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Volatile organic compounds (VOCs) are important precursors to the formation of ozone (O3) and secondary organic aero-sols (SOA) and can also have direct human health impacts. Generally, given the range and number of VOC species, their emissions are poorly characterised. The VOC levels in Beijing during two campaigns (APHH) were investigated using a dispersion model (NAME), and a chemical box model (AtChem2) in order to understand how chemistry and transport affect the VOC concentrations in Beijing. Emissions of VOCs in Beijing and contributions from outside Beijing were modelled using the NAME dispersion model combined with the emission inventories and were used to initialize the AtChem2 box model. The modelled concentrations of VOCs from the NAME-AtChem2 combination were then compared to the output of a chemical transport model (GEOS-Chem). The results from the emission inventories and the NAME air mass pathways suggest that industrial sources to the south of Beijing and within Beijing both in summer and winter are very important in con-trolling the VOC levels in Beijing. A number of scenarios with different nitrogen oxides to ozone ratios (NOx / O3) and hydroxyl (OH) levels were simulated to determine the changes in VOC levels. In Beijing over 80 % of VOC are emitted locally during winter, while during summer about 35 % of VOC concentrations (greater for some individual species) are transported into Beijing from the surrounding regions. Most winter scenarios are in good agreement with daily GEOS-Chem simulations, with the best agreements seen for the modelled concentrations of ethanol, benzene and propane with correlation coefficients of 0.67, 0.63 and 0.64 respectively. Furthermore, the production of formaldehyde within 24 hours air travel from Beijing was investigated, and it was determined that 90 % of formaldehyde in the winter and 83 % in the summer in Beijing is secondary, produced from oxidation of non-methane volatile organic compounds (NMVOCs). The benzene / CO and toluene / CO ratios during the campaign is very similar to the ratio derived from literature for 2014 in Beijing, however more data are needed to enable investigation of more species over longer timeframes to determine whether this ratio can be applied to predicting VOCs in Beijing. The results suggest that VOC concentrations in Beijing are driven predominantly by sources within Beijing and by local atmospheric chemistry during the winter, and by a combination of transport and chemistry during the summer. Moreover, the relationship of the NOx / VOC and O3 during winter and summer shows the need for season-specific policy measures.

This study delves into the photochemical atmospheric changes reported globally during the pandemic by analyzing the change in emissions from mobile sources and the contribution of local meteorology to ozone (O3) and particle formation in Bogotá (Colombia), Santiago (Chile), and São Paulo (Brazil). The impact of mobility reductions (50%–80%) produced by the early coronavirus-imposed lockdown was assessed through high-resolution vehicular emission inventories, surface measurements, aerosol optical depth and size, and satellite observations of tropospheric nitrogen dioxide (NO2) columns. A generalized additive model (GAM) technique was also used to separate the local meteorology and urban patterns from other drivers relevant for O3 and NO2 formation. Volatile organic compounds, nitrogen oxides (NOx), and fine particulate matter (PM2.5) decreased significantly due to motorized trip reductions. In situ nitrogen oxide median surface mixing ratios declined by 70%, 67%, and 67% in Bogotá, Santiago, and São Paulo, respectively. NO2 column medians from satellite observations decreased by 40%, 35%, and 47%, respectively, which was consistent with the changes in mobility and surface mixing ratio reductions of 34%, 25%, and 34%. However, the ambient NO2 to NOx ratio increased, denoting a shift of the O3 formation regime that led to a 51%, 36%, and 30% increase in the median O3 surface mixing ratios in the 3 respective cities. O3 showed high sensitivity to slight temperature changes during the pandemic lockdown period analyzed. However, the GAM results indicate that O3 increases were mainly caused by emission changes. The lockdown led to an increase in the median of the maximum daily 8-h average O3 of between 56% and 90% in these cities.

The rapid urbanization and industrialization of Northern China in recent decades has resulted in poor air quality in major cities like Beijing. Transport of air pollution plays a key role in determining the relative influence of local emissions and regional contributions to observed air pollution. In this paper, dispersion modelling (Numerical Atmospheric Modelling Environment, NAME model) is used with emission inventories and in-situ ground measurement data to track the pathways of air masses arriving at Beijing. The percentage of time the air masses spent over specific regions on their travel to Beijing is used to assess the effects of regional meteorology on carbon monoxide (CO), a good tracer of anthropogenic emissions. The NAME model is used with the MEIC (Multi-resolution Emission Inventory for China) emission inventories to determine the amount of pollution that is transported to Beijing from the immediate surrounding areas and regions further away. This approach captures the magnitude and variability of CO over Beijing and reveals that CO is strongly driven by transport processes. This study provides a more detailed understanding of relative contributions to air pollution in Beijing under different regional airflow conditions. Approximately 45 % over a 4 year average (2013–2016) of the total CO pollution that affects Beijing is transported from other regions, and about half of this contribution comes from beyond the Hebei and Tianjin regions that immediately surround Beijing. The industrial sector is the dominant emission source from the surrounding regions and contributes over 20 % of the total CO in Beijing. Finally, using PM2.5 to determine high pollution days, three pollution classification types of pollution were identified and used to analyse the APHH winter campaign and the 4 year period. The results can inform targeted control measures to be implemented by Beijing and the surrounding provinces to tackle air quality problems that affect Beijing and China.

