Roadside Air Quality Research Articles

Suitability of using carbon dioxide as a tracer gas for studying vehicle emission dispersion in a real street canyon

High-rise buildings form deep urban street canyons and restrict the dispersion of vehicle emissions, posing severe health risks to the public by aggravating roadside air quality. Field measurements are important for understanding the dispersion process of tailpipe emissions in street canyons, while a major challenge is the lack of a suitable tracer gas. Carbon dioxide (CO2), which is safe to the public and inexpensive to obtain, can be reliably measured by existing gas analysers. This study investigated the suitability of using CO2 as a tracer gas for characterising vehicle emission dispersion in a real-world street canyon. The tracer gas was released via a line or point source, whose dispersion was characterised by a sensors network comprising low-cost air quality sensors. The results showed that the CO2 contained in the exhaust gas of a test vehicle itself had unmeasurable effect at roadsides. Both the line and point sources produced obvious CO2 level elevations at approximately 30 s after the test vehicle passed by. In addition, for both line and point sources, the CO2 elevations were much more distinct at the roadside next to tailpipe exit than the opposite side, and were higher at 0.8 m than 1.6 m above the ground. The present study demonstrated that using CO2 as a tracer gas is feasible for investigating vehicle emission dispersion in real-world street canyons. Future studies are needed to improve the gas release rate of the developed tracer gas systems for more reliable measurements and larger street canyons.

Determination of the Spatial Distribution of Air Pollutants in Bucheon, Republic of Korea, in Winter Using a GIS-Based Mobile Laboratory.

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emission sources. In Republic of Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m3 in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM2.5 concentration was 161.7 µg/m3. The maximum NOx, NO2, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.

Roadside NO2/NOx and primary NO2 from individual vehicles

Traffic-derived nitrogen oxides (NOx) is a major component of kerbside pollution in large cities and represent an important source of exposure and health risk to urban populations, especially during the use of roadside pavements. The ratio of ambient NO2/NOx (xNO2) varies widely in urban environments, influenced by relative source strengths, but also oxidation. Primary NO2 emissions have changed over the years, with changes in engine and exhaust controls. Over the years 1998/2021 the NO2 fraction in exhaust (fNO2) in Hong Kong has changed from 0.05 in the late 1990s to values ∼0.2 in the years 2010/2015, and has fallen to ∼0.08 in recent years. To better understand fNO2 from individual on-road vehicles, this study monitored NO and NO2 at the kerb in a busy street canyon in Hong Kong, capturing the rapid concentration changes in response to vehicles passing by. The kerbside xNO2 was much lower (∼0.2) than that at the roadside air quality monitoring station (∼0.5) whose inlet was elevated some 3 m above the road nearby. Output from pollution sensors was deconvoluted to explore rapidly changing concentrations in the plume segments from individually identifiable vehicles as they passed and enable to estimate of the NO2 fraction in the exhaust for individual vehicle. Each vehicle was assigned to a type, Euro emission class and year of manufacture. The fNO2 from individual vehicles depends on emission class, increasing from Euro 4 to Euro 6, while fNO2 decreases as vehicles get older. The estimates of fNO2 made at the kerb were similar to those derived for the local air quality monitoring station, but not necessarily that of the city as a whole, where fNO2 varies by a factor of two.

Evaluation of roadside air quality using deep learning models after the application of the diesel vehicle policy (Euro 6)

Euro 6 is the latest vehicle emission standards for pollutants such as CO, NO2 and PM, that all new vehicles must comply, and it was introduced in September 2015 in South Korea. This study examined the effect of Euro 6 by comparing the measured pollutant concentrations after 2016 (Euro 6–era) to the estimated concentrations without Euro 6. The concentration without Euro 6 was estimated by first modeling the air quality using various environmental factors related to diesel vehicles, meteorological conditions, temporal information such as date and precursors in 2002–2015 (pre–Euro 6–era), and then applying the model to predict the concentration after 2016. In this study, we used both recurrent neural network (RNN) and random forest (RF) algorithms to model the air quality and showed that RNN can achieve higher R2 (0.634 ~ 0.759 depending on pollutants) than RF, making it more suitable for air quality modeling. According to our results, the measured concentrations during 2016–2019 were lower than the concentrations predicted using RNN by − 1.2%, − 3.4%, and − 4.8% for CO, NO2 and PM10. Such reduction can be attributed to the result of Euro 6.

