Marine Pollution Bulletin 62 (2011) 1198–1206

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Marine Pollution Bulletin

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ANN application for prediction of atmospheric nitrogen depositionto aquatic ecosystems

Sundarambal Palani a,c,⇑, Pavel Tkalich a, Rajasekhar Balasubramanian b, Jegathambal Palanichamy d

a Tropical Marine Science Institute, National University of Singapore, 18 Kent Ridge Road, Singapore 119227, Singaporeb Department of Civil and Environmental Engineering, National University of Singapore, Singapore 117576, Singaporec Department of Chemical and Biomolecular Engineering, National University of Singapore, Singapore 117576, Singapored Institute of Hydraulic Engineering and Water Resources Management, RWTH Aachen University, Aachen 52056, Germany

a r t i c l e i n f o

Keywords:Atmospheric depositionNitrogenAquatic ecosystemsEutrophicationNeural networkSoutheast Asia

0025-326X/$ - see front matter � 2011 Elsevier Ltd.doi:10.1016/j.marpolbul.2011.03.033

⇑ Corresponding author at: Tropical Marine Scienceof Singapore, 18 Kent Ridge Road, Singapore 119221455.

E-mail address: tmssp@nus.edu.sg (S. Palani).

a b s t r a c t

The occurrences of increased atmospheric nitrogen deposition (ADN) in Southeast Asia during smokehaze episodes have undesired consequences on receiving aquatic ecosystems. A successful predictionof episodic ADN will allow a quantitative understanding of its possible impacts. In this study, an artificialneural network (ANN) model is used to estimate atmospheric deposition of total nitrogen (TN) andorganic nitrogen (ON) concentrations to coastal aquatic ecosystems. The selected model input variableswere nitrogen species from atmospheric deposition, Total Suspended Particulates, Pollutant StandardsIndex and meteorological parameters. ANN models predictions were also compared with multiple linearregression model having the same inputs and output. ANN model performance was found relatively moreaccurate in its predictions and adequate even for high-concentration events with acceptable minimumerror. The developed ANN model can be used as a forecasting tool to complement the current TN andON analysis within the atmospheric deposition-monitoring program in the region.

� 2011 Elsevier Ltd. All rights reserved.

1. Introduction

Atmospheric deposition has recently gained attention as a sig-nificant additional source of nutrients (nitrogen, N and phospho-rous, P) to aquatic ecosystems such as coastal and marineecosystems, particularly in the context of the eutrophication. Ithas been reported that the atmospherically deposited nutrientshave undergone a tenfold increase in recent decades due to a di-verse array of industrial human activities and forest fires (Gallowayet al., 1994; Jickells, 1998; Smith, 2003; Galloway et al., 2004).Since the increased nitrogen pollution can cause severe degrada-tion in coastal ecosystems, the role of atmospheric deposition asa contributor of nitrogen species to coastal waters needs to be sys-tematically investigated (Fisher and Oppenheimer, 1991). Thenitrogen species consist of total nitrogen (TN), ammonium (NHþ4 ),nitrite (NO�2 ), nitrate (NO�3 ) and organic nitrogen (ON). Atmo-spheric deposition of these species can occur through precipitation(‘‘wet deposition’’) and/or fallout of particles (‘‘dry deposition’’).

The increasing amounts of atmospheric anthropogenic N enter-ing the ocean could increase annual new marine biological produc-tion by 3.5%; the excess nitrogen can deplete dissolved oxygen

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levels in the water and has significant effects on climate, foodproduction, and ecosystems all over the world (Duce et al., 2008).Evidence suggests that water soluble atmospheric ON is principallycontinental in origin and can contribute significantly to the totalsoluble nitrogen flux (Cornell et al., 1995, 2003), a significant frac-tion of which is available to phytoplankton growth as N source(Antia et al., 1991; Peierls and Paerl, 1997). Several research studieshave highlighted the importance of the atmospheric depositiononto the aquatic systems in Southeast Asia (SEA) (Cornell et al.,1995; Paerl, 1997; Balasubramanian et al., 1999; Abram et al.,2003; Sundarambal et al., 2007). The transboundary smoke hazeresulting from the land and prolonged forest fires in Indonesiaand neighboring countries (Balasubramanian et al., 2003) episodi-cally affect regional air quality in SEA. These haze episodes couldintroduce significant amounts of nitrogen species to coastal ecosys-tems through atmospheric deposition and contribute to coastalwater eutrophication (Sundarambal et al., 2009; Sundarambalet al., 2010a,b,c). The coastal ecosystems in Singapore are proneto be significantly affected by atmospheric nitrogen inputs,although delivered as a diffuse flux in contrast to localized river, be-cause of their small size with shallow depth. The mean concentra-tions of nitrogen species in the Singapore coastal waters derivedfrom atmospheric deposition are comparable to those fromland-based sources such as wastewater treatment plants(TN = 2.13 mg/l) and rivers (NHþ4 = 0.16 mg/l and NO�2 + NO�3 = 0.34 mg/l) (DHI, 2004; Sundarambal et al., 2009).

