270 research outputs found

    Prediction of bioavailability and toxicity of complex chemical mixtures through machine learning models

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    Empirical data from a 6-month mesocosms experiment were used to assess the ability and performance of two machine learning (ML) models, including artificial neural network (NN) and random forest (RF), to predict temporal bioavailability changes of complex chemical mixtures in contaminated soils amended with compost or biochar. From the predicted bioavailability data, toxicity response for relevant ecological receptors was then forecasted to establish environmental risk implications and determine acceptable end-point remediation. The dataset corresponds to replicate samples collected over 180 days and analysed for total and bioavailable petroleum hydrocarbons and heavy metals/metalloids content. Further to this, a range of biological indicators including bacteria count, soil respiration, microbial community fingerprint, seeds germination, earthworm's lethality, and bioluminescent bacteria were evaluated to inform the environmental risk assessment. Parameters such as soil type, amendment (biochar and compost), initial concentration of individual compounds, and incubation time were used as inputs of the ML models. The relative importance of the input variables was also analysed to better understand the drivers of temporal changes in bioavailability and toxicity. It showed that toxicity changes can be driven by multiple factors (combined effects), which may not be accounted for in classical linear regression analysis (correlation). The use of ML models could improve our understanding of rate-limiting processes affecting the freely available fraction (bioavailable) of contaminants in soil, therefore contributing to mitigate potential risks and to inform appropriate response and recovery methods

    In situ and ex situ bioremediation of heavy metals: the present scenario

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    Enhanced population growth, rapid industrialization, urbanization and hazardous industrial practices have resulted in the development of environmental pollution in the past few decades. Heavy metals are one of those pollutants that are related to environmental and public health concerns based on their toxicity. Effective bioremediation may be accomplished through “ex situ” and “in situ” processes, based on the type and concentration of pollutants, characteristics of the site but is not limited to cost. The recent developments in artificial neural network and microbial gene editing help to improve “in situ” bioremediation of heavy metals from the polluted sites. Multi-omics approaches are adopted for the effective removal of heavy metals by various indigenous microbes. This overview introspects two major bioremediation techniques, their principles, limitations and advantages, and the new aspects of nanobiotechnology, computational biology and DNA technology to improve the scenario

    Diagnostic strategy and risk assessment framework for complex chemical mixtures

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    Environmental contamination comprises a complex mixture of both organic and inorganic contaminants. Understanding their distribution, behaviour and chemical interactions provides the evidence necessary to make informed decision and implement robust remediation strategies. However most of the current risk assessment frameworks, used to manage land contamination, are based on the total contaminant concentration rather than the concentration likely to pose significant risk, the bioavailable concentration. Further to this, the exposure assessments embedded within the frameworks do not explicitly address the partitioning and bioavailability of chemical mixtures. This inability may contribute to an overestimation of both the eco-toxicological effects of the fractions and their mobility in air and water; leading to an overestimation of health and environmental effects. In turn, this may limit the efficacy of the risk assessment frameworks to inform targeted and proportionate remediation strategies. The aim of this PhD study was to address this gap by delivering an integrated risk assessment framework for sites contaminated with complex chemical mixtures. Specifically, this PhD study investigated the fate and behaviour of complex mixtures of petroleum hydrocarbons, metals and metalloids in soils and its implication for partitioning, bioavailability and risk assessment through a 12 month mesocosms study. Further to this, an integrated approach, where contaminants bioavailability and distribution changes along with a range of microbiological indicators and ecotoxicological bioassays, was used to provide multiple lines of evidence to support the risk characterisation and assess the remediation end-point over a 6 month study. From the empirical data obtained from the two mesocosm studies, two Machine Leaning (ML) approaches have been developed to provide a quick and reliable tool to assess multi-contaminated sites with Visible and Near-Infrared Spectroscopy (Vis-NIRS), and to predict bioavailability and toxicity changes occurring during bioremediation. Overall this PhD study shed light on the behaviour of bioavailability, and toxicity of complex chemical mixtures in soils genuinely contaminated. This was supported through a comprehensive and integrated analytical framework providing the necessary lines of evidence to evaluate the implications for risk assessment and identify the end point remediation. The developed framework can significantly help to identify optimal remediation strategies and contribute to change the over-conservative nature of the current risk assessments

