Concentrations of particulate matter (PM2.5) and contributions of tire wear particle to PM2.5 in an indoor parking garage: Comparison with the outside and the differences according to the sampling sites

Particulate matter (PM) is increasingly affecting the social-economic development of countries. An increase in PM2.5 concentration increases susceptibility to cardiovascular and respiratory diseases and cancer. Tire wear particles (TWP) contribute to airborne PM. In the present work, we investigated the variation in the concentration of TWP of <2.5 μm in aerodynamic diameter (TWP2.5) in an indoor parking garage depending on the sampling sites. PM2.5 samples were collected at four sites in an indoor parking garage of a college campus: the entrance of the parking garage (Ent), the second floor toward the third floor (2F), the front of the parking zone on the second floor (2FP), and the third floor toward the fourth floor (3F). Each PM2.5 sampling was performed for 4 days during the fall season. The PM2.5 concentrations at the 2F and 2FP were similar to the outside PM2.5 concentrations, whereas those at the Ent and 3F were higher than the outside PM2.5 concentrations. The TWP2.5 concentrations in the indoor parking garage were 0.61-0.73 μg/m3. The differences in the TWP2.5 concentrations depending on the sampling sites were due to the differences in traffic volumes. The TWP2.5 concentration at the 2FP was higher than those at the other sampling sites owing to air stagnation and TWPs produced by the high friction when parking and exiting a car in the parking zone. The contributions of TWP2.5 to the PM2.5 concentrations were 3.9-11.7%, in the order of 2FP ≫ Ent > 3F > 2F. A good air ventilation system can be recommended to reduce TWP2.5 concentrations in indoor parking garages.

Improving traffic-related air pollution estimates by modelling minor road traffic volumes

Accurately estimating annual average daily traffic (AADT) on minor roads is essential for assessing traffic-related air pollution (TRAP) exposure, particularly in areas where most people live. Our study assessed the direct and indirect external validity of three methods used to estimate AADT on minor roads in Melbourne, Australia. We estimated the minor road AADT using a fixed-value approach (assuming 600 vehicles/day) and linear and negative binomial (NB) models. The models were generated using road type, road importance index, AADT and distance of the nearest major road, population density, workplace density, and weighted road density. External measurements of traffic counts, as well as black carbon (BC) and ultrafine particles (UFP), were conducted at 201 sites for direct and indirect validation, respectively. Statistical tests included Akaike information criterion (AIC) to compare models' performance, the concordance correlation coefficient (CCC) for direct validation, and Spearman's correlation coefficient for indirect validation. Results show that 88.5% of the roads in Melbourne are minor, yet only 18.9% have AADT. The performance assessment of minor road models indicated comparable performance for both models (AIC of 1,023,686 vs. 1,058,502). In the direct validation with external traffic measurements, there was no difference between the three methods for overall minor roads. However, for minor roads within residential areas, CCC (95% confidence interval [CI]) values were -0.001 (-0.17; 0.18), 0.47 (0.32; 0.60), and 0.29 (0.18; 0.39) for the fixed-value approach, the linear model, and the NB model, respectively. In the indirect validation, we found differences only on UFP where the Spearman's correlation (95% CI) for both models and fixed-value approach were 0.50 (0.37; 0.62) and 0.34 (0.19; 0.48), respectively. In conclusion, our linear model outperformed the fixed-value approach when compared against traffic and TRAP measurements. The methodology followed in this study is relevant to locations with incomplete minor road AADT data.

