High-resolution modeling of the distribution of surface air pollutants and their intercontinental transport by a global tropospheric atmospheric chemistry source–receptor model (GNAQPMS-SM)

Abstract

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. We calculate the surface and planetary boundary layer (PBL) S-R relationships in 19 regions over the whole globe for ozone (O3), black carbon (BC), and non-sea-salt sulfate (nss-sulfate) by conducting a high-resolution (0.5&deg &times 0.5&deg) simulation for the year 2018. The model exhibits a realistic capacity in reproducing the spatial distributions and seasonal variations of tropospheric O3, carbon monoxide, and aerosols at global and regional scales – Europe (EUR), North America (NAM), and East Asia (EA). The correlation coefficient (R) and normalized mean bias (NMB) for seasonal O3 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 EUR, NAM, and EA 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 concentrations in the upper troposphere and stratosphere in the tropics, midlatitude, 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 surface O3 can travel a long distance and contributes a non-negligible fraction to downwind regions. Non-local source transport explains approximately 35 %–60 % of surface O3 in EA, South Asia (SAS), EUR, and NAM. The O3 exported from EUR can also be transported across the Arctic Ocean to the North Pacific and contributes nearly 5 %–7.5 % to the North Pacific. BC is directly linked to local emissions, and each BC source region mainly contributes to itself and surrounding regions. For nss-sulfate, contributions of long-range transport account for 15 %–30 % within the PBL in EA, SAS, EUR, and NAM. Our estimated international transport of BC and nss-sulfate is lower than that from the Hemispheric Transport of Air Pollution (HTAP) assessment report in 2010, but most surface O3 results are within the range. This difference may be related to the different simulation years, emission inventories, vertical and horizontal resolutions, and S-R revealing methods. Additional emission sensitivity simulation shows a negative O3 response in receptor region EA in January from EA. The difference between two methods in estimated S-R relationships of nss-sulfate and O3 are mainly due to ignoring the nonlinearity of pollutants during chemical processes. The S-R relationship of aerosols within EA 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.

Influences of stratospheric intrusions to high summer surface ozone over a heavily industrialized region in northern China

“…An accurate radiative-transfer model (TUV version 4.5) with an eight-stream discrete ordinate solver was coupled online with GNAQPMS to provide photolysis rates [49]. The model has been utilized to evaluate mercury transport [53], and more recently the global distributions of gaseous pollutants [54], aerosol components [55,56], and intercontinental transport [57].…”

Deep learning-based gas-phase chemical kinetics kernel emulator: Application in a global air quality simulation case

“…The horizontal resolution of the model is variable, and a resolution of 0.5 °× 0.5 °and 20 vertical layers were used in this study. Twenty photolysis rates of gas species including the gaseous, aerosol and cloud species effects on photolysis were calculated online using atmospheric radiative transport model (TUV) (Madronich, 1987;Li et al, 2011;Ye et al, 2021). Meteorological fields including convection and boundary layer diffusion process were derived from the Global Weather Research and Forecasting (GWRF v3.6) model driven by the final analyses (FNL) dataset of National Centers for Environmental Prediction (NCEP) (Zhang et al, 2012).…”

“…Meteorological fields including convection and boundary layer diffusion process were derived from the Global Weather Research and Forecasting (GWRF v3.6) model driven by the final analyses (FNL) dataset of National Centers for Environmental Prediction (NCEP) (Zhang et al, 2012). Top boundaries and initial conditions for each species were obtained from the climatic mean output from MOZART v2.4 (Ye et al, 2021). The CBM-Z gas phase chemistry module in GNAQPMS comprised 52 chemical species and 132 chemical reactions with a lifetime ranging from milliseconds to years.…”

“…Air pollution has adverse effects on ecology and human health, and potentially influences climate change (Kampa and Castanas, 2008;Fiore et al, 2012). Global atmospheric chemical transport model simulation (CTMs) is the main research method for air quality forecast, which can accurately and comprehensively elucidate the physical and chemical processes of various atmospheric pollutants at regional and global scales (Byun and Ching, 1999;Ye et al, 2021). Global CTMs comprise emission, advection transport, turbulent diffusion, gas-phase and aqueous chemistry, heterogeneous chemistry, and deposition (Wang et al, 2001;Binkowski and Roselle, 2003;Long et al, 2015).…”

NAQPMS-PDAF v2.0: A Novel Hybrid Nonlinear Data Assimilation System for Improved Simulation of PM2.5 Chemical Components

“…The Nested Air Quality Prediction Modeling System (NAQPMS), developed by the Institute of Atmospheric Physics (IAP), Chinese Academy of Sciences (CAS), is used to provide background fields of key aerosol chemical components in this study. NAQPMS is capable of characterizing the three-dimensional spatiotemporal distribution of various atmospheric compositions at global and regional scales through multiple physicochemical processes (shown in Table S1) and has been widely used in atmospheric pollution and chemistry research, such as O3 pollution, haze episodes (Wang et al, 2014;Du et al, 2021), regional transport (Wang et al, 2017;Wang et al, 2019), source identification (Li et al, 2022b), air quality simulation at global scale (Ye et al, 2021) and at urban-street scale (Wang et al, 2023), and acid deposition (Ge et al, 2014).…”