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Antibiotic-resistant microbial populations in urban receiving waters and wastewaters from Tanzania
Get PDFThis research article was published by Elsevier, 2023Antimicrobial resistance against certain medications in the pathogenic microbial community is globally increasing due to the continual discharge and disposal of pharmaceuticals in the environment. The phenomenon resulted in significant antibiotic resistance among several exposed Enterobacteriaceae species, with wastewater treatment plants (WWTPs) and rivers serving as significant reservoirs. Despite antibiotic resistance being a tragedy, particularly in treating diseases by using antibiotics, local and regional studies indicating the severity, resistant species and the molecular level insight into these pathogens are scarce, thus requiring immediate intervention. This study, therefore, investigated wastewater from wastewater treatment ponds and receiving waters for the presence of resistant pathogens through phenotypical and molecular approach screening of their genes. Among the 57 analyzed samples, 18 (67%, n = 27) isolates of Klebsiella spp.., 4 (80%, n = 5) isolates of Proteus spp., 1 (100%, n = 1) of isolated Pseudomonas aeruginosa and 6 (18%, n = 34) of E. coli found were resistant to at least 1 among the tested antibiotics. E. coli had an 83% higher proportion of multi-drug resistance (MDR) than Klebsiella spp.., which had 68.5%, and no MDR was shown by P. aeruginosa isolates. Among the 20 bacterial isolates of antibiotic-resistant genes, showed that E. coli harboured 39%, followed by 22% of Klebsiella spp. Eleven (11) isolates of these 20 (55%) contained sulphonamides resistant genes: Sul 1 (n = 4) and Sul 2 (n = 7). Ten (10) isolates (50%) contained the tetracycline-resistant genes in which 4 isolates showed the Tet A, Tet B – 1 and 5 isolates contained Tet D. β-lactamases (bla CTX-M and bla SHV) were found in 7 isolates (35%). The existence of these antibiotic-resistant species in the urban receiving and wastewater presents a threat of transmission of diseases to humans and animals that are not cured by the existing medications, jeopardizing public health safety
Dynamic modelling and optimal control analysis of a fractional order chikungunya disease model with temperature effects
Get PDFThis research article published by Elsevier, 2023Approximately 1.3 billion inhabitants in 94 countries are estimated to be at risk of chikungunya
virus infection. A mechanistic compartmental model based on fractional calculus, the Caputo
derivative has been proposed to evaluate the effects of temperature and multiple disease control
measures (larvicides use, insecticides and physical barriers) during an outbreak. The proposed
model was calibrated based on data from literature and validated with daily chikungunya fever
cases reported at Kadmat primary health centre, India. The transmission potential of the disease
was examined. Sensitive analyses were conducted through computing partial rank correlation
coefficients. Memory effects which are often neglected when mechanistic models are used to
model the transmission dynamics of infectious diseases, were found to have a significant effect
on the dynamics of chikungunya
Optimization of divalent mercury removal from synthetic wastewater using desirability function in central composite design of response surface methodology
Get PDFThis research article was published by Journal of Environmental Health Science and EngineeringHeavy metals exist in the ecosystem both naturally and due to anthropogenic activities and as recalcitrant pollutants; they are non-biodegradable and cause acute and chronic diseases to human beings and many lifeforms. A statistical experimental approach was applied in this current study to optimize the detoxification of mercury [Hg(II)] from mono-component biosorption system by a novel hybrid granular activated carbon (biosorbent) prepared from maize plant residues. The analysis of variance by the application of central composite design shows that all the studied independent factors greatly influence Hg(II) removal efficiency and uptake capacity. The optimum experimental condition of 30 min contact time, 0.5 g/L biosorbent dosage, and 15 mg/L initial Hg(II) concentration were achieved after seeking 20 optimization solutions at 0.903 desirability. The optimum percentage removal and uptake capacity of Hg(II) at the optimal experimental setup was 96.7% and 10.8 mg/g, respectively. To confirm the quadratic models developed for the prediction of the responses as a function of the independent factors, confirmatory laboratory experiments were performed at the optimum condition. The results show that at the established best experimental condition, the optimum Hg(II) removal efficiency of 98.3% and uptake capacity of 11.2 mg/g were attained, which were within the prediction intervals indicating the suitability of the quadratic models in predicting future cases. The TEM and XRD analyses show that the Hg(II) ions were adsorbed by the biosorbent successfully and this suggests the potential and applicability of this novel biosorbent in treating water contaminants, especially heavy metals
