Robust imputation method with context-aware voting ensemble model for management of water-quality data.

Water-quality monitoring and management are crucial for ensuring the safety and sustainability of water resources. However, missing data is a frequent problem in water-quality datasets, which can result in biased results in hydrological modeling and data analysis. While classic statistical methods and emerging machine/deep learning methods have been applied for imputing missing values, most existing studies perform well in specific missing scenarios, but not in universal scenarios. Therefore, existing imputation methods often fail to robustly impute missing values across various scenarios. To address the problem, we propose an imputation method that uses a context-aware voting-ensemble model to dynamically select optimal weights to integrate various imputation models across different missingness scenarios. For first identify the attributes of missingness scenarios that influence imputation accuracy. Then after introducing missing values in collected data according to the missingness scenarios, we measure the accuracy of various imputation models across the missingness scenarios. Weights of imputation models are optimized by estimating non-linear functions with regression model that can capture relationships between missingness scenarios and imputation accuracies of models. The final imputed value of the ensemble model for a missing scenario can be determined by multiplying each imputation model's weight by its imputed value, then summing the products. The method inherits the advantages of state-of-art imputation models, including the ability to learn long-term dependencies in time series, as well as the flexibility of using a dynamic weighting strategy to process various missingness scenarios. To validate the superiority of our method, we evaluate on real-world water-quality data from a river in South Korea. The proposed method achieves higher accuracy and lower variation of imputed values than baseline models across various missingness scenarios. Furthermore, we showed the applicability of our method to various hydrological environment by validating our method on industrial water quality dataset. This study highlights the potential value of the ensemble model with dynamic weighting in robust imputation of water-quality data.

Aortic Arch Variants and Anomalies: Embryology, Imaging Findings, and Clinical Considerations.

There is a wide spectrum of congenital anomalies or variations of the aortic arch, ranging from non-symptomatic variations that are mostly detected incidentally to clinically symptomatic variations that cause severe respiratory distress or esophageal compression. Some of these may be accompanied by other congenital heart diseases or chromosomal anomalies. The widespread use of multidetector computed tomography (CT) in clinical practice has resulted in incidental detection of several variations of the aortic arch in adults. Thus, radiologists and clinicians should be aware of the classification of aortic arch anomalies and carefully look for imaging features associated with a high risk of clinical symptoms. Understanding the embryological development of the aortic arch aids in the classification of various subtypes of aortic arch anomalies and variants. For accurate diagnosis and precise evaluation of aortic arch anomalies, cross-sectional imaging modalities, such as multidetector CT or magnetic resonance imaging, play an important role by providing three-dimensional reconstructed images. In this review, we describe the embryological development of the thoracic aorta and discuss variations and anomalies of the aortic arch along with their clinical implications.

Development of a Soil Organic Matter Content Prediction Model Based on Supervised Learning Using Vis-NIR/SWIR Spectroscopy.

In the current scenario of anthropogenic climate change, carbon credit security is becoming increasingly important worldwide. Topsoil is the terrestrial ecosystem component with the largest carbon sequestration capacity. Since soil organic matter (SOM), which is mostly composed of organic carbon, and can be affected by rainfall, cultivation, and pollutant inflow, predicting SOM content through regular monitoring is necessary to secure a stable carbon sink. In addition, topsoil in the Republic of Korea is vulnerable to erosion due to climate, topography, and natural and anthropogenic causes, which is also a serious issue worldwide. To mitigate topsoil erosion, establish an efficient topsoil management system, and maximize topsoil utilization, it is necessary to construct a database or gather data for the construction of a database of topsoil environmental factors and topsoil composition. Spectroscopic techniques have been used in recent studies to rapidly measure topsoil composition. In this study, we investigated the spectral characteristics of the topsoil from four major rivers in the Republic of Korea and developed a machine learning-based SOM content prediction model using spectroscopic techniques. A total of 138 topsoil samples were collected from the waterfront area and drinking water protection zone of each river. The reflection spectrum was measured under the condition of an exposure time of 136 ms using a spectroradiometer (Fieldspec4, ASD Inc., Alpharetta, GA, USA). The reflection spectrum was measured three times in wavelengths ranging from 350 to 2500 nm. To predict the SOM content, partial least squares regression and support vector regression were used. The performance of each model was evaluated through the coefficient of determination (R2) and root mean square error. The result of the SOM content prediction model for the total topsoil was R2 = 0.706. Our findings identified the important wavelength of SOM in topsoil using spectroscopic technology and confirmed the predictability of the SOM content. These results could be used for the construction of a national topsoil database.

