Modeling nitrate aerosol distributions and its direct radiative forcing in East Asia with RAMS-CMAQ

Modeling nitrate aerosol distributions and its direct radiative forcing in East Asia with RAMS-CMAQ

Particuology 11 (2013) 256–263 Contents lists available at SciVerse ScienceDirect Particuology journal homepage: Mod...

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Particuology 11 (2013) 256–263

Contents lists available at SciVerse ScienceDirect

Particuology journal homepage:

Modeling nitrate aerosol distributions and its direct radiative forcing in East Asia with RAMS-CMAQ Xiao Han a , Meigen Zhang a,∗ , Baorong Zhou b a b

State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China Xinghua Meteorological Bureau, Jiangsu 225700, China

a r t i c l e

i n f o

Article history: Received 19 May 2012 Received in revised form 15 August 2012 Accepted 10 September 2012 Keywords: Nitrate aerosol Direct radiative forcing CMAQ EANET AOD

a b s t r a c t The geographical and seasonal characteristics in nitrate aerosol and its direct radiative forcing over East Asia are analyzed by using the air quality modeling system RAMS-CMAQ coupled with an aerosol optical properties/radiative transfer module. For evaluating the model performance, nitrate ion concentration in precipitation, and mixing ratios of PM10 , and some gas precursors of aerosol during the whole year of 2007 are compared against surface observations at 17 stations located in Japan, Korea, and China, and the satellite retrieved NO2 columns. The comparison shows that the simulated values are generally in good agreement with the observed ones. Simulated monthly averaged values are mostly within a factor of 2 of the measurements at the observation stations. The distribution patterns of NO2 from simulation and satellite measurement are also similar with each other. Analysis of the distribution features of monthly and yearly averaged mass concentration and direct radiative forcing (DRF) of nitrate indicates that the nitrate aerosol could reach about 25–30% of the total aerosol mass concentration and DRF in Sichuan Basin, Southeast China, and East China where the high mass burden of all major aerosols concentrated. The highest mass concentration and strongest DRF of nitrate could exceed 40 ␮g/m3 and −5 W/m2 , respectively. It also indicates that other aerosol species, such as carbonaceous and mineral particles, could obviously influence the nitrate DRF for they are often internally mixed with each other. © 2013 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

1. Introduction Nitrogen oxides are important chemical species in both the stratosphere and troposphere playing a key role in catalytic production of ozone chemistry, gas-to-particle reaction, and other chemical processes. The nitrate aerosol is a secondary aerosol produced from nitrogen oxides and causes many environmental and

Abbreviations: ALDX, higher aldehyde; AOD, aerosol optical depth; CAM, community atmosphere model; CAMRT, radiative transfer scheme of community atmosphere model; CB05 or CB-IV, Carbon Bond mechanism; CMAQ, community multiscale air quality; DRF, direct radiative forcing; EANET, Acid Deposition Monitoring Network in East Asia; EDGAR, Emission Database for Global Atmospheric Research; GEIA, Global Emissions Inventory Activity; INTEX-B, Intercontinental Chemical Transport Experiment-Phase B; ISORROPIA, inorganic aerosol thermodynamic equilibrium model; MOZART-4, Model for Ozone and Related Chemical Tracers, version 4; NTR, organic nitrate; OMI, Ozone Monitoring Instrument; RAMS, regional atmospheric modeling system; CMAQ, regional air quality modeling system; RAQM, regional air quality Eulerian model; REAS, Regional Emission inventory in Asia; RegCM, regional climate model; SSA, single scattering albedo; TOA, top-ofatmosphere; VOC, volatile organic compound. ∗ Corresponding author. Tel.: +86 010 62379620. E-mail address: [email protected] (M. Zhang).

