Fate of ozone in marine environments has been receiving increased attention due to the tightening of ambient air quality standards. The role of deposition and halogen chemistry is examined through ...incorporation of an enhanced ozone deposition algorithm and inclusion of halogen chemistry in a comprehensive atmospheric modeling system. The enhanced ozone deposition treatment accounts for the interaction of iodide in seawater with ozone and increases deposition velocities by 1 order of magnitude. Halogen chemistry includes detailed chemical reactions of organic and inorganic bromine and iodine species. Two different simulations are completed with the halogen chemistry: without and with photochemical reactions of higher iodine oxides. Enhanced deposition reduces mean summer-time surface ozone by ∼3% over marine regions in the Northern Hemisphere. Halogen chemistry without the photochemical reactions of higher iodine oxides reduces surface ozone by ∼15% whereas simulations with the photochemical reactions of higher iodine oxides indicate ozone reductions of ∼48%. The model without these processes overpredicts ozone compared to observations whereas the inclusion of these processes improves predictions. The inclusion of photochemical reactions for higher iodine oxides leads to ozone predictions that are lower than observations, underscoring the need for further refinement of the halogen emissions and chemistry scheme in the model.
Coupled air quality and climate models can predict aerosol concentrations and properties, as well as aerosol direct and indirect effects that depend on aerosol chemistry and microphysics treatments. ...In this study, Weather Research and Forecasting with Chemistry (WRF/Chem) simulations are conducted over continental U.S. (CONUS) for January and July 2001 with the same gas‐phase mechanism (CB05) but three aerosol modules (Modal Aerosol Dynamics Model for Europe/Secondary Organic Aerosol Model (MADE/SORGAM), Model for Simulating Aerosol Interactions and Chemistry (MOSAIC), and Model of Aerosol Dynamics, Reaction, Ionization and Dissolution (MADRID)) to examine the impacts of aerosol treatments on predictions of aerosols and their effects on cloud properties and radiation. The simulations with the three aerosol modules give similar domain mean predictions of surface PM2.5 concentrations but exhibit a strong spatial variation in magnitudes with large differences in eastern U.S. Large discrepancies are found in the predicted concentrations of sulfate and organic matter due to different treatments in secondary inorganic and secondary organic aerosol (SOA) formation. In particular, the nucleation calculation in MADE/SORGAM causes mass buildup of sulfate which results in much higher sulfate concentrations that those predicted by WRF/Chem with the other two aerosol modules. Different PM mass concentrations and size representations lead to differences in the predicted aerosol number concentrations. The above differences in PM concentrations lead to large differences in simulated condensation nuclei (CCN) and cloud properties in both months. The simulated ranges of domain mean are (1.9–14.3) × 109 m−3 and (1.4–5.4) × 109 m−3 for PM2.5 number concentration, (1.6–3.9) × 108 cm−2 and (1.9–3.9) × 108 cm−2 for CCN, 102.9–208.2 cm−3 and 143.7–202.2 cm−3 for column cloud droplet number concentration (CDNC), and 4.5–6.4 and 3.6–6.7 for cloud optical depths (COT) in January and July, respectively. The sensitivity simulation for July 2001 using online biogenic emissions increases isoprene concentrations but decreases terpene concentrations, leading to a domain mean increase in O3 (1.5 ppb) and a decrease in biogenic SOA (−0.07 µg m−3) and PM2.5 (−0.2 µg m−3). Anthropogenic emissions contribute to O3, biogenic SOA (BSOA), and PM2.5 concentrations by 38.0%, 44.2%, and 53.6% domain mean and by up to 78.5%, 89.7%, and 96.3%, respectively, indicating that a large fraction of BSOA is controllable through controlling atmospheric oxidant levels in CONUS. Anthropogenic emissions also contribute to a decrease in downward shortwave flux at ground surface (−5.8 W m−2), temperature at 2 m (−0.05°C), wind speed at 10 m (−0.02 m s−1), planetary boundary layer height (−6.6 m), and precipitation (−0.08 mm), as well as an increase in CCN (+5.7 × 10−7 cm−2), in‐cloud CDNC (+40.4 cm−3), and COT (+0.6). This work indicates the need for an accurate representation of several aerosol processes such as SOA formation and aerosol‐cloud interactions in simulating aerosol direct and indirect effects in the online‐coupled models.
