Apportionment of Vehicle Fleet Emissions by Linear Regression, Positive Matrix Factorization, and Emission Modeling

Instantaneous CO2 emission modelling for a Euro 6 start-stop vehicle based on portable emission measurement system data and artificial intelligence methods

One of the increasingly common methods to counteract the increased fuel consumption of vehicles is start-stop technology. This paper introduces a methodology which presents the process of measuring and creating a computational model of CO2 emissions using artificial intelligence techniques for a vehicle equipped with start-stop technology. The method requires only measurement data of velocity, acceleration of vehicle, and gradient of road to predict the emission of CO2. In this paper, three methods of machine learning techniques were analyzed, while the best prediction results are shown by the gradient boosting method. For the developed models, the results were validated using the coefficient of determination, the mean squared error, and based on visual evaluation of residual and instantaneous emission plots and CO2 emission maps. The developed models present a novel methodology and can be used for microscale environmental analysis.

Evidence of non-tailpipe emission contributions to PM2.5 and PM10 near southern California highways

Particulate Matter (PM) concentrations near highways are influenced by vehicle tailpipe and non-tailpipe emissions, other emission sources, and urban background aerosols. This study collected PM_2.5 and PM₁₀ filter samples near two southern California highways (I-5 and I-710) over two weeks in winter 2020. Samples were analyzed for chemical source markers. Mean PM_2.5 and PM₁₀ concentrations were approximately 10-15 and 30 μg/m³, respectively. Organic matter, mineral dust, and elemental carbon (EC) were the most abundant PM components. EC and polycyclic aromatic hydrocarbons at I-710 were 19-26% and 47% higher than those at the I-5 sites, respectively, likely due to a larger proportion of diesel vehicles. High correlations were found for elements with common sources, such as markers for brake wear (e.g., Fe, Ba, Cu, and Zr) and road dust (e.g., Al, Si, Ca, and Mn). Based on rubber abundances, the contributions of tire tread particles to PM_2.5 and PM₁₀ mass were approximately 8.0% at I-5 and 5.5% at I-710. Two different tire brands showed significantly different Si, Zn, carbon, and natural rubber abundances.