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Fast and reliable source identification of criteria air pollutants in an industrial city
Atmospheric Environment 95 (2014) 239e248
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Fast and reliable source identification of criteria air pollutants in an industrial city Kevin Clarke, Hye-Ok Kwon, Sung-Deuk Choi* School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulju-gun, Ulsan, 689-798, Republic of Korea
h i g h l i g h t s The source identification of air pollutants is important for an industrial city. Criteria air pollutants were measured in the industrial city of Ulsan, South Korea. We developed a fast and reliable source identification method. Air pollution sources were identified using this new method. The automation of this procedure may allow a real-time diagnostic of the sources.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 April 2014 Received in revised form 16 June 2014 Accepted 19 June 2014 Available online 19 June 2014
Most of industrial cities in developed countries use automatic station networks for monitoring of Criteria Air Pollutants (CAPs), and the tremendous amount of data acquired are often used to verify that the concentrations are within safety levels and to warn the population in the other case. Furthermore, these data can be used to investigate the sourceereceptor relationship. In this study, the data were collected from automatic monitoring stations in Ulsan, the most industrialized city in South Korea. The dataset consists of hourly concentrations of five CAPs (SO2, CO, O3, NO2, and PM10) recorded at 13 monitoring stations in the city during a full year (March 2011eFebruary 2012). Different types of sources were identified by studying the temporal (daily and seasonal) trends and spatial distributions of CAPs with wind directions. It was confirmed that SO2 pollution in Ulsan originated mostly from local industrial areas, whereas CO and NO2 were also substantially influenced by mobile sources. The high PM10 levels resulted from both local industries and traffic sources as well as from remote sources. The originality of this work comes from the study of the high episodes of pollutions on a case by case basis as well as on average data. Moreover, rather simple statistical tools developed in this study can be used for a real-time diagnosis of the local pollution in large urban and industrial areas. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Criteria air pollutants Air pollution Source identification Industrial city Ulsan
1. Introduction Criteria Air Pollutants (CAPs) are a set of pollutants that cause human health and/or environmental hazard, either directly or indirectly. The United States Environmental Protection Agency (USEPA) lists six CAPs: sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), lead, and Particulate Matter (PM) (US-EPA, 2011). SO2 and NO2 are harmful to human health and lead to acid rains (US-EPA, 2012). CO and NO2 contribute to creating tropospheric ozone, which is both harmful to human health and a
* Corresponding author. E-mail address: [email protected] (S.-D. Choi). http://dx.doi.org/10.1016/j.atmosenv.2014.06.040 1352-2310/© 2014 Elsevier Ltd. All rights reserved.
smog precursor (Reeves et al., 2002; Spellman, 2010). High particle concentrations are linked to respiratory diseases (WHO, 2005). The World Health Organization (WHO) defines Air Quality Guidelines (AQG), which can be found in Table S1 in the Supplementary Information. SO2 is mainly a primary pollutant (Manahan, 2009), and local industries using heavy oil and coal may be a major emission source of SO2 in developed countries. CO and NO2 are mostly emitted by combustion sources, e.g., car engines, urban heating, industries, biomass burning, etc. NO2 is formed by the oxidation of NO, which itself is produced by the reaction between O2 and N2 at high temperatures during combustion (Manahan, 2009). Ozone is a secondary pollutant, formed by the presence of precursors such as NO2, CO, and Volatile Organic Compounds (VOCs). PM10 can be
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Fig. 1. Location of Automatic Weather Stations (AWS), Air Monitoring Stations (AMS), and the main industrial areas in Ulsan, Korea. The white squares represent the associations between AWS and AMS.
either a primary or a secondary pollutant. In the former case, its origins are numerous, e.g., (1) natural emissions by sand storms, forest fires, volcano eruptions, and sea sprays and (2) anthropogenic emissions mostly by combustion from industrial, agricultural, traffic, and urban sources. In the latter case, particles are sometimes referred to photochemical smog; this smog is produced in specific conditions when levels of VOCs, NOx, CO, and O3 are high (Manahan, 2009). This information on the types of sources and origins of CAPs is summarized in Table S2. For the past two decades, numerous studies have focused on the spatial distribution and the temporal trends of CAPs in urban areas (See references in Table S3). Other studies have focused on the relationship between levels of different air pollutants or the influence of meteorological conditions (Liu et al., 2002; Lei et al., 2004; Abdul-Wahab et al., 2005; Kova c-Andri c et al., 2009; Song et al., € 2010; Ozbay et al., 2011; Ling et al., 2014). For source identification of CAPs, various combinations of measurement and modeling tools have been applied. For example, a receptor modeling approach using backward trajectories was developed to identify the locations of sources of SO2 and estimate their contributions in Taiwan (Lin et al., 2004). The backward trajectory analysis and temporal variations of SO2 and NO2 were also used to reveal a major source in Shanghai Port (Zhao et al., 2013). Even aircraft measurements were conducted for O3, NOx, CO, VOCs, and SO2 in the Yangtze River Delta region (Geng et al., 2009), and their sources were identified based on the spatial and temporal variations and correlations of measurement data. The source apportionment of PM in Europe and India using receptor models and measurement data were reviewed (Viana et al., 2008; Pant and Harrison, 2012). A geographic information system (GIS) with passive air sampling data for NO2 and VOCs was used to predict source locations and concentrations in El Paso, Texas, USA (Smith et al., 2006). A similar approach using GIS
and passive air sampling data was applied in Montreal, Canada for NO2 (Crouse et al., 2009) and Hanoi, Vietnam for SO2 and NO2 (Hien et al., 2014). Furthermore, a source apportionment algorithm was adapted to the Community Multiscale Air Quality (CMAQ) Modeling System for tracking emission sources of PM sulfate, SO2, and elemental carbon (EC) in Hong Kong (Kwok et al., 2012). However, almost no study dealt with five CAPs simultaneously in industrial cities using a combination of several methods of source identification. In this study, therefore, we propose a more general approach to reach the above objective by studying spatial distributions and temporal trends of CAPs and taking into account meteorological conditions. For this purpose, the Ulsan metropolitan city (hereafter Ulsan) is chosen as a test bed, which is regarded as an optimal place for studying sourceereceptor relationships of pollutants in multimedia compartments (Lee et al., 2011; Choi et al., 2012b; Kwon and Choi, 2014). The ultimate goal of this study is to develop a fast and reliable source identification method for CAPs, allowing a real-time diagnosis in urban or industrial areas. LongRange Transport (LRT) of both CAPs and Hazardous Air Pollutants (HAPs) is also a critical issue in Korea (Choi et al., 2012a), but this topic is beyond the scope of the present study. Instead, we briefly dealt with it in the Supplementary Information (Text S1). 2. Material and methods 2.1. Study area Ulsan is located on the southeast coast of the Korean peninsula (Fig. 1), and it is the seventh largest city in South Korea with a population over 1.2 million inhabitants covering an area of over 1000 km2. It is considered as Korea's industrial hub with four main industrial areas: (1) Yangjeong district, where the world's largest
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automobile assembly plant is located, (2) Mipo district, home of the world's largest shipyard, (3) Yeocheon district, and (4) Onsan village, home of two of the world's largest oil refineries (http:// english.ulsan.go.kr/). Ulsan also has many motorways and large avenues with frequent traffic congestion. 2.2. Meteorological data Nine Automatic Weather Stations (AWS), operated by the Korea Meteorological Service (KMA), provide data such as wind speed, wind direction, temperature, and rainfall in Ulsan. Among them, four stations are located in rural areas, which are not of interest for this study. The remaining five are linked to 13 Air Monitoring Stations (AMS) as shown in Fig. 1. For example, meteorological data measured at AWS #3 were used for interpreting CAP data measured at AMS #2. The GPS coordinates of five AWS are shown in Table S4. For this study, hourly data collected from February 2011 to February 2012 were used, divided into four seasons as follows: spring (February 25June 10), summer (June 10August 24), fall (August 25November 18), and winter (November 19February 24, 2012). These dates were chosen to match Polycyclic Aromatic Hydrocarbon (PAH) sampling campaigns for an upcoming study (unpublishedein progress). The wind patterns in Ulsan during this period were consistent with those usually measured in Korea; the patterns were different regarding the seasons with prevailing winds from NW during the winter, SSW and SE during the summer, and in-between during the spring and fall (Fig. 2). Annual windroses from the five AWS are provided in the Supplementary Information (Fig. S1). The land-sea breeze circulation system in Ulsan seems to be very complicated due to the complex topography (Fig. 1). In this study, the influence of land-sea breezes on the levels of CAPs is briefly discussed in the following sections. We are currently investigating land-sea breeze circulations in Ulsan with observation and simulation of wind fields. Therefore, an in-depth discussion on the land-sea breezes is out of the scope of the present study. 2.3. Measurement of CAPs For routine monitoring purposes, 13 AMS were installed all over Ulsan, providing hourly data of CAPs such as SO2, CO, O3, NO2, and
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PM10, i.e., all the CAPs defined by US-EPA, except lead and PM2.5. This network is operated by the Ulsan Institute of Health and Environment (UIHE) supported by Ulsan metropolitan city and the Korean Ministry of Environment. Among the 13 stations (Fig. 1), four stations are located in four different industrial areas: Hyomun (#13), Yeocheon (#11), Bugok (#5), and Hwasan (#12). Seven stations are located in densely populated urban areas; among them, four stations are in the city centereSeongnam (#8), Samsan (#6), Sinjeong (#9), and Yaeum (#10)e, and three stations in peripheral districts located respectively north, west, and east: Nongso (#1), Mugeo (#4), and Daesong (#2). Lastly, two stations are located in peri-urban areas: Sangnam (#7) and Deoksin (#3). The locations of stations were chosen in order to cover most of the densely populated areas of the city as well as the main industrial areas. The GPS coordinates of 13 AMS are shown in Table S5. Measurements of PM10 were performed by a b-Ray absorption method using a continuous particulate monitor. SO2 measurements were performed using a pulse UV fluorescence method, CO measurements using a non-dispersive infrared method, NO2 using a chemiluminescent method, and O3 using a UV photometric method. Instruments and models for each compound, hourly Limits Of Detection (LOD), and upper limits are listed in Table S6.
