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Article

Impact of Precursor Compounds Associated with Long-Range Transport in the East Asia Region—Variation in CO/CO2 and VOCs

by
Minwook Kim
1,†,
Myoungki Song
2,†,
Sea-Ho Oh
2,
Geun-Hye Yu
2,
Seoyeong Choe
2,
Hajeong Jeon
2,
Jin-Ho Kim
1 and
Min-Suk Bae
2,*
1
Climate Change Assessment Division, National Institute of Agricultural Sciences, Wanju 55365, Republic of Korea
2
Department of Environmental Engineering, Mokpo National University, Muan 58554, Republic of Korea
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Appl. Sci. 2023, 13(19), 10783; https://doi.org/10.3390/app131910783
Submission received: 28 August 2023 / Revised: 23 September 2023 / Accepted: 26 September 2023 / Published: 28 September 2023
(This article belongs to the Special Issue Short- and Long-Term Air Pollution Analysis, Modeling and Prediction)
Browse Figures
Versions Notes

Abstract

:
In this study, the impact of long-range transport, one of the factors contributing to the presence of PM2.5, was examined, and an analysis of marker compounds associated with its long-range transport was conducted. Aerosol optical depth, wind field, CO/CO2 back-trajectory analysis, and satellite observation results were performed to determine PM2.5, volatile organic compound (VOC), CO, CO2, SO2, O3, NO, NO2, and NH3 levels at an orchard located in Jeollabuk-do, Republic of Korea. The characteristic of long-range transport at the observation area was evaluated during the research period. The concentrations for long-range transport based on concentration changes in gaseous materials and composition changes in PM2.5 were analyzed. A back-trajectory analysis for the ratio of CO to CO2 with satellite observation results was used to identify long-range transport. Furthermore, the proportionality between the ratio of 1,2-dichloroethane to naphthalene in VOCs and the quantity of precursor compounds linked to long-range transport were observed.

1. Introduction

Air pollution aggravated by urban development and modernization is of a significant public health concern due to its adverse effects on human health. According to recent studies, an increase in PM10 by about 10 µ/m3 in concentration causes an increase of 0.58% in respiratory mortality, and an increase in PM2.5 by 10 µ/m3 increases the incidence and hospitalization of respiratory diseases by 2.07% and 8%, respectively [1,2]. In addition, a previous study indicated that 9.98% of the total deaths in China in 2016 were associated with exposure to PM2.5 as well as an economic loss of 0.91% of the GDP (gross domestic product) [3]. Thus, nations around the world are making various efforts to reduce air pollutants.
Air pollutants are classified by gaseous and particulate matter. Chemical compounds of particulate matter include organic compounds, elemental carbon (EC), trace elements, and water-soluble ionic compounds [4,5,6]. Gaseous compounds such as VOCs (volatile organic compounds) including benzene and toluene are also mixed in the air, thus affecting air quality. These air pollutants can move in long-range transport depending on wind direction or speed, causing serious environmental issues and aging symptoms according to their retention time [7,8]. Thus, it is necessary to understand not only the features of regional air pollutants but also the external effects of long-range transport.
Various prior studies related to the long-range transport of atmospheric pollutants have been conducted. It has been argued that desert dust significantly contributes to the increase in PM concentrations across Europe and that long-range transport contributes to approximately 50% of urban atmospheric pollutants [9,10,11]. Furthermore, according to previous research, South Korea, the study area of this investigation, has been documented as a recipient of various atmospheric pollutants, including PM and secondary fine particulate matter, as well as gaseous substances such as CO, originating from China [12,13,14]. Therefore, in order to comprehend the sources of atmospheric pollutants for mitigation purposes, it is essential to understand the long-range transport of key pollutants, their potential origins, and the relationship between pollutants and meteorological parameters. Various studies related to the long-range transport of air pollutants have insisted that desert dust contributes to increases in PM concentrations all over Europe and that approximately 50% of air pollutants in cities are due to long-range transport [7,9,10,11]. Various air pollutants such as PM, secondary pollutants, and gaseous matters such as CO from East Asia can flow into Korea, the research area of this study [12,13,14,15,16,17,18]. It is necessary to understand the relationship of long-range transport with potential sources and weather parameters of pollutants to reduce air pollutants. Thus, the purpose of this study was to analyze precursor compounds of air pollutants associated with long-range transport. An agricultural location without major point sources was selected to measure air pollutants increased by long-range transport. The results of this study might explain the long-range transport of air pollutants.

2. Materials and Methods

2.1. Measuring Location

An observational study was executed in an apple orchard located at Jangsoo, Jeollabuk-do, Korea (35°62′04″ N 127°51′24″ E), to determine sources and features of PM2.5 in an agricultural area. Observations were conducted for a total of 14 days from 25 January 2021 to 7 February 2021, during the winter period when long-distance inflows mainly occur [12,13,14]. A trailer for air measurement was used for observation (Figure 1). This trailer could collect custom-made, three-hour integrated PM using filters, VOCs using thermal desorption tubes (a total of 112 samples for each PM and VOCs), and gaseous compounds (CO, CO2, SO2, O3, NO, NO2 (Thermo Scientific, Waltham, MA, USA) and NH3 (Los Gatos Research (LGR), ABB Inc., San Jose, CA, USA) using a real-time analyzer.

