We address inconsistent procedures and metrics used to evaluate photochemical model performance, recommend a specific set of statistical metrics, and develop updated quantitative performance benchmarks for those metrics. We promote quantitatively consistent evaluations across different applications, scales, models, inputs, and configurations, thereby (1) improving the user's ability to quantitatively place results in context and guide model improvements, and (2) better informing users, regulators, and stakeholders of model uncertainties and weaknesses prior to using results for policy assessments. While we primarily address U.S. modeling and regulatory settings, these recommendations are relevant to any such applications of state-of-the-science photochemical models.

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Errors in chemical transport models (CTMs) interpreting the relation between space-retrieved tropospheric column densities of nitrogen dioxide (NO₂) and emissions of nitrogen oxides (NO_x) have important consequences on the inverse modeling. They are however difficult to quantify due to lack of adequate in situ measurements, particularly over China and other developing countries. This study proposes an alternate approach for model evaluation over East China, by analyzing the sensitivity of modeled NO₂ columns to errors in meteorological and chemical parameters/processes important to the nitrogen abundance. As a demonstration, it evaluates the nested version of GEOS-Chem driven by the GEOS-5 meteorology and the INTEX-B anthropogenic emissions and used with retrievals from the Ozone Monitoring Instrument (OMI) to constrain emissions of NO_x. The CTM has been used extensively for such applications. Errors are examined for a comprehensive set of meteorological and chemical parameters using measurements and/or uncertainty analysis based on current knowledge. Results are exploited then for sensitivity simulations perturbing the respective parameters, as the basis of the following post-model linearized and localized first-order modification. It is found that the model meteorology likely contains errors of various magnitudes in cloud optical depth, air temperature, water vapor, boundary layer height and many other parameters. Model errors also exist in gaseous and heterogeneous reactions, aerosol optical properties and emissions of non-nitrogen species affecting the nitrogen chemistry. Modifications accounting for quantified errors in 10 selected parameters increase the NO₂ columns in most areas with an average positive impact of 18% in July and 8% in January, the most important factor being modified uptake of the hydroperoxyl radical (HO₂) on aerosols. This suggests a possible systematic model bias such that the top-down emissions will be overestimated by the same magnitude if the model is used for emission inversion without corrections. The modifications however cannot eliminate the large model underestimates in cities and other extremely polluted areas (particularly in the north) as compared to satellite retrievals, likely pointing to underestimates of the a priori emission inventory in these places with important implications for understanding of atmospheric chemistry and air quality. Note that these modifications are simplified and should be interpreted with caution for error apportionment

Abstract. This paper compares measurements of gaseous and particulate emissions from a wide range of biomassburning plumes intercepted by the NASA DC-8 research aircraft during the three phases of the ARCTAS-2008 experiment: ARCTAS-A, based out of Fairbanks, Alaska, USA (3 April to 19 April 2008); ARCTAS-B based out of Cold Lake, Alberta, Canada (29 June to 13 July 2008); and ARCTAS-CARB, based out of Palmdale, California, USA (18 June to 24 June 2008). Approximately 500 smoke plumes from biomass burning emissions that varied in age from minutes to days were segregated by fire source region and urban emission influences. The normalized excess mixing ratios (NEMR) of gaseous (carbon dioxide, acetonitrile, hydrogen cyanide, toluene, benzene, methane, oxides of nitrogen and ozone) and fine aerosol particulate components (nitrate, sulfate, ammonium, chloride, organic aerosols and water soluble organic carbon) of these plumes were compared. A detailed statistical analysis of the different plume categories for different gaseous and aerosol species is presented in this paper.Correspondence to: A. Hecobian (arsineh@gatech.edu)The comparison of NEMR values showed that CH 4 concentrations were higher in air-masses that were influenced by urban emissions. Fresh biomass burning plumes mixed with urban emissions showed a higher degree of oxidative processing in comparison with fresh biomass burning only plumes. This was evident in higher concentrations of inorganic aerosol components such as sulfate, nitrate and ammonium, but not reflected in the organic components. Lower NO x NEMRs combined with high sulfate, nitrate and ammonium NEMRs in aerosols of plumes subject to long-range transport, when comparing all plume categories, provided evidence of advanced processing of these plumes.

We analyzed satellite observations of nitrogen dioxide (NO2) columns by the Ozone Monitoring Instrument (OMI) over China from 2005 to 2010 in order to estimate the top-down anthropogenic nitrogen oxides (NOx) emission trends. Since NOx emissions were affected by the economic slowdown in 2009, we removed one year of abnormal data in the analysis. The estimated average emission trend is 4.01 ± 1.39% yr(-1), which is slower than the trend of 5.8-10.8% yr(-1) reported for previous years. We find large regional, seasonal, and urban-rural variations in emission trends. The average NOx emission trend of 3.47 ± 1.07% yr(-1) in warm season (June-September) is less than the trend of 5.03 ± 1.92% yr(-1) in cool season (October-May). The regional annual emission trends decrease from 4.76 ± 1.61% yr(-1) in North China Plain to 3.11 ± 0.98% yr(-1) in Yangtze River Delta and further down to -4.39 ± 1.81% yr(-1) in Pearl River Delta. The annual emission trends of the four largest megacities, Shanghai, Beijing, Guangzhou, and Shenzhen are -0.76 ± 0.29%, 0.69 ± 0.27%, -4.46 ± 1.22%, and -7.18 ± 2.88% yr(-1), considerably lower than the regional averages or surrounding rural regions. These results appear to suggest that a number of factors, including emission control measures of thermal power plants, increased hydro-power usage, vehicle emission regulations, and closure or migration of high-emission industries, have significantly reduced or even reversed the increasing trend of NOx emissions in more economically developed megacities and southern coastal regions, but their effects are not as significant in other major cities or less economically developed regions.

