Time Series Of Emissions Research Articles

Linking Electricity and Air Quality Models by Downscaling: Weather-Informed Hourly Dispatch of Generation Accounting for Renewable and Load Temporal Variability Scenarios.

National models of the electric sector typically consider a handful of generator operating periods per year, while pollutant fate and transport models have an hourly resolution. We bridge that scale gap by introducing a novel fundamental-based temporal downscaling method (TDM) for translating national or regional energy scenarios to hourly emissions. Optimization-based generator dispatch is used to account for variations in emissions stemming from weather-sensitive power demands and wind and solar generation. The TDM is demonstrated by downscaling emissions from the electricity market module in the National Energy Model System. As a case study, we implement the TDM in the Virginia-Carolinas region and compare its results with traditional statistical downscaling used in the Sparse Matrix Operator Kernel Emissions (SMOKE) processing model. We find that the TDM emission profiles respond to weather and that nitrogen oxide emissions are positively correlated with conditions conducive to ozone formation. In contrast, SMOKE emission time series, which are rooted in historical operating patterns, exhibit insensitivity to weather conditions and potential biases, particularly with high renewable penetration and climate change. Relying on SMOKE profiles can also obscure variations in emission patterns across different policy scenarios, potentially downplaying their impacts on power system operations and emissions.

Fractal Analysis of Mining Wastewater Time Series Parameters: Balkhash Urban Region and Sayak Ore District

The population life and health quality are significantly reduced due to water resources pollution caused by heavy metals, especially in urban agglomerations located close to metal ore mining and processing facilities. The greatest environmental pollution occurs during the extraction of Cu, Zn, and Pb. In this study, a fractal R/S analysis of wastewater discharge indicators time series from a metal ore mining facility located in the Sayak ore district in the Republic of Kazakhstan (turbidity, electrical conductivity, flow magnitude, and pH level) was carried out. A sharp increase in the flow rate was recorded from 10 to 15 July 2024 and an increase in the electrical conductivity from 4 to 26 July 2024. However, the latest type of indicator assessment does not exceed the critical level for life. The presence of electrical conductivity indicators time series long-term memory and persistence was also recorded (the Hurst exponent for the electrical conductivity time series is fixed in the 0.56 to 0.59 range and does not go below the threshold value for randomness according to the Anis-Lloyd formula). Thus, the value-changing process is controlled and stable, and minor changes in turbidity indicate that these releases do not significantly harm the environment. Despite this, the results obtained do not allow for a comprehensive analysis of the state of releases as the data from all deposits is not available. Therefore, due to the time constraints of the data provided for analysis, it is difficult to fully assess the impact of specific metal ore mining facilities on the environmental safety of the Balkhash urban region. In addition, many studies indicate very high risks of chronic diseases for the population living in this region. The findings of this study enable us to conclude that the application of fractal analysis and the calculation of fractal characteristics for time series of emissions can serve as an indicator of the environmental status within the given area. This information can be used by environmental services to build reliable environmental pollution monitoring systems.

Observing the interaction between energy generation carbon emissions and economic conditions could consider generation, intensity, and times of the day.

Management of carbon emissions of regional electricity generation involves resolving much nuanced relationships. However, current studies pertaining to the relationship between energy and the economy utilize only quarterly and annual data and focus on the long-run relationship but are oblivious to temporal associations. This study takes the context of the State of Texas and utilizes per 15min of data between 2007 and 2020 to derive the monthly time series of life-cycle emissions, emissions factor, and energy generation. The study also divides each day into three portions (0:00-8:00, 8:00-16:00, and 16:00-24:00) in order to descend precision down to the hourly level. Impulse-response analysis indicates that (1) employment conditions impact emissions by affecting the amount of electric energy generated, while demographic (population as reflected by property price) tends to impact emissions by affecting the carbon intensity of electricity, and (2) the emissions produced during 0:00-8:00 display different reaction to fluctuation of regional economic conditions compared to emissions produced during other periods, the effect emanates from either energy generation fluctuation or emissions factor shocks. The broader implication of the findings lies in an emphasis on time-interval-specific analysis and concurrent consideration of both energy generation and emissions factors that can lead to optimized policy implementation results.

