Source Contributions to Ozone Formation in the New South Wales Greater Metropolitan Region, Australia

atmosphere

Article Source Contributions to Ozone Formation in the New South Wales Greater Metropolitan Region, Australia

Hiep Nguyen Duc * , Lisa T.-C. Chang, Toan Trieu, David Salter and Yvonne Scorgie New South Wales Ofﬁce of Environment and Heritage, PO Box 29 Lidcombe, NSW 1825, Australia; [email protected] (L.T.-C.C.); [email protected] (T.T.); [email protected] (D.S.); [email protected] (Y.S.) * Correspondence: [email protected]; Tel.: +61-2-9995-5050; Fax: +61-2-9995-6250

Received: 27 September 2018; Accepted: 11 November 2018; Published: 13 November 2018

Abstract: Ozone and fine particles (PM2.5) are the two main air pollutants of concern in the New South Wales Greater Metropolitan Region (NSW GMR) due to their contribution to poor air quality days in the region. This paper focuses on source contributions to ambient ozone concentrations for different parts of the NSW GMR, based on source emissions across the greater Sydney region. The observation-based Integrated Empirical Rate model (IER) was applied to delineate the different regions within the GMR based on the photochemical smog profile of each region. Ozone source contribution was then modelled using the CCAM-CTM (Cubic Conformal Atmospheric model-Chemical Transport model) modelling system and the latest air emission inventory for the greater Sydney region. Source contributions to ozone varied between regions, and also varied depending on the air quality metric applied (e.g., average or maximum ozone). Biogenic volatile organic compound (VOC) emissions were found to contribute significantly to median and maximum ozone concentration in North West Sydney during summer. After commercial and domestic sources, power generation was found to be the next largest anthropogenic source of maximum ozone concentrations in North West Sydney. However, in South West Sydney, beside commercial and domestic sources, on-road vehicles were predicted to be the most significant contributor to maximum ozone levels, followed by biogenic sources and power stations. The results provide information that policy makers can use to devise various options to control ozone levels in different parts of the NSW Greater Metropolitan Region.

Keywords: ozone; Greater Metropolitan Region of Sydney; source contribution; source attribution; air quality model; Cubic Conformal Atmospheric model (CCAM); Chemical Transport model (CTM)

1. Introduction Ozone is one of the criterion pollutants monitored at many monitoring stations in the Sydney metropolitan area over the past few decades (from 1970 until now). It is a secondary pollutant produced from complex photochemical reaction of nitrogen oxides (NOx) and mainly volatile organic compounds (VOC) under sunlight. In the metropolitan area of Sydney, NOx emissions come from many different sources including motor vehicles, industrial, commercial-domestic, non-road mobile (e.g., shipping, rail, aircraft) and biogenic (soil) sources. Similarly, VOC emission comes from biogenic (vegetation) and various anthropogenic sources. From the early 1970s until the late 1990s, ozone exceedances at a number of sites in Sydney occurred frequently, especially during the summer period. From early 2000–2010, ozone concentration as measured at many monitoring stations tended to decrease. This is mainly due to a cleaner ﬂeet of motor vehicles compared to that in the past, despite an increase in the total number of vehicles. Recently, there has been a slight upward trend of ozone levels in the Sydney region.

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Controlling ozone (either maximum ozone or exposure), with least cost and best outcome, is a complex problem to solve and depends on the meteorological and emission characteristics within the region. A study to understand and determine the source contribution to ozone formation in various parts of the Sydney metropolitan region is helpful to policy makers to manage ozone levels. Sensitivity of ozone concentration to changes in emission rate of VOC and NOx sources has been shown to be nonlinear in many experimental and modelling studies [1]. As the ozone response to NOx and VOC sources is mostly nonlinear depending on the source region, which is either NOx or VOC limited, the nonlinear interaction among the impacts of emission sources leads to discrepancies between source contribution attributed from a group of emitting sources, and the sum of contribution attributed to each component [1]. For this reason, source contribution is studied for the impact of each source to different regions in Sydney, which is characterised by the base state of emission and the meteorology pattern in the region. Observation-based methods can be used to provide a means of assessing the potential extent of the photochemical smog problem in various regions [2,3]. The Integrated Empirical Rate (IER) model, developed by Johnson [3] based on smog chamber studies, is used in this study to determine and understand the photochemical smog in various regional sites in Sydney. Ambient measurements are used at the monitoring stations. The IER model have been used by Blanchard [4] and Blanchard and Fairley [5] in photochemical smog studies in Michigan and California to determine whether a site or a region is in a NOx-limited or VOC-limited regime. The contribution of the source to ambient ozone concentration is important for understanding the process of dispersion of pollutants from various sources to a receptor point using various approaches, such as the tagged species approach [6]. Interest in this area, especially ambient particulates has been expanded in recent years in numerous studies on particle characterisation or VOC via source fingerprints using the positive matrix factorisation (PMF) statistical method [7–9]. Rather than a backward receptor model, a forward source to receptor dispersion model can also be used to study the source contribution at the receptor point. To this end, an emission inventory and an air quality dispersion model can be used to predict the pollutant concentration at the receptor point, under various emission source profile scenarios, and hence, can isolate the source contribution of various sources [6,10–13]. For example, using an urban air quality model called SIRANE with a tagged species approach to determine the source contribution to NO2 concentration in Lyon, a large industrial city in France, Nguyen et al. [13] showed that traffic is the main cause of NO2 air pollution in Lyon. Likewise, Dunker et al. [10] using ozone source apportionment technology (OSAT) in the CAMx air quality model, attributed ozone in the Lake Michigan area to mainly biogenic sources, depending on the receptor region. The forward approach of using an air quality model for source contribution determination of ozone concentration at receptor points is used in this study by using the Cubic Conformal Atmospheric model-Chemical Transport model (CCAM-CTM). Other models such as the CAMx (Comprehensive Air Quality model with Extensions) or the CMAQ (Community Multiscale Air Quality Modeling System) have been used by various authors, such as Li et al. [11] who used CAMx with OSAT to study the source contribution to ozone in the Pearl River Delta (PRD) region of China and Wang et al. [14] who used CAMx with OSAT to study ozone source attribution during a smog episode in Beijing, China. The authors showed that a significant spatial distribution of ozone in Beijing has a strong regional contribution. Similar techniques have been used to study the source apportionment of PM2.5 using source-oriented CMAQ, such as Zhang et al. [15] in their study of source apportionment of PM2.5 secondary nitrate and sulfate in China from multiple emission sources across China. The CCAM-CTM air quality model is applied to the Sydney basin, which has a population of more than 5 million and has a diverse source of air emission pattern. Beside biogenic emissions, anthropogenic source emissions such as motor vehicles and industrial sources are two of the main contributors to ozone formation in Sydney. Duc et al. [16] used a modelling approach based on the Air Pollution model-Chemical Transport model (TAPM-CTM) to show that the motor vehicle morning Atmosphere 2018, 9, 443 3 of 32 peak emission in Sydney strongly influences the daily maximum ozone level in the afternoon at various sites in Sydney, especially downwind sites in the South West of Sydney. Duc et al., [12] recently used Cubic Conformal Atmospheric model-Chemical Transport model (CCAM-CT) to determine both the PM2.5 and ozone source contribution based on the Sydney particle study (SPS) period of January 2011. Their results show that biogenic emission contributed to about 15–25% of average PM2.5 and about 40–60% of the maximum ozone at several sites in the Greater Sydney Region (GMR) during January 2011. In addition, the source contribution to maximum ozone in Richmond (North West Sydney) is different from other sites in Sydney in that the industrial sources contribute more than mobile sources to the maximum ozone at this site while the reverse is true at other sites. This study extends the previous study [12] by using many additional sources including non-road mobile sources (shipping, locomotives, aircrafts), and commercial and domestic sources over the whole 12 month period of 2008 and determining the source contribution of ozone for different sub-regions which have different ozone characteristics in its response to NOx and VOC emission.

2. Methodology and Data

2.1. CCAM-CTM Modelling System The CCAM-CTM modelling system was used in this study to simulate the photochemical process that produces ozone over the NSW Greater Metropolitan Region (GMR). This chemical transport model (CTM) is driven by the meteorological data produced by the Cubic Conforming Atmospheric model (CCAM). CCAM can use the re-analysis global data sets such as NCEP or ERA-Interim as the host data to downscale into a higher resolution regional modelling domain and to produce meteorological data as input to the CTM model. Figure1 shows the overall structure of the emission modelling and CCAM-CTM framework. The Cubic Conformal Atmospheric model (CCAM) uses the global meteorological forecast or re-analysis data such as ERA-Interim or NCEP reanalysis data as a host model to downscale these data into a region of interest at higher resolution [17]. As a mesoscale model, CCAM is similar to the Weather Research Forecast (WRF) model in that they both use the host model data (such as NCEP, ERA-Interim, CMIP5) but it is different in a number of ways:

- It does not require boundary condition data as it is a global model that can be stretched to higher resolution at one particular region. - It can use CMIP5 climate projection data as host model data and does the climate projection based on the corresponding emission scenarios of the host model.

In this study, the CTM domain conﬁguration consists of 4 nested grids, as shown in Figure2, comprising the outermost Australia domain (75 × 65 grid cells) at 80 km × 80 km resolution, NSW (60 × 60 grid cells) at 27 km × 27 km, the Greater Metropolitan Region or GMR (60 × 60 grid cells) at 9 km × 9 km resolution and the innermost domain covering the Sydney region (60 × 60 grid cells) at 3 km × 3 km resolution. Atmosphere 2018, 9, x FOR PEER REVIEW 4 of 32 Atmosphere 2018, 9, 443 4 of 32 Atmosphere 2018, 9, x FOR PEER REVIEW 4 of 32

Figure 1. Schematic structure of the emission and chem chemicalical transport model (CTM) modelling system. Figure 1. Schematic structure of the emission and chemical transport model (CTM) modelling system.

