Roadside PM10 Research Articles

Defining the in vivo mechanism of air pollutant toxicity using murine stress response biomarkers

Air pollution can cause a wide range of serious human diseases. For the informed instigation of interventions which prevent these outcomes there is an urgent need to develop robust in vivo biomarkers which provide insights into mechanisms of toxicity and relate pollutants to specific adverse outcomes. We exemplify for a first time the application of in vivo stress response reporters in establishing mechanisms of air pollution toxicity and the application of this knowledge in epidemiological studies. We first demonstrated the utility of reporter mice to understand toxicity mechanisms of air pollutants using diesel exhaust particles compounds. We observed that nitro-PAHs induced Hmox1 and CYP1a1 reporters in a time- and dose-dependent, cell- and tissue-specific manner. Using in vivo genetic and pharmacological approaches we confirmed that the NRF2 pathway mediated this Hmox1-reporter induction stress reporter activity. We then correlated the activation of stress-reporter models (oxidative stress/inflammation, DNA damage and Ah receptor -AhR- activity) with responses in primary human nasal cells exposed to chemicals present in particulate matter (PM; PM2.5-SRM2975, PM10-SRM1648b) or fresh roadside PM10. To exemplify their use in clinical studies, Pneumococcal adhesion was assessed in exposed primary human nasal epithelial cells (HPNEpC). The combined use of HPNEpC and in vivo reporters demonstrated that London roadside PM10 particles induced pneumococcal infection in HPNEpC mediated by oxidative stress responses. The combined use of in vivo reporter models with human data thus provides a robust approach to define the relationship between air pollutant exposure and health risks. Moreover, these models can be used in epidemiological studies to hazard ranking environmental pollutants by considering the complexity of mechanisms of toxicity. These data will facilitate the relationship between toxic potential and the level of pollutant exposure in populations to be established and potentially extremely valuable tools for intervention studies for disease prevention.

TRAPSim: An agent-based model to estimate personal exposure to non-exhaust road emissions in central Seoul

Non-exhaust emissions (NEEs) from brake and tyre wear cause detrimental health effects, yet their relationship with mobility has not been examined rigorously. We constructed an agent-based traffic simulator to illustrate the coupled problems of emissions, behaviour, and the estimated exposure to PM10 for groups of drivers and subway commuters in Seoul CBD. Having calibrated the parameters, the results regarding the air quality revealed that roughly 25–30% of the roadside PM10 was significantly higher than the background PM10. Additionally, compared to intra-urban cars, pedestrians who commuted for longer periods of time and were exposed to more ambient particles suffered significant health losses; however, drivers only became aware of the health risk when PM10 levels were consistently high for a few days. Compared to the business-as-usual scenario of vehicle entry, a 90% vehicle restriction was able to reduce PM10 by 18–24% and cut the percentage of resident drivers who were at risk. However, it was not effective for subway commuters. Using an agent-based traffic simulator in a health context can provide insights into how exposure and health effects can vary depending on the time of exposure and the form of transportation.

Influence of roadside vegetation barriers on air quality inside urban street canyons

More evidence has shown that exposure to particulate matter (PMs) within urban streets increases adverse health risks, and vegetation barriers have the potential to improve near-road air quality. To gain insight into the influences of vegetation barrier characteristics on the dispersion of PMs (TSP, PM10, PM2.5), field measurements were performed in Wuhan, China. Twenty-four sample belts were selected within oblique wind canyons, on road and roadside TSP, PM10 and PM2.5 concentration were simultaneously monitored in steady periods. Layer shelterbelt porosity was used to represent the vertical configurations of the vegetation barriers. The result indicated that vegetation combination of trees, shrubs, and herbs is effective for reducing the concentration of PMs. Vegetation barriers can reduce TSP and PM10 concentrations to a certain level (5∼23 %) in the areas behind vegetation barriers compared to the control within oblique wind canyons. In contrast, the reduction effect of the vegetation barrier on PM2.5 could be positive or negative was inconsistent. Pearson correlation analysis results indicated that TSP and PM10 reduction efficiencies were negatively correlated with shelterbelt porosity in the 0∼2 m height section, but the vegetation barrier indicators had no obvious effects on the reduction efficiency of PM2.5. To improve roadside air quality, the use of shrubs or hedges with heights lower than 2 m should be encouraged, and large, dense trees should be avoided around roads with heavy traffic. These results provide insight on how to improve roadside air quality by mitigating PM pollution in urban street canyons.

