Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki

Voukantsis, Dimitris; Karatzas, Kostas; Kukkonen, Jaakko; Räsänen, Teemu; Karppinen, Ari; Kolehmainen, Mikko

doi:10.1016/j.scitotenv.2010.12.039

Cited by 222 publications

(113 citation statements)

References 31 publications

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“…Principal Component Analysis (PCA) is a source identification method that enables us to certify that the variables were ideally correlated with one sole component only without correlating with other component and produce a new set of variables called principal component (PCs) [15], [16]. The number of principal components will be less than or equals to the original variables.…”

Section: Principal Component Analysismentioning

confidence: 99%

Seasonal Variation of Criteria Pollutant in an Urban Coastal Environment: Kuala Terengganu

2017

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Abstract. The aim of this study are (i) to determine the yearly and monsoonal variations of the criteria pollutants in Kuala Terengganu, (ii) identify the influences of the meteorological factors and other minor criteria pollutants that impose on the concentration of the most significant criteria pollutant using Principal Component Analysis (PCA). The hourly data for the criteria pollutants (PM10, CO, O3, NO2, and SO2) and meteorological factors (temperature, relative humidity, windspeed) for the duration of 10 years procured from the Department of Environment, Malaysia were analyzed. PM10 is the only pollutant that frequently recorded concentrations exceeding the MAAQG in the length of the year 2001 to 2010 and the criteria pollutants reported differ significantly between the monsoon seasons. PCA showed that sources contributing to the most significant criteria pollutant (PM10) are meteorological factor influences and industrial emissions.

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Section: Principal Component Analysismentioning

confidence: 99%

Seasonal Variation of Criteria Pollutant in an Urban Coastal Environment: Kuala Terengganu

2017

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“…Also, we applied the Nash Sutcliffe Efficiency Coefficient (E), coefficient of determination (R 2 ) and the Index of Agreement (IA), between the observed and predicted data to illustrate the validity of the model (Feng et al, 2015;Voukantsis et al, 2011;Krause et al, 2005). (7) (8) (9) (10) (11) Where P and M are the predicted and the observed values of PM 2.5 at the time t, respectively, and -M and -P are the average of predicted and observed values, respectively and n is the number of data.…”

Section: Model Efficiencymentioning

confidence: 99%

“…6) which causes a good performance of predicting PM 2.5 in the Karaj City. Voukantsis et al (2011) used a prin cipal component analysis to select input parameters for MLP neural network and predicted PM 10 and PM 2.5 . They obtained IA = 0.8.…”

Section: The Effect Of Learning Rate Andmentioning

confidence: 99%

“…However, the R 2 in our study for MLP and RBF was 0.92 and 0.93, respectively. We compared the results our MLP neural network with the results of Voukantsis et al (2011) andFeng et al (2015) and found that our model presented a suit able performance in predicting PM 2.5 concentrations in Karaj City. Feng et al (2015) obtained RMSE rate between 28 to 36 for one day and two days PM 2.5 pre diction using an MLP neural network.…”

Section: The Effect Of Learning Rate Andmentioning

confidence: 99%

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Predicting PM2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City

Asadollahfardi¹,

Zangooei²,

Aria³

2016

Asian J. Atmos. Environ

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The forecasting of air pollution is an important and popular topic in environmental engineering. Due to health impacts caused by unacceptable particulate matter (PM) levels, it has become one of the great est concerns in metropolitan cities like Karaj City in Iran. In this study, the concentration of PM 2.5 was predicted by applying a multilayer percepteron (MLP) neural network, a radial basis function (RBF) neural network and a Markov chain model. Two months of hourly data including temperature, NO, NO 2 , NO x , CO, SO 2 and PM 10 were used as inputs to the artifi cial neural networks. From 1,488 data, 1,300 of data was used to train the models and the rest of the data were applied to test the models. The results of using artificial neural networks indicated that the models performed well in predicting PM 2.5 concen trations. The application of a Markov chain described the probable occurrences of unhealthy hours. The MLP neural network with two hidden layers including 19 neurons in the first layer and 16 neurons in the second layer provided the best results. The coeffi cient of determination (R 2 ), Index of Agreement (IA) and Efficiency (E) between the observed and the predicted data using an MLP neural network were 0.92, 0.93 and 0.981, respectively. In the MLP neu ral network, the MBE was 0.0546 which indicates the adequacy of the model. In the RBF neural net work, increasing the number of neurons to 1,488 caused the RMSE to decline from 7.88 to 0.00 and caused R 2 to reach 0.93. In the Markov chain model the absolute error was 0.014 which indicated an ac ceptable accuracy and precision. We concluded the probability of occurrence state duration and transi tion of PM 2.5 pollution is predictable using a Markov chain method.

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“…These models attempt to find patterns directly from the input data, rather than numerical simulations. Some of the widely used models are linear regression, Geographically Weighted Regression (Ma et al, 2014), Land Use Regression (Eeftens et al, 2012), Support Vector Machine (Osowski et al, 2007) and Artificial Neural Networks (Voukantsis et al, 2011, Feng et al, 2015. Various attempts have also been made to combine different methods in order to achieve better performance (Sanchez et al, 2013;Adams & Kanaroglou, 2016).…”

Section: Introductionmentioning

confidence: 99%

A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

Fan

Hou

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

128

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ABSTRACT:Time series data in practical applications always contain missing values due to sensor malfunction, network failure, outliers etc. In order to handle missing values in time series, as well as the lack of considering temporal properties in machine learning models, we propose a spatiotemporal prediction framework based on missing value processing algorithms and deep recurrent neural network (DRNN). By using missing tag and missing interval to represent time series patterns, we implement three different missing value fixing algorithms, which are further incorporated into deep neural network that consists of LSTM (Long Short-term Memory) layers and fully connected layers. Real-world air quality and meteorological datasets (Jingjinji area, China) are used for model training and testing. Deep feed forward neural networks (DFNN) and gradient boosting decision trees (GBDT) are trained as baseline models against the proposed DRNN. Performances of three missing value fixing algorithms, as well as different machine learning models are evaluated and analysed. Experiments show that the proposed DRNN framework outperforms both DFNN and GBDT, therefore validating the capacity of the proposed framework. Our results also provides useful insights for better understanding of different strategies that handle missing values.

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Intercomparison of air quality data using principal component analysis, and forecasting of PM10 and PM2.5 concentrations using artificial neural networks, in Thessaloniki and Helsinki

Cited by 222 publications

References 31 publications

Seasonal Variation of Criteria Pollutant in an Urban Coastal Environment: Kuala Terengganu

Seasonal Variation of Criteria Pollutant in an Urban Coastal Environment: Kuala Terengganu

Predicting PM2.5 Concentrations Using Artificial Neural Networks and Markov Chain, a Case Study Karaj City

A Spatiotemporal Prediction Framework for Air Pollution Based on Deep RNN

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