Non-exhaust traffic-derived particulate matter (PM) currently constitutes a significant fraction of urban environmental pollution, often surpassing exhaust emissions. Non-exhaust sources, originating primarily from tire and brake wear and road dust resuspension, account for an estimated 50–85% of total traffic-related PM10 emissions. Tire Wear Particles (TWPs), and Tire and Road Wear Particles (TRWPs), are fine particles (≤5 mm) generated by the frictional interaction between tires and road surfaces, representing a major source of microplastics (MPs) in the environment. TRWPs pose significant environmental and health risks, related not only to their particulate nature (contributing to PM10 and PM2.5) but also to the leaching of toxic chemical additives and transformation products (TPs). Therefore, assessing the contribution of TRWPs to aerosol burden is critical and also challenging, due to their heterogeneous nature, variable chemical composition and the lack of standardized protocols for determination in the atmosphere. This paper reviews and compares the chemical tracers and analytical approaches employed for TRWP identification and quantification. The current consensus highlights that relying on a single compound (e.g., rubber, Zn, BTHs, phthalates) lacks the necessary specificity. Therefore, the integration of multiple tracers and the cross-validation using different analytical techniques (e.g., combining spectroscopy, Pyrolysis Gas Chromatography – Mass Spectrometry (Py-GC/MS), and High Performance Liquid Chromatography – High Resolution Mass Spectromerty (HPLC-HRMS) for organic markers) is crucial to overcome methodological limitations and understand the input of TRWPs in environmental matrices. Future research must prioritize the development of standardized protocols, certified reference materials, and the continued evaluation of transformation products to fully understand the contribution and impact of TRWPs on atmospheric pollution.
Fugitive road dust (FRD) is a major contributor to airborne particulate matter (PM) in urban environments. Although routine road-cleaning operations are widely implemented to reduce FRD, their short-term air-quality benefits under real traffic conditions remain inadequately quantified. The Testing Re-entrained Aerosol Kinetic Emissions from Roads (TRAKER) system was used to evaluate the short-term effectiveness of vacuum sweeping in Baoding and sprinkling-vacuum sweeping in Weifang, China. Surface dust removal efficiency () and near-road PM reduction efficiency () were calculated for PM10 and PM2.5 from 10 min before cleaning to 1 h after treatment.Vacuum sweeping alone showed limited and unstable short-term effectiveness, with rebounds in both road-surface dust and near-road PM occurring within 20–30 min after treatment. Across the tested road classes, ranged from 24% to 27% for PM10-related surface dust, exceeding the corresponding of 20% for PM10, whereas for PM2.5-related surface dust and for PM2.5 were both relatively low, ranging from 11% to 13% and from 12% to 13%, respectively. In contrast, sprinkling–vacuum sweeping achieved substantially higher and more stable efficiencies across road classes. The values ranged from 75% to 92% for PM10-related surface dust and from 78% to 92% for PM2.5-related surface dust, while the corresponding values ranged from 75% to 91% for PM10 and from 76% to 90% for PM2.5. The mismatch between surface-dust removal and near-road PM reduction, particularly for vacuum sweeping alone, highlights the need to evaluate road-cleaning effectiveness using both source-oriented and air-quality-oriented indicators.
The background concentrations of PM2.5 were estimated from a 23-year time series of daily measurements collected at eleven rural monitoring stations across the Valencian Community (Spain), comprising more than 8,400 observation applying three different methods.Three approaches were applied: two types of clustering models based on Hidden Markov Models and Gaussian Mixture Models and the percentile-based approach. The estimated background PM2.5 ranged from 3.55 to 3.90 μg/m3, with both methods showing good agreement. The 20th percentile of the daily concentration closely matched the clustering estimates.The analysis reveals a mean value of 8.2 ± 6.2 μg/m3, and a declining trend from 2014 regarding the pollutant concentration. Although the highest values were typically observed during the summer season, all stations were within legal limits for the yearly PM2.5 values.This study represents the first attempt to establish a PM2.5 background in the Valencian Community and provides valuable information to policymakers to implement strategies for improving air quality.
