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This study investigated the occurrence of 11 pharmaceutical compounds in the rivers and groundwater systems of Arusha City, Tanzania. Each suspected individual residue of active pharmaceutical compounds in water matrices, was pre-concentrated using solid-phase extraction techniques and, then quantified using a liquid chromatography-mass spectrometer mass spectrometer (LC-MS/MS). The concentrations varied across the assessed rivers and groundwater systems. High concentrations of caffeine 520 ng/L were detected in the station downwards of a wastewater stabilization pond, discharging its partially treated effluent into the river, followed by stations whose rivers flowed through informal areas. Sampled points' located near the river's water sources reported fewer compounds with values below the detection limit, such as amoxicillin, paracetamols, and doxycycline. Except for sulfamethoxazole (94 ng/L) in the borehole, most of the concentrations detected in rivers were ten times higher than in boreholes. In addition, in boreholes, more compounds were identified in the monitoring than in the domestic ones, and concentration varied with depth of deep boreholes (25 m) were less abundant than shallow wells of less than 10 m. In conclusion, pharmaceutical compounds were frequently detected in both rivers and groundwater systems within Arusha City suggesting the need for understanding of their fates and associated risks.

The changes in landscape structure and functioning due to unprecedented human interference are hastening across the globe and it is thus a compelling necessity to preserve and restore our ecosystems. This study aimed to characterize levels of landscape fragmentation, habitat structure, driving forces, and perceptions of the residents on the most preferred reconfiguration approaches. The land use/land cover [LULC] change was first determined by interpreting the 1973, 1986, 1995, 2002, 2014, and 2022 Landsat images using the QGIS 3.26 while the selected landscape fragmentation metrics were analyzed using FRAGSTATS 4.2. Forests, shrubs, and grasslands showed a declining trend, except for agriculture, water, and built-up areas, which depicted high increases for the study periods [1973 to 2022]. The landscape of the study area is characterized as progressively fragmenting as signified by high escalated values of patch number [374 %], edge density [7828 %] between 1986 and 2002, contagion [10.3%], and a declined value of Shannon Diversity Index [SHDI] [-17.42%], Shannon evenness index [SHEI] [-25.8 %] and connectedness [-43.3%]. Considering these results, high losses of forests and grasslands coupled with expansive farmlands and built-up areas have led to unprecedented landscape fragmentation From field surveys and oral interviews, this has not only left streams vulnerable to massive sediment loads but has also triggered annual floods which occur during wet months even though change in onset of rainfall seasons was also reported. The findings call for restoration and integrated and sustainable restoration efforts, especially for the forests, grasslands, and riparian corridors along with sustainable urban planning and community-based sensitization on watershed management.

Abstract Glycans are the most abundant fundamental biomolecules, but profiling glycans is challenging due to their structural complexity. To address this, a novel glycan detection platform is developed by integrating surface‐enhanced Raman spectroscopy (SERS), boronic acid receptors, and machine learning tools. Boronic acid receptors bind with glycans, and the reaction influences molecular vibrations, leading to unique Raman spectral patterns. Unlike prior studies that focus on designing a boronic acid with high binding selectivity toward a target glycan, this sensor is designed to analyze overall changes in spectral patterns using machine learning algorithms. For proof‐of‐concept, 4‐mercaptophenylboronic acid (4MBA) and 1‐thianthrenylboronic acid (1TBA) are used for glycan detection. The sensing platform successfully recognizes the stereoisomers and the structural isomers with different glycosidic linkages. The collective spectra that combine the spectra from both boronic acid receptors improve the performance of the support vector machine model due to the enrichment of the structural information of glycans. In addition, this new sensor could quantify the mole fraction of sialic acid in lactose background using the machine learning regression technique. This low‐cost, rapid, and highly accessible sensor will provide the scientific community with another option for frequent comparative glycan screening in standard biological laboratories.

Recently, quantum dots (QDs) have garnered significant attention due to their superior photoelectronic properties, and widespread applications in high-resolution displays and solar cells. Generally, the optoelectronic properties of QDs are majorly dictated by their bandgap energy (related to their size). Thus, there is a large impetus on accurate modeling and control of size of QD nanocrystals. Unfortunately, unlike traditional protein or sugar crystallization, there are very few models that describe QD crystallization. That said, the existing few QD models are based on computationally demanding multiscale modeling approaches, which (a) makes extrapolation to different QD systems highly resource-intensive; and (b) makes them unsuitable for direct implementation in a controller framework. To address these challenges, we present a unified QD crystallization modeling and control framework. Specifically, mass-energy balance equations, population balance equations (PBEs), and crystallization kinetics are decomposed into a matrix of first-order ordinary differential equations (ODEs) that can be easily computed using native python solvers. Further, a model predictive controller (MPC) module is designed for set-point tracking of crystal size distribution (CSD). Also, unlike the multiscale QD models from the literature, the developed PBE-based QD model has high computational efficiency, and can be directly incorporated within the MPC without the requirement of a surrogate model. Then, to demonstrate the developed unified framework’s capabilities, batch crystallization of CdTe QDs is taken as a case study. It was observed that the simulation results are in good agreement with the experimental results, and the MPC shows effective set-point tracking performance for regulating size of QD crystals.

