Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models
To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM 2.5 and PM 2.5−10 particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black...
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| Veröffentlicht in: | Environmental monitoring and assessment 2016-10, Vol.188 (10), p.574-574, Article 574 |
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| creator | Ogundele, Lasun T. Owoade, Oyediran K. Olise, Felix S. Hopke, Philip K. |
| description | To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM
2.5
and PM
2.5−10
particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m
3
for PM
2.5
and 331.36, 190.01, and 184.60 μg/m
3
for PM
2.5−10
for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM
2.5
, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM
2.5−10
. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM
2.5
while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM
2.5−10
. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area. |
| doi_str_mv | 10.1007/s10661-016-5585-8 |
| format | Article |
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2.5
and PM
2.5−10
particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m
3
for PM
2.5
and 331.36, 190.01, and 184.60 μg/m
3
for PM
2.5−10
for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM
2.5
, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM
2.5−10
. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM
2.5
while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM
2.5−10
. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.</description><identifier>ISSN: 0167-6369</identifier><identifier>EISSN: 1573-2959</identifier><identifier>DOI: 10.1007/s10661-016-5585-8</identifier><identifier>PMID: 27645143</identifier><language>eng</language><publisher>Cham: Springer International Publishing</publisher><subject>Air Pollutants - analysis ; Air Pollution - analysis ; Atmospheric Protection/Air Quality Control/Air Pollution ; Carbon - analysis ; Coal - analysis ; Earth and Environmental Science ; Ecology ; Ecotoxicology ; Environment ; Environmental Management ; Environmental Monitoring - methods ; Environmental Pollution - analysis ; Iron - chemistry ; Models, Theoretical ; Monitoring/Environmental Analysis ; Particle Size ; Particulate Matter - analysis ; Principal Component Analysis ; Soil - chemistry ; Steel - chemistry</subject><ispartof>Environmental monitoring and assessment, 2016-10, Vol.188 (10), p.574-574, Article 574</ispartof><rights>Springer International Publishing Switzerland 2016</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c410t-f8110dae92afdd23c09bba6e6acc270095d21b6c332b2e2e3d6a4377959160163</citedby><cites>FETCH-LOGICAL-c410t-f8110dae92afdd23c09bba6e6acc270095d21b6c332b2e2e3d6a4377959160163</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://link.springer.com/content/pdf/10.1007/s10661-016-5585-8$$EPDF$$P50$$Gspringer$$H</linktopdf><linktohtml>$$Uhttps://link.springer.com/10.1007/s10661-016-5585-8$$EHTML$$P50$$Gspringer$$H</linktohtml><link.rule.ids>315,781,785,27929,27930,41493,42562,51324</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/27645143$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ogundele, Lasun T.</creatorcontrib><creatorcontrib>Owoade, Oyediran K.</creatorcontrib><creatorcontrib>Olise, Felix S.</creatorcontrib><creatorcontrib>Hopke, Philip K.</creatorcontrib><title>Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models</title><title>Environmental monitoring and assessment</title><addtitle>Environ Monit Assess</addtitle><addtitle>Environ Monit Assess</addtitle><description>To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM
2.5
and PM
2.5−10
particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m
3
for PM
2.5
and 331.36, 190.01, and 184.60 μg/m
3
for PM
2.5−10
for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM
2.5
, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM
2.5−10
. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM
2.5
while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM
2.5−10
. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.</description><subject>Air Pollutants - analysis</subject><subject>Air Pollution - analysis</subject><subject>Atmospheric Protection/Air Quality Control/Air Pollution</subject><subject>Carbon - analysis</subject><subject>Coal - analysis</subject><subject>Earth and Environmental Science</subject><subject>Ecology</subject><subject>Ecotoxicology</subject><subject>Environment</subject><subject>Environmental Management</subject><subject>Environmental Monitoring - methods</subject><subject>Environmental Pollution - analysis</subject><subject>Iron - chemistry</subject><subject>Models, Theoretical</subject><subject>Monitoring/Environmental Analysis</subject><subject>Particle Size</subject><subject>Particulate Matter - analysis</subject><subject>Principal Component Analysis</subject><subject>Soil - chemistry</subject><subject>Steel - chemistry</subject><issn>0167-6369</issn><issn>1573-2959</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp9kc1u1DAUhS1ERYfCA7BBXrJoin9iJ1lWI6ZU6sBIUImd5dg3lavEDnaC1Dfoug_Aw_EkODMDy658rfOdI917EHpHyQUlpPqYKJGSFoTKQohaFPULtKKi4gVrRPMSrbJQFZLL5hS9TumeENJUZfMKnbJKloKWfIV-fwtzNICdBT-5zhk9ueCx9hbrcQxx-Q1ZwqHDuy27EHtpP_15fKIEO49dPDrSBNDjZKIecRqgn5y_w502U4gPGPyvBdyHzWlRdtvNOd6tN5d78-2X7fUPHMHAmHk8BAt9eoNOOt0neHt8z9Dt5tP39efi5uvV9frypjAlJVPR1ZQSq6FhurOWcUOattUSpDaGVXltYRltpeGctQwYcCt1yasqn4nKfCV-hj4ccscYfs6QJjW4ZKDvtYcwJ0VrxigVZc0ySg-oiSGlCJ0aoxt0fFCUqKUWdahF5Vy11KLq7Hl_jJ_bAex_x78eMsAOQMqSv4Oo7nMvPq_8TOpfSd2YxQ</recordid><startdate>20161001</startdate><enddate>20161001</enddate><creator>Ogundele, Lasun T.