Particulate matter in a motorcycle-dominated urban area: Source apportionment and cancer risk of lung deposited surface area (LDSA) concentrations

Real-time assessment of PM1.0-bound trace metal cancer risk through high-resolution GED-ICP-MS and integrated ELCR analysis

This study employed a gas exchange device coupled with inductively coupled plasma mass spectrometry (GED-ICP-MS) for high-resolution analysis of trace metals in PM1.0 within an urban area. The system was validated against conventional filter-based measurements (R2=0.57-0.97) and effectively captured diurnal variations and episodic events typically overlooked by traditional sampling. Through positive matrix factorization (PMF), we identified six distinct sources by integrating trace metal concentrations and particle size distribution (PSD). By combining temporal slope factors of mixtures (SFm) with lung deposited surface area (LDSA), we provided insights into source-specific health risks. The average traffic-related SFm (7.5 ×10-4) was comparable to other traffic sites, though values increased significantly (1.0 ×10-3) during evening rush hours. These findings revealed that vehicle-emitted metals were predominantly in the PM1.0 size range, indicating that traditional PM10-based health risk assessments may underestimate their impact. The observed lack of correlation between LDSA and SFm emphasizes that comprehensive health risk assessment requires both PSD and toxicity measurements, particularly for particles below 100 nm. The overall excess lifetime cancer risk averaged 2.9 × 10-5, varying between peak (4.0 ×10-5) and off-peak (2.5 ×10-5) traffic periods, remaining within US EPA's acceptable range, while demonstrating the crucial role of trace metals in determining health risks despite their low concentrations.

Unveiling the Impact of Wildfires on Nanoparticle Characteristics and Exposure Disparities through Mobile and Fixed-Site Monitoring in Toronto, Canada

This study investigates the impacts of wildfires on nanoparticle characteristics and exposure disparities in Toronto, integrating data from a large-scale mobile monitoring campaign and fixed-site measurements during the unprecedented 2023 wildfire season. Our results reveal changes in particle characteristics during wildfire days, with particle number concentrations decreasing by 60% and particle diameter increasing by 30% compared to nonwildfire days. Moreover, the median lung deposited surface area (LDSA) levels rose by 31% during wildfire events. We employed gradient boosting models to estimate near-road LDSA levels on both wildfire and nonwildfire days. The LDSA ratio (wildfire/nonwildfire) exceeded 2.0 in certain areas along highways and in downtown Toronto. Furthermore, our findings show that marginalized communities faced greater LDSA increases than less marginalized ones. Under wildfire conditions, the LDSA ratio difference between the most and least marginalized groups was 16% for recent immigrants and visible minorities and 7% for seniors and children, both statistically significant. This study delivers critical insights into the spatiotemporal variations of nanoparticle characteristics during wildfire and nonwildfire periods, demonstrating the substantial health risks posed by increased LDSA levels and the inequitable distribution of these risks among Toronto's diverse population.

Urban emissions of fine and ultrafine particulate matter in Los Angeles: Sources and variations in lung-deposited surface area

Airborne particulate matter (PM) in urban environments poses significant health risks by penetrating the respiratory system, with concern over lung-deposited surface area (LDSA) as an indicator of particle exposure. This study aimed to investigate the diurnal trends and sources of LDSA, particle number concentration (PNC), elemental carbon (EC), and organic carbon (OC) concentrations in Los Angeles across different seasons to provide a comprehensive understanding of the contributions from primary and secondary sources of ultrafine particles (UFPs). Hourly measurements of PNC and LDSA were conducted using the DiSCmini and Scanning Mobility Particle Sizer (SMPS), while OC and EC concentrations were measured using the Sunset Lab EC/OC Monitor. The results showed distinct diurnal trends in PNC and EC, with peaks occurring in the early morning and evening, which were consistent with periods of increased traffic volume. During warmer periods, a midday increase in PNC was observed, attributed to photochemical reactions. In contrast, a nighttime peak during colder months suggested the formation of secondary aerosols through aqueous-phase chemistry. Additionally, the DiSCmini consistently reported higher LDSA values than SMPS, indicating the presence of irregularly shaped UFPs, particularly during periods of heavy traffic flow. Positive Matrix Factorization (PMF) analysis identified three primary sources. Factor 1 (photochemically influenced processes), driven by secondary organic aerosol formation during warmer periods, contributed to 19% of LDSA. Factor 2, in which primarily traffic influenced emissions were the dominant contributor, accounting for 70% of LDSA and associated with high loadings of OC (61%), EC (78%), and NOx (94%). Factor 3 (aqueous phase secondary process influenced emissions) during colder months, accounted for 11% of LDSA. Both Factor 1 and 3 sources exhibited comparable contributions of OC4 (52% and 48%, respectively), underscoring their roles in secondary aerosol formation. These findings emphasize the need to address both primary and secondary emissions to mitigate health risks associated with UFP exposure.

