Roadside Monitoring Research Articles

Characterizations of air pollutants at roadside monitoring stations and traffic emission effects: A case study in Tianjin, China

To investigate the response of Roadside Monitoring Stations (RSs) to traffic-related air pollution, traffic and pollutant characteristics, influencing factors, and potential source characterization in Tianjin, China were determined based on roadside monitoring of real-world data conducted at RSs in 2022. The diurnal variation trend of pollutants at RSs was consistent with that at the National Monitoring Station (NM), with notably higher pollutant fluctuations during the morning and evening peak traffic times at RSs, where the average diurnal concentration was 41.46 % higher than that at the NM. The generalized additive model (GAM) for nitrogen oxides (NOx) and carbon monoxide (CO), responding to the multiple influencing factors, performed well at RSs, with deviance explained by 86.6 and 61.4 %, respectively. The synergistic effects of wind direction and speed contributed to most of the variations in NOx and CO, which were 14.74 and 12.87 %, respectively. Pollutant concentrations were highest under windless conditions, with pollutants originating primarily from local vehicle emissions. The model results indicated that medium-duty truck (MDT) traffic flow predominantly contributed to the variability in NOx emissions, whereas passenger car (PC) traffic flow was the primary source of CO emissions from traffic variables. MDTs should be the focus of urban NOx traffic emissions control. Potential-source analysis validated the results obtained from the GAM, and both analyses showed that RSs can better characterize traffic-related air pollutants. Furthermore, more stringent emission standards have effectively mitigated the release of pollutants from motor vehicles and contributed to the modernization of vehicle fleet composition, effectively decreasing CO concentrations.

The method for evaluating the coverage rate of roadside monitoring cameras in road areas

Accurately assessing the coverage rate of roadside monitoring cameras in road areas is crucial for intelligent transportation systems and road risk assessment. This paper presents a new method that uses Carsim software to simulate different camera tilt angles and road geometry conditions to evaluate the coverage area of roadside monitoring cameras. By adjusting the camera tilt angles and the curve radius of the road, the coverage effect under different conditions is analyzed, and affine transformation is used to calculate the actual coverage area. The research results show that this method can effectively fit the road area, providing theoretical support for the deployment of monitoring cameras in traffic.

COMPREHENSIVE ASSESSMENT ROADSIDE INJURIES THROUGH HIGH-RESOLUTION THREE-DIMENSIONAL LASER SCANNING

Roadside injuries are a significant concern within transportation infrastructure, necessitating effective monitoring and assessment methodologies. In this study, we introduce a novel approach for evaluating roadside injuries using high-resolution three-dimensional laser scanning systems. Through a meticulous selection process based on road cover characteristics, we implement a localized roadside monitoring methodology. The aim is to accurately identify and assess injuries, leveraging the precise data captured by laser scanning systems. By analyzing this data, we seek to provide practical insights for informing future road surface restoration activities. This research contributes to enhancing the efficiency and precision of roadside injury evaluation, ultimately facilitating improved maintenance and safety measures within transportation networks.

A Virtual Method for Optimizing Deployment of Roadside Monitoring Lidars at As-Built Intersections

A Virtual Method for Optimizing Deployment of Roadside Monitoring Lidars at As-Built Intersections

YOLOv7-3D: A Monocular 3D Traffic Object Detection Method from a Roadside Perspective

Current autonomous driving systems predominantly focus on 3D object perception from the vehicle’s perspective. However, the single-camera 3D object detection algorithm in the roadside monitoring scenario provides stereo perception of traffic objects, offering more accurate collection and analysis of traffic information to ensure reliable support for urban traffic safety. In this paper, we propose the YOLOv7-3D algorithm specifically designed for single-camera 3D object detection from a roadside viewpoint. Our approach utilizes various information, including 2D bounding boxes, projected corner keypoints, and offset vectors relative to the center of the 2D bounding boxes, to enhance the accuracy of 3D object bounding box detection. Additionally, we introduce a 5-layer feature pyramid network (FPN) structure and a multi-scale spatial attention mechanism to improve feature saliency for objects of different scales, thereby enhancing the detection accuracy of the network. Experimental results demonstrate that our YOLOv7-3D network achieved significantly higher detection accuracy on the Rope3D dataset while reducing computational complexity by 60%.

