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.

We estimate monetized health benefits of phasing out the coal-fired power plants in Ontario and Alberta as well as in the US. We quantify these health impacts by accounting for reduced mortality due to chronic exposure to NO2 (In Canada) and PM2.5 (In Canada and the US).We apply the US EPA's Community Multi-Scale Air Quality (CMAQ-5.0) model and its adjoint to quantify the marginal benefits (MB) of NOx and PM2.5 emissions. The adjoint model traces mortality counts back to emissions for each single source location and time. The backward simulations of the model rely on non-linear concentration-response (C-R) functions of single (PM2.5) and three-pollutant (PM2.5, NO2 and O3) epidemiologic models. The simulations are done over a nested 12 km and 36 km domain, covering North America and for July 2010.Our preliminary results show health benefits for specific plants in Ontario and Alberta are between C$ 30k-310k/ton of PM2.5 and $30-270k/ton of NOx. These values range between $30k-580k/ton of PM2.5 for plants in the US. Retrospective analysis of coal phase-out in Ontario suggests benefits of $3.1 billion/yr, while societal benefits of the proposed phase-out in Alberta is approximated at $2.4 billion/yr.We find significant benefits from coal phase-out in both Ontario and Alberta, and even larger benefits in the US. For Ontario, our results suggest that most of the health benefits from Ontario coal phase-out materializes in the province, whereas Alberta phase-out entails larger out-of-province benefits.

Objective: We assess the societal benefits of reducing air pollutant emissions that contribute to ambient fine particulate matter (PM2.5), ozone (O3), and nitrogen dioxide (NO2) exposure and public health impacts. Recent evidence suggests that nonlinear, multi-pollutant concentration-response (C-R) models are more appropriate than traditional, linear forms used in epidemiology. We examine the implications of alternate C-R models in an emissions reduction framework.Methods: We integrate C-R models for non-accidental mortality due to PM2.5, O3, and NO2 into the Community Multiscale Air Quality Model (CMAQ). This sophisticated atmospheric model and its adjoint tool allow us to trace public health impacts back to sources of pollutant emissions. We compare the monetized public health benefits of reducing emissions from sources across Central Canada at a 12 km resolution for July 2010. We apply C-R models (single or multipollutant and linear or nonlinear) derived from the 2001 Canadian Census Health and Environment Cohort (CanCHEC).Results: Our preliminary results indicate significant and widespread benefits of PM2.5 and NOx (NO + NO2) emissions control, particularly in major urban areas of Central Canada. We find benefits ranging from $400,000-800,000 per ton of reduction in PM2.5 from sources in Toronto, while NOx control for the same location entails benefits of $2,000-200,000/ton depending on the choice of C-R model. Nonlinear models consistently produce larger benefit estimates than their linear counterparts. We estimate coefficients of variation based solely on the choice of C-R model to be 0.4-0.6 for PM2.5 and 0.6-1.6 for NOx.Conclusions: Our results show that the public health benefits of emission reductions are highly sensitive to C-R specification, and that traditional C-R models may significantly underestimate the benefits of air pollution controls. Further research is needed to determine the most appropriate C-R model to support public policy.

This study suggests a new modeling framework using a hybrid Eulerian–Lagrangian-based modeling tool (the Screening Trajectory Ozone Prediction System, STOPS) for a prediction of an Asian dust event in Korea. The new version of STOPS (v1.5) has been implemented into the Community Multi-scale Air Quality (CMAQ) model version 5.0.2. The STOPS modeling system is a moving nest (Lagrangian approach) between the source and the receptor inside the host Eulerian CMAQ model. The proposed model generates simulation results that are relatively consistent with those of CMAQ but within a comparatively shorter computational time period. We find that standard CMAQ generally underestimates PM10 concentrations during the simulation period (February 2015) and fails to capture PM10 peaks during Asian dust events (22–24 February 2015). The underestimation in PM10 concentration is very likely due to missing dust emissions in CMAQ rather than incorrectly simulated meteorology, as the model meteorology agrees well with the observations. To improve the underestimated PM10 results from CMAQ, we used the STOPS model with constrained PM concentrations based on aerosol optical depth (AOD) data from the Geostationary Ocean Color Imager (GOCI), reflecting real-time initial and boundary conditions of dust particles near the Korean Peninsula. The simulated PM10 from the STOPS simulations were improved significantly and closely matched the surface observations. With additional verification of the capabilities of the methodology on emission estimations and more STOPS simulations for various time periods, the STOPS model could prove to be a useful tool not just for the predictions of Asian dust but also for other unexpected events such as wildfires and oil spills.

