High contribution from outdoor air to personal exposure and potential inhaled dose of PM2.5 for indoor-active university students

Personal PM2.5 Exposure and Associated Factors Among Adults with Allergic Diseases in an Urban Environment: A Panel Study

This study analyzed the factors influencing personal PM2.5 exposure levels among adults with allergic diseases in Seoul using a linear mixed-effects (LMEs) model. The average personal PM2.5 exposure concentration of the study participants was 17.38 μg/m3, exceeding the World Health Organization (WHO) daily recommended guideline (15.00 μg/m3), though it was relatively low compared to global levels. Inter-individual exposure variability was approximately 43.5%, with exposure levels varying significantly depending on microenvironments. Notably, 58% of participants exhibited higher exposure on weekends compared to weekdays, likely associated with increased outdoor activities. The LMEs model results identified smoking (90.81% higher in smokers), temperature, relative humidity, outdoor pollutants (PM2.5, O3, CO), indoor PM2.5 and CO concentrations, and time spent in residential environments as factors increasing exposure, while rainfall (91.23% reduction), wind speed, and air purifier use were identified as factors reducing exposure. These findings suggest that individual activity patterns and environmental factors significantly influence exposure levels, highlighting the need for personalized mitigation strategies and national fine dust policies. This study is expected to provide scientific evidence contributing to the reduction in health risks and improvement of quality of life for individuals with allergic diseases.

The Association Between Indoor Air Pollution and Lung Cancer Risk in a Chinese Population

Though indoor air pollution (IAP) is associated with elevated lung cancer risk, an integrated measure is imperative to thoroughly investigate this association. The interplay between sex and IAP on lung cancer remains unclear. We conducted a population-based case-control study in Jiangsu Province, China, from 2003 to 2010, with 2871 lung cancer cases and 8019 controls. Exposures and covariates information were collected via in-person interviews using a standardized questionnaire. An integrated weighted risk score (WRS), accounting for the effect sizes of each source of IAP, was introduced. Unconditional logistic regression was employed to estimate adjusted odds ratios (aORs) and their 95% confidence intervals (CIs). Interactions between sex and IAP by tobacco smoking status were evaluated. Environmental tobacco smoking (ETS) (aOR = 1.54, 95% CI: 1.40, 1.69), poor ventilation (aOR = 1.18, 95% CI: 1.07, 1.30), and coal used for cooking (aOR = 1.27, 95% CI: 1.15, 1.41) were associated with lung cancer. Dose-response relationships between lung cancer and WRS were observed, with p for trend less than 0.001. aOR for individuals at the highest quartile of the WRS of IAP was 1.74 (95% CI: 1.52, 2.00) compared to the lowest quartile. The associations were more profound among never-smokers than ever-smokers. Females tended to be more vulnerable to IAP, and sex interacted with IAP beyond multiplicativity on the odds scale. IAP is associated with lung cancer, with a stronger impact among never-smokers. An interaction between IAP and sex was observed. These results underscore the importance of controlling IAP, especially ETS in order to reduce the risk of lung cancer.

Feasibility Study on the Use of NO2 and PM2.5 Sensors for Exposure Assessment and Indoor Source Apportionment at Fixed Locations

Recent advances in sensor technology for air pollution monitoring open new possibilities in the field of environmental epidemiology. The low spatial resolution of fixed outdoor measurement stations and modelling uncertainties currently limit the understanding of personal exposure. In this context, air quality sensor systems (AQSSs) offer significant potential to enhance personal exposure assessment. A pilot study was conducted to investigate the feasibility of the NO2 sensor model B43F and the particulate matter (PM) sensor model OPC-R1, both from Alphasense (UK), for use in epidemiological studies. Seven patients with chronic obstructive pulmonary disease (COPD) or asthma had built-for-purpose sensor systems placed inside and outside of their homes at fixed locations for one month. Participants documented their indoor activities, presence in the house, window status, and symptom severity and performed a peak expiratory flow test. The potential inhaled doses of PM2.5 and NO2 were calculated using different data sources such as outdoor data from air quality monitoring stations, indoor data from AQSSs, and generic inhalation rates (IR) or activity-specific IR. Moreover, the relation between indoor and outdoor air quality obtained with AQSSs, an indoor source apportionment study, and an evaluation of the suitability of the AQSS data for studying the relationship between air quality and health were investigated. The results highlight the value of the sensor data and the importance of monitoring indoor air quality and activity patterns to avoid exposure misclassification. The use of AQSSs at fixed locations shows promise for larger-scale and/or long-term epidemiological studies.

Simulating the impact of particulate matter exposure on health-related behaviour: A comparative study of stochastic modelling and personal monitoring data

Epidemiological and exposure studies concerning particulate matter (PM) often rely on data from sparse governmental stations. While low-cost personal monitors have some drawbacks, recent developments have shown that they can provide fairly accurate and fit-for-purpose data. Comparing a stochastic, i.e., agent-based model (ABM), with environmental, biometric and activity data, collected with personal monitors, could provide insight into how the two approaches assess PM exposure and dose. An ABM was constructed, simulating a PM exposure/dose assessment of 100 agents. Their actions were governed by inherent probabilities of performing an activity, based on population data. Each activity was associated with an intensity level, and a PM pollution level. The ABM results were compared with real-world results. Both approaches had comparable results, showing similar trends and a mean dose. Discrepancies were seen in the activities with the highest mean dose values. A stochastic model, based on population data, does not capture well some specifics of a local population. Combined, personal sensors could provide input for calibration, and an ABM approach can help offset a low number of participants. Implementing a function of agents influencing others transport choice, increased the importance of cycling/walking in the overall dose estimate. Activists, agents with an increased transport influence, did not play an important role at low PM levels. As concentrations rose, higher shares of activists (and their influence) caused the dose to increase. Simulating a person's PM exposure/dose in different scenarios and activities in a virtual environment provides researchers and policymakers with a valuable tool.

Outdoor Health Risk of Atmospheric Particulate Matter at Night in Xi'an, Northwestern China

The deterioration of air quality via anthropogenic activities during the night period has been deemed a serious concern among the scientific community. Thereby, we explored the outdoor particulate matter (PM) concentration and the contributions from various sources during the day and night in winter and spring 2021 in a megacity, northwestern China. The results revealed that the changes in chemical compositions of PM and sources (motor vehicles, industrial emissions, coal combustion) at night lead to substantial PM toxicity, oxidative potential (OP), and OP/PM per unit mass, indicating high oxidative toxicity and exposure risk at nighttime. Furthermore, higher environmentally persistent free radical (EPFR) concentration and its significant correlation with OP were observed, suggesting that EPFRs cause reactive oxygen species (ROS) formation. Moreover, the noncarcinogenic and carcinogenic risks were systematically explained and spatialized to children and adults, highlighting intensified hotspots to epidemiological researchers. This better understanding of day-night-based PM formation pathways and their hazardous impact will assist to guide measures to diminish the toxicity of PM and reduce the disease led by air pollution.