SHB2012-7th International Symposium on Sustainable Healthy Buildings, Seoul, Korea 18 May 2012

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1 A Prediction Model of Indoor Air Quality in Underground Space for Minimizing Energy Consumption Using Nonlinear Dynamic Neural Network External Analysis SeungChul Lee 1, Hongbin Liu 1, MinJeong Kim 1, OnYu Kang 1, easeok Oh 1, Jeong ai Kim 2, Kailas L. Wasewar 3, ChangKyoo Yoo 1,* 1 Department of Environmental Science & Engineering, College of Engineering, Kyung Hee University, Yongin, , Korea 2 Department of Architectural Engineering, College of Engineering, Kyung Hee University, Yongin, , Korea 3 Department of Chemical Engineering, Visvesvaraya National Institute of echnology (VNI) Nagpur , Nagpur, INDIA Abstract *Corresponding Author: ChangKyoo Yoo (ckyoo@khu.ac.kr) A ventilation system is widely used to control indoor air quality (IAQ) in subway stations. However, improper ventilation control strategy not only leads to unnecessary hazardous pollutants accumulation but also to energy losses. herefore an accurate predictive model is needed to control IAQ effectively as well as to minimize the energy consumption in underground subway stations. Level of outdoor air pollutants must be considered for the design of ventilation control system as there are nonlinear dynamic relations. A new nonlinear dynamic prediction model using external analysis is proposed in present paper. Neural network is used to capture the nonlinearity between outdoor air quality and IAQ. Concentration of particulate matters (PM 10 ) coming from outside is selected as external variable and two outdoor PM 10 at time t-1 and t-2 are selected for capturing the dynamic characteristic of outdoor PM 10. After carrying out the nonlinear dynamic external analysis, partial least squares (PLS) method is used to predict PM 10 concentration in underground station and the energy usage for the ventilation control. he proposed model is applied to real-time IAQ data in D-subway station of Korea. he result showed that the prediction performances are greatly improved for PM 10 concentration as well as energy usage of a ventilation system. Keywords: Indoor air quality (IAQ), Subway station, Non linear dynamic external analysis, External air pollutants, Partial least square (PLS) 391

2 1. Introduction Metro systems have been constructed to improve the quality of public transportation and considered to be the most convenient mode of commuter transportation in the world. Since people spend a considerable amount of time in the subway stations daily, there has been a growing concern over the regulation of indoor air quality (IAQ) in subway stations. Various types of hazardous pollutants, such as particulate matters (PMs) and volatile organic compounds (VOCs), remain accumulated in the subway (especially located in underground) space due to heavy use, overcrowding, and inadequate indoor ventilation system. o attempt to address this social concern, Korean Ministry of Environment (MOE) enacted Indoor Air Quality Control in Public Use Facilities Act (IAQCPUF) to control the pollutants in indoor environments including the underground subway station [1-7]. Recently, several researches [8-10] on monitoring and diagnosis of IAQ of different underground subway systems have been carried out. Cheng [8] have measured the concentration of PMs inside trains as well as platforms of aipei subway stations. hey have found that the concentrations of PMs are positively correlated with the concentration of PMs of outdoor (i.e., ambient) air. Lau [9] have monitored daily variations of IAQ in Hong Kong metro station using statistical methods, such as clustering analysis (CA). Kim [10] have used multivariate monitoring methods, such as principal component analysis (PCA) and partial least square (PLS) for monitoring IAQ. However, these researchers have mainly focused on the air pollutant inside the subway station, but not on other factors which influence on the IAQ. It is well known that the IAQ of underground subway station is influenced by two sources: 1) passenger movement and 2) outdoor air pollutant. he number of passengers utilized the facility has a correlation with variations of concentration of PMs, since the passenger movement creates the air flow (from outside to inside of the subway station) and re-suspends the PMs [11,12]. he outdoor air (or the air entering from ventilation system) also affects the concentrations of PMs inside indoor space due to the presence of PMs in the outdoor air [9,13]. In general, the outdoor air pollutants have been considered as the main source of indoor air pollutant of the underground subway station, since the outdoor air entering from the ventilation systems purifies the indoor air. If the ventilation systems are not operated sufficiently, the IAQ is decreased and the outdoor air pollutant might be accumulated inside the subway station [14]. It is also well-known that the outdoor air quality (OAQ) heavily dependents on the weather changes and seasonal variations, resulting in variations in the IAQ of subway systems. herefore, to monitor and predict the variations in IAQ of the underground subway systems precisely, a new method which is not influenced by the variations in OAQ is necessary. In the first part of the present study, external analysis is used to know whether OAQ affects the IAQ and to eliminate the influence of OAQ from the real dataset. o know the influence of OAQ on the IAQ, the data with the following two scenarios are compared: 1) the real data which has effect of outdoor air, and 2) the data obtained after taking out the effect of outdoor air. he first part (i.e., elimination of the influence of OAQ from IAQ of underground subway station) is the central theme of the present study. Later, PCA and PLS regression methods are applied to monitor and predict the IAQ, respectively. hese methods are applied on real time data obtained from tele-monitoring system (MS) in Y-station, Korea. PCA method helps to know the dependency between 392

