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PM2.5 Spatiotemporal Variations and the Relationship with Meteorological Factors during 2013-2014 in Beijing, China.

Huang F, Li X, Wang C, Xu Q, Wang W, Luo Y, Tao L, Gao Q, Guo J, Chen S, Cao K, Liu L, Gao N, Liu X, Yang K, Yan A, Guo X - PLoS ONE (2015)

Bottom Line: PM2.5 concentration was significantly higher in winter (P < 0.0083) and in the southern part of the city (P < 0.0167).PM2.5 concentration was significantly higher in the night than day (P < 0.0167).Meteorological factors were associated with daily PM2.5 concentration using the GAMM model (R2 = 0.59, AIC = 7373.84).

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.

ABSTRACT

Objective: Limited information is available regarding spatiotemporal variations of particles with median aerodynamic diameter < 2.5 μm (PM2.5) at high resolutions, and their relationships with meteorological factors in Beijing, China. This study aimed to detect spatiotemporal change patterns of PM2.5 from August 2013 to July 2014 in Beijing, and to assess the relationship between PM2.5 and meteorological factors.

Methods: Daily and hourly PM2.5 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 PM2.5. The Kruskal-Wallis H test, Bonferroni correction, and Mann-Whitney U test were used to assess differences in PM2.5 levels associated with spatial and temporal factors including season, region, daytime and day of week. Relationships between daily PM2.5 and meteorological variables were analyzed using the generalized additive mixed model (GAMM).

Results: Annual mean and median of PM2.5 concentrations were 88.07 μg/m3 and 71.00 μg/m3, respectively, from August 2013 to July 2014. PM2.5 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 PM2.5 showed a long-term trend of fluctuations, with 2-6 peaks each month. PM2.5 concentration was significantly higher in the night than day (P < 0.0167). Meteorological factors were associated with daily PM2.5 concentration using the GAMM model (R2 = 0.59, AIC = 7373.84).

Conclusion: PM2.5 pollution in Beijing shows strong spatiotemporal variations. Meteorological factors influence the PM2.5 concentration with certain patterns. Generally, prior day wind speed, sunlight hours and precipitation are negatively correlated with PM2.5, whereas relative humidity and air pressure three days earlier are positively correlated with PM2.5.

No MeSH data available.


Related in: MedlinePlus

Exposure-response curves for PM2.5 and meteorological variables, Beijing, 2013–2014.
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pone.0141642.g006: Exposure-response curves for PM2.5 and meteorological variables, Beijing, 2013–2014.

Mentions: Overall effect size measured by the adjusted R2 was 0.59 and goodness-of-fit assessed by the AIC was 7373.84 for the final GAMM model. The relationship between PM2.5 and prior day wind speed was monotonically decreasing (Fig 6). Similarly, an overall downward tendency was found for PM2.5 with increasing sunlight hours. On the contrary, PM2.5 was positively correlated to relative humidity. For the dichotomous precipitation variable, PM2.5 concentration was 85.68% (95% CI: 82.98%–88.47%) on days with precipitation, compared with those days of without precipitation. Air pressure had a 3-day lag effect on PM2.5, which was positively correlated with log-transformed PM2.5 concentration in linear from.


PM2.5 Spatiotemporal Variations and the Relationship with Meteorological Factors during 2013-2014 in Beijing, China.

Huang F, Li X, Wang C, Xu Q, Wang W, Luo Y, Tao L, Gao Q, Guo J, Chen S, Cao K, Liu L, Gao N, Liu X, Yang K, Yan A, Guo X - PLoS ONE (2015)

Exposure-response curves for PM2.5 and meteorological variables, Beijing, 2013–2014.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4631325&req=5

pone.0141642.g006: Exposure-response curves for PM2.5 and meteorological variables, Beijing, 2013–2014.
Mentions: Overall effect size measured by the adjusted R2 was 0.59 and goodness-of-fit assessed by the AIC was 7373.84 for the final GAMM model. The relationship between PM2.5 and prior day wind speed was monotonically decreasing (Fig 6). Similarly, an overall downward tendency was found for PM2.5 with increasing sunlight hours. On the contrary, PM2.5 was positively correlated to relative humidity. For the dichotomous precipitation variable, PM2.5 concentration was 85.68% (95% CI: 82.98%–88.47%) on days with precipitation, compared with those days of without precipitation. Air pressure had a 3-day lag effect on PM2.5, which was positively correlated with log-transformed PM2.5 concentration in linear from.

Bottom Line: PM2.5 concentration was significantly higher in winter (P < 0.0083) and in the southern part of the city (P < 0.0167).PM2.5 concentration was significantly higher in the night than day (P < 0.0167).Meteorological factors were associated with daily PM2.5 concentration using the GAMM model (R2 = 0.59, AIC = 7373.84).

View Article: PubMed Central - PubMed

Affiliation: Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.

ABSTRACT

Objective: Limited information is available regarding spatiotemporal variations of particles with median aerodynamic diameter < 2.5 μm (PM2.5) at high resolutions, and their relationships with meteorological factors in Beijing, China. This study aimed to detect spatiotemporal change patterns of PM2.5 from August 2013 to July 2014 in Beijing, and to assess the relationship between PM2.5 and meteorological factors.

Methods: Daily and hourly PM2.5 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 PM2.5. The Kruskal-Wallis H test, Bonferroni correction, and Mann-Whitney U test were used to assess differences in PM2.5 levels associated with spatial and temporal factors including season, region, daytime and day of week. Relationships between daily PM2.5 and meteorological variables were analyzed using the generalized additive mixed model (GAMM).

Results: Annual mean and median of PM2.5 concentrations were 88.07 μg/m3 and 71.00 μg/m3, respectively, from August 2013 to July 2014. PM2.5 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 PM2.5 showed a long-term trend of fluctuations, with 2-6 peaks each month. PM2.5 concentration was significantly higher in the night than day (P < 0.0167). Meteorological factors were associated with daily PM2.5 concentration using the GAMM model (R2 = 0.59, AIC = 7373.84).

Conclusion: PM2.5 pollution in Beijing shows strong spatiotemporal variations. Meteorological factors influence the PM2.5 concentration with certain patterns. Generally, prior day wind speed, sunlight hours and precipitation are negatively correlated with PM2.5, whereas relative humidity and air pressure three days earlier are positively correlated with PM2.5.

No MeSH data available.


Related in: MedlinePlus