Urban Traffic Areas Research Articles

Environmental Noise Mapping: GIS-Based Study of Urban Industrial Zones and Traffic Areas

Urbanization and industrialization present significant challenges to public health, particularly in managing environmental noise pollution. This study addresses the critical issue of noise pollution at the study area by developing a comprehensive noise map that encompasses industrial zones, construction sites, and areas with heavy traffic. Utilizing ArcGIS modeling software, the research visualizes the spatial dynamics of noise pollution, resulting in a detailed map that represents noise levels throughout the area. The analysis reveals that noise levels during weekday peak hours average 70.5 dBA, markedly higher than the 65.5 dBA average recorded during weekend peak hours. During weekdays morning peak, noise levels indicate three monitoring points in the red zone, two in the yellow zone, and five in the green zone. Notably, noise levels at all ten monitoring locations during weekdays peak hours exceeded the maximum permissible sound level of 60 dBA set by the Department of Environment for residential and commercial areas. These findings contribute to the development of targeted noise mitigation strategies and serve as a valuable resource for environmental authorities, policymakers, and urban planners. This research underscores the importance of a comprehensive approach to addressing environmental noise pollution and provides a practical framework for urban areas facing similar challenges.

Trends of Atmospheric Black Carbon Concentration over the Bengaluru

This study investigates the spatial and temporal variations of atmospheric black carbon (BC) concentrations in Bangalore, focusing on the period from June 2018 to May 2020. BC mass concentrations were measured using a portable micro-Aethalometer (model: AethLabs AE-51) at seven different locations, representing a range of land use types from rural to urban areas. The study aimed to assess BC pollution levels, identify hotspots, and understand the factors influencing BC concentrations. The results revealed significant variations in BC concentrations among the different locations. The highest BC concentrations were observed in urban traffic and industrial areas, with average concentrations of 6.21 µg/m³ and 6.78 µg/m³, respectively. Urban centres also exhibited high BC levels, with an average concentration of 7.40 µg/m³. In contrast, rural areas recorded the lowest BC concentrations, with an average of 2.51 µg/m³. These findings highlight the influence of anthropogenic activities and land use patterns on BC pollution levels. Diurnal variations in BC concentrations exhibited distinct patterns, with significant peaks during morning and evening hours, primarily associated with increased traffic emissions. The study also identified seasonal trends, with higher BC concentrations observed during winter months, attributed to stable atmospheric conditions and increased fuel consumption for heating purposes. Notably, BC concentrations were generally lower during summer months due to higher wind speeds and increased vertical mixing of pollutants. The findings of this study emphasize the need for targeted mitigation strategies to reduce BC pollution in Bangalore. The identified hotspots, such as urban traffic and industrial areas, require specific attention to mitigate the adverse health and environmental effects of BC emissions. Furthermore, the study underscores the importance of comprehensive air pollution management approaches that consider both local emissions and meteorological factors to effectively tackle BC pollution.

Influence of PAHs and Genetic Polymorphisms on Urinary PAHs Metabolite and DNA Damage among Malaysian Children in Urban Traffic Area

Influence of PAHs and Genetic Polymorphisms on Urinary PAHs Metabolite and DNA Damage among Malaysian Children in Urban Traffic Area

Feasibility of utilization of recycled HDPE in manhole covers for urban traffic areas and industrial zones

The consumption of plastic raw materials has been exponentially growing throughout the world in the last decade and so has, in the same proportion, their associated waste. Recycling through the mechanical treatment of the heavy fraction of these plastics is the most economic and the safest alternative to use this waste and to recirculate it for reuse within the same industry or for other related industries located near the place where the waste is generated. In this paper, a study of characterization and post-processing of recycled high density polyethylene received by a hazardous waste manager in southern Spain is presented, an area in which containers, Intermediate Bulk Containers, and drums, originally used for oil products from refineries and petrochemical industries nearby, are employed. These high density polyethylene materials are validated to meet the requirements of the harmonized standards to be used as self-supply in the production of manhole covers. EN 124–6:2015 standard allows the use of plastic materials such as PE or PP, even using material externally reprocessed, such as material derived from thermoplastic products that have not been previously used as lids or manhole covers. Class A-15 and B-125 of the standard are oriented for the construction of these types of elements to cover or close in certain urban spaces such as pedestrian areas, parking areas or multi-story car parks for vehicles. The study shows that the recycled material fully complies with the density; it keeps unalterable during fire test after application of the coating, there are no adverse effects on artificial ageing; the decrease of the average value of the tensile impact strength by 21.84%; and achieves 6 mm of maximum deflection against the test load Fp (45.3 MPa). Therefore, it can be considered a valid scenario for the effective use of solid plastic waste in products that make up part of the urban or industrial areas, as an example of complete circularity material and offers a valid alternative to the end-of-waste condition.

