Current Issue

Data on gaseous and particulate species concentrations and meteorological parameters are required to run and validate OCABOX. The Korea-United States Air Quality (KORUS-AQ) field study conducted jointly by the National Institute of Environmental Research of South Korea and the National Aeronautics and Space Administration of the United States in May-June 2016 offered extensive data sets measured from ground stations, aircrafts, and ships (Crawford et al., 2021). The collected data have been used by many researchers trying to understand the factors influencing the air quality of Korea (e.g., Kim et al., 2022; Park et al., 2021; Jordan et al., 2020; Oak et al., 2019; Kim et al., 2018; Nault et al., 2018) because the KORUS-AQ campaign provided unprecedented comprehensive measurements of air pollutants, including both gas- and particulate-phase species.

In this study, a set of hourly data on major ionic components of PM_2.5, including NH₄⁺, Na⁺, K⁺, Ca²⁺, Mg²⁺, NO₃^-, SO₄^2-, and Cl^-, and their gaseous precursors, such as HCl, SO₂, NO_x, HNO₃, and NH₃, measured at the Olympic Park (OP) ground site (37.53°N, 127.12°E) from 15 May to 17 June 2016 by Seoul Metropolitan Government Research Institute of Public Health and Environment (Choi et al., 2019) were used to run OCABOX in the observation-based mode and to compare the simulation results. The PM_2.5 mass concentration was measured using a radiometric particulate mass monitor, FH62C14 (Thermo Scientific, USA), whereas a Monitor for Aerosol & Gasses in Ambient Air, MAR GA ADI 2080 (Metrohm Applikon., Netherlands), was used to determine the concentrations of the ionic particulate species and related gaseous species HCl, NH₃, and HNO₃. Gas species analyzers were used to measure the CO (Ecotech model EC9830, Australia), O₃ (Ecotech model EC9810, Australia), SO₂ (Ecotech model EC9850, Australia), and NO_x (Ecotech model EC9841, Australia) concentrations. One can refer to the literature (Choi et al., 2019; Ghosh et al., 2012) for more detailed information on the measurement and data processing methods. Meteorological parameters, such as air temperature, relative humidity, and wind speed, were also measured at the OP site.

Fig. 1 shows the concentrations of several key pollutants measured during the period. There was a distinct event exceeding the Korean daily PM_2.5 standard (35 μg/m³) during 25-31 May, which will be called the LRT period because it was caused by enhanced long-range transport, and several relatively short events during which local emissions dominated due to stagnant air (Park et al., 2021; Peterson et al., 2019; National Institute of Environmental Research and National Aeronautics and Space Administration, 2017). During the KORUS-AQ campaign, the air quality of Seoul was reportedly influenced strongly by the formation of secondary PM (Park et al., 2021; Nault et al., 2018; Kim et al., 2018). This is supported by the high correlations among the PM_2.5 and ionic species concentrations shown after 25 May.

Fig. 1. 
Overview of the time evolutions of the concentrations of several key pollutants during the studied period.

As shown in Section 3, the supply of appropriate regional background concentrations appeared to be important for the reliable operation of OCABOX. The data measured at a marine background site in Baengnyeong Island (BI) (37.97°N, 124.63°E) collected during the same period were provided as the regional background concentrations in several sensitivity analyses. The Beta Attenuation Monitor Model 1020 (BAM1020) (Met One Ins., USA) was used to measure the PM_2.5 mass concentration, whereas the anion and cation particle and gas system, AIM URG-9000D (URG Corporation, USA), was used to measure the ionic species in PM_2.5. The measured data of several gaseous species, CO (TELEDYNE API model T300, USA), O₃ (TELEDYNE API model T400, USA), SO₂ (TELEDYNE API model 100A, USA), and NO₂ (TELEDYNE API model T200, USA), were also used.

A box model OCABOX, developed by modifying the 3D chemical transport model CMAQ v.5.3.2 into 0-dimensional box model, was used in this study. Carbon Bond 6 (CB6) (Yarwood et al., 2010) was used as the gas-phase mechanism and the 6th generation aerosol module (AERO6) was used as the aerosol mechanism including aqueous-phase and heterogeneous reactions. The horizontal dimension of the box was set at 30 km and the planetary boundary layer height (PBLH) predicted by a mesoscale meteorological model Weather Research and Forecasting (WRF) v3.8 (Powers et al., 2017), which can be a proxy of the mixing layer height (MLH) (Banks et al., 2015), was used as the vertical dimension of the box.

