Huang Fangfang, Li Xia, Wang Chao, Xu Qin, Wang Wei, Luo Yanxia, Tao Lixin, Gao Qi, Guo Jin, Chen Sipeng, Cao Kai, Liu Long, Gao Ni, Liu Xiangtong, Yang Kun, Yan Aoshuang, Guo Xiuhua
Department of Epidemiology and Health Statistics, School of Public Health, Capital Medical University, Beijing, China; Beijing Municipal Key Laboratory of Clinical Epidemiology, Beijing, China.
Graduate Entry Medical School, University of Limerick, Limerick, Ireland.
PLoS One. 2015 Nov 3;10(11):e0141642. doi: 10.1371/journal.pone.0141642. eCollection 2015.
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.
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).
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).
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.
关于中国北京地区空气动力学直径小于2.5μm的颗粒物（PM2.5）在高分辨率下的时空变化及其与气象因素的关系，目前可用信息有限。本研究旨在检测2013年8月至2014年7月北京地区PM2.5的时空变化模式，并评估PM2.5与气象因素之间的关系。
分别分析了北京市环境保护局（BJEPB）提供的每日和每小时PM2.5数据。采用普通克里金（OK）插值法、时间序列图、斯皮尔曼相关系数和离散系数（COD）来描述PM2.5的时空变化。使用Kruskal-Wallis H检验、Bonferroni校正和Mann-Whitney U检验来评估与季节、区域、白天和星期几等时空因素相关的PM2.5水平差异。采用广义相加混合模型（GAMM）分析每日PM2.5与气象变量之间的关系。
2013年8月至2014年7月，PM2.5浓度的年平均值和中位数分别为88.07μg/m3和71.00μg/m3。冬季（P < 0.0083）和城市南部地区（P < 0.0167）的PM2.5浓度显著更高。PM2.5的逐日变化呈现长期波动趋势，每月有2 - 6个峰值。夜间的PM2.5浓度显著高于白天（P < 0.0167）。使用GAMM模型分析发现气象因素与每日PM2.5浓度相关（R2 = 0.59，AIC = 7373.84）。
北京地区的PM2.5污染呈现出强烈的时空变化。气象因素对PM2.5浓度有一定的影响模式。一般来说，前一日风速、日照时数和降水量与PM2.5呈负相关，而三天前的相对湿度和气压与PM2.5呈正相关。
