Busy Highway Research Articles

COPD Unveiled: Insights from Dwellers along a Busy Highway in Kattankulathur Block

Background: Chronic obstructive pulmonary disease (COPD) is a serious lung condition that imposes a significant financial burden on individuals and society, straining the medical system. According to the World Health Organization, it was the third leading cause of death worldwide in 2020. In India, it ranks as the second leading cause of death, and in Tamil Nadu, it is the tenth. The Global Burden of Disease study in 2018 found that COPD accounted for 50% of all cases and 69% of disability. Outdoor air pollution plays a major role in India’s burden of chronic respiratory diseases. Early diagnosis and intervention can help to alleviate symptoms, prevent complications, and reduce morbidity and mortality, ultimately improving quality of life. Objectives: (1) To estimate the prevalence of COPD among the study population (2) To assess the risk factors associated with COPD among the study population. Methodology: A community-based cross-sectional study was conducted among adults along the roadside dwelling of a National Highway. Using a multi-stage random sampling technique 403 elderly was selected. Participants were interviewed by a pre-tested structured questionnaire followed by spirometry (pulmonary function test) evaluation. Data entry was performed in an Excel sheet and analyzed using SPSS. The odds ratio and Chi-square test were performed to determine the association between qualitative variables. Results: Among 403 study population 58% were male and 42% were female. The prevalence of COPD was found to be 22%, it was 11.1% among males and 10.9% among females. It is evident that individuals, who had exposure to risk factors such as cigarette smoking 78%, overcrowding 35.8%, lack of cross ventilation 10.9%, and living near a highway within 50 m of distance 36.2% are at more risk for developing COPD. Conclusion: Study revealed that people living in closer proximity with roadways increases the risk of COPD.

Full of sound and fury.

The subject of noise in the operating theatre was recognized as early as 1972 and has been compared to noise levels on a busy highway. While noise-induced hearing loss in orthopaedic surgery specifically has been recognized as early as the 1990s, it remains poorly studied. As a result, there has been renewed focus in this occupational hazard. Noise level is typically measured in decibels (dB), whereas noise adjusted for human perception uses A-weighted sound levels and is expressed in dBA. Mean operating theatre noise levels range between 51 and 75 dBA, with peak levels between 80 and 119 dBA. The greatest sources of noise emanate from powered surgical instruments, which can exceed levels as high as 140 dBA. Newer technology, such as robotic-assisted systems, contribute a potential new source of noise. This article is a narrative review of the deleterious effects of prolonged noise exposure, including noise-induced hearing loss in the operating theatre team and the patient, intraoperative miscommunication, and increased cognitive load and stress, all of which impact the surgical team's overall performance. Interventions to mitigate the effects of noise exposure include the use of quieter surgical equipment, the implementation of sound-absorbing personal protective equipment, or changes in communication protocols. Future research endeavours should use advanced research methods and embrace technological innovations to proactively mitigate the effects of operating theatre noise.

Томографический измеритель концентрации метана на приземных трассах

Using a prototype of a remote lidar created in the laboratory for measuring methane concentration, a study of the distribution of methane along a route ~ 2.25 km long was carried out in order to localize gas emissions and control leaks in unfavorable areas. The lidar prototype, operating at a wavelength of 1653 nm, included a powerful Raman amplifier and made it possible to measure the distance to the reflection point with an accuracy of no worse than 50 meters. The lidar scanned the track, starting from short distances and ending with measurements along the entire measured track. The route included a near zone with modern multi-storey buildings without gas infrastructure, a middle zone with multi-storey buildings and gas infrastructure, as well as an extended forest area ending with a busy highway with heavy traffic. That is, it was possible to assess the contribution of each zone to the background methane concentration along the route. Observations were carried out during the winter-spring period and were carried out at different times of the day under weather conditions that were recorded. Processing of the results showed a significantly different contribution of each zone to the average concentration on the route. The contribution of the middle zone with multi-storey buildings equipped with gas infrastructure turned out to be especially large, which suggests a possible gas leak from the gas distribution system in this area. On average, the background concentration of methane on this route repeated the value measured in 2023 during the same seasonal period in 2023.

