Improving estimates of air pollution exposure through ubiquitous sensing technologies

Associations between long-term air pollution exposure and mortality and cardiovascular morbidity: A comparison of mobility-integrated and residential-only exposure assessment

Epidemiological studies investigating the health effects of long-term air pollution exposure typically only consider the participants' residential addresses when determining exposure. Neglecting mobility may introduce measurement error, potentially leading to bias or reduced precision of exposure-health relationships in epidemiological studies. In this study we compared the exposure-health associations between residential-only and mobility-integrated air pollution exposures. We evaluated two major pollutants, NO2 and PM2.5, and four health outcomes, natural and cause-specific mortality and coronary and cerebrovascular events. Agent-based modeling (ABM) was used to simulate the mobility patterns of the participants in the EPIC-NL cohort in the Netherlands and the Swiss National Cohort (SNC) in Switzerland, based on travel survey information. To obtain mobility-integrated exposures, hourly air pollution surfaces were developed and overlaid with the time-dependent location data from the ABM. We used Cox proportional hazards models within each cohort separately to evaluate the association between residential-only and mobility-integrated exposure and mortality and cardiovascular events, adjusting for major individual and area-level covariates. The mobility-integrated exposure and the residential exposure showed very high correlations for both pollutants and cohorts (R2 > 0.97). The mean exposure was 1-2 % and the exposure contrast 10-20 % lower for the mobility-integrated exposure. For all health outcomes, both pollutants and both cohorts, there were only small differences between residential-only and mobility-integrated exposure effect estimates. For the SNC, Hazard ratios (HRs) for natural mortality were 1.04 (1.03 - 1.04) and 1.03 (1.03 - 1.04) per interquartile range (IQR) increase in NO2 for residential and mobility-integrated exposure, respectively. For PM2.5 the corresponding estimates were 1.01 (1.01 - 1.02) per IQR increase for both approaches. Our findings support the growing evidence that assessment of long-term air pollution exposure at the residential address only in epidemiological studies may not lead to substantial bias and loss of precision in health effects estimates.

A brief review of methods for determining time-activity patterns in California

Air pollution exposure has been found to be linked with numerous adverse human health effects. Because both air pollution concentrations and the location of human individuals change spatiotemporally, understanding the time-activity patterns (TAPs) is of utmost importance for the mitigation of adverse exposures and to improve the accuracy of air pollution and health analyses. "Time-activity patterns" outlined here broadly refer to the spatiotemporal positions of individuals. In this review paper, we briefly review past efforts on collecting individual TAP information for air pollution and health studies, with a specific focus on California efforts. We also critically summarize emerging technologies and approaches for collecting TAP data. Specifically, we critically reviewed five types of emerging TAP data sources, including call detail record, social media location data, Google Location History, iPhone Significant Location, and crowd-sourced location data. This review provides a comprehensive summary and critique of different methods to collect TAP information and offers recommendations for use in retrospective air pollution and health studies.Implications: In this review paper, we provide a comprehensive overview of approaches for collecting time-activity pattern (TAP) data from individuals, a crucial component in understanding human behavior and its implications across various fields such as urban planning, environmental science, and, particularly, public health in relation to air pollution exposures.Furthermore, our paper introduces and critically evaluates several emerging methods for TAP data collection. These novel approaches, including but not limited to Google Location History, iPhone Significant Locations, and crowdsourced smartphone location data, offer unprecedented granularity in tracking human activities. By showcasing these methodologies and their often not well-recognized weaknesses, we highlight both the potential and limitations of these tools to advance our understanding of human behavior patterns, especially in terms of how individuals interact with their environments. This discussion not only showcases the originality of our work but also sets the stage for future research directions that can benefit from these innovative data collection strategies.

Variability in mobility-based air pollution exposure assessment: Effects of GPS tracking duration and temporal resolution of air pollution maps

Mobility-based exposure assessment of air pollution has been proposed as a potentially more valid approach than home-based assessments. However, methodological uncertainties in operationalizing mobility-based assessment may still increase inaccuracies in estimating exposures. It remains unclear whether using short-term mobility data and yearly average air pollution concentrations is reliable for estimating personal air pollution exposure. This study aimed to assess variability in exposure estimates modeled by short- and long-term global positioning system (GPS) data and air pollution maps with yearly and monthly temporal resolutions. We tracked 428 participants for a short period (14 days) with a GPS device and for a long period (several months) with a smartphone application. Exposure estimates of nitrogen dioxide, ozone, and fine particulate matter (PM10 and PM2.5) were computed based on GPS data, air pollution maps, and temporal and indoor/outdoor adjustments. The concordance correlation coefficient (CCC) indicated excellent agreement (0.85-0.99) between exposure estimates based on short- and long-term GPS data from smartphones but ranged from moderate to excellent (0.57-0.99) when comparing exposure estimates based on data from different devices. Agreement between yearly and monthly map-based estimates was poor to moderate without temporal adjustment (CCC: 0-0.63) but excellent after temporal adjustment (CCC: 0.92-1.0). The findings suggest that using short-term (i.e., 7 or 14 days) GPS data and yearly average air pollution concentrations in mobility-based assessments can well represent long-term mobility and yearly averages for determining long-term exposures. However, GPS data collected via dedicated devices and smartphones may identify distinct indoor/outdoor patterns, affecting the indoor/outdoor adjustments of exposure estimates. Additionally, careful selection of using yearly or monthly maps is advised for assessing exposures within specific short periods.

Agent-based modelling: A stochastic approach to assessing personal exposure to environmental pollutants - Insights from the URBANOME project

In the context of the URBANOME project, aiming to assess European citizens' exposure to air pollutants (PM10, PM2.5, NO2) and noise, an extensive data collection process was undertaken. This involved the distribution of stationary home sensors, portable sensors, and smartphone applications, alongside participants logging their activities while using these devices. By leveraging socioeconomic and socio-demographic statistical data for the residents of Thessaloniki, we developed an agent-based model to estimate exposure levels based on the movement patterns, locations, and data collected from the URBANOME campaign. The model highlights that an individual's exposure is closely linked to the type of activities they perform, their location, age, and gender. Whether exposure occurs indoors, or outdoors is important for determining intake levels. Activity selections were found to be strongly influenced by income, age, and social connections, indicating that socio-economic factors significantly shape exposure patterns. The analysis also revealed considerable differences between PM measurements taken from fixed monitoring stations and the sensors used in the campaign. Notably, even agents residing in the same household displayed distinct exposure levels, underscoring the variability within localized environments. Preliminary results from the URBANOME campaign were compared with the ABM outputs, showing differences in median values of up to 20 % of both noise and inhalation intakes. This research emphasizes the importance of using such models for developing future scenarios in large cities aimed at fostering green transitions and enhancing citizens' quality of life. These models provide valuable insights for designing strategies to reduce exposure and improve urban living conditions.

Burning gig, rewarding risk: Effects of dual exposure to incentive structure and heat condition on risky driving among on-demand food-delivery motorcyclists in Kaohsiung, Taiwan

The gig economy, characterized by short-term, task-based work facilitated via digital platforms, has raised various occupational safety concerns, including road safety risks and heat exposure faced by on-demand food delivery (ODFD) workers. Often using open modes of transportation, such as motorcycles and bicycles, these workers have minimal physical protection and direct environmental exposure while working long hours on the road, interacting with larger vehicles. Prior research has suggested that their road risks result from prevalent risky driving incentivized by platform-established business models, but quantitative evidence is lacking. Furthermore, while prolonged heat exposure may contribute to increased risky driving, our understanding of this relationship remains limited. This study investigates the impact of dual exposures to incentive structure and heat condition on risky driving among ODFD motorcyclists in Kaohsiung, Taiwan. A wearable sensing scheme was implemented, tracking a cohort of 40 ODFD workers during their work shifts in real time, collecting data on their speed, acceleration/deceleration patterns, incentive issuances, and heat exposure. Through a case-crossover approach, generalized linear cross-level mixed-effects models were employed to demonstrate the impact of incentive issuance on increasing risky driving among ODFD workers, including faster driving speeds, higher risks of speeding, harsher acceleration and braking, and more erratic acceleration patterns. Additionally, this study reveals that heat exposure, characterized by higher temperatures and humidity levels, exacerbates speed-related risky driving. These findings advance our understanding of causal mechanisms in two key areas of literature: firstly, the road safety risks faced by ODFD gig workers, and secondly, the broader relationship between heat exposure and risky driving. This research offers insights for policymakers to mitigate risky driving among ODFD workers, which is crucial in the context of climate change, where such urban economic dynamics may amplify climate-related inequities and place disproportionate safety burdens on vulnerable workers within the rapidly evolving gig economy.

