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ABSTRACT
A major challenge for air quality forecasters is to reduce the uncertainty of air pollution emission inventory. Error in the emission data is a primary source of error in air quality forecasts, much like the effect of error in the initial conditions on the accuracy of weather forecasting. Data assimilation has been widely used to improve weather forecasting by correcting the initial conditions with weather observations. In a similar way, observed concentrations of air pollutants can be used to correct the errors in the emission data. In this study, a new method is developed for estimating air pollution emissions based on a Newtonian relaxation and nudging technique. case studies for the period of 1-25 August 2006 in 47 cities in China indicate that the nudging technique resulted in improved estimations of sulfur dioxide (SO^sub 2^) and nitrogen dioxide (NO^sub 2^) emissions in the majority of these cities. Predictions of SO^sub 2^ and NO^sub 2^ concentrations in January, April, August, and October using the emission estimations derived from the nudging technique showed remarkable improvements over those based on the original emission data.
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1. Introduction
As the date for the 2008 Olympic Summer Games was approaching, one of the biggest issues that faced the game's organizers was accurately forecasting the air quality in the host city of Beijing, China. Forecasters at the Beijing Meteorological Bureau relied on coupled numerical weather prediction (NWP) and atmospheric chemistry models, such as "Models-3" (Dennis et al. 1996), a widely used operational air quality forecast system developed by the U.S. Environmental Protection Agency, to accomplish their task. Models-3 consists of three components: 1) a regional NWP model, 2) an air pollution source initialization model, and 3) a multiscale atmospheric chemistry model [Community Multiscale Air Quality Model (CMAQ)]. The modeling system has demonstrated skill in simulating and forecasting air quality at local, city, regional, and continental scales in the United States (Byun 1999a,b). However, the forecast accuracy depends critically on the accuracy of the pollution emission inventory that the model uses as input. Thus, obtaining an accurate air pollution emission inventory is a prerequisite for improving air quality forecasts.
The results from the Beijing City Air Pollution Observation Experiment (BECAPEX; Xu et al. 2003) showed that...