When, Where, and What? Characterizing Personal PM2.5 Exposure in Periurban India by Integrating GPS, Wearable Camera, and Ambient and Personal Monitoring Data

Household concentrations and female and child exposures to air pollution in peri-urban sub-Saharan Africa: measurements from the CLEAN-Air(Africa) study

BACKGROUND

Relatively clean cooking fuels such as liquefied petroleum gas (LPG) emit less fine particulate matter (PM2·5) and carbon monoxide (CO) than polluting fuels (eg, wood, charcoal). Yet, some clean cooking interventions have not achieved substantial exposure reductions. This study evaluates determinants of between-community variability in exposures to household air pollution (HAP) across sub-Saharan Africa.

METHODS

In this measurement study, we recruited households cooking primarily with LPG or exclusively with wood or charcoal in peri-urban Cameroon, Ghana, and Kenya from previously surveyed households. In 2019-20, we conducted monitoring of 24 h PM2·5 and CO kitchen concentrations (n=256) and female cook (n=248) and child (n=124) exposures. PM2·5 measurements used gravimetric and light scattering methods. Stove use monitoring and surveys on cooking characteristics and ambient air pollution exposure (eg, walking time to main road) were also administered.

FINDINGS

The mean PM2·5 kitchen concentration was five times higher among households cooking with charcoal than those using LPG in the Kenyan community (297 μg/m3, 95% CI 216-406, vs 61 μg/m3, 49-76), but only 4 μg/m3 higher in the Ghanaian community (56 μg/m3, 45-70, vs 52 μg/m3, 40-68). The mean CO kitchen concentration in charcoal-using households was double the WHO guideline (6·11 parts per million [ppm]) in the Kenyan community (15·81 ppm, 95% CI 8·71-28·72), but below the guideline in the Ghanaian setting (1·77 ppm, 1·04-2·99). In all communities, mean PM2·5 cook exposures only met the WHO interim-1 target (35 μg/m3) among LPG users staying indoors and living more than 10 min walk from a road.

INTERPRETATION

Community-level variation in the relative difference in HAP exposures between LPG and polluting cooking fuel users in peri-urban sub-Saharan Africa might be attributed to differences in ambient air pollution levels. Thus, mitigation of indoor and outdoor PM2·5 sources will probably be critical for obtaining significant exposure reductions in rapidly urbanising settings of sub-Saharan Africa.

FUNDING

UK National Institute for Health and Care Research.

Systematic review of best practices for GPS data usage, processing, and linkage in health, exposure science and environmental context research

Global Positioning System (GPS) technology is increasingly used in health research to capture individual mobility and contextual and environmental exposures. However, the tools, techniques and decisions for using GPS data vary from study to study, making comparisons and reproducibility challenging.

OBJECTIVES

The objectives of this systematic review were to (1) identify best practices for GPS data collection and processing; (2) quantify reporting of best practices in published studies; and (3) discuss examples found in reviewed manuscripts that future researchers may employ for reporting GPS data usage, processing and linkage of GPS data in health studies.

DESIGN

A systematic review.

DATA SOURCES

Electronic databases searched (24 October 2023) were PubMed, Scopus and Web of Science (PROSPERO ID: CRD42022322166).

ELIGIBILITY CRITERIA

Included peer-reviewed studies published in English met at least one of the criteria: (1) protocols involving GPS for exposure/context and human health research purposes and containing empirical data; (2) linkage of GPS data to other data intended for research on contextual influences on health; (3) associations between GPS-measured mobility or exposures and health; (4) derived variable methods using GPS data in health research; or (5) comparison of GPS tracking with other methods (eg, travel diary).

DATA EXTRACTION AND SYNTHESIS

We examined 157 manuscripts for reporting of best practices including wear time, sampling frequency, data validity, noise/signal loss and data linkage to assess risk of bias.

RESULTS

We found that 6% of the studies did not disclose the GPS device model used, only 12.1% reported the per cent of GPS data lost by signal loss, only 15.7% reported the per cent of GPS data considered to be noise and only 68.2% reported the inclusion criteria for their data.

CONCLUSIONS

Our recommendations for reporting on GPS usage, processing and linkage may be transferrable to other geospatial devices, with the hope of promoting transparency and reproducibility in this research.

PROSPERO REGISTRATION NUMBER

CRD42022322166.

The contribution of the exposome to the burden of cardiovascular disease

Large epidemiological and health impact assessment studies at the global scale, such as the Global Burden of Disease project, indicate that chronic non-communicable diseases, such as atherosclerosis and diabetes mellitus, caused almost two-thirds of the annual global deaths in 2020. By 2030, 77% of all deaths are expected to be caused by non-communicable diseases. Although this increase is mainly due to the ageing of the general population in Western societies, other reasons include the increasing effects of soil, water, air and noise pollution on health, together with the effects of other environmental risk factors such as climate change, unhealthy city designs (including lack of green spaces), unhealthy lifestyle habits and psychosocial stress. The exposome concept was established in 2005 as a new strategy to study the effect of the environment on health. The exposome describes the harmful biochemical and metabolic changes that occur in our body owing to the totality of different environmental exposures throughout the life course, which ultimately lead to adverse health effects and premature deaths. In this Review, we describe the exposome concept with a focus on environmental physical and chemical exposures and their effects on the burden of cardiovascular disease. We discuss selected exposome studies and highlight the relevance of the exposome concept for future health research as well as preventive medicine. We also discuss the challenges and limitations of exposome studies.

Using time-resolved monitor wearing data to study the effect of clean cooking interventions on personal air pollution exposures

BACKGROUND

Personal monitoring can estimate individuals' exposures to environmental pollutants; however, accuracy depends on consistent monitor wearing, which is under evaluated.

OBJECTIVE

To study the association between device wearing and personal air pollution exposure.

METHODS

Using personal device accelerometry data collected in the context of a randomized cooking intervention in Ghana with three study arms (control, improved biomass, and liquified petroleum gas (LPG) arms; N = 1414), we account for device wearing to infer parameters of PM2.5 and CO exposure.

RESULTS

Device wearing was positively associated with exposure in the control and improved biomass arms, but weakly in the LPG arm. Inferred community-level air pollution was similar across study arms (~45 μg/m3). The estimated direct contribution of individuals' cooking to PM2.5 exposure was 64 μg/m3 for the control arm, 74 μg/m3 for improved biomass, and 6 μg/m3 for LPG. Arm-specific average PM2.5 exposure at near-maximum wearing was significantly lower in the LPG arm as compared to the improved biomass and control arms. Analysis of personal CO exposure mirrored PM2.5 results.

CONCLUSIONS

Personal monitor wearing was positively associated with average air pollution exposure, emphasizing the importance of high device wearing during monitoring periods and directly assessing device wearing for each deployment.

SIGNIFICANCE

We demonstrate that personal monitor wearing data can be used to refine exposure estimates and infer unobserved parameters related to the timing and source of environmental exposures.

IMPACT STATEMENTS

In a cookstove trial among pregnant women, time-resolved personal air pollution device wearing data were used to refine exposure estimates and infer unobserved exposure parameters, including community-level air pollution, the direct contribution of cooking to personal exposure, and the effect of clean cooking interventions on personal exposure. For example, in the control arm, while average 48 h personal PM2.5 exposure was 77 μg/m3, average predicted exposure at near-maximum daytime device wearing was 108 μg/m3 and 48 μg/m3 at zero daytime device wearing. Wearing-corrected average 48 h personal PM2.5 exposures were 50% lower in the LPG arm than the control and improved biomass and inferred direct cooking contributions to personal PM2.5 from LPG were 90% lower than the other arms. Our recommendation is that studies assessing personal exposures should examine the direct association between device wearing and estimated mean personal exposure.

