DEVELOPMENT OF A METHOD FOR PREDICTING THE RECURRENCE OF STATES OF ATMOSPHERIC AIR POLLUTION CONCENTRATION IN INDUSTRIAL CITIES

This paper reports the method developed for predicting the recurrence of states related to air pollution at industrial cities based on the modified window measure. The new scientific result implies that dangerous states of the urban air pollution should be identified and predicted based not on the prediction of the concentration of pollution as it is, but based on forecasting the recurrence of states of the concentration of atmospheric air pollutions. The proposed prediction method makes it possible to operatively forecast not only the clear but also hidden dangerous states of air pollution at industrial cities. This provides for an overall improvement in the effectiveness of interventions to prevent hazardous contamination of the atmosphere and the environment. The results of experimental testing indicate the feasibility of the proposed method. It was established that in the test interval of monitoring (between counts 12‒36) there were sharp characteristic changes in the predicted measure for the recurrence of state. It is noted that such changes are the predictors of hidden events involving hazardous air pollution at industrial cities. It was experimentally found that a more accurate forecast is ensured for the forecast horizon d=1 (6 hours). It is shown that in the considered case, in order to ensure the reliability of forecasting laminar states in the contaminated atmosphere, the smoothing parameter to be selected should not be less than 0.8. It is noted that in order to predict dangerous states of the atmosphere pollution based on the dynamics in the prediction of a state recurrence measure, there is no need in the information about meteorological conditions at the time of forecasting and in the future. This is the main distinguishing feature and advantage of the proposed prediction method. A given method for RS forecasting proves to be invariant to urban configuration, the types of stationary and mobile pollution sources, as well as meteorological condition