3 different IAQ variables. PLS regression method is used for predicting IAQ of a particular day using the data of a previous day. Root mean square of error (RMSE) values of predicted concentrations (of PMs) obtained using PLS regression model is compared to know the superiority of the regression model which takes out the effect of outdoor air. Outlines of this paper are as follows. he first section introduces the theories of multivariable statistical methods (PCA and PLS regression) and external analysis. In the materials and methods section, the proposed methods for monitoring and prediction of IAQ for data set 1) which has effect of outdoor air and 2) which takes out the effect of outdoor air are explained. he third section presents the results for the data obtained from Seoul metro station. Finally the conclusions of this article are addressed 2. heory Since environmental variables such as air pollutants are usually highly correlated, multivariate methods are necessary to find the dependency among different variables of IAQ. he multivariate statistical methods which are commonly used are principal component analysis (PCA) and partial least squares (PLS) to explain the relationships among different variables using complex data sets. PCA and PLS are used to explain the variance-covariance matrix structure using a small number of principal components (PCs). herefore, these methods help to reduce the dimensionality of the original data while retaining important information to display the data information in a format that can be easily interpreted [15] Principal component analysis (PCA) PCA is an optimal dimensionality reduction technique for capturing the variance of a dataset. he original matrix X is decomposed into a process and noise subspaces: he matrix X can be written as the sum of the outer product of vectors t i and p i and the residual matrix E: n = + = å i i + i=l X P E t p E (1) where t i is a score vector that contains information on the relationship between different samples, p i is a loading vector that contains information on the relation between different variables and n is the number of independent variables [15-17] Partial Least Squares (PLS) regression method PLS regression method is used to frame a model from the data set, which in turn helps for prediction of the data for as particular day. PLS regression reduces the dimensions of the input variables (X) and output variables (Y) by projecting the information into a low dimensional space that maximizes the covariance between them [18-19]. he matrices X and Y are decomposed using following equations: n = + = å i i + i=l X P E t p E (2) 393

4 n = + = å i i + i=l Y UQ F u q F (3) Where p and q are loading vectors that contain information on the relationship between variables, n is the number of latent variables (LVs), and U are the score matrices and E and F are the residual matrices. he PLS regression model which relates X and Y can be expressed in as Y= B LV+ F (4) where B is given by ( ) -1 B= W P W Q, in which = ( ) W X Yq [20-22] Statistical external analysis Usually, process variables are classified into three groups: external variables, main variables and other variables. he concentrations of main variables depend on external and other variables. Hence, the expressions for the main variables (such as PMs) can be subdivided in to two parts: a) the part affected by external variables [such as outdoor air and number of passengers] and b) the part affected by the other variables (other than external variables) [such as ventilation system and air filtration] [23-24]. In the present paper, external analysis is carried out on main variables to remove the effect of external variables. In external analysis, data matrix X can be written as combination of external and main variables X=[ G H ] (5) where G is the matrix of external variables and H is the matrix of main variables data. he main matrix H can be split into two parts, GC and E, where GC is a part affected by external variables and E is the part affected by other variables. hus, the main matrix H can be written as E= H-GC (6) where C is the coefficient matrix calculated using ( ) -1 C= G G G H [24]. In this study, we focused on E matrix, which is obtained by removing the effect of external variables (i.e., outdoor air quality in this study) on main variables (i.e., PM10 and PM2.5 in this study). 3. Materials and Methods 3.1. he proposed method A proposed framework for monitoring and prediction of IAQ is shown in Fig. 1. Monitoring of IAQ of the underground subway station is carried out using multivariate statistical analysis (i.e., PCA method). Prediction models are used to carry out prediction of IAQ. hese methods are applied to Y-subway station, in Korea. he data used in this study is collected from October, 2007 to April,

