[ Research Article ]
Asian Journal of Atmospheric Environment - Vol. 14, No. 4, pp.335-344
ISSN: 1976-6912 (Print) 2287-1160 (Online)
Print publication date 31 Dec 2020
Received 17 Feb 2020 Revised 23 Mar 2020 Accepted 08 Jul 2020

# Visibility Degradation and Its Contributors at an Urban Site in Korea

Chang-Jin Ma* ; Cheol-Soo Lim1) ; Gong-Unn Kang2) ; Sun-A Jung1) ; Mi-Ra Jo1)
Department of Environmental Science, Fukuoka Women’s University, Fukuoka 813-8529, Japan
1)Department of Air Quality Research, National Institute of Environmental Research, Incheon 22689, Republic of Korea
2)Department of Medical Administration, Wonkwang Health Science University, Iksan 54538, Republic of Korea

Correspondence to: * Tel: +81-90-9470-9293 E-mail: ma@fwu.ac.jp

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

## Abstract

In order to provide a better knowledge of visibility degradation during the PM2.5 event day (episodic high PM2.5 level, hereafter called as “event day”), the relationship between visibility and the chemical species of PM2.5 measured in Gwangju, Korea was estimated. Moreover, a visibility forecasting model was constructed by a statistical approach. The diurnal variation of visibility and PM2.5 concentration on the event day indicated that as the concentration of PM2.5 increased, more light was absorbed and scattered, resulting in visibility deterioration. The averaged visibility during the event day was 7.9 km, which was almost three times lower than that observed during a non-event day. Although the hygroscopic growth of aerosol was not considered in this study, it has been proved that NH4NO3 and organics dominantly contributed to the light scattering during the PM2.5 event day in Gwangju, Korea. The visibility determined in this study had also a negative correlation with PM10, nitrate, relative humidity, EC, OC, and sulfate. Meanwhile, visibility was positively linked with wind speed and temperature. The results of interrelationship and a multiple regression model suggest that among the meteorological variables, temperature was the main variable that influenced visibility.

## Keywords:

PM2.5, Visibility, Chemical composition, Light scattering, Nephelometer, Regression model

## 1. INTRODUCTION

Fine particles, especially those with diameter 2.5 micrometers or less (generally called as PM2.5), are believed to be the main cause of reduced visibility. When PM2.5 in the atmosphere scatters light, haze is caused and reduces the distance people can see and obscures the color and clarity of the objects. Visibility of less than 100 m is usually reported as zero. In this condition, roads and airports may be closed. Due to repeatedly poor visibility at many airports around the world, sometimes flights are delayed or cancelled.

Visibility degradation is mainly caused by the absorption and scattering of light by particles and gases. The field observation by Kim et al. (2008) suggested that the light scattering by particles accounted for 52.8 to 81.3% of the worsening of visibility at the urban site of Gwangju, Korea. Among various PM2.5 components, major four kinds are the most effective at reducing visibility. They are sulfates, nitrates, organic carbon (OC), and elemental carbon (EC, also called as BC (Black Carbon)) (Sloane et al., 1991).

The contribution of aerosol components to the light-scattering varies according to the region. Wang et al. (2015) reported that the apportionment contributions from organic, sulfate, ammonium nitrate and ammonium chloride were 54%, 24%, 12%, and 10%, respectively in Beijing, China during a wintertime intense haze episode. They also mentioned that the organic components contributed greatly to the light extinction (about 45% in Shenzhen, China (Yao et al., 2010) and 9-50% in the Eastern USA (Watson, 2002)) by quoting other research papers.

It is significantly meaningful to study visibility based on the chemical compositions of aerosol obtained through a real-time chemical analysis. However, the real-time or quasi-real-time monitored chemical compositions of PM2.5 have seldom been applied.

In the present study, the relationship between visibility and the simultaneously measured chemical composition of PM2.5 was estimated. Moreover, the simplified visibility forecasting model was constructed by a statistical approach based on the routinely monitored PM2.5 and meteorological parameters. If visibility can be easily forecasted through the routinely monitored data, we can protect ourselves from various risks of our health and our daily lives.

