Estimating the urban environmental impact of gasoline-ethanol blended fuels in a passenger vehicle engine.

A large portion of urban emissions in developing countries come from old gasoline vehicles driven in metropolitan areas. The present study aimed to develop models to estimate the environmental impact of different contents of gasoline and ethanol mixtures (pure gasoline; 25, 50, 75% ethanol blended to gasoline; and 100% ethanol) in a flex-fuel engine. We tested the blended fuel using three different speeds and recorded the GHG emissions and engine output data. The data mining approach was used to develop environmental impact predictive models. The ethanol content in gasoline; the engine rotational speed 900, 2000, and 3000 rpm; and λ were used as attributes. The classification target was the environmental impact concerning the CO2 emission ("low," "average," and "high"). We employed the Random forest algorithm to develop predictive models. The mean values of CO2 concentrations for all studied fuel content were above 2.47% of the volume. The trees' models (accuracy 73%, κ =0.61) showed three alternatives for predicting the environmental impact based on the ethanol blend, the engine rotation, λ, and the air-fuel ratio. Such models might help policymakers develop educational campaigns to reduce short- and medium-term urban commuter traffic pollution in countries that lack suitable urban transportation.

Environmental impact of the on-road transportation distance and product volume from farm to a fresh food distribution center: a case study in Brazil.

The pollutants' emissions from on-road transport are critical pressure on the climate change scenario, and most developing countries rely on mostly diesel transportation. The current study aimed to estimate the environmental impact of the distance from the agricultural production area of fresh food (papaya, potato, and tomato) to a fresh food distribution center located in Campinas, Sao Paulo, Brazil. The way the products were carried was assessed for calculating the total transported volume. The total amount carried was measured, considering the number of trips multiplied by the total distance traveled within a year of supply. An online calculator was used to evaluate the amount of CO2 emission, and to allow the estimative of the amount of CO2-eq, that is the Global Warming Impact (GWP) in 100 years. The highest CO2 emission was identified in the potato transported from Paraná State to the distribution center, with a CO2-eq emission of 3237 t/year (64% of contribution), followed by the papaya from Bahia State (2723 t/year, 42% of contribution), and the tomato from Sao Paulo State (625 t/year, 71% of contribution). However, when computing the GWP, the highest value was found in the transport of potato from the Minas Gerais State (8 × 10-2 in 100 years) followed by the papaya from Rio Grande do Norte State (5 × 10-2 in 100 years) and the papaya from Bahia (3 × 10-2 in 100 years). The higher the amount of product transported by a trip, the smaller the environmental impact in the long run. A proper strategy to reduce the environmental impact would be to have large freight volume when transporting food from vast distances within continental countries.