S. Palani et al. / Marine Pollution Bulletin 62 (2011) 1198–1206 1199

In order to protect coastal waters and prevent the occurrence ofeutrophication, models are needed with capability to predict theinfluence of atmospheric deposition of nitrogen species on waterquality, especially during air pollution episodes. Air pollutantsexhibit strong nonlinear behavior in the atmosphere due to thecombined influence of physical processes and chemical transfor-mations on their fate. The complexity of pollutant predictionsusing either deterministic or stochastic models has motivated theuse of an artificial neural network (ANN) model for the intendedapplication. ANNs have the ability to learn about nonlinear rela-tionships between the variables of interest and to identify complexpatterns in data sets that are not well described by a set of knownprocesses or simple mathematical formulae.

The present work was carried out to evaluate the potential of anANN model constructed with recurrent neural network (RNN)architecture in making reliable predictions of dry atmosphericdeposition of TN and ON concentrations, a task that is known topresent certain difficulties. The ANN model prediction is comparedto that from a multiple linear regression (MLR) model for the sameinputs and outputs. The former is a state-of-the-art techniquewhile the latter is a conventional one. Model inputs were derivedfrom continuous air quality monitoring conducted at an estab-lished sampling location in Singapore.

2. Materials and methods

2.1. Study area

Singapore is a small island with total land area of 710 km2 lo-cated at 137 km north of the equator (Fig. 1). The boundaries ofSingapore’s territorial water coincide closely with the port limits.The Singapore Strait is channel shaped, where there is a confluenceof three different water bodies (Java Sea, South China Sea and Ma-lacca Strait). Because of its geographical location, its climate ischaracterized by uniform temperature and pressure, high humidityand abundant rainfall. There are two main seasons, namely theNortheast Monsoon (NEM) (November to March) and the South-west Monsoon (SWM) (June to September). The ambient air tem-perature ranges from 22.1 to 34.6 �C, and the annual averagerainfall is 2753 mm. In general, dry weather is due to the lack ofconvection, which prevents the development of rain-bearingclouds. The primary sources of air pollutants in Singapore are de-rived from industrial, petrochemical and transportation activitieswithin the country and transboundary air pollution from neighbor-ing countries. During the NEM period, air mass masses might beinfluenced by transboundary transport of air pollutants from Chi-na, Myanmar, Cambodia, Vietnam, Laos and Thailand. During the

Fig. 1. Map of Singapore and sampling location.

SWM period, air masses pass by southern Sumatra, Borneo, Sura-baya and Java Islands of Indonesia and could potentially transportbiomass burning-impacted air masses from that region to the Sin-gapore area, Malacca Straits and Peninsular Malaysia (Sundaram-bal et al., 2009).