## 2. EXPERIMENTAL METHODS

### 2. 1 Description of the Monitoring Site

Gwangju Metropolitan City, the sixth largest city in South Korea, is located in between a mountainous area in the east and a plains area of the southwestern part of the Korean Peninsula. Gwangju has a population of 1.49 million people over an area of 501.2 km2. The annual average temperature is 15°C and the highest average is 33.1°C in August.

As a major source of air pollution, about 636,743 motor vehicles including 92,396 heavy duty are registered in Gwangju (The car registration number by year, 2016). The main industries of Gwangju are automobile, home appliances, high-tech components, semiconductor, and cultural contents.

Monitoring was carried out on the roof of a three-story building (13.5 m) of the Honam area air pollution intensive monitoring site (35.23N; 126.85E). The surroundings of this monitoring site are education and research facilities, and residential and commercial areas with some minor point sources such as electronic manufactories and precision machinery industries. Six-lane roads, usually with light traffic, are located 50 m all around the monitoring site.

### 2. 2 Instruments and Measurements

The hourly data of the ionic species in PM2.5 were directly obtained using the URG-9000D Ambient Ion Monitor (AIM, URG Co.). AIM has an additional ion detector to allow for time-resolved direct measurements of both cations and anions. PM2.5 is drawn into the AIM Monitor through a PM2.5 sharp-cut cyclone and then the sample is drawn through a Liquid Diffusion Denuder. PM2.5 laden air stream next enters the Aerosol Super-Saturation Chamber to enhance particle growth. The enlarged particles in the An Inertial Particle Separator are then injected into the Ion Chromatograph. Ion Chromatography (IC) (ICS-2000, Dionex Co.) was used to determine the concentrations of various ions present in PM2.5. Before running the collected PM samples, the IC system was calibrated using each standard solution. By comparing the data obtained from the PM samples to those obtained from the known standard solutions, the ionic species of PM samples could be identified and quantitated. Samples were analyzed for ammonium, nitrate, and sulfate. Detection limits of ammonium, nitrate, and sulfate are 0.339, 0.020, and 0.023 μg/m3, respectively.

The comparison study between the filter-based laboratory IC technique and AIM method conducted by Beccaceci et al. (2015) showed an overall good correlation (R2>0.83) for ammonium, nitrate, and sulfate.

The concentration of OC and EC in PM2.5 collected on the quartz filters for 45 minutes with a flow rate of 8 lpm was determined using the thermal-optical transmittance (TOT) method. TOT method was designed for the analysis of the carbonaceous fraction of particulate diesel exhaust based on the National Institute of Occupational Safety and Health method 5040 (NIOSH 5040) (Karanasiou et al., 2015). In order to estimate the carbon of the blank filter contaminated during handling and transport, the laboratory blank filters were also analyzed in the same way as samples. Carbon dioxide produced in the analytical procedure of OC and EC was measured by the Non-Dispersive Infra-Red (NDIR) detectors that are the industry standard method of measuring the concentration of carbon oxides (CO and CO2). The limit of detections (LODs) of OC and EC were calculated as 0.65 and 0.02 μg/m3, respectively. Karanasiou et al. (2015) gave a full detail of TOT method.

In order to measure a highly variable light-scattering coefficient, the three-wavelength (i.e., 450 nm (blue), 550 nm (green), and 700 nm (red)) Integrating Nephelometer with backscatter shutter (Model 3563, TSI Scientific Inc., USA) was applied. This unique instrument is one of well-known excellent devices for the measurement of short-term measurements of the light-scattering coefficient of ambient particles. Sensitivity to light-scattering coefficients is as low as 2.0×10-7 per meter (60-second averaging time) (Anderson and Ogren, 1998). Integrating Nephelometers measure the angular integral of light scattering that yields the quantity called the scattering coefficient, which is used in the Beer-Lambert Law to calculate total light extinction (Bodhaine et al., 1991).

Light-absorption coefficient of particles was measured by the Aethalometer (Magee Scientific Inc., USA). The aethalometer is the earliest and most common method used for a real-time readout of the concentration of BC in an air stream. Aethalometers are now constructed to perform their optical analyses simultaneously at multiple wavelengths, typically spanning the range from 370 nm (near-ultraviolet) to 950 nm (near-infrared). The Aethalometer collects the sample on a quartz fiber filter tape, and performs a continuous optical analysis, while the sample is collecting (Hansen et al., 1982).

