Locally weighted least squares kernel regression and statistical evaluation of LIDAR measurements

• The LIDAR technique is an efficient tool in monitoring the distribution of atmospheric species of importance. We study the concentration of atmospheric atomic mercury in an Italian geothermal field and discuss the possibility of using recent results from local polynomial kernel regression theory for the evaluation of the derivative of the DIAL curve. A MISE-optimal bandwidth selector, which takes account of the heteroscedasticity in the regression is suggested. Further, we estimate the integrated amount of mercury in a certain area.

Subject headings

NATURVETENSKAP  -- Fysik -- Atom- och molekylfysik och optik (hsv//swe)

NATURAL SCIENCES  -- Physical Sciences -- Atom and Molecular Physics and Optics (hsv//eng)

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

Keyword

LIDAR measurements

Locally weighted least squares regression

air pollution

atmospheric atomic mercury

geothermal field

Publication and Content Type

art (subject category)

ref (subject category)