A web tool for STORET/WQX water quality data retrieval and Best Management Practice scenario suggestion.

Total Maximum Daily Load is a water quality standard to regulate water quality of streams, rivers and lakes. A wide range of approaches are used currently to develop TMDLs for impaired streams and rivers. Flow and load duration curves (FDC and LDC) have been used in many states to evaluate the relationship between flow and pollutant loading along with other models and approaches. A web-based LDC Tool was developed to facilitate development of FDC and LDC as well as to support other hydrologic analyses. In this study, the FDC and LDC tool was enhanced to allow collection of water quality data via the web and to assist in establishing cost-effective Best Management Practice (BMP) implementations. The enhanced web-based tool provides use of water quality data not only from the US Geological Survey but also from the Water Quality Portal for the U.S. via web access. Moreover, the web-based tool identifies required pollutant reductions to meet standard loads and suggests a BMP scenario based on ability of BMPs to reduce pollutant loads, BMP establishment and maintenance costs. In the study, flow and water quality data were collected via web access to develop LDC and to identify the required reduction. The suggested BMP scenario from the web-based tool was evaluated using the EPA Spreadsheet Tool for the Estimation of Pollutant Load model to attain the required pollutant reduction at least cost.

Assessment of natural and calcined starfish for the amelioration of acidic soil.

Quality improvement of acidic soil (with an initial pH of approximately 4.5) with respect to soil pH, exchangeable cations, organic matter content, and maize growth was attempted using natural (NSF) and calcined starfish (CSF). Acidic soil was amended with NSF and CSF in the range of 1 to 10 wt.% to improve soil pH, organic matter content, and exchangeable cations. Following the treatment, the soil pH was monitored for periods up to 3 months. The exchangeable cations were measured after 1 month of curing. After a curing period of 1 month, the maize growth experiment was performed with selected treated samples to evaluate the effectiveness of the treatment. The results show that 1 wt.% of NSF and CSF (700 and 900 °C) were required to increase the soil pH to a value higher than 7. In the case of CSF (900 °C), 1 wt.% was sufficient to increase the soil pH value to 9 due to the strong alkalinity in the treatment. No significant changes in soil pHs were observed after 7 days of curing and up to 3 months of curing. Upon treatment, the cation exchange capacity values significantly increased as compared to the untreated samples. The organic content of the samples increased upon NSF treatment, but it remains virtually unchanged upon CSF treatment. Maize growth was greater in the treated samples rather than the untreated samples, except for the samples treated with 1 and 3 wt.% CSF (900 °C), where maize growth was limited due to strong alkalinity. This indicates that the amelioration of acidic soil using natural and calcined starfish is beneficial for plant growth as long as the application rate does not produce alkaline conditions outside the optimal pH range for maize growth.

Soil pollution assessment and identification of hyperaccumulating plants in chromated copper arsenate (CCA) contaminated sites, Korea.

