Abstract. The success of future geostationary (GEO) satellite observation missions depends on our ability to design instruments that address their key scientific objectives. In this study, an Observation System Simulation Experiment (OSSE) is performed to quantify the constraints on methane (CH 4 ) emissions in North America obtained from shortwave infrared (SWIR), thermal infrared (TIR), and multi-spectral (SWIR+TIR) measurements in geostationary orbit and from future SWIR low-Earth orbit (LEO) measurements. An efficient stochastic algorithm is used to compute the information content of the inverted emissions at high spatial resolution (0.5 • × 0.7 • ) in a variational framework using the GEOSChem chemistry-transport model and its adjoint. Our results show that at sub-weekly timescales, SWIR measurements in GEO orbit can constrain about twice as many independent flux patterns than in LEO orbit, with a degree of freedom for signal (DOF) for the inversion of 266 and 115, respectively. Comparisons between TIR GEO and SWIR LEO configurations reveal that poor boundary layer sensitivities for the TIR measurements cannot be compensated for by the high spatiotemporal sampling of a GEO orbit. The benefit of a multi-spectral instrument compared to current SWIR products in a GEO context is shown for sub-weekly timescale constraints, with an increase in the DOF of about 50 % for a 3-day inversion. Our results further suggest that both the SWIR and multi-spectral measurements on GEO orbits could almost fully resolve CH 4 fluxes at a spatial resolution of at least 100 km × 100 km over source hotspots (emissions > 4 × 10 5 kg day −1 ). The sensitivity of the optimized emission scaling factors to typical errors in boundary and initial conditions can reach 30 and 50 % for the SWIR GEO or SWIR LEO configurations, respectively, while it is smaller than 5 % in the case of a multi-spectral GEO system. Overall, our results demonstrate that multi-spectral measurements from a geostationary satellite platform would address the need for higher spatiotemporal constraints on CH 4 emissions while greatly mitigating the impact of inherent uncertainties in source inversion methods on the inferred fluxes.

Abstract. Approximately 3 billion people worldwide cook with solid fuels, such as wood, charcoal, and agricultural residues. These fuels, also used for residential heating, are often combusted in inefficient devices, producing carbonaceous emissions. Between 2.6 and 3.8 million premature deaths occur as a result of exposure to fine particulate matter from the resulting household air pollution (Health Effects Institute, 2018a; World Health Organization, 2018). Household air pollution also contributes to ambient air pollution; the magnitude of this contribution is uncertain. Here, we simulate the distribution of the two major health-damaging outdoor air pollutants (PM2.5 and O3) using state-of-the-science emissions databases and atmospheric chemical transport models to estimate the impact of household combustion on ambient air quality in India. The present study focuses on New Delhi and the SOMAARTH Demographic, Development, and Environmental Surveillance Site (DDESS) in the Palwal District of Haryana, located about 80 km south of New Delhi. The DDESS covers an approximate population of 200 000 within 52 villages. The emissions inventory used in the present study was prepared based on a national inventory in India (Sharma et al., 2015, 2016), an updated residential sector inventory prepared at the University of Illinois, updated cookstove emissions factors from Fleming et al. (2018b), and PM2.5 speciation from cooking fires from Jayarathne et al. (2018). Simulation of regional air quality was carried out using the US Environmental Protection Agency Community Multiscale Air Quality modeling system (CMAQ) in conjunction with the Weather Research and Forecasting modeling system (WRF) to simulate the meteorological inputs for CMAQ, and the global chemical transport model GEOS-Chem to generate concentrations on the boundary of the computational domain. Comparisons between observed and simulated O3 and PM2.5 levels are carried out to assess overall airborne levels and to estimate the contribution of household cooking emissions. Observed and predicted ozone levels over New Delhi during September 2015, December 2015, and September 2016 routinely exceeded the 8 h Indian standard of 100 µg m−3, and, on occasion, exceeded 180 µg m−3. PM2.5 levels are predicted over the SOMAARTH headquarters (September 2015 and September 2016), Bajada Pahari (a village in the surveillance site; September 2015, December 2015, and September 2016), and New Delhi (September 2015, December 2015, and September 2016). The predicted fractional impact of residential emissions on anthropogenic PM2.5 levels varies from about 0.27 in SOMAARTH HQ and Bajada Pahari to about 0.10 in New Delhi. The predicted secondary organic portion of PM2.5 produced by household emissions ranges from 16 % to 80 %. Predicted levels of secondary organic PM2.5 during the periods studied at the four locations averaged about 30 µg m−3, representing approximately 30 % and 20 % of total PM2.5 levels in the rural and urban stations, respectively.

