Integrating smart-phone based momentary location tracking with fixed site air quality monitoring for personal exposure assessment

Relationships of residential distance to greenhouse floriculture and organophosphate, pyrethroid, and neonicotinoid urinary metabolite concentration in Ecuadorian Adolescents

BACKGROUND

Adolescents living in agricultural areas are at higher risk of secondary pesticide exposure; however, there is limited evidence to confirm exposure by pesticide drift for greenhouse floriculture, like rose production.

METHODS

525 adolescents (12-17, 49% male) living in Pedro Moncayo, Ecuador were assessed in 2016. Urinary concentrations of creatinine and pesticide biomarkers (organophosphates, neonicotinoids, and pyrethroids) were measured using mass-spectrometry. Home distance to the nearest greenhouse and surface area of greenhouses within various buffer sizes around the home were calculated. Linear regression assessed whether home distance and surface area of greenhouses was associated with creatinine-adjusted metabolite concentration, adjusting for demographic, socioeconomic, and anthropometric variables. Geospatially weighted regression (GWR) was conducted, adjusting for similar covariates. Getis-ord Gi* identified hot and cold spots using a 1994 m distance band.

RESULTS

The associations between residential distance to greenhouses and urinary pesticide metabolites differed by metabolite type. The adjusted mean concentrations of OHIM (neonicotinoid) were greater (p-difference = 0.02) among participants living within 200 m (1.08 ug/g of creatinine) vs > 200 m (0.64 ug/g); however, the opposite was observed for 3,5,6-Trichloro-2-pyridinol (TCPy, organophosphate; 0-200 m: 3.63 ug/g vs > 200 m: 4.30 ug/g, p-diff = 0.05). In linear models, greater distances were negatively associated with para-nitrophenol (PNP, organophosphate; percent difference per 50% greater distance [95% CI]: - 2.5% [- 4.9%, - 0.1%]) and somewhat with 2-isopropyl-4-methyl-6-hydroxypyrimidine (IMPy, organophosphate; - 4.0% [- 8.3%, 0.4%]), among participants living within 200 m of greenhouses. Concurring with the adjusted means analyses, opposite (positive) associations were observed for TCPy (2.1% [95%CI 0.3%, 3.9%]). Organophosphate and pyrethroid hotspots were found in parishes with greater greenhouse density, whereas neonicotinoid hot spots were in parishes with the lowest greenhouse density.

CONCLUSION

We observed negative associations between residential distance to greenhouses with OHIM, PNP and to some extent IMPy, suggesting that imidacloprid, parathion and diazinon are drifting from floricultural greenhouses and reaching children living within 200 m. Positive TCPy associations suggest greenhouses weren't the chlorpyrifos source during this study period, which implies that non-floricultural open-air agriculture (e.g. corn, potatoes, strawberries, grains) may be a source. Further research incorporating diverse geospatial constructs of pesticide sources, pesticide use reports (if available), participant location tracking, and repeated metabolite measurements is recommended.

Piloting co-developed behaviour change interventions to reduce exposure to air pollution and improve self-reported asthma-related health

BACKGROUND

Exposure to air pollution can exacerbate asthma with immediate and long-term health consequences. Behaviour changes can reduce exposure to air pollution, yet its 'invisible' nature often leaves individuals unaware of their exposure, complicating the identification of appropriate behaviour modifications. Moreover, making health behaviour changes can be challenging, necessitating additional support from healthcare professionals.

OBJECTIVE

This pilot study used personal exposure monitoring, data feedback, and co-developed behaviour change interventions with individuals with asthma, with the goal of reducing personal exposure to PM2.5 and subsequently improving asthma-related health.

METHODS

Twenty-eight participants conducted baseline exposure monitoring for one-week, simultaneously keeping asthma symptom and medication diaries (previously published in McCarron et al., 2023). Participants were then randomised into control (n = 8) or intervention (n = 9) groups. Intervention participants received PM2.5 exposure feedback and worked with researchers to co-develop behaviour change interventions based on a health behaviour change programme which they implemented during the follow-up monitoring week. Control group participants received no feedback or intervention during the study.

RESULTS

All interventions focused on the home environment. Intervention group participants reduced their at-home exposure by an average of 5.7 µg/m³ over the monitoring week (-23.0 to +3.2 µg/m³), whereas the control group had a reduction of 4.7 µg/m³ (-15.6 to +0.4 µg/m³). Furthermore, intervention group participants experienced a 4.6% decrease in participant-hours with reported asthma symptoms, while the control group saw a 0.5% increase. Similarly, the intervention group's asthma-related quality of life improved compared to the control group.

IMPACT STATEMENT

This pilot study investigated a novel behaviour change intervention, utilising personal exposure monitoring, data feedback, and co-developed interventions guided by a health behaviour change programme. The study aimed to reduce personal exposure to fine particulate matter (PM2.5) and improve self-reported asthma-related health. Conducting a randomised controlled trial with 28 participants, co-developed intervention successfully targeted exposure peaks within participants' home microenvironments, resulting in a reduction in at-home personal exposure to PM2.5 and improving self-reported asthma-related health. The study contributes valuable insights into the environmental exposure-health relationship and highlights the potential of the intervention for individual-level decision-making to protect human health.

