Science of the Total Environment 382 (2007) 324 – 341
www.elsevier.com/locate/scitotenv
Long-term visibility trends in one highly urbanized, one highly
industrialized, and two Rural areas of Taiwan
Ying I. Tsai a,b,⁎, Su-Ching Kuo c , Wen-Jhy Lee b,d , Chien-Lung Chen e , Pei-Ti Chen f
a
c
Department of Environmental Engineering and Science, Chia Nan University of Pharmacy and Science,
60, Sec. 1, Erh-Jen Rd., Jen-Te, Tainan, Taiwan 717, ROC
b
Sustainable Environment Research Center, National Cheng Kung University, 1, University Rd., Tainan, Taiwan 701, ROC
Department of Applied Chemistry, Chia Nan University of Pharmacy and Science, 60, Sec. 1, Erh-Jen Rd., Jen-Te, Tainan, Taiwan 717, ROC
d
Department of Environmental Engineering, National Cheng Kung University, 1, University Rd., Tainan, Taiwan 701, ROC
e
Department of Finance, Fortune Institute of Technology, 1-10, Nwongchang Rd., Daliao, Kaohsiung, Taiwan 831, ROC
f
Department of Hospitality Management, Chung Hua University, 707, Sec. 2, Wu-Fu Rd., Hsinchu, Taiwan 300, ROC
Received 4 August 2006; received in revised form 24 April 2007; accepted 26 April 2007
Available online 4 June 2007
Abstract
Visibility trends on the island of Taiwan were investigated employing visibility and meteorological (1961–2003), and air pollutant
(1994–2003) data from one highly urbanized center (Taipei), one highly industrialized center (Kaohsiung), and two rural centers
(Hualien and Taitung). Average annual visibility (1961–2003) was significantly higher at the rural centers. Unlike at the other centers,
visibility in Taipei improved between 1992 (6.6 km) and 2003 (9.9 km), and this can be linked to the construction and expansion of a
mass transit rail system in Taipei, the use of which has helped reduce emissions of traffic related air pollutants, particles, and NO2. This
has left Kaohsiung with the lowest annual visibility since 1994, despite its 1961–2003 average being superior to that of Taipei.
Precipitation lowers visibility, as demonstrated by the all-centers correlation coefficient for visibility and precipitation of − 0.92.
Hence, frequency of precipitation is one of the factors contributing to the average annual visibility number. The poorest air quality
category (‘episode’), most commonly experienced in Taipei and Kaohsiung, was characterized by relatively high concentrations of
PM10 and NOx at those centers, with comparatively high atmospheric pressure and comparatively low visibility and wind speed.
Excepting O3, pollutant concentrations were slightly higher during weekdays, although there was no consistent, significant difference
in weekday–weekend visibility. Principal component analysis demonstrated that visibility was markedly reduced in Taipei,
Kaohsiung, and Hualien by increased vehicular emissions, road traffic dust, and industrial activity, but not in Taitung, where visibility
was as a result superior to that at the other centers and degradation in visibility was likely a response to long-range transport of
pollutants rather than local sources. Optimal empirical regression models indicated a negative impact on visibility for each of PM10,
SO2 and NO2, particularly so for PM10, and validity of these models for Taipei, Kaohsiung, and Hualien was confirmed by correlation
coefficients of simulated and observed average visibility of 0.63–0.72 for daily visibility and 0.85–0.88 for monthly visibility. For
Taitung these figures were only 0.46 and 0.50, respectively, indicating that simulations for Taitung should include long-range
transport as a pollutant source.
© 2007 Elsevier B.V. All rights reserved.
Keywords: Visibility deterioration; Air quality; Weekend effect; Principal component analysis; Empirical model
⁎ Corresponding author. Department of Environmental Engineering and Science, Chia Nan University of Pharmacy and Science, 60, Sec. 1, ErhJen Rd., Jen-Te, Tainan, Taiwan 717, ROC. Tel.: +886 6 266 0208; fax: +886 6 266 9090.
E-mail address: [email protected] (Y.I. Tsai).
0048-9697/$ - see front matter © 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.scitotenv.2007.04.048
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
1. Introduction
Visibility is related to the reduction in brightness as
photons are absorbed or scattered by particles in the
atmosphere between the viewer and the object; atmospheric extinction. Atmospheric species can therefore
be assigned an extinction coefficient, the sum of their
scattering and absorption coefficients. The scattering
coefficient of atmospheric pollutants and hence their
effect on visibility has been shown to differ according to
pollutant characteristics (Cohen, 1975; Horvath, 1995,
1996; Seinfeld and Pandis, 1998; Tsai, 2005). Visibility
is therefore a highly relevant factor in studies of atmospheric pollution, is used as a referent for differentiating good and poor air quality (Tsai and Cheng,
1999), and forms part of comprehensive meteorological
and climatological analyses.
Appel et al. (1985) found that the main culprits in
atmospheric visibility degradation are PM2.5 and NO2.
The specific content of PM2.5 is a most important factor
in its effect on visibility because the size and chemical
composition of each component particle affects its
ability to refract, scatter, and absorb light. Sulfates were
found to be responsible for about 60–70% of atmospheric extinction in the eastern US, while in the inner
mountain west sulfates were responsible for 30%, carbon aerosols for 35%, and coarse mass for between 15–
25% (Malm and Day, 2000). There is a strong association between presence of PM2.5 and presence of
PM10, to the extent that a targeted reduction of PM10 is
likely to lead to an increase in atmospheric visibility
(Malm and Day, 2000; Tsai et al., 2003). Indeed, Kim
et al. (2001) reported that during Asian dust storm
periods, when hourly averaged concentration of PM10
rose from 33 to 602 μg m− 3, hourly averaged visibility
decreased from 62 km to just 2 km. Chan et al. (1999)
found that poor visibility in Brisbane, Australia, was
mainly caused by emissions of pollutants from traffic
sources, followed by secondary sulfates and biomass
burning, while Lee and Sequeira (2002) indicated that
fine ammoniated sulfate aerosol is probably the predominant water-soluble inorganic visibility-reducing
component in Hong Kong.
Stuart and Hoff (1994) took long-term visibility data
from 140 observation stations and developed a novel
statistical technique by which the frequency distributions of the unbiased data and median visibilities were
consistent with expectations about the geographic behavior of visibility. Their results showed that the average summer visibility at the Inuvik and Dawson Creeks
within the Canadian Arctic Circle was up to 109 km and
222 km, respectively, reducing to 23 km and 76 km,
325
respectively, in winter, possibly due to the ‘arctic haze’
effect.
In the Visibility Impairment Due to Sulfur Transport
and Transformation in the Atmosphere (VISTTA) project (Blumenthal et al., 1981), it was found that NO2 in
the emissions from Arizona Navajo coal-fired power
plant led to brown clouds and the disappearance of blue
light. However, the secondary photochemical particles
did not affect air quality in the region beyond 100 km
from the plant at low relative humidity. The observed
aerosol in the study region was partially from wildfires
in southern California, to the southwest. Using data
from 36 monitoring stations across the US, the Interagency Monitoring of Protected Visual Environments
(IMPROVE) project (Sisler and Malm, 1994) discovered from the seasonal visibility variations that watersoluble aerosols cause the visibility to deteriorate and
that humidity is a major factor influencing light scattering of atmospheric aerosols. The scattering coefficient of atmospheric particles was implicated in the
results of the Region Visibility Experimental Assessment in the Lower Fraser Valley (REVEAL) project
(Pryor et al., 1997), which showed that although
atmospheric PM2.5 was b 20 μg m− 3, visibility was
reduced to b 10 km. It was calculated that sulfate and
nitrate particles contributed between 55–67% of the
light scattering effect of the fine particulate. Lowenthal
et al. (1997) also indicated in the REVEAL project that,
for towns in a basin about 50–80 km east of Vancouver,
40% of PM2.5 was from traffic emissions while secondary sulfate contributed about 26% of the total pollution. The contribution from nitrate varied dramatically
from 9–20%. Day et al. (1997) used the IMPROVE
project results to analyze the atmospheric aerosol
compositions from 1988 to 1994 for the Great Smoky
Mountains National Park and discovered that summer
SO42− and organic carbon concentrations were higher
than in other seasons; the aerosol was mostly acidic.
