Estimating minute ventilation and air pollution inhaled dose using heart rate, breath frequency, age, sex and forced vital capacity: A pooled-data analysis

TracMyAir smartphone application for modeling exposures to PM2.5 and ozone - Integration with air quality networks and location-activity sensors

Epidemiologic studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations from central-site monitors or air quality model estimates as exposure surrogates, which can result in exposure errors. We previously developed an exposure model called TracMyAir, which is an iPhone application that determines seven tiers of individual-level exposure metrics for ambient PM2.5 and O3 using outdoor concentrations, home building characteristics, weather, time-activities. The exposure metrics with increasing information needs and complexity include: outdoor concentration (Cout, Tier 1), building infiltration factor (Finf, Tier 2), indoor concentration (Cin, Tier 3), time spent in microenvironments (ME) (TME, Tier 4), personal exposure factor (Fpex, Tier 5), exposure (E, Tier 6), and inhaled dose (D, Tier 7). In this study, we extended TracMyAir with two sets of additional features: (1) time-resolved exposures using smartphone geolocations with a ME classification model (MicroTrac) and official PM2.5 and O3 monitoring network, and (2) exposures based on low-cost outdoor PurpleAir (PA) PM2.5 monitoring network, non-ambient indoor PM2.5 using indoor-outdoor PA monitors, and inhaled dose based on physical activity data from smartphone and smartwatch. To demonstrate the two sets of extended features, we applied TracMyAir to estimate hourly PM2.5 and O3 exposure metrics for two corresponding panel studies with participants living in central North Carolina, USA. For Tier 4, the MicroTrac estimates were compared with 24-h diary data, and correctly classified the ME for 97 % of the daily time spent by the participants. Overall, the TracMyAir estimates showed considerable temporal and building-to-building variability of Finf, and Cin (Tiers 2-3), and person-to-person variability of Cout, TME, Fpex, E, and D (Tiers 1, 4-7). Our study demonstrates the capability of extending TracMyAir with air quality monitors, location-activity sensors, and models to determine fine-scale exposures, in support of epidemiologic studies and public health strategies to help reduce exposures to air pollutants.

Construction of a minute ventilation model to address inter-individual inhaled dose variability within identical exposure scenarios using wearable devices

Inhaled dose is crucial for accurately assessing exposure to air pollution, determined by pollutant concentration and minute ventilation (VE). However, the VE predictive models and its application to assess the health effects of air pollution are still lacking. In this study, we developed VE predictive models using machine learning techniques, utilizing data obtained from eighty participants who underwent a laboratory cardiopulmonary exercise test (CPET). VE predictive models were developed using generalized additive model (GAM), random forest model (RF) and extreme gradient boosting (XGBoost) and analyzed for explanation of input variables. The Random Forest model, cross-validated, exhibited outstanding performance with an R2 of 0.986 and a MAE of 1.816 L/min. The median difference between the measured VE and the predicted VE was 0.18 L/min, and the median difference between the black carbon (BC) inhaled dose based on predicted VE and measured VE was 0.02 ng. Employing explainable machine learning, the results showed that metabolic equivalent (METs), heart rate, and body weight are the three top important variables, emphasizing the significance of incorporating METs variables when constructing VE models. Through multiple linear regression models and an adjusted stratified analysis model, the significant adverse association between BC concentration and inhaled dose on diastolic blood pressure (DBP) was only observed in female. The disparity in the effect of BC inhaled dose compared to BC concentration on DBP reached up to 115 %. This study is the first to explore the ability of different machine learning algorithms to construct VE prediction models and directly apply the models to assess health effects of an example pollutant. This study contributes to the accurate assessment of air pollution exposure leveraging wearable devices, an approach useful for environmental epidemiology studies.

Emerging concern on air pollution and health: Trade-off between air pollution exposure and physical activity

Air pollution is a major contributor to the global disease burden, especially affecting respiratory and cardiovascular health. However, physical activity is associated with improved lung function, a slower decline in lung function, and lower mortality. The public is more likely to be exposed to air pollution during outdoor physical activity. However, studies on how long-term and short-term exposure to air pollution interacts with physical activity yield inconsistent results, and the thresholds for air pollution and physical activity remain unclear. Thus, more studies are needed to provide sufficient evidence to guide the public to safely engage in outdoor physical activity when exposed to air pollution.