2.2. Chemical Analysis of PM2.5

PM2.5 was automatically collected every three hours during this study. The following compounds were analyzed to investigate the chemical compositions of PM2.5: organic carbon (OC), EC, equivalent black carbon (eBC), water-soluble organic carbon (WSOC), water-soluble ionic compounds including NO3−, SO42−, NH4+, and trace elements (i.e., Al, Si, Ca, and Ti). The NIOSH 5040 protocol (National Institute of Occupational Safety and Health, Washington, DC, USA) was used for the analysis of OC and EC. A multi-angle absorption photometer (MAAP) (Thermo Scientific 5012) was used for eBC analysis. After extracting collected filters with deionized distilled water (DDW), analyses of WSOC and water-soluble ionic compounds were performed with a total organic carbon (TOC) analyzer and ion chromatography (IC) (Metrohm 930, Herisau, Switzerland). Energy dispersive X-ray fluorescence (ED–XRF, ARL QUANT’X EDXRF Spectrometer, Thermo Inc., Waltham, MA, USA) was used to analyze trace elements.

2.3. Analysis of VOCs

A three-hour integrated sequence VOC sampler was implemented at a flow rate of 50 mL/min to minimize the loss of low VOCs, which were automatically collected. A total of 123 samples were collected and analyzed during the sampling periods. VOCs (Custom (Q-QC27-1), Kemidas (M-VAVOC 503M2), Gyeonggi-do, Republic of Korea) were used as standard material for analysis. The internal standard material was chlorobenzene-d5 manufactured by Kemidas. A solid adsorption tube (C2-CAXX-5149) (Markes International, Ltd., Bridgend, UK) was utilized for sample collection and the calibration curve. VOCs were analyzed using thermal desorption (Unity2, Markes International, Ltd., Bridgend, UK) gas chromatography (GC) (7890A, Agilent, Santa Clara, CA, USA) mass spectrometry (MS) (5975C, Agilent, Santa Clara, CA, USA), and the analysis method was US EPA TO-17 (solid adsorption method). The 33 compounds used for the analysis included benzene, toluene, ethylbenzene, and xylene both for agricultural chemical analyses and orchard ambient analyses. Briefly, 1 µL of a diluted solution was collected by mixing two standard VOCs in the solid adsorption tube using nitrogen gas at a flow rate of 50 mL/min. In addition, 1 µL of chlorobenzene-d5 at the concentration of 50 mg/L was injected into the adsorption tube, with all the samples at a flow rate of 50 mL/min, using nitrogen gas to finalize the calibration curve. The detailed analytical procedure can be found elsewhere [15].

2.4. Back-Trajectory Analysis of Meteorological and Satellite Data

To identify the source area of long-range transport air parcels, a concentration weighted trajectory (CWT) was applied with CO and CO2. Backward trajectories arriving in the sampling area were obtained using the HYSPLIT model with GDAS1 meteorological data. To further perform CWT analysis, 120 h backward trajectories that arrived at 1000 m above ground level (AGL) were used. CWT with CO/CO2 ratio was calculated as follows [16,17]:
C i j = ∑ n = 1 k C n t i j n ∑ n = 1 k t i j n
where Cij was the average ratio of grid ij; k was the total number of concentrations and trajectories; Cn was the hourly CO/CO2 ratio (ppb/ppm); and tijn was the residence time of the trajectory through grid ij. The trajectory distribution grid had a 1.0° × 1.0° resolution and a boundary of 110–140° E and 25–50° E.
Data from satellites and meteorology were analyzed during the long-range transport (LRT) period. Aerosol optical depth (AOD) of Terra and Aqua/MODIS and CO Column of Sentinel-5P/TROPOMI were used as satellite data [18,19,20]. Data passing through the Korean peninsula satellite orbit during the LRT period were calculated using the arithmetic mean [21]. Data on wind direction and speed in 850 hPa from ERA5 were used as meteorological data in this study. ERA5 data were calculated using the arithmetic mean every three hours during the LRT event period to indicate an overall current pattern of Northeast Asia. During the measurement period, MOD04/MYD04 satellite data were used. This parameter data was named “Optical_Depth_Land_And_Ocean”. An average MODIS was shown with an arithmetic mean after rearranging 0.25 degrees of spacing grids.