Source apportionment of PM2.5 and PM10 pollutants near an urban roadside site using positive matrix factorization

This paper presents results from a comprehensive study of source apportionment of particulate matter (PM) of size PM2.5 and PM10 near a busy highway in Sharjah, United Arab Emirates. Source apportionment was carried out using US Environmental Protection Agency (EPA) Positive Matrix Factorization model. Furthermore, backward trajectory analysis and Potential Source Contribution Function were used to assess air mass transport pathways and identify potential source regions, respectively. The results revealed six major sources for PM2.5, including traffic, sea salt, fugitive dust, secondary aerosols, heavy oil combustion and mineral dust. For PM10, four major sources were identified, including secondary aerosols, traffic, sea salt and mineral dust. Traffic emissions were found to be significant contributors to both PM2.5 and PM10 pollution, along with natural sources like sea salt and mineral dust. Backward trajectory analysis indicated the influence of different wind regimes on air mass transport, with contributions from regions like Arabian Gulf, Arabian Sea, Oman and Iran. The Conditional Bivariate Probability Function analysis further explained the impact of local traffic emissions and other sources on PM pollution under varying wind conditions.

Estimating near-roadway air pollution from multi-frequency noise measurements

Air pollution is a major environmental problem and its monitoring is essential for regulatory purposes, policy making, and protecting public health. However, dense networks of air quality monitoring equipment are prohibitively expensive due to equipment costs, labor requirements, and infrastructure needs. As a result, alternative lower-cost methods that reliably determine air quality levels near potent pollution sources such as freeways are desirable. We present an approach that couples noise frequency measurements with machine learning to estimate near-roadway particulate matter (PM2.5), nitrogen dioxide (NO2), and black carbon (BC) at 1-min temporal resolution. The models were based on data collected by co-located noise and air quality instruments near a busy freeway in Long Beach, California. Model performance was excellent for all three pollutants, e.g., NO2 predictions yielded Pearson's R = 0.87 with a root mean square error of 7.2 ppb; this error represents about 10 % of total morning rush hour concentrations. Among the best air pollutant predictors were noise frequencies at 40 Hz, 500 Hz, and 800 Hz, and meteorology, particularly wind direction. Overall, our method potentially provides a cost-effective and efficient approach to estimating and/or supplementing near-road air pollutant concentrations in urban areas at high temporal resolution.

Assessing the predictability of surrogate safety measures as crash precursors based on vehicle trajectory data prior to crashes

This study aims to investigate the predictability of surrogate safety measures (SSMs) for real-time crash risk prediction. We conducted a year-long drone video collection on a busy freeway in Nanjing, China, and collected 20 rear-end crashes. The predictability of SSMs was defined as the probability of crash occurrence when using SSMs as precursors to crashes. Ridge regression models were established to explore contributing factors to the predictability of SSMs. Four commonly used SSMs were tested in this study. It was found that modified time-to-collision (MTTC) outperformed other SSMs when the early warning capability was set at a minimum of 1 s. We further investigated the cost and benefit of SSMs in safety interventions by evaluating the number of necessary predictions for successful crash prediction and the proportion of crashes that can be predicted accurately. The result demonstrated these SSMs were most efficient in proactive safety management systems with an early warning capability of 1 s. In this case, 308, 131, 281, and 327,661 predictions needed to be made before a crash could be successfully predicted by TTC, MTTC, DRAC, and PICUD, respectively, achieving 75 %, 85 %, 35 %, and 100 % successful crash identifications. The ridge regression results indicated that the predefined threshold had the greatest impact on the predictability of all tested SSMs.

