Prasetya, Koni Dwi Tsai, Fuan International PhD Program in Environmental Science and Technology National Central University 300 Jhongda Rd. Jhongli Taoyuan32001 Taiwan Center for Space and Remote Sensing Research National Central University 300 Jhongda Rd. Jhongli Taoyuan32001 Taiwan

ISBN: (纸本)9781713893646

This study introduces a novel deep learning-based method for detecting open pit coal mines in medium high (MH)resolution satellite imagery and analyzing annual variations in coal mining areas. The study aims to monitor and precisely identify coal mines, given their vital function as a primary energy source and a significant contributor to greenhouse gas emissions. The proposed method employs the U-Net architecture and ResNet 34 as its backbone for accurate detection and classification. The applied dataset consists of multispectral imagery from Sentinel-2 and synthetic aperture radar (SAR) imagery from Sentinel-1. Manual labelling of known coal mine locations using mining data as a reference, subdividing Sentinel image patches for training U-Net convolutional neural networks (CNNs) to classify coal mine and non-coal mine areas, and training and testing U-Net architecture and ResNet as a backbone deep learning model are the three essential steps involved in the process. The classification accuracy of the coal mining detection deep learning model is 97%, and the kappa value is 0.91. Preliminary results indicate that the investigation demonstrates the evolution of coal mining from 2016 to 2021, with an increase of over 40% in coal mining area in 2019. In 2017 and 2020, the area mined for coal will decrease. Variations in annual coal mining variations emphasize the significance of replanting efforts. The proposed method uses deep learning and satellite imagery to provide an accurate and efficient solution for detecting and monitoring open-pit coal mines. Incorporating artificial intelligence into dynamic coal mining activities yields valuable insights that aid in making informed decisions for sustainability. © 2023 ACRS. All Rights Reserved.

关键词： Artificial Intelligence Coal Mine Deep Learning Monitoring Multispectral SAR Sentinel Supervised Classification U-Net

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Direct detection of adenovirus in environmental waste waters by portable optical fiber sensor platform

Direct detection of adenovirus in environmental waste waters...

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12th IEEE SENSORS 2013 Conference

作者： Yildirim, Nimet Li, Dan Long, Feng Gu, April Z. Bioengineering PhD Program Northeastern University Boston United States Department of Civil and Environmental Engineering Northeastern University Boston United States State Key Joint Laboratory of Environment Simulation and Pollution Control Environmental Science and Engineering Tsinghua University Beijing China Department of Environment Science and Engineering Tsinghua University Beijing China 360 Huntington Ave. Boston MA 02115 United States

ISBN: (纸本)9781467346405

Reusable and portable evanescent wave biosensor for rapid, sensitive and highly selective direct detection of adenoviruses was developed. Here, specific antibody for the hexon protein of the virus was used as a recognition element. Linear detection range was determined from 100PFU/ml to 2000PFU/ml. This direct detection system can be regenerated successfully with over 5 times without significant deterioration of the sensor performance. The developed biosensor system has high specificity towards adenovirus, and it was applied to environmental waste water samples for the detection of adenovirus with 95 to 117% recovery and 1.1 to 8.1% cv values (n=2). © 2013 IEEE.

关键词： Optical fibers

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Alginate modifies the physiological impact of CeO2nanoparticles in corn seedlings cultivated in soil

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Journal of environmental sciences 2014年第2期26卷 382-389页

作者： Lijuan Zhao Jose R.Peralta-Videa Bo Peng Susmita Bandyopadhyay Baltazar Corral-Diaz Pedro Osuna-Avila Milka O.Montes Arturo A.Keller Jorge L.Gardea-Torresdey Chemistry Department The University of Texas at El Paso 500 West University Avenue University of California Center for Environmental Implications of Nanotechnology The University of Texas at El Paso 500 West University Avenue Environmental Science and Engineering PhD program The University of Texas at El Paso 500 West University Avenue Department of Biological Sciences The University of Texas at El Paso 500 West University Avenue Universidad Aut'onoma De Ciudad Ju'arez Plutarco Elias Calles # 1210 Fovissste Chamizal. Ciudad Juarez Chihuahua 32310 Mexico Chemistry Department University of Texas of the Permian Basin Odessa Bren School of Environmental Sciences and Management 3420 Bren Hall University of California

