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
Open Access
- 14 October 2021
- journal article
- research article
- Published by Springer Science and Business Media LLC in BMC Public Health
- Vol. 21 (1), 1-10
- https://doi.org/10.1186/s12889-021-11832-3
Abstract
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 PM10 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. To analyse the impact of the first intervention, we analysed monthly PM10 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 PM10 in a neighbourhood 13 km away from the steel plant were used as a control series. To evaluate the second intervention, hourly concentrations of PM10, SO2 and polycyclic-aromatic-hydrocarbons (PAHs) were analysed. PM10 concentrations in the intervention neighbourhood showed a peak just a few months before the introduction of the law. When compared to the control series, PM10 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. 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.Keywords
Funding Information
- Universitätsmedizin der Johannes Gutenberg-Universität Mainz
This publication has 28 references indexed in Scilit:
- Interrupted time series regression for the evaluation of public health interventions: a tutorialInternational Journal of Epidemiology, 2016
- Regression based quasi-experimental approach when randomisation is not an option: interrupted time series analysisBMJ, 2015
- Goal specificity: A proxy measure for improvements in environmental outcomes in collaborative governanceJournal of Environmental Management, 2014
- Mortality analysis by neighbourhood in a city with high levels of industrial air pollutionInternational Journal of Public Health, 2014
- [Air pollution and urgent hospital admissions in 25 Italian cities: results from the EpiAir2 project].2013
- Air pollution and mortality in twenty-five Italian cities: results of the EpiAir2 Project2013
- Spatial variability of air pollutants in the city of Taranto, Italy and its potential impact on exposure assessmentEnvironmental Monitoring and Assessment, 2012
- Simulation-based power calculation for designing interrupted time series analyses of health policy interventionsJournal of Clinical Epidemiology, 2011
- Applying a propensity score-based weighting model to interrupted time series data: improving causal inference in programme evaluationJournal of Evaluation in Clinical Practice, 2010
- A Simple, Positive Semi-Definite, Heteroskedasticity and Autocorrelation Consistent Covariance MatrixEconometrica, 1987