Ion beam techniques for source fingerprinting fine particle air pollution in major Asian-Pacific cities

Nuclear Inst. and Methods in Physics Research B xxx (xxxx) xxx–xxx

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Ion beam techniques for source fingerprinting fine particle air pollution in major Asian-Pacific cities David D. Cohen , Armand Atanacio, Jagoda Crawford, Rainer Siegele ⁎

Australian Nuclear Science and Technology Organisation, New Illawarra Rd., Menai, NSW 2234, Australia

ARTICLE INFO

ABSTRACT

Keywords: IBA Fine particles Source apportionment Databases

Fine particle air pollution is a significant problem in large urbanised areas across the Asian region. With funding from the International Atomic Energy Agency (IAEA) fifteen countries in Asia have been collecting weekly samples on filters of fine and coarse particles in major cities for the past 15 years. These filters have been analysed for over 20 different chemical species from hydrogen to lead using a range of analytical techniques including accelerator based ion beam techniques such as PIXE, PIGE, PESA, RBS, as well as XRF and NAA. These data have been included into a major database, which is generally available, containing over 17,000 combined sampling days from these fifteen countries spanning an area of the globe from ± 50° latitude and from 70° to 180° longitude. That is, the sampling covers an area north-south from Mongolia to New Zealand and west-east from Islamabad, Pakistan to Wellington, NZ.

1. Introduction Urbanisation in many countries around the world is driving increased needs for energy and resources as populations demand higher standards of living requiring higher energy consumption which impacts the environment [1]. This is particularly true for the Asian region. Asia now contains over 60% of the world’s population and 13 of the world’s top 20 megacities, four of these are in India and three in China [2]. A megacity is a city with more than 10 million people. Since 2002 the International Atomic Energy Agency (IAEA) in Vienna has been funding a study of fine (PM2.5) and coarse (2.5–10 µm) particle pollution in Asia under a Regional Cooperative Agreement (RCA). The key features of this study were to:-

• Apply the same sampling techniques in all countries to characterise local air pollution. • Sample every week in each country for fine and coarse particles in urban and rural areas for more than a decade. • Apply nuclear techniques such as ion beam analysis (IBA), instru• • ⁎

mental neutron activation analysis (INAA) and x-ray fluorescence (XRF) to analyse each country’s filters for more than 20 different chemical species on the same filter type. Use these analyses to generate a decadal (or greater) elemental database for all Asian countries in the project. Apply the latest statistical techniques to this database to produce

• •

quantitative source apportionment data including source fingerprints and their contributions to the total measured gravimetric fine and coarse mass data measured at each site in each country. Apply wind speed and direction back trajectory methods to further identify the origin of the fine and coarse particle pollution. Identify key long range transport of both natural and anthropogenic air pollution sources.

Table 1 shows the 15 countries involved in the IAEA/RCA project since 2002 and the cities where sampling has been taking place together with their population estimates in 2017. The 15 countries being sampled represent 49% of the global population of 7.7 billion and the filters collected in these cities represented the air breathed by over 110 million people. The annual urban growth rates for each of these cities in 2015 ranged from 0.78% in Korea to 3.44% in Bangladesh. The average annual growth rate across all 15 IAEA/RCA project cities in 2015 was 2.3%. This corresponds to around 7000 people a day moving into the 15 cities being sampled by our project. For the whole of Asia, with 60% of the world’s population this same growth rate represents around 290,000 people a day moving into cities across Asia each day. The analysis methods used by each country are also provided in the table and generally cover more than 20 elements from hydrogen to lead for each country represented. The elemental database generated has been used with the positive matrix factorisation (PMF) source apportionment technique to generate

Corresponding author. E-mail address: [email protected] (D.D. Cohen).

https://doi.org/10.1016/j.nimb.2019.07.023 Received 8 May 2019; Received in revised form 22 July 2019; Accepted 22 July 2019 0168-583X/ Crown Copyright © 2019 Published by Elsevier B.V. All rights reserved.

Please cite this article as: David D. Cohen, et al., Nuclear Inst. and Methods in Physics Research B, https://doi.org/10.1016/j.nimb.2019.07.023

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Table 1 Countries involved in the IAEA/RCA project since 2002, the cities where sampling has been taking place together with their population estimates in 2017. Country

Population (M)

City

Population (M)

Urban Growth %/year

Analysis techniques

Australia Bangladesh China India Indonesia Korea Malaysia Mongolia Myanmar New Zealand Pakistan Philippines Sri Lanka Thailand Vietnam Total

24 158 1380 1280 261 52 32 3.1 55 4.5 205 104 23 69 97 3748M

Sydney Dhaka Beijing Mumbai Bandung Seoul Kuala Lumpur Ulaanbaatar Yangon Wellington Islamabad Manila Columbo Bangkok Hanoi

5 9 22 18.4 2.4 10 1.6 1.5 5.2 0.4 1.0 13 5.6 8.3 8 111 M

1.54 3.44 2.70 2.38 2.60 0.78 2.36 2.82 2.47 1.93 3.26 1.30 1.13 2.76 3.01 2.30%

IBA PIXE, XRF INAA INAA, XRF, PIXE XRF, PIXE, INAA INAA PIXE, INAA XRF, PIXE PIXE IBA IBA, INAA IBA XRF, PIXE INAA, XRF IBA

between 4 and 8 different source fingerprints contributing to the total measured mass at each sampling site on each day. The combination of these fingerprints with the wind speed and direction back trajectory method, Hybrid Single Particle Lagrangian Trajectory (HYSPLIT) [3–10], has identified a wide range of long range transport events including soil movements from the Gobi desert across China and into Vietnam, smoke from biomass burning in Indonesia being transported into Malaysia and Singapore as well as sulfate transported from China’s coal fired power stations into Hanoi. These data can be used by health and environment organisations to study the effects of air pollution on large regional populations as well as identifying the major pollution source contributors across urban Asia.

