TRACE: Tennessee Research and Creative TRACE: Tennessee Research and Creative Exchange Exchange Model development of dust emission and heterogeneous Model development of dust emission and heterogeneous chemistry within the Community Multiscale Air Quality modeling chemistry within the Community Multiscale Air Quality modeling system and its application over East Asia system and its application over East Asia

. The Community Multiscale Air Quality (CMAQ) model has been further developed in terms of simulating natural wind-blown dust in this study, with a series of modiﬁ-cations aimed at improving the model’s capability to predict the emission, transport, and chemical reactions of dust. The default parameterization of initial threshold friction velocity constants are revised to correct the double counting of the impact of soil moisture in CMAQ by the reanalysis of ﬁeld experiment data; source-dependent speciation proﬁles for dust emission are derived based on local measurements for the Gobi and Taklamakan deserts in East Asia; and dust heterogeneous chemistry is also implemented. The improved dust module in the CMAQ is applied over East Asia for March and April from 2006 to 2010. The model evaluation result shows that the simulation bias of PM 10 and aerosol optical depth (AOD) is reduced, respectively, from − 55.42 and − 31.97 % by the original CMAQ to − 16.05 and − 22.1 % by the revised CMAQ. Comparison with observations at the nearby Gobi stations of Duolun and Yulin indicates that applying a source-dependent proﬁle helps reduce simulation bias for trace metals. Implementing heterogeneous chemistry also results in better agreement with observations for sulfur dioxide (SO 2 ), sulfate (SO 2 − 4 ), nitric acid (HNO 3 ), nitrous oxides (NO x ) , and nitrate (NO − 3 ). The investigation of a severe dust storm episode from 19 to 21 March 2010 suggests that the revised CMAQ is capable of capturing the spatial distribution and temporal variation of dust. The model evaluation also indicates potential uncertainty within the excessive soil moisture used by meteorological simulation. The mass contribution of ﬁne-mode particles in dust emission may be underestimated by 50 %. The revised CMAQ model provides a useful tool for future studies to investigate the emission, transport, and impact of wind-blown dust over East Asia and elsewhere.


Introduction
Natural dust has a wide impact on many different aspects of the Earth's system. It reduces atmospheric visibility (Engelstaedter et al., 2003;Kurosaki and Mikami, 2005;Washington et al., 2003), deteriorates air quality (De Longueville et al., 2010;Prospero, 1999), alters the radiative forcing budget (Liao et al., 2004;Miller et al., 2006;Reddy et al., 2005), and also affects the cloud properties and precipitation (Rosenfeld et al., 2001;Forster et al., 2007). Over East Asia, spring time dust storms often lead to severe air pollution since the intensively elevated aerosol loadings are dumped over the most populated areas. The estimated global source of mineral dust with diameters below 10 µm is between 1000 and 4000 Tg year −1 on a global scale as reported by Intergovernmental Panel on Climate Change (IPCC), and  reported annual Asian dust emission as 800 Tg. East Asian dust mainly originates from two dominant source regions and their surrounding areas, including the Taklamakan Desert in northwest China and the Gobi Desert in Mongolia and northern China (Huang et al., 2010). In spring, the Mongolian cyclone associated with the East Asian trough often leads to strong northwesterly near-surface winds (Shao and Dong, 2006) that lift and transport the eolian dust particles. East Asian dust can be transported to densely populated areas over China (Qian et al., 2002), South Korea (Chun et al., 2001;Park and In, 2003), and Japan (Ma et al., 2001;Uno et al., 2001) and at times can even be transported across the Pacific Ocean, reaching as far as the west coast of North America (Fairlie et al., 2010;Zhao et al., 2010). Along the transport pathway, mineral dust particles also serve as carriers and reaction platforms by taking up reactive gases such as ozone (O 3 ), nitrogen oxides (NO x ), sulfur dioxide (SO 2 ), nitric acid (HNO 3 ), hydroxyl radicals (OH), and volatile organic compounds (VOCs). The heterogeneous dust chemistry may change the photochemistry, acid deposition, and production of secondary aerosols in the atmosphere. Moreover, East Asian dust is believed to contribute geochemically significant amounts of minerals that are deposited in the western part of the Pacific Ocean. These minerals may alter the oceanic primary productivity Zhuang et al., 1992) as well.
Since natural dust links the biogeochemical cycle of land, atmosphere, and ocean, understanding the emission, evolution, and transport of dust is essential for further examining its impacts on the Earth's system. Numerical modeling is one of the most important approaches for systematically investigating dust. Many global models simulate dust emissions, transport, and depositions. Huneeus et al. (2011) conducted intercomparisons of 15 global models and reported their simulated aerosol optical depth (AOD) and Ångström exponent (AE) within a factor of 2 and the deposition and surface concentration within a factor of 10 with respect to observations, indicating significant variations among different models. Regional models usually represent dust by following the same coherent manner as global models. For example, the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) (Grell et al., 2005) coupled with the GOCART scheme (Ginoux et al., 2001) has been applied to simulate dust emission over central East Asia (Kumar et al., 2014), the United States (Zhao et al., 2010), and East Asia (Chen et al., 2013). The Sulfur Transport and dEposition Model (STEM) (Carmichael et al., 2003) used the COAMPS scheme (Liu and Westphal, 2001) and has been applied over East Asia (Tang et al., 2004). Regional models have fine spatiotemporal resolution and multiple physical parameterizations at the cost of intensive computation. As compared to global models, regional models may provide more realistic representations of the surface roughness, soil moisture, and contents and also allow comparable validation against surface observations (Darmenova and Sokolik, 2008).
The Community Multiscale Air Quality (CMAQ) model is a state-of-the-science model and has been applied in numerous regional modeling studies worldwide. Unlike other models, in which dust is usually treated as a unique aerosol, the CMAQ distributes dust particles into 19 aerosol species, such as inorganic aerosols and trace metals. This approach is consistent with the original design of the CMAQ as an air quality model, and it also provides a potential platform to examine the diversities of chemical and physical properties within dust particles. It enables the model to examine the mixing status and net effect of natural dust and anthropogenic aerosols on air quality and regional climate. The validation of the CMAQ performance is not well understood due to limited research efforts. Appel et al. (2013) conducted a full year simulation with CMAQ over the continental United States for 2006 and reported good agreement between simulations and observations, with the mean bias around ±0.5 µg m −3 and 0.5-1.5 µg m −3 (∼ ±30 %) for soil concentrations over western and eastern United States, respectively. But the CMAQ simulations over other regions underestimate dust emissions significantly. Fu et al. (2014) reported that the default dust scheme in the CMAQ underestimated dust emission by 98 % during a 6-day dust storm episode in 2011. With the modeling domain covering the entire Northern Hemisphere, Xing et al. (2015) also suggested that the CMAQ underestimated AOD by 30-60 % in areas where dust is dominant, while the bias was less than ±15 % elsewhere.