climatic issues such as acid rain (Brimblecombe & Stedman, 1982), atmospheric haze (Deng et al., 2008; Wang, Zhuang, Sun, & An, 2006), radiation balance change, and cloud droplet formation (Li & Han, 2011; Li, Wang, Zhuang, & Han, 2009; Zhang, Wang, Guo, & Wang, 2009). The soluble aerosol particles, including nitrate aerosol, also act a part in the core-mantle particles by coating on mineral or black carbon particles (Mishchenko, Liu, Travis, & Lacis, 2004). This internal mixing state could obviously influence the radiative properties by enhancing the absorbing ability and then weakening the negative radiative forcing (Bauer et al., 2007; Jacobson, 2001). As a result of the rapid development of industry and commerce, present air pollution problems caused by aerosol and its gas precursor are becoming more and more serious in East Asia (Zhang, Han, & Zhu, 2007; Zhang, Han, Cheng, & Tao, 2009). Meanwhile, nitrogen oxide emissions from Asia are larger than those from Europe and North America, and they are expected to continue to increase (Akimoto, 2003). Especially anthropogenic emissions associated with fossil fuel burning in China have grown significantly as they experienced a period of rapid economic development and industrial expansion over the last three decades (e.g., Richter, Burrows, Nüss, Granier, & Niemeier, 2005; Wu, Jiang, Liu, & Tang, 2002; Zhang, Streets, et al., 2007). Thus, full

1674-2001/$ – see front matter © 2013 Chinese Society of Particuology and Institute of Process Engineering, Chinese Academy of Sciences. Published by Elsevier B.V. All rights reserved.

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consideration of nitrate aerosol should be important in the study about the atmospheric environment and regional climate change in East Asia. The modeling system which is capable of resolving the transport, transformation, and deposition mechanisms of nitrogen compounds could help to capture the comprehensive information of associated effects of nitrate aerosol. In recent years, modeling studies have paid more attention to this subject in East Asia. An et al. (2002) used the regional air quality Eulerian model (RAQM) to simulate four monthly nitrate concentrations in precipitation. Zhang, Gao, Ge, and Xu (2007) used the air quality modeling system named as regional atmospheric modeling system and community multiscale air quality model (RAMS-CMAQ) to reproduce the seasonal variation of nitrate mass burden. Li et al. (2009) tried to calculate the first indirect radiative effects of nitrate aerosol in China by using the regional climate model (RegCM3). These studies provided the preliminary release of distribution features of partial nitrate characterization in East Asia. However, the investigation about environmental influence and direct radiative forcing of nitrate is still insufficient for only few relative works have focused on it. Additionally, since the sulfate aerosol, absorbing soot, organic carbon, and soil dust also significantly scatter or absorb the atmospheric radiation flux, previous study has suggested that it is necessary to take all major aerosol compositions into account even though the physical and optical properties of one single aerosol species were simulated and studied (Ghan et al., 2001). In this paper, the nitrate mass concentration and direct radiative forcing (DRF) over East Asia in the whole year of 2007 are investigated by using the regional air quality modeling system RAMS-CMAQ coupled with an aerosol optical properties/radiative transfer module. All major aerosol components, including sulfate, ammonium, black carbon, organic carbon, dust, and sea salt, are considered in this modeling system, besides nitrate. The regional features of nitrate mass burden and DRF are focused upon and analyzed. The paper is organized as follows. The modeling system is described in Section 2. In Section 3, the modeled results are evaluated by comparing with the observation data, and the geographical and seasonal distributions of nitrate aerosol concentration and DRF over East Asia are discussed. Section 4 presents the conclusions. 2. Model description The regional air quality model CMAQ is used in this study to describe the physical and chemical processes of aerosol emission, coagulation, dry and wet deposition, formation of secondary particles from precursors, and other important characteristics. In this study, the updated and expanded version of the Carbon Bond mechanism CB05 (Sarwar, Luecken, Yarwood, Whitten, & Carter, 2008) was applied to describe the mechanisms of gas-phase chemistry and address the vapor phase precursors. Compared with CB-IV, the new version CB05 mechanism adds several nitrogen oxides (NOx ) recycling reactions to improve the representation of the life time of NOx over multiday scales (Zaveri & Peters, 1999). Additional radical reactions for improving nighttime chemistry of NO3 removal are also included. The description of gas-phase consumption of HNO3 is enhanced because of the inclusion of the photolysis of HNO3 via reactions of NO3 + HO2 , ALDX + NO3 , and NTR + OH with the CB05 mechanism. The CMAQ accounts for the gas-phase nitric acid (HNO3 ) production via homogeneous and heterogeneous reactions which can then be partitioned to form aerosol nitrate. An inorganic aerosol thermodynamic equilibrium model, “ISORROPIA” is used to determine partitioning of inorganic aerosols in the model (Nenes, Pandis, & Pilinis, 1999). Except nitrate aerosol, sulfate, ammonium, black carbon, organic carbon, dust, and sea salt are also simulated in this modeling system.