3. Results

3.1. Time Series Results of Measured Concentrations

Table 1 shows the averaged concentrations during the study period. The PM2.5 concentration was 21.87 µg/m3, meaning that it was observed at less than 25.00 µg/m3 of its Korea Environmental Standard. PM2.5 compositions accounted for 80.10% of the total PM2.5, including 24.09% carbons of OC and EC, 49.34% ions of SO42− and NO3−, and 6.63% trace elements. Based on results of previous studies, it was presumed that 19.90% of compositions except OC, EC, ions, and metals consisted of H, N, S, and O (which are organic matter except for carbon), unmeasured metals, and a small amount of water [4,5,6]. Gaseous compounds measured in the study area had seven compositions including CO, CO2, SO2, O3, NO, NO2, and NH3 at average concentrations of 202.18 ppb, 446.74 ppm, 4.95 ppb, 38.14 ppb, 0.95 ppb, 3.77 ppb, and 6.97 ppb, respectively.
The characteristic of the study area was investigated using the PM2.5 compositions. OC can be divided into water-insoluble organic carbon (WIOC) and water-soluble organic carbon (WSOC). WIOC is mainly generated through the combustion of fossil fuels as a primary origin, and WSOC is released as the primary particulate from sources of biomass burning (BB) or generated by creating a secondary organic aerosol (SOA) [22,23,24,25,26]. WSOC and WIOC accounted for 66% and 34% of the total OC in the study area, respectively. Considering that the study area was located in an agricultural site and the sampling period was in the winter season, it was presumed that WIOC was caused by the use of fossil fuels for heating in winter and that WSOC was caused by biomass burning and SOA formation. From these data, the ratios of K+/EC and K+/Cl, which are indicators of biological combustion, were inspected to understand the reason for WSOC. According to previous studies, the ratio of K+/EC generated by biomass combustion is >2.25 for soft plants such as rice straws, 1~2 for corn, and >0.19 for hard plants such as wood [27,28,29,30]. In addition, the ratio of K+/Cl generated by biomass combustion is 0.3~1 for crops and 2.8~5.4 for wood [28,31]. In this study, the ratios of K+/EC and K+/Cl were 0.19 and 0.26 lower than those of previous studies, respectively. The average ratio of K+/K in this study was 0.34. Thus, a large amount of K was present in water-insoluble materials. Its origin could be from soils rather than biomass combustion, as reported in previous studies (geological origin of 0.1 for K+/K, biomass combustion of 0.9 for K+/K) [28,32]. Meanwhile, the biological combustion of agricultural byproducts in Korea is performed during November and December after completing crop harvests. Thus, the period of this study was set to be from January to February. Based on the results reported above, WSOC might have a bigger influence due to SOA generations than biomass combustions.
Water-soluble ionic compounds accounted for 49.34% of PM2.5 compositions, which were the highest. The concentrations of ionic compounds were: 2.452 μg/m3 for NH4+, 5.149 μg/m3 for NO3−, 2.754 μg/m3 for SO42−, and 0.086 μg/m3 for K+. NH3 in the atmosphere is an alkaline gas that can form ammonium sulfate ((NH4)2SO4) or ammonium bisulfate ((NH4)HSO4) after it is neutralized by sulfuric acid (H2SO4). Some NH3 can form ammonium nitrate (NH4NO3) by reacting with nitric acid (HNO3) [33,34]. Ammonium sulfate, ammonium bisulfate, and ammonium nitrate are all SOAs. Their generations are dominated by NH3 in the atmosphere. The content of NH3 was analyzed using the mole ratio of NO3−, SO42−, and NH4+ in PM2.5. According to previous studies, the air can be defined as ammonium-rich if the mole ratio of NH4+/SO42− in PM2.5 is higher than 1.5 [35,36]. If the mole ratio of NH4+/SO42− in PM2.5 is lower than 1.5, it is regarded that the contribution of SO42− to SOA in PM2.5 is significant. When the mole ratio of NO3−/SO42− is high, the contribution of NO3− to SOA is dominant. Thus, the air can be defined as ammonium-rich. In this study, mole ratios of NH4+/SO42− and NO3−/SO42− were 4.75 and 2.90, respectively [37,38]. Therefore, NH4+ in the atmosphere was sufficient in the study area, and the generation of SOA would be relatively easy due to the high contributions of NO3− to SOA. Based on the results of WSOC, water-soluble ions, the ratio of K+/K, EC, and eBC, PM2.5 had the following main causes: SOA generation, combustion of fossil fuels for heating, scattering of earth crustal materials, and so on. However, because WSOC and water-soluble ions accounted for 15.84% and 49.34% of total PM2.5, respectively, it could be judged that SOA contributed to PM2.5 the most in the study area.
Figure 2 shows wind direction/speed and time-series concentrations. As shown in the graph, the temperature was −11~12 °C during the measurement period, and the relative humidity (RH) was 20~88%. From the measured concentrations, a gaseous material had a 7.55 times difference between minimum and maximum concentrations, and the average PM2.5 had a 55.69 times difference between minimum and maximum concentrations. Even within the short period of this study, this displayed a significant difference between compositions of gaseous materials and PM2.5. From results shown in Figure 2, significant differences in PM2.5 were found during the following periods: 1 p.m. on 30 January~3 p.m. on 31 January, 9 a.m. on 1 February~3 p.m. on 2 February, and 9 a.m. on 6 Feburary~3 p.m. on 7 February. The changes in concentrations during the above periods (event periods) might have been caused by external influxes due to several reasons. The first reason is that the CO concentration increased during the event period. The lifetime of atmospheric CO is about three months in winter. CO is normally an inert precursor for long-range transport for less than 10 days [39]. In addition, because CO reacts with OH radicals in photochemical reactions and produces CO2, it is known that its daily concentration is very low from 1 p.m. to 4 p.m. when photochemical reactions are definitely active [35,36]. In this study, CO concentrations during the event period and other periods were 283 ppb and 184 ppb, respectively, showing a higher concentration during the event period. CO concentrations by time indicated that its average concentration was about 180 ppb from 6 a.m. to 9 a.m. and about 150 ppb from 1 p.m. to 4 p.m. during other periods, showing lower concentrations when photochemical reactions were occurring. On the other hand, its concentration during the event period was 220 ppb from 6 a.m. to 9 a.m. and 270 ppb from 1 p.m. to 4 p.m. when photochemical reactions were dominating. Based on daily concentration changes in CO and CO concentrations by time at the event period and other periods, the study area might be affected by external influxes during the event period. The second reason is the increase in EC during the event period. According to previous studies, an increase in EC could result from external influxes if no specific EC sources are available. A 68% increase in EC has been reported [40]. Our study location was an orchard located in an agricultural region without any residential houses, highways, or industrial duplexes discharging EC nearby. However, EC was found to be increased about 2.1 times when comparing its concentrations between the event period and other periods. This increase in EC during the event period might be due to external influxes associated with the geological features of the study location. The third reason for an external influx is that water-soluble ions were the main cause of increased PM2.5. Previous studies have insisted that an increase in SOA is a big factor influencing long-range transport [40]. Generally, atmospheric SO2 and NO2 react with hydroxyl radicals and generate sulfuric acid and nitric acid. Then nano-particles such as (NH4)2SO4, (NH4)HSO4, and NH4NO3 are produced by reactions between ammonia and sulfuric and nitric acids [41,42].
Additionally, if ammonia is available after NH3 reacts with sulfuric acid first, it can react with nitric acid [31]. This study showed that the content of water-soluble ions within PM2.5 was 60.27% during the event period and 40.64% during other periods. Thus, the event period had a higher content of water-soluble ions than other periods. Concentrations of SO42−, NO3−, and NH4+ within PM2.5 were increased about 2.9 times, 7.2 times, and 4.7 times, respectively, during the event period compared with those during other periods. On the other hand, atmospheric concentrations of SO2, NO, and NO2 (factors to generate SOA such as SO42− and NO3−) in PM2.5 were increased by 0.97 times, 1.06 times, and 1.4 times, respectively. Their increases were much lower than the increases in water-soluble ions in PM2.5. Along with ammonium-rich SOA due to NO3−, SO42−, and NH4+, the mole ratio of NH4+/SO42− was 3.8 during other periods and 6.14 during the event period, indicating a stronger condition for SOA during the event period than during other periods. Based on the mole ratio of NO3−/SO42−, the contribution of NO3− to PM2.5 was also higher during the event period, which was 4.5 compared with 1.83 during other periods. Atmospheric NH3, a dominant factor for generating SOA by NO3−, SO42−, and NH4+, displayed about a 2.7 times difference between the event period and other periods. There were small differences in concentrations of SO2, NO, and NO2 during each period. Even without increasing concentrations of SO2, NO, and NO2 in the atmosphere, these materials could be caused by external influxes such as an increase in water-soluble ions like NO3−, SO42−, and NH4+ in PM2.5, ammonium-rich status increase during the event period, and increased contributions of NO3− and gaseous NH3. That is, types of water-soluble ion compounds were increased during the event period. After sufficient NH3 in the process of SOA formation reacted with sulfuric and nitric acids, the remaining NH3 might affect the study area. Some previous studies have suggested that an increase in NH3 is due to external influxes and that SOA compositions in PM2.5 and NH3 can be increased by long-range transport. External influxes can be classified into three types based on distance, including short distances (less than 100 Km), medium distances (100~550 Km), and long distances (longer than 550 Km). It has been shown that an external influx with an SOA increase is generated by medium and long distances [40].

