Implementation of regulatory standards has reduced exhaust emissions of particulate matter from road traffic substantially in the developed world. However, nonexhaust particle emissions arising from ...the wear of brakes, tires, and the road surface, together with the resuspension of road dust, are unregulated and exceed exhaust emissions in many jurisdictions. While knowledge of the sources of nonexhaust particles is fairly good, source-specific measurements of airborne concentrations are few, and studies of the toxicology and epidemiology do not give a clear picture of the health risk posed. This paper reviews the current state of knowledge, with a strong focus on health-related research, highlighting areas where further research is an essential prerequisite for developing focused policy responses to nonexhaust particles.
The study aims to develop simplified rational criteria for identification of accident blackspots along the National Highways, so that the practitioners can quickly identify the blackspots and ...intervene. A statistical approach has been used to identify the rational criteria, i.e., the threshold value of each of the different crash parameters were identified, above which a location will be treated as a blackspot. The analysis is based on past 3-years’ crash data of NH of West Bengal, India. The identified blackspots have been compared with the Ministry of Road Transport and Highways (MoRTH), Govt. of India guidelines and it is found that the number of blackspots by MoRTH guidelines is grossly over-estimated. Finally, the blackspots have been prioritized based on its severity as observed from the analysis. This will help the practitioners prioritize the blackspot locations while planning the countermeasures. Although the work has been demonstrated based on crash data of West Bengal, the technique can be adopted by other researchers or practitioners to determine the simplified rational criteria for other regions as well.
Computer vision has evolved in the last decade as a key technology for numerous applications replacing human supervision. Timely detection of traffic violations and abnormal behavior of pedestrians ...at public places through computer vision and visual surveillance can be highly effective for maintaining traffic order in cities. However, despite a handful of computer vision–based techniques proposed in recent times to understand the traffic violations or other types of on-road anomalies, no methodological survey is available that provides a detailed insight into the classification techniques, learning methods, datasets, and application contexts. Thus, this study aims to investigate the recent visual surveillance–related research on anomaly detection in public places, particularly on road. The study analyzes various vision-guided anomaly detection techniques using a generic framework such that the key technical components can be easily understood. Our survey includes definitions of related terminologies and concepts, judicious classifications of the vision-guided anomaly detection approaches, detailed analysis of anomaly detection methods including deep learning–based methods, descriptions of the relevant datasets with environmental conditions, and types of anomalies. The study also reveals vital gaps in the available datasets and anomaly detection capability in various contexts, and thus gives future directions to the computer vision–guided anomaly detection research. As anomaly detection is an important step in automatic road traffic surveillance, this survey can be a useful resource for interested researchers working on solving various issues of Intelligent Transportation Systems (ITS).
Fog computing extends the facility of cloud computing from the center to edge networks. Although fog computing has the advantages of location awareness and low latency, the rising requirements of ...ubiquitous connectivity and ultra-low latency challenge real-time traffic management for smart cities. As an integration of fog computing and vehicular networks, vehicular fog computing (VFC) is promising to achieve real-time and location-aware network responses. Since the concept and use case of VFC are in the initial phase, this article first constructs a three-layer VFC model to enable distributed traffic management in order to minimize the response time of citywide events collected and reported by vehicles. Furthermore, the VFC-enabled offloading scheme is formulated as an optimization problem by leveraging moving and parked vehicles as fog nodes. A real-world taxi-trajectory-based performance analysis validates our model. Finally, some research challenges and open issues toward VFC-enabled traffic management are summarized and highlighted.
Covid19-induced lockdown measures caused modifications in atmospheric pollutant and greenhouse gas emissions. Urban road traffic was the most impacted, with 48–60% average reduction in Italy. This ...offered an unprecedented opportunity to assess how a prolonged (∼2 months) and remarkable abatement of traffic emissions impacted on urban air quality. Six out of the eight most populated cities in Italy with different climatic conditions were analysed: Milan, Bologna, Florence, Rome, Naples, and Palermo. The selected scenario (24/02/2020–30/04/2020) was compared to a meteorologically comparable scenario in 2019 (25/02/2019–02/05/2019). NO2, O3, PM2.5 and PM10 observations from 58 air quality and meteorological stations were used, while traffic mobility was derived from municipality-scale big data.
