Recently, health damage to children exposed to synthetic polyurethane (PU) running tracks has aroused social panic in China. Some possible toxic volatiles may be responsible for these damages. However, the exact cause remains unclear. We have detected a low concentration of sulfur dioxide (SO₂; 1.80–3.30 mg/m³) on the surface of the PU running track. Surprisingly, we found that SO₂ was generated from the PU running track, and even such a low concentration of SO₂ could induce severe lung inflammation with hemorrhage, inflammatory cell infiltration, and inflammatory factor secretion in mice after 2-week exposure. Prolonged exposure (5 weeks) to the SO₂ caused chronic pulmonary inflammation and pulmonary fibrosis in the mice. Peripheral hemogram results showed that platelet concentration increased significantly in the SO₂ group compared to that in the control group, and the proportion of blood neutrophils and monocytes among total leukocytes was more imbalanced in the SO₂ group (16.6%) than in the control group (8.0%). Further histopathology results of sternal marrow demonstrated that hematopoietic hyperplasia was severely suppressed with increased reticular stroma and adipocytes under SO₂ exposure. These data indicate that a low concentration of SO₂ generated spontaneously from PU running track outdoors as a secondary product is still harmful to health, as it impairs the respiratory system, hematopoiesis, and immunologic function. This indicates that the low-concentration SO₂ could be a major cause of diseases induced by air pollution, such as chronic obstructive pulmonary disease.

The effect of ambient air pollution exposure on childhood hypertension has emerged as a concern in China, and previous studies suggested pet ownership is associated with lower blood pressure (BP). However, limited information exists on the interactive effects pet ownership and air pollution exposure has on hypertension. We investigated the interactions between exposure to pet ownership and air pollutants on hypertension in Chinese children. 9354 students in twenty-four elementary and middle schools (aged 5–17 years) in Northeastern China were evaluated during 2012–2013. Four-year average concentrations of particulate matter with aerodynamic diameter of ≤10 μm (PM10), SO2, NO2, and O3, were collected in the 24 districts from 2009 to 2012. Hypertension was defined as average diastolic or systolic BP (three time measurements) in the 95th percentile or higher based on height, age, and sex. To examine effects, two-level regression analysis was used, controlling covariates. Consistent interactions between exposure to pet and air pollutants were observed. Compared to children exposed to pet, those not exposed exhibited consistently stronger effects of air pollution. The highest odds ratios (ORs) per 30.6 μg/m3 increase in PM10 were 1.79 (95%confidence interval [95%CI]: 1.29–2.50) in children without current pet exposure compared to 1.24 (95%CI: 0.85–1.82) in children with current pet exposure. As for BP, only O3 had an interaction for all exposure to pet ownership types, and showed lower BP in children exposed to pet. The increases in mean diastolic BP per 46.3 μg/m3 increase in O3 were 0.60 mmHg (95%CI: 0.21, 0.48) in children without pet exposure in utero compared with 0.34 mmHg (95%CI: 0.21, 0.48) in their counterparts. When stratified by age, pet exposure was more protective among younger children. In conclusion, in this large population-based cohort, pet ownership is associated with smaller associations between air pollution and hypertension in children, suggesting pet ownership reduces susceptibility to the health effects of pollutants.

Air pollution forecasting is significant for public health and controlling pollution, and statistical methods are important air pollution forecasting techniques. Nevertheless, the research of AQI (air quality index) forecasting is very rare. So an accurate and stable AQI forecasting model is very urgent and necessary. For the high complex, volatile and nonlinear AQI series, this research presents a novel optimal-combined model based on CEEMD (complementary ensemble empirical mode decomposition), PSOGSA (particle swarm optimization and gravitational search algorithm), PSO (particle swarm optimization) and combined forecasting method. The proposed model effectively solves the blind combined forecasting. AQI series forecasts of five cities in North China show that the proposed model has the highest correct rate of forecasting classifications compared with the candidates. Totally, the presented model has the following advantages compared with the candidates: more robust forecasting performance, smaller forecasting error and better generalization ability.

