Abstract
Identifying changes in ambient air pollution levels and establishing causation is a research area of strategic importance to assess the effectiveness of air quality interventions. A major challenge in pursuing these objectives is represented by the confounding effects of the meteorological conditions which easily mask or emphasize changes in pollutants concentrations. In this study, a methodological procedure to analyze changes in pollutants concentrations levels after accounting for changes in meteorology over time was developed. The procedure integrated several statistical tools, such as the change points detection and trend analysis that are applied to the pollutants concentrations meteorologically normalized using a machine learning model. Data of air pollutants and meteorological parameters, collected over the period 2013–2019 in a rural area affected by anthropic emissive sources, were used to test the procedure. The joint analysis of the obtained results with the available metadata allowed providing plausible explanations of the observed air pollutants behavior. Consequently, the procedure appears promising in elucidating those changes in the air pollutant levels not easily identifiable in the original data, supplying valuable information to identify an atmospheric response after an intervention or an unplanned event.
Highlights
Published: 30 December 2021Air pollution is one of the biggest environmental threats to human health, alongside climate change [1]
The procedure appears promising in elucidating those changes in the air pollutant levels not identifiable in the original data, supplying valuable information to identify an atmospheric response after an intervention or an unplanned event
A new approach based on machine learning (ML) random forest (RF) predictive algorithms, having better performances than traditional statistical methods [12], Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Summary
Published: 30 December 2021Air pollution is one of the biggest environmental threats to human health, alongside climate change [1]. To safeguard people’s health, the design of effective and well-targeted strategies aimed at preventing or reducing health damages associated with the exposure to the atmospheric pollution [2], as well as the assessment of the effectiveness of air quality interventions [3], are required. Both these issues can profit by the development of tools that allow understanding the changes and behaviors of air pollutants over time and establishing whether a change can be attributed to a known cause [4]. A new approach based on machine learning (ML) random forest (RF) predictive algorithms, having better performances than traditional statistical methods [12], Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