Air pollution exposure in active versus passive travel modes across five continents: A Bayesian random-effects meta-analysis

Epidemiological studies on health effects of air pollution usually estimate exposure at the residential address. However, ignoring daily mobility patterns may lead to biased exposure estimates, as documented in previous exposure studies. To improve the reliable integration of exposure related to mobility patterns into epidemiological studies, we conducted a systematic review of studies across all continents that measured air pollution concentrations in various modes of transport using portable sensors. To compare personal exposure across different transport modes, specifically active versus motorized modes, we estimated pairwise exposure ratios using a Bayesian random-effects meta-analysis. Overall, we included measurements of six air pollutants (black carbon (BC), carbon monoxide (CO), nitrogen dioxide (NO2), particulate matter (PM10, PM2.5) and ultrafine particles (UFP)) for seven modes of transport (i.e., walking, cycling, bus, car, motorcycle, overground, underground) from 52 published studies. Compared to active modes, users of motorized modes were consistently the most exposed to gaseous pollutants (CO and NO2). Cycling and walking were the most exposed to UFP compared to other modes. Active vs passive mode contrasts were mostly inconsistent for other particle metrics. Compared to active modes, bus users were consistently more exposed to PM10 and PM2.5, while car users, on average, were less exposed than pedestrians. Rail modes experienced both some lower exposures (compared to cyclists for PM10 and pedestrians for UFP) and higher exposures (compared to cyclist for PM2.5 and BC). Ratios calculated for motorcycles should be considered carefully due to the small number of studies, mostly conducted in Asia. Computing exposure ratios overcomes the heterogeneity in pollutant levels that may exist between continents and countries. However, formulating ratios on a global scale remains challenging owing to the disparities in available data between countries.

Does residential address-based exposure assessment for outdoor air pollution lead to bias in epidemiological studies?

BACKGROUND

Epidemiological studies of long-term exposure to outdoor air pollution have consistently documented associations with morbidity and mortality. Air pollution exposure in these epidemiological studies is generally assessed at the residential address, because individual time-activity patterns are seldom known in large epidemiological studies. Ignoring time-activity patterns may result in bias in epidemiological studies. The aims of this paper are to assess the agreement between exposure assessed at the residential address and exposures estimated with time-activity integrated and the potential bias in epidemiological studies when exposure is estimated at the residential address.

MAIN BODY

We reviewed exposure studies that have compared residential and time-activity integrated exposures, with a focus on the correlation. We further discuss epidemiological studies that have compared health effect estimates between the residential and time-activity integrated exposure and studies that have indirectly estimated the potential bias in health effect estimates in epidemiological studies related to ignoring time-activity patterns. A large number of studies compared residential and time-activity integrated exposure, especially in Europe and North America, mostly focusing on differences in level. Eleven of these studies reported correlations, showing that the correlation between residential address-based and time-activity integrated long-term air pollution exposure was generally high to very high (R > 0.8). For individual subjects large differences were found between residential and time-activity integrated exposures. Consistent with the high correlation, five of six identified epidemiological studies found nearly identical health effects using residential and time-activity integrated exposure. Six additional studies in Europe and North America showed only small to moderate potential bias (9 to 30% potential underestimation) in estimated exposure response functions using residence-based exposures. Differences of average exposure level were generally small and in both directions. Exposure contrasts were smaller for time-activity integrated exposures in nearly all studies. The difference in exposure was not equally distributed across the population including between different socio-economic groups.

CONCLUSIONS

Overall, the bias in epidemiological studies related to assessing long-term exposure at the residential address only is likely small in populations comparable to those evaluated in the comparison studies. Further improvements in exposure assessment especially for large populations remain useful.

Hourly land-use regression modeling for NO2 and PM2.5 in the Netherlands

Annual average land-use regression (LUR) models have been widely used to assess spatial patterns of air pollution exposures. However, they fail to capture diurnal variability in air pollution and consequently might result in biased dynamic exposure assessments. In this study we aimed to model average hourly concentrations for two major pollutants, NO2 and PM2.5, for the Netherlands using the LUR algorithm. We modelled the spatial variation of average hourly concentrations for the years 2016-2019 combined, for two seasons, and for two weekday types. Two modelling approaches were used, supervised linear regression (SLR) and random forest (RF). The potential predictors included population, road, land use, satellite retrievals, and chemical transport model pollution estimates variables with different buffer sizes. We also temporally adjusted hourly concentrations from a 2019 annual model using the hourly monitoring data, to compare its performance with the hourly modelling approach. The results showed that hourly NO2 models performed overall well (5-fold cross validation R2 = 0.50-0.78), while the PM2.5 performed moderately (5-fold cross validation R2 = 0.24-0.62). Both for NO2 and PM2.5 the warm season models performed worse than the cold season ones, and the weekends' worse than weekdays'. The performance of the RF and SLR models was similar for both pollutants. For both SLR and RF, variables with larger buffer sizes representing variation in background concentrations, were selected more often in the weekend models compared to the weekdays, and in the warm season compared to the cold one. Temporal adjustment of annual average models performed overall worse than both modelling approaches (NO2 hourly R2 = 0.35-0.70; PM2.5 hourly R2 = 0.01-0.15). The difference in model performance and selection of variables across hours, seasons, and weekday types documents the benefit to develop independent hourly models when matching it to hourly time activity data.

Validity of Mobility-Based Exposure Assessment of Air Pollution: A Comparative Analysis with Home-Based Exposure Assessment

Air pollution exposure is typically assessed at the front door where people live in large-scale epidemiological studies, overlooking individuals' daily mobility out-of-home. However, there is limited evidence that incorporating mobility data into personal air pollution assessment improves exposure assessment compared to home-based assessments. This study aimed to compare the agreement between mobility-based and home-based assessments with personal exposure measurements. We measured repeatedly particulate matter (PM2.5) and black carbon (BC) using a sample of 41 older adults in the Netherlands. In total, 104 valid 24 h average personal measurements were collected. Home-based exposures were estimated by combining participants' home locations and temporal-adjusted air pollution maps. Mobility-based estimates of air pollution were computed based on smartphone-based tracking data, temporal-adjusted air pollution maps, indoor-outdoor penetration, and travel mode adjustment. Intraclass correlation coefficients (ICC) revealed that mobility-based estimates significantly improved agreement with personal measurements compared to home-based assessments. For PM2.5, agreement increased by 64% (ICC: 0.39-0.64), and for BC, it increased by 21% (ICC: 0.43-0.52). Our findings suggest that adjusting for indoor-outdoor pollutant ratios in mobility-based assessments can provide more valid estimates of air pollution than the commonly used home-based assessments, with no added value observed from travel mode adjustments.

Childhood exposure to outdoor air pollution in different microenvironments and cognitive and fine motor function in children from six European cohorts

BACKGROUND

Exposure to air pollution during childhood has been linked with adverse effects on cognitive development and motor function. However, limited research has been done on the associations of air pollution exposure in different microenvironments such as home, school, or while commuting with these outcomes.

OBJECTIVE

To analyze the association between childhood air pollution exposure in different microenvironments and cognitive and fine motor function from six European birth cohorts.

METHODS

We included 1301 children from six European birth cohorts aged 6-11 years from the HELIX project. Average outdoor air pollutants concentrations (NO2, PM2.5) were estimated using land use regression models for different microenvironments (home, school, and commute), for 1-year before the outcome assessment. Attentional function, cognitive flexibility, non-verbal intelligence, and fine motor function were assessed using the Attention Network Test, Trail Making Test A and B, Raven Colored Progressive Matrices test, and the Finger Tapping test, respectively. Adjusted linear regressions models were run to determine the association between each air pollutant from each microenvironment on each outcome.