The meteorological input file was generated using the output of WRF and measured data. Fig. 2 shows the WRF domain (WO) with a horizontal grid size of 30 km on a Lambert Conformal map projection covering the whole Korean Peninsula. The terrain-following sigma vertical coordinate was employed with 29 unevenly spaced vertical levels ranging from the surface to 50 hPa. ERA-Interim reanalysis data were used for the initial and boundary conditions. WRF was run from 0000 UTC 20 April to 0000 UTC 30 June 2016 (Fig. 3). The first 10 days of WRF simulation were treated as spin-up time, and only the fields from 0000 UTC 30 April were supplied to OCABOX simulations. Table 1 provides detailed information of the WRF domain and physics options used for the WRF run. The measured data for air temperature, relative humidity, and wind speed were overwritten to the WRF output file to replace the model prediction.

Fig. 2. 
Model domains for preparing meteorology and emissions inputs: the WRF domain (WO) and the SMOKE domain (SO).

Fig. 3. 
Simulation periods of the models used. CMAQ was run only for supplying the average ratios [NH₃]/[NH₄⁺] and [HNO₃]/[NO₃^-] in SA3.

The emission fields for the OCABOX run were generated using Sparse Matrix Operator Kernel Emissions (SMOKE) v4.5. The Korean national emissions inventory called Clean Air Policy Support System (CAPSS) for 2016 (National Institute of Environmental Research, 2019) was used as the raw emissions data. The natural emissions were determined using the Model of Emissions of Gases and Aerosols from Nature (MEGAN) v2.1. Emissions speciation was performed using the chemical mechanism CB6. SMOKE was run from 0000 UTC 30 April to 0000 UTC 20 June 2016 (Fig. 3).

Table 2 summarizes the SMOKE run configuration used in this study. Fig. 2 shows the domain for the SMOKE run (SO).

OCABOX was run from 0000 UTC 10 May to 0000 UTC 17 June 2016, including a five-day spin-up (Fig. 3) for the five scenarios shown in Table 3: a base run (BASE) and four sensitivity analysis runs (SA1, SA2, SA3, and SA4). For the initial condition, the “profile” concentrations provided by the 3D model CMAQ were used. The profile concentrations were also used as the boundary conditions except for the species for which the concentrations measured at the BI marine background site (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^-, SO₄^2-, NO₃^-, NH₄⁺, NH₃, and HNO₃) were used, as shown in Table 3. For the build-up of the species whose concentrations are not measured at the OP site during the episode periods, the first five days of the OCABOX simulations were taken as the spin up time. Fig. 3 presents the simulation time set for the models used in this study.

CMAQ v5.3 was needed to generate the species concentrations at the BI site to perform a sensitivity test for the marine background concentrations, which will be explained in Section 3.4. WRF v3.8 was used as the meteorological driver for this test. Fig. S1 shows the spatial domains used for WRF (WC1 and WC2) and CMAQ (C1 and C2) simulations for this test. Two-way nesting between Domain 1 (WC1 and C1) and Domain 2 (WC2 and C2) was used. A Lambert Conformal map projection was employed for all domains. Table 4 and Table 5 show detailed information on the run configurations for WRF and CMAQ, respectively, used for this sensitivity test. WRF was run from 0000 UTC 20 April to 0000 UTC 20 June 2016, whereas CMAQ was run from 0000 UTC 30 April to 0000 UTC 20 June, with the first 10 days for both WRF and CMAQ runs treated as spin-up time. The emission fields were prepared for the domain C2 in the same way described above.

As shown in Table 3, the CO, O₃, SO₂, SO₄^2-, Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^-, and HCl concentrations measured at the OP site were used as constraints and the CO, O₃, SO₂, and NO₂ concentrations measured at the BI site were used as regional background conditions for the BASE simulation.

Fig. 4 compares the OCABOX-predicted concentrations with observations. The results of the integrated process rate (IPR) analysis, which is a part of the process analysis (PA), indicating the effects of each process, such as gas-phase chemistry (CHEM), horizontal advection (HADV), vertical diffusion (VDIF), emission (EMIS), dry deposition (DDEP), and aerosol processes (AERO), on the species concentrations are shown together. NO was supplied mostly by emission and converted to NO₂ via chemistry (Fig. 4a), whereas NO₂ was produced from the chemical reaction of NO and removed by chemistry and horizontal advection (Fig. 4b). Compared to the observations, both NO and NO₂ were underestimated by OCABOX considerably. The reason for underpredicting NO and NO₂ may be either the underestimation of NO_x emissions or too low background concentrations provided by the CMAQ profile, as was indicated by the huge effect of HADV on NO₂. This will be discussed in more detail in the following subsections presenting the sensitivity analysis results.