APPLYING MACHINE LEARNING AND OBJECT DETECTION FORIDENTIFYING TRAFFIC RULE VIOLATION: OVER SPEEDING

We have observed that traffic offences are manually inspected by the traffic department in many developing nations, such as India. Such systems enable traffic management and traffic law enforcement. Following this, the present study focuses on Traffic violations as one of the key issues hindering the efficient operations and safety aspects in the urban traffic area. Currently, traffic violations are controlled using enforcement measure that demands a high manual workforce, and resource which entails high economic cost. Contrary, the use of automatic traffic violation detection systems in many of the urban cities of the developed and developing countries is being implemented for a higher degree of traffic enforcement and adherence to the traffic operation rules. Following this the present study focus on the applicationof machine learning for automatic traffic violation detection for weak- lane disciplined mixed traffic conditions. The resulting exhibit that the developed model can detect traffic violations of over-speeding at an accuracy of 95%. The traditional methods like speed camera use the basic principle of the Doppler Effect and RADAR technologies but at present,Due to their drawbacks which vary from the expense of purchasing equipment, increase in work and labour costs, and lack of monitoring, they are ineffective. As a result, it is a straightforward, affordable, and practical speed detecting method in Mixed traffic conditions. So here, we prefer automatic speed violation detection. The suggested solution will be highly accurate and cost-efficient.

Occurrence and risk assessment of PAHs from athletic fields under typical rainfall events.

Six polycyclic aromatic hydrocarbons (PAHs) including naphthalene (Nap), fluorene (Flu), phenanthrene (Phe), fluoranthene (Fla), pyrene (Pyr), and chrysene (Chr) were detected in runoff from five athletic fields during three rainfall events. The event mean concentration (EMC) of ∑6PAHs ranged from 3.96 to 23.23 μg/L, which was much higher than the EMC in urban traffic area runoff. Except for Nap, the PAH concentrations followed in the order of artificial turf > badminton court > basketball court > plastic runway > optennis court. The surface characteristics of the athletic fields, such as the composition of materials and roughness, played an essential role in the release of PAHs. ∑6PAHs concentration during the 2nd rainfall event (July 22nd) was the highest among the three rainfall events, indicating that high rainfall intensity facilitated the PAHs release. PAHs during three rainfall events showed little first flush effect except for the artificial turf during the 2nd (22nd July) and 3rd (29th July) rainfall events. The first flush effect could be affected by rainfall characters, PAH properties, and surface characteristics of athletic fields. Ecological risk assessment showed that PAHs in runoff corresponded to moderate-to-high risk, while health risk assessment showed that PAHs could pose a potential carcinogenic danger to human health via dermal contact.

Multi-output machine learning model for regional air pollution forecasting in Ho Chi Minh City, Vietnam

Air pollution concentrations in Ho Chi Minh City (HCMC) have been found to surpass the WHO standard, which has become a very serious problem affecting human health and the ecosystem. Various machine learning algorithms have recently been widely used in air quality forecasting studies to predict possible impacts. Training and constructing several machine learning models for different air pollutants, such as NO2, SO2, O3, and CO forecasts, is a time-consuming process that necessitates additional effort for deployment, maintenance, and monitoring. In this paper, an effort has been made to develop a multi-step multi-output multivariate model (a global model) for air quality forecasting, taking into account various parameters such as meteorological conditions, air quality data from urban traffic, residential, and industrial areas, urban space information, and time component for the prediction of NO2, SO2, O3, CO hourly (1 h to 24 h) concentrations. The global forecasting model can anticipate multiple air pollutant concentrations concurrently, based on past concentrations of covariate characteristics. The datasets on air pollution time series were gathered from six HealthyAir air quality monitoring sites in HCMC between February 2021 and August 2022. Darksky weather provided the hourly concentrations of meteorological conditions for the same period. This is the first model built using real-time air quality data for NO2, SO2, CO, and O3 forecasting in HCM city. To assess the effectiveness of the proposed model, it was evaluated using real data from HealthyAir stations and quantified using Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and correlation indices. The results show that the global air quality forecasting model beats earlier models built for air quality forecasting of each specific pollutant in HCMC.

