First Results From a Calibrated Network of Low-Cost PM2.5 Monitors in Mombasa, Kenya Show Exceedance of Healthy Guidelines.

The paucity of fine particulate matter (PM2.5) measurements limits estimates of air pollution mortality in Sub-Saharan Africa. Well calibrated low-cost sensors can provide reliable data especially where reference monitors are unavailable. We evaluate the performance of Clarity Node-S PM monitors against a Tapered element oscillating microbalance (TEOM) 1400a and develop a calibration model in Mombasa, Kenya's second largest city. As-reported Clarity Node-S data from January 2023 through April 2023 was moderately correlated with the TEOM-1400a measurements (R 2 = 0.61) and exhibited a mean absolute error (MAE) of 7.03 µg m-3. Employing three calibration models, namely, multiple linear regression (MLR), Gaussian mixture regression and random forest (RF) decreased the MAE to 4.28, 3.93, and 4.40 µg m-3 respectively. The R 2 value improved to 0.63 for the MLR model but all other models registered a decrease (R 2 = 0.44 and 0.60 respectively). Applying the correction factor to a five-sensor network in Mombasa that was operated between July 2021 and July 2022 gave insights to the air quality in the city. The average daily concentrations of PM2.5 within the city ranged from 12 to 18 µg m-3. The concentrations exceeded the WHO daily PM2.5 limits more than 50% of the time, in particular at the sites nearby frequent industrial activity. Higher averages were observed during the dry and cold seasons and during early morning and evening periods of high activity. These results represent some of the first air quality monitoring measurements in Mombasa and highlight the need for more study.

A wireless-controlled lab-scale system for automated gamma column scanning and radiotracer applications.

We present a novel wireless-controlled lab-scale system for gamma column scanning and radiotracer applications. The system utilizes a microcontroller for wireless movement of the source and detector along vertical and lateral axes. Acquired data is wirelessly transmitted to a handheld device, enabling real-time scan profiles. The system reduces scan duration, manpower involvement, and radiation exposure. Applicability to radiotracer applications, especially residence-time distribution measurements, is demonstrated. Finally, with its expanded scanning area and enhanced flexibility, the system holds potential for providing cost-effective solutions in both research and industrial settings.

Optimization of diffuse reflectance spectroscopy measurements for direct and rapid screening of pesticides: A case study of spinach.

Although diffuse reflectance spectroscopy (DRS) measurements can be collected rapidly and simultaneously, the resulting datasets are imbalanced and redundant due to the highly correlated spectral features collected on relatively few samples. Consequently, modelling these datasets using machine learning (ML) techniques is challenging and necessitates longer training times and more computational resources. Furthermore, models developed with such data are frequently prone to overfitting, resulting in promising but often non-reproducible results. We demonstrate the advantage of using an eigenvector decomposition principal component analysis (PCA) in reducing the dimensionality and data mining of DRS measurements in the short near-infrared region (750-900 nm). A total of 547 DRS measurements consisting of 151 wavelengths were acquired from spinach samples sprayed with two different pesticides and control samples. The measurements were later preprocessed with a Savitzky-Golay filter and multiplicative scatter analysis. After performing PCA on the preprocessed data, two principal components (PCs) that explained 77% of the cumulative variance and maximized the interclass variation were extracted and used as inputs to three ML models namely; artificial neural networks, support vector machine and random forest, to classify the samples. Re-sampling was used to tune the models and avoid overfitting. The performance of the models was compared using raw DRS data, pre-processed (PP) DRS data, and PCs data. The results show that pesticide classification using PCs data requires the least amount of training time (average 2.4 s) for all the models, and achieves 100% classification accuracy. In addition, it was observed that spectral data pre-processing improves accuracy and training time when compared to using raw spectral data. These findings are particularly encouraging since they demonstrate the possibility of developing rapid and accurate classification models for screening pesticide residues in fresh produce based on DRS measurements with minimal computational resources.

Quality and human health risk assessment of uranium and other heavy metals in drinking water from Kwale County, Kenya.

Heavy metal contamination in drinking water is a global health concern. Anthropogenic and geogenic activities exacerbate the concentrations of these metals in surface and groundwater. In this study, we sampled drinking water sourced from surface and groundwater resources at the environs of Mrima Hill and the Kwale heavy minerals sand deposit, Kwale County, Kenya. The concentrations of Cr, Ni, Cu, As, Cd, Pb, and U were measured using the inductively coupled plasma mass spectrometer. The water quality indices were evaluated using the weighted arithmetic index method, while the human health risks due to exposure to these heavy metals through the ingestion pathway were assessed using deterministic and probabilistic techniques. The concentrations of Cr and Cd in samples from both study areas exceeded the national and international maximum contaminant levels in drinking water. The concentration levels of Ni, Cu, As, and U in all samples from both study areas were within the recommended values in drinking water. Therefore, the quality of water from both study areas was unsuitable for human consumption due to Cd and Cr contamination. The non-carcinogenic risk assessment also showed that the hazard indices (HI) evaluated for both children and adults at the study areas were higher than unity. In addition, the estimated carcinogenic risks of both population groups were more than the recommended value of 10-4. This study shows that the residents near Mrima Hill and the Kwale heavy minerals sand deposit remain susceptible to carcinogenic and non-carcinogenic health risks emanating from exposure to these heavy metals in drinking water.

