{"id":"https://openalex.org/W7083188272","doi":"https://doi.org/10.5334/dsj-2025-027","title":"Robust Machine Learning Algorithmic Rules for Detecting Air Pollution in the Lower Parts of the Atmosphere","display_name":"Robust Machine Learning Algorithmic Rules for Detecting Air Pollution in the Lower Parts of the Atmosphere","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W7083188272","doi":"https://doi.org/10.5334/dsj-2025-027"},"language":"en","primary_location":{"id":"doi:10.5334/dsj-2025-027","is_oa":true,"landing_page_url":"https://doi.org/10.5334/dsj-2025-027","pdf_url":"https://datascience.codata.org/articles/1867/files/68d3e81f2d9bd.pdf","source":{"id":"https://openalex.org/S62969111","display_name":"Data Science Journal","issn_l":"1683-1470","issn":["1683-1470"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320511","host_organization_name":"Ubiquity Press","host_organization_lineage":["https://openalex.org/P4310320511"],"host_organization_lineage_names":["Ubiquity Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Science Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://datascience.codata.org/articles/1867/files/68d3e81f2d9bd.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Kassim Mwitondi","orcid":"https://orcid.org/0000-0003-1134-547X"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Kassim Mwitondi","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-1134-547X","affiliations":[]},{"author_position":"last","author":{"id":null,"display_name":"Hugo Wai Leung Mak","orcid":"https://orcid.org/0000-0002-7033-6218"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hugo Wai Leung Mak","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-7033-6218","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":350,"currency":"GBP","value_usd":429},"apc_paid":{"value":350,"currency":"GBP","value_usd":429},"fwci":6.2328,"has_fulltext":true,"cited_by_count":3,"citation_normalized_percentile":{"value":0.96636855,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":95,"max":98},"biblio":{"volume":"24","issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.33399999141693115,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.33399999141693115,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13177","display_name":"Geological and Geophysical Studies","score":0.05860000103712082,"subfield":{"id":"https://openalex.org/subfields/1907","display_name":"Geology"},"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/T13067","display_name":"Geological Modeling and Analysis","score":0.02979999966919422,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"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/randomness","display_name":"Randomness","score":0.7085999846458435},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.6832000017166138},{"id":"https://openalex.org/keywords/air-pollution","display_name":"Air pollution","score":0.5263000130653381},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.39100000262260437},{"id":"https://openalex.org/keywords/pollutant","display_name":"Pollutant","score":0.36649999022483826},{"id":"https://openalex.org/keywords/pollution","display_name":"Pollution","score":0.34279999136924744},{"id":"https://openalex.org/keywords/atmosphere","display_name":"Atmosphere (unit)","score":0.33980000019073486},{"id":"https://openalex.org/keywords/air-pollutant-concentrations","display_name":"Air pollutant concentrations","score":0.3377000093460083}],"concepts":[{"id":"https://openalex.org/C125112378","wikidata":"https://www.wikidata.org/wiki/Q176640","display_name":"Randomness","level":2,"score":0.7085999846458435},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6832000017166138},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6442999839782715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5350000262260437},{"id":"https://openalex.org/C559116025","wikidata":"https://www.wikidata.org/wiki/Q131123","display_name":"Air pollution","level":2,"score":0.5263000130653381},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4462999999523163},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.39100000262260437},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3894999921321869},{"id":"https://openalex.org/C82685317","wikidata":"https://www.wikidata.org/wiki/Q19829510","display_name":"Pollutant","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C521259446","wikidata":"https://www.wikidata.org/wiki/Q58734","display_name":"Pollution","level":2,"score":0.34279999136924744},{"id":"https://openalex.org/C65440619","wikidata":"https://www.wikidata.org/wiki/Q177974","display_name":"Atmosphere (unit)","level":2,"score":0.33980000019073486},{"id":"https://openalex.org/C52173716","wikidata":"https://www.wikidata.org/wiki/Q4698350","display_name":"Air pollutant concentrations","level":4,"score":0.3377000093460083},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.32280001044273376},{"id":"https://openalex.org/C126314574","wikidata":"https://www.wikidata.org/wiki/Q2364111","display_name":"Air quality index","level":2,"score":0.3181999921798706},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.29190000891685486},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.29089999198913574},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.259799987077713},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C24245907","wikidata":"https://www.wikidata.org/wiki/Q498957","display_name":"Particulates","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.2515999972820282}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.5334/dsj-2025-027","is_oa":true,"landing_page_url":"https://doi.org/10.5334/dsj-2025-027","pdf_url":"https://datascience.codata.org/articles/1867/files/68d3e81f2d9bd.pdf","source":{"id":"https://openalex.org/S62969111","display_name":"Data Science Journal","issn_l":"1683-1470","issn":["1683-1470"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320511","host_organization_name":"Ubiquity Press","host_organization_lineage":["https://openalex.org/P4310320511"],"host_organization_lineage_names":["Ubiquity Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Science Journal","raw_type":"journal-article"},{"id":"pmh:oai:repository.hkust.edu.hk:1783.1-166171","is_oa":false,"landing_page_url":"http://repository.hkust.edu.hk/ir/Record/1783.1-166171","pdf_url":null,"source":{"id":"https://openalex.org/S4306401796","display_name":"Rare & Special e-Zone (The Hong Kong University of Science and Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I200769079","host_organization_name":"Hong Kong University of Science and Technology","host_organization_lineage":["https://openalex.org/I200769079"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Article"},{"id":"pmh:oai:doaj.org/article:c03af268e7664d22aa91a842fe1644dd","is_oa":true,"landing_page_url":"https://doaj.org/article/c03af268e7664d22aa91a842fe1644dd","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":"Data Science Journal, Vol 24, Pp 27-27 (2025)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.5334/dsj-2025-027","is_oa":true,"landing_page_url":"https://doi.org/10.5334/dsj-2025-027","pdf_url":"https://datascience.codata.org/articles/1867/files/68d3e81f2d9bd.pdf","source":{"id":"https://openalex.org/S62969111","display_name":"Data Science Journal","issn_l":"1683-1470","issn":["1683-1470"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320511","host_organization_name":"Ubiquity Press","host_organization_lineage":["https://openalex.org/P4310320511"],"host_organization_lineage_names":["Ubiquity Press"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Data Science Journal","raw_type":"journal-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure","score":0.477836012840271}],"awards":[],"funders":[{"id":"https://openalex.org/F4320322472","display_name":"Qatar University","ror":"https://ror.org/00yhnba62"},{"id":"https://openalex.org/F4320322942","display_name":"Chinese University of Hong Kong","ror":"https://ror.org/00t33hh48"},{"id":"https://openalex.org/F4320323537","display_name":"Hong Kong University of Science and Technology","ror":"https://ror.org/00q4vv597"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7083188272.pdf","grobid_xml":"https://content.openalex.org/works/W7083188272.