Ineffective implementation of emergency reduction measures against high concentrations of particulate matter in Seoul, Republic of Korea

Since December 30, 2017, the Seoul Metropolitan Government, Republic of Korea, has been implementing emergency reduction measures (ERMs) restricting the operation of industrial sites, thermal power plants, and vehicles when air quality is expected to deteriorate. ERMs are implemented when the present observed concentration of particulate matter (PM) of aerodynamic diameter less than 2.5 μm (PM2.5) and/or the predicted values for the following day exceed a threshold value. In this study, the effectiveness of ERMs was evaluated for 33 days with and 6 days without ERM implementation but where the PM2.5 concentration exceeded the threshold value, until March 15, 2021. Of the 33 days of ERM implementation, on 7 days it was executed despite the thresholds not being met. The ERM on these days might have been properly executed because the pre-notice and implementation of ERM might have reduced the local emissions of air pollutants. Our major findings are that even on days of ERM implementation, there were marginal reductions in vehicle traffic, thermal power generation, and industrial emissions. Second, the concentrations of PM2.5 and related air pollutants in Seoul were almost unchanged for most ERM implementation episodes. Third, most of the 39 (= 33 + 6) days when the air quality worsened were caused by the transboundary transport of air pollutants from China. In conclusion, it was revealed that the currently executed ERM law is insufficient for effectively reducing PM2.5. To achieve the required reductions, it is necessary to undertake stricter policies in Seoul and its neighboring regions.

Comparison of polymeric components and tire wear particle contents in particulate matter collected at bus stop and college campus

Particulate matter (PM_2.5) samples were collected at two different places of a college campus (CC) and a bus stop (BS) nearby the college campus. The traffic volume of college campus was very low due to untact classes. Polymeric components and tire wear particle (TWP) contents in the PM_2.5 samples were analyzed using pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS). Various polymeric components such as natural rubber (NR), bitumen, saturated hydrocarbons, poly(ethylene terephthalate) (PET), and plant-related particles (PRPs) were observed. NR and bitumen are key components of TWP of bus tire tread and asphalt pavement wear particle (APWP), respectively. The TWP contents in the PM_2.5 samples collected at the bus stop were larger than those collected at the college campus. For the same sampling site, the TWP content in the PM_2.5 sample collected for higher fine dust concentration in the air was greater than that for lower one. The TWP_2.5 concentration in the air for the BS sampling was higher than those for the CC sampling, even when the PM_2.5 concentration in the air for the former was lower than those for the latter. It can be concluded that the TWPs and APWPs in the PM_2.5 samples collected at the college campus should be transferred mostly from the outside road.

Estimating long-term and short-term impact of COVID-19 activity restriction on regional highway traffic demand: A case study in Zhejiang Province, China

Since the outbreak of COVID-19 in China in late 2019, government administrators have implemented traffic restriction policies to prevent the spread of COVID-19. However, highway traffic volumes obtained from ETC data in some provinces did not return to the levels of previous years after the end of the traffic restriction policy, suggesting that traffic restriction policy may have long-term effects. This paper proposed a method that analyzes traffic restriction policies' long-term and short-term impact on highway traffic volume under COVID-19. This method first analyzes the long-term and short-term impacts of traffic restriction policies on the highway traffic volume using the Prophet model combined with the concept of traffic volume loss. It further investigates the relationship between COVID-19 cases and the long-term and short-term impacts of the traffic restriction policy using Granger causality and the impulse response function of the Bayesian vector autoregressive (BVAR) model. The results showed that during the COVID-19 pandemic, highway traffic in Zhejiang Province decreased by about 95.5%, and the short-term impact of COVID-19 cases was most pronounced on the second day. However, the long-term effects were relatively small when the traffic restriction policy ended and was verified by data from other provinces. These results will provide decision support for traffic management and provide recommendations for future traffic impact assessments in the event of similar epidemics.

