Quantifying human exposure to air pollution--moving from static monitoring to spatio-temporally resolved personal exposure assessment

Exposomics: a review of methodologies, applications, and future directions in molecular medicine

The exposome is the measure of all the exposures of an individual in a lifetime and how those exposures relate to health. Exposomics is the emerging field of research to measure and study the totality of the exposome. Exposomics can assist with molecular medicine by furthering our understanding of how the exposome influences cellular and molecular processes such as gene expression, epigenetic modifications, metabolic pathways, and immune responses. These molecular alterations can aid as biomarkers for the diagnosis, disease prediction, early detection, and treatment and offering new avenues for personalized medicine. Advances in high throughput omics and other technologies as well as increased computational analytics is enabling comprehensive measurement and sophisticated analysis of the exposome to elucidate their cumulative and combined impacts on health, which can enable individuals, communities, and policymakers to create programs, policies, and protections that promote healthier environments and people. This review provides an overview of the potential role of exposomics in molecular medicine, covering its history, methodologies, current research and applications, and future directions.

Durational Exposure to Particulate Matter and Changes in Fertility Intentions: A Study of Adults in China

OBJECTIVE

The effects of prolonged exposure to persistently elevated atmospheric pollutants, commonly termed air pollution waves, on fertility intentions remain inadequately understood. This study aims to investigate the association between particulate matter (PM) exposure and fertility intentions.

METHODS

In this nationwide cross-sectional study, we analyzed data from 10,747 participants (5496 females and 5251 males). PM waves were defined as periods lasting 3‒6 consecutive days during which the daily average concentrations exceeded China's Ambient Air Quality Standards Grade II thresholds (PM2.5 > 75 μg/m3 and PM10 > 150 μg/m3). We employed multivariate logistic regression models to assess the association between exposure to PM waves and fertility intentions.

RESULTS

Significant inverse associations were detected between exposure to PM2.5 wave events (characterized by concentrations exceeding 75 μg/m3 for durations of 4‒6 days, P < 0.05) and PM10 wave events (defined as concentrations exceeding 150 μg/m3 for 6 consecutive days, P < 0.05) and fertility intentions among females. In contrast, neither the PM2.5 wave nor the PM10 wave events demonstrated statistically significant correlations with fertility intentions in males (P > 0.05 for all comparisons). The potentially susceptible subgroup was identified as females aged 20-30 years.

CONCLUSIONS

Our results provide the first evidence that PM2.5 and PM10 waves are associated with a reduction in female fertility intentions, offering critical insights for the development of public health policies and strategies aimed at individual protection.

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

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

Using low-cost sensors to assess common air pollution sources across multiple residences

The rapid development of low-cost sensors provides the opportunity to greatly advance the scope and extent of monitoring of indoor air pollution. In this study, calibrated particle matter (PM) sensors and a non-negative matrix factorisation (NMF) source apportionment technique are used to investigate PM concentrations and source contributions across three households in an urban residential area. The NMF is applied to combined data from all houses to generate source profiles that can be used to understand how PM source characteristics are similar or differ between different households in the same urban area. PM2.5 and PM10 concentrations in all three houses were greater, more variable, and significantly different to ambient concentrations recorded at a nearby ambient monitoring site. Concentrations were also significantly different between houses, with the World Health Organisation 24-h guideline limits for PM2.5 breached in one household. The applied methodology was highly successful at modelling concentrations for all the houses (R2 ≥ 0.983), finding that across the houses the I/O (indoor to outdoor sources ratio) was the lowest for PM1 (down to 0.08), and greatest for PM10 (up to 4.93). Whilst the sources could not be clearly distinguished further than being outdoors or indoors, the methodology provides clear insights to source variability within and between the monitored houses. These results highlight the importance of monitoring indoor air pollution to improve pollution exposure estimates, as whilst people may live in areas with acceptable ambient air quality, they can be exposed to unhealthy concentrations in their own homes. This method may be applied in future studies for extended periods to investigate the influence of source seasonality on PM concentrations or scaled up to investigate source variability across larger geographical areas.

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

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

Feasibility of low-cost particulate matter sensors for long-term environmental monitoring: Field evaluation and calibration

Low-cost sensor networks offer the potential to reduce monitoring costs while providing high-resolution spatiotemporal data on pollutant levels. However, these sensors come with limitations, and many aspects of their field performance remain underexplored. During October to December 2023, this study deployed two identical low-cost sensor systems near an urban standard monitoring station to record PM2.5 and PM10 concentrations, along with environmental temperature and humidity. Our evaluation of the monitoring performance of these sensors revealed a broad data distribution with a systematic overestimation; this overestimation was more pronounced in PM10 readings. The sensors showed good consistency (R2 > 0.9, NRMSE<5 %), and normalization residuals were tracked to assess stability, which, despite occasional environmental influences, remained generally stable. A lateral comparison of four calibration models (MLR, SVR, RF, XGBoost) demonstrated superior performance of RF and XGBoost over others, particularly with RF showing enhanced effectiveness on the test set. SHAP analysis identified sensor readings as the most critical variable, underscoring their pivotal role in predictive modeling. Relative humidity consistently proved more significant than dew point and temperature, with higher RH levels typically having a positive impact on model outputs. The study indicates that, with appropriate calibration, sensors can supplement the sparse networks of regulatory-grade instruments, enabling dense neighborhood-scale monitoring and a better understanding of temporal air quality trends.

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

BACKGROUND

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

MAIN BODY

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

CONCLUSIONS

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

Exposure to airborne particulate matter during commuting using portable sensors: Effects of transport modes in a French metropolis study case

Outdoor exposure to particulate matter (PM2.5 and PM10) in urban areas can vary considerably depending on the mode of transport. This study aims to quantify this difference in exposure during daily travel, by carrying out a micro-sensor measurement campaign. The pollutant exposure was assessed simultaneously over predefined routes in order to allow comparison between different transport modes having the same starting and ending points. During the six-week measurement campaign, the average reference values for PM background concentrations were 13.72 and 17.92μg/m3 for the PM2.5 and PM10, respectively. The results revealed that the mode with the highest exposure to PM2.5 adjusted to background concentration (PM2.5Norm) was the bus (1.65) followed by metro (1.51), walking (1.33), tramway (1.31), car (1.09) and finally the bike (1.06). For PM10Norm, the tramway had the highest exposure (1.86), followed by walking (1.68), metro (1.65), bus (1.61), bike (1.43) and finally the car (1.39). The level of urbanization around the route and the presence of preferential lanes for public transportation influenced the concentration to which commuters were exposed. For the active modes (bike and walking), we observed frequent variations in concentrations during the trip, characterized by punctual peaks in concentration, depending on the local characteristics of road traffic and urban morphology. Fluctuations in particulate matter inside public transport vehicles were partly explained by the opening and closing of doors during stops, as well as the passenger flows, influencing the re-suspension of particles. The car was one of the least exposed modes overall, with the lowest concentration variability, although these concentrations can vary greatly depending on the ventilation parameters used. These results encourage measures to move the most exposed users away from road traffic, by developing a network of lanes entirely dedicated to cycling and walking, particularly in densely populated areas, as well as encouraging the renewal of motorized vehicles to use less polluting fuels with efficient ventilation systems.

