Analysis of model-based PM2.5 emission factors for on-road mobile sources in Mexico

Zavala, M.; Barrera, Hugo; Morante, J.; Molina, Luisa T.

doi:10.1016/s0187-6236(13)71065-8

Cited by 8 publications

(6 citation statements)

References 41 publications

(37 reference statements)

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“…Large numbers of vehicles on the road emit more particles than fewer vehicles. 42 However, variation of traffic volume was not strongly related to PM 2.5 variation, and was not a significant factor for the variations of increasing PM. 43 Also, older vehicles tend to emit more PM 2.5 to ambient air.…”

Section: Air Soil and Water Researchmentioning

confidence: 79%

“…43 Also, older vehicles tend to emit more PM 2.5 to ambient air. 42 Both emissions of gasoline- and diesel-fueled vehicles can contribute to PM 2.5. For example, gasoline-fueled vehicles contribute 17.2% and diesel vehicles contribute 8.1% to PM 2.5 in Seoul, Korea.…”

Section: Resultsmentioning

confidence: 99%

“…44 However, low sulfur content in fuel produces less amount of PM 2.5 emissions. 42 Therefore, vehicle population, vehicle age, and fuel quality are factors resulting in changed PM 2.5 levels.…”

Section: Potential Source Areamentioning

confidence: 99%

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Temporal Variations and Potential Source Areas of Fine Particulate Matter in Bangkok, Thailand

Kanchanasuta

Sooktawee²,

Patpai³

et al. 2020

Air, Soil and Water Research

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Particulate matter (PM) less than 2.5 micron (PM2.5) issue is 1 of the important targets of concern by the United Nations’ Sustainable Development Goals. Bangkok is a megacity and facing air pollution problems. This study analyzed PM, PM2.5 and PM less than 10 micron (PM10), monitoring data from stations located in Bangkok, and aimed to present their variations in diurnal, weekly, and intra-annual timescales. High PM concentrations are related to calm wind. The diurnal variation of PM2.5/PM10 suggested a greater accumulation of PM2.5 than PMcoarse during the low wind speed. Potential source areas affecting PM rising at each monitoring station were identified using statistical technique, bivariate polar plot, and conditional bivariate probability function. Results showed that Ratchathewi District Monitoring Station identified 3 potential source areas related to emissions from transportation sources creating rising PM concentrations. The first potential source was located in the northwest direction, namely, the Rama VI Road close to the conjunction with Ratchawithi Road. The second potential source area was located around the cross-section between Phaya Thai Road and Rama I Road, while the third was located at the intersection of the Phaya Thai Road to Yothi Street and Rang Nam Road. These potential source areas constitute useful information for managing and reducing PM.

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Section: Air Soil and Water Researchmentioning

confidence: 79%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Temporal Variations and Potential Source Areas of Fine Particulate Matter in Bangkok, Thailand

Kanchanasuta

Sooktawee²,

Patpai³

et al. 2020

Air, Soil and Water Research

View full text Add to dashboard Cite

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“…A number of studies indicate that the use of the model can improve the emissions estimates of inventories in Mexico with respect to older emission models (Zavala et al, 2013;Guevara et al, 2017). However, its efficient application to other countries requires the adjustment of multiple internal parameters, among which the emission factor databases are of key importance.…”

Section: Comparison With Moves2014 Mexico Modelmentioning

confidence: 99%

Emission factors of black carbon and co-pollutants from diesel vehicles in Mexico City

et al. 2017

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Abstract. Diesel-powered vehicles are intensively used in urban areas for transporting goods and people but can substantially contribute to high emissions of black carbon (BC), organic carbon (OC), and other gaseous pollutants. Strategies aimed at controlling mobile emissions sources thus have the potential to improve air quality and help mitigate the impacts of air pollutants on climate, ecosystems, and human health. However, in developing countries there are limited data on the BC and OC emission characteristics of diesel-powered vehicles, and thus there are large uncertainties in the estimation of the emission contributions from these sources. We measured BC, OC, and other inorganic components of fine particulate matter (PM), as well as carbon monoxide (CO), nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), ethane, acetylene, benzene, toluene, and C2-benzenes under real-world driving conditions for 20 diesel-powered vehicles encompassing multiple emission level technologies in Mexico City with the chasing technique using the Aerodyne mobile laboratory. Average BC emission factors ranged from 0.41-2.48 g kg −1 of fuel depending on vehicle type. The vehicles were also simultaneously measured using the cross-road remote sensing technique to obtain the emission factors of nitrogen oxide (NO), CO, total hydrocarbons, and fine PM, thus allowing for the intercomparison of the results from the two techniques. There is overall good agreement between the two techniques and both can identify high and low emitters, but substantial differences were found in some of the vehicles, probably due to the ability of the chasing technique to capture a larger diversity of driving conditions in comparison to the remote sensing technique. A comparison of the results with the US EPA MOVES2014b model showed that the model underestimates CO, OC, and selected VOC species, whereas there is better agreement for NO x and BC. Larger OC / BC ratios were found in comparison to ratios measured in California using the same technique, further demonstrating the need for using locally obtained diesel-powered vehicle emission factor database in developing countries in order to reduce the uncertainty in the emissions estimates and to improve the evaluation of the effectiveness of emissions reduction measures.

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“…First, the contribution of single human activity to PM 2.5 varies greatly. For instance, some have studied the relationship between vehicle emissions and PM 2.5 [30,31]. In addition, Guo et al [32] found that the expansion of logistic services had the greatest impact on air pollution, but Gross Domestic Product (GDP) and urban population growth were only weakly correlated with air pollution.…”

Section: Introductionmentioning

confidence: 99%

Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors

Sun

Zhou

Wu³

et al. 2019

IJERPH

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A chance constrained stochastic Data Envelopment Analysis (DEA) was developed for investigating the relations between PM2.5 pollution days and meteorological factors and human activities, incorporating with an empirical study for 13 cities in Jiangsu Province (China) to illustrate the model. This approach not only admits random input and output environment, but also allows the evaluation unit to exceed the front edge under the given probability constraint. Moreover, observing the change in outcome variables when a group of explanatory variables are deleted provides an additional strategic technique to measure the effect of the remaining explanatory variables. It is found that: (1) For 2013–2016, the influencing factors of PM2.5 pollution days included wind speed, no precipitation day, relative humidity, population density, construction area, transportation, coal consumption and green coverage rate. In 2016, the number of cities whose PM2.5 pollution days was affected by construction was decreased by three from 2015 but increased according to transportation and energy utilization. (2) The PM2.5 pollution days in southern and central Jiangsu Province were primarily affected by the combined effect of the meteorological factors and social progress, while the northern Jiangsu Province was largely impacted by the social progress. In 2013–2016, at different risk levels, 60% inland cities were of valid stochastic efficiency, while 33% coastal cities were of valid stochastic efficiency. (3) The chance constrained stochastic DEA, which incorporates the data distribution characteristics of meteorological factors and human activities, is valuable for exploring the essential features of data in investigating the influencing factors of PM2.5.

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Analysis of model-based PM2.5 emission factors for on-road mobile sources in Mexico

Cited by 8 publications

References 41 publications

Temporal Variations and Potential Source Areas of Fine Particulate Matter in Bangkok, Thailand

Temporal Variations and Potential Source Areas of Fine Particulate Matter in Bangkok, Thailand

Emission factors of black carbon and co-pollutants from diesel vehicles in Mexico City

Influencing Factors of PM2.5 Pollution: Disaster Points of Meteorological Factors

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