Next Article in Journal
Use of Foamed Cement Banking for Reducing Expressways Embankment Settlement
Previous Article in Journal
Load Classification: A Case Study for Applying Neural Networks in Hyper-Constrained Embedded Devices
Previous Article in Special Issue
Combination of Different Approaches to Infer Local or Regional Contributions to PM2.5 Burdens in Graz, Austria
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Applied Sciences
Volume 11
Issue 24
10.3390/app112411958
applsci-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Statistical Modeling for PM10, PM2.5 and PM1 at Gangneung Affected by Local Meteorological Variables and PM10 and PM2.5 at Beijing for Non- and Dust Periods

by
Soo-Min Choi
1 and
Hyo Choi
2,*
1
Department of Computer Engineering, Konkuk University, Chungju 27478, Korea
2
Atmospheric and Oceanic Disaster Research Institute, Gangneung 25563, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2021, 11(24), 11958; https://0-doi-org.brum.beds.ac.uk/10.3390/app112411958
Submission received: 20 November 2021 / Revised: 9 December 2021 / Accepted: 12 December 2021 / Published: 15 December 2021
(This article belongs to the Special Issue Characteristics of Air Pollution Assessment, Modeling and Reduction)
Browse Figures
Versions Notes

Abstract

:
Multiple statistical prediction modeling of PM10, PM2.5 and PM1 at Gangneung city, Korea, was performed in association with local meteorological parameters (air temperature, wind speed and relative humidity) and PM10 and PM2.5 concentrations of an upwind site in Beijing, China, in the transport route of Chinese yellow dusts which originated from the Gobi Desert and passed through Beijing to the city from 18 March to 27 March 2015. Before and after the dust periods, the PM10, PM2.5 and PM1 concentrations showed as being very high at 09:00 LST (the morning rush hour) by the increasing emitted pollutants from vehicles and flying dust from the road and their maxima occurred at 20:00 to 22:00 LST (the evening departure time) from the additional pollutants from resident heating boilers. During the dust period, these peak trends were not found due to the persistent accumulation of dust in the city from the Gobi Desert through Beijing, China, as shown in real-time COMS-AI satellite images. Multiple correlation coefficients among PM10, PM2.5 and PM1 at Gangneung were in the range of 0.916 to 0.998. Multiple statistical models were devised to predict each PM concentration, and the significant levels through multi-regression analyses were p < 0.001, showing all the coefficients to be significant. The observed and calculated PM concentrations were compared, and new linear regression models were sequentially suggested to reproduce the original observed PM values with improved correlation coefficients, to some extent.

1. Introduction

Atmospheric pollution consists of great amounts of different compounds, such as particulate matter and gas, mainly from vehicles and flying dusts on the road, industries, the burning of trash, forest fires and yellow dusts of the desert and arid areas to the atmosphere [1,2]. Generally, air pollutants are extremely harmful to the human body by reaching the lungs through the bronchi, which can cause respiratory, cardiovascular, and cerebrovascular disease [3,4,5].
The previous research studies mentioned that the particulate matter concentration in the ambient air of an urban area is greatly affected by not only meteorological parameters (air temperature, wind speed and direction, solar radiation, relative humidity and atmospheric pressure) [6,7,8,9], but also gaseous ones (CO, CO2, O3, SO2, and NO2) and so on [10,11,12,13]. Air pollution reduces visibility in smog and haze weather and influences the health threat to outdoor physical activity [14,15]. From numerical and experimental studies, it is well-known that, as meteorological conditions can promote or prevent the dispersion of air pollutants, the pollution state becomes much worse in atmospheric visibility [16].
During spring, when the wind speed is over 10 m/s and the relative humidity is less than 40% in the air near the surface of the Gobi Desert in southern Mongolia, the Kubuchi Desert in Inner Mongolia, the Ordos Desert, the Huangtu Plateau (the Loess Plateau) and the Taklamakan Desert in northwest China, huge amounts of yellow sands and dusts are raised from the ground surface up to a 3 km or 5 km height, and they are vertically and horizontally transported to the wide downwind regions, such as eastern China, Korea, Japan and southeastern Asian countries. This phenomenon is referred to in a variety of ways: Huang Chen, KOSA, Asian Dust, Yellow Sand, Sand Storm and Dust Storm [17,18,19,20,21,22].
Due to the persistent accumulation of dust particles in the lower atmosphere of the source area, visibility during the dust period in China and Mongolia is the worst, and it becomes rapidly worse in the further downwind countries of northeast and southeast Asia, including China, Taiwan, Korea, Japan and even the west coast of the U.S.A. by the deposition of a huge amount of transported dusts [23,24,25,26]. Many environmental researchers have explained the chemical composition of fine and coarse particles in relation with Asian Dust events in detail [27,28,29].
Furthermore, Zhao [30] mentioned more severe air pollution in the case of a higher ratio of PM2.5/PM10 in China. Choi [31,32] and Lee and Chung [33] indicated that, using a simple regression method, the coarse particle concentration (PM10–PM2.5) in Gangneung city, Korea, was greater than the fine particle concentration (PM2.5) during the dust period, and the coarse particulates larger than 2.5 μm could significantly contribute to the PM10 concentration, but PM2.5 decreased sharply.
We know that atmospheric circulation and atmospheric boundary layer structure from various numerical modeling on dust transportation and accumulation can greatly affect the dispersion and accumulation of particulate matters in the inland and coastal cities, resulting in very high pollutant concentrations [34,35,36,37,38,39]. In recent years, multivariate statistical modeling was also carried out to predict temporal and spatial analyses of particulate matter affected by gases or meteorological parameters [8,40].
Most studies for predicting the particulate matter concentrations for non-dust and dust periods have used simple regression statistical methods in Korea, except for numerical modeling [33]. We propose multiple regression statistical models to improve the predictions of PM10, PM2.5 and PM1 concentrations for Gangneung city, Korea affected by local meteorological variables (air temperature, wind speed and relative humidity) and PM10 and PM2.5 concentrations in Beijing city, China before, during and after the dust periods.

