Surface ozone at rural sites in the latrobe valley and Cape Grim, Australia

A~ic

&mvirminrnr Vol. 20. No. 12. pp. 2403-2422.

Printed in Gmt

1986.

ooM-6981/86 s3.oo+o.lm Rtpmon Joumlr Ltd.

Britain.

SURFACE OZONE AT RURAL SITES IN THE LATROBE VALLEY AND CAPE GRIM, AUSTRALIA I. E. GALBALLY*,A. J. MILLER?, R.

D. Hoyt,

S. AHMET& R.

C. Jouti$

and D. A-ITWOOD$

lCSIRO Division of Atmospheric Research, Aspendale, Victoria, Australia, tCSIR0 Division of Mathematics and Statistics on behalf of SIROMATH, Melbourne. Victoria, Australia, $Statc Ekctricity Commission of Victoria, Melbourne, Victoria, Australia and @nvironment Protection Authority of Victoria, Melbourne, Victoria, Australia (First receitmi 2ll October 1985 and received for publication 2 June 1986)

Abrtrrct--Ozone and other air quality data from five rural sites in the industrialized Latrobe Valley, Victoria, have been subject to statistical analyses including linear regression modelling The bchaviour of OS in the Latrok Valley is explained largely in terms of natural background atmospheric processes as observed at Cape Grim, Tasmania. The maximum l-h average concentration of naturally occurring 0, (obtained from a byear record at Cape Grim) is less than 40 ppb (v/v). In contrast the industrialized Latrobe Valley sites show 0, values exceeding 40 ppb between 1% and 3 % of the time. These higher concentrations occur in conditions consistent with local photo&mica] production of 0, via ‘smog’ type processes and appear preferentially at low NO. concentrations (3-4 ppb) during the afternoon (13-18 h) and at high temperatures (above 25°C). A comparison of observations from an elevated station (750 m) with those from the valley floor shows systematic differences in seasonal and diurnal 0, variations and the time of day of occurrence ofelevated 0, concentrations which can be explained in terms of the diurnal cycle of convective mixing and mountain/valley winds. A linear regression model incorporating this understanding has accounted for bctwan 43 % and 64 % of the variance of 0, concentration at the elevated and rural stations. The statistical model incorporates temperature, time ofday. month of year, wind speed, 0, concmtration 24-h earlier andNO. concentration as vatiabla in the regression quation, with temperature being the dominant variabk. The standard deviation of the residual 0, values (observed minus fitted) is around 5 ppb. Auto and cross correlations are used to show that perhaps half of the unexplained variance is coherent from site to site and hence potentially could be modelled. Key word index: Ozone, nitrogen oxides, background concentrations, rural air quality, regression model, Australia, statistical analysis, meteorology, photochemical processes, tropospheric ozone.

the Latrobe Valleyof Victoria. As a secondary aim-as part of this task-we particularly try to identify the During the last two decades much progress has been conditions under which the 0, concentrations present made in understanding the behaviour of ozone (0,) in are enhanced by local photochemical smog processes the lower atmosphere. Basic physical/chemical pro- as distinct from hemispheric background ‘natural cesses such as injection from the stratosphere (Junge, processes. A more complete report on this study is 1962;Danielsen, 1968),production in the background provided in Ahmet et al. (1985). troposphere (Crutzen, 1974, Liu et al., 1980), proWhile graphical presentations of observed relationduction in urban photochemical smog (Haagen-Smit, ships between 0, concentration and one or two factors 1952) and destruction by contact with the earth’s at a time can give some useful insight for this work, surface (Galbally, 1968) have been revealed. These such methods are limited baguse of the interrelationprocesses have been incorporated into complex ships between these factors. For instance, temperature physical/chemicalmodels of the troposphere (e.g.Levy is strongly related to time of day and season. These et ul., 1985),urban pollution models (cg. Reynolds ef relationships can be made clearer if the effects of al., 1973) and in one case a boundary layer model variations in other factors can be removed. This calls (Garland and Derwent, 1979).However, we still do not for a simultaneous model in all of the factors involved. have a good understanding of the physical/chemical A regression model has &en developed for this causes of hour by hour and day by day changes in 0, purpose making use of the known physical and observed in a non-urban environment. chemical processes affecting 0, production and In this paper we attempt such a task-that is, destruction. through physical/chemicalinterpretation and statistiThe model has been developed for explanatory cal analysis to explain the variations of O1 observed purposes, not for prediction. A predictive model for O3 on a time scale of 1 h to a year at several rural sites in concentration would need lo use predicted meteoro1. INTRODUCrION

*I

,0:,2-n

2403

I.

2404

E. GALBALLY

logical variables (temperature, wind speed, etc.) but we have not limited our variables to those that can be easily predicted. Other researchers have used regression techniques for 0, modelling. Ludwig and Martinez (1979) and Evans et al. (1983) conducted analyses similar to that presented here; Williamson (1979) and Chock et al. (1982) did analyses based on daytime (10 a.m.-4 p.m.) and daily maximum 0, concentrations, respectively; and Karl (1979) and Clark and Karl (1982) developed regression equations to forecast daily maximum 0, concentrations from prognostic (predicted) meteorological variables. The two studies most similar to that performed here, i.e. using hourly 0, data for every hour of the day (Ludwig and Martinez, 1979; Evans et al., 1983), find relationships with O3 that are positively correlated for temperature, solar radiation and wind speed, and that are negatively correlated for oxides of nitrogen (NO,), total hydrocarbons (THCs) and relative humidity. The regression studies of daytime and daily maximum 0, concentrations (Williamson, 1979; Chock et al., 1982) confirm the importance of surface temperature and relative humidity on 0, concentration. Williamson (1979) highlights the importance of the previous history of the air parcel (length of trajectory, time spent over land/ocean, integrated NO, and HC emissions along the trajectory, etc.) for determining 0, levels. It appears that the 0, level is negatively correlated with the NO, and HC emissions along the air parcel trajectory but that the 0, level observed is positively correlated with the highest NO, level observed at the same station from 5 a.m. to 2 p.m. on the same day. Most probably this correlation arises because the high NO, level is a surrogate for some other properties that are conditions conducive to photochemical smog formation. The study of Chock er al. (1982) shows that upper air temperatures (900 mb and 850 mb) on occasions can be a better indication of high 0, levels than surface temperature. The studies of O3 forecasts via regression relationships (Karl, 1979; Clark and Karl, 1982) relate prognostic meteorological variables to the forecast daily maximum I-h average 0, concentration. Again, in these studies temperature, wind speed, relative humidity, sea-level pressure and time of year are all important (Karl, 1979) and when available the ‘Quadrant of backward trajectory’ is a variable of major significance second only to temperature (Clark and Karl. 1982).

2. BACKGROUND

er ol

rises to typically 1000 m (with peaks to about I500 mj with a coastal range rising to typically 5OOm (with peaks to about 760 m) forming the Sedge of the region. To the E the valley opens to the Tasman Sea and on the W extremity land rises to only some 200 m. The central area of the valley contains the towns of Moe, Morwell and Traralgon and has a population of some 70,000. The main economic activities in the area are power generation (about 4000 MW installed capacity) and associated brown coal mining, paper manufacturing and general agriculture. The climate of the Latrobe Valley is temperate with some maritime effects especially at the E and W extremities. Surface winds in the valley are mainly E or W, as influenced by the topography, with W winds predominant (Tapp and Hoy, 1980). Sea breezes, mainly from the E, occur in the warmer months although their effects are not felt until late afternoon in thecentral valley area (Physick, 1983). Mean maximum mixing depths range from about 600 m in winter to about 15OOm in summer over the Latrobe Valley (LVASSSC, 1981; Jones, 1982). Estimated total annual air emissions in the Latrobe Valley for 1980 are given in Table I. As shown in this table, four of the five pollutants listed derive mainly from elevated industrial point sources. For example, about 98 y0 of NO, and almost all of SO2 are emitted by major point sources. There are no measurements of the natural emissions of NO, in the Latrobe Valley. However, measurements made at other locations in SE Australia (Galbally and Roy, 1978, 1981) suggest that the natural emissions of NO from the soil in the Latrobe Valley could be of the same magnitude (- 10’ ta-‘) as the NO, emissions from diffuse surface sources. Air quality in the Latrobe Valley of Victoria is being studied in detail to aid environmental impact assessment, having regard to further development of the brown coal resources in the area (Hart, 1981; Manins, 1984). General air quality in the Latrobe Valley has been reviewed in LVASSSC (1981) and Joynt (1983). Maximum l-h (and 8-h for 0,) concentrations recorded from the air monitoring network in the valley are shown in Table 2, together with the air quality objectives for the state of Victoria (Environment Protection Authority of Victoria, 1981, 1982). The present study of 0s in the Latrobe Valley was initiated because 0, levels, while not high in comparison with other industrial or urban areas, were close to or just exceeding the air quality objectives for the state of Victoria. Previous studies of 0, in the Latrobe Valley are Hoy (1979 and 1984) and Ahmet (1984).