Ten Years Variation of Air Quality over An Giang Province, Vietnam

The study aimed to assess the evolution of ambient air quality in An Giang province during 2010-2020. Air samples collected in urban areas, traffic, tourism, industrial clusters, brick kilns, quarries, landfills, and background areas were analyzed for temperature (T), noise, total suspended particulates (TSP), carbon monoxide (CO), nitrogen dioxide (NO2), sulfur dioxides (SO2), ozone (O3), ammonia (NH3), hydrogen sulfide (H2S), and hydrogen fluoride (HF). The findings were compared with the Vietnamese standard on air quality. Pearson correlation and principal component analysis (PCA) were performed to recognize the correlation between pollutants and potential pollution sources. The results showed that the average values of air quality parameters in An Giang province during the study period temporally fluctuated but within the allowable limits. Moreover, the air quality parameters were well correlated, especially temperature and ozone. Three PCs obtained from PCA could explain 87.50% of the air quality variations with a significant contribution of all parameters. It is found that CO, NO2 and SO2 originated mainly from traffic and urban areas. While NH3 and H2S were dominant in the landfill areas, HF was from brick kilns. Consequently, the traffic and urban areas are considered the most influential contributors to air pollution in the study area.

Study on Identification and Prevention of Traffic Congestion Zones Considering Resilience-Vulnerability of Urban Transportation Systems

In order to solve the problem of urban short-term traffic congestion and temporal and spatial heterogeneity, it is important to scientifically delineate urban traffic congestion response areas to alleviate regional traffic congestion and improve road network efficiency. Previous urban traffic congestion zoning is mostly divided by urban administrative divisions, which is difficult to reflect the difference of congestion degree within administrative divisions or traffic congestion zoning. In this paper, we introduce the Self-Organizing Feature Mapping (SOFM) model, construct the urban traffic congestion zoning index system based on the resilience and vulnerability of urban traffic systems, and establish the urban traffic congestion zoning model, which is divided into four, five, six, and seven according to the different structures of competition layer topology. The four vulnerability damage capacity indicators of traffic volume, severe congestion mileage, delay time and average operating speed, and two resilience supply capacity indicators of traffic systems, namely, road condition and number of lanes, are used as model input vectors; the data of Guiyang city from January to June 2021 are used as data sets to input four SOFM models for training and testing and the best SOFM model with six competitive topologies is constructed. Finally, the Support Vector Machine (SVM) is used to identify the optimal partition boundary line for traffic congestion. The results show that the four models predict the urban traffic congestion zoning level correctly over 95% on the test set, each traffic congestion zoning evaluation index in the urban area shows different obvious spatial clustering characteristics, the urban traffic congestion area is divided into six categories, and the city is divided into 16 zoning areas considering the urban traffic congestion control types (prevention zone, control zone, closure control zone). The spatial boundary is clear and credible, which helps to improve the spatial accuracy when predicting urban traffic congestion zoning and provides a new methodological approach for urban traffic congestion zoning and zoning boundary delineation.

Relevance of tyre wear particles to the total content of microplastics transported by runoff in a high-imperviousness and intense vehicle traffic urban area.

