Novel approaches to modelling and monitoring of heavy metal - contaminated sites

Soil contamination is becoming more prevalent, and with increasing global population, more people are being affected. Contaminated site assessment informs management of contaminant sources, affected soil and groundwater. Inaccuracy of assessment can lead to misclassification of sites, resulting in unnecessary remediation, or failing to remediate where it is required. The research presented in this thesis sought to reduce the risk of misclassification by addressing four key aspects of assessment; sampling, detection, mapping and monitoring. The study sought to refine sample size requirements by estimating the number of samples required to determine if the mean at a site exceeded Australian contamination thresholds. A large number of samples were required, yet this may be unrealistic due to time and cost. Portable X-ray Fluorescence spectroscopy (PXRF) provides real-time analysis of soil heavy metal concentrations, enabling more samples to be collected. There is room for improvement in the accuracy of PXRF measurements, so the study explored the potential of integrating these with spectra obtained from visible-near infrared spectroscopy (vis-NIR). Integration of the two spectral methods provided a measure of precision, yet only a marginal increase in accuracy. To improve mapping methods this study obtained measurements from within the Sydney estuary catchment and integrated these, alongside freely available covariates, into linear mixed models to predict lead and zinc concentrations in soil across the catchment. The final chapter of the thesis combined linear mixed models from two time points to predict change in heavy metal concentrations over time at a remediated Sydney parkland. The models provided a detailed snapshot of heavy metal distributions and factors influencing these distributions over time. It is evident in this thesis that much can be done to improve contaminated site assessment and help ensure land is safe and secured for future generations

Predicting bioavailability change of complex chemical mixtures in contaminated soils using visible and near-infrared spectroscopy and random forest regression

A number of studies have shown that visible and near infrared spectroscopy (VIS-NIRS) offers a rapid on-site measurement tool for the determination of total contaminant concentration of petroleum hydrocarbons compounds (PHC), heavy metals and metalloids (HM) in soil. However none of them have yet assessed the feasibility of using VIS-NIRS coupled to random forest (RF) regression for determining both the total and bioavailable concentrations of complex chemical mixtures. Results showed that the predictions of the total concentrations of polycyclic aromatic hydrocarbons (PAH), PHC, and alkanes (ALK) were very good, good and fair, and in contrast, the predictions of the bioavailable concentrations of the PAH and PHC were only fair, and poor for ALK. A large number of trace elements, mainly lead (Pb), aluminium (Al), nickel (Ni), chromium (Cr), cadmium (Cd), iron (Fe) and zinc (Zn) were predicted with very good or good accuracy. The prediction results of the total HMs were also better than those of the bioavailable concentrations. Overall, the results demonstrate that VIS-NIR DRS coupled to RF is a promising rapid measurement tool to inform both the distribution and bioavailability of complex chemical mixtures without the need of collecting soil samples and lengthy extraction for further analysis

Exposure and Exposure Modeling

Exposure to contaminants in the environment is quantified through the ecological risk assessment (ERA) process which provides a framework for the development and implementation of environmental management decisions. The ERA uses available toxicological and ecological information to estimate the probability of occurrence for a specified undesired ecological event or endpoint. The level for these endpoints depends on the objectives and the constraints imposed upon the risk assessment process; therefore, multiple endpoints at different scales may be necessary. ERAs Ecotoxicology | Exposure and Exposure Assessment 1527Author\u27s personal copy often rely on the link between these undesired endpoints to a threshold of exposure to specific toxicants and toxicant mixtures. Oral reference doses (RfD), inhalation reference concentrations (RfC), and carcinogenicity assessments are the usual way these links are expressed in the ERA, and unfortunately most of these thresholds have been developed for human health assessments and not ecosystem integrity. However, since these studies often use animal models, in many cases the original empirical data can be used when trying to apply these findings to ecological consequences or to establish ecological screening values (ESVs). The ecological exposure assessment often begins by comparing constituent concentrations in media (surface water, sediment, soil) to ESVs. The ESVs are derived from ecologically relevant criteria and standards. For example, in the United States the United States Environmental Protection Agency (USEPA) Screening Values and National Ambient Water Quality Criteria (NAWQC) are often used based on ‘no observed adverse effect levels’ (NOAELs) or ‘lowest observed adverse effect levels’ (LOAELs) derived from literature to assess exposure. Radionuclide comparisons for ecological screening are typically dose-based for population level effects. In addition to the ecological threshold comparison, constituents that may bioaccumulate/bioconcentrate are identified during initial screening processes. This is done to account for toxicants that may not be present at levels exceeding ESVs, but must be considered due to trophic transfer of toxicants that may concentrate in higher-trophic-level organisms. Constituents that exceed ESV comparisons (present with means, maximums, or 95% upper confidence levels (UCLs)) are evaluated using a lines-of-evidence approach based on (1) a background evaluation, (2) a bioaccumulation/ bioconcentration potential and ecotoxicity evaluation, (3) a frequency and pattern-of-exceedances evaluation based on review of exceedances to the ESVs, and (4) an evaluation of existing biological data. From this information, ecosystems can be prioritized in terms of risk and focused for proper exposure assessments. This article presents a scientific overview and review of how toxicant exposure is estimated and applied to assess ecosystem integrity

