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Nocturnal fine particulate nitrate formation by N2O5 heterogeneous chemistry in Seoul Metropolitan Area, Korea
Atmospheric Research 225 (2019) 58–69
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Nocturnal fine particulate nitrate formation by N2O5 heterogeneous chemistry in Seoul Metropolitan Area, Korea
T
Hyun-Young Joa, Hyo-Jung Leea, Yu-Jin Joa, Jong-Jae Leea, Soojin Banb, Jin-Ju Leeb, ⁎⁎ ⁎ Lim-Seok Changb, Gookyoung Heob, , Cheol-Hee Kima, a b
Department of Atmospheric Sciences, Pusan National University, Busan 609-735, Republic of Korea Air Quality Forecasting Center, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Republic of Korea
ARTICLE INFO
ABSTRACT
Keywords: N2O5 heterogeneous chemistry Nitrate aerosol Secondary inorganic aerosol PM2.5 forecasting Seoul Metropolitan Area CMAQ model
This study investigated the potential of fine nitrate (NO3− in PM2.5) formation in Seoul Metropolitan Area (SMA) by nighttime dinitrogen pentoxide (N2O5) heterogeneous chemistry during March 16–18, 2016, relatively dry and stagnant early spring days, by intervening N2O5 uptake coefficients (reactive uptake probability, γN2O5) in modeling with WRF-CMAQ. Simulations of a base case and two sensitivity tests with default (Davis et al., 2008), zero and decupled (tenfold) γN2O5 showed that impacts of γN2O5 on NO3− and PM2.5 are sensitive to relative humidity (RH) and sulfate-nitrate-ammonium (SNA) conditions. The base case simulation generally underestimated NO3− and PM2.5 levels in comparison to observations. Even with decupled γN2O5, modeled NO3− and PM2.5 concentrations showed relatively small increases under conditions that RH is relatively low in the range of 20 to 40% and SNA levels are severely underestimated (e.g., lower by one third) in the base case simulation. Comparisons of NO3− and PM2.5 concentrations in SMA between simulations with differently specified γN2O5 indicated that N2O5 heterogeneous chemistry has potential to (1) form additional nitric acid (HNO3), (2) further react with ammonia (NH3) emitted from various sources including agricultural sources outside of SMA urbancore areas, and (3) contribute to NO3− and PM2.5 formation in SMA. Additional modeling and observational studies on heterogeneous N2O5 chemistry are needed to improve our understanding of NO3− and PM2.5 formation and better forecast PM2.5 pollution levels over SMA or other urban areas with abundant nitrogen oxides emissions and ammonia emissions such as agricultural emissions from surrounding areas.
1. Introduction The Seoul Metropolitan Area (SMA) that includes Seoul, the capital of South Korea, and its surrounding areas, home of about 25 million people and 10 million cars (KOSIS, 2018), has experienced heavy particulate matter (PM) air pollution over the recent decade (Kim et al., 2017a,b; Seo et al., 2017). Since 2014, the South Korean government has provided air quality forecasts of pollution levels of inhalable particles with a diameter of 10 μm or less (PM10) and fine particles with a diameter of 2.5 μm or less (PM2.5) with four pollution grades (good, moderate, bad, and very bad) based on model-predicted daily mean concentrations and air quality forecasters' subjective analyses and decisions (Chang et al., 2016a). Despite considerable efforts to improve the accuracy of PM2.5 air
quality forecasting, some general biases, such as underestimating PM2.5 during late winter or early spring, still remain in the operational PM2.5 predictions, particularly under stagnant atmospheric conditions where surface wind is relatively weak and thus local emissions become more important than long-range transport (Kim et al., 2017a,b; Chang et al., 2016a). Inaccurate representation of complex PM2.5 formation chemistry in the air quality model used for PM2.5 forecasting often results in severely failed PM2.5 forecasts. One possible source of such failures has been recognized to be uncertainties in secondary formation mechanisms of inorganic species in PM2.5, involving sulfate (SO42−), nitrate (NO3−), and ammonium (NH4+) (hereafter collectively referred to as SNA), which are dominant inorganic species in PM2.5 (Pathak et al., 2009; Khan et al., 2010; Squizzato et al., 2012; Shin et al., 2016; Seo et al., 2017).
⁎ Correspondence to: C.-H. Kim, Department of Atmospheric Sciences, Pusan National University, 30 San, Jangjeon-Dong, Geumjeong-Gu, Busan 609-735, Republic of Korea. ⁎⁎ Correspondence to: G. Heo, Air Quality Forecasting Center, Climate and Air Quality Research Department, National Institute of Environmental Research, 42 Hwangyeong-ro, Seo-gu, Incheon 22689, Republic of Korea. E-mail addresses: [email protected], [email protected] (G. Heo), [email protected] (C.-H. Kim).
https://doi.org/10.1016/j.atmosres.2019.03.028 Received 13 January 2019; Received in revised form 6 March 2019; Accepted 20 March 2019 Available online 21 March 2019 0169-8095/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Modeling domains (27 km, 9 km, and 3 km) and terrian features over Seoul Metropolitan Area (SMA).
Many studies (e.g., Shin et al., 2016; Seo et al., 2017) have revealed that SNA is often the most abundant component of PM2.5 during pollution events in SMA. In particular, the NO3− fraction is becoming larger in PM2.5 in urban areas in South Korea (NIER, 2017), which suggests the importance of emitted nitrogen oxides (NOx, i.e., NO + NO2) in occurrences of high PM2.5 concentrations over SMA through the process of converting NOx into NO3−. Recent model predictions for PM2.5 forecasting have also indicated considerable uncertainties in representing NO3− formation, especially for morning peaks of PM2.5 episodes, which has been recognized to be associated with the nighttime heterogeneous chemistry of dinitrogen pentoxide (N2O5) (Prabhakar et al., 2017). Nitrate aerosols in PM2.5 can be formed in several pathways, and one is via the homogeneous reaction of gaseous nitric acid (HNO3) and ammonia (NH3) (Finlayson-Pitts and Pitts, 2000). HNO3 is primarily produced from the reaction between nitrogen dioxide (NO2) and hydroxyl radicals (OH) during daytime (Finlayson-Pitts and Pitts, 1997) and later combines with NH3 to produce particulate ammonium nitrate (NH4NO3). Another major pathway is the heterogeneous hydrolysis of N2O5 on aerosol surfaces at night (Ravishankara, 1997; Brown et al., 2006a,b; Kim et al., 2014). During nighttime, N2O5 primarily accumulates via the reversible reaction between NO2 and nitrate radicals (NO3), which are produced from the reaction of NO2 with ozone (O3) (Finlayson-Pitts and Pitts, 1997), and heterogeneous conversion of N2O5 into HNO3 on aerosol surfaces may occur (Chang et al., 2011), substantially contributing to high levels of fine particulate nitrate early on the next day. Many previous studies (e.g., Riemer et al., 2003; Brown et al., 2006b; Davis et al., 2008; Bertram et al., 2009; Chang et al., 2011, 2016b; Young et al., 2016; Wang et al., 2017; Osthoff et al., 2018) pointed out that one of major uncertainties in modeling nighttime N2O5 heterogeneous chemistry is its uptake coefficients (reactive uptake probability, γN2O5), which is highly variable with several orders of magnitude depending on meteorological conditions and aerosol compositions. At some sites, the contribution of N2O5 hydrolysis to the enhancement of fine particulate nitrate was estimated to reach 50–100% based on a thermodynamic model (Pathak et al., 2009, 2011) and assumptions on γN2O5. Another aspect of the uptake coefficients (γN2O5) associated with nighttime N2O5 is its reaction with chloride ion (Cl−) which is ubiquitously present in PM2.5. Cl− originates mostly
from sea salt aerosol and produces nitryl chloride (ClNO2), and then is degassed from the aerosol, reducing the NO3− and PM2.5 mass production (Sarwar et al., 2012, 2014). ClNO2 is stable at night, but is photolysed within approximately 1 h after sunrise. The chlorine atoms (Cl) liberated by photolysis react with volatile organic compounds (VOCs), enhancing VOC oxidation in morning hours. Therefore, the uncertainty still remains as to whether nighttime N2O5 heterogeneous chemistry can actually account for increases in PM2.5 mass concentrations under atmospheric conditions relevant to SMA, South Korea. With this background, the uncertainty of nitrate formation from nighttime N2O5 processing must be evaluated for accurate PM2.5 forecasting which is needed to provide PM2.5 pollution warnings to the public in South Korea. In this study, we investigated impacts of nighttime N2O5 heterogeneous chemistry on fine nitrate (NO3− in PM2.5) and PM2.5 formation under conditions where SNA levels are underestimated (hereafter, referred to as “SNA-underestimated conditions”) in SMA, during March 16–18, 2016, relatively dry and stagnant early spring days. In particular, the role of γN2O5 was quantified by simulating a base case and two sensitivity tests with default (Davis et al., 2008), killed (zero) and decupled (tenfold) γN2O5, respectively. Modeled NO3− and PM2.5 concentrations were compared and diagnosed against observations to improve our understanding of nitrate and PM2.5 formation and better predict PM2.5 pollution levels under atmospheric conditions relevant to SMA. 2. Method and data 2.1. Modeling system and domain The WRF-CMAQ model, which consists of Weather Research and Forecast model (WRF, https://www.mmm.ucar.edu/weather-researchand-forecasting-model) and Community Multi-scale Air Quality model (CMAQ, https://www.cmascenter.org/cmaq/), was employed. For meteorological modeling, WRF (v3.6.1) was used to generate meteorological fields to drive CMAQ (ver. 5.0.2) using the 1° × 1° Final Operational Global Analysis (FNL) data of the National Centers for Environmental Prediction (NCEP). Chemistry and transport processes of chemical species were simulated using CMAQ. The CMAQ configurations are based on SAPRC99 for gas phase chemistry, AERO5 aerosol 59
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were obtained by using the standard CMAQ profile provided in the CMAQ package and using a spin-up period, and the boundary conditions for the inner grids were updated by the model outputs from the outer grids. Anthropogenic emissions for Northeast Asia are based on the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) inventory for the year 2006 (Zhang et al., 2009; Li et al., 2014), and the Clean Air Policy Support System (CAPSS) inventory for the year 2007 was used for Korea (Kim et al., 2008; Lee et al., 2011). Biogenic emissions considered here are based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.04 (Guenther, 2006). 2.2. Reactions relevant to nighttime N2O5 chemistry During nighttime, NO2 photolysis shuts down, and NO2 becomes available to sequentially produce NO3, N2O5, and HNO3. Reactions relevant to N2O5 formation, its conversion to HNO3 and subsequent formation of NO3− in the form of NH4NO3 are shown in Reactions (R1) to (R6) below.