Dataset of banana leaves and stem images for object detection, classification and segmentation: A case of Tanzania
Get PDFThis research article was published by Elsevier in 2023Banana is among major crops cultivated by most smallholder farmers in Tanzania and other parts of Africa. This crop is very important in the household economy as well as food security since it serves as both food and cash crops. Despite these benefits, the majority of smallholder farmers are experiencing low yields which are attributed to diseases. The most problematic diseases are Black Sigatoka and Fusarium Wilt Race 1. Black Sigatoka is a disease that produces spots on the leaves of bananas and is caused by an air-borne fungus called Pseudocercospora fijiensis, formerly known as Mycosphaerella fijiensis. Fusarium Wilt Race 1 disease is one of the most destructive banana diseases that is caused by a soil-borne fungus called Fusarium oxysporum f.sp. Cubense (Foc). The dataset of curated banana crop image is presented in this article. Images of both healthy and diseased banana leaves and stems were taken in Tanzania and are included in the dataset. Smartphone cameras were used to take pictures of the banana leaves and stems. The dataset is the largest publicly accessible dataset for banana leaves and stems and includes 16,092 images. The dataset is significant and can be used to develop machine learning models for early detection of diseases affecting bananas. This dataset can be used for a number of computer vision applications, including object detection, classification, and image segmentation. The motivation for generating this dataset is to contribute to developing machine learning tools and spur innovations that will help to address the issue of crop diseases and help to eradicate the problem of food security in Africa
Tracing the dominant sources of sediment flowing towards Lake Victoria using geochemical tracers and a Bayesian mixing model
Get PDFThis research article was published by Springer Nature in 2023Purpose Lake Victoria has been increasingly silting over the past decades, impacting water quality and loss of biodiversity.
Sediment control strategies require information on the relative and absolute contributions of sediment from different sources.
However, to date, there is no continuous monitoring of sediment flux or water quality in any of the tributaries, prohibiting an
assessment of the scale of the problem. The aim of this study was to trace the dominant sources of riverine sediment using
geochemical fingerprinting, thereby generating a knowledge base for improving land management and reducing sediment
yields in Simiyu River catchment, one of the main contributing rivers to Lake Victoria.
Materials and methods Geochemical tracer concentrations were analyzed in transported sediment from the main river and
two tributaries (riverbed sediments) and from soils in five dominant land use types (agricultural land, bush land, forest
land, channel banks, and main river banks). Dominant sources to the Simiyu main river sediment were attributed using the
Bayesian MixSIAR model.
Results and discussion The mixing model outputs showed that the Simiyu tributary was the dominant source of sediment
to the Simiyu main river with 63.2%, while the Duma tributary accounted for 36.8%. Cultivated land was shown to be the
main land use source of riverine sediment, accounting for 80.0% and 86.4% in Simiyu and Duma sub-tributaries, respectively,
followed by channel banks with 9.0% in both sub-tributaries. Direct unmixing of the Simiyu main river sediment to the land
use sources yielded 64.7% contribution of cultivated land and 26.5% of channel banks.
Conclusion The demonstrated application of sediment source tracing provides an important pathway for quantifying the
dominant sources of sediment in the rivers flowing towards Lake Victoria. Eroded soil from agricultural areas is the biggest
contributor to transported sediment in the Simiyu River. This information is vital for the design of catchment wide man agement plans that should focus on reducing soil erosion and sediment delivery from farming areas to the river networks,
ultimately supporting both food security and water quality in the Lake Victoria Basin
Human-elephant interactions: exploring conflicts and drivers in enduimet wildlife management area, Tanzania
Get PDFA Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Master’s in Life Sciences of the Nelson Mandela African Institution of Science and TechnologyA globally rapid land use/land cover change in human-transformed landscapes alters the interface of
human-wildlife interactions due to shifting socio-ecological and environmental pressures.
