Data Management

Air Quality Data acquisition

The VAISALA Air Quality Transmitter AQT 400 series like most modern continuous air quality monitoring instruments contains its own data acquisition system as well as provision for digital output of data to an external data logger. Hence, utilising the instruments data acquisition system it is possible to transfer data to a laptop to collect and store monitoring data. Data can then be imported into Microsoft Excel spreadsheet for temporary storage purposes.

Air Quality data storage, archiving and retrieval

All data will be stored in a central database and will be regularly backed up. Each monitoring site and the parameter will be assigned a unique identifier that enables easy retrieval. It is preferable to store data in such a way that incoming data is appended to the archive file so it can be viewed as a continuous data set. Two parallel data sets will be maintained: one that preserves raw data in its original form and the other that has been quality assured and will be available for further analysis. Keeping a raw data set archived means that the data can be revisited and re-analysed if any problems arise with the original quality assurance process.

Daily data checks

The main advantage of regular data transfer by telemetry is that the data can be checked at least once a day so that instrument faults, systems failures, data spikes, human error, power failures, interference or other disturbances can be readily identified and promptly remedied to minimise instrument breakdown and data loss.  Daily data checks is recommended for each site and notes of events that may affect results such as bushfires, dust storms, roadworks, fireworks, etc. will be recorded. 

Data Analysis

Using the software analysis tool R-studio, the following analysis is recommended to sufficiently provide the desired results:

Time Series Regression

Time series regression is a Trend analysis method which looks at how a potential driver of change has developed over time, and how it is likely to develop in the future. It does not predict what the future will look like, however, it becomes a powerful tool for strategic planning by creating plausible, detailed pictures of what the future might look like \cite{oecd}. Trend detection is considered a key aspect of understanding the state of air quality based on past data\cite{Blanchard_1999}. Statistical methods for trend analysis of environmental data can be classified into parametric and non-parametric tests. Hence, Time series linear regression(a parametric method) is chosen due to its simplicity and straightforwardness of interpretation. In addition, parametric tests tend to be more powerful than nonparametric tests and they have an ability to quantify the magnitude of a trend\cite{oecd}. Time series regression will be employed to describe and analyse the trend of the air quality data.