Human health risks brought by fine atmospheric particles raise scholarly and policy awareness about the role of urban trees as bio-filters of air pollution. While a large number of empirical studies have focused on the characteristics of vegetation leaves and their effects on atmospheric particle retention, the dry deposition of particles on branches, which plays a significant role in capturing and retaining particles during the defoliation period and contributes substantially to total removal of atmospheric particles, is under-investigated. To fill in this knowledge gap, this case study examined the dry deposition velocities (Vd) of submicron particulate matters (PM₁) on the branches of six common deciduous species in Shanghai (China) using laboratory experiments. And the association between Vd and key branch anatomical traits (including surface roughness, perimeter, rind width proportion, lenticel density, peeling, and groove/ridge characteristics) was explored. It was found that surface roughness would increase Vd, as a rougher surface significantly increases turbulence, which is conducive to particle diffusion. By contrast, peeling, branch perimeter, and lenticel density would decrease Vd. Peeling represents the exfoliated remains on the branch surfaces which may flutter considerably with airflow, leading to particle resuspension and low Vd. When branch perimeter increases, the boundary layer of branches thickens and a wake area appears, increasing the difficulty of particles to reach branch surface, and reducing Vd. While lenticels can increase the roughness of branch surface, their pointy shape would uplift airflow and cause a leeward wake area, lowering Vd. This finely wrought study contributes to a better understanding of branch dry deposition during leaf-off seasons and potential of deciduous trees serving as nature-based air filters all year round in urban environments.

Exposure to suspended particulate matter (PM), found in the air, is one of the most acute environmental problems that affect the health of modern society. Among the different airborne pollutants, heavy metals (HMs) are particularly relevant because they are bioaccumulated, impairing the functions of living beings. This study aimed to establish a method to predict heavy metal concentrations in leaves and road dust, through their magnetic properties measurements. For this purpose, machine learning, automatic linear regression (MLR), and support vector machine (SVM) were used to establish models for the prediction of airborne heavy metals based on leaves and road dust magnetic properties. Road dust samples and leaves of two common evergreen species (Cupressus lusitanica/Casuarina equisetifolia) were sampled simultaneously during two different years in the Great Metropolitan Area (GMA) of Costa Rica. MLR and SVM algorithms were used to establish the relationship between airborne heavy metal concentrations based on single (χlf) and multiple (χlf y χdf) leaf magnetic properties and road dust. Results showed that Fe, Cu, Cr, V, and Zn concentrations were well-simulated by SVM prediction models, with adjusted R² values ≥ 0.7 in both training and test stages. By contrast, the concentrations of Pb and Ni were not well-simulated, with adjusted R² values < 0.7 in both training and test stages. Heavy metal predicción models using magnetic properties of leaves from Casuarina equisetifolia, as collectors, yielded better prediction results than those based on the leaves of Cupressus lusitanica and road dust, showing relatively higher adjusted R² values and lower errors (MAE and RMSE) in both training and test stages. SVM proved to be the best prediction model with variations between single (χlf) and multiple (χlf y χdf) magnetic properties depending on the element studied.