NO2 + O3
(R1)
N2 O5
(R2)
NO3 + NO2
(R3)
NO3 + NO2 N2 O5
NO3 + O2
N2 O5 + H2 O N2 O5 + Cl
HNO3 + NH3
2HNO3 Y·ClNO2 + (2 Y)·HNO3
NH 4 NO3
(R4) (R5) (R6)
N2O5 is formed from the gas-phase oxidation of NO2 by O3 (R1), followed by the reaction of NO2 with NO3 (R2). N2O5 thermally dissociates back to NO3 and NO2 (R3). N2O5 also heterogeneously reacts on aerosol surfaces and forms HNO3 through (R4) or (R5), and finally produces particulate nitrate via Reaction (R6). Reaction (R5) in which N2O5 reacts with chloride ion (Cl−) and forms nitryl chloride (ClNO2) was not used in this study because relatively lower Cl− concentrations (typically less than 0.5 μg/m3) were measured during the study period. Note that using the HNO3 yield of Reaction (R5), i.e., 2-Y where Y is the ClNO2 yield, instead of 2 for Reaction (R4) will lead to lower nitrate production and make the nitrate underestimation problem even worse during March 16–18, 2016. After sunrise, NO3 photolyzes almost immediately, and therefore NO3 is less important during daytime. Since N2O5 is not accumulated up to a relatively high concentration during the day, HNO3 formation by Reactions (R4) and (R5) becomes far less important during daytime.
Fig. 2. Time variations of PM2.5 and its relevent species measured at Bulkwang site during 14–22 March 2016. Top panel represents measured PM2.5 of daily averaged, minimum, 5/25/50/75/95 percentiles, and maximum concentrations; Bottom panel showed hourly variations of PM2.5, NO3−, SO42−, NH4+ concentrations.
module with ISORROPIA II for aerosol thermodynamics (Fountoukis and Nenes, 2007), Multiscale Horizontal diffusion, and Euler Backward Iterative (EBI) chemical solver. The modeling domains comprise three grids with horizontal resolutions of 27, 9, and 3 km over SMA, as shown in Fig. 1. As indicated in Fig. 1, several complex terrain features dominate to the north, to the east, and to the west of SMA. To the west, Yellow Sea is located, and induces sea and land breezes penetrating inland areas. Due to local circulations, SMA can be a domain of so called “closed basin” surrounded by mountains when synoptic wind is weak. Then, the precursors of NO3 and N2O5 are accumulated, and nitrate aerosols can be heterogeneously formed on aerosol surfaces during nighttime which results in relatively high NO3− and PM2.5 concentrations when the land breeze starts to blow. Around noon when the sea breeze sets in, nitrate aerosols that are heterogeneously produced at night and conserved under relatively high humidity around the coastal area can be brought back to Seoul, the central SMA area. In the WRF-CMAQ model, there were 15 layers vertically on a sigma coordinate up to 50 kPa with the lowest layer thickness of about 32 m. The initial and boundary conditions of chemical species' concentrations
2.3. Measurements and meteorological data To evaluate the performance of the WRF-CMAQ modeling system, we used measurements obtained from the Bulgwang site (126.98°E, 37.61°N, 67 m above sea level), as indicated in Fig. 1. The Bulgwang site is one of the intensive measurement stations operated by the Korean Ministry of Environment (KME) to provide comprehensive PM2.5 composition data in South Korea. The site is located northwest of Seoul which represents residential urban areas in SMA. Hourly PM2.5 concentrations were measured using a β-ray attenuation method (BAM-1020, Met one, USA). As major PM2.5 components, water soluble ions (NO3−, SO42−, NH4+; collectively SNA) were measured by an ambient ion monitor (AIM) (URG-9000D, URG
Table 1 Summary of three sensitivity tests for the analysis of N2O5 heterogeneous conversion to NO3−. Test
γN2O5
Description
N2O5_ON N2O5_OFF D_N2O5_ON
γN2O5 = ~0.02 γN2O5 = 0 Decupling of γN2O5
Default heterogenous reactions in CMAQ (ver. 5.0.1) (Davis et al., 2008) Neglecting the process of N2O5 heterogeneous conversion to NO3− Tenfold increase of N2O5 heterogeneous chemistry (The expected upper bound on the conversion effect)
60
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Fig. 3. Comparisons of time series of measurements (white dots) and base case simulations (black dots) of PM2.5, NO3−, SO42−, and NH4+ concentrations during the study period.
to examine the impacts of nighttime N2O5 heterogeneous chemistry on NO3− and PM2.5 concentrations, we selected March 16–18, 2016, atmospherically stagnant and relatively dry early spring days. Relative humidity was relatively low on these three days, which was used to test the model capability of simulating nitrate formation under relatively low RH conditions such as 20–40%, which has been reported to be a limitation of the aerosol thermodynamics module ISORROPIA II used for this study (Guo et al., 2016; Chang et al., 2016b). Stagnant synoptic cases were selected from the synoptic parameters suggested by Jo and Kim (2013), such as weak geostrophic wind speed no greater than 6.0 m/s and positive vorticity at 850 hPa. Accordingly, the period covering March 16 to March 18, 2016 was employed in this study, and the synoptic features of the selected case are described in the Supplementary Materials. Fig. 2 shows the time series of PM2.5 and SNA (i.e., NO3−, SO42− and NH4+) measured at the Bulgwang site (Fig. 1) in central SMA during March 14–23, 2016, including our selected study period. Overall, with an exception of slight decreases on March 18 and 22, 2016, daily mean PM2.5 concentrations steadily increased from March 16 to March 21, 2016, which is related to the local accumulation of air pollutants under stagnant atmospheric conditions particularly from March 16 to the morning of March 18, 2016 and the influence of longrange transport particularly from the night of March 18, 2016. In Fig. 2, it is interesting to note that PM2.5 and NO3− show similar temporal variations. Both PM2.5 and NO3− concentrations showed a peak value at night with an average ratio of NO3− to PM2.5 of 0.26, which is significantly higher than ratios of SO42− and NH4+ to PM2.5 of 0.13 and 0.13, respectively. Furthermore, the average ratio of NO3− to SO42− on a mass basis was found to be 2.0, suggesting that NO3− is a main factor for predicting PM2.5 concentrations. This nitrate fraction, 0.26, is relatively higher than fractions reported by previous studies such as Pathak et al. (2009), Khan et al. (2010), and Squizzato et al. (2012), which all reported relatively lower nitrate fractions with
Table 2 Comparison of observed and modeled PM2.5 and NO3− concentrations for March 17, 2016. NO3−
PM2.5
OBS D_N2O5_ON N2O5_ON N2O5_OFF
Daily mean (μg/m3)
Hourly max (μg/m3)
Daily mean (μg/m3)
Hourly max (μg/m3)
39.0 34.7 (−11%) 20.1 (−48%) 18.9 (−51%)
57.5 64.9 (13%) 44.6 (−23%) 40.9 (−29%)
10.7 11.9 (11%) 5.4 (−50%) 4.4 (−58%)
21.0 23.3 (11%) 11.8 (−44%) 7.7 (−63%)
Note: OBS, D_N2O5_ON, N2O5_ON, N2O5_OFF mean observation and model calculations for decupling, default and killed cases described in Table 1, respectively. Numbers in the parentheses are relative errors, (model – obs)/obs.
Corporation, USA) utilizing ion chromatography. More detailed information can be found in Jeon et al. (2015). Weather maps at 850 hPa geopotential level and meteorological surface observation data were also used for both case selection and verification of the meteorological model. Meteorological data used here include temperature, relative humidity, wind speed and wind direction provided by the Korea Meteorological Administration (KMA, 2016). 2.4. Case selection PM2.5 concentrations are affected by both local emissions and longrange transported air pollutants. Under stagnant meteorological conditions, local emissions primarily affect the formation of high PM2.5 concentrations via accumulation of directly emitted air pollutants and secondary formation such as fine nitrate formation by N2O5 heterogeneous process. In South Korea, stagnant atmospheric conditions typically occur by a stagnant or slowly moving anticyclone system accompanied by weak horizontal and vertical mixing processes. In order 61
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Fig. 4. Time series of vertical NO, O3, NO2, NO3, N2O5, and NO3− concentrations simulated at Bulwkang site (base case). Black dots (ㆍ) represent simulated boundary layer heights.
different features. We selected 3/16/2016 12 LST (03 UTC) to 3/18/ 2016 12 LST to further examine the impact of heterogeneous conversion of N2O5 to HNO3 on NO3− and PM2.5 concentrations under relatively stagnant atmospheric conditions over SMA.
test with decupling (tenfold increase) the γN2O5 can be regarded as an extreme case for conversion of N2O5 to nitrate particulate matter excessively. Using these three CMAQ simulations, we evaluated the N2O5 heterogeneous hydrolysis and explored its potential contribution to PM2.5 formation over SMA.
