Assessment of seasonal variability of PM, BC and UFP levels at a highway toll stations and their associated health risks

As a part of their occupation, workers at toll stations are exposed to traffic emissions during the working shift, which sometimes stretches to 12 h. To assess the exposure and subsequent health risk of these workers, a study was performed on a highway toll station in India. PM1, PM2.5, PM10, BC and UFP concentration were determined inside a toll collectors' cabin and outside in a free-flowing traffic section (125 m from the toll cabin). The concentrations varied in the following range: PM1 (40.69-226.13 μg m-3), PM2.5 (49.71-247.36 μg m-3), PM10 (83.15-458.14 μg m-3) and BC (2.1-87.5 μg m-3) and UFP: 101-53705 pt cm-3. The mean concentration inside the cabin was 1.34 (PM1), 1.35 (PM2.5), 1.16 (PM10) and 2.91 (BC) times the concentration outside for the summer season. The corresponding levels in the winter season were 1.14 (PM1), 1.11 (PM2.5), 1.11 (PM10), 2.50 (BC) and 1.82 (UFP). In addition to the exhaust emission, the non-exhaust emissions such as resuspension of crustal particles, fly ash and bioaerosols were identified. Using the Multiple Path Particle Dosimetry model for two groups - adults (18-21 years) and adults (21+ years), it was estimated that the pulmonary deposition of in-cabin workers were 50% (PM2.5) -75% (PM1) higher than the workers outside the cabin. Particle mass deposition was found to be higher for adults (21+ years) than adults (18-21 years) for both the seasons. The study quantitatively assessed the health risk faced by the workers in terms of exposure concentration and deposition in respiratory tract. More such studies at different traffic mix and climate can provide better estimates of health risk of toll workers that can be used to devise appropriate strategies for control of it.

Constructing transferable and interpretable machine learning models for black carbon concentrations

Black carbon (BC) has received increasing attention from researchers due to its adverse health effects. However, in-situ BC measurements are often not included as a regulated variable in air quality monitoring networks. Machine learning (ML) models have been studied extensively to serve as virtual sensors to complement the reference instruments. This study evaluates and compares three white-box (WB) and four black-box (BB) ML models to estimate BC concentrations, with the focus to show their transferability and interpretability. We train the models with the long-term air pollutant and weather measurements in Barcelona urban background site, and test them in other European urban and traffic sites. Despite the difference in geographical locations and measurement sites, BC correlates the strongest with particle number concentration of accumulation mode (PNacc, r = 0.73-0.85) and nitrogen dioxide (NO2, r = 0.68-0.85) and the weakest with meteorological parameters. Due to its similarity of correlation behaviour, the ML models trained in Barcelona performs prominently at the traffic site in Helsinki (R2 = 0.80-0.86; mean absolute error MAE = 3.90-4.73 %) and at the urban background site in Dresden (R2 = 0.79-0.84; MAE = 4.23-4.82 %). WB models appear to explain less variability of BC than BB models, long short-term memory (LSTM) model of which outperforms the rest of the models. In terms of interpretability, we adopt several methods for individual model to quantify and normalize the relative importance of each input feature. The overall static relative importance commonly used for WB models demonstrate varying results from the dynamic values utilized to show local contribution used for BB models. PNacc and NO2 on average have the strongest absolute static contribution; however, they simultaneously impact the estimation positively and negatively at different sites. This comprehensive analysis demonstrates that the possibility of these interpretable air pollutant ML models to be transfered across space and time.

Is biomagnetic leaf monitoring still an effective method for monitoring the heavy metal pollution of atmospheric particulate matter in clean cities?

The development of a reasonable method for predicting heavy metals (HMs) pollution in atmospheric particulate matter (PM) remains challenging. This paper presents an elution-filtration method to collect PM from the surface of Osmanthus fragrans in a very clean area (Guiyang, China). The aim is to evaluate the effectiveness of biomagnetic leaf monitoring as a simple and rapid method for assessing HMs pollution in clean cities. For this purpose, we determined the magnetic parameters and concentrations of selected HMs in PM samples to investigate their relationships. The results showed that the magnetic minerals in PM samples were mainly low coercivity ferrimagnetic minerals, with a small amount of high coercivity minerals. The types of magnetic minerals were generally single, and the magnetic domain state was pseudo-single domain (PSD). There was a significant correlation between magnetic parameters and the heavy metal (HM) concentrations in PM. Low-field magnetic susceptibility (χ) could be used as an ideal proxy for determining anthropogenic HM pollution. Traffic emissions were the main atmospheric pollution source in urban Guiyang. Due to the incomplete traffic network and large traffic flow, traffic congestion (TC) often occurred at road intersections in the northwest and southwest corners of the city, resulting in the highest concentration of magnetic minerals and the most severe PM pollution. To mitigate atmospheric PM pollution and protect public health, it is strongly recommended that municipal authorities prioritize urban planning and traffic management to address TC. Measures should be implemented urgently to alleviate stop-and-go traffic.

Monitoring trace element concentrations with environmentally friendly biomonitors in Artvin, Turkey

