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The prevalence of drought in the Horn of Africa has continued to threaten access to safe and affordable water for millions of people. In order to improve monitoring of water pump functionality, telemetry-connected sensors have been installed on 480 electrical groundwater pumps in arid regions of Kenya and Ethiopia, designed to improve monitoring and support operation and maintenance of these water supplies. In this paper, we describe the development and validation of two classification systems designed to identify the functionality and non-functionality of these electrical pumps, one an expert-informed conditional classifier and the other leveraging machine learning. Given a known relationship between surface water availability and groundwater pump use, the classifiers combine in-situ sensor data with remote sensing indicators for rainfall and surface water. Our validation indicates a overall pump status sensitivity (true positive rate) of 82% for the expert classifier and 84% for the machine learner. When the pump is being used, both classifiers have a 100% true positive rate performance. When a pump is not being used, the specificity (true negative rate) is about 50% for the expert classifier and over 65% for the machine learner. If these detection capabilities were integrated into a repair service, the typical uptime of pumps during drought periods in this region could potentially, if budget resources and institutional incentives for pump repairs were provided, result in a drought-period uptime improvement from 60% to nearly of 85% - a 40% reduction in the relative risk of pump downtime., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Authors Thomas, Wilson, Kathuni are compensated employees of SweetSense Inc., the company providing the sensor products used to collect the data analyzed in this paper. The authors declare no other conflicts of interest., (Copyright © 2021 The Authors. Published by Elsevier B.V. All rights reserved.)

This study investigated the capability of ultraviolet radiation at 254 nm and 185 nm (UV/VUV) to degrade cyanotoxin microcystin-LR (MC-LR). Results showed 70% toxin reduction solely by 254 nm direct photolysis (ε 254  = 13,225 ± 814 M -1 cm -1 ; Φ 254 = 0.29 ± 0.03 mol/Einstein). The addition of 185 nm increased MC-LR degradation through advanced oxidation by • OH (k •OH,MC-LR  = 2.25 ± 0.39 × 10 10 M -1 s -1 ). Alkalinity and organics (DOC) reduced MC-LR degradation by scavenging • OH (k obs,MilliQ  = 0.117 cm 2 /mJ; k obs,50ppmAlk.  = 0.0497 cm 2 /mJ; k obs,6ppmDOC  = 0.019 cm 2 /mJ). Chloride absorbed 185 nm, impacting • OH formation and generating Cl • , while also scavenging • OH. However, Cl • is reactive and • OH scavenging is reversible, resulting in relatively low impact on MC-LR degradation (k obs,50ppmCl = 0.0939 cm 2 /mJ). In natural water, MC-LR could be degraded from a typical concentration (˜15 μg/L) to below detection (<0.5 μg/L) with a UV 254 fluence of 200 mJ/cm 2 using UV/VUV. The presence of cyanobacterial cells impeded MC-LR degradation; however, 90% MC-LR degradation could still be achieved. UV/VUV is a promising chemical-free technology capable of MC-LR degradation in a variety of water conditions, and a potentially suitable treatment option for small, remote communities., (Copyright © 2019 Elsevier B.V. All rights reserved.)