Connected Waters Initiative Research Centre

"Trace amounts of sulphur in speleothems suggest that stalagmites may act as archives of sulphur deposition, thereby recording aspects of atmospheric variability in sulphur content. Accurate interpretation of this novel sulphur archive depends upon understanding how biogeochemical cycling in the soil and
epikarst above the cave may modify the precursor atmospheric values of sulphur concentration and isotopic composition prior to incorporation into the speleothem record. Dual isotope analysis of d34O-SO4 and d18O-SO4 is used to trace biogeochemical transformations of atmospheric sulphur through the cave system at Grotta di Ernesto in the Italian Alps and builds towards a framework for interpretation of speleothem sulphur archives which depends on overlying ecosystem dynamics and karst hydrological properties. A three component model of atmospheric
sulphate signal modification is proposed to be driven by (1). vegetation and soil cycling, (2). the degree of groundwater mixing in the karst aquifer; and (3). redox status. The relative influence of each process is specific to individual drip flow sites and associated stalagmites, rendering each sulphur archive a unique
signal of environmental conditions. Under conditions found in the soil and epikarst above Grotta di Ernesto, the dual isotope signatures of sulphate sulphur and oxygen incorporated into speleothem carbonate, closely reflect past conditions of industrial sulphur loading to the atmosphere and the extent of signal modification through biogeochemical cycling and
aquifer mixing."

Speleothems are primarily studied in order to generate archives of climatic change and results have led to significant advances in identifying and dating major shifts in the climate system. However, the climatological meaning of many speleothem records cannot be interpreted unequivocally; this is particularly so for more subtle shifts and shorter time periods, but the use of multiple proxies and improving understanding of formation mechanisms offers a clear way forward.

Annual, monthly and daily analyses of stable isotopes in precipitation are commonly made worldwide, yet only a few studies have explored the variations occurring on short timescales within individual precipitation events, particularly at mid-latitude locations. This study examines hydrogen isotope data from sequential, intra-event samples from sixteen precipitation events during different seasons and a range of synoptic conditions over an 18-month period in Birmingham, UK. Precipitation events were observed simultaneously using a vertically-pointing micro rain radar (MRR), which, for the first time at a mid-latitude location, allowed high resolution examination of the microphysical characteristics (e.g. rain rate, fall velocity, drop size distributions) that may influence the local isotopic composition of rainwater. The range in δD from 242 samples from 16 events was -87.0‰ to +9.2‰, whilst the largest variation observed in a single event was 55.4‰. In contrast to previous work, the results indicate that some mid-latitude precipitation events do indeed show significant intra-event trends that are strongly influenced by precipitation processes and parameters such as rain rate, melting level height and droplet sizes. Inverse relationships between rain rate and isotopic composition are observed, representing an example of a local type of ‘amount effect’, a still poorly-understood process occurring at different scales. For these particular events the mean δ value may therefore not provide all the relevant information. This work has significance for the testing and development of isotope-enabled cloud resolving models and land surface models at higher resolutions, and provides improved insights into a range of environmental processes that are influenced by sub-sampled precipitation events.

Organic matter preserved in speleothems has considerable potential to record changes in the surrounding environment, particularly in the overlying vegetation. Here, we review three types of organic matter analysis relevant to speleothems: organic fluorescence, lipid biomarker analysis, and amino acid racemisation. Organic matter luminescence provides a useful non-destructive and rapid method for assessing dissolved organic matter quantity and quality, while biomarker analysis (amino acids and lipids) has the potential to provide a more detailed signal related to specific parts of the surrounding ecosystem such as the dominant vegetation regime and bacterial activity. Amino acid analysis has yet to prove demonstrably useful in stalagmites, due to the inability to characterise the sources of proteinaceous matter. However, the small but increasing body of work on lipid biomarker analysis in stalagmites has shown that a wide variety of recognisable biomarkers are preserved over long periods of time (4100 ka), can be recovered at temporal resolutions of o10 yr, and show meaningful changes through time. This approach is therefore of considerable potential value to Quaternary science. r

