Non-rush Hours Research Articles

Grocery store accessibility: Different metrics – Different modal disparity results and spatial patterns

Most accessibility studies focus on within transport mode travel performance variations. However, modal accessibility disparity analysis adds value to the single-mode analysis by assessing the interaction between different transport modes and land use. A review of modal disparity studies shows that different accessibility metrics lead to different results, and so it is unclear how this impacts modal accessibility disparity variation. Moreover, the correspondence of the disparity spatial pattern between the different metrics is unclear. This research examines how three typical accessibility metrics (closest facility, cumulative opportunity, space-time constrained) impact modal disparity of grocery store accessibility in Warsaw, Poland. Further, local indicators of spatial association are used to identify areas of similarity and difference between the metrics. This study finds that cumulative opportunities during non-rush hours indicate the best car advantage for all travel times but indicate the best transit advantage during rush hours for 15 min. Generally, the space-time metric indicates better transit accessibility than the closest facility metric which in turn shows better transit accessibility than cumulative opportunities. The city center has significant spatial similarity while peripheral, especially dense, areas have significant spatial difference. Similarity areas have higher transit stop and population densities, while difference areas have average-to-low stop, population, road and store densities.

Environmental and health risks of VOCs in the longest inner–city tunnel in Xi’an, Northwest China: Implication of impact from new energy vehicles

Traffic source–dominated volatile organic compound (VOC) samples were collected during four time-intervals in a day (Ⅰ: 7:30–10:30, Ⅱ: 11:00–14:00, Ⅲ: 16:30–19:30, and Ⅳ: 20:00–23:00) in a tunnel in summer, 2019, in Xi’an, China. The total measured VOC (TVOC) in periods Ⅰ and Ⅲ (rush hours, 107.2 ± 8.2 parts per billion by volume [ppbv]) was 1.8 times that in periods Ⅱ and Ⅳ (non-rush hours, 58.6 ± 13.8 ppbv), consistent with the variation in vehicle numbers in the tunnel. The considerably elevated ethane and ethylbenzene levels could have been attributed to emissions from compressed natural gas vehicles and the rapid development of methanol-fueled taxis in Xi’an in 2019. The mixing ratios of benzene, toluene, ethylbenzene, and xylenes (BTEX) contributed 9.4%–12.7% to TVOCs, and the contributions were nearly 40% higher in periods Ⅰ and Ⅲ than in Ⅱ and Ⅳ, indicating that BTEX levels were strongly affected by vehicle emissions. The indicators of motor vehicle emission, namely ethylene, propylene, toluene, m/p-xylenes, o-xylene, and propane, contributed to more than half of the ozone formation potential in this study. The noncarcinogenic risks of VOCs in this study were within the international safety standard, whereas the carcinogenic risks exceeded the standard by 2.3–4.6 times, suggesting that carcinogenic risks were more serious than noncarcinogenic risks. VOCs presented 2.2 and 1.4 times noncarcinogenic and carcinogenic risks during rush hours than during non-rush hours, respectively. Notably, the carcinogenic risk in period Ⅳ was comparable with that in period Ⅲ; however, the vehicle numbers and VOC mixing ratios were the lowest at night, which may have attributed to the increasing number and proportion of methanol M100-fueled vehicles in the tunnel. Therefore, VOCs emitted by new energy vehicles should also be seriously considered while evaluating fossil fuel vehicle emissions.

