Dioxide Ratio Research Articles

Development of the NO2 ratio model for heavy-duty diesel engine two-stage SCR aftertreatment system

A diesel oxidation catalyst (DOC) outlet nitrogen dioxide (NO2) ratio model based on the model-based calibration (MBC) method was proposed, which is an important part of the two-stage selective catalytic reduction (SCR) control strategy for heavy-duty diesel engines. Emissions regulations for heavy-duty diesel engines around the world have become more stringent in limiting nitrogen oxide (NOx), especially when the engine is cold starting, which brings serious challenges to the aftertreatment system. The two-stage SCR system with the close coupled selective catalytic reduction-diesel oxidation catalyst-diesel particulate filter-selective catalytic reduction (ccSCR-DOC-DPF-SCR) layout has the potential to achieve ultra-low NOx emissions due to its high technology maturity, but the two-stage SCR urea injection control strategy based on the chemical reaction kinetics model presents a new functional requirement for the prediction of NO2 ratio at the DOC outlet. However, experiments show that the Euro VI method based on calibration to obtain DOC outlet NO2 ratio was not suitable for the two-stage SCR system, because of the temperature delay effect of ccSCR in transient conditions. Therefore, a quadratic polynomial model using the MBC method was constructed to predict the DOC outlet NO2 ratio in the two-stage SCR system. The proposed MBC model can predict the NO2 ratio by using exhaust mass flow rate, DOC inlet temperature, and DOC inlet NOx concentration. Experiments show that the proposed MBC model has wide applicability, for the two-stage SCR system under transient conditions, whether ccSCR urea injection is enabled will not affect the accuracy of the model’s prediction of the NO2 ratio for the DOC outlet or SCR inlet.

The next challenge in emissions control for heavy-duty diesel vehicles: From NOx to N2O

Vehicle emissions are a major source of greenhouse gases globally. Dual selective catalytic reduction (SCR), an advanced version of single SCR, is crucial under stricter nitrogen oxide (NOx) emission standards for heavy-duty diesel vehicles (HDDVs). However, the emission characteristics of nitrous oxide (N2O), a byproduct of SCR and a potent greenhouse gas, remain unclear. This study investigates the N2O emissions from HDDVs equipped with single or dual SCR systems using heavy-duty chassis dynamometers under various ambient temperatures, altitudes, and loading masses. The results showed that the brake-specific emissions (EFb) of N2O from HDDVs with single and dual SCRs were 76.28–269.65 mg/kWh and 147.50–170.22 mg/kWh, respectively. Notably, the dual SCR-equipped HDDV emitted 6–22 times more N2O than NOx under all tested conditions. As ambient temperature increased from −10 °C to 25 °C and from 25 °C to 40 °C, the average distance-based emission factors (EFd) of N2O for the single SCR-equipped HDDV increased by 87.73% and 48.26%, respectively. However, the variation was not significant for the dual SCR-equipped HDDV. Under half- and full-load conditions, the average EFd of N2O for the single SCR-equipped HDDV increased by 47.57% and 110.92%, respectively, compared to those without loading. Similarly, N2O emissions for dual SCR-equipped HDDV increased by 41.40% and 65.37% under the same loading variations. As altitude increased from 0 m to 3000 m, the average EFd of N2O for the single SCR-equipped HDDV decreased by 64.31%. Additionally, N2O emissions were significantly affected by SCR temperature, engine power, and nitric oxide (NO)/nitrogen dioxide (NO2) ratio. These findings are crucial for setting future greenhouse gas limits of HDDVs and informing carbon reduction strategies.

Preparing for future pandemics: Automated intensive care electronic health record data extraction to accelerate clinical insights