Winter saw higher concentrations of SO2 and NO3− than
other seasons, indicating that SO2 was not converted
into SO42− during winter. The winter aerosol was mostly
neutral. Finally, in the Measurement of Haze Visual
Effects (MOHAVE) project, Gebhart and Malm (1997)
discovered that the sulfur-containing material, organic
carbon, light-absorbing elemental carbon and some
trace elements in emissions from southern California
during summer may be transported to Southern Nevada/
Southwestern Arizona. During winter, however, these
contaminants did not disperse easily from their source,
instead lingering in regions to the north or the Grand
Canyon region to the east, and causing higher concentrations of pollutants in the Grand Canyon region.
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Malm and Gebhart (1997) additionally showed, based
on the relationship between sulfur-containing substances and Se, that about 50% of sulfur-containing
particles originated from a local coal-fired power plant.
In winter, about 50% of sulfur-containing substances
were from wood burning while in summer various
sources from cities became more important.
Of relevance to visibility and air quality studies is a
phenomenon known as the weekend effect, in which O3
concentration is higher on weekends than on weekdays
in spite of the fact that ozone pollutant precursors such
as NO and volatile organic compounds are lower on
weekends. The weekend effect has been reported in
America since the 1970s (Cleveland et al., 1974; Elkus
and Wilson, 1977; Qin et al., 2004) and was reported in
Tainan, Taiwan, in 2005 by Tsai, associated with higher
weekend visibility.
Situated off the south-east coast of China, Taiwan has
a warm and humid climate. The hottest month is July,
with average temperatures near 30 °C, and the coldest
month is January, with average temperatures near 20 °C.
As elsewhere in the region, chronic and acute episodes
of visibility degradation in Taiwan are readily evident to
the resident and visitor alike. However, there has been
no extensive cross-Taiwan study of long term visibility
degradation to date, perhaps in part because monitoring
of critical air pollutants and atmospheric chemical data
was not detailed until about 15 years ago.
In this research therefore, four geographically distant
centers across Taiwan; Taipei, Kaohsiung, Hualien, and
Taitung, representing three identifiably different local
pollutant mixes, were chosen for analysis of visibility
and air quality over time. Taipei, in the north of Taiwan,
is the largest metropolitan area and the capital of
Taiwan, with a population of approximately 2.7 million
and a population density of 9676 km− 2. It is surrounded
by hills or mountains and suffers significant emissions
of pollutants from automobiles and factories. Kaohsiung, on a flat plain on the south-west coast, is the
biggest port and most heavily industrialized city in
Taiwan, with a population of approximately 1.5 million
and a population density of 9333 km− 2. It suffers
significant emissions of pollutants from both automobiles and fuel combustion from numerous petrochemical
and metal processing industrial areas. Taipei and
Kaohsiung will henceforth be referred to as ‘metropolitan areas’. Both occasionally suffer 24-h PM10 levels of
N150 μg m− 3, leading to an ‘episode’ air quality classification under the Taiwan National Ambient Air Quality Standards (NAAQS) classification system. Such
events are also generally associated with visibility deterioration linked to chemical smog (Tsai et al., 2003;
Tsai, 2005). Hualien, in the east of Taiwan, is a typical
rural area, with a population of approximately 360,000
and a population density of 77 km− 2. Taitung, in the
southeast of Taiwan, is also typical rural, with a population of approximately 240,000 and a population density of 69 km− 2. Both of these ‘non-metropolitan’
centers are surrounded by hills or mountains and neither
has any significant emissions of industrial pollutants,
only low-density traffic emissions.
Two data sets, one covering the period 1961–2003
and one 1994–2003, were utilized to determine changes
in visibility over time and the specific factors affecting
visibility in the study area, aided by a Varimax-rotated
principal component analysis employed to establish the
relationships between major air pollutants, meteorological parameters, and visibility.
2. Materials and methods
2.1. Visibility observations
Visibility observations came from the N400,000 sets
of meteorological data and visibility observations made
between 1961 and 2003 by Weather Service Office
(WSO) staff at the Taipei, Kaohsiung, Hualien, and
Tawu (for Taitung) WSOs, shown in Fig. 1. Visibility
observations are made at a height of 20–36 m, on the
roof of the building, and require that an operator scans
the horizon of at least 180° for predetermined sets of
objects made up of easily identifiable structures and
objects such as tall buildings, towers, and mountain
ridges. The furthest distance at which it is possible to see
more than half the set of objects at that distance is
recorded as the prevailing visibility. Between 1961 and
2003, visibility observations were made five times a day
(0800, 0900, 1100, 1400 and 1700 h). The daily visibility has been continually observed for more than
40 years by different observers. Over this time, selection
of targets has at times changed as the horizon itself has
changed due to municipal or regional development.
Following World Meteorological Organization (WMO)
rules, observations were always made by two operators,
both specially trained, who arrived at a consensus for
each observation, so guarding against individual observer differences. Also, procedures remained constant
throughout the period, reducing the risk of variation
from non-operator factors to a minimum.
2.2. Air quality data
The air quality data in this study came from
approximately one million sets recorded between 1994
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
327
Fig. 1. Position of weather observation and air quality monitoring stations, major industrial areas, major emission sources and highways in four
centers in Taiwan.
and 2003 at seventeen monitoring stations that form part
of the Taiwan Air Quality Monitoring Network
(TAQMN). Nine of the monitoring stations (henceforth
referred to as ‘sites’) were in Taipei: Hsintien (HT),
Tucheng (TC), Panchiao (PC), Hsinchuang (HC),
Tsailiao (TL), Shinlin (SL), Chungshan (CS), Wanhua
(WH), and Kuting (KT). Six were in Kaohsiung: Jenwu
(JW), Nanzi (NZ), Tsoying (TY), Chiankin (CK),
Chiancheng (CC), and Hsiaokang (HK). One was in
Hualien (HL) and one was in Taitung (TT), and the
locations of all can be seen in Fig. 1. The TAQMN
stations monitor air pollutants according to reference or
equivalent United States Environmental Protection
Agency (US EPA) methods. PM10 is monitored at the
sites using a β-gauge automated particle sampler from
Kimoto Electric Co., Ltd., Japan, while sulfur dioxide
(SO2), carbon monoxide (CO), ozone (O3), and oxides
of nitrogen (NO and NO2) are monitored using
instruments from Thermo Environmental Instruments,
Inc., USA. Continuous 24 h trace gas measurements of
SO2, CO, O3 and nitrogen oxides (NOx) were made
using pulsed fluorescence, infrared absorption, absorption of ultraviolet light and chemiluminescence, respectively. Each site is equipped with an automated
calibration system identical to those used for the regulatory monitoring of air quality in Taiwan (Tsai et al.,
2003; Tsai, 2005). Minimum detectable limits provided
by the aforementioned companies and confirmed in the
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field are 1 ppbv for SO2, 0.1 ppmv for CO, 2 ppbv for
O3, 10 μg m− 3 for PM10, and 1 ppbv for NOx.
Unfortunately, there was no data available for PM2.5 or
for the chemical composition of PM10.
2.3. Treatment and categorization of data
The Pollutant Standard Index (PSI) in Taiwan is
based on daily observations of the five criteria pollutants. For each pollutant, a sub-index is calculated from
a segmented linear function that transforms ambient
concentrations onto a scale extending from 0 to 500. A
24 h PSI value of b 50 is categorized as a ‘clear’ air
quality day, one of 50–100 as ‘moderate,’ and one of
≥ 100 as ‘hazy and unhealthy’ or ‘episode’ air quality.
As discussed, PM10 is mostly a reliable surrogate for
PM2.5, and of the five criteria air pollutants monitored
by the TAQMN it is the one most closely correlated with
air quality. A 24 h PM10 concentration of b 50 μg m− 3
leads to a ‘clear’ categorization, and corresponds to a
PSI b50, while one of N 150 μg m− 3 leads to a ‘hazy and
unhealthy’ or ‘PM10 episode’ categorization.
2.4. Mixing layer height
Mixing layer height (Mix), the height at which
aerosol dispersion occurs in the atmospheric boundary
layer, is an important parameter in the assessment of
pollutant concentration and dispersal and a crucial input
parameter in local visibility models. Its calculation,
using relative humidity, pressure, wind vector, cloud
fraction, zenith angle, and rain duration data obtained by
interpolation of the surface weather station data, is based
on a Lagrangian model (Tu, 1999), which parameterizes
the mixing layer height (Holzworth, 1972), where the
land parameters are determined according to Tsuang and
Tu (2002). The input lapse rate above a mixing layer
height was either set at a default value of the wet lapse
rate (− 6.5 °K km − 1 ) or calculated according to
rawinsonde data.