</creator><creator>Owoade, Oyediran K.</creator><creator>Olise, Felix S.</creator><creator>Hopke, Philip K.</creator><general>Springer International Publishing</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20161001</creationdate><title>Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models</title><author>Ogundele, Lasun T. ; Owoade, Oyediran K. ; Olise, Felix S. ; Hopke, Philip K.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c410t-f8110dae92afdd23c09bba6e6acc270095d21b6c332b2e2e3d6a4377959160163</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Air Pollutants - analysis</topic><topic>Air Pollution - analysis</topic><topic>Atmospheric Protection/Air Quality Control/Air Pollution</topic><topic>Carbon - analysis</topic><topic>Coal - analysis</topic><topic>Earth and Environmental Science</topic><topic>Ecology</topic><topic>Ecotoxicology</topic><topic>Environment</topic><topic>Environmental Management</topic><topic>Environmental Monitoring - methods</topic><topic>Environmental Pollution - analysis</topic><topic>Iron - chemistry</topic><topic>Models, Theoretical</topic><topic>Monitoring/Environmental Analysis</topic><topic>Particle Size</topic><topic>Particulate Matter - analysis</topic><topic>Principal Component Analysis</topic><topic>Soil - chemistry</topic><topic>Steel - chemistry</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ogundele, Lasun T.</creatorcontrib><creatorcontrib>Owoade, Oyediran K.</creatorcontrib><creatorcontrib>Olise, Felix S.</creatorcontrib><creatorcontrib>Hopke, Philip K.</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Environmental monitoring and assessment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ogundele, Lasun T.</au><au>Owoade, Oyediran K.</au><au>Olise, Felix S.</au><au>Hopke, Philip K.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models</atitle><jtitle>Environmental monitoring and assessment</jtitle><stitle>Environ Monit Assess</stitle><addtitle>Environ Monit Assess</addtitle><date>2016-10-01</date><risdate>2016</risdate><volume>188</volume><issue>10</issue><spage>574</spage><epage>574</epage><pages>574-574</pages><artnum>574</artnum><issn>0167-6369</issn><eissn>1573-2959</eissn><abstract>To identify the potential sources responsible for the particulate matter emission from secondary iron and steel smelting factory environment, PM
2.5
and PM
2.5−10
particles were collected using the low-volume air samplers twice a week for a year. The samples were analyzed for the elemental and black carbon content using x-ray fluorescence spectrometer and optical transmissometer, respectively. The average mass concentrations were 216.26, 151.68, and 138. 62 μg/m
3
for PM
2.5
and 331.36, 190.01, and 184.60 μg/m
3
for PM
2.5−10
for the production, outside M1 and outside M2 sites, respectively. The same size resolved data set were used as input for the positive matrix factorization (PMF), principal component factor analysis (PCFA), and Unmix (UNMIX) receptor modeling in order to identify the possible sources of particulate matter and their contribution. The PMF resolved four sources with their respective contributions were metal processing (33 %), e-waste (33 %), diesel emission (22 %) and soil (12 %) for PM
2.5
, and coking (50 %), soil (29 %), metal processing (16 %) and diesel combustion (5 %) for PM
2.5−10
. PCFA identified soil, metal processing, Pb source, and diesel combustion contributing 45, 41, 9, and 5 %, respectively to PM
2.5
while metal processing, soil, coal combustion and open burning contributed 43, 38, 12, and 7 %, respectively to the PM
2.5−10
. Also, UNMIX identified metal processing, soil, and diesel emission with 43, 42 and 15 % contributions, respectively for the fine fraction, and metal processing (71 %), soil (21 %) and unidentified source (1 %) for the coarse fraction. The study concluded that metal processing and e-waste are the major sources contributing to the fine fraction while coking and soil contributed to the coarse fraction within the factory environment. The application of PMF, PCFA and UNMIX receptor models improved the source identification and apportionment of particulate matter drive in the study area.</abstract><cop>Cham</cop><pub>Springer International Publishing</pub><pmid>27645143</pmid><doi>10.1007/s10661-016-5585-8</doi><tpages>1</tpages></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0167-6369 |
| ispartof | Environmental monitoring and assessment, 2016-10, Vol.188 (10), p.574-574, Article 574 |
| issn | 0167-6369 1573-2959 |
| language | eng |
| recordid | cdi_proquest_miscellaneous_1822115482 |
| source | MEDLINE; SpringerLink Journals - AutoHoldings |
| subjects | Air Pollutants - analysis Air Pollution - analysis Atmospheric Protection/Air Quality Control/Air Pollution Carbon - analysis Coal - analysis Earth and Environmental Science Ecology Ecotoxicology Environment Environmental Management Environmental Monitoring - methods Environmental Pollution - analysis Iron - chemistry Models, Theoretical Monitoring/Environmental Analysis Particle Size Particulate Matter - analysis Principal Component Analysis Soil - chemistry Steel - chemistry |
| title | Source identification and apportionment of PM2.5 and PM2.5−10 in iron and steel scrap smelting factory environment using PMF, PCFA and UNMIX receptor models |
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