2. Study Area and Data Analysis

2.1. Study Area

Except for the summer, with its heavy precipitation, during spring, autumn, and winter, it is known that a large amount of yellow sand dusts, raised from the Gobi Desert, arid Inner Mongolia and dry regions in northern China by a strong northwesterly wind or northerly wind, are transported to the downwind regions, such as Korea, and greatly affect the local air quality of Gangneung city, showing very high concentrations of particulate matters during such dust periods [33,35,37].
As a clean city, Gangneung consists of a basin with an elevation of about 25 m above mean sea level, with high mountains over 900 m height in its west and the East Sea of Korea in its east (Figure 1). It has complicated characteristics of marine and continental climates. As this city has no special factories to emit a large amounts of air pollutants, the main atmospheric pollution sources are vehicles on the road and heating boilers in the resident area during both winter and the early spring; thus, it always maintains very low concentrations of PM, more or less 40 µg/m3, except for during the dust period.

2.2. Data and Analysis

Particulate matter concentrations of PM10, PM2.5 and PM1 were measured by a German GRIMM-1107 Dust Meter (approved by the Ministry of Environment, Korean Government), which was installed at the Gangwon Meteorological Administration (128.9° longitude; 37.75° latitude; 20 m above sea level) located in the downtown area of Gangneung city. The dust meter, devised to measure 15 sizes of dust particles (>0.3 µm to 20.0 µm), can measure the concentration of particulate matter (µg/m3) at a 5-min interval, and the concentrations are again summed into PM10 (the total amount of a particle size of 10 µm or less), PM2.5 (with the total amount of 2.5 µm size or less), and PM1 (with the total amounts of 1 µm size or less). Then the concentrations of PM10, PM2.5, and PM1 are recalculated as the 1 h-averaged value and these concentrations were used as the basic data for this study.
Hourly based PM10 and PM2.5 data measured at Beijing city, China (especially, the South-3rd ring road observation point) were acquired through the internet with a website address (https://quotsoft.net/air/ or http://www.bjmemc.com.cn/ accessed on 20 March 2021). Since most of the yellow dusts, originated from the Gobi Desert and arid Inner Mongolia, pass through Beijing city to the downwind region by the northerly wind or northwesterly wind and reach Gangneung city in the eastern coast of Korea, the PM concentrations at Gangneung city are directly affected by the PM concentrations of Beijing city, still containing much of the dusts originated from the desert and arid area of northern China.
Normally, the transport time of the dusts of Beijing city to Gangneung city takes approximately two days under a relatively strong northwesterly wind of about 8 m/s to 10 m/s (i.e., over 10 m/s in the origin of the yellow dust storm), because the distance between Beijing city and Gangneung city is about 1400 km. Thus, in order to investigate the effect of PMs of Beijing city on the air quality at Gangneung city, two days early pollution data of Beijing are needed. Hourly based meteorological data measured at Gangneung, Korea were obtained from a website address (https://data.kma.go.kr/ 15 February 2021).
In this study, the correlation coefficients and predictive regression equations among PM10, PM2.5 and PM1 of Gangneung city associated with PM10 and PM2.5 of Beijing city were investigated in three divided periods of 18 March to 28 March, before the inflow of yellow dust (00:00 LST, 18 March to 00:00 LST, 21 March 2015), during the inflow (01:00 LST, 21 March to 00:00 LST, 23 March), and after the inflow (01:00 LST, 23 March to 00:00 LST, 27 March). Thus, statistical models were obtained through multiple regression analyses using IBM SPSS Statistics-25. For testing the goodness of the predicted values of PM10, PM2.5 and PM10 concentrations, the predicted values were compared to the observed ones.

3. Results and Discussion

3.1. Satellite Images of Yellow Dust Transport

COMS-AI (the Communication, Ocean and Meteorological Satellite, Korean Meteorological Administration (KMA)) satellite images were used to more accurately understand the real-time transport of yellow sand particles toward downwind regions. These images are very useful to verify the effect of dust particles raised from Gobi Desert passing through Beijing city, China on the local air quality of Gangneung city, Korea.
Figure 2 shows the Korean COMS-AI satellite images reflecting dust which were originated from the Gobi Desert and spread in the Gobi Desert, within its vicinity, to Inner mongo of northern China and Beijing city, and to the northern Korean peninsula at 15:00 LST (Korean Local Standard Time = UTC + 9 h) 10 March 2015 (before dust period at Gangneung city). However, the yellow dusts did not reach Gangneung city. On the other hand, after the yellow dusts reached Gangneung city, the effect of yellow dust appeared in the city, showing rapid increases in PM10, PM2.5 and PM1 concentrations, especially in the case of PM10 at over 120 µg/m3 at 21:00 LST, 21 March, and 134.67 µg/m3 at 05:00 22 March.
COMS satellite images in Figure 2 show the real-time transport of dusts raised from Gobi Desert, passing by Beijing city, China (B in Table 1) and finally reaching Gangneung city, Korea (G in Table 1) at 09:00 LST, 21 March (the starting day of the dust period). Thus, the air quality at Gangneung city can be greatly influenced by the transport of large dust particles of the Gobi Desert combined with air pollutants of Beijing city. This kind of transport pattern of the dusts continued near the end of the dust period until 00:00 LST, 23 March. Thus, except for the dust period, the air quality of this city with no factories depends upon its own pollutants from vehicles on the road and residential heating boilers.