RESULTS

In pooled analysis we observed high correlation (rs = 0.9) between air pollution exposures levels at home and school. However, the cohort-by-cohort analysis revealed correlations ranging from low to moderate. Air pollution exposure levels while commuting were higher than at home or school. Exposure to air pollution in the different microenvironments was not associated with working memory, attentional function, non-verbal intelligence, and fine motor function. Results remained consistently null in random-effects meta-analysis.

CONCLUSIONS

No association was observed between outdoor air pollution exposure in different microenvironments (home, school, commute) and cognitive and fine motor function in children from six European birth cohorts. Future research should include a more detailed exposure assessment, considering personal measurements and time spent in different microenvironments.

Do we need high temporal resolution modelling of exposure in urban areas? A test case

Roadside concentrations of harmful pollutants such as NOx are highly variable in both space and time. This is rarely considered when assessing pedestrian and cyclist exposures. We aim to fully describe the spatio-temporal variability of exposures of pedestrians and cyclists travelling along a road at high resolution. We evaluate the value added of high spatio-temporal resolution compared to high spatial resolution only. We also compare high resolution vehicle emissions modelling to using a constant volume source. We highlight conditions of peak exposures, and discuss implications for health impact assessments. Using the large eddy simulation code Fluidity we simulate NOx concentrations at a resolution of 2 m and 1 s along a 350 m road segment in a complex real-world street geometry including an intersection and bus stops. We then simulate pedestrian and cyclist journeys for different routes and departure times. For the high spatio-temporal method, the standard deviation in 1 s concentration experienced by pedestrians (50.9 μg.m-3) is nearly three times greater than that predicted by the high-spatial only (17.5 μg.m-3) or constant volume source (17.6 μg.m-3) methods. This exposure is characterised by low concentrations punctuated by short duration, peak exposures which elevate the mean exposure and are not captured by the other two methods. We also find that the mean exposure of cyclists on the road (31.8 μg.m-3) is significantly greater than that of cyclists on a roadside path (25.6 μg.m-3) and that of pedestrians on a sidewalk (17.6 μg.m-3). We conclude that ignoring high resolution temporal air pollution variability experienced at the breathing time scale can lead to a mischaracterization of pedestrian and cyclist exposures, and therefore also potentially the harm caused. High resolution methods reveal that peaks, and hence mean exposures, can be meaningfully reduced by avoiding hyper-local hotspots such as bus stops and junctions.

AirPen: A Wearable Monitor for Characterizing Exposures to Particulate Matter and Volatile Organic Compounds

Exposure to air pollution is a leading risk factor for disease and premature death, but technologies for assessing personal exposure to particulate and gaseous air pollutants, including the timing and location of such exposures, are limited. We developed a small, quiet, wearable monitor, called the AirPen, to quantify personal exposures to fine particulate matter (PM2.5) and volatile organic compounds (VOCs). The AirPen combines physical sample collection (PM onto a filter and VOCs onto a sorbent tube) with a suite of low-cost sensors (for PM, VOCs, temperature, pressure, humidity, light intensity, location, and motion). We validated the AirPen against conventional personal sampling equipment in the laboratory and then conducted a field study to measure at-work and away-from-work exposures to PM2.5 and VOCs among employees at an agricultural facility in Colorado, USA. The resultant sampling and sensor data indicated that personal exposures to benzene, toluene, ethylbenzene, and xylenes were dominated by a specific workplace location. These results illustrate how the AirPen can be used to advance our understanding of personal exposure to air pollution as a function of time, location, source, and activity, even in the absence of detailed activity diary data.

Detecting Elevated Air Pollution Levels by Monitoring Web Search Queries: Algorithm Development and Validation

BACKGROUND

Real-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 (PM_2.5). 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.

OBJECTIVE

This 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.

METHODS

We 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 PM_2.5) 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.

RESULTS

The 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 O_3, 0.74 (F₁-score 0.41) for nitrogen dioxide, and 0.85 (F₁-score 0.27) for PM_2.5, when used for detecting elevated pollution levels. Compared with using only meteorological data, the proposed method achieved superior accuracy by incorporating web search data.

CONCLUSIONS

The 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.

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.

Particulate air pollution in the Copenhagen metro part 2: Low-cost sensors and micro-environment classification

In this study fine particulate matter (PM_2.5) levels throughout the Copenhagen metro system are measured for the first time and found to be ∼10 times the roadside levels in Copenhagen. In this Part 2 article, low-cost sensor (LCS) nodes designed for personal-exposure monitoring are tested against a conventional mid-range device (TSI DustTrak), and gravimetric methods. The nodes were found to be effective for personal exposure measurements inside the metro system, with R² values of > 0.8 at 1-min and > 0.9 at 5-min time-resolution, with an average slope of 1.01 in both cases, in comparison to the reference, which is impressive for this dynamic environment. Micro-environment (ME) classification techniques are also developed and tested, involving the use of auxiliary sensors, measuring light, carbon dioxide, humidity, temperature and motion. The output from these sensors is used to distinguish between specific MEs, namely, being aboard trains travelling above- or under- ground, with 83 % accuracy, and determining whether sensors were aboard a train or stationary at a platform with 92 % accuracy. This information was used to show a 143 % increase in mean PM_2.5 concentration for underground sections relative to overground, and 22 % increase for train vs. platform measurements. The ME classification method can also be used to improve calibration models, assist in accurate exposure assessment based on detailed time-activity patterns, and facilitate field studies that do not require personnel to record time-activity diaries.

The impact of GPS-derived activity spaces on personal PM2.5 exposures in the MADRES cohort

BACKGROUND

In-utero exposure to particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) is associated with low birth weight and health risks later in life. Pregnant women are mobile and locations they spend time in contribute to their personal PM2.5 exposures. Therefore, it is important to understand how mobility and exposures encountered within activity spaces contribute to personal PM2.5 exposures during pregnancy.

METHODS

We collected 48-h integrated personal PM2.5 samples and continuous geolocation (GPS) data for 213 predominantly Hispanic/Latina pregnant women in their 3rd trimester in Los Angeles, CA. We also collected questionnaires and modeled outdoor air pollution and meteorology in their residential neighborhood. We calculated three GPS-derived activity space measures of exposure to road networks, greenness (NDVI), parks, traffic volume, walkability, and outdoor PM2.5 and temperature. We used bivariate analyses to screen variables (GPS-extracted exposures in activity spaces, individual characteristics, and residential neighborhood exposures) based on their relationship with personal, 48-h integrated PM2.5 concentrations. We then built a generalized linear model to explain the variability in personal PM2.5 exposure and identify key contributing factors.

RESULTS

Indoor PM2.5 sources, parity, and home ventilation were significantly associated with personal exposure. Activity-space based exposure to roads was associated with significantly higher personal PM2.5 exposure, while greenness was associated with lower personal PM2.5 exposure (β = -3.09 μg/m3 per SD increase in NDVI, p-value = 0.018). The contribution of outdoor PM2.5 to personal exposure was positive but relatively lower (β = 2.05 μg/m3 per SD increase, p-value = 0.016) than exposures in activity spaces and the indoor environment. The final model explained 34% of the variability in personal PM2.5 concentrations.

CONCLUSIONS

Our findings highlight the importance of activity spaces and the indoor environment on personal PM2.5 exposures of pregnant women living in Los Angeles, CA. This work also showcases the multiple, complex factors that contribute to total personal PM2.5 exposure.

Revisiting the atmospheric particles: Connecting lines and changing paradigms

Historically, the atmospheric particles constitute the most primitive and recent class of air pollutants. The science of atmospheric particles erupted more than a century ago covering more than four decades of size, with past few years experiencing major advancements on both theoretic and data-based observational grounds. More recently, the plausible recognition between particulate matter (PM) and the diffusion of the COVID-19 pandemic has led to the accretion of interest in particle science. With motivation from diverse particle research interests, this paper is an 'old engineer's survey' beginning with the evolution of atmospheric particles and identifies along the way many of the global instances signaling the 'size concept' of PM. A theme that runs through the narrative is a 'previously known' generational evolution of particle science to the 'newly procured' portfolio of knowledge, with important gains on the application of unmet concepts and future approaches to PM exposure and epidemiological research.