1] From the Permian through to the modern day, stalagmites are an important archive of environmental change. Annually laminated stalagmites provide both a precise chronology and a paleoclimate proxy. The rate of annual vertical growth of stalagmites is recorded in changes of calcite fabric, annual fluxes of fluorescent organic matter or annual variations in trace element composition. The processes governing stalagmite growth are the flux of water, the CO 2 saturation of drip water relative to the cave atmosphere, and the temperature. Although these processes are well understood, they depend on the specific hydrogeological flow routing of individual stalagmites. Therefore, although past climates are recorded in the vertical growth lamina thickness, the climatic signal is perturbed by noise related to local hydrologic factors. To separate local from global factors, we used geostatistical tools to analyze annual growth rate data from eleven stalagmites located on four continents. Variogram analyses permit the quantification of the signal content contained within the growth rate records. The information content ranges from 23 to 87%. Analysis of the growth derivative shows a negative correlation at a 1 year lag, meaning that acceleration in growth rate tends to be systematically followed by deceleration in growth rate and vice versa. We call this behavior "flickering" growth, and argue that it is related to the size of the store feeding the stalagmite. Variogram analysis and flickering are used to screen which types of signals can potentially be recorded in a given speleothem. Citation: Mariethoz, G., B. F. J. Kelly, and A. Baker (2012), Quantifying the value of laminated stalagmites for paleoclimate reconstructions, Geophys. Res. Lett., 39, L05407,

1] This paper focuses on analyzing chaos in cave percolation water drip rates, which has implications for flow routing in fractured media and on the use of speleothems for paleoclimate reconstructions. It has been shown that the physics of dripping faucets involve a set of non-linear equations leading to chaotic drip rate, meaning that, for a given drip rate, the interval between individual drops can vary greatly. It can be expected that drip waters supplying stalagmites show similar properties, and consequently the dependency between water flux and stalagmite growth rate or geochemistry could be more complicated than usually assumed. We used high-frequency monitoring of two contrasting drips in a cave in Australia, and identified chaos in cave drip rate. Our findings also indicate that the occurrence of chaos can give insights into flow routing in fractured media.

Training image-based approaches for stochastic simulations have recently gained attention in surface and subsurface hydrology. This family of methods allows the creation of multiple realizations of a study domain, with a spatial continuity based on a training image (TI) that contains the variability, connectivity, and structural properties deemed realistic. A major drawback of these methods is their computational and/or memory cost, making certain applications challenging. It was found that similar methods, also based on training images or exemplars, have been proposed in computer graphics. One such method, image quilting (IQ), is introduced in this paper and adapted for hydrogeological applications. The main difficulty is that Image Quilting was originally not designed to produce conditional simulations and was restricted to 2-D images. In this paper, the original method developed in computer graphics has been modified to accommodate conditioning data and 3-D problems. This new conditional image quilting method (CIQ) is patch based, does not require constructing a pattern databases, and can be used with both categorical and continuous training images. The main concept is to optimally cut the patches such that they overlap with minimum discontinuity. The optimal cut is determined using a dynamic programming algorithm. Conditioning is accomplished by prior selection of patches that are compatible with the conditioning data. The performance of CIQ is tested for a variety of hydrogeological test cases. The results, when compared with previous multiplepoint statistics (MPS) methods, indicate an improvement in CPU time by a factor of at least 50.

Reverse osmosis (RO) permeates from three Australian water recycling plants were characterised using three-dimensional excitation-emission matrix (EEM) fluorescence spectroscopy. The plants differed in terms of their RO operational configurations: RO feed water and pretreatment processes. Intermediate permeates from multiple staged RO treatment processes could be distinguished using Peak C (l Ex/Em ¼ 340/426 nm) and Peak T 1 (l Ex/Em ¼ 285/350 nm) fluorescence. Monte-Carlo analysis of Peak C and Peak T 1 rejection showed typical rejection of over 98% and permeate fluorescence intensities were used to determine Peak C as the most suitable for RO monitoring purposes. The results of this work indicate that fluorescence monitoring is a promising technique for sensitive quantitative and qualitative performance monitoring of RO treatment processes.

