As the world strives to achieve universal health coverage, the sudden occurrence of the Covid-19 pandemic and the unpreparedness of countries to respond quickly and appropriately impose an additional burden on health systems and achieve the goals of sustainable health development Got into trouble. In such a situation, the only strategy against coronary heart disease seems to be to prevent infected people from coming into contact with others by finding infected cases or reducing contact. Public health surveillance or tracking systems are critical in preventing and control ling disease in population. Implementation of these systems is possible by using RTLS technology and database to analyze and monitor information.

This study investigated the processes responsible for perennial low pH in Nyaruzinga wetland water source in Western Uganda. Under anoxic conditions in wetlands, the redox potential and pH increase, thereby favouring dissolution of iron compounds, humic substances and release of copper, chromium, cobalt, nickel and lead. In situ temperature, pH, DO, EC and Eh were determined while the heavy metals, anions, cations, TOC, DOC and humic substances were analysed in the laboratory, for sediments, raw and treated water. The pH and DO increased from 70 cm deep in the wetland to the surface while the other parameters decreased. Copper (0.123-0.152 mg/L), chromium (0.002-0.299 mg/L), nickel (0.07-0.119 mg/L) and cobalt (0.006-0.081 mg/L) were determined at 70 cm deep, being trace (0.001-0.09 mg/L) at the wetland surface. High concentrations of TOC (14.7-28.3 mg/L) and DOC (0.03-0.71 mg/L) were measured in the wetland while traces of DOC (0.001-0.009 mg/L) and humic substances were determined in raw water but not in the treated water. The low pH in Nyaruzinga wetland is attributed to low molecular weight humic substances under perennial water logged conditions, and treatment processes should aim to remove them.

Chronic exposure to ambient Ozone (O3) air pollution induces respiratory inflammation and hyperreactivity, emphysema and interstitial lung fibrosis. O3-induced oxidative stress causes epithelial barrier injury and cell death activating Toll-like receptors, DNA sensing pathways and inflammasomes with production of a range of inflammatory chemokines with a mixed phenotype of COPD and asthma. O3 exposure is often associated with other pollutants causing exacerbation leading to severe respiratory disease. Here, we review mechanisms and therapeutic targets to control O3-induced COPD-like disease.

This study investigates heavy metal contamination in seawater and sediments in three coastal regions of Oman – Darsait, Azaibah and Seeb. In 2022, sample collections were made in subtidal ecosystems in these three areas, and the samples were analysed to assess the potential environmental impacts of treated wastewater. The study employed a comprehensive sampling strategy adhering to the Oman Environmental Impact Assessment (EIA) Guidelines to evaluate key parameters such as temperature, salinity, dissolved oxygen, and heavy metal concentrations see Appendix 1. Results indicate that the levels of most heavy metals contamination had not changed significantly since the baseline surveys in 2009 and 2012, but that some levels were still above EIA guidelines. Recommendations are made to reduce these levels, ensuring the protection of marine environments and humans while supporting industrial growth. This research contributes to our understanding of anthropogenic impacts on marine ecosystems in Oman and highlights the importance of regular monitoring and adaptive management practices.

The paper introduces a machine learning method of detecting multiple sources of water contamination caused by wildfire. The method includes changing the water flow regime, monitoring the time series of the contaminant concentration caused by regime changes, and associating the signature of the contaminant changes over time with sources locations. The contaminant signature from multiple sources starting at the moment of changing water velocity are defined by extending the approach for one contamination source. The intensity, location of each source, and diffusion coefficient are defined to satisfy the minimum square between monitoring and theoretical concentrations. The equations derived from the criteria of the best fit between experimental and modeling data are solved using the theory of hypernumbers. The initial values for hypernumber solutions are computed using the transient process of contaminant transport curve analysis. The defined in this paper algorithm can by used for detecting location of the arbitrary impurity in water network system.