2.5. Modeling nighttime N2O5 heterogeneous chemistry
3. Results and discussion
We carried out multiple CMAQ simulations for the period covering March 13 to March 23, 2016 with 3 days as a spin-up period, with a base case and two sensitivity tests while turning off or decupling the N2O5 uptake process, as listed in Table 1: (1) base case with the default chemical reactions and without modification in Reaction (R4) (Davis et al., 2008; henceforth referred to as “N2O5_ON”), (2) simulation with uptake coefficient, γN2O5 = 0 in reaction (R4) (henceforth referred to as “N2O5_OFF”), and (3) simulation with decupling γN2O5 (γN2O5 = 10 × default γN2O5) in reaction (R4) (henceforth referred to as “D_N2O5_ON”). Analyzing differences between “N2O5_ON” and “N2O5_OFF” would facilitate diagnosing the N2O5 heterogeneous chemistry represented in the CMAQ (ver. 5.0.2) based on Davis et al. (2008) under atmospheric conditions relevant to the SMA area. On the other hand, “D_ N2O5_ON”
3.1. Evaluation of meteorology and chemistry for base case simulation Meteorological modeling performance was evaluated by comparing the simulations and measurements at the Bulgwang site. The simulated meteorological variables including temperature at 2 m, RH, and wind speed at 10 m were extracted at the grid point in the 3 km domain nearest to the Bulgwang site. The resultant index of agreement (IOA) of 2 m temperature, RH, and 10 m wind speed are 0.92, 0.80, and 0.50, respectively, indicating that near-surface temperature was more reasonably simulated than RH and 10 m wind speed. Despite overall reasonable simulations of meteorological variables, sometimes there were large biases. RH was overestimated with a big difference against observation over the period 62
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Fig. 5. Spatial distributions of simulated (a) NO3− and (b) N2O5 concentrations over SMA during the study period (base case).
especially when the pressure system changed with a low pressure system passing through the north region of North Korea. Fig. 3 shows that modeled PM2.5 and SNA concentrations are overall underestimated for the period of 3/16/2016 12 LST to 3/18/2016 12 LST. For the morning of 3/18/2016, the model grossly underestimated PM2.5 and SNA concentrations mainly due to overestimated wind speed. The most interesting part of the study period is 3/17/2016 on which we concentrate our analysis in the following sections. In Fig. 3, both model and observation show morning and night peaks of PM2.5 and NO3− on 3/17/2016. The model underestimated the daily mean concentrations of PM2.5 and NO3− by 48% (20.1 versus 39.0 μg/m3) and 50% (5.4 versus 10.7 μg/m3), respectively while the maximum hourly concentrations of PM2.5 and NO3− are also underestimated by 23% (44.6 versus 57.5 μg/m3) and 44% (11.8 versus 21.0 μg/m3), respectively, as summarized in Table 2. Despite the uncertainty of heterogeneous nitrate formation, some differences between day and night were found. The “observed” nighttime NO3− concentration averaged from 18 LST to 06 LST accounted 33% of PM2.5 (daytime average ~25%), while nighttime SO42− and NH4+ together constituted 22% of PM2.5 (daytime average ~5% and 7%, respectively). However, the “simulated” nighttime NO3− concentration accounted only 28% (daytime average ~15%), indicating the SNA-underestimated environment with relative dominance of nitrate aerosols.
highest values above the boundary layer height (mixing height), at altitudes of around 300–900 m during the nighttime on March 17, 2016, which is consistent with the fact that NO3 is mainly formed from NO2 via Reaction (R1), and removed by photolysis and reaction with NO near the ground level (Finlayson-Pitts and Pitts, 2000). N2O5 showed higher concentrations during nighttime above the modeled mixing height at altitudes of around 100–700 m, at somewhat lower altitudes compared to NO3, which is explained by the fact that N2O5 formation by Reaction (R2) needs both NO3 and NO2, and that NO2 is more abundant at altitudes closer to the ground level (Fig. 4). NO3− is formed by Reaction (R6) and showed highest concentrations mostly within the mixing heights (Fig. 4). Therefore, for the Bulgwang site located in the central SMA area, the possibility of nighttime NO3− formation in the residual layer above the mixing height and then mixing-down toward the ground level in the next day morning (Young et al., 2016; Prabhakar et al., 2017) is not clearly demonstrated in Fig. 4. Fig. 5 shows the spatial distributions of NO3− and N2O5 during both day and night with wind vectors at surface over SMA. The surface N2O5 concentrations indicated strong diurnal variations: increased at night and decreased at daytime. Based on modeled spatial distributions of surface NO3− and N2O5 concentrations, overall both NO3− and N2O5 concentrations were simulated to be higher in areas outside of Seoul (central SMA). However NO3− formed outside of Seoul can be transported into Seoul by horizontal advection, resulting in higher levels of NO3− as illustrated by two NO3− peaks on March 17, 2016 (Fig. 3).
3.2. Vertical and spatial distributions of chemical species relevant to N2O5 chemistry
3.3. Comparison of NO3− and PM2.5 formation between simulations with different N2O5 uptake coefficients
Vertical and spatial distributions for the base case simulation were further analyzed for species relevant to nighttime N2O5 heterogeneous chemistry. Fig. 4 shows the vertical distributions of modeled NO, NO2, O3, NO3, N2O5, and NO3− concentrations. As expected for typical urban areas with abundant NOx emissions, the model-simulated NO and NO2 levels were relatively higher at night and early morning hours, but lower during daytime (Finlayson-Pitts and Pitts, 2000). O3 concentrations were simulated to be (1) low during nighttime due to removal by NO emitted near the ground level, (2) relatively higher on the afternoon of March 17, 2016 in comparison to other times, and (3) constantly higher above around 500 m due to lack of removal by NO. NO3 showed
Fig. 6 shows the time series of PM2.5, NO3−, and N2O5 concentrations from three CMAQ simulations: N2O5_ON (base run), N2O5_OFF (turning off N2O5 heterogeneous chemistry by setting γN2O5 = 0), and D_N2O5_ON (decupling γN2O5) tests. Compared with the N2O5_ON case, the N2O5_OFF case showed higher N2O5 concentrations and lower PM2.5 and NO3− concentrations but showed relatively minor changes in NO3− concentrations with exceptions of peak values over two relatively high relative humidity periods: morning (06–12 LST) and night (18–24 LST) on 17 March 2016. During these times, increased NO3− formation 63
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Fig. 6. Time series of measured and modeled PM2.5, NO3−, and N2O5 at Bulgwang site (model: D_ N2O5_ON (decupled), N2O5_ON (base case), and N2O5_OFF (killed)).
Underestimation of NO3− even for D_N2O5_ON is also related to the fact that the impact of γN2O5 on increasing NO3− formation is also influenced by the aerosol surface area available for N2O5's heterogeneous reaction (Chang et al., 2011). Thus, combination of underestimated SNA and PM2.5 concentrations together with low relative humidity on 17 March 2016 seems to hinder the simulation for D_N2O5_ON from narrowing discrepancies between modeled and observed NO3− concentrations.
by heterogeneous conversion of N2O5 resulted in increased PM2.5 for both N2O5_ON and D_N2O5_ON cases. On the other hand, N2O5 concentrations were higher in the N2O5_OFF (γN2O5 = 0) test because in this simulation N2O5 was not heterogeneously converted to HNO3, which indicates that N2O5 needs to be converted to HNO3 and subsequently to NO3− in order to contribute to increasing NO3− and PM2.5 concentrations. In the N2O5_OFF case, the peak NO3− concentration at 21 LST was reduced by 35%, i.e., from 11.8 μg/㎥ of the N2O5_ON case to 7.7 μg/㎥. Fig. 6 also shows noticeable differences between D_N2O5_ON and N2O5_ON at 09 and 21 LST on 17 March 2016, which indicates that N2O5 heterogeneous chemistry has potential to enhance NO3− formation and makes modeled NO3− and PM2.5 concentrations closer to measurements. For D_N2O5_ON, the daily average NO3− concentration on 17 March 2016 increased by a factor of 2.2 (a factor of 1.7 for PM2.5) in comparison to the base case (N2O5_ON), and the simulated PM2.5 concentrations were also in closer agreement with observations (Fig. 6 and Table 2). However, it is interesting to note that all three tests including even D_N2O5_ON underestimated NO3− concentrations against observations during 12–18 LST of 17 March 2016. This seems to be in part due to severely low relative humidity (i.e., mostly below 40%) in the afternoon, which is in the range that ISORROPIA II has shown some limitation in simulating aerosol thermodynamics (Chang et al., 2016b).
