AIR POLLUTION – MONITORING MODELLING AND HEALTH

Methodology to Assess Air Pollution
Impact on Human Health Using the Generalized Linear Model with Poisson Regression 299
Lag
days
AIC t-value
0 6,798.7 1.60x10 -03 2.54x10 -04 6.30***
1 6,809.6 9.88x10 -04 2.17x10 -04 4.56***
2 6,793.6 9.07x10 -04 1.98x10 -04 4.59***
3 6,786.0 8.91x10 -04 1.96x10 -04 4.55***
4 6,785.8 6.20x10 -04 1.94x10 -04 3.19**
5 6,785.0 3.78x10 -04 1.91x10 -04 1.97*
6 6,778.2 3.46x10 -04 1.91x10 -04 1.82
7 6,774.9 7.36x10 -05 1.92x10 -04 0.38
***= 0; **= 0.001; *= 0.01 (Statistical significance level - ).
Table 7. Results analysis for no lag to seven lag days for models with no residual inclusion.
Lag
days
AIC t-value
0 6,588.0 1.45x10 -03 2.54x10 -04 5.71***
1 6,602.4 6.98x10 -04 2.18x10 -04 3.20**
2 6,593.7 6.22x10 -04 2.00x10 -04 3.12**
3 # 6,584.8 5.82x10 -04 1.98x10 -04 2.94**
4 6,581.2 2.71x10 -04 1.96x10 -04 1.38
5 6,576.1 5.50x10 -05 1.94x10 -04 0.29
6 6,569.6 6.54x10 -05 1.93x10 -04 0.34
7 6,556.6 -1.94x10 -04 1.94x10 -04 -1.00
***= 0; **= 0.001; *= 0.01 (Statistical significance level - ); # = the better model
Table 8. Results analysis for no lag to seven lag days for models including residuals.
5.4 Relative risk analysis
To analyze and estimate the PM 10 impact on Sao Paulo’s population health, the relative risk
for the model considering the effects of three lag days including residuals was calculated.
The expression that represents it is given by:
0.000582x
e
RR x . (16)