Spatial distribution of heavy metal abundance at distance gradients of roadside agricultural soil from the busiest highway in Bangladesh: A multi-index integration approach

Bangladesh is currently experiencing significant infrastructural development in road networking system through the construction or reconstruction of multiple roads and highways. Consequently, there is a rise in traffic intensity on roads and highways, along with a significant contamination of adjacent agricultural soils with heavy metals. The purpose of this study was to evaluate the ecological risk, health risk and the abundance of seven heavy metals (Cu, Mn, Pb, Cd, Cr, As, and Ni) in three distance gradients (0, 300, and 500 m) of agricultural soil along the Dhaka-Chattogram highway. The concentration of heavy metals was measured with an Atomic Absorption Spectrophotometer (AAS) on a total of 36 soil samples that were taken from 12 different sampling sites. Based on the findings, Cd had a high contamination factor for all distance gradients, whereas Cr had a moderate contamination factor in 67% of the study areas. According to the Pollution Load Index (PLI), Cd, Cr, and Pb were the predominant pollutants. Principal component analysis (PCA) result shows these metals mainly came from anthropogenic sources. The considerable positive correlations between Cu–Pb, Cu–Cd, Pb–Cd, and Cr–Ni all pointed to shared anthropogenic origins. As per Potential Ecological Risk Assessment (PERI) analysis, Pb, Cd, Cr, and Ni each contribute significantly and pose a moderate threat. The Target Hazard Quotient (THQ) values for all pathways of exposure to Pb and Cr in soils were more than 1, which would pose a significant risk to human health in the following order: THQadult female > THQadult male > THQchildren. This study will help to evaluate the human health risk and develop a better understanding of the heavy metal abundance scenario in the agricultural fields adjacent to this highway.

Effects of light pollution on Bluegill foraging behavior

AbstractObjectiveArtificial light at night (ALAN) is one of the most pervasive and rapidly expanding sources of anthropogenic pollution. Aquatic ecosystems may be especially vulnerable to the effects of ALAN due to their disproportionate exposure to anthropogenic pressures. However, research on mechanisms of response to ALAN by aquatic species remains sparse. Our research investigated the extent to which ALAN influences the nocturnal feeding efficiency of Bluegill Lepomis macrochirus.MethodsUsing an array of outdoor mesocosm tanks, we assigned juvenile Bluegill to five nighttime lighting treatments (control dark, 1 lx, 4 lx, 12 lx, and intermittent 12 lx). We conducted weekly nighttime feeding trials for 6 weeks to assess four prey capture variables, including capture efficiency (the effectiveness of prey strikes), capture rate (the number of prey items captured), strike rate (the frequency of prey strikes), and latency (time elapsed before the first prey strike).ResultThe steady lighting treatments, which were selected based on nighttime urban light intensities we previously measured in the field, had no apparent effect on any of the prey capture variables. However, flashing high‐intensity lights (intended to mimic the effect of passing car headlights on a busy highway) had a significant negative effect on capture rate and strike rate, thereby inhibiting the ability of fish to strike at and capture prey.ConclusionOur results demonstrate the potential for light pollution (especially flashing or intermittent lights) to interfere with the foraging behavior of this ecologically and economically important sportfish.

Exploring the impact of transportation on heavy metal pollution: A comparative study of trains and cars

This study aimed to investigate the impact of road and rail traffic on the soil through the analysis of the presence of heavy metals in soil samples collected next to a busy highway, local roads, and next to an active railway line. Results showed that cars emitted higher levels of heavy metals than trains. Soil samples near the highway had higher levels of Cu, Ni, and Hg. The values of the calculated indices like geo-accumulation index, potential toxicity response index, ecological risk factor, contamination factor, pollution load index, Nemerow's pollution index, and degree of contamination confirm that the soil samples sampled near the highway are the most polluted and highway have the greatest negative impact on the soil environment. These results suggest that controlling car emissions through strict regulations and promoting public transportation could effectively reduce the heavy metal concentrations in soil, particularly from highway emissions.

Renovation of the Van Brienenoord Bridge, The Netherlands

Part of the Netherlands’ busiest highway, the Van Brienenoord Bridge comprises 12 lanes of traffic split across the eastbound bridge built in the 1960 s and the western bridge built in the 1990 s. The Van Brienenoord Bridge complex consisting of two parallel 300 m span steel arch bridges, approach structures and three parallel bascule bridges over the New Meuse. The bridges carry about 230,000 vehicles daily. A strengthening and replacement strategy was developed to reduce road closures to one of the two bridges at a time and reducing this time to weeks instead of months. The strengthening consists of plate stiffeners to the main girders and arches and a new deck. Construction begins in 2025 and will extend the bridge’s useful life to another 100 years. The strengthening instead of replacing is in line with RWS’ commitment to adopting circular economy principles for their infrastructure network.

