{"id":"https://openalex.org/W2900768820","doi":"https://doi.org/10.1080/10095020.2018.1523341","title":"Exploration of spatial and temporal characteristics of PM2.5 concentration in Guangzhou, China using wavelet analysis and modified land use regression model","display_name":"Exploration of spatial and temporal characteristics of PM2.5 concentration in Guangzhou, China using wavelet analysis and modified land use regression model","publication_year":2018,"publication_date":"2018-10-02","ids":{"openalex":"https://openalex.org/W2900768820","doi":"https://doi.org/10.1080/10095020.2018.1523341","mag":"2900768820"},"language":"en","primary_location":{"id":"doi:10.1080/10095020.2018.1523341","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10095020.2018.1523341","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10095020.2018.1523341?needAccess=true","source":{"id":"https://openalex.org/S36798160","display_name":"Geo-spatial Information Science","issn_l":"1009-5020","issn":["1009-5020","1993-5153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Geo-spatial Information Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/10095020.2018.1523341?needAccess=true","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5085862781","display_name":"Fenglei Fan","orcid":"https://orcid.org/0000-0001-6190-9072"},"institutions":[{"id":"https://openalex.org/I140786321","display_name":"Tibet University","ror":"https://ror.org/05petvd47","country_code":"CN","type":"education","lineage":["https://openalex.org/I140786321"]},{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fenglei Fan","raw_affiliation_strings":["Joint Laboratory of Plateau Surface Remote Sensing, Tibet University, Lhasa, China","School of Geography, South China Normal University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Joint Laboratory of Plateau Surface Remote Sensing, Tibet University, Lhasa, China","institution_ids":["https://openalex.org/I140786321"]},{"raw_affiliation_string":"School of Geography, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102082139","display_name":"Runping Liu","orcid":null},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Runping Liu","raw_affiliation_strings":["School of Geography, South China Normal University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Geography, South China Normal University, Guangzhou, China","institution_ids":["https://openalex.org/I187400657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085862781"],"corresponding_institution_ids":["https://openalex.org/I140786321","https://openalex.org/I187400657"],"apc_list":{"value":1625,"currency":"GBP","value_usd":1993},"apc_paid":{"value":1625,"currency":"GBP","value_usd":1993},"fwci":1.347,"has_fulltext":false,"cited_by_count":15,"citation_normalized_percentile":{"value":0.77574537,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":99},"biblio":{"volume":"21","issue":"4","first_page":"311","last_page":"321"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.9991999864578247,"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"}},"topics":[{"id":"https://openalex.org/T10190","display_name":"Air Quality and Health Impacts","score":0.9991999864578247,"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/T10766","display_name":"Urban Heat Island Mitigation","score":0.9990000128746033,"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/T12120","display_name":"Air Quality Monitoring and Forecasting","score":0.9950000047683716,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/impervious-surface","display_name":"Impervious surface","score":0.927070140838623},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.6067587733268738},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6057898998260498},{"id":"https://openalex.org/keywords/linear-regression","display_name":"Linear regression","score":0.538057267665863},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.495889276266098},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4446832239627838},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.4415528476238251},{"id":"https://openalex.org/keywords/geographic-information-system","display_name":"Geographic information system","score":0.43116289377212524},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.39552196860313416},{"id":"https://openalex.org/keywords/physical-geography","display_name":"Physical geography","score":0.3528518080711365},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2741139531135559},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.2230435311794281},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.19353187084197998},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.17108529806137085}],"concepts":[{"id":"https://openalex.org/C2668921","wikidata":"https://www.wikidata.org/wiki/Q1434713","display_name":"Impervious surface","level":2,"score":0.927070140838623},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.6067587733268738},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6057898998260498},{"id":"https://openalex.org/C48921125","wikidata":"https://www.wikidata.org/wiki/Q10861030","display_name":"Linear regression","level":2,"score":0.538057267665863},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.495889276266098},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4446832239627838},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.4415528476238251},{"id":"https://openalex.org/C41856607","wikidata":"https://www.wikidata.org/wiki/Q483130","display_name":"Geographic information