Evaluating the Role of Roadside Green Belts in Mitigating Particulate Matter-Bound Polycyclic Aromatic Hydrocarbon Pollution

Abstract

This study aimed to evaluate the effectiveness of roadside green infrastructure in reducing particulate matter-bound polycyclic aromatic hydrocarbons (PM-bound PAHs) in urban soils and to assess the associated human health risks in Sabzevar, Iran, a developing city characterized by limited green space and sparse environmental monitoring. We conducted soil sampling across 4 traffic-influenced zones with varying vegetation cover, including a non-vegetated control, during the summer of 2022. Sampling was performed at standardized distances along vegetated buffer strips to assess spatial distribution patterns of 15 priority PAHs. Soil extracts were analyzed via Gas Chromatography-Mass Spectrometry (GC-MS), and vegetation density was quantified using the Normalized Difference Vegetation Index (NDVI) from satellite imagery. Traffic dynamics were proxied by posted speed limits. Health risks, including lifetime cancer risk (LTCR), were probabilistically estimated using Monte Carlo simulations. Results demonstrated significant spatial variability in PAH concentrations. The highway and control sites exhibited the highest total and high-molecular-weight PAH levels, whereas the inner-city boulevard showed the lowest overall contamination but elevated phenanthrene concentrations. No statistically significant reduction in PAH concentrations was observed up to 50 m behind vegetative buffers. However, several carcinogenic PAHs (eg, benzo[a]pyrene and dibenzo[a,h]anthracene) were negatively correlated with NDVI and positively correlated with vehicle speed limits, indicating that vegetation density and traffic intensity significantly influence roadside PAH distribution. Health risk estimates remained below regulatory thresholds for all demographic groups, although relatively higher susceptibility was observed among children, the elderly, and adolescent males. In conclusion, roadside vegetation alone did not provide measurable short-distance attenuation of soil PAHs, but vegetation density and traffic characteristics were important determinants of contamination patterns. These findings suggest that effective mitigation of traffic-related PAHs in developing cities requires integrated urban green planning combined with traffic management strategies rather than reliance on vegetation buffers alone.

Keywords

polycyclic aromatic hydrocarbons air pollution vegetation soil pollution

Introduction

Rapid urbanization, population growth, and industrial development have led to a substantial increase in motor vehicle ownership and industrial activities worldwide.¹ This rapid transformation has fundamentally altered urban environmental quality, particularly in low- and middle-income countries where infrastructure expansion often precedes effective environmental governance.² In many developing cities, such as those in Iran, urban expansion has outpaced environmental regulations, resulting in congested traffic corridors and expanding industrial zones.^3,4 Consequently, urban residents are chronically exposed to elevated concentrations of traffic-related air pollutants, especially in densely populated roadside environments. These factors contribute to the continuous emission of air pollutants into the atmosphere, worsening ambient air quality on a daily basis.⁵ In urban transport corridors, the combination of high vehicle density, a predominance of older diesel and gasoline vehicles, mixed light- and heavy-duty fleets, and inadequate emission control measures exacerbates pollutant levels, posing a persistent challenge for public health and environmental management.⁶ Understanding pollutant behavior in such microenvironments is therefore critical for designing effective mitigation strategies.

Road traffic emissions are a major source of a wide range of air pollutants, including nitrogen oxides (NOₓ), carbon monoxide (CO), volatile organic compounds (VOCs), particulate matter (PM), and polycyclic aromatic hydrocarbons (PAHs).⁷ PAHs are of particular concern due to their toxic, mutagenic, and carcinogenic properties.⁸ They are predominantly emitted through incomplete combustion processes in vehicle engines and tend to adsorb onto particulate matter of varying sizes: coarse PM (PM₁₀-PM_2.5), which settles relatively quickly and deposits near roadways; fine PM (PM_2.5), which can remain suspended longer and penetrate deeper into the respiratory system; and ultrafine PM (PM_0.1), which exhibits high surface area and strong adsorption capacity for hydrophobic pollutants like PAHs, allowing for long-range transport and potential systemic exposure.⁹ These deposited particles accumulate in roadside soils, creating long-term contamination reservoirs that may re-suspend into the atmosphere or enter the human body through inhalation, ingestion, or dermal contact. Once inhaled, PM-bound PAHs can penetrate deep into the respiratory tract, contributing to respiratory and cardiovascular diseases, DNA damage, and increased cancer risk.^10,11 Sensitive groups such as children (<12 years) and the elderly (⩾65 years) are particularly vulnerable to these health effects due to higher inhalation rates or weaker physiological defenses.¹⁰ Therefore, assessing both environmental distribution and associated health risks of roadside PAHs is essential for evidence-based urban health protection.

In recent years, green infrastructure, such as roadside vegetation belts, green walls, and urban forests, has emerged as a promising, nature-based solution to mitigate air pollution.^12,13 Vegetation can act as a physical barrier, altering airflow patterns, trapping particles, and facilitating pollutant deposition on leaf surfaces.^14,15 Studies in various urban contexts have demonstrated that dense and continuous vegetation along roadways can significantly reduce downwind concentrations of PM, black carbon, and PAHs, particularly when plant species are selected for high leaf surface roughness and pollutant capture efficiency.^14,16,17 Research in both European and Asian cities has shown measurable decreases in roadside PM_2.5 and PM₁₀ levels when effective vegetation barriers are in place.¹⁴ Beyond pollutant reduction, green spaces offer co-benefits including heat island mitigation, noise reduction, and improved urban esthetics.¹⁸ However, reported mitigation efficiencies vary widely depending on vegetation structure, density, local meteorology, traffic intensity, and urban morphology, suggesting that effectiveness is highly context-specific.

Despite growing evidence for the pollutant-reducing role of green infrastructure, important knowledge gaps remain, particularly regarding the role of roadside vegetation in reducing coarse settleable particles carrying PAHs in developing cities, where green space is often limited and environmental monitoring is scarce. Previous research has largely focused on fine particulate matter (PM_2.5) or gaseous pollutants, with fewer studies integrating detailed spatial pollutant measurements, vegetation density indices, and site-specific traffic and industrial conditions. Moreover, limited research has quantitatively linked vegetation density metrics (eg, NDVI), traffic intensity proxies, and probabilistic health risk assessment within a unified analytical framework. To address these gaps, the present study aims to (1) quantify the spatial distribution of 15 priority PAHs in roadside soils across zones with varying vegetation density, (2) evaluate the effectiveness of roadside green belts in attenuating PM-bound PAHs at increasing distances from traffic sources, and (3) assess age- and sex-specific carcinogenic and non-carcinogenic health risks using probabilistic modeling. We systematically investigated the influence of green space belts along major roads in Sabzevar, Iran, on PAHs bound to air settleable suspended particles deposited in roadside soils. Sabzevar represents a rapidly developing urban setting with limited green infrastructure and sparse environmental monitoring, making it an informative case study for similar cities in the Global South. By integrating pollutant monitoring, satellite-derived vegetation density analysis, traffic and industrial context, and age-specific health risk assessment, this work provides a novel, context-specific evaluation of how roadside green infrastructure influences PAH contamination in urban soils. The findings contribute to a more nuanced understanding of the realistic mitigation potential of vegetation barriers and provide evidence to support integrated urban planning and pollution control strategies in developing cities.

Materials and Methods

Study Area

This study was conducted in Sabzevar, a northeastern Iranian city with an estimated population of about 250 000 based on the 2016 national census. Geographically, the city lies between longitudes 57°37′-57°46′ and latitudes 36°22′-36°90′. Sabzevar has a predominantly arid climate, characterized by an average annual precipitation of nearly 180 mm and a mean relative humidity of 45%. Temperature extremes range from an average of approximately 2°C in December, the coldest month, to as high as 45°C in July. The city’s traditional urban structure, featuring narrow streets, often leads to persistent traffic congestion throughout the day. Furthermore, the presence of one of Iran’s main highways passing through Sabzevar may contribute to elevated levels of air pollution. A detailed map illustrating the study region, sampling locations, and main roads is presented in Figure 1.¹⁹

Figure 1.

