Development and evaluation of a physics-based windblown dust emission scheme implemented in the CMAQ modeling system

Source contributions to two super dust storms over Northern China in March 2021 and the impact of soil moisture

Two extremely devastating super dust storms (SDS) hit Mongolia and Northern China in March 2021, causing many deaths and substantial economic damage. Accurate forecasting of dust storms is of great importance for avoiding or mitigating their effects. One of the most critical factors affecting dust emissions is soil moisture, but its value in desert exhibits significant uncertainty. In this study, model experiments were conducted to simulate dust emissions using four soil moisture datasets. The results were compared with observations to assess the effects of soil moisture on the dust emission strength. The Integrated Source Apportionment Method (ISAM) was used to track the dust sources and quantify the contribution from each source region to the dust load over the North China Plain (NCP), Korea peninsula, and western Japan. The results show large differences in the dust load depending on the soil moisture datasets used. The high soil moisture in the NCEP dataset results in substantial underestimation of the dust emission flux and PM10 concentration. Despite a minor overestimation of PM10 concentrations in many Northern China cities, the ERA5 dataset yields the best simulation performance. During the two SDS events, about 7.5 Mt dust was released from the deserts in Mongolia and 2.8 Mt from the deserts in China. Source apportionment indicates that the Mongolian Gobi Desert is the dominant source of PM10 in the NCP, Korea peninsula, and western Japan, accounting for 60 %-80 %, while Inner Mongolia contributed 10 %-20 %.

Long- and short-term health benefits attributable to PM2.5 constituents reductions from 2013 to 2021: A spatiotemporal analysis in China

Long- and short-term exposure to constituents of fine particulate matter (PM2.5) substantially affects human health. However, assessments of the health and economic benefits of reducing PM2.5 constituents are scarce. This study estimates the number of premature deaths from all-cause, cardiovascular (CVD), and respiratory diseases avoided due to reductions in daily and annual average concentrations of PM2.5 constituents. The Environmental Benefits Mapping and Analysis Program was used for two scenarios: we used yearly concentrations of PM2.5 constituents from 2013 to 2020 as the baseline concentration surface (Scenario I), and 2021 as the baseline year (Scenario II). With reductions in daily and annual average concentrations of PM2.5 constituents, 309,099 (95 % confidence interval [CI]: 37,265-571,485) and 195,297 (95 % CI: 178,192-211,914) premature deaths were avoided in Scenario I, respectively; meanwhile, 347,296 (95 % CI: 79,258-604,758) and 201,567 (95 % CI: 185,038-217,530) premature deaths were avoided in Scenario II, respectively. Moreover, economic benefits associated with the prevention of premature deaths were estimated using the willingness to pay (WTP) and modified human capital (AHC) methods. The total estimated economic benefits amounted to 563.32 billion RMB (WTP) and 322.03 billion RMB (AHC) in Scenario I. In Scenario II, the associated economic benefits were 751.48 billion RMB (WTP) and 427.56 billion RMB (AHC), accounting for 0.657 and 0.374 % of China's gross domestic product in 2021, respectively. Additionally, we analyzed the sensitivity of CVD-related premature deaths to the concentrations of PM2.5 constituents, and found that CVD-related premature deaths were more sensitive to black carbon.

Characterizing variations in ambient PM2.5 concentrations at the U.S. Embassy in Dhaka, Bangladesh using observations and the CMAQ modeling system

We analyze hourly PM2.5 (particles with an aerodynamic diameter of ≤ 2.5 μm) concentrations measured at the U.S. Embassy in Dhaka over the 2016 - 2021 time period and find that concentrations are seasonally dependent with the highest occurring in winter and the lowest in monsoon seasons. Mean winter PM2.5 concentrations reached ~165-175 μg/m3 while monsoon concentrations remained ~30-35 μg/m3. Annual mean PM2.5 concentration reached ~5-6 times greater than the Bangladesh annual PM2.5 standard of 15 μg/m3. The number of days exceeding the daily PM2.5 standard of 65 μg/m3 in a year approached nearly 50%. Daily-mean PM2.5 concentrations remained elevated (>65 μg/m3) for more than 80 consecutive days. Night-time concentrations were greater than daytime concentrations. The comparison of results obtained from the Community Multiscale Air Quality (CMAQ) model simulations over the Northern Hemisphere using 108-km horizontal grids with observed data suggests that the model can reproduce the seasonal variation of observed data but underpredicts observed PM2.5 in winter months with a normalized mean bias of 13-32%. In the model, organic aerosol is the largest component of PM2.5, of which secondary organic aerosol plays a dominant role. Transboundary pollution has a large impact on the PM2.5 concentration in Dhaka, with an annual mean contribution of ~40 μg/m3.

