Analiza i modelowanie pól imisji zanieczyszczeń powietrza w dużych miastach : (na przykładzie Łodzi)
Ładowanie...
Data
2002
Autorzy
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Polska Akademia Nauk. Oddział w Łodzi. Komisja Ochrony Środowiska
Abstrakt
This work outlines main research directions in air pollution monitoring programs and
deals with analysis and modelling methods of air pollution concentration fields in large
cities. Studies are carried out on experimental data derived from measurements of air
pollution concentrations and meteorological parameters obtained for the city of Łódź.
Specific local factors deciding about emissions and air pollutant concentration fields are
taken into account. Evaluation of the atmosphere monitoring system in terms of a number
and spatial distribution of monitoring stations was carried out by means of artificial
neural networks (ANN). Competitive layer network that was trained with the use of the
neural gas algorithm was applied for this task. Positive verification of atmosphere
monitoring system has justified further studies on modelling air pollutant concentration
fields. For a more comprehensive analysis and evaluation of air-sanitary conditions, atmospheric
circulation and weather factors influencing air concentration fields of S02 and
suspended particulates in the winter season were taken into account. For each of the
identified atmospheric circulation type, spatial maps of mean-daily and sub-maximum
daily air pollution concentrations were computed. For construction of air pollutant concentration
fields an interpolation method using biharmonic Green spline functions was
applied. In order to visualise differences between air pollution concentrations characteristic
for each of the atmospheric circulation type area histograms and area cumulative
distribution functions were calculated.
A method for prediction of mean daily air pollution concentrations of S02 and suspended
particulates was proposed that uses multilayer perceptron neural networks. Accurate
prediction results were obtained thanks to proper selection of the training data, network
topology, and gradient descent training algorithm. In particular, the technique of
principal component analysis (PCA) applied to vectors of meteorological factors proved
very useful in reducing data dimension at the network input. Correlation analysis of
mean daily SO2 and suspended particulates concentrations was used for identifying the
order of the dynamie process involved in the spread of air pollution concentration at day
by day time scale.
This study proposes a systematic analysis and modelling methodology of air pollution
concentration fields in urban area. The key part of the system is the time series prediction
neural based model of daily air pollution concentrations. This model takes into
account earlier analyses and simulations. The proposed system and other employed experimental
techniques are formulated in generał terms and can be used for other regions
and situations in environmental studies. Successful use of the developed models requires,
however, careful data preparation and analysis that takes into account local specificity
of the modelled environment and factors influencing air pollution concentration
fields.
Opis
Komitet redakcyjny:
Zarzycki, Roman
Pęczak, Tadeusz
Kurnatowska, Alicja
Chmielewski, Andrzej
Słowa kluczowe
zanieczyszczenie powietrza - modele matematyczne, zanieczyszczenie powietrza - Polska - Łódź - 1970-2000, zanieczyszczenie powietrza - studium przypadku, pola imisji zanieczyszczeń powietrza, sztuczne sieci neuronowe, air pollution - mathematical models, air pollution - Poland - Łódź - 1970-2000, air pollution - case study, fields of air pollution immission, artificial neural networks