Fig. 1. Geographic locations of the EANET* observation sites in East Asia. *Acid Deposition Monitoring Network in East Asia.

The particle size distribution is represented as the superposition of three lognormal subdistributions: Aitken mode, accumulation mode, and coarse mode. Whereas internal mixing of the aerosol species is assumed within each mode, the modes themselves are externally mixed. The highly versatile numerical code RAMS which could well simulate the boundary layer and the underlying surface is used to provide the meteorological fields for CMAQ. For the emission inventory, the emissions of nitrogen oxides, sulfur dioxide, carbon monoxide, black carbon, organic carbon from anthropogenic activities (power, industry, residential, and transportation) are obtained from the 0.5◦ × 0.5◦ emission inventory for INTEX-B (http://www.cgrer.uiowa. edu/EMISSION DATA new/index 16.html). The Regional Emission inventory for Asia domain (REAS, frsgc/research/d4/emission.htm) was used to provide the emission of NH3 . Nitrogen oxides and ammonia from soil are adopted from the Global Emissions Inventory Activity (GEIA) 1◦ × 1◦ monthly global inventory (Benkovitz et al., 1996). Aircraft emissions are based on the Emission Database for Global Atmospheric Research (EDGAR; Olivier, Bouwman, Maas, & Berdowski, 1994). The sea salt and dust emissions were calculated on-line by using an algorithm from Gong (2003) and an empirical mechanism that performs well for describing dust emissions in East Asia (Han et al., 2004), respectively. The boundary conditions for the modeling system were obtained from the MOZART-4 (Pfiser et al., 2008) three-hour output data field. The model domain (Fig. 1) is 6654 km × 5440 km with 64 km grid cells on a rotated polar stereographic map projection centered at 35◦ N, 116◦ E. Previous researches have demonstrated that this modeling system performs well on simulating tropospheric ozone, aerosols, and other pollutants in East Asia (Han, Ge, Tao, Zhang, & Zhang, 2012; Zhang, Xu, Zhang, & Han, 2005; Zhang et al., 2006; Zhang, Streets, et al., 2007). A parameterization of aerosol optical properties and a radiative transfer scheme are coupled into this modeling system. The parameterization could greatly simplify the calculation process of Mie theory by using a Chebyshev polynomial fitting. The water uptake and internal mixing state of aerosol were treated by Kohler theory (Pruppacher & Klett, 1997) and Maxwell–Garnett mixing rule (Chuang et al., 2002), respectively. In previous work, the comparison with satellite and ground-base in situ measurements showed that the modeled AOD and SSA are well consistent with observed results (Han, Zhang, Han, Xin, & Liu, 2011). The direct radiative


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Fig. 2. Scatter plots for observed and modeled monthly average mass concentrations of (a) NOx in ppbv, (b) NO2 in ppbv, (c) O3 in ppbv, (d) content of NO3 − in ␮mol/L, in precipitation, and (e) PM10 in ␮g/m3 at the EANET sites in China, Japan, and Korea. The solid lines are 1:1, and the dashed lines are 2:1 or 1:2.

forcing could be estimated by radiative transfer scheme CAMRT (Collins et al., 2006) coupled with RAMS-CMAQ. This scheme spanned the entire short wave band, and considered gaseous molecule absorptions as well as the cloud radiative effect of ice and water clouds. The detail description of this aerosol optical properties/radiative transfer module could be found in Han et al. (2011). 3. Results and discussion 3.1. Model evaluation The measurement data from EANET (the Acid Deposition Monitoring Network in East Asia; and OMI (Ozone Monitoring Instrument; http://daac.gsfc.nasa. gov/Aura/OMI/index.shtml) in the whole year of 2007 were applied in this paper for evaluating the model results (Han, Ueda, & Sakurai, 2006). EANET surface monitoring network was built and operationalized in April 1998. There are 12 countries in East Asia and more than 40 observation stations that carry out monitoring of aerosol mass, wet deposition, dry deposition, and soil and vegetation. We have chosen the stations with long-term data of the entire year to run a comparative analysis. The position of each station in model domain is shown in Fig. 1. It can be seen that most

