Road traffic emissions are an increasingly important source of particulate matter in urban and non-road environments, where non-tailpipe emissions can contribute substantially to elevated levels of metals associated with adverse health effects. Thus, better characterization and quantification of traffic-emitted metals is warranted. In this study, real-world emission factors for fine particulate metals were determined from hourly x-ray fluorescence measurements over a three-year period (2015–2018) at an urban roadway and busy highway. Inter-site differences and temporal trends in real-world emission factors for metals were explored. The emission factors at both sites were within the range of past studies, and it was found that Ti, Fe, Cu, and Ba emissions were 2.2–3.0 times higher at the highway site, consistent with the higher proportion of heavy-duty vehicles. Weekday emission factors for some metals were also higher by 2.0–3.5 times relative to Sundays for Mn, Zn, Ca, and Fe, illustrating a dependence on fleet composition and roadway activity. Metal emission factors were also inversely related to relative humidity and precipitation, due to reduced road dust resuspension under wetter conditions. Correlation analysis revealed groups of metals that were co-emitted by different traffic activities and sources. Determining emission factors enabled the isolation of traffic-related metal emissions and also revealed that human exposure to metals in ambient air can vary substantially both temporally and spatially depending on fleet composition and traffic volume.

Increasing levels of road noise are creating new anthropogenic soundscapes that may affect wildlife globally. Bats, which form about a third of all mammal species, are sensitive bioindicators, and may be particularly vulnerable because of their dependency on echolocation. Here we present the first controlled field experiment with free-living bats. Using a Before-After-Control-Impact phantom road experimental design, we examine the impacts of traffic noise on their activity and feeding behaviour. Disentangling the impacts of traffic noise from other co-varying exposures such as habitat quality, the experiment demonstrates a significant negative effect on the activity of each of the five, ecologically different, species (genus for Myotis spp.) examined. This suggests that the results are widely applicable. The negative effects are largely attributable to noise in the sonic spectrum, which elicited aversive responses in all bat species tested,whereas responses to ultrasoundwere restricted to a single species. Our findings demonstrate that traffic noise can affect bat activity at least 20m away from the noise source. For Pipistrellus pipistrellus and Pipistrellus pygmaeus, feeding behaviour, as well as overall activity, was negatively affected. Ecological Impact Assessments are therfore needed wherever there are significant increases in traffic flow, and not just when new roads are built. Further research is required to identify effective mitigation strategies, to delineate the zone of influence of road noise, and to assess whether there is any habituation over time.

Stormwater is viewed as an alternative resource to mitigate water shortages. However, stormwater reuse is constrained due to the presence of many toxic pollutants such as hydrocarbons. Effective mitigation requires robust mathematical models for stormwater quality prediction based on an understanding of pollutant processes. However, the rise in global temperatures will impose changes to pollutant processes. This study has proposed a new perspective on modelling the build-up process of hydrocarbons, with a focus on volatile organic compounds (VOCs). Among organic compounds, VOCs are the most susceptible to changes as a result of global warming due to their volatility. Seven VOCs, namely, benzene, toluene, ethylbenzene, para-xylene, meta-xylene, ortho-xylene and styrene in road dust were investigated. The outcomes are expected to lay the foundation to overcoming the limitations in current modelling approaches such as not considering the influence of temperature and volatility, on the build-up process. A new conceptualisation is proposed for the classical build-up model by mathematically defining the volatility of VOCs in terms of temperature. Uncertainty in the re-conceptualised build-up model was quantified and was used to understand the build-up patterns in the future scenarios of global warming. Results indicated that for the likely scenarios, the variability in VOCs build-up gradually increases at the beginning of the dry period and then rapidly increases after around seven days, while the build-up reaches a near-constant value in a shorter dry period, limiting the variability. These initial research outcomes need to be further investigated given the expected impacts of global warming into the future.