Air pollution is the change in air composition that disrupts human health and environmental balance. Although natural and anthropogenic processes include crustal movements, photosynthesis, and plant and animal emissions, other sources of contamination also include industrial operations, transportation activities, household resources, and the chemical and metal industries. Thus, biomonitoring can be employed as a quick, affordable, and efficient method for estimating air pollution. In this study, some inorganic pollutants were detected using olive trees (Olea europaea L.) at eleven different points, depending on the traffic density in Artvin, Turkey. Trace element concentrations (Cr, Ti, Fe, Ni, Co, Cu, Zn, Pb, Al, and Mn) were measured in soil once a year and seasonally in plant samples with ICP-OES. Furthermore, basic component analyses total carbon (TC), total nitrogen (TN), total hydrogen (TH), and total sulfur (TS) were done with an elemental analyzer, total chlorophyll contents with a portable chlorophyll meter, and morphological and particle-based plant analyses with SEM-EDS. The pollution levels of these metals were calculated using the enrichment factor (EF) and geoaccumulation index (Igeo) parameters. Furthermore, the accuracy and validity tests of the analyses for trace metals were tested by applying certified reference materials (CRM) (ERM-CD281) for the plant samples and CRM (LGC-6187) for soil samples. Results indicated that soil trace element pollution distributions were ranked according to the following descending order: Fe (37,873.33 mg/kg) > Al (13,300 mg/kg) > Mn (1101.33 mg/kg) > Ti (353.5 mg/kg) > Zn (252.86 mg/kg) > Cu (87.77 mg/kg) > Cr (30.52 mg/kg) > Pb (19.65 mg/kg) > Ni (17.07 mg/kg) > Co (7.65 mg/kg). Moreover, air pollution from anthropogenic sources substantially increased average trace metal concentrations and sulfur emissions in autumn and winter. The average highest values of Fe (321.08 mg/kg) > Al (304.05 mg/kg) > Mn (32.75 mg/kg) > Zn (31.01 mg/kg) > Cu (17.92 mg/kg) > Ti (11.07 mg/kg) Cr (2.57 mg/kg) > Ni (17.07 mg/kg) were found in leaf samples taken from the roadside in autumn and winter. According to the EF and Igeo values, the main polluting trace elements in the soil were Zn, Cu, and Pb, while in the plant, these were detected as Fe, Al, Ti, Cr, Ni, and Cu. Kruskal-Wallis and correlation analysis statistically supported this relationship among metals. Results show that olive leaves are an effective bioindicator for detecting urban air pollution.

High contribution of new particle formation to ultrafine particles in four seasons in an urban atmosphere in south China

Ultra fine particles (UFP) cover the size range of both nucleation mode particles (NUC, D_p < 25 nm) and Aitken mode particles (AIT, 25 nm < D_p < 100 nm), and play important roles in radiative forcing and human health. In this study, we identified new particle formation (NPF) events and undefined events, explored their potential formation mechanism, and quantified their contributions to UFP number concentration (N_UFP) in urban Dongguan of the Pearl River Delta (PRD) region. Field campaigns were carried out in four seasons in 2019 to measure particle number concentration in the size range of 4.7-673.2 nm, volatile organic compounds (VOCs), gaseous pollutants, chemical compositions in PM_2.5, and meteorological parameters. The frequency of the occurrence of NPF, as indicated by a significant increase in NUC number concentration (N_NUC), was 26 %, and that of the undefined event, as indicated by substantial increases in N_NUC or AIT number concentration (N_AIT), was 32 % during the whole campaign period. The NPF events mainly occurred in autumn (with a frequency of 59 %) and winter (33 %) and only occasionally in spring (4 %) and summer (4 %). On the contrary, the frequencies of the undefined events were higher in spring (52 %) and summer (38 %) than in autumn (19 %) and winter (22 %). The burst periods of the NPF events mainly occurred before 11:00 Local Time (LT), while those of the undefined events mainly occurred after 11:00 LT. Accompanied to NPF events were low concentrations of VOCs and high concentrations of O₃. The undefined events by NUC or AIT were associated with the upwind transport of newly formed particles. Source apportionment analysis suggested that NPF and undefined events were the largest contributor to N_NUC (51 ± 28 %), N_AIT (41 ± 26 %), and N_UFP (45 ± 27 %), while coal combustion and biomass burning, and traffic emission were the second largest contributor to N_NUC (22 ± 20 %) and N_AIT (39 ± 28 %), respectively.

Emission impacts of left-turn lane on light-heavy-duty mixed traffic in signalized intersections: Optimization and empirical analysis

Reducing emissions from the transport sector is one of the crucial countermeasures for climate action. This study focuses on the optimization and emission analysis regarding the impacts of left-turn lanes on the emissions of mixed traffic flow (CO, HC, and NO_x) with both heavy-duty vehicles (HDV) and light-duty vehicles (LDV) at urban intersections, combining high-resolution field emission data and simulation tools. Based on high-precision field emission data collected by Portable OBEAS-3000, this study first develops instantaneous emission models for HDV and LDV under various operating conditions. Then, a tailored model is formulated to determine the optimal left-lane length for mixed traffic. Afterward, we empirically validate the model and analyze the effect of the left-turn lane (before and after optimization) on the emissions at the intersections using the established emission models and VISSIM simulations. The proposed method can reduce CO, HC, and NO_x emissions crossing intersections by around 30% compared to the original scenario. The proposed method significantly reduces average traffic delays after optimization by 16.67% (North), 21.09% (South), 14.61% (West), and 2.68% (East) in different entrance directions. The maximum queue lengths decrease by 79.42%, 39.09%, and 37.02% in different directions. Even though HDVs account for only a minor traffic volume, they contribute the most to CO, HC, and NO_x emissions at the intersection. The optimality of the proposed method is validated through an enumeration process. Overall, the method provides useful guidance and design methods for traffic designers to alleviate traffic congestion and emissions at urban intersections by strengthening left-turn lanes and improving traffic efficiency.

