Patrick Keenan

Understanding nutrient biogeochemistry in agricultural catchments: the challenge of appropriate monitoring frequencies.

We evaluate different frequencies of riverine nutrient concentration measurement to interpret diffuse pollution in agricultural catchments. We focus on three nutrient fractions, nitrate-nitrogen (NO3-N), total reactive phosphorus (TRP) and total phosphorus (TP) observed using conventional remote laboratory-based, low-frequency sampling and automated, in situ high-frequency monitoring. We demonstrate the value of low-frequency routine nutrient monitoring in providing long-term data on changes in surface water and groundwater nutrient concentrations. By contrast, automated high-frequency nutrient observations provide insight into the fine temporal structure of nutrient dynamics in response to a full spectrum of flow dynamics. We found good agreement between concurrent in situ and laboratory-based determinations for nitrate-nitrogen (Pearsons R = 0.93, p < 0.01). For phosphorus fractions: TP (R = 0.84, p < 0.01) and TRP (R = 0.79, p < 0.01) the relationships were poorer due to the underestimation of P fractions observed in situ and storage-related changes of grab samples. A detailed comparison between concurrent nutrient data obtained by the hourly in situ automated monitoring and weekly-to-fortnightly grab sampling reveals a significant information loss at the extreme range of nutrient concentration for low-frequency sampling.

The eukaryote alternative: Advantages of using yeasts in place of bacteria in microbial biosensor development

The relationship between Man and yeast has been a successful and enduring one. The characteristics of yeast have made it an ideal tool in scientific research and as such, it has been used extensively. In this review some of the advantages, methods and applications of yeasts in the biosensor field are outlined. In doing so, we propose a eukaryotic alternative to the current battery of bacteria-based microbial biosensors.

Determining E. coli burden on pasture in a headwater catchment: Combined field and modelling approach

Empirical monitoring studies of catchment-scale Escherichia coli burden to land from agriculture are scarce. This is not surprising given the complexity associated with the temporal and spatial heterogeneity in the excretion of livestock faecal deposits and variability in microbial content of faeces. However, such information is needed to appreciate better how land management and landscape features impact on water quality draining agricultural landscapes. The aim of this study was to develop and test a field-based protocol for determining the burden of E. coli in a small headwater catchment in the UK. Predictions of E. coli burden using an empirical model based on previous best estimates of excretion and shedding rates were also evaluated against observed data. The results indicated that an empirical model utilising key parameters was able to satisfactorily predict E. coli burden on pasture most of the time, with 89% of observed values falling within the minimum and maximum range of predicted values. In particular, the overall temporal pattern of E. coli burden on pasture is captured by the model. The observed and predicted values recorded a disagreement of >1 order of magnitude on only one of the nine sampling dates throughout an annual period. While a first approximation of E. coli burden to land, this field-based protocol represents one of the first comprehensive approaches for providing a real estimate of a dynamic microbial reservoir at the headwater catchment scale and highlights the utility of a simple dynamic empirical model for a more economical prediction of catchment-scale E. coli burden.

Long-term macronutrient stoichiometry of UK ombrotrophic peatlands

In this paper we report new data on peat carbon (C), nitrogen (N) and phosphorus (P) concentrations and accumulation rates for 15 sites in the UK. Concentrations of C, N and P measured in peat from five ombrotrophic blanket mires, spanning 4000-10,000years to present were combined with existing nutrient data from ten Scottish ombrotrophic peat bogs to provide the first UK perspective on millennial scale macronutrient concentrations in ombrotrophic peats. Long-term average C, N and P concentrations (0-1.25m) for the UK are 54.8, 1.56 and 0.039wt%, of similar magnitude to the few published comparable sites worldwide. The uppermost peat (0-0.2m) is enriched in P and N (51.0, 1.86, and 0.070wt%) relative to the deeper peat (0.5-1.25m, 56.3, 1.39, and 0.027wt%). Long-term average (whole core) accumulation rates of C, N and P are 25.3±2.2gCm-2year-1 (mean±SE), 0.70±0.09gNm-2year-1 and 0.018±0.004gPm-2year-1, again similar to values reported elsewhere in the world. The two most significant findings are: 1) that a regression model of N concentration on P concentration and mean annual precipitation, based on global meta data for surface peat samples, can explain 54% of variance in N concentration in these UK peat profiles; and 2) budget calculations for the UK peat cores yield an estimate for long-term average N-fixation of 0.8gm-2year-1. Our UK results, and comparison with others sites, corroborate published estimates of N storage in northern boreal peatlands through the Holocene as ranging between 8 and 15Pg N. However, the observed correlation of N% with both mean annual precipitation and P concentration allows a potential bias in global estimates that do not take this into account. The peat sampling data set has been deposited at the NERC Data Centre (Toberman et al., 2016).

The contribution of algae to freshwater dissolved organic matter: implications for UV spectroscopic analysis

Abstract Dissolved organic matter (DOM) is an important constituent of freshwater that participates in a number of key ecological and biogeochemical processes but can be problematic during water treatment. Thus, the demand for rapid and reliable monitoring is growing, and spectroscopic methods are potentially useful. A model with 3 components—2 that absorb in the ultraviolet (UV) range and are present at variable concentrations and a third that does not absorb light and is present at a low constant concentration—was previously found to yield reliable predictions of dissolved organic carbon concentration [DOC]. The model underestimated [DOC] in shallow eutrophic lakes in the Yangtze Basin, China, however, raising the possibility that DOM derived from algae might be poorly estimated, an idea supported by new data reported here for eutrophic British lakes. We estimated the extinction coefficients in the UV range of algae-derived DOM from published data on algal cultures and from new data from outdoor mesocosm experiments in which high concentrations of DOC were generated under conditions comparable to those in eutrophic freshwaters. The results demonstrate the weak UV absorbance of DOM from algae compared to DOM from terrestrial sources. A modified model, in which the third component represents algae-derived DOM present at variable concentrations, allowed contributions of such DOM to be estimated by combining the spectroscopic data with [DOC] measured by laboratory combustion. Estimated concentrations of algae-derived DOC in 77 surface freshwater samples ranged from 0 to 8.6 mg L−1, and the fraction of algae-derived DOM ranged from 0% to 100%.