High Road Density Research Articles

Environmental and Atmospheric Influences on Academic Performance: The Role of Green Spaces, Roads, and Wildfires Around Schools and Homes in the Federal District, Brazil

Environmental characteristics, such as proximity to green spaces and exposure to roads, can significantly influence atmospheric factors like air quality. For instance, areas with abundant green spaces typically exhibit better air quality, while high road density often correlates with increased air pollution, both of which can affect students’ cognitive functioning and academic performance. This study aimed to evaluate the association between the environmental and atmospheric conditions—specifically green spaces (measured by the NDVI and green space area), roads (total road length), and wildfires—around students’ schools and homes in the Federal District (FD), Brazil, and their impact on academic performance. We analyzed data from 344,175 public school students across 256 schools in the FD, covering the years 2017 to 2020. Using a mixed-effects regression model, we investigated how neighborhood characteristics such as green spaces, road density, and wildfire frequency influence individual-level academic performance while controlling for temporal, socioeconomic, and school-specific factors. Our findings indicate that the environmental factors around schools, particularly green spaces and road density, have significant associations with academic outcomes. Specifically, a higher road density around schools was linked to lower academic performance, whereas green space presence had a generally positive impact, especially around schools. Wildfires, while negatively associated with performance around homes, had mixed effects around schools. These results underscore the importance of considering environmental and atmospheric factors in urban planning and education policy to enhance student performance.

Roads and Woody Vegetation Structure Are Associated With Chimpanzee (Pan troglodytes) Occurrence at Its Northern Range Limit

ABSTRACTAimThe purported defining features of chimpanzee (Pan troglodytes) biogeography are sometimes simplistic and contradictory, yet we rely on these metrics to shape conservation and management planning for this endangered ape. We analysed the cover and configuration of woody vegetation at the northern margins of the species' range in Senegal to elucidate key predictors of chimpanzee biogeography.LocationSoutheastern Senegal.TaxaWestern chimpanzee (Pan troglodytes verus).MethodsWe combined reconnaissance surveys and interviews with published accounts of chimpanzee presence to verify previous estimates of the range limits of the species. High‐resolution, multispectral satellite images were used to quantify the configuration of trees and shrubs at each site. Woody vegetation cover and physiognomy, river density and road density were compared within and outside of the species range with generalised linear mixed models.ResultsOur surveys and interviews support the current estimates of the northern limits of the species range. Chimpanzee‐occupied sites had more patchily‐distributed vegetation, greater patch edge complexity, and slightly higher river density than unoccupied sites. Unoccupied sites had more woody vegetation cover and higher road density. Of all predictors, roads had the highest impact on chimpanzee occupancy.Main ConclusionsIn line with previous findings, anthropogenic modifications of the landscape had a strong impact on chimpanzee occupancy, but surprisingly, woody vegetation cover was negatively associated with chimpanzee occupancy in this region. Instead, more specific metrics of vegetation configuration were positively associated with chimpanzee site occupancy, indicating that precise measures are needed for assessing chimpanzee biogeography at the landscape scale.

Fisher activity patterns show potential for behavioral adaptations to human modified landscapes

Animals alter their diel activity in response to physiological constraints and ecological conditions. Fisher (Pekania pennanti) activity is known to vary through the diel cycle and change in response to cold stress and generally through both the climatic and biological seasons. However, less is known whether thermoregulatory effects impact fisher activity in milder climates and in areas of high human disturbance. We focused on two distinct research objectives to understand the 1) physiological constraints, and 2) ecological components of fisher activity in a highly disturbed landscape with a relatively mild climate. We used accelerometer data from 34 individual live-captured fisher in Rhode Island, USA from 2021 to 2023. We found that fisher activity patterns were primarily driven by diel cycle with higher activity levels at night than during the day. We did not observe any physiological influence of ambient temperature on fisher activity; daily minimum temperatures did not constrain fisher activity in the colder months, nor did daily maximum temperatures in warmer months. We did find that female activity levels differed by breeding status with non-pregnant females having higher activity levels than pregnant females. Considering ecological components, we found fisher decreased activity levels in higher road density areas during warmer months that coincide with higher traffic volumes. For fisher living in areas with lower road densities, we saw higher activity in the breeding season and summer than in winter. In contrast, fisher living in areas with high road densities had lower activity in the breeding season and summer than in winter. We conclude that fisher largely do not shift their activity to mitigate thermoregulatory costs in areas where temperatures do not reach extremes for extended periods of time. However, our findings suggest that behavioral shifts in activity are impacted by human disturbance and fisher minimize activity in risky areas.

