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IJGI
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Spatial Analysis of Pollution and Risk in a Changing Climate
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Spatial Analysis of Pollution and Risk in a Changing Climate

A special issue of ISPRS International Journal of Geo-Information (ISSN 2220-9964).

Deadline for manuscript submissions: closed (31 July 2019) | Viewed by 21143

Special Issue Editor

Prof. Dr. Jason Levy

E-Mail Website
Guest Editor
Disaster Preparedness and Emergency Management, University of Hawaii, 2540 Dole Street, Honolulu, HI 96822, USA
Interests: epidemiology and prevention of congenital anomalies; psychosis and affective psychosis; cancer epidemiology and prevention; molecular and human genome epidemiology; evidence synthesis related to public health and health services research
Special Issues, Collections and Topics in MDPI journals

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

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. ISPRS International Journal of Geo-Information is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1700 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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20 pages, 11243 KiB  
Article
A Novel Method for Improving Air Pollution Prediction Based on Machine Learning Approaches: A Case Study Applied to the Capital City of Tehran
by Mahmoud Reza Delavar, Amin Gholami, Gholam Reza Shiran, Yousef Rashidi, Gholam Reza Nakhaeizadeh, Kurt Fedra and Smaeil Hatefi Afshar
ISPRS Int. J. Geo-Inf. 2019, 8(2), 99; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8020099 - 23 Feb 2019
Cited by 88 | Viewed by 9989
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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20 pages, 3089 KiB  
Article
Spatiotemporal Analysis of Carbon Emissions and Carbon Storage Using National Geography Census Data in Wuhan, China
by Yanan Liu, Xiangyun Hu, Hao Wu, Anqi Zhang, Jieting Feng and Jianya Gong
ISPRS Int. J. Geo-Inf. 2019, 8(1), 7; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi8010007 - 26 Dec 2018
Cited by 7 | Viewed by 3909
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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15 pages, 277 KiB  
Review
Critical Review of Methods to Estimate PM2.5 Concentrations within Specified Research Region
by Guangyuan Zhang, Xiaoping Rui and Yonglei Fan
ISPRS Int. J. Geo-Inf. 2018, 7(9), 368; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi7090368 - 07 Sep 2018
Cited by 40 | Viewed by 6026
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)
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