Introduction
Over the last 3 decades, Thailand has experienced rapid industrialization, urbanization
and economic growth (World Bank, 2018a). A majority of the country's
development has occurred within and around Bangkok (BKK) (13.7∘ N,
100.5∘ E), the capital city of Thailand, and in the Bangkok
Metropolitan Region (BMR). BMR is comprised of BKK and the five adjacent
provinces of BKK (World Bank, 2018a, b). The increase in emissions is due to
accelerated growth in automotive and industrial activities. As a major
metropolitan area, BMR is dominated by mobile emissions sources, which
contributes to the emissions of CO and NOx, precursors of
ozone (O3) formation. The emissions from industrial activities also
contribute to those emissions and to the emissions of sulfur dioxide
(SO2) and the formation of particulate matter. Since 1995, BMR has
begun to experience air quality degradation and experienced exceedances in
Thailand National Ambient Air Quality Standards (NAAQs) for particulate
matter (PM) and ozone (O3) (PCD, 2015) owing to strong solar
radiation (peak density of direct radiation ∼1350 kWh m-2 yr-1), high temperature (yearly average ∼29 ∘C) and high humidity (yearly average ∼64 %) (Kumar et
al., 2012).
The relationship between air pollution and public health in BMR has been
examined in several published studies. Ruchirawat et al. (2007) reported that
children who lived in BKK were exposed to high levels of carcinogenic air
pollutants which might cause an elevated cancer risk. Buadong et al. (2009)
reported that the exposure to elevated PM and O3 in elderly
patients (≥65 years) was associated with an increasing in the number of
hospital visits for cardiovascular diseases on the following
day. Jinsart et al. (2002, 2012) reported the police
personnel and drivers in BKK tended to be exposed to higher levels of PM
concentrations compared with the general environment.
Several studies have demonstrated the role of atmospheric processes in
elevating Thailand's O3. Long-range transport from the Asian
continent has enhanced O3 concentrations in Thailand compared to
the lesser O3 concentrations disbursed via long-range transports
from the Indian Ocean (Pochanart et al., 2001). This regional transport,
moreover, played an important role in seasonal fluctuations of O3
in this area (Zhang and Oahn, 2002). Another factor that enhanced
O3 concentrations was the atmospheric chemistry of volatile organic
compounds (VOCs). However, this process tended to be more important in
enhancing O3 concentrations in suburban areas than in urban areas
(Suthawaree et al., 2012).
Therefore, the availability and analysis of multiyear measurements of such
gaseous criteria pollutants in the BMR will improve our understanding of how
they contribute to the air quality of this area. In this study, we analyzed
diurnal variations, seasonal variations and interannual trends of gaseous
pollutants including carbon monoxide (CO), nitric oxide (NO), nitrogen
dioxide (NO2), SO2 and O3 in BMR from 2010 to
2014. Chemical and physical processes associated with high O3
concentrations have been investigated. Since the monitoring station mostly
measured concentrations of nitrogen oxide (NOx),
O3 precursors in this study are referred to as
NOx. The photochemical reaction for O3 was
investigated during the photostationary state. The effects of local emission
and regional contributions of Ox are presented. The severity
of air pollution concentrations in BMR in relation to human health is
assessed by using the air quality index (AQI).
Map of
BMR, the location of monitoring stations and two major monsoons winds (from
NOAA HYSPLIT back trajectory model). Three monitoring station types (BKK
sites, roadside sites and BKK suburb sites) are shown as light blue dots,
purple dots and blue dots, respectively. (Note: * the station has been closed
since 1 October 2013.)
