Roadside Air Quality Monitoring Stations Research Articles

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.

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.

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.

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.

Estimation of the traffic related anthropogenic heat release using BTEX measurements – A case study in Abu Dhabi

The rate of release of anthropogenic heat—heat that results from human activity (mainly motorized transportation and air-conditioning)—is a crucial input to urban microclimate models. Traffic related anthropogenic heat, in particular, is notoriously difficult to estimate and often evades systematic treatment, especially in developing countries where traffic intensity indicators are either not routinely recorded or not publicly available. The most challenging aspect is the derivation of a typical daily profile of the anthropogenic heat release rate, which is essential for the accuracy of the urban microclimate models, given the dynamic nature of the heat island phenomenon. The basic premise of this study is that the traffic intensity profile, and hence the traffic related anthropogenic heat rate profile(s), can be inferred from urban air pollutant concentration measurements. Volatile organic compounds such as Benzene, Toluene, Ethylbenzene and m-, p-, o-Xylenes (BTEX) are the most relevant in this context. A new methodology is proposed for deriving normalized daily profile(s) of traffic related heat release rates using one year of hourly BTEX measurements recorded by a road-side air quality monitoring station in downtown Abu Dhabi. These normalized profiles are calibrated using average traffic related heat release rates derived from a ‘top-down’ analysis of aggregate transportation data. The calibrated daily profiles are subsequently validated according to a ‘bottom-up’ approach whereby the total number of circulating vehicles at a specific time of the day is estimated from satellite images of several carefully selected representative urban districts. The daily traffic related anthropogenic heat rate profile(s) derived according to this methodology are consistent with expectations and comparable to those reported in the literature for comparable cities. The daily profile presents two peaks, the first at about 8am and the second, more pronounced, at about 9pm. The average workday sensible heat release rate is estimated to be 11.4W/m2 with an evening peak of about 19W/m2. The peak latent heat rate is approximately 1.9W/m2 which amounts to about 10% of the sensible rate.

Urban air quality simulation in a high-rise building area using a CFD model coupled with mesoscale meteorological and chemistry-transport models

An integrated urban air quality modeling system is established by coupling a computational fluid dynamics (CFD) model with mesoscale meteorological and chemistry-transport models. The mesoscale models used are the weather research and forecasting (WRF) model and the community multiscale air quality (CMAQ) model, which provide the initial and time-dependent boundary conditions for the CFD model. For the consistency of chemical processes in the CFD and CMAQ models, the same chemical mechanism used in the CMAQ model is implemented in the CFD model. Urban air quality simulations are performed from 0900 to 1800 LT on 3 June 2010 in a high-rise building area of Seoul, Republic of Korea, where mobile emission sources are concentrated. The NO2 and O3 concentrations in the CFD simulation are evaluated with data measured at a roadside air quality monitoring station, showing better agreements than those in the CMAQ simulation. The NO2 and O3 concentration fields exhibit high spatial variabilities in the high-rise building area. The spatial variabilities near the surfaces are strongly associated with the heterogeneity of mobile emission on roads, whereas the spatial variabilities near the top of high-rise buildings are strongly associated with the heterogeneity of building geometry. The average NO2 and O3 concentrations (46 and 30 ppb, respectively, at z = 30 m) near the surfaces are considerably different from the NO2 and O3 concentrations in the CMAQ simulation (17 and 44 ppb, respectively, at z = 30 m), implying the insufficient urban surface representation in the CMAQ simulation. The heterogeneity of building geometry is found to enhance the vertical pollutant transport, whereas the heterogeneity of mobile emission is found to confine emitted pollutants near the surfaces. When the vertical mixing is efficient, the O3 concentration decreases in substantial vertical ranges with the same amount of NOx emission. The integrated urban air quality modeling system realistically simulates the spatial variabilities associated with the local influences of building geometry and mobile emission. This is a promising modeling approach that accounts for multiscale influences on urban air quality.