The studies mentioned above indicate that the capability of the CMAQ to simulate natural dust emissions remains poorly understood. In addition, the current dust scheme in the CMAQ does not include dust heterogeneous chemistry, while some studies have revealed the important impact of dust chemistry on ambient air pollutants with both measurement (Krueger et al., 2004;Matsuki et al., 2005;Usher et al., 2003) and modeling evidence (Bauer et al., 2004;Bian and Zender, 2003;Dentener et al., 1996). The objective of this study is to evaluate and improve the model's capability to reproduce dust emission and also to enable the model to treat the dust heterogeneous chemistry. Section 2 introduces the method of applying new parameterizations and implementing dust heterogeneous chemistry in the CMAQ, whereas Sect. 3 summarizes the improved model performance. Section 4 discusses the enhanced model capability and remaining uncertainties, and Sect. 5 concludes the paper with a summary of the findings. The process of wind-blown dust emission is controlled by a number of environmental variables, including wind speed, soil texture, land use type, vegetation cover, and soil moisture. Dust deflation is favored by dry soil with low and sparse vegetation and constrained by high soil moisture. The dust emission scheme employed in the CMAQ is developed by Tong et al. (2016). The emission (vertical flux) of the dust F (g m −2 s −1 ) was estimated based on a modified Owen's equation (Owen, 1964): where M is the erodible land use type, N is the soil texture type, and K is the ratio of vertical to horizontal flux calculated based on the amount of clay (clay %) within the soil: K = 10 0.134×(clay %)−6 , when:clay % < 20 % 0.0002, when:clay % > 20% . (2) The value of K (0.0002) for clay percentage > 20 % is used here following the recommendation of Marticorena and Bergametti (1995). A is a scaling factor (set as 32.0 in this study), ρ is air density, g is gravitational acceleration (9.8 m s −2 ), S i is dust source area for land type i, and SEP is the soil erodibility factor, which is calculated based on the fraction of clay, silt, and sand of the soil as SEP = 0.08 × clay + 1.0 × silt + 0.12 × sand.
(3) u * is the friction velocity, and u * ti,j is the threshold friction velocity for soil type j and land use type i. More details of the dust emission algorithm have been given in Tong et al. (2016). Equation (1) is applied only when the modelcalculated friction velocity exceeds the designated threshold value. Therefore, the value of threshold friction velocity is critical to determine the onset and magnitude of dust emission in the CMAQ model. In the CMAQ dust module, the threshold friction velocity is dynamically calculated based on the presence of nonerodible elements and the change in soil moisture (Tong et al., 2016). The effect of non-erodible elements is represented by wind energy partitioning following Marticorena et al. (1997). The effect of soil moisture on dust emission is implemented following a two-step approach proposed by Fécan et al. (1999). First, the maximum water holding capacity (W max ) for each soil type is determined based on the amount of clay in the soil: In the case of soil moisture exceeding W max , the threshold friction velocity is then adjusted (Marticorena et al., 1997) as where u * ci,j is the initial threshold friction velocity constant, and Z i,j is the surface roughness adjusting factor calculated with surface roughness length from the meteorology field, calculated as The constants C 1 = 32 and C 2 = −5 used in this equation are derived from field measurement data from Gillette et al. (1980) and the relationship between roughness length and friction velocity described in Marticorena et al. (1997).
And f soilm i,j is the moisture adjustment factor calculated using a revised Fécan formulation (Fécan et al., 1999): where S m is soil moisture andS l is the saturation soil moisture limit determined by soil textures. The values of S l are given by the North American Mesoscale (NAM) modeling system (Rogers et al., 2009), as summarized in Table 1.
Previously, the values of initial threshold friction velocity constant were taken from observed data from wind tunnel experiments conducted by Gillette and coworkers (Gillette et al., 1980(Gillette et al., , 1982. Fu et al. (2014) reported the u * ci,j used in the CMAQ has an average value of 0.7 m s −1 among all soil types, which is too high to generate enough dust particles over East Asia. Fu et al. (2014) used a fixed value of 0.3 m s −1 based on a study of local measurements in a northern desert in China . Although this smaller threshold helps generate a higher production of dust emission during the 6-day simulation episode from 1 to 6 May 2011, the arbitrarily designated threshold value for all land covers and soil categories prevents the model from reproducing spatial and temporal variations of dust emission. In this study, we have conducted a reanalysis of the field data (Gillette et al., 1980(Gillette et al., , 1982 and revised the threshold friction velocities through removing the double counting of soil moisture in the CMAQ dust emission modeling. In these field campaigns, some of these experiments were performed under rather dry conditions, but for other samples the effect of soil moisture cannot be ignored. Therefore, these values reported from field experiments are not always suitable for being used directly as the initial threshold friction velocity constant, which is assumed to represent extremely dry conditions. Meanwhile, in the CMAQ dust module, dynamic soil moisture data are used to adjust threshold friction velocity. Therefore, we need to convert the wet-condition data into threshold values under dry conditions. Otherwise, there will be the double counting of soil moisture in some cases. We took a three-step approach to calibrate the threshold friction velocity. First, the value of soil moisture was extracted for each sample from the raw field dataset (courtesy from Dale Gillette, retired). Next, these data were used to feed the Fécan formula (Fécan et al., 1999) to derive the drycondition threshold velocity by casting soil moisture back to zero. We have reprocessed the data for the soil and land use types measured by Gillette (1980Gillette ( , 1982. In the case of missing data for certain soil types, we have chosen the values with the soil composition closest to a measured type following the USDA soil composition diagram ( Fig. 1 of Gillette et al., 1980). Finally, the revised values of u * ci,j are implemented in the CMAQ. To our knowledge the double-counting issue affects the dust emission module in CMAQ only because the microphysical parameterization is developed based on Gillette et al. (1980) data. It also explains the significant underestimations of dusty by CMAQ as reported in Appel et al. (2013) and Fu et al. (2014). The comparison of the default and revised constants is summarized in Fig. 1c. As the double-count of soil moisture has been corrected, the revised constants are lower than the default ones. The majority of land cover in the Gobi is categorized as shrubland, where the revised initial threshold friction velocity constants are significantly lower than the default values for all soil types as shown in Fig. 1c, indicating that the revised scheme is expected to produce higher dust emissions over the Gobi. The Taklamakan Desert is mainly configured as barren or sparsely vegetated land cover with a sandy soil type, which only shows a small drop in the threshold friction velocity constant from 0.28 to 0.23 m s −1 , indicating that the changes in dust emission over the Taklamakan may not be substantial. The CMAQ distributes dust emission to four size bins: 0.1-1.0, 1.0-2.5, 2.5-5.0, and 5.0-10.0 µm, with the mass contribution as 3, 17, 41, and 39 % within each bin, respectively. The first two bins represent the fine-mode aerosol and the larger two rep-resent the coarse mode. So the mass contribution is 20 % for fine-mode and 80 % for coarse-mode aerosol.