Mobile measurements of black carbon: Comparison of normal traffic with reduced traffic conditions during COVID-19 lock-down

A mobile monitoring campaign was conducted (by bicycle) to assess the black carbon (BC) concentrations in Cluj-Napoca city, Romania, in 2020, before, during and after COVID-19 lock-down. Over the entire study period, the BC concentrations ranged between 1.0 and 25.9 μg/m³ (averaged per street section and period characterized by different traffic conditions). Marked spatial and temporal differences were observed. Observed differences in BC concentrations between locations are attributed to traffic intensities, with average BC concentrations, under normal circumstances, of 6.6-14.3 μg/m³ at roads with high to intense traffic, compared to 2.8-3.1 μg/m³ at areas with reduced traffic, such as residential areas, parks and pedestrian streets. The COVID-19 measures impacted traffic volumes, and hence average BC concentrations decreased from 5.9 μg/m³ to 3.0 μg/m³ during lock-down and in a lower extent to 3.4 μg/m³ and 4.4 μg/m³ in post-lockdown periods with reduced and more normalized traffic. Two approaches to account for variations in background concentrations when comparing different situations in time are assessed. Subtracting background concentrations that are measured at background sites along the monitoring route is an appropriate method to assess spatio-temporal differences in concentrations. A reduction of about 1-2 μg/m³ was observed for the streets with low to medium traffic, and up to 6 μg/m³ at high traffic locations under lockdown. The approach presented in this study, using mobile measurements, is useful to understand the personal exposure to BC along the roads in different seasons and the influence of traffic reduction on BC pollution during prolonged restrictions. All these will support policymakers to reduce pollution and achieve EU directives targets and WHO recommendations.

An evaluation of the impact of traffic on the distribution of PAHs and oxygenated PAHs in the soils and moss of the southeast Tibetan Plateau

Contaminants in high-altitude mountains such as the Tibetan Plateau (TP) have attracted extensive attention due to their potential impact on fragile ecosystems. Rapid development of the economy and society has promoted pollution caused by local traffic emissions in the TP. Among the pollutants emitted by traffic, polycyclic aromatic hydrocarbons (PAHs) and oxygenated PAHs (OPAHs) are of particular concern due to their high toxicity. The TP provides an environment to explore the degree and range of contribution for traffic-induced PAHs and OPAHs. In this study, soils and moss were collected at different altitudes and distances from the G318 highway in the southeast TP. The total concentrations of PAHs (∑₁₆PAHs) and OPAHs (∑₆OPAHs) in soils were in the range of 3.29-119 ng/g dry weight (dw) and 0.54-9.65 ng/g dw, respectively. ∑₁₆PAH and ∑₆OPAH concentrations decreased logarithmically with increasing distance from traffic. A significantly positive correlation between ∑₁₆PAHs and altitude was found at sampling points closest to traffic. Dominant PAHs constituents in soil and moss included chrysene (CHR), benzo[g,h,i]perylene (BghiP), and benzo[b]fluoranthene (BbF); prevalent OPAH compounds were 9-fluorenone (9-FO) and 9,10-anthraquinone (ATQ). These compounds were related to characteristics of traffic emissions. The multiple diagnosis ratio and correlation analysis showed that exhaust emissions were the main source of the PAHs and OPAHs in the studied environment. PMF modeling quantification of the relative contribution of traffic emissions to PAHs in roadside soils was 45 % on average. The present study characterized the extent and range of traffic-induced PAH and OPAH emissions, providing valuable information for understanding the environmental behaviors and potential risks of traffic-related contaminants in high-altitude areas.

Tunnel measurements reveal significant reduction in traffic-related light-absorbing aerosol emissions in China

Light-absorbing aerosols (LAA), including black carbon (BC) and brown carbon (BrC), profoundly impact regional and global climate. Vehicle emission is an important source of LAA in urban areas, but real-world emission features of LAA from the urban vehicle fleet are not fully understood. This study evaluates traffic-related BC and BrC emission factors (EFs) and their vehicular emission inventories via road tunnel measurements in Tianjin, China, in 2017 and 2021. The distance-based and fuel-based EFs of BC for the mixed fleet were 1.05 ± 1.28 mg km^-1 veh^-1 and 0.057 ± 0.057 g (kg-fuel)^-1, respectively, in 2021, with a dramatic decrease of 80.6 % compared to those in 2017. The BC EFs for gasoline vehicles (GVs, including traditional gasoline and hybrid vehicles) and diesel vehicles (DVs) were 0.55 ± 0.065 mg km^-1 veh^-1 and 10.5 ± 2.52 mg km^-1 veh^-1, respectively, in 2021. Compared to 2017, the BrC EFs also decreased significantly in 2021, by 10.8-53.6 %, with 0.32 ± 0.45 mg km^-1 veh^-1 and 0.018 ± 0.020 g (kg-fuel)^-1 of distance-based and fuel-based EFs for mixed fleet. The BrC EFs for GVs and DVs were 0.091 ± 0.024 mg km^-1 veh^-1 and 3.06 ± 0.91 mg km^-1 veh^-1, respectively, in 2021. Based on the BC and BrC EFs for GVs and DVs and annual mileage for each vehicle category, the annual vehicular LAA emission inventories were estimated. From 2017 to 2021, the annual vehicular LAA emissions in Tianjin have been significantly reduced, by 69 % for BC and 10 % for BrC. DVs account for a small amount of the vehicle population (8.4 %), but contribute to most of the BC (83 %) and BrC (86 %). Our study demonstrates the significant reduction of vehicular light-absorbing aerosols emission due to vehicle pollution prevention and control in China.

Spatial and seasonal variations in the carbon and lead isotopes of PM2.5 in air of residential buildings and their applications for source identification

To understand isotope distributions of PM_2.5 in residential buildings and apply them for source identification, carbon (δ¹³C) and lead (Pb) isotope ratios in indoor and outdoor air of residential buildings were analyzed. Moreover, factor analysis (FA) was employed to investigate sources, which were compared through isotopic analyses. The average δ¹³C values of indoor air are -26.94 ± 1.22‰ and -27.04 ± 0.44‰ in warm (August to October) and cold (February to March) seasons, respectively, and the corresponding values for outdoor air are -26.77 ± 0.54‰ and -26.57 ± 0.39‰. The average ²⁰⁶Pb/²⁰⁷Pb (²⁰⁸Pb/²⁰⁷Pb) ratios of indoor air are 1.1584 ± 0.0091 (2.4309 ± 0.0125) and 1.1529 ± 0.0032 (2.4227 ± 0.0081) in warm and cold seasons, respectively, and the corresponding values for outdoor air are 1.1594 ± 0.0069 (2.4374 ± 0.0103) and 1.1538 ± 0.0077 (2.4222 ± 0.0085). Seasonal variation in δ¹³C values or Pb isotope ratios of indoor air was not significant, and similar results were obtained for outdoor air. Significant differences were not observed between δ¹³C values or Pb isotope ratios of indoor and outdoor air. Traffic emission is the major contributor to indoor and outdoor PM_2.5 based on isotopic analyses; this result was consistent with the results of FA. The δ¹³C values of indoor air in buildings with poor ventilation conditions were significantly lighter than those of outdoor air. In summary, the spatial and seasonal variations of isotopes were similar in residential buildings, which can be used to identify sources of indoor PM_2.5, and ventilation condition is an influencing factor.

