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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. [ABSTRACT FROM AUTHOR]

This study investigates the dynamic nature of rainfall observed at the Sustainable Agriculture Farming Systems (SAFS) site in California's Sacramento Valley, which was established to study the benefits of winter cover cropping in Mediterranean irrigated-arid systems. Rainfall data of four different temporal scales (i.e. daily, weekly, biweekly, and monthly) are analysed to determine the dynamic nature of precipitation in time. In an arid climate with seasonal precipitation this has large implications for land and water management, both in the short term and in the long term. A nonlinear dynamic technique (correlation dimension method) that uses the phase-space reconstruction and dimension concepts is employed. Bearing in mind the possible effects of the presence of zeros (i.e. no rain) on the outcomes of this analysis, an attempt is also made to compare the dynamic nature of all-year rainfall and winter rainfall. Analysis of 15 years of data suggests that rainfall dynamics at this site are dominated by a large number of variables, regardless of the scales and seasons studied. The dimension results also suggest that: (1) rainfall dynamics at coarser resolutions are more irregular than that at finer resolutions; (2) winter rainfall has a higher variability than all-year rainfall. These results are indeed useful to gain information about the complexity of the rainfall process at this site with respect to (temporal) scales and seasons and, hence, the appropriate model (high-dimensional) type. However, in view of the potential effects of certain rainfall data characteristics (e.g. zeros, measurement errors, scale effects) on the correlation dimension analysis, the discussion also emphasizes the need for further verification, and possibly confirmation, of these results. [ABSTRACT FROM AUTHOR]