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Changes in NOx and O3 concentrations over a decade at a central urban area of Seoul, Korea
Atmospheric Environment 112 (2015) 116e125
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Atmospheric Environment journal homepage: www.elsevier.com/locate/atmosenv
Changes in NOx and O3 concentrations over a decade at a central urban area of Seoul, Korea Kowsalya Vellingiri a, Ki-Hyun Kim a, *, Jin Yong Jeon b, Richard J.C. Brown c, Myung-Chae Jung d a
Dept. of Civil and Environmental Engineering, Hanyang University, 222, Wangsimni-Ro, Seoul 133-791, Republic of Korea Dept. of Architectural Engineering, Hanyang University, 222, Wangsimni-Ro, Seoul 133-791, Republic of Korea Analytical Science Division, National Physical Laboratory, Teddington TW11 0LW, UK d Dept. of Energy & Mineral Resources Engineering, Sejong University, Seoul 147-747, Republic of Korea b c
h i g h l i g h t s From the view point of air quality, NO2 is more important than NO due to its greater human health effects. Further, O3 is an important greenhouse gas making significant contributions to the climate change. A study has been undertaken to explain the long-term changes in NOx/O3 levels in urban air. Korea has been actively pursuing policies to reduce pollutant emissions from traffic-related sources. The decadal trend of NOx was dependent on traffic pollution, while that of O3 on NO and meteorology.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 March 2015 Received in revised form 7 April 2015 Accepted 13 April 2015 Available online 15 April 2015
This study aims to explore trends of NOx (NO þ NO2) pollution over ten years (2004e2013) at an urban monitoring station at Yongsan in Seoul, Korea. The mean concentrations (in nmol/mol) of NO, NO2, and O3 measured over the entire study period were 25.3 ± 7.30, 36.9 ± 1.76, and 17.5 ± 1.31, respectively. The decadal trend of these pollutants exhibited statistically significant, but contrasting, results with downward NO and NOx trends and upward O3 trends throughout the study period. Correlation studies and principal component analysis (PCA) explained association of NO and NO2 with traffic related pollutants (CO, PM, and SO) at a statistically significant level while O3 exhibited patterns correlated with meteorological parameters. The overall results of this study indicate that the decadal trend of NO and NO2 was highly dependent on automotive related pollution, while O3 concentration was influenced by both the availability of NO and meteorological conditions. © 2015 Published by Elsevier Ltd.
Keywords: Air quality NO2 NO Ozone Road traffic emissions
1. Introduction In highly populated urban areas, human activities (such as fossil fuel combustion) are classified as the major sources of airborne pollutants such as nitrogen oxides (NOx) (Barck et al., 2005). Emissions of NOx, defined as the sum of nitric oxide (NO) and nitrogen dioxide (NO2), have become topical in recent years due to their significance in air quality management and legislation. NOx is produced via diverse anthropogenic (gasoline (and diesel) engine combustion, industrial furnaces, and heating installations)
* Corresponding author. E-mail address: [email protected] (K.-H. Kim). http://dx.doi.org/10.1016/j.atmosenv.2015.04.032 1352-2310/© 2015 Published by Elsevier Ltd.
(Carslaw, 2005; Carslaw and Beevers, 2004a,b), and natural processes (agricultural (bacterial) activities in the soil, forest fires, production of biogenic compounds, and photochemical destruction of nitrogen compounds in the upper atmosphere) (USEPA, 2011). However, because of the prominent contribution of traffic sources in urban areas, NOx is often used as a tracer of road traffic emissions et al., 2004). The emitted NOx contributes to the formation (Lewne of secondary airborne pollutants like O3 and PM2.5 (Chai et al., 2014). It also has the potential to lead to nutrient overload and the deterioration of soil and water quality following deposition as nitrate in the surrounding environments (Bytnerowicz et al., 2007). From the view point of air quality management, the significance of NOx and O3 can be emphasized in many respects. According to Derwent and Hertel (1998), over 90% of nitrogen compounds are
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emitted in the form of nitric oxide (NO). Less than 10% of nitrogen is directly emitted as NO2. Hence, due to fast reaction of O3 with NO, the presence of O3 in the lower atmosphere depends upon the rates of NO generation and destruction. Note that NO is not detrimental directly but is important as the source of further NO2 and ozone in the atmosphere that can exert serious health effects. Further, O3 is also a greenhouse gas with an important role in climate change (ECOREA, 2013). In the atmosphere, both NOx and O3 species may react with other pollutant species through the complex photochemical reactions. For instance, O3 is produced in the troposphere when methane (CH4), non-methane hydrocarbons (NMHCs), and carbon monoxide (CO) are photochemically oxidized in the presence of NOx (Nishanth et al., 2014). Hence, the presence of NMHCs and NOx in the atmosphere indicates the influence of volatile organic compounds (VOCs) on O3 production in the troposphere, whereas at the surface NMHCs and NOx are related to the dissociation of NO2 molecules by sunlight. To understand the interrelationship between levels of NOx and O3, it is necessary to learn more about how these pollutant species interact with themselves and with meteorological parameters. In Korea, the monitoring of NOx (NO and NO2) and O3 in the national air quality management network has routinely been made by chemiluminescence and ultraviolet (UV) photometry, respectively (the detection limits of all targets ~ 1 ppb). In addition, Korea has also been actively pursuing policies to reduce pollutant emissions from traffic-related sources. As a major strand of these efforts, the Korean government launched the natural gas vehicle supply (NGVS) program from 2000 as a result of which a total of 20,000 diesel-fueled buses were replaced with natural gas-fueled buses by 2007 (Kang, 2004). These replacement plans were executed in the major urban areas of Korea including Seoul (prior to 2002 World Cup Games) and were extended further to small-scaled urban locations from 2003 onwards. From 2004, diesel particulate filters (DPF) or diesel oxidation catalysts (DOC) was also supplied for retrofitting on older vehicles. The Korean government enforced a pollution control strategy called the ‘Total Air Pollution Load Management System’ to improve the air quality in the Metropolitan area from 2005 onwards (Korean Ministry of Environment, 2006). This policy aimed to reduce the total anthropogenic NOx emissions in Seoul by 53% from 2001 (309, 387 tonne yr1) to 2014 (145, 412 tonne yr1) (Kim et al., 2013). As part of these efforts, cash incentives or tax reductions were offered for low emission and hybrid vehicles from 2005 (Shon and Kim, 2011). In addition, an emissions inventory, namely the, ‘Clean Air Policy Support System (CAPSS)’ was developed to monitor air pollutant emissions in South Korea. In this study, the concentrations of nitrogen oxides and ozone were investigated using the data sets of daily means collected from Yongsan (YS), a central urban area of Seoul in the Republic of Korea, from 2004 to 2013. To this end, the concentrations NOx and O3 were analyzed to assess their temporal variability (e.g., monthly, seasonal, and inter-annual intervals). Hence, the results of this study offer us an opportunity to assess the nitrogen oxide and ozone pollution trend in this highly urbanized environment. 2. Methodology 2.1. Site characteristics of the study area In this study, the temporal distribution trends of nitrogen oxides (i.e., NO2 and NO) and ozone were investigated using the data sets collected from Yongsan (YS), Seoul, Korea (37 320 18800 N and 126 570 5600 E) over a 10-year period (2004e2013). General information about the study area has been described in our recent study
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(Vellingiri et al., 2015) (Fig. 1). This area is located on the north of the Han River and comprises a land area of 21.87 km2 with a population density of 10,000/km2. The average daytime and night time temperatures were 25 and 13 C, respectively (http://en. wikipedia.org/wiki/Yongsan_District). The study site is well known for of the global ‘K-pop’ label (e.g., H2 media and TS entertainments). In addition, some major KOSPI 200 companies (e.g., Amore Pacific, Orion Confectionary, and Hyundai Development Company) have offices and factories in this district. The presence of the Korean Ministry of National Defense in this district adds to its administrative significance. 2.2. Experimental method The concentration data of different NOx fractions have been collected routinely along with other important pollutants (including CO, SO2, PM2.5, PM10, TSP, CH4, and NMHC) from both regional (urban) and roadside stations dispersed all across seven major metropolitan cities and a number of smaller cities in Korea (Iqbal et al., 2014; Shon and Kim, 2011; Shon et al., 2011). As our study site, YS belongs to one of these cities, all these monitoring tasks have been conducted by following the standard operating protocol for the ‘AIR QUALITY’ monitoring operation guide and regulations managed by the Korean Ministry of Environment (KMOE). Details of experimental conditions regarding operational conditions of instrumental setups (Table 1S) and the principles of data processing are provided in Supplementary Material (SM). 3. Results and discussion 3.1. General features of NOx and O3 A statistical summary of the NOx and O3 data measured from YS, district of Seoul, Korea during the entire study period (2004e2013) is presented in Table 1. The mean concentrations of NO, NO2, NOx and O3 measured over the entire study period were 25.3 ± 7.30, 36.9 ± 1.76, 62.1 ± 8.04, and 17.5 ± 1.31 ppb, respectively. As the concentration of NO2 is generally higher than NO, one may assume that NO2 contributes the larger portion to the increase in NOx concentration in the study area over the measurement period (Table 2). The annual mean values for each pollutant were computed using the daily measurement data. The observed annual values of NO exhibited a decreasing trend:from 38.0 ppb (2004) to 17.7 ppb (2012). Likewise, NO2 also decreased from 40.3 ppb (2004) to 33.4 ppb (2012). Such a decrease in NOx concentrations has been observed commonly elsewhere: in The Netherlands, observed concentrations have fallen from 40 ppb to 30 ppb, particularly after 2004 (Keuken et al., 2012). Note that the limit value for NO2 in ambient air Europe is 40 ppb as an annual mean (ETC, 2011). The frequency distribution patterns of each target compound were evaluated for each year using the relevant daily mean values (Fig. 2). The maximum frequencies of NO data were observed in the 1e10 ppb concentration range in the early years (2004e2007). Even though after 2007 the most common concentration was in the range 10e20 ppb, there were also fewer noticeable occurrences (10e30) of high concentrations (40e100). Likewise, NO2 generally exhibited the maximum frequency in the 30e40 ppb range in 2004, 2006, 2008, 2010, and 2012 but in recent years it was shifted to the slightly low range of 20e30 ppb. NO in the urban atmosphere should reflect the direct impacts of vehicular pollution in the study area, as it is a good indicator of fossil fuel based emissions (Lewne et al., 2004). In contrast, the O3 data showed a maximum frequency at 0e10 ppb in 2004. However, over the years this gradually shifted to higher concentrations but with lower total occurrences (with maximum frequencies around 10e30 ppb).
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Fig. 1. Aerial map of the Yongsan, Seoul, Korea.
