Air pollution in Chinese megacities has reached extremely hazardous levels, and human activities are responsible for the emission or production of large amounts of particulate matter (PM). In addition to PM from anthropogenic sources, natural phenomena, such as dust storms over Asian deserts, may also emit large amounts of PM, which lead episodically to poor air quality over Chinese megacities. In this paper, we quantify the degradation of air quality by dust over Beijing, Chengdu and Shanghai megacities using the three dimensions (3D) chemistry transport model CHIMERE, which simulates dust emission and transport online. In the first part of our work, we evaluate dust emissions using Moderate Resolution Imaging Spectroradiometer (MODIS) and Infrared Atmospheric Sounding Interferometer (IASI) satellite observations of aerosol optical depth, respectively, in the visible and the thermal infrared over source areas. PM simulations were also evaluated compared to surface monitoring stations. Then, mineral dust emissions and their impacts on particle composition of several Chinese megacities were analyzed. Dust emissions and transport over China were simulated during three years (2011, 2013 and 2015). Annual dust contributions to the PM ${}_{10}$ budget over Beijing, Chengdu and Shanghai were evaluated respectively as 6.6%, 9.5% and 9.3%. Dust outbreaks largely contribute to poor air quality events during springtime. Indeed it was found that dust significantly contribute for 22%, 52% and 43% of spring PM ${}_{10}$ events (for Beijing, Chengdu and Shanghai respectively).

We report the results of year-long PM_2.5 (particulate matter less than 2.5 µm in diameter) simulations over Northeast Asia for the base year of 2013 under the framework of the Long-range Transboundary Air Pollutants in Northeast Asia (LTP) project. LTP is a tripartite project launched by China, Japan, and Korea for cooperative monitoring and modeling of the long-range transport (LRT) of air pollutants. In the modeling aspect in the LTP project, each country’s modeling group employs its own original air quality model and options. The three regional air quality models employed by the modeling groups are WRF-CAMx, NHM-RAQM2, and WRF-CMAQ. PM_2.5 concentrations were simulated in remote exit-and-entrance areas associated with the LRT process over China, Japan, and Korea. The results showed apparent bias that remains unexplored due to a series of uncertainties from emission estimates and inherent model limitations. The simulated PM₁₀ levels at seven remote exit-and-entrance sites were underestimated with the normalized mean bias of 0.4 ± 0.2. Among the four chemical components of PM_2.5 (SO₄²⁻, NO₃⁻, organic carbon (OC), and elemental carbon (EC)), the largest inter-model variability was in OC, with the second largest discrepancy in NO₃⁻. Our simulation results also indicated that under considerable SO₄²⁻ levels, favorable environments for ammonium nitrate formation were found in exit-and-entrance areas between China and Korea, and gas-aerosol partitioning for semi-volatile species of ammonium nitrate could be fully achieved prior to arrival at the entrance areas. Other chemical characteristics, including NO₃⁻/SO₄²⁻ and OC/EC ratios, are discussed to diagnose the LRT characteristics of PM_2.5 in exit-and-entrance areas associated with transboundary transport over China, Japan, and Korea.

