Dataset Information

Development and validation of models to predict personal ventilation rate for air pollution research.

ABSTRACT: Air pollution intake represents the amount of pollution inhaled into the body and may be calculated by multiplying an individual's ventilation rate with the concentration of pollutant present in their breathing zone. Ventilation rate is difficult to measure directly, and methods for estimating ventilation rate (and intake) are lacking. Therefore, the goal of this work was to examine how well linear models using heart rate and other basic physiologic data can predict personal ventilation rate. We measured personal ventilation and heart rate among a panel of subjects (n?=?36) while they conducted a series of specified routine tasks of varying exertion levels. From these data, 136 candidate models were identified using a series of variable transformation and selection algorithms. A second "free?living" validation study (n?=?26) served as an independent validation dataset for these candidate models. The top?performing model, which included heart rate (Hr), resting heart rate (Hrest), age, sex, and hip circumference and interactions between sex with Hr, Hrest, age, and hip predicted ventilation rate (Ve) to within 11% and 33% for moderate (Ve?=?45?L/min) and low (Ve?=?15?L/min) intensity activities, respectively, based on the validation study. Many of the promising candidate models performed substantially worse under independent validation. Our results indicate that while measures of air pollution exposure and intake are highly correlated within tasks for a given individual, this correlation decreases substantially across tasks (i.e., as individuals go about a series of typical daily activities). This discordance between exposure and intake may influence exposure?response estimates in epidemiological studies. New air pollution studies should consider the trade?offs between the predictive ability of intake models and the error potentially introduced by not accounting for ventilation rate.

SUBMITTER: Good N

PROVIDER: S-EPMC6401349 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Development and validation of models to predict personal ventilation rate for air pollution research.

Good N N Carpenter T T Anderson G B GB Wilson A A Peel J L JL Browning R C RC Volckens J J

Journal of exposure science & environmental epidemiology 20180905 4

Air pollution intake represents the amount of pollution inhaled into the body and may be calculated by multiplying an individual's ventilation rate with the concentration of pollutant present in their breathing zone. Ventilation rate is difficult to measure directly, and methods for estimating ventilation rate (and intake) are lacking. Therefore, the goal of this work was to examine how well linear models using heart rate and other basic physiologic data can predict personal ventilation rate. We ...[more]

PMID: 30185945

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Project description:BackgroundReal-time air pollution monitoring is a valuable tool for public health and environmental surveillance. In recent years, there has been a dramatic increase in air pollution forecasting and monitoring research using artificial neural networks. Most prior work relied on modeling pollutant concentrations collected from ground-based monitors and meteorological data for long-term forecasting of outdoor ozone (O3), oxides of nitrogen, and fine particulate matter (PM2.5). Given that traditional, highly sophisticated air quality monitors are expensive and not universally available, these models cannot adequately serve those not living near pollutant monitoring sites. Furthermore, because prior models were built based on physical measurement data collected from sensors, they may not be suitable for predicting the public health effects of pollution exposure.ObjectiveThis study aimed to develop and validate models to nowcast the observed pollution levels using web search data, which are publicly available in near real time from major search engines.MethodsWe developed novel machine learning-based models using both traditional supervised classification methods and state-of-the-art deep learning methods to detect elevated air pollution levels at the US city level by using generally available meteorological data and aggregate web-based search volume data derived from Google Trends. We validated the performance of these methods by predicting 3 critical air pollutants (O3, nitrogen dioxide, and PM2.5) across 10 major US metropolitan statistical areas in 2017 and 2018. We also explore different variations of the long short-term memory model and propose a novel search term dictionary learner-long short-term memory model to learn sequential patterns across multiple search terms for prediction.ResultsThe top-performing model was a deep neural sequence model long short-term memory, using meteorological and web search data, and reached an accuracy of 0.82 (F1-score 0.51) for O3, 0.74 (F1-score 0.41) for nitrogen dioxide, and 0.85 (F1-score 0.27) for PM2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data.ConclusionsThe results show that incorporating web search data with meteorological data improves the nowcasting performance for all 3 pollutants and suggest promising novel applications for tracking global physical phenomena using web search data.