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

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

Multi-Scenario Validation and Assessment of a Particulate Matter Sensor Monitor Optimized by Machine Learning Methods

OBJECTIVE

The aim was to evaluate and optimize the performance of sensor monitors in measuring PM2.5 and PM10 under typical emission scenarios both indoors and outdoors.

METHOD

Parallel measurements and comparisons of PM2.5 and PM10 were carried out between sensor monitors and standard instruments in typical indoor (2 months) and outdoor environments (1 year) in Shanghai, respectively. The optimized validation model was determined by comparing six machining learning models, adjusting for meteorological and related factors. The intra- and inter-device variation, measurement accuracy, and stability of sensor monitors were calculated and compared before and after validation.

RESULTS

Indoor particles were measured in a range of 0.8-370.7 μg/m3 and 1.9-465.2 μg/m3 for PM2.5 and PM10, respectively, while the outdoor ones were in the ranges of 1.0-211.0 μg/m3 and 0.0-493.0 μg/m3, correspondingly. Compared to machine learning models including multivariate linear model (ML), K-nearest neighbor model (KNN), support vector machine model (SVM), decision tree model (DT), and neural network model (MLP), the random forest (RF) model showed the best validation after adjusting for temperature, relative humidity (RH), PM2.5/PM10 ratios, and measurement time lengths (months) for both PM2.5 and PM10, in indoor (R2: 0.97 and 0.91, root-mean-square error (RMSE) of 1.91 μg/m3 and 4.56 μg/m3, respectively) and outdoor environments (R2: 0.90 and 0.80, RMSE of 5.61 μg/m3 and 17.54 μg/m3, respectively), respectively.

CONCLUSIONS

Sensor monitors could provide reliable measurements of PM2.5 and PM10 with high accuracy and acceptable inter and intra-device consistency under typical indoor and outdoor scenarios after validation by RF model. Adjusting for both climate factors and the ratio of PM2.5/PM10 could improve the validation performance.

Selective Detection of Functionalized Carbon Particles based on Polymer Semiconducting and Conducting Devices as Potential Particulate Matter Sensors

This paper reports a new mechanism for particulate matter detection and identification. Three types of carbon particles are synthesized with different functional groups to mimic the real particulates in atmospheric aerosol. After exposing polymer-based organic devices in organic field effect transistor (OFET) architectures to the particle mist, the sensitivity and selectivity of the detection of different types of particles are shown by the current changes extracted from the transfer curves. The results indicate that the sensitivity of the devices is related to the structure and functional groups of the organic semiconducting layers, as well as the morphology. The predominant response is simulated by a model that yielded values of charge carrier density increase and charge carriers delivered per unit mass of particles. The research points out that polymer semiconductor devices have the ability to selectively detect particles with multiple functional groups, which reveals a future direction for selective detection of particulate matter.

Exercise-Mediated Protection against Air Pollution-Induced Immune Damage: Mechanisms, Challenges, and Future Directions

Air pollution, a serious risk factor for human health, can lead to immune damage and various diseases. Long-term exposure to air pollutants can trigger oxidative stress and inflammatory responses (the main sources of immune impairment) in the body. Exercise has been shown to modulate anti-inflammatory and antioxidant statuses, enhance immune cell activity, as well as protect against immune damage caused by air pollution. However, the underlying mechanisms involved in the protective effects of exercise on pollutant-induced damage and the safe threshold for exercise in polluted environments remain elusive. In contrast to the extensive research on the pathogenesis of air pollution and the preventive role of exercise in enhancing fitness, investigations into exercise resistance to injury caused by air pollution are still in their infancy. In this review, we analyze evidence from humans, animals, and cell experiments on the combined effects of exercise and air pollution on immune health outcomes, with an emphasis on oxidative stress, inflammatory responses, and immune cells. We also propose possible mechanisms and directions for future research on exercise resistance to pollutant-induced damage in the body. Furthermore, we suggest strengthening epidemiological studies at different population levels and investigations on immune cells to guide how to determine the safety thresholds for exercise in polluted environments.

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.

Concurrent Heat and Air Pollution Exposures among People Experiencing Homelessness

BACKGROUND

Extreme heat and air pollution are important human health concerns; exposure can affect mental and physical well-being, particularly during periods of co-occurrence. Yet, the impacts on people are largely determined by underlying health conditions, coupled with the length and intensity of exposure. Preexisting adverse health conditions and prolonged exposure times are more common for people experiencing homelessness, particularly those with intersectional identity characteristics (e.g., disease, ability, age, etc.). Partially due to methodological limitations, such as data scarcity, there is a lack of research at the intersection of this at-risk population within the climate-health domain.

OBJECTIVES

We have three distinct objectives throughout this article: a) to advance critical discussions around the state of concurrent high heat and air pollution exposure research as it relates to people experiencing homelessness; b) to assert the importance of heat and air pollution exposure research among a highly vulnerable, too-often homogenized population-people experiencing homelessness; and c) to underline challenges in this area of study while presenting potential ways to address such shortcomings.

DISCUSSION

The health insights from concurrent air pollution and heat exposure studies are consequential when studying unhoused communities who are already overexposed to harmful environmental conditions. Without holistic data sets and more advanced methods to study concurrent exposures, appropriate and targeted prevention and intervention strategies cannot be developed to protect this at-risk population. We highlight that a) concurrent high heat and air pollution exposure research among people experiencing homelessness is significantly underdeveloped considering the pressing human health implications; b) the severity of physiological responses elicited by high heat and air pollution are predicated on exposure intensity and time, and thus people without means of seeking climate-controlled shelter are most at risk; and c) collaboration among transdisciplinary teams is needed to resolve data resolution issues and enable targeted prevention and intervention strategies. https://doi.org/10.1289/EHP13402.

Empowering Participatory Research in Urban Health: Wearable Biometric and Environmental Sensors for Activity Recognition

Participatory exposure research, which tracks behaviour and assesses exposure to stressors like air pollution, traditionally relies on time-activity diaries. This study introduces a novel approach, employing machine learning (ML) to empower laypersons in human activity recognition (HAR), aiming to reduce dependence on manual recording by leveraging data from wearable sensors. Recognising complex activities such as smoking and cooking presents unique challenges due to specific environmental conditions. In this research, we combined wearable environment/ambient and wrist-worn activity/biometric sensors for complex activity recognition in an urban stressor exposure study, measuring parameters like particulate matter concentrations, temperature, and humidity. Two groups, Group H (88 individuals) and Group M (18 individuals), wore the devices and manually logged their activities hourly and minutely, respectively. Prioritising accessibility and inclusivity, we selected three classification algorithms: k-nearest neighbours (IBk), decision trees (J48), and random forests (RF), based on: (1) proven efficacy in existing literature, (2) understandability and transparency for laypersons, (3) availability on user-friendly platforms like WEKA, and (4) efficiency on basic devices such as office laptops or smartphones. Accuracy improved with finer temporal resolution and detailed activity categories. However, when compared to other published human activity recognition research, our accuracy rates, particularly for less complex activities, were not as competitive. Misclassifications were higher for vague activities (resting, playing), while well-defined activities (smoking, cooking, running) had few errors. Including environmental sensor data increased accuracy for all activities, especially playing, smoking, and running. Future work should consider exploring other explainable algorithms available on diverse tools and platforms. Our findings underscore ML's potential in exposure studies, emphasising its adaptability and significance for laypersons while also highlighting areas for improvement.

