Air pollution exposure prediction approaches used in air pollution epidemiology studies

The Association Between Personal Air Pollution Exposures and Fractional Exhaled Nitric Oxide (FeNO): A Systematic Review

PURPOSE OF REVIEW

Airway inflammation is a common biological response to many types of environmental exposures and can lead to increased nitric oxide (NO) concentrations in exhaled breath. In recent years, several studies have evaluated airway inflammation using fractional exhaled nitric oxide (FeNO) as a biomarker of exposures to a range of air pollutants. This systematic review aims to summarize the studies that collected personal-level air pollution data to assess the air pollution-induced FeNO responses and to determine if utilizing personal-level data resulted in an improved characterization of the relationship between air pollution exposures and FeNO compared to using only ambient air pollution exposure data.

RECENT FINDINGS

Thirty-six eligible studies were identified. Overall, the studies included in this review establish that an increase in personal exposure to particulate and gaseous air pollutants can significantly increase FeNO. Nine out of the 12 studies reported statistically significant FeNO increases with increasing personal PM2.5 exposures, and up to 11.5% increase in FeNO per IQR increase in exposure has also been reported between FeNO and exposure to gas-phase pollutants, such as ozone, NO2, and benzene. Furthermore, factors such as chronic respiratory diseases, allergies, and medication use were found to be effect modifiers for air pollution-induced FeNO responses. About half of the studies that compared the effect estimates using both personal and ambient air pollution exposure methods reported that only personal exposure yielded significant associations with FeNO response. The evidence from the reviewed studies confirms that FeNO is a sensitive biomarker for air pollutant-induced airway inflammation. Personal air pollution exposure assessment is recommended to accurately assess the air pollution-induced FeNO responses. Furthermore, comprehensive adjustments for the potential confounding factors including the personal exposures of the co-pollutants, respiratory disease status, allergy status, and usage of medications for asthma and allergies are recommended while assessing the air pollution-induced FeNO responses.

High spatial-resolved source-specific exposure and risk in the city scale: Influence of spatial interrelationship between PM2.5 sources and population on exposure

Research on High Spatial-Resolved Source-Specific Exposure and Risk (HSRSSER) was conducted based on multiple-year, multiple-site synchronous measurement of PM2.5-bound (particulate matter with aerodynamic diameter<2.5 μm) toxic components in a Chinese megacity. The developed HSRSSER model combined the Positive Matrix Factorization (PMF) and Land Use Regression (LUR) to predict high spatial-resolved source contributions, and estimated the source-specific exposure and risk by personal activity time- and population-weighting. A total of 287 PM2.5 samples were collected at ten sites in 2018-2020, and toxic species including heavy metals (HMs), polycyclic aromatic hydrocarbons (PAHs) and organophosphate esters (OPEs) were analyzed. The percentage non-cancer risk were in the order of traffic emission (48 %) > industrial emission (22 %) > coal combustion (12 %) > waste incineration (11 %) > resuspend dust (7 %) > OPE-related products (0 %) ≈ secondary particles (0 %). Similar orders were observed in cancer risk. For traffic emission, due to its higher source contributions and large population in central area, non-cancer and cancer risk fraction increased from 23 % to 48 % and 20 % to 46 % after exposure estimation; while for industrial emission, higher source contributions but small population in suburb area decreased the percentage non-cancer and cancer risk from 38 % to 22 % and 39 % to 24 %, respectively.

Geospatial artificial intelligence for estimating daytime and nighttime nitrogen dioxide concentration variations in Taiwan: A spatial prediction model

Nitrogen dioxide (NO2) is a major air pollutant primarily emitted from traffic and industrial activities, posing health risks. However, current air pollution models often underestimate exposure risks by neglecting the bimodal pattern of NO2 levels throughout the day. This study aimed to address this gap by developing ensemble mixed spatial models (EMSM) using geo-artificial intelligence (Geo-AI) to examine the spatial and temporal variations of NO2 concentrations at a high resolution of 50m. These EMSMs integrated spatial modelling methods, including kriging, land use regression, machine learning, and ensemble learning. The models utilized 26 years of observed NO2 measurements, meteorological parameters, geospatial layers, and social and season-dependent variables as representative of emission sources. Separate models were developed for daytime and nighttime periods, which achieved high reliability with adjusted R2 values of 0.92 and 0.93, respectively. The study revealed that mean NO2 concentrations were significantly higher at nighttime (9.60 ppb) compared to daytime (5.61 ppb). Additionally, winter exhibited the highest NO2 levels regardless of time period. The developed EMSMs were utilized to generate maps illustrating NO2 levels pre and during COVID restrictions in Taiwan. These findings could aid epidemiological research on exposure risks and support policy-making and environmental planning initiatives.