Fig. 4. 
OCABOX predictions and PA results of BASE run: (a) NO; (b) NO₂; (c) HNO₃; (d) NO₃^-; (e) NH₃; (f) NH₄⁺.

HNO₃ (Fig. 4c) and NH₃ (Fig. 4e) were also underpredicted, but the aspects were quite different; the underprediction of HNO₃ was remarkable mostly at night, whereas NH₃ was underpredicted during the day. The characteristic evolutions of NH₃ concentration and the emission rate (Fig. 4e) stem from the diurnal variation of mixing height (not shown); the low mixing height at night leads to a strong influence of emission, and the opposite is the case during the day. Regarding HNO₃, the story becomes completely different because HNO₃ is not a primary pollutant; the underestimation of HNO₃ is prominent at night.

During the day, HNO₃ is produced by

whereas during the night, its production takes place via the following reactions:

When Reactions (2)-(5) are not important, HNO₃ reaches its maximum concentration during the day and minimum during the night in the presence of basic components (Mehlmann and Warneck, 1995), which can be attributed to the photochemical production during the day by Reaction (1) followed by its conversion to particulate NO₃^- during the night at high humidity and low temperature. A similar pattern is shown in the time series of the model-predicted HNO₃ concentration but the measured HNO₃ concentration did not show the nighttime minimum (Fig. 4c), particularly during the LRT period.

Fig. 5 shows the result of integrated reaction rate (IRR) analysis, which is the other part of PA, for HNO₃. The two pathways shown above were clearly distinguished in terms of the time of day and HNO₃ production from N₂O₅ during the night was much weaker than that from OH during the day. The weak production of HNO₃ at night might be due to the low ozone concentration in the SMA with high NO_x emission. Subsection 3.4 will discuss the possibility of the transport of HNO₃ produced at night over the Yellow Sea located in the west of the SMA with abundant ozone because of little maritime NO_x emissions.

Fig. 5. 
HNO₃ production rates from the reactions with OH during the day and from the reactions with N₂O₅ during the night.

The underprediction of HNO₃ and NH₃ during the night and day, respectively, resulted in the corresponding underprediction of both NO₃^- and NH₄⁺ (Fig. 4d and Fig. 4f) all day long throughout the simulation period. Several sensitivity analyses were performed to determine how the improved model prediction can be achieved.

As shown in Fig. 4, the base run resulted in a general underestimation of NH₄⁺ and NO₃^-, due to the underestimation of primary precursors NH₃, NO, and NO₂. This can be attributed to two possible reasons: underestimated emissions and underestimated background concentrations.

A simple order-of-magnitude calculation based on the used emissions data showed the effects of the emissions to be ~0.1 ppb/h for both NH₃ and NO_x, which are too low to achieve the observed concentration levels. A previous study reported that even 50%-increased NO_x emissions led to an underestimation of the NO_x concentration in Seoul during the KORUS-AQ period (Oak et al., 2019). On the other hand, the underestimated vehicle-emitted ammonia may lead to an underestimation of NH₄NO₃ (Link et al., 2017). Therefore, the first sensitivity test conducted in this study was to increase NO, NO₂, and NH₃ emissions by a factor of 2 to see if this could reduce the gap between the model prediction and observation satisfactorily.

Fig. S2 shows the OCABOX predictions and PA results obtained with the doubled emissions of NO, NO₂, and NH₃. Simply doubling the emissions of the primary species did not improve the model performance very much. Although the NH₃ and HNO₃ concentrations were increased by emissions doubling, the underprediction of HNO₃ during the night and the underprediction of NH₃ during the day were not mitigated, resulting in a minor increase in the NH₄⁺ and NO₃^- concentrations.