Study area, sampling locations and street map. CTR (Control site): a non-vegetated section of the Sabzevar Beltway with traffic volume comparable to vegetated sites; BLT (Beltway): a moderately trafficked vegetated section of the same beltway; HWY (Highway): the Sabzevar–Tehran intercity highway, a high-traffic corridor adjacent to industrial areas; and ICB (Inner-city boulevard): an inner-city boulevard with dense, continuous green vegetation forming a defined buffer.

Study Settings and Sample Collection

Field sampling was conducted between June and August 2022, coinciding with the dry season to minimize precipitation-driven variability in contaminant levels. Sampling locations in Sabzevar, Iran, were selected to represent a range of roadside vegetation coverage, traffic intensities, and proximity to potential emission sources. Inclusion criteria required (i) the presence of a major road with consistent traffic flow, (ii) either a well-established roadside green belt or the complete absence of vegetation for control comparison, and (iii) safe accessibility for sampling. Sites with ongoing construction, recent soil disturbance, or visible contamination unrelated to vehicular or industrial emissions were excluded. Based on these criteria, 4 representative zones were identified: CTR (Control site): a non-vegetated section of the Sabzevar Beltway with traffic volume comparable to vegetated sites; BLT (Beltway): a moderately trafficked vegetated section of the same beltway; HWY (Highway): the Sabzevar–Tehran intercity highway, a high-traffic corridor adjacent to industrial areas; and ICB (Inner-city boulevard): an inner-city boulevard with dense, continuous green vegetation forming a defined buffer. To assess the pollutant filtering effect of these vegetated buffers, soil samples were collected at 4 standardized positions relative to the green space line: directly in front of the buffer on the traffic-facing side (sampling site 1 (SS1)), immediately behind the buffer (sampling site 2 (SS2)), 20 m behind the buffer (sampling site 20 m (SS 20 m), and 50 m behind the buffer (sampling site 50 m (SS 50 m). These distances were selected based on the average vegetation height at each site, ensuring proportional scaling of sampling positions to buffer structure. The same spatial design was applied across all vegetated road segments (BLT, HWY, and ICB) and the non-vegetated control site (CTR). In the HWY site, 2 additional samples were collected from the highway shoulder to evaluate the influence of industrial proximity on PAH accumulation. All sampling was conducted under dry weather conditions, with no rainfall in the preceding week. At each sampling point, the top ~2 cm of soil was carefully cleared of debris, stones, and plant material prior to collection. Approximately 50 g of surface soil was collected using a clean stainless steel spatula, placed into pre-labeled polyethylene bags, sealed to prevent contamination, and transported in cooled containers to the laboratory for subsequent analysis. The sampled area at each point covered roughly 0.25 m² to ensure a representative measure of PAH-bound dust particles near the surface. This stratified and site-specific sampling approach enabled a detailed evaluation of PAH distribution in relation to vegetation coverage, buffer structure, traffic intensity, and industrial influence.

Laboratory Analysis

Sample Preparation and Extraction Method

Following field collection, soil samples were immediately transferred into pre-labeled, clean polyethylene bags to prevent contamination and preserve their original composition. Samples were stored at 4°C and processed within 24 hours. Upon arrival at the laboratory, samples were air-dried at room temperature (20°C-25°C) for 48 hours in a dust-free environment. After drying, the soils were gently disaggregated and sieved through a 100-mesh stainless steel sieve (150 μm) to remove stones, coarse debris, and plant material, producing a uniform texture for extraction. For PAH extraction, 10 g of sieved soil was weighed into a clean 250 mL glass extraction vessel and mixed thoroughly with 10 g of anhydrous sodium sulfate (Na₂SO₄) to ensure complete dehydration and facilitate solvent penetration. Dichloromethane (CH₂Cl₂, ⩾99.8% purity) was used as the extraction solvent. Initially, 40 mL of CH₂Cl₂ was added to the soil–Na₂SO₄ mixture, which was then sonicated for 5 minutes at 40 kHz to promote the release of PAHs from soil particles. The mixture was then subjected to continuous Soxhlet extraction using 300 mL CH₂Cl₂ for 24 hours at reflux temperature (40°C-45°C). After extraction, the eluate was concentrated to ~2 mL under reduced pressure using a rotary evaporator (Heidolph Laborota 4000) at 35°C. The concentrated extract was filtered through a 0.22 μm PTFE syringe filter prior to GC–MS analysis.^20,21

Gas Chromatography–Mass Spectrometry (GC–MS) Analysis

Quantitative and qualitative determination of 15 U.S. EPA priority PAHs (naphthalene [NA], acenaphthylene [ACY], acenaphthene [ACE], fluorene [FLU], phenanthrene [PHE], anthracene [ANT], fluoranthene [FLT], pyrene [PYR], benzo[a]anthracene [BaA], chrysene [CRY], benzo[b]fluoranthene [BbF], benzo[a]pyrene [BaP], indeno[1,2,3-cd]pyrene [IND], dibenzo[a,h]anthracene [DBA], and benzo[k]fluoranthene [BkF]) was performed using a Shimadzu QP-2010 SE GC–MS equipped with an Rtx-5MS capillary column (30 m × 0.25 mm i.d., 0.25 μm film thickness).

GC oven program:

40°C for 1 minute (initial hold)

Ramp to 120°C at 25°C/minute

Ramp to 160°C at 10°C/minute

Ramp to 300°C at 5°C/min, hold for 15 minutes

Injection was performed in splitless mode at 300°C, and the MS ion source temperature was maintained at 280°C. Selected Ion Monitoring (SIM) mode was applied for enhanced sensitivity and specificity. PAHs were identified based on retention times and characteristic m/z ion fragments, with concentrations expressed as ng/g of dry soil.^19,22

Quality Control of PAH Determination

To maintain analytical reliability, the GC–MS system was calibrated daily using freshly prepared multi-point standard solutions. Each analytical series included procedural blanks, spiked blanks, matrix spikes, and replicate samples to monitor instrument stability and potential contamination. The detection limits for individual PAHs, calculated from the standard deviation of 10 blank determinations, were within the range of 0.001 to 0.004 ng g⁻¹. Method performance was further validated through recovery assessments and desorption efficiency tests, which showed acceptable recovery rates between 82% and 103%. Certified reference standards of PAHs (Sigma-Aldrich, Germany) were used to prepare a stock solution, which was subsequently diluted with dichloromethane to obtain working standards at concentrations of 10, 100, 200, 500, and 1000 ppb. Calibration curves generated from these standards demonstrated excellent linearity across the tested range, confirming the suitability of the method for quantitative analysis.

Green Space Index and Buffer Strip Characteristics

The density and structure of green space adjacent to each sampling location were quantified using the Normalized Difference Vegetation Index (NDVI) in combination with direct field measurements. NDVI values were derived from radiometrically corrected Landsat 8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) imagery at a spatial resolution of 30 × 30 m, providing standardized metrics of vegetation cover. The NDVI, which ranges from −1 to +1, was calculated from red and near-infrared reflectance, with higher values indicating denser and healthier vegetation. NDVI maps were generated for each site and integrated into a GIS environment (ArcGIS version 10.8.2), where they were spatially overlaid with soil sampling points to evaluate the association between vegetation density and the distribution of PM-bound PAHs.^23,24 In the studied roadside green belts, the vegetation was relatively homogeneous, with most trees belonging to the same species and age class, which minimized variability in canopy structure and pollutant interception potential. A species inventory was compiled during field visits, confirming that the dominant tree species was consistent across all sampling sites, and tree height and canopy size showed minimal variation. This uniformity ensured that differences in PAH concentrations could be more confidently attributed to traffic and environmental factors rather than variations in plant type or size.

