CO2 Levels Research Articles

A Comprehensive Review of Approaches in Carbon Capture, and Utilization to Reduce Greenhouse Gases

Addressing atmospheric CO2 levels is crucial for mitigating global warming and promoting sustainable fossil fuel use. This review explores various CO2 capture strategies, including pre-combustion, post-combustion, oxy-fuel combustion, direct air capture, chemical looping, and polymeric membranes. Each strategy is critically evaluated in terms of its advantages, limitations, and overall effectiveness. Additionally, this study discusses advanced separation techniques for captured CO2, emphasizing recent innovations in membrane technology integrated with cryogenic processes. This integration has the potential to economically extract CO2 from diverse industrial processes, offering significant benefits in terms of operational cost reduction and increased efficiency. A detailed market analysis is also presented to explore feasible CO2 utilization options, highlighting potential incentives and motivations for capturing CO2. Furthermore, the technological readiness level of various capture and separation techniques is assessed, offering insights into their development and progress over time. This comprehensive analysis aims to support the advancement of effective and economically viable CO2 management solutions, contributing to a more sustainable and climate-resilient future.

Predictive Powers of Carbon Emissions and Oil Price on Stock and Foreign Exchange (FX) Markets Using Multivariate Recurrent Neural Networks (RNN) Model

The predictive power of environmental factors such as CO2 levels and Brent oil price was evaluated on the stock market (S&P 500) and foreign exchange (FX) rates for market currency pairs (EUR/USD) and emerging market currency pairs (MXN/USD) with recurrent neural networks (RNN). The S&P 500 is a collection of well-established companies in the United States representing the macroeconomic health of the nation and the global economy. The FX market is a global decentralized over-the-counter (OTC) market used to determine the spot price of currency pairs. A highly leveraged market usually trades within a specific price range. Since stocks and FX pairs are highly correlated to macroeconomic factors, it was hypothesized that environmental factors such as CO2 levels and oil prices also have predictive power due to their close causal relationship with anthropological economic activities. To verify the predictive power, an RNN model was built, and a bi-directional neural network with an internal state was used to process data sequences. The performance of RNN was quantified by measuring the residual prediction from the true value. Although the study at its current state might need further statistical rigor, it concluded that environmental factors increased the predictive power for the S&P 500 while decreasing it for the DM FX pair and the EM FX pair showed mixed results.

Shadow Economy and Environmental Sustainability in Global Developing Countries: Do Governance Indicators Play a Role?

Environmental sustainability has been a challenging issue all over the globe, with air pollution posing a significant threat. One main factor contributing to air pollution is the growth of the shadow economies. This study investigates the effect of the shadow economy on air pollution and explores how these effects depend on the levels of governance indicators. We utilize key air pollution indicators: carbon dioxide (CO2) and nitrous oxide (N2O) emissions. Furthermore, we examine the role of key governance indicators: corruption control, the rule of law, and regulatory quality. The study utilizes an annual panel dataset of 107 selected developing countries worldwide, spanning from 2002 to 2020, and employs the System GMM technique, which effectively tackles the omitted variable bias, potential endogeneity, and simultaneity issues in the model. The estimation results indicate that a sizeable shadow economy significantly increases the levels of CO2 and N2O emissions. Moreover, the results reveal that robust governance frameworks, evidenced by enhanced corruption control, a stronger rule of law, and superior regularity quality, mitigate the adverse effects of the shadow economy on CO2 and N2O emissions. This highlights a significant substitutability between the shadow economy and governance indicators, indicating that improvements in governance formworks will not only reduce the size of the shadow economy but also weaken its harmful impact on air pollution. Policy initiatives should thus focus on strengthening governance mechanisms, particularly enhancing control of corruption and the rule of law to effectively reduce the environmental impact of the shadow economies in developing countries. Additionally, governments should prioritize reforms in regulations and legal frameworks to limit the expansion of the shadow economy, thereby decreasing their contribution to air pollution.

