Recent assessments have analyzed the health impacts of PM2.5 from emissions from different locations and sectors using simplified or reduced-form air quality models. Here we present an alternative approach using the adjoint of the Community Multiscale Air Quality (CMAQ) model, which provides source-receptor relationships at highly resolved sectoral, spatial, and temporal scales. While damage resulting from anthropogenic emissions of BC is strongly correlated with population and premature death, we found little correlation between damage and emission magnitude, suggesting that controls on the largest emissions may not be the most efficient means of reducing damage resulting from anthropogenic BC emissions. Rather, the best proxy for locations with damaging BC emissions is locations where premature deaths occur. Onroad diesel and nonroad vehicle emissions are the largest contributors to premature deaths attributed to exposure to BC, while onroad gasoline emissions cause the highest deaths per amount emitted. Emissions in fall and winter contribute to more premature deaths (and more per amount emitted) than emissions in spring and summer. Overall, these results show the value of the high-resolution source attribution for determining the locations, seasons, and sectors for which BC emission controls have the most effective health benefits.

Abstract. Ammonia (NH3) emissions have large impacts on air quality and nitrogen deposition, influencing human health and the well-being of sensitive ecosystems. Large uncertainties exist in the “bottom-up” NH3 emission inventories due to limited source information and a historical lack of measurements, hindering the assessment of NH3-related environmental impacts. The increasing capability of satellites to measure NH3 abundance and the development of modeling tools enable us to better constrain NH3 emission estimates at high spatial resolution. In this study, we constrain the NH3 emission estimates from the widely used 2011 National Emissions Inventory (2011 NEI) in the US using Infrared Atmospheric Sounding Interferometer NH3 column density measurements (IASI-NH3) gridded at a 36 km by 36 km horizontal resolution. With a hybrid inverse modeling approach, we use the Community Multiscale Air Quality Modeling System (CMAQ) and its multiphase adjoint model to optimize NH3 emission estimates in April, July, and October. Our optimized emission estimates suggest that the total NH3 emissions are biased low by 26 % in 2011 NEI in April with overestimation in the Midwest and underestimation in the Southern States. In July and October, the estimates from NEI agree well with the optimized emission estimates, despite a low bias in hotspot regions. Evaluation of the inversion performance using independent observations shows reduced underestimation in simulated ambient NH3 concentration in all 3 months and reduced underestimation in NH4+ wet deposition in April. Implementing the optimized NH3 emission estimates improves the model performance in simulating PM2.5 concentration in the Midwest in April. The model results suggest that the estimated contribution of ammonium nitrate would be biased high in a priori NEI-based assessments. The higher emission estimates in this study also imply a higher ecological impact of nitrogen deposition originating from NH3 emissions.

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Changing precursor emission patterns in conjunction with stringent health protective air quality standards necessitate accurate quantification of nonlocal contributions to ozone pollution at a location due to atmospheric transport, that by nature predominantly occurs aloft nocturnally. Concerted efforts to characterize ozone aloft on a continuous basis to quantify its contribution to ground-level concentrations, however, are lacking. By applying our classical understanding of air pollution dynamics to analyze variations in widespread surface-level ozone measurements, in conjunction with process-based interpretation from a comprehensive air pollution modeling system and detailed backward-sensitivity calculations that quantitatively link surface-level and aloft pollution, we show that accurate quantification of the amount of ozone in the air entrained from aloft every morning as the atmospheric boundary layer grows is the key missing component for characterizing background pollution at a location, and we propose a cost-effective continuous aloft ozone measurement strategy to address critical scientific gaps in current air quality management. Continuous aloft air pollution measurements can be achieved cost-effectively through leveraging advances in sensor technology and proliferation of tall telecommunications masts. Resultant improvements in ozone distribution characterization at 400-500 m altitude are estimated to be 3-4 times more effective in characterizing the surface-level daily maximum 8-h average ozone (DM8O) than improvements from surface measurements since they directly quantify the amount of pollution imported to a location and furnish key missing information on processes and sources regulating background ozone and its modulation of ground-level concentrations. Since >80% of the DM8O sensitivity to tropospheric ozone is potentially captured through measurements between 200 and 1200 m altitude (a possible design goal for future remote sensing instrumentation), their assimilation will dramatically improve air quality forecast and health advisories.

Abstract. We present the development of a multiphase adjoint for the Community Multiscale Air Quality (CMAQ) model, a widely used chemical transport model. The adjoint model provides location- and time-specific gradients that can be used in various applications such as backward sensitivity analysis, source attribution, optimal pollution control, data assimilation, and inverse modeling. The science processes of the CMAQ model include gas-phase chemistry, aerosol dynamics and thermodynamics, cloud chemistry and dynamics, diffusion, and advection. Discrete adjoints are implemented for all the science processes, with an additional continuous adjoint for advection. The development of discrete adjoints is assisted with algorithmic differentiation (AD) tools. Particularly, the Kinetic PreProcessor (KPP) is implemented for gas-phase and aqueous chemistry, and two different automatic differentiation tools are used for other processes such as clouds, aerosols, diffusion, and advection. The continuous adjoint of advection is developed manually. For adjoint validation, the brute-force or finite-difference method (FDM) is implemented process by process with box- or column-model simulations. Due to the inherent limitations of the FDM caused by numerical round-off errors, the complex variable method (CVM) is adopted where necessary. The adjoint model often shows better agreement with the CVM than with the FDM. The adjoints of all science processes compare favorably with the FDM and CVM. In an example application of the full multiphase adjoint model, we provide the first estimates of how emissions of particulate matter (PM2.5) affect public health across the US.