Nutrient runoff from agriculture practices poses a significant risk to waterway health and can have long-lasting and complex implications for the environment, ecosystems, and the human population. Consequently, a systematic quantitative literature review (SQLR) was conducted to identify different nutrient runoff mitigation strategies (NRMS) that are currently used globally to prevent or remediate environmental damage from excessive agricultural fertilisation. Empirical data on the outcomes from various NRMS from the reviewed studies were used to evaluate the strategies based on environmental benefit, implementation cost, and practicality perspectives. An overall assessment of the feasibility of NRMS was determined, and a macro-level assessment of the reported barriers preventing the widespread implementation of NRMS was provided. Identified research gaps and issues included a dearth of literature covering nutrient runoff mitigation, scepticism from agricultural landowners to voluntarily adopt policy without substantial incentives, and a general lack of cost/benefit analyses, including an understanding of the uncertainty associated with NRMS that can inform decision-makers about effective and efficient strategies for different site situations. Synthesis of SQLR data facilitated the development of a comprehensive nutrient runoff decision-making framework which addresses present limitations and provides site-specific NRMS recommendations for policymakers to implement.

A mobile monitoring station was developed to measure nitrate and physicochemical water quality parameters remotely, in real-time, and at very high frequencies (thirty minutes). Several calibration experiments were performed to validate the outputs of a real-time nutrient sensor, which can be affected by optical interferences such as turbidity, pH, temperature and salinity. Whilst most of these proved to play a minor role, a data-driven compensation model was developed to account for turbidity interferences. The reliability of real-time optical sensors has been questioned previously; however, this study has shown that following compensation, the readings can be more accurate than traditional laboratory-based equipment. In addition, significant benefits are offered by monitoring waterways at high frequencies, due to rapid changes in analyte concentrations over short time periods. This, combined with the versatility of the mobile station, provides opportunities for several beneficial monitoring applications, such as of fertiliser runoff in agricultural areas in rural regions, aquaculture runoff, and waterways in environmentally sensitive areas such as the Great Barrier Reef.

Governments around the world have introduced a number of stringent policies to try to contain COVID-19 outbreaks, but the relative importance of such measures, in comparison to the community response to these restrictions, the amount of testing conducted, and the interconnections between them, is not well understood yet. In this study, data were collected from numerous online sources, pre-processed and analysed, and a number of Bayesian Network models were developed, in an attempt to unpack such complexity. Results show that early, high-volume testing was the most crucial factor in successfully monitoring and controlling the outbreaks; when testing was low, early government and community responses were found to be both critical in predicting how rapidly cases and deaths grew in the first weeks of the outbreak. Results also highlight that in countries with low early test numbers, the undiagnosed cases could have been up to five times higher than the officially diagnosed cases. The conducted analysis and developed models can be refined in the future with more data and variables, to understand/model potential second waves of contagions.