3.2. Temporal pattern of nitrogen oxides and O3 The temporal variations of NO, NO2, and O3 were analyzed after dividing their data into monthly, seasonal, and annual intervals. The day-to-day variation patterns are plotted using the daily mean values (Fig. 3). According to the legislative guideline of KMOE, the maximum allowable daily mean for NO2 is 60 ppb. If the annual sum of days exceeding this threshold is compared, the results show gradual reduction through the years [1] 40 times (2004 and 2008), [2] 30 times (2009 and 2013), and [3] 20 times (2007 and 2010), and [4] 9 times (2012). Comparison of the monthly data, as seen in Fig. 1S, indicated the existence of strong seasonal trends with NO concentrations of: 10.1 (June) to 49.0 ppb (January) and NO2 concentrations of 25.8 (August) to 45.7 ppb (January). In contrast, O3 concentrations varied from 8.21 (December) to 27.9 ppb (May) (Table 2S).
The seasonal variations of NO, NO2, and O3 were also assessed by grouping the daily data into four seasons: spring (MarcheMay), summer (JuneeAugust), fall (SeptembereNovember), and winter (DecembereFebruary) (Fig. 2S). As shown in Table 3S, the highest seasonal mean concentrations (ppb) of NO, NO2, and O3 were: 41.6 ± 14.5 (in winter), 43.5 ± 5.56 (in winter), 24.5 ± 5.23 (in spring), respectively. The winter maximum of NO and NO2 can be explained by stagnant air conditions, lack of sunshine for photochemical conversion to ozone and paucity of rainfall to remove these gases from the atmosphere (Li et al., 2012). The spring maximum of O3 is well-known and due to photochemical activity when the sun's intensity is high (Iqbal et al., 2014; Klumpp et al., 2006; Stephens, 1969; Varshney and Aggarwal, 1992). The statistical significance of the seasonal relationships of each target compound was assessed by a KruskaleWallis (KW) test using the annual mean values obtained across each season. The KW non-
Table 1 Summary of nitrogen oxides and ozone concentrations, and relevant parameters measured at Yongsan district of Seoul, Korea between 2004 and 2013 (computations made using the daily mean values of each parameters). Year
2005 2006 2007 2008 2009 2010 2011 2012 2013 All data a b c
38.0 ± 41.0 1.0e238 33.1 ± 37.8 2.0e242 22.9 ± 25.1 3.0e153 25.2 ± 23.8 2.0e152 28.7 ± 25.6 2.0e147 24.3 ± 24.0 1.29e134 23.4 ± 22.7 1.38e214 18.5 ± 21.5 1.38e124 17.7 ± 18.3 1.04e152 21.3 ± 22.1 1.63e164 25.3 ± 7.30 1.0e242
NO2 (ppb) a
(23.0) (321)b (18.0) (344) (14.0) (287) (16)0.0 (299) (20.0) (366) (15.3) (360) (16.0) (364) (8.96) (362) (12.1) (362) (13.5) (361) (15.7) (2783)
40.3 ± 18.2 3.0e102 32.8 ± 16.4 3.0e87 35.1 ± 13.1 8.0e75 37.8 ± 14.8 9.0e93 42.6 ± 15.5 10e91.0 39.1 ± 14.5 9.42e89.0 37.6 ± 12.9 9.29e78.7 33.6 ± 14.6 9.29e79.2 33.4 ± 12.2 6.42e72.0 36.3 ± 14.2 7.71e87.1 36.9 ± 1.76 3.0e102
NOx (ppb) (37.0) (321) (32.0) (344) (34.0) (287) (37.0) (299) (40.0) (366) (37.1) (360) (36.5) (364) (31.6) (362) (32.6) (362) (33.1) (361) (35.2) (2783)
78.2 ± 55.6 6.0e339 65.8 ± 48.1 6.0e329 58.0 ± 36.3 11.0e228 63.0 ± 35.4 13e214 71.3 ± 38.0 12e218 63.4 ± 36.0 11.0e209 61.0 ± 32.5 10.7e293 52.1 ± 34.2 10.7e196 51.1 ± 28.1 7.46e219 57.6 ± 34.3 9.33e230 62.1 ± 8.04 6.0e339
O3 (ppb) (62.0) (321) (53.0) (344) (48.0) (287) (54.0) (299) (60.0) (366) (53.3) (360) (53.5) (364) (41.1) (362) (45.1) (362) (45.7) (361) (53.2) (2783)
13.6 ± 8.29 1.0e52.0 13.7 ± 8.20 1.0e50.0 17.1 ± 11.0 1.0e50 17.1 ± 10.3 1.0e51.0 15.6 ± 9.3 1.0e44 20.1 ± 11.5 2.33e61.0 18.0 ± 11.0 1.71e62.2 20.4 ± 11.1 1.71e69.8 19.4 ± 10.2 1.35e51.0 19.7 ± 12.0 0.67e83.3 17.5 ± 1.31 0.67e83.3
TEMP ( C)
WS (m/sec) (12.0) (350) (12.0) (351) (16.0) (278) (15.0) (298) (15.0) (366) (19.6) (360) (15.7) (359) (19.3) (361) (18.3) (364) (18.2) (361) (15.8) (2799)
0.76 ± 0.31 0.3e2.0 0.83e0.34 0.20e2.20 1.02e0.35 0.40e2.40 1.28 ± 0.48 0.50e3.10 2.21 ± 0.52 1.2e4.0 2.41 ± 0.58 1.43e4.52 2.46 ± 0.59 1.51e4.63 1.43 ± 0.58 0.45e3.19 2.53 ± 0.63 1.60e6.55 2.52 ± 0.61 1.57e4.68 1.74 ± 0.12 0.20e6.55
(0.70) (361) (0.80) (361) (0.90) (320) (1.20) (320) (2.10) (366) (2.29) (360) (2.32) (365) (1.36) (365) (2.38) (361) (2.37) (365) (1.73) (2859)
12.8 ± 9.64 13.8 to 29.4 11.7e10.8 10.5 to 29.4 14.0e9.84 11.3 to 29.8 12.9 ± 10.1 6.4 to 29.7 12.9 ± 9.99 8.5 to 29.3 13.1 ± 10.0 9.79 to 28.6 12.5 ± 10.9 12.1 to 29.3 12.9 ± 10.6 13.0e30.4 12.2 ± 11.4 13.4 to 31.3 12.6 ± 11.0 12.7 to 29.8 12.8 ± 0.56 13.8 to 31.3
HUM (%) (14.30) (361) (13.60) (361) (16.8) (320) (11.6) (317) (14.5) (366) (15.6) (360) (13.1) (365) (14.6) (365) (13.9) (361) (12.6) (365) (14.4) (2859)
64.2 ± 16.9 23.0e96.0 64.3 ± 17.9 26.0e96.0 63.1 ± 16.2 25.0e96.0 59.4 ± 15.1 20e93 59.9 ± 14.1 22e92 59.8 ± 13.9 28.8e90.3 60.8 ± 14.3 26.2e92.4 59.2 ± 17.1 25.1e95.7 55.5 ± 13.8 22.6 ± 87.7 58.5 ± 13.1 25.4e87.8 60.4 ± 1.69 20.0e96.0
(65.0) (360) (65.0) (361) (63.0) (320) (61.0) (314) (62) (366) (61.3) (360) (62.4) (365) (56.2) (365) (55.2) (361) (57.9) (365) (61.4) (2858)
UV (mW/cm2)
SR (W/m2)
0.05 ± 0.03 0.0e0.15 0.05 ± 0.04 0.0e0.21 0.15 ± 0.08 0.02e0.73 0.29 ± 0.23 0.02e0.94 0.38 ± 0.19 0.04e0.84 0.37 ± 0.20 0.01e0.86 0.37 ± 0.20 0.03e0.86 0.28 ± 0.20 0.01e1.03 0.39 ± 0.20 0.02e0.85 0.39 ± 0.21 0.02e0.89 0.27 ± 0.08 0.0e1.03
125 ± 63.5 8.0e293 123 ± 58.4 7.0e295 133 ± 70.8 19.0e305 113 ± 54.6 16.0e248 147 ± 74.6 7.0e309 145 ± 78.2 2.21e313 138 ± 77.6 7.50e340 133 ± 77.1 6.13e300 148 ± 75.6 3.46e307 146 ± 78.4 5.13e321 136 ± 8.91 2.21e321
(0.04) (361) (0.05) (292) (0.14) (275) (0.21) (307) (0.34) (366) (0.33) (360) (0.32) (365) (0.23) (365) (0.35) (361) (0.37) (365) (0.28) (2814)
(116) (361) (117) (361) (116) (320) (108) (307) (144) (366) (134) (360) (126) (365) (119) (365) (141) (361) (144) (365) (126) (2859)
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NO (ppb) 2004
B. Meteorological Parametersc
A. Pollutant species
Mean ± SD (Median), SD ¼ Standard Deviation. Range ¼ Minimum Maximum (N); and N ¼ number of seasonal data. Acronyms used for meteorological parameters denote: WD ¼ wind direction, WS ¼ wind speed, TEMP ¼ temperature, HUM ¼ humidity, UV ¼ ultraviolet, and SR ¼ solar radiation.
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Table 2 Trends in nitrogen oxides, ozone, and relevant meteorological parameters measured at Yongsan district of Seoul, Korea between 2004 and 2013 (using annual (A) and (B) monthly mean concentrations (for seasonal trend)). Pollutants Mean
(A) Annual trend NO 25.3 36.9 NO2 NOx 62.1 NO2/NOx 0.66 NO/NOx 0.40 O3 17.5 WS 1.74 TEMP 12.8 HUM 60.5 UV 0.27 SR 135 (B) Seasonal trend NO 24.8 NO2 36.3 NOx 61.1 NO2/NOx 0.63 NO/NOx 0.365 O3 17.5 WS 1.73 TEMP 12.8 HUM 60.0 UV 0.27 SR 134
SD
MK Sen's coefficient slope (Q) (tau)
6.30 0.689 3.22 ¡0.200 8.29 0.600 0.05 0.600 0.052 0.733 2.53 0.689 0.75 0.822 0.585 ¡0.156 2.73 0.683 0.14 0.644 11.9 0.467
1.852 0.273 2.103 0.012 0.013 0.682 0.208 0.044 0.727 0.038 2.502
18.7 0.393 9.79 ¡0.159 26.0 0.285 0.13 0.526 0.123 0.519 7.52 0.444 0.77 0.685 10.0 0.074 11.3 0.315 0.19 0.559 43.6 0.356
0.078 0.035 0.120 0.001 0.0009 0.061 0.017 0.018 0.035 0.003 0.24
Probability Trend at 95% (p) confident intervala 0.005 0.484 0.017 0.017 0.002 0.005 0.0004 0.601 0.005 0.009 0.073
£ þ þ þ £ þ þ
<0.0001 0.028 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.314 <0.0001 <0.0001 <0.0001
£ þ þ þ £ þ þ
a The signs of þ, , and denote increasing, decreasing, and no trends, respectively. Numbers in boldface denote the case of no changes ().
parametric method can be used to test whether there are differences between multiple independent samples. The test was used in preference to the one way analysis of variance (ANOVA) because it does not make assumptions concerning how the daily data are distributed (Carslaw, 2005). The results of the KW test indicated that NO, NO2, NOx, and O3 exhibited significant seasonal trends (p < 0.0001). In the case of meteorological parameters, all parameters except wind speed (p ¼ 0.399) showed statistical differences between seasons.
