ABSTRACT This article presents the results of the statistical modeling of the ground-level ozone concentration in the air in the close vicinity of the city of Zrenjanin (Serbia). This study is aimed at defining the dependence of ozone concentration on the following predictors: SO2, CO, H2S, NO, NO2, NOx, PM10, benzene, toluene, m,p-Xylene, o-Xylene and ethylbenzene concentration in the air, as well as on the meteorological parameters (the wind direction, the wind speed, air pressure, air temperature, solar radiation, and RH). Multiple linear regression analysis (MLRA) and artificial neural networks (ANNs) were used as the tools for the mathematical analysis of the indicated occurrence. The results have shown that ANNs provide better estimates of ozone concentration on the monitoring site, whereas the multilinear regression model once again has proven to be less efficient in the accurate prediction of ozone concentration.

中文翻译：

---

使用多元线性回归和人工神经网络方法预测环境空气中的臭氧浓度

摘要 本文介绍了兹雷尼亚宁（塞尔维亚）市附近空气中地面臭氧浓度的统计建模结果。本研究旨在确定臭氧浓度对以下预测因子的依赖性：SO2、CO、H2S、NO、NO2、NOx、PM10、苯、甲苯、间二甲苯、邻二甲苯和空气中的乙苯浓度，以及气象参数（风向、风速、气压、气温、太阳辐射和相对湿度）。多元线性回归分析 (MLRA) 和人工神经网络 (ANN) 被用作对指定事件进行数学分析的工具。结果表明，人工神经网络可以更好地估计监测现场的臭氧浓度，