Evaluating Dispersion Modeling of Inhalable Particulates (PM10) Emissions in Complex Terrain of Coal Mines
The dispersion of inhalable particulates (PM 10 ) in opencast mines needs to be identified precisely for controlling its atmospheric concentration. To date, misrepresented terrains in dispersion models resulted in over/under-estimated predictions. The present study aimed to model the dispersion of P...
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description | The dispersion of inhalable particulates (PM
10
) in opencast mines needs to be identified precisely for controlling its atmospheric concentration. To date, misrepresented terrains in dispersion models resulted in over/under-estimated predictions. The present study aimed to model the dispersion of PM
10
in coal mines using AERMOD and assess outcomes rendered by disparate digital elevation models (DEM). CartoDEM (10 m) generated using the rational polynomial coefficient method and publically available DEMs, i.e., SRTM (90 m), ASTER (30 m), CartoDEM (30 m), and FLAT, were processed for simulating complex terrain of coal mines. Modeled concentration predicted using different terrain inputs was compared with field measured values for evaluating performance metrics. This comparison suggested that SRTM and FLAT topography met lesser performance criteria in comparison with other input DEMs. The model performance was evaluated using Willmott’s index of agreement (
d
r
) being 0.39, 0.41, and 0.47 for SRTM, ASTER, and CartoDEM, respectively. However, CartoDEM (10 m) showed a slight improvement with
d
r
of 0.57. The results revealed that model performance improved due to the recentness of DEM rather than its resolution. Overburden dump, haulage routes, and railway siding shared the majority PM
10
concentration load invariably in all model runs where peak concentration varied from 454 to 680 µg/m
3
. Categorically, complex terrain simulations of coal mines influenced dispersion models by altering emission sources’ interaction with pre-processor calculations of meteorological data. The work will help improve the performance of models in complex terrain and the selection of topographic parameterization for risk-based decisions. |
doi_str_mv | 10.1007/s10666-021-09762-w |
format | Article |
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10
) in opencast mines needs to be identified precisely for controlling its atmospheric concentration. To date, misrepresented terrains in dispersion models resulted in over/under-estimated predictions. The present study aimed to model the dispersion of PM
10
in coal mines using AERMOD and assess outcomes rendered by disparate digital elevation models (DEM). CartoDEM (10 m) generated using the rational polynomial coefficient method and publically available DEMs, i.e., SRTM (90 m), ASTER (30 m), CartoDEM (30 m), and FLAT, were processed for simulating complex terrain of coal mines. Modeled concentration predicted using different terrain inputs was compared with field measured values for evaluating performance metrics. This comparison suggested that SRTM and FLAT topography met lesser performance criteria in comparison with other input DEMs. The model performance was evaluated using Willmott’s index of agreement (
d
r
) being 0.39, 0.41, and 0.47 for SRTM, ASTER, and CartoDEM, respectively. However, CartoDEM (10 m) showed a slight improvement with
d
r
of 0.57. The results revealed that model performance improved due to the recentness of DEM rather than its resolution. Overburden dump, haulage routes, and railway siding shared the majority PM
10
concentration load invariably in all model runs where peak concentration varied from 454 to 680 µg/m
3
. Categorically, complex terrain simulations of coal mines influenced dispersion models by altering emission sources’ interaction with pre-processor calculations of meteorological data. The work will help improve the performance of models in complex terrain and the selection of topographic parameterization for risk-based decisions.</description><identifier>ISSN: 1420-2026</identifier><identifier>EISSN: 1573-2967</identifier><identifier>DOI: 10.1007/s10666-021-09762-w</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Air pollution ; Airborne particulates ; Applications of Mathematics ; Atmospheric models ; Coal ; Coal industry ; Coal mines ; Coal mining ; Comparative analysis ; Digital Elevation Models ; Dispersion ; Earth and Environmental Science ; Emissions ; Environment ; International economic relations ; Math. Appl. in Environmental Science ; Mathematical analysis ; Mathematical Modeling and Industrial Mathematics ; Meteorological data ; Microprocessors ; Mines ; Operations Research/Decision Theory ; Overburden ; Parameterization ; Particulate matter ; Particulates ; Performance enhancement ; Performance evaluation ; Performance measurement ; Polynomials ; Siding ; Terrain</subject><ispartof>Environmental modeling & assessment, 2021-06, Vol.26 (3), p.385-403</ispartof><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021</rights><rights>COPYRIGHT 2021 Springer</rights><rights>The Author(s), under exclusive licence to Springer Nature Switzerland AG 2021.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c358t-6552e5db7ea8b9397e4d1938f000f3d7d3cdb7e140819806b19009ed0b4a59243</citedby><cites>FETCH-LOGICAL-c358t-6552e5db7ea8b9397e4d1938f000f3d7d3cdb7e140819806b19009ed0b4a59243</cites><orcidid>0000-0003-4104-1776</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10666-021-09762-w$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10666-021-09762-w$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>314,780,784,27924,27925,41488,42557,51319</link.rule.ids></links><search><creatorcontrib>Srivastava, Amartanshu</creatorcontrib><creatorcontrib>Kumar, Ambasht</creatorcontrib><creatorcontrib>Elumalai, Suresh Pandian</creatorcontrib><title>Evaluating Dispersion Modeling of Inhalable Particulates (PM10) Emissions in Complex Terrain of Coal Mines</title><title>Environmental modeling & assessment</title><addtitle>Environ Model Assess</addtitle><description>The dispersion of inhalable particulates (PM
