Applications of nonlinear dynamic tools for studying air pollution are gaining attention. Studies on ozone concentration in urban areas have reported the presence of low-dimensional deterministic natures and thus the possibility of good predictions of air pollution dynamics. In light of these encouraging results, a nonlinear deterministic approach is employed herein to study air pollution dynamics in a rural, and largely agricultural, setting in California. Specifically, air quality index (AQI) data observed at the University of California, Davis/National Oceanic and Atmospheric Administration (UCD/NOAA) climate station are studied. Four different daily AQI types of data are analyzed: maximum, minimum, difference (between maximum and minimum), and average. The correlation dimension method, a nonlinear dynamic technique that uses phase–space reconstruction and nearest neighbor concepts, is employed to identify the nature of the underlying dynamics, whether high-dimensional or low-dimensional. Correlation dimensions of 5.12, 6.20, 6.68, and 5.84 obtained for the above four series, respectively, indicate the presence of low-dimensional deterministic behavior, with six or seven dominant governing variables in the underlying dynamics. The dimension results and number of variables are in reasonable agreement with those reported by past studies, even though the studied data are different: rural versus urban, and AQI versus ozone concentration. Future efforts will focus on strengthening the present results on the nature of air pollution dynamics, identifying the actual governing variables, and predictions of air pollution dynamics.
