Spatial-temporal distribution characteristics of pollutants of heavy-duty diesel vehicles in urban road networks: a case study of Kunming City

With the continuous promotion of urbanization in China, the economic level of small and medium-sized cities has been further improved. The transportation industry is crucial in promoting urban-rural integration and construction. Still, motor vehicle emissions also bring air pollution problems to cities, with heavy-duty diesel vehicle emissions severely impacting the urban environment. This study used a bottom-up approach to analyze the spatial emission characteristics of heavy-duty diesel vehicles under different road types in Kunming, a typical medium-sized city in China. A high-resolution emission inventory (1 km × 1 km) of heavy-duty diesel vehicles was developed using the vehicle emission inventory model (VEIN) and ArcGIS, and the vehicle emission standards were determined by the Weibull survival rate curve. The VEIN emission model was optimized using a velocity correction curve. The results showed that heavy-duty vehicles had a more significant impact on the emissions during the morning and evening peak hours, with low emission levels during the day and high emission levels at night and early morning. The total daily emissions of carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NOx), and particulate matter (PM10 and PM2.5) from heavy-duty diesel vehicles in Motorway, Trunk, Primary, Secondary, and Tertiary were 14.44 tons, 5.26 tons, 4.78 tons, 7.02 tons, and 3.83 tons, respectively. China III heavy-duty diesel vehicles mainly contributed to CO, HC, NOx, and PM emissions. This study can be used as an essential reference for controlling the exhaust emissions of HDDVs in Kunming.

Evaluating the real-world emissions of diesel passenger Car in Indian heterogeneous traffic

A 30 portable emission measurement system (PEMS) test was conducted in this study to examine the effect of driving modes (aggressive and normal) and road type (urban and rural) on tailpipe emissions. Driving modes were assessed using relative positive acceleration and velocity × positive acceleration factors. The findings revealed that aggressive and normal driving modes differed significantly on urban and rural roads, as evident from paired sample t-test (p < 0.05). Furthermore, aggressive driving exhibited more prominent speed and acceleration on rural roads, while normal driving modes showed consistent acceleration or speed patterns regardless of road conditions as observed from kernel density estimation and box plot analysis. Emission rates (CO, CO2, HC, and NOx) significantly varied between aggressive and normal driving modes on urban and rural roads, as indicated by paired sample t-test analysis (p < 0.05). Aggressive driving increased CO2, CO, and HC emission rates for acceleration and deceleration modes by 18% to 40% compared to normal driving. Aggressive driving modes increased the emission factors (CO, HC, and CO2) by 5% to 25% compared to the normal driving mode on both urban and rural roads. Moreover, the NOx emission factors were also found significant during normal driving conditions on urban roads. This study provides real-world emission factors of diesel cars considering the impact of route, vehicle familiarity, and driving behavior induced by varying traffic conditions, which will contribute to improve the current emissions inventory on both a local and global level.

Transformation and degradation of polycyclic aromatic hydrocarbons (PAHs) in urban road surfaces: Influential factors, implications and recommendations

Polycyclic aromatic hydrocarbons (PAHs) are prone to post-emission transformation and degradation to yield transformed PAH products (TPPs) that are potentially more hazardous than parent PAHs. This review provides a comprehensive evaluation of the potential environmental processes of PAHs such as sorption, volatilisation, photo- and bio-transformation and degradation on road surfaces, a significant accumulation point of PAHs. The review primarily evaluates key influential factors, toxicity implications, PAHs and TPPs fate and viable options for mitigating environmental and human health impacts. Photolysis was identified as the most significant transformation and degradation process due to the light absorption capacity of most PAHs. Climate conditions, physicochemical properties of road dust (sorbent), PAHs and TPPs and the existence of heavy metals such as Fe (III) are notable underlying factors for photolysis. Available data points to the predominance of carbonyl TPPs than other products such as nitro and hydroxyl TPPs with decreasing concentration trend of 9-fluorenone > 9,10-anthraquinone > benzo[a]fluorenone on road surfaces. The review recommends conducting future investigations targeting the influential factors pertaining to the fate of road deposited PAHs and TPPs. Furthermore, development of cost and time effective modern analytical methods is needed to quantify PAHs and TPPs present in minute quantities of samples. The review also identified that the unavailability of toxicity equivalency factors (TEF) for the most critical TPPs can be addressed using quantitative structure-activity relationship (QSAR) models and bioassays simultaneously. The content of this review is significant to the future work of researchers across various fields including analytical and environmental chemistry, stormwater pollution and toxicology.