Quantification of green infrastructure effects on airborne nanoparticles dispersion at an urban scale

Many dispersion models are available to simulate the mass concentrations of particulate matter in an urban environment. Still, fewer are capable of simulating the effect of green infrastructure (GI) on the airborne nanoparticles represented by total particle number concentration (ToNC). We developed an integrated approach capable of simulating the dispersion of airborne nanoparticles under the various scenarios of green infrastructure. We demonstrated the usefulness of this approach by simulating a high-resolution spatial (250 × 250 m) concentration of traffic-emitted airborne nanoparticles at an urban scale under eight GI urban planning scenarios: the base year 2015 (2015-Rl-GI); business-as-usual for 2039 (2039-BAU-GI); three hypothetical future scenarios with maximum possible coniferous (2039-HMax-Con), deciduous (2039-HMax-Dec) trees, and grassland (2039-HMax-Grl) over the available land; and three alternative future scenarios by considering coniferous (2039-HNR-Con), deciduous (2039-HNR-Dec) trees, and grassland (2039-HNR-Grl) around traffic lanes. We assessed both the parametric and structural uncertainties due to particle transformation processes (nucleation, coagulation and deposition) and uncertainty in particle number emission factors (PNEFs) on ToNC, respectively. We also simulated the combined impact of deposition and aerodynamic dispersion of GI on ToNC reduction. The annual average ToN emission (ToNE) reduced from 5.36 × 10²² (2015) to 2.84 × 10²¹ (2039) particles due to the UK's air quality plan in future. Parametric uncertainty due to variable PNEFs might cause variation in annual ToNC from -57% to +60%. However, structural uncertainties in ToNC, due to particle transformation processes were up to -12%, -11% and +0.14% for deposition, coagulation, and nucleation, respectively. The annual ToN deposition (ToND) and concentration were 28-4800 × 10¹⁹ particles and 3.94-19.10 × 10³ # cm^-3, respectively, depending on the percentage share of GI type and annual traffic emissions. Planting maximum coniferous trees (2039-HMax-Con) simulated maximum reduction in annual ToNC. Coniferous trees near traffic lanes (2039-HNR-Con) also found to be more effective to reduce annual ToNC.

Airborne particle number concentrations in China: A critical review

Particle number concentration (PNC) is an important parameter for evaluating the environmental health and climate effects of particulate matter (PM). A good understanding of PNC is essential to control atmospheric ultrafine particles (UFP) and protect public health. In this study, we reviewed the PNC studies in the literature aimed to gain a comprehensive understanding about the levels, trends, and sources of PNC in China. The PNC levels at the urban, suburban, rural, remote, and coastal sites in China were 8500-52,200, 8600-30,300, 8600-28,400, 2100-16,100, and 5700-19,600 cm^-3, respectively. The wide ranges of PNC indicate significant heterogeneity in the spatial distribution of PNC, but also are partly due to the different measurement techniques deployed in different studies. In general, it still can be concluded that the PNC levels at urban > suburban > rural > coastal > remote sites. Except for Mt. Waliguan (a remote site of 3816 m a.s.l.), other cities had the highest PNC in spring or winter and the lowest in summer or autumn. Long-term changes of PNCs in Beijing and Nanjing indicated that PNCs of Nucleation and Aitken modes had substantially declined following stricter emission controls in recent years, but more frequent new particle formation (NPF) events were observed due to reduction in coagulation sink. Overall, traffic emission was the most dominant source of PNC in more than 94.4% of the selected cities around the world, while combustion2 (the energy production and industry related combustion source), background aerosol, and nucleation sources were also important contributors to PNC. This study provides insights about PNC and its sources around the world, especially in China. A few recommendations were suggested to further improve the understanding of PNC and to develop effective PNC control strategies.

Modeling of the effects of porous and solid barriers along the road from traffic emissions in idealized urban street canyons

In this paper, numerical modeling of concentration propagation using various types of barriers and trees with porosity properties in an idealized urban canyon to protect nearby houses was considered. To solve this problem, a modification of the Reynolds-averaged Navier-Stokes equations is used to take into account the porous medium. To validate the mathematical model and the numerical algorithm, a test problem was solved without taking into account various barriers with a source of pollution. After validation, the main problem was solved, describing the emission process of pollutants between houses using different types of grass barriers and trees with different porosity properties. The numerical simulation data were compared with the calculated values using various types of grass barriers and trees. Taking into account the optimal properties of porous trees in combination with barriers, it was found that height of the barrier itself has a minor role in the spread of pollutants.

Characteristics of iron-containing magnetic particles in household dust from an urban area: A case study in the megacity of Shanghai

In order to characterize the magnetic properties and trace sources of household dust particles, magnetic measurements, geochemical and SEM/TEM analyses were performed on vacuum dust from 40 homes in Shanghai, China. Iron-containing magnetic particles (IMPs) in the household dust were dominated by magnetite, while maghemite, hematite and metallic iron were also present. The IMPs were mainly composed of coarse-grained particles (e.g., >0.1 µm). Ultrafine superparamagnetic (SP) grains (<30 nm) increased proportionately with the abundance of the total IMPs. Household dust had more and coarser IMPs than background soil, but less and finer IMPs than street dust and industrial emissions (coal combustion and metallurgy). Metallic Fe and spherical IMPs, originating from brake wear abrasion and coal combustion, respectively, have been observed using the SEM/TEM. Contents of magnetic particles were positively correlated to Mo, Ni and Sb, while HIRM was associated with As, Mo, Pb and Sb. The multiple lines of evidence including magnetic measurements, geochemical and SEM/TEM analyses suggested that industrial and traffic emissions and street dust were dominant contributors to the IMPs. Such an approach can help to establish more precisely the sources of household dust particles and could be applied to other indoor contexts and further urban environments.

