{"id":"https://openalex.org/W3212210567","doi":"https://doi.org/10.3390/rs13224545","title":"Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale","display_name":"Estimation of PM2.5 Concentration Using Deep Bayesian Model Considering Spatial Multiscale","publication_year":2021,"publication_date":"2021-11-12","ids":{"openalex":"https://openalex.org/W3212210567","doi":"https://doi.org/10.3390/rs13224545","mag":"3212210567"},"language":"en","primary_location":{"id":"doi:10.3390/rs13224545","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13224545","pdf_url":"https://www.mdpi.com/2072-4292/13/22/4545/pdf?version=1636710667","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.mdpi.com/2072-4292/13/22/4545/pdf?version=1636710667","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057477280","display_name":"Xingdi Chen","orcid":"https://orcid.org/0000-0003-4187-8330"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingdi Chen","raw_affiliation_strings":["School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"],"affiliations":[{"raw_affiliation_string":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112395785","display_name":"Kong Peng","orcid":null},"institutions":[{"id":"https://openalex.org/I194716290","display_name":"China Academy of Space Technology","ror":"https://ror.org/025397a59","country_code":"CN","type":"government","lineage":["https://openalex.org/I194716290","https://openalex.org/I2802615301"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Kong","raw_affiliation_strings":["Institute of Spacecraft System Engineering, Beijing 100094, China"],"affiliations":[{"raw_affiliation_string":"Institute of Spacecraft System Engineering, Beijing 100094, China","institution_ids":["https://openalex.org/I194716290"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091117089","display_name":"Peng Jiang","orcid":"https://orcid.org/0000-0002-7342-7940"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Jiang","raw_affiliation_strings":["School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"],"affiliations":[{"raw_affiliation_string":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China","institution_ids":["https://openalex.org/I143868143"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100458040","display_name":"Yanlan Wu","orcid":"https://orcid.org/0000-0002-8983-3150"},"institutions":[{"id":"https://openalex.org/I143868143","display_name":"Anhui University","ror":"https://ror.org/05th6yx34","country_code":"CN","type":"education","lineage":["https://openalex.org/I143868143"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanlan Wu","raw_affiliation_strings":["Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei 230601, China","School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China"],"affiliations":[{"raw_affiliation_string":"Information Materials and Intelligent Sensing Laboratory of Anhui Province, Hefei 230601, China","institution_ids":["https://openalex.org/I143868143"]},{"raw_affiliation_string":"School of Resources and Environmental Engineering, Anhui University, Hefei 230601, China","institution_ids":["https://openalex.org/I143868143"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100458040"],"corresponding_institution_ids":["https://openalex.org/I143868143"],"apc_list":{"value":2500,"currency":"CHF","value_usd":2707},"apc_paid":{"value":2500,"currency":"CHF","value_usd":2707},"fwci":0.6372,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.64993263,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":100},"biblio":{"volume":"13","issue":"22","first_page":"4545","last_page":"4545"},"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":0.9997000098228455,"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/T12916","display_name":"COVID-19 impact on air quality","score":0.989300012588501,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/overfitting","display_name":"Overfitting","score":0.9047646522521973},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5823122262954712},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.4963584542274475},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4907902181148529},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4717491567134857},{"id":"https://openalex.org/keywords/moderate-resolution-imaging-spectroradiometer","display_name":"Moderate-resolution imaging