The relative impact of non-exhaust sources (i.e. road dust, tire wear, road wear and brake wear particles) on urban air quality is increasing. Among them, road dust resuspension has generally the highest impact on PM concentrations but its spatio-temporal variability has been rarely studied and modeled. Some recent studies attempted to observe and describe the time-variability but, as it is driven by traffic and meteorology, uncertainty remains on the seasonality of emissions. The knowledge gap on spatial variability is much wider, as several factors have been pointed out as responsible for road dust build-up: pavement characteristics, traffic intensity and speed, fleet composition, proximity to traffic lights, but also the presence of external sources. However, no parameterization is available as a function of these variables.We investigated mobile road dust smaller than 10 μm (MF10) in two cities with different climatic and traffic conditions (Barcelona and Turin), to explore MF10 seasonal variability and the relationship between MF10 and site characteristics (pavement macrotexture, traffic intensity and proximity to braking zone). Moreover, we provide the first estimates of emission factors in the Po Valley both in summer and winter conditions. Our results showed a good inverse relationship between MF10 and macro-texture, traffic intensity and distance from the nearest braking zone. We also found a clear seasonal effect of road dust emissions, with higher emission in summer, likely due to the lower pavement moisture. These results allowed building a simple empirical mode, predicting maximal dust loadings and, consequently, emission potential, based on the aforementioned data. This model will need to be scaled for meteorological effect, using methods accounting for weather and pavement moisture. This can significantly improve bottom-up emission inventory for spatial allocation of emissions and air quality management, to select those roads with higher emissions for mitigation measures.

Bogota registers frequent episodes of poor air quality from high PM₁₀ concentrations. It is one of the main Latin American megacities, located at 2600 m in the tropical Andes, but there is insufficient data on PM₁₀ source contribution. A characterization of the chemical composition and the source apportionment of PM₁₀ at an urban background site in Bogota was carried out in this study. Daily samples were collected from June 2015 to May 2016 (a total of 311 samples). Organic carbon (OC), elemental carbon (EC), water soluble compounds (SO₄²⁻, Cl⁻, NO₃⁻, NH₄⁺), major elements (Al, Fe, Mg, Ca, Na, K, P) and trace metals (V, Cd, Pb, Sr, Ba, among others) were analyzed. The results were interpreted in terms of their variability during the rainy season (RS) and the dry season (DS). The data obtained revealed that the carbonaceous fraction (∼51%) and mineral dust (23%) were the main PM₁₀ components, followed by others (15%), Secondary Inorganic Compounds (SIC) (11%) and sea salt (0.4%). The average concentrations of soil, SIC and OC were higher during RS than DS. However, peak values were observed during the DS due to photochemical activity and forest fires. Although trace metals represented <1% of PM₁₀, high concentrations of toxic elements such as Pb and Sb on RS, and Cu on DS, were obtained. By using a PMF model, six factors were identified (∼96% PM₁₀) including fugitive dust, road dust, metal processing, secondary PM, vehicles exhaust and industrial emissions. Traffic (exhaust emissions + road dust) was the major PM₁₀ source, accounting for ∼50% of the PM₁₀. The results provided novel data about PM₁₀ chemical composition, its sources and its seasonal variability during the year, which can help the local government to define control strategies for the main emission sources during the most critical periods.

PM₂.₅ is one of the most studied atmospheric pollutants due to its adverse impacts on human health and welfare and the environment. An improved model (the chemical mass balance gas constraint-Iteration: CMBGC-Iteration) is proposed and applied to identify source categories and estimate source contributions of PM₂.₅. The CMBGC-Iteration model uses the ratio of gases to PM as constraints and considers the uncertainties of source profiles and receptor datasets, which is crucial information for source apportionment. To apply this model, samples of PM₂.₅ were collected at Tianjin, a megacity in northern China. The ambient PM₂.₅ dataset, source information, and gas-to-particle ratios (such as SO₂/PM₂.₅, CO/PM₂.₅, and NOx/PM₂.₅ ratios) were introduced into the CMBGC-Iteration to identify the potential sources and their contributions. Six source categories were identified by this model and the order based on their contributions to PM₂.₅ was as follows: secondary sources (30%), crustal dust (25%), vehicle exhaust (16%), coal combustion (13%), SOC (7.6%), and cement dust (0.40%). In addition, the same dataset was also calculated by other receptor models (CMB, CMB-Iteration, CMB-GC, PMF, WALSPMF, and NCAPCA), and the results obtained were compared. Ensemble-average source impacts were calculated based on the seven source apportionment results: contributions of secondary sources (28%), crustal dust (20%), coal combustion (18%), vehicle exhaust (17%), SOC (11%), and cement dust (1.3%). The similar results of CMBGC-Iteration and ensemble method indicated that CMBGC-Iteration can produce relatively appropriate results.