Feature selection approaches for predictive modelling of cadmium sources and pollution levels in water springs

The World Health Organization lists cadmium (Cd) as one of the top ten chemicals of public health concern. Cd is toxic at relatively low exposure levels and has acute and chronic effects on both health and the environment. In this study, we investigate a suite of data-driven methods that could assist decision-makers in estimating Cd levels in water springs, and in identifying polluting sources. Machine learning (ML) regression models were used to identify sources of contamination and predict Cd levels based on support vector machines and a variety of tree-based models, including Random Forests, M5Tree, CatBoost, and gradient boosting. Feature selection analysis revealed that heavy traffic and distance to a major power plant in the sampled area play a leading role in springs Cd contamination, together with precipitation levels and average of slopes of the closest waste dumps upstream to sampled springs. Our best performing ML model was the Adaboost regression tree using all the features (RMSE = 19.36, R^2 = 0.64). Our findings highlight the effectiveness of predictive data-driven modeling in addressing environmental challenges, particularly in high-risk areas with low resources.

Impacts of vegetation on particle concentrations in roadside environments

In roadside environments, commuters are exposed to a high level of traffic-related pollution. Despite vegetation is often used to mitigate air pollution in road environments, its air quality impacts are complex and could be both positive or negative depending on specific conditions. This study conducted field measurements to assess the air quality impacts of roadside vegetation. Three common street vegetation configurations (dense vegetation, porous vegetation, and clearing) were selected and the concentrations of size-resolved particles and black carbon were measured. Results show that dense vegetation formed an accumulation area of particle pollutants on the sidewalk and bikeway, which was attributable to the increased deposition of pollutants. Compared with porous vegetation, the increase in particle concentrations before dense vegetation was 0-35% on the sidewalk (closer to vegetation) and 0-6% on the bikeway. Due to high homogeneity, fine particles (0.3-1 μm) showed low variability among different sample points, while coarse particles (>1 μm) showed high variability and presented a significant increase in concentration before dense vegetation. Porous vegetation showed weak interception effects on pollutants, and the particle concentrations before porous vegetation were close to those in the clearing. The horizontal decay of particle concentrations in porous and dense vegetation showed that particle pollutants were difficult to penetrate dense vegetation, which concentrations of particles presented a pronounced increase in the front part (0-5 m) of dense vegetation but also showed a large drop across it. These results suggest that vegetation serves as a good filter to clean the air and could improve the air quality away from the vegetation but could also worsen the air quality close to the vegetation. This study provides an insight into the environmental impacts of roadside vegetation, which could have practical implications in air pollution abatement.

Separating the impact of gradual lockdown measures on air pollutants from seasonal variability

Analysis of near-surface measurements at several measuring points in Graz, Austria, reveals the impact of restrictive measures during the COVID-19 pandemic on the emission of atmospheric pollutants. We quantify the effects at traffic hotspots, industrial and residential areas. Using historical data collected over several years, we are able to account for meteorological and seasonal confounders. Our analysis is based on daily means as well as intraday pollution level curves. Nitrogen dioxide (NO₂) has decreased drastically while the levels of particulate matter PM₁₀ and carbon monoxide (CO) mostly exhibit little change. Traffic data shows that the decrease in traffic frequency is parallel to the decline in the levels of NO₂ and NO.

Vehicle-derived ultrafine particulate contaminating bees and bee products

Despite adverse health effects, ultrafine particulate matter (UFP), i.e., PM less than 0.1 μm in diameter, is an emerging pollutant not subject to regulation. UFP may cause both lung inflammation and cardiopulmonary disease and may enter the brain directly via the olfactory bulb, affecting the nervous system. In highly urbanized environments, diesel and gasoline vehicles are among the major sources of UFP including combustion-generated solid particle pollutant and metal-based particles. Metal-based UFP are of much concern, as they may promote inflammation and DNA damage via oxidative stress with generation of free radicals and reactive oxygen species (ROS). We used the honeybee as an alternative sampling system of UFP in an area of the Po Valley (Northern Italy), which is subject to intense traffic. Worker bees are widely recognised as efficient samplers of air pollutants, including airborne PM. During flight and foraging activity, pubescence of the bees promotes the accumulation of electrical charge on the body's surface, enhancing attraction to air pollutants. Bees living near the main Italian highway, the Autostrada A1, displayed a contamination of nanosized Fe-oxides/hydroxides and baryte. Sources of Fe-bearing and baryte ultrafine particles are primarily the vehicles speeding on the motorway. Pollen collected by forager bees and honey produced by the bee colony displayed contamination by nanosized Fe-oxides/hydroxides and baryte. Such a contamination exposes pollinators and humans to UFP ingestion, endangering the safety of food produced at traffic-influenced sites. Given the global spread of traffic, our findings suggest that exposure and environmental impact of ultrafine Fe-oxides/hydroxides and baryte are potentially ubiquitous, although usually overlooked in environmental policy discussions.

