A 5-year Clean Air Action Plan was implemented in 2013 to reduce air pollutant emissions and improve ambient air quality in Beijing. Assessment of this action plan is an essential part of the decision-making process to review its efficacy and to develop new policies. Both statistical and chemical transport modelling have been previously applied to assess the efficacy of this action plan. However, inherent uncertainties in these methods mean that new and independent methods are required to support the assessment process. Here, we applied a machine-learning-based random forest technique to quantify the effectiveness of Beijing's action plan by decoupling the impact of meteorology on ambient air quality. Our results demonstrate that meteorological conditions have an important impact on the year-to-year variations in ambient air quality. Further analyses show that the PM_2.5 mass concentration would have broken the target of the plan (2017 annual PM_2.5<60 µg m⁻³) were it not for the meteorological conditions in winter 2017 favouring the dispersion of air pollutants. However, over the whole period (2013–2017), the primary emission controls required by the action plan have led to significant reductions in PM_2.5, PM₁₀, NO₂, SO₂, and CO from 2013 to 2017 of approximately 34 %, 24 %, 17 %, 68 %, and 33 %, respectively, after meteorological correction. The marked decrease in PM_2.5 and SO₂ is largely attributable to a reduction in coal combustion. Our results indicate that the action plan has been highly effective in reducing the primary pollution emissions and improving air quality in Beijing. The action plan offers a successful example for developing air quality policies in other regions of China and other developing countries.

The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO₂, O₃, and PM_2.5 concentrations arising from these emission changes in 11 cities globally by applying a deweathering machine learning technique. Sudden decreases in deweathered NO₂ concentrations and increases in O₃ were observed in almost all cities. However, the decline in NO₂ concentrations attributable to the lockdowns was not as large as expected, at reductions of 10 to 50%. Accordingly, O₃ increased by 2 to 30% (except for London), the total gaseous oxidant (O_x = NO₂ + O₃) showed limited change, and PM_2.5 concentrations decreased in most cities studied but increased in London and Paris. Our results demonstrate the need for a sophisticated analysis to quantify air quality impacts of interventions and indicate that true air quality improvements were notably more limited than some earlier reports or observational data suggested.

Aerosols produced from the oxidation of volatile/semi-volatile organic compounds (VOCs/SVOCs), known as secondary organic aerosol (SOA), account for a significant fraction of atmospheric airborne particles. This paper reviews the current understanding of SOA formation from gas-phase oxidation with a focus on anthropogenic precursors and their reaction products from atmospheric simulation chamber studies. The review summarises the major reaction products derived from main groups of SOA precursors (e.g., alkanes, aromatics), SOA yields and the factors controlling SOA formation. We highlight that lab-derived SOA yield depends strongly upon, not only the concentrations of SOA precursors and oxidants but also simulation conditions.

