Mapping Global Atmospheric CO2 Concentration at High Spatiotemporal Resolution

24-h continuous non-invasive multiparameter home monitoring of vitals in patients with Rett syndrome by an innovative wearable technology: evidence of an overlooked chronic fatigue status

Background

Sleep is disturbed in Rett syndrome (RTT), a rare and progressive neurodevelopmental disorder primarily affecting female patients (prevalence 7.1/100,000 female patients) linked to pathogenic variations in the X-linked methyl-CpG-binding protein 2 (MECP2) gene. Autonomic nervous system dysfunction with a predominance of the sympathetic nervous system (SNS) over the parasympathetic nervous system (PSNS) is reported in RTT, along with exercise fatigue and increased sudden death risk. The aim of the present study was to test the feasibility of a continuous 24 h non-invasive home monitoring of the biological vitals (biovitals) by an innovative wearable sensor device in pediatric and adolescent/adult RTT patients.

Methods

A total of 10 female patients (mean age 18.3 ± 9.4 years, range 4.7-35.5 years) with typical RTT and MECP2 pathogenic variations were enrolled. Clinical severity was assessed by validated scales. Heart rate (HR), respiratory rate (RR), and skin temperature (SkT) were monitored by the YouCare Wearable Medical Device (Accyourate Group SpA, L'Aquila, Italy). The average percentage of maximum HR (HRmax%) was calculated. Heart rate variability (HRV) was expressed by consolidated time-domain and frequency-domain parameters. The HR/LF (low frequency) ratio, indicating SNS activation under dynamic exercise, was calculated. Simultaneous continuous measurement of indoor air quality variables was performed and the patients' contributions to the surrounding water vapor partial pressure [PH2O (pt)] and carbon dioxide [PCO2 (pt)] were indirectly estimated.

Results

Of the 6,559.79 h of biovital recordings, 5051.03 h (77%) were valid for data interpretation. Sleep and wake hours were 9.0 ± 1.1 h and 14.9 ± 1.1 h, respectively. HRmax % [median: 71.86% (interquartile range 61.03-82%)] and HR/LF [median: 3.75 (interquartile range 3.19-5.05)] were elevated, independent from the wake-sleep cycle. The majority of HRV time- and frequency-domain parameters were significantly higher in the pediatric patients (p ≤ 0.031). The HRV HR/LF ratio was associated with phenotype severity, disease progression, clinical sleep disorder, subclinical hypoxia, and electroencephalographic observations of multifocal epileptic activity and general background slowing.

Conclusion

Our findings indicate the feasibility of a continuous 24-h non-invasive home monitoring of biovital parameters in RTT. Moreover, for the first time, HRmax% and the HR/LF ratio were identified as potential objective markers of fatigue, illness severity, and disease progression.

Impacts of different biomass burning emission inventories: Simulations of atmospheric CO2 concentrations based on GEOS-Chem

Biomass burning has substantial spatiotemporal variabilities. It contributes significantly to the dynamics of global CO₂ distributions and variances. Quantifying the impacts of biomass burning emissions on atmospheric CO₂ concentrations is essential for global and regional carbon cycles and budgets. In this study, we performed several numerical experiments by switching and replacing inventories to estimate the impacts of four biomass burning emission inventories on atmospheric CO₂ concentration simulations in 2006-2010 based on the global chemical transport model, GEOS-Chem. The results highlighted similarities and differences in the annual and seasonal variability of biomass burning emissions and simulated CO₂ concentrations at global and regional scales. Based on four different biomass burning emission inventories, we found that biomass burning emissions could lead to a global CO₂ concentration increase of 2.4 ppm annually. Africa contributed the largest global CO₂ emissions among all continental regions, where the maximum CO₂ concentration increase could reach 7.9-13.0 ppm in summer. Model evaluation results showed that simulation using the Quick Fire Emissions Database (QFED) as the model priori biomass burning emission inventory had the best performance compared with the satellite and surface observations. The sensitivity of simulated CO₂ concentrations to the uncertainties in different biomass burning emission inventories was high in southern South America and most areas of the Eurasian continent, and low in central Africa and Southeast Asia. This study furthers our understanding of the critical role of biomass burning in atmospheric CO₂ and indicates an urgent need to improve the accuracy of biomass burning emission estimates in CO₂ simulations.