The largest impact of land-use change on catchment hydrology can be linked to deforestation. This change, driven by exponential population growth, intensified food and industrial production, has resulted in alterations in river flow regimes such as high peaks, reduced base flows, and silt deposition. To reverse this trend more extensive management practices are becoming increasingly important, but can also lead to severe losses in agricultural production. Land-use optimization tools can help catchment managers to explore numerous land-use configurations for the evaluation of trade-offs amongst various uses. In this study, the Soil and water assessment tool (SWAT) model was coupled with a genetic algorithm to identify land-use/management configurations with minimal trade-offs between environmental objectives (reduced sediment load, increased stream low flow) and the crop yields of maize and soybean in Nyangores catchment (Kenya). During the land-use optimization, areas under conventional agriculture could either remain as they are or change to agroforestry or conservation agriculture (CA), where the latter was represented by introducing contour farming and vegetative filter strips. From the sets of the resulting Pareto-optimal solutions we selected mid-range solutions, representing a fair compromise among all objectives, for further analysis. We found that a combined measure implementation strategy (agroforestry on certain sites and conservation agriculture on other sites within the catchment) proved to be superior over single measure implementation strategies. On the catchment scale, a 3.6% change to forests combined with a 35% change to CA resulted in highly reduced sediment loads (−78%), increased low flow (+14%) and only slightly decreased crop yields (

Surface-enhanced Raman spectroscopy (SERS) is a powerful tool for molecule identification. However, profiling complex samples remains a challenge because SERS peaks are likely to overlap, confounding features when multiple analytes are present in a single sample. In addition, SERS often suffers from high variability in signal enhancement due to nonuniform SERS substrate. The machine learning classification techniques widely used for facial recognition are excellent tools to overcome the complexity of SERS data interpretation. Herein, we reported a sensor for classifying coffee beverages by integrating SERS, feature extractions, and machine learning classifiers. A versatile and low-cost SERS substrate, called nanopaper, was used to enhance Raman signals of dilute compounds in coffee beverages. Two classic multivariate analysis techniques, Principal Component Analysis (PCA) and Discriminant Analysis of Principal Components (DAPC), were used to extract the significant spectral features, and the performance of various machine learning classifiers was evaluated. The combination of DAPC with Support Vector Machine (SVM) or K-Nearest Neighbor (KNN) shows the best performance for classifying coffee beverages. This user-friendly and versatile sensor has the potential to be a practical quality-control tool for the food industry.

Tailored climate change information is essential to understand future climate risks and identify relevant adaptation strategies. However, distilling and effectively communicating decision-relevant information from climate science remains challenging. In this paper, we develop and apply an iterative stakeholder engagement approach and a Site Specific Synthesis of Projected Range (SPR), to co-produce future climate information for Africa’s largest tea producing nations - Kenya and Malawi - for the mid-and late-21st century. SPR provides a novel analysis approach, which combines long-term station observations with projections from 29 global climate models and the first convection-permitting high-resolution climate projection for Africa (CP4A). This addresses the mismatch between spatial scales of projections, large-scale modelling uncertainties and stakeholder need for site-specific information. Iterative stakeholder engagement and communication helped to build trust, allowed use of new observation data and improved visualisations of climate information for stakeholders. SPR demonstrates site-specificity in changes in all metrics, showing risks of large changes in tea crop sensitive metrics. All nine locations analysed show substantial (up to four times) increases in heatwave days and large decreases in cold nights by 2050s compared to the current climate. While tea producers are already witnessing changing climatic conditions, potential future changes will greatly affect the resilience of tea production, thereby affecting the sustainability and quality of tea production in the region. Site specific climate information iteratively co-produced with stakeholders helps them to identify location-specific adaptation strategies and investment priorities, potentially safeguarding supply-chains and millions of livelihoods.

Opportunities for sustainable agriculture development in Kenya which heavily depends on healthy soil and soil microbial diversity can be found in the promotion of organic farming. The increasing populations, higher demands for animal and plant products, climate change impacts, and weather conditions necessitate novel ideas and technologies to try reverse the adverse effects of excessive use of inorganic fertilizers and to maximize the potential of the land. Sapropel, a promising biological deposit from freshwater lakes, has found its limelight in its use in agricultural crop production as a soil conditioner and biofertilizer. This could be an ultimate practice for sustainable food and energy production in Kenya. This review aims at presenting information in the literature about the potential use of the organic fertilizer based on sapropels in intensified crop production and their main effects on plant growth. It discusses soil fertility, the composition of sapropels, and their application and advantages in agricultural production. It is evident from the review that sapropel can be considered as a resource that is valuable with wide application possibilities in agriculture. Sapropel can present an important contribution to the solution of the conservation of the fertility of the soil for integrated nutrient management systems to maintain agricultural productivity and help in environmental conservation.