서울시 교통체증이 미세먼지 농도에 미치는 영향 : 빅데이터를 활용하여

Particulate matter negatively affects personal health and daily living; hence, it has become a popular topic in academia. Transportation-related variables have thus far not been extensively explored, even though various factors associated with particulate matter have been investigated. In particular, the amount of particulate matter emitted from vehicles depends on the speed of travel, which can be adversely affected by traffic congestion. Hence, we aim to examine the causal relationships between concentrations of particulate as well as ultrafine particulate matter and traffic congestion. To investigate the effects of traffic congestion on concentration of particulate matter, we used big data regarding various urban parameters, such as travel speed, air pollution, and climatological data, by employing fixed-effects panel data analysis models. We use the real-time congestion index (RCI) to calculate traffic congestion, in addition to precipitation, wind speed, wind direction, and temperature as the control variables. The main findings from the panel models are as follows: First, traffic congestion is directly related to the concentrations of particulate and ultrafine particulate matter. In particular, an increase in one unit of RCI increases the concentration of the particulate matter by 3.3 ㎍/㎥ and ultrafine particulate matter by 1.7 ㎍/㎥, after accounting for other factors. Second, the impact of traffic congestion is greater when the concentration of particulate matter is less than 30 ㎍/㎜ (also called the “good condition“ of PM10) and that of ultrafine particulate matter is above 35 ㎍/㎜. (called the “bad condition” of PM2.5). Third, the effects of traffic congestion on particulate matter are greater during non-rush hours than during rush hours. This is probably because of the greater number of vehicles on the roads during non-rush hours, such as older/obsolete vehicles, trucks, and diesel-powered vehicles, which are likely to emit more pollutants. Our results suggest that alleviating traffic congestion in addition to existing air pollution policies could improve air quality, even though such effects may not be substantial.

Large-eddy simulation of traffic-related air pollution at a very high resolution in a mega-city: evaluation against mobile sensors and insights for influencing factors

Abstract. Urban air pollution has tremendous spatial variability at scales ranging from kilometers to meters due to unevenly distributed emission sources, complex flow patterns, and photochemical reactions. However, high-resolution air quality information is not available through traditional approaches such as ground-based measurements and regional air quality models (with typical resolution > 1 km). Here we develop a 10 m resolution air quality model for traffic-related CO pollution based on the Parallelized Large-Eddy Simulation Model (PALM). The model performance is evaluated with measurements obtained from sensors deployed on a taxi platform, which collects data with a comparable spatial resolution to our model. The very high resolution of the model reveals a detailed geographical dispersion pattern of air pollution in and out of the road network. The model results (0.92 ± 0.40 mg m−3) agree well with the measurements (0.90 ± 0.58 mg m−3, n=114 502). The model has similar spatial patterns to those of the measurements, and the r2 value of a linear regression between model and measurement data is 0.50 ± 0.07 during non-rush hours with middle and low wind speeds. A non-linear relationship is found between average modeled concentrations and wind speed with higher concentrations under calm wind speeds. The modeled concentrations are also 20 %–30 % higher in streets that align with the wind direction within ∼ 20∘. We find that streets with higher buildings downwind have lower modeled concentrations at the pedestrian level, and similar effects are found for the variability in building heights (including gaps between buildings). The modeled concentrations also decay fast in the first ∼ 50 m from the nearest highway and arterial road but change slower further away. This study demonstrates the potential of large-eddy simulation in urban air quality modeling, which is a vigorous part of the smart city system and could inform urban planning and air quality management.

Exposures and health impact for bicycle and electric scooter commuters in Taipei

Bicycle and scooter commuters are known to expose to more traffic-related pollutants. Benzene, toluene, ethylbenzene, xylenes (BTEXs), and PM2.5 were measured along four routes during rush hour and non-rush hour periods by cyclists and motorcyclists in Taipei, Taiwan. The health impacts were also evaluated using health impact modeling (HIM) approach. The average exposures of total BTEX and PM2.5 were 20.07 ppb and 23.27 μg/m3 for the cyclists and 33.96 ppb and 27.65 μg/m3 for the motorcyclists, respectively. Riding on an “alternative” route resulted in lower exposure. Scooters could contribute more to PM2.5 exposure than buses/trucks and passenger cars. Cyclists could have a lower risk of all-cause mortality and a single-pollutant HIM was found to result in underestimations. In conclusion, the number of scooters in traffic should be reduced, and commuters should be encouraged to shift from gasoline scooters to bicycles to reduce air pollution.

Social restriction effects on air pollution: How the PM2.5 concentration changed with lockdown management of COVID-19 pandemic control in Bangkok Thailand

