Traffic contributes to fine particulate matter (PM₂.₅) in the atmosphere through engine exhaust emissions and road dust generation. However, the evolution of traffic related PM₂.₅ emission over recent years remains unclear, especially when various efforts to reduce emission e.g., aftertreatment technologies and high emission standards from China IV to China V, have been implemented. In this study, hourly elemental carbon (EC), a marker of primary engine exhaust emissions, and trace element of calcium (Ca), a marker of road dust, were measured at a nearby highway sampling site in Shanghai from 2016 to 2019. A random forest-based machine learning algorithm was applied to decouple the influences of meteorological variables on the measured EC and Ca, revealing the deweathered trend in exhaust emissions and road dust. After meteorological normalization, we showed that non-exhaust emissions, i.e., road dust from traffic, increased their fractional contribution to PM₂.₅ over recent years. In particular, road dust was found to be more important, as revealed by the deweathered trend of Ca fraction in PM₂.₅, increasing at 6.1% year⁻¹, more than twice that of EC (2.9% year⁻¹). This study suggests that while various efforts have been successful in reducing vehicular exhaust emissions, road dust will not abate at a similar rate. The results of this study provide insights into the trend of traffic-related emissions over recent years based on high temporal resolution monitoring data, with important implications for policymaking.

Although road traffic noise is the most important source of environmental noise emission in large cities, little is known about health burden. The present study was conducted to estimate the burden of diseases attributed to traffic noise in the metropolis of Tehran in 2017. Using noise maps provided by the municipality of Tehran, we calculated population exposure distribution in term of Ldₙ and Lₙᵢgₕₜ and the number of DALYs lost due to ischemic heart disease, hypertension, high sleep disturbance, annoyance and stroke endpoints based on the World Health Organization Environmental Noise Guidelines for the European Region. We applied published dose-response functions to estimate the traffic noise burden for high sleep disturbance and annoyance. We estimated 61,284 DALYs or 697 DALYs per 100,000 population attributed to traffic noise in Tehran for the reference year 2017. Highly sleep disturbance with a share of 58.74% of the DALYs was recognized as the most important contributor of disease burden, and noise annoyance with a share of 23.12% was ranked next. Ischemic heart disease (11.71%), stroke (5.12%), and hypertension (1.31%) were ranked third to fourth, respectively, in terms of the burden of disease caused by environmental noise. A considerable fraction of the population of Tehran lives in areas with an environmental noise higher than the standard level. The findings showed that traffic noise pollution is an important environmental risk factor in Tehran imposes the greatest burden on the community, mainly through highly sleep disturbance and noise annoyance endpoints.

Many researches mention the need to identify the land-based sources of riverine macrolitter but few field data on litter amount, composition and sources are available in the scientific literature. Describing macrolitter hotspot dynamics would actually allow a better estimation of fluxes in the receiving environments and a better identification of the more appropriate mitigation strategies. This study provides new insights in roadway macrolitter production rates, typologies and input sources (i.e. deliberate or accidental). The macrolitter from an 800 m portion of a highly frequented roadway (around 90,000 vehicles per day) was collected during almost one year. Typologies were defined using the OSPAR/TGML classification. Results show high annual loads of macrolitter (42.8 kg/yr/ha), suggesting significant contributions of the road runoff to the litter fluxes in urban stormwater. Over the course of a year, 88.5 kg of debris were collected, including 53.2 kg (60%) of plastic debris. In total, 36,439 items were characterized, of which 84% were plastics. The macrodebris collected present a low diversity of components with Top 10 items accounting for 92% by count and a majority of small and lightweight items like plastic fragments (31%) or cigarette butts (18%). Input sources were estimated for 43% of the mass collected in which 37.2% were deliberately littered and 62.8% were accidental leaks, illustrating a major contribution of uncovered trucks and unsecured loads. The accumulation rates show a linear correlation with the road traffic. Such data are of prime interest since they enable to determine the potential contribution of road traffic to plastic fluxes to the environment.