system","level":2,"score":0.43116289377212524},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.39552196860313416},{"id":"https://openalex.org/C100970517","wikidata":"https://www.wikidata.org/wiki/Q52107","display_name":"Physical geography","level":1,"score":0.3528518080711365},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2741139531135559},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.2230435311794281},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.19353187084197998},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.17108529806137085},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1080/10095020.2018.1523341","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10095020.2018.1523341","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10095020.2018.1523341?needAccess=true","source":{"id":"https://openalex.org/S36798160","display_name":"Geo-spatial Information Science","issn_l":"1009-5020","issn":["1009-5020","1993-5153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Geo-spatial Information Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:9db8791799c0444a88b012f09bce19b3","is_oa":true,"landing_page_url":"https://doaj.org/article/9db8791799c0444a88b012f09bce19b3","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":"Geo-spatial Information Science, Vol 21, Iss 4, Pp 311-321 (2018)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/10095020.2018.1523341","is_oa":true,"landing_page_url":"https://doi.org/10.1080/10095020.2018.1523341","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/10095020.2018.1523341?needAccess=true","source":{"id":"https://openalex.org/S36798160","display_name":"Geo-spatial Information Science","issn_l":"1009-5020","issn":["1009-5020","1993-5153"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Geo-spatial Information Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G7302094890","display_name":null,"funder_award_id":"41201432","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":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2900768820.pdf","grobid_xml":"https://content.openalex.org/works/W2900768820.grobid-xml"},"referenced_works_count":37,"referenced_works":["https://openalex.org/W1965737908","https://openalex.org/W1979789940","https://openalex.org/W1991628772","https://openalex.org/W2003207330","https://openalex.org/W2017153448","https://openalex.org/W2020747033","https://openalex.org/W2021936090","https://openalex.org/W2023384536","https://openalex.org/W2023467953","https://openalex.org/W2025024664","https://openalex.org/W2026332487","https://openalex.org/W2027756597","https://openalex.org/W2028060078","https://openalex.org/W2031888471","https://openalex.org/W2032799636","https://openalex.org/W2034139177","https://openalex.org/W2044568947","https://openalex.org/W2046364576","https://openalex.org/W2065947772","https://openalex.org/W2071237226","https://openalex.org/W2076166095","https://openalex.org/W2079480608","https://openalex.org/W2091661380","https://openalex.org/W2092998278","https://openalex.org/W2095087545","https://openalex.org/W2096972703","https://openalex.org/W2101982704","https://openalex.org/W2110456190","https://openalex.org/W2135671319","https://openalex.org/W2138085930","https://openalex.org/W2199807383","https://openalex.org/W2335637623","https://openalex.org/W2416262024","https://openalex.org/W2480175994","https://openalex.org/W2951572255","https://openalex.org/W3122817556","https://openalex.org/W4235889631"],"related_works":["https://openalex.org/W2738109983","https://openalex.org/W2364341326","https://openalex.org/W2757433404","https://openalex.org/W4254235682","https://openalex.org/W2054563345","https://openalex.org/W2520989432","https://openalex.org/W3132993209","https://openalex.org/W2159637219","https://openalex.org/W2271813916","https://openalex.org/W2024488612"],"abstract_inverted_index":{"This":[0],"article":[1],"attempts":[2],"to":[3,18],"detail":[4],"time":[5],"series":[6],"characteristics":[7],"of":[8,86,97,114,128],"PM2.5":[9,98,115,129],"concentration":[10,91,113,127,144],"in":[11,99,117,122,130,135,146,151],"Guangzhou":[12,100],"(China)":[13],"from":[14,65],"1":[15],"June":[16],"2012":[17],"31":[19],"May":[20],"2013":[21],"based":[22],"on":[23],"wavelet":[24],"analysis":[25,74],"tools,":[26],"and":[27,37,93,106,119,138,148],"discuss":[28],"its":[29,87],"spatial":[30],"distribution":[31],"using":[32],"geographic":[33],"information":[34],"system":[35],"software":[36],"a":[38,102],"modified":[39,46],"land":[40,82],"use":[41,52,83],"regression":[42],"model.":[43],"In":[44],"this":[45],"model,":[47],"an":[48],"important":[49],"variable":[50],"(land":[51],"data)":[53],"is":[54,132],"substituted":[55],"for":[56],"impervious":[57],"surface":[58,77],"area,":[59],"which":[60],"can":[61],"be":[62],"obtained":[63],"conveniently":[64],"remote":[66],"sensing":[67],"imagery":[68],"through":[69],"the":[70,111,120],"linear":[71],"spectral":[72],"mixture":[73],"method.":[75],"Impervious":[76],"has":[78],"higher":[79,133],"precision":[80],"than":[81,134],"data":[84],"because":[85],"sub-pixel":[88],"level.":[89],"Seasonal":[90],"pattern":[92],"day-by-day":[94],"change":[95],"feature":[96],"with":[101],"micro-perspective":[103],"are":[104,141],"discussed":[105],"understood.":[107],"Results":[108],"include:":[109],"(1)":[110],"highest":[112],"occurs":[116],"October":[118],"lowest":[121],"July,":[123],"respectively;":[124],"(2)":[125],"average":[126],"winter":[131,147],"other":[136],"seasons;":[137],"(3)":[139],"there":[140],"two":[142],"high":[143],"zones":[145],"one":[149],"zone":[150],"spring.":[152]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":3}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
