Study of Visibility Reduction and its Causes in Hong Kong (Tender Ref. AS 01-286)

Final Report

By

Dr. WANG Tao

Research Centre for Environmental Technology and Management

Department of Civil and Structural Engineering The Hong Kong Polytechnic University

For

Air Services Group The Environmental Protection Department of HKSAR

March 2003 (Revised in September 2003)

Page 2 Acknowledgements

The author would like to thank Mr. Joey Kwok Yue Him, Mr. Ding Aijun, Mr. Steven Poon, and Mr. Joe Cheung Hing Cho for their assistance in processing and analysis of data and in preparation of this report. The author also thanks Professor Y. S. Li for his support of this study. WinHaze model and the modification work were carried out by Mr. John Molenar of Air Resources Specialists, Inc. We are grateful to the Hong Kong Observatory for providing the meteorological data.

Page 3 Table of Contents Acknowledgements ...... 3 Executive Summary ...... 13 1. Introduction ...... 15 1.1 Background ...... 15 1.2 Scope of the study and methodology ...... 15 2. Data Used in This Study...... 16 2.1 Data integration...... 16 2.2 Digital photos of visibility scenes in Victoria Harbor, Tung Chung, and Yuen Long 17 3. Data Analysis and Findings...... 18 3.1 Overall spatial and seasonal patterns of visibility impairment in Victoria Harbor, western Hong Kong, and urban Shenzhen ...... 18 3.2 Correlation of visibility reduction (expressed as Bext) in the three districts...... 19 3.3 Relation of visibility impairment (Bext) to ambient concentrations of aerosol mass and gaseous pollutants...... 20

3.4 Relation of visibility impairment to PM2.5 chemical composition...... 21

3.5 Contribution of PM2.5 composition and gaseous NO2 to light extinction ...... 25

3.5.1 NO2 absorption...... 25 3.5.2 A preliminary validation of IMPROVE formula...... 25 3.5.3 Multiple linear regression...... 26 3.5.4 Application of modified IMPROVE formula...... 26

3.5.5 Contribution of NO2 to light extinction in Hong Kong...... 31

3.6 Spatial distribution of PM2.5 and an assessment of visibility impairment in other areas of Hong Kong...... 32 3.7 Modified WinHaze model for simulating visibility degradation due to air pollution 35 3.8 Case studies of visibility impaired days...... 35 3.9 Effects of synoptic weather patterns on visibility impairment in Hong Kong...... 38 3.10 On the source(s) of aerosol sulfate observed in Hong Kong...... 41 3.10.1 The sulfate concentrations under different weather patterns...... 41 3.10.2 Use of back trajectories to identify possible source origin ...... 44 3.10.3 Case studies of sulfate in chemically aged air mass...... 46 3.11 Implications for management of visibility impairment in Hong Kong ...... 55 4. Conclusions and Suggested Future Work...... 56 4.1. Conclusions ...... 56 4.2. Suggested future work...... 57 References ...... 59

Page 4 List of Figures Figure 1 Scatter plots of measured Bext at Tai O versus inverse of the visual range at the Hong Kong International Airport (HKA): (a) hourly data, (b) daily data...... 16 Figure 2 Digital photos of (a)Central/Western, (b)Tung Chung and (c)Yuen Long ...... 17 Figure 3 Frequency of visibility impairment in Hong Kong and selected cities in Guangdong from 1991 to 2002...... 19 Figure 4 Scatter plots of daily Bext at Hong Kong Observatory (HKO), Hong Kong International Airport (HKA) and the Shenzhen City (SZC) during 2001 with RH≤80%20 Figure 5 Scatter plots of daily visibility at the three stations during 2001 with RH≤80% ..... 21 Figure 6 Scatter plots of hourly Tung Chung air quality data and HKA Bext during 2001 with RH≤80%...... 21 Figure 7 Scatter plots of hourly Tung Chung air quality data and HKA Bext during 2001 with RH≤80%...... 21

Figure 8 Scatter plots of daily PM2.5 chemical composition and Bext at HKO from November 2000 to October 2001 with RH≤80%...... 23 Figure 9 Hygroscopic growth curve (Dr. William Malm, personal communication, 1997) ... 26 Figure 10 Scatter plot of measured particle scattering coefficient versus reconstructed value ...... 26

Figure 11 Reconstructed Bext using modified IMPROVE formula for Tsuen Wan (RHKing’s

Park≤80%) ...... 28

Figure 12 Reconstructed Bext using modified IMPROVE formula for Hok Tsui (RHWaglan

Island≤80%) ...... 28 Figure 13 Average contribution to light extinction using modified IMPROVE formula at Tsuen Wan and Hok Tsui (RH≤80%) ...... 29 Figure 14 Average contribution to highest 20% light extinction using modified IMPROVE formula at Tsuen Wan and Hok Tsui (RH≤80%)...... 29 Figure 15 Average contribution to lowest 20% light extinction using modified IMPROVE formula at Tsuen Wan and Hok Tsui (RH≤80%)...... 29 Figure 16 Average contribution to highest and lowest 20% light extinction using modified IMPROVE formula at Tung Chung (RH≤80%)...... 30

Figure 17 Frequency distribution of NO2 contribution to light extinction in urban Tsuen Wan

from 2001 to 2002. The contribution is defined as NO2abs(TW)/Bext(HKO)...... 30

Figure 18 Time series of visibility absorption by NO2 at Tsuen Wan and Mong Kok, O3

concentrations at Tung Chung and the NO2 contribution to light extinction at Tsuen Wan on 1 January 2002 and 10 July 2002...... 31

Page 5 Figure 19 Statistics for continuous FSP data measured in Causeway Bay, Central Western, Tung Chung, Tap Mun and Tsuen Wan during year 2001...... 32

Figure 20 Average PM2.5 chemical composition at Tsuen Wan, Mong Kok, and Hok Tsui for samples collected from November 2000 to October 2001...... 32 Figure 21 Time series of sulfate concentrations measured from November 2000 to October 2001...... 32 Figure 22 Time series of nitrate concentrations measured from November 2000 to October 2001...... 33 Figure 23 Time series of ammonium concentrations measured from November 2000 to October 2001...... 33 Figure 24 Time series of elemental carbon concentrations measured from November 2000 to October 2001...... 33 Figure 25 Time series of organic carbon concentrations measured from November 2000 to October 2001...... 33 Figure 26 Time series of non-sea salt sulfate concentrations measured at Hok Tsui and Tsuen Wan and their ratio from November 2000 to October 2001...... 33

Figure 27 Average PM2.5 chemical composition at Tung Chung and Yuen Long for samples collected from January 2002 to September 2002...... 34 Figure 28 Statistics for sulfate data obtained in Hok Tsui, Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen Long and Tung Chung during 2002 ...... 34 Figure 29 Statistics for nitrate data obtained in Hok Tsui, Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen Long and Tung Chung during 2002 ...... 34 Figure 30 Statistics for ammonium data obtained in Hok Tsui, Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen Long and Tung Chung during 2002...... 34 Figure 31 Statistics for elemental carbon data obtained in Hok Tsui, Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen Long and Tung Chung during 2002 ...... 34 Figure 32 Statistics for organic carbon data obtained in Hok Tsui, Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen Long and Tung Chung during 2002...... 35 Figure 33 Time series of RSP and FSP concentrations measured at Tung Chung and Tap Mun from January 2001 to October 2002...... 35 Figure 34 Example of type 1: northerly (winter monsoon)...... 38 Figure 35 Example of type 2: northeasterly (winter monsoon) ...... 39 Figure 36 Example of type 3: easterly and southeasterly...... 39 Figure 37 Example of type 4: trough ...... 39 Figure 38 Example of type 5: southerly or southwesterly (summer monsoon) ...... 39 Figure 39 Example of type 6: cyclonic I (close to Hong Kong) ...... 39 Figure 40 Example of type 7: cyclonic II (distant from Hong Kong)...... 39

Page 6 Figure 41 Comparison with the meteorology grouping by Physick (2001)...... 40 Figure 42 Daily mean visibility statistics for the seven meteorological categories during year 2001...... 40 Figure 43 Daily mean visibility statistics for the seven meteorological categories during year 2001 with RH≤80%...... 40 Figure 44 Average number of hour of visibility≤8km per day during year 2001 ...... 41 Figure 45 Correlation of sulfate with other chemical components at Tsuen Wan and Hok Tsui in the winter monsoon type ...... 43 Figure 46 Correlation of sulfate with other chemical components at Tsuen Wan and Hok Tsui in the easterly and southeasterly type...... 43 Figure 47 Correlation of sulfate with other chemical components at Tsuen Wan and Hok Tsui in the summer monsoon type ...... 44 Figure 48 Four air mass categories from November 2000 to October 2001 (continental: red; coastal: green; coastal-Taiwan: yellow; marine: blue). The air-mass grouping was classified using ten-day back trajectories...... 45

Figure 49 Time series of PM2.5 samples collected at Hok Tsui and selected gaseous species at Tap Mun in the four air-mass categories...... 45

Figure 50 Comparison of PM2.5 between urban (Tsuen Wan) and upwind rural (Hok Tsui) stations in coastal airmass ...... 46

Figure 51 Comparison of PM2.5 between urban (Tsuen Wan) and upwind rural (Hok Tsui) stations in coastal airmass passing Taiwan...... 46 3 Figure 52 Back trajectories colored with daily mean PM2.5 mass concentrations (µg/m ) at Hok Tsui...... 48 Figure 53 Back trajectories colored with daily mean sulfate concentrations (µg/m3) in Hok

Tsui. The background is 1 deg. by 1deg. and large-point emissions of SO2 complied by Dr. David Streets (Streets et al., 2003) ...... 48 Figure 54 Back trajectories colored with nitrate concentrations (µg/m3) in Hok Tsui...... 48 Figure 55 Back trajectories colored with ammonium concentrations (µg/m3) in Hok Tsui ... 48 Figure 56 Back trajectories colored with daily mean EC concentrations (µg/m3) in Hok Tsui ...... 48 Figure 57 Back trajectories colored with daily mean OC concentrations (µg/m3) in Hok Tsui ...... 48

Figure 58 Back trajectories colored with daily mean SO2 concentrations (ppbv) in Tap Mun...... 49

Figure 59 Back trajectories colored with NOx concentrations (ppbv) in Tap Mun...... 49 Figure 60 Back trajectories colored with CO concentrations (ppbv) in Tap Mun...... 49 Figure 61 Synoptic weather chart on 24 March 2001 ...... 49

Page 7 Figure 62 Synoptic weather chart on 29 April 2001 ...... 49 Figure 63 Back Trajectory on 24 March 2001 ...... 49 Figure 64 Back Trajectory on 29 April 2001...... 50 Figure 65 Time series of vector winds at Tsuen Wan, King’s Park, Tai Mo Shan, HK Airport and Waglan Island from 23 to 24 March 2001...... 50 Figure 66 Time series of vector winds at Tsuen Wan, King’s Park, Tai Mo Shan, HK Airport and Waglan Island from 28 to 29 April 2001 ...... 50 Figure 67 Time series of visibility and air pollutants measured at Tap Mun from 23 to 24 March 2001 ...... 50 Figure 68 Time series of visibility and air pollutants measured at Tap Mun from 28 to 29 April 2001 ...... 50 Figure 69 Time series of air pollutants measured at Hok Tsui from 23 to 24 March 2001..... 50 Figure 70 Time series of air pollutants measured at Hok Tsui from 28 to 29 April 2001...... 51

Figure 71 Trace elements of PM2.5 at Tsuen Wan, Mong Kok and Hok Tsui for samples collected on 24 March 2001...... 52

Figure 72 Trace elements of PM2.5 at Tsuen Wan, Mong Kok and Hok Tsui for samples collected on 29 April 2001 ...... 52 Figure 73 Time series of vector winds at Tsuen Wan, King’s Park, Tai Mo Shan, HK Airport and Waglan Island on 4 July 2001...... 53 Figure 74 Synoptic weather chart on 4 July 2001...... 53 Figure 75 Time series of visibility and air pollutants measured at Tung Chung and Tap Mun on 4 July 2001 ...... 53 Figure 76 Time series of vector winds at Tsuen Wan, King’s Park, Tai Mo Shan, HK Airport and Waglan Island on 4 February 2001...... 54 Figure 77 Synoptic weather chart on 4 February 2001 ...... 54 Figure 78 Time series of visibility and air pollutants measured at Tung Chung and Tap Mun on 4 February 2001 ...... 54 Figure 79 Trend of visibility impairment vs concentrations of secondary aerosols (sulfate and nitrate) in Hong Kong ...... 54

Figure 80 Trend of sulfate and SO2 emission rate in Hong Kong...... 54

Figure 81 Trend of nitrate and NOx emission rate in Hong Kong...... 54

Page 8 List of Tables Table 1 Monthly mean visibility and % frequency of visibility<8km measured at the Hong Kong International Airport (HKA), Hong Kong Observatory (HKO) and Urban Shenzhen (SZC) for year 2001 (RH≤80%) ...... 18 Table 2 Correlation coefficients (r2) between hourly air quality data and Bext from October 2000 to January 2002 with RH≤80% ...... 22 2 Table 3 Correlation coefficients (r ) between PM2.5 chemical composition and Bext from November 2000 to October 2001 with RH≤80%...... 24 Table 4 Comparison of the results of different formulations in linear regression modeling, IMPROVE formula, and Chin’s model...... 27 3 Table 5 Average PM2.5 chemical composition (µg/m ) at Hok Tsui and Tsuen Wan in seven weather categories from November 2000 to October 2001 ...... 42 Table 6 Chemical composition for four air mass types determined using back trajectories from November 2000 to October 2001 ...... 46 Table 7 Molar ratio of [sulfate] to [sulphur dioxide + sulfate] at Tsuen Wan, Mong Kok, and Hok Tsui for samples collected on 24 March 2001 and 29 April 2001...... 51 3 Table 8 PM2.5 chemical concentrations (µg/m ) at Tsuen Wan, Mong Kok, and Hok Tsui for samples collected on 24 March 2001 and 29 April 2001 ...... 52 Table 9 Chemical concentrations (µg/m3) measured at Tsuen Wan, Mong Kok and Hok Tsui on 4 July 2001 ...... 53 Table 10 Chemical concentrations (µg/m3) measured at Tsuen Wan, Mong Kok and Hok Tsui on 4 February 2001 ...... 54

Page 9 List of Case-Figures Case-Fig 1 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 12 to 14 January 2001...... 61 Case-Fig 2 Synoptic weather charts from 12 to 13 January 2001...... 62 Case-Fig 3 Visibility contour maps in southern China from 12 to 14 January 2001 ...... 63 Case-Fig 4 RSP contour maps in Hong Kong from 12 to 13 January 2001...... 66 Case-Fig 5 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 26 February to 1 March 2001 ...... 67 Case-Fig 6 Synoptic weather charts from 27 to 28 February 2001...... 68 Case-Fig 7 Visibility contour maps in southern China from 26 February to 1 March 2001... 69 Case-Fig 8 RSP contour maps in Hong Kong from 27 to 28 February 2001...... 73

Case-Fig 9 Comparison of chemical composition and Trace elements of PM2.5 at Tsuen Wan on 28 February 2001 with annual average ...... 75

Case-Fig 10 Comparison of chemical composition and Trace elements of PM2.5 at Mong Kok on 28 February 2001 with annual average ...... 76

Case-Fig 11 Comparison of chemical composition and Trace elements of PM2.5 at Hok Tsui on 28 February 2001 with annual average ...... 77 Case-Fig 12 Concentrations of selected VOC compounds measured at Central Western and Tsuen Wan on 27 February 2001...... 78 Case-Fig 13 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 2 to 12 March 2001...... 79 Case-Fig 14 Synoptic weather charts from 3 to 12 March 2001...... 80 Case-Fig 15 Visibility contour maps in southern China from 3 to 12 March 2001 ...... 82 Case-Fig 16 RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001...... 92 Case-Fig 17 Mean potential height and streamlines at 1000 hPa on 3, 7, and 11 March 2001 ...... 101 Case-Fig 18 TOMS aerosol index on 3, 4, 6, 7, 9, 11 and 12 March 2001...... 102

Case-Fig 19 Comparison of chemical composition and trace elements of PM2.5 at Tsuen Wan on 6 March 2001 with annual average ...... 103

Case-Fig 20 Comparison of chemical composition and trace elements of PM2.5 at Mong Kok on 6 March 2001 with annual average ...... 104

Case-Fig 21 Comparison of chemical composition and trace elements of PM2.5 at Hok Tsui on 6 March 2001 with annual average ...... 105 Case-Fig 22 VOC concentrations measured at Tsuen Wan, Mong Kok, Central West and Hok Tsui on 11 March 2001...... 106

Page 10 Case-Fig 23 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 11 to 16 April 2001 ...... 107 Case-Fig 24 Synoptic weather charts from 11 to 15 April 2001...... 108 Case-Fig 25 Visibility contour maps in southern China from 11 to 16 April 2001...... 109 Case-Fig 26 RSP contour maps in Hong Kong on 12 and from 14 to 16 April 2001...... 115 Case-Fig 27 Mean potential height and streamlines at 1000 hPa on 12, 14 and 15 April 2001 ...... 123 Case-Fig 28 TOMS aerosol index from 12 to 16 April 2001...... 124 Case-Fig 29 Time series of Visibility and air pollutants measured at Tap Mun and Tung Chung from 13 to 20 September 2001 ...... 125 Case-Fig 30 Synoptic weather charts from 13 to 20 September 2001...... 126 Case-Fig 31 Visibility contour maps in southern China from 13 to 20 September 2001...... 128 Case-Fig 32 RSP contour maps in Hong Kong on 15 and 17 September 2001 ...... 136

Case-Fig 33 Comparison of chemical composition and trace elements of PM2.5 at Tsuen Wan on 14 September 2001 with annual average...... 140

Case-Fig 34 Comparison of chemical composition and trace elements of PM2.5 at Mong Kok on 14 September 2001 with annual average...... 141

Case-Fig 35 Comparison of chemical composition and trace elements of PM2.5 at Hok Tsui on 14 September 2001 with annual average...... 142 Case-Fig 36 VOC concentrations measured at Tsuen Wan, Mong Kok, Central West and Hok Tsui on 19 September 2001...... 143 Case-Fig 37 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 1 to 8 January 2002...... 144 Case-Fig 38 Synoptic weather charts from 1 to 7 January 2002...... 145 Case-Fig 39 Visibility contour maps in southern China from 1 to 8 January 2002 ...... 147 Case-Fig 40 RSP contour maps in Hong Kong on 1, 5 and 7 January 2002...... 155 Case-Fig 41 Satellite image by MODIS on 7 January 2002 ...... 161 Case-Fig 42 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 27 to 29August 2002...... 162 Case-Fig 43 Synoptic weather charts from 28 to 29 August 2002...... 163 Case-Fig 44 Visibility contour maps in southern China from 27 to 29 August 2002 ...... 164 Case-Fig 45 RSP contour maps in Hong Kong on 28 August 2002...... 167 Case-Fig 46 VOC concentrations measured at Tung Chung and Yuen Long on 27 August 2002...... 169 Case-Fig 47 Diurnal time series of visibility, air pollutants and VOC measured at Central Western on 30 August 2002...... 170

Page 11 Case-Fig 48 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 3 to 10 September 2002 ...... 171 Case-Fig 49 Synoptic weather charts from 4 to 10 September 2002...... 172 Case-Fig 50 Visibility contour maps in southern China from 3 to 10 September 2002...... 174 Case-Fig 51 RSP contour maps in Hong Kong on 6 and 10 September 2002 ...... 182

Case-Fig 52 Comparison of chemical composition of PM2.5 at Tung Chung and Yuen Long on 28 February 2002 with annual average ...... 186 Case-Fig 53 VOC concentrations measured at Tung Chung and Yuen Long on 04 September 2002...... 187 Case-Fig 54 Diurnal time series of visibility, air pollutants and VOC measured at Central Western on 5 September 2002...... 188

Page 12 z Transport of dust was occasionally found in spring causing degraded visibility in Hong Kong. But the Executive Summary worst visibility impairment was due to particulate matter of anthropogenic origin. This study was commissioned by the Hong Kong Environmental Protection Department (HKEPD) to z A modified IMPROVE formula was developed and better understand the meteorological and chemical compared with multiple linear regression method. factors causing visibility degradation in Hong Kong The IMPROVE formula was recommended for and to explore possible control measures for reducing assessing visibility impairment by air pollution for the impact of air pollution on visibility. The study Hong Kong. comprised an in-depth analysis of weather and air z The annual mean PM2.5 concentration was 23.5 quality related data, particularly the newly obtained 3 3 fine particulate chemical composition. Major findings µg/m at upwind Hok Tsui and 34.1 µg/m at urban of this study are summarized below. Tsuen Wan, according to the filter samples collected during November 2000-October 2001. z The frequency of visibility impairment in Hong PM2.5 mass in background air was dominated by Kong showed an increasing trend from 1991 to sulfate (accounting for 39% of the total mass) 2002. Visibility degradation in Shenzhen was followed by carbonaceous materials (24% for comparable to that observed in the western sector organic carbon and 8% for elemental carbon). of Hong Kong. Within the Hong Kong territory, Urban PM2.5 was dominated by carbonaceous visibility was poorer in the western airport than in compounds (35% for organic carbon and 20% for urban Victoria Harbor. elemental carbon), followed by sulfate (27%). Fine particle nitrate had small (3%-4%) contribution in z Visibility degradation moderately correlated both background and urban areas. among the two stations in Hong Kong and one station in Shenzhen, suggesting the influence of z Ammonium sulfate was the largest contributor to localized features, such as meteorology, pollution visibility degradation in Hong Kong, accounting emission and transport, on visibility impairment in for 51% and 33% on average of the total light each area. extinction in the upwind rural and urban areas, respectively. Organic carbon and elemental carbon z The passage of cold fronts/onset of winter contributed to 17% (in rural area)-21% (in urban monsoons and approaching tropical cyclones in area) and 12% (in rural area)-26% (in urban area), warm seasons were the main synoptic weather respectively. Ammonium nitrate had a minor patterns associated with poor visibility in Hong (4%-5%) contribution. The mean contribution of Kong. The frontal passage was the primary nitrogen dioxide was 5% in the urban center and mechanism for transporting regional pollution from was negligible (2%) in rural locations. the north, while atmospheric stagnation associated with distant tropical cyclones allowed pollution in z Available fine suspended particulate data Hong Kong and adjacent cities to accumulate. (determined by the TEOM instrument and referred to as FSP in this report) showed the lowest level of z During photochemical episodes, the temporal particles in the eastern area of Hong Kong. The evolution of poor-visibility zone in the southern annual mean PM2.5 concentration in 2001 was 30 µg/m3 at eastern Tap Mun, 37 µg/m3 in urban Tsuen Pearl River Delta (PRD) appeared to decouple 3 from that in the northern region, implying different Wan, and 35 µg/m in western Tung Chung. Such a meteorological conditions in inland and coastal distribution implies that visibility in the eastern regions of the PRD under large-scale stagnation. coastal area should be better than that in the urban (Similar finding was obtained by Wong (2000), and western areas. In the western sector, northwest based on the lack of correlations of visibility Yuen Long appeared to receive higher aerosol between coastal and inland areas.) loading than in the airport and therefore may experience more severe visibility degradation than z The poorer visibility in the western area was at the airport. attributed to the following two factors:(1) transport of regional pollution from the north apparently z Regional sources had significant contributions to affected the western part of Hong Kong more than the fine particles observed in Hong Kong. Under the urban areas owing to the existence of the the prevailing easterly and northeasterly flows, mountain ranges, and (2) air pollutants, emitted background PM2.5 concentrations (measured at from both Hong Kong and regional sources, were Hok Tsui) accounted for about 79% of the total trapped in the western sector by the convergence of mass measured in urban Tsuen Wan. The regional sea breezes under weak synoptic winds. sources contributed nearly 100% of the sulfates, 51% of organic carbon, and 62% of ammonium nitrate, and 37% of elemental carbon observed in Page 13 urban Hong Kong. Backward air trajectories pollutants to avoid undesirable side effects. indicated that the air masses arriving from N and Reduction of sulfate concentrations is not believed NE originated from the Asian continent, passing to cause an increase in fine nitrates and fine over eastern and southern coastal regions and particle mass during smog episodes in warm adjacent waters of China mainland and the island seasons and possibly in winter as well due to the of Taiwan. Enhanced chemical tracers from ship sub-tropical climate of Hong Kong which should exhausts were also observed in air masses from the favor the presence of nitrate in the gas phase. east. Finally, mitigation program for visibility (and PM) should also consider its implication for z Compared to transport from the northeast and east photochemical ozone and vice versa. directions, higher sulfate concentrations (up to 20 3 µg/m ) were found under weak northwesterly z Additional studies are needed to improve the winds and during atmospheric stagnation. These understanding of source attributions including conditions were often associated with severe smog regional contributions and the relationship between pollution which was due to sub-regional (i.e., the precursor reduction and visibility response. PRD) emissions as opposed to long-range Research tools integrating emission inventory and transport. ambient measurement analysis, receptor and chemical transport models are recommended. z A computer-imaging software program Additional field measurements are recommended (HK_WinHaze) was developed for simulation of in locations outside the HK-PRD region. visibility scenes in three areas of Hong Kong (HK) with different loadings of air pollutants. z Given ammonium sulfate is the dominant visibility-degrading pollutant, visibility management program should aim to lower the atmospheric concentrations of ammonium sulfate by reducing SO2 emissions. Although the air masses coming into the HK-PRD region contained high background sulfate, the most severe visibility impairments were due to PRD sub-regional pollution. Therefore, the initial step of mitigation strategy for the visibility impairment should consider reducing SO2 emission in the PRD. Long-term program need to reduce the levels of sulfate and other aerosols in regional background by reducing emissions of SO2 and other precursor gases from distance sources including marine vessels. This would require co-operation of regional authorities in China mainland and Taiwan, and international maritime organizations. z While ammonium sulfate observed in Hong Kong was of regional origin, organic carbon and elemental carbon — which also contributed significantly to visibility degradation — had a large (49%-63%) contribution from local sources. Reducing their emissions from sources in Hong Kong will help improve the visibility situation and also benefit meeting the Air Quality Objective for particulate matter since carbonaceous materials accounted for a large fraction of fine particle mass in Hong Kong. It was estimated that a 30% reduction of ambient concentration of organic and elemental carbon at urban Tsuen Wan will result in a 12% improvement in visibility and a larger 16% decrease in total PM2.5 mass.

