## 2 Materials and Methods
### Emission Estimation  
We studied emissions from road transportation in four groups of pollutants that affect climate change, air quality and health, namely, GHGs (CH\({}_{4}\), CO\({}_{2}\) and N\({}_{2}\)O), ozone precursor gases (CO, NMVOC and NOx), aerosols (BC, PM\({}_{10}\) and PM\({}_{2.5}\)), and acidifying gases (NH\({}_{3}\) and SO\({}_{2}\)). Several studies have been conducted to estimate the emissions from road transportation based on fuel consumption [17; 39; 40]. To estimate these emissions, we selected a standardized methodology consistent with the 2006 IPCC Guidelines for National Greenhouse Gas Inventories [41] and the method from the EEA/EMEP Emission Inventory Guidebook 2019. [42]. Thus, we used tier 1 methods that use activity data derived from available statistical information (energy statistics, production statistics, traffic counts, population size, etc.). In addition, tier 1 emission factors were chosen to represent \"typical\" or \"averaged\" process conditions; they tended to be independent of technology. Furthermore, we used an additional level of detail (tier 2) for the calculation of SO\({}_{2}\) emissions, since Colombian fuel emission factors were used [43]. This is consistent with previous studiesthat showed that this methodology was adequate to estimate inventories at the national level, when detailed information by city was not available [20; 44; 45; 46; 47]. Overall, the method was based on estimating emissions through a linear relationship between activity data and emission factors (Table A1). The calculation was made using Equation (1), as follows:  
\[E(p)=\sum_{p,f,v}\left(Fuel_{f,v}*Ef_{p,f,v}\right) \tag{1}\]  
where _E (p)_ is the total emission for species or pollutant \(p\), _Fuel (f,v)_ is the fuel sold (diesel, gasoline and CNG) for type of vehicle \(v\), _Ef (p,f,v)_ is the emission factor for pollutant species \(p\), for type of fuel \(f\) and vehicle \(v\). Therefore, the emission estimate for each polluting species was calculated using Equation (1) with the following data:  
_Fuel:_ We used the Statistical Bulletin by Ministry of Mines and Energy [48], which includes activities such as monthly sales of fuels for the first half of 2018. We used the Liquid Fuel Information System (SIGOM) [49], which includes monthly sales to fuel retail distributors for the first half of 2020. In addition, we used the Mercantile Exchange  
Figure 1: Location of Colombia in South America. Study area covers the entire territory of Colombia. Colombia [50] (as shown in Table 10), for data on the consumption of CNG (Figure 11). Furthermore, considering that consumption was only focused on the transport sector, fuel distribution data was obtained from automotive service stations, assuming 96% of the distribution of the total of this category (retail distributors) was the total of the fuel supply, according to SICOM data [49]. For fuel consumption by vehicle type, the consumption distribution percentages (Table 1) of the indicative action plan for energy efficiency [51] were selected, calculating consumption by vehicle category. _Number of vehicles:_ Census of number of vehicles by type (vehicle category) from the Single National Traffic Registry of Colombia [37]. _Emission factors:_ The emission factors considered were those established by the EMEP/EEE Joint Inventory Guide to Air Pollutant Emissions database [42], for vehicle type and pollutant (GHGs, ozone precursors, aerosols and acidifying gases). The SO\({}_{2}\) emission factor and the power calorific value by type of fuel was obtained from the 2016 UPME FECOC calculator (Colombian fuel emission factors) in energy units (Kg T)\({}^{-1}\)) [43], except the CNG power calorific value was taken from the PROMIGAS technical notes [52]. These values were assumed for all types of vehicle under study. Additionally, CO\({}_{2\text{eq}}\) from the main GHGs (CH\({}_{4}\) and N\({}_{2}\)O) was estimated.