Implementing source-dependent speciation profile
The emission of natural wind-blown dust particles is distributed to 19 aerosol species in the CMAQ, following the profile developed based on the EPA's SPECIATE database (Simon et al., 2010). As compared with other models that treat dust as a unique aerosol species, the CMAQ approach provides a more detailed description of dust mineralogy. This feature benefits CMAQ by enabling the model to maintain the consistency between dust and other types of existing aerosol in the modeling system. However, mass contributions of different chemical components may differ greatly among different source areas; thus, using a fixed profile for all dust sources may introduce uncertainty and lose the capacity to model the varieties of dust particles. The mass contribution of aluminum (Al) is around 5-8 % for pure minerals around the world, and the ratios between other trace metals and Al may vary substantially for different dust particles. Thus, the elemental mass ratio between calcium and aluminum (Ca / Al) is usually used to identify the source of dust samples (Huang et al., 2010;Sun et al., 2005). For example, the Ca / Al ratio for Saharan dust is around 0.9 and 1.0 for fine and coarse dust particles, respectively (Blanco et al., 2003;Formenti et al., 2003;Kandler et al., 2007;Reid et al., 2003); for Arabian dust, it is around 0.13 and 0.15 for coastal and inland dust, respectively (Krueger et al., 2004); for Taklamakan dust, it is about 1.5-1.9 (Huang et al., 2010); and for dust from the Gobi Desert, it is 0.4-1.1 (Arimoto et al., 2006;Zhang et al., 2003). To characterize the dust aerosols in the CMAQ better, source-dependent speciation profiles are developed in this study for the Gobi and Taklamakan deserts based on local measurement data collected by Huang et al. (2010). These two profiles are compared with the default one in the CMAQ as shown in Table 2. For the model species which are not measured in Huang et al. (2010), including primary organic carbons (APOC), noncarbon aerosols (APNCOM), elementary carbons (EC), silicon (ASI), and water (AH2O), their values for the Taklamakan and Gobi are kept the same as in the default profile. And for unspeciated (AOTHR) and non-anion dust (ASOIL), their values in the two new profiles are calculated based on the contributions of all other species, to keep the total mass contributions conservative. It is important to note that the model species refer to an anion or cation phase for sulfate (ASO4, SO 2− 4 ), nitrate (ANO3, NO − 3 ), chloride (ACL, Cl − ), ammonium (ANH4, NH + 4 ), sodium (ANA, Na + ), Ca + 2 (ACA), magnesium (AMG, Mg + 2 ), and potassium (AK, K + ) and an element phase for iron (AFE, Fe), Al(AAL), silicon (ASI, Si), titanium (ATI, Ti), and manganese (AMN, Mn). Mass contributions of different aerosols differ significantly among the profiles as shown in Table 2. For example, Ca + 2 accounts for 7.94 % of the total fine-particle mass in the default profile, which is much higher than the Taklamakan (2.063 %) and the Gobi (1.788 %). For Mg + 2 , the default profile assumes 0 % of mass contribution, yet the values for the Taklamakan and Gobi are 0.165 and 0.799 %, respectively. The K + contribution within the default profile is 3.77 %, while for the Taklamakan it is 0.153 % and for the Gobi it is 0.282 %. Si is one of the most abundant metals in the crust, yet the default speciation profile has an inappropriate assumption of zero Si content in coarse-mode dust particles. As no measurements are found for Si over the Taklamakan or Gobi, we use the element ratio of Al / Si as 8 % / 28 % to derive the mass contribution of Si in the coarse model, which is a conventional approach for trace metal analysis (Huang et al., 2010). Different configurations within the speciation profile will lead to significant differences in model predictions for these trace metals, demonstrated in more detail in Sect. 3.

Implementation of heterogeneous reactions
The default heterogeneous chemistry scheme within the CMAQ considered the conversions from N 2 O 5 to HNO 3 and from NO 2 to HONO and HNO 3 . These reactions play an important role in the nighttime production of nitrate aerosols (Dong et al., 2014;Pathak et al., 2011;Pun and Seigneur, 2001). Heterogeneous reactions are treated as irreversible in the model (Davis et al., 2008;Sarwar et al., 2008;Vogel et al., 2003). While dust particles serve as a platform for heterogeneous reaction, they also participate in some of the reactions to take up the gas-phase species. The uptake of gases onto the surface of dust particles is defined by a pseudo-first-order reaction rate K (Dentener et al., 1996;Heikes and Thompson, 1983) where r p is the radius of the particle, D g is the diffusion coefficient of gas molecules, v g is the mean molecular velocity of gas, A p is the surface area of the particle, and γ g is the uptake coefficient for gas. Many research efforts have been devoted to quantifying the uptake coefficients. The reported values of the uptake coefficient may differ by more than 2-3 orders of magnitude, depending on the source of the dust samples and analytical methods (Cwiertny et al., 2008;Usher et al., 2003). While this work focuses on East Asia, the uptake coefficients are mainly collected from Zhu et al. (2010), which summarized the estimations for dust samples from deserts in China. The "best guess" regarding uptake coefficients was suggested based on the analysis of different measurement studies summarized in Zhu and Zhang (2010). But in this study both the lower and upper limits of uptake coefficients are examined to evaluate their impacts. Table 3 lists the 13 dust heterogeneous reactions implemented in the CMAQ in this study, with the lower and upper boundaries of uptake coefficients.  Skamarock et al., 2008). Initial and boundary conditions are downscaled from the GEOS-Chem global model simulation for 2006-2010 by following the routines described in Lam and Fu (2009). Biogenic emission is from MEGAN2.1 (Guenther et al., 2006;Müller et al., 2008), biomass burning emission is from FLAMBE (Reid et al., 2009), and anthropogenic emission is from  over China and INTEX-B over other countries within the domain. More details about meteorology and emission datasets are given in Dong and Fu (2015a, b).
