DAFTAR PUSTAKA
Andayani N. 2002. Analisa polutan karbon monooksida dengan menggunakan metode statistik untuk data spatial [skripsi]. Surabaya : Fakultas Matematika dan Ilmu Pengetahuan, Institut Teknologi Sepuluh Nopember. Anderson RL, Bancroft TL.1952. Statistical Theory in Research. New York :
McGraw-Hill.
Bennett CA, Franklin NL 1954. Statistical Analysis in Chemistry and Chemical
Industry. New York : John Wiley & Sons.
Biro Pusat Statistik. 2002. Surabaya dalam Angka 2002. Jakarta : Biro Pusat Statistik.
Boer EPJ, Dekkers ALM, Stein A. 2002. Optimization of a Monitoring Network for Sulfur Dioxide. J Environmental Quality 31:121-128
Brumback BA, Rice JA. 1998. Smoothing spline models for the analysis of nested and crossed samples of curves (with discussion). J Amer Stat Ass 93: 961-994.
Brumback BA, Ruppert D, Wand MP. 1999. Comment on Variable selection and function estimation in additive nonparametric regression using a data-based prior by Shively, Kohn and Wood. J Amer Stat Ass 94: 794-797.
Chamida. 2004. Strategi Pengendalian Pencemaran Udara berupa Kebijakan
berdasarkan Pemanfaatan Model Matematik Pencemar Udara PM10 di Kota
Surabaya [tesis]. Surabaya : Pascasarjana Studi Teknik Lingkungan ITS. Christensen R. 1984. Plane Answers to Complex Questions. The Theory of Linear
Models. New York : Springer-Verlag.
Cohran WG. 1939. The use of analysis of variance in enumeration by sampling. J
Amer Stat Ass 34: 492-510.
Corbeil RR, Searle SR. 1976. A comparison of variance component estimators.
Biometrics 32: 779-791.
Carroll R et al. 1997. Ozone Exposure and Population density in Harris Country Texas. Journal of the American Statistical Association 92: 392-415.
Cressie N. 1993. Statistics for Spatial Data. New York : John Wiley and Sons. Cressie N, Huang CH. 1999. Classes of Nonseparable, Spatio-temporal
Stationary Covariance Function. Journal of the American Statistical Association 94:1330-1340.
Cressie N, Wikle CK. 2002. Space-time Kalman Filter. Encyclopedia of Environtmetrics 4 : 2045-2049. Chichester : John Wiley and Sons.
Crump SL. 1946. The estimation of variance components analysis of variance.
Biometrics Bull 2: 7-11.
Crump SL. 1951. The variance component analysis. Biometrics 7 : 1-16.
Daniel HE. 1939. The estimation of component of variance. J R Stat Soc Suppl 6:186-197.
115
Diggle PJ, Tawn JA, Moyeed RA. 1998. Model-based geostatistics (with discussion). Appl Stat 47:299-350.
[Din LH Surabaya] Dinas Lingkungan Hidup Kota Surabaya. 2002. Laporan evaluasi : Hasil pemantauan kualitas udara ambien tahun 2001. Surabaya : Din LH Surabaya
[Dinhub Surabaya] Dinas Perhubungan Kota Surabaya. 2002. Laporan kegiatan : Perhitungan dan analisa persimpangan yang dilengkapi dengan APILL maupun yang direncanakan. Surabaya : Dinhub Surabaya.
Djuraidah A, Aunuddin. 2006a. Pendugaan Regresi Spline Terpenalti dengan Pendekatan Model Linear Campuran. Statistika Jurnal Statistika FMIPA-UNISBA 6(1): 39-46.
Djuraidah A, Aunuddin. 2006b. Pendugaan Model Aditif untuk Data Deret Waktu dengan Pendekatan Model Linear Campuran. Inferensi Jurnal Statistika FMIPA-ITS 2(1):76-92.
Djuraidah A, Aunuddin. 2006c. Kriging dan Thin-Plate Spline dengan Pendekatan Model Campuran. Matematika Integratif Jurnal Matematika FMIPA-UNPAD 5(2):1-12.
Djuraidah A, Aunuddin. 2006d. Estimation of Spatio-temporal Additive Model Using Linear Mixed Model Approach with Application to Ozone Data in Surabaya. Proceedings of The first International Conference on Mathematics and Statistics, Bandung, June 19 – 21, 2006 (akan diterbitkan).
