toxicIntensity: toxicIntensity Method

Description

Simulate contaminants intensity over the landscape by two steps : dispersal of toxic particules and local intensity of particules after dispersal.

Usage

toxicIntensity(objectL, ...)
"toxicIntensity"(objectL, toxic_emission, mintime = 1, maxtime = 60, size_raster = 2^10, kernel = "NIG", kernel.options = list(a1 = 0.2073, a2 = 0.2073, b1 = 0.3971, b2 = 0.3971, b3  = 0.0649, theta = 0), beta = 0.4, alpha = list(minalpha = 0.1, maxalpha = 0.95, covariate_threshold = 30, simulate = T, covariate = NULL))

Arguments

objectL

A Landscape object

...

parameters

toxic_emission

Matrix of sources emissions, row as sources ID, col as time

mintime

Start simulation time (default=1)

maxtime

End simulation time

size_raster

raster size (default = 2^10)

kernel

dispersion kernel, function name (default = NIG)

kernel.options

parameters list for the kernel function

beta

toxic adherence parameter between 0 and 1 (default = 0.4)

alpha

list of toxic loss options

(default = list(minalpha=0.1,maxalpha=0.95,covariate_threshold=30,simulate=TRUE,covariate=NULL))

Value

A ToxicIntensityRaster, a 3D array as time matrix dispersion, [t,x,y]

Details

The dispersal of contaminants is implemented by rastering the landscape and by computing the convolution between sources emissions and a dispersal kernel.

The dispersion kernel by default is Normal Inverse Gaussian kernel ("NIG" function). Currently, two others are implemented "geometric" (with parameter a) and "2Dt" kernels (with parameters a, b, c1, c2).

Local intensity depends of beta and alpha parameters. Beta represents the toxic adherence between [0,1]. Alpha represents a list of parameters of the lost of toxic particules due to covariates (precipitation). There are two configurations to integrate the loss in the function : (i) simulating covariate (simulate=TRUE) or (ii) uploading covariate (simulate=FALSE). The covariate is linked to the loss by a linear regression with paramaters minalpha, maxalpha, covariate_threshold.