Spatio-Temporal Variation-Induced Group Disparity of Intra-Urban NO2 Exposure

Previous studies on exposure disparity have focused more on spatial variation but ignored the temporal variation of air pollution; thus, it is necessary to explore group disparity in terms of spatio-temporal variation to assist policy-making regarding public health. This study employed the dynamic land use regression (LUR) model and mobile phone signal data to illustrate the variation features of group disparity in Shanghai. The results showed that NO₂ exposure followed a bimodal, diurnal variation pattern and remained at a high level on weekdays but decreased on weekends. The most critical at-risk areas were within the central city in areas with a high population density. Moreover, women and the elderly proved to be more exposed to NO₂ pollution in Shanghai. Furthermore, the results of this study showed that it is vital to focus on land-use planning, transportation improvement programs, and population agglomeration to attenuate exposure inequality.

Low-cost monitoring of atmospheric PM-development and testing

Ambient particulate matter (PM) pollution is a significant problem in many urban and rural regions and has severe human health implications. Real-time, spatially dense monitoring using a network of low-cost sensors (LCS) was previously proposed as a way to alleviate the problem of PM. In this study, the performance of an LCS (Plantower PMS7003), a candidate for use in such a network, was investigated. The sensor was calibrated in a controlled climate chamber against a standard reference aerosol monitor. Reproducibility and calibration were evaluated in laboratory tests. Long-term, in-field performance was studied via deploying an LCS assembly at an environmental monitoring station. Results indicated excellent unit-to-unit consistency; however, each sensor needed to be calibrated individually as their characteristics varied slightly. Based on the results of a 15-month field test, quantitative and indicative LCS performance appeared promising: overall indicative accuracy was approximately 73-75% with comparable precision and recall. It is advised that the LCS are cleaned after 6-8 months of operation. Overall, the LCS appeared suitable for low-cost monitoring.

User-Centred Design of a Final Results Report for Participants in Multi-Sensor Personal Air Pollution Exposure Monitoring Campaigns

Using low-cost portable air quality (AQ) monitoring devices is a growing trend in personal exposure studies, enabling a higher spatio-temporal resolution and identifying acute exposure to high concentrations. Comprehension of the results by participants is not guaranteed in exposure studies. However, information on personal exposure is multiplex, which calls for participant involvement in information design to maximise communication output and comprehension. This study describes and proposes a model of a user-centred design (UCD) approach for preparing a final report for participants involved in a multi-sensor personal exposure monitoring study performed in seven cities within the EU Horizon 2020 ICARUS project. Using a combination of human-centred design (HCD), human-information interaction (HII) and design thinking approaches, we iteratively included participants in the framing and design of the final report. User needs were mapped using a survey (n = 82), and feedback on the draft report was obtained from a focus group (n = 5). User requirements were assessed and validated using a post-campaign survey (n = 31). The UCD research was conducted amongst participants in Ljubljana, Slovenia, and the results report was distributed among the participating cities across Europe. The feedback made it clear that the final report was well-received and helped participants better understand the influence of individual behaviours on personal exposure to air pollution.

Assessing the Current Integration of Multiple Personalised Wearable Sensors for Environment and Health Monitoring

The ever-growing development of sensor technology brings new opportunities to investigate impacts of the outdoor environment on human health at the individual level. However, there is limited literature on the use of multiple personalized sensors in urban environments. This review paper focuses on examining how multiple personalized sensors have been integrated to enhance the monitoring of co-exposures and health effects in the city. Following PRISMA guidelines, two reviewers screened 4898 studies from Scopus, Web of Science, ProQuest, Embase, and PubMed databases published from January 2010 to April 2021. In this case, 39 articles met the eligibility criteria. The review begins by examining the characteristics of the reviewed papers to assess the current situation of integrating multiple sensors for health and environment monitoring. Two main challenges were identified from the quality assessment: choosing sensors and integrating data. Lastly, we propose a checklist with feasible measures to improve the integration of multiple sensors for future studies.

Beyond air pollution at home: Assessment of personal exposure to PM2.5 using activity-based travel demand model and low-cost air sensor network data

Assessing personal exposure to air pollution is challenging due to the limited availability of human movement data and the complexity of modeling air pollution at high spatiotemporal resolution. Most health studies rely on residential estimates of outdoor air pollution instead which introduces exposure measurement error. Personal exposure for 100,784 individuals in Los Angeles County was estimated by integrating human movement data simulated from the Southern California Association of Governments (SCAG) activity-based travel demand model with hourly PM_2.5 predictions from my 500 m gridded model incorporating low-cost sensor monitoring data. Individual exposures were assigned considering PM_2.5 levels at homes, workplaces, and other activity locations. These dynamic exposures were compared to the residence-based exposures, which do not consider human movement, to examine the degree of exposure estimation bias. The results suggest that exposures were underestimated by 13% (range 5-22%) on average when human movement was not considered, and much of the error was eliminated by accounting for work location. Exposure estimation bias increased for people who exhibited higher mobility levels, especially for workers with long commute distances. Overall, the personal exposures of workers were underestimated by 22% (5-61%) relative to their residence-based exposures. For workers who commute >20 miles, their exposure levels can be at most underestimated by 61%. Omitting mobility resulted in underestimating exposures for people who reside in areas with cleaner air but work in more polluted areas. Similarly, exposures were overestimated for people living in areas with poorer air quality and working in cleaner areas. These could lead to differential estimation biases across racial, ethnic and socioeconomic lines that typically correlate with where people live and work and lead to important exposure and health disparities. This study demonstrates that ignoring human movement and spatiotemporal variability of air pollution could lead to differential exposure misclassification potentially biasing health risk assessments. These improved dynamic approaches can help planners and policymakers identify disadvantaged populations for which exposures are typically misrepresented and might lead to targeted policy and planning implications.

Environmental exposure during travel: A research review and suggestions forward

Daily travel through the urban fabric exposes urban dwellers to a range of environmental conditions that may have an impact on their health and wellbeing. Knowledge about exposures during travel, their associations with travel behavior, and their social and health outcomes are still limited. In our review, we aim to explain how the current environmental exposure research addresses the interactions between human and environmental systems during travel through their spatial, temporal and contextual dimensions. Based on the 104 selected studies, we identify significant recent advances in addressing the spatiotemporal dynamics of exposure during travel. However, the conceptual and methodological framework for understanding the role of multiple environmental exposures in travel environments is still in an early phase, and the health and wellbeing impacts at individual or population level are not well known. Further research with greater geographical balance is needed to fill the gaps in the empirical evidence, and linking environmental exposures during travel with the causal health and wellbeing outcomes. These advancements can enable evidence-based urban and transport planning to take the next step in advancing urban livability.

Short-term personal and outdoor exposure to ultrafine and fine particulate air pollution in association with blood pressure and lung function in healthy adults

Studies reporting on associations between short-term exposure to outdoor fine (PM_2.5), and ultrafine particles (UFP) and blood pressure and lung function have been inconsistent. Few studies have characterized exposure by personal monitoring, which especially for UFP may have resulted in substantial exposure measurement error. We investigated the association between 24-h average personal UFP, PM_2.5, and soot exposure and dose and the health parameters blood pressure and lung function. We further assessed the short-term associations between outdoor concentrations measured at a central monitoring site and near the residences and these health outcomes. We performed three 24-h personal exposure measurements for UFP, PM_2.5, and soot in 132 healthy adults from Basel (Switzerland), Amsterdam and Utrecht (the Netherlands), and Turin (Italy). Monitoring of each subject was conducted in different seasons in a one-year study period. Subject's activity levels and associated ventilation rates were measured using actigraphy to calculate the inhaled dose. After each 24-h monitoring session, blood pressure and lung function were measured. Contemporaneously with personal measurements, UFP, PM_2.5 and soot were measured outdoor at the subject's residential address and at a central site in the research area. Associations between short-term personal and outdoor exposure and dose to UFP, PM_2.5, and soot and health outcomes were tested using linear mixed effect models. The 24-h mean personal, residential and central site outdoor UFP exposures were not associated with blood pressure or lung function. UFP mean exposures in the 2-h prior to the health test was also not associated with blood pressure and lung function. Personal, central site and residential PM_2.5 exposure were positively associated with systolic blood pressure (about 1.4 mmHg increase per Interquartile range). Personal soot exposure and dose were positively associated with diastolic blood pressure (1.2 and 0.9 mmHg increase per Interquartile range). No consistent associations between PM_2.5 or soot exposure and lung function were observed. Short-term personal, residential outdoor or central site exposure to UFP was not associated with blood pressure or lung function. Short-term personal PM_2.5 and soot exposures were associated with blood pressure, but not lung function.