of the stations are located in the high anthropogenic emission or downwind regions. OMI is a 3-h actual satellite measurement monitoring O3 and NO2 for improving the understanding of Earth’s stratospheric ozone layer and tropospheric air quality with a space-borne spectroradiometer and newly developed near-realtime retrieval system (Boersma et al., 2007). The data set released publicly was validated in detail (Celarier et al., 2008) and widely used in related studies (Herron-Thorpe, Lamb, Mount, & Vaughan, 2010; Vasilkov et al., 2009). Fig. 2 compares results of monthly averaged values for simulation and EANET surface observation, as scatter plots. It is hard to collect the available observed data of nitrate aerosol. However, since most of the nitrate particles are converted from its precursor nitrogen oxides (Chang & Liao, 2009; Khoder, 2002), the evaluation of NOx , NO2 , and O3 could partly reflect the accuracy of nitrate simulation for they are the gaseous precursors or related species of nitrate aerosol formation. It can be found that ∼60% and ∼80% of the simulated values are within a multiplicative factor of 2 of measurements in Fig. 2(a) and (b), respectively. Most of them broadly gather around the 1:1 solid line. From Fig. 2(c), it can be seen that even thought the model obviously underestimates O3 at some sites, most of the simulated values are still within a factor of 2 of measurements. The possible reason of the low value

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Fig. 3. Monthly mean tropospheric NO2 columns (unit: ×1015 molecules/cm2 ) of (a)–(d) OMI monitoring, (e)–(h) model simulation, and (i)–(l) differences by subtracting OMI monitoring from model simulation, obtained in January (first row), April (second row), July (third row), and October (fourth row).

of modeled O3 may be the underestimation of VOC. As we know, the main source of tropospheric O3 is the photolysis of NO2 , and the oxidized groups generated by the oxidation reactions of VOC could reduce the consumption of O3 in the reaction of NO oxidation. Thus, the underestimation of VOC would lead to the decreased O3 . Additionally, the modeled nitrate ion (NO3 − ) mass concentration in precipitation, which refers to nitrate wet deposition, is also compared with EANET observation at six stations. Fig. 2(d) shows the simulation results agree well with observed values for ∼80%

of the simulated values fall within a factor of 2. The evaluation of PM10 could generally reflect the reasonability of aerosol particle simulation for it represents mass concentration of all major aerosol particles. Fig. 2(e) compares PM10 between simulation and EANET observation, showing that the model could well reproduce the values of observation because most of the scatter points gather around the 1:1 solid line, too. These comparisons indicate that the model basically performs well in simulating the surface mass burdens of NOx , NO2 , O3 , content of NO3 − in precipitation, and PM10 .


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Fig. 4. Modeled monthly mean mass concentration (unit: ␮g/m3 ) of nitrate aerosol and the wind field at surface in (a) January, (b) April, (c) July, and (d) October; (e)–(h) the direct radiative forcing (DRF; unit: W/m2 ) of nitrate aerosol at top of the atmosphere (TOA) simulated by the model in these four months.

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Fig. 5. Annual mean (a) mass concentration (unit: ␮g/m3 ) of PM10 at surface, (b) aerosol optical depth (AOD), and (c) DRF (unit: W/m2 ) of all major aerosols; (d) percentages (%) of nitrate mass concentration to total mass concentration, (e) nitrate aerosol optical depth, and (f) nitrate aerosol DRF simulated by the model.

