Exploring the Potential of Using Carbonyl Sulfide to Track the Urban Biosphere Signal

Unidad de excelencia María de Maeztu CEX2019-000940-MCities are implementing additional urban green as a means to capture CO and become more carbon neutral. However, cities are complex systems where anthropogenic and natural components of the CO budget interact with each other, and the ability to measure the efficacy of such measures is still not properly addressed. There is still a high degree of uncertainty in determining the contribution of the vegetation signal, which furthermore confounds the use of CO mole fraction measurements for inferring anthropogenic emissions of CO. Carbonyl sulfide (OCS) is a tracer of photosynthesis which can aid in constraining the biosphere signal. This study explores the potential of using OCS to track the urban biosphere signal. We used the Sulfur Transport and dEposition Model (STEM) to simulate the OCS concentrations and the Carnegie Ames Stanford Approach ecosystem model to simulate global CO fluxes over the Bay Area of San Francisco during March 2015. Two observation towers provided measurements of OCS and CO: The Sutro tower in San Francisco (upwind from the area of study providing background observations), and a tower located at Sandia National Laboratories in Livermore (downwind of the highly urbanized San Francisco region). Our results show that the STEM model works better under stable marine influence, and that the boundary layer height and entrainment are driving the diurnal changes in OCS and CO at the downwind Sandia site. However, the STEM model needs to better represent the transport and boundary layer variability, and improved estimates of gross primary productivity for characterizing the urban biosphere signal are needed

Estimating methane emissions in California's urban and rural regions using multitower observations

We present an analysis of methane (CH_4) emissions using atmospheric observations from 13 sites in California during June 2013 to May 2014. A hierarchical Bayesian inversion method is used to estimate CH_4 emissions for spatial regions (0.3° pixels for major regions) by comparing measured CH_4 mixing ratios with transport model (Weather Research and Forecasting and Stochastic Time-Inverted Lagrangian Transport) predictions based on seasonally varying California-specific CH_4 prior emission models. The transport model is assessed using a combination of meteorological and carbon monoxide (CO) measurements coupled with the gridded California Air Resources Board (CARB) CO emission inventory. The hierarchical Bayesian inversion suggests that state annual anthropogenic CH_4 emissions are 2.42 ± 0.49 Tg CH_4/yr (at 95% confidence), higher (1.2–1.8 times) than the current CARB inventory (1.64 Tg CH_4/yr in 2013). It should be noted that undiagnosed sources of errors or uncaptured errors in the model-measurement mismatch covariance may increase these uncertainty bounds beyond that indicated here. The CH_4 emissions from the Central Valley and urban regions (San Francisco Bay and South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, respectively. This study suggests that the livestock sector is likely the major contributor to the state total CH_4 emissions, in agreement with CARB's inventory. Attribution to source sectors for subregions of California using additional trace gas species would further improve the quantification of California's CH_4 emissions and mitigation efforts toward the California Global Warming Solutions Act of 2006 (Assembly Bill 32)

Local and Regional-Scale Racial and Ethnic Disparities in Air Pollution Determined by Long-Term Mobile Monitoring

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (10km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially-variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2, 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2(pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8%-30% higher than the population average; for white populations, average exposures to these pollutants are 9%-14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale

Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring.

Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on existing capabilities to systematically compare urban variation at several scales, from hyperlocal (<100 m) to regional (>10 km), and to assess consequences for outdoor air pollution experienced by residents of different races and ethnicities, by creating a set of uniquely extensive and high-resolution observations of spatially variable pollutants: NO, NO2, black carbon (BC), and ultrafine particles (UFP). We conducted full-coverage monitoring of a wide sample of urban and suburban neighborhoods (93 km2 and 450,000 residents) in four counties of the San Francisco Bay Area using Google Street View cars equipped with the Aclima mobile platform. Comparing scales of variation across the sampled population, greater differences arise from localized pollution gradients for BC and NO (pollutants dominated by primary sources) and from regional gradients for UFP and NO2 (pollutants dominated by secondary contributions). Median concentrations of UFP, NO, and NO2 are, for Hispanic and Black populations, 8 to 30% higher than the population average; for White populations, average exposures to these pollutants are 9 to 14% lower than the population average. Systematic racial/ethnic disparities are influenced by regional concentration gradients due to sharp contrasts in demographic composition among cities and urban districts, while within-group extremes arise from local peaks. Our results illustrate how detailed and extensive fine-scale pollution observations can add new insights about differences and disparities in air pollution exposures at the population scale

Estimating methane emissions from biological and fossil-fuel sources in the San Francisco Bay Area

We present the first sector-specific analysis of methane (CH4) emissions from the San Francisco Bay Area (SFBA) using CH4 and volatile organic compound (VOC) measurements from six sites during September – December 2015. We apply a hierarchical Bayesian inversion to separate the biological from fossil-fuel (natural gas and petroleum) sources using the measurements of CH4 and selected VOCs, a source-specific 1 km CH4 emission model, and an atmospheric transport model. We estimate that SFBA CH4 emissions are 166–289 Gg CH4/yr (at 95% confidence), 1.3–2.3 times higher than a recent inventory with much of the underestimation from landfill. Including the VOCs, 82 ± 27% of total posterior median CH4 emissions are biological and 17 ± 3% fossil fuel, where landfill and natural gas dominate the biological and fossil-fuel CH4 of prior emissions, respectively

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city's air quality using mobile monitors with "data-only" versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a "data-only" approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1-2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment

Estimating methane emissions in California’s urban and rural regions using multi-tower observations:

We present an analysis of methane (CH4) emissions using atmospheric observations from 36 thirteen sites in California during June 2013 – May 2014. A hierarchical Bayesian inversion 37 method is used to estimate CH4 emissions for spatial regions (0.3° pixels for major regions) by 38 comparing measured CH4 mixing ratios with transport model (WRF-STILT) predictions based 39 on seasonally varying California-specific CH4 prior emission models. The transport model is 40 assessed using a combination of meteorological and carbon monoxide (CO) measurements 41 coupled with the gridded California Air Resources Board (CARB) carbon monoxide (CO) 42 emission inventory. Hierarchical Bayesian inversion suggests that state annual anthropogenic 43 CH4 emissions are 2.42 ± 0.49 Tg CH4/yr (at 95% confidence, including transport bias 44 uncertainty), higher (1.2 - 1.8 times) than the CARB current inventory (1.64 Tg CH4/yr in 2013). 45 We note that the estimated CH4 emissions drop to 1.0 - 1.6 times the CARB inventory if we 46 correct for the 10% median CH4 emissions assuming the bias in CO analysis is applicable to 47 CH4. The CH4 emissions from the Central Valley and urban regions (San Francisco Bay and 48 South Coast Air Basins) account for ~58% and 26% of the total posterior emissions, 49 respectively. This study suggests that the livestock sector is likely the major contributor to the 50 state total CH4 emissions, in agreement with CARB’s inventory. Attribution to source sectors for 51 sub-regions of California using additional trace gas species would further improve the 52 quantification of California’s CH4 emissions and mitigation efforts towards the California Global 53 Warming Solutions Act of 2006 (AB-32)