Special Issue "Spatial Analysis of Pollution and Risk in a Changing Climate"

Special Issue Editor

Guest Editor
Prof. Dr. Jason K. Levy

Disaster Preparedness and Emergency Management, University of Hawaii, Kapolei, HI 96707, USA
Website | E-Mail
Interests: fuzzy systems; evolutionary algorithms; neural networks; artificial intelligence; network security

Special Issue Information

Dear Colleagues,

This Special Issue deals with spatial issues pertaining for the management of air, land and water pollution. This issue is particularly urgent and important in the wake of the destructive 2017 Hurricane Season. For example, Hurricane Harvey caused the release of hazardous materials into the environment. At US Gulf Coast industrial facilities, at least 14 toxic waste sites were flooded or damaged and 100 spills of hazardous substances have been reported. In Texas, USA, many plants released of hazardous airborne emissions. There are a number of advances in geospatial tools for analyzing environmental pollution. Remote sensing tools, such as Landsat enhanced thematic mapper (ETM+), shuttle radar topography mission (SRTM), and sequential Landsat satellite images, can be used to detect environmental pollution while spaceborne platforms, such as Terra, Aqua, Aura, and ENVISAT have provided new insights into the understanding of the Earth’s atmosphere.

Prof. Dr. Jason K. Levy
Guest Editor

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1000 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • pollution modeling
  • disaster risk
  • chemical hazards
  • satellite data
  • emissions

Published Papers (3 papers)

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Research

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Open AccessArticle A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran
ISPRS Int. J. Geo-Inf. 2019, 8(2), 99; https://doi.org/10.3390/ijgi8020099
Received: 23 January 2019 / Revised: 17 February 2019 / Accepted: 20 February 2019 / Published: 23 February 2019
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Abstract
Environmental pollution has mainly been attributed to urbanization and industrial developments across the globe. Air pollution has been marked as one of the major problems of metropolitan areas around the world, especially in Tehran, the capital of Iran, where its administrators and residents [...] Read more.
Environmental pollution has mainly been attributed to urbanization and industrial developments across the globe. Air pollution has been marked as one of the major problems of metropolitan areas around the world, especially in Tehran, the capital of Iran, where its administrators and residents have long been struggling with air pollution damage such as the health issues of its citizens. As far as the study area of this research is concerned, a considerable proportion of Tehran air pollution is attributed to PM10 and PM2.5 pollutants. Therefore, the present study was conducted to determine the prediction models to determine air pollutions based on PM10 and PM2.5 pollution concentrations in Tehran. To predict the air-pollution, the data related to day of week, month of year, topography, meteorology, and pollutant rate of two nearest neighbors as the input parameters and machine learning methods were used. These methods include a regression support vector machine, geographically weighted regression, artificial neural network and auto-regressive nonlinear neural network with an external input as the machine learning method for the air pollution prediction. A prediction model was then proposed to improve the afore-mentioned methods, by which the error percentage has been reduced and improved by 57%, 47%, 47% and 94%, respectively. The most reliable algorithm for the prediction of air pollution was autoregressive nonlinear neural network with external input using the proposed prediction model, where its one-day prediction error reached 1.79 µg/m3. Finally, using genetic algorithm, data for day of week, month of year, topography, wind direction, maximum temperature and pollutant rate of the two nearest neighbors were identified as the most effective parameters in the prediction of air pollution. Full article
(This article belongs to the Special Issue Spatial Analysis of Pollution and Risk in a Changing Climate)
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Open AccessArticle Spatiotemporal Analysis of Carbon Emissions and Carbon Storage Using National Geography Census Data in Wuhan, China
ISPRS Int. J. Geo-Inf. 2019, 8(1), 7; https://doi.org/10.3390/ijgi8010007
Received: 12 November 2018 / Revised: 21 December 2018 / Accepted: 21 December 2018 / Published: 26 December 2018
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Abstract
Mapping changes in carbon emissions and carbon storage (CECS) with high precision at a small scale (urban street-block level) can improve governmental policy decisions with respect to the construction of low-carbon cities. In this study, a methodological framework for assessing the carbon budget [...] Read more.
Mapping changes in carbon emissions and carbon storage (CECS) with high precision at a small scale (urban street-block level) can improve governmental policy decisions with respect to the construction of low-carbon cities. In this study, a methodological framework for assessing the carbon budget and its spatiotemporal changes from 2015 to 2017 in Wuhan is proposed, which is able to monitor a large area. To estimate the carbon storage, a comprehensive coefficient model was adopted with carbon density factors and corresponding land cover types. Details regarding land cover were extracted from the Geographic National Census Data (GNCD), including forests, grasslands, croplands, and gardens. For the carbon emissions, an emission-factor model was first used and a spatialization operation was subsequently performed using the geographic location that was obtained from the GNCD. The carbon emissions that were identified in the study are from fossil-fuel consumption, industrial production processes, disposal of urban domestic refuse, and transportation. The final dynamic changes in the CECS, in addition to the net carbon emissions, were monitored and analyzed, yielding temporal and spatial maps with a high-precision at a small scale. The results showed that the carbon storage in Wuhan declined by 2.70% over the three years, whereas the carbon emissions initially increased by 0.2%, and subsequently decreased by 3.1% over this period. The trend in the net carbon emission changes was similar to that of the carbon emissions, demonstrating that the efficiency of carbon reduction was improved during this period. Precise spatiotemporal results at the street-block level can offer insights to governments that are engaged in urban carbon cycle decision making processes, improving their capacities to more effectively manage the spatial distribution of CECS. Full article
(This article belongs to the Special Issue Spatial Analysis of Pollution and Risk in a Changing Climate)
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Review

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Open AccessReview Critical Review of Methods to Estimate PM2.5 Concentrations within Specified Research Region
ISPRS Int. J. Geo-Inf. 2018, 7(9), 368; https://doi.org/10.3390/ijgi7090368
Received: 15 June 2018 / Revised: 7 August 2018 / Accepted: 20 August 2018 / Published: 7 September 2018
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Abstract
Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points [...] Read more.
Obtaining PM2.5 data for the entirety of a research region underlies the study of the relationship between PM2.5 and human spatiotemporal activity. A professional sampler with a filter membrane is used to measure accurate values of PM2.5 at single points in space. However, there are numerous PM2.5 sampling and monitoring facilities that rely on data from only representative points, and which cannot measure the data for the whole region of research interest. This provides the motivation for researching the methods of estimation of particulate matter in areas having fewer monitors at a special scale, an approach now attracting considerable academic interest. The aim of this study is to (1) reclassify and particularize the most frequently used approaches for estimating the PM2.5 concentrations covering an entire research region; (2) list improvements to and integrations of traditional methods and their applications; and (3) compare existing approaches to PM2.5 estimation on the basis of accuracy and applicability. Full article
(This article belongs to the Special Issue Spatial Analysis of Pollution and Risk in a Changing Climate)
ISPRS Int. J. Geo-Inf. EISSN 2220-9964 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
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