14C as a tool to trace terrestrial carbon in a complex lake system: implications for foodweb structure and carbon cycling

Carbon and nitrogen stable isotope analysis (SIA) has identified the terrestrial subsidy of freshwater food-webs but relies on different 13C fractionation in aquatic and terrestrial primary producers. However dissolved inorganic carbon (DIC) is partly comprised of 13C depleted respiration of terrestrial C and ‘old’ C derived from weathering of catchment geology. SIA thus fails to differentiate between the contribution of old and recently fixed terrestrial C. DIC in alkaline lakes is partially derived from weathering of 14C-free carbonaceous bedrock This
yields an artificial age offset leading samples to appear significantly older than their actual age. As such, 14C can be used as a biomarker to identify the proportion of autochthonous C in the food-web. With terrestrial C inputs likely to increase, the origin and utilisation of ‘old’ or ‘recent’ allochthonous C in the food-web can also be determined. Stable isotopes and 14C were measured for biota, particulate organic matter (POM), DIC and dissolved organic carbon (DOC) from Lough Erne, Northern Ireland, a humic but alkaline lake. High winter δ15N values in calanoid zooplankton (δ15N =24‰) relative to phytoplankton and POM (δ15N =6‰ and 12‰ respectively) may reflect several microbial trophic levels between terrestrial C and calanoids. Furthermore winter calanoid 14C ages are consistent with DOC from inflowing rivers (87 and 75 years BP respectively) but not phytoplankton (355 years BP). Summer calanoid δ13N, δ15N and 14C (312 years BP) indicate greater reliance on phytoplankton. There is also temporal and spatial variation in DIC, DOC and POM C isotopes.

14C as a tool to trace terrestrial carbon in a complex lake system: implications for food-web structure and carbon cycling

Globally lakes bury and remineralise significant quantities of terrestrial C, and the associated flux of terrestrial C strongly influences their functioning. Changing deposition chemistry, land use and climate induced impacts on hydrology will affect soil biogeochemistry and terrestrial C export¹ and hence lake ecology with potential feedbacks for regional and global C cycling. C and nitrogen stable isotope analysis (SIA) has identified the terrestrial subsidy of freshwater food webs. The approach relies on different ¹³C fractionation in aquatic and terrestrial primary producers, but also that inorganic C demands of aquatic primary producers are partly met by ¹³C depleted C from respiration of terrestrial C, and ‘old’ C derived from weathering of catchment geology. SIA thus fails to differentiate between the contributions of old and recently fixed terrestrial C. Natural abundance ¹⁴C can be used as an additional biomarker to untangle riverine food webs² where aquatic and terrestrial δ ¹³C overlap, but may also be valuable for examining the age and origin of C in the lake. Primary production in lakes is based on dissolved inorganic C (DIC). DIC in alkaline lakes is partially derived from weathering of carbonaceous bedrock, a proportion of which is¹⁴C-free. The low ¹⁴C activity yields an artificial age offset leading samples to appear hundreds to thousands of years older than their actual age. As such, ¹⁴C can be used to identify the proportion of autochthonous C in the food-web. With terrestrial C inputs likely to increase, the origin and utilisation of ‘fossil’ or ‘recent’ allochthonous C in the food-web can also be determined. Stable isotopes and 14C were measured for biota, particulate organic matter (POM), DIC and dissolved organic carbon (DOC) from Lough Erne, Northern Ireland, a humic alkaline lake. Temporal and spatial variation was evident in DIC, DOC and POM C isotopes with implications for the fluctuation in terrestrial export processes. Ramped pyrolysis of lake surface sediment indicates the burial of two C components. ¹⁴C activity (507 ± 30 BP) of sediment combusted at 400˚C was consistent with algal values and younger than bulk sediment values (1097 ± 30 BP). The sample was subsequently combusted at 850˚C, yielding 14C values (1471 ± 30 BP) older than the bulk sediment age, suggesting that fossil terrestrial carbon is also buried in the sediment. Stable isotopes in the food web indicate that terrestrial organic C is also utilised by lake organisms. High winter δ ¹⁵N values in calanoid zooplankton (δ ¹⁵N = 24%¸) relative to phytoplankton and POM (δ ¹⁵N = 6h and 12h respectively) may reflect several microbial trophic levels between terrestrial C and calanoids. Furthermore winter calanoid 14C ages are consistent with DOC from an inflowing river (75 ± 24 BP), not phytoplankton (367 ± 70 BP). Summer calanoid δ ^13C, δ ¹⁵N and ¹⁴C (345 ± 80 BP) indicate greater reliance on phytoplankton.