There have been many studies on the relationship between fine particulate matter (PM₂.₅) and lung function. However, the impact of short-term or long-term PM₂.₅ exposures on lung function in children is still inconsistent globally, and the reasons for the inconsistency of the research results are not clear. Therefore, we searched the PubMed, Embase and Web of Science databases up to May 2022, and a total of 653 studies about PM₂.₅ exposures on children's lung function were identified. Random effects meta-analysis was used to estimate the combined effects of the 25 articles included. PM₂.₅ concentrations in short-term exposure studies mainly come from individual and site monitoring. And for every 10 μg/m³ increase, forced vital capacity (FVC), forced expiratory volume in the first second (FEV₁) and peak expiratory flow (PEF) decreased by 21.39 ml (95% CI: 13.87, 28.92), 25.66 ml (95% CI: 14.85, 36.47) and 1.76 L/min (95% CI: 1.04, 2.49), respectively. The effect of PM₂.₅ on lung function has a lag effect. For every 10 μg/m³ increase in the 1-day moving average PM₂.₅ concentration, FEV₁, FVC and PEF decreased by 14.81 ml, 15.40 ml and 1.18 L/min, respectively. PM₂.₅ concentrations in long-term exposure studies mainly obtained via ground monitoring stations. And for every 10 μg/m³ increase, FEV₁, FVC and PEF decreased by 61.00 ml (95% CI: 25.80, 96.21), 54.47 ml (95% CI: 7.29, 101.64) and 10.02 L/min (95% CI: 7.07, 12.98), respectively. The sex, body mass index (BMI), relative humidity (RH), temperature (Temp) and the average PM₂.₅ exposure level modify the relationship between short-term PM₂.₅ exposure and lung function. Our study provides further scientific evidence for the deleterious effects of PM₂.₅ exposures on children's lung function, suggesting that exposure to PM₂.₅ is detrimental to children's respiratory health. Appropriate protective measures should be taken to reduce the adverse impact of air pollution on children's health.

Household air pollution (HAP) arising from combustion of biomass fuel (BMF) is a leading cause of morbidity and mortality in low-income countries. Air pollution may stimulate pro-inflammatory responses by activating diverse immune cells and cyto/chemokine expression, thereby contributing to diseases. We aimed to study cellular immune responses among women chronically exposed to HAP through use of BMF for domestic cooking. Among 200 healthy, non-smoking women in rural Bangladesh, we assessed exposure to HAP by measuring particulate matter 2.5 (PM₂.₅), black carbon (BC) and carbon monoxide (CO), through use of personal monitors RTI MicroPEM™ and Lascar CO logger respectively, for 48 h. Blood samples were collected following HAP exposure assessment and were analyzed for immunoprofiling by flow cytometry, plasma IgE by immunoassay analyzer and cyto/chemokine response from monocyte-derived-macrophages (MDM) and -dendritic cells (MDDC) by multiplex immunoassay. In multivariate linear regression model, a doubling of PM₂.₅ was associated with small increments in immature/early B cells (CD19⁺CD38⁺) and plasmablasts (CD19⁺CD38⁺CD27⁺). In contrast, a doubling of CO was associated with 1.20% reduction in CD19⁺ B lymphocytes (95% confidence interval (CI) = -2.36, −0.01). A doubling of PM₂.₅ and BC each was associated with 3.12% (95%CI = −5.85, −0.38) and 4.07% (95%CI = −7.96, −0.17) decrements in memory B cells (CD19⁺CD27⁺), respectively. Exposure to CO was associated with increased plasma IgE levels (beta(β) = 240.4, 95%CI = 3.06, 477.8). PM₂.₅ and CO exposure was associated with increased MDM production of CXCL10 (β = 12287, 95%CI = 1038, 23536) and CCL5 (β = 835.7, 95%CI = 95.5, 1576), respectively. Conversely, BC exposure was associated with reduction in MDDC-produced CCL5 (β = −3583, 95%CI = −6358, −807.8) and TNF-α (β = −15521, 95%CI = −28968, −2074). Our findings suggest that chronic HAP exposure through BMF use adversely affects proportions of B lymphocytes, particularly memory B cells, plasma IgE levels and functions of antigen presenting cells in rural women.

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.

It is widely recognized that green infrastructures in urban ecosystems provides important ecosystem services, including air purification. The potential absorption of nitrogen oxides (NOₓ) by urban trees has not been fully quantified, although it is important for air pollution mitigation and the well-being of urban residents. In this study, four common tree species (Sophora japonica L., Fraxinus chinensis Roxb., Populus tomentosa Carrière, Sabina chinensis (L.)) in Beijing, China, were studied. The dual stable isotopes (¹⁵N and ¹⁸O) and a Bayesian isotope mixing model were applied to estimate the sources contributions of potential nitrogen sources to the roadside trees based on leaf and soil sampling in urban regions. The following order of sources contributions was determined: soil > dry deposition > traffic-related NOₓ. The capacity of urban trees for NOₓ removal in the city was estimated using a remote sensing and GIS approach, and the removal capacity was found to range from 0.79 to 1.11 g m⁻² a⁻¹ across administrative regions, indicating that 1304 tons of NOₓ could be potentially removed by urban trees in 2019. Our finding qualified the potential NOₓ removal by urban trees in terms of atmospheric pollution mitigation, highlighting the role of green infrastructure in air purification, which should be taken into account by stakeholders to manage green infrastructure as the basis of a nature-based approach.