In the study, personal PM₂.₅ exposures and their source contributions were characterized for 159 subjects living in the Beijing Metropolitan area. The exposures and sources were examined as functions of residential location, season, vocation, cigarette smoking, and time spent outdoors. Sampling was performed for two categories of volunteers, guards and students, that lived in urban and suburban areas of Beijing. Samples were collected using portable PM₂.₅ monitors during summer and winter. Exposure measurements were supplemented with a questionnaire that tracked personal activity and time spent in microenvironments that may have impacted exposures. Simultaneously, ambient PM₂.₅ data were obtained from national network stations located at the Gucheng and Huairouzhen sites. These data were used as a comparison against the personal PM₂.₅ exposures and produced poor correlations between personal and ambient PM₂.₅. These results demonstrate that individual behavior strongly affects personal PM₂.₅ exposure. Six primary sources of personal PM₂.₅ exposure were determined using a positive matrix factorization (PMF) source apportionment model. These sources included Roadway Transport Source, Soil/Dust Source, Industrial/Combustion Source, Secondary Inorganic Source, Cd Source, and Household Heating Source. Averaged across all subjects and seasons, the highest source contribution was Secondary Inorganic Source (24.8% ± 32.6%, AVG ± STD), whereas the largest primary ambient source was determined to be Roadway Transport (20.9% ± 13.6%). Subjects were classified according to the questionnaire and were used to help understand the relationship between personal activity and source contribution to PM₂.₅ exposure. In general, primary ambient sources showed only significant spatial and seasonal differences, while secondary sources differed significantly between populations with different personal behavior. In particular, Cd source was found to be related to smoking exposure and was the most unpredictable source, with significant differences between populations of different sites, vocations, smoking exposures, and outdoor time.

The urban nitrogen (N) and carbon (C) cycles are substantially influenced by human activity. Alterations to these cycles include increased inputs from fossil fuel combustion and fertilizer use. The leaf chemistry of urban trees can be used to distinguish between these different N and C sources. Here, we evaluated relationships between urban vegetation and different N and C sources in street and residential trees in the Salt Lake Valley, Utah. We tested three hypotheses: 1) unfertilized street trees on high traffic density roads will have higher leaf %N, more enriched δ¹⁵N and more depleted δ¹³C than unfertilized street trees on low traffic density roads; 2) trees in high income residential neighborhoods will have higher leaf %N, more depleted δ¹⁵N and more enriched δ¹³C than trees in lower income neighborhoods; and 3) unfertilized street trees will have lower leaf %N, more enriched δ¹⁵N and more depleted δ¹³C than fertilized residential trees. Leaf δ¹⁵N was more enriched near high traffic density roads for one study species. However, street tree δ¹⁵N and δ¹³C were largely influenced by vehicle emissions from primary and secondary roads within 1000 m radius rather than the immediately adjacent road. Leaf δ¹³C was correlated with neighborhood income, although this relationship may be the result of variations in irrigation practices rather than variations in C sources. Finally, unfertilized trees in downtown Salt Lake had lower leaf %N, more enriched δ¹⁵N and more depleted δ¹³C than fertilized trees. These results highlight that urban trees can serve as biomonitors of the environment. Moreover, they emphasize that roads can have large spatial footprints and that the leaf chemistry of urban vegetation may be influenced by the spatial patterns in roads and road densities at the landscape scale.

This paper analyses sources of sixteen PAHs – polycyclic aromatic hydrocarbons in urbanized areas by using selected diagnostic ratios. Simultaneously, an attempt was made to determine how sewage sludge changes PAHs content in urbanized areas soils. In the experiment three lawns along the main roads in Bialystok with different traffic intensity, three doses of sewage sludge and two years of study were considered. There was no effect of fertilization with sewage sludge on the sum of 16 PAHs in urban soil samples, nevertheless, the sum of 16 PAHs was reduced from 2.6 in 2011 to 2.3 mg/kg in 2012. Among 16 tested PAHs compounds, benzo[a]pyrene was the most dominant compound in samples collected in both years – about 15% of all PAHs. The results suggest that application of sludge into the soil did not influence the concentration of 2-3-ring, 4-ring and 5-6-ring PAHs. For the objects fertilized with a dose 150.0 Mg/ha, of sludge the total sum of potentially carcinogenic PAHs in the urban soil lowered by approximately 68% in comparison with the control plots. PAHs contamination of the urban soil samples resulted from the influence of coal, petroleum and biomass combustion. Moreover, PAHs can enter soil via at mospheric deposition.

