Abstract
New York City data indicate that seasonal and annual variations in dispersion-normalized air pollutant concentrations appear to accurately reflect changes in source emission patterns. The normalized concentrations make it possible to observe the impact of regulatory changes on ambient air quality without these impacts being obscured by meteorological fluctuations. It is found that numerical modeling techniques and regression analysis can be powerful tools for extracting information from large sets of air quality data. The use of differential, as opposed to absolute, pollutant concentration values will reduce artifact correlations caused by seasonal, weekly, or daily meteorological fluctuations and will permit more accurate estimation of the regression coefficients. This technique was successfully applied to a set of daily pollution measurements whose absolute concentrations were found not to yield a statistically significant fit by multiple regression.
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