A community-based study on associations between PM2.5 and PM1 exposure and heart rate variability using wearable low-cost sensing devices

Peaks, sources, and immediate health impacts of PM2.5 and PM1 exposure in Indonesia and Taiwan with microsensors

BACKGROUND

Microsensors have been used for the high-resolution particulate matter (PM) monitoring.

OBJECTIVES

This study applies PM and health microsensors with the objective of assessing the peak exposure, sources, and immediate health impacts of PM2.5 and PM1 in two Asian countries.

METHODS

Exposure assessment and health evaluation were carried out for 50 subjects in 2018 and 2019 in Bandung, Indonesia and for 55 subjects in 2019 and 2020 in Kaohsiung, Taiwan. Calibrated AS-LUNG sets and medical-certified RootiRx® sensors were used to assess PM and heart-rate variability (HRV), respectively.

RESULTS

Overall, the 5-min mean exposure of PM2.5 and PM1 was 30.4 ± 20.0 and 27.0 ± 15.7 µg/m3 in Indonesia and 14.9 ± 11.2 and 13.9 ± 9.8 µg/m3 in Taiwan, respectively. The maximum 5-min peak PM2.5 and PM1 exposures were 473.6 and 154.0 µg/m3 in Indonesia and 467.4 and 217.7 µg/m3 in Taiwan, respectively. Community factories and mosquito coil burning are the two most important exposure sources, resulting in, on average, 4.73 and 5.82 µg/m3 higher PM2.5 exposure increments for Indonesian subjects and 10.1 and 9.82 µg/m3 higher PM2.5 exposure for Taiwanese subjects compared to non-exposure periods, respectively. Moreover, agricultural waste burning and incense burning were another two important exposure sources, but only in Taiwan. Furthermore, 5-min PM2.5 and PM1 exposure had statistically significantly immediate impacts on the HRV indices and heart rates of all subjects in Taiwan and the scooter subjects in Indonesia with generalized additive mixed models. The HRV change for a 10 µg/m3 increase in PM2.5 and PM1 ranged from -0.9% to -2.5% except for ratio of low-high frequency, with greater impacts associated with PM1 than PM2.5 in both countries.

IMPACT STATEMENT

This work highlights the ability of microsensors to capture high peaks of PM2.5 and PM1, to identify exposure sources through the integration of activity records, and to assess immediate changes in heart rate variability for a panel of approximately 50 subjects in Indonesia and Taiwan. This study stands out as one of the few to demonstrate the immediate health impacts of peak PM, complementing to the short-term (days or weeks) or long-term effects (months or longer) assessed in most epidemiological studies. The technology/methodology employed offer great potential for researchers in the resource-limited countries with high PM2.5 and PM1 levels.

Characterization of spatial-temporal distribution and microenvironment source contribution of PM2.5 concentrations using a low-cost sensor network with artificial neural network/kriging techniques

Low-cost sensors (LCS) network is widely used to improve the resolution of spatial-temporal distribution of air pollutant concentrations in urban areas. However, studies on air pollution sources contribution to the microenvironment, especially in industrial and mix-used housing areas, still need to be completed. This study investigated the spatial-temporal distribution and source contributions of PM2.5 in the urban area based on 6-month of the LCS network datasets. The Artificial Neural Network (ANN) was used to calibrate the measured PM2.5 by the LCS network. The calibrated PM2.5 were shown to agree with reference PM2.5 measured by the BAM-1020 with R2 of 0.85, MNE of 30.91%, and RMSE of 3.73 μg/m3, which meet the criteria for hotspot identification and personal exposure study purposes. The Kriging method was further used to establish the spatial-temporal distribution of PM2.5 concentrations in the urban area. Results showed that the highest average PM2.5 concentration occurred during autumn and winter due to monsoon and topographic effects. From a diurnal perspective, the highest level of PM2.5 concentration was observed during the daytime due to heavy traffic emissions and industrial production. Based on the present ANN-based microenvironment source contribution assessment model, temples, fried chicken shops, traffic emissions in shopping and residential zones, and industrial activities such as the mechanical manufacturing and precision metal machining were identified as the sources of PM2.5. The numerical algorithm coupled with the LCS network presented in this study is a practical framework for PM2.5 hotspots and source identification, aiding decision-makers in reducing atmospheric PM2.5 concentrations and formulating regional air pollution control strategies.