Personal airborne particulate matter exposure and intake dose, indoor air quality, biometric, and activity dataset from the city of Ljubljana, Slovenia

To gain a comprehensive understanding of the effects of air pollution on human health, it is imperative to assess exposure at an individual level. This necessitates the use of personal monitoring equipment that can record exposure in real-time while simultaneously capturing relevant biometric data and the activities being undertaken by the individual. The dataset presented herein encompasses data from five distinct sources, collected in Ljubljana, Slovenia: (a) Questionnaire filled out by all participants for general information, i.e., age, height, weight. (b) Time Activity Diaries maintained by participants. (a) A personal particulate matter sensing unit that recorded data on PM1, PM2.5, PM10, ambient temperature, relative humidity, and geographical location coordinates. (b) A wrist-worn Smart Activity Tracker that captures heart rate and movement data. (c) An Indoor Air Quality sensing unit, specifically the uHoo device, which measures parameters such as Temperature, Relative Humidity, CO2, TVOC, PM2.5, CO, O3, NO2, and Air Pressure. Participants were equipped with these sensors for two separate 1-week durations in 2019, specifically during a heating season and a non-heating season, as part of the ICARUS H2020 project. Throughout these periods, data was recorded at a 1-minute resolution. Additionally, based on the collected data, an inhalation rate was determined, and an inhalation adjusted exposure (or intake dose) was computed. The richness and granularity of this dataset not only offer insights into the direct health implications of air pollution exposure but also provide a valuable resource for urban planners, environmental scientists, and policymakers aiming to design healthier living environments and implement effective air quality regulations.

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.

Comparative characteristics of developing morphofunctional features of schoolchildren from different climatic and geographical regions

OBJECTIVES

Adaptation processes in body of schoolchildren, along with study load and social factors, are determined by influence of climatic and geographical factors of residence region. This research has been undertaken to study the morphofunctional characteristics of schoolchildren at the age of 7-17 years.

METHODS

The research involved 880 schoolchildren of both sexes in age from 7 to 17 years, studying in secondary school №22 in Pavlodar (Kazakhstan) and secondary school №7 in Kyzylorda (Kazakhstan). The Quetelet index of northern schoolchildren was within the norm but higher compared to southern peers.

RESULTS

The Quetelet index was within the normal range for all schoolchildren, but higher among northerners. The indicator of the life index in the boys of the south was higher up to 10 years, then the adaptive possibilities of breathing decreased; in the girls in the north this indicator was higher in all periods, except for 8-9 years. The physical performance index (PWC170/kg) was higher in children from the southern region.

CONCLUSIONS

This study revealed significant geographical differences in the morphofunctional development of children. The obtained data served allowed establishing the "Electronic map of schoolchildren's health" and indicated the need for systematic accounting of schoolchildren's health indicators for effective development of differentiated prevention programs.

Personal strategies to mitigate the effects of air pollution exposure during sport and exercise: a narrative review and position statement by the Canadian Academy of Sport and Exercise Medicine and the Canadian Society for Exercise Physiology

Air pollution is among the leading environmental threats to health around the world today, particularly in the context of sports and exercise. With the effects of air pollution, pollution episodes (eg, wildfire conflagrations) and climate change becoming increasingly apparent to the general population, so have their impacts on sport and exercise. As such, there has been growing interest in the sporting community (ie, athletes, coaches, and sports science and medicine team members) in practical personal-level actions to reduce the exposure to and risk of air pollution. Limited evidence suggests the following strategies may be employed: minimising all exposures by time and distance, monitoring air pollution conditions for locations of interest, limiting outdoor exercise, using acclimation protocols, wearing N95 face masks and using antioxidant supplementation. The overarching purpose of this position statement by the Canadian Academy of Sport and Exercise Medicine and the Canadian Society for Exercise Physiology is to detail the current state of evidence and provide recommendations on implementing these personal strategies in preventing and mitigating the adverse health and performance effects of air pollution exposure during exercise while recognising the limited evidence base.