¹ Monteith, D.T et al., (2007) Dissolved organic carbon trends resulting from changes in atmospheric deposition chemistry. Nature, 450:537-535

² Caraco, N., et al.,(2010) Millennial-aged organic carbon subsidies to a modern river food web. Ecology,91: 2385-2393.

Artificial Neural Network for Compositional Ionic Liquid Viscosity Prediction

Being a new generation of green solvents and high-tech reaction media of the future, ionic liquids have increasingly attracted much attention. Of particular interest in this context are room temperature ionic liquids (in short as ILs in this paper). Due to the relatively high viscosity, ILs is expected to be used in the form of solvent diluted mixture with reduced viscosity in industrial application, where predicting the viscosity of IL mixture has been an important research issue. Different IL mixture and many modelling approaches have been investigated. The objective of this study is to provide an alternative model approach using soft computing technique, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of ILs [C _n-mim][NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0-328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity taking account of IL alkyl chain length, as well as temperature and compositions simultaneously, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. This illustrates the potential application of ANN in the case that the physical and thermodynamic properties are highly non-linear or too complex. © 2012 Copyright the authors.

Artificial neural network model to predict compositional viscosity over a broad range of temperatures

The objective of this study is to provide an alternative model approach, i.e., artificial neural network (ANN) model, to predict the compositional viscosity of binary mixtures of room temperature ionic liquids (in short as ILs) [C _n-mim] [NTf ₂] with n=4, 6, 8, 10 in methanol and ethanol over the entire range of molar fraction at a broad range of temperatures from T=293.0328.0K. The results show that the proposed ANN model provides alternative way to predict compositional viscosity successfully with highly improved accuracy and also show its potential to be extensively utilized to predict compositional viscosity over a wide range of temperatures and more complex viscosity compositions, i.e., more complex intermolecular interactions between components in which it would be hard or impossible to establish the analytical model. © 2010 IEEE.

Non-parametric bill of quantities estimation of concrete road bridges' superstructure: an artificial neural networks approach

Bridge construction responds to the need for environmentally friendly design of motorways and facilitates the passage through sensitive natural areas and the bypassing of urban areas. However, according to numerous research studies, bridge construction presents substantial budget overruns. Therefore, it is necessary early in the planning process for the decision makers to have reliable estimates of the final cost based on previously constructed projects. At the same time, the current European financial crisis reduces the available capital for investments and financial institutions are even less willing to finance transportation infrastructure. Consequently, it is even more necessary today to estimate the budget of high-cost construction projects -such as road bridges- with reasonable accuracy, in order for the state funds to be invested with lower risk and the projects to be designed with the highest possible efficiency. In this paper, a Bill-of-Quantities (BoQ) estimation tool for road bridges is developed in order to support the decisions made at the preliminary planning and design stages of highways. Specifically, a Feed-Forward Artificial Neural Network (ANN) with a hidden layer of 10 neurons is trained to predict the superstructure material quantities (concrete, pre-stressed steel and reinforcing steel) using the width of the deck, the adjusted length of span or cantilever and the type of the bridge as input variables. The training dataset includes actual data from 68 recently constructed concrete motorway bridges in Greece. According to the relevant metrics, the developed model captures very well the complex interrelations in the dataset and demonstrates strong generalisation capability. Furthermore, it outperforms the linear regression models developed for the same dataset. Therefore, the proposed cost estimation model stands as a useful and reliable tool for the construction industry as it enables planners to reach informed decisions for technical and economic planning of concrete bridge projects from their early implementation stages.