Field surveys were conducted to characterize the physical attributes of the roadside buffer strips, including vegetation composition, canopy height, and buffer width. Vegetation primarily consisted of shrubs and medium to tall trees, with widths varying between 5 and 12 m depending on site-specific constraints. These strips were situated between traffic lanes and pedestrian or residential zones, acting as potential physical and biological barriers to airborne pollutants.²⁵

In addition, the influence of vehicular dynamics on PAH dispersion was examined using posted speed limits from the municipal Traffic Organization as a proxy for average vehicle speed at each road segment. This parameter was incorporated to help interpret the interaction between traffic flow characteristics, vegetation density, and the spatial attenuation of PAH concentrations in roadside soils.²⁶

Health Risk Assessment

The potential health impact arising from combined exposure to multiple PAH compounds was assessed through the calculation of the benzo[a]pyrene equivalent concentration (BaPₑq), which expresses the cumulative carcinogenic potency of PAH mixtures relative to benzo[a]pyrene.¹⁹ The benzo[a]pyrene equivalent concentration for airborne PAHs (BEC) was computed according to the following equation:

B E C = \sum_{I = 1}^{n} Ci*TEFi

(1)

In this formula, Ci denotes the ambient concentration of the i PAH compound, while TEFi represents its corresponding toxicity equivalency factor (TEF), as summarized in Table S1 of the Supplemental Information. For compounds with measured concentrations below the analytical LOD, values were substituted with one-half of the LOD. The concentration term C in equation (1) was modeled as following a lognormal distribution to capture variability in environmental exposure. The health risk evaluation was stratified by sex and across 4 distinct age categories: children (4-10 years), adolescents (11-17 years), adults (18-60 years), and older adults (>60 years). The daily inhalation exposure (DIE) to PAHs for each subgroup was then calculated using the following expression:

DIE = \sum_{I = 1}^{n} B E C i * I R * T i

(2)

In this expression, Ti denotes the daily exposure duration for the i PAH, BECi represents the corresponding BaP-equivalent concentration in ambient air, and IRIRIR indicates the inhalation rate (m³/day). In our probabilistic framework (equation (2)), IR was modeled using a normal distribution, whereas BEC was assumed to follow a lognormal distribution to capture variability in daily inhalation exposure (DIE). Comprehensive details on all parameters applied for PAH risk estimation are provided in Table S2 of the Supplemental Materials.

Cancer Risk Assessment

The lifetime cancer risk (LTCR) associated with inhalation exposure to PAHs was calculated using the following equation:

L T C R = \frac{SF*DIE*EF*ED*CF}{BW*AT}

(3)

Where:

- SF is the cancer slope factor for benzo[a]pyrene (BaP), modeled as a lognormal distribution with a geometric mean of 3.14 mg/kg/day and a standard deviation of 1.80 mg/kg/day,¹⁹

- DIE is the daily inhalation exposure (ng/day),

- EF is exposure frequency (days/year),

- ED is exposure duration (years),

- CF is a unit conversion factor (10⁻⁶ mg/ng),

- BW is body weight (kg), modeled as a normal distribution,

- AT = EF × ED is the average exposure time (days).

A Monte Carlo simulation with 100 000 iterations was conducted using Crystal Ball (v11.1.2.4; Oracle Corp., USA) to estimate probabilistic LTCR values. Sensitivity analysis was also performed to identify which variables contributed most to the uncertainty in the risk estimates. In line with Xia et al. 2013,²⁷ BEC and SF were found to be the most influential parameters.

Statistical Analysis

A series of statistical procedures was applied to evaluate the relationships between PAH concentrations, vegetation characteristics, and traffic-related parameters, as well as to quantify the filtering effectiveness of green space buffers. Given the stratified sampling design, covering 4 sites (CTR, BLT, HWY, and ICB) and 4 standardized distances from the green space line at each site, the dataset allowed for both between-site and within-site comparisons. The Shapiro–Wilk test was first employed to assess the normality of PAH concentration distributions at each sampling position, given its suitability for small sample sizes. Based on these results, parametric or non-parametric tests were selected as appropriate for subsequent analyses. Descriptive statistics, including minimum, maximum, mean, and standard deviation, were calculated to summarize PAH levels across sites and distances. Pearson’s correlation coefficients were computed to assess linear associations between individual PAH compounds, total PAH concentrations, NDVI-derived vegetation density values, and posted maximum vehicle speed limits. To compare PAH concentrations among the 4 study sites, 1-way ANOVA was conducted, followed by Tukey’s post hoc tests to identify significant pairwise differences. Differences in PAH concentrations across the 4 sampling distances relative to the green space line (traffic-facing edge, immediately behind, 20 m behind, and 50 m behind) were examined using independent sample t-tests for selected pairwise comparisons, with particular focus on contrasts between roadside edge and farther distances, as well as between the 2 vegetated setbacks (20 and 50 m). All statistical analyses were performed using R software (version 4.5.1), with packages including “stats” for ANOVA and t-tests, “ggplot2” for data visualization, and “Hmisc” for correlation analyses.

Results

PAHs Concentration

Descriptive statistics of PAHs in all sampling sites are presented in Figure 2. In the CTR zone, fluorene had the highest average concentration at 1.75 ng/g (SD = 0.1), while ICB exhibited the lowest average concentration of benzo-alpha-anthracene at 0.01 ng/g (SD = 0.02). Additionally, benzo OH, anthracene, and acenaphthene reached their lowest mean concentrations in ICB at 0.17 ng/g. Conversely, fluorene peaked in BLT at 1.31 ng/g. HWY recorded the highest average concentration of chrysene at 1.5 ng/g, whereas indeno-1,2,3-cdpyrene had the lowest in this zone at 0.39 ng/g. The CTR zone had the lowest benzo-alpha-anthracene (0.03 ng/g), while ICB exhibited the highest mean level of phenanthrene at 0.65 ng/g.

Figure 2.

Descriptive statistics of particulate matter-bound PAH concentrations across sampling sites. (Dibenzo[a,h]anthracene (DBA), Indeno[1,2,3-cd]pyrene (IND), Benzo[a]pyrene (BaP), Benzo[b]fluoranthene (BbF), Chrysene (CRY), Benzo[a]anthracene (BaA), Pyrene (PYR), Fluoranthene (FLT), Anthracene (ANT), Phenanthrene (PHE), Fluorene (FLU), Acenaphthene (ACE), Acenaphthylene (ACY), Naphthalene (NA)).

Cumulative concentrations across all zones are presented in Figure 3. The HWY site exhibited the highest total PAH concentration (12.64 ng/g; SD = 1.19), followed closely by CTR (12.48 ng/g) and BLT (11.00 ng/g). The ICB site recorded the lowest contaminant load (2.51 ng/g; SD = 0.43).

Figure 3.

Total concentrations of high molecular weight (HMW) and low molecular weight (LMW) polycyclic aromatic hydrocarbons (PAHs) in surface soils across 4 sampling zones in Sabzevar, Iran. CTR (Control site): a non-vegetated section of the Sabzevar Beltway with traffic volume comparable to vegetated sites; BLT (Beltway): a moderately trafficked vegetated section of the same beltway; HWY (Highway): the Sabzevar–Tehran intercity highway, a high-traffic corridor adjacent to industrial areas; and ICB (Inner-city boulevard): an inner-city boulevard with dense, continuous green vegetation forming a defined buffer.

Figure 2 illustrates the total concentrations of high and low molecular weight PAHs (HMW-PAHs and LMW-PAHs) across all sampling locations. HWY and CTR showed higher contributions from HMW-PAHs, while BLT displayed a relatively more balanced distribution between LMW and HMW fractions. In contrast, ICB had predominantly HMW-PAHs with minimal LMW content. The figure is supported by descriptive statistics derived from the measured mean concentrations and LMW/HMW ratios.

Correlation Coefficient PM-Bound PAH Compounds

The overall Spearman correlation coefficients among PAH compounds were computed and are displayed in Figure 4. The strongest correlation (r = .94) was observed between naphthalene (NA) and acenaphthylene (ACY), as well as between NA and total PAH concentration. The weakest correlation was between acenaphthene (ACE) and indeno-1,2,3-cdpyrene (IND) at r = −0.4. A strong association was also noted between ACY and total LMW-PAH concentration, and between pyrene (PYR) and total HMW-PAH concentration.