Development of over 30-years of high spatiotemporal resolution air pollution models and surfaces for California

California's diverse geography and meteorological conditions necessitate models capturing fine-grained patterns of air pollution distribution. This study presents the development of high-resolution (100 m) daily land use regression (LUR) models spanning 1989-2021 for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California. These machine learning LUR algorithms integrated comprehensive data sources, including traffic, land use, land cover, meteorological conditions, vegetation dynamics, and satellite data. The modeling process incorporated historical air quality observations utilizing continuous regulatory, fixed site saturation, and Google Streetcar mobile monitoring data. The model performance (adjusted R2) for NO2, PM2.5, and O3 was 84 %, 65 %, and 92 %, respectively. Over the years, NO2 concentrations showed a consistent decline, attributed to regulatory efforts and reduced human activities on weekends. Traffic density and weather conditions significantly influenced NO2 levels. PM2.5 concentrations also decreased over time, influenced by aerosol optical depth (AOD), traffic density, weather, and land use patterns, such as developed open spaces and vegetation. Industrial activities and residential areas contributed to higher PM2.5 concentrations. O3 concentrations exhibited no significant annual trend, with higher levels observed on weekends and lower levels associated with traffic density due to the scavenger effect. Weather conditions and land use, such as commercial areas and water bodies, influenced O3 concentrations. To extend the prediction of daily NO2, PM2.5, and O3 to 1989, models were developed for predictors such as daily road traffic, normalized difference vegetation index (NDVI), Ozone Monitoring Instrument (OMI)-NO2, monthly AOD, and OMI-O3. These models enabled effective estimation for any period with known daily weather conditions. Longitudinal analysis revealed a consistent NO2 decline, regulatory-driven PM2.5 decreases countered by wildfire impacts, and spatially variable O3 concentrations with no long-term trend. This study enhances understanding of air pollution trends, aiding in identifying lifetime exposure for statewide populations and supporting informed policy decisions and environmental justice advocacy.

Examining air pollution exposure dynamics in disadvantaged communities through high-resolution mapping

This study bridges gaps in air pollution research by examining exposure dynamics in disadvantaged communities. Using cutting-edge machine learning and massive data processing, we produced high-resolution (100 meters) daily air pollution maps for nitrogen dioxide (NO2), fine particulate matter (PM2.5), and ozone (O3) across California for 2012-2019. Our findings revealed opposite spatial patterns of NO2 and PM2.5 to that of O3. We also identified consistent, higher pollutant exposure for disadvantaged communities from 2012 to 2019, although the most disadvantaged communities saw the largest NO2 and PM2.5 reductions and the advantaged neighborhoods experienced greatest rising O3 concentrations. Further, day-to-day exposure variations decreased for NO2 and O3. The disparity in NO2 exposure decreased, while it persisted for O3. In addition, PM2.5 showed increased day-to-day variations across all communities due to the increase in wildfire frequency and intensity, particularly affecting advantaged suburban and rural communities.

Health effects of air pollution on respiratory symptoms: A longitudinal study using digital health sensors

Previous studies of air pollution and respiratory disease often relied on aggregated or lagged acute respiratory disease outcome measures, such as emergency department (ED) visits or hospitalizations, which may lack temporal and spatial resolution. This study investigated the association between daily air pollution exposure and respiratory symptoms among participants with asthma and chronic obstructive pulmonary disease (COPD), using a unique dataset passively collected by digital sensors monitoring inhaled medication use. The aggregated dataset comprised 456,779 short-acting beta-agonist (SABA) puffs across 3,386 people with asthma or COPD, between 2012 and 2019, across the state of California. Each rescue use was assigned space-time air pollution values of nitrogen dioxide (NO2), fine particulate matter with diameter ≤ 2.5 µm (PM2.5) and ozone (O3), derived from highly spatially resolved air pollution surfaces generated for the state of California. Statistical analyses were conducted using linear mixed models and random forest machine learning. Results indicate that daily air pollution exposure is positively associated with an increase in daily SABA use, for individual pollutants and simultaneous exposure to multiple pollutants. The advanced linear mixed model found that a 10-ppb increase in NO2, a 10 μg m-3 increase in PM2.5, and a 30-ppb increase in O3 were respectively associated with incidence rate ratios of SABA use of 1.025 (95 % CI: 1.013-1.038), 1.054 (95 % CI: 1.041-1.068), and 1.161 (95 % CI: 1.127-1.233), equivalent to a respective 2.5 %, 5.4 % and 16 % increase in SABA puffs over the mean. The random forest machine learning approach showed similar results. This study highlights the potential of digital health sensors to provide valuable insights into the daily health impacts of environmental exposures, offering a novel approach to epidemiological research that goes beyond residential address. Further investigation is warranted to explore potential causal relationships and to inform public health strategies for respiratory disease management.

Using a smartphone application to capture daily work activities: a longitudinal pilot study in a farming population

OBJECTIVES

Smartphones are increasingly used to collect real-time information on time-varying exposures. We developed and deployed an application (app) to evaluate the feasibility of using smartphones to collect real-time information on intermittent agricultural activities and to characterize agricultural task variability in a longitudinal study of farmers.

METHODS

We recruited 19 male farmers, aged 50-60 years, to report their farming activities on 24 randomly selected days over 6 months using the Life in a Day app. Eligibility criteria include personal use of an iOS or Android smartphone and >4 h of farming activities at least two days per week. We developed a study-specific database of 350 farming tasks that were provided in the app; 152 were linked to questions that were asked when the activity ended. We report eligibility, study compliance, number of activities, duration of activities by day and task, and responses to the follow-up questions.

