{"id":"https://openalex.org/W7141736135","doi":"https://doi.org/10.48550/arxiv.2603.25495","title":"Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models","display_name":"Interpretable PM2.5 Forecasting for Urban Air Quality: A Comparative Study of Operational Time-Series Models","publication_year":2026,"publication_date":"2026-03-26","ids":{"openalex":"https://openalex.org/W7141736135","doi":"https://doi.org/10.48550/arxiv.2603.25495"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.25495","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25495","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.25495","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5130785124","display_name":"Moazzam Umer Gondal","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gondal, Moazzam Umer","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093953984","display_name":"Hamad ul Qudous","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Qudous, Hamad ul","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067561067","display_name":"Asma Ahmad Farhan","orcid":"https://orcid.org/0009-0004-4267-0253"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Farhan, Asma Ahmad","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130800765","display_name":"Sultan Alamri","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alamri, Sultan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5130785124"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9757999777793884,"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":0.9757999777793884,"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":0.01600000075995922,"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/T10075","display_name":"Atmospheric chemistry and aerosols","score":0.0024999999441206455,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.8120999932289124},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.6001999974250793},{"id":"https://openalex.org/keywords/workflow","display_name":"Workflow","score":0.5544999837875366},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5486999750137329},{"id":"https://openalex.org/keywords/forecast-error","display_name":"Forecast error","score":0.5127000212669373},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4602999985218048},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.460099995136261}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.8120999932289124},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.6001999974250793},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5758000016212463},{"id":"https://openalex.org/C177212765","wikidata":"https://www.wikidata.org/wiki/Q627335","display_name":"Workflow","level":2,"score":0.5544999837875366},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5486999750137329},{"id":"https://openalex.org/C174303752","wikidata":"https://www.wikidata.org/wiki/Q3624438","display_name":"Forecast error","level":2,"score":0.5127000212669373},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4602999985218048},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.460099995136261},{"id":"https://openalex.org/C167085575","wikidata":"https://www.wikidata.org/wiki/Q6803654","display_name":"Mean squared prediction error","level":2,"score":0.4047999978065491},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3804999887943268},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3790000081062317},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.33869999647140503},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.3336000144481659},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.28769999742507935},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.28619998693466187},{"id":"https://openalex.org/C170061395","wikidata":"https://www.wikidata.org/wiki/Q5468164","display_name":"Forecast skill","level":2,"score":0.27720001339912415},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2540999948978424},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.25495","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25495","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2603.25495","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.25495","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"article"},"sustainable_development_goals":[{"score":0.8362256288528442,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurate":[0],"short-term":[1],"air-quality":[2,228],"forecasting":[3,16,33,57,76,103,220],"is":[4],"essential":[5],"for":[6,39,226],"public":[7],"health":[8],"protection":[9],"and":[10,22,31,49,67,83,101,116,134,158,176,182],"urban":[11,227],"management,":[12],"yet":[13],"many":[14],"recent":[15],"frameworks":[17],"rely":[18],"on":[19],"complex,":[20],"data-intensive,":[21],"computationally":[23],"demanding":[24],"models.":[25],"This":[26],"study":[27],"investigates":[28],"whether":[29],"lightweight":[30,218],"interpretable":[32],"approaches":[34],"can":[35,222],"provide":[36],"competitive":[37,225],"performance":[38],"hourly":[40],"PM2.5":[41],"prediction":[42],"in":[43,112],"Beijing,":[44],"China.":[45],"Using":[46],"multi-year":[47],"pollutant":[48],"meteorological":[50],"time-series":[51],"data,":[52],"we":[53],"developed":[54],"a":[55,231],"leakage-aware":[56],"workflow":[58],"that":[59,217],"combined":[60],"chronological":[61],"data":[62],"partitioning,":[63],"preprocessing,":[64],"feature":[65],"selection,":[66],"exogenous-driver":[68],"modeling":[69],"under":[70,94],"the":[71,90,125,149,164,196],"Perfect":[72],"Prognosis":[73],"setting.":[74],"Three":[75],"families":[77],"were":[78,92],"evaluated:":[79],"SARIMAX,":[80,159],"Facebook":[81,122,156,192],"Prophet,":[82],"NeuralProphet.":[84,147],"To":[85],"assess":[86],"practical":[87,232],"deployment":[88],"behavior,":[89],"models":[91],"tested":[93],"two":[95],"adaptive":[96],"regimes:":[97],"weekly":[98],"walk-forward":[99,120,201],"refitting":[100],"frozen":[102],"with":[104,129,160],"online":[105,152],"residual":[106,153,183],"correction.":[107],"Results":[108],"showed":[109],"clear":[110],"differences":[111],"both":[113,180],"predictive":[114],"accuracy":[115],"computational":[117],"efficiency.":[118],"Under":[119],"refitting,":[121],"Prophet":[123,157,193],"achieved":[124],"strongest":[126],"completed":[127],"performance,":[128],"an":[130,135],"MAE":[131],"of":[132,137],"$37.61$":[133],"RMSE":[136,170],"$50.10$,":[138],"while":[139,203],"also":[140],"requiring":[141],"substantially":[142],"less":[143,174,177],"execution":[144],"time":[145],"than":[146],"In":[148],"frozen-model":[150],"regime,":[151],"correction":[154,184],"improved":[155],"corrected":[161,191],"SARIMAX":[162],"yielding":[163],"lowest":[165],"overall":[166],"error":[167,198],"(MAE":[168],"$32.50$;":[169],"$46.85$).":[171],"NeuralProphet":[172],"remained":[173],"accurate":[175],"stable":[178],"across":[179],"regimes,":[181],"did":[185],"not":[186],"improve":[187],"its":[188,200],"forecasts.":[189],"Notably,":[190],"reached":[194],"nearly":[195],"same":[197],"as":[199],"counterpart":[202],"reducing":[204],"runtime":[205],"from":[206],"$15$":[207],"min":[208],"$21.91$":[209],"sec":[210],"to":[211],"$46.60$":[212],"sec.":[213],"These":[214],"findings":[215],"show":[216],"additive":[219],"strategies":[221],"remain":[223],"highly":[224],"prediction,":[229],"offering":[230],"balance":[233],"between":[234],"accuracy,":[235],"interpretability,":[236],"...":[237]},"counts_by_year":[],"updated_date":"2026-03-28T06:16:51.555046","created_date":"2026-03-28T00:00:00"}