The treatment of produced waters (by-products of oil and gas extraction) with the innovative process of membrane distillation is challenging, because these highly saline streams contain high concentrations of organic compounds and hydrocarbons that cause membrane wetting and impairment of performance. To design the most compact treatment scheme and with the aim of obtaining an easier management of produced water for reuse purposes, Fenton oxidation is here investigated as a feed pre-treatment that may produce an effluent easily handled by membrane distillation. In high-recovery membrane distillation tests, we systematically investigate the detrimental effects of individual contaminants in a synthetic produced water mimicking the composition of a real sample. The recovery rate depends strongly on the initial salinity, which eventually causes scaling and pore blocking. Surfactants are found to be mainly responsible for membrane wetting, but volatile and hydrophobic organics also spoil the quality of the product water. A Fenton oxidation pre-treatment is thus performed to degrade the target organics, with the aim of enhancing the effectiveness of the following membrane distillation and to improve the quality of the final product. The combined oxidation-membrane distillation scheme has both advantages and limitations, which need to be carefully evaluated and further investigated.
Abstract. State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing satellites and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low − especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a Random Forest classifier to automatize the quality assurance/quality control (QA/QC) procedure of near-surface snow depth measurements collected through ultrasonic sensors, with particular reference to differentiate snow cover from grass or bare ground data and to detecting random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (north-western Italian Alps), and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, being it the most suited to describe the performances of a model in case of a multi-class imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %), yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow ground data, enhancing their use in snow models.
<p><span dir="ltr" role="presentation">A</span><span dir="ltr" role="presentation">dvanced environmental</span> <span dir="ltr" role="presentation">technologies have made available an increasing</span> <span dir="ltr" role="presentation">amount</span> <span dir="ltr" role="presentation">of dat</span><span dir="ltr" role="presentation">a</span> <span dir="ltr" role="presentation">from </span><span dir="ltr" role="presentation">remote sensing satellites, and more sophisticated ground data. Their assimilation into dynamic </span><span dir="ltr" role="presentation">models is progressively becoming the most frequent, and</span> <span dir="ltr" role="presentation">conceivably</span> <span dir="ltr" role="presentation">the most successful, solution </span><span dir="ltr" role="presentation">to estimate snow water</span> <span dir="ltr" role="presentation">resources. Models</span> <span dir="ltr" role="presentation">reliability is th</span><span dir="ltr" role="presentation">e</span><span dir="ltr" role="presentation">refore</span> <span dir="ltr" role="presentation">bounded to data</span> <span dir="ltr" role="presentation">quality</span><span dir="ltr" role="presentation">, which is </span><span dir="ltr" role="presentation">often low in mountain, high</span><span dir="ltr" role="presentation">-</span><span dir="ltr" role="presentation">elevation,</span> <span dir="ltr" role="presentation">and unattended settings. To add new value to snow</span><span dir="ltr" role="presentation">-</span><span dir="ltr" role="presentation">depth </span><span dir="ltr" role="presentation">sensor measurements,</span> <span dir="ltr" role="presentation">we</span> <span dir="ltr" role="presentation">developed</span> <span dir="ltr" role="presentation">a</span> <span dir="ltr" role="presentation">machine</span><span dir="ltr" role="presentation">-</span><span dir="ltr" role="presentation">learning</span> <span dir="ltr" role="presentation">algorithm</span> <span dir="ltr" role="presentation">to automatize the</span> <span dir="ltr" role="presentation">QA/QC </span><span dir="ltr" role="presentation">procedure</span> <span dir="ltr" role="presentation">of near</span><span dir="ltr" role="presentation">-</span><span dir="ltr" role="presentation">surface</span> <span dir="ltr" role="presentation">snow</span> <span dir="ltr" role="presentation">depth observations</span> <span dir="ltr" role="presentation">collected through</span> <span dir="ltr" role="presentation">ground</span> <span dir="ltr" role="presentation">stations</span> <span dir="ltr" role="presentation">data</span><span dir="ltr" role="presentation">. </span><span dir="ltr" role="presentation">Starting from a consolidated manual classification, based on the expert knowledge of hydrologist</span><span dir="ltr" role="presentation">s </span><span dir="ltr" role="presentation">in Valle D'Aosta, a</span> <span dir="ltr" role="presentation">R</span><span dir="ltr" role="presentation">andom</span> <span dir="ltr" role="presentation">F</span><span dir="ltr" role="presentation">orest classifier was developed to discriminate snow cover</span> <span dir="ltr" role="presentation">from</span> <span dir="ltr" role="presentation">grass </span><span dir="ltr" role="presentation">or bare ground data and detect</span> <span dir="ltr" role="presentation">random</span> <span dir="ltr" role="presentation">errors</span> <span dir="ltr" role="presentation">(e.g., spikes)</span><span dir="ltr" role="presentation">. The model was trained and tested on </span><span dir="ltr" role="presentation">Valle d&#8217;Aosta data and</span> <span dir="ltr" role="presentation">then</span> <span dir="ltr" role="presentation">validated on 3 years of data from 30 stations on the Italian territory. </span><span dir="ltr" role="presentation">The F1 score</span> <span dir="ltr" role="presentation">was</span> <span dir="ltr" role="presentation">used as scoring metric, being it most suited to describe the performances of a </span><span dir="ltr" role="presentation">model in case of a multiclass imbalanced classification</span> <span dir="ltr" role="presentation">problem. The</span> <span dir="ltr" role="presentation">model proved to be robust </span><span dir="ltr" role="presentation">and reliable in the classification of snow cover and</span> <span dir="ltr" role="presentation">grass</span><span dir="ltr" role="presentation">/bare ground discrimina</span><span dir="ltr" role="presentation">tion </span><span dir="ltr" role="presentation">(</span><span dir="ltr" role="presentation">F1 values </span><span dir="ltr" role="presentation">above 90%</span><span dir="ltr" role="presentation">)</span><span dir="ltr" role="presentation">, yet less reliable in</span> <span dir="ltr" role="presentation">random</span> <span dir="ltr" role="presentation">error detection,</span> <span dir="ltr" role="presentation">mostly due to the dataset imbalance. No </span><span dir="ltr" role="presentation">clear</span> <span dir="ltr" role="presentation">correlation with single year meteorology was</span> <span dir="ltr" role="presentation">found</span> <span dir="ltr" role="presentation">in the training domain, and the promising </span><span dir="ltr" role="presentation">results from the generalization to a larger</span> <span dir="ltr" role="presentation">domain corroborate</span><span dir="ltr" role="presentation">s</span> <span dir="ltr" role="presentation">the model robustness and</span> <span dir="ltr" role="presentation">reliability.</span><span dir="ltr" role="presentation">This machine</span> <span dir="ltr" role="presentation">learning</span> <span dir="ltr" role="presentation">application of</span> <span dir="ltr" role="presentation">data quality assessment provides more</span> <span dir="ltr" role="presentation">reliable snow</span> <span dir="ltr" role="presentation">ground </span><span dir="ltr" role="presentation">data,</span> <span dir="ltr" role="presentation">enhancing the quality of snow models.</span></p>
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