Spatial and PMF analysis of particle size distributions simultaneously measured at four locations at the roadside of highways

In urban areas, particulate matter emitted from vehicles directly affects the health of citizens near roads. Thus, in this study, particle size distribution was measured by the horizontal and vertical distances along a highway road with heavy traffic to characterize the dispersion phenomena of particulate matter emitted from vehicles. In addition, the contribution of pollution sources was analyzed using a source-receptor model. A concentration gradient was observed in which the concentration decreased with the increase in the distance from the road when the wind blew from the road to the monitoring locations. The concentration was slightly higher within 50 m of the road when the wind blows parallel to the road, and similar concentrations were found at the other monitoring locations further away from the roads. In particular, the higher the turbulence intensity of the wind, the lower is the concentration gradient coefficient because of the more enhanced mixing and dispersion. A positive matrix factorization (PMF) model with the measured particle size distribution data in the range of 9–300 nm resulted in a contribution of about 70 % (number) and 20 % (mass) to particle concentrations because of six types of vehicles including LPG, two gasoline vehicles (GDI, MPI), and three diesel vehicles with 3rd, 4th, and 5th emission classes. It showed a decrease in the vehicular contribution as the distance from the road increased. Particle number concentrations decreased with increasing altitude up to 30 m above the ground. The results of this study can be useful in deriving generalized gradient equations of particle concentrations exposed by distance and wind direction at the roadside using traffic and meteorological conditions and for establishing environmental policies, such as roadside exposure assessment, in the future. A capsule abstractDispersion of particles emitted from vehicles on a busy highway was characterized by roadside measurements of horizontal and vertical profiles of particle size distributions measured at four locations. The source profiles and contributions were estimated by major sources using a source-receptor model such as PMF.

Determinants of traffic related atmospheric particulate matter concentrations and their associated health risk at a highway toll plaza in India

People working at toll plazas are continuously exposed to high pollutant concentrations due to traffic congestion at toll booths. However, there are fewer studies available which focus on toll workers’ exposures. Therefore, a study was conducted at a toll plaza on a busy national highway (NH) in India to assess black carbon (BC), particulate matter with aerodynamic diameter ≤2.5 μm (PM2.5), and ultrafine particles (UFP) concentrations and their associated health risks on the toll workers inside toll cabin and kerbside. Higher BC (34 μg m−3 vs. 18 μg m−3) and PM2.5 (86 μg m−3 vs. 79 μg m−3) concentrations were observed inside the cabin than at kerbside. On the contrary, UFP concentrations were higher at the kerbside (22,790 pt cm−3) than in-cabin (18525 pt cm−3). All pollutant concentrations were higher during evening hours than in the morning hours. BC and PM2.5 concentrations were found to be higher during the weekdays. Positive correlations were found between traffic volume and all pollutant concentrations. Negative correlations were observed for solar radiation and boundary layer height with pollutant concentrations. Additionally, it was found that the health risk of toll workers was higher (risk quotient, RQ range: ∼2–5) than the prescribed limit (RQ < 1). The present study demonstrates that there is a need to control personal exposure to pollutants in the workplaces, especially locations with traffic proximity.

NSGA-III-SD based Fuzzy energy management system optimization for lithium battery/supercapacitor HEV

Hybrid energy storage system (HESS), a combination of lithium battery (LIB) and supercapacitor (SC) in hybrid electric vehicles (HEV), can slow down the degradation of battery performance and improve the peak current characteristics by using an optimal energy management system (EMS). A fuzzy EMS controller is first designed to allocate the energy between LIB and SC, then an improved NSGA-III algorithm based on similarity and diversity selection (NSGA-III-SD) is proposed to reduce energy consumption, lithium battery output current, peak power and prolong the life of LIB as well as to meet the SOC of SC constraint by optimizing the parameters of the fuzzy rule base and membership functions (MFs). Four typical driving patterns including busy city, urban, suburban and highway are selected to demonstrate the efficiency of the proposed method. The experimental results indicate that the optimal fuzzy EMS exhibits good adaptive power distribution capability under all driving conditions.