To what extent the traffic restriction policies can improve its air quality? An inspiration from COVID-19

In hazy days, several local authorities always implemented the strict traffic-restriction measures to improve the air quality. However, owing to lack of data, the quantitative relationships between them are still not clear. Coincidentally, traffic restriction measures during the COVID-19 pandemic provided an experimental setup for revealing such relationships. Hence, the changes in air quality in response to traffic restrictions during COVID-19 in Spain and United States was explored in this study. In contrast to pre-lockdown, the private traffic volume as well as public traffic during the lockdown period decreased within a range of 60-90%. The NO₂ concentration decreased by approximately 50%, while O₃ concentration increased by approximately 40%. Additionally, changes in air quality in response to traffic reduction were explored to reveal the contribution of transportation to air pollution. As the traffic volume decreased linearly, NO₂ concentration decreased exponentially, whereas O₃ concentration increased exponentially. Air pollutants did not change evidently until the traffic volume was reduced by less than 40%. The recovery process of the traffic volume and air pollutants during the post-lockdown period was also explored. The traffic volume was confirmed to return to background levels within four months, but air pollutants were found to recover randomly. This study highlights the exponential impact of traffic volume on air quality changes, which is of great significance to air pollution control in terms of traffic restriction policy.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s00477-022-02351-7.

Investigation of COVID-19-related lockdowns on the air pollution changes in augsburg in 2020, Germany

The COVID-19 pandemic in Germany in 2020 brought many regulations to impede its transmission such as lockdown. Hence, in this study, we compared the annual air pollutants (CO, NO, NO₂, O₃, PM₁₀, PM_2.5, and BC) in Augsburg in 2020 to the record data in 2010-2019. The annual air pollutants in 2020 were significantly (p < 0.001) lower than that in 2010-2019 except O₃, which was significantly (p = 0.02) higher than that in 2010-2019. In a depth perspective, we explored how lockdown impacted air pollutants in Augsburg. We simulated air pollutants based on the meteorological data, traffic density, and weekday and weekend/holiday by using four different models (i.e. Random Forest, K-nearest Neighbors, Linear Regression, and Lasso Regression). According to the best fitting effects, Random Forest was used to predict air pollutants during two lockdown periods (16/03/2020-19/04/2020, 1st lockdown and 02/11/2020-31/12/2020, 2nd lockdown) to explore how lockdown measures impacted air pollutants. Compared to the predicted values, the measured CO, NO₂, and BC significantly reduced 18.21%, 21.75%, and 48.92% in the 1st lockdown as well as 7.67%, 32.28%, and 79.08% in the 2nd lockdown. It could be owing to the reduction of traffic and industrial activities. O₃ significantly increased 15.62% in the 1st lockdown but decreased 40.39% in the 2nd lockdown, which may have relations with the fluctuations the NO titration effect and photochemistry effect. PM₁₀ and PM_2.5 were significantly increased 18.23% an 10.06% in the 1st lockdown but reduced 34.37% and 30.62% in the 2nd lockdown, which could be owing to their complex generation mechanisms.

Traffic emission dominates the spatial variations of metal contamination and ecological-health risks in urban park soil

Metals in urban park soil are closely related to traffic emissions, which adversely affect soil quality and human health. However, little is known about the quantitative impacts of traffic on the spatial variations of metals in park soil after the banning of leaded gasoline. Herein, concentrations of Cu, Pb and Zn in surface soil of four recreational parks of Sydney (Ashfield, Robson, Lamberts and Leichhardt) were measured to evaluate their spatial characteristics in contamination, ecological and health risks and relationships with traffic emissions. Contamination of metals are assessed by contamination factor (CF). Normalized metal concentrations (<63 μm) in the park soil were 24-614, 23-3520 and 99-3060 mg kg^-1 for Cu, Pb and Zn, respectively, and CFs ranged from 1.4 to 207, whose variations inter- and intra-parks were related to traffic volumes. Traffic emission accounted for 72-84% of metals contamination in soil of Ashfield, Robson and Lamberts by sites, whereas the values were 25-70% for Leichhardt due to the absence of a surrounding arterial road. In Ashfield and Robson Parks, metal concentrations from traffic decreased exponentially with distance from arterial roads. Metals in Lamberts Park and in areas near arterial roads in Ashfield and Robson Parks may raise ecological risk, and traffic sources contributed to 61-81% of the risk. The ranges of ecological risk zones away from arterial roads and average daily traffic volumes showed an exponential relationship. Copper and Zn in soil of the four parks have no non-carcinogenic health risk for children and adults, and Pb has negligible health risk for adults. Lead in Lamberts Park and in sites near arterial roads of Ashfield and Robson Parks may raise non-carcinogenic risk for children (HI > 1) due to traffic emissions. These results emphasize the remarkable influence of traffic emissions on urban soil metal, which can be predicated quantitatively by traffic volume.

