OZONE EXPOSURE AND POPULATION-DENSITY IN HARRIS COUNTY, TEXAS

We address the following question: What is the pattern of human exposu re to ozone in Harris County (Houston) since 1980? While there has bee n considerable research on characterizing ozone measured at fixed moni toring stations, little is known about ozone away from the monitoring stations, and whether areas of higher ozone correspond to areas of hig h population density. To address this question, we build a spatial-tem poral model for hourly ozone levels that predicts ozone at any locatio n in Harris County at any time between 1980 and 1993. Along with build ing the model, we develop a fast model-fitting method that can cope wi th the massive amounts of available data and takes into account the su bstantial number of missing observations. Having built the model, we c ombine it with census tract information, focusing on young children. W e conclude that the highest ozone levels occur at locations with relat ively small populations of young children. Using various measures of e xposure, we estimate that exposure of young children to ozone decrease d by approximately 20% from 1980 to 1993. An examination of the distri bution of population exposure has several policy implications. In part icular, we conclude that the current siting of monitors is not ideal i f one is concerned with population exposure assessment. Monitors appea r to be well sited in the downtown Houston and close-in southeast port ions of the county. However, the area of peak population is southwest of the urban center, coincident with a rapidly growing residential are a. Currently, only one monitor measures air quality in this area. The far north-central and northwest parts of the county are also experienc ing rapid population growth, and our model predicts relatively high le vels of population exposure in these areas. Again, only one monitor is sited to assess exposure over this large area. The model we developed for the ozone prediction consists of first using a square root transf ormation and then decomposing the transformed data into a trend part a nd an irregular part, the latter modeled as a Gaussian random field wi th both time and space correlations. Due to the large number of observ ations and high-dimensional optimization problem, we developed a fast method to estimate the parameters of the model. The model and estimati on method are general and can be used in many problems with space-time observations.