Regression model to project and mitigate vehicular emissions in Cochabamba, Bolivia

Date of Award

2017

Degree Name

M.S. in Renewable and Clean Energy

Department

Department of Mechanical and Aerospace Engineering. Graduate Renewable and Clean Energy Program

Advisor/Chair

Advisor: Robert J. Brecha

Abstract

The purpose of this study is to generate a regression model tying the vehicular emissions in Cochabamba, Bolivia to input factors including the current state of the public fleet, city population, weather, and GDP. The finished model and the process to generate it can act as a tool to project future emissions in the city, accounting for the aforementioned input factors. It can also be used to estimate the drop in city pollution levels in a scenario where the public transportation fleet is partially replaced by non-emitting, electric vehicles. The main pollutant focused on in this study is particulate matter (PM₁₀), but data also exists for ozone (O₃), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂).The model generation process explained in the study could be applied to these pollutants as well. The regression model is generated using the open source software, R. Its final form utilizes a random forest regression model, but neural net, gradient boosting, and support vector machine models were also explored.

Keywords

Waste gases Bolivia Cochabamba Measurement, Waste gases Forecasting, Air Pollution potential Simulation methods, Air Pollution Measurement, Air Pollution Research, Engineering, Environmental Engineering, Mechanical Engineering, Random Forest Model, Vehicular Fleet, Cochabamba, Bolivia, Vehicle Emissions, Predictive Ensemble Model

Rights Statement

Copyright © 2017, author

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