Tracking Daily Concentrations of PM2.5 Chemical Composition in China since 2000

PM_2.5 chemical components play significant roles in the climate, air quality, and public health, and the roles vary due to their different physicochemical properties. Obtaining accurate and timely updated information on China's PM_2.5 chemical composition is the basis for research and environmental management. Here, we developed a full-coverage near-real-time PM_2.5 chemical composition data set at 10 km spatial resolution since 2000, combining the Weather Research and Forecasting-Community Multiscale Air Quality modeling system, ground observations, a machine learning algorithm, and multisource-fusion PM_2.5 data. PM_2.5 chemical components in our data set are in good agreement with the available observations (correlation coefficients range from 0.64 to 0.75 at a monthly scale from 2000 to 2020 and from 0.67 to 0.80 at a daily scale from 2013 to 2020; most normalized mean biases within ±20%). Our data set reveals the long-term trends in PM_2.5 chemical composition in China, especially the rapid decreases after 2013 for sulfate, nitrate, ammonium, organic matter, and black carbon, at the rate of -9.0, -7.2, -8.1, -8.4, and -9.2% per year, respectively. The day-to-day variability is also well captured, including evolutions in spatial distribution and shares of PM_2.5 components. As part of Tracking Air Pollution in China (http://tapdata.org.cn), this daily-updated data set provides large opportunities for health and climate research as well as policy-making in China.

Role of Dust and Iron Solubility in Sulfate Formation during the Long-Range Transport in East Asia Evidenced by 17O-Excess Signatures

Numerical models have been developed to elucidate air pollution caused by sulfate aerosols (SO₄^2-). However, typical models generally underestimate SO₄^2-, and oxidation processes have not been validated. This study improves the modeling of SO₄^2- formation processes using the mass-independent oxygen isotopic composition [¹⁷O-excess; Δ¹⁷O(SO₄^2-)], which reflects pathways from sulfur dioxide (SO₂) to SO₄^2-, at the background site in Japan throughout 2015. The standard setting in the Community Multiscale Air Quality (CMAQ) model captured SO₄^2- concentration, whereas Δ¹⁷O(SO₄^2-) was underestimated, suggesting that oxidation processes were not correctly represented. The dust inline calculation improved Δ¹⁷O(SO₄^2-) because dust-derived increases in cloud-water pH promoted acidity-driven SO₄^2- production, but Δ¹⁷O(SO₄^2-) was still overestimated during winter as a result. Increasing solubilities of the transition-metal ions, such as iron, which are a highly uncertain modeling parameter, decreased the overestimated Δ¹⁷O(SO₄^2-) in winter. Thus, dust and high metal solubility are essential factors for SO₄^2- formation in the region downstream of China. It was estimated that the remaining mismatch of Δ¹⁷O(SO₄^2-) between the observation and model can be explained by the proposed SO₄^2- formation mechanisms in Chinese pollution. These accurately modeled SO₄^2- formation mechanisms validated by Δ¹⁷O(SO₄^2-) will contribute to emission regulation strategies required for better air quality and precise climate change predictions over East Asia.

Distinct transport mechanisms of East Asian dust and the impact on downwind marine and atmospheric environments

East Asian dust episodes have a multitude of impacts, including on human health, environment, and climate over near-source and receptor regions. However, the mechanistic understanding of the synoptic conditions of these outbreaks at different altitude layers, and their eventual environmental impacts are less studied. The present study analyzed the synoptic transport patterns of East Asian dust during multiple dust generation episodes that occurred over only a few days apart in northern China, and which eventually delivered high PM₁₀ concentrations to surface level and high-altitude locations in Taiwan. Whether the dust plume was uplifted ahead of or behind the 700 hPa trough over East Asia determined its trajectory and eventual impact on the environment downwind. The total dust (iron) deposition over the ocean surface preceding arrival to Taiwan was 2.4 mg m^-2 (0.95 μg m^-2) for the episode impacting the surface level and 5.0 mg m^-2 (4.6 μg m^-2) for the episode impacting high-altitude Taiwan. Dust deposition in marine areas east of China was more intense for the higher altitude transport event that was uplifted behind the 700 hPa trough and resulted in twice higher marine Chl-a concentrations. Furthermore, we estimated a dust-induced direct radiative effect over a high mountainous region in Taiwan of -6.2 to -8.2 W m^-2 at the surface, -1.9 to -2.9 W m^-2 at the top of the atmosphere and +3.9 to +5.3 W m^-2 in the atmosphere. This dust-induced atmospheric warming and surface cooling are non-negligible influences on the atmospheric thermal structure and biogeochemical cycle over the western North Pacific. Overall, this study highlights the significant impacts of dust particles on the marine ecosystem and atmospheric radiation budget over the downwind region, thus lays the foundation for linking these impacts to the initial synoptic conditions in the source area.