    Sustainable Agriculture and Soil Conservation

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    Soil degradation is one of the most topical environmental threats. A number of processes causing soil degradation, specifically erosion, compaction, salinization, pollution, and loss of both organic matter and soil biodiversity, are also strictly connected to agricultural activity and its intensification. The development and adoption of sustainable agronomic practices able to preserve and enhance the physical, chemical, and biological properties of soils and improve agroecosystem functions is a challenge for both scientists and farmers. The Special Issue entitled “Sustainable Agriculture and Soil Conservation” collects 12 original contributions addressing the state of the art of sustainable agriculture and soil conservation. The papers cover a wide range of topics, including organic agriculture, soil amendment and soil organic carbon (SOC) management, the impact of SOC on soil water repellency, the effects of soil tillage on the quantity of SOC associated with several fractions of soil particles and depth, and SOC prediction, using visible and near-infrared spectra and multivariate modeling. Moreover, the effects of some soil contaminants (e.g., crude oil, tungsten, copper, and polycyclic aromatic hydrocarbons) are discussed or reviewed in light of the recent literature. The collection of the manuscripts presented in this Special Issue provides a relevant knowledge contribution for improving our understanding on sustainable agriculture and soil conservation, thus stimulating new views on this main topic

    Implementation of spectroscopy as a rapid measurement tool (RMT) to inform risk assessment at petroleum contaminated sites in the Niger Delta, Nigeria.

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    The recent developments and applications of rapid measurement tools (RMT) such as visible near-infrared (vis-NR) spectroscopy can provide ‘fit for purpose’ and cost effective data for informing risk assessment and managing oil-contaminated sites. Infrared spectroscopy discriminates between chemical compounds by detecting the specific vibrational frequencies of molecular bonds, producing a unique infrared ‘spectral signal’ thereby enhancing its identification and quantification applying chemometrics. The aim of the research was therefore to assess the potential of vis-NIR and mid-infrared (MIR) diffuse reflectance spectroscopy (DRS) techniques as RMT to inform risk decision support for remediation of petroleum contaminated sites. The objectives of the study were to: critically review chromatographic and spectroscopic methods for petroleum hydrocarbon analysis in soils; evaluate vis-NIR sensitivity to detect hydrocarbon concentration differences throughout weathering; predict TPH, PAH and alkanes concentrations in contaminated soils using vis-NIR and MIR DRS coupled with regression techniques. The study further evaluated which spectroscopy technique (vis-NIR or MIR); and which modelling method (RF or PLSR) performs best. In this study, a series of 13 soil mesocosms was set up where each soil sample collected was spiked with 10 ml of Alaskan crude oil and allowed to equilibrate at room temperature for 48 h before analysis. The mesocosms were incubated for two years at roomntemperature in the dark. Soils scanning and gas chromatography coupled to mass spectrometry (GC-MS) analysis were carried out at T0, 4, 12, 16, 20 and 24 months. Prior to scanning, soil samples were air-dried at room temperature (21oC) to reduce the effect of moisture. The soil scanning was done simultaneously using an AgroSpec spectrometer with a spectral range of 305 to 2200 nm (tec5 Technology for Spectroscopy, Germany) and Analytical Spectral Devices LabSpec2500 spectrometer (ASD Inc, USA) with a spectral range of 305 to 2500 nm to assess and compare the sensitivity and response of the two instruments to weathering and hydrocarbon composition change overtime against GC-MS data. Partial least squares (PLS) and random forest (RF) regression models showed that ASD LabSpec2500 performed better than tec5 which may be attributed to the shorter wavelength spectra range of the tec5 spectrometer and therefore not detecting all significant hydrocarbon signals (e.g., 2207, 2220, 2240 and 2460 nm). The sensitivity of the two spectral devices to weathering and REWARD K. DOUGLAS Cranfield University PhD Thesis, 2018 hydrocarbon composition change was, however, comparable; and the predicted concentrations were also comparable to the hydrocarbons concentrations determined by GC-MS. The results (coefficient of determination, RÂČ=0.9; ratio of prediction deviation, RPD=3.79 and root mean square error of prediction, RMSEP=108.56 mg/kg) demonstrate that visible-near infrared diffuse reflectance spectroscopy (vis-NIR DRS) is a proven tool for rapid site investigation and monitoring without the need of collecting soil samples and lengthy hydrocarbon extraction for further analysis..To this end, 85 soil samples collected from three crude oil spill sites in the Niger Delta, Nigeria. Prior to spectral measurement, soil physiochemical properties such as pH, total organic carbon and textural analysis were carried out. Soil samples were scanned (field-moist) and assessed using ASD LabSpec2500 (wavelength 350-2500 nm) and MIR data was acquired with Agilent 4300 handheld Fourier transform infrared (FTIR) spectrometer (Agilent Technologies, Santa Clara, CA, United States) with a spectral range of 4000- 650 cmˉÂč. Specifically, detailed analysis of the hydrocarbon content including total petroleum hydrocarbons (TPH), aliphatic and aromatic hydrocarbon fractions were determined and quantified by GC-MS, vis-NIR and MIR DRS. MIR over-performed vis-NIR with RF modelling method performing better than PLSR in predicting TPH, PAH and alkanes. However, PLSR-vis-NIR produced slightly better results than PLSR- MIR in predicting TPH and alkanes. Overall, vis-NIR (wavelength 350-2500 nm) laboratory-scale study yields better TPH prediction than the field-scale study. The minimised moisture content may have improved the results, as laboratory-scale samples were air-dried. Based on the results, MIR spectroscopy coupled with RF is recommended for the analysis of hydrocarbon contaminated soil. Finally, spectroscopy approach was proposed as RMT for contaminated soil investigation and risk prioritisation.PhD in Environment and Agrifoo

