ABSTRACT With the rapid growth of artificial intelligence, machine learning has emerged as a useful tool in the construction industry for optimizing materials and predicting concrete properties. This study explores the use of steel fiber and recycled demolition waste (DW) to improve concrete’s mechanical properties. The main objective is to predict the compressive strength (CS) of DW-modified fiber-reinforced concrete (FRC) by assessing the effects of cement, RHA, fiber content, natural and demolished waste aggregates, water, and superplasticizer dosages. Seven mix designs with varying fiber and DW levels were experimentally tested. Machine learning methods, including adaptive boosting (ADB), extreme gradient boosting (XGB), random forest (RF), and stacking models (XGB-ADB, XGB-RF), were applied to analyze these variables’ impact on CS. A dataset of 405 points was compiled from literature via a systematic review. The hybrid XGB-RF and XGB models showed the best performance with R2 values of 0.849 and 0.845, respectively. SHAP analysis identified cement, water, and superplasticizer as key factors affecting CS. Experimental validation supported the modeling results and the development of a graphical user interface. The novelty lies in integrating hybrid ML, explainable analysis, and experimental validation to predict the CS of the modified concrete and support mix design through a user-friendly GUI.

Manually filling gallons of water causes many problems, such as volume inaccuracy, waste, and suboptimal water quality monitoring. This research aims to develop an Internet of Things (IoT)-based gallon water filling automation system using an ESP32 microcontroller and a flow meter sensor for accurate water volume measurement and real-time monitoring via a web dashboard and WhatsApp notifications. The methods used include literature review, hardware and software design, and comprehensive system testing. This system integrates RFID-based user authentication, automatic pump and solenoid valve control, transaction data recording in a MySQL database, and automatic user notifications. Test results show that the system can automatically and accurately fill gallons of water to the desired volume, stop the flow when the gallon is full, minimize human error, and allow users to monitor the filling process remotely. Thus, this system provides a more efficient, modern, and transparent gallon water filling solution for drinking water depot businesses

Mata Vaishno Devi Shrine in Jammu and Kashmir is the best visited religious site in India with millions of pilgrims every year thronging this site. Although its religious and economic importance is beyond measure, the sheer number of pilgrimage tourism traffic has already presented a lot of environmental pressure on the location. Among the serious issues are challenges of waste production, water preservation, land degradation and quality of air. The research will look at the effects of pilgrimage tourism on the environment of the shrine environment and suggest a competency model of sustainable tourism growth towards introduction of technological advancements and community integration consideration. The study uses a qualitative method to examine both environmental tasks, the fieldwork, and the interviews conducted with the stakeholders. It singles out practices of proper waste management, water recycling, and energy efficiency such as the adoption of smart waste system, solar energy solution, and control with RFID features. The paper is applicable to the United Nations Sustainable Development Goals (SDGs), especially SDG 12 (Responsible Consumption and Production) and SDG 13 (Climate Action) and it can be used to provide a transferable construct on sustainable pilgrimage tourism. In the paper policy recommendations are offered that can come into place by the local authorities and they are mainly concerned with controlling the number of pilgrims, the management of waste products as well as fully capitalising on water and energy consumption. This study will make a contribution to the overall discussion regarding sustainable tourism practices in pilgrimage destinations by providing practical remedies to the reduction of any environmental impact without compromising on culture and spiritual heritage.

A mixed culture, prepared by acclimatising Chlorella vulgaris (C. vulgaris) to municipal wastewater (MWW), was used in two reactors: one was inoculated with activated sludge (AS), and the other was not (NAS). Under 12:12h light: dark, mixing, and a hydraulic retention time of 1.17 d, both systems removed ∼92% of 270mg-COD/L, 57% of 70mg-N/L, and 27% of PO43--P, while achieving a sludge volume index of 42mL/g. C. vulgaris disappeared and was replaced by other algae, including cyanobacteria, indicating that inoculation is not necessary. Higher dissolved oxygen production, IC uptake, and nitrification occurred in the NAS reactor than in the AS reactor, supported by a higher abundance of the autotrophic/aerobic community in the NAS reactor. Genomic data revealed latent mechanisms (denitrification, N-fixation, nitrate/nitrite reduction, multiple phosphorus pathways) than mass balance. Pure algae seeding is not essential, but activated sludge seeding could affect performance.

A green nanotechnology approach based on coffee waste as a precursor for zero-valent iron in soil decontamination of venlafaxine.

Source-dependent composition and reactivity of effluent organic matter regulating disinfection byproduct formation and cytotoxicity.

Performance and energy balance of an anaerobic dynamic membrane bioreactor treating concentrated municipal wastewater.