Enhancing Alaskan wildfire prediction and carbon flux estimation: a two-stage deep learning approach within a process-based model

Abstract Wildfires in boreal forests release substantial amounts of carbon into the atmosphere. However, current land-surface models are limited in their representation of fire processes, including their ignition and spread. This study thus developed FireDL, a novel data-driven machine-learning model for the prediction of natural wildfires, and combined it with a land-surface model to better understand the impact of fire on carbon fluxes. FireDL has a two-stage deep learning structure that sequentially combines a long short-term memory (LSTM) algorithm and an artificial neural network (ANN). Preliminary random forest analysis identified fire duration as an important factor in predicting the burned area. Thus, in FireDL, the LSTM algorithm was employed to predict fire occurrence and duration, utilizing lightning, vegetation, and climate datasets. Subsequently, the ANN predicted the total burned area using the LTSM-derived fire duration predictions and climate datasets as input. FireDL produced a robust performance in predicting large fires (>10 000 ha), achieving a correlation coefficient of 0.72. The daily-scaled burned area predictions derived from FireDL were integrated into the Community Land Model version 5—Biogeochemistry (CLM5-BGC) to produce CLM5-BGC-FireDL. This integration considerably improved carbon emission estimations. Notably, the total net ecosystem exchange (NEE) estimated using CLM5-BGC-FireDL in 2019, the year with the highest recorded burned area during our study, was twice that estimated using the standard CLM5-BGC. Discrepancies in the NEE can significantly influence atmospheric CO2 levels, highlighting the importance of our fire prediction model in forecasting the burned area and carbon emissions. The use of FireDL with future climate scenarios is thus anticipated to yield valuable insights into ecosystem management and climate change mitigation strategies.

Design of Bimetallic Boronene Structures Leveraging Neighboring Effects for Efficient Electrocatalytic CO2 Reduction Reaction

AbstractElectrocatalytic CO2 reduction (CO2RR) offers a promising avenue to address rising atmospheric CO2 levels by producing high‐value chemicals. However, the development of efficient, long‐lasting catalysts remains challenging. In this study, particle swarm optimization is employed to design a novel bimetallic boronene structure, thereby enhancing CO2RR activity through precise tuning of the metal‐substrate microenvironment. Through high‐throughput screening, seven CuMB4 (M = V, Zn, Nb, Ag, Cd, Ta, Au) monolayers are identified as promising CO2RR catalysts based on stability, conductivity, catalytic activity, and selectivity. These materials, characterized by hyper‐coordination and neighboring bimetallic effects, are proposed for synthesis via self‐assembled surface growth. Notably, CuZnB4 exhibited exceptional theoretical catalytic activity, characterized by remarkably low limiting potentials (UL) of −0.16 V for CH4 and −0.27 V for C2H4, as well as low kinetic barriers of 0.65 and 0.53 eV, respectively. The enhanced activity results from neighboring effects optimizing densely populated boron active sites and the ability to suppress hydrogen evolution reactions. Additionally, this study explored intrinsic properties influencing catalytic activity using volcano plots, descriptor analysis, and electron density evaluation. Unlike traditional catalysts prone to oxidation, CuMB4 varieties possess self‐activating properties, facilitating the conversion of *O/*OH to H2O and enhancing CO2 conversion. This research introduces robust CO2RR catalysts and provides insights into manipulating neighboring effects, thus guiding future catalyst design.

Changes in central venous-to-arterial PCO2 difference and central venous oxygen saturation as markers to define fluid responsiveness in critically ill patients: a pot-hoc analysis of a multi-center prospective study