A recently identified chemical, 2-((4-Methylpentan-2-yl)amino)-5-(phenylamino)cyclohexa-2,5-diene-1,4-dione (6PPD-quinone; 6PPD-Q), is a transformation product of an additive used in the manufacture of tire rubber and causes acute lethality in coho salmon (Oncorhynchus kisutch) in urban watersheds. Despite its potential presence and ecotoxicity in receiving waters worldwide, information on the occurrence and fate of 6PPD-Q is limited. Here, we investigated the concentrations of 6PPD-Q and its parent chemical, 6PPD, in road dust collected from arterial and residential roads in Tokyo, Japan from May to October 2021. 6PPD-Q concentrations were highest from May to June, when atmospheric ozone concentrations are the highest in Japan; a correlation between 6PPD-Q and photochemical oxidants, as an alternative to ozone, corroborated this finding. We also found that 6PPD-Q concentrations at photochemical oxidant concentrations ranging from 35 to 47 ppbv were higher in dust collected from roads with high traffic volumes (i.e., arterial roads; median: 8.6 μg/g-OC) than in dust collected from roads with lower traffic volumes (i.e., residential roads; median: 6.3 μg/g-OC), indicating that 6PPD-Q is generated from traffic-related sources. We also found that 6PPD-Q was leached from dust particles within a few hours, with a log partitioning coefficient between organic carbon and water (KOC) of 3.2–3.5. The present results will help to understand the environmental occurrence, fate, and behavior of 6PPD-Q.

Many studies revealed the rapid decline of atmospheric PM₂.₅ in Beijing due to the emission control measures. The variation of particle number concentration (PN) which has important influences on regional climate and human health, however, was rarely reported. This study measured the particle number size distributions (PNSD) in 3–700 nm in winter of Beijing during 2013–2019. It was found that PN decreased by 58% from 2013 to 2017, but increased by 29% from 2017 to 2019. By Positive matrix factorization (PMF) analysis, five source factors of PNSD were identified as Nucleation, Fresh traffic, Aged traffic + Diesel, Coal + biomass burning and Secondary. Overall, factors associated with primary emissions were found to decrease continuously. Coal + biomass burning dominated the reduction (65%) among the three primary sources during 2013–2017, which resulted from the great efforts on emission control of coal combustion and biomass burning. Fresh traffic and Aged traffic + Diesel decreased by 43% and 66%, respectively, from 2013 to 2019, as a result of the upgrade of the vehicle emission standards in Beijing-Tianjin-Hebei area. On the other hand, the contribution from Nucleation and Secondary decreased with the reduction of gaseous precursors in 2013–2017, but due to the increased intensity of new particle formation (NPF) and secondary oxidation, they increased by 56% and 70%, respectively, from 2017 to 2019, which led to the simultaneously increase of PN and particle volume concentration. This study indicated that NPF may play an important role in urban atmosphere under continuous air quality improvement.

Plume chasing is cost-effective, measuring individual, on-road vehicular emissions. Whereas, wake-flow-generated turbulence results in intermittent, rapid pollutant dilution and substantial fluctuating concentrations right behind the vehicle being chased. The sampling duration is therefore one of the important factors for acquiring representative (average) concentrations, which, however, has been seldom addressed. This paper, which is based on the detailed spatio-temporal dispersion data after a heavy-duty truck calculated by large-eddy simulation (LES), examines how sampling duration affects the uncertainty of the measured concentrations in plume chasing. The tailpipe dispersion is largely driven by the jet-like flows through the vehicle underbody with approximate Gaussian concentration distribution for x ≤ 0.6h, where x is the distance after the vehicle and h the characteristic vehicle size. Thereafter for x ≥ 0.6h, the major recirculation plays an important role in near-wake pollutant transport whose concentrations are highly fluctuating and positively shewed. Plume chasing for a longer sampling duration is more favourable but is logistically impractical in busy traffic. Sampling duration, also known as averaging time in the statistical analysis, thus has a crucial role in sampling accuracy. With a longer sampling (averaging) duration, the sample mean concentration converges to the population mean, improving the sample reliability. However, this effect is less pronounced in long sampling duration. The sampling accuracy is also influenced by the locations of sampling points. For the region x > 0.6h, the sampling accuracy is degraded to a large extent. As a result, acceptable sample mean is hardly achievable. Finally, frequency analysis unveils the mechanism leading to the variance in concentration measurements which is attributed to sampling duration. Those data with frequency higher than the sampling frequency are filtered out by moving average in the statistical analyses.