2.4. Source identification methodology In this section, a systematic source identification method developed in the current study is described. The source identification method consists of three steps (Fig. 3). (1) The classification of the concentrations according to wind directions, divided into 16 categories (sorted clockwise from N to NNW), allows to visualize which site influences another as well as to illustrate whether the pollution measured at the 13 stations comes from the same wind directions or not. In the former case, it would indicate remote sources; in the latter case, local sources. Rather than a pollution rose, in which all directions are shown, we plotted arrows on the stations for the concentrations above the threshold of two and three standard deviations above the average value (x þ 2s and x þ 3s). In this way, we highlighted the
Fig. 2. Seasonal windroses at (a) the Ulsan meteorological observatory (AWS #2 in Fig. 1) in an urban area and (b) an industrial site (AWS #4 in Fig. 1) in Ulsan during February 2011eFebruary 2012.
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Fig. 3. Three-step source identification method developed in this study.
significant influences from one site to another and discarded the insignificant variations. (2) The Inverse Distance Weighed (IDW) interpolation tool from ArcMap 10.1 software (Esri's ArcGIS suite for spatial analysis) was used to provide detailed spatial patterns indicating pollution hot spots, potentially at the origin of local air pollution. It also can be used to visualize the inter-station variability of the levels of pollutants to detect the potential presence of point sources. The inter-station variability was also statistically evaluated using the t-test. (3) The hourly data were averaged according to the time of the day to provide 24-value daily patterns, which give information about the type of sources: mobile sources tend to peak during rush hours, i.e., morning and evening peaks during weekdays, industrial sources during office hours, and secondary pollutants react differently with respect to the solar radiation. The hourly data were also averaged according to the month to provide 12-value seasonal patterns, which gives information about the type of sources: urban sources tend to increase at winter time due to residential heating systems, whereas industrial sources usually do not. In this step, the t-test and correlation analysis were used to confirm significant variations of CAPs. Finally, in order to estimate the origin of CAPs on a day-to-day basis, we used some indexes calculating the spatial variability without averaging the data. Only the data containing concentrations above the guideline of the Korean government (Table S1) were considered. Indeed, the concentrations below these limits are not a major issue. If a source is remote, different districts of Ulsan located a few km from each other should be impacted the same way. Therefore, the spatial variability between different sites should be very low. If a source is local and non-point, like househeating or traffic, all urban sites should be impacted by their local emissions, and the spatial inter-site variability should also be quite low. At the opposite, point sources such as chemical industries or power plants will impact different areas in different ways, and they will lead to high spatial variability between sites. We used the RD index (ratios of the Relative Deviation to the median value), which are calculated as (maximum median)/ median at each station. The use of the median rather than the average is preferable because the median value is not impacted by
extreme values. We can speculate that the station located closest to the source usually shows the maximum concentration. The higher the index, the higher the probability to have a pollution coming from a single location. Three other indexes for the real-time diagnosis of CAPs developed during this work, leading to similar conclusions, are summarized in Text S2. 3. Results and discussion 3.1. Daily variations of CAPs The hourly levels of SO2 were generally higher during daytime at all the stations except Daesong and Yeocheon stations, for which the levels were roughly constant all day long (Fig. 4a). This behavior suggests a predominance of industrial sources, which are mainly active during office hours. The lack of drop at night-time in Yeocheon could suggest that some industries there operate non-stop. The main wind directions also played an important role in the daily patterns of SO2 concentrations. In the afternoon, the main winds were from south and east (i.e., sea breeze), causing the emission from Hwasan to impact Deoksin and to a lesser extent the city center. During the night and the morning, winds from the north and west could cause emissions from Yeocheon to impact Daesong. CO concentrations were maximal during rush hour times in general with two peaks: (1) a sharp one starting from 56 AM to 10 AMnoon with an apex around 89 AM and (2) a smaller one from 68 PM to 11 PM1 AM (Fig. 4b). This behavior suggests a predominance of traffic sources; car exhausts are mainly active during rush hours. A comparison of the data from Sundays to the other days of the week confirms this hypothesis: the rush hour peaks tend to fade on Sundays. Yeocheon was the exception, showing the same patterns as the other sites but with CO levels significantly higher at nighttime (p < 0.01 for an unpaired t-test performed on 400 randomly chosen samples). However, Yeocheon is an industrial area, and the traffic there is not denser than those in other districts of Ulsan; an important part of the emissions could be due to industrial sources. The higher concentrations at nighttime do not necessarily imply more important emissions at nighttime. As mentioned in the previous paragraph, the industries in Yeocheon seem to emit 24 h a day. In addition, mixing heights are low at night, preventing pollutants from diluting in the higher atmosphere. Considering that Yeocheon is located N of Deoksin, the
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Fig. 4. Hourly averaged levels of five CAPs in Ulsan during March 2011eFebruary 2012. Data from industrial sites and averaged values of urban sites are shown.
main wind directions could also explain why in Deoksin, the morning rush hour peak was much higher than the evening rush hour peak. NO2 levels were maximal during rush hours, in general with two peaks: the first one with an apex at 89 AM and the second one with an apex at 810 PM (Fig. 4c). The apex times suggest that the main source of NO2 is car exhausts (Teixeira et al., 2009). The exception is for Bugok and Hwasan, which showed the second peak much earlier, starting about 1 PM with an apex around 6 PM. This observation at the two sites might be due to both car exhaust and industrial activities. O3 is formed from NO2, O2, and solar radiation; its concentration was maximal when UV light was maximal in the early afternoon (for all the sources, the apex was between 1 PM and 4 PM). The curve started around 10 AM and the concentrations went back down and stabilized around 10 PM (Fig. 4f). The reverse reaction is the spontaneous decomposition of ozone. Note that in unpolluted air, these two reactions result in an equilibrium with no net formation or loss of O3. However, with the presence of catalytic species such as RO2 or HO2 radicals, formed during the oxidation of VOCs and CO respectively, the reverse reaction does not take place, resulting in the net formation of ozone (Atkinson, 2000; Reeves et al., 2002; Manahan, 2009; VanLoon and Duffy, 2011). CO, VOCs, and NO2 are, therefore, ozone precursors (Tu et al., 2007; Xu et al., 2011; Wang et al., 2013). The 13 stations showed a very similar pattern of ozone levels (Fig. 4d) with the lowest concentrations in
the morning (around 8 AM) because of the reverse reaction taking place at nighttime (Nishanth et al., 2014). At all the monitoring stations, the O3 levels stabilized in the middle of the night (between midnight and 6 AM) (Fig. 4d), before dropping again during the rush hours (68 AM), before the solar radiation is intense enough to trigger O3 formation again (Tu et al., 2007). In addition, the time trends of CO were also anti-correlated with those of O3 (Fig. 4b and d), indicating that CO is an ozone precursor. The behavior of PM10 concentration was more erratic than those of other pollutants. Nevertheless, we can observe a common pattern in industrial sites (Yeocheon, Bugok, and Hwasan), i.e., one peak in the morning (around 10 AM and noon) and the other in the afternoon between 34 PM and 78 PM. This pattern of PM10 looks a little bit similar to the pattern of NO2 concentrations with common sources both from vehicles and industries. On the contrary, at downtown sites (Daesong, Samsan, Seongnam, Sinjeong, and Yaeum), only the morning peak was visible between 9 AM and 1 PM. For all the 13 stations except Daesong, Bugok, and Yeocheon, the lowest PM10 levels were measured in the early morning (35 AM to 68 AM), suggesting emissions during daytime and slow decrease during nighttime (Fig. 4e). 3.2. Seasonal variations of CAPs In general, SO2 levels are higher in winter than other seasons owing to increased fossil fuel burning in cold seasons. However, all
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the stations in Ulsan showed significantly higher SO2 levels in the summer (Fig. 5a), especially in Hwasan (p < 0.01 for an unpaired ttest performed on 400 randomly chosen samples). These high levels of SO2 in summer are a unique phenomenon in Ulsan. In our former study (Lee et al., 2011), it was suggested that SO2 emitted from the industrial complex was transported to urban and residential areas by land-sea breeze and southeastern seasonal winds. This interpretation implies that major sources of SO2 are located on the east side of the AMS network. Considering all sites, CO levels for temperatures below 10 C were on average 9% and 12% higher than CO levels for temperatures ranging 1020 C and above 20 C, respectively. Even though the levels of CO were generally higher in the winter than those in the summer (Fig. 5b), seasonal variations at all the sites excluding Yeocheon were not so strong compared with those of other pollutants. The sharpest increase in CO levels in Yeocheon during October 2011eFebruary 2012 cannot be explained by house heating in cold seasons. Instead, Yeocheon station might be located near large-scale emission sources among petrochemical industries. These potential sources seem to release large amounts of CO seasonally. For example, the combustion reaction is likely to be much more incomplete in vehicle engines and industrial burners during cold weather (Martins et al., 2014). The main wind direction also might play an import role in substantially high CO levels. If the potential sources are located in the northwest side of Yeocheon station, prevailing northwestern winds in fall and winter may carry large amounts of CO to Yeocheon station. NO2 levels were slightly but significantly lower in the summer for all the stations and quite constant during the rest of the year (p < 0.05 for an unpaired t-test performed on 400 randomly chosen samples) with the smallest seasonal variations at Hwasan station (Fig. 5c). This apparent seasonal variation was probably due to the fact that NO2 is depleted during the tropospheric O3 formation, which is more important in summer (Ta et al., 2004). For all the stations, the highest O3 levels were found in the spring, and the lowest O3 levels were measured in the winter (Fig. 5d). O3 showed a definite correlation with duration of sunshine. The highest PM10 were found at all the stations in the spring mostly because of an episode of very high concentrations from May 1st to May 4th (reaching daily means over 400 mg/m3 on May 2nd), followed by two more episodes on May 13th and May 16th, when
