Chronic kidney disease of unknown etiology (CKDu) is spreading gradually in Sri Lanka. In the current research, 1,435 well water samples from all 25 districts of Sri Lanka, 91 rice samples, and 84 human urine samples from both CKDu-endemic and non-endemic areas in Sri Lanka were analyzed for arsenic, cadmium, lead, and chromium to detect whether toxic elements could be a cause of CKDu. The liver-type fatty acid binding protein (L-FABP) concentration and arsenic, cadmium, lead, and chromium concentrations of the urine samples were analyzed to determine the relation of L-FABP with arsenic, cadmium, lead, and chromium. High concentrations of arsenic, cadmium, lead, and chromium were not detected in the well water samples from CKDu-endemic areas. Arsenic, cadmium, and lead contents in the rice samples from both CKDu-endemic and non-endemic areas were well below the Codex standard. There were no relationships between the L-FABP concentration and concentrations of arsenic, cadmium, lead, and chromium in urine. In addition, arsenic, cadmium, lead, and chromium concentrations in human urine samples from CKDu-endemic areas were not significantly different from those from non-endemic areas. These findings indicated that arsenic, cadmium, lead, and chromium could not cause CKDu.

A diagnostic of fluorosis, such as dental and skeletal fluorosis caused by excess fluoride in drinking and cooking water, is endemic in at least 25 countries around the globe; the number of patients would be in the tens of millions. Other reports have estimated that more than 70 million people in the world suffer from dental or skeletal fluorosis. Since many people in developing countries rely on groundwater for their drinking water and are unable to access any other water resources, the removal of fluoride from drinking water is urgently needed. In the current research, fluoride (F⁻) was removed from well water using a novel batch electrolysis system composed of carbon electrodes and two cells separated by a membrane diaphragm with the goal of its application in developing countries. The removal of F⁻ was investigated using synthesized well water with various levels of F⁻ and magnesium (Mg²⁺) at a constant current. As the electrolysis progressed, Mg(OH)₂ precipitation appeared in the cathode cell, which co-precipitated with F⁻ by forming a complex. No chemicals are needed, since the Mg²⁺ already in the well water is utilized for the precipitation of magnesium hydroxide. The F⁻ removal rate gradually increased with the increase of Mg²⁺ concentration up to 80% when the Mg²⁺ concentration was 200 mg/L. The F⁻ concentration slightly affected the F⁻ removal rate above 50 mg/L of Mg²⁺ in the solution. The system’s capability to achieve F⁻ concentration of less than 1 mg/L, which is the Sri Lankan standard for drinking water, was examined using Sri Lankan groundwater with typical F⁻ and Mg²⁺ concentrations. Well-water samples—183 from the Anuradhapura and 56 from the Polonnaruwa regions—were used for the estimation. The system showed high capability as 84% and 72% for Anuradhapura well water and for Polonnaruwa well water, respectively. It was confirmed that a high percentage of well water having F⁻ concentrations of more than 1 mg/L could be treated by the electrolysis system without the addition of any chemicals.

Distribution of most prevalent disinfection by-products, trihalomethanes (THMs) in relation to treatment technology and common water quality parameters (turbidity, conductivity, color, pH, and residual chlorine) was examined for two water supply schemes (WSS) in Sri Lanka (locations: Greater Kandy-WSS (GKWSS) (80.56–80.66 °E, 7.28–7.38 °N) and Kandy South-WSS (KSWSS) (80.49–80.63 °E, 7.21–7.30 °N). In both treatment plants, only CHCl₃ and CHCl₂Br were detected in appreciable concentrations and total THMs (TTHMs) values were well below the WHO limits (80 μg/L). TTHMs variations ranged from 0 to 16 μg/L and 0 to 54 μg/L in GKWSS and KSWSS, respectively. Highest TTHM value (54 μg/L) was found in KSWSS which employs pulsation treatment technology. Correlations between CHCl₃ and CHCl₂Br in both water schemes are noteworthy, but THM levels relate to most of the water quality parameters ambiguously. However, a distinct relationship is observed between THM levels and degree of chlorination, resident time, pipeline corrosion, and temperature. THM formation increased towards the boundaries of most of the sub-water supply schemes (SWSS).

This study examines the influence of light wavelengths on the growth dynamics of five algal genera (Chlorella sp., Volvox sp., Gloeocapsa sp., Microspora sp., and Mougeotia sp.) in freshwater systems, using machine learning to optimize growth models. Natural light yielded the highest algal proliferation, increasing the total count from 90 to 1390 cells/mL in 30 days. Filtered wavelengths showed that blue light most effective (840 cells/mL), followed by red (490 cells/mL) and yellow (200 cells/mL), while green light minimally impacted growth (160 cells/mL). Genera-specific responses revealed that Gloeocapsa sp. and Mougeotia sp. thrived the most under blue light (240 and 750 cells/mL, respectively), with red and blue wavelengths generally enhancing growth across genera. Machine learning models achieved high accuracy (R² > 0.96 for total growth and R² > 0.8 for genera-specific and wavelength-based models), refining growth kinetics. These results suggest that spectral manipulation limiting blue/red wavelengths in water treatment to curb blooms while leveraging natural light for biofuel cultivation could optimize algal management. The integration of empirical data with machine learning offers a robust framework for predictive modeling in algal research and industrial applications.