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.

Quantifying source apportionment of potentially toxic elements (PTEs) in soils and associated human health risk (HHR) is essential for soil environment regulation and pollution risk mitigation. For this purpose, an integrated method was proposed, and applied to a dataset consisting of As, Cd, Cr, Cu, Hg, Ni, Pb, Se, and Zn in 273 soil surface samples. Positive matrix factorization (PMF) was used to quantitatively examine sources contributions of PTEs in soils; and the HHR arising from the identified source was determined by combining source profiles and health risk assessment; at last, sequential Gaussian simulation (SGS) was used to identify the areas with high HHR. Four sources were identified by PMF. Natural and agricultural sources affected all 9 PTEs contents with contributions ranging from 19.2% to 62.9%. 41.9% of Cd, 40.8% of Pb, 58.6% of Se, and 29.8% of Zn were controlled by industrial and traffic emissions. Metals smelting and mining explained 35.5%, 30.5%, and 24.9% of Cr, Cu, and Ni variations, respectively. Hg was dominated by atmospheric deposition from coal combustion and coking (58.7%). The mean values of the total non-carcinogenic risks of PTEs were 1.55 × 10⁻¹ and 9.40 × 10⁻¹ for adults and children, and the total carcinogenic risk of PTEs had an average value of 8.86 × 10⁻⁵. Based on source-oriented HHR calculation, natural and agricultural sources were the most important factor influencing HHR, explaining 51.0% and 49.1% of non-carcinogenic risks for children and adults, and 44.2% of carcinogenic risk. SGS indicated that 1.1% of the total area was identified as hazardous areas with non-carcinogens risk for children.

The Central Plains Economic Region (CPER) located along the transport path to the Beijing-Tianjin-Hebei area has experienced severe PM₂.₅ pollution in recent years. However, few modeling studies have been performed on the sources of PM₂.₅, especially the impacts of emission reduction strategies. In this study, the Nested Air Quality Prediction Model System (NAQPMS) with an online tracer-tagging module was adopted to investigate source sectors of PM₂.₅ and a series of sensitivity tests were conducted to investigate the impacts of different sector-based mitigation strategies on PM₂.₅ pollution. The response surfaces of pollutants to sector-based emission changes were built. The results showed that resident-related sector (resident and agriculture), fugitive dust, traffic and industry emissions were the main sources of PM₂.₅ in Zhengzhou, contributing 49%, 19%, 15% and 13%, respectively. Response surfaces of pollutants to sector-based emission changes in Henan revealed that the combined reduction of resident-related sector and industry emissions efficiently decreased PM₂.₅ in Zhengzhou. However, reduced emissions in only the Henan region barely satisfied the national air quality standard of 75 μg/m³, whereas a 50%–60% reduction in resident-related sector and industry emissions over the whole region could reach this goal. On severely polluted days, even a 60% reduction in these two sectors over the whole region was insufficient to satisfy the standard of 75 μg/m³. Moreover, a reduction in traffic emissions resulted in an increase in the O₃ concentration. The results of the response surface method showed that PM₂.₅ in Zhengzhou decreased by 19% in response to the COVID-19 lockdown, which approached the observed reduction of 21%, indicating that the response surface method could be employed to study the impacts of the COVID-19 lockdown on air pollution. This study provides a scientific reference for the formulation of pollution mitigation strategies in the CPER.