Abstract
AbstractSediment fingerprinting estimates sediment source contributions directly from river sediment. Despite being fundamental to the interpretation of sediment fingerprinting results, the classification of sediment sources and its impact on the accuracy of source apportionment remain underinvestigated. This study assessed the impact of source classification on sediment fingerprinting based on diffuse reflectance infrared Fourier transform spectrometry (DRIFTS), using individual, source‐specific partial least‐squares regression (PLSR) models. The objectives were to (a) perform a model sensitivity analysis through systematically omitting sediment sources and (b) investigate how sediment source‐group discrimination and the importance of the groups as actual sources relate to variations in results. Within the Aire catchment (United Kingdom), five sediment sources were classified and sampled (n = 117): grassland topsoil in three lithological areas (limestone, millstone grit, and coal measures); riverbanks; and street dust. Experimental mixtures (n = 54) of the sources were used to develop PLSR models between known quantities of a single source and DRIFTS spectra of the mixtures, which were applied to estimate source contributions from DRIFTS spectra of suspended (n = 200) and bed (n = 5) sediment samples. Dominant sediment sources were limestone topsoil (45 ± 12%) and street dust (43 ± 10%). Millstone and coals topsoil contributed on average 19 ± 13% and 14 ± 10%, and riverbanks 16 ± 18%. Due to the use of individual PLSR models, the sum of all contributions can deviate from 100%; thus, a model sensitivity analysis assessed the impact and accuracy of source classification. Omitting less important sources (e.g., coals topsoil) did not change the contributions of other sources, whereas omitting important, poorly‐discriminated sources (e.g., riverbank) increased the contributions of all sources. In other words, variation in source classification substantially alters source apportionment depending on source discrimination and source importance. These results will guide development of procedures for evaluating the appropriate type and number of sediment sources in DRIFTS‐PLSR sediment fingerprinting.
Highlights
Sediment occurs naturally in rivers across the world
The findings suggest that the degree of discrimination between the source classes, in combination with the importance of the source classes as actual sediment sources, determines the sensitivity of the model to the exclusion of a particular source
diffuse reflectance infrared Fourier transform spectrometry (DRIFTS)‐based sediment fingerprinting using individual, source‐ specific partial least‐ squares regression (PLSR) models was applied to assess the impact of sediment source classification on sediment fingerprinting results
Summary
Sediment occurs naturally in rivers across the world. Yet excessive sediment is damaging to the ecological and biochemical state of river systems and causes increased water treatment and infrastructural maintenance costs (Béjar, Gibbins, Vericat, & Batalla, 2017; Grove, Bilotta, Woockman, & Schwartz, 2015; Jones et al, 2012; Kemp, Sear, Collins, Naden, & Jones, 2011; Selbig, Bannerman, & Corsi, 2013; Taylor & Owens, 2009). Cooper, Krueger, et al, 2014; Moore & Semmens, 2008; Nosrati, Govers, Semmens, & Ward, 2014) or Markov Chain Monte Carlo algorithms (e.g., Collins et al, 2010; Palazón, Gaspar, Latorre, Blake, & Navas, 2015; Wilkinson, Olley, Furuichi, Burton, & Kinsey‐Henderson, 2015), important uncertainties remain concerning the impact of source classification on sediment fingerprinting results (Collins et al, 2017; Davis & Fox, 2009; Haddadchi et al, 2013; Laceby & Olley, 2015; Mukundan et al, 2012; Owens et al, 2016) To this end, research has investigated the possibility of classifying potential sediment sources more objectively using cluster analysis to distinguish statistically significant source groups (Walling & Woodward, 1995; Walling, Woodward, & Nicholas, 1993); select source groups based on the similarity between source material and river sediment (Pulley, Foster, & Collins, 2017); and test the effect of multiple source‐group configurations and different composite fingerprint properties (Pulley & Collins, 2018). The specific objectives are to (a) perform a model sensitivity analysis by systematically omitting sediment sources from the classification and (b) investigate how sediment source‐group discrimination and the importance of the groups as actual sources relate to variations in results
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
