{"id":"https://openalex.org/W7225459474","doi":"https://doi.org/10.1038/s41598-026-78701-0","title":"Machine learning-based PM2.5 estimation using NASA\u2019s MERRA-2 and comparison of ML- and SHAP-based meteorological normalization in Thailand","display_name":"Machine learning-based PM2.5 estimation using NASA\u2019s MERRA-2 and comparison of ML- and SHAP-based meteorological normalization in Thailand","publication_year":2026,"publication_date":"2026-11-07","ids":{"openalex":"https://openalex.org/W7225459474","doi":"https://doi.org/10.1038/s41598-026-78701-0"},"language":"en","primary_location":{"id":"doi:10.1038/s41598-026-78701-0","doi":"https://doi.org/10.1038/s41598-026-78701-0","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-78701-0","pdf_url":"https://www.nature.com/articles/s41598-026-78701-0_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.nature.com/articles/s41598-026-78701-0_reference.pdf","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024944526","display_name":"Nishit Aman","orcid":"https://orcid.org/0000-0001-5005-6414","observed_orcids":["https://orcid.org/0000-0001-5005-6414"]},"institutions":[{"id":"https://openalex.org/I4405260188","display_name":"Ministry of Higher Education, Science, Research and Innovation","ror":"https://ror.org/03cqd2v24","country_code":"TH","type":"government","lineage":["https://openalex.org/I2801080794","https://openalex.org/I4405260188"]}],"countries":["TH"],"is_corresponding":false,"raw_author_name":"Nishit Aman","raw_affiliation_strings":["Hydro-Informatics Innovation Division, Hydro-Informatics Institute, Ministry of Higher Education, Science, Research and Innovation, Bangkok, 10900, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydro-Informatics Innovation Division, Hydro-Informatics Institute, Ministry of Higher Education, Science, Research and Innovation, Bangkok, 10900, Thailand","institution_ids":["https://openalex.org/I4405260188"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049629682","display_name":"Kritanai Torsri","orcid":"https://orcid.org/0000-0003-2795-806X","observed_orcids":["https://orcid.org/0000-0003-2795-806X"]},"institutions":[{"id":"https://openalex.org/I4405260188","display_name":"Ministry of Higher Education, Science, Research and Innovation","ror":"https://ror.org/03cqd2v24","country_code":"TH","type":"government","lineage":["https://openalex.org/I2801080794","https://openalex.org/I4405260188"]}],"countries":["TH"],"is_corresponding":true,"raw_author_name":"Kritanai Torsri","raw_affiliation_strings":["Hydro-Informatics Innovation Division, Hydro-Informatics Institute, Ministry of Higher Education, Science, Research and Innovation, Bangkok, 10900, Thailand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hydro-Informatics Innovation Division, Hydro-Informatics Institute, Ministry of Higher Education, Science, Research and Innovation, Bangkok, 10900, Thailand","institution_ids":["https://openalex.org/I4405260188"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5049629682"],"corresponding_institution_ids":["https://openalex.org/I4405260188"],"apc_list":{"value":2690,"currency":"USD","value_usd":2690},"apc_paid":{"value":2690,"currency":"USD","value_usd":2690},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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":0.9179999828338623,"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":0.9179999828338623,"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/T10075","display_name":"Atmospheric chemistry and aerosols","score":0.0333000011742115,"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"}},{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.03150000050663948,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pm2-5-estimation","display_name":"PM2.5 estimation","score":0.9819999933242798},{"id":"https://openalex.org/keywords/merra-2","display_name":"MERRA-2","score":0.949999988079071},{"id":"https://openalex.org/keywords/air-quality","display_name":"air quality","score":0.5230000019073486},{"id":"https://openalex.org/keywords/thailand","display_name":"Thailand","score":0.8230000138282776},{"id":"https://openalex.org/keywords/lightgbm","display_name":"LightGBM","score":0.7409999966621399},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.8970000147819519},{"id":"https://openalex.org/keywords/meteorological-normalization","display_name":"meteorological normalization","score":0.9850000143051147},{"id":"https://openalex.org/keywords/emissions-trends","display_name":"emission trends","score":0.5339999794960022},{"id":"https://openalex.org/keywords/biomass-burning","display_name":"biomass burning","score":0.8320000171661377},{"id":"https://openalex.org/keywords/urban-air-pollution","display_name":"urban air pollution","score":0.7129999995231628},{"id":"https://openalex.org/keywords/long-term-air-quality-trends","display_name":"long-term