The spatiotemporal distribution pattern and influencing factors of PM 2.5 in Shaanxi Province

Q2 Environmental Science

环境科学研究 Pub Date : 2018-05-25 DOI:10.13198/j.issn.1001-6929.2018.01.16

南国卫, 孙虎, 朱一梅

引用次数: 0

摘要

PM2.5 is the main culprit causing haze in multiple provinces and cities in China. Clarifying its spatiotemporal distribution pattern and clarifying its influencing factors has profound implications for the comprehensive management of haze. Based on PM2.5 concentration data from 50 monitoring stations in Shaanxi Province in 2015, spatial data statistics, Kriging interpolation, and Morlet wavelet analysis were used to study the spatiotemporal distribution pattern of PM2.5 concentration in Shaanxi Province, And the grey correlation model was used to explore the influencing factors of PM2.5 concentration. The results showed that: ① the overall PM2.5 concentration in Shaanxi Province showed a seasonal variation pattern of 'high in winter, low in summer, and middle in spring and autumn', a monthly variation pattern of 'U-shaped' fluctuations, a daily variation pattern of periodic pulse fluctuations, and a temporal variation pattern of 'W-shaped' fluctuations; ② The concentration of PM2.5 in Shaanxi Province exhibits a spatial distribution characteristic of 'low in the north and high in the central and southern regions', with significant spatial agglomeration. High value areas in different seasons are concentrated in cities within the Guanzhong Basin with relatively low elevations. This is closely related to the difficulty of air diffusion within the basin, the high frequency of calm and stable weather, and the susceptibility to temperature inversion; ③ The indicator layer with the greatest impact on PM2.5 concentration in Shaanxi Province is the source of PM2.5 pollution (with a weight value of 0.49), followed by urbanization and land use (with a weight value of 0.37), and meteorological and topographic factors have the smallest impact (with a weight value of 0.15). The comprehensive correlation degree of each indicator layer in different cities varies greatly All indicator factors are strongly correlated with PM2.5 concentration. Precipitation, motor vehicle ownership, sulfur dioxide emissions, smoke (dust) emissions, built-up area, population density, and per capita GDP are the main factors affecting PM2.5 concentration in Shaanxi Province. The main factors affecting PM2.5 concentration in various cities have certain spatial differences. Research shows that human activities have a significant impact on PM2.5 in Shaanxi Province, especially with the rapid advancement of urbanization, The continuous growth of relevant indicators such as population, motor vehicles, energy consumption, and total industrial output value will further increase the diversity of PM2.5 sources and the emissions of related pollutants

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陕西省PM 2.5 时空分布规律及其影响因素

PM2.5是导致中国多省市发生灰霾的罪魁祸首，明确其时空分布规律，厘清其影响因素对灰霾的综合治理意义深远.基于陕西省2015年50个监测站点的PM2.5浓度数据，采用空间数据统计方法、克里金插值法以及Morlet小波分析法对陕西省PM2.5浓度的时空分布规律进行研究，并运用灰色关联模型来探讨PM2.5浓度的影响因素.结果显示：①陕西省PM2.5浓度整体呈'冬高夏低、春秋居中'的季节性变化规律，'U型'起伏的月变化规律，周期性脉冲波动型的日变化规律以及'W型'起伏的时变化规律；②陕西省PM2.5浓度呈'北部低，中南部高'的空间分布特征，并且空间集聚性显著.不同季节的高值区均集聚于海拔相对较低的关中盆地内部城市.这与盆地内部空气不易扩散，静稳天气出现频率较高，易出现逆温现象密切相关；③影响陕西省PM2.5浓度最大的指标层是PM2.5污染来源（权重值为0.49），其次是城市化与土地利用（权重值为0.37），气象与地形因子影响最小（权重值为0.15）.不同城市各指标层的综合关联度差异较大.④各指标因子与PM2.5浓度均为强度关联.降水量、机动车保有量、二氧化硫排放量、烟粉（尘）排放量、建成区面积、人口密度和人均GDP是影响陕西省PM2.5浓度的主要因子，影响各城市PM2.5浓度的主要因子具有一定的空间差异性.研究显示，人类活动对陕西省PM2.5的影响显著，尤其是城市化的快速推进，相关指标（如人口、机动车、能耗、工业总产值等）持续增长，将进一步加大PM2.5来源的多样性以及相关污染物的排放量.

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来源期刊

环境科学研究 Environmental Science-Environmental Science (miscellaneous)

CiteScore

3.80

自引率

0.00%

发文量

6496

期刊介绍：