RESULTS

Of the 143 farmers we reached out to for this study, 16 were not reached by phone or refused to answer eligibility questions, 69 were ineligible (limited smartphone use and/or farming time), 58 met study criteria, and 19 agreed to participate. Refusals were mostly related to uneasiness with the app and/or time commitment (32 of 39). Participation declined gradually over time, with 11 farmers reporting activities through the 24-week study period. We obtained data on 279 days (median 554 min/day; median 18 days per farmer) and 1,321 activities (median 61 min/activity; median 3 activities per day per farmer). The activities were predominantly related to animals (36%), transportation (12%), and equipment (10%). Planting crops and yard work had the longest median durations; short-duration tasks included fueling trucks, collecting/storing eggs, and tree work. Time period-specific variability was observed; for example, crop-related activities were reported for an average of 204 min/day during planting but only 28 min/day during pre-planting and 110 min/day during the growing period. We obtained additional information for 485 (37%) activities; the most frequently asked questions were related to "feed animals" (231 activities) and "operate fuel-powered vehicle (transportation)" (120 activities).

CONCLUSIONS

Our study demonstrated feasibility and good compliance in collecting longitudinal activity data over 6 months using smartphones in a relatively homogeneous population of farmers. We captured most of the farming day and observed substantial heterogeneity in activities, highlighting the need for individual activity data when characterizing exposure in farmers. We also identified several areas for improvement. In addition, future evaluations should include more diverse populations.

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.

Social Network Analysis of Nonprofits in Disaster Response: The Case of Twitter During the COVID-19 Pandemic in the United States

The COVID-19 pandemic has created complex problems that require organizations to collaborate within and across the sector line. Social media data can provide insights into how nonprofits interact for the pandemic response from both social network and geographical perspectives. This study innovatively investigated the connection and interaction patterns among 74 National Voluntary Organizations Active in Disaster (NVOAD) nonprofits and three government agencies based on structural analyses and content analyses of their Twitter communications during the long-term global COVID-19 pandemic. The daily tweeting quantities of all nonprofits were generally consistent with the pandemic severity in the United States before July 2020 and remained stable afterward. Nonprofits’ tweets can reflect their purposes of sharing information, building communities, and taking actions for disaster response. Government agencies played leadership roles in providing COVID-19 guidelines and information. Human services, International and Foreign Affairs, and Public and Societal Benefit nonprofits, especially American Red Cross played central roles in the nonprofit communication network. Possible explanations include the following: (1) Geographically, connections and interactions among nonprofits are more likely to happen within the same city or in neighboring states. (2) Both mission homophily and heterophily contribute to connections and interactions among nonprofits, depending on their subsectors. The findings not only help the public better understand how nonprofits are collaboratively fighting the pandemic, but also provide guidance for nonprofits to plan for better interactions and communications in future disaster response.

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.

Is Compact Urban Form Good for Air Quality? A Case Study from China Based on Hourly Smartphone Data

In previous studies, planners have debated extensively whether compact development can improve air quality in urban areas. Most of them estimated pollution exposure with stationary census data that linked exposures solely to residential locations, therefore overlooking residents’ space–time inhalation of air pollutants. In this study, we conducted an air pollution exposure assessment by scrutinizing one-hour resolution population distribution maps derived from hourly smartphone data and air pollutant concentrations derived from inverse distance weighted interpolation. We selected Wuhan as the study area and used Pearson correlation analysis to explore the effect of compactness on population-weighted concentrations. The results showed that even if a compact urban form helps to reduce pollution concentrations by decreasing vehicle traveling miles and tailpipe emissions, higher levels of building density and floor area ratios may increase population-weighted exposure. With regard to downtown areas with high population density, compact development may locate more people in areas with excessive air pollution. In all, reducing density in urban public centers and developing a polycentric urban structure may aid in the improvement of air quality in cities with compact urban forms.

Identification of superspreading environment under COVID-19 through human mobility data

COVID-19 reaffirms the vital role of superspreaders in a pandemic. We propose to broaden the research on superspreaders through integrating human mobility data and geographical factors to identify superspreading environment. Six types of popular public facilities were selected: bars, shopping centres, karaoke/cinemas, mega shopping malls, public libraries, and sports centres. A historical dataset on mobility was used to calculate the generalized activity space and space-time prism of individuals during a pre-pandemic period. Analysis of geographic interconnections of public facilities yielded locations by different classes of potential spatial risk. These risk surfaces were weighed and integrated into a "risk map of superspreading environment" (SE-risk map) at the city level. Overall, the proposed method can estimate empirical hot spots of superspreading environment with statistical accuracy. The SE-risk map of Hong Kong can pre-identify areas that overlap with the actual disease clusters of bar-related transmission. Our study presents first-of-its-kind research that combines data on facility location and human mobility to identify superspreading environment. The resultant SE-risk map steers the investigation away from pure human focus to include geographic environment, thereby enabling more differentiated non-pharmaceutical interventions and exit strategies to target some places more than others when complete city lockdown is not practicable.

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.

