scholarly journals Assessment of the Road Traffic Air Pollution in Urban Contexts: A Statistical Approach

2021 ◽  
Author(s):  
Concettina Marino ◽  
Antonino Nucara ◽  
Maria Francesca Panzera ◽  
Matilde Pietrafesa
Keyword(s):  
Air Pollution ◽  
Statistical Approach ◽  
Road Traffic ◽  
Pollutant Emission ◽  
Vehicle Speed ◽  
Emission Rates ◽  
Urban Context ◽  
The Road ◽  
Specific Category ◽  
Yearly Average

In the article a statistical approach to the assessment of the emission rates discharged by the road traffic in a spatial context is proposed. It exploits an indicator, the Yearly Average Vehicle, representing the pollutant emission rate of the average vehicle belonging to a specific category, and considers the statistical variability of most of the involved traffic parameters: vehicle speed and mileage travelled in the considered time period. Finally, indicators, assessing both the most probable value among the possible emission rates and the extent of their variability range, are proposed. They may also be used to underpin decision making-processes, when the effects of different policies addressing air pollution issues, are to be evaluated. Therefore, they are suitable for the analysis supporting urban planning activities, with a view to addressing and mitigating the effects and the consequences of pollution due to the transportation sector of the urban context.

Air pollution in an urban street canyon: Novel insights from highly resolved traffic information and meteorology

2021 ◽  
Author(s):  
Laura Ehrnsperger ◽  
Otto Klemm
Keyword(s):  
Air Pollution ◽  
Temporal Dynamics ◽  
Road Traffic ◽  
Ambient Air ◽  
Ambient Air Pollution ◽  
Traffic Information ◽  
Pollutant Emission ◽  
Urban Site ◽  
Gasoline Vehicles ◽  
Agricultural Areas

<p>Ambient air pollution caused by fine particulate matter (PM) and trace gases is a pressing topic as it affects the vast majority of the world's population, especially in densely populated urban environments. The main sources of ambient air pollution in cities are road traffic, industries and domestic heating. Alongside nitrogen oxides (NO<sub>x</sub>) and PM, ammonia (NH<sub>3</sub>) is also a relevant air pollutant due to its role as a precursor of particulate ammonium (NH<sub>4</sub><sup>+</sup>). To examine the temporal patterns and sources of air pollutants, this study used fast-response air quality measurements in combination with highly resolved traffic information in Münster, NW Germany. The temporal dynamics of NO<sub>x</sub> and the particle number concentration (PN<sub>10</sub>) were similar to the diurnal and weekly courses of the traffic density. On very short timescales, the real-world peak ratios of NO<sub>x</sub> and PM ≤ 10 µm diameter (PM<sub>10</sub>) exceeded the predicted pollutant emission ratios of the Handbook for Emission Factors for Road Transport (HBEFA) by a factor of 6.4 and 2.0, respectively. A relative importance model revealed that light-duty vehicles (LDVs) are the major relative contributor to PN<sub>10</sub> (38 %) despite their low abundance (4 %) in the local vehicle fleet.  Diesel and gasoline vehicles contributed similarly to the concentrations of PM<sub>10</sub> and PN<sub>10</sub>, while the impact of gasoline vehicles on the PM<sub>1</sub> concentration was greater than that of diesel vehicles by a factor of 4.4. The most recent emission class Euro 6 had the highest influence on PM<sub>10</sub>. Meteorological parameters explained a large portion of the variations in PM<sub>10</sub> and PM<sub>1</sub>, while meteorology had only a minor influence on PN<sub>10</sub>. We also studied the short-term temporal dynamics of urban NH<sub>3 </sub>concentrations, the role of road traffic and agriculture as NH<sub>3</sub> sources and the importance of ammonia for secondary particle formation (SPF). The NH<sub>3</sub> mixing ratio was rather high (mean: 17 ppb) compared to other urban areas and showed distinct diurnal maxima around 10 a.m. and 9 p.m. The main source for ammonia in Münster was agriculture, but road traffic also contributed through local emissions from vehicle catalysts. NH<sub>3</sub> from surrounding agricultural areas accumulated in the nocturnal boundary layer and contributed to SPF in the city center. The size-resolved chemical composition of inorganic ions in PM<sub>10</sub> was dominated by NH<sub>4</sub><sup>+</sup> (8.7 µg m<sup>-3</sup>), followed by NO<sub>3</sub><sup>-</sup> (3.9 µg m<sup>-3</sup>), SO<sub>4</sub><sup>2-</sup> (1.6 µg m<sup>-3</sup>) and Cl<sup>-</sup> (1.3 µg m<sup>-3</sup>). Particles in the accumulation range (diameter: 0.1 – 1 µm) showed the highest inorganic ion concentrations. The ammonium neutralization index J (111 %) indicated an excess of NH<sub>4</sub><sup>+</sup> leading to mostly alkaline PM. High ammonia emissions from surrounding agricultural areas combined with large amounts of NO<sub>x</sub> from road traffic play a crucial role for SPF in Münster. Our results further indicate that replacing fossil-fuelled LDVs with electrical vehicles would greatly reduce the PN<sub>10</sub> concentrations at this urban site.</p>

scholarly journals Quantification of the noise charges for the selected road network sections