Djuraidah A, Aunuddin. 2006e. Optimasi Penentuan Lokasi Stasiun Pemantau Kualitas Udara Ambien di Kota Surabaya. Forum Statistika dan Komputasi, 11(2):1-5.
Draghicescu D. 2003. A Model for Spatio-Temporal Prediction of Ground-Level
Ozone Mixing Ratios [terhubung berkala]. http : http://www.cgd.ucar.edu/
stats/Workshop2003/draghicescu.pdf
Eilers PHC, Marx BD. 1996. Flexible smoothing with B-splines and penalties (with discussion). Stat Sci 11: 89-121.
Eisenhart C. 1947. The assumptions underlying the analysis of variance.
Biometrics 3:1-21.
Eubank RL. 1988. Spline Smoothing and Nonparametric Regression. New York :
Marcel Deker.
Fan J, Zhang JT. 1998. Comment on Smoothing spline models for the analysis of nested and crossed samples of curves by Brumback and Rice. J Amer Stat
Ass 93: 961-994.
Fan J, Yao Q. 2003. Nonlinear Time Series. Nonparametric and Parametric
Methods. New York : Springer-Verlag
Fisher RA.1925. Statistical Methods for Research Workers. 1st edn. Edinburg and
London: Olyver & Boyd.
116
French JL, Kammann EE, Wand MP. 2001. Comment on Semiparametric nonlinear mixed-effects models and their applications by Ke and Wang.
J Amer Stat Ass 96:1285-1288.
Gaál M, et al. 2004. Simulations to evaluate optimal construction of monitoring networks. Applied Ecology and Environtmental Research 2(2):59-71. Green PJ, Silverman BW. 1994. Nonparametric Regression and Generalized
Linear Models : a Roughness Penalty Approach. London: Chapman & Hall.
[GTZ-SUTP].GTZ-Sustainable Urban Transportation Project. 2000. Transportasi
yang Berkelanjutan dan Kualitas Udara di Surabaya. Surabaya : Bappeda
KMS.
Gu, C, Wahba G. 1991. Minimizing GCV/GML scores with multiple smoothing parameters via the Newton method. SIAM J Sci Stat Comp 12: 383–398. Guo W. 2002. Functional mixed effects models. Biometrics, 58, 121-128.
Guttorp P, Meiring W, Sampson P. 1998. Space-time Estimation of a grid-cell
Hourly Ozone Levels for Assessment of a Deterministic Model.
Technical report 007 NRCSE. [terhubung berkala]. www.stat.washington.
edu/NCES/resorce/papers.html
Hamonangan E. 2004. Model simulasi pengelolaan kualitas udara. Makalah Diskusi Panel: PencemaranUdara dan Dampaknya terhadap Kesehatan
Manusia. Jakarta : Kerjasama antara JICA DEMS-PROJECT dengan
SARPEDAL KLH.
Handcock MS, Meier K, Nychka D. 1994. Comment on Kriging and spline : an empirical comparison of their predictive performance in some application by Laslett. J Amer Stat Ass 89 :401-403.
Hartley HO, Rao JNK.1967. Maximum likelihood estimation for the mixed analysis of variance model. Biometrika 54:93-108.
Harville DA.1977. Maximum likelihood approaches to variance component estimation and to related problems. J Amer Stat Ass 72:320-340.
Hastie TJ, Tibshirani RJ. 1990. Generalized Additive Models. London: Chapman & Hall.
Henderson CR. 1953. Estimation of variance and covariance components.
Biometrics 9:226-252.
Herbach LH. 1959. Properties of Model II type of analysis of variance tests. An optimum nature of F-test for Model II in balanced case. Ann Math Stat 30:030-959.
Huang JZ, Shen H. 2004. Functional Coefficient Regression Models for Nonlinear Time series: A Polynomial Spline Approach. Scand J Stat 31: 515-534 Hutchinson MF. 1993. On thin plate splines and Kriging. Interface Foundation of
North America. Berkeley. Comp Sci Sta 25:55-62.
Hutchinson MF. 1998 Interpolation of Rainfall Data with Thin Plate Smoothing
Splines. Part I : Two Dimensional Smoothing of Data with Short Range Correlation. J Geographic Information and Decision Analysis 2(2): 139-151
117
Jackson RWB.1939 Reliability of mental tests. Brit J Psycol 29:267-287. Kammann EE, Wand MP. 2003. Geoadditive models. Appl Stat 52:1-18.