50 40

Islamabad

30

Beijing

Hanoi Myanmar

India

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Sri Lanka

0

Malaysia

-10

Bandung Broome

Sydney

New Zealand

190

180

170

160

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150

140

130

Cape Grim

110

100

90

80

One of the key strengths of this regional IAEA/RCA project was to provide and train each country in the use of the fine and coarse sampling units using the same sampler and filter types. GENT stacked filter units which have been fully characterised previously [11] were provided to each country. Each GENT unit uses 47 mm Nuclepore filters having a nominal fine particle cut off of 2.5 µm (PM2.5) and a coarse cut off of 2.5–10 µm for a flow rate of 16 L/minute. The PM10 values were obtained by adding the fine and coarse values together. The aim was for each country to sample over a 24-hour period from midnight to midnight every Sunday and Wednesday of each week. The particle loadings were often very high in several countries at certain times of the year and sampling on some days often had to be reduced to a total of only 8 h spread evenly across a 24 h period to avoid filter clogging. Fig. 1 shows the locations of the sampling sites in each of the 15 IAEA/RCA project member states. These sites represent a unique curtain spanning a 30% area of the globe from ± 50° latitude and from 70° to 180° longitude. That is, the sampling covered an area north-south from Mongolia to New Zealand and west-east from Islamabad, Pakistan to Wellington, NZ. Several countries had more than one sampling site. Table 2 below summarise the fine and coarse filter sampling sites and dates sampled in the elemental database for each country. A full description of each site and its precise location can be found in the manual attached to the elemental dataset webpage on the ANSTO website [12] and, for brevity, are not discussed here. Each filter was weighed before and after exposure using a microbalance to ± 5 µg or better in a temperature and humidity controlled laboratory. Each filter was assigned an error code 0–9, 0 denotes a filter collected and analysed with no errors while 9 denotes a filter with terminal errors which should not be included in a general analysis. Typical filter errors included; not exposed for the required time, filter lost, damaged or destroyed at some stage in the process or particulate mass measurements less than zero. The number of filters having

60

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120

-30

-50

Manila

Bangkok

-20

2. Fine particle samplers and Sampling:

Korea

Bangladesh

20

Latitude

Ulaanbaatar

Fig. 1. Location of the 15 IAEA/RCA country sampling sites in Asia.

gravimetric mass greater than zero, the number of filters assigned with error code 9 and the range of elements measured for each filter are also provided in Table 2. The column marked ‘No.Elts’ provides information on the range of the number of elements measured on filters at each sampling site from the lowest number to the highest number. Up to January 2019 17,892 fine filters and 17,818 coarse filters were collected and analysed. In all, 91.7% of the fine filters and 87.9% of the coarse filters had an error code of 0. 3. Analysis techniques PIXE and PIGE have long been major techniques applied to the analysis of fine particle air filters [13–16] for many years. Here at ANSTO we have measured more than 60,000 filters over the past 25 years using multiple simultaneous IBA techniques including PIXE, PIGE, RBS and PESA [14,17–21]. PIXE typically provides elemental concentrations from aluminium to lead in a few minutes of irradiation time. Particle Induced Gamma ray Emission (PIGE) is useful for the determination of light element analyses such as sodium, fluorine and aluminium. The average GENT sampler pumps 23 m3 of air in 24 h at 16 L/ minute. For fine particle concentrations of between 10 µg/m3 and 50 µg/m3 this implies 24-hour filter masses as low as 230 µg and often higher than1500 µg. For MeV protons, used in PIXE, these filter masses can still be considered as thin since protons will only loose a few keV traversing them (depending on the collection area). For typical filter 2

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Table 2 Summary of fine and coarse filters collected in each country. Site

Start

Stop

Fine mass > 0

Fine Err = 9

Coarse Mass > 0

Coarse Err = 9

No. Elts

AUS33 AUS35 BAN880 CHN86 CHN86a CHN86b IND91a INO62 INO62a INO62b INO62c KOR82 KOR82a MAL60 MON73 NZ64c NZ64b NZ64e NZ64f NZ64a PAK92 PHI62 SRI94a SRI94 SRI94b THA66 THA66a VIE84

4-Feb-98 4-Feb-98 2-Jan-02 7-Jan-02 23-Mar-04 20-Nov-03 1-Jan-02 13-Jan-03 13-Jan-03 11-Sep-08 5-Mar-10 5-Jan-02 5-Jan-02 8-Jan-02 25-Oct-04 15-Jul-05 5-Jul-02 1-Sep-06 9-Feb-10 22-May-00 17-Apr-02 13-Oct-00 4-May-00 18-Jun-03 23-Jun-12 2-Jan-02 15-Jan-03 16-Jan-02

3-Sep-17 3-Sep-17 28-Jan-18 13-May-04 9-Oct-06 31-Jan-18 26-Dec-12 26-Dec-17 25-Jun-09 28-Dec-17 28-Dec-17 30-Nov-07 11-Dec-08 27-Dec-17 21-Dec-17 29-Jun-07 10-Jan-04 9-Dec-12 15-Dec-10 28-Jan-02 19-May-11 3-Sep-17 15-Aug-07 30-Mar-12 25-Nov-17 30-Dec-07 30-Dec-07 26-Oct-16 Total %

