Determination of VOC source signature of vehicle exhaust in a traffic tunnel

This study describes the methodology used to obtain the volatile organic compound (VOC) source signature of vehicle exhaust. To accomplish this, C(2)-C(9) VOCs were measured in a traffic tunnel located in Seoul, South Korea. The effect of VOC concentrations from the outside ambient air was considered in the determination of the source signature. To examine the effects of ambient air on VOC concentrations inside the tunnel, the ratio of propane to the total VOC concentrations was compared between the entrance and middle sites in the tunnel. Propane was used as a standard not only because of its insignificant contribution to vehicle exhaust gas, but also the fact that propane is the most abundant VOC in the atmosphere of Seoul. The ratio of propane to the total VOC concentrations was higher at the entrance site than at the middle location by, on average, 60%. This suggests that ambient air affects the inside tunnel air to a greater extent at the entrance site as compared to the middle site. The contribution of ambient air to the air inside the tunnel at the entrance location varied from 30% to 67%, with an average of 55%. This is 1.5 times higher than the value measured at the middle location, which ranged from 20% to 48%, with an average of 36%. This shows that ambient air substantially affects the inside air of the tunnel. Excluding the effects of ambient air on the air inside the tunnel can provide an improved chemical composition for vehicle exhaust using tunnel measurements. We believe that the concentration difference between the two sites within the tunnel provides a more accurate chemical composition of vehicle exhaust as compared to that obtained from a measurement taken at only one point inside the tunnel.     

公路交通噪声防治措施

随着交通运输业的发展及公路等级的不断提高,公路交通噪声越来越引起人们的关注,因此,解决公路交通噪声污染成为一个急待解决的问题,本文论述公路交通噪声造成的危害,并有针对性地提出降低公路交通噪声防治措施。     

Counting of visitors in the Meijendel dunes, The Netherlands

Accurate and sufficiently detailed information about recreation-related use of dune areas is necessary for their management. Long-term monitoring can provide this information. This paper presents the visitor counting programme used in the Meijendel dune area since 1992. The data collected during the first ten years are used to evaluate the method. The combination of mechanical vehicle counts and additional visual counts proved to be reliable and produced an accurate data set. Costs could be reduced through eventually reducing the number of counting locations and limiting the number of visual counts.     

Spatial gradients and source apportionment of volatile organic compounds near roadways

Concentrations of 55 volatile organic compounds (VOCs) (C₂–C₁₂) are reported near a highway in Raleigh, NC. Thirty-minute samples were collected at eight locations, ranging from approximately 10–100 m perpendicular from the roadway. The highest concentrations of VOCs were generally measured closest to the roadway, and concentrations decreased exponentially with increasing distance from the roadway. The highest mean concentration for individual VOCs were for ethylene (3.10 ppbv) (mean concentration at x = 13 m), propane (2.27 ppbv), ethane (1.91 ppbv), isopentane (1.54 ppbv), toluene (0.95 ppbv), and n-butane (0.89 ppbv). Concentrations at the nearest roadway location (x = 13 m) were generally between 2.0 and 1.5 times those from the farthest roadway location (x = 92 m). The data were apportioned into four source categories using the EPA Chemical Mass Balance Model (CMB8.2): motor vehicle exhaust, compressed natural gas, propane gas, and evaporative gasoline. The majority of the VOCs resulted from motor vehicle exhaust (67 ± 12%) (% of total VOC at x = 13 m ± S.D.). Compressed natural gas, propane gas, and evaporative gasoline accounted for approximately 15%, 7% and 1% of the total VOC emissions, respectively, at x = 13 m.     

Spatial distribution and risk assessment of soil heavy metal contamination along roadsides of the northwestern Sichuan Plateau,China北大核心CSCD

Traffic activities in roadways are the major source of heavy metal contamination on the northwestern Sichuan Plateau, China. To characterize the spatial pattern of heavy metal distribution, we collected soil samples and measured the concentrations of five heavy metals (Cd, Cu, Ni, Pb, and Zn) from nine sites in Hongyuan County, Sichuan Province, including three transects that reflected typical gradients of traffic density. Each transect consisted of three levels of traffic density sites. We calculated single pollution index, Nemerow multi-factor index, and potential ecological risk index, to assess the ecological risk of the heavy metal contamination. Results showed that the soils were contaminated by Cd and Zn, with higher concentrations than that of the natural soil background values in all sampling sites. Moreover, Cd and Zn concentrations increased with increasing traffic density, suggesting that traffic activities were potentially responsible for the metal contaminations. The single pollution index indicated that the study sites were heavily contaminated by Cd and slightly polluted by Ni, Pb, and Zn, and the Nemerow multi-factor and potential ecological risk indexes indicated moderate potential ecological risks at the study sites. Specifically, the sites with high traffic density were moderately contaminated and the sites with low traffic density were mildly contaminated. In general, Cd and Zn were highly accumulated in soils of the study region; therefore focus should be on the high ecological hazard associated with soil heavy metal contamination, even in undeveloped regions. © 2018 Science Press. All rights reserved.     