3.4. Enhanced NO3− and PM2.5 formation by heterogeneous N2O5 chemistry in SMA In this section, we further explain enhanced formation of NO3− and PM2.5 in the SMA area via heterogeneous conversion of N2O5 into HNO3 and subsequent reaction with NH3 by examining spatial distributions of NO3, N2O5, HNO3, NH3, NO3− and PM2.5 (Figs. 7, 9, 10) and briefly looking into the influence of meteorological conditions such as relative humidity (Fig. 8). Fig. 7 shows vertical variations of NO3, N2O5, NO3− and PM2.5 concentrations at the Bulgwang site simulated for N2O5_ON (base case) and N2O5_OFF (killed uptake) with the overlayed boundary layer heights (mixing heights) simulated here (black dot). Due to lack of additional removal of N2O5 by the heterogeneous reaction (R4), NO3 and N2O5 concentrations are higher in the N2O5_OFF case in 64
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Fig. 7. Time series of vertical NO3, N2O5, NO3−, and PM2.5 concentrations at Bulgwang site modeled by N2O5_ON (base case) and N2O5_OFF (killed) simulations, and their differences. Black dots (ㆍ) represent simulated boundary layer heights.
comparison to the N2O5_ON case. However, despite additional HNO3 formation via heterogeneous conversion of N2O5, differences in NO3− and PM2.5 are relatively small between N2O5_ON and N2O5_OFF with a noticeable exception during 18–24 LST on March 17, 2016. In Fig. 7, largest differences between the two cases, 4.1 μg/m3 in NO3− and 5.3 μg/m3 in PM2.5, are found at 21 LST. The model-estimated boundary layer height, which is shown by black dots in Fig. 7, starts to decrease from 17 LST, shows a minimum at 20 LST and increases until midnight. Note that N2O5 and NO3− concentrations become higher above the boundary layer height at around 18 to 21 LST. This is implying that increases in NO3− and PM2.5 concentrations are related to entrainment from the top of nocturnal boundary layer to some degree (Berkes et al., 2016; Trousdell et al., 2016). Fig. 8 also demonstrates the impact of relative humidity (RH) on NO3− formation via N2O5 heterogeneous hydrolysis as previous studies pointed out (e.g., Davis et al., 2008; Chang et al., 2016b). Fig. 8 shows the time series of NO3− concentrations for the three tests together with meteorological variables: temperature and relative humidity. Relatively noticeable differences in NO3− between N2O5_ON (or D_N2O5_ON) and N2O5_OFF are found only over two periods when the relative humidity was relatively higher on 17 March 2016: early morning (~09 LST) and late night (~21 LST). RH was overall low on March 17, 2016. The observed RH was below 40% at 01–02 and 09–19 LST and showed a maximum of 53% at 07 LST and a minimum of 21% at 12 LST. As shown in Fig. 8, the model clearly underestimated RH for 03–09 LST and the simulated RH was below 40% for 03–17 LST on March 17, 2016. Note that the aerosol thermodynamics module ISORROPIA II used for this study has been reported to show relatively poor performance in simulating NO3− concentrations under dry conditions with relative humidity lower than 40% (Chang et al., 2016b; Guo et al., 2016). Previous studies showed that the main hydrolysis product of N2O5 such as nitric acid (HNO3) can be partitioned more favorably to the aerosol phase at lower temperatures (Stelson and Seinfeld, 1982). No clear feature for temperature was found for this study. Chen et al. (2016) pointed out that SNA aerosols increased dramatically at some sites in Beijing at the time when sulfur-oxidation (SO2-to-H2SO4) rates and nitric-oxidation (NO2-/NO3-to-HNO3) rates increased simultaneously and that they were correlated with the high relative humidity
60–90% on those days. Therefore, additional studies covering both low and high relative humidity conditions are desirable to better predict fine nitrate concentrations under various relative humidity conditions relevant to the SMA area. NO3− exists in the form of NH4NO3 when NH3 is available for reaction with HNO3 via Reaction (R6) as described earlier. Thus, we show spatial distributions of HNO3, NH3, NO3− and PM2.5 concentrations for the base case at 03, 06, 09, 18, and 21 LST on 17 March 2016 when differences in NO3− and PM2.5 between N2O5_ON and N2O5_OFF were relatively higher (Fig. 9). The spatial distributions indicate that NO3−, and PM2.5 can be additionally formed by heterogeneous conversion of N2O5 into HNO3 and subsequent reaction wich NH3 which is emitted from various sources such as agricultural sources outside of SMA urbancore areas. Note that NO3− and PM2.5 formed in surburban SMA areas can also contribute to increasing NO3− and PM2.5 concentrations in the central SMA area via horizontal transport. Additional modeling and observational studies on heterogeneous N2O5 chemistry are needed to improve our understanding of NO3− and PM2.5 formation and better forecast PM2.5 pollution levels over SMA or other urban areas with abundant nitrogen oxides emissions together with NH3 emissions such as agricultural emissions from surrounding areas. In order to further analyze how N2O5 heterogeneous chemistry contributes to enhancing NO3− and PM2.5 formation in the SMA area, we also show differences in HNO3, NO3− and PM2.5 between D_N2O5_ON (decupling) and N2O5_ON (base case) in Fig. 10. HNO3 concentrations are somewhat higher for D_N2O5_ON compared to N2O5_ON, and relatively bigger differences in NO3− and PM2.5 are found where ammonia concentrations are relatively higher (Fig. 9, Fig. 10), which indicates that ammonia emissions such as agricultural ammonia emissions in the areas surrounding Seoul (central SMA) are also important in regard to NO3− and PM2.5 formation. Previous studies also reported that increases in NO3− were observed and simulated in some areas in California where ammonia emissions are abundant (Russell and Cass, 1986; Kelly et al., 2014; Brown et al., 2006a; Lurmann et al., 2006). Note that chlorine (Cl) chemistry was not considered in this study which focuses on the problem of nitrate underestimation under atmospheric conditions relevant to the SMA, South Korea. The 65
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Fig. 8. Time series of simulated temperature and relative humidity, and their biaes between predictions and obervations at Bulgwang site. Also shown are surface NO3− concentrations observed and modelded by D_ N2O5_ON (decupled), N2O5_ON (base case), and N2O5_OFF (killed) simulations.
heterogeneous uptake of N2O5 is confounded by Cl-ClNO2 chemistry which tends to reduce nitrate formation (see reaction (R5)) and PM2.5 concentrations (Wang et al., 2018; McDuffie et al., 2018; Fibiger et al., 2018). However, Cl emissions are also associated with combustion sources in and around Korea (Kim et al., 2017a), and some researchers
pointed out evidence of Cl emissions from biomass burning activities (Ahern et al., 2017). Therefore, diagnosing both nitrate underestimation and overestimation may be further needed by carrying out chemical model with the detailed Cl-ClNO2 chemistry.
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Fig. 9. Spatial distributions of simulated (a) HNO3, (b) NH3, (c) NO3−, and (d) PM2.5 concentrations (base case) over SMA during the study period.
between modeled and observed NO3− concentrations. These results indicate that solving nitrate underestimation does involve not only just re-parameterizing γN2O5 but also better modeling other major PM2.5 components such as sulfate and ammonium, which implies that accurately representing nitrate formation under atmospheric conditions relevant to the SMA would be a complex task. Analyzing the spatial distributions of modeled HNO3, NH3, NO3−, and PM2.5 on 17 March 2016 was useful to understand how NO3− and PM2.5 formation can be enhanced in the SMA by N2O5 heterogeneous hydrolysis. Our results indicate that (1) NO3− and PM2.5 can be additionally formed by heterogeneous conversion of N2O5 into HNO3 and subsequent reaction wich NH3 which is emitted from various sources such as agricultural sources outside of SMA urban-core areas and (2) these additionally formed NO3− and PM2.5 can result in increased concentrations of NO3− and PM2.5 in SMA urban-core areas by vertical mixing (entrainment) or horizontal advection. Additional modeling and observational studies are needed to improve our understanding of nitrate and PM2.5 formation mechanisms under various atmosperic conditions (e.g., dry and stagnant conditions in early spring days in SMA), to accurately predict PM2.5 concentrations and forecast PM2.5 pollution levels for the SMA and other areas where nitrogen oxides emissions are abundant and ammoinia is also relatively well supplied by various sources including agricultural sources in
4. Summary and conclusions This study carried out a series of WRF-CMAQ simulations with differently represented N2O5 uptake coefficient (γN2O5) to diagnose the role of heterogeneous N2O5 hydrolysis, and examine its potential contribution to fine nitrate and PM2.5 formation under relatively dry (relative humidity of 20–55%, mostly below 40%) and stagnant conditions relevant to early spring days in the Seoul Metropolitan Area (SMA), South Korea. Model results and model-measurement comparison indicate that the N2O5 scheme based on Davis et al. (2008) in CMAQ (ver. 5.0.2) needs to be improved to reasonably simulate fine nitrate formation at relatively high PM2.5 and nitrate concentrations under relatively dry conditions, particularly RH of 20–40%. Such improvement is particularly needed to accurately predict PM2.5 pollution levels in SMA in South Korea. Our detailed analysis of the case of March 17, 2016 clearly demonstrated that the impact of γN2O5 on enhancing NO3− formation is strongly influenced by the PM2.5 pollution level (which is related to providing aerosol surfaces available for N2O5's heterogeneous reaction) as well as relative humidity. Thus, even with tenfold increased γN2O5, combination of underestimated sulfate-nitrate-ammonium (SNA) and PM2.5 concentrations and low relative humidity on 17 March 2016 hindered the WRF-CMAQ simulation from narrowing discrepancies 67
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Fig. 10. Differences in spatial distributions of simulated (a) HNO3, (b) NO3−, and (c) PM2.5 concentrations between D_N2O5_ON (decupling) and N2O5_ON (base) over SMA during the study period.
adjacent areas.
