A synoptic climatology of tropospheric ozone episodes in Sydney, Australia

INTERNATIONAL JOURNAL OF CLIMATOLOGY

Int. J. Climatol. 26: 1635–1649 (2006)

Published online 2 May 2006 in Wiley InterScience

(www.interscience.wiley.com) DOI: 10.1002/joc.1332

A SYNOPTIC CLIMATOLOGY OF TROPOSPHERIC OZONE EPISODES INSYDNEY, AUSTRALIA

MELISSA HART,* RICHARD DE DEAR and ROBERT HYDE

Department of Physical Geography, Macquarie University, NSW, Australia

Received 21 June 2005Revised 3 February 2006

Accepted 16 February 2006

ABSTRACT

Concentrations of tropospheric ozone often exceed Australian air quality goals in Sydney during summer. However,features in the occurrence of ozone in Sydney are yet to be fully explained. Meteorological conditions associated withozone episodes in Sydney are caused by complex interactions between synoptic and meso-scale processes. This paperdiscusses the meteorological influences behind ozone pollution episodes in Sydney.

A synoptic climatology of ozone episodes in Sydney was generated using multivariate statistical techniques, includingprincipal component analysis (PCA) and a two-stage cluster analysis, to classify days into meteorologically homogeneoussynoptic categories. Surface and upper air meteorological data for warm months (Oct–Mar) over a 10-year period wereused as input into the statistical analyses. Eleven synoptic categories were identified in Sydney during the warm seasonand ozone concentrations associated with each of the synoptic categories were investigated. One synoptic category wasfound to be associated almost exclusively with high pollution concentrations. High ozone concentrations were found tobe associated with a high-pressure system located in the middle to eastern Tasman Sea producing light northwesterlygradient winds, an afternoon sea breeze, high afternoon temperatures, a shallow mixing height at the coast and warmingaloft during the day.

Over 90% of all days exceeding current air quality goals for ozone in Sydney fell within the synoptic category associatedwith the highest ozone concentrations. It is envisaged that results from this research will be useful to Australian regulatorybodies from both a forecast point of view and for the siting of future ozone precursor sources in Sydney and surroundingregions. Copyright 2006 Royal Meteorological Society.

KEY WORDS: ozone; synoptic climatology; principal component analysis; cluster analysis; Sydney

1. INTRODUCTION

Concentrations of ozone often exceed Australian air quality goals in Sydney during summer. The meteorolog-ical conditions associated with ozone in the Sydney region and surrounds are complex, with ozone episodesgenerally occurring from October to March. Sydney is located on the eastern coast of Australia at latitude33.8 °S, and experiences a subtropical climate with warm to hot summers and cool to cold winters (Bureauof Meteorology, 1991). The main urban areas are located within a basin bound by elevated terrain to thenorth, west and south, and the Tasman Sea to the east (Figure 1). The weather in the region is affected by thisrelatively complex topography, with overnight drainage flows, and frequent sea breezes during the warmermonths. High ozone peaks, particularly in the central and western regions of Sydney, are most commonlyassociated with this afternoon sea breeze that transports precursor emissions from the central business districtand eastern suburbs across the Sydney basin (Hyde et al., 1995).

* Correspondence to: Melissa Hart, Department of Mechanical and Materials Engineering, Portland State University, OR, 97207, USA;e-mail: [email protected]

Copyright 2006 Royal Meteorological Society

1636 M. HART, R. DE DEAR AND R. HYDE

Figure 1. Location map and topography of the Sydney basin and surrounds. NSW DEC air quality monitoring stations are shown

Sydney’s airshed is relatively isolated from those of other large urban centres. Therefore boundary layerflows generally contain background concentrations of ozone. Only occasionally are ozone and its precursorstransported into Sydney from industrial and rural sources to the north and south of Sydney.

In 1998, the Australian National Environment Protection Council (NEPC) produced a National EnvironmentProtection Measure for Air Quality (the Air NEPM) (NEPC, 1998). The Air NEPM provided, for the first timein Australia, a set of national ambient air quality standards. The Air NEPM standards for ozone are 100 ppb fora 1-h average concentration and 80 ppb for a 4-h average, with the goal allowed to exceed 1 day per year. Inaddition, the New South Wales (NSW) state environmental regulatory body, the Department of Environmentand Conservation (DEC), has set long-term goals for NSW based on the World Health Organization (WHO)ozone goals of 80 ppb (1-h average) and 60 ppb (4-h average). The number of days in which the Air NEPMozone goal of 100 ppb exceeded over the 10-year period 1992–2001 is shown in Figure 2. Days in which airquality goal has been exceeded and can be reasonably attributed to bushfires within Sydney and surroundingregions are also presented.

Most previous studies of ozone formation in Sydney have been episode-based and the main features inthe occurrence of ozone are yet to be fully explained. For example, on many occasions ozone increases tomedium and high levels during the morning at locations away from the coast before the arrival of the seabreeze. However, the highest ozone concentrations in the Sydney basin are usually associated with an abruptincrease in the concentrations coinciding with the onset of the sea breeze (Hyde et al., 1995, 2000).

Two previous analyses of synoptic scale processes leading to high pollution events in Sydney are those ofLeighton and Spark (1997) and Katestone Scientific (1997). Leighton and Spark (1997) undertook a manualclassification of mean sea-level synoptic maps to determine the relationship between synoptic climatologyand pollution events in Sydney. They identified the main synoptic situations over the eastern Australianregion that lead to moderate to high pollution events in winter and summer. They also related the number ofpollution days to the time an anticyclone remained in the region. It was found that summertime ozone eventswere associated with an anticyclone centred over the northern Tasman Sea combined with a light west tonorthwesterly gradient wind.

