Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors.

The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.

Forecasting of compound ocean-fluvial floods using machine learning.

Flood modelling and forecasting can enhance our understanding of flood mechanisms and facilitate effective management of flood risk. Conventional flood hazard and risk assessments usually consider one driver at a time, whether it is ocean, fluvial or pluvial, without considering the compound nature of flood events. In this paper, we developed a novel approach for modelling and forecasting compound coastal-fluvial floods using a two-step framework. In step one, a hydrodynamic model is used to simulate floodwater propagation; while in step two, machine learning (ML) models are used to generate flood forecasts. The architecture of hydrodynamic-ML forecasting system incorporates a hydrodynamic model covering a specific domain, with individual ML models trained for each pixel. In total 7 ML models including: Support Vector Regression (SVR), Support Vector Machine (SVM), Radial Basis Function (RBF), Linear Regression (LR), Gaussian Process Regression (GPR), Decision Tree (DT), and Artificial Neural Network (ANN) were applied in this study. Forecasting compound floods is achieved using two sets of inputs: timeseries of river discharges in the upstream fluvial section and downstream ocean water levels in the coastal areas. The accuracy of the flood forecasting system is demonstrated for Cork City, Ireland; and modelling performance was evaluated using several statistical tools. Results show that the proposed models can provide reliable estimates of flood inundation and associated water depths. Overall, the RBF model exhibits the best performance. Despite the complexity of compound multi-driver floods, this study shows that the coupled hydrodynamic-ML approach can forecast coastal-fluvial flood with limited hydraulic and hydrological input data. This system overcomes the limitations of traditional hydrodynamic model-based systems where trade-offs between the always competing numerical model accuracy and computational time prohibit the model to be used for short-term flood forecasting. Once trained, the ML component of the coupled system can perform flood forecasting in near real-time, potentially integrating into a flood early warning system. Accurate flood forecasting has a wide range of positive societal impacts, including improved flood preparedness, increased confidence, better resource allocation, reduced flood damage, and potentially even flood prevention.

In-situ detection of microplastics in the aquatic environment: A systematic literature review.

Microplastics are ubiquitous in the aquatic environment and have emerged as a significant environmental issue due to their potential impacts on human health and the ecosystem. Current laboratory-based microplastic detection methods suffer from various drawbacks, including a lack of standardisation, limited spatial and temporal coverage, high costs, and time-consuming procedures. Consequently, there is a need for the development of in-situ techniques to detect and monitor microplastics to effectively identify and understand their sources, pathways, and behaviours. Herein, we adopt a systematic literature review method to assess the development and application of experimental and field technologies designed for the in-situ detection and monitoring of aquatic microplastics, without the need for sample preparation. Four scientific databases were searched in March 2023, resulting in a review of 62 relevant studies. These studies were classified into seven sensor categories and their working principles were discussed. The sensor classes include optical devices, digital holography, Raman spectroscopy, other spectroscopy, hyperspectral imaging, remote sensing, and other methods. We also looked at how data from these technologies are integrated with machine learning models to develop classifiers capable of accurately characterising the physical and chemical properties of microplastics and discriminating them from other particles. This review concluded that in-situ detection of microplastics in aquatic environments is feasible and can be achieved with high accuracy, even though the methods are still in the early stages of development. Nonetheless, further research is still needed to enhance the in-situ detection of microplastics. This includes exploring the possibility of combining various detection methods and developing robust machine-learning classifiers. Additionally, there is a recommendation for in-situ implementation of the reviewed methods to assess their effectiveness in detecting microplastics and identify their limitations.

Management of Climate Resilience: Exploring the Potential of Digital Twin Technology, 3D City Modelling, and Early Warning Systems.

