Statistical Analysis of Trends in Urban Ozone Air Quality

Rao, S. Trivikrama; Sistla, Gopal; Henry, Robert F.

doi:10.1080/10473289.1992.10467069

Cited by 31 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Rao et al 2 demonstrated the use of some statistical methods for examining trends in ambient O 3 air quality downwind of major urban areas. However, the methods did not incorporate adjustments for meteorological variations.…”

Section: Ozone Trendsmentioning

confidence: 99%

“…First, changes in O 3 concentrations resulting from the implementation of an emission control policy are easily masked by year-to-year changes in meteorological conditions. [1][2][3][4] Since meteorological variables are known to have quasi-periodic behavior on time scales ranging from several weeks to years, there is reason to suspect the presence of natural cycles in O 3 concentrations occurring on similar scales. Second, the spatial and temporal extent of the databases of O 3 , its precursors, and meteorological variables places limits on the magnitude of changes that one can expect to detect.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Ozone Air Quality over North America: Part I—A Review of Reported Trends

Wolff

Dunker

Rao

et al. 2001

Journal of the Air & Waste Management Association

Self Cite

View full text Add to dashboard Cite

Ozone and precursor trends can be used to measure the effectiveness of regulatory programs that have been implemented. In this paper, we review trends in the concentrations of O 3 , NO x , and HCs over North America that have been reported in the literature. Although most existing trend studies are confounded by meteorological variability, both the raw data trends and the trends adjusted for meteorology collectively indicate a general decreasing trend in O 3 concentrations in most areas of the United IMPLICATIONS Despite the implementation of many emission control programs during the past three decades, ambient O 3 levels in some parts of the United States, Canada, and Mexico continue to exceed the National Ambient Air Quality Standard or the Air Quality Objective set forth for O 3 . Detecting and tracking changes in ambient O 3 levels is complicated because of the strong influence of meteorological fluctuations on O 3 concentrations. The attribution of changes in O 3 to specific emission control plans that have been implemented is even more difficult due to limitations imposed by the quality and quantity of available data on O 3 and its precursors and uncertainties in emission estimates. This paper reviews the trends in O 3 and precursor concentrations reported over North America.

show abstract

Section: Ozone Trendsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ozone Air Quality over North America: Part I—A Review of Reported Trends

Wolff

Dunker

Rao

et al. 2001

Journal of the Air & Waste Management Association

Self Cite

View full text Add to dashboard Cite

show abstract

“…The meteorological fluctuations in a long-term trend may suppress the underlying trend. In order to obtain meteorologically adjusted O 3 trend, Kolmogorov-Zurbenko filters (KZ filter) (introduced by Rao et al 1992) for time series decomposition was applied to separate both ozone and meteorological data into short-term, seasonal and long-term components. Separation helps in smoothening of data, which is amenable to further application of regression analysis.…”

Section: Meteorologically Adjusted Long-term O 3 Trendmentioning

confidence: 99%

“…The most recent method of decomposition STL (SeasonalTrend decomposition procedure based on Loess) (Cleveland et al 1990) is widely used method for the data having extreme observations. One of the important application of the decomposition method is to smoothen the exploratory variables that are responsible for the trend cycle by applying filters such as Kalman filters (Kalman 1960), wavelet decomposition (Nason and Silverman 1994) and Kolmogorov-Zurbenko filters (Rao et al 1992).…”

Section: Introductionmentioning

confidence: 99%

Analysis of long-term ozone trend over Delhi and its meteorological adjustment

Kumari

Jayaraman²,

Chirashree

2013

Int. J. Environ. Sci. Technol.

View full text Add to dashboard Cite

Present study deals with the statistical analysis of long-term ground level ozone (O 3 ) trend and the influence of meteorological variables on its variation over Delhi, India. Daily mean and maximum of O 3 and meteorological data, obtained from India Meteorological Department, were arranged for the period of 9 years (1998)(1999)(2000)(2001)(2002)(2003)(2004)(2005)(2006). Based on the preliminary correlation study of all the data with O 3 , six variables viz. daily maximum temperature, daily average relative humidity, dew point, wind speed, visibility, and total sunshine were selected. Classical additive time series decomposition technique was used to obtain seasonally adjusted long-term trend. To analyze the masking effect of meteorology, adjustment was made using Kolmogorov-Zurbenko filters followed by stepwise regression analysis to the smoothed series of O 3 maximum and meteorological variables, which showed that long-term trend was independent of sunshine duration. Results indicate a significant increasing trend with annual increase of 1.13 % for O 3 mean and 3 % for O 3 maximum. Annual deseasonalized trend for seasonal cycle shows bimodal oscillations. About 43 % of O 3 variation was explained by the selected meteorological factors and rest of variation attributed to factors like emission of precursor gases, pollutant transport, policy changes, etc. Among the three tested regression models, performance of Model 2 with variable temperature, wind speed, and visibility was found to be best that resulted in lowering of O 3 trend. Large variability (23 %) was explained by the variable visibility depicted that the emission of primary pollutants not only provides the precursor gases but also control the local photochemical reactions.

show abstract

“…The prediction of these extreme concentrations and the assessment of their contribution to atmospheric pollution are subjects of strong environmental concern. Therefore extreme value concepts have recently been used in the monitoring of environmental data such as ground-level ozone (Smith, 1989) or as a tool in modelling trends in the urban ozone air quality (Rao et al, 1992). During the summer season of the last few years German monitoring sites have registered, almost daily, the exceedances of the required national air quality standard for ozone.…”

Section: Introductionmentioning

confidence: 99%

Extreme value analysis of Munich air pollution data

Kütchenhoff¹,

Thamerus

1996

Environ Ecol Stat

View full text Add to dashboard Cite

We present three di erent approaches to model extreme values of daily air pollution data. We tted a generalized extreme value distribution to the monthly maxima of daily concentration measures. For the exceedances of a high threshold depending on the data the parameters of the generalized Pareto distribution were estimated. Accounting for autocorrelation clusters of exceedances were used. To get information about the relationship of the exceedance of the air quality standard and possible predictors we applied logistic regression. Results and their interpretation are given for daily average concentrations of O 3 and of NO 2 at two monitoring sites within the city of Munich.

show abstract

Statistical Analysis of Trends in Urban Ozone Air Quality

Cited by 31 publications

References 13 publications

Ozone Air Quality over North America: Part I—A Review of Reported Trends

Ozone Air Quality over North America: Part I—A Review of Reported Trends

Analysis of long-term ozone trend over Delhi and its meteorological adjustment

Extreme value analysis of Munich air pollution data

Contact Info

Product

Resources

About