Fine particulate matter (PM₂.₅) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM₂.₅, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM₂.₅, especially in areas with high spatiotemporal variability of PM₂.₅.In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

The city of London, UK, has seen in recent years an increase in the number of high-rise/multi-storey buildings (“skyscrapers”) with roof heights reaching 150 m and more, with the Shard being a prime example with a height of ∼310 m. This changing cityscape together with recent plans of local authorities of introducing Combined Heat and Power Plant (CHP) led to a detailed study in which CFD and wind tunnel studies were carried out to assess the effect of such high-rise buildings on the dispersion of air pollution in their vicinity. A new, open-source simulator, FLUIDITY, which incorporates the Large Eddy Simulation (LES) method, was implemented; the simulated results were subsequently validated against experimental measurements from the EnFlo wind tunnel. The novelty of the LES methodology within FLUIDITY is based on the combination of an adaptive, unstructured, mesh with an eddy-viscosity tensor (for the sub-grid scales) that is anisotropic. The simulated normalised mean concentrations results were compared to the corresponding wind tunnel measurements, showing for most detector locations good correlations, with differences ranging from 3% to 37%. The validation procedure was followed by the simulation of two further hypothetical scenarios, in which the heights of buildings surrounding the source building were increased. The results showed clearly how the high-rise buildings affected the surrounding air flows and dispersion patterns, with the generation of “dead-zones” and high-concentration “hotspots” in areas where these did not previously exist. The work clearly showed that complex CFD modelling can provide useful information to urban planners when changes to cityscapes are considered, so that design options can be tested against environmental quality criteria.

Ostrava in the Moravian-Silesian region (Czech Republic) is a European air pollution hot spot for airborne particulate matter (PM), polycyclic aromatic hydrocarbons (PAHs), and ultrafine particles (UFPs). Air pollution source apportionment is essential for implementation of successful abatement strategies. UFPs or nanoparticles of diameter <100 nm exhibit the highest deposition efficiency in human lungs. To permit apportionment of PM sources at the hot-spot including nanoparticles, Positive Matrix Factorization (PMF) was applied to highly time resolved particle number size distributions (NSD, 14 nm-10 μm) and PM₀.₀₉₋₁.₁₅ chemical composition. Diurnal patterns, meteorological variables, gaseous pollutants, organic markers, and associations between the NSD factors and chemical composition factors were used to identify the pollution sources. The PMF on the NSD reveals two factors in the ultrafine size range: industrial UFPs (28%, number mode diameter - NMD 45 nm), industrial/fresh road traffic nanoparticles (26%, NMD 26 nm); three factors in the accumulation size range: urban background (24%, NMD 93 nm), coal burning (14%, volume mode diameter - VMD 0.5 μm), regional pollution (3%, VMD 0.8 μm) and one factor in the coarse size range: industrial coarse particles/road dust (2%, VMD 5 μm). The PMF analysis of PM₀.₀₉₋₁.₁₅ revealed four factors: SIA/CC/BB (52%), road dust (18%), sinter/steel (16%), iron production (16%). The factors in the ultrafine size range resolved with NSD have a positive correlation with sinter/steel production and iron production factors resolved with chemical composition. Coal combustion factor resolved with NSD has moderate correlation with SIA/CC/BB factor. The organic markers homohopanes correlate with coal combustion and the levoglucosan correlates with urban background. The PMF applications to NSD and chemical composition datasets are complementary. PAHs in PM₁ were found to be associated with coal combustion factor.

This study presents the particle size distribution and respiratory deposition estimates of airborne perfluoroalkyl acids (PFAAs) during the haze period. Size-segregated haze aerosols were collected from an urban location in Shanghai using an eight-stage air sampler. The samples were analyzed for eight PFAAs using ultra-high-performance liquid chromatography tandem triple quadrupole mass spectrometry. The quantification results showed that the concentrations of particle-bound Σ 8PFAAs ranged from 0.26 to 1.90 ng m⁻³ (mean: 1.44 ng m⁻³). All of the measured PFAAs particle size distributions had a bimodal mode that peaked respectively in accumulation size range (0.4 < Dp < 2.1 μm) and coarse size ranges (Dp > 2.1 μm), but the width of each distribution somewhat varied by compound. The emission source, molecular weight, and volatility of the PFAAs were important factors influencing the size distribution of particle-bound PFAAs. Of these compounds, PFUnDA presented a strong accumulation in the fine size range (average 75% associated with particles <2.1 μm), followed by PFOA (69%) and PFDA (64%). The human risk assessment of PFOS via inhalation was addressed and followed the same pattern as the size distribution, with a 2-fold higher risk for the fine particle fraction compared to the coarse particle fraction at urban sites. Approximately 30.3–82.0% of PFAA deposition (∑PFAA: 72.5%) in the alveolar region was associated with particles <2.1 μm, although the contribution of fine particles to the total PFAAs concentration in urban air was only 28–57% (∑8PFAAs: 48%). These results suggested that fine particles are significant contributors to the deposition of PFAAs in the alveolar region of the lung.

