Journal Issue:
EnNRJ Vol. 21 No. 2

1

Journal Volume

Journal Volume
EnNRJ Volume 21
(2023)

Articles

PublicationOpen Access
Optimization and Kinetic Study of Phosphorus Dissolution from Primary Settled-Nightsoil Sludge
(2023) Wanida Pimpeach; Withida Patthanaissaranukool; Chongchin Polprasert; Supawadee Polprasert; Suwisa Mahasandana; Bunyarit Panyapinyopol
In this study, chemical extraction using different acid concentrations, solids concentrations, and reaction time with subsequent interactions mechanism were carried out to evaluate the potential of phosphorus (P) recovery from primary settled-nightsoil sludge (PSNS). The response surface methodology (RSM) with Box-Behnken experimental design and one-way ANOVA analysis were also employed to establish optimal P leaching conditions. The extraction efficiency relied mainly on acid and solids concentration. The second-order polynomial model was successfully developed for extracting process designs. Approximately 93% of P could effectively be extracted from PSNS of 20,000 mg/L with 0.5 M of H2SO4 at reaction time of 45 min (optimum condition). Kinetic studies showed that the pseudo-second order was fit to describe leaching of P and metals. Moreover, the rate of kinetic constants (k2) of the P, Fe, Mg, and Ca under optimum condition were found to be 0.1607, 0.1099, 0.0317, and 0.0053 g/mg·min, respectively. The 99% leaching of maximum extracted P concentration at the equilibrium (9.6673 mg/g) took place in less than one hour. The findings of a suitable simple and low-cost method P dissolution from PSNS not only provides understanding of leaching kinetics, but also helps to pave a way of recovering P from a renewable resource in the field of waste utilization.
PublicationOpen Access
Evaluation of Land Use Land Cover Changes in Nan Province, Thailand, Using Multi-Sensor Satellite Data and Google Earth Engine
(2023) Jiratiwan Kruasilp; Sura Pattanakiat; Thamarat Phutthai; Poonperm Vardhanabindu; Pisut Nakmuenwai
Land use and land cover (LULC) conversion has become a chronic problem in Nan province. The primary factors of changes are lacking arable land, agricultural practices, and agriculture expansion. This study evaluated the usefulness of multi-sensor Landsat-5 (LS5), Landsat-8 (LS8), Sentinel-1 (S1), and Sentinel-2 (S2) satellite data for monitoring changes in LULC in Nan province, Thailand during a 30-year period (1990-2019), using a random forest (RF) model and the cloud-based Google Earth Engine (GEE) platform. Information of established land management policies was also used to describe the LULC changes. The median composite of the input variables selection from multi-sensor data were used to generate datasets. A total of 36 datasets showed the overall accuracy (OA) ranged from 51.70% to 96.95%. Sentinel-2 satellite images combined with the Modified Soil-Adjusted Vegetation Index (MSAVI) and topographic variables provided the highest OA (96.95%). Combination of optical (i.e., S2 and LS8) and S1 Synthetic Aperture Radar (SAR) data expressed better classification accuracy than individual S1 data. Forest cover decreased continuously during five consecutive periods. Coverage of maize and Pará rubber trees rapidly expanded in 2010-2014. These changes indicate an adverse consequence of the established economic development promoted by industrial and export agriculture. The findings strongly support the use of the RF technique, GEE platform and multi-sensor satellite data to enhance LULC classification accuracy in mountainous area. This study recommended that certain informative and science-based evidence will encourage local policymakers to identify priority areas for land management and natural resource conservation.

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