A note on estimating safety performance functions with a flexible specification of traffic volume

In this note, a flexible approach to allow for variation in the impact of traffic volume in the estimation of Safety Performance Functions (SPFs) is proposed. The approach generalizes a recently proposed approach by Gayah and Donnell (2021) (GD) titled "Estimating safety performance functions for two-lane rural roads using an alternative functional form for traffic volume". GD approach proposes a multiple regime structure for AADT impact while explicitly constraining the impact at the regime threshold to be the same. While the GD approach provides a flexible structure, the framework as proposed calls for careful judgement for threshold selection and additional model estimation complexity for the AADT constraint. The current note establishes the equivalence of the proposed approach with the GD approach and subsequently presents a more flexible model structure that improves on the GD approach. Subsequently, we document the advantages of our proposed approach in terms of model estimation, parameter significance testing, flexibility to consider multiple traffic volume ranges and ease of accommodating random parameters for analysis. Finally, we present potential directions for future research.

Visual recognition for urban traffic data retrieval and analysis in major events using convolutional neural networks

Accurate and prompt traffic data are necessary for the successful management of major events. Computer vision techniques, such as convolutional neural network (CNN) applied on video monitoring data, can provide a cost-efficient and timely alternative to traditional data collection and analysis methods. This paper presents a framework designed to take videos as input and output traffic volume counts and intersection turning patterns. This framework comprises a CNN model and an object tracking algorithm to detect and track vehicles in the camera's pixel view first. Homographic projection then maps vehicle spatial-temporal information (including unique ID, location, and timestamp) onto an orthogonal real-scale map, from which the traffic counts and turns are computed. Several video data are manually labeled and compared with the framework output. The following results show a robust traffic volume count accuracy up to 96.91%. Moreover, this work investigates the performance influencing factors including lighting condition (over a 24-h-period), pixel size, and camera angle. Based on the analysis, it is suggested to place cameras such that detection pixel size is above 2343 and the view angle is below 22°, for more accurate counts. Next, previous and current traffic reports after Texas A&M home football games are compared with the framework output. Results suggest that the proposed framework is able to reproduce traffic volume change trends for different traffic directions. Lastly, this work also contributes a new intersection turning pattern, i.e., counts for each ingress-egress edge pair, with its optimization technique which result in an accuracy between 43% and 72%.

Spatial distribution and health risk of exposure to BTEX in urban area: a comparison study of different land-use types and traffic volumes

Many previous studies have investigated BTEX concentrations in urban areas; however, the available evidence on the association of different land-use types and BTEX concentrations is still scarce. In this study, the BTEX concentrations were measured and compared in different land-use types and traffic volumes of Mashhad metropolis, Iran. Sampling was conducted in summer and winter of 2018 based on NIOSH 1501 method in six land-use types, including Residential, Commercial/official, Industrial, Greenspace, Transportation, and Tourism. The spatial autocorrelation model was used to investigate the emission pattern. The Monte Carlo simulation technique and sensitivity analysis were used to assess the health risk of exposure to BTEX compounds. The median [interquartile range (IQR)] of benzene, toluene, ethylbenzene m-xylene, o-xylene and total BTEX concentrations based on overall mean were 4 (2.23), 8.37 (4.48), 1.2 (1.46), 0.89 (2.59), 0.8 (1.73) and 17.7 (8.19) µg/m³, respectively. Benzene and toluene had clustered emission patterns (z-score > 1.96). Exposure to benzene in the study area had a carcinogenic risk for inhabitants. The concentration of BTEX compounds was significantly different based on land-use type. The maximum and minimum concentrations of BTEX were observed in Transportation and Greenspace land uses, respectively. The BTEX concentrations in summer were significantly higher than in winter, and traffic had a significant effect on BTEX concentrations. Overall, our results supported a significant relationship between land-use type and BTEX concentrations in the urban area. Moreover, ambient benzene concentration had a carcinogenic risk potential for inhabitants of study area.