z Visibility management should be formulated synergistically with control measures for other Page 14 particle data. In 2000, the Hong Kong Environmental Protection Department (HKEPD) initiated a 12-month 1. Introduction sampling program for PM2.5 (particulate matter with aerodynamic diameter less than or equal to 2.5 micrometers) at three locations in Hong Kong. In 1.1 Background addition volatile organic compounds were collected at several sites. Louie et al. (2002) gave detailed descriptions of site characteristics, experimental Poor visibility is the most publicly perceived indicator techniques, and the overall measurement results. The of air pollution in Hong Kong and elsewhere. The present investigation was commissioned by the reduction of visibility by air pollution is due to HKEPD aiming to (1) assess the relationship between particles and gases in air that remove light from a sight visibility impairment and newly obtained fine path and scatter light into a sight path, thereby particulate matter and other air-quality data, (2) obscuring the contrast of a target with background air. identify the influence of different synoptic Watson (2002) has provided a comprehensive review meteorological conditions on visibility deterioration in of visibility science as well as U.S. regulations relating Hong Kong, (3) gain insights into the spatial to visibility. A large body of studies have demonstrated perspective of visibility in different districts of Hong a strong linkage between air pollution and visibility Kong, and (4) explore strategies for improving reduction in both urban areas — such as Denver visibility. (Groblicki et al., 1981), Los Angeles (Adams et al., 1990), and Mexico City (Eidels-dubovoi, 2002) — and remote wilderness areas and national parks (Malm et 1.2 Scope of the study and methodology al., 1994; Watson, 2002). These studies have shown the dominant roles of ammonium sulfate, This study involved an in-depth analysis of a carbonaceous materials, and ammonium nitrates in comprehensive air-quality and meteorological data set reducing visibility and that humidity is also an collected in Hong Kong and from other parts of important factor affecting visibility. southern China. Time series, correlations, chemical species ratios, multiple linear regressions and back Studies conducted in Hong Kong have indicated an trajectories were examined. Detailed analysis of eight overall increasing trend of visibility impairment in cases of poor visibility was also conducted. In addition, Hong Kong and the adjacent PRD in the recent a computer model (modified WinHaze) was developed decades (Chang and Koo, 1986; Cheng et al.,1997; to simulate visibility scenes in three districts of Hong Wong, 2000) and that air pollution was an important Kong — Victoria Harbor, Tung Chung, and Yuen Long, cause (Chin, 1997; Sequeira and Lai, 1998; Lee and using observed concentrations of particles and Sequeira, 2001; Lee and Sequeira 2002). The earlier nitrogen dioxide in each district. studies of the relationship between particles and visibility impairment relied on the use of coarse

* In this report, PM2.5 refers to the samples collected in Hong Kong using discrete filter technique, whereas fine suspended particulate (FSP) refers to PM2.5 mass measured by a continuous method (TEOM). Similarly, respirable suspended particulate (RSP) refers to continuous mass measurement of PM10 (particulate with aerodynamic diameter less than or equal to 10 micrometers).

Page 15 The visual range data were converted to light extinction coefficient (Bext) using the Koschmeider 2. Data Used in This Study equation:

Constant k Bext = z Air quality data from 14 HKEPD’s Air Quality Visibility (km) Monitoring Stations (AQMS). — Central/Western (CW), Eastern (EN), Kwai Chung (KC), Kwun As Bext is proportional to atmospheric concentrations Tong (KT), Sha Tin (ST), Sham Shui Po (SSP), Tai of aerosols and gases that directly contribute to Po (TP), Tap Mun (TM), Tsuen Wan (TW), Tung visibility reduction, it is thus more appropriate than Chung (TC), Yuen Long (YL), Causeway Bay visual range in correlation and apportionment studies. (CB), Central (CL), Mong Kok (MK). (October In addition, the variation of Bext is consistent with the 2000-December 2002) convention that a higher value represents a more z 12-month volatile organic compounds (VOCs), polluted situation. mass and chemical composition of PM2.5 collected at Hok Tsui (HT), Mong Kok, and Tsuen Wan. The constant k in the above formula has been found to (November 2000-October 2001) vary from place to place, depending on how visual z Visibility and other meteorological data from Hong ranges are observed as well as on atmospheric Kong and southern China provided by the Hong characteristics. A value of 3.912 has often been chosen Kong Observatory. (October 2000-December under the following assumptions (Cohen 1975): 2002) z The target is a perfectly black observed object; z z PM2.5 and VOC data collected at Tung Chung, and The threshold contrast ratio is 0.02; Yuen Long from a sampling project in 2002. z The atmosphere between the object and the (January-September 2002) observer is homogenous; z Earth’s curvature is negligible; z The sky has the same brightness at the object, the 2.1 Data integration background, and the observer. In reality, the above conditions are rarely fully met. The above data sets were merged into MS Excel and For example, the viewing target is often not a black the units of gaseous pollutants were converted to ppbv object, and the atmosphere may not be homogenous. (parts per billion by volume). The detailed procedures Some studies have shown a k value ranging from 1.6 for data integration and file structures can be found in to 2.9 (Cwalinski et al., 1975; Dzubay et al., 1982; Appendix XVII. Qzkaynak et al., 1985; Griffing., 1980).

800 600 (a) (b) Slope = 2.60 500 2 r = 0.78 600 ) )

-1 400 -1

400 300 Slope = 2.40 2 r = 0.63 Tai O Bext(Mm Tai O Bext (Mm 200 200 100

0 0 0 100 200 300 400 0 50 100 150 200 250 300 -1 1/HKA Visibility (Mm-1) 1/HKA Visibility (Mm )

Figure 1 Scatter plots of measured Bext at Tai O versus inverse of the visual range at the Hong Kong International Airport (HKA): (a) hourly data, (b) daily data.

Page 16 Therefore, selecting a k value specific for Hong Kong range of meteorological conditions were encountered requires a direct comparison of measurement of Bext and different air masses – ranging from very clean with visual-range observation conducted nearby. oceanic air to highly polluted urban plumes – were In-situ measurements of light extinction were recently sampled. Therefore, the value of 2.6 was adopted as made by researchers of the Hong Kong Polytechnic the conversion constant in the Koschmeder equation University at Tai O on Lantau Island. Bext was for Hong Kong. determined by the light extinction coefficient measured with an integrating nephelometer (Optec, 2.2 Digital photos of visibility scenes in Model NGN-2) and a particle soot absorption photometer (Radiance research, Model PSAP). The Victoria Harbor, Tung Chung, and Yuen time period for the optical measurement was from October to December 2002. Long

Figure 1 shows scatter plot of measured Bext at Tai O Photo images (figure 2) of the above three areas were versus inverse of the visual range at HKA for both taken using a high-resolution digital camera (Nikon hourly and daily observations. It can be seen that the k Coolpix 880). The locations were chosen to represent value was 2.4 and 2.6 for hourly and daily data, Hong Kong’s urban center, western, and northwestern respectively. There was a large degree of scatter in the districts. Photos were taken on 12 days in Tung Chung hourly data due to the poor resolution of visual range and 8 days in Yuen Long between 18 January and 28 observation on bad visibility days. Use of daily August 2002. Two sets of images were recorded at averaged data significantly improved the correlation Central West. The dates on which photos were taken and yielded a slope of 2.6 with r2 = 0.78. Although the are listed in Appendix VII. One photo from each site above k value is based on the data obtained from a under the clearest condition was selected to three-month period, it is believed to be representative incorporate into the WinHaze model by Air Resources and robust because during these three months, a wide Specialist Inc. in the United States.

(a)

(b) (c)

Figure 2 Digital photos of (a)Victoria Harbor, (b)Tung Chung and (c)Yuen Long

Page 17 be noted, however, that this process removed the cases in which high levels of particulate matter were present 3. Data Analysis and during high RH conditions. Because there was no direct reading of relative humidity for the s’tations in southern China, the 3-hourly relative humidity at the Findings Shenzhen site was calculated from the corresponding air temperature and dew-point temperature using the formula identical to that used by Wong (2000). 3.1 Overall spatial and seasonal patterns of Table 1 indicates that average visibility in Shenzhen in visibility impairment in Victoria Harbor, 2001 was better than that of the two stations in Hong western Hong Kong, and urban Shenzhen Kong. But the frequency of visibility impairment (i.e., visibility<8km) in Shenzhen was comparable to that in Monthly mean visibility for relative humidity (RH) the Hong Kong airport. It was noticed that the lowest equal or less than 80% and the percentage frequency visibility value in Shenzhen was 1.3 km compared to of hourly visibility (3-hourly for Shenzhen) less than 8 0.4 km in Hong Kong. It was unclear whether the km were computed using visibility observations made better average visibility in Shenzhen was due to the at the Hong Kong Observatory (HKO) headquarters in cut off of the lowest value at 1.3 km. It was decided to Kowloon, the Hong Kong International Airport (HKA) use the frequency for comparing visibility in the three in western Hong Kong, and urban Shenzhen (SZC) areas, which suggested visibility impairment in (Table 1). As recommended by the World Shenzhen was comparable to that at the Hong Kong Meteorological Organization, a cutoff at RH=80% was airport in the western sector of Hong Kong. used to select the periods when visibility was not strongly influenced by atmospheric moisture. It should

Table 1 Monthly mean visibility and % frequency of visibility<8km measured at the Hong Kong International Airport (HKA), Hong Kong Observatory (HKO) and Urban Shenzhen (SZC) for year 2001 (RH≤80%)

HKA HKO SZC

Jan 11.66 (13%)* 12.06 (12%) 15.26 (09%)

Feb 10.46 (21%) 11.85 (14%) 13.54 (11%)

Mar 12.04 (10%) 12.85 (09%) 14.53 (11%)

Apr 11.90 (07%) 12.58 (11%) 15.29 (10%)

May 12.96 (09%) 12.94 (13%) 14.02 (19%)

Jun 20.39 (05%) 20.58 (06%) 22.23 (04%)

Jul 17.04 (03%) 17.65 (04%) 17.57 (02%)

Aug 14.99 (13%) 16.21 (06%) 16.26 (17%)

Sep 11.05 (19%) 11.31 (17%) 11.69 (25%)

Oct 11.56 (12%) 11.99 (13%) 13.41 (10%)

Nov 11.26 (12%) 12.87 (05%) 14.12 (16%)

Dec 10.78 (19%) 13.42 (02%) 13.49 (17%)

Annual 12.73 (12%) 13.29 (09%) 14.68 (13%) * 1-hourly data for Hong Kong, 3-hourly data for Shenzhen * Frequency shown in parenthesis

Page 18 35

30 Macau Shenzhen 25 Hong Kong Shaoguan 20 Yangjiang 15 Shanwei

% Visibility < 8km Visibility % 10

5

0 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002

Note: The frequency of visibility impairment is defined as the percentage of hours having visibility<8km in total hours with RH≤80%

Figure 3 Frequency of visibility impairment in Hong Kong and selected cities in Guangdong from 1991 to 2002.

Within Hong Kong, the western area (HKA) showed as shown in Figure 3 for selected cities. It can be seen lower annual mean visibility (12.7 km) and more that the frequency of visibility impairment in Hong frequent visibility impairment (12%) than Victoria Kong and Shenzhen has shown a gradual increasing Harbor did (HKO: mean=13.3 km; frequency = 9%). trend since 1991. Both suggest that the western part of Hong Kong suffered from more serious visibility impairment than 3.2 Correlation of visibility reduction in the urban center. This feature is in contrast to the spatial patterns of primary pollutants showing higher (expressed as Bext) in the three districts concentrations in urban areas. This implies that the concentrations of fine particulates which are To see whether the variation of visibility degradation responsible for most of the light extinction were higher in one area correlated with the change in another in the western part or the interaction of fine particles district in Hong Kong and Shenzhen, scatter plots of with water vapor produced more visibility reducing daily Bext were made for year 2001, as shown in aerosols. Table 1 also reveals an interesting Figure 4. observation: the frequency of poor visibility at urban HKO drastically decreased in August and towards the Visibility degradation showed a moderate correlation end of year 2001 (November and December), implying (r2 = 0.34-0.37) among the three stations. At first much better visibility in the urban areas than at the glance, this result seems to differ from the result for Airport during these months. 1999 obtained by Wong (2000) which suggested a strong correlation between visibility in Hong Kong Visibility reduction exhibited a clear seasonal pattern. and Shenzhen (r2 = 0.68). Closer examination of As expected, much better visibility was observed in Figure 4 revealed that the lack of correlation was summer when there was the smallest difference in caused by the scatter in data points with large values visibility among all three stations. Visibility of Bext (corresponding to low visibility). When daily impairment was the worst in winter. visibility data were used in the scatter plots (Figure 5), a much better correlation was obtained for the two Wong (2000) examined the trend for visibility Hong Kong stations (r2 = 0.69). The apparent impairment in Hong Kong and a number of cities in discrepancy can be attributed to the following reason. Guangdong. In the present report, the statistics for the The strong correlation in the scatter plots of the visual recent years were added to update the trend analysis, range is dominated by good visibility associated with

Page 19 clean weather (e.g., summer monsoon) which 3.3 Relation of visibility impairment (Bext) influences both of the areas. The weaker correlation in Bext is, on the other hand, owing to pollution to ambient concentrations of aerosol mass episodes (with large Bext values) during which Bext in western Hong Kong and the Harbor area showed a and gaseous pollutants large degree of scattering. The latter result suggests that western Hong Kong and the Harbor areas were Hourly extinction coefficients at the three sites were often under the influence of air with different chemical correlated with hourly air quality data from all 14 and optical properties during high-pollution conditions. AQMS (Table 2). As expected, Bext correlated with This topic will be discussed further in a later section. atmospheric level of FSP, followed by respirable suspended particulate (RSP) and gaseous NO2. Bext The scatter plots of Bext under all humidity conditions observed at the airport showed a better correlation were also examined, yielding slightly improved with air quality in Tung Chung, visibility at HKO correlation coefficients (r2 = 0.39-0.57) compared to correlated with air quality in Tsuen Wan, and SZC the case with RH≤80%. This can be explained by the with Yuen Long. The correlations of visibility fact that overcast/rainy conditions normally occur over impairment at HKO and SZC with air quality were a large spatial scale, having similar influence on less significant than in the case of HKA. Figures 6 and visibility reduction in the three areas. 7 show the scatter plots of Bext at HKA and air quality data at TC.

1.0 1.0 Slope = 0.54 Slope = 0.59 2 0.8 r = 0.37 0.8 2 ) ) r = 0.34 -1 -1 0.6 0.6

0.4 0.4 HKO Bext (km Bext HKO (km Bext HKA 0.2 0.2

0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 -1 -1 SZC Bext (km ) SZC Bext (km )

1.0 Slope = 0.54 2 0.8 r = 0.35 ) -1 0.6

0.4 HKO Bext (km Bext HKO 0.2

0.0 0.0 0.2 0.4 0.6 0.8 1.0 -1 HKA Bext (km ) Figure 4 Scatter plots of daily Bext at Hong Kong Observatory (HKO), Hong Kong International Airport (HKA) and the Shenzhen City (SZC) during 2001 with RH≤80%

Page 20 30 3.4 Relation of visibility impairment to Slope = 0.92 PM2.5 chemical composition 2 25 r = 0.69 Daily Bext at HKO, HKA and SZC were correlated to 20 time-matched chemical composition of PM2.5 measured at Mong Kok, Hok Tsui, and Tsuen Wan. ility (km) 15 The correlation coefficients are shown in Table 3. It can be seen that visibility reduction at HKO strongly correlated with the loading of ammonium, sulfate, 10 nitrate, and to a lesser extent, organic carbon. The Bext

HKO Visib at the airport correlated more with the OC content. 5 Figure 8 shows scatter plots for Bext at HKO. These results are consistent with the findings obtained by 0 Chin (1997) and elsewhere, indicating fine particulates 0 5 10 15 20 25 30 (sulfate, nitrate, ammonium and organic carbon) are HKA Visibility (km) the major visibility reducing aerosol components in the polluted atmosphere.

1.6 [NO ] 30 Slope 2 = 0.003 2 Slope = 0.54 ) 1.4

-1 r = 0.24 2 25 r = 0.52 1.2 [SO ] Slope 2 = 0.007 1.0 2 20 r = 0.20

ility (km) 0.8 15 [SO ] 0.6 2 10 0.4 HK Airport Bext (km Bext HK Airport [NO ] HKA Visib 0.2 2 5 0.0 0 40 80 120 0 SO , NO (ppbv) 0 5 10 15 20 25 30 2 2 SZC Visibility (km) Figure 6 Scatter plots of hourly Tung Chung air quality data and HKA Bext during 2001 with RH≤80%

30 1.6 Slope [FSP] = 0.004 2 Slope = 0.59 ) 1.4 r = 0.53 25 2 -1 r = 0.50 1.2 20 Slope [RSP] = 0.002 2 1.0 r = 0.44

ility (km) [FSP] 15 0.8 0.6 10 0.4 HKO Visib [RSP] HK Airport Bext (km Bext HK Airport 5 0.2 0 0.0 0 5 10 15 20 25 30 0 50 100 150 200 250 3 SZC Visibility (km) FSP, RSP (ug/m ) Figure 5 Scatter plots of daily visibility at the three stations Figure 7 Scatter plots of hourly Tung Chung air quality data during 2001 with RH≤80% and HKA Bext during 2001 with RH≤80%

Page 21

Table 2 Correlation coefficients (r2) between hourly air quality data and Bext from October 2000 to January 2002 with RH≤80%

HKA Bext HKO Bext SZC Bext TC TW CL TM CB MK YL TC TW CL TM CB MK YL TC TW CL TM CB MK YL FSP 0.53 0.37 0.27 0.24 0.10 - - 0.28 0.44 0.39 0.32 0.23 - - 0.32 0.37 0.26 0.31 0.12 - - RSP 0.44 0.29 0.21 0.16 0.08 0.22 0.34 0.21 0.35 0.28 0.22 0.19 0.36 0.20 0.24 0.27 0.18 0.19 0.07 0.20 0.27

NO2 0.24 0.15 0.17 0.05 0.16 0.15 0.16 0.06 0.12 0.14 0.04 0.14 0.17 0.08 0.10 0.11 0.10 0.10 0.12 0.10 0.12 CO 0.21 0.09 0.01 0.03 0.07 0.06 - 0.07 0.08 0.01 0.06 0.05 0.06 - 0.03 0.05 0.01 0.00 0.06 0.01 -

SO2 0.20 0.04 0.08 0.04 0.05 0.05 0.15 0.10 0.05 0.09 0.02 0.07 0.09 0.09 0.09 0.06 0.08 0.05 0.08 0.08 0.12

NOx 0.18 0.04 0.04 0.04 0.03 0.01 - 0.04 0.03 0.01 0.04 0.01 0.02 - 0.06 0.02 0.01 0.09 0.01 0.00 - NO 0.05 0.00 0.02 0.00 0.01 0.00 - 0.00 0.00 0.00 0.01 0.00 0.00 - 0.00 0.00 0.00 0.00 0.00 0.00 -

O3 0.02 0.03 - 0.06 - - 0.04 0.02 0.02 - 0.06 - - 0.03 0.02 0.02 - 0.04 - - 0.03

HKA Bext HKO Bext SZC Bext EN CW KT SSP ST TP KC EN CW KT SSP ST TP KC EN CW KT SSP ST TP KC RSP 0.21 0.25 0.20 0.26 0.21 0.25 0.26 0.33 0.38 0.36 0.39 0.28 0.27 0.34 0.20 0.26 0.24 0.26 0.28 0.30 0.29

NO2 0.12 0.14 0.11 0.14 0.07 0.07 0.14 0.13 0.12 0.14 0.14 0.07 0.06 0.11 0.07 0.11 0.09 0.09 0.14 0.12 0.11

SO2 0.03 0.06 0.02 0.04 0.01 0.05 0.01 0.05 0.10 0.09 0.08 0.03 0.05 0.02 0.10 0.12 0.08 0.08 0.06 0.12 0.06

NOx - 0.06 0.02 0.06 0.01 - 0.02 - 0.06 0.04 0.06 0.01 - 0.01 - 0.06 0.01 0.03 0.04 - 0.02 NO - 0.01 0.00 0.02 0.00 - 0.00 - 0.01 0.01 0.02 0.00 - 0.00 - 0.01 0.00 0.00 0.01 - 0.00

O3 0.05 0.02 0.03 0.02 0.03 0.05 0.01 0.05 0.04 0.01 0.02 0.03 0.04 0.02 0.05 0.05 0.02 0.03 0.00 0.03 0.01

Page 22

0.8 0.8 ) ) Slope [TW] = 0.019 Slope [HT] = 0.072 -1 -1 2 2 r = 0.61 r = 0.31 0.6 0.6 Slope [MK] = 0.019 2 r = 0.67 0.4 0.4

Slope [TW] = 0.048 2 0.2 0.2 r = 0.40

Slope [HT] = 0.017 Slope [MK] = 0.056 HK ObservatoryHK Bext (km HK Observatory Bext(km 2 2 r = 0.49 r = 0.55 0.0 0.0 0 5 10 15 20 25 0 2 4 6 8 10 3 3 Sulfate (ug/m ) Nitrate (ug/m )

0.8 0.8

) ) Slope [HT] = 0.106 -1 -1 2 r = 0.49 0.6 Slope [HT] = 0.058 0.6 Slope [TW] = 0.013 2 2 r = 0.57 r = 0.02

Slope [MK] = -0.011 0.4 0.4 2 r = 0.13

Slope [TW] = 0.048 2 0.2 r = 0.67 0.2

Slope [MK] = 0.050 HK ObservatoryHK Bext (km 2 ObservatoryHK Bext (km r = 0.72 0.0 0.0 0 2 4 6 8 10 12 0 5 10 15 20 25 30 3 3 Ammonium (ug/m ) Elemental Carbon (ug/m )

0.8 Slope [TW] = 0.011

) 2 -1 r = 0.41 0.6 Slope [HT] = 0.014 2 r = 0.32

0.4

0.2 Slope [MK] = 0.007

HK ObservatoryHK Bext (km 2 r = 0.38 0.0 0 10 20 30 40 50 60 3 Organic Carbon (ug/m )

Figure 8 Scatter plots of daily PM2.5 chemical composition and Bext at HKO from November 2000 to October 2001 with RH≤80%

Page 23 2 Table 3 Correlation coefficients (r ) between PM2.5 chemical composition and Bext from November 2000 to October 2001 with RH≤80%

TW MK HT Bext 2- - + 2- - + 2- - + SO4 NO3 NH4 EC OC SO4 NO3 NH4 EC OC SO4 NO3 NH4 EC OC HKA HKO SZC 2- SO4 1 - NO3 0.20 1 + TW NH4 0.89 0.45 1 EC 0.06 0.00 0.05 1 OC 0.43 0.63 0.62 0.03 1

2- SO4 0.96 0.25 0.87 0.05 0.44 1 - NO3 0.30 0.92 0.52 0.02 0.66 0.33 1 + MK NH4 0.90 0.46 0.96 0.05 0.59 0.92 0.53 1 EC 0.09 0.16 0.14 0.03 0.14 0.12 0.12 0.16 1 OC 0.39 0.49 0.52 0.03 0.82 0.36 0.46 0.49 0.09 1

2- SO4 0.86 0.13 0.70 0.04 0.37 0.92 0.25 0.78 0.10 0.30 1 - NO3 0.12 0.81 0.32 0.01 0.56 0.13 0.81 0.30 0.11 0.39 0.08 1 + HT NH4 0.84 0.33 0.86 0.04 0.59 0.88 0.46 0.90 0.18 0.49 0.88 0.28 1 EC 0.59 0.29 0.63 0.06 0.55 0.61 0.41 0.66 0.16 0.50 0.56 0.30 0.70 1 OC 0.35 0.51 0.49 0.01 0.79 0.35 0.51 0.46 0.22 0.71 0.33 0.54 0.57 0.51 1

HKA 0.38 0.23 0.38 0.02 0.52 0.36 0.25 0.38 0.07 0.53 0.28 0.13 0.31 0.44 0.40 1 Bext HKO 0.61 0.40 0.67 0.02 0.41 0.67 0.54 0.72 0.13 0.38 0.49 0.31 0.57 0.50 0.32 0.45 1 SZC 0.58 0.14 0.57 0.04 0.42 0.51 0.32 0.56 0.03 0.34 0.37 0.04 0.41 0.46 0.27 0.54 0.44 1

Page 24 high NO2 concentrations in urban areas. For this 3.5 Contribution of PM2.5 composition and reason, a term for NO2 was added to the formula to gaseous NO2 to light extinction account for its absorption of light. The absorption -1 3 coefficient for NO2 (0.175 Mm /µg/m ) was adopted Particles and gases in the atmosphere reduce visibility from the work of Groblicke et al. (1981) who by scattering and absorbing light. The light extinction estimated the contribution of NO2 in Denver, Colorado. is measured by the extinction coefficient (Bext) which The final apportionment formula used in the present can be expressed as: study takes the following form:

Bext (Mm-1) = 3f(rh)[Sulfate] + 3f(rh)[Nitrate] Bext = Bray + Bag + Bsp + Bap +4[Organic] + 1[Soil] Where + 0.6[Coarse Mass] + 10[EC] + 0.175[NO ] + 10 Bray = Rayleigh scattering (light scattering by air 2 molecules) For the hygroscopic growth function, we adopted the Bag = light absorption by gases (mainly NO2) data (Figure 9) from Dr. William Malm, but also Bsp = light scattering by particles compared it with other data sets such as those used by Bap = light absorption by particles (e.g., black carbon). USEPA. These growth functions have shown very In this study, the formula developed in the IMPROVE small differences. The aerosol growth data from Dr. project (Interagency Monitoring of Protected Visual Malm has also been adopted in the WinHaze model.

