RESULTS AND DISCUSSION 1 Decolorization by immobilized enzymes MnP and 1,2-D

Bogor, 21-22 October 2015 245 2.5 Decolorization of wastewater dyes in a small-scale bioreactor The decolorization of wastewater dyes was evaluated in a small-scale bioreactor MaterFlesx ® LS ® Cole-parmer Instrument Company. The immobilized enzyme or fungus on alginate bead was packed into bioreactor coloum Ø = 2.5 cm, h = 10 cm, v = 49 cm 3 . The wastewater dyes 100 ml was place on Erlenmeyer flask and loaded to bioreactor coloum with flow rate 1.5 mL min -1 . Wastewater dyes decolorization was analysed by a UV –visible spectrophotometer at three wavelength of absorbance 400 nm, 500 nm and 600 nm. The evaluation of wastewater dyes absorbance was conducted for 1, 2, 3, 6, 24 hours reaction. The decolorization percentage was calculated for average from three percentage of decolorization at different absorbance, as the following equation: Decolorization = 1-At 400 Ao 400 + 1-At 500 Ao 500 + 1-At 600 Ao 600 3 × 100 Ao and At refers to initial and final absorbance at 400, 500, 600 nm units respectively. The sequencing batch decolorization of wastewater dyes was investigated to monitor the ability and lifetime immobilized fungus on repeating of recycling wastewater dyes. The first cycle the new immobilized bead in coloum bioreactor system was loaded with 100 mL of new wasterwater dyes at 1.5 mL min-1 flow rate to final reaction time 24 hours. The next cycle, the immobilized bead used was repeated and wastewater dyes was substituted. 3. RESULTS AND DISCUSSION 3.1 Decolorization by immobilized enzymes MnP and 1,2-D The potential of enzyme manganese peroxidase MnP from Trametes sp. U97 and 1,2- dioxygenase 1,2-D from Pestalotiopsis sp. NG007 to decolorize wastewater dyes was newly investigated. Their uses to decolorize of several synthetic dyes have been studied Yanto et al., 2014; Sari et al., 2012a. The ability of manganese peroxidase MnP from Trametes sp. U97 and 1,2-dioxygenase 1,2-D from Pestalotiopsis sp. NG007 in decolorizing wastewater dyes in 24 hours was shown in Table 1. The highest decolorization was obtained from immobilized enzyme MnP compared to immobilized enzyme 1,2-D. Likewise lignin peroxidase Lip, tyrosinases, H 2 O 2 -producing enzymes, and laccase, MnP are extra-cellular enzymes system produced by basidiomycetes fungi Haritash and Kaushik, 2009. They are usually called as ligninolytic enzymes system because their ability to degrade wood chemical properties Lignin, and celluloses as well as they have potential applications in the mineralization of environmental pollutants, wine stabilization, paper processing, the enzymatic conversion of chemical intermediates, and oxidation of several organic pollutants such as dyes Minussi et al., 2007. 1,2-D are intra-cellular enzymes system produced by ascomycetes fungi and bacteria. They was reported having ability to decolorize some dyes by other rule or mechanisms including of contribution of P450-Monooxygenase Ambrosia and Campos-Takaki, 2004. Table 1: Decolorization of wastewater dyes by immobilized enzymes MnP and 1,2-D Reaction time hours Decolorization MnP 0.36 U mL -1 from Trametes sp. U97 1,2-D 0.36 U mL -1 from Pestalotiopsis sp. NG007 1 26.33 ± 3.32 14.13 ± 5.02 2 28.76 ± 2.27 16.15 ± 4.48 3 30.86 ± 2.71 18.45 ± 5.15 6 34.78 ± 3.21 20.73 ± 6.11 24 48.14 ± 4.03 35.66 ± 5.23 Bogor, 21-22 October 2015 246 The decolorization of dyes is depending on the presence of enzymes. The high enzyme activity produced by microorganism was not always closely related to decolorization ability and oher factors still give significant contribution on degradation Hidayat and Tachibana, 2015a. In this study, biochemical feature of enzymes influenced the decolorization. The immobilized enzyme MnP reached 48 on decolorization of wastewater dyes, while 35 for immobilized enzyme 1,2-D. It revealed that MnP from Trametes sp. U97 to be more powerful than 1,2-D from Pestalotiopsis sp. NG007. 3.2 Decolorization by immobilized Fungus U97 The enzymes produced by fungus may affect the decolorization of wastewater dyes. Although one enzyme was produced and high activity by fungus, e.g. Trametes hirsuta and Pleurotus ostreatus produce the high laccase and MnP, but its fungus may be produced other enzymes that have take part in the decolorization or degradation Hidayat and Tachibana, 2015b. By extraction, the enzyme with high activities will be just isolated while minor enzyme activities will be eliminated. In this study, the decolorization of wastewater dyes directly use by immobilized cell of Trametes sp. U97 was investigated. Table 2: Decolorization of wastewater dyes by immobilized enzymes MnP and fungus U97 Reaction Time hours Decolorization Immobilized enzyme MnP 1.08 U mL -1 Immobilized cell of fungus U97 1 34.29 ± 4.73 47.22 ± 1.66 