甘肃省冬季颗粒物浓度的数值模拟

颗粒物浓度的数值模拟能够反映颗粒物的空间分布特征，对于防治大气颗粒物污染具有一定意义.利用MEIC清单和第二次全国污染源普查（简称“二污普”）数据统计的甘肃省工业源、电力源、农业源、民用源和交通源五类源的主要污染物排放量，分析了污染源排放的空间分布特征，利用WRF-Chem模式模拟了甘肃省2019年1月PM₁₀和PM_2.5浓度，将模拟结果与甘肃省33个环境空气质量国控监测点颗粒物日均监测数据进行对比，检验WRF-Chem模式模拟的性能，进一步分析了甘肃省颗粒物浓度的空间分布特征.结果表明:①甘肃省SO₂、NO_x、PM₁₀、PM_2.5、VOCs、NH₃和CO在1月的排放量分别为2.12×104、2.96×104、2.97×104、2.43×104、3.18×104、1.27×104和3.04×105 t，除NH₃外，其他污染物排放高值主要分布在兰州市、嘉峪关市等工业发达地区.②33个环境空气质量国控监测点模拟与监测的PM₁₀和PM_2.5浓度的相关系数分别为0.544和0.597，颗粒物的模拟值与监测值有较好的相关性；WRF-Chem模式模拟结果显示，PM₁₀和PM_2.5浓度高值分布在兰州市，次高值分布在天水市和庆阳市，甘南藏族自治州以及河西地区颗粒物浓度较低，这是甘肃省工业布局、扩散条件和地形条件综合作用的结果.研究显示，WRF-Chem模式可以较好地模拟甘肃省区域颗粒物浓度时空分布特征. 

Numerical Simulation Study on Concentration of Particulate Matter in Winter in Gansu Province

Numerical simulations to study the concentration of particulate matter (PM, both PM_2.5 and PM₁₀) play a crucial role in recognizing the spatiotemporal characteristics and further adopting emission control strategies. In this paper, emissions of various sectors such as industry, power, agriculture, residents and transportation in Gansu Province were calculated based on combination of the MEIC inventory and the second national investigation on pollution emissions, and the spatiotemporal characteristics of pollution emissions were subsequently analyzed. Then, WRF-Chem simulations were conducted to obtain the PM concentrations in Gasnsu Province in January 2019, and the daily outputs were validated against available observations from 33 national control environment monitoring stations. Finally, the spatiotemporal characteristics of PM concentrations were analyzed. The results indicated that: (1) The emissions of SO₂, NO_x, PM₁₀, PM_2.5, VOCs, NH₃ and CO in January were 2.12×104, 2.96×104, 2.97×104, 2.43×104, 3.18×104, 1.27×104 and 3.04×105 t, respectively. The largest pollutant emissions, except for NH₃, occurred in developed industrial areas, such as Lanzhou City and Jiayuguan City. (2) The simulated outputs were in good agreement with the observations, and the correlation coefficients between the modeled and observed PM₁₀ and PM_2.5 were 0.544 and 0.597, respectively. The study revealed that Lanzhou City had higher PM concentration values, followed by Tianshui City and Qingyang City, while Gannan Tibetan Autonomous Prefecture and Hexi Regions had the lowest PM concentrations. These characteristics were attributed to the industry distribution, topographical features, as well as meteorological and diffusion conditions. The results show that the WRF-Chem simulation satisfactorily represent the spatial and temporal characteristics of PM concentration in Gansu Province.