Figure 2. 2. The 4The nested 4 domains nested domainsfor the CCAM-CTM for the CCAM-CTM (Cubic Conformal (Cubic Atmospheric Conformal model-Chemical Atmospheric Figuremodel-ChemicalTransport 2. The model) 4 nested Transport simulation: domains model) for Australia, the simulation: CCAM-CTM NSW, Australia, GM (CubicR (Greater NSW,Conformal GMRMetropolitan Atmospheric (Greater MetropolitanRegion) model-Chemical and Region)Sydney Transportanddomains. Sydney model) domains. simulation: Australia, NSW, GMR (Greater Metropolitan Region) and Sydney domains. Further details of the domain setting, the model conﬁgurationsconfigurations as well as model validation as comparedFurther toto details observationsobservations of the domain cancan bebe foundfound setting, inin [the[18]18] model ofof thisthis co issueissuenfigurations ofof AtmosphereAtmosphere. as well. as model validation as compared to observations can be found in [18] of this issue of Atmosphere.

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2.2.2.2. Emission Emission Modelling Modelling TheThe emission emission modelling modelling embedded embedded in in the the CCAM-CTM CCAM‐CTM modelling modelling system system consists consists of of natural natural and and anthropogenicanthropogenic components. components. All All natural natural emissions emissions from from the the canopy, canopy, trees, trees, grass grass pasture, pasture, soil, soil, sea sea salt salt andand dust dust were were modelled modelled and and generated generated inline inline of of CTM CTM while while the the anthropogenic anthropogenic emissions emissions were were taken taken fromfrom the the EPA EPA NSW NSW GMR GMR Air Air Emissions Emissions Inventory Inventory for for 2008 2008 and and prepared prepared ofﬂine offline as as input input to to the the CTM. CTM. TheThe 2008 2008 NSW NSW GMR GMR Air Air Emissions Emissions Inventory Inventory data, data, which which is is the the latest latest inventory inventory available available to to this this study,study, was was segregated segregated into into 17 17 major major source source groups: groups power : power generation generation from from coal, powercoal, power generation generation from gas,from residential gas, residential wood heaters, wood heaters, on-road on vehicle‐road petrolvehicle exhaust, petrol exhaust, diesel exhaust, diesel otherexhaust, (e.g. other LPG) (e.g. exhaust, LPG) petrolexhaust, evaporation, petrol evaporation, non-exhaust non (e.g.,‐exhaust brake) (e.g., particulate brake) particulate matter, shippingmatter, shipping and commercial and commercial boats, industrialboats, industrial vehicles andvehicles equipment, and equipment, aircraft (ﬂight aircraft and (flight ground and operations), ground locomotives,operations), commerciallocomotives, non-roadcommercial equipment, non‐road other equipment, commercial-domestic other commercial areas‐ anddomestic industrial areas area and fugitive industrial sources, area biogenic fugitive sources,sources, andbiogenic non-biogenic sources, naturaland non sources.‐biogenic Figurenatural3, sources. as an example, Figure 3, shows as an theexample, anthropogenic shows the areaanthropogenic source NO area weekend source emission NO weekend for October emission 2008 for and October locations 2008 ofand power locations stations of power in the stations GMR. Thein the source-dependent GMR. The source fraction‐dependent for major fraction source for groups major source used in groups emission used modelling in emission is referenced modelling in is Appendixreferenced A in of Appendix [18] in this A issue of [18] of Atmospherein this issue. of Atmosphere.

Figure 3. Cont.

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Figure 3. Non-road area source NO weekend emissionemission ((aboveabove)) forfor OctoberOctober 20082008 (unit(unit inin kg/day)kg/day) and locations of power stations in the GMR ((bottom).).

The emission of NOx and various species of VOC from area sources and power station point sources in in the the GMR GMR for for a typical a typical summer summer month month (January (January 2008 2008weekday) weekday) is shown is shown in Table in 1. Table These1. Theseareas areassources sources are commercial-domestic, are commercial-domestic, locomoti locomotives,ves, shipping, shipping, non-road non-road commercial commercial vehicles, vehicles, industrial vehicles, passenger and light-duty petrol vehicles (VPX), passenger and light-duty diesel vehicles (VDX), all evaporative vehicle emission (VPV)(VPV) andand otherother vehiclevehicle exhaustexhaust (VLX).(VLX). The biogenic emission was modelled and generated inline in CTM. For ozone, the main biogenic contribution (VOC, NOxx and NH 3)) is is from from vegetation vegetation and and soil. The The biogenic biogenic VOC is the sum of these species, isoprene, monoterpene, ethanol, methanol,methanol, aldehydealdehyde andand acetone.acetone. The biogenic biogenic emission emission consists consists of of emission emission from from forest forest canopies, canopies, pasture pasture andand grass grass and andsoil. The soil. Thecanopy canopy model model divides divides the canopy the canopy into intoan arbitrary an arbitrary number number of vertical of vertical layers layers (typically (typically 10 layers 10 layers are areused). used). Biogenic Biogenic emissions emissions from from a forest a forest canopy canopy can can be be estimated estimated from from a aprescription prescription of of the the leaf leaf area 2 −2 −2 index, LAILAI (m(m2 m−2),), the the canopy canopy height height hc hc (m), (m), the the leaf leaf biomass Bm (g(g mm−2),), and and a a plant plant genera-specific genera-speciﬁc −1 −1 leaf level VOCVOC emissionemission raterate QQ ((µg-Cµg-C g g−1 h−1) )for for the the desired desired chemical chemical species. species. The The LAI LAI used used in CTM is based on Lu et al.’s al.’s [19] [19] scheme scheme which which consists consists of LAI LAI for for trees trees and and grass grass derived derived from from an an Advanced Advanced Very High-ResolutionHigh-Resolution radiometer radiometer (AVHRR) (AVHRR) normalized normalized difference difference vegetation vegetation index index (NDVI) (NDVI) data fromdata betweenfrom between 1981 and1981 1994. and 1994. The biogenic NOx emission model from soil is based on the temperature-dependent Equation (1) [20].

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The biogenic NOx emission model from soil is based on the temperature-dependent Equation (1) [20]. Q = Qnox exp[0.71(T − 30)] (1) Q = Qnox exp[0.71(T − 30)] (1) where Qnox is the NOx emission rate at 30 °C and T is the soil temperature (°C). ◦ ◦ whereAmmoniaQnox is the (NH NO3x) emission rateis based at 30 onC andBattyeT is and the Barro soil temperaturews’ scheme ( [21].C). This approach uses annualAmmonia average (NH emission3) emission factors is based prescribed on Battye andfor Barrows’four natural scheme la [21ndscapes]. This approach (forest, usesscrubland, annual averagepastureland, emission urban). factors prescribed for four natural landscapes (forest, scrubland, pastureland, urban). AsAs shown in in Figure Figure 4,4, biogenic biogenic VOC VOC emission emission occurs occurs mostly mostly in inthe the west west of Sydney of Sydney and and the Blue the BlueMountain Mountain area, area, the National the National Park Park areas areas in inthe the no north,rth, the the lower lower Hunter Hunter region region and and inland inland of thethe IllawarraIllawarra escarpment.escarpment. In general, isoprene andand monoterpenemonoterpene have similarsimilar spatialspatial patternspatterns asas VOCVOC distributiondistribution andand reﬂectreflect thethe LeafLeaf AreaArea IndexIndex (LAI)(LAI) distributiondistribution inin thethe modellingmodelling domaindomain [ 12[12].].

FigureFigure 4.4. VOC monthly 24-hourly averageaverage emissionemission (kg/hour)(kg/hour) ( left) and Leaf Area Index (LAI) (right) forfor FebruaryFebruary 2008.2008.

2.3.2.3. Emission ScenariosScenarios TheThe 20082008 NSWNSW GMRGMR AirAir Emissions Inventory data was segregated into 16 major source groups, thenthen re-groupedre-grouped in to 8 8 categories, categories, which which are are similar similar to to those those used used by by Karagulian, Karagulian, Belis Belis et etal. al.[22], [22 to], tomatch match the the six six identified identiﬁed categories categories using using the the component component profile proﬁle of of the the European European Guide on AirAir PollutionPollution SourceSource ApportionmentApportionment withwith ReceptorReceptor ModelsModels forfor particlesparticles [[23].23]. TheThe emissionsemissions scenariosscenarios usedused inin thisthis studystudy are:are: (1) All sources; (2) All anthropogenic—with emissionsemissions fromfrom allall anthropogenicanthropogenic sources;sources; (3)(3) AllAll natural—withnatural—with emissionsemissions fromfrom allall naturalnatural sources;sources; (4)(4) PowerPower stations:stations: emissionsemissions fromfrom coalcoal andand gasgas powerpower generations;generations; (5)(5) On-roadOn-road motormotor vehicles:vehicles: emissionsemissions fromfrom petrolpetrol exhaust,exhaust, dieseldiesel exhaust,exhaust, otherother exhaust,exhaust, petrolpetrol evaporativeevaporative andand non-exhaustnon-exhaust particulateparticulate matter;matter; (6)(6) Non-roadNon-road dieseldiesel andand marine:marine: with emissions from shipping and commercialcommercial boats, industrial vehicles and equipment, aircraft,aircraft, locomotives,locomotives, commercialcommercial non-roadnon-road equipment;equipment; (7)(7) Industry:Industry: with emissionsemissions fromfrom allall pointpoint sourcesource exceptexcept powerpower generationsgenerations fromfrom coalcoal andand gas;gas; andand (8)(8) Commercial-domestic:Commercial-domestic: withwith emissionsemissions fromfrom commercialcommercial andand domestic-commercialdomestic-commercial areaarea sourcesource excludingexcluding solid fuel burning (wood heaters). WoodWood heaterheater emissionemission isis treatedtreated asas aa separateseparate sourcesource categorycategory andand inin thisthis study,study, whichwhich focusedfocused onon summersummer ozone,ozone, therethere isis nono woodwood heatingheating emissionemission inin thethe GMR.GMR.

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Table 1. Emission of NOx and VOC species from various area sources in the GMR for weekdays in January 2008.