Abstract. The success of future geostationary (GEO) satellite observation missions depends on our ability to design instruments that address their key scientific objectives. In this study, an Observation System Simulation Experiment (OSSE) is performed to quantify the constraints on methane (CH4) emissions in North America obtained from Short Wave Infrared (SWIR), Thermal Infrared (TIR) and multi-spectral measurements in geostationary orbit compared to existing SWIR low earth (LEO) measurements. A stochastic algorithm is used to compute the information content of a variational inversion at high spatial resolution (0.5° × 0.7°) using the GEOS-Chem chemical transport model and its adjoint. Both the SWIR LEO and TIR GEO configurations generally provide poor constraints on CH4 emissions (error reduction <30 %), with the exception of a few hotspots (e.g., Los Angeles, Toronto urban areas and Appalachian Mountains) where the error reduction is greater than 50 %. On weekly time scales and for a GEO orbit, the degree of freedom for signal (DOFs) of the inversion from multi-spectral observations (500) is a factor of two higher than that obtained from a SWIR instrument (255) due to the increase in measurement sensitivity to boundary layer concentrations in the multi-spectral case. On a monthly time scale and for a GEO orbit, a SWIR instrument would reduce error in emission estimates by more than 70 % for hotspots of CH4 sources (emissions > 4 × 105 kg day−1 grid−1) at model grid scale, while a TIR instrument would provide a relative error reduction of 25–60 % over those areas. While performing similarly for monthly inversions, a multi-spectral instrument would allow for more than 70 % error reduction for these emissions for 7 or 3 day inversions. Sensitivity of the inversions to error in boundary conditions are found to be negligible. Moreover, estimates of the model resolution matrix over significant emitting regions (CH4 emissions > 2 × 105 kg day−1 grid−1) show that for all instrument configurations in GEO orbit the inversion is able to independently constrain CH4 sources at spatial scales smaller than 200 km. These results highlight the importance of using observations sensitive to boundary layer concentrations (i.e., SWIR) to achieve significant improvements in constraining CH4 sources compared to current LEO capabilities.

Abstract. The Northern California Camp Fire that took place in November 2018 was one of the most damaging environmental events in California history. Here, we analyze ground-based station observations of airborne particulate matter that has a diameter <2.5 µm (PM2.5) across Northern California and conduct numerical simulations of the Camp Fire using the Weather Research and Forecasting model online coupled with chemistry (WRF-Chem). Simulations are evaluated against ground-based observations of PM2.5, black carbon, and meteorology, as well as satellite measurements, such as Tropospheric Monitoring Instrument (TROPOMI) aerosol layer height and aerosol index. The Camp Fire led to an increase in Bay Area PM2.5 to over 50 µg m−3 for nearly 2 weeks, with localized peaks exceeding 300 µg m−3. Using the Visible Infrared Imaging Radiometer Suite (VIIRS) high-resolution fire detection products, the simulations reproduce the magnitude and evolution of surface PM2.5 concentrations, especially downwind of the wildfire. The overall spatial patterns of simulated aerosol plumes and their heights are comparable with the latest satellite products from TROPOMI. WRF-Chem sensitivity simulations are carried out to analyze uncertainties that arise from fire emissions, meteorological conditions, feedback of aerosol radiative effects on meteorology, and various physical parameterizations, including the planetary boundary layer model and the plume rise model. Downwind PM2.5 concentrations are sensitive to both flaming and smoldering emissions over the fire, so the uncertainty in the satellite-derived fire emission products can directly affect the air pollution simulations downwind. Our analysis also shows the importance of land surface and boundary layer parameterization in the fire simulation, which can result in large variations in magnitude and trend of surface PM2.5. Inclusion of aerosol radiative feedback moderately improves PM2.5 simulations, especially over the most polluted days. Results of this study can assist in the development of data assimilation systems as well as air quality forecasting of health exposures and economic impact studies.

A large concern with estimates of climate and health co-benefits of “clean” cookstoves from controlled emissions testing is whether results represent what actually happens in real homes during normal use. A growing body of evidence indicates that in-field emissions during daily cooking activities differ substantially from values obtained in laboratories, with correspondingly different estimates of co-benefits. We report PM2.5 emission factors from uncontrolled cooking (n = 7) and minimally controlled cooking tests (n = 51) using traditional chulha and angithi stoves in village kitchens in Haryana, India. Minimally controlled cooking tests (n = 13) in a village kitchen with mixed dung and brushwood fuels were representative of uncontrolled field tests for fine particulate matter (PM2.5), organic and elemental carbon (p > 0.5), but were substantially higher than previously published water boiling tests using dung or wood. When the fraction of nonrenewable biomass harvesting, elemental, and organic particulate emissions and modeled estimates of secondary organic aerosol (SOA) are included in 100 year global warming commitments (GWC100), the chulha had a net cooling impact using mixed fuels typical of the region. Correlation between PM2.5 emission factors and GWC (R 2 = 0.99) implies these stoves are climate neutral for primary PM2.5 emissions of 8.8 ± 0.7 and 9.8 ± 0.9 g PM2.5/kg dry fuel for GWC20 and GWC100, respectively, which is close to the mean for biomass stoves in global emission inventories.