Commercial Land Use Research Articles

Exploring the built environment impacts on Online Car-hailing waiting time: An empirical study in Beijing

Waiting time is a critical metric for evaluating the quality of online car-hailing services, with studies indicating a close association between Online Car-hailing waiting time (CWT) and the urban built environment (BE). Points of interest (POI) data is widely utilized to characterize the built environment. However, quantifying the proper combination or spatial relationships between different types of POIs to capture the functional features of the built environment within a region poses a challenge. This paper proposes a framework for analyzing built environment characteristics based on a semantic probabilistic topic model utilizing Latent Dirichlet Allocation, demonstrating that integrating the building area of POIs along with the travel activity intensity at their respective locations enables a more precise identification of regional functions. Moreover, while some studies have explored the correlation between the built environment and waiting time, few have evaluated the nonlinear interactions between them. On this foundation, employing the machine learning technique XGBoost model in conjunction with online car-hailing order data, we probe the relationship between the built environment and waiting time. The research indicates that CWT is comprehensively affected by multiple factors. Taking weekday evening peak period as an example, the CWT of Central-urban area, where has dense commercial and office land use, positively correlated with commercial and office topics, while negatively correlated with educational, residential, leisure and healthcare topics, leading to a longer CWT in the Central-urban. In addition, the interaction between BEs can weakens their individual effects on CWT. A higher degree of job-residence balance can mitigate the negative impact of residential and office topics on increasing CWT, particularly in Intermediate-urban areas. Additionally, BE topics may also suppress the positive effect of road density on reducing CWT. The relationship between the BE and CWT exhibits threshold effects and a V-shaped relationship, indicating that the BE is significantly associated with CWT only within specific ranges. This correlation also exhibits a gradient pattern from the Central-urban to the Sub-urban, especially concerning office and residential topics. These outcomes elucidate the salient ranges of the built environment that exert substantial impacts on waiting time, informing strategic planning for online car-hailing dispatch and urban development to augment passenger travel satisfaction.

Association between land use features and changes in walking patterns from pre-pandemic to post-pandemic: A case study of city of Sydney (2013–2023)

While the impact of the pandemic on active mobility patterns is widely studied in several cities, the underlying characteristics that describe the heterogeneity in changes in active mobility are less understood. This is particularly important for post-pandemic active mobility planning. This study aims to investigate and describe the association between urban population and land-use features, as well as changes in the spatio-temporal patterns of walking from pre-pandemic to post-pandemic through a case study of the city of Sydney, Australia, using 11 years of pedestrian count data from 2013 to 2023. The findings indicate that during the pandemic, the average daily pedestrian traffic in Sydney decreased significantly compared to the pre-pandemic period. However, since experiencing the lowest pedestrian traffic in 2020, activities in the study area have shown signs of partial recovery, with a 51% increase observed in 2023. The observed changes in pedestrian activities are, however, spatially heterogeneous. Modeling results reveal that areas with greater commercial land use, more points of interest (POIs), higher population density, and higher network connectivity experienced a significant negative change in the number of walking trips from the pre-pandemic to the pandemic period. Areas with higher percentages of educational and residential use and with higher personal income experienced smaller changes in pedestrian activities during the pandemic compared to the pre-pandemic period. During the post-pandemic recovery, the influential features remain mostly unchanged; however, the association direction is the opposite.

Built environment predictors of walking to school in a sprawled city

ABSTRACT Walking to school may provide a regular source of daily physical activity and help to reduce the prevalence of obesity, but walking has been on the decline for decades globally; this is particularly true in Saudi Arabia, especially with the major increase in auto-dependency. This cross-sectional study of students aged 6–18 from 57 schools collected 1218 responses to examine the potential associations between the built environment and walking to school in a sprawled city, namely Najran city, Saudi Arabia. Binomial logistic regression was used to highlight important built environment predictors of walking to school. The results showed that across all participating schools, the average proportion of students walking to school was 19%. Also, the findings of the study further revealed that several built environment attributes were associated with walking to school. Walking to school was positively associated with signalized intersections, distance to the central business district (CBD), increased commercial land use, the precence of a parking lot in front of the school and denser areas within the school neighborhood. However, walking to school is also negatively associated with commuting distance, the presence of industrial use, and the presence of hilly streets in school neighborhood. No significant correlations were found between walking to school and the population density of the individual student’s home neighborhood or the number of intersections, parks, streets, and speed bumps in the school neighborhood. Overall, the findings of this study can be used to guide and support the development of new policies to improve pedestrian infrastructure and establish new, smaller schools within some neighborhoods to increase active and safe commuting to schools in Najran city, Saudi Arabia.