air quality trends","score":0.7099999785423279},{"id":"https://openalex.org/keywords/air-quality-monitoring-gaps","display_name":"air quality monitoring gaps","score":0.5360000133514404},{"id":"https://openalex.org/keywords/southeast-asia","display_name":"Southeast Asia","score":0.6759999990463257}],"concepts":[{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.6607999801635742},{"id":"https://openalex.org/C24245907","wikidata":"https://www.wikidata.org/wiki/Q498957","display_name":"Particulates","level":2,"score":0.5128999948501587},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.5054000020027161},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.49459999799728394},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.4927000105381012},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4553000032901764},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.43970000743865967},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.43959999084472656},{"id":"https://openalex.org/C142259097","wikidata":"https://www.wikidata.org/wiki/Q5891314","display_name":"Homogeneity (statistics)","level":2,"score":0.3578999936580658},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.335099995136261},{"id":"https://openalex.org/C188154048","wikidata":"https://www.wikidata.org/wiki/Q6803609","display_name":"Mean absolute error","level":3,"score":0.3100000023841858},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.3098999857902527},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.30970001220703125},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.30660000443458557},{"id":"https://openalex.org/C194648359","wikidata":"https://www.wikidata.org/wiki/Q3318054","display_name":"Generalized additive model","level":2,"score":0.2870999872684479},{"id":"https://openalex.org/C114289077","wikidata":"https://www.wikidata.org/wiki/Q3284399","display_name":"Statistical model","level":2,"score":0.28139999508857727},{"id":"https://openalex.org/C2987857752","wikidata":"https://www.wikidata.org/wiki/Q12147","display_name":"Human health","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C133199616","wikidata":"https://www.wikidata.org/wiki/Q25386885","display_name":"Empirical modelling","level":2,"score":0.2777999937534332},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2743000090122223},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.26570001244544983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1038/s41598-026-78701-0","doi":"https://doi.org/10.1038/s41598-026-78701-0","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-78701-0","pdf_url":"https://www.nature.com/articles/s41598-026-78701-0_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1038/s41598-026-78701-0","doi":"https://doi.org/10.1038/s41598-026-78701-0","is_oa":true,"landing_page_url":"https://doi.org/10.1038/s41598-026-78701-0","pdf_url":"https://www.nature.com/articles/s41598-026-78701-0_reference.pdf","source":{"id":"https://openalex.org/S196734849","display_name":"Scientific Reports","issn_l":"2045-2322","issn":["2045-2322"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["anvur-class-a","anvur-scientific","cwts-core","doaj","dongbi-c","doyens","jufo-1","ki-jl-1","medline","norway-1","poland-140","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310319908","host_organization_name":"Nature Portfolio","host_organization_lineage":["https://openalex.org/P4310319908","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Nature Portfolio","Springer Nature"],"type":"journal"},"license":"cc-by-nc-nd","license_id":"https://openalex.org/licenses/cc-by-nc-nd","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Scientific Reports","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Climate action","score":0.821399986743927,"id":"https://metadata.un.org/sdg/13"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7225459474.pdf","grobid_xml":"https://content.openalex.org/works/W7225459474.grobid-xml"},"referenced_works_count":42,"referenced_works":["https://openalex.org/W1976991085","https://openalex.org/W1996771350","https://openalex.org/W2052319573","https://openalex.org/W2083128385","https://openalex.org/W2108079253","https://openalex.org/W2108162680","https://openalex.org/W2141970008","https://openalex.org/W2607030186","https://openalex.org/W2611772571","https://openalex.org/W2618851150","https://openalex.org/W2626032231","https://openalex.org/W2786588599","https://openalex.org/W2885226068","https://openalex.org/W2898556519","https://openalex.org/W2999615587","https://openalex.org/W3016070954","https://openalex.org/W3030898280","https://openalex.org/W3033013754","https://openalex.org/W3037375387","https://openalex.org/W3040292123","https://openalex.org/W3110093462","https://openalex.org/W3114304600