All monitoring and measurement instruments were intensively operated from May 26 to June 15, 2016.

### 2. 3 Determination of Visibility

Visibility is generally described as the maximum horizontal distance at which a target with a sky background can be visually observed by our eyes. As the early theory of visibility, Koschmieder (1924) developed below formula (i.e., Koschmieder’s formula).

where σext is extinction coefficient and it is the total attenuation of visible radiation due to scattering and absorption by gas molecules, aerosols, and other components (e.g. fog and cloud droplets) in the atmosphere.

The σext may be divided into a scattering coefficient, σscat and an absorption coefficient, σabs:

 ${\sigma }_{ext}={\sigma }_{scat}+{\sigma }_{abs}.$

The scattering and absorption coefficients may therefore be further subdivided into

Therefore, σext is ultimately composed as follows.

 ${\sigma }_{ext}={\sigma }_{sg}+{\sigma }_{sp}+{\sigma }_{ag}+{\sigma }_{ap}.$

where the subscripts of s, a, g, and p denote scattering, absorption, due to gas molecules, and due to PM, respectively.

The σsg, i.e., scattering due to gas molecules is also called ‘Rayleigh scattering’. It was calculated by Rayleigh scattering efficiency 0.12×10-4/m (Malm et al., 1996). The σag was determined by 3.3×NO2 concentration (ppm)×10-4 (Malm et al., 1994). The σsp was directly measured by a Nephelometer. The σap could be calculated by multiplying the BC concentration (ng/m3) measured by Aethalometer by a factor of 8.28 m2/g. The conversion factor of 8.28 m2/g used in this study was applied in Beijing previously by Yan et al. (2008) and He et al. (2009).

### 2. 4 Concept of Multivariate Analysis for Visibility Forecast

A multiple regression analysis was applied to the visibility forecasting. It can be performed based on the relationship between multiple explanatory variables and a response variable by fitting a linear equation as follows:

 ${Y}_{i}={C}_{0}+{C}_{1x1}+...+{C}_{4x4}+e$

where Yi, C0-4, x1-4, and e are response variable (visibility), constants, explanatory variables (PM2.5, temperature, wind speed, and relative humidity), and random error, respectively.

## 3. RESULTS AND DISCUSSION

### 3. 1 Diurnal Variation of Visibility and PM2.5

Photo 1 shows the surrounding from the ground station of this study on a non-event day (a clean day with the PM2.5 concentration of 21 μg/m3) (top) and the event day when PM2.5 reached 63 μg/m3 (bottom). As shown in the photo, it is possible to find out the clear difference between two scenic views.

Comparison of visual characteristics from the ground station of this study on a non-event day (PM2.5: 21 μg/m3) (top) and the event day (bottom) when PM2.5 reached 63 μg/m3.

The diurnal variations of visibility on the event day and a non-event day are drawn in Fig. 1. Visibility shows obvious diurnal variations on both the event day and a non-event day. The diurnal variations will be different depending on the situations of the day, e.g., human activities, the local meteorological elements, and PM2.5 (domestic and an inflow of from abroad). On a nonevent day, it ranged from 12.9 to 39.7 km with an average 22.9 km. The highest visibility appeared at around 5 p.m. and then rapidly decreased until around 3 a.m. local time. Meanwhile, during the event day, it showed a gentle time-variation with a significantly reduced value ranged 5.1-13.4 km with an average 7.9 km. The peak value occurred in the early afternoon between 1 p.m. and 2 p.m. local time and then it decreased until the morning rush hour of the next day.

Diurnal variation of visibility on the event day and a non-event day.

Fig. 2 shows the time dependent variation of the visibility with the measured PM2.5 during the event day. The environmental standard of PM2.5 in Korea is also displayed in the figure. The visibility was determined by the εsca measured by a Nephelometer.

Diurnal variation of visibility and PM2.5 concentration on the event day. The South Korea’s PM2.5 criteria is the daily mean value observed every hour.