Spatial Distribution of Shrubs Impacts Relationships among Saltation, Roughness, and Vegetation Structure in an East Asian Rangeland

Vegetation influences the occurrence of saltation through various mechanisms. Most previous studies have focused on the effects of vegetation on saltation occurrence under spatially homogeneous vegetation, whereas few field studies have examined how spatially heterogeneous cover affects saltation. To examine how spatial heterogeneity of vegetation influences saltation, we surveyed the vegetation and spatial distribution of shrubs and conducted roughness measurements at 11 sites at Tsogt-Ovoo, Gobi steppe of Mongolia, which are dominated by the shrubs Salsola passerina and Anabasis brevifolia. Saltation and meteorological observations were used to calculate the saltation flux, threshold friction velocity, and roughness length. The spatial distribution of shrubs was estimated from the intershrub distance obtained by calculating a semivariogram. Threshold friction velocity was well explained by roughness length. The relationships among roughness, saltation flux, and vegetation cover depended on the spatial distribution of shrubs. When the vegetation was distributed heterogeneously, roughness length increased as the vegetation cover decreased, and the saltation flux increased because the wake interference flow became dominant. When the vegetation was spatially homogeneous, however, the saltation flux was suppressed even when the vegetation cover was small. These field experiments show the importance of considering the spatial distribution of vegetation in evaluating saltation occurrence.

Nitrogen burden from atmospheric deposition in East Asian oceans in 2010 based on high-resolution regional numerical modeling

East Asian oceans are possibly affected by a high nitrogen (N) burden because of the intense anthropogenic emissions in this region. Based on high-resolution regional chemical transport modeling with horizontal grid scales of 36 and 12 km, we investigated the N burden into East Asian oceans via atmospheric deposition in 2010. We found a high N burden of 2-9 kg N ha^-1 yr^-1 over the Yellow Sea, East China Sea (ECS), and Sea of Japan. Emissions over East Asia were dominated by ammonia (NH₃) over land and nitrogen oxides (NO_x) over oceans, and N deposition was dominated by reduced N over most land and open ocean, whereas it was dominated by oxidized N over marginal seas and desert areas. The verified numerical modeling identified that the following processes were quantitatively important over East Asian oceans: the dry deposition of nitric acid (HNO₃), NH₃, and coarse-mode (aerodynamic diameter greater than 2.5 μm) NO₃^-, and wet deposition of fine-mode (aerodynamic diameter less than 2.5 μm) NO₃^- and NH₄⁺. The relative importance of the dry deposition of coarse-mode NO₃^- was higher over open ocean. The estimated N deposition to the whole ECS was 390 Gg N yr^-1; this is comparable to the discharge from the Yangtze River to the ECS, indicating the significant contribution of atmospheric deposition. Based on the high-resolution modeling over the ECS, a tendency of high deposition in the western ECS and low deposition in the eastern ECS was found, and a variety of deposition processes were estimated. The dry deposition of coarse-mode NO₃^- and wet deposition of fine-mode NH₄⁺ were the main factors, and the wet deposition of fine-mode NO₃^- over the northeastern ECS and wet deposition of coarse-mode NO₃^- over the southeastern ECS were also found to be significant processes determining N deposition over the ECS.

Estimating US Background Ozone Using Data Fusion

US background (US-B) ozone (O3) is the O3 that would be present in the absence of US anthropogenic (US-A) emissions. US-B O3 varies by location and season and can make up a large, sometimes dominant, portion of total O3. Typically, US-B O3 is quantified using a chemical transport model (CTM) though results are uncertain due to potential errors in model process descriptions and inputs, and there are significant differences in various model estimates of US-B O3. We develop and apply a method to fuse observed O3 with US-B O3 simulated by a regional CTM (CMAQ). We apportion the model bias as a function of space and time to US-B and US-A O3. Trends in O3 bias are explored across different simulation years and varying model scales. We found that the CTM US-B O3 estimate was typically biased low in spring and high in fall across years (2016-2017) and model scales. US-A O3 was biased high on average, with bias increasing for coarser resolution simulations. With the application of our data fusion bias adjustment method, we estimate a 28% improvement in the agreement of adjusted US-B O3. Across the four estimates, we found annual mean CTM-simulated US-B O3 ranging from 30 to 37 ppb with the spring mean ranging from 32 to 39 ppb. After applying the bias adjustment, we found annual mean US-B O3 ranging from 32 to 33 ppb with the spring mean ranging from 37 to 39 ppb.