Navigating the blame game: Investigating automated vehicle fault in collisions under mixed traffic conditions

Vehicle being at fault in a crash has extensively been associated with its driver's behaviors and other human errors for human-driven vehicles (HDV). The introduction of automated vehicles (AVs) is expected to eliminate such human errors due to the ability of AVs to communicate with the external environment. However, various reports have documented AVs being at fault in collisions. This study applied text mining and mixed-effects logistic regression (MELR) on crash data involving AVs collected between 2017 and 2022 in California to explore the likelihood of an AV being at fault during a collision. It was found that among 497 crashes, a relatively small percentage (14.29 %) involved AVs being at fault. The text network results revealed patterns of keywords associated with the AVs being at fault. Such patterns include conventional mode of operation, area of impact, and resulting injuries. Furthermore, with about a 93 % prediction accuracy and an 83 % sensitivity score, the MELR results revealed that the likelihood of AVs being at fault increases when they are operated in conventional mode or when disengagement is involved. Moreover, turning, merging, or changing lane movements, unclear weather conditions, and operating on roadways with four or more lanes significantly increased the odds of an AV being at fault during a crash. Conversely, AVs were less likely to be at fault in commercial land use than residential land use, at intersection locations, and when the crash involved a truck. The practical implications of the findings are presented to improve AV operations.

Human-derived food shrinks home ranges and alters resource selection of mammals at the urban-wild interface

Protected lands are an important source of food, shelter, and reproductive opportunities for wildlife, especially in urban landscapes. When urban development abuts the edges of undisturbed ecosystems, synanthropic species can alter their foraging behaviors and movement to utilize human-supplemented resources throughout the urban-wild interface. Therefore, urban development on the edges of protected lands can have pronounced effects on animal movement and ecosystem functions. Iconic urban adaptive mesopredators such as northern raccoons (Procyon lotor) and Virginia opossums (Didelphis virginiana) often benefit from human-supplemented food sources such as unsecured garbage, pet food, and fresh water when available. To investigate how urban edges affect the movements of urban-adapted omnivores within conservation lands, we estimated home ranges and third-order resource selection of 27 raccoons and 12 opossums with GPS collars throughout the protected areas of northern Key Largo, FL, USA between April 2022–October 2023. The proportion of urban development in an individual's home range was the most influential factor associated with home range size, followed by species and sex. Individuals with greater proportions of residential neighborhoods and commercial areas in their home ranges exhibited smaller home ranges. Third-order resource selection functions identified both mesopredator species using residential and commercial land use areas more than they were available on the landscape. These results indicate that urban areas attract urban-adapted mesopredators from protected areas and result in smaller home ranges in the face of abundant human-derived food. Reduced home ranges on edges can support higher densities of animals, which may increase rates of disease transmission, especially when the urban borders support populations of feral domestic species. Shifting foraging behaviors from the protected areas to urban edges could have cascading downward effects if seed-dispersing roles are diluted. As urbanization increases and the distance between wild lands and human disturbance decreases, it is increasingly important to study the mechanisms of how urban development on the edges of protected areas affect the movement of wildlife.

Occurrence, Concentration, and Distribution of 35 PFASs and Their Precursors Retained in 20 Stormwater Biofilters.