The impact of smart traffic interventions on roadside air quality employing machine learning approaches

In this paper, the impact of smart traffic interventions on air quality was assessed in Thatcham, West Berkshire, UK. The intervention linked NO2 levels with the cycle time of the traffic lights. When NO2 levels exceeded a certain threshold, the strategy was triggered, which reduced the traffic congestion by turning the traffic lights green. Eight Earthsense Zephyrs air quality sensors and nine inductive-loop traffic detectors were installed in Thatcham to simultaneously monitor the air quality and traffic flows, respectively. Compared to the pre-intervention period, the observed NO2 concentrations decreased in June, July and August and increased in September 2021, however, this does not reveal the true effect of smart traffic intervention. Using the observed data on the days with- and without-exceedances, we developed two machine learning models to predict the Business-as-usual (BAU) air quality level, i.e., a generalised additive model for average concentration and a quantile regression model for peak concentration. Our results demonstrated that average predicted concentrations (BAU) were lower than the observed concentrations (with intervention) by 12.45 %. However, we found that peak concentrations decreased by 20.54 %.

DATA-DRIVEN UNDERSTANDING OF AI-BASED TRAFFIC SIGNAL CONTROL AND ITS IMPLICATIONS ON LOW-CARBON URBAN TRANSPORT SYSTEMS

Emission by vehicles is one of the main sources causing the exceedance of the Air Pollution Index at roadside. Therefore, reducing emissions from vehicles in busy corridors has been a priority of the Government in improving roadside air quality. The existing pedestrian lights, which follow a predetermined signal timing for road crossings, are typical static traffic lights. The pushbutton has been added to improve the pedestrian crossing efficiency by rendering the green phase to the pedestrian as soon as possible. However, the current detection devices are not capable of streamlining the traffic flows and prioritizing pedestrians on the curb according to the actual needs. With the use of artificial intelligence (AI) techniques such as deep learning, this project can analyze the traffic conditions of a designated area, including the length of traffic queues and the number of pedestrians waiting at a crossroad. Our system can then manage traffic lights to facilitate efficient traffic flow between vehicles and pedestrians. In addition, the IoT technologies enable the system to collect traffic information for big data analysis and facilitate efficient traffic management. In the implementation stage of the project, a local field study of a crossroad with a school, bus stops, and shopping malls in the vicinity was conducted and relevant data for further analysis was collected. The results showed that the system could help to shorten traffic queues and pedestrian waiting time at the crossroad. The goals are to examine strategies in traffic signal control based on AI to reduce carbon footprint. The deliverables will assess how urban transport systems can reduce carbon emissions and adapt to climate change with a focus on traffic signal rationalization. It provides guidance, policy recommendations, and outlines research and information needs.

Long-term variation and evaluation of air quality across Hong Kong

Study of Air Quality Objectives (AQOs) and long-term changes of air pollution plays a decisive role in formulating and refining pollution control strategies. In this study, 10-year variations of six major air pollutants were analyzed at seven monitoring sites in Hong Kong. The continuous decrease of annual averaged concentrations of NO2, SO2, CO, PM2.5 and PM10 and numbers of days with severe pollution conditions validated the efficiency of the series of air pollution control schemes implemented by the Hong Kong government. However, there is still a big gap to meet the ultimate targets described by the World Health Organization. Besides, the concentration of O3 at roadside and urban stations increased by 135% ± 25% and 37% ± 18% from 2011 to 2020, respectively, meanwhile the highest 8 hr averaged O3 concentration was observed as 294 µg/m3 at background station in 2020, which pointed out the increasing ozone pollution in Hong Kong. There was a great decrease in the annual times of air quality health index (AQHI) laying in “high”, “very high” and “serious” categories from 2011 to 2020 with the decrease rate of 89.70%, 91.30% and 89.74% at roadside stations, and 79.03%, 95.98% and 72.73% at urban stations, respectively. Nevertheless, the number of days categorized as “high” or above at roadside station was twice more than that in the urban station during the past ten years. Thus, more policies and attentions should be given to the roadside air quality and its adverse health effect to pedestrians on street.