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Nocturnal fine particulate nitrate formation by N2O5 heterogeneous chemistry in Seoul Metropolitan Area, Korea
T
Hyun-Young Joa, Hyo-Jung Leea, Yu-Jin Joa, Jong-Jae Leea, Soojin Banb, Jin-Ju Leeb, ⁎⁎ ⁎ Lim-Seok Changb, Gookyoung Heob, , Cheol-Hee Kima, a b
Department of Atmospheric Sciences, Pusan National University, Busan 609-735, Republic of Korea Air Quality Forecasting Center, Climate and Air Quality Research Department, National Institute of Environmental Research, Incheon 22689, Republic of Korea
ARTICLE INFO
ABSTRACT
Keywords: N2O5 heterogeneous chemistry Nitrate aerosol Secondary inorganic aerosol PM2.5 forecasting Seoul Metropolitan Area CMAQ model
This study investigated the potential of fine nitrate (NO3− in PM2.5) formation in Seoul Metropolitan Area (SMA) by nighttime dinitrogen pentoxide (N2O5) heterogeneous chemistry during March 16–18, 2016, relatively dry and stagnant early spring days, by intervening N2O5 uptake coefficients (reactive uptake probability, γN2O5) in modeling with WRF-CMAQ. Simulations of a base case and two sensitivity tests with default (Davis et al., 2008), zero and decupled (tenfold) γN2O5 showed that impacts of γN2O5 on NO3− and PM2.5 are sensitive to relative humidity (RH) and sulfate-nitrate-ammonium (SNA) conditions. The base case simulation generally underestimated NO3− and PM2.5 levels in comparison to observations. Even with decupled γN2O5, modeled NO3− and PM2.5 concentrations showed relatively small increases under conditions that RH is relatively low in the range of 20 to 40% and SNA levels are severely underestimated (e.g., lower by one third) in the base case simulation. Comparisons of NO3− and PM2.5 concentrations in SMA between simulations with differently specified γN2O5 indicated that N2O5 heterogeneous chemistry has potential to (1) form additional nitric acid (HNO3), (2) further react with ammonia (NH3) emitted from various sources including agricultural sources outside of SMA urbancore areas, and (3) contribute to NO3− and PM2.5 formation in SMA. Additional modeling and observational studies on heterogeneous N2O5 chemistry are needed to improve our understanding of NO3− and PM2.5 formation and better forecast PM2.5 pollution levels over SMA or other urban areas with abundant nitrogen oxides emissions and ammonia emissions such as agricultural emissions from surrounding areas.
1. Introduction The Seoul Metropolitan Area (SMA) that includes Seoul, the capital of South Korea, and its surrounding areas, home of about 25 million people and 10 million cars (KOSIS, 2018), has experienced heavy particulate matter (PM) air pollution over the recent decade (Kim et al., 2017a,b; Seo et al., 2017). Since 2014, the South Korean government has provided air quality forecasts of pollution levels of inhalable particles with a diameter of 10 μm or less (PM10) and fine particles with a diameter of 2.5 μm or less (PM2.5) with four pollution grades (good, moderate, bad, and very bad) based on model-predicted daily mean concentrations and air quality forecasters' subjective analyses and decisions (Chang et al., 2016a). Despite considerable efforts to improve the accuracy of PM2.5 air
quality forecasting, some general biases, such as underestimating PM2.5 during late winter or early spring, still remain in the operational PM2.5 predictions, particularly under stagnant atmospheric conditions where surface wind is relatively weak and thus local emissions become more important than long-range transport (Kim et al., 2017a,b; Chang et al., 2016a). Inaccurate representation of complex PM2.5 formation chemistry in the air quality model used for PM2.5 forecasting often results in severely failed PM2.5 forecasts. One possible source of such failures has been recognized to be uncertainties in secondary formation mechanisms of inorganic species in PM2.5, involving sulfate (SO42−), nitrate (NO3−), and ammonium (NH4+) (hereafter collectively referred to as SNA), which are dominant inorganic species in PM2.5 (Pathak et al., 2009; Khan et al., 2010; Squizzato et al., 2012; Shin et al., 2016; Seo et al., 2017).
⁎ Correspondence to: C.-H. Kim, Department of Atmospheric Sciences, Pusan National University, 30 San, Jangjeon-Dong, Geumjeong-Gu, Busan 609-735, Republic of Korea. ⁎⁎ Correspondence to: G. Heo, Air Quality Forecasting Center, Climate and Air Quality Research Department, National Institute of Environmental Research, 42 Hwangyeong-ro, Seo-gu, Incheon 22689, Republic of Korea. E-mail addresses: [email protected], [email protected] (G. Heo), [email protected] (C.-H. Kim).
https://doi.org/10.1016/j.atmosres.2019.03.028 Received 13 January 2019; Received in revised form 6 March 2019; Accepted 20 March 2019 Available online 21 March 2019 0169-8095/ © 2019 Elsevier B.V. All rights reserved.
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Fig. 1. Modeling domains (27 km, 9 km, and 3 km) and terrian features over Seoul Metropolitan Area (SMA).
Many studies (e.g., Shin et al., 2016; Seo et al., 2017) have revealed that SNA is often the most abundant component of PM2.5 during pollution events in SMA. In particular, the NO3− fraction is becoming larger in PM2.5 in urban areas in South Korea (NIER, 2017), which suggests the importance of emitted nitrogen oxides (NOx, i.e., NO + NO2) in occurrences of high PM2.5 concentrations over SMA through the process of converting NOx into NO3−. Recent model predictions for PM2.5 forecasting have also indicated considerable uncertainties in representing NO3− formation, especially for morning peaks of PM2.5 episodes, which has been recognized to be associated with the nighttime heterogeneous chemistry of dinitrogen pentoxide (N2O5) (Prabhakar et al., 2017). Nitrate aerosols in PM2.5 can be formed in several pathways, and one is via the homogeneous reaction of gaseous nitric acid (HNO3) and ammonia (NH3) (Finlayson-Pitts and Pitts, 2000). HNO3 is primarily produced from the reaction between nitrogen dioxide (NO2) and hydroxyl radicals (OH) during daytime (Finlayson-Pitts and Pitts, 1997) and later combines with NH3 to produce particulate ammonium nitrate (NH4NO3). Another major pathway is the heterogeneous hydrolysis of N2O5 on aerosol surfaces at night (Ravishankara, 1997; Brown et al., 2006a,b; Kim et al., 2014). During nighttime, N2O5 primarily accumulates via the reversible reaction between NO2 and nitrate radicals (NO3), which are produced from the reaction of NO2 with ozone (O3) (Finlayson-Pitts and Pitts, 1997), and heterogeneous conversion of N2O5 into HNO3 on aerosol surfaces may occur (Chang et al., 2011), substantially contributing to high levels of fine particulate nitrate early on the next day. Many previous studies (e.g., Riemer et al., 2003; Brown et al., 2006b; Davis et al., 2008; Bertram et al., 2009; Chang et al., 2011, 2016b; Young et al., 2016; Wang et al., 2017; Osthoff et al., 2018) pointed out that one of major uncertainties in modeling nighttime N2O5 heterogeneous chemistry is its uptake coefficients (reactive uptake probability, γN2O5), which is highly variable with several orders of magnitude depending on meteorological conditions and aerosol compositions. At some sites, the contribution of N2O5 hydrolysis to the enhancement of fine particulate nitrate was estimated to reach 50–100% based on a thermodynamic model (Pathak et al., 2009, 2011) and assumptions on γN2O5. Another aspect of the uptake coefficients (γN2O5) associated with nighttime N2O5 is its reaction with chloride ion (Cl−) which is ubiquitously present in PM2.5. Cl− originates mostly
from sea salt aerosol and produces nitryl chloride (ClNO2), and then is degassed from the aerosol, reducing the NO3− and PM2.5 mass production (Sarwar et al., 2012, 2014). ClNO2 is stable at night, but is photolysed within approximately 1 h after sunrise. The chlorine atoms (Cl) liberated by photolysis react with volatile organic compounds (VOCs), enhancing VOC oxidation in morning hours. Therefore, the uncertainty still remains as to whether nighttime N2O5 heterogeneous chemistry can actually account for increases in PM2.5 mass concentrations under atmospheric conditions relevant to SMA, South Korea. With this background, the uncertainty of nitrate formation from nighttime N2O5 processing must be evaluated for accurate PM2.5 forecasting which is needed to provide PM2.5 pollution warnings to the public in South Korea. In this study, we investigated impacts of nighttime N2O5 heterogeneous chemistry on fine nitrate (NO3− in PM2.5) and PM2.5 formation under conditions where SNA levels are underestimated (hereafter, referred to as “SNA-underestimated conditions”) in SMA, during March 16–18, 2016, relatively dry and stagnant early spring days. In particular, the role of γN2O5 was quantified by simulating a base case and two sensitivity tests with default (Davis et al., 2008), killed (zero) and decupled (tenfold) γN2O5, respectively. Modeled NO3− and PM2.5 concentrations were compared and diagnosed against observations to improve our understanding of nitrate and PM2.5 formation and better predict PM2.5 pollution levels under atmospheric conditions relevant to SMA. 2. Method and data 2.1. Modeling system and domain The WRF-CMAQ model, which consists of Weather Research and Forecast model (WRF, https://www.mmm.ucar.edu/weather-researchand-forecasting-model) and Community Multi-scale Air Quality model (CMAQ, https://www.cmascenter.org/cmaq/), was employed. For meteorological modeling, WRF (v3.6.1) was used to generate meteorological fields to drive CMAQ (ver. 5.0.2) using the 1° × 1° Final Operational Global Analysis (FNL) data of the National Centers for Environmental Prediction (NCEP). Chemistry and transport processes of chemical species were simulated using CMAQ. The CMAQ configurations are based on SAPRC99 for gas phase chemistry, AERO5 aerosol 59
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were obtained by using the standard CMAQ profile provided in the CMAQ package and using a spin-up period, and the boundary conditions for the inner grids were updated by the model outputs from the outer grids. Anthropogenic emissions for Northeast Asia are based on the Intercontinental Chemical Transport Experiment-Phase B (INTEX-B) inventory for the year 2006 (Zhang et al., 2009; Li et al., 2014), and the Clean Air Policy Support System (CAPSS) inventory for the year 2007 was used for Korea (Kim et al., 2008; Lee et al., 2011). Biogenic emissions considered here are based on the Model of Emissions of Gases and Aerosols from Nature (MEGAN) version 2.04 (Guenther, 2006). 2.2. Reactions relevant to nighttime N2O5 chemistry During nighttime, NO2 photolysis shuts down, and NO2 becomes available to sequentially produce NO3, N2O5, and HNO3. Reactions relevant to N2O5 formation, its conversion to HNO3 and subsequent formation of NO3− in the form of NH4NO3 are shown in Reactions (R1) to (R6) below.