Current knowledge about the fate and transport behaviors of per- and polyfluoroalkyl substances (PFASs) in urban stormwater biofilter facilities is very limited. C5-14,16 perfluoroalkyl carboxylic acids [perfluorinated carboxylic acids (PFCAs)], C4,8,10 perfluoroalkanesulfonic acids (PFSAs), methyl-perfluorooctane sulfonamide acetic acid (MeFOSAA, a PFSA precursor), and unknown C6-8 PFCA and perfluorooctanesulfonic acid precursors were frequently found in bioretention media and forebay sediments at Σ35PFAS concentrations of <0.03-19 and 0.064-16 μg/kg-DW, respectively. Unknown C6-8 PFCA precursor concentrations were up to ten times higher than the corresponding PFCAs, especially at forebays and biofilters' top layer. No significant trend could be attributed to PFAS and precursor concentrations versus depth of filter media, though PFAS concentrations were 2-3 times higher in the upper layers on average (significant difference between the upper (0-5 cm) and deepest (35-50 cm) layer). PFASs had a similar spatial concentration distribution in each filter media (no clear difference between short- and long-chain PFASs). Commercial land use and organic matter were important factors explaining the concentration variations among the biofilters and between the sampling depths, respectively. Given the comparable PFAS accumulations in deeper and superficial layers and possible increased mobility after precursor biotransformation, designing shallow-depth, nonamended sand biofilters or maintaining only the top layer may be insufficient for stormwater PFAS management.

The spatial heterogeneity of urban green space distribution and configuration in Lilongwe City, Malawi.

Urban green spaces provide several benefits related to the quality of urban life. The existence and spatial arrangement of these spaces within neighbourhoods and functional land uses have significant implications for the well-being of urban dwellers. Previous studies on green spaces in urban areas of Malawi have focused on a broader and macro-level perspective, offering insightful information on general trends in different cities. However, there is a significant research shortage in localised understanding, which requires carrying out micro-level assessments concentrating on land use zones and neighbourhoods within these cities. In this study, we used remote sensing data and landscape metrics to understand the distribution and configuration of urban green spaces in the city's neighbourhoods and functional land uses and their relationship with urban form. The study revealed that 20% of neighbourhoods fail to meet the WHO-recommended standard of 9 m2 of green space per person, with a predominant concentration of these undersupplied areas in high-density and quasi-residential zones. In addition, 56.2% of Lilongwe City's total green area was contained under functional land uses. Particularly, high-rise residential, medium-density residential, low-density residential, quasi-residential, high-rise flat area, commercial class, high-rise commercial, heavy industry, light industry, and government land use zones contained 17.3%, 12.0%, 22.2%, 12.0%, 4.1%, 6.4%, 6.1%, 5.0%, 1.6%, and 13.3% of the total green spaces in functional land uses, respectively. Importantly, this research found significant correlations between urban form metrics, namely building coverage, building density, building perimeter area ratio, road density, and the distribution and configuration of urban green spaces. This necessitates an integrated approach to urban planning and design, emphasising the importance of balancing development with green space preservation.

Next Generation Public Supply Water Withdrawal Estimation for the Conterminous United States Using Machine Learning and Operational Frameworks

AbstractEstimation of human water withdrawals is more important now than ever due to uncertain water supplies, population growth, and climate change. Fourteen percent of the total water withdrawal in the United States is used for public supply, typically including deliveries to domestic, commercial, and occasionally including industrial, irrigation, and thermoelectric water withdrawal. Stewards of water resources in the USA require estimates of water withdrawals to manage and plan for future demands and sustainable water supplies. This study compiled the most comprehensive conterminous United States water withdrawal data set to date and developed a machine learning framework for estimating public supply withdrawals and associated uncertainty for the period 2000–2020. The modeling approach provides service area resolution estimates to allow for annual and monthly water withdrawal estimation while incorporating a complex array of driving factors that include hydroclimatic, demographic, socioeconomic, geographic, and land use factors. Model results reveal highly variable and lognormally distributed per‐capita water withdrawal, spanning from 30 to 650 gallons per capita per day (GPCD), across community, regional, and national scales, with pronounced seasonal variations. Analysis of estimated withdrawal trends indicates that the national annual average withdrawal experienced a decline at a rate of 0.58 GPCD/year during the period from 2000 to 2020. Model interpretation reveals a complex interplay between public supply withdrawal and key predictors, including population size, warm‐season precipitation, counts of large buildings and houses, and areas of urban and commercial land use. The developed models can forecast future public supply driven by various climate, demographic, and socioeconomic scenarios.