Environments) (Malm et al., 1994; Watson, 2002) was 2- The IMPROVE formula has assumed that SO4 and adopted to quantify the contributions of particles to the - + NO3 ions are fully neutralized by NH4 . Examination light extinction in Hong Kong. This formula 2- - + apportions the total light extinction to the following of scatter plots of [SO4 + NO3 ] and [NH4 ] obtained individual major aerosol component: sulfate, nitrate, during the 12-month PM study in Hong Kong indicated that such an assumption is suitable to Hong organics, light-absorbing carbon, soil, and coarse mass + Kong, since the measured NH4 was enough to (Malm et al., 1994). - 2- neutralize 80~100% of NO3 and SO4 ions at the Bext (Mm-1) = 3f(rh)[Sulfate] + 3f(rh)[Nitrate] three stations. + 4[Organic] + 1[Soil] + 0.6[Coarse Mass] +10[EC] + 10 3.5.2 A preliminary validation of

Where: [Sulfate] = (NH4)2SO4 IMPROVE formula [Nitrate] = NH4NO3 [Organic] = 1.4[OC] Although the IMPROVE formula has been adopted for [Soil] = 2.2[Al] + 2.19[Si] + 1.63[Ca] different geographical regions of the United States, it + 2.42[Fe] + 1.94[Ti] is nevertheless useful to check it’s applicability to [Coarse Mass] = [RSP] - [FSP] Hong Kong. A rigorous validation of the formula f(rh) = hygroscopic species growth function would require co-located measurements of light extinction and aerosol composition. Such information The dry extinction efficiency of each species in the was not available in Hong Kong until recently when above formula was determined based on critical some measurements were made at Tai O during reviews of numerous theoretical and field studies December 2002 by PolyU researchers. Here, we (Trijonis et al., 1988, 1990; White 1990; Trijonis and compared the reconstructed Bext due to particle Pitchford 1987). Malm et al. (1996) demonstrated a scattering to in-situ measurement of the light good agreement between measured and reconstructed scattering. PM2.5 samples at Tai O were collected by an Bext across the IMPROVE network over the entire Anderson Speciation Sampler, and the samples were United States. analyzed for organic carbon, elemental carbon, and major ions. The reconstructed Bsp took the following 3.5.1 NO absorption form: [Bsp] = 3f(rh)[ammonium sulfate] + 2 3f(rh)[ammonium nitrate] + 4[Organic]

The IMPROVE study was conducted primarily in Figure 10 shows a good agreement between the clean regions such as national parks where gaseous reconstructed and measured light scattering (Slope = NO2 played a negligible role in reducing visibility. As 1.12, r2 = 0.9). Given that particle scattering accounts a result, the formula used in that study did not include for a dominant portion of the total light extinction, the the contribution from NO2 (Malm et al., 1994). In above comparison suggests that the IMPROVE urban areas, however, absorption by NO2 may be an formula is applicable to Hong Kong. important cause of visibility reduction because of the

Page 25 7

6

5

4

f(rh) 3

2

1

0 0 20406080100 Relative Humidity (%)

Figure 9 Hygroscopic growth curve (Dr. William Malm, personal communication, 1997)

different treatment of aerosol components and with or 400 without a term for NO2. The outputs are shown in Table 4. The highlighted result was considered the most suitable. For comparison, the regression Slope = 1.1 coefficients determined by Chin (1997) were also

) 300 2 included in the table. -1 r = 0.90

It can be seen that the regression model yielded 200 coefficients similar to the IMPROVE formula, with an identical value for nitrate (3), a larger value for sulfate (4.2 versus 3), and a smaller value for organic carbon (3.2 versus 4) and elemental carbon (8 versus 10). But Tai O Bsp (Mm 100 the regression model suggested a much larger coefficient for soil (6.7 versus 1).

0 Judging from the fact that the IMPROVE formula has 0 100 200 300 400 been applied in many places and that the regression -1 approach gives a negative NO2 coefficient, we IMPROVE reconstructed Bsp (Mm ) recommend that the modified IMPROVE formula be adopted for Hong Kong. It should be pointed out, Figure 10 Scatter plot of measured particle scattering however, that the two approaches will give a similar coefficient versus reconstructed value result on the major species contributing to visibility reduction in Hong Kong.

3.5.3 Multiple linear regression 3.5.4 Application of modified IMPROVE formula Another way to establish the relationship between Bext and the concentrations of air pollutants is the use The modified IMPROVE formula was used to of multiple linear regression. This approach was reconstruct the light extinction coefficient (Bext) using applied to the Bext derived from the visual range at the PM2.5 composition obtained during the 12-month HKO and pollutants concentrations at TW. Eight PM study and gaseous NO2 at Tsuen Wan for daily different regression formulations were tested with average RH less than or equal to 80%.

Page 26 Table 4 Comparison of the results of different formulations in linear regression modeling, IMPROVE formula, and Chin’s model

Model 1 Model 2 Chin’s IMPROVE Without Without Without NO , Without Without Without NO , Coefficient All 2 All 2 (1997) formula NO2 CM & Soil CM & Soil NO2 CM & Soil CM & Soil

a 16.76 (25.2) 11.38 (23.38) 5.95 (24.23) 7.78 (21.93) 15.83 (22.62) 11.68 (21.39) 14.96 (22.89) 17.18 (20.36) 23.50 10 (constant)

b1 -0.31 (0.51) - 0.08 (0.02) - -0.30 (0.49) - 0.10 (0.43) - 0.47 - (for NO2)

b2 2- 4.21(2.24) 4.33 (2.21) 6.30 (1.90) 6.34 (1.86) 4.32 (0.48) 4.16 (0.40) 4.16 (0.45) 4.20 (0.40) 2.91 3 (for SO4 )

b3 - 2.05 (3.33) 2.75 (3.09) 4.86 (2.96) 4.74 (2.85) 2.65 (1.96) 2.97 (1.88) 3.39 (1.99) 3.28 (1.90) 0.70 3 (for NO3 )

b4 + 4.68 (6.70) 3.71 (6.45) -2.18 (5.54) -2.18 (5.47) - - - - 1.32 - (for NH4 )

b5 3.77 (1.52) 3.19 (1.17) 3.59 (1.53) 3.79 (1.10) 3.78 (1.49) 3.18 (1.10) 3.29 (1.51) 3.53 (1.08) 1.49 4 (Organic)

b6 6.10 (7.82) 6.61 (7.70) - - 5.88 (7.31) 6.70 (7.13) - - - 1 (Soil)

b7 0.27 (1.44) -0.06 (1.32) - - 0.27 (1.42) -0.07 (1.30) - - 1.09 0.6 (CM)

b8 8.80 (3.43) 8.28 (3.29) 8.00 (3.45) 8.14 (3.33) 8.77 (3.37) 8.28 (3.24) 8.09 (3.46) 8.26 (3.34) - 10 (EC)

* Note: PM2.5 data from Tsuen Wan, Visibility from HKO, RH from King’s Park + 2- - Model 1: NH4 , SO4 , NO3 are individual components + 2- - Model 2: NH4 is allocated to SO4 and NO3

Page 27 700

Gas Scattering 600 NO

) 2

-1 ) (NH4 2SO4 NH4NO3 500 OC EC Soil 400 Coarse Mass

300

200 Bext predicted by modified IMPROVE (Mm 100

0 Nov 00 Jan 01 Mar 01 May 01 Jul 01 Sep 01 Nov 01

Figure 11 Reconstructed Bext using modified IMPROVE formula for Tsuen Wan (RHKing’s Park≤80%)

500

Gas Scattering

NO2 )

-1 (NH ) SO 400 4 2 4 NH4NO3 OC EC Soil 300 Coarse Mass

200

100 Bext predicted by modified IMPROVE (Mm IMPROVE by modified predicted Bext

0 Nov 00 Jan 01 Mar 01 May 01 Jul 01 Sep 01 Nov 01

Figure 12 Reconstructed Bext using modified IMPROVE formula for Hok Tsui (RHWaglan Island≤80%)

Figures 11 and 12 give the average contribution of Ammonium sulfate ((NH4)2SO4) was found to be the each species to the light extinction for the above two largest contributor to light extinction, followed by stations. The contribution for days with highest 20% organic carbon (OC), and elemental carbon (EC). At Bext (representing poor visibility days) and lowest urban TW, sulfate, OC, and EC accounted for 33%, 20% Bext (representing good visibility days) are also 21%, and 26% of the total chemical extinction, shown. respectively. At the rural HT site, sulfate, OC, and EC contributed to 51%, 17% and 12% respectively. By Page 28 comparison, nitrate contributed to only 4-5%, and (Appel et al., 1985), and 4% in Sydney (Williams et NO2 accounted for 5% at TW, but only 2% at the rural al., 1982). site. (The NO2 data obtained from Tap Mun was used for the Hok Tsui site.) During top 20% poorest It should be pointed out that the above calculation of visibility days, contribution from sulfate was larger chemical extinction also suggests an important role of than the ensemble average, accounting for 37% at organic and elemental carbon at the urban TW station, urban TW and 62% at rural HT. which collectively contributed to 47% of the extinction for the annual mean and 43% during top The significant contribution of ammonium sulfate to 20% worst visibility days. visibility degradation in Hong Kong is due to a relatively high concentration of sulfate and its ability The large contribution of OC and EC at TW is to absorb water vapor which enhances light scattering. believed to be a localized effect, as the TW station The contribution of PM2.5 in Hong Kong is similar to subjects to a large emission of primary OC and EC. the situation in the eastern US where sulfate has been The high concentration of EC and OC may not found to be the dominant component for light represent the situation over a long path in the urban extinction (Watson 2002). Previously estimates of area based on which visibility is perceived. Fine NO2 contribution in other cities showed that NO2 sulfate, on the other hand, is believed to be the most accounted for 4.7% of light extinction in Houston, important aerosol component in visibility reduction Texas (Dzubay et al., 1982), 7% in Denver, Colorado over the sky of the Hong Kong area. (Groblicki et al., 1981), 7% in Los Angeles, California

Hok Tsui Tsuen Wan -1 Bext = 161.78 Mm Ammonium Sulfate Bext = 235.25 Mm-1 Ammonium Nitrate 4% 2% 5% 1% 5% 1% 8% EC 5% OC 17% 21% 33% Gas Scattering Nitrogen Dioxide 51% Coarse Mass 12% Soil 4% 5% 26%

Figure 13 Average contribution to light extinction using modified IMPROVE formula at Tsuen Wan and Hok Tsui (RH≤80%)

Hok Tsui Tsuen Wan -1 Bext = 262.92 Mm Ammonium Sulfate Bext = 370.39 Mm-1 Ammonium Nitrate 1% 3% 4% 1% 6% 1% EC 3% 4% 12% OC 25% 37% Gas Scattering Nitrogen Dioxide 14% Coarse Mass 1% 62% Soil 18% 8%

Figure 14 Average contribution to highest 20% light extinction using modified IMPROVE formula at Tsuen Wan and Hok Tsui (RH≤80%)

Hok Tsui Tsuen Wan -1 Bext = 74.51 Mm Ammonium Sulfate Bext = 132.03 Mm-1 Ammonium Nitrate 5% 4% 2% 1% EC 5% 1% 7% 8% 21% OC 24% 18% Gas Scattering Nitrogen Dioxide 44% Coarse Mass 4% Soil 12% 5% 39%

Figure 15 Average contribution to lowest 20% light extinction using modified IMPROVE formula at Tsuen Wan and Hok Tsui (RH≤80%)

Page 29 The dominant role of sulfate in visibility reduction can combined for the ensemble average (54% versus 43%) also be illustrated by ten PM2.5 samples collected in and even larger for the top 20% worst visibility days 2002 at Tung Chung where visibility has been found (65% versus 31%). These findings confirmed the poorer than in the urban area of Hong Kong. Figure 16 dominant role of fine sulfate in reducing visibility in below shows the relative contribution of sulfate, OC, Hong Kong. EC to the chemical extinction. It can be seen that sulfate’s contribution is larger than OC and EC

Average Bext = 143.61 Mm-1 Ammonium Sulfate 21% Ammonium Nitrate

EC 22% 54% OC 3%

Highest 20% Lowest 20% -1 Bext = 260.99 Mm-1 Bext = 51.33 Mm

16% 28%

15%

46% 4% 65% 23% 3% Figure 16 Average contribution to highest and lowest 20% light extinction using modified IMPROVE formula at Tung Chung (RH≤80%)

30

25 Total No. of hours with RH<=80%, Visibility<8km = 797 hrs

20

15

10 Number of hours

5 10 Jul 2002 0 0 2 4 6 8 10 12 14 [Tsuen Wan NO /HKO Bext] Ratio (%) 2

Figure 17 Frequency distribution of NO2 contribution to light extinction in urban Tsuen Wan from 2001 to 2002. The contribution is defined as NO2abs(TW)/Bext(HKO).

Page 30 3.5.5 Contribution of NO2 to light It can be seen that during the poor visibility days, the extinction in Hong Kong NO2 absorption of light never exceeded more than 15% of the total light extinction. (The highest

contribution was 14%.) The distribution centers at As shown in the previous section, gaseous NO 2 about 4%. This result is in accord with the estimate contributed an average of 5% to the light extinction in based on the IMPROVE formula, both indicating an the urban area. This result was based on the data insignificant role of NO in visibility degradation in collected on days with aerosol samples. It has been 2 Hong Kong. observed in Hong Kong that NO could exceed air 2 quality objective in urban areas during regional Two cases were examined below (figure 18) to photochemical episodes. To see whether NO could 2 illustrate the relationship between regional ozone, high have a larger contribution during the high NO events, 2 urban NO and light absorption. These two cases were we calculated percentage of hourly NO contribution 2 2 photochemical events — one in summer (10 July 2002) at Tsuen Wan to the total light extinction derived from and the other one in winter (1 January 2002). The the visibility at the Hong Kong Observatory figures showed that while high ozone was observed at according to the formula: NO /Bext(HKO), where 2abs suburban Tung Chung, elevated NO was recorded in NO =0.175 x [NO ] µg/m3. Figure 17 shows the 2 2abs 2 the urban area due to reaction: NO + O → NO + O . frequency distribution of this ratio during visibility 3 2 2 On July 10, NO contributed to 14% of total extinction impairment (i.e., visibility<8km and RH≤80%) for 2 which was the largest in the two-year period. On that 2001 and 2002. day hourly ozone reached 179 ppbv at Tung Chung.

12

HK Observatory Visibility 8

4 HKO Visibility (km) HKO Visibility 0 60 ) HK air quality objective -1 50 Mong Kok NO2 Absorption 40 Tsuen Wan NO2 Absorption 30 20 Absorption (Mm 2 10 [TW NO NO 0 Tung Chung O3 [TW NO2 / HKO Bext] Ratio 12 2 80 Bext] Ratio (%) / HKO (ppbv) 3 8 40 4

Tung Chung O Chung Tung 0 0 12:00 00:00 12:00 00:00 12:00 12/31/2001 01/01/2002 01/02/2002

12 HK Observatory Visibility 8

4 HKO Visibility (km) HKO Visibility 0 60 ) HK air quality objective -1 50 Mong Kok NO2 Absorption 40 Tsuen Wan NO2 Absorption 30 20 Absorption (Mm 2 10 [TW NO NO 0

Tung Chung O 2 160 3 12 Bext] Ratio (%) / HKO

(ppbv) [TW NO / HKO Bext] Ratio

3 2 120 8 80 4 40

Tung Chung O Chung Tung 0 0 12:00 00:00 12:00 00:00 12:00 07/09/2002 07/10/2002 07/11/2002

Figure 18 Time series of visibility absorption by NO2 at Tsuen Wan and Mong Kok, O3 concentrations at Tung Chung and the NO2 contribution to light extinction at Tsuen Wan on 1 January 2002 and 10 July 2002

Page 31 3.6 Spatial distribution of PM2.5 and an (The much larger contribution of sulfate to the assessment of visibility impairment in other chemical light extinction — discussed previously — compared to its contribution to the overall PM2.5 areas of Hong Kong composition was due to the ability of sulfate to absorb moisture and grow in the humid environment of Hong In Hong Kong, visibility observations were routinely Kong.) made only at two locations: one at the international airport in the western suburban area and the other at HKO’s headquarters in urban Kowloon. As discussed Sulfate Tsuen Wan Average concentration = 34.12 µg/m3 in the previous section, the data from these two areas Nitrate 9% 27% showed that visibility in urban Hong Kong was, for a Ammonium majority of the time in 2001, better than in the western area. It is of interest to know visibility in other sectors EC 4% of Hong Kong compare to the urban and western OC 35% 9% sectors. In order to have some insights about the Others 16% spatial perspective, continuously measured (with the TEOM instrument) fine particulate matter data at three general stations in Tung Chung, Tsuen Wan and Tap Mun were analyzed, together with those from two roadside stations in Causeway Bay and Central (Figure Sulfate Mong Kok Average concentration = 58.28 µg/m3 19). Nitrate 1% 16% Ammonium 3% 40% 5% 120 EC

OC 95%

) 100

3 75% 50% Others 35% 25% 80 5% Average 60 Sulfate Hok Tsui 40 3 Nitrate Average concentration = 25.53 µg/m 19% FSP concentration (ug/m FSP concentration 20 Ammonium 37%

EC 0 Causeway Bay Central Tung Chung Tap Mun Tsuen Wan OC 25% 3% Figure 19 Statistics for continuous FSP data measured in Others 7% 9% Causeway Bay, Central Western, Tung Chung, Tap Mun and Tsuen Wan during year 2001 Figure 20 Average PM2.5 chemical composition at Tsuen Wan, Mong Kok, and Hok Tsui for samples collected from As expected, the two roadside stations in the two November 2000 to October 2001 heavily trafficked areas showed the highest FSP concentrations. However it is interesting to see that the three general stations had comparable FSP levels. For 30 example, the upwind TM had an annual mean value of 3 Mong Kok 30 µg/m ; the urban TW site only showed an 25 Tsuen Wan 3 Hok Tsui enhancement of about 20% (annual mean: 37 µg/m ), ) and the suburban TC had a value of 35 µg/m3. 3 20 15 The 12-month PM2.5 study provides additional 10 information, particularly on chemical composition Sulfate (ug/m (Figures 21-25). Comparison of data at rural Hok Tsui and urban Tsuen Wan showed an annual mean 5 3 3 concentrations of 23.5 µg/m at HT and 34.1 µg/m at 0 TW (The roadside site at Mong Kok had a much Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov higher level of 58.3 µg/m3). It can be seen that carbonaceous compounds accounted for most of the Figure 21 Time series of sulfate concentrations measured PM2.5 mass at the roadside station and a major fraction from November 2000 to October 2001 at the general urban station. At the rural site sulfate was slightly higher than organic and elemental carbon.

Page 32 12 30 2.0 Hok Tsui sulfate Sulfate ratio [TW/ HT] 10 Mong Kok 25 Tsuen Wan sulfate Ratio 1.5 Tsuen Wan ) 3 ) 3 8 Hok Tsui 20

6 15 1.0 10

4 (ug/m Sulfate Nitrate (ug/m 0.5 5 2 0 0.0 0 Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Figure 26 Time series of non-sea salt sulfate concentrations Figure 22 Time series of nitrate concentrations measured measured at Hok Tsui and Tsuen Wan and their ratio from from November 2000 to October 2001 November 2000 to October 2001

12 Examination of chemical composition revealed an Mong Kok ) interesting feature: sulfate and, to a slightly lesser 3 10 Tsuen Wan Hok Tsui extent, ammonium ions, showed remarkably similar 8 concentrations at the three stations ranging from the 6 heavily polluted MK street to the remote HT. This indicated that (NH4)2SO4 was not emitted/formed from 4 surface (low-level) sources in Hong Kong, but was Ammonium (ug/m 2 associated in air masses that were advected into urban Hong Kong. Closer examination of the ratios of 0 Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov non-sea salt sulfate concentrations (Figure 26) at Hok Tsui to those at the urban Tsuen Wan indicated some Figure 23 Time series of ammonium concentrations measured seasonal dependence of the ratios. Non-sea salt sulfate from November 2000 to October 2001 2- (NSS-sulfate) was defined as ([SO4 ]-0.257[Na]), (Millero 1996) which accounted for>97% of the total 35 Mong Kok sulfate (99% at MK, 99% at TW and 97% at HT). The ) 3 30 Tsuen Wan ratios of NSS-Sulfate at HT to that at TW was ~0.6 in Hok Tsui 25 June-July when the summer monsoon prevailed and aerosol loading was low, whereas the ratios were close 20 to unity in other seasons. The near uniform distributing 15 sulfate implied transport of sulfate from outside sources. In a later section, a detailed analysis of the 10 possible sources of the sulfate will be presented.

Elemental Carbon (ug/m Carbon Elemental 5 0 In contrast to sulfate, carbon (organic and elemental) Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov and nitrate showed a large expected rural-to-urban Figure 24 Time series of elemental carbon concentrations increase, reflecting a strong local contribution to them. measured from November 2000 to October 2001 Additional PM2.5 data from Tung Chung and Yuen Long (10 samples collected from January to 60 Mong Kok September in 2002) provided additional information ) 3 50 Tsuen Wan about the PM2.5 distribution in other sites of Hong Hok Tsui Kong (Figure 27). The mean value was 74.3 µg/m3 at 40 Yuen Long compared to 41.5 µg/m3 at Tung Chung. 30 (Note: these samples at TC and YL were analyzed by a different laboratory and a large portion of PM2.5 mass 20 was not accounted for by the measured ions, OC, and

Organic Carbon (ug/m Carbon Organic 10 EC.) Similar to the previous comparison, (NH4)2SO4 showed a smaller difference at TC and YL than the 0 Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov other species did. With the caveat that sampling was conducted in different year and the number of samples Figure 25 Time series of organic carbon concentrations was different, the PM2.5 sulfate data from the above measured from November 2000 to October 2001 four general stations suggested an increasing trend from east to west: HT

Page 33

Sulfate Tung Chung 6 3 95% Nitrate Average concentration = 41.45 µg/m 75% Average 5 50% Ammonium 27% 25% 36% 5% EC 4 )

1% 3 OC 6% Others 7% 23% 3

Nitrate (ug/m 2 Sulfate Yuen Long 3 Nitrate Average concentration = 74.33 µg/m 1 20% Ammonium 36% 2% EC 0 Tsuen Wan Mong Kok Hok Tsui Tung Chung Yuen Long 6% OC 9% Figure 29 Statistics for nitrate data obtained in Hok Tsui, Others 27% Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen Long and Tung Chung during 2002

Figure 27 Average PM2.5 chemical composition at Tung Chung and Yuen Long for samples collected from January 2002 to September 2002 12 95% Given that YL appeared to have the highest 75% 10 Average 50% concentrations of both total mass and sulfate (Figures 25% ) 28-32), it is possible that the northwestern district 3 5% experiences the poorest visibility compared to TC and 8 TW. (Ongoing measurement at YL should provide valuable data to check this speculation.) By 6 comparison, the eastern area (HT), because of the

lowest PM2.5 concentrations, should have the best (ug/m Ammonium 4 visibility. 2 The sulfate data at TW and TC (with the same caveat that they were not collected in the same year and the 0 number of samples at TC was less than that at TW) Tsuen Wan Mong Kok Hok Tsui Tung Chung Yuen Long suggested that ammonium sulfate concentrations at Figure 30 Statistics for ammonium data obtained in Hok Tsui, western TC was higher than at TW, providing some Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen Long support to our previous speculation in the interim and Tung Chung during 2002 report that the secondary aerosols were present at higher levels in the western area, which could be responsible for the observed poorer visibility at the airport. 25 95% 30 75% Average 95% 50%

75% )

3 25% Average 50% 20 25 25% 5% 5%

) 20 3 15

15 10 Sulfate (ug/m 10 Elemental Carbon (ug/m Elemental

5 5

0 Tsuen Wan Mong Kok Hok Tsui Tung Chung Yuen Long 0 Tsuen Wan Mong Kok Hok Tsui Tung Chung Yuen Long Figure 28 Statistics for sulfate data obtained in Hok Tsui, Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen Long Figure 31 Statistics for elemental carbon data obtained in and Tung Chung during 2002 Hok Tsui, Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen Long and Tung Chung during 2002

Page 34 http://vista.cira.colostate.edu/improve/Tools/win_haze. htm). The model can be used for simulation of 40 95% visibility scenes of Hong Kong in three areas with 75% Average 50% different loadings of air pollutants. Three local images

) 25% 3 (Victoria Harbor, Tung Chung, and Yuen Long) were 30 5% included into the model. Some minor modifications of terminology and the screen looks were also made by Air Resources Specialist Inc. The modified model was 20 named “HK_WinHaze”.