Kleinschmidt I, Sharp BL, Clarke GPY, Curtis B, Fraser C. 2001. Use of Generalized linear mixed models in the spatial analysis of small-area malaria incidence rates in KwaZulu Natal, South Africa. Amer J Epidemiology 153(12): 1213-1221
[KLH] Kementrian Lingkungan Hidup. 2002. Status Lingkungan Hidup Indonesia
2002. Jakarta : KLH.
Laslett GM. 1994. Kriging and spline : an empirical comparison of their predictive performance in some application. J Amer Stat Ass 89:401-403. Miller JJ. 1973. Asymptotic properties of maximum likelihood estimates in the
mixed model of the analysis of variance. Ann Stat 5:746-762.
Milliken GA, Johnson DE. 1984. Analysis of Messy Data, Volume I : Design
Experiment. New York : Van Nostrand Reinhold Company.
Morawska K, Vishvakarman D, Mengersen K, Thomas S. 2002. Spatial variation of airborne pollutant concentrations in Brisbane, Australia and its potential impact on population exposure assessment. Atmospheric Environment 36:3545-3555.
Nychka DW. 2000. Spatial process estimates as smoothers. In Smoothing and Regression (M. Schimek, ed.). Heidelberg: Springer-Verlag.
Opsomer JD. 2005. Small area estimation using penalized spline regression. International Biometric Society, Eastern North American Region meeting,
Austin, TX, March 21, 2005. [terhubung berkala]. http://www.stat.
colostate.edu/~nsu/starmap/pps/Opsomer.Spline_survey ENAR.pdf
Pedan A. 2003. Smoothing with SAS PROC MIXED. SUGI 28: 268-28.
Pelangi on line. 2003. Udara bersih hak kita bersama. [terhubung berkala].
http://www.pelangi.or.id.
Pfeifer PE, Deutch SJ. 1980. A Three-State Iterative Procedure for Space-Time Modelling. Technometrics 22:35-47.
Prestiwati HY. 2002. Pemodelan statistik kualitas udara ambient berdasarkan pengukuran stasiun pemantau Taman Prestasi dan Perak Timur di Surabaya [skripsi]. Surabaya : Fakultas Matematika dan Ilmu Pengetahuan, Institut Teknologi Sepuluh Nopember.
Raffuse SM, et al. 2005. Analytical Techniques for Technical Assessments of
Ambient Air Monitoring Networks. Guidance Document
STI-905104.02-2805-GD September 2005. Prepared for U.S. Environmental Protection
Agency by Sonoma Technology. [terhubung berkala]. http://www.epa.gov/
ttn/amtic/files/ambient/criteria/nettech.pdf.
Ranalli, MG, Breidt FJ, Wang H.. 2005. Low-rank smoothing splines on complex domains. Seminar, Atlantic Ecology Division, EPA, Narragansett, RI.
March 1, 2005. [terhubung berkala]. http://www.stat.colostate.edu/~nsu/
118
Rao CR. 1972. Estimation of variance and covariance component in linear models. J Amer Stat Ass 67:112-115.
Roekmi RAK. 1997. Deskripsi konsentrasi zarrah tersuspensi di jalan Muhammad Husni Thamrin Jakarta [skripsi]. Bogor : Fakultas Matematika dan Ilmu Pengetahuan Alam, Institut Pertanian Bogor.
Ruppert D, Carroll RJ. 1997. Penalized regression splines. Unpublished
manuscript. [terhubung berkala]. http://www.orie.cornell.edu/~davidr
/papers/index/index/index.html
Ruppert D. 2002. Selecting the number of knots for penalized splines. J Comp
Graph Stat 11: 735–757.
Sahai H, Ageel, MI. 2000. The Analysis of Variance. Fixed, Random, and Mixed
Models. Boston : Birkhäuser.
Saksena S, Joshi V, Patil RS. 2002. Determining spatial patterns in Delhi’s ambient air quality data using cluster analysis. East-West Center Working
Papers: Environment Series 53:1-33. [terhubung berkala].
http://www.EastWestCenter.org.
Sanso B, Guenni L. 1999. Venezuelan Rainfall Data Analysis using a Bayesian Space-Time Model. Appl Stat 48:345-362.
[SARPEDAL KLH] Sarana Pengendalian Dampak Lingkungan Kementrian Lingkungan Hidup. 2003a. Indeks Standar Pencemar Udara (ISPU)
2001-2003. Jakarta : KLH.
