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PM(2.5) Spatiotemporal Variations and the Relationship with Meteorological Factors during 2013-2014 in Beijing, China
OBJECTIVE: Limited information is available regarding spatiotemporal variations of particles with median aerodynamic diameter < 2.5 μm (PM(2.5)) at high resolutions, and their relationships with meteorological factors in Beijing, China. This study aimed to detect spatiotemporal change patterns of...
Autores principales: | , , , , , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4631325/ https://www.ncbi.nlm.nih.gov/pubmed/26528542 http://dx.doi.org/10.1371/journal.pone.0141642 |
Sumario: | OBJECTIVE: Limited information is available regarding spatiotemporal variations of particles with median aerodynamic diameter < 2.5 μm (PM(2.5)) at high resolutions, and their relationships with meteorological factors in Beijing, China. This study aimed to detect spatiotemporal change patterns of PM(2.5) from August 2013 to July 2014 in Beijing, and to assess the relationship between PM(2.5) and meteorological factors. METHODS: Daily and hourly PM(2.5) data from the Beijing Environmental Protection Bureau (BJEPB) were analyzed separately. Ordinary kriging (OK) interpolation, time-series graphs, Spearman correlation coefficient and coefficient of divergence (COD) were used to describe the spatiotemporal variations of PM(2.5). The Kruskal-Wallis H test, Bonferroni correction, and Mann-Whitney U test were used to assess differences in PM(2.5) levels associated with spatial and temporal factors including season, region, daytime and day of week. Relationships between daily PM(2.5) and meteorological variables were analyzed using the generalized additive mixed model (GAMM). RESULTS: Annual mean and median of PM(2.5) concentrations were 88.07 μg/m(3) and 71.00 μg/m(3), respectively, from August 2013 to July 2014. PM(2.5) concentration was significantly higher in winter (P < 0.0083) and in the southern part of the city (P < 0.0167). Day to day variation of PM(2.5) showed a long-term trend of fluctuations, with 2–6 peaks each month. PM(2.5) concentration was significantly higher in the night than day (P < 0.0167). Meteorological factors were associated with daily PM(2.5) concentration using the GAMM model (R (2) = 0.59, AIC = 7373.84). CONCLUSION: PM(2.5) pollution in Beijing shows strong spatiotemporal variations. Meteorological factors influence the PM(2.5) concentration with certain patterns. Generally, prior day wind speed, sunlight hours and precipitation are negatively correlated with PM(2.5), whereas relative humidity and air pressure three days earlier are positively correlated with PM(2.5). |
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