Alginates are naturally occurring components of organic matter in natural soil whose effects on nanoparticle （NP） toxicity to plants is not well understood. In the present study, corn plants were grown for one month in soil spiked with 400 mg/kg CeO2 NPs with various alginate concentrations. After one month of growth in the NPs impacted soil, plants were harvested and analyzed for Ce and mineral element concentrations. Chlorophyll concentration and heat shock protein 70, used as biomarkers for oxidative stress, were also evaluated. Results showed that, compared to CeO2 NPs treatment, alginate at 10, 50, and 100 mg/kg increased Ce concentration in roots by approximately 46%, 38%, and 29% and by 115%, 45%, and 56% in shoots, respectively. CeO 2 NPs without alginate increased Mn accumulation in roots by 34% compared to control. CeO2 NPs with low and medium alginate increased Mn by ca. 92% respect to NPs without alginate and by ca. 155% respect to control. CeO2 NPs without/with alginate significantly increased accumulation of Fe and Al in roots. In addition, alginate at 50 mg/kg increased Zn accumulation in roots by 52% compared to control. In shoots, K increased at all NP treatments but the accumulation of other elements was not affected. Alginate enlarged the impact of CeO2 NPs to corn plants by reducing chlorophyll a content and triggering overexpression of heat shock protein 70.

关键词： CeO2 NPs alginate nutrient uptake chlorophyll heat shock protein

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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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environmental REVIEWS AND CASE STUDIES: Participatory Watershed Management: A Case Study from Maritime Canada

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environmental Practice 2012年第2期14卷 143-154页

作者： Guehlstorf, Nicholas Hallstrom, Lars K [a1]PhD Associate Professor Department of Political Science Environmental Sciences Program Southern Illinois University Edwardsville Illinois [a2]PhD Director Alberta Centre for Sustainable Rural Communities Associate Professor of Political Studies/Resource Economics and Environmental Sociology Department of Social Sciences (Augustana Faculty) and Faculty of Agricultural Life and Environmental Sciences University of Alberta Edmonton Canada

Canada has seen some community approaches to watershed management similar to network governance, citizen advisory boards, and participatory action mechanisms. These collaborative approaches are highlighted in scholarship, but are limited by context as implementation is not utilized well beyond local situations. The successes of Nova Scotia wetlands are generalizable, as they provide applicable knowledge about natural resource governance with limited federal guidelines, numerous exclusionary authorities, and new regional development tactics that are beneficial for many. In practice, this case study specifically explains the role of earnest, local, and inclusive participation that is well coordinated with multiple civic agencies. environmental Practice 14:143-153 (2012) [PUBLICATION ABSTRACT]

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Multispectral Drone Data Analysis on Coastal Dunes

Multispectral Drone Data Analysis on Coastal Dunes

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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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DEEP SOUTH URBAN AMBIENT PARTICULATE MATTER INDUCES CYTOTOXIC AND INFLAMMATORY RESPONSES IN A549 CELLS

Biomedical Sciences Instrumentation

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Biomedical sciences Instrumentation 2024年第2期60卷 188-201页

作者： Pacurari, M. Cox, I. Kafoury, R. Komolafe, K. Kodedoye, O. Farah, I. Department of Biology College of Science Engineering and Technology Jackson State University JacksonMS39217 United States Environmental Science PhD Program College of Science Engineering and Technology Jackson State University JacksonMS39217 United States RCMI Center for Environmental Health College of Science Engineering and Technology Jackson State University JacksonMS39217 United States