Toxicological Effects of Fine Particulate Matter (PM2.5): Health Risks and Associated Systemic Injuries-Systematic Review

Previous studies focused on investigating particulate matter with aerodynamic diameter ≤ 2.5 µm (PM2.5) have shown the risk of disease development, and association with increased morbidity and mortality rates. The current review investigate epidemiological and experimental findings from 2016 to 2021, which enabled the systemic overview of PM2.5's toxic impacts on human health. The Web of Science database search used descriptive terms to investigate the interaction among PM2.5 exposure, systemic effects, and COVID-19 disease. Analyzed studies have indicated that cardiovascular and respiratory systems have been extensively investigated and indicated as the main air pollution targets. Nevertheless, PM2.5 reaches other organic systems and harms the renal, neurological, gastrointestinal, and reproductive systems. Pathologies onset and/or get worse due to toxicological effects associated with the exposure to this particle type, since it can trigger several reactions, such as inflammatory responses, oxidative stress generation and genotoxicity. These cellular dysfunctions lead to organ malfunctions, as shown in the current review. In addition, the correlation between COVID-19/Sars-CoV-2 and PM2.5 exposure was also assessed to help better understand the role of atmospheric pollution in the pathophysiology of this disease. Despite the significant number of studies about PM2.5's effects on organic functions, available in the literature, there are still gaps in knowledge about how this particulate matter can hinder human health. The current review aimed to approach the main findings about the effect of PM2.5 exposure on different systems, and demonstrate the likely interaction of COVID-19/Sars-CoV-2 and PM2.5.

Changes in heart rate variability of healthy subjects shortly exposed to printing shop particles and the effect of air purifier intervention

Particulate matter (PM) released by printers may cause airway inflammation and cardiac electrophysiological changes. We conducted a two-stage crossover study to examine the association between short-term exposure to printing shop particles (PSPs) and the heart rate variability (HRV) among healthy volunteers, as well as to evaluate the effect of air purifier intervention. The on-site assessments of PSPs and individual HRV parameters of the volunteers were used to analyze the influence of size-fractionated PSPs and air purifier intervention on HRV at different lag times after PSPs exposure (0 min, 5 min, 15 min, and 30 min) by using the linear mixed-effects models. We observed that changes in 6 HRV parameters were associated with particle mass concentration (PMC) of PSPs, and changes in 8 HRV parameters were associated with particle number concentration (PNC) of PSPs. The sensitive HRV parameters were the square root of the mean of the sum of the squares of differences between adjacent NN intervals (rMSSD), NN50 count divided by the total number of all NN intervals (pNN50), normalized high frequency power (nHF), very high frequency power (VHF), normalized low frequency power (nLF), and the ratio of low frequency power to high frequency power (LF/HF). Most HRV parameters exhibited the strongest effect associated with PMC and PNC at a lag time of 30 min. The air purifier intervention markedly reduced the PNC and PMC of size-fractionated PSPs, enhanced 5 HRV parameters, and decreased the nLF and LF/HF. Our study suggests that short-term exposure to PSPs can affect HRV parameters, reflecting changes in cardiac autonomic nervous activity, and the use of an air purifier can reduce the concentration of PSPs and improve the autonomic nervous system activity of the exposed individuals.

Wearable Sensor-Based Monitoring of Environmental Exposures and the Associated Health Effects: A Review

Recent advances in sensor technology have facilitated the development and use of personalized sensors in monitoring environmental factors and the associated health effects. No studies have reviewed the research advancement in examining population-based health responses to environmental exposure via portable sensors/instruments. This study aims to review studies that use portable sensors to measure environmental factors and health responses while exploring the environmental effects on health. With a thorough literature review using two major English databases (Web of Science and PubMed), 24 eligible studies were included and analyzed out of 16,751 total records. The 24 studies include 5 on physical factors, 19 on chemical factors, and none on biological factors. The results show that particles were the most considered environmental factor among all of the physical, chemical, and biological factors, followed by total volatile organic compounds and carbon monoxide. Heart rate and heart rate variability were the most considered health indicators among all cardiopulmonary outcomes, followed by respiratory function. The studies mostly had a sample size of fewer than 100 participants and a study period of less than a week due to the challenges in accessing low-cost, small, and light wearable sensors. This review guides future sensor-based environmental health studies on project design and sensor selection.