Copyright 2006 Royal Meteorological Society Int. J. Climatol. 26: 1635–1649 (2006)DOI: 10.1002/joc

SYNOPTIC CLIMATOLOGY OF TROPOSPHERIC OZONE 1637

30

25

20

15

10

5

01992 92/93 93/94 94/95 95/96 96/97 97/98 98/99 99/00 00/01 2001

Ozone Season

Day

s

Exceedances of the ozonegoal attributed to bushfires

Days in which the ozonegoal is exceeded

Figure 2. The number of days the Australian Air NEPM 1-h ozone goal of 100 ppb was exceeded during the period 1992–2001. Daysin which the goal being exceeded can be attributable to bushfires are shown in dark grey

The study by Katestone Scientific (1997) using cluster analysis on 16 surface meteorological variablesindicated ozone conducive days for each Australian capital city. Three clusters were conducive to high ozoneconcentrations in Sydney, all associated with high maximum coastal temperatures (>28 °C), north-westerlywinds, and the presence of a sea breeze.

The investigations undertaken by Leighton and Spark (1997) and Katestone Scientific (1997) were limited todescribing the synoptic and meteorological conditions associated with high pollution days, with no discussionof their effectiveness in predicting episode days. Furthermore, neither study examined the interaction betweenthe synoptic conditions and associated meso-scale flows that are imperative for high ozone concentrations tooccur (Hyde et al., 1995).

This paper presents an objective synoptic climatology of ozone concentrations in Sydney. It has enhancedour understanding of the synoptic signatures associated with high concentrations of ozone and will assist inthe forecasting of these events. This climatology has been produced using multivariate statistical techniquesincluding principal component analysis (PCA) and cluster analysis. Previous applications of this synopticclimatological technique in air pollution studies have concentrated on surface meteorological variables asinputs to the statistical techniques. In contrast, the analysis described here uses both surface and upper airobservations for the warm months (October to March) over a 10-year period (1992–2001), and encompassesboth synoptic and meso-scale flows.

2. DATA SOURCES AND DESCRIPTION

2.1. Meteorological data

The common meteorological variables used in this type of synoptic climatological technique include: drybulb temperature, dew point temperature or relative humidity, mean sea-level pressure (MSLP), total cloudcover or global solar radiation, and u and v wind components. Some investigations have also includedvisibility (Kalkstein and Corrigan, 1986), vapour pressure (McGregor and Bamzelis, 1995), total daily solarinsolation, and morning and afternoon mixing heights (Davis et al., 1998; Eder et al., 1994). Studies by Eder



et al. (1994) and Davis et al. (1998) for Birmingham, Alabama, and Houston, Texas, respectively, have alsoincluded upper air temperature and wind parameters from 850 hPa.

To find the most appropriate suite of meteorological variables for use in the synoptic classification, aseries of experiments successively incorporated more meteorological parameters into the analysis, includingupper air measurements. Each configuration of variables was analysed using the multivariate statisticalprocedures discussed in Section 3. Ozone concentrations within each synoptic category were then analysedand configurations were compared by evaluating the maximum ozone concentrations observed in each of thesynoptic categories. The results showed that the inclusion of upper air variables was essential to achieve anunderstanding of the air mass characteristics behind ozone episodes in Sydney. Table I shows the suite ofmeteorological variables found to be the best at isolating the air mass characteristics behind ozone episodesin Sydney.

Observations taken at 6 a.m. and 3 p.m. were used to approximate diurnal minimum and maximum tempera-tures. These times also coincide with the twice-daily radiosonde ascents at Sydney Airport. Observations fromthe 850-hPa levels were considered representative of conditions above the planetary boundary layer. Mixingheight, which is important in the dispersion and dilution of pollutants, was derived graphically from upperair temperature traces as the point at which a parcel of air rising dry adiabatically crosses the environmentallapse rate (Holzworth, 1967). Midday mixing height was derived using the 6 a.m. radiosonde temperaturetrace and the 12 p.m. surface temperature, while the approximate height of the sea breeze was estimatedfrom the 3 p.m. radiosonde temperatures. The convention for the wind components is such that a positive u

component and a positive v component represent winds from the southwest quadrant.

2.2. Air quality data

Daily maximum ozone concentrations from DEC’s 18 ozone-monitoring sites across the Sydney basin wereobtained for the 10-year study period, 1992–2001. The maximum ozone concentration monitored within theSydney basin for each day was then used as input for further analyses after first removing those days in whichthe ozone levels were biased by known bushfires in the region.

Systematic records of bushfire activity in Sydney are scattered, but the most comprehensive data came fromthe NSW National Parks and Wildlife Service (NPWS) wildfire database, which covers wildfire activity from1995 to the present. Days with significant bushfire activity in the Sydney region were identified and dailymaximum observations of ozone and PM10 compared for bushfire and non-bushfire days. A simple linearregression model determined that particulate concentrations were significantly higher on days with bushfires.From this regression model known bushfire days that exceeded the NSW DEC long-term 1-h ozone goal of80 ppb ozone were found to have PM10 concentrations greater than 110 µg/m3. These thresholds, in additionto the data provided by the NPWS, were used to identify those days when Sydney air quality was probablyaffected by bushfires. Twenty-seven such days were identified between 1992 and 2001 and removed from allsubsequent analyses.

On a number of days when there were bushfires in the Sydney region, ozone and particulate concentrationswere below the criteria described above. Also, occasionally synoptic, regional and meso-scale winds mighthave transported some residual bushfire air into the Sydney Basin, but the impact on ozone concentrationswas low. Therefore, both groups of days were retained in the synoptic classification.