Predicting differential improvements in annual pollutant concentrations and exposures for regulatory policy assessment

Over the past decade, researchers and policy-makers have become increasingly interested in regulatory and policy interventions to reduce air pollution concentrations and improve human health. Studies have typically relied on relatively sparse environmental monitoring data that lack the spatial resolution to assess small-area improvements in air quality and health. Few studies have integrated multiple types of measures of an air pollutant into one single modeling framework that combines spatially- and temporally-rich monitoring data. In this paper, we investigated the differential effects of California emissions reduction plan on reducing air pollution between those living in the goods movement corridors (GMC) that are within 500 m of major highways that serve as truck routes to those farther away or adjacent to routes that prohibit trucks. A mixed effects Deletion/Substitution/Addition (D/S/A) machine learning algorithm was developed to model annual pollutant concentrations of nitrogen dioxide (NO₂) by taking repeated measures into consideration and by integrating multiple types of NO₂ measurements, including those through government regulatory and research-oriented saturation monitoring into a single modeling framework. Difference-in-difference analysis was conducted to identify whether those living in GMC demonstrated statistically larger reductions in air pollution exposure. The mixed effects D/S/A machine learning modeling result indicated that GMC had 2 ppb greater reductions in NO₂ concentrations from pre- to post-policy period than far away areas. The difference-in-difference analysis demonstrated that the subjects living in GMC experienced statistically significant greater reductions in NO₂ exposure than those living in the far away areas. This study contributes to scientific knowledge by providing empirical evidence that improvements in air quality via the emissions reductions plan policies impacted traffic-related air pollutant concentrations and associated exposures most among low-income Californians with chronic conditions living in GMC. The identified differences in pollutant reductions across different location domains may be applicable to other states or other countries if similar policies are enacted.

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.

Personal strategies to minimise effects of air pollution on respiratory health: advice for providers, patients and the public

As global awareness of air pollution rises, so does the imperative to provide evidence-based recommendations for strategies to mitigate its impact. While public policy has a central role in reducing air pollution, exposure can also be reduced by personal choices. Qualified evidence supports limiting physical exertion outdoors on high air pollution days and near air pollution sources, reducing near-roadway exposure while commuting, utilising air quality alert systems to plan activities, and wearing facemasks in prescribed circumstances. Other strategies include avoiding cooking with solid fuels, ventilating and isolating cooking areas, and using portable air cleaners fitted with high-efficiency particulate air filters. We detail recommendations to assist providers and public health officials when advising patients and the public regarding personal-level strategies to mitigate risk imposed by air pollution, while recognising that well-designed prospective studies are urgently needed to better establish and validate interventions that benefit respiratory health in this context.

On the accuracy and potential of Google Maps location history data to characterize individual mobility for air pollution health studies

Appropriately characterizing spatiotemporal individual mobility is important in many research areas, including epidemiological studies focusing on air pollution. However, in many retrospective air pollution health studies, exposure to air pollution is typically estimated at the subjects' residential addresses. Individual mobility is often neglected due to lack of data, and exposure misclassification errors are expected. In this study, we demonstrate the potential of using location history data collected from smartphones by the Google Maps application for characterizing historical individual mobility and exposure. Here, one subject carried a smartphone installed with Google Maps, and a reference GPS data logger which was configured to record location every 10 s, for a period of one week. The retrieved Google Maps Location History (GMLH) data were then compared with the GPS data to evaluate their effectiveness and accuracy of the GMLH data to capture individual mobility. We also conducted an online survey (n = 284) to assess the availability of GMLH data among smartphone users in the US. We found the GMLH data reasonably captured the spatial movement of the subject during the one-week time period at up to 200 m resolution. We were able to accurately estimate the time the subject spent in different microenvironments, as well as the time the subject spent driving during the week. The estimated time-weighted daily exposures to ambient particulate matter using GMLH and the GPS data logger were also similar (error less than 1.2%). Survey results showed that GMLH data may be available for 61% of the survey sample. Considering the popularity of smartphones and the Google Maps application, detailed historical location data are expected to be available for large portion of the population, and results from this study highlight the potential of these location history data to improve exposure estimation for retrospective epidemiological studies.

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.

Assessing exposure to outdoor air pollution for epidemiological studies: Model-based and personal sampling strategies

Epidemiologic studies have found air pollution to be causally linked to respiratory health including the exacerbation and development of childhood asthma. Accurately characterizing exposure is paramount in these studies to ensure valid estimates of health effects. Here, we provide a brief overview of the evolution of air pollution exposure assessment ranging from the use of ground-based, single-site air monitoring stations for population-level estimates to recent advances in spatiotemporal models, which use advanced machine learning algorithms and satellite-based data to accurately estimate individual-level daily exposures at high spatial resolutions. In addition, we review recent advances in sensor technology that enable the use of personal monitoring in epidemiologic studies, long-considered the "holy grail" of air pollution exposure assessment. Finally, we highlight key advantages and uses of each approach including the generalizability and public health relevance of air pollution models and the accuracy of personal monitors that are useful to guide personalized prevention strategies. Investigators and clinicians interested in the effects of air pollution on allergic disease and asthma should carefully consider the pros and cons of each approach to guide their application in research and practice.

The influence of urban planning factors on PM2.5 pollution exposure and implications: A case study in China based on remote sensing, LBS, and GIS data

In recent years, haze pollution has become a serious environmental problem affecting cities in China. Reducing PM_2.5 concentrations through urban planning is a promising method that has been a focus of recent multidisciplinary research. Most existing studies only analyze the relationship between urban planning factors and PM_2.5 concentration, and it is difficult to accurately reflect residents' actual air pollution exposure without considering their space-time behaviors. This study uses satellite remote sensing and location service data to measure PM_2.5 pollution exposure in Wuhan metropolitan area and explores the effects of urban spatial structure, land use, spatial form, transportation, and green space on pollution exposure. The results show that spatial structure, building density, road density, and green space coverage have a significant impact on PM_2.5 pollution exposure. In addition, this study proposes corresponding implications for urban planning to improve public respiratory health.

