Nitrogen species (NH3-N, NO3-N, and NO2-N) are found as one of the major dissolved constituents in wastewater or stormwater runoff. In this research, laboratory experiments were conducted to remove these pollutants from the water environment using radiata pine (Pinus radiata) sawdust. A series of batch tests was conducted by varying initial concentration, dosage, particle size, pH, and contact time to check the removal performance. Test results confirmed the effectiveness of radiata pine sawdust for removing these contaminants from the aqueous phase (100% removal of NO3-N, and NO2-N; 55% removal of NH3-N). The adsorbent dosage and initial concentration showed a significantly greater effect on the removal process over pH or particle sizes. The optimum dosage for contaminant removal on a laboratory scale was found to be 12 g. Next, the adsorption kinetics were studied using intraparticle diffusion, liquid-film diffusion, and a pseudo-first order and pseudo-second order model. The adsorption of all species followed a pseudo-second order model but NO2-N adsorption followed both models. In addition, the kinetics of NO2-N adsorption showed two-step adsorption following intraparticle diffusion and liquid-film diffusion. The isotherm study showed that NO3-N and NO2-N adsorption fitted slightly better with the Freundlich model but that NH3-N adsorption followed both Freundlich and Langmuir models.

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Abstract. Along with remote sensing technology development, vegetation monitoring can be performed using satellite imagery or Unmanned Aerial Vehicle (UAV) data. UAV imagery with a high resolution, between 3–5 cm at an altitude <100 m, is able to present specific land conditions without being affected by the weather. Information related to vegetation density is one of the components in the Environmental Impact Analysis (EIA) study of a proposed project development due to vegetation removal. In this study, information from consumer-grade cameras of a low-cost UAV platform was explored to classify vegetation density using the potential of RGB imagery-based vegetation index (VI). The correlation coefficient (R2) between field observation data and the seven different values of VI demonstrated moderate to strong correlation. The highest linier correlation of 80.16% (R2 = 0.64) was performed by the Green Red Vegetation Index (GRVI). Classification of the vegetation density was established by applying the object-based image analysis method through the combination of supervised machine learning algorithm of Support Vector Machine (SVM) and the GRVI vegetation index. The vegetation density classification consists of very low, low, medium, high, and very high-density classes. The data can be utilized in determining vegetation management efforts from the presence of a proposed project in the EIA study. The use of UAV imagery is considered effective in identifying vegetation density.

Amplapura is located in the highlands having a potential area of green open land which can absorb rainwater freely into the ground. However, in recent times, land conversion has begun to develop with new housing buildings, Griya Galiran Regency Housing. Furthermore, the effectiveness of the land area is still able to absorb water and maintain groundwater balance. A rainwater harvesting plan (RH) is needed, or, more popularly, rainwater harvesting. In this area. The aim is to provide a portion of residential land space for rainwater infiltration into the pores or soil cavities using the infiltration well method. The results showed that the Griya Galiran Regency Housing had an acceptable sand soil type and absorbed soil quickly, with a soil permeability coefficient (k) of 0.0014 cm/s. Designing the dimensions of the infiltration well at the Griya Galiran Regency Housing with an area of 70 m2 based on SNI 03-2453-2002 for a circular cross-section, an infiltration well with a diameter of 1.2 m with a depth of 2 m is made. In contrast, as a rectangular cross-section, an infiltration well has a side length of 1 m with a depth of 2 m.

Bendungan Tamblang dibangun untuk memenuhi kebutuhan irigasi dan air baku di Kecamatan Sawan dan Kecamatan Kubutamban Kabupaten Buleleng. Waduk ini berada di aliran Sungai Tukad Daya dan direncanakan mempunyai total tampungan sebesar 6,137,228 m 3 sedangkan tampungan waduk efektif sebesar 4,199,646 m 3. Fungsi Waduk Tamblang adalah untuk irigasi sebesar 584 ha serta penyediaan air baku sebesar 306 liter/detik. Simulasi operasi Waduk Tamblang menggunakan alternatif debit andalan 80% dan 90%. Data pendukung berupa data curah hujan harian, data debit permukaan aliran Sungai Tukad Daya dan data klimatologi dianalisis untuk mengetahui curah hujan andalan R50 dan R80, debit andalan, evapotranspirasi potensial, curah hujan efektif, kebutuhan air irigasi (KAI), serta kebutuhan air baku pada Kecamatan Sawan dan Kecamatan Kubutambahan. Operasi waduk disimulasikan ke dalam lima alternatif dengan keandalan inflow Q80 dan Q90. Hasil simulasi operasi Waduk Tamblang terjadi surplus pada simulasi alternatif 4 dan alternatif 5. Pola tanam alternatif 4 yaitu padijagung-kacang tanah dan alternatif 5 yaitu padi-buncis-bawang-kedelai dan ini sudah disesuaikan pada pola tanam eksisting. Hasil simulasi alternatif 4 dan 5 ini dapat memenuhi kebutuhan air baku dan air irigasi dengan persentase terlayani 100%.