Scale-up and performance evaluation of an electrodialysis process in a municipal wastewater treatment plant

A-OHO biological process for municipal wastewater treatment based on carbon-nitrogen element regulation: Total nitrogen removal, low-energy pathway, and microbial mechanism

Ion exchange membranes impact energy and treatment performance of low strength municipal wastewater in microbial fuel cells

Wastewater treatment plants are widely recognized as hotspots for antibiotic resistance. Although activated sludge processes are not designed to lower the abundance of pathogens or antibiotic resistance genes (ARGs), their effects on the pathobiome and antibiotic resistome require in-depth investigation. To this end, we collected wastewater samples before and after activated sludge process from five municipal wastewater treatment plants, each characterized by different inlet composition and treatment capacity. We extracted both intracellular and extracellular DNA and performed shotgun sequencing to characterize the bacterial community, pathobiome, and antibiotic resistome. Our aim was to assess the effects of activated sludge processes on bacterial community composition, the abundance of potentially pathogenic bacteria and high-risk ARGs, and the potential horizontal mobility of detected ARGs. Our results showed that activated sludge processes significantly reduced the abundance of potentially pathogenic bacteria and several high-risk ARGs. Notably, while the prevalence of plasmid-associated ARGs decreased following treatment, ARG-carrying contigs assigned to bacteriophages increased, particularly in extracellular DNA samples. Overall, activated sludge processes demonstrated a beneficial microbiological effect by lowering potentially pathogenic bacteria. However, the enrichment of viral particles carrying ARGs highlights a potential risk for ARG spreading during the following processes. These findings underscore the importance of analyzing both intracellular and extracellular DNA to fully understand the role of activated sludge in mitigating antibiotic resistance and pathogens in wastewater.

Smart optimization reveals high-efficiency operational strategies for the anaerobic-oxic-anoxic process in wastewater treatment.

The Baltic Sea remains one of the most contaminated marine seas globally, receiving diverse pollutant inputs from land-based and maritime sources. This study quantifies the concentrations and loads of microplastic (MP) in ship-generated greywater (GW) and evaluates their potential contribution to Baltic marine MP pollution. Eight GW streams from five vessels were sampled, and fifteen MP polymer types were identified and characterized. MP concentrations ranged from ≈38,000MP/m3 in mixed accommodation-laundry-galley (ALG) stream to≈602,000MP/m3 in laundry GW. Polyethylene terephthalate (PET) was the dominant polymer (58%), detected in all GW samples, while polypropylene (PP, 16%) appeared in only three streams from two vessels. Estimated annual MP loads from the studied Roll on - Roll off - Passenger (RoPax) vessels ranged between≈1.24 and 7.59 billion particles, which are typically delivered to municipal wastewater treatment plants (MWTPs) via Port Reception Facilities (PRFs). Considering the total Baltic fleet's greywater discharge of ≈5.4 million m3/yr in 2022, up to 1.1 trillion MP/yr could have been released directly to the sea, with ≈93% originating from passenger ships. If this volume generated in 2022 were instead delivered to PRFs and treated at MWTPs, ≈6 million - 651 billion MP/yr could still enter the Baltic environment, depending on the treatment efficiencies and technology configurations employed at the MWTPs. These results demonstrate that ship-generated GW is a significant yet understudied source of microplastic (MP) to the Baltic Sea. Moreover, while advanced systems with tertiary treatment technologies on board and ashore can remove ≈95 - 99.9% of MP, residual emissions remain substantial given the large wastewater volumes generated. Effective mitigation strategies should therefore focus on source identification and prevention within shipboard systems, particularly in laundry, galley and accommodation operations, to minimize MP inputs into GW streams and, ultimately, the marine environment.

Electrically enhanced microalgae-bacteria systems for wastewater treatment under low-temperature conditions.

Enhanced denitrification performance of constructed wetlands for wastewater treatment plant effluent using zero-valent iron-based mixing solid carbon sources.

High-selectivity phenol detection in cumene process wastewater via bromination and dynamic optical path.

A novel ternary peroxidase-like nanozyme Ag/MoS2/h-BN coupled self-powered electro-generated H2O2 for butyl xanthate degradation in mineral processing wastewater

Performance evaluation of sulfur autotrophic denitrification technology, AO process and anaerobic MBR for treating real mineral processing wastewater

Pilot study on an integrated anaerobic membrane bioreactor (AnMBR) and intermittent cycle extended aeration system (ICEAS) for seafood processing wastewater treatment.

Can fungal degradation replace conventional biological processes for treatment of highly acidic and saline preserved fruit processing wastewater by virtue of Candida, Pichia and Saccharomyces?