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Illicit discharges in urban stormwater drains are a major environmental concern that deteriorate downstream waterway health. Conventional detection methods such as stormwater drain visual inspection and dye testing have fundamental drawbacks and limitations which can prevent easy location and elimination of illegal discharges in a catchment. We deployed 22 novel low-cost level, temperature and conductivity sensors across an urban catchment in Melbourne for a year to monitor the distributed drainage network, thereby detecting likely illicit discharges ranging from a transitory flow with less than 10 minutes to persistent flows lasting longer than 20 hours. We discuss rapid deployment methods, real-time data collection and online processing. The ensemble analysis of all dry weather flow data across all sites indicates that: (i) large uncertainties are associated with discharge frequency, duration, and variation in water quality within industrial and residential land uses; (ii) most dry weather discharges are intermittent and transient flows which are difficult to detect and not simply due to cross-connections with the sewerage network; (iii) detectable diurnal discharge patterns can support mitigation efforts, including policies and regulatory measures (e.g., enforcement or education) to protect receiving waterways; and, (iv) that it is possible to cost effectively isolate sources of dry weather pollution using a distributed sensor network.

Waterways in the Southern Hemisphere, including on the Australian continent, are facing increasing levels of mercury contamination due to industrialization, agricultural intensification, energy production, urbanization, and mining. Mercury contamination undermines the use of waterways as a source of potable water and also has a deleterious effect on aquatic organisms. When developing management strategies to reduce mercury levels in waterways, it is crucial to set appropriate targets for the mitigation of these contaminated waterways. These mitigation targets could be (1) trigger values or default guideline values provided by water and sediment quality guidelines or (2) background (pre-industrialization) levels of mercury in waterways or sediments. The aims of this study were to (1) quantify the differences between existing environmental guideline values for mercury in freshwater lakes and background mercury concentrations and (2) determine the key factors affecting the spatial differences in background mercury concentrations in freshwater lake systems in Australia. Mercury concentrations were measured in background sediments from 21 lakes in Australia. These data indicate that background mercury concentrations in lake sediments can vary significantly across the continent and are up to nine times lower than current sediment quality guidelines in Australia and New Zealand. This indicates that if waterway managers are aiming to restore systems to ‘pre-industrialization’ mercury levels, it is highly important to quantify the site-specific background mercury concentration. Organic matter and precipitation were the main factors correlating with background mercury concentrations in lake sediments. We also found that the geology of the lake catchment correlates to the background mercury concentration of lake sediments. The highest mercury background concentrations were found in lakes in igneous mafic intrusive regions and the lowest in areas underlain by regolith. Taking into account these findings, we provide a preliminary map of predicted background mercury sediment concentrations across Australia that could be used by waterway managers for determining management targets.

Understanding concentration–discharge (C–Q) relationships can inform catchment solute and particulate export processes. Previous studies have shown that the extent to which baseflow contributes to streamflow can affect C–Q relationships in some catchments. However, the current understanding on the effects of baseflow contribution in shaping the C–Q patterns is largely derived from temperate catchments. As such, we still lack quantitative understanding of these effects across a wide range of climates (e.g. arid, tropical and subtropical). The study aims to assess how baseflow contributions, as defined by the median and the range of daily baseflow indices within individual catchments (BFI_m and BFI_range, respectively), influence C–Q slopes across 157 catchments in Australia spanning five climate zones. This study focuses on six water quality variables: electrical conductivity (EC), total phosphorus (TP), soluble reactive phosphorus (SRP), total suspended solids (TSS), the sum of nitrate and nitrite (NOx) and total nitrogen (TN). The impact of baseflow contributions is explored with a novel Bayesian hierarchical model. For sediments and nutrient species (TSS, NOx, TN and TP), we generally see largely positive C–Q slopes, which suggest a dominance of mobilization export patterns. Further, for TSS, NOx and TP we see stronger mobilization (steeper positive C–Q slopes) in catchments with higher values in both the BFI_m and BFI_range, as these two metrics are positively correlated for most catchments. The enhanced mobilization in catchments with higher BFI_m or BFI_range is likely due to the more variable flow pathways that occur in catchments with higher baseflow contributions. These variable flow pathways can lead to higher concentration gradients between low flows and high flows, where the former is generally dominated by groundwater/slow subsurface flow while the latter by surface water sources, respectively. This result highlights the crucial role of flow pathways in determining catchment exports of solutes and particulates. Our study also demonstrates the need for further studies on how the temporal variations of flow regimes and baseflow contributions influence flow pathways and the potential impacts of these flow pathways on catchment C–Q relationships.

