Outdoor and Indoor Ozone Concentration Estimation Based on Artificial Neural Network and Single Zone Mass Balance Model

• DOI: 10.1016/j.proeng.2017.10.253
• Publication Date: 2017-11-01T04:52:55Z
• Authors: Jialei Shen; Jie Chen; Xinyi Zhang; Sicong Zou; Zhi Gao
• Keywords: 13. Climate action; 01 natural sciences; 0105 earth and related environmental sciences

Abstract Both outdoor and indoor ozone concentrations have negative health effects on human. This paper proposes an artificial neural network to estimate the outdoor ozone concentrations and a mass balance model tool to estimate indoor ozone concentrations. The prediction of outdoor and indoor ozone concentration levels is of great significance for people’s health. The estimation models are validated by the measured data selected from the monitoring stations and field measurements in a room in Nanjing, respectively. The accuracy of the estimation models is evaluated. The neural network built in this paper can generally estimate the outdoor ozone concentrations to some extent, while the single zone mass balance model is useful for predicting indoor ozone concentration levels.