Micro-scale particle simulation and traffic-related particle exposure assessment in an Asian residential community

Conducting studies on sharp particulate matter (PM) gradients in Asian residential communities is difficult due to their complex building arrangements and various emission sources, particularly road traffic. In this study, a synthetic methodology, combining numerical simulations and minor field observations, was set up to investigate the dispersion of traffic-related PM in a typical Asian residential community and its contribution to PM exposure. A Lagrangian particle model (GRAL) was applied to estimate the spatiotemporal variation of the traffic-related PM increments within the community. A detailed topography dataset with 5 m horizontal resolution was used to simulate a micro-scale flow field. The model performance was comprehensively validated using both in-situ and mobile observations. The coefficient of determination (R²) of the simulated vs. observed PM_2.5 reached 0.81 by an artery road, and 0.85 in alleys without significant road traffic. The maximum increments of kerbside PM exposure concentration contributed by road traffic during rush hour were found to be 38% (PM₁₀) and 40% (PM_2.5). This synthetic method was used to assess the impact of synoptic wind and canyon orientation on residents' PM_2.5 exposure related to traffic exhaust. Perfect exponential decay curves of traffic-related PM_2.5 were found within canyons. The decrease of road-traffic PM_2.5 on five different floor levels, compared with that on kerbside levels, ranged between 42% and 100%. The results demonstrated that in complex Asian communities, Lagrangian particle models such as GRAL can simulate the spatial distribution of PM₁₀ and PM_2.5 and assess the residents' outdoor exposure.

Integrated dispersion-deposition modelling for air pollutant reduction via green infrastructure at an urban scale

Green infrastructure (GI) can reduce air pollutants concentrations via coupled effects of surface deposition and aerodynamic dispersion, yet their magnitudes and relative effectiveness in reducing pollutant concentration are less studied at the urban scale. Here, we develop and apply an integrated GI assessment approach to simulate the individual effects of GI along with their combined impact on pollutant concentration reduction under eight GI scenarios. These include current for year 2015 (2015-Base); business-as-usual for year 2039 (2039-BAU); three alternative future scenarios with maximum possible coniferous (2039-Max-Con), deciduous (2039-Max-Dec) trees, and grassland (2039-Max-Grl) over the available land; and another three alternative future scenarios by considering coniferous (2039-NR-Con), deciduous (2039-NR-Dec) trees, and grassland (2039-NR-Grl) around traffic lanes. A typical UK town, Guildford, is chosen as study area where we estimated current and future traffic emissions (NO_x, PM₁₀ and PM_2.5), annual deposited amount and pollutants concentration reductions and percentage shared by dispersion and deposition effect in concentration reduction under above scenarios. The annual pollutant deposition was found to vary from 0.27-2.77 t·yr^-1·km^-2 for NO_x, 0.46-1.03 t·yr^-1·km^-2 for PM₁₀ and 0.08-0.23 t·yr^-1·km^-2 for PM_2.5, depending on the percentage share of GI type and traffic emissions. The 2039-Max-Dec showed the aerodynamic effect of GI can reduce the annual pollutant concentration levels up to ~10% in NO_x, ~1% in PM₁₀ and ~0.8% in PM_2.5. Furthermore, the total reductions can be achieved, via GI's coupled effects of surface deposition and aerodynamic dispersion, up to ~35% in NO_x, ~21% in PM₁₀ and ~8% in PM_2.5 with ~75% GI cover in modelled domain under 2015-Base scenario. Coniferous trees (2039-Max-Con) were found to promote enhanced turbulence flow and offer more surface for deposition. Moreover, planting coniferous trees near traffic lanes (2039-NR-Con) was found to be a more effective solution to reduce annual pollutant concentration.

Tree distribution, morphology and modelled air pollution in urban parks of Hong Kong

Trees offer a range of ecosystem services and remain important in providing human benefits. However, emerging literature questions the long-accepted view of trees being able to improve air quality in urban parks. The aerodynamic effect of trees was identified as a major reason for the change of pollutant distribution in near-road parks, where trees can act as porous barriers and cause localised concentration increase. Although not yet fully developed, planting strategies aiming to mitigate the negative effect of vegetation on air quality should be encouraged in future park design. In this study, we explored the effect of tree planting design on pollutant diffusion by integrating field surveys in urban parks in Hong Kong with computational fluid dynamic (CFD) modelling. A series of indicators associated with tree morphology and landscape were derived from the surveys and their influence on air pollutant distribution in parks was examined using ENVI-MET. Dense trees with low crown base were found effective in improving air quality within parks when planted as barriers with a width of ~15 m at borders. However, more extensive planting led to a decrease in wind velocity and an increase in pollutant concentrations, which should be avoided. Tall trees tended to have little influence on airflow at the pedestrian level, which means they seem appropriate for small urban parks where wide barriers are not applicable and rapid ventilation should be encouraged. The tree distribution also altered the airflow and pollutant dispersion in parks. Our study provides clues for thoughtful planting strategies which can optimise air quality in urban parks.

Traffic contribution to polycyclic aromatic hydrocarbons in road dust: A source apportionment analysis under different antecedent dry-weather periods

Road dust (RD) and its adsorbed pollutants have been regarded as a leading source of diffuse stormwater pollution. Therefore, a source-oriented mitigation strategy of pollutants in RD is important for an integrated stormwater management. In this study, a total of 66 RD samples were collected from 22 asphalt roads with five traffic load categories under different antecedent dry-weather periods (ADPs) in the city of Dresden, Germany. The surface loads (0.1-30.91 μg m^-2) and solid-phase concentrations (0.95-27.83 μg g^-1) of polycyclic aromatic hydrocarbons (PAHs) in RD were determined. The results show that the Σ₁₆PAHs contents decreased with increasing distance from the city center to the city border. One-way ANOVA indicated that surface load was significantly dominated by ADPs and solid-phase concentration was statistically traffic-load dependent. According to the positive matrix factorization (PMF) receptor model, gasoline- and diesel-powered engine emissions always accounted for the highest proportions of total PAH contents. However, with an increasing ADP, the PAHs contents attributed to the incineration and tire debris became evident. The source-specific risks posed by PAHs were further estimated by the incremental lifetime cancer risk (ILCR) analysis. Traffic contributed to the majority of the carcinogenic substances. Moreover, the hazard quotient (HQ) and mean hazard quotient (MHQ) for the ecological risk assessment suggest that PAHs in RD had a 21% probability of being toxic to benthic organisms and aquatic environments. CAPSULE: Σ₁₆PAH content decreased with an increasing distance from the city center to border, and an increasing number of PAH sources was identified with an increasing residual time.

