Assessing the influence of the carbon oxidation-reduction state on organic pollutant biodegradation in algal-bacterial photobioreactors

The influence of the carbon oxidation-reduction state (CORS) of organic pollutants on their biodegradation in enclosed algal-bacterial photobioreactors was evaluated using a consortium of enriched wild-type methanotrophic bacteria and microalgae. Methane, methanol and glucose (with CORS -4, -2 and 0, respectively) were chosen as model organic pollutants. In the absence of external oxygen supply, microalgal photosynthesis was not capable of supporting a significant methane and methanol biodegradation due to their high oxygen demands per carbon unit, while glucose was fully oxidized by photosynthetic oxygenation. When bicarbonate was added, removal efficiencies of 37¿±¿4% (20 days), 65¿±¿4% (11 days) and 100% (2 days) were recorded for CH(4,) CH(3)OH and C(6)H(12)O(6), respectively due to the additional oxygen generated from photosynthetic bicarbonate assimilation. The use of NO(3)(-) instead of NH(4)(+) as nitrogen source (N oxidation-reduction state of +5 vs. -3) resulted in an increase in CH(4) degradation from 0 to 33¿±¿3% in the absence of bicarbonate and from 37¿±¿4% to 100% in the presence of bicarbonate, likely due to a decrease in the stoichiometric oxygen requirements and the higher photosynthetic oxygen production. Hypothetically, the CORS of the substrates might affect the CORS of the microalgal biomass composition (higher lipid content). However, the total lipid content of the algal-bacterial biomass was 19¿±¿7% in the absence and 16¿±¿2% in the presence of bicarbonat

Aerospace medicine and biology: A continuing bibliography with indexes, supplement 203

This bibliography lists 150 reports, articles, and other documents introduced into the NASA scientific and technical information system in January 1980

Estimating causal networks in biosphere–atmosphere interaction with the PCMCI approach

Local meteorological conditions and biospheric activity are tightly coupled. Understanding these links is an essential prerequisite for predicting the Earth system under climate change conditions. However, many empirical studies on the interaction between the biosphere and the atmosphere are based on correlative approaches that are not able to deduce causal paths, and only very few studies apply causal discovery methods. Here, we use a recently proposed causal graph discovery algorithm, which aims to reconstruct the causal dependency structure underlying a set of time series. We explore the potential of this method to infer temporal dependencies in biosphere-atmosphere interactions. Specifically we address the following questions: How do periodicity and heteroscedasticity influence causal detection rates, i.e. the detection of existing and non-existing links? How consistent are results for noise-contaminated data? Do results exhibit an increased information content that justifies the use of this causal-inference method? We explore the first question using artificial time series with well known dependencies that mimic real-world biosphere-atmosphere interactions. The two remaining questions are addressed jointly in two case studies utilizing observational data. Firstly, we analyse three replicated eddy covariance datasets from a Mediterranean ecosystem at half hourly time resolution allowing us to understand the impact of measurement uncertainties. Secondly, we analyse global NDVI time series (GIMMS 3g) along with gridded climate data to study large-scale climatic drivers of vegetation greenness. Overall, the results confirm the capacity of the causal discovery method to extract time-lagged linear dependencies under realistic settings. The violation of the method's assumptions increases the likelihood to detect false links. Nevertheless, we consistently identify interaction patterns in observational data. Our findings suggest that estimating a directed biosphere-atmosphere network at the ecosystem level can offer novel possibilities to unravel complex multi-directional interactions. Other than classical correlative approaches, our findings are constrained to a few meaningful set of relations which can be powerful insights for the evaluation of terrestrial ecosystem models

Multivariate calibration of CO2 Solubility in iethanolamine (DEA) using Raman Spectroscopy

The emission of greenhouse gas which is mainly from carbon dioxide (CO2) have cause phenomena of global warming and climate changes. Thus, this issues has become main focus in worldwide nowadays. CO2 removal process also is an essential step in many industrial processing especially in natural gas processing. Absorption based on alkanolamines like diethanolamine (DEA) have been widely applied in carbon capture plant. CO2 solubility has become an important parameter in the absorption process for proper process control in plant operation. Measurement of CO2 solubility in 10%, 20% and 30% DEA using Raman spectroscopy is the main focus in this research. Raman shift and intensity of different DEA concentration with respect to CO2 loading (mol CO2 / mol amine) are obtained by using Raman spectroscopy. Partial Least Square (PLS) regression approach will be utilized to develop a calibration model that relate the Raman spectroscopy data to CO2 solubility in different concentration of DEA. From the result obtained, 10%, 20% and 30% model have shown great performance and demonstrate good prediction of CO2 solubility. In addition, a combined concentration model and combined modified model is developed to predict CO2 solubility at different DEA concentration. From the study, with the inverse of 900 – 1100 cm-1 are added to predicting matrix, the validation R2 have been increased from 0.9004 to 0.9136. Combination of analytical instrument and multivariate calibration tools will aid the process of online monitoring CO2 solubility in DEA in process plant operation

Real-time quality assurance testing using photonic techniques: Application to iodine water system

A feasibility study of the use of inspection systems incorporating photonic sensors and multivariate analyses to provide an instrumentation system that in real-time assures quality and that the system in control has been conducted. A system is in control when the near future of the product quality is predictable. Off-line chemical analyses can be used for a chemical process when slow kinetics allows time to take a sample to the laboratory and the system provides a recovery mechanism that returns the system to statistical control without intervention of the operator. The objective for this study has been the implementation of do-it-right-the-first-time and just-in-time philosophies. The Environment Control and Life Support Systems (ECLSS) water reclamation system that adds iodine for biocidal control is an ideal candidate for the study and implementation of do-it-right-the-first-time technologies

Reduced functional measure of cardiovascular reserve predicts admission to critical care unit following kidney transplantation

Background: There is currently no effective preoperative assessment for patients undergoing kidney transplantation that is able to identify those at high perioperative risk requiring admission to critical care unit (CCU). We sought to determine if functional measures of cardiovascular reserve, in particular the anaerobic threshold (VO2AT) could identify these patients. Methods: Adult patients were assessed within 4 weeks prior to kidney transplantation in a University hospital with a 37-bed CCU, between April 2010 and June 2012. Cardiopulmonary exercise testing (CPET), echocardiography and arterial applanation tonometry were performed. Results: There were 70 participants (age 41.7614.5 years, 60% male, 91.4% living donor kidney recipients, 23.4% were desensitized). 14 patients (20%) required escalation of care from the ward to CCU following transplantation. Reduced anaerobic threshold (VO2AT) was the most significant predictor, independently (OR = 0.43; 95% CI 0.27–0.68; p,0.001) and in the multivariate logistic regression analysis (adjusted OR = 0.26; 95% CI 0.12–0.59; p = 0.001). The area under the receiveroperating- characteristic curve was 0.93, based on a risk prediction model that incorporated VO2AT, body mass index and desensitization status. Neither echocardiographic nor measures of aortic compliance were significantly associated with CCU admission. Conclusions: To our knowledge, this is the first prospective observational study to demonstrate the usefulness of CPET as a preoperative risk stratification tool for patients undergoing kidney transplantation. The study suggests that VO2AT has the potential to predict perioperative morbidity in kidney transplant recipients

The behavior of Particulate Matter (PM10) Concentrations at Industrial Sites in Malaysia

Particulate Matter (PM10) is one of the atmospheric pollutants that can cause significant effect to human health. Meteorological factors such as wind speed (WS), relative humidity (RH) and temperature (T), and gaseous pollutants namely surface layer ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2) and carbon monoxide (CO) are reported as some of the main factors that influence the concentration of PM10. Therefore, the aim of this study is to investigate the pattern and behaviour of PM10 concentration at three industrial sites which were Pasir Gudang in Johor, Perai in Penang and Nilai in Negeri Sembilan. In the current study, the descriptive statistics, correlation analysis and multiple linear regressions were used to analyse the hourly average data from 2010 to 2014. The maximum values of PM10 concentration recorded at Pasir Gudang, Nilai and Perai stations were 995 μg/m³, 711 μg/m³ and 232 μg/m³, respectively. Positive correlation was found between PM10 concentration and all gaseous pollutants. While for meteorological parameters, only wind speed had negative relations at all monitoring stations. The values of R2 for Pasir Gudang, Perai and Nilai were 0.539, 0.628 and 0.634, respectively. Overall, this study proved that most of the selected meteorological parameters and gaseous pollutants positively influenced the concentration of PM10

Multivariate Calibration of CO2 solubility in Methyldiethanolamine (MDEA) using Raman Spectroscopy

For decades, Carbon dioxide (CO2) capturing process had been an important issues since it is one of the major greenhouse gas (GHG) contributors which leads to the global warming. Alkanolamines such as Methyldiethanolamine (MDEA) had been widely used for CO2 capturing by absorption process. A study on carbon dioxide (CO2) solubility was done inside aqueous MDEA solution by using Raman Spectroscopy with the goal of calculating the CO2 loading. This is because, there was still no direct measurement to calculate the CO2 loading inside the MDEA solution. Therefore, a sensor or a measurement device is needed to calculate the CO2 loading. After a three careful experiment had been run on three different MDEA concentrations which are 10%, 20% and 30% concentration, the raw data from the Raman Spectrum had been obtained. Matlab simulation was used to construct a statistical calibration and validation models between the CO2 loading and the peak of Raman Shift by using Partial Least-Squares method (PLS). Results shows that lower MDEA concentration produce better Coefficient of Determination (R2) and Mean Square Error (MSE) for calibration models while the combination of the three MDEA concentrations has found as a good fit with R2 of 0.9651 and MSE of 0.0347 in CO2 loading prediction