INFORMATION

3. The Latrobe Valley of Victoria is a region some 100 km long by SOkm wide near the S extremity of the Great Dividing Range [see Fig. 1(a)]. The valley runs E-W with the floor at an elevation of SO-100 m above sea-level [Fig. l(b)]. The range to the N of the valley

DATA AVAILABLE

0, has been measured at 1I air quality monitoring stations in the Latrobe Valley but in this study attention was focused on five of these stations, Darnum North (DN), Hazelwood Estate (HE), Minniedale

Surface ozone at rural sites in the Latrobe Valley and Cape Grim, Australia

2405

(a)

TROBE VALLEY

W

Fig. L(a) Location map for places mentioned in the text and (b) topographic map of the Latrobc Valley, showing sites of air monitoring stations and power stations.

Road (MR), Boolarra (BO) and Mount Tassic (MT). By choosing five rural stations with differing characteristics it is taught that the bulk of the info~tion coRected by the rural monitoring network has been included, since concurrent 0, readings at the stations are highly correlated (MilIar, 1982).The five stations usad (refer to Fig, l(b) and Table 3 for locations) and the periods during which 0, data were available arc given in Tabk 4. One-hour average readings were used

in this study and are identified by the end of the period, e.g. hour 1 is CWIOh to 0059 h. The 0s data at Mount Tassie, as well as being generally not concurrent with the other valley stations, included an extreme drought period for Victoria during 1982 and ending early in 1983,It is not clear at present how much effbct this dry period may have had on the 0, data collected but these unusual climatic conditions favour photoehemical OS formation.

2406

1.

E.

GALBALLY

et al.

Table I. Annual air emissions (10’ t) for 1980 in the Latrobe Valley* Non-methanic hydrocarbons

NO, as NO1

SO2

Particulate matter

Major point sources7 Mobile sources Minor point sources Others$ (domestic, commercial agricultural tilling, etc.)

54.5 1.3 0.0

41.9 0.1 0.1

43.4 0.2 0.6

0.1

0.0

0.6

4.1

1.8

Total

55.9

48. I

44.8

77.5

4.9

co 54.1 18.6 0.1

0.1 2.9 0.1

l Derived from Latrobe Valley Air Emissions Inventory (Latrobe Valley Water and Sewerage Board, private communication) for typical weekday emissions in February and July 1980. The numberof significant figures quoted do not reflect the uncertainty of the inventory. t State Electricity Commission of Victoria power stations and other industry. $ Contributions from fires. road dust and other miscellaneous and natural sources excluded.

Table 2. Maximum air pollutant concentrations from I4 monitoring stations in the Latrobe Valley (September l979-August 1984) Victorian air quality objectives

Pollutants SOs (ppb) ND1 (ppb) 0~ (ppb) 0, (ppb) LVD (km)$ CD (ppm)

Averaging time

Maximum concentration

Acceptable level *

lh lh

56 100 100 70 4 12

170 150 120 50 20 30

K lh lh

Detrimental levelt 340 250 150 80 60

* May be exceeded on up to three days per year for all pollutants except one-hour 0, levels which may be exceeded on one day per year. t May not be equalled or exceeded. $ Local visual distance as measured by nephelometer; minimum visibility levels are shown.

Table 3. Station locations and elevations Station Damum North, Victoria Booiarra, Victoria Hazdwood Estate. Victoria Minniedale Road, Victoria Mount Tassie, Victoria Cape Grim, Tasmania

Latitude (S)

Longitude (E)

Elevation (m)

38” 08’S 38” 24’S 38’ 19’S 38” 13’S 38” 24’S 40”4l’S

146” OI’E 146” 16’E 146” 23’E 146” 35’E 146” 34’E 144” 4l’E

153 171 91 76 762 94

General details of air quality monitoring station design, instruments and data acquisition systemsare given for the Latrobe Valky by Laws-Herd (19&1). 0s is measured by D&ii 1003 AH U.V.absorption analyzers. As with the other gas analyzers, they are cheeked daily by automatic computer activated ehaeks of the zero level and a rdbmnce level. Data from stations failing to pass these cheeks are automatically invalidated. At quarterly intervals field calibrators are compared with a Dasibi 100s cahbration unit (which has been recognized by the USEPA as a primary

standard). The output of the Dasibi 1008 is also compared by gas phase titration against a U.S. National Bureau of Standards NO gold standard. Sample air is drawn from a height of approximately 5 m. Other data obtained included NO,, NOs, NO, SO,, local visual distance (LVD), tamperature, total solar radiation (TSR), wind sped (lo@, wind direo tion (10 m) and Pasquill stability ategory, fromthe air monitoring network and power station emission rates. Pasquill stability categories A-G (A very unstable, D neutral, G very stable) were determined

objectively by

Surface ozone at rural sites in the Latrobe Valky and Cape Grim, Australia

2407

Table 4. Ozone data available for this study Station

Period when data available

Damum North

July 197Q-June 1981 March 1983-February 1984 Bookrra May 198&August 1980 March 1983-February 1984 Hazelwood Estate May 197Q-June 1981 (October 1979; December 1979,January 1980and December 1980missing) March 1983-February 1984 Minniedak Road June 197Q-September1981 (December 1979,January 1980and February 1980 missing) March 1983-February 1984 Mount Tassic October 1981-August 1983 (December 1982,January, February, April and June 1983missing) October 1976December 1983 (June Cape Grim 1977,November 1977-January 1978, October 1978-December 1978, August 1979, November 197Q-January 1980 missing)

the procedure of Smith (1972).modified by Tapp and Hoy (1982). Power station emissions were estimated for regression purposes as proportional to hourly MW generated. No radiation measurements were made at Mount Tassie or Boolarra Surface 0, measurements have been made at the Australian Baseline Air Pollution Station, Cape Grim (CC), Tasmania [see Fig. l(a)] since October 1976. Cape Grim is a remote location, 50 km from the nearest small town. Ozone data from Cape Grim selected for baseline conditions, i.e. including only wind speeds greater than 20 km h-’ and wind directions from the Southern Ocean (190”-ZSO”), are used in this paper as a reference set for natural background lower tropospheric O1. The monitoring system at Cape Grim is described in Elsworth and Galbally (1984).O,, wind speed,wind direction and air temperature are the only measurements from Cape Grim that are used in this study.

Number of hours of data 13.567 8752 15,601

18.862

6816 9872 (baseline conditions)

strong turbulence during the daytime and on windy

nights 0, is continually carried down to the surface and the concentration in the near-surface air shows little or no diurnal variation. When the turbulence in the boundary layer diminisheson nights with low wind speedsand the destruction of 0, at the surface exceeds the downward transport into the near-surface air, then the O1 concentration at this level gradually decreases as is evident in Fig. 2. The rate of decrease is proportional to the Oa destruction rate at the underlying surface. These processes were first described by Auer (1939).This variation of OS with wind speed can be conveniently represented by a reciprocal relationship between 0, and wind speed according to time of day for the purpose of regression modelling; this is developed further in section 5. At Mount Tassie and Cape Grim there is no dependence of 0, concentration on wind speed. At some mountain sites an inverse diurnal cycle of 0s is found to be associated with upslope and downslope winds (Price and Pales, 1963). however at Mount 4. OZONE VARIATIONWITHPHYSICALANDCHEMICAL Tassie, the good exposure of the site to the free PARAMElXRS atmosphere even in tight wind conditions ensures little 0, variation due to diurnal influences.At Cape Grim 4.1. Diurnal variation the O1 concentration shows no day/night differences One of the best known features of 0, in the surface because the wind is generally from the ocean and water air is its diurnal variation. Figure 2 shows 0s concen- surfaces destroy O3 so slowly (Galblly and Roy, l!XlO) tration against time of day for high and low wind that no change in OS concentration is observed, in spite speeds. At the valley floor sites there is a marked of any diurnal change in mixing. diurnal cycle at low wind speeds while at high wind speeds there is little variation. This behaviour was examined using vertical 0, profiles in the boundary 4.2. Synoptic iy¶uences On a time scale of several days synoptic scale layer by Galbally (1968) for another rural site in southeastern Australia. Subsequently Galbally (1972) motions in the region covering the Lotrobe Valley can and Garland and Derwent (1979) described this be- affect the 0, concentration. Stagnant anticyclonic conditions may favour 0, formation; particular synhaviour. Generally there is a relatively constant 0, concentration in the free atmosphere. When there is optic conditions may promote the transport of 0, or

2408

I. E. GALEALLY

10

ef af.