5 Before carrying out the PCA on IAQ, external analysis is carried out for removing the influence of outdoor air pollutant from the IAQ data. It is well known that indoor air pollutants, especially concentration of PMs, will get affected due to the presence of PMs in the outdoor air (entering from ventilation system) [9]. herefore, in the external analysis, outdoor air pollutants (i.e., PM 10 and ) are referred as external variable and concentrations of PM 10 and inside the subway station are referred as main variables. Fig. 1. Framework for monitoring and prediction of IAQ PCA is used to develop the IAQ monitoring models. PCA monitoring is carried out to know whether outdoor air pollutants affect IAQ. o know the influence of outdoor air pollutants on the IAQ, the PCA monitoring models with the following two scenarios are compared: 1) the real data which has effect of outdoor air pollutants, and 2) the data obtained after taking out the effect of outdoor air pollutants from the real data by external analysis. In this study, the PCA monitoring model with the first scenario is named as conventional PCA model, while with the second scenario is named as external PCA model. In addition, another PCA monitoring model is developed to know whether installation of platform screen door (PSD) system affects IAQ. It is known that PSD system can improve IAQ, since it blocks the inflow of deteriorated air from tunnel of underground subway station. o know the influence of PSD installation on the IAQ, the whole data is divided into two data sets: 1) for previous period of PSD installation and 2) for after period of PSD installation. he durations for the first and second data sets are from October 2007 to December 2007 (the number of sample is 29) and from December 2007 to April 2008 (the number of sample is 42). Finally, PLS regression is used to develop prediction models for IAQ of underground subway systems. o develop the prediction models, it is not reflected to the models whether PSD is installed. In the development of prediction models of IAQ, output variables (Y) taken for this study are the current concentrations of PM 10 (t) and (t) on the platform of Y- station. hese variables are considered since concentrations of PMs are taken as criteria for monitoring IAQ [25]. Input variables (X) taken for this study are the concentration of outdoor PM 10, ratio of PM 10 between outdoor and indoor air, ratio between particulate matters (i.e., PM 10 and ) on platform of Y-station, number of passenger, temperature, humidity and wind-speed. o verify the accuracy of the prediction model taking out the effect of outdoor air pollutant, the performance of external prediction models (PLS models with the second 395

6 scenario) are compared with conventional prediction models (PLS models with the first scenario). o evaluate the predictive accuracy of prediction models, root mean square error (RMSE) values of the predicted concentrations (of PM 10 and ) obtained using PLS models are used. RMSE is a measurement method, frequently used to determine the measure of gap between model and observed values. RMSE is defined as: RMSE= å n $ ( y -y ) 2 i=1 i,observed i,model n-1 (7) where y, are the actual observed values, y, are the predicted values and n is the number of experiments [26] Y-station in Seoul metro system he objective system for this study is underground subway is station on line number 3, Seoul Metro (i.e., Y-stations). Indoor air pollutants data is collected from a real-time telemonitoring system (MS) installed in Y-station. MS system shown in Fig. 2 is located at the center of the platform and measures the concentration levels of seven air pollutants (NO, NO 2, NO X, PM 10,, CO, CO 2 ) within the fixed measurement intervals. Concentration of NO, NO 2 and NO X are measured by the chemiluminescence of nitro-oxide materials and ozone. able. 1 Characteristic features of measuring equipment of MP in S-station Device (component analyzer) NO X analyzer (NA-623) PM 10 analyzer (SPM-613D) analyzer (SPM-613) CO 2 analyzer (NDIR gas analyzer) Detection limits (measuring range) 0.5 ppb (0-1 ppb) Less than ±1 μg/m (0-0.5/ 1/ 2/ 5 mg/m 3 ) Less than ±1 μg/m (0-0.5/ 1/ 2/ 5 mg/m 3 ) 0.1 ppm ( ppm) Measurement accuracy (measurement repeatability) Within ±1% of span gas concentration Less than ±0.5% of full scale (FS) Less than ±2% FS Within ±1% FS PM 10 and are measured by β-ray attenuation principle with the corresponding size distribution filters. CO and CO 2 are measured using the wavelengths obtained by nondispersive infrared radiation absorption. he specific characteristic features (such as detection limit and measurement accuracy) of measuring equipments of MS are presented in able 1 [10]. 396