During the event day, the daily average mass concentration of PM2.5 ranged from 42 to 83 μg/m3 with an average concentration of 63 μg/m3. The time serial PM2.5 was severely fluctuated throughout the whole event day. Unlike non-event day, the rush-hour peak of PM2.5 was not found. This result indicates that, added to the domestic factors including the local sources, the inflow of PM2.5 from abroad had a significant influence on the diurnal variation of PM2.5 on event day. This unusual hourly variation of PM2.5 at Gwangju on the event day has also been introduced in a previous study (Park et al., 2013). Meanwhile, a symmetrical temporal variation with the concentration of PM2.5 indicates that visibility has a strong inverse proportion PM2.5. The opposite diurnal patterns between PM2.5 and visibility has also been measured in an urban area of Northeast China by Zhao et al. (2017).

### 3. 2 εscaNeph. and Three Kinds of Particles

To evaluate the contribution of each particle type to the εsca measured by a Nephelometer (εscaNeph.), the scatter plots of εscaNeph. and the theoretically reconstructed mass concentration of major three particle types (i.e., (NH4)2SO4, NH4NO3, and organics) were drawn Fig. 3 (on the event day) and Fig. 4 (on a non-event day). Sulfate was assumed to be fully neutralized (i.e., (NH4)2SO4).

Scatter plots of εscaNeph. and the mass concentrations of major three particle types (i.e., (NH4)2SO4, NH4NO3, and organics) on the event day.

Scatter plots of εscaNeph. and the mass concentrations of major three particle types (i.e., (NH4)2SO4, NH4NO3, and organics) on a non-event day.

The mass concentration (m) of (NH4)2SO4, NH4NO3, and organics was theoretically calculated. The m of NH4NO3, and (NH4)2SO4 described as following equations was calculated from the IC results of ammonium, nitrate, and sulfate ions under assumption that the combined forms of NH3 with nitrate and sulfate were NH4NO3 and (NH4)2SO4, respectively.

 ${m}_{{NH}_{4}N{O}_{3}}=N{O}_{{3}_{IC}}^{-}\frac{N{H}_{4}N{O}_{{3}_{M.W.}}}{N{O}_{{3}_{M.W.}}}$
 ${m}_{{\left({NH}_{4}\right)}_{2}S{O}_{4}}={S{O}_{4}^{2-}}_{IC}\frac{{\left(N{H}_{4}\right)}_{2}S{O}_{{4}_{M.W.}}}{S{O}_{{4}_{M.W.}}}$

where NO3-IC and SO42-IC are the IC results (μg/m3) of nitrate and sulfate, respectively. M.W. is molecular weight.

The mass of organics, mOrganics, was determined by multiplying the amount of OC concentration (μg/m3) by 1.6 (Cabada et al., 2004; Countess et al., 1980).

As shown in Fig. 3, the high correlations (r values varied from 0.74 to 0.91) between the εscaNeph. and three particle types appeared on the event day. The high correlation coefficients, especially, with NH4NO3 and organics, indicate that the nitrate and organic compounds dominantly contributed to the light scattering in Gwangju during PM2.5 event day. The similar results have been obtained in the studies carried out in the urban sites of Taiwan (Kuo et al., 2013) and China (Yu et al., 2019). These studies suggested that the visibility degradation due to nitrate was much higher than that due to sulfate. However, these results are a little bit different from that of Detroit’s summertime atmosphere reported by Wolff et al. (1982). They suggested that sulfate was the most efficient light-scattering species per unit mass of dry weight.

According to the study conducted by Tao et al. (2007) in Guangzhou, China, the correlation coefficients of sulfate, nitrate, and OC between visibility were -0.66, -0.64, and -0.63, respectively. Jung et al. (2009) also reported that (NH4)2SO4, NH4NO3, and organics contributed 42.2%, 24.9%, and 9.0% of light extinction coefficient (bext) in Beijing during the maximum polluted period in 2006.

Although there were slight differences in contribution rates in various studies reported previously, there is no doubt that the major secondary particles contribute greatly to visibility deterioration.

