{"id":"https://openalex.org/W4399177454","doi":"https://doi.org/10.3389/fdata.2024.1412837","title":"Particulate matter forecast and prediction in Curitiba using machine learning","display_name":"Particulate matter forecast and prediction in Curitiba using machine learning","publication_year":2024,"publication_date":"2024-05-30","ids":{"openalex":"https://openalex.org/W4399177454","doi":"https://doi.org/10.3389/fdata.2024.1412837","pmid":"https://pubmed.ncbi.nlm.nih.gov/38873282"},"language":"en","primary_location":{"id":"doi:10.3389/fdata.2024.1412837","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2024.1412837","pdf_url":"https://www.frontiersin.org/articles/10.3389/fdata.2024.1412837/pdf?isPublishedV2=False","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.frontiersin.org/articles/10.3389/fdata.2024.1412837/pdf?isPublishedV2=False","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5000895295","display_name":"Marianna Gon\u00e7alves Dias Chaves","orcid":null},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":true,"raw_author_name":"Marianna Gon\u00e7alves Dias Chaves","raw_affiliation_strings":["Graduate Program of Environmental Engineering, Federal University of Paran\u00e1, Curitiba, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate Program of Environmental Engineering, Federal University of Paran\u00e1, Curitiba, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104316646","display_name":"Adriel Bilharva da Silva","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adriel Bilharva da Silva","raw_affiliation_strings":["Perkons S.A., Curitiba, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Perkons S.A., Curitiba, Brazil","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080927706","display_name":"Em\u00edlio Graciliano Ferreira Mercuri","orcid":"https://orcid.org/0000-0003-3101-7537"},"institutions":[{"id":"https://openalex.org/I52418104","display_name":"Universidade Federal do Paran\u00e1","ror":"https://ror.org/05syd6y78","country_code":"BR","type":"education","lineage":["https://openalex.org/I52418104"]}],"countries":["BR"],"is_corresponding":false,"raw_author_name":"Em\u00edlio Graciliano Ferreira Mercuri","raw_affiliation_strings":["Department of Environmental Engineering, Federal University of Paran\u00e1, Curitiba, Brazil"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Environmental Engineering, Federal University of Paran\u00e1, Curitiba, Brazil","institution_ids":["https://openalex.org/I52418104"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051575275","display_name":"Steffen M. Noe","orcid":"https://orcid.org/0000-0003-1514-1140"},"institutions":[{"id":"https://openalex.org/I19409027","display_name":"Estonian University of Life Sciences","ror":"https://ror.org/00s67c790","country_code":"EE","type":"education","lineage":["https://openalex.org/I19409027"]}],"countries":["EE"],"is_corresponding":false,"raw_author_name":"Steffen Manfred Noe","raw_affiliation_strings":["Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, Estonia"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Forestry and Engineering, Estonian University of Life Sciences, Tartu, Estonia","institution_ids":["https://openalex.org/I19409027"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5000895295"],"corresponding_institution_ids":["https://openalex.org/I52418104"],"apc_list":{"value":1285,"currency":"USD","value_usd":1285},"apc_paid":{"value":1285,"currency":"USD","value_usd":1285},"fwci":0.5421,"has_fulltext":true,"cited_by_count":4,"citation_normalized_percentile":{"value":0.55733419,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":98},"biblio":{"volume":"7","issue":null,"first_page":"1412837","last_page":"1412837"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.890999972820282,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.890999972820282,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.06210000067949295,"subfield":{"id":"https://openalex.org/subfields/2307","display_name":"Health, Toxicology and Mutagenesis"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12095","display_name":"Vehicle emissions and performance","score":0.009399999864399433,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/particulates","display_name":"Particulates","score":0.6967135071754456},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.6393264532089233},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.6335417628288269},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.5937461853027344},{"id":"https://openalex.org/keywords/air-quality-index","display_name":"Air quality