To examine the performance of the CMAQ model development with revised parameterization and dust heterogeneous reactions, a total of six scenarios is conducted as listed in  Dust_Revised is designed to investigate the performance of applying the new parameterization of initial friction velocity threshold constants; Dust_Profile is designed to examine the improvement by applying source-dependent dust com- 0.02, otherwise where x 1 , x 2 , and x 3 and γ 1 , γ 2 , and γ 3 are the normalized molar concentrations and N 2 O 5 uptake coefficients on NH 4 HSO 4 , (NH 4 ) 2 SO 4 , and NH 4 NO 3 respectively, γ * d = min(γ d , 0.0124), where γ d is the uptake coefficient on dry particles determined by relative humidity and temperature, RH is relative humidity, CRH is crystallization relative humidity, IRH is ice formation relative humidity determined by temperature Davis et al. (2008) C2 Vogel et al. (2003) Implemented dust heterogeneous reactions in this work OH + dust → products 0.1∼ 1.0 Zhu and Zhang (2010)  R3 H Zhu and Zhang (2010)  R4 CH 3 COOH + dust → products 1.0 × 10 −3 Zhu and Zhang (2010)  R5 CH 3 OH + dust → products 1.0 × 10 −5 Zhu and Zhang (2010)  R6 CH 2 O + dust → products 1.0 × 10 −5 Zhu and Zhang (2010)  R7 HNO Phadnis and Carmichael (2000)  Revised initial friction velocity threshold constant in dust plume rise scheme Dust_Profile Same as Dust_Revised but with implemented source-dependent speciation profile Dust_Chem Same as Dust_Profile but with implemented dust chemistry with lower limit of uptake coefficient Dust_ChemHigh Same as Dust_Chem but with upper limit of uptake coefficients position profiles; and Dust_Chem and Dust_ChemHigh are designed to examine the impacts of implementing heterogeneous chemistry with the lower and upper estimations of uptake coefficients, respectively. The scenario Dust_Profile, Dust_Chem, and Dust_ChemHigh are all performed based on Dust_Revised.

Observations
Both ground-based measurements and satellite observations are used in this study to help examine the uncertainty and evaluate the performance of the model. The Air Pollution Index (API, http://datacenter.mep.gov.cn) reported by the Chinese Ministry of Environmental Protection (MEP) is used to evaluate the PM 10 predictions from the CMAQ. The API is reported on a daily basis with a national coverage of 86 middle size or larger cities in China and has been applied in many modeling studies for evaluation purposes . To investigate the transport of dust particles over downwind areas simulated by the model, we also used surface observations from the Acid Deposition Monitoring Network in East Asia (EANET; EANET, 2007) over Japan, and observations from the Taiwan Air Quality Monitoring Network (TAQMN; http://taqm.epa.gov.tw/taqm/en/default. aspx). EANET provides hourly or daily records of PM 10 , O 3 , NO x , and SO 2 and also biweekly (or longer-interval) records of HNO 3 , SO 2− 4 , and NO − 3 . Data from EANET is also employed in this study for examining model performance with dust heterogeneous chemistry. The TAQMN provides observations of most criteria air pollutants, yet only PM 10 observations at the Xinzhuang site are used in this study to focus on dust storm impact. These variables are used to exam-ine the simulation responses by implementing dust heterogeneous chemistry. Ground-based measurements of K + , Mg + 2 , Ca + 2 , and PM 2.5 at Duolun and Yunlin from Fudan University's observation network (Huang et al., 2010) are used to investigate the model's capability to simulate tracer metals by applying the source-dependent speciation profile for suspended dust particles. AOD from the AErosol RObotic NETwork (AERONET; http://aeronet.gsfc.nasa.gov/) operated by NASA is also collected to evaluate the CMAQ predictions. To examine the spatial distribution and column density of model-simulated dust particles, we also used AOD products from the Moderate Resolution Imaging Spectroradiometer  Table 5 summarizes detailed information on each observation network.

Improved model performance with revised friction velocity thresholds
To examine model improvement by implementing new initial threshold friction velocity constants, simulation results from Dust_Default and Dust_Revised are compared for the spatial distribution of PM 10 and evaluation bias against API. Dust_Default -Dust_Off and Dust_Revised -Dust_Off represent the PM 10 from the default dust scheme and the revised dust scheme, respectively, as shown in Fig. 3a and b. PM 10 concentrations generated from dust are averaged for March and April from 2006 to 2010. Figure 3a indicates that the default dust scheme produces a very limited amount of PM 10 only over the Gobi Desert (less than 70 µg m −3 ), which can hardly represent the East Asian dust storms. The revised scheme produces higher PM 10 concentrations of more than 400 µg m −3 in the Gobi source region, as shown in Fig. 3b. Dust plumes are generated by the revised model over the Gobi and Taklamakan deserts and also in sparse grassland over the northwest region of the Tibetan Plateau. Particles from dust plumes are transported southeastward and contribute 50-100 µg m −3 of PM 10 over northern and eastern China and less than 50 µg m −3 over southern China, South Korea, and Japan. Huang et al. (2010) demonstrated that there are two transport pathways for Asian dust: plumes from the Gobi and Taklamakan are either pushed by prevailing winds eastward towards South Korea and Japan or southeastward down towards southern China and Taiwan. With the revised dust scheme, the CMAQ generally reproduces the spatial distribution of Asian dust as shown in Fig. 3b, which indicates the most significant impact over northern and east-  ern China and relatively weak impacts over downwind areas along the transport pathway towards the southeast. Figure 3c and d demonstrate the evaluation bias at the API cities over China for the Dust_Default and Dust_Revised scenarios, respectively. With the default dust scheme, the CMAQ shows a large negative bias for the entire domain. The most serious underestimation is found over northern and western China, with a negative bias of more than −80 µg m −3 . Figure 3c also suggests that cities closer to the Taklamakan and Gobi deserts have a larger negative bias, indicating that the default scheme cannot generate sufficient dust emission to reproduce the observed PM 10 . With the revised scheme as shown in Fig. 3d, simulation biases for most of the cities are reduced down to ±20 µg m −3 . The largest overestimation is found at Hohhot (39 µg m −3 ), and the largest underestimation is found at Xining and Lanzhou (−60 µg m −3 ). Figure 3d also indicates that the model tends to have a larger simulation discrepancy in cities close to the desert. Figure 4a to c summarize the evaluation statistics for simulated PM 10 against observations from the API. Daily data pairs from observation and simulation are used here to calculate the statistics. The Dust_Off scenario underestimates PM 10 concentration by −56.74 %. Since dust storms from the Taklamakan and Gobi substantially contribute to suspended particle concentrations over East Asia during spring, a simulation with no dust emission should be responsible for the large underestimation. With the default dust emission module, model performance for the scenario Dust_Default is only slightly improved and PM 10 is still underestimated by −55.42 %, as shown by Fig. 4b. The comparison between Dust_Off and Dust_Default suggests that the default dust module is not able to generate sufficient elevated particles to match the observed PM 10 levels from the API. On the other hand, the Dust_Revised scenario shows a much better performance with an normalized mean bias (NMB) value of −16.06 %, as shown in Fig. 4c. Simulation results are also evaluated against AOD observations from the AERONET, with the statistics shown in Fig. 4d to f. Statistics for AOD evaluation against the AERONET suggest similar model performances as for PM 10 . The CMAQ simulation without dust rise underestimates AOD by −31.34 % at AERONET stations, and the default dust module offers almost no improvement for AOD prediction. The Dust_Revised scenario underestimates AOD by −22.1 %, indicating that the revised scheme is also able to improve the model performance for simulating fine-mode dust.