3.2. Hourly PM10, PM2.5 and PM1 Concentrations before, during and after the Dust Periods

In Figure 3, before the inflow of dusts from the Gobi Desert to Gangneung city (00:00 LST, March 18 to 00:00LST, March 21), the minimum concentration of PM10 (PM2.5; PM1) was 28.83 µg/m3 (22.02 µg/m3; 15.92 µg/m3) and its maximum was 82.17 µg/m3 (71.13 µg/m3; 57.95 µg/m3) for each hour. In contrast, during the dust period, the minimum concentration of PM10 (PM2.5; PM1) was 34.90 µg/m3 (17.27 µg/m3; 9.75 µg/m3) and its maximum was 134.67 µg/m3 (81.03 µg/m3; 64.42 µg/m3), respectively. After the dust period, the minimum concentration of PM10 (PM2.5; PM1) was 19.90 µg/m3 (1.93 µg/m3; 7.12 µg/m3) and its maximum was 61.58 µg/m3 (48.25 µg/m3; 41.53 µg/m3), slightly less than that before the dust period.
Since most of the yellow dusts raised from the Gobi Desert and arid Inner Mongolia pass through Beijing city, China, by a northerly wind or northwesterly wind and reach Gangneung city in the eastern coast of Korea, the PM concentrations in Gangneung are directly affected by the PM concentrations of Beijing, containing much of the dusts originated from the Gobi Desert and arid area of northern China. Normally, the transport of the dusts from Beijing city to Gangneung city takes approximately two days by a prevailing northwesterly wind of about 8 m/s to 10 m/s simulated by Weather Research and Forecasting Model (WRF-3.6.1), because the distance between Beijing city and Gangneung city is about 1400 km. Thus, as the two-day earlier PM data of Beijing city correspond to the PM data of Gangneung city (Figure 4), the PM data of Gangneung city from 01:00 LST, 21 March to 00:00 LST, 23 March (during the dust period) can be affected by the PM data of Beijing city from 00:00 LST, 19 March to 01:00 LST, 21 March.
In Gangneung city, vehicles on roads, boilers for heating and cooking in residential areas are the main sources of pollutant emissions, due to there being no factories emitting a large amount of air pollutants. The PM10 concentration before the yellow dust period appeared as 28.8333 to 82.1667 µg/m3, which was 10 µg/m3 lower or 20 µg/m3 higher than ones of the previous case studies. When PM10 increased, the concentrations of PM2.5 and PM1 increased at the same time, and vice versa.
A very high PM concentration appeared once each in the morning and afternoon, and the concentration was particularly high at 08:00 LST to 09:00 LST (the time to go to work). The maximum concentration was shown at 20:00 LST (the time to leave the office). The high PM concentrations from 08:00 to 09:00 LST were attributed to the emission of a large amount of air pollutants, such as particulate matters and gaseous substances through the combustion of vehicle fuel and a large amount of dust scattered by the movement of the vehicles on the road.
Similarly, at 20:00 LST or 22:00 LST (two or three hours after the time to leave work), the PM concentration was also very high, with a maximum concentration. It was due to a large amount of both particulate matters and gaseous emitted from vehicles and scattered dust flied from the road, and added air pollutants emitted from boilers for heating and cooking in the resident area under the relative cool weather in the early evening in the city in March.
During the daytime after 09:00 LST, PM concentration was generally low. Choi and Speer [16] reported that as the ground surface is heated by the solar radiation, the heated air rises vertically, accompanying the air pollutants emitted from vehicles and fling dusts on the road to the top of the atmospheric boundary layer d to about 1 km height, resulting in the low concentrations of particulate and gaseous. Differently, as the nocturnal surface inversion layer (NSIL) is formed with a very compressed thin layer of about 200 m in height, due to the ground surface cooling (called a strong stable layer). This temperature structure can induce the falling of air toward the ground surface and accumulation of pollutants near the surface, an increase in the pollutant concentration, occurs with a maximum concentration of PM at 22:00 LST, similar to previous cases with a deviated occurrence time [35,37]. After that, the PM concentration is lowered, because the amount of air pollutants emitted from the vehicles is decreased, due to the decreased number of vehicles at night after 22:00 LST.
Figure 5 shows the hourly distributions of the air temperature (°C), wind speed (m/s) and relative humidity (%), corresponding to PMs concentrations from 18 to 27 March, 2015 in Gangneung, Korea. It is well known that when yellow dusts are raised up from the dry ground surface of the Gobi Desert, the relative humidity in air near the ground surface is less than 40% and the wind speed is over 10 m/s. A northwesterly wind of more or less 10 m/s transports the yellow dusts toward the downwind regions, such as Gangneung city, Korea.
During the dust period in Gangneung city on 21 to 22 March, 2015, the air temperature was high, and visibility was low, but the wind speed was relatively high at about 4 m/s to 6m/s. Namely, as air temperature and wind speed increase, PM10, PM2.5 and PM1 concentrations also increase, concurrently, while relative humidity decreases (Figure 3 and Figure 5). Even though the northwesterly wind over 10 m/s in the source origin of yellow dusts was reduced to 4 m/s to 6 m/s in Gangneung city, still the strong wind could transport the yellow dusts into this city and simultaneously easily raise up dust particles on the road to the low atmosphere of the city.
For instance, on 19 March, as the relative humidity at Gangneung city was over 90%, PM10, PM2.5 and PM1 concentrations were very low, showing their minima. On the other hand, when the relative humidity was less than 30% on 21 and 22 March, very high concentrations of PM10, PM2.5 and PM1 were detected with their maxima. Thus, it means that those meteorological variables have strong correlations with the particulate matter concentrations.

3.3. Correlation Matrix and Predictive Regression Equations among PM10, PM2.5 and PM1 (Gangneung) Associated with PM10 and PM2.5 (Beijing)

In linear regression, there is only one independent and dependent variable involved, but, in the case of multiple regression, there is a set of independent variables Xj to explain better or predict the dependent variable, Yi. The applied models for the prediction of values have the forms of a multiple predictive regression equation of each PM10(G), PM2.5(G) and PM1(G) concentration of Gangneung city influenced by the local meteorological parameters and the PM10(B) and PM2.5(B) concentrations of Beijing city, shown in Table 2.
Y1 = a11X1 + a12X 2 + a13X3 + a14X4 + a15X5 + a16X6 + a17X7 + a18X8 + b1
Y2 = a21X1 + a22X 2 + a23X3 + a24X4 + a25X5 + a26X6 + a27X7 + a28X8 + b2
Y3 = a31X1 + a23X 2 + a33X3 + a43X4 + a53X5 + a63X6 + a73X7 + a83X8 + b3
or
Yi = aijXij + bi
where aij (i = 1 to 3, j = 1 to 8) means the coefficient of matrix (here, a11, a22 and a33 are zero). The observed values of Xj (j = 1 to 8) are PM10(G), PM2.5(G), PM1(G), T(G, air temperature), W(G, wind speed), RH(G, relative humidity), PM10(B) and PM2.5(B) as independent variables. The predictive values of Yi (i = 1 to 3) are PM10(G), PM2.5(G) and PM10(G)) as dependent variables. bi (i = 1 to 3) are intercepts in the equations of Yi as the error term, respectively. The final multiple regression equations are given in Table 1.
Table 1 indicates that multiple correlation coefficients are present among PMs, and meteorological parameters (air temperature, T; wind speed, W; and relative humidity, RH) of Gangneung city (Korea) are associated with the PMs of Beijing city. Additionally, predictive regression equations of each PM concentration at Gangneung city before, during after the dust periods and all periods are shown. Multivariate statistical modeling for evaluating the predicted values of the PMs of Gangneung was performed in four classifications of period via multiple regression analyses, using IBM SPSS Statistics-25.
Multiple correlation coefficients among PM10(G) PM2.5(G) and PM1(G) were in the range of 0.916 to 0.998. In particular, the correlation coefficients of PM10(G), PM2.5(G) and PM1(G) before, during and after the dust periods were 0.983 to 0.998, 0.916 to 0.998 and 0.941 to 0.998, respectively. However, for all periods, those coefficients were 0.954 to 0.998, showing perfect correlations. Except for the slightly lower coefficients (0.916, 0.941) of PM10 during and after the dust periods, the correlation coefficient of any PM concentration was in the range of 0.954 to 0.998.
Correlation analysis showed that PM10(G) PM2.5(G), PM1(G), T(G), W(G), RH(G), PM10(B), PM2.5(B) were significantly correlated at p < 0.001, though the correlation coefficients were different. Regardless of non-dust and dust periods, in particular, the correlation coefficients of both PM2.5 and PM1 were in the range of 0.997 to 0.998, showing perfect correlations.

