Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic

Digital Twins in Agriculture: Orchestration and Applications

Digital Twins have emerged as an outstanding opportunity for precision farming, digitally replicating in real-time the functionalities of objects and plants. A virtual replica of the crop, including key agronomic development aspects such as irrigation, optimal fertilization strategies, and pest management, can support decision-making and a step change in farm management, increasing overall sustainability and direct water, fertilizer, and pesticide savings. In this review, Digital Twin technology is critically reviewed and framed in the context of recent advances in precision agriculture and Agriculture 4.0. The review is organized for each step of agricultural lifecycle, edaphic, phytotechnologic, postharvest, and farm infrastructure, with supporting case studies demonstrating direct benefits for agriculture production and supply chain considering both benefits and limitations of such an approach. Challenges and limitations are disclosed regarding the complexity of managing such an amount of data and a multitude of (often) simultaneous operations and supports.

Four buildings and a flush: Lessons from degraded water quality and recommendations on building water management

A reduction in building occupancy can lead to stagnant water in plumbing, and the potential consequences for water quality have gained increasing attention. To investigate this, a study was conducted during the COVID-19 pandemic, focusing on water quality in four institutional buildings. Two of these buildings were old (>58 years) and large (>19,000 m2), while the other two were new (>13 years) and small (<11,000 m2). The study revealed significant decreases in water usage in the small buildings, whereas usage remained unchanged in the large buildings. Initial analysis found that residual chlorine was rarely detectable in cold/drinking water samples. Furthermore, the pH, dissolved oxygen, total organic carbon, and total cell count levels in the first draw of cold water samples were similar across all buildings. However, the ranges of heavy metal concentrations in large buildings were greater than observed in small buildings. Copper (Cu), lead (Pb), and manganese (Mn) sporadically exceeded drinking water limits at cold water fixtures, with maximum concentrations of 2.7 mg Cu L-1, 45.4 μg Pb L-1, 1.9 mg Mn L-1. Flushing the plumbing for 5 min resulted in detectable residual at fixtures in three buildings, but even after 125 min of flushing in largest and oldest building, no residual chlorine was detected at the fixture closest to the building's point of entry. During the pandemic, the building owner conducted fixture flushing, where one to a few fixtures were operated per visit in buildings with hundreds of fixtures and multiple floors. However, further research is needed to understand the fundamental processes that control faucet water quality from the service line to the faucet. In the absence of this knowledge, building owners should create and use as-built drawings to develop flushing plans and conduct periodic water testing.

Spatiotemporal optimization of water quality degradation monitoring in water distribution systems supplied by surface sources: A chronological and critical review

Drinking water may undergo spatiotemporal changes in quality as it leaves the treatment plant and enters the distribution system. This variability means that not all consumers receive water of the same quality. Monitoring water quality in distribution networks makes it possible to verify the compliance of current regulations and reduce consumption risks associated with water quality degradation. An inaccurate interpretation of the spatiotemporal variability of water quality affects the selection of monitoring locations and the sampling frequency, which may conceal problems with the water quality and increase consumers' risk. This paper presents a chronological and critical review of the literature on the evolution, benefits and limitations of methodologies for the optimization of water quality degradation monitoring in water distribution systems supplied by surface sources. This review compares the different methodologies and examines the types of approaches, optimization objectives, variables, and types of spatial and temporal analysis, as well as the main advantages and limitations. A cost-benefit analysis was conducted to assess applicability in different-sized municipalities (small, medium and large). Future research recommendations for optimal water quality monitoring in distribution networks are also provided.

Application of "Behind the Barriers" Model at Neighbourhood Scale to Improve Water Management under Multi-Risks Scenarios: A Case Study in Lyon, France

In modern urban areas, water management highly depends on the socio-ecological urban water cycle (UWC) that heavily relies on water infrastructures. However, increasing water-related hazards, natural and/or human-based, makes it difficult to balance water resources in the socio-ecological UWC. In the last decade, urban infrastructure resilience has rapidly become a popular topic in disaster risk management and inspired many studies and operational approaches. Among these theories and methods, the "Behind the Barriers" model (BB model), developed by Barroca and Serre in 2013, is considered a theory that allows effective and comprehensive analysis of urban infrastructure resilience through cognitive, functional, correlative, and organisational dimensions. Moreover, this analysis can be a reference to develop actions that improve infrastructure resilience under critical scenarios. Therefore, this study aims to study resilience design actions based on the BB model to achieve socio-ecological water balance and assess the performance of these actions. The study focuses on water management on a neighbourhood scale, which is considered the essential urban unit to study and improve the resilience of critical infrastructures, such as water services. The Part-Dieu neighbourhood in Lyon, France is selected as a case study, and it highlights the need to develop indicators to assess the performance of implemented actions in a structural and global resilience framework, to understand urban systems as complex and dynamic systems to provide decision support, and to strengthen crisis prevention and management perspectives in a dynamic approach.

