Proceedings of the Eastern Asia Society for Transportation Studies, Vol.8, 2011

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1 Modeling of Traffic-Induced NO 2 Concentration to Predict the Indoor Air Quality in Houses near Major Road Adyati Pradini YUDISON Research Assistant Air and Waste Management Research Group Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Jalan Ganesha no 10 Bandung, West Java, Indonesia adyati@gmail.com DRIEJANA* Associate Professor Air and Waste Management Research Group Faculty of Civil and Environmental Engineering Institut Teknologi Bandung Jalan Ganesha no 10 Bandung, West Java, Indonesia driejana@yahoo.com *Corresponding author Abstract: Health effects due to vehicle exhaust pollutant become a concern in Indonesia where many residential building located near the major roads in urban area. Previous researches that conducted in urban area in Bandung, Indonesia, found the average indoor to outdoor ratio (I/O) value of NO 2 pollutant of 0.90 indicated that indoor air quality in residential adjacent to road might be strongly influenced by outdoor air pollution. In order to measure the heath impact, it is necessary therefore to know the level of pollutant concentration inside the house. This study aims to develop model for predicting indoor air quality using outdoor air quality data. The measured NO 2 concentrations were mapped with appropriate spatial interpolation technique and analyzed by statistical method. The model resulted in this study is indoor outdoor with uncertainty of 33.3%. Keywords: health effects, air pollutant mapping, NO 2, model development 1. INTRODUCTION Transportation is one of the main activities that emit anthropogenic air pollutant in urban area. Air pollutants are generated the combustion process in the vehicle combustion chamber. The main pollutants that emitted from fossil fuel combustion are carbon dioxide (CO 2 ), Carbon monoxide (CO), Fine particulates (PM 10 and PM 2.5 ), Nitrogen oxides (NO x ), Sulfur oxides (SO x ) and Hydrocarbons. The existence of those anthropogenic pollutants on the atmosphere has been recognized to cause adverse effects on health and environment. Health effects due to vehicle exhaust pollutants become a concern in Indonesia since many residential buildings are located near the busy roads in urban area. Some researchers find that ambient environment can affect indoor environment and it was find that outdoor air pollution becomes the main source of indoor air quality (Yocum, 1982, EPA, 1987, Diesy, 1994, Perry, 1994 in Srivastava, 2002). The influence of outdoor air pollutant to indoor air quality is represented by Indoor to Outdoor ratio (I/O). I/O value less than 1 indicates that outdoor air quality influenced indoor air quality. If the value is higher than 1 there might be other significant pollutant sources that influenced the compound to contribute to indoor air quality (Godish, 2003). I/O value less than 1 is common in the area with tropical climate. Yang et al.

2 (2004) in Driejana et al. (2009), has reported that the I/O ratio in Brisbane, Australia of and Lawrence et al., (2004) found that I/O value of 0.58 in Agra, India. Recent study found that average I/O value obtained from 30 residential buildings located near the road on Karees Area, Bandung, Indonesia is 0.90 (Driejana et al., 2009). Other study based on 64 residential buildings found a value of 0.86 (Yudison and Driejana, 2011). Outdoor concentration in one area depends on many factors such as chemical reaction with other substance, the pollutant lifetime and meteorological factors. Meteorological factors, such as wind direction, wind velocity, atmospheric stability, and temperature, become important physical factors that determine the pollutant dispersion pattern (Wark and Warner, 1981). Due to dispersion mechanisms in the atmosphere emission loadings and patterns might be different from those of ambient air concentration. Meteorological factors cause air pollutant concentration to be distributed spatially. Many studies have found that the concentrations were spatially distributed. This phenomenon can be seen by mapping of concentration. Nowadays, Geographic Information System becomes a popular tools for air pollutant concentration mapping. Spatial interpolation method is used to predict the concentration in the area where the measurement data is absent. The most common methods to be used for in pollutant concentration mapping are Distance Weighing Methods (Greenland and Yorty,1985; De Leeuw and Van Zantvoort, 1997; Phillips et al., 1997 in Diem, 2002) and Krigging Methods (Lefohn et al., 1987, 1988, Casadoet et al., 1994, Loibl et al., 1994, Westenbarger and Frisvold, 1994, Liu and Rossini, 1996; Godzik, 1997; Phillips et al., 1997; Mulholland et al., 1998; Holland et al., 2000; Tayanc,2000 in Diem, 2002). This paper aims to present the result of a new method to predict indoor air quality using outdoor air quality data. Indoor air quality data are more difficult to obtain on a regular basis than that of outdoors as access to every compound will be needed in order to get the data from sampling points. On the other hand, measurements of outdoor air quality can be done more easily. Simplicity of the method will be important for further study to assess health impact caused by transportation activity. The health effect assessment is necessary effort as a basis for minimizing the health effects due to transportation activity. 2. METHODOLOGY For building a model, data were obtained from measurement of NO 2 concentration using passive samplers (Sari & Driejana, 2009; Driejana et al., 2009) on the study area. The pollutant that has selected for this model was NO 2 that measured using passive diffusion tube. The data obtained were mapping by appropriate spatial interpolation technique. The indoor and outdoor interpolation techniques were chosen based on their uncertainty levels. Furthermore, the results of interpolation were analyzed by statistical method to develop the model. The validity test of model was done by the measurement data from other area. 2.1 Study location This study was conducted in Karees Area that has high population and traffic densities. Transportation activity becomes the main source of ambient air pollutant since the residential is a dominant land use in the area. The main roads in the area of study are Jalan Laswi, Jalan