Development of trust-based autonomous driving framework in New Zealand

PurposeAutonomous vehicles (AVs) have the potential to transform the infrastructure, mobility and social well-being paradigms in New Zealand (NZ) amid its unprecedented population and road safety challenges. But, public acceptance, co-evolution of regulations and AV technology based on interpersonal and institutional trust perspectives pose significant challenges. Previous theories and models need to be more comprehensive to address trust influencing autonomous driving (AD) factors in natural settings. Therefore, this study aims to find key AD factors corresponding to the chain of human-machine interaction (HMI) events happening in real time and formulate a guiding framework for the successful deployment of AVs in NZ.Design/methodology/approachThis study utilized a comprehensive literature review complemented by an AV users’ study with 15 participants. AV driving sprints were conducted on low, medium and high-density roads in Auckland, followed by 15 ideation workshops to gather data about the users’ observations, feelings and attitudes towards the AVs during HMI.FindingsThis research study determined nine essential trust-influencing AD determinants in HMI and legal readiness domains. These AD determinants were analyzed, corresponding to eight AV events in three phases. Subsequently, a guiding framework was developed based on these factors, i.e. human-machine interaction autonomous driving events relationship identification framework (HMI-ADERIF) for the deployment of AVs in New Zealand.Research limitations/implicationsThis study was conducted only in specific Auckland areas.Practical implicationsThis study is significant for advanced design research and provides valuable insights, guidelines and deployment pathways for designers, practitioners and regulators when developing HMI Systems for AD vehicles.Originality/valueThis study is the first-ever AV user study in New Zealand in live traffic conditions. This user study also claimed its novelty due to AV trials in congested and fast-moving traffic on the four-lane motorway in New Zealand. Previously, none of the studies conducted AV user study on SUV BMW vehicle and motorway in real-time traffic conditions; all operations were completely autonomous without any input from the driver. Thus, it explored the essential autonomous driving (AD) trust influencing variables in human factors and legal readiness domains. This research is also unique in identifying critical AD determinants that affect the user trust, acceptance and adoption of AVs in New Zealand by bridging the socio-technical gap with futuristic research insights.

Road Extraction from Remote Sensing Imagery with Spatial Attention Based on Swin Transformer

Road extraction is a crucial aspect of remote sensing imagery processing that plays a significant role in various remote sensing applications, including automatic driving, urban planning, and path navigation. However, accurate road extraction is a challenging task due to factors such as high road density, building occlusion, and complex traffic environments. In this study, a Spatial Attention Swin Transformer (SASwin Transformer) architecture is proposed to create a robust encoder capable of extracting roads from remote sensing imagery. In this architecture, we have developed a spatial self-attention (SSA) module that captures efficient and rich spatial information through spatial self-attention to reconstruct the feature map. Following this, the module performs residual connections with the input, which helps reduce interference from unrelated regions. Additionally, we designed a Spatial MLP (SMLP) module to aggregate spatial feature information from multiple branches while simultaneously reducing computational complexity. Two public road datasets, the Massachusetts dataset and the DeepGlobe dataset, were used for extensive experiments. The results show that our proposed model has an improved overall performance compared to several state-of-the-art algorithms. In particular, on the two datasets, our model outperforms D-LinkNet with an increase in Intersection over Union (IoU) metrics of 1.88% and 1.84%, respectively.

Traffic Exposure and Breast Cancer Mortality by Area of Residence: Incorporating Clinical and Socioeconomic Data.

Incorporating clinical and environmental data holds promise for monitoring vulnerable populations at the community level. This spatial epidemiology study explores the link between traffic-related air pollution and breast cancer mortality in Seoul, using public socioeconomic and clinical data from Samsung Medical Center's registry (N=6,089). Traffic and socioeconomic status were collected from official sources and integrated for spatial analysis. The findings revealed a significant association between adult breast cancer mortality and districts with high road density, NO2 emissions, and family income (p<0.05). Significant spatial autocorrelation of residuals was observed (Moran's I test p<0.001).