3. SYNOPTIC CLIMATOLOGY METHODS

An analysis of the synoptic climatology of tropospheric ozone episodes in Sydney was undertaken usingthe meteorological variables listed in Table I. Surface and upper air meteorological data for warm months(Oct–Mar) over a 10-year period (1992–2001) were used as input into the statistical analysis. The analysisinvolved multivariate statistical techniques including PCA and a two-stage cluster analysis to classify daysinto meteorologically homogeneous synoptic categories. These synoptic categories were then related to theobserved ground level ozone concentrations in Sydney. This method of synoptic classification is known



Table I. Mean meteorological conditions associated with each synoptic category

Meteorological variables Category number (number of days in category)

1(182)

2(135)

3(200)

4(151)

5(47)

6(206)

7(48)

8(116)

9(11)

10(137)

11(172)

Mean (‘within-type’ standard deviation)Temperature (°C) 19.7 13.8 21.1 16.4 15.0 16.7 19.5 19.7 18.1 19.7 18.8

(1.8) (2.4) (1.7) (2.2) (2.2) (2.5) (2.7) (2.4) (3.4) (2.0) (1.9)Dew point (°C) 17.1 8.1 18.4 11.9 8.3 13.3 13.8 16.7 7.9 17.2 15.8

(2.2) (3.3) (2.1) (2.6) (4.2) (3.0) (4.0) (2.7) (5.4) (2.2) (2.4)u wind (m/s) 0.3 2.7 0.1 −0.5 2.5 0.6 1.0 0.5 4.0 −0.1 −2.1

(2.5) (2.6) (2.2) (3.1) (3.1) (1.3) (2.5) (2.2) (3.0) (3.5) (2.5)

Surf

ace

v wind (m/s) 1.0 0.8 −1.0 3.9 −0.9 −1.2 −1.7 −1.3 −2.9 5.1 0.5(1.7) (2.0) (1.4) (2.9) (2.7) (1.1) (1.4) (1.6) (3.2) (2.4) (2.9)

6:00

AM

MSLP (hPa) 1015 1016 1012 1021 1008 1019 1007 1010 999 1012 1019(4.5) (5.8) (4.6) (4.8) (4.5) (4.1) (5.6) (4.1) (4.6) (4.6) (4.0)

Total cloud cover 4.5 2.5 3.4 5.7 3.2 2.4 3.9 6.1 5.5 6.6 6.6(oktas) (2.6) (2.4) (2.7) (2.1) (2.6) (2.2) (3.1) (2.1) (2.0) (1.7) (1.6)850 hPa temperature (°C) 12.5 5.9 18.0 5.7 7.5 11.4 15.7 14.6 10.3 12.4 9.2

(2.9) (3.0) (2.7) (2.2) (2.6) (3.3) (3.1) (2.9) (5.0) (3.5) (2.0)850 hPa dew point 6.5 −1.9 4.7 2.8 −1.1 −2.0 0.8 6.2 1.4 8.5 6.9temperature (°C) (3.8) (4.7) (6.0) (3.5) (4.4) (8.1) (6.9) (4.6) (4.5) (3.5) (3.0)

12:0

0PM U

pper

air

Mixing height (m) 1021 1794 673 1705 1872 1341 1088 763 789 733 1390(594) (1018) (392) (907) (739) (500) (760) (581) (761) (711) (750)

Temperature (°C) 24.7 20.2 29.1 19.9 24.4 25.0 32.4 24.9 22.2 21.2 22.6(1.6) (2.2) (2.3) (2.0) (3.0) (2.0) (2.8) (2.6) (3.8) (2.1) (2.1)

Dew point (°C) 16.8 9.2 18.2 11.4 3.2 13.4 7.9 16.7 5.4 16.9 15.3(2.1) (2.9) (2.5) (2.5) (4.2) (2.6) (5.8) (2.8) (4.1) (2.3) (2.5)

u wind (m/s) −4.3 −4.2 −4.8 −3.4 6.4 −5.4 5.8 −2.1 9.5 −1.8 −4.7(4.1) (5.0) (4.2) (3.4) (3.3) (2.9) (3.7) (4.8) (2.6) (2.6) (3.9)

v wind (m/s) 0.8 1.9 −4.2 4.1 0.5 −5.2 −2.8 −3.1 −1.7 6.5 −1.6(2.0) (1.9) (1.6) (2.1) (4.1) (1.6) (3.8) (3.0) (3.2) (2.2) (2.2)

Surf

ace

MSLP (hPa) 1014 1015 1009 1021 1007 1016 1003 1007 999 1013 1018(4.4) (5.4) (5.0) (4.3) (3.6) (4.5) (5.4) (4.5) (4.8) (4.2) (4.6)

Total cloud cover 3.7 2.9 2.8 5.4 3.0 2.2 3.5 6.3 6.4 6.4 6.5(oktas) (2.4) (2.2) (2.2) (2.2) (2.3) (2.1) (2.6) (2.0) (1.2) (2.0) (1.7)Inland – coastal 2.6 3.0 4.5 1.1 −0.2 3.7 −0.6 2.0 0.1 1.4 0.5temperature (°C) (2.0) (1.3) (3.0) (1.6) (1.1) (1.7) (1.2) (3.0) (1.1) (2.2) (1.7)Mixing height (m) 692 757 316 1004 2147 581 2049 360 1436 388 728

(313) (376) (274) (450) (781) (364) (908) (423) (968) (349) (435)

3:00

PM

850 hPa temperature (°C) 14.0 8.2 19.9 6.5 9.1 14.2 17.2 15.3 8.8 11.7 10.5(2.5) (2.9) (2.4) (2.0) (2.8) (2.6) (2.7) (2.6) (3.4) (3.0) (1.9)

850 hPa dew point 5.6 −2.6 5.7 1.9 −1.6 −2.2 3.2 7.1 0.7 8.1 6.7temperature (°C) (3.9) (8.1) (4.7) (4.8) (3.6) (8.7) (4.2) (3.7) (3.0) (3.2) (3.0)850 hPa u wind (m/s) 2.6 6.6 5.0 −0.1 10.1 3.3 10.4 9.4 18.7 1.4 −0.4