Cities, and in particular those in coastal low-lying areas, are becoming increasingly susceptible to climate change, the impact of which is worsened by the tendency for population concentration in these areas. Therefore, comprehensive early warning systems are necessary to minimize harm from extreme climate events on communities. Ideally, such a system would allow all stakeholders to acquire accurate up-to-date information and respond effectively. This paper presents a systematic review that highlights the significance, potential, and future directions of 3D city modelling, early warning systems, and digital twins in the creation of technology for building climate resilience through the effective management of smart cities. In total, 68 papers were identified through the PRISMA approach. A total of 37 case studies were included, among which (n = 10) define the framework for a digital twin technology, (n = 14) involve the design of 3D virtual city models, and (n = 13) entail the generation of early warning alerts using the real-time sensor data. This review concludes that the bidirectional flow of data between a digital model and the real physical environment is an emerging concept for enhancing climate resilience. However, the research is primarily in the phase of theoretical concepts and discussion, and numerous research gaps remain regarding the implementation and use of a bidirectional data flow in a true digital twin. Nonetheless, ongoing innovative research projects are exploring the potential of digital twin technology to address the challenges faced by communities in vulnerable areas, which will hopefully lead to practical solutions for enhancing climate resilience in the near future.

Prediction of Actual from Climatic Precipitation with Data Collected from Northern Poland: A Statistical Approach.

Water is a basic element of the natural environment and the most important component in human water management. Rainfall is the main source of water. Therefore, determining the amount of precipitation reaching the ground using sensors is crucial information. Precise precipitation data are necessary for better modeling quality, as the observation data from weather stations are used as basics for weather model assessment. The authors compared precipitation from the Hellmann rain gauge (climatic precipitation, 1.0 m above the ground surface) measured throughout the year and the GGI 3000 rain gauge (actual precipitation on the ground level) measured from April to October. Measurement sequences from the years 2011-2020 were considered. The data for analysis were obtained from a weather station located in northern Poland. The authors analyzed the relationships between data from the two sensors. A comparative study showed that the measurements of actual precipitation are higher and there are strong relationships between actual and climatic rainfall (r = 0.99). Using the introduced coefficient it is possible to determine the full-year actual precipitation with high probability, taking into account the precipitation with a correction from the winter half-year and the actual precipitation from the summer half-year, which is of great importance in the calculation of the water balance.

Low-Cost Sensors for Monitoring Coastal Climate Hazards: A Systematic Review and Meta-Analysis.

Unequivocal change in the climate system has put coastal regions around the world at increasing risk from climate-related hazards. Monitoring the coast is often difficult and expensive, resulting in sparse monitoring equipment lacking in sufficient temporal and spatial coverage. Thus, low-cost methods to monitor the coast at finer temporal and spatial resolution are imperative for climate resilience along the world's coasts. Exploiting such low-cost methods for the development of early warning support could be invaluable to coastal settlements. This paper aims to provide the most up-to-date low-cost techniques developed and used in the last decade for monitoring coastal hazards and their forcing agents via systematic review of the peer-reviewed literature in three scientific databases: Scopus, Web of Science and ScienceDirect. A total of 60 papers retrieved from these databases through the preferred reporting items for systematic reviews and meta-analyses (PRISMA) protocol were analysed in detail to yield different categories of low-cost sensors. These sensors span the entire domain for monitoring coastal hazards, as they focus on monitoring coastal zone characteristics (e.g., topography), forcing agents (e.g., water levels), and the hazards themselves (e.g., coastal flooding). It was found from the meta-analysis of the retrieved papers that terrestrial photogrammetry, followed by aerial photogrammetry, was the most widely used technique for monitoring different coastal hazards, mainly coastal erosion and shoreline change. Different monitoring techniques are available to monitor the same hazard/forcing agent, for instance, unmanned aerial vehicles (UAVs), time-lapse cameras, and wireless sensor networks (WSNs) for monitoring coastal morphological changes such as beach erosion, creating opportunities to not only select but also combine different techniques to meet specific monitoring objectives. The sensors considered in this paper are useful for monitoring the most pressing challenges in coastal zones due to the changing climate. Such a review could be extended to encompass more sensors and variables in the future due to the systematic approach of this review. This study is the first to systematically review a wide range of low-cost sensors available for the monitoring of coastal zones in the context of changing climate and is expected to benefit coastal researchers and managers to choose suitable low-cost sensors to meet their desired objectives for the regular monitoring of the coast to increase climate resilience.