Species (kg/h) Commercial-Domestic Locomotives Shipping Comm-Veh Ind-Veh VPX VDX VLX VPV Power (coal) Power (gas) NO 4794 11,450 24,230 310 52,197 58,248 43,856 502 0 121,957 1681 NO2 387 924 1955 25 4213 3653 10,501 31 0 6036 89 ALD2 4527 57 784 6 741 841 280 39 5768 30 9 ETH 1631 159 503 9 1208 3167 303 150 0 0 11 FORM 785 47 197 9 1027 561 454 26 435 1 73 ISOP 2 0 0 0 0 12 7 1 19 0 0 OLE 2094 43 421 3 336 1506 122 72 626 21 20 PAR 94,905 291 14,195 22 2283 10,867 1915 518 46,536 583 287 TOL 14,480 24 3615 4 301 3523 172 167 1693 44 0 XYL 16,495 22 4851 4 244 3997 494 190 1048 120 0 ETOH 22,714 0 11 0 1 70 0 0 200 0 0 MEOH 4067 0 27 0 5 51 55 2 0 0 0 Atmosphere 2018, 9, 443 9 of 32

2.4. The Integrated Empirical Rate (IER) Model

To determine the ozone production sensitivity to NOx and VOC, the IER model was used in this study. Central to the IER model is the concept of smog produced (SP). Photochemical smog is quantiﬁed in terms of NO oxidation which deﬁnes SP as the quantity of NO consumed by photochemical processes plus the quantity of O3 produced.

t t t t t [SP]0 = [NO]0 − [NO] + [O 3] − [O 3]0

t t where [NO]0 and [O 3]0 denote the NO and O3 concentrations that would exist in the absence of t t atmospheric chemical reactions occurring after time t = 0 and [NO] and [O3] are the NO and O3 t concentrations existing at time t. [SP]0 denotes the concentration of smog produced by chemical reactions occurring during time t = 0 to time t = t. When there is no more NO2 to be photolysed, and no more NO to react with reactive organic compounds to produce NO2 and nitrogen products (such as peroxyacetyl nitrate (PAN) species), ozone production is stopped. This condition in the photochemical process is called the NOx-limited regime. The ratio of the current concentration of SP to the concentration that would be present if the NOx-limited regime existed is deﬁned as the parameter “Extent” of smog production (E). The extent value is indicative of how far toward attaining the NOx-limited regime the photochemical reactions have progressed. When E = 1, smog production is in the NOx-limited regime and the NO2 concentration approaches zero. When E < 1, smog production is in the light-limited (or VOC-limited) regime. Previous studies have shown that high ozone levels in the west, north west and south west of Sydney are mostly in the regime of NOx-limited while central east Sydney is mostly light-limited (VOC-limited) [24]. For sensitivity analysis, it is expected that in the NOx-limited region, sensitivity of ozone to NOx emission change (say per-ton) is positive while it is negative in the VOC-limited region.

2.5. Source Attribution to Ozone Formation in Different Sub-Regions Our study delineates the Greater Sydney Region into different sub-regions using the IER method and principal component analysis (PCA) statistical method. Source contribution to ozone formation was then determined in each of these sub-regions. Using the 2008 summer (November 2007–March 2008) observed hourly monitoring data (NO, NO2, ozone and temperature) in the IER method, the extent of photochemical smog production in various Sydney sites was determined and then classiﬁed using a clustering statistical method. At a low level of pollutant concentrations, near background levels, with little or no photochemical reaction, the extent can be equal to 1. For this reason, to better classify the degree of photochemical reaction, the ozone level or the SP (smog produced) concentration has to be taken into account in addition to the extent variable. Therefore, it is more informative to categorise the smog potential using both the extent value and the ozone concentration as:

- Category A: extent > 0.8 and ozone conc. > 7 pphm - Category B: 0.5 < extent < 0.8 and 5 pphm < ozone conc. < 7 pphm - Category C: 0.5 < extent < 0.8 and 3 pphm < ozone conc. < 5 pphm - Category D: extent > 0.9 and ozone conc. < 3 pphm

Category A represents a very high ozone episode, Category B is a high ozone episode, Category C is a medium smog potential and Category D represents background or no smog production. Using similar techniques as those of Yu and Chang [25], the principal component analysis (PCA) via correlation matrix on the 2007/2008 (1 November 2007 to 31 March 2008) summer ozone data was then used to delineate the regions in the Sydney area. The purpose of the PCA analysis is to transform the correlation coefficients between sites into component scores for each site based on the orthogonal principal components, in which the first component accounts for most of the variance and Atmosphere 2018, 9, x FOR PEER REVIEW 10 of 32 Atmosphere 2018, 9, 443 10 of 32 principal component the second most variance and so on. In this way, sites for which ozone are strongly related have similar component scores. Grouping the sites using a spatial contour plot of thecomponent second principal scores identifies component sites the with second similar most ozone variance characteristics. and so on. In this way, sites for which ozone are strongly related have similar component scores. Grouping the sites using a spatial contour plot of component3. Results scores identifies sites with similar ozone characteristics.

3.3.1. Results Regional Delineation Using Integrated Empirical Rate (IER) Model and Principal Component Analysis 3.1.(PCA) Regional Delineation Using Integrated Empirical Rate (IER) Model and Principal Component AnalysisThe (PCA) delineation of sub-regions in the GMR region based on their similar ozone formation patternsThe delineation was performed of sub-regions using the in clustering the GMR region method based and on PCA their method. similar ozoneThese formationmethods produced patterns wassimilar performed results. usingWe present the clustering here the PCA method results and wi PCAth component method. Thesescores, methods which show produced that there similar are 3 results.principle We components present here explaining the PCA results most withof the component variances. scores,The eigenvalues which show of the that correlation there are 3 matrix principle and componentsthe corresponding explaining principal most ofcomponent the variances. scores The for eigenvalues each site were of the derived. correlation The matrixcontour and plot the of correspondingcomponent scores principal for each component component scores was for used each to site delineate were derived. the regions. The contour plot of component scoresThe for eachmain component sub-regions was are usedwestern to delineate Sydney (North the regions. West, West, South West) and the coastal region consistingThe main of sub-regionsastern Sydney, are Lower western Hunter Sydney and (North the Illawarra West, West, as Southshown West) in Figu andre the5a. coastalThe patterns region of consistingsub-regional of astern delineation Sydney, were Lower similar Hunter to those and thefrom Illawarra summer as 1997/1998 shown in [26,27] Figure as5a. depicted The patterns in Figure of sub-regional5b. This means delineation that over were these similar 20 years, to those the from spatial summer pattern 1997/1998 of ozone [26 formation,27] as depicted did not in show Figure much5b. Thischange. means that over these 20 years, the spatial pattern of ozone formation did not show much change. FromFrom the the PCA PCA analysis analysis showing showing the the spatial spatial pattern pattern of of ozone ozone formation, formation, the the Lower Lower Hunter Hunter and and IllawarraIllawarra can can be be considered considered as as separate separate from from Sydney Sydney and and the the Sydney Sydney western western region region is is separate separate from from easterneastern and and central central Sydney. Sydney. The The western western Sydney Sydney region region is characterised is characterised as havingas having a high a high frequency frequency of of smog events (NOx-limited or extent of smog production higher than 0.8). This is shown in Table 2, smog events (NOx-limited or extent of smog production higher than 0.8). This is shown in Table2, whichwhich shows shows the the number number of of observed observed hourly hourly data data that that are are classiﬁed classified in in each each of of the the 4 smog4 smog potential potential categoriescategories for for January January 2008. 2008. Sites Sites near near the the coast coast such such as Randwick as Randwick and Rozelleand Rozelle are in are VOC-limited in VOC-limited area witharea low with smog low eventssmog events (zero number (zero number of observed of observ hourlyed hourly data in data Category in Category A and CategoryA and Category B). B).

(a)

Figure 5. Cont.

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0.5 0.5 1.5 1.5

(b) Principal component 1 score contour (summer 1997/1998) Principal component 1 score contour (summer 1997/1998)

6450 6450

6400 6400

Beresfield Beresfield Newcastle Newcastle 6350 6350

6300 6300 Richmond Richmond

Lindfield Lindfield Chullura Chullura 6250 Bringelly 0.3 Pacific Ocean 6250 Bringelly Pacific Ocean

0.25 0.2 6200 Wollongong 6200 Wollongong

Albion Park Albion Park

6150 6150

200 250 300 350 400 450 500 200 250 300 350 400 450 500 Principal component 1 score contour (summer 1997/1998) Photochemical smog regions for summer 1997/1998

6450 6450

6400 6400

Beresfield Beresfield Newcastle Newcastle 6350 6350

6300 6300 Richmond Richmond

Lindfield Lindfield Chullura Chullura 6250 Bringelly Pacific Ocean 6250 Bringelly 0.3 Pacific Ocean

0.25 1 1 0. 0. 0. 0.2 0.2 2 6200 Wollongong 6200 Wollongong

Albion Park Albion Park

6150 6150

200 250 300 350 400 450 500 200 250 300 350 400 450 500 FigureFigure 5. 5.Contour Contour plots plots of of the the ﬁrst first 3 principal3 principal component component scores scores depicting depicting the the regional regional delineation delineation basedbased on on similarity similarity of of ozone ozone formation formation pattern pattern in in (a) ( 2007/2008a) 2007/2008 and and in in (b )(b 1997/1998.) 1997/1998. The The last last plot plot in in eacheach graph graph is is the the combined combined plots plots of of the the 3 component3 component score score contours. contours.

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Table 2. Number of observed hourly data that are classiﬁed into each smog potential categories at different sites in the GMR for January 2008.

