Assessing the nonlinear response of fine particles to precursor emissions: development and application of an extended response surface modeling technique 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 based on the conventional response surface modeling (RSM) technique; it first quantifies the relationship between PM2.5 concentrations and the emissions of gaseous precursors from each single region using the conventional RSM technique, and then assesses the effects of inter-regional transport of PM2.5 and its gaseous precursors on PM2.5 concentrations in the target region. We apply this novel technique with a widely used regional chemical transport model (CTM) 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 CTM simulations; the correlation coefficients are larger than 0.98 and 0.99, and the mean normalized errors (MNEs) 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.

Local and Regional Contributions to Fine Particle Pollution in Winter of the Yangtze River Delta, China

“…For example, the non-linear response of pollutant concentrations to precursors' emissions has not been well considered in current zero-out method. More advanced sensitivity analysis methods such as high-order direct decoupled methods (Hakami et al, 2003;Cohan et al, 2005), response surface modeling (Wang et al, 2011b;Xing et al, 2011;Zhao et al, 2015) are recommended for further sensitivity studies. Besides, uncertainties still exists for the aerosol concentration predictions and future work is needed in to get better model performance.…”

Assessment of inter-city transport of particulate matter in the Beijing–Tianjin–Hebei region

“…Another commonly used method is the sensitivity analysis based on Euler 3-D models, such as CMAQ (Community Multiscale Air Quality model), which is undertaken by calculating the change in concentration due to a change in emissions. This method includes the brute force method (e.g., Wang et al, 2015), the decoupled direct method (DDM, Itahashi et al, 2012), and the response surface model (RSM, Zhao et al, 2015). These methods are all based on a chemical transport model, so that the physical and chemical processes can both be well considered.…”

An Integrated Air Quality Model and Optimization Model for Regional Economic and Environmental Development: A Case Study of Tangshan, China

“…Another important factor might cause the uncertainties of the coefficients was the nonlinearity. The non-linear relationship between pollutant concentrations to the changes precursors' emissions was complex and a challenge to deal with Zhao et al, 2015). To simplify the optimization model, we assume a linear relationship between them.…”