Impact of the COVID-19 pandemic on air pollution in Chinese megacities from the perspective of traffic volume and meteorological factors

During 2020, the COVID-19 pandemic resulted in a widespread lockdown in many cities in China. In this study, we assessed the impact of changes in human activities on air quality during the COVID-19 pandemic by determining the relationships between air quality, traffic volume, and meteorological conditions. The megacities of Wuhan, Beijing, Shanghai, and Guangzhou were selected as the study area, and the variation trends of air pollutants for the period January-May between 2016 and 2020 were analyzed. The passenger volume of public transportation (PVPT) and the passenger volume of taxis (PVT) along with data on precipitation, temperature, relative humidity, wind speed, and boundary layer height were used to identify and quantify the driving force of the air pollution variation. The results showed that the change rates of fine particulate matter (PM_2.5), NO₂, and SO₂ before and during the lockdown in the four megacities ranged from -49.9% to 78.2% (average: -9.4% ± 59.3%), -55.4% to -32.3% (average: -43.0% ± 9.7%), and - 21.1% to 11.9% (average: -10.9% ± 15.4%), respectively. The response of NO₂ to the lockdown was the most sensitive, while the response of PM_2.5 was smaller and more delayed. During the lockdown period, haze from uninterrupted industrial emissions and fireworks under the effect of air mass transport from surrounding areas and adverse climate conditions was probably the cause of abnormally high PM_2.5 concentrations in Beijing. In addition, the PVT was the most significant factor for NO₂, and meteorology had a greater impact on PM_2.5 than NO₂ and SO₂. There is a need for more national-level policies for limiting firework displays and traffic emissions, as well as further studies on the formation and transmission of secondary air pollutants.

Assessing the impact of COVID-19 pandemic on urban transportation and air quality in Canada

The global outbreak and spread of COVID-19 had a significant impact on the environment of urban areas. This study aimed to provide a new insight into the urban transportation and air pollutant emission of representative Canadian cities impacted by this pandemic. The consumption of urban transportation fuel was analyzed and the corresponding CO₂ emissions was evaluated. The changes in urban traffic volume and air pollutant concentrations before and after the outbreak of this pandemic was investigated. Due to the lockdown after the outbreak of COVID-19, the domestic consumption of motor gasoline and estimated CO₂ emissions from urban vehicles in Canada has continuously decreased with a lowest level in April 2020, and rebounded in May 2020. It will still take a long time to recover to pre-pandemic levels because of the upcoming second wave of pandemic and further change. The Air Quality Health Index (AQHI), level of urban congestion and concentration level of NO₂ and CO had strong relevance with the COVID-19 period while SO₂ did not show significant relation. The comprehensive analysis of changing fuel consumptions, traffic volume and emission levels can help the government assess the impact and make corresponding strategy for such a pandemic in the future.

COVID-19 restrictions provide a brief respite from the wildlife roadkill toll

The COVID-19 pandemic provides a rare opportunity to reveal the impact of reduced human activity on wildlife. I compared traffic volume and wildlife roadkill data along 18 km of highway before, during and after a 3-month period of COVID-19 restrictions with baseline data from the previous four years. Three marsupial herbivores comprised 89% of the 1820 roadkills recorded during the 4.5-year survey period: rufous-bellied pademelon Thylogale billardierii (31.5% of total), common brushtail possum Trichosurus vulpecula (29.8%) and red-necked wallaby Notamacropus rufogriseus (27.9%). During April 2020, when human activity was most restricted in the study area, traffic volume decreased by 36% (i.e. by an average 13,520 vehicle movements per day) and wildlife roadkill decreased by 48% (i.e. from 44 to 23 roadkills). However, when restrictions eased, traffic volume and wildlife roadkill returned to baseline levels indicating that the respite was brief in terms of animal welfare and of limited conservation value for these widespread and abundant species. Nevertheless, the results of this study suggest that even short periods of traffic reduction or road closures could be used as part of a management strategy for the conservation of endangered wildlife populations and re-wildling programs where roadkill is a risk factor.