Under rapid proceeding of COVID-19 pandemic, Thailand government announced lockdown and social restriction in March 2020. With the frustration of pandemic, anthropogenic etiology of air pollution was beneficially assessed on other hands. The study aims at exploring how PM2.5 concentration changed with lockdown management and social restriction as part of COVID-19 control in the Bangkok Metropolitan Region, Thailand. There was PM2.5 concentration reduction of roadside (18.6%) and ambient (9.2%) in COVID-19 lockdown period than the same months of previous consecutive year. Moreover, this study showed a clear decline of PM2.5 during lockdown in both rush and non-rush hours except one roadside area which has a non-significant rising PM2.5 because of trucks activities in some area. Additionally, the probable high concentration during the lockdowns period occurred at calm wind speed, mostly from the south direction, particularly in roadside area indicating the traffic source of PM2.5 in the Conditional Bivariate Probability Function (CBPF) plot which estimate probable direction and source of air pollutant. Although PM2.5 is significantly reduced in the lockdown period, it is still above 66% (33 microgram per cubic meter) of the Thailand standard in CBPF analysis. No specific minimum level of PM2.5 is safe for health. However, it highlights monitoring emission sources and encouraging the community to make concerns about their daily contributing activities of air pollution. © 2021, Thai Society of Higher Eduation Institutes on Environment. All rights reserved.

Vertical and horizontal distributions of traffic-related pollutants beside an urban arterial road based on unmanned aerial vehicle observations

The increased knowledge in distribution patterns of traffic-related pollutants can help address the problems of air pollution. Utilities of instrumented unmanned aerial vehicles (UAVs) who have high mobility are an effective approach to investigate the pollutant distributions. However, UAV-based observations are mostly conducted in open suburban areas and have been rare in urban areas where present severe pollution problems. In this study, an instrumented six-rotor UAV was carried out to investigate the spatiotemporal distributions of air pollutants beside an urban arterial road in Shanghai, China. Both vertical and horizontal distributions of particulate matters (PM), carbon monoxide (CO) within the height of 120 m were investigated. Results show that the vertical fall-off of roadside CO was mainly in a height range of 0–60 m and was 0–40 m for PM. PM and CO concentrations both presented exponential decay in the vertical direction and showed more significant decay in traffic rush hours compared with that in non-rush hours. Meanwhile, the concentration of fine particles (PM1) was mostly affected by the background concentration while the concentrations of coarse particles and CO were more related to traffic activities. Additionally, at the height of 40 m, the horizontal dispersion of CO could reach over 60 m away from the road, and the increase in pollutant concentrations was mainly in the downwind direction of the road. This study revealed the general trends of vertical and horizontal distributions of air pollutants beside an urban arterial road, which could provide implications in mitigating traffic-related air pollution.

Motorcyclists have much higher exposure to black carbon compared to other commuters in traffic of Hanoi, Vietnam

Exposure to air pollutants, including black carbon (BC), during transportation is an important contribution to the total daily respiratory exposure for humans. Exposure to BC is especially relevant in crowded cities of developing countries. Specifically, in Hanoi (Vietnam), air pollution including BC is of concern, particularly near traffic. Despite public interest, no studies have been undertaken to understand the levels of respiratory exposure of BC to commuters in Hanoi, who predominantly use motorcyles. In this study, we simultaneously measured BC concentrations using microAeth AE51 portable devices on motorcycles, and in the cabins of cars and buses. The mean BC concentration on motorcycles was 29.4 μg/m3 compared to 10.1 μg/m3 in the buses. BC concentrations were also shown to be influenced by traffic density. Measurements during peak traffic showed BC concentrations of 34.7 μg/m3 and 12.1 μg/m3 for motorcyclists and bus passengers, respectively. For non-rush hour periods (on weekdays), the corresponding BC levels were 27.7 and 8.4 μg/m3, respectively. BC levels inside of a passenger car with different ventilation modes was also examined. The concentrations of BC inside the cabin of a passenger car were 18.1 μg/m3 and 11.7 μg/m3, with the windows open and closed, respectively. It is estimated that motorcyclists are exposed to a significantly higher level of BC than users of buses and cars. Our findings are evidence for promoting the use of public transportation to protect public health in Hanoi, which may also be applicable to other cities in South-East Asia with similar traffic characteristics.