Site Category A Category B Category C Category D Randwick 0 0 12 1431 Rozelle 0 0 38 215 Lindﬁeld 0 8 88 414 Liverpool 6 5 83 198 Bringelly 12 3 46 1138 Chullora 3 7 92 214 Earlwood 0 7 68 300 Wallsend 0 2 52 2060 Newcastle 0 0 1 597 Beresﬁeld 0 0 26 598 Wollongong 0 1 24 797 Kemble Grange 0 0 53 1437 Richmond 4 1 9 863 Bargo 8 0 9 1061 St Marys 12 1 46 756 Vineyard 1 0 14 763 Prospect 13 2 62 225 McArthur 15 4 60 788 Oakdale 13 0 11 2007 Albion Park South 0 2 47 1441

The Sydney western region is also sub-divided into 3 sub-regions, North West Sydney, West Sydney and South West Sydney. The North West is also influenced by sources in the Lower Hunter and the South West is related to some extent to the Illawarra region, as shown by the PCA analysis. The results confirmed previous studies [24,26,27], which have shown that the GMR region can be divided into sub-regions (north west, south west, west and central east) based on the ozone smog characteristics formed from the influence of meteorology and emission patterns in the GMR. The GMR region was therefore divided into 6 sub-regions for this ozone source contribution study: Sydney North West, Sydney South West, Sydney West, Central and East Sydney, the Illawarra in the south and the Lower Hunter region in the north.

3.2. Source Contribution to Ozone over the Whole GMR The CCAM-CTM predicted daily maximum 1-h and 4-h ozone concentrations in the GMR domain (9 km × 9 km) for the entire calendar year of 2008 under various emission scenarios are extracted at grid points nearest to the location of the 18 NSW Ofﬁce of Environment and Heritage (OEH) air quality monitoring stations. Results from maximum 1-h and 4-h ozone analysis are very similar, therefore only results for 1-h predicted maximum ozone are presented. Table3 and Figure6 shows the average source contributions to daily maximum 1-h ozone over the whole GMR. Apparently, natural sources are more signiﬁcant in contributing to ozone concentration than all anthropogenic sources. During summer months (December, January and February), commercial and domestic, power stations and on-road motor vehicle sources contribute most to ozone formation in that order. During winter months (June, July and August), most of the anthropogenic sources contribute negatively to ozone formation (i.e., the presence of these sources decreases the level of ozone). Of the anthropogenic sources beside commercial-domestic sources, power stations contribute more to ozone formation within the whole GMR than that from on-road motor vehicles, industrial and non-diesel and marine sources in this order. AtmosphereAtmosphere2018 2018,,9 9,, 443 x FOR PEER REVIEW 1313 of 3232

Figure 6. Contribution of all anthropogenic and natural emissions (bar graph) and different anthropogenicFigure 6. Contribution sources (line ofgraph) all anthropogenic to the monthly and average natural of daily emissions maximum (bar 1-h graph) ozone concentrationand different inanthropogenic 2008 in the GMR. sources (line graph) to the monthly average of daily maximum 1-hour ozone concentration in 2008 in the GMR. The monthly average of daily maximum 1-h ozone concentration for January 2008 for the 4 emissionThe monthly scenarios: average base of case, daily commercial-domestic maximum 1-hour ozone sources concentration off, power for stations January sources 2008 for off the and 4 on-roademission mobile scenarios: sources base off case, is showncommercial-domestic in Figure7. The sources spatial off, pattern power in dailystations maximum sources off 1-h and ozone on- fromroad variousmobile sources source contributionoff is shown isin notFigure uniform 7. The across spatial the pattern GMR asin expected.daily maximum The spatial 1-hour pattern ozone offrom maximum various 1-hsource ozone contribution shows that is not the uniform relative importanceacross the GMR of source as expected. contribution The spatial is different pattern for of differentmaximum sub-regions. 1-hour ozone shows that the relative importance of source contribution is different for different sub-regions. 3.3. Source Contribution to Ozone Concentrations over Sub-Regions within GMR 3.3. Source Contribution to Ozone Concentrations over Sub-Regions within GMR The daily maximum 1-h and 4-h predicted ozone concentrations under various emission scenarios are furtherThe daily analysed maximum in this 1-hour section and to investigate4-hour predicted the source ozone contributions concentrations to ozone under formation various emission in each ofscenarios the sub-regions are further across analysed the GMR in forthis 2008. section As shownto investigate above from the thesource PCA contributions analysis, the to different ozone sub-regionsformation in have each differentof the sub-regions smog characteristics across the GM asR represented for 2008. As byshown the extent above offrom smog the productionPCA analysis, or NOthe xdifferent/VOC relationship sub-regions with have ozone different levelproduction. smog charac Theteristics 6 regions as represented of the GMR by are the Sydney extent Northwest of smog (Richmondproduction andor NO Vineyard),x/VOC relationship Sydney West with (Bringelly, ozone Prospect level pr andoduction. St Marys), The Sydney6 regions South of the West GMR (Bargo, are LiverpoolSydney Northwest and Oakdale), (Richmond Sydney Eastand (Chullora,Vineyard), Earlwood, Sydney West Randwick (Bringelly, and Rozelle), Prospect Illawarra and St (AlbionMarys), Park,Sydney Kembla South Grange West (Bargo, and Wollongong) Liverpool and and Oakdale), Lower Hunter Sydney or Newcastle East (Chullora, (Newcastle) Earlwood, regions. Randwick As for theand whole Rozelle), GMR Illawarra region analysis, (Albion only Park, results Kembla for 1-hGrange predicted and ozoneWollongong) are presented and Lower here since Hunter the 4-h or onesNewcastle are very (Newcastle) similar. regions. As for the whole GMR region analysis, only results for 1-hour predicted ozone are presented here since the 4-hour ones are very similar.

Atmosphere 2018, 9, 443 14 of 32

Table 3. Monthly average of daily maximum 1-h predicted ozone concentration (ppb) in the GMR for different months in 2008 under various emission scenarios.

All Power Commercial- On-Road Non-Road Diesel All Month Industry All Natural Sources Stations Domestic Motor Vehicles and Marine Anthropogenic January 33.41 3.30 8.38 2.53 0.63 0.36 15.21 18.20 February 26.29 1.70 5.54 1.06 0.18 0.38 8.86 17.43 March 28.42 1.43 7.62 0.79 0.16 0.37 10.37 18.05 April 18.39 0.16 2.32 −1.11 −0.11 0.011 1.29 17.10 May 23.03 0.22 3.94 −2.44 −0.30 −0.066 1.44 21.58 June 20.74 −0.012 0.88 −1.94 −0.30 −0.19 −1.55 22.29 July 18.81 −0.097 0.77 −1.75 −0.24 −0.13 −1.42 20.24 August 21.83 0.019 1.35 −1.44 −0.17 −0.082 −0.28 22.11 September 21.80 0.88 3.18 −0.95 −0.10 0.090 3.13 18.67 October 29.45 2.30 6.95 1.07 0.30 0.38 11.03 18.42 November 26.53 1.43 3.70 0.54 0.24 0.16 6.07 20.45 December 32.29 2.36 6.26 1.90 0.54 0.47 11.53 20.77 Annual average 25.11 1.14 4.25 −0.14 0.071 0.15 5.49 19.62 Atmosphere 2018, 9, 443 15 of 32 Atmosphere 2018, 9, x FOR PEER REVIEW 15 of 32

(a) (b)

Figure 7. Monthly average ofof dailydaily maximummaximum1-h 1-hour ozone ozone concentration concentration for January for January 2008 2008 in (a) inbase (a) case,base (case,b) commercial-domestic (b) commercial-domestic sources sources off, (c) poweroff, (c) stations power sourcesstations off sources and (d off) on-road and (d mobile) on-road sources mobile off emissionsources off scenarios. emission scenarios.

3.3.1. Sydney North West RegionRegion This region is characterised as mostly NO -limited during the ozone season (summer). Table4 This region is characterised as mostly NOxx-limited during the ozone season (summer). Table 4 and FigureFigure8 8 show show that that of of the the anthropogenic anthropogenic sources, sources, commercial-domestic commercial-domestic sourcessources (except(except woodwood heaters) contributecontribute mostmost toto thethe 1-h1-hour ozone ozone max, max, especially especially in the in summerthe summer months. months. Power Power stations stations and motorand motor vehicles vehicles are next are innext importance in importance in contributing in contributing to ozone to ozone in the Northin the West.North Interestingly,West. Interestingly, during coldduring winter cold months,winter months, on-road on-road motor vehiclesmotor vehicles have a have negative a negative contribution contribution to maximum to maximum 1-h ozone 1-hour in theozone North in the West North of Sydney West of (i.e., Sydney increase (i.e., in increase motor vehicle in motor emission vehicle actually emission decreases actually the decreases maximum the ozonemaximum level ozone in that level region). in that This region). is due to This the factis due that to photochemical the fact that photochemical ozone production ozone never production or rarely reachesnever or NOx-limited rarely reaches during NOx-limited daytime, during that is, daytime, mostly light-limited that is, mostly (or light-limited VOC-limited) (or regimes VOC-limited) exist in theregimes basin. exist in the basin.

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Figure 8. Contribution of all anthropogenic and natural emissions (bar graph) and different Figure 8. Contribution of all anthropogenic and natural emissions (bar graph) and different anthropogenic sources (line graph) to the monthly average of daily maximum 1-h ozone concentration anthropogenic sources (line graph) to the monthly average of daily maximum 1-hour ozone in 2008 in the Sydney northwest region. concentration in 2008 in the Sydney northwest region. To illustrate the change in ozone level in detail, Figure9 shows the difference in predicted 1-h ozoneTo illustrate (ppb) atthe Richmond change in forozone January level 2008in detai betweenl, Figure the 9 baseshows case the and difference for scenarios in predicted when no1- hourbiogenic ozone emissions (ppb) at are Richmond present and for whenJanuary there 2008 is nobetween emission the from base power case and stations. for scenarios Biogenic when emission no biogenicsigniﬁcantly emissions increases are present the ozone and level when and there is is the no most emission important from power source stations. contribution Biogenic tothe emission ozone significantlymaximum. Power increases station the emissionozone level from and the is neighbouring the most important region insource the Lower contribution Hunter to also the strongly ozone maximum.inﬂuences thePower 1-h maximumstation emission ozone from level the in Sydney neighbouring northwest, region as isin also the shownLower inHunter Figure also9. Note strongly that, influencesat night the the ozone 1-hour level maximum is higher ozone than thelevel base in Sydney case when northwest, coal-gas as power is also station shown emissions in Figure are 9. Note off as that, at night the ozone level is higher than the base case when coal-gas power station emissions are there is less NOx emission to scavenge the ozone. off asTo there summarise is less NO thex emission whole distribution to scavenge of the predicted ozone. ozone with minimum, maximum, and median values,To summarise Figure 10 showsthe whole the distribu box plotstion of of thepredicted ozone ozone prediction with mi fornimum, January maximum, 2008 under and different median values,emission Figure scenarios. 10 shows the box plots of the ozone prediction for January 2008 under different emission scenarios.