Membrane Monitoring a b s t r a c t A rapid, highly sensitive and selective detector is urgently required to detect contamination events in recycled water systems -for example, cross-connection events in dual reticulation pipes that recycle advanced treated sewage effluent -as existing technologies, including total organic carbon and conductivity monitoring, cannot always provide the sensitivity required. Fluorescence spectroscopy has been suggested as a potential monitoring tool given its high sensitivity and selectivity. A review of recent literature demonstrates that by monitoring the fluorescence of dissolved organic matter (DOM), the ratios of humic-like (Peak C) and protein-like (Peak T) fluorescence peaks can be used to identify trace sewage contamination in river waters and estuaries, a situation analogous to contamination detection in recycled water systems. Additionally, strong correlations have been shown between Peak T and biochemical oxygen demand (BOD) in rivers, which is indicative of water impacted by microbial activity and therefore of sewage impacted systems. Hence, this review concludes that the sensitive detection of contamination events in recycled water systems may be achieved by monitoring Peak T and/or Peak C fluorescence. However, in such systems, effluent is treated to a high standard resulting in much lower DOM concentrations and the impact of these advanced treatment processes on Peaks T and C fluorescence is largely unknown and requires investigation. This review has highlighted that further work is also required to determine (a) the stability and distinctiveness of recycled water fluorescence in relation to the treatment processes utilised, (b) the impact of matrix effects, particularly the impact of oxidation, (c) calibration issues for online monitoring, and (d) the advanced data analytical techniques required, if any, to improve detection of contamination events.

Carbohydrates Charge density Hydrophobicity Protein a b s t r a c t Algogenic organic matter (AOM) can interfere with drinking water treatment processes and comprehensive characterisation of AOM will be informative with respect to treatability. This paper characterises the AOM originating from four algae species (Chlorella vulgaris, Microcystis aeruginosa, Asterionella formosa and Melosira sp.) using techniques including dissolved organic carbon (DOC), specific UV absorbance (SUVA), zeta potential, charge density, hydrophobicity, protein and carbohydrate content, molecular weight and fluorescence. All AOM was predominantly hydrophilic with a low SUVA. AOM had negative zeta potential values in the range pH 2-10. The stationary phase charge density of AOM from C. vulgaris was greatest at 3.2 meq g À1 while that of M. aeruginosa and Melosira sp. was negligible. Lower charge density was related to higher hydrophobicity, while it was related in turn to increasing proteins 4500 kDa:carbohydrate ratio. This demonstrates that AOM is of a very different character to natural organic matter (NOM).

Drinking water treatment Fluorescence excitation emission matrix (FEEM) SUVA a b s t r a c t Organic matter (OM) causes many problems in drinking water treatment. It is difficult to monitor OM concentrations and character during treatment processes due to its complexity. Fluorescence spectroscopy is a promising tool for online monitoring. In this study, a unique dataset of fluorescence excitation emission matrixes (EEMs) (n ¼ 867) was collected from all treatment stages of five drinking water treatment plants (WTPs) situated in diverse locations from subtropical to temperate climate. The WTPs incorporated various water sources, treatment processes and OM removal efficiencies (DOC removal 0%e68%).

For the first time, the application of different robust data mining techniques to the assessment of water treatment performance is considered. Principal components analysis (PCA), parallel factor analysis (PARAFAC), and a self-organizing map (SOM) were used in the analysis of multivariate data characterising organic matter (OM) removal at 16 water treatment works. Decomposed fluorescence data from PCA, PARAFAC and SOM were used as input to calibrate fluorescence data with OM concentrations using stepwise regression (SR), partial least squares (PLS), multiple linear regression (MLR), and neural network with back-propagation algorithm (BPNN). The best results were obtained with combined PARAFAC/PLS and SOM/BPNN. Both the numerical accuracy and feasibility of the adopted solutions were compared and recommendations on the use of the above techniques for fluorescence data analysis are presented.

Large datasets are common in chemical and environmental engineering applications and tools for their analysis are in great demand. Here, the outputs of a series of fluorescence spectroscopy analyses are utilised to demonstrate the application of the self-organising map (SOM) technique for data analysis. Fluorescence spectroscopy is a wellestablished technique of organic matter fingerprinting in water. The technique can provide detailed information on the physico-chemical properties of water. However, analysis of fluorescence spectra requires the application of robust statistical and computational data pre-processing and analysis tools. This paper presents a tutorial for training engineering postgraduate researchers in the use of SOM techniques using MATLAB ® . Via a tutorial, the application of SOM to fluorescence spectra and, in particular, the characterisation of organic matter removal in water treatment, is presented. The tutorial presents a step-by-step example of the application of SOM to fluorescence data analysis and includes the source code for MATLAB ® , together with presentation and discussion of the results. With this tutorial we hope to popularise this robust pattern recognition technique for fluorescence data analysis and large data sets in general, and also to provide educational practitioners with a novel tool with which to train engineering students in SOM.