High-Coverage Reconstruction of XCO2 Using Multisource Satellite Remote Sensing Data in Beijing-Tianjin-Hebei Region

The extreme climate caused by global warming has had a great impact on the earth's ecology. As the main greenhouse gas, atmospheric CO₂ concentration change and its spatial distribution are among the main uncertain factors in climate change assessment. Remote sensing satellites can obtain changes in CO₂ concentration in the global atmosphere. However, some problems (e.g., low time resolution and incomplete coverage) caused by the satellite observation mode and clouds/aerosols still exist. By analyzing sources of atmospheric CO₂ and various factors affecting the spatial distribution of CO₂, this study used multisource satellite-based data and a random forest model to reconstruct the daily CO₂ column concentration (XCO₂) with full spatial coverage in the Beijing-Tianjin-Hebei region. Based on a matched data set from 1 January 2015, to 31 December 2019, the performance of the model is demonstrated by the determination coefficient (R²) = 0.96, root mean square error (RMSE) = 1.09 ppm, and mean absolute error (MAE) = 0.56 ppm. Meanwhile, the tenfold cross-validation (10-CV) results based on samples show R² = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm, and the 10-CV results based on spatial location show R² = 0.91, RMSE = 1.68 ppm, and MAE = 0.88 ppm. Finally, the spatially seamless mapping of daily XCO₂ concentrations from 2015 to 2019 in the Beijing-Tianjin-Hebei region was conducted using the established model. The study of the spatial distribution of XCO₂ concentration in the Beijing-Tianjin-Hebei region shows its spatial differentiation and seasonal variation characteristics. Moreover, daily XCO₂ map has the potential to monitor regional carbon emissions and evaluate emission reduction.

Comparative Evaluation of Top-Down GOSAT XCO2 vs. Bottom-Up National Reports in the European Countries

Submitting national inventory reports (NIRs) on emissions of greenhouse gases (GHGs) is obligatory for parties of the United Nations Framework Convention on Climate Change (UNFCCC). The NIR forms the basis for monitoring individual countries’ progress on mitigating climate change. Countries prepare NIRs using the default bottom–up methodology of the Intergovernmental Panel on Climate Change (IPCC), as approved by the Kyoto protocol. We provide tangible evidence of the discrepancy between official bottom–up NIR reporting (unit: tons) versus top–down XCO2 reporting (unit: ppm) within the European continent, as measured by the Greenhouse Gases Observing Satellite (GOSAT). Bottom–up NIR (annual growth rate of CO2 emission from 2010 to 2016: −1.55%) does not show meaningful correlation (geographically weighted regression coefficient = −0.001, R2 = 0.024) to top–down GOSAT XCO2 (annual growth rate: 0.59%) in the European countries. The top five countries within the European continent on carbon emissions in NIR do not match the top five countries on GOSAT XCO2 concentrations. NIR exhibits anthropogenic carbon-generating activity within country boundaries, whereas satellite signals reveal the trans-boundary movement of natural and anthropogenic carbon. Although bottom–up NIR reporting has already gained worldwide recognition as a method to track national follow-up for treaty obligations, the single approach based on bottom–up did not present background atmospheric CO2 density derived from the air mass movement between the countries. In conclusion, we suggest an integrated measuring, reporting, and verification (MRV) approach using top–down observation in combination with bottom–up NIR that can provide sufficient countrywide objective evidence for national follow-up activities.

Spatio-Temporal Mapping of Multi-Satellite Observed Column Atmospheric CO2 Using Precision-Weighted Kriging Method

Column-averaged dry air mole fraction of atmospheric CO2 (XCO2), obtained by multiple satellite observations since 2003 such as ENVISAT/SCIAMACHY, GOSAT, and OCO-2 satellite, is valuable for understanding the spatio-temporal variations of atmospheric CO2 concentrations which are related to carbon uptake and emissions. In order to construct long-term spatio-temporal continuous XCO2 from multiple satellites with different temporal and spatial periods of observations, we developed a precision-weighted spatio-temporal kriging method for integrating and mapping multi-satellite observed XCO2. The approach integrated XCO2 from different sensors considering differences in vertical sensitivity, overpass time, the field of view, repeat cycle and measurement precision. We produced globally mapped XCO2 (GM-XCO2) with spatial/temporal resolution of 1 × 1 degree every eight days from 2003 to 2016 with corresponding data precision and interpolation uncertainty in each grid. The predicted GM-XCO2 precision improved in most grids compared with conventional spatio-temporal kriging results, especially during the satellites overlapping period (0.3–0.5 ppm). The method showed good reliability with R2 of 0.97 from cross-validation. GM-XCO2 showed good accuracy with a standard deviation of bias from total carbon column observing network (TCCON) measurements of 1.05 ppm. This method has potential applications for integrating and mapping XCO2 or other similar datasets observed from multiple satellite sensors. The resulting GM-XCO2 product may be also used in different carbon cycle research applications with different precision requirements.