Exploring the impacts of built environment on bike-sharing trips on weekends: The case of Guangzhou

Shared mobility has brought many disruptive changes to urban transportation systems all over the world. Shared bikes have proven to be among the most successful and influential travel tools in attempting to alleviate the last-mile problem – the difficulty in getting people from transportation stations to their final destinations. This study aims to investigate the impacts of built environment factors on bike-sharing trips. Although many studies have explored these impacts, most have focused on the impacts of urban function, and have paid insufficient attention to the cycling environment. This study used multi-source data, including street view images (SVIs), points of interest (POIs), digital elevation models (DEMs), and road networks, to fully identify the influences of the built environment from five dimensions. The multiscale geographically weighted regression (MGWR) method was used to investigate the impacts of the urban built environment on bike-sharing usage. The results found that high-density roads, recreational POIs, and residential POIs all had positive impacts on the volume of bike-sharing trips in residential areas on weekends, whereas urban greenness negatively impacts bike-sharing usage in parks, because of strict regulations promulgated by local governments. Moreover, the impacts of high-density street networks and residential communities had strong spatial non-stationarity, while the influences of other built environment factors, including road gradient, eye-level greenness, and urban function mixture, were demonstrated spatial stationarity. These findings can facilitate local governments’ and enterprises’ efforts to improve the cycling environment and ensure the efficient management of shared bikes.

Loss of Traditional Orchards and Its Impact on the Occurrence of Threatened Tree-Dwelling Bird Species

Horticulture is one of the land use types in agricultural landscapes, which is beneficial for nature if traditional ways of management are implemented. Orchards are affected by three negative transformations: abandonment that leads to afforestation; grubbing as a result of the cessation of fruit plantation; or intensification with the use of chemicals. In this study, changes in orchard management and structure were examined over a decade (2014–2023) in southern Poland (the Carpathians). Additionally, changes in the distribution of Syrian woodpeckers were assessed—a rare species of special concern in the European Union being a major nest hole excavator in orchards. Over a decade, trees in nearly one-fourth of orchards were removed, 15% of orchards were overgrown by forests due to abandonment, and only 40% remained unchanged. The changes were most pronounced in already abandoned orchards and many traditionally used ones. Fruit trees were grubbed in orchards in areas with a high density of people and roads, whereas succession prevailed in orchards in the vicinity of forests. During the same period, around 40% of woodpecker territories vanished, and this phenomenon was associated with tree grubbing or succession by forests. As the Syrian woodpecker requires protection in Europe, it is recommended to preserve traditional horticulture. Moreover, conservative cultivation of traditional varieties of fruit trees and agro-tourism in traditional orchards could be implemented in synergy with nature conservation.

Habitat selection of resident and non-resident gray wolves: implications for habitat connectivity

Habitat selection studies facilitate assessing and predicting species distributions and habitat connectivity, but habitat selection can vary temporally and among individuals, which is often ignored. We used GPS telemetry data from 96 Gray wolves (Canis lupus) in the western Great Lakes region of the USA to assess differences in habitat selection while wolves exhibited resident (territorial) or non-resident (dispersing or floating) movements and discuss implications for habitat connectivity. We used a step-selection function (SSF) to assess habitat selection by wolves exhibiting resident or non-resident movements, and modeled circuit connectivity throughout the western Great Lakes region. Wolves selected for natural land cover and against areas with high road densities, with no differences in selection among wolves when resident, dispersing, or floating. Similar habitat selection between resident and non-resident wolves may be due to similarity in environmental conditions, when non-resident movements occur largely within established wolf range rather than near the periphery or beyond the species range. Alternatively, non-resident wolves may travel through occupied territories because higher food availability or lower human disturbance outweighs risks posed by conspecifics. Finally, an absence of differences in habitat selection between resident and non-resident wolf movements may be due to other unknown reasons. We recommend considering context-dependency when evaluating differences in movements and habitat use between resident and non-resident individuals. Our results also provide independent validation of a previous species distribution model and connectivity analysis suggesting most potential wolf habitat in the western Great Lakes region is occupied, with limited connectivity to unoccupied habitat.