However, discrepancies still exist at some stations. The simulation results obviously overestimate the mass burden of NOx at Yusuhara site. Since the model performs well at the nearby station Banryu, probably the selected data for comparison from simulation results are influenced by heavy anthropogenic emission from surrounding areas. The model also underestimates NOx and NO2 at Rishiri and Hongwen sites. Rishiri is located at the northwest corner, implying that it is relatively difficult to capture the range of pollution variation around the edge of the model domain. The discrepancies at Hongwen might be attributed to the biased emission inventory used in this simulation. Furthermore, direct comparison with point measurements is difficult because the simulation results

are interpreted as the average of each grid cell (64 km2 horizontal area). Fig. 3 compares monthly averaged column burden of tropospheric NO2 between OMI monitoring and simulation over model domain in January, April, July, and October. It can be seen that the distribution patterns are similar in each month, and model basically captures the high values around 10 × 1015 –20 × 1015 molecules/cm2 appeared in East China, Yangtze River Delta, Pearl River Delta in China, South Korea, and Southwest Japan. The seasonal change trend of modeled column burden of NO2 , which is higher in winter and lower in summer, also well follows that of measurement. However, the model results


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are relatively higher than those of OMI monitoring in East China and south part of East Asia. From Fig. 3(i)–(l) it can be found that the largest discrepancy generally reaches 5 × 1015 molecules/cm2 . It suggests that the model may overestimate column burden of tropospheric NO2 . Zhang, Gao, et al. (2007) used the modeling system RAMS-CMAQ to simulate the mass concentration of nitrate in 2001, and the model results were compared with the data of TRACEP aircraft in situ measurements. The relatively good agreement between simulations and observations indicated that the gas-phase chemistry and secondary aerosol formation in ambient atmosphere could be represented relatively well with this modeling system. Thus, the overestimation of NO2 is probably caused by the uncertainties of local emission inventory. On the other hand, previous study has shown that OMI underestimates by about 15% the tropospheric column burden as compared to ground-base and aircraft in situ measurements (Celarier et al., 2008), which could be another reason of bias between model and OMI results. 3.2. Geographical and seasonal distributions of nitrate mass concentrations and DRF Fig. 4(a)–(d) presents the monthly averaged mass concentration of nitrate aerosol at surface in January, April, July, and October in 2007. It can be found that the high values mainly concentrated in East China, Sichuan Basin, and Pearl River Delta in China up to about 20–40 ␮g/m3 . Strong anthropogenic emission was the main reason of the high mass burden in these regions. In addition, the seasonal variation of nitrate mass burden was significant due to the influence of precipitation and temperature. In January, the mass burden of nitrate in Southeast China, East China, and Sichuan Basin reached the highest levels (30–50 ␮g/m3 ). Generally, the photolysis of NO2 is weak during the winter time due to the weak photochemical activity. This feature could cause the accumulation of NO2 and more nitrate aerosol formation. The wet scavenging of nitrate aerosol is also relatively rare because precipitation is very low over China in winter. Thus, the meteorological field should be another important reason of the high mass burden of nitrate appearing in January besides the strong anthropogenic emission. In April, the photochemical activity was stronger than in January. The mass burden of nitrate became relatively lower (10–30 ␮g/m3 ) in the high value regions, and the highest value appeared only in part of East China (30–40 ␮g/m3 ). In July, the photochemical activity and precipitation could be the strongest over East Asia, implying that the photolysis of NO2 and wet scavenging of nitrate aerosol should be obviously enhanced. It can be seen that the mass burden of nitrate was lower than 5 ␮g/m3 in most regions of East Asia, and that the high value only appeared in part of East China (10–30 ␮g/m3 ) in July. The general distribution pattern in October was similar to that in January. Compared to January, the lower mass burden of nitrate (15–30 ␮g/m3 ) could be found in Sichuan Basin and Southeast China. However, the mass burden in East China (40–50 ␮g/m3 ) was obviously larger than that in January. Since the photochemical activity and precipitation was relatively stronger in October than in January, the influence of diffusion condition might be a factor determining this feature. From Fig. 4(a) and (d) we can see that wind speed in January was stronger than that in October over East China, implying that diffusion condition in October was worse, thus trapping more nitrate aerosol in this region. Fig. 4(e)–(h) presents the monthly averaged DRF of nitrate aerosol calculated by subtracting the radiation flux with nitrate from that without nitrate in January, April, July, and October at the top of atmosphere (TOA), showing that the seasonal variation of nitrate DRF was not quite similar to that of nitrate mass concentration. The largest range of strong negative effect appeared in April.

