The hourly data of PM concentrations at six monitoring stations were also used to understand the concentration changes of PM10 and PM2.5 simultaneously for 2 years from 2015 to 2016. Regarding hourly variations of PM10 and PM2.5 concentrations, both showed high levels at 9-11 due to rush hour.
INTRODUCTION
Particulate Matter
- Definition and risk of particulate matter
- Sources of particulate matter
- Standards of particulate matter by country
- Particulate matter forecast, warning and alert
In addition, PM2.5 has started the demonstration forecast in the metropolitan areas since May 2014 and until January 2015, 10 regions across the country were provided with the PM2.5 forecast. From January 1, 2015, the city and provincial governors have been allowed to issue air pollution warnings or warnings when the actual PM10 and PM2.5 concentrations exceed the specified standard according to the Air Environment Law.
Ulsan city, South Korea
- Overview about Ulsan metropolitan city
- Previous studies about particulate matter in Ulsan
The research on PM pollution in Ulsan city has been conducted since 2001, but it involved research on heavy metals in the atmosphere. Since then, many domestic newspapers have been published in Ulsan on this issue, but the assessment of health risks and heavy metals in particulate matter were more important than identifying the source.
Objectives of this study
MATERIALS AND METHODS
- Information about data
- Meteorological conditions in Ulsan
- Wind fields by season
- Traffic volume by region
- Yellow dust events and particulate matter warnings
- Amount of emission from CAPSS data
In this study, the concentrations of PM10 and PM2.5 were obtained from data of the last 2 years for six stations, including 4 residential areas (NS, DS, SAM and YE stations), 1 commercial area (SN) and 1 industrial area (YE) (Table 7). The data recorded every hour were used without any special filtering process except for errors in the measuring equipment. a) Location of PM10 and PM2.5 air monitoring stations and (b) the main industrial areas in Ulsan. In step 2, there is a procedure to interpret the data or perform smoothing, vertical velocity adjustment and divergence minimization for a summary analysis of the observed data for step 1 (Scire, 2000).
In this study, the CALMET model was used to simulate the three-dimensional wind fields in Ulsan using meteorological data at 9 meteorological stations in the city of Ulsan. Traffic volume data was obtained from the 2015 survey results on the website (http://www.road.re.kr/main/main.asp) of the Ministry of Land, Infrastructure and Transport (MLIT). The warning in 2015 occurred regardless of the day of the week, however warnings in 2016 could be observed on Monday, Saturday and Sunday (Table 11).
To identify the potential sources of PM, the amounts of PM emissions in Ulsan were considered.
RESULTS AND DISCUSSION
Priority assessment of air monitoring stations for PM 10
There was a recurring trend with the first rank of the SAM station and the third rank of the Sinjeong station. The SAM station which was the first site for 2 years is far away from other areas and there is no measuring station nearby, so it is difficult to predict the PM10 concentration of the SAM station. Therefore, if the government moves the gauging station later, it is necessary to abolish and transfer the gauging stations except SAM, Deoksin, Hwasan and Sinjeong stations.
Correlation analysis of particulate matter
- Correlation analysis of particulate matter concentrations among stations
- Relationship between particulate matter concentrations and meteorological parameters
In autumn, due to the high mixing height, PM10 was dispersed over long distances, but PM2.5 was not. Spearman correlation analysis between daily average concentrations of PM10, PM2.5 and meteorological factors such as temperature, precipitation, wind speed, relative humidity, duration of solar radiation and cloud cover for each season was performed to better understand the influences of local meteorologies on pollution levels in Ulsan. PM10 and PM2.5 concentrations were positively correlated with temperature in all seasons except autumn.
The transformation of secondary aerosol particles was also favored at higher air temperatures due to enhanced photochemical processes (Lu et al., 2017). However, there was a very weak positive correlation between wind speed and PM2.5 concentrations in spring. PM concentrations were negatively related to relative humidity because dust emissions can be inhibited due to minimum fractional velocity/wind speed under moist air conditions.
In addition, there were wet removal effects due to the abundance of rainfall in summer (Li et al., 2017a).
Annual variation of particulate matter
For square plots, the lower and upper bounds of the boxes indicate the 25th percentile and 75th percentile, respectively. The dashed line within the box represents the median, while the solid line represents the median. The red and blue lines on the graph show the average annual PM10 and PM2.5 standards, respectively.
Such consistency may be reflected in the effects of emissions control efforts (Sharma et al., 2014). The highest concentration of PM10 at the HS station exceeded the average annual concentration in 2 years.
Seasonal/Monthly variation of particulate matter
- Seasonal variations of particulate matter
- Monthly variation of particulate matter
The yellow dust blown from the desert in China and Mongolia by the westerly wind increased the concentrations of PM (Clarke et al., 2014; Kim et al., 2012; Wang and Lin, 2015). In comparison between yellow dust days and normal days, PM10 concentrations increased about 2.1-2.7 times and PM2.5 concentrations increased 1.3-1.5 times. Comparison of the concentrations of (a) PM10 and (b) PM2.5 between non-yellow dust and yellow dust periods.