Challenges in Observation of Ultrafine Particles: Addressing Estimation Miscalculations and the Necessity of Temporal Trends

Ultrafine particles (UFPs) pose a significant health risk, making comprehensive assessment essential. The influence of emission sources on particle concentrations is not only constrained by meteorological conditions but often intertwined with them, making it challenging to separate these effects. This study utilized valuable long-term particle number and size distribution (PNSD) data from 2018 to 2023 to develop a tree-based machine learning model enhanced with an interpretable component, incorporating temporal markers to characterize background or time series residuals. Our results demonstrated that, differing from PM2.5, which is significantly shaped by planetary boundary layer height, wind speed plays a crucial role in determining the particle number concentration (PNC), showing strong regional specificity. Furthermore, we systematically identified and analyzed anthropogenically influenced periodic trends. Notably, while Aitken mode observations are initially linked to traffic-related peaks, both Aitken and nucleation modes contribute to concentration peaks during rush hour periods on short-term impacts after deweather adjustment. Pollutant baseline concentrations are largely driven by human activities, with meteorological factors modulating their variability, and the secondary formation of UFPs is likely reflected in temporal residuals. This study provides a flexible framework for isolating meteorological effects, allowing more accurate assessment of anthropogenic impacts and targeted management strategies for UFP and PNC.

Quantifying the effects of the microphysical hygroscopic restructuring of soot on ensemble optical properties and satellite aerosol optical depth retrievals

Black carbon, or soot, significantly contributes to atmospheric light absorption due to its low single scattering albedo (SSA). This study investigates the impact of soot's hygroscopic restructuring on satellite remote sensing, focusing on radiative forcing, top-of-atmosphere (TOA) reflectance, and aerosol optical depth (AOD) retrievals. We characterized soot aging using relative humidity (RH) growth factor functions and modeled fresh and aging soot aggregates using a cluster-cluster aggregation algorithm. Bulk optical properties for each RH level were simulated using core-mantle generalized multi-sphere Mie-solution, weighted by the probability density function of soot monomer numbers. We incorporated soot aging models into the 6S radiative transfer model by adjusting the geometric mean radius of the soot component to match bulk SSA values at each aging degree. Extensive radiative transfer simulations were conducted, complemented by an analysis of 16 actual soot-containing cases in China from 2016 to 2019. Results showed that identical soot loads correspond to lower reflectance in dry environments and higher reflectance in humid environments. MODIS AOD retrieval errors approached zero when RH was close to 70 %, likely due to the similarity between the 6S default soot and the aging model at this humidity. Neglecting RH led to overestimations in AOD for cases with RH < 65 % and underestimations in high humidity cases (RH > 75 %). The average MODIS AOD retrieval errors for RH < 65 %, RH between 65 % and 75 %, and RH > 75 % were -33.15 %, -1.80 %, and +42.42 %, respectively. This study underscores the necessity of incorporating RH into satellite AOD retrieval algorithms to enhance accuracy and reduce uncertainties.

Pinpointing sources of pollution using citizen science and hyperlocal low-cost mobile source apportionment

Currently, methodologies for the identification and apportionment of air pollution sources are not widely applied due to their high cost. We present a new approach, combining mobile measurements from multiple sensors collected from the daily walks of citizen scientists, in a high population density area of Birmingham, UK. The methodology successfully pinpoints the different sources affecting the local air quality in the area using only a handful of measurements. It was found that regional sources of pollution were mostly responsible for the PM2.5 and PM1 concentrations. In contrast, PM10 was mostly associated with local sources. The total particle number and the lung deposited surface area of PM were almost solely associated with traffic, while black carbon was associated with both the sources from the urban background and local traffic. Our analysis showed that while the effect of the hyperlocal sources, such as emissions from construction works or traffic, do not exceed the distance of a couple of hundred meters, they can influence the health of thousands of people in densely populated areas. Thus, using this novel approach we illustrate the limitations of the present measurement network paradigm and offer an alternative and versatile approach to understanding the hyperlocal factors that affect urban air quality. Mobile monitoring by citizen scientists is shown to have huge potential to enhance spatiotemporal resolution of air quality data without the need of extensive and expensive campaigns.

