Assessing the nonlinear response of fine particles to precursor emissions: development and application of an Extended Response Surface Modeling technique (ERSM v1.0)

Abstract

Abstract. An innovative Extended Response Surface Modeling technique (ERSM v1.0) is developed to characterize the nonlinear response of fine particles (PM2.5) to large and simultaneous changes of multiple precursor emissions from multiple regions and sectors. The ERSM technique is developed starting from the conventional Response Surface Modeling (RSM) technique; it first quantifies the relationship between PM2.5 concentrations and precursor emissions in a single region with the conventional RSM technique, and then assesses the effects of inter-regional transport of PM2.5 and its precursors on PM2.5 concentrations in the target region. We apply this novel technique with a widely used regional air quality model over the Yangtze River Delta (YRD) region of China, and evaluate the response of PM2.5 and its inorganic components to the emissions of 36 pollutant-region-sector combinations. The predicted PM2.5 concentrations agree well with independent air quality model simulations; the correlation coefficients are larger than 0.98 and 0.99, and the mean normalized errors are less than 1 and 2% for January and August, respectively. It is also demonstrated that the ERSM technique could reproduce fairly well the response of PM2.5 to continuous changes of precursor emission levels between zero and 150%. Employing this new technique, we identify the major sources contributing to PM2.5 and its inorganic components in the YRD region. The nonlinearity in the response of PM2.5 to emission changes is characterized and the underlying chemical processes are illustrated.