3.4. Partial Correlation Matrix among PMs of Gangneung (G) and PMs of Beijing (B)

Table 2 presents the partial correlation coefficients (Pearson r) of PMs associated with the meteorological parameters of Gangneung (Korea) and PMs of Beijing (China), before, during, and after the dust periods and all periods. The partial correlation coefficients among PM10, PM2.5 and PM1 concentrations in Table 2 are much lower than the multiple correlation coefficients among them in Table 1, before, during and after the dust periods, except for the correlation coefficients of PM10(G) and PM2.5(G).
This implies that the calculated values of each PM concentration using multiple regression statistical models are much closer to the observed values for non-dust and dust periods, in Table 1. The highest coefficient (the lowest) between PM10 and PM2.5 at Gangneung city was 0.913 before the dust period (0.725 during the dust period), and the highest coefficient (the lowest) between PM2.5 and PM1 was 0.997 after the dust period (0.989 before the dust period).
Before the dust period, PM10 of Gangneung city is positively influenced by its relative humidity, but it is negatively by the local air temperature, wind speed, PM10 and PM2.5 of Beijing. PM2.5 of Gangneung city is positively influenced by its relative humidity and the PM10 of Beijing city, but it is negatively by others. PM1 is negatively influenced by the local air temperature and wind speed, except for others.
During the dust period, PM10 of Gangneung is positively influenced by the local air temperature, wind speed and PM10 of Beijing, except for others. The PM2.5 concentration is negatively influenced by the local wind speed and relative humidity, except for others. PM1 of Gangneung city is positively influenced by all variables. After the dust period, PM10 of Gangneung city is negatively influenced by the local relative humidity, except for others. PM2.5 and PM1 at Gangneung city are negatively influenced by the local wind speed and relative humidity, except for others. For all periods, PM10 of Gangneung city is negatively influenced by the local relative humidity, except for others, but PM2.5 and PM1 are negatively influenced by the local wind speed, except for others.
In particular, the air quality of Gangneung city during the dust period becomes seriously worse due to the huge amounts of transported dusts (mainly composed of coarse particles larger than 2.5 µm in diameter) from the Gobi Desert passing through Beijing city, as shown in Figure 2. Correlation coefficients of PM10 between PM2.5 and PM1 at Gangneung city are high with 0.913 and 0.854 (before the dust period), 0.725 and 0.689 (during the dust period), 0.732 and 0.709 (after the dust period), and 0.767 and 0.685 (all periods).
PM10, PM2.5 and PM1 at Gangneung during the dust period are highly correlated with PM10 at Beijing with 0.407, 0.567 and 0.574, in contrast to other periods. It means that when PM10 of Beijing increased, those concentrations of Gangneung also increased. As a result, the effect of PM10 of Beijing city exists, when the PMs of Gangneung increases. However, both PM2.5 and PM1 concentrations of Gangneung were still positively influenced by PM2.5 of Beijing, except for PM10, having a negative sign. From the above statement, the long-distance transport of dust particles from Beijing, China to Gangneung, Korea is very significant to the variation in the air quality of Gangneung.

3.5. Validation of Models

Figure 6 indicates the comparison between the observed values and the predicted values calculated by multiple predictive regression models in Table 1. In order to test the goodness of the estimations of each PM concentration, the scattered diagrams show model fitting with calculated and observed PM10, PM2.5 and PM1 concentrations. Their original observed values were reproduced by the comparison of observed values with calculated values, before, during, after the dust periods and for all periods. The reproduced observed values can be calculated by the linear regression equations in the scattered diagrams of Figure 6.
Figure 7 shows that each observed and calculated PM value with the date denotes the behavior throughout each period. The correlation coefficient of each PM concentration in the scattered diagram is slightly improved over the correlation coefficient in Table 1, in the case of the PM10 concentration after the dust period and all periods.

4. Conclusions

The multivariate statistical modeling among hourly PM10, PM2.5 and PM1 concentrations in Gangneung city, Korea, influenced by the local meteorological parameters and PM10 and PM2.5 concentrations in Beijing city, China, was performed for non-dust and dust periods in March, and it gave the following results.
  • Before and after the dust period, PM10, PM2.5 and PM1 concentrations showed as being very high at 09:00 LST of the morning rush hour by the increasing of the emitted pollutants from vehicles on the road. Their maxima values were detected at 20:00 to 22:00 LST of the evening departure time, with additional pollutants from resident heating boilers. However, during the dust period, these peak trends were not found under the persistent accumulation of dusts and particulate matters in Gangneung city from the Gobi Desert and Beijing city.
  • Correlation coefficients among PM10, PM2.5 and PM1 in Gangneung, using multiple regression statistical models, were in the range of 0.916 to 0.998, and the significant level of the regression was p < 0.001, showing all the coefficients to be significant.
  • Before the dust period, PM10 of Gangneung city is positively influenced by its relative humidity, but it is negatively by the local air temperature, wind speed, PM10 and PM2.5 of Beijing. PM2.5 of Gangneung city is positively influenced by its relative humidity and PM10 of Beijing, but it is negatively influenced by others. PM1 is negatively influenced by its air temperature and wind speed, except for others.
  • During the dust period, PM10 of Gangneung city is positively influenced by the local air temperature, wind speed and PM10 of Beijing, except for others. PM2.5 concentration is negatively influenced by the local wind speed and relative humidity, except for others. PM1 of Gangneung is positively influenced by all variables.
  • After the dust period, PM10 of Gangneung city is negatively influence by local relative humidity, except for others. PM2.5 and PM1 in Gangneung city are negatively influenced by the local wind speed and relative humidity, except for others. For all periods, PM10 of Gangneung city is negatively influence by the local relative humidity, except for others, but PM2.5 and PM1 are negatively influenced by the local wind speed, except for others.
  • Multivariate statistical models were devised to predict each PM concentration. The observed and calculated PM concentrations were compared each other, and new linear regression prediction models to reproduce the original observed PM values were also suggested.