Recent studies have indicated that urban streets can be hotspots for emissions of methane (CH4) from leaky natural gas lines, particularly in cities with older natural gas distribution systems. The objective of the current study was to determine whether leaking sewer pipes could also be a source of street-level CH4 as well as nitrous oxide (N2O) in Cincinnati, Ohio, a city with a relatively new gas pipeline network. To do this, we measured the carbon (δ13C) and hydrogen (δ2H) stable isotopic composition of CH4 to distinguish between biogenic CH4 from sewer gas and thermogenic CH4 from leaking natural gas pipelines and measured CH4 and N2O flux rates and concentrations at sites from a previous study of street-level CH4 enhancements (77 out of 104 sites) as well as additional sites found through surveying sewer grates and utility manholes (27 out of 104 sites). The average isotopic signatures for δ13C-CH4 and δ2H-CH4 were −48.5‰ ± 6.0‰ and −302‰ ± 142‰. The measured flux rates ranged from 0.0 to 282.5 mg CH4 day−1 and 0.0–14.1 mg N2O day−1 (n = 43). The average CH4 and N2O concentrations measured in our study were 4.0 ± 7.6 ppm and 392 ± 158 ppb, respectively (n = 104). 72% of sites where fluxes were measured were a source of biogenic CH4. Overall, 47% of the sampled sites had biogenic CH4, while only 13% of our sites had solely thermogenic CH4. The other sites were either a source of both biogenic and thermogenic CH4 (13%), and a relatively large portion of sites had an unresolved source (29%). Overall, this survey of emissions across a large urban area indicates that production and emission of biogenic CH4 and N2O is considerable, although CH4 fluxes are lower than those reported for cities with leaky natural gas distribution systems.

Methodology to quantify and distinguish the spatial distribution of the risks from multiple pollutants within the region was developed in this paper. An integrated quantitative risk assessment was conducted by utilizing a large amount of information available to explore spatial distribution of risk by single and multiple pollutants, and the magnitude of the overall risk from multiple pollutants based on the current concentrations of pollutants and toxicity data. Two target criteria levels – level I (NOEC/LOEC based) and level II (LC/EC/IC₅₀ based) – were employed, and thus, the regional and sub-regional risks were evaluated according to these two levels. The risk of multiple toxic metals (As, Cd, Cr, Hg and Pb) to a terrestrial ecosystem for the region around the Bohai Sea and the Yellow Sea were evaluated as a case. The total overall ecological risks from heavy metals in the region for level I and level II were 21.73% and 12.53%, respectively. The risks were ranked in the order of Cr > As > Pb > Cd > Hg with Cr posing the greatest ecological risk, which was 61.12% for level I. The top three cities according to the level II ecological risk were Cangzhou > Lianyungang > Panjin, while the top three cities of level I ecological risk were Cangzhou > Panjin > Lianyungang. This method provides a quantitative risk assessment with multiple and clear protection levels for risk management.

Air pollution, being especially severe in the fast-growing developing world, continues to post a threat to public health. Yet, few studies are capable of quantifying well how different groups of people in different places experience different levels of air pollution at the global scale. In this paper, we use worldwide Chinese as a lens to quantify the spatiotemporal variations and geographic differences in PM2.5 exposures using unprecedented mobile phone big data and air pollution records. The results show that Chinese in South and East Asia suffer relatively serious PM2.5 exposures, where the Chinese in China have the highest PM2.5 exposures (52.8 μg/m3/year), which is fourfold higher than the exposures in the United States (10.7 μg/m3/year). Overall, the Chinese in Asian cities (35.5 μg/m3/year) experienced the most serious PM2.5 exposures when compared with the Chinese in the cities of other continents. These results, partly presented as a spatiotemporally explicit map of PM2.5 exposures for worldwide Chinese, help researchers and governments to consider how to address the effects of air pollution on public health with respect to different population groups and geographic locations.

Potentially toxic elements are widespread soil contaminants, whose occurrence could entail a concern for human health upon ingestion of fruit and vegetables harvested in a polluted area. This work set out to evaluate the concentrations of lead and cadmium as well as the levels of thirteen heavy metals for which a limit value is yet to be established by the food safety authorities, in order to perform a risk characterization related to the dietary intake of these metals and to provide a scientific opinion with wider relevance in the light of current worldwide regulatory issues. The sampling consisted of fruit and vegetables grown in a potentially contaminated area of southern Italy due to the illegal dump of hazardous wastes. An evaluation of the dietary exposure through the calculation of the Hazard Index (HI), the Maximum Cumulative Ratio (MCR) and the Target Cancer Risk (TCR) was adopted to this end. The results revealed that about the 30% of samples showed quantifiable levels of chemicals and no significant difference emerged between the potentially polluted area and the nearby cities that were selected as a control landfill site. The overall risk characterization for non-carcinogenic endpoints showed that the HI did not reach unsafe values, except for a small number of samples mainly because of aberrant occurrences and, in any case, the cumulative toxicity was mainly driven by thallium and vanadium. As far as the carcinogenic effects of arsenic are concerned, the distribution of TCR values broadly lay below the safety threshold; a certain percentage of data, however, exceeded this limit and should be taken into account for the enforcement of future regulatory thresholds.