Unexpected air quality impacts from implementation of green infrastructure in urban environments: A Kansas City case study

Green infrastructure (GI) implementation can benefit an urban environment by reducing the impacts of urban stormwater on aquatic ecosystems and human health. However, few studies have systematically analyzed the biophysical effects on regional meteorology and air quality that are triggered by changes in the urban vegetative coverage. In this study we use a state-of-the-art high-resolution air quality model to simulate the effects of a hypothetically feasible vegetation-focused GI implementation scenario in Kansas City, MO/KS on regional meteorology and air quality. Full year simulations are conducted for both the base case and GI land use scenarios using two different land surface models (LSMs) schemes inside the meteorological model. While the magnitudes of the changes in air quality due to the GI implementation differ using the two LSMs, the model outputs consistently showed increases in summertime PM_2.5 (1.1 μg m^-3, approximately 10% increase using NOAH LSM), which occurred mostly during the night and arose from the primary components, due to the cooler surface temperatures and the decreased planetary boundary layer height (PBLH). Both the maximum daily 8-hour average ozone and 1 h daily maximum O₃ during summertime, decreased over the downtown areas (maximum decreases of 0.9 and 1.4 ppbv respectively). The largest ozone decreases were simulated to happen during the night, mainly caused by the titration effect of increased NO_x concentration from the lower PBLH. These results highlight the region-specific non-linear process feedback from GI on regional air quality, and further demonstrate the need for comprehensive coupled meteorological-air quality modeling systems and necessity of accurate land surface model for studying these impacts.

Aeolian Prokaryotic Communities of the Global Dust Belt Over the Red Sea

Aeolian prokaryotic communities (APC) are important components of bioaerosols that are transported freely or attached to dust particles suspended in the atmosphere. Terrestrial and marine ecosystems are known to release and receive significant prokaryote loads into and from the surrounded atmospheric air. However, compared to terrestrial systems, there is a lack of microbial characterization of atmospheric dust over marine systems, such as the Red Sea, which receives significant terrestrial dust loads and is centrally located within the Global Dust Belt. Prokaryotic communities are likely to be particularly important in the Global Dust Belt, the area between the west coast of North Africa and Central Asia that supports the highest dust fluxes on the planet. Here we characterize the diversity and richness of the APC over the Red Sea ecosystem, the only sea fully contained within the Global Dust Belt. MiSeq sequencing was used to target 16S ribosomal DNA of two hundred and forty aeolian dust samples. These samples were collected at ∼7.5 m high above the sea level at coastal and offshore sampling sites over a 2-year period (2015–2017). The sequencing outcomes revealed that the APC in the atmospheric dust is dominated by Proteobacteria (42.69%), Firmicutes (41.11%), Actinobacteria, (7.69%), and Bacteroidetes (3.49%). The dust-associated prokaryotes were transported from different geographical sources and found to be more diverse than prokaryotic communities of the Red Sea surface water. Marine and soil originated prokaryotes were detected in APC. Hence, depending on the season, these groups may have traveled from other distant sources during storm events in the Red Sea region, where the APC structure is influenced by the origin and the concentration of aeolian dust particles. Accordingly, further studies of the impact of atmospheric organic aerosols on the recipient environments are required.

Stepwise Assessment of Different Saltation Theories in Comparison with Field Observation Data

Wind-blown dust models use input data, including soil conditions and meteorology, to interpret the multi-step wind erosion process and predict the quantity of dust emission. Therefore, the accuracy of the wind-blown dust models is dependent on the accuracy of each input condition and the robustness of the model schemes for each elemental step of wind erosion. A thorough evaluation of a wind-blown model thus requires validation of the input conditions and the elemental model schemes. However, most model evaluations and intercomparisons have focused on the final output of the models, i.e., the vertical dust emission. Recently, a delicate set of measurement data for saltation flux and friction velocity was reported from the Japan-Australia Dust Experiment (JADE) Project, which enabled the step-by-step evaluation of wind-blown dust models up to the saltation step. When all the input parameters were provided from the observations, both the two widely used saltation schemes showed very good agreement with measurements, with the correlation coefficient and the agreement of index both being larger than 0.9, which demonstrated the strong robustness of the physical schemes for saltation. However, using the meteorology model to estimate the input conditions such as weather and soil conditions, considerably degraded the models’ performance. The critical reason for the model failure was determined to be the inaccuracy in the estimation of the threshold friction velocity (representing soil condition), followed by inaccurate estimation of surface wind speed. It was not possible to determine which of the two saltation schemes was superior, based on the present study results. Such differentiation will require further evaluation studies using more measurements of saltation flux and vertical dust emissions.