Monitoring vs. modeled exposure data in time-series studies of ambient air pollution and acute health outcomes

BACKGROUND

Population-based short-term air pollution health studies often have limited spatiotemporally representative exposure data, leading to concerns of exposure measurement error.

OBJECTIVE

To compare the use of monitoring and modeled exposure metrics in time-series analyses of air pollution and cardiorespiratory emergency department (ED) visits.

METHODS

We obtained daily counts of ED visits for Atlanta, GA during 2009-2013. We leveraged daily ZIP code level concentration estimates for eight pollutants from nine exposure metrics. Metrics included central monitor (CM), monitor-based (inverse distance weighting, kriging), model-based [community multiscale air quality (CMAQ), land use regression (LUR)], and satellite-based measures. We used Poisson models to estimate air pollution health associations using the different exposure metrics. The approach involved: (1) assessing CM-based associations, (2) determining if non-CM metrics can reproduce CM-based associations, and (3) identifying potential value added of incorporating full spatiotemporal information provided by non-CM metrics.

RESULTS

Using CM exposures, we observed associations between cardiovascular ED visits and carbon monoxide, nitrogen dioxide, fine particulate matter, elemental and organic carbon, and between respiratory ED visits and ozone. Non-CM metrics were largely able to reproduce CM-based associations, although some unexpected results using CMAQ- and LUR-based metrics reduced confidence in these data for some spatiotemporally-variable pollutants. Associations with nitrogen dioxide and sulfur dioxide were only detected, or were stronger, when using metrics that incorporate all available monitoring data (i.e., inverse distance weighting and kriging).

SIGNIFICANCE

The use of routinely-collected ambient monitoring data for exposure assignment in time-series studies of large metropolitan areas is a sound approach, particularly when data from multiple monitors are available. More sophisticated approaches derived from CMAQ, LUR, or satellites may add value when monitoring data are inadequate and if paired with thorough data characterization. These results are useful for interpretation of existing literature and for improving exposure assessment in future studies.

IMPACT STATEMENT

This study compared and interpreted the use of monitoring and modeled exposure metrics in a daily time-series analysis of air pollution and cardiorespiratory emergency department visits. The results suggest that the use of routinely-collected ambient monitoring data in population-based short-term air pollution and health studies is a sound approach for exposure assignment in large metropolitan regions. CMAQ-, LUR-, and satellite-based metrics may allow for health effects estimation when monitoring data are sparse, if paired with thorough data characterization. These results are useful for interpretation of existing health effects literature and for improving exposure assessment in future air pollution epidemiology studies.

Exposure Assessment of Ambient PM2.5 Levels during a Sequence of Dust Episodes: A Case Study Coupling the WRF-Chem Model with GIS-Based Postprocessing

A sequence of dust intrusions occurred from the Sahara Desert to the central Mediterranean in the second half of June 2021. This event was simulated by means of the Weather Research and Forecasting coupled with chemistry (WRF-Chem) regional chemical transport model (CTM). The population exposure to the dust surface PM2.5 was evaluated with the open-source quantum geographical information system (QGIS) by combining the output of the CTM with the resident population map of Italy. WRF-Chem analyses were compared with spaceborne aerosol observations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and, for the PM2.5 surface dust concentration, with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Considering the full-period (17-24 June) and area-averaged statistics, the WRF-Chem simulations showed a general underestimation for both the aerosol optical depth (AOD) and the PM2.5 surface dust concentration. The comparison of exposure classes calculated for Italy and its macro-regions showed that the dust sequence exposure varies with the location and entity of the resident population amount. The lowest exposure class (up to 5 µg m^-3) had the highest percentage (38%) of the population of Italy and most of the population of north Italy, whereas more than a half of the population of central, south and insular Italy had been exposed to dust PM2.5 in the range of 15-25 µg m^-3. The coupling of the WRF-Chem model with QGIS is a promising tool for the management of risks posed by extreme pollution and/or severe meteorological events. Specifically, the present methodology can also be applied for operational dust forecasting purposes, to deliver safety alarm messages to areas with the most exposed population.

