Chronic nitrogen legacy in the aquifers of China

Communications Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1)

Published: Jan. 28, 2025

Language: Английский

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Spatial variations in groundwater hydrochemistry, sources, and controls across catchments on Chinese Loess Plateau

Journal of Hydrology Regional Studies, Journal Year: 2024, Volume and Issue: 53, P. 101791 - 101791

Published: April 26, 2024

This study was carried out at the Chinese Loess Plateau (CLP), which is located in middle reach of Yellow River northern China. Understanding groundwater hydrochemistry and its controlling factors regions like CLP with thick unsaturated zones challenging due to limited accessibility. The primary objective this investigate hydrochemistry, sources, controls, assess suitability for drinking irrigation purposes across 14 major catchments CLP. Our results showed that composition dissolved solutes primarily influenced by weathering rock, particularly silicate rock. However, human activities, especially agriculture urban areas, also have a notable impact on solutes. presence elevated nitrate various underscores urgency addressing long-term nitrogen pollution. Environmental changes such as climate change, erosion rates, land-use patterns are key shaping quality. As air temperature decreases intensifies, along more intensive land use, ion concentrations tend rise. Note that, approximately 91% suitable direct drinking, but ensuring proper drainage crucial prevent sodium damage during irrigation. These findings enhance our understanding influencing essential effectively managing quality

Language: Английский

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Study on the prediction performance of AIDS monthly incidence in Xinjiang based on time series and deep learning models

BMC Public Health, Journal Year: 2025, Volume and Issue: 25(1)

Published: Feb. 25, 2025

AIDS is a highly fatal infectious disease of Class B, and Xinjiang high-incidence region for in China. The core prevention control lies early monitoring warning. This study aims to identify the best model predicting monthly incidence Xinjiang, providing scientific evidence control. Monthly data from January 2004 December 2020 were collected. Six different models, including ARIMA (2,1,2) model, (2,1,2)-EGARCH (2,2) combined (2,1,2)-TGARCH (1,1) ETS (A, A, A) XGBoost LSTM used fitting forecasting. All models able capture overall trend Xinjiang. In terms RMSE MAE, performed best, achieving smallest values. For MAPE metric, best. Considering RMSE, together, was best-performing this study. also showed good predictive performance, while relatively poorly. Deep learning (such as LSTM) have significant potential time series may limitations when handling data, future improvements or combinations could enhance prediction performance.

Language: Английский

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A comparison of several cyclo-stationary and fractionally differenced time-varying autoregressive models in runoff simulation and prediction

Journal of Hydrology, Journal Year: 2024, Volume and Issue: 638, P. 131509 - 131509

Published: June 15, 2024

Language: Английский

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Hourly Asymmetric Multifractality and Dynamic Efficiency in Cryptocurrency Markets: The Effects of COVID‐19 and Russia–Ukraine Tension

Australian Economic Papers, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 7, 2025

ABSTRACT This paper examines the hourly downward/upward multifractality and dynamic efficiency of four cryptocurrencies—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), Litecoin (LTC)— before during COVID‐19 pandemic, Russia–Ukraine tension. Using asymmetric multifractal detrended fluctuation analysis method, results show significant in all series, which intensifies for BTC only throughout crisis narrows ETH, XRP, LTC. Moreover, we that cryptocurrency markets are more inefficient upward (downward) trend (during) crisis. LTC is least market pre COVID‐19, whereas XRP pandemic The evidence excessive crypto markets. Before crisis, positive values excess asymmetry have been identified markets, were negative ETH showed wider fluctuations compared to indicating a stronger reaction war's impact.

Language: Английский

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How small is big enough? Big data-driven machine learning predictions for a full-scale wastewater treatment plant

Water Research, Journal Year: 2024, Volume and Issue: unknown, P. 123041 - 123041

Published: Dec. 1, 2024

Language: Английский

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Unveiling Industrial Influence: Statistical Insights into Brahmani River Water Quality

Environmental earth sciences, Journal Year: 2025, Volume and Issue: unknown, P. 181 - 192

Published: Jan. 1, 2025

Language: Английский

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Unravelling nitrate transformation mechanisms in karst catchments through the coupling of high-frequency sensor data and machine learning

Water Research, Journal Year: 2024, Volume and Issue: 267, P. 122507 - 122507

Published: Sept. 23, 2024

Language: Английский

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Advances and perspectives of research on soil moisture response to afforestation: A review

Hydrological Sciences Journal, Journal Year: 2024, Volume and Issue: 69(14), P. 2090 - 2107

Published: Aug. 28, 2024

The soil moisture (SM) balance was affected by afforestation. In this study, the CiteSpace software used to review literatures on impact of afforestation SM, and we further present research findings Loess Plateau (LP) as follows: 1) spatio-temporal heterogeneity SM varies depending scale; 2) presence a dried layer significantly hinders deep recharge; 3) accumulation net primary productivity (NPP) may be occurring at expense SM. following topics are discussed: recharge consumption in LP; green water footprint its shortage degree non-crop greenery for evaluating allocation use; development inclusive models encompassing different spatial scales assess limits Soil Water Carrying Capacity Vegetation (SWCCV).

Language: Английский

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Machine Learning-based Optimal Nitrate Dynamics and Flux Monitoring Frequency in Karst Catchment

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Oct. 1, 2024

Language: Английский

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