BackgroundThe main aim of the study whether changes in central venous-to-arterial CO2 difference (ΔP(v–a)CO2) and central venous oxygen saturation (ΔScvO2) induced by volume expansion (VE) are reliable parameters to define fluid responsiveness (FR) in sedated and mechanically ventilated septic patients. We also sought to determine whether the degree of FR was related to baseline ScvO2 and P(v–a)CO2 levels.MethodsThis was a post-hoc analysis of a multicenter prospective study. We included 205 mechanically ventilated patients with acute circulatory failure. Cardiac index (CI), P(v–a)CO2, ScvO2, and other hemodynamic variables were measured before and after VE. A VE-induced increase in CI > 15% defined fluid responders. Areas under the receiver operating characteristic curves (AUCs) and the gray zones were determined for ΔP(v–a)CO2 and ΔScvO2.ResultsOne hundred fifteen patients (56.1%) were classified as fluid responders. The AUCs for ΔP(v–a)CO2 and ΔScvO2 to define FR were 0.831 (95% CI 0.772–0.880) (p < 0.001) and 0.801 (95% CI 0.739–0.853) (p < 0.001), respectively. ΔP(v–a)CO2 ≤ 2.1 mmHg and ΔScvO2 ≥ 3.4% after VE allowed the categorization between responders and non-responders with positive predictive values of 90% and 86% and negative predictive values of 58% and 64%, respectively. The gray zones for ΔP(v–a)CO2 (− 2 to 0 mmHg) and ΔScvO2 (− 1 to 5%) included 22% and 40.5% of patients, respectively. ΔP(v–a)CO2 and ΔScvO2 were independently associated with FR in multivariable analysis. No significant relationships were found between pre-infusion ScvO2 and P(v–a)CO2 levels and FR.ConclusionIn mechanically critically ill patients, ΔP(v–a)CO2 and ΔScvO2 are reliable parameters to define FR and can be used in the absence of CI measurement. The response to VE was independent of baseline ScvO2 and P(v–a)CO2 levels.Clinical trial registration The study was registered in the ClinicalTrials.gov registry: NCT03225378, date: July 20, 2017.

Dynamic Indoor Environmental Quality Assessment in Residential Buildings: Real-Time Monitoring of Comfort Parameters Using LoRaWAN

This study addresses an identified literature gap regarding indoor environmental quality in residential buildings, where the primary focus has traditionally been on energy performance rather than comfort optimization. Leveraging the low-cost and easy-to-implement LoRaWAN protocol, this research collects and analyses real-time data on comfort parameters, including temperature, CO2 levels, humidity, lighting, atmospheric pressure, and total volatile organic compounds (TVOC) across various buildings within the INCUBE EU project. The results highlight the dynamic nature of the parameters and emphasize the importance of continuous monitoring to enhance comfort and energy efficiency in smart residential buildings. The findings advocate for integrating technologies like LoRaWAN to optimize indoor environmental quality, ultimately improving residential comfort and occupant well-being.

INCUBATOR DESIGN FORREVIVALS SILKWORM SEEDS

This article analyzes the effectiveness of a new incubator design for reviving silkworm eggs. In the course of the study, an incubator based on a rotating disk was created to control the parameters of the microclimate and ensure uniform development of eggs. In this system, temperature, humidity and CO2 levels are monitored using the SCD 41 and other sensors. The results showed that using the new incubator, silkworm eggs were revived 4.1% faster than using traditional methods, and the cocoon yield increased by 5.8%. Thanks to the high efficiency of the new construction, the economic efficiency of silk production will increase and the possibility of producing high-quality silk will expand. Thus, the construction of an innovative incubator opens up opportunities for using new approaches in sericulture.

Efficient utilization of xylose requires CO2 fixation in Synechococcus elongatus PCC 7942

Cyanobacteria show great promise as autotrophic hosts for the renewable biosynthesis of useful chemicals from CO2 and light. While they can efficiently fix CO2, cyanobacteria are generally outperformed by heterotrophic production hosts in terms of productivity and titer. Photomixotrophy, or co-utilization of sugars and CO2 as carbon feedstocks, has been implemented in cyanobacteria to greatly improve productivity and titers of several chemical products. We introduced xylose photomixotrophy to a 2,3-butanediol producing strain of Synechococcus elongatus PCC 7942 and characterized the effect of gene knockouts, changing pathway expression levels, and changing growth conditions on chemical production. Interestingly, 2,3-butanediol production was almost completely inhibited in the absence of added CO2. Untargeted metabolomics implied that RuBisCO was a significant bottleneck, especially at ambient CO2 levels, restricting the supply of lower glycolysis metabolites needed for 2,3-butanediol production. The dependence of the strain on elevated CO2 levels suggests some practical limitations on how xylose photomixotrophy can be efficiently carried out in S. elongatus.