Driving factors behind the continuous increase of long-term PM2.5-attributable health burden in India using the high-resolution global datasets from 2001 to 2020

Air pollution is the fourth leading global risk factor, whereas in India air pollution is reported as the highest risk factor with millions of premature deaths every year. Despite implementation of several air pollution control plans, PM_2.5 levels over India have not noticeably reduced. PM_2.5-associated health burdens in India have increased significantly in past decades. A fine resolution (0·01° × 0·01°) analysis of PM_2.5-attribulable premature deaths (rather than the coarse-level analysis) may elucidate the reason for this increase and inform and effective start-of-the-art state-level and national emission control strategies. This study quantified the spatiotemporal dynamics of PM_2.5-attributable premature deaths from 2001 to 2020 and applied a decomposition analysis to dissect the contribution of various associated parameters, such as PM_2.5 concentration, population distribution and disease-specific baseline death rate. Results show significant spatiotemporal variations of PM_2.5 and associated health burden in India. During the study period, population weighted PM_2.5 value increased from 46.0 to 59.5 μg/m³ and associated non-communicable death increased around 87.6 %, from 1050 [95 % (CI): 880-1210] thousand to 1970 (95 % CI: 1658-2259) thousand. The states of Uttar Pradesh, Bihar, West Bengal, Maharashtra, Rajasthan, and Madhya Pradesh had the highest PM_2.5-attributable deaths. In these states, non-accidental deaths increased from 232.1, 112.7, 81.4, 79.1, 66.3 and 58.5 thousand in 2001 to 424.1, 226.7, 156.2, 154.5, 123.3 and 119.7 thousand in 2020. In per capita population (/10⁵ population), the highest PM_2.5-attributable deaths were observed in Delhi, Uttar Pradesh, Bihar, Haryana and Punjab. Throughout the study period, demographic changes outweighed the health burden and were responsible for ~62.8 % increase of PM_2.5-related non-accidental deaths across India, whereas the change in PM_2.5 concentration influenced only 18.7 %. The change in baseline mortality rate impacts differently for the estimation of disease-specific mortality changes. Our findings suggest more dynamic and comprehensive policies at state-specific level, especially for North India is very indispensable for the overall decrease of PM_2.5-related deaths in India.

Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution

BACKGROUND

Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity.

METHODS

We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants.

RESULTS

Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs.

CONCLUSIONS

Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies.

Effect of short-term exposure to ambient nitrogen dioxide and particulate matter on repeated lung function measures in infancy: A South African birth cohort

BACKGROUND

The developing lung is highly susceptible to environmental toxicants, with both short- and long-term exposure to ambient air pollutants linked to early childhood effects. This study assessed the short-term exposure effects of nitrogen dioxide (NO₂) and particulate matter (PM₁₀) on lung function in infants aged 6 weeks, 6, 12 and 24 months, the early developmental phase of child growth.

METHODS

Lung function was determined by multiple breath washout and tidal breathing measurement in non-sedated infants. Individual exposure to NO₂ and PM₁₀ was determined by hybrid land use regression and dispersion modelling, with two-week average estimates (preceding the test date). Linear mixed models were used to adjust for the repeated measures design and an age*exposure interaction was introduced to obtain effect estimates for each age group.

RESULTS

There were 165 infants that had lung function testing, with 82 of them having more than one test occasion. Exposure to PM₁₀ (μg/m³) resulted in a decline in tidal volume at 6 weeks [-0.4 ml (-0.9; 0.0), p = 0.065], 6 months [-0.5 ml (-1.0; 0.0), p = 0.046] and 12 months [-0.3 ml (-0.7; 0.0), p = 0.045]. PM₁₀ was related to an increase in respiratory rate and minute ventilation, while a decline was observed for functional residual capacity for the same age groups, though not statistically significant for these outcomes. Such associations were however less evident for exposure to NO₂, with inconsistent changes observed across measurement parameters and age groups.

CONCLUSION

Our study suggests that PM₁₀ results in acute lung function impairments among infants from a low-socioeconomic setting, while the association with NO₂ is less convincing.

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.