Monitoring Earth's atmosphere with Sentinel-5 TROPOMI and Artificial Intelligence: Quantifying volcanic SO2 emissions

Identifying changes in volcanic unrest and tracking eruptive activity are fundamental for volcanic surveillance and monitoring. Magmatic gases, particularly sulphur dioxide (SO2), play a crucial role in influencing eruptive styles, making the monitoring of SO2 emissions essential. Recent advancements in satellite remote sensing technology, including higher spatial resolution and sensitivity, have enhanced our ability to detect SO2 emissions from volcanoes worldwide. However, traditional fixed-threshold algorithms struggle to automatically distinguish volcanic SO2 emissions from non-volcanic sources. Additionally, accurately quantifying SO2 emissions is challenging due to their dependence on plume height, particularly when reaching high altitudes. To address these challenges, we developed an Artificial Intelligence (AI) algorithm that detects and quantifies volcanic SO2 emissions in near real-time. Our approach utilizes a Random Forest (RF) model, a supervised Machine Learning (ML) algorithm, to identify volcanic SO2 emissions and integrates Cloud Top Height (CTH) data to enhance the accuracy of SO2 mass quantification during intense volcanic eruptions. This AI algorithm, fully implemented in Google Earth Engine (GEE), leverages data from the TROPOspheric Monitoring Instrument (TROPOMI) aboard the Copernicus Sentinel-5 Precursor (S5P) satellite to automatically retrieve daily volcanic SO2 plumes and CTH. We validated the model's performance against the Radius classifier, a state-of-the-art tool, and generalized its application across various volcanoes (Etna, Villarrica, Fuego, Pacaya, and Cumbre Vieja) with differing degassing activities, SO2 emission rates, and plume geometries. Our findings demonstrate that the proposed AI approach effectively identifies and quantifies SO2 plumes emitted by different volcanoes, enabling the investigation of SO2 emission time series that reflect magma dynamics.

Magma recharge at Manam volcano, Papua New Guinea, identified through thermal and SO2 satellite remote sensing of open-vent emissions

Manam is one of the most frequently active volcanoes in Papua New Guinea and is a top contributor to global volcanic volatile emissions due to its persistent open-vent degassing. Here, we present a multi-year time series (2018–2021) of thermal and SO2 emissions for Manam from satellite remote sensing, which we interpret in the context of open-vent feedback between magma supply, reservoir pressure, and outgassing. We classify the time series into four phases based on the varying SO2 flux and observe a transient, yet substantial, increase in time-averaged SO2 flux from background levels of ~ 0.6 to ~ 4.72 kt day−1 between March and July 2019. We also identify a transition from temporally coupled to decoupled gas and thermal emissions during this period which we explain in the context of a magma recharge event that supplied new, volatile-rich magma to the shallow plumbing system beneath Manam. We infer that the arrival of this recharge magma triggered the series of eruptions between August 2018 and March 2019. These explosive events collectively removed 0.18 km3 of degassed residual magma and signalled the onset of a renewed period of unrest that ultimately culminated in a major eruption on 28 June 2019. We quantify the magnitude of “excess” degassing at Manam after the removal of the inferred residual magma. SO2 emissions reveal that ~ 0.18 km3 of magma was supplied, but only ~ 0.08 km3 was erupted between April 2019 and December 2021. We highlight how multi-parameter remote sensing observations over months to years enable the interpretation of open-vent processes that may be missed by short-duration campaign measurements.

Dual channel visible graph convolutional neural network for microleakage monitoring of pipeline weld homalographic cracks

When using a single sensor to monitor early microleakage of nuclear power pressure pipeline leakage, there are problems such as low monitoring accuracy and poor early warning reliability due to the limitations of the monitoring range and weak difference between the leakage signals. To address these challenges, this paper proposes a dual channel visible graph convolutional neural network (DCV-GCN). Firstly, the acoustic emission time-series data of each channel are truncated and divided, and the significant frequency bands are selected based on the envelope spectrum. On this basis, the sequence group is averaged to obtain the graph structure sequence. Then, the limited penetrable visibility (LPV) graph construction algorithm is used to calculate the adjacency matrix, and the important nodes is reserved according to the eigenvector centrality. Furthermore, the inverse ratio of the distance from the sensor in each single channel to the center of the crack is used as the fusion weight, and the adjacency matrices are merged after normalization to transform the construction of the graph structure dataset. Finally, the dataset is input into the graph convolutional neural network, and the effectiveness of the method is verified by carefully designing three homalographic cracks. The results show that the proposed method can effectively extract the distinguishing features with similar frequency components and similar leakage rates, and the recognition accuracy of different leakage states can reach 98.56 %. In addition, through ablation experiments and different parameter strategy settings, the operating mechanism is explained, which can provide a reference for monitoring and analysis by industrial technicians.