Project description:BackgroundPersonal and indoor air pollution monitors represent two ways to assess acute air pollution exposures; however, few reproductive epidemiology studies have incorporated these tools.ObjectiveTo provide an overview of the unique challenges and opportunities that arise when measuring acute exposure to air pollution in two ongoing reproductive epidemiology studies.MethodsThe Air Pollution, In Vitro Fertilization (IVF), and Reproductive Outcomes (AIR) Study recruits women undergoing IVF to wear a personal particulate matter (PM) air pollution monitor (AirBeam2©) for the 72-hour period following the start of controlled ovarian stimulation. The Reproductive Effects of Chemicals and Air Pollutants (RECAP) Study recruits men across the United States to place an air pollution monitor (emmET) in their home for 3 months, use a smartphone application, and provide a semen sample. We highlight the key issues identified in implementing exposure assessment for both studies.ResultsThe main advantages of using the AirBeam2© personal monitor are as follows: (a) the low cost, (b) the ability to collect multiple size fractions of PM data every second, (c) the portability, (d) its capability to track GPS location, and (e) the ability for the participant to observe their real-time exposure information. The limited battery life, incompatibility with iOS-based smartphones, and frequent connection issues that arise between the AirBeam2© and smartphone are the main disadvantages. The main advantages of the emmET are the ability to measure multiple air pollutants at a high level of accuracy, collect data for a long period of time without burdening the participant, and ship monitors to participants around the country without the need for in-person set-up by trained technicians; however, the monitor only measures the indoor home environment.ConclusionsNovel methods can be utilised to characterise short-term air pollution exposure in reproductive epidemiology studies and represent an exciting area for future research.

Project description:Air pollution inhaled dose is the product of pollutant concentration and minute ventilation ([Formula: see text]). Previous studies have parameterized the relationship between [Formula: see text] and variables such as heart rate (HR) and have observed substantial inter-subject variability. In this paper, we evaluate a method to estimate [Formula: see text] 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 [Formula: see text] with HR, breath frequency (fB), 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 [Formula: see text] = e-9.59HR2.39age0.274sex-0.204FVC0.520 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 [Formula: see text] = e-8.57HR1.72fB0.611age0.298sex-0.206FVC0.614, where fB 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 [Formula: see text], 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 [Formula: see text] 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.

Project description:BackgroundEchocardiography (echo) based machine learning (ML) models may be useful in identifying patients at high-risk of all-cause mortality.MethodsWe developed ML models (ResNet deep learning using echo videos and CatBoost gradient boosting using echo measurements) to predict 1-year, 3-year, and 5-year mortality. Models were trained on the Mackay dataset, Taiwan (6083 echos, 3626 patients) and validated in the Alberta HEART dataset, Canada (997 echos, 595 patients). We examined the performance of the models overall, and in subgroups (healthy controls, at risk of heart failure (HF), HF with reduced ejection fraction (HFrEF) and HF with preserved ejection fraction (HFpEF)). We compared the models' performance to the MAGGIC risk score, and examined the correlation between the models' predicted probability of death and baseline quality of life as measured by the Kansas City Cardiomyopathy Questionnaire (KCCQ).FindingsMortality rates at 1-, 3- and 5-years were 14.9%, 28.6%, and 42.5% in the Mackay cohort, and 3.0%, 10.3%, and 18.7%, in the Alberta HEART cohort. The ResNet and CatBoost models achieved area under the receiver-operating curve (AUROC) between 85% and 92% in internal validation. In external validation, the AUROCs for the ResNet (82%, 82%, and 78%) were significantly better than CatBoost (78%, 73%, and 75%), for 1-, 3- and 5-year mortality prediction respectively, with better or comparable performance to the MAGGIC score. ResNet models predicted higher probability of death in the HFpEF and HFrEF (30%-50%) subgroups than in controls and at risk patients (5%-20%). The predicted probabilities of death correlated with KCCQ scores (all p < 0.05).InterpretationEcho-based ML models to predict mortality had good internal and external validity, were generalizable, correlated with patients' quality of life, and are comparable to an established HF risk score. These models can be leveraged for automated risk stratification at point-of-care.FundingFunding for Alberta HEART was provided by an Alberta Innovates - Health Solutions Interdisciplinary Team Grant no. AHFMRITG 200801018. P.K. holds a Canadian Institutes of Health Research (CIHR) Sex and Gender Science Chair and a Heart & Stroke Foundation Chair in Cardiovascular Research. A.V. and V.S. received funding from the Mitacs Globalink Research Internship.

