Component temperature and emissivity are crucial for understanding plant physiology and urban thermal dynamics. However, existing thermal infrared unmixing methods face challenges in simultaneous retrieval and multicomponent analysis. We propose Thermal Remote sensing Unmixing for Subpixel Temperature and emissivity with the Discrete Anisotropic Radiative Transfer model (TRUST-DART), a gradient-based multi-pixel physical method that simultaneously separates component temperature and emissivity from non-isothermal mixed pixels over urban areas. TRUST-DART utilizes the DART model and requires inputs including at-surface radiance imagery, downwelling sky irradiance, a 3D mock-up with component classification, and standard DART parameters (e.g., spatial resolution and skylight ratio). This method produces maps of component emissivity and temperature. The accuracy of TRUST-DART is evaluated using both vegetation and urban scenes, employing Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images and DART-simulated pseudo-ASTER images. Results show a residual radiance error is approximately 0.05 W/(m2 & sdot;sr). In absence of the co-registration and sensor noise errors, the median residual error of emissivity is approximately 0.02, and the median residual error of temperature is within 1 K. This novel approach significantly advances our ability to analyze thermal properties of urban areas, offering potential breakthroughs in urban environmental monitoring and planning. The source code of TRUSTDART is distributed together with DART (https://dart.omp.eu).

Hazardous waste from metal processing industries increases heavy metal contamination in ecosystems, threatening environmental health and regional sustainability. This study suggests a resilient and human-centered environmental monitoring approach that incorporates machine learning and decision analytics to address these challenges in line with Industry 5.0's goals. By utilising a PRINCIPAL COMPONENT REGRESSION (PCR)-based predictive model, the approach addresses variability in environmental data, predicting levels of heavy metals like lead, zinc, nickel, arsenic, and cadmium, frequently beyond regulatory thresholds. The suggested PCR-based model outperforms conventional models by lowering mean absolute error (MAE) to 2.9339, mean absolute percentage error (MAPE) to 0.0358, and nearly the same mean square error (MSE). This study introduces a more interpretable and computationally efficient alternative to existing predictive models by introducing a novel integration of PCR with machine learning for environmental monitoring. By predicting and optimising environmental outcomes, validation against test datasets confirmed its ability to optimise impurity control. After process adjustments, the average concentrations of lead, nickel, and cadmium were reduced from 13.23 to 11.26 mg/L, 2.83 to 2.70 mg/L, and 2.15 to 1.88 mg/L, respectively. This research supports sustainability, resilience, and decisionmaking aligned with Industry 5.0, offering scalable solutions and insights for global industries.HighlightsChemical plants' environmental risk is evaluated using a machine learning algorithmFor better monitoring, the PCR method forecasts process variables and interactionsIt identifies the key factors that affect the environmental risks in soil and waterAs a result, the local ecosystem's levels of toxic metals have notably decreasedInsights for managing environmental risks aligned with Industry 5.0 principles

Open-pit coal mining poses a severe threat to regional ecological security. Rapid and accurate monitoring of ecological quality changes is crucial for regional ecological restoration. In this study, taking the Wujiata open-pit coal mine as an example, the Red-Edge Normalized Difference Vegetation Index (RENDVI), Salinity Index (SI-T), WETness index (WET), Normalized Differential Built Soil Index (NDBSI), Land Surface Temperature (LST), and Desertification Index (DI) were used to construct the Open-pit Mine Remote Sensing Ecological Index (OM-RSEI) through Principal Component Analysis (PCA). The ecological quality and restoration conditions of typical mining areas in arid and semi-arid regions were monitored and evaluated. The results shown that: (1) The contribution rates and eigenvalues of OM-RSEI were higher than those of conventional RSEI, OM-RSEI was more applicable in open-pit mining areas. (2) From 2018 to 2023, the OM-RSEI of the Wujiata open-pit coal mine showed a 'V' shaped fluctuation that was damaged and then gradually recovered. (3) The degraded area of Wujiata open-pit coal mine and its 5 km buffer zone accounted for 78.02%, and the improved area accounted for 19.16%. (4) The average Moran's I index of OM-RSEI in the study area was 0.8189, and the high-high clustering corresponded to the 'good' and 'excellent' distributions, while the low-low clustering corresponded to the 'poor' and 'less-poor' distributions. The OM-RSEI provided a new indicator for monitoring and evaluation of ecological restoration in open-pit coal mines, which can provide theoretical support for ecological restoration in open-pit coal mining areas.

The threat power transmission and distribution projects pose to the ecological environment has been widely discussed by researchers. The scarcity of early environmental monitoring and supervision technologies, particularly the lack of effective real-time monitoring mechanisms and feedback systems, has hindered the timely quantitative identification of potential early-stage environmental risks. This study aims to comprehensively review the literature and analyze the research context and shortcomings of the advance warning technologies of power transmission and distribution projects construction period using the integrated space-sky-ground system approach. The key contributions of this research include (1) listing ten environmental risks and categorizing the environmental risks associated with the construction cycle of power transmission and distribution projects; (2) categorizing the monitoring data into one-dimensional, two-dimensional, and three-dimensional frameworks; and (3) constructing the potential environmental risk knowledge system by employing the knowledge graph technology and visualizing it. This review study provides a panoramic view of knowledge in a certain field and reveals the issues that have not been fully explored in the research field of monitoring technologies for potential environmental damage caused by power transmission and transformation projects.