PM10 levels were over 100 mg/m3. These very high concentrations were mainly due to yellow sand storms coming from China. The general trend was the same for all the stations with high PM10 levels in the spring and a decrease in the summer (Fig. 5e). The fast decreasing trend after May can be partially explained by increasing precipitation (i.e., enhanced wet deposition of PM10) in June (294 mm) and July (285 mm), accounting for 47% of the total precipitation (1233 mm) in Ulsan in 2011 (KMA, 2012). A further decreasing trend of PM10 in August and September 2011 with much less precipitation (71 mm and 97 mm for each month) can be explained by prevailing wind directions (respectively SSW and NNE) carrying relatively clean air from the East Sea to the city. 3.3. Year-round spatial distribution of CAPs The contour map (Fig. 6) using the IDW interpolation tool allows us to draw several conclusions. SO2 levels were generally homogenous on all the stations except in Yeocheon, Hwasan, and to a lesser extent, Bugok, where they were on average twice as high as those at the other stations. Yeocheon and Hwasan areas are, therefore, local sources of SO2, and it is more likely that Bugok was impacted by nearby industrial areas rather than a source itself (Fig. 6a). The lowest CO concentrations were found in the peripheral areas (Nongso, Sangnam, and Deoksin, all away from the city center). This result suggests that downtown Ulsan is an important local source of CO, even though the concentrations were lower than those in Yeocheon, which is a primary local source of CO (Fig. 6b). NO2 levels were roughly constant all year long and for all the stations, but slightly higher in the industrial areas (Hyomun and Bugok) and a residential/commercial area (Mugeo), indicating local anthropogenic sources: mobile, urban, and industrial (Fig. 6c). O3, as a secondary pollutant, showed little local variations and was anti-correlated to NO2. The levels were slightly lower in districts where NO2 levels were high, such as Mugeo and Hyomun, and slightly higher in districts where NO2 levels were low, such as Daesong (Fig. 6d). PM10 levels were generally higher in the industrial areas than those in other urban and rural areas. Especially, the levels of PM10 in Hyomun were significantly above the “all station average” (p < 0.01, for an unpaired t-test performed on 400 randomly chosen samples)
Fig. 5. Monthly averaged levels of five CAPs in Ulsan during March 2011eFebruary 2012. Data from industrial sites and averaged values of urban sites are shown.
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Fig. 6. Spatial distribution of yearly-averaged levels of five CAPs in Ulsan during March 2011eFebruary 2012. Locations of 9 AWS (triangles) and 13 AMS (stars) are also shown.
throughout the year, indicating this site as an important local source of PM10 (Fig. 6e). Because of the proximity of automobile industries in Hyomun, it is probable that the PM10 pollution originated here, either directly emitted by these industries and heavy vehicles or as a secondary pollutant created by the VOCs and NOx released by them. 3.4. Influence of wind directions on the spatial distribution of CAPs In previous sections, we briefly mentioned the influence of major wind directions on the temporal and spatial variations of
CAPs. In this section, we further provide detailed interpretations of wind effects using seasonal contour maps of each pollutant (Fig. 7). SO2 emissions from Yeocheon and Hwasan (petrochemical and non-ferrous industries) seem to have made a big impact on nearby residential areas; all the northern parts of Ulsan were impacted by Yeocheon emissions, including Nongso in the north, Daesong in the east, and Mugeo in the west, which showed sharp peaks in concentrations with winds coming from Yeocheon. Hwasan emissions strongly impacted the southern part of Ulsan: Bugok, Sangnam, and Deoksin (Fig. S2). Considering that the pollution originated E and
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Fig. 7. Spatial distribution of seasonally-averaged levels of five CAPs. Thin and thick arrows represent wind directions resulting in higher concentrations above x þ 2s and x þ 3s, respectively at each AMS.
ENE directions from Deoksin, the wind directions alone explain the higher concentrations in Deoksin in the summer compared with those in the winter (Fig. 7a). The wind directions also explain the seasonal pattern in Yeocheon; the concentrations were the lowest when the winds came from N, NNW, and NW, and the amount of winds from these directions was much more important in the winter (over 900 h in the winter, 700800 h in the spring and fall, and less than 350 h in the summer). In the case of CO pollution, Yeocheon seems to have made only a limited impact on neighbor sites. The CO levels in Yeocheon were significantly higher than those of the surrounding sites (p < 0.01 for unpaired t-test performed on 400 randomly chosen samples) both in the summer and winter. However, according to the wind directions, in the fall and winter, none of them seem to be substantially impacted by the very high CO emissions from Yeocheon. Higher concentrations in Yeocheon were observed when winds were coming from N, NNW, and NW directions,
which were much more frequent in the winter. The fact that other stations did not show higher CO levels indicates that major CO emission sources are surely located very close to Yeocheon station (Fig. 7b). In the spring and summer, winds from the E and ESE directions resulted in higher concentrations of NO2 over the city center. Even though the same wind directions for all sites could indicate a remote pollution rather than a local one, the fact that the sites located upwind of the industrial zones showed lower concentrations suggests that a large portion of the NO2 emission originated from Hyomun, Yeochon, and Bugok (Fig. 7c). Because of the homogeneity of the concentrations at all the stations in the fall and winter, wind directions seem to have made little to no impact on the concentrations. The highest NO2 levels were measured at Mugeo station throughout the year regardless of wind directions, and there are no industrial sources around the station, indicating that the main source of NO2 in Mugeo is vehicles.
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In the case of ozone pollution, higher O3 levels were measured at five stations (Mugeo, Seongnam, Sinjeong, Yaeum, and Deoksin) in the fall when the winds came from the shore, and for all the seasons, the highest levels were measured in Daesong, which is probably due to its proximity to the shore. Throughout the year except fall, wind directions seem to have made little impact on measured concentrations, and when it did, it seems to have come mostly from the shore (sea breeze) rather than the city (Fig. 7d). Explanations for this behavior might be the recirculation of polluted air masses during sea breeze events (Oh et al., 2006; Ji et al., 2013) and the reactions between ozone and nitrous oxides, causing higher O3 concentrations in rural areas next to major cities than in the cities themselves (Jeon et al., 2014). In the spring, higher PM10 concentrations were measured when the winds came from W, WNW, or WSW directions in the west part of Ulsan, indicating the influence of remote sources (Fig. 7e). When winds blew from NW direction, higher concentrations were observed in Daesong. The origin of the pollution could be Hyomun, which showed the highest PM10 concentrations. In the summer, higher PM10 levels in Sangnam, Yeocheon, and Bugok came from Hwasan area, but the fact that Hwasan did not show higher PM10 levels than average levels in Ulsan and the similar wind direction at these three sites (from S to N) could indicate the influence of remote sources. However, the increase of PM10 levels in Nongso when winds blew from S to N was probably due to local origins, as Nongso was located downwind from Hyomun, which showed very high PM10 concentrations. In the fall, higher PM10 levels with S/SSE winds in Deoksin and Sangnam seems to have been due to the petrochemical/non-ferrous industries. In the winter, higher PM10 levels came with S winds in Yaeum, Samsan, and Hyomun. Here also, both local and remote sources (or a mixture of both) are possible. 3.5. Application for the real-time diagnosis SO2 seems almost exclusively a local point source pollutant in Ulsan, and O3 is an almost exclusively secondary pollutant, not coming from a point source. According to the Relative Deviation to the median value (RD index), almost all (98%) the RD indexes of SO2 were higher than 1.0. At the opposite, only 4% of the O3 data lead to those values (RD > 1.0). This value (RD ¼ 1) was chosen as a threshold to discriminate local point sources to non-point sources or remote sources for other CAPs. According to the RD index, local point sources were the main cause of 97% of the hourly episodes of high CO concentrations in Ulsan. As no CO concentration data exceeded the Korean government's upper limit (Table S1), a limit of three times the standard deviation above the all-data averaged was arbitrary chosen, corresponding to the level of 1650 ppb. Yeocheon, Nongso, and Bugok contributed 70%, 18%, and 5% of these high episodes, respectively. According to these indexes, therefore, industries located in Yeocheon are probably the main source of CO. It was estimated that local point sources accounted for 23% of the episodes of high NO2 levels exceeding the upper limit. These point sources are distributed rather homogeneously over Ulsan, in the city center (Mugeo: 15%, Seongnam: 16%, and Sinjeong: 10%) as well as in industrial areas (Yeocheon: 15% and Hyomun: 12%). This result indicates the predominance of non-point or remote sources. Since NO2 is a reactive species with a short lifetime in the atmosphere, pollution from remote sources is unlikely. Moreover, the non-point sources showed a vast majority of values exceeding the upper limit for downtown Ulsan (Mugeo, Seongnam, Yaeum, and Sinjeong), and vehicles were probably the main contributors. Local point sources were the main cause of 26% of episodes of high levels (above x þ 2s) of PM10 in Ulsan. Among these high level
