Air pollution inhaled dose is the product of pollutant concentration and minute ventilation ( ). Previous studies have parameterized the relationship between and variables such as heart rate (HR) and have observed substantial inter-subject variability. In this paper, we evaluate a method to estimate with easy-to-measure variables in an analysis of pooled-data from eight independent studies. We compiled a large diverse data set that is balanced with respect to age, sex and fitness level. We used linear mixed models to estimate with HR, breath frequency (f_B), age, sex, height, and forced vital capacity (FVC) as predictors. FVC was estimated using the Global Lung Function Initiative method. We log-transformed the dependent and independent variables to produce a model in the form of a power function and assessed model performance using a ten-fold cross-validation procedure. The best performing model using HR as the only field-measured parameter was = e^-9.59HR^2.39age^0.274sex^-0.204FVC^0.520 with HR in beats per minute, age in years, sex is 1 for males and 2 for females, FVC in liters, and a median(IQR) cross-validated percent error of 0.664(45.4)%. The best performing model overall was = e^-8.57HR^1.72f_B^0.611age^0.298sex^-0.206FVC^0.614, where f_B is breaths per minute, and a median(IQR) percent error of 1.20(37.9)%. The performance of these models is substantially better than any previously-published model when evaluated using this large pooled-data set. We did not observe an independent effect of height on , nor an effect of race, though this may have been due to insufficient numbers of non-white participants. We did observe an effect of FVC such that these models over- or under-predict in persons whose measured FVC was substantially lower or higher than estimated FVC, respectively. Although additional measurements are necessary to confirm this finding regarding FVC, we recommend using measured FVC when possible.

Electronic nicotine delivery systems (ENDS) aerosols are complex mixtures of chemicals, metals, and particles that may present inhalation hazards and adverse respiratory health risks. Despite being considered a safer alternative to tobacco cigarettes, metal exposure levels and respiratory effects associated with device aging and vaping frequency have not been fully characterized. In this study, we utilize an automated multi-channel ENDS aerosol generation system (EAGS) to generate aerosols from JUUL pod-type ENDS using tobacco-flavored e-liquid. Aerosol puff fractions (1–50) and (101–150) are monitored and sampled using various collection media. Extracted aerosols are prepared for metal and toxicological analysis using human primary small airway epithelial cells (SAEC). ENDS aerosol-mediated cellular responses, including reactive oxygen species (ROS), oxidative stress, cell viability, and DNA damage, are evaluated after 24 h and 7-day exposures. Our results show higher particle concentrations in later puff fractions (0.135 mg/m³) than in initial puff fractions (0.00212 mg/m³). Later puff fraction aerosols contain higher toxic metal concentrations, including chromium, copper, and lead, which elicit increased levels of ROS followed by significant declines in total glutathione and cell viability. Notably, a 30% increase in DNA damage was observed after 7 days because of later puff fraction exposures. This work is consistent with ENDS aerosols becoming more hazardous across the use of pre-filled pod devices, which may threaten respiratory health.

Evaluating vaping parameters that influence electronic nicotine delivery system (ENDS) emission profiles and potentially hazardous exposure levels is essential to protecting human health. We developed an automated multi-channel ENDS aerosol generation system (EAGS) for characterizing size-resolved particle emissions across pod- and mod-type devices using real-time monitoring instruments, an exposure chamber, and vaping parameters including different ventilation rates, device type and age, e-liquid formulation, and atomizer setup. Results show the ENDS device type, e-liquid flavoring, and nicotine content can affect particle emissions. In general, pod-type devices have unimodal particle size distributions and higher number emissions, while mod-type devices have bimodal size distributions and higher mass emissions. For pod-type devices, later puff fractions emit lower aerosols, which is potentially associated with the change of coil resistance and power during ageing. For a mod-type device, an atomizer with a lower resistance coil and higher power generates larger particle emissions than an atomizer with a greater resistance coil and lower power. The unventilated scenario produces higher particle emission factors, except for particle mass emission from pod-type devices. The data provided herein indicate the EAGS can produce realistic and reproducible puff profiles of pod- and mod-type ENDS devices and therefore is a suitable platform for characterizing ENDS-associated exposure risks.

To better understand the interaction of physical activity and air pollution exposure, it is important to quantify the change in ventilation rate incurred by activity. In this paper, we describe a method for estimating ventilation using easily-measured variables such as heart rate (HR), breathing rate (f_B), and forced vital capacity (FVC). We recruited healthy adolescents to use a treadmill while we continuously measured HR, f_B, and the tidal volume (V_T) of each breath. Participants began at rest then walked and ran at increasing speed until HR was 160–180 beats per minute followed by a cool down period. The novel feature of this method is that minute ventilation ( ) was normalized by FVC. We used general linear mixed models with a random effect for subject and identified nine potential predictor variables that influence either or FVC. We assessed predictive performance with a five-fold cross-validation procedure. We used a brute force selection process to identify the best performing models based on cross-validation percent error, the Akaike Information Criterion and the p-value of parameter estimates. We found a two-predictor model including HR and f_B to have the best predictive performance ( /FVC = -4.247+0.0595HR+0.226f_B, mean percent error = 8.1±29%); however, given the ubiquity of HR measurements, a one-predictor model including HR may also be useful ( /FVC = -3.859+0.101HR, mean percent error = 11.3±36%).

Persons with communication disabilities including persons with post-stroke aphasia (PWAs) possess a vulnerability to climate change as a result of their communication impairments. The disproportionate effects of climate change are likely to exacerbate preexisting inequities in social determinants of health. Communication disability intersecting with other characteristics subject to discrimination (e.g., race, age, sex, income) may lead to inequities in climate-related adaptive capacity. This article echoes earlier concerns related to climate change and further educates healthcare professionals about the impact of climate change on the global human population, with particular consideration of PWAs. The aims of this article are the following: (1) to broaden the understanding of aphasiologists and clinicians caring for PWAs about climate change and the contributions of human activity (anthropogenic) to this crisis; (2) to describe climate change and its impact on health; (3) to detail the intersectionality of climate and health; (4) to explore climate change and its potential effects on PWAs; and (5) to offer hope through emissions reduction, adaptation, resilience, and immediate change.

Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences. Environmental Health Perspectives is an Open Access journal published by the National Institute of Environmental Health Sciences.