NO2, BC and PM Exposure of Participants in the Polluscope Autumn 2019 Campaign in the Paris Region

The Polluscope project aims to better understand the personal exposure to air pollutants in the Paris region. This article is based on one campaign from the project, which was conducted in the autumn of 2019 and involved 63 participants equipped with portable sensors (i.e., NO₂, BC and PM) for one week. After a phase of data curation, analyses were performed on the results from all participants, as well as on individual participants' data for case studies. A machine learning algorithm was used to allocate the data to different environments (e.g., transportation, indoor, home, office, and outdoor). The results of the campaign showed that the participants' exposure to air pollutants depended very much on their lifestyle and the sources of pollution that may be present in the vicinity. Individuals' use of transportation was found to be associated with higher levels of pollutants, even when the time spent on transport was relatively short. In contrast, homes and offices were environments with the lowest concentrations of pollutants. However, some activities performed in indoor air (e.g., cooking) also showed a high levels of pollution over a relatively short period.

Size fraction of hazardous particulate matter governing the respiratory deposition and inhalation risk in the highly polluted city Delhi

Delhi has been identified as one of the highly polluted cities in the world and recently associated with the highest population weighted PM_2.5 concentration. However, the unavailability of the health risk estimations using long-term data for Indian cities has been pointed out as a hurdle in performing the correct assessment. The present work estimated deposition of particles in different regions of respiratory systems (head airway = 67% deposition for 2.5 µm particles; tracheo-bronchiolar (TB) = 73% deposition for 1.0 µm particles; alveolar (AL) = 17% deposition for 0.5 µm, 0.25 µm, and < 0.25 µm particles) using PM samples collected at a breathing height of 1.5 m near the major ring road in New Delhi (India). The calculated risk index (RI) varied considerably between winter (1.21 ± 0.26 to 1.33 ± 0.50) and pre-monsoon-southwest monsoon months (0.34 ± 0.08 to 0.96 ± 0.27). Respiratory deposition dose of nanosized particles (≤ 500 nm) in the alveoli region of the lung was found to be considerable (35%) indicating the need for understanding the role of these particles in posing health risk. Although the calculated values of risk metric for exposures of PM-associated metals indicated no risk to IIT Delhi population (hazard quotient < 1 and excess risk of getting cancer < 10^-6-10^-9), continuous monitoring for particles of different sizes at inhalation height are required for protecting human health.

Agent-based modeling of urban exposome interventions: prospects, model architectures, and methodological challenges

With ever more people living in cities worldwide, it becomes increasingly important to understand and improve the impact of the urban habitat on livability, health behaviors, and health outcomes. However, implementing interventions that tackle the exposome in complex urban systems can be costly and have long-term, sometimes unforeseen, impacts. Hence, it is crucial to assess the health impact, cost-effectiveness, and social distributional impacts of possible urban exposome interventions (UEIs) before implementing them. Spatial agent-based modeling (ABM) can capture complex behavior-environment interactions, exposure dynamics, and social outcomes in a spatial context. This article discusses model architectures and methodological challenges for successfully modeling UEIs using spatial ABM. We review the potential and limitations of the method; model components required to capture active and passive exposure and intervention effects; human-environment interactions and their integration into the macro-level health impact assessment and social costs benefit analysis; and strategies for model calibration. Major challenges for a successful application of ABM to UEI assessment are (1) the design of realistic behavioral models that can capture different types of exposure and that respond to urban interventions, (2) the mismatch between the possible granularity of exposure estimates and the evidence for corresponding exposure-response functions, (3) the scalability issues that emerge when aiming to estimate long-term effects such as health and social impacts based on high-resolution models of human-environment interactions, (4) as well as the data- and computational complexity of calibrating the resulting agent-based model. Although challenges exist, strategies are proposed to improve the implementation of ABM in exposome research.

The relationship between greenspace and personal exposure to PM2.5 during walking trips in Delhi, India

The presence of urban greenspace may lead to reduced personal exposure to air pollution via several mechanisms, for example, increased dispersion of airborne particulates; however, there is a lack of real-time evidence across different urban contexts. Study participants were 79 adolescents with asthma who lived in Delhi, India and were recruited to the Delhi Air Pollution and Health Effects (DAPHNE) study. Participants were monitored continuously for exposure to PM_2.5 (particulate matter with an aerodynamic diameter of less than 2.5 μm) for 48 h. We isolated normal day-to-day walking journeys (n = 199) from the personal monitoring dataset and assessed the relationship between greenspace and personal PM_2.5 using different spatial scales of the mean Normalised Difference Vegetation Index (NDVI), mean tree cover (TC), and proportion of surrounding green land use (GLU) and parks or forests (PF). The journeys had a mean duration of 12.7 (range 5, 53) min and mean PM_2.5 personal exposure of 133.9 (standard deviation = 114.8) μg/m³. The within-trip analysis showed weak inverse associations between greenspace markers and PM_2.5 concentrations only in the spring/summer/monsoon season, with statistically significant associations for TC at the 25 and 50 m buffers in adjusted models. Between-trip analysis also indicated inverse associations for NDVI and TC, but suggested positive associations for GLU and PF in the spring/summer/monsoon season; no overall patterns of association were evident in the autumn/winter season. Associations between greenspace and personal PM_2.5 during walking trips in Delhi varied across metrics, spatial scales, and season, but were most consistent for TC. These mixed findings may partly relate to journeys being dominated by walking along roads and small effects on PM_2.5 of small pockets of greenspace. Larger areas of greenspace may, however, give rise to observable spatial effects on PM_2.5, which vary by season.

Association of ambient and household air pollution with lung function in young adults in an peri-urban area of South-India: A cross-sectional study

OBJECTIVE

Although there is evidence for the association between air pollution and decreased lung function in children, evidence for adolescents and young adults is scarce. For a peri-urban area in India, we evaluated the association of ambient PM_2.5 and household air pollution with lung function for young adults who had recently attained their expected maximum lung function.

METHODS

We measured, using a standardized protocol, forced expiratory volume in the first second (FEV₁) and forced vital capacity (FVC) in participants aged 20-26 years from the third follow-up of the population-based APCAPCS cohort (2010-2012) in 28 Indian villages. We estimated annual average PM_2.5outdoors at residence using land-use regression. Biomass cooking fuel (a proxy for levels of household air pollution) was self-reported. We fitted a within-between linear-mixed model with random intercepts by village, adjusting for potential confounders.

RESULTS

We evaluated 1,044 participants with mean age of 22.8 (SD = 1) years (range 20-26 years); 327 participants (31%) were female. Only males reported use of tobacco smoking (9% of all participants, 13% of males). The mean ambient PM_2.5 exposure was 32.9 (SD = 2.8) µg/m³; 76% reported use of biomass as cooking fuel. The adjusted association between 1 µg/m³ increase in PM_2.5 was -27 ml (95% CI, -89 to 34) for FEV₁ and -5 ml (95% CI, -93 to 76) for FVC. The adjusted association between use of biomass was -112 ml (95% CI, -211 to -13) for FEV₁ and -142 ml (95% CI, -285 to 0) for FVC. The adjusted association was of greater magnitude for those with unvented stove (-158 ml, 95% CI, -279 to -36 for FEV₁ and -211 ml, 95% CI, -386 to -36 for FVC).