Quantifying population exposure to air pollution using individual mobility patterns inferred from mobile phone data

A critical question in environmental epidemiology is whether air pollution exposures of large populations can be refined using individual mobile-device-based mobility patterns. Cellular network data has become an essential tool for understanding the movements of human populations. As such, through inferring the daily home and work locations of 407,435 mobile phone users whose positions are determined, we assess exposure to PM_2.5. Spatiotemporal PM_2.5 concentrations are predicted using an Aerosol Optical Depth- and Land Use Regression-combined model. Air pollution exposures of subjects are assigned considering modeled PM_2.5 levels at both their home and work locations. These exposures are then compared to residence-only exposure metric, which does not consider daily mobility. In our study, we demonstrate that individual air pollution exposures can be quantified using mobile device data, for populations of unprecedented size. In examining mean annual PM_2.5 exposures determined, bias for the residence-based exposures was 0.91, relative to the exposure metric considering the work location. Thus, we find that ignoring daily mobility potentially contributes to misclassification in health effect estimates. Our framework for understanding population exposure to environmental pollution could play a key role in prospective environmental epidemiological studies.

Environmental and behavioral determinants affecting the association of airway macrophages carbon load with distance to major roads and traffic density

Biomarkers are promising indicators to evaluate human exposure to air pollutants and to predict the health outcomes. Area of Airway macrophages that is occupied by Black Carbon could be used as a biomarker of personal long term exposure to traffic related air pollution. Association of airway macrophages carbon load with weighted average distance and environmental and subject-specific behavior are considered in this study. Sputum samples were taken from 160 healthy adult women and airway macrophages carbon load (AMCL) were determined in 93 subjects, which represent a success rate of 62% in sputum induction. Nearest distance of the subjects to major roads and average weighted distance were calculated for each subject. A questionnaire was field according to general and behavioral characteristics of the participants. There was not any significant difference (P-value >0.05) between induced and non-induced subjects. Subjects with indoor kitchen without separation wall, passive smokers and those with longer presence time in high traffic streets showed higher carbon area. Weighted average distance had a better association (β = -0.186, 95%CI: -0.139, -0.230, P-value = 0.00) with AMCL than nearest distance to major roads (β = -0.155, 95%CI: -0.109, -0.201, P-value = 0.19). Association of Weighted average distance with AMCL was interrupted in subjects with a garage connected to house environment, those with IK kitchen, those with a hood above the stove and passive smokers. The findings indicated that more generation and distribution of indoor air pollutants can completely enhance the internal exposure and indoor pollution has the same importance as outdoor pollution.

Towards an Integrated Science of Movement: Converging Research on Animal Movement Ecology and Human Mobility Science

There is long-standing scientific interest in understanding purposeful movement by animals and humans. Traditionally, collecting data on individual moving entities was difficult and time-consuming, limiting scientific progress. The growth of location-aware and other geospatial technologies for capturing, managing and analyzing moving objects data are shattering these limitations, leading to revolutions in animal movement ecology and human mobility science. Despite parallel transitions towards massive individual-level data collected automatically via sensors, there is little scientific cross-fertilization across the animal and human divide. There are potential synergies from converging these separate domains towards an integrated science of movement. This paper discusses the data-driven revolutions in the animal movement ecology and human mobility science, their contrasting worldviews and, as examples of complementarity, transdisciplinary questions that span both fields. We also identify research challenges that should be met to develop an integrated science of movement trajectories.

Hourly land-use regression models based on low-cost PM monitor data

Land-use regression (LUR) models provide location and time specific estimates of exposure to air pollution and thereby improve the sensitivity of health effects models. However, they require pollutant concentrations at multiple locations along with land-use variables. Often, monitoring is performed over short durations using mobile monitoring with research-grade instruments. Low-cost PM monitors provide an alternative approach that increases the spatial and temporal resolution of the air quality data. LUR models were developed to predict hourly PM concentrations across a metropolitan area using PM concentrations measured simultaneously at multiple locations with low-cost monitors. Monitors were placed at 23 sites during the 2015/16 heating season. Monitors were externally calibrated using co-located measurements including a reference instrument (GRIMM particle spectrometer). LUR models for each hour of the day and weekdays/weekend days were developed using the deletion/substitution/addition algorithm. Coefficients of determination for hourly PM predictions ranged from 0.66 and 0.76 (average 0.7). The hourly-resolved LUR model results will be used in epidemiological studies to examine if and how quickly, increases in ambient PM concentrations trigger adverse health events by reducing the exposure misclassification that arises from using less time resolved exposure estimates.

Review of modelling air pollution from traffic at street-level - The state of the science

Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses.