Microplastics (MPs) are an emerging pollutant and a worldwide issue. A wide variety of MPs and tyre wear particles (TWPs) are entering and spreading in the environment. TWPs can reach waterbodies through runoff, where main contributing particulate matter comes from impervious areas. In this paper, TWPs and other types of MPs that were transported with the runoff of a high populated-impervious urban area were characterised. Briefly, MPs were sampled from sediments in a stormwater detention reservoir (SDR) used for flood control of a catchment area of ∼36 km2, of which 73% was impervious. The sampled SDR is located in São Paulo, the most populated city in South America. TWPs were the most common type of MPs in this SDR, accounting for 53% of the total MPs; followed by fragments (30%), fibres (9%), films (4%) and pellets (4%). In particular, MPs in the size range 0.1 mm–0.5 mm were mostly TWPs. Such a profile of MPs in the SDR is unlike what is reported in environmental compartments elsewhere. TWPs were found at levels of 2160 units/(kg sediment·km2 of impervious area) and 87.8 units/(kg sediment·km street length); MP and TWP loadings are introduced here for the first time. The annual flux of MPs and TWPs were 7.8 × 1011 and 4.1 × 1011 units/(km2·year), respectively, and TWP emissions varied from 43.3 to 205.5 kg/day. SDRs can be sites to intercept MP pollution in urban areas. This study suggests that future research on MP monitoring in urban areas and design should consider both imperviousness and street length as important factors to normalize TWP contribution to urban pollution.

Urban ecosystems and heavy rainfall – A Flood Regulating Ecosystem Service modelling approach for extreme events on the local scale

Increasing urbanisation in combination with a rise in the frequency and intensity of heavy rain events increase the risk of urban flooding. Flood Regulating Ecosystem Services (FRES) address the capacity of ecosystems to reduce the flood hazard and lower damage. FRES can be estimated by quantification of supply (provision of a service by an ecosystem) and demand (need for specific ES by society). However, FRES for pluvial floods in cities have rarely been studied and there is a gap in research and methods on FRES supply and demand quantification. In this study, we assessed FRES of an urban district in the City of Rostock (Germany) for a one-hour heavy rainfall event using the hydrological model LEAFlood. The hydrological model delivered the FRES supply indicators of soil water retention and water retained by canopies (interception). An intersection of the potential demand (based on indicators of population density, land reference value, monuments and infrastructure) and the modelled surface water depth revealed the actual demand. Comparing the actual demand and supply indicated the budget of FRES to identify unmet demand and supply surplus. Results show highest mean FRES supply on greened areas of forests, woodlands and green areas, resulting in a supply surplus. Whereas, sealed areas (paved surface where water cannot infiltrate into the soil), such as settlements, urban dense areas, traffic areas and industry, have an unmet demand resulting from low supply and relatively high actual demand. With the hydrological model LEAFlood, single landscape elements on the urban scale can be evaluated regarding their FRES and interception can be considered. Both are important for FRES assessment in urban areas. In contrast to flood risk maps, the study of FRES gives the opportunity to take into account the contribution of nature to flood regulation benefits for the socio-economic system. The visualisation of FRES supply and demand balance helps urban planners to identify hotspots and reduce potential impacts of urban pluvial flooding with ecosystem-based adaptations.

A new ensemble spatio-temporal PM2.5 prediction method based on graph attention recursive networks and reinforcement learning

Inhalable particulate matter with a diameter of less than 2.5 μm spatio-temporal prediction technology is an important tool for environmental governance in urban traffic congestion areas. A new Ensemble Graph Attention Reinforcement Learning Recursive Network is proposed to create a multi-data-driven spatio-temporal prediction method with excellent application value. The modeling process includes three basic steps. In step I, the graph attention network is used to effectively aggregate the spatio-temporal correlation characteristics of the original air pollutant data. In step II, the features extracted from the graph attention network are transferred to the long short-term memory network and the temporal convolutional network and the prediction models are constructed respectively. In step III, the reinforcement learning algorithm effectively analyzes the adaptability of the two different models to the data sets and realizes ensemble based on continuous optimization of weights. By comparing the experimental results of the listed cases, the following points can be summarized: (a) the graph attention network can effectively aggregate the spatio-temporal correlation characteristics of the original data and optimize the performance of the predictor. (b) The reinforcement learning algorithm effectively realizes the integration of several neural networks and improves the comprehensive adaptability and generalization capabilities of the model. (c) The proposed model in this paper has great application potential and value in spatial and temporal prediction and has achieved better performance than the other 25 benchmark models.