Organic Carbon (ug/m Organic Carbon 3.8 Case studies of visibility impaired days 10 Eight cases were analyzed to gain insight into the meteorological and chemical characteristics associated 0 Tsuen Wan Mong Kok Hok Tsui Tung Chung Yuen Long with these poor visibility events. They include five cases in 2001: January 12-13, February 27-28, March Figure 32 Statistics for organic carbon data obtained in Hok Tsui, Mong Kok, and Tsuen Wan from 2000 to 2001, and Yuen 3-11, April 11-18, September 13-19, and three events Long and Tung Chung during 2002 in 2002: January 1-7, August 28, and September 4-10 (see Figure 33). These events were selected to represent high pollution/poor visibility cases that (1) 3.7 Modified WinHaze model for occurred in different seasons, (2) had anthropogenic simulating visibility degradation due to air origin and from transport of dust particles, and (3) pollution were associated with different meteorological conditions (e.g., tropical cyclones and cold fronts). As discussed in section 3.5, the modified IMPROVE Time series of gaseous pollutants, aerosol composition, and meteorological data were used in the study of formula (with NO2 added) was selected for estimate of contribution of air pollutants to light extinction for these cases. In addition, visibility data from southern Hong Kong. The modified formula was incorporated China and satellite images were used when into the WinHaze model (Molenar et al., 1994; also see appropriate. website: 300 ) 3 250 RSP in blue FSP in red 200 150 100 50 Tap Mun RSP & FSPRSP (ug/m & Mun Tap 3000 ) Case 2 Case 3 Case 8 3 Case 1 Case 5 Case 6 Case 4 250 Case 7 200 150 100 50

Tung Chung RSP & FSP (ug/m FSP & RSP Chung Tung 0 Jan/01 Apr/01 Jul/01 Oct/01 Jan/02 Apr/02 Jul/02 Oct/02

Figure 33 Time series of RSP and FSP concentrations measured at Tung Chung and Tap Mun from January 2001 to October 2002

Page 35 Case 1: 12-13 January 2001: (Transport of PRD The visibility contour maps (Case-Fig 7) show that the regional pollution) low visibility zone was present in the northwestern PRD and was moving along a southeastward direction This is a typical case of transport of regional pollution under the strong northwesterly wind. The RSP contour initiated by a cold front. During this event, visibility at (Case-Fig 8) shows that the first plume affected only the airport dropped to 2.5 km. (Visibility at HKO the western sector in the afternoon of the 27th whereas dropped to a low level for a much shorter period of the second one impacted only the eastern side of Hong time, with an obvious time lag when compared to Kong on the 28th. Interestingly the visibility at HKA HKA.) Case-Fig 1 shows the time series of visibility remained low throughout the two periods. This case and air quality observations for January 12-13, 2003, shows the complex spatial pattern of air pollutants due including visibility recorded at HKA, HKO, and to the topography of Hong Kong. The chemical th Shenzhen, as well as air quality data recorded at the composition of PM2.5 collected on the 28 and VOC western Tung Chung station and the northeastern Tap samples collected on the 27th are shown in Case-Figs 9, Mun station. The synoptic charts (Case-Fig 2) show a 10, 11 and 12. Compared to the annual mean cold front passed over Hong Kong on January 12 composition, the episode day showed a smaller bringing strong northerly winds. The time series plots difference in aerosol concentrations among the three clearly show that the drop in HKA’s visibility was stations. More interestingly, the PM2.5 chemical coincident with the increasing air pollutants at Tung composition at all three stations seems different during Chung where RSP concentrations increased from 30 the episode. The sulfate to nitrate mass ratio on the µg/m3 to 262 µg/m3. The mass ratio of FSP to RSP 28th was about 2, compared to much lower values of kept at 0.7-0.8, indicating that the majority of particle 5-12 for the annual means at the three sites. In addition, mass was in the fine fraction. there is a larger fraction of species that are unaccounted for by summing sulfate, nitrate, To gain insight into the regional picture of visibility ammonium, OC and EC at the two urban sites. degradation and possible regional transport, 3-hourly Comparison of trace elements shows very large visibility and wind data from a number of weather enrichment of chlorine, lead, and zinc at Tsuen Wan stations in southern China were plotted in contour and Mong Kok, suggesting contribution of unusual format (Case-Fig 3). These plots reveal that the poor source(s) during this case. Comparison of some VOC visibility zone was located on the northern part of compounds (Case-Fig 12) at Tsuen Wan and Central PRD moving to the south towards Hong Kong under West, however, did not reveal obvious difference the northerly wind. In the coastal region, the wind was between episode and annual mean composition. from the east. Case 3: 3-11 March 2001 (Mix of dust and To see how pollution plumes move across Hong Kong, anthropogenic aerosols) the contour maps of RSP recorded in Hong Kong were examined (Case-Fig 4). They clearly show that only During the above period, a moderate dust episode was Tung Chung suffered from serious RSP pollution encountered under strong northeasterly winds in Hong while other areas in Hong Kong received low RSP Kong. Time series of air quality data for eastern Tap concentrations. (The time series plots for Tung Chung Mun and western Tung Chung are shown in Case-Fig also show relatively low levels of air pollutants.) This 13. The streamlines and the synoptic weather charts at case shows that regional pollution can have a larger 1000 hPa are shown in Case-Figs 14 and 17. The impact on the western part of Hong Kong, compared evidence for dust transport was a decreased mass ratio to urban and eastern areas, due to the mountain ranges of FSP to RSP (~0.4) observed on March 5, 6, and 9 in the New Territories blocking the pollutants and also owing to an increased loading of large particles. The due to the easterly wind in the eastern sector of Hong PM2.5 samples collected on March 6 showed Kong. significant enhancement in soil elements such as Fe, Si, and Al (Case-Figs 19-21). Examination of TOMS Case 2: 27-28 February 2001 (Transport of PRD satellite aerosol index (Case-Fig 18) and observations regional pollution) of ground stations in China mainland and Taiwan confirmed the presence of large scale dusts in the Similar to the previous case, poor visibility in Hong eastern portion of China and the adjacent Yellow Sea Kong was due to the transport of regional pollution and the East China Sea. The VOC composition data with a cold front coming from the northwest (Case-Fig collected on March 11 at four sites (Case-Fig 22) did 6). However it differs from the previous case in that not suggest obvious difference between the episode this regional pollution affected both western and and the annual mean, although the concentrations at eastern parts of Hong Kong, but in different period of the episode day were higher. time. The time series plots (Case-Fig 5) show a clear time lag in the drop of visibility at HKA and HKO and The arrival of this dust event, although causing an in peak concentrations of air pollutants at Tung Chung increase in RSP concentrations in Hong Kong (hourly and Tap Mun. mass concentrations reached 187 µg/m3 at Tap Mun on March 9), did not significantly impair visibility. Page 36 Visibility only dropped to 9 km in Hong Kong. This Case 5: 13-19 September 2001 (Photochemical smog) observation highlights the fact that large particles are less efficient in reducing visibility on a per mass basis. A persistent photochemical pollution episode occurred In contrast, the sharp decreases in visibility prior to in Hong Kong under the influence of a semi-stationary and after the dust episode (on March 3 and 11, typhoon, Nari, situated over the East China Sea. respectively) were due to the transport from the north Case-Figs 29 and 30 show the time series of visibility of anthropogenic aerosols associated with strong and air pollutants, and synoptic weather charts northerly winds. Hourly visibility reduced to 2.5 km respectively. and 1.2 km at the airport on March 3 and 11, respectively. The anthropogenic nature of aerosols was Previous studies have shown that the stagnation indicated by an increasing ratio of FSP to RSP and caused by the descending air mass at the outskirt of a enhancement of gaseous pollutants on March 3 and 11. low-pressure system is the dominant synoptic cause of photochemical pollution in Hong Kong, with high Three-hourly visibility data from a number of weather concentrations of oxidants like O3 and NO2. What was stations in southern China and RSP concentrations unusual for the present case was that Nari was moving from EPD air monitoring stations were plotted in very slowly resulting in the period of stagnation in contour format (Case-Figs 15 and 16). Hong Kong much longer than in the previous cases. Ozone levels at Tung Chung remained high (hourly Some interesting features can be seen from the value>90 ppbv) for 6 consecutive days (September transport pattern of dust and anthropogenic pollution. 14-19). At a rural coastal site in Tai O, PolyU On March 3, southern part of the PRD (Hong Kong researchers measured ozone levels exceeding 120 and Shenzhen) started out (e.g., see the 11 am contour) ppbv on all six days (Wang and Kwok, 2003a). The with better visual air quality than the inner area of highest hourly O3 at TC was 179 ppbv which was the PRD (Guangzhou). But as time advanced, polluted air highest that had been recorded in the EPD’s AQMS. mass in the inner PRD moved southward (with the (The highest O3 at Tai O was 191 ppbv). Particulate northerly winds) and influenced Hong Kong causing a matter (both FSP and RSP) also showed high levels drastic reduction in visibility in Hong Kong at 5 pm. during September 14-19. Fine particles dominate total Visibility contour plots also indicated southward aerosol mass with FSP/RSP of about 0.8, suggesting transport of PRD regional pollution. anthropogenic origin of the observed particles.

For the case of dust transport, the contour plots Chemical composition of aerosol samples collected on revealed a different picture. There was no indication of September 14 (Case-Figs 33-35) indicated a larger horizontal time lag in visibility change across the fraction of sulfates. Aerosol composition at MK region, perhaps suggesting that dust was transported showed increased contribution of organic carbon. primarily from high altitudes to the surface. The VOC Trace element data suggested a reduced contribution data collected on March 11 did not indicate significant from soil and sea salt. Auto tracers (Pb and Zn) and changes in chemical composition (Case-Fig 22). coal burning tracers (As and Se) also showed comparable levels during this episode. The VOC data Case 4: 11-18 April 2001 (Dust) (Case-Fig 36) indicated more abundant toluene, benzene and butane. This case is similar to the previous one in terms of meteorological and chemical characteristics The photochemical smog seriously impaired visibility (Case-Figs 23-28). The main meteorological feature in Hong Kong. The visual range recorded at HKA was the passage of a cold front on April 11 followed dropped to as low as 1.8 km on September 15. The by the strong NNE winds. The elevated RSP was regional visibility contour plots and RSP contour plots observed on April 12 with concentrations in the range (Case-Figs 31 and 32) revealed that the smog of 130-150 µg/m3 in the ground stations of Hong Kong. apparently was restricted to the southern part of the The FSP to RSP mass ratio reduced to ~0.4 on April PRD, while Guangzhou showed much better visibility. 12. TOMS Satellite data suggested dust was This provides observational evidence that visibility transported to Hong Kong from direct north. As in the and, by inference, air quality in the northern and March 9 case, the dust did not cause significant southern parts do not necessarily closely link to each reduction in visibility on April 12 (lowest 1h visibility other during a pollution episode. Wong (2000) also was 11 km). The signature of mineral dust, i.e., showed that visibility in Hong Kong was poorly enriching Si, Al, and Fe, was observed in a PM2.5 filter correlated with that of inland Guangzhou during sample collected on April 17 at Hok Tsui. Visibility visibility impaired days. contour maps also indicated southward movement of air mass to Hong Kong during the April 12 event. This case also illustrated poorer visibility in the western sector than at Victoria Harbor, which has been observed on other occasions as shown later.

Page 37 Case 6: 1-7 January 2002 (Wintertime smog pollution) 6 and 10 with visibility dropping below 2 km at HKA. The hourly concentrations of particulate matter The first week of year 2002 saw several smog measured at the Tung Chung station reached to a peak episodes on January 1, 5 and 7 that were marked by of >200 µg/m3 on these 2 days; ozone concentrations high concentrations of aerosols and gaseous pollutants exceeded 120 ppbv for 5 days (Case-Fig 48). This (Case-Fig 37). Hourly visibility dropped as low as 1.7 episode was induced by the typhoon Sinlaku over the km, 2.2 km, and 1.6 km on 1st, 5th, and 7th respectively. East China Sea (Case-Fig 49). Similar to the previous The pollution smog was caused by large-scale two photochemical smog cases, the visibility contour stagnation. Measurements at Tai O by PolyU maps showed only southern PRD suffered from poor researchers suggested mixed local and sub-regional visibility (Case-Figs 50 & 51). Case-Fig 52 shows that pollution (Wang et al., 2003b). Different from the the PM2.5 samples collected on September 4 at Yuen September case, the calm condition for this episode Long and Tung Chung indicated similar composition was caused by a slow moving anticyclone situated in major components such as sulfate, OC and EC. (The over northern China (Case-Fig 38). Satellite image trace metals were not available for these two samples.) acquired by the NASA MODIS instrument on 1/7 VOC data was shown in Case-Figs 53 & 54. (Case-Fig 41) revealed the presence of smog covering southeastern China and adjacent oceans. 3.9 Effects of synoptic weather patterns on visibility impairment in Hong Kong The visibility maps reveal interesting patterns of spatial evolution in visibility. In the morning hours, In this activity, synoptic weather patterns were poor visibility was indicated in both southern and classified into seven categories based on the northern cities in the PRD, presumably due to classification scheme used in the PATH validation accumulation of local emissions from morning traffic. study (Physick et al., 2001). Two-year (October In the later period of the day (afternoon) visibility in 2000-October 2002) weather maps were examined and the northern part of the PRD improved but the divided into the following groups: (1) Northerly southern portion (Hong Kong-Shenzhen-Macau) (winter monsoon), (2) Northeasterly (winter monsoon), continued to experience poor visibility. This (3) Easterly and Southeasterly, (4) Trough, (5) phenomenon could be related to an enhanced Southerly/southwesterly (summer monsoon), (6) dispersion in the inner PRD in the afternoon (leading Cyclonic I (close to Hong Kong,) and (7) Cyclonic II to better visibility and by inference better air quality) (distant from Hong Kong). Figures 34-40 show an and the development of sea breezes in the afternoon example for each group. The visibility data from HKO trapping pollutants in the southern part of PRD. and HKA were grouped according to the above Previous studies (e.g., Wang et al., 2001) showed that weather patterns. Figure 41 shows a comparison of the under weak synoptic conditions, winds in western result of grouping from our study and that by Physick Hong Kong often shifted to NNW which could (2001) for 1990-1995. Figures 42 and 43 show the facilitate transport of adjacent regional pollution to statistics of visibility in each of the weather patterns Hong Kong. The RSP and visibility contour maps for all RH and RH≤80%. (Case-Figs 39 and 40) for Hong Kong indicated contributions of both local and regional pollution.

Case 7: 28 August 2002 (Single-day photochemical . smog)

Similar to the September 2001 case, the poor visibility on this day was associated with the formation of photochemical smog induced by the typhoon Rusa (Case-Fig 43). Visibility dropped to 3.5 km in the morning and the ozone levels reached 188 ppbv at Tung Chung (Case-Fig 42). The visibility contour maps showed poor visibility in the PRD cities. (Case-Figs 44 & 45) Other south China cities, such as Guangzhou had good visibility in the afternoon. The VOC level showed diurnal variations. (Case-Figs 46 & 47)

Case 8: 4-10 September 2002 (Multi-day photochemical smog) Figure 34 Example of type 1: northerly (winter monsoon)

This case is similar to the multi-day photochemical smog during September 13-19 in previous year. Extremely poor visibility was observed on September Page 38

Figure 38 Example of type 5: southerly or southwesterly (summer monsoon) Figure 35 Example of type 2: northeasterly (winter monsoon)

Figure 39 Example of type 6: cyclonic I (close to Hong Kong) Figure 36 Example of type 3: easterly and southeasterly

Figure 40 Example of type 7: cyclonic II (distant from Hong Figure 37 Example of type 4: trough Kong)

Page 39

Figure 41 Comparison with the meteorology grouping by Physick (2001) Examination of the plots shows that: z The summer monsoons (Type 5) — which bring in 30 95% clean maritime air to coastal South China and are 75% 25 50% also associated with good dispersion and wet 25% 5% conditions-attributed to the best visibility in 20 summer (see Figures 42-43). 15

10 z The worst pollution — related visibility

impairments were found during winter monsoons Daily visibility (km) 5 HKO HKA (Type 1 and 2) (particularly with the passage of 0 cold fronts) and when tropical cyclones were N NE E-SE Trough S-SW C1 C2 situated in the East China Sea in the warm months (Type 7). The former synoptic process is known to Figure 42 Daily mean visibility statistics for the seven transport PRD pollution and large-scale polluted meteorological categories during year 2001 continental air to Hong Kong, whereas the latter case of stagnation allows pollutants emitted from 30 95% HK and adjacent areas to accumulate, thus the poor 75% 25 50% visibility is most likely to be due to emissions of 25% 5% sub-regional scales (i.e., southern part of the PRD). 20 15 z During the poor visibility days, the western part (i.e., airport) experienced more severe visibility 10

reduction than in the urban center (Kowloon). This Daily visibility (km) 5 HKO HKA can be attributed to (1) mountain ranges that 0 blocked regional pollution and (2) pollution is N NE E-SE Trough S-SW C1 C2 trapped in the western part due to the convergence of sea breezes. Figure 43 Daily mean visibility statistics for the seven meteorological categories during year 2001 with RH≤80% The above findings also can be obtained by examining the statistics for hours of visibility below≤8 km, as shown in Figure 44.

Page 40 The PM2.5 data obtained in the 12-month sampling project were classified into one of the above weather — related groups based on the weather chart of the day when the aerosol sample was taken. For the easterly/southeasterly and the southerly/southwesterly types, aerosol samples taken under light and variable winds and with high ozone and NO levels were excluded in order to reduce the influence of local pollution on regional air chemistry. Four cases were found in the former type and three for the latter. The statistics of major aerosol components are shown in Table 5.

It can be seen that the dominant weather patterns were Figure 44 Average number of hour of visibility≤8km per day easterly/southeasterly (16 cases). Under the E-SE flow, during year 2001 Hok Tsui was upwind of urban Tsuen Wan. Yet, sulfate showed remarkably similar concentrations at the upwind and urban sites (8.53 µg/m3 at HT versus 8.45 µg/m3 at TW). This strongly suggested that the 3.10 On the source(s) of aerosol sulfate observed sulfates were transported from outside Hong observed in Hong Kong Kong. In comparison, elemental carbon, organic carbon, and nitrate exhibited expected large It has been shown (in Figure 21) that the sulfate upwind-to-urban increasing trends, reflecting a strong concentrations was comparable in roadside Mong Kok, local contribution to these species in the urban area. urban Tsuen Wan, and upwind coastal Hok Tsui, (Elaborated discussions of regional contributions and suggesting that the sulfate aerosols in Hong Kong sources of aerosols are given in next section on were derived from sources outside Hong Kong. To back-trajectory analysis.) better understand the regional contribution and to gain insight into possible source origins, we further Table 5 reveals another interesting feature, namely, the sulfate concentrations in Hok Tsui was higher in the analyzed the PM2.5 composition data using three different approaches, including grouping the aerosol easterly flow than in the northeast monsoon group (but data according to weather patterns, back trajectories be aware that the latter type only had three samples). and examinations of individual cases. (As will be This result could imply the contributions from island shown below, a large fraction of the sulfate observed of Taiwan and from ship emissions. in Hong Kong were transported from outside sources.) Table 5 also shows that EC and OC at Tsuen Wan had 3.10.1 The sulfate concentrations under different seasonal profiles. While the OC showed a large difference in northerly winter monsoon and different weather patterns southerly summer monsoon (18.01 µg/m3 versus 7.17 µg/m3), EC had comparable levels (5.10 µg/m3 versus The aim of this analysis was to see whether and how 5.91 µg/m3). This could be due to a combination of the concentration of fine sulfate (and of other PM2.5 factors, including (a) a strong EC emission in Hong components) varied with the seven weather types Kong, (b) southerly wind transport EC from urban discussed in section 3.9. The motivation for this centre to TW (explaining a small seasonality of EC), (c) activity was that these weather types represent major regional contribution of OC in winter and (d) loss of large-scale dynamic transport patterns in South China volatile OC in summer (both contributing to a large and thus the relations between sulfate and the weather seasonal difference of OC). types could yield valuable insights into source origins of sulfate. The northerly (type 1) and northeasterly Grouping the aerosol data according to calculated back (type 2) monsoons are mainly associated with the trajectories gave similar findings, as shown later. outflow of continental pollution, whereas the easterly/southeasterly (type 3), trough (type 4), and To gain insight into the source signatures of southerly/southwesterly (type 5) groups are sulfate-laden air masses, correlations between sulfate characteristics of inflows of maritime (clean) air. Type and other individual components were examined and 6 is often accompanied with rainy and windy weather the correlation coefficients are shown in Figures 45-47 and thus is expected to have lowest levels of air below. pollutants. The type 7, which is associated with a distant low-pressure system, can lead to accumulation of local and sub-regional (Pearl River Delta) pollutants.

Page 41 3 Table 5 Average PM2.5 chemical composition (µg/m ) at Hok Tsui and Tsuen Wan in seven weather categories from November 2000 to October 2001 No. of Sulfate Nitrate Ammonium EC OC Others Total samples Type 1 Tsuen Wan 11.74 3.16 4.78 5.10 18.01 6.91 49.70 7 (Northerly winter monsoon) Hok Tsui 11.04 2.26 3.69 2.60 10.83 5.43 35.84 6

Type 2 Tsuen Wan 10.49 3.40 4.23 5.47 21.97 6.69 52.24 5 (Northeasterly winter monsoon) Hok Tsui 9.88 1.52 3.09 2.08 13.90 8.00 38.48 4

Type 3 Tsuen Wan 8.45 1.08 2.49 4.70 10.48 3.64 30.84 16 (Easterly and Southeasterly) Hok Tsui 8.53 0.67 2.04 1.52 5.15 5.93 23.84 18

Type 4 Tsuen Wan 6.63 0.55 1.64 5.72 7.23 0.00 21.65 6 (Trough) Hok Tsui 5.99 0.23 1.08 1.03 2.25 1.62 12.21 6

Type 5 Tsuen Wan 5.76 0.60 1.51 5.91 7.17 0.00 20.17 8 (Southerly or Southwesterly summer monsoon) Hok Tsui 6.78 0.18 1.36 1.29 2.27 1.68 13.56 9

Type 6 Tsuen Wan 10.97 0.46 3.20 7.59 10.00 4.90 37.13 1 (Cyclonic I) Hok Tsui 11.05 0.32 3.05 2.77 7.77 5.79 30.75 1

Type 7 Tsuen Wan 9.77 0.78 2.93 6.45 14.54 4.00 38.49 3 (Cyclonic II) Hok Tsui 10.87 0.36 2.99 2.29 9.75 4.02 27.65 3

Total Tsuen Wan 9.12 1.43 2.97 5.85 12.77 3.73 35.75 46 Average Hok Tsui 9.16 0.79 2.47 1.94 7.42 4.64 26.05 47

Page 42 U U Tl Tl Hg Hg La La Hok Tsui Au Tsuen Wan Au Ba Ba Sb Sb Sn Sn In In Cd Cd Ag Ag Pd Pd Mo Mo Zr Zr Ga Ga Cu Cu Co Co Rb Rb K bio K bio Se Se As As Ni Ni V V Y Y Sr Sr

Chemical species Chemical Br species Chemical Br Zn Zn 2.5 Pb 2.5 Pb Cr Cr

PM K PM K P P Fe Fe Mn Mn Ti Ti Ca Ca Si Si Al Al Mg Mg Cl Cl Na Na EC EC OC OC Ammonium Ammonium Nitrate Nitrate 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 2 2 CorrelationCorrelation coefficien coeffectiont (r 2()r ) Correlation coeffectioncoefficient (r(r2)) Figure 45 Correlation of sulfate with other chemical components at Tsuen Wan and Hok Tsui in the winter monsoon type

U U Tl Tl Hg Hg La La Au Tsuen Wan Au Hok Tsui Ba Ba Sb Sb Sn Sn In In Cd Cd Ag Ag Pd Pd Mo Mo Zr Zr Ga Ga Cu Cu Co Co Rb Rb K bio K bio Se Se As As Ni Ni V V Y Y Sr Sr

Chemical species Br Chemical species Br Zn Zn 2.5 Pb 2.5 Pb Cr Cr

PM K PM K P P Fe Fe Mn Mn Ti Ti Ca Ca Si Si Al Al Mg Mg Cl Cl Na Na EC EC OC OC Ammonium Ammonium Nitrate Nitrate 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 2 2 CorrelationCorrelation coefficien coeffectiont (r 2()r ) Correlation coeffectioncoefficient (r(r2)) Figure 46 Correlation of sulfate with other chemical components at Tsuen Wan and Hok Tsui in the easterly and southeasterly type

Page 43 U U Tl Tl Hg Hg La Tsuen Wan La Hok Tsui Au Au Ba Ba Sb Sb Sn Sn In In Cd Cd Ag Ag Pd Pd Mo Mo Zr Zr Ga Ga Cu Cu Co Co Rb Rb K bio K bio Se Se As As Ni Ni V V Y Y Sr Sr

Chemical species Br Chemical species Br Zn Zn 2.5 Pb 2.5 Pb Cr Cr

PM K PM K P P Fe Fe Mn Mn Ti Ti Ca Ca Si Si Al Al Mg Mg Cl Cl Na Na EC EC OC OC Ammonium Ammonium Nitrate Nitrate 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 2 2 CorrelationCorrelation coefficiencoeffectiont (r (r2) ) CorrelationCorrelation coefficien coeffectiont (r (2)r ) Figure 47 Correlation of sulfate with other chemical components at Tsuen Wan and Hok Tsui in the summer monsoon type

It can be seen that during winter monsoon period, the model (Version. 4.6) for all the days with PM2.5 filter sulfate was strongly correlated with chemical tracers measurements (i.e., November 2000-October 2001). of vehicle emissions (K, Cr, Zn, Br), oil combustion (V, Ni), coal-burning power plants (As and Se), and Back trajectory calculation: the meteorological data biomass burning (non-soil K). The urban TW site used in the HYSPLIT model was the output from a also had signature of soil. This result implies that meso-scale meteorological model, MM5v3.6 (The sulfate may be formed by a mixture of sources in Fifth-Generation NCAR/Penn State Mesoscale model direct continental outflow. During the summer version 3.6). The analyses data used in MM5 was monsoon, sulfate also showed strong correlation with 6-hourly NCEP/NCAR global re-analysis data with tracers of oil combustion indicating contribution from horizontal resolution of 2.5 degree and 17 pressure ships. Interestingly, sulfate was correlated with layers. All the simulations were run with tracers of coal burning and vehicle emissions in the non-hydrostatic dynamics, and four-dimensional data E-SE weather type. One would not expect to see assimilation (FDDA) “grid analyses nudging” was these signatures as there are no power plants and conducted during the simulations. In most of the cases, vehicles immediately upwind of Hong Kong in the the trajectory at UTC 00 o’clock (Hong Kong local time east and southeast directions. This result implies 08:00) was chosen. But for some cases with passages of transport of urban pollution from distant sources. cold front, back trajectories of different time were used.