Substantial efforts have been made in recent years to investigate the horizontal variability of air pollutants at regional and urban scales and epidemiological studies have taken advantage of resulting improvements in exposure assessment. On the contrary, only a few studies have investigated the vertical variability and their results are not consistent. In this study, a field experiment has been conducted to evaluate the variation of concentrations of different particle metrics and gaseous pollutants on the basis of floor height at a high rise building. Two 15-day monitoring campaigns were conducted in the urban area of Bologna, Northern Italy, one of the most polluted areas in Europe. Measurements sites were operated simultaneously at 2, 15, 26, 44 and 65 m a.g.l. Several particulate matter metrics including PM₂.₅ mass and chemical composition, particle number concentration and size distribution were measured. Time integrated measurement of NO₂ and BTEX were also included in the monitoring campaigns. Measurements showed relevant vertical gradients for most traffic related pollutants. A monotonic gradient of PM₂.₅ was found with ground-to-top differences of 4% during the warm period and 11% during the cold period. Larger gradients were found for UFP (∼30% during both seasons) with a substantial loss of particles from ground to top in the sub-50 nm size range. The largest drops in concentrations for chemical components were found for Elemental Carbon (−27%), iron (−11%) and tin (−36%) during winter. The ground-to-top decline of concentrations for NO₂ and benzene during winter was equal to 74% and 35%, respectively. In conclusion, our findings emphasize the need to include vertical variations of urban air pollutants when evaluating population exposure and associated health effects, especially in relation to some traffic related pollutants and particle metrics.

Pollutant emissions from incomplete combustion of raw coal in low-efficiency residential heating stoves greatly contribute to winter haze in China. Semi-coke coals and improved heating stoves are expected to lower air pollutant emissions and are vigorously promoted by the Chinese government in many national and local plans. In this study, the thermal performance and air pollutant emissions from semi-coke combustion in improved heating stoves were measured in a pilot rural county and compared to the baseline of burning raw coal to quantify the mitigation potential of air pollutant emissions. A total of five stove-fuel combinations were tested, and 27 samples from 27 different volunteered households were obtained. The heating efficiency of improved stoves increased, but fuel consumption appeared higher with more useful energy output compared to traditional stoves. The emission factors of PM2.5, SO2, and CO2 of semi-coke burning in specified improved stoves were lower than the baseline of burning raw coal chunk, but no significant NOx and CO decreases were observed. The total amount of PM2.5 and SO2 emissions per household in one heating season was lower, but CO, CO2, and NOx increased when semi-coke coal and specified improved stoves were deployed. Most differences were not statistically significant due to the limited samples and large variation, indicating that further evaluation would be needed to make conclusions that could be considered for policy.

Excess exposure to fluoride causes substantive health burden in humans and livestock globally. However, few studies have assessed the distribution and controls of variability of ambient background concentrations of fluoride in soil. Ambient background concentrations of fluoride in soil were collated for Greater Melbourne, Greater Geelong, Ballarat and Mitchell in Victoria, Australia (n = 1005). Correlation analysis and machine learning techniques were used to identify environmental and anthropogenic influences of fluoride variability in soil. Sub-soils (>0.3 m deep), in some areas overlying siltstone and sandstone, and to a lesser extent, overlying basalt, were naturally enriched with fluoride at concentrations above ecological thresholds for grazing animals. Soil fluoride enrichment was predominantly influenced by parent material (mineralogy), precipitation (illuviation), leaching during palaeoclimates and marine inputs. Industrial air pollution did not significantly influence ambient background concentrations of fluoride at a regional scale. However, agricultural practices (potentially the use of phosphate fertilisers) were indicated to have resulted in added fluoride to surface soils overlying sediments. Geospatial variables alone were not sufficient to accurately model ambient background soil fluoride concentrations. A multiple regression model based on soil chemistry and parent material was shown to accurately predict ambient background fluoride concentrations in soils and support assessment of fluoride enrichment in the environment.