Understanding the time-activity pattern to improve the measurement of personal exposure: An exploratory and experimental research

Although ambient fine particulate matter (PM2.5) concentrations and their components are commonly used as proxies for personal exposure monitoring, developing an accurate and cost-effective method to use these proxies for personal exposure measurement continues to pose a significant challenge. Herein, we propose a scenario-based exposure model to precisely estimate personal exposure level of heavy metal(loid)s (HMs) using scenario HMs concentrations and time-activity patterns. Personal exposure levels and ambient pollution levels for PM2.5 and HMs differed significantly with corresponding personal/ambient ratios of approximately 2, and exposure scenarios could narrow the assessment error gap by 26.1-45.4%. Using a scenario-based exposure model, we assessed the associated health risks of a large sample population and identified that the carcinogenic risk of As exceeded 1 × 10-6, while we observed non-carcinogenic risks from As, Cd, Ni, and Mn in personal exposure to PM2.5. We conclude that the scenario-based exposure model is a preferential alternative for monitoring personal exposure compared to ambient concentrations. This method ensures the feasibility of personal exposure monitoring and health risk assessments in large-scale studies.

A pruned feed-forward neural network (pruned-FNN) approach to measure air pollution exposure

Environmental epidemiology studies require accurate estimations of exposure intensities to air pollution. The process from air pollutant emission to individual exposure is however complex and nonlinear, which poses significant modeling challenges. This study aims to develop an exposure assessment model that can strike a balance between accuracy, complexity, and usability. In this regard, neural networks offer one possible approach. This study employed a custom-designed pruned feed-forward neural network (pruned-FNN) approach to calculate the air pollution exposure index based on emission time and rates, terrain factors, meteorological conditions, and proximity measurements. The model's performance was evaluated by cross-validating the estimated exposure indexes with ground-based monitoring records. The pruned FNN can predict pollution exposure indexes (PEIs) that are highly and stably correlated with the monitored air pollutant concentrations (Spearman's rank correlation coefficients for tenfold cross-validation (mean ± standard deviation: 0.906 ± 0.028) and for random cross-validation (0.913 ± 0.024)). The predicted values are also close to the ground truth in most cases (95.5% of the predicted PEIs have relative errors smaller than 10%) when the training datasets are sufficiently large and well-covered. The pruned-FNN method can make accurate exposure estimations using a flexible number of variables and less extensive data in a less money/time-consuming manner. Compared to other exposure assessment models, the pruned FNN is an appropriate and effective approach for exposure assessment that covers a large geographic area over a long period of time.

Long term spatiotemporal trends and health risk assessment of remotely sensed PM2.5 concentrations in Nigeria

PM_2.5 is an important indicator reflecting air quality variations. Currently, environmental pollution related issues have become more severe that significantly threaten human health. The current study is an attempt to analyze the spatio-dynamic characteristics of PM_2.5 in Nigeria based on the directional distribution and trend clustering analysis from 2001 to 2019. The results indicated that PM_2.5 concentration increased in most of the Nigerian states, particularly in mid-northern and southern states. The lowest PM_2.5 concentration in Nigeria is even beyond the interim target-1 (35 μg/m³) of the WHO. During the study period, the average PM_2.5 concentration increased at a growth rate of 0.2 μg/m³/yr from 69 μg/m³ to 81 μg/m³. The growth rate varied from region to region. Kano, Jigawa, Katsina, Bauchi, Yobe, and Zamfara experienced the fastest growth rate of 0.9 μg/m³/yr with 77.9 μg/m³ mean concentration. The median center of the national average PM_2.5 moved toward the north indicating the highest PM_2.5 concentration in northern states. The Saharan desert dust is the dominant source of PM_2.5 in northern areas. Moreover, agricultural practices and deforestation activities along with low rainfall increase desertification and air pollution in these regions. Health risks increased in most of the mid-northern and southern states. The extent of ultra-high health risk (UHR) areas corresponding to the 8×10⁴-7.3×10⁶ μg⋅person/m³ increased from 1.5% to 2.8%. Mainly Kano, Lagos, Oyo, Edo, Osun, Ekiti, southeastern Kwara, Kogi, Enugu, Anambra, Northeastern Imo, Abia, River, Delta, northeastern Bayelsa, Akwa Ibom, Ebonyi, Abuja, Northern Kaduna, Katsina, Jigawa, central Sokoto, northeastern Zamfara, central Borno, central Adamawa, and northwestern Plateau are under UHR areas.

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.

Participant engagement to develop report-back materials for personal air monitoring

Background

Studies that measure environmental exposures in biological samples frequently provide participants their results. In contrast, studies using personal air monitors do not typically provide participants their monitoring results. The objective of this study was to engage adolescents who completed personal air sampling and their caregivers to develop understandable and actionable report-back documents containing the results of their personal air sampling.

Methods

Adolescents and their caregivers who previously completed personal air sampling participated in focus groups to guide the development of report-back materials. We conducted thematic analyses of focus group data to guide the design of the report-back document and convened experts in community engagement, reporting study results, and human subjects research to provide feedback. Final revisions to the report-back document were made based on follow-up focus group feedback.

Results

Focus groups identified critical components of an air-monitoring report-back document to include an overview of the pollutant being measured, a comparison of individual personal sampling data to the overall study population, a guide to interpreting results, visualization of individual data, and additional information on pollution sources, health risks, and exposure reduction strategies. Participants also indicated their desire to receive study results in an electronic and interactive format. The final report-back document was electronic and included background information, participants' results presented using interactive maps and figures, and additional material regarding pollution sources.

Conclusion

Studies using personal air monitoring technology should provide research participants their results in an understandable and meaningful way to empower participants with increased knowledge to guide exposure reduction strategies.

Is residential proximity to polluted sites during pregnancy associated with preterm birth or low birth weight? Results from an integrated exposure database in North Carolina (2003-2015)

BACKGROUND

Preterm birth (PTB) and term low birth weight (LBW) have been associated with pollution and other environmental exposures, but the relationship between these adverse outcomes and specific characteristics of polluted sites is not well studied.

OBJECTIVES

We conducted a retrospective cohort study to examine relationships between residential proximity to polluted sites in North Carolina (NC) and PTB and LBW. We further stratified exposure to polluted sites by route of contaminant emissions and specific contaminants released at each site.

METHODS

We created an integrated exposure geodatabase of polluted sites in NC from 2002 to 2015 including all landfills, Superfund sites, and industrial sites. Using birth certificates, we assembled a cohort of 1,494,651 singleton births in NC from 2003 to 2015. We geocoded the gestational parent residential address on the birth certificate, and defined exposure to polluted sites as residence within one mile of a site. We used log-binomial regression models to estimate adjusted risk ratios (aRR) and 95% confidence intervals (CI). Binomial models were used to estimate adjusted risk differences (aRD) per 10,000 births and 95% CIs for associations between exposure to polluted sites and PTB or LBW.

RESULTS

We observed weak associations between residential proximity to polluted sites and PTB [aRR(95% CI): 1.07(1.06,1.09); aRD(95% CI): 61(48,74)] and LBW [aRR(95% CI): 1.09(1.06,1.12); aRD(95% CI): 24(17,31)]. Secondary analyses showed increased risk of both PTB and LBW among births exposed to sites characterized by water emissions, air emissions, and land impoundment. In analyses of specific contaminants, increased risk of PTB was associated with proximity to sites containing arsenic, benzene, cadmium, lead, mercury, and polycyclic aromatic hydrocarbons. LBW was associated with exposure to arsenic, benzene, cadmium, lead, and mercury.

