Modeling transmission of hexavalent chromium concentration and its health cost with a water quality analysis simulation program

Chen, Ying-Chu; Tseng, Chwan-Lu; Chen, Yun-Ting

doi:10.1002/wer.1569

Cited by 3 publications

(3 citation statements)

References 42 publications

(68 reference statements)

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“…In Taiwan, the transmission of hexavalent chromium (Cr(VI)) contamination in a water-sediment system and its flux into cultivated soils was used to determine a link to gastric cancer. In this study, highest concentrations of Cr(VI) correlated with greater risk of gastric cancer [31]. However, a systematic review and meta-analysis by Suh et al published a year earlier had concluded that Cr(VI) was not associated with gastric cancer risk [32].…”

Section: Water Sources and Gastric Cancer Riskcontrasting

confidence: 53%

Environmental factors associated with gastric carcinogenesis

Kayamba

Kelly

2021

Current Opinion in Gastroenterology

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Purpose of reviewGastric cancer is one of the leading causes of cancer-related deaths globally. Several modifiable environmental factors have been linked to gastric carcinogenesis but in many cases, evidence is either weak or fragmented. In this review, we interrogate the latest evidence implicating environmental factors in the development of gastric cancer. Recent findingsWe are writing this review at a time when technological advancements are enabling scientists to effectively conduct large epidemiological studies with better tools for exposure estimations. We have highlighted risk factors that do not yet have enough evidence to be included as definite carcinogens in the International Agency for Research on Cancer monographs but have the potential for inclusion in the near future. Considered in our review are the links between gastric cancer and exposure to biomass smoke, particulate matter, occupational hazards and water contamination.

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Section: Water Sources and Gastric Cancer Riskcontrasting

confidence: 53%

Environmental factors associated with gastric carcinogenesis

Kayamba

Kelly

2021

Current Opinion in Gastroenterology

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“…Existing water quality prediction models can be divided into two types: mechanistic and non-mechanistic models [7,8] . Mechanistic models, such as the Streeter Phelps (S-P) model [9][10][11][12] , QUAL2E model [13][14][15] , Water Quality Analysis Simulation Program (WASP) [15][16][17] , Soil and Water Assessment Tool (SWAT) [18][19][20] , etc., are based on physical and chemical principles and require a large amount of basic and long-term monitoring data. However, such data are often unavailable or incomplete in practice, which limits the applicability and accuracy of mechanistic models.…”

Section: Introductionmentioning

confidence: 99%

Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

Pang,

Luo,

Yao

et al. 2024

Water Resour Manage

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The prediction of water quality in urban rivers plays a crucial role in supporting water environment management. This study collected and organized realtime water quality monitoring data from four water quality monitoring stations in the Fenjiang River Basin of Foshan City, spanning from 2016 to 2021. To reduce noise interference in historical monitoring data, we applied a wavelet packet denoising (WPD) technique. Subsequently, we developed a single-factor water quality prediction model based on Long Short-Term Memory (LSTM), focusing on two key factors of water quality degradation: chemical oxygen demand (COD) and ammonia nitrogen (NH3-N).The results of this study demonstrate that the integration of WPD with LSTM, referred to as WPD-LSTM, outperformed conventional LSTM models in terms of predictive accuracy. Notably, the WPD-LSTM model exhibited superior performance in predicting the impact of COD and NH3-N on water quality in the Fenjiang River, surpassing the traditional LSTM model over a prediction period of 12 hours and 3 days.In the 12-hour prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 55% to 67%, with an average decrease of 61%; the RMSE values of COD predictions in the four monitoring sections decreased by 18% to 51%, with an average decrease of 29%. In the 3-day prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 40% to 83%, with an average decrease of 65%; the RMSE values of COD predictions in the four monitoring sections decreased by 50% to 69%, with an average decrease of 60%. By employing the WPD-LSTM method, this study contributes to improving the precision of water quality prediction, thereby providing valuable insights for effective water environment management in urban river systems.

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Section: Introductionmentioning

confidence: 99%

Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

pang,

Luo,

et al. 2023

Preprint

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The prediction of water quality in urban rivers plays a crucial role in supporting water environment management. This study collected and organized real-time water quality monitoring data from four water quality monitoring stations in the Fenjiang River Basin of Foshan City, spanning from 2016 to 2021. To reduce noise interference in historical monitoring data, we applied a wavelet packet denoising (WPD) technique. Subsequently, we developed a single-factor water quality prediction model based on Long Short-Term Memory (LSTM), focusing on two key factors of water quality degradation: chemical oxygen demand (COD) and ammonia nitrogen (NH3-N). The results of this study demonstrate that the integration of WPD with LSTM, referred to as WPD-LSTM, outperformed conventional LSTM models in terms of predictive accuracy. Notably, the WPD-LSTM model exhibited superior performance in predicting the impact of COD and NH3-N on water quality in the Fenjiang River, surpassing the traditional LSTM model over a prediction period of 12 hours and 3 days. In the 12-hour prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 55% to 67%, with an average decrease of 61%; the RMSE values of COD predictions in the four monitoring sections decreased by 18% to 51%, with an average decrease of 29%. In the 3-day prediction, the RMSE values of NH3-N predictions in the four monitoring sections decreased by 40% to 83%, with an average decrease of 65%; the RMSE values of COD predictions in the four monitoring sections decreased by 50% to 69%, with an average decrease of 60%. By employing the WPD-LSTM method, this study contributes to improving the precision of water quality prediction, thereby providing valuable insights for effective water environment management in urban river systems.

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Modeling transmission of hexavalent chromium concentration and its health cost with a water quality analysis simulation program

Cited by 3 publications

References 42 publications

Environmental factors associated with gastric carcinogenesis

Environmental factors associated with gastric carcinogenesis

Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

Water Quality Prediction in Urban Waterways Based on Wavelet Packet Denoising and LSTM

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