{"id":"https://openalex.org/W4399154727","doi":"https://doi.org/10.1109/wf-iot58464.2023.10539442","title":"A Meta-Graph Deep Learning Framework for Forecasting Air Pollutants in Stockholm","display_name":"A Meta-Graph Deep Learning Framework for Forecasting Air Pollutants in Stockholm","publication_year":2023,"publication_date":"2023-10-12","ids":{"openalex":"https://openalex.org/W4399154727","doi":"https://doi.org/10.1109/wf-iot58464.2023.10539442"},"language":"en","primary_location":{"id":"doi:10.1109/wf-iot58464.2023.10539442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wf-iot58464.2023.10539442","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 9th World Forum on Internet of Things (WF-IoT)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100363359","display_name":"Zhiguo Zhang","orcid":"https://orcid.org/0000-0003-1681-4853"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Zhiguo Zhang","raw_affiliation_strings":["KTH Royal Institute of Technology,Department of Civil and Architectural Engineering,Stockholm,Sweden,10044"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,Department of Civil and Architectural Engineering,Stockholm,Sweden,10044","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103171792","display_name":"Xiaoliang Ma","orcid":"https://orcid.org/0000-0001-5526-4511"},"institutions":[{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Xiaoliang Ma","raw_affiliation_strings":["KTH Royal Institute of Technology,Department of Civil and Architectural Engineering,Stockholm,Sweden,10044"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,Department of Civil and Architectural Engineering,Stockholm,Sweden,10044","institution_ids":["https://openalex.org/I86987016"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024948416","display_name":"Christer Johansson","orcid":"https://orcid.org/0000-0002-8459-9852"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Christer Johansson","raw_affiliation_strings":["Environmental and Health Administration, City of Stockholm,Stockholm,10420"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Environmental and Health Administration, City of Stockholm,Stockholm,10420","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039097899","display_name":"Junchen Jin","orcid":null},"institutions":[{"id":"https://openalex.org/I76130692","display_name":"Zhejiang University","ror":"https://ror.org/00a2xv884","country_code":"CN","type":"education","lineage":["https://openalex.org/I76130692"]},{"id":"https://openalex.org/I86987016","display_name":"KTH Royal Institute of Technology","ror":"https://ror.org/026vcq606","country_code":"SE","type":"education","lineage":["https://openalex.org/I86987016"]}],"countries":["CN","SE"],"is_corresponding":false,"raw_author_name":"Junchen Jin","raw_affiliation_strings":["KTH Royal Institute of Technology,Department of Civil and Architectural Engineering,Stockholm,Sweden,10044","College of Electrical Engineering, Zhejiang University, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"KTH Royal Institute of Technology,Department of Civil and Architectural Engineering,Stockholm,Sweden,10044","institution_ids":["https://openalex.org/I86987016"]},{"raw_affiliation_string":"College of Electrical Engineering, Zhejiang University, Hangzhou, China","institution_ids":["https://openalex.org/I76130692"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066306079","display_name":"Magnuz Engardt","orcid":"https://orcid.org/0000-0002-4695-1106"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Magnuz Engardt","raw_affiliation_strings":["Environmental and Health Administration, City of Stockholm,Stockholm,10420"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Environmental and Health Administration, City of Stockholm,Stockholm,10420","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.4545,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.61063996,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"01","last_page":"06"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9968000054359436,"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":0.9918000102043152,"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/air-quality-index","display_name":"Air quality index","score":0.699369490146637},{"id":"https://openalex.org/keywords/air-pollution","display_name":"Air pollution","score":0.6434434652328491},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5778424739837646},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5389677882194519},{"id":"https://openalex.org/keywords/pollutant","display_name":"Pollutant","score":0.5331422686576843},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.4941282570362091},{"id":"https://openalex.org/keywords/air-pollutants","display_name":"Air pollutants","score":0.4886203408241272},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.4273775815963745},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.4240679144859314},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42348945140838623},{"id":"https://openalex.org/keywords/air-pollutant-concentrations","display_name":"Air pollutant