Impact of Personal, Subhourly Exposure to Ultrafine Particles on Respiratory Health in Adolescents with Asthma

Rationale: Ultrafine particle (UFP; particles <0.1 μm in diameter) concentrations exhibit high spatiotemporal variability; thus, individual-level exposures and health risks are difficult to estimate. Objectives: To determine the effects of recent UFP exposures on respiratory health outcomes in children and to determine if children with asthma are at increased risk. Methods: Personal sampling of UFPs was completed by adolescents in combination with repeated personal spirometry measurements and ecological momentary assessment of respiratory symptoms (wheeze, cough, and/or shortness of breath). We assessed the association between UFP exposures every 30 minutes up to 150 minutes before measuring forced expiratory volume in 1 second (FEV1), peak expiratory flow, and respiratory symptoms using mixed-effects models and interaction with asthma diagnosis. Results: Participants (N = 105; 43% with asthma) completed an average of 11 spirometry measurements and 16 symptom responses throughout sampling. After adjustments (maternal education, physical activity, season, and distance to nearest roadway), a 10-fold increase in UFP exposure was significantly associated with a 0.04-L decrease (95% confidence interval [CI], -0.07 to -0.001) in FEV1 90 minutes later. Asthma status modified this association in which participants with asthma had significantly lower FEV1 values in response to UFP exposures 30 minutes earlier than participants without asthma. We found a significant increase in the odds of reporting a respiratory symptom 30 minutes after increased UFP exposure (odds ratio, 1.8; 95% CI, 1.00 to 3.00). Conclusions: Greater UFP exposure conferred deleterious effects on lung function and respiratory symptoms within 90 minutes of exposure and was more pronounced among participants with asthma.

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

BACKGROUND

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

OBJECTIVE

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

METHODS

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

RESULTS

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

CONCLUSION

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

Validity of self-reported air pollution annoyance to assess long-term exposure to air pollutants in Belgium

In epidemiological studies, assessment of long term exposure to air pollution is often estimated using air pollution measurements at fixed monitoring stations, and interpolated to the residence of survey participants through Geographical Information Systems (GIS). However, obtaining georeferenced address data from national registries requires a long and cumbersome administrative procedure, since this kind of personal data is protected by privacy regulations. This paper aims to assess whether information collected in health interview surveys, including air pollution annoyance, could be used to build prediction models for assessing individual long term exposure to air pollution, removing the need for data on personal residence address. Analyses were carried out based on data from the Belgian Health Interview Survey (BHIS) 2013 linked to GIS-modelled air pollution exposure at the residence place of participants older than 15 years (n = 9347). First, univariate linear regressions were performed to assess the relationship between air pollution annoyance and modelled exposure to each air pollutant. Secondly, a multivariable linear regression was performed for each air pollutant based on a set of variables selected with elastic net cross-validation, including variables related to environmental annoyance, socio-economic and health status of participants. Finally, the performance of the models to classify individuals in three levels of exposure was assessed by means of a confusion matrix. Our results suggest a limited validity of self-reported air pollution annoyance as a direct proxy for air pollution exposure and a weak contribution of environmental annoyance variables in prediction models. Models using variables related to the socio-economic status, region, urban level and environmental annoyance allow to predict individual air pollution exposure with a percentage of error ranging from 8% to 18%. Although these models do not provide very accurate predictions in terms of absolute exposure to air pollution, they do allow to classify individuals in groups of relative exposure levels, ranking participants from low over medium to high air pollution exposure. This model represents a rapid assessment tool to identify groups within the BHIS participants undergoing the highest levels of environmental stress.

Developing air pollution concentration fields for health studies using multiple methods: Cross-comparison and evaluation