Author Contributions

Conceptualization, S.-M.C. and H.C.; methodology, S.-M.C. and H.C.; software, S.-M.C. and H.C.; validation, S.-M.C. and H.C.; formal analysis S.-M.C. and H.C.; investigation, S.-M.C. and H.C.; resources, S.-M.C. and H.C.; data curation, S.-M.C. and H.C.; writing—original draft preparation, S.-M.C. and H.C.; writing—review and editing, H.C.; supervision, H.C.; project administration, H.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

Hourly based PM10 and PM2.5 data measured at Beijing (especially, the South-3rd ring road observation point), China were acquired through internet with a website address (https://quotsoft.net/air/ or http://www.bjmemc.com.cn/ accessed on 20 March 2021). Hourly based meteorological data measured at Gangneung, Korea and satellite images were obtained from website addresses of the KMA (https://data.kma.go.kr/; http://www.kma.go.kr accessed on 15 February 2021).

Acknowledgments

The authors express much thanks to the Beijing Municipal Ecological and Environmental Monitoring Center for obtaining air quality data in the Beijing district and to the Korea Meteorological Administration for the meteorological data and satellite images in support of this work.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Westerholm, R.; Almen, J.; Li, H.; Rannug, U.; Rosen, A. Exhaust emission from gasoline-fulled light duty vehicle operated in different driving conditions; a chemical and biological characterization. Atmos. Environ. B 1992, 26, 79–90. [Google Scholar] [CrossRef]
  2. Li, C.; Dai, Z.; Yang, L.; Ma, Z. Spatiotemporal characteristics of air quality across Weifang from 2014–2018. Int. J. Environ. Res. Public Health 2019, 16, 3122. [Google Scholar] [CrossRef] [Green Version]
  3. Chow, J.C. Health effects of fine particulate air pollution; Lines that connect. J. Air Waste Manag. Assoc. 2006, 56, 707–708. [Google Scholar] [CrossRef] [PubMed]
  4. Huang, D.; Xu, J.; Zhang, S. Valuing the health risks of particulate air pollution in the Pearl River Delta, China. Environ. Sci. Policy. 2012, 15, 38–47. [Google Scholar] [CrossRef]
  5. Shen, F.; Ge, X.; Hu, J.; Nie, D.; Tian, L.; Chen, M. Air pollution characteristics and health risks in Henan Province, China. Environ. Res. Sci. 2017, 156, 625–634. [Google Scholar] [CrossRef] [PubMed]
  6. Bhaskar, B.V.; Mehta, V.M. Atmospheric particulate pollutants and their relationship with meteorology in Ahmedabad. Aerosol Air. Qual. Res. 2010, 10, 301–315. [Google Scholar] [CrossRef] [Green Version]
  7. Cheng, Y.; He, K.B.; Du, Z.Y.; Zheng, M.; Duan, F.K.; Ma, Y.L. Humidity plays an important role in the PM2.5 pollution in Beijing. Environ. Pollut. 2015, 197, 68–75. [Google Scholar] [CrossRef]
  8. Li, X.; Ma, Y.; Wang, Y.; Liu, N.; Hong, Y. Temporal and spatial analyses of particulate matter (PM10 and PM2.5) and its relationship with meteorological parameters over an urban city in northeast China, 2017. Atmos. Res. 2017, 198, 185–193. [Google Scholar] [CrossRef]
  9. Shi, C.; Yuan, R.; Wu, B.; Meng, Y.; Zhang, H.; Zhang, H.; Gong, Z. Meteorological conditions to PM2.5 pollution in winter 2016/2017 in the western Yangtze River Delta, China. Sci. Total Environ. 2018, 642, 1221–1232. [Google Scholar] [CrossRef] [PubMed]
  10. Park, D.; Ha, K. Characteristics of PM10, PM2.5, CO2 and CO monitored in interiors and platforms of subway train in Seoul, Korea. Environ. Int. 2008, 34, 629–634. [Google Scholar] [CrossRef] [PubMed]
  11. Zhao, S.; Yu, Y.; Yin, D.; He, J.; Liu, N.; Qu, J.; Xiao, J. Annual and diurnal variations of gaseous and particulate pollutants in 31 provincial capital cities based on in sit air quality monitoring data from China National Environmental Monitoring Center. Environ. Int. 2016, 86, 92–106. [Google Scholar] [CrossRef] [PubMed]
  12. Ma, X.; Jia, H. Particulate matter and gaseous pollutions in three megacities over China: Situation and implication. Atmos. Environ. 2016, 140, 476–494. [Google Scholar] [CrossRef]
  13. Xiao, K.; Wang, Y.K.; Wu, G.; Fu, B.; Zhu, Y. Spatiotemporal characteristics of air pollutions (PM10, PM2.5, SO2, NO2, O3, and CO) in the inland basin city of Chengdu, Southwest China. Atmosphere 2018, 9, 74. [Google Scholar] [CrossRef] [Green Version]
  14. Li, F.; Liu, Y.; Lu, J.J.; Liang, L.; Harmer, P. Ambient air pollution in China poses a multi-faceted health threat to outdoor physical activity. J. Epidemiol. Community Health 2015, 69, 201–204. [Google Scholar] [CrossRef] [Green Version]
  15. He, J.Q.; Yu, X.N.; Zhu, B.; Yuan, L.; Ma, J.; Shen, L.; Zhu, J. Characteristics of Aerosol Extinction and Low Visibility in Haze Weather in Winter of Nanjing, China. Environ. Sci. 2016, 36, 1645–1653. Available online: http://www.zghjkx.com.cn/EN/abstract/abstract14655.shtml (accessed on 20 March 2021).
  16. Choi, H.; Speer, M.S. Effects of atmospheric circulation and boundary layer structure on the dispersion of suspended particulates in the Seoul metropolitan area. Meteorol. Atmos. Phys. 2006, 92, 239–254. [Google Scholar] [CrossRef]
  17. Zhang, X.; Arimoto, R.; An, Z.; Chen, T.; Zhang, G.; Zhu, G.; Wang, X. Atmospheric trace elements over source regions for Chinese dust: Concentrations, sources and atmospheric deposition on the Loess Plateau. Atmos. Environ.-A 1993, 27, 2051–2067. [Google Scholar] [CrossRef]