3.3. Long term trends in NOx and O3 concentrations in Yongsan Analysis of long-term trends of airborne pollutants in specific regions is important to understand changes in the pollution climate. To learn more about this, the variation of the NOx was investigated in relation to other parameters (Fig. 3S). For instance, NO decreased form 38 (2004) to 21.3 ppb (2013), whereas the CO levels increased from 0.13 (2004) to 0.31 ppm (2013). SO2 levels also showed a similarly decreasing trend. On the other hand, when we compared the relationship of O3 with UV and SR, they were highly comparable each other. These results support the hypothesis that meteorological parameters also play a vital role in affecting O3 levels in this location. Moreover, to explore the local contribution, long term trends of NOx and O3 were analyzed using MK method (Table 2). The MK method is a non-parametric test that can be applied to abnormally distributed data with missing points (Carslaw, 2005). The test detects only the presence of a trend of which quantification is made by the calculation of a slope using the Sen method (Gilbert, 1987). To analyze the recent trends of NOx and O3 in our study site, the MK test was conducted using the monthly (and yearly) mean data sets (Figs. 1S and 3S). The results of the MK test on the annual data confirmed the constant reductions in NO levels across the years from 38.0 (2004)
to 21.3 ppb (2013) with a negative slope of 1.852 ppb yr1. In contrast, NO2 did not show any discernible changes over a study period. According to this test, the annual trend of NO2 appears to be less significant (p ¼ 0.484), while NOx showed a downward trend (p ¼ 0.017 level) clearly driven mainly by the effect of NO in the overall NOx sum. This pattern of NO reduction is explained by reduction in primary NO emissions driven by several factors such as the improvement in fuel types (e.g., natural gas) and vehicle engine types (e.g., (DOC) or (DPF)) together with legislative amendments to control emissions (e.g., tax reduction on low emission and hybrid vehicles) (Carslaw, 2005). The annual concentrations of O3 showed a strong upward trend (p ¼ 0.005) for the entire study period which is closely related to the trends of some meteorological parameters (e.g., UV and SR). This pattern may reflect the interactive role between strong photochemical aging and upward NO2/NOx ratios (Shon et al., 2011). For the meteorological parameters, the annual trend showed statistically significant upward trends for wind speed (WS), SR, and UV. In contrast, humidity (HUM) showed downward trend over a decadal period, while there was no apparent trend for temperature. If the relative role of the regional contribution to the global NOx emission inventory is assessed, the significance of the Asian continent has changed very rapidly through the years. Although it represented only a minor fraction (~10,000 kt y1) in the 19th century its proportion has increased rapidly (~29,000 kt y1) fueled by enormous population growth and cultural developments in Asian continent (Akimoto, 2003), (Fig. 4S(a)). In fact, the rate of urbanization in Asia was about 1.6% between the years of 1975 and 2000 but is projected to decrease slightly to 1.4% between 2000 and 2025 (Fig. 4S(b)). In recent years, due to urbanization and industrialization, emissions of air pollutants have increased substantially. The rate of the population migration toward urban areas has also caused rapid increases in the number of vehicles and the rate of energy consumption (Lee et al., 2011). This type of a temporal pattern has in fact been observed generally at most urban sites around the world, as reported by many previous studies (Carslaw, 2005; Keuken et al., 2012; Shon et al., 2011). 3.4. Factors affecting the distribution of NOx and O3 In order to study the factors and processes influencing the NOx and O3 concentrations, a correlation analysis was conducted. For this analysis, both (1) daily data sets and (2) seasonal datasets were used (Table 4S). Strong correlations were found between annual means of NO and those of CO (r ¼ 0.52, p < 0.1), CH4 (r ¼ 0.50, p < 0.1), and THC (r ¼ 0.47, p < 0.1). In the case of seasonal data, the strongest correlations with NO were found for SO2, CO, PM2.5 and PM10 in fall, but with CH4 and NMHC in winter. It thus suggests that CO and SO2 should be affected similarly by primary combustion products such as motor vehicle emissions, while THC concentrations may also reflect the emissions from biomass burning processes (Smit et al., 2010; Uherek et al., 2010). Strong correlations were observed between NO2 and both CO (p < 0.01), PM2.5 (p < 0.01), and SO2 (p < 0.01) for all data. The seasonal correlation of NO2 showed significantly strong correlations with most pollutant species in winter, followed by fall, and then the other two seasons. All meteorological parameters showed inverse weak correlations with NO and NO2. Progiou and Ziomas (2011) reported that passenger cars in Athens, Greece were the major sources (>50%) of CO, NMVOCs, and CH4. On the other hand, they found that PM10 and NOx are connected to heavy duty vehicles with a contribution of 66 and 65%, respectively. A strong inverse correlation was also observed between O3 and all fractions of NOx. In contrast with NO and NO2, O3 showed significant correlation with UV (r ¼ 0.53, p < 0.01) and SR (r ¼ 0.58, p < 0.01). Interestingly,
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Fig. 2. Frequency distribution patterns of (A) NO, NO2, and O3 (ppb) and (B) UV and SR measured from Yongsan between 2004 and 2013 (using daily data).
O3 showed only strong inverse correlations with SO2, CO, and NMHC in winter, while WS showed positive correlations throughout all seasons. This observation supports the well-known role of photochemical reduction of NO and simultaneous production of NO2. It has been reported that ozone production is generally controlled by HOxeNOxeVOC (or NMHC) chemistry (Arens et al., 2001). Although pollution levels in urban areas are mainly governed by emissions of major pollutants, meteorology also plays an important role in their dispersion, photochemical transformation, and transport (Melkonyan and Kuttler, 2012). In addition, the winds blowing towards Korea tend to originate from specific directions (e.g., NW, N, or SE). These directions can reflect the effect of the ocean (East China Sea) and highly industrialized areas (e.g., Beijing) (Lim et al., 2010). Hence, our results support the hypothesis that O3 levels are influenced to some meteorological variables like wind speed and direction. To identify the most dominant groups of pollutants and meteorological parameters, one can use statistical tools like PCA. In order to study the reciprocity of all parameters, whole daily datasets and seasonally grouped daily datasets were used. The principal
components (PC) extracted are ordered in such a way that the PC1 explains most of the variance, and the following PCs account for the largest proportion of variability that has not been accounted by its predecessors (Abdul-Wahab et al., 2005). According to the Kaiser criterion, PCs with an eigen value 1 are considered as being of statistical significance and factor loadings greater than 0.75 were considered strong; 0.75 to 0.50 as moderate and 0.49 to 0.30 as weak variables. The PCA results produced five main components to explain a total variance of 80.8% of all data measured for the entire period (2004e2013: Table 3). PC1 explained 28.8% of the total variance, with NO, NO2, PM2.5, PM10, TSP, CO, and SO2 all contributing to its loading. PC2, which accounted for about 21% of the total variance, was related to THC (CH4 and NMHC) and UV. This kind of strong association of NOx with other pollutants has often been observed as a function of land use type, traffic patterns, local topography, atmospheric chemistry, and physical processes (Beckerman et al., 2008). PC3, PC4, and PC5 mainly comprised contributions from O3, TEMP, and HUM, respectively. The close relationship between O3 and those parameters suggest the role of photochemistry in its
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Fig. 3. Plot of daily mean concentrations of NO, NO2, and O3 (ppb) measured at Yongsan from 2004 to 2013.
formation (Melkonyan and Kuttler, 2012). The seasonally extracted PCs are also described in Table 3. These results showed a dominant variability of 45.2% accounted for by most pollutants (except SO2 and CO) in winter and 40.2% in fall (except O3) season. The spring season showed 29.0% variability with PC1 mainly coming from NO, NO2, and NMHC, while summer showed the potential contribution of photochemistry (e.g., NO2, O3, and PM) in the range of 25.8%. Owing to the chemical coupling of O3 and NOx (¼NO þ NO2), the concentration levels of O3 and NO2 are inextricably linked. Consequently, the relationships between NOx and O3 exhibit highly nonlinear trends, and any resultant reduction in the level of NO2 is invariably accompanied by an increase in the level of O3 (Clapp and Jenkin, 2001). Fig. 4 depicts the annual variation of NOx together with ozone. Accordingly, it is evident that O3 showed an increase in concentration as a function of the consumption of NO by photochemistry. Hence, NO plays the critical role in the photochemical process of ozone. The ratio of NO/NOx (0.34) indicated a strong downward trend (at p ¼ 0.002 level) (Tables 1 and 2). This relative
decrease in NO/NOx ratio may be attributed to the aforementioned chemical processes or the increased use of DOC. According to the policy of Special Act on Metropolitan Air Quality Improvement by the Korean Ministry of Environment (2004e2012), the use of low pollution devices (such as DPF or DOC) has been implemented for diesel vehicles (ECOREA, 2013) (http://eng.me.go.kr/eng/web/ index.do?menuId¼ 30&findDepth ¼ 1). A total of 1,046,492 DOC devices were supplied from 2004 to 2012 in Seoul; the maximum distributions of nearly 42% (62,234) and 36% (53,461) of these were made in 2006 and 2007, respectively. The DOCs can catalytically oxidize CO and hydrocarbons by effectively converting NO to NO2 which may lead to increase in the NO2 fraction in NOx (Shon et al., 2011; Tian et al., 2011). 3.5. Comparison of NOx and O3 levels between different studies The status of NOx and O3 pollution at Yongsan was compared with previous datasets by considering both spatial and temporal
