Development and Application of Artificial Neural ... - Springer Link

Water Air Soil Pollut (2013) 224:1634 DOI 10.1007/s11270-013-1634-x

Development and Application of Artificial Neural Network Modeling in Forecasting PM10 Levels in a Mediterranean City K. P. Moustris & I. K. Larissi & P. T. Nastos & K. V. Koukouletsos & A. G. Paliatsos

Received: 1 December 2012 / Accepted: 18 June 2013 / Published online: 3 July 2013 # Springer Science+Business Media Dordrecht 2013

Abstract The study of atmospheric concentration levels at a local scale is one of the most important topics in environmental sciences. Multivariate analysis, fuzzy logic, and neural networks have been introduced in forecasting procedures in order to elaborate operational techniques for level characterization of specific atmospheric pollutants at different spatial and temporal scales. K. P. Moustris (*) Department of Mechanical Engineering, Technological and Education Institute of Piraeus, Athens, Greece e-mail: [email protected] I. K. Larissi Laboratory of Environmental Technology, Electronic Computer Systems Engineering Department, Technological and Education Institute of Piraeus, Athens, Greece e-mail: [email protected] P. T. Nastos Laboratory of Climatology and Atmospheric Environment, Department of Geology and Geoenvironment, University of Athens, Athens, Greece e-mail: [email protected] K. V. Koukouletsos : A. G. Paliatsos

Particularly, approaches based on artificial neural networks (ANNs) have been proposed and successfully applied for forecasting concentration levels of PM10, NO2, SO2, CO, and O3. The present study explores the development and application of ANN models for forecasting, 24 h ahead, not only the daily concentration levels of PM10 but also the number of hours exceeding the PM10 concentration threshold during the day in five different regions within the greater Athens area (GAA). The ANN modeling was based on measurements and estimates of the mean daily PM10 concentration, the maximum hourly NO2 concentration, air temperature, relative humidity, wind speed, and the mode daily value of wind direction from five different monitoring stations for the period 2001–2005. The evaluation of the model performance showed the risk of daily PM10 concentration levels exceeding certain thresholds as well as the duration of the exceedances can be successfully predicted. Despite the limitations of the model, the results indicate that ANNs, when adequately trained, have considerable potential to be used for 1 day ahead PM10 concentration forecasting and the duration within the GAA. Keywords PM10 forecasting . Artificial neural networks . Athens . Greece

General Department of Mathematics, Technological Education Institute of Piraeus, Athens, Greece K. V. Koukouletsos e-mail: [email protected]

1 Introduction

A. G. Paliatsos e-mail: [email protected]

PM10 has been identified as one of the major air pollutants (Hrdličková et al. 2008; Sfetsos and Vlachogiannis 2010;

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WHO 2013). Several epidemiological studies have demonstrated that PM10 concentrations are responsible for various health problems and especially respiratory diseases (Schwartz et al. 1991; Dockery et al. 1992, 1993; Dockery and Pope 1994; Pope et al. 1995; Seaton et al. 1995; Ostro et al. 1999; Pope 2000; Grigoropoulos et al. 2008; Nastos et al. 2010). It should be noted that the generating sources and the formation mechanism of PM10 are very complex. The persistence of the PM10 pollution is a result of industrial and societal developments (e.g., urbanization) but is also influenced by meteorological factors (temperature, wind direction, wind speed, humidity, duration, and amount of precipitation, atmospheric pressure, and insolation) and their day to day variability (Van der Wal and Janssen 2000). Concentration, composition, and particle size of suspended particulate matter at a given site are affected by a number of factors, such as meteorological conditions, topographical influences, emission sources, and particle parameters (density, shape, hygroscopicity). Furthermore, a number of controlling factors, such as wind speed, surface temperature, humidity, surface atmospheric pressure, and dew point temperature, influence the concentration levels of PM10 (Sanchez et al. 1990; Lu and Fang 2002; Lee et al. 2003). During the last decade, many attempts have been made to model PM10 concentrations by analyzing meteorological data in order to predict PM10 concentration levels by employing statistical methods and artificial neural networks (ANNs). Perez and Reyes (2002) developed a neural network based model that uses values of PM10 concentrations measured until 6 p.m. on a day and also measured and forecasted values of meteorological variables as input in order to predict the level reached by the maximum of the 24-h moving average of PM10 concentration on the next day. The parameters of the model were adjusted using 1998 data to forecast 1999 conditions and 1999 data to forecast 2000 maximum concentrations. Several variables were considered as input to the neural network model and it was found that, among the relevant meteorological input variables, the forecasted difference between maximum and minimum temperature is the most important. The final results showed that the performance of the developed neural network is better than that of a linear model with the same inputs; however, the difference is not large which should be an indication that the decision to use a linear or a