In recent decades, heavy metal contamination in soil adjacent to chromated copper arsenate (CCA) treated wood has received increasing attention. This study was conducted to determine the pollution level (PL) based on the concentrations of Cr, Cu and As in soils and to evaluate the remediative capacity of native plant species grown in the CCA contaminated site, Gangwon Province, Korea. The pollution index (PI), integrated pollution index (IPI), bioaccumulation factors (BAF(shoots) and BAF(roots)) and translocation factor (TF) were determined to ensure soil contamination and phytoremediation availability. The 19 soil samples from 10 locations possibly contaminated with Cr, Cu and As were collected. The concentrations of Cr, Cu and As in the soil samples ranged from 50.56-94.13 mg kg(-1), 27.78-120.83 mg kg(-1), and 0.13-9.43 mg kg(-1), respectively. Generally, the metal concentrations decreased as the distance between the CCA-treated wood structure and sampling point increased. For investigating phytoremediative capacity, the 19 native plant species were also collected in the same area with soil samples. Our results showed that only one plant species of Iris ensata, which presented the highest accumulations of Cr (1120 mg kg(-1)) in its shoot, was identified as a hyperaccumulator. Moreover, the relatively higher values of BAF(shoot) (3.23-22.10) were observed for Typha orientalis, Iris ensata and Scirpus radicans Schk, suggesting that these plant species might be applicable for selective metal extraction from the soils. For phytostabilization, the 15 plant species with BAF(root) values>1 and TF values<1 were suitable; however, Typha orientalis was the best for Cr.

Development of Web-based Load Duration Curve system for analysis of total maximum daily load and water quality characteristics in a waterbody.

In many states of the US, the total maximum daily load program has been widely developed for watershed water quality restoration and management. However, the total maximum daily load is often represented as an average daily pollutant load based on average long-term flow conditions, and as such, it does not adequately describe the problems they aim to address. Without an adequate characterization of water quality problems, appropriate solutions cannot be identified and implemented. The total maximum daily load approach should consider adequate water quality characterizations based on overall flow conditions rather than on a single flow event such as average daily flow. The Load Duration Curve, which provides opportunities for enhanced pollutant source and best management practice targeting both in the total maximum daily load development and in water quality restoration efforts, has been used for the determination of appropriate total maximum daily load targets. However, at least 30 min to an hour is needed for unskilled people based on our experiences to generate the Load Duration Curve using a desktop-based spreadsheet computer program. Therefore, in this study, the Web-based Load Duration Curve system (https://engineering.purdue.edu/∼ldc/) was developed and applied to a study watershed for an analysis of the total maximum daily load and water quality characteristics in the watershed. This system provides diverse options for Flow Duration Curve and Load Duration Curve analysis of a watershed of interest in a brief time. The Web-based Load Duration Curve system is useful for characterizing the problem according to flow regimes, and for providing a visual representation that enables an easy understanding of the problem and the total maximum daily load targets. In addition, this system will be able to help researchers identify appropriate best management practices within watersheds.

Monitoring of selected veterinary antibiotics in environmental compartments near a composting facility in Gangwon Province, Korea.

Many studies have been recently reported that veterinary antibiotics released into the environment have a detrimental effect on humans such as the occurrence of antibiotic-resistant bacteria. However, only limited information is available regarding to the release of antibiotics in environmental compartments in Korea. Objectives of this study were to evaluate the concentrations of antibiotics in water, sediment, and soil adjacent to a composting facility in Korea and to determine the dilution effects of antibiotics when released into the environment. Seven antibiotics of chlortetracycline, oxytetracycline, tetracycline, sulfamethazine, sulfamethoxazole, sulfathiazole, and tylosin were evaluated by high-performance liquid chromatography-tandem mass spectrometry following pretreatment using solid-phase extraction to clean the samples. Results showed that the highest concentration of each antibiotic in both aqueous and solid samples was detected from a site adjacent to the composting facility. We also found that the studied water, sediment, and soil samples are contaminated by veterinary antibiotics throughout comparison with studies from other countries. However, relatively lower concentrations of each antibiotic were observed from the rice paddy soil located at the bottom of the water stream. Further research is necessary to continuously monitor the antibiotics release into ecosystems, thereby developing an environmental risk assessment.