Meanwhile, as shown in Fig. 4, the εscaNeph. has a strong correlation (r=0.94) with only NH4NO3 in the nonevent day. However, a related study carried out by Yu et al. (2019) in an urban-industrial site in China during the recent wintertime suggested that sulfate was the largest contributor for the clean period. The results in Fig. 4 also indicate that there was no apparent correlation between OM and the εscaNeph. (r=0.47) on a non-event day.

### 3. 3 Interrelationships among Particle Components, Meteorological factors, and Visibility

The interrelationships among components (visibility, sulfate, nitrate, OC, EC, temperature (Temp.), wind speed (W.S.), relative humidity (Rh), and PM2.5) on the event day were analyzed (Fig. 5). The visibility on the event day had negative correlations with PM2.5, PM10, nitrate, relative humidity, EC, OC, and sulfate. Especially, PM2.5 and PM10 showed fairly strong negative correlation coefficients of -0.76 and -0.73 with visibility. Meanwhile, visibility had a slightly high correlation coefficient with temperature (r=0.65) and wind speed (r=0.59), while it has a negative correlation with relative humidity (r=-0.58). This result is remarkably consistent with that of Xue et al. (2015). Xue et al. (2015) reported that the visibility of Shanghai reached about 25 km at a time when temperature and wind speed were high, while visibility decreased to 16 km under the weather type of low wind speed and temperature, and high relative humidity. High temperature can promote the dispersion of the air pollutants, as a result, cause higher visibility. High wind leads to unstable meteorological conditions which accelerate the dispersion of pollutants, and then contribute to improved visibility. High relative humidity can induce the increasing of PM concentration because aerosol hygroscopic increases significantly. Finally, the increasing of PM, especially PM2.5, can induce the aerosol scattering capability and the visibility decreasing.

The interrelationships among components (visibility, sulfate, nitrate, OC, EC, temperature (Temp.), wind speed (W.S.), relative humidity (Rh), and PM2.5) on the event day. The numbers in plots mean Pearson’s correlation coefficiency.

### 3. 4 Visibility Forecasting Model Based on the Routinely Collected Measurement Data

Ann et al. (2000) reported that visibility showed strong seasonal fluctuations in Seoul, Korea with the worst in spring. According to their study, visibility was less than 10 km in Seoul on most days of spring. Therefore, if it is possible to easily predict the visibility even in spring, it will be very helpful in our daily lives.

In this study, a simple visibility forecasting model was constructed by a statistical approach. The advantage of our model is that it does not require special observations using professional measuring devices to obtain the data needed to build the model. Our statistical model is based on the routinely monitored PM2.5 and meteorological data from the ambient air monitoring stations widely distributed throughout the country.

3. 4. 1 Multicollinearity Test among Explanatory Variables

Fig. 6 shows the statistical procedure for the multi-step regression model with step-wise induction of variables. PM2.5, temperature, wind speed, and relative humidity data were selected as the explanatory variables. These data can be taken in routinely from the Honam area air pollution intensive monitoring site in Gwangju Metropolitan City.

Flowchart for multi step regression model with step-wise induction of variables.

At first, in order to check the multicollinearity among explanatory variables, a multicollinearity index known as the variance inflation factor (VIF) was calculated by the following equation (Hossain et al., 2010):

where n is the number of predictor variables and R2i is the square of the multiple correlation coefficient of the i th variable with the remaining (n-1) variables.

If VIF ranges from 0 to 5, there is not the multicollinearity problem (Hossain et al., 2010). The calculated VIFs among the combination of four explanatory variables are listed in Table 1 and they varied from 1.00 to 1.03. It can therefore be said that there is no correlation among four selected explanatory variables.

The coefficients for four kinds variables by multi step regression model.

3. 4. 2 The Built Model Formula

The coefficients for four kinds explanatory variables determined by a multi-step regression model are also listed in Table 1. The constructed visibility forecasting model was as follow:

As expected, the constructed model suggests that PM2.5 is the most important contributor to worsening visibility. Among the three potential meteorological variables, temperature is the most influential factor in determining the visibility reduction. Our model also indicates that wind speed, along with temperature, contributes to the improvement of visibility. This may seem reasonable because the wind plays a major role in spreading pollutants. Meanwhile, relative humidity, as shown already in Fig. 5, has a role to play in reducing visibility.