Impact of transient truck and train traffic on ambient air and noise levels in underserved communities

Traffic-related air and noise pollution on or near major roadways have been examined but these pollutants have not been extensively investigated away from major roadways in residential communities, especially in the United States. To evaluate the impact of trucks and trains passing nearby on air and noise pollution in residential areas during non-rush hours, we simultaneously measured concentrations of size-resolved airborne particulate matter (PM) and sound pressure levels as A-weighted equivalent (dBA) with frequencies in three underserved communities adjacent to industrial facilities in Houston, TX. We found that median concentrations for PM1 (particle size ≤ 1 μm) and PM10 (particle size ≤ 10 μm) were highest when trucks passed by at sampling locations, followed by periods when trains passed by. PM1 and PM10 concentrations were lowest at background (defined when there was no truck or train traffic near the monitoring location). Median PM2.5 (particle size ≤ 2.5 μm) mass concentrations were 19.8 μg/m3 (trains), 16.5 μg/m3 (trucks), and 13.9 μg/m3 (background). Short-term increases in noise were attributed to trains and trucks passing nearby as well. The median noise levels were the highest when trains passed by (66.7 dBA) followed by periods when trucks were in the vicinity of the monitoring locations (62.5 dBA); background levels were 58.2 dBA. The overall Spearman correlation coefficients between air and noise pollution were between 0.09 and 0.46. Hence, we recommend that both air pollutant and noise levels be concurrently evaluated for accurate exposure assessment related to traffic sources in residential communities.

Spatial and Temporal Dynamics in Air Pollution Exposure Assessment

Analyzing individual exposure in urban areas offers several challenges where both the individual's activities and air pollution levels demonstrate a large degree of spatial and temporal dynamics. This review article discusses the concepts, key elements, current developments in assessing personal exposure to urban air pollution (seventy-two studies reviewed) and respective advantages and disadvantages. A new conceptual structure to organize personal exposure assessment methods is proposed according to two classification criteria: (i) spatial-temporal variations of individuals' activities (point-fixed or trajectory based) and (ii) characterization of air quality (variable or uniform). This review suggests that the spatial and temporal variability of urban air pollution levels in combination with indoor exposures and individual's time-activity patterns are key elements of personal exposure assessment. In the literature review, the majority of revised studies (44 studies) indicate that the trajectory based with variable air quality approach provides a promising framework for tackling the important question of inter- and intra-variability of individual exposure. However, future quantitative comparison between the different approaches should be performed, and the selection of the most appropriate approach for exposure quantification should take into account the purpose of the health study. This review provides a structured basis for the intercomparing of different methodologies and to make their advantages and limitations more transparent in addressing specific research objectives.

The Canadian Urban Environmental Health Research Consortium - a protocol for building a national environmental exposure data platform for integrated analyses of urban form and health

BACKGROUND

Multiple external environmental exposures related to residential location and urban form including, air pollutants, noise, greenness, and walkability have been linked to health impacts or benefits. The Canadian Urban Environmental Health Research Consortium (CANUE) was established to facilitate the linkage of extensive geospatial exposure data to existing Canadian cohorts and administrative health data holdings. We hypothesize that this linkage will enable investigators to test a variety of their own hypotheses related to the interdependent associations of built environment features with diverse health outcomes encompassed by the cohorts and administrative data.

METHODS

We developed a protocol for compiling measures of built environment features that quantify exposure; vary spatially on the urban and suburban scale; and can be modified through changes in policy or individual behaviour to benefit health. These measures fall into six domains: air quality, noise, greenness, weather/climate, and transportation and neighbourhood factors; and will be indexed to six-digit postal codes to facilitate merging with health databases. Initial efforts focus on existing data and include estimates of air pollutants, greenness, temperature extremes, and neighbourhood walkability and socioeconomic characteristics. Key gaps will be addressed for noise exposure, with a new national model being developed, and for transportation-related exposures, with detailed estimates of truck volumes and diesel emissions now underway in selected cities. Improvements to existing exposure estimates are planned, primarily by increasing temporal and/or spatial resolution given new satellite-based sensors and more detailed national air quality modelling. Novel metrics are also planned for walkability and food environments, green space access and function and life-long climate-related exposures based on local climate zones. Critical challenges exist, for example, the quantity and quality of input data to many of the models and metrics has changed over time, making it difficult to develop and validate historical exposures.

DISCUSSION

CANUE represents a unique effort to coordinate and leverage substantial research investments and will enable a more focused effort on filling gaps in exposure information, improving the range of exposures quantified, their precision and mechanistic relevance to health. Epidemiological studies may be better able to explore the common theme of urban form and health in an integrated manner, ultimately contributing new knowledge informing policies that enhance healthy urban living.

Multi-Contextual Segregation and Environmental Justice Research: Toward Fine-Scale Spatiotemporal Approaches

Many environmental justice studies have sought to examine the effect of residential segregation on unequal exposure to environmental factors among different social groups, but little is known about how segregation in non-residential contexts affects such disparity. Based on a review of the relevant literature, this paper discusses the limitations of traditional residence-based approaches in examining the association between socioeconomic or racial/ethnic segregation and unequal environmental exposure in environmental justice research. It emphasizes that future research needs to go beyond residential segregation by considering the full spectrum of segregation experienced by people in various geographic and temporal contexts of everyday life. Along with this comprehensive understanding of segregation, the paper also highlights the importance of assessing environmental exposure at a high spatiotemporal resolution in environmental justice research. The successful integration of a comprehensive concept of segregation, high-resolution data and fine-grained spatiotemporal approaches to assessing segregation and environmental exposure would provide more nuanced and robust findings on the associations between segregation and disparities in environmental exposure and their health impacts. Moreover, it would also contribute to significantly expanding the scope of environmental justice research.

Opportunistic Environmental Sensing with Smartphones: a Critical Review of Current Literature and Applications

PURPOSE OF REVIEW

This review sought to summarize recent literature and applications of passive, or opportunistic, mobile sensing in the fields of exposure science in built environment settings; highlight innovative opportunistic sensing systems; and analyze their functionality, significant features, and limitations.