  18. Chung, Y.; Yoon, M. On the Occurrence of Yellow Sand and Atmospheric Loadings. Atmos. Environ. 1996, 30, 2387–2397. Available online: http://www.koreascience.or.kr/article/JAKO199411921388674.page (accessed on 20 June 2021). [CrossRef]
  19. Chung, Y.S.; Kim, H.S.; Natsagdorj, L.; Jugder, D.; Chen, S.J. On Yellow Sand Occurred During 1997–2000. J. Korean Meteorol. Sci. 2001, 37, 305–316. Available online: https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE00952163 (accessed on 20 June 2021).
  20. Wang, X.; Ma, Y.; Chen, H.; Wen, G.; Chen, S.; Tao, Z.; Chung, Y. The relation between sandstorms and strong winds in Xinjiang, China. Water Air Soil Pollut. 2003, 3, 67–79. [Google Scholar] [CrossRef]
  21. Zhang, Y.; Zhong, Y. The simulation and diagnosis for a strong wind associated northeast low. Acta Meteorol. Sin. 1985, 43, 97–105. [Google Scholar]
  22. Choi, H.; Zhang, Y.H. Predicting duststorm evolution with the vorticity theory. Atmos. Res. 2008, 89, 338–350. [Google Scholar] [CrossRef]
  23. Tegen, I.; Fung, I. Modeling of mineral dust in the atmosphere: Sources, transport, and optical thickness. J. Geophys. Res.-D 1994, 99, 22897–22914. [Google Scholar] [CrossRef]
  24. Kotamarthi, V.R.; Carmichael, G.R. The long range transport of pollutants in the Pacific Rim region. Atmos. Environ.-A 1990, 24, 1521–1534. [Google Scholar] [CrossRef]
  25. McKendry, I.G.; Hacker, J.P.; Stull, R.; Sakiyama, S.; Mignacca, D.; Reid, K. Long-range transport of Asian dust to the Lower Fraser Valley, British Columbia, Canada. J. Geophys. Res. Atmos.-D 2001, 106, 18361–18370. [Google Scholar] [CrossRef]
  26. Lin, T. Long-range transport of yellow sand to Taiwan in Spring 2000: Observed evidence and simulation. Atmos. Environ. 2001, 35, 5873–5882. [Google Scholar] [CrossRef]
  27. Carmichael, G.R.; Hong, M.; Ueda, H.; Chen, L.; Murano, K.; Park, J.K.; Lee, H.; Kim, Y.; Kang, C.; Shim, S. Aerosol composition at Cheju Island, Korea. J. Geophys. Res.-D 1997, 102, 6047–6061. [Google Scholar] [CrossRef]
  28. Kim, Y.J.; Kim, K.W.; Kim, S.D.; Lee, B.K.; Han, J.S. Fine particulate matter characteristics and its impact on visibility impairment at two sites in Korea: Seoul and Incheon. Atmos. Environ. 2000, 40, 593–609. [Google Scholar] [CrossRef]
  29. Choi, H. Characteristics of Hourly Variation of Gaseous Pollutant Concentration at Gangneung, Korea for Yellow Sand Event Period in Winter Case Study of 14–16 February 2005. J. Clim. Res. 2011, 6, 59–76. Available online: https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART001533093 (accessed on 20 June 2021).
  30. Zhao, D.; Chen, H.; Yu, E.; Luo, T. PM2.5/PM10 ratios in eight economic regions and their relationship with meteorology in China. Adv. Meteorol. 2019, 15, 5296725. [Google Scholar] [CrossRef] [Green Version]
  31. Choi, H. Comparison of PM1, PM2.5 and PM10 concentrations in a mountainous coastal city, Gangneung before and after the Yellow Dust event in spring. J. Environ. Sci. 2008, 17, 633–645. [Google Scholar] [CrossRef]
  32. Choi, H. Impact of Fine Particulate Matters Transported from Gobi Desert to Particulate Concentrations (PM10, PM2.5, PM1) for Yellow Sand Event Period in Fall-Case study of 27 October 2003. J. Climate Res. 2010, 5, 219–233. Available online: https://www.kci.go.kr/kciportal/ci/sereArticleSearch/ciSereArtiView.kci?sereArticleSearchBean.artiId=ART001493480 (accessed on 20 June 2021).
  33. Lee, M.S.; Chung, J.D. Impact of yellow dust transport from Gobi Desert on fractional ratio and correlations of temporal PM10, PM2.5 and PM1 at Gangneung city in fall. J. Environ. Sci. 2012, 21, 217–231. [Google Scholar] [CrossRef] [Green Version]
  34. Choi, H.; Zhang, Y.H.; Takahashi, S. Recycling of suspended particulates by the interaction of sea-land breeze circulation and complex coastal terrain. Meteorol. Atmos. Phys. 2004, 87, 109–120. [Google Scholar] [CrossRef]
  35. Choi, H.; Choi, D.S. Concentrations of PM10, PM2.5, and PM1 influenced by atmospheric circulation and atmospheric boundary layer in the Korean mountainous coast during a duststorm. Atmos. Res. 2008, 89, 330–337. [Google Scholar] [CrossRef]
  36. Choi, H.; Zhang, Y.H.; Kim, K.H. Sudden high concentration of TSP affected by atmospheric boundary layer in Seoul metropolitan area during duststorm period. Environ. Int. 2008, 34, 635–647. [Google Scholar] [CrossRef] [PubMed]
  37. Choi, H.; Choi, D.S.; Choi, S.M. Meteorological condition and atmospheric boundary layer influenced upon temporal concentrations of PM1, PM2.5 at a Coastal City, Korea for Yellow Sand Event from Gobi Desert. Dis. Adv. 2010, 3, 309–315. [Google Scholar]
  38. Uzan, L.; Alpert, P. The coastal boundary layer and air pollution—A high temporal resolution analysis in the East Mediterranean coast. Open Atmos. Sci. J. 2012, 6, 9–18. [Google Scholar] [CrossRef] [Green Version]
  39. Choi, H. Trapping effect of a calm zone by lee side-internal gravity waves and cyclonic winds on sudden high concentrations of particulate matters combined with the Yellow Dusts from Gobi Desert in the Korean eastern coast. Dis. Adv. 2013, 6, 101–111. [Google Scholar]