10
) in opencast mines needs to be identified precisely for controlling its atmospheric concentration. To date, misrepresented terrains in dispersion models resulted in over/under-estimated predictions. The present study aimed to model the dispersion of PM
10
in coal mines using AERMOD and assess outcomes rendered by disparate digital elevation models (DEM). CartoDEM (10 m) generated using the rational polynomial coefficient method and publically available DEMs, i.e., SRTM (90 m), ASTER (30 m), CartoDEM (30 m), and FLAT, were processed for simulating complex terrain of coal mines. Modeled concentration predicted using different terrain inputs was compared with field measured values for evaluating performance metrics. This comparison suggested that SRTM and FLAT topography met lesser performance criteria in comparison with other input DEMs. The model performance was evaluated using Willmott’s index of agreement (
d
r
) being 0.39, 0.41, and 0.47 for SRTM, ASTER, and CartoDEM, respectively. However, CartoDEM (10 m) showed a slight improvement with
d
r
of 0.57. The results revealed that model performance improved due to the recentness of DEM rather than its resolution. Overburden dump, haulage routes, and railway siding shared the majority PM
10
concentration load invariably in all model runs where peak concentration varied from 454 to 680 µg/m
3
. Categorically, complex terrain simulations of coal mines influenced dispersion models by altering emission sources’ interaction with pre-processor calculations of meteorological data. The work will help improve the performance of models in complex terrain and the selection of topographic parameterization for risk-based decisions.</description><subject>Air pollution</subject><subject>Airborne particulates</subject><subject>Applications of Mathematics</subject><subject>Atmospheric models</subject><subject>Coal</subject><subject>Coal industry</subject><subject>Coal mines</subject><subject>Coal mining</subject><subject>Comparative analysis</subject><subject>Digital Elevation Models</subject><subject>Dispersion</subject><subject>Earth and Environmental Science</subject><subject>Emissions</subject><subject>Environment</subject><subject>International economic relations</subject><subject>Math. Appl. in Environmental Science</subject><subject>Mathematical analysis</subject><subject>Mathematical Modeling and Industrial Mathematics</subject><subject>Meteorological data</subject><subject>Microprocessors</subject><subject>Mines</subject><subject>Operations Research/Decision Theory</subject><subject>Overburden</subject><subject>Parameterization</subject><subject>Particulate matter</subject><subject>Particulates</subject><subject>Performance enhancement</subject><subject>Performance evaluation</subject><subject>Performance measurement</subject><subject>Polynomials</subject><subject>Siding</subject><subject>Terrain</subject><issn>1420-2026</issn><issn>1573-2967</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kUFv3CAQha0qlZqk_QM9IfWSHJwOYMAco822jZRVc0jPCNvjLSsWtuBNmn9fXEfKLeIAM7xv5kmvqj5TuKIA6mumIKWsgdEatJKsfnpXnVKheM20VCfl3TCoGTD5oTrLeQdQ9CBOq9360fqjnVzYkhuXD5iyi4Fs4oB-7sWR3Ibf1tvOI7m3aXL90dsJM7m431C4JOu9yzOSiQtkFfcHj3_JA6ZkS13oVbSebFzA_LF6P1qf8dPLfV79-rZ-WP2o735-v11d39U9F-1USyEYiqFTaNtOc62wGajm7Vg8j3xQA-_nT9pAS3ULsqMaQOMAXWOFZg0_r74scw8p_jlinswuHlMoKw0TrJVcNUIX1dWi2lqPxoUxTsn25Qy4d30MOLrSv1a0OBCtmseyBehTzDnhaA7J7W16NhTMHIJZQjAlBPM_BPNUIL5AuYjDFtOrlzeofznTiVo</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>Srivastava, Amartanshu</creator><creator>Kumar, Ambasht</creator><creator>Elumalai, Suresh Pandian</creator><general>Springer International Publishing</general><general>Springer</general><general>Springer Nature B.V</general><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7SC</scope><scope>7ST</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AL</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K60</scope><scope>K6~</scope><scope>K7-</scope><scope>KR7</scope><scope>L.-</scope><scope>L6V</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>M0C</scope><scope>M0N</scope><scope>M2P</scope><scope>M7S</scope><scope>P5Z</scope><scope>P62</scope><scope>PATMY</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>Q9U</scope><scope>SOI</scope><orcidid>https://orcid.org/0000-0003-4104-1776</orcidid></search><sort><creationdate>20210601</creationdate><title>Evaluating Dispersion Modeling of Inhalable Particulates (PM10) Emissions in Complex Terrain of Coal Mines</title><author>Srivastava, Amartanshu ; Kumar, Ambasht ; Elumalai, Suresh Pandian</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c358t-6552e5db7ea8b9397e4d1938f000f3d7d3cdb7e140819806b19009ed0b4a59243</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Air pollution</topic><topic>Airborne particulates</topic><topic>Applications of Mathematics</topic><topic>Atmospheric models</topic><topic>Coal</topic><topic>Coal industry</topic><topic>Coal mines</topic><topic>Coal mining</topic><topic>Comparative