3.2. Satellite Observations for Long-Range Transport

To identify the external influx into the study area, we looked over the results of satellite observations provided in aerosol optical depth (AOD), ERA wind field during the event period, back-trajectory analysis, and Sentinel for CO/CO2. Because AOD provides information on the relative weights of pollutants existing in the atmospheric column, it is useful to investigate changes in spatial contributions for particulates. AOD uses MOD04/MYD04 data from TERRA, a representative satellite, to observe the Earth. MODIS sensor observation data are loaded on the Aqua satellite. All data that corresponded to the event period were rearranged into 0.25-degree grids after obtaining arithmetic means. However, we could not obtain all data during the entire event period due to orbit limitations of the Terra and Aqua satellites, and thus the results were calculated using data from 12 p.m. to 2 p.m. on 30 January, 10 a.m. to 2 p.m. on 1 February, and 11 a.m. to 4 p.m. on 6 February. Figure 3 shows the observation data for AOD. As shown in this graph, AOD had a wide range of 0.7~1.4 from West China centered in Shanghai, China, to the study area.
Data from Sentinel-5 Precursor (S-5 P) observed during the study period were confirmed. S-5 P is a satellite with a single fade is used in GMES (Global Monitoring for Environment and Security) that is operated by the EU community and the European Space Agency for common goals [18]. To provide high-quality data, information, service, and knowledge in time, GMES aims to support European goals for sustainable development and environmental global governance. S-5 P provides daily information for air quality and concentrations of trace gases and aerosols that are important for climate forcing and ozone. In detail, S-5 P observes air quality using TROPOMI (tropospheric monitoring instrument, co-developed by the Netherlands and European Space Agency) and a spectrometer having spectrum bands of UV, visible light (VIS), near-infrared (NIR), and short wavelength infrared (SWIR). Major atmospheric components such as O3, NO2, CO, SO2, methane (CH4), formaldehyde (CH2O), aerosols, and clouds can be observed using selected wavelength ranges in TROPOMI [43]. Thus, analyzed CO from data from S-5 P observed during the period of this study. Figure 4 indicates the concentration gradient of CO observed by S-5 P. As shown in the figure, CO appeared in high concentrations in China. High levels of CO from the study area to Japan were also confirmed.
Figure 5 shows data from the ERA wind field and meteorological field. We illustrated the ERA wind field and meteorological field on the same map of the back-trajectory graph using the U and V components of ERA5 data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). These data confirmed that the study area was influenced by the west wind from China on 30 January, 1~2 February, and 6~7 February.
A back-trajectory analysis was performed using the ratio of CO/CO2 to confirm the external influx in the study area. The reason why the ratio of CO/CO2 was used for the back-trajectory analysis is because CO can be used as an inert precursor for long-range transport. In addition, it is a gaseous material that showed a high concentration during the event period, which can be estimated during the period of external influx. Its concentration in the study area only showed a 1.01 times difference between the event period and other periods. Thus, the ratio of CO/CO2 can be used as a marker for external influxes. In this study, the residence time-weighted concentration (RTWC) model was used for the back-trajectory analysis. By complementing the disadvantages of the potential source contribution function (PSCF) model, the RTWC model can impose a weighted concentration by the source on each grid for calculating each average concentration of grid cells. In detail, the PSCF model can indicate the possibility of arriving at a detention place after a component with some pollutants passes through the source by a specific wind. However, it has a disadvantage in that it cannot apply concentration weight by time because movements of sources are generated by the same retention time even when source materials have some concentration differences [44,45,46,47]. The RTWC model can impose a weighted concentration by the source, thus overcoming the disadvantage of the PSCF model. Figure 6 shows the results of the RTWC model realized for the ratio of CO/CO2 during the total study period. It does not display the actual contribution because its concentration of RTWC by area is expanded by the number of trajectory segments whenever concentrations are distributed in each grid cell [46]. Therefore, it is difficult to understand the actual concentration by area with the concentrations shown in the figure only. However, the RTWC model suggests a relative importance of source locations measured in the study area [46]. In the case of interpreting the relative importance of source locations, the ratio of CO/CO2 was introduced from the west and northwest of the study area. A similar result for the wind field was found, as shown in Figure 6.
As a result of identifying external influxes in the study area and analyzing data from AOD and S-5 P, high concentrations of aerosol and CO from China (west of the study area) and ERA wind fields flowing from the west and northwest of the study area were confirmed. Additionally, the ratio of CO/CO2 from the RTWC model had an external influence from the same direction as the ERA wind field. Conclusively, results of back-trajectory analysis using AOD, S-5 P, ERA wind field, and RTWC confirmed that external influxes during the event period affected the study area.