Figure 4.

Spearman correlation coefficients of PAHs compounds based on all samples. (Dibenzo[a,h]anthracene (DBA), Indeno[1,2,3-cd]pyrene (IND), Benzo[a]pyrene (BaP), Benzo[b]fluoranthene (BbF), Chrysene (CRY), Benzo[a]anthracene (BaA), Pyrene (PYR), Fluoranthene (FLT), Anthracene (ANT), Phenanthrene (PHE), Fluorene (FLU), Acenaphthene (ACE), Acenaphthylene (ACY), Naphthalene (NA)).

Regional Comparison of PAH Concentrations

As shown in Figure 5, several statistically significant differences in PAH concentrations were observed between sites. For DBA, levels were significantly lower at HWY compared with BLT (mean difference = −0.59, P < .001) and higher at HWY compared with ICB (0.76, P < .001). IND concentrations were significantly higher at BLT versus HWY (0.85, P = .010) and BLT versus ICB (1.22, P < .001). BaP levels were elevated at CTR compared with BLT (0.73, P < .001), and higher at ICB relative to HWY (0.86, P < .001). CRY showed strong contrasts, with CTR higher than BLT (0.80, P = .010) and ICB (1.38, P < .001), and BLT lower than HWY (−0.83, P < .001). Significant differences were also observed for PYR (CTR > ICB, 1.16, P < .001), FLT (CTR > ICB, 0.78, P = .040; BLT > ICB, 0.84, P = .010), ANT (CTR > ICB, 0.67, P < .001; BLT > ICB, 0.44, P = .040), and FLU (CTR > ICB, 1.52, P < .001; BLT > ICB, 1.07, P = .020; HWY > ICB, 1.00, P = .030). ACE concentrations were significantly lower at BLT compared with HWY (−0.61, P < .001) and higher at HWY compared with ICB (0.61, P < .001). For ACY and NA, CTR and BLT had significantly lower levels than ICB (all P < .001). Total PAHs, as well as low-weight (LW) and high-weight (HW) PAHs, were markedly higher at ICB compared with CTR, BLT, and HWY (all P < .001), with differences ranging from 3.95 to 10.13 µg/m³.

Figure 5.

Mean differences in particulate matter-bound polycyclic aromatic hydrocarbons (PM-bound PAHs) concentrations between sampling sites in Sabzevar, Iran. Statistically significant differences (P < .05) are indicated, highlighting spatial variability of PAH pollution across urban zones. CTR (Control site): a non-vegetated section of the Sabzevar Beltway with traffic volume comparable to vegetated sites; BLT (Beltway): a moderately trafficked vegetated section of the same beltway; HWY (Highway): the Sabzevar–Tehran intercity highway, a high-traffic corridor adjacent to industrial areas; and ICB (Inner-city boulevard): an inner-city boulevard with dense, continuous green vegetation forming a defined buffer (Dibenzo[a,h]anthracene (DBA), Indeno[1,2,3-cd]pyrene (IND), Benzo[a]pyrene (BaP), Benzo[b]fluoranthene (BbF), Chrysene (CRY), Benzo[a]anthracene (BaA), Pyrene (PYR), Fluoranthene (FLT), Anthracene (ANT), Phenanthrene (PHE), Fluorene (FLU), Acenaphthene (ACE), Acenaphthylene (ACY), Naphthalene (NA)).

Comparison of PM-Bound PAHs Across Green Space Sampling Sites

Figure 6 presents the comparisons of PM-bound PAHs measured at 4 sampling sites along the green space line adjacent to the road: directly in front of the buffer (SS1), immediately behind the buffer (SS2), 20 m behind (SS 20 m), and 50 m behind the buffer (SS 50 m). Statistical analysis of mean differences in PAH concentrations between these sites revealed no significant differences for the majority of individual PAHs and their grouped totals (low-weight and high-weight PAHs), with all P-values exceeding .05. Specifically, for DBA, mean differences between sites ranged from −0.17 to 0.19, with P-values near 1.0, indicating no significant variation in concentrations between any 2 sampling locations along the green space line. Similarly, IND and BaP showed minimal differences between sites, with mean differences close to zero and confidence intervals encompassing zero. For BbF, although a trend toward slightly lower concentrations was observed at SS50 m compared to SS1 (mean difference = −0.33), this difference did not reach statistical significance (P = .15). Concentrations of CRY, BaA, PYR, FLT, ANT, PHE, FLU, ACE, ACY, and NA also exhibited no statistically significant differences between any sites. Total PAHs, total low-weight PAHs, and total high-weight PAHs similarly showed stable concentrations across all sampling points, with mean differences ranging between −1.66 and 0.34 and P-values consistently equal to or above .85, reinforcing the absence of significant spatial gradients within the green space line. These findings suggest that the vegetative buffer and distances up to 50 m behind the buffer do not produce statistically significant differences in PM-bound PAH concentrations under the conditions studied. While minor trends of decreasing PAH concentrations with increasing distance from the traffic-facing side are observed, these were not statistically supported, indicating a relatively uniform distribution of these pollutants across the sampled green space gradient.

Figure 6.

Comparison of particulate matter-bound polycyclic aromatic hydrocarbon (PAH) concentrations at 4 sampling positions relative to roadside green space buffers in Sabzevar, Iran: directly in front of the buffer (SS1), immediately behind the buffer (SS2), 20 m behind (SS 20 m), and 50 m behind the buffer (SS 50 m). (Dibenzo[a,h]anthracene (DBA), Indeno[1,2,3-cd]pyrene (IND), Benzo[a]pyrene (BaP), Benzo[b]fluoranthene (BbF), Chrysene (CRY), Benzo[a]anthracene (BaA), Pyrene (PYR), Fluoranthene (FLT), Anthracene (ANT), Phenanthrene (PHE), Fluorene (FLU), Acenaphthene (ACE), Acenaphthylene (ACY), Naphthalene (NA)).

Correlation of PM-Bound PAHs With NDVI and Vehicle Speed Limits

Table 1 presents the Spearman correlation coefficients between PM-bound PAH concentrations and two road-related variables: green space index (NDVI) and maximum vehicle speed limit. Several PAH compounds exhibited significant negative correlations with NDVI, including DBA (r = −0.52, P < .05), BaP (r = −0.64, P < .01), CRY (r = −0.61, P < .01), and ACE (r = −0.68, P < .01), suggesting lower concentrations of these compounds in areas with higher vegetation cover. In contrast, IND showed a significant positive correlation with NDVI (r = .60, P < .01), while other compounds demonstrated weak or non-significant associations.

Table 1.

Spearman Correlation Coefficients Between Individual PM-Bound PAH Compounds and Road-Related Environmental Variables: Green Space Index (NDVI) and Maximum Vehicle Speed Limit.

PM-bound PAHs	NDVI	Vehicle speed
Dibenzo[a,h]anthracene (DBA)	−0.52^*	0.54^*
Indeno[1,2,3-cd]pyrene (IND)	0.6^**	−0.52^*
Benzo[a]pyrene (BaP)	−0.64^**	0.73^**
Benzo[b]fluoranthene (BbF)	−0.13	0
Chrysene (CRY)	−0.61^**	0.62^**
Benzo[a]anthracene (BaA)	−0.24	0.02
Pyrene (PYR)	−0.13	0.16
Fluoranthene (FLT)	0.31	−0.22
Anthracene (ANT)	−0.15	0.4
Phenanthrene (PHE)	0.14	−0.25
Fluorene (FLU)	0.24	0.14
Acenaphthene (ACE)	−0.68^**	0.65^**
Acenaphthylene (ACY)	0.21	0.06
Naphthalene (NA)	0.16	0.06
Total PAHs	−0.12	0.25
Total LMW PAHs	0.05	0.2
Total HMW PAHs	−0.24	0.25

Asterisks indicate significance levels (^*P < .05, ^**P < .01). Negative values reflect inverse relationships, while positive values indicate direct associations.

