Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations

Abstract

Recent progress in developing artificial neural network (ANN) metamodels has paved the way for reliable use of these models in the prediction of air pollutant concentrations in urban atmosphere. However, improvement of prediction performance, proper selection of input parameters and model architecture, and quantification of model uncertainties remain key challenges to their practical use. This study has three main objectives: to select an ensemble of input parameters for ANN metamodels consisting of meteorological variables that are predictable by conventional weather forecast models and variables that properly describe the complex nature of pollutant source conditions in a major city, to optimize the ANN models to achieve the most accurate hourly prediction for a case study (city of Tehran), and to examine a methodology to analyze uncertainties based on ANN and Monte Carlo simulations (MCS). In the current study, the ANNs were constructed to predict criteria pollutants of nitrogen oxides (NOx), nitrogen dioxide (NO2), nitrogen monoxide (NO), ozone (O3), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM10) in Tehran based on the data collected at a monitoring station in the densely populated central area of the city. The best combination of input variables was comprehensively investigated taking into account the predictability of meteorological input variables and the study of model performance, correlation coefficients, and spectral analysis. Among numerous meteorological variables, wind speed, air temperature, relative humidity and wind direction were chosen as input variables for the ANN models. The complex nature of pollutant source conditions was reflected through the use of hour of the day and month of the year as input variables and the development of different models for each day of the week. After that, ANN models were constructed and validated, and a methodology of computing prediction intervals (PI) and probability of exceeding air quality thresholds was developed by combining ANNs and MCSs based on Latin Hypercube Sampling (LHS). The results showed that proper ANN models can be used as reliable metamodels for the prediction of hourly air pollutants in urban environments. High correlations were obtained with R (2) of more than 0.82 between modeled and observed hourly pollutant levels for CO, NOx, NO2, NO, and PM10. However, predicted O3 levels were less accurate. The combined use of ANNs and MCSs seems very promising in analyzing air pollution prediction uncertainties. Replacing deterministic predictions with probabilistic PIs can enhance the reliability of ANN models and provide a means of quantifying prediction uncertainties.

Multi-Source Information Fusion Based on Neural Networks in Air Quality Forecasting

“…Take the mean absolute error (MAE), mean relative error (MRE), root mean square error (RMSE), and the average accuracy E of the predicted values of four pollutants as the evaluation standard of judging prediction effect [10] . The three models according to the results of the experiments are given a contrastive analysis.…”

“…daily meteorological data from June 30, 2014 to July 30, 2014 as the predictive input to predict the daily values of NO 2 , O 3 , PM 10 , and AQI during the whole month. According to the experiment, the best hidden layer neuron number of NO 2 , O 3 , AQI and PM 10 forecast model is identified as 10,10,10,12 respectively.…”

Tehran Air Pollutants Prediction Based on Random Forest Feature Selection Method

“…In order to improve the performance of models, month related parameter indicating the changes in emissions due to changes in atmospheric conditions during the year was calculated using Equation 2 (Arhami et al, 2013). …”

“…• The inability of input attributes to form an efficient model for prediction of this pollutant (Arhami et al, 2013). In fact air pollutant prediction accuracy depends upon the accuracy of the measured data, sources of release and spatial variation of pollutant concentration (Perez et al, 2011).…”

Prediction Model of Air Pollutant Levels Using Linear Model with Component Analysis

“…On this research, wind direction was considered as independent variables but they did not separate the effect of wind direction to each prediction of pollutant, moreover, serial error correlation due to time series model was not taken into account which might cause result bias. Reference [6] also used ANN to predict pollutants, but they noted less accuracy for O 3 prediction in Tehran, Iran.…”