Exposure characteristics of ultrafine particles on urban streets and its impact on pedestrians

In order to investigate the pedestrian exposure characteristics of ultrafine particles (UFPs) on urban streets, both mobile and fixed-point monitoring experiments were conducted. A generalized additive model and a respiratory deposition dose model were used to quantify the influencing factors and potential harm of UFPs, respectively. The results showed that UFPs' hotspots were more likely to manifest at places where vehicles tend to cluster, namely at road intersections and bus stops. The pedestrian bridge had the lowest number concentration of UFPs in comparison with the pedestrian crossing and underground passage at the same intersection. Aboveground, a "weekend effect" acting upon urban streets and evidence for periodicity at the intersections were found. The UFPs' number concentration was comprehensively explained-about 62.7% of its variation-by traffic volume, wind speed, temperature, and relative humidity. The UFPs were mainly deposited in the alveolar region of the respiratory system, but the deposition doses of males exceeded those of females under the same conditions. Based on these findings, the study also provides appropriate suggestions for better managing traffic pollution sources, traffic infrastructure, and traffic organization.

Living near greenness is associated with higher bone strength: A large cross-sectional epidemiological study in China

BACKGROUND

Living near green spaces may benefit various health outcomes. However, no studies have investigated the greenness-bone linkage in the general population. Moreover, to which extent ambient air pollution (AAP), physical activity (PA), and body mass index (BMI) mediate this relationship remains unclear. We aimed to explore the association between greenness and bone strength and the potential mediating roles of AAP, PA, and BMI in Chinese adults.

METHODS

This cross-sectional analysis enrolled 66,053 adults from the China Multi-Ethnic Cohort in 2018-2019. The normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) were employed to define residential greenness. The calcaneus quantitative ultrasound index (QUI) was used to indicate bone strength. Multiple linear regression models and mediation analyses were used to estimate the residential greenness-bone strength association and potential pathways operating through AAP (represented by PM_2.5 [particulate matter <2.5 μm in diameter]), PA, and BMI. Stratification analyses were performed to identify susceptible populations.

RESULTS

Higher residential exposure to greenness was significantly associated with an increase in QUI, with changes (95% confidence interval) of 3.28 (3.05, 3.50), 3.57 (3.34, 3.80), 2.68 (2.46, 2.90), and 2.93 (2.71, 3.15) for every interquartile range increase in NDVI_500m, NDVI_1000m, EVI_500m, and EVI_1000m, respectively. Sex, urbanicity, annual family income, smoking, and drinking significantly modified the association of greenness-bone strength, with more remarkable associations in males, urban residents, subjects from wealthier families, smokers, and drinkers. For the NDVI_500m/EVI_500m-QUI relationship, the positive mediating roles of PM_2.5 and PA were 6.70%/8.50 and 2.43%/2.69%, respectively, whereas those negative for BMI and PA-BMI were 0.88%/1.06% and 0.05%/0.05%, respectively.

CONCLUSION

Living in a greener area may predict higher bone strength, particularly among males, urban residents, wealthier people, smokers, and drinkers. AAP, PA, BMI, and other factors may partially mediate the positive association. Our findings underscore the importance of optimizing greenness planning and management policies.

Modeling the impact of indoor relative humidity on the infection risk of five respiratory airborne viruses