The Impact of Individual Mobility on Long-Term Exposure to Ambient PM2.5: Assessing Effect Modification by Travel Patterns and Spatial Variability of PM2.5

The impact of individuals' mobility on the degree of error in estimates of exposure to ambient PM2.5 concentrations is increasingly reported in the literature. However, the degree to which accounting for mobility reduces error likely varies as a function of two related factors-individuals' routine travel patterns and the local variations of air pollution fields. We investigated whether individuals' routine travel patterns moderate the impact of mobility on individual long-term exposure assessment. Here, we have used real-world time-activity data collected from 2013 participants in Erie/Niagara counties, New York, USA, matched with daily PM2.5 predictions obtained from two spatial exposure models. We further examined the role of the spatiotemporal representation of ambient PM2.5 as a second moderator in the relationship between an individual's mobility and the exposure measurement error using a random effect model. We found that the effect of mobility on the long-term exposure estimates was significant, but that this effect was modified by individuals' routine travel patterns. Further, this effect modification was pronounced when the local variations of ambient PM2.5 concentrations were captured from multiple sources of air pollution data ('a multi-sourced exposure model'). In contrast, the mobility effect and its modification were not detected when ambient PM2.5 concentration was estimated solely from sparse monitoring data ('a single-sourced exposure model'). This study showed that there was a significant association between individuals' mobility and the long-term exposure measurement error. However, the effect could be modified by individuals' routine travel patterns and the error-prone representation of spatiotemporal variability of PM2.5.

Assessing personal exposure using Agent Based Modelling informed by sensors technology

Technology innovations create possibilities to capture exposure-related data at a great depth and breadth. Considering, though, the substantial hurdles involved in collecting individual data for whole populations, this study introduces a first approach of simulating human movement and interaction behaviour, using Agent Based Modelling (ABM). A city scale ABM was developed for urban Thessaloniki, Greece that feeds into population-based exposure assessment without imposing prior bias, basing its estimations onto emerging properties of the behaviour of the computerised autonomous decision makers (agents) that compose the city-system. Population statistics, road and buildings networks data were transformed into human, road and building agents, respectively. Survey outputs with time-use patterns were associated with human agent rules, aiming to model representative to real-world behaviours. Moreover, time-geography of exposure data, derived from a local sensors campaign, was used to inform and enhance the model. As a prevalence of an agent-specific decision-making, virtual individuals of different sociodemographic backgrounds express different spatiotemporal behaviours and their trajectories are coupled with spatially resolved pollution levels. Personal exposure was evaluated by assigning PM concentrations to human agents based on coordinates, type of location and intensity of encountered activities. Study results indicated that PM2.5 inhalation adjusted exposure between housemates can differ by 56.5% whereas exposure between two neighbours can vary by as much as 87%, due to the prevalence of different behaviours. This study provides details of a new methodology that permits the cost-effective construction of refined time-activity diaries and daily exposure profiles, taking into account different microenvironments and sociodemographic characteristics. The proposed method leads to a refined exposure assessment model, addressing effectively vulnerable subgroups of population. It can be used for evaluating the probable impacts of different public health policies prior to implementation reducing, therefore, the time and expense required to identify efficient measures.

Real-time measurements of PM2.5 and ozone to assess the effectiveness of residential indoor air filtration in Shanghai homes

Portable air cleaners are increasingly used in polluted areas in an attempt to reduce human exposure; however, there has been limited work characterizing their effectiveness at reducing exposure. With this in mind, we recruited forty-three children with asthma from suburban Shanghai and deployed air cleaners (with HEPA and activated carbon filters) in their bedrooms. During both 2-week filtration and non-filtration periods, low-cost PM_2.5 and O₃ air monitors were used to measure pollutants indoors, outdoors, and for personal exposure. Indoor PM_2.5 concentrations were reduced substantially with the use of air cleaners, from 34 ± 17 to 10 ± 8 µg/m³ , with roughly 80% of indoor PM_2.5 estimated to come from outdoor sources. Personal exposure to PM_2.5 was reduced from 40 ± 17 to 25 ± 14 µg/m³ . The more modest reductions in personal exposure and high contribution of outdoor PM_2.5 to indoor concentrations highlight the need to reduce outdoor PM_2.5 and/or to clean indoor air in multiple locations. Indoor O₃ concentrations were generally low (mean = 8±4 ppb), and no significant difference was seen by filtration status. The concentrations of pollutants and the air cleaner effectiveness were highly variable over time and across homes, highlighting the usefulness of real-time air monitors for understanding individual exposure reduction strategies.

Spatial and Temporal Exposure Assessment to PM2.5 in a Community Using Sensor-Based Air Monitoring Instruments and Dynamic Population Distributions

This research was to conduct a pilot study for two consecutive days in order to assess fine particulate matter (PM2.5) exposure of an entire population in a community. We aimed to construct a surveillance system by analyzing the observed spatio-temporal variation of exposure. Guro-gu in Seoul, South Korea, was divided into 2,204 scale grids of 100 m each. Hourly exposure concentrations of PM2.5 were modeled by the inverse distance weighted method, using 24 sensor-based air monitoring instruments and the indoor-to-outdoor concentration ratio. Population distribution was assessed using mobile phone network data and indoor residential rates, according to sex and age over time. Exposure concentration, population distribution, and population exposure were visualized to present spatio-temporal variation. The PM2.5 exposure of the entire population of Guro-gu was calculated by population-weighted average exposure concentration. The average concentration of outdoor PM2.5 was 42.1 µg/m3, which was lower than the value of the beta attenuation monitor measured by fixed monitoring station. Indoor concentration was estimated using an indoor-to-outdoor PM2.5 concentration ratio of 0.747. The population-weighted average exposure concentration of PM2.5 was 32.4 µg/m3. Thirty-one percent of the population exceeded the Korean Atmospheric Environmental Standard for PM2.5 over a 24 h average period. The results of this study can be used in a long-term aggregate and cumulative PM2.5 exposure assessment, and as a basis for policy decisions on public health management among policymakers and stakeholders.

Application of smartphone-based spectroscopy to biosample analysis: A review

The emergence of the smartphones has brought extensive changes to our lifestyles, from communicating with one another, to shopping and enjoyment of entertainment, and from studying to functioning at the workplace (and in the field). At the same time, this portable device has also provided new possibilities in scientific research and applications. Based on the growing awareness of good health management, researchers have coupled health monitoring to smartphone sensing technologies. Along the way, there have been developed a variety of smartphone-based optical detection platforms for analyzing biological samples, including standalone smartphone units and integrated smartphone sensing systems. In this review, we outline the applications of smartphone-based optical sensors for biosamples. These applications focus mainly on three aspects: Microscopic imaging sensing, colorimetric sensing and luminescence sensing. We also discuss briefly some limitations of the current state of smartphone-based spectroscopy and present prospects of the future applicability of smartphone sensors.

Children's microenvironmental exposure to PM2.5 and ozone and the impact of indoor air filtration

BACKGROUND

In highly polluted urban areas, personal exposure to PM_2.5 and O₃ occur daily in various microenvironments. Identifying which microenvironments contribute most to exposure can pinpoint effective exposure reduction strategies and mitigate adverse health impacts.

METHODS

This work uses real-time sensors to assess the exposures of children with asthma (N = 39) in Shanghai, quantifying microenvironmental exposure to PM_2.5 and O₃. An air cleaner was deployed in participants' bedrooms where we hypothesized exposure could be most efficiently reduced. Monitoring occurred for two 48-h periods: one with bedroom filtration (portable air cleaner with HEPA and activated carbon filters) and the other without.

RESULTS

Children spent 91% of their time indoors with the majority spent in their bedroom (47%). Without filtration, the bedroom and classroom environments were the largest contributors to PM_2.5 exposure. With filtration, bedroom PM_2.5 exposure was reduced by 75% (45% of total exposure). Although filtration status did not impact O₃, the largest contribution of O₃ exposure also came from the bedroom.