Atmosphere 2018, 9, 443 17 of 32

Table 4. Monthly average of daily maximum 1-h ozone concentration (ppb) in the Sydney northwest region for different months in 2008 under various emission scenarios.

All Power Commercial- On-Road Non-Road Diesel All Month Industry All Natural Sources Stations Domestic Motor Vehicles and Marine Anthropogenic January 26.26 2.72 4.39 1.29 0.49 0.22 9.11 17.15 February 21.81 1.39 3.29 0.28 0.15 0.15 5.26 16.54 March 23.27 1.15 4.95 −0.28 0.11 0.14 6.08 17.19 April 16.01 0.09 1.59 −1.51 −0.20 −0.11 −0.12 16.13 May 18.99 0.08 2.89 −3.23 −0.38 −0.21 −0.79 19.78 June 17.57 −0.08 0.58 −2.77 −0.37 −0.26 −2.89 20.46 July 15.81 −0.16 0.48 −2.46 −0.29 −0.18 −2.61 18.42 August 19.02 −0.07 0.97 −1.94 −0.24 −0.16 −1.40 20.42 September 18.67 0.44 2.33 −1.36 −0.19 −0.04 1.20 17.47 October 24.49 2.00 4.48 0.23 0.21 0.23 7.17 17.32 November 22.25 1.15 2.40 −0.10 0.15 0.09 3.69 18.56 December 26.54 1.99 3.29 1.02 0.41 0.30 7.00 19.54 Annual average 20.91 0.90 2.64 −0.90 −0.01 0.01 2.65 18.26 Atmosphere 2018, 9, x FOR PEER REVIEW 18 of 32

AtmosphereAtmosphere2018 2018, 9,, 4439, x FOR PEER REVIEW 18 of18 32 of 32

Figure 9. Time series of difference in predicted 1-hour ozone (ppb) at Richmond for January 2008 FigureFigure 9. 9.Time Time series series of differenceof difference in predictedin predicted 1-h 1-ho ozoneur ozone (ppb) at(ppb) Richmond at Richmond for January for January 2008 between 2008 between the base case and biogenic emissions off (blue) and between the base case and power stations thebetween base case the and base biogenic case and emissions biogenic emissions off (blue) andoff (b betweenlue) and thebetween base casethe base and case power and stations power stations emissions emissions off (red). offemissions (red). off (red).

Figure 10. Box plot for 1-hour ozone concentration (ppb) in January 2008 under different emission Figurescenarios 10.10. BoxBox at Richmond. plotplot for for 1-h 1-hour ozone ozone concentration concentration (ppb) in(ppb) January in January 2008 under 2008 different under different emission scenariosemission atscenarios Richmond. at Richmond. 3.3.2.3.3.2. Sydney Sydney West, West, Sydney Sydney South South West, West, Sydney Sydney EastEast Regions 3.3.2. SydneyIn the Sydney West, Sydney West region, South the West, on-road Sydney motor East vehi Regionscle emission has slightly more contribution In the Sydney West region, the on-road motor vehicle emission has slightly more contribution to to ozone 1-hour max than that from power stations in the summer months. However, during cooler ozoneIn 1-h the max Sydney than West that from region, power the stationson-road inmotor the summer vehicle months.emission However, has slight duringly more cooler contribution months months in autumn and winter, motor vehicles have a negative contribution to daily maximum 1-hour into autumnozone 1-hour and winter, max than motor that vehicles from power have a statio negativens in contribution the summer to months. daily maximum However, 1-h during ozone cooler levels. ozone levels. monthsAppendix in autumnA shows and winter, the tables moto andr vehicles graphs have summarising a negative thecontri effectbution of eachto daily source maximum to the ozone1-hour ozone levels.Appendix A shows the tables and graphs summarising the effect of each source to the ozone formationformation in in each each of of the the regions. regions. As As shownshown inin FigureFigure A6A6 (Appendix(Appendix A)A) of of the the ozone ozone concentration concentration at at Appendix A shows the tables and graphs summarising the effect of each source to the ozone ChulloraChullora for for January January 2008, 2008, even even though though thethe monthlymonthly maximum maximum is is reduced reduced when when the the mobile mobile source source is is formation in each of the regions. As shown in Figure A6 (Appendix A) of the ozone concentration at turnedturned off, off, the the median median ozone ozone increased increased slightly.slightly. ThisThis is is because because the the night night time time ozone ozone increases increases when when Chullora for January 2008, even though the monthly maximum is reduced when the mobile source is there is less NOx emission to scavenge the ozone. The ozone level also increases during the day time, whenturned ozone off, the level median is low ozone and inincreased a VOC-limited slightly. photochemical This is because process, the night if NOxtime emissionozone increases is decreased. when

Atmosphere 2018, 9, x FOR PEER REVIEW 19 of 32 Atmosphere 2018, 9, 443 19 of 32 there is less NOx emission to scavenge the ozone. The ozone level also increases during the day time, when ozone level is low and in a VOC-limited photochemical process, if NOx emission is decreased. ThisThis can can be be seen seen in in thethe timetime seriesseries ofof differencedifference ininpredicted predicted ozone ozone levels levels at atChullora Chullora for forJanuary January2008 2008 underunder the the base base case case and and no no motormotor vehiclevehicle emissionemission scenario, scenario, as as shown shown in in Figure Figure 11 11.. Maximum Maximum ozone ozone isis reduced reduced for for days days where where the the ozone ozone is is high, high, but but for for days days when when ozone ozone is is low low and and at at night, night, the the ozone ozone levellevel is is higher higher when when motor motor vehicle vehicle emission emission is is turned turned off. off.The Thetime timeseries series shows showsthat that for for most most of of the the timetime during during January January 2008, 2008, Chullora Chullora is is in in a a VOC-limited VOC-limited regime. regime. AlsoAlso shownshown inin FigureFigure 11 11,, is is the the time time seriesseries ofof differencedifference inin predictedpredicted ozoneozone underunder thethe basebase casecase andand whenwhen nono powerpower stationstation emissionemission isis present.present. HigherHigher levelslevels ofof locallocal NOxNOx duedue toto emissionemissionfrom frommotor motorvehicles vehiclescompared comparedto tothat thatreceived received downwinddownwind from from power power stations stations has has a a more more dramatic dramatic effect effect on on ozone ozone level level at at Chullora. Chullora.

FigureFigure 11. 11.Time Timeseries seriesof ofdifference difference in inpredicted predicted 1-h 1-h ozone ozone (ppb) (ppb) at at ChulloraChullora for for JanuaryJanuary 2008 2008 betweenbetween thethe basebase casecase andand motormotor vehiclevehicle emissionsemissions offoff (red)(red) andand betweenbetween thethe basebase casecase andand powerpower stationstation emissionsemissions off off (blue). (blue).

3.3.3.3.3.3. IllawarraIllawarra andand LowerLower HunterHunter RegionsRegions TheThe results for for the the Illawarra Illawarra and and Lower Lower Hunter Hunter regions regions are described are described in Appendix in Appendix TablesA A4 Tablesand A5. A4 and A5. TableTable5 summarises5 summarises the the order order of importanceof importance in source in source contribution contribution to 1-h to ozone 1-hour maximum ozone maximum in each ofinthe each 6 sub-regionsof the 6 sub-regions of the GMR of the Sydney GMR region.Sydney region.

TableTable 5. 5.Ranked Rankedorder orderof of contribution contributionfrom fromanthropogenic anthropogenic sourcessources toto maximummaximum 1-h1-h ozoneozone (warm(warm months).months). For For Central Central and and East East Sydney, Sydney, motor motor vehicle vehicle or industryor industry emission emission decreases decreases the maximumthe maximum 1-h ozone1-hour (negative ozone (negative contribution) contribution) and is denoted and is denoted by x in the by table.x in the table.

RankedRanked Order Order of Contribution of Contribution from from Anthropogenic Sources Sources to Maximum to Maximum 1-h 1-h Ozone (Warm Months of December, January and February of 2008) RegionRegion Ozone (Warm Months of December, January and February of 2008) First Second Third Fourth First Second Third Fourth Non-road diesel SydneySydney North North West CommercialCommercial domesticPower Power stations Motor vehicles Non-road diesel and Motor vehicles and shipping WestSydney Westdomestic Commercial domesticstations Power stations Motor vehicles Industryshipping Commercial Power SydneySydney West South West Commercial domestic Motor vehiclesMotor Power vehicles stations Industry Industry domestic stations Sydney Central Commercial domestic Power stations x x Sydney Southand East Commercial Motor Power stations Industry West domestic vehicles Non-road diesel Lower Hunter Commercial domestic Power stations Motor vehicles Sydney and shipping Commercial Power Central and x Non-road dieselx Illawarradomestic Commercial domesticstations Power stations Motor vehicles East and shipping