","https://openalex.org/W3134337617","https://openalex.org/W3169935594","https://openalex.org/W3189536807","https://openalex.org/W3199885704","https://openalex.org/W4200605469","https://openalex.org/W4220990433","https://openalex.org/W4221048984","https://openalex.org/W4221133795","https://openalex.org/W4284894614","https://openalex.org/W4321095123","https://openalex.org/W4327723562","https://openalex.org/W4365813376","https://openalex.org/W4377098129","https://openalex.org/W4383558249","https://openalex.org/W4383563419","https://openalex.org/W4383961591","https://openalex.org/W4385267876","https://openalex.org/W4396907000","https://openalex.org/W4409903086","https://openalex.org/W4411001255"],"related_works":["https://openalex.org/W3199885704","https://openalex.org/W4307022846","https://openalex.org/W4409903086","https://openalex.org/W4315607616","https://openalex.org/W4220990433","https://openalex.org/W4386169972","https://openalex.org/W7150158045","https://openalex.org/W4383961591","https://openalex.org/W4390012797","https://openalex.org/W4392455035"],"abstract_inverted_index":{"Fine":[0],"particulate":[1],"matter":[2],"(PM":[3],"2.5":[4,32,45,139,168,202,218,226],")":[5],"poses":[6],"severe":[7],"health":[8,282],"risks":[9],"in":[10,104,153,166,199,223,262],"Thailand;":[11],"however,":[12,234],"sparse":[13],"ground":[14],"monitoring":[15],"has":[16],"left":[17],"long-term":[18,200],"trends":[19,192,222,246],"poorly":[20],"quantified.":[21],"This":[22],"study":[23,214],"utilizes":[24],"NASA\u2019s":[25],"MERRA-2":[26],"reanalysis":[27,272],"to":[28,83,90,98,182,252,257,278],"estimate":[29],"historical":[30],"PM":[31,44,138,167,201,217,225],"levels":[33],"across":[34,68,110,141,169,229],"Thailand":[35,69,170,230],"through":[36],"two":[37],"approaches:":[38],"an":[39],"empirical":[40],"formulation":[41],"based":[42],"on":[43],"chemical":[46],"components":[47],"(MERRA2_EF)":[48],"and":[49,94,125,149,155,177,189,231,240,242,284],"a":[50,258],"machine-learning":[51],"model":[52,73,105],"(MERRA2_ML)":[53],"utilizing":[54],"the":[55,71,76,111,117,129,134,243,254],"Light":[56],"Gradient":[57],"Boosting":[58],"Machine":[59],"algorithm.":[60],"Validated":[61],"against":[62],"58":[63],"reference":[64],"air":[65,285],"quality":[66,286],"stations":[67],"(2012\u20132022),":[70],"MERRA2_ML":[72,118,131,185],"significantly":[74],"outperforms":[75],"MERRA2_EF,":[77,194],"increasing":[78],"temporal":[79,123],"correlation":[80],"from":[81,88,96,180],"0.48":[82],"0.84,":[84],"while":[85],"reducing":[86],"MAE":[87],"14.99":[89],"8.64":[91],"\u00b5g":[92,100,172],"m\u207b\u00b3":[93],"RMSE":[95],"24.95":[97],"13.39":[99],"m\u207b\u00b3.":[101],"Similar":[102],"improvements":[103],"performance":[106],"were":[107,227],"also":[108],"observed":[109,137,228,255],"different":[112],"regions":[113,179],"of":[114,136],"Thailand,":[115,142],"with":[116,159,184],"estimates":[119],"generally":[120],"showing":[121,186],"stronger":[122],"agreement":[124],"lower":[126,188],"errors":[127],"than":[128,193],"MERRA2_EF.":[130,160],"substantially":[132],"improved":[133],"representation":[135],"variability":[140],"accurately":[143],"capturing":[144],"diurnal":[145],"peaks,":[146],"dry-season":[147],"enhancements,":[148],"interannual":[150],"fluctuations,":[151],"particularly":[152],"biomass-burning-":[154],"urban-influenced":[156],"regions,":[157],"compared":[158],"Long-term":[161],"analysis":[162],"revealed":[163],"significant":[164],"increases":[165],"(1.97":[171],"m":[173],"\u207b3":[174],"per":[175],"decade)":[176],"most":[178],"1981":[181],"2022,":[183],"consistently":[187],"more":[190],"moderate":[191],"highlighting":[195],"substantial":[196],"spatial":[197],"heterogeneity":[198],"changes.":[203],"By":[204],"employing":[205],"Shapley":[206],"Additive":[207],"Explanation":[208],"(SHAP)":[209],"for":[210],"meteorological":[211],"normalization,":[212],"this":[213],"isolated":[215],"emission-driven":[216],"variations.":[219],"Overall,":[220],"decreasing":[221],"emission-related":[224],"several":[232],"regions;":[233],"statistical":[235],"significance":[236],"varied":[237],"by":[238],"region":[239],"method,":[241],"non-significant":[244],"national":[245],"do":[247],"not":[248],"provide":[249],"sufficient":[250],"evidence":[251],"attribute":[253],"decline":[256],"sustained":[259],"nationwide":[260],"reduction":[261],"emissions":[263],"or":[264],"policy":[265],"interventions.":[266],"These":[267],"findings":[268],"demonstrate":[269],"that":[270],"ML-enhanced":[271],"provides":[273],"robust,":[274],"spatially":[275],"explicit":[276],"data":[277],"support":[279],"evidence-based":[280],"public":[281],"decisions":[283],"management":[287],"throughout":[288],"Southeast":[289],"Asia.":[290]},"counts_by_year":[],"updated_date":"2026-11-09T13:22:03.289127","created_date":"2026-11-08T00:00:00"}
