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Effects of sulfate and humidity on visibility in the Taichung harbor area (Taiwan)
J.AemsolSci.Vol. 29,Suppl. l.pp.S1213-S1214. 1998 0 1998 Published by Elsevier Science Ltd. All rights reserved Printed in Great Britain 0021-8502/98 $19.00 + 0.00
Pergamon
EFFECTS OF SULFATE
AND HUMIDITY HARBOR
ON VISIBILITY
IN THE TAICHUNG
AREA (TAIWAN)
Y. I. TSAI, and M. T. CHENG Department of Environmental Engineering, National Chung Hsing University, Taichung 402, Taiwan KEYWORDS Sulfate, Humidity, Visibility Visibility is one of the major air quality concerns in the Taichung harbor area, which is located in the central Taiwan. In this area, large emission of sulfur dioxide and particulate comes from a coal-fired power plant. This study intends to understand the affecting factors on the local visibility and to develop a regression model based on these factors. Six intensive ambient samplings were conducted at three sampling stations near the harbor area. The power plant is about 2-5 km away from these sampling sites. The work was carried out during the period of December 1996 to October 1997. Totally 85 samples of PM2.5 and PM2 5-10were obtained with a Dichotomous sampler. The samples were further analyzed with an elemental analyzer and an ion chromatography. The data of chemical composition of these samples were analyzed with the local meteorological and air quality data. It was found that the average visual range of smoggy day can be as low as 5-6 km, and the corresponding concentrations of PM10 and sulfate were approximately 121.6k23.8 ug/m3 and 17.6f4.0 ug/m3, respectively. Furthermore, the VARIMAX-rotated principal component analysis was employed to identify the major factors which influenced the visibility. In Table 1, the six components Table 1. Summary of principal component analysis of visibility, aerosol chemical components, and meteorological factors in the Taichung harbor area. Variable Visibility ClNO;S04'Na+ NH4+ K’ Mg*+ Ca” Total Carbon Temp RH ws so2 co 01 PM,o NO NO2 Eigenvalue % variance Source
Factor
1
Factor
-0.90 0.12 0.76
-0.03 0.67
0.89 0.12
0.03 0.84 0.32 0.76 0.75 0.71 0.19 -0.38 -0.13 0.28 -0.07
0.80 -0.01 0.04 0.32 0.22 -0.42 0.61 -0.36 0.29 0.32 0.27 0.69
-0.01 0.43 5.93 32.92
Secondary aerosols
0.18
0.01 0.10 0.18 0.23 0.13 3.13
17.40 Marine spray
2
Factor 0.03 -0.09 -0.19
0.10 0.15 0.05 -0.04 0.05 0.23 -0.02
0.11 -0.63 0.72 0.16 -0.02 0.89 0.04 -0.65 -0.28 1.94 10.78
Photochemical related
S1213
3
Factor
4
Factor
5
-0.28
-0.08
0.08 -0.01
0.00
0.27 -0.06
0.35
0.05 0.38 0.58 0.74
0.16 -0.03 0.05 0.40 -0.02 -0.09 0.85 0.43 -0.15 -0.24 -0.12 0.12 0.13 0.40 0.17 0.19
1.47 8.16
1.35 7.52
0.11 0.10 0.23 -0.06 0.02 -0.29 -0.09 -0.04 0.86
0.08
Coal-fired related
Diesel burning
Factor
6
-0.09 0.35 0.30 0.02 0.07
0.1 I -0.16 -0.5 1 0.22
0.11 -0.47 0.05 0.07
-0.11 0.88 -0.01 0.29 0.24 0.24 1.14 6.31
Traffic related
S1214
Abstracts
of the 5th International
Aerosol
Conference
1998
accounted for 83.3% of the total variance. The identified major sources included secondary aerosols, marine spray, photochemical related aerosols, coal-tired related aerosols, diesel burning, and traffic related aerosols. According to the high-loading variables in the group of factor 1 and the major chemical species affecting light extinction coefficient (Pratsinis et al, 1984) a regression model based on sulfate and humidity has been developed for this area. The comparison of simulated and observed visibility is shown in Fig. 1. The correlation coefficient is r=O.902 and the correlation is significant at the 95% confidence level. The result indicates the variation of visibility in the harbor area was well-simulated by this regression model. The regression equation was then compared with the equations developed by Leaderer et al (1979). Discrepancy was found among these equations. The reason may be due to the different type of sampling sites. The equation in this work was derived from the coastal area where humidity has a pronounced effect and the Leaderer’s equations was obtained from the metropolitan area. regression coefficient ~0.902 24
‘s
Regression
0 0
4
8
12
16
20
24
Simulatedvisibility (km) Fig. 1. Simulated and observed visibility in the Taichung harbor area
ACKNOWLEDGEMENTS This work was supported by the Environmental Protection Bureau, Taichung County, and the National Science Council, R.O.C. through a grant number NSC86-221 l-E005-007.
REFERENCES Pratsinis, S., E. C. Ellis, T. Novakov, and S. K. Friedlander, (1984) The carbon containing component of the Los Angeles aerosol: source apportionment and contributions to the visibility budget, JAPCA, 34, 643-650. Leaderer, B. P., T. R. Holford, and J. A. K. Stolwijk, (1979) Relationship between sulfate aerosol and visibility, JAPCA, 29, 154- 157.