3.10.2 Use of back trajectories to identify Removal of local influence: As the goal of this activity possible source origin was to examine sulfate and other aerosol components in air mass transported to Hong Kong, a data filter was

used to remove the influence of local and sub-regional The above examinations of aerosol data in relation to pollution. The filter included surface winds recorded by different weather types have provided clear evidence the Hong Kong Observatory at several locations in and of regional transport of sulfate from outside Hong around Hong Kong and EPD’s air-quality data. High Kong. To confirm these findings and more ozone, high NO, and light/variable winds were taken as importantly to identify possible source regions for the indications of local pollution impact. A total of 9 cases sulfates, we calculated backward air trajectories using were found. These cases and 3 additional cases with the latest version of HYSPLIT (Hybrid rainy weather were excluded in the following analysis Single-Particle Lagrangian Integrated Trajectory) using the back trajectory results.

Page 44 Air chemistry and transport patterns: The trajectories Tsui, Tsuen Wan, Mong Kok, and gaseous pollutants at were classified into four major categories according Tap Mun color-coded in different air-mass groups. The to the pathways of the trajectories. Figure 48 below gaseous pollutants provide additional chemical shows the four air-mass groups representing air mass information on air masses transported to coastal Hong transported from the continent, coastal region, coastal Kong. The average concentrations for each air-mass waters passing over Taiwan, and maritime regions. group are given in Table 6. Figure 49 shows the concentrations of PM2.5 at Hok

Figure 48 Four air mass categories from November 2000 to October 2001 (continental: red; coastal: green; coastal-Taiwan: yellow; marine: blue). The air-mass grouping was classified using ten-day back trajectories.

40 30 20 10 NOx at TM 0 6 4 2 Nitrate atHT 0 1200 800 400 CO atCO TM 0 Marine air 12 Coastal air 8 Coastal air passed Taiwan Continental air 4 SO2 atSO2 TM 0 70 60

50 40 30 20

FSP and Sulfate and FSP atHT 10 0 11-1-2000 12-1-2000 1-1-2001 2-1-2001 3-1-2001 4-1-2001 5-1-2001 6-1-2001 7-1-2001 8-1-2001 9-1-2001 10-1-2001 Date

Figure 49 Time series of PM2.5 samples collected at Hok Tsui and selected gaseous species at Tap Mun in the four air-mass categories

Page 45 Table 6 shows that the sulfate levels were high in air 40 masses transported to Hong Kong from N, NE and E 35 Rural (Hok Tsui) directions, with a mean concentration ranging from Urban (Tsuen Wan)

3 3 g/m3) 30

8.54 µg/m to 12.79 µg/m . This result was in µ agreement with the finding using weather-pattern 25 classification. The trajectory-based approach further 20 suggested that pollution emission from Taiwan may 15 have caused elevated sulfate concentrations in the coastal-Taiwan air-mass group. 10

( Composition Chemical 5 Under the influence of coastal and coastal-Taiwan air 0 masses (corresponding to wind flows from NE and E), Sulfate Nitrate Ammonium EC OC PM 2.5 Hok Tsui (and Tap Mun) was upwind of the urban areas of Hong Kong. By comparing the pollutants Figure 51 Comparison of PM2.5 between urban (Tsuen Wan) and upwind rural (Hok Tsui) stations in coastal airmass concentrations at the upwind site and the urban TW passing Taiwan station, contribution of regional transport under the NE and E flows can be estimated. As shown in the table 6, nearly 100% of sulfate observed in urban Hong Kong Individual air trajectories were color-coded according was from outside (inferred from the Hok Tsui-to-Tsuen to the concentration of PM2.5 mass, individual aerosol Wan concentration ratio of sulfate in the coastal and component measured at Hok Tsui, as well as gaseous coastal-Taiwan air-mass group). Similarly, about 82% pollutants measured at Tap Mun, as shown in Figures of ammonium (77%-87%) was from external transport. 52-60 below. The plots show that higher aerosol For other major PM2.5 components, regional transport loadings were found in air masses originating from the accounted for ~50% of organic carbon, 37% of continent and in those traveling along the coastal elemental carbon, and 62% of nitrate. For the total regions from the east, whereas reduced levels of PM2.5 mass, regional sources contributed to ~79%. aerosol were observed for trajectories coming from the These results revealed significant contribution to the south. Carbon monoxide at Tap Mun showed a similar fine particulate in Hong Kong, particularly those picture. But SO2 and NOx was elevated in some cases secondary aerosols. Figures 50-51 highlight the above in the southerly flow indicating emissions from marine results in a graphical form. vessels in the coastal waters of Hong Kong and possibly the larger South China Sea region. Similar estimates of external contribution can be made during the inflow of marine air associated with 3.10.3 Case studies of sulfate in chemically southerly and southwesterly flow. Background air aged air mass from the South China Sea contained about 87% of sulfate, 53% of ammonium, 26% of OC, 13% of EC This analysis aimed to investigate sulfate and 46% nitrate observed at the TW site. For PM2.5, concentrations in regional background air mass which background contribution was 47%. Again, background was identified using radon measurement made at Hok air had a very significant contribution to fine sulfate in Tsui in spring 2001 (February 21-April 30) when an Hong Kong. The finding of this study on regional intensive measurement study was conducted at the contribution was in contrast to the result from a local station (Wang et al., 2003c). Radon222 has been used as study (Pathak et al., 2003) stating that 40% of sulfate a tracer of air that has had recent contact to land before and ammonium in PM2.5 in Hong Kong was from sampling. Our objective was to see what the sulfate outside sources. That study assumed that sulfate level was in aged air mass with very low Rn222 concentration in marine air masses was zero. concentrations. The procedure for identifying the study 40 cases was as follows. The Rn data were first 35 Rural (Hok Tsui) transformed logarithmically. The periods with lnRn Urban (Tsuen Wan)

g/m3) 30 µ and were considered representative of chemically aged 25 air masses. Two aerosol samples were collected during 20 the identified periods. They were obtained on 24 15 March and 29 April 2001. Below we will show relevant meteorological and chemical data to prove 10 that elevated levels of sulfate were indeed present in Chemical Composition ( Composition Chemical 5 marine air mass or aged continental air transported to 0 Hong Kong from the east. Sulfate Nitrate Ammonium EC OC PM 2.5

Figure 50 Comparison of PM2.5 between urban (Tsuen Wan) and upwind rural (Hok Tsui) stations in coastal airmass Table 6 Chemical composition for four air mass types determined using back trajectories from November 2000 to October 2001

Page 46 Marine Coastal Coastal TW Continental Hok Tsui 3.49 8.54 12.79 10.94 Sulfate (µg/m3) Tsuen Wan 4.03 8.94 11.91 12.22 Hok Tsui/Tsuen Wan 0.87 0.96 1.07 0.90

Hok Tsui 1.61 6.55 5.16 12.04 OC (µg/m3) Tsuen Wan 6.14 12.67 10.45 20.89 Hok Tsui/Tsuen Wan 0.26 0.52 0.49 0.58

Hok Tsui 0.76 1.82 1.98 2.14 EC (µg/m3) Tsuen Wan 5.87 5.00 5.25 5.28 Hok Tsui/Tsuen Wan 0.13 0.36 0.38 0.41

Hok Tsui 0.51 2.17 2.98 3.40 Ammonium (µg/m3) Tsuen Wan 0.96 2.80 3.44 4.87 Hok Tsui/Tsuen Wan 0.53 0.77 0.87 0.70

Hok Tsui 0.24 0.82 0.73 1.56 Nitrate (µg/m3) Tsuen Wan 0.51 1.14 1.39 3.34 Hok Tsui/Tsuen Wan 0.46 0.72 0.52 0.47

Hok Tsui 7.76 25.18 30.62 37.23 3 PM2.5 (µg/m ) Tsuen Wan 16.61 33.96 37.08 54.48 Hok Tsui/Tsuen Wan 0.47 0.74 0.83 0.68

Tap Mun 4.97 5.19 4.18 17.36

NOx (ppbv) Tsuen Wan 63.46 65.97 68.99 76.68 Tap Mun/Tsuen Wan 0.08 0.08 0.06 0.23

Tap Mun 258 355 319 675 CO (ppbv) Tsuen Wan 744 993 721 1249 Tap Mun/Tsuen Wan 0.35 0.36 0.44 0.54

Tap Mun 2.88 3.73 2.29 5.16

SO2 (ppbv) Tsuen Wan 7.60 4.47 6.22 8.92 Tap Mun/Tsuen Wan 0.38 0.83 0.37 0.58 Hok Tsui 14 17 7 10 No. of samples Tsuen Wan 14 15 7 10

Page 47

Figure 52 Back trajectories colored with daily mean PM2.5 Figure 55 Back trajectories colored with ammonium mass concentrations (µg/m3) at Hok Tsui. concentrations (µg/m3) in Hok Tsui

Figure 53 Back trajectories colored with daily mean sulfate Figure 56 Back trajectories colored with daily mean EC concentrations (µg/m3) in Hok Tsui. The background is 1 deg. concentrations (µg/m3) in Hok Tsui by 1deg. and large-point emissions of SO2 complied by Dr. David Streets (Streets et al., 2003)

Figure 57 Back trajectories colored with daily mean OC 3 Figure 54 Back trajectories colored with nitrate concentrations (µg/m ) in Hok Tsui concentrations (µg/m3) in Hok Tsui

Page 48

Figure 58 Back trajectories colored with daily mean SO2 concentrations (ppbv) in Tap Mun. Figure 61 Synoptic weather chart on 24 March 2001

Figure 62 Synoptic weather chart on 29 April 2001

Figure 59 Back trajectories colored with NOx concentrations (ppbv) in Tap Mun.

40

30

Latitude 20

10

90 100 110 120 130 140 Longitude Figure 63 Back Trajectory on 24 March 2001

Figure 60 Back trajectories colored with CO concentrations (ppbv) in Tap Mun.

Page 49 25 25

(km) 20 HKA 20 15 HKO 15 10 10 5 5 Visibility ) 3 60 60

40 (ug/m 40 40 RSP 20 FSP 20

RSP, FSP FSP RSP, 0 0 SO 30 12 2 12 NO (ppbv) x

2 8 8 , SO x 4 4 Latitude 20 NO 0 0 600 60 CO 400

40 (ppbv) 10 (ppbv) O3 O3 20 O3 200 CO 0 0 90 100 110 120 130 140 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 Longitude 03/23/01 03/24/01 03/25/01 Tap Mun Figure 64 Back Trajectory on 29 April 2001 Figure 67 Time series of visibility and air pollutants measured at Tap Mun from 23 to 24 March 2001

-1 Wind Speed = 2.5 ms Tsuen Wan6

25 HKA 25 (km) 20 HKO 20 15 15 Sai Kung 5 10 10

Visibility Visibility 5 5 ) 3 RSP 80 80 FSP Lau Fau Shan4 (ug/m 60 60 40 40 20 20

RSP, FSP 0 0

King's Park3 12 SO2 12 NO

(ppbv) x

2 8 8 , SO HK Airport2 x 4 4 NO

0 0 400 60 Waglan Island1

300 CO 40 (ppbv) (ppbv) 200 00:00 12:00 00:00 12:00 00:00 O3 O3 O 24 March 2001 20 3 100 23 March 2001 CO 0 0 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 Figure 65 Time series of vector winds at Tsuen Wan, King’s 04/28/01 04/29/01 04/30/01 Park, Tai Mo Shan, HK Airport and Waglan Island from 23 to 24 March 2001 Figure 68 Time series of visibility and air pollutants measured at Tap Mun from 28 to 29 April 2001

-1 Wind Speed = 2.5 ms Tsuen Wan6 6000 6000

4000 Rn 4000 2000 2000 Sai Kung 5 0 0

40 NOy 40 NO

(ppbv) 30 SO 30 Lau Fau Shan4 2 2

20 20 , NO, SO , NO, y King's Park 3 10 10 NO

0 0 60 500 HK Airport2 50 400 CO (ppbv) CO 40 O3 300

(ppbv) CO Waglan Island1 3 30

O 200 20 00:00 12:00 00:00 12:00 00:00 10 100 28 April 2001 29 April 2001 0 0 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 03/23/01 03/24/01 03/25/01 Figure 66 Time series of vector winds at Tsuen Wan, King’s Park, Tai Mo Shan, HK Airport and Waglan Island from 28 to Figure 69 Time series of air pollutants measured at Hok Tsui 29 April 2001 from 23 to 24 March 2001

Page 50 3000 Rn 3000 measured at Hok Tsui, accounting for more than 50% 2000 2000 of the total mass. Despite the high NO concentrations, 1000 1000 y 0 0 aerosol nitrate was very low, suggesting little 25 NO 25 y gas-to-particle conversion from the freshly emitted 20 NO 20

(ppbv) SO 2 2 15 15 NOx. The minimal influence of the large NOy on 10 10 aerosols was also indicated in the continuously , NO, SO , NO, y

NO 5 5 measured PM2.5 mass at Tap Mun, which showed little 0 0 350 enhancement of particle mass during the period of the 60 300

CO (ppbv) CO NO peak. 250 y O 40 3 200 (ppbv) 3 CO 150 O 20 100 The concentrations of minor species for the two cases 50 are compared with the annual means to get insights 0 0 00:00 06:00 12:00 18:00 00:00 06:00 12:00 18:00 00:00 about the source characteristics of the two samples 04/28/01 04/29/01 04/30/01 (Figures 71 & 72). In these two cases, soil signature Figure 70 Time series of air pollutants measured at Hok Tsui (Si, Al, Fe, etc) was smaller compared to the annual from 28 to 29 April 2001 mean samples, but oil burning tracers (V and Ni) Observations and results: Figures 61-70 show the became enhanced in the aerosol samples, again weather chart, back trajectory, surface winds at several indicating contribution from ship emissions. locations of Hong Kong, and air-quality data measured at Hok Tsui and Tap Mun in the above two cases. The As sulfate is produced from oxidation of SO2, the 2- meteorological data indicated that Hong Kong was fraction of sulfate in total sulfur (i.e., SO2+SO4 ) can under the influence of maritime air from south and be used to indicate the degree of chemical processing southeast. Radon, CO, FSP, and ozone all showed very of air mass after recent injection of SO2. The larger the low concentrations on March 24 and April 29, when sulfate fraction, the more SO2 being converted to the aerosol samples were collected. The very low sulfate. Table 7 shows the molar ratios of sulfate to concentrations of these primary and secondary total sulfur. pollutants suggested that air mass sampled on these two days was not re-circulating urban plume. However, Table 7 Molar ratio of [sulfate] to [sulphur dioxide + sulfate] elevated levels of NOy was observed in the morning at Tsuen Wan, Mong Kok, and Hok Tsui for samples collected periods at Hok Tsui. NOy is a sum of NOx and its on 24 March 2001 and 29 April 2001 oxidation products and intermediates (i.e., NOy = NO - 2- + NO + NO + HNO , + PAN + NO + HONO + SO4 2 3 3 3 24 March 29 April HO2NO2 + alkyl nitrates +…). Because NOy is more 2- SO2 + SO4 conserved than NOx, it has been used in studying chemical transformation of reactive nitrogen, chemical Tsuen Wan 0.11 0.17 age of air masses, and emission ratios of urban plumes Mong Kok 0.19 0.23 [e.g., Wang et al., 2003c]. In these two cases, the large

NO/NOy ratios indicated fresh emissions with respect Hok Tsui 0.50 0.57 to NOy suggesting the NOy was from adjacent sources in coastal waters (possibly the marine vessels traveling It can be seen that about 50% of sulfur at Hok Tsui in the main shipping route south of Po Tai Island). was in the form of sulfate, whereas the two urban sites had much smaller fractions. This implies that the urban stations had abundant fresh SO2 and higher sulfate Table 8 shows major PM2.5 components at Hok Tsui, would be expected in downwind locations as the SO2 Tsuen Wan, and Mong Kok on March 24 and April 29 is converted to sulfate. 2001. It can be seen that sulfate had a very similar concentration at the three sites with very different To summarize, the above cases showed that when emission characteristics. In fact the upwind HK site Hong Kong received air from south and southeast and showed sulfate higher than that observed at urban TW. when tracers of urban pollution/land emission (e.g., This result again showed that the sulfate was derived CO, O3, Rn) were at minimal levels, elevated sulfate from sources transported from outside Hong Kong. (~9 µg/m3) was indeed present in such air masses. The Ammonium also showed relatively small variation enhancement of the chemical tracers from oil burning between the upwind site and the heavily polluted (V and Ni) and freshly emitted NOy was observed, roadside station, revealing that it was also largely from signaling the influence of ship emissions. However, it outside sources. In comparison, large local was difficult to determine the relative contributions to contributions to OC, EC, and nitrate were evident as the observed sulfate from ships of various distances indicated by the large upwind-roadside concentrations and from emissions in Taiwan, due to lack of gradients. information on sulfate distribution over the large South China Sea region. Specifically designed field and On these two days, sulfate dominated the PM2.5 mass modeling investigations will be needed.

Page 51 3 Table 8 PM2.5 chemical concentrations (µg/m ) at Tsuen Wan, Mong Kok, and Hok Tsui for samples collected on 24 March 2001 and 29 April 2001

24 March 2001 29 April 2001

TW MK HT TW MK HT Nitrate 0.58 0.72 0.00 0.85 0.73 0.08 Sulfate 7.59 9.18 8.74 8.87 9.08 8.99 Ammonium 2.46 2.84 1.77 2.63 2.69 1.98 OC 8.25 9.73 2.27 8.13 12.19 3.43 EC 5.59 17.05 1.30 5.99 20.09 1.95 Others 0 0 0 0 0 0.16 Total 23.21 37.34 13.83 24.83 42.78 16.58

1.4 1.4 Annual Average Annual Average 24 Mar 2001 29 Apr 2001 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2

Relative contribution percentage (%) percentage contribution Relative 0.0 (%) percentage contribution Relative 0.0 As Se Al Si Fe V Ni Pb Zn Cd Br Na Cl As Se Al Si Fe V Ni Pb Zn Cd Br Na Cl Tsuen Wan PM2.5 Trace Element Tsuen Wan PM2.5 Trace Element

1.4 Annual Average 1.4 Annual Average 24 Mar 2001 29 Apr 2001 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2

Relative contribution (%) percentage 0.0 Relative contribution (%) percentage 0.0 As Se Al Si Fe V Ni Pb Zn Cd Br Na Cl As Se Al Si Fe V Ni Pb Zn Cd Br Na Cl Mong Kok PM2.5 Trace Element Mong Kok PM2.5 Trace Element

1.4 Annual Average 1.4 Annual Average 24 Mar 2001 29 Apr 2001 1.2 1.2 1.0 1.0 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2

Relative contribution percentage (%) percentage contribution Relative 0.0 (%) percentage contribution Relative 0.0 As Se Al Si Fe V Ni Pb Zn Cd Br Na Cl As Se Al Si Fe V Ni Pb Zn Cd Br Na Cl Hok Tsui PM2.5 Trace Element Hok Tsui PM2.5 Trace Element

Figure 71 Trace elements of PM2.5 at Tsuen Wan, Mong Kok Figure 72 Trace elements of PM2.5 at Tsuen Wan, Mong Kok and Hok Tsui for samples collected on 24 March 2001 and Hok Tsui for samples collected on 29 April 2001

Page 52 Comparison with winds from N and NW: We have shown that air masses arriving from the prevailing northeast and east directions contained elevated concentrations of sulfate, confirming the importance of regional transport from those directions. It is of interest to compare the sulfate levels from these wind sectors with those from north and northwest. One may expect higher pollutants concentrations when an air mass passes over the adjacent industrialized PRD which lies north and northwest of Hong Kong.

The prevailing wind arriving at Hong Kong is from south/southwest in summer, northeast/east in the other seasons. Northerly/northwesterly winds are often observed during the passages of cold fronts/onset of winter monsoons as well as when tropical cyclones Figure 74 Synoptic weather chart on 4 July 2001 approach Hong Kong. As shown before, these weather 30

(km) HKA patterns are often associated with severe pollution and 20 HKO poor visibility. Below we show one case of cold front 10 Visibility Visibility 0 )

3 200 and one tropical cyclone event. 150 (ug/m RSP 100 FSP 50 0 Northwesterly wind — 4 July 2001: The weather chart, RSP, FSP Tap Mun (TM) in blue 120 surface winds, visibility and air quality data are shown O

Tung Chung (TC) in red 3 in Figures 73-75. This was a high-ozone day when 80 (ppbv) hourly ozone reached 130 ppbv in Tung Chung. 40 120 0 Northwesterly winds were observed at several stations 60 80 NOx SO 2

40 (ppbv) (ppbv) in Hong Kong. Sulfate again showed comparable SO 3 x 2 concentrations (13.80-14.76 µg/m ) in the roadside, NO 40 20 urban and rural stations. These values were only 0 0 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 moderately higher than the averaged concentrations in 07/04/01 07/05/01 northeasterly and easterly flow (~9 µg/m3). The Figure 75 Time series of visibility and air pollutants measured northwesterly winds and observed high SO2/NOx at Tung Chung and Tap Mun on 4 July 2001 ratios suggested pollution transported from the PRD to Hong Kong. Other cases of photochemical pollution with lighter winds were examined and the sulfate Table 9 Chemical concentrations (µg/m3) measured at Tsuen concentrations at Hok Tsui was found to be 10.35 Wan, Mong Kok and Hok Tsui on 4 July 2001 µg/m3 on 15 August 2001, 11.89 µg/m3 on 21 August 3 2001, 18.16 µg/m on 14 September 2001. 2- - + Total SO4 NO3 NH4 EC OC Others

-1 Wind Speed = 2.5 ms Tsuen Wan6 Tsuen Wan 49.71 13.80 0.68 3.83 4.89 21.05 5.46

Sai Kung 5 Mong Kok 75.06 14.74 0.84 4.32 12.35 42.95 -0.14

Hok Tsui 43.25 14.76 0.49 4.05 3.00 16.55 4.41 Lau Fau Shan4

King's Park 3 Northerly wind — 4 February 2001: On this day, northerly winds prevailed following the passage of a HK Airport2 cold front (Figures 76-78). The sulfate concentrations ranged from 10.95 to 11.96 µg/m3 in the three stations. Waglan Island1 These values were quite similar to those when winds 00:00 06:00 12:00 18:00 00:00 were from NE/E. During an extremely polluted case 04 July 2001 caused by the passage of a cold front on 28 February Figure 73 Time series of vector winds at Tsuen Wan, King’s 2001, sulfate at Hok Tsui was 18.67 µg/m3. Park, Tai Mo Shan, HK Airport and Waglan Island on 4 July Examinations of additional northerly wind cases 2001 indicated that the sulfate levels could also be low if there were rainfalls in the upwind regions. For example, sulfate dropped to 3.87 µg/m3 at Hok Tsui on 12 December 2000.

