Organic aerosol (OA) emitted from biomass burning (BB) impacts air quality and global radiation balance. However, the comprehensive characterization of OA remains poorly understood because of the complex evolutionary behavior of OA in atmospheric processes. In this work, smoke particles were generated from rice straw combustion. The effect of OH radicals photooxidation on size distribution, light absorption, and molecular compositions of smoke particles was systematically investigated. The results showed that the median diameters of smoke particles increased by a factor of approximately 1.2 after photooxidation. In the particle compositions, although both non-polar fractions (n-hexane-soluble organic carbon, HSOC) and polar fractions (water-soluble organic carbon, WSOC) underwent photobleaching after aging, the photobleaching properties of HSOC (1.87–2.19) was always higher than that of WSOC (1.52–1.33). Besides, the light-absorbing properties of HSOC were higher than that of WSOC, showing a factor of approximately 1.75 times for mass absorption efficiency at 365 nm (MAE₃₆₅). Consequently, the simple forcing efficiency (SFE) caused by absorption showed that HSOC has higher radiation effects than WSOC. After photooxidation, the concentration of 16 PAHs in HSOC fractions significantly decreased by 15.3%–72.5%. In WSOC fractions, the content of CHO, CHONS, and CHOS compounds decreased slightly, while the content of CHON compounds increased. Meantime, the variations in molecular properties supported the decrease in light absorption of WSOC fractions. These results reveal the aging behavior of smoke particles, then stress the importance of non-polar organic fractions in particles, providing new insights into understanding the atmospheric pollution caused by BB smoke particles.

Air pollution and extreme heat have been responsible for more than a million deaths in China every year, especially in densely urbanized regions. While previous studies intensively evaluated air pollution episodes and extreme heat events, a limited number of studies comprehensively assessed atmospheric hot-and-polluted-episodes (HPE) – an episode with simultaneously high levels of air pollution and temperature – which have potential adverse synergic impacts on human health. This study focused on the Pearl River Delta (PRD) region of China due to its high temperature in summer and poor air quality throughout a year. We employed geostatistical downscaling to model meteorology at a spatial resolution of 1 km, and applied a machine learning algorithm (XGBoost) to estimate a high-resolution (1 km) daily concentration of particulate matter with an aerodynamic diameter ≤2.5 μm (PM₂.₅) and ozone (O₃) for June to October over 20 years (2000–2019). Our results indicate an increasing trend (∼50%) in the frequency of HPE occurrence in the first decade (2000–2010). Conversely, the annual frequency of HPE occurrence reduced (16.7%), but its intensity increased during the second decade (2010–2019). The northern cities in the PRD region had higher levels of PM₂.₅ and O₃ than their southern counterparts. During HPEs, regional daily PM₂.₅ exceeded the World Health Organization (WHO) and Chinese guideline levels by 75% and 25%, respectively, while the O₃ exceeded the WHO O₃ standard by up to 69%. Overall, 567,063 (95% confidence interval (CI): 510,357–623,770) and 52,231 (95%CI: 26,116–78,346) excessive deaths were respectively attributable to exposure to PM₂.₅ and O₃ in the PRD region. Our findings imply the necessity and urgency to formulate co-benefit policies to mitigate the region's air pollution and heat problems.

The volatilities of ambient organic aerosol (OA) components are important to forecasting OA formation with models. However, providing the OA volatility distribution inputs for models is challenging, and models often rely on measurements from chamber experiments. We measured the volatility of submicron ambient OA in Seoul during May/June of 2019 by connecting a thermodenuder to an Aerodyne Time-of-Flight Aerosol Mass Spectrometer (AMS). We calculated a volatility basis set (VBS) of the organic aerosol with a thermodenuder mass transfer model and data from the thermodenuder set to various temperatures (30–200 °C). We found a large discrepancy between the measured ambient VBS and a reference VBS used in air quality models, with the ambient organics being less volatile. The results suggest that a modeling study that tries to account for this discrepancy may be needed to identify the impact it has on modeling outcomes. Chamber experiments aiming to determine VBSs for specific chemical systems should address limitations caused by wall losses and incomplete modeling parameters.

