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Modifications  of  BME Analysis               193

        EXAMPLE 9.15:  Participate matter  has been associated with public  health risks
        by  a  number  of  authors  (e.g.,  Anderson  et al,  1992; USEPA,  1996; 1997).
        These  risks are associated mainly with  paniculate matter  of  aerodynamic par-
        ticle size of  lOfim  or smaller.  The  PMio  is a possible confounding factor, since
        it  may  have  a causal  association with  death  rate,  while  being  correlated with
        temperature  data.  The  PMio  data  were obtained  from  the  Aerometric  Infor-
        mation  Retrieval System (AIRS)  of the  USEPA (U.S. Environmental  Protection
        Agency).  The data  used  in this study  were collected  at  47 monitoring  stations
        located  throughout  the  State  of  North  Carolina  (Fig. 9.12).  Also  shown  in
                                                                3
         Figure  9.12  are the  contour  lines of  PMio  concentration  (in /^g/m ),  e.g., for
        August  31,  1995.  In order to  assess the  confounding  effect  of  PMio, we define
        the PEP parameter /3 DXp  =  (EDXP  -  E DP)/E D,  where E DXp  is the mor-
        tality  prediction  error  using temperature exposure, PMio, and death-rate data,
        while EDP  is the  prediction  error using only death-rate  and PM 10 data.  As be-
        fore, the D(s, t), X(s, t),  and P(s, t)  represent death-rate,  cold  temperature
        exposure,  and PMio  distributions,  respectively.






















         Figure  9.12. Locations of the 47 North  Carolina monitoring  stations for PMio
               data  (circles).  Also shown are the  contour  lines of  PMio  concentration
                       3
               (in  /xg/m )  for  August  31,  1995.

             Including  PMio  in  the  analysis  resulted  in  a total  of  six  covariance and
         cross-covariance  models  (as  compared to  only  three  covariance models  used
         in  Example  9.14).  All  six  covariance and  cross-covariance models were spa-
         tially  isotropic  and temporally  stationary  (Christakos  and  Serre,  2000a).  The
         physical  variables  (temperature  exposure  and  PMio  distributions)  exhibited
         considerably  larger  spatial correlation  ranges than  the  death-rate  distribution.
        The  spatiotemporal  distributions  of  exposure to  cold  temperature  X  and of
         PMio  concentrations  P  were found  to  be  negatively  correlated  (i.e.,  as the
        temperature  T  drops, the  exposure to  cold  temperature  X  increases  and the
         concentration  P  decreases).  Moreover,  exposure to  colder  temperatures and
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