Assessment of Individual-Level Exposure to Airborne Particulate Matter during Periods of Atmospheric Thermal Inversion

Air pollution exposure is harmful to human health and reducing it at the level of an individual requires measurements and assessments that capture the spatiotemporal variability of different microenvironments and the influence of specific activities. In this paper, activity-specific and general indoor and outdoor exposure during and after a period of high concentrations of particulate matter (PM), e.g., an atmospheric thermal inversion (ATI) in the Ljubljana subalpine basin, Slovenia, was assessed. To this end, personal particulate matter monitors (PPM) were used, worn by participants of the H2020 ICARUS sampling campaigns in spring 2019 who also recorded their hourly activities. ATI period(s) were determined based on data collected from two meteorological stations managed by the Slovenian Environmental Agency (SEA). Results showed that indoor and outdoor exposure to PM was significantly higher during the ATI period, and that the difference between mean indoor and outdoor exposure to PM was much higher during the ATI period (23.0 µg/m3) than after (6.5 µg/m3). Indoor activities generally were associated with smaller differences, with cooking and cleaning even having higher values in the post-ATI period. On the other hand, all outdoor activities had higher PM values during the ATI than after, with larger differences, mostly >30.0 µg/m3. Overall, this work demonstrated that an individual-level approach can provide better spatiotemporal resolution and evaluate the relative importance of specific high-exposure events, and in this way provide an ancillary tool for exposure assessments.

Research Priorities of Applying Low-Cost PM2.5 Sensors in Southeast Asian Countries

The low-cost and easy-to-use nature of rapidly developed PM_2.5 sensors provide an opportunity to bring breakthroughs in PM_2.5 research to resource-limited countries in Southeast Asia (SEA). This review provides an evaluation of the currently available literature and identifies research priorities in applying low-cost sensors (LCS) in PM_2.5 environmental and health research in SEA. The research priority is an outcome of a series of participatory workshops under the umbrella of the International Global Atmospheric Chemistry Project–Monsoon Asia and Oceania Networking Group (IGAC–MANGO). A literature review and research prioritization are conducted with a transdisciplinary perspective of providing useful scientific evidence in assisting authorities in formulating targeted strategies to reduce severe PM_2.5 pollution and health risks in this region. The PM_2.5 research gaps that could be filled by LCS application are identified in five categories: source evaluation, especially for the distinctive sources in the SEA countries; hot spot investigation; peak exposure assessment; exposure–health evaluation on acute health impacts; and short-term standards. The affordability of LCS, methodology transferability, international collaboration, and stakeholder engagement are keys to success in such transdisciplinary PM_2.5 research. Unique contributions to the international science community and challenges with LCS application in PM_2.5 research in SEA are also discussed.

Exposure to black carbon is associated with symptoms of depression: A retrospective cohort study in college students

BACKGROUND

Previous studies have revealed a significant association of fine particulate matter (PM_2.5) with emotional disorders. However, as a crucial component of PM_2.5, little is known about the potential effect of exposure to black carbon (BC) on the symptoms of depression and anxiety.

OBJECTIVES

To explore the associations of long-term exposure to BC during the past six years with the current symptoms of depression and anxiety in a group of incoming college students.

METHODS

This was a retrospective cohort study of incoming students in five universities of China. Symptoms of depression and anxiety during the past two weeks were measured by the Patient Health Questionnaire-2 (PHQ-2) and Generalized Anxiety Disorder Scale-2 (GAD-2), respectively. Levels of BC and other environmental factors during 2013 ∼ 2018 (six years prior to the recruitment) was obtained from public repositories and linked to individual data by home addresses. Averagely daily dose of BC exposure was estimated according to the respiratory rate. Demographic and behavioral variables were collected through a questionnaire. The associations of BC with symptoms of depression and anxiety were estimated by mixed linear models adjusting for socioeconomic and behavioral characteristics, and the principal components of multiple environmental exposures. Subgroup analysis was conducted to assess the effect modification by covariates. Overall effect of environmental mixture was evaluated by weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR).

