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Article: Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011

TitleSpatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011
Authors
KeywordsAir pollution
Meteorological factors
Seasonal-Trend Decomposition Procedure Based on Loess
Timescale-dependent relationship
Wavelet analysis
Issue Date2014
Citation
Environmental Pollution, 2014, v. 190, p. 75-81 How to Cite?
AbstractThere is an increasing interest in spatial and temporal variation of air pollution and its association with weather conditions. We presented the spatial and temporal variation of Air Pollution Index (API) and examined the associations between API and meteorological factors during 2001-2011 in Guangzhou, China. A Seasonal-Trend Decomposition Procedure Based on Loess (STL) was used to decompose API. Wavelet analyses were performed to examine the relationships between API and several meteorological factors. Air quality has improved since 2005. APIs were highly correlated among five monitoring stations, and there were substantial temporal variations. Timescale-dependent relationships were found between API and a variety of meteorological factors. Temperature, relative humidity, precipitation and wind speed were negatively correlated with API, while diurnal temperature range and atmospheric pressure were positively correlated with API in the annual cycle. Our findings should be taken into account when determining air quality forecasts and pollution control measures. © 2014 Elsevier Ltd. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/323911
ISSN
2023 Impact Factor: 7.6
2023 SCImago Journal Rankings: 2.132
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorLi, Li-
dc.contributor.authorQian, Jun-
dc.contributor.authorOu, Chun Quan-
dc.contributor.authorZhou, Ying Xue-
dc.contributor.authorGuo, Cui-
dc.contributor.authorGuo, Yuming-
dc.date.accessioned2023-01-13T03:00:11Z-
dc.date.available2023-01-13T03:00:11Z-
dc.date.issued2014-
dc.identifier.citationEnvironmental Pollution, 2014, v. 190, p. 75-81-
dc.identifier.issn0269-7491-
dc.identifier.urihttp://hdl.handle.net/10722/323911-
dc.description.abstractThere is an increasing interest in spatial and temporal variation of air pollution and its association with weather conditions. We presented the spatial and temporal variation of Air Pollution Index (API) and examined the associations between API and meteorological factors during 2001-2011 in Guangzhou, China. A Seasonal-Trend Decomposition Procedure Based on Loess (STL) was used to decompose API. Wavelet analyses were performed to examine the relationships between API and several meteorological factors. Air quality has improved since 2005. APIs were highly correlated among five monitoring stations, and there were substantial temporal variations. Timescale-dependent relationships were found between API and a variety of meteorological factors. Temperature, relative humidity, precipitation and wind speed were negatively correlated with API, while diurnal temperature range and atmospheric pressure were positively correlated with API in the annual cycle. Our findings should be taken into account when determining air quality forecasts and pollution control measures. © 2014 Elsevier Ltd. All rights reserved.-
dc.languageeng-
dc.relation.ispartofEnvironmental Pollution-
dc.subjectAir pollution-
dc.subjectMeteorological factors-
dc.subjectSeasonal-Trend Decomposition Procedure Based on Loess-
dc.subjectTimescale-dependent relationship-
dc.subjectWavelet analysis-
dc.titleSpatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001-2011-
dc.typeArticle-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1016/j.envpol.2014.03.020-
dc.identifier.pmid24732883-
dc.identifier.scopuseid_2-s2.0-84898637711-
dc.identifier.volume190-
dc.identifier.spage75-
dc.identifier.epage81-
dc.identifier.eissn1873-6424-
dc.identifier.isiWOS:000336709100010-

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