2.2. Data

Total Suspended Particulates (TSP) samples were collected forestimation of dry atmospheric deposition from September 2006to December 2006 at the Tropical Marine Science Institute (TMSI)in St. John’s Island (SJI) (latitude 1�1301000 North of the Equatorand longitude 103�5005400 East), Singapore (Fig. 1). The samplingstation ‘‘SJI’’ was selected because of its close proximity to the opencoastal area in the southern part of Singapore and the absence oflocal pollution sources. The ambient air over the sampling locationis not strongly influenced by major air pollution sources in the ab-sence of regional smoke haze episodes. The TSP samples were col-lected every 24 h on 20.3 � 25.4 cm size Whatman QM-A highpurity quartz (SiO2) microfibre filter (CAT No. 1851-865, Whatmanplc, Middlesex, UK) during dry weather conditions using a high vol-ume air sampler (model 3800 AFC: HI-Q Environmental ProductsCompany, USA). A detailed description of the dry deposition sam-pler can be found elsewhere (See et al., 2007; Sundarambal et al.,2009). The filters were conditioned in a dry box at 30% relativehumidity and 25 �C temperature for 24 h prior to and after air sam-pling. The difference between pre- and post-sampling weights wasused to estimate the mass of particulates collected on the filters.The TSP mass concentration (lg/m3) was then calculated by divid-ing the mass of particulates (lg) collected by the volume of airpassed through the filter (m3) during sampling period. The col-lected airborne particulate samples were processed to determineTN and water-soluble nitrogen species such as NHþ4 , NO�2 , NO�3and ON by an established analytical laboratory procedure devel-oped for dry atmospheric deposition samples in the tropical region(Sundarambal et al., 2007, 2009; Karthikeyan et al., 2009). The Uni-ted States Environmental Protection Agency (US EPA) developed anair quality index called Pollutant Standards Index (PSI), which hasbeen adopted by the National Environment Agency (NEA) in Singa-pore. The PSI was developed primarily to provide the public withinformation about daily ambient air quality (i.e. good (0–50), mod-erate (51–100), unhealthy (101–200), very unhealthy (201–300) orhazardous (>300)). During smoke haze episodes, the main contrib-utor to the PSI was PM10 (airborne particles6 10 lm) because thispollutant far exceeded the concentration of other air pollutants.During the sampling period, meteorological parameters and thePSI were obtained from the National University of Singapore(NUS) weather station (Department of Geography, NUS) and NEA,respectively. The measured nitrogen species in TSP, meteorologicalparameters and the PSI measured during the sampling period aregiven in Table 1.

2.3. Artificial neural network

Although the concept of artificial neurons was first introducedin 1943 (McCulloch and Pitts, 1943), research into the applicationof ANNs has intensified since the introduction of the Back Propaga-tion (BP) training algorithm for feed forward ANNs in 1986(Rumelhart et al., 1986). Though a variety of linear and nonlinearmodeling techniques (Lek et al., 1996) could be applied, ANNs withactive neurons, are believed to be a more appropriate predictionalgorithm for noisy and short time-series. The most important fea-ture of ANN models is their capability to provide the correct re-sponse even for the input values not presented in the trainingprocess. The developed models can therefore be used for predictionof atmospheric pollutants over a wide range of input parameters.

Table 1The range of measured nitrogen species from dry atmospheric deposition, meteorological parameters and PSI during the sampling period.

Parameters Unit Minimum Maximum Mean SD Median

Air temperature �C 24 28.3 26.9 0.88 26.8Pressure kPa 100.1 100.6 100.3 0.10 100.2Relative humidity % RH 68.4 89.2 75.9 4.05 76.1Wind speed m/s 0.6 4.6 1.3 0.62 1.2Wind direction 0–360� (c.w) 44.8 231.2 108.8 43.4 97Incoming radiation W/m2 37.7 199.6 137.7 32 136Rainfall mm/day 0 121 13.9 23.5 4.1NHþ4 mg/l 0.001 2.43 0.58 0.701 0.32NO�2 + NO�3 mg/l 0.1 6.63 1.11 1.07 0.83ON mg/l 0.044 8.16 1.83 1.53 1.38TN mg/l 1.12 14.9 3.47 2.64 2.8PSI 18.5 118.5 50 22 43TSP lg/m3 13.4 137.6 39.6 25.4 30.7

Note: c.w – clockwise.

1200 S. Palani et al. / Marine Pollution Bulletin 62 (2011) 1198–1206

ANN training methods generally fall into the categories of super-vised (Multilayer Perceptron (MLP), Radial basis function andRecurrent BP method), unsupervised methods (Kohonen and Hop-field method) and various hybrid approaches (combination of thebest features of two different methods). The main differences be-tween the various types of ANNs include network architectureand methods used to determine the weights and functions for in-puts and neurodes (training) (Caudill and Butler, 1992). The MLPneural network has been designed to function well in nonlinearphenomena while a feed forward MLP network consists of an inputlayer and an output layer with one or more hidden layers in be-tween them. Each of such a layer contains a certain number of arti-ficial neurons. The fundamental building blocks, called the neural,for a neural network are shown in Fig. 2. A set of inputs is appliedeither from the outside or from a previous layer. Each of these in-puts is multiplied by a weight (W), and the products are summedup. This summation of product is termed the NET N and must becalculated for each neural in the network. After NET is calculated,an activation function (f) is applied to modify it, thereby producingthe output signal OUT O1.

