Spatio-temporal modelling of <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" altimg="si1.svg"><mml:mrow><mml:msub><mml:mtext>PM</mml:mtext><mml:mn>10</mml:mn></mml:msub></mml:mrow></mml:math> daily concentrations in Italy using the SPDE approach

Fioravanti, Guido; Martino, Sara; Cameletti, Michela; Cattani, Giorgio

doi:10.1016/j.atmosenv.2021.118192

Cited by 21 publications

(21 citation statements)

References 51 publications

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“… 27 In fact, due to a high computational burden, previous GR models have provided gridded PM concentrations either for smaller areas of investigation or for one specific year. 24 , 25 , 28 Pan-European estimates of the reduction in pollutant concentrations and of changes in population exposure, which take into account the geographical distribution of pollutants at high spatial resolution, are not available.…”

Section: Introductionmentioning

confidence: 99%

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

Beloconi

Vounatsou

2021

Environ. Sci. Technol.

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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 (PM 2.5 ) and coarse (PM 10 ) particles at 1 km 2 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, PM 10 and PM 2.5 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 PM 10 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 PM 2.5 , 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.

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Section: Introductionmentioning

confidence: 99%

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

Beloconi

Vounatsou

2021

Environ. Sci. Technol.

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“…Unfortunately, none of the thesis researches provided adequate methodologies for including time aspects, ensuring that the sequential measurement is handled "equitably" in comparison to the spatial dimension. Samal et al [10] were referred to this issue as the "time scale issue" and later, over the past decades Fioravanti et al [11] were revised the scaling ratio is known as "spatiotemporal anisotropy parameter". To estimate this parameter, just a few basic approaches were proposed.…”

Section: Introductionmentioning

confidence: 99%

A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting

Zulqarnain¹,

Ghazali²,

Shah³

et al. 2022

Computers, Materials &Amp; Continua

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Air pollution is a significant problem in modern societies since it has a serious impact on human health and the environment. Particulate Matter (PM 2.5 ) is a type of air pollution that contains of interrupted elements with a diameter less than or equal to 2.5 m. For risk assessment and epidemiological investigations, a better knowledge of the spatiotemporal variation of PM 2.5 concentration in a constant space-time area is essential. Conventional spatiotemporal interpolation approaches commonly relying on robust presumption by limiting interpolation algorithms to those with explicit and basic mathematical expression, ignoring a plethora of hidden but crucial manipulating aspects. Many advanced deep learning approaches have been proposed to forecast Particulate Matter (PM 2.5 ). Recurrent neural network (RNN) is one of the popular deep learning architectures which is widely employed in PM 2.5 concentration forecasting. In this research, we proposed a Two-State Gated Recurrent Unit (TS-GRU) for monitoring and estimating the PM 2.5 concentration forecasting system. The proposed algorithm is capable of considering both spatial and temporal hidden affecting elements spontaneously. We tested our model using data from daily PM 2.5 dimensions taken in the contactual southeast area of the United States in 2009. In the studies, three evaluation matrices were utilized to compare the overall performance of each algorithm: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The experimental results revealed that our proposed TS-GRU model outperformed compared to the other deep learning approaches in terms of forecasting performance.

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“…Integrated nested Laplace approximations (INLA) ( Rue et al, 2009 ) allow fast computation for Bayesian inference and enable the use of the SPDE approach for spatial modelling ( Lindgren et al, 2011 ). These methods have been used in modelling of air pollutant levels in Italy ( Cameletti et al, 2013 ; Fioravanti et al, 2021 ) and England ( Blangiardo et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

“…However, the locations of weather monitors did not coincide with the pollutant monitors. Previous work ( Blangiardo et al, 2016 ; Cameletti et al, 2013 ; Fioravanti et al, 2021 ) has used covariates that are fixed over space, or used the geographically closest available measurements. Given the limited number and placement of weather monitors, we used a two-stage approach to address misalignment of weather covariate data, and compare four models for including weather covariates in the pollutant models.…”

Section: Introductionmentioning

confidence: 99%

“…This form of spatio–temporal model is well described in Chapter 7 of Blangiardo and Cameletti (2015) and Chapter 10 of Moraga (2019) . These methods have previously been used for spatio–temporal modelling of PM 10 levels in Italy ( Cameletti et al, 2013 ; Fioravanti et al, 2021 ) and NO 2 levels in England ( Blangiardo et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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

Wright

Newell

Lam

et al. 2021

International Journal of Hygiene and Environmental Health

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Spatio–temporal models of ambient air pollution can be used to predict pollutant levels across a geographical region. These predictions may then be used as estimates of exposure for individuals in analyses of the health effects of air pollution. Integrated nested Laplace approximations is a method for Bayesian inference, and a fast alternative to Markov chain Monte Carlo methods. It also facilitates the SPDE approach to spatial modelling, which has been used for modelling of air pollutant levels, and is available in the R-INLA package for the R statistics software. Covariates such as meteorological variables may be useful predictors in such models, but covariate misalignment must be dealt with. This paper describes a flexible method used to estimate pollutant levels for six pollutants in Suzhou, a city in China with dispersed air pollutant monitors and weather stations. A two-stage approach is used to address misalignment of weather covariate data.

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

Cited by 21 publications

References 51 publications

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

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

A Deep Two-State Gated Recurrent Unit for Particulate Matter (PM2.5) Concentration Forecasting

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

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