This study suggests a new modeling framework using a hybrid Lagrangian-Eulerian based modeling tool (the Screening Trajectory Ozone Prediction System, STOPS) for a more accurate prediction of Asian dust event in Korea. The new version of STOPS (v1.5) has been implemented into the Community Multi-scale Air Quality (CMAQ) model version 5.0.2. We apply STOPS to PM10 20 simulations in the East Asia during Asian dust events (22-24 February, 2015). The STOPS modeling system is a moving nest (Lagrangian approach) between the source and the receptor inside a CMAQ structure (Eulerian model). The proposed model generates simulation results that are relatively consistent with those of CMAQ but within a comparatively shorter computational time period. We evaluate the performance of standard CMAQ for the PM10 simulations and investigate the impact of 25 STOPS modeling with constrained PM concentration based on space-derived measurement (by using alternative PM emissions) on the improved accuracy of the PM10 prediction. We find that standard CMAQ generally underestimates PM10 concentrations during the simulation period (February, 2015) and fails to capture PM10 peaks during Asian dust events. Accurately simulated meteorology implies that the underestimated PM10 concentration is not due to the meteorology but to poorly estimated dust 30 emissions for the CMAQ simulation. To improve the underestimated PM10 results from standard CMAQ, we use the STOPS modeling system inside of the CMAQ model, and instead of running the costly, time-consuming Eulerian model, CMAQ, we run several STOPS simulations using constrained PM concentration based on aerosol optical depth (AOD) data from Geostationary Ocean Color Imager Geosci. Model Dev. Discuss., doi:10.5194/gmd-2016-180, 2016 Manuscript under review for journal Geosci. Model Dev. Published: 21 July 2016 c © Author(s) 2016. CC-BY 3.0 License.

A hybrid Lagrangian–Eulerian based modeling tool has been developed using the Eulerian framework of the Community Multiscale Air Quality (CMAQ) model. It is a moving nest that utilizes saved original CMAQ simulation results to provide boundary conditions, initial conditions, as well as emissions and meteorological parameters necessary for a simulation. Given that these files are available, this tool can run independently of the CMAQ whole domain simulation, and it is designed to simulate source–receptor relationships upon changes in emissions. In this tool, the original CMAQ's horizontal domain is reduced to a small sub-domain that follows a trajectory defined by the mean mixed-layer wind. It has the same vertical structure and physical and chemical interactions as CMAQ except advection calculation. The advantage of this tool compared to other Lagrangian models is its capability of utilizing realistic boundary conditions that change with space and time as well as detailed chemistry treatment. The correctness of the algorithms and the overall performance was evaluated against CMAQ simulation results. Its performance depends on the atmospheric conditions occurring during the simulation period, with the comparisons being most similar to CMAQ results under uniform wind conditions. The mean bias for surface ozone mixing ratios varies between −0.03 and −0.78 ppbV and the slope is between 0.99 and 1.01 for different analyzed cases. For complicated meteorological conditions, such as wind circulation, the simulated mixing ratios deviate from CMAQ values as a result of the Lagrangian approach of using mean wind for its movement, but are still close, with the mean bias for ozone varying between 0.07 and −4.29 ppbV and the slope varying between 0.95 and 1.06 for different analyzed cases. For historical reasons, this hybrid Lagrangian–Eulerian based tool is named the Screening Trajectory Ozone Prediction System (STOPS), but its use is not limited to ozone prediction as, similarly to CMAQ, it can simulate concentrations of many species, including particulate matter and some toxic compounds, such as formaldehyde and 1,3-butadiene.