Heavy Metals Phytoremediation and Its Impact on Photosynthetic Pigments and Metabolic Content in Some Plant Species Grown in the Streets of Jeddah Governorate, Saudi Arabia

In Jeddah governorate, increased industrialization, urbanization and vehicular traffic may raise levels of heavy metals in the air and soil, threatening food safety and human health. Roadside vegetation traffic barriers might be an effective way to enhance roadside air quality and reduce human exposure to vehicular pollution. This study was conducted in order to uncover the best-cultivated plant species for purifying polluted air in terms of environmental parameters. The samples for this study were obtained from several locations throughout Jeddah governorate streets. The main causes of air pollution in the study area are traffic and automobile emissions. Photosynthetic pigments, soluble sugars, soluble protein, amino acids, and proline in plant extracts were investigated in the plant species used in this study. Also, the ionic composition (K, Na Ca and Mg), as well as the heavy metals (Zn, Cr, Ni, Cd, Pb, Co and Ba) in the sampling sites, as well as roots and leaves of the collected plant samples were all assessed. Plant examination revealed that I. coccinea possessed the highest value of Chl b, soluble proteins and sugars, while T. stans exhibited the highest amount of Chl a, carotenoids and amino acids. Furthermore, the most common contaminants in all sites were Zn, Ba, and Ni, with Zn and Ba being the most actively accumulated in the leaves of the studied plants. The findings revealed that the root of C. lancifolius is a powerful phytoaccumulator of Zn, Ni, and Co. In addition, C. lancifolius, I. coccinea, Z. spina-christi, and B. spectabilis were shown to exhibit high accumulating potential of various polluting metals in their roots. Also, Zn, Ba, Ni, and Cr were the most efficiently transmitted metals to the leaves of the studied plants. Consequently, these plant species can be employed in phytoremediation approaches at contaminated sites.

Characterization of roadside air pollutants: An artery road of Lanzhou city in northwest China

Due to the lack of air and traffic monitoring data in China, this study employed a remote sensing (RS) system and air quality monitoring sensors to investigate the air pollutant concentrations, traffic profiles and meteorological data in order to characterize roadside pollutant concentrations and identify their influence factors for a major road in Lanzhou. The results suggested that the temporal variations of concentrations of SO2, NO2, PM2.5, PM10 and CO peaked at 5:00-7:00 while O3 peaked at 17:00. The concentrations of roadside air pollutants were lower on weekdays than weekends except O3. The concentrations of PM2.5 and PM10 declined with the height of the monitoring sites increased, peaking at 2-meter height whereas SO2, NO2, CO, and O3 reached their highest concentrations at 4-meter height. The concentrations of roadside NO2 and CO show positive correlations with the volume of heavy-duty trucks (HDTs) and light-duty passenger vehicles (LDPVs), respectively. This study provided scientific support to the establishment of roadside air quality monitoring systems and the assessment of the environmental and health impact from vehicle emissions.

A machine learning model for predicting PM2.5 and nitrate concentrations based on long-term water-soluble inorganic salts datasets at a road site station

In this study, long-term variations in the concentrations of PM2.5, water-soluble inorganic salts (WIS), and gaseous precursors measured by a roadside air quality monitoring station were investigated from 2017 to February 2021 to examine the formation mechanism of secondary inorganic PM2.5. A new machine learning model using WIS data as input variables was further developed to predict PM2.5 and nitrate concentrations for source tracing and effective control strategy development. The results showed that a reduction in the NOx concentration under VOC-limited O3 formation regime could offset the consumption of OH and O3, causing an increase in secondary NO3− and PM2.5 formation during fall and winter seasons. A good agreement was obtained between the predicted and measured PM2.5 values, with R2, root mean square error (RMSE), and mean absolute error (MAE) values of 0.81, 6.81 μg/m3, and 5.10 μg/m3, respectively. The nitrate ([NO3−]) prediction model could predict ∼59% of the atmospheric nitrate concentration. The sensitivity analysis of the input variables in the present model further revealed that NO3− and VOC were two important pollutants dominating the variation trend of PM2.5. It is recommended that decision makers should focus more on the reduction of VOC and O3 to reduce secondary PM2.5 formation during winter in central Taiwan. Real-time measurements of the chemical composition of PM2.5, taken as the regulatory air quality monitoring items are needed in the future.

