河南省PM2.5时空分布特征研究

PM2.5是影响河南省空气质量的首要污染物，秋冬季节灰霾污染严重。为了解河南省PM2.5污染的特征，对河南省的17个城市，运用统计学方法和ARCGIS技术分析其时空分布特征。结果表明：从2015年1月至12月，河南省17个城市日均质量浓度达标天数比例为57.16%，冬季整体污染严重，超标天数比例为73.68%，春季超标天数比例为44.37%，秋季超标天数比例为34.52%，夏季超标天数比例为20.08%。在去除气象记录的空气质量重污染日之后，周末的PM2.5平均值比工作日高 8.04%，表现出“逆周末效应”。PM2.5/PM10值在0.50~0.65之间，且PM2.5与SO2相关性较高，表明河南省受传统煤烟型污染影响较大，粗粒子污染明显。 PM2.5与PM10、SO2、NO2均呈现显著的相关性，说明河南省的污染主要是由燃煤及机动车尾气造成。由于温度及光照变化的影响，河南省PM2.5与O3在不同季节呈现显著差异，其在冬季和秋季的相关性分别为-0.315(p=0.05)、-0.353(p=0.05)，而在夏季的相关性为0.496(p=0.01)，春季为0.003。

Spatio-Temporal Distribution Characteristics of PM2.5 in Henan Province

PM2.5 is the primary pollutant affecting the air quality of Henan Province, Chian. In addition, dust haze pollution is serious in autumn and winter. In order to master the pollution feature of PM2.5, the spatio-temporal distribution characteristics of PM2.5 in 17 cities of Henan Province were studied by using statistical methods and ARCGIS. The results show that 57.16% of the entire days in 2015 met Chinese national standard on daily average concentration of PM2.5 in those 17 cities. However, from the seasonal perspective, 73.68% of the days in winter, 44.37% of the days in spring, 34.53% of the days in autumn and 20.08% of the days in summer failed to meet the standard. The analysis results show that the average concentration of PM2.5 at weekends was 8.04% higher than that of working days. This reveals an interesting phenomenon named inverse weekend effect when the extremely polluted days due to heavy haze mainly controlled by meteorological conditions have not been taken into consideration. There was a high relevance between the PM2.5 concentration and SO2 concentration. It suggests that the main air pollutants are coarse-particles which are mainly from coal-burning. There is higher correlation in the range of PM2.5 and SO2, it show that the coal-burning has a major influence on Henan Province. PM2.5 show a positive correlation with PM10 and NO2. It illustrates that the pollution is mainly caused by coal burning and motor vehicle exhaust. Owing to the influence of the temperature and sunlight changes, the correlativity of PM2.5 concentrations and O3 concentrations presents remarkable difference in different seasons. The correlation coefficient is 0.003 for spring, 0.496 (p=0.01) for summer, -0.353 (p=0.05) for autumn and -0.315 (p=0.05) for winter, respectively.