Assessment of Road Noise Pollution in Urban Residential Areas—A Case Study in Piteşti, Romania

The paper addresses the problem of urban road noise, in the context of the European legislative requirements regarding noise pollution. The second noise mapping in Pitesti city, revealed that despite the proposed action plan after the first noise mapping, the noise pollution increased instead of decreasing. Considering that the proposed measures were insufficient to control road noise in the conditions of the estimated increase in road traffic, the authors developed complex research to identify how the road noise level is determined by the way of regulation of road traffic at intersections of the residential zone. Thus, noise and traffic measurements are made at the main road intersections in the central part of the city, determining the most relevant noise indicators for the specifics of urban traffic and residential areas. The results obtained lead to the conclusion that roundabouts bring a reduction in noise pollution compared to traffic light intersections only if the speed of vehicles in the roundabout is predictable: on preselected lanes and with speed timing.

The Influence of Environmental Polycyclic Aromatic Hydrocarbons (PAHs) Exposure on DNA Damage among School Children in Urban Traffic Area, Malaysia.

This study aimed to investigate the association between particulate PAHs exposure and DNA damage in Malaysian schoolchildren in heavy traffic (HT) and low traffic (LT) areas. PAH samples at eight schools were collected using a low volume sampler for 24 h and quantified using Gas Chromatography-Mass Spectrometry. Two hundred and twenty-eight buccal cells of children were assessed for DNA damage using Comet Assay. Monte-Carlo simulation was performed to determine incremental lifetime cancer risk (ILCR) and to check the uncertainty and sensitivity of the estimated risk. Total PAH concentrations in the schools in HT area were higher than LT area ranging from 4.4 to 5.76 ng m−3 and 1.36 to 3.79 ng m−3, respectively. The source diagnostic ratio showed that PAHs in the HT area is pyrogenic, mainly from diesel emission. The 95th percentile of the ILCR for children in HT and LT area were 2.80 × 10−7 and 1.43 × 10−7, respectively. The degree of DNA damage was significantly more severe in children in the HT group compared to LT group. This study shows that total indoor PAH exposure was the most significant factor that influenced the DNA damage among children. Further investigation of the relationship between PAH exposure and genomic integrity in children is required to shed additional light on potential health risks.

Relevance of Tyre Wear Particles to the Total Content of Microplastics Transported by Runoff in a High-Imperviousness and Intense Vehicle Traffic Urban Area

Relevance of Tyre Wear Particles to the Total Content of Microplastics Transported by Runoff in a High-Imperviousness and Intense Vehicle Traffic Urban Area

Assessment of Heavy Metal Residues in Excreta of Rock Pigeon (Columba Livia) and Domestic Pigeon (Columba livia Domestica) in Faisalabad, Punjab, Pakistan

Pollution is the main cause that effects the environment in a degradable condition. For the presence of heavy metals in the atmosphere birds are considered as tremendous bio indicator. Blue pigeon and domestic pigeon (Coloumba livia) of Faisalabad are selected to examine the level of heavy metals including Manganese (Mn), zinc (Zn), Cobalt (Co), lead (Pb) cadmium (Cd) and Copper (Cu) in several tissues such as (heart, lungs, bones, muscles and feathers. Six adult birds were collected rural, urban and residential and traffic areas of Faisalabad. After collection of sample fresh weight will be measured of each sample, then digestion of sample was done by conventional acid digestion method and digested samples were analyzed in the lab by Atomic Absorption Spectrophotometer for the determination of heavy metals. It was concluded that accumulation of heavy metals in the birds depends upon the environmental conditions. Our result showed that concentration of heavy metals more in rock pigeon as compared to domestic pigeon which may be due to more exposure of industrial waste to rock pigeon. It has inferred from our study that high concentration of heavy metals in the excreta of rock pigeon was due to its high trophic level in food chain.

Large spatio-temporal variations of size-resolved particulate matter and volatile organic compounds in urban area with heavy traffic.