Mining urban sustainable performance: Spatio-temporal emission potential changes of urban transit buses in post-COVID-19 future

Emission benefits of transit buses depend on ridership. Declines in ridership caused by COVID-19 leads uncertainty about the emission reduction capacity of buses. This paper provides a method framework for analyzing spatio-temporal emission patterns of buses in combination with real-time ridership and potential emission changes in the post-COVID-19 future. Based on GPS trajectory and Smart Card data of 2056 buses from 278 routes covering 1.5 million ridership in Qingdao, China, spatio-temporal emissions characteristics of buses are studied. 7589 taxis with 0.2 million passengers’ trips are used for acquiring private cars’ emissions to evaluate the emissions difference between buses and cars. Empirical results show that the average difference between buses and cars with 2 persons can reach up to 117 g/km-person during 7:00–8:59 and 115 g/km-person during 17:00–18:59. However, buses have various emission benefits around the city at different periods. A double increase in emissions during non-rush hours can be observed compared with rush hours. 224 online survey data are used to study the potential ridership reduction trend in post-COVID-19. Results show that 56.3% of respondents would decrease the usage of buses in the post-COVID-19 future. Based on this figure, our analysis shows that per kilometer-person emissions of buses are higher than cars during non-rush hours, however, still lower than cars during rush hours. We conclude that when ridership reduces by more than 40%, buses cannot be “greener” travel modal than cars as before. Finally, several feasible policies are suggested for this potential challenge. Our study provides convincing evidence for understanding the emission patterns of buses, to support better buses investment decisions and promotion on eco-friendly public transport service in the post-COVID-19 future.

Traffic-Related Airborne VOC Profiles Variation on Road Sites and Residential Area within a Microscale in Urban Area in Southern Taiwan

The sampling sites, including roadsides and residential areas, were set up to collect ambient air and determine the volatile organic species it contained. For the roadside air, the average VOCs (volatile organic compounds) abundant at rush hour periods was two times that at non-rush hour periods. In the residential area, the VOC concentrationswere106 and 129 ppb during rush hour periods. The VOC concentration ratios of roadside and residential areas were in the range of 1.08–1.75 and the traffic emissions were related to the VOCs abundant in air. The highest VOC concentration was 168 ppb at midnight at residential sites and the VOC abundance could be two times that of roadside sites. This level of concentration could be attributed to the application of solvents and to human activity in a nearby motorcycle/vehicle maintenance plant, laundry rooms, etc. High abundant species were similar in both the roadside and residential air samples. These highly abundant species included toluene, acetone, acetonitrile, m,p-xylene and n-pentane, all of which can be emitted from traffic exhaust. Benzene, acrolein, formaldehyde, vinyl chloride and 1,3-butadiene were the main species with health impacts collected at both sites. In the micro-scale environment, the residential ambient air was affected by traffic flow from morning to night. In the midnight period, some local activities (a motorcycle/vehicle maintenance shop and laundry shops) affected the concentrations of certain VOCs (acetonitrile, toluene, hexane, 2-methylpentane, methyl cyclopentane and 3-methylpentane). The traffic and motor vehicles’ effects were determined, which could be useful for air quality management and strategy development in an urban area.

Diurnal and seasonal variations in water-soluble inorganic ions and nitrate dual isotopes of PM2.5: Implications for source apportionment and formation processes of urban aerosol nitrate

Recently, increasing urban aerosol NO3− deposition have aroused widespread attention. To reveal its sources and formation processes, the water-soluble inorganic ions (WSIIs) and NO3− dual isotopes (i.e., δ15N and δ18O) were measured in fine particles (i.e., PM2.5), which were collected during rush hours in the morning and afternoon, and non-rush hours at noon, discretely in winter and in summer at urban Guiyang, southwest China. Results showed the sulfate, nitrate, and ammonium together accounted for more than 90% of the total WSIIs in both seasons. Both NO3− and its dual isotopes were distinctly higher in winter than in summer. While only NO3− and δ15N were observed significantly higher during rush hours than the non-rush hours in summer, which could be potentially controlled by the temperature, relative humidity and ambient NH3 contents. The gradual but insignificant increase of δ18O during from morning to afternoon were ascribed to the decreasing contributions of •OH oxidation pathway. By using the Bayesian isotope mixing model, we revealed the coal combustion and biomass burning accounted for the predominant NOx sources in contributing to the particulate NO3− in winter, followed by vehicular and soil biogenic emissions. Whereas in summer, biomass burning played the most important role, since the coal combustion proportion sharply decreased. Moreover, the particle NO3− source contributions showed minor difference during diurnal periods in winter, while the difference was larger in summer, especially for that during between morning rush and noon non-rush hours, which were likely due to the changes of NOx emissions (e.g. from vehicular and soil biogenic source), as well as the meteorological conditions.

