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Predicting hourly air pollutant levels using artificial neural networks coupled with uncertainty analysis by Monte Carlo simulations.
MedLine Citation:
PMID:  23292230     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
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 (NO(x)), nitrogen dioxide (NO(2)), nitrogen monoxide (NO), ozone (O(3)), carbon monoxide (CO), and particulate matter with aerodynamic diameter of less than 10 μm (PM(10)) 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, NO(x), NO(2), NO, and PM(10). However, predicted O(3) 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.
Authors:
Mohammad Arhami; Nima Kamali; Mohammad Mahdi Rajabi
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2013-1-6
Journal Detail:
Title:  Environmental science and pollution research international     Volume:  -     ISSN:  1614-7499     ISO Abbreviation:  Environ Sci Pollut Res Int     Publication Date:  2013 Jan 
Date Detail:
Created Date:  2013-1-7     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9441769     Medline TA:  Environ Sci Pollut Res Int     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
Department of Civil Engineering, Sharif University of Technology, P.O. Box 11155-9313, Azadi Ave, Tehran, Iran, arhami@sharif.edu.
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