Prediction of air pollutants on roadside of the elevated roads with combination of pollutants periodicity and deep learning method

The socio-economic effects of urban elevated roads have been well-documented in previous studies. Nevertheless, the environmental impacts of the elevated road location are rarely considered. In this study, we quantified such impacts on four traffic pollutants (e.g., NO, NO2, CO, and O3) in Shanghai, using the two-year observation data from Shanghai roadside air quality monitoring stations—under the elevated road and on the side of the elevated road. Auto-correlation analysis was employed to identify the periodic patterns of air pollutants. The results showed that the severer traffic pollution and more notable daily periodic characteristics of air pollutants were observed under the elevated road. This result may be attributed to the elevated “cap” structure, which has become a barrier to prevent the pollutant diffusion in street canyon. Further, a novel Long Short-Term Memory model with the identified periodicity was proposed to predict air quality. The proposed model achieved higher goodness of fit and lower prediction error in prediction of four pollutants compared to other baseline models, and that the forecasting accuracy was higher under the elevated road. These findings ascertain the effect of the elevated road location on the variations of atmospheric pollutants and could provide implications in taking control measures to mitigate traffic pollution under the elevated road.

Determination of local traffic emission and non-local background source contribution to on-road air pollution using fixed-route mobile air sensor network

Traffic-related air pollutants are major contributors to deteriorating urban air quality and pose a serious threat to pedestrians. From both a scientific and a regulatory standpoint, it is important and challenging to understand the contributions of local and non-local sources to accurately apportion specific sources such as traffic emissions contribution to on-road and near-road microenvironment air quality. In this study, we deployed mobile sensors on-board buses to monitor NO, NO2, CO and PM2.5 along ten important routes in Hong Kong. The measurements include two seasons: April 2017 and July 2017. Two types of baseline extraction methods were evaluated and applied to separate local and background concentrations. The results show NO and NO2 are locally dominated air pollutants in spring, constituting 72%–84% and 58%–71%, respectively, with large inter-road variation. PM2.5 and CO largely arise from background sources, which contribute 55%–65% and 73%–79% respectively. PM2.5 displays a homogeneous spatial pattern, and the contributions show seasonal change, decreasing during summer. Regional transport pollution is the primary contributor during high pollution episodes. Isolated vehicle plumes show highly skewed concentration distributions. There are characteristic polluted segments on routes and they are most evident at rush hours. The most polluted road segments (top 10%) cluster at tunnel entrances and congested points. Some of these polluted locations were observed in Hong Kong's Low Emission Zones and suggest limitations to the existing control strategies, which only address larger buses. Our work gives new insights in the importance of regional cooperation to improve background air pollution combined with local control strategies to improve roadside air quality in Hong Kong.

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.

Introducing the Green Infrastructure for Roadside Air Quality (GI4RAQ) Platform: Estimating Site-Specific Changes in the Dispersion of Vehicular Pollution Close to Source