Investigating the effect of trees on urban quality in Dublin by combining air monitoring with i-Tree Eco model

Fine particulate matter (PM2.5) is a common air pollutant that represents a significant threat to public health. In Dublin, a major source of PM2.5 is diesel exhaust, brake dust, and rubber tire particles from vehicles. Particulate matter can be deposited onto the surface of vegetation thereby removing it from the atmosphere. The presence of trees can increase the amount of surface area on which deposition can occur, but they can also alter wind flow in a way that increases local pollutant concentrations. This study assesses the effect of urban trees on removing PM2.5 using a novel combination of air quality monitoring and the i-Tree Eco (UFORE) deposition model during rush and non-rush hours with different traffic volumes. Monitoring was conducted in a tree alley and an area of no vegetation along the Drumcondra Road Lower in Dublin. Results indicated that there was no difference in PM2.5 concentrations outside of rush hours, but concentrations were significantly lower in the tree alley during rush hours compared to the street section with no trees. The i-Tree Eco model calculated that the tree alley could remove approximately 3 kg of PM2.5 on a yearly basis. Air monitoring found that the presence of trees resulted in a maximum air quality improvement of 126 %, more than double what was predicted by i-Tree Eco. While this study provides an initial exploration of trees and air quality in Dublin, further research combining these techniques with the measurement of local wind speed and direction would allow for a better understanding of how Dublin’s trees can affect its air quality. The results from this study suggest that they do cause a degree of air quality improvement and can be used to inform national clean air strategies aimed at reducing pollutant emissions.

Characteristics of exposure and health risk air pollutants in public buses in Korea.

The objective of this study was to estimate the concentrations of indoor air pollutants in unregulated public transport means in Korea and to determine the factors affecting the air quality as well as to assess the harmful effects of indoor air pollutants on the health of passengers. The correlation between particulate matter (PM10) and number of passengers on intra-city buses, express buses, town shuttle buses, or rural buses was insignificant whether it was during rush hours or non-rush hours. In regard to PM10 on express buses, there was no case where the standard limit was exceeded during the winter and summer seasons. In winter, however, the average concentration of PM10 increased by the travel distance. In regard to CO2, there was a statistically significant correlation (p < 0.05) between CO2 concentrations and the number of passengers, owing to human respiration. Unlike the case of PM10, there was a difference between rush hours and non-rush hours. Regarding volatile organic compounds (VOCs) and aldehydes, the values were high among recently manufactured vehicles regardless of bus types. The standard limit was exceeded during the summer season, which seemed to be related to the temperature and humidity in both indoor and outdoor. The results of this study indicate that the excess mortality resulting from PM10 was as high as the safety margin during both non-rush hours and rush hours among all unregulated public transportation means. The excess cancer risk and non-cancer risk of VOCs and aldehydes were as high as the safety margin during both non-rush hours and rush hours.

Analysis of roadside air pollutant concentrations and potential health risk of exposure in Hanoi, Vietnam

The large number of vehicles plying in roads is the main cause of traffic jam and air pollution in Hanoi. In this study, the vehicle density and shares of different vehicle types, the traffic flow velocity and roadside air pollutants concentrations were monitored in Chua Boc street, a typical arterial road in the city. The shares of the motorcycle, car and bus fleets in the total on-road traffic in the street were 78.4-87.3, 12.3-20.2 and 0.4-1.4%, respectively. The high density of vehicles caused traffic jam during rush hours and considerably reduced the vehicle speed. The traffic flow velocity during non-rush and rush hours was found to vary from 26.4-34.5 and 10.3-12.1 km/h, respectively. The average concentrations of PM10, PM2.5, SO2, NO2, CO and NMVOC during the rush hours were the following: 117.1 ± 8.5, 65.2 ± 10.6, 113.5 ± 10.9, 138.5 ± 16.0, 6792 ± 998 and 451 ± 71 µg/m3, respectively, which were about 1.9-2.6 times above the levels during non-rush hours. The decrease in vehicle speed during rush hours were strongly correlated with the increase in concentration of PM10 (R2 = 0.732), PM2.5 (R2 = 0.685), SO2 (R2 = 0.578), NO2 (R2 = 0.738), CO (R2 = 0.689) and NMVOC (R2 = 0.747). High levels of these toxic air pollutants in Hanoi city posed a high health risk to humans. Facemask use was more popular among the motorcycle commuters and pedestrians, especially during rush hours, than among the people working for extended time period alongside of the street.