concentrations","score":0.41913196444511414},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39203402400016785},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3830346465110779},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14688241481781006}],"concepts":[{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.699369490146637},{"id":"https://openalex.org/C559116025","wikidata":"https://www.wikidata.org/wiki/Q131123","display_name":"Air pollution","level":2,"score":0.6434434652328491},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5778424739837646},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5389677882194519},{"id":"https://openalex.org/C82685317","wikidata":"https://www.wikidata.org/wiki/Q19829510","display_name":"Pollutant","level":2,"score":0.5331422686576843},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.4941282570362091},{"id":"https://openalex.org/C2987853052","wikidata":"https://www.wikidata.org/wiki/Q131123","display_name":"Air pollutants","level":3,"score":0.4886203408241272},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.4273775815963745},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.4240679144859314},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42348945140838623},{"id":"https://openalex.org/C52173716","wikidata":"https://www.wikidata.org/wiki/Q4698350","display_name":"Air pollutant concentrations","level":4,"score":0.41913196444511414},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39203402400016785},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3830346465110779},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14688241481781006},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wf-iot58464.2023.10539442","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wf-iot58464.2023.10539442","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE 9th World Forum on Internet of Things (WF-IoT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W1993133847","https://openalex.org/W2150165932","https://openalex.org/W2911964244","https://openalex.org/W2982277720","https://openalex.org/W2990955039","https://openalex.org/W2990974130","https://openalex.org/W3000499162","https://openalex.org/W3009131034","https://openalex.org/W3012108457","https://openalex.org/W3019419694","https://openalex.org/W3038462551","https://openalex.org/W3109471654","https://openalex.org/W3140300776","https://openalex.org/W4280493402","https://openalex.org/W4283022696","https://openalex.org/W4385245566","https://openalex.org/W4391024573","https://openalex.org/W4399169362","https://openalex.org/W4399647672","https://openalex.org/W6681608319","https://openalex.org/W6869608176"],"related_works":["https://openalex.org/W2991488401","https://openalex.org/W2415702836","https://openalex.org/W2082703639","https://openalex.org/W3080344894","https://openalex.org/W4234591446","https://openalex.org/W2182784829","https://openalex.org/W2900439491","https://openalex.org/W2206479993","https://openalex.org/W3120396838","https://openalex.org/W1567165692"],"abstract_inverted_index":{"Forecasting":[0],"air":[1,17,50,67,95,99,141,146,194],"pollution":[2,51,100],"is":[3,55],"an":[4],"important":[5],"activity":[6],"for":[7,75,91,139,191],"developing":[8],"sustainable":[9],"and":[10,33,63,121,130,152,177],"smart":[11],"cities.":[12],"Generated":[13],"by":[14,71],"various":[15],"sources,":[16],"pollutants":[18,96,142],"distribute":[19],"in":[20,48,115,143,198],"the":[21,26,38,60,79,116,160,163,174,183,186,192],"atmospheric":[22],"environment":[23],"due":[24],"to":[25,43,58,127,158,188],"complex":[27],"dispersion":[28,180],"processes.":[29],"The":[30,112,167],"emerging":[31],"sensor":[32],"data":[34,151],"technologies":[35],"have":[36],"promoted":[37],"development":[39],"of":[40,66,78,94,108,162,185],"data-driven":[41],"approaches":[42],"replace":[44],"conventional":[45,178],"physical":[46],"models":[47,176],"urban":[49],"forecasting.":[52],"Nevertheless,":[53],"it":[54,172],"still":[56],"challenging":[57],"capture":[59],"intricate":[61],"spatial":[62,131],"temporal":[64,129],"patterns":[65],"pollutant":[68],"concentrations":[69,97],"measured":[70],"heterogeneous":[72],"sensors,":[73],"especially":[74],"long-term":[76],"prediction":[77],"multi-variate":[80],"time":[81],"series":[82],"data.":[83],"This":[84],"paper":[85],"proposes":[86],"a":[87,105],"deep":[88,110],"learning":[89],"framework":[90,107,117,187],"longer-term":[92],"forecast":[93],"using":[98,145],"sensing":[101,148],"data,":[102,149],"based":[103],"on":[104],"conceptual":[106],"meta-graph":[109,119],"learning.":[111],"key":[113],"modules":[114],"include":[118],"units":[120],"fusion":[122],"layers,":[123],"which":[124],"are":[125],"designed":[126],"learn":[128],"correlations":[132],"respectively.":[133],"A":[134],"detailed":[135],"case":[136],"was":[137],"formulated":[138],"forecasting":[140],"Stockholm":[144],"quality":[147,195],"meteorological":[150],"so":[153],"on.":[154],"Experiments":[155],"were":[156],"conducted":[157],"evaluate":[159],"performance":[161],"proposed":[164],"modelling":[165],"framework.":[166],"computational":[168],"results":[169],"show":[170],"that":[171],"outperforms":[173],"baseline":[175],"deterministic":[179],"models,":[181],"demonstrating":[182],"potential":[184],"be":[189],"deployed":[190],"real":[193],"information":[196],"systems":[197],"Stockholm.":[199]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}