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

Abstract

The World Health Organization has estimated that air pollution will be one of the most significant challenges related to the environment in the following years, and air quality monitoring and climate change mitigation actions have been promoted due to the Paris Agreement because of their impact on mortality risk. Thus, generating a methodology that supports experts in making decisions based on exposure data, identifying exposure-related activities, and proposing mitigation scenarios is essential. In this context, the emergence of Interactive Process Mining-a discipline that has progressed in the last years in healthcare-could help to develop a methodology based on human knowledge. For this reason, we propose a new methodology for a sequence-oriented sensitive analysis to identify the best activities and parameters to offer a mitigation policy. This methodology is innovative in the following points: i) we present in this paper the first application of Interactive Process Mining pollution personal exposure mitigation; ii) our solution reduces the computation cost and time of the traditional sensitive analysis; iii) the methodology is human-oriented in the sense that the process should be done with the environmental expert; and iv) our solution has been tested with synthetic data to explore the viability before the move to physical exposure measurements, taking the city of Valencia as the use case, and overcoming the difficulty of performing exposure measurements. This dataset has been generated with a model that considers the city of Valencia's demographic and epidemiological statistics. We have demonstrated that the assessments done using sequence-oriented sensitive analysis can identify target activities. The proposed scenarios can improve the initial KPIs-in the best scenario; we reduce the population exposure by 18% and the relative risk by 12%. Consequently, our proposal could be used with real data in future steps, becoming an innovative point for air pollution mitigation and environmental improvement.

Fig 1. Synthetic data generation workflow.

First, collected data from different sources is mapped in the domain geoJSON, generating gridded air quality data—after applying a dispersion model—and demographic data. This is combined with the dose-response functions from WHO to generate sequence activity data related with personal exposure and mortality relative risk.

Fig 2. The first step of the IP is the preparation phase, where the simulations are performed; then, the generated data is processed through interactive process mining in constant discussion with domains expert, and the proposed scenarios are validated with domain experts once the KPIs are calculated again.

Fig 3. Dashboard with base KPIs of the sample population.

In concrete, 24 h population exposure; mortality relative risk; percentage of risky activities and time spent in risky activities.

Fig 4. ANOVA analysis for the exposure among activities.

The ANOVA table is shown in addition to the heatmap with the exposure difference between activities and the plot of the Tukey test.

Fig 5. ANOVA analysis for the mortality relative risk among activities.

The ANOVA table is shown in addition to the heatmap with the mortality relative risk difference between activities and the plot of the Tukey test.

Fig 6. Process map of the daily activities in the populations.

Redder nodes represent activities with a high spent time and green nodes show activities with a low spent time.

Fig 7. Process map of the traces (citizens) with high exposures.

Highlighted nodes represent nodes with a significative difference in the spent time in comparison with the process map in Fig 6.

Fig 8. Dashboard with KPIs for Scenario 1, in which population exposure and percentage of risky activities are reduced.

Fig 9. Dashboard with KPIs for Scenario 2, in which population exposure, mortality relative risk and percentage of risky activities are reduced.

Fig 10. Dashboard with KPIs for Scenario 3, in which population exposure is reduced.

Fig 11. Dashboard with KPIs for Scenario 4, in which all KPIs are reduced.

Fig 12. Dashboard with KPIs for Scenario 5, in which all KPIs are reduced in a greater percentage in comparison with Fig 11.

Fig 13. IoT architecture.