[SARPEDAL KLH] Sarana Pengendalian Dampak Lingkungan Kementrian Lingkungan Hidup. 2003b. Air Quality Monitoring 2003. Jakarta : KLH. Satterthwaite FE. 1946. An approximate distribution of estimates of variance
components. Biometric Bull 2:110-114.
Searle SR, Casella G, McCulloch CE. 1992. Variance Component. New York : John Wiley & Sons.
Shaddick G, Wakefield J. 2002. Modelling Daily Multivariate Pollutant Data at Multiple Sides. J R Stat Soc, Series C 51:351-373.
Smith RL, Kolenikov S. 2004. Spatiotemporal modeling of PM2.5 data with
missing values. J Geophysical Research 108 STS :11-1 - 11-11.
Soedomo M. 2001. Pencemaran Udara. Kumpulan Karya Ilmiah. Bandung : Penerbit ITB.
Simonoff JS. 1996. Smoothing Methods in Statistics. New York : Springer-Verlag.
Stein ML. 1999. Interpolation of Spatial Data. Some Theory for Kriging. New York : Springer-Verlag.
Stoffer DS. 1986. Estimation and identification of Space-time ARMAX models in the presence of missing data. J Amer Stat Ass 81: 762-772.
Stone CJ. 1985. Additive Regression and Other Nonparametric Models. Ann Stat 13: 689–705.
119
Stroud JR, Muller P, Sanso B. 2001. Dynamic Models for Spatio-Temporal Data.
J R Stat Soc, Series B 63: 673-689.
Tonellato SF. 2001. A Multivariate Time Serie Model for the Analysis and Prediction of Carbon Monoxide Atmospheric Concentrations. Appl Stat 50: 187-200.
Thompson WA. 1962. The problem of negative estimates of variance components. Ann Math Stat 33:273-289.
Tippett LHC. 1931. The Method of Statistics. 1st edn. London : Williams and
Norgate.
Tsimikas JF, Ledolter J. 1997. Mixed model representation of state space models : New smoothing results and their application to REML estimation. Stat
Sinica 7: 973-991
[USEPA] U.S. Environmental Protection Agency. 2003. Region 5 network
assessment. Presented at the Air Monitoring & Quality Assurance
Workshop, Atlanta, CA, September 9-11 by the U.S. Environmental
Protection Agency, Region 5. [terhubung berkala] http://www.epa.gov/ttn/
amtic/files/ambient/pm25/workshop/atlanta/r5netas.pdft.
Venegas LE, Mazzeo NA. 2003. Design methodology for background air pollution monitoring site selection in an urban area. Int J Environ Poll 20:185-195.
Verbyla AP, Cullis BR, Kenward MG, Welham SJ. 1999. The analysis of designed experiments and longitudinal data by using smoothing splines (with discussion). J R Stat Soc, Series C 48: 269-312.
Wald A. 1941. On the analysis of variance in case of multiple classifications with unequal class frequencies. Ann Mathc Stat 12:346-350.
Wand M. 2003. Smoothing and mixed models. Comp Stat 18:223–249.
Wang Y, 1998. Mixed effects smoothing spline analysis of variance. J R Stat Soc, Series B 60:159-174.
Watson JG, et al. 1997. Guidance for Network Design and Optimum Site
Exposure for PM2.5 and PM10. Prepared for U.S. Environmental Protection
Agency by Desert Research Institute of the University and Community College System of Nevada.
Wu H, Zhang JT. 2002 Local polynomial mixed-effects models for longitudinal data. J Amer Stat Ass 97:883-897.
Zhang, D, Lin X, Raz, J, Sower MF. 1998. Semiparametric stochastic mixed models for longitudinal data. J Amer Stat Ass 93:710-719.