2.5. Statistical methods
Variation in visibility can be described using a
Weibull distribution, a standardized exponential distribution that uses a cumulative frequency, F, of a parameter V to simulate the visibility:
F ¼ 1 e ð C Þ
V k
ð1Þ
where k is defined as the form factor and C is defined as
the scale factor. Both k and C can be either normal or
log-normal distributions. Stuart and Hoff (1994) applied
the Weibull distribution to fit the frequency of occurrence of visibility between 0.8–24.1 km. At the confidence level of 95%, the correlation coefficient of the
fit, r, can be as high as 0.997, indicating that the Weibull
distribution offers an excellent estimation of the actual
mean visibility.
In contrast to the Wiebull distribution, the principal
component analysis (PCA) eigenvector model is a
relatively simple and effective tool for the identification
of relationships in voluminous environmental data containing multiple factors using the eigenvector decomposition of a matrix of pairwise correlations among
many variables (Miller et al., 2002). It was first applied
to estimate sources of urban ambient inhalable particulate in Boston (Thurston and Spengler, 1985). PCA has
since then been successfully applied in numerous source
apportionment studies of air pollutants and environmental issues (Statheropoulos et al., 1998; Tsai et al.,
2003; Tsai, 2005).
Pryor et al. (1994) identified the sources of visibilitydegrading aerosol for the impaired visibility observed in
the Lower Fraser Valley (LFV) of British Columbia,
Canada. They discovered that five significant principal
components accounted for over 70% of the explainable
variance in the fine particle speciation dataset. The
loading correlation coefficients of these components
were used in evaluating the sources contributing to
particulate light scattering and it was found that traffic
emissions and wood burning contributed most of the
light scattering.
The PCA technique is, in principle, a data reduction
exercise and simplifies possible models describing the
data set. Data reduction is achieved by finding linear
combinations (principal components, PCs) of the
original variables, which account for as much of the
original total variance as possible. It was used in this
study to identify which pollutants and meteorological
factors influenced visibility and to rank their importance, in so doing identifying relationships and trends.
To determine the major factors correlated with visibility,
a Varimax-rotated PCA, solved by eigenvector decomposition and used to redistribute the variance as well as
to provide a more interpretable structure to the factors,
was performed on dimensionless standardized data sets
from the air quality monitoring stations and the weather
service office data collected between 1994 and 2003.
Air quality and meteorological data were calculated with
PCs from a correlation matrix of visibility observations,
meteorological parameters, and air pollutant concentrations. Only principal components with eigen values
N1.0 were considered to be meaningful in the analysis
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
and interpretation of visibility and causative factors.
Loading correlation coefficients of larger than or equal
to 0.6 between the PCs and the original variables were
considered as influential source identity to each PC.
Further details of the statistical technique, including
theory and procedure, have been presented by Thurston
and Spengler (1985), Richman (1986) and by us in
earlier publications (Tsai et al., 2003; Tsai, 2005).
3. Results and discussion
3.1. Annual and monthly visibilities
It can be seen from Fig. 2 that the general trend in
visibility for Kaohsiung, Hualien, and Taitung for the
entire period 1961 to 2003 is one of decline. The exception is Taipei, which has seen an improving trend in
visibility from 1994. Between 1961 and 2003 the metropolitan centers (Taipei and Kaohsiung) experienced
the poorest annual average visibility (8.8 ± 1.5 km and
13.9 ± 5.9 km, respectively). In Taipei, visibility was
11.1 km in 1960 and had dropped to 6.6 km in 1992.
Since then the trend has apparently reversed, with
visibility in 2003 averaging 9.9 km. This reversal can be
linked to the construction and expansion of a mass transit
rail system in Taipei, the use of which has helped reduce
emissions of traffic related air pollutants, particles, and
NO2. In Kaohsiung, meanwhile, visibility worsened
from 25.7 km in 1960 to an annual low of 4.9 km in
2000, subsequent to which the annual averages suggest a
relatively unchanging picture going forward.
In the non-metropolitan centers average annual
visibility between 1961 and 2003 was 22.8 ± 6.9 km
(Hualien) and 25.7 ± 5.0 km (Taitung). Both centers
experienced a continued worsening of visibility from the
329
start to the end of the period, although in 2003 visibility
still remained significantly better than for Taipei and
Kaohsiung. In Hualien visibility dropped from 33.7 km
in 1960 to a low of 13.1 in 2003 while in Taitung it
dropped from 35.3 km in 1960 to a low of 16.2 km in
2003. This unabated worsening reflects increasing local
traffic density and industrial emissions (albeit still at a
much lower level than in the metropolitan centers) and
long range transport pollutants, mostly from industrial
and urban centers to the west.
The best month for visibility (highest monthly
average) was June or July for all centers. The worst
month for visibility showed more variability: for Taipei
it was March, for Kaohsiung December, for Hualien
March using the 1961–2003 data set and February using
the 1994–2003 data set, and for Taitung February using
1961–2003 and May using 1994–2003.
3.2. Precipitation and visibility
Visibility was reduced by precipitation. This can be
seen from Table 1, which shows that when precipitation
days are included in the data set the average annual
visibility for the period 1961–2003 suffers a higher
percentage reduction for the centers with higher precipitation frequency (i.e. more days with precipitation).
Average annual visibility for 1961–2003 for Taipei, the
center with the highest precipitation frequency of
13.2%, is reduced by 0.58 km or 6.63% of the ‘all
observations’ visibility with precipitation days included,
whereas for Kaohsiung, the center with the lowest
precipitation frequency of 5.58%, visibility is reduced
by only 0.25 km or 1.79% of the ‘all observations’
visibility. These results are similar to those obtained by
Stuart and Hoff (1994) at Sault Ste. Marie, Canada.
During rain, the median visibility in Sault Ste. Marie
dropped from 33.5 km for all hours to 8.5 km. Median
visibility fell further to 7.3 km during smoky or hazy
days and to 3.5 km in foggy conditions.
3.3. Air quality and visibility
Fig. 2. Time series of mean annual visibilities in Taiwan, 1961–2003.
Table 2 shows air quality data from nine monitoring
stations in Taipei, six in Kaohsiung, and one in Hualien
and Taitung in the period 1994–2003, classified using
24-h PM10 into the three PSI categories of ‘clear,’
‘moderate,’ and ‘episode.’ Visibility was highly correlated with air quality categories at the metropolitan
centers of Taipei and Kaohsiung whereas visibility associated with ‘moderate’ air quality was slightly worse
than that associated with ‘episode’ air quality at Hualien
and Taitung.
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Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
Table 1
Degradation in visibility attributable to precipitation, 1961–2003
Area
Visibility during all
observations (km)
Change in visibility
due to inclusion of days
with precipitation (km)
Change in
visibility (%) a, y
Precipitation
frequencies (%), x
Correlation
coefficient (r) b
and relationship
Taipei
Kaohsiung
Hualien
Taitung
8.78
13.95
22.81
25.75
− 0.58
− 0.25
− 1.33
− 0.89
−6.63
−1.79
−5.82
−3.47
13.20
5.58
10.23
9.83
−0.922
y = −0.650x + 1.884
a ðvisibility for all observationsvisbility for not rainingÞ
b
100k.
visibility for all observations
Relationship between precipitation frequency and percentage change in visibility.
The data for Taipei show that visibility was lowest
(5.7 to 6.6 km) in ‘episode’ air quality, characterized by
relatively high concentrations of PM10 and NOx, a
relatively high atmospheric pressure, and a relatively
low wind speed. Taipei has a higher average concentration of NOx than the other centers and concentrations of
PM10 and NOx in Taipei during ‘clear’ air quality are
lower than during ‘moderate’ or ‘episode’ air quality.
Indeed, the PM10 concentration associated with ‘episode’ air quality is approximately five to six times that
associated with ‘clear’ air quality (169.0–189.6 μg m− 3
compared with 29.4–34.9 μg m− 3), while the NOx
concentration is about three times greater. ‘Clear’ air
quality in Taipei (visibility 11.4 to 12.0 km) is also
characterized by a relatively low atmospheric pressure
and relatively high wind speed. Site TL experienced the
most ‘episode’ air quality hours (698), while HT
experienced the least (43). Site TL also experienced
the highest NOx concentration and HT the lowest,
demonstrating good correlation between NOx and
‘episode’ air quality. Conversely, no correlation is demonstrated between PM10 concentration and air quality
within the Taipei ‘episode’ data as site HT experienced
the highest PM10 concentration but the best visibility,
although it is worth noting that wind speed was extraordinarily high (3.7) at this site.