Table 3 The results of principal component analysis using all the environmental parameters measured at Yongsan between 2004 and 2013 (Using daily data). (I) pollutant species NO
NO2
O3
SO2
CO
PM2.5
PM10
TSP
CH4
NMHC
WS
TEMP
0.723 0.400 0.062 0.339 0.164
0.392 0.258 0.668 0.089 0.134
0.596 0.320 0.136 0.022 0.070
0.616 0.361 0.046 0.137 0.103
0.699 0.268 0.364 0.141 0.254
0.664 0.215 0.415 0.422 0.269
0.625 0.162 0.416 0.504 0.191
0.333 0.816 0.315 0.011 0.192
0.351 0.760 0.335 0.086 0.112
0.307 0.757 0.022 0.046 0.112
0.362 0.095 0.602 0.519 0.250
0.802 0.201 0.293 0.310 0.039
¡0.800 0.314 0.026 0.014 0.214
0.477 0.600 0.060 0.211 0.021
0.425 0.545 0.167 0.013 0.344
0.704 0.543 0.167 0.007 0.297
0.708 0.389 0.347 0.360 0.274
0.665 0.360 0.319 0.405 0.324
0.706 0.284 0.303 0.280 0.315
0.641 0.248 0.07 0.082 0.484
¡0.664 0.381 0.144 0.293 0.184
0.685 0.449 0.230 0.262 0.154 0.323
0.71 0.443 0.117 0.023 0.123 0.159
0.422 0.412 0.050 0.028 0.107 0.065
0.101 0.495 0.050 0.150 0.430 0.542
0.255 0.491 0.454 0.342 0.057 0.147
0.246 0.587 0.66 0.002 0.172 0.313
0.213 0.568 ¡0.653 0.056 0.122 0.340
0.186 0.237 0.391 0.563 0.536 0.129
0.748 0.089 0.390 0.033 0.002 0.261
0.700 0.304 0.267 0.433 0.027 0.228
0.514 ¡0.669 0.160 0.017 0.129 0.021
0.468 0.153 0.067 0.213 0.017 0.589
0.525 0.218 0.134 0.293 0.517 0.198
0.787 0.276 0.348 0.229 0.005 0.256
0.782 0.256 0.317 0.322 0.084 0.267
0.812 0.226 0.296 0.272 0.070 0.248
0.384 0.029 0.597 0.169 0.594 0.099
0.842 0.156 0.12 0.011 0.045
0.612 0.492 0.118 0.277 0.088
0.505 0.360 0.010 0.052 0.456
0.546 0.448 0.133 0.222 0.442
0.765 0.129 0.247 0.499 0.009
0.757 0.132 0.119 0.591 0.007
0.680 0.158 0.127 0.631 0.005
0.586 0.498 0.097 0.250 0.118
HUM
UV
SR
Eigen value
% of variance
Cumulative %
0.078 0.128 0.276 0.597 0.662
0.467 ¡0.560 0.462 0.206 0.267
0.265 0.299 0.703 0.050 0.455
4.901 3.634 2.520 1.507 1.182
28.8 21.4 14.8 8.86 6.96
28.8 50.2 65.0 73.9 80.8
0.378 0.310 0.445 0.038 0.407
0.375 0.070 0.704 0.332 0.058
0.263 0.715 0.491 0.042 0.122
0.369 0.300 0.524 0.555 0.033
8.594 2.702 1.900 1.255 1.031
45.2 14.2 10.0 6.61 5.43
45.2 59.5 69.5 76.1 81.5
¡0.662 0.233 0.096 0.263 0.204 0.297
0.103 0.474 0.408 0.491 0.186 0.313
0.032 0.187 0.121 0.690 0.480 0.185
0.455 0.577 0.218 0.272 0.034 0.053
0.142 0.410 0.062 0.457 ¡0.655 0.126
5.504 3.146 2.127 1.932 1.463 1.220
29.0 16.6 11.2 10.2 7.70 6.42
29.0 45.5 56.7 66.9 74.6 81.0
0.084 0.653 0.345 0.445 0.251 0.101
0.031 ¡0.606 0.142 0.214 0.252 0.124
0.192 0.227 0.49 0.067 0.392 0.530
¡0.576 0.301 0.284 0.225 0.455 0.180
0.338 ¡0.597 0.480 0.318 0.082 0.12
0.617 0.163 0.316 0.125 ¡0.514 0.040
4.897 4.439 2.154 1.842 1.459 1.091
25.8 23.4 11.3 9.70 7.68 5.74
25.8 49.1 60.5 70.2 77.8 83.6
0.402 0.196 0.669 0.367 0.23
¡0.508 0.286 0.468 0.068 0.354
0.399 0.099 0.772 0.062 0.140
0.072 ¡0.739 0.383 0.165 0.168
0.337 0.664 0.005 0.205 0.268
0.228 0.563 0.417 0.091 0.478
7.647 2.456 2.147 1.705 1.288
40.2 12.9 11.3 8.97 6.78
40.2 53.2 64.5 73.4 80.2
K. Vellingiri et al. / Atmospheric Environment 112 (2015) 116e125
(A) All data PC1 0.782 PC2 0.153 PC3 0.283 PC4 0.323 PC5 0.190 (B) Winter data PC1 0.878 PC2 0.135 PC3 0.219 PC4 0.075 PC5 0.114 (C) Spring data PC1 0.899 PC2 0.003 PC3 0.043 PC4 0.026 PC5 0.145 PC6 0.238 (D) Summer data PC1 0.149 PC2 0.787 PC3 0.039 PC4 0.547 PC5 0.064 PC6 0.021 (E) Fall data PC1 0.890 PC2 0.003 PC3 0.213 PC4 0.062 PC5 0.235
(II) meteorological parameters
Only factors loadings 0.5 are highlighted in boldface. Only factors with Eigen value 1 are shown.
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K. Vellingiri et al. / Atmospheric Environment 112 (2015) 116e125
Fig. 4. Comparison of annual mean concentrations of NO and NO2 with O3 (ppb) at the Yongsan for the period 2004e2013 (using daily data).
factors (Table 5S). First, if the results for the similar areas are compared between different time bands, we can observe noticeable increase of NO, NO2, and O3 levels. For instance, Esen et al. (2005) observed 28 ppb of O3 in Bursa, Turkey during 2001e2002. In contrast, Civan et al. (2015) reported 39 ppb of O3 in Aliga, Western Turkey during 2005e2007. The spatial trends of target pollutant levels can also be compared using the datasets obtained in a similar time band across different regions, e.g., Asian vs European countries (Table 5S). In the case of NO2, Mavroidis and Ilia (2012) reported 22 ppb in Athens, Greece, while Hwang et al. (2015) observed 17 ppb in Taiwan during the similar time period (2007e2009). In recent years, noticeable reductions in NO2 levels have been observed in European countries relative to Asian countries. For instance, in Korea, NO2 levels are often found to approach or exceed 40 ppb (annual guideline values), as seen in this work. In contrast, in European cities, its values tend to be much reduced, as seen from Athens, Greece (22 ppb) (Mavroidis and Ilia, 2012) or Nevada, USA (24.1 ppb) (Kimbrough et al., 2013). As seen from previous studies (Clapp and Jenkin, 2001), we may observe that NO2 is the major component of NOx contributions at low concentration levels, while NO tends to be more dominant at higher mixing ratios of NOx. As such, reduced concentrations of nitrogen oxide (NO and NO2) in developed countries like USA are attributed mainly to cleaner environments and less automotive related pollution. In contrast, in the case of developing countries such as China, traffic congestion accompanied by low vehicular speed contributes to the build-up of NO2 (Carslaw and Beevers,
2004b). Moreover, the occurrence of low O3 concentrations in some countries may be due to the formation of NO2 species by wellknown photochemical reactions (Yao et al., 2005). To this end, to reduce global NOx and O3 pollution, it is important to reduce the local and regional concentrations of these pollutants. It is necessary therefore to continue to implement strict administrative regulations to lower the contribution of local pollution to the global emissions budget. 4. Conclusion The environmental behavior of nitrogen oxides and ozone concentrations in urban environment was examined using the data sets collected from Seoul, Korea between 2004 and 2013. The data were analyzed to evaluate their distribution at various temporal scales (daily, monthly, seasonal, and annual intervals). The NO and NO2 indicated similar seasonal patterns of maximum peaks in the winter (caused by the increase in the consumption of fuels, poorer dispersion conditions, and less photochemical activity) and minimum levels in summer (e.g., the washout effect and greater photochemical activity). In contrast, due to increased photochemical activity, ozone concentration tends to soar in spring, while being reduced in winter. The long term analysis indicated a decreasing trend in NO and NOx, while ozone recorded an increasing trend throughout the decadal period. The ratios of NO/ NOx and NO2/NOx showed downward and upward trends, respectively. In addition, the ratio of NO2/NOx (0.66) also indicated the
K. Vellingiri et al. / Atmospheric Environment 112 (2015) 116e125
interrelationship of O3 with NO and NO2 through their photochemical reactions. The results of correlation studies indicated a clear interrelationship between NOx with most of the automotive related pollutants (e.g., CO, SO2, THC, PM2.5, and PM10). Conversely, O3 exhibited close relationships with some meteorological parameters (e.g., UV and SR). In addition, PCA analysis showed that CO, PM10, PM2.5, and TSP were the major components contributing to air pollution in the study area of YS. Moreover, the concentration of the NO and NO2 was mainly influenced by the local automotive-related pollution sources, whereas the O3 concentration was affected more by the meteorological parameters. Hence, we recommend that future studies assess the contribution of different source processes to observed pollutant levels, especially between the local and long range sources. The richness and quantity of information in the data presented has also clearly demonstrated the benefits of long term ambient air monitoring endeavors to the understanding of atmospheric processes and local pollution climate. Acknowledgment This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (grant number 2013004624). This work was also carried out with the support of the “Cooperative Research Program for Agriculture Science & Technology Development (Project title: Study on model development to control odor from pigpen, Project No. PJ01052101)” Rural Development Administration, Republic of Korea. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2015.04.032. 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. Akimoto, H., 2003. Global air quality and pollution. Science 302, 1716e1719. €ggeler, H.W., Ammann, M., 2001. HetArens, F., Gutzwiller, L., Baltensperger, U., Ga erogeneous reaction of NO2 on diesel soot particles. Environ. Sci. Technol. 35, 2191e2199. Barck, C., Lundahl, J., Hallden, G., Bylin, G., 2005. Brief exposures to NO2 augment the allergic inflammation in asthmatics. Environ. Res. 97, 58e66. Beckerman, B., Jerrett, M., Brook, J.R., Verma, D.K., Arain, M.A., Finkelstein, M.M., 2008. Correlation of nitrogen dioxide with other traffic pollutants near a major expressway. Atmos. Environ. 42, 275e290. Bytnerowicz, A., Omasa, K., Paoletti, E., 2007. Integrated effects of air pollution and climate change on forests: a northern hemisphere perspective. Environ. Pollut. 147, 438e445. Carslaw, D.C., 2005. Evidence of an increasing NO2/NOx emissions ratio from road traffic emissions. Atmos. Environ. 39, 4793e4802. Carslaw, D.C., Beevers, S.D., 2004a. Investigating the potential importance of primary NO2 emissions in a street Canyon. Atmos. Environ. 38, 3585e3594. Carslaw, D.C., Beevers, S.D., 2004b. New directions: should road vehicle emissions legislation consider primary NO2? Atmos. Environ. 38, 1233e1234. Chai, F., Gao, J., Chen, Z., Wang, S., Zhang, Y., Zhang, J., Zhang, H., Yun, Y., Ren, C., 2014. Spatial and temporal variation of particulate matter and gaseous pollutants in 26 cities in China. J. Environ. Sci. 26, 75e82. € Civan, M.Y., Elbir, T., Seyfioglu, R., Kuntasal, O.O., Bayram, A., Dogan, G., Yurdakul, S., € Müezzinoglu, A., Sofuoglu, S.C., 2015. Spatial and temporal variations Andiç, O., in atmospheric VOCs, NO2, SO2, and O3 concentrations at a heavily industrialized region in Western Turkey, and assessment of the carcinogenic risk levels of benzene. Atmos. Environ. 103, e113. Clapp, L.J., Jenkin, M.E., 2001. Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in the UK. Atmos. Environ. 35, 6391e6405.