1888 1999 1395 79 133 561 676 884 573 439 256 490 583 1023 933 142 77 568 100 77 898 1612 237 305 230 556 425 753 17,892 100

250 136 25 32 29 63 39 102 151 6 21 101 88 104 25 20 10 166 29 56 8 85 38 17 10 6 46 7 1670 9.3

1889 1999 1395 77 131 543 676 881 573 432 254 490 583 1023 941 151 87 507 100 77 899 1612 237 305 222 556 425 753 17,818 99. 6

192 149 47 34 44 53 36 96 111 15 5 136 198 250 13 9 14 158 10 24 59 332 55 56 14 23 29 6 2168 12.1

22-22 22-22 18-29 22-36 10-37 10-37 1-30 1-33 1-33 6-25 7-18 8-24 11-24 21-21 17-33 32-32 32-33 32-32 36-36 33-33 54-54 22-22 26-38 28-41 27-41 19-26 19-25 21-23

loadings the absorption of low energy x-rays was calculated to be less than 15% for aluminium and 5% for sulfur Hence quantitative elemental concentrations can be readily calculated without making significant corrections for ion energy loss or absorption of emerging xrays. In recent times, a range of other ion beam analysis (IBA) techniques have been applied simultaneously with PIXE such as PIGE, RBS and PESA to extend the range of elements to below aluminium even down to

hydrogen [14–16]. Typical spectra for these IBA techniques are shown in Fig. 2. Not all countries in this project were able to provide all the measurements provided by these four IBA techniques. If these IBA techniques are combined with Instrumental Neutron Activation Analysis (INAA), X-ray Fluorescence (XRF), Ion Chromatography (IC) or synchrotron XRF measurements then analysis can be extended to well over 30 different chemical species making source apportionment and source fingerprinting techniques more effective.

Fig. 2. Typical IBA spectra for (a) PIXE, (b) PIGE, (c) RBS and (d) PESA collected on 25 mm stretched Teflon filters. 3

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If a sufficient number of elements are measured, then estimates can be generated of the possible chemical composition using the following equations [15,22]:

Salt = 2.54[Na]

(1)

Ammonium Sulfate = 4.125[S]

(2)

Soil = 2.20[Al] + 2.49[Si] + 1.63[Ca] + 1.94[Ti] + 2.42[Fe]

(3)

Smoke = [K]

(4)

0.6[Fe]

Organics = 11([H] Non

0.25[S])

sea salt Sulfur = S

0.08[Na]

(5) (6)

Fig. 3. Sampling sites and dates included in the APAD elemental database.

RCM = Organics + Salt + Ammonium Sulfate + Soil + Smoke + BC (7)

+ traces

where the square brackets represent the concentration of that element. It should be noted that:-

• Eq. (1) assumes the total Na concentration is all related to sea salt as NaCl. • Eq. (2) assumes the total sulfur concentration is all related to fully neutralised (NH ) SO . • Eq. (3) assumes that all five elements Al, Si, Ca, Ti and Fe are in their common oxide forms and associated with windblown soils only. • Eq. (4) is a smoke from biomass burning indicator only. It assumes 4 2

• • •

4

that a fraction of the total fine K is associated with biomass burning. This fraction is calculated by removing fine K associated with soil (i.e. 0.6*[Fe]). If ‘Smoke’ is negative, it implies there is no smoke from biomass burning. Eq. (5) is an estimate of the total organics. It assumes the total H concentration has been measured, the average organic particle is 9% hydrogen and the total H includes H in (NH4)2SO4 and H in NH4NO3. Eq. (6) is an estimate of the non-sea salt sulfur, usually associated with anthropogenic fine particles. It assumes the [S/Na] ratio for sea salt particles is 8%. Eq. (7) is an estimate of the reconstructed mass (RCM). It does not include any possible contributions from water vapour, water of crystallisation, carbonate compounds or nitrate compounds like NH4NO3, consequently it should typically be less than 100% of the measured gravimetric mass. Generally, a requirement for successful source apportionment studies is that RCM be greater than 50% of the gravimetric mass and the elements measure span all the expected sources.

Fig. 4. The total number of sampling days in the APAD database for each country and the number of days with error code 9.

concentrations, errors and minimum detectable limits (MDLs) for the fine particles, the next three worksheets contain the corresponding data for the coarse particles (2.5 μm-10 μm) and the last three provide a summary of the known problems and errors associated with each country’s dataset. Error estimates were calculated using Eq. (8) by summing in quadrature the calibration errors (typically 5%), the experimental measurement errors (typically 5–15%), the statistical counting errors (typically 3–30%) and any other errors impacting the analysis.

Error2 = Calib2 + Expt2 + Stats2 + …….

(8)

MDL = 3(Bkg)1/2

(9)

For typical X-ray peaks in PIXE and γ-ray peaks in PIGE spectra sitting on a background the minimum detectable limit (MDL) was defined by Eq. (9), where ‘Bkg’ was the area under that peak. The APAD also contains a black carbon (BC) measurement for each filter using the Smoke Stained Reflectometry method or the laser absorption method [23] with an assumed mass absorption coefficient (ε, m2/g) for each member country. This generally ranged from 5 m2/g to 10 m2/g with a recommended value for the APAD of 7 m2/g. This mass absorption coefficient depends critically on average black carbon particle size, its density and refractive index. For this reason it should really only be applied to the fine fraction as mass absorption coefficients vary strongly with particle sizes above 2 μm and fall off quickly with values well below 2 m2/g for the larger size fractions [23].