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W2007565321","https://openalex.org/W2049633694","https://openalex.org/W2107327484","https://openalex.org/W2144428405","https://openalex.org/W2154259122","https://openalex.org/W2580719731","https://openalex.org/W2885419738","https://openalex.org/W2910807252","https://openalex.org/W3010956878","https://openalex.org/W3015814720","https://openalex.org/W3036284577","https://openalex.org/W3086419524","https://openalex.org/W3127554451","https://openalex.org/W3139592822","https://openalex.org/W3170211481","https://openalex.org/W3195167447","https://openalex.org/W3199773854","https://openalex.org/W3201890964","https://openalex.org/W4206721750","https://openalex.org/W4210538803","https://openalex.org/W4211081523","https://openalex.org/W4280536936","https://openalex.org/W4281722347","https://openalex.org/W4285085630","https://openalex.org/W4293329630","https://openalex.org/W4306645406","https://openalex.org/W4309660897","https://openalex.org/W4321370723","https://openalex.org/W4361274840","https://openalex.org/W4366205470","https://openalex.org/W4377987118","https://openalex.org/W4381678571","https://openalex.org/W4390751343","https://openalex.org/W4391403580","https://openalex.org/W4392662912","https://openalex.org/W4392971772","https://openalex.org/W4399592825"],"related_works":[],"abstract_inverted_index":{"Sophisticated":[0],"data-intensive":[1],"approaches":[2],"have":[3],"been":[4],"widely":[5],"applied":[6],"in":[7,48,139,194],"addressing":[8],"air":[9,131],"pollution":[10,132,193],"problems,":[11],"with":[12,29,87,98,259],"applications":[13],"ranging":[14],"from":[15,45,127,136],"remote":[16],"sensing":[17],"quantification":[18],"of":[19,22,106,158,192,202,226,236,255,265,271],"ground-level":[20,130],"concentrations":[21],"atmospheric":[23,30],"pollutants":[24,147,228],"to":[25,35,57,72,102,167,221,223,241,252,257],"associating":[26],"particulate":[27],"matter":[28],"CO2.":[31],"The":[32,112,153,174,197],"biggest":[33],"challenge":[34],"such":[36],"applications,":[37],"however,":[38],"remains":[39],"model":[40,243],"optimisation\u2014a":[41],"problem":[42,169],"that":[43,69,120,204],"derives":[44],"inherent":[46],"randomness":[47,60],"training,":[49],"validation":[50],"and":[51,65,108,124,148,163,171,182,215,245,268],"test":[52],"data.":[53],"A":[54],"standard":[55],"approach":[56,86,114],"address":[58],"data":[59,63,66,133],"hinges":[61],"on":[62],"harmonisation":[64],"augmentation\u2014two":[67],"concepts":[68],"naturally":[70],"appeal":[71],"the":[73,93,146,156,190,233,237,253,263,272],"highly":[74],"\u201cnon-orthogonal\u201d":[75],"17":[76],"Sustainable":[77],"Development":[78],"Goals":[79],"(SDG).":[80],"This":[81,217],"paper":[82],"proposes":[83],"a":[84,206],"novel":[85],"built-in":[88],"robust":[89],"mechanisms":[90],"for":[91,209],"generating":[92],"\u201cmost":[94],"parsimonious":[95],"model\u201d":[96],"\u2013":[97],"potential":[99,208],"\u201cglobal":[100],"representativeness\u201d":[101],"highlight":[103],"data-driven":[104],"solutions":[105],"regional":[107],"global":[109],"environmental":[110,269],"challenges.":[111],"proposed":[113],"is":[115,219],"powered":[116],"by":[117,262],"two":[118],"algorithms":[119,154],"sequentially":[121],"estimate,":[122],"maximise":[123],"optimise":[125],"parameters":[126],"thirty":[128],"thousand":[129],"points":[134],"obtained":[135],"different":[137],"locations":[138],"southern":[140,195],"China;":[141],"generate":[142],"statistical":[143],"associations":[144,178],"among":[145],"present":[149],"interpretable":[150],"visual":[151],"outputs.":[152],"balance":[155],"power":[157],"data,":[159],"machine":[160],"learning":[161],"techniques":[162],"underlying":[164],"domain":[165],"knowledge":[166],"enhance":[168],"identification":[170],"solution":[172],"development.":[173],"results":[175],"show":[176],"optimal":[177],"between":[179],"spatio-temporal":[180],"attributes":[181,270],"relevant":[183],"pollutants,":[184],"thus":[185],"provide":[186],"useful":[187],"insights":[188],"into":[189],"state":[191],"China.":[196],"findings":[198],"also":[199,250],"indicate":[200],"robustness":[201],"features":[203],"exhibit":[205],"great":[207],"building":[210],"analytical":[211],"bridges":[212],"across":[213],"disciplines":[214],"sectors.":[216],"research":[218],"expected":[220],"contribute":[222,251],"our":[224],"understanding":[225],"how":[227],"are":[229],"spatially":[230],"distributed":[231],"within":[232],"lower":[234],"part":[235],"atmosphere,":[238],"potentially":[239],"leading":[240],"improved":[242],"performance":[244],"innovation.":[246],"Further,":[247],"it":[248],"will":[249],"design":[254],"methods":[256],"deal":[258],"challenges":[260],"posed":[261],"\u201cnon-orthogonality\u201d":[264],"socio-economic,":[266],"technical":[267],"SDG.":[273]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-05-06T08:25:59.206177","created_date":"2025-10-10T00:00:00"}