2013─2014年武汉市PM_(2.5)的时空分布特征及其与气象条件的关系

Spatio-temporal Distribution of PM_(2.5) in Wuhan and Its Relationship with Meteorological Conditionsin, 2013─2014

霾天气的频繁发生降低了武汉空气质量,明晰致霾污染物PM2.5的污染机理、污染时空分布特征、影响因素是科学认识和解决霾污染天气的重要前提。在全国大范围内开展PM2.5源解析的背景下,探讨其时空分布特征,厘清与气象因子的相互关系,是对源解析工作的响应,也是污染防治决策的科学依据。本文以2013─2014年武汉市10个国控点的PM2.5监测数据和中国地面气象交换站气象数据为基础,运用统计学方法,基于不同时间尺度和空间分布类型研究PM2.5污染分布特征,并探讨其与气象条件的相互关系。结果表明:(1)武汉市PM2.5污染表现出显著的时间分布规律。就年变化而言,PM2.5污染程度2014年(年均值121μg·m-3)>2013年(107μg·m-3),且PM2.5污染主要为良好和轻度污染,季节尺度上冬季>秋季>春季>夏季,月均值规律体现为4─9月优于10─翌年3月,以周为单位表现出显著的正周末效应,即周日>周六>周一>周五>周四>周三>周二;(2)武汉市ρ(PM2.5)的空间差异与区域类型、城市布局有关,工业区、居民区、交通区较风景区污染严重,二环和三环较一环严重;(3)ρ(PM2.5)月均值分布与降雨量、气温、平均风速、相对湿度呈负相关关系(P<0.05),与气压呈正相关关系(P<0.01),监测点S2、S3、S9与平均相对湿度相关性更显著。PM2.5污染的减轻与气象条件有一定关系,也离不开城市环境空气治理。

Haze is one of the main reasons for the decline of air quality in Wuhan. To thoroughly understand the PM2.5 pollution mechanism, spatio-temporal characteristics of pollution and influencing factors is an important prerequisite to understand and solve this problem scientificly. Within the background of carring out a wide range of source apportionment of PM2.5 in the whole country, probing the spatial and temporal distribution characteristics, and clarifing the relationship between meteorological factors, is not only a response to the work of source analytic, but also the scientific basis of decision-making for pollution prevention. Based on PM2.5 data of 10 Chinese state-controlled sites and meteorological data from China Ground Meteorological Exchange Stations druing 2013 and 2014 in Wuhan, the statistical method was used to study the spatio-temporal distribution of PM2.5 pollution from different temporal scales and discuss its relationship with meteorological conditionsin. The results showed that: (1) PM2.5 pollution showed significant temporal distribution characteristics in Wuhan. On the variation of interannual scale, annual daily average concentration of PM2.5 decreased from 121μg·m-3 to 107μg·m-3, and PM2.5 pollution is mainly in good and slightly polluted condition. And the seasonal average concentration showed a order of winter > autumn (spring) > summer. Simultaneously, the monthly average concentration from April to September was superior to that from October to March of next year, and January to March. Furthermore, a significant positive weekend effect was found, namely Sunday > Saturday > Monday > Friday > Thursday > Wednesday > Tuesday. Generally, PM2.5 pollution turn to mitigation and alleviation. (2) The spatial differences of PM2.5 pollution were ralated to the regional types and the layout of the city, presenting that industrial zone, residential area and transport area were worse than the scenic. Moreover, the pollution in second and third ring was more serious than that in the first ring. And (3) the monthly average concentration of PM2.5 prensented a negatively correlation (P<0.05) with temperature, average wind speed and relative humidity, but a positively correlation (P<0.01) with the pressure. There was a significant correlation between monitoring points S2, S3, S9 and the average relative humidity. There is a certain relationship between reducing PM2.5 pollution and meteorological conditions, and the management of urban environment and air should not be neglected.