Parsimonious velocity inversion applied to the Los Angeles Basin, CA

The proliferation of dense arrays promises to improve our ability to image geological structures at the scales necessary for accurate assessment of seismic hazard. However, combining the resulting local high-resolution tomography with existing regional models presents an ongoing challenge. We developed a framework based on the level-set method that provides a means to infer where local data provides meaningful constraints beyond those found in regional models - e.g. the Community Velocity Models (CVMs) of southern California. This technique defines a volume within which updates are made to a reference CVM, with the boundary of the volume being part of the inversion rather than explicitly defined. By penalizing the complexity of the boundary, a minimal update that sufficiently explains the data is achieved. To test this framework, we use data from the Community Seismic Network, a dense permanent urban deployment. We inverted Love wave dispersion and amplification data, from the Mw 6.4 and 7.1 2019 Ridgecrest earthquakes. We invert for an update to CVM-S4.26 using the Tikhonov Ensemble Sampling scheme, a highly efficient derivative-free approximate Bayesian method. We find the data is best explained by a deepening of the Los Angeles Basin with its deepest part south of downtown Los Angeles, along with a steeper northeastern basin wall. This result offers new progress towards the parsimonious incorporation of detailed local basin models within regional reference models utilizing an objective framework and highlights the importance of accurate basin models when accounting for the amplification of surface waves in the high-rise building response band.

Assessing sub-grid variability within satellite pixels over urban regions using airborne mapping spectrometer measurements

Sub-grid variability (SGV) in atmospheric trace gases within satellite pixels is a key issue in satellite design and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250 m×250 m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by the mean of the sub-grid GeoTASO values within the same satellite pixel) for different hypothetical satellite pixel sizes over urban regions. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS-AQ field campaign and over the Los Angeles Basin, USA, during the 2017 Student Airborne Research Program (SARP) field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ∼10 % for 0.5 km×0.5 km pixel size to ∼35 % for 25 km×25 km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability in the retrieved NO2 VC within the same hypothetical satellite pixels (represented by the difference of retrieved values at two or more different times in a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over the SMA. For a given small (e.g., ≤4 h) sampling time difference within the same satellite pixels, the temporal variability in the retrieved NO2 VC increases with the increasing spatial resolution over the SMA, Busan region, and the Los Angeles Basin. The results of this study have implications for future satellite design and retrieval interpretation and validation when comparing pixel data with local observations. In addition, the analyses presented in this study are equally applicable in model evaluation when comparing model grid values to local observations. Results from the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) model indicate that the normalized satellite SGV of tropospheric NO2 VC calculated in this study could serve as an upper bound to the satellite SGV of other species (e.g., CO and SO2) that share common source(s) with NO2 but have relatively longer lifetime.

From COVID-19 to future electrification: Assessing traffic impacts on air quality by a machine-learning model

The large fluctuations in traffic during the COVID-19 pandemic provide an unparalleled opportunity to assess vehicle emission control efficacy. Here we develop a random-forest regression model, based on the large volume of real-time observational data during COVID-19, to predict surface-level NO2, O3, and fine particle concentration in the Los Angeles megacity. Our model exhibits high fidelity in reproducing pollutant concentrations in the Los Angeles Basin and identifies major factors controlling each species. During the strictest lockdown period, traffic reduction led to decreases in NO2 and particulate matter with aerodynamic diameters <2.5 μm by –30.1% and –17.5%, respectively, but a 5.7% increase in O3. Heavy-duty truck emissions contribute primarily to these variations. Future traffic-emission controls are estimated to impose similar effects as observed during the COVID-19 lockdown, but with smaller magnitude. Vehicular electrification will achieve further alleviation of NO2 levels.

Ambient noise Love wave attenuation tomography for the LASSIE array across the Los Angeles basin

The Los Angeles basin is located within the North America–Pacific plate boundary and contains multiple earthquake faults that threaten greater Los Angeles. Seismic attenuation tomography has the potential to provide important constraints on wave propagation in the basin and to provide supplementary information on structure in the form of the distribution of anelastic properties. On the basis of the amplitude information from seismic interferometry from the linear LASSIE array in the Los Angeles basin, we apply station-triplet attenuation tomography to obtain a 2D depth profile for the attenuation structure of the uppermost 0.6 km. The array crosses four Quaternary faults, three of which are blind. The attenuation tomography resolves strong attenuation (shear attenuation Qs ~ 20) for the fault zones and is consistent with sharp boundaries across them.

