Satellite remote sensing in epidemiological studies

When the smoke gets in your lungs: short-term effects of Indonesia's 2015 forest fires on health care use

BACKGROUND

The forest fires that ravaged parts of Indonesia in 2015 were the most severely polluting of this century but little is known about their effects on health care utilization of the affected population. We estimate their short-term impact on visit rates to primary and hospital care with particular focus on visits for specific smoke-related conditions (respiratory disease, acute respiratory tract infection (ARTI) and common cold).

METHOD

We estimate the short-term impact of the 2015 forest fire on visit rates to primary and hospital care by combining satellite data on Aerosol Optical Depth (AOD) with administrative records from Indonesian National Health Insurance Agency (BPJS Kesehatan) from January 2015-April  2016. The 16 months of panel data cover 203 districts in the islands of Sumatra and Kalimantan before, during and after the forest fires. We use the (more efficient) ANCOVA version adaptation of a fixed effects model to compare the trends in healthcare use of affected districts (with AOD value above 0.75) with control districts (AOD value below 0.75). Considering the higher vulnerability of children's lungs, we do this separately for children under 5 and the rest of the population adults (> 5), and for both urban and rural areas, and for both the period during and after the forest fires.

RESULTS

We find little effects for adults. For young children we estimate positive effects for care related to respiratory problems in primary health care facilities in urban areas. Hospital care visits in general, on the other hand, are negatively affected in rural areas. We argue that these patterns arise because accessibility of care during fires is more restricted for rural than for urban areas.

CONCLUSION

The severity of the fires and the absence of positive impact on health care utilization for adults and children in rural areas indicate large missed opportunities for receiving necessary care. This is particularly worrisome for children, whose lungs are most vulnerable to the effects. Our findings underscore the need to ensure ongoing access to medical services during forest fires and emphasize the necessity of catching up with essential care for children after the fires, particularly in rural areas.

Estimation of PM2.5 using high-resolution satellite data and its mortality risk in an area of Iran

Satellite-based exposure of fine particulate matters has been seldom used as a predictor of mortality. PM2.5 was predicted using Aerosol Optical Depths (AOD) through a two-stage regression model. The predicted PM2.5 was corrected for the bias using two approaches. We estimated the impact by two different scenarios of PM2.5 in the model. We statistically found different distributions of the predicted PM2.5 over the region. Compared to the reference value (5 µg/m3), 90th and 95th percentiles had significant adverse effect on total mortality (RR 90th percentile:1.45; CI 95%: 1.08-1.95 and RR 95th percentile:1.53; CI 95%: 1.11-2.1). Nearly 1050 deaths were attributed to any range of the air pollution (unhealthy range), of which more than half were attributed to high concentration range. Given the adverse effect of extreme values compared to the both scenarios, more efforts are suggested to define local-specific reference values and preventive strategies.

Advancing application of satellite remote sensing technologies for linking atmospheric and built environment to health

Background

The intricate interplay between human well-being and the surrounding environment underscores contemporary discourse. Within this paradigm, comprehensive environmental monitoring holds the key to unraveling the intricate connections linking population health to environmental exposures. The advent of satellite remote sensing monitoring (SRSM) has revolutionized traditional monitoring constraints, particularly limited spatial coverage and resolution. This innovation finds profound utility in quantifying land covers and air pollution data, casting new light on epidemiological and geographical investigations. This dynamic application reveals the intricate web connecting public health, environmental pollution, and the built environment.

Objective

This comprehensive review navigates the evolving trajectory of SRSM technology, casting light on its role in addressing environmental and geographic health issues. The discussion hones in on how SRSM has recently magnified our understanding of the relationship between air pollutant exposure and population health. Additionally, this discourse delves into public health challenges stemming from shifts in urban morphology.

Methods

Utilizing the strategic keywords "SRSM," "air pollutant health risk," and "built environment," an exhaustive search unfolded across prestigious databases including the China National Knowledge Network (CNKI), PubMed and Web of Science. The Citespace tool further unveiled interconnections among resultant articles and research trends.

Results

Synthesizing insights from a myriad of articles spanning 1988 to 2023, our findings unveil how SRMS bridges gaps in ground-based monitoring through continuous spatial observations, empowering global air quality surveillance. High-resolution SRSM advances data precision, capturing multiple built environment impact factors. Its application to epidemiological health exposure holds promise as a pioneering tool for contemporary health research.

Conclusion

This review underscores SRSM's pivotal role in enriching geographic health studies, particularly in atmospheric pollution domains. The study illuminates how SRSM overcomes spatial resolution and data loss hurdles, enriching environmental monitoring tools and datasets. The path forward envisions the integration of cutting-edge remote sensing technologies, novel explorations of urban-public health associations, and an enriched assessment of built environment characteristics on public well-being.

Benefits of Space Medicine Research for Healthcare on Earth

Space research has brought various discoveries and benefits in the fields of health, transportation, safety measures, industry, and many more. Additionally, space research has provided a large number of discoveries and inventions in the field of medicine. Many of these inventions benefit humanity in multiple ways, especially with regard to well-being. Research objectives range from the early detection of illnesses to statistical studies that help in epidemiology. Furthermore, there are potential future opportunities that might help in the development of mankind in general and Earth medicine in particular. This review presents some of the significant inventions that were made through the journey to space and elaborate on how those inventions helped develop Earth medicine and other fields.

Environmental neuroscience linking exposome to brain structure and function underlying cognition and behavior

Individual differences in human brain structure, function, and behavior can be attributed to genetic variations, environmental exposures, and their interactions. Although genome-wide association studies have identified many genetic variants associated with brain imaging phenotypes, environmental exposures associated with these phenotypes remain largely unknown. Here, we propose that environmental neuroscience should pay more attention on exploring the associations between lifetime environmental exposures (exposome) and brain imaging phenotypes and identifying both cumulative environmental effects and their vulnerable age windows during the life course. Exposome-neuroimaging association studies face several challenges including the accurate measurement of the totality of environmental exposures varied in space and time, the highly correlated structure of the exposome, and the lack of standardized approaches for exposome-wide association studies. By agnostically scanning the effects of environmental exposures on brain imaging phenotypes and their interactions with genomic variations, exposome-neuroimaging association analyses will improve our understanding of causal factors associated with individual differences in brain structure and function as well as their relations with cognitive abilities and neuropsychiatric disorders.