RECENT FINDINGS

Fifty-two papers related to opportunistic environmental sensing from 2009 or later were related to this review, of which 27 were included. An array of applications have emerged in environmental monitoring, employing anywhere from one to six of the phone's on-board sensors. The viability of an application is determined by several key factors: the number and quality of sensors on-board the smartphone; power and processing demand; algorithm complexity; data security; mobile network coverage; reliance on external data sources; minimum number of users required; and degree of user burden when using the application. Some factors are universal, while others are more context-specific. Future research should assess sensing applications based on these factors.

How Sensors Might Help Define the External Exposome

The advent of the exposome concept, the advancement of mobile technology, sensors, and the “internet of things” bring exciting opportunities to exposure science. Smartphone apps, wireless devices, the downsizing of monitoring technologies, along with lower costs for such equipment makes it possible for various aspects of exposure to be measured more easily and frequently. We discuss possibilities and lay out several criteria for using smart technologies for external exposome studies. Smart technologies are evolving quickly, and while they provide great promise for advancing exposure science, many are still in developmental stages and their use in epidemiology and risk studies must be carefully considered. The most useable technologies for exposure studies at this time relate to gathering exposure-factor data, such as location and activities. Development of some environmental sensors (e.g., for some air pollutants, noise, UV) is moving towards making the use of these more reliable and accessible to research studies. The possibility of accessing such an unprecedented amount of personal data also comes with various limitations and challenges, which are discussed. The advantage of improving the collection of long term exposure factor data is that this can be combined with more “traditional” measurement data to model exposures to numerous environmental factors.

Benefits of Mobile Phone Technology for Personal Environmental Monitoring

Background

Tracking individuals in environmental epidemiological studies using novel mobile phone technologies can provide valuable information on geolocation and physical activity, which will improve our understanding of environmental exposures.

Objective

The objective of this study was to assess the performance of one of the least expensive mobile phones on the market to track people's travel-activity pattern.

Methods

Adults living and working in Barcelona (72/162 bicycle commuters) carried simultaneously a mobile phone and a Global Positioning System (GPS) tracker and filled in a travel-activity diary (TAD) for 1 week (N=162). The CalFit app for mobile phones was used to log participants’ geographical location and physical activity. The geographical location data were assigned to different microenvironments (home, work or school, in transit, others) with a newly developed spatiotemporal map-matching algorithm. The tracking performance of the mobile phones was compared with that of the GPS trackers using chi-square test and Kruskal-Wallis rank sum test. The minute agreement across all microenvironments between the TAD and the algorithm was compared using the Gwet agreement coefficient (AC1).

Results

The mobile phone acquired locations for 905 (29.2%) more trips reported in travel diaries than the GPS tracker (P<.001) and had a median accuracy of 25 m. Subjects spent on average 57.9%, 19.9%, 9.0%, and 13.2% of time at home, work, in transit, and other places, respectively, according to the TAD and 57.5%, 18.8%, 11.6%, and 12.1%, respectively, according to the map-matching algorithm. The overall minute agreement between both methods was high (AC1 .811, 95% CI .810-.812).

Conclusions

The use of mobile phones running the CalFit app provides better information on which microenvironments people spend their time in than previous approaches based only on GPS trackers. The improvements of mobile phone technology in microenvironment determination are because the mobile phones are faster at identifying first locations and capable of getting location in challenging environments thanks to the combination of assisted-GPS technology and network positioning systems. Moreover, collecting location information from mobile phones, which are already carried by individuals, allows monitoring more people with a cheaper and less burdensome method than deploying GPS trackers.

Air pollution, epigenetics, and asthma

Exposure to traffic-related air pollution (TRAP) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant as large segments of the global population resides in zones that are most impacted by TRAP and schools are often located in high TRAP exposure areas. Recent findings shed new light on the epigenetic mechanisms by which exposure to traffic pollution may contribute to the development and persistence of asthma. In order to delineate TRAP induced effects on the epigenome, utilization of newly available innovative methods to assess and quantify traffic pollution will be needed to accurately quantify exposure. This review will summarize the most recent findings in each of these areas. Although there is considerable evidence that TRAP plays a role in asthma, heterogeneity in both the definitions of TRAP exposure and asthma outcomes has led to confusion in the field. Novel information regarding molecular characterization of asthma phenotypes, TRAP exposure assessment methods, and epigenetics are revolutionizing the field. Application of these new findings will accelerate the field and the development of new strategies for interventions to combat TRAP-induced asthma.

Impact of air pollution on the burden of chronic respiratory diseases in China: time for urgent action

In China, where air pollution has become a major threat to public health, public awareness of the detrimental effects of air pollution on respiratory health is increasing-particularly in relation to haze days. Air pollutant emission levels in China remain substantially higher than are those in developed countries. Moreover, industry, traffic, and household biomass combustion have become major sources of air pollutant emissions, with substantial spatial and temporal variations. In this Review, we focus on the major constituents of air pollutants and their impacts on chronic respiratory diseases. We highlight targets for interventions and recommendations for pollution reduction through industrial upgrading, vehicle and fuel renovation, improvements in public transportation, lowering of personal exposure, mitigation of the direct effects of air pollution through healthy city development, intervention at population-based level (systematic health education, intensive and individualised intervention, pre-emptive measures, and rehabilitation), and improvement in air quality. The implementation of a national environmental protection policy has become urgent.