4. The IAEA elemental database The following countries; Australia, Bangladesh, China, India, Indonesia, Korea, Malaysia, Mongolia, New Zealand, Pakistan, Philippines Sri Lanka, Thailand and Vietnam provided data for the IAEA/RCA elemental database. The bulk of this data was collected between 2002 and 2017 in three stages, stage 1 was 2002-13, stage 2 was 2013-15 and stage 3 included all data up to 2017 and beyond (see Figs. 3 and 4). This elemental dataset is known as the Asia-Pacific Aerosol Database or APAD. The APAD contains over 17,000 fine and coarse sampling days from 28 sampling sites across 14 countries in the Asian region and spans more than 15 years from 2002 to 2017. Currently, we are not aware of any other dataset of fine and coarse particulate matter measurements using nuclear multi-elemental analyses and spanning at least 15 years and 14 countries in the Asia-Pacific Region. The APAD database resides in a standard EXCEL workbook containing nine worksheets. The first three worksheets contain the

5. Results Given an elemental database containing over 17,000 sampling days for both fine and coarse filters from 14 countries and each filter analysed for over 20 different elements it is not possible to present all of the results here. The reader is referred to the database manual and the ANSTO WEB page for a more complete information and analysis. Here we just present major significant country variations across the region 4

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Fig. 5. Fine particle (a) mass, (b) ammonium sulfate, (c) soil and (d) black carbon for all years by country.

over the 15 year time frame. The mass concentrations for each country are presented and contrasted with the current US EPA goals for fine and coarse particles and annual trends presented for key components of the total mass. We have chosen the box and whisker plot format to present much of the data below. Each shaded box contains 25–75% of the data, the horizontal bar is the median, the (+) symbol is the average of the data, the upper and lower vertical whiskers represent the 95% confidence intervals and the points outside the whiskers are the outlier or extreme events for each x-axis bin. This is a most informative way to express the data as it provides additional information pictorially about the distribution of the data. This is important as environmental data such as this in nature typically follows a lognormal distribution rather than Gaussian, so averages and standard deviations can be misleading. Eqs. (1)–(7) above have been used to provide the selected data in the plots below. Fig. 5. shows the fine particle (a) mass, (b) ammonium sulfate, (c) soil and (d) black carbon for all years by country. Clearly, Bangladesh, China, India, Mongolia, Philippines and Vietnam have significant fine mass (PM2.5) problems with annual averages well above the US EPA goal of 12 µg/m3 and a 24-hour maximum of 36 µg/ m3. Bangladesh, China, Mongolia, Philippines, Thailand and Vietnam have significant ammonium sulfate issues, probably related to motor vehicles and fossil fuel burning for electricity. Bangladesh, China, India, Mongolia and Pakistan have significant windblown dust issues mainly associated with major seasonal dust storms. Black carbon from diesel vehicles and biomass burning was an issue at all sites except Australia and New Zealand with average concentrations in the Philippines and Sri Lanka over 10 µg/m3. Fig. 6 shows the fine particle (a) mass, (b) ammonium sulfate, (c) soil and (d) black carbon for all countries by year. These yearly plots are useful to show if key components of the fine mass are increasing over time across all sites and countries in the project. Generally, across all countries and sites, the average fine mass shows a moderate increase over time from 20 µg/m3 to around 30 µg/m3. Well above the US EPA annual goal of 12 µg/m3, while the median value is relatively constant from 2002 to 2017 at 20 µg/m3. The fine mass also shows more extreme events > 60 µg/m3 as time progresses. There has been a significant increase from 3 µg/m3 to 5 µg/m3 in the average ammonium sulfate across all sites and countries, produced mainly by increased outlier days as the median values each year are relatively constant. The average windblown soil across all sites and countries shows a moderate increase from just above 2 µg/m3 to around 3 µg/m3, between 2002 and 2017

produced both by increased outlier days and an increase in the median values with time. This suggests both local and long range soil may have contributed. The increase in fine mass with time between 2002 and 2017 was not due to black carbon as it remained basically constant with time across all sites and countries at ~5 µg/m3. Figs. 7 and 8 show the fine particle percentage reconstructed mass for all years, all sites by country and the fine particle percentage reconstructed mass for all countries, all sites by year respectively. All countries except Korea and Sri Lanka had percentage reconstructed masses within the acceptable 50–150%. Reasons for this are discussed further below. Values of RCM% > 100% may occur if the assumptions given above for Eq. (7) are not met. This most often occurs from extreme events such as major dust storms, smoke from large bushfires or significant biomass burning near the sampling site. Analyses with 100% < RCM % < 150% may still be acceptable after detailed investigation to determine the cause and whether inclusion in the source apportionment analysis is justified. The fact that the mean and the median values of RCM% (Fig. 7) for all sites and countries are essentially constant with time from 2002 to 2017 demonstrates that generally the analysis and gravimetric weighing techniques have been at least consistent and reproducible from year to year for each participating country. Coarse particles occur mainly in the mechanical mode with diameters greater than 2.5 µm. The coarse mass is mainly composed of windblown soil and sea spray if the sampling site is within a hundred kilometres of the ocean. Windblown soil is defined by Eq. (3) and sea salt by Eq. (1) above. Fig. 9 shows the coarse particle (a) mass, (b) percentage reconstructed mass (c) soil and (d) sea salt for all years by country. As expected the coarse mass was greatest in Bangladesh, China and Mongolia, all affected by dust storms from the Gobi and Taklamakan deserts in western China. India Pakistan and Vietnam also had high coarse masses. The coarse masses were measured on Nuclepore filters which are mainly carbon, hydrogen and oxygen making an organic estimate of the organic content on these filters difficult. Hence the percentage RCM defined by Eq. (7) above may be less than 50% as shown in Fig. 9(b). The coarse sea salt component in Fig. 9(d) were relatively high in Australia, China, India, New Zealand and the Philippines as these sampling sites were near coastal regions. Mongolia is land locked so its high sea salt estimate is due entirely to sodium content of its windblown 5

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Fine Mass (µg/m3)

D.D. Cohen, et al.

