Corpus ID: 233346958

A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK

@inproceedings{Pinder2021APA,
  title={A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK},
  author={Thomas Pinder and M. Hollaway and C. Nemeth and P. Young and David Leslie},
  year={2021}
}
In March 2020 the United Kingdom (UK) entered a nationwide lockdown period due to the Covid-19 pandemic. As a result, levels of NO2 in the atmosphere dropped. In this work, we use over 550,134 NO2 data points from 237 stations in the UK to build a spatiotemporal Gaussian process (GP) capable of predicting NO2 levels across the entire UK. We integrate several covariate datasets to enhance the model’s ability to capture the complex spatiotemporal dynamics of NO2. Our numerical analyses show that… Expand

Figures from this paper

References

SHOWING 1-10 OF 32 REFERENCES
Changes in U.S. air pollution during the COVID-19 pandemic
The COVID-19 global pandemic has likely affected air quality due to extreme changes in human behavior. We assessed air quality during the COVID-19 pandemic for fine particulate matter (PM2.5) andExpand
Current and future global climate impacts resulting from COVID-19
The global response to the COVID-19 pandemic has led to a sudden reduction of both GHG emissions and air pollutants. Here, using national mobility data, we estimate global emission reductions for tenExpand
Random forest meteorological normalisation models for Swiss PM 10 trend analysis
Abstract. Meteorological normalisation is a technique which accounts for changes in meteorology over time in an air quality time series. Controlling for such changes helps support robust trendExpand
openair - An R package for air quality data analysis
TLDR
It is demonstrated how air pollution data can be analysed quickly and efficiently and in an interactive way, freeing time to consider the problem at hand. Expand
Health Risks of Air Pollution in Europe HRAPIE Project
This document presents recommendations for concentration–response functions for key pollutants to be included in cost–benefit analysis supporting the revision of the European Union’s air qualityExpand
Estimates of the Global Burden of Ambient PM2.5, Ozone, and NO2 on Asthma Incidence and Emergency Room Visits
TLDR
The magnitude of the global asthma burden that could be avoided by reducing ambient air pollution is estimated and key uncertainties and data limitations to be addressed to enable refined estimation are identified. Expand
Convergence of Sparse Variational Inference in Gaussian Processes Regression
TLDR
It is shown that the KL-divergence between the approximate model and the exact posterior arbitrarily small for a Gaussian-noise regression model with M needs to grow with N to ensure high quality approximations. Expand
Stein Variational Gaussian Processes
TLDR
SVGD provides a non-parametric alternative to variational inference which is substantially faster than MCMC but unhindered by parametric assumptions, and it is proved that for GP models with Lipschitz gradients the SVGD algorithm monotonically decreases the Kullback-Leibler divergence from the sampling distribution to the true posterior. Expand
Sparse Gaussian Processes using Pseudo-inputs
TLDR
It is shown that this new Gaussian process (GP) regression model can match full GP performance with small M, i.e. very sparse solutions, and it significantly outperforms other approaches in this regime. Expand
Adam: A Method for Stochastic Optimization
TLDR
This work introduces Adam, an algorithm for first-order gradient-based optimization of stochastic objective functions, based on adaptive estimates of lower-order moments, and provides a regret bound on the convergence rate that is comparable to the best known results under the online convex optimization framework. Expand
...
1
2
3
4
...