Limited impact of diesel particle filters on road traffic emissions of ultrafine particles

Diesel engines are a major contributor to emissions of both Black Carbon (BC) and ultrafine particles. Analysis of data from the only roadside monitoring site in Europe with a continuous dataset for size-segregated particle number count (Marylebone Road, London) from 2010 to 2021 reveals that the growing number of vehicles fitted with a Diesel Oxidation Catalyst (DOC) and Diesel Particle Filter (DPF) has been very effective in controlling the emissions of solid particles and hence BC, but that there has been little change in the liquid mode (<30 nm) particles, and that concentrations of ultrafine particles (<100 nm) still well exceed the threshold for “high” concentrations (>104 cm−3 /24-hour mean) defined by WHO. BC declined by 81% between 2014 and 2021, but the ultrafine particle (<100 nm) count declined by only 26%. Consequently, in locations worldwide with heavy diesel traffic, concentrations of ultrafine particles are likely to remain “high” for the foreseeable future unless more effective abatement technologies are implemented.

Simulation of Traffic-Born Pollutant Dispersion and Personal Exposure Using High-Resolution Computational Fluid Dynamics

Road vehicles are a large contributor to nitrogen oxides (NOx) pollution. The routine roadside monitoring stations, however, may underrepresent the severity of personal exposure in urban areas because long-term average readings cannot capture the effects of momentary, high peaks of air pollution. While numerical modelling tools historically have been used to propose an improved distribution of monitoring stations, ultra-high resolution Computational Fluid Dynamics models can further assist the relevant stakeholders in understanding the important details of pollutant dispersion and exposure at a local level. This study deploys a 10-cm-resolution CFD model to evaluate actual high peaks of personal exposure to NOx from traffic by tracking the gases emitted from the tailpipe of moving vehicles being dispersed towards the roadside. The investigation shows that a set of four Euro 5-rated diesel vehicles travelling at a constant speed may generate momentary roadside concentrations of NOx as high as 1.25 mg/m3, with a 25% expected increase for doubling the number of vehicles and approximately 50% reduction when considering Euro 6-rated vehicles. The paper demonstrates how the numerical tool can be used to identify the impact of measures to reduce personal exposure, such as protective urban furniture, as traffic patterns and environmental conditions change.

Association between short-term NOx exposure and asthma exacerbations in East London: A time series regression model

BackgroundThere is strong interest in the relationship between short-term air pollution exposure and human health. Most studies in this field focus on serious health effects such as death or hospital admission, but air pollution exposure affects many people with less severe impacts such as exacerbations of respiratory conditions. MethodWe developed a time series regression model to quantify the relationship between daily NOx concentration and asthma exacerbations requiring oral steroids from primary care settings. Explanatory variables include daily NOx concentration measurements extracted from 8 available background and roadside monitoring stations in east London, and daily ambient temperature extracted for London City Airport, located in east London. Lags of NOx concentrations up to 21 days (3 weeks) were used in the model. ResultResults of the time series modelling showed a significant relationship between NOx concentrations on each day and the number of oral steroid courses prescribed in the following three weeks. Atmospheric concentrations of NOx measured at roadside stations are a more effective indicator of future prescriptions of oral steroid courses than are measurements at background stations. This relationship has two main components, one during the first week and the other towards the end of the second week. We find that an increase of 1 μgm−3 in atmospheric concentration of NOx (mean 95.75 μgm−3) leads to an increase of about 2.7% in the mean of 30.9 prescriptions of oral steroid courses per day in the study area. There is a negative correlation between ambient temperature and asthma exacerbation, with a reduction of 1C∘ leading to an increase of about 1.4% in the mean number of prescriptions per day. We did not find any effects of daily precipitation or relative humidity additional to annual seasonal ones.

Field Calibration and Evaluation of an Internet-of-Things-Based Particulate Matter Sensor