Carbon emissions forecasting based on tensor decomposition with multi-source data fusion

Accurately forecasting carbon dioxide emissions is crucial for policymakers and researchers aiming to combat climate change and develop effective emission reduction strategies. This study introduces an innovative method that leverages multi-source social media information to address the challenges of insufficient information and data uncertainty in carbon emission time series forecasting. We propose a combined Tensor-LSTM-ARIMA model for predicting carbon emissions, utilizing tensor decomposition data analysis methods. The results indicate that this combined model effectively captures the complex relationships within heterogeneous data, outperforming baseline models in prediction accuracy. Furthermore, the study demonstrates that unstructured social media data can enhance structured time series data, providing a new perspective for comprehensively understanding the variables influencing carbon emission predictions.

Pathways toward Climate-Neutral Red Meat Production

Ruminant livestock industries can support the climate stabilization ambitions of the Paris Agreement through interventions that reduce GHG emissions (predominantly biogenic methane) and sequester carbon in landscapes. This study explored pathways for the Australian red meat industry (grazing, feedlot finishing, and domestic processing) to become climate neutral, whereby the radiative forcing (RF) footprint is plateaued and there is no additional forcing contribution. Emissions timeseries (CO2, N2O, CH4) were compiled for 1990 to 2020 and projected to 2030 under a business-as-usual scenario (including an 18% increase in sheep and 13% increase in beef cattle) and with a range of production system and vegetation management interventions. The RF footprint peaked in 2018 at 7.13 mW/m2 and decreased to 7.07 mW/m2 in 2020. With the future expansion of the herd/flock and under business-as-usual conditions, the RF footprint is projected to increase by 2.8% by 2030. However, with a combination of interventions, production has the potential to increase with a decreasing RF footprint, a condition that can be described as climate neutral. The Australian red meat industry has made an historical contribution to global RF increase. However, with ongoing RF management, it is possible to increase food production within climate-neutral limits.

Identifying impending failure in heterogeneous materials: A study on acoustic emission time series

In the present paper, precursors to identify impending failure in heterogeneous materials are investigated by analyzing Acoustic Emission (AE) time series. Three main approaches are considered: (1) long-range correlations using the Hurst (H) analysis, (2) Natural Time (NT) analysis, and (3) a proposed Method of Critical Fluctuations-Based (MCF-B) approach. The above approaches are employed to data collected from an experimental AE test on a concrete cubic specimen subjected to an axial compression loading. The results indicate that: persistent long-range correlations tend towards near-random behavior prior to imminent failure; critical regions, characterized by the convergence of natural time parameters, show a direct correlation with imminent increments in the acoustic emission activity and the structure's collapse; the proposed MCF-B approach as precursor, is capable to identify critical states, being the results in agreement with those from NT and Hurst analysis.

Modeling and forecasting carbon dioxide emission in Pakistan using a hybrid combination of regression and time series models

Carbon dioxide (CO2) emissions continue to rise globally despite efforts to combat climate change. Energy industry emissions are a pressing global issue, causing devastating impacts. Hence, it is vital to accurately and efficiently forecast CO2 emissions. Thus, this study comprehensively analyzes forecasting CO2 emissions by comparing various hybrid combinations of regression and time series methods to explore the CO2 emissions in Pakistan. First, divide the yearly time series of CO2 emissions into the long-run curve trend series and the residual subseries. The long-run curve trend subseries is modeled using parametric and nonparametric regression methods, while various standard time series models are used to forecast the residual subseries. However, the forecasts of each subseries will be combined to obtain the final forecast of CO2 emissions. This work used four different accuracy mean errors, a statistical test, and a graphical analysis as performance measures to evaluate the proposed hybrid forecasting technique. The findings confirmed that the proposed hybrid combination forecasting technique is highly accurate and efficient in forecasting CO2 emissions. Likewise, according to the proposed final optimal hybrid combination forecasting model, Pakistan's per capita CO2 emissions will be 1.130215 metric tons in 2030. Pakistan's escalating emission trend signals that creative solutions must be implemented to curb it. Thus, the government must price carbon footprints, regulate electricity from zero-carbon sources, reduce population, encourage afforestation in densely populated areas, adopt clean technology, and fund research.

