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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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

It is often assumed that a small proportion of a given vehicle fleet produces a disproportionate amount of air pollution emissions. If true, policy actions to target the highly polluting section of the fleet could lead to significant improvements in air quality. In this paper, high-emitter vehicle subsets are defined and their contributions to the total fleet emission are assessed. A new approach, using enrichment factor in cumulative Pareto analysis is proposed for detecting high emitter vehicle subsets within the vehicle fleet. A large dataset (over 94,000 remote-sensing measurements) from five UK-based EDAR (emission detecting and reporting system) field campaigns for the years 2016-17 is used as the test data. In addition to discussions about the high emitter screening criteria, the data analysis procedure and future issues of implementation are discussed. The results show different high emitter trends dependent on the pollutant investigated, and the vehicle type investigated. For example, the analysis indicates that 23 % and 51 % of petrol and diesel cars were responsible for 80 % of NO emissions within that subset of the fleet, respectively. Overall, the contributions of vehicles that account for 80 % of total fleet emissions usually reduce with EURO class improvement, with the subset fleet emissions becoming more homogenous. The high emitter constituent was more noticeable for pollutant PM compared with the other gaseous pollutants, and it was also more prominent for petrol cars when compared to diesel ones., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

This paper reports upon and analyses vehicle emissions measured by the Emissions Detecting and Reporting (EDAR) system, a Vehicle Emissions Remote Sensing System (VERSS) type device, used in five UK based field campaigns in 2016 and 2017. In total 94,940 measurements were made of 75,622 individual vehicles during the five campaigns. The measurements are subset into vehicle type (bus, car, HGV, minibus, motorcycle, other, plant, taxi, van, and unknown), fuel type for car (petrol and diesel), and EURO class, and particulate matter (PM), nitric oxide (NO) and nitrogen dioxide (NO 2 ) are reported. In terms of recent EURO class emission trends, NO and NO x emissions decrease from EURO 5 to EURO 6 for nearly all vehicle categories. Interestingly, taxis show a marked increase in NO 2 emissions from EURO 5 to EURO 6. Perhaps most concerningly is a marked increase in PM emissions from EURO 5 to EURO 6 for HGVs. Another noteworthy observation was that vans, buses and HGVs of unknown EURO class were often the dirtiest vehicles in their classes, suggesting that where counts of such vehicles are high, they will likely make a significant contribution to local emissions. Using Vehicle Specific Power (VSP) weighting we provide an indication of the magnitude of the on-site VERSS bias and also a closer estimate of the regulatory test/on-road emissions differences. Finally, a new 'EURO Updating Potential' (EUP) factor is introduced, to assess the effect of a range of air pollutant emissions restricted zones either currently in use or marked for future introduction. In particular, the effects of the London based Low Emission Zone (LEZ) and Ultra-Low Emissions Zone (ULEZ), and the proposed Birmingham based Clean Air Zone (CAZ) are estimated. With the current vehicle fleet, the impacts of the ULEZ and CAZ will be far more significant than the LEZ, which was introduced in 2008., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Size-resolved composition of particulate matter (PM) sampled in the industrial town of Port Talbot (PT), UK was determined in comparison to a typical urban background site in Birmingham (EROS). A Micro-Orifice Uniform Deposit Impactor (MOUDI) sampler was deployed for two separate sampling campaigns with the addition of a Grimm optical spectrometer at the PT site. MOUDI samples were analysed for water-soluble anions (Cl(-), NO₃(-) and SO₄(2-)) and cations (Na(+), NH4(+), K(+), Mg(2+) and Ca(2+)) and trace metals (Al, V, Cr, Mn, Fe, Cu, Zn, Sb, Ba and Pb). The PM mass distribution showed a predominance of fine particle (PM₂.₅) mass at EROS whereas the PT samples were dominated by the coarse fraction (PM₂.₅₋₁₀). SO₄(2-), Cl(-), NH4(+), Na(+), NO₃(-), and Ca(2+) were the predominant ionic species at both sites while Al and Fe were the metals with highest concentrations at both sites. Mean concentrations of Cl(-), Na(+), K(+), Ca(2+), Mg(2+), Cr, Mn, Fe and Zn were higher at PT than EROS due to industrial and marine influences. The contribution of regional pollution by sulphate, ammonium and nitrate was greater at EROS relative to PT. The traffic signatures of Cu, Sb, Ba and Pb were particularly prominent at EROS. Overall, PM at EROS was dominated by secondary aerosol and traffic-related particles while PT was heavily influenced by industrial activities and marine aerosol. Profound influences of wind direction are seen in the 72-hour data, especially in relation to the PT local sources. Measurements of particle number in 14 separate size bins plotted as a function of wind direction and speed are highly indicative of contributing sources, with local traffic dominant below 0.5 μm, steelworks emissions from 0.5 to 15 μm, and marine aerosol above 15 μm., (Copyright © 2013 Elsevier B.V. All rights reserved.)

In order to assess the contribution of a Municipal Solid Waste incinerator to the area's total contamination, metals and soluble ions have been determined in bulk deposition collected at sites affected by different levels of plant emissions, according to the results of the Calpuff air dispersion model. Results show that in general fluxes monitored at the different sites during the same period are quite similar for each analyte. Deposition fluxes of nitrite and ammonium are significantly lower at the more distant site, while copper is significantly higher at this site, possibly because of copper fungicide used on the nearby agriculture land. The presence of sea spray and resuspended soil dust can be inferred from Pearson correlation coefficients, while enrichment factors indicate that Cu, Pb and Zn have a probable anthropogenic origin. A more complete evaluation of the sources affecting the area was obtained with PMF analysis. The sources associated with each factor were identified from the source profile and temporal trends. Six factors were identified, three sources associate with natural matrices, while three factors represent anthropogenic sources. The greatest contribution of heavy metals, the most toxic and persistent components determined, is associated with resuspended soil dust, especially when weighted according to their toxicity. The anthropogenic source contribution is similar at all sites, and therefore the incinerator's relative contribution to the total pollutant load appears to be negligible compared to other sources affecting the area., (Copyright © 2013 Elsevier B.V. All rights reserved.)

Epidemiological studies typically using wide size range mass metrics (e.g. PM(10)) have demonstrated associations between airborne particulate matter and several adverse health outcomes. This approach ignores the fact that mass concentration may not correlate with regional lung dose, unlike the case of trace gases. When using measured particle size distributions as the basis for calculating regional lung dose, PM(10) mass concentration is found to be a good predictor of the mass dose in all regions of the lung, but is far less predictive of the surface area and particle number dose. On the other hand, measurements of particle number do not well predict mass dose, indicating that the chosen particle metric is likely to determine the health outcomes detectable by an epidemiological study. Consequently, epidemiological studies using mass metrics (PM(2.5) and PM(10)) may fail to recognise important health consequences of particulate matter exposure, leading to an underestimate of the public health consequences of particle exposure., (Copyright © 2010 Elsevier B.V. All rights reserved.)