Meteorological Normalisation Using Boosted Regression Trees to Estimate the Impact of COVID-19 Restrictions on Air Quality Levels

Abstract

The global COVID-19 pandemic that began in late December 2019 led to unprecedented lockdowns worldwide, providing a unique opportunity to investigate in detail the impacts of restricted anthropogenic emissions on air quality. A wide range of strategies and approaches exist to achieve this. In this paper, we use the “deweather” R package, based on Boosted Regression Tree (BRT) models, first to remove the influences of meteorology and emission trend patterns from NO, NO2, PM10 and O3 data series, and then to calculate the relative changes in air pollutant levels in 2020 with respect to the previous seven years (2013–2019). Data from a northern Spanish region, Cantabria, with all types of monitoring stations (traffic, urban background, industrial and rural) were used, dividing the calendar year into eight periods according to the intensity of government restrictions. The results showed mean reductions in the lockdown period above −50% for NOx, around −10% for PM10 and below −5% for O3. Small differences were found between the relative changes obtained from normalised data with respect to those from observations. These results highlight the importance of developing an integrated policy to reduce anthropogenic emissions and the need to move towards sustainable mobility to ensure safer air quality levels, as pre-existing concentrations in some cases exceed the safe threshold.

Machine Learning and Meteorological Normalization for Assessment of Particulate Matter Changes during the COVID-19 Lockdown in Zagreb, Croatia

“…Regarding the changes in air pollution, the results obtained in this study are in line with the other published work [ 13 , 14 , 34 , 73 ]. In a previous study carried out in Zagreb, average concentrations of NO 2 , PM 1 and PAHs in PM 1 during the lockdown period were compared with the average concentrations for the same period in 2019.…”

“…The given results are in accordance also with a study by Etchie et al [ 33 ] who observes no effect on PM by the lockdown. A study that also used RF and meteorological normalization shows only a moderate decrease for PM 10 [ 73 ]. The results imply that in Zagreb, Croatia, traffic is not the main contributor in such a site to air pollution by means of particulate matter.…”

Ensemble Deep Learning for Classification of Pollution Peaks

“…When all trees are generated, the final output from this algorithm is obtained by eqn (1). 𝑓 𝑥 is the output from regression tree n. Considering its high predictive power, more and more authors have been using GBM to predict air quality [3], [4].…”

“…Moreover, Grange et al used ML and meteorological parameters to develop weather normalized models (WNMs) for developing air contaminant prediction models [5]. Rybarczyk and Zalakeviciute [4], Barré et al [2] and Ceballos-Santos et al [3] developed WNMs using gradient boosting machine (GBM) to simulate and quantify the effects of human activities on the environment in the context of COVID-19 lockdowns.…”