Author
Lidia Morawska
Other affiliations: University of Surrey, Jinan University, Thomas Jefferson University ...read more
Bio: Lidia Morawska is an academic researcher from Queensland University of Technology. The author has contributed to research in topics: Particle number & Ultrafine particle. The author has an hindex of 100, co-authored 746 publications receiving 95412 citations. Previous affiliations of Lidia Morawska include University of Surrey & Jinan University.
Papers published on a yearly basis
Papers
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Journal Article•
TL;DR: The authors regret the following editing errors: the measurement unit adopted for the surface area dose ( μm2) should be replaced by nm2, and the label of the y-axis (μm2), and the authors would like to apologise for any inconvenience caused.
01 Jan 2003
TL;DR: The emission factors of a bus fleet consisting of approximately three hundreds diesel powered vehicles were measured in a tunnel study under well controlled conditions during a two-day monitoring campaign in Brisbane.
Abstract: The emission factors of a bus fleet consisting of approximately three hundreds diesel powered
buses were measured in a tunnel study under well controlled conditions during a two-day
monitoring campaign in Brisbane. The number concentration of particles in the size range
0.017-0.7 m was monitored simultaneously by two Scanning Mobility Particle Sizers
located at the tunnel’s entrance and exit. The mean value of the number emission factors was
found to be (2.44±1.41)×1014 particles km-1. The results are in good agreement with the
emission factors determined from steady-state dynamometer testing of 12 buses from the
same Brisbane City bus fleet, thus indicating that when carefully designed, both approaches,
the dynamometer and on-road studies, can provide comparable results, applicable for the
assessment of the effect of traffic emissions on airborne particle pollution.
18 Oct 2017
TL;DR: The accuracy and reliability of low-cost particulate matter (PM) sensors have been investigated in this article, showing that they have serious limitations under ambient conditions, especially at low PM concentrations and small particle sizes.
Abstract: There has been a rapid increase in the number of low cost particulate matter (PM) sensors available on the market over the last two years (Carminati et al. 2015, Yi et al. 2015, Thompson 2016). These sensors come in compact sizes and cost just a few tens of dollars each. Many of them use the principle of infra-red (Patel et al. 2017, Sousan et al. 2017) or laser (Kelly et al. 2017) light scattering from particles and operate by drawing air through a small chamber within the device. While these sensors provide a reading that generally increases linearly with PM concentration, their accuracy and reliability are very much in question. In particular, they have serious limitations under ambient conditions, especially at low PM concentrations and small particle sizes (Austin et al. 2015, Wang et al. 2015)...
TL;DR: In this paper , a unique statistic model was developed where the sum of PM2.5 and the weighted precursor gases was predicted as a function of meteorology and a proxy of primary emissions, where the input data of PM10, CO, O3, NOx, and SO2 were obtained from routine measurements.
Abstract: Quantifying the threat that climate change poses to fine particle (PM2.5) pollution is hampered by large uncertainties in the relationship between PM2.5 and meteorology. To constrain the impact of climate change on PM2.5, statistical models are often employed in a different manner than physical-chemical models to reduce the requirement of input data. A majority of statistical models predict PM2.5 concentration (often log-transformed) as a simple function of meteorology, which could be biased due to the conversion of precursor gases to PM2.5. We reduced this bias by developing a unique statistic model where the sum of PM2.5 and the weighted precursor gases, rather than the PM2.5 alone, was predicted as a function of meteorology and a proxy of primary emissions, where the input data of PM10, CO, O3, NOx, and SO2 were obtained from routine measurements. This modification, without losing the simplicity of statistical models, reduced the mean-square error from 27 to 17% and increased the coefficient of determination from 47 to 67% in the model cross-validation using daily PM2.5 observations during 2013-2018 for 74 cities over China. We found a previously unrecognized mechanism that synoptic climate change in the past half-century might have increased low quantiles of PM2.5 more strenuously than the upper quantiles in large cities over China. Climate change during 1971-2018 was projected to increase the annual mean concentration of PM2.5 at a degree that could be comparable with the toughest-ever clean air policy during 2013-2018 had counteracted it, as inferred from the decline in the daily concentration of carbon monoxide as an inert gas. Our estimate of the impact of climate change on PM2.5 is higher than previous statistical models, suggesting that aerosol chemistry might play a more important role than previously thought in the interaction between climate change and air pollution. Our result indicated that air quality might degrade if the future synoptic climate change could continue interacting with aerosol chemistry as it had occurred in the past half-century.
