Live London Air Quality Charts

Live London Air Quality Charts

Charts of recent air quality measurements

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REST API

REST API

Recent and historical data for over 120 air pollution monitoring sites

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Learn Data Analysis in Python

Learn Data Analysis in Python

Examples of air pollution data analysis using Python

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Wind Direction and Particulate Air Pollution

Particulate matter (PM) levels are strongly influenced by wind direction. A clear trend can be shown by plotting this variable against PM concentrations, averaged according to their associated wind directions: The graph shows a marked increase in average PM levels when there is an easterly wind direction. These findings are consistent with published literature which

Long term trends in air pollution

Time series plots can be considered as being the ‘bread and butter’ of air pollution analysis. They are useful for visualising long term trends in pollution levels, thereby enabling the evaluation of control measures and highlighting areas for improvement. An example of this is in visualising changes in the increments in particulate matter (PM) concentrations