NO2 levels remarkably dropped over all urban areas (from −24.9% in Milan to −59.1% in Naples), to an extent roughly proportional but lower than traffic reduction. Conversely, O3 concentrations remained unchanged or even increased (up to 13.7% in Palermo and 14.7% in Rome), likely because of the reduced O3 titration triggered by lower NO emissions from vehicles, and lower NOx emissions over typical VOCs-limited environments such as urban areas, not compensated by comparable VOCs emissions reductions. PM10 exhibited reductions up to 31.5% (Palermo) and increases up to 7.3% (Naples), while PM2.5 showed reductions of ∼13–17% counterbalanced by increases up to ∼9%. Higher household heating usage (+16–19% in March), also driven by colder weather conditions than 2019 (−0.2 to −0.8 °C) may partly explain primary PM emissions increase, while an increase in agriculture activities may account for the NH3 emissions increase leading to secondary aerosol formation. This study confirmed the complex nature of atmospheric pollution even when a major emission source is clearly isolated and controlled, and the need for consistent decarbonisation efforts across all emission sectors to really improve air quality and public health.
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•Air quality impacts of Covid19 lockdown over 6 major Italian cities were assessed.•Daily observations of air quality, meteorological parameters and mobility were used.•Against a dramatic road traffic reduction (48–60%), NO2 levels reduced by 24.9–59.1%.•O3 levels remained basically unchanged or slightly increased (up to 11.4–14.7%).•PM daily concentrations slightly decreased, at most by 17% (PM2.5) and 32.1% (PM10).
Main finding A 2-month urban traffic ban extended to the whole Italy only significantly reduced NO2 levels, while O3, PM2.5 and PM10 concentrations were affected to a minor extent.
•A higher accuracy electric vehicle performance simulation model is developed.•An electric vehicle model is calibrated by the experimental data.•Average vehicle speed of driving cycle has main impact ...on energy consumption.•Vehicle with regenerative braking saves 2.43% energy under congested traffic.
In order to predict and study the effects of different parameters on performance characteristics of electric vehicles. A vehicle simulation model of pure battery electric vehicles equipped with single pedal control system is established and calibrated by the experimental data based on vehicle energy flow and driving range analysis, the simulation doesn’t include thermal aspect of the battery/vehicle. Next, the effects of different environmental and control parameters on energy consumption and driving range of pure electric vehicles are analyzed. The main findings are: (1) for the single driving cycle, the relative error of battery power and current is below 5%, and the absolute error of battery voltage is below 2.5 V. For the whole driving range, the absolute error of driving range is only about 5.75 km. (2) The main factors influencing energy consumption and driving range are average vehicle speed, running time and the frequency distribution of braking process, besides, the energy consumption of congested traffic with/without regenerative brake control system are 46.07 kW·h/100 km and 47.19 kW·h/100 km, respectively, meanwhile, vehicle with regenerative braking saves 2.43% energy under congested traffic. (3) The threshold of quitting the working condition of energy recovery for the motor can be set in a certain value based on the safety of driver in the emergencies and energy conversion. Further, the model and data in the paper can be applied to evaluate and optimize the energy consumption and driving range by changing different technologies or strategies in the future.
In the Republic of Korea, Environmental Impact Assessment (EIAs) precedes development projects to predict and analyze potential environmental effects. Generally, EIA noise evaluations utilize 2D ...noise prediction equations and correction coefficients. This method, however, offers only a sectional noise evaluation and has limitations in complex environments with diverse noise sources. Moreover, the determination of various variables during the EIA process based on subjective human judgment raises concerns about the reliability of the results. Thus, this study aims to develop software accessible via a web environment for user-friendly EIA noise evaluations. This software supports integrated data management and generates a 3D noise prediction model for more precise and realistic analysis of noise impacts, specifically focusing on road-traffic noise at this stage of development. The 3D noise prediction model and noise map generated by the developed software have been validated against through comparison with the results of onsite noise measurements and commercial EIA software, SoundPLAN. This validation aimed to assess the practical utility of the application.
