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Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010)

Noel Cressie and Christopher WikleHardcover: 624 pagesYear: 2011Publisher: John WileyISBN-13: 978-0471692744Here is the new reference book about spatial and spatio-temporal statistical modeling! No...

Statistics for Spatio-Temporal Data

Air pollution poses the largest environmental health risk in Europe. Particulate matter (PM) concentrations are the most harmful pollutants representing the main air quality indicator in the Sustainable Development Goals (SDGs). The air quality surveillance in Europe is based on a monitoring network that is too coarse for a comprehensive evaluation of the air pollution burden. We link raw pollutant data with remotely sensed products using Bayesian geostatistical models and for the first time estimate pan-European near-surface concentrations of both fine (PM2.5) and coarse (PM10) particles at 1 km2 spatial resolution during 2006-2019. We evaluate the compliance with the air quality thresholds set by the World Health Organization (WHO) and the European Union (EU) and assess country-wise trends. The results show that during the last 14 years, PM10 and PM2.5 concentrations declined by 36.5% (95% credible interval: 30.3, 41.9%) and 39.1% (26.6, 50.5%), respectively. The number of people exposed to PM10 levels above the WHO thresholds decreased from 78.3% (52.6, 91.8%) in 2006 to 28.4% (16.2, 43.7%) in 2019; for PM2.5, the decrease was smaller: from 91.0% (61.3, 99.1%) exposed in 2006 to 53.6% (33.5, 76.3%) in 2019. Although there is a clear improvement in the overall picture, stricter measures are needed to ensure compliance with the WHO guidelines.

Substantial Reduction in Particulate Matter Air Pollution across Europe during 2006-2019: A Spatiotemporal Modeling Analysis.

Estimating ambient air pollutant levels in Suzhou through the SPDE approach with R-INLA.

Our objective in this work was to present a hierarchical Bayesian spatiotemporal model that allowed us to make spatial predictions of air pollution levels effectively and with very few computational costs. We specified a hierarchical spatiotemporal model using the Stochastic Partial Differential Equations of the integrated nested Laplace approximations approximation. This approach allowed us to spatially predict in the territory of Catalonia (Spain) the levels of the four pollutants for which there is the most evidence of an adverse health effect. Our model allowed us to make fairly accurate spatial predictions of both long-term and short-term exposure to air pollutants with a relatively low density of monitoring stations and at a much lower computation time. The only requirements of our method are the minimum number of stations distributed throughout the territory where the predictions are to be made, and that the spatial and temporal dimensions are either independent or separable. • We show a hierarchical Bayesian spatiotemporal model. • Our model provides predictions of both long-term and short-term exposure. • The computational cost is low. • The model only needs a minimum number of stations being distributed throughout the territory. • The other requirement of our model is that the spatial and temporal dimensions are either independent or separable. 

Spatial prediction of air pollution levels using a hierarchical Bayesian spatiotemporal model in Catalonia, Spain

Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach

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.

Guido Fioravanti

Papers

Spatio-temporal modelling of PM10 daily concentrations in Italy using the SPDE approach

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