Impacts of applying source-dependent profile
The speciation of dust particles determines the contributions of crust species and trace metal concentrations predicted by the model. As described in Sect. 2.2, we modified the fixed speciation profile within the CMAQ to be source-dependent for the Gobi and Taklamakan, and in this section groundbased observations collected at Duolun and Yulin are used to investigate the impacts by applying different speciation profiles. The model simulations from the Dust_Revised and Dust_Profile scenarios are compared with observations for K + , Mg + 2 , and Ca + 2 with data pairs, as shown in Fig. 5 (sim-ulations and observations for K + and Mg + 2 are upscaled by 5 and 10 time, respectively, to make them comparable with Ca + 2 in the same figure), and evaluation statistics are summarized in Table 6. The two cities are close to the Gobi Desert, as shown by Fig. 2. Huang et al. (2010) used back trajectory and Ca / Al ratio analysis to demonstrate that the Gobi Desert is the main contributor of dust particles at Duolun and Yulin. Figure 5a indicates that with the default speciation profile, the CMAQ overestimates K + and Ca + 2 by 208.9 and 36.69 %, respectively. Predicted Mg + 2 concentration is almost zero because there is no emission of Mg + 2 in the anthropogenic emission inventories, and the default profile indicated a 0 % mass contribution of Mg + 2 from dust emission. With the source-dependent speciation profile, the CMAQ simulation of Mg + 2 is increased significantly as shown in Fig. 5b. The revised CMAQ underestimates K + and Ca + 2 by −47.83 and −53.12 %, respectively. A consistent negative bias for trace metals should be due to the underestimation of total finemode aerosol within dust. Figure 5c shows the compari-  Table 6. Note that simulations and observations of K + and Mg + 2 are upscaled by 5 and 10 times, respectively, to make them comparable with Ca + 2 in the same plot. (c) The evaluation of CMAQ-simulated PM 2.5 at Duolun and Yulin. son between observed and simulated PM 2.5 concentrations at Duolun and Yulin. The Dust_Revised and Dust_Profile scenarios only differ in terms of their speciation profile for particles. The predicted PM 2.5 from the two scenarios are almost identical, so only Dust_Profile is shown in Fig. 5c. The concentration of PM 2.5 is underestimated by −45.59 %, which is consistent with the underestimations of Mg + 2 , K + , and Ca + 2 . The evaluation statistics suggest that total PM 2.5 is underestimated at Duolun and Yunlin, but Ca + 2 is overestimated under the default profile. So although the NMB value changes from 36.69 % by the default profile to −53.12 % for Ca + 2 by the revised profile, it is highly possible that the revised profile provides a better estimation of the calcium mass contribution since the evaluation statistics for trace metals should be consistent with that for total PM 2.5 . The overall underestimation of PM 2.5 and trace metals is probably due to the underestimation of fine-mode aerosol mass contribution (20 %) to total dust emission. On the other hand, the simulation bias shown in Fig. 3d suggests a slight overestimation of PM 10 at cities close to the Gobi Desert. So it is highly possible that the fine-particle mass contribution configured within CMAQ may be underestimated. Unfortunately, measurements made by Huang et al. (2010) only collected data for total suspended particles (TSPs) and no PM 10 observa-tion is found for Duolun or Yulin. So no reliable conclusion is drawn at this point because the PM 10 data are not available at Duolun or Yulin, although the TSPs are measured and indicate that the fine-mode aerosol should have a larger mass contribution (about 40 % in TSP).

Impacts of heterogeneous chemistry
Dust heterogeneous chemistry involves the uptake of gasphase species and the production of secondary inorganic aerosols. In this section we investigate the impacts of implementing dust chemistry in the CMAQ by examining the simulation difference between Dust_Chem and Dust_Profile and the difference between Dust_ChemHigh and Dust_Profile. Figure 6 shows the concentration changes (color contours represent the absolute concentration changes, and dashed lines represent the percentage changes) under heterogeneous chemistry with lower (left column) and upper (right column) limits of uptake coefficients for O 3 (first row), SO 2 (second row), SO 2− 4 (third row), HNO 3 (fourth row), NO x (fifth row), and NO − 3 (sixth row). All variables are averaged for March and April from 2006 to 2010. The spatial distributions shown in Fig. 6 suggest that impacts of dust chemistry are more pronounced in eastern China over the downwind areas than in the dust source area near the deserts. This is because eastern China has intense anthropogenic emissions that help to accelerate the dust chemistry, while the desert areas in northern and western China have much lower concentrations of the reactive gases. Dong and Fu (2015a) reported that in spring, O 3 concentration is around 30 ppbv and less than 5 ppbv for NO 2 and SO 2 over the Gobi, while the concentrations over eastern China are 50-60 ppbv for O 3 and 10-40 ppbv for NO 2 and SO 2 . A greater proportion of these reactive gases participates in the heterogeneous reactions on the surface of dust particles transported from deserts and thus lead to a more significant impact of dust chemistry over the downwind area instead of over the deserts. Li et al. (2012) also reported that the impact of dust chemistry for O 3 , SO 2 , and NO 2 is less than 5 % over the Gobi but up to 30-40 % in eastern China and even higher over the western Pacific. O 3 concentration is reduced by 3-6 ppbv (2-10 %) and 5-11 ppbv (4-20 %) with the lower and upper limits of uptake coefficients, respectively, due to irreversible Reaction (R1) as listed in Table 3, which agrees well with the values reported by Tang et al. (2004;20 % and Li et al. (2012;5-20 %. Wang et al. (2012) reported lower O 3 reduction due to dust chemistry by 3.8 and 7.3 ppbv with lower and upper uptake coefficients, respectively, which could be because of using a different simulation year (2001) with a lower baseline O 3 over East Asia. The concentration of SO 2 is reduced by ∼ 2 ppbv (10 %) and ∼ 6 ppbv (30 %) with lower and upper limits of uptake coefficients, respectively, due to the consumption in reaction R13, which also leads to the increase in SO 2− 4 concentration by ∼ 3 µg m −3 (8 %) and more than 5 µg m −3 (15 %) under Dust_Chem and Dust_ChemHigh, respectively. Impacts on SO 2 reported by other studies differ moderately by a factor of 2 or more, varying from 55 % by Tang et al. (2004) as the