Page 53 -1 Wind Speed = 2.5 ms The above results have shown while the primary Tsuen Wan6 pollutants at the Hok Tsui site can decrease by 1-2 orders of magnitude as the wind directions change Sai Kung 5 from NW to NE and to E, sulfate only drops by about 0-50%. This highlights the significant contribution of Lau Fau Shan4 regional sulfate.

King's Park 3 The regional contribution to visibility degradation in Hong Kong was also suggested by the available trend HK Airport 2 data (Figures 79-81). Although emissions of precursor gases such as SO and NO were reduced significantly Waglan Island1 2 x in the past ten years since 1991(53% reduction for SO2 00:00 06:00 12:00 18:00 00:00 and 46% decrease for NO ), sulfate, nitrate, and 04 February 2001 x visibility impairment showed an opposite upward or Figure 76 Time series of vector winds at Tsuen Wan, King’s stable trend. The result implies that a significant Park, Tai Mo Shan, HK Airport and Waglan Island on 4 fraction of the sulfate and nitrate in Hong Kong were February 2001 ‘imported’.

Figure 79 Trend of visibility impairment vs concentrations of secondary aerosols (sulfate and nitrate) in Hong Kong Figure 77 Synoptic weather chart on 4 February 2001

8

(km) 6 HKA 4 HKO 2 Visibility Visibility

) 0 3 120 RSP (ug/m 80 FSP 40

RSP, FSP RSP, FSP 0 20 Tap Mun (TM) in blue 15 O

Tung Chung (TC) in red 3

10 (ppbv)

5

0 120 60 SO

2

80 40 (ppbv)

NO (ppbv) x x SO NO 40 2 20 Figure 80 Trend of sulfate and SO2 emission rate in Hong

0 0 Kong 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 02/04/01 02/05/01 Figure 78 Time series of visibility and air pollutants measured at Tung Chung and Tap Mun on 4 February 2001

Table 10 Chemical concentrations (µg/m3) measured at Tsuen Wan, Mong Kok and Hok Tsui on 4 February 2001

2- - + Total SO4 NO3 NH4 EC OC Others

Tsuen Wan 58.33 11.21 4.64 5.53 6.34 25.00 5.61 Mong Kok 75.02 11.96 5.05 5.78 15.18 35.19 1.87

Hok Tsui 48.33 10.95 4.31 4.96 4.56 18.01 5.54 Figure 81 Trend of nitrate and NOx emission rate in Hong Kong

Page 54 3.11 Implications for management of present (e.g., Ansari and Pandis, 1998). This would visibility impairment in Hong Kong result in an increase in the ambient levels of nitrate which would not be beneficial for visibility

improvement. The effect of reduction of SO emission This study has shown that ammonium sulfate is the 2 on ammonium nitrate (thus the total PM) is dependent most important air pollutants contributing visibility on factors, such as temperature, relative humidity, degradation in Hong Kong, followed by organic and availability of gaseous ammonia (NH ) and nitric acid elemental carbon. It is well known that almost all fine 3 (HNO ). The side effect (i.e., reducing sulfates leading particle sulfates originate from SO oxidation and are 3 2 to an increase of nitrates) tends to be significant under associated with ammonium. The major source of SO 2 low temperature, high humidity and the presence of is combustion of fossil fuels from power generations abundant NH and HNO , which favor the formation and industrial operations, which account for 93% of 3 3 of particle-phase ammonium nitrate in the the total SO emission in Hong Kong and the PRD 2 HNO (g)-NH (g)-NH NO (s) equilibrium. (CH2M Hill report, 2002). NH is generally emitted 3 3 4 3 3 from agricultural activities and to a lesser extent, from Accurate assessment of how this system operates in transportation in urban area. Organic carbon can be of Hong Kong’s environment is difficult at present owing primary and secondary origin, while the majority of to a lack of ambient data on NH and HNO . elemental carbon (or black carbon) is primary. Motor 3 3 Nevertheless, we can make some speculations. The vehicles are believed to be the dominant primary subtropical warm climate in Hong Kong is expected to source for both OC and EC in Hong Kong. favor the presence of nitrate in the gas phase. The

12-month data provided support to this contention, This study has revealed significant contribution to fine showing very low levels of nitrates during particles in Hong Kong from emissions in adjacent photochemical smog episodes. For examples, on 4 PRD cities and more distant regions. The regional July 2001, sulfate concentration at Tsuen Wan was sources located in the upwind E-NE directions of 13.8 µg/m3 compared to 0.68 µg/m3 of nitrate. Hong Kong have varying contributions to different However, the same data set also indicated a sharp aerosol components. Sulfate has been shown to be of increase of nitrate concentrations in winter, especially regional origin under the prevailing meteorological during the passage of cold fronts. For instance, on 4 conditions, while local Hong Kong’s emission has February 2001, sulfate was 11.21 µg/m3 and nitrate large impact on elemental carbon and organic carbon, was 4.64 µg/m3. Overall, reducing SO emission the latter also has significant contribution from distant 2 should not cause increase in particle nitrate during sources. While the aerosols in the regional background summer smog episodes (thus will improve visibility), are responsible for large-scale regional haze, but the net impact during winter is less clear and particularly during the autumn to winter seasons, the requires further investigations. In case reducing SO most severe visibility impairments (i.e., smoggy days) 2 results in an increase in nitrate, combined control of are primarily due to sub-regional pollution sources gaseous precursors for sulfate and nitrate (SO , NO , (i.e., the PRD and Hong Kong). It has been estimated 2 x VOC, NH ) will be needed in winter. that emission from the PRD contributes to 87% of the 3 total SO emission in the HK-PRD region (CH2M Hill 2 Although ammonium sulfate dominates light report, 2002). extinction in Hong Kong, organic and elemental

carbon also make important contribution in the urban The initial effort of visibility management program atmosphere. Visibility management program should should aim to improve visibility on the worst smoggy thus also aim to lower their atmospheric abundance. days by reducing emissions of sulfur dioxide and As mentioned previously, organic carbon can be both organic and elemental carbon in Hong Kong and in primary and secondary. The secondary OC is produced PRD cities adjacent to Hong Kong. Long-term primarily from oxidation of aromatic compounds like program will need to reduce the levels of sulfate and toluene, xylenes, and trimethylbezenes and of biogenic other aerosols in regional background air by reducing compounds like α- and β-pinene and lemonene etc. emissions of SO and other precursors gases from 2 VOC data collected in Hong Kong revealed that distance sources including marine vessels. This would toluene is the most dominant non-methane require co-operation of regional authorities in China hydrocarbon, thus reducing its emission should reduce mainland, Taiwan, and international maritime the secondary fraction of OC. Reducing emission of organizations. OC and EC will also be beneficial for meeting the PM

Air Quality Objectives given that they account for a It is important to consider the potential undesirable dominant fraction of PM mass. The visibility side effects of controlling one pollutant on the level of 2.5 management will also need to consider its implications another pollutant. Reducing emission of SO will lead 2 for photochemical ozone pollution due to the complex to a decrease in sulfate concentrations. However, the non-linear interactions between gaseous precursors decline of sulfate can leave more gaseous ammonia and secondary products. available to react with nitric acid to form particle nitrate, if sufficient ammonia and nitric acid are

Page 55 z A modified IMPROVE formula was developed and 4. Conclusions and compared with multiple linear regression method. The IMPROVE formula was recommended for Suggested Future Work assessing visibility impairment by air pollution for Hong Kong. 4.1. Conclusions z The annual mean PM2.5 concentration was 23.5 Following conclusions can be drawn based on this µg/m3 at upwind Hok Tsui and 34.1 µg/m3 at urban study. Tsuen Wan, according to the filter samples collected during November 2000-October 2001. z The frequency of visibility impairment in Hong PM2.5 mass in background air was dominated by Kong showed an increasing trend from 1991 to sulfate (accounting for 39% of the total mass) 2002. Visibility degradation in Shenzhen was followed by carbonaceous materials (24% for comparable to that observed in the western sector organic carbon and 8% for elemental carbon). of Hong Kong. Within the Hong Kong territory, Urban PM2.5 was dominated by carbonaceous visibility was poorer in the western airport than in compounds (35% for organic carbon and 20% for urban Victoria Harbor. elemental carbon), followed by sulfate (27%). Fine particle nitrate had small (3%-4%) contribution in z Visibility degradation moderately correlated both background and urban areas. among the two stations in Hong Kong and one station in Shenzhen, suggesting the influence of z Ammonium sulfate was the largest contributor to localized features, such as meteorology, pollution visibility degradation in Hong Kong, accounting emission and transport, on visibility impairment in for 51% and 33% on average of the total light each area. extinction in the upwind rural and urban areas, respectively. Organic carbon and elemental carbon z The passage of cold fronts/onset of winter contributed to 17% (in rural area)-21% (in urban monsoons and approaching tropical cyclones in area) and 12% (in rural area)-26% (in urban area), warm seasons were the main synoptic weather respectively. Ammonium nitrate had a minor patterns associated with poor visibility in Hong (4%-5%) contribution. The mean contribution of Kong. The frontal passage was the primary nitrogen dioxide was 5% in the urban center and mechanism for transporting regional pollution from was negligible (2%) in rural locations. the north, while atmospheric stagnation associated with distant tropical cyclones allowed pollution in z Available fine suspended particulate data Hong Kong and adjacent cities to accumulate. (determined by the TEOM instrument and referred to as FSP in this report) showed the lowest level of z During photochemical episodes, the temporal particles in the eastern area of Hong Kong. The evolution of poor-visibility zone in the southern annual mean PM2.5 concentration in 2001 was 30 Pearl River Delta (PRD) appeared to decouple µg/m3 at eastern Tap Mun, 37 µg/m3 in urban Tsuen from that in the northern region, implying different Wan, and 35 µg/m3 in western Tung Chung. Such a meteorological conditions in inland and coastal distribution implies that visibility in the eastern regions of the PRD under large-scale stagnation. coastal area should be better than that in the urban (Similar finding was obtained by Wong (2000), and western areas. In the western sector, northwest based on the lack of correlations of visibility Yuen Long appeared to receive higher aerosol between coastal and inland areas.) loading than in the airport and therefore may experience more severe visibility degradation than z The poorer visibility in the western area was at the airport. attributed to the following two factors:(1) transport of regional pollution from the north apparently z Regional sources had significant contributions to affected the western part of Hong Kong more than the fine particles observed in Hong Kong. Under the urban areas owing to the existence of the the prevailing easterly and northeasterly flows, mountain ranges, and (2) air pollutants, emitted background PM2.5 concentrations (measured at from both Hong Kong and regional sources, were Hok Tsui) accounted for about 79% of the total trapped in the western sector by the convergence of mass measured in urban Tsuen Wan. The regional sea breezes under weak synoptic winds. sources contributed nearly 100% of the sulfates, 51% of organic carbon, and 62% of ammonium z Transport of dust was occasionally found in spring nitrate, and 37% of elemental carbon observed in causing degraded visibility in Hong Kong. But the urban Hong Kong. Backward air trajectories worst visibility impairment was due to particulate indicated that the air masses arriving from N and matter of anthropogenic origin. NE originated from the Asian continent, passing over eastern and southern coastal regions and adjacent waters of China mainland and the island Page 56 of Taiwan. Enhanced chemical tracers from ship favor the presence of nitrate in the gas phase. exhausts were also observed in air masses from the Finally, mitigation program for visibility (and PM) east. should also consider its implication for photochemical ozone and vice versa. z Compared to transport from the northeast and east directions, higher sulfate concentrations (up to 20 3 4.2. Suggested future work µg/m ) were found under weak northwesterly winds and during atmospheric stagnation. These Additional studies are needed to improve the conditions were often associated with severe smog understanding of the scientific issues that are directly pollution which was due to sub-regional (i.e., the relevant to the formulation of a visibility-management PRD) emissions as opposed to long-range program specific for Hong Kong and the adjacent PRD transport. region. Some areas are suggested for further investigations, as outlined below. z A computer-imaging software program (HK_WinHaze) was developed for simulation of z Source attributions: More research is needed on the visibility scenes in three areas of Hong Kong (HK) source attributions of visibility impairment and on with different loadings of air pollutants. the quantitative relationship between precursor reduction and visibility response. These issues z Given ammonium sulfate is the dominant should be best addressed using corroborative visibility-degrading pollutant, visibility approaches including emission inventory and management program should aim to lower the atmospheric concentrations of ammonium sulfate ambient measurement analysis, receptor and chemical transport models. by reducing SO2 emissions. Although the air masses coming into the HK-PRD region contained high background sulfate, the most severe visibility z Multiple approaches are needed because each of impairments were due to PRD sub-regional the above tools has its strength and inherent pollution. Therefore, the initial step of mitigation limitations. For example, receptor models (e.g, strategy for the visibility impairment should chemical mass balance models) work well for consider reducing SO2 emission in the PRD. primary and non-reactive particles but cannot Long-term program need to reduce the levels of identify source categories for secondary particles sulfate and other aerosols in regional background which are shown to be the dominant contributors to by reducing emissions of SO2 and other precursor visibility impairment in Hong Kong. Chemical gases from distance sources including marine transport models, though having comprehensive vessels. This would require co-operation of descriptions of atmospheric physics and chemistry, regional authorities in China mainland and Taiwan, require detailed and up-to-date emission and international maritime organizations. inventories and atmospheric measurements for inputs and/or validation. z While ammonium sulfate observed in Hong Kong was of regional origin, organic carbon and z Regional contributions: More research is required elemental carbon — which also contributed on the relative contributions from major significantly to visibility degradation — had a continental source regions (outside HK-PRD) and large (49%-63%) contribution from local sources. ship emissions from different water zones. Reducing their emissions from sources in Hong Specifically designed field measurement in Kong will help improve the visibility situation and conjunction with chemical transport models is also benefit meeting the Air Quality Objective for particulate matter since carbonaceous materials recommended. Measurement program over the accounted for a large fraction of fine particle mass South China Sea at locations far away from Hong in Hong Kong. It was estimated that a 30% Kong would provide valuable information on the reduction of ambient concentration of organic and contribution from ship emissions and from other elemental carbon at urban Tsuen Wan will result in sources upwind. Scheduled cruise ships and a 12% improvement in visibility and a larger 16% container vessels may provide a versatile mobile decrease in total PM2.5 mass. platform for making measurements over the South China Sea. Similarly, measurement at locations z Visibility management should be formulated north of the Pearl River Delta (near Shaoguan) synergistically with control measures for other could provide useful data for assessing transport pollutants to avoid undesirable side effects. from the central region of China. Reduction of sulfate concentrations is not believed to cause an increase in fine nitrates and fine z Measurements of nitric acid and ammonium should particle mass during smog episodes in warm be made along with particulate chemical seasons and possibly in winter as well due to the composition for evaluation of the effect of sulfate sub-tropical climate of Hong Kong which should Page 57 reduction on fine particle nitrate. Chemical transport models should also be used for this purpose and for evaluating the impacts of aerosol reduction on photochemical ozone pollution and vice versa.

Page 58 Groblicki, P.J., Wolff, G.T., and Countess, R.J., (1981) References Visibility-reducing species in the Denver “brown cloud”-I. Relationship between extinction and Adams, K.M., Davis, L.I., and Japar, S.M., (1990) chemical composition, Atmospheric Environment, 15, Real-Time, in situ measurements of atmospheric 2473-2484. optical absorption in the visible via photoacoustic spectroscopy-IV, Visibility degradation and aerosol Lee, Y.L., and Sequeira, R., (2002) Water-soluble optical properties in Los Angeles, Atmospheric aerosol and visibility degradation in Hong Kong Environment, 24A, 605-610. during autumn and early winter 1998, Environmental Pollution, 116, 225-233. Ansari, A., and Pandis, S.N., (1998) On the response of atmospheric particulate matter concentrations to Lee, Y.L., and Sequeira, R., (2001) Visibility precursors concentrations, Environmental Science and degradation across Hong Kong: its components, and Technology, 32, 2706-2714. their relative contribution, Atmospheric Environmental 35, Atmospheric Environment, 35, 5861-5872. Appel, B.R., Tokiwa, Y., Hsu, J., Kothny, E.L., and Hahn, E., (1985) Visibility as related to atmospheric Louie, P.K.K., Leung, G.S.P., Yeung, B.T.W., Sin, aerosol constituents, Atmospheric Environment 9, D.W.M., Yu, J., Lau, A., Bergin, M., Zheng, M., Chow 1525. J., and Watson, J., (2002) Twelve-month particulate matter study in Hong Kong, Hong Kong CH2M Hill (China) Limited, (2002) Study of air Environmental Protection Department. quality in the Pearl River Delta Region, Environmental Protection Department HKSAR, Executive Summary, Malm, W.C., Molenar, J.V., Eldred, R.A., and Sisler, Agreement No. CE106/98. J.F., (1996) Examining the relationship among atmospheric aerosols and light scattering and Chang, W.L, and Koo, E.H., (1986) A study of extinction in the Grand Canyon Area, Journal of visibility trends in Hong Kong. Atmospheric Geophysical Research 101(D14), 19251-19265. Environment, 20, 1847-1858. Malm, W.C., Sisler, J.F., Huffman, D., Eldred, R.A., Cheng, C.M., Chan S.T., and Chan, C.C., (1997) and Cahill, T.A., (1994) Spatial and seasonal trends in Visibility trends in Hong Kong, Technical Notes, particle concentration and optical extinction in the Royal Observatory, No. 69. United States, Journal of Geophysical Research, 99(D1), 1347-1370. Chin, C.P., (1997) Visibility impairment in Hong Kong, Report No. EPD TP1/97. Millero, F.J., (1996) Chemical oceanography, CRC Press, Boca Raton. Cohen, A., (1975) Horizontal visibility and the measurement of atmospheric optical depth of Lidar. Molenar, J. V., Malm, W.C., and Johnson, C. E., (1994) Applied Optics 14, 2878-2882. Visual air quality simulation techniques, Atmospheric Environment, 28, 1055-1063. Cwalinski, R., Lansinger, J.M., and Tank, W.G., (1975) Field testing and evaluation of methods for measuring Ozkaynak, H., Schatz, A.D., Thurston, G.D., Isaacs, visibility. Final report EPA-650/2-75-039, U.S. R.G., and Husar, R.B., (1985) Relationship between Environmental Protection Agency, Office of Research aerosol extinction coefficients derived from airport and Development, Washington, DC, 20460. visual range observations and alternative measures of airborne particulate mass. Journal of the Air Pollution Dzubay, T.G., Stevens, R.K., Lewis, C.W., Hern, D.H., Control Association 35, 1176-1185. Countney, W.J., Tesch, J.W., and Mason, M.A., (1982) Visibility and aerosol in Houston, Texas, Pathak, R. P., Yao, X.H., Lau, K.H., and Chan, C.K., Environmental Science and Technology, 16, 514-525. (2003) Acidity and concentrations of ionic species of

PM2.5 in Hong Kong, Atmospheric Environment, 37, Eidels-Dubovoi, S., (2002) Aerosol impacts on visible 1113-1124. light extinction in the atmosphere of Mexico City, The Science of the Total Environmental, 287, 213-220. Physick, W.L., and Goudey, R., (2001) Estimating an annual-average RSP concentration for Hong Kong Griffing, G.W., (1980) Relations between the using days characteristic of the dominant weather prevailing visibility, nephelometer scattering patterns, Atmospheric Environment, 35, 2697-2705. coefficient and sunphotometer turbidity coefficient. Atmospheric Environment 14, 577-584.

Page 59 Sequeira, R., and Lai, K.H., (1998) The effect of Wong, S., (2000) Preliminary Assessment of Visibility meteorological parameters and aerosol constituents on for Hong Kong and other Cities in the Region, Report visibility in urban Hong Kong, Atmospheric No. EPD ITP16/00. Environment, 32, 2865-2871.

Trijonis, J.C., and Pitchford, M., (1987) Preliminary extinction budget results from the RESOLVE program edited by P.S. Bhardwaja, Air and Waste Management Association, Pittsburgh, PA.

Trijonis, J.C., McGown, M., Pitchford, M., Blumenthal, D., Robert, P., White, W., Macias, E., Weiss, R., Waggoner, A., Watson, J., Chow, J., and Flocchini, R., (1988) RESOLVE Project Final Report: Visibility Conditions and Causes of Visbility Degradation in the Mojave Desert of California, NWC TP #6869, Naval Weapons Center, China Lake, CA.

Trijonis, J.C., Malm, W.C., Pitchford, M., White, W.H., Charlson, R., and Husar, R., (1990) Visibility: Existing and historical conditions-causes and effects: State of Science and Technology Report 24, National Acid Precipitation Assessment Program, Washington, DC.

Wang, T., Wu, Y.Y., Cheung, T.F., Lam, K.S., (2001) A study of surface ozone and the relation to complex wind flow in Hong Kong, Atmospheric Environment, 35, 3203-3215.

Wang, T., and Kwok, Y.H.J., (2003a) Measurement and analysis of a multi-day photochemical smog episode in Pearl River Delta of China, Journal of Applied Meteorology, 42, 404-416.

Wang, T., Poon, C.N., Kwok, Y.H., and Li, Y.S., (2003b) Characterizing the temporal variability and emission patterns of pollution plumes in the Pearl River Delta of China, Atmospheric Environment, 37, 3539-3550.

Wang, T., Ding, A.J., Black, D.R., Zahorowski, W., Poon, C.N., and Li, Y.S., (2003c) Chemical characterization of the boundary layer outflow of air pollution to Hong Kong during February-April 2001, Journal of Geophysical Research, 108(D20), 8787.

Watson, J.G., (2002) Critical review-visibility: science and regulation, Journal of The Air and Waste Management Association, 52.

Williams, D.J., Milne, J.W., Roberts, D.B., and Jones, D.J.A., (1982) The optical properties of Sydney’s brown haze. In: Carras, Johnson (Eds.), Urban Atmosphere — Sydney, A Case Study, 125-140.

White, W.H., (1990) Contribution to light scattering, In: Acidic Deposition: State of Science and Technology Report 24, Trijonis, J., (lead author), National Acid Precipitation Assessment Program, Washington, DC, 85-102.