The value of DRF could reach −4 to −8 W/m2 in Sichuan Basin, East China, and Korea Peninsula. The main reason of this feature could be the high mass burden of nitrate in these regions. Another important reason could be the nitrate internally mixed with carbonaceous and mineral aerosols by coating on the surfaces, thus obviously enhancing the impact ability to radiation balance as mentioned in Zhang, Shi, Iwasaka, and Hu (2000) and Jacobson (2001). This strong negative effect in Fig. 4(f) of nitrate aerosol DRF could be mainly caused by the interaction between nitrate and other mixed aerosol components, especially the mineral aerosols from the dust storm as frequently happened in East Asia during spring. Fig. 5(a)–(c) shows respectively the yearly averaged mass concentration of PM10 at surface, aerosol optical depth (AOD), and DRF of all major aerosol components at TOA. The contribution of nitrate mass concentration, optical depth, and DRF to those of the total aerosols are also shown in Fig. 5(d)–(f), respectively. It can be seen that the relatively high percentages (>30%) of nitrate mass burden, optical depth, and DRF were mainly concentrated in Southeast China, East China, Korea peninsula, and Southwest Japan. The mass concentration of PM10 was not very high in South Korea and Central Japan, but the ratio of nitrate mass concentration to PM10 mass concentration still exceeded 30% in these two regions. These features indicate that the nitrate aerosol is a major aerosol component in all of the regions with significant anthropogenic impact in East Asia, not just in the regions with high aerosol mass burden. On the other hand, the distribution patterns in Fig. 5(e) and (f) are similar to that in Fig. 5(d), clearly meaning that the optical and radiative effects of nitrate were mainly determined by its mass concentration. In Northeast China, the percentage of nitrate DRF was ∼10% higher than that of nitrate mass burden. Considering the mass burden of PM10 in Northeast China was relatively lower than in Central China and Southeast China, as shown in Fig. 5(a), the probable reason may be that the aerosol composition in this region is different from those in Central China and Southeast China. It also indicates that except nitrate, the component of other aerosol particles which have strong atmospheric extinction effect may be less in Northeast China. 4. Conclusions In this study, the air quality modeling system RAMS-CMAQ coupled with a module for calculating aerosol optical and radiative properties was applied to simulate the mass concentration, optical depth, and DRF of nitrate aerosol in East Asia through the whole year of 2007. The new version of Carbon Bond mechanism CB05, which contains relatively comprehensive description of gas-phase consumption of HNO3 , NOx recycling reactions in multiday scales, and nighttime chemistry of NO3 removal, is applied in the modeling system for improving the simulation accuracy of nitrate aerosol. The modeled mass concentrations of NO2 , NOx , O3 , content of NO3 − in precipitation, and PM10 were compared with ground-base measurements and satellite monitoring for evaluating the rationality of model simulation. The result of comparison shows that the modeling system can capture their values and distribution features relatively well, thus also partly supporting the validity of nitrate simulation. Then the modeled monthly averaged mass concentration at surface and DRF at TOA of nitrate in January, April, July, and October in 2007 are presented. The analysis of the mass concentration shows that the high burden of nitrate mainly concentrated in Sichuan Basin, Southeast China, and East China with values ranging around 20–40 ␮g/m3 . The maximum burden which could exceed 40 ␮g/m3 was broadly located in East China in January and October. The strong local anthropogenic emission and seasonal change of meteorological field could be the main reasons of such distribution features. The yearly averaged percentage of nitrate mass