Fluorescence spectroscopy enables fast and sensitive analysis of environmental samples containing various organic matter constituents. However, to retrieve valuable information from fluorescence spectra, robust techniques for data analysis should be employed. Here, different multivariate analysis methods and artificial neural networks (ANNs) were applied for decomposition and calibration of fluorescence excitation-emission matrices (EEMs). This is the first paper summarizing the application of different data mining methods, from multiway analysis to ANNs, for fluorescence EEMs technique employed to characterize organic matter properties and removal in the field of drinking water treatment. Fluorescence analysis was carried out on municipal water treatment samples of raw and partially-treated water. Parallel factor analysis (PARAFAC) method and self-organizing maps were used to analyse EEMs, extract information on the organic matter constituents and reduce the dimensionality of the data to enhance the efficiency of calibration methods. Partial least squares (PLS), multiple linear regression (MLR) and neural network with back-propagation were employed for calibration of fluorescence data with actual total organic carbon (TOC) concentrations. All models except PARAFAC-MLR produced consistent results with correlation coefficient R 2 ¼ 0.93 for validation dataset. This is the first such comparative analysis of fluorescence data modelling that clarifies fundamental fluorescence data analysis questions regarding the suitability of different decomposition and calibration methods.

Key to effective disinfection byproduct (DBP) management is source water control and management, and more specifically, organic matter (OM) control and management. However, the content and character of OM in source waters is spatially and temporally variable, and the prediction of its composition is challenging. Water treatment companies require adequate analytical techniques for OM characterisation to maintain the operation of the water supply and treatment systems adjusted to constantly changing environmental conditions. There is a requirement, therefore, for an improved understanding of OM composition and character in source water, how that composition and character varies with flow conditions, and how this impacts on drinking water treatment. This paper demonstrates that fluorescence spectroscopy offers a potential alternative to other analytical methods of OM characterisation. The advantages of fluorescence include rapid, sensitive and selective characterisation of OM, no sample pre-treatment, small sample volume, and the potential for on-line monitoring incorporation. Fluorescence can provide useful information on OM reactivity and treatability together with an indication of the OM sources (allochthonous or autochthonous). The paper discusses a body of literature which has identified relationships between fluorescence spectra and OM physico-chemical properties (i.e. degree of hydrophobicity, microbial content), has applied fluorescence spectroscopy to characterise the changes in OM upon disinfection, and has related the fluorescence properties to DBP formation. Further work is required in the robust management of data arising from fluorescence spectroscopy analysis and, in particular, Excitation Emission Matrices. Consideration must be given as to how the data might best be employed to greatest effect on a routine basis at WTW.

For the first time, the application of different robust data mining techniques to the assessment of water treatment performance is considered. Principal components analysis (PCA), parallel factor analysis (PARAFAC), and a self-organizing map (SOM) were used in the analysis of multivariate data characterising organic matter (OM) removal at 16 water treatment works. Decomposed fluorescence data from PCA, PARAFAC and SOM were used as input to calibrate fluorescence data with OM concentrations using stepwise regression (SR), partial least squares (PLS), multiple linear regression (MLR), and neural network with back-propagation algorithm (BPNN). The best results were obtained with combined PARAFAC/PLS and SOM/BPNN. Both the numerical accuracy and feasibility of the adopted solutions were compared and recommendations on the use of the above techniques for fluorescence data analysis are presented.

Key to effective disinfection byproduct (DBP) management is source water control and management, and more specifically, organic matter (OM) control and management. However, the content and character of OM in source waters is spatially and temporally variable, and the prediction of its composition is challenging. Water treatment companies require adequate analytical techniques for OM characterisation to maintain the operation of the water supply and treatment systems adjusted to constantly changing environmental conditions. There is a requirement, therefore, for an improved understanding of OM composition and character in source water, how that composition and character varies with flow conditions, and how this impacts on drinking water treatment. This paper demonstrates that fluorescence spectroscopy offers a potential alternative to other analytical methods of OM characterisation. The advantages of fluorescence include rapid, sensitive and selective characterisation of OM, no sample pre-treatment, small sample volume, and the potential for on-line monitoring incorporation. Fluorescence can provide useful information on OM reactivity and treatability together with an indication of the OM sources (allochthonous or autochthonous). The paper discusses a body of literature which has identified relationships between fluorescence spectra and OM physico-chemical properties (i.e. degree of hydrophobicity, microbial content), has applied fluorescence spectroscopy to characterise the changes in OM upon disinfection, and has related the fluorescence properties to DBP formation. Further work is required in the robust management of data arising from fluorescence spectroscopy analysis and, in particular, Excitation Emission Matrices. Consideration must be given as to how the data might best be employed to greatest effect on a routine basis at WTW.