Statistical assessment of air quality interventions

Fassò, Alessandro

doi:10.1007/s00477-013-0702-5

Cited by 22 publications

(20 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Calculli et al, 2015;Fassò, 2013;Fassò et al, 2016;Finazzi, 2013;Finazzi et al, 2013), so it is not clear 261 15 that this slight deficiency is belonging to D-STEM software or it is an accidental observation that 262 happened in our study. 263 However, the temporal prediction of D-STEM LUR model was very well.…”

mentioning

confidence: 78%

Concurrent Spatiotemporal Daily Land Use Regression Modeling and Missing Data Imputation of Fine Particulate Matter Using Distributed Space Time Expectation Maximization

Shahri

Fassò

Mahaki

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

Highlights 25 -First Land Use Regression using D-STEM, a recently introduced statistical software 26 -Assess D-STEM in spatiotemporal modeling, mapping, and missing data imputation 27 -Estimate high resolution (20x20 m) daily maps for exposure assessment in a megacity 28 -Provide both short-and long-term exposure assessment for epidemiological studies 29 30 31 32 Graphical Abstract 33 34 35 3 Abstract 36 Land use regression (LUR) has been widely applied in epidemiologic research for exposure assessment. 37 In this study, for the first time, we aimed to develop a spatiotemporal LUR model using Distributed 38 Space Time Expectation Maximization (D-STEM). This spatiotemporal LUR model examined with daily 39 particulate matter ≤ 2.5 µm (PM2.5) within the megacity of Tehran, capital of Iran. Moreover, D-STEM 40 missing data imputation was compared with mean substitution in each monitoring station, as it is 41 equivalent to ignoring of missing data, which is common in LUR studies that employ regulatory 42 monitoring stations' data. The amount of missing data was 28% of the total number of observations, in 43 Tehran in 2015. The annual mean of PM2.5 concentrations was 33 µg/m 3 . Spatiotemporal R-squared of 44 the D-STEM final daily LUR model was 78%, and leave-one-out cross-validation (LOOCV) R-squared was 45 66%. Spatial R-squared and LOOCV R-squared were 89% and 72%, respectively. Temporal R-squared and 46 LOOCV R-squared were 99.5% and 99.3%, respectively. Mean absolute error decreased 26% in 47 imputation of missing data by using the D-STEM final LUR model instead of mean substitution. This 48 study reveals competence of the D-STEM software in spatiotemporal missing data imputation, 49 estimation of temporal trend, and mapping of small scale (20 x 20 meters) within-city spatial variations, 50 in the LUR context. The estimated PM2.5 concentrations maps could be used in future studies on short-51 and/or long-term health effects. Overall, we suggest using D-STEM capabilities in increasing LUR studies 52 that employ data of regulatory network monitoring stations. 53 54 55 Keywords 56 Air pollution; D-STEM; LUR; Missing data; PM2.5 57 58 4

show abstract

mentioning

confidence: 78%

Concurrent Spatiotemporal Daily Land Use Regression Modeling and Missing Data Imputation of Fine Particulate Matter Using Distributed Space Time Expectation Maximization

Shahri

Fassò

Mahaki

et al. 2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Moreover, similar problems have been studied for London "sulphur-free zone" [23] and the "low emission zone" in Munich [24]. In Fassò [20], the author considered both particulates and nitrogen oxides and observed the presence of a more pronounced permanent reduction of the latter within the restricted area, despite the data showing a strong spatial variability depending on the type of pollutant. This is consistent with the known emissions pattern of particulate matters and nitrogen oxides.…”

Section: Introductionmentioning

confidence: 99%

“…The previous Milan limited traffic zone, known as Area C, has already been treated in literature by Fassò [20], who analyzed its introduction through spatio-temporal models, by Invernizzi et al [21], who considered its impact on black carbon, and by Percoco [22] who considered its effect on traffic. Moreover, similar problems have been studied for London "sulphur-free zone" [23] and the "low emission zone" in Munich [24].…”