Methodology
Study area
Figure 1 shows a map of BMR, the location of monitoring stations in this
study and major monsoon winds over this region. BMR refers to BKK and the
five adjacent provinces, i.e., Nakhon Pathom, Pathum Thani, Nonthaburi, Samut
Prakan and Samut Sakhon. These provinces are linked to BKK in terms of
traffic and industrial development (Zhang and Oanh, 2002). Thailand has three
official seasons – local summer (February to May), rainy seasons (May to
October) and local winter (October to February) as per the Thai
Meteorological Department (TMD) (TMD, 2015). During the rainy season, this
region's weather is influenced by southwest monsoon wind that travels from
the Indian Ocean to Thailand. This marine air mass contains a large amount of
moisture, resulting in the wet season in Thailand. During this season,
Thailand is characterized by cloudy weather with high precipitation and high
humidity. From October to April, this region is influenced by northeast
monsoon wind that travels from the northeastern and the northern parts of
Asia (China and Mongolia). This monsoon wind brings a cold and dry air mass,
which leads to the dry season (local summer and local winter) in Thailand.
The local winter in Thailand is characterized by cool and dry weather, while
the local summer is characterized by hot (35 to 40∘) to extremely hot
weather (> 40∘) due to strong solar radiation. During the
dry season, storms may occur during the seasonal transition (TMD, 2015).
Transportation and industrial sectors are considered to be the major sources
of air pollutants in the study area (Watcharavitoon et al., 2013). In 2014,
∼36 million new vehicles were registered in Thailand, and 29 % of
these cars were registered in BKK (DLT, 2015). About 56 % and 28 % of
the registered vehicles in BKK were gasoline and diesel engines,
respectively. The remaining 16 % were run
on compressed natural gas (CNG) (DLT, 2017). There are a variety of metal,
auto parts, paper, plastic, food and chemical manufacturing facilities and
power plants in the outskirts of BKK (DIW, 2016a, b, c, d, e).
Data collection and data analysis
Over the 5-year period (1 January 2010 to 31 December 2014), hourly
observations from 15 Pollution Control Department (PCD) monitoring stations
were analyzed. The monitoring stations are assigned to three categories: BKK
sites, roadside sites and BKK suburb sites. BKK sites refer to the monitoring
stations that are located within BKK's residential, commercial, industrial
and mixed areas. They are within ∼50 to 100 m of the road. Roadside
sites refer to the monitoring stations that are located in BKK within 2 to
5 m of the road (Zhang and Oanh, 2002). BKK suburb sites refer to the
monitoring stations that are located in the provinces adjacent to BKK
(Fig. 1). Quality assurance and quality control on the data set were
performed by PCD prior to receiving the data. Hourly observations of the
gaseous pollutants and meteorological parameters were automatically collected
with autocalibration at the monitoring stations. Manual quality control was
performed when unusual observations were found. An external audit of the
equipment and monitoring stations was done every year. The data availability
and details of equipment calibrations are provided in Fig. S1, Sect. S1,
Supplement.
Gaseous species were measured at 3 m above ground level (a.g.l.). CO was
measured using nondispersive infrared detection (Thermo Scientific 48i). NO
and NO2 were measured using chemiluminescence detection (Thermo
Scientific 42i). SO2 was measured using ultraviolet (UV)
fluorescence detection (Thermo Scientific 43i), and O3 is measured
by using UV absorption photometry detection (Thermo Scientific 49i). The
meteorological parameters including wind speed (WS) and wind direction (WD)
were measured at 10 m a.g.l. by a cup anemometer and
potentiometer wind vanes. Temperature (T) and relative humidity (RH) were
measured at 2 m a.g.l. by a thermistor and thin film capacitor,
respectively (Watchravitoon et al., 2013). All the meteorological
measurements were made by Met One or an equivalent method.
Data analysis, statistical analysis and plots were developed using Excel
2016. Predominant wind directions related to O3 concentrations are
obtained using the Openair package (tool for the analysis of air pollution
data) on the RStudio program (https://www.rstudio.com/, last access:
6 February 2018).
Maximum (vertical bars) and average (solid line) concentrations of
(a) CO, (b) SO2, (c) NO2
(d) O3 and (e) NO from the 15 monitoring
stations, from 2010 to 2014, are compared with the hourly NAAQs (dotted line)
of Thailand (except NO, which is not a criteria pollutant). The number of
hourly O3 exceedances is shown by (f) locations and
(g) seasons.