CONCLUSIONS

We observed negative associations between ambient PM_2.5 and household air pollution and lung function in young adults who had recently attained their maximum lung function.

Prediction of personal exposure to PM2.5 in mother-child pairs in rural Ghana

BACKGROUND

Air pollution epidemiological studies usually rely on estimates of long-term exposure to air pollutants, which are difficult to ascertain. This problem is accentuated in settings where sources of personal exposure differ from those of ambient concentrations, including household air pollution environments where cooking is an important source.

OBJECTIVE

The objective of this study was to assess the feasibility of estimating usual exposure to PM_2.5 based on short-term measurements.

METHODS

We leveraged three types of short-term measurements from a cohort of mother-child pairs in 26 communities in rural Ghana: (A) personal exposure to PM_2.5 in mothers and age four children, ambient PM_2.5 concentrations (B) at the community level, and (C) at a central site. Baseline models were linear mixed models with a random intercept for community or for participant. Lowest root-mean-square-error (RMSE) was used to select the best-performing model.

RESULTS

We analyzed 240 community-days and 251 participant-days of PM_2.5. Medians (IQR) of PM_2.5 were 19.5 (36.5) μg/m³ for the central site, 28.7 (41.5) μg/m³ for the communities, 70.6 (56.9) μg/m³ for mothers, and 80.9 (74.1) μg/m³ for children. The ICCs (95% CI) for community ambient and personal exposure were 0.30 (0.17, 0.47) and 0.74 (0.65, 0.81) respectively. The sources of variability differed during the Harmattan season. Children's daily exposure was best predicted by models that used community ambient compared to mother's exposure as a predictor (log-scale RMSE: 0.165 vs 0.325).

CONCLUSION

Our results support the feasibility of predicting usual personal exposure to PM_2.5 using short-term measurements in settings where household air pollution is an important source of exposure. Our results also suggest that mother's exposure may not be the best proxy for child's exposure at age four.

A stochastic exposure model integrating random forest and agent-based approaches: Evaluation for PM2.5 in Jiangsu, China

This research proposes an Activity Pattern embedded Air Pollution Exposure Model (AP2EM), based on survey data of when, where, and how people spend their time and indoor/outdoor ratios for microenvironments. AP2EM integrates random forest and agent-based approaches to simulate the stochastic exposure to outdoor fine particulate matter (PM_2.5) along with indoor and in-vehicle PM_2.5 of outdoor origin. The R² of the linear regression between the model's calculations and personal measurement was 0.65, which was more accurate than the commonly-used aggregated exposure (AE) model and the outdoor exposure (OE) model. The population-weighted PM_2.5 exposure estimated by the AP2EM was 36.7 μg/m³ in Jiangsu, China, during 2014-2017. The OE model overestimated exposure by 54.0%, and the AE model underestimated exposure by 6.5%. These misestimate reflect ignorance of traditional studies on effects posed from time spent indoors (~85%) and doing low respiratory rate activities (~93%), problems of biased sampling, and neglecting low probability events. The proposed AP2EM treats activity patterns of individuals as chains and uses stochastic estimates to model activity choices, providing a more comprehensive understanding of human activity and exposure characteristics. Overall, the AP2EM is applicable for other air pollutants in different regions and benefits China's air pollution control policy designs.

Ambient Air Pollution and Socioeconomic Status in China

BACKGROUND

Air pollution disparities by socioeconomic status (SES) are well documented for the United States, with most literature indicating an inverse relationship (i.e., higher concentrations for lower-SES populations). Few studies exist for China, a country accounting for 26% of global premature deaths from ambient air pollution.

OBJECTIVE

Our objective was to test the relationship between ambient air pollution exposures and SES in China.

METHODS

We combined estimated year 2015 annual-average ambient levels of nitrogen dioxide (NO 2 ) and fine particulate matter [PM ≤ 2.5 μ m in aerodynamic diameter (PM 2.5 )] with national demographic information. Pollution estimates were derived from a national empirical model for China at 1 -km spatial resolution; demographic estimates were derived from national gridded gross national product (GDP) per capita at 1 -km resolution, and (separately) a national representative sample of 21,095 individuals from the China Health and Retirement Longitudinal Study (CHARLS) 2015 cohort. Our use of global data on population density and cohort data on where people live helped avoid the spatial imprecision found in publicly available census data for China. We quantified air pollution disparities among individual's rural-to-urban migration status; SES factors (education, occupation, and income); and minority status. We compared results using three approaches to SES measurement: individual SES score, community-averaged SES score, and gridded GDP per capita.

RESULTS

Ambient NO 2 and PM 2.5 levels were higher for higher-SES populations than for lower-SES population, higher for long-standing urban residents than for rural-to-urban migrant populations, and higher for the majority ethnic group (Han) than for the average across nine minority groups. For the three SES measurements (individual SES score, community-averaged SES score, gridded GDP per capita), a 1-interquartile range higher SES corresponded to higher concentrations of 6 - 9   μ g / m 3 NO 2 and 3 - 6   μ g / m 3 PM 2.5 ; average concentrations for the highest and lowest 20th percentile of SES differed by 41-89% for NO 2 and 12-25% for PM 2.5 . This pattern held in rural and urban locations, across geographic regions, across a wide range of spatial resolution, and for modeled vs. measured pollution concentrations.

CONCLUSIONS

Multiple analyses here reveal that in China, ambient NO 2 and PM 2.5 concentrations are higher for high-SES than for low-SES individuals; these results are robust to multiple sensitivity analyses. Our findings are consistent with the idea that in China's current industrialization and urbanization stage, economic development is correlated with both SES and air pollution. To our knowledge, our study provides the most comprehensive picture to date of ambient air pollution disparities in China; the results differ dramatically from results and from theories to explain conditions in the United States. https://doi.org/10.1289/EHP9872.

Measuring PM2.5 Concentrations from a Single Smartphone Photograph

PM2.5 participates in light scattering, leading to degraded outdoor views, which forms the basis for estimating PM2.5 from photographs. This paper devises an algorithm to estimate PM2.5 concentrations by extracting visual cues and atmospheric indices from a single photograph. While air quality measurements in the context of complex urban scenes are particularly challenging, when only a single atmospheric index or cue is given, each one can reinforce others to yield a more robust estimator. Therefore, we selected an appropriate atmospheric index in various outdoor scenes to identify reasonable cue combinations for measuring PM2.5. A PM2.5 dataset (PhotoPM-daytime) was built and used to evaluate performance and validate efficacy of cue combinations. Furthermore, a city-wide experiment was conducted using photographs crawled from the Internet to demonstrate the applicability of the algorithm in large-area PM2.5 monitoring. Results show that smartphones equipped with the developed method could potentially be used as PM2.5 sensors.

