High-resolution modeling the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source-receptor model (GNAQPMS-SM)
- 1State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
- 2College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
- 3Center for Excellence in Regional Atmospheric Environment, Institute of Urban Environment, Chinese Academy of Science, Xiamen 361021, China
- 4China National Environmental Monitoring Center, Beijing 100012, China
Abstract. Many efforts have been devoted to quantifying the impact of intercontinental transport on global air quality by using global chemical transport models with horizontal resolutions of hundreds of kilometers in recent decades. In this study, a global online air quality source-receptor model (GNAQPMS-SM) is designed to effectively compute the contributions of various regions to ambient pollutant concentrations. The newly developed model is able to quantify source-receptor (S-R) relationships in one simulation without introducing errors by nonlinear chemistry, which largely reduces the computation costs compared to the brute force method. We calculate the surface and planetary boundary layer (PBL) S-R relationships in 19 regions over the whole globe for ozone, black carbon (BC) and non-sea-salt sulphate (nss-sulphate) by conducting a high-resolution (0.5° × 0.5°) simulation for the year 2018. The model exhibits a realistic capacity in reproducing the spatial distributions and seasonal variations of tropospheric ozone, carbon monoxide, and aerosols at global and regional scales (Europe, North America and East Asia). The correlation coefficient (R) and normalized mean bias (NMB) for seasonal ozone at global background and urban-rural sites ranged from 0.49 to 0.87 and −2 % to 14.97 %, respectively. For aerosols, the R and NMB in Europe, North America and East Asia mostly exceed 0.6 and are within ±15 %. These statistical parameters based on this global simulation can match those of regional models in key regions. The simulated tropospheric nitrogen dioxide and aerosol optical depths are generally in agreement with satellite observations. The model overestimates ozone mixing ratios in the upper troposphere and stratosphere in the tropics, mid-latitude and polar regions of the Southern Hemisphere due to the use of a simplified stratospheric ozone scheme and/or biases in estimated stratosphere-troposphere exchange dynamics. We find that O3 in the surface layer can travel a long distance and contributes a nonnegligible fraction to downwind regions. Nonlocal source transport explains approximately 35–60 % of surface O3 in East Asia, South Asia, Europe and North America. The O3 exported from Europe can also be transported across the Arctic Ocean to the North Pacific and contributes nearly 5–7.5 % to the North Pacific. BC, as a primary aerosol, is directly linked to local emissions, and each BC source region mainly contributes to itself and surrounding regions. For nss-sulphate, contributions of long-range transport account for 15–30 % within the PBL in East Asia, South Asia, Europe and North America. Our estimated international transport is lower than that from the Hemispheric Transport of Air Pollution (HTAP) assessment report in 2010. In this study, local contributions to surface nss-sulphate and BC exceed the ranges given in the HTAP model, while local contributions to nss-sulphate and BC within the PBL are mainly within the ranges. This difference may be related to the different simulation years, emission inventories, horizontal resolutions and S-R revealing methods. The S-R relationship of aerosols within the East Asia subcontinent is also assessed. The model that we developed creates a link between the scientific community and policymakers. Finally, the results are discussed in the context of future model development and analysis opportunities.
Qian Ye et al.
Qian Ye et al.
Qian Ye et al.
Viewed (geographical distribution)