Spatial Disaggregate Module

The Spatial Disaggregation Module mainly deals with the prediction of pollution concentrations at a disaggregate level. SMARTPLANS performs these predictions at the grid cell level. The user can define the spatial resolution of the input grid cells to be used in the estimation of pollution concentration. The estimated concentrations will also be used as input into the Health Benefits Module. The pollutant included in the model are: : Hydro Carbon (HC), Carbon Monoxide (CO), Nitrogen Oxide (NOx), fine particulate matter (PM2.5), particulate matter (PM10), ozone (O3), Sulfur Dioxide (SO2), and Carbon Dioxide (CO2). The Spatial Disaggregation Module allows the user to choose one of two different Pollution Concentration Models to predict the concentrations of the various pollutants at a disaggregate level: (1) Land Use Regression (LUR) model and (2) Gaussian Dispersion model.

Grid Cell Level Land Use and Transportation Disaggregation

This module also disaggregates the zonal population and job predictions obtained from the Land Use Module to the grid cell level. It also disaggregates the link level predictions for each modeled vehicle class that are obtained from the Transportation Module to the grid cell level. The transportation predictions that are disaggregated include: traffic flows, vehicle kilometers traveled (VKT) and vehicle minutes traveled (VMT) to the grid cell level. This disaggregated land use and transportation information is primarily used as input variables to the Land Use Regression (LUR) Models.

The process of disaggregation is done based on the concordance of IDs between the Grid Cells Layer and the Zones Layer in the case of population and jobs. On the other hand, the process is done based on concordance of IDs between the Grid Cells Layer and Links Layer in the case of traffic volume, VKT, and VMT. Here, the software relies on input information provided by t he user which defines the presence of a particular land use type (e.g., residential or commercial) in the used grid cell layer. The fields storing the information are set up as categorical variables taking on the value of 1 if a particular land use type is found in the grid cell, 0 otherwise. For instance, if a field, say RESDLU is introduced to represent the residential land use, then the values in this field will be set to 1 if the grid cell intersects with residential land use, 0 otherwise. The program will assign population to the grid cells with RESDLU equal to 1 within the zone evenly.

Alternatively, the field for all the grid cells that fall within a particular zone could be setup to have weights that add up to 1 for the specific land use type. In this case, the population in the zone is distributed to the grid cells based on the provided weights. The same is done for the jobs using a field representing the non-residential land use in the grid cells. In the case of traffic flow, VKT, and VMT, the program will assign link level values to the grid cells where the links and grids intersected. For example, if a grid cell has two roads (links) going through it, then the traffic flow pertaining to the two links is added and assigned to the grid cell. The same is done for the VKT and VMT.

Land Use Regression (LUR) Modeling Approach

The LUR approach utilizes a multivariate linear regression model to estimate pollution concentration. Here, the concentration is calculated as follows:

where betas are the estimated parameters for pollutant p, while X’s are significant variables that influence pollutant p.

Gaussian Dispersion Modeling Approach

The Gaussian Dispersion modeling approach estimates the concentration of pollutant p at a receptor point (x, y, z) (i.e., centroid of a grid cell) from a given source (i.e., center of road links) that lie at an angle (theta > 0) along the wind direction w. Ground Level concentration C for pollutant p at crosswind distance y and downwind distance x is calculated as follows:

where:

  • Cp is the concentration of a specific pollutant p at location x, y, and z
  • H is the height (m) of the release of the emissions from emission sources (i.e., vehicles). This is set to 0.035 m
  • Y is the distance (m) along the normal from the emission source to the plume center line
  • Qp is the simulated road link emission rate (g/m/s) of pollutant p
  • μ is the average wind speed (m/s)
  • σy is the standard deviation of the horizontal concentration in the plume
  • σz is the standard deviation of the vertical concentration in the plume
  • Dp is an optional term capturing the molecular weight of pollutant p

Dp can be set to either a power or exponential function.


where Ep is a positive parameter > 0; β is less than or equal to 0; and Wp is the molecular weight of p.

The dispersion parameter σy and σz vary by “Stability Class”, which are dependent on weather conditions and average wind speed. For example, if the stability class is set to Day with moderate incoming solar radiation and wind speed greater than 5 m/s, then the stability class will be D. Consequently, the standard deviations of the horizonal and vertical concentrations will be calculated as follows:


RWCModule