Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution.

BACKGROUND: Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. METHODS: We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. RESULTS: Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. CONCLUSIONS: Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.

Difference in ambient-personal exposure to PM2.5 and its inflammatory effect in local residents in urban and peri-urban Beijing, China: results of the AIRLESS project.

Measurement of ambient fine particulate matter (PM2.5) is often used as a proxy of personal exposure in epidemiological studies. However, the difference between personal and ambient exposure, and whether it biases the estimates of health effects remain unknown. Based on an epidemiological study (AIRLESS) and simultaneously launched intensive monitoring campaigns (APHH), we quantified and compared the personal and ambient exposure to PM2.5 and the related health impact among residents in Beijing, China. In total, 123 urban and 128 peri-urban non-smoking participants were recruited from two well-established cohorts in Beijing. During winter 2016 and summer 2017, each participant was instructed to carry a validated personal air monitor (PAM) to measure PM2.5 concentration at high spatiotemporal resolution for seven consecutive days in each season. Multiple inflammatory biomarkers were measured, including exhaled NO, blood monocytes counts and C-reactive protein. Linear mixed-effect models were used for the associations between exposure and health outcomes with adjustment for confounders. The average level of daily personal exposure to PM2.5 was consistently lower than using corresponding ambient concentration, and the difference is greater during the winter. The personal to ambient (P/A) ratio of exposure to PM2.5 exhibited an exponentially declining trend, and showed larger variations when ambient PM2.5 levels < 25 µg m-3. Personal exposure to PM2.5 was significantly associated with the increase in respiratory and systemic inflammatory biomarkers; however, the associations were weaker or became insignificant when ambient concentrations were used. Exposure to ambient PM2.5 might not be a good proxy to estimate the health effect of exposure to personal PM2.5.

Susceptibility of hypertensive individuals to acute blood pressure increases in response to personal-level environmental temperature decrease.

BACKGROUND: Environmental temperature is negatively associated with blood pressure (BP), and hypertension may exacerbate this association. The aim of this study is to investigate whether hypertensive individuals are more susceptible to acute BP increases following temperature decrease than non-hypertensive individuals. METHODS: The study panel consisted of 126 hypertensive and 125 non-hypertensive (n = 251) elderly participants who completed 940 clinical visits during the winter of 2016 and summer of 2017 in Beijing, China. Personal-level environmental temperature (PET) was continuously monitored for each participant with a portable sensor platform. We associated systolic BP (SBP) and diastolic BP (DBP) with the average PET over 24 h before clinical visits using linear mixed-effects models and explored hourly lag patterns for the associations using distributed lag models. RESULTS: We found that per 1 °C decrease in PET, hypertensive individuals showed an average (95 % confidence interval) increase of 0.96 (0.72, 1.19) and 0.28 (0.13, 0.42) mmHg for SBP and DBP, respectively; and non-hypertensive participants showed significantly smaller increases of 0.28 (0.03, 0.53) mmHg SBP and 0.14 (-0.01, 0.30) mmHg DBP. A lag pattern analysis showed that for hypertensive individuals, the increases in SBP and DBP were greatest following lag 1 h PET decrease and gradually attenuated up to lag 10 h exposure. No significant BP change was observed in non-hypertensive individuals associated with lag 1-24 h PET exposure. The enhanced increase in PET-associated BP in hypertensive participants (i.e., susceptibility) was more significant in winter than in summer. CONCLUSIONS: We found that a decrease in environmental temperature was associated with acute BP increases and these associations diminished over time, disappearing after approximately 10 hours. This implies that any intervention measures to prevent BP increases due to temperature drop should be implemented as soon as possible. Such timely interventions are particularly needed for hypertensive individuals especially during the cold season due to their increased susceptibility.

Maintaining eye fixation relieves pressure of cognitive action control.

Cognitive control enables humans to behave guided by their current goals and intentions. Cognitive control in one task generally suffers when humans try to engage in another task on top. However, we discovered an additional task that supports conflict resolution. In two experiments, participants performed a spatial cognitive control task. For different blocks of trials, they either received no instruction regarding eye movements or were asked to maintain the eyes fixated on a stimulus. The additional eye fixation task did not reduce task performance, but selectively ameliorated the adverse effects of cognitive conflicts on reaction times (Experiment 1). Likewise, in urgent situations, the additional task reduced performance impairments due to stimulus-driven processing overpowering cognitive control (Experiment 2). These findings suggest that maintaining eye fixation locks attentional resources that would otherwise induce spatial cognitive conflicts. This reveals an attentional disinhibition that boosts goal-directed action by relieving pressure from cognitive control.

Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project.

BACKGROUND: Air pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations. METHODS: Taking advantage of recent advancements in sensor technologies and computational techniques, this paper presents a novel methodological approach that improves dose estimations of multiple air pollutants in large-scale health studies. We show the results of an intensive field campaign that measured personal exposures to gaseous pollutants and particulate matter of a health panel of 251 participants residing in urban and peri-urban Beijing with 60 personal air quality monitors (PAMs). Outdoor air pollution measurements were collected in monitoring stations close to the participants' residential addresses. Based on parameters collected with the PAMs, we developed an advanced computational model that automatically classified time-activity-location patterns of each individual during daily life at high spatial and temporal resolution. RESULTS: Applying this methodological approach in two established cohorts, we found substantial differences between doses estimated from outdoor and personal air quality measurements. The PAM measurements also significantly reduced the correlation between pollutant species often observed in static outdoor measurements, reducing confounding effects. CONCLUSIONS: Future work will utilise these improved dose estimations to investigate the underlying mechanisms of air pollution on cardio-pulmonary health outcomes using detailed medical biomarkers in a way that has not been possible before.

Characterising low-cost sensors in highly portable platforms to quantify personal exposure in diverse environments.

The inaccurate quantification of personal exposure to air pollution introduces error and bias in health estimations, severely limiting causal inference in epidemiological research worldwide. Rapid advancements in affordable, miniaturised air pollution sensor technologies offer the potential to address this limitation by capturing the high variability of personal exposure during daily life in large-scale studies with unprecedented spatial and temporal resolution. However, concerns remain regarding the suitability of novel sensing technologies for scientific and policy purposes. In this paper we characterise the performance of a portable personal air quality monitor (PAM) that integrates multiple miniaturised sensors for nitrogen oxides (NO x ), carbon monoxide (CO), ozone (O3) and particulate matter (PM) measurements along with temperature, relative humidity, acceleration, noise and GPS sensors. Overall, the air pollution sensors showed high reproducibility (mean R ¯ 2 = 0.93, min-max: 0.80-1.00) and excellent agreement with standard instrumentation (mean R ¯ 2 = 0.82, min-max: 0.54-0.99) in outdoor, indoor and commuting microenvironments across seasons and different geographical settings. An important outcome of this study is that the error of the PAM is significantly smaller than the error introduced when estimating personal exposure based on sparsely distributed outdoor fixed monitoring stations. Hence, novel sensing technologies such as the ones demonstrated here can revolutionise health studies by providing highly resolved reliable exposure metrics at a large scale to investigate the underlying mechanisms of the effects of air pollution on health.