Rapid in-situ radiometric assessment of the Mrima-Kiruku high background radiation anomaly complex of Kenya.

This paper presents the radiometric survey results of the Mrima-Kiruku high background radiation (HBR) anomaly complex of south coastal Kenya. Utilizing a portable γ-ray spectrometer consisting of a 2.0 l NaI(Tl) backpack detector integrated with GPS to perform the relevant in-situ radiometric measurements, a novel geospatial gating method was devised to represent the measurements. The goal of this study was to assess radiation exposure and associated natural radioactivity levels in the complex and to compare the results obtained with those from previous preliminary related studies. Absorbed dose-rates in air were found to range <60-2368 nGy h-1. These rates were observed to correspond with the spatial variability of the underlying geology and terrain, increasing toward the summits of both Mrima and Kiruku Hills which implies that the complex is a geogenic HBR anomaly. The activity concentrations of 232Th in the study area are generally higher than those of 40K and 238U: The means of 40K, 238U and 232Th ranged 235±19-603±28 Bq kg-1, 68±6-326±24 Bq kg-1 and 386±12-1817±51 Bq kg-1 respectively. It was concluded that the high air absorbed dose-rate values that were measured (>600 nGy h-1) are due to elevated activity concentrations of 232Th. Therefore there is significant (>1 mSv/y) radiological hazard to the inhabitants of the area particularly those who reside at the foothills of both Mrima and Kiruku Hills.

Energy dispersive X-ray fluorescence and scattering assessment of soil quality via partial least squares and artificial neural networks analytical modeling approaches.

Soil quality assessment (SQA) calls for rapid, simple and affordable but accurate analysis of soil quality indicators (SQIs). Routine methods of soil analysis are tedious and expensive. Energy dispersive X-ray fluorescence and scattering (EDXRFS) spectrometry in conjunction with chemometrics is a potentially powerful method for rapid SQA. In this study, a 25 m Ci (109)Cd isotope source XRF spectrometer was used to realize EDXRFS spectrometry of soils. Glycerol (a simulate of "organic" soil solution) and kaolin (a model clay soil) doped with soil micro (Fe, Cu, Zn) and macro (NO(3)(-), SO(4)(2-), H(2)PO(4)(-)) nutrients were used to train multivariate chemometric calibration models for direct (non-invasive) analysis of SQIs based on partial least squares (PLS) and artificial neural networks (ANN). The techniques were compared for each SQI with respect to speed, robustness, correction ability for matrix effects, and resolution of spectral overlap. The method was then applied to perform direct rapid analysis of SQIs in field soils. A one-way ANOVA test showed no statistical difference at 95% confidence interval between PLS and ANN results compared to reference soil nutrients. PLS was more accurate analyzing C, N, Na, P and Zn (R(2)>0.9) and low SEP of (0.05%, 0.01%, 0.01%, and 1.98 µg g(-1)respectively), while ANN was better suited for analysis of Mg, Cu and Fe (R(2)>0.9 and SEP of 0.08%, 4.02 µg g(-1), and 0.88 µg g(-1) respectively).

Direct rapid analysis of trace bioavailable soil macronutrients by chemometrics-assisted energy dispersive X-ray fluorescence and scattering spectrometry.

Precision agriculture depends on the knowledge and management of soil quality (SQ), which calls for affordable, simple and rapid but accurate analysis of bioavailable soil nutrients. Conventional SQ analysis methods are tedious and expensive. We demonstrate the utility of a new chemometrics-assisted energy dispersive X-ray fluorescence and scattering (EDXRFS) spectroscopy method we have developed for direct rapid analysis of trace 'bioavailable' macronutrients (i.e. C, N, Na, Mg, P) in soils. The method exploits, in addition to X-ray fluorescence, the scatter peaks detected from soil pellets to develop a model for SQ analysis. Spectra were acquired from soil samples held in a Teflon holder analyzed using (109)Cd isotope source EDXRF spectrometer for 200 s. Chemometric techniques namely principal component analysis (PCA), partial least squares (PLS) and artificial neural networks (ANNs) were utilized for pattern recognition based on fluorescence and Compton scatter peaks regions, and to develop multivariate quantitative calibration models based on Compton scatter peak respectively. SQ analyses were realized with high CMD (R(2)>0.9) and low SEP (0.01% for N and Na, 0.05% for C, 0.08% for Mg and 1.98 µg g(-1) for P). Comparison of predicted macronutrients with reference standards using a one-way ANOVA test showed no statistical difference at 95% confidence level. To the best of the authors' knowledge, this is the first time that an XRF method has demonstrated utility in trace analysis of macronutrients in soil or related matrices.