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

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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