Simulation of airborne trace metals in fine particulate matter over North America

Trace metal distributions are of relevance to understand sources of fine particulate matter (PM2.5), PM2.5-related health effects, and atmospheric chemistry. However, knowledge of trace metal distributions is lacking due to limited ground-based measurements and model simulations. This study develops a simulation of 12 trace metal concentrations (Si, Ca, Al, Fe, Ti, Mn, K, Mg, As, Cd, Ni and Pb) over continental North America for 2013 using the GEOS-Chem chemical transport model. Evaluation of modeled trace metal concentrations with observations indicates a spatial consistency within a factor of 2, an improvement over previous studies that were within a factor of 3-6. The spatial distribution of trace metal concentrations reflects their primary emission sources. Crustal element (Si, Ca, Al, Fe, Ti, Mn, K) concentrations are enhanced over the central US from anthropogenic fugitive dust and over the southwestern U.S. due to natural mineral dust. Heavy metal (As, Cd, Ni and Pb) concentrations are high over the eastern U.S. from industry. K is abundance in the southeast from biomass burning and high concentrations of Mg is observed along the coast from sea spray. The spatial pattern of PM2.5 mass is most strongly correlated with Pb, Ni, As and K due to their signature emission sources. Challenges remain in accurately simulating observed trace metal concentrations. Halving anthropogenic fugitive dust emissions in the 2011 National Air Toxic Assessment (NATA) inventory and doubling natural dust emissions in the default GEOS-Chem simulation was necessary to reduce biases in crustal element concentrations. A fivefold increase of anthropogenic emissions of As and Pb was necessary in the NATA inventory to reduce the national-scale bias versus observations by more than 80 %, potentially reflecting missing sources.

Reflecting on progress since the 2005 NARSTO emissions inventory report

Emission inventories are the foundation for cost-effective air quality management activities. In 2005, a report by the public/private partnership North American Research Strategy for Tropospheric Ozone (NARSTO) evaluated the strengths and weaknesses of North American emissions inventories and made recommendations for improving their effectiveness. This paper reviews the recommendation areas and briefly discusses what has been addressed, what remains unchanged, and new questions that have arisen. The findings reveal that all emissions inventory improvement areas identified by the 2005 NARSTO publication have been explored and implemented to some degree. The U.S. National Emissions Inventory has become more detailed and has incorporated new research into previously under-characterized sources such as fine particles and biomass burning. Additionally, it is now easier to access the emissions inventory and the documentation of the inventory via the internet. However, many emissions-related research needs exist, on topics such as emission estimation methods, speciation, scalable emission factor development, incorporation of new emission measurement techniques, estimation of uncertainty, top-down verification, and analysis of uncharacterized sources. A common theme throughout this retrospective summary is the need for increased coordination among stakeholders. Researchers and inventory developers must work together to ensure that planned emissions research and new findings can be used to update the emissions inventory. To continue to address emissions inventory challenges, industry, the scientific community, and government agencies need to continue to leverage resources and collaborate as often as possible. As evidenced by the progress noted, continued investment in and coordination of emissions inventory activities will provide dividends to air quality management programs across the country, continent, and world. Implications: In 2005, a report by the public/private partnership North American Research Strategy for Tropospheric Ozone (NARSTO) evaluated the strengths and weaknesses of North American air pollution emissions inventories. This paper reviews the eight recommendation areas and briefly discusses what has been addressed, what remains unchanged, and new questions that have arisen. Although progress has been made, many opportunities exist for the scientific agencies, industry, and government agencies to leverage resources and collaborate to continue improving emissions inventories.

Simulating Performance of CHIMERE on a Late Autumnal Dust Storm over Northern China

The accurate forecasting of dust emission and transport is a societal demand worldwide as dust pollution is part of many health, economic, and environment issues, which significantly impact sustainable development. The dust forecasting ability of present air quality forecast systems is mainly focused on spring dust events in East Asia, but further improvement may be needed as there is still difficulty in forecasting autumn dust activities, such as failing to predict the serious dust storm that occurred on 25 to 26 November 2018. In this study, a state-of-the-art air quality model, CHIMERE, with three coupled dust schemes was introduced for the first time to simulate the dust emissions during this event to qualitatively and quantitatively validate its dust simulating performance over Northern China. The model results reported that two of the three dust schemes were able to capture the dust emission source located in Gansu Province and reproduce the easterly dust transport path, showing moderately close agreement in the horizontal and vertical distribution patterns with the ground-based and satellite observations. The simulated PM10 concentration had a better relationship with the observed values with a correlation coefficient up to 0.96, while it was lower in the transported areas. Meanwhile, the simulations also presented incorrect dust emission positions such as in areas between the Hulun Buir sandy land and Horqin sandy land. Our results indicate that CHIMERE exhibits reasonably good performance regarding its dust simulation and forecast ability over this area, and its application would help to improve the dust analysis and forecast abilities in Northern China.