Human Health Risks and Interference of Urban Landscape and Meteorological Parameters in the Distribution of Pollutant: A Case Study of Nakhon Si Thammarat Province, Thailand

Recently, the air quality in urban areas has declined because of increasing traffic emissions. This paper aimed to determine the toxicity from exposure to pollutants among three sensitive groups of residents in urban areas. Moreover, this study also estimated the impacts of landscape and meteorological conditions on the accumulation of air pollutants in these areas. The results showed that the annual average concentration in the town exceeded the WHO air quality guidelines. Other areas that had a high traffic density also presented unacceptable levels according to the hazard quotient (HQ value ≥ 1). It was found that the air quality in the town had declined. This study also found that people living in a tropical monsoon climate should avoid exposure to air pollution in both the summer and rainy seasons; even though the pollutant concentration is lower in the rainy season, the longer exposure time causes unacceptable health risks. Humidity showed a strong impact on gas pollutant reduction (rs = −0.943). The pollutants tended to increase in areas with a high density of main roads. Additionally, building density affected the accumulation of pollutants in near-source areas and blocked pollutants in receptor areas. Therefore, this study suggests that local authorities should provide vegetation infrastructure for a sustainable air quality improvement in urban areas.

A modelling approach to inform regional cumulative effects assessment in northern Ontario

Cumulatively, the effects of resource extraction and climate change have the potential to cause an unprecedented change to the ecosystems and livelihoods of Indigenous communities in Canada’s northern regions. Maintaining environmental and community values in the presence of such change will require a comprehensive understanding of potential long-term risks and opportunities to prevent or mitigate risks at the regional level. However, assessing the cumulative impact and benefits of the multiple disturbances at the regional level is complex due to the interaction of numerous drivers, values, actors, assessment scales, planning, and decision-making processes. We develop an integrated risk and cumulative effects (CE) management methodology to inform regional assessment by combining conceptual modelling through bowtie analysis and quantitative scenario analysis using ALCES Online (AO) simulation. We applied the framework using a case example in the Ring of Fire region of northern Ontario, Canada, to assess the CE of climate change, land use change, and wildlife harvest on the moose (Alces alces)-wolf (Canis lupus)-caribou (Rangifer tarandus) prey-predator system. The bowtie risk analysis provided a mechanism to define the management problem by identifying threats that contribute to risk, associated consequences, and specific management strategies that could be pursued under legislative frameworks and changes to maintain the sustainable dynamics of the moose-wolf-caribou system (MWC). The AO simulation of different levels of development and moose harvest, as well a baseline scenario that excluded climate change and development, allowed for a comprehensive examination of the complex processes driving the social-ecological system. The scenario analysis suggests that moose harvest can promote balance in the MWC system but only if applied in a sophisticated manner that limits moose harvest in areas with high road density, increases moose harvest in more remote regions susceptible to moose population expansion with climate change, and prioritizes Indigenous moose harvest to ensure a sustainable supply of moose for subsistence harvest. The case example shows how conceptual and quantitative modelling can provide the strategic perspective required for regional assessment.

How do density, employment and transit affect the prevalence of COVID-19 pandemic? A study of 3,141 counties across the United States

Previous research has explored the effect of the built environment on the spread of the coronavirus disease (COVID-19) pandemic. This study extends the existing literature by examining the relationship between pandemic prevalence and density, employment, and transit factors at the county level. Using multilinear spatial-lag regressions and time series clustering analyses on the Smart Location Database encompassing 3141 counties in the United States, our findings reveal the following: (1) Density, employment, and transit variables yield heterogeneous effects to infection rate, death rate, and mortality rate. (2) Pedestrian-oriented road density is positively correlated to the prevalence of COVID-19, every 0.011 miles/acre increase is associated with 1% increase in the infection rate. (3) A consistent negative correlation is observed between jobs per household and infection rate, while a decrease in unemployment rate leads to an increase in the death rate. (4) The results from time series analysis suggest that areas characterized by low auto-oriented intersection density but high pedestrian-oriented road density are more susceptible to the impacts of pandemics. This highlights the need to prioritize pandemic prevention efforts in the suburban and rural areas with low population density, as emphasized in existing literature emphasized.

