一个基于中尺度数值模式的城市分区空气污染预报方法

A DISTRICT-PARTITIONED CITY AIR POLUTION PREDICTION METHOD BASED ON MESO-SCALE NUMERICAL MODEL

以深圳市为例,对污染物浓度与各种大气参数的定量联系进行了细致的统计及物理分析,在此基础上建立起分区的城市空气污染潜势等级预报方案。该方案定量考虑了地面和边界层共十几个因子的影响,特别是考虑了地理环境各向异性的效应,将风向作为一个独立的影响因子。方案还针对各污染物稀释特性的差异对不同污染物分别建立潜势预报方案。另外,方案还考虑了相同大气条件下大气对城市不同区域污染物稀释特性可能存在的差异,对不同区域分别建立潜势预报方案。最后用高分辨的中尺度数值模式对大气参数的未来演变作出高时空分辨的预报,进而作出分区、分时段的城市空气污染潜势预报。本方法完全客观定量,物理意义明确,可制作高时间分辨的空气污染潜势预报。

Taking Shenzhen city as an example, the statistical and physical relationship between density of pollutants and various atmospheric parameters are analyzed in detail, and a space-partitioned city air pollution potential prediction scheme is established based on it. The scheme considers quantitatively more than ten factors at surface and planetary boundary layer (PBL), especially the effects of anisotropy of geographical environment, and treats wind direction as an independent impact factor. While the scheme treats the prediction equation respectively for different pollutants according to their differences in dilute properties, it considers as well the possible differences in dilute properties at different district of the city under the same atmospheric condition, treating predictions respectively for different district. Finally, the temporally and spatially high resolution predictions for the atmospheric factors are made with a high resolution numerical model, and further the space-partitioned and time-variational city pollution potential predictions are made. The scheme is objective and quantitative, and with clear physical meaning, so it is suitable to be used in making high resolution air pollution predictions.