Assessing the inhaled dose of nanomaterials by nanoparticle tracking analysis (NTA) of exhaled breath condensate (EBC) and its relationship with lung inflammatory biomarkers

The widespread and increasing use of nanomaterials has resulted in a higher likelihood of exposure by inhalation for nanotechnology workers. However, tracking the internal dose of nanoparticles deposited at the airways level, is still challenging. To assess the suitability of particle number concentration determination as biomarker of internal dose, we carried out a cross sectional investigation involving 80 workers handling nanomaterials. External exposure was characterized by portable counters of particles DISCminiTM (Testo, DE), allowing to categorize 51 workers as exposed and 29 as non-exposed (NE) to nanoparticles. Each subject filled in a questionnaire reporting working practices and health status. Exhaled breath condensate was collected and analysed for the number of particles/ml as well as for inflammatory biomarkers. A clear-cut relationship between the number of airborne particles in the nano-size range determined by the particle counters and the particle concentration in exhaled breath condensate (EBC) was apparent. Moreover, inflammatory cytokines (IL-1β, IL-10, and TNF-α) measured in EBC, were significantly higher in the exposed subjects as compared to not exposed. Finally, significant correlations were found between external exposure, the number concentration of particles measured by the nanoparticle tracking analysis (NTA) and inflammatory cytokines. As a whole, the present study, suggests that NTA can be regarded as a reliable tool to assess the inhaled dose of particles and that this dose can effectively elicit inflammatory effects.

VOC and IVOC emission features and inventory of motorcycles in China

How did the motorcycle emissions evolve during the economic development in China? To address data gaps, this study firstly measured the volatile organic compound (VOC) and intermediate-volatility organic compound (IVOC) emissions from motorcycles. The results confirmed that the emission control of motorcycles, especially small-displacement motorcycles, significantly lagged behind other gasoline-powered vehicles. For the China IV motorcycles, the average VOC and IVOC emission factors (EFs) were 2.74 and 7.78 times higher than the China V-VI light-duty gasoline vehicles, respectively. The notable high IVOC emissions were attributed to a dual influence from gasoline and lubricating oil. Furthermore, based on the complete EF dataset and economy-related activity data, a county-level emission inventory was developed in China. Motorcycle VOC and IVOC emissions changed from 2536.48 Gg and 197.19 Gg in 2006 to 594.21 Gg and 12.66 Gg in 2020, respectively. The absence of motorcycle IVOC emissions in the existed vehicular inventories led to an underestimation of up to 20%. Across the 15 years, the motorcycle VOC and IVOC emission hotspots were concentrated in the undeveloped regions, with the rural emissions reaching 5.81-10.14 times those of the urban emissions. This study provides the first-hand and close-to-realistic data to support motorcycle emission management and accurate air quality simulations.

Biomonitoring potentially toxic elements in atmospheric particulate matter of greater Dhaka region using leaves of higher plants

