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      Environmental assessment of interventions to restrain the impact of industrial pollution using a quasi-experimental design: limitations of the interventions and recommendations for public health policy

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          Abstract

          Background

          In an industrial area, the asymmetry between the weights of the economic interests compared to the public-health needs can determine which interests are represented in decision-making processes. This might lead to partial interventions, whose impacts are not always evaluated. This study focuses on two interventions implemented in Taranto, Italy, a city hosting one of the largest steel plants in Europe. The first intervention deals with measures industrial plants must implement by law to reduce emissions during so called “wind days” in order to reduce PM 10 and benzo [a] pyrene concentrations. The second one is a warning to the population with recommendations to aerate indoor spaces from 12 pm to 6 pm, when pollutant concentrations are believed to be lower.

          Methods

          To analyse the impact of the first intervention, we analysed monthly PM 10 data in the period 2009–2016 from two monitoring stations and conducted an interrupted-time-series analysis. Coefficients of time-based covariates are estimated in the regression model. To minimise potential confounding, monthly concentrations of PM 10 in a neighbourhood 13 km away from the steel plant were used as a control series. To evaluate the second intervention, hourly concentrations of PM 10, SO 2 and polycyclic-aromatic-hydrocarbons (PAHs) were analysed.

          Results

          PM 10 concentrations in the intervention neighbourhood showed a peak just a few months before the introduction of the law. When compared to the control series, PM 10 concentrations were constantly higher throughout the entire study period. After the intervention, there was a reduction in the difference between the two time-series (− 25.6%). During “wind days” results suggested no reduction in concentrations of air pollutants from 12 pm to 18 pm.

          Conclusion

          Results of our study suggest revising the warning to the population. Furthermore, they evidence that in complex highly industrialised areas, air quality interventions cannot focus on only a single pollutant, but rather should consider the complex relationships between the different contaminants. Environmental interventions should be reviewed periodically, particularly when they have implications for social constraints. While the results of our study can be related only to the specific situation reported in the article, the methodology applied might be useful for the environmental management in industrial areas with similar features.

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          Most cited references31

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          Interrupted time series regression for the evaluation of public health interventions: a tutorial

          Abstract Interrupted time series (ITS) analysis is a valuable study design for evaluating the effectiveness of population-level health interventions that have been implemented at a clearly defined point in time. It is increasingly being used to evaluate the effectiveness of interventions ranging from clinical therapy to national public health legislation. Whereas the design shares many properties of regression-based approaches in other epidemiological studies, there are a range of unique features of time series data that require additional methodological considerations. In this tutorial we use a worked example to demonstrate a robust approach to ITS analysis using segmented regression. We begin by describing the design and considering when ITS is an appropriate design choice. We then discuss the essential, yet often omitted, step of proposing the impact model a priori. Subsequently, we demonstrate the approach to statistical analysis including the main segmented regression model. Finally we describe the main methodological issues associated with ITS analysis: over-dispersion of time series data, autocorrelation, adjusting for seasonal trends and controlling for time-varying confounders, and we also outline some of the more complex design adaptations that can be used to strengthen the basic ITS design.
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            A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix

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              Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysis

              Interrupted time series analysis is a quasi-experimental design that can evaluate an intervention effect, using longitudinal data. The advantages, disadvantages, and underlying assumptions of various modelling approaches are discussed using published examples
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                Author and article information

                Contributors
                emilio.gianicolo@uni-mainz.de
                Journal
                BMC Public Health
                BMC Public Health
                BMC Public Health
                BioMed Central (London )
                1471-2458
                14 October 2021
                14 October 2021
                2021
                : 21
                : 1856
                Affiliations
                [1 ]GRID grid.410607.4, Division of Epidemiology and Health Services Research, Working Group for the Evaluation of Political Intervention, Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), , University Medical Center of the Johannes Gutenberg University Mainz, ; Obere Zahlbacher Str. 69, 55131 Mainz, Germany
                [2 ]GRID grid.5326.2, ISNI 0000 0001 1940 4177, Istitute of Clinical Physiology, National Research Council, ; Lecce, Italy
                [3 ]GRID grid.5326.2, ISNI 0000 0001 1940 4177, Istitute of Atmospheric Sciences and Climate, National Research Council, ; Bologna, Italy
                [4 ]GRID grid.5326.2, ISNI 0000 0001 1940 4177, Istitute of Atmospheric Sciences and Climate, National Research Council, ; Lecce, Italy
                Author information
                http://orcid.org/0000-0002-3473-0752
                Article
                11832
                10.1186/s12889-021-11832-3
                8515703
                34649551
                49a28cd4-f6ed-4eb7-9962-0dbfc27763d2
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 1 February 2021
                : 22 September 2021
                Funding
                Funded by: Universitätsmedizin der Johannes Gutenberg-Universität Mainz (8974)
                Categories
                Research Article
                Custom metadata
                © The Author(s) 2021

                Public health
                evaluation of interventions,air quality,steel industry,taranto (southern italy),interrupted time series

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