Browsing by Author "Antancio, AJ"

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    Fingerprinting Australian soils based on their source location
    (Elsevier B. V., 2021-03-03) Crawford, J; Cohen, DD; Antancio, AJ; Manohar, M; Siegele, R
    Sampling of PM2.5 has been undertaken twice per week at the Liverpool and Mascot sites (in Sydney, Australia) since 1998. Ion Beam Analysis (IBA) was applied to each sample to determine the concentrations of 21 elements from hydrogen to lead and the black carbon concentration was determined using photon transmission techniques. Sampling days that displayed high and low airborne soil concentrations were identified and three distinct sets of soil fingerprints were determined using Positive Matrix Factorisation (PMF) source apportionment techniques. A fingerprint for all sampling days (representing the average soil fingerprint for each site), a fingerprint corresponding to low soil days associated with local retrained road dust and a fingerprint for high soil days associated with agricultural activities. The ratios of key soil elements (i.e. Si, Al, Fe) displayed larger temporal variation for the high soil days, whereas lower variation was observed for low (local) soil days. Furthermore, it was found that the El Niño-Southern Oscillation (ENSO) affected the concentration of windblown soil dust in the atmosphere. The average soil fingerprint, for all data, was heavily influenced by sampling days containing higher concentrations of soil dust, thus representing the dominant soil type. However, we did observe differences in the K/Fe and Ca/Si ratios to be a distinguishing factor between the average soil fingerprint and the high soil day fingerprint. The Soil fingerprint for the low soil concentration days had a large fraction of black carbon associated with vehicle emissions, represented retrained road dust. © 2021 Turkish National Committee for Air Pollution Research and Control. Production and hosting by Elsevier B.V.
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    Ion beam techniques for source fingerprinting fine particle air pollution in major Asian-Pacific cities
    (Elsevier B. V., 2020-08-15) Cohen, DD; Antancio, AJ; Crawford, J; Siegele, R
    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. Crown Copyright © 2019 Published by Elsevier B.V
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    Robust observational constraint of uncertain aerosol processes and emissions in a climate model and the effect on aerosol radiative forcing
    (Copernicus Publications, 2020-08-13) Johnson, JS; Regayre, LA; Yoshioka, M; Pringle, KJ; Turnock, ST; Browse, J; Sexton, DMH; Rostron, JW; Schutgens, NAJ; Partridge, DG; Liu, DT; Allan, JD; Coe, H; Ding, AJ; Cohen, DD; Antancio, AJ; Kakkari, V; Asmi, E; Carslaw, KS
    The effect of observational constraint on the ranges of uncertain physical and chemical process parameters was explored in a global aerosol–climate model. The study uses 1 million variants of the Hadley Centre General Environment Model version 3 (HadGEM3) that sample 26 sources of uncertainty, together with over 9000 monthly aggregated grid-box measurements of aerosol optical depth, PM2.5, particle number concentrations, sulfate and organic mass concentrations. Despite many compensating effects in the model, the procedure constrains the probability distributions of parameters related to secondary organic aerosol, anthropogenic SO2 emissions, residential emissions, sea spray emissions, dry deposition rates of SO2 and aerosols, new particle formation, cloud droplet pH and the diameter of primary combustion particles. Observational constraint rules out nearly 98 % of the model variants. On constraint, the ±1σ (standard deviation) range of global annual mean direct radiative forcing (RFari) is reduced by 33 % to −0.14 to −0.26 W m−2, and the 95 % credible interval (CI) is reduced by 34 % to −0.1 to −0.32 W m−2. For the global annual mean aerosol–cloud radiative forcing, RFaci, the ±1σ range is reduced by 7 % to −1.66 to −2.48 W m−2, and the 95 % CI by 6 % to −1.28 to −2.88 W m−2. The tightness of the constraint is limited by parameter cancellation effects (model equifinality) as well as the large and poorly defined “representativeness error” associated with comparing point measurements with a global model. The constraint could also be narrowed if model structural errors that prevent simultaneous agreement with different measurement types in multiple locations and seasons could be improved. For example, constraints using either sulfate or PM2.5 measurements individually result in RFari±1σ ranges that only just overlap, which shows that emergent constraints based on one measurement type may be overconfident. © The Authors CC-BY Licence 4.0