10 A hybrid Lagrangian-Eulerian based modeling tool has been developed using the 11 Eulerian framework of the Community Multiscale Air Quality (CMAQ) model. It is a 12 moving nest that utilizes saved original CMAQ simulation results to provide boundary 13 conditions, initial conditions, as well as emissions and meteorological parameters 14 necessary for a simulation. Given that these file are available, this tool can run 15 independently from the CMAQ whole domain simulation and it is designed to simulate 16 source – receptor relationship upon changes in emissions. In this tool, the original 17 CMAQ’s horizontal domain is reduced to a small sub-domain that follows a trajectory 18 defined by the mean mixed-layer wind. It has the same vertical structure and physical 19 and chemical interactions as CMAQ except advection calculation. The advantage of this 20 tool compared to other Lagrangian models is its capability of utilizing realistic 21 boundary conditions that change with space and time as well as detailed chemistry 22 treatment. The correctness of the algorithms and the overall performance was evaluated 23 against CMAQ simulation results. Its performance depends on the atmospheric 24 conditions occurring during the simulation period with the comparisons being most 25 similar to CMAQ results under uniform wind conditions. The mean bias for surface 26 ozone mixing ratios varies between -0.03 ppbV and -0.78 ppbV and the slope is 27 between 0.99 and 1.01 for different analyzed cases. For complicated meteorological 28 condition, such as wind circulation, the simulated mixing ratios deviate from CMAQ 29 values as a result of Lagrangian approach of using mean wind for its movement, but are 30 still close, with the mean bias for ozone varying between 0.07 ppbV and -4.29 ppbV 31 and slope varying between 0.95 and 1.06 for different analyzed cases. For historical 32 reasons this hybrid Lagrangian – Eulerian based tool is named the Screening Trajectory 33 Ozone Prediction System (STOPS) but its use is not limited to ozone prediction as 34 similarly to CMAQ it can simulate concentrations of many species, including 35 particulate matter and some toxic compounds, such as formaldehyde and 1,3-butadiene. 36 37

Recent studies have shown that exposure to particulate black carbon (BC) has significant adverse health effects and may be more detrimental to human health than exposure to PM2.5 as a whole. Mobile source BC emission controls, mostly on diesel-burning vehicles, have successfully decreased mobile source BC emissions to less than half of what they were 30 years ago. Quantification of the benefits of previous emissions controls conveys the value of these regulatory actions and provides a method by which future control alternatives could be evaluated. In this study we use the adjoint of the Community Multiscale Air Quality (CMAQ) model to estimate highly-resolved spatial distributions of benefits related to emission reductions for six urban regions within the continental US. Emissions from outside each of the six chosen regions account for between 7% and 27% of the premature deaths attributed to exposure to BC within the region. While we estimate that nonroad mobile and onroad diesel emissions account for the largest number of premature deaths attributable to exposure to BC, onroad gasoline is shown to have more than double the benefit per unit emission relative to that of nonroad mobile and onroad diesel. Within the region encompassing New York City and Philadelphia, reductions in emissions from large industrial combustion sources that are not classified as EGUs (i.e., non-EGU) are estimated to have up to triple the benefits per unit emission relative to reductions to onroad diesel sectors, and provide similar benefits per unit emission to that of onroad gasoline emissions in the region. While onroad mobile emissions have been decreasing in the past 30 years and a majority of vehicle emission controls that regulate PM focus on diesel emissions, our analysis shows the most efficient target for stricter controls is actually onroad gasoline emissions.

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.

