Optimal estimators for ambient air quality levels

Abstract

Procedures to estimate missing data, determine extrema, and derive uncertainties for data collected in ambient air monitoring networks are presented. The optimal linear estimators used obtain unbiased, minimum variance results based on the temporal and spatial correlation of the data and estimates of sample uncertainty. The first estimator interpolates missing data. The second estimator derives extrema, e.g. minimum and maximum concentrations, from the completed data set. Together the estimators can be used to check the validity of monitored observations, identify outliers, and estimate regional and local components of pollutant levels. The estimators are evaluated using data collected in urban air quality monitoring networks in Houston, Philadelphia and St Louis.