Long-Term Exposure to Ambient Fine Particulate Matter and Incidence of Major Cardiovascular Diseases: A Prospective Study of 0.5 Million Adults in China

Few cohort studies explored the long-term effects of ambient fine particulate matter (PM_2.5) on incidence of cardiovascular diseases (CVDs), especially in countries with higher levels of air pollution. We aimed to evaluate the association between long-term exposure to PM_2.5 and incidence of CVD in China. We performed a prospective cohort study in ten regions that recruited 512,689 adults during 2004-2008, with follow-up until 2017. Annual PM_2.5 concentrations were estimated using a satellite-based model with national coverage and 1 x 1 km spatial resolution. Time-varying Cox proportional hazard regression models were used to estimate hazard ratios (HRs) for all-cause and cause-specific CVDs associated with PM_2.5, adjusting for conventional covariates. During 5.08 million person-years of follow-up, 148,030 incident cases of CVD were identified. Long-term exposure to PM_2.5 showed positive and linear association with incidence of CVD, without a threshold below any concentration. The adjusted HRs per 10 μg/m³ increase in PM_2.5 was 1.04 (95%CI: 1.02, 1.07) for total CVD. The risk estimates differed between certain population subgroups, with greater HRs in men, in household with higher income, and in people using unclean heating fuels. This prospective study of large Chinese population provided essential epidemiological evidence for CVD incident risk associated with PM_2.5.

Using wildland fire smoke modeling data in gerontological health research (California, 2007-2018)

Widespread population exposure to wildland fire smoke underscores the urgent need for new techniques to characterize fire-derived pollution for epidemiologic studies and to build climate-resilient communities especially for aging populations. Using atmospheric chemical transport modeling, we examined air quality with and without wildland fire smoke PM_2.5. In 12-km gridded output, the 24-hour average concentration of all-source PM_2.5 in California (2007-2018) was 5.16 μg/m³ (S.D. 4.66 μg/m³). The average concentration of fire-PM_2.5 in California by year was 1.61 μg/m³ (~30% of total PM_2.5). The contribution of fire-source PM_2.5 ranged from 6.8% to 49%. We define a "smokewave" as two or more consecutive days with modeled levels above 35 μg/m³. Based on model-derived fire-PM_2.5, 99.5% of California's population lived in a county that experienced at least one smokewave from 2007 to 2018, yet understanding of the impact of smoke on the health of aging populations is limited. Approximately 2.7 million (56%) of California residents aged 65+ years lived in counties representing the top 3 quartiles of fire-PM_2.5 concentrations (2007-2018). For each year (2007-2018), grid cells containing skilled nursing facilities had significantly higher mean concentrations of all-source PM_2.5 than cells without those facilities, but they also had generally lower mean concentrations of wildland fire-specific PM_2.5. Compared to rural monitors in California, model predictions of wildland fire impacts on daily average PM_2.5 carbon (organic and elemental) performed well most years but tended to overestimate wildland fire impacts for high-fire years. The modeling system isolated wildland fire PM_2.5 from other sources at monitored and unmonitored locations, which is important for understanding exposures for aging population in health studies.

What you see is what you breathe? Estimating air pollution spatial variation using street level imagery

High spatial resolution information on urban air pollution levels is unavailable in many areas globally, partially due to high input data needs of existing estimation approaches. Here we introduce a computer vision method to estimate annual means for air pollution levels from street level images. We used annual mean estimates of NO2 and PM2.5 concentrations from locally calibrated models as labels from London, New York, and Vancouver to allow for compilation of a sufficiently large dataset (~250k images for each city). Our experimental setup is designed to quantify intra and intercity transferability of image-based model estimates. Performances were high and comparable to traditional land-use regression (LUR) and dispersion models when training and testing on images from the same city (R2 values between 0.51 and 0.95 when validated on data from ground monitoring stations). Like LUR models, transferability of models between cities in different geographies is more difficult. Specifically, transferability between the three cities i.e., London, New York, and Vancouver, which have similar pollution source profiles were moderately successful (R2 values between zero and 0.67). Comparatively, performances when transferring models trained on these cities with very different source profiles i.e., Accra in Ghana and Hong Kong were lower (R2 between zero and 0.21) suggesting the need for local calibration with local calibration using additional measurement data from cities that share similar source profiles.

Ambient Air Pollution Exposure Assessments in Fertility Studies: a Systematic Review and Guide for Reproductive Epidemiologists

Purpose of Review

We reviewed the exposure assessments of ambient air pollution used in studies of fertility, fecundability, and pregnancy loss.

Recent Findings

Comprehensive literature searches were performed in the PUBMED, Web of Science, and Scopus databases. Of 168 total studies, 45 met the eligibility criteria and were included in the review. We find that 69% of fertility and pregnancy loss studies have used one-dimensional proximity models or surface monitor data, while only 35% have used the improved models, such as land-use regression models (4%), dispersion/chemical transport models (11%), or fusion models (20%). No published studies have used personal air monitors.

Summary

While air pollution exposure models have vastly improved over the past decade from a simple, one-dimensional distance or air monitor data to models that incorporate physiochemical properties leading to better predictive accuracy, precision, and increased spatiotemporal variability and resolution, the fertility literature has yet to fully incorporate these new methods. We provide descriptions of each of these air pollution exposure models and assess the strengths and limitations of each model, while summarizing the findings of the literature on ambient air pollution and fertility that apply each method.

