In 2016, there were 317 serious water pollution incidents in the UK, with 78,000 locations where businesses discharge controlled quantities of pollutants into rivers; therefore, continuous monitoring is vital. Since 1998, the environment agency has taken over 50 million water samples for water quality monitoring. The Internet of Things has grown phenomenally in recent years, reaching all aspects of our lives, many of these connected devices use wireless sensor networks to relay data to internet-connected nodes, where data can be processed, analyzed and consumed. However, Underwater wireless communications rely mainly on alternative communication methods such as optical and acoustic, with radio frequencies being an under-exploited method. This research presents real world results conducted in the Leeds and Liverpool Canal for the novel use of the 433 MHz radio frequency combined with a bowtie antenna in underwater communications in raw water, achieving distances of 7 m at 1.2 kbps and 5 m at 25 kbps.

Geosmin contamination in water is a leading cause of odor related complaints to water companies in UK, tainting water with an earthy smell that is detectable by humans in quantities as low as 4 nanograms per liter. Current Geosmin detection methods depend on lab-based equipment, requiring samples to be collected and transported before Geosmin can be tested. This research presents a novel method for the detection of Geosmin in water using Microwave spectroscopy capable of detecting differentiating between four levels of Geosmin contamination: 5 ng/L, 10 ng/L, 0.5 mg/L and 1 mg/L as well as control samples. Frequencies within the 5.4 GHz to 5.9, 6.4 GHz to 6.5 GHz and 7.2 GHz to 7.5 GHz ranges showed significant separation between the sample classes.

Machine learning feature space reduction techniques allow for vast feature spaces to be reduced with little loss or even significant improvements in the reliability of predictions of models. Microwave spectroscopy with feature spaces of over 8000 are not uncommon when considering magnitude and phase. Applying Machine learning techniques to this type of feature space allows for a quicker reduction and helps to identify the most suitable predictive features. The control of insect vectors that transmit diseases including malaria, visceral leishmaniasis and zika rely on the use of insecticide. These diseases affect millions, malaria alone accounted for 214 million new cases resulting in 438,000 deaths in 2015. One method used in controlling the vectors is through indoor residual spraying, applying insecticide to the wall surface inside houses. Alphacypermethrin is one of the insecticides that is currently sprayed in several countries for vector control. Quality assurance and monitoring of the control activities is challenging relying on the use of laboratory-reared insects. This was improved with a chemical based Insecticide Quantification Kit, but these assays have been challenging to operationalise. An electromagnetic sensor is being developed to investigate the potential to detect alpha-cypermethrin. Preliminary experiments were carried out to differentiate tiles sprayed with Technical Grade alpha-cypermethrin, wettable powder containing 5% alpha-cypermethrin and wettable powder with over dose of alpha-cypermethrin using a horn antenna at a frequency range between 1 GHz to 6 GHz. The experimental results indicated the potential use of electromagnetic waves to determine alpha-cypermethrin in a non-destructive manner.

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