In this Section:

	Background
	Project Overview
	Research Methods
	Status of Project
	References
	Related Links
	Contacts

Research and Products:
Air Quality

Research Methods

Daily meteorological and air quality data (Figure 3.) were collected and analyzed for the time period 1990–2003, where available. Meteorological variables of interest that were analyzed in this study are maximum daily temperature, average daily wind speed, average daily dew point temperature, mixing height, average daily relative humidity, incoming solar radiation, and precipitation.

This study utilized a technique called the KZ filter to remove meteorological signals from the air quality time-series. This was done to reveal underlying air quality trends and contribute to an understanding of the mechanisms controlling the trends. The KZ filter is capable of separating a time-series into different temporal components by varying the length of the filter window and the number of iterations.

Although the KZ filter has been widely used for this type of trend separation in ozone studies in the eastern United States, this project aimed to extend the method in three key ways. First, the study was developed through a partnership between academic researchers and air quality planners and managers, and the output was tailored to be more applicable to decision makers’ needs. Second, the KZ filter was applied to PM in this study in order to determine the method’s effectiveness on this pollutant. Third, the method was applied to the Southwest in order to evaluate the effectiveness of the method in a region with weaker weather controls than the eastern United States.

Figure 4. The KZ filter [more details]

Statistical techniques were used to evaluate the relationship between pollutants and meteorological variables, producing a set of recommended models for ozone and PM. The KZ filter was then used to separate meteorological and air quality data into short-term, seasonal, and long-term trend components (Figure 4.). Statistical techniques and the KZ filter method were further applied to produce long-term ozone and PM trends that have had the influence of weather removed. These trends represent changes in pollutant concentrations attributable to sources other than the removed meteorological variables, such as emissions changes.