Investigating the influence of streetscape environmental characteristics on pedestrian crashes at intersections using street view images and explainable machine learning

Examining the relationship between streetscape features and road traffic accidents is pivotal for enhancing roadway safety. While previous studies have primarily focused on the influence of street design characteristics, sociodemographic features, and land use features on crash occurrence, the impact of streetscape features on pedestrian crashes has not been thoroughly investigated. Furthermore, while machine learning models demonstrate high accuracy in prediction and are increasingly utilized in traffic safety research, understanding the prediction results poses challenges. To address these gaps, this study extracts streetscape environment characteristics from street view images (SVIs) using a combination of semantic segmentation and object detection deep learning networks. These characteristics are then incorporated into the eXtreme Gradient Boosting (XGBoost) algorithm, along with a set of control variables, to model the occurrence of pedestrian crashes at intersections. Subsequently, the SHapley Additive exPlanations (SHAP) method is integrated with XGBoost to establish an interpretable framework for exploring the association between pedestrian crash occurrence and the surrounding streetscape built environment. The results are interpreted from global, local, and regional perspectives. The findings indicate that, from a global perspective, traffic volume and commercial land use are significant contributors to pedestrian–vehicle collisions at intersections, while road, person, and vehicle elements extracted from SVIs are associated with higher risks of pedestrian crash onset. At a local level, the XGBoost-SHAP framework enables quantification of features’ local contributions for individual intersections, revealing spatial heterogeneity in factors influencing pedestrian crashes. From a regional perspective, similar intersections can be grouped to define geographical regions, facilitating the formulation of spatially responsive strategies for distinct regions to reduce traffic accidents. This approach can potentially enhance the quality and accuracy of local policy making. These findings underscore the underlying relationship between streetscape-level environmental characteristics and vehicle–pedestrian crashes. The integration of SVIs and deep learning techniques offers a visually descriptive portrayal of the streetscape environment at locations where traffic crashes occur at eye level. The proposed framework not only achieves excellent prediction performance but also enhances understanding of traffic crash occurrences, offering guidance for optimizing traffic accident prevention and treatment programs.

Development of a Land Price Model for a Medium Sized Indian City

Land price plays a crucial role in the development of a region, which serves as an indicator of the features of a property. The primary goal of this paper is to explore the relationship between land price and various geographical and accessibility parameters and thereby arrive at a model to predict the land price. Based on literature surveys, the parameters that influence land price are identified further through which data collection from primary surveys, the creation of a road network map, a geographic information system (GIS) analysis to determine the distance to the central business district (CBD), measurement of road density and access road width, assessment of employment opportunities through establishment surveys, and identification of various land use parcels in the study region are accomplished. The land prices are collected from recently sold parcels in each of the zones in the study region. A negative and significant correlation is observed between land price and distance to the CBD. Positive correlations are observed between land price and other factors considered, such as road density, availability of educational facilities, employment opportunities, and the extent of commercial and residential land use areas. A non-linear regression model is developed that can predict land price depending on the significant parameters.

The effect of climatic variables and techno-structural factors on the water pollution caused by nicotine leakage from littered cigarette butts.

Nicotine is one of the main pollutants in cigarette smoke that trapped in the filter. Nicotine leakage from cigarette butts is one of the concerns associated with this hazardous waste. In this study, the concentration of nicotine leaked from cigarette butts into the urban environment was investigated under the influence of three parameters including rainfall, density, and the durability of the littered cigarette butt. The leakage of nicotine into the environment was estimated in six scenarios based on the difference in the durability of cigarette butts in the urban environment and the humidity ratio. The results showed that the density of littered cigarette butts was 0.0019-0.294 number/m2. The density of littered cigarette butts in residential land-use was 52.38% lower than commercial land-use. Nicotine leakage from cigarette butts in commercial, residential, and recreational land-uses was 1.1, 0.484, and .0065 mg/m2, respectively. In the studied scenarios, in the best and worst case, 335,070 and 481,950 gr/year of nicotine will leak from the cigarette butt into the water resources. Considering that cigarette butts are a serious source of toxins including nicotine to the environment, control measures are necessary to reduce its density in the urban environment.

