Analýza znecistení ovzdusí prachovými cásticemi PM10 zalozená na zobecneném lineárním modelu s maticí plánu silne neúplné hodnosti

Analysis of PM10 air pollution in Brno based on a GLM with strongly rank-deficient design matrix.

abstract

en

A family of complex (generalized) linear models has been suggested exhibiting strong rank-deficiency in the design matrix to allow for more precise modeling involving identification of significant air pollution sources, among others. From each of them the parameter estimates were obtained using both standard estimation procedure and a new sparse parameter estimation technique based on BPA4 – a four-step modification of the Basis Pursuit Algorithm originally suggested in [Chen S. S., Donoho D. L., Saunders M. A.. Atomic decomposition by basis pursuit.SIAM J. Sci. Comput., 20(1):33-61,1998.] for time-scale analysis of digital signals. The goal of the analysis was to identify the model and algorithm yielding most precise one-day forecasts of the level of pollution by PM10 with regard to the meteorological and seasonal covariates.

Byla navrzena trída komplexních zobecnených lineárních modelu vykazujících silnou neúplnost hodnosti matice plánu v dusledku snah o dosazení zvýsené presnosti modelu, který umoznuje krome jiného identifikovat významné zdroje znecistení. Pro kazdý model byly nalezeny odhady parametru jak standardním postupem tak i novou estimacní technikou pro hledání rídkých odhadu zalozenou na BPA4 – ctyrkrokové modifikaci algoritmu "Basis Pursuit" [Chen S. S., Donoho D. L., Saunders M. A. Atomic decomposition by basis pursuit. SIAM J. Sci. Comput., 20(1):33-61,1998.]. Cílem analýzy je nalezení modelu a algoritmu, který dává nejpresnejsi jednodenní predpovedi znecistení cásticemi PM10 v závislosti na meteorologických a sezónních faktorech.

A family of complex (generalized) linear models has been suggested exhibiting strong rank-deficiency in the design matrix to allow for more precise modeling involving identification of significant air pollution sources, among others. From each of them the parameter estimates were obtained using both standard estimation procedure and a new sparse parameter estimation technique based on BPA4 – a four-step modification of the Basis Pursuit Algorithm originally suggested in [Chen S. S., Donoho D. L., Saunders M. A.. Atomic decomposition by basis pursuit.SIAM J. Sci. Comput., 20(1):33-61,1998.] for time-scale analysis of digital signals. The goal of the analysis was to identify the model and algorithm yielding most precise one-day forecasts of the level of pollution by PM10 with regard to the meteorological and seasonal covariates.

Air Pollution; Dust Aerosols PM10; Generalized autoregressive linear model; sparse estimator; Basis Pursuit Algorithm

16.08.2007

TIES

Brno, C(eská republika

118–118

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