Mapping Mobility: Utilizing Local-Knowledge-Derived Activity Space to Estimate Exposure to Ambient Air Pollution among Individuals Experiencing Unsheltered Homelessness

Individuals experiencing homelessness represent a growing population in the United States. Air pollution exposure among individuals experiencing homelessness has not been quantified. Utilizing local knowledge mapping, we generated activity spaces for 62 individuals experiencing homelessness residing in a semi-rural county within the United States. Satellite derived measurements of fine particulate matter (PM2.5) were utilized to estimate annual exposure to air pollution experienced by our participants, as well as differences in the variation in estimated PM2.5 at the local scale compared with stationary monitor data and point location estimates for the same period. Spatial variation in exposure to PM2.5 was detected between participants at both the point and activity space level. Among all participants, annual median PM2.5 exposure was 16.22 μg/m³, exceeding the National Air Quality Standard. Local knowledge mapping represents a novel mechanism to capture mobility patterns and investigate exposure to air pollution within vulnerable populations. Reliance on stationary monitor data to estimate air pollution exposure may lead to exposure misclassification, particularly in rural and semirural regions where monitoring is limited.

New Homogeneous Spatial Areas Identified Using Case-Crossover Spatial Lag Grid Differences between Aerosol Optical Depth-PM2.5 and Respiratory-Cardiovascular Emergency Department Visits and Hospitalizations

Optimal use of Hierarchical Bayesian Model (HBM)-assembled aerosol optical depth (AOD)-PM2.5 fused surfaces in epidemiologic studies requires homogeneous temporal and spatial fused surfaces. No analytical method is available to evaluate spatial heterogeneity. The temporal case-crossover design was modified to assess the spatial association between four experimental AOD-PM2.5 fused surfaces and four respiratory–cardiovascular hospital events in 12 km2 grids. The maximum number of adjacent lag grids with significant odds ratios (ORs) identified homogeneous spatial areas (HOSAs). The largest HOSA included five grids (lag grids 04; 720 km2) and the smallest HOSA contained two grids (lag grids 01; 288 km2). Emergency department asthma and inpatient asthma, myocardial infarction, and heart failure ORs were significantly higher in rural grids without air monitors than in urban grids with air monitors at lag grids 0, 1, and 01. Rural grids had higher AOD-PM2.5 concentration levels, population density, and poverty percentages than urban grids. Warm season ORs were significantly higher than cold season ORs for all health outcomes at lag grids 0, 1, 01, and 04. The possibility of elevated fine and ultrafine PM and other demographic and environmental risk factors synergistically contributing to elevated respiratory–cardiovascular chronic diseases in persons residing in rural areas was discussed.

An exposomic framework to uncover environmental drivers of aging

The exposome, the environmental complement of the genome, is an omics level characterization of an individual’s exposures. There is growing interest in uncovering the role of the environment in human health using an exposomic framework that provides a systematic and unbiased analysis of the non-genetic drivers of health and disease. Many environmental toxicants are associated with molecular hallmarks of aging. An exposomic framework has potential to advance understanding of these associations and how modifications to the environment can promote healthy aging in the population. However, few studies have used this framework to study biological aging. We provide an overview of approaches and challenges in using an exposomic framework to investigate environmental drivers of aging. While capturing exposures over a life course is a daunting and expensive task, the use of historical data can be a practical way to approach this research.

Operationalizing the Exposome Using Passive Silicone Samplers

The exposome, which is defined as the cumulative effect of environmental exposures and corresponding biological responses, aims to provide a comprehensive measure for evaluating non-genetic causes of disease. Operationalization of the exposome for environmental health and precision medicine has been limited by the lack of a universal approach for characterizing complex exposures, particularly as they vary temporally and geographically. To overcome these challenges, passive sampling devices (PSDs) provide a key measurement strategy for deep exposome phenotyping, which aims to provide comprehensive chemical assessment using untargeted high-resolution mass spectrometry for exposome-wide association studies. To highlight the advantages of silicone PSDs, we review their use in population studies and evaluate the broad range of applications and chemical classes characterized using these samplers. We assess key aspects of incorporating PSDs within observational studies, including the need to preclean samplers prior to use to remove impurities that interfere with compound detection, analytical considerations, and cost. We close with strategies on how to incorporate measures of the external exposome using PSDs, and their advantages for reducing variability in exposure measures and providing a more thorough accounting of the exposome. Continued development and application of silicone PSDs will facilitate greater understanding of how environmental exposures drive disease risk, while providing a feasible strategy for incorporating untargeted, high-resolution characterization of the external exposome in human studies.

PM2.5 exposure as a risk factor for type 2 diabetes mellitus in the Mexico City metropolitan area

BACKGROUND

Exposure to air pollution is the main risk factor for morbidity and mortality in the world. Exposure to particulate matter with aerodynamic diameter ≤ 2.5 μm (PM2.5) is associated with cardiovascular and respiratory conditions, as well as with lung cancer, and there is evidence to suggest that it is also associated with type II diabetes (DM). The Mexico City Metropolitan Area (MCMA) is home to more than 20 million people, where PM2.5 levels exceed national and international standards every day. Likewise, DM represents a growing public health problem with prevalence around 12%. In this study, the objective was to evaluate the association between exposure to PM2.5 and DM in adults living in the MCMA.

METHODS

Data from the 2006 or 2012 National Health and Nutrition Surveys (ENSANUT) were used to identify subjects with DM and year of diagnosis. We estimated PM2.5 exposure at a residence level, based on information from the air quality monitoring system (monitors), as well as satellite measurements (satellite). We analyzed the relationship through a cross-sectional approach and as a case - control study.

RESULTS

For every 10 μg/m3 increase of PM2.5 we found an OR = 3.09 (95% CI 1.17-8.15) in the 2012 sample. These results were not conclusive for the 2006 data or for the case - control approach.

CONCLUSIONS

Our results add to the evidence linking PM2.5 exposure to DM in Mexican adults. Studies in low- and middle-income countries, where PM2.5 atmospheric concentrations exceed WHO standards, are required to strengthen the evidence.