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nonlinear model is less important than the right choice of input variables. Hooyberghs et al. (2005) developed a neural network tool in order to forecast the daily average PM10 concentrations in Belgium 1 day ahead. The research was based upon measurements from ten monitoring sites during the period 1997–2001 and upon ECMWF simulations of meteorological parameters. After selecting the most important input variables to communicate useful information for the prediction of PM10 concentrations, it was clear that the most important input variable was the boundary layer height. A model based on this parameter, currently operational online, monitors the daily average threshold of 100 μg/m3. By extending the model with other input parameters, they were able to increase, although slightly, the performance. It is therefore concluded that that day-to-day fluctuations of PM10 concentrations in Belgian urban areas are mainly driven, by meteorological conditions and secondarily by changes in anthropogenic sources. Slini et al. (2006) developed a module for operational concentration levels of particulate matter with aerodynamic diameter up to 10 μm (PM10) for the city of Thessaloniki. The air quality data sets examined corresponded to PM10 concentrations for the years 1994– 2000. In order to provide an operational air quality forecasting module for PM10, statistical methods were investigated and applied. The presented results demonstrate that Classification and Regression Trees (CART) and ANN methods are capable of capturing PM10 concentration trends but there were not able to forecast peak values. Between the two of them CART may have a better performance concerning the index of agreement. The methods studied demonstrate promising operational forecasting capabilities, although highly related to data availability. Grivas and Chaloulakou (2006) developed various artificial neural network models in order to provide reliable predictions of PM10 hourly concentrations at four measurement locations within the GAA. The PM10 data used cover the period of 2001–2002. ANNs were developed using a combination of meteorological and time-scale input variables. A genetic algorithm optimization procedure for the selection of the input variables was also evaluated. The results of the neural network models were rather satisfactory, with values of the coefficient of determination for independent test sets ranging between 0.50 and 0.67 for the four sites and values of the index of agreement between 0.80 and 0.89. The performance of examined neural network models


was superior in comparison with multiple linear regression models that were developed in parallel. Their performance was also found adequate in the case of highconcentration events, with acceptable probabilities of detection and low false alarm rates. Papanastasiou et al. (2007) developed models using multiple regression and neural network (NN) methods that might produce accurate 24-h predictions of daily average value of PM10 concentration. Pollution and meteorological data were collected in the urban area of Volos, a medium-sized coastal city in central Greece. Both models utilize five variables as inputs, which incorporate meteorology (difference between daily maximum and minimum hourly value of ground temperature and daily average value of wind speed), persistency in PM10 levels, and weekly and annual variation of PM10 concentration. After a comparative assessment of the two models it was shown that the NN model showed slightly better skills in forecasting PM10 concentrations: the regression can forecast 55 % while the NN model 61 % of the variance of the data. Several statistical indices were employed in order to verify the quality and reliability of the developed models. The results indicated that their skill scores are satisfying, presenting minor differences, and both models are capable in predicting exceedances above the threshold of 50 μg/m3 at a satisfactory level, which means that 80 % of the observed exceedances has been predicted successfully. Sfetsos and Vlachogiannis (2009; 2010) in their work introduced a new methodology for the prediction of daily PM10 concentrations, in line with the regulatory framework introduced through the EU Directive 2008/50/EC. The proposed approach was based on the efficient utilization of the data collected over short-time intervals (hourly) rather than the daily values used to derive the daily regulatory threshold. The application of the proposed methodology was demonstrated using data from five monitoring stations of air pollutants located in Athens, over a 5-year period (2000–2004) as well as compatible meteorological data from the NCEP (National Centers for Environmental Protection). A set of different models have been tested at the same time to reveal the effectiveness of the proposed approach, both univariate and multivariate, and linear and non-linear models. The analysis of all examined datasets has shown conclusive evidence that the introduction of the newly developed procedure which utilizes data collected over a shorter horizon can significantly increase the forecasting ability of any developed model using daily historic PM10 data, under all examined metrics.

