The Community Multiscale Air Quality (CMAQ) model version 5.3 (CMAQ53), released to the public in August 2019 and followed by version 5.3.1 (CMAQ531) in December 2019, contains numerous science ...updates, enhanced functionality, and improved computation efficiency relative to the previous version of the model, 5.2.1 (CMAQ521). Major science advances in the new model include a new aerosol module (AERO7) with significant updates to secondary organic aerosol (SOA) chemistry, updated chlorine chemistry, updated detailed bromine and iodine chemistry, updated simple halogen chemistry, the addition of dimethyl sulfide (DMS) chemistry in the CB6r3 chemical mechanism, updated M3Dry bidirectional deposition model, and the new Surface Tiled Aerosol and Gaseous Exchange (STAGE) bidirectional deposition model. In addition, support for the Weather Research and Forecasting (WRF) model's hybrid vertical coordinate (HVC) was added to CMAQ53 and the Meteorology-Chemistry Interface Processor (MCIP) version 5.0 (MCIP50). Enhanced functionality in CMAQ53 includes the new Detailed Emissions Scaling, Isolation and Diagnostic (DESID) system for scaling incoming emissions to CMAQ and reading multiple gridded input emission files. Evaluation of CMAQ531 was performed by comparing monthly and seasonal mean daily 8 h average (MDA8) O
and daily PM
values from several CMAQ531 simulations to a similarly configured CMAQ521 simulation encompassing 2016. For MDA8 O
, CMAQ531 has higher O
in the winter versus CMAQ521, due primarily to reduced dry deposition to snow, which strongly reduces wintertime O
bias (2-4 ppbv monthly average). MDA8 O
is lower with CMAQ531 throughout the rest of the year, particularly in spring, due in part to reduced O
from the lateral boundary conditions (BCs), which generally increases MDA8 O
bias in spring and fall ( 0.5 μg m
). For daily 24 h average PM
, CMAQ531 has lower concentrations on average in spring and fall, higher concentrations in summer, and similar concentrations in winter to CMAQ521, which slightly increases bias in spring and fall and reduces bias in summer. Comparisons were also performed to isolate updates to several specific aspects of the modeling system, namely the lateral BCs, meteorology model version, and the deposition model used. Transitioning from a hemispheric CMAQ (HCMAQ) version 5.2.1 simulation to a HCMAQ version 5.3 simulation to provide lateral BCs contributes to higher O
mixing ratios in the regional CMAQ simulation in higher latitudes during winter (due to the decreased O
dry deposition to snow in CMAQ53) and lower O
mixing ratios in middle and lower latitudes year-round (due to reduced O
over the ocean with CMAQ53). Transitioning from WRF version 3.8 to WRF version 4.1.1 with the HVC resulted in consistently higher (1.0-1.5 ppbv) MDA8 O
mixing ratios and higher PM
concentrations (0.1-0.25 μg m
) throughout the year. Finally, comparisons of the M3Dry and STAGE deposition models showed that MDA8 O
is generally higher with M3Dry outside of summer, while PM
is consistently higher with STAGE due to differences in the assumptions of particle deposition velocities to non-vegetated surfaces and land use with short vegetation (e.g., grasslands) between the two models. For ambient NH
, STAGE has slightly higher concentrations and smaller bias in the winter, spring, and fall, while M3Dry has higher concentrations and smaller bias but larger error and lower correlation in the summer.
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.