Defining the Scope of Exposome Studies and Research Needs from a Multidisciplinary Perspective

The concept of the exposome was introduced over 15 years ago to reflect the important role that the environment exerts on health and disease. While originally viewed as a call-to-arms to develop more comprehensive exposure assessment methods applicable at the individual level and throughout the life course, the scope of the exposome has now expanded to include the associated biological response. In order to explore these concepts, a workshop was hosted by the Gunma University Initiative for Advanced Research (GIAR, Japan) to discuss the scope of exposomics from an international and multidisciplinary perspective. This Global Perspective is a summary of the discussions with emphasis on (1) top-down, bottom-up, and functional approaches to exposomics, (2) the need for integration and standardization of LC- and GC-based high-resolution mass spectrometry methods for untargeted exposome analyses, (3) the design of an exposomics study, (4) the requirement for open science workflows including mass spectral libraries and public databases, (5) the necessity for large investments in mass spectrometry infrastructure in order to sequence the exposome, and (6) the role of the exposome in precision medicine and nutrition to create personalized environmental exposure profiles. Recommendations are made on key issues to encourage continued advancement and cooperation in exposomics.

Pandemic meets pollution: Poor air quality increases deaths by COVID-19

We study the impact of short-term exposure to ambient air pollution on the spread and severity of COVID-19 in Germany. We combine data at the county-by-day level on confirmed cases and deaths with information on local air quality and weather conditions. Following Deryugina et al. (2019), we instrument short-term variation in local concentrations of particulate matter (PM10) by region-specific daily variation in wind directions. We find significant positive effects of PM10 concentration on death numbers from four days before to ten days after the onset of symptoms. Specifically, for elderly patients (80+ years) an increase in ambient PM10 concentration by one standard deviation between two and four days after developing symptoms increases the number of deaths by 19 percent of a standard deviation. In addition, higher levels air pollution raise the number of confirmed cases of COVID-19 for all age groups. The timing of effects surrounding the onset of illness suggests that air pollution affects the severity of already-realized infections. We discuss the implications of our results for immediate policy levers to reduce the exposure and level of ambient air pollution, as well as for cost-benefit considerations of policies aiming at sustainable longer-term reductions of pollution levels.

Fine-Scale Air Pollution Models for Epidemiologic Research: Insights From Approaches Developed in the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air)

PURPOSE OF REVIEW

Epidemiological studies of short- and long-term health impacts of ambient air pollutants require accurate exposure estimates. We describe the evolution in exposure assessment and assignment in air pollution epidemiology, with a focus on spatiotemporal techniques first developed to meet the needs of the Multi-ethnic Study of Atherosclerosis and Air Pollution (MESA Air). Initially designed to capture the substantial variation in pollutant levels and potential health impacts that can occur over small spatial and temporal scales in metropolitan areas, these methods have now matured to permit fine-scale exposure characterization across the contiguous USA and can be used for understanding long- and short-term health effects of exposure across the lifespan. For context, we highlight how the MESA Air models compare to other available exposure models.

RECENT FINDINGS

Newer model-based exposure assessment techniques provide predictions of pollutant concentrations with fine spatial and temporal resolution. These validated models can predict concentrations of several pollutants, including particulate matter less than 2.5 μm in diameter (PM2.5), oxides of nitrogen, and ozone, at specific locations (such as at residential addresses) over short time intervals (such as 2 weeks) across the contiguous USA between 1980 and the present. Advances in statistical methods, incorporation of supplemental pollutant monitoring campaigns, improved geographic information systems, and integration of more complete satellite and chemical transport model outputs have contributed to the increasing validity and refined spatiotemporal spans of available models. Modern models for predicting levels of outdoor concentrations of air pollutants can explain a substantial amount of the spatiotemporal variation in observations and are being used to provide critical insights into effects of air pollutants on the prevalence, incidence, progression, and prognosis of diseases across the lifespan. Additional enhancements in model inputs and model design, such as incorporation of better traffic data, novel monitoring platforms, and deployment of machine learning techniques, will allow even further improvements in the performance of pollutant prediction models.

Prenatal air pollution exposure and neurodevelopment: A review and blueprint for a harmonized approach within ECHO

BACKGROUND

Air pollution exposure is ubiquitous with demonstrated effects on morbidity and mortality. A growing literature suggests that prenatal air pollution exposure impacts neurodevelopment. We posit that the Environmental influences on Child Health Outcomes (ECHO) program will provide unique opportunities to fill critical knowledge gaps given the wide spatial and temporal variability of ECHO participants.

OBJECTIVES

We briefly describe current methods for air pollution exposure assessment, summarize existing studies of air pollution and neurodevelopment, and synthesize this information as a basis for recommendations, or a blueprint, for evaluating air pollution effects on neurodevelopmental outcomes in ECHO.

METHODS

We review peer-reviewed literature on prenatal air pollution exposure and neurodevelopmental outcomes, including autism spectrum disorder, attention deficit hyperactivity disorder, intelligence, general cognition, mood, and imaging measures. ECHO meta-data were compiled and evaluated to assess frequency of neurodevelopmental assessments and prenatal and infancy residential address locations. Cohort recruitment locations and enrollment years were summarized to examine potential spatial and temporal variation present in ECHO.

DISCUSSION

While the literature provides compelling evidence that prenatal air pollution affects neurodevelopment, limitations in spatial and temporal exposure variation exist for current published studies. As >90% of the ECHO cohorts have collected a prenatal or infancy address, application of advanced geographic information systems-based models for common air pollutant exposures may be ideal to address limitations of published research.

CONCLUSIONS

In ECHO we have the opportunity to pioneer unifying exposure assessment and evaluate effects across multiple periods of development and neurodevelopmental outcomes, setting the standard for evaluation of prenatal air pollution exposures with the goal of improving children's health.

Coming Together for Climate and Health: Proceedings of the Second Annual Clinical Climate Change Meeting, January 24, 2020

Climate change is imposing increasingly severe impacts on public health. Addressing these impacts requires heightened awareness of climate-driven health conditions and appropriate clinical practices to manage these conditions. Within this context, the 2nd Annual Clinical Climate Change Conference, held January 24, 2020 at the New York Academy of Medicine, brought together more than 150 allied health practitioners from across the United States for a one-day conference showcasing the state of the science on the climate and health. Eight platform presentations—including a keynote address from Karenna Gore of the Center for Earth Ethics at Union Theological Seminary—covered a range of environmentally induced, climate-related disease areas as well as topics related to environmental justice. Additionally, key workshops engaged participants in the clinical management of climate-related health conditions. Communicating the existing evidence base for climate change-driven impacts on human health is crucial for preparing practitioners to identify and address these impacts. Further partnership between researchers and practitioners to extend and disseminate this evidence base will yield important advancements toward protecting patients and improving health outcomes in an era of climate crisis.