Concentration of particulate matter and atmospheric pollutants in the residential area of Kathmandu Valley: a case study of March–April 2021 Forest fire events

Forest fires have become more intense and frequent in recently changing climates. The wide variety of pollutants released by forest fire include greenhouse gases, photochemically reactive compounds, and fine and coarse particulate matter. This study investigated the impact of forest fire events on air quality in the Kathmandu Valley during March-April 2021 using ground air quality monitoring stations and satellite data. The three fire periods were studied (a) Pre-fire from 21st - 23rd March (b) first-fire episode from 24th -27th March and (c) second fire episode from 1st – 5th April of 2021. The concentrations of PM2.5 reached to maximum 199 μg/m3 during pre-fire period, 371 μg/m3 and 280 μg/m3 during first and second fire event respectively. The second fire episode had lower PM2.5 concentration despite higher fire counts (449) compared to the first episode suggesting influence of fire activities near to vicinity of Kathmandu valley during second fire episode. There was a two-day lag between the beginning of forest fire events and an increase in PM2.5 levels in Kathmandu. Satellite observation showed varying patterns for different pollutants. HCHO levels responded quickly to fire activity, while AOD and CO levels increased after a few days. Also, low wind speed, low temperature, and low relative humidity additionally elevated these pollutants in Kathmandu. This study emphasizes the extent of the impact of forest fires on air quality and the importance of considering meteorological and satellite data to understand the distribution of pollutants during such events.

A machine learning based regression methods to predicting syngas composition for plasma gasification system

Plasma gasification is considered a promising technology that converts waste into energy through an environmentally friendly process. This research focuses on predicting the outputs of this process, utilizing ML regression techniques. Data from previous studies involving different solid fuels were collected, and four regression techniques Random Forest Regression, Gaussian Process Regression, Decision Tree Regression, and Support Vector Regression were employed to predict the levels of CO2, CO, N2, O2, H2, and CH4 in the plasma gasification process. The experimental dataset was gathered using a microwave gasifier with varying air flow rates (50–100 sL/min) and plasma power (3–6 kW). GPR a coefficient of determination (R2) values exceeding 0.983 for all outputs and low NRMSE and MSLE values (<0.082 and <0.00123, respectively). RFR also performed well, with R2 values >0.98 for all outputs except CH4 and CO2. SVR exhibited the least favorable performance, while DTR showed intermediate results. Plasma gasification Machine Learning modeling has proven to enable the prediction of the high-complexity chemical reaction chain in gasification. These models hold promise for applications in simulation environments and can be integrated into microcontroller-based systems for practical use for optimization and control. By this means, the cost of plasma gasification processes would be reduced, and so the plasma gasification system would be more flexible.

Defining a phenotype of severe COPD patients who develop chronic hypercapnia

IntroductionChronic hypercapnia, defined by elevated blood CO2 levels, is a serious complication most prevalent in severe COPD. It negatively impacts quality of life, increases hospitalization rates, and elevates mortality risks. However, not all severe COPD patients develop chronic hypercapnia, and its underlying mechanisms remain unclear. Identifying clinical and pathophysiological predictors of hypercapnia is essential for tailored treatment strategies. This study investigates the relationship between hypercapnia and patient characteristics, lung function, and CT scan features to identify potential therapeutic targets. MethodsThis cross-sectional study included 1,526 COPD patients from three cohorts: a standard care cohort and two research cohorts (NCT04023409; NCT03053973). Data collected included demographic and clinical information, blood gases, lung function (FEV1, FVC, TLC, RV, DLCOc), and high-resolution CT scans (lung volumes, air trapping, emphysema scores, airway wall thickness (Pi10), and diaphragm indices). ResultsHypercapnia prevalence increased with COPD severity. Hypercapnic patients were older, more likely to smoke, and had more comorbidities. They exhibited lower FEV1 and FVC, and higher RV/TLC ratios, with CT scans showing lower emphysema scores and greater Pi10. Multivariate analysis identified lower PaO2, FEV1% predicted, and emphysema scores, along with higher RV/TLC ratios and NT-proBNP levels, as independent predictors of PaCO2, collectively explaining 46.3% of the variance. ConclusionCOPD patients with chronic hypercapnia are characterized by higher smoking rates, lower PaO2 levels, poorer lung function, less emphysema, and increased airway pathology. These findings underscore the multifactorial nature of hypercapnia in COPD, highlighting the need for personalized therapeutic strategies targeting these factors to improve outcomes.