Key Points
Comparison of three aerosol modules in a fully coupled meteorology‐chemistry model
Different aerosol modules lead to different concentrations and climatic feedbacks
Anthropogenic emissions contribute largely to air quality and climate predictions
The Community Multiscale Air Quality (CMAQ) model is a comprehensive multipollutant air quality modeling system developed and maintained by the US Environmental Protection Agency's (EPA) Office of ...Research and Development (ORD). Recently, version 5.1 of the CMAQ model (v5.1) was released to the public, incorporating a large number of science updates and extended capabilities over the previous release version of the model (v5.0.2). These updates include the following: improvements in the meteorological calculations in both CMAQ and the Weather Research and Forecast (WRF) model used to provide meteorological fields to CMAQ, updates to the gas and aerosol chemistry, revisions to the calculations of clouds and photolysis, and improvements to the dry and wet deposition in the model. Sensitivity simulations isolating several of the major updates to the modeling system show that changes to the meteorological calculations result in enhanced afternoon and early evening mixing in the model, periods when the model historically underestimates mixing. This enhanced mixing results in higher ozone (O
) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM
) concentrations due to greater dilution of primary pollutants (e.g., elemental and organic carbon). Updates to the clouds and photolysis calculations greatly improve consistency between the WRF and CMAQ models and result in generally higher O
mixing ratios, primarily due to reduced cloudiness and attenuation of photolysis in the model. Updates to the aerosol chemistry result in higher secondary organic aerosol (SOA) concentrations in the summer, thereby reducing summertime PM
bias (PM
is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O
and PM
on average in January and July. Overall, the seasonal variation in simulated PM
generally improves in CMAQv5.1 (when considering all model updates), as simulated PM
concentrations decrease in the winter (when PM
is generally overestimated by CMAQ) and increase in the summer (when PM
is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O
mean bias, as O
tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O
is low); however, O
correlation is largely improved with v5.1. Sensitivity simulations for several hypothetical emission reduction scenarios show that v5.1 tends to be slightly more responsive to reductions in NO
(NO + NO
), VOC and SO
(SO
+ SO
) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O
due to large, widespread reductions in observed emissions.
Surface layer resistance plays an important role in determining ozone deposition velocity over sea-water and can be influenced by chemical interactions at the air-water interface. Here, we examine ...the effect of chemical interactions of iodide, dimethylsulfide, dissolved organic carbon, and bromide in seawater on ozone deposition. We perform a series of simulations using the hemispheric Community Multiscale Air Quality model for summer months in the Northern Hemisphere. Our results suggest that each chemical interaction enhances the ozone deposition velocity and decreases the atmospheric ozone mixing ratio over seawater. Iodide enhances the median deposition velocity over seawater by 0.023 cm s−1, dissolved organic carbon by 0.021 cm s−1, dimethylsulfide by 0.002 cm s−1, and bromide by ∼0.0006 cm s−1. Consequently, iodide decreases the median atmospheric ozone mixing ratio over seawater by 0.7 ppb, dissolved organic carbon by 0.8 ppb, dimethylsulfide by 0.1 ppb, and bromide by 0.02 ppb. In a separate model simulation, we account for the effect of dissolved salts in seawater on the Henry’s law constant for ozone and find that it reduces the median deposition velocity by 0.007 cm s−1 and increases surface ozone mixing ratio by 0.2 ppb. The combined effect of these processes increases the median ozone deposition velocity over seawater by 0.040 cm s−1, lowers the atmospheric ozone mixing ratio by 5%, and slightly improves model performance relative to observations.