With a modified version of the Wells-Riley model, we simulated the size distribution and dynamics of five airborne viruses (measles, influenza, SARS-CoV-2, human rhinovirus, and adenovirus) emitted from a speaking person in a typical residential setting over a relative humidity (RH) range of 20-80% and air temperature of 20-25 °C. Besides the size transformation of virus-containing droplets due to evaporation, respiratory absorption, and then removal by gravitational settling, the modified model also considered the removal mechanism by ventilation. The trend and magnitude of RH impact depended on the respiratory virus. For rhinovirus and adenovirus humidifying the indoor air from 20/30 to 50% will be increasing the relative infection risk, however, this relative infection risk increase will be negligible for rhinovirus and weak for adenovirus. Humidification will have a potential benefit in decreasing the infection risk only for influenza when there is a large infection risk decrease for humidifying from 20 to 50%. Regardless of the dry solution composition, humidification will overall increase the infection risk via long-range airborne transmission of SARS-CoV-2. Compared to humidification at a constant ventilation rate, increasing the ventilation rate to moderate levels 0.5 → 2.0 h-1 will have a more beneficial infection risk decrease for all viruses except for influenza. Increasing the ventilation rate from low values of 0.5 h-1 to higher levels of 6 h-1 will have a dominating effect on reducing the infection risk regardless of virus type.

Airborne magnetic nanoparticles may contribute to COVID-19 outbreak: Relationships in Greece and Iran

This work attempts to shed light on whether the COVID-19 pandemic rides on airborne pollution. In particular, a two-city study provides evidence that PM_2.5 contributes to the timing and severity of the epidemic, without adjustment for confounders. The publicly available data of deaths between March and October 2020, updated it on May 30, 2021, and the average seasonal concentrations of PM_2.5 pollution over the previous years in Thessaloniki, the second-largest city of Greece, were investigated. It was found that changes in coronavirus-related deaths follow changes in air pollution and that the correlation between the two data sets is maximized at the lag time of one month. Similar data from Tehran were gathered for comparison. The results of this study underscore that it is possible, if not likely, that pollution nanoparticles are related to COVID-19 fatalities (Granger causality, p < 0.05), contributing to the understanding of the environmental impact on pandemics.

Children's exposure to BC and PM pollution, and respiratory tract deposits during commuting trips to school

Although children have been identified as a vulnerable group highly susceptible to traffic-related air pollution, their exposure during school commutes to traffic-related pollutants and the relevant health impact is rarely studied. In this study, we measured black carbon (BC) and particulate matter (PM: PM₁, PM_2.5, and PM₁₀) concentrations that children are exposed to during their multi-modal (walking, private cars, and e-bikes) commuting trips to schools in Xi'an, China. A multi-parameter inhalation rate assessment model was developed in combination with the Multi-Path Particle Dosimetry (MPPD) model to quantify the deposition dose in different parts of children's respiratory system (head, tracheobronchial (TB), pulmonary (PUL)). Results show that walking to school exposed children to the lowest PM₁, PM_2.5, and BC concentrations, whereas riding an e-bike led to significantly elevated exposure to PM₁ and BC than the other two modes. This is due to children's closer proximity to vehicle tail pipe emissions when they bike to school on road or roadside. The PM and BC concentrations showed remarkable increases in comparison to background concentrations during children's school commutes. Urban background (UB) concentration, traffic volume (TV), time of day, and meteorological parameters could influence a child's personal exposure, and the impact of each factor vary across different transportation modes. Particle size of the pollutant affects its deposition site in the respiratory system. Deposition fractions (DFs) and deposition doses in the head region (DF > 50%) were the highest for PM and BC, for which fine particles (BC, PM₁, and PM_2.5) were then most easily deposited in the PUL region while coarse particles rarely reach PUL. Children inhaled higher doses of polluted air during active commuting (walking) than passive commuting (private cars, e-bikes), due to longer times of exposure coupled with more active breathing.

Gasoline fume inhalation induces apoptosis, inflammation, and favors Th2 polarization in C57BL/6 mice