NO2 + O3
(R1)
N2 O5
(R2)
NO3 + NO2
(R3)
NO3 + NO2 N2 O5
NO3 + O2
N2 O5 + H2 O N2 O5 + Cl
HNO3 + NH3
2HNO3 Y·ClNO2 + (2 Y)·HNO3
NH 4 NO3
(R4) (R5) (R6)
N2O5 is formed from the gas-phase oxidation of NO2 by O3 (R1), followed by the reaction of NO2 with NO3 (R2). N2O5 thermally dissociates back to NO3 and NO2 (R3). N2O5 also heterogeneously reacts on aerosol surfaces and forms HNO3 through (R4) or (R5), and finally produces particulate nitrate via Reaction (R6). Reaction (R5) in which N2O5 reacts with chloride ion (Cl−) and forms nitryl chloride (ClNO2) was not used in this study because relatively lower Cl− concentrations (typically less than 0.5 μg/m3) were measured during the study period. Note that using the HNO3 yield of Reaction (R5), i.e., 2-Y where Y is the ClNO2 yield, instead of 2 for Reaction (R4) will lead to lower nitrate production and make the nitrate underestimation problem even worse during March 16–18, 2016. After sunrise, NO3 photolyzes almost immediately, and therefore NO3 is less important during daytime. Since N2O5 is not accumulated up to a relatively high concentration during the day, HNO3 formation by Reactions (R4) and (R5) becomes far less important during daytime.
Fig. 2. Time variations of PM2.5 and its relevent species measured at Bulkwang site during 14–22 March 2016. Top panel represents measured PM2.5 of daily averaged, minimum, 5/25/50/75/95 percentiles, and maximum concentrations; Bottom panel showed hourly variations of PM2.5, NO3−, SO42−, NH4+ concentrations.
module with ISORROPIA II for aerosol thermodynamics (Fountoukis and Nenes, 2007), Multiscale Horizontal diffusion, and Euler Backward Iterative (EBI) chemical solver. The modeling domains comprise three grids with horizontal resolutions of 27, 9, and 3 km over SMA, as shown in Fig. 1. As indicated in Fig. 1, several complex terrain features dominate to the north, to the east, and to the west of SMA. To the west, Yellow Sea is located, and induces sea and land breezes penetrating inland areas. Due to local circulations, SMA can be a domain of so called “closed basin” surrounded by mountains when synoptic wind is weak. Then, the precursors of NO3 and N2O5 are accumulated, and nitrate aerosols can be heterogeneously formed on aerosol surfaces during nighttime which results in relatively high NO3− and PM2.5 concentrations when the land breeze starts to blow. Around noon when the sea breeze sets in, nitrate aerosols that are heterogeneously produced at night and conserved under relatively high humidity around the coastal area can be brought back to Seoul, the central SMA area. In the WRF-CMAQ model, there were 15 layers vertically on a sigma coordinate up to 50 kPa with the lowest layer thickness of about 32 m. The initial and boundary conditions of chemical species' concentrations
2.3. Measurements and meteorological data To evaluate the performance of the WRF-CMAQ modeling system, we used measurements obtained from the Bulgwang site (126.98°E, 37.61°N, 67 m above sea level), as indicated in Fig. 1. The Bulgwang site is one of the intensive measurement stations operated by the Korean Ministry of Environment (KME) to provide comprehensive PM2.5 composition data in South Korea. The site is located northwest of Seoul which represents residential urban areas in SMA. Hourly PM2.5 concentrations were measured using a β-ray attenuation method (BAM-1020, Met one, USA). As major PM2.5 components, water soluble ions (NO3−, SO42−, NH4+; collectively SNA) were measured by an ambient ion monitor (AIM) (URG-9000D, URG
Table 1 Summary of three sensitivity tests for the analysis of N2O5 heterogeneous conversion to NO3−. Test
γN2O5
Description
N2O5_ON N2O5_OFF D_N2O5_ON
γN2O5 = ~0.02 γN2O5 = 0 Decupling of γN2O5
Default heterogenous reactions in CMAQ (ver. 5.0.1) (Davis et al., 2008) Neglecting the process of N2O5 heterogeneous conversion to NO3− Tenfold increase of N2O5 heterogeneous chemistry (The expected upper bound on the conversion effect)
60
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Fig. 3. Comparisons of time series of measurements (white dots) and base case simulations (black dots) of PM2.5, NO3−, SO42−, and NH4+ concentrations during the study period.
to examine the impacts of nighttime N2O5 heterogeneous chemistry on NO3− and PM2.5 concentrations, we selected March 16–18, 2016, atmospherically stagnant and relatively dry early spring days. Relative humidity was relatively low on these three days, which was used to test the model capability of simulating nitrate formation under relatively low RH conditions such as 20–40%, which has been reported to be a limitation of the aerosol thermodynamics module ISORROPIA II used for this study (Guo et al., 2016; Chang et al., 2016b). Stagnant synoptic cases were selected from the synoptic parameters suggested by Jo and Kim (2013), such as weak geostrophic wind speed no greater than 6.0 m/s and positive vorticity at 850 hPa. Accordingly, the period covering March 16 to March 18, 2016 was employed in this study, and the synoptic features of the selected case are described in the Supplementary Materials. Fig. 2 shows the time series of PM2.5 and SNA (i.e., NO3−, SO42− and NH4+) measured at the Bulgwang site (Fig. 1) in central SMA during March 14–23, 2016, including our selected study period. Overall, with an exception of slight decreases on March 18 and 22, 2016, daily mean PM2.5 concentrations steadily increased from March 16 to March 21, 2016, which is related to the local accumulation of air pollutants under stagnant atmospheric conditions particularly from March 16 to the morning of March 18, 2016 and the influence of longrange transport particularly from the night of March 18, 2016. In Fig. 2, it is interesting to note that PM2.5 and NO3− show similar temporal variations. Both PM2.5 and NO3− concentrations showed a peak value at night with an average ratio of NO3− to PM2.5 of 0.26, which is significantly higher than ratios of SO42− and NH4+ to PM2.5 of 0.13 and 0.13, respectively. Furthermore, the average ratio of NO3− to SO42− on a mass basis was found to be 2.0, suggesting that NO3− is a main factor for predicting PM2.5 concentrations. This nitrate fraction, 0.26, is relatively higher than fractions reported by previous studies such as Pathak et al. (2009), Khan et al. (2010), and Squizzato et al. (2012), which all reported relatively lower nitrate fractions with
Table 2 Comparison of observed and modeled PM2.5 and NO3− concentrations for March 17, 2016. NO3−
PM2.5
OBS D_N2O5_ON N2O5_ON N2O5_OFF
Daily mean (μg/m3)
Hourly max (μg/m3)
Daily mean (μg/m3)
Hourly max (μg/m3)
39.0 34.7 (−11%) 20.1 (−48%) 18.9 (−51%)
57.5 64.9 (13%) 44.6 (−23%) 40.9 (−29%)
10.7 11.9 (11%) 5.4 (−50%) 4.4 (−58%)
21.0 23.3 (11%) 11.8 (−44%) 7.7 (−63%)
Note: OBS, D_N2O5_ON, N2O5_ON, N2O5_OFF mean observation and model calculations for decupling, default and killed cases described in Table 1, respectively. Numbers in the parentheses are relative errors, (model – obs)/obs.
Corporation, USA) utilizing ion chromatography. More detailed information can be found in Jeon et al. (2015). Weather maps at 850 hPa geopotential level and meteorological surface observation data were also used for both case selection and verification of the meteorological model. Meteorological data used here include temperature, relative humidity, wind speed and wind direction provided by the Korea Meteorological Administration (KMA, 2016). 2.4. Case selection PM2.5 concentrations are affected by both local emissions and longrange transported air pollutants. Under stagnant meteorological conditions, local emissions primarily affect the formation of high PM2.5 concentrations via accumulation of directly emitted air pollutants and secondary formation such as fine nitrate formation by N2O5 heterogeneous process. In South Korea, stagnant atmospheric conditions typically occur by a stagnant or slowly moving anticyclone system accompanied by weak horizontal and vertical mixing processes. In order 61
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Fig. 4. Time series of vertical NO, O3, NO2, NO3, N2O5, and NO3− concentrations simulated at Bulwkang site (base case). Black dots (ㆍ) represent simulated boundary layer heights.
different features. We selected 3/16/2016 12 LST (03 UTC) to 3/18/ 2016 12 LST to further examine the impact of heterogeneous conversion of N2O5 to HNO3 on NO3− and PM2.5 concentrations under relatively stagnant atmospheric conditions over SMA.
test with decupling (tenfold increase) the γN2O5 can be regarded as an extreme case for conversion of N2O5 to nitrate particulate matter excessively. Using these three CMAQ simulations, we evaluated the N2O5 heterogeneous hydrolysis and explored its potential contribution to PM2.5 formation over SMA.