2018 ◽  
Vol 196 ◽  
pp. 04047
Author(s):  
Martina Margorínová ◽  
Marjan Lep ◽  
Mária Trojanová
Keyword(s):  
Air Pollution ◽  
Member State ◽  
Road Network ◽  
Road Traffic ◽  
Road Transport ◽  
European Parliament ◽  
Health Problems ◽  
External Costs ◽  
The Road ◽  
Negative Impacts

Road traffic is the most used kind of transportation which has a lot of benefits. Except of these benefits, the road traffic causes a lot of negative impacts like a congestions, air pollution and noise. The proposal of European Parliament is reduction of these impacts by their inclusion to the road charges. In the annex of amending directive 1999/62ES are stated prices for congestion and external costs. If the member state wants higher amount like are stated, they have to be calculated. One element of external costs is noise costs. Noise from road transport causes health problems and has annoying effect to people. In this article, it was processed proposal of noise charges quantification, which consist of a few steps. This process was applied for quantifying noise charges with real values for Slovakia and Slovenia.

scholarly journals Characterization of Emission Factors Concerning Gasoline, LPG, and Diesel Vehicles via Transient Chassis-Dynamometer Tests

2019 ◽  
Vol 9 (8) ◽  
pp. 1573 ◽  
Author(s):  
Gyutae Park ◽  
Sunhee Mun ◽  
Heekyoung Hong ◽  
Taekho Chung ◽  
Sungwoon Jung ◽  
...  
Keyword(s):  
Air Pollution ◽  
Gasoline Direct Injection ◽  
Environmental Research ◽  
Pollutant Emission ◽  
Vehicle Speed ◽  
Gaseous Emissions ◽  
Liquefied Petroleum Gas ◽  
Diesel Vehicles ◽  
Study Results ◽  
Secondary Air

Gaseous emissions from vehicles contribute substantially to air pollution and climate change. Vehicular emissions also contain secondary pollutants produced via chemical reactions that occur between the emitted gases and atmospheric air. This study aims at understanding patterns concerning emission of regulated, greenhouse, and precursor gases, which demonstrate potential for secondary aerosol (SA) formation, from vehicles incorporating different engine technologies—multi-point injection (MPI) and gasoline direct injection (GDI)—and using different fuels—gasoline, liquefied petroleum gas (LPG), and diesel. Drive cycles from the National Institute of Environmental Research (NIER) were used in this study. Results obtained from drive cycle tests demonstrate a decline in aggregate gas emissions corresponding to an increase in average vehicle speed. CO2 accounts for more than 99% of aggregate gaseous emissions. In terms of concentration, CO and NH3 form predominantly non-CO2 emissions from gasoline and LPG vehicles, whereas nitrogen oxides (NOx) and non-methane hydrocarbons (NMHC) dominate diesel-vehicle emissions. A higher percentage of SO2 is emitted from diesel vehicles compared to their gasoline- and LPG-powered counterparts. EURO-5- and EURO-6-compliant vehicles equipped with diesel particulate filters (DPFs) tend to emit higher amounts of NO2 compared to EURO-3-compliant vehicles, which are not equipped with DPFs. Vehicles incorporating GDI tend to emit less CO2 compared to those incorporating MPI, albeit at the expense of increased CO emissions. The authors believe that results reported in this paper concerning regulated and unregulated pollutant-emission monitoring can contribute towards an accurate evaluation of both primary and secondary air-pollution scenarios in Korea.

scholarly journals Analysis of the Contribution of the Road Traffic Industry to the PM2.5 Emission for Different Land-Use Types

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Peng Xu ◽  
Wei Wang ◽  
Jiawei Ji ◽  
Shunyu Yao
Keyword(s):  
Land Use ◽  
Air Pollution ◽  
Road Traffic ◽  
Urban Air Pollution ◽  
Urban Pollution ◽  
Road Dust ◽  
Chinese Society ◽  
Vehicle Exhaust ◽  
Analytic Hierarchy ◽  
The Road

Road dust and vehicle exhaust are the main sources of air pollution in cities, especially in recent years with the quantity of vehicles and transportation construction continuously soaring; the hazy weather has been a dominant urban pollution form which is widely concerned by the Chinese society. By establishing a relationship model between traffic and land use, then applying analytic hierarchy process on the data from air quality monitoring station, this paper concludes the influence of different traffic behavior on air pollution which provides support to abate urban air pollution caused by traffic reasons through taking measures to control traffic.