Project description:OBJECTIVE:Air pollution is a leading preventable risk factor for cardiovascular diseases. Previous studies mostly relied on concentrations at residence, which might not represent personal exposure. Personal air pollution exposure has a greater variability compared with levels of ambient air pollution, facilitating evaluation of exposure-response functions and vascular pathophysiology. We aimed to evaluate the association between predicted annual personal exposure to PM2.5 and black carbon (BC) and three vascular damage markers in peri-urban South India. METHODS:We analyzed the third wave of the APCAPS cohort (2010-2012), which recruited participants from 28 villages. We used predicted personal exposure to PM2.5 and BC derived from 610 participant-days of 24 h average gravimetric PM2.5 and BC measurements and predictors related to usual time-activity. Outcomes included carotid intima-media thickness (CIMT), carotid-femoral pulse wave velocity (cf-PWV) and augmentation index (AIx). We fit linear mixed models, adjusting for potential confounders and accounting for the clustered data structure. We evaluated nonlinear associations using generalized additive mixed models. RESULTS:Of the 3017 participants (mean age 38 years), 1453 (48%) were women. The average PM2.5 exposure was 51 µg/m3 (range 13-85) for men, and 61 µg/m3 (range 40-120) for women, while the average BC was 4 µg/m3 (range 3-7) for men and 8 µg/m3 (range 3-22) for women. A 10 ?g/m3 increase of PM2.5 was positively associated with CIMT (0.026 mm, 95% CI 0.014, 0.037), cf-PWV (0.069 m/s, 95% CI 0.008, 0.131) and AIx (0.8%, 95% CI 0.3, 1.3) among men. The exposure-response function for PM2.5 and AIx among men showed non-linearity, particularly within the exposure range dominated by tobacco smoking and occupational exposures. Both PM2.5 and BC were positively associated with AIx among women (0.6%, 95% CI 0.2, 1.0, per 10 ?g/m3 PM2.5; 0.5%, 95% CI 0.1, 0.8, per 2 ?g/m3 BC). CONCLUSIONS:Personal exposure to particulate matter was associated with vascular damage in a peri-urban population in South India. Personal exposure to particulate matter appears to have gender-specific effects on the type of vascular damage, potentially reflecting differences in sources of personal exposure by gender.

Project description:Mobile phones have a large spectrum of applications, aiding in risk prevention and improving health and wellbeing of their owners. So far, however, they have not been used for direct assessment of personal exposure to air pollution. In this study, we comprehensively evaluated the first, and the only available, mobile phone-BROAD Life-equipped with air pollution sensors (PM2.5 and VOC), to answer the question whether this technology is a viable option in the quest of reducing the burden of disease to air pollution. We tested its performance, applicability and suitability for the purpose by subjecting it to varied concentrations of different types of aerosol particles (cigarette smoke, petrol exhaust and concrete dust) and formaldehyde under controlled laboratory conditions, as well as to ambient particles during field measurements. Six reference instruments were used in the study: AEROTRAK Optical Particle Counter (OPC model number 9306), DustTrak, Aerodynamic Particle Counter (APS), Scanning Mobility Particle Sizer (SMPS), Tapered Element Oscillating Microbalance (TEOM) and Formaldehyde Analyser. Overall, we found that the phone's response was linear at higher particle number concentrations in the chamber, above 5 and 10 μg m-3, for combustion and concrete dust particles, respectively, and for higher formaldehyde concentrations, making it potentially suitable for applications in polluted environments. At lower ambient concentrations of particles around 10 ug m-3 and 20 μg m-3 for PM2.5 and PM10, respectively, the phone's response was below its noise level, suggesting that it is not suitable for ambient monitoring under relatively clean urban conditions. This mobile phone has a number of limitations that may hinder its use in personal exposure and for continuous monitoring. Despite these limitations, it may be used for comparative assessments, for example when comparing outcomes of intervention measures or local impacts of air pollution sources. It should be kept in mind, however, that a mobile phone measuring air quality alone cannot as such 'reduce the burden of disease to air pollution, as knowing ambient concentrations is only one of the building block in this quest. As long as individuals cannot avoid exposure e.g. in urban areas, knowing concentrations is not sufficient to reduce potential adverse effects. Yet, there are many situations and microenvironments, which individuals could avoid knowing the concentrations and also being aware of the risk caused by exposure to them. This includes for example to proximity to vehicle emissions, either for social purposes (e.g. street cafes) or exercising (e.g. walking or jogging along busy roads)or indoor environments affected by combustion emissions (smoking, candle burning, open fire).

Dataset Information

Development and validation of models to predict personal ventilation rate for air pollution research.

Publications

Development and validation of models to predict personal ventilation rate for air pollution research.

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