Atmosphere 2018, 9, 443 20 of 32

4. Discussion Air quality models have been used recently by various authors to study the source contribution to air pollution concentration at a receptor. The main models that are currently used are the source-oriented CMAQ model and the CAMx model with OSAT. The main advantage of these models is that they treat emitted species from different sources separately, and hence, can identify source contributions to ozone in a single model run rather than the multiple runs required by chemical transport models (CTM), using brute force or zero-out method as used in this study [11]. Caiazzo et al. [28], in their study to quantify the impact of major sectors on air pollution and early death in the whole US in 2005 using WRF/CMAQ model, estimated that ~10,000 (90% CI: −1000 to 21,000) premature deaths per year were due to changes in maximum ozone concentrations. The largest contributor to early deaths due to increases in maximum ozone is road transportation with ~5000 (90% CI: −900 to 11,000) followed by power generation with ~2000 (90% CI: −300 to 4000) early deaths per year. Our study on source contribution to ozone formation in the Greater Metropolitan Region (GMR) of Sydney in 2008 identified biogenic sources (mainly vegetation via isoprene and monoterpene emission) as the major contributor to ozone formation in all regions of the GMR. This result confirms the previous study of Duc et al. [8] on ozone source contribution in the Sydney Particle Study (SPS) in January 2011. Similarly, Ying and Krishnan [29] in their study of source contribution of VOC in southeast Texas based on simulation using the CMAQ model, found that for maximum 8-h ozone occurrences from 16 August to 7 September 2000, the median of relative contributions from biogenic sources was approximately 60% compared to 40% from anthropogenic ones. As biogenic emission is a major contribution to ozone formation, it is important for the model to account for it as accurately as possible. Emmerson et al. [30] found that the Australian Biogenic Canopy and Grass Emission Model (ABCGEM), as implemented in the CCAM-CTM model used in this study, accounts for biogenic VOC emission (especially monoterpenes) from Australian eucalyptus vegetation better than the model of Emissions of Gases and Aerosols from Nature (MEGAN) when these modelled emissions are compared with observations. Among the anthropogenic sources, commercial and domestic sources are the main contributors to ozone concentration in all regions. Power station or motor vehicle sources, depending on the region, are the next main contributors to ozone formation. However, in the cooler months, these sources (power stations and motor vehicles) decrease the level of ozone. From Table1, commercial and domestic sources (except wood heaters) have much higher emission of most VOC species than other sources, even though they emit less NOx than other mobile sources. For this reason, it is not surprising that commercial and domestic sources contribute most to the ozone formation in all the metropolitan regions. As it is not possible or practical to control biogenic emission sources, the implication of this study is that to control the level of ozone in the GMR, commercial and domestic sources should be the focus for policy makers to manage the ozone level in the GMR. As shown in Figure7, when commercial domestic sources are turned off, a reduction in ozone is attained in all regions in the GMR. A reduction in power station emission will significantly improve the ozone concentration in the North West of Sydney, but less so in other regions of the GMR. Gégo et al. [31] in their study of the effects of change in nitrogen oxide emissions from the electric power sector on ozone levels in the eastern U.S, using the CMAQ model for 2002, have shown that ozone concentrations would have been much higher in much of the eastern United States if NOx emission controls had not been implemented by the power sector, and exceptions occurred in the immediate vicinity of major point sources where increased NO titration tends to lower ozone levels. Motor vehicles are less a problem compared to commercial-domestic and power station sources except for the South West of Sydney. However, the South West of Sydney has more smog episodes than other regions in the GMR, therefore, to reduce the high frequency of ozone events, control of motor vehicle emission should be given more priority than control of emission from power station sources. Atmosphere 2018, 9, 443 21 of 32

As shown in Figure7, compared to other regions, a significant reduction of ozone in the South West is achieved when on-road mobile sources were turned off. This region is downwind from the urban area of Sydney, where the largest emission source of NOx and VOC precursors is motor vehicles, and hence, causes elevated ozone downwind in the South West of Sydney. Various studies have shown that areas downwind from the upwind emission of precursors experience high ozone [32,33]. Reduction in motor vehicle emission benefits the South West of Sydney but instead increases the daily average ozone level in the east of the Sydney region where most motor vehicle emission occurs even though this does not affect the maximum ozone level in this region. Similar to our study, the source contribution to ozone in the Pearl River Delta (PRD) region of China was studied by Li et al. [11] who divided the whole modelling domain including the PRD and Hong Kong regions, south China region and the whole of China into 12 source areas and 7 source categories which include anthropogenic sources outside PRD, biogenic, shipping, point sources, areas sources such as domestic and commercial and mobile sources in PRD, and in Hong Kong. Their results showed that under mean ozone conditions, super-regional (outside regional and PRD areas) contribution is dominant but, for high ozone episodes, elevated regional and local sources are still the main causative factors. Among all the sources, the mobile source is the category that contributes most to ozone in the PRD region. They also showed that ozone in the upwind area of the Pearl River Delta is mostly affected by super-regional sources and local sources while the downwind area has higher ozone and is affected by regional sources in its upwind area besides the super-regional and local source contribution. This is not surprising as the mobile source is the dominant local source emission in the PRD area, and hence, downwind areas are mostly affected by this source in the ozone formation. It should be noted that our results on the source contribution to ozone is only applied to the base 2008 emission inventory data. If this “base state” of emission is changed so that there is a different composition of VOC and NOx emissions (such as the 2013 or 2018 emission inventories), then the source contribution results could be different because the relation of ozone formation with VOC and NOx precursors emissions at various regions in the GMR is nonlinear and complex. As ozone response to emitting sources of VOC and NOx is nonlinear, the contribution of each source to maximum ozone is dependent on the “base state” consisting of all other emitting sources in the region. Therefore, uncertainty in the emission inventory of these sources also affects the contribution of ozone attributed to this source. Cohan et al. [1] has shown that underestimates of NOx emission rates lead to underprediction of the total source contribution but overprediction of per-ton sensitivity. Fujita et al. [34] in their study of source contributions of ozone precursors in California’s South Coast Air Basin to ozone weekday and weekend variation showed that reduced NOx emission of weekend emission from motor vehicle caused higher level of ozone than during the weekday due to the higher ratio of NMHC (non-methane hydrocarbon) to NOx in this VOC-limited basin. Conversely, Collet et al. [35] in their study of ozone source attribution and sensitivity analysis across the U.S showed that, in most of the locations, reduction in NOx emission is more effective than reduction in VOC emission in reducing ozone levels. It is therefore important to determine and understand which region in the GMR is NOx-limited or VOC-limited. The observation-based IER method which uses monitoring station data has proved to be useful in delineation of the different regions of photochemical smog profiles, within the GMR where monitoring stations are located. Based on the results from the IER method, the results of source contribution to maximum ozone in the GMR using an air quality model for different emission scenarios can be understood and explained adequately from an emission point of view, and therefore, can facilitate policy implementation. Atmosphere 2018, 9, 443 22 of 32

As for policy implementation in reducing ozone, it should be pointed out that even though Sydney ozone trend in the past decade has decreased, the background ozone trend is actually increasing [36], which is part of a world-wide trend of increasing background ozone levels. This will make the further reduction of ozone in the future much more difﬁcult, as shown and discussed by Parrish et al. [37] in their study of background ozone contribution to the California air basins.

5. Conclusions This study provides a detailed source contribution to ozone formation in different regions of the Greater Metropolitan Regions of Sydney. The importance of each source in these regions is depending on the ozone formation potential of each of these regions, and whether they are in NOx-limited or VOC-limited photochemical regimes in the ozone season. Extensive monitoring data available from the OEH monitoring network and many previous studies using observation-based methods have provided an understanding of the ozone formation potential in various sub-regions of the GMR. Biogenic emission is the major contributor to ozone formation in all regions of the GMR due to their large emission of VOC. Of anthropogenic emissions, commercial and domestic sources are the main contributors to ozone concentration in all regions. These commercial and domestic sources (except wood heaters) have much higher emission of most VOC species but less NOx than other sources, such as mobile sources, and hence contribute more to ozone formation in all the metropolitan regions compared to other sources. The commercial and domestic sources should be the focus for policy makers to manage the ozone level in the GMR. Furthermore, based on the results of this study, the following policy-relevant suggestions are proposed to regulate the ozone level in particular Sydney regions:

- In the North West of Sydney, power station emission control will improve the ozone concentration there as well as in the Lower Hunter and the Illawarra regions. - In the South West of Sydney, which has more smog episodes than other regions in the GMR, control of motor vehicle emissions should be given priority to reduce the high frequency of ozone events in the Sydney basin.

Our next study in relation to source contribution to ozone formation will focus on the health impact of each emission source on various health endpoints from their contribution to poor air quality (e.g., ozone and PM2.5) in the GMR.

Author Contributions: Conceptualization, H.N.D.; methodology, H.N.D., L.T.-T.C.; software, H.N.D., T.T., D.S.; validation, L.T.-T.C., T.T. and D.S.; formal analysis, H.N.D.; investigation, H.N.D., L.T.-T.C.; resources, H.N.D., D.S., Y.S.; data curation, H.N.D., L.T.-T.C., T.T., D.S.; writing—original draft preparation, H.N.D.; writing—review and editing, H.N.D., L.T.-T.C.; visualization, H.N.D., L.T.-T.C.; supervision, H.N.D.; project administration, Y.S.; funding acquisition, Clare Murphy. Funding: This research received no external funding except support from Clean Air and Urban Landscapes (CAUL) Research Hub for the retreat conference. Acknowledgments: Open access funding for this work was provided from the Office of Environment & Heritage, New South Wales (OEH, NSW). Conflicts of Interest: The authors declare no conflict of interest. Atmosphere 2018, 9, x FOR PEER REVIEW 23 of 32

Appendix A

AtmosphereA.1. Sydney2018 West, 9, 443 Region 23 of 32

Table A1. Monthly average of daily maximum 1-hour predicted ozone concentration (ppb) in the Appendix A Sydney west region for different months in 2008 under various emission scenarios.

