A dynamic evaluation framework for ambient air pollution monitoring.

Highlights • A novel dynamic evaluation system is proposed for monitoring urban ambient air condition. • The enhanced sine cosine algorithm is proposed to tune the parameters of forecast engine. • The fuzzy synthetic evaluation model with entropy weight is used to perform the air quality levels. • Three real data examples are conducted to investigate the proposed monitoring system. Abstract Accurate real-time prediction of urban air quality is one of the most important problems in control and improve ambient air condition globally. Therefore, the modeling and applications of air pollutant forecasting and evaluation has attracted the attention of researchers in recent years. Based on the method of fuzzy mathematical synthetic evaluation, this paper built a dynamic evaluation model for the purpose of mastering the future air quality immediately. A newly proposed computational intelligence optimization algorithm is improved to optimize the least square support vector machine, which can generate rolling forecasts of six air pollutants concentration. The information of future air quality status is built by the fuzzy synthetic assessment model based on entropy weighing method. The results and analysis of air quality monitoring show that accurate and reliable forecast of urban air pollutants concentration are possible and the air quality conditions can be evaluated objectively. Through the simulation design, it proves that the proposed dynamic evaluation model can provide a practical tool for ambient air ambient quality evaluation. [ABSTRACT FROM AUTHOR]