The general formulation for the transformation between thetwo layers in a neural network is given by Eq. (1):

NM ¼XI

i¼1

Wi;m þ hm where hm is a bias ð1Þ

Om ¼ f ðNmÞ ¼1

1þ e�Nð2Þ

where layer I is the input to layer M and the output from layer M isO (i and m refer to the i-th and m-th elements of the layers I and M,respectively). The values of W are constants, which are determinedby the neural network software as part of calibration. Most neuralnetworks use the Logistic Activation Function (Eq. (2)) and the mostcommon training algorithm ‘‘BP’’ is used in the ANN literature. TheANN modeling steps include selection of performance criteria, datapre-processing (if necessary) and data division (a training set con-

W3

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biasWb

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biasWb

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Fig. 2. An artificial neural network building block with activation function.

taining all possible extreme cases, overfitting test set and an inde-pendent validation set), selection of model inputs, outputs andnetwork architecture, optimization of connection weights, training,testing and validation (Sundarambal et al., 2008). ANN’s ‘‘learn’’ byexample as long as the input dataset contains a wide range of thetypes of patterns that the ANN will be asked to predict, and themodel uses them successfully to predict the output using those pat-terns. Thus, if extreme data range events occur, the models must berecalibrated and revalidated.

In this paper, an ANN model with recurrent neural network(RNN) architecture is proposed for the prediction of TN and ONconcentrations from atmospheric deposition to Singapore coastalwaters. RNN is a modification of the feed forward neural networkarchitecture to allow for temporal classification (Fig. 3). While afeed forward network propagates data linearly from inputs to out-puts, RNN also propagates data from later processing stages to ear-lier stages. In RNN, a ‘‘context’’ layer is added to the structure,which retains information between observations. At each timestep, new inputs are fed into the RNN. The previous contents ofthe hidden layer are passed into the context layer which are thenfed back into the hidden layer in the next time step. In an algo-rithm similar to the BP algorithm, called BP through time (BPTT)(Rumelhart et al., 1986), the weights of the hidden layers and con-text layers are set. While training a network, the effect that each ofthe network inputs has on the network output should be studied.This provides feedback as to which input parameters are the mostsignificant. Based on this feedback, it may be decided to prune theinput space by removing the insignificant parameters. By adoptingone set of values for learning rate as 0.2 (range 0–1) and momen-tum as 0.5 (range 0–0.9), the training process is started and thenadjusted if necessary. The learning process is controlled by themethod of internal validation (about 20% of calibration data washeld back and used to test the error at the end of each epoch)(Tsoukalas and Uhrig, 1997) and performance evaluation test (Le-

Output Layer

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Fig. 3. Typical recurrent neural network architecture.

S. Palani et al. / Marine Pollution Bulletin 62 (2011) 1198–1206 1201

gates and McCabe, 1999). The ANN with the best performancewhen applied to the validation set is selected. Two commonly usedstopping criteria are (i) stop after a certain number of runs throughall the training data (each run through all the training data is calledan epoch); (ii) stop when the total sum-squared (target) errorreaches some low level. The performances of the models are eval-uated using Nash–Sutcliffe coefficient of efficiency (E) (Nash andSutcliffe, 1970), the square root of the mean square error (RMSE),mean square error (MSE), mean absolute error (MAE), mean abso-lute deviation (MAD) and correlation coefficient (r). In addition,scatter plots and time series plots are used for visual comparisonof measured and predicted values.