Fig. 6. Fine particle (a) mass, (b) ammonium sulfate, (c) soil and (d) black carbon for all countries, all sites by year.

300

As stated previously the US EPA fine mass goals are 12 µg/m3 for annual average and 35 µg/m3 for 24 maximum. Only Australia, Korea, New Zealand and Pakistan consistently meet these annual US EPA mass goals. All 14 countries have at some time in the past 15 years had extreme events with fine masses above 50 µg/m3, thus exceeding the 24 h US EPA goal. Note there are currently no standards or goals for fine ammonium sulfate, windblown soils or black carbon content in the fine mass fractions. In urban areas a primary source of ammonium sulfate is the burning of fossil fuels containing sulfur. The sulfur dioxide produced is oxidised to sulfuric acid in the presence of water vapour and sunlight which is then neutralised to ammonium sulfate in the presence of ammonia. The ammonium sulfate concentrations ranged from 10% to 30% of the fine mass with an average of 18% across all countries. Note Korea has no ammonium sulfate measurements in the database as their analyses were performed using instrumental neutron activation analysis (INAA) which is not sensitive to sulfur. As sulfur can be 20% of the fine mass this also affected their final reconstructed mass estimates and meant that they had no signature element for ammonium sulfate in their source apportionment calculations as well. This was a major drawback for Korea in both fully characterising their aerosol and in identifying possible sources contributing to their air pollution. The soil component of the fine mass is mainly windblown soil uplifted by motor vehicles in urban areas or transported in from large distances during major dust events. The soil concentrations ranged from 5% to 20% of the fine mass with an average of 10% across all countries. Pakistan had the highest average percentage soil at 21% of the fine mass. Mongolia has significant dust transported from the Gobi desert regions at certain times of the year usually from March to April. This accounts for its significant maximum value of 1210 µg/m3 during the 15-year study period. The black carbon concentrations ranged from 5% to 45% of the fine mass with an average of 20% across all countries. Sri Lanka had the highest percentage of black carbon with 46% of its fine mass being black carbon. This was probably due to the large number of diesel vehicles and to the burning of biomass and rubbish in local urban areas. Mongolia had the highest maximum black carbon with a massive 188 µg/m3, followed by the Philippines with 61 µg/m3 then Bangladesh with 43 µg/m3.

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PAK

NZ

MAL

MON

KOR

IND

INO

CHN

0

AUS

50 BAN

Fine RCM (%)

6. Discussion

All Years all Sites by Country

Fig. 7. Fine particle reconstructed mass for all years, all sites by country.

All Countries by Year

100

2015 2016 2017

2012 2013 2014

2009 2010 2011

2007 2008

0

2004 2005 2006

50

2001 2002 2003

Fine RCM (%)

150

Fig. 8. Fine particle reconstructed mass for all countries, all sites by year.

soils and not sea salt. Fig. 10 shows the coarse particle (a) mass, (b) percentage reconstructed mass (c) soil and (d) sea salt for all countries by year. The median and average coarse mass for all countries and all sites were relatively constant below 50 µg/m3 between 2002 and 2017. The high average mass compared with the median mass was driven by the large number of extreme events occurring each year with concentration well above 100 µg/m3. The coarse soil of Fig. 10(c) shows a similar pattern to the coarse mass as expected with relatively constant median and average values over the 15 year study period from 2002 to 2017. The sea spray plot with time, Fig. 10(d), shows a significant increase in the median and the average values between 2010 and 2014. This was probably driven by major cyclone events affecting coastal sites in those years in the Philippines, China and Vietnam. 6

Nuclear Inst. and Methods in Physics Research B xxx (xxxx) xxx–xxx

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D.D. Cohen, et al.

Fig. 9. Coarse particle (a) mass, (b) percentage reconstructed mass (c) soil and (d) sea salt for all years by country.

7. Source fingerprinting and apportionment Recent source apportionment techniques have combined a nominal two-step process of looking for correlations to generate an elemental fingerprint and then using linear least squared statistical methods to determine these fingerprint contributions to the total mass with a one step process known as Positive Matrix Factorisation (PMF) [24–27]. This PMF process is based on matrix algebra, providing both quantitative estimates of the fingerprints as well as their contributions. It assures that the fingerprint contributions to the total mass are always positive. It should be noted that the original DOS version of the PMF analysis codes developed by Paatero [27] was used in this work and not the modified US-EPA version 5.0 PMF codes based on multi-linear engine (ME) available on the internet [28]. We utilised the PMF-DOS version

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Coarse Soil (µg/m3)

Coarse Mass (µg/m3)

The average percentage reconstructed mass (RCM), as defined by Eq. (7), was generally between 50% and 70% for all countries except Korea (42%) and Sri Lanka (132%). Sri Lanka had a median average RCM of 83% which was more reasonable with a maximum value of 224%. This significant shift in the average RCM versus the median RCM for Sri Lanka indicates that either several fine gravimetric mass measurements in their dataset were under estimated or that their XRF analysis has over estimated several key elemental concentrations. Again, most countries except Korea had a broad suite of elements spanning all expected urban pollution sources making source apportionment estimates possible. For brevity, an equivalent summary table for coarse data in the APAD database will not be reproduced here. Instead the reader is referred to the APAD manual that can be downloaded from the ANSTO web page for more information [12].