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episodes due to local point sources, 90% originated in one of the four national industrial complexes: Hyomun (39%), Yeocheon (28%), Bugok (14%), and Hwasan (9%). 3.6. Use of the national emission data for source identification According to the annual report on air quality in Korea (NIER, 2012), Ulsan shows higher levels of SO2 than anywhere else in Korea (Text S3 and Fig. S3), demonstrating the local character of the pollutant. This gap between the levels in Ulsan and the rest of Korea is almost entirely explained by the industrial activities, which represent more than half of the total emissions in Ulsan for the year 2011 (Fig. S4) and between 0% and 90% in other provinces (NIER, 2013). The CO emissions in Ulsan for the year 2011 are mainly due to production processes and vehicles, respectively 30% and 35% of the total emissions (Fig. S4), which is in agreement with the main sources identified in our study. The emission and monitoring data both highlight the fact that Ulsan is different from other Korean provinces, for which vehicles are the main source of CO with contributions ranging from 29% to 81% (NIER, 2013). The comparison of NO2 throughout Korea (Fig. S3) shows that Ulsan is a spot with concentrations slightly higher than the surrounding areas, demonstrating the local character of the pollutant, but the emissions in Ulsan for the year 2011 are in the same range as in other provinces and are evenly distributed between following different sources: energy industrial combustion, vehicles and nonroad mobile sources, and manufacturing combustion. The weak contribution from production processes is confirmed by the emission data (Fig. S4). The PM10 emissions for the year 2011 in Ulsan are largely due to both mobile (vehicles and non-road) sources (41%) and industrial activities. This result confirms the estimated value of source contribution; local industrial sources accounted for 26% of high PM10 concentration episodes. Among the estimated 74% of high PM10 level episodes due to remote sources, we need to investigate the portion of LRT. The study of back trajectories and the photochemical age of air mass should be performed in order to draw definitive conclusions, but this is out of the scope of this study. 4. Conclusions This study showed that the data collected from AMS could be used to establish a diagnosis for the identification and localization of the main sources of CAPs in an industrial city. The main sources of CAPs identified in Ulsan are as follows: The main sources of SO2 were exclusively local industries located in the petrochemical and non-ferrous industrial complexes. Those of CO were both industries in the petrochemical industrial complex and traffic sources in downtown Ulsan. The sources of NO2 were industries located in the east part of Ulsan and vehicles, especially in Mugeo district. The temporal and spatial distributions of O3 were inversely correlated with those of NO2, indicating a photochemical reaction. In addition, O3 levels seem to have been affected by the circulation of winds in the shore. The main sources of PM10 were remote sources through LRT as well as local sources such as automobile industries. These results were obtained by using simple statistical tools (standard deviation, t-test, RD index, and correlation analysis), meteorological data (wind direction and precipitation), and a GIS tool. Thus, the methodology described here is applicable to any other cities with a monitoring station network. Combining these different tools, we can make a fast and reliable diagnosis on the type and location of the sources impacting a specific site at a specific time.
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Further improvements could be made by trying to correlate the levels of CAPs to those of other air pollutants such as PAHs and VOCs. With the extra information brought by the measurement of other pollutants, which have sources in common with CAPs, an accurate source apportionment study is worth considering. Acknowledgments This work was supported by a Ulsan National Institute of Science and Technology (UNIST) grant funded in 2013. The authors would like to thank Dr. Erwan Simon for his assistance in the design of the indexes and his support in handling of a large database. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2014.06.040. References Abdul-Wahab, S.A., Bakheit, C.S., Al-Alawi, S.M., 2005. Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ. Model. Softw. 20, 1263e1271. Atkinson, R., 2000. Atmospheric chemistry of VOCs and NOx. Atmos. Environ. 34, 2063e2101. Choi, S.-D., Ghim, Y.S., Lee, J.Y., Kim, J.Y., Kim, Y.P., 2012a. Factors affecting the level and pattern of polycyclic aromatic hydrocarbons (PAHs) at Gosan, Korea during a dust period. J. Hazard. Mater. 227e228, 79e87. Choi, S.-D., Kwon, H.-O., Lee, Y.-S., Park, E.-J., Oh, J.-Y., 2012b. Improving the spatial resolution of atmospheric polycyclic aromatic hydrocarbons using passive air samplers in a multi-industrial city. J. Hazard. Mater. 241, 252e258. Crouse, D.L., Goldberg, M.S., Ross, N.A., 2009. A prediction-based approach to modelling temporal and spatial variability of traffic-related air pollution in Montreal, Canada. Atmos. Environ. 43, 5075e5084. Geng, F., Zhang, Q., Tie, X., Huang, M., Ma, X., Deng, Z., Yu, Q., Quan, J., Zhao, C., 2009. Aircraft measurements of O3, NOx, CO, VOCs, and SO2 in the Yangtze river delta region. Atmos. Environ. 43, 584e593. Hien, P.D., Hangartner, M., Fabian, S., Tan, P.M., 2014. Concentrations of NO2, SO2, and benzene across Hanoi measured by passive diffusion samplers. Atmos. Environ. 88, 66e73. Jeon, W.-B., Lee, S.-H., Lee, H., Park, C., Kim, D.-H., Park, S.-Y., 2014. A study on high ozone formation mechanism associated with change of NOx/VOCs ratio at a rural area in the Korean Peninsula. Atmos. Environ. 89, 10e21. Ji, H.E., Lee, S.H., Lee, H.W., 2013. Characteristics of sea breeze front development with various synoptic conditions and its impact on lower troposphere ozone formation. Adv. Atmos. Sci. 30, 1461e1478. KMA, 2012. 2011 Annual Climatological Report (in Korean language). Kova c-Andri c, E., Brana, J., Gvozdi c, V., 2009. Impact of meteorological factors on ozone concentrations modelled by time series analysis and multivariate statistical methods. Ecol. Inform. 4, 117e122. Kwok, R., Fung, J.C.H., Lau, A.K.H., Wang, Z.S., 2012. Tracking emission sources of sulfur and elemental carbon in Hong Kong/Pearl river delta region. J. Atmos. Chem. 69, 1e22. Kwon, H.-O., Choi, S.-D., 2014. Polycyclic aromatic hydrocarbons (PAHs) in soils from a multi-industrial city, South Korea. Sci. Total Environ. 470e471, 1494e1501. Lee, Y.-S., Choi, S.-D., Kwon, H.-O., Kim, C.-S., Son, H.-S., Ye, J., 2011. Temporal and spatial distribution of sulfur dioxide in Ulsan, Korea (In Korean language). J. Korean Soc. Environ. Anal. 14, 249e256. Lei, W.F., Zhang, R.Y., Tie, X.X., Hess, P., 2004. Chemical characterization of ozone formation in the Houston-Galveston area: a chemical transport model study. J. Geophys. Res. Atmos. 109.
Lin, C.-H., Wu, Y.-L., Chang, K.-H., Lai, C.-H., 2004. A method for locating influential pollution sources and estimating their contributions. Environ. Model. Assess. 9, 129e136. Ling, H., Sch€ afer, K., Xin, J., Qin, M., Suppan, P., Wang, Y., 2014. Small-scale spatial variations of gaseous air pollutants e a comparison of path-integrated and in situ measurement methods. Atmos. Environ. 92, 566e575. Liu, K.-Y., Wang, Z., Hsiao, L.-F., 2002. A modeling of the sea breeze and its impacts on ozone distribution in northern Taiwan. Environ. Model. Softw. 17, 21e27. Manahan, S.E., 2009. Fundamentals of Environmental Chemistry, third ed. CRC Press. , J.R., 2014. Cold start and full cycle emissions from Martins, A.A., Rocha, R.A.D., Sodre a flexible fuel vehicle operating with natural gas, ethanol and gasoline. J. Nat. Gas Sci. Eng. 17, 94e98. NIER, 2012. Annual Report on Air Quality in Korea, 2011 (in Korean language). NIER, 2013. National Air Pollutants Emission 2011 (in Korean language). Nishanth, T., Praseed, K.M., Kumar, M.K.S., Valsaraj, K.T., 2014. Influence of ozone precursors and PM10 on the variation of surface O3 over Kannur, India. Atmos. Res. 138, 112e124. Oh, I.-B., Kim, Y.-K., Lee, H.W., Kim, C.-H., 2006. An observational and numerical study of the effects of the late sea breeze on ozone distributions in the Busan metropolitan area, Korea. Atmos. Environ. 40, 1284e1298. € ruparmak, S¸.Ç., Ayberk, S., 2011. Multivariate methods for Ozbay, B., Keskin, G.A., Dog ground-level ozone modeling. Atmos. Res. 102, 57e65. Pant, P., Harrison, R.M., 2012. Critical review of receptor modelling for particulate matter: a case study of India. Atmos. Environ. 49, 1e12. Reeves, C.E., Penkett, S.A., Bauguitte, S., Law, K.S., Evans, M.J., Bandy, B.J., Monks, P.S., Edwards, G.D., Phillips, G., Barjat, H., Kent, J., Dewey, K., Schmitgen, S., Kley, D., 2002. Potential for photochemical ozone formation in the troposphere over the North Atlantic as derived from aircraft observations during ACSOE. J. Geophys. Res. Atmos. 107, 4707. € Smith, L., Mukerjee, S., Gonzales, M., Stallings, C., Neas, L., Norris, G., Ozkaynak, H., 2006. Use of GIS and ancillary variables to predict volatile organic compound and nitrogen dioxide levels at unmonitored locations. Atmos. Environ. 40, 3773e3787. Song, S.-K., Shon, Z.-H., Kim, Y.-K., Kang, Y.-H., Oh, I.-B., Jung, C.-H., 2010. Influence of ship emissions on ozone concentrations around coastal areas during summer season. Atmos. Environ. 44, 713e723. Spellman, F.R., 2010. The Science of Environmental Pollution, second ed. CRC Press. Ta, W., Wang, T., Xiao, H., Zhu, X., Xiao, Z., 2004. Gaseous and particulate air pollution in the Lanzhou Valley, China. Sci. Total Environ. 320, 163e176. Teixeira, E.C., de Santana, E.R., Wiegand, F., Fachel, J., 2009. Measurement of surface ozone and its precursors in an urban area in South Brazil. Atmos. Environ. 43, 2213e2220. Tu, J., Xia, Z.-G., Wang, H., Li, W., 2007. Temporal variations in surface ozone and its precursors and meteorological effects at an urban site in China. Atmos. Res. 85, 310e337. US-EPA, 2011. National Ambient Air Quality Standards (NAAQS). http://epa.gov/air/ criteria.html. US-EPA, 2012. Effects of Acid Rain e Human Health. http://www.epa.gov/acidrain/ effects/health.html. VanLoon, G.W., Duffy, S.J., 2011. Environmental Chemistry - a Global Perspective, third ed. Oxford University Press. Viana, M., Kuhlbusch, T.A.J., Querol, X., Alastuey, A., Harrison, R.M., Hopke, P.K., vo ^t, A.S.H., Hueglin, C., Bloemen, H., Winiwarter, W., Vallius, M., Szidat, S., Pre Wåhlin, P., Vecchi, R., Miranda, A.I., Kasper-Giebl, A., Maenhaut, W., Hitzenberger, R., 2008. Source apportionment of particulate matter in Europe: a review of methods and results. J. Aerosol Sci. 39, 827e849. Wang, Y., Hu, B., Tang, G., Ji, D., Zhang, H., Bai, J., Wang, X., Wang, Y., 2013. Characteristics of ozone and its precursors in Northern China: a comparative study of three sites. Atmos. Res. 132e133, 450e459. WHO, 2005. WHO Air Quality Guidelines for Particulate Matter, Ozone, Nitrogen Dioxide and Sulfur Dioxide e Summary of Risk Assessment. Xu, J., Zhang, X., Xu, X., Zhao, X., Meng, W., Pu, W., 2011. Measurement of surface ozone and its precursors in urban and rural sites in Beijing. Procedia Earth Planet. Sci. 2, 255e261. Zhao, M., Zhang, Y., Ma, W., Fu, Q., Yang, X., Li, C., Zhou, B., Yu, Q., Chen, L., 2013. Characteristics and ship traffic source identification of air pollutants in China's largest port. Atmos. Environ. 64, 277e286.
