Tropospheric ozone data assimilation in the NASA GEOS Composition Forecast modeling system (GEOS-CF v2.0) using satellite data for ozone vertical profiles (MLS), total ozone columns (OMI), and thermal infrared radiances (AIRS, IASI)

The NASA Goddard Earth Observing System Composition Forecast system (GEOS-CF) provides global near-real-time analyses and forecasts of atmospheric composition. The current version of GEOS-CF builds on the GEOS general circulation model with Forward Processing assimilation of meteorological data (GEOS-FP) and includes detailed GEOS-Chem tropospheric and stratospheric chemistry. Here we add 3D variational data assimilation in GEOS-CF to assimilate satellite observations of ozone including MLS vertical profiles, OMI total columns, and AIRS and IASI hyperspectral 9.6 μ m radiances. We focus our evaluations on the troposphere. We find that the detailed tropospheric chemistry in GEOS-CF significantly improves the simulated background ozone fields relative to previous versions of the GEOS model, allowing for specification of smaller background errors in assimilation and resulting in smaller assimilation increments to correct the simulated ozone. Assimilation increments are largest in the upper troposphere and are consistent between satellite data sets. The OMI and MLS ozone data generally provide more information than the AIRS and IASI radiances except at high latitudes where the radiances provide more information. Comparisons to independent ozonesonde and aircraft (ATom-4) observations for 2018 show significant GEOS-CF improvement from the assimilation, particularly in the extratropical upper troposphere

Prescribed Burns as a Tool to Mitigate Future Wildfire Smoke Exposure: Lessons for States and Rural Environmental Justice Communities

Abstract Smoke from wildfires presents one of the greatest threats to air quality, public health, and ecosystems in the United States, especially in the West. Here we quantify the efficacy of prescribed burning as an intervention for mitigating smoke exposure downwind of wildfires across the West during the 2018 and 2020 fire seasons. Using the adjoint of the GEOS‐Chem chemical transport model, we calculate the sensitivities of population‐weighted smoke concentrations in receptor regions, including states and rural environmental justice communities, to fire emissions upwind of the receptors. We find that the population‐weighted smoke exposure across the West during the September 2020 fires was 44 μg/m3 but would have been 20%–30% greater had these wildfires occurred in October or November. We further simulate a set of prescribed burn scenarios and find that controlled burning interventions in northern California and the Pacific Northwest could have reduced the population‐weighted smoke exposure across the western United States by 21 μg/m3 in September 2020, while doing so in all other states would have reduced smoke exposure by only 1.5 μg/m3. Satellite records of large, prescribed burns (>1,000 acres, or 4 km2) reveal that northern California and western Oregon conducted only seven such prescribed fires over a 6‐year period (2015–2020), even though these regions have a disproportionate impact on smoke exposure for rural environmental justice communities and population centers across the West. Our analysis suggests that prioritizing northern California and the Pacific Northwest for prescribed burns might mitigate future smoke exposure

Real-time indoor measurement of health and climate-relevant air pollution concentrations during a carbon-finance-approved cookstove intervention in rural India

Biomass combustion in residential cookstoves is a major source of air pollution and a large contributor to the global burden of disease. Carbon financing offers a potential funding source for health-relevant energy technologies in low-income countries. We conducted a randomized intervention study to evaluate air pollution impacts of a carbon-finance-approved cookstove in rural South India. Prior research on this topic often has used time-integrated measures of indoor air quality. Here, we employed real-time monitors (∼24 h measurement at ∼ minute temporal resolution), thereby allowing investigation of minutely and hourly temporal patterns. We measured indoor concentrations of fine particulate matter (PM2.5), black carbon (BC) and carbon monoxide (CO) in intervention households (used newer, rocket-type stoves) and control households (“nonintervention”; continued using traditional open fire stoves). Some intervention households elected not to use only the new, intervention stoves (i.e., elected not to follow the study-design protocol); we therefore conducted analysis for “per protocol” versus “intent to treat.” We compared 24 h averages of air pollutants versus cooking hours only averages. Implementation of the per protocol intervention cookstove decreased median concentrations of CO (by 1.5 ppm (2.8 − 1.3; control − per protocol), p = 0.28) and PM2.5 (by 148 μg/m3 (365 − 217), p = 0.46) but increased BC concentration (by 39 μg/m3 (26 − −12), p < 0.05) and the ratio of BC/PM2.5 (by 0.25 (−0.28 − −0.03), p < 0.05) during cooking-relevant hours-of-day relative to controls. Calculated median effective air exchange rates based on decay in CO concentrations were stable between seasons (season 1: 2.5 h−1, season 2: 2.8 h−1). Finally, we discuss an analytical framework for evaluating real-time indoor datasets with limited sample sizes. For the present study, use of real-time (versus time-averaged) equipment substantially reduced the number of households we were able to monitor. Keywords: HAP, DustTrak, MicroAeth, Indoor, Exposure concentratio

Data‐Driven Placement of PM2.5 Air Quality Sensors in the United States: An Approach to Target Urban Environmental Injustice

Abstract In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low‐cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low‐cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low‐cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost‐constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM2.5 extremes and increasing pollution monitoring in low‐income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low‐income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low‐cost sensors in less privileged communities