Toll-like receptor signaling in neurons modulates C. elegans feeding behavior in a hunger state-dependent manner

Animals face the risk of encountering pathogenic microbes while foraging for resources. Assessing the risk of nutrition vs. infection can result in the behavioral regulation of immune processes. Behavioral immunity in the nematode roundworm Caenorhabditis elegans (C. elegans) is regulated, in part, by the innate immune molecule TOL-1: a homolog of vertebrate Toll-like Receptor (TLR) proteins that influences C. elegans pathogen avoidance behaviors by promoting the development of CO2-detecting chemosensory neurons. While TOL-1′s role in pathogen avoidance is well established, its role in an opposing behavior – foraging – has not been examined. In addition to pathogenic bacteria, preferred food for C. elegans, such as Escherichia coli (E. coli), create significant and aversive environmental CO2 levels which may limit feeding behaviors in a tol-1 dependent manner. We have found that in addition to conferring antibacterial immunity, TOL-1 signals in neurons through the p38 MAPK PMK-1 to promote turning behavior and limit foraging when food is abundant and that the anorectic TOL-1/PMK-1 pathway is attenuated during starvation to promote foraging These data highlight the dynamic role of a conserved innate immune cascade in neurons during both high and low hunger states and identify mechanisms underlying the neuro-immune control of feeding strategies.

Growth inhibition and recovery of Pinus massoniana in Chongqing since the 1980s

Over recent decades, the forest ecosystems of southern China have been substantially affected by acid rain, a principal biogeochemical driver. Despite recent efforts to mitigate acid rain, the long-term adaptive responses of trees of varying ages remain insufficiently understood. In this study, we employed the tree-ring basal area increment (BAI), δ13C, δ15N, and δ18O values to identify patterns in growth inhibition and recovery in young and mature Pinus massoniana within acid rain-afflicted zones in China. A marked growth inhibition phase occurred from 1983 to 2010, characterized by a significant decline in BAI, particularly in mature trees. A growth recovery phase occurred from 2011 to 2020, with considerable BAI increases. Throughout the growth-inhibition phase, the intrinsic water-use efficiency (iWUE) increased, whereas δ15N levels were stable, suggesting gradual stomatal closure influenced by both acid rain and elevated CO2 levels. During this phase, mature and young trees exhibited differing strategies in response to environmental stressors. The growth recovery phase was characterized by significant reductions in both iWUE and δ15N, leading to a more closed nitrogen cycle. Structural equation modeling and correlational analyses identified SO42−, NO3−, and precipitation as key determinants of growth variations in both young and mature trees. Our findings underscore the effect of prolonged acid rain exposure on growth suppression across P. massoniana age groups, highlighting iWUE as a reliable predictor of tree growth, although age should be considered. With the ongoing reduction in acidic gas emissions, subtropical P. massoniana forests have exhibited significant resilience, highlighting rapid ecological recovery. We advocate for continued large-scale monitoring and model-based assessments of acid rain effects, emphasizing the need for long-term evaluations to comprehend the ecological resurgence and carbon sequestration potential within these ecosystems.