highest, 10-20 % as the medium (Li et al., 2012), and 5-8 % as the lowest  impact. Different impacts caused by heterogeneous chemistry reported by different studies should mainly be attributed to the different simulation episodes. Tang et al. (2004) focused on dust episodes only in 2001 with lower baseline pollutants from anthropogenic emission; Li et al. (2012) also simulated dust episodes but in 2010; and  simulated the year 2001 but focused on the entirety of April, when the monthly averages of particles are apparently smaller than the values from dust episodes only. Reaction (R7) indicates the consumption and Reaction (R11) indicates the production of HNO 3 , while the net effect of dust chemistry is found to decrease HNO 3 concentration by 0.2-0.8 ppbv (8-30 %) as shown in Fig. 6e and f. Our result is comparable with the values reported by Li et al. (2012) as 5-40 % but smaller than that reported by Tang et al. (2004) as 30-70 %. Although the reaction R9 indicates the uptake of NO x by dust particles, simulation results suggest that NO x concentration is increased by 0.2-1 ppbv over eastern China and the west Pacific. The elevation of NO x concentration should be attributed to the depletion of O 3 and the conversion of gas-phase HNO 3 back to NO x (Yarwood et al., 2005). As a result of excessive SO 2− 4 production from dust chemistry, the concentration of NO − 3 is decreased under the Dust_Chem scenario due to the thermal-dynamic equilibrium between SO 2− 4 -NH + 4 -NO − 3 . The equilibrium drives the inorganic aerosols to convert from NH 4 NO 3 to (NH 4 ) 2 SO 4 over eastern China, with intensive anthropogenic SO 2 and NO x emissions from industry and power sector but insufficient NH 3 to neutralize all the acid anions. On the other hand, over the western Pacific and Japan, where SO 2 and NO x emissions are less intensive, the concentration of NO − 3 is increased slightly due to the production indicated by Reactions (R8) and (R10). Consequently, over eastern and central China, removed NO − 3 evaporates back to HNO 3 , which again pushes the gas-phase equilibrium towards the production of NO x and thus leads to the increase in NO x but a decrease in HNO 3 and NO − 3 . Meanwhile, with the upper limit of uptake coefficients, the production rate of HNO 3 catches up with the removal rate of NO − 3 , which helps to slow down the decrease in NO − 3 over China and accelerate the increase in NO − 3 over the western Pacific and Japan. Our result is consistent with the findings from other studies.  also reported the increase in NO x and decrease in HNO 3 and NO − 3 concentrations due to dust chemistry over East Asia. Li et al. (2012) reported that NO − 3 concentration with a lower uptake coefficient is about 5 µg m −3 (30 %) lower than the base case simulation (with a "best guess" uptake coefficient suggested in Zhu et al., 2010) and NO − 3 predicted by high uptake coefficient is about 12 µg m −3 (100 %) higher than the base case at Shanghai and Xiamen.
The impact of dust chemistry shown in Fig. 6 suggests comparable results to other modeling assessments, but very few previous studies incorporated observation data to validate further the impact indicated by the model. To evaluate the model's capability to represent dust chemistry and also determine the best-fit uptake coefficients, observations from EANET are used to compared with simulations from Dust_Profile, Dust_Chem, and Dust_ChemHigh. Evaluation statistics are summarized in Table 7. Simulation results from different scenarios for O 3 all agree well with observations, as indicated by the statistics. O 3 is overpredicted by 1.26 % without dust chemistry in the CMAQ and underpredicted by −1.97 and −4.43 % with lower and upper uptake coefficients, respectively. SO 2 is overpredicted in all scenarios, but the NMB value is reduced from 90.7 % without dust chemistry to 69.8 and 63.7 % with lower and upper uptake coefficients, respectively. Evaluation statistics for HNO 3 and NO x show a similar response to SO 2 , where heterogeneous chemistry helps to reduce the large overestimations from 109.03 % without dust chemistry to 85.17 and 81.24 % with lower and upper limits of uptake coefficients, respectively. The positive bias for SO 2 and HNO 3 should be attributed to the overestimated anthropogenic emissions (Dong and Fu, 2015a;Wang et al., 2011). For NO x evaluation, however, model overes-  (Dong and Fu, 2015a), but implementing dust chemistry in the model leads to a larger overprediction of NO x . The concentration of SO 2− 4 is underpredicted by 16.28 % without dust chemistry, yet the simulation overpredicts SO 2− 4 by 13.74 and 29.43 % under the Dust_Chem and Dust_ChemHigh scenarios, respectively. For NO − 3 predictions, dust chemistry helps to reduce the underprediction from 13.07 % under the Dust_Profile scenario to −1.97 % under Dust_Chem scenario. But the simulation is boosted too much with the upper limit of coefficients and it overpredicts NO − 3 concentration by 24.09 % under the Dust_ChemHigh scenario. Note that EANET data are collected from Japanese sites so that Dust_Chem and Dust_ChemHigh show consistent increases in NO − 3 , as shown in Fig. 6. Statistics shown in Table 7 suggest that implementing heterogeneous chemistry seems able to improve the CMAQ performance for most of the species except O 3 and NO x , but the lower limit of uptake coefficients favors the prediction of SO 2− 4 and NO − 3 , and the upper limit of uptake coefficients has a better prediction for SO 2− 4 and HNO 3 . Although these statistics show competitive performance between Dust_Chem and Dust_ChemHigh, the lower limit of the uptake coefficient may be more appropriate if we consider the uncertainty within the baseline anthropogenic emissions. With both surface observations and satellite retrievals, Dong and Fu (2015a) demonstrated that the CMAQ overpredicted NO x and SO 2 over East Asia between 2006 and 2010 by around 30 and 20 %, respectively, due to overestimation in anthropogenic emissions. Wang et al. (2011) also report the overestimation of SO 2 by 14 % over China. Implementing dust chemistry helps to reduce simulated concentrations of SO 2 , NO x , and HNO 3 , so it can balance part of the positive bias caused by anthropogenic emissions, but the statistics for SO 2− 4 and NO − 3 indicate that the counter effect caused by using the upper limit of uptake coefficients may be too excessive and push the balance towards an overestimation of aerosols as a side effect. Consequently, without explicitly excluding the bias within anthropogenic emissions, no reliable conclusion could be achieved regarding the preference of uptake coefficients.