Determinants of carbon load in airway macrophages in pregnant women

The airway macrophages carbon loading (AMCL) has been suggested to be a biomarker of the long-term exposure to air pollution; however, to date no study has characterized AMCL for the pregnancy period. Therefore, this study aimed to assess the determinants of AMCL during pregnancy in Iran, a middle-income country. This study was based on a sample of 234 pregnant women with term and normal vaginal delivery who were residing in Sabzevar, Iran (2019). We characterized 35 potential determinants of personal exposure to air pollution for each participant, including six personal, nine indoor, and 20 home-outdoor factors. We applied Deletion/Substitution/Addition algorithm to identify the most relevant determinants that could predict AMCL levels. The median (IQR) of AMCL level was 0.12 (0.30) μm² with a successful sputum induction in 82.9% (194) of participants. Ambient residential PM_2.5 levels were positively associated with higher AMCL levels. On the other hand, increased residential distance to the traffic lights, squares and ring-roads, the duration of opening window per day, and opening window during cooking were inversely associated with AMCL levels. Our findings provide novel insights on the different personal, indoor, and outdoor determinants of personal exposure to air pollution during pregnancy in a middle-income country.

A review and analysis of personal and ambient PM2.5 measurements: Implications for epidemiology studies

BACKGROUND

In epidemiology studies, ambient measurements of PM_2.5 are often used as surrogates for personal exposures. However, it is unclear the degree to which ambient PM_2.5 reflects personal exposures.

OBJECTIVE

In order to examine potential sources of bias in epidemiology studies, we conducted a review and meta-analysis of studies to determine the extent to which short-term measurements of ambient PM_2.5 levels are related to short-term measurements of personal PM_2.5 levels.

METHODS

We conducted a literature search of studies reporting both personal and ambient measurements of PM_2.5 published in the last 10 years (2009-2019) and incorporated studies published prior to 2009 from reviews.

RESULTS

Seventy-one studies were identified. Based on 17 studies reporting slopes, a meta-analysis revealed an overall slope of 0.56 μg/m³ (95% CI: [0.39, 0.73]) personal PM_2.5 per μg/m³ increase in ambient PM_2.5. Slopes for summer months were higher (slope = 0.73, 95% CI: [0.64, 0.81]) than for winter (slope = 0.46, 95% CI: [0.36, 0.57]). Based on 44 studies reporting correlations, we calculated an overall personal-ambient PM_2.5 correlation of 0.63 (95% CI: [0.55, 0.71]). Correlations were stronger in studies conducted in Canada (r = 0.86, 95% CI: [0.67, 0.94]) compared to the USA (r = 0.60, 95% CI: [0.49, 0.70]) and China (r = 0.60, 95% CI: [0.46, 0.71]). Correlations also were stronger in urban areas (r = 0.53, 95% CI: [0.43, 0.62]) vs. suburban areas (r = 0.36, 95% CI: [0.21, 0.49]).

SIGNIFICANCE

Our results suggest a large degree of variability in the personal-ambient PM_2.5 association and the potential for exposure misclassification and measurement error in PM_2.5 epidemiology studies.

Personal Exposure to Black Carbon, Particulate Matter and Nitrogen Dioxide in the Paris Region Measured by Portable Sensors Worn by Volunteers

Portable sensors have emerged as a promising solution for personal exposure (PE) measurement. For the first time in Île-de-France, PE to black carbon (BC), particulate matter (PM), and nitrogen dioxide (NO₂) was quantified based on three field campaigns involving 37 volunteers from the general public wearing the sensors all day long for a week. This successful deployment demonstrated its ability to quantify PE on a large scale, in various environments (from dense urban to suburban, indoor and outdoor) and in all seasons. The impact of the visited environments was investigated. The proximity to road traffic (for BC and NO₂), as well as cooking activities and tobacco smoke (for PM), made significant contributions to total exposure (up to 34%, 26%, and 44%, respectively), even though the time spent in these environments was short. Finally, even if ambient outdoor levels played a role in PE, the prominent impact of the different environments suggests that traditional ambient monitoring stations is not a proper surrogate for PE quantification.

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mobile health applications, and wearable electronic devices. Such advancements have enabled accurate and updated tracking and modeling of health conditions. For instance, digital health technologies are capable of measuring environmental pollution and predicting its adverse health effects. Several health conditions, including chronic airway diseases such as asthma and chronic obstructive pulmonary disease, can be exacerbated by pollution. These diseases impose substantial health burdens with high morbidity and mortality. Recently, efforts have been made to develop digital technologies to alleviate such conditions. Moreover, the COVID-19 pandemic has facilitated the application of telemedicine and telemonitoring for patients with chronic airway diseases. This article reviews current trends and studies in digital technology utilization for investigating and managing environmental exposure and chronic airway diseases. First, we discussed the recent progression of digital technologies in general environmental healthcare. Then, we summarized the capacity of digital technologies in predicting exacerbation and self-management of airway diseases. Concluding these reviews, we provided suggestions to improve digital health technologies' abilities to reduce the adverse effects of environmental exposure in chronic airway diseases, based on personal exposure-response modeling.

Chemical Investigation of Household Solid Fuel Use and Outdoor Air Pollution Contributions to Personal PM2.5 Exposures

In communities with household solid fuel use, transitioning to clean stoves/fuels often results in only moderate reductions in fine particulate matter (PM2.5) exposures; the chemical composition of those exposures may help explain why. We collected personal exposure (men and women) and outdoor PM2.5 samples in villages in three Chinese provinces (Shanxi, Beijing, and Guangxi) and measured chemical components, including water-soluble organic carbon (WSOC), ions, elements, and organic tracers. Source contributions from chemical mass balance modeling (biomass burning, coal combustion, vehicles, dust, and secondary inorganic aerosol) were similar between outdoor and personal PM2.5 samples. Principal component analysis of organic and inorganic components identified analogous sources, including a regional ambient source. Chemical components of PM2.5 exposures did not differ significantly by gender. Participants using coal had higher personal/outdoor (P/O) ratios of coal combustion tracers (picene, sulfate, As, and Pb) than those not using coal, but no such trend was observed for biomass burning tracers (levoglucosan, K+, WSOC). Picene and most levoglucosan P/O ratios exceeded 1 even among participants not using coal and biomass, respectively, indicating substantial indirect exposure to solid fuel emissions from other homes. Contributions of community-level emissions to exposures suggest that meaningful exposure reductions will likely require extensive fuel use changes within communities.

Wearable Devices for Environmental Monitoring in the Built Environment: A Systematic Review

The so-called Internet of Things (IoT), which is rapidly increasing the number of network-connected and interconnected objects, could have a far-reaching impact in identifying the link between human health, well-being, and environmental concerns. In line with the IoT concept, many commercial wearables have been introduced in recent years, which differ from the usual devices in that they use the term "smart" alongside the terms "watches", "glasses", and "jewellery". Commercially available wearables aim to enhance smartphone functionality by enabling payment for commercial items or monitoring physical activity. However, what is the trend of scientific production about the concept of wearables regarding environmental monitoring issues? What are the main areas of interest covered by scientific production? What are the main findings and limitations of the developed solution in this field? The methodology used to answer the above questions is based on a systematic review. The data were acquired following a reproducible methodology. The main result is that, among the thermal, visual, acoustic, and air quality environmental factors, the last one is the most considered when using wearables even though in combination with some others. Another relevant finding is that of the acquired studies; in only one, the authors shared their wearables as an open-source device, and it will probably be necessary to encourage researchers to consider open-source as a means to promote scalability and proliferation of new wearables customized to cover different domains.