A Spatiotemporal Prediction Model for Black Carbon in the Denver Metropolitan Area, 2009-2020

Studies on health effects of air pollution from local sources require exposure assessments that capture spatial and temporal trends. To facilitate intraurban studies in Denver, Colorado, we developed a spatiotemporal prediction model for black carbon (BC). To inform our model, we collected more than 700 weekly BC samples using personal air samplers from 2018 to 2020. The model incorporated spatial and spatiotemporal predictors and smoothed time trends to generate point-level weekly predictions of BC concentrations for the years 2009-2020. Our results indicate that our model reliably predicted weekly BC concentrations across the region during the year in which we collected data. We achieved a 10-fold cross-validation R² of 0.83 and a root-mean-square error of 0.15 μg/m³ for weekly BC concentrations predicted at our sampling locations. Predicted concentrations displayed expected temporal trends, with the highest concentrations predicted during winter months. Thus, our prediction model improves on typical land use regression models that generally only capture spatial gradients. However, our model is limited by a lack of long-term BC monitoring data for full validation of historical predictions. BC predictions from the weekly spatiotemporal model will be used in traffic-related air pollution exposure-disease associations more precisely than previous models for the region have allowed.

Impact of air pollution on renal outcomes: a systematic review and meta-analysis protocol

INTRODUCTION

Chronic kidney disease is a serious and a frequent disease associated with a high risk of morbi-mortality. Although several risk factors have already been well addressed, mostly diabetes and hypertension, many remain underappreciated, such as chronic exposure to air pollution.

METHODS AND ANALYSIS

We will search EMBASE, PubMed, Web of Science, Cochrane Library and CINAHL database, from inception to 31 March 2020, for relevant records using a combination of keywords related to the type of exposure (ozone, carbon monoxide, nitrogen oxides and dioxide, sulfur dioxide, PM_2.5, PM_coarse and PM₁₀) and to the type of outcome (chronic kidney disease, end-stage renal/kidney disease, kidney failure, proteinuria/albuminuria, renal function, renal transplant, kidney graft, kidney transplant failure, nephrotic syndrome and kidney cancer). The review will be reported according to the guidelines of the Meta-analysis Of Observational Studies in Epidemiology. Two independent reviewers will select studies without design or language restrictions, using original data and investigating the association between exposure to one or more of the prespecified air pollutants and subsequent risk of renal outcomes. Using random-effects meta-analyses, we will present pooled summary statistics (HR, OR or beta-coefficients with their respective 95% CI) associated with a standardised increase in each pollutant level. The results will be presented by air pollutant and outcome. Heterogeneity will be assessed using the χ² test on Cochran's Q statistic and quantified by calculating I². The Egger's test and visual inspection of funnel plots will be used to assess publication bias.

ETHICS AND DISSEMINATION

Since primary data are not collected in this study, ethical approval is not required. This review is expected to provide relevant data on the associations between various air pollutants' exposure and renal outcomes. The final report will be published in an international peer-reviewed journal.

PROSPERO REGISTRATION NUMBER

CRD42020187956.

External Environmental Pollution as a Risk Factor for Asthma

Air pollution is a worrisome risk factor for global morbidity and mortality and plays a special role in many respiratory conditions. It contributes to around 8 million deaths/year, with outdoor exposure being responsible for more than 4.2 million deaths throughout the world, while more than 3.8 million die from situations related to indoor pollution. Pollutant agents induce several respiratory symptoms. In addition, there is a clear interference in numerous asthma outcomes, such as incidence, prevalence, hospital admission, visits to emergency departments, mortality, and asthma attacks, among others. The particulate matter group of pollutants includes coarse particles/PM₁₀, fine particles/PM_2.5, and ultrafine particles/PM_0.1. The gaseous components include ground-level ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide. The timing, load, and route of allergen exposure are other items affecting allergic disease phenotypes. The complex interaction between pollutant exposures and human host factors has an implication in the development and rise of asthma as a public health problem. However, there are hiatuses in the understanding of the pathways in this disease. The routes through which pollutants induce asthma are multiple, and include the epigenetic changes that occur in the respiratory tract microbiome, oxidative stress, and immune dysregulation. In addition, the expansion of the modern Westernized lifestyle, which is characterized by intense urbanization and more time spent indoors, resulted in greater exposure to polluted air. Another point to consider is the different role of the environment according to age groups. Children growing up in economically disadvantaged neighborhoods suffer more important negative health impacts. This narrative review highlights the principal polluting agents, their sources of emission, epidemiological findings, and mechanistic evidence that links environmental exposures to asthma.

Imputing Satellite-Derived Aerosol Optical Depth Using a Multi-Resolution Spatial Model and Random Forest for PM2.5 Prediction

A task for environmental health research is to produce complete pollution exposure maps despite limited monitoring data. Satellite-derived aerosol optical depth (AOD) is frequently used as a predictor in various models to improve PM2.5 estimation, despite significant gaps in coverage. We analyze PM2.5 and AOD from July 2011 in the contiguous United States. We examine two methods to aid in gap-filling AOD: (1) lattice kriging, a spatial statistical method adapted to handle large amounts data, and (2) random forest, a tree-based machine learning method. First, we evaluate each model’s performance in the spatial prediction of AOD, and we additionally consider ensemble methods for combining the predictors. In order to accurately assess the predictive performance of these methods, we construct spatially clustered holdouts to mimic the observed patterns of missing data. Finally, we assess whether gap-filling AOD through one of the proposed ensemble methods can improve prediction of PM2.5 in a random forest model. Our results suggest that ensemble methods of combining lattice kriging and random forest can improve AOD gap-filling. Based on summary metrics of performance, PM2.5 predictions based on random forest models were largely similar regardless of the inclusion of gap-filled AOD, but there was some variability in daily model predictions.