A spatiotemporal graph neural network (STGNN) framework combining CNN–LSTM temporal encoding with Graph Attention Networks v2 (GATv2) spatial message passing was evaluated for retrospective bias correction of Community Multiscale Air Quality (CMAQ) simulations under day-ahead observational information constraints. Using five years of hourly EPA observations from 235 monitoring stations across the continental United States, t improved model performance under retrospective, reanalysis-driven conditions, while restricting all observation-derived inputs to information available at least 24 h before the target prediction hour. The reported skill should therefore be interpreted as an upper bound on operational forecast performance, as the WRF–CMAQ inputs were derived from retrospective reanalysis rather than forecast-mode simulations. The STGNN achieves a root–mean–square error (RMSE) of 4.53 ppb for NO2 and 7.32 ppb for O3, representing relative error reductions of 52.4% and 65.0%, respectively, over raw CMAQ fields. The framework increases the coefficient of determination, R2, from –0.15 to 0.74 for NO2 and from –0.72 to 0.79 for O3, while substantially minimizing systematic model bias and improving diurnal variability representations. Comprehensive ablation experiments show that the CNN–LSTM temporal encoder provides most of the predictive skill, while graph message passing yields modest additional RMSE reductions of 2.2% for NO2 and 3.0% for O3, with station-cluster bootstrap confidence intervals excluding zero. Wind–modulated edge weight variants provide negligible predictive benefit over static distance-based connectivity. Across four regional holdout experiments, the complete framework produced lower point-estimate RMSE than raw CMAQ at stations excluded from training, with reductions of 17.4–32.5% for NO2 and 40.4–60.0% for O3. Paired station-cluster bootstrap intervals excluded zero in seven of the eight region–pollutant comparisons. Permutation feature importance analysis isolates raw model predictions and hybrid 24–hour lagged temperature errors as the dominant drivers of predictive utility, highlighting the strong predictive association between lagged meteorological-error information and CMAQ bias within the evaluated framework.
This study examines the chemical composition, source contributions, and oxidative potential of PM2.5 at the Daebul National Industrial Complex, South Korea. Detailed measurements of ions, carbonaceous fractions, metals, and organic molecular tracers were integrated with a Positive Matrix Factorization (PMF) model and source-direction analysis. Six main sources were identified: waste polyethylene terephthalate (PET) and wood combustion, industrial steel processing, industrial transition-metal emissions, road dust, secondary nitrate, and secondary sulfate and organic aerosol. Combustion-related sources were predominant, contributing nearly 60 % of the PMF-resolved mass, with a pronounced increase during nighttime due to stagnant southwesterly winds. In contrast, daytime aerosols largely contained Fe-, Mn-, and Si-rich particles originating from industrial and crustal sources. The oxidative potential of PM2.5 closely tracked changes in combustion and steel-processing factors, supporting the view that quinone-enriched organics and transition metals substantially influence particle redox activity. Significant associations between PMF sources and volatile organic compounds (VOCs) such as naphthalene, tert-butylbenzene, and chlorinated solvents underscore this mechanistic relationship. These findings demonstrate that mixed plastic–biomass combustion and industrial metal emissions are the major sources affecting both PM2.5 mass and oxidative potential, assessed by the dithiothreitol (DTT) assay and expressed as 9,10-phenanthrenequinone-equivalent oxidative potential (QDTT-OP), providing a scientific basis for targeted emission-control strategies in industrialized areas.
Ship emissions are an important sources of atmospheric pollutants in coastal regions, yet their spatio-temporal variability in harbour residential areas remains poorly characterized. This study assessed ambient PM and particle number concentration (PNC) in the Baltic Sea harbour city of Warnemünde–Rostock (Germany), where maritime and touristic activity occur in close proximity to residential areas. A stroller-based mobile platform measured PM, PNC, lung-deposited surface area, black carbon (eBC) and ozone during 56 repeated surveys covering 238 km over 14 days, complemented by rooftop stationary PM monitoring. For campaign-wide analysis, the common 3.76 km route was divided into 20 m segments. Walk–segment medians and 90th percentiles were used to distinguish recurrent spatial patterns from isolated plume encounters, and predefined micro-environments were compared using walk-specific zone statistics. Whole-survey median PNC ranged from 2.4 × 103 to 1.2 × 104 cm-3, while short-duration peaks exceeded 2.4 × 105 cm-3. The strongest campaign-wide upper-tail hot-spot occurred along the Seepromenade during a temporary festival. Among maritime locations, the Warnow ferry terminal represented the clearest recurrent hot-spot, whereas the cruise-terminal vicinity showed episodic enhancements dependent on vessel activity, wind direction, and other factors. During the unfavorable downwind conditions, the high PNC plume peaks reached the urban and residential route sections. In contrast, PM2.5 varied only narrowly among the predefined microenvironments, while stationary daily PM2.5 remained generally low. Repeated high-resolution mobile monitoring therefore provides essential complementary information to fixed-site measurements by resolving short-lived plume encounters and fine-scale spatial concentration gradients and supports targeted mitigation measures such as ferry electrification and greater use of shore power.