Cooperative Location Privacy in Vehicular Networks: Why Simple Mix Zones are Not Enough

Vehicular communications disclose rich information about the vehicles and their whereabouts. Pseudonymous authentication secures communication while enhancing user privacy. To enhance location privacy, cryptographic mix zones were proposed to facilitate vehicles covertly transition to new ephemeral credentials. The resilience to (syntactic and semantic) pseudonym linking (attacks) highly depends on the geometry of the mix zones, mobility patterns, vehicle density, and arrival rates. We introduce a tracking algorithm for linking pseudonyms before and after a cryptographically protected mix zone. Our experimental results show that an eavesdropper, leveraging standardized vehicular communication messages and road layout, could successfully link ≈73% of pseudonyms during nonrush hours and ≈62% of pseudonyms during rush hours after vehicles change their pseudonyms in a mix zone. To mitigate such inference attacks, we present a novel cooperative mix zone scheme that enhances user privacy regardless of the vehicle mobility patterns, vehicle density, and arrival rate to the mix zone. A subset of vehicles, termed relaying vehicles, is selected to be responsible for emulating nonexisting vehicles. Such vehicles cooperatively disseminate decoy traffic without affecting safety-critical operations: with 50% of vehicles as relaying vehicles, the probability of linking pseudonyms (for the entire interval) drops from ≈68% to ≈18%. On average, this imposes 28 ms extra computation overhead, per second, on the roadside unit (RSU) and 4.67 ms extra computation overhead, per second, on the (relaying) vehicle side; it also introduces 1.46 kB/s extra communication overhead by (relaying) vehicles and 45 kB/s by RSUs for the dissemination of decoy traffic. Thus, user privacy is enhanced at the cost of low computation and communication overheads.

A Deep Adaptive Traffic Signal Controller With Long-Term Planning Horizon and Spatial-Temporal State Definition Under Dynamic Traffic Fluctuations

This study proposes a new adaptive traffic signal control scheme to effectively manage dynamically fluctuating traffic flows through intersections. A spatial-temporal representation of the traffic state at an intersection has been designed to efficiently identify traffic patterns from complex intersection environments, and a deep neural network (long short-term memory network, LSTM) is used to determine look-ahead signal control decisions based on the estimated long-term feedback from a given traffic state. The actor-critic algorithm, one of the reinforcement learning-based algorithms, is adopted to obtain the essential parameters of the LSTM deep neural network through multiple interactions between a simulated environment and the corresponding adaptive traffic signal controller. A realistic model environment comprising a 24-hour time-varying traffic demand including rush hour and non-rush hour situations served as the basis for traffic generation in the numerical experiments to confirm the effectiveness of the proposed scheme. The results of these experiments show that, compared to an optimized fixed time plan (Synchro), the proposed scheme can reduce waiting times at intersections by an astounding 50% with consequential benefits of reducing fuel consumptions, emissions, queue lengths, and vehicle delays whilst increasing mean speeds.

Spatiotemporal short-term traffic forecasting using the network weight matrix and systematic detrending

This study examines the spatiotemporal dependency between traffic links. We model the traffic flow of 140 traffic links in a sub-network of the Minneapolis-St. Paul highway system for both rush hour and non-rush hour time intervals, and validate the extracted network weight matrix. The results of the modeling indicate: (1) the spatial weight matrix is unstable over time-of-day, while the network weight matrix is robust in all cases and (2) the performance of the network weight matrix in non-rush hour traffic regimes is significantly better than rush hour traffic regimes. The results of the validation show the network weight matrix outperforms the traditional way of capturing spatial dependency between traffic links. Averaging over all traffic links and time, this superiority is about 13.2% in rush hour and 15.3% in non-rush hour, when only the first-order neighboring links are embedded in modeling. In addition, this study proposes a two-step algorithm to search and identify the best look-back time window for upstream links. We indicate the best look-back time window depends on the travel time between two study detectors, and it varies by time-of-day and traffic link.

