REMI model: Bottom-up emissions inventories for cities with lack of data

Abstract

Abstract Emissions inventorying is a complex task with regulatory and/or scientific environmental purposes. In South American cities, when the task is performed, the common denominator is lack of data and documentation, and vehicles are usually the main source of pollutant of emerging and consolidated megacities. Therefore, emissions inventories is becoming more important, especially for mobile sources. In this manuscript we present the model REMI (R-EMssions-Inventory) for developing bottom-up emissions inventory for vehicles in cities with lack of data (Ibarra & Ynoue, 2016). The program was written in R (R CORE TEAM 2016) using several libraries. The program consists in several R scripts organized in folders with Inputs& Outputs. For traffic inputs uses counts or simulations, and also, it can be as a top-down method with statistical traffic information. REMI classifies vehicule data by fuel, size of motor, use and gross weight anually up to 50 years, according to EEA/EMEP guidelines and Copert (Ntziachristos, 2014). REMI has several options for emission factors, 1) Emission factors from Ntziachristos (2014), 2) local emission factors or 3) mixed emission factors. In the future REMI will include HBEFA emission factors. REMI also incorporates deterioration factors. Currently REMI estimate hot-engine emissions of 27 pollutants. Keywords: REMI, vehicular, emissions inventory, R, bottom-up.