(3.4) (3.6) (4.2) (3.9) (2.9) (3.8) (4.1) (5.2) (5.7) (4.3) (3.6)

Upp

erai

r

850 hPa v wind (m/s) 1.1 0.9 −1.2 2.6 0.1 −0.8 −3.4 −4.2 −4.0 2.5 −1.9(4.4) (3.6) (4.0) (3.5) (3.5) (4.0) (3.5) (5.1) (8.5) (5.1) (4.8)





as the circulation-to-environment approach, in which the synoptic classification is produced first and thenrelated to the environmental variable in question (Yarnal, 1993). This technique is becoming more frequentin air pollution climatology studies, particularly in relation to ozone. For example, Cheng et al. (1992) usedthese methods to produce a synoptic climatology from surface meteorological variables to study ozone andtotal suspended particles (TSP) concentrations during summer months in Philadelphia. They evaluated theeffectiveness of their classification in predicting high pollution days by comparing daily mean pollutionconcentrations for each synoptic category along with the percentage of the top 50 polluted days falling withineach category. This approach has also been used to study ozone concentrations in Birmingham, Alabama, byEder et al. (1994), where both surface and upper air variables were used as input for their statistical analyses.They found that 33.2% of days experiencing ozone concentrations greater than 80 ppb fell within the synopticcategory associated with the highest ozone concentrations. Other examples of these statistical methods beingused to identify conditions favourable to ozone episodes include Schreiber (2003) for Lancaster Country,Pennsylvania; Smoyer et al. (2000) for Birmingham, Alabama, and Philadelphia; and Greene et al. (1999) forBirmingham, Cleveland, Philadelphia, and Seattle.

3.1. Principal component analysis

P-mode PCA was used as a data reduction technique to investigate fluctuations over time of a suite ofmeteorological variables at one point in space. The raw data were standardised by using a correlation matrixto take into account the inconsistent units of the meteorological variables (Preisendorfer and Mobley, 1988;Yarnal, 1993). Data reduction is achieved in PCA by finding linear combinations (principal components,(PC)) of the original variables, which account for as much of the total variance in the original variables aspossible. The components are ordered such that the first explains the greatest proportion of the varianceand the second accounts for as much of the residual variance as possible, while remaining orthogonal(i.e. completely unrelated) to the first in data-space. Subsequent components explain less variance than thepreceeding components. By retaining the leading PC, a large amount of the original variance can be explainedwhile reducing the size of the data matrix for subsequent analysis to as many columns as retained components.

The number of PC to be retained was decided using two methods: graphically with a scree test and byexamining a scree plot of eigennumber versus eigenvalue for a major break in the plot (Cattell, 1966), andmore simply, retaining PC with an eigenvector value greater than 1.0 (Preisendorfer and Mobley, 1988;Yarnal, 1993). Both methods suggested retaining five PC, which explained 73.2% of the variance in theoriginal matrix of meteorological variables described earlier as Suite 3.

Each PC comprises a series of loadings that represent the correlation between the principal componentand the original meteorological parameter. A larger component loading indicates greater importance of themeteorological parameter in interpreting that principal component. Component scores, values for each day’sweather observations based on the PC, ascertain the relationships between the original meteorological variablesfor each day and the PC. Thus, days with similar meteorological conditions will exhibit similar principalcomponent scores (Shahgedanova et al., 1998).

3.2. Cluster analysis

To generate synoptic categories, cluster analysis was applied to the principal component scores for each day.Clustering procedures group days with similar component scores and assign each day into a meteorologicallyhomogeneous subset of the data (Greene et al., 1999; Kalkstein and Corrigan, 1986; Shahgedanova et al.,1998). The component scores matrix (number of days times the number of retained PCs) obtained fromPCA served as the input matrix for cluster analysis. The reduced size and the absence of co-linearity in thecomponent scores matrix make it ideal data for clustering (Davis et al., 1998).

A two-stage clustering technique was used. First, average-linkage (a hierarchical and agglomerative method)determined the number of clusters and the mean conditions within each cluster. Then these initial clusterswere used as input in to k-means clustering, a non-hierarchical iterative method. This two-stage approachis considered superior to a one stage approach, i.e. using average-linkage alone (Eder et al., 1994). In thefirst stage, hierarchical clustering techniques calculate the Euclidean distance between all daily component



scores. The two closest component scores are then merged to form a new cluster, distances are recalculated,and this process is repeated until all component scores are merged into one final cluster (Everitt and Der,1996). Pseudo-F and pseudo-t2 statistics were used to find the optimal number of clusters that minimisedwithin cluster variance and maximised between cluster separations, thus indicating when to stop the clusteringprocedure. The pseudo-F statistic is the ratio of between cluster and within cluster variance, where peaks inthe pseudo-F statistic are indicators of greatest cluster separation (Eder et al., 1994). The pseudo-t2 statisticis the ratio of the sum of square errors within two clusters and the sum of square errors within one cluster,quantifying the difference between two clusters merged at each step. To determine the optimum number ofclusters to preserve, a large increase in pseudo-F statistics should be observed followed by a large increase inpseudo-t2 statistic as the number of clusters decreases (Everitt and Der, 1996). These statistical tests showedthat the optimal number of clusters for this climatology is 11.