Appendix A.1. Sydney West Region On-Road Non-Road All All Power Commercial- All Month Motor Diesel and Industry Anthropo Sources Stations Domestic Natural Vehicles Marine genic Table A1. Monthly average of daily maximum 1-h predicted ozone concentration (ppb) in the Sydney January 38.53 4.02 10.23 5.24 0.52 0.28 20.29 18.23 west region for different months in 2008 under various emission scenarios. February 29.33 2.52 6.83 1.72 0.11 0.64 11.82 17.51 On-Road Non-Road March 31.89All 1.24Power Commercial- 10.57 1.66 −0.07 0.32 All 13.72 All 18.17 Month Motor Diesel and Industry Sources Stations Domestic Anthropogenic Natural April 18.61 0.09 2.95 Vehicles−1.44 Marine−0.08 0.09 1.63 16.97 MayJanuary 23.6 38.53 0.36 4.02 10.23 4.8 5.24−2.94 0.52−0.16 0.280.02 20.292.19 18.2321.41 JuneFebruary 20.68 29.33 0.13 2.52 6.830.88 1.72−1.97 0.11−0.18 0.64−0.14 11.82−1.26 17.5121.94 March 31.89 1.24 10.57 1.66 −0.07 0.32 13.72 18.17 JulyApril 18.99 18.61 −0.04 0.09 2.950.86 −1.44−1.42 −0.08−0.09 0.09−0.04 1.63−0.72 16.9719.71 AugustMay 22.27 23.6 0.01 0.36 1.58 4.8 −2.94−1.25 −0.16−0.09 0.02−0.01 2.190.29 21.4121.98 June 20.68 0.13 0.88 −1.97 −0.18 −0.14 −1.26 21.94 September 23.02 1.53 3.47 −0.84 −0.01 0.19 4.38 18.63 July 18.99 −0.04 0.86 −1.42 −0.09 −0.04 −0.72 19.71 OctoberAugust 32.28 22.27 2.39 0.01 1.588.24 −1.25 2.5 −0.09 0.24 −0.01 0.48 0.29 13.87 21.98 18.41 September 23.02 1.53 3.47 −0.84 −0.01 0.19 4.38 18.63 November 28.36 1.69 4.8 0.96 0.15 0.19 7.79 20.57 October 32.28 2.39 8.24 2.5 0.24 0.48 13.87 18.41 DecemberNovember 34.12 28.36 2.33 1.69 6.62 4.8 0.96 3.24 0.150.36 0.190.47 7.7913.03 20.5721.09 AverageDecember 26.84 34.12 1.35 2.33 6.625.17 3.24 0.46 0.360.06 0.470.21 13.037.27 21.0919.56 Average 26.84 1.35 5.17 0.46 0.06 0.21 7.27 19.56

Figure A1. Contribution of all anthropogenic and natural emissions (bar graph) and different anthropogenicFigure A1. Contribution sources (line of graph) all anthropogenic to the monthly averageand natu ofral daily emissions maximum (bar 1-h graph) ozone concentrationand different inanthropogenic 2008 in the Sydney sources west (line region. graph) to the monthly average of daily maximum 1-hour ozone concentration in 2008 in the Sydney west region.

Atmosphere 2018, 9, 443 24 of 32 Atmosphere 2018, 9, x FOR PEER REVIEW 24 of 32

Figure A2. Figure A2. BoxBox plot plot for for 1-hour 1-h ozone ozone concentration concentration (ppb) (ppb) in in January January 20082008 underunder differentdifferent emission scenarios at St Marys. scenarios at St Marys. Appendix A.2. Sydney South West Region A.2. Sydney South West Region In the Sydney south west region, which is mostly NOx-limited during the ozone season and is downwindIn the Sydney from the south Sydney west central region, east which region, is mostly the on-road NOx-limited motor during vehicle the emission ozone season has a greater and is contributiondownwind from to daily the maximumSydney central 1-h ozone east thanregion, that the from on-road power motor stations vehicle in the emission summer months.has a greater Next iscontribution industry and to daily then maximum non-diesel 1-hour and shipping ozone than sources. that from in that power order stations as the in ranked the summer contribution months. to ozoneNext is concentration. industry and then non-diesel and shipping sources. in that order as the ranked contribution to ozone concentration. Table A2. Monthly average of daily maximum 1-h predicted ozone concentration (ppb) in the Sydney southTable westA2. Monthly region for average different of months daily maximum in 2008 under 1-hour various predicted emission ozone scenarios. concentration (ppb) in the Sydney south west region for different months in 2008 under various emission scenarios. On-Road Non-Road All Power Commercial- All All Month Motor Diesel and Industry Sources Stations Domestic Non-Road Anthropogenic Natural VehiclesOn-Road Marine All All Power Commercial- Diesel All MonthJanuary 38.05 3.12 8.76 5.41Motor 0.79 1.31Industry 19.4Anthropo 18.65 FebruarySources 27.07 Stations 1.54 Domestic 4.79 1.78 0.4and 0.95 9.46 17.61Natural March 31.66 1.75 8.17 1.96Vehicles 0.35 1.11 13.35genic 18.32 Marine April 19.76 0.23 2.01 −0.11 0.11 0.26 2.52 17.24 JanuaryMay 38.05 24.62 3.12 0.54 2.918.76 −1.15.41 0.070.79 0.271.31 2.7619.4 21.8618.65 June 21.71 0.11 0.63 −1.29 −0.06 −0.05 −0.64 22.35 February 27.07 1.54 4.79 1.78 0.4 0.95 9.46 17.61 July 19.7 −0.01 0.62 −1.26 −0.07 −0.09 −0.8 20.5 MarchAugust 31.66 22.37 1.75 0.05 0.858.17 −0.811.96 −0.020.35 0.051.11 0.1613.35 22.2118.32 September 21.97 0.89 2.25 −0.51 0.05 0.21 2.92 19.06 April 19.76 0.23 2.01 −0.11 0.11 0.26 2.52 17.24 October 29.87 1.81 5.66 2.22 0.46 0.83 11 18.86 MayNovember 24.62 27.91 0.54 1.18 4.29 2.91 1.1−1.1 0.21 0.07 0.50.27 7.282.76 20.6321.86 JuneDecember 21.71 31.40.11 1.62 4.83 0.63 2.26−1.29 0.49−0.06 0.74−0.05 9.95−0.64 21.4522.35 Average 26.37 1.07 3.83 0.81 0.23 0.51 6.47 19.91 July 19.7 −0.01 0.62 −1.26 −0.07 −0.09 −0.8 20.5 August 22.37 0.05 0.85 −0.81 −0.02 0.05 0.16 22.21 September 21.97 0.89 2.25 -0.51 0.05 0.21 2.92 19.06 October 29.87 1.81 5.66 2.22 0.46 0.83 11 18.86 November 27.91 1.18 4.29 1.1 0.21 0.5 7.28 20.63 December 31.4 1.62 4.83 2.26 0.49 0.74 9.95 21.45

Atmosphere 2018, 9, x FOR PEER REVIEW 25 of 32 Atmosphere 2018, 9, x FOR PEER REVIEW 25 of 32 Average 26.37 1.07 3.83 0.81 0.23 0.51 6.47 19.91 AtmosphereAverage 2018, 9,26.37 443 1.07 3.83 0.81 0.23 0.51 6.47 2519.91 of 32

Figure A3. Contribution of all anthropogenic and natural emissions (bar graph) and different Figure A3. Contribution of all anthropogenic and natural emissions (bar graph) and different anthropogenic sources (line graph) to the monthly average of daily maximum 1-hour ozone anthropogenicFigure A3. Contribution sources (line of graph) all anthropogenic to the monthly averageand natu ofral daily emissions maximum (bar 1-h graph) ozone concentrationand different concentration in 2008 in the Sydney South West region. inanthropogenic 2008 in the Sydney sources South (line West graph) region. to the monthly average of daily maximum 1-hour ozone concentration in 2008 in the Sydney South West region.

Figure A4. Box plot for 1-h ozone concentration (ppb) in January 2008 under different emission Figure A4. Box plot for 1-hour ozone concentration (ppb) in January 2008 under different emission scenarios at Bargo. scenariosFigure A4. at BoxBargo. plot for 1-hour ozone concentration (ppb) in January 2008 under different emission scenarios at Bargo.

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A.3. Sydney East Region In the Sydney east region, the motor vehicle emission, on average for each month, contributes negatively to the 1-hour maximum ozone concentration in all months of 2008 in this region (as Atmosphererepresented2018, by9, 443 the 3 sites listed above). As this region is in a light-limited regime (or VOC-limited),26 of 32 an increase in NOx emission from motor vehicle sources will reduce the ozone level.

AppendixTable A.3. A3. Sydney Monthly East average Region of daily maximum 1-hour predicted ozone concentration (ppb) in the InSydney the Sydney east region east for region, different the months motor in vehicle 2008 under emission, various on emission average scenarios. for each month, contributes negatively to the 1-h maximum ozone concentration in all monthsNon-Road of 2008 in this region (as represented Commerci On-Road All by the 3 sites listedAll above).Power As this region is in a light-limitedDiesel regime (or VOC-limited), an increaseAll in Month al- Motor Industry Anthropo NOx emissionSources from motor Stations vehicle sources will reduce the ozoneand level. Natural Domestic Vehicles genic Marine Table A3. Monthly average of daily maximum 1-h predicted ozone concentration (ppb) in the Sydney January 27.91 2.29 8.63 −0.88 0.09 0.16 10.29 17.61 east region for different months in 2008 under various emission scenarios. February 23.5 1.41 6.07 −0.82 −0.3 0.17 6.53 16.97 On-Road Non-Road March 22.79All Power 0.93 Commercial- 6.59 −1.96 −0.37 0.12 All 5.32 All17.47 Month Motor Diesel and Industry Sources Stations Domestic Anthropogenic Natural April 15.82 0.09 1.98 Vehicles−2.37 Marine−0.46 −0.1 −0.84 16.66 MayJanuary 18.94 27.91 2.290.05 8.63 4.05 −−0.885.08 0.09−0.88 0.16−0.17 10.29−1.9 17.6120.85 JuneFebruary 17.77 23.5 − 1.410.01 6.070.82 −−0.823.67 −−0.30.77 0.17−0.26 6.53−3.88 16.9721.65 March 22.79 0.93 6.59 −1.96 −0.37 0.12 5.32 17.47 JulyApril 15.79 15.82 − 0.090.11 1.980.81 −−2.373.46 −−0.460.68 −−0.10.18 −0.84−3.6 16.6619.39 AugustMay 19.39 18.94 0.05 0.03 4.05 1.43 −−5.083.02 −−0.880.51 −0.17−0.13 −1.9−2.16 20.8521.55 June 17.77 −0.01 0.82 −3.67 −0.77 −0.26 −3.88 21.65 September 19.32 0.29 3.91 −2.39 −0.62 −0.02 1.2 18.12 July 15.79 −0.11 0.81 −3.46 −0.68 −0.18 −3.6 19.39 OctoberAugust 26.23 19.39 0.03 2.04 1.43 8.14 −−3.021.61 −−0.510.32 −0.130.12 −2.16 8.39 21.5517.85 September 19.32 0.29 3.91 −2.39 −0.62 −0.02 1.2 18.12 November 23.79 1.11 3.38 −0.85 −0.09 0.13 3.68 20.11 October 26.23 2.04 8.14 −1.61 −0.32 0.12 8.39 17.85 DecemberNovember 30.73 23.79 1.11 2.26 3.38 8.31 −−0.850.37 −0.090.17 0.13 0.34 3.6810.71 20.1120.01 − AverageDecember 21.85 30.73 2.260.87 8.31 4.52 −0.372.21 0.17−0.39 0.340.02 10.71 2.82 20.0119.03 Average 21.85 0.87 4.52 −2.21 −0.39 0.02 2.82 19.03