Wildland fires are a major source of fine particulate matter (PM2.5), one of the most harmful ambient pollutants for human health globally. To represent the influence of wildland fire emissions on ...atmospheric composition, regional and global chemical transport models rely on emission inventories developed from estimates of burned area (i.e. fire size and location). While different methods of estimating annual burned area agree reasonably well in the western U.S. (within 20–30% for most years during 2002–2014), estimates for the southern U.S. can vary by more than a factor of five. These differences in burned area lead to significant variability in the spatial and temporal allocation of emissions across fire emission inventory platforms. In this work, we implement wildland fire emission estimates for 2011 from three different products - the USEPA National Emission Inventory (NEI), the Fire Inventory of NCAR (FINN), and the Global Fire Emission Database (GFED4s) - into the Community Multiscale Air Quality (CMAQ) model to quantify and characterize differences in simulated PM and ozone concentrations across the contiguous U.S. (CONUS) due to the fire emission inventory used. The NEI is developed specifically for the U.S., while both FINN and GFED4s are available globally. We find that NEI emissions lead to the largest increases in modeled annual average PM2.5 (0.85 μg m−3) and April–September maximum daily 8-h ozone (0.28 ppb) nationally compared to a “no fire” baseline, followed by FINN (0.33 μg m−3 and 0.22 ppb) and GFED4s (0.12 μg m−3 and 0.17 ppb). Annual mean enhancements in wildland fire pollution are highest in the southern U.S. across all three inventories (over 4 μg m−3 and 2 ppb in some areas), but show considerable spatial variability within these regions. We also examine the representation of five individual fire events during 2011 and find that of the two global inventories, FINN reproduces more of the acute changes in pollutant concentrations modeled with NEI and shown in surface observations during each of the episodes investigated compared to GFED4s. Understanding the sensitivity of modeling fire-related PM2.5 and ozone in the U.S. to burned area estimation approaches will inform future efforts to assess the implications of present and future fire activity for air quality and human health at national and global scales.
•Estimated burned area varies widely in the southern US where small fires are common.•Mean simulated smoke PM2.5 varies by a factor of seven across inventories.•Modeled smoke plume rise is sensitive to the spatial allocation of burned area.•Wildland fire smoke affects most of the US, even areas with low local fire activity.
In November 2016, a large area of wildfire occurred in the southeastern United States, concomitant with the occurrence of severe drought during the same period. Whereas the previous studies on ...biomass burning over this region mainly focused on the prescribed fire, this study investigated the impact of wildfire using the two-way-coupled Weather Research and Forecasting model and Community Multiscale Air Quality model. Two episodic wildfire burning events (November 6 to 9 and November 13 to 16, 2016) were selected, and the mean contribution to fine particulate matter (PM2.5) in the southeastern United States from wildfires reached 9.6 to 42.5 μg m−3 and 10.9 to 26.1 μg m−3, with mean relative contributions of 41% and 49%, respectively, during these two events. The effect of wildfire propagates along the path of the smoke plume, which is determined by the wind speed and direction. For instance, during the first event, the dominant low-altitude wind vector displayed an anticyclonic-type flow with low wind speed, resulting in relatively localized influence and high intensity. In contrast, during the second event, relatively fast eastward wind, particularly over the latter part of the event, strengthened the diffusion and affected larger areas in comparison with the first event. Moreover, differently from the previous studies, this study took a further step to reveal the mechanism of the aerosol direct effect on the deterioration of air quality during wildfire, mainly through the modulation of reduction in surface downward shortwave radiation, planetary boundary layer height and wind speed, subsequently, facilitating pollution accumulation. Quantification analysis showed an average of 10% to 14% extra enhancement of PM2.5 during the November 6 to 8 episode. Considering that more frequent drought is projected to occur in the southeastern United States, wildfire may play an even more important role in modulating the air quality in this region.
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•The impact of wildfire on PM2.5 concentrations over southeastern U.S. was quantified.•During the episodes in November 2016, the contribution of wildfire to PM2.5 is 45%.•The radiative effect of aerosol may further enhance the impact resulting from wildfire.
To What Extent Can Biogenic SOA be Controlled? Carlton, Annmarie G; Pinder, Robert W; Bhave, Prakash V ...