spectroradiometer","score":0.4710376560688019},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46633172035217285},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.43525370955467224},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4302513897418976},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.4274338483810425},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4264729917049408},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.42423728108406067},{"id":"https://openalex.org/keywords/mean-squared-error","display_name":"Mean squared error","score":0.41564011573791504},{"id":"https://openalex.org/keywords/satellite","display_name":"Satellite","score":0.3281742334365845},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.24816390872001648},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2038382589817047}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.9047646522521973},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5823122262954712},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4963584542274475},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4907902181148529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4717491567134857},{"id":"https://openalex.org/C2777007095","wikidata":"https://www.wikidata.org/wiki/Q676840","display_name":"Moderate-resolution imaging spectroradiometer","level":3,"score":0.4710376560688019},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46633172035217285},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.43525370955467224},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4302513897418976},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.4274338483810425},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4264729917049408},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.42423728108406067},{"id":"https://openalex.org/C139945424","wikidata":"https://www.wikidata.org/wiki/Q1940696","display_name":"Mean squared error","level":2,"score":0.41564011573791504},{"id":"https://openalex.org/C19269812","wikidata":"https://www.wikidata.org/wiki/Q26540","display_name":"Satellite","level":2,"score":0.3281742334365845},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.24816390872001648},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2038382589817047},{"id":"https://openalex.org/C146978453","wikidata":"https://www.wikidata.org/wiki/Q3798668","display_name":"Aerospace engineering","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.3390/rs13224545","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13224545","pdf_url":"https://www.mdpi.com/2072-4292/13/22/4545/pdf?version=1636710667","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:897952685233496aaaf89e6fa4e8cde3","is_oa":true,"landing_page_url":"https://doaj.org/article/897952685233496aaaf89e6fa4e8cde3","pdf_url":null,"source":{"id":"https://openalex.org/S112646816","display_name":"SHILAP Revista de lepidopterolog\u00eda","issn_l":"0300-5267","issn":["0300-5267","2340-4078"],"is_oa":true,"is_in_doaj":true,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Remote Sensing, Vol 13, Iss 22, p 4545 (2021)","raw_type":"article"},{"id":"pmh:oai:mdpi.com:/2072-4292/13/22/4545/","is_oa":true,"landing_page_url":"https://dx.doi.org/10.3390/rs13224545","pdf_url":null,"source":{"id":"https://openalex.org/S4306400947","display_name":"MDPI (MDPI AG)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210097602","host_organization_name":"Multidisciplinary Digital Publishing Institute (Switzerland)","host_organization_lineage":["https://openalex.org/I4210097602"],"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":"Remote Sensing; Volume 13; Issue 22; Pages: 4545","raw_type":"Text"}],"best_oa_location":{"id":"doi:10.3390/rs13224545","is_oa":true,"landing_page_url":"https://doi.org/10.3390/rs13224545","pdf_url":"https://www.mdpi.com/2072-4292/13/22/4545/pdf?version=1636710667","source":{"id":"https://openalex.org/S43295729","display_name":"Remote Sensing","issn_l":"2072-4292","issn":["2072-4292"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Remote Sensing","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Life in Land","score":0.44999998807907104,"id":"https://metadata.un.org/sdg/15"}],"awards":[{"id":"https://openalex.org/G6593640913","display_name":null,"funder_award_id":"No. 