Carbon Monitoring System Research Articles

Characterizing and communicating uncertainty: lessons from NASA’s Carbon Monitoring System

Abstract Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: addressing its origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities.

Support Carbon Neutral Goal with a High-resolution Carbon Monitoring System in Beijing

Abstract China has committed to peak its carbon emissions before 2030 and achieve carbon neutrality before 2060, known as the Dual Carbon Goals (DCG). To support the DCG, monitoring and assessment of progresses in emissions reduction have become a central research focus. Here we describe the Beijing-Tianjin-Hebei (JJJ) Carbon Monitoring System, established over the last six years through a collaborative effort of 16 research institutions and universities. The monitoring capability includes ground-based high-precision reference stations, a dense network of medium-accuracy low-cost sensors, carbon satellites, vertical profile samplers, and mobile platforms. Observations using the network have shown CO2 variations from diurnal, synoptic to seasonal-interannual timescales, as well as dramatic on-road changes during the COVID-19 lockdown. High-resolution modeling and data assimilation at km-scale reveal major deficiencies in global carbon emissions datasets at regional/city scale. The inversion found the total fossil fuel CO2 emissions of the City of Beijing to be 117 megatonne (Mt), but much lower than the national-data-based inventories (152 MtCO2). The results have been used in emissions assessment by national and local environmental protection agencies. Similar systems are now being developed in other Chinese cities.

CODS: Carbon Monoxide Pollution Detection and Monitoring System Based on The Internet of Things Towards Urban Health Enhancement

CODS: Carbon Monoxide Pollution Detection and Monitoring System Based on The Internet of Things Towards Urban Health Enhancement

Design of Carbon Monitoring System Platform for Agricultural Energy Internet

As global climate change and carbon emission issues become increasingly severe, the agricultural energy internet plays a crucial role in achieving low-carbon development. The merging of new energy with modern agriculture is progressively being realized, making effective carbon monitoring and accounting especially critical. This paper presents a novel design for a carbon monitoring system platform for the agricultural energy internet, using aquaponics greenhouses as the research object. Addressing the difficulties presented by the diversity and magnitude of agricultural carbon sources, the paper proposes a carbon accounting model applicable to such greenhouse environments. This model aims to enhance energy utilization efficiency and reduce carbon emissions through precise accounting and monitoring. Subsequently, a carbon monitoring system applicable to the agricultural energy internet is designed. This system enables real-time and accurate monitoring and assessment of carbon emissions and absorption in agricultural parks. The platform, developed using a B/S architecture with a separation model between front-end and back-end employing Vue and Spring Boot frameworks, provides a comprehensive solution for monitoring and managing carbon emissions in agricultural energy.

If Some Critical Regions Achieve Carbon Neutrality, How Will the Global Atmospheric CO2 Concentration Change?

Due to anthropogenic emissions, the global CO2 concentration increases at a rate of approximately 2 ppm per year. With over 130 countries and regions committing to carbon neutrality goals and continuously reducing anthropogenic CO2 emissions, understanding how atmospheric CO2 concentrations will change globally and in other regions has become an intriguing question. Examining different regions’ efforts to reduce anthropogenic CO2 emissions through atmospheric CO2 observations is also meaningful. We used prior and posterior fluxes to drive the TM5 model. The posterior fluxes were based on the China Carbon Monitoring, Verification and Support System for Global (CCMVS-G), which assimilated the atmospheric CO2 concentration data from ground-based observation and satellite observation. We found that the CO2 concentration obtained using the posterior fluxes was more in line with the actual situation. Then, we presented some experiments to estimate how global and regional CO2 concentrations would change if certain key regions and the whole world achieved net zero emissions of anthropogenic CO2. After removing carbon fluxes from China, North America, and Europe, global CO2 concentrations decreased by around 0.58 ppm, 0.22 ppm, and 0.10 ppm, respectively. The most significant decrease occurred in the regions where fluxes were removed, followed by other areas at the same latitude affected by westerly winds. This indicates that fossil fuel flux is the main factor affecting CO2 concentrations, and that meteorological-driven transportation also significantly impacts CO2 concentrations. Most importantly, using this method, it is possible to quantitatively estimate the impact of achieving carbon neutrality in one region on CO2 concentrations in local regions as well as globally.

