Ozone exposure and population density in Harris County, Texas

We address the following question: What is the pattern of human exposure to ozone in Harris County (Houston) since 1980? While there has been considerable research on characterizing ozone measured at fixed monitoring stations, little is known about ozone away from the monitoring stations, and whether areas of higher ozone correspond to areas of high population density. To address this question, we build a spatial-temporal model for hourly ozone levels that predicts ozone at any location in Harris County at any time between 1980 and 1993. Along with building the model, we develop a fast model-fitting method that can cope with the massive amounts of available data and takes into account the substantial number of missing observations. Having built the model, we combine it with census tract information, focusing on young children. We conclude that the highest ozone levels occur at locations with relatively small populations of young children. Using various measures of exposure, we estimate that exposure of young children to ozone decreased by approximately 20% from 1980 to 1993. An examination of the distribution of population exposure has several policy implications. In particular, we conclude that the current siting of monitors is not ideal if one is concerned with population exposure assessment. Monitors appear to be well sited in the downtown Houston and close-in southeast portions of the county. However, the area of peak population is southwest of the urban center, coincident with a rapidly growing residential area. Currently, only one monitor measures air quality in this area. The far north-central and northwest parts of the county are also experiencing rapid population growth, and our model predicts relatively high levels 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 transformation and then decomposing the transformed data into a trend part and an irregular part, the latter modeled as a Gaussian random field with both time and space correlations. Due to the large number of observations and high-dimensional optimization problem, we developed a fast method to estimate the parameters of the model. The model and estimation method are general and can be used in many problems with space-time observations.