Heavy metal soil contamination detection using combined geochemistry and field spectroradiometry in the United Kingdom

Technological advances in hyperspectral remote sensing have been widely applied in heavy metal soil contamination studies, as they are able to provide assessments in a rapid and cost-effective way. The present work investigates the potential role of combining field and laboratory spectroradiometry with geochemical data of lead (Pb), zinc (Zn), copper (Cu) and cadmium (Cd) in quantifying and modelling heavy metal soil contamination (HMSC) for a floodplain site located in Wales, United Kingdom. The study objectives were to: (i) collect field- and lab-based spectra from contaminated soils by using ASD FieldSpec® 3, where the spectrum varies between 350 and 2500 nm; (ii) build field- and lab-based spectral libraries; (iii) conduct geochemical analyses of Pb, Zn, Cu and Cd using atomic absorption spectrometer; (iv) identify the specific spectral regions associated to the modelling of HMSC; and (v) develop and validate heavy metal prediction models (HMPM) for the aforementioned contaminants, by considering their spectral features and concentrations in the soil. Herein, the field- and lab-based spectral features derived from 85 soil samples were used successfully to develop two spectral libraries, which along with the concentrations of Pb, Zn, Cu and Cd were combined to build eight HMPMs using stepwise multiple linear regression. The results showed, for the first time, the feasibility to predict HMSC in a highly contaminated floodplain site by combining soil geochemistry analyses and field spectroradiometry. The generated models help for mapping heavy metal concentrations over a huge area by using space-borne hyperspectral sensors. The results further demonstrated the feasibility of combining geochemistry analyses with filed spectroradiometric data to generate models that can predict heavy metal concentrations

Application of multi-scale assessment and modelling of landfill leachate migration: implications for risk-based contaminated land assessment, landfill remediation, and groundwater protection

There are a large number of unlined and historical landfill sites across Britain, contaminating groundwater and soil resources as well as posing a threat to human health and local communities. There is an essential requirement for robust methodology when carrying out risk-based site investigations prior to risk assessment and remediation of landfill sites. This research has focused upon the methods used during site investigations for two reasons. Firstly, the site investigation is often conducted using field instruments and methods that do not account for the heterogeneous conditions found at landfill sites. Interpreting geophysical conditions between sampled points is a common practise. Given the complex and heterogeneous conditions at landfill sites, such methodology introduces uncertainty into data sets. Secondly, risk estimation models that simulate groundwater flow and contaminant transport require extensive field information. The data used during model construction will significantly impact contaminant transport simulations. Modelling guidelines also need further development, ensuring that sound modelling practises are adhered toduring model construction.To address these concerns, four research objectives were identified: (1) Two new multi-spatial field assessment methods (remote sensing and ground penetrating radar), previously applied in other fields of science, were tested on landfill sites; (2) Kriging was used as a tool to improve landfill-sampling strategies; (3 & 4) Groundwater flow and contaminant transport models were used to evaluate whether different scales of field data and modelling practises influenced modelling assumptions and simulation.The utility of novel field- and airborne-based remote sensing methodologies in identifying the location and intensity of vegetation stress caused by leachate migration and inferring pathways of near surface contamination using patterns of vegetation stress was proven. The results from the kriging investigations demonstrated that additional insight into field conditions could be resolved to identify locations of additional sampling points, and provide information about variability in hydrological data sets. The Ground Penetrating Radar investigations provided three types of valuable near-surface information that could assist in determining landfill risks: buried landfill features, leachate plume locations and local hydrogeological conditions. These combined methods provided detailed synoptic geophysical and contaminant information that would otherwise be difficult to determine. Their application and acceptance as site assessment methods (used under certain landfill conditions) could increase the accuracy of assessing risks posed by landfill leachate.These applications also demonstrated that the most effective site assessments are achieved when integrated with other field data such as soil, vegetation, and groundwater quantity measurements, contaminant concentrations and aerial photographs, providing comprehensive information needed for risk estimation modelling.The modelling analyses found that close attention must be paid to site-specific and model-specific characteristics, as well as modelling practises. These factors influenced model results. By using additional data to infer model parameters, it was evident that the amount of data available will influence the way in which risk will be perceived. The more data that was available during model construction, the higher the risk prediction. This was the case for some seventy- percent of the models.By improving the accuracy of site investigation methodology, and by adhering to robust assessment and modelling practices, a higher level of quality assurance can be achieved in the risk assessment and remediation of contaminating landfill sites. If the improvements and recommendations presented in this research are considered, uncertainties inherent in the site investigation could be reduced, therefore enhancing the accuracy of landfill risk assessment and remedial decisions