Diurnal variations in gaseous species. The plots provide the average
concentrations of O3, NO and NO2 in ppb, the average
concentrations of CO in ppm, and the average concentrations of SO2
in ppb at (a) BKK sites, (b) roadside sites and
(c) BKK suburb sites. Vertical bars provide ±1 standard
deviation of the species concentrations.
Result and discussion
Status of pollution in BMR from 2010 to 2014
Figure 2a to e show the maximum and average concentrations of gaseous
pollutants, from 2010 to 2014 from the 15 monitoring stations. These
concentrations are compared with the hourly NAAQs of Thailand (NAAQs of
Thailand for hourly CO, NO2, SO2 and O3 are
30 ppm, 170 ppb, 300 ppb and 100 ppb, respectively (PCD, 2018)). Since NO
is not a criteria pollutant, only the maximum and average concentrations are
presented. During the study period, the maximum concentrations of CO,
NO2 and SO2 were mostly at their hourly standards (an
exceedance of NO2 was found at monitoring station 52T during 2013).
However, the maximum concentrations of O3 exceeded its standard.
Elevated CO, NO and NO2 concentrations were observed more
frequently at roadside sites than other sites. The average concentrations of
CO, NO and NO2 at roadside sites were ∼1.0±0.1 ppm, ∼60.5±42.7 ppb, and ∼30.9±8.1 ppb, respectively. Elevated
SO2 was more commonly observed at BKK suburb sites than other
sites. The average concentrations of SO2 at BKK suburb sites were
∼4.0±2.3 ppb. The average concentrations of O3 during the
daytime (06:00 to 18:00 LT) over BKK sites, roadside sites and BKK suburb
sites were ∼24.4±13.5, ∼18.2±12.3 and ∼27.7±14.7 ppb, and their values during the nighttime (18:00 to 6:00 LT) were
∼11.3±3.3, ∼9.1±4.9 and ∼14.2±5.4 ppb,
respectively. The 24 h average O3 concentrations were highest at
BKK suburb sites (∼21.4±3.3 ppb), followed by BKK sites
(18.6±2.3 ppb) and roadside sites (13.9±8.6 ppb). Statistical
analyses of the concentrations of gaseous pollutants from the three
monitoring station
types are provided in Table S1, Sect. B, Supplement.
The seasonal variations in the gaseous pollutants reveal that, in general,
elevated concentrations were observed during dry seasons and they decreased
during wet seasons (Fig. S2, Sect. S3). Interannual variations in the gaseous
pollutants reveal that while the concentrations of CO, NO2 and
SO2 decreased or remained constant, the concentration of
O3 tended to increase during the study period (Fig. S3, Sect. S4).
An O3 exceedance was recorded when an hourly concentration of
O3 was greater than 100 ppb (hourly O3 standard).
Figure 2f and g illustrate the number of hourly O3 exceedances,
which are shown by location and by seasons. The hourly O3
exceedances at BKK suburb sites were more frequently observed than at the
other sites. The average number of hourly O3 exceedances was ∼16 h yr-1 at BKK sites, ∼9 h yr-1 at roadside sites and
∼43 h yr-1 at BKK suburb sites. The hourly O3
exceedances were commonly observed during the dry season, less so during the
transitional period between the seasons (May) and rarely during the wet
season.