Currently, the HBCDs were listed in Annex A by the Stockholm Convention, and the knowledge on the contamination of TBBPA and HBCDs in soil and road dust in China is still limited, and it is unclear what role is played by dust particle size distribution. In this study, a total of 81 soil and 43 road dust samples were analyzed with TBBPA and ΣHBCDs concentrations ranging from < LOQ to 33.8 ng/g dw (dry weight) and 0.43–15.2 ng/g dw in soil, and from < LOQ to 74.1 ng/g dw and 7.25–14.0 ng/g dw in road dust, respectively. TBBPA and HBCDs exhibited different spatial distribution patterns in soil, where relatively high levels of HBCDs were found in industrial area and commercial area, while high levels of TBBPA were detceted in residential area. However, no distinct variation in spatial distribution of these two compounds was observed in road dust. Different diastereoisomer profiles of HBCDs were also found with γ-HBCD predominating in soil and α-HBCD occupying a large proportion in road dust. The α-/γ-HBCD values in road dust were significantly greater (T-test, P < 0.05) than those in soil, which suggested that γ-HBCD in road dust were likely to transform into α-HBCD compared with soil. The distribution of dust particle size showed that HBCDs levels were increasing with the decreasing in particle sizes, while the TBBPA showed some “accidental” peak values in specific diameter ranges. The estimated daily intakes (EDIs) of TBBPA and HBCDs were assessed through dust ingestion, dermal absorption and inhalation via road dust, and all the exposure estimates were well below the reference dose (RfD), but the toddlers were more vulnerable to TBBPA and HBCDs intakes, which should be paid more attention.

Heavy metal soil contamination is associated with potential toxicity to humans or ecotoxicity. Scholars have increasingly used a combination of geographical information science (GIS) with geostatistical and multivariate statistical analysis techniques to examine the spatial distribution of heavy metals in soils at a regional scale. A review of such studies showed that most soil sampling programs were based on grid patterns and composite sampling methodologies. Many programs intended to characterize various soil types and land use types. The most often used sampling depth intervals were 0–0.10 m, or 0–0.20 m, below surface; and the sampling densities used ranged from 0.0004 to 6.1 samples per km², with a median of 0.4 samples per km². The most widely used spatial interpolators were inverse distance weighted interpolation and ordinary kriging; and the most often used multivariate statistical analysis techniques were principal component analysis and cluster analysis. The review also identified several determining and correlating factors in heavy metal distribution in soils, including soil type, soil pH, soil organic matter, land use type, Fe, Al, and heavy metal concentrations. The major natural and anthropogenic sources of heavy metals were found to derive from lithogenic origin, roadway and transportation, atmospheric deposition, wastewater and runoff from industrial and mining facilities, fertilizer application, livestock manure, and sewage sludge. This review argues that the full potential of integrated GIS and multivariate statistical analysis for assessing heavy metal distribution in soils on a regional scale has not yet been fully realized. It is proposed that future research be conducted to map multivariate results in GIS to pinpoint specific anthropogenic sources, to analyze temporal trends in addition to spatial patterns, to optimize modeling parameters, and to expand the use of different multivariate analysis tools beyond principal component analysis (PCA) and cluster analysis (CA).

Modeled atmospheric pollution removal by trees based on eddy flux, leaf, and chamber studies of relatively few species may not scale up to adequately assess landscape-level air pollution effects of the urban forest. A land use regression (LUR) model (R2 = 0.70) based on NO(2) measured at 144 sites in Portland, Oregon (USA), after controlling for roads, railroads, and elevation, estimated every 10 ha (20%) of tree canopy within 400 m of a site was associated with a 0.57 ppb decrease in NO(2). Using BenMAP and a 200 m resolution NO(2) model, we estimated that the NO(2) reduction associated with trees in Portland could result in significantly fewer incidences of respiratory problems, providing a $7 million USD benefit annually. These in-situ urban measurements predict a significantly higher reduction of NO(2) by urban trees than do existing models. Further studies are needed to maximize the potential of urban trees in improving air quality.

Traffic is a major source of pollutants in cities. In this well-replicated study we analysed a broad array of contaminants in snowpacks along roads of different traffic intensities. The majority of pollutants showed a similar pattern with respect to traffic intensity: pH and conductivity as well as concentrations of PAHs, total suspended solids, phosphorus and most heavy metals were higher next to high intensity roads compared to low intensity roads. These pollutant levels also decreased considerably up to 5 m distance from the roads. Furthermore, apart from nitrogen, these variables increased in concentration from control sites in urban forest patches to road bank sites next to roads of low, intermediate and high traffic intensities. The deposition pattern of various traffic-derived pollutants – whether gaseous or particle-bound – was the same. Such information can be useful for the purposes of managing pollutants in urban areas.