Figure A5. Contribution of all anthropogenic and natural emissions (bar graph) and different anthropogenicFigure A5. Contribution sources (line graph)of all anthropogenic to the monthly averageand natu ofral daily emissions maximum (bar 1-h graph) ozone concentrationand different inanthropogenic 2008 in the Sydney sources east (line region. graph) to the monthly average of daily maximum 1-hour ozone concentration in 2008 in the Sydney east region.

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Figure A6. Box plot for 1-h ozone concentration (ppb) in January 2008 under different emission Figure A6. Box plot for 1-hour ozone concentration (ppb) in January 2008 under different emission scenarios at Chullora. scenarios at Chullora. Appendix A.4. the Illawarra Region A.4. the Illawarra Region In the Illawarra region, after commercial and domestic sources, power stations and motor vehicles are nextIn the in importance Illawarra region, in contributing after commercial to ozone formation. and domestic sources, power stations and motor vehicles are next in importance in contributing to ozone formation. Table A4. Monthly average of daily maximum 1-h predicted ozone concentration (ppb) in the Illawarra regionTable forA4. differentMonthly months average in of 2008 daily under maximum various 1-hour emission predicted scenarios. ozone concentration (ppb) in the Illawarra region for different months in 2008 under various emission scenarios. On-road Non-Road All Power Commercial- All All Month Motor DieselNon-Road and Industry Sources Stations CommerciDomestic On-road AnthropogenicAll Natural All Power vehicles MarineDiesel All Month al- Motor Industry Anthropo JanuarySources 33.99 Stations 4.02 8.99 2.48 0.48and −0.1 15.86Natural 18.13 Domestic vehicles genic February 27.45 2 5.87 1.82Marine 0.14 0.21 10.05 17.41 March 29.34 1.75 8.02 1.4 0.08 0 11.25 18.1 JanuaryApril 33.99 18.36 4.02 0.19 8.992.44 − 2.481.05 − 0.480.1 −−0.120.1 1.3715.86 16.9918.13 FebruaryMay 27.45 23.35 0.43 2 5.873.84 − 1.822.09 − 0.140.2 − 0.210.25 1.82 10.05 21.53 17.41 MarchJune 29.34 20.78 1.75 0.05 8.02 0.82 − 1.41.9 − 0.080.22 −0.21 0 − 11.251.45 22.23 18.1 AprilJuly 18.36 19.04 − 0.190.02 2.44 0.72 −−1.051.59 −−0.140.1 −−0.110.12 −1.121.37 20.1616.99 August 22.22 0.12 1.21 −1.2 −0.07 −0.04 0.06 22.16 May 23.35 0.43 3.84 −2.09 −0.2 −0.25 1.82 21.53 September 21.85 1.22 2.96 −0.92 −0.08 −0.01 3.2 18.65 JuneOctober 20.78 29.7 0.05 2.69 0.826.94 1.28−1.9 − 0.230.22 0.18−0.21 11.35−1.45 18.3522.23 JulyNovember 19.04 26.68 −0.02 1.76 0.72 3.65 − 0.621.59 − 0.220.14 −−0.110.11 6.14−1.12 20.5420.16 AugustDecember 22.22 32.17 0.12 2.39 1.215.95 2.23−1.2 − 0.520.07 0.25−0.04 11.330.06 20.8422.16 − SeptemberAverage 21.8525.44 1.22 1.39 2.96 4.3 − 0.090.92 − 0.070.08 −0.030.01 5.843.2 19.618.65 October 29.7 2.69 6.94 1.28 0.23 0.18 11.35 18.35 November 26.68 1.76 3.65 0.62 0.22 −0.11 6.14 20.54 December 32.17 2.39 5.95 2.23 0.52 0.25 11.33 20.84 Average 25.44 1.39 4.3 0.09 0.07 −0.03 5.84 19.6

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Figure A7. Contribution of all anthropogenic and natural emissions (bar graph) and different Figure A7. Contribution of all anthropogenic and natural emissions (bar graph) and different anthropogenicFigure A7. Contribution sources (line of graph)all anthropogenic to the mo nthlyand naaveragetural emissions of daily (barmaximum graph) 1-hour and different ozone anthropogenic sources (line graph) to the monthly average of daily maximum 1-h ozone concentration concentrationanthropogenic in 2008sources in the(line Illawarra graph) region. to the monthly average of daily maximum 1-hour ozone in 2008 in the Illawarra region. concentration in 2008 in the Illawarra region.

Figure A8. BoxBox plot plot for for 1-hour 1-h ozone ozone concentration concentration (ppb) (ppb) in in January January 20082008 underunder differentdifferent emissionemission scenariosFigure A8. at Wollongong.Box plot for 1-hour ozone concentration (ppb) in January 2008 under different emission Appendixscenarios A.5. Theat Wollongong. Lower Hunter (Newcastle) Region In the Lower Hunter (Newcastle) region, power stations, non-road diesel and shipping sources are more important than motor vehicle and industrial sources.

Atmosphere 2018, 9, x FOR PEER REVIEW 29 of 32

A.5. the Lower Hunter (Newcastle) Region In the Lower Hunter (Newcastle) region, power stations, non-road diesel and shipping sources are more important than motor vehicle and industrial sources.

Table A5. Monthly average of daily maximum 1-hour predicted ozone concentration (ppb) in the Newcastle region for different months in 2008 under various emission scenarios.

Non-Road Atmosphere 2018, 9, 443 Commerci On-Road All 29 of 32 All Power Diesel All Month al- Motor Industry Anthropo Sources Stations and Natural Domestic Vehicles genic Table A5. Monthly average of daily maximum 1-h predictedMarine ozone concentration (ppb) in the JanuaryNewcastle region24.91 for different0.78 months3.65 in 2008 under0.33 various emission1.64 scenarios.0.25 6.67 18.25 February 25.55 1.10 5.00 0.92 0.73 0.25 8.00 17.55 On-Road Non-Road March 25.52All Power0.95 Commercial-4.12 0.67 1.19 0.40 All7.35 All18.17 Month Motor Diesel and Industry Sources Stations Domestic Anthropogenic Natural April 20.82 −0.10 3.31 Vehicles−0.19 Marine−0.05 −0.01 3.01 17.80 MayJanuary 25.49 24.91 −0.98 0.78 5.44 3.65 − 0.330.62 − 1.640.38 − 0.250.02 6.673.51 18.2521.98 February 25.55 1.10 5.00 0.92 0.73 0.25 8.00 17.55 June 23.24 −1.18 2.47 −0.62 −0.38 −0.09 0.23 23.00 March 25.52 0.95 4.12 0.67 1.19 0.40 7.35 18.17 JulyApril 20.91 20.82 −−1.000.10 1.46 3.31 −0.520.19 −−0.310.05 −−0.010.04 3.01−0.38 17.8021.30 − − − − AugustMay 23.77 25.49 −1.150.98 3.05 5.44 −0.440.62 −0.180.38 0.000.02 3.51 1.37 21.9822.40 June 23.24 −1.18 2.47 −0.62 −0.38 −0.09 0.23 23.00 SeptemberJuly 24.69 20.91 −0.981.00 3.95 1.46 −0.260.52 −0.300.31 −0.130.04 −0.385.65 21.3019.04 OctoberAugust 29.86 23.77 −3.281.15 6.16 3.05 −0.430.44 −1.000.18 0.000.26 1.3711.16 22.4018.70 September 24.69 0.98 3.95 0.26 0.30 0.13 5.65 19.04 NovemberOctober 26.06 29.86 1.63 3.28 3.27 6.16 0.52 0.43 1.000.60 0.260.22 11.166.24 18.7019.82 DecemberNovember 34.31 26.06 3.91 1.63 7.81 3.27 1.12 0.52 0.601.37 0.220.45 6.2414.66 19.8219.65 December 34.31 3.91 7.81 1.12 1.37 0.45 14.66 19.65 AverageAverage 25.4525.45 0.69 0.69 4.15 4.15 0.15 0.46 0.150.15 5.635.63 19.8219.82

Figure A9. Contribution of all anthropogenic and natural emissions (bar graph) and different Figure A9. Contribution of all anthropogenic and natural emissions (bar graph) and different anthropogenic sources (line graph) to the monthly average of daily maximum 1-h ozone concentration anthropogenic sources (line graph) to the monthly average of daily maximum 1-hour ozone in 2008 in the Newcastle region. concentration in 2008 in the Newcastle region.

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Figure A10. BoxBox plot plot for for 1-hour 1-h ozone ozone concentration concentration (ppb) (ppb) in in January January 2008 2008 underunder differentdifferent emissionemission scenariosscenarios atat Newcastle.Newcastle.

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