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Fast and reliable source identification of criteria air pollutants in an industrial city Kevin Clarke, Hye-Ok Kwon, Sung-Deuk Choi* School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, 50 UNIST-gil, Ulju-gun, Ulsan, 689-798, Republic of Korea
h i g h l i g h t s The source identification of air pollutants is important for an industrial city. Criteria air pollutants were measured in the industrial city of Ulsan, South Korea. We developed a fast and reliable source identification method. Air pollution sources were identified using this new method. The automation of this procedure may allow a real-time diagnostic of the sources.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 1 April 2014 Received in revised form 16 June 2014 Accepted 19 June 2014 Available online 19 June 2014
Most of industrial cities in developed countries use automatic station networks for monitoring of Criteria Air Pollutants (CAPs), and the tremendous amount of data acquired are often used to verify that the concentrations are within safety levels and to warn the population in the other case. Furthermore, these data can be used to investigate the sourceereceptor relationship. In this study, the data were collected from automatic monitoring stations in Ulsan, the most industrialized city in South Korea. The dataset consists of hourly concentrations of five CAPs (SO2, CO, O3, NO2, and PM10) recorded at 13 monitoring stations in the city during a full year (March 2011eFebruary 2012). Different types of sources were identified by studying the temporal (daily and seasonal) trends and spatial distributions of CAPs with wind directions. It was confirmed that SO2 pollution in Ulsan originated mostly from local industrial areas, whereas CO and NO2 were also substantially influenced by mobile sources. The high PM10 levels resulted from both local industries and traffic sources as well as from remote sources. The originality of this work comes from the study of the high episodes of pollutions on a case by case basis as well as on average data. Moreover, rather simple statistical tools developed in this study can be used for a real-time diagnosis of the local pollution in large urban and industrial areas. © 2014 Elsevier Ltd. All rights reserved.
Keywords: Criteria air pollutants Air pollution Source identification Industrial city Ulsan
1. Introduction Criteria Air Pollutants (CAPs) are a set of pollutants that cause human health and/or environmental hazard, either directly or indirectly. The United States Environmental Protection Agency (USEPA) lists six CAPs: sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), ozone (O3), lead, and Particulate Matter (PM) (US-EPA, 2011). SO2 and NO2 are harmful to human health and lead to acid rains (US-EPA, 2012). CO and NO2 contribute to creating tropospheric ozone, which is both harmful to human health and a
* Corresponding author. E-mail address: [email protected] (S.-D. Choi). http://dx.doi.org/10.1016/j.atmosenv.2014.06.040 1352-2310/© 2014 Elsevier Ltd. All rights reserved.
smog precursor (Reeves et al., 2002; Spellman, 2010). High particle concentrations are linked to respiratory diseases (WHO, 2005). The World Health Organization (WHO) defines Air Quality Guidelines (AQG), which can be found in Table S1 in the Supplementary Information. SO2 is mainly a primary pollutant (Manahan, 2009), and local industries using heavy oil and coal may be a major emission source of SO2 in developed countries. CO and NO2 are mostly emitted by combustion sources, e.g., car engines, urban heating, industries, biomass burning, etc. NO2 is formed by the oxidation of NO, which itself is produced by the reaction between O2 and N2 at high temperatures during combustion (Manahan, 2009). Ozone is a secondary pollutant, formed by the presence of precursors such as NO2, CO, and Volatile Organic Compounds (VOCs). PM10 can be
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Fig. 1. Location of Automatic Weather Stations (AWS), Air Monitoring Stations (AMS), and the main industrial areas in Ulsan, Korea. The white squares represent the associations between AWS and AMS.
either a primary or a secondary pollutant. In the former case, its origins are numerous, e.g., (1) natural emissions by sand storms, forest fires, volcano eruptions, and sea sprays and (2) anthropogenic emissions mostly by combustion from industrial, agricultural, traffic, and urban sources. In the latter case, particles are sometimes referred to photochemical smog; this smog is produced in specific conditions when levels of VOCs, NOx, CO, and O3 are high (Manahan, 2009). This information on the types of sources and origins of CAPs is summarized in Table S2. For the past two decades, numerous studies have focused on the spatial distribution and the temporal trends of CAPs in urban areas (See references in Table S3). Other studies have focused on the relationship between levels of different air pollutants or the influence of meteorological conditions (Liu et al., 2002; Lei et al., 2004; Abdul-Wahab et al., 2005; Kova c-Andri c et al., 2009; Song et al., € 2010; Ozbay et al., 2011; Ling et al., 2014). For source identification of CAPs, various combinations of measurement and modeling tools have been applied. For example, a receptor modeling approach using backward trajectories was developed to identify the locations of sources of SO2 and estimate their contributions in Taiwan (Lin et al., 2004). The backward trajectory analysis and temporal variations of SO2 and NO2 were also used to reveal a major source in Shanghai Port (Zhao et al., 2013). Even aircraft measurements were conducted for O3, NOx, CO, VOCs, and SO2 in the Yangtze River Delta region (Geng et al., 2009), and their sources were identified based on the spatial and temporal variations and correlations of measurement data. The source apportionment of PM in Europe and India using receptor models and measurement data were reviewed (Viana et al., 2008; Pant and Harrison, 2012). A geographic information system (GIS) with passive air sampling data for NO2 and VOCs was used to predict source locations and concentrations in El Paso, Texas, USA (Smith et al., 2006). A similar approach using GIS
and passive air sampling data was applied in Montreal, Canada for NO2 (Crouse et al., 2009) and Hanoi, Vietnam for SO2 and NO2 (Hien et al., 2014). Furthermore, a source apportionment algorithm was adapted to the Community Multiscale Air Quality (CMAQ) Modeling System for tracking emission sources of PM sulfate, SO2, and elemental carbon (EC) in Hong Kong (Kwok et al., 2012). However, almost no study dealt with five CAPs simultaneously in industrial cities using a combination of several methods of source identification. In this study, therefore, we propose a more general approach to reach the above objective by studying spatial distributions and temporal trends of CAPs and taking into account meteorological conditions. For this purpose, the Ulsan metropolitan city (hereafter Ulsan) is chosen as a test bed, which is regarded as an optimal place for studying sourceereceptor relationships of pollutants in multimedia compartments (Lee et al., 2011; Choi et al., 2012b; Kwon and Choi, 2014). The ultimate goal of this study is to develop a fast and reliable source identification method for CAPs, allowing a real-time diagnosis in urban or industrial areas. LongRange Transport (LRT) of both CAPs and Hazardous Air Pollutants (HAPs) is also a critical issue in Korea (Choi et al., 2012a), but this topic is beyond the scope of the present study. Instead, we briefly dealt with it in the Supplementary Information (Text S1). 2. Material and methods 2.1. Study area Ulsan is located on the southeast coast of the Korean peninsula (Fig. 1), and it is the seventh largest city in South Korea with a population over 1.2 million inhabitants covering an area of over 1000 km2. It is considered as Korea's industrial hub with four main industrial areas: (1) Yangjeong district, where the world's largest
