Ministry of Water and Environment

Abstract Background Despite the limited evidence, desludging operators remain at a heightened risk of work-related musculoskeletal disorders (WMSDs). This study established the prevalence and predictors of WMSDs among desludging operators in Uganda. Methods A digitalized structured questionnaire was used to collect cross-sectional data on musculoskeletal disorders and routine workplace activities from 303 desludging operators in Uganda. Data were analyzed using STATA version 15.0. Results The prevalence of WMSDs among desludging operators was 29.7%. The background characteristics associated with WMSDs included; attending safety training in the last 12 months (APR = 0.55, 95% CI: 0.31–0.98) and undertaking regular health check-ups (APR = 1.68, 95% CI: 1.16–2.44). The ergonomic factors associated with WMSDs were; working with a bent wrist (APR = 2.64, 95%CI: 1.54–4.52), working with hands above the head (APR = 0.40, 95% CI: 0.26–0.62), and pinching unsupported objects (APR = 0.87, 95% CI: 0.59–1.29). Psychosocial factors associated with WMSDs included; neither agreeing nor disagreeing about being able to influence the availability of equipment needed to do their work = 0.43, 95% CI: 0.20–0.94), having much influence over the amount of work done (APR = 1.75, 95% CI: 1.05–2.91), and sometimes feeling that everything done was an effort (APR = 1.71, 95% CI: 1.01–2.89). Conclusion The prevalence of WMSDs was high among desludging operators in Uganda. We recommend providing comprehensive training on occupational health and safety, with a specific focus on ergonomic hazards. In addition, personal protective equipment uses, regular exercise programs, and considerations for work pace, rest, and recovery opportunities should be prioritized to reduce the risk of WMSDs.

Land use, land cover, and climate change impacts are current global challenges that are affecting many sectors, like agricultural production, socio-economic development, water quality, and causing land fragmentation. In developing countries like Uganda, rural areas with high populations dependent on agriculture are the most affected. The development of sustainable management measures requires proper identification of drivers and impacts on the environment and livelihoods of the affected communities. This study applied drivers, pressure, state, impact, and response model in the L. Kyoga basin to determine the drivers and impacts of land use, land cover, and climate change on livelihoods and the environment. The objective of this study was to determine the drivers and impacts of land use, land cover, and climate changes on the environment and livelihoods in the L. Kyoga basin and suggest sustainable mitigation measures. Focus group discussions, key informant interviews, field observations, and literature reviews were used to collect data. Population increase and climate change were the leading drivers, while agriculture and urbanization were the primary pressures, leading to degraded land, wetlands, and forests; loss of soil fertility, hunger, poverty, poor water quality, which are getting worse. The local communities, government, and non-government institutions had responses to impacts, including agrochemicals, restoration, and conservation approaches. Although most responses were at a small/pilot scale level, most responses had promising results. The application of policies and regulations to manage impacts was also found to be weak. Land use, land cover changes, and climate change occur in the L. Kyoga basin with major impacts on land, water, and community livelihoods. With the observed increase in climate change and population growth, drivers and impacts are potentially getting worse. Therefore, it is essential to expand interventions, provide relief, review policies and regulations, and enforce them. The findings are helpful for decisions and policy-makers to design appropriate management options.

In this study, how well the climate models reproduce variability in observed rainfall was assessed based on General Circulation Models (GCMs) from phase 3 and phase 5 of the Coupled Model Inter-comparison Project, i.e., CMIP3 and CMIP5, respectively as well as the Regional Climate Models (RCMs) of COordinated Regional Climate Downscaling EXperiment (CORDEX) over Africa. Observed and climate model based daily rainfall across the Lake Victoria Basin, which is one of the wettest parts of Africa, was considered. Temporal variability was assessed based on the coefficient of variation of daily and annual rainfall, and the maximum dry and wet spell in each year. Furthermore, variation in daily rainfall was assessed in terms of the long-range dependence. Comparison of variability results from observed and climate model based rainfall was made. It was found that the capacity to reproduce variability in observed wet and dry conditions depends on the specific GCM (of CMIP3 or CMIP5) or CORDEX RCM. However, the CORDEX RCMs replicated variability in observed daily rainfall better than the CMIP3 and CMIP5 GCMs. This was due to the spatial resolutions of the CORDEX RCMs which are higher than those of the CMIP3 and CMIP5 GCMs. The ensemble mean of the coefficients of correlation between the variability in observed and that of climate model based rainfall was close to zero for both the GCMs or RCMs. This suggests that analyses can be done on a case by case basis. In other words, GCMs or RCMs which adequately reproduce variability in observed wet and dry conditions can be considered for further statistical analysis of the changes especially on the basis of statistical methods for downscaling. For daily timescale, both the GCMs and RCMs from all the three sets of climate models generally exhibited poor performance in capturing the time of occurrences and the magnitudes of rainfall events (when considered in a combined way). To reliably assess long-term rainfall changes, it is vital to characterize natural variation in terms of the statistical dependence. With respect to natural variability of rainfall at local scales, there is room for further improvement of the climate models; however, whether theory of fractals and/or concepts of scaling behavior or self-similarity can explicitly contribute in that respect is a crucial consideration. Results from this study gave some insights in the reasonableness of the future rainfall projections.