Faculty Publications
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Item The role of atmospheric correction algorithms in the prediction of soil organic carbon from hyperion data(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2017) Minu, S.; Shetty, A.; Minasny, B.; Gomez, C.In this study, the role of atmospheric correction algorithm in the prediction of soil organic carbon (SOC) from spaceborne hyperspectral sensor (Hyperion) visible near-infrared (vis-NIR, 400–2500 nm) data was analysed in fields located in two different geographical settings, viz. Karnataka in India and Narrabri in Australia. Atmospheric correction algorithms, (1) ATmospheric CORection (ATCOR), (2) Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH), (3) 6S, and (4) QUick Atmospheric Correction (QUAC), were employed for retrieving spectral reflectance from radiance image. The results showed that ATCOR corrected spectra coupled with partial least square regression prediction model, produced the best SOC prediction performances, irrespective of the study area. Comparing the results across study areas, Karnataka region gave lower prediction accuracy than Narrabri region. This may be explained due to difference in spatial arrangement of field conditions. A spectral similarity comparison of atmospherically corrected Hyperion spectra of soil samples with field-measured vis-NIR spectra was performed. Among the atmospheric correction algorithms, ATCOR corrected spectra found to capture the pattern in soil reflectance curve near 2200 nm. ATCOR’s finer spectral sampling distance in shortwave infrared wavelength region compared to other models may be the main reason for its better performance. This work would open up a great scope for accurate SOC mapping when future hyperspectral missions are realized. © 2017 Informa UK Limited, trading as Taylor & Francis Group.Item Prediction accuracy of soil organic carbon from ground based visible near-infrared reflectance spectroscopy(Springer, 2018) Minu, S.; Shetty, A.The present study was conducted to predict soil organic carbon (SOC) from ground visible near-infrared (Vis-NIR, 400- 2500 nm) spectroradiometer reflectance spectra. The objective was to study the effect of various pre-processing methods and prediction models on the accuracy of SOC estimated. Measured SOC content and reflectance spectra from pasture and cotton fields of Narrabri, Australia were used in the analysis. Reflectance spectra were pretreated with different smoothing methods such as: moving average, median filtering, gaussian smoothing and Savitzky Golay smoothing. A comparison between principal component regression, partial least square regression (PLSR) and artificial neural network models was carried out to get an optimum model for organic carbon prediction. The results indicate that PLSR model performs better with Savitzky Golay as the best pre-processing method for the study area, yielding R2cal = 0:84, RPDcal = 2.55 and RPIQcal = 4.02 in the calibration set and R2val = 0:77, RPDval = 2.17 and RPIQval = 3.19 in the validation set. The study recommends a suitable method in case of limited number of soil data. Based on the study, it can be said that properly pretreated reflectance spectra show tremendous potential in soil organic carbon prediction. © Indian Society of Remote Sensing 2017.Item Acetaminophen micropollutant: Historical and current occurrences, toxicity, removal strategies and transformation pathways in different environments(Elsevier Ltd, 2019) Vo, H.N.; Le, G.K.; Nguyen, T.M.; Bui, X.-T.; Nguyen, K.H.; Rene, E.R.; Vo, T.D.H.; Cao Ngoc, N.-D.; Mohan, R.Acetaminophen (ACT) is commonly used as a counter painkiller and nowadays, it is increasingly present in the natural water environment. Although its concentrations are usually at the ppt to ppm levels, ACT can transform into various intermediates depending on the environmental conditions. Due to the complexity of the ACT degradation products and the intermediates, it poses a major challenge for monitoring, detection and to propose adequate treatment technologies. The main objectives of this review study were to assess (i) the occurrences and toxicities, (2) the removal technologies and (3) the transformation pathways and intermediates of ACT in four environmental compartments namely wastewater, surface water, ground water, and soil/sediments. Based on the review, it was observed that the ACT concentrations in wastewater can reach up to several hundreds of ppb. Amongst the different countries, China and the USA showed the highest ACT concentration in wastewater (?300 ?g/L), with a very high detection frequency (81–100%). Concerning surface water, the ACT concentrations were found to be at the ppt level. Some regions in France, Spain, Germany, Korea, USA, and UK comply with the recommended ACT concentration for drinking water (71 ng/L). Notably, ACT can transform and degrade into various metabolites such as aromatic derivatives or organic acids. Some of them (e.g., hydroquinone and benzoquinone) are toxic to human and other life forms. Thus, in water and wastewater treatment plants, tertiary treatment systems such as advanced oxidation, membrane separation, and hybrid processes should be used to remove the toxic metabolites of ACT. © 2019 Elsevier LtdItem Investigation of performance and technical assessments of hybrid source electric vehicles under different locations and driving conditions(Taylor and Francis Ltd., 2024) Sidharthan P, V.; Kashyap, Y.Sustainable transportation is a significant concept followed by nations implementing Nationally Determined Contributions (NDCs) that reduce emissions and adapt to climate change impacts. Electric vehicle (EV) adoption has accelerated; however, a trade-off exists between EV adoption and EV batteries-Battery charging from the grid (conventional energy sources) and e-wastes from retired batteries deposited in landfills. Thus, EVs associated with renewable energy sources (RES) are an alternate solution. This paper proposes a hybrid source electric vehicle (HSEV) with a high energy-dense supercapacitor (SC) as the primary source and PV energy as the secondary source. An energy management algorithm (EMA) with a modified controller is implemented in a Matlab/Simulink environment. Analysis of HSEV under varying locations (Australia, India, and Scotland), driving profiles (WLTP class-1, IDC, and ECE), and driving times (daytime, nighttime) highlights the importance of the proposed EMA. Grid charging instants are reduced to 3 times per month in Australia under WLTP class-1 cycle employing PV energy. Moreover, SC degradation is least compared to the lithium-ion battery in a BEV (Battery Electric Vehicle), hence avoiding the chances of maintenance and replacements. The proposed HSEV exhibits improved performance compared to BEVs of a similar type under different locations, driving, and environmental conditions. © 2023 Taylor & Francis Group, LLC.