Design of an optimum air monitoring network for exposure assessments |
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| Institution: | 1. School of Physics and Centre for Climate and Air Pollution Studies, Ryan Institute, National University of Ireland Galway, Galway, Ireland;2. Institut de Ciències del Mar, CSIC, Pg Marítim de la Barceloneta 37–49 Barcelona, Spain;3. Department of Chemistry and Environmental Research Institute, University College Cork, Cork, Ireland;4. Laboratoire Chimie Environment, Aix Marseille Université, Marseille 13 331, France;5. Southern Ontario Centre for Atmospheric Aerosol Research, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Canada;6. Department of Chemistry, University of Cambridge, Cambridge, UK;1. Clinical Trial Service Unit and Epidemiological Studies Unit (CTSU), Nuffield Department of Population Health, University of Oxford, Big Data Institute, Old Road Campus, Oxford OX3 7LF, UK;2. Chinese Academy of Medical Sciences, Beijing, China;3. Medical Research Council Population Health Research Unit (MRC PHRU), Nuffield Department of Population Health, University of Oxford, UK;4. Department of Epidemiology, School of Public Health, Peking University Health Science Center, Peking University Center for Public Health and Epidemic Preparedness and Response, Beijing, China;5. NCDs Prevention and Control Department, Jiangsu CDC, Zhejiang Province, China |
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| Abstract: | Nonlinear programming techniques are frequently used to design optimum monitoring networks. These mathematically rigorous techniques are difficult to implement or cumbersome when considering other design criteria. This paper presents a more pragmatic approach to the design of an optimal monitoring network to estimate human exposure to hazardous air pollutants. In this approach, an air quality simulation model is used to produce representative air quality patterns, which are then combined with population patterns to obtain typical exposure patterns. These combined patterns are used to determine ‘figures of merit’ for each potential monitoring site, which are used to identify and rank the most favorable sites. The spatial covariance structure of the air quality patterns is used to draw a ‘sphere of influence’ around each site to identify and eliminate redundant monitoring sites. This procedure determines the minimum number of sites required to achieve the desired spatial coverage. This methodology was used to design an optimal ambient air monitoring network for assessing population exposure to hazardous pollutants in the southeastern Ohio River valley. |
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