I

A 80

6

18

12

TIME OF

DAY

(hi

TtME

DAY

(h)

?4

-_

I(b)

OF

Fig. 2. Diurnal variation of 0, for high ( > 6 m s” ‘, ---) and low ((r2m s- I, -) wind speeds. (a) Latrobc Valley sites and (b) Mount Tnssie and Cape Grim. precursors from Melbourne, a city of about 3 million people approximately 150 km W of the Valley; and particular wind directions may bring large NO, sources upwind of a monitoring station causing deplemd OJ concentrations on these occasions. AItmtt (1984) showed that on days of 0, exceeding SOppb, the most frequent situations were those of NE, N and NW synoptic flows resulting from high pressure systems centred in the Tasman Sea. The variability of 0, concentrations with 9OOmb (about 1000m) and surface winds was further examined to investigate synoptic scale inffuences on O1 kvels in the Latrobe Valley. {Lack of upper air observations in the Valley required spatial cxtrqolations of data from !We East, just outside the Latrobe Valley.) No marked dependence of mean 0, concentrations and 0, > 40 ppb on either 900mb or surface wind direction could be found. Variability with direction was less than stationto-station anility. Thus, the highest 0s concentrations ( > 50 ppb) occur in stagmmt or @tit windconditions to the rear of anticyclonic systems; there is no evidence to suggest a s&&ant influence of the Melbourne Airshed on O3

levels in the Valley via long range transport of 0, or its precursors (in agreement with Hay, 1984); nor any evidence of major OJ variations with wind direction due to local NO, sources in the Valley.

4.3. Seusona~ changes There are regular seasonal variations of 0, in the surface air as shown in Fig. 3. Conditions of higher wind speed ( > 6 m s-l), selected to include wellmixed conditions, showed a maximum at all stations in winter. This winter maximum in surface O2 is observed throu~out the S Hemisphere (~ltmans and Komhyr, 1976; Galbally ~lnd Roy, l98t; Logan, 1986; Levy Y$~1.. 1985). The Cape Grim data, which have been selected for clean air oonditions.are 5-10 ppb higher during the winter months than the other stations. We have no quantitative explanation of this phenomenon. it is likety that NO from local sources in the Latrobe Vafley would iower 0, concentrations there, espe&aliy during winter when no 0, creation by photochemical processes would be likely. Ozone-sonde soundings at Aspendale [see

Surface ozone at rural sites in the Latrobe Valley and Cape Grim, Australia

2409

4.4. Temperature variations

11

’

*

’

*

JFMAMJ

(b)

*

*

3

’

’

)

JASON0

I

WIN0 SPEEO -a 6 m s-’

I1

6

.

.

I

08

J

FMAMJ

JA

J

FMAMJ

JASOND

*a

SOND

Fig. 3. Variation of 0, with month. (a) Mean 0, concentration (all Cape Grim data rather than selected baseline data were used); (b) mean 0, coneentmtion for wind speed > 6ms-’ (Cape Grim baseline data) and (c) relative frequency of 0, > 40 ppb.

Fig. l(a)], presented by Pittock (1977), provide an explanation of these winter-time maxima. At the 800 mb (about 2 km) level over Aspendale, maximum 0, occurs in spring/summer and a minimum in autumn/winter but the amplitude of the seasonal cycle is very small ( f 3 ppb). In the surface air there is a broad winter maximum and a summer minimum. This is the opposite to what one would expect from a view of purely photochemical production of 0, in the troposphere, and more consistent with influence by stratospheric/tropospheric exchange and vertical mixing in the atmosphere, see Levy et al. (1985). Considering the near constant 0, mixing ratio at the 800 mb level, it is suggested that the winter maximum in 0, in the surface air is caused by the seasonal variation of mixing across the lowest 2 km of the atmosphere and of destruction at and near the underiying surface, the downward mixing being most efficient in winter and destruction more efficient in summer. This is consistent with what is observed in the Valley.

Another physical parameter influenced by synoptic processes (as well as by diurnal and seasonal ones) is air temperature. In anticyclonic systems with clear skies and light winds, both 0, and surface temperature decrease rapidly at night, whereas, in frontal or cycIonic weather, wind speeds tend to be higher, the sky cloudy and there is no 0, decrease at night. Figure 4 shows 0, concentration against temperature, for high and low wind speeds. At the Valley floor sites, over the range 16> 30°C there is no difference between the low and high wind speed cases with both showing increasing 0, with increasing temperature. At temperatures < 16°C and low wind speeds, there is a decrease in oxone with decreasing temperature as is explained above. However, at higher wind speeds and below 16°C there appears to be some increase in 0, with decreasing temperature. Cross tabulations of the data show that this Oa increase, with decreasing temperature below 1620°C in the high wind case, is unrelated to time of day or NO, variations. It is due to the occurrence, at winds > 6 m s- I, of higher 0, values in the winter (cooler) months, and lower 0, values in warmer, unpolluted (windy) conditions in the summer months. This is borne out by the Cape Grim baseline data which shows a clear negative relationship between 0s concentration and air temperature in clean air. The rise in 0s concentrations at 26°C to > 31°C is associated with photochemical production of 0,. Variability of 0, with total solar radiation and Pasquill stability category were also examined. It should be noted that temperature, Pasquill category and total solar radiation (TSR) are all indicative of vertical mixing in the planetary boundary layer and are inter-related to some extent. Temperature is a measure of the integrated effect of TSR, is closely related to stability, and was in this data set the most effective of these variables in sorting the 0, data. 4.5. Photochemical formation An important aspect of the physical/chemical relationships in this data involves the formation of 0, in the valley by photochemical smog processes from locally emitted precursors. Chamber studies of photochemical smog using ambient air from Sydney, Australia, with ambient light levels and temperatures, indicated that the highest levels of primary smog products (which in this case we can identify with photochemically produced 0,) are approximately four times the initial NO, concentration. Furthermore the yield of primary smog products appears to double for an increase of ambient temperature from 20°C to 40°C for constant HC and light levels (Johnson, 1983,1984). Studies of 0, at a site in the Rocky Mountains in Colarado, U.S.A., showed that OS concentrations of up to 100 ppb may occur at Rnal NO, concentrations of 2-8 ppb but that at lower NO, concentrations lower levels of 0, occur (Fehsenfeld et al., 1983). Apart from the fact that there are higher natural levels of 0, at

1.

E. GALBALLY

et al.

