Efficiency of diesel-contaminated soil washing with different tween 80 surfactant concentrations, pH, and bentonite ratios.

Soil contaminated with diesel fuel is a hazard to the environment and people; therefore, it needs to be remediated. Soil washing enhanced with Tween 80 (TW80), non-toxic and non-ionic surfactant, can effectively remove diesel from contaminated soils. In this study, the effects of 0.01%, 0.1%, 0.5%, 1%, and 1.5% (v/v) [TW80] concentrations; 0%, 5%, and 15% (w/w) bentonite; and variation in pH on washing efficiency were examined in a batch test. The prepared samples were physiochemically characterized on the basis of particle size, zeta potential, cation exchange capacity (CEC), scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) analysis. When the bentonite content in soil was 5% or 15%, 1.5% [TW80] solution exhibited the highest washing efficiency. The diesel removal efficiencies in soil with 0% bentonite were slightly higher than those in soils with 5% and 15% bentonite because of the increase in adsorption sites by bentonite; consequently, diesel could not be easily washed out. The extracted n-alkanes showed that the percentage of carbon number 20 was higher than that of the other even-numbered carbons in the retained washed samples analyzed by gas chromatography-mass spectrometry (GC-MS). In all the washing tests, the diesel removal efficiencies in soil with 15% bentonite and 0.1% [TW80] were lower than those in soil with 15% bentonite and water because of adsorption. The bentonite samples washed with TW80 have different morphologies, with a voluminous structure composed of the fusion of all layered structures, as supported by SEM results. Changes in the diesel content and residual TW80 content in the soil before and after washing were shown by the carbon content in the EDS results. The mechanism of the washing effect was investigated by CEC and zeta potential measurements. This study may aid in selecting appropriate conditions for improving washing efficiencies in future field applications.

Preeminently Robust Neural PPG Denoiser.

Photoplethysmography (PPG) is a simple and cost-efficient technique that effectively measures cardiovascular response by detecting blood volume changes in a noninvasive manner. A practical challenge in the use of PPGs in real-world applications is noise reduction. PPG signals are likely to be compromised by various types of noise, such as scattering or motion artifacts, and removing such compounding noises using a monotonous method is not easy. To this end, this paper proposes a neural PPG denoiser that can robustly remove multiple types of noise from a PPG signal. By casting the noise reduction problem into a signal restoration approach, we aim to achieve a solid performance in the reduction of different noise types using a single neural denoiser built upon transformer-based deep generative models. Using this proposed method, we conducted the experiments on the noise reduction of a PPG signal synthetically contaminated with five types of noise. Following this, we performed a comparative study using six different noise reduction algorithms, each of which is known to be the best model for each noise. Evaluation results of the peak signal-to-noise ratio (PSNR) show that the neural PPG denoiser is superior in three out of five noise types to the performance of conventional noise reduction algorithms. The salt-and-pepper noise type showed the best performance, with the PSNR of the neural PPG denoiser being 36.6080, and the PSNRs of the other methods were 19.8160 and 32.8234. The Poisson noise type performed the worst, showing a PSNR of 33.0090; the PSNRs of other methods were 35.1822 and 33.4795, respectively. Thereafter, an experiment to recover a signal synthesized with two or more of the five noise types was conducted. When the number of mixed noises was two, three, four, and five, the PSNRs were 29.2759, 27.8759, 26.5608, and 25.9402, respectively. Finally, an experiment to recover motion artifacts was also conducted. The synthesized motion artifact signal was created by synthesizing only a certain ratio of the total signal length. As a result of the motion artifact signal restoration, the PSNRs were 25.2872, 22.8240, 21.2901, and 19.9577 at 30%, 50%, 70%, and 90% motion artifact ratios, respectively. In the three experiments conducted, the neural PPG denoiser showed that various types of noise were effectively removed. This proposal contributes to the universal denoising of continuous PPG signals and can be further expanded to denoise continuous signals in the general domain.