Fig. 10. Coarse particle (a) mass, (b) percentage reconstructed mass (c) soil and (d) sea salt for all countries, all sites by year.

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number of factors or fingerprints the PMF analysis produced. It demonstrates that the fine mass in each country could be subdivided into between 4 and 8 individual fingerprints each with their own contributions to the total gravimetric fine mass. The PMF analysis process does not automatically assign a receptor source fingerprint name to each resulting fingerprint. It only provides the contributing correlating elements which form that fingerprint. Each fingerprint is subsequently given a suitable name by the data analyst using local sampling site knowledge and experience in identifying the most likely source associated with the elemental fingerprints in each factor. Also, when interpreting these PMF fingerprints it is important to be mindful of the distinction between emission source fingerprints and receptor source fingerprints, the latter related to the PMF data in our ASFID database. Emission source fingerprints are the elements emitted directly from a specific source. For example, emissions directly from the exhaust pipe of a car. On the other hand, receptor source fingerprints relate to the elements measured at a site location which is often very far from the original emission source. Clearly the receptor fingerprint would be significantly different to the original emission fingerprint depending on the chemistry that may take place during transport to the receptor site. The assigned receptor fingerprint names in this database are therefore not definitive, but a best attempt by each member state to identify the dominant source/s in each mixed source receptor fingerprint. Table 4 lists the main fingerprint naming abbreviations used in ASFID. Many of the fingerprint names used in the database are also combinations of abbreviations, for example, IndFe would refer to an industrial source dominated by iron (Fe) or AutoRoad would refer to reentrained road dust from automobiles. Again due to brevity, the full ASFID database outputs will not be discussed further here. The reader is referred to the ASFID manual and its associated ANSTO WEB page for more details [12]. Instead we demonstrate the potential of combining elemental data, PMF analysis with wind speed and direction data from HYSPLIT [9,10]. Fig. 12 shows the daily contributions of secondary sulfate fingerprint obtained from an eight factor PMF fit to the elemental data from 2001 to 18 for the Hanoi site in Vietnam. The 17 June 2012 was a high secondary sulfate day with concentrations approaching 100 µg/m3. Fig. 13 shows the locations, as boxes, of the 29 major coal fired power stations in eastern China and out to Korea together with the 24 one hour, 3-day wind back trajectories from the Hanoi for the 17 June 2012. These 17 June hourly back trajectories clearly intersect with selected power station sites on this day showing that emissions from them were transported to the Hanoi site within 3 days. We repeated this process for every hour of every sampling day from 2001 to 2018 and every time a trajectory passes over a power station box put a dot in that box only for the 556 days in this 17-year period with daily secondary sulfate threshold greater than 20 µg/m3. To show consistency this was performed for two starting back trajectory heights, 300 m (blue dots) and 500 m (red dots) above the Hanoi sampling site. By counting the number of dots in each box for each starting height we can estimate contributions from each coal fired power station to the sampling site in Hanoi on the all sampling days above our threshold. It is not important to distinguish the symbols inside the boxes but to have some feel for the density of symbols within each power station box across all boxes. Noting that almost all boxes/power stations have some contribution to measurements at the Hanoi site at some point during the extensive sampling period and the large distances considered. It is just a demonstration of long range transport of fine particles. Results showed that the for all secondary sulfate events between 2001 and 2018 in Hanoi, above the threshold of 20 µg/m3, the six coal fired power stations labelled 3, 6, 8, 20, 27 and 28 accounted for 58% of all the back trajectories that arrived at the Hanoi site. These six coal fired power station sites between 500 and 1500 km from the Hanoi sampling site provide a clear demonstration of long range transport of

Fig. 11. Sampling sites and dates included in the ASFID elemental database.

as, for us, it was a more flexible code designed for researchers. In addition, the project participants have invested significant time developing VBA software scripts for running this executable code as well as standardising and plotting many of its output files in Microsoft Excel. A recent collaborative study [29] involving ANSTO and CSIRO in Australia showed very good agreement between the PMF solutions obtained independently using the two different PMF versions. This is important as the solutions to the equations utilised in PMF are not unique, but are produced by a least squares iterative process based on the following matrix equation: (10)

Mik = Fij*Gjk + Err

where source fingerprints (Fij) and their contributions (Gjk) are calculated directly from the original input measurement matrix (Mik) and there are i measurements, j sources and k samples and ‘Err’ is an error term which is minimized by the PMF process. Matrices F and G are both forced to be positive. PMF techniques are very powerful for modeling large amounts of data typically with, i > 20, j > 4 and k ≫ 50. The elemental data within APAD has been used to generate elemental source fingerprints and quantitative source contributions to the total fine and coarse masses using Eq. (10) above. This process generated a second IAEA/RCA database of country fingerprints and their contributions known as the Asia-Pacific Source Fingerprint Database (ASFID). The ASFID results for each country are summarised in Fig. 11. The full ASFID database can be found at the ANSTO WEB page [12]. The source fingerprints and fingerprint contributions have to date have been completed for selected sites in 14 countries only and mainly for first and second stages of the project from 2002 to 2014. But this is still representative of typical sources that occur in each urban area of for each country. Table 3 shows a summary of the start and stop dates used in the PMF analyses of the fine mass at selected sites in each country and the Table 3 Summary of the start and stop dates used for PMF analyses of the fine mass at selected sites in each country and the number of factors or fingerprints the PMF analysis produced. #