For vehicle speed limits, several PAHs were significantly positively correlated, including DBA (r = .54, P < .05), BaP (r = .73, P < .01), CRY (r = .62, P < .01), and ACE (r = .65, P < .01), indicating higher concentrations in roads with greater speed limits. IND was negatively correlated with speed (r = −0.52, P < .05). The remaining compounds and total PAH groups (total, LMW, and HMW) showed generally weak, non-significant relationships with both NDVI and vehicle speed.

Assessment of Carcinogenic Risk

Figure 7 presents the results of the probabilistic health risk assessment for different age and sex groups exposed to atmospheric PAHs. The lifetime cancer risk (LTCR) estimates, based on the 95th percentile of the Monte Carlo simulations, showed that the highest carcinogenic risk was found among adolescent males, with an LTCR of 1.78 × 10⁻⁸, whereas the lowest LTCR was identified for adolescent females, at 1.07 × 10⁻⁸. All estimated values were significantly below the USEPA threshold of 1 × 10⁻⁶, indicating an acceptable level of cancer risk due to PAH inhalation across all population groups. Risk estimates varied by age group, reflecting physiological, metabolic, and behavioral differences. Although adolescents showed slightly elevated exposure-adjusted risks due to higher activity and inhalation rates, children and the elderly are biologically more vulnerable, and thus remain priority groups for public health protection.

Figure 7.

Estimated lifetime cancer risk (LTCR) from inhalation exposure to ambient particulate matter-bound polycyclic aromatic hydrocarbons (PAHs) across different age and sex groups in Sabzevar, Iran. All values are below the USEPA risk threshold of 1 × 10⁻⁶.

In addition to carcinogenic risk, we also evaluated the non-carcinogenic (morbidity) risk through B[a]Peq-based daily inhalation exposure (DIE). While these risks remained below reference values for all groups, children and the elderly exhibited relatively higher DIE levels, suggesting potential susceptibility to non-cancer health outcomes, including respiratory inflammation, oxidative stress, and endocrine disruption under chronic exposure conditions.

Discussion

This study provides a comprehensive multi-scale assessment of PM-bound PAHs across urban functional zones, green space buffers, and road-related environmental variables, combined with probabilistic health risk estimation, an approach rarely integrated in a single investigation. We found clear spatial variability in PAH profiles, with HWY and CTR showing the highest total and HMW-PAH concentrations, while ICB recorded the lowest load but distinct compound-specific peaks (eg, phenanthrene). Significant inter-site differences emerged for several carcinogenic PAHs, underscoring localized emission sources and traffic-related influences. Contrary to expectations, PAH concentrations showed no statistically significant attenuation within 50 m behind vegetative buffers, indicating limited barrier efficiency under the studied conditions. Correlation analyses revealed divergent relationships with green space (NDVI) and vehicle speed limits, most notably, strong negative associations of DBA, BaP, CRY, and ACE with NDVI, and positive correlations of these same compounds with speed, suggesting both vegetation density and traffic dynamics as key determinants. Finally, the health risk assessment indicated that both carcinogenic and non-carcinogenic risks were well below regulatory thresholds, though children, the elderly, and adolescent males exhibited comparatively higher susceptibility patterns, warranting continued exposure mitigation strategies in high-traffic urban zones. These results emphasize the need for context-specific assessment of urban pollution, as localized traffic emissions and micro-scale environmental conditions strongly shape PAH distribution patterns. Our findings also highlight the importance of evaluating individual PAH compounds rather than relying solely on total PM metrics, as high-toxicity PAHs persist even when overall PM levels appear reduced.

Comparison With Previous Studies

Our results align with global findings indicating that PAH concentrations are highest in areas with dense traffic and industrial activity. In particular, the HWY site exhibited the highest total PAH levels (mean: 12.64 ng/g), consistent with the findings of Sun et al in Hangzhou, where proximity to highways and industrial corridors was significantly associated with elevated PAH accumulation in roadside dust.²⁸ Wang et al also observed increased levels of high-molecular-weight PAHs (eg, chrysene, benzo[a]pyrene) near high-traffic roads in Shanghai. In our study, chrysene (1.5 ng/g) and BaP (0.90 ng/g) concentrations peaked at HWY, significantly exceeding values measured in vegetated zones like ICB (P < .001).²⁹ Kosari et al in Sabzevar highlighted traffic emissions as dominant PAH sources in ambient air, using receptor modeling. Our soil-based results corroborate their findings, with strong correlations between PAH markers such as NA–ACY (r = .94), and spatial trends reinforcing traffic proximity as a primary determinant of PAH distribution.¹⁹ The comparison of our soil-based data with previous airborne and dust studies strengthens the argument that traffic emissions contribute consistently across environmental matrices. Furthermore, the pronounced differences in HMW-PAH levels between vegetated and non-vegetated sites suggest that localized emission control strategies can be effective in reducing high-toxicity PAH exposure.

Numerous studies have emphasized the potential of vegetation to act as a natural filter for airborne pollutants. Diener and Mudu³⁰ noted that vegetation effectiveness depends on continuity, height, and structural density. Our results support this, as sites with dense vegetation (NDVI > 0.3), particularly ICB, showed significantly reduced PAH concentrations (mean: 2.51 ng/g) and strong negative correlations with NDVI, notably for BaP (r = −0.64) and ACE (r = −0.68**).³⁰ Moreover, Balmer et al. (2019) reported that vegetated zones tend to reduce the prevalence of pyrogenic PAHs, especially high-molecular-weight fractions. In our study, total HMW-PAH concentrations in ICB were more than 5.4 ng/g lower than in HWY, emphasizing the filtration capacity of well-structured green buffers.³¹

Our findings contribute to a nuanced understanding of the role of urban vegetation in mitigating airborne pollutants, particularly PM-bound PAHs, and place them in the context of a diverse body of existing literature. Vashist et al reviewed the mechanisms by which vegetation reduces particulate matter, through deposition on leaf surfaces, altered dispersion patterns, and microclimatic modifications, and emphasized that effectiveness depends heavily on species type, planting density, and spatial placement. They also highlighted substantial research gaps in quantifying pollutant-specific responses in different urban settings.³² Our observation of no significant PAH reduction across distances behind the vegetative buffer, despite visible green coverage, mirrors this perspective: placement and aerodynamic context likely constrained the buffer’s capacity to intercept traffic-derived PAHs in our study area. This reinforces the view that vegetation cannot be considered a universal solution; its pollutant removal potential is strongly conditioned by local wind patterns, vegetation morphology, and site geometry. These findings suggest that while urban vegetation contributes to pollutant mitigation, the efficiency of buffers is highly dependent on local aerodynamic conditions, leaf morphology, and buffer width. Future urban planning should consider both micro-scale vegetation placement and species selection to maximize PAH interception. Similarly, Gong et al, using a Bayesian meta-analysis, reported notable mean reductions in PM (16%-27%), NOx (14%-36%), and SO₂ (20%-48%), yet found no improvement, and in some cases increases, in ground-level O₃. They also stressed that mitigation efficiency depends on both distance from the emission source and pollutant characteristics.³³ Our lack of significant PAH reduction within 50 m of the roadway is consistent with their conclusion that proximity to high-intensity sources limits removal efficiency. Moreover, while Gong et al focused on total PM mass reductions, our compound-specific results reveal that even when PM mass might decline, the toxic subfractions such as PAHs can persist, underscoring the need for pollutant-specific evaluations.³³ Venter et al further complicate the picture by showing that street-level vegetation effects on air quality are often weak, highly variable, and context-dependent. In some cases, vegetation can trap pollutants and restrict ventilation, leading to higher concentrations in the immediate vicinity. This interpretation aligns closely with our null results for PAH reduction across the vegetative buffer and suggests that, in our study, vegetation may have acted more as a barrier to airflow than as an efficient sink, particularly at the 15 to 60 m scale where such effects are most likely to manifest.³⁴ At a broader spatial scale, Meo et al demonstrated that countries with greater green space coverage tend to have significantly lower PM_2.5, PM₁₀, and CO concentrations, with associated reductions in COVID-19 incidence and mortality. While their work suggests clear public health benefits of greenery at the macro scale, our contrasting micro-scale results underscore the scale dependency of vegetation-pollution interactions: benefits evident at regional or national scales may not necessarily emerge within short distances from heavy traffic sources, where localized aerodynamic effects dominate.³⁵ The importance of vegetation configuration is further illustrated by Moreira Junior et al., who reported ~33% lower PM_2.5 levels inside a botanical garden compared with a traffic tunnel environment. Their case demonstrates that enclosed, dense green areas with limited pollutant inflow can provide strong localized benefits. In contrast, our roadside buffer, which is both aerodynamically open and relatively narrow, lacked the same degree of enclosure and thus may have been less effective at pollutant interception.³⁶ Overall, our study advances the literature by providing compound-specific, micro-scale evidence that complements the predominantly PM mass focused research base. Consistent with Venter et al and partially with Gong et al, we find that vegetation’s ability to mitigate airborne pollutants is highly site-specific and may be minimal at street scale for fine-bound organic contaminants like PAHs. In contrast to macro-scale studies such as Meo et al, our work emphasizes that local microclimate, vegetation structure, and spatial arrangement can neutralize, or even reverse, expected air quality benefits. By integrating both NDVI and traffic speed as explanatory variables, we also extend prior work by jointly evaluating green space metrics and roadway dynamics in determining pollutant concentrations, offering a more holistic view of the interacting factors that govern urban PAH distributions.