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

Measurement of ambient fine particulate matter (PM_2.5) is often used as a proxy of personal exposure in epidemiological studies. However, the difference between personal and ambient exposure, and whether it biases the estimates of health effects remain unknown. Based on an epidemiological study (AIRLESS) and simultaneously launched intensive monitoring campaigns (APHH), we quantified and compared the personal and ambient exposure to PM_2.5 and the related health impact among residents in Beijing, China. In total, 123 urban and 128 peri-urban non-smoking participants were recruited from two well-established cohorts in Beijing. During winter 2016 and summer 2017, each participant was instructed to carry a validated personal air monitor (PAM) to measure PM_2.5 concentration at high spatiotemporal resolution for seven consecutive days in each season. Multiple inflammatory biomarkers were measured, including exhaled NO, blood monocytes counts and C-reactive protein. Linear mixed-effect models were used for the associations between exposure and health outcomes with adjustment for confounders. The average level of daily personal exposure to PM_2.5 was consistently lower than using corresponding ambient concentration, and the difference is greater during the winter. The personal to ambient (P/A) ratio of exposure to PM_2.5 exhibited an exponentially declining trend, and showed larger variations when ambient PM_2.5 levels < 25 μg m^-3. Personal exposure to PM_2.5 was significantly associated with the increase in respiratory and systemic inflammatory biomarkers; however, the associations were weaker or became insignificant when ambient concentrations were used. Exposure to ambient PM_2.5 might not be a good proxy to estimate the health effect of exposure to personal PM_2.5.

Peak Inhalation Exposure Metrics Used in Occupational Epidemiologic and Exposure Studies

Peak exposures are of concern because they can potentially overwhelm normal defense mechanisms and induce adverse health effects. Metrics of peak exposure have been used in epidemiologic and exposure studies, but consensus is lacking on its definition. The relevant characteristics of peak exposure are dependent upon exposure patterns, biokinetics of exposure, and disease mechanisms. The objective of this review was to summarize the use of peak metrics in epidemiologic and exposure studies. A comprehensive search of Medline, Embase, Web of Science, and NIOSHTIC-2 databases was conducted using keywords related to peak exposures. The retrieved references were reviewed and selected for indexing if they included a peak metric and met additional criteria. Information on health outcomes and peak exposure metrics was extracted from each reference. A total of 1,215 epidemiologic or exposure references were identified, of which 182 were indexed and summarized. For the 72 epidemiologic studies, the health outcomes most frequently evaluated were: chronic respiratory effects, cancer and acute respiratory symptoms. Exposures were frequently assessed using task-based and full-shift time-integrated methods, qualitative methods, and real-time instruments. Peak exposure summary metrics included the presence or absence of a peak event, highest exposure intensity and frequency greater than a target. Peak metrics in the 110 exposure studies most frequently included highest exposure intensity, average short-duration intensity, and graphical presentation of the real-time data (plots). This review provides a framework for considering biologically relevant peak exposure metrics for epidemiologic and exposure studies to help inform risk assessment and exposure mitigation.

Determinants of personal exposure to PM2.5 and black carbon in Chinese adults: A repeated-measures study in villages using solid fuel energy

Exposure to air pollution is a leading health risk factor. The variance components and contributions of indoor versus outdoor source determinants of personal exposure to air pollution are poorly understood, especially in settings of household solid fuel use. We conducted a panel study with up to 4 days of repeated measures of integrated gravimetric personal exposure to PM2.5 and black carbon in 787 men and women (ages 40-79) living in peri-urban villages in northern (Beijing and Shanxi) and southern (Guangxi) China. We simultaneously measured outdoor PM2.5 and collected questionnaire data on sociodemographic characteristics and indoor pollution sources including tobacco smoking and solid fuel stove use. We obtained over 2000 days of personal exposure monitoring which showed higher exposures in the heating season (geometric mean (GM): 108 versus 65 μg/m3 in the non-heating season for PM2.5) and among northern participants (GM: 90 versus 59 μg/m3 in southern China in the non-heating season for PM2.5). We used mixed-effects models to estimate within- and between-participant variance components and to assess the determinants of exposures. Within-participant variance in exposure dominated the total variability (68-95%). Outdoor PM2.5 was the dominant variable for explaining within-participant variance in exposure to PM2.5 (16%). Household fuel use (PM2.5: 8%; black carbon: 10%) and smoking status (PM2.5: 27%; black carbon: 5%) explained the most between-participant variance. Indoor sources (solid fuel stoves, tobacco smoking) were associated with 13-30% higher exposures to air pollution and each 10 μg/m3 increase in outdoor PM2.5 was associated with 6-8% higher exposure. Our findings indicate that repeated measurements of daily exposure are likely needed to capture longer-term exposures in settings of household solid fuel use, even within a single season, and that reducing air pollution from both outdoor and indoor sources is likely needed to achieve measurable reductions in exposures to air pollution.

Defining pathways to healthy sustainable urban development

Goals and pathways to achieve sustainable urban development have multiple interlinkages with human health and wellbeing. However, these interlinkages have not been examined in depth in recent discussions on urban sustainability and global urban science. This paper fills that gap by elaborating in detail the multiple links between urban sustainability and human health and by mapping research gaps at the interface of health and urban sustainability sciences. As researchers from a broad range of disciplines, we aimed to: 1) define the process of urbanization, highlighting distinctions from related concepts to support improved conceptual rigour in health research; 2) review the evidence linking health with urbanization, urbanicity, and cities and identify cross-cutting issues; and 3) highlight new research approaches needed to study complex urban systems and their links with health. This novel, comprehensive knowledge synthesis addresses issue of interest across multiple disciplines. Our review of concepts of urban development should be of particular value to researchers and practitioners in the health sciences, while our review of the links between urban environments and health should be of particular interest to those outside of public health. We identify specific actions to promote health through sustainable urban development that leaves no one behind, including: integrated planning; evidence-informed policy-making; and monitoring the implementation of policies. We also highlight the critical role of effective governance and equity-driven planning in progress towards sustainable, healthy, and just urban development.

The relationship between personal exposure and ambient PM2.5 and black carbon in Beijing

This study is part of the "Air Polluion Impacts on Cardiopulmonary disease in Beijing: an integrated study of Exposure Science, Toxicologenomics & Environmental Epidemiology (APIC-ESTEE)" project under the UK-China joint research programme "Atmospheric Pollution and Human Health in a Chinese Megacity (APHH-China)". The aim is to capture the spatio-temporal variability in people's exposure to fine particles (PM_2.5) and black carbon (BC) air pollution in Beijing, China. A total of 120 students were recruited for a panel study from ten universities in Haidian District in northwestern Beijing from December 2017 to June 2018. Real-time personal concentrations of PM_2.5 and BC were measured over a 24-h period with two research-grade portable personal exposure monitors. Personal microenvironments (MEs) were determined by applying an algorithm to the handheld GPS unit data. On average, the participants spent the most time indoors (79% in Residence and 16% in Workplace), and much less time travelling by Walking, Cycling, Bus and Metro. Similar patterns were observed across participant gender and body-mass index classifications. The participants were exposed to 33.8 ± 27.8 μg m^-3 PM_2.5 and to 1.9 ± 1.2 μg m^-3 BC over the 24-h monitoring period, on average 24.3 μg m^-3 (42%) and 0.8 μg m^-3 (28%) lower, respectively, than the concurrent fixed-site ambient measurements. Relative differences between personal and ambient BC concentrations showed greater variability across the MEs, highlighting significant contributions from Dining and travelling by Bus, which involve potential combustion of fuels. This study demonstrates the potential value of personal exposure monitoring in investigating air pollution related health effects, and in evaluating the effectiveness of pollution control and intervention measures.

