What are the impacts of fracking operations on local water quality?
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Impact of transboundary water flows on quality-induced water pressure in China
Quality-induced water pressure (P) is gaining increased attention. With the flows of transboundary water, P can be transferred among upstream and downstream regions. Here, we quantified the magnitude of pollutant transmission, and assessed its impact on individual provinces in China. On the annual basis, P was mitigated in 61% of provinces for Chemical Oxygen Demand, 87% for Ammonia Nitrogen, and 84% for Total Phosphorus, while it was intensified for 77% for Total Nitrogen in 2021. The aggregated P were mitigated in 68% of provinces, while intensified in 32% provinces. Furthermore, the monthly assessment has found that the impact of transboundary water on P varies seasonally, generally alleviating in winter and exacerbating in summer. This fluctuation was attributed to the comparatively higher quality of transboundary inflows during winter relative to local water quality. This study provides a scientific foundation for effective water management and quality control.
Solar-driven interfacial evaporation technologies for food, energy and water
Solar-driven interfacial evaporation technologies use solar energy to heat materials that drive water evaporation. These technologies are versatile and do not require electricity, which enables their potential application across the food, energy and water nexus. In this Review, we assess the potential of solar-driven interfacial evaporation technologies in food, energy and clean-water production, in wastewater treatment, and in resource recovery. Interfacial evaporation technologies can produce up to 5.3 l m–2 h−1 of drinking water using sunlight as the energy source. Systems designed for food production in coastal regions desalinate water to irrigate crops or wash contaminated soils. Technologies are being developed to simultaneously produce both clean energy and water through interfacial evaporation and have reached up to 204 W m–2 for electricity and 2.5 l m–2 h–1 for water in separate systems. Other solar evaporation approaches or combinations of approaches could potentially use the full solar spectrum to generate multiple products (such as water, food, electricity, heating or cooling, and/or fuels). In the future, solar evaporation technologies could aid in food, energy and water provision in low-resource or rural settings that lack reliable access to these essentials, but the systems must first undergo rigorous, scaled-up field testing to understand their performance, stability and competitiveness.
Brine management with zero and minimal liquid discharge
Zero liquid discharge (ZLD) and minimal liquid discharge (MLD) are brine management approaches that aim to reduce the environmental impacts of brine discharge and recover water for reuse. ZLD maximizes water recovery and avoids the needs for brine disposal, but is expensive and energy-intensive. MLD (which reduces the brine volume and recovers some water) has been proposed as a practical and cost-effective alternative to ZLD, but brine disposal is needed. In this Review, we examine the concepts, technologies and industrial applications of ZLD and MLD. These brine management strategies have current and potential applications in the desalination, energy, mining and semiconductor industries, all of which produce large volumes of brine. Brine concentration and crystallization in ZLD and MLD often rely on mechanical vapour compression and thermal crystallizers, which are effective but energy-intensive. Novel engineered systems for brine volume reduction and crystallization are under active development to achieve MLD and/or ZLD. These emerging systems, such as membrane distillation, electrodialytic crystallization and solvent extraction desalination, still face challenges to outcompete mechanical vapour compression and thermal crystallizers, underscoring the critical need to maximize the full potential of reverse osmosis to attain ultrahigh water recovery. Brine valorization has potential to partially offset the cost of ZLD and MLD, provided that resource recovery can be integrated into treatment trains economically and in accordance with regulations.
Configural processing as an optimized strategy for robust object recognition in neural networks
Configural processing, the perception of spatial relationships among an object’s components, is crucial for object recognition, yet its teleology and underlying mechanisms remain unclear. We hypothesize that configural processing drives robust recognition under varying conditions. Using identification tasks with composite letter stimuli, we compare neural network models trained with either configural or local cues. We find that configural cues support robust generalization across geometric transformations (e.g., rotation, scaling) and novel feature sets. When both cues are available, configural cues dominate local features. Layerwise analysis reveals that sensitivity to configural cues emerges later in processing, likely enhancing robustness to pixel-level transformations. Notably, this occurs in a purely feedforward manner without recurrent computations. These findings with letter stimuli successfully extend to naturalistic face images. Our results demonstrate that configural processing emerges in a naíve network based on task contingencies, and is beneficial for robust object processing under varying viewing conditions.
Secure and federated genome-wide association studies for biobank-scale datasets
Sharing data across institutions for genome-wide association studies (GWAS) would enhance the discovery of genetic variation linked to health and disease1,2. However, existing data-sharing regulations limit the scope of such collaborations3. Although cryptographic tools for secure computation promise to enable collaborative analysis with formal privacy guarantees, existing approaches either are computationally impractical or do not implement current state-of-the-art methods4,5,6. We introduce secure federated genome-wide association studies (SF-GWAS), a combination of secure computation frameworks and distributed algorithms that empowers efficient and accurate GWAS on private data held by multiple entities while ensuring data confidentiality. SF-GWAS supports widely used GWAS pipelines based on principal-component analysis or linear mixed models. We demonstrate the accuracy and practical runtimes of SF-GWAS on five datasets, including a UK Biobank cohort of 410,000 individuals, showcasing an order-of-magnitude improvement in runtime compared to previous methods. Our work enables secure collaborative genomic studies at unprecedented scale.
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