3 Sukabumi, Jalan Gatot Subroto, Jalan Kiara Condong, Jalan Jakarta and Jalan Ahmad Yani (Figure 1). Figure 1 Study location 2.2 Data measurement NO 2 passive sampler is commonly used to measure NO 2 indoor and outdoor concentration (Plaisance et al., 2002, Tania and Driejana, 2009, Driejana et al, 2009, Yudison and Driejana, 2011). This passive diffusion tube was exposed on the sampling point for 1 week. Temperature and pressure data were taken on the beginning and the end of sampling. There are 64 sampling point to measure indoor NO 2 and 94 sampling points to measure outdoor NO 2 which contain 30 roadside sampling points and 64 sampling points at residential building terraces (Figure 2).

4 Figure 2 Measurement point locations for NO 2 concentration model Data for testing the model validity were carried out by comparing spatial pattern developed by the model with actual measurement. In order to do the validity test, empirical model resulted from this study is applied to the data obtained in different area in Bandung. There are 100 measurement points for both indoor and outdoor NO 2 concentration. The location and measurement point distribution is presented on Figure 3. Figure 3 Measurements points location for validity test 2.3 Air pollutant mapping The mapping of NO 2 data are generated by Geographic Information System using two different spatial interpolation methods, Inverse Distance to Power and Krigging. The analyses

5 were done by 150 m x 150 m grid to store the spatial data. The particular grid size was chosen since study area is relatively small. Uncertainty analysis has been developed on previous study (Yudison and Driejana, 2011) and the accuracy value of each method can be seen in Table 1. Table 1 RMSe and SD value Method Outdoor Indoor RMSE (µg/m 3 ) SD (µg/m 3 ) RMSE (µg/m 3 ) SD (µg/m 3 ) IDW KRIGING Model Development The model for predicting indoor air pollutant concentration was developed by analyzing the association between indoor and outdoor NO 2 concentration. Data used were indoor and outdoor gridded data resulted of spatial interpolation analysis. The gridded data were chosen based on the value of RMSE and SD. The smaller the value of RMSE and SD the more accurate the mapping method. Average value of outdoor and indoor interpolated concentration data at each grid is extracted to obtained gridded concentration value. The indoor data were paired up with that of outdoor from the same grid. Before the statistical analysis was taken, the association between indoor and outdoor pattern was investigated to determine appropriate statistical method to be used. The similarity of spatial pattern of indoor and outdoor concentrations was analyzed using scatter plot diagram. 2.5 Validity test The model validity was tested in order to know the uncertainty of the model. The empirical model obtained then was use to predict indoor NO 2 concentration using data that measured in other area. To obtain the uncertainty of the model, the values of the predicted concentration were compared with the values from measurement data. 3. RESULT AND DISCUSSION 3.1 Selection of Interpolation Method From the previous study, there were two different interpolation methods that were used for NO 2 mapping in Karees area. Namely Inverse Distance Weighing and Krigging interpolation methods (Adyati and Driejana, 2011). Since indoor air quality was influenced by outdoor air quality, the distribution of NO 2 indoor and outdoor concentration should be similar. Furthermore, same interpolation method for indoor and outdoor concentration mapping should be selected.

6 Basically, interpolation method for mapping is selected based on the smallest value of Root Mean Square Error (RMSE) and Standard Deviation (SD). In this study, interpolation method with the smallest value of RMSE and SD is different for indoor and outdoor concentration mapping. Krigging method is the best for Outdoor concentration mapping and IDW method is for Indoor NO 2 concentration mapping. The difference of RMSE and SD values of the two interpolation methods used for Indoor NO 2 concentration mapping were greater than of outdoor concentration mappings (Table 2). For indoor concentration mapping, IDW method has the smallest RMSE and SD value. Thus, the selected interpolation method for developing the model was IDW method. Figure 4 and Figure 5 show NO 2 concentration mapping with IDW interpolation method of outdoor concentration and indoor concentration. Table 2 RMSE and SD values difference (µg/m 3 ) IDW and Krigging RMSe Difference IDW and Krigging SD Difference Outdoor Indoor Figure 4 IDW NO 2 Outdoor Concentration Mapping

7 Figure 5 IDW Indoor NO 2 Concentration Mapping The spatial patterns are not exactly the same, however it shows that indoor concentration value is lower than that of outdoor in the same point. 3.2 Model Development In this study, model was developed by analyzing the association between indoor and outdoor concentrations. The average concentration value from each grid is extracted. The data were able to be paired up since there are indoor and outdoor average concentrations for each grid. Figure 6 illustrate how average NO 2 concentration is extracted from each grid.

