CALINE4 is a line source air quality model developed by the California Department of Transportation (Caltrans). It is based on the Gaussian diffusion equation and employs a mixing zone concept to characterize pollutant dispersion over the roadway.
The purpose of the model is to assess air quality impacts near transportation facilities. Given source strength, meteorology and site geometry, CALINE4 can predict pollutant concentrations for receptors located within 500 meters of the roadway. In addition to predicting concentrations of relatively inert pollutants such as carbon monoxide (CO), the model can predict nitrogen dioxide (N02) and suspended particle concentrations. It also has special options for modeling air quality near intersections, street canyons, and parking facilities.
Historically, the CALINE series of models required relatively minimal input from the user. Spatial and temporal arrays of wind direction, wind speed and diffusivity were not needed by the models. While CALINE4 uses more input parameters than its predecessors, it must still be considered an extremely easy model to implement. For most applications, optional inputs can be bypassed and many other inputs can be assigned assumed worst case values. More complex approaches to dispersion modeling are unnecessary for most applications because of the uncertainties in estimating emission factors and traffic volumes for future years.
CALINE4's accuracy is well balanced with the accuracy of state-of-the-art predictive models for emissions and traffic. The model also possesses greater flexibility than earlier versions at little cost to the user in terms of input complexity.
CALINE4 should be thought of as an updated and expanded version of CALINE3. While the models use different methods for developing their vertical and horizontal dispersion curves, the final results differ very little by air quality modeling standards. For the most part, the technical differences between the two models represent "fine tuning" of the Gaussian method (as applied to line source modeling) and the mixing zone model. The real differences between the two models are in the areas of improved input / output flexibility and expanded capabilities.
A roadway model that predicts air pollutant concentrations near highways and arterial streets due to emissions from motor vehicles operating under free-flow conditions and idling vehicles. In addition, CAL3QHC incorporates methods for estimating traffic queue lengths at roadway intersections.
CAL3QHC is a consolidation of the CALINE3 line source dispersion model and an algorithm that estimates the length of the queues formed by idling vehicles at signalized intersections. The contribution of the emissions from idling vehicles is estimated and converted into line sources using the CALINE3 link format. CAL3QHC requires all input parameters necessary to run CALINE3 plus the following additional inputs:
- idling emission rates
- the number of "moving" lanes in each approach link
- the signal timing of the intersection
CAL3QHC also includes three additional traffic parameters that must be provided by the user:
- Saturation Flow Rate,
- Signal Type, and
- Arrival Type
BREEZE CAL3QHC permits the user to specify roadway links and receptor locations within an XYZ plane. The Y-axis is aligned due north, with wind angle inputs to the model following accepted meteorological convention (e.g. 270E represents a wind from the west). The positive X-axis is aligned due east. A link can be specified as either a free flow or a queue link. The program automatically sums the contributions from each link to each receptor. Surface roughness and meteorological variables (such as atmospheric stability, wind speed, and wind direction) are assumed to be spatially constant over the entire study area.
The dispersion component used in CAL3QHC is CALINE3, a line source dispersion model developed by the California Department of Transportation. CALINE3 estimates air pollutant concentrations resulting from moving vehicles on a roadway based on the assumptions that pollutants emitted from motor vehicles traveling along a segment of roadway can be represented as a "line source" of emissions, and that pollutants will disperse in a Gaussian distribution from a defined "mixing zone" over the roadway being modeled.
An enhanced version of CAL3QHC, this version can process up to a year of hourly meteorological, vehicular emissions, and traffic volume and signalization data in one model run. In addition, 1-hour and running 8-hour averages of CO or 24-hour and annual block averages of PM can be calculated.
CAL3QHCR is an enhanced, but separate, version of CAL3QHC. CAL3QHCR has been programmed to process up to a year of hourly meteorological (MET), and vehicular emissions, traffic volume, and signalization (ETS) data in one run using the basic algorithms from CAL3QHC. Daily to seasonal runs can also be made with CAL3QHCR, whereas CAL3QHC was designed to process one hour of ETS and MET data.
Flexibility has been built into CAL3QHCR to allow a two-tiered approach. These two approaches (Tier I and Tier II) are enhancements to the basic approach used in CAL3QHC.
In Tier I:
- A full year of hourly meteorological data are entered into the model in place of the one hour of artificial meteorological data commonly entered into CAL3QHC
- One hour of ETS data are entered
In Tier II:
- The same hourly meteorological data as used in Tier I approach are entered into the model
- ETS data contains more detailed information and reflect traffic conditions for each hour of a week
CAL3QHCR reads the ETS data up to 7 sets of hourly ETS data (in the form of diurnal patterns) and processes the data into a week of hourly ETS data. The weekly ETS data are synchronized to the day of the week of the meteorological data year. The weekly traffic conditions are assumed to be the same for each week throughout the modeled period.
While CAL3QHC only prints maximum hourly averages, CAL3QHCR calculates 1-hour and running 8-hour averaged CO or 24-hour and annual block averaged PM concentrations. In addition, CAL3QHCR output contains: 1) a table of calm wind durations with their respective frequencies, 2) identification of truncated queues due to queues exceeding the physical constraints of the intersection, 3) optional link contribution results for each printed average, and 4) optional use of variable ambient background concentration data in calculating the various maxima concentrations. This output, when produced by either of the two tier approaches, provides a more detailed synopsis of MET and traffic conditions on air quality than could be derived from CAL3QHC.