Транспортный фактор в субурбанизационных процессах Иркутской агломерации

The paper deals with the problems of the Irkutsk agglomeration associated with the increasing role of personal motor transport in the interaction of the city with suburban settlements. The study area includes a number of suburban municipalities most involved in the agglomeration process. The dynamics of the growth of personal vehicles in the context of municipalities is considered. Measurements of the intensity of traffic on the main highways leaving the city, and the ratio of personal and public transport, providing communication within the agglomeration, were carried out. The most intensively developing suburban settlements and the busiest highways - Kultuksky and Baikalsky, connecting the city with the nearest settlements on the coast of Lake Baikal and the waters of the Angara bays, along which there is an increase in cottage and garden-dacha construction, have been identified. At the same time, the share of public transport in the structure of flows is directly proportional to its intensity, which contributes to congestion and traffic jams at the exits from the city. The factors and reasons influencing the development of different areas of suburban areas are considered. Based on the results of the analysis, recommendations are given for optimizing the transport network in order to minimize existing problems and further systematic development of the agglomeration.

Artificial Neural Network Modeling on PM10, PM2.5, and NO2 Concentrations between Two Megacities without a Lockdown in Korea, for the COVID-19 Pandemic Period of 2020

The mutual relationship among daily averaged PM10, PM2.5, and NO2 concentrations in two megacities (Seoul and Busan) connected by the busiest highway in Korea was investigated using an artificial neural network model (ANN)-sigmoid function, for a novel coronavirus (COVID-19) pandemic period from 1 January to 31 December 2020. Daily and weekly mean concentrations of NO2 in 2020 under neither locked down cities, nor limitation of the activities of vehicles and people by the Korean Government have decreased by about 15%, and 12% in Seoul, and Busan cities, than the ones in 2019, respectively. PM 10 (PM2.5) concentration has also decreased by 15% (10%), and 12% (10%) in Seoul, and Busan, with a similar decline of NO2, causing an improvement in air quality in each city. Multilayer perception (MLP), which has a back-propagation training algorithm for a feed-forward artificial neural network technique with a sigmoid activation function was adopted to predict daily averaged PM10, PM2.5, and NO2 concentrations in two cities with their interplay. Root mean square error (RMSE) with the coefficient of determination (R2) evaluates the performance of the model between the predicted and measured values of daily mean PM10, PM2.5, and NO2, in Seoul were 2.251 with 0.882 (1.909 with 0.896; 1.913 with 0.892), 0.717 with 0.925 (0.955 with 0.930; 0.955 with 0.922), and 3.502 with 0.729 (2.808 with 0.746; 3.481 with 0.734), in 2 (5; 7) nodes in a single hidden layer. Similarly, they in Busan were 2.155 with 0.853 (1.519 with 0.896; 1.649 with 0.869), 0.692 with 0.914 (0.891 with 0.910; 1.211 with 0.883), and 2.747 with 0.667 (2.277 with 0.669; 2.137 with 0.689), respectively. The closeness of the predicted values to the observed ones shows a very high Pearson r correlation coefficient of over 0.932, except for 0.818 of NO2 in Busan. Modeling performance using IBM SPSS-v27 software on daily averaged PM10, PM2.5, and NO2 concentrations in each city were compared by scatter plots and their daily distributions between predicted and observed values.