Stream water quality is highly variable both across space and time. Water quality monitoring programmes have collected a large amount of data that provide a good basis for investigating the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provide an opportunity to further our understanding of water quality dynamics in subtropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of (1) identifying the influential environmental drivers of temporal variation in flow event concentrations and (2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g. runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations and between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.

Our current capacity to model stream water quality is limited – particularly at large spatial scales across multiple catchments. To address this, we developed a Bayesian hierarchical statistical model to simulate the spatiotemporal variability in stream water quality across the state of Victoria, Australia. The model was developed using monthly water quality monitoring data over 21 years and across 102 catchments (which span over 130 000 km2). The modeling focused on six key water quality constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate–nitrite (NOx) and electrical conductivity (EC). The model structure was informed by knowledge of the key factors driving water quality variation, which were identified in two preceding studies using the same dataset. Apart from FRP, which is hardly explained (19.9 %), the model explains 38.2 % (NOx) to 88.6 % (EC) of the total spatiotemporal variability in water quality. Across constituents, the model generally captures over half of the observed spatial variability; the temporal variability remains largely unexplained across all catchments, although long-term trends are well captured. The model is best used to predict proportional changes in water quality on a Box–Cox-transformed scale, but it can have substantial bias if used to predict absolute values for high concentrations. This model can assist catchment management by (1) identifying hot spots and hot moments for waterway pollution; (2) predicting the effects of catchment changes on water quality, e.g., urbanization or forestation; and (3) identifying and explaining major water quality trends and changes. Further model improvements should focus on the following: (1) alternative statistical model structures to improve fitting for truncated data (for constituents where a large amount of data fall below the detection limit); and (2) better representation of nonconservative constituents (e.g., FRP) by accounting for important biogeochemical processes.

Understanding carbon (C) cycling in riverine ecosystems is crucial for accurate estimates of regional and global C budgets. However, the slow progress in identifying spatial and temporal patterns and drivers of riverine C has been largely driven by limited data availability. This lack of information is particularly acute in Australia. To address this issue, we compile the first comprehensive database (OzRiCa) of concentrations, stable isotopes, and fluxes of particulate organic C (POC), dissolved organic (DOC) and inorganic C (DIC), carbon dioxide (CO2) and methane (CH4) for streams and rivers across Australia, along with concurrent physical and chemical data. The OzRiCa database contains 54 843 observations from 2879 unique sites derived from state agencies, scientific publications, and newly collected, previously unpublished data. The measurements span the period between 1966 to 2024. The database reveals that most observations and sites are located in the Mediterranean, temperate and subtropical regions of the country, with fewer data in semi-arid and tropical regions. We also highlight patterns in temporal data coverage, with half of the sites limited to a single measurement. The scarcity of long-term and high-resolution temporal data for CO2 and CH4, along with the limited number of direct measurements for DIC and CO2, emphasises the need for more direct, frequent and long-term monitoring efforts to capture the extreme temporal variability of Australian climatic regions. We also identify limited stable isotopic data and concurrent measurement of multiple C species, both limiting our ability to better understand C sources and sinks, as well as in-stream C processes. Future research should prioritise these critical gaps to improve our understanding of riverine C dynamics in Australia. OzRiCa provides a baseline for future research, paving the way for studies of fluvial C fluxes at regional, national, and global scales. It will also be a valuable resource to studying ecosystem health, water quality, and other biogeochemical processes. The database can be freely accessed at https://doi.org/10.4211/hs.9aa735254e7e424ca18603c047d02f50 (Ulloa Cedamanos et al., 2026).

A short overview is given about the potential of polarized beams at future colliders is given. In particular the baseline design for polarized beams at the ILC is presented and the physics case for polarized e− and e+ is discussed. In order to fulfil the precision requirements spin tracking from the source to the interaction point is needed. Updates concerning the theoretical calculations as well as their implementation in simulation codes are reported.

A novel single-particle technique to measure emittance has been developed and used to characterise seventeen different muon beams for the Muon Ionisation Cooling Experiment (MICE). The muon beams, whose mean momenta vary from 171 to 281 MeV/c, have emittances of approximately 1.2-2.3 π mm-rad horizontally and 0.6-1.0 π mm-rad vertically, a horizontal dispersion of 90-190 mm and momentum spreads of about 25 MeV/c. There is reasonable agreement between the measured parameters of the beams and the results of simulations. The beams are found to meet the requirements of MICE.

