Spatiotemporally resolved urban fossil fuel CO₂ (FFCO₂) emissions are critical to urban carbon cycle research and urban climate policy. Two general scientific approaches have been taken to estimate spatiotemporally explicit urban FFCO₂ fluxes, referred to here as “downscaling” and “bottom‐up.” Bottom‐up approaches can specifically characterize the CO₂‐emitting infrastructure in cities but are labor‐intensive to build and currently available in few U.S. cities. Downscaling approaches, often available globally, require proxy information to allocate or distribute emissions resulting in additional uncertainty. We present a comparison of a downscaled FFCO₂ emission data product (Open‐source Data Inventory for Anthropogenic CO₂ (ODIAC)) to a bottom‐up estimate (Hestia) in four U.S. urban areas in an effort to better isolate and understand differences between the approaches. We find whole‐city differences ranging from −1.5% (Los Angeles Basin) to +20.8% (Salt Lake City). At the 1 km × 1 km spatial scale, comparisons reveal a low‐emission limit in ODIAC driven by saturation of the nighttime light spatial proxy. At this resolution, the median difference between the two approaches ranged from 47 to 84% depending upon city with correlations ranging from 0.34 to 0.68. The largest discrepancies were found for large point sources and the on‐road sector, suggesting that downscaled FFCO₂ data products could be improved by incorporating independent large point‐source estimates and estimating on‐road sources with a relevant spatial surrogate. Progressively coarsening the spatial resolution improves agreement but greater than approximately 25 km², there were diminishing returns to agreement suggesting a practical resolution when using downscaled approaches.

Hemispherical (fisheye) photography is a well-established approach for estimating the sky view factor (SVF). High-resolution urban models from LiDAR and oblique airborne photogrammetry can provide continuous SVF estimates over a large urban area, but such data are not always available and are difficult to acquire. Street view panoramas have become widely available in urban areas worldwide: Google Street View (GSV) maintains a global network of panoramas excluding China and several other countries; Baidu Street View (BSV) and Tencent Street View (TSV) focus their panorama acquisition efforts within China, and have covered hundreds of cities therein. In this paper, we approach this issue from a big data perspective by presenting and validating a method for automatic estimation of SVF from massive amounts of street view photographs. Comparisons were made with SVF estimates derived from two independent sources: a LiDAR-based Digital Surface Model (DSM) and an oblique airborne photogrammetry-based 3D city model (OAP3D), resulting in a correlation coefficient of 0.863 and 0.987, respectively. The comparisons demonstrated the capacity of the proposed method to provide reliable SVF estimates. Additionally, we present an application of the proposed method with about 12,000 GSV panoramas to characterize the spatial distribution of SVF over Manhattan Island in New York City. Although this is a proof-of-concept study, it has shown the potential of the proposed approach to assist urban climate and urban planning research. However, further development is needed before this approach can be finally delivered to the urban climate and urban planning communities for practical applications.

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.

Legislation in the State of California mandates reductions in emissions of short‐lived climate pollutants of 40% from 2013 levels by 2030 for CH₄. Identification of the sector(s) responsible for these emissions and their temporal and spatial variability is a key step in achieving these goals. Here, we determine the emissions of CH₄ in Los Angeles from 2011–2017 using a mountaintop remote sensing mapping spectrometer. We show that the pattern of CH₄ emissions contains both seasonal and nonseasonal contributions. We find that the seasonal component peaks in the winter and is correlated (R² = 0.58) with utility natural gas consumption from the residential and commercial sectors and not from the industrial and gas‐fired power plant sectors. The nonseasonal component is (22.9 ± 1.4) Gg CH₄/month. If the seasonal correlation is causal, about (1.4 ± 0.1)% of the commercial and residential natural gas consumption in Los Angeles is released into the atmosphere.