Size-fractionated particulate matter (PM) analysis is critical for air pollution research, yet remains limited in Southeast Asia, particularly in rapidly urbanizing and industrializing regions. Our year-round study investigated 20 elements in PM<0.5, PM0.5-1, PM1-2.5, PM2.5-10, and PM>10 at urban (UB) and industrial (IN) sites in Southern Vietnam. At both sites, elements with concentrations >100 ng m-3 included Al, Ca, Fe, Zn, K, and Mg. Elements occurring at 10-100 ng m-3 comprised Ti, Pb, Mn, Cu, and Ba, while the remaining elements were present at <10 ng m-3. Overall, most elemental concentrations were substantially higher at the IN site than at UB. Pronounced seasonal variability was observed, with elemental concentrations during the dry season being approximately twice as high as those in the rainy season, particularly in PM1. Crustal elements were mainly associated with coarse particles, whereas anthropogenic elements were mainly associated with fine particles. The enrichment factor and principal component analysis indicated that UB PM was influenced mainly by traffic emissions, while IN PM was contributed by mechanical industrial activities, surface treatment processes, and combustion sources. Deposition modeling suggested predominant head-region deposition for crustal elements (77%), whereas anthropogenic elements exhibited higher tracheobronchial and alveolar deposition (7-13%). Health risk assessment revealed substantially elevated non-carcinogenic (HI=1.56) and carcinogenic risk (TCR=2.45×10-3) for children at the IN site, with Pb, Ni, and Cr as the principal contributors. These findings highlight the urgent need for size-specific monitoring and targeted emission control strategies, particularly in industrial areas, in rapidly developing regions of Southeast Asia.
Airborne and settled dust pose a significant environmental problem in areas with disturbed crustal material due to human-related or natural activities. Climate and landscape changes may increase dust issues globally. Fort McKay, an Indigenous and Métis community in the Canadian Athabasca Oil Sands Region has experienced elevated dust levels for decades, with growing concerns. This study combines two years of ambient particulate matter (PM) measurements resolving different particle sizes and time scales with monthly dustfall samples to document community-level burden and to better characterize dust deposition events to improve monitoring and mitigation strategies. Monthly dustfall was high throughout Fort McKay, frequently exceeded residential guidelines, and was higher than other monitoring locations in the region. Deposition estimates based on ambient PM data show deposition is dominated by PM>10 though consistent underestimation highlights methodological limitations. Analysis of 24-hr integrated PM samples reveals strong episodicity in dust deposition events: ∼40% of days account for 75% of cumulative PM mass, where hourly data show that 30% of hours accounted for the same proportion of PM2.5-10 mass. Dust events last on average 3.5 ± 1.2 hours and are characterized by short-lived but intense peaks. Moderate correlations (r=0.62) and similar episodicity between PM2.5-10 and PM>10 suggest that PM2.5-10 can be a reasonable surrogate for PM>10, helping identify dust events and track deposition changes in real time. These findings highlight the need to capture episodic, coarse-mode PM exposures in dust-impacted regions and implement targeted monitoring and regulatory frameworks to address disproportionate exposure burdens in nearby communities.