In the second stage of the clustering technique, cluster seed values, the mean component score for eachcluster, were calculated for each of the 11 clusters and then used as input to k-mean clustering. The non-hierarchical iterative technique of k-means clustering refines the cluster solution by reclassifying days afterthey have been placed in a cluster. However, the method when used in collaboration with a hierarchicaltechnique such as average-linkage requires initial cluster number and cluster seed values. The resultingclustering solutions grouped days with similar meteorological characteristics. Mean meteorological conditionswithin each cluster and the associated synoptic weather charts were then examined. As clusters exhibit adistinctive air mass and synoptic signature, and as weather conditions determine the dispersion or accumulationof air pollutants, we would expect each cluster to show a characteristic regime of pollution concentrationsowing to the critical impact of weather conditions on the dispersion or accumulation of air pollutants (Chenget al., 1992).

4. SYNOPTIC CLIMATOLOGY RESULTS AND DISCUSSION

The mean ozone concentrations associated with each of the 11 synoptic categories identified in Section 3.2are presented in Table II, along with the percentage of the total number of very high days, defined as thoseexceeding the current air quality goal (days greater than 100 ppb), experienced during the study period fallinginto each category. The number of days in which the daily maximum 1-h average ozone concentration fellinto the following classes are illustrated in Figure 3:

• Low pollution days – days less than 60 ppb• Medium pollution days – days between 60 and 80 ppb• High pollution days – days between 80 and 100 ppb (80 ppb is the NSW DEC’s long-term goal for 1-h

average ozone concentrations)• Very high days – days that exceed the current Australian Air NEPM for 1-h average ozone of 100 ppb.

Table II indicates that Synoptic Category 3 was associated with higher ozone concentrations than the othercategories, with a mean daily maximum 1-h average ozone concentration of 86 ppb. Furthermore 91.2% ofall days, which exceeded the Australian Air NEPM for daily maximum 1-h average ozone, fell within thiscategory. Of the 57 very high days occurring during the 10-year study period, only five fell outside SynopticCategory 3; two in Category 1; and one in each of Categories 6, 7, and 8.

Figure 4 presents a box plot showing the within-group variance of maximum daily 1-h average ozoneconcentrations for each of the eleven synoptic categories. This graph presents the median, maximum,minimum, lower (QL), and upper (QU) quartile of ozone concentrations for each synoptic category. Outliersare also indicated as any point falling below QL – 1.5 × inter quartile range (IQR) or above QU – 1.5 × IQR,where IQR is the difference between the two quartiles (SAS Institute, 1999). Synoptic Category 3 can beseen in Figure 4 to be associated with significantly higher pollution concentrations than all other categories.

A one-way analysis of variance (ANOVA) was undertaken to determine whether the mean ozoneconcentration within the synoptic categories were significantly different. Results from the ANOVA rejected



Table II. Mean ozone concentration associated with each synoptic category and number and percentage of medium, high,and very high days

Category number(n)

1(182)

2(135)

3(200)

4(151)

5(47)

6(206)

7(48)

8(116)

9(11)

10(137)

11(172)

Mean daily ozoneconcentration (ppb) forcategory

54 42 86 31 33 59 49 47 30 34 32

(Standard deviation) (18) (11) (25) (9) (10) (14) (14) (17) (8) (14) (10)Number low pollution days(O3 < 60 ppb)

127 128 42 150 46 114 41 94 11 128 171

Percentage of low pollutiondays (O3 < 60 ppb)

70% 95% 15% 99% 99% 46% 85% 80% 100% 93% 99%

Number medium pollution days(60 ppb ≤ O3 < 80 ppb)

38 6 63 1 1 75 6 18 0 9 1

Percentage of mediumpollution days(60 ppb ≤ O3 < 80 ppb)

21% 4% 34% 1% 2% 37% 13% 16% 0% 7% 1%

Number of high pollution days(80 ppb ≤ O3 < 100 ppb)

15 1 43 0 0 16 0 3 0 0 0

Percentage of high pollutiondays (80 ppb ≤ O3 < 100 ppb)

8% 1% 23% 0% 0% 8% 0% 3% 0% 0% 0%

Number of very high days(O3 ≥ 100 ppb)

2 0 52 0 0 1 1 1 0 0 0

Percentage of very high days(O3 ≥ 100 ppb)

1% 0% 28% 0% 0% 1% 2% 1% 0% 0% 0%

Percentage of very high days(O3 ≥ 100 ppb) falling incategory

3.5% 0.0% 91.2% 0.0% 0.0% 1.8% 1.8% 1.8% 0.0% 0.0% 0.0%

250

200

150

100

50

01 2 3 4 5 6 7 8 9 10 11

Synoptic Category

No.

of d

ays

in c

ateg

ory

O3 < 60 ppb 60 ppb ≤ O3 < 80 ppb 80 ppb ≤ O3 < 100 ppb O3 ≥ 100 ppb

Figure 3. The number of days and percentage of total days in which the daily maximum 1-h average ozone concentration fell into thefollowing classes: low, medium, high and very high



160

140

120

100

80

60

40

20

0C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11

Synoptic Category

Max

imum

dai

ly 1

-hou

r av

erag

e oz

one

conc

entr

atio

n (p

pb)

Box Plot - Ozone concetrations for each synoptic category

Figure 4. Box plot showing the within-group variance of maximum daily 1-h average ozone concentrations experienced in each of the11 synoptic categories. This graph presents the median, maximum, minimum, lower and upper quartile of ozone concentrations, outliers

are presented as crosses

the null hypothesis that all category means were equal (F = 179, d.f. = 10, P =< 0.01). As there was astatistically significant difference in mean ozone concentrations across the synoptic categories, a multiplecomparisons method was used to identify which categories were different. The Tukey–Kramer multiplecomparisons method was used. This approach provides confidence intervals for all pair-wise differencesbetween means and calculates the maximum probability of the true difference falling outside the estimatedconfidence interval. This method of multiple comparisons was chosen for its suitability in analyses involvinguneven sample sizes (Miller, 1986) because there were different numbers of days falling within each synopticcategory. Results from the Tukey–Kramer test show that mean ozone concentrations in Synoptic Category 3were significantly different (p < 0.05) and greater than the remaining ten synoptic categories. The followingassemblages of synoptic categories exhibited no significant difference between mean ozone concentrations:

• Synoptic Category 1 with Category 2 and 5• Synoptic Category 2 with Category 7 and 8• Synoptic Category 7 with Category 8• Synoptic Category 9 with Category 1, 2, 4, and 5

4.1. Discussion of synoptic categories

Representative MSLP charts (surface pressure standardised to sea-level) for each synoptic category arepresented in Figure 5. For each synoptic category average meteorological conditions for each of the parameters



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used in the statistical analyses are presented in Table I, while the occurrence of Low, Medium, High, andVery High events over the 10-year period are presented in Table II.

