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

Abstract

ObjectiveLimited 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.MethodsDaily 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).ResultsAnnual 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 (R 2 = 0.59, AIC = 7373.84).ConclusionPM2.5 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.

Temporal Patterns in Fine Particulate Matter Time Series in Beijing: A Calendar View

“…1a,c) and the PM 2.5 concentrations during these days usually peaks at noon. These results show that the diurnal patterns of PM 2.5 vary from day to day through the year, and PM 2.5 concentration in the daytime could be higher than at night in many days, which complement previous studies concluding that diurnal variation of PM 2.5 change by seasons and PM 2.5 concentration at night is higher than that in the daytime1016.…”

“…Days in L3 cluster usually occur near winter (in February, March, October, November and December but not January) although days in L1 and L2 clusters can be found in any month throughout the full year which doesn’t exhibit very clear seasonal pattern. There may exist significant differences in PM 2.5 concentration levels between different seasons101516, however we argue that the arbitrary seasonal division of variation in PM 2.5 concentration may result in information loss and conceal potentially important insights. The calendar visualization used in our study, however, provides an informative and straightforward way to look into variation patterns of air pollutants.…”

Understanding meteorological influences on PM&lt;sub&gt;2.5&lt;/sub&gt; concentrations across China: a temporal and spatial perspective

“…For Nanjing (T. , a mega-city in the Yangtze River, and Hong Kong (Fung et al, 2014), a mega coastal city, precipitation exerted the strongest influence, whilst wind speed exerted a weak influence on PM 2.5 concentrations in winter. On the other hand, for winter, wind speed was the dominant meteorological factor for PM 2.5 concentrations in Beijing (Huang et al, 2015), a mega-city in North China, and precipitation played a weak role in affecting local PM 2.5 concentrations. Compared with studies at a local or regional scale, this research conducted at the national scale provided a better understanding of spatial and temporal patterns of meteorological influences on PM 2.5 concentrations across China, for the following reasons.…”

“…Correlations between individual meteorological factors and PM 2.5 concentrations have been analyzed in such mega-cities as Nanjing (T. Shen and Li, 2016), Beijing (Huang et al, 2015;Yin et al, 2016), Wuhan , Hangzhou (Jian et al, 2012), Chengdu (Zeng and Zhang et al, 2017) and Hong Kong (Fung et al, 2014). These studies suggested that meteorological influences on PM 2.5 concentrations varied significantly across regions.…”

“…Yao (2017) revealed a generally negative correlation between evaporation and PM 2.5 concentrations in a series of cities within the North China Plain. Huang et al (2015) and Yin et al (2016) found a negative influence of sunshine duration and a positive influence of relative humidity on PM 2.5 concentrations in Beijing. Li et al (2015b) suggested that air pressure and temperature were positively correlated with PM 2.5 concentrations in Chengdu.…”