Gentrification of Communities Surrounding Chedi Luang Worawiharn Temple, Sri Phum Subdistrict, Muang District, Chiang Mai Province

Modification of building structure and use by new investors to achieve increased economic returns by pushing former residents out of the areas is the process of gentrification.The purpose of this article is to investigate the gentrification of communities surrounding Chedi Luang Worawiharn Temple, Sri Phum subdistrict, Muang district, Chiang Mai province, and to analyze changes in the gentrification phenomenon during the coronavirus disease 2019 situation. Data collection was composed of physical surveying, interviews, and questionnaires. Three major groups were interviewed including 18 persons from the original community, 15 newcomers, and 10 community leaders and government officials. The 268 questionnaires were sampled in three communities including the Phra Chao Mengrai Samakkhi community, the Chang Taem community, and the Phranon community. The study found that there is mostly commercial land use in all three communities. Most of the new occupants used to live in the central region. The study area has three stages of gentrification. The development process begins with the first stage, early development, and then completes development in the second stage, and lastly to the third stage, which is the community's adaptation to the emergence of the gentrification phenomenon. The study also discovered that, before the emergence of coronavirus disease in 2019, the communities were significantly impacted by the economy due to gentrification, such as increased living costs and rent/land prices. While the rise of commercial and service businesses is a beneficial result of gentrification. During coronavirus epidemics, certain localities witnessed population declines whereas land prices climbed with a stagnant economy, increased speculation, and a drop in commerce and services. However, gentrification improves better relationships between former inhabitants and newcomers for overcoming the 2019 coronavirus disease outbreak.

Associations of neighbourhood attributes with depression in mid-age and older adults: the moderating role of traffic-related air pollution and neighbourhood socioeconomic status

ABSTRACT We aimed to examine whether features of the natural, built and socioeconomic neighbourhood environments were related to depressive symptoms in Australian mid-age and older adults; and whether traffic-related air pollution (TRAP) (NO2) and area-level SES moderated the associations. A total of 4141 of participants (M age = 61 years) were included in this cross-sectional study. Neighbourhood environmental characteristics included percentages of parkland, blue space and commercial land use, population density (persons/hectare), street intersection density (intersections/km2) within 1 km residential buffers. Annual mean concentrations of nitrogen dioxide (NO2) were included. In total-effect models, greater street intersection connectivity was associated with fewer depressive symptoms. Population density was positively associated with depressive symptoms in direct-effect models. Residents from disadvantaged areas with low levels of NO2 and with greater commercial land use coverage reported fewer depressive symptoms. In contrast, residents were more likely to report depressive symptoms with greater coverage of commercial land use in high SES neighbourhoods with low levels of NO2. Street connectivity and population density play a role in the maintenance of mental health in older adults. NO2 and area-level SES are moderators of commercial land use-depression associations, and this knowledge can better inform planning of commercial destinations and facilities in urban environments.

Spatial Transformation Analysis in Menganti Subdistrict: A Study of Peri-Urban Area Typologies in the Face of Urban Expansion

The swift urbanization of Indonesian cities, notably Surabaya, has spurred population growth and a heightened demand for land, leading to the expansion of urban areas into peri-urban regions like Menganti District. This district, bordering Surabaya, experiences significant land-use change shaped by the city’s expansion, making it a compelling area for analysis due to its complex dynamics. This study aims to analyze Menganti’s peri-urban spatial transformation and its typology based on the land-use triangle continuum theory. Using indicators like land-use proportions, building density, and educational and healthcare facility distribution, the analysis delineates four zones: Urban fringe, Urban-Rural fringe, Rural-Urban fringe, and Rural-fringe zones. The analysis further involves comparing land-use maps from 2017 and 2022, revealing that Urban fringe and Rural fringe zones primarily witness residential land use growth, while Urban-Rural fringe and Rural-Urban fringe display a mix of residential, industrial, commercial, and warehouse land use growth. Analyzing factors influencing spatial transformation in Menganti’s peri-urban area through Delphi and AHP techniques underscores elements like trend of land-use change, road networks, trade facilities, distance from the district center and Surabaya city, and land prices as pivotal influences. Understanding these dynamics aids in comprehending the evolving landscape and guiding sustainable development strategies in peri-urban regions experiencing urban expansion.