Seventeen polycyclic aromatic hydrocarbons (PAHs) and thirty-three n-alkanes were studied in 128 PM2.5 samples collected seasonally at urban and suburban sites of Wuhu, a rapidly developing city in the subtropical monsoon climate zone of China. The concentrations, spatiotemporal distributions, meteorological correlations, emission source apportionment, and spatial source regions were discussed. The average total PAHs concentrations were 9.72 ± 10.83ng/m3 and 9.90±10.29 ng/m3 at urban and suburban sites, respectively, while the corresponding annual average concentrations of n-alkanes were 36.59 ± 25.69 ng/m3 and 29.52 ± 28.00 ng/m3. No significant urban-suburban differences were observed for either TPAH or n-alkane concentrations. Such spatial homogeneity may be attributable to rapid urbanization-induced pollution homogenization or intensive regional pollutant transport, which offsets local urban-suburban concentration gradients. Both pollutants exhibited pronounced seasonal patterns consistent with regional climatic characteristics, with concentrations peaking in winter and declining to the lowest levels in summer. Meteorological correlation analysis further confirmed that temperature, atmospheric pressure, and wind speed were dominant factors modulating PAH and n-alkane variations. PMF-based source apportionment results revealed that the PAHs in Wuhu was predominantly derived from coal combustion, followed by vehicle exhaust and biomass burning, with petroleum volatilization contributing minimally. For n-alkanes, CPI and WNA analyses indicated that anthropogenic fossil fuel combustion constituted the dominant source across seasons. Spatial source analysis further demonstrated that regional pollutant accumulation was primarily attributed to emissions from surrounding urban agglomerations, with long-range transport from the northwest and northeast of Wuhu playing a critical role in pollutant loading. Collectively, these findings provide scientific support for seasonal and wind-direction-based early warning and targeted pollution control strategies for local atmospheric environmental management.
Low-cost particulate matter (PM) sensors are increasingly used for air quality monitoring and exposure assessment, but their performance across aerosol types, particle-size fractions, and concentration ranges remains uncertain for newer sensor models. We measured the concentrations of three aerosols (salt, Arizona road dust, and kerosene) with four low-cost sensors (OPC-N3, IPS-7100, SEN66, and PurpleAir PIXEL), a photometer (pDR-1500), and reference instruments (SMPS and APS) at environmental (0–40 μg/m3) and elevated (50–2,500 μg/m3) concentrations. Monodisperse DEHS particles (0.1–5.0 μm) were used to assess detection efficiency and particle sizing accuracy. At environmental concentrations, all sensors were highly linear with the reference (R2 > 0.93) across aerosol types, with the SEN66 exhibiting the highest inter-sensor precision (CV < 10%). The IPS-7100 showed strong agreement with the reference for PM1 salt at occupational concentrations (slope = 1.26, R2 = 0.99, bias = −20.6%), while the OPC-N3 achieved near-unity performance for kerosene PM10 (slope = 1.20, R2 = 1.00, bias = −0.6%) and kerosene PM4 (slope = 1.39, R2 = 1.00, bias = 1.8%) at occupational concentrations. Detection efficiency, evaluated from number concentrations, was below 50% for most sensor and size combinations, with the OPC-N3 exceeding 100% at 2.5 and 4.0 μm. Aerosol type strongly influenced sensor response, underscoring the need for aerosol-specific calibration before low-cost sensors are used for air quality monitoring, indoor or near-source exposure assessment, or elevated-concentration applications. The high linearity observed across several sensor-aerosol combinations indicates the potential for aerosol-specific calibration.
The Iberian Peninsula is highly susceptible to Saharan dust intrusions, which have become increasingly frequent and intense in recent years. In March 2022, an exceptional winter dust outbreak affected north-western Spain, producing record PM10 levels across several locations. This study characterises the chemical, optical, and morphological properties of aerosols associated with the event that impacted León (NW Spain) between 14 and 16 March 2022. A month-long sampling campaign (1 – 31 March) was conducted downtown, and PM10 samples were analysed for water-soluble inorganic ions, trace elements, and organic and elemental carbon. Aerosol light absorption and column-integrated optical properties were also evaluated, and selected samples were examined using SEM-EDS. During the intrusion, PM10 reached 370 μg m-3, far exceeding the WHO 24-hour guideline. Average concentrations increased from 16 ± 7.7 μg m-3 under non-dust conditions to 89 ± 115 μg m-3 during the outbreak. Organic carbon rose markedly, reaching 11 μg m-3 compared with 2.2 ± 0.9 μg m-3 in background air. Strong enhancements in crustal elements (Si, Ca, Al, Fe, K, Mg) and major ions (SO42-, Ca2+, NO3-) were consistent with intense mineral dust transport. Aethalometer-derived iron concentrations confirmed substantial dust loading, while an increase in the Ångström Absorption Exponent indicated enhanced UV–visible absorption by dust particles. These results demonstrate severe air-quality deterioration during winter Saharan intrusions and highlight the value of integrating chemical, optical, and morphological analyses to characterise extreme dust events affecting south-western Europe.