SIGNIFICANCE

This study provides evidence for potential reproductive health effects of polluted sites, and underscores the importance of accounting for heterogeneity between polluted sites when considering these exposures.

IMPACT STATEMENT

We documented an overall increased risk of both PTB and LBW in births with gestational exposure to polluted sites using a harmonized geodatabase of three site types, and further examined exposures stratified by site characteristics (route of emission, specific contaminants present). We observed increased risk of both PTB and LBW among births exposed to sites with water emissions or air emissions, across site types. Increased risk of PTB was associated with gestational proximity to sites containing arsenic, benzene, cadmium, lead, mercury, and polycyclic aromatic hydrocarbons; increased risk of LBW was associated with exposure to arsenic, benzene, cadmium, lead, and mercury.

Responses of schoolchildren with asthma to recommendations to reduce desert dust exposure: Results from the LIFE-MEDEA intervention project using wearable technology

Current public health recommendations for desert dust storms (DDS) events focus on vulnerable population groups, such as children with asthma, and include advice to stay indoors and limit outdoor physical activity. To date, no scientific evidence exists on the efficacy of these recommendations in reducing DDS exposure. We aimed to objectively assess the behavioral responses of children with asthma to recommendations for reduction of DDS exposure. In two heavily affected by DDS Mediterranean regions (Cyprus & Crete, Greece), schoolchildren with asthma (6-11 years) were recruited from primary schools and were randomized to control (business as usual scenario) and intervention groups. All children were equipped with pedometer and GPS sensors embedded in smartwatches for objective real-time data collection from inside and outside their classroom and household settings. Interventions included the timely communication of personal DDS alerts accompanied by exposure reduction recommendations to both the parents and school-teachers of children in the intervention group. A mixed effect model was used to assess changes in daily levels of time spent, and steps performed outside classrooms and households, between non-DDS and DDS days across the study groups. The change in the time spent outside classrooms and homes, between non-DDS and DDS days, was 37.2 min (p_value = 0.098) in the control group and -62.4 min (p_value < 0.001) in the intervention group. The difference in the effects between the two groups was statistically significant (interaction p_value < 0.001). The change in daily steps performed outside classrooms and homes, was -495.1 steps (p_value = 0.350) in the control group and -1039.5 (p_value = 0.003) in the intervention group (interaction p_value = 0.575). The effects on both the time and steps performed outside were more profound during after-school hours. To summarize, among children with asthma, we demonstrated that timely personal DDS alerts and detailed recommendations lead to significant behavioral changes in contrast to the usual public health recommendations.

A systematic review of evidence for maternal preconception exposure to outdoor air pollution on Children's health

The preconception period is a critical window for gametogenesis, therefore preconception exposure to air pollutants may have long-term effects on children. We systematically reviewed epidemiological evidence concerning the effects of preconception ambient air pollution exposure on children's health outcomes and identified research gaps for future investigations. We searched PubMed and Web of Science from journal inception up to October 2022 based on an established protocol (PROSPERO: CRD42022277608). We then identified 162 articles based on searching strategy, 22 of which met the inclusion criteria. Studies covered a wide range of health outcomes including birth defects, preterm birth, birthweight, respiratory outcomes, and developmental outcomes. Findings suggested that exposure to outdoor air pollutants during maternal preconception period were associated with various health outcomes, of which birth defects has the most consistent findings. A meta-analysis revealed that during 3-month preconception period, a 10 μg/m3 increase in PM10 and PM2.5 was associated with relative risk (RR) of birth defects of 1.06 (95% confidence interval (CI): 1.00, 1.02) and 1.14 (95% CI: 0.82, 1.59), respectively. Preterm birth, low birthweight, and autism have also been associated with maternal preconception exposure to PM2.5, PM10, O3 and SO2. However, the significance of associations and effect sizes varied substantially across studies, partly due to the heterogeneity in exposure and outcome assessments. Future studies should use more accurate exposure assessment methods to obtain individual-level exposures with high temporal resolution. This will allow the exploration of which specific time window (weeks or months) during the preconception period has the strongest effect. In future epidemiologic studies, integrating pathophysiologic biomarkers relevant to clinical outcomes may help improve the causal inference of associations between preconception exposure and health outcomes suggested by the current limited literature. Additionally, potential effects of paternal preconception exposure need to be studied.

Time trends and persistence in PM2.5 in 20 megacities: evidence for the time period 2018-2020

The degree of persistence in daily data for PM_2.5 in 20 relevant megacities such as Bangkok, Beijing, Mumbai, Calcutta, Canton, Dhaka, Delhi, Jakarta, London, Los Angeles, Mexico City, Moscow, New York, Osaka. Paris, Sao Paulo, Seoul, Shanghai, Tientsin, and Tokyo is examined in this work. The analysis developed is based on fractional integration techniques. Specifically, the differentiation parameter is used to measure the degree of persistence in the series under study, which collects data on daily measurements carried out from January 1, 2018, to December 31, 2020. The results obtained show that the estimated values for the differentiation parameter are restricted to the interval (0, 1) in all cases, which allows us to conclude that there is a mean reverting pattern and, therefore, transitory effects of shocks.

Environmental noise and health in low-middle-income-countries: A systematic review of epidemiological evidence

Evidence of the health impacts from environmental noise has largely been drawn from studies in high-income countries, which has then been used to inform development of noise guidelines. It is unclear whether findings in high-income countries can be readily translated into policy contexts in low-middle-income-countries (LMICs). We conducted this systematic review to summarise noise epidemiological studies in LMICs. We conducted a literature search of studies in Medline and Web of Science published during 2009-2021, supplemented with specialist journal hand searches. Screening, data extraction, assessment of risk of bias as well as overall quality and strength of evidence were conducted following established guidelines (e.g. Navigation Guide). 58 studies were identified, 53% of which were from India, China and Bulgaria. Most (92%) were cross-sectional studies. 53% of studies assessed noise exposure based on fixed-site measurements using sound level meters and 17% from propagation-based noise models. Mean noise exposure among all studies ranged from 48 to 120 dB (L_eq), with over half of the studies (52%) reporting the mean between 60 and 80 dB. The most studied health outcome was noise annoyance (43% of studies), followed by cardiovascular (17%) and mental health outcomes (17%). Studies generally reported a positive (i.e. adverse) relationship between noise exposure and annoyance. Some limited evidence based on only two studies showing that long-term noise exposure may be associated with higher prevalence of cardiovascular outcomes in adults. Findings on mental health outcomes were inconsistent across the studies. Overall, 4 studies (6%) had "probably low", 18 (31%) had "probably high" and 36 (62%) had "high" risk of bias. Quality of evidence was rated as 'low' for mental health outcomes and 'very low' for all other outcomes. Strength of evidence for each outcome was assessed as 'inadequate', highlighting high-quality epidemiological studies are urgently needed in LMICs to strengthen the evidence base.