Studies have reported significant reductions in air pollutant levels due to the COVID-19 outbreak worldwide global lockdowns. Nevertheless, all of the reports are limited compared to data from the same period over the past few years, providing mainly an overview of past events, with no future predictions. Lockdown level can be directly related to the number of new COVID-19 cases, air pollution, and economic restriction. As lockdown status varies considerably across the globe, there is a window for mega-cities to determine the optimum lockdown flexibility. To that end, firstly, we employed four different Artificial Neural Networks (ANN) to examine the compatibility to the original levels of CO, O₃, NO₂, NO, PM₂.₅, and PM₁₀, for São Paulo City, the current Pandemic epicenter in South America. After checking compatibility, we simulated four hypothetical scenarios: 10%, 30%, 70%, and 90% lockdown to predict air pollution levels. To our knowledge, ANN have not been applied to air pollution prediction by lockdown level. Using a limited database, the Multilayer Perceptron neural network has proven to be robust (with Mean Absolute Percentage Error ∼ 30%), with acceptable predictive power to estimate air pollution changes. We illustrate that air pollutant levels can effectively be controlled and predicted when flexible lockdown measures are implemented. The models will be a useful tool for governments to manage the delicate balance among lockdown, number of COVID-19 cases, and air pollution.

The UK implemented a lockdown in Spring (2020) to curtail the person-to-person transmission of the SARS-CoV-2 virus. Measures restricted movements to one outing per day for exercise and shopping, otherwise most people were restricted to their dwelling except for key workers (e.g. medical, supermarkets, and transport). In this study, we quantified changes to air quality across the United Kingdom from 30/03/2020 to 03/05/2020 (weeks 14–18), the period of most stringent travel restrictions. Daily pollutant measurements of NO₂, O₃ and PM₂.₅ from the national network of monitoring sites during this period were compared with measurements over the same period during 2017–19. Comparisons were also made with predicted concentrations for the 2020 period from business-as-usual (BAU) modelling, where the contributions of normal anthropogenic activities were estimated under the observed meteorological conditions. During the lockdown study period there was a 69% reduction in traffic overall (74% reduction in light and 35% in heavy vehicles). Measurements from 129 monitoring stations, identified mean reductions in NO₂ of 38.3% (−8.8 μg/m³) and PM₂.₅ of 16.5% (−2.2 μg/m³). Improvements in NO₂ and PM₂.₅ were largest at urban traffic sites and more modest at background locations where a large proportion of the population live. In contrast, O₃ concentrations on average increased by 7.6% (+4.8 μg/m³) with the largest increases at roadside sites due to reductions in local emissions of NO. A lack of VOC monitoring limited our capacity to interpret changes in O₃ at urban background locations. BAU models predicted comparable NO₂ reductions and O₃ gains, although PM₂.₅ episodes would have been more prominent without lockdown. Results demonstrate the relatively modest contribution of traffic to air quality, suggesting that sustained improvements in air quality require actions across various sectors, including working with international and European initiatives on long-range transport air pollutants, especially PM₂.₅ and O₃.

The limited evidence available suggests that the interaction between chemical pollutants and biological particles may intensify respiratory diseases caused by air pollution in urban areas. Unlike air pollutants, which are routinely measured, records of biotic component are scarce. While pollen concentrations are daily surveyed in most cities, data related to airborne bacteria or fungi are not usually available. This work presents the first effort to understand atmospheric pollution integrating both biotic and abiotic agents, trying to identify relationships among the Proteobacteria, Actinobacteria and Ascomycota phyla with palynological, meteorological and air quality variables using all biological historical records available in the Madrid Greater Region. The tools employed involve statistical hypothesis contrast tests such as Kruskal-Wallis and machine learning algorithms. A cluster analysis was performed to analyse which abiotic variables were able to separate the biotic variables into groups. Significant relationships were found for temperature and relative humidity. In addition, the relative abundance of the biological phyla studied was affected by PM₁₀ and O₃ ambient concentration. Preliminary Generalized Additive Models (GAMs) to predict the biotic relative abundances based on these atmospheric variables were developed. The results (r = 0.70) were acceptable taking into account the scarcity of the available data. These models can be used as an indication of the biotic composition when no measurements are available. They are also a good starting point to continue working in the development of more accurate models and to investigate causal relationships.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO₂) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15 min sampling frequency) made by an array of low-cost sensor nodes, with the sensors’ readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3 ppb, smaller than the RMSE of the two base products independently (LUR: 14.87 ppb, WDSN: 10.45 ppb). The normalized Moran’s I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO₂ concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