Spatial distribution of XCO2 using OCO-2 data in growing seasons

The purpose of this research is to assess the spatial distribution of CO₂ concentration during the growing seasons (April to September) in 2015 over Iran. The XCO₂ data belonging to orbiting carbon observatory-2 (OCO-2) and eight environmental variables data consist of normalized difference vegetation index (NDVI), net primary productivity (NPP), land surface temperature (LST), leaf area index (LAI), air temperature, wind speed, wind direction, and national land cover map were modeled by multi-layer perceptron (MLP). The values of R² and RMSE indices show the good performance of the multi-layer perceptron model for monthly models. Based on sensitivity analysis results, land cover and wind direction had the most important role in the spatial distribution of XCO₂. Also, the results revealed that the maximum values of XCO₂ observed in the east, south east, and desert areas in central of Iran due to the lack of vegetation cover, lack of local wind current, and high temperature. The western, northwestern and northern regions of Iran have the minimum amounts of XCO₂ because of existing valuable ecosystem such as Hyrcanian and Zagrous forests, rangeland, air currents, and low temperature. The findings of this study indicated that the manageable factors such as land cover and vegetation cover play very important roles in the spatial distribution of CO₂ and finding carbon dioxide source and sink at national scale. Therefore, policymakers and managers by the logical management of these resources are able to control or even reduce the concentration of carbon dioxide in different areas.

Important evidence of constant low CO2 windows and impacts on the non-closure of the greenhouse effect

The CO₂ distribution in the atmosphere remains unclear for the complexity of the long-range vertical transport process and other influencing factors. In this work, regression analysis was used to verify the accuracy of CO₂ concentrations datasets. Geostatistical analyses were used to investigate the spatiotemporal distributions of CO₂ at 7 levels from near the surface to the mid-troposphere (0~5 km). Spatial correlation and time series analyses were used to further determine the diffusion characteristics of the CO₂ concentration based on the horizontal wind (NCEP R2), which is one of the main driving factors. The results showed that the horizontal, not vertical, diffusion of CO₂ becomes increasingly more prominent with the decrease in atmospheric pressure to the mid-troposphere, whereas many regions, such as the Rocky Mountains and Qinghai-Tibet Plateau, have constant low values throughout the year due to the influence of high topography (up to 10.756 ppmv lower than that near the surface). These areas form low CO₂ concentration 'windows' keeping letting thermal infrared energy out into space. This study is the first to question the existing view of the closure of the 'greenhouse effect'. Future research studies should more precisely determine the closure threshold and the uncertainties about the surface fluxes.

The Global Spatiotemporal Distribution of the Mid-Tropospheric CO2 Concentration and Analysis of the Controlling Factors

The atmospheric infrared sounder (AIRS) provides a robust and accurate data source to investigate the variability of mid-tropospheric CO2 globally. In this paper, we use the AIRS CO2 product and other auxiliary data to survey the spatiotemporal distribution characteristics of mid-tropospheric CO2 and the controlling factors using linear regression, empirical orthogonal functions (EOFs), geostatistical analysis, and correlation analysis. The results show that areas with low mid-tropospheric CO2 concentrations (20°S–5°N) (384.2 ppm) are formed as a result of subsidence in the atmosphere, the presence of the Amazon rainforest, and the lack of high CO2 emission areas. The areas with high mid-tropospheric CO2 concentrations (30°N–70°N) (382.1 ppm) are formed due to high CO2 emissions. The global mid-tropospheric CO2 concentrations increased gradually (the annual average rate of increase in CO2 concentration is 2.11 ppm/a), with the highest concentration occurring in spring (384.0 ppm) and the lowest value in winter (382.5 ppm). The amplitude of the seasonal variation retrieved from AIRS (average: 1.38 ppm) is consistent with that of comprehensive observation network for trace gases (CONTRAIL), but smaller than the surface ground stations, which is related to altitude and coverage. These results contribute to a comprehensive understanding of the spatiotemporal distribution of mid-tropospheric CO2 and related mechanisms.