Pergamon
EFFECTS OF SULFATE
AND HUMIDITY HARBOR
ON VISIBILITY
IN THE TAICHUNG
AREA (TAIWAN)
Y. I. TSAI, and M. T. CHENG Department of Environmental Engineering, National Chung Hsing University, Taichung 402, Taiwan KEYWORDS Sulfate, Humidity, Visibility Visibility is one of the major air quality concerns in the Taichung harbor area, which is located in the central Taiwan. In this area, large emission of sulfur dioxide and particulate comes from a coal-fired power plant. This study intends to understand the affecting factors on the local visibility and to develop a regression model based on these factors. Six intensive ambient samplings were conducted at three sampling stations near the harbor area. The power plant is about 2-5 km away from these sampling sites. The work was carried out during the period of December 1996 to October 1997. Totally 85 samples of PM2.5 and PM2 5-10were obtained with a Dichotomous sampler. The samples were further analyzed with an elemental analyzer and an ion chromatography. The data of chemical composition of these samples were analyzed with the local meteorological and air quality data. It was found that the average visual range of smoggy day can be as low as 5-6 km, and the corresponding concentrations of PM10 and sulfate were approximately 121.6k23.8 ug/m3 and 17.6f4.0 ug/m3, respectively. Furthermore, the VARIMAX-rotated principal component analysis was employed to identify the major factors which influenced the visibility. In Table 1, the six components Table 1. Summary of principal component analysis of visibility, aerosol chemical components, and meteorological factors in the Taichung harbor area. Variable Visibility ClNO;S04'Na+ NH4+ K’ Mg*+ Ca” Total Carbon Temp RH ws so2 co 01 PM,o NO NO2 Eigenvalue % variance Source
Factor
1
Factor
-0.90 0.12 0.76
-0.03 0.67
0.89 0.12
0.03 0.84 0.32 0.76 0.75 0.71 0.19 -0.38 -0.13 0.28 -0.07
0.80 -0.01 0.04 0.32 0.22 -0.42 0.61 -0.36 0.29 0.32 0.27 0.69
-0.01 0.43 5.93 32.92
Secondary aerosols
0.18
0.01 0.10 0.18 0.23 0.13 3.13
17.40 Marine spray
2
Factor 0.03 -0.09 -0.19
0.10 0.15 0.05 -0.04 0.05 0.23 -0.02
0.11 -0.63 0.72 0.16 -0.02 0.89 0.04 -0.65 -0.28 1.94 10.78
Photochemical related
S1213
3
Factor
4
Factor
5
-0.28
-0.08
0.08 -0.01
0.00
0.27 -0.06
0.35
0.05 0.38 0.58 0.74
0.16 -0.03 0.05 0.40 -0.02 -0.09 0.85 0.43 -0.15 -0.24 -0.12 0.12 0.13 0.40 0.17 0.19
1.47 8.16
1.35 7.52
0.11 0.10 0.23 -0.06 0.02 -0.29 -0.09 -0.04 0.86
0.08
Coal-fired related
Diesel burning
Factor
6
-0.09 0.35 0.30 0.02 0.07
0.1 I -0.16 -0.5 1 0.22
0.11 -0.47 0.05 0.07
-0.11 0.88 -0.01 0.29 0.24 0.24 1.14 6.31
Traffic related
S1214
Abstracts
of the 5th International
Aerosol
Conference
1998
accounted for 83.3% of the total variance. The identified major sources included secondary aerosols, marine spray, photochemical related aerosols, coal-tired related aerosols, diesel burning, and traffic related aerosols. According to the high-loading variables in the group of factor 1 and the major chemical species affecting light extinction coefficient (Pratsinis et al, 1984) a regression model based on sulfate and humidity has been developed for this area. The comparison of simulated and observed visibility is shown in Fig. 1. The correlation coefficient is r=O.902 and the correlation is significant at the 95% confidence level. The result indicates the variation of visibility in the harbor area was well-simulated by this regression model. The regression equation was then compared with the equations developed by Leaderer et al (1979). Discrepancy was found among these equations. The reason may be due to the different type of sampling sites. The equation in this work was derived from the coastal area where humidity has a pronounced effect and the Leaderer’s equations was obtained from the metropolitan area. regression coefficient ~0.902 24
‘s
Regression
0 0
4
8
12
16
20
24
Simulatedvisibility (km) Fig. 1. Simulated and observed visibility in the Taichung harbor area
ACKNOWLEDGEMENTS This work was supported by the Environmental Protection Bureau, Taichung County, and the National Science Council, R.O.C. through a grant number NSC86-221 l-E005-007.
REFERENCES Pratsinis, S., E. C. Ellis, T. Novakov, and S. K. Friedlander, (1984) The carbon containing component of the Los Angeles aerosol: source apportionment and contributions to the visibility budget, JAPCA, 34, 643-650. Leaderer, B. P., T. R. Holford, and J. A. K. Stolwijk, (1979) Relationship between sulfate aerosol and visibility, JAPCA, 29, 154- 157.