Ensemble prediction of air quality using the WRF/CMAQ model system for health effect studies in China

Jianlin Hu,Xun Li,Lin Huang,Bin Zhao,Qi Ying,Shuxiao Wang,Hongliang Zhang,Qiang Zhang

doi:10.5194/acp-17-13103-2017

Abstract

Abstract. Accurate exposure estimates are required for health effect analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used to provide spatial distribution, chemical composition, particle size fractions, and source origins of air pollutants. The accuracy of air quality predictions in China is greatly affected by the uncertainties of emission inventories. The Community Multiscale Air Quality (CMAQ) model with meteorological inputs from the Weather Research and Forecasting (WRF) model were used in this study to simulate air pollutants in China in 2013. Four simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance of each simulation was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 generally meet the model performance criteria, but performance differences exist in different regions, for different pollutants, and among inventories. Ensemble predictions were calculated by linearly combining the results from different inventories to minimize the sum of the squared errors between the ensemble results and the observations in all cities. The ensemble concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFEs) of the ensemble annual PM2.5 in the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25 to −0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual daily maximum 1 h O3 (O3-1h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1h. The study demonstrates that ensemble predictions from combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories, and the results are publicly available for future health effect studies.

Highlights

A significant portion of the population in China has been exposed to severe air pollution in recent decades as the consequence of intensive energy use without efficient control measures
Based on ambient air pollution data published by the China National Environmental Monitoring Center (CNEMC), most of the major cities are in violation of the Chinese Ambient Air Quality Standards grade II standard (35 μg m−3) for annual average particulate matter with diameter of 2.5 μm or less
O3 from School of Environment at Tsinghua University (SOE) is 7.2 parts per billion lower than the mean observed concentration while the underpredictions of the other three inventories are less than 2 ppb

Summary

Introduction

A significant portion of the population in China has been exposed to severe air pollution in recent decades as the consequence of intensive energy use without efficient control measures. Recent studies have suggested that approximately more than 1 million premature deaths can be attributed to outdoor air pollution each year in China (Lelieveld et al, 2015; Liu et al, 2016; Hu et al, 2017a). Accurate exposure estimates are required in health effect studies. Ambient air quality is usually measured at monitoring sites and used to represent the exposure of the population in the surrounding areas. Chemical transport models (CTMs) have been widely used in health effect studies to overcome the limitations in central monitoring measurements for exposure estimates (Philip et al, 2014; Lelieveld et al, 2015; Liu et al, 2016; Laurent et al, 2016a, b; Ostro et al, 2015). Different emission inventories focus on specific geographical regions in the urban, regional (Zhao et al, 2012; Zhang et al, 2008), and national or continental (Zhang et al, 2009; Kurokawa et al, 2013) scales, and/or focus on specific pollutants (Su et al, 2011; Ou et al, 2015) and specific sectors (Zhao et al, 2008; Xu et al, 2017)

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