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A spatio-temporal analysis of NO$_2$ concentrations during the Italian 2020 COVID-19 lockdown

TL;DR: In this article, the lockdown measures taken worldwide in 2020 to reduce the spread of the SARS-CoV-2 virus can be envisioned as a policy intervention with an indirect effect on air quality.
Abstract: When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify - in space and time - the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020 to reduce the spread of the SARS-CoV- 2 virus can be envisioned as a policy intervention with an indirect effect on air quality. In this paper we propose a statistical spatio-temporal model as a tool for intervention analysis, able to take into account the effect of weather and other confounding factors, as well as the spatial and temporal correlation existing in the data. In particular, we focus here on the 2019/2020 relative change in nitrogen dioxide (NO$_2$) concentrations in the north of Italy, for the period of March and April during which the lockdown measure was in force. As an output, we provide a collection of weekly continuous maps, describing the spatial pattern of the NO$_2$ 2019/2020 relative changes. We found that during March and April 2020 most of the studied area is characterized by negative relative changes (median values around -25%), with the exception of the first week of March and the fourth week of April (median values around 5%). As these changes cannot be attributed to a weather effect, it is likely that they are a byproduct of the lockdown measures.
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Journal ArticleDOI
TL;DR: In this article , the lockdown measures taken worldwide in 2020 to reduce the spread of the SARS-CoV-2 virus can be envisioned as a policy intervention with an indirect effect on air quality.
Abstract: When a new environmental policy or a specific intervention is taken in order to improve air quality, it is paramount to assess and quantify—in space and time—the effectiveness of the adopted strategy. The lockdown measures taken worldwide in 2020 to reduce the spread of the SARS‐CoV‐2 virus can be envisioned as a policy intervention with an indirect effect on air quality. In this paper we propose a statistical spatiotemporal model as a tool for intervention analysis, able to take into account the effect of weather and other confounding factor, as well as the spatial and temporal correlation existing in the data. In particular, we focus here on the 2019/2020 relative change in nitrogen dioxide (NO 2 ) concentrations in the north of Italy, for the period of March and April during which the lockdown measure was in force. We found that during March and April 2020 most of the studied area is characterized by negative relative changes (median values around − 25%), with the exception of the first week of March and the fourth week of April (median values around 5%). As these changes cannot be attributed to a weather effect, it is likely that they are a byproduct of the lockdown measures. There are two aspects of our research that are equally interesting. First, we provide a unique statistical perspective for calculating the relative change in the NO 2 by jointly modeling pollutant concentrations time series. Second, as an output we provide a collection of weekly continuous maps, describing the spatial pattern of the NO 2 2019/2020 relative changes.

4 citations

References
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TL;DR: In this paper, the average NO2 column drop over all Chinese cities amounts to -40% relative to the same period in 2019, and reaches up to a factor of ~2 at heavily hit cities, e.g. Wuhan, Jinan, while the decreases in Western Europe and the U.S. are also significant.
Abstract: Spaceborne NO2 column observations from two high-resolution instruments, TROPOMI onboard Sentinel-5 Precursor and OMI on Aura, reveal unprecedented NO2 decreases over China, South Korea, Western Europe and the U.S. as a result of public health measures enforced to contain the coronavirus disease outbreak (Covid-19) in January-April 2020. The average NO2 column drop over all Chinese cities amounts to -40% relative to the same period in 2019, and reaches up to a factor of ~2 at heavily hit cities, e.g. Wuhan, Jinan, while the decreases in Western Europe and the U.S. are also significant (-20 to -38%). In contrast with this, although Iran is also strongly affected by the disease, the observations do not show evidence of lower emissions, reflecting more limited health measures.

468 citations

Journal ArticleDOI
TL;DR: The reduction of air pollution was strongly associated with travel restrictions during this pandemic—on average, the air quality index (AQI) decreased and five air pollutants decreased, and SO2, PM10, and NO2 were completely mediated.

463 citations

Journal ArticleDOI
TL;DR: It is shown how scientifically derived colour maps report true data variations, reduce complexity, and are accessible for people with colour-vision deficiencies, in a simple guide for the scientific use of colour.
Abstract: The accurate representation of data is essential in science communication. However, colour maps that visually distort data through uneven colour gradients or are unreadable to those with colour-vision deficiency remain prevalent in science. These include, but are not limited to, rainbow-like and red-green colour maps. Here, we present a simple guide for the scientific use of colour. We show how scientifically derived colour maps report true data variations, reduce complexity, and are accessible for people with colour-vision deficiencies. We highlight ways for the scientific community to identify and prevent the misuse of colour in science, and call for a proactive step away from colour misuse among the community, publishers, and the press.

351 citations

Journal ArticleDOI
TL;DR: This work considers a hierarchical spatio-temporal model for particulate matter (PM) concentration in the North-Italian region Piemonte and proposes a strategy to represent a GF with Matérn covariance function as a Gaussian Markov Random Field (GMRF) through the SPDE approach.
Abstract: In this work, we consider a hierarchical spatio-temporal model for particulate matter (PM) concentration in the North-Italian region Piemonte. The model involves a Gaussian Field (GF), affected by a measurement error, and a state process characterized by a first order autoregressive dynamic model and spatially correlated innovations. This kind of model is well discussed and widely used in the air quality literature thanks to its flexibility in modelling the effect of relevant covariates (i.e. meteorological and geographical variables) as well as time and space dependence. However, Bayesian inference—through Markov chain Monte Carlo (MCMC) techniques—can be a challenge due to convergence problems and heavy computational loads. In particular, the computational issue refers to the infeasibility of linear algebra operations involving the big dense covariance matrices which occur when large spatio-temporal datasets are present. The main goal of this work is to present an effective estimating and spatial prediction strategy for the considered spatio-temporal model. This proposal consists in representing a GF with Matern covariance function as a Gaussian Markov Random Field (GMRF) through the Stochastic Partial Differential Equations (SPDE) approach. The main advantage of moving from a GF to a GMRF stems from the good computational properties that the latter enjoys. In fact, GMRFs are defined by sparse matrices that allow for computationally effective numerical methods. Moreover, when dealing with Bayesian inference for GMRFs, it is possible to adopt the Integrated Nested Laplace Approximation (INLA) algorithm as an alternative to MCMC methods giving rise to additional computational advantages. The implementation of the SPDE approach through the R-library INLA ( www.r-inla.org ) is illustrated with reference to the Piemonte PM data. In particular, providing the step-by-step R-code, we show how it is easy to get prediction and probability of exceedance maps in a reasonable computing time.

337 citations

Journal ArticleDOI
TL;DR: A principled joint prior is developed for the range and the marginal variance of one-dimensional, two- dimensional, and three-dimensional Matérn GRFs with fixed smoothness and is applied to a dataset of annual precipitation in southern Norway, leading to conservative estimates of nonstationarity and improved predictive performance over the stationary model.
Abstract: Priors are important for achieving proper posteriors with physically meaningful covariance structures for Gaussian random fields (GRFs) since the likelihood typically only provides limited informat...

216 citations