Page 60 35 30 HKA HKO 25 SZC (km) 20 15 10 Visibility 5 0 300 ) 3 250 RSP

(ug/m 200 FSP 150 100

RSP, FSP 50 0

0.9 0.8 0.7 0.6 FSP/RSP 0.5 Tung Chung (TC) in red 0.4 O3 100 3000 Tap Mun (TM) in blue 80 2500 CO O 3

2000 60 (ppbv)

(ppbv) 1500 40

CO 1000 20 500 0 0

60 150 NOx SO 2

40 (ppbv) 100 SO

(ppbv) 2 x

NO 50 20

0 0 1/12 1/13 1/14 1/15 Case 1

Case-Fig 1 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 12 to 14 January 2001

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Case-Fig 2 Synoptic weather charts from 12 to 13 January 2001

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Case-Fig 3 Visibility contour maps in southern China from 12 to 14 January 2001

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Case-Fig 3-b Visibility contour maps in southern China from 12 to 14 January 2001

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Case-Fig 3-c Visibility contour maps in southern China from 12 to 14 January 2001

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Case-Fig 4 RSP contour maps in Hong Kong from 12 to 13 January 2001

Page 66 30 25 HKA HKO SZC

(km) 20 15 10

Visibility 5

200

) RSP 3 150 FSP (ug/m 100

50 RSP, FSP 0 1.0 0.9 0.8 0.7

FSP/RSP 0.6 0.5 0.4 Tung Chung (TC) in red O3 2500 Tap Mun (TM) in blue 60

2000 CO O 3

1500 40 (ppbv) (ppbv) 1000 CO 20 500 0 0 60 160 NOx 50 SO 120 40 SO 2 (ppbv)

(ppbv) 2 30

x 80 20 NO 40 10 0 0 2/26 2/27 2/28 3/1 3/2 Case 2

Case-Fig 5 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 26 February to 1 March 2001

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Case-Fig 6 Synoptic weather charts from 27 to 28 February 2001

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Case-Fig 7 Visibility contour maps in southern China from 26 February to 1 March 2001

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Case-Fig 7-b Visibility contour maps in southern China from 26 February to 1 March 2001

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Case-Fig 7-c Visibility contour maps in southern China from 26 February to 1 March 2001

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Case-Fig 7-d Visibility contour maps in southern China from 26 February to 1 March 2001

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Case-Fig 8 RSP contour maps in Hong Kong from 27 to 28 February 2001

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Case-Fig 8-b RSP contour maps in Hong Kong from 27 to 28 February 2001

Page 74 Tsuen Wan annual average Sulphate Tsuen Wan, 28 Feb 2001 Sulphate 3 3 Total Concentration = 34.12 µg/m Nitrate Total Concentration = 122.04 µg/m Nitrate Ammonium Ammonium EC EC OC OC Others Others 9% 23% 19% 27%

9%

35% 4% 10% 9% 34% 5% 16%

2.0 Annual Average 28 Feb 2001 1.5

1.0

0.5

0.0 As Se Si Al Ca Mn Fe V Ni Cd Pb Br Na Cl Mg K Relative contribution percentage (%) Tsuen Wan PM2.5 Trace Element

Case-Fig 9 Comparison of chemical composition and Trace elements of PM2.5 at Tsuen Wan on 28 February 2001 with annual average

Page 75 Mong Kok annual average Sulphate Mong Kok, 28 Feb 2001 Sulphate Total Concentration = 58.28 µg/m3 Nitrate Total Concentration = 131.41 µg/m3 Nitrate Ammonium Ammonium EC EC OC OC Others Others 1% 10% 16% 19%

3% 40% 5% 8%

46% 9%

35% 8%

2.0 Annual Average 28 Feb 2001 1.5

1.0

0.5

0.0 As Se Si Al Ca Mn Fe V Ni Cd Pb Br Na Cl Mg K Relative contribution percentage (%) Mong Kok PM2.5 Trace Element

Case-Fig 10 Comparison of chemical composition and Trace elements of PM2.5 at Mong Kok on 28 February 2001 with annual average

Page 76 Hok Tsui annual average Sulphate Hok Tsui, 28 Feb 2001 Sulphate 3 Total Concentration = 23.53 µg/m3 Nitrate Total Concentration = 68.29 µg/m Nitrate Ammonium Ammonium EC EC OC OC Others Others 12% 19% 27% 37%

34% 25% 10%

3% 7% 9% 6% 11%

2.5 Annual Average 2.0 28 Feb 2001

1.5

1.0

0.5

0.0 As Se Si Al Ca Mn Fe V Ni Cd Pb Br Na Cl Mg K Relative contribution percentage (%) Hok Tsui PM2.5 Trace Element

Case-Fig 11 Comparison of chemical composition and Trace elements of PM2.5 at Hok Tsui on 28 February 2001 with annual average

Page 77 20 20 Annual average Annual average

) 27 Feb 2001 ) 27 Feb 2001 3 Central Western 3 Tsuen Wan 15 15

10 10

5 5 VOC concentration (ug/m VOC concentration (ug/m

0 0 Butane Butane Propane Toluene Propane Toluene Isoprene Isoprene Benzene Benzene o-Xylene o-Xylene m,p-Xylene m,p-Xylene Ethylbenzene Ethylbenzene Chloromethane Chloromethane Tetrachloroethene Tetrachloroethene

Case-Fig 12 Concentrations of selected VOC compounds measured at Central Western and Tsuen Wan on 27 February 2001

Page 78 30 25 HKA HKO SZC

(km) 20 15 10

Visibility 5 0 200 )

3 RSP 150

(ug/m FSP 100

50 RSP, FSP 0 1.0 Tung Chung (TC) in red 0.9 0.8 Tap Mun (TM) in blue 0.7 0.6

FSP/RSP 0.5 0.4 2000 O3 80

1500 60 CO O 3 (ppbv) 1000 40 (ppbv)

CO 500 20

0 0 40 120 NOx 30 SO 2

80 SO (ppbv)

(ppbv) 2

x 20 NO 40 10

0 0 3/2 3/3 3/4 3/5 3/6 3/7 3/8 3/9 3/10 3/11 3/12 3/13 Case 3

Case-Fig 13 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 2 to 12 March 2001

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Case-Fig 14 Synoptic weather charts from 3 to 12 March 2001

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Case-Fig 14-b Synoptic weather charts from 3 to 12 March 2001

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Case-Fig 15 Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 15-b Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 15-c Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 15-d Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 15-e Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 15-f Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 15-g Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 15-h Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 15-i Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 15-j Visibility contour maps in southern China from 3 to 12 March 2001

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Case-Fig 16 RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001

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Case-Fig 16-b RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001

Page 93

Case-Fig 16-c RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001

Page 94

Case-Fig 16-d RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001

Page 95

Case-Fig 16-e RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001

Page 96

Case-Fig 16-f RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001

Page 97

Case-Fig 16-g RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001

Page 98

Case-Fig 16-h RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001

Page 99

Case-Fig 16-i RSP contour maps in Hong Kong on 3 and from 8 to 11 March 2001

Page 100

Case-Fig 17 Mean potential height and streamlines at 1000 hPa on 3, 7, and 11 March 2001

Page 101

Case-Fig 18 TOMS aerosol index on 3, 4, 6, 7, 9, 11 and 12 March 2001

Page 102 Tsuen Wan, 6 Mar 2001 Tsuen Wan annual average Sulphate Sulphate 3 3 Total Concentration = 34.12 µg/m Nitrate Total Concentration = 37.08 µg/m Nitrate Ammonium Ammonium EC EC OC OC Others Others 9% 19% 27% 29%

6%

35% 4% 4%

9% 12%

16% 30%

6 Annual Average 06 Mar 2001 5 4 3 2 1 0 As Se Si Al Ca Mn Fe V Ni Cd Pb Br Na Cl Mg K Relative contribution percentage (%) Tsuen Wan PM2.5 Trace Element

Case-Fig 19 Comparison of chemical composition and trace elements of PM2.5 at Tsuen Wan on 6 March 2001 with annual average

Page 103 Mong Kok annual average Sulphate Mong Kok, 6 Mar 2001 Sulphate 3 Total Concentration = 58.28 µg/m 3 Nitrate Total Concentration = 68.17 µg/m Nitrate Ammonium Ammonium EC EC OC OC Others Others 1% 16% 13% 13% 4% 3% 40% 4% 5%

31%

35% 35%

3.5 3.0 Annual Average 06 Mar 2001 2.5 2.0 1.5 1.0 0.5 0.0 As Se Si Al Ca Mn Fe V Ni Cd Pb Br Na Cl Mg K Relative contribution percentage (%) Mong Kok PM2.5 Trace Element

Case-Fig 20 Comparison of chemical composition and trace elements of PM2.5 at Mong Kok on 6 March 2001 with annual average

Page 104 Hok Tsui annual average Sulphate Hok Tsui, 6 Mar 2001 Sulphate 3 Total Concentration = 23.53 µg/m 3 Nitrate Total Concentration = 38.75 µg/m Nitrate Ammonium Ammonium EC EC OC OC Others Others 19% 22% 37%

43% 5%

25% 5% 5% 3% 7% 9% 20%

6 Annual Average 5 06 Mar 2001

4

3

2

1

0 As Se Si Al Ca Mn Fe V Ni Cd Pb Br Na Cl Mg K Relative contribution percentage (%) Hok Tsui PM2.5 Trace Element

Case-Fig 21 Comparison of chemical composition and trace elements of PM2.5 at Hok Tsui on 6 March 2001 with annual average

Page 105 40 40 Annual average Annual average

) 11 March 2001 ) 11 March 2001 3 Hok Tsui 3 Mong Kok 30 30

20 20

10 10 VOC concentration (ug/m VOC concentration VOC concentration (ug/m VOC concentration

0 0 Butane Butane Propane Toluene Propane Toluene Isoprene Isoprene Benzene Benzene o-Xylene o-Xylene m,p-Xylene m,p-Xylene Ethylbenzene Ethylbenzene Chloromethane Chloromethane Tetrachloroethene Tetrachloroethene

40 40 Annual average Annual average

) 11 Mar 2001 ) 11 March 2001 3 Tsuen Wan 3 Central Western 30 30

20 20

10 10 VOC concentration (ug/m VOC concentration (ug/m VOC concentration

0 0 Butane Butane Propane Toluene Propane Toluene Isoprene Isoprene Benzene Benzene o-Xylene o-Xylene m,p-Xylene m,p-Xylene Ethylbenzene Ethylbenzene Chloromethane Chloromethane Tetrachloroethene Tetrachloroethene

Case-Fig 22 VOC concentrations measured at Tsuen Wan, Mong Kok, Central West and Hok Tsui on 11 March 2001

Page 106 25 HKO 20 HKA SZC (km)

ty 15

ibili 10 s i

V 5

0 200 RSP ) 3 150 FSP (ug/m 100

50 RSP, FSP RSP, 0 0.9 0.8 0.7 RSP / 0.6

FSP 0.5 0.4 O3 Tung Chung (TC) in red 80 CO 1200 60

Tap Mun (TM) in blue O 3 (ppbv) 800 40 (ppbv)

CO 400 20

0 0 40

120 NOx 30 SO 2

80 (ppbv) (ppbv) 20 x SO2

NO 40 10

0 0 4/11 4/12 4/13 4/14 4/15 4/16 4/17 Case 4

Case-Fig 23 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 11 to 16 April 2001

Page 107

Case-Fig 24 Synoptic weather charts from 11 to 15 April 2001

Page 108

Case-Fig 25 Visibility contour maps in southern China from 11 to 16 April 2001

Page 109

Case-Fig 25-b Visibility contour maps in southern China from 11 to 16 April 2001

Page 110

Case-Fig 25-c Visibility contour maps in southern China from 11 to 16 April 2001

Page 111

Case-Fig 25-d Visibility contour maps in southern China from 11 to 16 April 2001

Page 112

Case-Fig 25-e Visibility contour maps in southern China from 11 to 16 April 2001

Page 113

Case-Fig 25-f Visibility contour maps in southern China from 11 to 16 April 2001

Page 114

Case-Fig 26 RSP contour maps in Hong Kong on 12 and from 14 to 16 April 2001

Page 115

Case-Fig 26-b RSP contour maps in Hong Kong on 12 and from 14 to 16 April 2001

Page 116

Case-Fig 26-c RSP contour maps in Hong Kong on 12 and from 14 to 16 April 2001

Page 117

Case-Fig 26-d RSP contour maps in Hong Kong on 12 and from 14 to 16 April 2001

Page 118

Case-Fig 26-e RSP contour maps in Hong Kong on 12 and from 14 to 16 April 2001

Page 119

Case-Fig 26-f RSP contour maps in Hong Kong on 12 and from 14 to 16 April 2001

Page 120

Case-Fig 26-g RSP contour maps in Hong Kong on 12 and from 14 to 16 April 2001

Page 121

Case-Fig 26-h RSP contour maps in Hong Kong on 12 and from 14 to 16 April 2001

Page 122

Case-Fig 27 Mean potential height and streamlines at 1000 hPa on 12, 14 and 15 April 2001

Page 123

Case-Fig 28 TOMS aerosol index from 12 to 16 April 2001

Page 124 16

12 HKA HKO SZC (km) ty 8 ibili s 4 Vi

0 200 RSP ) 3

150 FSP (ug/m 100 FSP , 50 RSP 0

0.9

0.8

0.7

FSP/RSP 0.6 O3 0.5 Tung Chung (TC) in red 1200 CO Tap Mun (TM) in blue 160 1000

800 120 O 3 (ppbv) 600 (ppbv) 80

CO 400 40 200 0 NO 0 120 x 60 SO SO

2 2

80 40 (ppbv) (ppbv) x

NO 40 20

0 0 9/13 9/14 9/15 9/16 9/17 9/18 9/19 9/20 9/21 Case 5

Case-Fig 29 Time series of Visibility and air pollutants measured at Tap Mun and Tung Chung from 13 to 20 September 2001

Page 125

Case-Fig 30 Synoptic weather charts from 13 to 20 September 2001

Page 126

Case-Fig 30-b Synoptic weather charts from 13 to 20 September 2001

Page 127

Case-Fig 31 Visibility contour maps in southern China from 13 to 20 September 2001

Page 128

Case-Fig 31-b Visibility contour maps in southern China from 13 to 20 September 2001

Page 129

Case-Fig 31-c Visibility contour maps in southern China from 13 to 20 September 2001

Page 130

Case-Fig 31-d Visibility contour maps in southern China from 13 to 20 September 2001

Page 131

Case-Fig 31-e Visibility contour maps in southern China from 13 to 20 September 2001

Page 132

Case-Fig 31-f Visibility contour maps in southern China from 13 to 20 September 2001

Page 133

Case-Fig 31-g Visibility contour maps in southern China from 13 to 20 September 2001

Page 134

Case-Fig 31-h Visibility contour maps in southern China from 13 to 20 September 2001

Page 135

Case-Fig 32 RSP contour maps in Hong Kong on 15 and 17 September 2001

Page 136

Case-Fig 32-b RSP contour maps in Hong Kong on 15 and 17 September 2001

Page 137

Case-Fig 32-c RSP contour maps in Hong Kong on 15 and 17 September 2001

Page 138

Case-Fig 32-d RSP contour maps in Hong Kong on 15 and 17 September 2001

Page 139 Tsuen Wan annual average Sulphate Tsuen Wan, 14 Sep 2001 Sulphate 3 3 Total Concentration = 34.12 µg/m Nitrate Total Concentration = 68.54 µg/m Nitrate Ammonium Ammonium EC EC OC OC Others Others 9% 12% 27% 32%

35% 4% 34%

9% 2% 12% 16% 8%

Annual Average 1.5 14 Sep 2001

1.0

0.5

0.0 As Se Si Al Ca Mn Fe V Ni Cd Pb Br Na Cl Mg K Relative contribution percentage (%) Tsuen Wan PM2.5 Trace Element

Case-Fig 33 Comparison of chemical composition and trace elements of PM2.5 at Tsuen Wan on 14 September 2001 with annual average

Page 140 Mong Kok annual average Sulphate Mong Kok, 14 Sep 2001 Sulphate Total Concentration = 58.28 µg/m3 Nitrate Total Concentration = 97.67 µg/m3 Nitrate Ammonium Ammonium EC EC OC OC Others Others 1% 0% 16% 24%

3% 40% 5% 43%

3%

9%

35% 21%

1.0 Annual Average 0.8 14 Sep 2001

0.6

0.4

0.2

0.0 As Se Si Al Ca Mn Fe V Ni Cd Pb Br Na Cl Mg K Relative contribution percentage (%) Mong Kok PM2.5 Trace Element

Case-Fig 34 Comparison of chemical composition and trace elements of PM2.5 at Mong Kok on 14 September 2001 with annual average

Page 141 Hok Tsui annual average Sulphate Hok Tsui, 14 Sep 2001 Sulphate Total Concentration = 23.53 µg/m3 Nitrate Total Concentration = 38.21 µg/m3 Nitrate Ammonium Ammonium EC EC OC OC Others Others 19% 14% 37%

14% 48%

25% 11% 3% 13% 7% 9% 0%

2.0 Annual Average 14 Sep 2001 1.5

1.0

0.5

0.0 As Se Si Al Ca Mn Fe V Ni Cd Pb Br Na Cl Mg K Relative contribution percentage (%) Hok Tsui PM2.5 Trace Element

Case-Fig 35 Comparison of chemical composition and trace elements of PM2.5 at Hok Tsui on 14 September 2001 with annual average

Page 142 Annual average Annual average 40 40 ) 19 Sep 2001 ) 19 Sep 2001 3 3 Hok Tsui Mong Kok

30 30

20 20

10 10 VOC concentration (ug/m concentration VOC VOC concentration (ug/m concentration VOC

0 0 Butane Butane Propane Toluene Propane Toluene Isoprene Isoprene Benzene Benzene o-Xylene o-Xylene m,p-Xylene m,p-Xylene Ethylbenzene Ethylbenzene Chloromethane Chloromethane Tetrachloroethene Tetrachloroethene

Annual average Annual average 40 40 ) 19 Sep 2001 ) 19 Sep 2001 3 Tsuen Wan 3 Central Western

30 30

20 20

10 10 VOC concentration (ug/m concentration VOC (ug/m concentration VOC

0 0 Butane Butane Propane Toluene Propane Toluene Isoprene Isoprene Benzene Benzene o-Xylene o-Xylene m,p-Xylene m,p-Xylene Ethylbenzene Ethylbenzene Chloromethane Chloromethane Tetrachloroethene Tetrachloroethene

Case-Fig 36 VOC concentrations measured at Tsuen Wan, Mong Kok, Central West and Hok Tsui on 19 September 2001

Page 143 30 SZC 25 HKO HKA (km) 20 ty 15 ibili s 10 Vi 5 0 300 ) 3 RSP 250

(ug/m 200 FSP P

S 150

P, F 100 S

R 50 0

0.9

0.8

0.7 FSP/RSP 0.6 Tung Chung (TC) in red 0.5 O3 Tap Mun (TM) in blue 80 2000 CO 60 1500 O 3 (ppbv) 40 (ppbv) 1000 CO 500 20

0 0 250 100 200 NOx 80 SO

150 2

SO 60 (ppbv)

(ppbv) 2 x 100 40 NO 50 20

0 0 1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 Case 6

Case-Fig 37 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 1 to 8 January 2002

Page 144

Case-Fig 38 Synoptic weather charts from 1 to 7 January 2002

Page 145

Case-Fig 38-b Synoptic weather charts from 1-7 January 2002

Page 146

Case-Fig 39 Visibility contour maps in southern China from 1 to 8 January 2002

Page 147

Case-Fig 39-b Visibility contour maps in southern China from 1 to 8 January 2002

Page 148

Case-Fig 39-c Visibility contour maps in southern China from 1 to 8 January 2002

Page 149

Case-Fig 39-d Visibility contour maps in southern China from 1 to 8 January 2002

Page 150

Case-Fig 39-e Visibility contour maps in southern China from 1 to 8 January 2002

Page 151

Case-Fig 39-f Visibility contour maps in southern China from 1 to 8 January 2002

Page 152

Case-Fig 39-g Visibility contour maps in southern China from 1 to 8 January 2002

Page 153

Case-Fig 39-h Visibility contour maps in southern China from 1 to 8 January 2002

Page 154

Case-Fig 40 RSP contour maps in Hong Kong on 1, 5 and 7 January 2002

Page 155

Case-Fig 40-b RSP contour maps in Hong Kong on 1, 5 and 7 January 2002

Page 156

Case-Fig 40-c RSP contour maps in Hong Kong on 1, 5 and 7 January 2002

Page 157

Case-Fig 40-d RSP contour maps in Hong Kong on 1, 5 and 7 January 2002

Page 158

Case-Fig 40-e RSP contour maps in Hong Kong on 1, 5 and 7 January 2002

Page 159

Case-Fig 40-f RSP contour maps in Hong Kong on 1, 5 and 7 January 2002

Page 160

Case-Fig 41 Satellite image by MODIS on 7 January 2002

Page 161 25 HKO HKA 20 SZC (km)

ty 15

ibili 10 s

Vi 5 0 Tung Chung (TC) in red ) 3 RSP 150 Tap Mun (TM) in blue (ug/m 100 FSP FSP , 50 RSP 0

0.9 0.8 0.7 0.6 FSP/RSP 0.5 0.4

160 O

120 3 (ppbv)

80

40

0 100 120 NO

x 80 SO 2 80 SO 60 (ppbv) (ppbv) 2 x 40 NO 40 20

0 0 8/27 8/28 8/29 8/30 Case 7

Case-Fig 42 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 27 to 29August 2002

Page 162

Case-Fig 43 Synoptic weather charts from 28 to 29 August 2002

Page 163

Case-Fig 44 Visibility contour maps in southern China from 27 to 29 August 2002

Page 164

Case-Fig 44-b Visibility contour maps in southern China from 27 to 29 August 2002

Page 165

Case-Fig 44-c Visibility contour maps in southern China from 27 to 29 August 2002

Page 166

Case-Fig 45 RSP contour maps in Hong Kong on 28 August 2002

Page 167

Case-Fig 45-b RSP contour maps in Hong Kong on 28 August 2002

Page 168 25 25 Annual average Annual average ) ) 27 August 2002 27 August 2002 3 3 20 Tung Chung 20 Yuen Long

15 15

10 10

5 5 VOC concentration (ug/m VOC concentration VOC concentration (ug/m VOC concentration

0 0 Butane Butane Propane Toluene Propane Toluene Isoprene Isoprene Benzene Benzene o-Xylene o-Xylene m,p-Xylene m,p-Xylene Ethylbenzene Ethylbenzene Chloromethane Chloromethane Tetrachloroethene Tetrachloroethene

Case-Fig 46 VOC concentrations measured at Tung Chung and Yuen Long on 27 August 2002

Page 169 16

HKO Visibility 3.0 Xylene (pptv) 14 2.0 12 1.0 Visibility (km) 10

0.0 Ethylbenzene (pptv) 160

) 1.5 3 120 RSP 1.0 80 0.5 RSP (ug/m RSP 40

0 0.0 200 10 SO2 (ppbv)Toluene NOx 8 (ppbv) 150 2 NO 6 100 , SO 2 4 50 2

NO, NO 0 0

2.5 Benzene (ppbv)

) 80 v O3 2.0 b

pp 60

( 1.5 40 1.0 zone

O 20 0.5 0 0.0 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 08/29/2002 08/30/2002

Case-Fig 47 Diurnal time series of visibility, air pollutants and VOC measured at Central Western on 30 August 2002

Page 170 30 25 HKA HKO SZC

(km) 20 ty 15 ibili s 10 i

V 5 0 250 )

3 RSP 200 FSP (ug/m 150

100

50 RSP, FSP RSP, 0 1.0

0.8

RSP 0.6 /

FSP 0.4 0.2 Tung Chung (TC) in red 200

Tap Mun (TM) in blue 160 O 3

120 (ppbv)

80

40

0 100 NO x 60 80 SO SO 60 2 2 40 (ppbv) (ppbv) x 40

NO 20 20

0 0 9/3 9/4 9/5 9/6 9/7 9/8 9/9 9/10 9/11 Case 8

Case-Fig 48 Time series of visibility and air pollutants measured at Tap Mun and Tung Chung from 3 to 10 September 2002

Page 171

Case-Fig 49 Synoptic weather charts from 4 to 10 September 2002

Page 172

Case-Fig 49-b Synoptic weather charts from 4 to 10 September 2002

Page 173

Case-Fig 50 Visibility contour maps in southern China from 3 to 10 September 2002

Page 174

Case-Fig 50-b Visibility contour maps in southern China from 3 to 10 September 2002

Page 175

Case-Fig 50-c Visibility contour maps in southern China from 3 to 10 September 2002

Page 176

Case-Fig 50-d Visibility contour maps in southern China from 3 to 10 September 2002

Page 177

Case-Fig 508-e Visibility contour maps in southern China from 3 to 10 September 2002

Page 178

Case-Fig 50-f Visibility contour maps in southern China from 3 to 10 September 2002

Page 179

Case-Fig 50-g Visibility contour maps in southern China from 3 to 10 September 2002

Page 180

Case-Fig 50-h Visibility contour maps in southern China from 3 to 10 September 2002

Page 181

Case-Fig 51 RSP contour maps in Hong Kong on 6 and 10 September 2002

Page 182

Case-Fig 51-b RSP contour maps in Hong Kong on 6 and 10 September 2002

Page 183

Case-Fig 51-c RSP contour maps in Hong Kong on 6 and 10 September 2002

Page 184

Case-Fig 51-d RSP contour maps in Hong Kong on 6 and 10 September 2002

Page 185 Tung Chung, 04 Sep 2002 Tung Chung annual average Sulphate Sulphate 3 3 Total Concentration = 41.45 µg/m Nitrate Total Concentration = 49.41 µg/m Nitrate Ammonium Ammonium EC EC OC OC Others Others 26% 27% 31% 36%

1% 0% 5% 8% 7% 26% 9% 24%

Yuen Long annual average Sulphate Yuen Long 04 Sep 2002 Sulphate 3 3 Total Concentration = 74.33 µg/m Nitrate Total Concentration = 66.02 µg/m Nitrate Ammonium Ammonium EC EC OC OC Others Others 20% 13% 30% 35% 1%

5%

34% 10% 1%

7% 29% 15%

Case-Fig 52 Comparison of chemical composition of PM2.5 at Tung Chung and Yuen Long on 28 February 2002 with annual average

Page 186 60 60 Annual average Annual average ) ) 4 September 2002 4 September 2002 3 3 50 Tung Chung 50 Yuen Long

40 40

30 30

20 20 VOC concentration (ug/m VOC concentration VOC concentration (ug/m VOC concentration 10 10

0 0 Butane Butane Propane Toluene Propane Toluene Isoprene Isoprene Benzene Benzene o-Xylene o-Xylene m,p-Xylene m,p-Xylene Ethylbenzene Ethylbenzene Chloromethane Chloromethane Tetrachloroethene Tetrachloroethene

Case-Fig 53 VOC concentrations measured at Tung Chung and Yuen Long on 04 September 2002

Page 187 12 4 Xylene (ppbv)

10 3 2 8

Visibility (km) 1

6 0 (ppbv) Ethylbenzene 160 3.0 ) 3 120 2.0 80 RSP 1.0

RSP (ug/m RSP 40

0 0.0 200 12 (ppbv)Toluene SO2

(ppbv) 150 2 NOx NO 8 100 , SO 2 50 4

NO, NO 0 0

80 Benzene (ppbv) 0.6 60 O3 0.4

(ppbv) 40 3 O 20 0.2

0 0.0 00:00 03:00 06:00 09:00 12:00 15:00 18:00 21:00 00:00 09/05/2002 09/06/2002

Case-Fig 54 Diurnal time series of visibility, air pollutants and VOC measured at Central Western on 5 September 2002

