基于地面监测数据的2013~2015年长三角地区PM2.5时空特征

doi:10.11870/cjlyzyyhj201605015

摘要/Abstract

摘要： 近年来,长三角地区灰霾天气持续增多,空气细颗粒物污染问题日益突出。基于2013年1月至2015年5月长三角地区及周边缓冲区内共214个空气质量监测站点PM_2.5逐时监测数据,运用普通克里金插值方法,从年、季、月尺度上分析了PM_2.5的空间分布格局和时间动态变化。结果表明:(1)2 a来,长三角地区PM_2.5浓度空间分布明显呈现整体北部高南部低,局部地区略有突出的分布特征;长三角地区PM_2.5浓度年均值为57.08μg/m³;其中,江苏省PM_2.5的年均值为三省市最高,为65.84μg/m³;其次为上海市,年均值为53.87μg/m³;浙江省PM_2.5的年均值较小,为51.53μg/m³。(2)从季节尺度分析,长三角地区PM_2.5浓度变化表现出冬春季高,夏秋季低的变化趋势;这与区域内冬季风向来源、降水稀少、气象扩散条件差有着密切的关系; (3)长三角地区月浓度变化大致呈U形分布; 12月份PM_2.5浓度最高; 3月份以后, PM_2.5浓度开始呈逐步下降趋势;在5~9月份,区域PM_2.5处于"U"字的谷底,其中6月份夏收时期秸秆焚烧、气象等因素导致PM_2.5浓度有略微升高;进入10月份后迅速攀升,且11、12月份呈现持续升高态势。

关键词: PM_2.5浓度, 地面监测, 空间分布, 时间动态变化, 长三角

Abstract: As one of the three economic hubs in China, the Yangtze River Delta (YRD) experiences serious air pollution due to the huge energy consumption in recent years. The air pollution in this region became more and more serious with increasingly frequent haze. The main pollution source is respirable particulate matters (_PM2.5). Based on this, data of _PM2.5 from 214 automatic air quality monitoring stations of YRD and surrounding buffer regions from January 2013 to May 2015 were analyzed to examine the temporal and spatial characteristics of _PM2.5 at year, season, month scales. Using Ordinary Kriging interpolation method, we found that (1) The distribution of _PM2.5 concentration was obvious in the YRD region and displayed high values in the north and low values in the south. The annual average concentrations of _PM2.5 was 57.08 μg·m^-3 in the YRD region. From a regional perspective, the annual average concentrations of _PM2.5 in different sub-regions decreased in the order of Jiangsu province, Shanghai Municipality and Zhejiang province, which were 65.84μg·m^-3, 47.31μg·m^-3 and 51.53 μg·m^-3, respectively. The average concentrations of _PM2.5 in this three regions all exceeded the national ambient air quality standard of 35 μg·m^-3. The main reason may be related with the regional industrial structure, regional agricultural pollution, meteorological characteristics and etc. (2) The average concentration of _PM2.5 in different seasons decreased in the order of winter, spring, autumn and summer, which were 82.73 μg/m^-3,54.02 μg/m^-3, 49.71 μg/m^-3 and 41.72 μg/m^-3, respectively. The main reasons caused the obvious differences in seasons were the atmosphere conditions (air mass source, precipitation, etc.), human activities and the condition of natural ecosystems. For example, in winter more stable atmosphere, high frequency and intensity temperature inversion were not conducive to pollution dilution. But in summer more plants grow and flourish, this was conducive to the adsorption of particulate matter in the atmosphere; secondly, the precipitation increase in summer can also conducive to the wet deposition and dilution of atmospheric pollutants; therefore, the concentration of _PM2.5 was the lowest in summer relatively.(3) Monthly average concentrations of _PM2.5 showed a U-shaped curve; the peaks of which appeared in December; after March, the average concentration of _PM2.5 showed a gradual decline; In 5-9 months, the average concentration of _PM2.5 in the bottom of U-shaped curve and reached to the minimum in September; but in June, the average concentration of _PM2.5 concentrations increased slightly because of the straw burning; in October the average concentrations of _PM2.5 rose rapidly and continued to rise in November and December. In this study, the result based on the method of GIS spatial interpolation may cause a few errors; the achievements based on the space statistical method may not represent the real phenomenon completely. But overall, the spatial interpolation method based on GIS provided the way to understand the spatial distribution characteristics of regional _PM2.5; the spatial statistical method based on GIS provided the basic data and material science for the discrimination of regional _PM2.5 concentration level. This paper has important practical significance for the remote sensing of atmospheric pollutants, product validation and local governments to carry out environment management decision-making and so on.

Key words: the concentrations of PM_2.5, ground monitoring, spacedistribution, temporal dynamics, Yangtze River Delta region

中图分类号:

X513

戴昭鑫, 张云芝, 胡云锋, 董昱. 基于地面监测数据的2013~2015年长三角地区PM_2.5时空特征[J]. 长江流域资源与环境, 2016, 25(05): 813-821.

DAI Zhao-xin, ZHANG Yun-zhi, HU Yun-feng, DONG Yu. SPATIAL-TEMPORAL CHARACTERISTICS OF PM 2.5 IN YANGTZE RIVER DELTA (YRD) REGION BASED ON THE GROUND MONITORING DATA FROM 2013-2015[J]. RESOURCES AND ENVIRONMENT IN THE YANGTZE BASIN, 2016, 25(05): 813-821.

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