Road-kills in New Zealand: long-term effects track population changes and reveal colour blindness

Road-kills were recorded at random throughout New Zealand, on 96359 km of roads, avoiding towns and busy motorways, from 1963-2018. Traffic increase from 1.04 m to 4.33 million vehicles during the study had little effect on mortality, even at the greater traffic density in the North Island. Seasonal changes measured on 8435 km (151 trips) between Lower Hutt and Otaki from 1985-2015 showed lowest mortality in winter. Major differences in species identification between two independent observers on the same route, from 2009-2014, resulted from one being red/green colourblind. Possum (Trichosurus vulpecula) numbers dipped briefly in the 1970s, peaked in the 1990s, and have declined since then where there has been widespread poisoning to protect trees, birds, and limit bovine TB. Rabbits (Oryctolagus cuniculus) increased steadily after control was lifted in the 1980s and now dominate the road-kills; the effect of RHD, introduced in 1997, does not register, probably because it causes short-term local oscillations. Hedgehog (Erinaceus europaeus) numbers show no clear trend and, unlike the other species, North and South Island patterns differ; the lower numbers in the South may reflect the cooler climate. Brown hares (Lepus europaeus) remain relatively stable, with a doubling in numbers since the 1980s in parallel with rabbits. The predators, cats (Felis catus) and mustelids (Mustela furo, M.erminea, M. nivalis), followed their prey increase until the 1990s when extensive predator control began; they then declined, although rabbit and rat (Rattus rattus, R. norvegicus) numbers continued to rise. In the 1950-60s, far more live mammals were being seen on and from roads, and adaptations to traffic have evolved. These historical records may be useful to assess future changes in road-kill following the adoption of silent electric cars, driverless vehicles, and public transport.

On airborne tire wear particles along roads with different traffic characteristics using passive sampling and optical microscopy, single particle SEM/EDX, and µ-ATR-FTIR analyses

Tire wear particles (TWPs) are a major category of microplastic pollution produced by friction between tires and road surfaces. This non-exhaust particulate matter (PM) is transported through the air and with runoff leading to environmental pollution and health concerns. Here, we collected airborne PM along paved roads with different traffic volumes and speeds using Sigma-2 passive samplers. Particles entering the samplers deposit onto substrates for analysis, or, as we modified it, directly into small (60 ml) separatory funnels, which is particularly useful with high particle loads, where a density separation aids in isolating the microplastics. We quantified putative TWPs (∼10–80 µm) deposited on the substrates (primarily adhesive tape on glass slides) and in the funnels using stereomicroscopy. Putative TWP deposition rates (particles/cm2/day ± SD) at 5 m from the road were highest near a busy highway (324 ± 129), followed by a boulevard with moderate traffic (184 ± 93), and a slow traffic avenue (29 ± 7). We observed that deposition rates increased within proximity to the highway: 99 ± 54, 180 ± 88, and 340 ± 145 at 30, 15, and 5 m, respectively. We show that TWP abundances (i.e., deposition and mass concentration) increase with vehicle braking (driving behavior). We observed no differences (p &amp;gt; 0.05) between the separatory funnel and adhesive tape collection methods. In addition, we were able to obtain FTIR spectra of TWPs (&amp;gt;10 µm) using µ-ATR-FTIR. Both deserve further scrutiny as novel sampling and analytical approaches. In a separate sampling campaign, we differentiated 1438 particles (∼1–80 µm) deposited on boron substrates into TWP, metal, mineral, and biogenic/organic classes with single particle SEM/EDX analysis based on morpho-textural-chemical classification and machine learning. The results revealed similar concentration trends with traffic (high &amp;gt; moderate &amp;gt; low), with the distribution of particle sources alike for the highway and the moderate road: TWPs (∼38–39%) &amp;gt; biogenic (∼34–35%) &amp;gt; minerals (∼23–26%), and metallic particles (∼2–3%). The low traffic road yielded a much different distribution: biogenic (65%) &amp;gt; minerals (27%) &amp;gt; TWPs (7%) &amp;gt; metallic particles (1%). Overall, this work provides much-needed empirical data on airborne TWPs along different types of roads.