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Changes in NOx and O3 concentrations over a decade at a central urban area of Seoul, Korea Kowsalya Vellingiri a, Ki-Hyun Kim a, *, Jin Yong Jeon b, Richard J.C. Brown c, Myung-Chae Jung d a
Dept. of Civil and Environmental Engineering, Hanyang University, 222, Wangsimni-Ro, Seoul 133-791, Republic of Korea Dept. of Architectural Engineering, Hanyang University, 222, Wangsimni-Ro, Seoul 133-791, Republic of Korea Analytical Science Division, National Physical Laboratory, Teddington TW11 0LW, UK d Dept. of Energy & Mineral Resources Engineering, Sejong University, Seoul 147-747, Republic of Korea b c
h i g h l i g h t s From the view point of air quality, NO2 is more important than NO due to its greater human health effects. Further, O3 is an important greenhouse gas making significant contributions to the climate change. A study has been undertaken to explain the long-term changes in NOx/O3 levels in urban air. Korea has been actively pursuing policies to reduce pollutant emissions from traffic-related sources. The decadal trend of NOx was dependent on traffic pollution, while that of O3 on NO and meteorology.
a r t i c l e i n f o
a b s t r a c t
Article history: Received 5 March 2015 Received in revised form 7 April 2015 Accepted 13 April 2015 Available online 15 April 2015
This study aims to explore trends of NOx (NO þ NO2) pollution over ten years (2004e2013) at an urban monitoring station at Yongsan in Seoul, Korea. The mean concentrations (in nmol/mol) of NO, NO2, and O3 measured over the entire study period were 25.3 ± 7.30, 36.9 ± 1.76, and 17.5 ± 1.31, respectively. The decadal trend of these pollutants exhibited statistically significant, but contrasting, results with downward NO and NOx trends and upward O3 trends throughout the study period. Correlation studies and principal component analysis (PCA) explained association of NO and NO2 with traffic related pollutants (CO, PM, and SO) at a statistically significant level while O3 exhibited patterns correlated with meteorological parameters. The overall results of this study indicate that the decadal trend of NO and NO2 was highly dependent on automotive related pollution, while O3 concentration was influenced by both the availability of NO and meteorological conditions. © 2015 Published by Elsevier Ltd.
Keywords: Air quality NO2 NO Ozone Road traffic emissions
1. Introduction In highly populated urban areas, human activities (such as fossil fuel combustion) are classified as the major sources of airborne pollutants such as nitrogen oxides (NOx) (Barck et al., 2005). Emissions of NOx, defined as the sum of nitric oxide (NO) and nitrogen dioxide (NO2), have become topical in recent years due to their significance in air quality management and legislation. NOx is produced via diverse anthropogenic (gasoline (and diesel) engine combustion, industrial furnaces, and heating installations)
* Corresponding author. E-mail address: [email protected] (K.-H. Kim). http://dx.doi.org/10.1016/j.atmosenv.2015.04.032 1352-2310/© 2015 Published by Elsevier Ltd.
(Carslaw, 2005; Carslaw and Beevers, 2004a,b), and natural processes (agricultural (bacterial) activities in the soil, forest fires, production of biogenic compounds, and photochemical destruction of nitrogen compounds in the upper atmosphere) (USEPA, 2011). However, because of the prominent contribution of traffic sources in urban areas, NOx is often used as a tracer of road traffic emissions et al., 2004). The emitted NOx contributes to the formation (Lewne of secondary airborne pollutants like O3 and PM2.5 (Chai et al., 2014). It also has the potential to lead to nutrient overload and the deterioration of soil and water quality following deposition as nitrate in the surrounding environments (Bytnerowicz et al., 2007). From the view point of air quality management, the significance of NOx and O3 can be emphasized in many respects. According to Derwent and Hertel (1998), over 90% of nitrogen compounds are
K. Vellingiri et al. / Atmospheric Environment 112 (2015) 116e125
emitted in the form of nitric oxide (NO). Less than 10% of nitrogen is directly emitted as NO2. Hence, due to fast reaction of O3 with NO, the presence of O3 in the lower atmosphere depends upon the rates of NO generation and destruction. Note that NO is not detrimental directly but is important as the source of further NO2 and ozone in the atmosphere that can exert serious health effects. Further, O3 is also a greenhouse gas with an important role in climate change (ECOREA, 2013). In the atmosphere, both NOx and O3 species may react with other pollutant species through the complex photochemical reactions. For instance, O3 is produced in the troposphere when methane (CH4), non-methane hydrocarbons (NMHCs), and carbon monoxide (CO) are photochemically oxidized in the presence of NOx (Nishanth et al., 2014). Hence, the presence of NMHCs and NOx in the atmosphere indicates the influence of volatile organic compounds (VOCs) on O3 production in the troposphere, whereas at the surface NMHCs and NOx are related to the dissociation of NO2 molecules by sunlight. To understand the interrelationship between levels of NOx and O3, it is necessary to learn more about how these pollutant species interact with themselves and with meteorological parameters. In Korea, the monitoring of NOx (NO and NO2) and O3 in the national air quality management network has routinely been made by chemiluminescence and ultraviolet (UV) photometry, respectively (the detection limits of all targets ~ 1 ppb). In addition, Korea has also been actively pursuing policies to reduce pollutant emissions from traffic-related sources. As a major strand of these efforts, the Korean government launched the natural gas vehicle supply (NGVS) program from 2000 as a result of which a total of 20,000 diesel-fueled buses were replaced with natural gas-fueled buses by 2007 (Kang, 2004). These replacement plans were executed in the major urban areas of Korea including Seoul (prior to 2002 World Cup Games) and were extended further to small-scaled urban locations from 2003 onwards. From 2004, diesel particulate filters (DPF) or diesel oxidation catalysts (DOC) was also supplied for retrofitting on older vehicles. The Korean government enforced a pollution control strategy called the ‘Total Air Pollution Load Management System’ to improve the air quality in the Metropolitan area from 2005 onwards (Korean Ministry of Environment, 2006). This policy aimed to reduce the total anthropogenic NOx emissions in Seoul by 53% from 2001 (309, 387 tonne yr1) to 2014 (145, 412 tonne yr1) (Kim et al., 2013). As part of these efforts, cash incentives or tax reductions were offered for low emission and hybrid vehicles from 2005 (Shon and Kim, 2011). In addition, an emissions inventory, namely the, ‘Clean Air Policy Support System (CAPSS)’ was developed to monitor air pollutant emissions in South Korea. In this study, the concentrations of nitrogen oxides and ozone were investigated using the data sets of daily means collected from Yongsan (YS), a central urban area of Seoul in the Republic of Korea, from 2004 to 2013. To this end, the concentrations NOx and O3 were analyzed to assess their temporal variability (e.g., monthly, seasonal, and inter-annual intervals). Hence, the results of this study offer us an opportunity to assess the nitrogen oxide and ozone pollution trend in this highly urbanized environment. 2. Methodology 2.1. Site characteristics of the study area In this study, the temporal distribution trends of nitrogen oxides (i.e., NO2 and NO) and ozone were investigated using the data sets collected from Yongsan (YS), Seoul, Korea (37 320 18800 N and 126 570 5600 E) over a 10-year period (2004e2013). General information about the study area has been described in our recent study
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(Vellingiri et al., 2015) (Fig. 1). This area is located on the north of the Han River and comprises a land area of 21.87 km2 with a population density of 10,000/km2. The average daytime and night time temperatures were 25 and 13 C, respectively (http://en. wikipedia.org/wiki/Yongsan_District). The study site is well known for of the global ‘K-pop’ label (e.g., H2 media and TS entertainments). In addition, some major KOSPI 200 companies (e.g., Amore Pacific, Orion Confectionary, and Hyundai Development Company) have offices and factories in this district. The presence of the Korean Ministry of National Defense in this district adds to its administrative significance. 2.2. Experimental method The concentration data of different NOx fractions have been collected routinely along with other important pollutants (including CO, SO2, PM2.5, PM10, TSP, CH4, and NMHC) from both regional (urban) and roadside stations dispersed all across seven major metropolitan cities and a number of smaller cities in Korea (Iqbal et al., 2014; Shon and Kim, 2011; Shon et al., 2011). As our study site, YS belongs to one of these cities, all these monitoring tasks have been conducted by following the standard operating protocol for the ‘AIR QUALITY’ monitoring operation guide and regulations managed by the Korean Ministry of Environment (KMOE). Details of experimental conditions regarding operational conditions of instrumental setups (Table 1S) and the principles of data processing are provided in Supplementary Material (SM). 3. Results and discussion 3.1. General features of NOx and O3 A statistical summary of the NOx and O3 data measured from YS, district of Seoul, Korea during the entire study period (2004e2013) is presented in Table 1. The mean concentrations of NO, NO2, NOx and O3 measured over the entire study period were 25.3 ± 7.30, 36.9 ± 1.76, 62.1 ± 8.04, and 17.5 ± 1.31 ppb, respectively. As the concentration of NO2 is generally higher than NO, one may assume that NO2 contributes the larger portion to the increase in NOx concentration in the study area over the measurement period (Table 2). The annual mean values for each pollutant were computed using the daily measurement data. The observed annual values of NO exhibited a decreasing trend:from 38.0 ppb (2004) to 17.7 ppb (2012). Likewise, NO2 also decreased from 40.3 ppb (2004) to 33.4 ppb (2012). Such a decrease in NOx concentrations has been observed commonly elsewhere: in The Netherlands, observed concentrations have fallen from 40 ppb to 30 ppb, particularly after 2004 (Keuken et al., 2012). Note that the limit value for NO2 in ambient air Europe is 40 ppb as an annual mean (ETC, 2011). The frequency distribution patterns of each target compound were evaluated for each year using the relevant daily mean values (Fig. 2). The maximum frequencies of NO data were observed in the 1e10 ppb concentration range in the early years (2004e2007). Even though after 2007 the most common concentration was in the range 10e20 ppb, there were also fewer noticeable occurrences (10e30) of high concentrations (40e100). Likewise, NO2 generally exhibited the maximum frequency in the 30e40 ppb range in 2004, 2006, 2008, 2010, and 2012 but in recent years it was shifted to the slightly low range of 20e30 ppb. NO in the urban atmosphere should reflect the direct impacts of vehicular pollution in the study area, as it is a good indicator of fossil fuel based emissions (Lewne et al., 2004). In contrast, the O3 data showed a maximum frequency at 0e10 ppb in 2004. However, over the years this gradually shifted to higher concentrations but with lower total occurrences (with maximum frequencies around 10e30 ppb).
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Fig. 1. Aerial map of the Yongsan, Seoul, Korea.
3.2. Temporal pattern of nitrogen oxides and O3 The temporal variations of NO, NO2, and O3 were analyzed after dividing their data into monthly, seasonal, and annual intervals. The day-to-day variation patterns are plotted using the daily mean values (Fig. 3). According to the legislative guideline of KMOE, the maximum allowable daily mean for NO2 is 60 ppb. If the annual sum of days exceeding this threshold is compared, the results show gradual reduction through the years [1] 40 times (2004 and 2008), [2] 30 times (2009 and 2013), and [3] 20 times (2007 and 2010), and [4] 9 times (2012). Comparison of the monthly data, as seen in Fig. 1S, indicated the existence of strong seasonal trends with NO concentrations of: 10.1 (June) to 49.0 ppb (January) and NO2 concentrations of 25.8 (August) to 45.7 ppb (January). In contrast, O3 concentrations varied from 8.21 (December) to 27.9 ppb (May) (Table 2S).