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

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

Visualization and Analysis of Air Pollution and Human Health Based on Cluster Analysis: A Bibliometric Review from 2001 to 2021

Bibliometric techniques and social network analysis are employed in this study to evaluate 14,955 papers on air pollution and health that were published from 2001 to 2021. To track the research hotspots, the principle of machine learning is applied in this study to divide 10,212 records of keywords into 96 clusters through OmniViz software. Our findings highlight strong research interests and the practical need to control air pollution to improve human health, as evidenced by an annual growth rate of over 15.8% in the related publications. The cluster analysis showed that clusters C22 (exposure, model, mortality) and C8 (health, environment, risk) are the most popular topics in this field of research. Furthermore, we develop co-occurrence networks based on the cluster analysis results in which a more specific keyword classification was obtained. These key areas include: "Air pollutant source", "Exposure-Response relationship", "Public & Occupational Health", and so on. Future research hotspots are analyzed through characteristics of the cluster groups, including the advancement of health risk assessment techniques, an interdisciplinary approach to quantifying human exposure to air pollution, and strategies in health risk assessment.

Comparing human exposure to fine particulate matter in low and high-income countries: A systematic review of studies measuring personal PM2.5 exposure

BACKGROUND

Due to the adverse health effects of air pollution, researchers have advocated for personal exposure measurements whereby individuals carry portable monitors in order to better characterise and understand the sources of people's pollution exposure.

OBJECTIVES

The aim of this systematic review is to assess the differences in the magnitude and sources of personal PM2.5 exposures experienced between countries at contrasting levels of income.

METHODS

This review summarised studies that measured participants personal exposure by carrying a PM2.5 monitor throughout their typical day. Personal PM2.5 exposures were summarised to indicate the distribution of exposures measured within each country income category (based on low (LIC), lower-middle (LMIC), upper-middle (UMIC), and high (HIC) income countries) and between different groups (i.e. gender, age, urban or rural residents).

RESULTS

From the 2259 search results, there were 140 studies that met our criteria. Overall, personal PM2.5 exposures in HICs were lower compared to other countries, with UMICs exposures being slightly lower than exposures measured in LMICs or LICs. 34% of measured groups in HICs reported below the ambient World Health Organisation 24-h PM2.5 guideline of 15 μg/m3, compared to only 1% of UMICs and 0% of LMICs and LICs. There was no difference between rural and urban participant exposures in HICs, but there were noticeably higher exposures recorded in rural areas compared to urban areas in non-HICs, due to significant household sources of PM2.5 in rural locations. In HICs, studies reported that secondhand smoke, ambient pollution infiltrating indoors, and traffic emissions were the dominant contributors to personal exposures. While, in non-HICs, household cooking and heating with biomass and coal were reported as the most important sources.

CONCLUSION

This review revealed a growing literature of personal PM2.5 exposure studies, which highlighted a large variability in exposures recorded and severe inequalities in geographical and social population subgroups.

Prenatal exposure to ambient air pollution and adverse birth outcomes: An umbrella review of 36 systematic reviews and meta-analyses

Multiple systematic reviews and meta-analyses linked prenatal exposure to ambient air pollutants to adverse birth outcomes with mixed findings, including results indicating positive, negative, and null associations across the pregnancy periods. The objective of this study was to systematically summarise systematic reviews and meta-analyses on air pollutants and birth outcomes to assess the overall epidemiological evidence. Systematic reviews with/without meta-analyses on the association between air pollutants (NO2, CO, O3, SO2, PM2.5, and PM10) and birth outcomes (preterm birth; stillbirth; spontaneous abortion; birth weight; low birth weight, LBW; small-for-gestational-age) up to March 30, 2022 were included. We searched PubMed, CINAHL, Scopus, Medline, Embase, and the Web of Science Core Collection, systematic reviews repositories, grey literature databases, internet search engines, and references of included studies. The consistency in the directions of the effect estimates was classified as more consistent positive or negative, less consistent positive or negative, unclear, and consistently null. Next, the confidence in the direction was rated as either convincing, probable, limited-suggestive, or limited non-conclusive evidence. Final synthesis included 36 systematic reviews (21 with and 15 without meta-analyses) that contained 295 distinct primary studies. PM2.5 showed more consistent positive associations than other pollutants. The positive exposure-outcome associations based on the entire pregnancy period were more consistent than trimester-specific exposure averages. For whole pregnancy exposure, a more consistent positive association was found for PM2.5 and birth weight reductions, particulate matter and spontaneous abortion, and SO2 and LBW. Other exposure-outcome associations mostly showed less consistent positive associations and few unclear directions of associations. Almost all associations showed probable evidence. The available evidence indicates plausible causal effects of criteria air pollutants on birth outcomes. To strengthen the evidence, more high-quality studies are required, particularly from understudied settings, such as low-and-middle-income countries. However, the current evidence may warrant the adoption of the precautionary principle.

A systematic literature review on indoor PM2.5 concentrations and personal exposure in urban residential buildings

Particulate matter with an aerodynamic diameter less than 2.5μm (PM_2.5) is currently a major air pollutant that has been raising public attention. Studies have found that short/long-term exposure to PM_2.5 lead detrimental health effects. Since people in most region of the world spend a large proportion of time in dwellings, personal exposure to PM_2.5 in home microenvironment should be carefully investigated. The objective of this review is to investigate and summary studies in terms of personal exposure to indoor PM_2.5 pollutants from the literature between 2000 and 2021. Factors from both outdoor and indoor environment that have impact on indoor PM_2.5 levels were explicated. Exposure studies were verified relating to individual activity pattern and exposure models. It was found that abundant investigations in terms of personal exposure to indoor PM_2.5 is affected by factors including concentration level, exposure duration and personal diversity. Personal exposure models, including microenvironment model, mathematical model, stochastic model and other simulation models of particle deposition in different regions of human airway are reviewed. Further studies joining indoor measurement and simulation of PM_2.5 concentration and estimation of deposition in human respiratory tract are necessary for individual health protection.

Evaluating Ensemble Learning Methods for Multi-Modal Emotion Recognition Using Sensor Data Fusion

Automatic recognition of human emotions is not a trivial process. There are many factors affecting emotions internally and externally. Expressing emotions could also be performed in many ways such as text, speech, body gestures or even physiologically by physiological body responses. Emotion detection enables many applications such as adaptive user interfaces, interactive games, and human robot interaction and many more. The availability of advanced technologies such as mobiles, sensors, and data analytics tools led to the ability to collect data from various sources, which enabled researchers to predict human emotions accurately. Most current research uses them in the lab experiments for data collection. In this work, we use direct and real time sensor data to construct a subject-independent (generic) multi-modal emotion prediction model. This research integrates both on-body physiological markers, surrounding sensory data, and emotion measurements to achieve the following goals: (1) Collecting a multi-modal data set including environmental, body responses, and emotions. (2) Creating subject-independent Predictive models of emotional states based on fusing environmental and physiological variables. (3) Assessing ensemble learning methods and comparing their performance for creating a generic subject-independent model for emotion recognition with high accuracy and comparing the results with previous similar research. To achieve that, we conducted a real-world study “in the wild” with physiological and mobile sensors. Collecting the data-set is coming from participants walking around Minia university campus to create accurate predictive models. Various ensemble learning models (Bagging, Boosting, and Stacking) have been used, combining the following base algorithms (K Nearest Neighbor KNN, Decision Tree DT, Random Forest RF, and Support Vector Machine SVM) as base learners and DT as a meta-classifier. The results showed that, the ensemble stacking learner technique gave the best accuracy of 98.2% compared with other variants of ensemble learning methods. On the contrary, bagging and boosting methods gave (96.4%) and (96.6%) accuracy levels respectively.