Diurnal variation in the gaseous species
Diurnal variations in gaseous pollutant are shown in Fig. 3a to c. The
diurnal variations in O3 show a single-peak pattern (Aneja et al.,
2001) with the concentrations increasing after sunrise and reaching the peak
∼ 15:00 local time (LT). The concentrations begin to decline in the
evening and reach the minimum concentrations ∼ 07:00 LT the next
morning. The concentrations of O3 at the peaks were ∼40 ppb
at BKK sites, ∼30 ppb at roadside sites and ∼45 ppb at BKK
suburb sites. The diurnal variations in NO show a bimodal pattern with the
concentrations reaching the first and the second peak ∼ 07:00 to 09:00
and ∼ 21:00 to 22:00 LT, respectively. The concentrations of NO at
the first and the second peak were ∼40 and ∼23 ppb at BKK sites,
∼110 and ∼73 ppb at roadside sites, and ∼30 and ∼13 ppb at BKK suburb sites. The concentrations of NO2 at the
first and the second peak were ∼23 and ∼28 ppb at BKK sites,
∼33 and ∼37 ppb at roadside sites, and ∼20 and ∼22 ppb at BKK suburb sites. Even the diurnal variations in
NOx show a bimodal pattern; at roadside sites the pattern
was flatter than at other sites. The flatter pattern of NOx
at roadside sites reveals that this monitoring station type was affected by a
high concentration of NOx all day. The diurnal variations in
CO show a bimodal pattern with the first and the second peak occurring
∼ 08:00 and 21:00 LT, respectively. The concentrations of CO at the
first and the second peak were ∼1 ppm (both peaks) at BKK sites, ∼2 and ∼1.5 ppm at roadside sites, and ∼1 ppm (both peaks) at
BKK suburb sites. The first peak of the diurnal variations in NO,
NO2 and CO corresponds with the morning rush hour in BKK (07:00 to
09:00 LT). The second peak occurred ∼3 to 5 h after the evening
traffic rush hour (16:00 to 18:00 LT) (Leong et al., 2002), due to a
combination of pollutant emissions and the collapse of the planetary boundary
layer (weak turbulence and diffusion) during this time. The diurnal
variations in SO2 show a bimodal pattern with the first and the
second peak of SO2 occurring ∼ 08:00 and 21:00 LT,
respectively. The concentrations of SO2 at the first and the second
peak were ∼4 and ∼3 ppb at BKK sites and roadside sites and
∼6 and ∼3 ppb at BKK suburb sites. At the roadside sites, the
peaks are more obvious than at the other sites. The result indicates that at
this monitoring station type, SO2 is primarily influenced by
emissions from vehicle exhaust using a high sulfur content fuel (Henschel et
al., 2013). It is noteworthy that BKK has a large diesel engine fleet (an
estimated 25 % of registered vehicles) (DLT, 2015). Diesel fuel contains
∼0.035 % wt sulfur (DOEB, 2017). The season-wise
distributions of the diurnal variations are provided in Fig. S4, Sect. S5.
Diurnal variations in the rate of change in O3
concentration (Δ[O3]/dt) during (a) local
summers, (b) wet seasons and (c) local winters.
Figure 4a to c shows diurnal variations in the rate of change in O3
concentration (Δ[O3]/dt) during dry seasons (local summer
and local winter) and wet seasons at the three monitoring station types (the
data have been averaged for each monitoring station type to capture the rate
of change in O3 concentration characteristics). The diurnal
variations in Δ[O3]/dt are a combination of O3
chemistry and meteorology. In general, Δ[O3]/dt during
the wet season were lower than those during dry season. However, during local
winter, the rates of change in O3 concentration were the highest.
The Δ[O3]/dt at the three monitoring station types, from
10:00 to 11:00 LT, were 4.5 to 7.0 ppb h-1 during wet seasons, 6.7 to
7.5 ppb h-1 during local summers and 5.7 to 9.2 ppb h-1 during
local winters. The Δ[O3]/dt became negative from 14:00 to
15:00 LT. As expected, the rate of change in O3 concentration was
nearly constant during the nighttime. Rapid changes in the mixing height and
solar insolation during the morning increases Δ[O3]/dt.
After sunset, the formation of O3 is inhibited and the planetary
boundary layer becomes more stable resulting in O3 reduction
through chemical reactions (for example, the oxidation of O3 by
NOx) and physical processes (for example, dry deposition to
the earth surface) (Naja and Lal, 2002).