Numerical modelling of pollutant transport and dispersion in urban street canyons is widely used to assess roadside air quality. However, many existing studies rely on idealized geometries and assume chemically inert pollutants, limiting understanding of reactive traffic emissions such as NOx in realistic urban environments. This study investigates the coupled dispersion and chemical transformation of passive CO and reactive NOx and O3 within a morphologically irregular real urban street canyon. A URANS-based modelling framework is used, incorporating NOx-O3 photochemistry with explicit representation of solar radiation effects. The results indicate that passive and reactive species exhibit similar dispersion behaviour, highlighting the dominant role of canyon-scale flow structures in pollutant distribution. Chemical reaction contribution for NO2 displays an inverse relationship with O3 formation. O3 concentrations rise gradually from ground to roof level due to diminished NO-O3 titration with increasing height. Transport budget analysis shows that CO is primarily governed by advection, whereas NO2 and O3 are influenced by both advection and turbulent diffusion, with chemical processes introducing coupled source-sink effects. The inverse NO2-O3 relationship highlights the role of photochemistry in locally modifying concentrations, despite its smaller contribution to the overall transport budget. Overall, photochemical processes, though secondary in magnitude, play a critical role in shaping the spatial distribution of reactive pollutants within the canyon.
Accurately predicting PM2.5 levels is critical for addressing this central challenge of PM2.5 pollution in atmospheric environmental management and protecting public health. This study proposes a novel dual-path parallel hybrid deep learning architecture, termed CABT-Net (CNN-Attention-BiLSTM-Transformer), for accurate 12-h PM2.5 concentration prediction at four sites (Adams, Boulder, Denver, Douglas) in Colorado's Front Range region. The model employs CNN to extract local temporal structures from PM2.5 concentration sequences, and integrates an Attention Mechanism to focus on key influencing factors and strengthen the focus on effective information. BiLSTM captures long- and short-term dynamic dependencies within the sequences, while Transformer models global temporal correlations via self-attention. These components complement each other to achieve comprehensive capture of complex temporal dependencies. The model demonstrates excellent performance across the four sites. Notably, R2 remains no less than 0.81 for 10th-step predictions. Compared to single models, this parallel architecture utilizes dual-path sub-models and employs a linear aggregation layer outputs the PM2.5 concentration sequence, enhancing the accuracy of long-term 12-h predictions. To elucidate the underlying causes of pollution patterns identified by the model, the study further combines HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory Model) backward trajectory analysis and meteorological data to analyze site-specific pollution characteristics and the causes of special surge events. It exhibits strong robustness in PM2.5 concentration forecasting and provides a transferable methodological framework for short-term PM2.5 prediction studies in similar regions.
Carbonaceous particulate matter plays a central role in urban air quality, yet integrated molecular-level characterization of phenolic compounds and carboxylic acids within the same actively sampled PM10 matrix remains scarce for temperate European cities. This study investigates the seasonal variability and source relationships of 22 carboxylic acids, comprising 14 dicarboxylic acids and 8 fatty acids, and 53 phenolic compounds across five structural families (alkylphenols, nitrophenols, chlorophenols, bromophenols, and aminophenols) in weekly PM10 samples collected at an urban background site in Strasbourg, France, over a 36-week campaign. Of the targeted compounds, 19 carboxylic acids and 20 phenols were detected and quantified. Total DCA concentrations ranged from 0.02 to 55.4 ng m−3, with phthalic acid as the dominant species (median 3.3 ng m−3), reflecting strong anthropogenic and secondary photochemical inputs. FAs accounted for approximately 70% of the total measured organic acid mass and were dominated by hexadecenoic acid (C16), nonanoic (C9), and decanoic (C10) acids, the latter two exhibiting severely episodic distributions driven by intermittent cooking-emission events. Within the phenomenon, chlorophenols constituted the largest annual contribution (44.9%), followed by alkylphenols (22%) and nitrophenols (21%), pointing to the importance of secondary halogenation chemistry alongside combustion-related primary and nitration pathways. Seasonal analysis revealed a clear compositional restructuring: DCAs shifted from dominance by phthalic and succinic acids in winter to malonic acid prevalence in summer, consistent with enhanced photochemical oxidation, phenolic profiles transitioned from co-dominance of nitrophenol and dichlorophenol in winter toward progressively more chlorinated and brominated species in summer and autumn. Spearman rank correlation analysis confirmed strong intra-family associations within both the mid-chain DCA cluster (C5-C8; ρ = 0.84-0.97) and the chlorophenol subgroup (2,4-DCP vs. 2,6-DCP; ρ = 0.93), while cross-family correlations between organic acids and phenols were uniformly weak (|ρ| < 0.35), indicating largely independent source influences and formation mechanisms. Comparison with previously published fogwater, rainwater, and passive air monitoring data from the same region highlights matrix-dependent partitioning, particularly the pronounced aqueous-phase enrichment of nitrophenols relative to the particulate fraction. Together, these findings provide the first integrated seasonal characterization of both compound families in actively sampled urban PM10 for a representative temperate European environment, establishing a molecular baseline for future receptor modeling and long-term trend assessment.