Changes in U.S. air pollution during the COVID-19 pandemic

The COVID-19 global pandemic has likely affected air quality due to extreme changes in human behavior. We assessed air quality during the COVID-19 pandemic for fine particulate matter (PM2.5) and nitrogen dioxide (NO2) in the continental United States from January 8th-April 21st in 2017-2020. We considered pollution during the COVID-19 period (March 13-April 21st) and the pre-COVID-19 period (January 8th-March 12th) with 2020 representing 'current' data and 2017-2019 representing 'historical' data. County-level pollution concentrations were compared between historical versus current periods, and counties were stratified by institution of early or late non-essential business closures. Statistically significant NO2 declines were observed during the current COVID-19 period compared to historical data: a 25.5% reduction with absolute decrease of 4.8 ppb. PM2.5 also showed decreases during the COVID-19 period, and the reduction is statistically significant in urban counties and counties from states instituting early non-essential business closures. Understanding how air pollution is affected during COVID-19 pandemic will provide important clues regarding health effects and control of emissions. Further investigation is warranted to link this finding with health implications.

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

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

Estimating Daily PM2.5 and PM10 over Italy Using an Ensemble Model

Spatiotemporally resolved particulate matter (PM) estimates are essential for reconstructing long and short-term exposures in epidemiological research. Improved estimates of PM_2.5 and PM₁₀ concentrations were produced over Italy for 2013-2015 using satellite remote-sensing data and an ensemble modeling approach. The following modeling stages were used: (1) missing values of the satellite-based aerosol optical depth (AOD) product were imputed using a spatiotemporal land-use random-forest (RF) model incorporating AOD data from atmospheric ensemble models; (2) daily PM estimations were produced using four modeling approaches: linear mixed effects, RF, extreme gradient boosting, and a chemical transport model, the flexible air quality regional model. The filled-in MAIAC AOD together with additional spatial and temporal predictors were used as inputs in the three first models; (3) a geographically weighted generalized additive model (GAM) ensemble model was used to fuse the estimations from the four models by allowing the weights of each model to vary over space and time. The GAM ensemble model outperformed the four separate models, decreasing the cross-validated root mean squared error by 1-42%, depending on the model. The spatiotemporally resolved PM estimations produced by the suggested model can be applied in future epidemiological studies across Italy.

Children's acute respiratory symptoms associated with PM2.5 estimates in two sequential representative surveys from the Mexico City Metropolitan Area

BACKGROUND

Respiratory diseases are a major component of morbidity in children and their symptoms may be spatially and temporally exacerbated by exposure gradients of fine particulate matter (PM_2.5) in large polluted urban areas, like the Mexico City Metropolitan Area (MCMA).

OBJECTIVES

To analyze the association between satellite-derived and interpolated PM_2.5 estimates with children's (≤9 years old) acute respiratory symptoms (ARS) in two probabilistic samples representing the MCMA.

METHODS

We obtained ARS data from the 2006 and 2012 National Surveys for Health and Nutrition (ENSaNut). Two week average exposure to PM_2.5 was assessed for each household with spatial estimates from a hybrid model with satellite measurements of aerosol optical depth (AOD-PM_2.5) and also with interpolated PM_2.5 measurements from ground stations, from the Mexico City monitoring network (MNW-PM_2.5). We used survey-adjusted logistic regressions to analyze the association between PM_2.5 estimates and ARS reported on children.

RESULTS

A total of 1,005 and 1,233 children were surveyed in 2006 and 2012 representing 3.1 and 3.5 million children, respectively. For the same years and over the periods of study, the estimated prevalence of ARS decreased from 49.4% (95% CI: 44.9,53.9%) to 37.8% (95% CI: 34,41.7%). AOD-PM_2.5 and MNW-PM_2.5 estimates were associated with significantly higher reports of ARS in children 0-4 years old [OR₂₀₀₆ = 1.29 (95% (CI): 0.99,1.68) and OR₂₀₀₆ = 1.24 (95% CI: 1.08,1.42), respectively]. We observed positive non-significant associations in 2012 in both age groups and in 2006 for children 5-9 years old. No statistically significant differences in health effect estimates of PM_2.5 were found comparing AOD-PM_2.5 or MNW-PM_2.5 for exposure assessment.

CONCLUSIONS

Our findings suggest that PM_2.5 is a risk factor for the prevalence of ARS in children and expand the growing evidence of the utility of new satellite AOD-based methods for estimating health effects from acute exposure to PM_2.5.

Contribution of low-cost sensor measurements to the prediction of PM2.5 levels: A case study in Imperial County, California, USA

Regulatory monitoring networks are often too sparse to support community-scale PM2.5 exposure assessment while emerging low-cost sensors have the potential to fill in the gaps. To date, limited studies, if any, have been conducted to utilize low-cost sensor measurements to improve PM2.5 prediction with high spatiotemporal resolutions based on statistical models. Imperial County in California is an exemplary region with sparse Air Quality System (AQS) monitors and a community-operated low-cost network entitled Identifying Violations Affecting Neighborhoods (IVAN). This study aims to evaluate the contribution of IVAN measurements to the quality of PM2.5 prediction. We adopted the Random Forest algorithm to estimate daily PM2.5 concentrations at a 1-km spatial resolution using three different PM2.5 datasets (AQS-only, IVAN-only, and AQS/IVAN combined). The results show that the integration of low-cost sensor measurements is an effective way to significantly improve the quality of PM2.5 prediction with an increase of cross-validation (CV) R2 by ~0.2. The IVAN measurements also contributed to the increased importance of emission source-related covariates and more reasonable spatial patterns of PM2.5. The remaining uncertainty in the calibrated IVAN measurements could still cause apparent outliers in the prediction model, highlighting the need for more effective calibration or integration methods to relieve its negative impact.

