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In this work, we propose our newly developed wafer-type plasma monitoring sensor based on a floating-type double probe method that can be useful for two-dimensional (2D) in situ plasma diagnosis within a semiconductor processing chamber. A key achievement of this work is the first realization of an ultra-thin plasma monitoring sensor with a system thickness of ~1.4 mm, which supports a fully automated robot arm transfer capability for in situ plasma diagnosis. To the best of our knowledge, it is the thinnest accomplishment among all wafer-type plasma monitoring sensors. Our proposed sensor is assembled with two Si wafers and SiO 2 -based probes; accordingly, it makes it possible to monitor the actual dynamics of processing plasmas under electrostatic chucking (ESC) conditions. Also, it allows for the prevention of chamber contamination issues after continuously exposing the radio frequency (RF) to various processing gases. Using a test-bed chamber, we successfully demonstrated the feasibility and system performance of the proposed sensor, including robot arm transfer capability, vacuum and thermal stress durability, and data integrity and reproducibility. Consequently, compared with the conventional plasma diagnostic tools, we expect that our proposed sensor will be highly beneficial for tool-to-tool matching (TTTM) and/or for studying various plasma-related items by more accurately providing the parameters of processing plasmas, further saving both time and manpower resources required for preventive maintenance (PM) routines as well.

We proposed a novel approach based on dual ammonium and nitrate nitrogen isotope ratios (δ 15 N NH4 and δ 15 N NO3 , respectively) axes to identify nitrogen sources in intensive livestock farming watersheds, especially those with swine excreta treatment facilities. The δ 15 N NH4 and δ 15 N NO3 values in water samples were measured monthly in 2016-2017. Soil and mineral fertilizers, sewage, sewage effluent, manure, and swine effluents were the five sources considered to identify nitrogen sources. The results showed that nitrogen pollution from agricultural activities was well reflected by the seasonal δ 15 N NH4 and δ 15 N NO3 patterns in the river, and microbial nitrification was suggested as the dominant nitrogen transformation process in the river. This study revealed that δ 15 N NH4 and δ 15 N NO3 axes provided better results than the traditionally used nitrate oxygen (δ 18 O NO3 ) and δ 15 N NO3 axes for identifying nitrogen sources in agricultural watersheds with swine excreta treatment facilities. The mixing model results showed that stream water was severely contaminated with swine effluents (e.g., a mean minimum contribution of 31%), thus affecting the quality of the mainstream (p = 0.068 < 0.10). This study was the first successful application of dual δ 15 N NH4 and δ 15 N NO3 axes to better understand nitrogen sources in intensive livestock farming watersheds with swine excreta treatment facilities., Competing Interests: Declaration of competing interest The authors declare that no competing financial interests exist., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Machine learning modeling techniques have emerged as a potential means for predicting algal blooms. In this study, synthetic spatio-temporal water quality data for a river section were generated with a 3D water quality model and used to investigate the capability of a convolutional neural network (CNN) for predicting harmful cyanobacterial blooms. The CNN model displayed a reasonable capacity for short-term predictions of cyanobacteria (Microcystis) biomass. In the nowcasting of Microcystis, the CNN performance had a Nash-Sutcliffe Efficiency (NSE) of 0.87. An increase in the forecast lead time resulted in a decrease in the prediction accuracy, reducing the NSE from 0.87 to 0.58. As the spatial observation density increased from 20% to 100% of the input image grids, the CNN prediction NSE had improved from 0.70 to 0.84. Adding noise to the data resulted in accuracy deterioration, but even at the noise amplitude of 10%, the accuracy was acceptable for some applications, with NSE = 0.76. Visualization of the CNN results characterized its performance variations across the studied river reach. Overall, this study successfully demonstrated the capability of the CNN model for cyanobacterial bloom prediction using high temporal frequency images., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Data assimilation in complex water quality modeling is inevitably multivariate because several water quality variables interact and correlate. In ensemble Kalman filter applications, determining which variables to include and the structure of the relationships among these variables is important to achieve accurate forecast results. In this study, various analysis methods with different combinations of variables and interaction structures were evaluated under two different simulation conditions: synthetic and real. In the former, a synthetic experimental setting was formulated to ensure that issues, including incorrect model error assumption problem, spurious correlation between variables, and observational data inconsistency, would not distort the analysis results. The latter did not have such considerations. Therefore, this process could demonstrate the undistorted effects of the different analysis methods on the assimilated outputs and how these effects might diminish in real applications. Under synthetic conditions, updating a single active variable was found to improve the accuracy of the other active variables, and updating multiple active variables in a multivariate manner mutually enhanced the accuracy of the variables if proper ensemble covariance and observation data consistency were ensured. The results of the real case indicated a weakened mutual enhancement effect, and the methods in which variable localization were applied yielded the best analysis results. However, the multivariate analysis methods produced more accurate forecasting results, indicating that these methods could be superior. Therefore, it is suggested that multivariate analysis methods be considered first for water quality modeling, and the application of variable localization should be considered if significant spurious correlations and data inconsistency are present., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier Ltd.)

River water quality is one of the main challenges that societies face during the 21st century. Accurate and reliable real-time prediction of water quality is an effective adaptation measure to counteract water quality issues such as accidental spill and harmful algae blooms. To improve accuracy and skill of water quality forecasts along the Yeongsan River in South Korea three different ensemble data assimilation (DA) methods have been investigated: the traditional Ensemble Kalman Filter (EnKF) and two related algorithms (Dud-EnKF and EnKF-GS) that offer either possibilities to improve initial conditions for non-linear models or reduce computation time (important for real-time forecasting) by using a (smaller) time-lagged ensemble to estimate the Kalman gain. Twin experiments, assimilating synthetic observations of three algae species and phosphate concentrations, with relatively small ensemble sizes showed that all three DA methods improved forecast accuracy and skill with only subtle difference between the methods. They all improved the model accuracy at downstream locations with very similar performances but due to spurious correlation, the accuracy at upstream locations was somewhat deteriorated. The experiments also showed no clear trend of improvement by increasing the ensemble size from 8 to 64. The real world experiments, assimilating real observations of three algae species and phosphate concentrations, showed that less improvement was achieved compared to the twin experiments. Further improvement of the model accuracy may be achieved with different state variable definitions, use of different perturbation and error modelling settings and/or better calibration of the deterministic water quality model., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 The Authors. Published by Elsevier Ltd.. All rights reserved.)