Country

Site

ID

Start

Stop

Factors

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

AUL BAN CHN IND INO KOR MAL MON NZ PAK PHI SRI THA VIE

33 880 86b 91a 62 82a 60 73 64e 92 63 94 66 84

AUL33 BAN880 CHN86b IND91a INO62 KOR82a MAL60 MON73 NZ64e PAK92 PHI63 SRI94 THA66 VIE84

3-Jan-02 2-Jan-02 19-May-07 2-Feb-09 2-Jan-06 5-Jan-02 8-Jan-02 28-Oct-04 1-Sep-06 12-Nov-03 16-Jan-01 18-Jun-03 2-Jan-03 16-Jan-02

29-Dec-13 1-Mar-09 16-Jul-13 26-Dec-12 27-Dec-13 11-Dec-08 30-Dec-13 11-Nov-13 9-Dec-12 19-May-11 28-Apr-13 28-Feb-06 30-Dec-07 20-May-13

7 5 5 7 7 7 6 5 5 7 6 4 5 8

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Table 4 Key abbreviations used for PMF fingerprint naming and their associated descriptions in the ASFID database. Fingerprint abbreviation

Fingerprint name

Key elements and description

Sea Soil

Sea salt or Sea spray Windblown soil

2ndryS

Secondary Sulfate

Smoke Ind

Biomass burning Industry

Auto

Vehicular related

IndSAged

Industrial Sulfur Aged Seaspray

Na or Cl as the main driving elements in fresh sea spray. Typically also contains Mg, Br and S. Al, Si, Ca, Ti, Fe are the crustal matter elements typically associated with windblown soil. May also include Ba, Sr and Rb. S as the main driving element, sometimes with traces of Na, Si, P, Ca, Ti, V, Fe, Ni, Zn, Se and Br. H and S are the dominant elements associated with ammonium sulfate. K and BC, sometimes with P and Ca, as the main driving elements for smoke associated with biomass burning. Dominant elements such as (but not limited to) Al, Ti, Cr, Ni, Co, Cu, Zn, Pb, S, As, Mo and Sb are often associated with a general industry source. S, Zn, Cl, Ca, Mn, Fe, Cr, V, Ni, Cu, Br, Pb. When a number of auto related fingerprints are identified, e.g. related to leaded, unleaded or diesel fuel and retrained road dust because of its relationship to automobiles emissions such as fuel combustion, brake and tyre wear; they are often numbered as Auto1, Auto2 or AutoRoad etc. S, Na and BC were the dominant elements. Probably formed by chemical reactions of sea spray particles with sulfate particles from industry. The use of Aged in the abbreviation is due to the reduced Cl in the fingerprint associated with the ageing of fresh sea spray.

Fig. 12. Secondary sulfate fingerprint at Hanoi between 2001 and 2018.

Fig. 14. Fine mass 20 sites across Asia December 2006.

8. Elemental contour mapping Another application of our regional databases is to produce elemental and source fingerprint contour maps using the large number of sampling sites. Contour mapping at the surface is a key component in trying to reconcile surface mappings with mappings using data from satellites. Figs. 14 and 15 show such contour maps for average monthly fine mass and fine black carbon for the Asian region. Similarly plots can be produced for daily data as well for all elements and sources in the APAD and ASFID databases. These contour maps are very crude but they do cover over 108 square kilometres. They assume a flat topology over the region and require more than 15 sites if the interpolation is to be meaningful. But with refinements [30] they have the potential to provide estimates of the particle mass and its sub-components at locations between samplings sites and over much larger areas. This could be particularly useful for calibrating satellite data and for estimating fine particle pollution exposures across and around areas where there are currently no data.

Fig. 13. HYSPLIT back trajectories from Hanoi showing intersections with coalfired power stations in eastern China and beyond.

secondary sulfate from eastern China into Hanoi. There are three major local coal fired power stations within the greater Hanoi urban area which will under favourable wind conditions also contribute to the secondary sulfate concentrations. But even if these were turned off, the results demonstrate that secondary sulfate levels above our threshold would still significantly occur at the Hanoi site due to long range transport from eastern China and beyond.

9. Summary Urbanisation is a key driver of fine particle air pollution. Asia contains 60% of the current global population of 7.7 billion people and many of these now live in megacities which have populations over 10 million people. Recognising this in 2002 the International Atomic 9

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relatively constant. The average windblown soil across all sites and countries showed a moderate increase from just above 2 µg/m3 to around 3 µg/m3, between 2002 and 2017 produced both by increased outlier days and an increase in the median values with time. This suggests both local and long range soil may have contributed. The increase in fine mass with time between 2002 and 2017 was not due to black carbon as it was basically constant with time across all sites and countries at ~5 µg/m3. Application of HYSPLIT back trajectory methods to both the APAD elemental data and the ASFID source data is a powerful tool to identify the origin of both local and regional air pollution sources. Using these methods and our databases we have been able to track long range transport (LRT) of particle pollution over hundreds of kilometers. By looking at extreme daily events over many years we have been able to identify and quantify LRT of secondary sulfur from coal fired power stations in China into Vietnam, smoke from forest fires in Indonesia into Malaysia, dust from the Gobi desert in China across to Korea, smog haze from India into Pakistan and sulfur emissions from Indonesian volcanoes across the Indonesia archipelago. Both the APAD and the ASFID datasets should be useful to pollution managers and to researchers not just in the Asian region but also globally and we believe they will be a valuable resource for many years to come.

Fig. 15. Black carbon 20 sites across Asia December 2006.