Page 188

Appendixes

Page 189

28 Chang Sha Jiangxi Province

Ji'an Hunan Province

26 Chenzhou Ganzhou Fujian Province Lianxian Shaoguan Lianping Meixian

Latitude (deg) Guangdong Province 24 Guangxi Qingyuan Fogang Guangzhou Heyuan Shantou

Gaoyao Shenzhen Shanwei Xinyi 22 Yangjiang Hong Kong Macau Zhanjiang Shangchuan Dao

110 112 114 116 118 Longitude (deg)

Appendix I Locations of visibility monitoring stations in Southern China

Page 190

*Trace Gas Monitoring Stations: Central, Central/Western, Causeway Bay, Eastern, Kwai Chung, Kwun Tong, Mong Kok, Sha Tin, Sham Shui Po, Tai Po, Tap Mun, Tung Chung, Tsuen Wan, Yuen Long VOC Sampling Stations: Central, Hok Tsui, Mong Kok, Tsuen Wan, Yuen Long, Tung Chung PM2.5 Sampling Stations: Hok Tsui, Mong Kok, Tung Chung, Tsuen Wan, Yuen Long

Appendix II Locations where visibility and air quality data were collected

Page 191

No Parameter SO2 NOx NO NO2 CO O3 WS WD OT SR IT RSP FSP O3lig 1 Causeway Bay (CB) X X X X X X X X 2 Central (CL) X X X X X X X X 3 Central/Western (CW) X X X X X X X X X X X 4 Eastern (EN) X X X X X X X X 5 Kwai Chung (KC) X X X X X X X X X X X 6 Kwun Tong (KT) X X X X X X X X X X X 7 Mong Kok (MK) X X X X X X X X X X X 8 Sham Shui Po (SSP) X X X X X X X X X X X 9 Sha Tin (ST) X X X X X X X X X X X 10 Tung Chung (TC) X X X X X X X X X X X X X 11 Tap Mun (TM) X X X X X X X X X 12 Tai Po (TP) X X X X X X X X X X 13 Tsuen Wan (TW) X X X X X X X X X X X X X 14 Yuen Long (YL) X X X X X X X X X X

General Station: 3 - 6, 8 - 14 Roadside Station: 1, 2, 7

X The Obtained Data

Appendix III HK EPD air quality data from January 2001 to December 2002

Page 192

No Parameter Vis WDIR WSP RH Temp Cloud Sun Rad Pres Rain 1 HK Airport X X X X X 2 HK Observatory X X X X X 3 King's Park X X X X X X 4 SZ Airport X X X X 5 SZ City X X X X X 6 Guang Zhou X X X X X 7 Macau X X X X X 8 Lian Xian X X X X X 9 Shao Guan X X X X X 10 Fo Gang X X X X X 11 Lian Ping X X X X X 12 Mei Xian X X X X X 13 Gao Yao X X X X X 14 He Yuan X X X X X 15 Xin Yi X X X X X 16 Shan Wei X X X X X 17 Zhan Jian X X X X X 18 Yang Jiang X X X X X 19 Shang Chuan Dao X X X X X

X The Obtained Data

Appendix IV Visibility and meteorology data of HK and PRD cities from October 2000 to December 2002

Page 193

Air mass Air mass Date MK HT TW Date MK HT TW category category 06 Nov 2000 Coastal 05 May 2001 Local

12 Nov 2000 Rain 11 May 2001 Local

18 Nov 2000 Coastal 17 May 2001 Marine

24 Nov 2000 Coastal 23 May 2001 Coastal

30 Nov 2000 Coastal 29 May 2001 Coastal TW

06 Dec 2000 Continental 04 Jun 2001 Marine

12 Dec 2000 Rain 10 Jun 2001 Marine

18 Dec 2000 Continental 16 Jun 2001 Marine

24 Dec 2000 Continental 22 Jun 2001 Marine

30 Dec 2000 Continental 28 Jun 2001 Marine

05 Jan 2001 Coastal 04 Jul 2001 Local

11 Jan 2001 Continental 10 Jul 2001 Marine

17 Jan 2001 Coastal 16 Jul 2001 Marine

23 Jan 2001 Coastal 22 Jul 2001 Marine

29 Jan 2001 Continental 28 Jul 2001 Marine

04 Feb 2001 Coastal 03 Aug 2001 Marine

10 Feb 2001 Continental 09 Aug 2001 Marine

16 Feb 2001 Coastal 15 Aug 2001 Local

22 Feb 2001 Local 21 Aug 2001 Local

28 Feb 2001 Continental 27 Aug 2001 Coastal

06 Mar 2001 Coastal TW 02 Sep 2001 Marine

12 Mar 2001 Continental 08 Sep 2001 Rain

18 Mar 2001 Coastal TW 14 Sep 2001 Local

24 Mar 2001 Coastal TW 20 Sep 2001 Coastal

30 Mar 2001 Coastal 26 Sep 2001 Continental

05 Apr 2001 Coastal 02 Oct 2001 Coastal

11 Apr 2001 Local 08 Oct 2001 Local

17 Apr 2001 Coastal TW 14 Oct 2001 Coastal

23 Apr 2001 Coastal TW 20 Oct 2001 Coastal

29 Apr 2001 Marine 26 Oct 2001 Coastal TW

MK: Mong Kok, HT: Hok Tsui, TW: Tsuen Wan The Obtained Data

Appendix V PM2.5 sampling dates at the three air sheds for the 12-month sampling project

Page 194

TVOC Teflon filter volume (m3) 23 Fe Iron concentration QVOC Quartz filter volume (m3) 24 Co Cobalt concentration TMSGC Teflon Mass concentration (µg/m3) 25 Ni Nickel concentration QMSGC QMA Mass concentration (µg/m3) 26 Cu Copper concentration 1 Cl- Chloride concentration 27 Zn Zinc concentration - 2 NO3 Nitrate concentration 28 Ga Gallium concentration 2- 3 SO4 Sulfate concentration 29 As Arsenic concentration + 4 NH4 Ammonium concentration 30 Se Selenium concentration 5 Na+ Soluble Sodium concentration 31 Br Bromine concentration 6 K+ Soluble Potassium concentration 32 Rb Rubidium concentration 7 OC Organic Carbon concentration 33 Sr Strontium concentration 8 EC Elemental Carbon concentration 34 Y Yttrium concentration 9 TC Total Carbon concentration 35 Zr Zirconium concentration 10 Na Sodium concentration 36 Mo Molybdenum concentration 11 Mg Magnesium concentration 37 Pd Palladium concentration 12 Al Aluminum concentration 38 Ag Silver concentration 13 Si Silicon concentration 39 Cd Cadmium concentration 14 P Phosphorous concentration 40 In Indium concentration 15 S Sulfur concentration 41 Sn Tin concentration 16 Cl Chlorine concentration 42 Sb Antimony concentration 17 K Potassium concentration 43 Ba Barium concentration 18 Ca Calcium concentration 44 La Lanthanum concentration 19 Ti Titanium concentration 45 Au Gold concentration 20 V Vanadium concentration 46 Hg Mercury concentration 21 Cr Chromium concentration 47 Tl Thallium concentration 22 Mn Manganese concentration 48 Pb Lead concentration 49 U Uranium concentration

Appendix VI Chemical composition analyzed for the 12-month PM2.5 sampling project

Page 195

Date Tung Chung Yuen Long Central Western 18 Jan 02 X X 22 Jan 02 X 29 Jan 02 X X 01 Feb 02 X 05 Feb 02 X 15 Feb 02 X 22 Feb 02 X 26 Feb 02 X 06 Mar 02 X 22 Apr 02 X X 10 May 02 X 31 May 02 X 07 Jun 02 X 24 Jun 02 X X 25 Jun 02 X 27 Jun 02 X 24 Jul 02 X 28 Aug 02 X

Appendix VII Dates of photos taken at selected locations

Page 196

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Aug,2002 8 8 8 8 Sep,2002 1 8 2 1 1 1 1 1 Oct,2002 1 1 1 1 1 Nov,2002 1 1 1 1 1 Dec,2002 1 1 1 1 1 1 Jan,2003 1 1 1 1 1 Feb,2003 1 1

Appendix VIII VOC sampling dates at Central/Western in Civic Exchange Project

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Aug,2002 1 1 1 2 Sep,2002 1 1 2 1 2 1 1 1 Oct,2002 1 1 1 1 1 Nov,2002 1 1 1 1 1 Dec,2002 1 1 1 1 1 1 Jan,2003 1 1 1 1 1 Feb,2003 1 1

Appendix IX VOC sampling dates at Tap Mun in Civic Exchange Project

Page 197

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Aug,2002 1 1 1 Sep,2002 1 1 1 1 1 1 Oct,2002 1 1 1 1 1 Nov,2002 1 1 1 1 1 Dec,2002 1 1 1 1 1 1 Jan,2003 1 1 1 1 1 Feb,2003 1 1

Appendix X VOC sampling dates at Tung Chung in Civic Exchange Project

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Aug,2002 1 1 1 2 Sep,2002 1 1 2 1 2 1 1 1 Oct,2002 1 1 1 1 1 1 Nov,2002 1 1 1 1 1 Dec,2002 1 1 1 1 1 1 Jan,2003 1 1 1 1 1 Feb,2003 1 1

Appendix XI VOC sampling dates at Yuen Long in Civic Exchange Project

Page 198

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Jan,2002 1 1 Feb,2002 1 1 Mar,2002 1 Apr,2002 1 May,2002 1 Jun,2002 Jul,2002 1 Aug,2002 1 Sep,2002 1

Appendix XII VOC and PM2.5 sampling dates at Tung Chung and Yuen Long in 10-round sampling project

Page 199

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Nov,2000 1 1 1 1 1 Dec,2000 1 1 1 1 1 Jan,2001 1 1 1 1 Feb,2001 1 1 1 1 1 Mar,2001 1 1 1 1 1 Apr,2001 1 1 1 1 1 May,2001 1 1 1 1 1 Jun,2001 1 1 1 1 1 Jul,2001 1 1 1 1 1 Aug,2001 1 1 1 1 1 1 Sep,2001 1 1 1 1 1 Oct,2001 1 1 1 1 1 Nov,2001 1 1 1 1 Dec,2001 1 1 1 1 1 1 1

Appendix XIII VOC sampling dates at Central/Western in 12-month project

Page 200

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Nov,2000 1 1 1 1 1 Dec,2000 1 1 1 1 1 Jan,2001 1 1 1 1 Feb,2001 1 1 1 1 1 Mar,2001 1 1 1 1 1 Apr,2001 1 1 1 1 May,2001 1 1 1 1 1 1 Jun,2001 1 1 1 1 1 Jul,2001 1 1 1 1 1 Aug,2001 1 1 1 1 1 Sep,2001 1 1 1 1 1 Oct,2001 1 1 1 1 1 1 Nov,2001 1 1 1 1 1 Dec,2001 1 1 1 1 1

Appendix XIV VOC sampling dates at Tsuen Wan in 12-month project

Page 201

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 Nov,2000 1 1 Dec,2000 1 1 Jan,2001 1 Feb,2001 1 1 Mar,2001 1 1 Apr,2001 1 1 May,2001 1 1 Jun,2001 1 1 Jul,2001 1 1 Aug,2001 1 1 Sep,2001 1 1 Oct,2001 1 1

Appendix XV VOC sampling dates at Hok Tsui and Mong Kok in 12-month project

Page 202 1 1,1,1-Trichloroethane 49 2-Methylhexane 97 Freon 12 2 1,1,2,2-Tetrachloroethane 50 2-Methylpentane 98 Freon 22 3 1,1,2-Trichloroethane 51 3,6-Dimethyloctane 99 Heptane 4 1,1-Dichloroethane 52 3-Chlolopropene 100 Hexachlorobutadiene 5 1,1-Dichloroethene 53 3-Ethyltoluene 101 Hexane 6 1,2,3,5-Tetramethylbenzene 54 3-Methyl-1-Pentene 102 Hexylbenzene 7 1,2,3-Trimethylbenzene 55 3-Methylheptane 103 Indan 8 1,2,4,5-Tetramethylbenzene 56 3-Methylhexane 104 Iso-Butane 9 1,2,4-Trichlorobenzene 57 3-Methylpentane 105 Iso-Butylbenzene 10 1,2,4-Trimethylbenzene 58 4-Ethyltoluene 106 Isoprene 11 1,2,4-Trimethylcyclohexane 59 4-Methyl-1-Pentene 107 Iso-Propylbenzene 12 1,2-Dibromoethane 60 4-Methylheptane 108 m,p-Xylene 13 1,2-Dichloroethane 61 Benzene 109 m-/p-Chlorotoluene 14 1,2-Dichloropropane 62 Benzyl chloride 110 m-Dichlorobenzene 15 1,2-Diethylbenzene 63 Bromodichloromethane111 Methylcyclohexane 16 1,3,5-Trimethylbenzene 64 Bromoethane 112 Methylcyclopentane 17 1,3-Butadiene 65 Bromoform 113 Methylene chloride 18 1,3-Diethylbenzene 66 Bromomethane 114 Naphthalene 19 1,4-Dichlorobutane 67 Bromotrichloromethane 115 n-Butylbenzene 20 1,4-Diethylbenzene 68 Butane 116 Nonane 21 1-Butene/Iso-Butylene 69 Carbontetrachloride 117 n-Propylbenzene 22 1-Butyne 70 Chlorobenzene118 o-Chlorotoluene 23 1-Decene 71 Chloroethane 119 Octane 24 1-Heptene 72 Chloroethene 120 o-Dichlorobenzene 25 1-Methylcyclohexene 73 Chloroform 121 o-Xylene 26 1-Methylcyclopentene 74 Chloromethane 122 p-Cymene 27 1-Nonene 75 cis-1,2-Dichloroethene 123 p-Dichlorobenzene 28 1-Octene 76 cis-1,2-Dimethylcyclohexane 124 Pentane 29 1-Pentene 77 cis-1,3-Dichloropropene 125 Propane 30 1-Propyne 78 cis-2-Butene 126 Propylene 31 2,2,3-Trimethylbutane 79 cis-2-Heptene 127 Sec-Butylbenzene 32 2,2,4-Trimethylpentane 80 cis-2-Hexene 128 Styrene 33 2,2,5-Trimethylhexane 81 cis-2-Pentene 129 Tert-Butylbenzene 34 2,2-Dimethylbutane 82 cis-3-Heptene 130 Tetrachloroethene 35 2,2-Dimethylhexane 83 cis-3-Methyl-2-Pentene 131 Toluene 36 2,2-Dimethylpropane 84 cis-4-Methyl-2-Pentene 132 trans-1,2-Dichloroethene 37 2,3,4-Trimethylpentane 85 Cyclohexane 133 trans-1,2-Dimethylcyclohexane 38 2,3-Dimethylbutane 86 Cyclohexene 134 trans-1,3-Dichloropropene 39 2,3-Dimethylpentane 87 Cyclopentane 135 trans-2-Butene 40 2,4-Dimethylhexane 88 Cyclopentene 136 trans-2-Heptene 41 2,4-Dimethylpentane 89 Decane 137 trans-2-Hexene 42 2,5-Dimethylhexane 90 Dibromochloromethane138 trans-2-Pentene 43 2-Ethyl-1-Butene 91 Dibromomethane 139 trans-3-Heptene 44 2-Ethyltoluene 92 Dodecane 140 trans-3-Methyl-2-Pentene 45 2-Methyl-1-Butene 93 Ethylbenzene 141 trans-4-Methyl-2-Pentene 46 2-Methyl-2-Butene 94 Freon 11 142 Trichloroethene 47 2-Methylbutane 95 Freon 113 143 Undecane 48 2-Methylheptane 96 Freon 114

Appendix XVI Chemical species identified in VOC samples

Page 203 Objective:

To merge different types of raw air quality data files to an hourly and daily time series table. (Master.xls)

Raw data format:

EPD provided us 3 yearly folders. Each yearly folder contains 12 monthly folders. Each monthly folder contains air quality data (AQD) from 14 stations in MTS-format files. Each mts file contains all data and parameters from a particular station.

Procedures for data processing: For year 2001 data, air quality data (AQD) from EPD were saved in “Raw” folders. Open mts files with NotePad. Read the format. (Fig 1) Open mts files with Excel. Separate data into different cells. (Fig 2) Create a Visual Basic Application (VBA) programs to process the mts file into Excel file. Rearrange them into a time series versus station parameters and change the parameter units. (Fig 3) For each station, merge 12 monthly Excel files into one Excel file with 12 monthly table sheets. (Fig 4) For each station, create 2 new table sheets. Merge 12 monthly tables into a yearly table and separate them into hourly data table sheet and daily data table sheet. (Fig 5 & 6) Create a Master.xls file. Append hourly data table sheet and daily data table sheet of the 14 stations into 2 sheets in Excel (hourly data table sheet and daily data table sheet). (Fig 7 & 8)

Appendix XVII Processing raw air quality data (AQD) and creation of a master database

Page 204 Fig 1

Fig 2

Page 205 Fig 3

Fig 4

Page 206 Fig 5

Fig 6

Page 207 Fig 7

Fig 8

Page 208 Objective: To merge different meteorological data collected by different stations to a daily time series table. (Master.xls)

Raw data format: For HK data (i.e. HKA, HKO & King’s Park) EPD provided data in sp format. For PRD data (i.e. PRD cities) EPD provided 3-hourly data. HKUST, through EPD, provided data in station codes format.

Procedure: For HK data Save data from EPD to “Raw” folders. Open the sp files with NotePad and validate the data. (Fig 9) Open the sp files with Excel. Separate data into different columns. (Fig 10) Merge the sp files of different stations into a table. (Fig 11) For PRD data Open the files with NotePad and check the data. (Fig 12) Open the files with Excel. Separate data into different columns. (Fig 13) Insert data to Igor Pro.(Fig 14) Run program to synchronize the time of 3-hourly data to 1-hourly data. (Fig 15) Export the files to Excel. For Master Meteorology Data Merge HK data and PRD data into Igor Pro. Run program to synchronize the time series of different data. (Fig 16) Export to Excel and save as “Master.xls”.

Appendix XVIII Processing raw meteorology data and creation of a master database

Page 209 Fig 9

Fig 10

Page 210 Fig 11

Fig 12

Page 211 Fig 13

Fig 14

Page 212 Fig 15

Fig 16

Page 213

Objective:

To merge different PM2.5 data collected by different stations to a daily time series table. (Master.xls)

Raw data format:

EPD provided PM2.5 data named as “HKEPDreport##.xls” which ## is range from 01 to 04, total 4 Excel worksheets.

Procedure:

Open the “HKEPDreport##.xls” under PM2.5 folder and check the data format. (Fig17)

Open all “HKEPDreport##.xls” and merge them into a time series “Master.xls”. (Fig 18)

Appendix XIX Processing PM2.5 sampling master database

Page 214 Fig 17

Fig 18

Page 215 Within an easy-to-use operating environment, the HK_WinHaze for Windows software provides powerful visibility simulations for three urban area scenes in Hong Kong, namely Victoria Habour Vista, Yuen Long Vista and Tung Chung Vista.

HK_WinHaze is a 32 bit program designed to operate under Windows 98 and Windows XP only.

System Requirements: 150 MB free disk space; 800 × 600 at 16 bit color video display (1024 × 768 at 24 bit color/true color recommended); 16 MB RAM (32 MB RAM recommended).

Note: The HK_WinHaze software requires approximately 150 MB of free space on the PC's hard drive.

Startup: To load HK_WinHaze, insert the CD and run HK_WinHaze Setup.exe. All program and image files will be copied to your local hard drive. Double-click the HK_WinHaze desktop icon to activate the program.

Software Interface Features: HK_WinHaze for Windows runs in the Microsoft Windows operating environment (Screen 1.1). Applications and system functions are initiated by clicking on icons. Programs utilize standard user-friendly Windows features such as option buttons, choose lists and image scroll pointers. The mouse can be used throughout the software.

Appendix XX HK_WinHaze Model reference manual

Page 216 of 236 Generation of Visual Air Quality Images in Hong Kong

HK_WinHaze Overview HK_WinHaze for Windows is a Visual Air Quality Imaging Model which consists of two primary components: 1. The first component is to model Hong Kong Visual Air Quality by Directly Enter. 2. The second component is to Calculate bext From Aerosol Species.

Enter: extinction, visual range or deciview Step 1.1 Double-click the HK_WinHaze desktop icon to activate the program (Screen 1.1).

Screen 1.1

Step 1.2 Choose vista from the Choose Vista To Model icon (Screen 1.1). There are 3 vista to choose from, namely Victoria Harbour (Screen 1.2), Yuen Long (Screen 1.3) and Tung Chung (Screen 1.4). Only one vista can be chosen at a time.

Page 217 of 236

Screen 1.2

Page 218 of 236 Screen 1.3

Page 219 of 236

Screen 1.4

Step 1.3 On Select How to Input Extinction icon, select Directly Enter: extinction, visual range or deciview (Screen 1.4).

Step 1.4 On Screen 1.5a, 1.5b or 1.5c, click on one of the following choices: • Extinction (Default) • Visual Range • deciView as well as choosing either Increment (Default) or Percent for different model combination.

For example, upon the entry of values on the BLANK icons of the Extinction selection, New Image Total bext (Mm-1) will be generated (Screen 1.5d). This is similar when Visual Range (Screen 1.5e) or deciView (Screen 1.5f) is chosen.

Click either Accept This Data For Modeling or Return Without Using This Data.

Page 220 of 236

Screen 1.5a Screen 1.5b

Screen 1.5c Screen 1.5d

Screen 1.5e Screen 1.5f

Page 221 of 236 Step 1.5 Upon acceptance of the data, Model Parameters icon will be filled up with values. Click Model Images icon will activate the program to model Base Image and then the New Image in about 5 to 6 seconds (Screen 1.6 & 1.7a).

Screen 1.6

Step 1.6 To save modeled images for future reference, just click Save Base (Top) Image for the Base Image, or click Save New (Bottom) Image for the New Image.

Screen 1.7a Page 222 of 236

Page 223 of 236 Step 1.7 To compare modeled images (Top & Bottom), click Split Images icon to apportion the two images on screen (Screen 1.7b & 1.8).

Screen 1.7b Step 1.8 Click left mouse button on slider and move to choose vertical position where images are split. Click OK icon will return back to Screen 1.9 showing split images on both the Top and Bottom Images.

Page 224 of 236 Screen 1.8 Step 1.9 To save split images for future reference, just click Save Top Spit Image for the top split image, or click Save Bottom Spit Image for the bottom split image.

Page 225 of 236

Screen 1.9

Step 1.10 When both Model and Split Images exist, View Modeled Images icon is available for swapping between Model Images and Split Images (Screen 1.9).

Page 226 of 236 Calculate Bext from Aerosol Species Step 2.1 Double-click the HK_WinHaze desktop icon to activate the program.

Step 2.2 Choose vista from the Choose Vista To Model icon. There are 3 vista to choose from, namely Victoria Habour Vista, Yuen Long Vista and Tung Chung Vista. Only one vista can be chosen at a time (Screen 2.1).

Screen 2.1

Page 227 of 236 Step 2.3 On Select How to Input Extinction icon, select Calculate bext From Aerosol Species (Screen 2.2).

Screen 2.2

An image enter screen will prompt out for the inputs of optical parameters (Screen 2.3).

Page 228 of 236 Step 2.4 For each image, enter: 1. Species extinction efficiency if different values are to be used. It is very important that the same set of efficiency values is applied to both the Base and New Images for a valid comparison. * (initial values on Base & New Images are IMPROVE extinction efficiency) 2. Species concentrations * (initially set as zero values on Base Image, there is no difference for having the entries remained blank or filled with zero) 3. Relative humidity * (initially set to 50 % on both Images) 4. % of Organic Carbon that is hygroscopic * (initially set to 0 % on both Images)

Screen 2.3

Page 229 of 236 Step 2.5 Click Calculate Optical Parameters to generate bext, Visual Range, etc. on Calculated Parameters icon (Screen 2.4a).

Screen 2.4a

Step 2.6 Click Accept this data for modeling or re-enter different aerosol data and re-calculate (Screen 2.4b).

Page 230 of 236 Screen 2.4b

Page 231 of 236 Step 2.7 Upon acceptance of the parameters, Model Parameters on Screen 2.2 will be filled up with values. Click Model Images icon will activate the program to model Base Image and then the New Image in about 5 to 6 seconds (Screen 2.5 & 2.6).

Screen 2.5

Page 232 of 236 Screen 2.6 Step 2.8 To save modeled images for future reference, just click Save Base (Top) Image for the Base Image, or click Save New (Bottom) Image for the New Image (Screen 2.6).

Step 2.9 To compare modeled images (Top & Bottom), click Split Images icon to apportion the two images on screen (Screen 2.7 & 2.8).

Page 233 of 236

Screen 2.7

Page 234 of 236 Step 2.10 Click left mouse button on slider and move to choose vertical position where images are split. Click OK icon will turn to Screen 2.9 showing split images on both the Top and Bottom Images.

Screen 2.8

Page 235 of 236 Step 2.11 To save split images for future reference, just click Save Top Spit Image for the top split image, or click Save Bottom Spit Image for the bottom split image.

Screen 2.9

Step 2.12 When both Model and Split Images exist, View Modeled Images icon is available for swapping between Model Images and Split Images (Screen 2.16).

Page 236 of 236