The impact of air pollutant deposition on solar energy system efficiency: An approach to estimate PV soiling effects with the Community Multiscale Air Quality (CMAQ) model

Deposition and accumulation of aerosol particles on photovoltaics (PV) panels, which is commonly referred to as "soiling of PV panels," impacts the performance of the PV energy system. It is desirable to estimate the soiling effect at different locations and times for modeling the PV system performance and devising cost-effective mitigation. This study presents an approach to estimate the soiling effect by utilizing particulate matter (PM) dry deposition estimates from air quality model simulations. The Community Multiscale Air Quality (CMAQ) modeling system used in this study was developed by the U.S. Environmental Protection Agency (U.S. EPA) for air quality assessments, rule-making, and research. Three deposition estimates based on different surface roughness length parameters assumed in CMAQ were used to illustrate the soling effect in different land-use types. The results were analyzed for three locations in the U.S. for year 2011. One urban and one suburban location in Colorado were selected because there have been field measurements of particle deposition on solar panels and analysis on the consequent soiling effect performed at these locations. The third location is a coastal city in Texas, the City of Brownsville. These three locations have distinct ambient environments. CMAQ underestimates particle deposition by 40% to 80% when compared to the field measurements at the two sites in Colorado due to the underestimations in both the ambient PM₁₀ concentration and deposition velocity. The estimated panel transmittance sensitivity due to the deposited particles is higher than the sensitivity obtained from the measurements in Colorado. The final soiling effect, which is transmittance loss, is estimated as 3.17 ± 4.20% for the Texas site, 0.45 ± 0.33%, and 0.31 ± 0.25% for the Colorado sites. Although the numbers are lower compared to the measurements in Colorado, the results are comparable with the soiling effects observed in U.S.

Updates to the Noah Land Surface Model in WRF-CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition

Regional, state, and local environmental regulatory agencies often use Eulerian models to investigate the potential impacts on pollutant deposition and air quality from changes in land use, anthropogenic and natural emissions, and climate. The Noah land surface model (LSM) in the Weather Research and Forecasting (WRF) model is widely used with the Community Multiscale Air Quality (CMAQ) model for such investigations, but there are many inconsistencies that need to be changed so that they are consistent with dry deposition and emission processes. In this work, the Noah LSM in WRFv3.8.1 is improved in its linkage to CMAQv5.2 by adding important parameters to the WRF/Noah output, updating the WRF soil and vegetation reference tables that influence CMAQ wet and dry photochemical deposition processes, and decreasing WRF/Noah's top soil layer depth to be consistent with CMAQ processes (e.g., windblown dust and bidirectional ammonia exchange). The modified WRF/Noah-CMAQ system (both off-line and coupled) impacts meteorological predictions of 2-m temperature (T2; increases and decreases), 2-m mixing ratio (Q2; decreases), and 10-m wind speed (WSPD10; decreases) in the United States. These changes are mostly driven by leaf area index values and aerodynamic roughness lengths updated in the vegetation tables based on satellite data, with additional impacts from soil tables updated based on recent soil data. Improvements in the consistency in the treatment of land surface processes between CMAQ and WRF resulted in improvements in both estimated meteorological (e.g., T2, WSPD10, and latent heat fluxes) and chemical (e.g., ozone, sulfur dioxide, and windblown dust) model estimates.

Updates to the Noah Land Surface Model in WRF-CMAQ to Improve Simulated Meteorology, Air Quality, and Deposition

Regional, state, and local environmental regulatory agencies often use Eulerian models to investigate the potential impacts on pollutant deposition and air quality from changes in land use, anthropogenic and natural emissions, and climate. The Noah land surface model (LSM) in the Weather Research and Forecasting (WRF) model is widely used with the Community Multiscale Air Quality (CMAQ) model for such investigations, but there are many inconsistencies that need to be changed so that they are consistent with dry deposition and emission processes. In this work, the Noah LSM in WRFv3.8.1 is improved in its linkage to CMAQv5.2 by adding important parameters to the WRF/Noah output, updating the WRF soil and vegetation reference tables that influence CMAQ wet and dry photochemical deposition processes, and decreasing WRF/Noah's top soil layer depth to be consistent with CMAQ processes (e.g., windblown dust and bidirectional ammonia exchange). The modified WRF/Noah-CMAQ system (both off-line and coupled) impacts meteorological predictions of 2-m temperature (T2; increases and decreases), 2-m mixing ratio (Q2; decreases), and 10-m wind speed (WSPD10; decreases) in the United States. These changes are mostly driven by leaf area index values and aerodynamic roughness lengths updated in the vegetation tables based on satellite data, with additional impacts from soil tables updated based on recent soil data. Improvements in the consistency in the treatment of land surface processes between CMAQ and WRF resulted in improvements in both estimated meteorological (e.g., T2, WSPD10, and latent heat fluxes) and chemical (e.g., ozone, sulfur dioxide, and windblown dust) model estimates.