Invert global and China's terrestrial carbon fluxes over 2019–2021 based on assimilating richer atmospheric CO2 observations

With China's commitment to reach carbon peak by 2030 and achieve carbon neutrality by 2060, it is particularly important to obtain terrestrial ecosystem carbon fluxes with low uncertainty both globally and in China. The use of more observation data may help reduce the uncertainty of inverting carbon fluxes. This study uses the observation data from global stations, background stations and provincial stations in China, as well as the OCO-2 satellite, and uses the China Carbon Monitoring, Verification and Supporting System for Global (CCMVS-G) to estimate the carbon fluxes of global and Chinese terrestrial ecosystems from 2019 to 2021. The results revealed that the global terrestrial ecosystem carbon sink was approximately −3.40 Pg C/yr from 2019 to 2021. The carbon sinks in the Northern Hemisphere are large, especially in Asia, North America, and Europe. From 2019 to 2021, the carbon sink of China's terrestrial ecosystem was approximately −0.44 Pg C/yr. Carbon sinks exhibit significant seasonal and interannual variations in China. After assimilating the observation data, the uncertainty of the posterior flux is smaller than that of the prior flux, a more reasonable distribution of carbon sources and sinks can be obtained, and more accurate boundary conditions can be provided for the China Carbon Monitoring, Verification and Supporting System for Regional (CCMVS-R). In the future, it is important to establish a well-designed CO2 ground-based observation network.

Satellite-based solar-induced fluorescence tracks seasonal and elevational patterns of photosynthesis in California’s Sierra Nevada mountains

Robust carbon monitoring systems are needed for land managers to assess and mitigate the changing effects of ecosystem stress on western United States forests, where most aboveground carbon is stored in mountainous areas. Atmospheric carbon uptake via gross primary productivity (GPP) is an important indicator of ecosystem function and is particularly relevant to carbon monitoring systems. However, limited ground-based observations in remote areas with complex topography represent a significant challenge for tracking regional-scale GPP. Satellite observations can help bridge these monitoring gaps, but the accuracy of remote sensing methods for inferring GPP is still limited in montane evergreen needleleaf biomes, where (a) photosynthetic activity is largely decoupled from canopy structure and chlorophyll content, and (b) strong heterogeneity in phenology and atmospheric conditions is difficult to resolve in space and time. Using monthly solar-induced chlorophyll fluorescence (SIF) sampled at ∼4 km from the TROPOspheric Monitoring Instrument (TROPOMI), we show that high-resolution satellite-observed SIF followed ecological expectations of seasonal and elevational patterns of GPP across a 3000 m elevation gradient in the Sierra Nevada mountains of California. After accounting for the effects of high reflected radiance in TROPOMI SIF due to snow cover, the seasonal and elevational patterns of SIF were well correlated with GPP estimates from a machine-learning model (FLUXCOM) and a land surface model (CLM5.0-SP), outperforming other spectral vegetation indices. Differences in the seasonality of TROPOMI SIF and GPP estimates were likely attributed to misrepresentation of moisture limitation and winter photosynthetic activity in FLUXCOM and CLM5.0 respectively, as indicated by discrepancies with GPP derived from eddy covariance observations in the southern Sierra Nevada. These results suggest that satellite-observed SIF can serve as a useful diagnostic and constraint to improve upon estimates of GPP toward multiscale carbon monitoring systems in montane, evergreen conifer biomes at regional scales.

