Detecting and Tracking Changes in Ozone Air Quality

Rao, S. Trivikrama; Žurbenko, Igor G.

doi:10.1080/10473289.1994.10467303

Cited by 195 publications

(148 citation statements)

References 5 publications

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“…For the purpose of trend estimation, regression modelling has been widely used to model pollutants as a function of meteorological parameters; this approach has demonstrated capability of detecting trends that are disguised by meteorological variations. Vingarzan and Taylor [12] and Rao and Zurbenko [13] provide applications of regression models to study trends in ozone concentrations. CO is a pollutant that is known to be involved, although in a minor way, in chemical reactions leading to the production of photochemical smog.…”

Section: Analysis Of Temporal Componentsmentioning

confidence: 99%

Application of Statistical Methods to Assess Carbon Monoxide Pollution Variations within an Urban Area

Capilla

2012

IJG

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In recent years there have been considerable new legislation and efforts by vehicle manufactures aimed at reducing pollutant emission to improve air quality in urban areas. Carbon monoxide is a major pollutant in urban areas, and in this study we analyze monthly carbon monoxide (CO) data from Valencia City, a representative Mediterranean city in terms of its structure and climatology. Temporal and spatial trends in pollution were recorded from a monitoring network that consisted of five monitoring sites. A multiple linear model, incorporating meteorological parameters, annual cycles, and random error due to serial correlation, was used to estimate the temporal changes in pollution. An analysis performed on the meteorologically adjusted data reveals a significant decreasing trend in CO concentrations and an annual seasonal cycle. The model parameters are estimated by applying the least-squares method. The standard error of the parameters is determined while taking into account the serial correlation in the residuals. The decreasing trend implies to a certain extent an improvement in the air quality of the study area. The seasonal cycle shows variations that are mainly associated with traffic and meteorological patterns. Analysis of the stochastic spatial component shows that most of the intersite covariances can be analyzed using an exponential variogram model.

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Section: Analysis Of Temporal Componentsmentioning

confidence: 99%

Application of Statistical Methods to Assess Carbon Monoxide Pollution Variations within an Urban Area

Capilla

2012

IJG

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“…In an effort to forecast air quality (AQ) daily, many different kinds of models have been developed (Robeson and Steyn, 1990;Yi and Prybutok, 1996;Millionis and Davies, 1994a, b;Rao and Zurbenko, 1994;Zannetti, 1990). The worldwide concern about this continuing environmental problem reinforces the need for more accurate and, at the same time, more easily applied models.…”

Section: Introductionmentioning

confidence: 99%

Regression Analysis and urban air quality forecasting: an application for the city of Athens

2013

Global NEST Journal

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Air pollution forecasts in major urban areas are becoming a problem concerning the day to day environmental management for city authorities. This paper describes the development of an application to forecast the peak ozone levels with the aid of meteorological and air quality variables, in the Greater Athens Area. For this purpose, a number of regression models were considered, while the selection of the final model was based on extensive analysis and on literature. The model adapted includes variables that are available on a daily basis, so as daily operational maximum ozone concentration level forecast can be achieved.

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“…In this section, according to Rao and Zurbenko (1994), the shortterm component is attributed to weather and short-term fluctuations in precursor emissions. Seasonal component is a result of changes in solar angle, and the long-term trend results from changes in overall emissions and pollutant transport, climate and policies.…”

Section: Meteorologically Adjusted Long-term O 3 Trendmentioning

confidence: 99%

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

Kumari

Jayaraman²,

Chirashree

2013

Int. J. Environ. Sci. Technol.

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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.

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Detecting and Tracking Changes in Ozone Air Quality

Cited by 195 publications

References 5 publications

Application of Statistical Methods to Assess Carbon Monoxide Pollution Variations within an Urban Area

Application of Statistical Methods to Assess Carbon Monoxide Pollution Variations within an Urban Area

Regression Analysis and urban air quality forecasting: an application for the city of Athens

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

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