Spatiotemporal variation in nitrogen loads and their impacts on river water quality in the upper Yangtze River basin

Ai, Wang; Yang, Dawen; Tang, Lihua

doi:10.1016/j.jhydrol.2020.125487

Cited by 47 publications

(18 citation statements)

References 52 publications

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“…daily or every few hours by automatic samplers) that were taken during runoff events and grab samples (e.g. monthly) that were taken under baseflow conditions (Orr et al, 2014;Waters and Packett, 2007). As EC data from the loads monitoring programme were limited, we extracted additional EC data from the Water Monitoring Information Portal provided by the Department of Natural Resources, Mines and Energy of Queensland (DNRME, 2016) to complement the loads monitoring programme records.…”

Section: Water Quality Datamentioning

confidence: 99%

“…On average, there were 14 samples per event across the nine constituents (ranging from 12 for DOP to 16 for EC; Table S3 in the Supplement). This ensured that the water quality dynamics over a runoff event were reasonably well captured and that the derived EMCs were reliable (Waters and Packett, 2007). For each event, the EMC of a constituent was calculated as the total load per unit flow volume within the event using the following (Bartley et al, 2012):…”

Section: Event Mean Concentrationmentioning

confidence: 99%

“…4.1.1). The effect of event temperature can be also attributed to the fact that the higher temperatures could lead to more recent mineralisation of nutrients, increasing readily transportable dissolved nutrient sources (Liu et al, 2017;Wang et al, 2020). Temperature is one controlling factor that affects pollution transformation (Barnard et al, 2005).…”

Section: Temperaturementioning

confidence: 99%

“…This study attempted to address these knowledge gaps in statistical water quality models, taking advantages of eventbased water quality monitoring data from the Great Barrier Reef (GBR) catchments in northern Australia, where landderived pollutants have posed threats to ecosystem health of the GBR lagoon (Brodie et al, 2012;McKergow et al, 2005b). We address the limitations in statistical water quality models by using the following: (1) Bayesian hierarchical modelling was used to investigate water quality temporal variation, which allowed the prediction of water quality in multiple catchments and, simultaneously, quantified parameter uncertainty (Gelman et al, 2013;Rode et al, 2010;Webb and King, 2009); and (2) Bayesian model averaging (BMA) approaches were used to identify the relative importance of the different environmental factors and provide multi-model weighted predictions, which have been shown to better quan-tify the uncertainty arising from model selection (Höge et al, 2019;Raftery et al, 1997;Wang et al, 2012). We targeted nine common water quality indicators, including sediments, nutrients and salinity.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments

Liu

Ryu

Webb

et al. 2021

Hydrol. Earth Syst. Sci.

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Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programmes have collected a large amount of data that provide a good basis for investigating the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provide an opportunity to further our understanding of water quality dynamics in subtropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of (1) identifying the influential environmental drivers of temporal variation in flow event concentrations and (2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g. runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations and between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.

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Section: Water Quality Datamentioning

confidence: 99%

Section: Event Mean Concentrationmentioning

confidence: 99%

Section: Temperaturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments

Liu

Ryu

Webb

et al. 2021

Hydrol. Earth Syst. Sci.

View full text Add to dashboard Cite

show abstract

“…4.1.1). The effect of event temperature can be also attributed to the fact that the higher temperatures could lead to more recent mineralisation of nutrients, increasing readily transportable dissolved nutrient sources (Liu et al, 2017;Wang et al, 2020). In addition, higher event temperature might be associated with higher pre-event temperature, resulting in poor groundcover, potentially lowering the dissolved nutrients losses through plant assimilation/uptake (Sect.…”

Section: Temperaturementioning

confidence: 99%

A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality: a case study in the Great Barrier Reef catchments

Liu¹,

Ryu²,

Webb³

et al. 2021

Preprint

View full text Add to dashboard Cite

Abstract. Stream water quality is highly variable both across space and time. Water quality monitoring programs have collected a large amount of data that provide a good basis to investigate the key drivers of spatial and temporal variability. Event-based water quality monitoring data in the Great Barrier Reef catchments in northern Australia provides an opportunity to further our understanding of water quality dynamics in sub-tropical and tropical regions. This study investigated nine water quality constituents, including sediments, nutrients and salinity, with the aim of: 1) identifying the influential environmental drivers of temporal variation in flow event concentrations; and 2) developing a modelling framework to predict the temporal variation in water quality at multiple sites simultaneously. This study used a hierarchical Bayesian model averaging framework to explore the relationship between event concentration and catchment-scale environmental variables (e.g., runoff, rainfall and groundcover conditions). Key factors affecting the temporal changes in water quality varied among constituent concentrations, as well as between catchments. Catchment rainfall and runoff affected in-stream particulate constituents, while catchment wetness and vegetation cover had more impact on dissolved nutrient concentration and salinity. In addition, in large dry catchments, antecedent catchment soil moisture and vegetation had a large influence on dissolved nutrients, which highlights the important effect of catchment hydrological connectivity on pollutant mobilisation and delivery.

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Developing an Approach for Integrating Chemical Analysis and Transcriptional Changes to Assess Contaminants in Water, Sediment, and Fish

Cardenas Perez,

Challis,

Alcaraz

et al. 2024

Enviro Toxic and Chemistry

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Pharmaceuticals in aquatic environments pose threats to aquatic organisms because of their continuous release and potential accumulation. Monitoring methods for these contaminants are inadequate, with targeted analyses falling short in assessing water quality's impact on biota. The present study advocates for integrated strategies combining suspect and targeted chemical analyses with molecular biomarker approaches to better understand the risks posed by complex chemical mixtures to nontarget organisms. The research aimed to integrate chemical analysis and transcriptome changes in fathead minnows to prioritize contaminants, assess their effects, and apply this strategy in Wascana Creek, Canada. Analysis revealed higher pharmaceutical concentrations downstream of a wastewater‐treatment plant, with clozapine being the most abundant in fathead minnows, showing notable bioavailability from water and sediment sources. Considering the importance of bioaccumulation factor and biota–sediment accumulation factor in risk assessment, these coefficients were calculated based on field data collected during spring, summer, and fall seasons in 2021. Bioaccumulation was classified as very bioaccumulative with values >5000 L kg–1, suggesting the ability of pharmaceuticals to accumulate in aquatic organisms. The study highlighted the intricate relationship between nutrient availability, water quality, and key pathways affected by pharmaceuticals, personal care products, and rubber components. Prioritization of these chemicals was done through suspect analysis, supported by identifying perturbed pathways (specifically signaling and cellular processes) using transcriptomic analysis in exposed fish. This strategy not only aids in environmental risk assessment but also serves as a practical model for other watersheds, streamlining risk‐assessment processes to identify environmental hazards and work toward reducing risks from contaminants of emerging concern. Environ Toxicol Chem 2024;00:1–22. © 2024 SETAC

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Spatiotemporal variation in nitrogen loads and their impacts on river water quality in the upper Yangtze River basin

Cited by 47 publications

References 52 publications

A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments

A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality – a case study in the Great Barrier Reef catchments

A Bayesian approach to understanding the key factors influencing temporal variability in stream water quality: a case study in the Great Barrier Reef catchments

Developing an Approach for Integrating Chemical Analysis and Transcriptional Changes to Assess Contaminants in Water, Sediment, and Fish

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