Air pollution and urban road transport: evidence from the world’s largest low-emission zone in London

Low-emission zones (LEZs) have been implemented widely in Europe to tackle air pollution sourced from vehicular emissions. We quantify the effectiveness of the world's largest LEZ—London's LEZ—in reducing its target pollutant, PM10. Using a difference-in-difference (DID) framework, we find that the least stringent phase I of London's LEZ induced a short-term increase in the roadside PM10 within the zone by about 14.8%, whereas the longer and more restrictive phase II significantly drove down the PM10 by 5.5%. We explore the underlying reasons behind the disparity in policy effect across stages. We show that upon the introduction of phase I, the traffic volume of targeted heavy goods vehicles (HGVs) and temporarily exempted light goods vehicles (LGVs) has substantially increased, outweighing the environmental effect of a higher proportion of greener vehicles. We provide possible behavioral explanations for this phenomenon.

A comparative study of using Random Forests (RF), Extreme Learning Machine (ELM) and Deep Learning (DL) algorithms in modelling Roadside Particulate Matter (PM10 & PM2.5)

The effects of traffic-derived air pollution can be controlled through the provision of adequate and effective air quality control and mitigation measures with the aid of air quality models. This paper examines the application of Machine Learning (ML) methods (Random Forests (RF), Extreme Learning Machine (ELM) and Deep Learning (DL)) algorithms in air quality modelling. Data collected from continuous monitoring stations in London comprising air pollutants, traffic and meteorological variables were used for training the models. The selected ML methods were trained to predict roadside PM10 and PM2.5 concentrations. The results obtained showed that all the methods can be used for training models for the prediction of the PM10 and PM2.5 concentrations. The RF, ELM and DL algorithms were found to be suitable for this purpose due to their predictive accuracy and faster training speed. The models performed slightly better in predicting PM2.5 than the PM10 concentrations. The average performance of the machine learning models was found to be 0.90 and 0.94 (R); 99% and 98% (FAC2); 9.2 and 4.5 (RMSE) and 0.83 and 0.84(IOA) for the prediction of PM10, PM2.5. The advantages of using the Deep Learning, ELM and RF algorithms over traditional ANN are the faster training speed and scalability especially when using high-performance computing.

A tale of two cities: is air pollution improving in Paris and London?

Paris and London are Europe's two megacities and both experience poor air quality with systemic breaches of the NO2 limit value. Policy initiatives have been taken to address this: some European-wide (e.g. Euro emission standards); others local (e.g. Low Emission Zone, LEZ). Trends in NOX, NO2 and particulate matter (PM10, PM2.5) for 2005–2016 in background and roadside locations; and trends in traffic increments were calculated in both cities to address their impact. Trends in traffic counts and the distribution in Euro standards for diesel vehicles were also evaluated. Linear-mixed effect models were built to determine the main determinants of traffic concentrations. There was an overall increase in roadside NO2 in 2005–2009 in both cities followed by a decrease of ∼5% year−1 from 2010. Downward trends were associated with the introduction of Euro V heavy vehicles. Despite NO2 decreasing, at current rates, roads will need 20 (Paris) and 193 years (London) to achieve the European Limit Value (40 μg m−3 annual mean). Euro 5 light diesel vehicles were associated with the decrease in roadside PM10. An increase in motorcycles in London since 2010 contributed to the lack of significant trend in PM2.5 roadside increment in 2010–16.

Assessment and prediction of the impact of road transport on ambient concentrations of particulate matter PM10

The main challenge facing the air quality management authorities in most cities is meeting the air quality limits and objectives in areas where road traffic is high. The difficulty and uncertainties associated with the estimation and prediction of the road traffic contribution to the overall air quality levels is the major contributing factor. In this paper, particulate matter (PM10) data from 10 monitoring sites in London was investigated with a view to estimating and developing Artificial Neural Network models (ANN) for predicting the impact of the road traffic on the levels of PM10 concentration in London. Twin studies in conjunction with bivariate polar plots were used to identify and estimate the contribution of road traffic and other sources of PM10 at the monitoring sites. The road traffic was found to have contributed between 24% and 62% of the hourly average roadside PM10 concentrations. The ANN models performed well in predicting the road contributions with their R-values ranging between 0.6 and 0.9, FAC2 between 0.6 and 0.95, and the normalised mean bias between 0.01 and 0.11. The hourly emission rates of the vehicles were found to be the most contributing input variables to the outputs of the ANN models followed by background PM10, gaseous pollutants and meteorological variables respectively.