Extension of Japan’s Prefectural Emission Accounting and Enrichment of Socioeconomic Data from 1990 to 2020

With the continuous increase in carbon dioxide emissions due to human activities and the resulting severe climate issues, there is global concern about energy conservation and emission reduction. However, detailed data on energy consumption and emissions at a fine-grained scale, particularly regarding spatial dimensions and sector-specific emissions, remains insufficient and in need of refinement and timely updates. In Japan, following the Fukushima nuclear disaster, there has been a significant shift from nuclear power generation to reliance on fossil fuels across various sectors, highlighting disparities in emissions data across different regions and industries. Our work extends the emissions time series for Japan’s 47 prefectures, incorporating their socioeconomic characteristics over a broader time frame and with a more detailed sectoral classification. The emissions inventory, covering the period from 1990 to 2020, is based on the consumption of the three main fossil fuels across 32 sectors, with emissions carefully allocated for regional power generation. This dataset, presented in a unified format, is expanded to include longer time scales and more detailed socioeconomic data. It is anticipated to offer crucial insights for establishing regional emission reduction targets and identifying sectoral priorities for decarbonization.

Characterisation of the stellar activity of M dwarfs

Context. The chromospheric emission estimated in the core of different lines, such as Ca II H & K, Na D1 and D2, and Ha, is not always correlated between lines. In particular, the Ca II H & K and Ha emission time series are anti-correlated for a few percent of the stars, contrary to what is observed on the Sun. This puzzling result has been observed for both solar-type stars and M stars. Aims. Our objective is to characterise these relationships in more detail using complementary criteria, and based on a large set of spectra obtained with HARPS for a large sample of M dwarfs. This should allow to evaluate whether or not additional processes are required to explain the observations. Methods. We analysed the time average and variability of the Ca, Na, and Hα emissions for 177 M stars ranging from subspectral types M0 to M8, paying particular attention to their (anti-)correlations on both short and long timescales as well as slopes between indices. We also computed synthetic Hα time series based on different assumptions of plage properties. We compared our findings with observations in order to evaluate whether or not the main observed properties could be reproduced. Results. The statistical properties of our sample, in terms of correlations and slopes between indices at different timescales, differ from what we previously obtained for FGK stars: there are fewer stars with a null correlation, and the correlations we find show a weaker dependence on timescale. However, there can be a large dispersion from one season to another for stars with a well identified low or negative correlation. We also specify the complex relationship between the average activity levels, with a clear indication of a change in the sign of the slope from the relation between Ca and Hα (and between Na and Ha) for the most massive M dwarfs. In addition, we observe a change in slope in the Na–Ca relation at an intermediate activity level. At this stage, we are not able to find simple plage properties that, alone, are sufficient to reproduce the observations. However, the simulations already allow us to point out that it is not straightforward to compare the temporal variability correlation and the integrated indices. Our findings also demonstrate the need for complex activity patterns to explain some of the observations. Conclusions. We conclude that the relation between the three indices examined here exhibits a large diversity in behaviour over the sample studied. More detailed simulations with complex activity patterns are necessary to understand these observations. This will teach us about plage properties for this type of star.

The multifractal characteristics of acoustic emission for composite samples subject to simulated field loading condition

ABSTRACT The composite strata structure is a common geological formation in western China. The study of the failure characteristics of the composite structure is of great help to the stability evaluation of stope and the prediction of coal mine dynamic disasters. In order to study the influence of different hard rock coefficients on the instability characteristics of composite samples, the triaxial tests with progressive varying confining pressure simulating the field loading condition were conducted on eight groups of composite samples with different hard rock coefficients. The bearing capacity of different samples was tested using the RMT-150B triaxial compression equipment, while the DS-5 type eight-channel acoustic emission monitoring system was employed for simultaneous monitoring of acoustic emission parameters. Additionally, the multifractal theory in mathematics is applied to the analysis of acoustic emission signals, exploring the multifractal characteristics of the time series of acoustic emission signals. The results show that the peak strength of the composite sample decreases first and then increases as the hard rock coefficient increases, which is consistent with the trend of the multifractal spectrum parameter Δ f α of the acoustic emission time series. A significant increase in the low-frequency and high-amplitude signal ratio can be used as a precursor to early indicate the instability in composite samples. The dynamic changes of multifractal parameters at different stages of the progressive varying confining pressure can reflect the influence of axial pressure and confining pressure on the crack propagation in the composite samples, and the two parameters at the hydrostatic pressure stage can be used as a rule-of-thumb for screening the testing samples. Simultaneously, the multifractal parameters can early warn the rock failure much more ahead of time than the conventional approach associated with the warning point obtained in the acoustic emission curves, and its head of time can be reaching 0.015 failure progress.