Zoppou C, Roberts S, Hegland M. 2000. Spatial and temporal approximation using additive models. ANZIAM Journal. 42(E):C1599-C1611. [terhubung
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Lampiran 6. Prediksi temporal dari model aditif spatio-temporal terbaik PM10
J a m K o n se n tr a si O zo n ( ln ) d i S U F -1 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 2 0 5 4 0 3 6 0 1 8 0 1 7 5 3 1 7 2 0 5 4 0 3 6 0 1 8 0 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2 J a m K o n se n tr a si O zo n ( ln ) d i S U F -2 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 2 0 5 4 0 3 6 0 1 8 0 1 7 5 3 1 7 2 0 5 4 0 3 6 0 1 8 0 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2 J a m K o n se n tr a si O zo n ( ln ) d i S U F -3 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 2 0 5 4 0 3 6 0 1 8 0 1 7 5 3 1 7 2 0 5 4 0 3 6 0 1 8 0 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2
126 Lampiran 6. Lanjutan J a m K o n s e n tr a s i O z o n ( ln ) d i S U F -4 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 2 0 5 4 0 3 60 18 0 1 7 5 3 1 7 2 0 5 40 36 0 1 8 0 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2 J a m K o n se n tr a si Oz o n d i S U F -5 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 5 3 1 7 20 54 0 3 6 0 1 8 0 1 7 5 3 1 7 2 0 5 4 0 3 6 0 1 80 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2
127
Lampiran 7. Kontur dari model aditif spatio-temporal terbaik untuk Ozon pada jam 1 sampai jam 24
112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 4.8 11.4 18.1 24.7 24.7 31.4 38.0 38.0 44.6 44.6 51.3 57.9 Jam 1 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 6.0 9.2 9.2 12.4 15.7 18.9 22.1 2 2 .1 25.3 25.3 25.3 28. 5 31 .7 31.7 Jam2 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 6.0 6.0 9.2 12.3 15.4 15.4 18.6 21.7 24.8 28 .0 3 1 .1 31.1 Jam 3 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 4.1 4.1 7.9 11.6 15.3 19.0 22.7 26. 5 30.2 33 .9 33.9 Jam 4 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 4.2 4.2 7.5 10.7 14.0 14.0 17.3 20.5 23.8 27. 0 30 .3 30.3 Jam 5 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 3.8 3.8 7.3 7.3 7.3 10 .8 14.2 14.2 17.7 17.7 21.2 24. 6 28. 1 31 .6 31.6 Jam 6 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 7.5 7.5 11.2 11.2 14 .9 18.5 18.5 22.2 22.2 25.9 29.5 29.5 33 .2 33.2 Jam 7 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 23. 8 29.3 29.3 34.7 34.7 40.2 45.7 5 1.1 51.1 56.6 56. 6 62 .1 62.1 Jam 8 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 85.5 93.8 102.0 110.2 110.2 118.5 11 8 .5 126.7 134.9 143.2 15 1.4 Jam 9 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 106.3 111.2 111.2 116 .0 116.0 120.8 120 .8 125.7 12 5.7 130.5 13 0.5 135.4 13 5 .4 140.2 Jam 10 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 102.4 106.4 110 .3 114.3 118.3 11 8.3 122.2 12 2.2 126.2 12 6.2 130.1 13 0 .1 130.1 Jam 11 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 94.6 98 .4 98.4 102.3 10 6 .2 11 0.0 113.9 117.8 1 17.8 121.6 125.5 129.4 Jam 12 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 92.6 96.7 96.7 100.7 10 4 .8 108 .9 113.0 117.0 121.1 125.2 129.3 Jam 13 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 85.8 89.7 89.7 93.6 93.6 97.5 97.5 10 1.4 101.4 105.3 1 0 5 .3 109.2 113.1 117.0 120 .9 Jam 14 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 85.8 89.7 89.7 93.6 93.6 97.5 97.5 10 1.4 101.4 105.3 1 0 5 .3 109.2 113.1 117.0 120 .9 Jam 15 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 77.8 82 .3 82.3 86.9 91 .5 9 1.5 96. 