"Exposure Track"-The Impact of Mobile-Device-Based Mobility Patterns on Quantifying Population Exposure to Air Pollution

Air pollution is now recognized as the world's single largest environmental and human health threat. Indeed, a large number of environmental epidemiological studies have quantified the health impacts of population exposure to pollution. In previous studies, exposure estimates at the population level have not considered spatially- and temporally varying populations present in study regions. Therefore, in the first study of it is kind, we use measured population activity patterns representing several million people to evaluate population-weighted exposure to air pollution on a city-wide scale. Mobile and wireless devices yield information about where and when people are present, thus collective activity patterns were determined using counts of connections to the cellular network. Population-weighted exposure to PM2.5 in New York City (NYC), herein termed "Active Population Exposure" was evaluated using population activity patterns and spatiotemporal PM2.5 concentration levels, and compared to "Home Population Exposure", which assumed a static population distribution as per Census data. Areas of relatively higher population-weighted exposures were concentrated in different districts within NYC in both scenarios. These were more centralized for the "Active Population Exposure" scenario. Population-weighted exposure computed in each district of NYC for the "Active" scenario were found to be statistically significantly (p < 0.05) different to the "Home" scenario for most districts. In investigating the temporal variability of the "Active" population-weighted exposures determined in districts, these were found to be significantly different (p < 0.05) during the daytime and the nighttime. Evaluating population exposure to air pollution using spatiotemporal population mobility patterns warrants consideration in future environmental epidemiological studies linking air quality and human health.

Identification of Effects of Regulatory Actions on Air Quality in Goods Movement Corridors in California

Few studies have assessed the impact of regulatory actions on air quality improvement through a comprehensive monitoring effort. In this study, we designed saturation sampling of nitrogen oxides (NOX) for the counties of Los Angeles and Alameda (San Francisco Bay) before (2003-2007) and after (2008-2013) implementation of goods movement actions in California. We further separated the research regions into three location categories, including goods movement corridors (GMCs), nongoods movement corridors (NGMCs), and control areas (CTRLs). Linear mixed models were developed to identify whether reductions in NOX were greater in GMCs than in other areas, after controlling for potential confounding, including weather conditions (e.g., wind speed and temperature) and season of sampling. We also considered factors that might confound the relationship, including traffic and cargo volumes that may have changed due to economic downturn impacts. Compared to the pre-policy period, we found reductions of average pollutant concentrations for nitrogen dioxide (NO2) and NOX in GMCs of 6.4 and 21.7 ppb. The reductions were smaller in NGMCs (5.9 and 16.3 ppb, respectively) and in CTRLs (4.6 and 12.1 ppb, respectively). After controlling for potential confounding from weather conditions, season of sampling, and the economic downturn in 2008, the linear mixed models demonstrated that reductions in NO2 and NOX were significantly greater in GMCs compared to reductions observed in CTRLs; there were no statistically significant differences between NGMCs and CTRLs. These results indicate that policies regulating goods movement are achieving the desired outcome of improving air quality for the state, particularly in goods movement corridors where most disadvantaged communities live.

Dynamic assessment of exposure to air pollution using mobile phone data

Background

Exposure to air pollution can have major health impacts, such as respiratory and cardiovascular diseases. Traditionally, only the air pollution concentration at the home location is taken into account in health impact assessments and epidemiological studies. Neglecting individual travel patterns can lead to a bias in air pollution exposure assessments.

Methods

In this work, we present a novel approach to calculate the daily exposure to air pollution using mobile phone data of approximately 5 million mobile phone users living in Belgium. At present, this data is collected and stored by telecom operators mainly for management of the mobile network. Yet it represents a major source of information in the study of human mobility. We calculate the exposure to NO₂ using two approaches: assuming people stay at home the entire day (traditional static approach), and incorporating individual travel patterns using their location inferred from their use of the mobile phone network (dynamic approach).

Results

The mean exposure to NO₂ increases with 1.27 μg/m³ (4.3 %) during the week and with 0.12 μg/m³ (0.4 %) during the weekend when incorporating individual travel patterns. During the week, mostly people living in municipalities surrounding larger cities experience the highest increase in NO₂ exposure when incorporating their travel patterns, probably because most of them work in these larger cities with higher NO₂ concentrations.

Conclusions

It is relevant for health impact assessments and epidemiological studies to incorporate individual travel patterns in estimating air pollution exposure. Mobile phone data is a promising data source to determine individual travel patterns, because of the advantages (e.g. low costs, large sample size, passive data collection) compared to travel surveys, GPS, and smartphone data (i.e. data captured by applications on smartphones).

Air pollution and allergic diseases

PURPOSE OF REVIEW

Exposure to traffic-related air pollutants (TRAPs) has been implicated in asthma development, persistence, and exacerbation. This exposure is highly significant because increasingly large segments of the population worldwide reside in zones that have high levels of TRAP, including children, as schools are often located in high traffic pollution exposure areas.

RECENT FINDINGS

Recent findings include epidemiologic and mechanistic studies that shed new light on the impact of traffic pollution on allergic diseases and the biology underlying this impact. In addition, new innovative methods to assess and quantify traffic pollution have been developed to assess exposure and identify vulnerable populations and individuals.

SUMMARY

This review will summarize the most recent findings in each of these areas. These findings will have a substantial impact on clinical practice and research by the development of novel methods to quantify exposure and identify at-risk individuals, as well as mechanistic studies that identify new targets for intervention for individuals most adversely affected by TRAP exposure.