12 research outputs found
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Estimating the burden of disease attributable to ambient air pollution (ambient PM2.5 and ambient ozone) in South Africa for 2000, 2006 and 2012
Characteristics of Droughts in South Africa: A Case Study of Free State and North West Provinces
The Free State (FS) and North West (NW) Provinces are often hard hit by droughts with impacts on water availability, farm production and livestock holdings. The South African government declared the two Provinces drought disaster areas in the 2015/2016 hydrological year. This is a major drawback, since both the Provinces play an important role to South African economy as they are a haven to agricultural production and have major water reservoirs in South Africa. This study was undertaken to investigate the historical evolution of drought within the FS and NW Provinces over the past 30 years. The Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) calculated based on monthly meteorological data from 14 weather/climate stations within the FS and NW Provinces were used to explore and characterize variation in drought intensity, duration, frequency and severity in FS and NW Provinces during 1985–2015. Results indicate that there exist localized positive and negative trends with spatial dependence across the selected stations. In particular, about 60% of the weather stations exhibiting a decreasing trend are located in FS Province, suggesting that FS has being experiencing increasing drought during the analyzed period compared to NW Province. Results from the analysis of drought evaluation indicators (DEIs) calculated from SPEI suggest that drought severity and frequency was more pronounced in FS while the intensity of the drought was more in NW Province during 1985–2015. In addition, based on SPEI calculations, moderate drought occurrences increased during 1985–1994 and 1995–2004 periods and decreased thereafter (2005–2015) in both Provinces. Drought classification based on parameters derived from SPEI produced similar results for mild drought occurrences during the same time scales
Climatic Variables and Malaria Morbidity in Mutale Local Municipality, South Africa: A 19-Year Data Analysis
The north-eastern parts of South Africa, comprising the Limpopo Province, have recorded a sudden rise in the rate of malaria morbidity and mortality in the 2017 malaria season. The epidemiological profiles of malaria, as well as other vector-borne diseases, are strongly associated with climate and environmental conditions. A retrospective understanding of the relationship between climate and the occurrence of malaria may provide insight into the dynamics of the disease’s transmission and its persistence in the north-eastern region. In this paper, the association between climatic variables and the occurrence of malaria was studied in the Mutale local municipality in South Africa over a period of 19-year. Time series analysis was conducted on monthly climatic variables and monthly malaria cases in the Mutale municipality for the period of 1998–2017. Spearman correlation analysis was performed and the Seasonal Autoregressive Integrated Moving Average (SARIMA) model was developed. Microsoft Excel was used for data cleaning, and statistical software R was used to analyse the data and develop the model. Results show that both climatic variables’ and malaria cases’ time series exhibited seasonal patterns, showing a number of peaks and fluctuations. Spearman correlation analysis indicated that monthly total rainfall, mean minimum temperature, mean maximum temperature, mean average temperature, and mean relative humidity were significantly and positively correlated with monthly malaria cases in the study area. Regression analysis showed that monthly total rainfall and monthly mean minimum temperature (R2 = 0.65), at a two-month lagged effect, are the most significant climatic predictors of malaria transmission in Mutale local municipality. A SARIMA (2,1,2) (1,1,1) model fitted with only malaria cases has a prediction performance of about 51%, and the SARIMAX (2,1,2) (1,1,1) model with climatic variables as exogenous factors has a prediction performance of about 72% in malaria cases. The model gives a close comparison between the predicted and observed number of malaria cases, hence indicating that the model provides an acceptable fit to predict the number of malaria cases in the municipality. To sum up, the association between the climatic variables and malaria cases provides clues to better understand the dynamics of malaria transmission. The lagged effect detected in this study can help in adequate planning for malaria intervention
A bibliometric analysis of solar energy forecasting studies in Africa