BackgroundManual data abstraction from electronic health records (EHRs) for research on intensive care patients is time-intensive and challenging, especially during high-pressure periods such as pandemics. Automated data extraction is a potential alternative but may raise quality concerns. This study assessed the feasibility and credibility of automated data extraction during the coronavirus disease 2019 (COVID-19) pandemic. MethodsWe retrieved routinely collected data from the COVID-Predict Dutch Data Warehouse, a multicenter database containing the following data on intensive care patients with COVID-19: demographic, medication, laboratory results, and data from monitoring and life support devices. These data were sourced from EHRs using automated data extraction. We used these data to determine indices of wasted ventilation and their prognostic value and compared our findings to a previously published original study that relied on manual data abstraction largely from the same hospitals. ResultsUsing automatically extracted data, we replicated the original study. Among 1515 patients intubated for over 2 days, Harris–Benedict (HB) estimates of dead space fraction increased over time and were higher in non-survivors at each time point: at the start of ventilation (0.70±0.13 vs. 0.67±0.15, P <0.001), day 1 (0.74±0.10 vs. 0.71±0.11, P<0.001), day 2 (0.77±0.09 vs. 0.73±0.11, P<0.001), and day 3 (0.78±0.09 vs. 0.74±0.10, P<0.001). Patients with HB dead space fraction above the median had an increased mortality rate of 13.5%, compared to 10.1% in those with values below the median (P<0.005). Ventilatory ratio showed similar trends, with mortality increasing from 10.8% to 12.9% (P=0.040). Conversely, the end-tidal-to-arterial partial pressure of carbon dioxide (PaCO₂) ratio was inversely related to mortality, with a lower 28-day mortality in the higher than median group (8.5% vs. 15.1%, P<0.001). After adjusting for base risk, impaired ventilation markers showed no significant association with 28-day mortality. ConclusionManual data abstraction from EHRs may be unnecessary for reliable research on intensive care patients, highlighting the feasibility and credibility of automated data extraction as a trustworthy and scalable solution to accelerate clinical insights, especially during future pandemics.

Effect of Ga-Promoted on Ni/Zr + Al2O3 Catalysts for Enhanced CO2 Reforming and Process Optimization

AbstractIn this study, zirconia-modified alumina support (S) was used to investigate Ga-promoted Ni catalysts for dry reforming of methane (DRM). The catalysts (Ni + (0–3) wt% Ga/S) were prepared using the wet impregnation method and calcined at 700 °C for 3 h. The inclusion of Ga enhanced the surface area, basicity, and metal-support interaction of the Ni-Ga/S catalysts. Smaller Ni particles containing Ga were seen in the TEM. The most active and stable catalyst was Ni + 2.0 Ga/S, having a conversion of 35% CO2 and 28% CH4 at 600 °C and displaying less (17%) carbon deposition. Furthermore, the DRM process was optimized by a mathematical model. The model determined the optimal conditions as follows: temperature (800 °C), gas flow rate (GHSV—30,000 ml h−1gcat−1), and methane to carbon dioxide ratio (1:1). The model predicts CH4 and CO2 conversions of 76.76% and 82.0%, respectively, and an H2/CO ratio of 1.02, compared to experimental results showing CH4 conversion at 74.56%, CO2 conversion at 83.25%, and an H2/CO ratio of 1.01. The model demonstrates excellent agreement with the experimental observations, exhibiting less than 3% error. Graphical Abstract

A Comprehensive Review of Syngas Production, Fuel Properties, and Operational Parameters for Biomass Conversion

This study aims to provide an overview of the growing need for renewable energy conversion and aligns with the broader context of environmentally friendly energy, specifically through producing syngas from biomass. Unlike natural gas, which is mainly composed of methane, syngas contains a mixture of combustible CO, H2, and CnHm. Therefore, optimizing its production requires a thorough examination of various operational parameters such as the gasifying agent, the equivalence ratio, the biofuel type, and the state, particularly in densified forms like pellets or briquettes. As new biomass sources are continually discovered and tested, operational parameters are also constantly evaluated, and new techniques are continuously developed. Indeed, these techniques include different gasifier types and the use or non-use of catalysts during biofuel conversion. The present study focuses on these critical aspects to examine their effect on the efficiency of syngas production. It is worth mentioning that syngas is the primary gaseous product from gasification. Moreover, it is essential to note that the pyrolysis process (prior to gasification) can produce, in addition to tar and char, a mixture of gases. The common feature among these gases is their versatility in energy generation, heat production, and chemical synthesis. The analysis encompasses the resulting gas features, including the yield and composition, mainly through the hydrogen-to-carbon monoxide ratio and the carbon monoxide-to-carbon dioxide ratio, as well as the lower heating value and considerations of the tar yield.