  40. He, J.H.; Ding, S.; Liu, D.F. Exploring the spatiotemporal pattern of PM2.5 distribution and its determinants in Chinese cities based on a multilevel analysis approach. Sci. Total Environ. 2019, 659, 1513–1525. [Google Scholar] [CrossRef]
Applsci 11 11958 g001 550
Figure 1. Topographical feature in the north-eastern Asia including the Gobi Desert (large red ellipse), Beijing city (China; red square) and Gangneung city (Korea; small red circle).
Figure 1. Topographical feature in the north-eastern Asia including the Gobi Desert (large red ellipse), Beijing city (China; red square) and Gangneung city (Korea; small red circle).
Applsci 11 11958 g001
Applsci 11 11958 g002 550
Figure 2. Korean COMS satellite images reflecting dusts at (a) 15:00 LST 20 March 2015 (before dust period at Gangneung city) and (b) 21:00 LST, 21 March (for dust period). Red small circle, small and big squares denote Gangneung (Korea), Beijing (China) and the Gobi Desert (Mongolia). (The Korea Meteorological Administration (KMA) supplied the images.) (http://www.kma.go.kr, accessed on 15 February 2021).
Figure 2. Korean COMS satellite images reflecting dusts at (a) 15:00 LST 20 March 2015 (before dust period at Gangneung city) and (b) 21:00 LST, 21 March (for dust period). Red small circle, small and big squares denote Gangneung (Korea), Beijing (China) and the Gobi Desert (Mongolia). (The Korea Meteorological Administration (KMA) supplied the images.) (http://www.kma.go.kr, accessed on 15 February 2021).
Applsci 11 11958 g002
Applsci 11 11958 g003 550
Figure 3. Hourly distribution of PM10, PM2.5 and PM1 concentrations, before, during and after the dust period from 18 to 27 March, 2015 at Gangneung city, Korea.
Figure 3. Hourly distribution of PM10, PM2.5 and PM1 concentrations, before, during and after the dust period from 18 to 27 March, 2015 at Gangneung city, Korea.
Applsci 11 11958 g003
Applsci 11 11958 g004 550
Figure 4. Hourly distribution of PM10, PM2.5 and PM1 concentrations from 16 March to 25 March, 2015 in Beijing city, China. As the transport of the dust of Beijing city to Gangneung city takes two days by 8 m/s northwesterly wind simulated by WRF-3.6.1 numerical model, and Choi and Zhang [22], two-day-earlier PM data (01:00 LST, 19 March to 00:00 LST, 21) correspond to PM data of Gangneung city for the dust period (01:00 LST, 21 March to 00:00 LST, 23) in Figure 3.
Figure 4. Hourly distribution of PM10, PM2.5 and PM1 concentrations from 16 March to 25 March, 2015 in Beijing city, China. As the transport of the dust of Beijing city to Gangneung city takes two days by 8 m/s northwesterly wind simulated by WRF-3.6.1 numerical model, and Choi and Zhang [22], two-day-earlier PM data (01:00 LST, 19 March to 00:00 LST, 21) correspond to PM data of Gangneung city for the dust period (01:00 LST, 21 March to 00:00 LST, 23) in Figure 3.
Applsci 11 11958 g004
Applsci 11 11958 g005 550
Figure 5. Hourly distributions of (a) air temperature (°C), (b) wind speed (m/s) and (c) relative humidity (%) corresponding to PMs concentrations from 18 March to 27, 2015 in Gangneung city, Korea.
Figure 5. Hourly distributions of (a) air temperature (°C), (b) wind speed (m/s) and (c) relative humidity (%) corresponding to PMs concentrations from 18 March to 27, 2015 in Gangneung city, Korea.
Applsci 11 11958 g005
Applsci 11 11958 g006a 550Applsci 11 11958 g006b 550
Figure 6. Comparison of observed with calculated PM10, PM2.5 and PM1 values in (a–c) figures before, during and after the dust periods and all periods from 18 March to 21, 2015 at Gangneung city, Korea.
Figure 6. Comparison of observed with calculated PM10, PM2.5 and PM1 values in (a–c) figures before, during and after the dust periods and all periods from 18 March to 21, 2015 at Gangneung city, Korea.
Applsci 11 11958 g006aApplsci 11 11958 g006b
Applsci 11 11958 g007a 550Applsci 11 11958 g007b 550
Figure 7. The comparison of observed vs. calculated PM10, PM2.5 and PM1 values with date in (a–c) figures through model fitting, before, during, after the dust periods and all periods from 18 to 27 March, 2015 in Gangneung city, Korea.
Figure 7. The comparison of observed vs. calculated PM10, PM2.5 and PM1 values with date in (a–c) figures through model fitting, before, during, after the dust periods and all periods from 18 to 27 March, 2015 in Gangneung city, Korea.
Applsci 11 11958 g007aApplsci 11 11958 g007b
Table
Table 1. Multiple correlation coefficients and predictive regression equations among PMs of Gangneung city (G) with PMs of Beijing city (B) under the influence of meteorological parameters. T, W and RH denote air temperature (°C), wind speed (m/s) and relative humidity (%).
Table 1. Multiple correlation coefficients and predictive regression equations among PMs of Gangneung city (G) with PMs of Beijing city (B) under the influence of meteorological parameters. T, W and RH denote air temperature (°C), wind speed (m/s) and relative humidity (%).
PeriodMulti-Correlation
Coefficient		Predictive Regression Equation
18/03/2015 to 21/03/2015
(Before dust period)		0.983PM10(G) =
 
