A new collaborative multi‐agent Monte Carlo simulation model for spatial correlation of air pollution global risk assessment

Air pollution risk assessment is complex due to dynamic data change and pollution source distribution. Air quality index concentration level prediction is an effective method of protecting public health by providing the means for an early warning against harmful air pollution. However, air quality index‐based prediction is challenging as it depends on several complicated factors resulting from dynamic nonlinear air quality time‐series data, such as dynamic weather patterns and the verity and distribution of air pollution sources. Subsequently, some minimal models have incorporated a time series‐based predicting air quality index at a global level (for a particular city or various cities). These models require interaction between the multiple air pollution sensing sources and additional parameters like wind direction and wind speed. The existing methods in predicting air quality index cannot handle short‐term dependencies. These methods also mostly neglect the spatial correlations between the different parameters. Moreover, the assumption of selecting the most recent part of the air quality time series is not valid considering that pollution is cyclic behavior according to various events and conditions due to the high possibility of falling into the trap of local minimum and poor generalization. Therefore, this paper proposes a new air pollution global risk assessment (APGRA) prediction model for an air quality index of spatial correlations to address these issues. The APGRA model incorporates an autoregressive integrated moving average (ARIMA), a Monte Carlo simulation, a collaborative multi‐agent system, and a prediction algorithm for reducing air quality index prediction error and processing time. The proposed APGRA model is evaluated based on Malaysia and China real‐world air quality datasets. The proposed APGRA model improves the average root mean squared error by 41%, mean and absolute error by 47.10% compared with the conventional ARIMA and ANFIS models.