Development of Reduction Scenarios Based on Urban Emission Estimation and Dispersion of Exhaust Pollutants from Light Duty Public Transport: Case of Tabriz, Iran

Kalajahi, Mina Jamshidi; Khazini, Leila; Rashidi, Yousef; Heris, Saeed Zeinali

doi:10.1007/s40825-019-00135-0

Cited by 13 publications

(6 citation statements)

References 47 publications

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“…A perfect model comprises MG, VG, and FAC2 equal to 1.0, and FB and NMSE equal to 0 46,47 . There is no ideal model for analyzing air quality problems because of the unstable nature and effect of random atmospheric processes 48 …”

Section: Methodsmentioning

confidence: 99%

“…46,47 There is no ideal model for analyzing air quality problems because of the unstable nature and effect of random atmospheric processes. 48 The root mean squared error (RMSE) and the mean absolute error (MAE) are two standard metrics for evaluating models. In order to calculate the MAE and RMSE, we need a sample set of n observations, y (y i , i = 1,2, …, n) and n the corresponding model predictions b y i .…”

Section: Modeling Ammonia Performancementioning

confidence: 99%

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Ammonia emissions from livestock facilities around an international airport, Tehran, Iran: An experimental and mathematical study

Asadollahfardi,

Khoormehr,

Sarmadi

et al. 2024

Env Prog and Sustain Energy

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Ammonia emissions from livestock farms are a major problem for staff and residents near these centers. Anaerobic decomposition of livestock waste and the collection and stacking of waste are the main sources of ammonia gas on livestock farms. This article examined ammonia emissions from livestock facilities near Tehran Imam Khomeini International Airport. First, field measurements were made in the summer (30°C), and collected samples were examined using the indophenol technique. AERMOD, based on Gaussian fluctuating plume theory, was constructed to model ammonia emissions. According to AERMOD, the maximum periodic‐seasonal 2‐hourly and 8‐hourly ammonia concentrations were 15.9, 229, and 63.7 OU (odor unit per unit volume)/m3, respectively. The hourly ammonia emission rate from livestock sources is also 0.0168 gm−2 h−1. AERMOD results mathematical model indicated reasonable agreement with the field measurement, which the coefficient of determination (R2), index of agreement (d), and efficiency coefficient (E) were 0.95, 0.97, and 0.91, respectively. Moreover, Statistical indices, including geometric mean bias (MG), geometric variance (VG), fraction of predictions within a factor of two of observations (FAC2), and normalized mean square error (NMSE), the root mean square error (RMSE) and mean absolute error (MAE) are also indicated by 1.36 and 0.67, respectively, were used to assess the reliability of the model results showed that AERMOD had a good agreement with the field measurement.

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Section: Methodsmentioning

confidence: 99%

Section: Modeling Ammonia Performancementioning

confidence: 99%

Ammonia emissions from livestock facilities around an international airport, Tehran, Iran: An experimental and mathematical study

Asadollahfardi,

Khoormehr,

Sarmadi

et al. 2024

Env Prog and Sustain Energy

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“…To draw future projections on emissions, it is common to use multilinear regression (MLR) and multiple polynomial regression (MPR) based on business as usual (BAU), and projections are subject to assigned goals or policy that may be verified through these techniques [ 35 ]. These models connect emission models with transport, environment, and other integrated overlapping models [ 43 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation

Rauf,

Umer

2023

PLoS ONE

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Transport sector contribution to global emissions is a known fact, however, the mitigation path to achieve nationally determined goals for carbon reduction is often not specified, A simplified technique based on minimax optimization using Grey relational grade and Random forest narrows down on most contributing input variables from twelve road transport modes. This is a region-specific, scenario-based technique applied to north Punjab, Province of Pakistan that first categorizes modes based on their emission and then integrates with AI modeling using Deep Neural Network to develop sustainable trade-offs for carbon reduction. The output parameter translates the problem into a systematic iterative technique that predicts optimization options with different scenarios to bring out an environment-friendly transport mix. A 25% reduction applied to the five most emission-releasing modes like Diesel Light and Heavy Duty vehicles, Gas Light and heavy-duty vehicles, and Gas-Cars results in 16.54 MT of Carbon dioxide which is 54.35% reduced to the predicted 36.24 MT for the year 2044. Similarly in another scenario replacing 25% Gas and Diesel Light Duty vehicles respectively by adding 50% Petrol Light Duty vehicles leads to 18.94 MT of emissions which brings the emission value in 2044 at par with emission releases of the year 2014. The technique offers a forward path that allows environment-friendly modal mix combinations based on business-as-usual to offer transport mix solutions for carbon reduction. It is a generalized model that is based on a customized transport mix. Future studies can also be applied to intermodal tradeoffs like rail, air, waterways, etc.

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“…Currently, transportation systems, which are the second highest source of pollution in cities [1], must deal with many issues to meet user expectations in terms of efficient, healthy, environmentally friendly, and convenient trips. For example, greenhouse gas (GHG) emissions [2], road congestion [3], and accidents [4] place a heavy burden on governments to provide an adequate environment for their population [5].…”

Section: Introductionmentioning

confidence: 99%

Game theoretic approach for public multi‐mode transportation in smart cities

2021

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Here, a dynamic multi-mode transportation model is developed in which the passenger, for his trip, can use one or a combination of the transportation forms such as a car, a bus or a bicycle. This model is based on the game theory concept-based trip costoptimization and it is called game theory multi-mode transportation. The proposed system is implemented through a realistic scenario in a specific city using the OMNET++ and the OpenStreetMap software tools. The results show that the average trip price and the average trip time are improved when using the proposed model.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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Development of Reduction Scenarios Based on Urban Emission Estimation and Dispersion of Exhaust Pollutants from Light Duty Public Transport: Case of Tabriz, Iran

Cited by 13 publications

References 47 publications

Ammonia emissions from livestock facilities around an international airport, Tehran, Iran: An experimental and mathematical study

Ammonia emissions from livestock facilities around an international airport, Tehran, Iran: An experimental and mathematical study

Development of AI-Augmented optimization technique for analysis & prediction of modal mix in road transportation

Game theoretic approach for public multi‐mode transportation in smart cities

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