Kaohsiung ‘episode’ visibility was the worst visibility experienced at any of the four centers, at a mere 3.2
to 3.4 km. Like Taipei, the concomitant air quality was
characterized by relatively high concentrations of PM10
and NOx, a relatively high atmospheric pressure, and a
relatively low wind speed. PM10 concentration associated with ‘episode’ air quality is approximately five to
six times that associated with ‘clear’ air quality, while
the NOx concentration is about 3.6 times greater. ‘Clear’
air quality is characterized by a relatively low atmospheric pressure and relatively high wind speed as well
as relatively lower concentrations of PM10 and NOx.
Visibility in Kaohsiung, which almost exactly
matched visibility in Taipei in 1994, has deteriorated
in the period since then at the same time as visibility in
Taipei has improved (Fig. 2). Air quality in the period
1994–2003 was also worse in Kaohsiung: ‘episode’ air
quality accounted for between 3.8 and 9.1% of observations, depending on site, compared with just 0.1–
1.7% of observations in Taipei. Furthermore, ‘moderate’
air quality accounted for another 59.9–66.4% of
observations in Kaohsiung and ‘clear’ air quality for
only 25.8–35.5%, whereas in Taipei ‘moderate’ air
quality accounted for 21.1–50.5% of observations and
‘clear’ air quality for a very significant 47.8–78.8% of
observations. In ‘clear’ air quality, visibility in Taipei
was on average just 1.4 times visibility in Kaohsiung.
The deterioration in visibility in Kaohsiung from ‘clear’
to ‘episode’ air quality was, however, more severe than
the equivalent deterioration in Taipei, such that ‘episode’ visibility in Taipei was 1.87 times that in
Kaohsiung. This effect occurred in spite of overall
higher average PM10 and NOx concentrations in Taipei
during ‘episode’ air quality, indicating that the pollutant
mix from industrial sources (in Kaohsiung) has a much
stronger extinction effect than the pollutant mix from
traffic sources (in Taipei).
Air quality in the non-metropolitan centers was
significantly better than in Kaohsiung or Taipei:
‘episode’ air quality accounted for only 0.13% of
observations in Hualien and 0.03% in Taitung, while
‘clear’ air quality accounted for 86.1% and 91.3% of
observations. Only PM10 concentrations and air pressure (highest in ‘episode’ air quality) showed unidirectional change through the air quality categories in
Hualien and Taitung. Visibility was significantly better
in all air quality categories, but it did not increase from
‘episode’ to ‘moderate’ to ‘clear’ air quality categories,
as it had done for Kaohsiung and Taipei. It was 14.4,
13.0, and 15.0 km in Hualien for ‘episode,’ ‘moderate,’
and ‘clear’ air quality, respectively, and 18.8, 18.3, and
20.6 km in Taitung. There was a further absence of
unidirectional change through air quality categories in
the NOx and wind speed data, bringing into question the
Table 2
Summary of average visibility (km), 24 h PM10 (μg m-3), 1 h NOx (ppbv), wind speed (m s-1) and atmospheric pressure (hPa) within three air quality categories between 1994 and 2003 at 17 sites in
two metropolitan areas and two rural areas
Air quality
Taipei
HT
TC
HC
TL
SL
CS
WH
KT
264
672
463
698
120
343
220
179
6.2
169.0
53.1
2.6
1012.5
6.0
180.9
71.4
2.2
1013.2
6.4
174.2
60.3
2.2
1013.4
6.1
178.2
72.2
2.2
1013.6
6.4
181.7
36.0
2.7
1013.1
6.3
180.9
70.5
2.4
1013.7
5.7
181.7
59.1
2.4
1014.4
5.8
185.2
62.9
3.1
1014.2
(b) Moderate days (PSI 100–50)
Occurrence
8689
14346
Number
(h) a
Visibility
7.7
8.6
24 h PM10
69.1
73.5
29.4
40.1
1 h NOx
Wind speed
2.8
2.6
Pressure
1012.9
1011.8
(c) Clear days (PSI b 50)
Occurrence 32467
26226
Number
(h) a
Visibility
11.4
11.7
24 h PM10
29.4
31.4
1 h NOx
23.8
30.6
Wind speed
3.1
3.2
Pressure
1012.1
1012.6
a
PC
18368
9.2
76.5
45.6
2.7
1012.2
20567
12.0
34.1
40.4
3.3
1012.6
16165
9.1
75.2
36.9
2.6
1011.8
24009
11.7
32.0
31.1
3.3
1012.5
20576
9.4
75.6
46.3
2.7
1012.0
19454
12.0
34.9
38.6
3.3
1012.6
11420
8.0
70.8
35.1
2.7
1012.6
29204
11.6
30.8
27.5
3.1
1012.1
17660
8.8
74.7
54.6
2.7
1012.5
22222
12.0
34.2
49.1
3.2
1012.4
13180
8.2
72.6
40.4
2.7
1012.5
27223
11.7
31.8
35.5
3.1
1012.2
12919
8.1
72.0
55.4
2.8
1012.6
26830
11.9
32.4
47.7
3.1
1011.9
JW
NZ
TY
CK
CC
HK
3009
1528
1835
3224
2659
3653
3.4
171.2
58.1
2.5
1015.1
3.4
167.3
45.9
2.5
1015.4
3.3
170.9
41.5
2.4
1015.2
3.3
172.3
44.2
2.4
1015.7
3.2
170.2
64.2
2.4
1015.6
3.2
170.5
74.6
2.4
1015.7
25485
4.9
96.5
45.9
2.6
1013.5
12240
10.2
33.0
24.5
2.8
1008.1
Occurrence number is defined as the hours those PSI values are greater than 100, the range of 50–100 or less than 50. The priority pollutants are PM
26328
4.9
91.1
34.1
2.6
1013.5
12715
10.0
35.3
25.4
2.8
1008.2
24411
4.5
92.4
32.3
2.5
1013.9
14476
9.9
31.6
20.4
2.8
1008.1
26034
4.9
95.7
31.9
2.6
1013.2
10703
10.8
36.6
21.4
2.8
1008.1
26711
5.1
93.6
45.6
2.6
1013.2
10837
10.5
36.8
30.3
2.8
1008.0
25606
5.1
95.7
51.6
2.6
1013.2
10951
10.3
37.2
31.1
2.7
1007.9
Hualien
Taitung
HL
TT
53
13
14.4
178.3
13.4
3.3
1017.0
18.8
184.9
15.9
2.7
1014.5
5578
3313
13.0
64.7
22.8
2.7
1013.8
18.3
63.8
13.1
2.9
1013.8
34991
15.0
29.6
19.4
2.8
1010.9
34954
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
(a) Episode (PSI ≥ 100)
43
Occurrence
Number
(h) a
Visibility
6.6
24 h PM10
189.6
1 h NOx
35.8
Wind speed
3.7
Pressure
1013.7
Kaohsiung
20.6
28.6
12.1
2.4
1011.3
.