2 38.84 ± 6.03 66.41 ± 2.18 3 39.04 ± 5.31 68.42 ± 1.57 6 44.74 ± 5.86 80.55 ± 1.63 24 57.62 ± 7.16 94.34 ± 4.08 Table 2 shows the decolorization of wastewater dyes by immobilized enzymes MnP and fungus U97. For comparison, enzyme activity in the inner of alginate bead was increased 3 fold 1.08 U mL -1 . Generally, the high MnP activity increased decolorization yield 19.69 from 48.14 to 57.62. Although the decolorization yield increased but it was approximately lower compare to addition of MnP activities. We suggest that decolorization yield is caused not only by contribution of high activities of MnP enzyme but also in several cases because of contribution of other enzymes. In contrast, when immobilized cell fungus U97 was applied, the decolorization of wastewater increased to be 94.34. The result is proposed to be the effect of presence of lignin peroxidase LiP, laccase, and MnP in the inner of alginate bead. Sari et al 2012b reported that in preparation liquid culture, fungus U97 produced laccase 0.001 U mL -1 , MnP 0.01 U mL -1 , and LiP 0.09 U mL -1 . Therefore, immobilized cell of fungus U97 was selected for the further studies. 3.3 Effect of flow rate and pH The yield of decolorization is also depending on how long dyes interact with alginate bead. As shown in Figure 1, faster flow rate resulted on lower decolorization yield. Flow rate of 1.5 mL min -1 was achieved maximum decolorization 94.34, while the fastest flow rate 3 mL min -1 was just resulted in 81.4 of decolorization. The decolorization yield for both flow rate was not significantly different for different decolorization time 1 to 3 hours. According to this result, suitable flow rate is an important factor to obtain maximum decolorization yield. Bogor, 21-22 October 2015 247 25 50 75 100 12 24 36 48 60 72 84 96 108 120 D ec ol or iz at ion Reaction Time hours 25 50 75 100 12 24 D ec ol or iz at ion Reaction Time hours Flow rate 3 mLMin + Adjusted pH 4.5 Flow rate 1.5 mLMin + Non Adjusted pH Flow rate 1.5 mLMin + Adjusted pH 4.5 The original pH of wastewater dyes used on this study was about 7.12. This pH is alkaline, so it will inhibit the decolorization yield because ligninolytic enzymes system produced fungus U97 suitable to decolorize in pH around 4.5. Figure 1 showed, when wastewater dyes directly load to immobilized fungus U97, the yield of decolorization decreased 83.75. The decolorization yield will achieve its maximum result when the pH of wastewater dyes was adjusted to be 4,5. The decolorization of original wastewater dyes decreased but decolorization yield at intial time until 12 hours reaction was not significantly different compared with that of adjusted pH. Figure 1: Effect of flow rate and pH on decolorization wastewater dyes by immobilized fungus U97 Figure 2: Sequencing batch wastewater dyes decolorization by immobilized fungus U97 in flow rate 1.5 mL -1 and adjusted pH 4.5 Bogor, 21-22 October 2015 248 3.4 Sequencing batch wastewater dyes decolorization Decolorization by immobilized cell was influenced by some internal and external factors such as pH, fungi strain, biochemical feature, and dyes types Hidayat Tachibana, 2015a; Yanto et al., 2014. Furthermore, an efficient application of decolorization of wastewater dyes requires the performance of alginate bead longevity. Thus, in this section the longevity of alginate bead was applied by the reaction time 24 hours for one cycle to final fifth time cycles in small-scale bioreactor. Based on the previous results, the immobilized cell of fungus U97 at flow rate 1.5 mL -1 and pH 4.5 was chosen to do this experiment. Decolorization process was initially started by adsorption step and catalytic action of enzymes, thus as shown in Figure 2, decolorization was slower decolorization rate after 1-2 hour reaction. However, after this initial process, decolorization only run by catalytic action of enzyme itself. Almost all repeated cycle shown the start up period until 24 hours reaction except for final cycle which was of 0-6 hour start up period and 6 hours steady period. Figure 2 also showed the immobilized fungus U97 in alginate bead could be used until fifth cycles although the decolorization yield would decline to 15, 16, 24 and 33 for 2 nd , 3 rd , 4 th and 5 th cycles compared to 1 st cycles. Elimination of the enzyme inside bead caused the decreasing of decolorization yield. Protection of enzyme could be performed by addition of coating agent in the outer bead such as polyallylamine hydrochloride, poly-sodium 4- stryrenesulfonate, glutaradehyde etc Osma et al., 2010. 