Matching Items (3)

128236-Thumbnail Image.png

Los Angeles megacity: a high-resolution land–atmosphere modelling system for urban CO2 emissions

Description

Megacities are major sources of anthropogenic fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions. The spatial extents of these large urban systems cover areas of 10 000 km[superscript 2] or more with complex

Megacities are major sources of anthropogenic fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions. The spatial extents of these large urban systems cover areas of 10 000 km[superscript 2] or more with complex topography and changing landscapes. We present a high-resolution land–atmosphere modelling system for urban CO[subscript 2] emissions over the Los Angeles (LA) megacity area. The Weather Research and Forecasting (WRF)-Chem model was coupled to a very high-resolution FFCO[subscript 2] emission product, Hestia-LA, to simulate atmospheric CO[subscript 2] concentrations across the LA megacity at spatial resolutions as fine as  ∼  1 km. We evaluated multiple WRF configurations, selecting one that minimized errors in wind speed, wind direction, and boundary layer height as evaluated by its performance against meteorological data collected during the CalNex-LA campaign (May–June 2010). Our results show no significant difference between moderate-resolution (4 km) and high-resolution (1.3 km) simulations when evaluated against surface meteorological data, but the high-resolution configurations better resolved planetary boundary layer heights and vertical gradients in the horizontal mean winds. We coupled our WRF configuration with the Vulcan 2.2 (10 km resolution) and Hestia-LA (1.3 km resolution) fossil fuel CO[subscript 2] emission products to evaluate the impact of the spatial resolution of the CO[subscript 2] emission products and the meteorological transport model on the representation of spatiotemporal variability in simulated atmospheric CO[subscript 2] concentrations. We find that high spatial resolution in the fossil fuel CO[subscript 2] emissions is more important than in the atmospheric model to capture CO[subscript 2] concentration variability across the LA megacity. Finally, we present a novel approach that employs simultaneous correlations of the simulated atmospheric CO[subscript 2] fields to qualitatively evaluate the greenhouse gas measurement network over the LA megacity. Spatial correlations in the atmospheric CO[subscript 2] fields reflect the coverage of individual measurement sites when a statistically significant number of sites observe emissions from a specific source or location. We conclude that elevated atmospheric CO[subscript 2] concentrations over the LA megacity are composed of multiple fine-scale plumes rather than a single homogenous urban dome. Furthermore, we conclude that FFCO[subscript 2] emissions monitoring in the LA megacity requires FFCO[subscript 2] emissions modelling with  ∼  1 km resolution because coarser-resolution emissions modelling tends to overestimate the observational constraints on the emissions estimates.

Contributors

Agent

Created

Date Created
  • 2016-07-22

128031-Thumbnail Image.png

Optimizing the Spatial Resolution for Urban CO2 Flux Studies Using the Shannon Entropy

Description

The ‘Hestia Project’ uses a bottom-up approach to quantify fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions spatially at the building/street level and temporally at the hourly level. Hestia FFCO[subscript 2]

The ‘Hestia Project’ uses a bottom-up approach to quantify fossil fuel CO[subscript 2] (FFCO[subscript 2]) emissions spatially at the building/street level and temporally at the hourly level. Hestia FFCO[subscript 2] emissions are provided in the form of a group of sector-specific vector layers with point, line, and polygon sources to support carbon cycle science and climate policy. Application to carbon cycle science, in particular, requires regular gridded data in order to link surface carbon fluxes to atmospheric transport models. However, the heterogeneity and complexity of FFCO[subscript 2] sources within regular grids is sensitive to spatial resolution. From the perspective of a data provider, we need to find a balance between resolution and data volume so that the gridded data product retains the maximum amount of information content while maintaining an efficient data volume. The Shannon entropy determines the minimum bits that are needed to encode an information source and can serve as a metric for the effective information content. In this paper, we present an analysis of the Shannon entropy of gridded FFCO[subscript 2] emissions with varying resolutions in four Hestia study areas, and find: (1) the Shannon entropy increases with smaller grid resolution until it reaches a maximum value (the max-entropy resolution); (2) total emissions (the sum of several sector-specific emission fields) show a finer max-entropy resolution than each of the sector-specific fields; (3) the residential emissions show a finer max-entropy resolution than the commercial emissions; (4) the max-entropy resolution of the onroad emissions grid is closely correlated to the density of the road network. These findings suggest that the Shannon entropy can detect the information effectiveness of the spatial resolution of gridded FFCO[subscript 2] emissions. Hence, the resolution-entropy relationship can be used to assist in determining an appropriate spatial resolution for urban CO[subscript 2] flux studies. We conclude that the optimal spatial resolution for providing Hestia total FFCO[subscript 2] emissions products is centered around 100 m, at which the FFCO[subscript 2] emissions data can not only fully meet the requirement of urban flux integration, but also be effectively used in understanding the relationships between FFCO[subscript 2] emissions and various social-economic variables at the U.S. census block group level.

Contributors

Agent

Created

Date Created
  • 2017-05-19

156901-Thumbnail Image.png

Relationships between on-road FFCO₂ emission and socio-economics/urban form factors

Description

Fossil fuel CO2 (FFCO2) emissions are recognized as the dominant greenhouse gas driving climate change (Enting et. al., 1995; Conway et al., 1994; Francey et al., 1995; Bousquet et. al.,

Fossil fuel CO2 (FFCO2) emissions are recognized as the dominant greenhouse gas driving climate change (Enting et. al., 1995; Conway et al., 1994; Francey et al., 1995; Bousquet et. al., 1999). Transportation is a major component of FFCO2 emissions, especially in urban areas. An improved understanding of on-road FFCO2 emission at high spatial resolution is essential to both carbon science and mitigation policy. Though considerable research has been accomplished within a few high-income portions of the planet such as the United States and Western Europe, little work has attempted to comprehensively quantify high-resolution on-road FFCO2 emissions globally. Key questions for such a global quantification are: (1) What are the driving factors for on-road FFCO2 emissions? (2) How robust are the relationships? and (3) How do on-road FFCO2 emissions vary with urban form at fine spatial scales?

This study used urban form/socio-economic data combined with self-reported on-road FFCO2 emissions for a sample of global cities to estimate relationships within a multivariate regression framework based on an adjusted STIRPAT model. The on-road high-resolution (whole-city) regression FFCO2 model robustness was evaluated by introducing artificial error, conducting cross-validation, and assessing relationship sensitivity under various model specifications. Results indicated that fuel economy, vehicle ownership, road density and population density were statistically significant factors that correlate with on-road FFCO2 emissions. Of these four variables, fuel economy and vehicle ownership had the most robust relationships.

A second regression model was constructed to examine the relationship between global on-road FFCO2 emissions and urban form factors (described by population

ii

density, road density, and distance to activity centers) at sub-city spatial scales (1 km2). Results showed that: 1) Road density is the most significant (p<2.66e-037) predictor of on-road FFCO2 emissions at the 1 km2 spatial scale; 2) The correlation between population density and on-road FFCO2 emissions for interstates/freeways varies little by city type. For arterials, on-road FFCO2 emissions show a stronger relationship to population density in clustered cities (slope = 0.24) than dispersed cities (slope = 0.13). FFCO2 3) The distance to activity centers has a significant positive relationship with on-road FFCO2 emission for the interstate and freeway toad types, but an insignificant relationship with the arterial road type.

Contributors

Agent

Created

Date Created
  • 2018