Understanding these shifts is crucial for mitigating repeated negative interactions that escalate
conflict states between people and wildlife. This study aimed to understand land use/land cover
change changes between1989–2019, with more recent spatio-temporal patterns of high pressure at
the human-elephant interface, and potentially underlying environmental and human driven factors
that affect elephant movement patterns. The study analyzed a dataset of 923 human-elephant conflict
occurrences, mainly crop foraging incidents in the Enduimet between the years 2016 and 2020 and
combined these data with land use/land cover change for year 2019 to understand potential drivers
of conflict. Furthermore, GPS datasets of elephants collared between 2019 to 2020 used to
understand elephant movement patterns in changing land use types. Landsat image study revealed
that 41% of the area had been converted into farmlands and settlements within the last three decades,
which creates elephant-intolerant habitats and the potential to increase pressure at the human elephant interface. The collared elephants using Enduimet moved through all land use types and did
not avoid settlements, although they moved through these at higher speeds, reflecting perception of
risk. Elephants travelled slightly more slowly in farmland, likely reflecting the availability of
foraging opportunities. Conclusively, communities in land use/land cover change urgently need
support to increase the effective distance between their farming activities and the protected areas
Risk of Exposure to Aflatoxin M1 through Consumption of Cow’s Milk among Children in Magadu Morogoro
Get PDFThis research article was published in the Research Square in 2023Aatoxins M1 contamination of milk affect the general population, and with particular attention to children
who frequently consume milk as part of complementary food. This study determined AFM1 contamination
of cow’s milk and estimated the risk of exposure to these toxins through consumption of cow’s milk among
children (6 to 36 month) in Magadu ward of Morogoro region in Tanzania.
A total of 165 mother-baby pairs were recruited and interviewed on child feeding practices with focus to
feeding of cow’s milk in the past 24-hours. Alongside interview, 100 raw cows’ milk samples were collected
from subsampled respondent households and were analyzed for AFM1 using Enzyme Linked
Immunosorbent Assay (ELISA).
The results showed that, about 35% of the surveyed children consumed cows’ milk in form of plain milk,
incorporated in porridge and/or tea. Amount consumed varied from 62.5-500 ml with median of 125 (125,
250) ml at a frequency of 1 to 2 times a day. All raw cows’ milk (100%) samples (n=100) were found
contaminated with AFM1 at concentration ranging from 0.052 to 9.310 μg/L, median 2.076 μg/L (1.27,
2.48). All samples were contaminated by AFM1 at levels above the limits of 0.05 μg/L of raw milk set by
Tanzania Bureau of Standard and the European Union, while 97% exceeded 0.5 μg/L set by the US Food
and Drug Authority. Exposure to AFM1 due to consumption of cow’s milk ranged from 0.0024- 0.077 μg/kg
bw per day with a median of 0.019 (0.0016, 0.026) μg/kg bw per day, while the Margin of Exposure (MOE)
ranged from 5.19- 166.76, median 20.68 (15.33, 25.40) implying high risk of public health concern. This
study recommends that, advocacy for consumption of cows’ milk to combat undernutrition in children
should consider a holistic approach that takes into account the safety aspect of the milk
Extreme Rainfall Events Classification Using Machine Learning for Kikuletwa River Floods
Get PDFA research article was submitted by artificial intelligence and machine learningAdvancements in Machine Learning techniques, availability of more data-sets, and 1 increased computing power have enabled a significant growth in a number research areas. Predicting, 2 detecting and classifying complex events in earth systems which by nature are difficult to model 3 is one of such areas. In this work, we investigate the application of different machine learning 4 techniques for detecting and classifying extreme rainfall events in a sub-catchment within Pangani 5 River Basin, found in Northern Tanzania. Identification and classification of extreme rainfall event 6is a preliminary crucial task towards success in predicting rainfall-induced river floods. To identify 7 a rain condition in the selected sub-catchment, we use data from five weather stations which have 8 been labeled for the whole sub-catchment. In order to assess which Machine Learning technique 9 suits better for rainfall classification, we apply five different algorithms in a historical dataset for the 10 period of 1979 to 2014. We evaluate the performance of the models in terms of precision and recall, 11 reporting Random Forest and XGBoost as the ones with best overall performance. However, since the 12 class distribution is imbalanced, the generic Multi-layer Perceptron performs best when identifying 13 the heavy rainfall events, which are eventually the main cause of rainfall-induced river floods in the 14 Pangani River Basin