10
331
332
Table 3
T-test and the mean (± standard deviation) levels of air pollutants and visibility during weekend and weekday periods at nine air quality monitoring stations in Taipei, 1994–2003
Taipei
Hsintien
2.8 ± 3.8 b
(n = 5714)
CO (ppmv)
0.58 ± 0.30
(n = 5843)
33 ± 22
O3 (ppbv)
(n = 5614)
PM10 (μg m– 3) 36 ± 25
(n = 5828)
NOx (ppbv)
20 ± 13
(n = 5672)
SO2 (ppbv)
Tucheng
Panchiao
Hsinchuang
Tsailiao
Shinlin
Weekday
t-test a Weekend
Weekday
t-test Weekend
Weekday
t-test Weekend
Weekday
t-test Weekend
Weekday
t-test Weekend
Weekday
t-test
3.4 ± 4.6
(n = 28,445)
0.68 ± 0.40
(n = 28,543)
30 ± 24
(n = 27,422)
41 ± 29
(n = 28,303)
26 ± 19
(n = 27,554)
10.5
6.3 ± 7.4
(n = 28,514)
0.79 ± 0.47
(n = 28,579)
28 ± 24
(n = 26,742)
50 ± 36
(n = 27,828)
36 ± 31
(n = 27,148)
16.9
7.7 ± 8.4
(n = 27,561)
1.00 ± 0.59
(n = 27,372)
25 ± 23
(n = 26,178)
58 ± 40
(n = 26,832)
45 ± 29
(n = 26,275)
17.6
8.0 ± 9.1
(n = 28,668)
0.78 ± 0.52
(n = 28,601)
30 ± 23
(n = 27,305)
51 ± 37
(n = 27,650)
36 ± 28
(n = 27,016)
24.4
5.7 ± 5.9
(n = 28,268)
1.10 ± 0.70
(n = 28,190)
23 ± 19
(n = 27,915)
59 ± 42
(n = 27,390)
45 ± 33
(n = 27,002)
15.4
3.6 ± 4.3
(n = 27,403)
0.72 ± 0.49
(n = 28,412)
27 ± 19
(n = 26,865)
45 ± 33
(n = 27,759)
31 ± 28
(n = 26,472)
10.5
17.6
5.9
10.5
25.8
4.5 ± 5.7
(n = 5820)
0.68 ± 0.36
(n = 5854)
30 ± 22
(n = 5528)
43 ± 31
(n = 5751)
27 ± 23
(n = 5634)
17.1
7.2
13.4
20.9
5.6 ± 6.5
(n = 5694)
0.80 ± 0.43
(n = 5678)
28 ± 21
(n = 5449)
49 ± 35
(n = 5597)
34 ± 21
(n = 5486)
24.7
7.3
15.0
27.5
5.0 ± 6.4
(n = 5904)
0.61 ± 0.37
(n = 5911)
33 ± 22
(n = 5632)
44 ± 33
(n = 5762)
25 ± 21
(n = 5608)
22.8
11.3
14.1
28.1
4.4 ± 5.1
(n = 5824)
0.88 ± 0.51
(n = 5834)
25 ± 19
(n = 5798)
52 ± 36
(n = 5686)
34 ± 25
(n = 5616)
22.4
9.8
12.2
23.5
2.9 ± 3.7
(n = 5571)
0.62 ± 0.37
(n = 5876)
29 ± 18
(n = 5508)
40 ± 28
(n = 5716)
24 ± 22
(n = 5567)
15.1
7.0
10.1
16.8
Taipei
Chungshan
SO2 (ppbv)
CO (ppmv)
O3 (ppbv)
−3
PM10 (μg m )
NOx (ppbv)
a
b
Wanhua
Kuting
Visibility (km)
Weekend
Weekday
t-test
Weekend
Weekday
t-test
Weekend
Weekday
t-test
Weekend
Weekday
t-test
4.4 ± 5.2
(n = 5883)
0.85 ± 0.46
(n = 5849)
25 ± 19
(n = 5600)
48 ± 33
(n = 5681)
40 ± 26
(n = 5643)
5.5 ± 5. 8
(n = 28,569)
1.13 ± 0.68
(n = 28,522)
22 ± 20
(n = 27,151)
55 ± 38
(n = 27,124)
54 ± 34
(n = 27,080)
13.2
3.8 ± 4.6
(n = 5843)
0.80 ± 0.38
(n = 5793)
29 ± 21
(n = 5824)
42 ± 30
(n = 5709)
29 ± 17
(n = 5620)
4.9 ± 5.5
(n = 28,240)
1.03 ± 0.57
(n = 28,189)
26 ± 23
(n = 28,431)
49 ± 35
(n = 27,630)
39 ± 24
(n = 26,771)
15.2
3.6 ± 4. 6
(n = 5811)
0.81 ± 0.44
(n = 5890)
26 ± 20
(n = 5591)
42 ± 29
(n = 5657)
39 ± 23
(n = 5689)
4.5 ± 5.0
(n = 28,348)
1.10 ± 0.68
(n = 28,673)
23 ± 22
(n = 27,254)
50 ± 35
(n = 27,100)
53 ± 32
(n = 27,199)
12.9
10.7 ± 6.2 (n = 2541)
10.6 ± 6.1 (n = 12,753)
1.1
30.0
11.4
12.9
28.6
For a level of significance of 0.05, tcritical (0.975; ∞) = 1.96.
Associated standard deviation.
29.3
8.6
14.0
29.1
31.1
7.8
16.5
31.9
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
Weekend
Table 4
T-test and the mean (± standard deviation) levels of air pollutants and visibility during weekend and weekday periods at eight air quality monitoring stations in Kaohsiung, Hualien and Taitung, 1994–2003
Kaohsiung
Weekend
SO2 (ppbv)
13 ± 16
(n = 5591)
CO (ppmv)
0.61 ± 0.28
(n = 5797)
O3 (ppbv)
37 ± 27
(n = 5558)
PM10 (μg m− 3) 77 ± 50
(n = 5774)
33 ± 25
NOx (ppbv)
(n = 5550)
Nanzi
Weekday
t-test
16 ± 18
9.7
(n = 27,654)
0.65 ± 0.31
9.8
(n = 28,272)
35 ± 26
6.2
(n = 26,906)
84 ± 53
9.4
(n = 27,840)
42 ± 30
19.5
(n = 26,640)
a
Weekend
Weekday
6.0 ± 7.4
(n = 5660)
0.54 ± 0.23
(n = 5729)
38 ± 28
(n = 5367)
73 ± 44
(n = 5554)
26 ± 19
(n = 5347)
7.1 ± 7.5
9.6
(n = 28,243)
0.57 ± 0.25
9.1
(n = 28,608)
37 ± 29
3.3
(n = 26,619)
80 ± 48
10.7
(n = 27,801)
33 ± 23
20.1
(n = 26,634)
Hualien
CO (ppmv)
O3 (ppbv)
PM10 (μg m− 3)
NOx (ppbv)
a
b
Chiankin
t-test Weekend
7.7 ± 7.6
(n = 5835)
0.74 ± 0.56
(n = 5675)
43 ± 29
(n = 5433)
71 ± 49
(n = 5754)
25 ± 21
(n = 5600)
Taitung
Hualien
SO2 (ppbv)
Tsoying
Taitung
t-test
Weekend
Weekday
0.72 ± 0.93
(n = 5728)
0.58 ± 0.27
(n = 5859)
23 ± 14
(n = 5441)
32 ± 23
(n = 5744)
17 ± 12
(n = 5503)
0.92 ± 0.96
(n = 28,125)
0.67 ± 0.36
(n = 28,466)
21 ± 13
(n = 26,290)
35 ± 23
(n = 27,730)
21 ± 14
(n = 26,560)
14.0
0.44 ± 0.50
(n = 5227)
0.49 ± 0.24
(n = 5505)
27 ± 13
(n = 5504)
31 ± 22
(n = 5392)
9.9 ± 5.2
(n = 5338)
0.6 ± 1.5
(n = 26,239)
0.59 ± 0.29
(n = 26,724)
26 ± 13
(n = 26,767)
33 ± 23
(n = 26,307)
12.7 ± 7.0
(n = 25,720)
9.5
19.1
8.7 ± 8.9
(n = 5669)
0.74 ± 0.37
(n = 5595)
41 ± 28
(n = 5387)
73 ± 49
(n = 5511)
27 ± 22
(n = 5439)
Weekday
t-test
9. 5 ± 9.3
5.8
(n = 27,712)
0.83 ± 0.50 12.0
(n = 27,559)
40 ± 29
0.89
(n = 26,224)
79 ± 52
8.1
(n = 27,462)
31 ± 25
11.0
(n = 26,265)
Weekend
Hsiaokang
Weekday
t-test Weekend
14 ± 12
15 ± 13
6.2
(n = 5180) (n = 25,508)
–
–
–b
–
–
–
80 ± 47
(n = 5626)
38 ± 28
(n = 5462)
87 ± 51
10.5
(n = 27,581)
44 ± 33
32.1
(26544)
Hualien
Visibility (km)
Weekday
9.1
t-test Weekend
8.7 ± 8.1
8.1
(n = 28,356)
0.76 ± 0.52
3.4
(n = 27,783)
42 ± 32
2.4
(n = 26,418)
75 ± 51
4.5
(n = 27,939)
29 ± 25
12.6
(n = 26,887)
Kaohsiung
Weekend
18.1
Weekday
Chiancheng
Weekday
15 ± 14
(n = 5702)
0.78 ± 0.53
(n = 5550)
37 ± 30
(n = 5424)
81 ± 47
(n = 5624)
40 ± 33
(n = 5502)
t-test
16 ± 15
4.9
(n = 28,109)
0.87 ± 0.59 10.2
(n = 27,326)
35 ± 29
6.2
(n = 26,535)
89 ± 51
10.5
(n = 27,736)
50 ± 39
17.8
(n = 26,734)
Taitung
Visibility (km)
Visibility (km)
t-test
Weekend
Weekday
t-test
Weekend
Weekday
t-test
Weekend
Weekday
t-test
8.1
6.5 ± 4.6
(n = 2482)
6.3 ± 4.8
(n = 12,393)
1.7
14.5 ± 4.7
(n = 2481)
14.8 ± 4.6
(n = 12,386)
2.2
20.5 ± 5.2
(n = 2518)
20.3 ± 5.0
(n = 12,712)
1.3
25.3
5.3
5.6
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
Jenwu
27.9
For a level of significance of 0.05, tcritical (0.975; ∞) = 1.96.