Photochemical reaction and interconversion between O3, NO and
NO2
The primary precursors for tropospheric O3, in the urban
environment, are NOx and non-methane volatile organic
compounds (VOCs), methane or CO (The Royal Society, 2008; Monks et al., 2009; Cooper et al., 2014). While NOx was
measured continuously at all the monitoring sites, VOCs were measured
periodically only at one monitoring station, limiting its usefulness as part
of this study. In this study, the photostationary state (PSS) is applied
through the chemical reactions of O3 formation from 10:00 to
16:00 LT. This time window is chosen due to the fully developed planetary
boundary layer with well-mixed condition (Pochanart et al., 2001) to avoid
the accumulation of air pollutants by surface inversion. Analysis and
calculation are performed only during the dry season to eliminate the effects
of the removal process by wet deposition.
The relationship among NO, NO2 and O3 under PSS is presented by
Eq. (1) (Seinfeld and Pandis, 1998).
[O3]PSS=j1[NO2]k3[NO],
where [O3]PSS is the concentration of O3 at
PSS and j1 and k3 are the reaction rate coefficient of the
photochemical reaction of NO2 and the reaction rate coefficient of
the chemical reaction between NO and O3, respectively.
The values for k3 (ppm-1 min-1) are calculated by Eq. (2)
(Seinfeld and Pandis, 1998; Tiwari et al., 2015).
k3=3.23×103exp[-1430/T]
During dry seasons, the values of j1 ranged from 0.12 to
1.22 min-1, and the average of those at BKK sites, roadside sites and
BKK suburb sites were 0.74±0.2, 0.64±0.3 and 0.55±0.3 min-1, respectively. The rate coefficients are calculated using
the NCAR TUV model for 10:00 to 16:00 LT during the 2010 dry season at the
latitude and longitude of 13.76∘ N and 100.50∘ E. The
average j1 value calculated from the NCAR TUV model is
0.021±0.0024 s-1, which is similar to the calculated j1 values
from Eq. (1) (j1 ranges from 0.008 to 0.013 s-1). The values of
j1 from this study are similar to those values at an urban background
site in Delhi, India (values of j1 ranged from 0.4 to 1.8 min-1
and the average was 0.8 min-1) (Tiwari et al., 2015) and those values
collected during daytime in November in the UK (value of j1 was ∼0.14 min-1) (Clapp and Jenkin, 2001).
The values of k3, during dry seasons, ranged from 28.3 to
30.9 ppm-1 min-1, and the average of those at BKK sites, roadside
sites and BKK suburb sites were 29.8±0.7, 29.7 and 29.8±0.7 ppm-1 min-1, respectively. The ratio of [NO2] and
[NO] was ∼1.9. The statistical analysis of j1 (min-1 and
s-1) and k3 (ppm-1 min-1 and
cm3 molecule-1 s-1) at the three monitoring station types
using Eq. (1) and the average j1 calculated from the NCAR TUV model are
provided in Table S2, Sect. F.
Relationships and crossover points of NO, NO2 and
O3 at (a) BKK sites, (b) roadside sites and
(c) BKK suburb sites and concentration distributions of those
species at (d) BKK sites, (e) roadside sites and
(f) BKK suburb sites.
Figure 5a to c shows the relationships between NO, NO2 and
O3, their crossover points, and concentration distributions. The
crossover point among species occurs when the concentration of
NOx is ∼60 ppb. At this point, two regimes are
identified, including a low-NOx regime and a
high-NOx regime. Under the low-NOx regime
([NOx] < 60 ppb), O3 is the dominant
species and NO2 concentrations are higher than NO for
NOx species. Conversely, under the high-NOx
regime ([NOx]> 60 ppb), NO and NO2
increase and the concentrations of O3 rapidly decrease. Under the
high-NOx regime, the decline in O3 trend lines may
describe the O3 removal process through the titration of
O3 by NO.
