|Data quality. Information from collection and chemical
analysis such as standard operating procedures, audits, accuracy and
precision, and data validation provide insight into sample and collection
biases and errors. This information is
necessary for data validation.
Metadata such as precision and accuracy are required for other
analyses (e.g., receptor modeling).
|Data availability. The number of species and amount of data
above detection give insight into what analyses can be performed and provide
a starting point for planning data analysis.
|Sampling duration. Duration provides information about
analysis possibilities, for example, 24-hr data cannot be used to investigate
diurnal patterns. This information may
also be necessary for calculating completeness criteria when aggregating
frequency. Frequency information
provides further insight into what analyses will be possible; for example,
one year of 1-in-6-day data may not be sufficient to investigate day-of-week
tendencies. Sample frequency will also
be necessary to calculate data completeness and to aggregate data.
data. Additional data for criteria
pollutants, speciated PM, and non-toxic hydrocarbons and meteorological data
can be useful in a variety of analyses such as data validation, understanding
transport, and source identification.