Assessing PM2.5 Model Performance for the Conterminous U.S. with Comparison to Model Performance Statistics from 2007-2015

Previous studies have proposed that model performance statistics from earlier photochemical grid model (PGM) applications can be used to benchmark performance in new PGM applications. A challenge in implementing this approach is that limited information is available on consistently calculated model performance statistics that vary spatially and temporally over the U.S. Here, a consistent set of model performance statistics are calculated by year, season, region, and monitoring network for PM2.5 and its major components using simulations from versions 4.7.1-5.2.1 of the Community Multiscale Air Quality (CMAQ) model for years 2007-2015. The multi-year set of statistics is then used to provide quantitative context for model performance results from the 2015 simulation. Model performance for PM2.5 organic carbon in the 2015 simulation ranked high (i.e., favorable performance) in the multi-year dataset, due to factors including recent improvements in biogenic secondary organic aerosol and atmospheric mixing parameterizations in CMAQ. Model performance statistics for the Northwest region in 2015 ranked low (i.e., unfavorable performance) for many species in comparison to the 2007-2015 dataset. This finding motivated additional investigation that suggests a need for improved speciation of wildfire PM2.5emissions and modeling of boundary layer dynamics near water bodies. Several limitations were identified in the approach of benchmarking new model performance results with previous results. Since performance statistics vary widely by region and season, a simple set of national performance benchmarks (e.g., one or two targets per species and statistic) as proposed previously are inadequate to assess model performance throughout the U.S. Also, trends in model performance statistics for sulfate over the 2007 to 2015 period suggest that model performance for earlier years may not be a useful reference for assessing model performance for recent years in some cases. Comparisons of results from the 2015 base case with results from five sensitivity simulations demonstrated the importance of parameterizations of NH3 surface exchange, organic aerosol volatility and production, and emissions of crustal cations for predicting PM2.5 species concentrations.

Modeling emissions for three-dimensional atmospheric chemistry transport models

Poor air quality is still a threat for human health in many parts of the world. In order to assess measures for emission reductions and improved air quality, three-dimensional atmospheric chemistry transport modeling systems are used in numerous research institutions and public authorities. These models need accurate emission data in appropriate spatial and temporal resolution as input. This paper reviews the most widely used emission inventories on global and regional scales and looks into the methods used to make the inventory data model ready. Shortcomings of using standard temporal profiles for each emission sector are discussed, and new methods to improve the spatiotemporal distribution of the emissions are presented. These methods are often neither top-down nor bottom-up approaches but can be seen as hybrid methods that use detailed information about the emission process to derive spatially varying temporal emission profiles. These profiles are subsequently used to distribute bulk emissions such as national totals on appropriate grids. The wide area of natural emissions is also summarized, and the calculation methods are described. Almost all types of natural emissions depend on meteorological information, which is why they are highly variable in time and space and frequently calculated within the chemistry transport models themselves. The paper closes with an outlook for new ways to improve model ready emission data, for example, by using external databases about road traffic flow or satellite data to determine actual land use or leaf area. In a world where emission patterns change rapidly, it seems appropriate to use new types of statistical and observational data to create detailed emission data sets and keep emission inventories up-to-date.

IMPLICATIONS

Emission data are probably the most important input for chemistry transport model (CTM) systems. They need to be provided in high spatial and temporal resolution and on a grid that is in agreement with the CTM grid. Simple methods to distribute the emissions in time and space need to be replaced by sophisticated emission models in order to improve the CTM results. New methods, e.g., for ammonia emissions, provide grid cell-dependent temporal profiles. In the future, large data fields from traffic observations or satellite observations could be used for more detailed emission data.

Coupling of organic and inorganic aerosol systems and the effect on gas-particle partitioning in the southeastern US

Several models were used to describe the partitioning of ammonia, water, and organic compounds between the gas and particle phases for conditions in the southeastern US during summer 2013. Existing equilibrium models and frameworks were found to be sufficient, although additional improvements in terms of estimating pure-species vapor pressures are needed. Thermodynamic model predictions were consistent, to first order, with a molar ratio of ammonium to sulfate of approximately 1.6 to 1.8 (ratio of ammonium to 2× sulfate, RN/2S ≈ 0.8 to 0.9) with approximately 70% of total ammonia and ammonium (NH x ) in the particle. Southeastern Aerosol Research and Characterization Network (SEARCH) gas and aerosol and Southern Oxidant and Aerosol Study (SOAS) Monitor for AeRosols and Gases in Ambient air (MARGA) aerosol measurements were consistent with these conditions. CMAQv5.2 regional chemical transport model predictions did not reflect these conditions due to a factor of 3 overestimate of the nonvolatile cations. In addition, gas-phase ammonia was overestimated in the CMAQ model leading to an even lower fraction of total ammonia in the particle. Chemical Speciation Network (CSN) and aerosol mass spectrometer (AMS) measurements indicated less ammonium per sulfate than SEARCH and MARGA measurements and were inconsistent with thermodynamic model predictions. Organic compounds were predicted to be present to some extent in the same phase as inorganic constituents, modifying their activity and resulting in a decrease in [H+]air (H+ in μgm-3 air), increase in ammonia partitioning to the gas phase, and increase in pH compared to complete organic vs. inorganic liquid-liquid phase separation. In addition, accounting for nonideal mixing modified the pH such that a fully interactive inorganic-organic system had a pH roughly 0.7 units higher than predicted using traditional methods (pH = 1.5 vs. 0.7). Particle-phase interactions of organic and inorganic compounds were found to increase partitioning towards the particle phase (vs. gas phase) for highly oxygenated (O : C≥0.6) compounds including several isoprene-derived tracers as well as levoglu-cosan but decrease particle-phase partitioning for low O: C, monoterpene-derived species.