Contributions of vehicular carbonaceous aerosols to PM<sub>2.5</sub> in a roadside environment in Hong Kong

Abstract. Hourly measurements of elemental carbon (EC) and organic carbon (OC) were made at Mong Kok, a roadside air quality monitoring station in Hong Kong, for a year, from May 2011 to April 2012. The monthly average EC concentrations were 3.8–4.9 μg C m−3, accounting for 9.2–17.7% of the PM2.5 mass (21.5–49.7 μg m−3). The EC concentrations showed little seasonal variation and peaked twice daily, coinciding with the traffic rush hours of a day. Strong correlations were found between EC and NOx concentrations, especially during the rush hours in the morning, confirming vehicular emissions as the dominant source of EC at this site. The analysis by means of the minimum OC / EC ratio approach to determine the OC / EC ratio representative of primary vehicular emissions yields a value of 0.5 for (OC / EC)vehicle. By applying the derived (OC / EC)vehicle ratio to the data set, the monthly average vehicle-related OC was estimated to account for 17–64% of the measured OC throughout the year. Vehicle-related OC was also estimated using receptor modeling of a combined data set of hourly NOx, OC, EC and volatile organic compounds characteristic of different types of vehicular emissions. The OCvehicle estimations by the two different approaches were in good agreement. When both EC and vehicle-derived organic matter (OM) (assuming an OM-to-OC ratio of 1.4) are considered, vehicular carbonaceous aerosols contributed ~ 7.3 μg m−3 to PM2.5, accounting for ~ 20% of PM2.5 mass (38.3 μg m−3) during winter, when Hong Kong received significant influence of air pollutants transported from outside, and ~ 30% of PM2.5 mass (28.2 μg m−3) during summertime, when local emission sources were dominant. A reduction of 3.8 μg m−3 in vehicular carbonaceous aerosols was estimated during 07:00–11:00 (i.e., rush hours on weekdays) on Sundays and public holidays. This could mainly be attributed to less on-road public transportation (e.g., diesel-powered buses) in comparison with non-holidays. These multiple lines of evidence confirm local vehicular emissions as an important source of PM2.5 in an urban roadside environment and suggest the importance of vehicular emission control in reducing exposure to PM2.5 in busy roadside environments.

Temporal Variations of Major Air Pollutants and Pollution Standard Index in the Great Tehran Area

In this study, the trends of Pollution Standard Index (PSI) and the levels of related air pollutants are analyzed based on the database monitored at two selected roadside air quality monitoring stations: Aghdasieh and Fatemi in Tehran during 2000–2006. The original measured pollutant data and the resultant PSIs are statistically analyzed in different time series, including daily, monthly and seasonal patterns. The daily mean PSIs in seasonal period can be regarded as nonstationary time series. The autoregressive integrated moving average (ARIMA) method implemented by the Box–Jenkins model is used to forecast the PSI time series. The performance evaluations of the adopted models are also carried out and discussed according to Akaike Information Criterion (AIC) and Bayesian information criteria (BIC). The results indicate that both ARIMA (1, 1, 1) and ARIMA (2, 1, 1) models can provide reliable satisfactory predictions for both time series.

Seasonal variation of air pollution index: Hong Kong case study

Air pollution is an important and popular topic in Hong Kong as concerns have been raised about the health impacts caused by vehicle exhausts in recent years. In Hong Kong, sulphur dioxide SO 2, nitrogen dioxide (NO 2), nitric oxide (NO), carbon monoxide (CO), and respirable suspended particulates (RSP) are major air pollutants caused by the dominant usage of diesel fuel by goods vehicles and buses. These major pollutants and the related secondary pollutant, e.g., ozone (O 3), become and impose harmful impact on human health in Hong Kong area after the northern shifting of major industries to Mainland China. The air pollution index (API), a referential parameter describing air pollution levels, provides information to enhance the public awareness of air pollutions in time series since 1995. In this study, the varying trends of API and the levels of related air pollutants are analyzed based on the database monitored at a selected roadside air quality monitoring station, i.e., Causeway Bay, during 1999–2003. Firstly, the original measured pollutant data and the resultant APIs are analyzed statistically in different time series including daily, monthly, seasonal patterns. It is found that the daily mean APIs in seasonal period can be regarded as stationary time series. Secondly, the auto-regressive moving average (ARMA) method, implemented by Box–Jenkins model, is used to forecast the API time series in different seasonal specifications. The performance evaluations of the adopted models are also carried out and discussed according to Bayesian information criteria (BIC) and root mean square error (RMSE). The results indicate that the ARMA model can provide reliable, satisfactory predictions for the problem interested and is expecting to be an alternative tool for practical assessment and justification.