Solar energy forecasting is considered an essential scientific aspect in supporting efforts to integrate solar energy into power grids. Moreover, solar energy forecasting plays an essential role in mitigating greenhouse gas emissions and conserving energy for future use. This study conducted a bibliometric analysis to assess solar energy forecasting research studies evolution at the continental (Africa) and southern Africa levels. Key aspects of analysis included (i) scientific research trends, (ii) nature of collaboration networks, (iii) co-occurrence of keywords and (iv) emerging themes in solar energy forecasting over the last two decades, between the years 2000–2021. The results indicate that solar energy forecasting research has, on average, expanded by 6.4% and 3.3% in Africa and southern Africa, respectively. Based on the study context, solar energy forecasting research only gained momentum in 2015, peaking in 2019, but it is generally still subtle. The scientific mapping illustrated that only South Africa ranks among the leading countries that have produced high numbers of published documents and also leads in contributions to the research area in both Africa and southern Africa. Three emerging topics were identified from the thematic map analysis— namely, “solar irradiance”, “artificial intelligence” and “clear sky”, which implies that researchers are paying attention to solar irradiance, using modelling techniques that incorporate machine learning techniques. Overall, this study contributes to scientific information on the potential bankability of renewable energy projects that could assist power utilities, governments and policymakers in Africa to enforce the green economy through accelerated decarbonisation of the energy systems and building relationships with developed countries for support and better transitioning to solar energy. From a Water–Energy–Food nexus perspective, the results of this work could assist the scientific community in Africa to take advantage of the inherent interconnectedness of water, energy and food resources, whilst also advancing the use of integrated solutions to shape the focus of solar energy research into a more systems thinking and transdisciplinary approach involving the interconnected primary resources and stakeholders pursuit of the Sustainable Development Goals
Analysis of mid-twentieth century rainfall trends and variability over southwestern Uganda
A methodology has been applied to investigate
the spatial variability and trends existent in a mid-twentieth
century climatic time series (for the period 1943–1977)
recorded by 58 climatic stations in the Albert–Victoria water
management area in Uganda. Data were subjected to quality
checks before further processing. In the present work, temporal
trends were analyzed using Mann–Kendall and linear
regression methods. Heterogeneity of monthly rainfall was
investigated using the precipitation concentration index
(PCI). Results revealed that 53 % of stations have positive
trends where 25 % are statistically significant and 45 % of
stations have negative trends with 23 % being statistically
significant. Very strong trends at 99 % significance level
were revealed at 12 stations. Positive trends in January,
February, and November at 40 stations were observed. The
highest rainfall was recorded in April, while January, June,
and July had the lowest rainfall. Spatial analysis results
showed that stations close to Lake Victoria recorded high
amounts of rainfall. Average annual coefficient of variability
was 19 %, signifying low variability. Rainfall distribution is
bimodal with maximums experienced in March–April–May
and September–October–November seasons of the year.
Analysis also revealed that PCI values showed a moderate
to seasonal rainfall distribution. Spectral analysis of the time components reveals the existence of a major period around
3, 6, and 10 years. The 6- and 10-year period is a characteristic
of September–October–November, March–April–
May, and annual time series.http://link.springer.com/journal/704hb201
Space Applications
Signals transmitted by global navigation satellite
system (GNSS) satellites are not confined to the
surface of the Earth but can likewise be used for
navigation in space. Satellites in low Earth orbits,
in particular, benefit from a similar signal strength
and experience a full-sky visibility. On the other
hand, the harsh space environment, long-term
reliability requirements and the high dynamics
of the host platform pose specific challenges to
the design and operation of space-borne GNSS
receivers. Despite these constraints, satellite manufacturers
and scientists have early on started to
exploit the benefits of GNSS technology. From the
first flight of a Global Positioning System (GPS) receiver
on Landsat-4, GNSS receivers have evolved
into indispensable and ubiquitous tools for navigation
and control of space vehicles.
Following a general introduction, the chapter
first describes the specific aspects of GNSS signal
tracking in space and highlights the technological
challenges of space-borne receiver design. Subsequently,
the use of GNSS for spacecraft navigation is
discussed taking into account both real-time navigation
and precise orbit determination. Relevant
algorithms and software tools are discussed and
the currently achieved performance is presented
based on actual missions and flight results.
A dedicated section is devoted to the use of spaceborne
GNSS for relative navigation of formation
flying satellites.
The chapter concludes with an outlook on
special applications such as spacecraft attitude
determination, GNSS tracking of ballistic vehicles
as well as GNSS radio science