Greening the concrete jungle: Unveiling the co-mitigation of greenspace configuration on PM2.5 and land surface temperature with explanatory machine learning

In densely populated urban areas, the amount of urban green space (UGS) is limited and increasing it can be challenging due to the high proportion of unvegetated land. Therefore, it is crucial to determine the optimal spatial configuration of UGS to achieve environmental benefits such as reducing PM2.5 concentrations and land surface temperature (LST). However, there is limited research on this topic. This study employed an explanatory machine learning method to identify the non-linear relationships between contributing factors and the co-mitigation of PM2.5 and LST in highly-dense urban areas, which had three key advantages: improved accuracy, spatially explicit information, and enhanced understanding of complex relationships. The study found that maintaining a UGS proportion of 25–30 % was desirable for mitigating PM2.5 and LST. Additionally, maintaining an aggregation index above 97, a patch density above 1650, and a largest UGS patch proportion between 2.00 % and 4.85 % was beneficial for co-mitigation. However, the study found conflicting results between shape complexity and co-mitigation. Surprisingly, higher road density appeared to mitigate both PM2.5 and LSTs. Overall, the study highlights the potential of explanatory machine learning methods for sustainable urban environmental management, providing insights into the co-mitigation effects of UGS and urban morphology.

Environmental impact assessment of transportation and land alteration using Earth observational datasets: Comparative study between cities in Asia and Europe

Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991–2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.

Meso Safety Analysis of Major Arterials: The Impact of Intersection Spacing and Network Connection Compatibility

With the increase in high-density road network construction in many urban areas, neither macro- nor micro-level safety analysis can fully explain the impact of network characteristics on safety. This study employed a meso-level analysis unit, which combines intersections with adjacent road segments, to investigate the safety impact of intersection spacing and network connection compatibility along 19 major arterials in Suzhou, China. Three main intersection spacing characteristics, the density of intersection spacing (DOIS), the standard deviation of intersection spacing (SDOIS), and the variation of intersection spacing (VOIS), were used to represent the interaction between arterials and other roads in the network. This study introduced the concept of hierarchical differences to quantitatively analyze the safety effects of connections between roads with different accessibility and mobility functions. To manage the hierarchical structure of the data (which has both within-arterial and between-arterial correlations), a two-level hierarchical negative binomial conditional autoregressive model was adopted to examine the safety effects of network characteristics at the meso level. The results showed the existence of unobserved heterogeneities among the selected arterials. The SDOIS and VOIS were found to have positive effects on crash frequency (more crashes on units with higher SDOIS and VOIS), while the DOIS was negatively associated with crash frequency. The meso units with moderately compatible network connections were found to have more crashes than those with compatible connections, while the units with the highest hierarchical differences had fewer crashes. The results from this study are expected to provide suggestions on potential safety hazard screening and crash prevention.

Pathways from street network design to symptoms of depression among emerging adults in China

Streets comprise over 80% of all urban public space, while previous studies associated street network attributes with traffic and transport model choice, they did not examine network design in conjunction with symptoms of depression. This paper developed a path analysis model to examine the direct and indirect effects of street network designs on symptoms of depression among undergraduates. Road network density, road intersection density and public transit density were measured within 1 km buffers centered on university campuses. A survey that included the 9-item patient health questionnaire (PHQ9) addressed these effects and measured the incidence of symptoms of depression among a random sample of 22,060 Chinese undergraduates. After controlling for individual- and campus-level covariates, the results revealed that exposure to PM2.5, poor sleep quality and unhealthy dietary pattern (excluding transport-related physical activity) mediated the relationship between specific street network attributes and symptoms of depression. Higher road density was found to alleviate symptoms of depression by increasing exposure to PM2.5. Greater road connectivity tended to alleviate symptoms of depression by reducing exposure to PM2.5 but to exacerbate symptoms of depression by worsening sleep quality and increasing the incidence of unhealthy dietary patterns. Better access to public transit inclined to ameliorate symptoms of depression by improving sleep quality but to exacerbate symptoms by increasing PM2.5 exposure. These findings emphasize the need for strategies aimed at improving street network designs to include assessments of the aggregate effects on campus environs and the associated impacts on undergraduate mental health.