Development of Inland Waterway Transport as a Key to Ensure Sustainability: A Case Study of Lithuania

To ensure the development of sustainability, the Europe Union (EU) set the goal of becoming a climate-neutral economy by 2050. The EU’s ambitious new target cannot be achieved without the involvement of all modes of transport in order to reduce emissions. According to it, the development of inland waterway transport is a key to ensuring sustainability. Calculations proving the benefits of developing the use of inland waterway transport for ensuring sustainability using the example of the Republic of Lithuania were carried out in this paper. Namely, the calculation of the benefit from the transfer of part of the cargo flow from the busy highway A1 Kaunas–Klaipeda to Neman River (inland waterway of international importance E41). Modeling and detailed comparison using the method of calculating the impact on the main external costs (accidents, air pollution, climate change, noise, habitat damage, congestion) showed that external costs are lower when transporting cargo by river. Increasing the barge capacity from 1000 t to 1800 t reduces the external cost of climate change by 25%. Moreover, replacing diesel with liquefied natural gas can reduce the external costs of climate change by 15% to 25%. The greatest effect is achieved by reducing the level of noise, congestion, and safety. It is possible to enlarge the positive effect by using energy generated by solar or wind power plants for electric-powered ships.

Characterization of winter air pollutant gradients near a major highway

Traffic-related air pollutants (TRAP) including nitric oxide (NO), nitrogen oxide (NOx), carbon monoxide (CO), ultrafine particles (UFP), black carbon (BC), and fine particulate matter (PM2.5) were simultaneously measured at near-road sites located at 10 m (NR10) and 150 m (NR150) from the same side of a busy highway to provide insights into the influence of winter time meteorology on exposure to TRAP near major roads. The spatial variabilities of TRAP were examined for ambient temperatures ranging from −11 °C to +19 °C under downwind, upwind, and stagnant air conditions. The downwind TRAP concentrations at NR10 were higher than the upwind concentrations by a factor of 1.4 for CO to 13 for NO. Despite steep downwind reductions of 38 % to 75 % within 150 m, the downwind concentrations at NR150 were still well above upwind concentrations. Near-road concentrations of NOx and UFP increased as ambient temperatures decreased due to elevated emissions of NOx and UFP from vehicles under colder temperatures. Traffic-related PM2.5 sources were identified using hourly PM2.5 chemical components including organic/inorganic aerosol and trace metals at both sites. The downwind concentrations of primary PM2.5 species related to tailpipe and non-tailpipe emissions at NR10 were substantially higher than the upwind concentrations by a factor of 4 and 32, respectively. Traffic-related PM2.5 sources accounted for almost half of total PM2.5 mass under downwind conditions, leading to a rapid change of PM2.5 chemical composition. Under stagnant air conditions, the concentrations of most TRAP and related PM2.5 including tailpipe emissions, secondary nitrate, and organic aerosol were comparable to, or even greater than, the downwind concentrations under windy conditions, especially at NR150. This study demonstrates that stagnant air conditions further widen the traffic-influenced area and people living near major roadways may experience increased risks from elevated exposure to traffic emissions during cold and stagnant winter conditions.

Impacts of heavy duty truck emissions on the vertical distributions of soil polycyclic aromatic hydrocarbons at a big haulage vehicle park

Nigeria relies heavily on heavy duty haulage vehicles to convey commodities from one end of the nation to the other, owing to the decades-long collapse of the rail transportation infrastructure. This is resulting in settings with very high concentrations of the trucks on the country's key roads. Diesel engine exhausts have long been recognized to be detrimental to human health. The concentrations of polycyclic aromatic hydrocarbons (PAHs) in the top and bottom soil samples from an extensive haulage vehicle stop located at Ogere, a roadside town along the ever busy Lagos-Ibadan highway were investigated. Soil samples taken at depths of 0–15 cm and 15–30 cm were subjected gas-chromatographic/mass spectrometer analysis. The Ʃ priority PAHs in the top and bottom soils at four sampling locations ranged between 16.4 and 32.0 mg/kg and 18.6–26.6 mg/kg respectively. The corresponding Ʃ carcinogenic PAHs for top and bottom soils ranged between 9.2 and 17.4 mg/kg and 8.7–12.0 mg/kg respectively. The concentrations of PAHs obtained in this study clearly exceeded the permissible limits set for soil PAHs in most developed nations of the world. The diagnostic ratio attributed the observed PAHs to diesel emissions. The study concluded that the emissions from the trucks at the haulage vehicle stop have tremendous impact on the soil PAH concentrations.