Measurements of OH, HO₂, complex RO₂ (alkene- and aromatic-related RO₂) and total RO₂ radicals taken during the integrated Study of AIR Pollution PROcesses in Beijing (AIRPRO) campaign in central Beijing in the summer of 2017, alongside observations of OH reactivity, are presented. The concentrations of radicals were elevated, with OH reaching up to $\mathrm{2.8}\times {\mathrm{10}}^{\mathrm{7}}\mathrm{molecule}{\mathrm{cm}}^{-\mathrm{3}}$ , HO₂ peaking at $\mathrm{1}\times {\mathrm{10}}^{\mathrm{9}}\mathrm{molecule}{\mathrm{cm}}^{-\mathrm{3}}$ and the total RO₂ concentration reaching $\mathrm{5.5}\times {\mathrm{10}}^{\mathrm{9}}\mathrm{molecule}{\mathrm{cm}}^{-\mathrm{3}}$ . OH reactivity (k(OH)) peaked at 89 s⁻¹ during the night, with a minimum during the afternoon of $\approx \mathrm{22}{\mathrm{s}}^{-\mathrm{1}}$ on average. An experimental budget analysis, in which the rates of production and destruction of the radicals are compared, highlighted that although the sources and sinks of OH were balanced under high NO concentrations, the OH sinks exceeded the known sources (by 15 ppbv h⁻¹) under the very low NO conditions (<0.5 ppbv) experienced in the afternoons, demonstrating a missing OH source consistent with previous studies under high volatile organic compound (VOC) emissions and low NO loadings. Under the highest NO mixing ratios (104 ppbv), the HO₂ production rate exceeded the rate of destruction by $\approx \mathrm{50}\mathrm{ppbv}{\mathrm{h}}^{-\mathrm{1}}$ , whilst the rate of destruction of total RO₂ exceeded the production by the same rate, indicating that the net propagation rate of RO₂ to HO₂ may be substantially slower than assumed. If just 10 % of the RO₂ radicals propagate to HO₂ upon reaction with NO, the HO₂ and RO₂ budgets could be closed at high NO, but at low NO this lower RO₂ to HO₂ propagation rate revealed a missing RO₂ sink that was similar in magnitude to the missing OH source. A detailed box model that incorporated the latest Master Chemical Mechanism (MCM3.3.1) reproduced the observed OH concentrations well but over-predicted the observed HO₂ under low concentrations of NO (<1 ppbv) and under-predicted RO₂ (both the complex RO₂ fraction and other RO₂ types which we classify as simple RO₂) most significantly at the highest NO concentrations. The model also under-predicted the observed k(OH) consistently by $\approx \mathrm{10}{\mathrm{s}}^{-\mathrm{1}}$ across all NO_x levels, highlighting that the good agreement for OH was fortuitous due to a cancellation of missing OH source and sink terms in its budget. Including heterogeneous loss of HO₂ to aerosol surfaces did reduce the modelled HO₂ concentrations in line with the observations but only at NO mixing ratios <0.3 ppbv. The inclusion of Cl atoms, formed from the photolysis of nitryl chloride, enhanced the modelled RO₂ concentration on several mornings when the Cl atom concentration was calculated to exceed $\mathrm{1}\times {\mathrm{10}}^{\mathrm{4}}\mathrm{atoms}{\mathrm{cm}}^{-\mathrm{3}}$ and could reconcile the modelled and measured RO₂ concentrations at these times. However, on other mornings, when the Cl atom concentration was lower, large under-predictions in total RO₂ remained. Furthermore, the inclusion of Cl atom chemistry did not enhance the modelled RO₂ beyond the first few hours after sunrise and so was unable to resolve the modelled under-prediction in RO₂ observed at other times of the day. Model scenarios, in which missing VOC reactivity was included as an additional reaction that converted OH to RO₂, highlighted that the modelled OH, HO₂ and RO₂ concentrations were sensitive to the choice of RO₂ product. The level of modelled to measured agreement for HO₂ and RO₂ (both complex and simple) could be improved if the missing OH reactivity formed a larger RO₂ species that was able to undergo reaction with NO, followed by isomerisation reactions reforming other RO₂ species, before eventually generating HO₂. In this work an α-pinene-derived RO₂ species was used as an example. In this simulation, consistent with the experimental budget analysis, the model underestimated the observed OH, indicating a missing OH source. The model uncertainty, with regards to the types of RO₂ species present and the radicals they form upon reaction with NO (HO₂ directly or another RO₂ species), leads to over an order of magnitude less O₃ production calculated from the predicted peroxy radicals than calculated from the observed peroxy radicals at the highest NO concentrations. This demonstrates the rate at which the larger RO₂ species propagate to HO₂, to another RO₂ or indeed to OH needs to be understood to accurately simulate the rate of ozone production in environments such as Beijing, where large multifunctional VOCs are likely present.

The Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-Beijing) programme is an international collaborative project focusing on understanding the sources, processes and health effects of air pollution in the Beijing megacity. APHH-Beijing brings together leading China and UK research groups, state-of-the-art infrastructure and air quality models to work on four research themes: (1) sources and emissions of air pollutants; (2) atmospheric processes affecting urban air pollution; (3) air pollution exposure and health impacts; and (4) interventions and solutions. Themes 1 and 2 are closely integrated and support Theme 3, while Themes 1–3 provide scientific data for Theme 4 to develop cost-effective air pollution mitigation solutions. This paper provides an introduction to (i) the rationale of the APHH-Beijing programme and (ii) the measurement and modelling activities performed as part of it. In addition, this paper introduces the meteorology and air quality conditions during two joint intensive field campaigns – a core integration activity in APHH-Beijing. The coordinated campaigns provided observations of the atmospheric chemistry and physics at two sites: (i) the Institute of Atmospheric Physics in central Beijing and (ii) Pinggu in rural Beijing during 10 November–10 December 2016 (winter) and 21 May–22 June 2017 (summer). The campaigns were complemented by numerical modelling and automatic air quality and low-cost sensor observations in the Beijing megacity. In summary, the paper provides background information on the APHH-Beijing programme and sets the scene for more focused papers addressing specific aspects, processes and effects of air pollution in Beijing.