Environmental science & technology,
05/2010, Letnik:
44, Številka:
9
Journal Article
Recenzirano
The implicit assumption that biogenic secondary organic aerosol (SOA) is natural and can not be controlled hinders effective air quality management. Anthropogenic pollution facilitates transformation ...of naturally emitted volatile organic compounds (VOCs) to the particle phase, enhancing the ambient concentrations of biogenic secondary organic aerosol (SOA). It is therefore conceivable that some portion of ambient biogenic SOA can be removed by controlling emissions of anthropogenic pollutants. Direct measurement of the controllable fraction of biogenic SOA is not possible, but can be estimated through 3-dimensional photochemical air quality modeling. To examine this in detail, 22 CMAQ model simulations were conducted over the continental U.S. (August 15 to September 4, 2003). The relative contributions of five emitted pollution classes (i.e., NO x , NH3, SO x , reactive non methane carbon (RNMC) and primary carbonaceous particulate matter (PCM)) on biogenic SOA were estimated by removing anthropogenic emissions of these pollutants, one at a time and all together. Model results demonstrate a strong influence of anthropogenic emissions on predicted biogenic SOA concentrations, suggesting more than 50% of biogenic SOA in the eastern U.S. can be controlled. Because biogenic SOA is substantially enhanced by controllable emissions, classification of SOA as biogenic or anthropogenic based solely on VOC origin is not sufficient to describe the controllable fraction.
This paper presents the first National Emissions Inventory (NEI) of fine particulate matter (PM2.5) that includes the full suite of PM2.5 trace elements (atomic number >10) measured at ambient ...monitoring sites across the U.S. PM2.5 emissions in the NEI were organized and aggregated into a set of 84 source categories for which chemical speciation profiles are available (e.g., Unpaved Road Dust, Agricultural Soil, Wildfires). Emission estimates for ten metals classified as Hazardous Air Pollutants (HAP) were refined using data from a recent HAP NEI. All emissions were spatially gridded, and U.S. emissions maps for dozens of trace elements (e.g., Fe, Ti) are presented for the first time. Nationally, the trace elements emitted in the highest quantities are silicon (3.8 × 105 ton/yr), aluminum (1.4 × 105 ton/yr), and calcium (1.3 × 105 ton/yr). Our chemical characterization of the PM2.5 inventory shows that most of the previously unspeciated emissions are comprised of crustal elements, potassium, sodium, chlorine, and metal-bound oxygen. This work also reveals that the largest PM2.5 sources lacking specific speciation data are off-road diesel-powered mobile equipment, road construction dust, marine vessels, gasoline-powered boats, and railroad locomotives.
The Community Multiscale Air Quality (CMAQ) modeling system is extended to simulate ozone, particulate matter, and related precursor distributions throughout the Northern Hemisphere. Modelled ...processes were examined and enhanced to suitably represent the extended space and time scales for such applications. Hemispheric scale simulations with CMAQ and the Weather Research and Forecasting (WRF) model are performed for multiple years. Model capabilities for a range of applications including episodic long-range pollutant transport, long-term trends in air pollution across the Northern Hemisphere, and air pollution-climate interactions are evaluated through detailed comparison with available surface, aloft, and remotely sensed observations. The expansion of CMAQ to simulate the hemispheric scales provides a framework to examine interactions between atmospheric processes occurring at various spatial and temporal scales with physical, chemical, and dynamical consistency.
Numerous scientific upgrades to the representation of secondary organic aerosol (SOA) are incorporated into the Community Multiscale Air Quality (CMAQ) modeling system. Additions include several ...recently identified SOA precursors: benzene, isoprene, and sesquiterpenes; and pathways: in-cloud oxidation of glyoxal and methylglyoxal, particle-phase oligomerization, and acid enhancement of isoprene SOA. NO x -dependent aromatic SOA yields are also added along with new empirical measurements of the enthalpies of vaporization and organic mass-to-carbon ratios. For the first time, these SOA precursors, pathways and empirical parameters are included simultaneously in an air quality model for an annual simulation spanning the continental U.S. Comparisons of CMAQ-modeled secondary organic carbon (OCsec) with semiempirical estimates screened from 165 routine monitoring sites across the U.S. indicate the new SOA module substantially improves model performance. The most notable improvement occurs in the central and southeastern U.S. where the regionally averaged temporal correlations (r) between modeled and semiempirical OCsec increase from −0.5 to 0.8 and −0.3 to 0.8, respectively, when the new SOA module is employed. Wintertime OCsec results improve in all regions of the continental U.S. and the seasonal and regional patterns of biogenic SOA are better represented.