Muon beams of low emittance provide the basis for the intense, well-characterized neutrino beams necessary to elucidate the physics of flavor at a neutrino factory and to provide lepton-antilepton collisions at energies of up to several TeV at a muon collider. The international Muon Ionization Cooling Experiment (MICE) aims to demonstrate ionization cooling, the technique by which it is proposed to reduce the phase-space volume occupied by the muon beam at such facilities. In an ionization-cooling channel, the muon beam passes through a material in which it loses energy. The energy lost is then replaced using rf cavities. The combined effect of energy loss and reacceleration is to reduce the transverse emittance of the beam (transverse cooling). A major revision of the scope of the project was carried out over the summer of 2014. The revised experiment can deliver a demonstration of ionization cooling. The design of the cooling demonstration experiment will be described together with its predicted cooling performance.

With increasing costs of fossil fuels and intensified environmental awareness, low carbon vehicles, including hybrid electric vehicles (HEVs), are becoming more popular for car buyers due to their lower running costs. HEVs are sensitive to the driving conditions under which they are used however, and real-world driving can be very different to the legislative test cycles. On the road there are higher speeds, faster accelerations and more changes in speed, plus additional factors that are not taken into account in laboratory tests, all leading to poorer fuel economy. Future trends in the automotive industry are predicted to include a large focus on increased hybridisation of passenger cars in the coming years, so this is an important current research area. The aims of this project were to determine the energy consumption of a HEV in real-world driving, and investigate the differences in this compared to other standard drive cycles, and also compared to testing in laboratory conditions. A second generation Toyota Prius equipped with a GPS (Global Positioning System) data logging system collected driving data while in use by Loughborough University Security over a period of 9 months. The journey data was used for the development of a drive cycle, the Loughborough University Urban Drive Cycle 2 (LUUDC2), representing urban driving around the university campus and local town roads. It will also have a likeness to other similar driving routines. Vehicle testing was carried out on a chassis dynamometer on the real-world LUUDC2 and other existing drive cycles for comparison, including ECE-15, UDDS (Urban Dynamometer Driving Schedule) and Artemis Urban. Comparisons were made between real-world driving test results and chassis dynamometer real-world cycle test results. Comparison was also made with a pure electric vehicle (EV) that was tested in a similar way. To verify the test results and investigate the energy consumption inside the system, a Prius model in Autonomie vehicle simulation software was used. There were two main areas of results outcomes; the first of which was higher fuel consumption on the LUUDC2 compared to other cycles due to cycle effects, with the former having greater accelerations and a more transient speed profile. In a drive cycle acceleration effect study, for the cycle with 80% higher average acceleration than the other the difference in fuel consumption was about 32%, of which around half of this was discovered to be as a result of an increased average acceleration and deceleration rate. Compared to the standard ECE-15 urban drive cycle, fuel consumption was 20% higher on the LUUDC2. The second main area of outcomes is the factors that give greater energy consumption in real-world driving compared to in a laboratory and in simulations being determined and quantified. There was found to be a significant difference in fuel consumption for the HEV of over a third between on-road real-world driving and chassis dynamometer testing on the developed real-world cycle. Contributors to the difference were identified and explored further to quantify their impact. Firstly, validation of the drive cycle accuracy by statistical comparison to the original dataset using acceleration magnitude distributions highlighted that the cycle could be better matched. Chassis dynamometer testing of a new refined cycle showed that this had a significant impact, contributing approximately 16% of the difference to the real-world driving, bringing this gap down to 21%. This showed how important accurate cycle production from the data set is to give a representative and meaningful output. Road gradient was investigated as a possible contributor to the difference. The Prius was driven on repeated circuits of the campus to produce a simplified real-world driving cycle that could be directly linked with the corresponding gradients, which were obtained by surveying the land. This cycle was run on the chassis dynamometer and Autonomie was also used to simulate driving this cycle with and without its gradients. This study showed that gradient had a negligible contribution to fuel consumption of the HEV in the case of a circular route where returning to the start point. A main factor in the difference to real-world driving was found to be the use of climate control auxiliaries with associated ambient temperature. Investigation found this element is estimated to contribute over 15% to the difference in real-world fuel consumption, by running the heater in low temperatures and the air conditioning in high temperatures. This leaves a 6% remainder made up of a collection of other small real-world factors. Equivalent tests carried out in simulations to those carried out on the chassis dynamometer gave 20% lower fuel consumption. This is accounted for by degradation of the test vehicle at approximately 7%, and the other part by inaccuracy of the simulation model. Laboratory testing of the high voltage battery pack found it constituted around 2% of the vehicle degradation factor, plus an additional 5% due to imbalance of the battery cell voltages, on top of the 7% stated above. From this investigation it can be concluded that the driving cycle and environment have a substantial impact of the energy use of a HEV. Therefore they could be better designed by incorporating real-world driving into the development process, for example by basing control strategies on real-world drive cycles. Vehicles would also benefit from being developed for use in a particular application to improve their fuel consumption. Alternatively, factors for each of the contributing elements of real-world driving could be included in published fuel economy figures to give prospective users more representative values.