Derivation of Time-Activity Data Using Wearable Cameras and Measures of Personal Inhalation Exposure among Workers at an Informal Electronic-Waste Recovery Site in Ghana

OBJECTIVES

Approximately 2 billion workers globally are employed in informal settings, which are characterized by substantial risk from hazardous exposures and varying job tasks and schedules. Existing methods for identifying occupational hazards must be adapted for unregulated and challenging work environments. We designed and applied a method for objectively deriving time-activity patterns from wearable camera data and matched images with continuous measurements of personal inhalation exposure to size-specific particulate matter (PM) among workers at an informal electronic-waste (e-waste) recovery site.

METHODS

One hundred and forty-two workers at the Agbogbloshie e-waste site in Accra, Ghana, wore sampling backpacks equipped with wearable cameras and real-time particle monitors during a total of 171 shifts. Self-reported recall of time-activity (30-min resolution) was collected during the end of shift interviews. Images (N = 35,588) and simultaneously measured PM2.5 were collected each minute and processed to identify activities established through worker interviews, observation, and existing literature. Descriptive statistics were generated for activity types, frequencies, and associated PM2.5 exposures. A kappa statistic measured agreement between self-reported and image-based time-activity data.

RESULTS

Based on image-based time-activity patterns, workers primarily dismantled, sorted/loaded, burned, and transported e-waste materials for metal recovery with high variability in activity duration. Image-based and self-reported time-activity data had poor agreement (kappa = 0.17). Most measured exposures (90%) exceeded the World Health Organization (WHO) 24-h ambient PM2.5 target of 25 µg m-3. The average on-site PM2.5 was 81 µg m-3 (SD: 94). PM2.5 levels were highest during burning, sorting/loading and dismantling (203, 89, 83 µg m-3, respectively). PM2.5 exposure during long periods of non-work-related activities also exceeded the WHO standard in 88% of measured data.

CONCLUSIONS

In complex, informal work environments, wearable cameras can improve occupational exposure assessments and, in conjunction with monitoring equipment, identify activities associated with high exposures to workplace hazards by providing high-resolution time-activity data.

Personal exposure to particulate matter in peri-urban India: predictors and association with ambient concentration at residence

Scalable exposure assessment approaches that capture personal exposure to particles for purposes of epidemiology are currently limited, but valuable, particularly in low-/middle-income countries where sources of personal exposure are often distinct from those of ambient concentrations. We measured 2 × 24-h integrated personal exposure to PM_2.5 and black carbon in two seasons in 402 participants living in peri-urban South India. Means (sd) of PM_2.5 personal exposure were 55.1(82.8) µg/m³ for men and 58.5(58.8) µg/m³ for women; corresponding figures for black carbon were 4.6(7.0) µg/m³ and 6.1(9.6) µg/m³. Most variability in personal exposure was within participant (intra-class correlation ~20%). Personal exposure measurements were not correlated (R_spearman < 0.2) with annual ambient concentration at residence modeled by land-use regression; no subgroup with moderate or good agreement could be identified (weighted kappa ≤ 0.3 in all subgroups). We developed models to predict personal exposure in men and women separately, based on time-invariant characteristics collected at baseline (individual, household, and general time-activity) using forward stepwise model building with mixed models. Models for women included cooking activities and household socio-economic position, while models for men included smoking and occupation. Models performed moderately in terms of between-participant variance explained (38-53%) and correlations between predictions and measurements (R_spearman: 0.30-0.50). More detailed, time-varying time-activity data did not substantially improve the performance of the models. Our results demonstrate the feasibility of predicting personal exposure in support of epidemiological studies investigating long-term particulate matter exposure in settings characterized by solid fuel use and high occupational exposure to particles.

Long-standing LPG subsidies, cooking fuel stacking, and personal exposure to air pollution in rural and peri-urban Ecuador

Ecuador presents a unique case study for evaluating personal air pollution exposure in a middle-income country where a clean cooking fuel has been available at low cost for several decades. We measured personal PM_2.5 exposure, stove use, and participant location during a 48-h monitoring period for 157 rural and peri-urban households in coastal and Andean Ecuador. While nearly all households owned a liquefied petroleum gas (LPG) stove and used it as their primary cooking fuel, one-quarter of households utilized firewood as a secondary fuel and 10% used induction stoves secondary to LPG. Stove use monitoring demonstrated clear within- and across-meal fuel stacking patterns. Firewood-owning participants had higher distributions of 48-h and 10-min PM_2.5 exposure as compared with primary LPG and induction stove users, and this effect became more pronounced with firewood use during monitoring.Accounting for within-subject clustering, contemporaneous firewood stove use was associated with 101 μg/m³ higher 10-min PM_2.5 exposure (95% CI: 94-108 μg/m³). LPG and induction cooking events were largely not associated with contemporaneous PM_2.5 exposure. Our results suggest that firewood use is associated with average and short-term personal air pollution exposure above the WHO interim-I guideline, even when LPG is the primary cooking fuel.

Personal exposure to particulate air pollution and vascular damage in peri-urban South India

OBJECTIVE

Air pollution is a leading preventable risk factor for cardiovascular diseases. Previous studies mostly relied on concentrations at residence, which might not represent personal exposure. Personal air pollution exposure has a greater variability compared with levels of ambient air pollution, facilitating evaluation of exposure-response functions and vascular pathophysiology. We aimed to evaluate the association between predicted annual personal exposure to PM2.5 and black carbon (BC) and three vascular damage markers in peri-urban South India.

METHODS

We analyzed the third wave of the APCAPS cohort (2010-2012), which recruited participants from 28 villages. We used predicted personal exposure to PM2.5 and BC derived from 610 participant-days of 24 h average gravimetric PM2.5 and BC measurements and predictors related to usual time-activity. Outcomes included carotid intima-media thickness (CIMT), carotid-femoral pulse wave velocity (cf-PWV) and augmentation index (AIx). We fit linear mixed models, adjusting for potential confounders and accounting for the clustered data structure. We evaluated nonlinear associations using generalized additive mixed models.

RESULTS

Of the 3017 participants (mean age 38 years), 1453 (48%) were women. The average PM2.5 exposure was 51 µg/m3 (range 13-85) for men, and 61 µg/m3 (range 40-120) for women, while the average BC was 4 µg/m3 (range 3-7) for men and 8 µg/m3 (range 3-22) for women. A 10 μg/m3 increase of PM2.5 was positively associated with CIMT (0.026 mm, 95% CI 0.014, 0.037), cf-PWV (0.069 m/s, 95% CI 0.008, 0.131) and AIx (0.8%, 95% CI 0.3, 1.3) among men. The exposure-response function for PM2.5 and AIx among men showed non-linearity, particularly within the exposure range dominated by tobacco smoking and occupational exposures. Both PM2.5 and BC were positively associated with AIx among women (0.6%, 95% CI 0.2, 1.0, per 10 μg/m3 PM2.5; 0.5%, 95% CI 0.1, 0.8, per 2 μg/m3 BC).

CONCLUSIONS

Personal exposure to particulate matter was associated with vascular damage in a peri-urban population in South India. Personal exposure to particulate matter appears to have gender-specific effects on the type of vascular damage, potentially reflecting differences in sources of personal exposure by gender.

