Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userMethane Emissions from Super-emitting Coal Mines in Australia quantified using TROPOMI Satellite Observations
168
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
Two years of satellite observations were used to estimate the methane emission from coal mines in Queensland, the largest coal-producing state in Australia. The six analyzed surface and underground coal mines are estimated to emit 570+/-98 Gg a-1 in 2018-2019. Together, they account for 7% of the national coal production, while emitting 55+/-10% of the reported methane emission from coal mining in Australia. Most remarkably, 40% of the quantified emission came from a single surface mine (Hail Creek). While surface coal mining is generally assumed to be a relatively minor source of methane, this suggests we may have to rethink its importance in the global methane budget. Our findings call for increased monitoring and investment in methane recovery technologies for both surface and underground mines. Our results indicate that for two of the three locations our satellite-based estimates are significantly higher than reported to the Australian government.
rate research
Read More
Coal mines are globally an important source of methane and also one of the largest point sources of methane. We present a high-resolution 0.1deg x 0.1deg bottom-up gridded emission inventory for methane emissions from coal mines in India and Australia, which are among the top five coal-producing countries in 2018. The aim is to reduce the uncertainty in local coal mine methane emissions and to improve the spatial localization to support monitoring and mitigation of these emissions. For India, we improve the spatial allocation of the emissions by identifying the exact location of surface and underground coal mines and, we use a tier-2 Intergovernmental Panel on Climate Change (IPCC) methodology to estimate the emissions from each coal mine using country-specific measured emission factors. For Australia, we estimate the emission for each coal mine by distributing the state-level reported total emissions using proxies of coal production and the coal basin-specific gas content profile of underground mines. Comparison of our total coal mine methane emission from India with existing global inventories showed our estimates are about a factor 3 lower, but well within the range of the national Indian estimate reported to the United Nations framework convention on climate change (UNFCCC). For both countries, the new spatial distribution of the emissions shows a large difference from the global inventories. Our improved emissions dataset will be useful for air quality or climate modeling and while assessing the satellite methane observations.
1) The annual cycle of atmospheric methane in southern high latitudes is extremely highly correlated with Antarctic sea ice extent. 2) The annual cycle of atmospheric methane in the Arctic is highly correlated with Antarctic or Arctic plus Antarctic sea ice extent. 3) We propose the global annual cycle of atmospheric methane is largely driven by Antarctic sea ice dynamics, with relatively stronger influence from other fluxes (probably the biota) in the Northern Hemisphere. 4) We propose degassing during sea ice freeze and temperature dependent solubility in the ocean dominate the annual methane cycle. 5) Results provide evidence that carbon cycle pathways, parameters and predictions must be reassessed.
Almost all remote sensing atmospheric PM2.5 estimation methods need satellite aerosol optical depth (AOD) products, which are often retrieved from top-of-atmosphere (TOA) reflectance via an atmospheric radiative transfer model. Then, is it possible to estimate ground-level PM2.5 directly from satellite TOA reflectance without a physical model? In this study, this challenging work are achieved based on a machine learning model. Specifically, we establish the relationship between PM2.5, satellite TOA reflectance, observation angles, and meteorological factors in a deep learning architecture (denoted as Ref-PM modeling). Taking the Wuhan Urban Agglomeration (WUA) as a case study, the results demonstrate that compared with the AOD-PM modeling, the Ref-PM modeling obtains a competitive performance, with out-of-sample cross-validated R2 and RMSE values of 0.87 and 9.89 ug/m3 respectively. Also, the TOA-reflectance-derived PM2.5 have a finer resolution and larger spatial coverage than the AOD-derived PM2.5. This work updates the traditional cognition of remote sensing PM2.5 estimation and has the potential to promote the application in atmospheric environmental monitoring.
The core properties of the wave climate and its changes in the Caspian Sea are established in terms of the annual mean significant wave height and its regional changes in 2002-2013 based on the outcome of the satellite altimetry mission JASON-1. Remotely estimated wave heights are validated against properties of the empirical distribution of instrumentally measured wave heights in the southern Caspian Sea and monthly averages of visually observed wave heights at three locations. A correction for systematic differences leads to very good correspondence between monthly averaged in situ and satellite data with a typical root mean square difference of 0.06 m. The average significant wave height in the Caspian Sea is 0.5-0.7 m in the northern basin of the sea, around 1.2 m in large parts of the central and southern basins and reaches up to 1.8 m in the northern segment of the central basin. The basin-wide average wave intensity varied insignificantly in the range of 1.02-1.14 m in 2002-2013. These estimates overestimate the wave heights by about 30% because low wave conditions are ignored. Substantial and statistically significant changes in the wave height occurred in certain areas. The wave height decreased by 0.019 +- 0.007 m/yr in the eastern segment of the central basin and by 0.04 +- 0.04 m/yr in the western segment of the southern basin. These changes can be explained by an increase in the frequency of westerly winds at the expence of southerly winds. Both basin-wide and regional extreme wave heights exhibit large interannual variations but do not show any significant trend. The patterns of changes in mean and extreme wave height are different. The average wave height has increased while the extreme wave height has decreased in the eastern segment of the southern basin.
Integrated assessment models (IAMs) are valuable tools that consider the interactions between socioeconomic systems and the climate system. Decision-makers and policy analysts employ IAMs to calculate the marginalized monetary cost of climate damages resulting from an incremental emission of a greenhouse gas. Used within the context of regulating anthropogenic methane emissions, this metric is called the social cost of methane (SC-CH$_4$). Because several key IAMs used for social cost estimation contain a simplified model structure that prevents the endogenous modeling of non-CO$_2$ greenhouse gases, very few estimates of the SC-CH$_4$ exist. For this reason, IAMs should be updated to better represent methane cycle dynamics that are consistent with comprehensive Earth System Models. We include feedbacks of climate change on the methane cycle to estimate the SC-CH$_4$. Our expected value for the SC-CH$_4$ is $1163/t-CH$_4$ under a constant 3.0% discount rate. This represents a 44% increase relative to a mean estimate without feedbacks on the methane cycle.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