Traffic noise prediction model of an Indian road: an increased scenario of vehicles and honking

Noise is considered as an underrated and underemphasized pollutant in contrast to other pollutants of the environment. Due to the non-acute response of health effects, people are not vigilant towards consequences regarding noise pollution. The expansion of the transportation industry is contributing towards the increment in the public and private vehicular volume which causes an increment in noise pollution. For evaluation of respective scenario, the research study has been conducted on one of the minor roads of Nagpur, India; for 2 years, viz., 2012 and 2019. The study concludes an increment of 5-6 dB(A) in noise level, 4-6 times in honking, and 1.7 times in traffic volume. The study confirms increment in sound pressure by 65.9% and 81.9% for the year 2012 and 2019 during morning and evening sessions, respectively. Noise prediction model has also been developed for the abovementioned years, using multiple regression analysis, considering traffic volume, honking, and speed against noise equivalent level. Honking has been further characterized into honk by light and medium category vehicles as acoustical properties of horns vary with respect to category of vehicle and introduced into the noise prediction model. Noise prediction model for 2019 has predicted the noise level in a range of - 1.7 to + 1.4 dB (Leq) with 84% of observations in the range of - 1 to + 1 dB (Leq), when compared with observed Leq on the field. For proper management of noise pollution, a noise prediction model is essentially needed so that the noise level can be anticipated, and accordingly, measures can be outlined and executed. This increased noise level has serious impacts on human hearing capacity and overall health. Accordingly, noise mitigation preventive measures are recommended to control traffic noise in the urban environment.

Traffic volume and crashes and how crash and road characteristics affect their relationship - A meta-analysis

The present study has investigated the relationship between traffic volume and crash numbers by means of meta-analysis, based on 521 crash prediction models from 118 studies. The weighted pooled volume coefficient for all crashes and all levels of crash severity (excluding fatal crashes) is 0.875. The most important moderator variable is crash type. Pooled volume coefficients are systematically greater for multi vehicle crashes (1.210) than for single vehicle crashes (0.552). Regarding crash severity, the results indicate that volume coefficients are smaller for more fatal crashes (0.777 for all fatal crashes) than for injury crashes but no systematic differences were found between volume coefficients for injury and property-damage-only crashes. At higher levels of volume and on divided roads, volume coefficients tend to be greater than at lower levels of volume and on undivided roads. This is consistent with the finding that freeways on average have greater volume coefficients than other types of road and that two-lane roads are the road type with the smallest average volume coefficients. The results indicate that results from crash prediction models are likely to be more precise when crashes are disaggregated by crash type, crash severity, and road type. Disaggregating models by volume level and distinguishing between divided and undivided roads may also improve the precision of the results. The results indicate further that crash prediction models may be misleading if they are used to predict crash numbers on roads that differ from those that were used for model development with respect to composition of crash types, share of fatal or serious injury crashes, road types, and volume levels.