The results in Table II show that Synoptic Category 3 had the highest number of High and Very Highdays (43 and 52 days, respectively) and was associated with 91.2% of the Very High ozone event days in the10-year period analysed.

Whereas days within Synoptic Category 3 were associated with the majority of high and very high ozoneevents, and a significant number of medium ozone days, Categories 1 and 6 are also of interest with respect toozone concentrations. Synoptic Categories 1 and 6 together experienced 31 out of the total of 78 High ozonedays and both categories had a significant number of medium ozone events. A discussion of the averagesynoptic and associated meteorological conditions experienced in each category is presented below.

4.1.1. Synoptic category 1. This category of days is characterised by an anticyclone centred over the easternGreat Australian Bight with a region of low pressure to the south of Tasmania and New Zealand. Winds at850 hPa are light west-southwest, and average 6 a.m. temperatures at the coast are 19.7 °C increasing to24.7 °C at the coast by 3 p.m. The average mixing height at midday is 1014 m, while the average depth of thesea breeze at 3 p.m. is 692 m, compared with 581 m for Category 6 and 316 m for Category 3. The averageafternoon temperature at the coast on these days is 24.7 °C while inland it is 27.3 °C. These temperatures aretoo low for the production of high values of photochemical smog. In this category there were only 17 dayswhen ozone exceeded the NSW DEC long-term goal of 80 ppb, and only 2 days that exceeded the currentAir NEPM of 100 ppb.

4.1.2. Synoptic category 2. In this category there was only 1 day in the 10-year study period when ozoneconcentrations were greater than 80 ppb, and only 6 days when concentrations were between 60 and 80 ppb.Days in Category 2 were associated with a synoptic pattern similar to Category 1, except that the influence ofthe region of low pressure between Australia and New Zealand extended further north resulting in strongercooler gradient winds. Average early morning temperatures at the coast of 13.8 °C were the lowest of allcategories, usually because of a cold front passing across Sydney the preceding day. Winds at 850 hPa areon average from the west-southwest with similar direction winds experienced at the coast. Coastal and inlandtemperatures remained relatively cool during the day, while at midday the average mixing height was 1794 mand in the afternoon there was a deep east-southeast sea breeze with average height of 757 m. On these daysthe combination of cool air, which is not conducive to ozone formation, and a deep mixing layer at middaywould result in low concentrations of ozone and precursors across the basin.

4.1.3. Synoptic category 3. As discussed above most High and Very High ozone events occurred within thiscategory. The characteristic synoptic situation associated with all days in this category was an anticyclone inthe central Tasman Sea and a MSLP ridge extending northeast across northern NSW and southern Queensland.Table I shows average 6 a.m. surface winds at the coast were light northerly, while at 850 hPa winds werewest-northwesterly.

Except for Category 7, days in Category 3 were warmest at the coast both at 6 a.m. and 12 p.m. andhad the highest afternoon inland – coastal temperature difference between Richmond (∼60 km inland) andSydney airport at the coast. In addition to high temperatures, mixing depths at the coast both before and afterthe onset of the sea breeze were lower than in other categories with an average mixing height of 673 m atmidday and 316 m within the northeast sea breeze.

Therefore, the combination of shallow mixing depths before and after the sea breeze and high temperaturesat the coast and inland would inhibit the dispersion of photochemical smog precursors and on many daysprovide a favourable environment for the formation of ozone.

4.1.4. Synoptic category 4. This group of days is characterised by an anticyclone located to the south ofthe continent, centred near Tasmania or to the south of the Great Australian Bight. These days have a surfaceridge of high pressure extending north along the coast of NSW. Temperatures in Sydney are cool because ofthe southerly gradient winds and moderate cloud cover (5.7 oktas). Average midday mixing heights on these



days are deep at 1705 m with winds turning southeasterly during the afternoon because of a deep sea breeze.With cool temperatures, southerly direction winds, and a deep mixed layer, these days are not conducive tothe formation of ozone as reflected by the data in Table II which shows only 1 day in the 10-year period withmaximum concentrations greater that 60 ppb.

4.1.5. Synoptic category 5. This category is characterised by a region of low pressure located in the south ofthe Tasman Sea between Tasmania and southern New Zealand. This situation results in strong westerly windsaloft (e.g. 10 m/s at 850 hPa), low surface temperatures in the morning, mild surface temperatures inlandand at the coast in the afternoon (24.2 °C and 24.4 °C, respectively), a deep mixing layer in the morningwith an average value of 1872 m increasing to 2147 m during the afternoon, and no afternoon sea breeze.In this situation, low surface temperatures combined with an offshore surface wind and deep mixing heightsare not conducive for the formation of ozone, and only 1 day in the 10-year period had maximum ozoneconcentrations greater than 60 ppb.

4.1.6. Synoptic category 6. The representative surface chart for this category of days (Figure 5) shows aregion of high pressure located off the coast of southern NSW resulting, on average, in west-northwesterlywinds at 850 hPa above Sydney and a strong north-easterly afternoon sea breeze. While days in this categoryaccounted for only one Very High ozone event, it had the second highest number of High ozone events andthe highest number of Medium ozone events.