Application of Machine Learning to Estimate Ammonia Atmospheric Emissions and Concentrations

This paper describes an innovative method that recursively applies the machine learning Random Forest to an assumed homogeneous aerographic domain around measurement sites to predict concentrations and emissions of ammonia, an atmospheric pollutant that causes acidification and eutrophication of soil and water and contributes to secondary PM2.5. The methodology was implemented to understand the effects of weather and emission changes on atmospheric ammonia concentrations. The model was trained and tested by hourly measurements of ammonia concentrations and atmospheric turbulence parameters, starting from a constant emission scenario. The initial values of emissions were calculated based on a bottom-up emission inventory detailed at the municipal level and considering a circular area of about 4 km radius centered on measurement sites. By comparing predicted and measured concentrations for each iteration, the emissions were modified, the model’s training and testing were repeated, and the model converged to a very high performance in predicting ammonia concentrations and establishing hourly time-varying emission profiles. The ammonia concentration predictions were extremely accurate and reliable compared to the measured values. The relationship between NH3 concentrations and the calculated emissions rates is compatible with physical atmospheric turbulence parameters. The site-specific emissions profiles, estimated by the proposed methodology, clearly show a nonlinear relation with measured concentrations and allow the identification of the effect of atmospheric turbulence on pollutant accumulation. The proposed methodology is suitable for validating and confirming emission time series and defining highly accurate emission profiles for the improvement of the performances of chemical and transport models (CTMs) in combination with in situ measurements and/or optical depth from satellite observation.

Multivariate Analysis of CO2 Emissions by Energy Generation in IEA Member Countries

The concentration of carbon dioxide (), the main catalyst for global warming, reached an average of 414.7 parts per million in 2021 and 2.3 ppm more than in 2020. This concentration of is the highest recorded in at least 800,000 years, according to the International Energy Agency. The increase in the concentration of is a growing concern in the scientific community and environmental organizations since the increase in global temperature resulting from this phenomenon can lead to serious and irreversible effects. This topic has gained space in global analyse and has become increasingly recurrent. This research aimed to deal with and analysed essential points in this discussion. With the study, it was possible to point out evidence that Brazil is a country that has been significantly reducing its emissions, being in the cluster along with IEA member countries that least issue . With this article, it was possible to point out similar characteristics between the emissions of the countries, making it possible to highlight three latent variables: countries that have a constant tall or low trend in emissions, Grouping of countries that showed an upward trend at the beginning of the analysed period, but with a significant reduction over time, and countries that have had more discrepant behaviour from the others, these being the countries that have a structural breakdown in their time series of emissions by power generation.

The Start Matters: A Comparative Analysis of Climate Equity Among UNFCCC Country Parties and Country Groups

Incorrect indicators and starting years for emission cumulation can lead to confusion regarding the concepts of climate equity and climate responsibility. This article examines the variations in the results obtained by using different indicators and starting years to calculate climate equity and climate responsibilities among country parties and country groups of the UNFCCC. The article utilizes historical greenhouse gas (GHG) emissions data from 193 countries spanning the period 1850 to 2021. The data is aggregated from various sources including EDGAR, Climate Watch, and Global Carbon Budget (GCB). The article calculates cumulative GHG emissions and cumulative GHG emissions per capita, with starting years 1850, 1970, and 1990. By highlighting differences in various indicators, the article aims to provide a better understanding of climate responsibilities, climate beneficiaries, and climate equity. The results demonstrate that cumulative emissions and cumulative emissions per capita are scientific indicators that reveal a country’s level of climate responsibility and climate equity. Negotiators can achieve consensus more easily in the complex system if they have a comprehensive and scientific understanding of climate equity. It is suggested that country groups under the UNFCCC use scientific indicators and methodologies to reveal climate responsibilities and climate equity.

Application of recurrence quantification analysis of acoustic emission time series to analysis of a plastic flow of metals.

As a result of the application of recurrence quantification analysis to acoustic emission time series obtained in uniaxial tensile testing of copper and silver, we detected the existence of a characteristic interval in which the Shannon information entropy (as a parameter of the quantitative analysis of recurrence plots) increased rapidly. Using statistical analysis of the behavior of the dislocation ensemble, we established a relation between the physical parameters of the given interval and the global stability loss parameters of the plastic flow of metals, indicating the predictability of the distinctive point determined long before the critical state was attained.