3.3. Ratio Characteristic of Long-Range Transport Component

Based on the chemical compositions of PM2.5, AOD, S-5 P, RTWC, and the ERA wind field during the event period in this study, it can be concluded that a high concentration of PM2.5 generated by long-range transport from the west of the study area had external effects. Thus, this study identified precursor components for long-range transport based on concentration differences between the event period and other periods. Chemical compounds that influenced the event period were selected based on concentrations measured during the event period and other periods. The analytical determination was the following: (1) a compound that was observed to show an increase in concentration during the event period; (2) a compound that affected SOA formation; and (3) a compound that satisfied the condition suggested by a previous study as an inert precursor of long-range transport. As a result, the indicators for long-distance tracking included gaseous materials (including CO and NH3) and PM2.5 compositions (such as eBC, EC, WSOC, OC, NO3−, SO42−, and NH4+). Table 2 shows the results for the selected long-range indicators and PM2.5. When results were compared between the event period and other periods, PM2.5 and gaseous materials including CO and NH3 were about 3.3 times, 1.5 times, and 2.7 times higher, respectively, during the event period. For PM2.5 compositions, NO3−, SO42−, and NH4+ were increased by about 7.16 times, 2.9 times, and 4.7 times, respectively. These results confirmed that the PM2.5 concentration increase during the event period was influenced by SOA from long-range transport. In addition, WSOC was influenced by SOA introduced by long-range transport because WSOC had about a 2 times increase during the event period and a dominant effect on SOA formation.
Among the PM2.5 components, eBC and EC were compared between the event period and other periods. eBC and EC are primarily derived from the combustion of fossil fuels and incomplete combustion of biomass. Regarding their analytical methods, EC can be determined using the thermo-optical method, and eBC can be determined using optical technology [48,49]. In the case of eBC/EC with the NIOSH5040 protocol, PM2.5 was generally in the range of 1.4~1.7. It might have a maximum difference of two times depending on particle size distribution, mixing status, and chemical compositions [50,51,52]. Except for the difference in analytical methods between eBC and EC, there are two examples that show differences between eBC and EC. The first is that brown carbon generated by biomass combustion increases eBC more than EC [53,54]. The second is an underestimation of EC using char-OC, which is contained in samples. Previous studies have reported that methanol extraction of char-OC contained within filters can increase EC by more than two times [49,55]. In other words, char-OC within filters can cause an underestimation of EC and an increase in the eBC/EC ratio. The measured ratio of eBC/EC in this study was 2.02 during other periods and 2.46 during the event period. Study areas specified by ratios of K+/K and K+/Cl had relatively low combustions of biomass. The orchard did not have any sources generating char-OC. Thus, the eBC/EC increase during the event period should be due to external influxes.
To evaluate the above results more accurately, we analyzed correlations between CO and CO2 and SOA formation indicators including SO42−, NO3−, NH4+ (SNA), eBC, and EC (Figure 7). Regarding the correlation between CO and CO2, CO2 displayed a relatively homogenous concentration throughout the study period. However, CO concentration was obviously increased during the event period. That is, the increasing rate of CO as an origin of CO2 was not related to the CO2 increase during the event period, meaning that the ratio of CO/CO2 could be used as an indicator for external influxes. To examine SOA inflow by SNA, we examined the correlation between [NO3−/SO42−] and [NH4+/SO42−] and the correlation between NH3 and NO. Figure 7 shows a linear relationship between [NO3−/SO42−] and [NO4+/SO42−]. Meanwhile, it was confirmed that NH3 increased more than NO during the event period. In PM2.5 compositions, PM2.5 introduced during the event period flowed into the study area in a type of SOA based on SNA. However, NO2 and SO2 were not introduced in gas states because they completely reacted with NH3. On the other hand, it was confirmed that the remaining NH3 was introduced into the study area after reacting with atmospheric NO2 and SO2. Therefore, a linear relationship between [NO3−/SO42−] and [NH4+/SO42−] in PM2.5 could play a role as an indicator that NH3/NO increases atmospheric NH3 due to external influxes. The correlation between eBC and EC during the event period also showed a linear proportional relationship, with an eBC/EC ratio of approximately 2.0. It was hard to evaluate external influxes based on the ratio of eBC to EC. However, when data on 28 January and 1 February, which had strong external influxes during the event period, were analyzed, the ratio of eBC/EC could be used as an evaluation index for strong external influxes because it increased from 2.0 to 2.4 or higher.