8 Figure 6 Grid average concentration value extraction The paired data is presented in scatter plot diagram. So that the association pattern between indoor and outdoor could be seen. It can be seen that the association between outdoor and indoor data tends to be linear (Figure 7), therefore linear regression analysis was undertaken. Figure 7 Outdoor and indoor concentration scatter plot diagram The linear regression analysis found that outdoor NO 2 concentration has strong linear association with indoor concentration. It shows that the higher the outdoor NO 2 concentration, the higher the indoor concentration. This association has coefficient determination value (R 2 ) of means 77.6% of indoor NO 2 concentration variation can be explained by outdoor concentration. Mathematical model obtained from this association is presented in equation (1).

9 y 0.857x (1) P=0.000 P=0.920 Where : x = Outdoor NO 2 concentration y = Predicted Indoor NO 2 concentration Since the p-value of outdoor NO 2 concentration is 0.000, it might be said that outdoor NO 2 concentration is a useful predictor for predicting indoor NO 2 concentration at α=0.01 or 99% confident level. 3.3 Validity Test The developed model then is used to predict the indoor NO 2 concentration with outdoor NO 2 measurement data in other area. The predicted value of NO 2 resulted from model calculation is compared with the actual measurement on Figure 8. The ratio value of 1:1, 1:1.5, and 1:2 express the ratio of predicted value to the measurement value. The figure shows that the predicted value dominantly located within the 1:1.5 area, means that model has uncertainty degree of 33.3%. Figure 8 Comparison of predicted and measured indoor NO 2 concentration

10 4. CONCLUSION In this study outdoor pollutant concentration data were used to predict indoor pollutant concentration data. The air pollutant parameter that was selected for developing the model was NO 2 since it is one of the main transport-induced air pollutants. Interpolated data were used for developing this model with IDW as the selected interpolation method. The outdoor concentration has a strong linear association with that of indoor (p=0.000), so that the model can be built using linear regression analysis. The model that was developed in this study is y 0.857x where x is outdoor NO 2 concentration and y is indoor NO 2 concentration. The association has coefficient determination (R 2 ) of The uncertainty of 33.3% was found to be reasonable. Therefore it is concluded that the model can be used to predict indoor NO 2 concentration for the residential building located near major roads with no significant indoor source (I/O < 1). The prediction of indoor air pollutant concentration is important to assess health impact on the residents living near the major road. In the practical use, this model become one of alternative methods to predict indoor air quality in a simple way since it is easier to conducted continuous outdoor air quality monitoring than that of indoor. ACKNOWLEDGEMENTS This study is a part of the research entitled Transport-Induced Air Pollutant Emission and Its Correlation with Indoor Air Quality. Research Grant funded by Hibah Riset International ITB, The authors wish to thank to Ratih Retno Purwaningrum and Febi Adrianti for their help in data collection for model validity test and Dr. Akhmad Riqqi and Intan Sofiyanti for their advice given on GIS. REFERENCES Diem, J. E., Comrie, A. C. (2002) Predictive mapping of air pollution involving sparse spatial observations, Environmental Pollution 119, Driejana, Putri, A. R., Watson, A. F. R. (2009) Influence of Traffic-related Emissions on Indoor Air Quality in Residential Buildings Adjacent to Roads, Sustainable Infrastructure and Built Environment 2009 International Conference Proceedings. Godis, T. (2003) Air Quality, 4th Edition, Lewis Publishers Inc, Michigan. Lawrence, A. J., Masih, A., Taneja, A. (2004) Indoor/outdoor relationships of carbon monoxide and oxides of nitrogen in domestic homes with roadside, urban and rural location in a central Indian region, Indoor Air 2004, 15: Plaisance, H., Sagnier, I., Saison, J. Y., Galloo, J. C., Guillermo, R. (2002) Performances and application of passive sampling methods for the simultaneous determination of nitrogen dioxide in ambient air, Environmental Monitoring and Assessment 79:

11 Sari, P. T, Driejana (2009) Measurement of Nitrogen Dioxide Concentrations along Major Roads in Bandung, Sustainable Infrastructure and Built Environment 2009 International Conference Proceedings. Srivastava, A., Jain, V. K. (2003) Relationship between Indoor and Outdoor Air Quality in Delhi, Indoor Built Environment 2003, 12: Wark, K., Warner C. F. (1981) Air Pollution, Its Origin and Control, Harper & Row Publisher, New York. Yudison, A. P., Driejana (2011) Predicting Indoor Air Pollution Concentration from Outdoor Levels by Mapping Approach, Manuscript in preparation to be submitted to ITB Journal.