Incomplete data for analysis.
333
334
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
relationships of these factors with air quality apparently
identified in the Kaohsiung and Taipei data. Moreover,
contrary to observations for Kaohsiung and Taipei, the
average wind speed during ‘episode’ air quality was
higher than in ‘clear’ air quality for both Hualien and
Taitung, meaning either that wind speed is not a
causative factor in ‘clear’ air quality or that it was
overridden by another factor or factors in Hualien and
Taitung. It is proposed that there is ‘another factor’ that
explains the association of higher wind speeds with
‘episode’ air quality in Hualien and Taitung: long range
transport of PM2.5–10 within Asian dust storm winds
(Mori et al., 2002; Chen et al., 2004; Nakamura et al.,
2005; Tsai and Chen, 2006a). PM2.5–10 contributes
significantly to the local PM10 mass, leading to an
‘episode’ air quality categorization, but does not
interfere with visibility as fine particles (PM2.5) do
(Cheng and Tsai, 2000), and hence ‘episode’ air quality
is not associated with a deterioration in visibility in
Hualien and Taitung.
Hualien, and for the most part unsupported by statistical
significance.
A statistically significant weekend effect, with
ozone concentrations higher during weekends than
during weekdays, was observed at all sites other than
Chiankin in Kaohsiung. This result is similar to that
obtained by Pryor and Steyn (1995) at LFV, Canada,
where ozone concentrations varied by days of the week
and were uniformly higher on weekends. Additionally,
typical NOx concentrations were higher, though not
uniformly, during weekdays, while concentrations of
both ozone and NOx increased notably between 1984
and 1991. Their hebdomadal cycles exhibited a negative
correlation. Pryor (1998) also noted that although both
NOx and volatile organic compounds (VOCs) are
precursors to the formation of O3, the ozone concentrations in the LFV area was VOC-sensitive but not NOxsensitive.
3.4. Weekend versus weekday visibility and air quality
Tables 5 and 6 show correlation coefficients of
mixing layer height (Mix) with air quality/meteorological factors at the moment when Mix changes, one hour
after a change, and two hours after a change. Correlation
coefficients of Mix with visibility show that at all
centers a change in visibility is most correlated with Mix
one hour after a change in Mix, that is, that the greatest
change in visibility has occurred at this time. Of the
pollutants, O3 has a correlation coefficient at a change in
Mix of between 0.31 and 0.47, and continues to show
positive correlation at one and two hours after a change,
indicating that changes in O3 concentration are
synchronized with changes in the Mix. This outcome
is indicative of a higher Mix leading to higher solar
radiation leading to stronger photochemical reactions
and more O3. The concentration patterns of CO, PM10
and NOx, meanwhile, show a negative correlation with
change in Mix, indicative of falling levels of these
pollutants as Mix rises. In Taipei, Hualien, and Taitung,
except for NOx at site Hsintien and PM10 at site Shinlin,
the negative correlation between these pollutants and
Mix weakens with time from the change in Mix, indicating that these pollutants are easily dispersed
into the atmosphere as Mix rises. In contrast to all
other sites (except Shinlin, as already noted), all sites
in Kaohsiung bar Nanzi experienced a negative correlation between PM10 and Mix that was stronger at one
hour than it was at the time of a change in Mix,
indicative of a lag in the diffusion of PM10 on a rise in
Mix. This is because the stronger photochemical reactions that result from a higher Mix lead in Kaohsiung
Average weekend and weekday visibilities and air
pollutant levels for the period 1994 to 2003 were calculated and subjected to a t-test (Tables 3 and 4).
Standard deviations for SO2 were slightly greater than
± 100% of the arithmetic mean, whereas those of all
other parameters fell within ± 100% of the arithmetic
mean. This indicates that when using SO2 as a visibility
parameter in further investigation of empirical models
for visibility studies (Section 3.7), the log-normal distribution of SO2 must be considered while the other
parameters may be regarded as normal distributions
when carrying out regression analyses of these models.
Hence, the quantile–quantile plots and Mann–Whitney
tests were not used in discussing visibility in relation to
SO2 (Pryor and Steyn, 1995; Pryor, 1998). Average
weekend and weekday visibilities showed slight
differences at all four centers. At Taipei, Kaohsiung,
and Taitung visibility was slightly higher at weekends,
but not statistically significant, whereas at Hualien
visibility was slightly lower at weekends, and this difference was statistically significant, with a t-test value
of 2.2. Levels of SO2, CO, PM10, and NOx were slightly
lower at weekends at all centers, indicating that all
centers experienced less anthropogenic pollution from
automobile, industrial and other sources at this time,
and differences were statistically significant. No definitive conclusion can be drawn from these data regarding any difference in weekend visibility because
the differences noted are slight, contrary in the case of
3.5. Mixing layer height and air quality/visibility
Taipei
Hsintien
Tucheng
Panchiao
Hsinchuang
Tsailiao
Shinlin
Mixing layer height
Mixing layer height
Mixing layer height
Mixing layer height
Mixing layer height
Mixing layer height
Realtime
CO
−0.01
O3
0.41
PM10 −0.34
NOx −0.02
After
1h
After
2h
Realtime
After
1h
After
2h
Realtime
After
1h
After
2h
Realtime
After
1h
After
2h
Realtime
After
1h
After
2h
Realtime
After
1h
After
2h
− 0.09
0.31
− 0.33
− 0.09
− 0.09
0.22
− 0.32
− 0.10
− 0.15
0.46
− 0.28
− 0.12
− 0.01
0.36
− 0.25
− 0.02
−0.01
0.27
−0.19
−0.01
− 0.20
0.47
− 0.23
− 0.24
−0.07
0.37
−0.21
−0.19
− 0.07
0.26
− 0.16
− 0.18
− 0.19
0.42
− 0.20
− 0.17
− 0.09
0.31
− 0.18
− 0.10
− 0.11
0.21
− 0.09
− 0.12
− 0.11
0.39
− 0.25
− 0.09
− 0.04
0.26
− 0.23
− 0.03
− 0.02
0.16
− 0.18
− 0.02
−0.12
0.32
−0.25
−0.12
− 0.02
0.20
− 0.26
− 0.01
−0.01
0.10
−0.19
−0.05
Taipei
CO
O3
PM10
NOx
a
Chungshan
Wanhua
Kuting
Mixing layer height
Mixing layer height
Mixing layer height
Mixing layer height
Real-time
After 1 h
After 2 h
Real-time
After 1 h
After 2 h
Real-time
After 1 h
After 2 h
− 0.13
0.41
− 0.23
− 0.18
− 0.01
0.28
− 0.22
− 0.07
− 0.02
0.17
− 0.16
− 0.08
− 0.17
0.45
− 0.28
− 0.16
− 0.03
0.32
− 0.24
− 0.09
− 0.03
0.21
− 0.20
− 0.09
−0.13
0.44
−0.26
−0.19
− 0.03
0.33
− 0.22
− 0.11
−0.02
0.21
−0.18
−0.12
Ventilation index: multiply by mixing layer height and wind speed.
