Abstract
This paper presents a general spatio-temporal model for assessing the air quality impact of environmental policies which are introduced as abrupt changes. The estimation method is based on the EM algorithm and the model allows to estimate the impact on air quality over a region and the reduction of human exposure following the considered environmental policy. Moreover, impact testing is proposed as a likelihood ratio test and the number of observations after intervention is computed in order to achieve a certain power for a minimal reduction. An extensive case study is related to the introduction of the congestion charge in Milan city. The consequent estimated reduction of airborne particulate matters and total nitrogen oxides motivates the methods introduced while its derivation illustrates both implementation and inferential issues.
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Notes
At each replication, the initial values have been rescaled between 0 and three times their value by multiplication with random numbers 3B where \(B^{\prime}s\) have been drawn from the Beta distribution with parameters 4 and 8, so that E(3B) = 1.
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Acknowledgements
This research is part of Project EN17, ‘Methods for the integration of different renewable energy sources and impact monitoring with satellite data’, funded by Lombardy Region government under ‘Frame Agreement 2009’. The help of Angela Locatelli and Francesco Miazzo for data handling has been much appreciated by the author.
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Fassò, A. Statistical assessment of air quality interventions. Stoch Environ Res Risk Assess 27, 1651–1660 (2013). https://doi.org/10.1007/s00477-013-0702-5
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DOI: https://doi.org/10.1007/s00477-013-0702-5