A monitoring campaign, the first of this kind for a heavy traffic urban area of Vietnam, was conducted which generated nearly 200 daily filter samples of PM2.5, PM10, and black carbon (BC), 1300 online hourly PMx (PM10, PM2.5, and PM1), 900 hourly/bi-hourly BTEX data, 700h of traffic counts, and online meteorology records. PMx and BTEX levels show large horizontal gradients across this small urban area of 300m width suggesting that the pollution data should be generated with sufficient spatial resolutions for assessment of the exposure and health effects. This paper focuses on analyzing PMx with reference to the previously published BTEX to provide a more complete picture of the traffic-related pollution in the area. Spatio-temporal variations of pollutants are analyzed in relation to traffic flows and fleet compositions, weekday-weekend effects, local and regional meteorology. PM10 and BTEX levels had larger variations between the sites indicating their stronger associations with the traffic activities than the finer particles. Twenty-four-hour(24 h) PM2.5 levels ranged between 19 and 191µg/m3 with high PM1/PM2.5 ratios of above 0.8 at ambient site (AA) and above 0.7 at roadsides. Multivariate relationship analysis (PCA) for the bi-hourly datasets of meteorology, traffic flows, and pollutant levels indicated overwhelming influence of on-road traffic fleet compositions on the roadside pollutants levels. At AA, PCA results showed a complex interaction between local emissions, meteorological conditions, and regional/long-range transport. Higher pollution levels were associated with the airmass types having the continental origin and pathways.

Subdivision of Urban Traffic Area Based on the Combination of Static Zoning and Dynamic Zoning

In this paper, the traffic area of subzone division in urban road network is studied and a subzone division method based on the combination of static partition and dynamic partition is proposed. The static partition is carried out for the road network when the traffic flow is in a noncongested state, so as to provide the decision-making basis for the traffic green wave signal control strategy. At the same time, aiming at the road network when the traffic flow is congested, the dynamic partition is carried out on the basis of static partition to provide the decision-making basis for the traffic maximum flow signal control strategy. In view of the fact that it is difficult to determine the clustering center point during the initial division, this method proposes to determine the clustering center point according to the value of nodes of betweenness centrality. In order to solve the problem that it is difficult to collect traffic data, a method for estimating traffic flow density is proposed. In order to solve the problem of normalization of different probability distribution among various parameters, Mahalanobis distance is used as the fusion index of subzone division. Model verification shows that the method is feasible and effective.

Iterative learning perimeter control method for traffic sub-region considering disturbances

Most of the existing perimeter control methods in urban traffic regions are only suitable for the road network in an ideal state, and the impact of various uncertain factors and disturbances in the actual traffic system on the control performance is not considered. In this paper, a disturbance term is introduced into the vehicle balance equation of the road network, and an iterative learning perimeter control method of urban traffic area considering the disturbance is proposed by using the repeatability of the macroscopic traffic flow. Through iterative learning control of the perimeter intersections, the cumulative number of vehicles in the sub-region is stabilized near the expected value, and it is demonstrated that the tracking error of the system converges to a boundary under bounded disturbances. Finally, it is verified through simulation experiments that the proposed method can effectively suppress the effects of different levels of disturbances on the performance of the road network and improve the traffic conditions.

Determinants of Internal-Migration in Ethiopia: Qualitative Review

Internal migration is a critical problem in Ethiopia. This review focused on determinants of internal migration in Ethiopia. Therefore, the objective of this review was to assess determinants of internal migration in Ethiopia. The review used secondary data sources from research articles and reports from different researchers and organizations who have written on the determinants of internal migration and related topic in Ethiopia and elsewhere. Depending on the reviewed document, the most significant determinants of internal migration in Ethiopia are encapsulating with household and individual demographic, socio-economic and structural and institutional factors. Besides, food insecurity, environmental degradation, lower agricultural productivity, demographic pressure, famine, unemployment, conflicts, drought, poor living conditions, inadequate social services, land shortages in rural areas were identified as the major push factors of internal migration in Ethiopia. Although “push” factors predominate, there are some significant “pull” factors that attract rural people to traffic urban areas such as better employment opportunities, higher incomes, better healthcare and education, urban facilities and way of life, security, and other urban amenities. So, further investigations should be conducted by governmental and non-governmental institutions to suggest possible solutions by focusing on the above aforementioned significant variables that affect internal migration in Ethiopia. Keywords: Internal migration, Determinants, push and pull factors, Ethiopia. DOI: 10.7176/DCS10-9-03 Publication date: September 30 th 2020