    Environmental engineering applications of electronic nose systems based on MOX gas sensors

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    Nowadays, the electronic nose (e-nose) has gained a huge amount of attention due to its ability to detect and differentiate mixtures of various gases and odors using a limited number of sensors. Its applications in the environmental fields include analysis of the parameters for environmental control, process control, and confirming the efficiency of the odor-control systems. The e-nose has been developed by mimicking the olfactory system of mammals. This paper investigates e-noses and their sensors for the detection of environmental contaminants. Among different types of gas chemical sensors, metal oxide semiconductor sensors (MOXs) can be used for the detection of volatile compounds in air at ppm and sub-ppm levels. In this regard, the advantages and disadvantages of MOX sensors and the solutions to solve the problems arising upon these sensors’ applications are addressed, and the research works in the field of environmental contamination monitoring are overviewed. These studies have revealed the suitability of e-noses for most of the reported applications, especially when the tools were specifically developed for that application, e.g., in the facilities of water and wastewater management systems. As a general rule, the literature review discusses the aspects related to various applications as well as the development of effective solutions. However, the main limitation in the expansion of the use of e-noses as an environmental monitoring tool is their complexity and lack of specific standards, which can be corrected through appropriate data processing methods applications

    Field programmable gate array based sigmoid function implementation using differential lookup table and second order nonlinear function

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    Artificial neural network (ANN) is an established artificial intelligence technique that is widely used for solving numerous problems such as classification and clustering in various fields. However, the major problem with ANN is a factor of time. ANN takes a longer time to execute a huge number of neurons. In order to overcome this, ANN is implemented into hardware namely field-programmable-gate-array (FPGA). However, implementing the ANN into a field-programmable gate array (FPGA) has led to a new problem related to the sigmoid function implementation. Often used as the activation function for ANN, a sigmoid function cannot be directly implemented in FPGA. Owing to its accuracy, the lookup table (LUT) has always been used to implement the sigmoid function in FPGA. In this case, obtaining the high accuracy of LUT is expensive particularly in terms of its memory requirements in FPGA. Second-order nonlinear function (SONF) is an appealing replacement for LUT due to its small memory requirement. Although there is a trade-off between accuracy and memory size. Taking the advantage of the aforementioned approaches, this thesis proposed a combination of SONF and a modified LUT namely differential lookup table (dLUT). The deviation values between SONF and sigmoid function are used to create the dLUT. SONF is used as the first step to approximate the sigmoid function. Then it is followed by adding or deducting with the value that has been stored in the dLUT as a second step as demonstrated via simulation. This combination has successfully reduced the deviation value. The reduction value is significant as compared to previous implementations such as SONF, and LUT itself. Further simulation has been carried out to evaluate the accuracy of the ANN in detecting the object in an indoor environment by using the proposed method as a sigmoid function. The result has proven that the proposed method has produced the output almost as accurately as software implementation in detecting the target in indoor positioning problems. Therefore, the proposed method can be applied in any field that demands higher processing and high accuracy in sigmoid function outpu

    Optimal design of negative emission hybrid renewable energy systems with biochar production

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    To tackle the increasing global energy demand the climate change problem, the integration of renewable energy and negative emission technologies is a promising solution. In this work, a novel concept called “negative emission hybrid renewable energy system” is proposed for the first time. It is a hybrid solar-wind-biomass renewable energy system with biochar production, which could potentially provide energy generation, carbon sequestration, and waste treatment services within one system. The optimization and the conflicting economic and environmental trade-off of such system has not yet been fully investigated in the literature. To fill the research gap, this paper aims to propose a stochastic multi-objective decision-support framework to identify optimal design of the energy mix and discuss the economic and environmental feasibilities of a negative emission hybrid renewable energy system. This approach maximizes energy output and minimizes greenhouse gas emissions by the optimal sizing of the solar, wind, combustion, gasification, pyrolysis, and energy storage components in the system. A case study on Carabao Island in the Philippines, which is representative of an island-mode energy system, is conducted based on the aim of achieving net-zero emission for the whole island. For the island with a population of 10,881 people and an area of 22.05 km2, the proposed optimal system have significant negative emission capability and promising profitability with a carbon sequestration potential of 2795 kg CO2-eq/day and a predicted daily profit of 455 US$/day
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