The oceans are the main source of natural halogen and sulfur compounds, which have a significant influence on the oxidizing capacity of the marine atmosphere; however, their impact on the air quality ...of coastal cities is currently unknown. We explore the effect of marine halogens (Cl, Br and I) and dimethyl sulfide (DMS) on the air quality of a large coastal city through a set of high-resolution (4-km) air quality simulations for the urban area of Los Angeles, US, using the Community Multiscale Air Quality (CMAQ model). The results indicate that marine halogen emissions decrease ozone and nitrogen dioxide levels up to 5ppbv and 2.5ppbv, respectively, in the city of Los Angeles. Previous studies suggested that the inclusion of chlorine in air quality models leads to the generation of ozone in urban areas through photolysis of nitryl chloride (ClNO2). However, we find that when considering the chemistry of Cl, Br and I together the net effect is a reduction of surface ozone concentrations. Furthermore, combined ocean emissions of halogens and DMS cause substantial changes in the levels of key urban atmospheric oxidants such as OH, HO2 and NO3, and in the composition and mass of fine particles. Although the levels of ozone, NO3 and HOx are reduced, we find a 10% increase in secondary organic aerosol (SOA) mean concentration, attributed to the increase in aerosol acidity and sulfate aerosol formation when combining DMS and bromine. Therefore, this new pathway for enhanced SOA formation may potentially help with current model under predictions of urban SOA. Although further observations and research are needed to establish these preliminary conclusions, this first city-scale investigation suggests that the inclusion of oceanic halogens and DMS in air quality models may improve regional air quality predictions over coastal cities around the world.
Display omitted
•Natural marine emissions (Cl, Br, I and DMS) included in CMAQ simulations over LA.•Oceanic halogens and DMS may play an important role on coastal urban areas.•Substantial changes in the levels of key urban atmospheric oxidants (OH, HO2 and NO3).•O3 and NO2 ambient levels decreased by 5ppbv and 2.5ppbv in Los Angeles city.•10% increase in secondary organic aerosol (SOA) due to dimethyl sulphide (DMS).
The air quality of many large coastal areas in the United States is affected by the confluence of polluted urban and relatively clean marine airmasses, each with distinct atmospheric chemistry. In ...this context, the role of iodide-mediated ozone (O3) deposition over seawater and marine halogen chemistry accounted for in both the lateral boundary conditions and coastal waters surrounding the continental U.S. is examined using the Community Multiscale Air Quality (CMAQ) model. Several nested simulations are conducted in which these halogen processes are implemented separately in the continental U.S. and hemispheric CMAQ domains, the latter providing lateral boundary conditions for the former. Overall, it is the combination of these processes within both the continental U.S. domain and from lateral boundary conditions that lead to the largest reductions in modeled surface O3 concentrations. Predicted reductions in surface O3 concentrations occur mainly along the coast where CMAQ typically has large overpredictions. These results suggest that a realistic representation of halogen processes in marine regions can improve model prediction of O3 concentrations near the coast.
The United States Environmental Protection Agency (EPA) has implemented a Bayesian spatial data fusion model called the Downscaler (DS) model to generate daily air quality surfaces for PM2.5 across ...the contiguous U.S. Previous implementations of DS relied on monitoring data from EPA’s Air Quality System (AQS) network, which is largely concentrated in urban areas. In this work, we introduce to the DS modeling framework an additional PM2.5 input dataset from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network located mainly in remote sites. In the western U.S. where IMPROVE sites are relatively dense (compared to the eastern U.S.), the inclusion of IMPROVE PM2.5 data to the DS model runs reduces predicted annual averages and 98th percentile concentrations by as much as 1.0 and 4 μg m−3, respectively. Some urban areas in the western U.S., such as Denver, Colorado, had moderate increases in the predicted annual average concentrations, which led to a sharpening of the gradient between urban and remote areas. Comparison of observed and DS-predicted concentrations for the grid cells containing IMPROVE and AQS sites revealed consistent improvement at the IMPROVE sites but some degradation at the AQS sites. Cross-validation results of common site-days withheld in both simulations show a slight reduction in the mean bias but a slight increase in the mean square error when the IMPROVE data is included. These results indicate that the output of the DS model (and presumably other Bayesian data fusion models) is sensitive to the addition of geographically distinct input data, and that the application of such models should consider the prediction domain (national or urban focused) when deciding to include new input data.