RESULTS

A total of 20,079 participants was included in the current study. After adjustment for covariates, long-term BC exposure was significantly associated with symptoms of depression (β = 0.17, P < 0.001) but not anxiety (β = 0.07, P = 0.125). Effect modification by sex and parental educational level: BC was correlated with depressive symptoms in women (β = 0.23, P < 0.001) but not in men (β = 0.04, P = 0.581), and higher educational level was associated with decreased effect sizes of BC. Sensitivity analysis showed that the acute and short-term effects of BC on depression was consistent with its long-term exposure (β varied from 0.18 to 0.20). WQS identified BC as the primary pollutant in association with symptoms of depression but not anxiety. BKMR identified no significant interaction between BC and other exposures.

CONCLUSION

Exposure to BC is associated with symptoms of depression but not anxiety in college students, and the relationship is modified by sex and education.

Health risk assessment of PM2.5 on walking trips

PM_2.5 has an impact on residents' physical health during travelling, especially walking completely exposed to the environment. In order to obtain the specific impact of PM_2.5 on walking, 368 healthy volunteers were selected and they were grouped according to gender and age. In the experiment, the heart rate change rate (HR%) is taken as test variable. According to receiver operating characteristic (ROC) curve, the travel is divided into two states: safety and risk. Based on this, a binary logit model considering Body Mass Index (BMI) is established to determine the contribution of PM_2.5 concentration and body characteristics to travel risk. The experiment was conducted on Chang'an Middle Road in Xi'an City. The analysis results show that the threshold of HR% for safety and risk ranges from 31.1 to 40.1%, and that of PM_2.5 concentration ranges from 81 to 168 μg/m³. The probability of risk rises 5.8% and 11.4%, respectively, for every unit increase in PM_2.5 concentration and HR%. Under same conditions, the probability of risk for male is 76.8% of that for female. The probability of risk for youth is 67.5% of that for middle-aged people, and the probability of risk for people with BMI in healthy range is 72.1% of that for non-healthy range. The research evaluates risk characteristics of walking in particular polluted weather, which can improve residents' health level and provide suggestions for travel decision while walking.

Effects of low-frequency noise from wind turbines on heart rate variability in healthy individuals

Wind turbines generate low-frequency noise (LFN, 20-200 Hz), which poses health risks to nearby residents. This study aimed to assess heart rate variability (HRV) responses to LFN exposure and to evaluate the LFN exposure (dB, L_Aeq) inside households located near wind turbines. Thirty subjects living within a 500 m radius of wind turbines were recruited. The field campaigns for LFN (L_Aeq) and HRV monitoring were carried out in July and December 2018. A generalized additive mixed model was employed to evaluate the relationship between HRV changes and LFN. The results suggested that the standard deviations of all the normal to normal R-R intervals were reduced significantly, by 3.39%, with a 95% CI = (0.15%, 6.52%) per 7.86 dB (L_Aeq) of LFN in the exposure range of 38.2-57.1 dB (L_Aeq). The indoor LFN exposure (L_Aeq) ranged between 30.7 and 43.4 dB (L_Aeq) at a distance of 124-330 m from wind turbines. Moreover, households built with concrete and equipped with airtight windows showed the highest LFN difference of 13.7 dB between indoors and outdoors. In view of the adverse health impacts of LFN exposure, there should be regulations on the requisite distances of wind turbines from residential communities for health protection.

Demonstrating the Applicability of Smartwatches in PM2.5 Health Impact Assessment

Smartwatches are being increasingly used in research to monitor heart rate (HR). However, it is debatable whether the data from smartwatches are of high enough quality to be applied in assessing the health impacts of air pollutants. The objective of this study was to assess whether smartwatches are useful complements to certified medical devices for assessing PM_2.5 health impacts. Smartwatches and medical devices were used to measure HR for 7 and 2 days consecutively, respectively, for 49 subjects in 2020 in Taiwan. Their associations with PM_2.5 from low-cost sensing devices were assessed. Good correlations in HR were found between smartwatches and certified medical devices (r_s > 0.6, except for exercise, commuting, and worshipping). The health damage coefficients obtained from smartwatches (0.282% increase per 10 μg/m³ increase in PM_2.5) showed the same direction, with a difference of only 8.74% in magnitude compared to those obtained from certified medical devices. Additionally, with large sample sizes, the health impacts during high-intensity activities were assessed. Our work demonstrates that smartwatches are useful complements to certified medical devices in PM_2.5 health assessment, which can be replicated in developing countries.