Epidemiological studies have shown that exposure to traffic-related pollutants increases incidence of adverse health outcomes. Transit users in cities across the globe commonly spend 15–45 min or more waiting at transit stops each day, often at locations with high levels of pollution from traffic. Here, we investigate the characteristics of concentration profiles of ultrafine particles (UFP) with 5 m spatial resolution across intersections, to determine the best place to site transit stops to minimize exposures. Cross-intersection UFP profiles were derived from 1744 profiles covering 90 m before and after each intersection center with a mobile monitoring platform. Measurements were made at 10 signalized intersections located at six urban sites, each with a distinct built environment, during both mornings and afternoons. Measurements were made within 1.5 m of the sidewalk and approximately at breathing height (1.5 m above ground level) to approximate sidewalk exposures. UFP profiles were strongly influenced by high emissions from vehicle stops and accelerations, and peaked within 30 m of intersection centers; from there concentrations decreased sharply with distance. Peak concentrations averaged about 90% higher than the minima along the block. They were accompanied by more frequent and larger transient concentration spikes, increasing the chance of people near the intersection being exposed to both short-term extremely high concentration spikes and higher average concentrations. The decays are somewhat larger before the intersection than after the intersection, however as siting transit stops after intersections is preferred for smooth traffic flow, we focus on after the intersection. Simple time-duration exposure calculations combined with breathing rates suggest moving a bus stop from 20 to 40–50 m after the intersection can reduce transit-users' exposure levels to total UFP substantially, in proportion to the reciprocal of the magnitude of elevation at the intersection.

Polybrominated diphenyl ethers (PBDEs) are widely used as flame retardants for various products and have become ubiquitous pollutants in environmental media. However, little is known about PBDE levels in Shenzhen, a manufacturing center of electronic products. This study aimed to investigate spatiotemporal variability of PBDE concentration in atmospheric fine particles (PM2.5) and to estimate the daily inhalation exposure doses for local residents in Shenzhen, China. A total of 36 samples were collected and 8 PBDE compounds (BDE-28, 47, 99, 100, 153, 154, 183 and 209) were analyzed by isotope dilution high-resolution gas chromatograph/high-resolution mass spectrometer (HRGC/HRMS). Mean concentrations of Σ8PBDEs and BDE-209 in PM2.5 in Shenzhen were 33.47 pg/m3 and 24.75 pg/m3, respectively, which were lower than those for other reported cities from China. The mean concentration of Σ8PBDEs was higher in the winter + spring than that in summer + autumn, and both concentrations of BDE-28 and BDE-47 in PM2.5 were significantly higher in winter + spring than those in summer + autumn. Among the 8 congeners, BDE-209 was predominant, accounting for 73.9% of the Σ8PBDEs concentrations. Traffic area, industrial area, residential area and discharge of electronic industries had significant positive influences on PBDE concentrations in PM2.5. Both vegetation area and water area were significantly negatively correlated with PBDE levels in PM2.5. Significantly negative correlation was also found between PBDE concentrations in PM2.5 and the relative humidity. The ranking of estimated inhalation exposure doses of PBDEs via PM2.5 inhalation were toddlers (1.74 pg/kg b.w./day) > children (1.33 pg/kg b.w./day) > adults (1.26 pg/kg b.w./day) > teenagers (0.64 pg/kg b.w./day), and toddlers had a highest risk to expose to PBDEs by PM2.5. To our knowledge, the present study is the first to reveal the spatiotemporal variability of PBDEs in PM2.5 of Shenzhen, China.

While increasing evidence suggested that PM₂.₅ is the most harmful fraction of the particle pollutants, the health effects of coarse particles (PM₁₀–₂.₅) have been inconclusive, especially on cerebrovascular diseases, we thus evaluated the effects of PM₁₀, PM₂.₅, and PM₁₀–₂.₅ on stroke mortality in six Chinese subtropical cities using generalized additive models. We also conducted random-effects meta-analyses to estimate the overall effects across the six cities. We found that PM₁₀, PM₂.₅, and PM₁₀₋₂.₅ were significantly associated with stroke mortality. Each 10 μg/m³ increase of PM₁₀, PM₂.₅ and PM₁₀₋₂.₅ (lag03) was associated with an increase of 1.88% (95% CI: 1.37%, 2.39%), 3.07% (95% CI: 2.35%, 3.79%), and 5.72% (95% CI: 3.82%, 7.65%) in overall stroke mortality. Using the World Health Organization's guideline as reference concentration, we estimated that 3.21% (95% CI: 1.65%, 3.01%) of stroke mortality (corresponding to 1743 stroke mortalities, 95% CI: 896, 1633) were attributed to PM₁₀, 5.57% (95% CI: 0.50%, 1.23%) stroke mortality (3019, 95% CI: 2286, 3777) were attributed to PM₂.₅, and 2.02% (95% CI: 1.85%, 3.08%) of stroke mortality (1097, 95% CI: 1005, 1673) could be attributed to PM₁₀₋₂.₅. Our analysis indicates that both PM₂.₅ and PM₁₀₋₂.₅ are important risk factors of stroke mortality and should be considered in the prevention and control of stroke in the study area.