Contamination Strength Research Articles

A semi-probabilistic Bayesian method to identify the number and location of potential sources in 3D unconfined aquifer using limited observed concentration

Source identification of a contaminant has always been challenging for accurately modeling groundwater transport. Source identification problems are classified into several parts, such as identifying the location of contamination, the strength of contamination, the time the contaminant is introduced into the groundwater, and the duration of its activity. Identifying the sources considering all the parts as variables increases the computational complexity. Reducing the number of variables in source identification problems is necessary for a swift solution through optimization approaches. The most challenging variable in source identification modeling is the location of contamination, as it is a discrete variable for almost all the numerical solutions of groundwater models. In this research study, we have created a methodology to narrow the location of contamination from a random distribution throughout the aquifer to a reasonable number of probable locations. Although methods to identify the location of contamination were devised earlier, we have attempted an approach of combining a particle tracking approach with Bayesian method of updating the probabilities as a novel approach, where the observation data is limited. We have considered the aquifer parameters and observation well data and devised a method with a Lagrangian approach to particle movement to identify the potential source locations. We have refined the source locations to a narrower probability distribution using the Bayesian method of updating the probability through new information of refined grid space. We have tested the models to identify the potential sources with different hypothetical problems and identified the sources in advective dominant transport with an average probability of 0.53, diffusion dominant transport with an average probability of 0.62, heterogenous soils with an average probability of 0.99, anisotropic aquifer with an average probability of 0.91, and aquifer with irregular boundary with an average probability of 0.96 to identify the location nearest to the actual contaminant source. The results are satisfactory in identifying the number of potential sources with an accuracy of 88 % (15 identified out of 17 sources with a probability greater than 0.4) and their locations in the aquifer with a probability of 0.223 for exact location identification. The probability of finding a source nearest to the actual location is 0.745 at an average distance of 11.6 m from the actual source location.

Comparison of multi-criteria analysis methodologies for the prioritization of arsenic-contaminated sites in the southwest of China.

The issue of contaminated sites has been highlighted as an immediate priority in the 13th Five-Year Plan of China. Identification and prioritization of contaminated sites are of key importance for proposing effective strategies for the regional management of contaminated sites. In this study, three advanced multi-attribute methodologies, the risk-based priority methodology, the regional risk assessment methodology, and the dominance-based rough set approach (DRSA), were comparatively employed to screen contaminated sites in, Guangxi, Southwest of China. The results of the three prioritizations show that the highest ranking site identified by the three methods had great agreement. In regard to the screening attributers, while the risk-based prioritization methodology and regional risk assessment methodology allowed a high discrimination in the screening of contaminated sites associated with different attributes, such as farmland, residential areas, contaminant level, number of people, area, storage quality, site service life, and surrounding communities, the DRSA allowed the identification of contamination strength (CS) and contamination potential (CP).

Efficient detection and correction of variable strength ECG artifact from single channel EEG

The electroencephalogram (EEG) electrodes are susceptible to electrocardiogram (ECG) artifacts, misleading the analysis and diagnosis from the EEG recording. This paper mainly focuses on detection and correction of contaminating ECG artifact of various strengths, from a single channel EEG in the absence of a reference ECG channel. The algorithm involves two stages i.e. detection of ECG artifacts and correction of those artifacts. For detection of ECG artifact, modified S-transform (MST) is used on the bandpass filtered contaminated EEG to localize the higher energy QRS segments in time scale. For improved energy concentration of MST around the instantaneous frequency, artifact detection proximity in time scale is.05s. To correct ECG artifacts from EEG, a modified ensembled average subtraction is proposed, which restrict the overcompensation of the EEG in the correction process. The proposed algorithm is tested on MIT/BIH Polysomnography dataset and synthetic data set with various contamination strength, together more than 45 hours of EEG data. The proposed method achieves mean positive predictive value (PPV) and failed detection rate (FDR) of 97.87% and 2.13% respectively for MIT/BIH Polysomnography EEG recordings. The mean PPV and FDR for the CAP sleep EEG recordings with various contamination levels of ECG are found to be 98.77% and 1.22% respectively. The proposed algorithm is compared against existing Continuous wavelet transform (CWT) and Empirical ensembled mode decomposition (EEMD) based algorithms where the proposed algorithm found to be performing better in terms of spike to energy ratio (SER), spike to background energy ratio (SBR), correlation factors, Mutual information (MI), Mean absolute error (MAE) and Mean square error (MSE) along with a lower change in power spectral density (ΔPSD) in various brain frequency bands.

Instantaneous mode contamination and parametric combination instability of spinning cyclically symmetric ring structures with expanding application to planetary gear ring

This work addresses the free and parametric elastic vibrations of the spinning cyclically symmetric ring structures. The focus is on the instantaneous mode contamination, parametric combination instability and their connections. An analytical model is developed by using the Hamilton’s principle for the in-plane bending deflection, the distinction of which is in the arbitrary distributions of the attached mass and stiffness. A special case with equally-spaced discrete mass particles and spinning springs is detailed. The uneven tangential force and the time-invariant deflection caused by the mass particles are formulated. The results imply that the order of such deflection is equal to the number of the mass particles. The instantaneous mode contamination and parametric combination instability are captured by the perturbation and superposition mode shapes of the stationary smooth ring by introducing complex coefficients. The contamination rule is similar to that of the stationary structure but the contamination strength is time-variant due to the spinning springs. New analytical results and quantitative explanations on the contamination and instability especially their connections are presented. As an application of the proposed method, the free and parametric vibrations of the planetary gear ring are formulated. Main results are demonstrated by means of the numerical simulations and compared with the existing studies.

Kinetic Study of Carbon Dioxide (CO2) Respiration Rate in Bioremediation of Soil Contaminated with Spent Motor Oil

ABSTRACT This study was carried out to investigate the kinetics of CO2 generation from the bioremediation of soil contaminated with spent motor oil (SCSMO) in an aerobic fixed-bed bioreactor designed and fabricated using indigenous technology. Six contaminated soil samples with different compositions at the same contaminant strength were investigated under a controlled air flow rate of 10 L/h. Results obtained revealed that treatment option “6” (i.e., the biostimulation option) gave the best result, with 75% reduction in the initial oil and grease content (O&G) and CO2 generation of 6,242 mg/kg contaminated soil (CS). Therefore, the biostimulation option was chosen as the treatment technology and kinetic investigation was based on the treatment technology. Results of kinetic investigation of the treatment technology showed that the growth rate can be represented by the popular Monod equation and the kinetic parameters are μmax = 1.115 × 10−16 h−1; K O&G = 37,490.9 mg/kg; yield of CO2 = 42.59%; and yield of biomass = 57.41%. The rate equation and kinetic parameters can be used to design a bioreactor and monitor biodegradation reaction during bioremediation process.