Local and regional contribution to Ox
The Ox concentration is the summation of O3 and
NO2 concentration. Under the PSS condition, the concentration of
NO, NO2 and O3 approaches an equilibrium and the
concentration of Ox may be considered constant (Keuken et
al., 2009). Since the conversion between O3 and NO2 in
the urban and suburban atmosphere is rapid, the use of Ox to
represent the production of oxidants is more appropriate than only using
O3 (Lu et al, 2010). The local or NOx-dependent
contribution refers to Ox concentration that are influenced
by a concentration of the local pollutants. The regional or NOx-independent contribution
refers to the background
concentration of Ox that is not influenced by changes in the
local pollutants (Clapp and Jenkin, 2001; Tiwari et al., 2015).
The comparison of fitted linear regression lines from this study,
including at BKK sites, roadside sites and BKK suburb sites with fitted
linear regression lines from other studies.
Non-episode
Episode
This study
– BKK sites
[Ox]=0.33[NOx]+44.39
[Ox]=0.48[NOx]+91.10
– roadside sites
[Ox]=0.13[NOx]+53.89
[Ox]=0.29[NOx]+104.45
– BKK suburb sites
[Ox]=0.31[NOx]+47.0
[Ox]=0.68[NOx]+82.89
UKa
[Ox]=0.097[NOx]+38.2
[Ox]=0.112[NOx]+55.5
Buenos Aires, Argentinab
[Ox]=0.099[NOx]+22.0
Delhi, Indiac
[Ox]=0.54[NOx]+28.89
a Clapp and Jenkin (2001). b Mazzeo et al. (2005). c Tiwari et al. (2015).
The effects of local and regional contributions on
Ox during non-episode and episode days at (a) BKK
sites, (b) roadside sites and (c) BKK suburb sites.
Figure 6a to c show the local and regional contributions of
Ox at the three monitoring station types. The effects of the
local and regional contributions to Ox concentration are
analyzed by plotting Ox concentrations against
NOx concentrations and fitting the plot with a linear
regression (y=mx+c). The concentrations of NOx and
Ox are referred to as x and y, respectively. The slope
of the linear regression (m) implies the local contribution, and the
intercept with the y axis (c) implies the regional (background)
contribution (Aneja et al., 2000; Clapp and Jerkin, 2001; Notario et al.,
2012). Table 1 shows the comparison between fitted linear regressions from
this study and fitted linear regression lines from other studies. The average
background Ox concentrations over BMR during non-episodes
([O3]hourly < 100 ppb) and episodes
([O3]hourly > 100 ppb) were ∼48 and
∼95 ppb, respectively. The local and regional contributions during the
episode days, in general, were about double of those during the non-episode
days. The results reveal that elevated O3 concentrations during the
episode days are influenced by both the local and regional contributions of
Ox. It is noteworthy that the pattern of the local and
regional contributions at roadside sites during non-episode periods is
composed of two NOx concentration regimes. The
low-NOx regime (NOx < 60 ppb)
resembles the local and regional contributions during non-episodes over BKK
suburb sites. The high-NOx regime
(NOx > 60 ppb) may represent the typical
characteristics of air quality near roads.
Backward
trajectories from the HYSPLIT model reveal a (a) NE wind direction
(13 January 2010) and (b) SW wind direction (1 January 2010).
The local contributions from the fitted linear regressions are compared with
the local contribution that is calculated from the delta O3 method.