7 Fig. 2. ele-monitoring system (MS) located in Y-Station, Korea he daily mean values of each MP variables reported during October 2007 to April 2008 are used in the present study. Average values of indoor air pollutants obtained using the above data are shown in able 2. he meteorological conditions (i.e., temperature, humidity and wind-speed) collected from 1) Seoul Metropolitan Research Institute of Public Health and Environment and 2) internet material from the meteorological office are used in this study. able. 2 Correlation coefficients for original data obtained from Y-station Without PSD O-PM 10 P/O PM 10 em Hum Ws P-PM 10 /PM 10 Passenger O-PM P/O PM em Hum Ws P-PM /PM Passenger 1.00 With PSD O-PM 10 P/O PM 10 em Hum Ws P-PM 10 /PM 10 Passenger O-PM P/O PM em Hum Ws P-PM /PM Passenger

8 4. Results and discussion 4.1 Monitoring of IAQ using univariate statistical analysis Univeriate statistical analysis is used to monitor and analyze the variations in separate IAQ variables. Fig.3 shows the results of univariate analysis about IAQ in Y-subway station. In Fig.3, x-axis represents the number of samples (i.e., observations) and y-axis represents the individual content of IAQ. he concentrations of PMs on platform show similar trend with the concentration of PM 10 of outdoor air, that is, similar peak points around the 32 nd and 60 th observations. It implies that the outdoor air pollutants are the main source of indoor air pollutant of the underground subway station. However, the univariate analysis cannot consider the correlation among the variables of IAQ. herefore, to consider the correlation among the IAQ variables, multivariate statistical analysis should be applied. Fig. 3. Monitoring results of IAQ using univariate analysis 4.2 Monitoring and prediction of IAQ using multivariate statistical analysis Monitoring of indoor air pollutants using PCA o contemplate the correlations among the IAQ variables of underground subway station, PCA monitoring models are developed. 398

9 (a) (b) Fig. 4. IAQ monitoring using conventional PCA for whole dataset: (a) score plot and (b) loading plot Fig. 4 shows the IAQ monitoring results using the conventional PCA model (i.e., with of outdoor air on IAQ) for the whole dataset. Fig 4a is a score plot (in the PC 1 -PC 2 ), which represents the relationships among the samples. In general, the clustered samples (within similar regions) in score plot represent the samples which are under the similar condition of IAQ. (a) (b) Fig. 5. Loading plots using conventional PCA: (a) un-installation of PSD and (b) installation of PSD In Fig 5a, the whole samples are divided into two classes according to the installation of PSD. he samples which are measured before the installation of PSD are located on the bottom-left side of score plot, while the samples which are measured after installation of PSD are positioned on the top-right side. It implies that the IAQ is influenced by PSD system, since PSD blocks the inflow of deteriorated air from tunnel of underground subway station. Fig 4b shows a loading plot (in the PC 1 -PC 2 ), and is used to interpret the relations between the variables. In general, the clustered variables (shown in different circles) in loading plot represent the variables which have strong dependency. Fig 4b represents that the nine variables of IAQ in the Y-station are grouped into three clusters: 1) the first cluster contains 399

