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Water eutrophication has become one of the prominent problems of environmental protection in inland watersheds. Turbidity, total phosphorus (TP) and total nitrogen (TN) concentrations are key water quality parameters (WQPs) that reflect the level of water eutrophication in inland waters. Due to the complex interaction effects between different water quality in urban rivers, the water quality retrieval models still have the problem of single input features and poor applicability. This paper proposed a robust feature selection method based on machine learning and utilized Sentinel-2 remote sensing images for water quality retrieval of Chan and Ba rivers in Xi'an City. The ReliefF and global sensitivity analysis (GSA) methods (ReliefF-GSA) were used to select the optimal feature combination from the potential feature dataset. Based on the optimal feature combination, Random Forest regression (RFR), LightGBM and XGboost models were constructed for the three WQPs retrieval, respectively. The optimal models were then used to invert the three WQPs and the spatial-temporal variation of WQPs from January 2021 to January 2022 was analysed. The results show that (1) The RelieF-GSA method is suitable for high-dimensional feature filtration and enables optimal feature selection for specific WQPs retrieval. It is revealed that the BOI index (black odour water index) is the key feature for the retrieval of turbidity and TN concentration. (2) The RFR model was found to be better than other models and more appropriate for Chan and Ba rivers, with coefficients of determination (R2) of 0.90, 0.89 and 0.81, respectively. (3) It was found that the water qualities in the Chan and Ba rivers have prominent seasonal characteristics. Turbidity and TP concentrations showed higher, while TN concentration showed relatively low in autumn. The method and conclusions of this paper can further provide a reference for WPQs retrieval in urban rivers. [ABSTRACT FROM AUTHOR]

Atmospheric pollution affects air quality and can pose a serious risk to public health. Traditional PM2.5 monitoring is limited by the uneven distribution of ground stations, which makes it difficult to obtain spatially continuous and accurate PM2.5 concentrations information. Using aerosol optical depth (AOD) retrieved from satellite remote sensing to study the temporal and spatial variation characteristics of PM2.5 can accurately predict the PM2.5 concentrations in a wide range and provide a basis for atmospheric pollution prevention and control. Considering the spatio-temporal correlations among the data, this study uses AOD data, introduces geographic location data, temporal data, meteorological data, and elevation data to construct an Optimized XGBoost (O-XGBoost) model with spatial and temporal characteristics to estimate PM2.5 concentrations in the Guanzhong region from 2019 to 2021 and analyse its spatial and temporal distribution characteristics. The results show that compared with the random forest (RF) and XGBoost models, the O-XGBoost model has higher estimation accuracy, with $$ {R^2}$$ R 2 , $$ RMSE$$ RMSE , and $$MAE$$ MAE of 0.873, 11.460 $$\mu g\cdot{m^{ - 3}}$$ μg ⋅ m − 3 , and 8.061 $$\mu g\cdot{m^{ - 3}}$$ μg ⋅ m − 3 , respectively. The estimation of PM2.5 concentrations based on the O-XGBoost model makes up for the uneven distribution of ground monitoring stations and improves the estimation accuracy. [ABSTRACT FROM AUTHOR]

Long-term exposure to PM2.5 pollution increases the risk of cardiovascular diseases, particularly ischemic heart disease (IHD). Current assessments of the health effects related to PM2.5 exposure are limited by sparse ground monitoring stations and applicable disease research cohorts, making accurate health effect evaluations challenging. Using satellite-observed aerosol optical depth (AOD) data and the XGBoost-PM25 model, we obtained 1 km scale PM2.5 exposure levels across China. We quantified the premature mortality caused by PM2.5-exposure-induced IHD using the Global Exposure Mortality Model (GEMM) and baseline mortality data. Furthermore, we employed the Gini coefficient, a measure from economics to quantify inequality, to evaluate the distribution differences in health impacts due to PM2.5 exposure under varying socioeconomic conditions. The results indicate that PM2.5 concentrations in China are higher in the central and eastern regions. From 2007 to 2022, the national overall level showed a decreasing trend, dropping from 47.41 μg/m3 to 25.16 μg/m3. The number of premature deaths attributable to PM2.5 exposure increased from 819 thousand in 2007 to 870 thousand in 2022, with fluctuations in certain regions. This increase is linked to population growth and aging because PM2.5 levels have decreased. The results also indicate disparities in premature mortality from IHD among different economic groups in China from 2007 to 2022, with middle-income groups having a higher cumulative proportion of IHD-related premature deaths compared with high- and low-income groups. Despite narrowing GDP gaps across regions from 2007 to 2022, IHD consistently "favored" the middle-income groups. The highest Gini coefficient was observed in the Northwest (0.035), and the lowest was in the South (0.019). Targeted policy interventions are essential to establish a more equitable atmospheric environment. [ABSTRACT FROM AUTHOR]