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Shekarrizfard et al. (2011) introduced a wavelet transform-based artificial neural networks (WT-ANN) method in the estimation and prediction of PM10. The application of wavelet transform was selected for its temporal shift properties and multiresolution analysis characteristics enabling it to reduce disturbing perturbations in the input training set data (the removal of noise from data series before being used in ANN modeling is of great importance). Meteorological data gathered from previous days from an Iranian metropolitan area were used as an input vector for the models. Appropriate statistical indices were used in order to investigate the relation between PM10 levels and meteorological variables. The results of the simulation of PM10 based on WT-ANN method outperformed those of the traditional ANN models and showed a noticeable increased accuracy and speed in PM10 estimation-prediction. Vlachogianni et al. (2011) developed forecasting models based on stepwise multiple linear regression (MLR) for Athens and Helsinki. The variables to be forecasted were the maximum hourly concentrations of both PM10 and NO as well as the daily average PM10 concentration of the next day. The meteorological preprocessing model MPP-FMI was used for computing the Monin–Obukhov length and the mixing height. Input data for the model were selected from two stations, representative of urban background and urban traffic station both in Athens and Helsinki in 2005. Several statistical evaluation parameters were employed to analyze and compare the performance of the models. Forecasts from the MLR model was also compared to those from an ANN model in order to investigate if an increase in performance might justify the additional computational effort. The best predictor variables for both cities were the concentrations of NOx and PM10 during evening hours as well as wind speed and the Monin-Obukhov length. For Helsinki the model accuracy was expectedly better on the average, when the Monin–Obukhov length and mixing height were included as predictor variables. The forecasts of the daily average concentration were better than the forecasts of maximum hourly concentration for PM10. The authors concluded that since the results produced by the ANN model where just slightly better than the ones produced by the MLR methodology the MLR methodology can be a useful and fairly accurate tool. Finally, artificial neural networks are a branch of artificial intelligence developed in the 1950s aiming at imitating the biological brain architecture. They are an

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approach to the description of functioning of human nervous system through mathematical functions. Typical ANNs use very simple models of neurons. These artificial neurons models retain only very rough characteristics of biological neurons of the human brain (McCulloh and Pitts 1943). According to Tu (1996), artificial neural networks are algorithms that can be used to perform nonlinear statistical modeling and provide a new alternative to logistic regression. Artificial neural networks offer a number of advantages, including requiring less formal statistical training, ability to implicitly detect complex nonlinear relationships between dependent and independent variables, ability to detect all possible interactions between predictor variables, and the availability of multiple training algorithms. Disadvantages include its “black box” nature, greater computational burden, and the proneness to overfitting. Black box nature means that we are not able to know the exactly process and the final relationship between the parameters of the problem. Greater computational burden meaning that for the appropriate ANN training a large number of exemplars (the magnitude of the training set) is required. This leads to an increase of the training time. Overfitting otherwise overtraining is meaning that is possible during the training phase the ANN do not learn how to solve the problem but to memorize it. For that purpose, the cross validation methodology is applied (Moustris et al. 2010; Sfetsos and Vlachogiannis 2010). In the present study, the objective is to develop innovating ANN models in order to simultaneously predict 24 h ahead the mean daily PM10 concentrations as well as the number of hours during the next day where PM10 concentration is above the threshold value ([PM10]≥50 μg/m3) according to the European Union Directive 2008/50/EC, at five different monitoring sites within the greater Athens area (GAA).

2 Data and Methodology The city of Athens is located in an area of complex topography within the Athens basin (∼450 km2). A detailed description of the Athens basin topography and climate is found in various publications (e.g. Larissi et al. 2010). The geography of the area does not favor the dispersion of air pollutants. The sources of atmospheric particles can be either natural or anthropogenic and car traffic is an important source in urban environments


(Finlayson-Pitts and Pitts 2000; Grivas and Chaloulakou 2006). The five examined monitoring sites, part of the monitoring network of the Hellenic Ministry of the Energy, Environment and Climatic Change (HMEECC), can be classified in two categories (Fig. 1): The city-center station Aristotelous (ARI) and the peripheral stations Agia Paraskevi (AGP), Maroussi (MAR), Lykovrissi (LYK), and Thrakomakedones (THR). For each of the five stations, hourly concentrations of ambient air pollutants of PM10 and NO2 covering the period 2001–2005 as well as meteorological data concerning hourly values of the air temperature, relative humidity, wind speed and wind direction for the same period have been used. A question that always arises in prediction is which are the appropriate parameters-variables for the model? For answering this question, the Stepwise Multi Linear Regression Analysis (SMLRA) was applied (Murtoniemi et al. 1994; Grivas and Chaloulakou 2006; Xu et al. 2008). The mean daily PM10 concentration was considered as the dependent variable and the maximum hourly NO2 concentration, the mean daily air temperature, the mean daily relative humidity, the mean daily wind speed and the mode daily wind direction were considered as the independent variables. Applying the SMLRA only significant terms were included (p