In this study, four different plant species, namely Artocarpus heterophyllus, Mangifera indica, Psidium guajava, and Swietenia mahagoni, were selected from seven different locations to assess the feasibility of using them as a cost-effective alternative for biomonitoring air quality. Atmospheric coarse particulate matter (PM10), soil samples, and leaf samples were collected from residential, industrial, and traffic-congested sites located in the greater Dhaka region. The heavy metal concentrations (Cd, Cr, Cu, Fe, Mn, Ni, Pb, and Zn) in the leaves of the different species, PM10, and soil samples were analyzed. The highest Pb (718 ng/m3) and Zn (15,956 ng/m3) concentrations were found in PM10 of Kodomtoli which is an industrial area. On the other hand, the highest Fe (6,152 ng/m3) and Ni (61.1 ng/m3) concentrations were recorded in the PM10 of Gabtoli, a heavy-traffic area. A significant positive correlation (r = 0.74; p < 0.01) between Pb content in plant leaves and PM fraction was found which indicated that atmospheric PM-bound Pb may contribute to the uptake of Pb by plant leaves. The analysis of the enrichment factor (EF) revealed that soils were contaminated with Cd, Ni, Pb, and Zn. The abaxial leaf surfaces of Psidium guajava growing at the polluted site exhibited up to a 40% decrease in stomatal pores compared to the control site. Saet's summary index (Zc) demonstrated that Mangifera indica had the highest bioaccumulation capacity. The metal accumulation index (MAI) was also evaluated to assess the overall metal accumulation capacity of the selected plants. Of the four species, Swietenia mahagoni (3.05) exhibited the highest MAI value followed by Mangifera indica (2.97). Mangifera indica and Swietenia mahagoni were also found to accumulate high concentrations of Pb and Cr in their leaves and are deemed to be good candidates to biomonitor Pb and Cr contents in ambient air.

High-time resolution PM2.5 source apportionment assisted by spectrum-based characteristics analysis

Characteristics extraction and anomaly analysis based on frequency spectrum can provide crucial support for source apportionment of PM2.5 pollution. In this study, an effective source apportionment framework combining the Fast Fourier Transform (FFT)- and Continuous Wavelet Transform (CWT)-based spectral analyses and Positive Matrix Factorization (PMF) receptor model is developed for spectrum characteristics extraction and source contribution assessment. The developed framework is applied to Beijing during the winter heating period with 1-h time resolution. The spectrum characteristics of anomaly frequency, location, duration and intensity of PM2.5 pollution can be captured to gain an in-depth understanding of source-oriented information and provide necessary indicators for reliable PMF source apportionment. The combined analysis demonstrates that the secondary inorganic aerosols make relatively high contributions (50.59 %) to PM2.5 pollution during the winter heating period in Beijing, followed by biomass burning, vehicle emission, coal combustion, road dust, industrial process and firework emission sources accounting for 15.01 %, 11.00 %, 10.70 %, 5.31 %, 3.88 %, and 3.51 %, respectively. The source apportionment result suggests that combining frequency spectrum characteristics with source apportionment can provide consistent rationales for understanding the temporal evolution of PM2.5 pollution, identifying the potential source types and quantifying the related contributions.

Ambient air particulate total lung deposited surface area (LDSA) levels in urban Europe

This study aims to picture the phenomenology of urban ambient total lung deposited surface area (LDSA) (including head/throat (HA), tracheobronchial (TB), and alveolar (ALV) regions) based on multiple path particle dosimetry (MPPD) model during 2017-2019 period collected from urban background (UB, n = 15), traffic (TR, n = 6), suburban background (SUB, n = 4), and regional background (RB, n = 1) monitoring sites in Europe (25) and USA (1). Briefly, the spatial-temporal distribution characteristics of the deposition of LDSA, including diel, weekly, and seasonal patterns, were analyzed. Then, the relationship between LDSA and other air quality metrics at each monitoring site was investigated. The result showed that the peak concentrations of LDSA at UB and TR sites are commonly observed in the morning (06:00-8:00 UTC) and late evening (19:00-22:00 UTC), coinciding with traffic rush hours, biomass burning, and atmospheric stagnation periods. The only LDSA night-time peaks are observed on weekends. Due to the variability of emission sources and meteorology, the seasonal variability of the LDSA concentration revealed significant differences (p = 0.01) between the four seasons at all monitoring sites. Meanwhile, the correlations of LDSA with other pollutant metrics suggested that Aitken and accumulation mode particles play a significant role in the total LDSA concentration. The results also indicated that the main proportion of total LDSA is attributed to the ALV fraction (50 %), followed by the TB (34 %) and HA (16 %). Overall, this study provides valuable information of LDSA as a predictor in epidemiological studies and for the first time presenting total LDSA in a variety of European urban environments.