Ozone (O3) pollution in traditional industrial cities of Northeast China remains under-characterized. We analyzed daily maximum 8-h average ozone (MDA8 O3) at ten national monitoring stations in Shenyang during 2022–2024 and site-based volatile organic compound (VOC) observations at the Shenyang Atmospheric Compound Pollution Three-Dimensional Monitoring Supersite during warm seasons. Annual 90th-percentile MDA8 O3 concentrations were 146, 154, and 150 μg m−3 in 2022, 2023, and 2024, respectively. Although below the Grade II limit, 25, 26, and 21 exceedance days occurred, mainly in the warm season. Monthly MDA8 O3 peaked in May–June. A stepwise multiple linear regression model explained 64.5% of warm-season MDA8 O3 variance and identified solar radiation and temperature as the strongest positive associations. Five HYSPLIT trajectory clusters showed regional air-mass transport from multiple directions. At the supersite, aromatics dominated measured VOCs. Positive matrix factorization resolved seven factors, with the toluene-rich aromatic and fuel evaporation-related factors contributing the largest shares of resolved VOC mass. MIR-based reactivity ranking differed from mass-based source contributions, with reactive aromatics, alkenes, and isoprene contributing relatively more to calculated ozone formation potential. These results show that Shenyang ozone pollution reflects radiation-temperature conditions, regional transport, and a mixed urban-industrial VOC source structure. VOC and reactivity results provide site-specific evidence, not estimates of citywide ozone-production sensitivity.
In 2020, several wildfires in the western United States (US) affected regional air quality in northern Nevada for weeks. To assess the contribution of wildfire smoke to air quality in the Reno-Sparks (Nevada) area, air pollutants were measured and analyzed during a smoke-free period (August to October 2019) and compared to a smoke-affected period (August to October 2020). The ambient concentrations of fine particulate matter (PM2.5), organic carbon (OC), and elemental carbon (EC) measured at a US Environmental Protection Agency (EPA) monitoring station in the Reno-Sparks area during smoke-affected days were on average 35.17 ± 25.60 μg m−3, 16.76 ± 11.95 μg m−3, and 4.91 ± 4.03 μg m−3, respectively. These concentrations are approximately 8 to 9.6 times higher than the concentrations collected on smoke-free days. In contrast, ozone (O3) concentrations increased by only approximately 12% during smoke-affected days. On 18 of the 50 smoke-affected days, PM2.5 concentrations exceeded the primary 24-h EPA National Ambient Air Quality Standard of 35 μg m−3. Particle-phase, PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) were collected on filters over a 24-h period, extracted, and quantitatively analyzed for 104 PAHs using gas chromatography-mass spectrometry (GC-MS). Total PM2.5-bound PAHs were approximately 6.3 times higher during smoke-affected days (mean: 4.87 ± 3.49 ng m−3) than during smoke-free days (mean: 0.77 ± 0.65 ng m−3), with dimethylnaphthalenes (1.4-, 1.5-, and 2,3-dimethylnaphthalene) dominating the smoke-affected PM2.5 samples. Analysis of gas-phase PAHs in smoke-affected samples collected using XAD-resin cartridges revealed that the total concentration was approximately 47 times more than in PM2.5 samples, with naphthalene being the most abundant gas-phase PAH.