2.4. ANN in air quality

Applications of ANN in the areas of water engineering, ecologi-cal and environmental sciences have been reported since thebeginning of the 1990s. In recent years, ANNs have been usedintensively for prediction and forecasting in a number of areas suchas water resources (Liong et al., 1999; Maier and Dandy, 2000; Sun-darambal et al., 2008), oceanography (Lee, 2004; Makarynskyy,2004), chemical engineering (Quek et al., 2000), environmental sci-ence (Grubert, 2003) and atmospheric sciences (Gardner and Dor-ling, 1998, 1999; Kukkonen et al., 2003; Jiang et al., 2004; Brunelliet al., 2007). ANNs are able to map the nonlinear relationships thatare characteristic of aquatic ecosystems (Lek et al., 1996). MLPmodel is used to predict short-term atmospheric sulfur dioxideconcentration (Boznar et al., 1993) and surface ozone concentra-tions (Yi and Prybutok, 1996) in industrialized area. Comrie(1997) compared ozone forecasts made by MLP and regressionmodels. Konovalov (2003) applied ANN for analysis of nonlinearrelationships between TSP and its gaseous precursors based onobservational data. ANN was employed to predict the nitrogendioxide concentration at industrial, commercial and residentialactivity places (Chelani and Hasan, 2001). Limited air quality dataand high air quality monitoring cost often pose serious problems toprocess-based modeling approaches. ANN is particularly an optionas it is computationally very fast and requires much less inputparameters/conditions compared to the deterministic models.ANN requires, however, a good number of representative data fortraining the network. Even though ANN method has been exten-sively used in air and water quality modeling, it has not being ap-plied in water-soluble bioavailable nitrogen species present inaerosol until now as evident from the literature review. In the pres-ent study, ANN modeling of atmospheric TN and ON deposition inSingapore is attempted for the first time.

Table 2Pearson correlation coefficient of parameters used in the present study.

Parameter TSP NHþ4 NO�2 + NO�3 PSI Pr

TSP 1NHþ4 0.53 1NO�2 + NO�3 0.74 0.37 1PSI 0.87 0.63 0.59 1Pr 0.47 0.23 0.33 0.53 1AT 0.63 0.35 0.44 0.74 0.37RH �0.67 �0.40 �0.44 �0.78 �0.37WS 0.26 �0.15 0.26 0.11 0.03WD 0.34 �0.07 0.42 0.27 0.27IR 0.41 0.12 0.31 0.48 0.21RF �0.22 0.09 �0.13 �0.26 �0.22TN 0.82 0.54 0.84 0.67 0.30ON 0.67 0.25 0.58 0.46 0.19

Note: TSP – Total Suspended Particulate Matter; Pr – pressure; AT – air temperature; RH –RF – rainfall; TN – total nitrogen.

While conceptual models require extensive data sets andlengthy model calibrations, MLR and ANN models offer simplerand faster solutions. The general purpose of the MLR (the termwas first used by Pearson (1908)) equation is to learn more aboutthe relationship between several independent variables and adependent variable. ANNs are more flexible than regression mod-els, and require less prior knowledge of the system under study.Unlike ANNs, the regression theory imposes strict conditions forerror statistics, such as normal distribution and constant (homo-scedastic) variance. On the other hand, it is difficult to interpret aphysical meaning of the ANN parameters. In addition, ANN alsoeasily handles more input variables and is extremely helpful whenthere are a large number of experiments, but in the case of MLR, alarge number of input variables lead to a polynomial with manycoefficients that involves tedious computation. Regression coeffi-cients can reveal useful information about the system under study,but there are no established techniques to obtain such informationfrom the ANN parameters. MLR model was also developed for com-paring its predictions accuracy with ANN model.

In the present study, a commercial neural net software packageNeuroShell 2™ Release 4.0 (Neuroshell, 2000) which implementsseveral ANN algorithms including RNN was used to develop theANN model. To use the program, a set of inputs and outputs mustbe defined, and a suitable training set must be developed. Thechoice of input variables in the present study is based on a statis-tical correlation analysis of the field data, the prediction accuracyof TN or ON, and the domain knowledge. In general, the concentra-tion of nitrogen species appeared to have a stronger correlationwith TSP, PSI, relative humidity and air temperature than any otherparameters (Table 2). ANN models were developed using meteoro-logical parameters and nitrogen species from dry atmosphericdeposition, TSP and PSI as input variables (Table 3). The dry atmo-spheric deposition data (4 months, a total of 60 patterns) from Sta-tion SJI (Fig. 1) were divided into three sets; training set contains60% of the record size (36 patterns) while the remaining 20% foroverfitting test set (12 patterns) and 20% for the independent val-idation set (12 patterns). The general approach of selecting a goodtraining set from an available data set is to include all extremeevents so that all possible minimum and maximum values presentin the data set. The representative data, which was never seen be-fore by trained ANN, were used as the validation set. The ANNmodel was also trained and tested by specifying minimum andmaximum values of each input parameter that are above and be-low values in the data set (example, expand the input data rangeby 25%) to allow a wider range of anticipated future measured datafor future predictions and improve accuracy on extreme values.The sensitivity analysis is carried out by removing each of the input