TL;DR: In this paper , the health risk indices caused by PM10 inhalation by adults, children, and infants in 158 European cities between 2013 and 2019 were studied to determine if Europeans were adversely affected by carcinogenic and non-carcinogenic factors or not.
Cited by
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TL;DR: Authors/Task Force Members: Piotr Ponikowski* (Chairperson) (Poland), Adriaan A. Voors* (Co-Chair person) (The Netherlands), Stefan D. Anker (Germany), Héctor Bueno (Spain), John G. F. Cleland (UK), Andrew J. S. Coats (UK)
13,400 citations
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2010 aimed to estimate annual deaths for the world and 21 regions between 1980 and 2010 for 235 causes, with uncertainty intervals (UIs), separately by age and sex, using the Cause of Death Ensemble model.
11,809 citations
Theo Vos1, Amanuel Alemu Abajobir, Kalkidan Hassen Abate2, Cristiana Abbafati3 +775 more•Institutions (305)
TL;DR: The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016.
10,401 citations
TL;DR: In this paper, the authors estimated deaths and disability-adjusted life years (DALYs; sum of years lived with disability [YLD] and years of life lost [YLL]) attributable to the independent effects of 67 risk factors and clusters of risk factors for 21 regions in 1990 and 2010.
9,324 citations
University of Washington1, Sapienza University of Rome2, Mekelle University3, University of Texas at San Antonio4, King Saud bin Abdulaziz University for Health Sciences5, Debre markos University6, Emory University7, University of Oxford8, University of Cartagena9, United Nations Population Fund10, University of Birmingham11, Stanford University12, Aga Khan University13, University of Melbourne14, National Taiwan University15, University of Cambridge16, University of California, San Diego17, Public Health Foundation of India18, Public Health England19, University of Peradeniya20, Harvard University21, National Institutes of Health22, Tehran University of Medical Sciences23, Auckland University of Technology24, University of Sheffield25, University of Western Australia26, Karolinska Institutet27, Birzeit University28, Brandeis University29, American Cancer Society30, Ochsner Medical Center31, Yonsei University32, University of Bristol33, Heidelberg University34, Vanderbilt University35, South African Medical Research Council36, Jordan University of Science and Technology37, New Generation University College38, Northeastern University39, Simmons College40, Norwegian Institute of Public Health41, Boston University42, Chinese Center for Disease Control and Prevention43, University of Bari44, University of São Paulo45, University of Otago46, University of Crete47, International Centre for Diarrhoeal Disease Research, Bangladesh48, Fred Hutchinson Cancer Research Center49, Teikyo University50, Bhabha Atomic Research Centre51, University of Tokyo52, Finnish Institute of Occupational Health53, Heriot-Watt University54, University of Alabama at Birmingham55, Griffith University56, National Center for Disease Control and Public Health57, University of California, Irvine58, Johns Hopkins University59, New York University60, University of Queensland61, Universidade Federal de Minas Gerais62, National Research University – Higher School of Economics63, University of Bergen64, Columbia University65, Shandong University66, University of North Carolina at Chapel Hill67, Fujita Health University68, Korea University69, Chongqing Medical University70, Zhejiang University71
TL;DR: The global, regional, and national prevalence of overweight and obesity in children and adults during 1980-2013 is estimated using a spatiotemporal Gaussian process regression model to estimate prevalence with 95% uncertainty intervals (UIs).
9,180 citations