Design of Multi-Layer Graphene Membrane with Descending Pore Size for 100% Water Desalination by Simulation Using ReaxFF.

The performance of a desalination membrane depends on a specific pore size suitable for both water permeability and salt rejection. To increase membrane permeability, the applied pressure should be increased, which creates the need to improve membrane stability. In this research article, a molecular dynamics (MD) simulation was performed using ReaxFF module from Amsterdam Modeling suite (AMS) software to simulate water desalination efficiency using a single and multi-layer graphene membrane. The graphene membrane with different pore sizes and a multi-layer graphene membrane with descending pore size in each layer were designed and studied under different pressures. The stability of the membrane was checked using Material Studio 2019 by studying the dynamics summary. The single-layer graphene membrane was evaluated under pressures ranging from 100 to 500 MPa, with the salt rejection ranging from 95% to 82% with a water permeability of 0.347 × 10-9 to 2.94 × 10-9 (mm.g.cm-2s-1.bar-1), respectively. Almost 100% salt rejection was achieved for the multi-layer graphene membrane. This study successfully demonstrated the design and optimization of graphene membrane performance without functionalization.

A Literature Review of Modelling and Experimental Studies of Water Treatment by Adsorption Processes on Nanomaterials.

A significant growth in the future demand for water resources is expected. Hence researchers have focused on finding new technologies to develop water filtration systems by using experimental and simulation methods. These developments were mainly on membrane-based separation technology, and photocatalytic degradation of organic pollutants which play an important role in wastewater treatment by means of adsorption technology. In this work, we provide valuable critical review of the latest experimental and simulation methods on wastewater treatment by adsorption on nanomaterials for the removal of pollutants. First, we review the wastewater treatment processes that were carried out using membranes and nanoparticles. These processes are highlighted and discussed in detail according to the rate of pollutant expulsion, the adsorption capacity, and the effect of adsorption on nanoscale surfaces. Then we review the role of the adsorption process in the photocatalytic degradation of pollutants in wastewater. We summarise the comparison based on decomposition ratios and degradation efficiency of pollutants. Therefore, the present article gives an evidence-based review of the rapid development of experimental and theoretical studies on wastewater treatment by adsorption processes. Lastly, the future direction of adsorption methods on water filtration processes is indicated.

The electronic properties and water desalination performance of a photocatalytic TiO2/MoS2 nanocomposites bilayer membrane: a molecular dynamic simulation.

Due to the rapid depletion of water resources, more interest is paid for the efficient desalination process in recent years. MoS2 membrane aroused attention due to its high mechanical stability and electronic properties, which can sustain extra-large strains. In this study, the electronic properties and water desalination performance of TiO2/MoS2-hexagonal, and TiO2/MoS2-rhombohedral nanocomposites bilayer membranes were studied and simulated for the first time. The effect of TiO2 in the performance of MoS2 was observed in water desalination under the defined applied pressure ranging from 50 to 250 MPa with a 6.4 Å pore diameter. The membrane structure is created and optimized. The energy minimized for TiO2 from - 19,596.4282 kcal/mol for the initial structure to - 19,605.1611 kcal/mol for the final structure. For TiO2/MoS2-hexagonal, the energy minimized from - 4955.54271 eV) to - 4955.62091 eV and TiO2/MoS2-rhombohedral from - 6042.26925 eV to - 6046.91835 eV. A molecular dynamic (MD) simulation was performed using Material Studio 2019 to study the electronic properties under 0-1 eV electric field using the CASTEP code. The results showed a better photocatalytic performance under the external electric field. The effect of external electric field significantly intensifies absorption in the visible range and achieved a high photocatalytic activity on TiO2/MoS2. TiO2, TiO2/MoS2-hexagonal and TiO2/MoS2-rhombohedral nanocomposites bilayer membranes are simulated and evaluated for the water desalination using ReaxFF software. Both MoS2 phases with TiO2 have achieved a high salt rejection up to 97% (P-value = 0.0036, R2 = 0.958), while TiO2/MoS2-rhombohedral achieved the highest permeability (6.0*10-8 mm g cm-2 s-1 bar-1) (P-value = 0.000296, R2 = 0.972) under 250 MPa applied pressure.