The seasonal variations of NO, NO2, and O3 were also assessed by grouping the daily data into four seasons: spring (MarcheMay), summer (JuneeAugust), fall (SeptembereNovember), and winter (DecembereFebruary) (Fig. 2S). As shown in Table 3S, the highest seasonal mean concentrations (ppb) of NO, NO2, and O3 were: 41.6 ± 14.5 (in winter), 43.5 ± 5.56 (in winter), 24.5 ± 5.23 (in spring), respectively. The winter maximum of NO and NO2 can be explained by stagnant air conditions, lack of sunshine for photochemical conversion to ozone and paucity of rainfall to remove these gases from the atmosphere (Li et al., 2012). The spring maximum of O3 is well-known and due to photochemical activity when the sun's intensity is high (Iqbal et al., 2014; Klumpp et al., 2006; Stephens, 1969; Varshney and Aggarwal, 1992). The statistical significance of the seasonal relationships of each target compound was assessed by a KruskaleWallis (KW) test using the annual mean values obtained across each season. The KW non-
Table 1 Summary of nitrogen oxides and ozone concentrations, and relevant parameters measured at Yongsan district of Seoul, Korea between 2004 and 2013 (computations made using the daily mean values of each parameters). Year
2005 2006 2007 2008 2009 2010 2011 2012 2013 All data a b c
38.0 ± 41.0 1.0e238 33.1 ± 37.8 2.0e242 22.9 ± 25.1 3.0e153 25.2 ± 23.8 2.0e152 28.7 ± 25.6 2.0e147 24.3 ± 24.0 1.29e134 23.4 ± 22.7 1.38e214 18.5 ± 21.5 1.38e124 17.7 ± 18.3 1.04e152 21.3 ± 22.1 1.63e164 25.3 ± 7.30 1.0e242
NO2 (ppb) a
(23.0) (321)b (18.0) (344) (14.0) (287) (16)0.0 (299) (20.0) (366) (15.3) (360) (16.0) (364) (8.96) (362) (12.1) (362) (13.5) (361) (15.7) (2783)
40.3 ± 18.2 3.0e102 32.8 ± 16.4 3.0e87 35.1 ± 13.1 8.0e75 37.8 ± 14.8 9.0e93 42.6 ± 15.5 10e91.0 39.1 ± 14.5 9.42e89.0 37.6 ± 12.9 9.29e78.7 33.6 ± 14.6 9.29e79.2 33.4 ± 12.2 6.42e72.0 36.3 ± 14.2 7.71e87.1 36.9 ± 1.76 3.0e102
NOx (ppb) (37.0) (321) (32.0) (344) (34.0) (287) (37.0) (299) (40.0) (366) (37.1) (360) (36.5) (364) (31.6) (362) (32.6) (362) (33.1) (361) (35.2) (2783)
78.2 ± 55.6 6.0e339 65.8 ± 48.1 6.0e329 58.0 ± 36.3 11.0e228 63.0 ± 35.4 13e214 71.3 ± 38.0 12e218 63.4 ± 36.0 11.0e209 61.0 ± 32.5 10.7e293 52.1 ± 34.2 10.7e196 51.1 ± 28.1 7.46e219 57.6 ± 34.3 9.33e230 62.1 ± 8.04 6.0e339
O3 (ppb) (62.0) (321) (53.0) (344) (48.0) (287) (54.0) (299) (60.0) (366) (53.3) (360) (53.5) (364) (41.1) (362) (45.1) (362) (45.7) (361) (53.2) (2783)
13.6 ± 8.29 1.0e52.0 13.7 ± 8.20 1.0e50.0 17.1 ± 11.0 1.0e50 17.1 ± 10.3 1.0e51.0 15.6 ± 9.3 1.0e44 20.1 ± 11.5 2.33e61.0 18.0 ± 11.0 1.71e62.2 20.4 ± 11.1 1.71e69.8 19.4 ± 10.2 1.35e51.0 19.7 ± 12.0 0.67e83.3 17.5 ± 1.31 0.67e83.3
TEMP ( C)
WS (m/sec) (12.0) (350) (12.0) (351) (16.0) (278) (15.0) (298) (15.0) (366) (19.6) (360) (15.7) (359) (19.3) (361) (18.3) (364) (18.2) (361) (15.8) (2799)
0.76 ± 0.31 0.3e2.0 0.83e0.34 0.20e2.20 1.02e0.35 0.40e2.40 1.28 ± 0.48 0.50e3.10 2.21 ± 0.52 1.2e4.0 2.41 ± 0.58 1.43e4.52 2.46 ± 0.59 1.51e4.63 1.43 ± 0.58 0.45e3.19 2.53 ± 0.63 1.60e6.55 2.52 ± 0.61 1.57e4.68 1.74 ± 0.12 0.20e6.55
(0.70) (361) (0.80) (361) (0.90) (320) (1.20) (320) (2.10) (366) (2.29) (360) (2.32) (365) (1.36) (365) (2.38) (361) (2.37) (365) (1.73) (2859)
12.8 ± 9.64 13.8 to 29.4 11.7e10.8 10.5 to 29.4 14.0e9.84 11.3 to 29.8 12.9 ± 10.1 6.4 to 29.7 12.9 ± 9.99 8.5 to 29.3 13.1 ± 10.0 9.79 to 28.6 12.5 ± 10.9 12.1 to 29.3 12.9 ± 10.6 13.0e30.4 12.2 ± 11.4 13.4 to 31.3 12.6 ± 11.0 12.7 to 29.8 12.8 ± 0.56 13.8 to 31.3
HUM (%) (14.30) (361) (13.60) (361) (16.8) (320) (11.6) (317) (14.5) (366) (15.6) (360) (13.1) (365) (14.6) (365) (13.9) (361) (12.6) (365) (14.4) (2859)
64.2 ± 16.9 23.0e96.0 64.3 ± 17.9 26.0e96.0 63.1 ± 16.2 25.0e96.0 59.4 ± 15.1 20e93 59.9 ± 14.1 22e92 59.8 ± 13.9 28.8e90.3 60.8 ± 14.3 26.2e92.4 59.2 ± 17.1 25.1e95.7 55.5 ± 13.8 22.6 ± 87.7 58.5 ± 13.1 25.4e87.8 60.4 ± 1.69 20.0e96.0
(65.0) (360) (65.0) (361) (63.0) (320) (61.0) (314) (62) (366) (61.3) (360) (62.4) (365) (56.2) (365) (55.2) (361) (57.9) (365) (61.4) (2858)
UV (mW/cm2)
SR (W/m2)
0.05 ± 0.03 0.0e0.15 0.05 ± 0.04 0.0e0.21 0.15 ± 0.08 0.02e0.73 0.29 ± 0.23 0.02e0.94 0.38 ± 0.19 0.04e0.84 0.37 ± 0.20 0.01e0.86 0.37 ± 0.20 0.03e0.86 0.28 ± 0.20 0.01e1.03 0.39 ± 0.20 0.02e0.85 0.39 ± 0.21 0.02e0.89 0.27 ± 0.08 0.0e1.03
125 ± 63.5 8.0e293 123 ± 58.4 7.0e295 133 ± 70.8 19.0e305 113 ± 54.6 16.0e248 147 ± 74.6 7.0e309 145 ± 78.2 2.21e313 138 ± 77.6 7.50e340 133 ± 77.1 6.13e300 148 ± 75.6 3.46e307 146 ± 78.4 5.13e321 136 ± 8.91 2.21e321
(0.04) (361) (0.05) (292) (0.14) (275) (0.21) (307) (0.34) (366) (0.33) (360) (0.32) (365) (0.23) (365) (0.35) (361) (0.37) (365) (0.28) (2814)
(116) (361) (117) (361) (116) (320) (108) (307) (144) (366) (134) (360) (126) (365) (119) (365) (141) (361) (144) (365) (126) (2859)
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NO (ppb) 2004
B. Meteorological Parametersc
A. Pollutant species
Mean ± SD (Median), SD ¼ Standard Deviation. Range ¼ Minimum Maximum (N); and N ¼ number of seasonal data. Acronyms used for meteorological parameters denote: WD ¼ wind direction, WS ¼ wind speed, TEMP ¼ temperature, HUM ¼ humidity, UV ¼ ultraviolet, and SR ¼ solar radiation.
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Table 2 Trends in nitrogen oxides, ozone, and relevant meteorological parameters measured at Yongsan district of Seoul, Korea between 2004 and 2013 (using annual (A) and (B) monthly mean concentrations (for seasonal trend)). Pollutants Mean
(A) Annual trend NO 25.3 36.9 NO2 NOx 62.1 NO2/NOx 0.66 NO/NOx 0.40 O3 17.5 WS 1.74 TEMP 12.8 HUM 60.5 UV 0.27 SR 135 (B) Seasonal trend NO 24.8 NO2 36.3 NOx 61.1 NO2/NOx 0.63 NO/NOx 0.365 O3 17.5 WS 1.73 TEMP 12.8 HUM 60.0 UV 0.27 SR 134
SD
MK Sen's coefficient slope (Q) (tau)
6.30 0.689 3.22 ¡0.200 8.29 0.600 0.05 0.600 0.052 0.733 2.53 0.689 0.75 0.822 0.585 ¡0.156 2.73 0.683 0.14 0.644 11.9 0.467
1.852 0.273 2.103 0.012 0.013 0.682 0.208 0.044 0.727 0.038 2.502
18.7 0.393 9.79 ¡0.159 26.0 0.285 0.13 0.526 0.123 0.519 7.52 0.444 0.77 0.685 10.0 0.074 11.3 0.315 0.19 0.559 43.6 0.356
0.078 0.035 0.120 0.001 0.0009 0.061 0.017 0.018 0.035 0.003 0.24
Probability Trend at 95% (p) confident intervala 0.005 0.484 0.017 0.017 0.002 0.005 0.0004 0.601 0.005 0.009 0.073
£ þ þ þ £ þ þ
<0.0001 0.028 <0.0001 <0.0001 <0.0001 <0.0001 <0.0001 0.314 <0.0001 <0.0001 <0.0001
£ þ þ þ £ þ þ
a The signs of þ, , and denote increasing, decreasing, and no trends, respectively. Numbers in boldface denote the case of no changes ().
parametric method can be used to test whether there are differences between multiple independent samples. The test was used in preference to the one way analysis of variance (ANOVA) because it does not make assumptions concerning how the daily data are distributed (Carslaw, 2005). The results of the KW test indicated that NO, NO2, NOx, and O3 exhibited significant seasonal trends (p < 0.0001). In the case of meteorological parameters, all parameters except wind speed (p ¼ 0.399) showed statistical differences between seasons.