41971311","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3212210567.pdf","grobid_xml":"https://content.openalex.org/works/W3212210567.grobid-xml"},"referenced_works_count":44,"referenced_works":["https://openalex.org/W1875061881","https://openalex.org/W1980891198","https://openalex.org/W1990797640","https://openalex.org/W2005792051","https://openalex.org/W2083944525","https://openalex.org/W2102799873","https://openalex.org/W2108162680","https://openalex.org/W2110673467","https://openalex.org/W2153992435","https://openalex.org/W2155261478","https://openalex.org/W2172839472","https://openalex.org/W2310114729","https://openalex.org/W2322236889","https://openalex.org/W2405742917","https://openalex.org/W2480175994","https://openalex.org/W2739486717","https://openalex.org/W2776069591","https://openalex.org/W2790202404","https://openalex.org/W2796464444","https://openalex.org/W2801996267","https://openalex.org/W2885419738","https://openalex.org/W2917616628","https://openalex.org/W2932464630","https://openalex.org/W2937452004","https://openalex.org/W2946269013","https://openalex.org/W2948834265","https://openalex.org/W2955301595","https://openalex.org/W2996132844","https://openalex.org/W3008431026","https://openalex.org/W3008966418","https://openalex.org/W3011053038","https://openalex.org/W3015407791","https://openalex.org/W3025949386","https://openalex.org/W3031304377","https://openalex.org/W3044909247","https://openalex.org/W3093144165","https://openalex.org/W3099729076","https://openalex.org/W3105234146","https://openalex.org/W3108864973","https://openalex.org/W3114327799","https://openalex.org/W3128578677","https://openalex.org/W3134325823","https://openalex.org/W3161619952","https://openalex.org/W3185271969"],"related_works":["https://openalex.org/W4362597605","https://openalex.org/W1574414179","https://openalex.org/W3009056573","https://openalex.org/W2922073769","https://openalex.org/W4297676672","https://openalex.org/W4281702477","https://openalex.org/W4378510483","https://openalex.org/W4376166922","https://openalex.org/W2490526372","https://openalex.org/W4221142204"],"abstract_inverted_index":{"Directly":[0],"establishing":[1],"the":[2,30,50,92,104,114,122,126,146,150,157,165,178,183,189,201,212,215,219,229,234,243,247,254,257,269],"relationship":[3],"between":[4],"satellite":[5,135],"data":[6,107,136,140],"and":[7,100,119,137,170,205,278],"PM2.5":[8,15,25,53,66,167],"concentration":[9,16],"through":[10,98],"deep":[11,37,43,64],"learning":[12,38,44],"methods":[13,40],"for":[14,22],"estimation":[17,67,168,248],"is":[18,110],"an":[19],"important":[20],"means":[21],"estimating":[23],"regional":[24],"concentration.":[26],"However,":[27],"due":[28],"to":[29,57,82,91,112,120,145,148,162],"lack":[31],"of":[32,34,52,106,116,125,153,156,185,200,214,236,256],"consideration":[33],"uncertainty":[35],"in":[36,49,239,259,271],"methods,":[39],"based":[41],"on":[42,188],"have":[45],"certain":[46],"overfitting":[47,118],"problems":[48],"process":[51],"estimation.":[54],"In":[55,128,233],"response":[56],"this":[58,60,186,260,272],"problem,":[59],"paper":[61,261,273],"designs":[62],"a":[63,78,86,275],"Bayesian":[65,79],"model":[68,76,117,147,258,270],"that":[69,182,199,208],"takes":[70],"into":[71,102],"account":[72,103],"multiple":[73],"scales.":[74],"The":[75],"uses":[77],"neural":[80,93],"network":[81],"describe":[83],"key":[84],"parameters":[85],"priori,":[87],"provide":[88],"regularization":[89],"effects":[90],"network,":[94],"perform":[95],"posterior":[96],"inference":[97],"parameters,":[99],"take":[101],"characteristics":[105],"uncertainty,":[108],"which":[109,195,224],"used":[111,142],"alleviate":[113],"problem":[115],"improve":[121],"generalization":[123,171,280],"ability":[124],"model.":[127],"addition,":[129],"different-scale":[130,154],"Moderate-Resolution":[131],"Imaging":[132],"Spectroradiometer":[133],"(MODIS)":[134],"ERA5":[138],"reanalysis":[139],"were":[141],"as":[143,159,161,177],"input":[144],"strengthen":[149],"model\u2019s":[151,166],"perception":[152],"features":[155],"atmosphere,":[158],"well":[160],"further":[163],"enhance":[164],"accuracy":[169,249,277],"ability.":[172,281],"Experiments":[173],"with":[174,242],"Anhui":[175],"Province":[176],"research":[179],"area":[180],"showed":[181],"R2":[184,255],"method":[187],"independent":[190],"test":[191],"set":[192],"was":[193,196,221,225,250],"0.78,":[194],"higher":[197,276],"than":[198,228],"DNN,":[202],"random":[203],"forest,":[204],"BNN":[206],"models":[207],"do":[209],"not":[210],"consider":[211],"impact":[213],"surrounding":[216],"environment;":[217],"moreover,":[218],"RMSE":[220],"19.45":[222],"\u03bcg\u00b7m\u22123,":[223],"also":[226],"lower":[227],"three":[230,245],"compared":[231,241],"models.":[232],"experiment":[235],"different":[237],"seasons":[238],"2019,":[240],"other":[244],"models,":[246],"significantly":[251],"reduced;":[252],"however,":[253],"could":[262],"still":[263],"reach":[264],"0.66":[265],"or":[266],"more.":[267],"Thus,":[268],"has":[274],"better":[279]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-03-14T08:43:22.919905","created_date":"2025-10-10T00:00:00"}
