A NEURAL-NETWORK-BASED METHOD FOR SHORT-TERM PREDICTIONS OF AMBIENT SO2 CONCENTRATIONS IN HIGHLY POLLUTED INDUSTRIAL-AREAS OF COMPLEX TERRAIN

被引：220

作者：

BOZNAR, M

LESJAK, M

MLAKAR, P

机构：

[1] Laboratory for Measuring Systems, Department of Nuclear Physics, Jozef Stefan Institute, 61000 Ljubljana

来源：

ATMOSPHERIC ENVIRONMENT PART B-URBAN ATMOSPHERE | 1993年 / 27卷 / 02期

关键词：

AIR POLLUTION PREDICTION; NEURAL NETWORKS; PATTERN; LEARNING; SO2; POLLUTION; MONITORING SYSTEM; THERMAL POWER PLANT (TPP);

D O I：

10.1016/0957-1272(93)90007-S

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

A new method for short-term air pollution prediction is described, based on the neural network. It was developed for prediction for SO2 pollution around the biggest Slovenian thermal power plant at Sostanj. Because of the high SO2 emissions, there is a need for a reliable air pollution prediction method that would enable lowering the peaks of pollutant concentrations in critical meteorological situations. In complex topography, classical methods for air pollution modelling are not reliable enough. The results obtained by this new method are very promising. The method can also be used, with slight modifications, for other important air pollutants, the concentrations of which can be measured continuously.

引用

页码：221 / 230

页数：10