2.5. Modeling nighttime N2O5 heterogeneous chemistry
3. Results and discussion
We carried out multiple CMAQ simulations for the period covering March 13 to March 23, 2016 with 3 days as a spin-up period, with a base case and two sensitivity tests while turning off or decupling the N2O5 uptake process, as listed in Table 1: (1) base case with the default chemical reactions and without modification in Reaction (R4) (Davis et al., 2008; henceforth referred to as “N2O5_ON”), (2) simulation with uptake coefficient, γN2O5 = 0 in reaction (R4) (henceforth referred to as “N2O5_OFF”), and (3) simulation with decupling γN2O5 (γN2O5 = 10 × default γN2O5) in reaction (R4) (henceforth referred to as “D_N2O5_ON”). Analyzing differences between “N2O5_ON” and “N2O5_OFF” would facilitate diagnosing the N2O5 heterogeneous chemistry represented in the CMAQ (ver. 5.0.2) based on Davis et al. (2008) under atmospheric conditions relevant to the SMA area. On the other hand, “D_ N2O5_ON”
3.1. Evaluation of meteorology and chemistry for base case simulation Meteorological modeling performance was evaluated by comparing the simulations and measurements at the Bulgwang site. The simulated meteorological variables including temperature at 2 m, RH, and wind speed at 10 m were extracted at the grid point in the 3 km domain nearest to the Bulgwang site. The resultant index of agreement (IOA) of 2 m temperature, RH, and 10 m wind speed are 0.92, 0.80, and 0.50, respectively, indicating that near-surface temperature was more reasonably simulated than RH and 10 m wind speed. Despite overall reasonable simulations of meteorological variables, sometimes there were large biases. RH was overestimated with a big difference against observation over the period 62
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Fig. 5. Spatial distributions of simulated (a) NO3− and (b) N2O5 concentrations over SMA during the study period (base case).
especially when the pressure system changed with a low pressure system passing through the north region of North Korea. Fig. 3 shows that modeled PM2.5 and SNA concentrations are overall underestimated for the period of 3/16/2016 12 LST to 3/18/2016 12 LST. For the morning of 3/18/2016, the model grossly underestimated PM2.5 and SNA concentrations mainly due to overestimated wind speed. The most interesting part of the study period is 3/17/2016 on which we concentrate our analysis in the following sections. In Fig. 3, both model and observation show morning and night peaks of PM2.5 and NO3− on 3/17/2016. The model underestimated the daily mean concentrations of PM2.5 and NO3− by 48% (20.1 versus 39.0 μg/m3) and 50% (5.4 versus 10.7 μg/m3), respectively while the maximum hourly concentrations of PM2.5 and NO3− are also underestimated by 23% (44.6 versus 57.5 μg/m3) and 44% (11.8 versus 21.0 μg/m3), respectively, as summarized in Table 2. Despite the uncertainty of heterogeneous nitrate formation, some differences between day and night were found. The “observed” nighttime NO3− concentration averaged from 18 LST to 06 LST accounted 33% of PM2.5 (daytime average ~25%), while nighttime SO42− and NH4+ together constituted 22% of PM2.5 (daytime average ~5% and 7%, respectively). However, the “simulated” nighttime NO3− concentration accounted only 28% (daytime average ~15%), indicating the SNA-underestimated environment with relative dominance of nitrate aerosols.
highest values above the boundary layer height (mixing height), at altitudes of around 300–900 m during the nighttime on March 17, 2016, which is consistent with the fact that NO3 is mainly formed from NO2 via Reaction (R1), and removed by photolysis and reaction with NO near the ground level (Finlayson-Pitts and Pitts, 2000). N2O5 showed higher concentrations during nighttime above the modeled mixing height at altitudes of around 100–700 m, at somewhat lower altitudes compared to NO3, which is explained by the fact that N2O5 formation by Reaction (R2) needs both NO3 and NO2, and that NO2 is more abundant at altitudes closer to the ground level (Fig. 4). NO3− is formed by Reaction (R6) and showed highest concentrations mostly within the mixing heights (Fig. 4). Therefore, for the Bulgwang site located in the central SMA area, the possibility of nighttime NO3− formation in the residual layer above the mixing height and then mixing-down toward the ground level in the next day morning (Young et al., 2016; Prabhakar et al., 2017) is not clearly demonstrated in Fig. 4. Fig. 5 shows the spatial distributions of NO3− and N2O5 during both day and night with wind vectors at surface over SMA. The surface N2O5 concentrations indicated strong diurnal variations: increased at night and decreased at daytime. Based on modeled spatial distributions of surface NO3− and N2O5 concentrations, overall both NO3− and N2O5 concentrations were simulated to be higher in areas outside of Seoul (central SMA). However NO3− formed outside of Seoul can be transported into Seoul by horizontal advection, resulting in higher levels of NO3− as illustrated by two NO3− peaks on March 17, 2016 (Fig. 3).
3.2. Vertical and spatial distributions of chemical species relevant to N2O5 chemistry
3.3. Comparison of NO3− and PM2.5 formation between simulations with different N2O5 uptake coefficients
Vertical and spatial distributions for the base case simulation were further analyzed for species relevant to nighttime N2O5 heterogeneous chemistry. Fig. 4 shows the vertical distributions of modeled NO, NO2, O3, NO3, N2O5, and NO3− concentrations. As expected for typical urban areas with abundant NOx emissions, the model-simulated NO and NO2 levels were relatively higher at night and early morning hours, but lower during daytime (Finlayson-Pitts and Pitts, 2000). O3 concentrations were simulated to be (1) low during nighttime due to removal by NO emitted near the ground level, (2) relatively higher on the afternoon of March 17, 2016 in comparison to other times, and (3) constantly higher above around 500 m due to lack of removal by NO. NO3 showed
Fig. 6 shows the time series of PM2.5, NO3−, and N2O5 concentrations from three CMAQ simulations: N2O5_ON (base run), N2O5_OFF (turning off N2O5 heterogeneous chemistry by setting γN2O5 = 0), and D_N2O5_ON (decupling γN2O5) tests. Compared with the N2O5_ON case, the N2O5_OFF case showed higher N2O5 concentrations and lower PM2.5 and NO3− concentrations but showed relatively minor changes in NO3− concentrations with exceptions of peak values over two relatively high relative humidity periods: morning (06–12 LST) and night (18–24 LST) on 17 March 2016. During these times, increased NO3− formation 63
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Fig. 6. Time series of measured and modeled PM2.5, NO3−, and N2O5 at Bulgwang site (model: D_ N2O5_ON (decupled), N2O5_ON (base case), and N2O5_OFF (killed)).
Underestimation of NO3− even for D_N2O5_ON is also related to the fact that the impact of γN2O5 on increasing NO3− formation is also influenced by the aerosol surface area available for N2O5's heterogeneous reaction (Chang et al., 2011). Thus, combination of underestimated SNA and PM2.5 concentrations together with low relative humidity on 17 March 2016 seems to hinder the simulation for D_N2O5_ON from narrowing discrepancies between modeled and observed NO3− concentrations.
by heterogeneous conversion of N2O5 resulted in increased PM2.5 for both N2O5_ON and D_N2O5_ON cases. On the other hand, N2O5 concentrations were higher in the N2O5_OFF (γN2O5 = 0) test because in this simulation N2O5 was not heterogeneously converted to HNO3, which indicates that N2O5 needs to be converted to HNO3 and subsequently to NO3− in order to contribute to increasing NO3− and PM2.5 concentrations. In the N2O5_OFF case, the peak NO3− concentration at 21 LST was reduced by 35%, i.e., from 11.8 μg/㎥ of the N2O5_ON case to 7.7 μg/㎥. Fig. 6 also shows noticeable differences between D_N2O5_ON and N2O5_ON at 09 and 21 LST on 17 March 2016, which indicates that N2O5 heterogeneous chemistry has potential to enhance NO3− formation and makes modeled NO3− and PM2.5 concentrations closer to measurements. For D_N2O5_ON, the daily average NO3− concentration on 17 March 2016 increased by a factor of 2.2 (a factor of 1.7 for PM2.5) in comparison to the base case (N2O5_ON), and the simulated PM2.5 concentrations were also in closer agreement with observations (Fig. 6 and Table 2). However, it is interesting to note that all three tests including even D_N2O5_ON underestimated NO3− concentrations against observations during 12–18 LST of 17 March 2016. This seems to be in part due to severely low relative humidity (i.e., mostly below 40%) in the afternoon, which is in the range that ISORROPIA II has shown some limitation in simulating aerosol thermodynamics (Chang et al., 2016b).