A comprehensive study on spatio-temporal distribution, health risk assessment and ozone formation potential of BTEX emissions in ambient air of Delhi, India

The hazardous air pollutants like benzene, toluene, ethylbenzene and xylene (BTEX) are considered as toxic because of their role in ozone formation and adverse effects on human health. Owing to this, the present study was carried out at six spatially distributed sites in Delhi from November 2017- June 2018. Activated charcoal tubes were used to collect samples of BTEX and were further analyzed using GC-FID. The minimum BTEX concentration was found at institutional site (9.94 μg/m3) and maximum at roadside site (103.12 μg/m3) with the average of 46.66 μg/m3. Also, the levels of BTEX were 1.18–1.74 times higher during rush hours as compared to non-rush hours. The high T/B ratio (2.26–3.41) observed is the indication of the traffic-originated sources of emission. The cancer risks calculated for benzene at probability 0.50 ranged as 1.29E-06 – 1.80E-05, whereas 4.09E-06 – 3.40E-05 at probability 0.95, which were higher than the acceptable value of 1.0E-06. The non-cancer health risks in terms of hazard index were observed less than unity i.e. within acceptable limit. The total ozone formation potential (OFP) was obtained as 207.51 ± 123.40 μg/m3 with maximum potential by toluene. Such high levels of BTEX, cancer risks and OFP obtained in the study especially at roadside and connectivity hub are harmful for people residing near these areas, and also to large commuters, who are exposed to such emissions during travelling.

Switch between critical percolation modes in city traffic dynamics

Percolation transition is widely observed in networks ranging from biology to engineering. While much attention has been paid to network topologies, studies rarely focus on critical percolation phenomena driven by network dynamics. Using extensive real data, we study the critical percolation properties in city traffic dynamics. Our results suggest that two modes of different critical percolation behaviors are switching in the same network topology under different traffic dynamics. One mode of city traffic (during nonrush hours or days off) has similar critical percolation characteristics as small world networks, while the other mode (during rush hours on working days) tends to behave as a 2D lattice. This switching behavior can be understood by the fact that the high-speed urban roads during nonrush hours or days off (that are congested during rush hours) represent effective long-range connections, like in small world networks. Our results might be useful for understanding and improving traffic resilience.

Seatbelt wearing rate in a Chinese city: Results from multi-round cross-sectional studies

ObjectiveRoad traffic crashes impose a heavy burden in China’s most populous city, Shanghai. Since early 2016, Shanghai police have started deploying high-resolution cameras and an object-detection algorithm in seatbelt enforcement. Around the same time, an international consortium on road safety launched a series of capacity trainings for law enforcement accompanied by infrastructure renovation and social marketing campaigns in Shanghai. Our objective is to assess the level and trend of seatbelt use in Shanghai over 2015-2017. MethodsOngoing observational studies evaluate seatbelt use within the city. Data are collected at eight randomly selected locations throughout the city, stratified by road type and geographic locations. Data collection covers most times of the day on both business days and weekends, providing a full representation of varying traffic models during both rush hours and non-rush hours. At each site, trained field workers observe seatbelt use of occupants in all passing motorized vehicles. A multivariate logistic regression model was developed to analyze the data. FindingsFrom October 2015 to December 2017, six rounds of data collection were completed with a total sample size of 77,641 drivers and passengers of motor vehicles. The seatbelt use rate has steadily increased over time, from 60.8% (95% CI: 59.9%–61.7%) in round 1 to 84.9% (84.3%–85.4%) in round 6 (p-value < 0.01). The increase is substantial and statistically significant for both drivers and passengers (p-value < 0.01). Results from the multivariate logistic regression show an annual growth rate of seatbelt use of 8.8% (8.4%–9.1%); the wearing rate was 49.8% (49.0%–50.5%) lower among passengers than drivers; 3.5% (2.9%–4.1%) lower in peri-urban areas than central urban areas; 16.3% (14.3%–18.4%) higher among 18–24-year and 18.9% (17.0%–20.9%) higher among 25–59-year olds than other age groups. Conclusion and policy implicationsSeatbelt use in Shanghai has been boosted during a short period of time through a combined strategy that includes training of police, social marketing campaign, and the deployment of computer vision assisted enforcement. Large cities in China and other countries facing similar challenges with seatbelt wearing may learn from Shanghai’s experience.