Hydrologic responses to climate and land use/cover changes in world heritage site of Ngorongoro conservation area and surrounding catchments, northern Tanzania
Get PDFA Dissertation submitted in Partial Fulfilment of the Requirements for the Degree of
Doctor of Philosophy in Hydrology and Water Resources Engineering of the Nelson
Mandela African Institution of Science and TechnologyIn Tanzania, various studies have analyzed the impact of climate and land use/cover changes
on water resources. However, information on the interactions between climate and land
use/cover change, temporal and spatial variability of hydrological components and water
quality at the local scale is insufficient. The objective of this study was to evaluate the
hydrological response to climate and land use/cover changes in Ngorongoro Conservation Area
(NCA) and surroundings. The study performed climate change analysis using outputs from a
multi-model ensemble of Regional Climate Models (RCMs) and statistically downscaled
Global Climate Models (GCMs). The CA–Markov model applied to project Land use/cover
for the future 2025 and 2035. This study further used the Soil Water Assessment Tool (SWAT)
modelling approaches to analyse the hydrological responses and HYDRUS 1D to determine
the change in Groundwater quality due to climate and land use/cover changes. The analysis of
climate change between historical period (1982-2011) and future period (2021-2050) indicated
an increase in the mean annual rainfall and temperature, seasonal rainfall except June to
September (JJAS) season which showed a decreasing trend. Spatially, rainfall and temperatures
would increase over the entire area. The projected Land use/cover change for the period 2025
to 2035 compared to the baseline 2016, showed a reduction in bushland, forest, water, and
woodland, but an intensification in cultivated land, grassland, bare land, and the built-up area.
The surface runoff, evapotranspiration, lateral flow, and water yield would significantly
increase in the future, while groundwater would decrease under combined climate and land
use/cover change. It is predicted that two anions (Cl−
and PO4
−3
) and two cations (Na+
and K+
)
would exceed the permissible limits for the drinking water set by the World Health organisation
(WHO) and Tanzania Bureau of Standards (TBS), from 2036 to 2050. Changes in groundwater
quality due to major cations and anions is significantly correlated to evapotranspiration and
temperature with Pearson correlation (r) between 0.35 and 0.85. Furthermore, correlate to the
changes in all land use/ cover types with Pearson correlation (r) between 0.56 and 0.96. The
results obtained provide further insight into future water resources management planning and
adaptation strategi
Posterior Distribution of the Unknown Parameter of Poisson Distribution Under Different Priors: An Application to Under-Fivemortality Data
Get PDFA research article was submitted to SSRN 4411393Under-five mortality rate is one of the most essential indicator of acountry’s socio-economic well-being and public health status. Poissondistribution under different priors such as conjugate/Gamma prior,uniform and Jeffrey’s prior is used to obtain posterior distribution ofthe unknown parameter with an application to under-five mortalitydata in six East Africa countries from 1960 to 2020. The estimatesare examined through a Bayesian analysis while all the calculationsare carried out the R-statistical software and MS Excel. Among allpriors used in this study, conjugate prior was found to be compatiblefor the unknown parameters of the Poisson distribution. The casesof under-five mortality are found to reduce over time. East Africacountries through East Africa Community (EAC) should build strongand resilient health systems, identify and prioritize interventions tomitigate under-five mortality among Member States