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automobile assembly plant is located, (2) Mipo district, home of the world's largest shipyard, (3) Yeocheon district, and (4) Onsan village, home of two of the world's largest oil refineries (http:// english.ulsan.go.kr/). Ulsan also has many motorways and large avenues with frequent traffic congestion. 2.2. Meteorological data Nine Automatic Weather Stations (AWS), operated by the Korea Meteorological Service (KMA), provide data such as wind speed, wind direction, temperature, and rainfall in Ulsan. Among them, four stations are located in rural areas, which are not of interest for this study. The remaining five are linked to 13 Air Monitoring Stations (AMS) as shown in Fig. 1. For example, meteorological data measured at AWS #3 were used for interpreting CAP data measured at AMS #2. The GPS coordinates of five AWS are shown in Table S4. For this study, hourly data collected from February 2011 to February 2012 were used, divided into four seasons as follows: spring (February 25June 10), summer (June 10August 24), fall (August 25November 18), and winter (November 19February 24, 2012). These dates were chosen to match Polycyclic Aromatic Hydrocarbon (PAH) sampling campaigns for an upcoming study (unpublishedein progress). The wind patterns in Ulsan during this period were consistent with those usually measured in Korea; the patterns were different regarding the seasons with prevailing winds from NW during the winter, SSW and SE during the summer, and in-between during the spring and fall (Fig. 2). Annual windroses from the five AWS are provided in the Supplementary Information (Fig. S1). The land-sea breeze circulation system in Ulsan seems to be very complicated due to the complex topography (Fig. 1). In this study, the influence of land-sea breezes on the levels of CAPs is briefly discussed in the following sections. We are currently investigating land-sea breeze circulations in Ulsan with observation and simulation of wind fields. Therefore, an in-depth discussion on the land-sea breezes is out of the scope of the present study. 2.3. Measurement of CAPs For routine monitoring purposes, 13 AMS were installed all over Ulsan, providing hourly data of CAPs such as SO2, CO, O3, NO2, and
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PM10, i.e., all the CAPs defined by US-EPA, except lead and PM2.5. This network is operated by the Ulsan Institute of Health and Environment (UIHE) supported by Ulsan metropolitan city and the Korean Ministry of Environment. Among the 13 stations (Fig. 1), four stations are located in four different industrial areas: Hyomun (#13), Yeocheon (#11), Bugok (#5), and Hwasan (#12). Seven stations are located in densely populated urban areas; among them, four stations are in the city centereSeongnam (#8), Samsan (#6), Sinjeong (#9), and Yaeum (#10)e, and three stations in peripheral districts located respectively north, west, and east: Nongso (#1), Mugeo (#4), and Daesong (#2). Lastly, two stations are located in peri-urban areas: Sangnam (#7) and Deoksin (#3). The locations of stations were chosen in order to cover most of the densely populated areas of the city as well as the main industrial areas. The GPS coordinates of 13 AMS are shown in Table S5. Measurements of PM10 were performed by a b-Ray absorption method using a continuous particulate monitor. SO2 measurements were performed using a pulse UV fluorescence method, CO measurements using a non-dispersive infrared method, NO2 using a chemiluminescent method, and O3 using a UV photometric method. Instruments and models for each compound, hourly Limits Of Detection (LOD), and upper limits are listed in Table S6.
2.4. Source identification methodology In this section, a systematic source identification method developed in the current study is described. The source identification method consists of three steps (Fig. 3). (1) The classification of the concentrations according to wind directions, divided into 16 categories (sorted clockwise from N to NNW), allows to visualize which site influences another as well as to illustrate whether the pollution measured at the 13 stations comes from the same wind directions or not. In the former case, it would indicate remote sources; in the latter case, local sources. Rather than a pollution rose, in which all directions are shown, we plotted arrows on the stations for the concentrations above the threshold of two and three standard deviations above the average value (x þ 2s and x þ 3s). In this way, we highlighted the
Fig. 2. Seasonal windroses at (a) the Ulsan meteorological observatory (AWS #2 in Fig. 1) in an urban area and (b) an industrial site (AWS #4 in Fig. 1) in Ulsan during February 2011eFebruary 2012.
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Fig. 3. Three-step source identification method developed in this study.
significant influences from one site to another and discarded the insignificant variations. (2) The Inverse Distance Weighed (IDW) interpolation tool from ArcMap 10.1 software (Esri's ArcGIS suite for spatial analysis) was used to provide detailed spatial patterns indicating pollution hot spots, potentially at the origin of local air pollution. It also can be used to visualize the inter-station variability of the levels of pollutants to detect the potential presence of point sources. The inter-station variability was also statistically evaluated using the t-test. (3) The hourly data were averaged according to the time of the day to provide 24-value daily patterns, which give information about the type of sources: mobile sources tend to peak during rush hours, i.e., morning and evening peaks during weekdays, industrial sources during office hours, and secondary pollutants react differently with respect to the solar radiation. The hourly data were also averaged according to the month to provide 12-value seasonal patterns, which gives information about the type of sources: urban sources tend to increase at winter time due to residential heating systems, whereas industrial sources usually do not. In this step, the t-test and correlation analysis were used to confirm significant variations of CAPs. Finally, in order to estimate the origin of CAPs on a day-to-day basis, we used some indexes calculating the spatial variability without averaging the data. Only the data containing concentrations above the guideline of the Korean government (Table S1) were considered. Indeed, the concentrations below these limits are not a major issue. If a source is remote, different districts of Ulsan located a few km from each other should be impacted the same way. Therefore, the spatial variability between different sites should be very low. If a source is local and non-point, like househeating or traffic, all urban sites should be impacted by their local emissions, and the spatial inter-site variability should also be quite low. At the opposite, point sources such as chemical industries or power plants will impact different areas in different ways, and they will lead to high spatial variability between sites. We used the RD index (ratios of the Relative Deviation to the median value), which are calculated as (maximum median)/ median at each station. The use of the median rather than the average is preferable because the median value is not impacted by
extreme values. We can speculate that the station located closest to the source usually shows the maximum concentration. The higher the index, the higher the probability to have a pollution coming from a single location. Three other indexes for the real-time diagnosis of CAPs developed during this work, leading to similar conclusions, are summarized in Text S2. 3. Results and discussion 3.1. Daily variations of CAPs The hourly levels of SO2 were generally higher during daytime at all the stations except Daesong and Yeocheon stations, for which the levels were roughly constant all day long (Fig. 4a). This behavior suggests a predominance of industrial sources, which are mainly active during office hours. The lack of drop at night-time in Yeocheon could suggest that some industries there operate non-stop. The main wind directions also played an important role in the daily patterns of SO2 concentrations. In the afternoon, the main winds were from south and east (i.e., sea breeze), causing the emission from Hwasan to impact Deoksin and to a lesser extent the city center. During the night and the morning, winds from the north and west could cause emissions from Yeocheon to impact Daesong. CO concentrations were maximal during rush hour times in general with two peaks: (1) a sharp one starting from 56 AM to 10 AMnoon with an apex around 89 AM and (2) a smaller one from 68 PM to 11 PM1 AM (Fig. 4b). This behavior suggests a predominance of traffic sources; car exhausts are mainly active during rush hours. A comparison of the data from Sundays to the other days of the week confirms this hypothesis: the rush hour peaks tend to fade on Sundays. Yeocheon was the exception, showing the same patterns as the other sites but with CO levels significantly higher at nighttime (p < 0.01 for an unpaired t-test performed on 400 randomly chosen samples). However, Yeocheon is an industrial area, and the traffic there is not denser than those in other districts of Ulsan; an important part of the emissions could be due to industrial sources. The higher concentrations at nighttime do not necessarily imply more important emissions at nighttime. As mentioned in the previous paragraph, the industries in Yeocheon seem to emit 24 h a day. In addition, mixing heights are low at night, preventing pollutants from diluting in the higher atmosphere. Considering that Yeocheon is located N of Deoksin, the
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Fig. 4. Hourly averaged levels of five CAPs in Ulsan during March 2011eFebruary 2012. Data from industrial sites and averaged values of urban sites are shown.