Constraining Sector‐Specific CO2 Fluxes Using Space‐Based XCO2 Observations Over the Los Angeles Basin

AbstractThe concentration of carbon dioxide (CO2) in Earth's atmosphere is increasing due to human activities and the resulting effects on the global climate system have initiated several policy‐driven approaches to reduce emissions of this greenhouse gas. Quantifying the effectiveness of such policies requires both bottom‐up and top‐down approaches to estimate CO2 emissions. This work investigates, for the first time, the potential of using Snapshot Area Map observations from NASA's OCO‐3 instrument to disaggregate sector‐specific emissions from instrument observations. Optimized sector‐specific timeseries were produced using Bayesian inversion techniques and compared to proxy activity data from transportation, commercial maritime, and industrial sectors in the Los Angeles Basin. Results demonstrate that dense space‐based observations of atmospheric CO2 are capable of disentangling sector‐specific CO2 fluxes, paving the way for accurate monitoring of the effects of carbon‐reduction policies and operational carbon monitoring systems.

Low-cost urban carbon monitoring network and implications for china: a comprehensive review.

The development and renewal of gas sensor technology have enabled more and more low-cost gas sensors to form a carbon monitoring network to meet the requirements of the city. In the context of China's commitment to achieving the "double carbon" target by 2060, this paper reviews the principles of four standard gas sensors and the application of several low-cost sensors in urban carbon monitoring networks, with the aim of providing a practical reference for the future deployment of carbon monitoring networks in Chinese cities. Moreover, the types, prices, and deployment of the sensors used in each project are summarized. Based on this review, non-dispersive infrared sensors have the best performance among the sensors and are commonly used in many cities. Lots of urban climate networks in cities were summarized by many reviews in the literature, but only a few sensors were studied, and they did not consider carbon dioxide (CO2) sensors. This review focuses on the dense CO2 urban monitoring network, and some case studies are also discussed, such as Seoul and San Francisco. To address the issue of how to better ensure the balance between cost and accuracy in the deployment of sensor networks, this paper proposes a method of simultaneously deploying medium-precision and high-precision fixed sensors and mobile sensors to form an urban carbon monitoring network. Finally, the prospects and recommendations, such as different ways to mitigate CO2 and develop an entire carbon monitoring system for future urban carbon monitoring in China, are also presented.

Design of energy big data and carbon emission monitoring system based on perceptron model in the context of carbon neutral and carbon peaking

Abstract The big energy data and carbon emission monitoring system is designed to collect carbon emission-related data for pollution gas management. This paper constructs a carbon emission monitoring system in the context of carbon neutrality and peaking. A multi-layer perceptron algorithm is introduced based on the principle of perception, and a BP-MLP neural network model is proposed by optimizing the perceptron weights using BP neural network. For the sensors in the carbon emission monitoring system, the node redundancy is processed, and the optimal sensor distribution is proved by using the correlation coefficient. Finally, the evaluation analysis of the carbon monitoring system was carried out in three aspects: relevance coefficient de-redundancy, number of iterations and daily emissions. The results show that when the correlation threshold is 0.8, the sensor distribution of the monitoring system can satisfy the monitoring under various wind conditions, and when the number of iterations is 600, the difference between the real value and the monitored value is only 3.63% and the daily emission peaks at 5.243 mg/m3 at 14:00 a.m. This shows that the carbon emission monitoring system constructed based on the BP-MLP model can effectively collect and analyze carbon emission data. Data collection and analysis, and provide corresponding data support for the management of gas pollution.