A delta O3 (ΔO3) analysis was performed to
reflect on the intensity of O3 production in the BMR area (Lindsay
and Chameides, 1988). Lindsay et al. (1989) analyzed high-O3 events
in Atlanta, GA, USA, and showed that rural background O3 during
high O3 concentrations ([O3] > 80 ppb) in
the Atlanta metropolitan area was higher than its average and the
concentration of O3 increased from ∼15 to 20 ppb when the
air mass traveled across the city. This enhanced the total O3
concentration from 80 to 85 ppb. In our study, the differences in the
concentrations of O3 at the upwind and downwind monitoring stations
(monitoring stations 20T and 27T) are averaged. The conditions
to calculate ΔO3 in this study are as follows (1) high
O3 concentrations ([O3] > 80 ppb) were
observed at at least one of the two monitoring stations; (2) the calculation
is performed from 10:00 to 16:00 LT during the dry season to avoid the
accumulation of air pollutants by surface inversion and the effects of the
removal process by wet deposition; (3) National Oceanic and Atmospheric
Administration (NOAA) HYSPLIT model backward trajectories revealed N–NE,
S–SW wind directions (Fig. 7). Even the O3 concentrations at the
downwind monitoring stations are expected to be greater than the O3
concentrations at the upwind monitoring stations, a negative ΔO3 may be found. The negative ΔO3 suggests the
deposition of O3 and/or that O3 was consumed as it passes
over the city and/or that there may have been a wind reversal so that air
already polluted by the metropolitan area was brought back in to the city
(Lindsay et al., 1989). The ΔO3 in BMR ranged from -53 to
86 ppb (average ∼10.4 ppb) and ranged from -66 to 96 ppb (average
∼9.4 ppb) when the predominant wind directions advecting into the city
were from NE and SW, respectively. Thus, we find that there was a ∼10 ppb enhancement of the O3 concentration during the air
pollution high O3 concentration in BMR
([O3] > 80 ppb), which corroborates local
O3 production analysis based on linear regression.
Correlation of air pollutants
Local sources analysis
The characteristics of emission sources are often determined by the ratios
between CO and NOx (CO / NOx) and
SO2 and NOx
(SO2 / NOx). In general, the major sources of
NOx are point sources and mobile sources. However,
NOx from point sources is more likely correlated with
SO2. NOx from mobile sources is more likely
correlated with CO (Parrish et al., 1991). Therefore, the characteristics of
mobile sources are high CO / NOx ratios and low
SO2 / NOx ratios. In contrast to mobile
sources, the characteristics of point sources are low
CO / NOx ratios and high
SO2 / NOx ratios (Parrish et al., 1991;
Rasheed et al., 2014).
The comparison of CO / NOx and
SO2 / NOx ratios from this study with other
studies (modified from Rasheed et al., 2014).
Region
Source
CO / NOx
SO2 / NOx
This study
19.8
0.1
– BKK sites
18.25
0.09
– roadside sites
21.15
0.11
– BKK suburb sites
19.20
0.09
Eastern US
4.3
0.94
Mobile
8.4
0.05
Point
0.95
1.8
Pennsylvania
2.6
1.7
Mobile
7.8
0.05
Point
0.8
2.3
Western US
6.7
0.41
Mobile
10.2
0.05
Point
1.2
1.1
Denver metropolitan area
7.3
0.19
Mobile
10.5
0.05
Point
0.18
0.44
Raleigh, NC
16.3
0.73
New Delhi, India
50
0.58
Guwahati and Nagpur, Indiac
> 0.3
Kolkata and Durgapur,
≤0.13
Indiac
Madrid, Spaina
13.3
0.29
Rouen, Franceb
12–18
Islamabad, Pakistan
– based on Emission Inventory (2010)
Mobile
4.94
0.34
Point
0.63
7.0
– based on ambient data
10
0.01
a Fernandez-Jiménez et al. (2003).
b Coppalle et al. (2001).
c Mallik and Lal (2014).
Table 2 shows the comparison between the CO / NOx and
SO2 / NOx ratios from this study when compared
with other studies. The ratio of CO / NOx is 19.8, and
the ratio of SO2 / NOx is 0.1 over BMR. This
suggests that the major contributors of primary pollutants over the BMR are
mobile sources. However, this region may be influenced by manufacturing
facilities' point sources (SO2 contributor) on the outskirts of the
BKK. These point sources will impact the concentrations of SO2,
NOx and CO. Correlations among species are provided in
Table S3, Sect. G.
Relationship between the concentrations of O3, wind speeds
and wind directions during (a) O3 episodes
([O3]hourly > 100 ppb) and
(b) during non-O3-episodes
([O3]hourly≤100 ppb) over BMR from 2010 to 2014.