How do households perceive flood-risk? The impact of flooding on the cost of accommodation in Dublin, Ireland.

Climate change and human behaviour, such as building on floodplains, are increasing the incidence of floods in urban areas. This paper investigates the relationship between flood risk and residential accommodation costs, both sales and rental, using a detailed dataset of over 650,000 sale and rental listings in Dublin, Ireland over the period 2006-2015. These are combined with detailed data for the Dodder river on 1% flood risk and past flooding events. Research to date suggested that the lack of a persistent effect may have an impact on buyers' and sellers' risk perceptions by changing with the prevalence of hazard events and that homebuyers are unaware of flood risks and insurance requirements when bidding on properties. Using hedonic regression techniques, the presented work shows opposite results: flood events are found to have a negative impact, particularly on sale prices, while being at 1% risk has no effect once past flood events are controlled for. For past flood events, however, there is evidence to suggest that this impacts on property values, certainly in the areas affected and up to 200 m away. Before the institutional flood risk maps were published, the assessment was based on existing Ordnance Survey maps which showed areas 'Liable to flooding' generated with land surveys carried out around the 1830. Set against these devices for raising awareness of flooding is the Irish constitution which regards property rights almost the same as human rights, which obvious impacts on the ability of planners to implement development/zoning plans. On the basis of this evidence, it is reasonable to conclude that households pay more attention to past flood events than to scientific assessments of flood risk, has important policy implications about communicating flood risk to consumers.

Spatially distributed potential evapotranspiration modeling and climate projections.

Evapotranspiration integrates energy and mass transfer between the Earth's surface and atmosphere and is the most active mechanism linking the atmosphere, hydrosphsophere, lithosphere and biosphere. This study focuses on the fine resolution modeling and projection of spatially distributed potential evapotranspiration on the large catchment scale as response to climate change. Six potential evapotranspiration designed algorithms, systematically selected based on a structured criteria and data availability, have been applied and then validated to long-term mean monthly data for the Shannon River catchment with a 50m2 cell size. The best validated algorithm was therefore applied to evaluate the possible effect of future climate change on potential evapotranspiration rates. Spatially distributed potential evapotranspiration projections have been modeled based on climate change projections from multi-GCM ensembles for three future time intervals (2020, 2050 and 2080) using a range of different Representative Concentration Pathways producing four scenarios for each time interval. Finally, seasonal results have been compared to baseline results to evaluate the impact of climate change on the potential evapotranspiration and therefor on the catchment dynamical water balance. The results present evidence that the modeled climate change scenarios would have a significant impact on the future potential evapotranspiration rates. All the simulated scenarios predicted an increase in potential evapotranspiration for each modeled future time interval, which would significantly affect the dynamical catchment water balance. This study addresses the gap in the literature of using GIS-based algorithms to model fine-scale spatially distributed potential evapotranspiration on the large catchment systems based on climatological observations and simulations in different climatological zones. Providing fine-scale potential evapotranspiration data is very crucial to assess the dynamical catchment water balance to setup management scenarios for the water abstractions. This study illustrates a transferable systematic method to design GIS-based algorithms to simulate spatially distributed potential evapotranspiration on the large catchment systems.