Exploring the influence of two inventories on simulated air pollutants during winter over the Yangtze River Delta. (1st June 2019) - Sha, Tong; Ma, Xiaoyan; Jia, Hailing; van der A, Ronald J.; Ding, Jieying; Zhang, Yanling; Chang, Yunhua
Abstract: Currently, haze events in winter occur more frequently than decades ago, especially in Eastern and Central China, including the Yangtze River Delta (YRD). WRF-Chem is applied in this study to explore the discrepancies of the simulated air pollutants induced by employing different emission inventories, particularly during haze events. Two inventories are involved in this study, MEIC (Multi-resolution Emission Inventory for China) and GlobEmission (inventory from GlobEmission project emission estimates), representing the emission inventories based on quite different ways. We first compared monthly emissions of SO2 and NOx in two inventories during January over YRD, and found the mean differences of SO2 (NOx ) are 20% (7%), with the ranges of 0%–47% (−100%–100%). The largest differences are both found in Shanghai, with 47% for SO2 (MEIC in 2010, GlobEmission in 2014), and 45% for NOx (MEIC in 2010, GlobEmission in 2015), respectively, partly because the reduction of emissions was large during the 12th Five-Year Plan (2011–2015) in this area. By comparing the simulated air pollutants mass concentrations using two inventories with in-situ observations during January 2015, we found that the simulated SO2 using MEIC and GlobEmission are both higher than observations, with mean normalized biases of 207% and 121% over YRD, respectively, and much larger in the city cluster of Nanjing-Shanghai, where are nearly four (MEIC) and three times (GlobEmission) higher than observations. In contrast, NO2 simulations using GlobEmission are lower than observations (22%) and MEIC simulations (45%) over YRD. The largest biases of GlobEmission simulation are found in Zhejiang province (over 70%). The biases of simulated monthly mean PM2.5 are 38% (MEIC) and 30% (GlobEmission) over YRD, respectively. A case study during heavy haze event shows that the biases increase to 61% (MEIC) and 39% (GlobEmission), and spatial correlation coefficient in the simulation using GlobEmission increase to 0.69 over YRD. Temporal correlation coefficients in the city cluster of Nanjing-Shanghai increase from 0.5 (MEIC) to 0.7 (GlobEmission). Highlights: Emissions of SO2 (NOx ) in MEIC inventory are 20% (7%) higher than GlobE. Simulated SO2 (NO2 ) are 207% (49%) in MEIC and 121% (−22%) in GlobE. than observed. Simulated PM2.5 are 38% (MEIC) and 30% (GlobE.) higher than observations. Larger discrepancies (22%) of simulated PM2.5 by two inventories are found in haze. Temporal R of PM2.5 is better from simulation with GlobE. (0.7) than with MEIC (0.5).