Evaluating the multi-variable influence on O3, NO2, and HCHO using BRTs and RF model

This study addresses significant knowledge gaps in understanding the complex interplay between atmospheric chemistry and synoptic conditions. Using emerging machine learning techniques—Boosted Regression Trees (BRTs) and Random Forest (RF) models—we investigate the influence of synoptic conditions on pollutant levels. Several BRTs and RF models are developed to estimate surface concentrations of ozone (O3), nitrogen dioxide (NO2), and formaldehyde (HCHO). By considering a range of algorithmic structures and explanatory variables for each pollutant, the research aims to identify the most skillful predictive approaches and influential factors governing pollutant levels. The design seeks to highlight key determinants of concentration patterns without constraining the investigation to pre-defined model structures or explanatory variable sets. Introducing a novel methodology, Correlation Coefficient Differential Evaluation (C2DE), we quantitatively assess the influence of explanatory variables. C2DE reveals significant contributions from spatial variables (i.e., trajectory clusters at varying altitudes), formaldehyde to nitrogen dioxide ratio (FNR), and meteorological parameters. Specifically, spatial variables contribute approximately 28 % to O3 concentrations, while the FNR accounts for around 5.2–9.8 % of the overall influence. For NO2 and HCHO, spatial variables contribute around 26.5 % and 32.1 %, respectively. Moreover, when considering the combined influence of meteorological parameters, these collectively explain about 45.34 %, 35.31 %, and 45.41 % of the variations in O3, NO2, and HCHO concentrations, respectively. Thus, C2DE provides valuable insights into the relative contributions of these factors, aiding in the comprehensive evaluation of air quality dynamics. This underscores the need for a multifaceted approach to comprehending and effectively addressing air pollution before devising its control strategies.

Analysis of Ozone Formation Sensitivity in Chinese Representative Regions Using Satellite and Ground-Based Data

O3 poses a significant threat to human health and the ecological environment. In recent years, O3 pollution has become increasingly serious, making it difficult to accurately control O3 precursor emissions. Satellite indicator methods, such as the FNR (formaldehyde-to-nitrogen dioxide ratio (HCHO/NO2 ratio)), provide an effective way to identify ozone pollution control areas on a large geographical scale due to their simple acquisition of datasets. This can help determine the primary factors contributing to O3 pollution and assist in managing it. Based on TROPOMI data from May 2018 to December 2022, combined with ground-based monitoring data from the China National Environmental Monitoring Centre, we explored the uncertainty associated with using the HCHO/NO2 ratio (FNR) as an indicator in ozone control area determination. We focused on the four representative regions in China: Jing-Jin-Ji-Lu-Yu (JJJLY), Jiang-Zhe-Hu-Wan (JZHW), Chuan-Yu (CY), and South China. By using the statistical curve-fitting method, we found that the FNR thresholds were 3.5–5.1, 2.0–4.0, 2.5–4.2, and 1.7–3.5, respectively. Meanwhile, we analyzed the spatial and temporal characteristics of the HCHO, NO2, and O3 control areas. The HCHO concentrations and NO2 concentrations had obvious cyclical patterns, with higher HCHO column densities occurring in summer and higher NO2 concentrations in winter. These high values always appeared in areas with dense population activities and well-developed economies. The distribution characteristics of the ozone control areas indicated that during O3 pollution periods, the urban areas with industrial activities and high population densities were primarily controlled by VOCs, and the suburban areas gradually shifted from VOC-limited regimes to transitional regimes and eventually reverted back to VOC-limited regimes. In contrast, the rural and other remote areas with relatively less development were mainly controlled by NOx. The FNR also exhibited periodic variations, with higher values mostly appearing in summer and lower values appearing in winter. This study identifies the main factors contributing to O3 pollution in different regions of China and can serve as a valuable reference for O3 pollution control.

Variation among individual beef cattle in methane-to-carbon dioxide ratio measured under on-farm conditions using the sniffer method.

This study proposed a method for measuring the methane (CH4 )/carbon dioxide (CO2 ) ratio from individual beef cattle under on-farm conditions and estimated the variance components of the CH4 /CO2 ratio. Gas measurements were conducted using 166 Japanese Black cattle group-housed in pens equipped with individual feed bins. The gas containing the animal's breath was measured individually after concentrate feeding by covering the feed bin with a sheet with sampling inlets. Measurements were performed six times (three consecutive days, twice daily) per individual. Most of the sampled gas contained more than 1000 ppm of the mean background-corrected CO2 , suggesting that the method proposed in this study successfully collected sufficient breath concentration to accurately measure the CH4 /CO2 ratio. The between-animal variance accounted for 31.7% of the total variance in the CH4 /CO2 ratio. The results showed that the gas collection method proposed in this study could be a useful tool for measuring the CH4 /CO2 ratio under on-farm conditions. The variance component obtained from this study will help to establish protocols for generating data for genetic evaluation and performing dietary experiments with sufficient statistical power.