Comparison of next-generation portable pollution monitors to measure exposure to PM2.5 from household air pollution in Puno, Peru

Assessment of personal exposure to PM_2.5 is critical for understanding intervention effectiveness and exposure-response relationships in household air pollution studies. In this pilot study, we compared PM_2.5 concentrations obtained from two next-generation personal exposure monitors (the Enhanced Children MicroPEM or ECM; and the Ultrasonic Personal Air Sampler or UPAS) to those obtained with a traditional Triplex Cyclone and SKC Air Pump (a gravimetric cyclone/pump sampler). We co-located cyclone/pumps with an ECM and UPAS to obtain 24-hour kitchen concentrations and personal exposure measurements. We measured Spearmen correlations and evaluated agreement using the Bland-Altman method. We obtained 215 filters from 72 ECM and 71 UPAS co-locations. Overall, the ECM and the UPAS had similar correlation (ECM ρ = 0.91 vs UPAS ρ = 0.88) and agreement (ECM mean difference of 121.7 µg/m³ vs UPAS mean difference of 93.9 µg/m³ ) with overlapping confidence intervals when compared against the cyclone/pump. When adjusted for the limit of detection, agreement between the devices and the cyclone/pump was also similar for all samples (ECM mean difference of 68.8 µg/m³ vs UPAS mean difference of 65.4 µg/m³ ) and personal exposure samples (ECM mean difference of -3.8 µg/m³ vs UPAS mean difference of -12.9 µg/m³ ). Both the ECM and UPAS produced comparable measurements when compared against a cyclone/pump setup.

Identifying predictors of personal exposure to air temperature in peri-urban India

Characterizing personal exposure to air temperature is critical to understanding exposure measurement error in epidemiologic studies using fixed-site exposure data and to identify strategies to protect public health. To date, no study evaluating personal air temperature in the general population has been conducted in a low-and-middle income country. We used data from the CHAI study consisting of 50 adults monitored in up to six non-consecutive 24 h sessions in peri-urban south India. We quantified the agreement and association between fixed-site ambient and personal air temperature, and identified predictors of personal air temperature based on housing assessment, self-reported, GPS, remote sensing, and wearable camera data. Mean (SD) daytime (6 am-10 pm) average personal air temperature was 31.2 (2.6) °C and mean nighttime (10 pm-6 am) average temperature was 28.8 (2.8) °C. Agreement between average personal air and fixed-site ambient temperatures was limited, especially at night when personal air temperatures were underestimated by fixed-site temperatures (MBE = -5.6 °C). The proportion of average personal nighttime temperature variability explained by ambient fixed-site temperatures was moderate (R²_mar = 0.39); daytime associations were stronger for women (R²_mar = 0.51) than for men (R²_mar = 0.3). Other predictors of average nighttime personal air temperature included residential altitude, ceiling height, and household income. Predictors of average daytime personal air temperature included roof materials, GPS-tracked altitude, time working in agriculture (for women), and time travelling (for men). No biomass cooking, urban heat island, or greenspace effects were identified. R²_mar between ambient fixed-site and personal air temperature indicate that ambient fixed-site temperature is only a moderately useful proxy of personal air temperature in the context of peri-urban India. Our findings suggest that people living in houses at lower altitude, with lower ceiling height and asbestos roofing sheets might be more vulnerable to heat. We also identified households with higher income, women working in agriculture and men with long commutes as disproportionately exposed to high temperatures.

Association between ambient and household air pollution with carotid intima-media thickness in peri-urban South India: CHAI-Project

BACKGROUND

Evidence linking ambient air pollution with atherosclerosis is lacking from low- and middle-income countries. Additionally, evidence regarding the association between household air pollution and atherosclerosis is limited. We evaluated the association between ambient fine particulate matter [particulate matter with an aerodynamic diameter of ≤2.5 µm (PM2.5)] and biomass fuel use on carotid intima-media thickness (CIMT), a surrogate of atherosclerosis, in India.

METHODS

We analysed the third follow-up of the Andhra Pradesh Children and Parent Study cohort (2010-2012), which recruited participants from 28 peri-urban villages. Our primary outcome was mean CIMT, measured using a standardized protocol. We estimated annual average PM2.5 outdoors at residence using land-use regression. Biomass cooking fuel was self-reported. We fitted a within-between linear-mixed model adjusting for potential confounders.

RESULTS

Among 3278 participants (48% women, mean age 38 years), mean PM2.5 was 32.7 [range 24.4-38.2] µg/m3, and 60% used biomass. After confounder adjustment, we observed positive associations between within-village variation in PM2.5 and CIMT in all participants [1.79%, 95% confidence interval (CI), -0.31 to 3.90 per 1  µg/m3 of PM2.5] and in men (2.98%, 95% CI, 0.23-5.72, per 1  µg/m3 of PM2.5). Use of biomass cooking fuel was associated with CIMT in all participants (1.60%, 95% CI, -0.46 to 3.65), especially in women with an unvented stove (6.14%, 95% CI, 1.40-10.89). The point-estimate for the PM2.5 association was larger in sub-groups with higher cardiometabolic risk profile.

CONCLUSIONS

Ambient and household air pollution were positively associated with CIMT in a peri-urban population of India, although with limited precision for some estimates. We observed differences in the association between ambient and household air pollution and CIMT by gender.

Association of Ambient and Household Air Pollution With Bone Mineral Content Among Adults in Peri-urban South India

IMPORTANCE

Air pollution is a major threat to global health. Osteoporosis is responsible for a substantial burden of disease globally and is expected to increase in prevalence because of population aging. Few studies have investigated the association between air pollution and bone health, and their findings were inconclusive.

OBJECTIVE

To quantify the association between ambient and household air pollution and bone mass in a sample of the general population in peri-urban India.

DESIGN, SETTING, AND PARTICIPANTS

This was a population-based cross-sectional analysis of the Andhra Pradesh Children and Parents Study cohort, which recruited participants from 28 villages near Hyderabad, South India, during 2009 to 2012. Separate linear mixed models were fitted with nested random intercepts (household within villages) for each exposure-outcome pair and were sequentially adjusted for potential confounders. Data analysis was conducted between April 2019 and July 2019.

EXPOSURES

Annual mean ambient particulate matter air pollution less than 2.5 µm in aerodynamic diameter (PM2.5) and black carbon (BC) levels at the residence estimated by land-use regression and self-reported use of biomass cooking fuel.

MAIN OUTCOMES AND MEASURES

The primary outcome was bone mineral content (BMC) measured in grams, corrected by bone area at the lumbar spine and left hip, as measured by dual-energy x-ray absorptiometry. The secondary outcome was bone mineral density measured in grams per centimeters squared.

