A high resolution vehicular emissions inventory for Ecuador using the IVE modelling system

Viteri, Rafaela; Borge, Rafael; Paredes, Marcel; Pérez, Miguel Ángel Ávalos

doi:10.1016/j.chemosphere.2022.137634

Cited by 6 publications

(3 citation statements)

References 21 publications

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“…To explore efficient tactics for diminishing GHG and specific air pollutant emissions within the urban transportation sector, a vehicle emission model has been formulated as a valuable instrument for conducting assessments of vehicle emission inventories in diverse developing Asian urban areas, such as Hanoi, Vietnam [54]; Bandung, Indonesia [55]; alongside cities in Ecuador [38,56] and India [57].…”

Section: Methodsmentioning

confidence: 99%

Assessing the Additional Benefits of Thailand’s Approaches to Reduce Motor Vehicle Emissions

Thanatrakolsri,

Sirithian

2024

Energies

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Air pollutants and greenhouse gases (GHGs) represent major challenges in our era, contributing to climate change and global health issues. These problems arise from a variety of well-known sources, including motor vehicles. Almost all nations, Thailand included, have formulated and implemented policies to curb greenhouse gas (GHG) emissions in line with the requirements and commitments of the Paris Agreement. The evaluation of specific air pollutants and GHG emissions originating from road vehicles utilises the Thailand database, referencing the year 2019. Data intersections from 2019 to 2022 are grounded in actual data collected from relevant departments in Thailand, while projections for 2023–2030 are forecasted based on the baseline year. The secondary database used in the International Vehicle Emission model is adjusted according to real-world driving data to accurately reflect country-specific emission factors. Dynamic emission factors for specific air pollutants and GHGs are evaluated and integrated with the average Vehicle Kilometres Travelled (VKT) for each vehicle category. The Business-As-Usual (BAU) scenario is then examined, based on existing policies aimed at reducing air pollutants and GHG emissions in Thailand’s transport sector. These policies include strategies for the adoption of electric vehicles and the promotion of public transport to reduce VKT. Under the BAU scenario, the overall number of road vehicles in Thailand, including passenger cars, motorcycles, pickups, vans, trucks, and buses, is expected to increase by approximately 6.58% by 2030, leading to a rise in specific air pollutants and GHG emissions compared to the 2019 baseline. However, by adhering to Thailand’s strategies and transitioning to new electric passenger cars and buses, greenhouse gas emissions and specific air pollutants from the road transport sector will be significantly reduced.

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Section: Methodsmentioning

confidence: 99%

Assessing the Additional Benefits of Thailand’s Approaches to Reduce Motor Vehicle Emissions

Thanatrakolsri,

Sirithian

2024

Energies

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“…Therefore, governments must develop sustainable environmental policies, in addition to boosting economic growth. This involves minimizing emissions from thermoelectric generation, manufacturing, and the road and off-road transport sectors (both passengers and goods) [ [2] , [3] , [4] , [5] ], as they are directly linked to air quality and exposure in urban areas and strongly impact morbidity and mortality in societies [ 6 ], subject to particular conditions [ 7 ]. As a result, the World Health Organization has developed air quality guidelines for pollutants such as CO, NO 2 , SO 2 , O 3 , PM 2.5 and PM 10 [ 8 ], which have key implications for public health because of their association with cardiovascular and respiratory diseases and other health outcomes, including premature mortality [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Source apportionment of ambient pollution levels in Guayaquil, Ecuador

Patiño-Aroca,

Hernández-Paredes,

Panchana-López

et al. 2024

Heliyon

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“…Quantification of urban road traffic emissions with low uncertainty is fundamental to effective emission reduction policies [4,5]. Due to the high cost of the largescale direct monitoring of vehicle exhaust, current research on urban road traffic emissions primarily uses emission models (such as IVE and MOVES) for emission quantification [6][7][8]. These models define various vehicle types for which emissions per unit distance can differ significantly by several or even hundreds of times with parameters such as fuel type, emission standards, weight, and displacement [9,10].…”

Section: Introductionmentioning

confidence: 99%

Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage

Cai,

Zeng,

et al. 2024

Sustainability

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With pronounced differences in emission factors among vehicle types and marked spatiotemporal heterogeneity of vehicle fleet composition, extrapolating fleet composition from insufficient sample hour periods and road segments will introduce significant uncertainty in calculating regional daily road traffic emissions. We proposed a framework to manage uncertainty in urban road traffic emissions associated with vehicle fleet composition from the perspective of spatiotemporal sampling coverage. Initially, the respective relationships of the temporal and spatial sampling coverages of fleet composition with the resulting regional daily road traffic emission uncertainties were determined, using the core area of a typical small and medium-sized city in China with the widely-used International Vehicle Emissions (IVE) model as example. Subsequently, function models were developed to explore the determination of the spatiotemporal sampling coverage of fleet composition. These results of emission uncertainties and function models implied that gases with larger emission factor discrepancies between vehicle types, such as NOx, required greater spatiotemporal sampling coverage than gases with smaller discrepancies, such as CO2, under the same uncertainties target. Therefore, sampling efforts should be prioritized for gases with larger emission factor discrepancies. Additionally, increasing sampling coverage in one dimension (either spatial or temporal) can reduce the minimum required coverage in the other dimension. To further reduce uncertainty, enhancing both spatial and temporal sampling coverage of the fleet composition is more effective than enhancing one type of coverage alone. The framework and results proposed in this work can reduce the uncertainty of emissions calculations caused by insufficient sampling coverage and contribute to more accurate transport emission reduction policy formulation.

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A high resolution vehicular emissions inventory for Ecuador using the IVE modelling system

Cited by 6 publications

References 21 publications

Assessing the Additional Benefits of Thailand’s Approaches to Reduce Motor Vehicle Emissions

Assessing the Additional Benefits of Thailand’s Approaches to Reduce Motor Vehicle Emissions

Source apportionment of ambient pollution levels in Guayaquil, Ecuador

Managing Uncertainty in Urban Road Traffic Emissions Associated with Vehicle Fleet Composition: From the Perspective of Spatiotemporal Sampling Coverage

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