Assessment of the NASA carbon monitoring system wet carbon stakeholder community: data needs, gaps, and opportunities

Wet carbon (WC) ecosystems are a critical part of the carbon cycle, yet they are underrepresented in many policy and science communities due to the relative under-investment in stakeholder and boundary organizations. WC systems include the hydrosphere and carbon cycling systems that operate in wetlands, oceans, rivers, streams, lakes, ponds, and permafrost. In this article, we provide evidence from a desk review of WC stakeholders, that includes individuals, groups or organizations that are affected by climate change, and utilize carbon data. These stakeholders are involved in decision-making processes in WC ecosystems, and can be private companies, non-governmental organizations, government agencies ranging in scope from local to federal, parastatals, international organizations, and more. In this paper, we identify and describe the links and interests of WC stakeholders and analyze the gaps between scientific understanding and information needs. A continued focus on WC systems could lead to increased stakeholder engagement and methodological and scientific progress. Our study revealed that stakeholder interest in WC systems was not primarily determined by its role in the carbon cycle, but rather by its significance for local policy, economics, or ecology. To bridge the gap between stakeholders and available WC data, we need improved communication of data availability and uncertainty, capacity building, engagement between stakeholder groups, and data continuity. Enhanced stakeholder engagement across various systems will facilitate greater utilization of carbon monitoring data derived from remote sensing; thereby creating more informed stakeholders as well as more effective decision-making processes.

Multi-scale observations of mangrove blue carbon ecosystem fluxes: The NASA Carbon Monitoring System BlueFlux field campaign

The BlueFlux field campaign, supported by NASA’s Carbon Monitoring System, will develop prototype blue carbon products to inform coastal carbon management. While blue carbon has been suggested as a nature-based climate solution (NBS) to remove carbon dioxide (CO2) from the atmosphere, these ecosystems also release additional greenhouse gases (GHGs) such as methane (CH4) and are sensitive to disturbances including hurricanes and sea-level rise. To understand blue carbon as an NBS, BlueFlux is conducting multi-scale measurements of CO2 and CH4 fluxes across coastal landscapes, combined with long-term carbon burial, in Southern Florida using chambers, flux towers, and aircraft combined with remote-sensing observations for regional upscaling. During the first deployment in April 2022, CO2 uptake and CH4 emissions across the Everglades National Park averaged −4.9 ± 4.7 μmol CO2 m−2 s−1 and 19.8 ± 41.1 nmol CH4 m−2 s−1, respectively. When scaled to the region, mangrove CH4 emissions offset the mangrove CO2 uptake by about 5% (assuming a 100 year CH4 global warming potential of 28), leading to total net uptake of 31.8 Tg CO2-eq y−1. Subsequent field campaigns will measure diurnal and seasonal changes in emissions and integrate measurements of long-term carbon burial to develop comprehensive annual and long-term GHG budgets to inform blue carbon as a climate solution.

An approach to estimating forest biomass while quantifying estimate uncertainty and correcting bias in machine learning maps

Providing forest biomass estimates with desired accuracy and precision for small areas is a key challenge to incorporating forest carbon offsets into commodity trading programs. Enrolled forest carbon projects and verification entities typically rely on probabilistically sampled field data and design-based (DB) estimators to estimate carbon storage and characterize uncertainty. However, this methodology requires a large amount of field data to achieve sufficient precision and collection of these data can be prohibitively expensive. This has spurred interest in developing regional-scale maps of forest biomass that incorporate remote sensing data as an alternative to collecting expensive plot data. These maps are often generated using machine learning (ML) algorithms that combine remote sensing products and field measurements. While these maps can produce estimates across large geographic regions at fine spatial resolutions, the estimates are prone to bias and do not have associated uncertainty estimates. Here, we assess one such map developed by the National Aeronautics and Space Administration's Carbon Monitoring System. We consider model-assisted (MA) and geostatistical model-based (GMB) estimators to address map bias and uncertainty quantification. The MA and GMB estimators use a sample of field observations as the response, and the ML-produced map as an auxiliary variable to achieve statistically defensible predictions. We compare MA and GMB estimator performance to DB and direct (DR) estimators. This assessment considers both counties and a small areal extent experimental forest, all within Oregon USA. Results suggest the MA and GMB estimators perform similar to the DB estimator at the state level and in counties containing many field plots. But in counties with moderate to small field sample sizes, the GMB and MA estimators are more precise than the DB estimator. As within-county sample sizes get smaller, the GMB estimator tends to outperform MA. Results also show the DR estimator's state-level estimates are substantially larger than the DB, MA and GMB estimates, indicating that that the DR estimator may be biased. When assessing the GMB estimator for the experimental forest, we find the GMB estimator has sufficient precision for stand-level carbon accounting even when no field observations are available within the stand. Plot-level GMB uncertainty interval coverage probabilities were estimated and showed adequate coverage. This suggests that the GMB estimator is producing statistically rigorous uncertainty estimates.