Simulations of 108 emission reduction scenarios for NO _x and VOCs using Comprehensive Air Quality Model with Extensions (CAMx) were conducted for eight cities in the Chengdu metropolitan area (CMA). The isopleth diagrams were drawn to explore the responses and differences of ozone (O ₃ ) concentrations to NO _x and VOCs emission changes under Chengdu, CMA and Sichuan Province emission reduction scenarios. The results show that the O ₃ -sensitive regimes of eight cities may change under different emission reduction scenarios. Under Chengdu emission reduction scenario, the Chengdu city is in the transition regime and O ₃ formation will shift from transition to VOC-limited when the VOCs emissions decreased by 50%, and the decreases in O ₃ concentrations caused by VOCs emission reductions are small. For the CMA and Sichuan Province emission reduction scenarios, all cities are NO _x -limited in the baseline cases and with at least a 66% and a 77% reduction in NO _x emissions, respectively, the daily maximum 8-h average O ₃ (MDA8) can attain the O ₃ standard (160 μg m ⁻³ ). Although reductions in VOCs emissions can also lessen the O ₃ concentration, the effectiveness is relatively small. The changes in O ₃ concentrations under different VOCs to NO _x emission reduction ratios indicate that all cities achieve a relatively high O ₃ concentration decrement with low VOCs to NO _x emission reduction ratios and that the decreasing O ₃ concentrations caused by non-local emission reductions are much higher than those achieved by local emission reductions. In addition, the decreases in O ₃ concentrations in Chengdu are quite close when the total NO _x and VOCs emissions reduction percentages are less than 30% under the CMA and Sichuan emission reduction scenarios.

This study conducted an intensive air quality model evaluation as a response to the urgent need to understand the reliability, consistency, and uncertainty of air quality models supporting the implementation of the PM_2.5 Air Pollution Control Action Plan in China. Five regional air quality models of CMAQ version 5.02, CMAQ version 5.3.2, CAMx version 6.2, CAMx version 7.1, and NAQPMS have been evaluated for the CO, SO₂, NO₂, O₃, PM₁₀, and PM_2.5 concentration and components. A unified statistical method and the same observational data set of 2017, comprising 17 air pollution episodes collected from four super monitoring stations in the regions of Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing in China, have been used for the evaluation. All the participating models performed well in simulating the mean PM_2.5 concentrations, with an NMB ranging from −0.29 to −0.04, showing that the participating models are basically suitable for simulation and as evaluation tools for PM_2.5 in regulatory applications. However, the participating models showed a great variability for PM_2.5 components, with the NME ranging from 0.48 to 0.53. The models performed reasonably well in simulating the mean sulfate, nitrate, BC, and NH₄⁺ concentration in PM_2.5, while they were diversified in simulating the mean OC concentrations. The participating models also consistently performed well in simulating the concentration of NO₂, CO, and O₃. However, the models generally overestimated SO₂ concentrations, and to some extent underestimated PM₁₀ concentrations, which is likely attributable to uncertainties in emission sources and the rapid implementation of strict control policies for SO₂. The evaluation work of this study shows that there remains significant potential for further enhancement. Updating and improving the emission inventory should be prioritized to achieve better results, and further investigations into the uncertainties associated with the meteorological simulations, chemical mechanisms, and physical parameterization options of air quality models should also be conducted in future work.

This work discusses the theory of PM2.5 source apportionment and presents an integrated modeling approach, which combined receptor models based on PM2.5 component measurement data and CTM air quality models using emission inventory. This approach obtains refined source apportionment results through a three-step modeling process. Firstly, this approach takes advantage of the CMB receptor model's ability to resolve the contributing source sectors of the primary PM2.5 components. This approach also used relatively accurate PM2.5 component measurement data, especially for secondary components and dust of PM2.5, which present high uncertainty for the CTM model due to the challenges with model mechanisms, emission inventory, and model resolution. Secondly, this approach utilizes CTM’s source apportionment tools to estimate the contributions of specific emission sources by simulating atmospheric physical and chemical processes and tracking the emission sources groups of specific spatial locations for complete components of PM2.5. Finally, the CMB receptor model source apportionment results for primary PM2.5 components, along with the measured secondary components PM2.5 concentrations, and the source apportionment results including local and long-range contributions estimated by the CTM model are used to obtain the refined integrated source apportionment. The integrated approach developed in this work was applied to conduct source apportionment in the 2+26 Cities Region during the autumn and winter of 2017-2018. This work provided refined source apportionment results for all PM2.5 components, including the local contributions and mutual contributions among different cities and regions, which are crucial for policy and action plans. This work directly supported the PM2.5 pollution control action plans in the region.