This paper presents a field evaluation of IoT-enabled Plantower PMS5003 particulate matter sensors in Birmingham, United Kingdom. Commercial, off the shelf, sensors were adapted to utilise Low Power Wide Area Network (LPWAN) IoT technology enabling batteries to be used as a power source. The devices are capable of measuring and communicating data to an online platform with a battery life of ∼2 months, at a measurement interval of 15 min, allowing for automated air quality monitoring for extended periods at high density. The sensors demonstrate success at being integrated into a wireless sensor network, with a high presence of readings. The average correlation coefficients (r2) between raw PMS device data and reference instrumentation are 0.718, 0.703, and 0.543 for PM1, PM2.5, and PM10, respectively. The devices also demonstrate good intersensor consistency, with Pearson’s r values between pairs ranging from 0.92 to 0.99 across all size ranges. Relative humidity (RH) clearly influences the response of the sensors, especially for RH &amp;gt;85%, in keeping with previous laboratory evaluations and evaluations of similar devices. The development of a multi-linear correction factor that accounts for humidity effects on the performance of the sensors is described; using this model, Pearson’s r values range from 0.81 to 0.91 compared to 0.73–0.85 from uncorrected values. There is also some evidence of drift at high humidity over an 8-week period, suggesting that such sensors will (at least currently) need recalibration approximately bimonthly. The limit of detection (LoD) (1.60–4.75 μg m−3) calculated from this study also demonstrates that the sensors are suitable for capturing concentrations typical of a moderately polluted United Kingdom urban environment—LoDs of PM2.5 in this study would have allowed for capture of 94.7% of the concentrations recorded at a typical United Kingdom urban roadside monitoring site between 2017 and 2020.

Camera Assisted Roadside Monitoring for Invasive Alien Plant Species Using Deep Learning.

Invasive alien plant species (IAPS) pose a threat to biodiversity as they propagate and outcompete natural vegetation. In this study, a system for monitoring IAPS on the roadside is presented. The system consists of a camera that acquires images at high speed mounted on a vehicle that follows the traffic. Images of seven IAPS (Cytisus scoparius, Heracleum, Lupinus polyphyllus, Pastinaca sativa, Reynoutria, Rosa rugosa, and Solidago) were collected on Danish motorways. Three deep convolutional neural networks for classification (ResNet50V2 and MobileNetV2) and object detection (YOLOv3) were trained and evaluated at different image sizes. The results showed that the performance of the networks varied with the input image size and also the size of the IAPS in the images. Binary classification of IAPS vs. non-IAPS showed an increased performance, compared to the classification of individual IAPS. This study shows that automatic detection and mapping of invasive plants along the roadside is possible at high speeds.

Winter air quality improvement in Beijing by clean air actions from 2014 to 2018

In order to cope with heavy haze pollution in Beijing, the Air Pollution Prevention and Control Action Plan was issued in 2013. In this study, we took January observations from 2014 to 2018 as a case study to assess wintertime air quality improvement in Beijing after five years of continuous emission control measures and to investigate the causes of haze formation. SO2 and NO2 concentrations in January 2018 decreased by 84% and 41% from January 2014, while NH3 concentrations remained relatively stable during these years with an average of 7.28 μg m−3. The mean concentrations of PM2.5 and water-soluble inorganic ions declined over the study period, while compared with 2014, decreased SO42− ratio and increased NO3− contribution in PM2.5 were observed, highlighting the importance of reactive nitrogen (Nr) pollution control. High concentrations of NH3, NO2, as well as NH4+ and NO3− in PM2.5 at roadside monitoring sites, indicated that vehicle sources played a vital role in local urban Nr emissions. Finally, we found most NH3 existed in the gas phase in the Beijing atmosphere, even on polluted winter days, suggesting a considerable risk for additional secondary aerosol formation. Our findings demonstrate the effectiveness of the emissions control in alleviating Beijing's air pollution. Significant reductions of precursor emissions are needed to achieve the goals set by air quality standards in the future, especially for NH3. It is especially crucial to jointly consider and control multiple pollution sources to effectively reduce the high levels of air pollution that remain in Beijing.

Generative adversarial network-based atmospheric scattering model for image dehazing

This paper presents a trainable Generative Adversarial Network (GAN)-based end-to-end system for image dehazing, which is named the DehazeGAN. DehazeGAN can be used for edge computing-based applications, such as roadside monitoring. It adopts two networks: one is generator (G), and the other is discriminator (D). The G adopts the U-Net architecture, whose layers are particularly designed to incorporate the atmospheric scattering model of image dehazing. By using a reformulated atmospheric scattering model, the weights of the generator network are initialized by the coarse transmission map, and the biases are adaptively adjusted by using the previous round’s trained weights. Since the details may be blurry after the fog is removed, the contrast loss is added to enhance the visibility actively. Aside from the typical GAN adversarial loss, the pixel-wise Mean Square Error (MSE) loss, the contrast loss and the dark channel loss are introduced into the generator loss function. Extensive experiments on benchmark images, the results of which are compared with those of several state-of-the-art methods, demonstrate that the proposed DehazeGAN performs better and is more effective.