Differences in fine particle chemical composition on clear and cloudy days

Clouds are prevalent and alter PM_2.5 mass and chemical composition. Cloud-affected satellite retrievals are often removed from data products, hindering estimates of tropospheric chemical composition during cloudy times. We examine surface fine particulate matter (PM_2.5) chemical constituent concentrations in the Interagency Monitoring of PROtected Visual Environments network during Cloudy and Clear Sky times defined using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud flags from 2010-2014 with a focus on differences in particle hygroscopicity and aerosol liquid water (ALW). Cloudy and Clear Sky periods exhibit significant differences in PM_2.5 and chemical composition that vary regionally and seasonally. In the eastern US, relative humidity alone cannot explain differences in ALW, suggesting emissions and in situ chemistry exert determining impacts. An implicit clear sky bias may hinder efforts to quantitatively to understand and improve model representation of aerosol-cloud interactions.

Tackling the Complexity of the Exposome: Considerations from the Gunma University Initiative for Advanced Research (GIAR) Exposome Symposium

The attempt to describe complex diseases by solely genetic determination has not been successful. There is increasing recognition that the development of disease is often a consequence of interactions between multiple genetic and environmental factors. To date, much of the research on environmental determinants of disease has focused on single exposures generally measured at a single time point. In order to address this limitation, the concept of the exposome has been introduced as a comprehensive approach, studying the full complement of environmental exposures from conception onwards. However, exposures are vast, dynamic, and diverse, and only a small proportion can be reasonably measured due to limitations in technology and feasibility. In addition, the interplay between genes and exposure as well as between different exposures is complicated and multifaceted, which leads to difficulties in linking disease or health outcomes with exposures. The large numbers of collected samples require well-designed logistics. Furthermore, the immense data sets generated from exposome studies require a significant computational investment for both data analysis and data storage. This report summarizes discussions during an international exposome symposium held at Gunma University in Japan regarding the concept of the exposome, challenges in exposome research, and future perspectives in the field.

Air pollution, ambient temperature, green space and preterm birth

PURPOSE OF REVIEW

Preterm birth is the leading cause of global child mortality, and survivors are at increased risk of multiple morbidities that can continue into adulthood. Recent studies have suggested that maternal exposure to air pollution and high and low ambient temperatures may increase the risk of preterm birth, whereas proximity to green space may decrease it. This review summarizes these findings and suggests avenues for further research.

RECENT FINDINGS

Particulate matter may be associated with an increased risk of preterm birth, but the magnitude of the effect remains unclear. Heat and cold likely increase the risk of preterm birth, with stronger evidence for heat. The first and third trimesters may be sensitive periods for exposure to both temperature and particulate matter, but the underlying biological mechanisms are incompletely understood. Context-appropriate green space can substantially reduce particulate matter levels and mitigate urban heat islands.

SUMMARY

In a warming, urbanizing world, exposure to unusual temperatures and elevated particulate matter levels represent an increasing risk for pregnant women. Green infrastructure might help mitigate this risk, but further research is needed to confirm its effects in complex urban environments and evaluate the contribution of both indoor and outdoor particulate matter and air temperature to personal exposure and preterm birth.

Impacts of snow and cloud covers on satellite-derived PM2.5 levels

Satellite aerosol optical depth (AOD) has been widely employed to evaluate ground fine particle (PM2.5) levels, whereas snow/cloud covers often lead to a large proportion of non-random missing AOD values. As a result, the fully covered and unbiased PM2.5 estimates will be hard to generate. Among the current approaches to deal with the data gap issue, few have considered the cloud-AOD relationship and none of them have considered the snow-AOD relationship. This study examined the impacts of snow and cloud covers on AOD and PM2.5 and made full- coverage PM2.5 predictions by considering these impacts. To estimate missing AOD values, daily gap-filling models with snow/cloud fractions and meteorological covariates were developed using the random forest algorithm. By using these models in New York State, a daily AOD data set with a 1-km resolution was generated with a complete coverage. The "out-of-bag" R2 of the gap-filling models averaged 0.93 with an interquartile range from 0.90 to 0.95. Subsequently, a random forest-based PM2.5 prediction model with the gap-filled AOD and covariates was built to predict fully covered PM2.5 estimates. A ten-fold cross-validation for the prediction model showed a good performance with an R2 of 0.82. In the gap-filling models, the snow fraction was of higher significance to the snow season compared with the rest of the year. The prediction models fitted with/without the snow fraction also suggested the discernible changes in PM2.5 patterns, further confirming the significance of this parameter. Compared with the methods without considering snow and cloud covers, our PM2.5 prediction surfaces showed more spatial details and reflected small-scale terrain-driven PM2.5 patterns. The proposed methods can be generalized to the areas with extensive snow/cloud covers and large proportions of missing satellite AOD data for predicting PM2.5 levels with high resolutions and complete coverage.

An Ensemble Machine-Learning Model To Predict Historical PM2.5 Concentrations in China from Satellite Data

The long satellite aerosol data record enables assessments of historical PM_2.5 level in regions where routine PM_2.5 monitoring began only recently. However, most previous models reported decreased prediction accuracy when predicting PM_2.5 levels outside the model-training period. In this study, we proposed an ensemble machine learning approach that provided reliable PM_2.5 hindcast capabilities. The missing satellite data were first filled by multiple imputation. Then the modeling domain, China, was divided into seven regions using a spatial clustering method to control for unobserved spatial heterogeneity. A set of machine learning models including random forest, generalized additive model, and extreme gradient boosting were trained in each region separately. Finally, a generalized additive ensemble model was developed to combine predictions from different algorithms. The ensemble prediction characterized the spatiotemporal distribution of daily PM_2.5 well with the cross-validation (CV) R² (RMSE) of 0.79 (21 μg/m³). The cluster-based subregion models outperformed national models and improved the CV R² by ∼0.05. Compared with previous studies, our model provided more accurate out-of-range predictions at the daily level ( R² = 0.58, RMSE = 29 μg/m³) and monthly level ( R² = 0.76, RMSE = 16 μg/m³). Our hindcast modeling system allows for the construction of unbiased historical PM_2.5 levels.

