Time Is Ripe for Targeting Per- and Polyfluoroalkyl Substances-Induced Hormesis: Global Aquatic Hotspots and Implications for Ecological Risk Assessment.

Globally implemented ecological risk assessment (ERA) guidelines marginalize hormesis, a biphasic dose-response relationship characterized by low-dose stimulation and high-dose inhibition. The present study illuminated the promise of hormesis as a scientific dose-response model for ERA of per- and polyfluoroalkyl substances (PFAS) represented by perfluorooctanoic acid (PFOA) and perfluorooctanesulfonic acid (PFOS). A total of 266 hormetic dose-response relationships were recompiled from 1237 observations, covering 30 species from nine representative taxonomic groups. The standardized hormetic amplitudes followed the log-normal probability distribution, being subject to the limits of biological plasticity but independent of stress inducers. The SHapley Additive exPlanations algorithm revealed that the target endpoint was the most important variable explaining the hormetic amplitudes. Subsequently, quantitative frameworks were established to incorporate hormesis into the predicted no-effect concentration levels, with a lower induction dose and a zero-equivalent point but a broader hormetic zone for PFOS. Realistically, 10,117 observed concentrations of PFOA and PFOS were gathered worldwide, 4% of which fell within hormetic zones, highlighting the environmental relevance of hormesis. Additionally, the hormesis induction potential was identified in other legacy and emerging PFAS as well as their alternatives and mixtures. Collectively, it is time to incorporate the hormesis concept into PFAS studies to facilitate more realistic risk characterizations.

Risk assessment of trace metals and polycyclic aromatic hydrocarbons in seawater of typical bays in the Bohai Sea.

The ecological risks of trace metals (Cu, Zn, As, Cd, Pb, and Hg) and PAHs in seawater from three typical bays of the Bohai Sea (the Liaodong Bay, Bohai Bay, and Laizhou Bay) were comprehensively assessed by recompiling 637 sites. Results highlighted that scrutiny should be given to the ecological risks of Cu (3.80 µg/L) in the Bohai Bay and Hg (0.23 µg/L) in the Laizhou Bay. Conversely, the Liaodong Bay exhibited negligible ecological risks related to trace metals. The risks of ΣPAHs in the Liaodong Bay, Bohai Bay, and Laizhou Bay were moderate, with mean concentrations of 368.16 ng/L, 731.93 ng/L, and 187.58 ng/L, respectively. The source allocation of trace metals and PAHs required consideration of spatial variability and anthropogenic factors, which greatly affected the distribution and composition of these pollutants. The combined ecological risks in the Bohai Bay (6.80 %) and Laizhou Bay (5.43 %) deserved more attention.

Coupling strength of human-natural systems mediates the response of ecosystem services to land use change.

Addressing the dynamics of human-natural systems (HNS) driven by land use change (LC) is a key challenge for the sustainable development of ecosystem services (ES). However, how changes to the HNS coupling relationships affect ES is rarely reported. We used network analysis methods to construct an HNS correlation network in the Loess Plateau based on the correlation between the main components of HNS, such as ES, human factors, landscape pattern, vegetation cover, climate change and geomorphic characteristics, and quantitatively described the HNS coupling relationships through key network attributes. We analyzed the variation in HNS network attributes and their relationships with ES along an LC intensity gradient. The results show that carbon storage and soil conservation in the Loess Plateau increased by 0.56% and 0.26%, respectively, during the study period, while the habitat quality and water yield decreased by 0.11% and 0.18%, respectively. An increase in LC intensity reduces connectivity and density in the HNS network, which results in looser connections among HNS components. Importantly, we found that HNS network attributes explained 85% of ES variation across different LC intensity gradients and that connectivity and density had the strongest explanatory power. This means that LC mainly affects ES dynamics by changing the coupling strength of HNS. Our research offers a new perspective for linking LC-HNS-ES, which will help guide practitioners toward establishing and maintaining the sustainability of human well-being amidst changing HNS.

Throughput Maximization Using Deep Complex Networks for Industrial Internet of Things.

The high-density Industrial Internet of Things needs to meet the requirements of high-density device access and massive data transmission, which requires the support of multiple-input multiple-output (MIMO) antenna cognitive systems to keep high throughput. In such a system, spectral efficiency (SE) optimization based on dynamic power allocation is an effective way to enhance the network throughput as the channel quality variations significantly affect the spectral efficiency performance. Deep learning methods have illustrated the ability to efficiently solve the non-convexity of resource allocation problems induced by the channel multi-path and inter-user interference effects. However, current real-valued deep-learning-based power allocation methods have failed to utilize the representational capacity of complex-valued data as they regard the complex-valued channel data as two parts: real and imaginary data. In this paper, we propose a complex-valued power allocation network (AttCVNN) with cross-channel and in-channel attention mechanisms to improve the model performance where the former considers the relationship between cognitive users and the primary user, i.e., inter-network users, while the latter focuses on the relationship among cognitive users, i.e., intra-network users. Comparison experiments indicate that the proposed AttCVNN notably outperforms both the equal power allocation method (EPM) and the real-valued and the complex-valued fully connected network (FNN, CVFNN) and shows a better convergence rate in the training phase than the real-valued convolutional neural network (AttCNN).

Reconciling the actual and nominal exposure concentrations of microplastics in aqueous phase: Implications for risk assessment and deviation control.

The deviation between actual and nominal concentrations of microplastics (MPs), as a long-standing issue, has been critically commented. However, there is still a lack of quantitative assessment and reconciling practice on the deviation. In this study, a total of 210 deviations were recompiled to thoroughly examine this issue. It was shown that up to 81 (39%) deviations exceeded the recommended ± 20% variation specification, highlighting that the deviation of MPs should not be neglected. This study attempted to reconcile the deviation based on the most prominent driving factors. Specifically, the game theory-based SHapley Additive exPlanations (SHAP) algorithm identified that the particle size was the most important factor affecting the deviation. Subsequently, at each size magnitude, a significant linear correlation between the logarithmic actual and nominal concentrations was determined, which provided a sound basis for estimating the actual concentration from the nominal one. Furthermore, deviations of different size classes were simulated through 10, 000 points, suggesting that the ± 20% deviation variation could be well maintained within a specific concentration range. Moreover, the potential interaction effects between factors were quantified by SHAP interaction values, with more detailed conversion bases proposed. Additionally, several control measures were recommended to reduce the deviation of MPs.