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A Trend Analysis Approach
for Air Quality Network Data
Polly Lang
Why is long term trend analysis important?
● Measure the change in air pollutant concentration over time
● Evaluate the effects of long term changes, e.g. policy interventions on air
quality
● Identify unexpected ‘signals’
○ Lack of expected response in pollutant concentrations
○ Unexpected changes enabling exploration of drivers
Methods for long term trend analysis
● Analysis of time series of a single monitoring site
○ Not representative of a large area
○ Local variation
● Compare long term trends from multiple sites individually
○ Not practical for large monitoring networks
● Average concentration from multiple sites
○ Trend may be leveraged by sites opening and closing
● Filter data to only use data from sites measuring over entire period of analysis
○ Prohibitive for long time periods/sparse monitoring networks
○ Limits the length of the time period
Monitoring site counts in the London network over time
Methods for long term trend analysis
● Analysis of time series of a single monitoring site
○ Not representative of a large area
○ Local variation
● Compare long term trends from multiple sites individually
○ Not practical for large monitoring networks
● Average concentration from multiple sites
○ Trend may be leveraged by sites opening and closing
● Filter data to only use data from sites measuring over entire period of analysis
○ Prohibitive for long time periods/sparse monitoring networks
○ Limits the length of the time period
All monitoring sites measuring NO2
between 2000-2017
Long term monitoring sites measuring
NO2 for the entire period 2000-2017
Long term trends in London: 3 different methods
The rolling change method
Rolling change trends in roadside NOx, NO2 concentration
and NO2/NOx ratio in UK 2000-2017
Rolling change trends in roadside NOx, NO2 concentration
and NO2/NOx ratio in Europe 2000-2017
Concluding Remarks
● Long term trend analysis is important for measuring changes in air quality
over time and evaluating the effects of policy interventions
● Aggregating data from multiple monitoring sites ‘averages out’ local variability
● This is complicated by site flux in monitoring networks introducing bias to the
average trend
● The rolling change method is a technique for long term trend analysis using
data from sparse and/or biased monitoring networks
Rolling change method can be implemented using the ‘aqtrends’ R package
available here: https://github.com/pollylang/aqtrends
Further Information
More detail on the rolling change method can be found in the following paper:
Lang, P. E., Carslaw, D. C., & Moller, S. J. (2019), ‘A trend analysis approach for
air quality network data’, Atmospheric Environment:X (2)
I would like to acknowledge Defra, King's College London, and Ricardo Energy &
Environment for providing the data used in this presentation.

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A trend analysis approach for air quality network data - Polly Lang

  • 1. A Trend Analysis Approach for Air Quality Network Data Polly Lang
  • 2. Why is long term trend analysis important? ● Measure the change in air pollutant concentration over time ● Evaluate the effects of long term changes, e.g. policy interventions on air quality ● Identify unexpected ‘signals’ ○ Lack of expected response in pollutant concentrations ○ Unexpected changes enabling exploration of drivers
  • 3. Methods for long term trend analysis ● Analysis of time series of a single monitoring site ○ Not representative of a large area ○ Local variation ● Compare long term trends from multiple sites individually ○ Not practical for large monitoring networks ● Average concentration from multiple sites ○ Trend may be leveraged by sites opening and closing ● Filter data to only use data from sites measuring over entire period of analysis ○ Prohibitive for long time periods/sparse monitoring networks ○ Limits the length of the time period
  • 4. Monitoring site counts in the London network over time
  • 5. Methods for long term trend analysis ● Analysis of time series of a single monitoring site ○ Not representative of a large area ○ Local variation ● Compare long term trends from multiple sites individually ○ Not practical for large monitoring networks ● Average concentration from multiple sites ○ Trend may be leveraged by sites opening and closing ● Filter data to only use data from sites measuring over entire period of analysis ○ Prohibitive for long time periods/sparse monitoring networks ○ Limits the length of the time period
  • 6. All monitoring sites measuring NO2 between 2000-2017 Long term monitoring sites measuring NO2 for the entire period 2000-2017
  • 7. Long term trends in London: 3 different methods
  • 9. Rolling change trends in roadside NOx, NO2 concentration and NO2/NOx ratio in UK 2000-2017
  • 10. Rolling change trends in roadside NOx, NO2 concentration and NO2/NOx ratio in Europe 2000-2017
  • 11. Concluding Remarks ● Long term trend analysis is important for measuring changes in air quality over time and evaluating the effects of policy interventions ● Aggregating data from multiple monitoring sites ‘averages out’ local variability ● This is complicated by site flux in monitoring networks introducing bias to the average trend ● The rolling change method is a technique for long term trend analysis using data from sparse and/or biased monitoring networks Rolling change method can be implemented using the ‘aqtrends’ R package available here: https://github.com/pollylang/aqtrends
  • 12. Further Information More detail on the rolling change method can be found in the following paper: Lang, P. E., Carslaw, D. C., & Moller, S. J. (2019), ‘A trend analysis approach for air quality network data’, Atmospheric Environment:X (2) I would like to acknowledge Defra, King's College London, and Ricardo Energy & Environment for providing the data used in this presentation.