Air pollution exposure assessment at schools and playgrounds in Williamsburg Brooklyn NYC, with a view to developing a set of policy solutions

Community science offers unique opportunities for non-professional involvement of volunteers in the scientific process, not just during the data acquisition, but also in other phases, like problem definition, quality assurance, data analysis and interpretation, and the dissemination of results. Moreover, community science can be a powerful tool for public engagement and empowerment during policy formulation. This paper aims to present a pilot study on personal exposure to fine particulate matter (PM2.5) and raises awareness of the hazards of air pollution. As part of data acquisition conducted in 2019, high school students gathered data at their schools, schoolyards, and playgrounds using low-cost monitors AirBeam2. The data was automatically uploaded every second onto the AirCasting mobile app. Besides, a stationary network of air monitors (fixed stations) was deployed in the neighborhood to collect real-time ambient air concentrations of PM2.5. Students involved in the project attended workshops, training sessions, and researched to better understand air pollution, as part of their science class curriculum and portfolio. This air quality monitoring was incorporated into the "Our Air/Nuestro Aire" - El Puente grassroots campaign. The main goals of this campaign included sharing the data collected with the community, engaging academic partners to develop a set of policy and urban design solutions, and to be considered into a 5-point policy platform.

Laboratory Chamber Evaluation of Flow Air Quality Sensor PM2.5 and PM10 Measurements

The emergence of low-cost air quality sensors as viable tools for the monitoring of air quality at population and individual levels necessitates the evaluation of these instruments. The Flow air quality tracker, a product of Plume Labs, is one such sensor. To evaluate these sensors, we assessed 34 of them in a controlled laboratory setting by exposing them to PM₁₀ and PM_2.5 and compared the response with Plantower A003 measurements. The overall coefficient of determination (R²) of measured PM_2.5 was 0.76 and of PM₁₀ it was 0.73, but the Flows’ accuracy improved after each introduction of incense. Overall, these findings suggest that the Flow can be a useful air quality monitoring tool in air pollution areas with higher concentrations, when incorporated into other monitoring frameworks and when used in aggregate. The broader environmental implications of this work are that it is possible for individuals and groups to monitor their individual exposure to particulate matter pollution.

Sensing the Nighttime Economy–Housing Imbalance from a Mobile Phone Data Perspective: A Case Study in Shanghai

Sensing the nighttime economy–housing imbalance is of great importance for urban planning and commerce. As an efficient tool of social sensing and human observation, mobile phone data provides an effective way to address this issue. In this paper, an indicator, mobile phone data-based nighttime economy–housing imbalance intensity, is proposed to measure the degree of the nighttime economy–housing imbalance. This indicator can distinguish vitality variations between sleep periods and nighttime activity periods, which are highly related to the nighttime economy–housing imbalance. The spatial pattern of the nighttime economy–housing imbalance was explored, and its association with the built environment was investigated through city-scale geographical regression analysis in Shanghai, China. The results showed that the sub-districts of Shanghai with high-positive-imbalance intensities displayed structures with superimposed rings and striped shapes, and the sub-districts with negative imbalance intensities were distributed around high positive-intensity areas. There were significant linear correlations between imbalance intensity and the built environment. The multiple influences of built environment factors and related mechanisms were explored from a geographical perspective. Our study utilized the social sensing data to provide a more comprehensive understanding of the nighttime economy–housing imbalance. These findings will be useful for fostering the nighttime economy and supporting urban renewal.

Evaluation and prevention and control measures of urban public transport exposure risk under the influence of COVID-19—Taking Wuhan as an example

Background

Since December 2019, COVID-19 began to spread throughout the world for nearly two years. During the epidemic, the travel intensity of most urban residents has dropped significantly, and they can only complete inflexible travel such as "home to designated hospital" and "home to supermarket" and some special commuting trips. While ensuring basic travel of residents under major public health emergency, there is also a problem of high risk of infection caused by exposure of the population to the public transport network. For the discipline of urban transport, how to use planning methods to promote public health and reduce the potential spread of diseases has become a common problem faced by the government, academia and industry.

Method

Based on the mobility perspective of travel agents, the spatial analysis methods such as topological model of bus network structure, centrality model of public transport network and nuclear density analysis are used to obtain the exposure risk and spatial distribution characteristics of public transport from two aspects of bus stops and epidemic sites.

Results

The overall spatial exposure risk of Wuhan city presents an obvious "multi center circle" structure at the level of bus stops. The high and relatively high risk stops are mainly transport hubs, shopping malls and other sites, accounting for 35.63%. The medium and low-risk stops are mainly the villages and communities outside the core areas of each administrative region, accounting for 64.37%. On the other hand, at the scale of epidemic sites, the coverage covers 4018 bus stops in Wuhan, accounting for 36.5% of all bus stops, and 169 bus lines, accounting for 39.9% of all routes. High risk epidemic sites are mainly concentrated in the core areas within the jurisdiction of Wuhan City, and in the direction of urban outer circle diffusion, they are mainly distributed in the low and medium risk epidemic sites. According to the difference of the risk level of public transport exposure, the hierarchical public transport control measures are formulated.

Discussion

This paper proposes differentiated prevention and control countermeasures according to the difference of risk levels, and provides theoretical basis and decision-making reference for urban traffic management departments in emergency management and formulation of prevention and control countermeasures.

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.

Children's exposure to size-fractioned particulate matter: Chemical composition and internal dose

The health effects of the particulate matter (PM) depend not only on its aerodynamic diameter (AD) and chemical composition, but also on the time activity pattern of the individuals and on their age. The main objective of this work was to assess the exposure of children to aerosol particles by using personal instruments, to study the particle size and composition of the inhaled PM, and to estimate their transport and deposition into the human respiratory tract (HRT). The average daily PM2.5 exposure was 19 μg/m³ and the size fractions with the greatest contribution to PM2.5 concentrations were 1 < AD <2.5 μm and AD <0.25 μm. Results indicated a contribution of 9% from the mineral aerosol, 7.2% from anthropogenic sulphate, 6.7% from black carbon and 5% from anthropogenic trace elements to the daily exposure to PM2.5. The levels of mineral and marine elements increased with increasing particle size, while anthropogenic elements were present in higher concentrations in the finest particles. Particle size has been shown to influence the variability of daily dose deposited between the extrathoracic and alveolar-interstitial zones. On average, 3% of the PM deposited in the bronchial region, whereas 5% to 8% were found in the bronchiolar region. The level of physical activity had a significant contribution to the total daily dose.

Estimates of population exposure to atmospheric pollution and health-related externalities in a real city: The impact of spatial resolution on the accuracy of results

Health impacts of atmospheric pollution is an important issue in urban environments. Its magnitude depends on population exposure which have been frequently estimated by considering different approaches relating pollutant concentration and population exposed to it. However, the uncertainties due to the spatial resolution of the model used to estimate the pollutant concentration or due to the lack of representativeness of urban air quality monitoring station (AQMS) have not been evaluated in detail. In this context, NO₂ annual average concentration at pedestrian level in the whole city of Pamplona (Spain) modelled at high spatial resolution (~1 m) by Computational Fluid Dynamic (CFD) simulations is used to estimate the total population exposure and health-related externalities by using different approaches. Air pollutant concentration and population are aggregated at different spatial resolutions ranging from a horizontal grid cell size of 100 m × 100 m to a coarser resolution where the whole city is covered by only one cell (6 km × 5 km). In addition, concentrations at AQMS locations are also extracted to assess the representativeness of those AQMS. The case with a spatial resolution of 100 m × 100 m for both pollutant-concentration distribution and population data is used as a reference (Base case) and compared with those obtained with the other approaches. This study indicates that the spatial resolution of concentration and population distribution in the city should be 1 km × 1 km or finer to obtain appropriate estimates of total population exposure (underestimations <13%) and health-related externalities (underestimations <37%). For the cases with coarser resolutions, a strong underestimation of total population exposure (>31%) and health-related externalities (>76%) was found. On the other hand, the use of AQMS concentrations can induce important errors due to the limited spatial representativeness, in particular in terms of population exposure.

