> summary(v.cast1)

Call:
   c("inla(formula = no2 ~ -1 + Intercept + nadmvyska + no2_y_camx + ", " 
   no2_y_sym + f(i_back, model = spdemod_back) + f(i_urb, model = 
   spdemod_urb), ", " family = \"gaussian\", data = inla.stack.data(stack, 
   spdemod_back = spdemod_back, ", " spedemod_urb = spdemod_urb), 
   quantiles = NULL, control.compute = list(dic = TRUE, ", " waic = T), 
   control.predictor = list(A = inla.stack.A(stack), ", " compute = TRUE), 
   control.results = list(return.marginals.random = FALSE, ", " 
   return.marginals.predictor = FALSE))") 
Time used:
    Pre = 3.58, Running = 1643, Post = 2.31, Total = 1648 
Fixed effects:
             mean    sd   mode kld
Intercept  15.451 1.772 15.461   0
nadmvyska  -0.009 0.002 -0.009   0
no2_y_camx  0.358 0.363  0.360   0
no2_y_sym   1.253 1.221  1.239   0

Random effects:
  Name	  Model
    i_back SPDE2 model
   i_urb SPDE2 model

Model hyperparameters:
                                           mean      sd    mode
Precision for the Gaussian observations   0.141   0.018   0.136
Range for i_back                         66.895  41.997  41.431
Stdev for i_back                          1.163   0.537   0.857
Range for i_urb                         968.163 491.011 695.075
Stdev for i_urb                           2.044   0.568   1.780

Expected number of effective parameters(stdev): 10.16(3.19)
Number of equivalent replicates : 5.91 

Deviance Information Criterion (DIC) ...............: 303.08
Deviance Information Criterion (DIC, saturated) ....: 73.65
Effective number of parameters .....................: 11.26

Watanabe-Akaike information criterion (WAIC) ...: 304.49
Effective number of parameters .................: 10.96

Marginal log-Likelihood:  -190.78 
Posterior marginals for the linear predictor and
 the fitted values are computed

> summary(v.cast2)

Call:
   c("inla(formula = no2 ~ -1 + Intercept + nadmvyska + no2_y_camx + ", " 
   no2_y_sym + f(i_back, model = spdemod_back) + f(i_urb, model = 
   spdemod_urb), ", " family = \"gaussian\", data = inla.stack.data(stack, 
   spdemod_back = spdemod_back, ", " spedemod_urb = spdemod_urb), 
   quantiles = NULL, control.compute = list(dic = TRUE, ", " waic = T), 
   control.predictor = list(A = inla.stack.A(stack), ", " compute = TRUE), 
   control.results = list(return.marginals.random = FALSE, ", " 
   return.marginals.predictor = FALSE))") 
Time used:
    Pre = 3.72, Running = 1449, Post = 0.838, Total = 1454 
Fixed effects:
             mean    sd   mode kld
Intercept  15.464 1.801 15.472   0
nadmvyska  -0.010 0.003 -0.010   0
no2_y_camx  0.362 0.365  0.362   0
no2_y_sym   1.240 1.221  1.234   0

Random effects:
  Name	  Model
    i_back SPDE2 model
   i_urb SPDE2 model

Model hyperparameters:
                                            mean       sd    mode
Precision for the Gaussian observations    0.138    0.028   0.133
Range for i_back                         125.305  249.139  26.120
Stdev for i_back                           0.692    0.411   0.380
Range for i_urb                         1242.644 1132.606 540.236
Stdev for i_urb                            2.420    1.570   1.444

Expected number of effective parameters(stdev): 10.80(3.86)
Number of equivalent replicates : 5.55 

Deviance Information Criterion (DIC) ...............: 303.62
Deviance Information Criterion (DIC, saturated) ....: 75.12
Effective number of parameters .....................: 12.07

Watanabe-Akaike information criterion (WAIC) ...: 304.70
Effective number of parameters .................: 11.36

Marginal log-Likelihood:  -190.42 
Posterior marginals for the linear predictor and
 the fitted values are computed