RESULTS

A total of 3717 participants were analyzed (mean [SD] age, 35.7 [14.0] years; 1711 [46.0%] women). The annual mean (SD) PM2.5 exposure was 32.8 (2.5) μg/m3, and the annual mean (SD) BC exposure was 2.5 (0.2) μg/m3; 57.8% of participants used biomass cooking fuels. In fully adjusted models, PM2.5 was associated with lower BMC in the spine (mean difference, -0.57 g per 3 μg/m3 increase in PM2.5; 95% CI, -1.06 to -0.07 g per 3 μg/m3 increase in PM2.5) and hip (mean difference, -0.13 g per 3 μg/m3 increase in PM2.5; 95% CI, -0.3 to 0.03 g per 3 μg/m3 increase in PM2.5). After confounder adjustment, exposure to PM2.5 was also associated with lower bone mineral density in the spine (mean difference, -0.011 g/cm2 per 3 μg/m3 increase in PM2.5; 95% CI, -0.021 to 0 g/cm2 per 3 μg/m3 increase in PM2.5) and hip (mean difference, -0.004 g/cm2 per 3 μg/m3 increase in PM2.5; 95% CI, -0.008 to 0.001 g/cm2 per 3 μg/m3 increase in PM2.5). Exposure to BC was associated with lower BMC in the spine (mean difference, -1.13 g per 1 μg/m3 increase in BC; 95% CI, -2.81 to 0.54 g per 1 μg/m3 increase in BC) and hip (mean difference, -0.35 g per 1 μg/m3 increase in BC; 95% CI, -0.96 to 0.25 g per 1 μg/m3 increase in BC), although the confidence intervals were wider. There was no association between biomass fuel use and spine BMC (mean difference, 0.12 g; 95% CI, -0.45 to 0.68 g).

CONCLUSIONS AND RELEVANCE

In a cross-sectional analysis of a population-based cohort, ambient air pollution was associated with lower BMC in a young adult population in a peri-urban area of South India.

Calibration of a low-cost PM2.5 monitor using a random forest model

BACKGROUND

Particle air pollution has adverse health effects, and low-cost monitoring among a large population group is an effective method for performing environmental health studies. However, concern about the accuracy of low-cost monitors has affected their popularization in monitoring projects.

OBJECTIVE

To calibrate a low-cost particle monitor (HK-B3, Hike, China) through a controlled exposure experiment.

METHODS

Our study used a MicroPEM monitor (RTI, America) as a standard particle concentration measurement device to calibrate the Hike monitors. A machine learning model was established to calibrate the particle concentration obtained by the low-cost PM_2.5 monitors, and ten-fold validation was used to test the model. In addition, we used a linear regression model to compare the results of the machine learning model. A calibration method was established for the low-cost monitors, and it can be used to apply the monitors in future air pollution monitoring projects.

RESULTS

The values of the random forest model calibration results and observations were more condensed around the regression line y = 0.99x + 0.05, and the R squared value (R² = 0.98) was higher than that for the linear regression (R² = 0.87). The random forest model showed better performance than the traditional linear regression model.

CONCLUSIONS

Our study provided an effective calibration method to support the accuracy of low-cost monitors. The machine learning method based on the calibration model established in our study can increase the effectiveness of future air pollution and health studies.

Cooking/Window Opening and Associated Increases of Indoor PM2.5 and NO2 Concentrations of Children’s Houses in Kaohsiung, Taiwan

High concentrations of air pollutants and increased morbidity and mortality rates are found in industrial areas, especially for the susceptible group, children; however, most studies use atmospheric dispersion modeling to estimate household air pollutants. Therefore, the aim of this study was to assess the indoor air quality, e.g., CO, CO2, NO2, SO2, O3, particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5), and their influence factors in children’s homes in an industrial city. Children in the “general school”, “traffic school”, and “industrial school” were randomly and proportionally selected. Air pollutants were sampled for 24 h in the living rooms and on the balcony of their houses and questionnaires of time–microenvironment–activity-diary were recorded. The indoor CO concentration of the traffic area was significantly higher than that of the industrial area and the general area. In regard to the effects of window opening, household NO2 and PM2.5 concentrations during window opening periods were significantly higher than of the reference periods. For the influence of cooking, indoor CO2, NO2, and PM2.5 levels during the cooking periods were significantly higher than that of the reference periods. The indoor air quality of children in industrial cities were affected by residential areas and household activities.

Predicting gestational personal exposure to PM2.5 from satellite-driven ambient concentrations in Shanghai

BACKGROUND

It has been widely reported that gestational exposure to fine particulate matters (PM_2.5) is associated with a series of adverse birth outcomes. However, the discrepancy between ambient PM_2.5 concentrations and personal PM_2.5 exposure would significantly affect the estimation of exposure-response relationship.

OBJECTIVE

Our study aimed to predict gestational personal exposure to PM_2.5 from the satellite-driven ambient concentrations and analyze the influence of other potential determinants.

METHOD

We collected 762 72-h personal exposure samples from a panel of 329 pregnant women in Shanghai, China as well as their time-activity patterns from Feb 2017 to Jun 2018. We established an ambient PM_2.5 model based on MAIAC AOD at 1 km resolution, then used its output as a major predictor to develop a personal exposure model.

RESULTS

Our ambient PM_2.5 model yielded a cross-validation R² of 0.96. Personal PM_2.5 exposure levels were almost identical to the corresponding ambient concentrations. After adjusting for time-activity patterns and meteorological factors, our personal exposure has a CV R² of 0.76.

CONCLUSION

We established a prediction model for gestational personal exposure to PM_2.5 from satellite-based ambient concentrations and provided a methodological reference for further epidemiological studies.

Lack of association between particulate air pollution and blood glucose levels and diabetic status in peri-urban India

BACKGROUND

Limited evidence exists on the effect of particulate air pollution on blood glucose levels. We evaluated the associations of residential and personal levels of fine particulate matter (PM_2.5) and black carbon (BC) with blood glucose and diabetic status among residents of 28 peri-urban villages in South India.

METHODS

We used cross-sectional data from 5065 adults (≥18 years, 54% men) included in the Andhra Pradesh Children and Parents Study. Fasting plasma glucose was measured once in 2010-2012 and prevalent prediabetes and diabetes were defined following the American Diabetes Association criteria. We estimated annual ambient PM_2.5 and BC levels at residence using land-use regression models and annual personal exposure to PM_2.5 and BC using prediction models based on direct measurements from a subsample of 402 participants. We used linear and logistic nested mixed-effect models to assess the association between exposure metrics and health outcomes. For personal exposures, we stratified analyses by sex.

RESULTS

Mean (SD) residential PM_2.5 and BC were 32.9 (2.6) μg/m³ and 2.5 (2.6) μg/m³, respectively; personal exposures to PM_2.5 and BC were 54.5 (11.5) μg/m³ and 5.8 (2.5) μg/m³, respectively. Average (SD) fasting blood glucose was 5.3 (1.3) mmol/l, 16% of participants had prediabetes, and 5.5% had diabetes. Residential PM_2.5 and BC were not associated with higher blood glucose levels. Personal PM_2.5 (20 μg/m³ increase) and BC (1 μg/m³ increase) were negatively associated with blood glucose levels in women (PM_2.5: -1.93, 95%CI: -3.12, -0.73; BC: -0.63, 95%CI: -0.90, -0.37). In men, associations were negative for personal PM_2.5 (-1.99, 95%CI: -3.56, -0.39) and positive for personal BC (0.49, 95%CI: -0.44, 1.43). We observed no evidence of associations between any exposure and prevalence of prediabetes/diabetes.

CONCLUSIONS

Our results do not provide evidence that residential exposures to PM_2.5 or BC are associated with blood glucose or prevalence of prediabetes/diabetes in this population. Associations with personal exposure may have been affected by unmeasured confounding, highlighting a challenge in using personal exposure estimates in air pollution epidemiology. These associations should be further examined in longitudinal studies.