The benefits of ‘green infrastructure’ are multi-faceted and well-documented, but estimating those of individual street-scale planting schemes at planning can be challenging. This is crucial to avoid undervaluing proposed schemes in cost–benefit analyses, and ensure they are resilient to ‘value engineering’ between planning and implementation. Here, we introduce prototype software enabling urban practitioners to estimate the site-specific air quality impacts of roadside vegetation barriers: highly localised changes in pollutant concentrations due to changes in the dispersion of vehicular emissions close to source. We summarise the recent shift in understanding regarding the impacts of vegetation on urban air pollution towards changes in pollutant dispersion (cf. deposition) and describe our prototype software, offering rapid estimates thereof. First tests of the underlying model’s performance are promising, reproducing: annual mean NO2 and PM2.5 concentrations in a street canyon (Marylebone Road, London, UK) to within 10% and 25%, respectively; and changes in pollutant concentrations of the right order of magnitude behind roadside barriers in a wind tunnel simulation of a street canyon and a real open-road environment. However, the model underestimates the benefits of a barrier in a simulated street canyon under perpendicular wind conditions. The prototype software is a first step towards informing practitioners of the site-specific impacts of vegetation barriers, which should always be additional to (i.e., no substitute for) essential emission reductions. The code is open-source to engage further researchers in its continued development.

A review of strategies for mitigating roadside air pollution in urban street canyons.

Urban street canyons formed by high-rise buildings restrict the dispersion of vehicle emissions, which pose severe health risks to the public by aggravating roadside air quality. However, this issue is often overlooked in city planning. This paper reviews the mechanisms controlling vehicle emission dispersion in urban street canyons and the strategies for managing roadside air pollution. Studies have shown that air pollution hotspots are not all attributed to heavy traffic and proper urban design can mitigate air pollution. The key factors include traffic conditions, canyon geometry, weather conditions and chemical reactions. Two categories of mitigation strategies are identified, namely traffic interventions and city planning. Popular traffic interventions for street canyons include low emission zones and congestion charges which can moderately improve roadside air quality. In comparison, city planning in terms of building geometry can significantly promote pollutant dispersion in street canyons. General design guidelines, such as lower canyon aspect ratio, alignment between streets and prevailing winds, non-uniform building heights and ground-level building porosity, may be encompassed in new development. Concurrently, in-street barriers are widely applicable to rectify the poor roadside air quality in existing street canyons. They are broadly classified into porous (e.g. trees and hedges) and solid (e.g. kerbside parked cars, noise fences and viaducts) barriers that utilize their aerodynamic advantages to ease roadside air pollution. Post-evaluations are needed to review these strategies by real-world field experiments and more detailed modelling in the practical perspective.

Assessing air pollution tolerance of plant species in vegetation traffic barriers in Kathmandu Valley, Nepal

Vegetation traffic barriers along roads can be an effective structure to improve roadside air quality and to reduce human exposure to traffic air pollutants. However, the selection of the plant species should be considered as an important design parameter for vegetation traffic barriers because different plant species demonstrate different levels of tolerance to air pollutants. This study compares the air pollution tolerance of different plant species found in the vegetation traffic barriers in the Kathmandu valley. Four biochemical parameters (relative water content, leaf extract pH, total chlorophyll and ascorbic acid) and the dust-capturing potential of plants were analyzed. Out of the nine selected species, Cinnamomum camphora showed the highest tolerance to air pollution based on the air pollution tolerance index. Similarly, Schefflera pueckleri, Psidium guajava and Ficus benjamina were found to be the sensitive species, while Ficus sp., Nerium oleander, Thuja sp., Dypsis lutescens and Albizia julibrissin were found to have a moderate level of tolerance to air pollution. N. oleander had the highest dust-capturing potential. Considering both air pollution tolerance index and dust-capturing potential, C. camphora, N. oleander and A. julibrissin were found to be the most suitable species for the roadside plantation. The findings of this study might have important implications for plant species selection for vegetation traffic barriers.

Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model.

The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident’s outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In order to take the periodicity of pollutants into empirical air quality forecasting models, this study evaluates the temporal variations of air pollutants and develops a novel sequence to sequence model with weekly periodicity to forecast air quality. Two-year observation data from Shanghai roadside air quality monitoring stations are employed to support analyzing and modeling. The results conclude that the fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations show obvious daily and weekly variations, and the temporal patterns are nearly consistent with the periodicity of traffic flow in Shanghai. Compared with PM2.5, the CO concentrations are more affected by traffic variation. The proposed model outperforms the baseline model in terms of accuracy, and presents a higher linear consistency in PM2.5 prediction and lower errors in CO prediction. This study could assist environmental researchers to further improve the technologies for urban air quality forecasting, and serve as tools for supporting policymakers to implement related traffic management and emission control policies.

