A Focus on Particulate Matter and Health

Russell, Armistead G.; Brunekreef, Bert

doi:10.1021/es9005459

Cited by 219 publications

(100 citation statements)

References 55 publications

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“…The other 3 minor inorganic types were rich in transition metallic species, including Fe-rich, Cu-rich, and Vrich, accounting for 2.8%, 2.5%, and 0.9%, respectively. These metallic particle types are highlighted due to their potential health effects (Russell and Brunekreef, 2009). As discussed in Section 3.2, their Nf varied in a wide range and could reach as high as~20-30% under specific meteorological condition.…”

Section: Resultsmentioning

confidence: 99%

Characteristics of individual particles in the atmosphere of Guangzhou by single particle mass spectrometry

Zhang

Han

et al. 2015

Atmospheric Research

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Section: Resultsmentioning

confidence: 99%

Characteristics of individual particles in the atmosphere of Guangzhou by single particle mass spectrometry

Zhang

Han

et al. 2015

Atmospheric Research

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“…PM plays a major role in the global radiative balance (e.g., Charlson et al, 1992;Hubert et al, 2003;Jiang et al, 2013b) and is detrimental to human health (e.g., Dockery et al, 1993;Moloi et al, 2002;Russell and Brunekreef, 2009). Because human activities represent a major source of PM, many administrations have developed policies with the aim of limiting the release of aerosols and precursor compounds (e.g., USEPA, 2007;NSW, 2011;VES, 2012).…”

Section: Introductionmentioning

confidence: 99%

“…The concentration and composition of aerosols can be monitored in near real time using mass spectrometry (Russell and Brunekreef, 2009;Hartonen et al, 2011), or, more commonly, by collecting samples on filters and then analyzing using off-line techniques (e.g., Ion Beam Analysis; Cohen 1996). The latter technique was used in this study.…”

Section: Introductionmentioning

confidence: 99%

Particulate Pollution in the Sydney Region: Source Diagnostics and Synoptic Controls

Crawford

Griffiths

Cohen

et al. 2016

Aerosol Air Qual. Res.

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Airborne particulate matter (PM 2.5 ) was sampled at Richmond and Liverpool, located in the Sydney Basin, Australia, and ion beam analysis was used to obtain the elemental composition. Using self-organising maps to classify synoptic weather systems, it was found that high PM 2.5 concentrations were associated with high pressure systems located to the east of the sampling sites. The highest median sulfur was associated with weak synoptic conditions and high soil dust days were more often associated with frontal systems.To investigate the effect of local flows in the Sydney Basin, the Weather Research and Forecasting model (WRF) was used to generate meteorological data of 12 km resolution. A comparison was made between back trajectories generated using the higher-resolution WRF data, the 0.5° by 0.5° Climate Forecast System data and the 1° by 1° Global Data Assimilation System data. It was found that for high soil dust days, there were small differences between the different back trajectories. However, under weak synoptic conditions (high sulfur days), the back trajectories generated from higher resolution data showed larger variations over a 24 hr period. This was attributed to the meandering of local winds and seabreezes.Lower altitude back trajectories, generated from low resolution data, passed more often over the power stations located on the western side of the Great Dividing Range (while the sampling sites are on the east). This demonstrates the need for higher resolution meteorological data for generating low altitude back trajectories when the source and receptor are separated by hilly terrain.In estimating the number of high sulfur days for which a power station was crossed, there was up to 20% difference at Liverpool and up to 10% difference at Richmond, between back trajectories starting at different altitudes and generated from meteorological data of three different resolutions.

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“…It is well established nowadays that short and long term exposure to high particulate matter concentrations causes adverse health effects and reduction in population's life expectancy in both developed and developing countries. A number of toxicological and epidemiological studies reported that exposure to particulate matter can cause health problems ranging from respiratory to cardiovascular illnesses (Pope III and Dockery, 2006;Perez et al, 2009;Russell and Brunekreef, 2009). …”

Section: General Overview On Pm10mentioning

confidence: 99%

Variable Selection Based on Statistical Learning Approaches to Improve PM10 Concentration Forecasting

Ishak¹

2016

J ENV INFORM

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ABSTRACT. In this work, the problem of variable selection for regression is investigated in order to improve the forecasting accuracy. To that effect, the support vector regression (SVR) and the random forests (RF) are used to assess the variable importance. Then, a stepwise algorithm is built to select the best subset of predictors. An intensive comparative study is conducted on simulated and real datasets. The real datasets expose the problem of particulate matter concentration forecasting in two monitoring stations from Tunisia. We have proposed a combined approach using SVR and RF for variable importance assessment and for variable selection. We have achieved a significant improvement in forecasts accuracy for the two stations when using only a reduced number of selected predictors.

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A Focus on Particulate Matter and Health

Cited by 219 publications

References 55 publications

Characteristics of individual particles in the atmosphere of Guangzhou by single particle mass spectrometry

Characteristics of individual particles in the atmosphere of Guangzhou by single particle mass spectrometry

Particulate Pollution in the Sydney Region: Source Diagnostics and Synoptic Controls

Variable Selection Based on Statistical Learning Approaches to Improve PM10 Concentration Forecasting

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