Monitoring Excess Exposure to Air Pollution for Professional Drivers in London Using Low-Cost Sensors

In this pilot study, low-cost air pollution sensor nodes were fitted in waste removal trucks, hospital vans and taxis to record drivers’ exposure to air pollution in Central London. Particulate matter (PM 2.5 and PM 10 ), CO 2 , NO 2 , temperature and humidity were recorded in real-time with nodes containing low-cost sensors, an electrochemical gas sensor for NO 2 , an optical particle counter for PM 2.5 and PM 10 and a non-dispersive infrared (NDIR) sensor for CO 2 , temperature and relative humidity. An intervention using a pollution filter to trap PM and NO 2 was also evaluated. The measurements were compared with urban background and roadside monitoring stations at Honor Oak Park and Marylebone Road, respectively. The vehicle records show PM and NO 2 concentrations similar to Marylebone Road and a higher NO 2 -to-PM ratio than at Honor Oak Park. Drivers are exposed to elevated pollution levels relative to Honor Oak Park: 1.72 μ g m − 3 , 1.92 μ g m − 3 and 58.38 ppb for PM 2.5 , PM 10 , and NO 2 , respectively. The CO 2 levels ranged from 410 to over 4000 ppm. There is a significant difference in average concentrations of PM 2.5 and PM 10 between the vehicle types and a non-significant difference in the average concentrations measured with and without the pollution filter within the sectors. In conclusion, drivers face elevated air pollution exposure as part of their jobs.

Street protests and air pollution in Hong Kong

June 2019 saw large-scale street protests in Hong Kong that impeded traffic flow along streets in areas around to the Legislative Council building. These had the potential to reduce overall air pollutant emissions from traffic and lower their concentrations. Two roadside monitoring stations relatively close to the Legislative Council reveal that measured concentrations of nitrogen dioxide declined during the protests compared with measurements from other sites by at least 50% on many occasions. There were only subtle changes in particulate loads and no evidence of any reduction in carbon monoxide concentrations. Pedestrianisation and bus route rationalisation are often seen as methods to reduce exposure in congested areas, but the observations here suggest that the substantial improvements in the nitrogen dioxide levels might not be matched by improvements in other pollutants. Plans for changes to street layouts to improve air quality need careful investigation before they are implemented.

Vehicle weight identification system for spatiotemporal load distribution on bridges based on non-contact machine vision technology and deep learning algorithms

Accurate information regarding the weight of vehicle loads plays a significant role in maintaining the structural health of bridges. However, the only method currently available for ascertaining the weight of loads is the bridge weigh-in-motion (BWIM) system, which is not widely used because of the high cost of the large device involved. There is therefore a need to develop an effective, low-cost technology to ascertain vehicle loads in relation to spatiotemporal load distribution on long-span bridges. This paper proposes a non-contact vehicle identification methodology to distinguish a vehicle from its load based on machine vision technology and deep learning algorithms. The vehicle information (i.e., type, weight, position, and motion trajectory, etc.) is conveniently obtained from a roadside monitoring surveillance camera, while the axle-weight distribution interval for nine classified vehicle types is obtained from the statistical information of 8402 delivery vehicles from which the relationship between a unique vehicle type and the corresponding weight information is established. Meanwhile, a dataset containing 8624 vehicle images was established for training the deep convolutional neural network (DCNN), where nine rough-grained vehicle classifications were contained in order to enhance the generalizability of the network. Optimization analysis was conducted to improve the network accuracy in vehicle types identification. The position of vehicles can also be effectively detected by a faster region-based convolutional neural network (Faster R-CNN), where the pre-trained DCNN with 98.17% vehicle types classification accuracy is employed as the co-shared network layer to enhance computation efficiency. Utilizing the object detection results from the Faster R-CNN and utilizing a Kalman filter, a vehicle in motion could also be simultaneously real-time tracked by the monitoring video, while a graphical user interface (GUI) incorporated into the video camera enabled automatic identification. A post-processing module has been established based on the proposed method, and a field test was conducted to validate the reliability of the system.