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.

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.

Evidence has shown associations between air pollution and traffic-related exposure with accelerated aging, but no study to date has linked the exposure with age at natural menopause, an important indicator of reproductive aging. In this study, we sought to examine the associations of residential exposure to ambient particulate matter (PM) and distance to major roadways with age at natural menopause in the Nurses’ Health Study II (NHS II), a large, prospective female cohort in US. A total of 105,996 premenopausal participants in NHS II were included at age 40 and followed through 2015. Time-varying residential exposures to PM₁₀, PM₂.₅₋₁₀, and PM₂.₅ and distance to roads was estimated. We calculated hazard ratios (HR) and 95% confidence intervals (CIs) for natural menopause using Cox proportional hazard models adjusting for potential confounders and predictors of age at menopause. We also examined effect modification by region, smoking, body mass, physical activity, menstrual cycle length, and population density. There were 64,340 reports of natural menopause throughout 1,059,229 person-years of follow-up. In fully adjusted models, a 10 μg/m³ increase in the cumulative average exposure to PM₁₀ (HR: 1.02, 95% CI: 1.00, 1.04), PM₂.₅₋₁₀ (HR: 1.03, 95% CI: 1.00, 1.05), and PM₂.₅ (HR: 1.03, 95% CI: 1.00, 1.06) and living within 50 m to a major road at age 40 (HR: 1.03, 95%CI: 1.00, 1.06) were associated with slightly earlier menopause. No statistically significant effect modification was found, although the associations of PM were slightly stronger for women who lived in the West and for never smokers. To conclude, we found exposure to ambient PM and traffic in midlife was associated with slightly earlier onset of natural menopause. Our results support previous evidence that exposure to air pollution and traffic may accelerate reproductive aging.

Atmospheric peroxyacetyl nitrate (PAN) and ozone (O₃) are two typical indicators for photochemical pollution that have adverse effects on the ecosystem and human health. Observation networks for these pollutants have been expanding in developed regions of China, such as North China Plain (NCP) and Pearl River Delta (PRD), but are sparse in Yangtze River Delta (YRD), meaning their concentration and influencing factors remain poorly understood. Here, we performed a one-year measurement of atmospheric PAN, O₃, particulate matter with aerodynamic diameter smaller than 2.5 μm (PM₂.₅), nitrogen oxides (NOₓ), carbon monoxide (CO), and meteorological parameters from December 2016 to November 2017 in Shanghai. Overall, high hourly maximum PAN and O₃ were found to be 7.0 and 185 ppbv in summer, 6.2 and 146 ppbv in autumn, 5.8 and 137 ppbv in spring, and 6.0 and 76.7 ppbv in winter, respectively. Continental air masses probably carried atmospheric pollutants to the sampling site, while frequent maritime winds brought in less polluted air masses. Furthermore, positive correlations (R: 0.72–0.85) between PAN and O₃ were found in summer, indicating a predominant role of photochemistry in their formation. Unlike in summer, weak or no correlations between PAN and O₃ were featured during the other seasons, especially in winter, due to their different loss pathways. Unexpectedly, positive correlations between PAN and PM₂.₅ were found in all seasons. During summer, moderate correlation could be attributed to the strong photochemistry acting as a common driver in the formation of secondary aerosols and PAN. During winter, high PM₂.₅ might promote PAN production through HONO production, hence resulting in a good positive correlation. Additionally, the loss of PAN by thermal decomposition (TPAN) only accounted for a small fraction (ca. 1%) of the total (PAN + TPAN) during a typical winter episode, while it significantly reached 14.4 ppbv (71.1% of the total) in summer.