Upper limits for road salt pollution in lakes

AbstractWidespread and increasing use of road deicing salt is a major driver of increasing lake chloride concentrations, which can negatively impact aquatic organisms and ecosystems. We used a simple model to explore the controls on road salt concentrations and predict equilibrium concentrations in lakes across the contiguous United States. The model suggests that equilibrium salt concentration depends on three quantities: salt application rate, road density, and runoff (precipitation minus evapotranspiration). High application combined with high road density leads to high equilibrium salt concentrations regardless of runoff. Yet if application can be held at current rates or reduced, concentrations in many lakes situated in lightly to moderately urbanized watersheds should equilibrate at levels below currently recommended thresholds. In particular, our model predicts that, given 2010–2015 road salt application rates, equilibrium chloride concentrations in the contiguous United States will exceed the current regulatory chronic exposure threshold of 230 mg L−1 in over 2000 lakes; will exceed 120 mg L−1 in over 9000 lakes; and will be below 120 mg L−1 in hundreds of thousands of lakes. Our analysis helps to contextualize current trends in road salt pollution of lakes, and suggests that stabilization of equilibrium chloride concentrations below thresholds designed to protect aquatic organisms should be an achievable goal.

Risk assessment and zoning of flood disaster in Wuchengxiyu Region, China

Based on the data of physical geography and social economy, an index system of Wuchengxiyu Region was constructed with the disaster-inducing factors, disaster-pregnant environment, and disaster-bearing body as indicators by analyzing the formation and impact factors of flood disasters. The weight of each index was given using the analytic hierarchy process, and the comprehensive evaluation index was constructed by the weighted comprehensive method, and the flood disaster evaluation model was established. The flood risk zoning of Wuchengxiyu Region was implemented by means of Geographic Information System spatial analysis methods. The results showed that the high-risk areas in Wuchengxiyu Region are mainly distributed in Liangxi District, Binhu District, Xishan District of Wuxi City, Zhonglou District, Tianning District, and Xinbei District of Changzhou City, and Jiangyin City. The high-risk area in Wuxi City is highly exposed due to its low terrain. The high-risk area in Changzhou City is highly vulnerable due to the high density of population, buildings and roads. And the high-risk area in Jiangyin City is located in the high-risk area of disaster-causing factors due to its abundant rainfall.

Investigating the Nonlinear Relationship Between Car Dependency and the Built Environment

Car-dominated daily travel has caused many severe and urgent urban problems across the world, and such travel patterns have been found to be related to the built environment. However, few existing studies have uncovered the nonlinear relationship between the built environment and car dependency using a machine learning method, thus failing to provide policymakers with nuanced evidence-based guidance on reducing car dependency. Using data from Puget Sound regional household travel surveys, this study analyzes the complicated relationship between car dependency and the built environment using the gradient boost decision tree method. The results show that people living in high-density areas are less likely to rely on private cars than those living in low-density neighborhoods. Both threshold and nonlinear effects are observed in the relationships between the built environment and car dependency. Increasing road density promotes car usage when the road density is below 6 km/km&lt;sup&gt;2&lt;/sup&gt;. However, the positive association between road density and car use is not observed in areas with high road density. Increasing pedestrian-oriented road density decreases the likelihood of using cars as the main mode. Such a negative effect is most effective when the pedestrian-oriented road density is over 14.5 km/km&lt;sup&gt;2&lt;/sup&gt;. More diverse land use also discourages people’s car use, probably because those areas are more likely to promote active modes. Destination accessibility has an overall negative effect and a significant threshold effect on car dependency. These findings can help urban planners formulate tailored land-use interventions to reduce car dependency.

The role of baseflow and stormwater in transport of tire and bitumen particles in Tehran city: A dense urban environment

Urban stormwater runoff is the main terrestrial source of tire and bitumen particles as emerging pollutants, which have adverse effects on receiving aquatic and terrestrial environments. In this study, the occurrence and characteristics of tire and bitumen particles in four rainfall events and three baseflow had been measured at the end of a dense urban catchment in Tehran metropolis. Particles were classified to 37–300, 300–500, and 500–5000 μm with stainless steel sieves, and hydrogen peroxide 30% was used for digestion of organic matter, following density separation with ZnCl2 (1.7–1.75 g/mL) to separate tire and bitumen particles from minerals. The type of tire and bitumen particles was determined by Micro-Raman and FTIR ATR. The number of tire and bitumen particles in rainfall events was 3.3–60.5 and 3.5–73 particles/L, respectively, and in the base flow were 0.5–3 and 0.8–6.5 particles/L, respectively. The most abundant size of tire and bitumen particles was 37–300 μm. The highest abundance of tire and bitumen particles was observed during a rainfall event in peak discharge. The results indicate the important role of urban stormwater runoff, where there are high vehicle traffic and high road density, in the release into the environment of bitumen and rubber.