Modelling street level PM<sub>10</sub> concentrations across Europe: source apportionment and possible futures

Abstract. Despite increasing emission controls, particulate matter (PM) has remained a critical issue for European air quality in recent years. The various sources of PM, both from primary particulate emissions as well as secondary formation from precursor gases, make this a complex problem to tackle. In order to allow for credible predictions of future concentrations under policy assumptions, a modelling approach is needed that considers all chemical processes and spatial dimensions involved, from long-range transport of pollution to local emissions in street canyons. Here we describe a modelling scheme which has been implemented in the GAINS integrated assessment model to assess compliance with PM10 (PM with aerodynamic diameter <10 μm) limit values at individual air quality monitoring stations reporting to the AirBase database. The modelling approach relies on a combination of bottom up modelling of emissions, simplified atmospheric chemistry and dispersion calculations, and a traffic increment calculation wherever applicable. At each monitoring station fulfilling a few data coverage criteria, measured concentrations in the base year 2009 are explained to the extent possible and then modelled for the past and future. More than 1850 monitoring stations are covered, including more than 300 traffic stations and 80% of the stations which exceeded the EU air quality limit values in 2009. As a validation, we compare modelled trends in the period 2000–2008 to observations, which are well reproduced. The modelling scheme is applied here to quantify explicitly source contributions to ambient concentrations at several critical monitoring stations, displaying the differences in spatial origin and chemical composition of urban roadside PM10 across Europe. Furthermore, we analyse the predicted evolution of PM10 concentrations in the European Union until 2030 under different policy scenarios. Significant improvements in ambient PM10 concentrations are expected assuming successful implementation of already agreed legislation; however, these will not be large enough to ensure attainment of PM10 limit values in hot spot locations such as Southern Poland and major European cities. Remaining issues are largely eliminated in a scenario applying the best available emission control technologies to the maximal technically feasible extent.

Roadside PM10 and associated metals in Constantine, Algeria

Samples of particulate matter less than or equal to 10 μm (PM10) were collected every other day using an Airmetrics MiniVol portable air sampler in Constantine, the third largest and most densely populated city of Algeria with a population of 600,000. The main objective of this study was to assess the concentrations of particulate matter (PM) with an aerodynamic diameter ≤10 μm (PM10), and dust-laden trace metals in a residential area with heavy traffic during two months, from 25 March 2010 to 24 May 2010. Furthermore, the present work examines the daily levels of metallic elements Fe, Zn and Cu. The PM10 concentrations ranged from 7.6 to 159.4 μg/m3 during the study period. WHO’s daily PM10 average guide value of 50 μg/m3 was exceeded 10 times over 31 samples. HYSPLIT back trajectories were used to identify the source locations of the pollutants. Rain scavenging of atmospheric particulate matter led to a substantial decrease in PM10 concentrations. During the study period, Zn was detected in one sample at a concentration of 0.78 μg/m3. It is believed that air masses originating from Iceland’s Eyjafjöll volcano may have transported clouds of ashes rich in Zn to the sampling site on that occasion. A maximum of 2.92 μg/m3 of Cu was observed when the volcanic cloud reached the sampling site four times during the corresponding 24-h sampling period.

Insights into the composition and sources of rural, urban and roadside carbonaceous PM₁₀.

