A 5-month campaign (from May to September 2017) was conducted to characterize volatile organic compounds (VOCs) for the first time at four sites in Zhengzhou, Henan Province, China, where ground level ozone (O₃) concentration has shown an increasing trend in recent years. Canister samples were collected for the measurement of 57 VOCs, which, along with reactive nitrogen oxides (NO_x), are the most important O₃ precursors. During the same period, O₃ and its precursor gases were monitored online simultaneously. The results indicated that the average mixing ratio of total quantified VOCs (${\mathrm{\Sigma }}_{\mathrm{VOCs}}=\mathrm{28.8}\pm \mathrm{22.1}$ ppbv) in Zhengzhou was lower than that in the other Chinese megacities, while alkyne comprised a higher proportion of Σ_VOCs. The abundances, compositions and ratios of typical VOCs showed clear spatial and temporal variations. Cluster analysis indicates that air masses from the south of Zhengzhou were cleaner than from other directions. The molar ratio of VOCs to NO_x indicated that, in general, O₃ formation was more sensitive to VOCs than NO_x formation in Zhengzhou. The source apportionment was conducted with positive matrix factorization (PMF), and it was found that vehicle exhaust, coal and biomass burning and solvent usage were the major sources for ambient VOCs at all four sites. From potential source contribution function (PSCF) analysis, the strong emissions from coal + biomass burning and solvent usage were concentrated in the southwest of Shanxi and Henan provinces. This study gathers scientific evidence on the pollution sources for Zhengzhou, benefiting the government to establish efficient environmental control measures, particularly for O₃ pollution.

The development of chemical transport models with advanced physics and chemical schemes could improve air-quality forecasts. In this study, the China Meteorological Administration Unified Atmospheric Chemistry Environment (CUACE) model, a comprehensive chemistry module incorporating gaseous chemistry and a size-segregated multicomponent aerosol algorithm, was coupled to the Weather Research and Forecasting (WRF) framework with chemistry (WRF-Chem) using an interface procedure to build the WRF/CUACE v1.0 model. The latest version of CUACE includes an updated aerosol dry deposition scheme and the introduction of heterogeneous chemical reactions on aerosol surfaces. We evaluated the WRF/CUACE v1.0 model by simulating PM_2.5, O₃, NO₂, and SO₂ concentrations for January, April, July, and October (representing winter, spring, summer and autumn, respectively) in 2013, 2015, and 2017 and comparing them with ground-based observations. Secondary inorganic aerosol simulations for the North China Plain (NCP), Yangtze River Delta (YRD), and Sichuan Basin (SCB) were also evaluated. The model captured well the variations of PM_2.5, O₃, and NO₂ concentrations in all seasons in eastern China. However, it is difficult to accurately reproduce the variations of air pollutants over SCB, due to its deep basin terrain. The simulations of SO₂ were generally reasonable in the NCP and YRD with the bias at −15.5 % and 24.55 %, respectively, while they were poor in the Pearl River Delta (PRD) and SCB. The sulfate and nitrate simulations were substantially improved by introducing heterogeneous chemical reactions into the CUACE model (e.g., change in bias from −95.0 % to 4.1 % for sulfate and from 124.1 % to 96.0 % for nitrate in the NCP). Additionally, The WRF/CUACE v1.0 model was revealed with better performance in simulating chemical species relative to the coupled Fifth-Generation Penn State/NCAR Mesoscale Model (MM5) and CUACE model. The development of the WRF/CUACE v1.0 model represents an important step towards improving air-quality modeling and forecasts in China.

A framework was developed to quantitatively assess the contribution of meteorology variations to the trend of fine particular matter (PM_2.5) concentrations and to separate the impacts of meteorology from the control measures in the trend, based upon the Environmental Meteorology Index (EMI). The model-based EMI realistically reflects the role of meteorology in the trend of PM_2.5 and is explicitly attributed to three major factors: deposition, vertical accumulation and horizontal transports. Based on the 2013–2019 PM_2.5 observation data and re-analysis meteorological data in China, the contributions of meteorology and control measures in nine regions of China were assessed separately by the EMI-based framework. Monitoring network observations show that the PM_2.5 concentrations have declined by about 50 % on the national average and by about 35 % to 53 % for various regions. It is found that the nationwide emission control measures were the dominant factor in the declining trend of China PM_2.5 concentrations, contributing about 47 % of the PM_2.5 decrease from 2013 to 2019 on the national average and 32 % to 52 % for various regions. The meteorology has a variable and sometimes critical contribution to the year-by-year variations of PM_2.5 concentrations, 5 % on the annual average and 10 %–20 % for the fall–winter heavy pollution seasons.