Energy Agency (IAEA) funded a unique regional cooperative agreement (RCA) to quantify and characterize fine particle pollution in 15 countries in the greater Asia-Pacific region. This study has been operating continuously between 2002 and 2018 and was one of the longest running air pollution projects funded by the IAEA in this area. Each of the 15 countries has sampled fine and coarse particulate matter, using the same samplers, the same sampling techniques and applying nuclear techniques to analyse all the filters collected. This IAEA/RCA project has collected and analysed over 34,000 fine and coarse filters for over 20 different elemental species spanning the time period 2002–2017. This is a unique collection of data for the Asian-Pacific region and has been added to a Microsoft EXCEL based dataset. This elemental database is called the Asia-Pacific Aerosol Database (APAD) and is freely available for download with manuals from the ANSTO WEB page [12]. APAD’s format has been standardised across all 14 countries with fine and coarse concentrations, errors and minimum detectable limits (MDL). APAD has been used in conjunction with positive matrix factorization (PMF) techniques to determine source fingerprints and source contributions to fine and coarse particulate pollution. These fingerprints and source contributions have been added to a further database called ASFID. This, like the APAD database is a quantitative database. ASFID and its instruction manuals are also readily available on the ANSTO WEB page [12]. The number of sources identified in each country ranged from four to eight and included the natural sources like windblown soils and sea spray and the anthropogenic sources like motor vehicles, secondary sulfates and industrial emissions. Data in APAD shows that only 4 of the 14 countries, Australia, Korea, New Zealand and Pakistan consistently had average annual fine particle masses that meet the current US EPA goal of 12 µg/m3. All 14 countries had at some time in the past 15 years had extreme events with fine masses above 50 µg/m3 thus exceeding the 24 h US EPA goal of 34 µg/m3. This has significant health implications for millions of people in the region, shortening the life expectancy of many of them by between 3 and 5 years. Between 2002 and 2017 the average fine particle mass concentrations for all sites across all countries showed a moderate increase over time from 20 µg/m3 to around 30 µg/m3. Again above the US EPA annual goal, while the median value was relatively constant from 2002 to 2017 at 20 µg/m3. The fine mass also shows more extreme events with masses greater than 60 µg/m3 as time progressed. There has been a significant increase from 3 µg/m3 to 5 µg/m3 in the average ammonium sulfate across all sites and countries, produced mainly by increase outlier days as the median values each year are

Acknowledgements The authors would like to acknowledge the initial extensive work of Professor Philip Hopke in establishing the first dataset for all countries in this IAEA/RCA project and to all countries for providing their long term data. Professor Willy Maenhaut for providing the GENT stacked filter units and for training during the course of this project. The authors would like to acknowledge National Collaborative Research Infrastructure Strategies (NCRIS) for funding of the Centre for Accelerator Science (CAS) and to CAS staff for access to the ion beam analysis facilities. This work has been performed under contract and with support from the IAEA and its technical staff in Vienna, Austria. References [1] The 37 Megacities and Largest Cities: Demographia World Urban Areas: 2017. Available at: http://www.newgeography.com/content/005593-the-largest-citiesdemographia-world-urban-areas-2017. Last accessed: Feb 2019. [2] Urban population growth (annual %). https://www.populationpyramid.net/hnp/ urban-population-growth/2015/. Last accessed: Feb 2019. [3] D.D. Cohen, J. Crawford, E. Stelcer, A. Atanacio, A new approach to the combination of IBA techniques and wind back trajectory data to determine source contributions to long range transport of fine particle air pollution, Nucl. Instrum. Methods Phys. Res. Sect. B 273 (2012) 186–188, https://doi.org/10.1016/j.nimb. 2011.07.071. [4] B.A. Begum, S.K. Biswas, G.G. Pandit, I.V. Saradhi, S. Waheed, N. Siddique, M.C.S. Seneviratne, D.D. Cohen, A. Markwitz, P.K. Hopke, Long-range transport of soil dust and smoke pollution in the South Asian region, Atmos Pollut. Res. 2 (2011) 151–157, https://doi.org/10.5094/APR.2011.020. [5] Y.-C. Chan, O. Hawas, D. Hawker, P. Vowles, D.D. Cohen, E. Stelcer, R. Simpson, G. Golding, E. Christensen, Using multiple type composition data and wind data in PMF analysis to apportion and locate sources of air pollutants, Atmos Environ. 45 (2011) 439–449, https://doi.org/10.1016/j.atmosenv.2010.09.060. [6] D.D. Cohen, J. Crawford, E. Stelcer, A.J. Atanacio, Application of positive matrix factorization, multi-linear engine and back trajectory techniques to the quantification of coal-fired power station pollution in metropolitan Sydney, Atmos Environ. 61 (2012) 204–211, https://doi.org/10.1016/j.atmosenv.2012.07.037. [7] D.D. Cohen, E. Stelcer, D. Garton, J. Crawford, Fine particle characterisation, source apportionment and long-range dust transport into the Sydney Basin: a long term study between 1998 and 2009, Atmos. Pollut. Res. 2 (2011) 182–189, https://doi. org/10.5094/apr.2011.023. [8] J. Crawford, D.D. Cohen, A. Atanacio, The impact of closure of coal-fired power stations on aerosol concentrations in the Sydney Basin, Atmos. Pollut. Res. 9 (2018) 1167–1176, https://doi.org/10.1016/j.apr.2018.05.002. [9] D.D. Cohen, J. Crawford, E. Stelcer, V.T. Bac, Characterisation and source apportionment of fine particulate sources at Hanoi from to 2008, Atmos. Environ. 44 (2010) (2001) 320–328, https://doi.org/10.1016/j.atmosenv.2009.10.037. [10] D.D. Cohen, J. Crawford, E. Stelcer, T.B. Vuong, Long range transport of fine

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