ABSTRACT
Heavy metal pollution in urban rivers corresponds to anthropogenic impacts. Considering the environmental importance of the Winongo River for domestic use, agriculture, and fisheries, a comprehensive study of heavy metal contamination in this river needs to be conducted. This research focused on the assessment of heavy metal in the water and sediment using the enrichment factor (EF), geo-accumulation index (Igeo), Ecological Risk Index (Er), and Potential ecological risk index (RI). Results showed that the concentrations of the heavy metals Pb, Cu, Cd, Al, and Fe in the water samples exceeded thresholds. Based on EF, Igeo, and Er assessment, the level of contamination by the heavy metals Pb, Cu, Cr, and Cd was found to be low, and that by Fe and Al was found to be moderate to high. The mean values of heavy metals in sediment in the descending order are as follows Fe > Al > Pb > Cu > Cr > Cd (1,445, 2692.42, 0.17, 0.048, 0.016, 0 mg/kg) respectively. Meanwhile, the mean values of heavy metals in the water in descending were Al (1.208), Fe (0.857), Pb (0.155), Cu (0.018), Cr (0.009), and Cd (0 mg/L) respectively. The sources pollution of Cu, Cd, and Pb were identified as anthropogenic sources such as city effluent, road, fisheries, and mechanic workshops. Fe and Al from sediment exhibit strong correlation (r = 0.688). This suggests that Fe and Al possibly comes from same sources originating from earth materials. In general, the potential risk assessment showed that in the Winongo River, the midstream area had higher pollution levels than the downstream and upstream areas (pollution in midstream > downstream > upstream). The sources of pollution in the midstream were identified as city effluent, roads, fisheries, and mechanic workshops. For this reason, the findings of this research are expected to provide a scientific basis for pollution control.
ABSTRACT
Background: The unpredictability of the progression of coronavirus disease 2019 (COVID-19) may be attributed to the low precision of the tools used to predict the prognosis of this disease. Objective: To identify the predictors associated with poor clinical outcomes in patients with COVID-19. Methods: Relevant articles from PubMed, Embase, Cochrane, and Web of Science were searched as of April 5, 2020. The quality of the included papers was appraised using the Newcastle-Ottawa scale (NOS). Data of interest were collected and evaluated for their compatibility for the meta-analysis. Cumulative calculations to determine the correlation and effect estimates were performed using the Z test. Results: In total, 19 papers recording 1,934 mild and 1,644 severe cases of COVID-19 were included. Based on the initial evaluation, 62 potential risk factors were identified for the meta-analysis. Several comorbidities, including chronic respiratory disease, cardiovascular disease, diabetes mellitus, and hypertension were observed more frequent among patients with severe COVID-19 than with the mild ones. Compared to the mild form, severe COVID-19 was associated with symptoms such as dyspnea, anorexia, fatigue, increased respiratory rate, and high systolic blood pressure. Lower levels of lymphocytes and hemoglobin; elevated levels of leukocytes, aspartate aminotransferase, alanine aminotransferase, blood creatinine, blood urea nitrogen, high-sensitivity troponin, creatine kinase, high-sensitivity C-reactive protein, interleukin 6, D-dimer, ferritin, lactate dehydrogenase, and procalcitonin; and a high erythrocyte sedimentation rate were also associated with severe COVID-19. Conclusion: More than 30 risk factors are associated with a higher risk of severe COVID-19. These may serve as useful baseline parameters in the development of prediction tools for COVID-19 prognosis.
