Extending the Kolmogorov–Zurbenko Filter: Application to Ozone, Particulate Matter, and Meteorological Trends

Abstract

Tropospheric ozone (O3) and particulate matter (PM) are pollutants of great concern to air quality managers. Federal standards for these pollutants have been promulgated in recent years because of the known adverse effects of the pollutants on human health, the environment, and visibility. Local meteorological conditions exert a strong influence over day-to-day variations in pollutant concentrations; therefore, the meteorological signal must be removed in order for air quality planners and managers to examine underlying emissions-related trends and make better air quality management decisions for the future. Although the Kolmogorov-Zurbenko (KZ) filter has been widely used for this type of trend separation in O3 studies in the eastern United States, this article aims to extend the method in three key ways. First, whereas the KZ filter is known as a useful tool for O3 analysis, this study also evaluates its effectiveness when applied to PM. Second, the method was applied to Tucson, AZ, a city in the semi-arid southwestern United States (Southwest), to evaluate the appropriateness of the method in a region with weaker synoptic weather controls on air quality than the eastern United States. Third, additional forms of output were developed and tailored to be more applicable to decision-makers' needs through a partnership between academic researchers and air quality planners and managers. Results of the study indicate that the KZ filter is a useful method for examining emissions-related PM trends, resulting in small, but potentially significant, differences after adjustment. For the Tucson situation with weaker synoptic controls, the KZ method identified mixing height as a more important variable than has been found in other cities.

Low modeled ozone production suggests underestimation of precursor emissions (especially NO<sub><i>x</i></sub>) in Europe

“…Increased vertical mixing dilutes the ozone precursors inhibiting ozone production, but it also reduces the NO x titration of ozone (especially in urban areas) and enhances the downward trans-port of ozone from the enriched-ozone upper layers in the evening-morning which in turn influences the ozone mixing ratios and chemistry the next day (Kleeman, 2008;Lin et al, 2008). It is probably due to these reasons that the correlation of ozone mixing ratios with the mixing depth is reported to be weak (Wise and Comrie, 2005;Ordóñez et al, 2005;Jacob and Winner, 2009). Therefore, we do not expect that our PBLH uncertainties (Table 4, Fig.…”

<i>E pluribus unum</i>*: ensemble air quality predictions

“…In particular, by taking the difference between two KZ-f corresponding to different parameters (m, p), a band-pass filter is created. The applications of KZ-f in the field of chemical weather is expanding and includes, among others, the diagnosis of the meteorological and air quality measurements and model results (Rao et al, 1997;Hogrefe et al, 2000), the diagnosis of trends (Wise et al, 2005;Papanastasiou et al, 2012) and the bias adjustment of ozone forecasts (Kang et al, 2008). The filter has been proven in several occasions to be capable of capturing the fundamental time scales of regional models without having to perform a full Fourier analysis.…”

Temporal trends of spatial correlation within the PM<SUB align="right">10 time series of the AirBase ambient air quality database

“…The mid-term (seasonal) component S(t) can be interpreted as a result of changes in the solar angle (induced variations of emissions and temperature dependencies). The long-term signal e(t) can be interpreted to result from long-term changes in overall emissions, pollutant transport, climate, economics, and environmental policies (Wise and Comrie, 2005). e(t) is as well supposed to be influenced by evolutions in the operational principles of the monitoring network.…”