CALRoads View – Hourly Concentration Output in CAL3QHCR
June 2023
CALRoads View includes three mobile source air dispersion models: CALINE4, CAL3QHC, and CAL3QHCR. These models are used for predicting air pollution concentrations of carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM), and other inert gases from idle or moving vehicles.
The CAL3QHCR model is enhanced to process up to one year of hourly meteorological data. However, the output produced by the model is coded to produce values for specific averaging periods. The periods selected for each pollutant may not correspond to local guidelines for modelers outside the United States. To assist these modelers, developers at Lakes Software enhanced the CAL3QHCR executable to produce an Hourly Concentration File. This file is generated automatically with each run performed using Lakes’ CAL3QHCR executable, so there’s no need to enable special options to use it.
Go to the File | Preferences menu and select the CAL3QHCR option from the EPA Models/Limits menu. The default executable is CAL3QHCR_LK.EXE.
The CAL3QHCR model is enhanced to process up to one year of hourly meteorological data. However, the output produced by the model is coded to produce values for specific averaging periods. The periods selected for each pollutant may not correspond to local guidelines for modelers outside the United States. To assist these modelers, developers at Lakes Software enhanced the CAL3QHCR executable to produce an Hourly Concentration File. This file is generated automatically with each run performed using Lakes’ CAL3QHCR executable, so there’s no need to enable special options to use it.
Go to the File | Preferences menu and select the CAL3QHCR option from the EPA Models/Limits menu. The default executable is CAL3QHCR_LK.EXE.
After performing a CAL3QHCR model run, go to the Output menu and select Hourly Concentration File to open the file in a text editor. This is a CSV file so it can be opened directly in spreadsheet programs such as Microsoft Excel for additional data analysis (e.g., calculating non-standard averaging periods).