Urban green spaces can mitigate traffic-related PAHs through a combination of physical and biological processes. Vegetation intercepts PM-bound PAHs via dry deposition on leaves, bark, and stems, with broad-leaved species, such as Platanus acerifolia, shown to accumulate more particulates due to larger surface area and complex microstructures.³⁷ This surface capture, enhanced by high leaf area index and favorable wind conditions, can reduce ambient particle concentrations by up to 20% in some settings. Vegetation also modifies local airflow, with dense, multi-layered buffers slowing wind speeds and increasing turbulence, thereby extending particle residence time and promoting deposition.³⁸ Low-porosity barriers (<40%) in particular have been linked to substantial reductions in downwind particulate matter and black carbon levels. Beyond mechanical capture, phytoremediation processes enable plants to absorb PAHs into their tissues and stimulate rhizosphere microbes that degrade these otherwise persistent pollutants. In our study, these mechanisms were reflected in significantly lower PAH concentrations at vegetated sites (ICB and BLT) compared to the highway-adjacent location, with the greatest reductions observed 20 to 50 m beyond the buffer strip.^39,40 A strong negative correlation between NDVI and PAH levels, especially for carcinogenic compounds like benzo[a]pyrene, underscores the role of vegetation density in pollutant mitigation. Moreover, the reduced abundance of high-molecular-weight PAHs in vegetated areas (5.4 ng/g less than HWY) suggests that aerodynamic filtering and surface retention are particularly effective for coarse particle-bound contaminants, aligning with established deposition and barrier function models. These findings suggest that while urban vegetation contributes to pollutant mitigation, the efficiency of buffers is highly dependent on local aerodynamic conditions, leaf morphology, and buffer width. Future urban planning should consider both micro-scale vegetation placement and species selection to maximize PAH interception.

It is important to acknowledge that our study was conducted exclusively during the dry summer season, which may influence the observed PAH concentrations and distribution patterns. During periods of higher precipitation, PAH deposition could increase due to wet scavenging, potentially reducing airborne concentrations but enhancing accumulation in soil and vegetated surfaces.⁴¹ Conversely, stronger winds in transitional or cold seasons may enhance dispersion, leading to more widespread but lower-concentration PAH deposition across urban zones. Lower temperatures in winter may also slow PAH volatilization and microbial degradation, potentially increasing the persistence of high-molecular-weight compounds in soil.⁴² Additionally, many urban green spaces lose foliage or exhibit minimal vegetation cover during winter, which may reduce the interception and filtration capacity of roadside buffers, further altering the effectiveness of green belts in mitigating PAH pollution.⁴³ These seasonal dynamics suggest that PAH accumulation and the mitigating effect of green belts could vary substantially throughout the year, underscoring the need for multi-seasonal monitoring to fully capture temporal variations in urban pollutant patterns.

Health Risk Related PAHs Exposure

Our cancer risk modeling complements prior work by Xia et al,²⁷ who identified children as the most susceptible demographic due to their higher exposure-to-body-mass ratio. The highest LTCR in our study was observed among adolescent males (1.78 × 10⁻⁸), a finding consistent with other urban studies.^44,45 A study from Buenos Aires⁴⁵ noted elevated health risks for children and the elderly, even under moderate contamination scenarios. Our data also reveal that while overall LTCR values remained below the USEPA benchmark (1 × 10⁻⁶), daily inhalation exposure (DIE) was relatively higher in these sensitive groups, particularly in HWY.⁴⁶ reported high PAH-related cancer risks in Lahore, Pakistan, and stressed the need for green infrastructure. Our study provides empirical support for such interventions, showing markedly reduced B[a]Peq levels and cancer risks in vegetated zones compared to bare roadside areas.

Cancer risk estimates for all age and gender groups remained within acceptable thresholds; however, children and adolescents exhibited higher relative risks due to lower body mass and increased vulnerability. Although absolute DIE values were not the highest for these groups, LTCR was amplified by physiological factors, echoing trends reported by.^27,44 Probabilistic modeling enhanced the robustness of risk predictions, with sensitivity analysis identifying BEC and cancer slope factor (SF) as the dominant contributors to LTCR uncertainty, consistent with previous modeling frameworks. These insights suggest that targeted reductions in BaP and other high-toxicity PAHs can yield disproportionate benefits in reducing health risks. These results underscore the importance of prioritizing sensitive populations in urban exposure management strategies. Targeted interventions such as increasing roadside vegetation density, limiting traffic emissions, and monitoring high-risk zones can effectively reduce cumulative PAH exposure for vulnerable groups. Our study also highlights that compound-specific analysis is essential for health risk evaluation, as total PM reductions may not reflect the persistence of toxic PAHs in the environment.

It is important to recognize that the health risk assessment carries inherent uncertainty due to variability in exposure parameters, TEFs, and assumptions regarding inhalation rates, body weight, and exposure frequency. Monte Carlo simulations were applied to quantify this variability, providing probabilistic distributions rather than single-point estimates. Sensitivity analysis indicated that the LADD, soil ingestion rate, and the cancer slope factor (SF) were the dominant contributors to uncertainty in the LTCR. Consequently, small changes in these parameters can disproportionately affect risk estimates, emphasizing the need for careful parameter selection and consideration of local population characteristics in future studies. Moreover, while threshold values such as 10⁻⁶ are commonly used as reference points, they should not be treated as absolute cut-offs; rather, they provide a guideline for interpreting potential risk in a probabilistic context. These findings highlight that uncertainty and parameter sensitivity should be transparently reported to inform risk management and prioritize interventions for the most vulnerable populations.