Characteristics and influencing factors of traffic pollutant emission concentration in Shenzhen City

With the increasing number of motor vehicles, exhaust pollution has become increasingly serious. In order to grasp the roadside air pollution caused by traffic emissions in Shenzhen City and explore the relationship between traffic and air pollution, we design a series of roadside monitoring experiments to study the law and key influencing factors of traffic emissions in air pollution. The experiments monitor the traffic flow, meteorological data, and air pollutant mass concentration of typical roads. By comparing the monitoring results with those of the national air quality control points, which reflects the impact of road moving sources on air quality, we find that when there is less traffic, the roadside monitoring results are consistent with those of the state control point; when the traffic flow (especially freight) is large, the roadside monitoring results are significantly higher than those of the state control station. Based on the correlation analysis of nitrogen oxide (NOx) mass concentration and carbon monoxide (CO) mass concentration with the main influencing factors, the effects of traffic flow and weather on the mass concentration and trend of pollutants emitted by traffic are revealed. The overall trend is more consistent with the total traffic volume. The results show that the mass concentration of NOx and CO in various districts of Shenzhen are mainly affected by the emissions of motor vehicles (especially trucks), and the light intensity, wind direction, and wind speed have a significant effect on the emission diffusion of pollutants.

The Long-term Characteristics of PM10 and PM2.5 in Bangkok, Thailand

The long-term characteristics of non-roadside (residential) PM10 and PM2.5 in Bangkok, Thailand was analyzed by using hourly concentrations of PM10 and PM2.5 which had been collected from 10 monitoring stations by the Pollution Control Department (PCD) of Thailand from 2006 to 2016. The results showed that most of the stations showed either the decreasing trend or no trend characters. The PM2.5 and PM10 during weekdays and dry season were higher than during weekends and wet season, respectively. The diurnal variations of both PM2.5 and PM10 exhibited multi-peaks characteristic, mostly 2 peaks during a day for PM2.5 and 2 to 3 peaks depending on the locations for PM10. The PM2.5 to PM10 ratio of our residential monitoring stations was 0.61 in average which was in the same range as the PM2.5/PM10 ratio from the roadside monitoring stations. This shows that the common sources of PM2.5 and PM10 at both types of monitoring station were similar, probably mainly from the traffic and transportations. However, it was found that PM2.5/PM10 ratio during wet season was lower than during dry season indicating the role of emission sources and removal process in each season.

Characteristics of PM2.5-Bound Polycyclic Aromatic Hydrocarbons and Nitro-Polycyclic Aromatic Hydrocarbons at A Roadside Air Pollution Monitoring Station in Kanazawa, Japan.

Polycyclic aromatic hydrocarbons (PAHs) and nitro-PAHs (NPAHs) in PM2.5 samples were collected at a roadside monitoring station in Kanazawa, Japan, in every season from 2017 to 2018. Nine PAHs and five NPAHs were determined using high-performance liquid chromatography with fluorescence detection and chemiluminescence detection, respectively. The mean concentrations of PAHs and NPAHs were highest in winter and lowest in summer. Fluoranthene and pyrene were the dominant PAHs and 1-nitropyrene was the dominant NPAH in all seasons, and these compounds were mainly emitted by diesel vehicles. The concentration ratio of benzo(a)pyrene (BaP) to benzo(ghi)perylene (BgPe) ((BaP)/(BgPe)) and of indeno(1,2,3-cd)pyrene (IDP) to the sum of IDP and benzo(ghi)perylene (BgPe) ((IDP)/((IDP)+(BgPe0) might still be useful indicators for identifying traffic emission sources today. Moreover, our results showed that the carcinogenic risk in all seasons was below the acceptable limit set by the WHO.

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.

Determinants of Public Acceptance for Traffic-Reducing Policies to Improve Urban Air Quality

Air pollution remains a problem in German cities. In particular, the nitrogen dioxide (NO2) annual limit-value set by the European Union of 40 µg/m3 was not met at ~40% of roadside monitoring stations across German cities in 2018. In response to this issue, many cities are experimenting with various traffic-reducing measures targeting diesel passenger vehicles so as to reduce emissions of NO2 and improve air quality. Identifying the determinants of public acceptance for these measures using a systematic approach can help inform policy-makers in other German cities. Survey data generated from a questionnaire in Potsdam, Germany, were used in predictive models to quantify support for investments in traffic-reducing measures generally and to quantify support for a specific traffic-reducing measure implemented in Potsdam in 2017. This exploratory analysis found that general support for investments in such measures was most strongly predicted by environmental and air pollution perception variables, whereas specific support for the actual traffic measure was most strongly predicted by mobility habits and preferences. With such measures becoming more common in German cities and across Europe, these results exemplify the complexity of factors influencing public acceptance of traffic-reducing policies, highlight the contrasting roles environmental beliefs and mobility habits play in determining support for such measures, and emphasize the connections between mobility, air pollution, and human health.