We present global direct radiative effect (DRE) calculations of carbonaceous aerosols emitted from biomass/biofuel burning addressing the interplay between two poorly constrained contributions to DRE: mixing state of black carbon (lensing) and light absorption by organic aerosol (OA) due to the presence of brown carbon (BrC). We use the parameterization of Saleh et al. (2014) which captures the variability in biomass/biofuel OA absorption. The global mean effect of OA absorption is +0.22 W/m2 and +0.12 W/m2 for externally and internally mixed cases, while the effect of lensing is +0.39 W/m2 and +0.29 W/m2 for nonabsorbing and absorbing OA cases, signifying the nonlinear interplay between OA absorption and lensing. These two effects can be overestimated if not treated simultaneously in radiative transfer calculations. The combined effect of OA absorption and lensing increases the global mean DRE of biomass/biofuel aerosols from −0.46 W/m2 to +0.05 W/m2 and appears to reduce the gap between existing model‐based and observationally constrained DRE estimates. We observed a strong sensitivity to these parameters in key regions, where DRE shifts from strongly negative (< −1 W/m2) to strongly positive (> +1 W/m2) when accounting for lensing and OA absorption.

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Reliable estimates of externality costs-such as the costs arising from premature mortality due to exposure to fine particulate matter (PM 2.5 )-are critical for policy analysis. To facilitate broader analysis, several datasets of the social costs of air quality have been produced by a set of reducedcomplexity models (RCMs). It is much easier to use the tabulated marginal costs derived from RCMs than it is to run 'state-of-the-science' chemical transport models (CTMs). However, the differences between these datasets have not been systematically examined, leaving analysts with no guidance on how and when these differences matter. Here, we compare per-tonne marginal costs from ground level and elevated emission sources for each county in the United States for sulfur dioxide (SO 2 ), nitrogen oxides (NO x ), ammonia (NH 3 ) and inert primary PM 2.5 from three RCMs: Air Pollution Emission Experiments and Policy (AP2), Estimating Air pollution Social Impacts Using Regression (EASIUR) and the Intervention Model for Air Pollution (InMAP). National emission-weighted average damages vary among models by approximately 21%, 31%, 28% and 12% for inert primary PM 2.5 , SO 2 , NO x and NH 3 emissions, respectively, for ground-level sources. For elevated sources, emission-weighted damages vary by approximately 42%, 26%, 42% and 20% for inert primary PM 2.5 , SO 2 , NO x and NH 3 emissions, respectively. Despite fundamental structural differences, the three models predict marginal costs that are within the same order of magnitude. That different and independent methods have converged on similar results bolsters confidence in the RCMs. Policy analyzes of national-level air quality policies that sum over pollutants and geographical locations are often robust to these differences, although the differences may matter for more source-or locationspecific analyzes. Overall, the loss of fidelity caused by using RCMs and their social cost datasets in place of CTMs is modest.

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Current methods of estimating the public health effects of emissions are computationally too expensive or do not fully address complex atmospheric processes, frequently limiting their applications to policy research. Using a reduced-form model derived from tagged chemical transport model (CTM) simulations, we present PM2.5 mortality costs per tonne of inorganic air pollutants with the 36 km × 36 km spatial resolution of source location in the United States, providing the most comprehensive set of such estimates comparable to CTM-based estimates. Our estimates vary by 2 orders of magnitude. Emission-weighted seasonal averages were estimated at $88,000-130,000/t PM2.5 (inert primary), $14,000-24,000/t SO2, $3,800-14,000/t NOx, and $23,000-66,000/t NH3. The aggregate social costs for year 2005 emissions were estimated at $1.0 trillion dollars. Compared to other studies, our estimates have similar magnitudes and spatial distributions for primary PM2.5 but substantially different spatial patterns for precursor species where secondary chemistry is important. For example, differences of more than a factor of 10 were found in many areas of Texas, New Mexico, and New England states for NOx and of California, Texas, and Maine for NH3. Our method allows for updates as emissions inventories and CTMs improve, enhancing the potential to link policy research to up-to-date atmospheric science.