Insights into the nature and sources of the urban and roadside increments in carbonaceous PM10 are gained from bulk chemical analyses on daily filter samples collected at a roadside, urban background and rural site in Edinburgh, UK (not all sampling contemporaneous). The concentrations of PM10 water-soluble organic matter (WSOM) at the three sites were similar, and (where measured concurrently) strongly correlated, indicating a uniform background source, in contrast to the black carbon component (quantified by filter optical reflectance) whose average concentrations at urban background and roadside were, respectively, about 3 and 7 times greater than at the rural site, indicating local urban sources. BC was not a major component of PM10 but was a major component of the urban and roadside PM10 increments (∼50% and ∼60% respectively). The roadside WSOM had greater hydrophobicity than the urban background WSOM. UV-vis spectra indicated increased prevalence of unsaturated bonds and conjugation in urban background WSOM in winter compared with summer. This is consistent with both summertime photochemical production of particle OM and maritime primary aliphatic WSOM. Raman microscopy of a small subset of samples indicated carbon functionality ranged between diesel-like material and more complex humic-like material. Results overall indicate the presence of a background functionalized carbonaceous material, with local BC sources superimposed.

Estimation of PM10 in the traffic-related atmosphere for three road types in Beijing and Guangzhou, China

The levels of roadside PM10 in Beijing, China, were investigated in 2011 and 2012 on a seasonal basis to estimate the population exposure to particulates for three road types. The measurements of PM10 were also conducted in the southern Chinese megacity of Guangzhou for comparison purposes. The results showed that roadside PM10 in Beijing correlated strongly with the PM10 background in the urban atmosphere. The levels of PM10 in street canyons were markedly higher than those along the open roads and in crossroad areas because of limited ventilation. An elevation of PM10 was observed in April, which was possibly due to the sand storms that frequently occur in the spring. Based on these observations, roadside PM10 in Beijing could have multiple origins and was to some extent dispersion-governed. In Guangzhou, the roadside PM10 did not closely relate to the background values. The PM10 pollution was greatly affected by local traffic conditions. The simulation of PM10 for different road types was completed during the study period using the Motor Vehicle Emissions Factor Model (MOBILE6.2) as an emission model and the California Line Source Dispersion Model (CALINE4) and Operational Street Pollution Model (OSPM) as dispersion models. The MOBILE6.2/CALINE4 software package was demonstrated to be sufficient for the simulation of PM10 in the open roads and crossroad areas in both Beijing and Guangzhou, and the simulation results of roadside PM10 in the street canyons by the MOBILE6.2/OSPM package were in close agreement with those of the measurements.

Monitoring PM10 and Ultrafine Particles in Urban Environments Using Mobile Measurements

Mobile platforms are increasingly used to acquire air quality data at a high spatial and temporal resolution in complex urban environments. As such, mobile measurements provide a solution for short-term studies to acquire a spatially spread data set that would not be feasible if using stationary measurements. Mobile monitoring campaigns were carried out with a bicycle platform at two different urban locations, consisting of 20 and 24 repeated runs along a fixed route over a three-week period. The measurement runs were carried out on different days and at different times of the day, without systematical temporal coverage. Significant differences in UFP concentration were found within the day and between days, and also between several streets along the measurement route. These differences were related to traffic intensity and street characteristics. In contrast, PM10 concentrations differed between measurement days, but the within-day variability of PM10 was mostly non-significant. Additionally, the spatial variability was limited and the PM10 concentrations were only significantly different between busy streets, with high concentrations, and quiet background streets, with low ones. The results indicate that for most streets the number of runs was sufficient to give a good approximation of median daytime UFP concentration levels for the measurement period, and for some streets this number could even be reduced to less than 10. However, for PM10 a higher number of runs is needed, and this may be attributed to the significant background contribution to the roadside PM10 concentration, and the high variability of this. We conclude that a limited set of mobile measurements makes it possible to map locations with systematically higher or lower UFP and PM10 concentrations in urban environments.

