CO2 Levels Research Articles

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.

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.

Environmental factors and the incidence of pediatric epistaxis: A systematic review with meta-analysis.

A growing body of literature explores environmental risk factors for pediatric epistaxis, yielding variable results. We aim to clarify these associations through a systematic review and meta-analysis. PubMed, Scopus, Cochrane Central Register of Control Trials, Web of Science, Medline, Google Scholars, and Embase were systematically searched up to April 2024. Eligible articles were reviewed, and the quality was assessed. A systematic review and meta-analysis was conducted to clarify correlations between the incidence of epistaxis and multiple environmental factors according to Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines. A total of 8 studies, comprising 55,176 participants, met the inclusion criteria. The incidence of epistaxis peaked during the summer months (Proportion=12.73%, CI: 9.629%-16.201%). Significant risk factors included environmental variables elevated in the summer, including higher monthly mean temperatures, increased sunlight exposure, elevated O3 levels, and lower atmospheric pressure. In contrast, factors like mean monthly humidity, wind speed, SO2, CO, NO2, and PM-10 levels were not associated with an increased risk of epistaxis. This meta-analysis underscores the significant impact of multiple environmental factors, particularly those more pronounced during the summer months, on the incidence of pediatric epistaxis.

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.

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.

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.

The Physics Behind Climate Change: Understanding Greenhouse Gases

Natural environments have a direct or indirect impact on all living things' well-being, growth, nourishment, and development. Globally, the main contributors to climate change include industrialisation, agriculture, urbanisation, and greenhouse effects. Every year, the earth's surface temperature and carbon dioxide (CO2) levels rise due to these climatic changes. The primary goal of this review article is to study the effect of greenhouse gases in climate change. The pace at which the planet's temperature has increased over the last 50 years has deeply alarmed a number of scientists, engineers, and environmentalists. More flexibility in their adaptation will help plant species withstand fluctuations in the frequency of harsh weather occurrences. One of the factors causing climate change is GHG. The results show how climate change affects the environment, which usually shows up as rising temperatures and CO2 levels.

Indoor Air Quality System: A Design of Monitoring CO and CO2 Substances for Elderly Home Using IoT

The use of an IoT-based monitoring system for CO and CO2 gases in elderly homes is a necessary innovation for remotely monitoring air quality. This work was successful in establishing a remote monitoring system that can measure CO and CO2 gases utilizing IoT technology combined with Blynk software. The MQ-7 sensors detect CO gas, while the MQ-135 sensors detect CO2. Data from these sensors is delivered to a central server, which then sends it to the I2C 16X2 LCD panel and Blynk software, allowing data visualization via an internet connection. The study's findings indicate that this system can detect and monitor CO and CO2 gas levels with a high degree of precision. This device can also monitor air quality in real time using the Blynk software. As a result, users may accurately monitor air quality (CO and CO2 gases) in elderly homes.

Bamboo Forests: Unleashing the Potential for Carbon Abatement and Local Income Improvements

Bamboo forests exhibit a unique efficient growth pattern that makes them invaluable in reducing atmospheric CO2 levels. Additionally, bamboo forests offer a diverse range of products, thus holding the potential to bolster local income. Despite these benefits, the comprehensive assessment of bamboo forests’ potential in both carbon abatement and improving local income enhancement has been hindered by the absence of a detailed bamboo biomass map. In this study, we address this gap by amalgamating a bamboo aboveground biomass (AGB) map covering three prominent producing provinces in southern China, utilizing multi-source remote sensing datasets. The results not only demonstrate a satisfactory consistency with China’s Ninth National Forest Inventory but also provide a more detailed spatial distribution. Based on this AGB estimation, we project an approximately threefold potential increase in annual bamboo culm harvest from existing bamboo forests. This represents a significant opportunity for expanding carbon abatement efforts, elevating local income levels, and facilitating the production of bamboo-derived biofuels. Furthermore, the adoption of an optimized management strategy has the potential to further enhance bamboo production. This study generates the first high-resolution bamboo AGB map and underscores the substantial potential of China’s bamboo forests in contributing to carbon sequestration and improving local income. The favorable income generated for local residents can serve as a compelling incentive for the implementation of sustainable forest management practices, offering a promising pathway toward achieving carbon-related objectives within the forestry sector and providing necessary support for forestry designation projects.

A participatory study of indoor environment quality in homes of children and youth in Kanehsatake First Nation

Indoor air quality is an important determinant for the health of children and youth, but the conditions within Indigenous communities are understudied. We collaborated with Kanehsatake First Nation in Quebec, Canada, to address this gap using a community-based participatory research approach. Levels of key indoor air indicators, including particulate matter (PM2.5), CO2, and relative humidity, were measured in 31 randomly selected households between June 2021 and January 2022. Questionnaires were administered remotely to collect information on housing conditions. Excessive humidity was common, with 52% of households having a relative humidity above 55%. The mean PM2.5 concentration was 21.0 (standard deviation 38.5) µg/m3, with higher mean levels observed in smoking compared to non-smoking households (36.1 µg/m3 and 10.1 µg/m3, respectively). The mean CO2 level in participating households was 881 ppm (standard deviation 256), with 30% (n = 9) of homes exceeding 1000 ppm. Flooding rates were high, with 55% of households reporting at least one past flood. One-third of houses were inadequately ventilated relative to occupancy, and over one-quarter reported needing major repairs. The results indicate the value and importance of characterizing the indoor environment in First Nations households and the viability of data collection through community-based participatory research in environmental health research.

