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Impact Assessment of Repeated Exposure of Organotypic 3D Bronchial and Nasal Tissue Culture Models to Whole Cigarette Smoke

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Sequence-oriented sensitive analysis for PM2.5 exposure and risk assessment using interactive process mining.

Eduardo Illueca Fernández^1,2, Carlos Fernández Llatas^2,3, Antonio Jesús Jara Valera⁴

¹Department of Informatics and Systems, University of Murcia, Murcia, Spain.

|August 24, 2023

Summary

This summary is machine-generated.

Interactive Process Mining offers a new human-centered approach to personal air pollution exposure mitigation. This method identifies key activities and parameters, reducing exposure by 18% and relative risk by 12%.

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Area of Science:

Environmental Science and Public Health
Data Science and Process Mining

Background:

Air pollution is a major global environmental and health challenge, impacting mortality risk.
Effective air quality monitoring and climate change mitigation are crucial, as highlighted by the Paris Agreement.
Existing methods for analyzing exposure data and proposing mitigation strategies require enhancement.

Purpose of the Study:

To propose a novel methodology for sequence-oriented sensitive analysis to support air pollution personal exposure mitigation.
To develop a human-oriented approach integrating environmental experts' knowledge with Interactive Process Mining.
To identify optimal activities and parameters for effective mitigation policies.

Main Methods:

Application of Interactive Process Mining for personal air pollution exposure mitigation, a first in this field.
Development of a sequence-oriented sensitive analysis technique.
Testing the methodology with synthetic data generated using demographic and epidemiological statistics from Valencia, Spain.

Main Results:

The methodology successfully identified target activities for mitigation through sequence-oriented sensitive analysis.
The proposed scenarios demonstrated significant improvements in Key Performance Indicators (KPIs).
In the best-case scenario, population exposure was reduced by 18%, and relative risk decreased by 12%.

Conclusions:

The proposed Interactive Process Mining-based methodology is a viable and innovative tool for air pollution mitigation.
This approach reduces computational costs and time compared to traditional sensitive analysis.
The methodology's human-oriented nature and proven effectiveness with synthetic data support its future application with real-world measurements for environmental improvement.