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Fenton-mediated solar-driven photocatalysis of industrial dye effluent with polyaniline impregnated with activated TiO2-Nps
Get PDFThis research article was published in the Journal of Photochemistry and Photobiology 2024Various integrated technologies have been investigated for the remediation of heavily polluted industrial dye
effluent. Also, more than 70 % of these dyes are known to be solely azo dyes used in the textile industry with
5–30 % presence in the effluent as loose dye molecules which are recalcitrant to treatment. These challenges led
to the investigation of energy-efficient processes (solar) and the fabrication of high-performance nano-photo-
catalysts for proficient photocatalysis of dye effluent while mediating the process with Fenton reagents. The
study fabricated nanopolymeric catalyst composites (P-AKT) via novel in situ coupling and impregnation of the
polyaniline (PANI) with surface-activated TiO2 NPs. This fabrication is aimed at developing a high-performance
catalyst with rapid and proficient photocatalytic activities to photons from sunlight irradiation. The photo-
catalytic process was mediated using a novel Fenton reagent to enhance the generation of radical species for dye
degradation. Various instrumental characterization methods were used to study the structural, molecular,
elemental, functional and optoelectronic properties of the fabricated nanocomposite photocatalysts. The result
reveals functional groups aiding dye-catalyst bonding and morphological interaction reveal a surface-activated
tetragonal crystalline mixture of anatase and rutile from TiO2
Nps embedded in the macromolecular chain of
PANI. It also reveals the optimal conditions of 20 mg dosage, 10 mg/L initial concentration with substantial
effectiveness at pH of 5 and 7. However, the most efficient photocatalyst recorded was P-AKT-2 % and P-AKT-3 %
having 95 % and 94 % efficiencies at 90 min of solar irradiation. The photocatalyst equally demonstrated its
capacity for effluent treatability up to 4 cycles of us
Common beans imagery dataset for early detection of bean rust and bean anthracnose diseases
Get PDFThis research article was published by ElsevierCommon bean plays a crucial role in the agricultural sector in Tanzania. To most smallholder farmers, the crop serves as a principal source of protein and an essential source of income. Despite its significance, common bean production is often affected by diseases, particularly bean rust and bean anthracnose, resulting in low yields and diminished economic returns. To address this challenge, a comprehensive dataset of common bean leaf images has been collected by using smartphone cameras to capture the visual characteristics of healthy and diseased leaves. The dataset contains more than 59,072 labeled images, offering a valuable resource for developing machine learning models and user-friendly tools capable of early detection and diagnosis of bean rust and bean anthracnose diseases. The aim of generating this dataset is to facilitate the development of machine learning tools that will empower agricultural extension officers, smallholder farmers, and other stakeholders in agriculture to promptly identify and diagnose affected crops, enabling timely and effective interventions before causing significant economic loss. By equipping farmers with the knowledge and tools to combat these diseases, we can safeguard bean production, enhance food security, and strengthen the economic well-being of smallholder farmers in Tanzania and other parts of Africa
Kinetics and optimization modeling of Fenton-mediated photocatalysis of dye effluent with novel PANI/AK-TiO2 nanocomposites
Get PDFThis Research article was published by the International Journal of Environmental Science and Technology,2024The quest for the application of a high solar-photon sensitive photocatalysts for rapid photodegradation of recalcitrant dye molecules with deleterious environmental impacts remains a major setback for the adoption of photocatalysis in the treatment of industrial effluent. This brought about the development and use of a novel solar-driven photocatalyst mediated with Fenton-reagents for the treatability of dye effluent. However, the degradation efficiency of the process hinges on the reaction kinetics, synergistic or antagonistic interactive effects of three independent process variables such as the pH of the system (5–7), photocatalyst dosage (20–50 mg/L) and irradiation time (30–90 min) on the modeling and optimization photocatalysis of methylene blue dye in effluent. The result of the statistical study suggested the quadratic model which accurately predicts the response variables, having strong correlation values of 0.9984 and 0.9994 and variances < 0.2. The optimized variables for the photocatalytic process investigated by the analysis of variance were shown to be statistically significant (p-values < 0.0001), with the main interaction effects on the percentage degradation of dye being the pH, catalyst dosage, and irradiation period. Experimental optimum conditions attained were at pH = 5, dosage = 20 mg/L and irradiation time of 90 min for 97.019% degradation of methylene blue dye
Novel use of portable gamma sensors to rapidly assess soil status and recovery in degraded East African agro-pastoral land
Get PDFThis Research Article was published by the University of Plymouth Research Outputs , 2024Soil resources in East African agro-pastoral lands are being rapidly depleted by erosion, threatening food, water and livelihood security. Here we explore the utility of innovation in portable gamma sensors to rapidly assess soil health via proxy measurement of soil organic matter (SOM) providing visual information that enables local communities to take action to mitigate land degradation before it reaches a critical tipping point.
