Search

Climate change impact assessment is critical for creating adequate climate change mitigation and adaptation policies and plans. Critical infrastructure systems (e.g., water and energy systems) are particularly at risk for climate change impacts. In fact, a better understanding of climate change impacts on the water and energy systems would facilitate the fulfillment of SDG2 (end hunger), SDG 6 (clean water and sanitation), SDG 7 (affordable and clean energy), SDG 11 (sustainable cities and communities), and SDG 13 (climate action), with many indirect benefits across many other areas. Nonetheless, conducting climate change impact assessment, particularly at the community-level, is not an easy task. Often, the impact assessment models require access to substantial computational resources to run the complex models, as well as the expertise to work with those models and interpret their results, which may not be possible for all communities. As such, there is a need to expand climate change impact assessment to include more accessible models that can handle high-resolution, local data that is of interest to communities. Here, we highlight how climate impact assessment studies can benefit from the power of artificial intelligence (AI). The report details the use of AI model to conduct a computationally efficient climate change impact assessment. This model is applied to a case study across the United States of America (U.S.) as an example to showcase the insights it generates in real-world applications. To demonstrate this process, the study will focus on the impacts on coupled water and electricity demand (e.g., the water-electricity demand nexus). To conduct the impact assessment, the report demonstrates two different means of collecting future climate data—Coupled Model Intercomparison Project 5 (CMIP5) Earth System Models (ESMs) and contemporary climate analogs. Our results show significant increases across the Midwestern U.S. when using ESM-derived data. Similar resu

As the climate crisis intensifies, it is becoming increasingly important to conduct research aimed at fully understanding the climate change impacts on various infrastructure systems. In particular, the water-electricity demand nexus is a growing area of focus. However, research on the water-electricity demand nexus requires the use of demand data, which can be difficult to obtain, especially across large spatial extents. Here, we present a dataset containing over a decade (2007–2018) of monthly water and electricity consumption data for 46 major US cities (2018 population >250,000). Additionally, we include pre-processed climate data from the North American Regional Reanalysis (NARR) to supplement studies on the relationship between the water-electricity demand nexus and the local climate. This data can be used for a number of studies that require water and/or electricity demand data across long time frames and large spatial extents. The data can also be used to evaluate the possible impacts of climate change on the water-electricity demand nexus by leveraging the relationship between the observed values.

Coronavirus illness (COVID-19) is an infectious pathology generated by intense severe respiratory syndrome coronavirus 2 (SARS-CoV-2). This infectious disease has emerged in 2019. The COVID-19-associated pandemic has considerably affected the way of life and the economy in the world. It is consequently crucial to find solutions allowing remedying or alleviating the effects of this infectious disease. Natural products have been in perpetual application from immemorial time given that they are attested to be efficient towards several illnesses without major side effects. Various studies have shown that plant extracts or purified molecules have a promising inhibiting impact towards coronavirus. In addition, it is substantial to understand the characteristics, susceptibility and impact of diet on patients infected with COVID-19. In this review, we recapitulate the influence of extracts or pure molecules from medicinal plants on COVID-19. We approach the possibilities of plant treatment/co-treatment and feeding applied to COVID-19. We also show coronavirus susceptibility and complications associated with nutrient deficiencies and then discuss the major food groups efficient on COVID-19 pathogenesis. Then, we covered emerging technologies using plant-based SARS-CoV-2 vaccine. We conclude by giving nutrient and plants curative therapy recommendations which are of potential interest in the COVID-19 infection and could pave the way for pharmacological treatments or co-treatments of COVID-19.

Water and electricity systems are highly interconnected in terms of supply (e.g., electricity is used in the treatment and distribution of water) and demand (e.g., appliances that use water within homes also use electricity). Understanding the impact that climate change may have on coupled water and electricity demand is a crucial task for ensuring resilient planning and sustainable management of water and energy infrastructure. Here, we leverage an efficient methodology that integrates contemporary climate analogs and machine learning to project the city-level coupled water and electricity demand of 46 major US cities into the future. The results show strong regional differences, with some cities possibly experiencing increases in summer water and electricity demand of up to 15% and 20%, respectively, because of climate change. Obtaining projections of future water and electricity demand under climate change is critical for building resilient infrastructure in the face of climate change.

Machine learning and mathematical modeling techniques have been conducted to model the thin layer drying kinetics of pea pods, under either microwave or conventional air drying,. The effect of nine different microwave output powers (200-1000 W) and five different ventilated oven temperatures (40, 60, 80, 100, and 120 & DEG;C) on drying kinetics was studied. The experimental drying rates were fitted to 11 literature semi-empirical models to determine the kinetic parameters, finding the higher goodness-of-fit for the Midilli et al. model (average R-2 = 0.999 for both drying methods). Moreover, the data were modeled using support vector machine (SVM) for regression which was optimized with dragonfly algorithm (DA) technique. The best result was obtained by Gaussian kernel with the optimal parameters sigma, C, and epsilon values estimated as 0.2871, 78.45, and 0, respectively. The small root mean square error (RMSE = 0.0132) and the high determination coefficient (R-2 = 0.9983) values proved how robust the SVM model is. DA-SVM techniques can reliably be utilized to describe the thin layer drying kinetics of pea pods. It is useful to provide models that can assist in the development of food process control algorithms, and provided insights into complex processes, for the technological design of microwave or convective drying for pea pods preservation. Practical applications Drying of by-products from pea processing industry was investigated as a critical step prior to their valorization. The drying of pea pods has never been investigated before which is the case of the present study whose objective was to study and model the microwave and convective drying kinetics of pea pods. Our research work reported that the Midilli et al. model was the most appropriate to describe the thin layer drying kinetics of pea pods for both drying methods, but mathematical drying models, although a useful tool, remains empirical in nature and product specific. Because of these limitatio

Traditional, mainstream definitions of drought describe it as deficit in water-related variables or water-dependent activities (e.g., precipitation, soil moisture, surface and groundwater storage, and irrigation) due to natural variabilities that are out of the control of local decision-makers. Here, we argue that within coupled human-water systems, drought must be defined and understood as a process as opposed to a product to help better frame and describe the complex and interrelated dynamics of both natural and human-induced changes that define anthropogenic drought as a compound multidimensional and multiscale phenomenon, governed by the combination of natural water variability, climate change, human decisions and activities, and altered micro-climate conditions due to changes in land and water management. This definition considers the full spectrum of dynamic feedbacks and processes (e.g., land-atmosphere interactions and water and energy balance) within human-nature systems that drive the development of anthropogenic drought. This process magnifies the water supply demand gap and can lead to water bankruptcy, which will become more rampant around the globe in the coming decades due to continuously growing water demands under compounding effects of climate change and global environmental degradation. This challenge has de facto implications for both short-term and long-term water resources planning and management, water governance, and policymaking. Herein, after a brief overview of the anthropogenic drought concept and its examples, we discuss existing research gaps and opportunities for better understanding, modeling, and management of this phenomenon.