Section: Introductionmentioning

confidence: 99%

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

Maranzano

Fassò

Pelagatti

et al. 2020

IJERPH

Self Cite

View full text Add to dashboard Cite

Most urban areas of the Po basin in the North of Italy are persistently affected by poor air quality and difficulty in disposing of airborne pollutants. In this context, the municipality of Milan started a multi-year progressive policy based on an extended limited traffic zone (Area B). Starting on 25 February 2019, the first phase partially restricted the circulation of some classes of highly polluting vehicles on the territory, in particular, Euro 0 petrol vehicles and Euro 0 to 3 diesel vehicles, excluding public transport. This is the early-stage of a long term policy that will restrict access to an increasing number of vehicles. The goal of this paper is to evaluate the early-stage impact of this policy on two specific vehicle-generated pollutants: total nitrogen oxides (NO x ) and nitrogen dioxide (NO 2 ), which are gathered by Lombardy Regional Agency for Environmental Protection (ARPA Lombardia). We use a statistical model for time series intervention analysis based on unobservable components. We use data from 2014 to 2018 for pre-policy model selection and the relatively short period up to September 2019 for early-stage policy assessment. We include weather conditions, socio-economic factors, and a counter-factual, given by the concentration of the same pollutant in other important neighbouring cities. Although the average concentrations reduced after the policy introduction, this paper argues that this could be due to other factors. Considering that the short time window may be not long enough for social adaptation to the new rules, our model does not provide statistical evidence of a positive policy effect for NO x and NO 2 . Instead, in one of the most central monitoring stations, a significant negative impact is found.

show abstract

“…As far as spatio‐temporal pollutant concentration data are concerned, univariate hierarchical models were used in several case studies. In the context of maximum likelihood estimation, the expectation maximization (EM) algorithm has been largely used, for example, Fassò () considers traffic policy assessment, while Smith et al . () deal with high percentages of missing particulate matter concentration data.…”

Section: Introductionmentioning

confidence: 99%

“…As far as spatio-temporal pollutant concentration data are concerned, univariate hierarchical models were used in several case studies. In the context of maximum likelihood estimation, the expectation maximization (EM) algorithm has been largely used, for example, Fassò (2013) considers traffic policy assessment, while Smith et al (2003) deal with high percentages of missing particulate matter concentration data. In the Bayesian framework, Cameletti et al (2011) compare six models for PM 10 in Piedmont (Italy), featuring different levels of complexity either in the hierarchical structure or in the spatio-temporal covariance function.…”

Section: Introductionmentioning

confidence: 99%

Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in Apulia, Italy

et al. 2015

Self Cite

View full text Add to dashboard Cite

e Multivariate spatio-temporal statistical models are deserving for increasing attention for environmental data in general and for air quality data in particular because they can reveal dependencies and spatio-temporal dynamics across multiple variables and can be used to obtain dynamic concentration maps over specified areas. In this frame, we introduce a multivariate generalization of a known spatio-temporal model referred to as the hidden dynamic geostatistical model. Maximum likelihood parameter estimates are obtained implementing the expectation maximization algorithm and suitably extending the D-STEM software, recently introduced for alternative model specifications, allowing to handle multiple variables with heterogeneous spatial support, covariates, and missing data. A case study illustrates some of the statistical issues typical of a medium complexity problem related to air quality data modeling. Considering air quality and meteorological data over the Apulia region, Italy, the usual approach using meteorological variables as regressors is not possible because these data are observed on different monitoring networks, and preliminary spatial interpolation to co-locate the data is to be avoided. Hence, an eight-variate model is considered for understanding the relations between daily concentrations of particulate matters (PM 10 ) and nitrogen dioxides (NO 2 ) and six non co-located meteorological variables. Model interpretation is given, and its use for dynamic map construction, uncertainty included, is illustrated. Moreover, some preliminary evidence of the model capability to detect a Saharan dust event is presented.

show abstract

Statistical assessment of air quality interventions

Cited by 22 publications

References 16 publications

Concurrent Spatiotemporal Daily Land Use Regression Modeling and Missing Data Imputation of Fine Particulate Matter Using Distributed Space Time Expectation Maximization

Concurrent Spatiotemporal Daily Land Use Regression Modeling and Missing Data Imputation of Fine Particulate Matter Using Distributed Space Time Expectation Maximization

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

Maximum likelihood estimation of the multivariate hidden dynamic geostatistical model with application to air quality in Apulia, Italy

Contact Info

Product

Resources

About