This study is grounded in the outcomes of an integrated, interdisciplinary approach to support co-design of land management policy tailored to the needs of specific communities and places. The work has shown that limitations to delivering socially acceptable and environmentally desirable solutions can be addressed by (1) closing fundamental gaps between the evidence bases of different disciplines and indigenous knowledge and (2) addressing, through participatory action, the implementation gap between science-based recommendations, policy makers and practitioners. Key adaptations implemented in the study region include new bylaws to enforce altered grazing regimes, grassland recovery and tree planting.
Against this context, we report a first trial of a portable gamma spectrometer to rapidly assess spatial variability in soil health using total and radionuclide-specific gamma emissions from naturally occurring radioisotopes as a proxy for soil organic matter. A Medusa MS-700 portable gamma spectrometer was deployed on foot across a landscape of known variability in soil health status encompassing a spectrum of impact from severely gullied soil/subsoil, heavily grazed surface soil, recovered grazed soil (ca 3 years exclusion of livestock) and conservation agriculture plots. In-situ field results showed a clear gradient in raw total gamma count rate with sample areas in each zone at 1200 ± 100, 980 ± 70, 814 ± 60 and 720 ± 60 counts per second across the above four areas respectively. Correlations between radioisotope-specific gamma spectrometer data and organic matter (range 15 ± 2 to 30 ± 3 g kg-1 from degraded land to conservation agriculture) were evaluated to explore the dominant control on sensor response. Further comparisons are made to major and minor element geochemistry. Feedback from local Maasai community members who participated in the research further underpins the value of the sensor as a qualitative assessment tool e.g. using visual colour coding in the live data feed in the field. Quantitative comparison of sensor and laboratory data will permit development of protocols for airborne (drone) gamma spectrometry that offers community scale evaluation of grazing pressure on soil health to inform livestock future exclusion policy in common land prone to soil erosion
Mathematical model to assess the impacts of aflatoxin contamination in crops, livestock and humans
Get PDFA research article was published by Scientific African Volume 23, March 2024Aflatoxin contamination poses a significant challenge in food safety and security as it affects
both the health of consumers and supply chains. Due to the health impacts associated with
aflatoxin contamination, countries have set standards and restrictions for importing food
crops and animal feed, resulting in greater economic losses to farmers, transporters, and crop
processors. This study aimed to develop a mathematical model that tracks the contamination
status of crops, livestock and humans in supporting efforts to control aflatoxin. The analysis
of the mathematical model shows that both aflatoxin contamination-free equilibrium (ACFE)
and aflatoxin contamination-persistence equilibrium (ACPE) exist. To study the dynamics of
contamination, we derived the basic aflatoxin contamination number, 0 which is analogous
to the basic reproduction number in epidemiological models. When 0 < 1, the ACFE is
globally asymptotically stable, whereas when 0 > 1 the ACPE is globally asymptotically stable.
Partial Rank Correlation Coefficients (PRCCs) for global sensitivity analysis were calculated
using Latin Hypercube Sampling (LHS) to see how sensitive and significant the parameter is on
each variable. Results from numerical simulations showed that decreasing crop contamination
and shading rates and increasing the death rate of aflatoxin fungi in soil by 50% can reduce
the basic contamination number by above 92%. Thus, it is important to introduce control
measures that target crop contamination, shading and death rates of aflatoxin fungi in soil to
reduce contamination in the population. Compared to other studies in aflatoxin contamination,
the current study provides a thoroughly global sensitivity analysis of parameters involved in
contamination and indicated the most important ones for control strategie
Determinants of Dogs’ Helminth Treatment-Seeking Behavior among Dog Owners in Rural Northern Tanzania: Towards Control of Taenia multiceps, an Emerging Threat to Small Ruminants’ Productivity
Get PDFA research article was published by Preprints, 2024Taeniid infections pose a significant threat to both animal and public health, as certain
tapeworms within this group can also infect humans, potentially leading to severe health
conditions. Therefore, this calls for preventive and control measures, such as regular deworming of
dogs. While the efficacy of deworming has been established in developed countries, there is a
paucity of reported deworming practices in rural areas of developing countries, such as Tanzania.