An automated project carbon planning, monitoring and forecasting system integrating building information model and earned value method

Building and construction account for significant embodied carbon related to materials and construction processes. Carbon monitoring and forecasting during project execution to track performance is instrumental given the inherent uncertainty and complexity. This study aims at a carbon monitoring system integrating building information model (BIM) and earned value method (EVM) to facilitate automated carbon control and management in the construction process. First, a carbon-loaded planning technique is formulated incorporating an ad-hoc building element-level carbon database. Then, an enhanced EVM model is built into the system for dynamic carbon monitoring and forecasting during construction. Next, the planning technique and the EVM model are embedded in Navisworks as plugins for automated data exchange and visualization. Finally, a case study is carried out to demonstrate the application of this system and validate its functions.

A carbon-monitoring strategy through near-real–time data and space technology

In this perspective, we proposed an innovative strategy that coupled near-real-time emission data with satellite observations to make a reliable and precise global carbon-monitoring system.

Internet of Things (IoT)-Integrated Embodied Carbon Assessment and Monitoring of Prefabricated Buildings

Buildings contribute significantly to carbon emissions over their life cycle. Recently, embodied carbon (EC) accounts for an increasing share of life cycle carbon emissions of new buildings as their energy efficiency is improving. However, traditional methods of data collection and communication from diverse carbon sources such as manual recording lead to low-efficient and prone-to-error estimation of embodied carbon emissions. Therefore, this paper aims to propose an Internet of Things (IoT)-integrated embodied carbon assessment and monitoring system (ECAMS) for prefabricated buildings. This system involves three layers, i.e., of data collection, data communication, and data analysis. To provide a theoretical foundation, the EC assessment model is firstly modified to distribute carbon emissions into twelve construction statuses and at five levels of analysis of prefabricated buildings. IoT sensors including radio frequency identification (RFID), acceleration transducer, and global positioning system (GPS) are employed for automated real-time data collection. IoT data will be communicated with building information modelling (BIM) and carbon assessment software through application programming interfaces (APIs). Laboratory tests were conducted to demonstrate the feasibility of sensor-based data collection and communication. The proposed system facilitates more efficient and accurate estimations of prefabricated buildings’ embodied carbon, which should help practitioners to explore effective carbon reduction strategies.

Study of the mechanism of digitalization boosting urban low-carbon transformation

The climate problem is severe and many urban climates are poor. To deal with a series of climate problems, low-carbon measures are indispensable. Digital development is becoming a new driving force for social development. Therefore, the development of urban low-carbon transformation should be based on digital construction. This paper explores the significance and theoretical mechanism of the research of the mechanism of digitalization boosting urban low-carbon transition. Theoretically, digitalization can reduce carbon emissions through mechanisms such as optimizing industrial structure, adjusting energy structure, improving government governance efficiency, and changing people’s way of production and life. Finally, from the perspective of carbon neutrality, this paper puts forward relevant countermeasures, from the following six aspects: building a carbon emission measurement and monitoring system, promoting the digital transformation of enterprises, increasing the research and development of new energy technologies, improving the carbon emission market trading system, optimizing the carbon emission administrative management system, and practicing the low-carbon concept.

