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Data for assessment of sediment, soil, and water quality at Ashfield flats reserve, Western Australia.

ABSTRACT: Sediment and water samples were collected using transects and grids within sampling strata, in 2019, 2020, and 2021 from a riparian reserve adjoining the Swan River estuary in Western Australia. Different sampling designs were used each year, with transects and/or grids designed to assess changes in sediment and water quality across assumed environmental gradients such as salinity or distance from possible contaminant sources. Sediments were from 0-10cm; pH and electrical conductivity were measured on suspensions, 32 elements measured by ICP-OES on HNO₃/HCl digests, and microplastics counted microscopically after Fenton digestion and density separation. Surface water was from wetland ponds and stormwater drains, with pH, EC measured in-situ. Filtered acidified water subsamples used to measure nitrate + nitrite and dissolved phosphate spectrophotometrically and 26 elements using ICP-OES. Reported data include metadata and are for 231 sediment/soil samples and 172 water samples, including sampling strata categories and UTM and Longitude-Latitude coordinates. Elemental concentrations have been censored based on blank subtraction and calculated lower detection limits, with censored data presented with missing value codes.

SUBMITTER: Rate AW

PROVIDER: S-EPMC8885578 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Data for assessment of sediment, soil, and water quality at Ashfield flats reserve, Western Australia.

Rate Andrew W AW McGrath Gavan S GS

Data in brief 20220219

Sediment and water samples were collected using transects and grids within sampling strata, in 2019, 2020, and 2021 from a riparian reserve adjoining the Swan River estuary in Western Australia. Different sampling designs were used each year, with transects and/or grids designed to assess changes in sediment and water quality across assumed environmental gradients such as salinity or distance from possible contaminant sources. Sediments were from 0-10cm; pH and electrical conductivity were measu ...[more]

PMID: 35242948

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Project description:Water-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.

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Dataset Information

Data for assessment of sediment, soil, and water quality at Ashfield flats reserve, Western Australia.

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Data for assessment of sediment, soil, and water quality at Ashfield flats reserve, Western Australia.

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