Secondary organic aerosol (SOA) formed from the atmospheric oxidation of nonmethane organic gases (NMOG) is a major contributor to atmospheric aerosol mass. Emissions and smog chamber experiments ...were performed to investigate SOA formation from gasoline vehicles, diesel vehicles, and biomass burning. About 10–20% of NMOG emissions from these major combustion sources are not routinely speciated and therefore are currently misclassified in emission inventories and chemical transport models. The smog chamber data demonstrate that this misclassification biases model predictions of SOA production low because the unspeciated NMOG produce more SOA per unit mass than the speciated NMOG. We present new source-specific SOA yield parameterizations for these unspeciated emissions. These parameterizations and associated source profiles are designed for implementation in chemical transport models. Box model calculations using these new parameterizations predict that NMOG emissions from the top six combustion sources form 0.7 Tg y ⁻¹ of first-generation SOA in the United States, almost 90% of which is from biomass burning and gasoline vehicles. About 85% of this SOA comes from unspeciated NMOG, demonstrating that chemical transport models need improved treatment of combustion emissions to accurately predict ambient SOA concentrations.
This study analyzes simulated regional-scale ozone burdens both near the surface and aloft, estimates process contributions to these burdens, and calculates the sensitivity of the simulated ...regional-scale ozone burden to several key model inputs with a particular emphasis on boundary conditions derived from hemispheric or global-scale models. The Community Multiscale Air Quality (CMAQ) model simulations supporting this analysis were performed over the continental US for the year 2010 within the context of the Air Quality Model Evaluation International Initiative (AQMEII) and Task Force on Hemispheric Transport of Air Pollution (TF-HTAP) activities. CMAQ process analysis (PA) results highlight the dominant role of horizontal and vertical advection on the ozone burden in the mid-to-upper troposphere and lower stratosphere. Vertical mixing, including mixing by convective clouds, couples fluctuations in free-tropospheric ozone to ozone in lower layers. Hypothetical bounding scenarios were performed to quantify the effects of emissions, boundary conditions, and ozone dry deposition on the simulated ozone burden. Analysis of these simulations confirms that the characterization of ozone outside the regional-scale modeling domain can have a profound impact on simulated regional-scale ozone. This was further investigated by using data from four hemispheric or global modeling systems (Chemistry - Integrated Forecasting Model (C-IFS), CMAQ extended for hemispheric applications (H-CMAQ), the Goddard Earth Observing System model coupled to chemistry (GEOS-Chem), and AM3) to derive alternate boundary conditions for the regional-scale CMAQ simulations. The regional-scale CMAQ simulations using these four different boundary conditions showed that the largest ozone abundance in the upper layers was simulated when using boundary conditions from GEOS-Chem, followed by the simulations using C-IFS, AM3, and H-CMAQ boundary conditions, consistent with the analysis of the ozone fields from the global models along the CMAQ boundaries. Using boundary conditions from AM3 yielded higher springtime ozone columns burdens in the middle and lower troposphere compared to boundary conditions from the other models. For surface ozone, the differences between the AM3-driven CMAQ simulations and the CMAQ simulations driven by other large-scale models are especially pronounced during spring and winter where they can reach more than 10 ppb for seasonal mean ozone mixing ratios and as much as 15 ppb for domain-averaged daily maximum 8 h average ozone on individual days. In contrast, the differences between the C-IFS-, GEOS-Chem-, and H-CMAQ-driven regional-scale CMAQ simulations are typically smaller. Comparing simulated sur face ozone mixing ratios to observations and computing seasonal and regional model performance statistics revealed that boundary conditions can have a substantial impact on model performance. Further analysis showed that boundary conditions can affect model performance across the entire range of the observed distribution, although the impacts tend to be lower during summer and for the very highest observed percentiles. The results are discussed in the context of future model development and analysis opportunities.
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