Assessing the effects of trans-boundary aerosol transport between various city clusters on regional haze episodes in spring over East China

Abstract

Regional haze episodes have been frequently reported in east China since 2000. In the present study, two regional haze episodes over east China in the spring of 2011 were examined by observations and simulations conducted by a three-dimensional regional chemical transport model (NAQPMS) with an on-line tracer-tagged module. The model reproduced accurately the observed PM2.5 with correlation coefficient ranging from 0.52 to 0.76 and root mean square error (RMSE) of 20–50µg/m3 in four city clusters (Yangtze River Delta, Shandong Peninsula, Huabei Plain and Central Liaoning) over east China. Our results indicate that a northward cross-border transport from the Yangtze River Delta to Central Liaoning below 2 km above ground played an important role in the formation of these regional high PM2.5 episodes. Contributions of regional transport from outside city clusters presented an increasing trend from south to north. In the northernmost cluster (Central Liaoning), the contribution from other city clusters reached 40–50% during the two episodes. In contrast, it was below 10% in the Yangtze River Delta (southernmost cluster). Mixing accumulation of pollutants from various city clusters during transport was responsible for this trend. Furthermore, a preliminary estimate shows that cross-border transport of PM2.5 might increase 0.5–3% daily mortality during the high PM2.5 episodes

Haze Days in North China and the associated atmospheric circulations based on daily visibility data from 1960 to 2012

“…In recent years, rapid economic development has led to heavy atmospheric aerosol loadings in this region, which has caused severe environmental and climate problems [ Giorgi et al ., ; Qian et al ., ; Han et al ., ; Zhang et al ., ; Li et al ., ; Liao et al ., ]. The characteristics of the aerosol particles on haze days and the aerosol particles' effects on the formation of haze have been well documented for this region [ Niu et al ., ; Quan et al ., ; Ji et al ., ; Li et al ., ]. However, the related atmospheric background fields on haze days in this region have not been clearly understood from climatic perspectives, although the ambient conditions have been explored in some case studies.…”

Modeling of aerosol property evolution during winter haze episodes over a megacity cluster in northern China: roles of regional transport and heterogeneous reactions of SO<sub>2</sub>

“…Further details regarding the NAQPMS can be found in J. Li et al (2013Li et al ( , 2014, and numerous subsequent papers have been published describing recent updates.…”

“…The mass contribution from precursors emitted in different locations, referred to as C 1 , was then tagged. More technical details can be found in Li et al (2014) and Wu et al (2017). The following equation can be employed to calculate the degree of chemical conversion during transport (TC): Table 1. where i represents region, n is the total number of regions, and n is 10 in this study.…”

MP CBM-Z V1.0: design for a new Carbon Bond Mechanism Z (CBM-Z) gas-phase chemical mechanism architecture for next-generation processors

“…The gas-phase chemistry module is described as a system of ordinary differential equations (ODEs) to simulate the chemical kinetics of trace gases in an atmosphere model (Seinfeld and Pandis, 2012). Linford et al (2009) reported that the Regional Acid Deposition Model version 2 (RADM2) (Zimmermann and Poppe, 1994;Chang et al, 1987), a chemical kinetics kernel, accounted for 90 % of the computational time in the Weather Forecasting and Research/Chemistry (WRFchem) model (Grell et al, 2005). Another widely used chemical kinetics kernel, the Carbon Bond Mechanism version Z (CBM-Z) (Zaveri and Peters, 1999), accounts for approximately 68 % of the computation time in the Global Nested Air Quality Prediction Model System (GNAQPMS) (Chen et al, 2015;Wang et al, 2017).…”

“…Xu et al (2015) rewrote the Princeton Ocean Model (POM) using Compute Unified Device Architecture-C (CUDA-C) to port it from a CPU to a GPU platform. Linford et al (2009) also tried to solve the computation bottleneck of RADM2 mentioned above by using a heterogeneous platform such as GPU-CPU. In addition, our previous work showed the primary optimizations we performed to accelerate the GNAQPMS on the new generation CPU and Intel MIC platforms (Knights Landing, KNL; Sodani et al, 2016) and had a significant performance improvement on both platforms, a 2.77× speedup on CPU and a 3.51× speedup on the KNL node (Wang et al, 2017).…”