Abstract
Trend analysis of air pollutant concentrations becomes problematic when applied to data from air quality monitoring networks containing time series of differing lengths. The average trend from such data can be misleading due to biases in the monitoring network. For example, if new monitoring sites located in more polluted locations are added to a network, the introduction of these time series can leverage the trend upwards. A method for resolving this problem was developed, using rolling window regression to recursively calculate the change in pollutant concentration as a function of time, which can be used as a proxy for the true trend. The efficacy of the method was established by conducting simulations with known trends. The rolling change trend was shown to more accurately reflect the true trend than simply averaging the time series. Application of the technique to estimate trends in NOx, NO2 and NO2/NOx concentrations at London roadside monitoring sites over the period 2000–2017 revealed clear differences from the simple average. In particular, a significant monotonic downward trend in NOx concentration was observed, in stark contrast to the average trend, which suggested little change in NOx concentration had occurred over the same period. By accurately representing trends using time series of different lengths, this method has the benefit of being able to describe changes in air quality for locations and time periods with otherwise insufficient data.
Highlights
IntroductionAir quality monitoring networks are instrumental to the evaluation and management of air pollution by governments, policy makers and regulatory bodies
Simulations were carried out to compare the effectiveness of the average trend and the rolling change trend to display the true change in pollutant concentration over time
Variation in the concentrations of different time series was simulated by sampling the concentration in the first year of the time series from a normal distribution with a mean equal to the concentration of the true trend in that year and a standard deviation of 10 (X ∼ N). (b) Short term monitoring sites without a time-dependent bias in concentration
Summary
Air quality monitoring networks are instrumental to the evaluation and management of air pollution by governments, policy makers and regulatory bodies While other tools, such as emission inventories, are often used to track changes in emissions, the complex nature of atmospheric processes and local conditions mean that emissions data are not necessarily an accurate indicator of pollutant concentration or exposure. Ambient data from monitoring networks, subject to rigorous analysis, can reveal the pollutant concentrations, correlations and trends at measurement locations. Such information is invaluable for estimating the actual effects of social and infrastructure changes, and policy interventions on air quality. Cluster analysis has been used to look at trends across a large number of sites allowing potential drivers for observed changes to be investigated and differences within and across regions to be explored (Malley et al, 2018)
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