downwind of small cities with populations of about 100,000 people (e.g. Antell, 1979; Spicer et al., 1982).0, concentrations at rural sites in the U.S.A. were up to 125 ppb with 95th percentile levels in the range 40-75 ppb (Evans et al., 1983). For comparison 95th percentile 0, concentrations in the Latrobe Valley were in the range 29-36 ppb (see Fig. 5). The physical conditions under which this local production of 0, occurs in the valley are examined in Figs 6, 7, 3 and 8. We see that it generally occurs during the daytime, from mid spring to mid autumn, over a wide range of wind speeds and at temperatures greater than 21°C at the Valley floor sites. At Mount Tassie these elevated levels occur more frequently than in the valley, over much of the night as well as the daytime and at temperatures down to 16°C. We suggest that these differences are explained by 0, decrease due to diminished turbulence and on-going destruction overnight at the low-lying stations compared with the almost unvarying good exposure of the Mount Tassie site to the surrounding atmosphere. The transport of photochemical 0, to Mount Tassie is thought to occur through convective mixing of photochemical pollutants from the valley floor during the daytimeand the transport of these pollutants by synoptic or mesoscale flows10 the Mount Tassicsitcduringbothdaytimcand night-time. Because this air is separated from the Fig. 4. Variation of 0, with temperature. (a) Wind speed earth’s surface, it retains elevated 0, concentrations > 6ms-’ and (b) wind speed < 2ms-‘. after sunset and so 0, concentrations > 40 ppb can be observed at Mount Tassie at night. No data are shown for Cape Grim because, as discussed earlier, the 0, these heights (3 km) in the Rocky Mountains of about concentration in baseline air never reaches 40 ppb at 40 ppb, the chemical processes affecting this regional this location. (Cape Grim does experience higher 0, pollution in Colorado are similar to those in the concentrations, up to 60 ppb, in air transported from Melbourne, however such occurrences are specifically Latrobe Valley. Cumulative frequency distributions of 0, for lo- excluded when we select the data by considering only cations in the Latrobe Valley and at Cape Grim are occasions with winds from the Southern Ocean.) NO, and non-methane HCs (NMHCs) are the key shown in Fig. 5. The highest hourly 0, concentration observed in baseline (clean air) conditions at Cape chemical factors in this local production of 03. There Grim was 38 ppb. 0, production processes in clean air are few measurements of NMHCs in the Valley are very slow but destruction processes particularly at (Ardern, 1983; Hooper et al., 1984); they show average the surface are fast; whereas in air containing photoconcentrations of around 0.4 ppmC at rural sites. However, this NMHC data is too sparse to be of use in chemically produced O,, production and destruction processes are both fast. Therefore in clean air near the sorting the 0, observations. NO, can lead to either diminished 0, levels near surface the relative frequency of 0, > 40 ppb could intense sources of NO, or enhanced 0, levels after decrease but not increase due to natural processes active smog chemistry has commenced. In Fig. 9 we see whereas, in photochemically polluted air, the relative frequency could both decrease or increase relative to that at night the 0, levels decrease with increasing clean air. As we are most interested in the occurrence of NO,, presumably due to the reaction NO+ 0, + NO, + 0, which leads to the destruction of Oj and high 0s values, we have a criterion to identify the production of NO,. However, during the daytime the occurrence of photochcmically produced 0, in the variation is much less consistent, reflecting the action valley-that is when the concentration of 0, is greater than 40 ppb. Most probably all the 0, observations in of photochemical reactions producing 0, from NO2 the Latrobe Valley with concentrationsabove this level and HCs during the daytime. The variation of the are due to photochemical production of 0,. From 0.6 relative frequency of 0, > 40 ppb with NO, concentration during the day is seen in Fig. 9. In general, the to 2.5% of the time 0, concentrations measured at highest relative frequencies of 0, > 40 ppb appear to these five rural sites in the Latrobe Valley were greater occur at NO, concentrations of 3-4 ppb. The overall than 40 ppb (Fig. 5). The highest 0, concentration recorded in the Latrobe Valley was 100 ppb (Table 3) trend is still for 0, to decrease with NO, during the day suggesting that, while photochemical production is and this is consistent with estimates of 0, occurrences

Surface ozone at rural sites in the Latrok

Valley and Cape Grim, Australia

2411

1 ON 80 HE l -+ D---a

MR MT

cc IBASELINE CONOITIONS)

10

0

30

10

O3

CONCENTRATION

50

LO

(ppb)

Fig. 5. Cumulative frequency distributions of OS on a normal probability scale.

0 b

10

l

ON

80 HE

x HR l

1-3

MT

r-6

7-9

11-15

IO-12

LOCAL

TIME

16-18

l9-21

n-14

(h)

Fig. 6. Diurnal variation of relative frequency of 0, z 40 ppb.

dominant during a small fraction of the time, it has a relatively small influence on the long-term average 0, concentration. In general in this data set, 0, decreases as NO, NO, or NO, increases.

Also we find in examining the full data set that most of the NO, present is in the form of NO,, not NO. This arises because the NO, concentration is generally less than the background 0, concentration and therefore

1. E. GALBALLY ef al.

2412

0-l

z-4

WIND

4-6

SPEED

TEMPERATURE

26

(m 5“ I

Fig 7. Variation of relative frequency of 0, > 40 ppb with wind speed.

O-2

3-4 NO2

5-6

7-e

(‘C)

Fig. 8. Variation of the relative frequency of 0, > 40 ppb with temperature.

58

NO2 ( ppb)

(ppb)

Fig. 9. Variation of(a)OJ at night (01-03 h)and (b)relativefrequencyofO, day (13-18 h) with NO1.

any NO present will be promptly oxidized to NO, by the reaction of 0, with NO. NO buildup only occurs on the rare occasions when 0, disappears from the air. Consequently, in this data set, 0, is positively correlated with the ratio NOJNO,,. To assessthe contribution of power station NO, to total NO, present when photochcmicd pollution occurs we note that the largest fraction of NO, in the Valley comes from large elevated point sources, generally power stations CTablc 2). Thcrcforc. as SO2 ;Ind NO, have similar removal mechanisms in this situation, i.e. dry deposition and advation out of the valley. WCcan identify the power station contribution to the total NO, present by considering the amount of SO, simultaneously present, given that SO, has few other sources. Unfortunately NO, and SO, concentrations are below the instrumental detection knits (2 ppb and 5 ppb, reap&vely) when O3 is greater than 40 ppb, i.e. when local photochemically produced O3 is present, on

> 40 ppbduring the

between 30 % and 80 % of occasions (depending on the site). Thus we are in fact limited by the instrument detection limit. An alternative approach involves examination of the 50,90.95 and 99 percentiles of NO, and SO, at the sites of Minniedale Road, Darnum North, Hazelwood Estate and Mount Tassie. This shows that, when mcasurablc, the SO2 lcvcls arc on avcragc approximately one half of the NO, levels. The inventory given in Table 2 indicates that major elevated point sourccscmit approximutely ~qllidcO~~ntrittions of NO, and SO,. Assuming that NO, and SO, have removal processes that operate at comparable rates, then it app;lrs, from the itvct;tgc NO, to SO, ratio ;I( the vatiey sites, that the NO, observed at these rural sites has contributions of the same order of magnitude from diffuse surhcc sourcw a@ rmtjor point sources, in contrast with the 1: 6Umth of NO, emissions from surfpce and elevated sources. However. it is not certain whether this ‘average’ situation applies to those few “/, of occasions when 0, > 40 ppb. More precise

Surface ozone at rural sites in the Latrobc Valley and Cape Grim,

measurements of NO, and SO1 are needed to examine this point. S. REGReSSIONMODELLING 5.1,

Ozone distributions

and correlations

aim of the modelling is not only to describe the average 0s concentration for a given combination of factors (season, time of day, temperature, wind speed, etc.), but also to describe the variation about that average. The distribution of 0, concentration at the valley floor sites is positively skewed (skewed to the right), as is shown in Fig. 5. y-, Weibull or log-normal distributions fit the data fairly well; it requires very large sample sizes to discriminate between them. For any given set ofconditions (e.g. a small temperature range), the distributions tend to be more symmetric, though an interesting result is found in the case of temperature. Using the data for Minniedale Road, the coefficients of skew by tem~rature ranges were calculated and are presented in Table 5. This shows that the coefficients of skew tend to increase with increasing temperature except for temperatures less than 10°C. Thecorresponding data for other sites gave negative coeficients of skew for temperatures under 10°C and values close to zero for the tO-15°C range. Thus the skewness of OJ distributions in the Latrobe Valley increases fairly steadily with temperature. The overall skewness for Minniedale Road was 0.64, which is typical of the high temperature skewnesses. After fitting the regression mode1 to be described, the skewness of the variation about the regression line was found to be 0.09 for Minniedale Road. ff the regression model allows for y- distributions of residuals about the regression line with constant shape parameter but varying scale parameter, then the maximum likelihood equations to be solved for the regression coefficients are (weighted) least-squares equations. This property applies to certain other distributions also, including the Poisson and binomial distributions (Green, 1984). If the true model is known then the use of the weights guarantees asymptotic efficiency. However, the properties of weighted least squares, using estimated weights, are not well understood, and in the few cases where they have been investigated for finite sample sizes (Sowden, 1971), the regression coeficients have often been found to be substantially biased. The

In this study, large weights would be assigned to cases giving low O3 con~tratio~ and small weights would beassigned to the high 03 cases of most interest.

Australia

Unweighted least squares was used resulting in un-

biased estimates of regression coefficients but some loss of efficiency. In particular, the model does not fit as well as it could at low O3 concentrations, and the models have occasionally estimated negative concentrations. Figure 10 shows autocorrelations in time of 0~ concentration at five stations. The contrasts between Mount Tassie and Cape Grim, which have almost no diurnal cycle and the other stations, are very marked. This large 24-h periodicity in the auto-correlations at the valley floor stations reflects the physical processes that regulate the dominant Oa variations of O5 increase during the daytime and decrease at night at low level ~ntin~tal sites as discussed in section 4.1. The auto-correlations for the valley sites decrease rapidly over the first 2-3 days, indicating that this is a typical period of synoptic OJ variation. This is also the time at which the auto-correlations for Mount Tassie reach a minimum. After about 3 days, the ‘centreline’ of the auto~orrelations for the valley sites is at about +O.l, indicating that the contribution from longer-term ‘seasonal’ variation is fairly small compared with the diurnal variation. The auto-correlation function at Cape Grim shows no sign of a diurnal variation. This is due to the coastal location and the predominance of winds from the ocean where destruction processes are so slow that diurnal variations in OJ are not observed. The high residual auto-correlation at Cape Grim after 2-3 days reflects the’fact that the seasonal variation at Cape Grim dominates over all other variations, including those on diurnal and synoptic time scales. Figure fl shows the cross-correlations lagged to 100 h between three of the stations, two in close proximity to each other, MR and HE, and a third at the opposite end of the valley, DN, see Fig. l(a). These cross-correlations show that 38 %-56 % of the variance in the data at zero lag is coherent between stations across the valley and that at a lag of 24 h the correlation is atmost as large between sites as it is for data from the one site. Around 16 % of the variance in the data is accounted for by the correlation of 24 h lag. A special feature of these cross-correlations that is not shown in Fig. 1I is that they aresymmetricaround zero lag. This means that there are no systematic advective

processes that affect OS variations in the valley. Overall, the cross-correlations show that between onehalfand one-third of the variance in the OJ data occurs as a valley-wide phenomenon and should be explained by variables that have common behaviour at all of the stations. The other one-half to two-thirds of the variance in the data is associated with local variations

Table 5. The coefficientsof skew by temperature range for OJ data from Minnicdale Road Tc~w~rature (“C) Sample size

< 10

to-15

2413

15-20 20-25 0.54 1458

25-30 0.86 492

>30 0.74 212

2414

1. E. GALBALLY et a/.

k0

LAG

80

60

100

(h)

Fig. 10. Auto-correlations of OS observations (all Cape Grim data rather than s&&d baseline data were used).

1.0 ON-HE

0.5

-

-

-_

-

-

_ _ _ _.

ON-HR “C_MR

0.0

2

0.7

w

u E t;

s

0.6

0.5

$ s id

06.

E ou

0.1

z ::

0.1

0.1

0

-0.1

-0.1 PO

LO

LAG

Fi.g

(h)

II. Cross-correlation of O3 observations between sckcted stations.

2415

Surface ozone at rural sites in the Latrobc Valley and Cape Grim, Australia

occurring at individual monitoring stations and, of course, errors in individual measuring instruments. The high auto-correlations cause substantial problems in regression modelling For uncorrelated samples from a distribution which has variance u*, the variance of the mean of a sample of n observations is o’/n. If consecutive samples are correlated with the auto-correlation of lag I being p’. then the variance of the sample mean is approximately ~‘(1 + p)/[n(l - p)]. That is, the elfective sample size is reduced from n to n(1 - p)/( I+ p). In our case, with the value of p in excess of 0.9, the effective sample sixe is reduced by a factor of about 24 or to about the equivalent of one independent observation per day. Some of the autocorrelation arises from the diurnal pattern, from the high auto-correlation of temperature and other factors from 1 h to the next. With high auto-correlations, ordinary least squares (OLS) gives unbiased but inefficient estimates of regression coefficients, but the usual estimates of standard errors are in error by an order of magnitude. The method of Cochrane and Orcutt (1949)was used to overcome these problems. A brief introduction to the method is given in the Appendix. OLS was used with caution for the preliminary screening of alternative models. 5.2. Regression model and results The basic model fitted was: Ozone concentration = b. + bl T+ b4di/(w + 1) + b,,mj+ b4Or4

(1)

where bO, b, , b,, b,,, b4 are regression coefficients T = temperature in “C di = dummy variable for time of day (see below) w-windspeedinms-’ m, = dummy variable for month 0 r4 = 03 concentration 24 h earlier.

Categorical variables have been represented in the model by using dummy variables.For instance, for the 12 months of the year, 11 dummy variables (one lees than the number of categories) were used. If we call these variables ml, m2, . . . ml I, then for January, m, = 1 while all the others = 0, for February, ml = 1 while all the others = 0. December is identified by having all the dummy variables = 0. In the basic model, wind speed is represented in the term di/(w+ 1) where generally d, is negative. Regression models for pollution concentration have often incorporated wind speed as a reciprocal power (e.g. Tiao et al., 1975; Annand and Hudson, 1981; Chock et al., 1982).Figure 2 shows that at high wind speeds there is very little diurnal variation in ozone, and this suggested dividing the time of day dummy variables by some function of wind speed. Dividing by (wf 1) was found to give slightly better fits than dividing by (w+ 9) (where the units of w are m s- ‘). Table 6 shows the residual standard deviations, with sample sixesin brackets, for a range of models fitted by OLS. The first model is the basic model which is contained in all of the other models. The effectiveness of adding the extra factors can be roughly gauged by comparing the residual standard deviations with those for Model 1. The use of OLS and the varying sample sixesin this table (due to missingobservations)prevent rigorous statisticalcomparisons between models based upon these results. The Cochrane-Orcutt method has been used on the more promising models. For three of the five sites, Model 5 is the best-fitting of the 15 models shown in Table 6. The fit of Model 7 indicates that little or no improvement can be obtained by adding wind speed as a variable; it is already in the model in the time-of-day factors. Model 10 uses a surrogate variable for NO, from diffuse surface sources equal to the difference NO,-SO2 or 0 when this is negative. It is interesting to note that it has reduced the residual standard deviation by between 0.4and 0.5 ppb

Table6. Residualstandard deviations of 0, (ppb) for a range of models fitted by ordinary least squares (with sample sizes in brackets)

1 2 3 4 5 6 7 8 9 10 11 12 13 14

Raaic model [Equation (l)] Rasic + tamparature2 Rasic+NO, Rasic+NO+NOz &sic + NO, + temperature2 Rasic+ Pasauill Rasic+wind spaed &sic + wind direction Raaic+TRR for each of last 24 h Raaic+ surface NO, Raaic+ NO,/NO, ratio Basic + power ganerati011 Rasic+NO,*timeofday Rasic+NO..timcofday

+NOf*tirneofdav 15 Rasic-+NOx.t&ofday + NO. l temperature

Hazelwood Estate

Minniedale

4.63 (5155) 5.11(4587) 5.04 (6822) 4.72 (6429)

5.18 (13,110) 5.18 (13.110) 4.76(11%8j 4.63 (10.332) 4.75 ii l;968j 4.84 (7120) 5.18(13,110) 5.09(13.110) 5.12(11.184) 4.74 (10,905) 4.94 (8180) 5.17(13,110) 4.73 (11,968)

5.24(16,411) 5.14(16,411) 4.76(15,101) 4.76 (14,679) 4.64(15,101) 5.25 (9030) 5.23 (16,411) 5.20 (15411) 5.09(15291) 4.86 (11,772) S.lS(12.518) 5.19 (16411) 4,74(15,101)

4.83 (4507)

5.43 f8645) . ,

4.63 (6429)

4.69(11,968)

4.71 (l&101)

4.75 (4507)

5.44 (8645)

4.54 (6429)

4.63 (1 l,%S)

4.74(15,101)

4.82 (4507)

Darnum North

Roolarra

5.79 (10,631) 5.64(10,631) 5.47 (8645) 5.61(5445) 5.31(6645j 5.88 14112) 5.78 (10,631) 5.62 (10.631) 5.86(9149) 5.65 (7552) 5.65 (6594) 5.79 (10,631) 5.45 (8645)

5.05 (6822) 4.96 (6822) 4.75 (6429) 4.68 (6119) 4.66 (6429) 5.05 (6822) 4.84 (6822)

Road

Mount Tassic 5.48 (5Kt8) 5.22((5008) 4.87 (4507) 4.84 (4407) 4.59 (4567) 5.45 (5008) 5.38 (5008) 5.00(4158) 5.87 (1934)

2416

I. E. GALBALLY

except at Damum North. Model 12 includes the power generation of each of the different power stations in the valley for each hour. Its inclusion made a negligible improvement in the fit of the model except at Minniedale Road where there was a reduction of 0.05 ppb in the residual standard deviation. Table 7 gives regression coeficients using one iteration of the Cochrane-Orcutt method for the Model 5 in Table 6 with a first-order auto-regressive model for residuals. Model 5 is: Ozone concentration = bo + h, T+ 627” + b,,dJ(w + 1) + b,,mj + b5024 + &NO,

(2)

where NO, = NO, concentration (ppb) and the bs are the regressioncoefficients.

From Table 7 we see that the relationship with temperature and NO, is very strong with the standard errors of the regression coefficients for these variables (which are shown in brackets in the table) being very much smaller than the coeficients themselves. At the valley floor sites the multiple regression gives roughly a I-ppb increase in O3 for every 1°C increase in air temperature over the range 5-35°C. For each I-ppb increase in NO, the OJ concentration is decreased by an average of0.5 ppb. This isconsistent with the results in Fig. 9 (for NO*). The time-of-day factors are fairly small; for instance, for a light wind of 2 m s - ’ the value of I/(w + I) is one-third which means that the average

et ol.

time-of-day variation in OJ concentration after allowing for temperature, NO,, etc., is about f 1 ppb at this wind speed. The monthly factors give a range of about 10-12 ppb with a fairly smooth pattern which could be represented well by one or two Fourier terms. As the regression coefficient for the 0~ concentration 24 h earlier is only between 2% and IO%, it does not contribute much to improving the fit. The residual standard deviations are all about 5 ppb. A residual standard deviation of 5 ppb means that in about 95 % of cases, the fitted OJ concentration was within + 10 ppb of that observed. Table 8 shows that the scatter about the regression line is smaller at low concentrations than at high concentrations. The residual standard deviation ranges from about 2-4 ppb for low estimated concentrations to 7-10ppb for estimated concentrations over 30 ppb. The model results in some estimated 0, concentrations being less than zero, as discussed in the previous section. Figure 12 shows the manner in which OJ, estimated from the regression Model 5, varies with temperature, time-of-day, wind speed and season for the five sites. Thus the terms (b, + b, T+ b2 T’) are shown for temperature, b,di/(w+ 1) for time of day and zero wind sped, and bdimj for month. The same vertical scale has been used for each of these factors, thus showing the dominance of temperature as an explanatory variable. The monthly factor shows a strong seasonal pattern at the valley floor sites identical to that observed at Cape Grim in clean air. The mountain top site, Mount Tassie, shows a lesser seasonal cycle and this is

Table I. Regression coefficients and standard errors (in brackets) for Model 5 fitted using the Cochrane-Orcutt method

Variable

Darnum North

4.1 (0.8) Temperature (“C) 0.52 (0.06) (Temperature)* (“C*) 0.011(0.001) Time- 1-3 a.m. - 1.3 (0.3) of-day 4-6 a.m. -2.6 (0.3) factors l-9 a.m. -4.1 (0.3) divided IO-12 noon -2.6 (0.4) l-3 p.m. 0.0 (0.5) by 0.8 (0.4j (w+ I) 4-6 p.m. 0.2 10.4) l-9 p.m. IO-12 mdngt -0.9 io.4j -2.0 (0.8) Month Jan. factors Feb. - 1.6 (0.9) -0.4 (1.1) Mar. 5.4 (1.0) Apr. 5.1 (0.9) May luoe 1.0 (1.0) 10.1 (0.9) July 6.1 (1.0) Aug. 5.2 (0.9) Sept. 6.0 (0.9) Oct. 3.0 (0.8) Nov. 0.069(0.0!39) Oz;;p(22) h before) (ppb) -0.64 (0.01) I

Intercepttbd tppb)

Sample size (h) Residual std. devn. (ppb) % Variance explained (R’)

8645 5.6 54

Boolarra 0.4 (0.8) 0.84 (0.06) 0.001(0.002) - 1.1 (0.3) -2.0 (0.3) -3.4 (0.3j -2.5

- 1.0

(0.4)

io.4j

- I.2 (0.4) -0.9 (0.3) - 1.4 (0.3) -0.2 iO.8j 0.3 10.9) 3.2 iO.9j 6.3 (0.9) 1.3 (0.9) 9.6 (0.9) Il.1 (0.9) I I.4 (0.9) Il.1 (1.1) 8.8 (0.9) 4.5 (0.9) 0.095(0.010) - 0.53 (0.02) 6 429 5.0 51

Hazelwood Estate

Minniedale Road

Mount Tassie

- 1.2 (0.1) 1.13 (0.04) -0.003(0.001) -3.3 (0.2) -3.5 (0.2) -3.8 (0.2) -2.0 (0.3) 0.2 (0.3) -0.3 (0.3) - I.5 (0.3) -2.3 (0.2) -1.1 (0.8) -0.5 (0.8) 0.1 (0.8) 3.5 (0.8) 4.8 (0.7) 8.6 (0.7) 10.6 (0.g) 10.3 10.8) 10.5 io.8j 9.5 (0.8) 6.0 (0.7) 0.030(0.ao1) - 0.52 (0.01)

I.8 (0.6) 0.51 (0.03) 0.0l5(0.001) - 1.9 (0.3) -2.1 (0.3) -2.8 (0.3) -0.2 (0.3) I.9 (0.3) 0.2 (0.3) -0.1 (0.3) -1.5 (0.3) - 1.6 (0.6) -0.8 (0.1) 2.1 (0.6) 5.0 (0.6) 6.8 (0.6) 8.7 (0.6) II.3 (0.6j 9.8 (0.6) 10.8 iO.6j 9.8 (0.6) 6.5 (0.6) 007 l(O.006) - 0.46 (0.01)

22.6 (1.1) - 0.20 (0.06) 0.029(0.002) 0.6 (0.9) 0.3 (0.8) - 1.3 (0.8) -2.1 (0.8) -3.4 (0.8) -3.6 (0.8) - 1.1 (0.8l

I I 968 5.1 62

I5 I01 4.9 64

4501 5.0 43

-0.1

-4.6

io.9j

(1.6) -5.6 il.;) 1.6 (1.1) 1.1 (1.1) 2.1 (1.8) 4.1 i1.7j 1.2 (1.8) 2.8 (1.1) 3.4 (1.8) 1.3 (1.1) -2.3 (1.6) 0.018(O.OlO) -0.51 (0.01)

2417

Surface ozone at rural sites in the L&robe Valley and Cape Grim, Australia

--.--

MR -lo

Fig. 12. Fitted ckts in the model of 0, concentration vs (a) temperature, (b) time of day and (c) month.

consistent with the near absence of a seasonal cycle observed at Aspendale [Fig. l(a)] from 03-sonde flights at 800 mb pressure height ( - 2 km) described in Pittock (1977). It is surprising that the time of day factors are so small compared with the diurnal variation in O3 concentration seen in Fig. 2(a). The explanation of this is that the diurnal variation of OS in the valley-in particular the nocturnal decrease in 03-is more closely coupled with temperature than with time of day. The physical mechanism involves OS decreases that are coupled to clear skies, low surface temperatures and the presence of a strong nocturnal inversion (Galbally, 1968). The percentage of the variance explained by this model ranged from 43 % for Mount Tassie to 64 % for Minniedale Road. The low result at Mount Tassie is probably due to the lower variability of O3 about its mean concentration. The fraction of the variance explained in a regression relationship such as Equation (2) depends on the length of data records analyxed, the selection of the data (e.g. all data or only daily maxima) and other factors affecting the statistical characteristics of the data sets chosen. Ludwig and Martina (1979) show that for 1 month of data at one site 80 % of the variance in O3 was accounted for by the correlation with air temperature, but the fraction of variance explained at another site by air temperature

2418

I.

E. GALB~SLLY

was less than 1.Ye.Evans er nl. (1983) used a year’s data and separately examined six sites. While at three of the sites they only accounted for IS % of the variance in the Oa data, at two other sites they accounted for 32 % and 52 % of the variance, with the temperature departure from the monthly mean and relative humidity as the best variables. The studies of daytime or daily maximum l-h 0, concentration (Williamson, 1979; Karl, 1979; Chock et al., 1982; Clark and Karl, 1982) show that a much higher percentage of the variance in this selected O3 data set can be explained by regression analysis. The most effective variables are air temperature and air parcel trajectory. Three of the studies show regressions that account for between 50 % and 80 % of the variance in the data (Williamson, 1979; Karl, 1979; Chock el al., 1982), while the fourth study (Clark and Karl, 1982), the most comprehensive, only gives the component of the variance explained when the regression equations are used for predictive purposes. Naturally, this is lower than that obtained in the analysis mode and lies between 20 % and 50 % for the 27 stations examined. There has been no attempt so far to apply regression analysis to the daily maximum l-h Oj concentrations that occur in the Latrobe Valley. Figures 13 and 14 show the auto-correlations lagged to 100 hand the cross-correlations between stations of the residuals from Model 5. These can be compared

Fig. 13. Auto-correlations

af.

with the auto-correlations and cross-correlations of the raw ozone data presented in Figs 10 and 11. Because of small sample sizes for observations jointly available at pairs of points, this analysis of residuals was confined to three sites, two in close proximity to each other, MR and HE, and a third at the opposite end of the valley, DN [see Fig. l(a)]. The autocorrelations of residuals show that around 74 x-83 % of the variance in the residuals occurs on a time scale longer than 1 h but only 10 % of this variance occurs on a time scale longer than around 10 h. Furthermore the cross+orrelations of residuals between the sites show that between 21% and 39% of the variance in the residuals is coherent between the sites for a zero time lag but only 10% of the variance in the residuals is coherent between the stations on a timescale ofaround 7 h or more. At a pair of sites not shown in Fig. 14, HE and 30, the cross-correlation at zero lag is 0.78, the covariance between these two sites is 61%. These figures indicate that there could be at least two processes causing the unexplained variance. Most of this variance occurs on time scales of less than 10 h, hence the driving mechanisms must vary on similarly short time scales. One of the processes, which could explain between 30% and 60% of the residual variance, is site specific (i.e. it acts independentfy at each site), whereas the other process which acts uniformly across the valley, could explain between 10% and M %

ro LAG

ef

60

00

(h) of 0,

residuals (observed-modelled).

2419

Surface oxone at rural sites in the Latrobe Valley and Cape Grim, Australia 1.0 ON-HE ---

a9 -

-

___-----

40

LAG

Fig. 14. Cross-correlations

60

DN-“R M-MN

100

60

(h)

of 0, residuals (observed-modelled) selected locations.

of the variance in the residuals over periods of 7 h and IeSS. An analysis of the factors contributing to the variance in the Ox data is presented in Table 9. At all the stations except Mount Tassic,the regression mode1 accounts for more than 50% of the variance in the data. The variance from instrumental causes was evaluated from simultaneous measurements with two instruments at Cape Grim (Elsworth and Galbally, 1984)and is an insignificant cause of variability. The variance of residualsunaccounted for by the regression mode1 appears to be equally divided between that arising from site specific effects and another component that is coherent from site to site, as determined by the intersite correlations of these residuals. This indicates that there is still room for significant improvement in the explanatory model which could probably be achieved by the introduction of new independent variables. This may be possible using 900 mb wind speed and direction, mixing heights and HC concentrations if such data were available. The results of Chock et al. (1982)suggest that the USCof an upper air temperature, say at 850 mb, may provide a worthwhile improvement in the fit of the model. Trajectories of air masses, inversions and synoptic features are some of the additional parameters rquiring investigation. Cubic terms in temperature have

between

Table 9. The variance in 0, at each station Site Total variance (ppb)’ Variance accounted for by model (%) Variance from instrumental causes (%) Unexplained variance (%) (a) inter station (b) site specific

DN 64

BO 50

HE 67

MR 67

MT 44

54

51

62

64

43

2

2

2

2

2

20 26

30 19

23 15

19 17

17 40

been fitted, as also have cross-products of temperature and l/(w + 1).Both gave small but statistically significant improvements in fit. 6. CONCLUSIONS The bchaviour of O3 in the L&robe Valley is explained largely in terms of natural background atmospheric processes.The O3 variations with a nighttime minimum and daytime maximum, along with a seasonal maximum in well-mixedcondftions in winter, are almost indistinguishable from those expected for clean air at these rural sites.

2420

1. E. GALBALLY et al.

The maximum concentration of naturally occurring O3 in clean, near surface air over SE Australia (obtained from a 6-year record at Cape Grim, Tasmania) is less than 40 ppb. Five rural sites in the Latrobe Valley show OJ values exceeding 40 ppb between 1% and 3% of the time. This is evidence of local photochemically produced 03. This photochemitally produced 0, appears preferentially at low NO, concentrations (3-4 ppb) during the afternoon (hours 13- 18) and at high temperatures (above 25°C). The Oa data for the Latrobe Valley are highly autocorrelated. Site to site correlations between valley floor sites around 30 km apart range from 0.65 to 0.81, indicating a uniform behaviour of Oa in rural air in the L&robe Valley. When observations from an elevated station (Mount Tassie-750 m) adjacent to the valley are compared with those from the valley floor stations, systematic differences in seasonal and diurnal O3 variations and the time of day ofoccurrence ofelevated 0, concentrations are observed which can be explained in terms of the diurnal cycle of convective mixing and mountain/valley winds. An explanatory statistical model has been developed to incorporate this physical understanding into linear regression analysts of the data. Due IO the high autocorrelation both in the raw data and the residuals the method of Cochrane-Orcutt was preferable to standard least squares fitting methods. The final model explains between 43 and 64% of the variance at the various sites with a residual standard deviation of 5 ppb: Ozone concentration

= intercept + linear function (temperature, temperature’, time of day/[wind speed + 11, month of year, ozone 24 h earlier, NO,).

Many other variables w&e tested but none gave a worthwhile improvement to the model. Between station correlations of the residuals at zero lag are about 0.5-0.6, which implies that there are still some spatially coherent processes that are unaccounted for in these models. Air temperature is the most powerful variable in explaining 0, variations in the Latrobe Valley. The basic explanatory statistical model developed here shows about a I-ppb 0, increase for each 1°C temperature rise over the range 0 to > 30°C. Statistically, temperature was a surrogate for total solar radiation and stability. Month of year was the second most significant variable. The model showed about a IO-ppb seasonal range in 0, with the maximum in the winter monthsat the L&robe Valley sites. This is identical to the seasonal variation of OJ observed in clean air at Cape Grim, Tasmania. Ac&aowledgemetus-We thank all those peopk who have assisted in gathwing and processing the data kom the Latrok Wky Air Monitoring Network and the Cape Grim Baseline Air Pollution Station. We thank Dr C. H. B.

Pries&y, past Chairman of the Latrobe Valley Air Shed Study Steering Committee, who initiated this study.

REFERENCES

Ahmct S. (1984) Synoptic and mesoscale weather features associated with 0, and SO2 peaks in the Latrobe Valley. Proceedings of the 8th Internarional Clean Air Conference, Melbourne, 1984. Clean Air Society of Australia and New Zealand, pp. 351-360. Ahmet S., Attwood D.. Gaibally 1. E., Hoy R. D.. Joynt R. C. and Miller A. (1985) A statistical study of ozone in the Latrobc Valley. Report to the Latrobe Valley Airshed Study Steering Committee (available from Secretary. LVASSSC. C/o Environment Protection Authority of Victoria, Melbourne). Annand W. J. D. and Hudson A. M. (1981) Meteorological etTectson smoke and sulphur dioxideconcentrations in the Manchester area. Atmospheric Enoironmenr 15, 799406. Antell M. R. (1979) A model of ozone production in urban plumes. Enoir. Int. 2, 157-166. Ardern F. (1983) Ambient hydrocarbons in the Lutrobe Valley. SECV, Research and Development Department, Reoort No SO/83/61. . Auer R. (1939) Uber der taglichen Gang des Ozonegehalts der bodennahen Luft. Beir. zur Geophvs. 54. 137-145. Chock D. P., Kumar S. and He&ma& k. W. (1982) An analysis of trends in oxidant air quality in the South Coast Air Basin of Calil’ornia. A/moqlwric~ /~twirvnmcw/ 16. 2615-2624. Clark T. L. and Karl T. R. (1982) Application ol’ prognostic meteorological variables to forecasts of daily maximum one-hour ozoneconcentrations in the Northeastern United States. J. appl. Met. 21, 1662-1671. Cochrane D.-&d Orcutt-G. H. (1949) Application of least squares regression to relationships containing autocorrelated error terms. J. Am. Srarisl. Ass. 44, 32-61. Crutzen P. J. (1974) Photochemical reactions initiated by and influencing ozone in unpolluted tropospheric air. Tel/us 26, 58-70. Danielsen E. F. (1968) Stratospheric-tropospheric exchange based on radioactivity, ozone and potential vorticity. J. afmos. Sci. 25, 502-518. Elsworth C. M. and Galbally 1. E. (1984) Accurate surface ozone measurements in clean air: fact or fiction. Proceedingsof the 8th International Clean Air Conference, MG~~OUIIIC.1984. Clean Air Society of Australia and New Zealand, pi. IO!&-1112. Environment Protection Authority of Victoria (1981) State Environment Protection Policy (The Air Environment). Victoria Gowwnent Gazette 63, 2293-2305.

Environment Protection Authority of Victoria (1982) Amendments to the State Environment Protection Policy (The Air Environment). Victoria Gownment Gazette IZO, 3895-3902. Evans G., Finkelrtein P, Martin B., Pessiel N. and Groves M. (1983) Ozone measurements from a network of remote sites. J. Air Pollur. Conrrol Ass. 33, 291-296. Fehsenfeid F. C.. Bollinger M. J., Liu S. C., Parrish D. D.. McFarland M, Train& M, Kky D, Murphy P. C. and Albritton D. L. f1983LA study of ozone in the Colorado Mountains. J. (Itmos. Chem. 1; 87-105. Galbally 1. E (1968) Some measurements of ozone variation and daatruotion in the atmospheric surface layer. Nature, Land. 281.456457. Galbally 1. E. (1972) Ozone and oxidants in the surface air near Metbourne, Victoria. Froceediflgs c$Uie /n&rnufionu/ CIPan Air Conf&nce, Melbourne, 1972. Clean Air society of Australia and New Zealand, pp. 192-198. G&ally 1. E. and Roy C. R. (197X) Loss ol’ tixcd nitrogen from soils by nitric oxide exhakdtion. Nufure, L.ond. 275, 734-735.

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Surface ozone at rural sites in the Latrobc Valley and Cape Grim, Australia G&ally 1. E. and Roy C. R. (1980) Destruction of ozone at the earth’s surface. Q. II R. nut. Sot. 106,599-620. Galbally I. E. and Roy C. R. (198 1) Ozone and nitrogen oxides in the Southern Hemisphere troposphere. Proceedings o/ the Quadrennial Ozone Symposium, Boulder, Colorado, 1980. International Ozone Commission, Vol. I. pp. 431-438. Garland J. A. and Derwent R. G. (1979) Destruction at the ground and the diurnal cycle of concentration of ozone and other gases. Q. JI R. met. Sot. 105, 169-183. Green P. J. (1984) Iteratively reweighted least squares for maximum likelihood estimation, and some robust and resistant alternatives. 1. R. sW.sr. Sot.. II 46, 149-192. Haagen-Smit A. J. (1952) Chemistry and physiology of Los Angeles smog. Iad. Engng Cheat. 44, 1342-1346. Hart J. A. (1981) The Latrobe Valley Airshed Study: its significance as a tool for future planning. C/eon Air (Ausf.) 15,4!&55. Hooper M. A., Body P. J.and Lyall R. J. (1984) Hydrocarbons in the Latrobe Valley Airshed. Victoria, Ministry for Conservation, Environmental Study Series Publication Number 405. Hoy R. D. (1979) Ozone in the Latrobe Valley. State Electricity Commission of Victoria, Research and Development Department Report 362. Hoy R. D. (1984) Aspects of high pollutant concentrations in the Latrobe Valley. Proceedings of the 8th International Clean Air Conjerence, Melbourne, 1984. Clean Air Society of Australia and New Zealand, pp. 319-336. Johnson G. M. (1983) Factors affecting oxidant formation in Sydney air. In The Urban Atmosphere: Sydney, A Case Srudy (edited by Carras J. N. and Johnson G. M.), pp. _. 393-1408. CSIRO, Melbourne. Johnson G. M. (1984) A simple model for predicting the ozone concentration in ambient air. Proceedings ofthe8rh Inrernational Clean Air Conference, Melbourne, 1984. Clean Air Society of Australia and New Zealand. DD. .. 715-731. Jona D. E. (1982) Drainage flows and mixing depth at Loy Yang (Minniedale Road) from acoustic sounding records. State Electricity Commission of Victoria, Research and Development Department Report SO/82/47. Joynt R. C. (1983) Latrobe Valley pollution characteristics. Clean Air (Aust.) 17, 4248. Jungc C. E. (1962) Global ozone budget and exchange between stratosphere and troposphere. Tellus 14.363-377. Karl T. R. (1979) Potential application of model output statistics (MOS) to forecasts of surface ozone concentration. J. appl. Met. 18, 254-265. Laws-Herd K. A. (1984) The Latrobe Valley air monitoring network: criteria pollutant measurements and results. Proceedings ofthe 8rh lnrernational Clean Air Conference, Melbourne, 1984. Clean Air Society of Australia and New Zealand, pp. 4714113. Levy H, II, Mahlman J. D, Moxim W. J. and Liu S. C. (1985) Tropospheric ozone, the role of transport. J. geophys. Res. 90,3753-3772. Liu S. C, McFarland M, Mahhnan J. D. and Levy H. (1980) On the origin of tropospheric ozone. J. geophys. Res. 85, 7546-7552. Lqan J. A. (1986) Tropospheric ozone: seasonal behaviour, trends and anthropogenic influence. J. geophys. Res. (in press). Ludwig F. Land Martinez J. R. (I 979) Analysis ofacrometric data from the Houston Area Oxidant Study (HAOS)-I. In Proceedings of the Confirence on OzoneJOxidunts: lnreracrions with the Total Environment. 14-17 October 1979, Houston, Texas, pp. 127-142, Air Pollution Control Association, U.S.A. LVASSSC (1981) Air quality and related factors in the Latrobe Valley: report on the present state of knowledge. Latrobe Valley Airshed Study Steering Committee (available ffom Secretary, LVASSSC, Environment

Protection Authority of Victoria, Melbourne). Manins P. C. (1984) Concomitant researchfrom the Latrobc

Valley Air&cd Study. AI&. Met. Msg. 32,9S-104. Milkr A. J. (1982) Statistical investigation of pollutiott in the Latrobe Valley. Siromath report to Latrobe Valley Airshcd Study Working Group. Oltmans S. and Komhyr W. D. (1976) Surface ozone in Antarctica. 1. geophys. Res. 81, 5359-5364. Physick W. L. (1983) An initial assessment of pollutant dispersion by sea breezes in the L&robe Valley. Clean Air (Ausr). 17, 24-28. Pittock B. (1977) Climatology of the vertical distribution 01 ozone over Aspendale (385,145”E). Q. JI R. met. Sot. 103,

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APPENDIX: THE COCHRANE-DRCUTT

METHOD

The autocorrelation of consecutive O> concentrations is typically just over 90%. Standard least-squares regression theory is for the case of uncorrelated r&duals when the estimates obtained can be shown to have certain optimal properties. If the true relationship between the ‘Y-variable’, i.e. ozone concentration in our case, and the ‘X-variables’ is Y=Xg+E where /l is the vector of regression coefficients, and c represents the residual variation, and if Z is the covariance matrix of the residuals, then an equation with tutcorrelated residualscan be obtained as follows: firstly, find the Cholesky factorization

I=

LL’

where L is a lower triangular matrix. (The matrix E is an n x n matrix where n is the number of observations; for Minniodak Road this means that Z is a 15,101 x 15,101 matrix.)Thcn we

can write (L_‘Y) = (L-‘x)B+L-‘&, and the residuals in the vector L-‘c are uncorrelated.

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I. E. GALBALLY et al.

In practice, to use this method some simplifying assump tionsmust bcmadcabout theformofZ. Ifwcassumca simple first-order auto-regressive bchaviour for the residuals. then L-t has a very simple form. If p is the first-order autocorrelation of the residuals, then CL-’ has the form

for certain constants u and c. This means that in our multiple

regressions, we replace Y = the ozone concentration in the ith hour by (Y, - pY, _, ) and similarly we replace the i-th value Xi of any predictor variable by (Xi - pXi _, ). The method actualiy used has been to first fit the model by OIL3 (ordinary least squares) and then to estimate p from the residuals. This estimate of p was then used in the next regression. Experiments were conducted to test whether a second-order auto-regressive model fitted better; it did fit significantly better, but the improvement was small. Experiments were also carried out in which further iterations were used to estimate p. The use of this method has usually only made small changes in the values of regression coegkients compared with OLS, but has substantially changed the estimated standard errors; the usual OLS estimates of the standard errors are invalid.