Characterizing emission factors and oxidative potential of motorcycle emissions in a real-world tunnel environment

Transportation emissions significantly affect human health, air quality, and climate in urban areas. This study conducted experiments in an urban tunnel in Taipei, Taiwan, to characterize vehicle emissions under real driving conditions, providing emission factors of PM2.5, eBC, CO, and CO2. By applying multiple linear regression, it derives individual emission factors for heavy-duty vehicles (HDVs), light-duty vehicles (LDVs), and motorcycles (MCs). Additionally, the oxidative potential using dithiothreitol assay (OPDTT) was established to understand PM2.5 toxicity. Results showed HDVs dominated PM2.5 and eBC concentrations, while LDVs and MCs influenced CO and CO2 levels. The CO emission factor for transportation inside the tunnel was found to be higher than those in previous studies, likely owing to the increased fraction of MCs, which generally emit higher CO levels. Among the three vehicle types, HDVs exhibited the highest PM2.5 and eBC emission factors, while CO and CO2 levels were relatively higher for LDVs and MCs. The OPDTTm demonstrated that fresh traffic emissions were less toxic than aged aerosols, but higher OPDTTv indicated the impact on human health cannot be ignored. This study updates emission factors for various vehicle types, aiding in accurate assessment of transportation emissions' effects on air quality and human health, and providing a guideline for formulating mitigation strategies.

Particulate Matter and Its Components Induce Alteration on the T-Cell Response: A Population Biomarker Study

Compared with the T-cell potential of particulate matter (PM) in animal studies, comprehensive evaluation on the impairments of T-cell response and exposure-response from PM and its components in human population is limited. There were 768 participants in this study. We measured environmental PM and its polycyclic aromatic hydrocarbons (PAHs) and metals and urinary metabolite levels of PAHs and metals among population. T lymphocyte and its subpopulation (CD4⁺ T cells and CD8⁺ T cells) and the expressions of T-bet, GATA3, RORγt, and FoxP3 were measured. We explored the exposure-response of PM compositions by principal component analysis and mode of action by mediation analysis. There was a significant decreasing trend for T lymphocytes and the levels of T-bet and GATA3 with increased PM levels. Generally, there was a negative correlation between PM, urinary 1-hydroxypyrene, urinary metals, and the levels of T-bet and GATA3 expression. Additionally, CD4⁺ T lymphocytes were found to mediate the associations of PM_2.5 with T-bet expression. PM and its bound PAHs and metals could induce immune impairments by altering the T lymphocytes and genes of T-bet and GATA3.

Measuring the effect of fireworks on air quality in Minneapolis, Minnesota

Abstract

Air quality was measured before, during, and after a 4th of July fireworks display in downtown Minneapolis, Minnesota using a mix of low-cost sensors (CO, CO2, and NO) for gases and portable moderate cost instruments for particle measurements (PM2.5, lung deposited surface area, and number weighted particle size distributions). Meteorological conditions—temperature, humidity, and vertical temperature profile were also monitored. Concentrations of particles and most gaseous species peak between 10 pm and midnight on July 4th, decrease in the middle of the night but increase again and by between 6 and 7 am reach concentrations as high or higher than during fireworks. This overnight increase is likely due to a temperature inversion trapping emissions. Between 10 pm and midnight on July 4th the measures of particle concentration increase by 180–600% compared to the same period on July 3rd. Particle size distributions are strongly influenced by fireworks, shifting from traffic-like bimodal distributions before to a nearly unimodal distribution dominated by a large accumulation mode during and after. The shape of the size distribution measured during the early morning peak is nearly identical to that observed during fireworks, suggesting that the early morning peak is mainly due to trapped fireworks emissions not early morning traffic. Gaseous species are less strongly influenced by fireworks than particles. Comparing measurements made between 10 pm and midnight on July 4th and the same period on July 3rd, the concentration of CO increases 32% while the CO2 increases only 2% but increases by another 15% overnight. The NO concentration behaves oddly, decreasing during fireworks, but then recovering the next morning, more than doubling overnight. Our measurements of CO, NO, and PM2.5 are compared with those made at the nearest (~ 2 km away) Minnesota Pollution Control Agency Air Monitoring Station. Their NO results are quite different from ours with much lower concentrations before fireworks, a distinct peak during, followed by a strong overnight increase and an early morning peak somewhat similar in shape and concentration to ours. These differences are likely due mainly to malfunction of our low-cost NO sensor. Concentrations of CO and PM2.5 track ours within 25% but peak shapes are somewhat different, which is not unexpected given the spatial separation of the measurements.

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