3.4. Long-Distance Transport Using VOCs

We then determined whether the 32 VOCs measured in this study could be used as an evaluation index for long-range transport. It is well known that VOCs are generally removed by the physical adsorption process of wet and dry deposition in the troposphere and transformed by photolysis and OH radical reactions [56]. Adsorption removal of VOCs in wet and dry environments is determined by temperature, humidity, and pressure in the troposphere. The change in VOCs is influenced by oxidation speed for OH radicals. This oxidation speed for OH radical is different in VOC compounds involved in the lifetime of VOCs [57]. According to a previous study, KOH values of m-xylene and p-xylene are 23.6 and 14.3, respectively. The lifetime of m-xylene and p-xylene in the air are 11.8 h and 19.4 h, respectively. KOH and the lifetime of ethylbenzene are 6.05 and 1.6 days, respectively [57]. These data were used as an evaluation index for long-range transport in this study using VOC lifetime in the air and components that increased during the event period.
Table 3 indicates concentrations of VOCs in this study. The average VOC concentration during the total study period was 2.588 ppb, including 2.118 ppb during the event period and 2.682 ppb during other periods. The concentrations of VOCs were about 22% lower during the event period than during other periods. However, the concentrations were higher during the event period than during other periods for chloroform (Cf), 1,2-dichloroethane (12Dice), tetrachloroethene (TcCE), and chlorobenzene (CB). For comparisons between the event and other periods, the concentrations of Cf, 12Dice, TcCE, and CB were found to be 1.92 times, 3.74 times, 1.67 times, and 2.00 times higher, respectively, during the event period. Considering the four VOCs mentioned above, previous studies indicate that Cf, 12Dice, TcCE, and CB have a lifetime in the air of 146 days, 90 days, 200 days, and 13 days, respectively [58,59,60]. In addition, the time for VOCs to arrive at the study area was 12~48 h based on the back-trajectory analysis. Thus, Cf, 12Dice, TcCE, and CB might have been introduced into the study area by long-range transport based on the overall concentration increases, lifetime in the air, and arrival time calculated using the back-trajectory analysis during the event period.
Cf, 12Dice, TcCE, and CB transported into the study area by long-range transport can be released artificially or naturally [61]. It is particularly difficult to investigate natural and artificial sources of Cf. In addition, CB is an ingredient of pesticides [60,61]. The study area was an orchard located in an agricultural region. Only comparing concentrations between events and other periods is not enough to evaluate external influxes due to the natural generation of VOCs and the frequent usage of pesticides. Therefore, concentrations of Cf, 12Dice, TcCE, CB, and naphthalene (NaT) during the event period were determined in this study. The reason for selecting NaT is because NaT, in an artificial atmospheric environment, is evaluated as the most abundant component of PAHs and that gaseous NaT is eliminated in a short time [62]. A previous study indicated that its lifetime in the air is 48 min~9 h with the OH radical, according to specific circumstances [62]. Therefore, it is expected that NaT will disappear within 12 h, which is the minimum time for an external influx. As indicated in Table 3, the deviation in NaT concentration during other periods was very low at 0.014. This means that gaseous NaT was abundantly distributed in the atmospheric environment. It is a compound with a short lifetime and a relatively low deviation in the study area. Ratios of Cf, 12Dice, TcCE, and CB to NaT were 2.047 times, 3.967 times, 2.005 times, and 1.974 times higher during the event period than during other periods, respectively. These results confirmed that concentrations of Cf, 12Dice, TcCE, and CB were increased not by emission sources in the study area but by external influxes.
Table 4 and Figure 7 show relationships between Cf/NaT, 12Dice/NaT, TcCE/NaT, and CB/NaT and CO, eBC, EC, OC, WSOC, NH4+, NO3−, and PM2.5 during the event period. As indicated in the table, r2 for Cf/NaT, 12Dice/NaT, TcCE/NaT, and CB/NaT were 0.170, 0.783, 0.507, and 0.568, respectively. This means that Cf/NaT had no relationship with long-range transport. However, TcCE/NaT and CB/NaT were partially related to long-range transport. Meanwhile, 12Dice/NaT presented a high correlation with long-range transport. Thus, 12Dice can be used as an indicator for long-range transport. Because the r2 of CO and 12Dice/NaT, as a direct index for long-range transport, was higher than 0.9, 12Dice/NaT can be used as an indicator for long-range transport with CO/CO2.
These results clarified the possibility of using VOCs as an indicator for long-range transport. It was confirmed that 12Dice could be used as an evaluation index for long-range transport based on the overall results of VOC lifetime in the air, increases during event period, and correlations with other long-distance indices.

