Abstract. This research has been conducted to develop the use of Passive Acoustic Monitoring (PAM) in rivers, a surrogate method for bedload monitoring. PAM consists in measuring the underwater noise naturally generated by bedload particles when impacting the river bed. Monitored bedload acoustic signals depend on bedload characteristics (e.g. grain size distribution, fluxes) but are also affected by the environment in which the acoustic waves are propagated. This study focuses on the determination of propagation effects in rivers. An experimental approach has been conducted in several streams to estimate acoustic propagation laws in field conditions. It is found that acoustic waves are differently propagated according to their frequency. As reported in other studies, acoustic waves are affected by the existence of a cutoff frequency in the kHz region. This cutoff frequency is inversely proportional to the water depth: larger water depth enables a better propagation of the acoustic waves at low frequency. Above the cutoff frequency, attenuation coefficients are found to increase linearly with frequency. The power of bedload sounds is more attenuated at higher frequencies than at low frequencies which means that, above the cutoff frequency, sounds of big particles are better propagated than sounds of small particles. Finally, it is observed that attenuation coefficients are variable within 2 orders of magnitude from one river to another. Attenuation coefficients are compared to several characteristics of the river (e.g. bed slope, bed rugosity). It is found that acoustic waves are better propagated in rivers characterised by smaller bed slopes. Bed rugosity and the presence of air bubbles in the water column are suspected to constrain the attenuation of acoustic wave in rivers.
Coal pyrolysis experiments were performed in the post-flame region of a CH4/H2/air flat-flame burner operating under fuel-rich conditions, where the temperature and gas compositions were similar to those found in the near-burner region of an industrial pulverized coal-fired furnace. Volatiles released from the coal particles formed a cloud of soot particles surrounding a centrally fed coal/char particle stream. Soot samples were collected from the cloud at different residence times using a water-cooled, nitrogen-quenched probe. The soot samples were then analyzed for their elemental compositions of carbon, hydrogen, nitrogen, sulfur, and (by difference) oxygen plus inorganic matter. Soot from three parent coals (Pittsburgh #8, Illinois #6, and Utah Hiawatha) and two gaseous hydrocarbon fuels (propane and acetylene) were investigated at temperatures of 1650, 1800, and 1900 K. The results reveal that the yield of coal-derived soot decreases with increasing reactor temperature, even though the total volatiles yield increased only slightly with temperature. The coal-derived soot yield at each reactor temperature condition also increased slightly with residence time. The carbon content in the coal-derived soot decreased with increasing particle residence time (at a given reactor temperature) and with increasing reactor temperature (at a given residence time) for all three coals. Carbon content remained constant with residence time for the gaseous hydrocarbon-fuel-derived soot. It is suggested that the observed decrease in coal-derived soot yield with increasing temperature is due to reactions of radical species from the flame with the soot precursors (i.e., the tar molecules). The slight increase in coal-derived soot yield with increasing residence time is due to attachment of light gas species such as acetylene which are richer in hydrogen than the local soot particles. The different behavior of soot from coal and the gaseous hydrocarbon fuels is explained in terms of their different chemical structures; coal-derived soot molecules have more aliphatic attachments and heteroatoms than soot from acetylene or propane. Carbon/hydrogen ratios in the soot samples were observed to be significantly different for the different soot types depending on parent fuel.
Improving the prediction of sand transport downstream of dams requires characterization of the interaction between turbulent flow and near-surface interstitial sands. The advanced age and impending decommissioning of many dams have brought increased attention to the fate of sediments stored in reservoirs. Sands can be reintroduced to coarse substrates that have available pore space resulting from periods of sediment-starved flow. The roughness and porosity of the coarse substrate are both affected by sand elevation relative to the coarse substrate; therefore, the turbulence characteristics and sediment transport over and through these beds are significantly altered after sediment is reintroduced. Past work by the writers on flow over sand-filled gravel beds revealed that the fine-sediment level controls the volume of material available for transport and the area of sediment exposed to the flow. The present work expands on the gravel-bed experiments by conducting similar measurements of turbulent flow and sand transport over a bed of cobbles with a median diameter of approximately 150 mm. This change in scale expands the generality of the previous experiments and broadens the range of sand transport and turbulence measurements. It was found that the same relationship between bed shear stress and sand elevation was valid for both gravel and cobble systems. Reductions in bed shear stress, relative to the clear-water case, were observed as the sand elevation was increased, although the highest sand elevation did not yield the lowest shear stress. Quadrant analysis showed that, for stronger turbulent events, there was an increase of sweeps and a decrease in bursts as the sand level was raised. This effect was observed for a region with a height of approximately 1.4 times the thickness of the roughness layer.
Manganese (Mn) concentrations and the probability of arsenic (As) exceeding the drinking-water standard of 10 μg/L were predicted in the Mississippi River Valley alluvial aquifer (MRVA) using boosted regression trees (BRT). BRT, a type of ensemble-tree machine-learning model, were created using predictor variables that affect Mn and As distribution in groundwater. These variables included iron (Fe) concentrations and specific conductance predicted from previously developed BRT models, groundwater flux and age estimates from MODFLOW, and hydrologic characteristics. The models also included results from the first airborne geophysical survey conducted in the United States to target an entire aquifer system. Predictions of high Mn and As occurred where Fe was high. Predicted high Mn concentrations were correlated with fraction of young groundwater (less than 65 years) computed from MODFLOW results. High probabilities of As exceedance were predicted where groundwater was relatively old and airborne electromagnetic resistivity was high, typically proximal to streams. Two-variable partial-dependence plots and sensitivity analysis were used to provide insight into the factors controlling Mn and As distribution in groundwater. The maps of predicted Mn concentrations and As exceedance probabilities can be used to identify areas where these constituents may be high, and that could be targeted for further study. This paper shows that incorporation of a selected set of process-informed data, such as MODFLOW results and airborne geophysics, into a machine-learning model improves model interpretability. Incorporation of process-rich information into machine-learning models will likely be useful for addressing a wide range of problems of interest to groundwater hydrologists.