Exposure to ambient air pollution component particulate matter (PM) in urban environments has been associated with adverse health effects to the exposed population. This study aimed to investigate the cytotoxic and inflammatory responses of lung A549 cells to ambient particulate matter (PM) extracts (EE) collected from a geographical site in south of city limits of Jackson, Mississippi. PM was collected from the site, extracted, and characterized for chemical composition and microscopic analysis using SEM. Following characterization, the EE was used to treat A549 cells, and various cellular responses were assessed. Our SEM analysis of EE indicated the presence of micrometer-sized particles, and elemental analysis indicated the presence of elements such as C, Si, Fe, S, N, P, and Ca. Cell viability decreased in a dose- and timedependent manner following exposure to EE. Inflammatory gene IL-1β, chemokine CCL-18 and MIP-1, and transcription factor NF-кB were upregulated by EE. EE also increased the level of ROS and mitochondria. In conclusion, our data indicates that EE is cytotoxic, inflammatory, and an oxidant to lung cells Also, the findings of the present study highlight the potential health risks associated with exposure to air pollution and inflammatory lung disease. © 2024 IAE All rights reserved.

关键词： Lung cancer

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Plant-based green synthesis of metallic nanoparticles: scientific curiosity or a realistic alternative to chemical synthesis?

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Nanotechnology for environmental Engineering 2016年第1期1卷 1-29页

作者： Peralta-Videa, Jose R. Huang, Yuxiong Parsons, Jason G. Zhao, Lijuan Lopez-Moreno, Laura Hernandez-Viezcas, Jose A. Gardea-Torresdey, Jorge L. Chemistry Department University of Texas at El Paso 500 West University Ave. El PasoTX79968 United States Environmental Science and Engineering PhD Program University of Texas at El Paso 500 West University Ave. El PasoTX79968 United States University of Texas at El Paso 500 West University Ave. El PasoTX79968 United States Bren School of Environmental Science and Management University of California Santa Barbara Santa BarbaraCA United States Department of Chemistry The University of Texas Rio Grande Valley 1201 W University Dr EdinburgTX78539 United States Department of Chemistry University of Puerto Rico at Mayagüez Mayagüez PR00680 United States

Currently, thousands of tons of metallic nanoparticles (MNPs) are produced and utilized in nano-enabled devises, personal care, medicinal, food and agricultural products. It is generally accepted that the reaction compounds and the techniques used in industrial production of MNPs are not environmentally friendly. The green synthesis has been proposed as an alternative to reduce the use of hazardous compounds and harsh reaction conditions in the production of MNPs. In this endeavor, investigators have used organic compounds, microbes, plants and plant-derived materials as reducing agents. Research papers are published every year, and each one of them stresses the benefits of the green approach and the advantages over the traditional syntheses. However, after almost two decades since the explosion of the reports about the new approach, the commercial production of green-synthesized nanoparticles does not seem to find a way to scale up commercial production. This review includes descriptions of the traditional and green synthesis and applications of MNPs and highlights the factors limiting the use of plant-based synthesis as a real alternative to the traditional synthesis of MNPs. © 2016, Springer International Publishing Switzerland.

关键词： Synthesis (chemical)

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

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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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ALIENS INVADE AND DROP OFF LITTER

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science Scope 2015年第5期38卷 16-23页

作者： Akasha Faist Pam Collinge Ian Schwartz Sharon Collinge PhD candidate in the ecology and evolutionary biology department at the University of Colorado Boulder sixth-grade science and technology teacher at Marshall Elementary in Eureka Kansas Casey Middle School in Boulder Colorado professor in the environmental studies program at the University of Colorado Boulder.

In the experiment described in this article, students gain an awareness of the environmental problems caused by invasive species. Students simulate invasive plant-litter depths, using pots containing germinating native plant seeds, soil, and plant litter, to understand how human-in- troduced invasive species can hinder the germination and growth of native plants. This lesson is geared toward grades 6-8 and was originally taught near the end of the school year as part of a unit on ecology and invasive species. Prior to this activity, students should have learned about data analysis, the structure and function of a cell, meiosis and mitosis, genetics, and evolution. Teachers Should allow three to four weeks for setup and monitoring of and reporting on the lab during the month of May. (Because each classroom has unique requirements and time constraints, this experiment can be formatted to fit within those guidelines; however, a detailed sample time line can be found with the online version of this article at ***/mid- dleschool/connections. aspx.) by Akasha Faist, Pam Collinge, lan Schwartz, and Sharon Collinge.

关键词： Invasive species Plant litter Seeds Poultry litter Native plants Germination Native species Ecosystems Leaves

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