4. Conclusions

This study investigated the influence of long-range transport as one of the PM2.5 factors in agricultural regions and estimated an indicator of long-range transport. There was an influence of long-range transport during the observation period. Concentrations of gaseous materials and PM compositions were inspected to find proof of long-range transport. The obtain results imply long-range transport based on changes in gas concentrations and PM compositions. In addition, the AOD, ERA wind field, back-trajectory analysis for CO/CO2, and satellite observation results were used to identify additional proof for long-range transport. The specific characteristic of long-range transport in the study area during this study was then determined. Long-range transport can be analyzed along with concentrations of CO, NH3, OC, and water-soluble ions in the future. Furthermore, we confirmed that the ratio of 12Dice/NaT in VOCs had a proportional relationship with an indicator of long-range transport. This suggests that VOCs can be used as markers for long-range transport. Atmospheric pollutants are influenced not only by local factors but also by wide-ranging factors such as intercountry transport. It is hoped that, based on the results presented in this study, a further understanding of the impact of atmospheric pollutants between regions and countries can be achieved, facilitating not only regional atmospheric pollutant management but also global atmospheric pollutant management.

Author Contributions

M.K. and M.S. equally contributed to this work by completing the experiment measurements, data analysis, and manuscript preparation. S.-H.O., G.-H.Y., S.C., H.J. and J.-H.K. contributed to research sample analyses. M.-S.B. contributed to the experimental planning and data analysis. All authors have read and agreed to the published version of the manuscript.

Funding

This study was supported by the Cooperative Research Program for Agriculture Science & Technology Development (PJ0170302023) Rural Development Administration, Republic of Korea.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data will be available upon request.

Conflicts of Interest

The authors declare no conflict of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PMparticulate matter
PM2.5particles that are 2.5 microns or less in diameter
PM10particles that are 10 microns or less in diameter
VOCsvolatile organic compounds
ECelemental carbon
OCorganic carbon
WSOCwater soluble organic carbon
MAAPmulti-angle absorption photometer
DDWdeionized distilled water
ICion chromatography
TOCtotal organic carbon
ED-XRFenergy dispersive X-ray fluorescence
GCgas chromatography
MSmass spectrometry
CWTconcentration weighted trajectory
LRTlong-range transport
AODaerosol optical depth
WIOCwater insoluble organic carbon
BBbiomass burning
SOAsecondary organic aerosol
RHrelative humidity
VISvisible light
NIRnear infrared
SWIRshort wavelength infrared
ECMWFEuropean Centre for Medium-Range Weather Forecasts
RTWCresidence time weighted concentration
PSCFpotential Source Contribution Function
SNASO42−, NO3−, NH4+
Cfchloroform
12Dice1,2-dichloroethane
TcCEtetrachloroethene
CBchlorobenzene
NaTnaphthalene

