The COVID-19 lockdowns led to major reductions in air pollutant emissions. Here, we quantitatively evaluate changes in ambient NO2, O3, and PM2.5 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 (Ox = NO2 + O3) showed limited change, and PM2.5 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.

Abstract. 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 PM2.5 mass concentration would have broken the target of the plan (2017 annual PM2.5<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 PM2.5, 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 PM2.5 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 particle number size distribution (PNSD) of airborne particles not only provides us with information about sources and atmospheric processing of particles, but also plays an important role in determining regional lung dose. As a result, urban particles and their size distributions have received much attention with a rapid increase of publications in recent years. The object of this review is to synthesise and analyse existing knowledge on particles in urban environments with a focus on their number concentration and size distribution. This study briefly reviews the characterization of PNSD from seven major sources of urban particles including traffic emissions, industrial emissions, biomass burning, cooking, transported aerosol, marine aerosol and nucleation. It then discusses atmospheric physical processes such as coagulation or condensation which have a strong influence on PNSD. Finally, the implications of PNSD datasets for source modelling are briefly discussed. Based on this review, it is concluded that the concentrations, modal structures and temporal patterns of urban particles are strongly influenced by traffic emissions, which are identified as the main source of particle number in urban environments. Information derived from particle number size distributions is beginning to play an important role in source apportionment studies.

Abstract. 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.

Abstract. Wintertime in situ measurements of OH, HO2 and RO2 radicals and OH reactivity were made in central Beijing during November and December 2016. Exceptionally elevated NO was observed on occasions, up to ∼250 ppbv. The daily maximum mixing ratios for radical species varied significantly day-to-day over the ranges 1–8×106 cm−3 (OH), 0.2–1.5×108 cm−3 (HO2) and 0.3–2.5×108 cm−3 (RO2). Averaged over the full observation period, the mean daytime peak in radicals was 2.7×106, 0.39×108 and 0.88×108 cm−3 for OH, HO2 and total RO2, respectively. The main daytime source of new radicals via initiation processes (primary production) was the photolysis of HONO (∼83 %), and the dominant termination pathways were the reactions of OH with NO and NO2, particularly under polluted haze conditions. The Master Chemical Mechanism (MCM) v3.3.1 operating within a box model was used to simulate the concentrations of OH, HO2 and RO2. The model underpredicted OH, HO2 and RO2, especially when NO mixing ratios were high (above 6 ppbv). The observation-to-model ratio of OH, HO2 and RO2 increased from ∼1 (for all radicals) at 3 ppbv of NO to a factor of ∼3, ∼20 and ∼91 for OH, HO2 and RO2, respectively, at ∼200 ppbv of NO. The significant underprediction of radical concentrations by the MCM suggests a deficiency in the representation of gas-phase chemistry at high NOx. The OH concentrations were surprisingly similar (within 20 % during the day) in and outside of haze events, despite j(O1D) decreasing by 50 % during haze periods. These observations provide strong evidence that gas-phase oxidation by OH can continue to generate secondary pollutants even under high-pollution episodes, despite the reduction in photolysis rates within haze.