Abstract
The concentration of nitrogen dioxide in the air along a major route in a large city is affected by very many factors, which are also interdependent. As an alternative to complicated deterministic models based on these complex processes, in this study a probabilistic model for predicting NO2 concentrations is proposed, using a simple accounting cluster-based method for determining probability distributions for tabulated values of ambient factors. Using the example of hourly values of NO2 concentration and data on wind speed and traffic flow for the main intersection in Wrocław (Poland), a model is constructed to predict the frequency of occurrence of concentrations in the form of a probability distribution, for given values of the input variables. The model was successfully verified on data for the first six months of 2018. A mean continuous rank probability score (CRPS) of 9.15 μg/m3 was obtained. In spite of the greater impact of traffic volume on urban NO2 concentrations, as measured by Pearson’s correlation coefficient, for instance, the model indicates that wind speed is also a very important factor—wind being the principal mechanism causing the evacuation of pollutants. This underlines the importance of sustainable city planning with regard to ensuring suitable conditions for the passage of air.
Highlights
With the increase in levels of anthropogenic air pollution, there is growing interest among scientists in modelling the relationship between pollutant concentrations and various ambient factors
Models that have been applied include artificial neural networks [9,10], which may be combined with a multiple regression model [11]
There is an increasing number of reports on the use of random tree methods to model pollutant concentrations. These include both single random trees [12] and more complex structures based on them: Random forest (RF) [13,14] and boosted regression trees (BRT) [15]
Summary
With the increase in levels of anthropogenic air pollution, there is growing interest among scientists in modelling the relationship between pollutant concentrations and various ambient factors. A probabilistic analysis is performed, and a forecast is obtained This takes the form of a probability distribution for NO2 concentrations or the probability of the exceeding of a set threshold of pollutant concentration (e.g., limit, warning, alarm levels), for given values of the explanatory variables. The model makes it possible, in a simple manner, to forecast probability distributions for pollution levels depending on the values of the input parameters. This makes it easy to determine what changes in air quality can be expected to result from a given reduction in the number of vehicles driving into the city center. This will enable estimation of the benefits resulting from, for example, the introduction of a charge for vehicles entering the central zone
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