Assessment of Societal Health Risks: Spatial Distribution and Potential Hazards of Toxic Metals in Street Dust Across Diverse Communities

On a global scale, the urban design of city centers is a topic of discussion concerning various aspects such development and its impact on public health. This research examines the health effects of urban agglomeration in city centers with compact, close development. In this work, the potentially toxic metals in street dust were studied by collecting and measuring street dust samples, measuring trace metal concentrations, and using index assessment, spatial analysis, correlation analysis, and health risk assessment models. Eskişehir, located in the part of Central Anatolia close to the Aegean region, west Türkiye, has been widely recognized as one of the most popular, known for having many narrow and old buildings in urban environments. The present paper investigates the atmospheric dust-related chemical speciation, urban environmental pollution, and human health risks in Eskişehir City by studying 66 dust samples collected at 11 points in the selected streets in August 2023. the study found that the concentrations of trace elements followed the order Cr > Ni > Pb > Cd > Cu. The primary source of these high levels is believed to be traffic-related contamination involving Cd, Pb, and Ni. The assessment of non-carcinogenic health risks has shown that the significant sources of potential toxic metals exposure for both children and adults are i) through ingestion and ii) dermal contact. The Hazard index (HI) for selected metals decreased in the order Cr > Pb > Ni > Cd > Cu for both children and adults without imposing possible non-carcinogenic risk (HI<1). On the contrary, Cr posed cancer risks above the safety threshold (> 10-4) through ingestion. Based on the available findings, Eskişehir still suffers from considerable environmental and ecological degradation and severe health risks due to street dust contamination. However, while high pollution was detected in the city center, where there is commercial land use, low values were observed in the region, rich with the water surface, bicycle paths, landscape design, and where traffic is slowed down.

Exploring an Interaction Model for Land Used Intensity-traffic Congestion

Traffic flow is a result of the connection between the derived demand and land use. The derived demand varies in space and time, this study explores how traffic congestion is correlated with land use patterns. Historically, statistical models were used to predict and analyze these patterns. The methodology of this study is to investigate this interaction by statistical methods such as linear regression modeling. This analysis was performed using various land use types that could influence the demand. From the regression analysis, the best influence variables that affect the model are land use variables. The strong statistical parameter is commercial land use, which affects traffic volume, and causes the highest traffic congestion. In addition, correlation values are negative, meaning that as commercial land use increases, traffic flow increases and road capacity decreases. When modeling with the commercial land use variable, we conclude the value of R-Squared = 0.87 and that the relationship is an inverse strong relationship between traffic volumes and commercial land use. Mostly, land use govern traffic demand.

Dynamics of Population and Land Use towards Sustainable Development in Patna Planning Area

Abstract: This study examines the impact of population growth on the land use pattern of Patna Planning Area, in the context of urban geography. Patna, the capital city of Bihar state in India, has experienced significant population growth over the past few decades, leading to changes in its land use pattern. The objectives of this research include analyzing the historical population growth trends in Patna, identifying the key factors that have contributed to this growth, and assessing its impact on land use. The study takes into account various factors such as residential, industrial, commercial, and agricultural land use, as well as the infrastructural developments associated with population growth. To achieve these objectives, both primary and secondary data sources are utilized. Primary data is collected through surveys, interviews, and observations, while secondary data is obtained from existing literature, government reports, and demographic databases. The findings of this study reveal that the population growth in Patna has led to a changing land use pattern. The expansion of residential areas and the conversion of agricultural land into residential and commercial zones are noted as significant transformations. Additionally, industrial growth has resulted in the emergence of new industrial zones and the conversion of agricultural land into industrial areas in the suburbs. Furthermore, it is observed that population growth has put pressure on infrastructure such as transportation, sewage and water supply systems, leading to the need for their expansion and development. This study highlights the need for urban planning and policies to accommodate the growing population while ensuring sustainable land use.