3.3. Long term trends in NOx and O3 concentrations in Yongsan Analysis of long-term trends of airborne pollutants in specific regions is important to understand changes in the pollution climate. To learn more about this, the variation of the NOx was investigated in relation to other parameters (Fig. 3S). For instance, NO decreased form 38 (2004) to 21.3 ppb (2013), whereas the CO levels increased from 0.13 (2004) to 0.31 ppm (2013). SO2 levels also showed a similarly decreasing trend. On the other hand, when we compared the relationship of O3 with UV and SR, they were highly comparable each other. These results support the hypothesis that meteorological parameters also play a vital role in affecting O3 levels in this location. Moreover, to explore the local contribution, long term trends of NOx and O3 were analyzed using MK method (Table 2). The MK method is a non-parametric test that can be applied to abnormally distributed data with missing points (Carslaw, 2005). The test detects only the presence of a trend of which quantification is made by the calculation of a slope using the Sen method (Gilbert, 1987). To analyze the recent trends of NOx and O3 in our study site, the MK test was conducted using the monthly (and yearly) mean data sets (Figs. 1S and 3S). The results of the MK test on the annual data confirmed the constant reductions in NO levels across the years from 38.0 (2004)
to 21.3 ppb (2013) with a negative slope of 1.852 ppb yr1. In contrast, NO2 did not show any discernible changes over a study period. According to this test, the annual trend of NO2 appears to be less significant (p ¼ 0.484), while NOx showed a downward trend (p ¼ 0.017 level) clearly driven mainly by the effect of NO in the overall NOx sum. This pattern of NO reduction is explained by reduction in primary NO emissions driven by several factors such as the improvement in fuel types (e.g., natural gas) and vehicle engine types (e.g., (DOC) or (DPF)) together with legislative amendments to control emissions (e.g., tax reduction on low emission and hybrid vehicles) (Carslaw, 2005). The annual concentrations of O3 showed a strong upward trend (p ¼ 0.005) for the entire study period which is closely related to the trends of some meteorological parameters (e.g., UV and SR). This pattern may reflect the interactive role between strong photochemical aging and upward NO2/NOx ratios (Shon et al., 2011). For the meteorological parameters, the annual trend showed statistically significant upward trends for wind speed (WS), SR, and UV. In contrast, humidity (HUM) showed downward trend over a decadal period, while there was no apparent trend for temperature. If the relative role of the regional contribution to the global NOx emission inventory is assessed, the significance of the Asian continent has changed very rapidly through the years. Although it represented only a minor fraction (~10,000 kt y1) in the 19th century its proportion has increased rapidly (~29,000 kt y1) fueled by enormous population growth and cultural developments in Asian continent (Akimoto, 2003), (Fig. 4S(a)). In fact, the rate of urbanization in Asia was about 1.6% between the years of 1975 and 2000 but is projected to decrease slightly to 1.4% between 2000 and 2025 (Fig. 4S(b)). In recent years, due to urbanization and industrialization, emissions of air pollutants have increased substantially. The rate of the population migration toward urban areas has also caused rapid increases in the number of vehicles and the rate of energy consumption (Lee et al., 2011). This type of a temporal pattern has in fact been observed generally at most urban sites around the world, as reported by many previous studies (Carslaw, 2005; Keuken et al., 2012; Shon et al., 2011). 3.4. Factors affecting the distribution of NOx and O3 In order to study the factors and processes influencing the NOx and O3 concentrations, a correlation analysis was conducted. For this analysis, both (1) daily data sets and (2) seasonal datasets were used (Table 4S). Strong correlations were found between annual means of NO and those of CO (r ¼ 0.52, p < 0.1), CH4 (r ¼ 0.50, p < 0.1), and THC (r ¼ 0.47, p < 0.1). In the case of seasonal data, the strongest correlations with NO were found for SO2, CO, PM2.5 and PM10 in fall, but with CH4 and NMHC in winter. It thus suggests that CO and SO2 should be affected similarly by primary combustion products such as motor vehicle emissions, while THC concentrations may also reflect the emissions from biomass burning processes (Smit et al., 2010; Uherek et al., 2010). Strong correlations were observed between NO2 and both CO (p < 0.01), PM2.5 (p < 0.01), and SO2 (p < 0.01) for all data. The seasonal correlation of NO2 showed significantly strong correlations with most pollutant species in winter, followed by fall, and then the other two seasons. All meteorological parameters showed inverse weak correlations with NO and NO2. Progiou and Ziomas (2011) reported that passenger cars in Athens, Greece were the major sources (>50%) of CO, NMVOCs, and CH4. On the other hand, they found that PM10 and NOx are connected to heavy duty vehicles with a contribution of 66 and 65%, respectively. A strong inverse correlation was also observed between O3 and all fractions of NOx. In contrast with NO and NO2, O3 showed significant correlation with UV (r ¼ 0.53, p < 0.01) and SR (r ¼ 0.58, p < 0.01). Interestingly,
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Fig. 2. Frequency distribution patterns of (A) NO, NO2, and O3 (ppb) and (B) UV and SR measured from Yongsan between 2004 and 2013 (using daily data).
O3 showed only strong inverse correlations with SO2, CO, and NMHC in winter, while WS showed positive correlations throughout all seasons. This observation supports the well-known role of photochemical reduction of NO and simultaneous production of NO2. It has been reported that ozone production is generally controlled by HOxeNOxeVOC (or NMHC) chemistry (Arens et al., 2001). Although pollution levels in urban areas are mainly governed by emissions of major pollutants, meteorology also plays an important role in their dispersion, photochemical transformation, and transport (Melkonyan and Kuttler, 2012). In addition, the winds blowing towards Korea tend to originate from specific directions (e.g., NW, N, or SE). These directions can reflect the effect of the ocean (East China Sea) and highly industrialized areas (e.g., Beijing) (Lim et al., 2010). Hence, our results support the hypothesis that O3 levels are influenced to some meteorological variables like wind speed and direction. To identify the most dominant groups of pollutants and meteorological parameters, one can use statistical tools like PCA. In order to study the reciprocity of all parameters, whole daily datasets and seasonally grouped daily datasets were used. The principal
components (PC) extracted are ordered in such a way that the PC1 explains most of the variance, and the following PCs account for the largest proportion of variability that has not been accounted by its predecessors (Abdul-Wahab et al., 2005). According to the Kaiser criterion, PCs with an eigen value 1 are considered as being of statistical significance and factor loadings greater than 0.75 were considered strong; 0.75 to 0.50 as moderate and 0.49 to 0.30 as weak variables. The PCA results produced five main components to explain a total variance of 80.8% of all data measured for the entire period (2004e2013: Table 3). PC1 explained 28.8% of the total variance, with NO, NO2, PM2.5, PM10, TSP, CO, and SO2 all contributing to its loading. PC2, which accounted for about 21% of the total variance, was related to THC (CH4 and NMHC) and UV. This kind of strong association of NOx with other pollutants has often been observed as a function of land use type, traffic patterns, local topography, atmospheric chemistry, and physical processes (Beckerman et al., 2008). PC3, PC4, and PC5 mainly comprised contributions from O3, TEMP, and HUM, respectively. The close relationship between O3 and those parameters suggest the role of photochemistry in its
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Fig. 3. Plot of daily mean concentrations of NO, NO2, and O3 (ppb) measured at Yongsan from 2004 to 2013.
formation (Melkonyan and Kuttler, 2012). The seasonally extracted PCs are also described in Table 3. These results showed a dominant variability of 45.2% accounted for by most pollutants (except SO2 and CO) in winter and 40.2% in fall (except O3) season. The spring season showed 29.0% variability with PC1 mainly coming from NO, NO2, and NMHC, while summer showed the potential contribution of photochemistry (e.g., NO2, O3, and PM) in the range of 25.8%. Owing to the chemical coupling of O3 and NOx (¼NO þ NO2), the concentration levels of O3 and NO2 are inextricably linked. Consequently, the relationships between NOx and O3 exhibit highly nonlinear trends, and any resultant reduction in the level of NO2 is invariably accompanied by an increase in the level of O3 (Clapp and Jenkin, 2001). Fig. 4 depicts the annual variation of NOx together with ozone. Accordingly, it is evident that O3 showed an increase in concentration as a function of the consumption of NO by photochemistry. Hence, NO plays the critical role in the photochemical process of ozone. The ratio of NO/NOx (0.34) indicated a strong downward trend (at p ¼ 0.002 level) (Tables 1 and 2). This relative
decrease in NO/NOx ratio may be attributed to the aforementioned chemical processes or the increased use of DOC. According to the policy of Special Act on Metropolitan Air Quality Improvement by the Korean Ministry of Environment (2004e2012), the use of low pollution devices (such as DPF or DOC) has been implemented for diesel vehicles (ECOREA, 2013) (http://eng.me.go.kr/eng/web/ index.do?menuId¼ 30&findDepth ¼ 1). A total of 1,046,492 DOC devices were supplied from 2004 to 2012 in Seoul; the maximum distributions of nearly 42% (62,234) and 36% (53,461) of these were made in 2006 and 2007, respectively. The DOCs can catalytically oxidize CO and hydrocarbons by effectively converting NO to NO2 which may lead to increase in the NO2 fraction in NOx (Shon et al., 2011; Tian et al., 2011). 3.5. Comparison of NOx and O3 levels between different studies The status of NOx and O3 pollution at Yongsan was compared with previous datasets by considering both spatial and temporal
Table 3 The results of principal component analysis using all the environmental parameters measured at Yongsan between 2004 and 2013 (Using daily data). (I) pollutant species NO
