Completing missing data in air monitoring stations using diurnal courses of regional pollution concentrations

• Autorzy: Hoffman S. , Jasiński R.
• Języki publikacji: EN

Abstrakty

EN

Data sets gathered continuously in air monitoring systems are never entirely complete. The problem of missing data in monitoring measure series often has to be solved by modeling. A new method of air monitoring data modelling was tested in the paper. Regional diurnal concentration courses (RDCCs) were used as the main source of knowledge of predicted time series during specified days. The paper presents a comparison of predicted and measured diurnal concentration patterns of two frequently used parameters in air monitoring (PMio and NO2). The analysis was based on hourly time series of these air pollutants collected in a 3-year period at nine monitoring stations in the Lodz Region. It was shown that well determined regional diurnal concentration patterns could be useful to missing data modelling at the specified monitoring site. Improvement of modelling accuracy is possible after modification of modelling results by adding local difference vectors (LDVs), describing the specificity of the monitoring station.

Słowa kluczowe

EN

air monitoring data; missing data modelling; pollution concentrations; diurnal courses

PL

monitoring zanieczyszczeń powietrza; koncentracja zanieczyszczeń; przebieg dobowy

• Wydawca: Institute of Environmental Engineering, Polish Academy of Sciences
• Czasopismo: Archives of Environmental Protection
• Rocznik: 2008
• Tom: Vol. 34, no. 3
• Strony: 133--142
• Opis fizyczny: bibliogr. 9 poz., tab., wykr.

Twórcy

autor

Hoffman S.

• Technical University of Częstochowa Department of Chemistry, Water and Wastewater Technology 69 Dąbrowskiego St., 42-200 Częstochowa

autor

Jasiński R.

• Technical University of Częstochowa Department of Chemistry, Water and Wastewater Technology 69 Dąbrowskiego St., 42-200 Częstochowa,

Bibliografia

• [1] Gardner M.W., Dorling S.R., 1998. Artificial neural networks (the multilayer perceptron) - a review of applications in the atmospheric sciences. Atmospheric Environment 32, 2627-2636.
• [2] Hadjiiski L., Geladi P., Hopke P., 1999. A comparison of modeling nonlinear systems with artificial neural networks and partial least squares. Chemometrics and Intelligent Laboratory Systems 49,91-103.
• [3] Hauck H., Kromp-Kolb H., Petz E., 1999. Requirements for the completeness of ambient air quality data sets with respect to derived parameters. Atmospheric Environment 33, 2059-2066.
• [4] Hoffman S., 2003. Regression modelling of ground level ozone concentration. In L. Pawtowski, M.R. Dudzińska, A. Pawlowski, (Eds.), Environmental Engineering Studies. Polish Research on the way to EU. Kluwer Academic/Plenum Publishers, New York, 53-60
• [5] Hoffman S., 2006. Short-Time Forecasting of Atmospheric NOX Concentration by neural networks. Environmental Engineering Science 23(4), 603-609.
• [6] Hoffman S., 2007. Treating missing data at air monitoring stations. In L. Pawlowski, M.R. Dudzińska, A. Pawtowski (eds.),,,Environmental engineering", Taylor & Francis Group, London, 349-353.
• [7] Karppinen A., Kukkonen J., Elolahde T., Konttinen M., Koskentalo T., 2000. A modelling system for predicting urban air pollution: comparison of model predictions with the data of an urban measurement network in Helsinki. Atmospheric Environment 34, 3735-3743.
• [8] Kolehmainen M., Martikainen H., Ruuskanen J., 2001. Neural networks and periodic components used in air quality forecasting. Atmospheric Environment 35, 815-825.
• [9] Plaia A., Bondi A.L., 2006. Single imputation method of missing values in environmental pollution data sets. Atmospheric Environment 40, 7316-7330