Methane (CH 4 ) is the primary component of natural gas and has a larger global warming potential than CO 2 . Recent top-down studies based on observations showed CH 4 emissions in California's South Coast Air Basin (SoCAB) were greater than those expected from population-apportioned bottom-up state inventories. In this study, we quantify CH 4 emissions with an advanced mesoscale inverse modeling system at a resolution of 8 km × 8 km, using aircraft measurements in the SoCAB during the 2010 Nexus of Air Quality and Climate Change campaign to constrain the inversion. To simulate atmospheric transport, we use the FLEXible PARTicle-Weather Research and Forecasting (FLEXPART-WRF) Lagrangian particle dispersion model driven by three configurations of the Weather Research and Forecasting (WRF) mesoscale model. We determine surface fluxes of CH 4 using a Bayesian least squares method in a four-dimensional inversion. Simulated CH 4 concentrations with the posterior emission inventory achieve much better correlations with the measurements (R 2 = 0.7) than using the prior inventory (U.S. Environmental Protection Agency's National Emission Inventory 2005, R 2 = 0.5). The emission estimates for CH 4 in the posterior, 46.3 ± 9.2 Mg CH 4 /h, are consistent with published observation-based estimates. Changes in the spatial distribution of CH 4 emissions in the SoCAB between the prior and posterior inventories are discussed. Missing or underestimated emissions from dairies, the oil/gas system, and landfills in the SoCAB seem to explain the differences between the prior and posterior inventories. We estimate that dairies contributed 5.9 ± 1.7 Mg CH 4 /h and the two sectors of oil and gas industries (production and downstream) and landfills together contributed 39.6 ± 8.1 Mg CH 4 /h in the SoCAB.

We quantify methane (CH4) emissions in California's San Joaquin Valley (SJV) by using 4 days of aircraft measurements from a field campaign during May–June 2010 together with a Bayesian inversion method and a mass balance approach. For the inversion estimates, we use the FLEXible PARTicle dispersion model (FLEXPART) to establish the source‐receptor relationship between sampled atmospheric concentrations and surface fluxes. Our prior CH4 emission estimates are from the California Greenhouse Gas Emissions Measurements (CALGEM) inventory. We use three meteorological configurations to drive FLEXPART and subsequently construct three inversions to analyze the final optimized estimates and their uncertainty (one standard deviation). We conduct May and June inversions independently and derive similar total CH4 emission estimates for the SJV: 135 ± 28 Mg/h in May and 135 ± 19 Mg/h in June. The inversion result is 1.7 times higher than the prior estimate from CALGEM. We also use an independent mass balance approach to estimate CH4 emissions in the northern SJV for one flight when meteorological conditions allowed. The mass balance estimate provides a confirmation of our inversion results, and these two independent estimates of the total CH4 emissions in the SJV are consistent with previous studies. In this study, we provide optimized CH4 emissions estimates at 0.1° horizontal resolution. Using independent spatial information on major CH4 sources, we estimate that livestock contribute 75–77% and oil/gas production contributes 15–18% of the total CH4 emissions in the SJV. Livestock explain most of the discrepancies between the prior and the optimized emissions from our inversion.

Agricultural ammonia (NH3) emissions are highly uncertain, with high spatiotemporal variability and a lack of widespread in situ measurements. Regional NH3 emission estimates using mass balance or emission ratio approaches are uncertain due to variable NH3 sources and sinks as well as unknown plume correlations with other dairy source tracers. We characterize the spatial distributions of NH3 and methane (CH4) dairy plumes using in situ surface and airborne measurements in the Tulare dairy feedlot region of the San Joaquin Valley, California, during the NASA Deriving Information on Surface conditions from Column and Vertically Resolved Observations Relevant to Air Quality 2013 field campaign. Surface NH3 and CH4 mixing ratios exhibit large variability with maxima localized downwind of individual dairy feedlots. The geometric mean NH3:CH4 enhancement ratio derived from surface measurements is 0.15 ± 0.03 ppmv ppmv−1. Individual dairy feedlots with spatially distinct NH3 and CH4 source pathways led to statistically significant correlations between NH3 and CH4 in 68% of the 69 downwind plumes sampled. At longer sampling distances, the NH3:CH4 enhancement ratio decreases 20–30%, suggesting the potential for NH3 deposition as a loss term for plumes within a few kilometers downwind of feedlots. Aircraft boundary layer transect measurements directly above surface mobile measurements in the dairy region show comparable gradients and geometric mean enhancement ratios within measurement uncertainties, even when including NH3 partitioning to submicron particles. Individual NH3 and CH4 plumes sampled at close proximity where losses are minimal are not necessarily correlated due to lack of mixing and distinct source pathways. Our analyses have important implications for constraining NH3 sink and plume variability influences on regional NH3 emission estimates and for improving NH3 emission inventory spatial allocations.