Utilization of dissolved CO2 to control methane and acetate production in methanation reactor

This study investigated the influence of dissolved CO2 on the selection of metabolic pathway using a methanation membrane bioreactor supplied with H2/CO2. Various ratios of H2/CO2 were applied (3.3, 3.8, 4.0, 4.5, and 5.0 (v/v)) to manipulate dissolved CO2 levels in the medium. The findings revealed a correlation between the concentration of dissolved CO2 and the production of CH4 (positive) and acetate (negative). Specifically, at a dissolved concentration of CO2 above 2.0 ± 0.2 mmol/L, production of CH4 was favored. At the opposite, acetate production was favored at lower dissolved CO2 concentrations, with a maximum concentration of 1.9 g/L observed at 0.9 mmol/L of dissolved CO2. This study demonstrates that the modification of dissolved CO2 levels in a methanation bioreactor can provide a strategy for the selection of metabolic pathways and microbial communities, thereby offering a promising opportunity for optimizing the conversion of CO2 into high-value products such as CH4 and acetate.

Variations in air pollution before, during and after the COVID-19 lockdown in Peruvian cities.

The high concentrations of air pollutants in Peru remain a persistent problem, significantly impacting public health. Understanding the extent to which the COVID-19 lockdown affected these contaminants is crucial. To determine variations in NO2, O3, CO, and SO2 concentrations in 10 Peruvian cities before, during, and after lockdown. A comparative ecological study was conducted in urban areas of 10 major Peruvian cities using the Google Earth Engine (GEE) platform. Data on atmospheric pollutant concentrations were extracted from the Sentinel-5P/TROPOMI satellite images for the period between March 16 and June 30, across the years 2019, 2020, 2021, and 2022, for comparative analysis. The Wilcoxon test was used to evaluate changes between the study periods. We included 10 urban cities located across three geographic regions of Peru. Most urban cities experienced a decrease in NO2 concentrations and an increase in O3 and CO levels during the lockdown, while SO2 concentrations remained relatively constant. The lockdown has caused variations in NO2, O3 and CO concentrations. Future studies with accurate data on air pollutant concentrations are needed to ensure targeted and effective interventions.

Computational Prediction of Co-firing with Various Biomass Waste Using Turbulent Non-Premixed Combustion

Co-firing in coal power plants has limitations because the existing combustion systems are designed to provide optimal performance only with coal. Therefore, investigating the combustion aspects of co-firing by mixing coal with biomass before applying it to existing coal power plants is necessary. To address this, a new numerical model was developed to predict the co-firing behavior of coal with various types of biomass waste, specifically focusing on temperature and pollutant behavior. This study developed a co-firing model in a Drop Tube Furnace (DTF) using a composition of 25% Wood Chips (WC), 25% Solid Recovered Fuel (SRF), 25% Empty Fruit Bunch Fibers (EFFR), and 25% Rice Husk (RH). A structured grid arrangement and the Probability Density Function (PDF) were utilized to depict the relationship between chemical combustion and turbulence. The distributions of temperature and mass fractions of pollutants along the furnace axis were predicted. The highest temperature was observed with 25% EFFR, attributed to its highest volatile matter content. The simulation predicted that 25% RH would be the lowest SO2 emitter. However, it also showed a slight increase in NO and CO levels due to the increased oxygen content when coal was mixed with biomass. The simulation with 25% EFFR predicted a decrease in CO2 emissions compared to other biomass types. The results of this parametric investigation could support the implementation of biomass co-firing technology in existing coal-fired power plants.

Two Sustainable Pathways of MOF-Catalyzed Room Temperature Chemical Fixation of CO2 inside Alkynes under Atmospheric Pressure.

The rising atmospheric CO2 levels necessitate the development of effective materials for its mitigation. Utilization of adsorbent materials for the reversible physisorption of CO2 has a significantly less impact. Recognizing this need, herein, we present a nitrogen-rich, aqua-stable, Ag(0)-nanoparticle-doped metal-organic framework (MOF) designed for the irreversible chemical conversion of CO2 into valuable fine chemicals. We demonstrate two sustainable pathways for CO2 fixation, utilizing the catalyst, 1'@Ag NPs. The designed catalyst facilitates the cyclization of propargylic amines and alcohols under ambient temperature and pressure conditions. Remarkably, this is the first MOF-based catalyst that allows for quantitative conversion of propargylic amines into 2-oxazolidinones at room temperature with atmospheric CO2 pressure. The process successfully transforms various propargylic amines and alcohols into 2-oxazolidinones and α-alkylidene cyclic carbonates under the CO2 atmosphere. Additionally, the catalyst shows excellent recyclability, maintaining its activity and structural integrity across multiple reuse cycles. Control experiments revealed that the catalytic efficiency of 1'@Ag NPs is attributed to the highly exposed alkynophilic Ag(0) sites on its pore walls. Computational studies further elucidate the mechanistic pathway for CO2 fixation. This work highlights the potential of 1'@Ag NPs to enhance environmental sustainability by converting CO2 into valuable bioactive chemicals under mild conditions.