To what extent are greenhouse-gas emissions offset by trees in a Sahelian silvopastoral system?

To assess the extent to which trees in a semi-arid silvopastoral system (SPS) can offset the greenhouse-gas (GHG) emissions of the system's livestock, this study used two process-based models (STEP-GENDEC-N2O and DynACof) to simulate 9 years of agricultural activity and resulting emissions in a SPS that has been operating in sahelian Senegal. STEP-GENDEC-N2O simulated soil N2O and CO2 fluxes, plus growth of the herbaceous layer, while DynACof focused on the tree layer. Outputs from the models included simulated time series of vegetative growth, water fluxes, and emissions. This output was validated through the use of published data, and measurements that were made at the SPS. Overall, the outputs from STEP-GENDEC-N2O agreed well with validation data for water fluxes, soil N, soil C, herbaceous biomass, and N2O emissions. Good agreement was also found between the measured fluxes of the SPS ecosystem, and the simulated values that were generated by combining STEP-GENDEC-N2O's simulations (of the herbaceous layer's heterotrophic respiration, autotrophic respiration, and gross primary productivity (GPP)) with DynACof's simulations of the tree layer's autotrophic respiration and GPP. Among the insights gained from the simulations was that in this SPS's sandy soils, nitrification was the dominant process that leads to N2O emissions. Our results show that the trees, at their current density (81 ha−1) offset 18 % to 41 % of the GHG emissions from livestock. With further development, the model set-up can be used for estimating the GHG offset at other tree densities, and will be useful for guiding future policies regarding climate-change adaptation and mitigation in the management of the Sahel's SPSs.

A Gridded Inventory of Annual 2012-2018 U.S. Anthropogenic Methane Emissions.

Nationally reported greenhouse gas inventories are a core component of the Paris Agreement's transparency framework. Comparisons with emission estimates derived from atmospheric observations help identify improvements to reduce uncertainties and increase the confidence in reported values. To facilitate comparisons over the contiguous United States, we present a 0.1° × 0.1° gridded inventory of annual 2012-2018 anthropogenic methane emissions, allocated to 26 individual source categories, with scale-dependent error estimates. Our inventory is consistent with the U.S. Environmental Protection Agency (EPA) Inventory of U.S. Greenhouse Gas Emissions and Sinks (GHGI), submitted to the United Nations in 2020. Total emissions and patterns (spatial/temporal) reflect the activity and emission factor data underlying the GHGI, including many updates relative to a previous gridded version of the GHGI that has been extensively compared with observations. These underlying data are not generally available in global gridded inventories, and comparison to EDGAR version 6 shows large spatial differences, particularly for the oil and gas sectors. We also find strong regional variability across all sources in annual 2012-2018 spatial trends, highlighting the importance of understanding regional- and facility-level activities. Our inventory represents the first time series of gridded GHGI methane emissions and enables robust comparisons of emissions and their trends with atmospheric observations.

Quantification of Diffusive Methane Emissions from a Large Eutrophic Lake with Satellite Imagery.

Lakes are major emitters of methane (CH4); however, a longstanding challenge with quantifying the magnitude of emissions remains as a result of large spatial and temporal variability. This study was designed to address the issue using satellite remote sensing with the advantages of spatial coverage and temporal resolution. Using Aqua/MODIS imagery (2003-2020) and in situ measured data (2011-2017) in eutrophic Lake Taihu, we compared the performance of eight machine learning models to predict diffusive CH4 emissions and found that the random forest (RF) model achieved the best fitting accuracy (R2 = 0.65 and mean relative error = 21%). On the basis of input satellite variables (chlorophyll a, water surface temperature, diffuse attenuation coefficient, and photosynthetically active radiation), we assessed how and why they help predict the CH4 emissions with the RF model. Overall, these variables mechanistically controlled the emissions, leading to the model capturing well the variability of diffusive CH4 emissions from the lake. Additionally, we found climate warming and associated algal blooms boosted the long-term increase in the emissions via reconstructing historical (2003-2020) daily time series of CH4 emissions. This study demonstrates the great potential of satellites to map lake CH4 emissions by providing spatiotemporal continuous data, with new and timely insights into accurately understanding the magnitude of aquatic greenhouse gas emissions.