Traffic data in air quality modeling: A review of key variables, improvements in results, open problems and challenges in current research

Pinto, Janaina Antonino; Kumar, Prashant; Alonso, Marcelo Félix; Andreão, Willian Lemker; Pedruzzi, Rizzieri; Santos, Fábio Soares dos; Moreira, Davidson Martins; Albuquerque, Taciana Toledo de Almeida

doi:10.1016/j.apr.2019.11.018

Cited by 44 publications

(16 citation statements)

References 115 publications

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“…In traffic-related emission modeling and assessment of the emission impact on air quality basic parameters characterising road traffic are used, such as traffic volume, vehicle categories and traffic speed [34]. Using the OnDynamic system, two of these parameters can be estimated: traffic volume and traffic speed.…”

Section: Data Processingmentioning

confidence: 99%

An Integrated System for Simultaneous Monitoring of Traffic and Pollution Concentration—Lessons Learned for Bielsko-Biała, Poland

2021

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The challenge of maintaining the required level of mobility and air quality in cities can be met by deploying an appropriate management system in which the immediate vicinity of roads is monitored to identify potential pollution hotspots. This paper presents an integrated low-cost system which can be used to study the impact of traffic related emission on air quality at intersections. The system was used for three months in 2017 at five locations covering intersections in the centre of a mid-sized city. Depending on the location, pollution hotspots with high PM2.5 and PM10 concentrations occurred 5–10% of the time. It was shown that despite the close mutual proximity of the locations, traffic and the immediate surroundings lead to significant variation in air quality. At locations with adverse ventilation conditions a tendency towards more frequent occurrences of moderate and sufficient air quality was observed than at other locations (even those with more traffic). Based on the results, a practical extension of the system was also proposed by formulating a model for the prediction of PM2.5 concentration using a neural network. Information on transit times, meteorological data and the background level of PM10 concentration were used as model input parameters.

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Section: Data Processingmentioning

confidence: 99%

An Integrated System for Simultaneous Monitoring of Traffic and Pollution Concentration—Lessons Learned for Bielsko-Biała, Poland

2021

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“…Pinto et al investigated the effect of traffic data on air pollution modeling. They found that road transportation vehicles are the major contributor of the civic air pollution [6]. Mobile source emission markedly increases smallparticle pollution, PM 2.5 , in urban areas.…”

Section: Introductionmentioning

confidence: 99%

Air pollution distribution in Arak city considering the effects of neighboring pollutant industries and urban traffics

Mostafavi

Safikhani

Salehfard

2021

Int J Energy Environ Eng

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Having heavy impacts on human's life, pollution adversely influences life. In this paper, a comprehensive model of the Arak's air pollution for different pollutant chemicals NO x , CO, and SO 2 is developed. Here, all effective fixed and mobile sources of pollution are within 50 km distance of the city. The topology of ground surface and also climate patterns of different local areas were carefully considered to accurately model the distribution of pollution. Then, comparing the results with data taken from the ambient air monitoring, the model was validated. According to our findings maximum of NO x concentration in Arak city, Iran was 7.7 times of standard value. This figure is 2.2 and 17.5 times for CO and SO 2 pollutants. Share of industry, for spring season, is 51%, 86% and 100% for NO x , CO, and SO 2 , respectively. Moreover, IRALCO itself has 31% share in total NO x pollution, while its share for CO and SO 2 pollution dramatically increases to 85% and 100%, respectively.

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“…There is no single model capable of meeting all the requirements across various spatial and temporal scales (Pinto et al, 2020). However, transparency, simplicity, and a user-friendly interface are requirements for those who mainly work in transportation policy and air quality modeling development (Fallahshorshani et al, 2012;Kaewunruen et al, 2016;Sallis et al, 2016; https://doi.org/10.5194/gmd-2021-135 Preprint.…”

Section: Introductionmentioning

confidence: 99%

Comprehensive Automobile Research System (CARS) – a Python-based Automobile Emissions Inventory Model

Baek

Pedruzzi

Park

et al. 2021

Preprint

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Abstract. The Comprehensive Automobile Research System (CARS) is an open-source python-based automobile emissions inventory model designed to efficiently estimate high quality emissions from motor-vehicle emission sources. It can estimate the criteria air pollutants, greenhouse gases, and air toxics in various temporal resolutions at the national, state, county, and any spatial resolution based on the spatiotemporal resolutions of input datasets. The CARS is designed to utilize the local vehicle activity database, such as vehicle travel distance, road link-level network Geographic Information System (GIS) information, and vehicle-specific average speed by road type, to generate a temporally and spatially enhanced automobile emissions inventory for policymakers, stakeholders, and the air quality modeling community. The CARS model adopted the European Environment Agency's (EEA) onroad automobile emissions calculation methodologies to estimate the hot exhaust, cold start, and evaporative emissions from onroad automobile sources. It can optionally utilize road link-specific average speed distribution (ASD) inputs to reflect more realistic vehicle speed variations by road type than a road-specific single averaged speed approach. Also, utilizing high-resolution road GIS data allows the CARS to estimate the road link-level emissions to improve the inventory's spatial resolution. When we compared the official 2015 national mobile emissions from Korea's Clean Air Policy Support System (CAPSS) against the ones estimated by the CARS, there is a moderate increase of VOC (33 %), CO (52 %), and fine particulate matter (PM2.5) (15 %) emissions while NOx and SOx are reduced by 24 % and 17 % in the CARS estimates. The main differences are driven by the usage of different vehicle activities and the incorporation of road-specific ASD, which plays a critical role in hot exhaust emission estimates but wasn’t implemented in Korea’s CAPSS mobile emissions inventory. While 52% of vehicles use gasoline fuel and 35 % use diesel, gasoline vehicles only contribute 7.7 % of total NOx emissions while diesel vehicles contribute 85.3 %. But for VOC emissions, gasoline vehicles contribute 52.1 % while diesel vehicles are limited to 23 %. While diesel buses are only 0.3 % of vehicles, each vehicle has the largest contribution to NOx emissions (8.51 % of NOx total) due to its longest daily VKT. For VOC, CNG buses are the largest contributor with 19.5 % of total VOC emissions. It indicates that the CNG bus is better for the rural area while the diesel bus is better applicable for the urban area for a better ozone control strategy because the rural area is usually NOx limited for ozone formation and urban area is VOC limited region. For primary PM2.5, more than 98.5 % is from diesel vehicles. The CARS model's in-depth analysis feature can assist government policymakers and stakeholders develop the best emission abatement strategies.

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Traffic data in air quality modeling: A review of key variables, improvements in results, open problems and challenges in current research

Cited by 44 publications

References 115 publications

An Integrated System for Simultaneous Monitoring of Traffic and Pollution Concentration—Lessons Learned for Bielsko-Biała, Poland

An Integrated System for Simultaneous Monitoring of Traffic and Pollution Concentration—Lessons Learned for Bielsko-Biała, Poland

Air pollution distribution in Arak city considering the effects of neighboring pollutant industries and urban traffics

Comprehensive Automobile Research System (CARS) – a Python-based Automobile Emissions Inventory Model

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