CONCLUSIONS

Actions taken to reduce bedroom PM_2.5 and O₃ concentrations can most efficiently reduce total exposure. As real-time pollutant monitors become more accessible, similar analyses can be used to evaluate new interventions and optimize exposure reductions for a variety of populations.

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.

Using a network of lower-cost monitors to identify the influence of modifiable factors driving spatial patterns in fine particulate matter concentrations in an urban environment

BACKGROUND

There is substantial interest in using networks of lower-cost air quality sensors to characterize urban population exposure to fine particulate matter mass (PM_2.5). However, sensor uncertainty is a concern with these monitors.

OBJECTIVES

(1) Quantify the uncertainty of lower-cost PM_2.5 sensors; (2) Use the high spatiotemporal resolution of a lower-cost sensor network to quantify the contribution of different modifiable and non-modifiable factors to urban PM_2.5.

METHODS

A network of 64 lower-cost monitors was deployed across Pittsburgh, PA, USA. Measurement and sampling uncertainties were quantified by comparison to local reference monitors. Data were sorted by land-use characteristics, time of day, and wind direction.

RESULTS

Careful calibration, temporal averaging, and reference site corrections reduced sensor uncertainty to 1 μg/m³, ~10% of typical long-term average PM_2.5 concentrations in Pittsburgh. Episodic and long-term enhancements to urban PM_2.5 due to a nearby large metallurgical coke manufacturing facility were 1.6 ± 0.36 μg/m³ and 0.3 ± 0.2 μg/m³, respectively. Daytime land-use regression models identified restaurants as an important local contributor to urban PM_2.5. PM_2.5 above EPA and WHO daily health standards was observed at several sites across the city.

SIGNIFICANCE

With proper management, a large network of lower-cost sensors can identify statistically significant trends and factors in urban exposure.

Prediction of human active mobility in rural areas: development and validity tests of three different approaches

BACKGROUND/AIM

Active mobility may play a relevant role in the assessment of environmental exposures (e.g. traffic-related air pollution, livestock emissions), but data about actual mobility patterns are work intensive to collect, especially in large study populations, therefore estimation methods for active mobility may be relevant for exposure assessment in different types of studies. We previously collected mobility patterns in a group of 941 participants in a rural setting in the Netherlands, using week-long GPS tracking. We had information regarding personal characteristics, self-reported data regarding weekly mobility patterns and spatial characteristics. The goal of this study was to develop versatile estimates of active mobility, test their accuracy using GPS measurements and explore the implications for exposure assessment studies.

METHODS

We estimated hours/week spent on active mobility based on personal characteristics (e.g. age, sex, pre-existing conditions), self-reported data (e.g. hours spent commuting per bike) or spatial predictors such as home and work address. Estimated hours/week spent on active mobility were compared with GPS measured hours/week, using linear regression and kappa statistics.

RESULTS

Estimated and measured hours/week spent on active mobility had low correspondence, even the best predicting estimation method based on self-reported data, resulted in a R² of 0.09 and Cohen's kappa of 0.07. A visual check indicated that, although predicted routes to work appeared to match GPS measured tracks, only a small proportion of active mobility was captured in this way, thus resulting in a low validity of overall predicted active mobility.

CONCLUSIONS

We were unable to develop a method that could accurately estimate active mobility, the best performing method was based on detailed self-reported information but still resulted in low correspondence. For future studies aiming to evaluate the contribution of home-work traffic to exposure, applying spatial predictors may be appropriate. Measurements still represent the best possible tool to evaluate mobility patterns.

Who are more exposed to PM2.5 pollution: A mobile phone data approach

BACKGROUND

Few studies have examined exposure disparity to ambient air pollution outside North America and Europe. Moreover, very few studies have investigated exposure disparity in terms of individual-level data or at multi-temporal scales.

OBJECTIVES

This work aims to examine the associations between individual- and neighbourhood-level economic statuses and individual exposure to PM2.5 across multi-temporal scales.

METHODS

The study population included 742,220 mobile phone users on a weekday in Shenzhen, China. A geo-informed backward propagation neural network model was developed to estimate hourly PM2.5 concentrations by the use of remote sensing and geospatial big data, which were then combined with individual trajectories to estimate individual total exposure during weekdays at multi-temporal scales. Coupling the estimated PM2.5 exposure with housing price, we examined the associations between individual- and neighbourhood-level economic statuses and individual exposure using linear regression and two-level hierarchical linear models. Furthermore, we performed five sensitivity analyses to test the robustness of the two-level effects.

RESULTS

We found positive associations between individual- and neighbourhood-level economic statuses and individual PM2.5 exposure at a daytime, daily, weekly, monthly, seasonal or annual scale. Findings on the effects of the two-level economic statuses were generally robust in the five sensitivity analyses. In particular, despite the insignificant effects observed in three of newly selected time periods in the sensitivity analysis, individual- and neighbourhood-level economic statuses were still positively associated with individual total exposure during each of other newly selected periods (including three other seasons).

CONCLUSIONS

There are statistically positive associations of individual PM2.5 exposure with individual- and neighbourhood-level economic statuses. That is, people living in areas with higher residential property prices are more exposed to PM2.5 pollution. Findings emphasize the need for public health intervention and urban planning initiatives targeting socio-economic disparity in ambient air pollution exposure, thus alleviating health disparities across socioeconomic groups.

Environmental Health Surveillance System for a Population Using Advanced Exposure Assessment

Human exposure to air pollution is a major public health concern. Environmental policymakers have been implementing various strategies to reduce exposure, including the 10th-day-no-driving system. To assess exposure of an entire population of a community in a highly polluted area, pollutant concentrations in microenvironments and population time–activity patterns are required. To date, population exposure to air pollutants has been assessed using air monitoring data from fixed atmospheric monitoring stations, atmospheric dispersion modeling, or spatial interpolation techniques for pollutant concentrations. This is coupled with census data, administrative registers, and data on the patterns of the time-based activities at the individual scale. Recent technologies such as sensors, the Internet of Things (IoT), communications technology, and artificial intelligence enable the accurate evaluation of air pollution exposure for a population in an environmental health context. In this study, the latest trends in published papers on the assessment of population exposure to air pollution were reviewed. Subsequently, this study proposes a methodology that will enable policymakers to develop an environmental health surveillance system that evaluates the distribution of air pollution exposure for a population within a target area and establish countermeasures based on advanced exposure assessment.

Quality of hybrid location data drawn from GPS-enabled mobile phones: Does it matter?

Despite their increasing popularity in human mobility studies, few studies have investigated the geo-spatial quality of GPS-enabled mobile phone data in which phone location is determined by special queries designed to collect location data with predetermined sampling intervals (hereafter "active mobile phone data"). We focus on two key issues in active mobile phone data-systematic gaps in tracking records and positioning uncertainty-and investigate their effects on human mobility pattern analyses. To address gaps in records, we develop an imputation strategy that utilizes local environment information, such as parcel boundaries, and recording time intervals. We evaluate the performance of the proposed imputation strategy by comparing raw versus imputed data with participants' online survey responses. The results indicate that imputed data are superior to raw data in identifying individuals' frequently visited places on a weekly basis. To assess the location accuracy of active mobile phone data, we investigate the spatial and temporal patterns of the positional uncertainty of each record and examine via Monte Carlo simulation how inaccurate location information might affect human mobility pattern indicators. Results suggest that the level of uncertainty varies as a function of time of day and the type of land use at which the position was determined, both of which are closely related to the location technology used to determine the location. Our study highlights the importance of understanding and addressing limitations of mobile phone derived positioning data prior to their use in human mobility studies.

Availability, access, analysis and dissemination of small-area data

In this era of 'big data', there is growing recognition of the value of environmental, health, social and demographic data for research. Open government data initiatives are growing in number and in terms of content. Remote sensing data are finding widespread use in environmental research, including in low- and middle-income settings. While our ability to study environment and health associations across countries and continents grows, data protection rules and greater patient control over the use of their data present new challenges to using health data in research. Innovative tools that circumvent the need for the physical sharing of data by supporting non-disclosive sharing of information, or that permit spatial analysis without researchers needing access to underlying patient data can be used to support analyses while protecting data confidentiality. User-friendly visualizations, allowing small-area data to be seen and understood by non-expert audiences, are revolutionizing public and researcher interactions with data. The UK Small Area Health Statistics Unit's Environment and Health Atlas for England and Wales, and the US National Environmental Public Health Tracking Network offer good examples. Open data facilitates user-generated outputs, and 'mash-ups', and user-generated inputs from social media, mobile devices and wearable tech are new data streams that will find utility in future studies, and bring novel dimensions with respect to ethical use of small-area data.

