Background: Hermetia illucens L. have gained popularity in recent years as an environmentally friendly response to both the present and potential future food/feed crisis. The larvae of H. illucens L., or black soldier fly larvae (BSFL), is an alternative solution to tackle the issue of organic waste bioconversion. However, understanding the environmental loads associated with biowaste bioconversion using BSFL to produce dried BSFL is a pivotal point to keep the environment sustainable. This study reported a life cycle assessment (LCA) of the biowaste bioconversion process of BSFL and determined the environment impact analysis to make recommendations for modifications to lessen environmental consequences. Methods: The methodology used is life cycle assessment (LCA), which includes: (a) system boundary determination (gate-to-gate), starting from biowaste production, biowaste bioconversion, prepupae and BSFL frass production. The system boundary of the dried BSFL production is designed for both the processing and production of one cycle of BSFL; (b) life cycle inventory activities carried out at PT Biomagg Sinergi Internasional, Depok, West Java, Indonesia; (c) conducting life cycle impact assessment on five environmental impact categories namely global warming potential (GWP), acidification (AC), terrestrial eutrophication (TE), fossil fuel depletion (FFE), eco-toxicity (ET); and (d) interpretation of the assessment result. The LCA is conducted using openLCA 1.11 software and TRACI 2.1 impact assessment method. Results: The impact values of GWP, AC, TE, FFE, and ET, per 100 kg of BSFL dried production was 6.687 kg CO2 eq; 0.029 kg SO2-eq; 0.092 kg N-eq; 16.732 MJ surplus; 121.231 CTUe. Production of prepupa had the highest hotspots in these emissions, followed dried BSFL production. Conclusions: Efforts to reduce environmental impacts that can be done are by implementing an integrated rearing system using substrate from a single type of known substrate for BSFL and using alternative drying methods for BSFL dried production.

The increasing importance of water quality management in water supply systems requires the development of efficient methodologies for the early detection of water quality incidents related to the detection of anomalies in water quality parameters. Research aims to analyze real-time water quality data (pH, turbidity, electrical conductivity, temperature, and chlorine), perform anomaly detection across parameters, and conduct a comprehensive investigation of water quality incidents that correlate with detected anomalies in water supply systems. This study can contribute to the development of an early detection and response system related to water quality incidents in water supply systems. Future work will focus on enhancing the application of systems for early detection of water quality incidents by expanding the data, developing anomaly detection methods by applying machine learning techniques, and figuring out the correlations between anomalies and water quality incidents.