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

AbstractEmissions inventorying is a complex task with regulatory and/or scientificenvironmental purposes. In South American cities, when the task is performed, thecommon denominator is lack of data and documentation, and vehicles are usuallythe 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) fordeveloping 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 infolders with Inputs& Outputs. For traffic inputs uses counts or simulations, andalso, it can be as a top-down method with statistical traffic information. REMIclassifies vehicule data by fuel, size of motor, use and gross weight anually up to50 years, according to EEA/EMEP guidelines and Copert (Ntziachristos, 2014).REMI has several options for emission factors, 1) Emission factors fromNtziachristos (2014), 2) local emission factors or 3) mixed emission factors. In thefuture REMI will include HBEFA emission factors. REMI also incorporatesdeterioration factors. Currently REMI estimate hot-engine emissions of 27pollutants.Keywords: REMI, vehicular, emissions inventory, R, bottom-up.