Dataset on physicochemical and microbial properties of raw water in four drinking water treatment plants based in South Africa

Smart Water Quality Monitoring with IoT Wireless Sensor Networks

Traditional laboratory-based water quality monitoring and testing approaches are soon to be outdated, mainly because of the need for real-time feedback and immediate responses to emergencies. The more recent wireless sensor network (WSN)-based techniques are evolving to alleviate the problems of monitoring, coverage, and energy management, among others. The inclusion of the Internet of Things (IoT) in WSN techniques can further lead to their improvement in delivering, in real time, effective and efficient water-monitoring systems, reaping from the benefits of IoT wireless systems. However, they still suffer from the inability to deliver accurate real-time data, a lack of reconfigurability, the need to be deployed in ad hoc harsh environments, and their limited acceptability within industry. Electronic sensors are required for them to be effectively incorporated into the IoT WSN water-quality-monitoring system. Very few electronic sensors exist for parameter measurement. This necessitates the incorporation of artificial intelligence (AI) sensory techniques for smart water-quality-monitoring systems for indicators without actual electronic sensors by relating with available sensor data. This approach is in its infancy and is still not yet accepted nor standardized by the industry. This work presents a smart water-quality-monitoring framework featuring an intelligent IoT WSN monitoring system. The system uses AI sensors for indicators without electronic sensors, as the design of electronic sensors is lagging behind monitoring systems. In particular, machine learning algorithms are used to predict E. coli concentrations in water. Six different machine learning models (ridge regression, random forest regressor, stochastic gradient boosting, support vector machine, k-nearest neighbors, and AdaBoost regressor) are used on a sourced dataset. From the results, the best-performing model on average during testing was the AdaBoost regressor (a MAE¯ of 14.37 counts/100 mL), and the worst-performing model was stochastic gradient boosting (a MAE¯ of 42.27 counts/100 mL). The development and application of such a system is not trivial. The best-performing water parameter set (Set A) contained pH, conductivity, chloride, turbidity, nitrates, and chlorophyll.

An update on sustainabilities and challenges on the removal of ammonia from aqueous solutions: A state-of-the-art review

An insightful attempt has been made in this review and the primary objective was to meticulously provide an update on the sustainabilities, advances and challenges pertaining the removal of ammonia from water and wastewater. Specifically, ammonia is a versatile compound that prevails in various spheres of the environment, and if not properly managed, this chemical species could pose severe ecological pressure and toxicity to different receiving environments and its biota. The notorious footprints of ammonia could be traced to anoxic conditions, an infestation of aquatic ecosystems, hyperactivity, convulsion, and methaemoglobin, popularly known as the "blue baby syndrome". In this review, latest updates regarding the sustainabilities, advancements and challenges for the removal of ammonia from aqueous solutions, i.e., river and waste waters, are briefly elucidated in light of future perspectives. Viable routes and ideal hotspots, i.e., wastewater and drinking water, for ammonia removal under the cost-effective options have been unpacked. Key mechanisms for the removal of ammonia were grossly bioremediation, oxidation, adsorption, filtration, precipitation, and ion exchange. Finally, this review denoted biological nutrient removal, struvite precipitation, and breakpoint chlorination as the most effective and promising technologies for the removal of ammonia from aquatic environments, although at the expense of energy and operational cost. Lastly, the future perspective, avenues of exploitation, and technical facets that deserve in-depth exploration are duly underscored.

Effective removal of ammonia from aqueous solution through struvite synthesis and breakpoint chlorination: Insights into the synergistic effects of the hybrid system

The ever-growing contamination of surface water due to various catchment activities poses threats and stress to downstream water treatment entities. Specifically, the presence of ammonia, microbial contaminants, organic matter, and heavy metals has been an issue of paramount concern to water treatment entities since stringent regulatory frameworks require these pollutants to be removed prior to water consumption. Herein, a hybrid approach that integrates struvite crystallization (precipitation) and breakpoint chlorination (stripping) for the removal of ammonia from aqueous solution was evaluated. To fulfil the goals of this study, batch experimental studies were pursued through the adoption of the well-known one-factor-at-a-time (AFAAT) method, specifically the effects of time, concentration/dosage, and mixing speed. The fate of chemical species was underpinned using the state-of-the-art analytical instruments and accredited standard methods. Cryptocrystalline magnesium oxide nanoparticles (MgO-NPs) were used as the magnesium source while the high-test hypochlorite (HTH) was used as the source of chlorine. From the experimental results, the optimum conditions were observed to be, i.e., Stage 1 - struvite synthesis, 110 mg/L of Mg and P dosage (concentration), 150 rpm of mixing speed, 60 min of contact time, and lastly, 120 min of sedimentation while optimum condition for the breakpoint chlorination (Stage 2) were 30 min of mixing and 8:1 Cl₂:NH₃ weight ratio. Specifically, in Stage 1, i.e., MgO-NPs, the pH increased from 6.7 to ≥9.6, while the turbidity was reduced from 9.1 to ≤1.3 NTU. Mn removal efficacy attained ≥97.70% (reduced from 174 μg/L to 4 μg/L) and Fe attained ≥96.64% (reduced from 11 mg/L to 0.37 mg/L). Elevated pH also led to the deactivation of bacteria. In Stage 2, i.e. breakpoint chlorination, the product water was further polished by eliminating residual ammonia and TPC at 8:1 Cl₂-NH₃ weight ratio. Interestingly, ammonia was reduced from 6.51 to 2.1 mg/L in Stage 1 (67.74% removal) and then from 2.1 to 0.002 mg/L post breakpoint chlorination (99.96% removal), i.e., stage 2. Overall, synergistic and complementary effects of integrating struvite synthesis and breakpoint chlorination hold great promise for the removal of ammonia from aqueous solutions thus confirming that this technology could potentially be used to curtail the effects of ammonia in the receiving environments and drinking water.