analysis</topic><topic>Digital Elevation Models</topic><topic>Dispersion</topic><topic>Earth and Environmental Science</topic><topic>Emissions</topic><topic>Environment</topic><topic>International economic relations</topic><topic>Math. Appl. in Environmental Science</topic><topic>Mathematical analysis</topic><topic>Mathematical Modeling and Industrial Mathematics</topic><topic>Meteorological data</topic><topic>Microprocessors</topic><topic>Mines</topic><topic>Operations Research/Decision Theory</topic><topic>Overburden</topic><topic>Parameterization</topic><topic>Particulate matter</topic><topic>Particulates</topic><topic>Performance enhancement</topic><topic>Performance evaluation</topic><topic>Performance measurement</topic><topic>Polynomials</topic><topic>Siding</topic><topic>Terrain</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Srivastava, Amartanshu</creatorcontrib><creatorcontrib>Kumar, Ambasht</creatorcontrib><creatorcontrib>Elumalai, Suresh Pandian</creatorcontrib><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Computer and Information Systems Abstracts</collection><collection>Environment Abstracts</collection><collection>Access via ABI/INFORM (ProQuest)</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Global (Alumni Edition)</collection><collection>Science Database (Alumni Edition)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Business Premium Collection</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>Computer Science Database</collection><collection>Civil Engineering Abstracts</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ProQuest Engineering Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>ABI/INFORM Global</collection><collection>Computing Database</collection><collection>Science Database</collection><collection>Engineering Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Environmental Science Database</collection><collection>ProQuest One Business</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering Collection</collection><collection>Environmental Science Collection</collection><collection>ProQuest Central Basic</collection><collection>Environment Abstracts</collection><jtitle>Environmental modeling & assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Srivastava, Amartanshu</au><au>Kumar, Ambasht</au><au>Elumalai, Suresh Pandian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Evaluating Dispersion Modeling of Inhalable Particulates (PM10) Emissions in Complex Terrain of Coal Mines</atitle><jtitle>Environmental modeling & assessment</jtitle><stitle>Environ Model Assess</stitle><date>2021-06-01</date><risdate>2021</risdate><volume>26</volume><issue>3</issue><spage>385</spage><epage>403</epage><pages>385-403</pages><issn>1420-2026</issn><eissn>1573-2967</eissn><abstract>The dispersion of inhalable particulates (PM
10
) in opencast mines needs to be identified precisely for controlling its atmospheric concentration. To date, misrepresented terrains in dispersion models resulted in over/under-estimated predictions. The present study aimed to model the dispersion of PM
10
in coal mines using AERMOD and assess outcomes rendered by disparate digital elevation models (DEM). CartoDEM (10 m) generated using the rational polynomial coefficient method and publically available DEMs, i.e., SRTM (90 m), ASTER (30 m), CartoDEM (30 m), and FLAT, were processed for simulating complex terrain of coal mines. Modeled concentration predicted using different terrain inputs was compared with field measured values for evaluating performance metrics. This comparison suggested that SRTM and FLAT topography met lesser performance criteria in comparison with other input DEMs. The model performance was evaluated using Willmott’s index of agreement (
d
r
) being 0.39, 0.41, and 0.47 for SRTM, ASTER, and CartoDEM, respectively. However, CartoDEM (10 m) showed a slight improvement with
d
r
of 0.57. The results revealed that model performance improved due to the recentness of DEM rather than its resolution. Overburden dump, haulage routes, and railway siding shared the majority PM
10
concentration load invariably in all model runs where peak concentration varied from 454 to 680 µg/m
3
. Categorically, complex terrain simulations of coal mines influenced dispersion models by altering emission sources’ interaction with pre-processor calculations of meteorological data. The work will help improve the performance of models in complex terrain and the selection of topographic parameterization for risk-based decisions.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><doi>10.1007/s10666-021-09762-w</doi><tpages>19</tpages><orcidid>https://orcid.org/0000-0003-4104-1776</orcidid></addata></record> |
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subjects | Air pollution Airborne particulates Applications of Mathematics Atmospheric models Coal Coal industry Coal mines Coal mining Comparative analysis Digital Elevation Models Dispersion Earth and Environmental Science Emissions Environment International economic relations Math. Appl. in Environmental Science Mathematical analysis Mathematical Modeling and Industrial Mathematics Meteorological data Microprocessors Mines Operations Research/Decision Theory Overburden Parameterization Particulate matter Particulates Performance enhancement Performance evaluation Performance measurement Polynomials Siding Terrain |
title | Evaluating Dispersion Modeling of Inhalable Particulates (PM10) Emissions in Complex Terrain of Coal Mines |
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