 		3.353 × PM2.5(G) − 2.708 × PM1(G)
+ 0.601 × T(G) − 1.240 × W(G) + 0.009 × RH(G)
− 0.006 × PM10(B) + 0.002 × PM2.5(B) − 2.206
0.998PM2.5(G) =
 
 		0.211 × PM10(G) + 0.917 × PM1(G)
− 0.245 × T(G) + 0.269 × W(G) − 0.035 × RH(G)
+ 0.006 × PM10(B) − 0.007 × PM2.5(B) + 4.638
0.997PM1(G) =
 
 		− 0.193 × PM10(G) + 1.038 × PM2.5(G)
+ 0.258 × T(G) − 0.240 × W(G) + 0.049 × RH(G)
− 0.005 × PM10(B) + 0.006 × PM2.5(B) − 5.710
21/03/2015 to 23/03/2015
(During dust period)		0.916PM10(G) =
 
 		3.560 × PM2.5(G) − 3.114 × PM1(G)
− 0.914 × T(G) + 4.444 × W(G) − 0.360 × RH(G)
+ 0.082 × PM10(B) − 0.252 × PM2.5(B) + 42.893
0.998PM2.5(G) =
 
 		0.047 × PM10(G) + 1.166 × PM1(G)
− 0.043 × T(G) − 0.525 × W(G) − 0.009 × RH(G)
− 0.001 × PM10(B) − 0.019 × PM2.5(B) + 7.359
0.998PM1(G) =
 
 		− 0.030 × PM10(G) + 0.842 × PM2.5(G)
+ 0.042 × T(G) + 0.406 × W(G) + 0.009 × RH(G)
+ 0.001 × PM10(B) + 0.020 × PM2.5(B) − 6.874
23/03/2015 to 27/03/2015
(After dust period)		0.941PM10(G) =
 
 		6.841 × PM2.5(G) − 6.102 × PM1(G)
− 0.548 × T(G) + 1.859 × W(G) − 0.433 × RH(G)
− 0.008 × PM10(B) + 0.002 × PM2.5(B) − 1.628
0.998PM2.5(G) =
 
 		0.090 × PM10(G) + 0.946 × PM1(G)
+ 0.061 × T(G) − 0.182 × W(G) + 0.044 × RH(G)
+ 0.002 × PM10(B) − 0.001 × PM2.5(B) + 1.747
0.998PM1(G) =
 
 		− 0.088 × PM10(G) + 1.042 × PM2.5(G)
− 0.060 × T(G) + 0.185 × W(G) − 0.043 × RH(G)
− 0.002 × PM10(B) + 0.003 × PM2.5(B) − 1.908
18/03/2015 to 27/03/2015
(all periods)		0.954PM10(G) =
 
 		5.826 × PM2.5(G) − 5.670 × PM1(G)
+ 0.554 × T(G) + 3.456 × W(G) − 0.112 × RH(G)
+ 0.039 × PM10(B) − 0.018 × PM2.5(B) − 12.128
0.998PM2.5(G) =
 
 		0.104 × PM10(G) + 1.059 × PM1(G)
− 0.003 × T(G) − 0.328 × W(G) + 0.019 × RH(G)
+ 0.005 × PM10(B) − 0.010 × PM2.5(B) + 1.350
0.997PM1(G) =
 
 		− 0.088 × PM10(G) + 0.924 × PM2.5(G)
+ 0.006 × T(G) + 0.268 × W(G) − 0.014 × RH(G)
− 0.005 × PM10(B) + 0.010 × PM2.5(B) − 1.246
Table
Table 2. Partial correlation matrix among PMs associated with meteorological parameters of Gangneung city (G) and PMs of Beijing city (B), before, during and after the dust periods.
Table 2. Partial correlation matrix among PMs associated with meteorological parameters of Gangneung city (G) and PMs of Beijing city (B), before, during and after the dust periods.
PeriodItemPM10
(G)		PM2.5
(G)		PM1
(G)		Temp
(G)		Wind
(G)		RH
(G)		PM10
(B)		PM2.5
(B)
BeforePM10 (G)1.0000.9130.854−0.112−0.2180.019−0.035−0.260
PM2.5 (G) 1.0000.989−0.385−0.1500.3200.210−0.003
PM1 (G) 1.000−0.444−0.1320.4030.2670.067
Temp (G) 1.0000.100−0.897−0.701−0.694
Wind (G) 1.000−0.0090.0840.131
RH (G) 1.0000.6230.644
PM10 (B) 1.0000.952
PM2.5 (B) 1.000
DuringPM10 (G)1.0000.7250.6890.1170.244−0.2860.407−0.119
PM2.5 (G) 1.0000.9960.030−0.013−0.0070.5670.411
PM1 (G) 1.0000.0410.005−0.0050.5740.466
Temp (G) 1.0000.395−0.7940.060−0.021
Wind (G) 1.000−0.651−0.103−0.056
RH (G) 1.0000.1120.226
PM10 (B) 1.0000.459
PM2.5 (B) 1.000
AfterPM10 (G)1.0000.7320.7090.4190.212−0.4700.2790.584
PM2.5 (G) 1.0000.9970.346−0.180−0.1110.3910.847
PM1 (G) 1.0000.356−0.171−0.1270.3720.846
Temp (G) 1.0000.209−0.741−0.1670.344
Wind (G) 1.000−0.475−0.095−0.225
RH (G) 1.0000.190−0.110
PM10 (B) 1.0000.551
PM2.5 (B) 1.000
AllPM10 (G)1.0000.7670.6850.4530.247−0.1660.4580.091
PM2.5 (G) 1.0000.9900.217−0.1210.2730.5830.425
PM1 (G) 1.0000.179−0.1580.3250.5800.479
Temp (G) 1.0000.355−0.548−0.036−0.133
Wind (G) 1.000−0.623−0.100−0.281
RH (G) 1.0000.3910.629
PM10 (B) 1.0000.780
PM2.5 (B) 1.000
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Choi, S.-M.; Choi, H. Statistical Modeling for PM10, PM2.5 and PM1 at Gangneung Affected by Local Meteorological Variables and PM10 and PM2.5 at Beijing for Non- and Dust Periods. Appl. Sci. 2021, 11, 11958. https://0-doi-org.brum.beds.ac.uk/10.3390/app112411958

AMA Style

Choi S-M, Choi H. Statistical Modeling for PM10, PM2.5 and PM1 at Gangneung Affected by Local Meteorological Variables and PM10 and PM2.5 at Beijing for Non- and Dust Periods. Applied Sciences. 2021; 11(24):11958. https://0-doi-org.brum.beds.ac.uk/10.3390/app112411958

Chicago/Turabian Style

Choi, Soo-Min, and Hyo Choi. 2021. "Statistical Modeling for PM10, PM2.5 and PM1 at Gangneung Affected by Local Meteorological Variables and PM10 and PM2.5 at Beijing for Non- and Dust Periods" Applied Sciences 11, no. 24: 11958. https://0-doi-org.brum.beds.ac.uk/10.3390/app112411958

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop