DERIVING SPATIAL MAPS OF MEAN WINTER-TIME PM2.5 IN LAUNCESTON: STEPSTOWARDS PARTICLE POPULATION-EXPOSURE ESTIMATION

John Innis1, Maj-Britt di Folco1 , Alex Bell1 , Ellis Cox1, Andrew Cunningham1 , Bob Hyde1 ,Mark Stanborough1 , and Elzbeita Chelkowska1

1 EPA Tasmania, GPO Box 1550 Hobart TAS 7001 Australia

Abstract Determination of population exposure to particles is an air quality topic ofincreasing interest, not the least because of the requirement for Australianjurisdictions to report this under the varied National Environmental ProtectionMeasure.

The topic is of special importance in Tasmanian, where elevated PM2.5 levelsoccur in many cities and towns in winter, due to smoke from the use of woodheaters (wood burners) for residential heating, coupled with cold weatherand local microclimates. Launceston, for example, is well known asexperiencing poor winter air quality, albeit much improved from severaldecades ago.

The air monitoring station at Ti Tree Bend in Launceston was established in1992. The South Launceston BLANkET (Base-Line Air Network of EPATasmania) station was installed in mid 2010. Typically each station recordsaround 10 to 15 winter exceedences of the Australian 24-hour PM2.5

standard of 25 μg/m3. Time-series of day-averaged PM2.5 data from thesestations are similar, and suggested the measured air quality may berepresentative of the broader area.

More recently however, car-based smoke measurement surveys revealedlarge spatial variation in night-time PM2.5 around Launceston. Some areas(such as Ravenswood and Mayfield) were consistently smokier thanelsewhere.

Launceston smoke surveys from winter 2015 and 2017 have been used, inconjunction with the fixed-location station data, to derive spatial maps ofmean winter-time PM2.5. The survey data were spatially-gridded andcorrelated with the contemporaneous PM2.5 levels recorded at the station.The linear relationship between PM2.5 levels in a grid cell and at the stationsare derived. The gradient of this relation provides a ‘grid–cell multiplier’. Amean winter–time PM2.5 is found by applying the cell-specific multiplier to thewinter–time mean PM2.5 from the fixed station. The method potentiallyprovides a direct measurement-based approach towards particle population-exposure estimation.

Keywords: PM2.5; woodsmoke; population exposure; Tasmania

1. IntroductionAn emerging air quality theme of increasingimportance is the estimation of population exposureto pollutants. Approaches using measurements,inventories, land-use information, remote-sensingand dispersion modelling are all under activedevelopment.

In the Tasmanian context the major pollutant ofconcern is PM2.5, as woodsmoke, arising fromresidential heating, planned burning, and bushfires.

Launceston (pop ~100,000) is well known asexperiencing poor winter-time air quality due tosmoke from residential woodheaters. In recentdecades the air quality in this city has improvedsignificantly, but exceedences of the 24-hour PM2.5

standard of 25 μg/m3 still occur, typically about 10to 15 instances in a winter, as measured at thereference level air station at Ti Tree Bend.

A second air station was established at SouthLaunceston, about 3.5 km south of Ti Tree Bend, in

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July 2010. This was one of the early BLANkET(Base-Line Air Network of EPA Tasmania) stations,using a specifically-calibrated DRX DustTrak forparticle measurements. The time-series of winter-time day-averaged PM2.5 for Ti Tree Bend andSouth Launceston were found to be very similar.

However, smoke measurement surveys using thecar-based 'Travel BLANkET' instrument in 2012and 2013 provided evidence that some areas ofLaunceston appeared to often exhibit higher smokelevels that were recorded at either of theLaunceston stations. Consequently, in winter 2015a comprehensive series of evening surveys wereconducted in Launceston to attempt to map out thespatial distribution of winter-time smoke. A furthersurvey programme was conducted in Launceston inwinter 2017. This paper presents the survey dataand results, and introduces an analytical methodthat looks to combine the spatial information fromthe surveys with the temporal information from thefixed station data in order to derive estimates ofwinter-time PM2.5 across Launceston.

2. The Launceston surveys, 2015 and 2017

2.1 Travel BLANkETThe Travel BLANkET instrument was designed andbuilt by EPA Tasmania as a means of mapping thespatial distribution of smoke in Tasmanian towns. Adescription is given in Innis et al. (2013). A DRXDustTrak (TSIinc.) samples the ambient air,recording a PM2.5 measurement every 5-seconds. AGPS device provides geo-location information. Alaptop with purpose-written software logs the PM2.5

value and the location of the measurement.

2.2 2015 surveysIn winter 2015 a total of 10 surveys were conductedin Launceston between the 22nd of May and the 9th

of September. Over 30,000 individual PM2.5

measurements were made. The surveys typicallycommenced around 20-h (8 pm) in the evening andcontinued to 01-h the next morning, to coincide withthe typical diurnal peak in PM2.5 concentrations.

2.3 2017 surveysIn winter 2017 surveys were conducted on 10separate nights between the 3rd of May and the 20th

of July. On five of these nights two cars were used,each with a Travel BLANkET instrument, effectivelymeaning 15 separate surveys were conducted.Surveys usually ran from 20-h (8 pm) to 02-h. Over50,000 separate PM2.5 measurements were made.

2.4 Survey results

The raw survey measurement data are shown inFigure 1, with the 2015 survey in the upper paneland the 2017 survey below. Individual PM2.5

measurements are colour-coded (see the key in thefigures for representative values) and also arerepresented by height above local ground level.Some some suburbs have been identified in theupper panel.

In general the winter 2015 surveys encounteredmore smoke than in 2017, probably in part as thesurveys in 2015 tended to be on colder nights,hence with presumed stronger inversion layers. Ingeneral however there is a large measure ofsimilarity in that the smokiest areas seen in 2015(e.g. Mayfield, Ravenswood, Newstead) tend to bethe smokiest areas seen in 2017.

Note for 2017: On one survey night smoke wasmeasured from a small (less than 1 ha) plannedburn in a reserve adjoining a residential area in theSummerhill area (purple symbols in Summerhill,lower panel). Many of these measurements wereover 150 μg/m3. These data have been excludedfrom the analysis to follow.

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Illustration 1: Smoke surveys ofLaunceston. Instantaneous PM2.5

measurements are represented by symbolcolour and height above local ground level.Upper panel: winter 2015; lower panel:winter 2017.

3. Analysis – rationale and implementation

3.1 Correlation of hour-averaged PM2.5 at Ti Tree Bend and South Launceston Figure 2 shows 'composite'-day hour-averagedwinter 2015 PM2.5 data from Ti Tree Bend (upperpanel) and South Launceston (lower). These havebeen formed by combining all data for a given hourof the day, regardless of date. The composite-dayfor all winter days (May-August) is shown, alongwith the composite-day for days with a day-averaged temperature below 10 C ('cold days').

The diurnal cycle at both stations is well defined.PM2.5 concentrations rise in the late afternoon andpeak at 23:00. A smaller subsidiary peak occursnear 08:00. The diurnal pattern almost certainlyarises from woodheater use, combined withmeteorological contributions such as the formationof a near-ground inversion layer after sunset.

An inter-station correlation plot of these composite-day, hour-averaged, PM2.5 values for winter 2015and winter 2017 (to mid July) are shown in Figure3. The hour-averaged PM2.5 values at each stationare highly correlated, and are very similar for bothyears. Correlation plots between hour-averageddata for individual nights do not show this level ofsimilarity. However, when averaging over theseveral months the inter-station correlationbecomes apparent.

The locations of these two stations were chosenindependently of each other, and were not selectedwith any knowledge of the level of correlation ofwinter PM2.5 at each site. The correlation that existssuggests that other locations around Launcestonmay also show similar behaviour. The analysis ofthe 2015 and 2017 proceeds on this basis.

3.2 Survey AnalysisThe analysis of the 2015 survey data wasdeveloped prior to the collection of the 2017 data,and was subsequently also applied to these laterdata. The area of greater Launceston was dividedinto a 12x12 grid, with each grid square beingapproximately 1 km on each side. PM2.5 survey datafrom a given night that fell within the grid wereformed into hour averages, and were correlatedwith contemporaneous hour-averaged PM2.5 stationdata from South Launceston (being the stationnearer to the geographic centre of the city). Gridcells were included in the analysis only if five or

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Illustration 2: Composite-day hour-averaged PM2.5 for Ti Tree Bend (upper)and South Launceston (lower) air stations,for winter 2015.

Illustration 3: Correlation plot of 'compositeday' hour-averaged PM2.5 for Ti Tree Bendand South Launceston for winter 2015(circle symbols) and winter 2017(squares). Note: Winter 2017 data are tomid July.

more independent survey measurements wereobtained in the cell. The correlation coefficient andthe significance of the correlation (expressed as theprobability of the correlation occurring by chance)were calculated.

In many cases the significance of the correlationwas found to be better than 10% (i.e. a probabilityof the correlation coefficient of that value occurringby chance of less than 0.1). An example of such acorrelation for the 2015 survey for a grid squarefrom the central suburb of Newstead is shown inFigure 4. The top panel shows the correlationbetween the hour-averaged PM2.5 survey data(vertical axis) and the hour-averaged PM2.5 stationdata (horizontal axis). The correlation co-efficient is0.638, with a probability of chance occurrence of

0.011 (~1%). The gradient of the line of minimumabsolute deviation (which is forced to pass throughthe origin of the axes) is 1.23. The lower panelshows the original (5-second) survey PM2.5

measurements plotted against hour-of-day (i.e.ignoring the date of the measurement) to show thelevel of cover over the evening hours.

Figure 5 shows the probability of chance correlationfor each grid cell over the Launceston area for the2015 survey. As noted, many cells showscorrelations significant at the 10% level or better.The pattern of green dots show the locations of theindividual survey measurements and delineate theLaunceston streetscape. The existence of thesesignificant correlations is considered to have alargely statistical origin: When meteorologicalconditions allow elevated smoke concentrations tooccur in one part of the city they are also conduciveto smoke concentrations increasing in other parts oftown.

The analysis was repeated for the more extensive2017 surveys. The 2017 results for the Newsteadgrid cell are given in Figure 6, as was presented forthis grid cell in Figure 4 for the 2015 survey. Thecorrelation coefficient found from the 2017 survey is0.647, with a significance of better than 1%.

The gradient of the line of minimum absolutedeviation, found for these 2017 data, is 1.25. This isvery similar to the value of 1.23 derived for this cellfrom the 2015 survey (see Figure 4).

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Illustration 4: Example of a grid cellcorrelation plot from the 2015 Launcestonsurvey. Top panel: hour averaged surveyPM2.5 data (vertical axis) plotted againstcontemporaneous hour-averaged PM2.5

data from the South Launceston station.Lower panel: Survey PM2.5 (5-second) dataplotted against hour of day (i.e.independent of the date).

Illustration 5: Launceston 2015 surveydata-grid showing the significance of thecorrelation for each cell. The green linesare locations of the survey measurementsand hence show the streetscape.

The consistency between the 2015 and 2017surveys for the derived gradients for theLaunceston grid cells is in general considered to bevery good. This is shown in Figure 7, where thegrid cell gradients derived in 2015 are plottedagainst the grid cell gradients found for 2017. Alsoshown is a 1:1 line. The figure includes all cellswhere five or more independent surveymeasurements were made in each year. A smallnumber of data appear far from a 1:1 relationship.In some cases these arise for grid cells thatencompass only a few streets, and often adjoin amore populated area.

With respect to Figure 7, and the relationshipbetween the survey results, it is worth notingexplicitly that each data pair of (2015, 2017) gridcell gradients comes from a comparison of thatyears' survey data, recorded each in cell, comparedto the contemporaneous hour-averaged SouthLaunceston station data. Hence the two data sets(2015 and 2017) are completely independent,excepting that some common instrumentation wasused.

3.3 Estimating the spatial distribution of ambient winter-time PM2.5

From the above results, it appears that, on average,the PM2.5 measured in a given grid cell is linearlyrelated to the hour-averaged PM2.5 measured at theSouth Launceston station. The multiplicative factorthat relates these quantities is taken as the gradientof the line of minimum deviation as found using theabove approach. It is postulated that this factorprovides a means to relate the station PM2.5 to thegrid cell PM2.5 for other times, i.e. for nights whensurveys were not conducted. Hence it is suggestedthat an approach to estimate the mean winter-timePM2.5 for a given grid cell is to multiply the stationmean winter PM2.5 by the relevant factor, being thederived gradient for that cell.

Figure 8 shows the result of using the derived gridcell multipliers to estimate the mean winter-timePM2.5 for the Launceston grid cells where thecorrelation significance was 10% or better.

The consistency of the relationships between the2015 and the 2017 surveys, and the inter-yearconsistency between the composite-day hour-

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Illustration 7: Scatter plot of grid cellgradients for 2015 (horizontal axis) and2017 (vertical axis). The 1:1 relationship isalso shown as the dashed line forguidance.

Illustration 6: Grid cell data from the 2017survey for the Newstead cell in Figure 4.

averaged South Launceston and Ti Tree Bend,indicate that the grid cell gradients (i.e. the adoptedmultiplicative factors) are likely to be applicable inother years as well.

3.4 Error estimatesAn investigation into the types and magnitudes ofthe errors that are likely to contribute to the finaluncertainty in the spatial estimate of winter PM2.5

has been conducted, but cannot be presented in fullhere. For the grid cells, the standard error of thesurvey measurements about the fitted line wasfound to be approximately 10% of the meanmeasured PM2.5 in the cell, as shown in Figure 9.This can be taken as an estimate of the statisticaluncertainty in the winter mean PM2.5 in each cell.

4. Discussion The analysis presented here has combined thespatial information provided by a relatively smallnumber of smoke measurement surveys with thecontinuous time-series of PM2.5 data from a fixedstation to derive an estimate of the spatial variationin mean ambient winter PM2.5 in Launceston, albeiton a relatively coarse grid of approximately 1 kmsquare.

The rationale for seeking a linear correlationbetween PM2.5 measured during the survey, in agiven grid cell, with the contemporaneous PM2.5measured at South Launceston station was in partprovided by the observed correlation seen between'composite day' hour-averaged PM2.5 at Ti Tree

Bend and South Launceston. An exploration as towhy linear correlations may be expected betweenthe averaged PM2.5 measured at any two locationsin Launceston is presented in Appendix A.

It is considered likely that there is variation in meanwinter PM2.5 within such a grid cell, so the workpresented here should be regarded as progresstowards a more complete understanding ofpopulation exposure to PM2.5 in Launceston.

AcknowledgmentsWe thank TasWater, the site owner the locations ofthe Ti Tree Bend and South Launceston stations.We also acknowledge the Launceston City Councilfor their on-going interest and work related to airquality issues in this city.

ReferencesInnis, J., Bell, A., Cox, E., Cunningham, A., Hyde,

B., and Smeal, A., 'Car-based surveys of wintersmoke-concentrations in some Tasmanian towns,2010-2012'. CASANZ Sydney 2013, ISBN9780987455321.

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Illustration 8: Spatial map of estimatedmean winter PM2.5 for Launceston 2015,derived using the grid cell multipliers andthe mean South Launceston station2015 winter PM2.5.

Illustration 9: Standard error about thefitted line for the grid cells versus meanmeasured PM2.5 in the cell, for the 2015survey data.

Appendix A – Why should winter PM2.5

concentrations at different Launcestonlocations appear to be linearly related?

Evidence presented in this paper suggests that,averaged over time (i.e. weeks to months), there isa linear relationship between PM2.5 concentrationindifferent areas of Launceston. This appendix offerssome discussion as to how this may arise.

For a given volume element (however defined) ofthe city, the PM2.5 concentration in the element canbe considered, in a simple sense, to change withtime as:

C(t)=(1/V) ∫ E(t) – L(t) dt (1)

where C(t) is the PM2.5 concentration at a giventime t, V is the volume, E(t) is a measure of theparticle emission into the volume, and L(t)represents the loss of particles from the box. In themost general sense L(t) could be considered asnegative, at times, if more particles flow into thevolume from adjoining volume elements than arelost. The hypothetical integration would becalculated over an appropriate time interval.

As a further simplification, given that C(t) increasesfrom low levels (clean air) to high concentration(~100 μg/m3) during the evening, assume that forthis interval L(t) is smaller than E(t), and can beneglected. This leaves C(t) as dependent on E(t)only.

E(t) in this case depends on the ensembleemissions of the woodheaters contributing smoketo the volume, V. The emission rate from any givenwoodheater is likely to vary greatly from day to dayand hour to hour. However the seemingly constantlinear relationship present in the spatial distributionof PM2.5 concentrations in Launceston suggeststhat the ensemble-average of the heater emissionsinto a given volume, averaged over time, isrelatively constant. This appears to be the simplestinterpretation of the origin of the spatialcorrelations. The number of operating woodheatersin a given volume (e.g. a suburb) could presumablychange over time, but the change from year to yearis likely to be relatively small.

The hypothesis proposed here is that the linearrelationships arise, in large part, due to the numberof woodheaters contributing smoke to each gridcell. A test of the hypothesis would be to see if thegradients (the adopted 'multiplicative factors') foundfor each grid cell bear any relation to the number ofwoodheaters in the cell.

In 2011 EPA Tasmania commissioned a home-heating telephone survey over a wide area of

Tasmania, including Launceston. From these dataa spatial map (250 m x 250 m resolution) ofwoodheater numbers was derived for Launceston.The greatest number of woodheaters were in thesuburbs of Mayfield, Invermay, Waverley andRavenswood. (Data for open fireplaces were alsocollected. The number of residences with openfireplaces was low overall.)

The woodheater survey data were re-binned intothe approximately 1 km grid squares used for the2015 and 2017 Travel BLANkET smoke surveys.Figure 10 shows in the top panel the grid cellgradients, averaged for the 2015 and 2017 surveys.The lower panel shows the woodheater numbers ineach smoke survey grid cell. While there aresimilarities between the pattern in each panel thereare also differences, most notably that the grid cellgradients appear higher than would be expected,based on woodheater numbers alone, in cells witha low woodheater total but adjoining cells withrelatively high woodheater numbers. This could beinterpreted as indicating that smoke from highwoodheater cells can move into adjacent cells

A scatter plot of woodheater number and grid cellgradient (averaged from the 2015 and 2017surveys) is given in Figure 11. There is a measureof a linear relation between these quantities, with acorrelation coefficient of 0.566, with a highsignificance. Hence, in some way, a high grid cellgradient is associated with a greater number ofwoodheaters in the cell, which is at least a measureof consistency with the hypothesis noted earlier.

Inspection of the individual data point pairs inFigure 11 revealed that some of the pointsfurtherest from a simple linear relation were fromthe outer edge of the grid, while others were, asnoted, in cells with a relatively low woodheater totalbut adjacent to a 'high woodheater' cell.

As a very crude first attempt to account for possiblemovement of smoke from a high woodheater cell toan adjacent cell, the following 'adjustment' wasmade to the woodheater cell count. If a given cellhad less than 50 woodheaters, the woodheatercount in the adjacent 6 cells was found. If the oneof the neighbouring cells had a woodheater countover 100, 25% of this cells' count was added to theoriginal cell. The scatter plot of 'adjusted'woodheater count and grid cell gradient is shown inFigure 12. The linearity and correlation coefficienthave slightly improved. The improvement is similarif a percentage contribution in the range of 15% to50% is used instead of 25%.

This 'adjustment' is presented for completeness,and to indicate that a full air-dispersion model studyappears warranted, and could prove veryinformative.

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Illustration 10: Top panel: grid cellgradients, averaged from the 2015 and2017 surveys. Lower panel: Woodheaternumbers in each grid cell, derived from the2011 telephone survey commissioned byEPA Tasmania.

Illustration 11: Scatter plot of grid cellgradients (average of 2015 and 2017) andwoodheater numbers in each survey cell.

Illustration 12: Scatter plot of 'adjusted'woodheater number and grid cell gradient(average of 2015 and 2017). See theappendix text for explanation.