Nitrous acid (HONO) mixing ratios for the Houston metropolitan area were simulated with the Community Multiscale Air Quality (CMAQ) Model for an episode during the Texas Air Quality Study (TexAQS) II in August/September 2006 and compared to in-situ MC/IC (mistchamber/ion chromatograph) and long path DOAS (Differential Optical Absorption Spectroscopy) measurements at three different altitude ranges. Several HONO sources were accounted for in simulations, such as gas phase formation, direct emissions, nitrogen dioxide (NO2) hydrolysis, photoinduced formation from excited NO2 and photo-induced conversion of NO2 into HONO on surfaces covered with organic materials. Compared to the gas-phase HONO formation there was about a tenfold increase in HONO mixing ratios when additional HONO sources were taken into account, which improved the correlation between modeled and measured values. Concentrations of HONO simulated with only gas phase chemistry did not change with altitude, while measured HONO concentrations decrease with height. A trend of decreasing HONO concentration with altitude was well captured with CMAQ predicted concentrations when heterogeneous chemistry and photolytic sources of HONO were taken into account. Heterogeneous HONO production mainly accelerated morning ozone formation, albeit slightly. Also HONO formation from excited NO2 only slightly affected HONO and ozone (O-3) concentrations. Photo-induced conversion of NO2 into HONO on surfaces covered with organic materials turned out to be a strong source of daytime HONO. Since HONO immediately photo-dissociates during daytime its ambient mixing ratios were only marginally altered (up to 0.5 ppbv), but significant increase in the hydroxyl radical (OH) and ozone concentration was obtained. In contrast to heterogeneous HONO formation that mainly accelerated morning ozone formation, inclusion of photo-induced surface chemistry influenced ozone throughout the day.

Numerical diffusion is a serious problem for Eulerian advection algorithms typically used in chemical transport simulators for air quality modeling. This extra diffusion damps local extremes and smears steep gradients in trace species concentrations, for example, those due to localized emissions of primary pollutants such as NOx and VOC’s. This, in turn, compromises the model’s chemistry calculations of the production of secondary pollutants, such as ozone, which depend nonlinearly on the relative concentrations of the precursors. In order to remove this source of inaccuracy, a Lagrangian advection algorithm called Trajectory-Grid (TG) has been implemented in the EPA’s Community Multiscale Air Quality (CMAQ) model. The TG algorithm follows representative air “packets” along trajectories determined by the wind field. The air composition in a packet is unchanged by advection. The resulting prototype simulation model is called CMAQ-TG. CMAQ-TG was compared to standard CMAQ for two-dimensional rotating, shearing, and stretching flows and a realistic three-dimensional wind field. Compared with the standard CMAQ advection algorithm, i.e., the piecewise parabolic method (PPM), CMAQ-TG advection produces a non-diffusive solution and maintains linearity of the advection process. Initial implementation has been limited to the simulation of photochemical air quality with an emphasis on the transport processes in CMAQ.

Recently, NOAA (National Oceanic and Atmospheric Administration) National Weather Service (NWS) and Office of Oceanic and Atmospheric Research (OAR) in collaboration with the U.S. EPA have been testing an initial capability for numerical air quality forecasting. The backbone of the initial capability was a computer modeling system based on the NWS Eta mesoscale meteorological forecast model and the NOAA/EPA Community Multiscale Air Quality (CMAQ) model (Byun and Ching, 1999; Byun and Schere, 2006). In 2006 the Eta model was replaced by the WRF/nmm model. An initial implementation linking the WRF/nmm and the CMAQ models was deployed in 2006. However there still exists a need to develop a more consistent coupling between the models wherein the chemistry/transport calculations in CMAQ are performed using the same grid and coordinate structure as the WRF/nmm. The fully compressible governing set of equations used in CMAQ can maintain the dynamic description of WRF just by replacing the Jacobian defining the vertical coordinate transformation and by carefully writing out dynamic and thermodynamic variables.