Effects of pollutant transport
In general, O3 has a short lifetime in the polluted urban
atmosphere (approximately hours). However, O3 has a longer lifetime
of several weeks in the free troposphere. This occurrence may allow
O3 to be transported over continental scales (Stevenson et al.,
2006: CO / NOx; Young et al., 2013:
CO / NOx; Monks et al., 2015). Figure 8 shows
O3 concentrations, during episodes and non-episodes, with
predominant wind directions and wind speeds. The results show that
O3 exceedances are associated with low wind speed and predominant
wind directions, i.e., the origins of the air masses. In general, elevated
O3 concentrations were observed with a wind speed lower than
4 ms-1 with northerly winds (station 22T), southerly winds (stations
3T, 10T, 19T, 20T and 61T) and westerly winds (station 52T). It is noteworthy
that the southerly winds, generally, bring cleaner marine air mass to the
land. However, under a stagnant condition (i.e., low wind speed), elevated
O3 concentrations were observed during southerly winds (Sahu et
al., 2013a, b).
Number of hours for the different AQI categories of
O3 over the BMR from 2010 to 2014.
AQI
Hour
BKK sites
Roadside sites
BKK suburb sites
3T
5T
10T
11T
12T
15T
61T
52T
54T
13T
14T
19T
20T
22T
27T
Good
39 018
32 021
27 959
40 715
26 606
33 628
26 442
32 665
40 231
31 070
35 429
33 592
30 793
34 301
26 873
Moderate
310
713
1023
556
367
479
1178
807
27
1620
944
1687
1340
1466
719
Unhealthy for
88
139
225
109
82
108
295
151
0
454
288
515
632
448
218
sensitive groups
Unhealthy
19
40
61
30
29
38
85
36
0
195
87
184
209
109
96
Very unhealthy
0
6
12
0
0
10
26
0
0
59
2
51
28
23
9
Hazardous
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Air quality index for O3 management
In the US, the AQI for air pollutants is divided into six categories (good,
moderate, unhealthy for sensitive groups, unhealthy, very unhealthy and
hazardous). These categories are nonlinear and relate to human health
(U.S. EPA, 2017a, b, c). In Thailand, the NAAQs for the air pollutant species
are pegged at an AQI value of 100. In this study, the severity of
O3 concentrations in BMR is evaluated by AQI for O3.
Table 3 provides the ambient air quality over BMR from 2010 to 2014 based on
the AQI of O3. Based on the AQI for O3, during the study
period, the majority of air quality over BMR was in the good AQI category
(∼97 %) followed by the moderate air quality category (∼2.3 %). However, the unhealthy for sensitive groups (∼0.7 %),
unhealthy (∼0.3 %) and very unhealthy (∼0.04 %)
O3 air quality categories were observed. Generally, BKK suburb
sites have a higher number of hours that were categorized as unhealthy for
sensitive groups, unhealthy and very unhealthy than BKK and roadside sites.
The average number of hours that were categorized as unhealthy for sensitive
groups, unhealthy and very unhealthy over BKK suburb sites were 425.8, 146.7
and 28.7 h. The calculation of the AQI for O3 can be found in
Figs. S5 and S6, Sect. S8.
This study provides measurements and analysis for the gaseous criteria
pollutants. However, in order to provide a well-established air quality
management policy, the integration of multidisciplinary analysis is needed.
This will include scientific, socioeconomic and policy analysis (Aneja et al,
2001). The results from this study revealed evidence of O3 air
quality standards being breached. This resulted in adverse health effects,
human welfare, economics, and environment over BMR. Ratio analysis suggests
that the first priority should be controlling pollution emissions from local
sources that are primarily mobile. The complex relationship between
O3 and its precursors and the effects of pollution transport show
that decreasing only NOx emissions and/or local emissions
may not be an effective policy to reduce O3 because of regional air
pollution transport (i.e., ozone and its precursors contribute to
O3 exceedances). To identify the proportional contribution
between local and regional sources of O3 concentrations during
selected O3 episode days, atmospheric modeling is needed to
quantify various processes that contribute to the ambient concentration at
specific locations. This scientific analysis provides a framework for the
process of establishing an air quality policy while
analyzing socioeconomic impacts.