NO2
O3
SO2
CO
PM2.5
PM10
TSP
CH4
NMHC
WS
TEMP
0.723 0.400 0.062 0.339 0.164
0.392 0.258 0.668 0.089 0.134
0.596 0.320 0.136 0.022 0.070
0.616 0.361 0.046 0.137 0.103
0.699 0.268 0.364 0.141 0.254
0.664 0.215 0.415 0.422 0.269
0.625 0.162 0.416 0.504 0.191
0.333 0.816 0.315 0.011 0.192
0.351 0.760 0.335 0.086 0.112
0.307 0.757 0.022 0.046 0.112
0.362 0.095 0.602 0.519 0.250
0.802 0.201 0.293 0.310 0.039
¡0.800 0.314 0.026 0.014 0.214
0.477 0.600 0.060 0.211 0.021
0.425 0.545 0.167 0.013 0.344
0.704 0.543 0.167 0.007 0.297
0.708 0.389 0.347 0.360 0.274
0.665 0.360 0.319 0.405 0.324
0.706 0.284 0.303 0.280 0.315
0.641 0.248 0.07 0.082 0.484
¡0.664 0.381 0.144 0.293 0.184
0.685 0.449 0.230 0.262 0.154 0.323
0.71 0.443 0.117 0.023 0.123 0.159
0.422 0.412 0.050 0.028 0.107 0.065
0.101 0.495 0.050 0.150 0.430 0.542
0.255 0.491 0.454 0.342 0.057 0.147
0.246 0.587 0.66 0.002 0.172 0.313
0.213 0.568 ¡0.653 0.056 0.122 0.340
0.186 0.237 0.391 0.563 0.536 0.129
0.748 0.089 0.390 0.033 0.002 0.261
0.700 0.304 0.267 0.433 0.027 0.228
0.514 ¡0.669 0.160 0.017 0.129 0.021
0.468 0.153 0.067 0.213 0.017 0.589
0.525 0.218 0.134 0.293 0.517 0.198
0.787 0.276 0.348 0.229 0.005 0.256
0.782 0.256 0.317 0.322 0.084 0.267
0.812 0.226 0.296 0.272 0.070 0.248
0.384 0.029 0.597 0.169 0.594 0.099
0.842 0.156 0.12 0.011 0.045
0.612 0.492 0.118 0.277 0.088
0.505 0.360 0.010 0.052 0.456
0.546 0.448 0.133 0.222 0.442
0.765 0.129 0.247 0.499 0.009
0.757 0.132 0.119 0.591 0.007
0.680 0.158 0.127 0.631 0.005
0.586 0.498 0.097 0.250 0.118
HUM
UV
SR
Eigen value
% of variance
Cumulative %
0.078 0.128 0.276 0.597 0.662
0.467 ¡0.560 0.462 0.206 0.267
0.265 0.299 0.703 0.050 0.455
4.901 3.634 2.520 1.507 1.182
28.8 21.4 14.8 8.86 6.96
28.8 50.2 65.0 73.9 80.8
0.378 0.310 0.445 0.038 0.407
0.375 0.070 0.704 0.332 0.058
0.263 0.715 0.491 0.042 0.122
0.369 0.300 0.524 0.555 0.033
8.594 2.702 1.900 1.255 1.031
45.2 14.2 10.0 6.61 5.43
45.2 59.5 69.5 76.1 81.5
¡0.662 0.233 0.096 0.263 0.204 0.297
0.103 0.474 0.408 0.491 0.186 0.313
0.032 0.187 0.121 0.690 0.480 0.185
0.455 0.577 0.218 0.272 0.034 0.053
0.142 0.410 0.062 0.457 ¡0.655 0.126
5.504 3.146 2.127 1.932 1.463 1.220
29.0 16.6 11.2 10.2 7.70 6.42
29.0 45.5 56.7 66.9 74.6 81.0
0.084 0.653 0.345 0.445 0.251 0.101
0.031 ¡0.606 0.142 0.214 0.252 0.124
0.192 0.227 0.49 0.067 0.392 0.530
¡0.576 0.301 0.284 0.225 0.455 0.180
0.338 ¡0.597 0.480 0.318 0.082 0.12
0.617 0.163 0.316 0.125 ¡0.514 0.040
4.897 4.439 2.154 1.842 1.459 1.091
25.8 23.4 11.3 9.70 7.68 5.74
25.8 49.1 60.5 70.2 77.8 83.6
0.402 0.196 0.669 0.367 0.23
¡0.508 0.286 0.468 0.068 0.354
0.399 0.099 0.772 0.062 0.140
0.072 ¡0.739 0.383 0.165 0.168
0.337 0.664 0.005 0.205 0.268
0.228 0.563 0.417 0.091 0.478
7.647 2.456 2.147 1.705 1.288
40.2 12.9 11.3 8.97 6.78
40.2 53.2 64.5 73.4 80.2
K. Vellingiri et al. / Atmospheric Environment 112 (2015) 116e125
(A) All data PC1 0.782 PC2 0.153 PC3 0.283 PC4 0.323 PC5 0.190 (B) Winter data PC1 0.878 PC2 0.135 PC3 0.219 PC4 0.075 PC5 0.114 (C) Spring data PC1 0.899 PC2 0.003 PC3 0.043 PC4 0.026 PC5 0.145 PC6 0.238 (D) Summer data PC1 0.149 PC2 0.787 PC3 0.039 PC4 0.547 PC5 0.064 PC6 0.021 (E) Fall data PC1 0.890 PC2 0.003 PC3 0.213 PC4 0.062 PC5 0.235
(II) meteorological parameters
Only factors loadings 0.5 are highlighted in boldface. Only factors with Eigen value 1 are shown.
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124
K. Vellingiri et al. / Atmospheric Environment 112 (2015) 116e125
Fig. 4. Comparison of annual mean concentrations of NO and NO2 with O3 (ppb) at the Yongsan for the period 2004e2013 (using daily data).
factors (Table 5S). First, if the results for the similar areas are compared between different time bands, we can observe noticeable increase of NO, NO2, and O3 levels. For instance, Esen et al. (2005) observed 28 ppb of O3 in Bursa, Turkey during 2001e2002. In contrast, Civan et al. (2015) reported 39 ppb of O3 in Aliga, Western Turkey during 2005e2007. The spatial trends of target pollutant levels can also be compared using the datasets obtained in a similar time band across different regions, e.g., Asian vs European countries (Table 5S). In the case of NO2, Mavroidis and Ilia (2012) reported 22 ppb in Athens, Greece, while Hwang et al. (2015) observed 17 ppb in Taiwan during the similar time period (2007e2009). In recent years, noticeable reductions in NO2 levels have been observed in European countries relative to Asian countries. For instance, in Korea, NO2 levels are often found to approach or exceed 40 ppb (annual guideline values), as seen in this work. In contrast, in European cities, its values tend to be much reduced, as seen from Athens, Greece (22 ppb) (Mavroidis and Ilia, 2012) or Nevada, USA (24.1 ppb) (Kimbrough et al., 2013). As seen from previous studies (Clapp and Jenkin, 2001), we may observe that NO2 is the major component of NOx contributions at low concentration levels, while NO tends to be more dominant at higher mixing ratios of NOx. As such, reduced concentrations of nitrogen oxide (NO and NO2) in developed countries like USA are attributed mainly to cleaner environments and less automotive related pollution. In contrast, in the case of developing countries such as China, traffic congestion accompanied by low vehicular speed contributes to the build-up of NO2 (Carslaw and Beevers,
2004b). Moreover, the occurrence of low O3 concentrations in some countries may be due to the formation of NO2 species by wellknown photochemical reactions (Yao et al., 2005). To this end, to reduce global NOx and O3 pollution, it is important to reduce the local and regional concentrations of these pollutants. It is necessary therefore to continue to implement strict administrative regulations to lower the contribution of local pollution to the global emissions budget. 4. Conclusion The environmental behavior of nitrogen oxides and ozone concentrations in urban environment was examined using the data sets collected from Seoul, Korea between 2004 and 2013. The data were analyzed to evaluate their distribution at various temporal scales (daily, monthly, seasonal, and annual intervals). The NO and NO2 indicated similar seasonal patterns of maximum peaks in the winter (caused by the increase in the consumption of fuels, poorer dispersion conditions, and less photochemical activity) and minimum levels in summer (e.g., the washout effect and greater photochemical activity). In contrast, due to increased photochemical activity, ozone concentration tends to soar in spring, while being reduced in winter. The long term analysis indicated a decreasing trend in NO and NOx, while ozone recorded an increasing trend throughout the decadal period. The ratios of NO/ NOx and NO2/NOx showed downward and upward trends, respectively. In addition, the ratio of NO2/NOx (0.66) also indicated the
K. Vellingiri et al. / Atmospheric Environment 112 (2015) 116e125
interrelationship of O3 with NO and NO2 through their photochemical reactions. The results of correlation studies indicated a clear interrelationship between NOx with most of the automotive related pollutants (e.g., CO, SO2, THC, PM2.5, and PM10). Conversely, O3 exhibited close relationships with some meteorological parameters (e.g., UV and SR). In addition, PCA analysis showed that CO, PM10, PM2.5, and TSP were the major components contributing to air pollution in the study area of YS. Moreover, the concentration of the NO and NO2 was mainly influenced by the local automotive-related pollution sources, whereas the O3 concentration was affected more by the meteorological parameters. Hence, we recommend that future studies assess the contribution of different source processes to observed pollutant levels, especially between the local and long range sources. The richness and quantity of information in the data presented has also clearly demonstrated the benefits of long term ambient air monitoring endeavors to the understanding of atmospheric processes and local pollution climate. Acknowledgment This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT and Future Planning (grant number 2013004624). This work was also carried out with the support of the “Cooperative Research Program for Agriculture Science & Technology Development (Project title: Study on model development to control odor from pigpen, Project No. PJ01052101)” Rural Development Administration, Republic of Korea. Appendix A. Supplementary data Supplementary data related to this article can be found at http:// dx.doi.org/10.1016/j.atmosenv.2015.04.032. 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. Akimoto, H., 2003. Global air quality and pollution. Science 302, 1716e1719. €ggeler, H.W., Ammann, M., 2001. HetArens, F., Gutzwiller, L., Baltensperger, U., Ga erogeneous reaction of NO2 on diesel soot particles. Environ. Sci. Technol. 35, 2191e2199. Barck, C., Lundahl, J., Hallden, G., Bylin, G., 2005. Brief exposures to NO2 augment the allergic inflammation in asthmatics. Environ. Res. 97, 58e66. Beckerman, B., Jerrett, M., Brook, J.R., Verma, D.K., Arain, M.A., Finkelstein, M.M., 2008. Correlation of nitrogen dioxide with other traffic pollutants near a major expressway. Atmos. Environ. 42, 275e290. Bytnerowicz, A., Omasa, K., Paoletti, E., 2007. Integrated effects of air pollution and climate change on forests: a northern hemisphere perspective. Environ. Pollut. 147, 438e445. Carslaw, D.C., 2005. Evidence of an increasing NO2/NOx emissions ratio from road traffic emissions. Atmos. Environ. 39, 4793e4802. Carslaw, D.C., Beevers, S.D., 2004a. Investigating the potential importance of primary NO2 emissions in a street Canyon. Atmos. Environ. 38, 3585e3594. Carslaw, D.C., Beevers, S.D., 2004b. New directions: should road vehicle emissions legislation consider primary NO2? Atmos. Environ. 38, 1233e1234. Chai, F., Gao, J., Chen, Z., Wang, S., Zhang, Y., Zhang, J., Zhang, H., Yun, Y., Ren, C., 2014. Spatial and temporal variation of particulate matter and gaseous pollutants in 26 cities in China. J. Environ. Sci. 26, 75e82. € Civan, M.Y., Elbir, T., Seyfioglu, R., Kuntasal, O.O., Bayram, A., Dogan, G., Yurdakul, S., € Müezzinoglu, A., Sofuoglu, S.C., 2015. Spatial and temporal variations Andiç, O., in atmospheric VOCs, NO2, SO2, and O3 concentrations at a heavily industrialized region in Western Turkey, and assessment of the carcinogenic risk levels of benzene. Atmos. Environ. 103, e113. Clapp, L.J., Jenkin, M.E., 2001. Analysis of the relationship between ambient levels of O3, NO2 and NO as a function of NOx in the UK. Atmos. Environ. 35, 6391e6405.
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