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2003
Approaching complexities in health and environment. Proceedings from HENVINET (Health and Environment Network) final conference. Brussels, Belgium 14-15 April 2010. Environmental Health, vol. 11, suppl. 1
2012
This report presents a review of data assimilation methods applicable to air quality. In the introduction, we first describe a brief history of data assimilation method development in the context of numerical weather prediction (NWP), and then we highlight key differences when applying data assimilation methods to air quality prediction from NWP applications. Based on these differences, we outline a set of key requirements for data assimilation when applied to air quality. Following this, we review the available data assimilation algorithms and attempt to identify suitable data assimilation methods that could be applied with air quality models. This review and its findings form the basis of the developments to be carried out in the Urban Data Assimilation Systems project.
NILU
2021
Whereas inhalation exposure to organic contaminants can negatively impact human health, knowledge of their spatial variability in the ambient atmosphere remains limited. We analyzed the extracts of passive air samplers deployed at 119 unique sites in Southern Canada between 2019 and 2022 for 353 organic vapors. Hierarchical clustering of the obtained data set revealed four archetypes of spatial concentration variability in the outdoor atmosphere, which are indicative of common sources and similar atmospheric dispersion behavior. “Point Source” signatures are characterized by elevated concentration in the vicinity of major release locations. A “Population” signature applies to compounds whose air concentrations are highly correlated with population density, and is associated with emissions from consumer products. The “Water Source” signature applies to substances with elevated levels in the vicinity of water bodies from which they evaporate. Another group of compounds displays a “Uniform” signature, indicative of a lack of major sources within the study area. We illustrate how such a data set, and the derived spatial patterns, can be applied to support the identification of sources, the quantification of atmospheric emissions, the modeling of air quality, and the investigation of potential inequities in inhalation exposure.
2024
2013
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