10 the temperature and humidity, 2) the second cluster is having the concentration of PM 10 and at platform, and 3) the last one consists the PM 10 concentration of outdoor air, ratio between particulate matters at platform and number of passengers. he first cluster corresponds to the general correlation between the temperature and humidity, that is, the humidity decreases as the temperature increases (due to the increase of saturated vapor). In the second cluster, since the concentration of is proportional to the concentration of PM 10. In the third cluster, it has been hypothesized that the concentrations of PMs at platform are influenced by the outdoor air entering from ventilation system as well as movement of passengers. Fig 5 shows loading plots (in the PC 1 -PC 2 ) obtained for data sets using the conventional PCA. Fig 5a shows the IAQ monitoring result for data obtained before installation of PSD, while Fig 5b is for data obtained after installation of PSD. he number of groups of dependency variables in loading plot (a) and (b) are different. In Fig 5a, nine variables of IAQ are grouped into three clusters: 1) the first one has the temperature and humidity, 2) the second one consists the concentration of PM 10 of outdoor air, concentrations of PMs at platform, and ration between PMs at platform, and 3) the last one contains the wind-speed and ratio of PM 10 between outdoor and indoor air. On the other hand, in Fig 5b, one cluster is existed and contains the concentrations of PMs at platform. It implies that the effect of outdoor air on concentrations of PMs at platform is reduced, since the inflow of outdoor air is blocked by PSD. he influence of outdoor air pollutant on IAQ (according to installation of PSD) is supplemented by correlation coefficients of IAQ variables (as shown in able 2). For data obtained before the installation of PSD, the concentrations of PM 10 and at platform show strong correlations with the outdoor air pollutant, of which correlation coefficients are 0.78 and 0.82, respectively. On the other hand, after the construction of PSD, the correlation coefficients between the PMs at platform and outdoor air pollutant are reduced (0.66 for PM 10 and 0.70 for ). It means that the influences of outdoor air on IAQ are decreased due to the installation of PSD system. he movement of passenger is another source of deterioration of IAQ, however, the correlations between the number of passenger and IAQ are weak (0.20 for PM 10 and ). It implies that the outdoor air quality is the most contributed source to IAQ than others. (a) (b) Fig. 6. Loading plots using external PCA: (a) un-installation of PSD and (b) installation of PSD 400

11 Fig. 6 shows the loading plots (in the PC 1 -PC 2 plane) using the external PCA model. In Fig 6a for data obtained before the installation of PSD, eight variables (without concentration of PM 10 of outdoor air) are grouped into two clusters: 1) the first one contains the temperature and humidity, and 2) the second one is having concentration of PMs and ratio of PMs at platform. able 3 shows the correlation coefficients of IAQ variables. he coefficients between the wind-speed and concentration of PMs at platform are reduced than the coefficients using conventional model (from to for PM 10 and from to for ). able. 3 Correlation coefficients for external data obtained taking out effect of outdoor air from original data Without PSD P/O PM 10 em Hum Ws P-PM 10 / PM 10 Passenger P/O PM em Hum Ws P-PM /PM Passenger 1.00 With PSD P/O PM 10 em Hum Ws P-PM 10 /PM 10 Passenger P/O PM em Hum Ws P-PM /PM Passenger 1.00 It implies that the effects of wind-speed (which makes the outdoor air inflows into underground subway station) on IAQ is also taken out by external analysis. In Fig 6b for data obtained after the installation of PSD, eight variables are grouped into two clusters: 1) the first one contains the concentration of PMs at platform and number of passengers, and 2) the second one is having concentration of PM 10 at platform and ratio of PM 10 between outdoor and indoor air. In able 3, the correlation coefficients between the IAQ and number of passengers are increased than those using conventional model (from 0.00 to 0.12 for PM 10 and from 0.11 to 0.30 for ). It means that, if the effect of outdoor air on IAQ is taken out (by installation of PSD as well as external analysis), the passengers might be main source of deterioration of IAQ of underground subway system. his result corresponds to research of Cheng et al. [8] and Lau et al. [9]. 401

12 Prediction of indoor air pollutants using PCL regression method PLS regression method is used to develop the IAQ prediction model. o demonstrate the effectiveness of the proposed prediction model, the whole data is divided into two parts, 1) training data set and 2) test data set. he prediction model which was built based on training data is tested using test data (which is not used for training). Among 71 samples obtained for whole data set, 59 samples are used for training set and 12 samples are used for test set. (a) (b) Fig. 7. Indoor air quality (IAQ) prediction results obtained using conventional prediction model for (a) PM 10 and (b) Fig 7 shows the predicted concentrations of PM 10 and using the conventional PLS models. In this model, PLS regression is constructed with four latent variables (LVs), capturing about 87.9% of the original data. It means that seven independent variables (concentration of outdoor PM 10, ratio of PM 10 between outdoor and indoor air, ratio between PMs on platform, number of passenger, temperature, humidity and wind-speed) of X are reduced into four LVs, and represent a strong correlation with Y variables (PMs) linearly, explaining about 88% of original data. he PLS regression models for PM 10 and are expressed as: = ( ) ( ) ( ) ( ) +. = ( ) ( ) ( ) ( ). +. where PM 10 : A vector of concentrations of PM 10 of all data samples (LV n ) PM10 : A vector of the values of latent variables which corresponds to the component PM 10 F PM10 : A vector of the residuals which corresponds to the component PM 10 : A vector of concentrations of of all data samples (LV n ) PM2.5 : A vector of the values of latent variables which corresponds to the component F PM2.5 : A vector of the residuals which corresponds to the component 402