AT RH WS WD IR RF TN

1�0.93 10.22 �0.23 10.21 �0.27 0.21 10.70 �0.74 0.27 0.17 1�0.54 0.50 �0.32 0.01 �0.44 10.43 �0.46 0.17 0.21 0.29 �0.15 10.28 �0.32 0.18 0.08 0.23 �0.22 0.88

relative humidity; WS – wind speed; WD – wind direction; IR – incoming radiation;

Table 3ANN model input/output for prediction of TN and ON from dry atmosphericdeposition.

ANNmodel

Inputs Output Method

TN Air temperature, pressure, relative humidity,wind speed, wind direction, incomingradiation, rainfall, NHþ4 , NO�2 + NO�3 , PSI, TSP

TN RNNMLR

ON Air temperature, pressure, relative humidity,wind speed, wind direction, incomingradiation, rainfall, NHþ4 , NO�2 + NO�3 , PSI, TSP,TN

ON RNNMLR

1202 S. Palani et al. / Marine Pollution Bulletin 62 (2011) 1198–1206

parameters from that of the initial ANN model and then comparingthe performance indicators. The greater the effect observed in theoutput, the greater is the sensitivity of that particular inputparameter.

3. Results and discussion

3.1. Total nitrogen model

ANN model was developed to simulate TN from dry atmo-spheric deposition onto the Singapore coastal waters using RNNarchitecture with 3 hidden layers having different activation func-tions (initial weight of 0.3, the optimum learning rate of 0.1 andmomentum of 0.1). Both ANN and MLR models used eleven inde-pendent variables as inputs and a dependent variable as an outputvariable (Table 3). The sensitivity of the input/output parameterson the TN prediction was relatively smaller for training and testdata set than validation data set. ANN model that produces the‘‘best result’’ for validation data set was retained. TN is mainlydetermined by the amount of inorganic and organic nitrogen pres-ent in the particulate matter and meteorological conditions. The

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contribution of the selected eleven input parameters to TN outputfor the best ANN model is given in a decreasing order of theirimportance as NO�3 + NO�2 TSP, PSI, NHþ4 , wind speed, incomingradiation and other input variables (Table 3). TN concentration cor-relates well with TSP (r = 0.82), NO�3 + NO�2 (r = 0.84) and PSI(r = 0.62) and meteorological conditions (Table 2) which showthe importance of the above mentioned physical and chemicalvariables in controlling nitrogen deposition behavior. The variationof atmospheric TN deposition is mainly dependent on air pollutionfrom local sources such as industrial and traffic emissions and re-gional sources like biomass burning. Atmospheric TN deposition isone of the most important measurements to be considered whenexamining its impact as new nitrogen input into surface watercausing excess algal growth and eutrophication. Smoke hazeevents due to biomass burning can dramatically affect the ratesof TN deposition to aquatic ecosystems. Fig. 4a and b show themeasured and predicted TN concentration respectively for the bestprediction ANN and MLR models. The scatter diagram betweenpredicted and measured TN concentration from ANN (Fig. 5a)and MLR (Fig. 5b) models illustrates how network performancechanges over the range of TN values. The data points cluster closelyaround the 45� fitted line and show the best performance of TNmodels between measured and predicted values. The data pointsin the scatter plot (Fig. 5a) are tightly clustered with TN < 4 lg/m3 and displayed nearly equal dispersion above and below the45� fitted line. The scatter plot (Fig. 5b) shows some tendency forthe network to under- or overestimate TN concentration. Datapoints for MLR model are more widely dispersed about the 45� fit-ted line than with ANN, however. This greater dispersion corre-sponds to a relatively large MSE value for MLR model.

The performance indicators for ANN and MLR models (Table 4)show that the TN prediction by best-fit ANN model is slightly supe-rior to MLR obtained with selected input data. The trained TN mod-el is appropriate, giving small values of RMSE, MSE, MAE and MAD.

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Table 4Performance indicators for dry atmospheric deposition of TN and ON prediction ANNand MLR model.