Consistent patterns of vehicle collision risk for six mammal species

The occurrence and rate of wildlife-vehicle collisions are related to both anthropocentric and environmental variables, however, few studies compare collision risks for multiple species within a model framework that is adaptable and transferable. Our research compares collision risk for multiple species across a large geographic area using a conceptually simple risk framework. We used six species of native terrestrial mammal often involved with wildlife-vehicle collisions in south-east Australia. We related collisions reported to a wildlife organisation to the co-occurrence of each species and a threatening process (presence and movement of road vehicles). For each species, we constructed statistical models from wildlife atlas data to predict occurrence across geographic space. Traffic volume and speed on road segments (also modelled) characterised the magnitude of threatening processes. The species occurrence models made plausible spatial predictions. Each model reduced the unexplained variation in patterns and distributions of species between 29.5% (black wallaby) and 34.3% (koala). The collision models reduced the unexplained variation in collision event data between 7.4% (koala) and 19.4% (common ringtail possum) with predictor variables correlating similarly with collision risk across species. Road authorities and environmental managers need simple and flexible tools to inform projects. Our model framework is useful for directing mitigation efforts (e.g. on road effects or species presence), predicting risk across differing spatial and temporal scales and target species, inferring patterns of threat, and identifying areas warranting additional data collection, analysis, and study.

An estimation of vehicle kilometer traveled and on-road emissions using the traffic volume and travel speed on road links in Incheon City

The objective of this study is to estimate the vehicle kilometer traveled (VKT) and on-road emissions using the traffic volume in urban. We estimated two VKT; one is based on registered vehicles and the other is based on traffic volumes. VKT for registered vehicles was 2.11 times greater than that of the applied traffic volumes because each VKT estimation method is different. Therefore, we had to define the inner VKT is moved VKT inner in urban to compare two values. Also, we focused on freight modes because these are discharged much air pollutant emissions. From analysis results, we found middle and large trucks registered in other regions traveled to target city in order to carry freight, target city has included many industrial and logistics areas. Freight is transferred through the harbors, large logistics centers, or via locations before being moved to the final destination. During this process, most freight is moved by middle and large trucks, and trailers rather than small trucks for freight import and export. Therefore, these trucks from other areas are inflow more than registered vehicles. Most emissions from diesel trucks had been overestimated in comparison to VKT from applied traffic volumes in target city. From these findings, VKT is essential based on traffic volume and travel speed on road links in order to estimate accurately the emissions of diesel trucks in target city. Our findings support the estimation of the effect of on-road emissions on urban air quality in Korea.

Taxonomy of factors which influence heavy metal build-up on urban road surfaces

Heavy metals build-up on urban road surfaces is a complex process and influenced by a diverse range of factors. Although numerous research studies have been conducted in the area of heavy metals build-up, limited research has been undertaken to rank these factors in terms of their influence on the build-up process. This results in limitations in the identification of the most critical factor/s for accurately estimating heavy metal loads and for designing effective stormwater treatment measures. The research study undertook an in-depth analysis of the factors which influence heavy metals build-up based on data generated from a number of different geographical locations around the world. Traffic volume was found to be the highest ranked factor in terms of influencing heavy metals build-up while land use was ranked the second. Proximity to arterial roads, antecedent dry days and road surface roughness has a relatively lower ranking. Furthermore, the study outcomes advances the conceptual understanding of heavy metals build-up based on the finding that with increasing traffic volume, total heavy metal build-up load increases while the variability decreases. The outcomes from this research study are expected to contribute to more accurate estimation of heavy metals build-up loads leading to more effective stormwater treatment design.

Assessment of honking impact on traffic noise in urban traffic environment of Nagpur, India

BACKGROUND

In context of increasing traffic noise in urban India, the objective of the research study is to assess noise due to heterogeneous traffic conditions and the impact of honking on it.

METHOD

Traffic volume, noise levels, honking, road geometry and vehicular speed were measured on national highway, major and minor roads in Nagpur, India.

RESULTS

Initial study showed lack of correlation between traffic volume and equivalent noise due to some factors, later identified as honking, road geometry and vehicular speed. Further, frequency analysis of traffic noise showed that honking contributed an additional 2 to 5 dB (A) noise, which is quite significant. Vehicular speed was also found to increase traffic noise. Statistical method of analysis of variance (ANOVA) confirms that frequent honking (p < 0.01) and vehicular speed (p < 0.05) have substantial impact on traffic noise apart from traffic volume and type of road.

CONCLUSIONS

The study suggests that honking must also be a component in traffic noise assessment and to identify and monitor "No Honking" zones in urban agglomerations.