Application of nonparametric regression methods to study the relationship between NO2 concentrations and local wind direction and speed at background sites.

Background concentrations of nitrogen dioxide (NO(2)) are not constant but vary temporally and spatially. The current paper presents a powerful tool for the quantification of the effects of wind direction and wind speed on background NO(2) concentrations, particularly in cases where monitoring data are limited. In contrast to previous studies which applied similar methods to sites directly affected by local pollution sources, the current study focuses on background sites with the aim of improving methods for predicting background concentrations adopted in air quality modelling studies. The relationship between measured NO(2) concentration in air at three such sites in Ireland and locally measured wind direction has been quantified using nonparametric regression methods. The major aim was to analyse a method for quantifying the effects of local wind direction on background levels of NO(2) in Ireland. The method was expanded to include wind speed as an added predictor variable. A Gaussian kernel function is used in the analysis and circular statistics employed for the wind direction variable. Wind direction and wind speed were both found to have a statistically significant effect on background levels of NO(2) at all three sites. Frequently environmental impact assessments are based on short term baseline monitoring producing a limited dataset. The presented non-parametric regression methods, in contrast to the frequently used methods such as binning of the data, allow concentrations for missing data pairs to be estimated and distinction between spurious and true peaks in concentrations to be made. The methods were found to provide a realistic estimation of long term concentration variation with wind direction and speed, even for cases where the data set is limited. Accurate identification of the actual variation at each location and causative factors could be made, thus supporting the improved definition of background concentrations for use in air quality modelling studies.

[1]  W. Härdle,et al.  Applied Nonparametric Regression , 1991 .

[2]  Shaibal Mukerjee,et al.  Statistical approaches in wind sector analyses for assessing local source impacts , 1994 .

[3]  Shaibal Mukerjee,et al.  ESTIMATING THE WIND DIRECTION OF MAXIMUM AIR POLLUTANT CONCENTRATION , 1996 .

[4]  Anthony J. Jakeman,et al.  Modeling distributions of air pollutant concentrations—II. Estimation of one and two parameter statistical distributions , 1986 .

[5]  K. Takezawa,et al.  Introduction to Nonparametric Regression , 2005 .

[6]  Philip K. Hopke,et al.  Comparison between Conditional Probability Function and Nonparametric Regression for Fine Particle Source Directions , 2004 .

[7]  J H Seinfeld,et al.  On frequency distributions of air pollutant concentrations. , 1976, Atmospheric environment.

[8]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[9]  John Fox.  Multiple and Generalized Nonparametric Regression , 2000 .

[10]  Ronald C. Henry,et al.  Locating nearby sources of air pollution by nonparametric regression of atmospheric concentrations on wind direction , 2002 .

[11]  Frederic Paik Schoenberg,et al.  Kernel Regression of Directional Data with Application to Wind and Wildfire Data in Los Angeles County, California , 2007, Forest Science.

[12]  W. Härdle.  Applied Nonparametric Regression , 1991 .

[13]  M. Bell,et al.  Detecting and quantifying aircraft and other on-airport contributions to ambient nitrogen oxides in the vicinity of a large international airport , 2006 .

[14]  Roy M. Harrison,et al.  Regression modelling of hourly NOx and NO2 concentrations in urban air in London , 1997 .

[15]  The relationship between extreme nitrogen oxide (NOx) concentrations in Dublin's atmosphere and meteorological conditions , 1998 .

[16]  John,et al.  Nonparametric Simple Regression: Smoothing Scatterplots , 2000 .

[17]  D. W. Scott,et al.  Multivariate Density Estimation, Theory, Practice and Visualization , 1992 .

[18]  C. Kirby,et al.  Temporal and Spatial Variations in Nitrogen Dioxide Concentrations Across an Urban Landscape: Cambridge, UK , 1998 .

[19]  R. Henry,et al.  Identifying the impact of large urban airports on local air quality by nonparametric regression , 2004 .

[20]  Ram Vedantham,et al.  Source region identification using kernel smoothing. , 2009, Environmental science & technology.