Human Settlements, Infrastructure, and Spatial Planning

Change, Intergovernmental Panel on Climate

doi:10.1017/cbo9781107415416.018

Cited by 59 publications

(15 citation statements)

References 498 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…North America [Seto et al, 2014]. Because some CO 2 emissions related with urban use are from outside urban areas (e.g., due to imported electricity), primary or direct CO 2 emissions from urban areas are lower (30-56%, central estimate 43%).…”

Section: Supporting Informationmentioning

confidence: 99%

Emissions and topographic effects on column CO₂ () variations, with a focus on the Southern California Megacity

et al. 2017

View full text Add to dashboard Cite

Within the California South Coast Air Basin (SoCAB), XnormalCO2 varies significantly due to atmospheric dynamics and the nonuniform distribution of sources. XnormalCO2 measurements within the basin have seasonal variation compared to the “background” due primarily to dynamics, or the origins of air masses coming into the basin. We observe basin‐background differences that are in close agreement for three observing systems: Total Carbon Column Observing Network (TCCON) 2.3 ± 1.2 ppm, Orbiting Carbon Observatory‐2 (OCO‐2) 2.4 ± 1.5 ppm, and Greenhouse gases Observing Satellite 2.4 ± 1.6 ppm (errors are 1σ). We further observe persistent significant differences (∼0.9 ppm) in XnormalCO2 between two TCCON sites located only 9 km apart within the SoCAB. We estimate that 20% (±1σ confidence interval (CI): 0%, 58%) of the variance is explained by a difference in elevation using a full physics and emissions model and 36% (±1σ CI: 10%, 101%) using a simple, fixed mixed layer model. This effect arises in the presence of a sharp gradient in any species (here we focus on CO2) between the mixed layer (ML) and free troposphere. Column differences between nearby locations arise when the change in elevation is greater than the change in ML height. This affects the fraction of atmosphere that is in the ML above each site. We show that such topographic effects produce significant variation in XnormalCO2 across the SoCAB as well.

show abstract

Section: Supporting Informationmentioning

confidence: 99%

Emissions and topographic effects on column CO₂ () variations, with a focus on the Southern California Megacity

et al. 2017

View full text Add to dashboard Cite

show abstract

“…More than 50% of the world's citizens are city dwellers inhabiting urban environments (Seto et al, 2014). These environments may triple in size between 2000 and 2030 if current predictions of urbanization continue (Seto et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

et al. 2021

View full text Add to dashboard Cite

Litter decomposition plays a pivotal role in the global carbon cycle, but is difficult to measure on a global scale, especially by citizen scientists. Here, citizen scientists, i.e., school students with their teachers, used the globally applied and standardized Tea Bag Index (TBI) method to collect data on litter decomposition in urban areas in Austria. They also sampled soils to investigate the linkages between litter decomposition and soil attributes. For this study, 54 sites were selected from the school experiments and assembled into a TBI dataset comprising litter decomposition rates (k), stabilization factors (S), as well as soil and environmental attributes. An extensive pre-processing procedure was applied to the dataset, including attribute selection and discretization of the decomposition rates and stabilization factors into three categories each. Data mining analyses of the TBI data helped reveal trends in litter decomposition. We generated predictive models (classification trees) that identified the soil attributes governing litter decomposition. Classification trees were developed for both of the litter decomposition parameters: decomposition rate (k) and stabilization factor (S). The main governing factor for both decomposition rate (k) and stabilization factor (S) was the sand content of the soils. The data mining models achieved an accuracy of 54.0 and 66.7% for decomposition rates and stabilization factors, respectively. The data mining results enhance our knowledge about the driving forces of litter decomposition in urban soils, which are underrepresented in soil monitoring schemes. The models are very informative for understanding and describing litter decomposition in urban settings in general. This approach may also further encourage participatory researcher-teacher-student interactions and thus help create an enabling environment for cooperation for further citizen science research in urban school settings.

show abstract

“…Various networks, such as the Total Carbon Column Observing Network (TCCON) and the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) in situ CO 2 network can aid in long-term measurements but are too sparse to track emissions from more than a few cities. Some urban areas have groundbased networks (e.g., Lauvaux et al, 2016;Shusterman et al, 2016;Verhulst et al, 2017;Mitchell et al, 2018;Sargent et al, 2018). Significant progress has been made in minimizing the cost, deployment time, and data delivery from these networks.…”

Section: Introductionmentioning

confidence: 99%

Southern California megacity CO<sub>2</sub>, CH<sub>4</sub>, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model

et al. 2018

View full text Add to dashboard Cite

Abstract. We estimate the overall CO2, CH4, and CO flux from the South Coast Air Basin using an inversion that couples Total Carbon Column Observing Network (TCCON) and Orbiting Carbon Observatory-2 (OCO-2) observations, with the Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model and the Open-source Data Inventory for Anthropogenic CO2 (ODIAC). Using TCCON data we estimate the direct net CO2 flux from the SoCAB to be 104 ± 26 Tg CO2 yr−1 for the study period of July 2013–August 2016. We obtain a slightly higher estimate of 120 ± 30 Tg CO2 yr−1 using OCO-2 data. These CO2 emission estimates are on the low end of previous work. Our net CH4 (360 ± 90 Gg CH4 yr−1) flux estimate is in agreement with central values from previous top-down studies going back to 2010 (342–440 Gg CH4 yr−1). CO emissions are estimated at 487 ± 122 Gg CO yr−1, much lower than previous top-down estimates (1440 Gg CO yr−1). Given the decreasing emissions of CO, this finding is not unexpected. We perform sensitivity tests to estimate how much errors in the prior, errors in the covariance, different inversion schemes, or a coarser dynamical model influence the emission estimates. Overall, the uncertainty is estimated to be 25 %, with the largest contribution from the dynamical model. Lessons learned here may help in future inversions of satellite data over urban areas.

show abstract

Human Settlements, Infrastructure, and Spatial Planning

Cited by 59 publications

References 498 publications

Emissions and topographic effects on column CO₂ () variations, with a focus on the Southern California Megacity

Emissions and topographic effects on column CO₂ () variations, with a focus on the Southern California Megacity

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

Southern California megacity CO<sub>2</sub>, CH<sub>4</sub>, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model

Contact Info

Product

Resources

About

Human Settlements, Infrastructure, and Spatial Planning

Cited by 59 publications

References 498 publications

Emissions and topographic effects on column CO2 () variations, with a focus on the Southern California Megacity

Emissions and topographic effects on column CO2 () variations, with a focus on the Southern California Megacity

TeaTime4Schools: Using Data Mining Techniques to Model Litter Decomposition in Austrian Urban School Soils

Southern California megacity CO&lt;sub&gt;2&lt;/sub&gt;, CH&lt;sub&gt;4&lt;/sub&gt;, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model

Contact Info

Product

Resources

About

Emissions and topographic effects on column CO₂ () variations, with a focus on the Southern California Megacity

Emissions and topographic effects on column CO₂ () variations, with a focus on the Southern California Megacity

Southern California megacity CO<sub>2</sub>, CH<sub>4</sub>, and CO flux estimates using ground- and space-based remote sensing and a Lagrangian model