Layout Methods of Monitoring Stations for Diesel Freight Trucks Emission Supervision Using Highway Traffic Survey Data—— Taking Shanxi Province as an Example

The in-use diesel trucks have high use intensity, high mobility, and frequent excessive emissions, which have become one of the important air pollution sources. The Action Plan for Tackling the Challenges of Diesel Truck Pollution Control issued by the Chinese government requires the establishment of a national monitoring network for transport-related air pollution and the use of various means to monitor diesel truck emissions. This study briefly summarized the requirements for diesel truck emission monitoring and supervision. Relying on the highway network traffic survey data, a method for identifying the main route of regional highway freight transportation was proposed. Then referring to the experience of highway air quality monitoring networks at home and abroad, the article proposed layout methods for roadside air quality monitoring stations, vehicle remote sensing monitoring stations, and road inspection stations, which fills the gap in domestic methods. At last, a demonstration study on the layout plan for the in-use diesel truck emissions monitoring stations in Shanxi Province was carried out. Taking Shanxi’s main highway freight corridors as the key supervision area, this article screened out 44 roadside air quality monitoring stations, 24 vehicle remote sensing monitoring stations, and 15 road inspection stations.

Effects of short-duration vehicular traffic control on volatile organic compounds in roadside atmosphere

Traffic air pollution control is an important aspect of air quality management in urban areas. In this study, we investigated whether short-duration vehicular traffic control could be applied as a temporary tool to improve the roadside air quality based on concentrations of roadside volatile organic compounds (VOCs). Experiments were conducted targeting a 30-min “no-vehicle” event observed on 30th May 2019 (13:30–14:00) in Central Taiwan. Although the event was not imposed for air quality management, it provided an excellent opportunity to evaluate it as a potential tool of air quality management. Roadside air samples were collected before, during and after the no-vehicle event from two locations in Central Taiwan. Samples were analyzed for a total of 87 target VOCs. Results showed that alkanes, aromatics and oxygenated organics were the major contributors of roadside VOCs. Mean concentrations of the sum of detected VOCs (∑VOCs) were about 8%–24% lower during the no-vehicle event compared to those before or after the event. Correlation analysis showed that a total of 20 VOCs were related to traffic emissions. During-event concentrations of the sum of those traffic related VOCs were less than pre- or post-event concentrations by 40.77%–83.20%. Diagnostic ratios among VOCs revealed a notable impact of no-vehicle event on the composition and concentrations of the roadside VOCs. Although the short-duration vehicular traffic control had only a limited impact on ∑VOCs due to the presence of multiple emission sources, it had significant impact on the traffic related VOCs.

High spatiotemporal characterization of on-road PM2.5 concentrations in high-density urban areas using mobile monitoring

Mobile air quality monitoring reports air pollutant concentrations at a high spatiotemporal resolution, enabling the characterization of heterogeneous human exposure and localized pollution hotspots. In this study, on-road concentrations of fine particulate matter (PM2.5) in a high-density urban area in Hong Kong were measured in December 2014 and January 2015 (winter) and June and July 2015 (summer) using a tramcar mobile monitoring platform. We developed a method of mapping the winter and summer on-road PM2.5 concentrations along a tramcar route at a 50-m spatial resolution, using mobile measurements. In addition, the minimum number of days required to precisely estimate on-road PM2.5 concentrations was estimated. The results showed that the on-road PM2.5 concentrations were highly correlated with PM2.5 concentrations measured at a nearby roadside air quality monitoring station (AQMS) in both winter and summer, with Pearson correlation coefficients of 0.89–0.93. The resulting maps of winter and summer on-road PM2.5 concentrations revealed small-scale spatial patterns used to identify more polluted areas. In addition, approximately 12 and 4 days were required to precisely capture spatial patterns of PM2.5 concentrations, with R2 higher than 0.6 in winter and in summer. The findings of this study offer valuable information on air pollution control and exposure reduction by highlighting localized pollution hotspots, and provide insights into the minimum sampling duration for mobile sampling campaigns.