In this study, a series of biodegradable poly(butylene alkylene carboxylate‐co‐terephthalate) copolymers with varying methylene numbers in the alkylene units (0, 2, 4, and 8) are synthesized. These copolymers, namely, poly(butylene oxalate‐co‐terephthalate) (PBOT), poly(butylene succinate‐co‐terephthalate) (PBST), poly(butylene adipate‐co‐terephthalate) (PBAT), and poly(butylene sebacate‐co‐terephthalate) (PBSeT), are prepared with nearly identical structural and molar compositions. The objective of this study is to comprehensively examine the impact of alkylene unit length on the barrier properties of the materials, delving into aspects such as crystallinity, free volume, molecular chain mobility, as well as adsorption and diffusion of gases within the materials. The findings indicate a decrease in crystallinity from 17.2% for PBOT to 10.2% for PBSeT as the alkylene chain length increases, while maintaining the same chemical composition. Concurrently, the fractional free volume increases from 0.9% to 2.6%, and the melt flow activation energy decreases from 106.6 to 52.1 kJ mol−1. Importantly, theoretical calculations are performed, demonstrating that the predominant site for gas adsorption and diffusion is within the material's free volume. These combined observations indicate a gradual decrease in barrier properties as the number of methylene groups increases. [ABSTRACT FROM AUTHOR]

Personalized travel itinerary recommendation algorithms are the focus of research in smart tourism and tourism GIS. Aiming to address issues present in travel itinerary recommendations for the increasingly popular "self-drive tour" mode, this study proposes an algorithm based on comprehensive tourist satisfaction to mitigate problems such as the neglect of important relevant factors and low degree of personalization. First, we construct a model of tourist satisfaction for travel itineraries by comprehensively considering factors including time utilization, the attractiveness of attractions, itinerary feasibility, and the diversity of attraction types. Unlike previous studies, we consider dining and accommodation time during the itinerary, the physical condition of tourists, and the diversity of attraction types, and establish penalty functions to flexibly constrain deviations from the expected conditions in itinerary planning. Then, with the optimization of comprehensive tourist satisfaction as the objective, we design a new algorithm to address the itinerary recommendation problem, supporting tourists in selecting must-visit attractions, restaurants, and hotels, as well as personalized preferences such as the sightseeing sequence. The experimental results demonstrate that our proposed algorithm outperforms two baseline algorithms, providing higher comprehensive tourist satisfaction while also exhibiting greater feasibility in itinerary planning. The proposed algorithm effectively addresses the issue of personalized travel itinerary recommendation, presenting an efficient, feasible, and practical solution. [ABSTRACT FROM AUTHOR]

The spin Hall effect (SHE) allows efficient generation of spin polarization or spin current through charge current and plays a crucial role in the development of spintronics. While SHE typically occurs in non-magnetic materials and is time-reversal even, exploring time-reversal-odd (T-odd) SHE, which couples SHE to magnetization in ferromagnetic materials, offers a new charge-spin conversion mechanism with new functionalities. Here, we report the observation of giant T-odd SHE in Fe3GeTe2/MoTe2 van der Waals heterostructure, representing a previously unidentified interfacial magnetic spin Hall effect (interfacial-MSHE). Through rigorous symmetry analysis and theoretical calculations, we attribute the interfacial-MSHE to a symmetry-breaking induced spin current dipole at the vdW interface. Furthermore, we show that this linear effect can be used for implementing multiply-accumulate operations and binary convolutional neural networks with cascaded multi-terminal devices. Our findings uncover an interfacial T-odd charge-spin conversion mechanism with promising potential for energy-efficient in-memory computing. Charge-to-spin conversion allows for the generation and control of spin polarization via a charge current. Typically, this is done with non-magnetic materials with large spin-orbit interactions such as Platinum. Herein, Dai et al demonstrate an intriguing charge-to-spin mechanism, a magnetic spin Hall effect, in a van der Waals heterostructure. [ABSTRACT FROM AUTHOR]