Visible and near infrared spectroscopy in soil science

This chapter provides a review on the state of soil visible–near infrared (vis–NIR) spectroscopy. Our intention is for the review to serve as a source of up-to date information on the past and current role of vis–NIR spectroscopy in soil science. It should also provide critical discussion on issues surrounding the use of vis–NIR for soil analysis and on future directions. To this end, we describe the fundamentals of visible and infrared diffuse reflectance spectroscopy and spectroscopic multivariate calibrations. A review of the past and current role of vis–NIR spectroscopy in soil analysis is provided, focusing on important soil attributes such as soil organic matter (SOM), minerals, texture, nutrients, water, pH, and heavy metals. We then discuss the performance and generalization capacity of vis–NIR calibrations, with particular attention on sample pre-tratments, co-variations in data sets, and mathematical data preprocessing. Field analyses and strategies for the practical use of vis–NIR are considered. We conclude that the technique is useful to measure soil water and mineral composition and to derive robust calibrations for SOM and clay content. Many studies show that we also can predict properties such as pH and nutrients, although their robustness may be questioned. For future work we recommend that research should focus on: (i) moving forward with more theoretical calibrations, (ii) better understanding of the complexity of soil and the physical basis for soil reflection, and (iii) applications and the use of spectra for soil mapping and monitoring, and for making inferences about soils quality, fertility and function. To do this, research in soil spectroscopy needs to be more collaborative and strategic. The development of the Global Soil Spectral Library might be a step in the right direction

Chemical characterization of clastic cave sediments and insights into particle transport and storage in karst aquifers

Abstract Chemical characterization of clastic cave sediments and insights into particle transport and storage in karst aquifers Jill L. Riddell Cave sediments can be divided into two groups: precipitates and clastics. Precipitates are speleothems, or lithologic or mineral features that are chemically precipitated in the cave environment. Clastic cave sediments are frequently described by depositional facies, sorting, and particle size (Bosch and White, 2004). Robust analytical chemical analyses of these sediments to quantify their physical and chemical components is rarely performed although some chemical characterization of mineralogy and paleomagnetism has become prevalent in recent years (Chess et al., 2010; Sasowsky et al., 2007). The organic carbon content of cave sediments can be representative of organic carbon concentrations in the larger karst system and concentrations of organic carbon in cave sediments can be used to estimate the potential retardation of organic contaminants through the entire karst system. The ability of karst sediments to be a sorbent for metals and organic contaminants, and store and transport contaminants is positively correlated with the amount of organic carbon in the sediment; yet these concentrations are rarely reported in karst sediments. This dissertation seeks to fill the gap in the mineralogy and chemical components of cave sediments; quantify the organic carbon content of cave sediments relative to depositional facies; and measure the adsorption of an organic microsphere onto a cave sediment to explore sediment-contaminant interactions. A case study from Dropping Lick Cave in Monroe County, WV, is presented where a variety of analytical techniques were used to determine the active fraction ( \u3c 2mm) mineralogy and chemical components of the sediment The sediments were silt and sand-sized particles consisting of quartz, some clay or silicate minerals, dolomite, and amorphous materials. The particle size and total carbon was within the same range reported for the \u3c 2mm fraction in other clastic cave sediments in this region, in the central United States, and in Puerto Rico. The preliminary mineralogy of the sediments is congruent with the mineralogy of surrounding siliciclastic rocks indicating that the source of the sediment is erosional products from nearby Peters Mountain and its slopes. Particle size, TOC, and total nitrogen were measured in sediments representing different facies in Butler Cave, Virginia, USA. TOC concentrations ranged from 0.08 – 0.87 weight percent and C:N molar ratio ranged from 3 – 15, indicating a possible terrestrial source of organic carbon in these sediments. TOC concentrations measured in Butler Cave were within the same range as those observed in above water, eogenetic clastic cave sediments from two caves in Puerto Rico. Estimated retardation factors calculated based on the TOC concentrations in the Butler Cave sediments indicate the range of TOC in this cave could be responsible for 39 – 987% increase in retardation of selected contaminants. This study highlights the importance of measuring the ranges of TOC in clastic cave sediments across different facies and their role in contaminant fate and transport. In this study, The adherence of carboxylated and nonfunctionalized polystyrene microspheres onto a clastic cave sediment was quantified for microsphere dilutions in three water types – deionized water, a 25 mg/L CaCO3 solution, and a karst spring water. Regardless of water type, both types of microspheres adhered to the sediment. Infrared absorbance data of different microsphere-solution-sediment mixtures indicated the potential presence of sediment minerals and microspheres in the solution. Analysis of solution pH and infrared spectra suggested pH and mineral constituents of the sediment are the most important factors in microsphere adherence. Using the adherence data, estimated KOC values for both types of microspheres were calculated and were in the same ranges as phthalates, a known contaminant in karst aquifers that is also considered a plastic, like polystyrene. The chemical and physical commonalities between microspheres and organic and microplastic (MP) contaminants warrant further investigation of microspheres as a proxy for contaminants in sediment-contaminant experiments. The results of these experiments suggest that consideration of MPs adhered to sediments should be considered when quantifying MP contamination in karst systems