A possible reason why the increased emissions could not improve the model performance may be found by examining the free ammonia ratio (FAR) (Blanchard and Hidy, 2003):

where [NH₃]^T=[NH₃]+[NH₄⁺] and [HNO₃]^T=[HNO₃]+[NO₃^-] are the total ammonia and nitric acid molar concentrations, respectively. In the right-hand side of Eq. (6), the numerator means the amount of ammonia remaining after neutralizing SO₄^2-. Therefore, FAR is the ratio of free ammonia over total nitric acid. If FAR>1, the atmosphere is in an ammonia-rich condition. Fig. 6 shows the FAR values predicted by the BASE and SA1 runs with observed values. The observation gave the FAR values always larger than 1 (in the range of 1.27-138), often exceeding 10, except for the missing data, indicating an ammonia-rich condition that prevailed in SMA during the studied period. This is in agreement with the previous reports that total ammonia is in excess in the atmosphere of Seoul due to the local emissions from vehicles, agricultural activities, and industry (Kim et al., 2018, 2017; Han and Kim, 2015; Kim, 2006). On the other hand, the model-predicted FAR values often decreased to below 1 for both BASE and SA1 runs, indicating that the model did not provide sufficient ammonia. The possibility of underestimating the emissions by more than a factor of 2 does not appear feasible. Therefore, additional sensitivity analyses needed to be conducted.

Fig. 6. 
Comparison of observed and OCABOX-predicted free ammonia ratios.

In the second sensitivity analysis, NO, NO₂, NH₃, and NH₄⁺ were also constrained by the measurements (Table 3) to mitigate the effects of inaccurate emissions of NO_x and total ammonia. Fig. 7 and Fig. S3 (for NO and NO₂) present the results of the SA2 run. Although this led to naturally improved NH₃ and NH₄⁺, NH₃ was overpredicted, while NH₄⁺ was underpredicted, accompanied by an underprediction of HNO₃ and NO₃^- during the LRT period. This result can be interpreted as the excessive evaporation of NH₄⁺ to NH₃ owing to the insufficient supply of HNO₃ by the model, again raising the possibility of the omission of HNO₃ transported from the Yellow Sea. On the other hand, the predictions of both NO₃^- and NH₄⁺ were improved considerably during relatively short high PM event periods reportedly influenced by stagnant air (e.g., 7-8 June). Based on these results, it is important to test the hypothesis that the reason for the underprediction of nighttime total HNO₃ during the LRT period is due to the omission of the inflow of HNO₃ produced over the Yellow Sea.

Fig. 7. 
OCABOX predictions and PA results of SA2 run: (a) HNO₃; (b) NO₃^-; (c) NH₃; (d) NH₄⁺.

Until now, the data measured at the BI site (37.97°N, 124.63°E) for CO, O₃, SO₂, and NO₂ and the ‘CMAQ profile’ values for the other species were used as the boundary conditions for horizontal advection. Because the measured concentrations at the BI site and the CMAQ profile values are much lower than the concentrations in SMA except for O₃, the use of these boundary conditions may lead to strong ventilation for the box modeling, resulting in considerable underestimations. Therefore, two sensitivity tests were performed to account for the effects of polluted background.

The first test for the ‘polluted background’ hypothesis was conducted with respect to the possibility of the inflow of HNO₃ produced over the nearby Yellow Sea during the night. As was mentioned above, Reactions (2)-(5) are generally not crucial inside the SMA because the nighttime O₃ concentration tends to be low due to the active ozone titration by high NO emission. Over the Yellow Sea, which is located to the west of SMA, Reactions (2)-(5) can be significant because the maritime ozone tends to survive during the night due to negligible NO emission. If HNO₃ produced in this way flows into the SMA, where the ammonia-rich condition prevails, it may react efficiently with NH₃ to produce NH₄NO₃.

In SA3, all the measured data on relevant species (Na⁺, K⁺, Ca²⁺, Mg²⁺, Cl^-, HCl, SO₄^2-, NO₃^-, and NH₄⁺) made at the BI site were added to the background field. The problem was that NH₃ and HNO₃ were not measured at the BI site. Therefore, their concentrations were estimated from the measured concentrations of NH₄⁺ and NO₃^- as follows. First, the 3D model CMAQ was run, as described in Subsection 2.2. The average NH₃, HNO₃, NH₄⁺, and NO₃^- concentrations for each hour during the whole period were calculated, and the average [NH₃]/[NH₄⁺] and [HNO₃]/[NO₃^-] ratios were determined at each hour. Finally, the NH₃ and HNO₃ concentrations were estimated by multiplying these ratios by the measured concentrations of NH₄⁺ and NO₃^-, respectively.