The development of urbanised areas together with the growing transport infrastructure and traffic volume are the main cause of air quality deterioration due to the increasing concentrations of particulate matter. Dust pollution is a threat to human health. It can cause the development of lung, larynx or circulatory system cancer. Due to the ability to accumulate dust particles on the leaf surface, the contribution of trees in the process of phytoremediation of air pollution has started to be appreciated. An analysis of the elemental composition of particulate matter (PM) stored on the leaves surface was also carried out, which showed high average concentration of: C > O > Si > Fe (above 8wt.%). It was also observed single particles with a high concentration of heavy metals: Ti, Mn, Ba, Zn, Cr, Pb, Sn, Ni and REE (rare earth elements). The major origin of PM are vehicular emissions, soil and re-suspended road dust. This paper presents also a comparison of selected tree, shrub and vine species differing in their ability to accumulate particulate matter. It was experimentally determined the average leaf surface of individual plant species and established the amount of particulate matter with aerodynamic diameter between 10 and 100 μm, 2.5 and 10 μm, and 0.2 and 2.5 μm deposited on the leaf surface and in waxes.Some species of vines (Parthenocissus quinquefolia), shrubs (Forsythia x intermediata) and coniferous trees, such as Betula pendula ‘Youngii’, Quercus rubra, Cratageus monogyna, Acer pseduoplatanus, Tilia cordata Mill. or Platanus orientalis turned out to be the most efficient in the process of phylloremediation.

Ozone (O₃) is a harmful pollutant when present in the lowermost layer of the atmosphere. Therefore, the European Commission formulated directives to regulate O₃ concentrations in near-surface air. However, almost 50% of the 5068 air quality stations in Europe do not monitor O₃ concentrations. This study aims to provide a hybrid modeling system that fills these gaps in the hourly surface O₃ observations on a site scale with much higher accuracy than existing O₃ models. This hybrid model was developed using estimations from multiple linear regression-based eXtreme Gradient Boosting Machines (MLR-XGBM) and O₃ reanalysis from European regional air quality models (CAMS-EU). The binary classification of extremely high O₃ events and the 1- and 24-h forecasts of hourly O₃ were investigated as secondary aims. In this study thirteen stations in Northern Bavaria, out of which six do not monitor O₃, were chosen as test sites. Considering the computational complexity of machine learning algorithms (MLAs), we also applied two recent MLA interpretation methods, namely SHapley Additive exPlanations (SHAP) and Local interpretable model-agnostic explanations (LIME).With SHAP, we showed an increasing effect of temperature on O₃ concentrations which intensifies for temperatures exceeding 17 °C. According to LIME, O₃ concentration peaks are mainly governed by meteorological factors under dry and warm conditions on a regional scale, whereas local nitrogen oxide concentrations control base O₃ concentrations during cold and wet periods.While recently developed MLAs for the spatial estimation of hourly O₃ concentrations had a station-based root-mean-square error (RMSE) above 27 μg/m³, our proposed model significantly reduced the estimation errors by about 66% with an RMSE of 9.49 μg/m³. We also found that logistic regression (LR) and MLR-XGBM performed best in the site-scale classification and 24-h forecast of O₃ concentrations (with a station-averaged accuracy and RMSE of 0.95 and 19.34 μg/m³, respectively).

Commuters are exposed to a variety of physicochemical and microbiological pollutants that can lead to adverse health effects. This study aims to evaluate the indoor air quality (IAQ) in cars, buses and trains in Lisbon, to estimate inhaled doses while commuting and to evaluate the impacts of cleaning and ventilation on the IAQ. Particulate matter with diameter lower than 1, 2.5 and 10 μm (PM₁, PM₂.₅ and PM₁₀), black carbon (BC), carbon monoxide (CO), carbon dioxide (CO₂) volatile organic compounds (VOCs), formaldehyde (CH₂O) and total airborne bacteria and fungi were measured and bacterial isolates were identified. Results showed that the type of ventilation is the main factor affecting the IAQ in vehicle cabins. Under the fan off condition, the concentration of BC was lower, but the concentration of gases such as CO₂, CO and VOC tended to accumulate rapidly. When the ventilation was used, the coarse particles were filtered originating the decrease of indoor concentrations. Commuters travelling in trains received the lowest dose for all chemical pollutants, except VOC, mainly because railways are further away from the direct vehicular emissions. Commuters travelling in cars without ventilation received the highest inhaled dose for almost all pollutants despite having the lowest travel duration. Airborne microbiota was highly affected by the occupancy of the vehicles and therefore, the fungi and bacterial loads were higher in trains and buses. Most of the isolated species were human associated bacteria and some of the most abundant species have been linked to respiratory tract infections.