Quantifying nitrate pollution sources of the drinking water source area using a Bayesian isotope mixing model in the northeastern suburbs of Beijing, China

Ren, Liangsuo; Cheng, Long; Zhang, Shurong; Ding, Aizhong; Zhu, Yi; Lu, Chunlei; Li, Yu; Yang, Qinke; Li, Jie

doi:10.1080/10256016.2021.1937149

Cited by 11 publications

(1 citation statement)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…CC BY 4.0 License. rivers, including genetic algorithms combined with groundwater models (Han et al, 2020;Habiyakare et al, 2022) or 490 optimization models (Ayaz et al, 2022), the modified export coefficient model combined with SWAT (Guo et al, 2022), physical/stochastic inverse models (Moghaddam et al, 2021), isotope mixing models (Wiegner et al, 2021;Ren et al, 2021), deep learning models (Kontos et al, 2021;Pan et al, 2021), the model-based backward probability method (Khoshgou and Neyshabouri, 2022), and the Null space Monte Carlo stochastic model (Pollicino et al, 2021), among many other models.…”

Section: Identify Pollutant Source Location Using Backward Location P...mentioning

confidence: 99%

Adjoint subordination to calculate backward travel time probability of pollutants in water with various velocity resolutions

Zhang

Fogg

Sun

et al. 2023

Preprint

View full text Add to dashboard Cite

Abstract. Backward probabilities such as backward travel time probability density function for pollutants in natural aquifers/rivers had been used by hydrologists for decades in water-quality related applications. Reliable calculation of backward probabilities, however, has been challenged by non-Fickian pollutant transport dynamics and variability in the resolution of velocity at study sites. To address these two issues, we built an adjoint model by deriving a backward-in-time fractional-derivative transport equation subordinated to regional flow, developed a Lagrangian solver, and applied the model/solver to backtrack pollutant transport in various flow systems. The adjoint model applies subordination to a reversed regional flow field, converts forward-in-time boundaries to either absorbing or reflective boundaries, and reverses the tempered stable density to define backward mechanical dispersion. The corresponding Lagrangian solver is computationally efficient in projecting backward super-diffusive mechanical dispersion along streamlines. Field applications demonstrate that the adjoint subordination model can successfully recover release history, dated groundwater age, and spatial location(s) of pollutant source(s) for flow systems with either upscaled constant velocity, non-uniform divergent flow field, or fine-resolution velocities in a non-stationary, regional-scale aquifer, where non-Fickian transport significantly affects pollutant dynamics and backward probability characteristics. Caution is needed when identifying the phase-sensitive (aqueous versus absorbed) pollutant source in natural media. Possible extensions of the adjoint subordination model are also discussed and tested for quantifying backward probabilities of pollutants in more complex media, such as discrete fracture networks.

show abstract

Section: Identify Pollutant Source Location Using Backward Location P...mentioning

confidence: 99%

Adjoint subordination to calculate backward travel time probability of pollutants in water with various velocity resolutions

Zhang

Fogg

Sun

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

Isotopic source identification of nitrogen pollution in the Pi River in Chengdu

Ding

Shi²,

Ouyang³

et al. 2022

Integr Envir Assess & Manag

View full text Add to dashboard Cite

This study used stable isotope (δ 15 N-NO 3 − and δ 18 O-NO 3 − ) ratios, modeled by means of a Bayesian stable isotope analysis in R (SIAR) approach, to identify nitrate sources in the Pi River, which flows through the megacity Chengdu. The goal was to determine where management resources should be applied to reduce nitrogen pollution. Results revealed that NO 3 − was the primary nitrogen species throughout the study area; that it originated in manure and sewage, as well as nitrification of fertilizer and soil nitrogen; and that the nitrogen in the main stream came primarily from the tributaries. Notably, the nitrogen concentration in the tributaries exhibited no evident seasonal variations, further demonstrating that its source was intensive anthropogenic activity. Results of Bayesian model (SIAR) estimation indicated that manure and sewage were the dominant nitrate contributors in the watershed and that the nitrate concentration decreased from 54.19% to 39.57% in response to water treatment. These results empirically demonstrate that the methodology described in this work can be used effectively in catchments affected by intensive anthropogenic activity to determine where management resources should be applied to reduce nitrogen pollution.

show abstract

Quantifying nitrate pollution sources of shallow groundwater and related health risks based on deterministic and Monte Carlo models: A study in Huaibei mining area, Huaibei coalfield, China

Qiu

Gui²,

et al. 2023

Ecotoxicology and Environmental Safety

View full text Add to dashboard Cite

Quantifying nitrate pollution sources of the drinking water source area using a Bayesian isotope mixing model in the northeastern suburbs of Beijing, China

Cited by 11 publications

References 52 publications

Adjoint subordination to calculate backward travel time probability of pollutants in water with various velocity resolutions

Adjoint subordination to calculate backward travel time probability of pollutants in water with various velocity resolutions

Isotopic source identification of nitrogen pollution in the Pi River in Chengdu

Quantifying nitrate pollution sources of shallow groundwater and related health risks based on deterministic and Monte Carlo models: A study in Huaibei mining area, Huaibei coalfield, China

Contact Info

Product

Resources

About