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concentration contained in all major aerosols could reach 25–40% in the high pollution burden regions. Analysis also shows that the nitrate DRF at TOA exhibited quite large negative effect with values ranging around −5 W/m2 in April and October, which was about 25–30% of the total aerosol DRF over the regions with high pollution burden in East Asia. These simulation results suggest that nitrate may be a major aerosol component in East Asia, especially in the area where strong anthropogenic activity concentrates. The mass concentration of nitrate aerosol may also be an important impact factor determining the strength of optical properties and radiative effects for their distribution patterns were broadly similar to each other in most parts of the model domain. However, it has been found that the strength of nitrate DRF did not coincide quite well with that of nitrate mass concentration in April, indicating that other aerosol species, such as mineral aerosol from dust storm frequently appearing in spring time in East Asia, could also obviously influence the nitrate DRF for they are often internally mixed with each other. This feature again suggested that the radiative forcing estimation of any aerosol species calls for taking into account the influence of other important aerosol components. Acknowledgements This work was supported by the “Strategic Priority Research Program” of the Chinese Academy of Sciences (Grant No. XDA05100502), National Department Public Benefit Research Foundation (Ministry of Environmental Protection of the People’s Republic of China) (No. 201109002), and National Natural Science Foundation of China (Grant Nos. 20937001, 41105106). References Akimoto, H. (2003). Global air quality and pollution. Science, 302, 1716–1719. An, J. L., Ueda, H., Wang, Z. F., Matsuda, K., Kajino, M., & Cheng, X. J. (2002). Simulations of monthly mean nitrate concentrations in precipitation over East Asia. Atmospheric Environment, 36, 4159–4171. Bauer, S. E., Mishchenko, M. I., Lacis, A. A., Zhang, S., Perlwitz, J., & Metzger, S. M. (2007). Do sulfate and nitrate coatings on mineral dust have important effects on radiative properties and climate modeling? Journal of Geophysical Research, 112, D06307. Benkovitz, C. M., Schultz, M. T., Pacyna, J., Tarrason, L., Dignon, J., Voldner, E. C., et al. (1996). Global gridded inventories of anthropogenic emissions of sulfur and nitrogen. Journal of Geophysical Research, 101, 29239–29253. Boersma, K. F., Eskes, H. J., Veefkind, J. P., Brinksma, E. J., van der A., R. J., Sneep, M., et al. (2007). Near-real time retrieval of tropospheric NO2 from OMI. Atmospheric Chemistry and Physics, 7, 2103–2118. Brimblecombe, P., & Stedman, D. H. (1982). Historical evidence for a dramatic increase in the nitrate component of acid rain. Nature, 298, 460–462. Celarier, E. A., Brinksma, E. J., Gleason, J. F., Veefkind, J. P., Cede, A., Hernam, J. P., et al. (2008). Validation of ozone monitoring instrument nitrogen dioxide columns. Journal of Geophysical Research, 113, D15S15. Chang, W., & Liao, H. (2009). Anthropogenic direct radiative forcing of tropospheric ozone and aerosols from 1850 to 2000 estimated with IPCC AR5 emissions inventories. Atmospheric and Oceanic Science Letters, 2(4), 201–207. Chuang, C. C., Penner, J. E., Prospero, J. M., Grant, K. E., Rau, G. H., & Kawamoto, K. (2002). Cloud susceptibility and the first aerosol indirect forcing: Sensitivity to black carbon and aerosol concentrations. Journal of Geophysical Research, 107, 4564. Collins, W. D., Rasch, P. J., Boville, B. A., Hack, J. J., McCaa, J. R., Williamson, D. L., et al. (2006). The formulation and atmospheric simulation of the community atmosphere model version 3 (CAM3). Journal of Climate, 19, 2144–2161. Deng, X., Tie, X., Wu, D., Zhou, X., Bi, X., Tan, H., et al. (2008). Long-term trend of visibility and its characterizations in the Pearl River Delta (PRD) region, China. Atmospheric Environment, 42, 1424–1435. Ghan, S. J., Easter, R. C., Chapman, E. G., Abdul-Razzak, H., Zhang, Y., Leung, L. R., et al. (2001). A physically based estimate of radiative forcing by anthropogenic sulfate aerosol. Journal of Geophysical Research, 106, 5279–5293. Gong, S. L. (2003). A parameterization of sea-salt aerosol source function for sub- and super-micron particles. Global Biogeochemical Cycles, 17(4), 1097. Han, X., Ge, C., Tao, J., Zhang, M., & Zhang, R. (2012). Air quality modeling for of a strong dust event in East Asia in March 2010. Aerosol and Air Quality Research, 12, 615–628.


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