Visibility
Pressure
Wind speed
Ventilation index
Real-time
After 1 h
After 2 h
0.08
0.35
0.10
0.68
0.09
0.32
0.18
0.62
0.07
0.31
0.12
0.59
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
Table 5
Correlation coefficients of visibility, air qualities and meteorological parameters affected by mixing layer height real-time, and 1 and 2 h lagged in Taipei, 1994–2003
335
336
Kaohsiung
Hualien
Jenwu
Nanzi
Tsoying
Chiankin
Hsiaokang
Hualien
Mixing layer height
Mixing layer height
Mixing layer height
Mixing layer height
Mixing layer height
Mixing layer height
Realtime
CO
− 0.12
O3
0.39
PM10 − 0.22
NOx − 0.06
After
1h
After
2h
Realtime
After
1h
After
2h
Realtime
After
1h
After
2h
Realtime
After
1h
After
2h
Realtime
After
1h
After
2h
Realtime
After
1h
After
2h
− 0.02
0.24
− 0.23
− 0.04
−0.06
0.21
−0.13
−0.07
− 0.06
0.46
− 0.27
− 0.15
− 0.06
0.30
− 0.27
− 0.04
− 0.01
0.29
− 0.17
− 0.12
−0.19
0.44
−0.21
−0.20
− 0.01
0.30
− 0.25
− 0.05
− 0.08
0.28
− 0.13
− 0.17
− 0.18
0.44
− 0.22
− 0.17
− 0.01
0.31
− 0.26
− 0.04
−0.08
0.27
−0.13
−0.15
− 0.16
0.43
− 0.21
− 0.17
− 0.01
0.28
− 0.22
− 0.06
− 0.07
0.25
− 0.11
− 0.15
− 0.21
0.33
− 0.06
− 0.22
−0.17
0.24
−0.01
−0.20
− 0.11
0.23
− 0.01
− 0.16
Taitung
Kaohsiung
Hualien
Taitung
Mixing layer height
Mixing layer height
Mixing layer height
Taitung
Mixing layer height
CO
O3
PM10
NOx
a
Real-time
After 1 h
After 2 h
− 0.13
0.31
− 0.12
− 0.13
− 0.10
0.24
− 0.08
− 0.08
−0.12
0.21
−0.09
−0.11
Visibility
Pressure
Wind speed
Ventilation index
Ventilation index: multiply by mixing layer height and wind speed.
Real-time
After 1 h
After 2 h
Real-time
After 1 h
After 2 h
Real-time
After 1 h
After 2 h
0.12
0.25
0.19
0.78
0.17
0.35
0.04
0.71
0.12
0.24
0.09
0.66
0.11
0.02
0.02
0.65
0.15
0.01
0.11
0.57
0.02
0.02
0.07
0.53
0.04
0.09
0.02
0.63
0.04
0.17
0.03
0.51
0.01
0.09
0.05
0.41
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
Table 6
Correlation coefficients of visibility, air qualities and meteorological parameters affected by mixing layer height real-time, and 1 and 2 h lagged in Kaohsiung, Hualien and Taitung, 1994–2003
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
to the formation of photochemical particulates such as
sulfate and nitrate that are not as rapidly or evenly
diffused as aerosol (Tsai and Chen, 2006b) and, as a
result, PM10 has its greatest impact on visibility (i.e.
improving visibility by its absence) in Kaohsiung one
hour after a rise in Mix.
A high correlation coefficient of 0.63–0.78 for the
ventilation index indicates that changes in this index are
synchronized with the change of the mixing layer height
and also indicates that changes in ventilation index are
associated with different rates of vertical and horizontal
dispersion of pollutants (Seibert et al., 2000).
3.6. Principal component analysis for visibility
The major factors determining visibility in each
center were identified via PCA, using the data obtained
between 1994 and 2003. In Taipei, there were nine PCs
with eigen values N1.0, representing a total 88.5% of the
variance, while in Kaohsiung there were four, representing a total 89.0% of the variance. There were three in
Hualien and Taitung, representing a total of 60.3% and
52.7% of the variance, respectively.
In Taipei and Kaohsiung the first PC (PC1) with
loading correction coefficients ≥ 0.6 consisted of SO2,
CO, NO2 and PM10. It accounts for 33.9% of the
explainable variance in Taipei, where variation in these
four species was consistent at all nine sites, indicating
that they originate from the same sources. CO and NO2
both come from transportation emissions. CO represents
emissions of black carbon and is thus related to the
absorption coefficient. Its concentration at site Chungshan in Taipei was closely related with visibility as
observed at the nearby Taipei weather service office.
SO2, from the burning of heavy fuels, is converted into
sulfate via photochemical reaction and, as sulfate can
influence visibility, SO2 and visibility exhibited a
moderate correlation. PC1 accounts for 62.0% of the
explainable variance in Kaohsiung, where variation in
PM10 concentration was the main factor affecting visibility, a relationship that was stronger than in Taipei.
In Hualien and Taitung PC1 consisted of only CO
and NO2, and accounts for 33.5% of the explainable
variance in Hualien and 20.4% in Taitung, indicative of
the impact on visibility from transportation emissions.
In addition to the low percentage figure for PC1 in
Taitung, PC3 (13.5% of the variance) contains none of
the pollutants, SO2, CO, NO2, PM10 and O3, indicating
that these pollutants do not affect visibility significantly
in Taitung. Hence, visibility in Hualien is affected to
some extent by traffic pollution, while visibility in
Taitung is the least affected by air pollution in general.
337
O3 was highly correlated between centers and was
the primary component of PC2 at all four centers,
indicating that it is evenly distributed and that all four
centers were equally influenced by photochemical reactions. In Taitung, once again worthy of special comment, both O3 and PM10 are predominantly PC2-related
species, revealing that PM10 is not likely sourced locally
but rather from long-range transport.
It is concluded that visibility in Taipei and Kaohsiung
in the period was determined mostly by fugitive dust,
transportation emissions, the products of photochemical
reactions, and industrial emissions as there are clear
correlations between relevant air pollutants and visibility. In Kaohsiung, especially, visibility has high
correlation (N0.90) with PC1, PM10, and NO2. Furthermore, as the explainable variance for Hualien and
Taitung is low (PCs with eigen values N1.0 account for
just 60.3% and 52.7% of the variance, respectively), it is
concluded that undefined parameters representing
pollutants from sources outside the study area are important determining factors in visibility at these centers.
3.7. Empirical models for visibility
The optimal empirical model for each center, using
data from the period 1994 to June 2002 (one year prior
to the end of the available data set), is shown in Table 7.
Because the light scattering cross section of particles is
significantly increased at relative humidity (RH) N90%
(e.g. that of an ammonium sulfate particle by a factor of
five or more above that of the dry particle at 35–40%
RH) (Malm and Day, 2001; Tsai and Kuo, 2005),
causing a degradation in visibility (Tang et al., 1981),
data collected at RH N 90% was excluded from the PCA
in developing empirical models in this study capable
of relating visibility to meteorological factors and pollutant concentrations, as was data collected during
precipitation. With only visibility and the meteorological factors of temperature, relative humidity and wind
speed included in the analysis, regression coefficients
were 0.44 for Taipei, 0.53 for Kaohsiung, 0.44 for
Hualien, and 0.37 for Taitung, indicating a low or
moderate correlation. Correlation rose with the average
airborne pollutant concentrations included in the
analysis, and with the PCs for each center included it
rose further, represented by a coefficient of 0.69 for
Taipei, 0.77 for Kaohsiung, 0.63 for Hualien, and 0.47
for Taitung.
The regression coefficients of the individual contributors to each empirical model are also listed in
Table 7. All have p values b0.0001 and, moreover,
within the 95% confidence level the parameters
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Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
fluencing the photochemical effect on visibility. Moreover, although the extent of the influence varies,
correlation of temperature and wind speed with visibility is positive while correlation of RH with visibility
is negative. The models developed for rural Hualien and
Taitung are inferior, and this is especially true of the
model for Taitung, in which only 22.4% of the variance
(r2) is explained by air pollutants and meteorological
parameters. More suitable models are likely to include
estimated using the model are not equal to 0, demonstrating that each meets the requirements for multivariate regression analyses. PM10, O3, SO2 and NO2 are the
most influential air pollutants, all having a negative
correlation with visibility. Whereas variations of ozone
concentration will not change the visibility, they represent the potential yield of photochemicals, e.g.
carbon, sulfate and nitrate, that cause light extinction.
