基于静风期污染物的PM2.5排放清单空间精细化方法

有利气象条件之后的静风期,极大降低了PM_2.5跨区域传输的影响,能够揭示本地源的排放状况。本文尝试性引入了静风期污染物分布揭示本地源排放特征的概念,提出了一种基于遥感数据的PM_2.5排放清单空间精细化方法：首先,利用 MODIS MCD19A2反演的ChinaHighPM_2.5数据,构建高时空分辨率PM_2.5数据融合方法;然后,构建唐山市有利气象条件之后的静风期污染物遴选方法（合理风向和风速：有利气象条件为东风,地面10 m高度风速大于3 m/s,其他风向,持续的较大风力5~10 m/s;静风期风速小于1.5~2.0 m/s）;其次,基于遴选的静风期PM_2.5数据分配MEIC清单中的PM_2.5总排放量,同时对比传统插值方法：基于GDP、人口密度、路网、土地利用类型数据,实现清单各污染源PM_2.5的1 km×1 km空间分配;最后,利用WRF-CMAQ模拟数据和地面台站实测数据进行真实性检验。研究结果表明：① PM_2.5数据填补融合方法能够有效提高PM_2.5监测数据的时空分辨率,且与地面监测值显著相关（R²=0.94,RMSE=4.64 µg/m³,NMB=2%,NME=7%）;② 引入有利气象条件后的静风期概念,提出了静风期污染物的遴选方法,有效降低了PM_2.5跨区域传输的影响,更好地反映了本地源排放的空间分布特征;③ WRF-CMAQ模拟方法的精度验证结果表明,该方法较传统面积插值法NME降低7%,NMB降低10%,RMSE降低1.54 µg/m³,R²提高11%。该方法为排放清单的空间精细化提供了新的研究思路。

Spatial Refinement Method of the PM2.5 Emission Inventory based on the Quiescent Period Pollutant

The quiescent period after favorable meteorological conditions greatly reduces the influence of PM_2.5 transmission across regions, thus it can reveal emissions of local sources. In this paper, the concept of the pollutant distribution in the quiescent period is introduced to reveal characteristics of local source emissions. A spatial refinement method of the PM_2.5 emission inventory based on remote sensing data is also proposed. Firstly, the high spatial and temporal resolution PM_2.5 data fusion method was constructed using the ChinaHighPM_2.5 data retrieved by MODIS MCD19A2. Then, the selection method of pollutants for the quiescent period after favorable meteorological conditions in Tangshan was established. The favorable meteorological condition is east wind, with a wind speed above 3 m/s at 10-m height. For other wind directions, it should be sustained strong wind, with a wind speed between 5~10 m/s. For quiescent period, the wind speed should be below 1.5~2.0 m/s. Furthermore, the total PM_2.5 emissions from the MEIC Inventory were allocated based on the PM_2.5 data of the selected quiescent period. At the same time, referring to the traditional interpolation methods, based on the data of GDP, population density, road network, and land use type, the spatial distribution of PM_2.5 in each pollution sources of the inventory was allocated into 1 km×1 km grids. Finally, the simulation data of WRF-CMAQ and the measured data of ground stations were used in the validation. The results show that, firstly, the method of PM_2.5 data fusion can effectively improve the temporal and spatial resolution of PM_2.5 observational data, and it is significantly correlated with the observational data on the ground (R²=0.94, RMSE=4.64 µg/m ³, NMB=2%, NME=7%). Secondly, the concept of the quiescent period after favorable meteorological conditions is introduced, and the selection method of the quiescent period is established. The cross-region transmission of PM_2.5 is effectively reduced, thus better reflecting the spatial distribution characteristics of local source emissions. Thirdly, the accuracy verification results based on WRF-CMAQ simulation method show that compared with the traditional area interpolation method, NME decreased 7%, NMB decreased 10%, RMSE decreased 1.54 µg/m ³, and R ² increased 11%. This method provides a new idea for the spatial refinement of the emission inventory.