Terrestrial and marine photosynthetic organisms emit trace gases, including isoprene and monoterpenes. The resulting emissions can impact the atmosphere through oxidative chemistry and formation of ...secondary organic aerosol. Large uncertainty exists as to the magnitude of the marine sources of these compounds, their controlling factors, and contribution to marine aerosol. In recent years, the number of relevant studies has increased substantially, necessitating the review of this topic. Isoprene emissions vary with plankton species, chlorophyll concentration, light, and other factors. Remote marine boundary layer isoprene mixing ratios can reach >300 pptv, and extrapolated global ocean fluxes range from <1 to >10 Tg C year-1. Modeling studies using surface chlorophyll concentration as an isoprene emissions proxy suggest variable atmospheric impacts. More information is needed, including emission fluxes of isoprene and monoterpenes from various biogeographical areas, the effects of species and nutrient limitation on emissions, and the aerosol yields via condensation and nucleation, in order to better quantify the atmospheric impacts of marine isoprene and monoterpenes.
The impact of marine isoprene emissions on summertime surface concentrations of isoprene, secondary organic aerosols (SOA), and ozone (O
3) in the coastal areas of the continental United States is ...studied using the U.S. Environmental Protection Agency regional-scale Community Multiscale Air Quality (CMAQ) modeling system. Marine isoprene emission rates are based on the following five parameters: laboratory measurements of isoprene production from phytoplankton under a range of light conditions, remotely-sensed chlorophyll-
a concentration (Chl–
a), incoming solar radiation, surface wind speed, and sea-water optical properties. Model simulations show that marine isoprene emissions are sensitive to meteorology and ocean ecosystem productivity, with the highest rates simulated over the Gulf of Mexico. Simulated offshore surface layer marine isoprene concentration is less than 10 ppt and significantly dwarfed by terrestrial emissions over the continental United States. With the isoprene reactions included in this study, the average contribution of marine isoprene to SOA and O
3 concentrations is predicted to be small, up to 0.004 μg m
−3 for SOA and 0.2 ppb for O
3 in coastal urban areas. The light-sensitivity of isoprene production from phytoplankton results in a midday maximum for marine isoprene emissions and a corresponding daytime increase in isoprene and O
3 concentrations in coastal locations. The potential impact of the daily variability in Chl-
a on O
3 and SOA concentrations is simulated in a sensitivity study with Chl-
a increased and decreased by a factor of five. Our results indicate that marine emissions of isoprene cause minor changes to coastal SOA and O
3 concentrations. Comparison of model simulations with few available measurements shows that the model underestimates marine boundary layer isoprene concentration. This underestimation is likely due to the limitations in current treatment of marine isoprene emission and a coarse spatial resolution used in the model simulations.
PM2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient ...methods that incorporate predictions from photochemical grid models (PGM) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM2.5 spatial fields to correspond with just meeting the PM2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM2.5 tends to be most responsive to primary PM2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO2 emission changes. On average, PM2.5 is more responsive to changes in anthropogenic primary PM2.5 emissions than NOx and SO2 emissions in the contiguous U.S.PM2.5 concentration fields that correspond to just meeting national ambient air quality standards (NAAQS) are useful for characterizing exposure in regulatory assessments. Computationally efficient methods that incorporate predictions from photochemical grid models (PGM) are needed to realistically project baseline concentration fields for these assessments. Thorough cross validation (CV) of hybrid spatial prediction models is also needed to better assess their predictive capability in sparsely monitored areas. In this study, a system for generating, evaluating, and projecting PM2.5 spatial fields to correspond with just meeting the PM2.5 NAAQS is developed and demonstrated. Results of ten-fold CV based on standard and spatial cluster withholding approaches indicate that performance of three spatial prediction models improves with decreasing distance to the nearest neighboring monitor, improved PGM performance, and increasing distance from sources of PM2.5 heterogeneity (e.g., complex terrain and fire). An air quality projection tool developed here is demonstrated to be effective for quickly projecting PM2.5 spatial fields to just meet NAAQS using realistic spatial response patterns based on air quality modeling. PM2.5 tends to be most responsive to primary PM2.5 emissions in urban areas, whereas response patterns are relatively smooth for NOx and SO2 emission changes. On average, PM2.5 is more responsive to changes in anthropogenic primary PM2.5 emissions than NOx and SO2 emissions in the contiguous U.S.
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