The impact of meteorology on visibility was also revealed in the field study conducted in Shanghai, China by Xue et al. (2015). They reported that visibility was reduced to 16 km under the weather condition of low wind speed and temperature, and high relative humidity.

3. 4. 3 Test for Residual Normality

A residual plot check is a key part of next steps after model-building. Here, the residual means the values for the difference between the observed data and the predicted data. Examining residual normality suggests that the assumptions were reasonable in the process of building the model and that the model selection was appropriate. In general, the overall pattern of the residuals should be normally distributed.

As shown in Fig. 7, the bell-shaped frequency of regression standardized residuals with a normal density function on the histogram indicates that the model built in this study is very reasonable.

Approximately normal distribution of standardized residuals produced by a model for a calibration process.

3. 4. 4 Model Validation

To validate the built model in this study, the predicted visibilities by model were compared with those of actually observed at the Gwangju Regional Meteorological Administrations (GRMA). The GRMA (35.17N; 126.89E) is about 9 km away from the Honam area air pollution intensive monitoring site.

Fig. 8 shows the scatter plot of the observed and predicted visibilities with a dotted regression line. A very high correlation (r2=0.892) between the observed and predicted visibilities has been proved. It can therefore be said that the model designed in this study can predict visibility with accuracy, and predicted results can be useful in our daily lives.

Scatter plot of the observed and forecasted visibilities.

## 4. CONCLUSIONS

The results of this study verified that a high PM2.5 mass concentration is one of the important factors in the deterioration of visibility once again. The visibility during PM2.5 episode was also closely linked to particle kinds, specially the fine secondary particles. Through the correlation analysis between the measured εsca by a Nephelometer and the theoretically reconstructed mass concentration of three major types of particles, it can be concluded that NH4NO3 and organics dominantly contributed to light scattering during the PM2.5 event day in Gwangju. Although a large amount of measured data must be used to build a good regression prediction model, the model built in this study used the monitored data over a limited period of spring. Therefore, it is not reasonable to predict the visibility of the whole season. However, it is expected that our results will contribute to establishing measures for our daily life by predicting visibility during the springtime when episodically high PM2.5 concentrations are often observed.

## Acknowledgments

This study was supported in part the study on the source contribution of pollutants and the characteristics of regional air parcel movement in the Korean Peninsula (II).

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### Photo 1.

Comparison of visual characteristics from the ground station of this study on a non-event day (PM2.5: 21 μg/m3) (top) and the event day (bottom) when PM2.5 reached 63 μg/m3.

### Fig. 1.

Diurnal variation of visibility on the event day and a non-event day.

### Fig. 2.

Diurnal variation of visibility and PM2.5 concentration on the event day. The South Korea’s PM2.5 criteria is the daily mean value observed every hour.

### Fig. 3.

Scatter plots of εscaNeph. and the mass concentrations of major three particle types (i.e., (NH4)2SO4, NH4NO3, and organics) on the event day.

### Fig. 4.

Scatter plots of εscaNeph. and the mass concentrations of major three particle types (i.e., (NH4)2SO4, NH4NO3, and organics) on a non-event day.

### Fig. 5.

The interrelationships among components (visibility, sulfate, nitrate, OC, EC, temperature (Temp.), wind speed (W.S.), relative humidity (Rh), and PM2.5) on the event day. The numbers in plots mean Pearson’s correlation coefficiency.

### Fig. 6.

Flowchart for multi step regression model with step-wise induction of variables.

### Fig. 7.

Approximately normal distribution of standardized residuals produced by a model for a calibration process.

### Fig. 8.

Scatter plot of the observed and forecasted visibilities.

### Table 1.

The coefficients for four kinds variables by multi step regression model.

Model Unstandardized coefficients Standardized
coefficients
t Sig. VIF
B Std. Error Beta
Constant 29.562 0.92 8.57 0.00
PM2.5 -0.268 0.02 -0.88 -12.73 0.00 1.03
Temperature 0.211 0.10 -0.06 4.64 0.65 1.00
Wind speed 0.161 0.45 -0.10 2.52 0.28 1.01
Relatve
humidity
-0.072 0.02 -0.46 -4.34 0.00 1.00