Estimating exposure to fine particulate matter emissions from vehicle traffic: Exposure misclassification and daily activity patterns in a large, sprawling region

Vehicle traffic is responsible for a significant portion of toxic air pollution in urban areas that has been linked to a wide range of adverse health outcomes. Most vehicle air quality analyses used for transportation planning and health effect studies estimate exposure from the measured or modeled concentration of an air pollutant at a person's home. This study evaluates exposure to fine particulate matter from vehicle traffic and the magnitude and cause of exposure misclassification that result from not accounting for population mobility during the day in a large, sprawling region. We develop a dynamic exposure model by integrating activity-based travel demand, vehicle emission, and air dispersion models to evaluate the magnitude, components and spatial patterns of vehicle exposure misclassification in the Atlanta, Georgia metropolitan area. Overall, we find that population exposure estimates increase by 51% when population mobility is accounted for. Errors are much larger in suburban and rural areas where exposure is underestimated while exposure may be overestimated near high volume roadways and in the urban core. Exposure while at work and traveling account for much of the error. We find much larger errors than prior studies, all of which have focused on more compact urban regions. Since many people spend a large part of their day away from their homes and vehicle emissions are known to create "hotspots" along roadways, home-based exposure is unlikely to be a robust estimator of a person's actual exposure. Accounting for population mobility in vehicle emission exposure studies may reveal more effective mitigation strategies, important differences in exposure between population groups with different travel patterns, and reduce exposure misclassification in health studies.

Internet of Things and Enhanced Living Environments: Measuring and Mapping Air Quality Using Cyber-physical Systems and Mobile Computing Technologies

This paper presents a real-time air quality monitoring system based on Internet of Things. Air quality is particularly relevant for enhanced living environments and well-being. The Environmental Protection Agency and the World Health Organization have acknowledged the material impact of air quality on public health and defined standards and policies to regulate and improve air quality. However, there is a significant need for cost-effective methods to monitor and control air quality which provide modularity, scalability, portability, easy installation and configuration features, and mobile computing technologies integration. The proposed method allows the measuring and mapping of air quality levels considering the spatial-temporal information. This system incorporates a cyber-physical system for data collection and mobile computing software for data consulting. Moreover, this method provides a cost-effective and efficient solution for air quality supervision and can be installed in vehicles to monitor air quality while travelling. The results obtained confirm the implementation of the system and present a relevant contribution to enhanced living environments in smart cities. This supervision solution provides real-time identification of unhealthy behaviours and supports the planning of possible interventions to increase air quality.

Novel Approaches to Air Pollution Exposure and Clinical Outcomes Assessment in Environmental Health Studies

An accurate assessment of pollutants’ exposure and precise evaluation of the clinical outcomes pose two major challenges to the contemporary environmental health research. The common methods for exposure assessment are based on residential addresses and are prone to many biases. Pollution levels are defined based on monitoring stations that are sparsely distributed and frequently distanced far from residential addresses. In addition, the degree of an association between outdoor and indoor air pollution levels is not fully elucidated, making the exposure assessment all the more inaccurate. Clinical outcomes’ assessment, on the other hand, mostly relies on the access to medical records from hospital admissions and outpatients’ visits in clinics. This method differentiates by health care seeking behavior and is therefore, problematic in evaluation of an onset, duration, and severity of an outcome. In the current paper, we review a number of novel solutions aimed to mitigate the aforementioned biases. First, a hybrid satellite-based modeling approach provides daily continuous spatiotemporal estimations with improved spatial resolution of 1 × 1 km2 and 200 × 200 m2 grid, and thus allows a more accurate exposure assessment. Utilizing low-cost air pollution sensors allowing a direct measurement of indoor air pollution levels can further validate these models. Furthermore, the real temporal-spatial activity can be assessed by GPS tracking devices within the individuals’ smartphones. A widespread use of smart devices can help with obtaining objective measurements of some of the clinical outcomes such as vital signs and glucose levels. Finally, human biomonitoring can be efficiently done at a population level, providing accurate estimates of in-vivo absorbed pollutants and allowing for the evaluation of body responses, by biomarkers examination. We suggest that the adoption of these novel methods will change the research paradigm heavily relying on ecological methodology and support development of the new clinical practices preventing adverse environmental effects on human health.

Youth Engaged Participatory Air Monitoring: A 'Day in the Life' in Urban Environmental Justice Communities

Air pollution in Southern California does not impact all communities equally; communities of color are disproportionately burdened by poor air quality and more likely to live near industrial facilities and freeways. Government regulatory monitors do not have the spatial resolution to provide air quality information at the neighborhood or personal scale. We describe the A Day in the Life program, an approach to participatory air monitoring that engages youth in collecting data that they can then analyze and use to take action. Academics partnered with Los Angeles-based youth environmental justice organizations to combine personal air monitoring, participatory science, and digital storytelling to build capacity to address local air quality issues. Eighteen youth participants from four different neighborhoods wore portable personal PM2.5 (fine particles <2.5 µm in diameter) monitors for a day in each of their respective communities, documenting and mapping their exposure to PM2.5 during their daily routine. Air monitoring was coupled with photography and videos to document what they experienced over the course of their day. The PM2.5 exposure during the day for participants averaged 10.7 µg/m3, although the range stretched from <1 to 180 µg/m3. One-third of all measurements were taken <300 m from a freeway. Overall, we demonstrate a method to increase local youth-centered understanding of personal exposures, pollution sources, and vulnerability to air quality.

The Use of the Internet of Things for Estimating Personal Pollution Exposure

This paper proposes a framework for an Air Quality Decision Support System (AQDSS), and as a proof of concept, develops an Internet of Things (IoT) application based on this framework. This application was assessed by means of a case study in the City of Madrid. We employed different sensors and combined outdoor and indoor data with spatiotemporal activity patterns to estimate the Personal Air Pollution Exposure (PAPE) of an individual. This pilot case study presents evidence that PAPE can be estimated by employing indoor air quality monitors and e-beacon technology that have not previously been used in similar studies and have the advantages of being low-cost and unobtrusive to the individual. In future work, our IoT application can be extended to include prediction models, enabling dynamic feedback about PAPE risks. Furthermore, PAPE data from this type of application could be useful for air quality policy development as well as in epidemiological studies that explore the effects of air pollution on certain diseases.

Evaluation of wearable sensors for physiologic monitoring of individually experienced temperatures in outdoor workers in southeastern U.S

Climate-related increases in global mean temperature and the intensification of heat waves present a significant threat to outdoor workers. Limited research has been completed to assess the potential differences in heat exposures that exist between individuals within similar microenvironments. Yet, there is a paucity of individual data characterizing patterns of individually experienced temperatures in workers and the associated physiologic heat strain response. The objective of this study was to apply a wearable sensor-based approach to examine the occupational, environmental, and behavioral factors that contribute to individual-level variations in heat strain in grounds maintenance workers. Outdoor workers from three diverse climatic locations in the southeastern United States - high temperature, high temperature + high humidity, and moderate temperature environments - participated in personal heat exposure monitoring during a 5-day work period in the summer. We performed Cox proportional hazards modeling to estimate associations between multiple heat strain events per worker and changes in individually experienced temperatures. Heat strain risk was higher among workers with a place to cool-off, higher education, and who worked in hotter temperatures. A mismatch was observed between workers' perceptions of heat strain and actual heat strain prevalence across exposure groups. We also used a quasi-Poisson regression with distributed lag non-linear function to estimate the non-linear and lag effects of individually experienced temperatures on risk of heat strain. The association between increasing temperature and heat strain was nonlinear and exhibited an U-shaped relationship. Heat strain was less common during issued heat warnings demonstrating behavioral adaptive actions taken by workers. This study is one of the first temperature monitoring studies to quantify the individual-level exposure-response function in this vulnerable population and highlights the elevated risk of heat strain both immediately and several days after worker exposure to high temperatures.