The status and development proposal of carbon sources and sinks monitoring satellite system

In order to mitigate global warming, the international communities actively explore low-carbon and green development methods. According to the Paris Agreement that came into effect in 2016, there will be a global stocktaking plan to carry out every 5 years from 2023 onwards. In September 2020, China proposed a "double carbon" target of carbon peaking before 2030 and carbon neutrality before 2060. Achieving carbon peaking and carbon neutrality goals requires accurate carbon emissions and carbon absorptions. China's existing carbon monitoring methods have insufficient detection accuracy, low spatial resolution, and narrow swath, which are difficult to meet the monitoring requirement of carbon sources and sinks monitoring. In order to meet the needs of carbon stocktaking and support the monitoring and supervision of carbon sources and sinks, it is recommended to make full use of the foundation of the existing satellites, improve the detection technical specifications of carbon sources and sinks monitoring measures, and build a multi-means and comprehensive, LEO-GEO orbit carbon monitoring satellite system to achieve higher precision, higher resolution and multi-dimensional carbon monitoring. On this basis, it is recommended to strengthen international cooperation, improve data sharing policy, actively participate in the development of carbon retrieval algorithm and the setting of international carbon monitoring standards, establish an independent and controllable global carbon monitoring and evaluation system, and contribute China's strength to the global realization of carbon peaking and carbon neutrality.

Scientist-stakeholder relationships drive carbon data product transfer effectiveness within NASA program

Carbon cycle science is at the heart of research on global climate change and its long-term impacts, as it examines the exchange of carbon between the atmosphere, oceans, land, and the impact of fossil fuel emissions on this cycling. Given the urgency of the climate challenge, NASA’s Carbon Monitoring System (CMS) requires all funded investigators to identify and work with stakeholder organizations at project inception to accelerate the transfer of the products developed by funded research into decision making systems. In this study, we contribute to the literature through the implementation of a quantitative analysis of 908 unique survey responses from funded investigators to explore the maturity of the scientist-stakeholder engagement. The paper employs multiple correspondence analysis to provide evidence to support policy options to increase stakeholder integration into research programs. Despite limitations of the dataset used, we demonstrated that multiple funding rounds, long-standing relationships between the stakeholder and scientist, and the scientific productivity of the Principal Investigator, including the ability to produce datasets and research papers on these datasets, all contribute to carbon products moving from research to operational use. The maturity of relationships between scientists and stakeholders was shown to result improved stakeholder engagement. The use of carbon products should be identified in every stage of the program, and that capacity building is needed to support both existing and newly identified stakeholders better understand and use CMS products. As federal, state, and local policy on climate adaptation and mitigation matures, the need for information on carbon will expand. Building of stakeholder-scientist relationships in CMS results in an effective generation and use of datasets to support this need and prototype ways that improved information needed for decision making can be created.

Carbon monitoring and above ground biomass trends: Anchor forest opportunities for tribal, private and federal relationships

There are more than 300 million hectares of forested land within the conterminous United States essential to sustaining the myriad social/cultural, economic, and ecologic benefits society enjoys from these lands. Nationwide, millions of forested hectares, both private and public, are disappearing functionally and physically through serve wildfire fire and land conversion. On many of these lands, management, centered on fire suppression, has led to reductions in forest resilience to wildfire. Lands, overstocked with accumulated fuel and faced with a changing climate, are expected to continue this legacy of fire and deteriorating health. A paradigm shift is needed to face the challenges confronting forests and enhance collaborative efforts across multiple forest ownerships. Our ability to leverage emerging technologies and pair them with the knowledge of indigenous peoples presents new opportunities for success. The objectives of this study were to 1) assess the Anchor Forest concept as a framework to leverage collaborative motivations and leadership by indigenous peoples (Tribes) in eastern Washington State to improve forest ecosystem health across legal and political boundaries, ‘cross-boundary’ management, and 2) demonstrate how the NASA carbon monitoring system (CMS) mapping products of regional forestland above ground biomass (AGB) density and temporal trends can provide information that supports decisionmakers in their efforts to collaboratively approach improving forest health conditions through management activities.