Spatiotemporal variations in aerosol optical depth and associated risks for populations in the arid region of Central Asia

With the progress of urbanization, atmospheric pollution and physical health issues caused by the increase of aerosol optical depth (AOD) become more and more prominent. Hence, population exposure risk to AOD becomes a research hotspot. The arid Central Asia (ACA) has a generally high AOD and is a major source area for dust aerosols in the world. Only few studies have discussed population exposure risk to AOD in ACA. Based on multisource remote sensing data, and used population exposure risk model, this study evaluated population exposure risk to AOD in six ecological zones (Northern steppe region of ACA (NSCA), Aral Sea desert area (ASDA), Tianshan Mountains (TSMT), Junggar Basin desert area (JBDA), Tarim Basin desert area (TBDA) and Hexi corridor desert area (HCDA)). Generally, AOD in ACA was kept increasing from 2000 to 2015, and it increased mostly in HCDA and areas near the Aral Sea (p < 0.001). With respect to seasonal variations, the maximum AOD was observed in spring and autumn, and the minimum was in winter. Considering land use changes, AOD was mainly manifested by the reduction of water bodies and expansion of construction lands. This was the mostly significant in NSCA and ASDA (p < 0.01). The population exposure risk to AOD in ACA was increasing continuously from 2000 to 2015, and high-value regions (>9) concentrated in oases, specifically, in the Aral Sea basin and Tarim River basin.The Aral Sea basin became the major AOD source region in ACA due to the shrinking water area after unreasonable development and utilization of water resources. These further increase population exposure risk to AOD in the Aral Sea area. Hence, ecological restoration in terminal lakes of ACA will become the key to lower population exposure risk to AOD practically.

Environmental contaminants and child development: Developmentally-informed opportunities and recommendations for integrating and informing child environmental health science

Child environmental health (CEH) science has identified numerous effects of early life exposures to common, ubiquitous environmental toxicants. CEH scientists have documented the costs not only to individual children but also to population-level health effects of such exposures. Importantly, such risks are unequally distributed in the population, with historically marginalized communities and the children living in these communities receiving the most damaging exposures. Developmental science offers a lens and set of methodologies to identify nuanced biological and behavioral processes that drive child development across physical, cognitive, and socioemotional domains. Developmental scientists are also experts in considering the multiple, hierarchically-layered contexts that shape development alongside toxicant exposure. Such contexts and the individuals acting within them make up an overarching "child serving ecosystem" spanning systems and sectors that serve children directly and indirectly. Articulating how biobehavioral mechanisms and social-ecological contexts unfold from a developmental perspective are needed in order to inform CEH translation and intervention efforts across this child-serving ecosystem. Developmentalists can also benefit from integrating CEH science findings in their work by considering the role of the physical environment, and environmental toxicants specifically, on child health and development. Building on themes that were laid out by Trentacosta and Mulligan in 2020, this commentary presents recommendations for connecting developmental and CEH science and for translating such work so that it can be used to promote child development in an equitable manner across this child-serving ecosystem. These opportunities include (1) Using Developmentally-Informed Conceptual Models; (2) Applying Creative, Sophisticated, and Rigorous Methods; (3) Integrating Developmentally-Sensitive Intervention Considerations; and (4) Establishing Interdisciplinary Collaborations and Cross-Sector Partnerships.

Longitudinal assessment of personal air pollution clouds in ten home and office environments

Elevated exposure to indoor air pollution is associated with negative human health and well-being outcomes. Inhalation exposure studies commonly rely on stationary monitors in combination with human time-activity patterns; however, this method is susceptible to exposure misclassification. We tracked ten participants during five consecutive workdays with stationary air pollutant monitors at their homes and offices, and wearable personal monitors. Real-time measures of size-resolved particulate matter (within range 0.3-10 μm) and CO₂ , and integrated samples of PM₁₀ , VOCs, and aldehydes were collected. The PM₁₀  cloud magnitude (excess of PM₁₀ beyond stationary room concentration) was detected for all participants in homes and offices. The PM₁₀  cloud magnitude ranged within 5-37 μg/m³ and was the most discernible in the coarse particle size fraction. Particles associated with "Urban mix," "Traffic," and "Human activities" sources contributed the most to PM₁₀ exposures. The personal CO₂  clouds were detected for participants with the SEMs in their living rooms and private or low-occupancy offices. The stationary monitors placed in bedrooms were better predictors of personal PM₁₀ and CO₂ exposures. An overall of 33 VOCs and aldehydes were detected in both microenvironments, with the majority exhibiting high correlation between personal and stationary stations.

Exposure and dose assessment of school children to air pollutants in a tropical coastal-urban area

This study estimates exposure and inhaled dose to air pollutants of children residing in a tropical coastal-urban area in Southeast Brazil. For that, twenty-one children filled their time-activities diaries and wore the passive samplers to monitor NO₂. The personal exposure was also estimated using data provided by the combination of WRF-Urban/GEOS-Chem/CMAQ models, and the nearby monitoring station. Indoor/outdoor ratios were used to consider the amount of time spent indoors by children in homes and schools. The model's performance was assessed by comparing the modelled data with concentrations measured by urban monitoring stations. A sensitivity analyses was also performed to evaluate the impact of the model's height on the air pollutant concentrations. The results showed that the mean children's personal exposure to NO₂ predicted by the model (22.3 μg/m³) was nearly twice to those measured by the passive samplers (12.3 μg/m³). In contrast, the nearest urban monitoring station did not represent the personal exposure to NO₂ (9.3 μg/m³), suggesting a bias in the quantification of previous epidemiological studies. The building effect parameterisation (BEP) together with the lowering of the model height enhanced the air pollutant concentrations and the exposure of children to air pollutants. With the use of the CMAQ model, exposure to O₃, PM₁₀, PM_2.5, and PM₁ was also estimated and revealed that the daily children's personal exposure was 13.4, 38.9, 32.9, and 9.6 μg/m³, respectively. Meanwhile, the potential inhalation daily dose was 570-667 μg for PM_2.5, 684-789 μg for PM₁₀, and 163-194 μg for PM₁, showing to be favourable to cause adverse health effects. The exposure of children to air pollutants estimated by the numerical model in this work was comparable to other studies found in the literature, showing one of the advantages of using the modelling approach since some air pollutants are poorly spatially represented and/or are not routinely monitored by environmental agencies in many regions.

Association of exposure to air pollutants with gestational diabetes mellitus in Chiayi City, Taiwan

INTRODUCTION

We investigated the associations of exposure to particulate matter with an aerodynamic diameter less than 2.5 μm (PM_2.5) and several gaseous pollutants with risk of gestational diabetes mellitus (GDM) in Taiwan.