Morphology and composition of particles emitted from a port fuel injection gasoline vehicle under real-world driving test cycles

Traffic vehicles, many of which are powered by port fuel injection (PFI) engines, are major sources of particulate matter in the urban atmosphere. We studied particles from the emission of a commercial PFI-engine vehicle when it was running under the states of cold start, hot start, hot stabilized running, idle and acceleration, using a transmission electron microscope and an energy-dispersive X-ray detector. Results showed that the particles were mainly composed of organic, soot, and Ca-rich particles, with a small amount of S-rich and metal-containing particles, and displayed a unimodal size distribution with the peak at 600 nm. The emissions were highest under the cold start running state, followed by the hot start, hot stabilized, acceleration, and idle running states. Organic particles under the hot start and hot stabilized running states were higher than those of other running states. Soot particles were highest under the cold start running state. Under the idle running state, the relative number fraction of Ca-rich particles was high although their absolute number was low. These results indicate that PFI-engine vehicles emit substantial primary particles, which favor the formation of secondary aerosols via providing reaction sites and reaction catalysts, as well as supplying soot, organic, mineral and metal particles in the size range of the accumulation mode. In addition, the contents of Ca, P, and Zn in organic particles may serve as fingerprints for source apportionment of particles from PFI-engine vehicles.

Exploratory assessment of outdoor and indoor airborne black carbon in different locations of Hanoi, Vietnam

Black carbon (BC) is a significant component of atmospheric particulate matter, especially in areas affected by combustion emissions. Despite the fact that air pollution is a great concern in Vietnam, there are no studies on the level of BC in the outdoor and indoor environment. In this exploratory study, an assessment of urban BC concentrations was conducted through monitoring of both outdoor and indoor BC concentrations in three households and one working office at different locations across Hanoi. PM_2.5 and meteorology data were also obtained for this monitoring period to evaluate the association between them and the outdoor BC concentration. Overall, the mean indoor and mean outdoor BC concentrations by 30 second-logs for the monitoring period were 4.42 μg/m³ and 4.89 μg/m³, respectively. Time-series analysis of paired indoor and outdoor BC concentrations suggested that indoor BC level was usually influenced by outdoor BC level (r = 0.78, p < 0.001). In this study, we observed a significant positive association between outdoor BC and PM_2.5 (r = 0.39, p < 0.001) while outdoor BC negatively correlated with wind speed (r = -0.34, p < 0.001). The level of outdoor BC in Hanoi measured in this study is relatively high and should be confirmed by further studies.

Pro-inflammatory responses to PM0.25 from airport and urban traffic emissions

Air traffic is rapidly growing, raising concerns about the air pollution in the surroundings of airports and its impact on public health. However, little is known about the impact of air pollution sources on air quality and health in the vicinity of airports. In this study, the sources and adverse health effects of airport-related particulate matter (PM) were investigated and compared to those of urban traffic emissions. Ambient PM_0.25 were collected at the Los Angeles International Airport (LAX) and at a central Los Angeles site (USC campus), along with PM_2.5 collected directly from turbine and diesel engines. The particle chemical composition, oxidative potential (OP) (ascorbic acid (AA), and electron spin resonance (ESR) assay) as well as their reactive oxygen species (ROS) activity, inflammatory potential (interleukin (IL) 6 and 8 and tumor necrosis factor (TNF)-α) and cytotoxicity on human bronchial epithelial (16HBE) cells were assessed. Chemical composition measurements confirmed that aircraft emissions were the major source to LAX PM_0.25, while the sources of the USC samples were more complex, including traffic emissions, suspended road and soil dust, and secondary aerosols. The traffic-related transition metals (Fe and Cu) in LAX and USC samples mainly affected OP values of particles, while multiple factors such as composition, size distribution and internalized amount of particles contributed to the promotion of ROS generation in 16HBE cells during 4 h exposure. Internalized particles in cells might also play an important role in activating inflammatory responses during cell recovery period, with LAX particles being more potent. Our results demonstrated considerable toxicity of airport-related particles, even at low exposure concentrations, suggesting that airport emission as source of PM_0.25 may also contribute to the adverse effects on public health attributable to PM. The potency of such particles is in the same range as those collected at a site in urban area impacted heavily by traffic emissions.

Characteristics and aging of traffic-derived particles in a highway tunnel at a coastal city in southern China

Road traffic is one of the major sources of particulate matters in the atmosphere. Tunnels provide a semi-closed place to measure traffic-derived particles before the particles were photo-chemically modified in the open air. In this study, aerosol particles were collected in a tunnel, and an urban site for comparison at a coastal city in south China. The particles were analyzed by using a transmission electron microscope coupled with an energy-dispersive X-ray spectrometry. There were four groups of particles according to sources: tailpipe-emitted particles, wear debris, road dust, and secondary particles. Tailpipe-emitted particles included soot, organic, and a part of sulfate and metal particles. Wear debris were characterized by their distinct metal components. Road dust was composed of mineral particles and fly ash. Secondary particles were some sulfate particles and mixture particles. Sulfate particles were further divided into two subtypes: with and without organic coating. Sulfate particles with organic coating accounted for 56.2% of total sulfate particles in the tunnel, while the percentage was 36.9% at the urban site, indicating that sulfate particles were more easily coated by organics in the tunnel than the urban site. However, the aging degree of sulfate particles in the tunnel was weaker than that at the urban site, which was attributed to the absence of photochemical reactions in the tunnel environment. Some mixture particles had a core-shell structure (C-S particles). The composition and morphologies of the cores of the C-S particles were similar to those of mineral, metal, and mixture particles. The shells of the C-S particles were mainly composed of organics. The C-S particles were more aged than the sulfate particles with coating in the tunnel environment, suggesting that mineral and metal components could efficiently enhance particle aging in the absence of photochemical reactions.

