A pragmatic mass closure model for airborne particulate matter at urban background and roadside sites

2003 ◽  
Vol 37 (35) ◽  
pp. 4927-4933 ◽  
Author(s):  
Roy M. Harrison ◽  
Alan M. Jones ◽  
Royston G. Lawrence
2010 ◽  
Vol 30 (5) ◽  
pp. 321-333 ◽  
Author(s):  
Yuki Kojima ◽  
Koji Inazu ◽  
Yoshiharu Hisamatsu ◽  
Hiroshi Okochi ◽  
Toshihide Baba ◽  
...  

2014 ◽  
Vol 14 (18) ◽  
pp. 9977-9991 ◽  
Author(s):  
S. M. Gaita ◽  
J. Boman ◽  
M. J. Gatari ◽  
J. B. C. Pettersson ◽  
S. Janhäll

Abstract. Sources of airborne particulate matter and their seasonal variation in urban areas in Sub-Saharan Africa are poorly understood due to lack of long-term measurement data. In view of this, filter samples of airborne particulate matter (particle diameter ≤2.5 μm, PM2.5) were collected between May 2008 and April 2010 at two sites (urban background site and suburban site) within the Nairobi metropolitan area. A total of 780 samples were collected and analyzed for particulate mass, black carbon (BC) and 13 trace elements. The average PM2.5 concentration at the urban background site was 21±9.5 μg m−3, whereas the concentration at the suburban site was 13±7.3 μg m−3. The daily PM2.5 concentrations exceeded 25 μg m−3 (the World Health Organization 24 h guideline value) on 29% of the days at the urban background site and 7% of the days at the suburban site. At both sites, BC, Fe, S and Cl accounted for approximately 80% of all detected elements. Positive matrix factorization analysis identified five source factors that contribute to PM2.5 in Nairobi, namely traffic, mineral dust, industry, combustion and a mixed factor (composed of biomass burning, secondary aerosol and aged sea salt). Mineral dust and traffic factors were related to approximately 74% of PM2.5. The identified source factors exhibited seasonal variation, apart from the traffic factor, which was prominently consistent throughout the sampling period. Weekly variations were observed in all factors, with weekdays having higher concentrations than weekends. The results provide information that can be exploited for policy formulation and mitigation strategies to control air pollution in Sub-Saharan African cities.


Animals ◽  
2021 ◽  
Vol 11 (3) ◽  
pp. 848
Author(s):  
Benjamin Eid ◽  
David Beggs ◽  
Peter Mansell

In 2019–2020, a particularly bad bushfire season in Australia resulted in cattle being exposed to prolonged periods of smoke haze and reduced air quality. Bushfire smoke contains many harmful pollutants, and impacts on regions far from the fire front, with smoke haze persisting for weeks. Particulate matter (PM) is one of the major components of bushfire smoke known to have a negative impact on human health. However, little has been reported about the potential effects that bushfire smoke has on cattle exposed to smoke haze for extended periods. We explored the current literature to investigate evidence for likely effects on cattle from prolonged exposure to smoke generated from bushfires in Australia. We conducted a search for papers related to the impacts of smoke on cattle. Initial searching returned no relevant articles through either CAB Direct or PubMed databases, whilst Google Scholar provided a small number of results. The search was then expanded to look at two sub-questions: the type of pollution that is found in bushfire smoke, and the reported effects of both humans and cattle being exposed to these types of pollutants. The primary mechanism for damage due to bushfire smoke is due to small airborne particulate matter (PM). Although evidence demonstrates that PM from bushfire smoke has a measurable impact on both human mortality and cardiorespiratory morbidities, there is little evidence regarding the impact of chronic bushfire smoke exposure in cattle. We hypothesize that cattle are not severely affected by chronic exposure to smoke haze, as evidenced by the lack of reports. This may be because cattle do not tend to suffer from the co-morbidities that, in the human population, seem to be made worse by smoke and pollution. Further, small changes to background mortality rates or transient morbidity may also go unreported.


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