METHODS

We retrospectively identified pregnant women who underwent a two-step approach to screen for GDM between 2006 and 2014. Information on concentrations of air pollutants (including PM_2.5, sulfur dioxide [SO₂], nitrogen oxides [NO_x], and ozone [O₃]) were collected from a single fixed-site monitoring station. We conducted logistic regression analyses to determine the associations between exposure to air pollutants and risk of GDM.

RESULTS

A total of 11210 women were analyzed, and 705 were diagnosed with GDM. Exposure to PM_2.5 during the second trimester was associated with a nearly 50% higher risk of GDM (odds ratio [OR] 1.47, 95% CI 0.96 to 2.24, p=0.077). The associations were consistent in the two-pollutant model (PM_2.5 + SO₂ [OR 1.73, p=0.038], PM_2.5 + NO_x [OR 1.52, p=0.064], PM_2.5 + O₃ [OR 1.96, p=0.015]), and were more prominent in women with age <30 years and body mass index <25 kg/m² (interaction p values <0.01).

DISCUSSION

Exposure to PM_2.5 was associated with risk of GDM, especially in women who were younger or had a normal body mass index.

The influence of outdoor PM2.5 concentration at workplace on nonaccidental mortality estimates in a Canadian census-based cohort

Background

Associations between mortality and exposure to ambient air pollution are usually explored using concentrations of residential outdoor fine particulate matter (PM_2.5) to estimate individual exposure. Such studies all have an important limitation in that they do not capture data on individual mobility throughout the day to areas where concentrations may be substantially different, leading to possible exposure misclassification. We examine the possible role of outdoor PM_2.5 concentrations at work for a large population-based mortality cohort.

Methods

Using the 2001 Canadian Census Health and Environment Cohort (CanCHEC), we created a time-weighted average that incorporates employment hours worked in the past week and outdoor PM_2.5 concentration at work and home. We used a Cox proportional hazard model with a 15-year follow-up (2001 to 2016) to explore whether inclusion of workplace estimates had an impact on hazard ratios for mortality for this cohort.

Results

Hazard ratios relying on outdoor PM_2.5 concentration at home were not significantly different from those using a time-weighted estimate, for the full cohort, nor for those who commute to a regular workplace. When exploring cohort subgroups according to neighborhood type and commute distance, there was a notable but insignificant change in risk of nonaccidental death for those living in car-oriented neighborhoods, and with commutes greater than 10 km.

Conclusions

Risk analyses performed with large cohorts in low-pollution environments do not seem to be biased if relying solely on outdoor PM_2.5 concentrations at home to estimate exposure.

Clean Air, Smart Cities, Healthy Hearts: Action on Air Pollution for Cardiovascular Health

More than twenty percent of all cardiovascular disease (CVD) deaths are caused by air pollution — more than three million deaths every year — and these numbers will continue to rise unless the global community takes action. Nine out of ten people worldwide breathe polluted air, which disproportionately affects those living in low-resource settings. The World Heart Federation (WHF) is committed to reducing the impact of air pollution on people’s health and has made this a priority area of its global advocacy efforts. In pursuit of this goal, WHF has formed an Air Pollution Expert Group to inform action on air pollution for CVD health and recommend changes to public health policy. This policy paper lays out the health impacts of air pollution, examines its position on the global policy agenda, demonstrates its relevance to the cardiovascular community, and proposes actionable policy measures to mitigate this deadly risk factor to health. The paper considers the important roles to be played by the Members of WHF, including scientific societies and the physicians that constitute them, heart health foundations, and patient advocacy groups. The paper concludes with a detailed table of recommendations for the various sub-target groups at the global, national, local, and patient level.

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

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

"I am an air quality scientist"- Using citizen science to characterise school children's exposure to air pollution

Children are particularly vulnerable to the harmful effects of air pollution. To tackle this issue and implement effective strategies to reduce child exposure, it is important to understand how children are exposed to this risk. This study followed a citizen science approach to air pollution monitoring, aiming to characterise school children's exposure to air pollution and to analyse how a citizen science approach to data collection could contribute to and enhance the research process. 258 children across five London primary schools attended air pollution education sessions and measured air pollution for a week using backpacks with built-in air quality sensors. Children received a summary of the results, advice and information on how to reduce exposure to air pollution. Data on the impact of the approach on the school community were collected using surveys and focus groups with children and their parents and interviews with the teachers involved. The unique data set obtained permitted us to map different routes and modes of transport used by the children and quantify different exposure levels. We identified that, on average, children were exposed to higher levels of air pollution when travelling to and from school, particularly during the morning journey where air pollution levels were on average 52% higher than exposures at school. Children who walked to and from school through busy main roads were exposed to 33% higher levels of air pollution than those who travelled through back streets. The findings from this study showed that using a citizen science approach to data collection, where children are actively involved in the research process, not only facilitated the gathering of a large data set by encouraging participation and stimulating adherence with the study protocol, but also increased children's awareness of air pollution, encouraging them to adopt positive behaviour changes to reduce their exposure.

Crowdsensing IoT Architecture for Pervasive Air Quality and Exposome Monitoring: Design, Development, Calibration, and Long-Term Validation

A pervasive assessment of air quality in an urban or mobile scenario is paramount for personal or city-wide exposure reduction action design and implementation. The capability to deploy a high-resolution hybrid network of regulatory grade and low-cost fixed and mobile devices is a primary enabler for the development of such knowledge, both as a primary source of information and for validating high-resolution air quality predictive models. The capability of real-time and cumulative personal exposure monitoring is also considered a primary driver for exposome monitoring and future predictive medicine approaches. Leveraging on chemical sensing, machine learning, and Internet of Things (IoT) expertise, we developed an integrated architecture capable of meeting the demanding requirements of this challenging problem. A detailed account of the design, development, and validation procedures is reported here, along with the results of a two-year field validation effort.

The Exposome in the Era of the Quantified Self

Human health is regulated by complex interactions among the genome, the microbiome, and the environment. While extensive research has been conducted on the human genome and microbiome, little is known about the human exposome. The exposome comprises the totality of chemical, biological, and physical exposures that individuals encounter over their lifetimes. Traditional environmental and biological monitoring only targets specific substances, whereas exposomic approaches identify and quantify thousands of substances simultaneously using nontargeted high-throughput and high-resolution analyses. The quantified self (QS) aims at enhancing our understanding of human health and disease through self-tracking. QS measurements are critical in exposome research, as external exposures impact an individual's health, behavior, and biology. This review discusses both the achievements and the shortcomings of current research and methodologies on the QS and the exposome and proposes future research directions.

Residential indoor air quality interventions through a social-ecological systems lens: A systematic review

Indoor air quality (IAQ) is an important consideration for health and well-being as people spend most of their time indoors. Multi-disciplinary interest in IAQ is growing, resulting in more empirical research, especially in affordable housing settings, given disproportionate impacts on vulnerable populations. Conceptually, there is little coherency among these case studies; they traverse diverse spatial scales, indoor and outdoor environments, and populations, making it difficult to implement research findings in any given setting. We employ a social-ecological systems (SES) framework to review and categorize existing interventions and other literature findings to elucidate relationships among spatially and otherwise diverse IAQ factors. This perspective is highly attentive to the role of agency, highlighting individual, household, and organizational behaviors and constraints in managing IAQ. When combined with scientific knowledge about the effectiveness of IAQ interventions, this approach favors actionable strategies for reducing the presence of indoor pollutants and personal exposures.