MEDFORD/SOMERVILLE, MASS. In a study that analyzed relationships between air quality and unemployment levels, a Tufts University researcher has developed a new statistical model that retrospectively estimates air pollution exposure for previous time periods where such information is not available.
Mary Davis, an associate professor of urban and environmental policy and planning at Tufts University School of Arts and Sciences, analyzed traffic-related air pollution levels and unemployment rates in four separate regions of California for which extensive air monitoring data was available: San Francisco Bay, Sacramento Valley, San Joaquin Valley, and the south-central coast between 1980 and 2000.
Davis focused on predicting trends in pollutants that are emitted by engines and known to negatively impact human healthhaze, carbon monoxide, and nitrogen dioxide. She integrated into her model other variables such as weather, population density, unemployment levels, and environmental regulations. Air pollution data came from the California Air Resources Board.
The unemployment data was provided by the federal Bureau of Labor Statistics. It showed broad shifts in overall unemployment with highs in the early 1980s and 1990s. Unemployment levels for the trucking industry, which were tracked separately, matched the overall trend.
Davis's analysis revealed a pattern. During the highest periods of unemploymentearly 1980s and early 1990sconcentrations of the three pollutants decreased in small but discernible amounts for every one percent increase in unemployment. The reason for the decrease, she says, was due to slowdowns in commercial trucking and car trips to work, shopping malls, or recreational destinations.
Importantly, Davis says the model allows epidemiologists to look backwards in time to predict public exposure to pollution in a geographical area. This data would ultimately help researchers discern public health trends.
|Contact: Alex Reid|