Method ANN MLR

Training Overfitting test Validation overall

TN modelE 0.94 0.98 0.93 0.86MSE 0.34 0.20 0.35 0.99RMSE 0.61 0.44 0.60 0.99r 0.97 0.99 0.96 0.93MAE 0.02 0.03 0.06 0.03MAD 1.33 1.60 1.23 1.39

ON modelE 0.99 0.97 0.91 0.99MSE 0.01 0.06 0.17 0.03RMSE 0.11 0.25 0.41 0.17r 1.00 0.99 0.96 0.99MAE 0.0009 0.029 0.024 0.0004MAD 1.00 1.17 1.00 1.02

S. Palani et al. / Marine Pollution Bulletin 62 (2011) 1198–1206 1203

Both ANN and MLR are able to simulate TN within 1 lg/m3 or less(MSE < 0.99 lg/m3 and E > 0.86). The ANN model shows good TNprediction with RMSE ranging from 0.44 to 0.61 lg/m3 and E is0.98 and 0.93 respectively for independent overfitting test and val-idation sets. Their performance was also found adequate even inthe case of highly polluted days during the episodic smoke hazeevents with acceptable minimum error. In addition, the correlationcoefficient ranges from 0.93 to 0.99 for both model predicted TN,underlying a small difference between the predicted and the mea-sured values. The results from ANN model trained with the inputparameter value range expanded by 25% show that the extra effort,however, has insignificant improvement in the prediction accu-racy. The regression equation obtained for TN from MLR model isgiven below.

TN ¼ 173þ 0:0612 TSPþ 0:673 ðNHþ4 Þ þ 1:40 ðNO�3 þ NO�2 Þ� 0:0188 PSI� 1:53 pressure� 0:601 air temperature� 0:028 relative humidity� 0:298 wind speed� 0:00697 wind directionþ 0:00346 incoming radiation� 0:0122 rainfall ð3Þ

3.2. Organic nitrogen model

ANN model was developed to simulate ON from atmosphericdeposition in Singapore using RNN architecture with three hidden

layers having different activation functions, initial weights (0.3),the optimum learning rate (0.1) and momentum (0.1). In ONmodeling, twelve explanatory input variables and one output var-iable (Table 3) were used for both ANN and MLR models. The sen-sitivity of the model parameters on the ON prediction arerelatively smaller for training and test data set than validationdata set. The model parameters that produce the ‘‘best result’’for validation data set often prove to be robust and these param-eters were retained for the final ON prediction. Usually, ON iscomputed by subtracting inorganic nitrogen from TN, those val-ues obtained from validated laboratory techniques (Sundarambalet al., 2007, 2009). The contribution of the selected twelve inputparameters was estimated by the best ANN model and is given ina decreasing order of their importance as TN, NO�3 + NO�2 , NHþ4 ,PSI, TSP, pressure and other input variables (Table 3). TN pre-dicted from the developed ANN model (Section 3.1) was usedfor ON prediction model. A similar trend was observed in theMLR model. The correlation (r) between ON concentration withTN, TSP, NO�3 + NO�2 and PSI are 0.88, 0.67, 0.58 and 0.46, respec-tively (Table 2). The nitrogen species (TN, NO�3 + NO�2 , NHþ4 ) arestatistically significant for ON prediction. This shows that theabove-mentioned variables and meteorological conditions playan important role in atmospheric ON deposition. Fig. 6a and bshow the measured and predicted concentrations of ON forANN and MLR models respectively. Fig. 7a and b present scatterplots between the measured and predicted concentration of dryatmospheric ON deposition by ANN and MLR models, respec-tively. Data points in Fig. 7 were tightly clustered withON < 3.5 lg/m3 and displayed nearly equal dispersion above andbelow the 45� fitted line. ANN is able to simulate the ON withMSE < 0.18 lg/m3 while MLR is able to simulate it withMSE < 0.03 lg/m3. This appeared to confirm a strong correlationbetween the selected input parameters and the atmospheric ONdeposition. The overall results of MLR are comparable to ANN.The performance indicators for ON prediction by ANN and MLRmodels were found adequate even in the case of high pollutantconcentration during episodic haze events (Table 4 and Fig. 6).Once validated TN and ON models are ready for prediction, thelaboratory procedure can be optimized by only analyzing NHþ4 ,NO�2 and NO�3 through the laboratory techniques excluding TNas it requires tedious laboratory procedures. The developed ANNmodel could be used as a new prediction tool, which comple-ments current TN and ON laboratory analysis inside the atmo-spheric deposition-monitoring program carried out in theregion. The regression equation obtained for ON from the MLRmodel is given below.