Days in Category 6 have a similar average afternoon wind speed and direction at 850 hPa to Category 3, butmorning temperatures are relatively low (16.7 °C) and average afternoon temperatures inland are only 28.7 °C.Although these days have relatively low values of total cloud (2.4 oktas), average morning mixing heightsof 1341 m are twice the height of those in Category 3. Consequently, although sea breeze mixing heights of581 m are relatively low, increased dispersion and dilution compared with Category 3 and lower overall airtemperatures will reduce the likelihood of High and Very High concentrations occurring in Category 6.

4.1.7. Synoptic category 7. This group of days has a low-pressure system southeast of Tasmania with atrough of low pressure extending northeast along the coast of southeast Australia. Under these conditionsSydney experiences hot dry west-southwest winds with average coastal and inland temperatures of 32.4 °Cand 31.8 °C, respectively, and an average midday mixing height of 1088 m increasing to 2049 m at the coastin the afternoon. On average, despite the high temperatures that were conducive to the formation of ozone,the absence of an afternoon sea breeze and the presence of high mixing depths resulted in few ozone events.Out of a sample of 48 days, there was one Very High ozone event, no High ozone days, and only 6 dayswhen ozone was Medium. The remaining 41 days had ozone less than 60 ppb.

4.1.8. Synoptic category 8. This synoptic category exhibits similar features similar to those of Category3, with a centre of high pressure in the Tasman Sea; however, in this category the anticyclone is displacedfurther east than in Category 3. In Synoptic Category 8, there is a centre of low pressure to the south ofTasmania and a region of high pressure in the west of the Great Australian Bight. Under these conditionsthere is a strong pressure gradient in southeast Australia, which results in strong west-southwest winds at850 hPa and mild surface temperatures at the coast. The morning and afternoon mixing heights are similar tothose in Category 3 (e.g. 763 m and 360 m in the sea breeze); however, winds are lighter within the northeastafternoon sea breeze. The main differences between categories are the higher coastal and inland afternoontemperatures in Category 3 (29.1 °C and 33.6 °C), which are favourable to ozone formation compared withthe lower Category 8 values of 24.9 and 27.0 °C; and wind speeds aloft at 850 hPa which are approximatelytwice as fast in Category 8 compared to Synoptic Category 3. However, although the number of elevatedozone values in Category 8 was significantly lower than Category 3, there was one Very High ozone event,3 High ozone days, and 18 days when ozone concentrations were between 60 and 80 ppb.

4.1.9. Synoptic category 9. This category was the smallest, occurring on less than 1% of days in the studyperiod. It is similar to Category 8 with a centre of low pressure south of Tasmania and a region of high



pressure in the western Australian Bight, but in Category 9 the trough of low pressure extends further northresulting in the strongest gradient winds of all categories, (approximately 19 m/s from the south at 850 hPa).Average cloud cover is reasonably high (5.5 oktas), the diurnal variation of temperature across the Sydneybasin is small, and with a strong southerly wind at the coast in the afternoon there is no sea breeze. The lowtemperatures combined with strong southerly winds were unfavourable for ozone formation. All days in thiscategory had low concentrations of ozone with an average of 30 ppb.

4.1.10. Synoptic category 10. This group of days has an anticyclone in the western region of the GreatAustralian Bight. Average winds at 850 hPa are a light southwesterly with morning and afternoon winds atthe coast being a moderate southerly. Average cloud cover is high (6.6 oktas) and the daytime variation intemperature across the Sydney basin is small. Average mixing height at midday is 733 m, and 388 m in theafternoon, indicating that on some days there is a southeasterly sea breeze. With afternoon temperatures at thecoast and inland of 21.2 and 22.6 °C, respectively, in general no photochemical production would be expected.There were no Very High or High events in this category, and only 9 days when ozone concentrations werebetween 60 and 80 ppb.

4.1.11. Synoptic category 11. Category 11 is characterised by an anticyclone centred to the south ofTasmania in the Southern Ocean resulting in light southwest winds at 850 hPa and south-southeast windsat the coast in the morning. Average mixing heights at midday are 1390 m but decreased to 728 m in theafternoon, and winds at the coast backed to the east-southeast. Average cloud cover is high (6.6 oktas) anddaytime variation in temperature at the coast is low (18.8 to 22.6 °C) and average afternoon temperaturesinland are only 23.1 °C. With the deep morning mixing layer, southerly winds, low average temperatures,and high average cloud cover, ozone production would not be expected to occur and the results in Table IIshow that there were no Very High or High ozone days, and only one event when ozone was between 60 and80 ppb.

4.2. Meteorological conditions associated with ozone episodes

This study has used statistical techniques to identify, for the first time, the three-dimensional characteristicsof air masses required to produce high concentrations of ozone in Sydney. The climatology is able to describethese characteristics for more than 90% of days that exceeded current ozone goals over a ten-year period.

The majority of ozone episodes in Sydney are associated with Synoptic Category 3, which is characterisedby an anticyclone centred in the Tasman Sea, with a ridge extending northeast into northern NSW and southernQueensland. A statistical analysis of synoptic and meso-scale flows present on Category 3 days showed thatthese conditions produce light north to northwest gradient winds, high surface temperatures during the dayacross the Sydney basin, and a shallow east to northeast sea breeze that can transport precursor emissionsand ozone into western Sydney.