Size and time-resolved roadside enrichment of atmospheric particulate pollutants

Abstract. Size and time-resolved roadside enrichments of atmospheric particulate pollutants in PM10 were detected and quantified in a Mediterranean urban environment (Barcelona, Spain). Simultaneous data from one urban background (UB), one traffic (T) and one heavy traffic (HT) location were analysed, and roadside PM10 enrichments (RE) in a number of elements arising from vehicular emissions were calculated. Tracers of primary traffic emissions (EC, Fe, Ba, Cu, Sb, Cr, Sn) showed the largest REs (>70%). Other traffic tracers (Zr, Cd) showed lower but still consistent REs (25–40%), similar to those obtained for mineral matter resulting from road dust resuspension (Ca, La, Ce, Ti, Ga, Sr, 30–40%). The sum of primary and secondary organic carbon showed a RE of 41%, with contributions of secondary OC (SOC) to total OC ranging from 46% at the HT site, 63% at the T site, and 78% in the UB. Finally, other trace elements (As, Co, Bi) showed unexpected but consistent roadside enrichments (23% up to 69%), suggesting a link to traffic emissions even though the emission process is unclear. Hourly-resolved PM speciation data proved to be a highly resourceful tool to determine the source origin of atmospheric pollutants in urban environments. At the HT site, up to 62% of fine Mn was attributable to industrial plumes, whereas coarse Mn levels were mainly attributed to traffic. Similarly, even though Zn showed on average no roadside enrichment and thus was classified as industrial, the hourly-resolved data proved that at least 15% of coarse Zn may be attributed to road traffic emissions. In addition, our results indicate that secondary nitrate formation occurs within the city-scale, even in the absence of long atmospheric residence times or long-range atmospheric transport processes. Characteristic tracer ratios of road traffic emissions were identified: Cu/Sb = 6.8–8.0, Cu/Sn = 4.7–5.4 and Sn/Sb = 1.5.

Analysis of Roadside Inhalable Particulate Matter (PM10) in Major Korean Cities

A data analysis of three major Korean cities was conducted to assess roadside inhalable particulate matter 10 microm or smaller in aerodynamic diameter (PM10), including temporal and meteorological variations, over a recent period of 4 to 6 years. The yearly roadside PM10 concentrations presented a well-defined increasing trend or no trend depending on the roadside monitoring station. Most mean values exceeded or approximated the Korean standard of 70 microg/m3 per year for PM10. A representative roadside diurnal trend was characterized by a distinct morning maximum. In most cases, the Sunday roadside concentrations were similar to or somewhat lower than the weekday concentrations, and the PM10 concentrations presented a well-defined seasonal variation, with the maximum concentration in March. The monthly maximum concentrations observed in March were most likely attributable to Asian dust storms. In two metropolitan cities (Seoul and Busan), the frequency of days with roadside PM10 concentrations exceeding the standard of 150 microg/m3 per 24 h was much lower for the roadside monitoring stations than for the residential monitoring station, whereas in the third city (Daegu), this result was reversed. Interestingly, the average maximum concentrations observed for the roadside sites in Seoul and Busan during March were higher than those for the residential sites, suggesting that the roadside concentrations responded more to the dust storms than the residential areas. The relationship between the pollutant concentrations and five important meteorological parameters (solar radiation, wind speed, air temperature, relative humidity, and precipitation) showed that the number and type of meteorological variables included in the equations varied according to the monitoring station or season. Finally, the current results confirmed that attention should be given to the PM10 exposure of residents living near roadways.

Characterization of airborne individual particles collected in an urban area, a satellite city and a clean air area in Beijing, 2001

Collection campaigns for PM10 and PM2.5 have been conducted in a northwestern Beijing urban area in monthly periods over 2001, with 7 days collection per month. The samples were also collected simultaneously in a satellite city, Nankou, and a clean air area near the Ming Tombs Reservoir (MTR) over the domestic heating (March) and non-heating (July/August) periods in 2001 (both for one week). To assist the analysis, three types of ‘source’ particulate matter (PM) samples were taken. These consisted of coal combustion ash collected on top of a coke oven; dust storm particles collected during dust storm periods; and roadside PM10 collected on a major road in Beijing. Monitoring results reveal that, in the urban area, particle mass levels were higher in winter than in other seasons. The 1-week/month average PM10 mass levels were over 250 ?gm?3 in winter. The particle mass levels in the satellite city were slightly lower than those at the urban site, and the lowest mass levels occurred at the MTR site. The morphology and chemical composition of individual airborne particles were determined by scanning electron microscopy, and image analysis was employed to study the number-size distributions. The number-size distributions of mineral particles showed that those in the Asia-Dust storm (ADS) collections are mostly coarser than 1 ?m, while mineral particles of the non-ADS collections are predominately finer than 1 ?m. The particles in the respirable (<2.5 ?m) fraction accounted for 99% of the total particles in airborne PM samples. Soot aggregates were generally the most abundant components in airborne PM samples at all three sites. The fly ash (spherical) particles at the MTR site were significantly enriched over the heating period, indicating a domestic coal-burning source.