•Developed WONSE, an innovative EIA (Environment Impact Assessment) software for noise.•Practicality of WONSE verified through field noise measurements and SoundPLAN comparison.•Offers cost-effective, transparent noise EIA solutions.
Background. The spatial features of the structure of earthworm communities in the area of influence of motor vehicles were analyzed. Five species of lumbricides belonging to three families were found ...in the studied biocenosis located near the M06 Kyiv–Chop motorway (Ukraine): Aporrectodea caliginosa (Savigny, 1826), A. rosea (Savigny, 1826), A. trapezoidеs (Dugesi, 1828), Lumbricus terrestris (Linnaeus, 1758) and Dendrobaena octaedra (Savigny, 1826). Materials and Methods. Earthworms were collected during 2021–2022 in the biocenosis near the M06 Kyiv–Chop motorway (Berezyna village, Zhytomyr region). The material was collected by excavation and layer-by-layer analysis of soil samples. The thickness of each layer was 10 cm. The maximum depth – 0.5 m. Samples were taken every 10 m from the road to a distance of 210 m. The distance between the rows of samples along the road was 30 m. STATISTICA software package was used for statistical analysis of the data. Biodiversity assessments were calculated using the PAST software package. SAGA and Q-GIS software packages were used for spatial analysis and mapping of the data. Results and Discussion. The key factor that influences the structure of earthworm communities in the area of road transport impact is the distance from the source of impact. The maximum values of the dominance, Margalef and Berger–Parker indexes and the number of species are observed in areas near the motorway, while the values of the Shannon, Simpson, Menhinik and Brillouin indexes have the opposite trend. There is a correlation between the spatial variability of the structure of earthworm communities and the values of reflectance in the bands B3, B5, B11 of the Sentinel-2 satellite image. It allowed us to apply a geographically weighted regression algorithm with several predictors that indirectly reflect environmental parameters to the data. Conclusion. The results obtained show that the use of predictors allows us to obtain a more mosaic model of the distribution of indicator values compared to interpolation by kriging, which can be used to predict the values of earthworm biodiversity indicators within the study area.
Abstract
Aims
The present study aimed to disentangle the risk of the three major transportation noise sources—road, railway, and aircraft traffic—and the air pollutants NO2 and PM2.5 on myocardial ...infarction (MI) mortality in Switzerland based on high quality/fine resolution exposure modelling.
Methods and results
We modelled long-term exposure to outdoor road traffic, railway, and aircraft noise levels, as well as NO2 and PM2.5 concentration for each address of the 4.40 million adults (>30 years) in the Swiss National Cohort (SNC). We investigated the association between transportation noise/air pollution exposure and death due to MI during the follow-up period 2000–08, by adjusting noise Lden(Road), Lden(Railway), and Lden(Air) estimates for NO2 and/or PM2.5 and vice versa by multipollutant Cox regression models considering potential confounders. Adjusting noise risk estimates of MI for NO2 and/or PM2.5 did not change the hazard ratios (HRs) per 10 dB increase in road traffic (without air pollution: 1.032, 95% CI: 1.014–1.051, adjusted for NO2 and PM2.5: 1.034, 95% CI: 1.014–1.055), railway traffic (1.020, 95% CI: 1.007–1.033 vs. 1.020, 95% CI: 1.007–1.033), and aircraft traffic noise (1.025, 95% CI: 1.006–1.045 vs. 1.025, 95% CI: 1.005–1.046). Conversely, noise adjusted HRs for air pollutants were lower than corresponding estimates without noise adjustment. Hazard ratio per 10 μg/m³ increase with and without noise adjustment were 1.024 (1.005–1.043) vs. 0.990 (0.965–1.016) for NO2 and 1.054 (1.013–1.093) vs. 1.019 (0.971–1.071) for PM2.5.
Conclusion
Our study suggests that transportation noise is associated with MI mortality, independent from air pollution. Air pollution studies not adequately adjusting for transportation noise exposure may overestimate the cardiovascular disease burden of air pollution.
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