Improving the simulation of convective dust storms in regional-to-global models

Convective dust storms have significant impacts on atmospheric conditions and air quality and are a major source of dust uplift in summertime. However, regional-to-global models generally do not accurately simulate these storms, a limitation that can be attributed to (1) using a single mean value for wind speed per grid box, i.e., not accounting for subgrid wind variability and (2) using convective parametrizations that poorly simulate cold pool outflows. This study aims to improve the simulation of convective dust storms by tackling these two issues. Specifically, we incorporate a probability distribution function for surface wind in each grid box to account for subgrid wind variability due to dry and moist convection. Furthermore, we use lightning assimilation to increase the accuracy of the convective parameterization and simulated cold pool outflows. This updated model framework is used to simulate a massive convective dust storm that hit Phoenix, AZ, on 6 July 2011. The results show that lightning assimilation provides a more realistic simulation of precipitation features, including timing and location, and the resulting cold pool outflows that generated the dust storm. When those results are combined with a dust model that accounts for subgrid wind variability, the prediction of dust uplift and concentrations are considerably improved compared to the default model results. This modeling framework could potentially improve the simulation of convective dust storms in global models, regional climate simulations, and retrospective air quality studies.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

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 (O₃) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM_2.5) 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 O₃ 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_2.5 bias (PM_2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O₃ and PM_2.5 on average in January and July. Overall, the seasonal variation in simulated PM_2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM_2.5 concentrations decrease in the winter (when PM_2.5 is generally overestimated by CMAQ) and increase in the summer (when PM_2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O₃ mean bias, as O₃ tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O₃ is low); however, O₃ 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 _x (NO + NO₂), VOC and SO _x (SO₂ + SO₄) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O₃ due to large, widespread reductions in observed emissions.

Extending the Community Multiscale Air Quality (CMAQ) Modeling System to Hemispheric Scales: Overview of Process Considerations and Initial Applications

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.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

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 (O₃) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM_2.5) 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 O₃ 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_2.5 bias (PM_2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O₃ and PM_2.5 on average in January and July. Overall, the seasonal variation in simulated PM_2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM_2.5 concentrations decrease in the winter (when PM_2.5 is generally overestimated by CMAQ) and increase in the summer (when PM_2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O₃ mean bias, as O₃ tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O₃ is low); however, O₃ 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 _x (NO + NO₂), VOC and SO _x (SO₂ + SO₄) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O₃ due to large, widespread reductions in observed emissions.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

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 (O₃) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM_2.5) 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 O₃ 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_2.5 bias (PM_2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O₃ and PM_2.5 on average in January and July. Overall, the seasonal variation in simulated PM_2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM_2.5 concentrations decrease in the winter (when PM_2.5 is generally overestimated by CMAQ) and increase in the summer (when PM_2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O₃ mean bias, as O₃ tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O₃ is low); however, O₃ 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 _x (NO + NO₂), VOC and SO _x (SO₂ + SO₄) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O₃ due to large, widespread reductions in observed emissions.

Description and evaluation of the Community Multiscale Air Quality (CMAQ) modeling system version 5.1

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 (O3) mixing ratios on average due to reduced NO titration, and lower fine particulate matter (PM2.5) 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 O3 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 PM2.5 bias (PM2.5 is typically underestimated by CMAQ in the summer), while updates to the gas chemistry result in slightly higher O3 and PM2.5 on average in January and July. Overall, the seasonal variation in simulated PM2.5 generally improves in CMAQv5.1 (when considering all model updates), as simulated PM2.5 concentrations decrease in the winter (when PM2.5 is generally overestimated by CMAQ) and increase in the summer (when PM2.5 is generally underestimated by CMAQ). Ozone mixing ratios are higher on average with v5.1 vs. v5.0.2, resulting in higher O3 mean bias, as O3 tends to be overestimated by CMAQ throughout most of the year (especially at locations where the observed O3 is low); however, O3 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 x (NO + NO2), VOC and SO x (SO2 + SO4) emissions than v5.0.2, representing an improvement as previous studies have shown CMAQ to underestimate the observed reduction in O3 due to large, widespread reductions in observed emissions.