Bridging the capability gap in environmental gamma-ray spectrometry

Environmental gamma-ray spectroscopy provides a powerful tool that can be used in environmental monitoring given that it offers a compromise between measurement time and accuracy allowing for large areas to be surveyed quickly and relatively inexpensively. Depending on monitoring objectives, spectral information can then be analysed in real-time or post survey to characterise contamination and identify potential anomalies. Smaller volume detectors are of particular worth to environmental surveys as they can be operated in the most demanding environments. However, difficulties are encountered in the selection of an appropriate detector that is robust enough for environmental surveying yet still provides a high quality signal. Furthermore, shortcomings remain with methods employed for robust spectral processing since a number of complexities need to be overcome including: the non-linearity in detector response with source burial depth, large counting uncertainties, accounting for the heterogeneity in the natural background and unreliable methods for detector calibration. This thesis aimed to investigate the application of machine learning algorithms to environmental gamma-ray spectroscopy data to identify changes in spectral shape within large Monte Carlo calibration libraries to estimate source characteristics for unseen field results. Additionally, a 71 × 71 mm lanthanum bromide detector was tested alongside a conventional 71 × 71 mm sodium iodide to assess whether its higher energy efficiency and resolution could make it more reliable in handheld surveys. The research presented in this thesis demonstrates that machine learning algorithms could be successfully applied to noisy spectra to produce valuable source estimates. Of note, were the novel characterisation estimates made on borehole and handheld detector measurements taken from land historically contaminated with 226Ra. Through a novel combination of noise suppression and neural networks the burial depth, activity and source extent of contamination was estimated and mapped. Furthermore, it was demonstrated that Machine Learning techniques could be operated in real-time to identify hazardous 226Ra containing hot particles with much greater confidence than current deterministic approaches such as the gross counting algorithm. It was concluded that remediation of 226Ra contaminated legacy sites could be greatly improved using the methods described in this thesis. Finally, Neural Networks were also applied to estimate the activity distribution of 137Cs, derived from the nuclear industry, in an estuarine environment. Findings demonstrated the method to be theoretically sound, but practically inconclusive, given that much of the contamination at the site was buried beyond the detection limits of the method. It was generally concluded that the noise posed by intrinsic counts in the 71 × 71 mm lanthanum bromide was too substantial to make any significant improvements over a comparable sodium iodide in contamination characterisation using 1 second counts

Advancing Urban Flood Resilience With Smart Water Infrastructure

Advances in wireless communications and low-power electronics are enabling a new generation of smart water systems that will employ real-time sensing and control to solve our most pressing water challenges. In a future characterized by these systems, networks of sensors will detect and communicate flood events at the neighborhood scale to improve disaster response. Meanwhile, wirelessly-controlled valves and pumps will coordinate reservoir releases to halt combined sewer overflows and restore water quality in urban streams. While these technologies promise to transform the field of water resources engineering, considerable knowledge gaps remain with regards to how smart water systems should be designed and operated. This dissertation presents foundational work towards building the smart water systems of the future, with a particular focus on applications to urban flooding. First, I introduce a first-of-its-kind embedded platform for real-time sensing and control of stormwater systems that will enable emergency managers to detect and respond to urban flood events in real-time. Next, I introduce new methods for hydrologic data assimilation that will enable real-time geolocation of floods and water quality hazards. Finally, I present theoretical contributions to the problem of controller placement in hydraulic networks that will help guide the design of future decentralized flood control systems. Taken together, these contributions pave the way for adaptive stormwater infrastructure that will mitigate the impacts of urban flooding through real-time response.PHDCivil EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/163144/1/mdbartos_1.pd