0 96 .0 100.6 105.2 109.7 Jam 16 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 69 .1 73 .7 73.7 78.3 82 .8 8 2 .8 87.4 8 7.4 92.0 96.6 Jam 17 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 53 .4 53.4 57.1 57.1 60.8 60 .8 64 .5 64.5 68 .2 6 8 .2 71.8 75.5 79.2 8 2 .9 82.9 Jam 18 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 32.5 37.0 37.0 41.5 41.5 46.0 50.4 54.9 54.9 59 .4 59.4 5 9.4 63 .8 63.8 68.3 72.8 7 7 .3 81.7 Jam 19 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 23.9 29. 2 29.2 34 .6 34.6 39.9 45.2 50.6 50.6 55.9 55.9 55.9 61 .2 61.2 66 .6 71.9 77.2 82.6 Jam 20 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 37.2 41. 3 41.3 45.4 45.4 49.6 49.6 53.7 53 .7 57.8 57.8 61 .9 61.9 6 6.0 70 .1 74.2 78.3 8 2 .5 86.6 Jam 21 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 39.9 42.6 45.3 47.9 4 7.9 50.6 50.6 53.3 53.3 5 3 .3 55.9 58 .6 61.2 Jam 22 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 31.8 34.9 38.0 41.1 44.3 44.3 47.4 47.4 50.5 50.5 53.6 56. 7 59.8 Jam 23 112.68 112.70 112.72 112.74 112.76 112.78 Longitude 7.22 7.24 7.26 7.28 7.30 7.32 L a ti tu d e 22.0 26.9 31.7 36.6 36.6 41.5 41.5 41.5 46 .3 4 6.3 51 .2 56 .1 60 .9 65 .8 70.7 75.5 80.4 Jam 24
128
Lampiran 8. Prediksi temporal dari model aditif spatio-temporal terbaik Ozon
J a m K o n se n tr a si O zo n ( ln ) d i S U F -1 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2 J a m K o n se n tr a si O zo n ( ln ) d i S U F -2 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2 J a m K o n se n tr a si O zo n ( ln ) d i S U F -3 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2
129 Lampiran 8. Lanjutan J a m K o n s e n tr a s i O z o n ( ln ) d i S U F -4 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2 J a m K o n se n tr a si O zo n d i S U F -5 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 6 4 2 0 7 2 0 5 4 0 3 6 0 1 8 0 1 B L N = 1 B L N = 2 B L N = 3 B L N = 4 B L N = 5 B L N = 6 B L N = 7 B L N = 8 B L N = 9 B L N = 1 0 B L N = 1 1 B L N = 1 2
130
Lampiran 9. Daftar titik contoh dari lokasi SUF baru
Titik Contoh SUF-B2 NO Longitude Latitude Jarak dengan SUF-B1 1 112.763 7.288 5.150 2 112.768 7.288 5.467 3* 112.768 7.292 5.100 4 112.773 7.292 5.466 5 112.773 7.297 5.149 6 112.778 7.297 5.557 7 112.773 7.301 4.862 8 112.778 7.301 5.293 9 112.778 7.305 5.064
Titik Contoh SUF-B3 NO Longitude Latitude Jarak dengan SUF-B2 1 112.754 7.250 5.245 2 112.758 7.250 5.124 3 112.763 7.250 5.049 4 112.749 7.254 4.947 5 112.754 7.254 4.766 6 112.758 7.254 4.632 7 112.763 7.254 4.550 8 112.734 7.258 5.338 9* 112.739 7.258 5.022 10 112.744 7.258 4.738 11 112.749 7.258 4.493
131
Lampiran 9. Lanjutan
Titik Contoh SUF-B4 Titik Contoh SUF-B5
NO
Longitude Latitude Longitude Latitude
Jarak antar kedua SUF 1 112.754 7.220 112.709 7.220 4.516 2 112.758 7.220 112.709 7.220 5.018 3 112.763 7.220 112.709 7.220 5.520 4 112.758 7.224 112.709 7.220 5.043 5 112.763 7.224 112.709 7.220 5.542 6 112.758 7.220 112.714 7.220 4.516 7 112.763 7.220 112.714 7.220 5.018 8 112.758 7.224 112.714 7.220 4.544 9 112.763 7.224 112.714 7.220 5.043 10 112.768 7.224 112.714 7.220 5.542 11 112.749 7.220 112.705 7.224 4.544 12 112.754 7.220 112.705 7.224 5.043 13 112.758 7.220 112.705 7.224 5.543 14 112.758 7.224 112.705 7.224 5.520 15 112.754 7.220 112.709 7.224 4.544 16 112.758 7.220 112.709 7.224 5.043 17 112.763 7.220 112.709 7.224 5.542 18 112.758 7.224 112.709 7.224 5.018 19 112.763 7.224 112.709 7.224 5.520 20 112.749 7.220 112.700 7.229 5.118 21 112.749 7.220 112.705 7.229 4.627 22* 112.754 7.220 112.705 7.229 5.118 23 112.754 7.220 112.709 7.229 4.627 24 112.758 7.220 112.709 7.229 5.118 25 112.758 7.224 112.709 7.229 5.043 26 112.763 7.224 112.709 7.229 5.542 27 112.749 7.220 112.700 7.233 5.240 28 112.749 7.220 112.705 7.233 4.761 29 112.754 7.220 112.705 7.233 5.240 30 112.749 7.220 112.700 7.237 5.406