4. CONCLUSION The immobilized fungus Trametes sp. U97 was more efficient to decolorize wastewater dyes than enzyme itself, manganese peroxidase especially. The maximum decolorization could be archived in flow rate 1.5 mL and pH 4.5 adjusted for origin pH. The alginate bead could be reused until fifth time cycling, although longevity will decline for one cycle to other. According to this study, the immobilized cell of fungus U97 is a potential alternative to handle colored wastewater of textile industries in small-scale bioreactor. REFERENCES Ambrosio, S.T., Campos-Takaki, G.M. 2004. Decolorization of reactive azo dyes by Cunninghamella elegans UCP 542 under co-metabolic condition. Bioresource Technology, 911: 69-75. Asgher, M., Shah, S.A.H., Ali, M., Legge, R.L. 2006. Decolorization of some reactive dyes by white rot fungi isolated in Pakistan. World Journal of Microbiology Biotechnology, 221, 89 – 93. Cerniglia, C.E., Sutherland, J.B. 2001. 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Yanto, D.H.Y., Tachibana, S., Itoh, K. 2014. Biodecolorization of Textile Dyes by Immobilized Enzymes in a Vertical Bioreactor System. The 4th International Conference on Sustainable Future for Human Security SUSTAIN 2013, Procedia Environmental Sciences, 20: 235-244. Bogor, 21-22 October 2015 250 PAPER C2 - Improving Land UseLand Cover Classification Accuracy Using Rule-Based Image Classification Agus Wuryanta 1 , Nunung Puji Nugroho 1 1 Forestry Technology Research Institute for Watershed Management Jln. Jend. A. Yani Pabelan, Kartasura, Kotak Pos 295, Surakarta, Central Jawa, 57012 Corresponding E-mail: agus_july1065yahoo.com ABSTRACT Current and accurate information on land useland cover is one of the important parameters in watershed management. Therefore, land useland cover needs to be monitored periodically. Remote sensing technology offers an effective way to do such activity. This research was conducted in Citanduy Watershed, which is one of the degraded watersheds which need to be restored in Indonesia. Objective of the study is to classify the land cover in the study area based on three classification techniques unsupervised, supervisedmaximum likelihood and rule-based that utilized multi-source data, both spectral and spatialthematic using Landsat 8 OLI satellite imagery. The results showed that the unsupervised classification technique result in the lowest level of classification accuracy, which is 34.43 Kappa statistics: 0.26, while supervisedmaximum likelihood technique result 63.08 Kappa statistics: 0.59 of classification accuracy and rule-based classification technique result in the highest level of classification accuracy, which is 83.20 Kappa statistics: 0.78. Land useland cover in the Citanduy Watershed is dominated by mixed garden 175,572 ha and paddy field 153,085 ha, with a percentage of about 71, while forests natural forests, mangrove forests and plantations only occupy an area of 86,174 ha 18.6. Keywords: Land useland cover, watershed, Landsat 8 OLI sattelite image, classification technique, accuracy 1. INTRODUCTION Land usecover is dynamics in nature, thus it needs to be monitored periodically. Remote sensing RS technology together with geographic information system GIS and global positioning system GPS is widely used to map and detect land use and land cover dynamics in an effective and efficient way Lampitlaw,D and Woldai., T.2000. The advantages of using RS are: 1 large areas can be observed; 2 both periodical and continuous measurement are provided; 3 the spectral data are generally consistent within and between sensors allowing multi-source data to be used; 4 the digital data format of many allows processing, standardization, archiving, and online distribution; and 5 a long-term and consistent archive is provided allowing chrono sequential analysis Inoue, 2010. Classification of satellite imageries to produce land usecover map is a complex process and requires consideration of many factors. Digital image classification comprises of seven major steps, which are: 1 selection of suitable classification system, 2 selection of training samples, 3 image pre-processing, 4 feature extraction, 5 selection of suitable classification approaches, 6 post-classification processing and 7 accuracy assessment Lu Weng, 2007. The common limitation of digital image classification that relies on spectral values is the low level of classification accuracy due to similar spectral signatures of different land uses and land Bogor, 21-22 October 2015 251 covers Francois M. J, and Ramires.I. 1996. Classification technique that combines spectral and thematic information, such as slope, elevation, and vegetation indices is expected to improve the accuracy of classification results Danoedoro, P. 2003. Objective of the study is to classify the land cover in the study area based on three classification techniques unsupervised, supervisedmaximum likelihood and rule-based that utilized multi-source data, both spectral and spatialthematic using Landsat 8 OLI satellite imagery.

2. METHOD 4.1 Location