Simulating a severe dust storm event
In this section we probe the capability of the CMAQ to reproduce a severe dust event. Many studies have reported that spring 2010 had the most severe dust storms in recent decades (Bian et al., 2011;Li et al., 2012) due to a nationwide drought in China. PM 10 observations were more than 1000 µg m −3 at Beijing (Han et al., 2012), 1600 µg m −3 at Seoul (Tatarov et al., 2012), and 1200 µg m −3 at Taiwan (Tsai et al., 2013). These studies mainly focused on the impact of dust storms on a local scale and the understanding of the emission and transport of the dust event on a regional scale is not well-developed. Here we examined this severe dust event with model simulations, satellite observations, and also surface measurements from multiple networks. Figure 7 displays the MODIS AOD and simulated AOD from the CMAQ Dust_Chem scenario during the severe dust storm episode from 19 to 21 March 2010. Simulated AOD is derived by following the approach described in Huang et al. (2013) for 11:00 LT only to make it consistent with the nadir view time by the MODIS. Spatial distributions of the satellite data agree well with the simulation on a daily scale, indicating that the model can generally reproduce the column density and long-range transport of dust particles. As shown in Fig. 7b, the CMAQ simulation suggests that a large amount of dust  To further examine the dust event, a forward trajectory is analyzed to characterize the transport pathway of dust plumes with the Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model from the NOAA/Air Resources Laboratory (Draxler and Rolph, 2015;Rolph, 2015). The movement of air mass was analyzed for 72 h, starting from 00:00 UTC (08:00 LT) on 19 March 2010 in the Gobi, with the forward trajectories shown in Fig. 8f. Air masses at 500 (red line), 1000 (blue line), and 2000 m (green line) moved southeastward until 20 March, when the higher plume turned east and moved across Japan and the west Pacific, while the lower plumes continued towards the eastern coastal area of China and finally reached Taiwan on 21 March. The HYSPLIT trajectory showed the same consistent transport of dust plumes as the MODIS and the CMAQ AOD analysis.
To understand the impact of dust storms along the transport pathway, we compare the simulated and observed surface level PM 10 on a daily scale for all of March 2010 at selected stations along the transport pathway, as indicated  Fig. 8a-e and g-o) are analyzed to examine the model performance. Temporal variations of PM 10 with observations from the API (red circles) are examined at Beijing, Lanzhou, Nanjing, Fuzhou, Lianyungang, and Shanghai as shown in Fig. 8a, b, c, d, e, and k, respectively. Observed PM 10 concentrations increased rapidly from less than 300 µg m −3 on 18 March to more than 600 µg m −3 at Beijing and 480 µg m −3 at Lianyungang on 19 March. In central and eastern China, concentrations of PM 10 peaked on 21 March at Nanjing and Shanghai, and the API reached the measurement ceiling value of 600 µg m −3 . In southern China, PM 10 was also elevated to 600 µg m −3 at Xiamen on 22 March. Temporal variation in these cities suggested that PM 10 concentrations were elevated with the onset of the dust storm, which moved from the Gobi to southeastern China from 19 to 21 March. PM 10 concentrations fell back under 300 µg m −3 after the event. Lanzhou reached a peak of PM 10 concentration with 500 µg m −3 on 14 March, which should be attributed to the impact of the dust storm that originated from the Taklamakan. Ling et al. (2011) also reported an observed 507 µg m −3 PM 10 on 14 March at Lanzhou. Observations from EANET and the TAQMN are also employed to examine the long-range transport of dust over the west Pacific and Taiwan. Temporal variations of PM 10 at three EANET sites (green diamonds) including Oki, Ogasawara, and Hedo are shown in Fig. 8h, i, and j, respectively. PM 10 concentrations at these sites all showed a consistent increase with the onset of dust on 21 or 22 March. At Xinzhuang, as shown in Fig. 8g, observations from the TAQMN (purple circles) demonstrated that local PM 10 was increased from less than 100 µg m −3 on 20 March to more than 700 µg m −3 on 21 March due to the impact of the dust storm. Simulated PM 10 from the Dust_Chem scenario agreed well with observations from different networks all over the domain. Predictions from the Dust_Chem and Dust_Off scenarios were almost the same at all stations during the non-dust period from 1 to 10 March, yet the Dust_Chem scenario was able to reproduce the rapid elevation of PM 10 during the dust event. However, a noticeable discrepancy was also found between the Dust_Chem prediction and observations. In general, the CMAQ overpredicted PM 10 slightly during the dust event at most of the API sites in China but failed to reproduce the high concentrations at Lanzhou before 15 March and after 20 March. To help understand the model performance in predicting fine particles from dust, daily variations of AOD from the AERONET observations and the CMAQ simulations are also examined at four stations, including Beijing, the Semi-Arid Climate Observatory Laboratory (SACOL) station at Lanzhou (Ling et al., 2011), Osaka, and EPA-NCU (Taiwan Environment Protection Agency station at National Central University), as shown in Fig. 9l, m, n, and o, respectively. Temporal variations of AOD were consistent with the daily changes in PM 10 at these cities along the dust plume move-ment trajectory. The highest AOD was found on 14 March at the SACOL station, which was consistent with the rapid increase in PM 10 concentrations at Lanzhou. Moderate underestimations of AOD were also found at Lanzhou and EPA-NCU stations during the dust events, indicating that finemode aerosols were also underestimated over this region. In general, comparisons between the CMAQ and observations from MODIS and surface networks suggest that the model is capable of reproducing the severe dust storm event in terms of the spatial distribution, transport, and concentration of dust particles, with a possible underestimation of dust emission from the Taklamakan.