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Figure 1. A trailer for air measurement located at Jangsoo in Jeollabuk-do, Korea (Left: Internal structure, right: installation view).
Figure 1. A trailer for air measurement located at Jangsoo in Jeollabuk-do, Korea (Left: Internal structure, right: installation view).
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Figure 2. Time-series concentrations of PM2.5, CO, CO2, EC, eBC, and ionic compounds.
Figure 2. Time-series concentrations of PM2.5, CO, CO2, EC, eBC, and ionic compounds.
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Figure 3. Levels of aerosols in the area at the average of the event periods in AOD satellite observation.
Figure 3. Levels of aerosols in the area at the average of the event periods in AOD satellite observation.
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Figure 4. The concentration gradient in CO observed by S-5P.
Figure 4. The concentration gradient in CO observed by S-5P.
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Figure 5. ERA Wind Field and Meteorological Field Data (European ECMWF).
Figure 5. ERA Wind Field and Meteorological Field Data (European ECMWF).
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Figure 6. The ratio of CO and CO2 and the result of the residence time-weighted concentration (RTWC) model in the study area.
Figure 6. The ratio of CO and CO2 and the result of the residence time-weighted concentration (RTWC) model in the study area.
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Figure 7. Scatter plots between (a) CO and CO2, (b) NH3 and NO, (c) eBC and EC, (d) [NO3−/SO42−] and [NH4+/SO42−], (e) PM2.5 and 12Dice/Nat, (f) NO3− and 12Dice/Nat, (g) NH4+ and 12Dice/Nat, and (h) CO and 12Dice/Nat.
Figure 7. Scatter plots between (a) CO and CO2, (b) NH3 and NO, (c) eBC and EC, (d) [NO3−/SO42−] and [NH4+/SO42−], (e) PM2.5 and 12Dice/Nat, (f) NO3− and 12Dice/Nat, (g) NH4+ and 12Dice/Nat, and (h) CO and 12Dice/Nat.
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Table 1. Chemical concentration results in the orchard area.
Table 1. Chemical concentration results in the orchard area.
CompoundUnitOverall AverageLRT (1) PeriodOther PeriodsRatio (2)
GasCOppb202.175283.010184.5211.534
CO2ppm446.742452.842445.4101.017
SO2ppb4.9534.8234.9810.968
O3ppb38.14042.25437.2411.135
NOppb0.9500.9940.9401.058
NO2ppb3.7704.9573.5101.412
NH3ppb6.97414.7485.3662.749
PM2.5PM2.5µg/m321.86950.86315.3883.305
eBCµg/m30.9161.9430.6922.807
PM2.5sumµg/m317.49941.54511.9953.464
OCµg/m34.8167.5774.1841.811
ECµg/m30.4530.7890.3762.101
WSOCµg/m33.1865.2392.7161.929
NO3−µg/m35.14917.1722.3977.163
SO42−µg/m32.7545.9282.0282.923
NH4+µg/m32.4526.8211.4524.696
K+µg/m30.0860.1990.0613.277
Naµg/m30.2780.4180.2471.691
Mgµg/m30.0770.1150.0681.688
Alµg/m30.1780.2510.1621.546
Siµg/m30.3510.4950.3191.553
Clµg/m30.3370.7360.2502.939
Kµg/m30.2530.5200.1942.679
Caµg/m30.1130.1820.0981.844
Tiµg/m30.0110.0160.0101.641
Crµg/m30.0020.0030.0021.252
Mnµg/m30.0110.0190.0092.140
Feµg/m30.1600.2440.1421.719
Niµg/m30.0030.0030.0031.245
Cuµg/m30.0010.0010.0002.555
Znµg/m30.0200.0380.0162.421
Brµg/m30.0030.0060.0022.340
Pbµg/m30.0080.0140.0071.978
(1) Long-range transport; (2) ratio of long-range transport to other periods.
Table 2. Concentrations of tracer components for long-range transport.
Table 2. Concentrations of tracer components for long-range transport.
CompoundUnitOverall AverageLRT (1) PeriodsOther PeriodsRatio (2)
GasCOppb202.175283.010184.5211.534
NH3ppb6.97414.7485.3662.749
PM2.5PM2.5 massµg/m321.86950.86315.3883.305
eBCµg/m30.9161.9430.6922.807
OCµg/m34.8167.5774.1841.811
ECµg/m30.4530.7890.3762.101
WSOCµg/m33.1865.2392.7161.929
NO3−µg/m35.14917.1722.3977.163
SO42−µg/m32.7545.9282.0282.923
NH4+µg/m32.4526.8211.4524.696
(1) Long-range transport; (2) ratio of long-range transport to other periods.
Table 3. VOCs concentrations measured during the study period.
Table 3. VOCs concentrations measured during the study period.
Compound (1)Overall AverageLRT (2) PeriodsOther PeriodsRatio (3)Non
Event
STD (4)
Acrylonitrile0.0210.0160.0220.7270.029
1,1-Dichloroethane0.2340.2660.2211.2040.289
Chloroform0.0450.0750.0391.9230.041
1,2-Dichloroethane0.0440.1160.0313.7420.025
Benzene0.7290.4830.7810.6180.782
Carbon tetrachloride0.0480.0440.0490.8980.018
Trichloroethene0.0070.0040.0080.5000.017
Toluene0.9860.6771.0520.6441.699
Tetrachloroethene0.0060.0100.0061.6670.007
Chlorobenzene0.0070.0120.0062.0000.012
Ethylbenzene0.1000.0980.1010.9700.166
m&p-Xylene0.0400.0420.0401.0500.079
Styrene0.0800.0600.0840.7140.057
o-Xylene0.0470.0450.0470.9570.078
Bromobenzene0.0510.0350.0530.6600.067
Isopropylbenzene0.0070.0070.0071.0000.006
2-Chlorotoluene0.0020.0010.0020.5000.004
1,3,5-Trimethylbenzene0.0040.0030.0040.7500.004
p-Isopropylbenzene0.0100.0110.0091.2220.010
1,2,4-Trimethylbenzene0.0170.0180.0171.0590.007
1,2-DiChlorobenzene0.0040.0040.0041.0000.002
1,3-DiChlorobenzene0.0010.001--0.002
tert-Butylbenzene0.001-0.001-0.002
1,4-DiChlorobenzene-0.001--0.001
n-Butylbenzene0.0070.0070.0071.0000.003
Naphthalene0.0900.0820.0910.9010.014
∑VOCs2.5882.1182.682
(1) Unit: ppb; (2) long-range transport; (3) ratio of long-range transport to other periods; (4) standard deviation.
Table 4. Correlation coefficient of determinations between VOC ratios for long-range transport and measured compounds.
Table 4. Correlation coefficient of determinations between VOC ratios for long-range transport and measured compounds.
r2Cf/NaT12Dice/NaTTcCE/NaTCB/NaT
PM2.50.3230.8790.3820.505
CO0.3210.9070.4360.525
eBC0.0840.7920.6300.663
EC0.0260.6100.6100.609
OC0.1040.7830.6290.641
WSOC0.1010.6470.4420.509
NH4+0.2200.7800.4090.504
NO3−0.1800.8340.5190.585
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Kim, M.; Song, M.; Oh, S.-H.; Yu, G.-H.; Choe, S.; Jeon, H.; Kim, J.-H.; Bae, M.-S. Impact of Precursor Compounds Associated with Long-Range Transport in the East Asia Region—Variation in CO/CO2 and VOCs. Appl. Sci. 2023, 13, 10783. https://doi.org/10.3390/app131910783

AMA Style

Kim M, Song M, Oh S-H, Yu G-H, Choe S, Jeon H, Kim J-H, Bae M-S. Impact of Precursor Compounds Associated with Long-Range Transport in the East Asia Region—Variation in CO/CO2 and VOCs. Applied Sciences. 2023; 13(19):10783. https://doi.org/10.3390/app131910783

Chicago/Turabian Style

Kim, Minwook, Myoungki Song, Sea-Ho Oh, Geun-Hye Yu, Seoyeong Choe, Hajeong Jeon, Jin-Ho Kim, and Min-Suk Bae. 2023. "Impact of Precursor Compounds Associated with Long-Range Transport in the East Asia Region—Variation in CO/CO2 and VOCs" Applied Sciences 13, no. 19: 10783. https://doi.org/10.3390/app131910783

APA Style

Kim, M., Song, M., Oh, S. -H., Yu, G. -H., Choe, S., Jeon, H., Kim, J. -H., & Bae, M. -S. (2023). Impact of Precursor Compounds Associated with Long-Range Transport in the East Asia Region—Variation in CO/CO2 and VOCs. Applied Sciences, 13(19), 10783. https://doi.org/10.3390/app131910783

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