A model for population exposure to PM2.5: Identification of determinants for high population exposure in Seoul

Temporal trend of microenvironmental time-activity patterns of the Seoul population from 2004 to 2022 and its potential impact on exposure assessment

BACKGROUND

Time-activity pattern (TAP) is an important parameter for determining personal exposure to environmental pollutants. Changes in TAPs could have significant implications for the alterations in outcomes of exposure assessments.

OBJECTIVE

This study aimed to evaluate the Seoul population's long-term change in TAPs, along with variations by sociodemographic group.

METHODS

In 2004, 2009, 2014, and 2019, the Time Use Survey of Statistics Korea collected the TAP information of 4036, 2610, 3337, and 2793 Seoul residents, respectively. In 2022, the TAP information of 4401 Seoul residents was collected for Korean Air Pollutant Exposure (KAPEX) research. The microenvironmental TAP changes in the Seoul population from 2004 to 2022 were assessed based on age, gender, work status, and day type.

RESULTS

From 2004 to 2022, Seoul people increasingly spent more time in indoor residences (from 14.8 ± 5.1 h to 15.8 ± 4.5 h) and less time in other indoors (from 7.2 ± 4.5 h to 5.9 ± 4.2 h). Their transit time constantly decreased from 2004 (1.4 ± 1.8 h) to 2022 (1.2 ± 1.3 h), whereas the outdoor time fluctuated throughout the years. From 2004 to 2022, the time of the day spent by Seoul people in residential indoor shifted to later in the morning (2004: 8:30 am; 2022: 9:00 am) and earlier in the evening (2004: 9:30 pm; 2022: 7:00 pm); however, the opposite was true for other indoors (2004: from 8:30 am to 9:30 pm; 2022: from 9:00 am to 7:00 pm) and transits (2004: 7:30-9:30 am and 3:00-8:00 pm; 2022: 7:30-9:00 pm and 5:00-9:00). The time of the day spent in outdoors increased from 2004 to 2019, with a distinct peak observed in 2022 (12:00 pm-2:00 pm). The microenvironmental time trends of adolescents and late-adulthoods differed from those of the other age groups, while those of males differed from females. Also, the microenvironmental time trends of the employed differed from those of the unemployed, and those during weekdays differed from those during weekends.

IMPACT STATEMENT

Microenvironmental TAP should be essentially considered to estimate the actual exposure to pollutants. This study demonstrates the Seoul population's long-term changes in TAP throughout the 18 years as the significant parameter in exposure assessment. Notably, the microenvironmental TAPs of Seoul people shifted, with variations across different sociodemographic groups. Previous studies in Korea did not consider the TAP shifts in exposure assessment; this study highlights the importance of aligning TAP data with concurrent environmental pollutant data and emphasizes the need for refined data collection in future exposure assessments.

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

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.

Personal exposure to PM2.5 in different microenvironments and activities for retired adults in two megacities, China

Microenvironmental concentrations and time-activity patterns influence personal exposure to fine particulate matter (PM_2.5). However, the variations and contributions of PM_2.5 exposures from various microenvironments (MEs) and activities remain unclear. In this study, gravimetrically corrected real-time personal PM_2.5 measurements were collected during routine activities in different MEs from 66 non-smoking retired adults. Exposure data were collected for five consecutive days over two seasons in Nanjing (NJ) and Beijing (BJ), China. Measured PM_2.5 concentrations varied substantially both between and within different MEs and activities. The highest average concentrations were observed in restaurants (NJ: mean 192 μg/m³, SD 242 μg/m³; BJ: mean 91 μg/m³, SD 79 μg/m³) and were associated with sources such as passive smoking and cooking emissions. Overall, PM_2.5 concentrations in different MEs and activities were moderately to highly correlated with outdoor PM_2.5 concentrations (Spearman's r = 0.51-0.97) except in restaurants and during passive smoking. The at-home ME contributed approximately 85 % of the total PM_2.5 exposure, corresponding to the participants spending about 87 % of their time there. The majority of household exposures occurred during sleeping, cooking, and other home-based activities. Transportation accounted for <5 % of total exposure. Our results indicate that improving indoor air quality, especially residential indoors, is important to reduce personal exposure to PM_2.5.

Exposed to NO2 in the center, NOx polluters in the periphery: Evidence from the Paris region

Air pollution is the cause of many health problems. In cities, combustion vehicles are a major contributor to emissions of key air pollutants. While many studies have focused on populations exposed to pollutants and the resulting environmental and social inequalities, few compare exposures and contributions. In this research, the population of the Household Travel Survey of the Paris region is studied by confronting two elements: the average individual exposure to NO₂ during an average working day and the average traffic NO_x emitted during a day by the motorized trips for each resident surveyed. The dynamic exposure to NO₂ of each resident is estimated according to activities in an average working day. The results confirm an environmental inequality according to the place of residence: on average, the center residents contribute little to pollutant emissions but are highly exposed. Some categories of the population, including women and the socially disadvantaged, are the most affected by these inequalities.

Ambient PM Concentrations as a Precursor of Emergency Visits for Respiratory Complaints: Roles of Deep Learning and Multi-Point Real-Time Monitoring

Despite ample evidence that high levels of particulate matter (PM) are associated with increased emergency visits related to respiratory diseases, little has been understood about how prediction processes could be improved by incorporating real-time data from multipoint monitoring stations. While previous studies use traditional statistical models, this study explored the feasibility of deep learning algorithms to improve the accuracy of predicting daily emergency hospital visits by tracking their spatiotemporal association with PM concentrations. We compared the predictive accuracy of the models based on PM datasets collected between 1 December 2019 and 31 December 2021 from a single but more accurate air monitoring station in each district (Air Korea) and multiple but less accurate monitoring sites (Korea Testing & Research Institute; KTR) within Guro District in Seoul, South Korea. We used MLP (multilayer perceptron) to integrate PM data from multiple locations and then LSTM (long short-term memory) models to incorporate the intrinsic temporal PM trends into the learning process. The results reveal evidence that predictive accuracy is improved from 1.67 to 0.79 in RMSE when spatial variations of air pollutants from multi-point stations are incorporated in the algorithm as a 9-day time window. The findings suggest guidelines on how environmental and health policymakers can arrange limited resources for emergency care and design ambient air monitoring and prevention strategies.