Intelligent traffic control system based on open IoT and machine learning

2020 ◽  
pp. 1-12
Author(s):  
Jiaona Chen ◽  
Hailong Liu
Keyword(s):  
Machine Learning ◽  
Control System ◽  
Traffic Control ◽  
Road Traffic ◽  
Control Process ◽  
Vehicle Speed ◽  
Smart Transportation ◽  
The Road ◽  
Traffic Control System ◽  
On The Road

Smart transportation relies on data collection, transmission, processing, and release, involving various terminal devices, control systems, central platforms, and communication links, so its control process is more complicated. In order to improve the operation efficiency of the intelligent traffic control system, based on the open Internet of Things and machine learning, this paper builds an intelligent three-way intelligent traffic control system, sets various parameters, and builds a simulation model using cellular automata as a platform. Moreover, in order to study the performance of the model, the model constructed in this paper is compared with the model of the traditional road traffic control system. In addition, this paper analyzes the model constructed in this paper through the statistics of the highest vehicle flow on the road and the relationship between road occupancy and vehicle speed. The research results show that the model constructed in this paper has good performance and can be applied to intelligent traffic control.

scholarly journals CORRELATION ANALYSIS OF DRIVING CONDITIONS AND ON-ROAD EMISSIONS TRENDS FOR VEHICLES

2017 ◽  
pp. 63-72
Author(s):  
Jawad Hilmi Al-rifai
Keyword(s):  
Vehicle Emissions ◽  
Measurement Unit ◽  
Vehicle Speed ◽  
Emission Rates ◽  
Gas Emissions ◽  
The Road ◽  
Grade Parameter ◽  
Road Grade ◽  
Driving Conditions ◽  
The Impact

This paper presents the impact of road grade, vehicle speed, number of vehicles and vehicle type on vehicle emissions. ANOVA analyses were conducted among different driving conditions and vehicle emissions to discover the significant effects of driving conditions on measured emission rates. This study is intended to improve the understanding of vehicle emission levels in Jordan. Gas emissions in real-world driving conditions were measured by a portable emissions measurement unit over six sections of an urban road. The road grade, speed, type and number of vehicles were found to have a significant influence on the rate of gas emissions. Road grade and diesel-fueled vehicles were positively correlated with average emission rates. The average emission rates were higher at speeds ranging between 60–69 km/hr than at three other speed ranges. The results of ANOVA showed a strong and consistent regression between rates of emissions measured and grade, speed and diesel vehicle parameters. The grade parameter contributed the most to the rate of emissions compared to other parameters. Gasoline vehicles contributed the least.

scholarly journals Assessment of the level of noise and chemical air pollution in two distinct urban areas

2020 ◽  
Vol 2 (2) ◽  
pp. 130-137
Author(s):  
Mihai Bratu ◽  
◽  
Elena Bucur ◽  
Valeriu Danciulescu ◽  
Mihaela Petrescu ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Road Traffic ◽  
Industrial Area ◽  
The Other ◽  
Noise Levels ◽  
Industrial Noise ◽  
The Road

In the paper are presented the results of tests on the evaluation of the level of noise and chemical air pollution in two distinct urban areas: an industrial area and an area characterized by heavy road traffic, with a focus on the novelty elements introduced by the regulations in force on the measurement and management of the level of ambient and industrial noise by periodically developing specific noise maps. The results of direct tests and noise maps developed in the case of the studied areas indicated higher values of the indicators measured near the road artery compared to the other measuring points, highlighting the influence of vegetation in urban areas to reduce noise levels and reduce air pollution.

scholarly journals Analysis of the Impact of Traffic Violation Monitoring on the Vehicle Speeds of Urban Main Road: Taking China as an Example

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Fuquan Pan ◽  
Yongzheng Yang ◽  
Lixia Zhang ◽  
Changxi Ma ◽  
Jinshun Yang ◽  
...  
Keyword(s):  
Probability Density ◽  
Road Traffic ◽  
Maximum Speed ◽  
Vehicle Speed ◽  
Main Road ◽  
The Road ◽  
Before And After ◽  
Traffic Violation ◽  
The Impact ◽  
Monitoring Area

In recent years, there are more and more applications of traffic violation monitoring in some countries. The present work aims to analyze the vehicle speeds nearby road traffic violation monitoring area on urban main roads and find out the impact of road traffic violation monitoring on the vehicle speeds. A representative urban main road section was selected and the traffic flow was recorded by camera method. The vehicle speeds before, within, and after the road traffic violation monitoring area were obtained by the calculation method. The speed data was classified and processed by SPSS software and mathematical method to establish the vehicle speed probability density models before, within, and after the road traffic violation monitoring area. The results show that the average speed and maximum speed within the traffic violation monitoring area are significantly slower than those before and after the traffic violation monitoring area. 70.1% of the vehicles before the road traffic violation monitoring area were speeding, and 80.2% of the vehicles after the road traffic violation monitoring area were speeding, while within the road traffic violation monitoring area, the speeding vehicles were reduced to 15.9%. When vehicles pass through the road traffic violation monitoring area, the vehicle speeds tend to first decrease and subsequently increase. In its active area, road traffic violation monitoring can effectively regulate driving behaviors and reduce speeding, but this effect is limited to the vicinity of the traffic violation monitoring. The distribution of vehicle speeds can be calculated from vehicle speed probability density models.

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