Thus, ozone is carefully considered as a parameter in-
Table 7
Regression results for the optimal empirical models of visibility in Taipei, Kaohsiung, Hualien and Taitung, 1994–June 2002
Region
Empirical model
Taipei
Vis = 0.195 × Temp − 0.274 × RH + 0.419 × Ws − 0.126 ×
[O3]e − 4.07 × ln[PM10]f − 0.845 × ln[SO2]g + 45.03h
Vis = 0.486 × Temp − 0.159 × RH + 0.310 × Ws − 0.064 ×
[O3] − 0.025 × [NO2]i − 3.117 × ln[PM10] + 20.84
Vis = 0.195 × Temp − 0.146 × RH − 0.123 ×
[O3] − 0.149 × [NO2] − 0.791 × ln[PM10] + 28.32
Vis = 0.303 × Temp − 0.106 × RH − 0.075 ×
[O3] − 0.128 × [NO2] − 0.787 × ln[PM10] + 26.31
Kaohsiung
Hualien
Taitung
a
Regression coefficient (r)
b
c
d
0.69
0.77
0.63
0.47
Regression coefficient
Estimated regression coefficient
Estimated standard deviation
t testj
p value
Taipei
β1 [Temp]
β2 [RH]
β3 [Ws]
β4 [O3]
β5 ln[PM10]
β6 ln[SO2]
β7
0.195
− 0.274
0.419
− 0.126
− 4.07
− 0.845
45.03
0.008
0.004
0.025
0.003
0.10
0.069
0.66
24.3
− 61.0
16.5
− 43.4
− 39.4
− 12.3
68.6
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
Kaohsiung
β1 [Temp]
β2 [RH]
β3 [Ws]
β4 [O3]
β5 [NO2]
β6 ln[PM10]
β7
0.486
− 0.159
0.310
− 0.064
− 0.025
− 3.117
20.84
0.009
0.004
0.028
0.002
0.003
0.079
0.56
54.3
− 38.1
11.2
− 39.0
− 7.3
− 39.3
37.4
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
Hualien
β1 [Temp]
β2 [RH]
β3 [O3]
β4 [NO2]
β5 ln[PM10]
β6
0.195
− 0.146
− 0.123
− 0.149
− 0.791
28.32
0.007
0.004
0.003
0.004
0.047
0.42
26.1
− 39.2
− 46.7
− 34.5
− 16.7
67.5
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
Taitung
β1 [Temp]
β2 [RH]
β3 [O3]
β4 [NO2]
β5 ln[PM10]
β6
0.303
− 0.106
− 0.075
− 0.128
− 0.787
26.31
0.010
0.004
0.003
0.009
0.049
0.46
31.2
− 27.6
− 25.5
− 13.7
− 16.2
57.4
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
b0.0001
Visibility (km). bAtmospheric temperature (°C). cRelative humidity (%). dWind speed (m s− 1). eO3 in ppbv. fPM10 in μg m− 3. gSO2 in ppbv.
empirical constant (km). iNO2 in ppbv. jFor a level of significance of 0.05, tcritical (0.975; ∞) = 1.96.
a
h
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
Table 8
Rise in visibility due to a 50% drop in concentration of PM10, O3 and
NO2, based on the optimal empirical model
Region
Taipei
Kaohsiung
Taitung
Hualien
Increase of visibility (km)
A 50% drop in
PM10 value
A 50% drop in
O3 value
A 50% drop in
NO2 value
2.8
2.2
0.6
0.5
0.13
0.06
0.09
0.12
–
0.03
0.12
0.15
the impact of long-range transport pollutants extant in
these centers, and additional studies on these pollutants
are necessary in order to develop such models.
Using the optimal empirical model, the improvement in visibility resulting from a 50% fall in PM10,
O3, and NO2 concentration was estimated (Table 8). This
339
revealed that visibility is most sensitive to changes in
PM10 concentration, especially in Taipei and Kaohsiung,
where a 50% drop in PM10 would improve visibility by
2.8 km and 2.2 km, respectively. Comparatively, the
effect of a 50% drop in O3 or NO2 on visibility would be
almost inconsequential: an additional 0.06–0.13 km for
O3 and 0.03–0.15 km for NO2. Moreover, the regression
data in Table 7 show that visibility is not significantly
sensitive to NO2 concentration and that O3 has the least
influence on visibility.
3.8. Verification of the optimal empirical models for
local visibilities
To verify the validity of the empirical models, they
were applied to the period July 2002 to June 2003, being
the data period excluded from the input into the models,
Fig. 3. Simulated and observed monthly mean hourly visibility in Taiwan using the optimal empirical model of visibility in this study, July 2002–June
2003. (a) Taipei, (b) Kaohsiung, (c) Hualien, (d) Taitung.
340
Y.I. Tsai et al. / Science of the Total Environment 382 (2007) 324–341
and the simulated results compared with the observed
data for that period. The correlation coefficient of the
simulated and observed average daily visibility was 0.71
for Taipei, 0.72 for Kaohsiung, 0.63 for Hualien, and
0.46 for Taitung, whilst that of the average monthly
visibility (Fig. 3) was as high as 0.88 for Taipei, 0.87 for
Kaohsiung, 0.85 for Hualien, but a still low 0.50 for
Taitung. These data show that the models for Taipei,
Kaohsiung, and Hualien were highly effective in simulating visibility observations at those centers between
July 2002 and June 2003, but that the model for Taitung
was not, and a potential solution for this has already
been posited here, indicating that simulations for
Taitung should include long-range transport as a pollutant source.
4. Conclusions
For the period 1961–2003 the average annual
visibility in a highly urbanized center in Taiwan (Taipei)
was worse than that in a highly industrialized center
(Kaohsiung), which in turn was worse than that in two
rural centers (Hualien and Taitung). Unlike at the other
centers, however, visibility in Taipei showed a rising
trend from 1992 to the end of the study period, such that
average annual visibility since 1994 has been superior to
that in Kaohsiung. This improvement in Taipei can be
linked to the construction and expansion of a mass
transit rail system in the city, the use of which has helped
reduce emissions of traffic related air pollutants, particles, and NO2. At all centers, visibility deteriorated in
the rain. Hence, frequency of precipitation is one of the
factors contributing to the average annual visibility
number. High atmospheric pressure and low wind speed
are unfavorable to dispersion and transportation of pollutants. Under these conditions, visibility was adversely
affected by high concentrations of pollutants (PM10 and
NOx), resulting in ‘episode’ visibility as low as 5.7–
6.6 km in Taipei and 3.2–3.4 km in Kaohsiung. Conversely, ‘clear’ air quality was characterized by low
atmospheric pressure, high wind speeds, low PM10, and
visibility as high as 11.4–12.0 km in Taipei and 9.9–
10.8 km in Kaohsiung. Excepting O3, which showed the
‘weekend effect’ concentration pattern, air pollutant
concentrations were slightly higher on weekdays than
on weekends. This did not, however, translate into
statistically significant weekday–weekend visibility
trends. A change in Mix had in the main a negative
correlation with pollutant concentrations that weakened
at one hour from the change, indicative of immediate
dispersal of pollutants. In Kaohsiung, however, the
negative correlation with PM10 strengthened at one hour
after the change, indicative of a delay in dispersal, and as
a result PM10 had its greatest impact on visibility (i.e.
improving visibility by its absence) in Kaohsiung one
hour after a rise in Mix. In general, the greatest change
in visibility was noted one hour after Mix changed.
Visibility in both Taipei and Kaohsiung is affected by
fugitive dust, mobile emissions, and industrial emissions, the latter especially in Kaohsiung where the
visibility is seriously affected by PM10 and NO2. In
Taitung, air pollutants influence visibility the least.
Optimum empirical models demonstrated that of PM10,
O3, SO2 and NO2, PM10 has the most significant
influence on visibility. A 50% drop in PM10 was shown
to raise the visibility by between 0.5 km (for Hualien)
and 2.8 km (for Taipei). The models were shown to
be highly effective in simulating visibility observations
for Taipei, Kaohsiung, and Hualien, but not for Taitung.
A model for this latter center is likely to benefit from the
inclusion of long-range transport sources of pollution.
Acknowledgements
This work was financially supported by the National
Science Council of the ROC under Contract Nos. NSC
89-2211-E-041-012, NSC 89-2211-E-041-020 and NSC
93-2211-E-041-003. We also thank the Central Weather
Bureau, Taiwan for the visibility data. In addition, thank
are due to Mr. Chiung-Lung Tai in the Department of
Environmental Engineering and Science, Chia Nan
University of Pharmacy and Science, for preliminary
contribution with data collection and analysis.
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