Explosives, especially those used for military weapons, have a short lifespan and their performance noticeably deteriorates over time. These old explosives need to be disposed of safely. Fluidized bed incinerators (FBIs) are safe for disposal of explosive waste (such as TNT) and produce fewer gas emissions compared to conventional methods, such as the rotary kiln. However, previous studies on this FBI process have only focused on minimizing the amount of NOx emissions without considering the operating and unitality costs (i.e., total cost) associated with the process. It is important to note that, in general, a number of different operating conditions are available to achieve a target NOx emission concentration and, thus, it requires a significant computational requirement to compare the total costs among those candidate operating conditions using a computational fluid dynamics simulation. To this end, a novel framework is proposed to quickly determine the most economically viable FBI process operating condition for a target NOx concentration. First, a surrogate model was developed to replace the high-fidelity model of an FBI process, and utilized to determine a set of possible operating conditions that may lead to a target NOx emission concentration. Second, the candidate operating conditions were fed to the Aspen Plus™ process simulation program to determine the most economically competitive option with respect to its total cost. The developed framework can provide operational guidelines for a clean and economical incineration process of explosive waste.

Urbanization has caused limitations on water resources, while climate change has reduced amounts of surface water in some parts of the world. Kikuyu, a suburban area in Kiambu county, Kenya, is facing this challenge. The major challenge in the study is scarcity of potable water, resulting in inadequate water supply to Kikuyu residents. Currently, only 63.6% of the population is being supplied with water by Kikuyu Water Company, the company mandated to supply water to the area. Water demand was 2972 m3/day in 2015 and was projected to be 3834 m3/day by 2025. This has put pressure on the already exploited clean water resources, making it necessary to seek additional sources of domestic water. Storage capacity and water quality of surface water bodies, especially small reservoirs whose water can be used to ease the demand, need to be assessed for supplemental water supply. This study aimed at assessing the suitability of the abandoned quarry reservoir as a source of potable urban water by determining its storage capacity characteristics and water quality status. Volume characteristics were determined using bathymetry survey in January 2019. Water samples were collected in January and August 2019 and analyzed for chemical, physical, and bacteriological quality, as per the American Public Health Association (APHA) standard methods for water and wastewater. Parameters were evaluated based on World Health Organization (WHO) and Kenya Bureau of Standards (KEBS) guidelines for drinking water, and rated based on the drinking water quality index (WQI). The reservoir’s maximum storage capacity was found to be 128,385 m3, the surface area was 17,699 m2, and the maximum depth was 15.11 m. Nineteen of the twenty-five investigated parameters were within the acceptable standards. However, the concentrations of manganese (Mn), cadmium (Cd), iron (Fe), turbidity, total coliforms, and Escherichia coli (E. coli) were above the acceptable limits. Manganese and iron levels increased with depth. The overall WQI of the reservoir was 82.51 and 85.85 in January and August, respectively. Therefore, based on WQI rating, the water scored a good quality rating and could be used for domestic supply upon treatment. The original achievement of this study is establishment of the volume of the water in the quarry as an additional source of water to the nearby community, along with water quality status.

The failing ecosystem services in Thika-Chania catchment is manifested in the deterioration of water quality, sedimentation of reservoirs, and subsequent increase in water treatment costs due to high turbidity. The services can be restored by implementing relevant soil and water conservation practices to enhance flow regulation and control sediment yield. The impacts of contour farming and filter strips on water and sediment yield were evaluated using Soil Water and Assessment Tool (SWAT), Texas A&M University, USA. Sediment calibration and validation was achieved using data obtained from a bathymetric survey. Model parameters were adjusted to simulate the conservation impacts of contour farming and filter strips. Results indicated the average annual sediment yield as 22 t/ha at the outlet of the catchment and average annual surface runoff of 202 mm. The simulation results showed that filter strips of 5 m width would reduce the average annual sediment yield from the catchment by 54%. The efficacy of filter strips in reducing sediment yield was observed to increase with increasing filter width. Three-meter filter strips and contour farming reduced the average annual sediment yield at catchment outlet by 46% and 36%, respectively. It was concluded that the implementation of contour farming and filters strips reduced sediments by 63% from the base value. Water yield at the sub-basin level was only influenced by contour farming. The total water yield at the catchment outlet experienced no significant change.