main wind directions could also explain why in Deoksin, the morning rush hour peak was much higher than the evening rush hour peak. NO2 levels were maximal during rush hours, in general with two peaks: the first one with an apex at 89 AM and the second one with an apex at 810 PM (Fig. 4c). The apex times suggest that the main source of NO2 is car exhausts (Teixeira et al., 2009). The exception is for Bugok and Hwasan, which showed the second peak much earlier, starting about 1 PM with an apex around 6 PM. This observation at the two sites might be due to both car exhaust and industrial activities. O3 is formed from NO2, O2, and solar radiation; its concentration was maximal when UV light was maximal in the early afternoon (for all the sources, the apex was between 1 PM and 4 PM). The curve started around 10 AM and the concentrations went back down and stabilized around 10 PM (Fig. 4f). The reverse reaction is the spontaneous decomposition of ozone. Note that in unpolluted air, these two reactions result in an equilibrium with no net formation or loss of O3. However, with the presence of catalytic species such as RO2 or HO2 radicals, formed during the oxidation of VOCs and CO respectively, the reverse reaction does not take place, resulting in the net formation of ozone (Atkinson, 2000; Reeves et al., 2002; Manahan, 2009; VanLoon and Duffy, 2011). CO, VOCs, and NO2 are, therefore, ozone precursors (Tu et al., 2007; Xu et al., 2011; Wang et al., 2013). The 13 stations showed a very similar pattern of ozone levels (Fig. 4d) with the lowest concentrations in
the morning (around 8 AM) because of the reverse reaction taking place at nighttime (Nishanth et al., 2014). At all the monitoring stations, the O3 levels stabilized in the middle of the night (between midnight and 6 AM) (Fig. 4d), before dropping again during the rush hours (68 AM), before the solar radiation is intense enough to trigger O3 formation again (Tu et al., 2007). In addition, the time trends of CO were also anti-correlated with those of O3 (Fig. 4b and d), indicating that CO is an ozone precursor. The behavior of PM10 concentration was more erratic than those of other pollutants. Nevertheless, we can observe a common pattern in industrial sites (Yeocheon, Bugok, and Hwasan), i.e., one peak in the morning (around 10 AM and noon) and the other in the afternoon between 34 PM and 78 PM. This pattern of PM10 looks a little bit similar to the pattern of NO2 concentrations with common sources both from vehicles and industries. On the contrary, at downtown sites (Daesong, Samsan, Seongnam, Sinjeong, and Yaeum), only the morning peak was visible between 9 AM and 1 PM. For all the 13 stations except Daesong, Bugok, and Yeocheon, the lowest PM10 levels were measured in the early morning (35 AM to 68 AM), suggesting emissions during daytime and slow decrease during nighttime (Fig. 4e). 3.2. Seasonal variations of CAPs In general, SO2 levels are higher in winter than other seasons owing to increased fossil fuel burning in cold seasons. However, all
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the stations in Ulsan showed significantly higher SO2 levels in the summer (Fig. 5a), especially in Hwasan (p < 0.01 for an unpaired ttest performed on 400 randomly chosen samples). These high levels of SO2 in summer are a unique phenomenon in Ulsan. In our former study (Lee et al., 2011), it was suggested that SO2 emitted from the industrial complex was transported to urban and residential areas by land-sea breeze and southeastern seasonal winds. This interpretation implies that major sources of SO2 are located on the east side of the AMS network. Considering all sites, CO levels for temperatures below 10 C were on average 9% and 12% higher than CO levels for temperatures ranging 1020 C and above 20 C, respectively. Even though the levels of CO were generally higher in the winter than those in the summer (Fig. 5b), seasonal variations at all the sites excluding Yeocheon were not so strong compared with those of other pollutants. The sharpest increase in CO levels in Yeocheon during October 2011eFebruary 2012 cannot be explained by house heating in cold seasons. Instead, Yeocheon station might be located near large-scale emission sources among petrochemical industries. These potential sources seem to release large amounts of CO seasonally. For example, the combustion reaction is likely to be much more incomplete in vehicle engines and industrial burners during cold weather (Martins et al., 2014). The main wind direction also might play an import role in substantially high CO levels. If the potential sources are located in the northwest side of Yeocheon station, prevailing northwestern winds in fall and winter may carry large amounts of CO to Yeocheon station. NO2 levels were slightly but significantly lower in the summer for all the stations and quite constant during the rest of the year (p < 0.05 for an unpaired t-test performed on 400 randomly chosen samples) with the smallest seasonal variations at Hwasan station (Fig. 5c). This apparent seasonal variation was probably due to the fact that NO2 is depleted during the tropospheric O3 formation, which is more important in summer (Ta et al., 2004). For all the stations, the highest O3 levels were found in the spring, and the lowest O3 levels were measured in the winter (Fig. 5d). O3 showed a definite correlation with duration of sunshine. The highest PM10 were found at all the stations in the spring mostly because of an episode of very high concentrations from May 1st to May 4th (reaching daily means over 400 mg/m3 on May 2nd), followed by two more episodes on May 13th and May 16th, when
PM10 levels were over 100 mg/m3. These very high concentrations were mainly due to yellow sand storms coming from China. The general trend was the same for all the stations with high PM10 levels in the spring and a decrease in the summer (Fig. 5e). The fast decreasing trend after May can be partially explained by increasing precipitation (i.e., enhanced wet deposition of PM10) in June (294 mm) and July (285 mm), accounting for 47% of the total precipitation (1233 mm) in Ulsan in 2011 (KMA, 2012). A further decreasing trend of PM10 in August and September 2011 with much less precipitation (71 mm and 97 mm for each month) can be explained by prevailing wind directions (respectively SSW and NNE) carrying relatively clean air from the East Sea to the city. 3.3. Year-round spatial distribution of CAPs The contour map (Fig. 6) using the IDW interpolation tool allows us to draw several conclusions. SO2 levels were generally homogenous on all the stations except in Yeocheon, Hwasan, and to a lesser extent, Bugok, where they were on average twice as high as those at the other stations. Yeocheon and Hwasan areas are, therefore, local sources of SO2, and it is more likely that Bugok was impacted by nearby industrial areas rather than a source itself (Fig. 6a). The lowest CO concentrations were found in the peripheral areas (Nongso, Sangnam, and Deoksin, all away from the city center). This result suggests that downtown Ulsan is an important local source of CO, even though the concentrations were lower than those in Yeocheon, which is a primary local source of CO (Fig. 6b). NO2 levels were roughly constant all year long and for all the stations, but slightly higher in the industrial areas (Hyomun and Bugok) and a residential/commercial area (Mugeo), indicating local anthropogenic sources: mobile, urban, and industrial (Fig. 6c). O3, as a secondary pollutant, showed little local variations and was anti-correlated to NO2. The levels were slightly lower in districts where NO2 levels were high, such as Mugeo and Hyomun, and slightly higher in districts where NO2 levels were low, such as Daesong (Fig. 6d). PM10 levels were generally higher in the industrial areas than those in other urban and rural areas. Especially, the levels of PM10 in Hyomun were significantly above the “all station average” (p < 0.01, for an unpaired t-test performed on 400 randomly chosen samples)
Fig. 5. Monthly averaged levels of five CAPs in Ulsan during March 2011eFebruary 2012. Data from industrial sites and averaged values of urban sites are shown.
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Fig. 6. Spatial distribution of yearly-averaged levels of five CAPs in Ulsan during March 2011eFebruary 2012. Locations of 9 AWS (triangles) and 13 AMS (stars) are also shown.
throughout the year, indicating this site as an important local source of PM10 (Fig. 6e). Because of the proximity of automobile industries in Hyomun, it is probable that the PM10 pollution originated here, either directly emitted by these industries and heavy vehicles or as a secondary pollutant created by the VOCs and NOx released by them. 3.4. Influence of wind directions on the spatial distribution of CAPs In previous sections, we briefly mentioned the influence of major wind directions on the temporal and spatial variations of
CAPs. In this section, we further provide detailed interpretations of wind effects using seasonal contour maps of each pollutant (Fig. 7). SO2 emissions from Yeocheon and Hwasan (petrochemical and non-ferrous industries) seem to have made a big impact on nearby residential areas; all the northern parts of Ulsan were impacted by Yeocheon emissions, including Nongso in the north, Daesong in the east, and Mugeo in the west, which showed sharp peaks in concentrations with winds coming from Yeocheon. Hwasan emissions strongly impacted the southern part of Ulsan: Bugok, Sangnam, and Deoksin (Fig. S2). Considering that the pollution originated E and
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Fig. 7. Spatial distribution of seasonally-averaged levels of five CAPs. Thin and thick arrows represent wind directions resulting in higher concentrations above x þ 2s and x þ 3s, respectively at each AMS.
ENE directions from Deoksin, the wind directions alone explain the higher concentrations in Deoksin in the summer compared with those in the winter (Fig. 7a). The wind directions also explain the seasonal pattern in Yeocheon; the concentrations were the lowest when the winds came from N, NNW, and NW, and the amount of winds from these directions was much more important in the winter (over 900 h in the winter, 700800 h in the spring and fall, and less than 350 h in the summer). In the case of CO pollution, Yeocheon seems to have made only a limited impact on neighbor sites. The CO levels in Yeocheon were significantly higher than those of the surrounding sites (p < 0.01 for unpaired t-test performed on 400 randomly chosen samples) both in the summer and winter. However, according to the wind directions, in the fall and winter, none of them seem to be substantially impacted by the very high CO emissions from Yeocheon. Higher concentrations in Yeocheon were observed when winds were coming from N, NNW, and NW directions,
which were much more frequent in the winter. The fact that other stations did not show higher CO levels indicates that major CO emission sources are surely located very close to Yeocheon station (Fig. 7b). In the spring and summer, winds from the E and ESE directions resulted in higher concentrations of NO2 over the city center. Even though the same wind directions for all sites could indicate a remote pollution rather than a local one, the fact that the sites located upwind of the industrial zones showed lower concentrations suggests that a large portion of the NO2 emission originated from Hyomun, Yeochon, and Bugok (Fig. 7c). Because of the homogeneity of the concentrations at all the stations in the fall and winter, wind directions seem to have made little to no impact on the concentrations. The highest NO2 levels were measured at Mugeo station throughout the year regardless of wind directions, and there are no industrial sources around the station, indicating that the main source of NO2 in Mugeo is vehicles.