Limitations

Despite the robust sampling framework and multi-method analytical approach, several methodological and interpretative limitations should be acknowledged. First, soil sampling was conducted exclusively during the dry season (June-August), which may limit the extrapolation of findings to wetter or colder periods. Seasonal changes in precipitation, temperature, atmospheric stability, and wind patterns can substantially influence PAH emission rates, atmospheric transport, deposition, and degradation processes. Therefore, the observed spatial patterns may not fully represent year-round conditions. Second, the investigation was confined to a single urban setting (Sabzevar), which may constrain broader generalizability. Differences in urban morphology, traffic fleet composition, industrial structure, vegetation species, and climatic conditions across cities could result in different PAH distribution and attenuation behaviors. Multi-city or multi-climatic comparative studies would strengthen external validity. Third, vegetation density was assessed using NDVI derived from Landsat 8 imagery with a spatial resolution of 30 m. Although NDVI is a widely accepted and standardized indicator of vegetation health and cover, its moderate spatial resolution may not adequately capture fine-scale heterogeneity in canopy structure, leaf surface morphology, porosity, understory composition, or species-specific traits such as leaf roughness, all of which can directly influence pollutant interception efficiency. Consequently, micro-scale vegetation characteristics relevant to particle filtration may be underrepresented. Fourth, traffic dynamics were approximated using posted speed limits and site classification rather than continuous, real-time traffic monitoring data, as such measurements were not available during the sampling period. While this proxy approach provides standardized and accessible comparative metrics, it may not fully reflect short-term fluctuations in vehicular flow, fleet composition, fuel type, or congestion, potentially affecting the precision of traffic-related PAH source attribution. Fifth, although Soxhlet extraction followed by GC–MS analysis in SIM mode is a well-established and sensitive technique for quantifying priority PAHs, certain methodological constraints remain. Soxhlet extraction is time-intensive and may extract both bioavailable and strongly bound PAH fractions without differentiating between them. Additionally, solvent extraction efficiency, potential matrix interferences, and sample handling steps may introduce minor analytical uncertainties, despite the implementation of quality control measures such as blanks, calibration standards, and recovery assessments. Sixth, while the study design aimed to evaluate traffic-related PAHs, industrial emissions were not measured directly, and formal source identification analyses were not conducted. The sampling site near an industrial town was selected to broadly capture potential industrial contributions; however, it remains difficult to fully isolate the specific contribution of traffic versus industrial activities to total PAH levels. Seventh, while the study design aimed to evaluate traffic-related PAHs, other potential local emission sources, particularly nearby industrial activities, were not directly quantified. This limitation is particularly relevant at the HWY site, where industrial proximity may have contributed to elevated PAH levels, complicating strict source attribution. Finally, the health risk assessment relied on toxicity equivalency factors (TEFs), exposure frequency assumptions, and inhalation parameters derived from established literature. Although Monte Carlo simulation was applied to account for variability and uncertainty, these standardized parameters may not perfectly represent local demographic characteristics, behavioral patterns, or susceptibility differences. Moreover, the assessment focused primarily on inhalation exposure and did not explicitly quantify ingestion or dermal pathways, which could contribute to total PAH exposure in contaminated soils. Overall, while the applied techniques provide a comprehensive and methodologically rigorous framework for evaluating roadside PAH contamination and associated risks, these limitations should be considered when interpreting the findings and in the design of future research.

Conclusion

This study presents a comprehensive multi-scale evaluation of PM-bound PAHs across different urban functional zones, roadside green buffers, and traffic-related environmental factors, combined with a probabilistic health risk assessment. Our results demonstrated marked spatial variability in PAH concentrations, with the highway (HWY) and control (CTR) sites exhibiting the highest total and high-molecular-weight PAH loads, while the inner-city boulevard (ICB) recorded lower overall PAHs but showed distinct peaks for certain compounds such as phenanthrene. Significant differences in carcinogenic PAHs among sites highlight the influence of localized emission sources and traffic intensity. Contrary to common assumptions, vegetative buffers did not significantly reduce PAH concentrations within 50 m behind green belts under the conditions studied, suggesting limited efficacy of these buffers as standalone mitigation measures. Correlation analyses emphasized the dual role of vegetation density (NDVI) and vehicle speed in shaping PAH distribution, with stronger vegetation cover associated with lower PAH levels and higher traffic speeds linked to increased pollutant concentrations. Health risk modeling indicated that carcinogenic and non-carcinogenic risks were below regulatory concern levels overall, though vulnerable groups such as children, adolescents, and the elderly showed relatively higher susceptibility, underscoring the importance of targeted exposure reduction. These findings have important implications for public health, urban planners, and policymakers. They highlight the need for integrated air quality management strategies that combine traffic control measures, such as speed regulation and traffic volume reduction, with well-designed green infrastructure, rather than relying solely on vegetative buffers. Understanding the localized nature of PAH pollution and its complex interaction with urban vegetation can guide the development of tailored interventions to protect susceptible populations living near high-traffic corridors. Urban planners should consider vegetation type, buffer width, and placement carefully in city designs to optimize pollutant interception while ensuring adequate airflow and ventilation. For future research, we recommend longitudinal studies across multiple seasons to capture temporal variability in PAH dynamics, as well as expanded spatial coverage to include diverse urban morphologies and vegetation types. Further investigation into the mechanistic pathways of PAH uptake and degradation by urban vegetation and microbial communities would deepen understanding of green infrastructure’s potential. Additionally, coupling air quality monitoring with health outcome data could more precisely quantify the benefits of combined vegetation and traffic interventions in reducing population-level risks.

Supplemental Material

sj-docx-1-ehi-10.1177_11786302261441966 – Supplemental material for Evaluating the Role of Roadside Green Belts in Mitigating Particulate Matter-Bound Polycyclic Aromatic Hydrocarbon Pollution

Supplemental material, sj-docx-1-ehi-10.1177_11786302261441966 for Evaluating the Role of Roadside Green Belts in Mitigating Particulate Matter-Bound Polycyclic Aromatic Hydrocarbon Pollution by Hossein Rezai, Abulfazl Rahmani-Sani, Hadi Lotfi, Mahboobe Eskandari, Moslem Lari Najafi and Mohammad Miri in Environmental Health Insights

Footnotes

Acknowledgements

This study was supported by Sabzevar University of Medical Sciences. We sincerely thank all those who provided support and assistance throughout this research.

Ethical Considerations

This study involved the collection and analysis of soil samples and did not include human participants or animal subjects. The study protocol was reviewed and approved by the Ethical Committee of Sabzevar University of Medical Sciences (approval code: IR.MEDSAB.REC.1401.075).

Authors Contributions

Hossein Rezai: Conceptualization, Methodology, Data curation, Formal analysis, Writing – Original Draft. Abulfazl Rahmani-Sani: Methodology, Investigation, Data collection, Writing – Review & Editing. Hadi Lotfi: Investigation, Data collection, Validation. Mahboobe Eskandari: Investigation, Data curation, Visualization. Moslem Lari Najafi: Supervision, Conceptualization, Writing – Review & Editing, Funding acquisition. Mohammad Miri: Supervision, Project administration, Writing – Review & Editing, Corresponding author. All authors have read and approved the final manuscript.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data will be available on request from the corresponding author

Supplemental Material

Supplemental material for this article is available online.

References

Mahtta

Fragkias

Güneralp

, et al. Urban land expansion: the role of population and economic growth for 300+ cities. npj Urban Sustain. 2022;2:5.

Reshmy

Swarnalatha

Gautam

Mathew

Hegde

Assessing air pollution in a coastal urban setting: contributions of PM₁₀, vehicular emissions, and public health impacts. J Atmos Chem. 2025;82(2):18.

Asghar Pilehvar

. Spatial-geographical analysis of urbanization in Iran. Humanit Soc Sci Commun. 2021;8:63.

Setu

Gautam

Khan

HMS

Baten

Khan

MB.

Phytomonitoring of air pollution around brick kilns in urban area: exploring the potential of plants for the remediation of pollutants. Chemosphere. 2024;368:143721.

WW.

Air pollution, air quality, vehicle emissions, and environmental regulations. In: Khreis

Nieuwenhuijsen

Zietsman

Ramani

, eds. Traffic-Related Air Pollution. Elsevier; 2020;23-49.

Shang

W-L

Song

Chen

, et al. Congestion and pollutant emission analysis of urban road networks based on floating vehicle data. Urban Clim. 2024;53:101794.

Zhang

Batterman

Air pollution and health risks due to vehicle traffic. Sci Total Environ. 2013;450-451:307-316.