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Fig. 7. Scatter plots between measured and predicted concentrations of ON by models (a) ANN and (b) MLR.

1204 S. Palani et al. / Marine Pollution Bulletin 62 (2011) 1198–1206

ON ¼ 25:1þ 0:00380 TSP� 0:919 ðNHþ4 Þ� 0:945 ðNO�3 þ NO�2 Þ þ 0:00119 PSI� 0:233 pressure� 0:0673 air temperatureþ 0:0007 relative humidity� 0:0161 wind speed� 0:000709 wind directionþ 0:00002 incoming radiation� 0:00208 rainfallþ 0:941 TN ð4Þ

The uncertainty in results might be due to dynamic characteris-tics of air quality in and around Singapore, meteorological forcingand occurrences of pollution due to unknown sources in the regionof interest within short period. Refinements may include the pre-diction of nitrogen species by including various other physical,chemical and meteorological parameters as model input. The esti-

mated TN and ON from atmospheric deposition could contribute asubstantial fraction of bioavailable nitrogen to the euphotic zone inthe aquatic ecosystems and using which possible environmentaleffects of ‘‘new’’ atmospheric deposition of nitrogen compoundson the aquatic ecosystems can be assessed. Using the ANN modelpredicted atmospheric deposition of TN and ON can be used asdiffused source nitrogen loading input into the process based mod-el NEUTRO (Tkalich and Sundarambal, 2003) to analyze and fore-cast the impact on coastal aquatic ecosystem due to the episodicatmospheric deposition event (Sundarambal et al., 2010a,c).

4. Summary and conclusion

ANN models were developed to simulate nutrients TN and ONfrom dry atmospheric deposition to the Singapore coastal waters

S. Palani et al. / Marine Pollution Bulletin 62 (2011) 1198–1206 1205

with an acceptable accuracy. The ANN model performance is foundto make good predictions with MSE less than 1 lg/m3 and 0.2 lg/m3 for TN and ON respectively and are comparable with MLR mod-el. With largely still unknown factors of air quality variation andlimited data size, a relatively good correlation was observed be-tween the measured and predicted concentration. In addition,the correlation coefficient ranges from 0.93 to 1 for both predictedN species, underlying a small difference between predicted andmeasured values. The results showed that the complex nonlinearbehavior in nitrogen deposition process from atmospheric couldbe modeled accurately using ANN technique at an instance of timewithout attempting to explain the nature of the phenomena. ANNhas performed well with better accuracy when compared withMLR techniques for TN prediction whereas the results from bothmethods are comparable for ON prediction in spite of their prosand cons. The developed ANN models could be used as a new pre-diction tool, which complements current TN, and ON laboratoryanalysis inside the atmospheric deposition-monitoring programcarried out in the region. If extreme data range events (i.e. outsidethe current modeled data range) occur in the future measured data,the models must be trained and tested again by incorporating newmeasured data set.

Many real-world processes are multidimensional, highly non-linear and complex. An accurate analytical model developmentbased on known mathematical and scientific principles is extre-mely difficult and time-consuming. ANN has learning capabilityand can be trained, using examples, to perform complex functions,input–output mapping, it can be re-trained to adapt to the environ-ment and it is able to generalize. The successful on-time predic-tions of episodic atmospheric nitrogen deposition concentrationare of particular importance for the pollution loading estimationonto surface water and forecasting the possible impact on aquaticecosystems. ANN modeling is a promising and useful tool to opti-mize monitoring network by identifying essential parameters tomonitor (thus, cost reduction), and to analyze the control factorsfor the occurrence of episodic atmospheric deposition and its im-pact on aquatic ecosystems.

Acknowledgments

We would like to thank the Division of Environmental Scienceand Engineering for providing laboratory facilities and TMSI, NUSfor the computational facilities. We are grateful to Dr. Serena Teoand Er. Lim Chin Sing for sample collections at TMSI, St. John’s Is-land, Singapore.

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