The impact of Los Angeles Basin pollution and stratospheric intrusions on the surrounding San Gabriel Mountains as seen by surface measurements, lidar, and numerical models

In this work, the impact of Los Angeles Basin pollution transport and stratospheric intrusions on the surface ozone levels observed in the San Gabriel Mountains is investigated based on a combination of surface and lidar measurements as well as WRF-Chem (Weather Research and Forecasting with Chemistry) and WACCM (Whole Atmosphere Community Climate Model) runs. The number of days with observed surface ozone levels exceeding the National Ambient Air Quality Standards exhibit a clear seasonal pattern, with a maximum during summer, when models suggest a minimum influence of stratospheric intrusions and the largest impact from Los Angeles Basin pollution transport. Additionally, measured and modeled surface ozone and PM10 were analyzed as a function of season, time of the day, and wind direction. Measurements and models are in good qualitative agreement, with maximum surface ozone observed for southwest and west winds. For the prevailing summer wind direction, slightly south of the ozone maximum and corresponding to south-southwest winds, lower ozone levels were observed. Back trajectories suggest that this is associated with transport from the central Los Angeles Basin, where titration limits the amount of surface ozone. A quantitative comparison of the lidar profiles with WRF-Chem and WACCM models revealed good agreement near the surface, with models showing an increasing positive bias as function of altitude, reaching 75 % at 15 km above sea level. Finally, three selected case studies covering the different mechanisms affecting the near-surface ozone concentration over the San Gabriel Mountains, namely stratospheric intrusions and pollution transport, are analyzed based on surface and ozone lidar measurements, as well as co-located ceilometer measurements and models.

Toward CO2 and CH4 measurements by ground-based observations of surface-scattered sunlight

&lt;div&gt; &lt;div&gt;Precise knowledge of sources and sinks in the carbon cycle is desired to understand its sensitivity to climate change and to account and verify man-made emissions. In this context, extended sources like urban areas play an important role. While in-situ measurements of carbon dioxide (CO&lt;sub&gt;2&lt;/sub&gt;) and methane (CH&lt;sub&gt;4&lt;/sub&gt;) are highly accurate but localized, satellites measure column-integrated concentrations over an extended footprint. The CLARS-FTS [1, 2] stationed at the Mt. Wilson observatory looking downward into the Los Angeles basin has pioneered an innovative measurement technique that fills the sensitivity gap between in-situ and satellite measurements. The technique enables mapping the urban greenhouse gas concentration fields by collecting spectra of ground scattered sunlight and scanning through the region.&lt;/div&gt; &lt;div&gt;&amp;#160;&lt;/div&gt; &lt;div&gt;Here, we report on progress developing a portable setup for a CLARS-FTS-like measurement geometry. The instrument is based on the EM27/SUN FTS with a modified pointing technique and a more sensitive detector. The retrieval algorithm is based on the RemoTeC software, previously employed for solar backscatter satellite measurements. We discuss first steps in terms of instrument performance and retrieval exercises. For the latter, we have carried out simulations on how the neglect of scattering by the retrieval affects the retrieved boundary layer concentrations of CO&lt;sub&gt;2&lt;/sub&gt; and CH&lt;sub&gt;4&lt;/sub&gt; for an ensemble of hypothetical scenes with variable complexity in aeorsol loadings and viewing geometry. We also report on a test to apply RemoTeC to a small set of CLARS-FTS spectra collected throughout the Los Angeles basin.&lt;/div&gt; &lt;div&gt;&amp;#160;&lt;/div&gt; &lt;div&gt;&lt;em&gt;References&lt;/em&gt;&lt;/div&gt; &lt;div&gt;[1] Fu, D. et al., 2014: Near-infrared remote sensing of Los Angeles trace gas distributions from a mountaintop site, Atmos. Meas. Tech., 7, 713&amp;#8211;729, https://doi.org/10.5194/amt-7-713-2014&lt;/div&gt; [2] Wong, K. W. et al., 2015: Mapping CH4 : CO2 ratios in Los Angeles with CLARS-FTS from Mount Wilson, California, Atmos. Chem. Phys., 15, 241&amp;#8211;252, https://doi.org/10.5194/acp-15-241-2015&lt;/div&gt;

Assessing sub-grid variability within satellite pixels using airborne mapping spectrometer measurements

Sub-grid variability (SGV) of atmospheric trace gases within satellite pixels is a key issue in satellite design, and interpretation and validation of retrieval products. However, characterizing this variability is challenging due to the lack of independent high-resolution measurements. Here we use tropospheric NO2 vertical column (VC) measurements from the Geostationary Trace gas and Aerosol Sensor Optimization (GeoTASO) airborne instrument with a spatial resolution of about 250 m 250 m to quantify the normalized SGV (i.e., the standard deviation of the sub-grid GeoTASO values within the sampled satellite pixel divided by their mean of the sub-grid GeoTASO values within the sampled satellite pixel) for different satellite pixel sizes. We use the GeoTASO measurements over the Seoul Metropolitan Area (SMA) and Busan region of South Korea during the 2016 KORUS‐AQ field campaign, and over the Los Angeles Basin, USA during the 2017 SARP field campaign. We find that the normalized SGV of NO2 VC increases with increasing satellite pixel sizes (from ~10 % for 0.5 km × 0.5 km pixel size to ~35 % for 25 km × 25 km pixel size), and this relationship holds for the three study regions, which are also within the domains of upcoming geostationary satellite air quality missions. We also quantify the temporal variability of the retrieved NO2 VC within the same satellite pixels (represented by the difference of retrieved values at two different times of a day). For a given satellite pixel size, the temporal variability within the same satellite pixels increases with the sampling time difference over SMA. For a given small (e.g.,