Analysis of ventilation and infiltration rates using physics-informed neural networks: Impact of space operation and meteorological factors

This study presents the development and application of a Physics-Informed Neural Network (PINN) model to estimate ventilation and infiltration rates using long-term observation data, addressing the challenge of dynamically varying space operations and meteorological conditions. A central research equestion is: How can we accurately estimate ventilation rates while accounting for these time-varying factors? Traditional tracer gas methods require numerous measurements to accurately characterize air change rates (ACR) under dynamic space operations and varying meteorological conditions. Our PINN model integrates these fluctuating factors, providing a more precise analysis of their transient effects on ACR. We employed Shapley Additive Explanations (SHAP) to interpret the sensitivity and contributions of each influencing factor. Our findings indicate that the state of windows and doors significantly affects spatial operations, while wind speed and direction are the most impactful meteorological factors. The interaction between open windows and doors results in higher ventilation rates compared to their individual effects. Wind-related factors cause ACR variations exceeding 200 %, with the wind direction relative to the office window playing a crucial role. Additionally, external temperature and indoor-outdoor temperature differences show a strong correlation with ACR. However, limitations include the lack of outdoor CO2 measurements and the assumption of uniform indoor CO2 levels, which may affect accuracy. Generalizability is also limited due to the specificity of the space studied. Future work should incorporate outdoor CO2 data and multiple spaces to enhance model applicability. This study contributes to optimizing ventilation strategies for better indoor air quality and energy efficiency.

Evaluating Indoor Air Quality in Residential Environments: A Study of PM2.5 and CO2 Dynamics Using Low-Cost Sensors

Indoor air quality (IAQ) poses a significant public health concern, and exposures to high levels of fine particulate matter (PM2.5) and carbon dioxide (CO2) could have detrimental health impacts. This study focused on assessing the indoor air pollutants in a residential house located in the town of Mission, Hidalgo County, South Texas, USA. The PM2.5 and CO2 were monitored indoors: the kitchen and the bedroom. This investigation also aimed to elucidate the effects of household activities such as cooking and human occupancy on these pollutants. Low-cost sensors (LCSs) from TSI AirAssure™ were used in this study. They were deployed within the breathing zone at approximately 1.5 m above the ground. Calibration of the low-cost sensors against Federal Equivalent Method (FEM) instruments was undertaken using a multiple linear regression method (MLR) model to improve the data accuracy. The indoor PM2.5 levels were significantly influenced by cooking activities, with the peak PM2.5 concentrations reaching up to 118.45 μg/m3. The CO2 levels in the bedroom increased during the occupant’s sleeping period, reaching as high as 1149.73 ppm. The health risk assessment was assessed through toxicity potential (TP) calculations for the PM2.5 concentrations. TP values of 0.21 and 0.20 were obtained in the kitchen and bedroom, respectively. The TP values were below the health hazard threshold (i.e., TP &lt; 1). These low TP values could be attributed to the use of electric stoves and efficient ventilation systems. This research highlights the effectiveness of low-cost sensors for continuous IAQ monitoring and helps promote better awareness of and necessary interventions for salubrious indoor microenvironments.

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 (&gt;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.

A 4IR-Driven operational risk model for CO2 storage in deepwater abandoned hydrocarbon reservoirs

This study presents the development of an advanced operational risk model that leverages Fourth Industrial Revolution (4IR) technologies to optimize carbon dioxide (CO2) storage within deepwater abandoned hydrocarbon reservoirs. The model systematically combines Artificial Neural Networks (ANN) with the optimization capabilities of Genetic Algorithms (GA) and the probabilistic analysis strengths of a Bayesian Network (BN) to perform dynamic and comprehensive risk assessments. By applying the model to a dataset covering a 200-year timeframe, it effectively forecasts CO2 storage capacities while simultaneously evaluating associated risks across different operational scenarios. One of the key innovations of this model is the introduction of a novel loss function designed to precisely manage forecast deviations and enhance the efficiency of operational processes. This function is critical in ensuring that the model remains robust and accurate in real-time risk assessments, allowing for more reliable decision-making in CO2 storage operations. In addition, the study conducts an economic evaluation that underscores the crucial role of 45Q tax credits in bolstering the financial sustainability of carbon sequestration projects. The analysis highlights how these credits significantly reduce the economic barriers to adopting carbon utilization, storage, and sequestration (CUSS) technologies, making large-scale implementation more feasible. The model's performance is underscored by its ability to achieve a 49% CO2 retention rate over two centuries, with an impressively low average error margin of 0.249%. These results highlight the model's impressive efficiency and accuracy, while also demonstrating its capacity to markedly improve the predictability of CO2 storage outcomes. The findings suggest that this model could play a pivotal role in advancing global sustainability efforts by optimizing CO2 storage processes, thereby contributing to the reduction of atmospheric CO2 levels and supporting long-term climate goals.