Covid-19 and its related environmental quality issues along ganga river, Varanasi, India

COVID-19 made people around the world in terrifying and appalling circumstance which held back all collaborative stitches that is the government decreed people to stay in homes for suppressing the disease spread amid people. Pandemic situation due to Covid-19 has lead to worldwide lockdown. On the other hand, it has increased the quality of the environment especially in hydrosphere and atmosphere progressively throughout the world. The global shutdown from March to June 2020 made liable in assessing the environmental condition which is eccentric accomplishment when collating with past two decades. This study demonstrates change in the water and aerosol quality with the aid of remote sensing in Ganga River of Varanasi city, Uttar Pradesh state of India. Sentinel data obtained from February to July of 2020 used to determine the variation of pollution levels during different phases of lockdown. NO2 levels derived from Sentinel data shows hazardous level of 5.64x10-5mol m-2 in March which is progressively decreased to 5.29 x10-5mol m-2 for the month of April. NO2 level further gradually increased after the unlock 1.0. Similarly, CO levels obtained from Sentinel data shows hazardous level of 0.0517mol m-2 in February which is progressively decreased to 0.0490 mol m-2 for the lockdown periods and it was gradually increased after the unlock phase. Further, Landsat data has been used to show Suspended Particulate Matter and pollution variation between lockdown phases. The high concentration of SPM value for March month ranges from -48.7773 to -48.7812 ,there is drastic decrease in concentration of SPM in which reflection value ranges from -48.7772 to -48.7806 in month of April noticed in the study area.Again there is drastic increase in July values ranging from -48.7753 to -48.7796 which is very high when compared to the lockdown SPM levels.The present study brought to light significant variations in the pollution level during the lockdown phases.

Oman’s Green Horizon: Steering Towards Sustainability Through Decarbonization and Energy Transition

This paper examines the determinants of CO2 emissions in Oman from 1990 to 2024, focusing on the impacts of energy consumption, economic growth, urbanization, financial development, and foreign direct investment. The analysis utilizes stepwise regression to systematically identify the most significant predictors, ensuring a parsimonious model. Robust least squares (ROLSs) are employed to account for potential outliers and heteroscedasticity in the data, providing more reliable estimates. Fully Modified Least Squares (FMOLSs) is applied to address issues of endogeneity and serial correlation, offering robust long-term coefficient estimates. Canonical cointegrating regression (CCR) further refines these estimates by handling non-stationary variables and ensuring consistency in the presence of cointegration. Cointegration tests, including the Johansen and Engle–Granger methods, confirm long-term equilibrium relationships among the variables; this study reveals several key findings. Energy use per capita (ENGY) and real GDP per capita (RGDPC) are consistently significant positive predictors of CO2 emissions. Urbanization (URB) also significantly contributes to higher emissions. Conversely, the Financial Development Index (FDX) and foreign direct investment (FDI) do not show significant effects on CO2 levels. The high R-squared values across models indicate that these variables explain a substantial portion of the variation in emissions. Cointegration tests confirm long-term equilibrium relationships among the variables, with the Johansen test identifying two cointegrating equations and the Engle–Granger test showing significant tau-statistics for FDX, ENGY, and URB. The VEC model further highlights the short-term dynamics and adjustment mechanisms. These findings underscore the importance of energy policy, economic development, and urban planning in Oman’s efforts towards sustainable development and decarbonization.