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Calculated K, L, and M shell X-ray line intensities for light ion impact on selected targets from Z=6 to 100
(Australian Nuclear Science and Technology Organisation, 2011-09-01) Crawford, J; Cohen, DD; Doherty, G; Atanacio, AJ
A computer code to calculate the K, L, and M α, β and γ X-ray line intensities, KLMabgRatios, is described together with the input tables used to calculate these intensities for light ion bombardment of targets with atomic numbers from Z=6 to 100. The KLMabgRatios program was written with the main aim of updating the 1980’s data files used up till now (Clayton AAEC M113/1986), with more recent experimental and theoretical datasets published in the last 2 years or so. Preferred recommended K, L and M X-ray line intensities for light ion impact on selected targets for atomic numbers between Z=6 and 100 are given for 8 K lines, 17 L lines and 22 M lines as well as their corresponding ωK, ωL and ωM total shell fluorescence yields. In addition a program, wexplore, has been written to carry out Gaussian fits to experimental K, L and M X-ray spectra to better determine L and M X-ray production subshell cross sections for light ion bombardment. A section on the use of this wexplore program is also included in this report.
The IAEA/RCA fine and coarse PMF receptor fingerprint database
(Australian Nuclear Science and Technology Organisation, 2016-02-01) Atanacio, AJ; Cohen, DD
This document accompanies the IAEA Master Positive Matrix Factorisation (PMF) Databases (fine and coarse). These databases have been generated from the 14 member state RCA Project RAC/07/015, “Characterization and Source Identification of Particulate Air Pollution in the Asia Region”. It fulfils the obligation under an IAEA contract to provide a fine and coarse ambient air PMF database with explanatory notes by February 2016 to the IAEA. The aim of this document is to provide instructional steps and related information necessary for navigating and utilising the IAEA Master Positive Matrix Factorisation (PMF) databases. It is important to note that interpretation of the PMF fingerprints and apportionment contained in either the coarse or fine databases are beyond the scope of this document. However, several countries have already published peer-reviewed papers related to their sites PMF source fingerprinting and source apportionment results. Comprehensive lists of publications are provided in Appendix 2.
Sydney particle characterisation study PM2.5 source apportionment in the Sydney Region between 2000 and 2014
(Australian Nuclear Science and Technology Organisation, 2016) Cohen, DD; Atanacio, AJ; Stelcer, E; Garton, D
The Australian Nuclear Science and Technology Organisation (ANSTO) has been applying accelerator based nuclear techniques to the characterisation of fine PM2.5 ambient air pollution since the early 1990s. Over the decades large long-term databases have been acquired at dozens of sites both in Australia and internationally on the PM2.5 mass together with over 23 different elemental and chemical species that make up this fine particle pollution. In this study we used data previously collected by ANSTO from four of our long-term sampling sites covering the period from 1 January 2000 to 31 December 2014. Positive matrix factorisation (PMF) source apportionment techniques were applied to this data to identify seven different source components or fingerprints that make up the measured total PM2.5mass at each of these four sites. The primary aim of this study was to: - convert the existing 15-year PM2.5 mass and elemental datasets for four given sites in the Sydney basin into identifiable source fingerprints - quantify the absolute and the percentage contribution of each of these fingerprints to the total fine PM2.5 mass - provide seasonal and annual variations for each of the source fingerprints - provide a readily accessible database containing the daily source fingerprints and their contributions covering the 15-year period from 2000–2014 for four given sites in the Sydney basin if possible, identify and quantify the major contributors of fine particle pollution to the ambient air quality in Sydney. Typically fine particles were collected over 24-hour periods twice a week (104 filters per year) at Lucas Heights, Richmond, Mascot and Liverpool sites over a 15-year period from 2000 to 2014. In all, around 6000 sampling days are represented by this study. Each of these filters was analysed for the 23 elemental and chemical species: hydrogen (H), sodium (Na), al uminium (Al), silicon (Si), phosphorous (P), sulfur (S), chlorine (Cl), potassium (K), calcium (Ca), titanium (Ti), vanadium (V), chromium (Cr), manganese (Mn), iron (Fe), cobalt (Co), nickel (Ni), copper (Cu), zinc (Zn), selenium (Se), bromium (Br), lead (Pb), bl ack carbon (BC) and total nitrogen (TotN) to concentrations down to 1ngm–3 of air sampled. TotN is the total nitrogen from ammonium and nitrate ions. © 2016 Australian Nuclear Science and Technology Organisation
Upper Hunter Valley particle characterization study: final report
(CSIRO Publishing, 2013-09-17) Hibberd, MF; Selleck, PW; Keywood, MD; Cohen, DD; Stelcer, E; Atanacio, AJ
This study provides an analysis of the composition of PM2.5 (particulate matter with a diameter of less than 2.5 micrometres) in the two main population centres in the Upper Hunter, namely Muswellbrook and Singleton, during 012.The finer PM2.5 particles have been studied because they are of greatest concern owing to their impact on health. Samples were collected for 24 hours every third day and analysed for the components of PM2.5, specifically twenty elements, fourteen soluble ions, two anhydrous sugars (levoglucosan and mannosan) that are found in woodsmoke, organic carbon (OC), and black carbon (BC), as well as gravimetric mass. The chemical composition of all the samples from each site was analysed using a mathematical technique called Positive Matrix Factorisation (PMF), which is widely used in air pollution source apportionment studies. This identified eight factors (also called ‘fingerprints’) which represent the mix of components that tend to vary together in time. Further analysis, using information about known sources and knowledge of atmospheric chemistry as well as wind sector and seasonal analysis, was undertaken to identify the most likely source of emissions for each factor and hence the contribution that each source makes to the measured PM2.5 concentrations. © 2013 CSIRO

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