The length of Synoptic Category 3 situations is highly variable. During the 10-year period examined,this synoptic situation persisted for 7 days on one occasion and for 6 days on another. More frequentlyobserved durations were 4 days (four occasions), 3 days (12 occasions), and 2 days (20 occasions). However,as shown in Table II, Synoptic Category 3 conditions do not necessarily lead to simultaneously high ozoneconcentrations. For example, 2 days examined by Hart et al. (2005) – the 21st and 22nd December 2000 – hadmarked differences in the direction and structure of meso-scale flows at the coast in the morning. The maximumozone concentration on 21st December was 158.4 ppb. In contrast, on 22nd December, stronger gradient windsand a rapid breakdown of the elevated inversion at the coast produced a maximum ozone concentration ofonly 62 ppb.

Complex interactions between synoptic and meso-scale flows have been widely recognised in north America(see the review by McKendry and Lundgren (2000)). A similar situation for the greater Sydney region duringSynoptic Category 3 situations was examined by Hess et al. (2004). They compared model predictions ofozone concentrations with observations from six monitoring stations across the Sydney basin for the 7-dayepisode 22nd to 27th January 2001, when maximum ozone concentrations ranged from 97.6 to 174.8 ppb.



Maximum concentrations on each day were associated with the onset of the sea breeze. However, morningand afternoon increases in ozone observed before the arrival of the sea breeze show a number of genericpatterns, including the dilution of ambient air with cleaner tropospheric air above (Hyde et al., 1995; Hydeet al., 2000).

No air quality monitoring stations are located on high ground around the boundary of the Sydney basin.Consequently, it is difficult to estimate the extent that ozone within the sea breeze front is diluted by cleanertropospheric air as it travels downwind into higher topography. Furthermore, the lack of vertical profilesof ozone concentrations above the Sydney basin precludes the identification of elevated layers of ozone.However, modelling of this episode by Hess et al. (2004) illustrates the potential complexity of processesoccurring under these Synoptic 3 category conditions. These processes include: the recirculation of ozonewithin the sea breeze front; the dispersion of stratified layers of ozone aloft by the gradient wind; the dilutionof ozone within the deepening boundary layer during the morning and early afternoon; transport of ozone andprecursors offshore within drainage flows during the night and early morning and their subsequent recirculationonshore within the sea breeze; and the long-range transport and dilution of ozone as the sea breeze continuesto propagate during the late afternoon and evening to locations well outside the Sydney basin.

5. SUMMARY AND CONCLUSIONS

This synoptic climatology of ozone episodes in Sydney, Australia, was produced using multivariate statisticaltechniques, with PCA used for data reduction and removing co-linearity between meteorological inputs. A two-stage clustering technique classified days into meteorologically homogeneous synoptic categories. Analysisof maximum daily 1-h averaged ozone concentrations associated with each synoptic category identified themeteorological processes behind ozone episodes.

Synoptic patterns for each category were compared with the corresponding air mass characteristics, synopticscale circulation, and pollution regimes, with one synoptic category clearly associated with the majority ofozone episodes in Sydney. The synoptic and meso-scale processes present in this category are linked toan anticyclone centred in the Tasman Sea, ridging back across the continent bringing light north to north-westerly gradient winds, warm temperatures, reduced mixing, low cloud cover, and a north-easterly sea breezeto Sydney. This climatology has isolated ozone episodes in Sydney, showing that more than 90% of daysduring the study period exceeding current air quality goals fell within the one synoptic category.

Previous research by Leighton and Spark (1997) and Katestone Scientific (1997) identified gradient windand temperature conditions associated with high ozone episodes in Sydney. Both studies suggested north-westerly gradient winds, high afternoon surface temperatures, and the occurrence of an afternoon sea breezeas prerequisites for high ozone formation and accumulation in Sydney. The current research confirms theseresults and enhances them through the use of multivariate statistical analyses. The inclusion of upper air datain the statistical analyses has for the first time elucidated the three-dimensional processes associated with highozone events, at both synoptic and meso-scale levels.

Moderate to high concentrations of ozone can form in the boundary layer during the morning and earlyafternoon in central and western Sydney. However, the highest concentrations coincide with the arrival ofthe sea breeze front, as precursor emissions and ozone from eastern Sydney, trapped within a shallow seabreeze, are advected westwards across the basin. Occasionally, the sea breeze may prolong the persistenceof pollution episodes within the basin by overnight recirculation within drainage flows. More commonly,however, as shown by Hess et al. (2004), ozone within the sea breeze front can be carried out of the basin,or it can be trapped aloft and then either recirculated offshore within the return flow of the sea breeze, ordispersed and diluted by the gradient wind.

This research has identified synoptic conditions typically associated with high concentrations of ozone andits transport inland across the Sydney region. These results will be useful to Australian regulatory bodies forforecasting the likely occurrence of high concentrations of ozone under these synoptic conditions, and for plan-ning future emission sources in Sydney’s and neighbouring airsheds. Work in progress, investigating the mete-orological differences between days with high ozone concentrations and days with similar synoptic (i.e. Synop-tic Category 3) conditions yet relatively low ozone, will further refine the analysis in the present study. These



investigations will use a combination of a 3D Eulerian grid prognostic meteorological model (TAPM) (Hurley,2002) and an analysis of high resolution, temporal and spatial, meteorological, and air quality observations.

Essential future work includes investigating the processes that determine the formation of ozone within theboundary layer during the morning and early afternoon on Synoptic Category 3 events, and their interactionwith ozone within the sea breeze front. Also, in order that these high ozone events can be predicted moreaccurately, the synoptic categories that precede the onset of Synoptic Category 3 need to be identified.

ACKNOWLEDGEMENTS

The authors thank the Australian Bureau of Meteorology for access to meteorological data, the NSW Depart-ment of Environmental and Conservation for access to pollution data, the National Parks and Wildfire Servicefor access to their wildfire database, and Alison Basden for her helpful comments during the preparation ofthis manuscript.

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A synoptic climatology of tropospheric ozone episodes in Sydney, Australia