基于WRF模式的兰州秋冬季大气污染预报模型研究

WRF-Based Forecast Model for Autumn and Winter Air Quality in Lanzhou

随着城市化进程的加快,城市大气环境问题成为人们关注的热点问题之一。诸多研究表明,特殊气象条件是造成城市大气污染事件的主要因子之一。本文利用WRF模式模拟得到的高时空分辨率气象场,结合污染物浓度监测数据,分析了风速、稳定能量、Froude数、边界层高度、位温递减率、输送指数和梯度理查森数与兰州大气污染物浓度的关系,并根据WRF的模拟要素建立了污染物浓度与气象影响因子的回归方程。研究发现,兰州城区边界层高度和位温递减率与大气污染物浓度的相关系数高,NO_2与气象影响因子的相关性较PM_(10)好。建立的回归方程对NO_2的模拟效果好于对PM_(10)的模拟效果,其对城区污染物浓度的模拟效果好于郊区。通过与不同地区空气质量数值模式模拟效果对比,结果表明:回归方程对污染物的模拟效果与数值模式模拟效果相当,甚至好于部分地区空气质量数值模式的模拟效果。因此,该研究方法为我国城市空气质量预报和大气污染研究提供了科学依据。

This paper investigates the relationship between air pollutant concentrations and seven meteorological parameters （wind speed, stable energy, Froude number, atmospheric boundary layer height, potential temperature lapse rate, transport index and gradient Richardson number） based on the near ground air pollutant concentrations and the high temporal and spatial resolution meteorological data from the Weather Research and Forecast （WRF） model and develops a regression model for predicting ground level daily mean PM10 （particulate matter with aerodynamic diameter less than 10 μm） and NO2 （nitrogen dioxide） concentrations for urban Lanzhou. The results show that in urban Lanzhou, pollutant concentrations correlate better with atmospheric boundary layer height and potential temperature lapse rate, and the correlations between NO2 and meteorological parameters are better than that between PM10 and meteorological parameters. The developed regression model performs better for NO2 than for PM10. The fitting degree of the developed regression model is higher in urban area than that in rural area, leading to the better performance in urban area. The overall performance of the regression model is as good as widely used comprehensive air quality models. The method provides a scientific basis for urban air quality forecasting and air pollution study.