Characterizing petroleum hydrocarbons deposited on road surfaces in urban environments

Petroleum hydrocarbons are a toxic pollutant group, primarily including volatile organic compounds (VOC), semi-volatile organic compounds (SVOC) and non-volatile organic compounds (NVOC). These pollutants can be accumulated on urban roads during dry periods and then washed-off by stormwater runoff in rainy days. Unlike heavy metals and polycyclic aromatic hydrocarbons, petroleum hydrocarbons have not received an equal attention in the field of stormwater pollutant processes. This paper investigated characteristics of VOC, SVOC and NVOC pollutant loads deposited on urban roads and their influential factors using a forward stepwise regression and PROMETHEE-GAIA analysis techniques. The results indicate that the loads deposited on urban roads were NVOC > SVOC > VOC. It is also noted that the degrees of factors in influencing petroleum hydrocarbons deposited on urban roads did not equal and their order was total solids > land use type > vehicular traffic > roughness of road surfaces. The research results also showed that petroleum hydrocarbons on urban road surfaces tend to be source limiting rather than transport limiting. These outcomes can contribute to petroleum hydrocarbons polluted stormwater management, such as treatment system design and stormwater modelling approach improvement.

Virtual testing of speed reduction schemes on urban collector roads

Urban collector roads are complex driving environments often encompassing both the mobility and the access road functions. In these conditions motorized traffic and vulnerable road users compete continually. Speed reduction measures may play a relevant role in these contexts, provided that such measures are also designed in compliance with the driver's capabilities and expectations. The paper describes a test procedure using driving simulation experiments, designed to evaluate the reconfiguration project of Via Pistoiese, an urban road collector located in Florence (Italy). The road improvement design consisted of several engineering treatments aimed to reduce and homogenize the driving speed, as well as to manage the co-existence of the different road users and mainly to protect pedestrians. The main focus of the research was to understand if the drivers' behaviour was according to the design hypothesis before the safety treatments are implemented in the real world. Due to the multiple engineering treatments included in the reconfiguration project, the evaluation of the overall safety effectiveness of the project rather than the single treatment safety impact was the main concern of the research study. In addition, the study aimed to assess the usefulness of the considered testing method to understand how to integrate road design with drivers' performances, especially in heterogeneous traffic environments where drivers' behaviour plays a decisive role in the success of the proposed design solutions. Fifty-eight participants drove through two immersive virtual environments, reproducing the existing configuration and the project reconfiguration, while data relating to different driving aspects were collected. Two analyses were performed. The first was focused on the analysis of the mean speed profiles and revealed that the considered engineering treatments are able to control the speeding behaviour without providing a too high discomfort to the drivers. The second analysis was finalized to evaluate the driver's behaviour approaching zebra crossing, evaluating the impact of countermeasures allowing the drivers to perceive in advance a critical situation (a pedestrian that suddenly crossed the street) and consequently to perform a smoother and safer manoeuvre. The experiments confirmed the validity of the considered engineering treatments, allowing expecting the improvement of the traffic safety in via Pistoiese, and support the usefulness of virtual reality experimentations to predict of the safety effectiveness of design solutions, taking into account the drivers' behaviour.

Analysis of factors affecting the severity of crashes in urban road intersections

Road crashes are events which depend on a variety of factors and which exhibit different magnitudes of outputs when evaluated with respect to the effects on road users. Despite a lot of research into the evaluation of crash likelihood and frequency, only a few works have focused exclusively on crash severity with these limited to sections of freeways and multilane highways. Hence, at present there is a large gap in knowledge on factors affecting the severity of crashes for other road categories, facilities, and scenarios. The paper deals with the identification of factors affecting crash severity level at urban road intersections. Two official crash records together with a weather database, a traffic data source with data aggregated into 5min intervals, and further information characterising the investigated urban intersections were used. Analyses were performed by using a back propagation neural network model and a generalized linear mixed model that enable the impact assessment of flow and other variables. Both methods demonstrate that flows play a role in the prediction of severity levels.

Applicability of models to estimate traffic noise for urban roads

BACKGROUND

Traffic noise is a highly relevant environmental impact in cities. Models to estimate traffic noise, in turn, can be useful tools to guide mitigation measures. In this paper, the applicability of models to estimate noise levels produced by a continuous flow of vehicles on urban roads is investigated. The aim is to identify which models are more appropriate to estimate traffic noise in urban areas since several models available were conceived to estimate noise from highway traffic.

RESULTS

First, measurements of traffic noise, vehicle count and speed were carried out in five arterial urban roads of a brazilian city. Together with geometric measurements of width of lanes and distance from noise meter to lanes, these data were input in several models to estimate traffic noise. The predicted noise levels were then compared to the respective measured counterparts for each road investigated. In addition, a chart showing mean differences in noise between estimations and measurements is presented, to evaluate the overall performance of the models. Measured Leq values varied from 69 to 79 dB(A) for traffic flows varying from 1618 to 5220 vehicles/h. Mean noise level differences between estimations and measurements for all urban roads investigated ranged from -3.5 to 5.5 dB(A).

CONCLUSIONS

According to the results, deficiencies of some models are discussed while other models are identified as applicable to noise estimations on urban roads in a condition of continuous flow. Key issues to apply such models to urban roads are highlighted.