3.4. Enhanced NO3− and PM2.5 formation by heterogeneous N2O5 chemistry in SMA In this section, we further explain enhanced formation of NO3− and PM2.5 in the SMA area via heterogeneous conversion of N2O5 into HNO3 and subsequent reaction with NH3 by examining spatial distributions of NO3, N2O5, HNO3, NH3, NO3− and PM2.5 (Figs. 7, 9, 10) and briefly looking into the influence of meteorological conditions such as relative humidity (Fig. 8). Fig. 7 shows vertical variations of NO3, N2O5, NO3− and PM2.5 concentrations at the Bulgwang site simulated for N2O5_ON (base case) and N2O5_OFF (killed uptake) with the overlayed boundary layer heights (mixing heights) simulated here (black dot). Due to lack of additional removal of N2O5 by the heterogeneous reaction (R4), NO3 and N2O5 concentrations are higher in the N2O5_OFF case in 64
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Fig. 7. Time series of vertical NO3, N2O5, NO3−, and PM2.5 concentrations at Bulgwang site modeled by N2O5_ON (base case) and N2O5_OFF (killed) simulations, and their differences. Black dots (ㆍ) represent simulated boundary layer heights.
comparison to the N2O5_ON case. However, despite additional HNO3 formation via heterogeneous conversion of N2O5, differences in NO3− and PM2.5 are relatively small between N2O5_ON and N2O5_OFF with a noticeable exception during 18–24 LST on March 17, 2016. In Fig. 7, largest differences between the two cases, 4.1 μg/m3 in NO3− and 5.3 μg/m3 in PM2.5, are found at 21 LST. The model-estimated boundary layer height, which is shown by black dots in Fig. 7, starts to decrease from 17 LST, shows a minimum at 20 LST and increases until midnight. Note that N2O5 and NO3− concentrations become higher above the boundary layer height at around 18 to 21 LST. This is implying that increases in NO3− and PM2.5 concentrations are related to entrainment from the top of nocturnal boundary layer to some degree (Berkes et al., 2016; Trousdell et al., 2016). Fig. 8 also demonstrates the impact of relative humidity (RH) on NO3− formation via N2O5 heterogeneous hydrolysis as previous studies pointed out (e.g., Davis et al., 2008; Chang et al., 2016b). Fig. 8 shows the time series of NO3− concentrations for the three tests together with meteorological variables: temperature and relative humidity. Relatively noticeable differences in NO3− between N2O5_ON (or D_N2O5_ON) and N2O5_OFF are found only over two periods when the relative humidity was relatively higher on 17 March 2016: early morning (~09 LST) and late night (~21 LST). RH was overall low on March 17, 2016. The observed RH was below 40% at 01–02 and 09–19 LST and showed a maximum of 53% at 07 LST and a minimum of 21% at 12 LST. As shown in Fig. 8, the model clearly underestimated RH for 03–09 LST and the simulated RH was below 40% for 03–17 LST on March 17, 2016. Note that the aerosol thermodynamics module ISORROPIA II used for this study has been reported to show relatively poor performance in simulating NO3− concentrations under dry conditions with relative humidity lower than 40% (Chang et al., 2016b; Guo et al., 2016). Previous studies showed that the main hydrolysis product of N2O5 such as nitric acid (HNO3) can be partitioned more favorably to the aerosol phase at lower temperatures (Stelson and Seinfeld, 1982). No clear feature for temperature was found for this study. Chen et al. (2016) pointed out that SNA aerosols increased dramatically at some sites in Beijing at the time when sulfur-oxidation (SO2-to-H2SO4) rates and nitric-oxidation (NO2-/NO3-to-HNO3) rates increased simultaneously and that they were correlated with the high relative humidity
60–90% on those days. Therefore, additional studies covering both low and high relative humidity conditions are desirable to better predict fine nitrate concentrations under various relative humidity conditions relevant to the SMA area. NO3− exists in the form of NH4NO3 when NH3 is available for reaction with HNO3 via Reaction (R6) as described earlier. Thus, we show spatial distributions of HNO3, NH3, NO3− and PM2.5 concentrations for the base case at 03, 06, 09, 18, and 21 LST on 17 March 2016 when differences in NO3− and PM2.5 between N2O5_ON and N2O5_OFF were relatively higher (Fig. 9). The spatial distributions indicate that NO3−, and PM2.5 can be additionally formed by heterogeneous conversion of N2O5 into HNO3 and subsequent reaction wich NH3 which is emitted from various sources such as agricultural sources outside of SMA urbancore areas. Note that NO3− and PM2.5 formed in surburban SMA areas can also contribute to increasing NO3− and PM2.5 concentrations in the central SMA area via horizontal transport. Additional modeling and observational studies on heterogeneous N2O5 chemistry are needed to improve our understanding of NO3− and PM2.5 formation and better forecast PM2.5 pollution levels over SMA or other urban areas with abundant nitrogen oxides emissions together with NH3 emissions such as agricultural emissions from surrounding areas. In order to further analyze how N2O5 heterogeneous chemistry contributes to enhancing NO3− and PM2.5 formation in the SMA area, we also show differences in HNO3, NO3− and PM2.5 between D_N2O5_ON (decupling) and N2O5_ON (base case) in Fig. 10. HNO3 concentrations are somewhat higher for D_N2O5_ON compared to N2O5_ON, and relatively bigger differences in NO3− and PM2.5 are found where ammonia concentrations are relatively higher (Fig. 9, Fig. 10), which indicates that ammonia emissions such as agricultural ammonia emissions in the areas surrounding Seoul (central SMA) are also important in regard to NO3− and PM2.5 formation. Previous studies also reported that increases in NO3− were observed and simulated in some areas in California where ammonia emissions are abundant (Russell and Cass, 1986; Kelly et al., 2014; Brown et al., 2006a; Lurmann et al., 2006). Note that chlorine (Cl) chemistry was not considered in this study which focuses on the problem of nitrate underestimation under atmospheric conditions relevant to the SMA, South Korea. The 65
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Fig. 8. Time series of simulated temperature and relative humidity, and their biaes between predictions and obervations at Bulgwang site. Also shown are surface NO3− concentrations observed and modelded by D_ N2O5_ON (decupled), N2O5_ON (base case), and N2O5_OFF (killed) simulations.
heterogeneous uptake of N2O5 is confounded by Cl-ClNO2 chemistry which tends to reduce nitrate formation (see reaction (R5)) and PM2.5 concentrations (Wang et al., 2018; McDuffie et al., 2018; Fibiger et al., 2018). However, Cl emissions are also associated with combustion sources in and around Korea (Kim et al., 2017a), and some researchers
pointed out evidence of Cl emissions from biomass burning activities (Ahern et al., 2017). Therefore, diagnosing both nitrate underestimation and overestimation may be further needed by carrying out chemical model with the detailed Cl-ClNO2 chemistry.
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Fig. 9. Spatial distributions of simulated (a) HNO3, (b) NH3, (c) NO3−, and (d) PM2.5 concentrations (base case) over SMA during the study period.
between modeled and observed NO3− concentrations. These results indicate that solving nitrate underestimation does involve not only just re-parameterizing γN2O5 but also better modeling other major PM2.5 components such as sulfate and ammonium, which implies that accurately representing nitrate formation under atmospheric conditions relevant to the SMA would be a complex task. Analyzing the spatial distributions of modeled HNO3, NH3, NO3−, and PM2.5 on 17 March 2016 was useful to understand how NO3− and PM2.5 formation can be enhanced in the SMA by N2O5 heterogeneous hydrolysis. Our results indicate that (1) NO3− and PM2.5 can be additionally formed by heterogeneous conversion of N2O5 into HNO3 and subsequent reaction wich NH3 which is emitted from various sources such as agricultural sources outside of SMA urban-core areas and (2) these additionally formed NO3− and PM2.5 can result in increased concentrations of NO3− and PM2.5 in SMA urban-core areas by vertical mixing (entrainment) or horizontal advection. Additional modeling and observational studies are needed to improve our understanding of nitrate and PM2.5 formation mechanisms under various atmosperic conditions (e.g., dry and stagnant conditions in early spring days in SMA), to accurately predict PM2.5 concentrations and forecast PM2.5 pollution levels for the SMA and other areas where nitrogen oxides emissions are abundant and ammoinia is also relatively well supplied by various sources including agricultural sources in
4. Summary and conclusions This study carried out a series of WRF-CMAQ simulations with differently represented N2O5 uptake coefficient (γN2O5) to diagnose the role of heterogeneous N2O5 hydrolysis, and examine its potential contribution to fine nitrate and PM2.5 formation under relatively dry (relative humidity of 20–55%, mostly below 40%) and stagnant conditions relevant to early spring days in the Seoul Metropolitan Area (SMA), South Korea. Model results and model-measurement comparison indicate that the N2O5 scheme based on Davis et al. (2008) in CMAQ (ver. 5.0.2) needs to be improved to reasonably simulate fine nitrate formation at relatively high PM2.5 and nitrate concentrations under relatively dry conditions, particularly RH of 20–40%. Such improvement is particularly needed to accurately predict PM2.5 pollution levels in SMA in South Korea. Our detailed analysis of the case of March 17, 2016 clearly demonstrated that the impact of γN2O5 on enhancing NO3− formation is strongly influenced by the PM2.5 pollution level (which is related to providing aerosol surfaces available for N2O5's heterogeneous reaction) as well as relative humidity. Thus, even with tenfold increased γN2O5, combination of underestimated sulfate-nitrate-ammonium (SNA) and PM2.5 concentrations and low relative humidity on 17 March 2016 hindered the WRF-CMAQ simulation from narrowing discrepancies 67
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Fig. 10. Differences in spatial distributions of simulated (a) HNO3, (b) NO3−, and (c) PM2.5 concentrations between D_N2O5_ON (decupling) and N2O5_ON (base) over SMA during the study period.
adjacent areas.
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