Addition of cellulolytic enzymes and phytase for improving ethanol fermentation performance and oil recovery in corn dry grind process

Application of hydrolytic and other enzymes for improving fermentation performance and oil recovery in corn dry-grind process was optimized. Non-starch polysaccharide enzymes (BluZy-P XL; predominantly xylanase activity) were added at stages prior to fermentation at optimum conditions of 50 ◦C and pH 5.2 and compared with conventional fermentation (30 ◦C, pH 4.0). Enzyme applications resulted in faster ethanol production rates with a slight increase in yield compared to control. The thin stillage yield increased by 0.7–5% w/w wet basis with corresponding increase in solids content with enzyme treatment after liquefaction. The oil partitioned in thin stillage was at 67.7% dry basis after treatment with hydrolytic enzymes during fermentation. Further addition of protease and phytase during simultaneous saccharification and fermentation increased thin stillage oil partitioning to 77.8%. It also influenced other fermentation parameters, e.g., ethanol production rate increased to 1.16 g/g dry corn per hour, and thin stillage wet solids increased by 2% w/w. This study indicated that treatments with non-starch hydrolytic enzymes have potential to improve the performance of corn dry-grind process including oil partitioning into thin stillage. The novelty of this research is the addition of protease and phytase enzymes during simultaneous saccharification and fermentation of corn dry-grind process, which further improved ethanol yields and oil partitioning into thin stillage

Pole-to-pole validation of GOME WFDOAS total ozone with groundbased data

International audienceThis paper summarises the validation of GOME total ozone retrieved using the Weighting Function Differential Optical Absorption Spectroscopy (WFDOAS) algorithm Version 1.0. This algorithm has been described in detail in a companion paper by Coldewey-Egbers et al. (2005). Compared to the operational GDP (GOME Data Processor) V3, several improvements to the total ozone retrieval have been introduced that account for the varying ozone dependent contribution to rotational Raman scattering, includes a new cloud scheme, and uses the GOME measured effective albedo in the retrieval. In this paper the WFDOAS results have been compared with selected ground-based measurements from the WOUDC (World Ozone and UV Radiation Data Centre) that collects total ozone measurements from a global network of stations covering all seasons. From the global validation excellent agreement between WFDOAS and ground data was observed. The agreement lies within ±1%, and very little seasonal variations in the differences are found. In the polar regions and at high solar zenith angles, however, a positive bias varying between 5 and 8% is found near the polar night period. As a function of solar zenith angle as well as of the retrieved total ozone, the WFDOAS differences to ground polar data, however, show a much weaker dependence as compared to the operational GOME Data Processor Version 3 of GOME that represents a significant improvement. Very few stations carry out simultaneous measurements by Brewer and Dobson spectrometers over an extended period (three years or more). Simultaneous Brewer and Dobson measurements from Hradec Kralove, Czech Republic (50.2N, 15.8E) and Hohenpeissenberg, Germany (47.8N, 11.0E) covering the period 1996-1999 have been compared with our GOME results. Agreement with Brewers are generally better than with the simultaneous Dobson measurements and this may be explained by the neglect of stratospheric (ozone) temperature correction in the standard ozone retrieval from the ground

Inverse Modeling of Texas NOx Emissions Using Space-Based and Ground-Based NO2 Observations

Inverse modeling of nitrogen oxide (NOx) emissions using satellite-based NO2 observations has become more prevalent in recent years, but has rarely been applied to regulatory modeling at regional scales. In this study, OMI satellite observations of NO2 column densities are used to conduct inverse modeling of NOx emission inventories for two Texas State Implementation Plan (SIP) modeling episodes. Addition of lightning, aircraft, and soil NOx emissions to the regulatory inventory narrowed but did not close the gap between modeled and satellite observed NO2 over rural regions. Satellitebased top-down emission inventories are created with the regional Comprehensive Air Quality Model with extensions (CAMx) using two techniques: the direct scaling method and discrete Kalman filter (DKF) with Decoupled Direct Method (DDM) sensitivity analysis. The simulations with satellite-inverted inventories are compared to the modeling results using the a priori inventory as well as an inventory created by a ground-level NO2 based DKF inversion. The DKF inversions yield conflicting results: the satellite based inversion scales up the a priori NOx emissions in most regions by factors of 1.02 to 1.84, leading to 3-55% increase in modeled NO2 column densities and 1-7 ppb increase in ground 8 h ozone concentrations, while the ground-based inversion indicates the a priori NOx emissions should be scaled by factors of 0.34 to 0.57 in each region. However, none of the inversions improve the model performance in simulating aircraft-observed NO2 or ground-level ozone (O3) concentrations

Using Satellite Remote Sensing and Modelling for Insights into N02 Air Pollution and NO2 Emissions

Nitrogen oxides (NO(x)) are key actors in air quality and climate change. Satellite remote sensing of tropospheric NO2 has developed rapidly with enhanced spatial and temporal resolution since initial observations in 1995. We have developed an improved algorithm and retrieved tropospheric NO2 columns from Ozone Monitoring Instrument. Column observations of tropospheric NO2 from the nadir-viewing satellite sensors contain large contributions from the boundary layer due to strong enhancement of NO2 in the boundary layer. We infer ground-level NO2 concentrations from the OMI satellite instrument which demonstrate significant agreement with in-situ surface measurements. We examine how NO2 columns measured by satellite, ground-level NO2 derived from satellite, and NO(x) emissions obtained from bottom-up inventories relate to world's urban population. We perform inverse modeling analysis of NO2 measurements from OMI to estimate "top-down" surface NO(x) emissions, which are used to evaluate and improve "bottom-up" emission inventories. We use NO2 column observations from OMI and the relationship between NO2 columns and NO(x) emissions from a GEOS-Chem model simulation to estimate the annual change in bottom-up NO(x) emissions. The emission updates offer an improved estimate of NO(x) that are critical to our understanding of air quality, acid deposition, and climate change

Ultrasonication in Soy Processing for Enhanced Protein and Sugar Yields and Subsequent Bacterial Nisin Production

Soy protein recovery from hexane-defatted soybean flakes using conventional methods is generally low. Importantly, some tightly-bound sugar in the soy flakes ends up in soy protein, thereby deteriorating the usefulness and quality of soy protein as a food ingredient. This research investigated the use of high-power ultrasound prior to soy protein extraction to simultaneously enhance protein yield and facilitate more sugar release in soy whey. The nutrient-rich soy whey was then used as a cheap growth medium to produce high-value nisin using Lactococcus lactis subsp. lactis. A nisin sensitive organism Micrococcus luteus was used as an indicator organism for international unit determination of nisin production as compared to standard. Soy flakes and water was mixed at the ratio of 1:10 (w/w). The slurry was then sonicated for 15, 30, 60 and 120 sec at a frequency of 20 kHz. The ultrasonic amplitude was maintained at 84 µmpp (peak to peak amplitude in µm) for all sonication durations. The results showed that with ultrasound pretreatment, the protein yield improved as much as by 46% in soy extract and sugar release by 50% with respect to nonsonicated samples (control). To maximize nisin production from soy whey, different parameters, such as aeration/agitation and incubation period were optimized. Nisin production from standard medium, DeMan, Rogosa and Sharpe (MRS) and soy whey was tested and compared. Maximum nisin production was achieved in stationary conditions and showed a continuous increase in yield till 48h of incubation (incubation period beyond that was not tested). Maximum nisin yield of 1.78 g/L of soy whey was obtained at 30°C and pH of 4.5 as opposed to 2.96 g/L of nisin with MRS medium

Magnetic Ordering in the Rare Earth Intermetallic Compound Tb₂Ti₃Ge₄: Magnetization and Neutron Diffraction Studies

Magnetization and neutron diffraction studies on a polycrystalline Tb2Ti3Ge4 sample (orthorhombic Sm5Ge4-type structure, space group Pnma, No. 62) have been carried out. This compound is found to order antiferromagnetically at ~18 K (TN). The magnetization (M) versus field (H) isotherms obtained at 2, 3, 5, and 10 K indicate a field-induced antiferromagnetic to ferromagnetic transition in fields of the order of 0.5 T. The saturation magnetization value at 2.5 K (M extrapolated to 1/H--\u3e0) is only ~5.6µB/Tb3+, suggesting the possible presence of crystal field effects with or without a persisting antiferromagnetic component. Neutron powder diffraction data at 10 K confirm the existence of a magnetic long range order. Modeling of the magnetic scattering reveals a complex and incommensurate antiferromagnetic spin structure below TN

Use of machine learning and principal component analysis to retrieve nitrogen dioxide (NO₂) with hyperspectral imagers and reduce noise in spectral fitting

Nitrogen dioxide (NO2) is an important trace-gas pollutant and climate agent whose presence also leads to spectral interference in ocean color retrievals. NO2 column densities have been retrieved with satellite UV–Vis spectrometers such as the Ozone Monitoring Instrument (OMI) and the Tropospheric Monitoring Instrument (TROPOMI) that typically have spectral resolutions of the order of 0.5 nm or better and spatial footprints as small as 3.6 km × 5.6 km. These NO2 observations are used to estimate emissions, monitor pollution trends, and study effects on human health. Here, we investigate whether it is possible to retrieve NO2 amounts with lower-spectral-resolution hyperspectral imagers such as the Ocean Color Instrument (OCI) that will fly on the Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite set for launch in early 2024. OCI will have a spectral resolution of 5 nm and a spatial resolution of ∼ 1 km with global coverage in 1–2 d. At this spectral resolution, small-scale spectral structure from NO2 absorption is still present. We use real spectra from the OMI to simulate OCI spectra that are in turn used to estimate NO2 slant column densities (SCDs) with an artificial neural network (NN) trained on target OMI retrievals. While we obtain good results with no noise added to the OCI simulated spectra, we find that the expected instrumental noise substantially degrades the OCI NO2 retrievals. Nevertheless, the NO2 information from OCI may be of value for ocean color retrievals. OCI retrievals can also be temporally averaged over timescales of the order of months to reduce noise and provide higher-spatial-resolution maps that may be useful for downscaling lower-spatial-resolution data provided by instruments such as OMI and TROPOMI; this downscaling could potentially enable higher-resolution emissions estimates and be useful for other applications. In addition, we show that NNs that use coefficients of leading modes of a principal component analysis of radiance spectra as inputs appear to enable noise reduction in NO2 retrievals. Once trained, NNs can also substantially speed up NO2 spectral fitting algorithms as applied to OMI, TROPOMI, and similar instruments that are flying or will soon fly in geostationary orbit.</p

Improved Satellite Retrievals of NO2 and SO2 over the Canadian Oil Sands and Comparisons with Surface Measurements

Satellite remote sensing is increasingly being used to monitor air quality over localized sources such as the Canadian oil sands. Following an initial study, significantly low biases have been identified in current NO2 and SO2 retrieval products from the Ozone Monitoring Instrument (OMI) satellite sensor over this location resulting from a combination of its rapid development and small spatial scale. Air mass factors (AMFs) used to convert line-of-sight "slant" columns to vertical columns were re-calculated for this region based on updated and higher resolution input information including absorber profiles from a regional-scale (15 km 15 km resolution) air quality model, higher spatial and temporal resolution surface reflectivity, and an improved treatment of snow. The overall impact of these new Environment Canada (EC) AMFs led to substantial increases in the peak NO2 and SO2 average vertical column density (VCD), occurring over an area of intensive surface mining, by factors of 2 and 1.4, respectively, relative to estimates made with previous AMFs. Comparisons are made with long-term averages of NO2 and SO2 (2005-2011) from in situ surface monitors by using the air quality model to map the OMI VCDs to surface concentrations. This new OMI-EC product is able to capture the spatial distribution of the in situ instruments (slopes of 0.65 to 1.0, correlation coefficients of greater than 0.9). The concentration absolute values from surface network observations were in reasonable agreement, with OMI-EC NO2 and SO2 biased low by roughly 30%. Several complications were addressed including correction for the interference effect in the surface NO2 instruments and smoothing and clear-sky biases in the OMI measurements. Overall these results highlight the importance of using input information that accounts for the spatial and temporal variability of the location of interest when performing retrievals