{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T21:13:05Z","timestamp":1792530785040,"version":"4.5.0"},"reference-count":28,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2018,1,1]],"date-time":"2018-01-01T00:00:00Z","timestamp":1514764800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"funder":[{"DOI":"10.13039\/501100003565","name":"Korea Ministry of Land, Infrastructure and Transport","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100003565","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Hazardous Materials"],"published-print":{"date-parts":[[2018,1]]},"DOI":"10.1016\/j.jhazmat.2017.07.050","type":"journal-article","created":{"date-parts":[[2017,7,25]],"date-time":"2017-07-25T04:30:58Z","timestamp":1500957058000},"page":"75-82","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":165,"special_numbering":"C","title":["Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)"],"prefix":"10.1016","volume":"341","author":[{"given":"Sechan","family":"Park","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minjeong","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Minhae","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyeong-Gyu","family":"Namgung","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ki-Tae","family":"Kim","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kyung Hwa","family":"Cho","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soon-Bark","family":"Kwon","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jhazmat.2017.07.050_bib0005","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1136\/oem.2004.014332","article-title":"The London underground dust and hazards to health","volume":"62","author":"Seaton","year":"2005","journal-title":"Occup. Environ. Med."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0010","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1021\/tx049723c","article-title":"Subway particles are more genotoxic than street particles and induce oxidative stress in cultured human lung cells","volume":"18","author":"Karlsson","year":"2005","journal-title":"Chem. Res. Toxicol."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0015","doi-asserted-by":"crossref","first-page":"709","DOI":"10.1080\/10473289.2006.10464485","article-title":"Health effects of fine particulate air pollution: lines that connect","volume":"56","author":"Pope","year":"2006","journal-title":"J. Air Waste Manage. Assoc."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0020","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1126\/science.1108752","article-title":"Air pollution-related illness:effects of particles","volume":"308","author":"Nel Atmosphere","year":"2005","journal-title":"Science"},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0025","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.scitotenv.2014.10.013","article-title":"A new look at inhalable metalliferous airborne particles on rail subway platforms","volume":"505","author":"Moreno","year":"2015","journal-title":"Sci. Total Environ."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0030","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1016\/j.jhazmat.2011.08.005","article-title":"source apportionment of PM1 emissions at a roadside station","volume":"195","author":"Cheng","year":"2011","journal-title":"J. Hazard. Mater."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0035","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.jhazmat.2015.05.015","article-title":"A multivariate study for characterizing particulate matter (PM10 PM2.5, and PM1) in Seoul metropolitan subway stations","volume":"297","author":"Kwon","year":"2015","journal-title":"Korean J. Hazard. Mater."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0040","doi-asserted-by":"crossref","first-page":"395","DOI":"10.1007\/s12273-013-0127-6","article-title":"A method to identify the point source of indoor gaseous contaminant based on limited on-site steady concentration measurements","volume":"6","author":"Wang","year":"2013","journal-title":"Build. Simul."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0045","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1016\/j.buildenv.2015.05.010","article-title":"Indoor air quality control for improving passenger health in subway platforms using an outdoor air quality dependent ventilation system","volume":"92","author":"Kim","year":"2015","journal-title":"Build. Environ."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0050","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.atmosenv.2014.04.043","article-title":"Subway platform air quality: assessing the influences of tunnel ventilation: train piston effect and station design","volume":"92","author":"Moreno","year":"2014","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0055","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1081\/TMA-100107583","article-title":"Seasonal variation and their characterization of suspended particulate matter in the air of subway stations","volume":"19","author":"Furuya","year":"2001","journal-title":"J. Trace Microprobe Tech."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0060","doi-asserted-by":"crossref","first-page":"1850","DOI":"10.1016\/j.buildenv.2007.11.001","article-title":"HVAC air-quaity model and its use to test a PM2 5 control strategy","volume":"43","author":"Marsika","year":"2008","journal-title":"Build. Environ."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0065","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1016\/j.buildenv.2014.03.004","article-title":"Influence of mechanical ventilation system on indoor carbon dioxide and particulate matter concentration","volume":"76","author":"Yu","year":"2014","journal-title":"Build. Environ."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0070","doi-asserted-by":"crossref","first-page":"697","DOI":"10.1007\/s11869-015-0369-9","article-title":"Prediction of particulate matter concentration profile in an opencast copper mine in India using an artificial neural network model","volume":"9","author":"Patra","year":"2016","journal-title":"Air Qual. Atmos. Health."