Fig. 8 compares the NH₃ and HNO₃ concentrations estimated in this way for the BI site with those measured at the OP site. The estimated BI NH₃ concentration was much lower than that measured at the OP site, as expected, because of the low NH₃ emission over the sea. On the other hand, the estimated BI HNO₃ concentration was as high as or even higher than the measured OP concentration during the LRT period. By contrast, during the non-LRT period, it was generally lower than the OP concentration. These results are very similar to the report of another research group who measured HNO₃ in both the BI and OP sites during the KORUS-AQ campaign (Korean Society for Atmospheric Environment, 2016).

Fig. 8. 
Comparison of the concentrations measured at OP and BI sites: (a) NH₃; (b) HNO₃. The concentrations for the BI site were estimated using the measured [NO₃^-] and [NH₄⁺] concentrations and the [HNO₃]/[NO₃^-] and [NH₃]/[NH₄⁺] ratios predicted by CMAQ.

Fig. 9 and Fig. S4 (for NO, NO₂, and NH₃) show the results of SA3. Despite the unchanged underestimation of NO and NO₂ compared to the BASE run, the HNO₃ and NO₃^- concentrations were increased significantly. This, in turn, led to considerable improvement in the prediction of high NH₄⁺ concentrations during the LRT period. The degree of agreement with observation in terms of NH₄⁺ concentration predicted for the LRT period was even better than SA2, in which NH₄⁺ was constrained directly by observation. This result strongly suggests that the model-predicted NH₄NO₃ formation in that period was hindered by the shortage in supply of HNO₃ from the sea. On the other hand, the NH₄⁺ prediction of SA3 during the non-LRT period was no better than that of SA2.

Fig. 9. 
OCABOX predictions and PA results of SA3 run: (a) HNO₃; (b) NO₃^-; (c) NH₄⁺.

SA4 was the last sensitivity analysis and the second test for polluted background. In this test, the effects of horizontal advection were removed. The SMA surrounding the box domain of this study is much larger than the box itself and usually maintains a high pollution level. Therefore, the air flowing into the box may have similar levels of pollutant concentrations, having little effect of horizontal advection.

In SA4, the background concentrations of NO, NO₂, NH₃, and NH₄⁺, which may be influenced strongly by the emission sources in the vicinity, were replaced by the measured data at the OP site. This method was selected to incapacitate the ventilating effect of horizontal advection instead of simply skipping the horizontal advection process in the model because the latter may lead to the steady build-up of emitted primary pollutants in the box.

Fig. 10 presents the results of the SA4 run. The use of observations made at OP site as the regional background concentrations, without constraining them directly, led to accurate predictions of NO and NO₂, showing that the inflow from the polluted background is sufficient to maintain the high concentrations inside the box even without a large emission. This result suggests that the main reason for the underprediction of NO_x in the BASE run might not be the low emissions but the ventilation by unrealistically clean background air. The predictions for the concentrations of NH₃, HNO₃, NH₄⁺, and NO₃^- all were improved for both the LRT and non-LRT periods because the supply of both the primary and secondary precursors from the regional background was accounted for.

Fig. 10. 
OCABOX predictions and PA results of SA4 run: (a) NO; (b) NO₂; (c) HNO₃; (d) NO₃^-; (e) NH₃; (f) NH₄⁺.

The results of SA4 show that the assignment of appropriate regional background concentrations may be necessary for reliable box model predictions. For the secondary precursor HNO₃, for which nighttime production over the sea may be significant, the measurement made at the BI site appears adequate as the marine background condition, whereas the observations made inside the SMA may be better for the primary precursors, such as NO, NO₂, and NH₃. Overall, the box model OCA BOX can be a reliable tool for analyzing the formation of secondary inorganic aerosol if the assignment of regional background concentrations and the use of observation-based constraints are conducted appropriately. This conclusion needs to be verified by further investigations for the other high PM episodes in various regions in the future.

In order to show how the appropriate setting of boundary conditions can improve the model predictions, scatter plots comparing the NH₄⁺ and NO₃^- concentrations predicted by the BASE and SA4 runs with observations are presented in Fig. 11. The statistic assessment results are summarized in Table 7. It is demonstrated by the scatter plots and statistical indices that the use of appropriate boundary conditions resolved the model underprediction of secondary inorganic PM species significantly.

Fig. 11. 
Scatter plots comparing OCABOX predictions for NH₄⁺ and NO₃^- with observations: (a) BASE run; (b) SA4.