13 (a) (b) Fig. 8. Indoor air quality (IAQ) prediction results obtained using external prediction model for (a) PM 10 and (b) Fig 8 shows the prediction result of the concentrations of PMs using the external PLS model. he prediction model is developed similar to the procedure mentioned in conventional PLS model. his model has captured 74% of the original data. able. 4 Comparison of RMSE values of different component prediction curves obtained using (a) conventional prediction model and (b) external prediction model (a) Conventional PLS prediction model RMSE raining data est data PM RMSE total (b) External PLS prediction model RMSE raining data est data PM RMSE total o know the superiority of the developed external regression models over conventional, RMSE values of predicted concentration (of PM 10 and ) obtained using PLS external and conventional regression models are compared. he procedure described in the development of seasonal regression models is used to develop the PLS global regression model. he values of RMSE obtained using external PLS regression models are lower than the conventional regression model, as shown in able 4. his means that external model which takes out the effects of outdoor air pollutant on IAQ results in more accurate prediction of IAQ, since the effects of OAQ (which is varied by variation in weather or traffic) is taken out so that the variations of IAQ variables are stabilized. 403

14 5. Conclusions o evaluate the variations in indoor air quality of the underground subway station which is influenced by outdoor air pollutants, the external models for monitoring and prediction of IAQ are developed. o find whether outdoor air pollutants affect the IAQ, external analysis is used and takes out the effects of outdoor air pollutants on IAQ. he external models (which take out the influence of outdoor air on IAQ) are developed for monitoring and prediction the IAQ using PCA and PLS methods, respectively. o find the superiority of the external regression model over conventional, the RMSE value of the predicted concentrations of PMs obtained using PLS external and conventional models are compared. he prediction results obtained using the external models are robust when compared with conventional prediction model (i.e., lower RMSE values than conventional model). In addition, once the influence of outdoor air pollutant on is taken out, the effect of passenger movement on IAQ is increased and this result corresponds to other researches (which show the correlation between the number of passenger and IAQ). he proposed external model can effectively predict the future concentrations of indoor particulate matters in underground subway station, and it could contribute to better management strategy for IAQ of subway systems. Acknowledgements his work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MES) (No ). References [1] Guo Z: Development of a windows-based indoor air quality simulation software package; Environmental Modelling & Software 2000; 15: [2] Furuya K, Kudo Y, Okinaga K, Yamukki M, akahashi S, Araki Y and Hisamatsu Y: Seasonal variation and their characterization of suspended particulate matter in the air of subway stations; Journal of race and Microprobe echniques 2001; 19: [3] Paivi A, arja Y., Anu K, imo M, Anne H, Kaarle H, Mika R.I, Risto H, arja K and Matti J: he concentrations and composition of and exposure to fine particles ( ) in the Helsinki subway system; Atmospheric Environment 2005; 39: [4] Kim N.J, Lee S.S, Jeon J.S, Kim J.H and Kim M.Y: Evaluation of factors to affect PM-10 concentration in subway station; Proceeding of Korean Society for Atmospheric Environment 2006; [5] Nieuwenhuijsen M.J, Gomez-Perales J.E and Colvile R.N: Levels of particulate air pollution, its elemental compositions, determinants and health effects in metro systems; Atmospheric Environment 2007; 41: [6] Murruni L.G, Solanes V, Debray M, Kreiner A.J, Davidson J and Davidson M: Concentrations and elemental composition of particulate matter in the Buenos Aires underground system; Atmospheric Environment 2009; 43: [7] Hooff.V and Blocken B: Coupled urban wind flow and indoor natural ventilation modeling on a high-resolution grid: A case study for the Amsterdam Arena stadium; Environmental Modelling & Software 2010; 25: [8] Cheng Y, Lin Y and Liu C: Levels of PM 10 and in aipei rapid transit system; Atmospheric Environment 2008; 42: [9] Lau J, Hung W. and Cheung C.S: Interpretation of air quality in relation to monitoring station s surroundings; Atmospheric Environment 2009; 43: [10] Kim Y.S, Kim M.J, Lim J.J, Kim J. and Yoo C.K: Predictive monitoring and diagnosis of 404

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