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0075","doi-asserted-by":"crossref","first-page":"5535","DOI":"10.1016\/j.watres.2011.08.010","article-title":"Prediction of contamination potential of groundwater arsenic in Cambodia Laos, and Thailand using artificial neural network","volume":"45","author":"Cho","year":"2011","journal-title":"Warter Res."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0080","doi-asserted-by":"crossref","first-page":"14008","DOI":"10.1007\/s11356-016-6565-9","article-title":"Artificial neural network models for prediction of daily fine particulate matter concentrations in Algiers","volume":"23","author":"Chellali","year":"2016","journal-title":"Environ. Sci. Pollut. Res."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0085","doi-asserted-by":"crossref","first-page":"1216","DOI":"10.1016\/j.atmosenv.2005.10.036","article-title":"Artificial neural network models for prediction of PM10 hourly concentrations in the Greater Area of Athens, Greece","volume":"40","author":"Grivas","year":"2006","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0090","doi-asserted-by":"crossref","first-page":"325","DOI":"10.1007\/s11270-007-9341-0","article-title":"Development and assessment of neural network and multiple regression models in order to predict PM10 levels in a medium-sized Mediterranean city","volume":"182","author":"Papanastasiou","year":"2007","journal-title":"Water Air Soil Pollut."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0095","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/S1352-2310(99)00316-7","article-title":"Prediction of PM2.5 concentrations several hours in advance using neural networks in Santiago","volume":"134","author":"Perez","year":"2000","journal-title":"Chile. Atmos. Envrion."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0100","doi-asserted-by":"crossref","first-page":"1183","DOI":"10.1080\/10473289.2003.10466276","article-title":"Neural network and multiple regression models for PM10 prediction in Athens: a comparative assessment","volume":"53","author":"Chaloulakou","year":"2003","journal-title":"J. Air Waste Manage. Assoc."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0105","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.atmosenv.2011.11.058","article-title":"Anoticeable shift in particulate matter levels after platform screen door installation in a Korean subway station","volume":"49","author":"Kim","year":"2012","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0110","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/j.envsoft.2004.03.010","article-title":"Neural network prediction model for fine particulate matter (PM 2. 5) on the US\u2013Mexico border in El Paso (Texas) and Ciudad Ju\u00e1rez (Chihuahua)","volume":"20","author":"Ordieres","year":"2005","journal-title":"Environ. Modell. Software"},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0115","doi-asserted-by":"crossref","first-page":"8331","DOI":"10.1016\/j.atmosenv.2008.07.020","article-title":"A hybrid ARIMA and artificial neural networks model to forecast particulate matter in urban areas: the case of Temuco, Chile","volume":"42","author":"D\u00edaz-Robles","year":"2008","journal-title":"Atmos. Environ."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0120","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1109\/iNIS.2015.56","article-title":"Gradient descent with momentum based neural network pattern classification for the prediction of soil moisture content in precision agriculture","author":"Lenka","year":"2015","journal-title":"Nanoelectronic and Information Systems (iNIS), 2015 IEEE International Symposium On. IEEE"},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0125","doi-asserted-by":"crossref","first-page":"6847","DOI":"10.3390\/w7126663","article-title":"Comparative studies of different imputation methods for recovering streamflow observation","volume":"7","author":"Kim","year":"2015","journal-title":"Water"},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0130","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.enbuild.2013.09.022","article-title":"Online monitoring and interpretation of periodic diurnal and seasonal variations of indoor air pollutants in a subway station using parallel factor analysis(PARAFAC)","volume":"68","author":"Lee","year":"2014","journal-title":"Energy Build."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0135","doi-asserted-by":"crossref","first-page":"362","DOI":"10.1007\/s10661-016-5373-5","article-title":"Transient variation of aerosol size distribution in an underground subway station","volume":"188","author":"Kwon","year":"2016","journal-title":"Environ. Monit. Assess."},{"key":"10.1016\/j.jhazmat.2017.07.050_bib0140","doi-asserted-by":"crossref","first-page":"5588","DOI":"10.1016\/j.atmosenv.2009.07.048","article-title":"Neural network forecasting of air pollutants hourly concentrations using optimized temporal averages of meteorological variables and pollutant concentration","volume":"43","author":"Hurst","year":"2009","journal-title":"Atmos. Environ."}],"container-title":["Journal of Hazardous Materials"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0304389417305599?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0304389417305599?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2018,9,5]],"date-time":"2018-09-05T07:25:47Z","timestamp":1536132347000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0304389417305599"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,1]]},"references-count":28,"alternative-id":["S0304389417305599"],"URL":"https:\/\/doi.org\/10.1016\/j.jhazmat.2017.07.050","relation":{},"ISSN":["0304-3894"],"issn-type":[{"value":"0304-3894","type":"print"}],"subject":[],"published":{"date-parts":[[2018,1]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Predicting PM10 concentration in Seoul metropolitan subway stations using artificial neural network (ANN)","name":"articletitle","label":"Article Title"},{"value":"Journal of Hazardous Materials","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jhazmat.2017.07.050","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2017 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}