A New Combined Stepwise-Based High-Order Decoupled Direct and Reduced-Form Method To Improve Uncertainty Analysis in PM_2.5 Simulations

Abstract

The traditional reduced-form model (RFM) based on the high-order decoupled direct method (HDDM), is an efficient uncertainty analysis approach for air quality models, but it has large biases in uncertainty propagation due to the limitation of the HDDM in predicting nonlinear responses to large perturbations of model inputs. To overcome the limitation, a new stepwise-based RFM method that combines several sets of local sensitive coefficients under different conditions is proposed. Evaluations reveal that the new RFM improves the prediction of nonlinear responses. The new method is applied to quantify uncertainties in simulated PM concentrations in the Pearl River Delta (PRD) region of China as a case study. Results show that the average uncertainty range of hourly PM concentrations is -28% to 57%, which can cover approximately 70% of the observed PM concentrations, while the traditional RFM underestimates the upper bound of the uncertainty range by 1-6%. Using a variance-based method, the PM boundary conditions and primary PM emissions are found to be the two major uncertainty sources in PM simulations. The new RFM better quantifies the uncertainty range in model simulations and can be applied to improve applications that rely on uncertainty information.

Novel Method for Ozone Isopleth Construction and Diagnosis for the Ozone Control Strategy of Chinese Cities

“…In addition to HIM, we adopt alternative methods to derive the isopleths, including the first-order integral method (IM) where we only use the first-order sensitivities to generate the isopleths (i.e., DDM), the stepwise approach as proposed by previous studies, , and the brute-force approach where we directly interpolate the isopleth diagrams based on C O3,pop values of the 16 vital points via nearest, linear, cubic, and natural-neighbor interpolation, respectively, without involving any sensitivities. The performances of HIM, IM, stepwise approach, and brute-force approach are evaluated (see the Results and Discussion section).…”

Fine particulate matter and cardiovascular disease: Comparison of assessment methods for long-term exposure

“…The CMAQ model fields are used in conjunction with observations in a multi-step process, which has been described in detail previously (Huang et al 2017). The final outputs capture spatially detailed information by the air quality model, as well as the coarser scale spatial and fine scale temporal information from the observations.…”

A Feasible Methodological Framework for Uncertainty Analysis and Diagnosis of Atmospheric Chemical Transport Models

“…Various sources of uncertainty exist in developing and applying CTMs models, including the parametric uncertainty associated with input data or parameters and the structural uncertainty arising from simplifications of complex chemical and physical processes 4 . Uncertainty analysis is an effective mean to improve model performance by identifying and diagnosing key sources of uncertainty 2,[5][6][7] . Although some attempts have been made to characterize uncertainties of atmospheric models in recent decades, 2,8 better quantification of uncertainties in CTMs remains a top research priority for atmospheric scientists 9,10 .…”

“…Stochastic Response Surface Model (SRSM) 1 and Probabilistic Collocation Method (PCM) [14][15][16][17] based on the polymonial chaos expansions (PCEs), the reduced-form model based on High-order Decoupled Direct Method (RFM-HDDM) 6,8,18 and the recently developed stepwise-based HDDM (SB-HDDM) 7 . These approaches all use an polynomial expansion instead of the original CTM to propagate uncertainties.…”

“…These approaches all use an polynomial expansion instead of the original CTM to propagate uncertainties. However, the RFM-HDDM has significant biases in predicting nonlinear responses when there are high uncertainties in model inputs 7,19 . The SB-HDDM partly overcomes this limitation but still has biases because it ignores the highorder cross sensitivities and assumes that the interaction among inputs is linear.…”