Air pollution has been shown to have a direct effect on human health. In particular, PM2.5 has been proven to be related to cardiovascular and respiratory problems. Therefore, it is important to have accurate models to predict high pollution events for this and other pollutants. We present different models that forecast PM2.5 maximum concentrations using a Long Short-Term Memory (LSTM) based neural network and a Deep Feedforward Neural Network (DFFNN). Ten years of air pollution and meteorological measurements from the network of monitoring stations in the city of Santiago, Chile were used, focusing on the behaviour of three zones of the city. All missing values were rebuilt using a method based on discrete cosine transforms and photochemical predictors selected through unsupervised clustering. Deep learning techniques provide significant improvements compared to a traditional multi-layer neural networks, particularly the LSTM model configured with a 7-day memory window (synoptic scale of pollution patterns) can capture critical pollution events at sites with both primary and secondary air pollution problems. Furthermore, the LSTM model consistently outperform deterministic models currently used in Santiago, Chile.

For three austral summer seasons (2013–2016, each from December to February) aerosol particles arriving at the Belgian Antarctic research station Princess Elisabeth (PE), in Dronning Maud Land in East Antarctica were characterized in terms of number concentrations of total aerosol particles (NCN) and cloud condensation nuclei (NCCN), the particle number size distribution (PNSD), the aerosol particle hygroscopicity and the influence of the air mass origin on NCN and NCCN. In general NCN was found to range from 40 to 6700 cm−3 with a median of 333 cm−3, while NCCN was found to cover a range between less than 10 and 1300 cm−3 for supersaturations (SS) between 0.1 and 0.7 %. It is shown that the aerosol is Aitken mode dominated and is characterized by a significant amount of freshly, secondarily formed aerosol particles, with 94 % and 36 % of the aerosol particles are smaller than 90 nm and ≈ 35 nm, respectively. Measurements of the basic meteorological parameters as well as the history of the air masses arriving at the measurement station indicate that the station is influenced by both, continental air masses originating from the Antarctic inland ice sheet (continental events – CE) and marine air masses originating from the Southern Ocean (marine events – ME). CEs came along with rather constant NCN and NCCN values, which we denote to be Antarctic continental background concentrations. MEs however cause large fluctuations in NCN and NCCN caused by scavenging due to precipitation or new particle formation based on marine precursors. The application of Hysplit back trajectories in form of the potential source contribution function (PSCF) analysis indicate, that the region of the Southern Ocean is a potential source of Aitken mode particles. For particles larger than ≈ 110 nm (CCN measured at SS of 0.1 %) the Antarctic ice shelf regions were found to be a potential source region, most likely due to the emission of sea salt aerosol particles, released from snow particles from surface snow layers by sublimation, e.g., during periods of high wind speed, leading to drifting or blowing snow. On the basis of the PNSDs and NCCN, the critical diameter for cloud droplet activation and the aerosol particle hygroscopicity parameter κ were determined to be 110 nm and 1, respectively, for a SS of 0.1 %. The region of the Antarctic inland plateau however was not found to feature a significant source region for CN and CCN measured at the PE station in austral summer.

Measurements of the concentration and variability of ice nucleating particles in the subtropical, maritime boundary layer are reported. Filter samples collected in Cape Verde over the period 2009–2013 are analyzed with a drop freezing experiment sensitive to detect the few, rare ice nuclei active at low supercooling. The data-set is augmented with continuous flow diffusion chamber (SPIN) measurements at temperatures below −24 °C from a two month field campaign at Cape Verde in 2016. The data set is used to address the questions: What are typical concentrations of ice nucleating particles active at a certain temperature, what affects their concentration, what is their composition and where are their sources? To investigate what the most common ice nuclei are and to identify the sources, bulk chemical aerosol composition obtained from the utilized filter samples is tested for correlations with ice nuclei concentration. It is shown that no significant correlation between the rare ice nuclei and the bulk aerosol chemical composition, which could serve as tracer for a specific aerosol class e.g. of maritime origin, can be made. Concentration of ice nuclei is found to increase exponentially with decreasing temperature. It indicates that several groups of particles with different ice nucleation properties (size, composition) are contributing to the ice nuclei concentration at different temperatures. The concentration of ice nuclei active at a specific temperature varies over a wide range. The frequency with which a certain ice nuclei concentration is measured within this range is found to follow a log-normal distribution. The log-normal frequency distribution can be explained by random dilution associated with turbulent, long-distance transport. To investigate the geographic origin of ice nuclei, source attribution of air masses from dispersion modeling is used to classify the data into 7 typical situations. While no source could be attributed to the ice nuclei active at temperatures higher than −12 °C, concentrations at lower temperatures tend to be elevated in air masses originating from the Saharan desert.