This repository contents Jupyter Notebooks files useful to process information related to fleet features, use intensity and road length.
fleet features and use intensity are processing in Fleet and Use intensity.ipynb. There are details about how we can process information to get useful data to feed to wrfchemi_cbmz_fc.ncl based on emissions pre-processor model Andrade et al. 2015. Here, I'm considering emission factors based on field experiment, mainly by Pérez-Martinez et al. (2014) and by Andrade et al. 2015, shown as following:
&fator_emissao ! VEIC 1, VEIC 2, VEIC 3, VEIC4A, VEIC4B, VEIC4C, VEIC 5, VEIC6A, VEIC6B
exa_co = 5.8000, 12.000, 5.8000, 3.6000, 3.6000, 3.6000, 0.0000, 9.1500, 9.0200,
exa_co2 = 219.00, 219.00, 219.00, 1422.0, 1422.0, 1422.0, 0.0000, 0.0000, 0.0000,
exa_nox = 0.3000, 1.1200, 0.3000, 9.2000, 9.2000, 9.2000, 0.0000, 0.1320, 0.1290,
exa_so2 = 0.0290, 0.0140, 0.0210, 0.6100, 0.6100, 0.6100, 0.0000, 0.0097, 0.0093,
exa_c2h5oh = 0.5080, 0.2500, 0.5080, 0.6100, 0.6100, 0.6100, 0.0000, 0.0790, 0.3050,
exa_hcho = 0.0089, 0.0110, 0.0098, 0.6100, 0.6100, 0.6100, 0.0000, 0.0152, 0.0155,
exa_ald = 0.0140, 0.0300, 0.0220, 0.6100, 0.6100, 0.6100, 0.0000, 0.0164, 0.0188,
exa_pm = 0.0200, 0.0200, 0.0200, 0.2770, 0.2770, 0.2770, 0.0000, 0.0500, 0.0500,
exa_voc = 0.4250, 1.3000, 0.4340, 2.0500, 2.0500, 2.0500, 0.0000, 1.0800, 1.0800,
vap_voc = 0.2300, 0.2500, 0.2400, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000, 0.0000,
liq_voc = 2.0000, 1.5000, 1.7500, 0.0000, 0.0000, 0.0000, 0.0000, 1.2000, 1.2000,
Where VEIC corresponds a specific type of vehicle and his fuel use:
- Light-duty vehicles (LDV): VEIC 1 (gasohol fuel), VEIC 2 (ethanol fuel), and VEIC 3 (Flex Gasohol and Ethanol fuel).
- Heavy-duty vehicles (HDV): VEIC4A (trucks diesel fuel), VEIC4B (urban bus, diesel fuel), VEIC4C (articulated urban bus, diesel fuel).
- Motorcycle (MC): VEIC6A (gasohol fuel), VEIC6B (Flex-G and Flex-Eth fuel).
- Others: VEIC 5 that represents taxis with fuel consumption of natural gas.
Also, this notebook contents Emissions Calculation for the Modeling Domain section that it's important to calculate emissions rates considering the number of vehicles inside the modeling domain, based on Top-Down approximation. With this information, we can compare if the bottom_up approximation (using wrfchemi_cbmz_fc.ncl program) results are similar to Top-Down results.
The second file Road_Length.ipynb is useful to process the output file from QGIS in *.csv format to *.txt.