Remaining uncertainties within the modeling system
Despite the improvements of model performance demonstrated in the previous sections, it is necessary to note that there are some important remaining uncertainties within the modeling system. The first type of uncertainty is related to the anthropogenic emissions. The assessment of the dust prediction capability of the model was primarily performed by comparing the simulation with observations, yet the bias caused by anthropogenic emissions would affect the bias from the dust prediction. So it is difficult to distinguish the uncertainties that arise from dust treatment in the model. Figure 9 displays the dust emission rate (Tg day −1 ) from the Dust_Chem scenario (blue rectangles, with blue dashed line indicating the trend), the simulation bias of PM 10 at the API stations (red circles, with red dashed line indicating the trend), and the simulation bias of PM 10 at EANET stations (green diamonds, with green dashed line indicating the trend), with all variables averaged on a monthly scale. The prediction from the CMAQ suggests a slightly increasing trend of dust emission from 2006 to 2010, which is consistent with the decadal increase in dust reported by Kurosaki et al. (2011) due to changes in soil erodibility over Mongolia and northeastern China. The recent decadal trend of dust emission over East Asia remains an open-ended question that will rely on investigations with longer-term simulations. Simulation biases of PM 10 agree fairly well with the dust emission at both the API and EANET stations, indicating that the overall underprediction of PM 10 over East Asia has a smaller discrepancy for years with stronger dust events. Assuming there is a persistent underestimation of primary PM emissions in the anthropogenic inventory, more dust emissions will apparently help to reduce the modeling bias for total PM 10 . This is also consistent with previous studies Dong and Fu, 2015a) which reported a systematic underestimation of anthropogenic emission of primary particles over China. The second type of uncertainty lies within the friction velocity threshold u * t , which is affected by the soil moisture fraction, which may be overestimated by the WRF. Although in this study the simulation performance is improved with the initial threshold friction velocity constants u * c adjusted by avoiding the double counting of the soil moisture effect, there are still non-negligible biases as shown in Sect. 3. Both the 5-year average modeling bias shown in Fig. 3d and temporal variations shown in Fig. 8 suggest possible overestimated dust emission from the Gobi and underestimated dust from the Taklamakan. The averaged u * t calculated by the CMAQ is 0.19 and 0.14 m s −1 over the Taklamakan and Gobi, respectively, with the soil moisture factor f soilm being 1.21 and 1.13, respectively, indicating that the Taklamakan needs higher friction velocity in order to generate dust because of a more significant soil moisture impact. However, some recent field measurement studies suggest that the u * t in the Taklamakan is lower than that over the Gobi.   reported the value of u * t as 0.34-0.42 m s −1 based on measurements made in April 2006 and 2008. Field measurements defined u * t as equal to the value of friction velocity u * when dust concentration is increased by 20 % for at least half an hour ; thus, the reported values of u * t from the measurement studies are higher than the calculations from the model. But the comparison between the Taklamakan and Gobi measurements suggests that the model may either underestimate u * t in the Gobi or overestimate it in the Taklamakan. Since f soilm is determined by the soil moisture fraction, we compare the soil moisture from FNL (NCEP final analysis data), which is used to drive WRF in this study, with another reanalysis dataset GLDAS (Global Land Data Assimilation System; Rodell et al., 2004). Figure 10 demonstrates the 5-year averages (for March and April) of the soil moisture fraction in the top 10 cm from (a) FNL and (b) GLDAS. Soil moisture is estimated to be 10-15 % by FNL in both deserts, while the values from GLDAS are less than 5 % in the Taklamakan and 5-10 % in the Gobi. Zender et al. (2003) reported that soil moisture from NCEP is too high over active dust emission areas and leads to a negative AOD bias of the model on a global scale. With the WRF-NMMB/BSC-Dust model, Haustein et al. (2012) conducted simulations with meteorology driven by FNL and GLDAS over north Africa and reported that the predictions with GLDAS had better agreement with the AERONET's AOD observations due to smaller friction velocity and slightly faster surface wind speed due to lower values of soil moisture. But no such sensitivity studies have been made over East Asia, and unfortunately there are no publicly available observation data for the period of 2006-2010 to examine the potential overestimation of soil moisture by FNL in our modeling domain. Although we previously reported a consistent negative bias of surface temperature at 2 m height for 2006-2010, which may be due to excessive soil moisture (Dong and Fu, 2015b), more research efforts are required to verify the uncertainties caused by using FNL soil data.
The last type of uncertainty lies within the mass contribution of fine aerosols within dust emission. Elevated dust particles are distributed into fine-and coarse-mode aerosols with mass ratios of 0.2 and 0.8, respectively, in the CMAQ dust scheme. In this study, however, the ratio of PM 2.5 / TSP derived from observations at Duolun and Yulin is 0.42 and 0.39, respectively, indicating that fine particles should have a higher mass contribution within East Asian dust. The data from Huang et al. (2010) indicated that the ratio of PM 2.5 / TSP at Tazhong was 0.45 in spring 2007, which suggested an even higher fine-particle mass contribution in the Taklamakan. Model evaluation results shown in Fig. 5 also demonstrate the systematic underestimations of both trace metals and total PM 2.5 concentrations in both dust source regions and downwind areas, while the concentrations of PM 10 are slightly overestimated near the source region as demonstrated in Fig. 3. Consequently, it is highly possible that the ratio of fine particles within dust emission should be higher. But since TSP also includes all large particles > 10 µm, observations of both PM 2.5 and PM 10 in active dust regions are urgently needed to help clearly characterize the ratio in the model.

Summary
The dust module in CMAQ has been further developed in this study. The initial threshold friction velocity constants are revised by removing the double counting of soil moisture in the default parameters; two source-dependent speciation profiles are derived based on local observations of dust emission from the Taklamakan and Gobi deserts; and dust heterogeneous chemistry is implemented. The CMAQ with its revised dust scheme is applied over East Asia for March and April from 2006 to 2010. Based on model evaluations with observation data from both ground-surface networks and satellite retrievals, the revised dust scheme is demonstrated to improve the model performance. Evaluation statistics suggest that NMB for PM 10 simulation is reduced from −55 % by the default model to −16 % by the revised model, and NMB for AOD is reduced from −31 to −22 %. Applying source-dependent speciation profiles improves the simulation of trace metals. Heterogeneous chemistry with lower and upper limits of uptake coefficients is also investigated. Although simulations with dust chemistry are demonstrated to agree better with observations than those without chemistry for most of the pollutants, no solid conclusion could be made regarding the preference of uptake coefficients without explicitly excluding the uncertainty caused by anthropogenic emission. This is because simulation with lower coefficients has better agreement with observations for O 3 , SO 2− 4 , and NO − 3 , while simulation with upper coefficients has better performance for SO 2 and NO 2 .
A severe dust storm episode from 19 to 21 March 2010 is investigated to examine the model performance during extreme dust event. The revised CMAQ modeling system successfully reproduces most of the PM 10 and AOD observations in both near-source (China) and downwind areas (Japan and Taiwan). But some notable discrepancies are also found, indicating the slight overestimation of dust from the Gobi and underestimation of dust from the Taklamakan. The comparison of the FNL and GLDAS soil moisture fractions indicates that the excessive soil moisture within FNL may be responsible for the higher friction velocity threshold and lower dust emissions calculated by the CMAQ over the Taklamakan. But more sensitive studies with different reanalysis data inputs for WRF and the local soil moisture measurements in the deserts are needed to reach a solid conclusion. In addition, potential uncertainty is also identified within the mass contributions of fine-and coarse-mode particles from dust emission. Evaluation results indicate a consistent underestimation of trace metals and PM 2.5 by 30-50 % at Duolun and Yulin close to the Gobi desert, yet PM 10 is generally overestimated slightly in neighboring cities. While measurements from Huang et al. (2010) suggested mass contribution to be ∼ 40 % of fine particles in TSP, the value of 20 % used in the current CMAQ may be too low for dust emissions from the Gobi and Taklamakan. In summary, the model development employed in this study has been demonstrated to enhance the capability of the CMAQ to simulate dust over East Asia regarding the chemical and physical processes involved, which can serve as a useful tool for further investigating the impacts of dust on regional climate over East Asia and elsewhere.