This study aims to understand determinants for helminth treatment-seeking behavior towards
control of cestodial taeniid infections in rural settings in northern Tanzania. A cross-sectional study
was undertaken in agropastoral and pastoral areas of northern Tanzania. Comprehensive data from
household surveys in selected sub-villages were collected to ascertain dog ownership, dog
deworming practices, and the availability of dewormers. Analytical methods were employed to
discern how various determinants influenced deworming practices among dog owners in these
communities. Awareness of dewormers specifically formulated for dogs emerged as a pivotal factor
affecting dog deworming practices. Dog owners who were informed about appropriate dog
dewormers were nearly two times more likely to engage in deworming compared to those with
limited knowledge (OR = 1.78, 95% CI 1.77 - 4.18, p < 0.001). A majority 32(51.6%, 95% CI: 38.7-64.3)
acknowledged accessing dewormers for livestock within their wards. Praziquantel a potent
dewormer on cestodes was least known to most dog owners 2(13%, 95% CI: 2.3-41.6). Praziquantel,
a potent and common dewormer effective against cestodial taeniid infections, remains relatively
unknown in rural areas of northern Tanzania. Therefore, more awareness on appropriate
deworming agents against taeniids in dogs should be raised in rural dog-keeping communitie
Computer Science Education in Selected Countries from Sub-Saharan Africa
Get PDFThis research article was published in ACM Inroads, Volume 15, Issue 1, 2024Computer Science education in sub-Saharan Africa has
evolved over the past decades. The number of institutions
offering distinct undergraduate programs has grown, thus
increasing the number of students enrolling in the computer
science discipline. Several computer science degree programs
have emerged with one of the objectives being to satisfy the
growing demand for local talent and skills. In this paper, we
provide a snapshot of the evolution of undergraduate computer
science education in selected countries in Sub-Saharan Africa
over the past 20+ years and an overview of the developments
in computer science education and observed trends. The
setup of educational institutions in Africa and the operational
context requires unique modalities for the design and delivery
of computer science education that meets the demands of the
industry, amongst others. This paper provides insights into
the best practices in the computer science curricula in the
selected countries, as well as an overview of the pedagogical
and delivery approaches to computer science education. The
paper highlights case studies from institutions in the selected
countries, namely Uganda, South Africa, Ghana, Tanzania,
and Kenya with a consolidated summary of the current and
emerging challenges and opportunities in all these countries.
The paper concludes by providing persectives on the future
landscape of computer science in Sub-Saharan Africa
Development of a web-based system to enhance monitoring and evaluation of higher education centers of excellence in Africa
Get PDFThe World Bank launched the Eastern and Southern Africa Higher Education Centers of
Excellence Project (ACE II) in 2016 to establish regional education and research centers in
Africa. The initiative aims to train a generation of Africans to solve African challenges in
industry, health, agriculture, applied statistics, and education. The ACE II seeks to improve
post-graduate education and promote joint research at 29 ACEs in Ethiopia, Kenya, Malawi,
Mozambique, Rwanda, Tanzania, Uganda, and Zambia. Monitoring and evaluation of ACE II
thrives on data. However, using Microsoft Excel sheets for data capturing and storage is
challenging due to its disintegration per center and time-consuming to analyze. The ACE II
lacks cumulative and informative visual data for impact demonstration, a feedback mechanism
for the beneficiaries, and a dedicated communication channel for stakeholders. The project
report presents ACE II Insight Hub, a system developed to enhance the monitoring and
evaluation of Higher Education Centers of Excellence in Africa. The study utilized qualitative
and quantitative research methods, structured interviews and survey questionnaires for data
collection. Twenty-five (25) participants were selected using purposive sampling. Participation
was voluntary and anonymous. Quantitative data were analyzed using Google Forms as graphs
and pie charts. Thematic analysis and document analysis were applied to qualitative data and
review of documents previously submitted. The system was deployed on the IUCEA intranet,
user was trained and validated the system by filling out an online questionnaire. The ACE II
Insight Hub aims to enhance stakeholder engagement, evidence-based decision-making,
project performance, impact assessment, and accountability for higher education Centers of
Excellence in Africa
Process modeling and optimization of photocatalytic treatment of dye-polluted effluent using novel polyaniline/graphene oxide-Fe3O4-Ag nanocomposites