Correcting Measurement Error in Satellite Aerosol Optical Depth with Machine Learning for Modeling PM2.5 in the Northeastern USA

Satellite-derived estimates of aerosol optical depth (AOD) are key predictors in particulate air pollution models. The multi-step retrieval algorithms that estimate AOD also produce quality control variables but these have not been systematically used to address the measurement error in AOD. We compare three machine-learning methods: random forests, gradient boosting, and extreme gradient boosting (XGBoost) to characterize and correct measurement error in the Multi-Angle Implementation of Atmospheric Correction (MAIAC) 1 × 1 km AOD product for Aqua and Terra satellites across the Northeastern/Mid-Atlantic USA versus collocated measures from 79 ground-based AERONET stations over 14 years. Models included 52 quality control, land use, meteorology, and spatially-derived features. Variable importance measures suggest relative azimuth, AOD uncertainty, and the AOD difference in 30-210 km moving windows are among the most important features for predicting measurement error. XGBoost outperformed the other machine-learning approaches, decreasing the root mean squared error in withheld testing data by 43% and 44% for Aqua and Terra. After correction using XGBoost, the correlation of collocated AOD and daily PM2.5 monitors across the region increased by 10 and 9 percentage points for Aqua and Terra. We demonstrate how machine learning with quality control and spatial features substantially improves satellite-derived AOD products for air pollution modeling.

Effects of personal air pollution exposure on asthma symptoms, lung function and airway inflammation

BACKGROUND

There is evidence that air pollution increases the risk of asthma hospitalizations and healthcare utilization, but the effects on day-to-day asthma control are not fully understood.

OBJECTIVE

We undertook a prospective single-centre panel study to test the hypothesis that personal air pollution exposure is associated with asthma symptoms, lung function and airway inflammation.

METHODS

Thirty-two patients with a clinical diagnosis of asthma were provided with a personal air pollution monitor (Cairclip NO₂ /O₃ ) which was kept on or around their person throughout the 12-week follow-up period. Ambient levels of NO₂ and particulate matter were modelled based upon satellite imaging data. Directly measured ozone, NO₂ and particulate matter levels were obtained from a monitoring station in central Leicester. Participants made daily electronic records of asthma symptoms, peak expiratory flow and exhaled nitric oxide. Spirometry and asthma symptom questionnaires were completed at fortnightly study visits. Data were analysed using linear mixed effects models and cross-correlation.

RESULTS

Cairclip exposure data were of good quality with clear evidence of diurnal variability and a missing data rate of approximately 20%. We were unable to detect consistent relationships between personal air pollution exposure and clinical outcomes in the group as a whole. In an exploratory subgroup analysis, total oxidant exposure was associated with increased daytime symptoms in women but not men.

CONCLUSIONS AND CLINICAL RELEVANCE

We did not find compelling evidence that air pollution exposure impacts on day-to-day clinical control in an unselected asthma population, but further studies are required in larger populations with higher exposure levels. Women may be more susceptible than men to the effects of air pollution, an observation which requires confirmation in future studies.

Methods for Assessing Long-Term Exposures to Outdoor Air Pollutants

Purpose of Review

Epidemiological studies of health effects of long-term exposure to outdoor air pollution rely on different exposure assessment methods. This review discusses widely used methods with a special focus on new developments.

Recent Findings

New data and study designs have been applied, including satellite measurements of fine particles and nitrogen dioxide (NO₂). The methods to apply satellite data for epidemiological studies are improving rapidly and have already contributed significantly to national-, continental- and global-scale models. Spatiotemporal models have been developed allowing more detailed temporal resolution compared to spatial models. The development of hybrid models combining dispersion models, satellite observations, land use and surface monitoring has improved models substantially. Mobile monitoring designs to develop models for long-term UFP exposure have been conducted.

Summary

Methods to assess long-term exposure to outdoor air pollution have improved significantly over the past decade. Application of satellite data and mobile monitoring designs is promising new methods.

Design of an Air Pollution Monitoring Campaign in Beijing for Application to Cohort Health Studies

No cohort studies in China on the health effects of long-term air pollution exposure have employed exposure estimates at the fine spatial scales desirable for cohort studies with individual-level health outcome data. Here we assess an array of modern air pollution exposure estimation approaches for assigning within-city exposure estimates in Beijing for individual pollutants and pollutant sources to individual members of a cohort. Issues considered in selecting specific monitoring data or new monitoring campaigns include: needed spatial resolution, exposure measurement error and its impact on health effect estimates, spatial alignment and compatibility with the cohort, and feasibility and expense. Sources of existing data largely include administrative monitoring data, predictions from air dispersion or chemical transport models and remote sensing (specifically satellite) data. New air monitoring campaigns include additional fixed site monitoring, snapshot monitoring, passive badge or micro-sensor saturation monitoring and mobile monitoring, as well as combinations of these. Each of these has relative advantages and disadvantages. It is concluded that a campaign in Beijing that at least includes a mobile monitoring component, when coupled with currently available spatio-temporal modeling methods, should be strongly considered. Such a campaign is economical and capable of providing the desired fine-scale spatial resolution for pollutants and sources.

The Potential Impact of Satellite-Retrieved Cloud Parameters on Ground-Level PM2.5 Mass and Composition

Satellite-retrieved aerosol optical properties have been extensively used to estimate ground-level fine particulate matter (PM2.5) concentrations in support of air pollution health effects research and air quality assessment at the urban to global scales. However, a large proportion, ~70%, of satellite observations of aerosols are missing as a result of cloud-cover, surface brightness, and snow-cover. The resulting PM2.5 estimates could therefore be biased due to this non-random data missingness. Cloud-cover in particular has the potential to impact ground-level PM2.5 concentrations through complex chemical and physical processes. We developed a series of statistical models using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product at 1 km resolution with information from the MODIS cloud product and meteorological information to investigate the extent to which cloud parameters and associated meteorological conditions impact ground-level aerosols at two urban sites in the US: Atlanta and San Francisco. We find that changes in temperature, wind speed, relative humidity, planetary boundary layer height, convective available potential energy, precipitation, cloud effective radius, cloud optical depth, and cloud emissivity are associated with changes in PM2.5 concentration and composition, and the changes differ by overpass time and cloud phase as well as between the San Francisco and Atlanta sites. A case-study at the San Francisco site confirmed that accounting for cloud-cover and associated meteorological conditions could substantially alter the spatial distribution of monthly ground-level PM2.5 concentrations.