A Distributed Network of 100 Black Carbon Sensors for 100 Days of Air Quality Monitoring in West Oakland, California

Ambient particulate matter (PM) pollution is a major environmental health risk in urban areas. Dense networks of low-cost air quality sensors are emerging to characterize the spatially heterogeneous concentrations that are typical of urban settings, but are not adequately captured using traditional regulatory monitors at central sites. In this study, we present the 100×100 BC Network, a 100-day deployment of low-cost black carbon (BC) sensors across 100 locations in West Oakland, California. This 15 km² community is surrounded by freeways and affected by emissions associated with local port and industrial activities. We assess the reliability of the sensor hardware and data collection systems, and identify modes of failure to both quantify and qualify network performance. We illustrate how dynamic, local emission sources build upon background BC concentrations. BC concentrations varied sharply over short distances (∼100 m) and timespans (∼1 hour), depending on surrounding land use, traffic patterns, and downwind distance from pollution sources. Strong BC concentration fluctuations were periodically observed over the diurnal and weekly cycles, reflecting the impact of localized traffic emissions and industrial facilities in the neighborhood. Overall, the results demonstrate how distributed sensor networks can reveal the complex spatiotemporal dynamics of combustion-related air pollution within urban neighborhoods.

Personal assessment of the external exposome during pregnancy and childhood in Europe

The human exposome affects child development and health later in life, but its personal external levels, variability, and correlations are largely unknown. We characterized the personal external exposome of pregnant women and children in eight European cities. Panel studies included 167 pregnant women and 183 children (aged 6-11 years). A personal exposure monitoring kit composed of smartphone, accelerometer, ultraviolet (UV) dosimeter, and two air pollution monitors were used to monitor physical activity (PA), fine particulate matter (PM_2.5), black carbon, traffic-related noise, UV-B radiation, and natural outdoor environments (NOE). 77% of women performed the adult recommendation of ≥150 min/week of moderate to vigorous PA (MVPA), while only 3% of children achieved the childhood recommendation of ≥60 min/day MVPA. 11% of women and 17% of children were exposed to daily PM_2.5 levels higher than recommended (≥25μg/m³). Mean exposure to noise ranged from Lden 51.1 dB in Kaunas to Lden 65.2 dB in Barcelona. 4% of women and 23% of children exceeded the recommended maximum of 2 Standard-Erythemal-Dose of UV-B at least once a week. 33% of women and 43% of children never reached the minimum NOE contact recommendation of ≥30 min/week. The variations in air and noise pollution exposure were dominated by between-city variability, while most of the variation observed for NOE contact and PA was between-participants. The correlations between all personal exposures ranged from very low to low (Rho < 0.30). The levels of personal external exposures in both pregnant women and children are above the health recommendations, and there is little correlation between the different exposures. The assessment of the personal external exposome is feasible but sampling requires from one day to more than one year depending on exposure due to high variability between and within cities and participants.

Smartphone and a freely available application as a new tool to record locomotor activity rhythm in large mammals and humans

Daily pattern of locomotor activity (LA), one of the most studied rhythms in humans and rodents, has not been widely investigated in large mammals. This is partly due to the high cost and breakability of used automatic devices. Since last decade, smartphones are becoming ubiquitous. Meanwhile, several applications detecting activity by using internal sensors were made available. In this study, we assumed that this device could be a cheaper and easier way to measure the LA rhythm in humans and large mammals, like camel and goat. A smartphone application (Nokia Mate Health), normally used to quantify physical activities in humans, was chosen for the study. To validate the rhythm data obtained from the smartphone, LA rhythm was simultaneously recorded using an automatic device, the Actiwatch-Mini®. Results showed that the smartphone provided a clear and significant daily rhythm of LA. The visual assessment of the superimposed LA rhythm's curves in all three species showed that the smartphone application displayed similar rhythms as those recorded by the Actiwatch-Mini. Highly significant positive correlation (p≤ 0.0001) exists between the two recording rhythms. The daily periods were both the same at 24.0 h. Acrophases were also significantly similar and occurring around mid-day: 11:40 ± 0.35 h vs 11:41 ± 0.35 h for the camel, 11:25 ± 0.19 h vs 11:37 ± 0.25 h for the goat and 13:04 ± 0.11 h vs 13:51 ± 0.28 h for humans using smartphone and Actiwatch, respectively. The related mesor and amplitude were also close between the two recording devices. Results indicate clearly that using smartphones constitutes a reliable cheap tool to study LA rhythm for chronobiology studies, especially in laboratories facing lack of funding.

Facilitating Inclusion of Geocoded Pollution Data into Health Studies

Exposure to pollutants impacts health and has been associated with a range of diseases, including respiratory and heart diseases, as well as all-cause mortality. Because taking exposure measures for individual studies is costly and impractical, most rely on data from sources such as the Environmental Protection Agency (EPA), which provides a wealth of publicly available pollution measures taken at over two thousand monitoring sites across the United States. While EPA data is readily available, estimating pollution exposure at a given latitude-longitude location remains computationally intensive. We developed Pollution-Associated Risk Geospatial Analysis SITE (PARGASITE), an online web-application and R package, that can be used to estimate levels of pollutants in the U.S. for 2005 through 2017 at user-defined geographic locations and time ranges. We demonstrate how PARGASITE can facilitate the study of associations between exposures and health outcomes using as an example an analysis of asthma risk factors among adults.

ExpoApp: An integrated system to assess multiple personal environmental exposures

To assess environmental exposures at the individual level, new assessment methods and tools are required. We developed an exposure assessment system (ExpoApp) for smartphones. ExpoApp integrates: (i) geo-location and accelerometry measurements from a waist attached smartphone, (ii) data from portable monitors, (iii) geographic information systems, and (iv) individual's information. ExpoApp calculates time spent in microenvironments, physical activity level, inhalation rate, and environmental exposures and doses (e.g., green spaces, inhaled ultrafine particles- UFP). We deployed ExpoApp in a panel study of 158 adults from five cities (Amsterdam and Utrecht- the Netherlands, Basel- Switzerland, Norwich- UK, and Torino- Italy) with an UFP monitor. To evaluate ExpoApp, participants also carried a reference accelerometer (ActiGraph) and completed a travel-activity diary (TAD). System reliability and validity of measurements were evaluated by comparing the monitoring failure rate and the agreement on time spent in microenvironments and physical activity with the reference tools. There were only significant failure rate differences between ExpoApp and ActiGraph in Norwich. Agreement on time in microenvironments and physical activity level between ExpoApp and reference tools was 86.6% (86.5-86.7) and 75.7% (71.5-79.4), respectively. ExpoApp estimated that participants inhaled 16.5 × 10¹⁰ particles/day of UFP and had almost no contact with green spaces (24% of participants spent ≥30 min/day in green spaces). Participants with more contact with green spaces had higher inhaled dose of UFP, except for the Netherlands, where the relationship was the inverse. ExpoApp is a reliable system and provides accurate individual's measurements, which may help to understand the role of environmental exposures on the origin and course of diseases.

The impact of particulate matter on allergy risk among adults: integrated exposure assessment

Exposure assessment is an important part in environmental epidemiology for determining the associations of environmental factors with health effects. One of the greatest challenges for personal exposure assessment is associated with peoples' mobility during the day and spatial and temporal dynamics of air pollution. In this study, the impact of PM₁₀ (particulate matter less than 10 μm) on allergy risk among adults was assessed using objective methods of exposure assessment. The primary objective of the present study was to estimate personal exposure to PM₁₀ based on individual daily movement patterns. Significant differences between the concentration of PM₁₀ in different microenvironments (MEs) and personal exposure to PM₁₀ were determined. Home exposure accounted for the largest part of PM₁₀ exposure. Thirty-five percent of PM₁₀ exposure was received in other non-home MEs. Allergy risk increased significantly with increasing exposure to PM₁₀. Adults exposed to the highest levels of PM₁₀ exposure had a twice-higher risk of allergies than adults exposed to the lowest levels of PM₁₀ exposure. The study results have practical relevance for exposure assessment to environmental factors and its impact on health effects.