Using a novel data linkage approach to investigate potential reductions in motor vehicle crash severity – An evaluation of strategic highway safety plan emphasis areas

Introduction: With the significant number of motor-vehicle fatalities occurring on the nation’s roadways in recent years, there exists a need to integrate a more complete range of data sources, available at a regional or statewide level, to effectively evaluate existing safety concerns and quantify their impacts. Crash data alone does not provide ample crash-associated citation, injury, and roadway characteristics; therefore, a more cohesive dataset is required to accurately and completely analyze the true impacts of motor-vehicle crashes. Previously developed strategies linked crash data with citation and roadway inventory data to enhance the identification and optimization of highway safety strategies. Method: The main objective of this research focused on developing a new deterministic linkage between crash and Emergency Medical Services (EMS) data, by utilizing the Massachusetts Crash Data System (CDS) and the Massachusetts Ambulance Trip Record Information System (MATRIS). Results: After several iterations of match criterion, the validated linkage successfully matched 58.3% of MATRIS records (containing an Injury Cause of Motor Vehicle Crash) to a CDS person record (55011 linked pairs, between 2014 and 2016). The data linkage provided significant insight into injury trends in several highway safety emphasis areas such as roadway departure, speeding-related, and distraction-affected crashes. The findings from this research are twofold: (1) an established process for linking previously separate data sets, and (2) a mechanism for analysis that provides decision-makers and safety professionals with a better measure of crash outcomes.     

Modelling the relationship of driver license and offense history with fatal and serious injury (FSI) crash involvement

Introduction: Previous research has indicated that increases in traffic offenses are linked to increased crash involvement rates, making reductions in offending an appropriate measure for evaluating road safety interventions in the short-term. However, the extent to which traffic offending predicts fatal and serious injury (FSI) crash involvement risk is not well established, prompting this new Victorian (Australia) study. Method: A preliminary cluster analysis was performed to describe the offense data and assess FSI crash involvement risk for each cluster. While controlling demographic and licensing variables, the key traffic offenses that predict future FSI crash involvement were then identified. The large sample size allowed the use of machine learning methods such as random forests, gradient boosting, and Least Absolute Shrinkage and Selection Operator (LASSO) regression. This was done for the ‘all driver’ sample and five sometimes overlapping groups of drivers; the young, the elderly, and those with a motorcycle license, a heavy vehicle license endorsement and/or a history of license bans. Results: With the exception of the group of drivers who had a history of bans, offense history significantly improved the accuracy of models predicting future FSI crash involvement using demographic and licensing data, suggesting that traffic offenses may be an important factor to consider when analyzing FSI crash involvement risk and the effects of road safety countermeasures. Conclusions: The results are helpful for identifying driver groups to target with further road safety countermeasures, and for showing that machine learning methods have an important role to play in research of this nature. Practical Application: This research indicates with whom road safety interventions should particularly be applied. Changes to driver demerit policies to better target offenses related to FSI crash involvement and repeat traffic offenders, who are at greater risk of FSI crash involvement, are recommended.     

Structural anatomy and temporal trends of road accident research: Full-scope analyses of the field

Introduction: Scholarly research on road accidents over the past 50 years has generated substantial literature. We propose a robust search strategy to retrieve and analyze this literature. Method: Analyses was focused on estimating the size of this literature and examining its intellectual anatomy and temporal trends using bibliometric indicators of its articles. Results: The size of the literature is estimated to have exceeded N = 25,000 items as of 2020. At the highest level of aggregation, patterns of term co-occurrence in road accident articles point to the presence of six major divisions: (i) law, legislation & road trauma statistics; (ii) vehicular safety technology; (iii) statistical modelling; (iv) driving simulator experiments of driving behavior; (v) driver style and personality (social psychology); and (vi) vehicle crashworthiness and occupant protection division. Analyses identify the emergence of various research clusters and their progress over time along with their respective influential entities. For example, driver injury severity ” and crash frequency show distinct characteristics of trending topics, with research activities in those areas notably intensified since 2015 Also, two developing clusters labelled autonomous vehicle and automated vehicle show distinct signs of becoming emerging streams of road accident literature. Conclusions: By objectively documenting temporal patterns in the development of the field, these analyses could offer new levels of insight into the intellectual composition of this field, its future directions, and knowledge gaps. Practical Applications: The proposed search strategy can be modified to generate specific subsets of this literature and assist future conventional reviews. The findings of temporal analyses could also be instrumental in informing and enriching literature review sections of original research articles. Analyses of authorships can facilitate collaborations, particularly across various divisions of accident research field.     

Children’s fear in traffic and its association with pedestrian decisions

Introduction: Research on risk for child pedestrian injury risk focuses primarily on cognitive risk factors, but emotional states such as fear may also be relevant to injury risk. The current study examined children's perception of fear in various traffic situations and the relationship between fear perception and pedestrian decisions. Method: 150 children aged 6–12-years old made pedestrian decisions using a table-top road model. Their perceived fear in the pedestrian context was assessed. Results: Children reported greater emotional fear when they faced quicker traffic, shorter distances from approaching traffic, and red rather than green traffic signals. Children who were more fearful made safer pedestrian decisions in more challenging traffic situations. However, when the least risky traffic situation was presented, fear was associated with more errors in children’s pedestrian decisions: fearful children failed to cross the street when they could have done so safely. Perception of fear did not vary by child age, although safe pedestrian decisions were more common among the older children. Conclusions: Children’s emotional fear may predict risk-taking in traffic. When traffic situations are challenging to cross within, fear may appropriately create safer decisions. However, when the traffic situation is less risky, feelings of fear could lead to excessive caution and inefficiency. Practical applications: Child pedestrian safety interventions may benefit by incorporating activities that introduce realistic fear of traffic risks into broader safety lessons.     

Primary and secondary components of PM2.5 in Milan (Italy)

In sampling campaigns--carried out by means of a high-volume gravimetric sampler--performed between August 2002 and December 2003, 24-h PM2.5 samples have been collected at an urban background site in downtown Milan and analyzed for elemental and organic carbon, ionic species (i.e., chloride, nitrates, sulfates and ammonium) and some elemental species. Chemical speciation data are evaluated also in terms of primary and secondary components of fine particulate matter: in particular, the contribution of secondary organic aerosols (SOA) and of the primary contribution from traffic to observed PM2.5 concentration levels are evaluated by means of the EC tracer method.