Abstract
The amount of air quality data collected around the world is enormous and continues to grow. This data is expensive to collect and manage, requiring specialist equipment, trained staff and a commitment over many years. Despite the large investment in collecting data, the analysis of data is more adhoc, fragmented and limited in ambition. Often, these data are analysed in very basic ways such as to compare measured concentrations with air quality standards and other guidelines. While such analysis is useful from a compliance perspective, it is also a wasted opportunity to gain more insight into the underlying characteristics of air pollution. An improved understanding of the causes of air pollution ultimately leads to improved air quality management.
Highlights
The amount of air quality data collected around the world is enormous and continues to grow
Despite the large investment in collecting data, the analysis of data is more adhoc, fragmented and limited in ambition. These data are analysed in very basic ways such as to compare measured concentrations with air quality standards and other guidelines. While such analysis is useful from a compliance perspective, it is a wasted opportunity to gain more insight into the underlying characteristics of air pollution
An improved understanding of the causes of air pollution leads to improved air quality management
Summary
The amount of air quality data collected around the world is enormous and continues to grow. Openair to produce a series of pollution roses that split the data by season, year, day of the week, hour of the day – or by any other variable in the data set of interest. This flexibility, which is at the heart of R and openair, immediately allows the user to follow different lines of enquiry in a fast and efficient way; building up a more complete picture about the characteristics of air pollution.
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