In this study, we examined the sources and temporal variability of 16 polycyclic aromatic hydrocarbons (PAHs) found in fine particulate matter (PM) in a typical industrial city in northern China. We also evaluated the incremental lifetime cancer risk (ILCR) from the inhalation of these PAHs. Atmospheric PM samples were collected for 7 consecutive days each month from 2014 to 2019, and the 16 PAHs were measured using multiplex gas chromatography-tandem mass spectrometry. The carcinogenic risk of PAH exposure was assessed using the inhalation unit risk (IUR) and cancer slope factor (CSF) methods. The annual average concentrations of PM for each year from 2014 to 2019 were 102.87±55.25, 86.92±60.43, 69.17±37.74, 58.20±59.15, 56.01±34.52, and 52.54±58.15 µg m, and the annual average ΣPAH concentrations were 56.03±81.09, 47.99±79.30, 40.41±57.31, 33.57±51.79, 43.23±74.80, and 25.20±50.91 ng m, respectively. Source identification, using diagnostic ratio analysis, indicated that the major PAH sources were coal/biomass combustion, fuel combustion, and traffic emissions. A health risk assessment showed that the ILCR from PAH inhalation decreased throughout the study period and varied with age. The IUR and CSF methods both showed that the adult ILCR exceeded 1.0×10. These findings demonstrate the importance of addressing the carcinogenic risk of PM-bound PAHs, particularly in adults.
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Sci Data
January 2025
Department of Earth and Environmental Engineering, Columbia University, New York, USA.
The Gravity Recovery and Climate Experiment (GRACE) and its follow-on (GRACE-FO) missions have provided estimates of Terrestrial Water Storage Anomalies (TWSA) since 2002, enabling the monitoring of global hydrological changes. However, temporal gaps within these datasets and the lack of TWSA observations prior to 2002 limit our understanding of long-term freshwater variability. In this study, we develop GRAiCE, a set of four global monthly TWSA reconstructions from 1984 to 2021 at 0.
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January 2025
Remote Sensing Centre for Earth System Research (RSC4Earth), Leipzig University, Leipzig, 04103, Germany.
With climate extremes' rising frequency and intensity, robust analytical tools are crucial to predict their impacts on terrestrial ecosystems. Machine learning techniques show promise but require well-structured, high-quality, and curated analysis-ready datasets. Earth observation datasets comprehensively monitor ecosystem dynamics and responses to climatic extremes, yet the data complexity can challenge the effectiveness of machine learning models.
View Article and Find Full Text PDFJ Environ Manage
January 2025
Advanced Institute of Natural Sciences, Beijing Normal University, Zhuhai, 519087, China; State Key Laboratory of Wetland Conservation and Restoration, School of Environment, Beijing Normal University, Beijing, 100875, China; Key Laboratory of Coastal Water Environmental Management and Water Ecological Restoration of Guang-dong Higher Education Institutes, Beijing Normal University, Zhuhai, 519087, China.
Since the Industrial Revolution, anthropogenic activities have substantially increased the input of nitrogen (N) and phosphorus (P) into river watersheds, exacerbated by uncertainties stemming from climate change. This study provided a detailed analysis of N and P inputs within the Dawen River Watershed in China from 2000 to 2021. The Net Anthropogenic Nitrogen Input (NANI) and Net Anthropogenic Phosphorus Input (NAPI) methods were used in study, which aimed to investigate how they respond to various climate change factors.
View Article and Find Full Text PDFJ Environ Manage
January 2025
School of Water Conservancy and Transportation, Zhengzhou University, Zhengzhou City, 450001, Henan Province, China. Electronic address:
Enhancing the understanding of the rainfall-runoff temporal dynamics in semi-arid and semi-humid regions is crucial for flood disaster mitigation. Loess Plateau is a unique environment within semi-arid and semi-humid regions, characterized by its deep loess soil, prevalent short-duration intense rainfall, and changes in underlying surface conditions. In this research, 25 catchments from the Loess Plateau were chosen to examine the temporal variations in event runoff responses across different time scales.
View Article and Find Full Text PDFNeural Netw
January 2025
Defense Innovation Institute, Chinese Academy of Military Science, Beijing 100071, China; Intelligent Game and Decision Laboratory, China.
The Physics-informed Neural Network (PINN) has been a popular method for solving partial differential equations (PDEs) due to its flexibility. However, PINN still faces challenges in characterizing spatio-temporal correlations when solving parametric PDEs due to network limitations. To address this issue, we propose a Physics-Informed Neural Implicit Flow (PINIF) framework, which enables a meshless low-rank representation of the parametric spatio-temporal field based on the expressiveness of the Neural Implicit Flow (NIF), enabling a meshless low-rank representation.
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