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In the case of ozone pollution, higher O3 levels were measured at five stations (Mugeo, Seongnam, Sinjeong, Yaeum, and Deoksin) in the fall when the winds came from the shore, and for all the seasons, the highest levels were measured in Daesong, which is probably due to its proximity to the shore. Throughout the year except fall, wind directions seem to have made little impact on measured concentrations, and when it did, it seems to have come mostly from the shore (sea breeze) rather than the city (Fig. 7d). Explanations for this behavior might be the recirculation of polluted air masses during sea breeze events (Oh et al., 2006; Ji et al., 2013) and the reactions between ozone and nitrous oxides, causing higher O3 concentrations in rural areas next to major cities than in the cities themselves (Jeon et al., 2014). In the spring, higher PM10 concentrations were measured when the winds came from W, WNW, or WSW directions in the west part of Ulsan, indicating the influence of remote sources (Fig. 7e). When winds blew from NW direction, higher concentrations were observed in Daesong. The origin of the pollution could be Hyomun, which showed the highest PM10 concentrations. In the summer, higher PM10 levels in Sangnam, Yeocheon, and Bugok came from Hwasan area, but the fact that Hwasan did not show higher PM10 levels than average levels in Ulsan and the similar wind direction at these three sites (from S to N) could indicate the influence of remote sources. However, the increase of PM10 levels in Nongso when winds blew from S to N was probably due to local origins, as Nongso was located downwind from Hyomun, which showed very high PM10 concentrations. In the fall, higher PM10 levels with S/SSE winds in Deoksin and Sangnam seems to have been due to the petrochemical/non-ferrous industries. In the winter, higher PM10 levels came with S winds in Yaeum, Samsan, and Hyomun. Here also, both local and remote sources (or a mixture of both) are possible. 3.5. Application for the real-time diagnosis SO2 seems almost exclusively a local point source pollutant in Ulsan, and O3 is an almost exclusively secondary pollutant, not coming from a point source. According to the Relative Deviation to the median value (RD index), almost all (98%) the RD indexes of SO2 were higher than 1.0. At the opposite, only 4% of the O3 data lead to those values (RD > 1.0). This value (RD ¼ 1) was chosen as a threshold to discriminate local point sources to non-point sources or remote sources for other CAPs. According to the RD index, local point sources were the main cause of 97% of the hourly episodes of high CO concentrations in Ulsan. As no CO concentration data exceeded the Korean government's upper limit (Table S1), a limit of three times the standard deviation above the all-data averaged was arbitrary chosen, corresponding to the level of 1650 ppb. Yeocheon, Nongso, and Bugok contributed 70%, 18%, and 5% of these high episodes, respectively. According to these indexes, therefore, industries located in Yeocheon are probably the main source of CO. It was estimated that local point sources accounted for 23% of the episodes of high NO2 levels exceeding the upper limit. These point sources are distributed rather homogeneously over Ulsan, in the city center (Mugeo: 15%, Seongnam: 16%, and Sinjeong: 10%) as well as in industrial areas (Yeocheon: 15% and Hyomun: 12%). This result indicates the predominance of non-point or remote sources. Since NO2 is a reactive species with a short lifetime in the atmosphere, pollution from remote sources is unlikely. Moreover, the non-point sources showed a vast majority of values exceeding the upper limit for downtown Ulsan (Mugeo, Seongnam, Yaeum, and Sinjeong), and vehicles were probably the main contributors. Local point sources were the main cause of 26% of episodes of high levels (above x þ 2s) of PM10 in Ulsan. Among these high level
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episodes due to local point sources, 90% originated in one of the four national industrial complexes: Hyomun (39%), Yeocheon (28%), Bugok (14%), and Hwasan (9%). 3.6. Use of the national emission data for source identification According to the annual report on air quality in Korea (NIER, 2012), Ulsan shows higher levels of SO2 than anywhere else in Korea (Text S3 and Fig. S3), demonstrating the local character of the pollutant. This gap between the levels in Ulsan and the rest of Korea is almost entirely explained by the industrial activities, which represent more than half of the total emissions in Ulsan for the year 2011 (Fig. S4) and between 0% and 90% in other provinces (NIER, 2013). The CO emissions in Ulsan for the year 2011 are mainly due to production processes and vehicles, respectively 30% and 35% of the total emissions (Fig. S4), which is in agreement with the main sources identified in our study. The emission and monitoring data both highlight the fact that Ulsan is different from other Korean provinces, for which vehicles are the main source of CO with contributions ranging from 29% to 81% (NIER, 2013). The comparison of NO2 throughout Korea (Fig. S3) shows that Ulsan is a spot with concentrations slightly higher than the surrounding areas, demonstrating the local character of the pollutant, but the emissions in Ulsan for the year 2011 are in the same range as in other provinces and are evenly distributed between following different sources: energy industrial combustion, vehicles and nonroad mobile sources, and manufacturing combustion. The weak contribution from production processes is confirmed by the emission data (Fig. S4). The PM10 emissions for the year 2011 in Ulsan are largely due to both mobile (vehicles and non-road) sources (41%) and industrial activities. This result confirms the estimated value of source contribution; local industrial sources accounted for 26% of high PM10 concentration episodes. Among the estimated 74% of high PM10 level episodes due to remote sources, we need to investigate the portion of LRT. The study of back trajectories and the photochemical age of air mass should be performed in order to draw definitive conclusions, but this is out of the scope of this study. 4. Conclusions This study showed that the data collected from AMS could be used to establish a diagnosis for the identification and localization of the main sources of CAPs in an industrial city. The main sources of CAPs identified in Ulsan are as follows: The main sources of SO2 were exclusively local industries located in the petrochemical and non-ferrous industrial complexes. Those of CO were both industries in the petrochemical industrial complex and traffic sources in downtown Ulsan. The sources of NO2 were industries located in the east part of Ulsan and vehicles, especially in Mugeo district. The temporal and spatial distributions of O3 were inversely correlated with those of NO2, indicating a photochemical reaction. In addition, O3 levels seem to have been affected by the circulation of winds in the shore. The main sources of PM10 were remote sources through LRT as well as local sources such as automobile industries. These results were obtained by using simple statistical tools (standard deviation, t-test, RD index, and correlation analysis), meteorological data (wind direction and precipitation), and a GIS tool. Thus, the methodology described here is applicable to any other cities with a monitoring station network. Combining these different tools, we can make a fast and reliable diagnosis on the type and location of the sources impacting a specific site at a specific time.
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Further improvements could be made by trying to correlate the levels of CAPs to those of other air pollutants such as PAHs and VOCs. With the extra information brought by the measurement of other pollutants, which have sources in common with CAPs, an accurate source apportionment study is worth considering. Acknowledgments This work was supported by a Ulsan National Institute of Science and Technology (UNIST) grant funded in 2013. The authors would like to thank Dr. Erwan Simon for his assistance in the design of the indexes and his support in handling of a large database. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2014.06.040. References Abdul-Wahab, S.A., Bakheit, C.S., Al-Alawi, S.M., 2005. Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations. Environ. Model. Softw. 20, 1263e1271. Atkinson, R., 2000. Atmospheric chemistry of VOCs and NOx. Atmos. Environ. 34, 2063e2101. Choi, S.-D., Ghim, Y.S., Lee, J.Y., Kim, J.Y., Kim, Y.P., 2012a. Factors affecting the level and pattern of polycyclic aromatic hydrocarbons (PAHs) at Gosan, Korea during a dust period. J. Hazard. Mater. 227e228, 79e87. Choi, S.-D., Kwon, H.-O., Lee, Y.-S., Park, E.-J., Oh, J.-Y., 2012b. Improving the spatial resolution of atmospheric polycyclic aromatic hydrocarbons using passive air samplers in a multi-industrial city. J. Hazard. Mater. 241, 252e258. Crouse, D.L., Goldberg, M.S., Ross, N.A., 2009. A prediction-based approach to modelling temporal and spatial variability of traffic-related air pollution in Montreal, Canada. Atmos. Environ. 43, 5075e5084. Geng, F., Zhang, Q., Tie, X., Huang, M., Ma, X., Deng, Z., Yu, Q., Quan, J., Zhao, C., 2009. Aircraft measurements of O3, NOx, CO, VOCs, and SO2 in the Yangtze river delta region. Atmos. Environ. 43, 584e593. Hien, P.D., Hangartner, M., Fabian, S., Tan, P.M., 2014. Concentrations of NO2, SO2, and benzene across Hanoi measured by passive diffusion samplers. Atmos. Environ. 88, 66e73. Jeon, W.-B., Lee, S.-H., Lee, H., Park, C., Kim, D.-H., Park, S.-Y., 2014. A study on high ozone formation mechanism associated with change of NOx/VOCs ratio at a rural area in the Korean Peninsula. Atmos. Environ. 89, 10e21. Ji, H.E., Lee, S.H., Lee, H.W., 2013. Characteristics of sea breeze front development with various synoptic conditions and its impact on lower troposphere ozone formation. Adv. Atmos. Sci. 30, 1461e1478. KMA, 2012. 2011 Annual Climatological Report (in Korean language). Kova c-Andri c, E., Brana, J., Gvozdi c, V., 2009. Impact of meteorological factors on ozone concentrations modelled by time series analysis and multivariate statistical methods. Ecol. Inform. 4, 117e122. Kwok, R., Fung, J.C.H., Lau, A.K.H., Wang, Z.S., 2012. Tracking emission sources of sulfur and elemental carbon in Hong Kong/Pearl river delta region. J. Atmos. Chem. 69, 1e22. Kwon, H.-O., Choi, S.-D., 2014. Polycyclic aromatic hydrocarbons (PAHs) in soils from a multi-industrial city, South Korea. Sci. Total Environ. 470e471, 1494e1501. Lee, Y.-S., Choi, S.-D., Kwon, H.-O., Kim, C.-S., Son, H.-S., Ye, J., 2011. Temporal and spatial distribution of sulfur dioxide in Ulsan, Korea (In Korean language). J. Korean Soc. Environ. Anal. 14, 249e256. Lei, W.F., Zhang, R.Y., Tie, X.X., Hess, P., 2004. Chemical characterization of ozone formation in the Houston-Galveston area: a chemical transport model study. J. Geophys. Res. Atmos. 109.
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