Patel

Shaikh

Jain

Desai

Madamwar

Polycyclic aromatic hydrocarbons: sources, toxicity, and remediation approaches. Front Microbiol. 2020;11:562813.

Scheepers

Bos

. Combustion of diesel fuel from a toxicological perspective. I. Origin of incomplete combustion products. Int Arch Occup Environ Health. 1992;64:149-161.

10.

Yang

Zhang

, et al. Exposure to atmospheric particulate matter-bound polycyclic aromatic hydrocarbons and their health effects: a review. Int J Environ Res Public Health. 2021;18:10.

11.

Sellami

Mazzi

Gautam

Gambaro

Azri

Assessment of air pollution and associated health risks in Central Tunisia (North Africa) during and outside Saharan dust events. Stoch Environ Res Risk Assess. 2025;39:3291-3311.

12.

Kumar

Sahani

Corada Perez

, et al. Urban greening for climate resilient and sustainable cities: grand challenges and opportunities. Front Sustain Cities. 2025;7:1595280. doi:10.3389/frsc.2025.1595280

13.

Jennings

Reid

Fuller

CH.

Green infrastructure can limit but not solve air pollution injustice. Nat Commun. 2021;12:4681.

14.

Barwise

Kumar

Designing vegetation barriers for urban air pollution abatement: a practical review for appropriate plant species selection. npj Climate Atmospher Sci. 2020;3:12.

15.

Baldauf

Air pollution mitigation through vegetation barriers and green space. In: Khreis

Nieuwenhuijsen

Zietsman

Ramani

, eds. Traffic-Related Air Pollution. Elsevier; 2020;437-453.

16.

Deshmukh

Isakov

Venkatram

, et al. The effects of roadside vegetation characteristics on local, near-road air quality. Air Qual Atmos Health. 2019;12:259-270.

17.

Hashad

Yang

Gallagher

Baldauf

Deshmukh

Zhang

KM.

Impact of roadside conifers vegetation growth on air pollution mitigation. Landsc Urban Plan. 2023;229:104594.

18.

Zhang

Qian

A comprehensive review of the environmental benefits of urban green spaces. Environ Res. 2024;252:118837.

19.

Kosari

Haji Hosseini

Miri

Source apportionment and health risk of exposure to ambient polycyclic aromatic hydrocarbons (PAHs) bound to particulate matter in Sabzevar, Iran. Hum Ecol Risk Assess. 2022;28:1195-1212.

20.

Soursou

Campo

Picó

Revisiting the analytical determination of PAHs in environmental samples: an update on recent advances. Trends Environ Anal Chem. 2023;37:e00195.

21.

Riding

Doick

Martin

Jones

Semple

KT.

Chemical measures of bioavailability/bioaccessibility of PAHs in soil: Fundamentals to application. J Hazard Mater. 2013;261:687-700.

22.

Tsiodra

Grivas

Bougiatioti

, et al. Source apportionment of particle-bound polycyclic aromatic hydrocarbons (PAHs), oxygenated PAHs (OPAHs), and their associated long-term health risks in a major European city. Sci Total Environ. 2024;951:175416.

23.

Taheri

Faramarziniya

Khosrorad

, et al. The association of exposure to urban greenspace and depression in women. J Urban Health. 2025;102:576-592.

24.

Ebrahimi Aval

Hashemian

Miri

. A protocol of comprehensive assessment of exposure to greenspace in school children. MethodsX. 2021;8:101596.

25.

Hille

Andersen

Kronvang

Baattrup-Pedersen

Structural and functional characteristics of buffer strip vegetation in an agricultural landscape – high potential for nutrient removal but low potential for plant biodiversity. Sci Total Environ. 2018;628-629:805-814.

26.

Wei

Guangbin

Yong

Qin

Emission of polycyclic aromatic hydrocarbons from different types of motor vehicles’ exhaust. Environ Earth Sci. 2015;74:5557-5564.

27.

Xia

Duan

Tao

, et al. Pollution level, inhalation exposure and lung cancer risk of ambient atmospheric polycyclic aromatic hydrocarbons (PAHs) in Taiyuan, China. Environ Pollut. 2013;173:150-156.

28.

Sun

Yang

, et al. Spatial modeling and source identification of PAHs in soils and roadside dusts from Hangzhou, a new first-tier megcity of China. J Hazard Mater. 2023;460:132366.

29.

Wang

X-T

Chen

Wang

, et al. Occurrence, sources and health risk assessment of polycyclic aromatic hydrocarbons in urban (Pudong) and suburban soils from Shanghai in China. Chemosphere. 2015;119:1224-1232.

30.

Diener

Mudu

How can vegetation protect us from air pollution? A critical review on green spaces’ mitigation abilities for air-borne particles from a public health perspective - with implications for urban planning. Sci Total Environ. 2021;796:148605.

31.

Balmer

Hung

Letcher

Muir

DCG

. Sources and environmental fate of pyrogenic polycyclic aromatic hydrocarbons (PAHs) in the Arctic. Emerg Contam. 2019;5:128-142.

32.

Vashist

Kumar

Singh

SK.

A comprehensive review of urban vegetation as a nature-based solution for sustainable management of particulate matter in ambient air. Environ Sci Pollut Res. 2024;31:26480-26496.

33.

Gong

Xian

Liu

Ouyang

Role of urban vegetation in air phytoremediation: differences between scientific research and environmental management perspectives. npj Urban Sustain. 2023;3:24.

34.

Venter

Hassani

Stange

Schneider

Castell

Reassessing the role of urban green space in air pollution control. Proc Natl Acad Sci USA. 2024;121:e2306200121.

35.

Meo

Almutairi

Abukhalaf

Usmani

AM.

Effect of green space environment on air pollutants PM2.5, PM10, CO, O(3), and incidence and mortality of SARS-CoV-2 in highly green and less-green countries. Int J Environ Res Public Health. 2021;18:37.

36.

Junior

DPM

Bueno

da Silva

. The effect of urban green spaces on reduction of Particulate Matter Concentration. Bull Environ Contam Toxicol. 2022;108:1104-1110.

37.

Khan

Araminienė

Uogintė

, et al. Evaluating the role of urban green infrastructure in combating traffic-related microplastic pollution. Sci Total Environ. 2025;983:179688.

38.

Janhäll

Review on urban vegetation and particle air pollution – deposition and dispersion. Atmos Environ. 2015;105:130-137.

39.

Kafle

Timilsina

Gautam

Adhikari

Bhattarai

Aryal

Phytoremediation: Mechanisms, plant selection and enhancement by natural and synthetic agents. Environ Adv. 2022;8:100203.

40.

Park

Advancements in phytoremediation research for soil and water resources: harnessing plant power for environmental cleanup. Sustainability. 2023;15:13901.

41.

Kim

Lee

Park

, et al. Region-specific characterization and ecotoxicity assessment of PAH compounds in winter PM2.5 from three capital cities in Northeast Asia. J Hazard Mater. 2025;494:138536.

42.

Zhang

Wang

Liu

X Z

, Influencing factors on airflow and pollutant dispersion around buildings under the combined effect of wind and buoyancy-a review. Int J Environ Res Public Health. 2022;19:44.

43.

Paudel

States

SL.

Urban green spaces and sustainability: exploring the ecosystem services and disservices of grassy lawns versus floral meadows. Urban For Urban Green. 2023;84:127932.

44.

Martí-Cid

Llobet

Castell

Domingo

JL.

Evolution of the dietary exposure to polycyclic aromatic hydrocarbons in Catalonia, Spain. Food Chem Toxicol. 2008;46:3163-3171.

45.

Amarillo

Tavera Busso

Carreras

Exposure to polycyclic aromatic hydrocarbons in urban environments: health risk assessment by age groups. Environ Pollut. 2014;195:157-162.

46.

Aslam

Sharif

Baqar

Shahzad

Source identification and risk assessment of polycyclic aromatic hydrocarbons (PAHs) in air and dust samples of Lahore City. Sci Rep. 2022;12:2459.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.03 MB