High spatial- and temporal-resolution anthropogenic heat discharge estimation in Los Angeles County, California

Anthropogenic heat flux (Q_f), which originates through energy consumption from buildings, industrial plants, vehicle exhausts, and human metabolism releases, is an important component in the urban Surface Energy Balance (SEB) system, and is key to understanding of many urban environmental issues. The present study provided a hybrid Q_f modeling approach, which combined the inventory and GIS approach to create a 365-day hourly Q_f profile at 120 m spatial resolution in Los Angeles County, California, USA. Q_f was estimated by separate calculation of heat release from buildings, traffics, and human metabolism, respectively. The results indicated that Q_f showed different magnitudes and diurnal patterns between workdays (dual-peak shape) and weekends/holidays, and also varied with seasons, and land use types. Qf yielded the highest values in the summer workdays, with its maximum value of 7.76 w/m². Q_f in hot summer workdays was obviously higher than that in the average summer workdays, which caused by higher demands for space cooling in buildings, and can reach 8.14 w/m² at maximum. Building energy consumption was identified as the dominant contributor to the Q_f in Downtown Los Angeles, which was found to have the largest mean Q_f throughout the year among all neighborhoods. It can be concluded that Q_f in the downtown was more significant in workdays than that in non-workdays, and its maximum value can reach 100 w/m². It is suggested that our approach may have wider applicability for Q_f estimation in large areas compared with the existing studies, as all the data used were available to the public. A high spatial and temporal Q_f profile, which can readily be incorporated into urban energy balance and Urban Heat Island (UHI) studies, provides valuable data and information for pertinent government agencies and researchers.

Refined 2013-based vehicle emission inventory and its spatial and temporal characteristics in Zhengzhou, China

Vehicle emission is becoming one of the most important pollution sources because of the increase in vehicle population and activity in China. A more reasonable and complete vehicle emission inventory in Zhengzhou for the year 2013 was developed in this study. This inventory is suitable for local emission factors and vehicle kilometers of travel. Estimates show that the total carbon monoxide (CO), hydrocarbon (HC), nitrogen oxide (NO_X), particulate matter (PM_2.5 and PM₁₀) and sulfur dioxide (SO₂) emissions were 291Gg, 35Gg, 106Gg, 6Gg, 7Gg, and 3Gg, respectively. Approximately 55% of CO and HC emissions were from light duty gasoline vehicles and normal gasoline motorcycles, whereas approximately 60% of NO_X, PM_2.5, PM₁₀ and SO₂ were from heavy duty diesel vehicles, heavy duty diesel trucks, and medium duty diesel trucks. The spatial distribution of emissions was allocated in grid cells based on a road network and traffic flows with a resolution of 1km×1km at different road types and locations, which shows that the six aforementioned air pollutants have similar characteristics in administrative districts. Emissions are mainly concentrated on the central grid cells of each part and in good agreement with line sources. The spatial characteristics were compared at a resolution of 3km×3km and in a population-based approach. The network approach yields better level estimates in this study. Meanwhile, the preliminary temporal profiles were also established for on-road mobile source.

Characteristics of ammonia gas and fine particulate ammonium from two distinct urban areas: Osaka, Japan, and Ho Chi Minh City, Vietnam

Continuous and simultaneous measurements of ammonia gas (NH₃) and fine particulate ammonium (PM_2.5NH₄⁺) were performed in two distinct urban areas: Osaka, Japan, and Ho Chi Minh City (HCMC), Vietnam. Measurements were performed using a new online instrument. Two measurement periods were conducted during February 11-March 12, 2015 (cold period), and July 1-September 14, 2015 (warm period), at the urban site in Osaka, while 17 days of measurements, from May 21 to June 8, 2015, were conducted at the urban site in HCMC. The average NH₃ concentration at the HCMC site was much higher than that at the Osaka site. The differences in the NH₃ levels between the two cities are a result of their different emission sources. Traffic emission is a significant contributor to the NH₃ levels within the urban area in Osaka. Conversely, the contribution of traffic emission to the NH₃ levels in the HCMC urban area is negligible. With a population of around 8.5 million people living in the urban area of HCMC, the high NH₃ level is due to human sources and poor waste management systems, especially because of the high temperature (30 °C) and dense population of the city (density up to 42,000 inhabitants per km²). In contrast to the NH₃ levels, the highest PM_2.5NH₄⁺ level occurred during the cold period at the Osaka site, and the average level at this site was higher than that at the HCMC site. The availability of atmospheric acids, low temperature, and high humidity facilitates the formation of ammonium. Our results indicate that NH₃ plays a key role in secondary inorganic aerosol formation; therefore, it contributes to a significant amount of PM_2.5 at the Osaka site. In contrast, the high levels of PM_2.5 observed at the HCMC site are likely from road traffic emission, mainly motorcycles, rather than secondary inorganic aerosol formation.

A method to estimate spatiotemporal air quality in an urban traffic corridor

Air quality exposure assessment using personal exposure sampling or direct measurement of spatiotemporal air pollutant concentrations has difficulty and limitations. Most statistical methods used for estimating spatiotemporal air quality do not account for the source characteristics (e.g. emissions). In this study, a prediction method, based on the lognormal probability distribution of hourly-average-spatial concentrations of carbon monoxide (CO) obtained by a CALINE4 model, has been developed and validated in an urban traffic corridor. The data on CO concentrations were collected at three locations and traffic and meteorology within the urban traffic corridor.(1) The method has been developed with the data of one location and validated at other two locations. The method estimated the CO concentrations reasonably well (correlation coefficient, r≥0.96). Later, the method has been applied to estimate the probability of occurrence [P(C≥Cstd] of the spatial CO concentrations in the corridor. The results have been promising and, therefore, may be useful to quantifying spatiotemporal air quality within an urban area.