Spatial–temporal modellization of the $$\hbox {NO}_{2}$$ NO 2 concentration data through geostatistical tools

Abstract

The nitrogen dioxide is a primary pollutant, regarded for the estimation of the air quality index, whose excessive presence may cause significant environmental and health problems. In the current work, we suggest characterizing the evolution of NO 2 levels, by using geostatistical approaches that deal with both the space and time coordinates. To develop our proposal, a first exploratory analysis was carried out on daily values of the target variable, daily measured in Portugal from 2004 to 2012, which led to identify three influential covariates (type of site, environment and month of measurement). In a second step, appropriate geostatistical tools were applied to model the trend and the space-time variability, thus enabling us to use the kriging techniques for prediction, without requiring data from a dense monitoring network. This methodology has valuable applications, as it can provide accurate assessment of the nitrogen dioxide concentrations at sites where either data have been lost or there is no monitoring station nearby.

Advanced methods for space–time environmental data

“…Adopting a Bayesian framework, MCMC algorithm is easily implemented using BUGS package, giving parameter estimates and kriging interpolation. This paper has some relations to above mentioned paper of Menezes et al (2015) which also has asymmetric distributed response variables. However, the latter is a space-time model with two stage estimation, while Boojari et al do not consider time and use a hierarchical model instead.…”

Statistical Modeling of the Early-Stage Impact of a New Traffic Policy in Milan, Italy

“…When the territory under study is large and spatial correlation is important, spatio-temporal models are appropriate. See, for example, the multivariate state space approach of Calculli et al [16], which is capable of handling jointly PM 10 , NO 2 and weather variables, the approach of Menezes et al [17] for modeling daily NO 2 trends in Portugal. Moreover, the land-use regression model (LUR) under a state space approach has been used for modeling air pollution in Tehran [18].…”

Modelling spatio-temporal data with multiple seasonalities: The NO2 Portuguese case

“…From the 49 stations, 33 are classified as background, 10 as traffic and 6 as industrial, 29 are located in urban areas, 11 in rural areas and 9 in suburban areas. The selected period corresponds to the highest NO 2 levels along the year, according (Menezes et al, 2016), who analysed NO 2 data during 8 years. This study has about 18% of missing data in the hourly levels of NO 2 .…”

“…NO 2 and other nitrogen oxides are also precursor of ozone and particulate matter, whose effects on human health and the environment are well documented. Concentrations of NO 2 have been analysed extensively in many urban areas (Carslaw, 2005;Grice et al, 2009;Roberts-Semple et al, 2012) as well as in background sites (Donnelly et al, 2011;Menezes et al, 2016). Moreover, these studies acknowledge that meteorological conditions influence NO 2 levels (Shi and Harrison, 1997;Donnelly et al, 2011;Russo and Soares, 2014).…”

“…Cameletti et al (2011) and, in our particular case, at obtaining hourly concentration predictions over the country. To this end the model by Menezes et al (2016) is extended to hourly data and meteorological variables are included. A block bootstrap procedure is proposed to correctly assess uncertainty of parameters estimates, as well as to produce reliable confidence regions for (space-time) NO 2 concentrations.…”