Get PDFThis research article was published by Journal of Water Process Engineering Volume 68, December 2024This study developed a novel photocatalytic nanocomposite via in situ polymerization with 3 % blend of graphene oxide (GO), magnetite (Fe3O4) and silver nanoparticle (Ag-Nps) from AgNO3. Also, the process applied a new Fenton mediative approach for efficient abatement of toxic dye molecules in textile effluent under an energy-efficient source. The research focused on modeling and optimizing photocatalytic degradation of methylene blue dye (10 mg/L), using the most influencing variables such as pH (3–7), photocatalyst dosage (10–30 mg/100 mL) and irradiation time (20–90 min). The study demonstrated high photocatalytic efficiency for a 2.27 eV bandgap photocatalyst under 18 W visible LED light irradiation. The selected statistical model at optimized conditions allowed effective treatment of heavily polluted dye effluent from the flax textile industry, assessing efficiency via physicochemical property changes. The result suggested the selected quadratic model whose value of R2Adjusted was close to R2predicted with good correlation and reliability. All experimental variables via the analysis of variance were statistically significant (p-values <0.0001), with irradiation time and photocatalyst dosage having the greatest interactive influence on dye-effluent photocatalysis. Also, the optimization of the response gave an efficiency of 96.10 % which was validated by 5 repetitive experimental runs having an efficiency of 95.8 % ± 0.05 at selected optimized conditions of 79.24 min, 5.25 pH and dose of 21.76 mg/ 100 mL effluent. Thereafter, at the optimized conditions and 360 min of irradiation, 95.99 % dye effluent treatment were achieved. Thus, the study presents versatile industrial photocatalysts for treating heavily polluted dye effluent
Potential use of zero-valent iron in enhancing performance and resource recovery during the anaerobic digestion of domestic sewage
Get PDFA Dissertation Submitted in Fulfillment of the Requirements for the Degree of Doctor of Philosophy in Environmental Sciences and Engineering of the Nelson Mandela African Institution of Science and TechnologyIncorporating metallic iron (Fe
0
) into anaerobic digesters can improve organics (chemical
oxygen demand (COD)), phosphorus, and nitrogen from contaminated water. However, no
study has systematically assessed Fe0
-supported anaerobic digestion (AD) systems for
removing organic compounds and nutrients from domestic sewage (DS), limiting our
understanding of their potential to replace tertiary treatment units. Besides, existing studies
often focus on single contaminants at high concentrations, which may not reflect real-world
effluents with multiple pollutants. Variations in experimental conditions and the type of
wastewater effluent treated complicate comparisons across studies. Additionally, there is a lack
of comprehensive evaluations of predictive models for methane (CH₄) yields in Fe0
-supported
AD systems, hindering the identification of the most effective model and affecting future
research and applications. Moreover, there is little information on sludge characteristics from
Fe0
-aided AD systems and their potential applications. This research focused on three primary
objectives: (i) assessing the impact of Fe0
type and dosage in AD systems for the simultaneous
removal of COD and nutrients (orthophosphate (PO4
3-
), ammonium (NH4
+
), nitrate (NO3
-
)),
and (ii) characterizing the solids and biogas in Fe0
-supported AD of DS, and (iii) evaluating
the Gompertz, Logistic, and Richard models for methane yield prediction. Two distinct
experiments were conducted at various scales. In the first experiment, lab-scale reactors
containing DS were subjected to varying dosages of Fe0
(0 to 30 g/L) over 32 experimental
runs conducted for 76 days at a constant temperature of 37 ± 0.5℃. In the second experiment,
bench-scale reactors with DS were fed with Fe0 and operated over 15 experimental runs for 53
days at 24 ± 3℃ temperature. Iron scraps (SI) and steel wool (SW) were used as the Fe0
sources.
A control experiment was also conducted. It was found out that: (a) the optimal Fe0
dosage for
organic and nutrient removal was 10 g/L SI, (b) NH4
+
and NO3
-
removal showed the lowest
removal efficiency, and (c) maximum removal efficiencies for COD, PO4
3-
, and NH4
+ + NO3
-
were 88.0%, 98.0%, and 40.0% for 10 g/L SI; 88.2%, 99.9%, and 25.1% for 10 g/L SW; and
68.9%, 7.3%, and 0.7% for the control system. Fe0
significantly enriched nutrients in the
sludge, improved settling characteristics, and increased the percentage of methane content in
biogas by over 12%. All tested methane prediction models showed good accuracy (error <
10%), with the Richard model demonstrating the highest level of fit (error < 1.6%). These
findings confirm the effectiveness of Fe0
-supported AD in removing organics and nutrients
from DS, producing agriculturally suitable sludge, and enhancing biogas methane content for
potential energy recover