Gasoline exposure has been widely reported in the literature as being toxic to human health. However, the exact underlying molecular mechanisms triggered by its inhalation have not been thoroughly investigated. We herein present a model of sub-chronic, static gasoline vapor inhalation in adult female C57BL/6 mice. Animals were exposed daily to either gasoline vapors (0.86 g/animal/90 minutes) or ambient air for five days/week over seven consecutive weeks. At the end of the study period, toxic and molecular mechanisms, underlying the inflammatory, oxidative, and apoptotic effects triggered by gasoline vapors, were examined in the lungs and liver of gasoline exposed mice. Static gasoline exposure induced a significant increase (+21 %) in lungs/body weight ratio in gasoline-exposed (GE) versus control (CON) mice along with a pulmonary inflammation attested by histological staining. The latter was consistent with increases in the transcript levels of proinflammatory cytokines [Interleukins (ILs) 4 and 6], respectively by ~ 6-, 4-fold in the lungs of GE mice compared to CON. Interestingly, IL-10 expression was also increased by ~ 10-fold in the lungs of GE mice suggesting an attempt to counterbalance the established inflammation. Moreover, the pulmonary expression of IL-12 and TNF-α was downregulated by 2- and 4-fold, respectively, suggesting the skewing toward Th2 phenotype. Additionally, GE mice showed a significant upregulation in Bax/Bcl-2 ratio, caspases 3, 8 and 9 with no change in JNK expression in the lungs, suggesting the activation of both intrinsic and extrinsic apoptotic pathways. Static gasoline exposure over seven consecutive weeks had a minor hepatic portal inflammation attested by H&E staining along with an increase in the hepatic expression of the mitochondrial complexes in GE mice. Therefore, tissue damage biomarkers highlight the health risks associated with vapor exposure and may present potential therapeutic targets for recovery from gasoline intoxication.

Influence of methodology on the estimation of the particle surface area dose received by a population in all-day activities

In everyday life, people are exposed to different concentrations of airborne particles depending on the microenvironment where they perform their different activities. Such exposure can lead to high sub-micron particle doses. The received dose depends on particle concentration to which people are exposed (typically expressed in terms of number or surface area), time spent in each activity or microenvironment (time activity pattern) and amount of air inhaled (inhalation rate). To estimate an actual value of the received dose, all these parameters should be measured under real-life conditions; in fact, the concentrations should be measured on a personal scale (i.e. through a direct exposure assessment), whereas time activity patterns and inhalation rates specific to the activity performed should be considered. The difficulties in obtaining direct measurements of these parameters usually lead to adopt time activity patterns and inhalation rates already available in scientific literature for typical populations, and local outdoor particle concentrations measured with fixed monitoring stations and extrapolated for all the other microenvironments. To overcome these limitations, we propose a full-field method for estimating the received dose of a population sample, in which all the parameters (concentration levels, time activity patterns and inhalation rates) are measured under real-life conditions (also including the inhalation rates, that were evaluated on the basis of the measured heart rates). Specifically, 34 volunteers were continuously monitored for seven days and the data of sub-micron particle concentrations, activities performed, and inhalation rates were recorded. The received dose was calculated with the proposed method and compared with those obtained from different simplified methodologies that consider typical data of particle concentrations, time activity patterns and inhalation rates obtained from literature. The results show that, depending on the methodology used, the differences in the received daily dose can be significant, with a general underestimation of the most simplified method.

Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data

Low-cost sensors can be used to improve the temporal and spatial resolution of an individual's particulate matter (PM) intake dose assessment. In this work, personal activity monitors were used to measure heart rate (proxy for minute ventilation), and low-cost PM sensors were used to measure concentrations of PM. Intake dose was assessed as a product of PM concentration and minute ventilation, using four models with increasing complexity. The two models that use heart rate as a variable had the most consistent results and showed a good response to variations in PM concentrations and heart rate. On the other hand, the two models using generalized population data of minute ventilation expectably yielded more coarse information on the intake dose. Aggregated weekly intake doses did not vary significantly between the models (6-22%). Propagation of uncertainty was assessed for each model, however, differences in their underlying assumptions made them incomparable. The most complex minute ventilation model, with heart rate as a variable, has shown slightly lower uncertainty than the model using fewer variables. Similarly, among the non-heart rate models, the one using real-time activity data has less uncertainty. Minute ventilation models contribute the most to the overall intake dose model uncertainty, followed closely by the low-cost personal activity monitors. The lack of a common methodology to assess the intake dose and quantifying related uncertainties is evident and should be a subject of further research.