scholarly journals Atmospheric trace gas trends obtained from FTIR column measurements in Toronto, Canada from 2002-2019

2021 ◽  
Vol 3 (5) ◽  
pp. 051002
Author(s):  
Shoma Yamanouchi ◽  
Kimberly Strong ◽  
Orfeo Colebatch ◽  
Stephanie Conway ◽  
B A Jones Dylan ◽  
...  
2016 ◽  
Vol 9 (9) ◽  
pp. 3213-3229 ◽  
Author(s):  
Mark F. Lunt ◽  
Matt Rigby ◽  
Anita L. Ganesan ◽  
Alistair J. Manning

Abstract. Atmospheric trace gas inversions often attempt to attribute fluxes to a high-dimensional grid using observations. To make this problem computationally feasible, and to reduce the degree of under-determination, some form of dimension reduction is usually performed. Here, we present an objective method for reducing the spatial dimension of the parameter space in atmospheric trace gas inversions. In addition to solving for a set of unknowns that govern emissions of a trace gas, we set out a framework that considers the number of unknowns to itself be an unknown. We rely on the well-established reversible-jump Markov chain Monte Carlo algorithm to use the data to determine the dimension of the parameter space. This framework provides a single-step process that solves for both the resolution of the inversion grid, as well as the magnitude of fluxes from this grid. Therefore, the uncertainty that surrounds the choice of aggregation is accounted for in the posterior parameter distribution. The posterior distribution of this transdimensional Markov chain provides a naturally smoothed solution, formed from an ensemble of coarser partitions of the spatial domain. We describe the form of the reversible-jump algorithm and how it may be applied to trace gas inversions. We build the system into a hierarchical Bayesian framework in which other unknown factors, such as the magnitude of the model uncertainty, can also be explored. A pseudo-data example is used to show the usefulness of this approach when compared to a subjectively chosen partitioning of a spatial domain. An inversion using real data is also shown to illustrate the scales at which the data allow for methane emissions over north-west Europe to be resolved.


1984 ◽  
Vol 2 (1) ◽  
pp. 65-81 ◽  
Author(s):  
E. Robinson ◽  
W. L. Bamesberger ◽  
F. A. Menzia ◽  
A. S. Waylett ◽  
S. F. Waylett

2018 ◽  
Vol 25 (28) ◽  
pp. 28431-28444 ◽  
Author(s):  
Xia Chen ◽  
Ping Zhao ◽  
Yanting Hu ◽  
Xiuhua Zhao ◽  
Lei Ouyang ◽  
...  

2015 ◽  
Vol 42 (2) ◽  
pp. 0215003 ◽  
Author(s):  
姚路 Yao Lu ◽  
刘文清 Liu Wenqing ◽  
刘建国 Liu Jianguo ◽  
阚瑞峰 Kan Ruifeng ◽  
许振宇 Xu Zhenyu ◽  
...  

2015 ◽  
Vol 8 (8) ◽  
pp. 3481-3492 ◽  
Author(s):  
S. E. Bush ◽  
F. M. Hopkins ◽  
J. T. Randerson ◽  
C.-T. Lai ◽  
J. R. Ehleringer

Abstract. Ground-based measurements of atmospheric trace gas species and criteria pollutants are essential for understanding emissions dynamics across space and time. Gas composition in the lower 50 m of the atmosphere has the greatest direct impacts on human health as well as ecosystem processes; hence data at this level are necessary for addressing carbon-cycle- and public-health-related questions. However, such surface data are generally associated with stationary measurement towers, where spatial representation is limited due to the high cost of establishing and maintaining an extensive network of measurement stations. We describe here a compact mobile laboratory equipped to provide high-precision, high-frequency, continuous, on-road synchronous measurements of CO2, CO, CH4, H2O, NOx, O3, aerosol, meteorological, and geospatial position data. The mobile laboratory has been deployed across the western USA. In addition to describing the vehicle and its capacity, we present data that illustrate the use of the laboratory as a powerful tool for investigating the spatial structure of urban trace gas emissions and criteria pollutants at spatial scales ranging from single streets to whole ecosystem and regional scales. We assess the magnitude of known point sources of CH4 and also identify fugitive urban CH4 emissions. We illustrate how such a mobile laboratory can be used to better understand emissions dynamics and quantify emissions ratios associated with trace gas emissions from wildfire incidents. Lastly, we discuss additional mobile laboratory applications in health and urban metabolism.


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