Description Usage Arguments Details Value
Simulate contaminants intensity over the landscape by two steps : dispersal of toxic particules and local intensity of particules after dispersal.
1 2 3 4 5 6 7 8 | toxicIntensity(objectL, ...)
## S4 method for signature 'Landscape'
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))
|
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)) |
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.
A ToxicIntensityRaster, a 3D array as time matrix dispersion, [t,x,y]
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