index","score":0.4820740818977356},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.4682061970233917},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4622158408164978},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.42583441734313965},{"id":"https://openalex.org/keywords/cmaq","display_name":"CMAQ","score":0.42498356103897095},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3084143400192261},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.21281519532203674},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1854037344455719}],"concepts":[{"id":"https://openalex.org/C24245907","wikidata":"https://www.wikidata.org/wiki/Q498957","display_name":"Particulates","level":2,"score":0.6967135071754456},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.6393264532089233},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.6335417628288269},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.5937461853027344},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.4820740818977356},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.4682061970233917},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4622158408164978},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.42583441734313965},{"id":"https://openalex.org/C2776845762","wikidata":"https://www.wikidata.org/wiki/Q23579663","display_name":"CMAQ","level":3,"score":0.42498356103897095},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3084143400192261},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.21281519532203674},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1854037344455719},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C149923435","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demography","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3389/fdata.2024.1412837","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2024.1412837","pdf_url":"https://www.frontiersin.org/articles/10.3389/fdata.2024.1412837/pdf?isPublishedV2=False","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},{"id":"pmid:38873282","is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/38873282","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in big data","raw_type":null},{"id":"pmh:oai:pubmedcentral.nih.gov:11169811","is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/11169811","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC11169811/pdf/fdata-07-1412837.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Front Big Data","raw_type":"Text"},{"id":"pmh:oai:doaj.org/article:6b360032c3b24d80924660e1cdcc9def","is_oa":true,"landing_page_url":"https://doaj.org/article/6b360032c3b24d80924660e1cdcc9def","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Frontiers in Big Data, Vol 7 (2024)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3389/fdata.2024.1412837","is_oa":true,"landing_page_url":"https://doi.org/10.3389/fdata.2024.1412837","pdf_url":"https://www.frontiersin.org/articles/10.3389/fdata.2024.1412837/pdf?isPublishedV2=False","source":{"id":"https://openalex.org/S4210201220","display_name":"Frontiers in Big Data","issn_l":"2624-909X","issn":["2624-909X"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320527","host_organization_name":"Frontiers Media","host_organization_lineage":["https://openalex.org/P4310320527"],"host_organization_lineage_names":["Frontiers Media"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Frontiers in Big Data","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.6299999952316284}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321091","display_name":"Coordena\u00e7\u00e3o de Aperfei\u00e7oamento de Pessoal de N\u00edvel Superior","ror":"https://ror.org/00x0ma614"},{"id":"https://openalex.org/F4320324872","display_name":"Eesti Maa\u00fclikool","ror":"https://ror.org/00s67c790"},{"id":"https://openalex.org/F4320324953","display_name":"Universidade Federal do Paran\u00e1","ror":null},{"id":"https://openalex.org/F4320335551","display_name":"Erasmus+","ror":null}],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4399177454.pdf"},"referenced_works_count":41,"referenced_works":["https://openalex.org/W1547692020","https://openalex.org/W1973791799","https://openalex.org/W2000410918","https://openalex.org/W2009329344","https://openalex.org/W2038073395","https://openalex.org/W2068192848","https://openalex.org/W2073912316","https://openalex.org/W2111700869","https://openalex.org/W2612864158","https://openalex.org/W2773231373","https://openalex.org/W2790202404","https://openalex.org/W2795609575","https://openalex.org/W2812669263","https://openalex.org/W2891910989","https://openalex.org/W2897897407","https://openalex.org/W2909938372","https://openalex.org/W2911930990","https://openalex.org/W2913323966","https://openalex.org/W2944545717","https://openalex.org/W2947044383","https://openalex.org/W3006107415","https://openalex.org/W3007092013","https://openalex.org/W3013768458","https://openalex.org/W3016414288","https://openalex.org/W3022712462","https://openalex.org/W3025949386","https://openalex.org/W3080185158","https://openalex.org/W3109716077","https://openalex.org/W3163275348","https://openalex.org/W4200026345","https://openalex.org/W4213049208","https://openalex.org/W4220937221","https://openalex.org/W4280581714","https://openalex.org/W4360980325","https://openalex.org/W4386141646","https://openalex.org/W4387611636","https://openalex.org/W4388534895","https://openalex.org/W4388761961","https://openalex.org/W4390581969","https://openalex.org/W4396934698","https://openalex.org/W6854122279"],"related_works":["https://openalex.org/W2040416759","https://openalex.org/W57125652","https://openalex.org/W4385980749","https://openalex.org/W1966181985","https://openalex.org/W1982564595","https://openalex.org/W72254309","https://openalex.org/W2094094734","https://openalex.org/W2112537734","https://openalex.org/W4317914702","https://openalex.org/W2610262283"],"abstract_inverted_index":{"Introduction:":[0],"Air":[1],"quality":[2],"is":[3,21,32],"directly":[4],"affected":[5],"by":[6,187],"pollutant":[7,317],"emission":[8,27],"from":[9,38,93,104,319],"vehicles,":[10],"especially":[11],"in":[12,41,48,76,108,117,193,285,287,302,326,345],"large":[13,346],"cities":[14],"and":[15,45,53,72,84,89,99,113,119,123,135,156,172,190,243,251,256,274,276,299],"metropolitan":[16],"areas":[17],"or":[18,195],"when":[19],"there":[20,293],"no":[22],"compliance":[23],"check":[24],"for":[25,82,145,157,226],"vehicle":[26,90,320,343],"standards.":[28],"Particulate":[29],"Matter":[30],"(PM)":[31],"one":[33],"of":[34,96,233,269,283,297,305,314,334,342],"the":[35,49,58,65,77,94,109,150,161,176,182,208,217,223,267,270,281,289,303,311,323,332,340],"pollutants":[36],"emitted":[37],"fuel":[39],"burning":[40],"internal":[42],"combustion":[43],"engines":[44],"remains":[46],"suspended":[47],"atmosphere,":[50,79],"causing":[51],"respiratory":[52],"cardiovascular":[54],"health":[55],"problems":[56],"to":[57,248,266,279,338],"population.":[59],"In":[60],"this":[61],"study,":[62],"we":[63,159],"analyzed":[64],"interaction":[66],"between":[67,111],"vehicular":[68],"emissions,":[69],"meteorological":[70],"variables,":[71],"particulate":[73,100],"matter":[74,101],"concentrations":[75],"lower":[78,324],"presenting":[80],"methods":[81],"predicting":[83],"forecasting":[85,124,252],"PM2.5.":[86,306],"Methods:":[87],"Meteorological":[88],"flow":[91],"data":[92,103,235,264,290],"city":[95,110],"Curitiba,":[97],"Brazil,":[98],"concentration":[102,282,304],"optical":[105],"sensors":[106],"installed":[107],"2020":[112],"2022":[114],"were":[115,125,246],"organized":[116],"hourly":[118,171,194],"daily":[120,173,196,213],"averages.":[121],"Prediction":[122],"based":[126],"on":[127,170,211],"two":[128],"machine":[129],"learning":[130],"models:":[131],"Random":[132],"Forest":[133],"(RF)":[134],"Long":[136],"Short-Term":[137],"Memory":[138],"(LSTM)":[139],"neural":[140],"network.":[141],"The":[142,199,241,259],"baseline":[143],"model":[144,210,225],"prediction":[146,174,202,250],"was":[147,181,205,220,261,277,294],"chosen":[148],"as":[149,164,237],"Multiple":[151],"Linear":[152],"Regression":[153],"(MLR)":[154],"model,":[155],"forecast,":[158],"used":[160,236],"naive":[162],"estimation":[163],"baseline.":[165],"Results:":[166],"RF":[167,209,242],"showed":[168],"that":[169,292],"scales,":[175],"planetary":[177],"boundary":[178],"layer":[179],"height":[180],"most":[183],"important":[184],"variable,":[185],"followed":[186],"wind":[188,191],"gust":[189],"velocity":[192],"cases,":[197],"respectively.":[198,258],"highest":[200,218],"PM":[201],"accuracy":[203,219],"(99.37%)":[204],"found":[206],"using":[207,222],"a":[212],"scale.":[214],"For":[215],"forecasting,":[216],"99.71%":[221],"LSTM":[224,244,260],"1-h":[227],"forecast":[228,280],"horizon":[229],"with":[230,254,263],"5":[231],"h":[232],"previous":[234],"input":[238],"variables.":[239],"Discussion:":[240],"models":[245],"able":[247,278],"improve":[249],"compared":[253],"MLR":[255],"Naive,":[257],"trained":[262],"corresponding":[265],"period":[268],"COVID-19":[271],"pandemic":[272],"(2020":[273],"2021)":[275],"PM2.5":[284],"2022,":[286],"which":[288],"show":[291],"greater":[295],"circulation":[296],"vehicles":[298],"higher":[300],"peaks":[301],"Our":[307],"results":[308],"can":[309],"help":[310],"physical":[312],"understanding":[313],"factors":[315],"influencing":[316],"dispersion":[318],"emissions":[321,344],"at":[322],"atmosphere":[325],"urban":[327],"environment.":[328],"This":[329],"study":[330],"supports":[331],"formulation":[333],"new":[335],"government":[336],"policies":[337],"mitigate":[339],"impact":[341],"cities.":[347]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2024,"cited_by_count":2}],"updated_date":"2026-07-23T08:03:31.855105","created_date":"2025-10-10T00:00:00"}