Dynamic impact assessment of urban floods on the compound spatial network of buildings-roads-emergency service facilities

There are complex interactions among urban spatial elements in rainstorm scenarios. Road interruptions may cause isolation between partially submerged buildings and emergency service facilities, thereby affecting the recovery capability of flooded buildings and the accessibility of emergency service facilities. This study constructed a compound spatial network of urban buildings-roads-emergency service facilities, and analyzed the complex dynamic impacts of time-varying dynamic floods on building risk, road risk, and emergency service accessibility. Firstly, a refined flood risk assessment at the building scale was carried out in combination with flood inundation, building vulnerability, population, vulnerable areas, underground buildings, and life facilities. Secondly, betweenness centrality indicator in complex networks was used to calculate road traffic capacity and collapse threshold, and the accessibility of emergency service facilities is calculated based on road traffic capacity. Finally, the interaction and feedback relationships between the compound spatial network were analyzed. The results show that: (1) Flooded building comprehensive risk analysis reveals that floods have a significant impact on residential and commercial land use, and high-risk flood areas are mainly concentrated in the central region of study area. (2) The road network has a collapse threshold, and the fluctuating state of emergency service accessibility is significantly influenced by road traffic capacity. (3) Roads act as “bridges” connecting buildings and emergency service facilities, leading to the redistribution of building flood comprehensive risk and emergency service accessibility, presenting complex dynamic changes over time. Additionally, submerged roads also affect the accessibility of emergency service facilities around flooded buildings, reducing the recovery capacity of flooded buildings, and exhibiting significant heterogeneity characteristics. Future research should consider the complex impacts of floods on urban elements in order to better manage dynamic flood risk.

Herbicides and pesticides synergistically interact at low concentrations in complex mixtures

Assessing a complex mixture of pesticides at the impacted sites has been challenging for risk assessors for 50 years. The default assumption is that at low concentrations, pesticides interact additively with one another; thus, the risk posed by each component of a complex mixture could be simply added up. The EPA interaction-based hazard index (HIInteraction) modifies this assumption using a binary weight-of-evidence (BINWOE). However, data gaps often preclude HIInteraction use at most sites. This study evaluated these assumptions using the BINWOE to estimate the hazard index (HI) of select pesticide mixtures. The lack of in vivo binary interaction data led us to use a cell line, SH-SY5Y, to obtain the data necessary for the BINWOE approach. In the risk assessment, we considered the most active exposure scenario inhaling a mixture of volatile pesticides from contaminated soil and groundwater. The potential interactions between pesticides in 15 binary mixtures were investigated using the MTT assay in SH-SY5Y cells. Our findings showed that 60% of the binary mixtures elicited synergism (in at least one concentration), 27% displayed antagonism, and 13% showed additive effects in SH-SY5Y cells. Combining human safety data with in vitro interaction data indicated that adults and toddlers were at the highest risk when considering industrial and commercial land use, respectively, compared to other subpopulations. Incorporating interaction data into the risk assessment either increased the risk by up to 20% or decreased the risk by 2%, depending on the mixture. Our results demonstrate the predominant synergistic interactions, even at low concentrations, altered risk characterization at the complex operating site. Most concerning, organochlorine pesticides with the same mechanism of action did not follow dose additivity when evaluated by SH-SY5Y cell lines. Based on our observations, we caution that current HI methods based on additivity assumptions may underestimate the risk of organochlorine mixtures.

Driving Stress Partial Model Considering Time Domain Applying for Two Wheelers Drivers: A Case of Primary Arterial Roads

The physiological response of motorcyclists, which is substantially correlated with driving stress, can be utilised to assess traffic safety. Heart rate and respiratory rate can be used to measure driver tension using the heart rate variability method. This investigation was conducted in Aceh province along the urban arterial roads Jl. T. Nyak Arief and Jl. T. Muhammad Daud Bereueh. This study seeks to provide the anticipated empirical findings for determining the stress threshold of motorcyclists on Banda Aceh's primary arterial roads. The Polar Vantage V2 Smartwatch Multisport measures the pulse rate and respiratory rate of voluntarily participating motorcycle riders. This research was conducted using direct observation and direct observation related to volunteer travel activities as a sample, with volunteers selected at random and questioned about their willingness to participate. Direct interview surveys with volunteers were used to collect socioeconomic information about volunteers. The Multiple Indicators Multiple Causes (MIMIC) model empirically examined which road conditions and land use variables influence driving tension among motorists. The results revealed that the highest heart rate (HR) was recorded in the commercial land use area, at 94.029 beats per minute, while the highest HR was recorded at road bends, at 94.244 beats per minute. The time domain stress model is influenced by land use variables, with the most significant types of land use being offices and education, with the forming variables consisting of SDNN, RMSSD, and TINN.