LOCALLY WEIGHTED LEAST SQUARES KERNEL REGRESSION AND STATISTICAL EVALUATION OF LIDAR MEASUREMENTS

• Published in: Environmetrics (London, Ont.) Vol. 7; no. 4; pp. 401 - 416
• Main Authors: HOLST, ULLA, HÖSSJER, OLA, BJÖRKLUND, CLAES, RAGNARSON, PÄR, EDNER, HANS
• Format: Journal Article
• Language: English
• Published: Chichester, UK John Wiley & Sons, Ltd 01.07.1996; Wiley
• Subjects: air pollution; Applied sciences; atmospheric atomic mercury; Atmospheric pollution; Atom and Molecular Physics and Optics; Atom- och molekylfysik och optik; Exact sciences and technology; Fysik; geothermal field; LIDAR measurements; Locally weighted least squares regression; Matematik; Mathematics; Natural Sciences; Naturvetenskap; Physical Sciences; Pollutants physicochemistry study: properties, effects, reactions, transport and distribution; Pollution; Probability Theory and Statistics; Sannolikhetsteori och statistik; Kernel function; Least squares method; Statistical model; Laser; Lidar; Air pollution; Mercury; Remote sensing
• ISSN: 1180-4009; 1099-095X; 1099-095X
• DOI: 10.1002/(SICI)1099-095X(199607)7:4<401::AID-ENV221>3.0.CO;2-D

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.