In the case of PM2.5, due to unstable atmospheric conditions, they can enter the upper atmosphere and act as condensation nuclei that form raindrops (Spracklen et al., 2011). In autumn, the PM was actively dispersed due to the high mixing height (Gupta et al., 2007). The reduction rates of PM10 and PM2.5 were similar, showing that rainfall had strongly influenced PM concentrations (Li et al., 2015).
In particular, PM10 concentrations continued to increase in spring due to yellow dust, while PM2.5 concentrations tended to decrease in April.
Daily variation of particulate matter
- The exceedance pattern of daily particulate matter concentrations
- Comparison between weekday and weekend
Average concentrations in six stations were highest in May (30.2 µg/m3) and lowest in September (16.4 µg/m3). PM10 concentrations were highest on Saturdays at five stations, except HS station, and lowest on Fridays at four stations, except DS and HS stations. The results were different from previous studies in which weekend concentrations were lower than weekdays (Johansson and Johansson, 2003).
This can be explained by high concentrations of PM during the weekend in 2016 due to yellow dust events (three times). As a result of summing the concentration during the day without yellow dust, the concentration was the highest on Tuesday, when the economic activities were very active, and the lowest concentration was on Sunday. This may be caused by the reduction of car emissions due to lower operating costs of factories on Sundays compared to weekdays.
The reduction rate was highest in HS station (13% decrease) of industrial area, followed by residential area (4% decrease) and business area (3% decrease), as the difference between weekdays and weekends was more evident in industrial areas due to more active traffic volume and population activity.
Hourly variation of particulate matter
The PM10 concentration differences between weekday and weekend followed the regional characteristics (Leena et al., 2017). The effect of the reduction between weekday and weekend can be attributed to artificial activities rather than natural causes (Jones et al., 2008). Occasionally, the high PM concentrations at 10:00 PM were observed due to the reverse layer formation due to the drop in surface temperature and low mixing height, which cannot smoothly disperse pollutants from industrial activities.
Spatial distributions of particulate matter
The reason for difference in the seasonal concentration range may be the geographical location and local influence with the meteorological factors for each season (Lee et al., 2006). For PM10 in spring and summer, southeast and east winds prevailed, respectively, and air pollution released from the industrial complexes can flow to residential areas in the western side of Ulsan. Therefore, in spring and summer, the highest concentration was found in HS station located in the industrial complex, and followed by residential area and the lowest in SAM station.
In the autumn concentration tear, the HS station was the highest, followed by the NS, YE, SN, SAM and DS stations. In winter, the northwest wind dominated and the concentrations of PM at SAM stations were influenced by the SN area. Therefore, it was the highest in HS station in winter, followed by NS, SN, DS and SAM and YE stations.
It indicates that the high PM2.5 concentrations at the SN station may not affect other stations due to the relatively low wind speed during these two years.
Source identification of particulate matter
The SAM station is affected by NW and NW winds and does not have many points. Stations SN and YE showed similar pollution roses and were affected by wind from the northwest direction but there was no point source in the northwest area, so there may be other sources. There is a mobile source to the left of the NS station, but it may be less affected by that source because it is affected by northerly winds.
Station YE, near SE, appeared to be affected by mobile sources due to NW winds. The DS station was affected by Hyundai Motor and Hyundai Heavy Industries in the surrounding area, but the petrochemical complex was the most affected by the northwest and southwest winds. In addition, station YE was mainly affected by northwesterly winds from the mobile source and was affected by southeasterly winds from the petrochemical complex.
The NS station was influenced by the general industrial complex from the north, but this complex was not included in the CAPSS data, the main sources of PM may be difficult to identify.
CONCLUSIONS
Air quality characteristics and sources affecting particulate matter (PM2.5) levels in the city of Red Deer, Canada. Characteristics of particulate matter and metals in the ambient air of a residential area in Korea's largest industrial city. Effects of particulate matter on respiratory diseases and the impact of meteorological factors in Busan, Korea.
Weekday–weekend difference and assessment of non-vehicular contributions to urban air particulate growth. Comparison of particle characteristics before, during and after the Asian dust events in Incheon and Ulsan, Korea. Analyzing the temporal variability of particulate matter and potential contributing factors over Mahabaleshwar, a high-altitude station in the Western Ghats, India.
Temporal and spatial analyzes of particulate matter (PM10 and PM2.5) and its relationship with meteorological parameters over an urban city in northeast China. A method to estimate the fraction of mineral dust in particulate matter using PM2.5-to-PM10 ratios. Urban air matter in central and southern Chile: Effects of weather conditions on fine and coarse particles.