Carbon dioxide hydrogenation to value-added products using Cu-based catalytic materials

In this work, we report on the synthesis and evaluation of catalytic materials composed of copper/calcium oxide/alumina (Cu/CaO/Al2O3) for carbon dioxide conversion to value-added products. Catalytic materials were synthesized using sequential incipient wetness impregnation method. Synthesized materials with different compositions of copper and adsorbent were tested in a high-pressure packed bed reactor under a pressure P =60 bars, temperature T=300 °C, and hydrogen to carbon dioxide ratio (H2/CO2 = 3) to determine their performances and catalytic activities. The results of the activity revealed that CaO-based catalysts were very active in converting CO2 to methanol and carbon monoxide. Catalyst containing 30%Cu10%CaO achieved the highest CO2 conversion of 16.5% and a maximum methanol selectivity of 17.8%, while the lowest selectivity towards carbon monoxide (CO) were obtained using 20%Cu-containing catalyst. Reducing CaO content from 10wt% to 5wt% boosted the CO2 conversion to 18% with higher selectivity towards CO compared to the catalyst containing 10%CaO.

Machine learning-based life cycle optimization for the carbon dioxide methanation process: Achieving environmental and productivity efficiency

One of the eye-catching procedures for pollution reduction is converting carbon dioxide to valuable materials and chemicals as methane, which is known as carbon dioxide methanation. Although the carbon dioxide hydrogenation process can help the environment by reducing carbon in the atmosphere, it can also release toxic emissions into the environment, which needs urgent assessment. In this research, the final goal is to determine the best path for this process in order to reach a minimum level of environmental pollution and maximum yield. Based on that, machine learning approaches including artificial neural networks and Bayesian optimization-based support vector machine kernel were employed for the carbon dioxide methanation process with Ni/Al2O3 catalyst to find the first objective function, and then an environmental model was formulated based on life cycle assessment results in order to generate global warming potential. The multi-objective optimization problem, which was based on four decision parameters (temperature, pressure, hydrogen to carbon ration, and gas velocity), was computed using a genetic algorithm, and decision-making techniques were used for finding the best solution. For the hydrogen/carbon dioxide ratio, the optimal ratio was approximately 4–4.5. Regarding temperature, the range of 340–360 °C has been determined, and for the optimal gas velocity, the range of 6600–7000 L/gcat.h has been computed. The best optimal pressure obtained by the CODAS, COPRAS, MOORA, MABAC, SAW, and TOPSIS methods was 7.69 bar, and the optimal pressure value computed by other methods has been around 1–2 bar.

Examining the Summertime Ozone Formation Regime in Southeast Michigan Using MOOSE Ground‐Based HCHO/NO2 Measurements and F0AM Box Model

AbstractAmbient ozone (O3) concentrations in Southeast Michigan (SEMI) can exceed the U.S. National Ambient Air Quality Standard. Despite past efforts to measure O3 precursors and elucidate reaction mechanisms, changing emission patterns and atmospheric composition in SEMI warrant new measurements and updated mechanisms to understand the causes of observed O3 exceedances. In this study, we examine the chemical drivers of O3 exceedances in SEMI, based on the Phase I MOOSE (Michigan‐Ontario Ozone Source Experiment) field study performed during May to June 2021. A zero‐dimensional (0‐D) box model is constrained with measurement data of meteorology and trace gas concentrations. Box model sensitivity simulations suggest that the formaldehyde to nitrogen dioxide ratio (HCHO/NO2) for the transition between the volatile organic compounds (VOCs)‐ and nitrogen oxides (NOx)‐limited O3 production regimes is 3.0 ± 0.3 in SEMI. The midday (12:00–16:00) averaged HCHO/NO2 ratio during the MOOSE Phase I study is 1.62 ± 1.03, suggesting that O3 production in SEMI is limited by VOC emissions. This finding implies that imposing stricter regulations on VOC emissions should be prioritized for the SEMI O3 nonattainment area. This study, through its use of ground‐based HCHO/NO2 ratios and box modeling to assess O3‐VOC‐NOx sensitivities, has significant implications for air quality policy and the design of effective O3 pollution control strategies, especially in O3 nonattainment areas.

Exploring indicators of genetic selection using the sniffer method to reduce methane emissions from Holstein cows.

This study aimed to evaluate whether the methane (CH4) to carbon dioxide (CO2) ratio (CH4/CO2) and methane-related traits obtained by the sniffer method can be used as indicators for genetic selection of Holstein cows with lower CH4 emissions. The sniffer method was used to simultaneously measure the concentrations of CH4 and CO2 during milking in each milking box of the automatic milking system to obtain CH4/CO2. Methane-related traits, which included CH4 emissions, CH4 per energy-corrected milk, methane conversion factor (MCF), and residual CH4, were calculated. First, we investigated the impact of the model with and without body weight (BW) on the lactation stage and parity for predicting methane-related traits using a first on-farm dataset (Farm 1; 400 records for 74 Holstein cows). Second, we estimated the genetic parameters for CH4/CO2 and methane-related traits using a second on-farm dataset (Farm 2; 520 records for 182 Holstein cows). Third, we compared the repeatability and environmental effects on these traits in both farm datasets. The data from Farm 1 revealed that MCF can be reliably evaluated during the lactation stage and parity, even when BW is excluded from the model. Farm 2 data revealed low heritability and moderate repeatability for CH4/CO2 (0.12 and 0.46, respectively) and MCF (0.13 and 0.38, respectively). In addition, the estimated genetic correlation of milk yield with CH4/CO2 was low (0.07) and that with MCF was moderate (-0.53). The on-farm data indicated that CH4/CO2 and MCF could be evaluated consistently during the lactation stage and parity with moderate repeatability on both farms. This study demonstrated the on-farm applicability of the sniffer method for selecting cows with low CH4 emissions.

Simulating the number of spot-samples required to estimate the methane to carbon dioxide ratio in lambs and its relationship with methane yield

ABSTRACT Measuring the methane to carbon dioxide ratio (CH4/CO2) from animals could be useful to predict CH4 yield when dry matter intake (DMI) cannot be measured. The objectives were to (1) evaluate the relationship of CH4/CO2 with CH4 yield; (2) compare the CH4/CO2 of 10–60 simulated spot-samples with the CH4/CO2 calculated with data from 48-h of respiration chamber measurements. The DMI and CH4 and CO2 emissions measured every 5–6 min in respiration chambers from a previous experiment with 70 lambs fed ryegrass substituted with 0%, 25%, 50%, 75% and 100% of forage rape were retrieved. Emission data were used to perform simulations of 10, 20, 30, 40, 50 and 60 randomly selected spot-samples per lamb. The CH4/CO2 of 20 or more spot-samples was useful to predict CH4 yield and detect differences between dietary treatments, while the precision of the prediction increased when increasing the number of spot-samples up to 50 samples per lamb. Twenty spot-samples were sufficient to obtain accurate CH4/CO2 estimates; however, variance decreased (precision improved) with an increasing number of spot-samples per lamb up to 50 spot-samples. The CH4/CO2 of 10–60 simulated spot-samples explained 61% to 66% of variation in the 48-h measured CH4 yield.

Performing moderate to severe activity is safe and tolerable for healthy youth while wearing a cloth facemask.

To investigate if a cloth facemask could affect physiological and perceptual responses to exercise at distinct exercise intensities in healthy young individuals. Nine participants (sex, female/male: 6/3; age: 13±1 years; VO2peak: 44.5±5.5 mL/kg/min) underwent a progressive square-wave test at four intensities: (1) 80% of ventilatory anaerobic threshold (VAT), (2) VAT, and (3) 40% between VAT and [Formula: see text] wearing a triple-layered cloth facemask or not. Participants then completed a final stage to exhaustion at a running speed equivalent to the maximum achieved during the cardio-respiratory exercise test (Peak). Physiological, metabolic, and perceptual measures were measured. Mask did not affect spirometry (forced vital capacity, peak expiratory flow, forced expiratory volume; all p≥0.27), respiratory (inspiratory capacity, end-expiratory volume [EELV] to functional vital capacity ratio, EELV, respiratory frequency [Rf], tidal volume [VT], Rf/VT, end-tidal carbo dioxide pressure, ventilatory equivalent to carbon dioxide ratio; all p≥0.196), hemodynamic (heart rate, systolic and diastolic blood pressure; all p>0.41), ratings of perceived exertion (p = 0.04) or metabolic measures (lactate; p = 0.78) at rest or at any exercise intensity. This study shows that performing moderate to severe activity is safe and tolerable for healthy youth while wearing a cloth facemask. ClinicalTrials.gov: NCT04887714.

Effect of Steam to Carbon Dioxide Ratio on the Performance of a Solid Oxide Cell for H2O/CO2 Co-Electrolysis

The mixture of H2 and CO, the so-called syngas, is the value-added product of H2O and CO2 co-electrolysis and the feedstock for the production of value-added chemicals (mainly through Fischer-Tropsch). The H2/CO ratio determines the process in which syngas will be utilized and the type of chemicals it will produce. In the present work, we investigate the effect of H2O/CO2 (steam/carbon dioxide, S/C) ratio of 0.5, 1 and 2 in the feed, on the electrochemical performance of an 8YSZ electrolyte-supported solid oxide cell and the H2/CO ratio in the outlet, under co-electrolysis at 900 °C. The B-site iron doped lanthanum strontium chromite La0.75Sr0.25Cr0.9Fe0.1O3-δ (LSCF) is used as fuel electrode material while as oxygen electrode the state-of-the art LSM perovskite is employed. LSCF is a mixed ionic-electronic conductor (MIEC) operating both under a reducing and oxidizing atmosphere. The cell is electrochemically characterized under co-electrolysis conditions both in the presence and absence of hydrogen in the feed of the steam and carbon dioxide mixtures. The results indicate that under the same concentration of hydrogen and different S/C ratios, the same electrochemical performance with a maximum current density of approximately 400 mA cm-2 is observed. However, increasing p(H2) in the feed results in higher OCV, smaller iV slope and Rp values. Furthermore, the maximum current density obtained from the cell does not seem to be affected by whether H2 is present or absent from the fuel electrode feed but has a significant effect on the H2/CO ratio in the analyzed outlet stream. Moreover, the H2/CO ratio seems to be identical under polarization at different current density values. Remarkably, the performance of the LSCF perovskite fuel electrode is not compromised by the exposure to oxidizing conditions, showcasing that this class of electrocatalysts retains their reactivity in oxidizing, reducing, and humid environments.

Prediction of methane emissions from fattening cattle using the methane-to-carbon dioxide ratio.

This study aimed to develop a prediction equation for methane (CH4 ) emissions from fattening cattle based on the CH4 /carbon dioxide (CO2 ) ratio and validate the predictive ability of the developed equation. The prediction equation was developed using the CH4 /CO2 ratio combined with oxygen consumption and respiratory quotient estimations that were theoretically calculated from the relation between gas emissions and energy metabolism. To validate the prediction equation, gas measurements in the headboxes were conducted using eight Japanese Black steers. The predictive ability of the developed equation was compared with that of two previously reported equations. As a result, the developed and reported equations had significant (P < 0.01) linear relationships between the observed and predicted CH4 emissions. Notably, only the developed equation had a significant (P < 0.01) linear relationship between the observed and predicted CH4 emissions when expressed per unit of dry matter intake. The results suggest that the developed prediction equation has a higher predictive ability than previously reported equations, particularly in evaluating the efficiency of CH4 emissions. Although further validation is required, the equation developed in this study can be a valuable tool for on-farm estimations of individual CH4 emissions from fattening cattle.

Ozone Pollution of Megacity Shanghai during City-Wide Lockdown Assessed Using TROPOMI Observations of NO2 and HCHO

An unprecedented city-wide lockdown took place in Shanghai from April to May 2022 to curb the spread of COVID-19, which caused socio-economic disruption but a significant reduction of anthropogenic emissions in this metropolis. However, the ground-based monitoring data showed that the concentration of ozone (O3) remained at a high level. This study applied Tropospheric Monitoring Instrument (TROPOMI) observations to examine changes in tropospheric vertical column density (VCD) of nitrogen dioxide (NO2) and formaldehyde (HCHO), which are precursors of O3. Compared with the same period in 2019–2021, VCDs of NO2 and HCHO decreased respectively by ~50% and ~20%. Multiple regression analysis showed that the lockdown effect played a dominant role in this dramatic decline rather than meteorological impacts. Using the exponentially-modified Gaussian method, this study quantified nitrogen oxides (NOX) emission in Shanghai as 32.60 mol/s with a decrease of 50–80%, which was mainly contributed by the transportation and industrial sectors. The significant reduction of NOX emission in Shanghai is much higher than that of volatile organic compounds (VOCs), which led to dramatic changes in formaldehyde-to-nitrogen dioxide ratio (HCHO/NO2, FNR). Thus, when enforcing regulation on NOx emission control in the future, coordinately reducing VOCs emission should be implemented to mitigate urban O3 pollution.

RSM modeling of different amounts of nano-TiO2 supplementation to a diesel engine running with hemp seed oil biodiesel/diesel fuel blends

In this study, experiments were performed on a single-cylinder diesel engine to define the optimum nanoparticle ratio using the response surface methodology. The experimental fuels used were a mixture of hemp seed oil biodiesel (30% by volume) and diesel (70% by volume) mixed with titanium dioxide nanoparticles at various amounts (25, 50, 75, and 100 ppm). The addition of 100 ppm titanium dioxide increased brake thermal efficiency by 29.65% and decreased brake specific fuel consumption by 5.16%. Furthermore, the addition of titanium dioxide up to 50 ppm reduced hydrocarbon emission by 12.07%, and up to 75 ppm reduced the carbon monoxide by 40.15%. In contrast, the titanium dioxide caused an average of 27% increase in nitrogen oxide emissions. On the other hand, the optimum titanium dioxide ratio and engine load were determined as 75 ppm and 2000 W, respectively. Under these conditions, brake thermal efficiency, brake specific fuel consumption, carbon monoxide, hydrocarbon, nitrogen oxide, and smoke emissions are 27.942%, 1081.51 g/kWh, 0.057%, 40.293 ppm, 257.3742 ppm, and 0.7064%, respectively. Optimum results were achieved with an overall high desirability value of 0.7665. A good harmony among the experimental and estimated response values demonstrates the acceptability of the developed models.

Development of mathematical model for predicting methane-to-carbon dioxide proportion in anaerobic biodegradability of cattle blood and rumen content

• Prediction of methane-to-carbon dioxide within mesophilic range. • Production of methane for use as renewable energy source. • Optimal methane-to-carbon dioxide ratio in relation to temperature and hydraulic retention time. The potential of methane yield depends on the composition of feedstock which is characterized by a diversity of biodegradable fractions. This study aims at constructing and using the novel kinetic model as a tool to predict methane-to-carbon dioxide ratio from slaughterhouse solid waste using hydraulic retention time and temperature as parameters. The slurries prepared were composed of cattle blood, rumen content and requisite nutrient solution which were fed into a continuously stirred tank reactors. The process was monitored at regular intervals of 3 days for a retention time of 54 days for thermostatically controlled temperatures of 35, 37 and 40 °C. The percentage methane and carbon dioxide gas concentrations in the headspace were measured. The phenomenon was idealized with scatter plots and co-efficient of determination used to establish a reasonable correlation between the identified parameters. In this study, simple, robust and reliable kinetic models have been developed using the Microsoft Excel Add-in Solver Tool with the Generalized Reduced Gradient. The optimum solution of seven unknown constants represented by adjusted values where simulated values adequately matched the observed values for the three zones of this study. Validation of the models also showed that the phenomenon can be produced in a continuum at any fixed temperature.

Effect of Sacubitril-Valsartan on Quality of Life, Functional and Exercise Capacity in Heart Failure with Preserved Ejection Fraction (HFpEF): A Systematic Review of Randomized Clinical Trials

Background: Sacubitril/Valsartan use in heart failure has shown promising results in early trials. However, the effects on the overall functional capacity, exercise capacity, and quality of life are unknown. Aims: We aimed to understand the results of studies that attempted to measure these outcomes that affect the mobility and day-to-day life of these patients. Methods: MEDLINE, PubMed, PubMed Central (PMC), Google Scholar, ClinicalTrials.gov, and ISRCTN were explored to look for clinical trials relevant to the literature. Results: A total of three high-quality randomized controlled trials were discovered that evaluated the effect of sacubitril-valsartan on functional capacity, exercise capacity, or quality of life. All of them were industry-funded and revealed no statistical difference in the mentioned outcomes. No study measured peak oxygen uptake or ventilation/carbon dioxide ratio slope. Conclusion: Sacubitril-valsartan had minimal to no impact on functional capacity, exercise capacity, or quality of life. However, future prospective studies with more sensitive outcome measures should be conducted to validate the findings.