Improving the City-scale Emission Inventory of Anthropogenic Air Pollutants: A Case Study of Nanjing

Monday, 15 December 2014
Liping Qiu1, Yu Zhao1, Runying Xu1, Fangjian Xie2, Haikun Wang1, Haixu Qin2, Xiucai Wu1 and Jie Zhang3, (1)Nanjing University, Nanjing, China, (2)Nanjing Academy of Environmental Protection Science, Nanjing, China, (3)Jiangsu Provincial Academy of Environmental Science, Nanjing, China
To evaluate the improvement of city-scale emission inventory, a high-resolution emission inventory of air pollutants for Nanjing is first developed combining detailed source information, and then justified through quantitative analysis with observations. The best available domestic emission factors and unit-/facility-based activity level data were compiled based on a thorough field survey on major emission sources. Totally 1089 individual emission sources were identified as point sources and all the emission-related parameters including burner type, combustion technology, fuel quality, and removal efficiency of pollution control devices, are carefully investigated and analyzed. Some new data such as detailed information of city fueling-gas stations, construction sites, monthly activity level, data from continuous emission monitoring systems and traffic flow information were combined to improve spatiotemporal distribution of this inventory. For SO2, NOX and CO, good spatial correlations were found between ground observation (9 state controlling air sampling sites in Nanjing) and city-scale emission inventory (R2=0.34, 0.38 and 0.74, respectively). For TSP, PM10 and PM2.5, however, poorer correlation was found due to relatively weaker accuracy in emission estimation and spatial distribution of road dust. The mixing ratios between specific pollutants including OC/EC, BC/CO and CO2/CO, are well correlated between those from ground observation and emission. Compared to MEIC (Multi-resolution Emission Inventory for China), there is a better spatial consistence between this city-scale emission inventory and NO2 measured by OMI (Ozone Monitoring Instrument). In particular, the city-scale emission inventory still correlated well with satellite observations (R2=0.28) while the regional emission inventory showed little correlation with satellite observations (R2=0.09) when grids containing power plants are excluded. It thus confirms the improvement of city-scale emission inventory on industrial and transportation sources other than big power plants. Through the inventory evaluation, the necessity to develop high-resolution emission inventory with comprehensive emission source information is revealed for atmospheric science studies and air quality improvement at local scale.