Interpretation of roadside PM10 monitoring data from Sunderland, United Kingdom.

Roadside PM10 has been monitored by Partisol at three sites in Sunderland between August 1997 and February 1998. The sites chosen were an inner city kerbside site; a roadside site adjacent to a dual carriageway on the outskirts of Sunderland with an open aspect; and a rural site. The results indicate that there is a seasonal variation in the relationship between the sites in terms of monitored PM10. In the winter there is a poor correlation between the sites whereas in the summer significant correlations are obtained. Of the sites monitored PM10 is consistently highest at the inner city roadside site. During the summer, exceedances of the U.K. 50 microg m(-3) standard (DETR, 2000) are associated with conditions suitable for the build-up of photochemical pollution however during the winter period exceedances are recorded during a variety of weather conditions. At the dual carriageway site PM2.5 has also been recorded and contributions to measured PM10 are 77% in summer and 68% in winter. The results illustrate a number of inconsistencies between this study utilising the Partisol and others reporting results where PM10 has been monitored by TEOM.

Roadside Particulate Air Pollution in Bangkok

Airborne fine particles of PM2.5-10 and PM2.5 in Bangkok, Nonthaburi, and Ayutthaya were measured from December 22, 1998, to March 26, 1999, and from November 30, 1999, to December 2, 1999. Almost all the PM10 values in the high-polluted (H) area exceeded the Thailand National Ambient Air Quality Standards (NAAQS) of 120 μg/m3. The low-polluted (L) area showed low PM10 (34–74 μg/m3 in the daytime and 54–89 μg/m3 at night). PM2.5 in the H area varied between 82 and 143 μg/m3 in the daytime and between 45 and 146 μg/m3 at night. In the L area, PM2.5 was quite low both day and night and varied between 24 and 54 μg/m3, lower than the U.S. Environmental Protection Agency (EPA) standard (65 μg/m3). The personal exposure results showed a significantly higher proportion of PM2.5 to PM10 in the H area than in the L area (H = 0.80 ± 0.08 and L = 0.65 ± 0.04). Roadside PM10 was measured simultaneously with the Thailand Pollution Control Department (PCD) monitoring station at the same site and at the intersections where police work. The result from dual simultaneous measurements of PM10 showed a good correlation (correlation coefficient: r = 0.93); however, PM levels near the roadside at the intersections were higher than the concentrations at the monitoring station. The relationship between ambient PM level and actual personal exposures was examined. Correlation coefficients between the general ambient outdoors and personal exposure levels were 0.92 for both PM2.5 and PM10. Bangkok air quality data for 1997–2000, including 24-hr average PM10, NO2, SO2, and O3 from eight PCD monitoring stations, were analyzed and validated. The annual arithmetic mean PM10 of the PCD data at the roadside monitoring stations for the last 3 years decreased from 130 to 73 μg/m3, whereas the corresponding levels at the general monitoring stations decreased from 90 to 49 μg/m3. The proportion of days when the level of the 24-hr average PM10 exceeded the NAAQS was between 13 and 26% at roadside stations. PCD data showed PM10 was well correlated with NO2 but not with SO2, suggesting that automobile exhaust is the main source of the particulate air pollution. The results obtained from the simultaneous measurement of PM2.5 and PM10 indicate the potential environmental health hazard of fine particles. In conclusion, Bangkok traffic police were exposed to high levels of automobile-derived particulate air pollution.

Roadside particulate levels at 30 locations in the Kathmandu Valley, Nepal

Roadside particulate levels (PM10) at 30 locations throughout the Kathmandu Valley, Nepal are reported. There were seven sampling cycles from January to December 1999. PM10 levels ranged between 1000 and 6000 µ/m³ for 1-h sampling. There was no consistent trend in PM10 levels with respect to monitoring cycle, average precipitation or wind speed. There is no WHO guideline for PM10 (no safe exposure threshold), but the EPA 24-h standard is 150 µ/m³. The major sources of roadside PM10 include dust re-suspension from vehicular movement and human activity, and particulate emissions from old vehicles. Other sources of PM10 in the valley include cement and brick factories. Studies are currently being conducted to determine the lead content of PM10 from roadside samples. Additional studies are warranted to assess the potential adverse human health effects of acute or chronic exposure to high particulate levels reported in this study.