Investigation of pre and post environmental impact of the lockdown (COVID-19) on the water quality of the Capibaribe and Tejipió rivers, Recife metropolitan region, Brazil

The coronavirus pandemic has seriously affected human health, although some improvements on environmental indexes have temporarily occurred, due to changes on socio-cultural and economic standards. The objective of this study was to evaluate the impacts of the coronavirus and the influence of the lockdown associated with rainfall on the water quality of the Capibaribe and Tejipió rivers, Recife, Northeast Brazil, using cloud remote sensing on the Google Earth Engine (GEE) platform. The study was carried out based on eight representative images from Sentinel-2. Among the selected images, two refer to the year 2019 (before the pandemic), three refer to 2020 (during a pandemic), two from the lockdown period (2020), and one for the year 2021. The land use and land cover (LULC) and slope of the study region were determined and classified. Water turbidity data were subjected to descriptive and multivariate statistics. When analyzing the data on LULC for the riparian margin of the Capibaribe and Tejipió rivers, a low permanent preservation area was found, with a predominance of almost 100% of the urban area to which the deposition of soil particles in rivers are minimal. The results indicated that turbidity values in the water bodies varied from 6 mg. L-1 up to 40 mg. L-1. Overall, the reduction in human-based activities generated by the lockdown enabled improvements in water quality of these urban rivers.

The role of deep learning in urban water management: A critical review

Deep learning techniques and algorithms are emerging as a disruptive technology with the potential to transform global economies, environments and societies. They have been applied to planning and management problems of urban water systems in general, however, there is lack of a systematic review of the current state of deep learning applications and an examination of potential directions where deep learning can contribute to solving urban water challenges. Here we provide such a review, covering water demand forecasting, leakage and contamination detection, sewer defect assessment, wastewater system state prediction, asset monitoring and urban flooding. We find that the application of deep learning techniques is still at an early stage as most studies used benchmark networks, synthetic data, laboratory or pilot systems to test the performance of deep learning methods with no practical adoption reported. Leakage detection is perhaps at the forefront of receiving practical implementation into day-to-day operation and management of urban water systems, compared with other problems reviewed. Five research challenges, i.e., data privacy, algorithmic development, explainability and trustworthiness, multi-agent systems and digital twins, are identified as key areas to advance the application and implementation of deep learning in urban water management. Future research and application of deep learning systems are expected to drive urban water systems towards high intelligence and autonomy. We hope this review will inspire research and development that can harness the power of deep learning to help achieve sustainable water management and digitalise the water sector across the world.

Using advanced metering infrastructure data to evaluate consumer compliance with water advisories during a water service interruption

Water main breaks disrupt services provided by utilities and result in Water Service Interruptions (WSIs). Water utilities can manage WSIs through water advisories, which request that consumers limit their water use. The performance of water advisories depends on consumer compliance and decisions to conserve water. This research explores customer compliance with water advisories using water consumption data collected through Advanced Metering Infrastructure (AMI). AMI provides high temporal and spatial resolution of water consumption data, which is analyzed to identify changes in water use behaviors. This research explores water use changes during a major water main break in Orange County, North Carolina, that caused a significant WSI, limiting water supply for more than 80,000 people. Customers were asked to reduce water use to essential purposes only and to boil water over the course of two days in November 2018. This research analyzes hourly consumption data to evaluate water consumption trends during the WSI and in response to water advisories. Statistical analysis is used to estimate the number of consumers who complied with utility notifications and to evaluate the volume of water saved. Regression analysis is applied to explore compliance across different user segments. Results provide insight about the level and variation of water conservation that can be expected during a WSI.

A Case Study of the Relationship Between Vegetation Coverage and Urban Heat Island in a Coastal City by Applying Digital Twins

While urban vegetation affects the urban thermal environment directly, the effects of different plant layouts and vegetation cover on urban microclimate regulation are different. This study has applied digital technologies to advance urban environmental research and forestry analysis. With a focus on a coastal city located on the eastern coast of the North Temperate Zone as a study area, this study collected the Landsat archive satellite remote sensing image data covering the study area in 2000-2020 and analyzed the temporal and spatial distribution characteristics of vegetation coverage, land surface temperature, and urban heat island (UHI) ratio index. The study results included the following findings: (1) The area of high fractional vegetation cover (FVC) (0.8-1.0) in the study area is increasing. Those areas are located in the mountain forests in the near-coastal area. The lowest temperature was also detected in the mountain area. (2) The distance from the coastline causes a negative correlation between land surface temperature and FVC. The land surface temperature in the regions with a distance of more than 25 km from the coastline decreases obviously with increasing FVC in summer. However, the correlation between the land surface temperature and FVC showed a slight change in the winter period. (3) UHI ratio index decreases along with the area of high FVC (H-FVC) area. The influence of ocean climate on seasons is different, which results in the reduced effect of the H-FVC area and differences in the UHI ratio index. (4) The distance from the coastline should be considered as an important factor in the forestry development planning of the coastal cities.

New Challenges towards Smart Systems’ Efficiency by Digital Twin in Water Distribution Networks

Nowadays, in the management of water distribution networks (WDNs), particular attention is paid to digital transition and the improvement of the energy efficiency of these systems. New technologies have been developed in the recent years and their implementation can be crucial to achieve a sustainable level of water networks, namely, in water and energy losses. In particular, Digital Twins (DT) represents a very innovative technology, which relies on the integration of virtual network models, optimization algorithms, real time data collection, and smart actuators information with Geographic Information System (GIS) data. This research defines a new methodology for an efficient application of DT expertise within water distribution networks. Assuming a DMA of a real water distribution network as a case study, it was demonstrated that a fast detection of leakage along with an optimal setting of pressure control valves by means of DT together with an optimization procedure can ensure up to 28% of water savings, contributing to significantly increase the efficiency of the whole system.

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.