Past air pollution epidemiological studies have used a wide range of methods to develop concentration fields for health analyses. The fields developed differ considerably among these methods. The reasons for these differences and comparisons of their strengths, as well as the limitations for estimating exposures, remains under-investigated. Here, we applied nine methods to develop fields of eight pollutants (carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), fine particulate matter (PM_2.5), and three speciated PM_2.5 constituents including elemental carbon (EC), organic carbon (OC), and sulfate (SO₄)) for the metropolitan Atlanta region for five years. The nine methods are Central Monitor (CM), Site Average (SA), Inverse Distance Weighting (IDW), Kriging (KRIG), Land Use Regression (LUR), satellite Aerosol Optical Depth (AOD), CMAQ model, CMAQ with kriging adjustment (CMAQ-KRIG), and CMAQ based data fusion (CMAQ-DF). Additionally, we applied an increasingly popular method, Random Forest (RF), and compared its results for NO₂ and PM_2.5 with other methods. For statistical evaluation, we focused our discussion on the temporal coefficient of determination, although other metrics are also calculated. Raw output from the CMAQ model contains modeling biases and errors, which are partially mitigated by fusing observational data in the CMAQ-KRIG and CMAQ-DF methods. Based on analyses that simulated model responses to more limited input data, the RF model is more robust and outperforms LUR for PM_2.5. These results suggest RF may have potential in air pollution health studies, especially when limited measurement data are available. The RF method has several important weaknesses, including a relatively poor performance for NO₂, diagnostic challenges, and computational intensiveness. The results of this study will help to improve our understanding of the strengths and weaknesses of different methods for estimating air pollutant exposures in epidemiological studies.

Using land use variable information and a random forest approach to correct spatial mean bias in fused CMAQ fields for particulate and gas species

Accurate spatiotemporal air pollution fields are essential for health impact and epidemiologic studies. There are an increasing number of studies that have combined observational data with spatiotemporally complete air pollution simulations. Land-use, speciated gaseous and particulate pollutant concentrations and chemical transport modeling are fused using a random forest approach to construct daily air quality fields for 12 pollutants (CO, NOx, NO2, SO2, O3, PM2.5, PM10, and PM2.5 constituents: SO42-, NO3-, NH4+, EC and OC) between 2005 and 2014 for the continental United States with little spatial or temporal bias. R2 ranged from 0.45 to 0.96, depending upon pollutant. Additional analysis found that temporal R2 ranged from 0.84 to 0.99 and spatial R2 values ranged from 0.76 to 0.96 across species. Four-fold cross-validation was performed to assess the model's predictive power, and ranged from 0.40 for PM10 to 0.94 for SO4 with other pollutants falling within this range. Largest improvements were found for PM10 which had substantial bias in the CMAQ fields that varied east-to-west; smallest improvements were for SO4 which was already well simulated. The random forest model results to correct the simulation biases, while largely consistent year-to-year, did show slight variation due in part to changes in the distribution of monitors and changes in CMAQ simulation inputs.

The Prediction of Influenza-like Illness and Respiratory Disease Using LSTM and ARIMA

This paper proposed the forecasting model of Influenza-like Illness (ILI) and respiratory disease. The dataset was extracted from the Taiwan Environmental Protection Administration (EPA) for air pollutants data and the Centers for Disease Control (CDC) for disease cases from 2009 to 2018. First, this paper applied the ARIMA method, which trained based on the weekly number of disease cases in time series. Second, we implemented the Long short-term memory (LSTM) method, which trained based on the correlation between the weekly number of diseases and air pollutants. The models were also trained and evaluated based on five and ten years of historical data. Autoregressive integrated moving average (ARIMA) has an excellent model in the five-year dataset of ILI at 2564.9 compared to ten years at 8173.6 of RMSE value. This accuracy is similar to the Respiratory dataset, which gets 15,656.7 in the five-year dataset and 22,680.4 of RMSE value in the ten-year dataset. On the contrary, LSTM has better accuracy in the ten-year dataset than the five-year dataset. For example, on average of RMSE in the ILI dataset, LSTM has 720.2 RMSE value in five years and 517.0 in ten years dataset. Also, in the Respiratory disease dataset, LSTM gets 4768.6 of five years of data and 3254.3 of the ten-year dataset. These experiments revealed that the LSTM model generally outperforms ARIMA by three to seven times higher model performance.

Digital Healthcare for Airway Diseases from Personal Environmental Exposure

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

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.

Predicting Within-City Spatial Variations in Outdoor Ultrafine Particle and Black Carbon Concentrations in Bucaramanga, Colombia: A Hybrid Approach Using Open-Source Geographic Data and Digital Images

Outdoor ultrafine particles (UFP, <0.1 μm) and black carbon (BC) vary greatly within cities and may have adverse impacts on human health. In this study, we used a hybrid approach to develop new models to estimate within-city spatial variations in outdoor UFP and BC concentrations across Bucaramanga, Colombia. We conducted a mobile monitoring campaign over 20 days in 2019. Regression models were trained on land use data and combined with predictions from convolutional neural networks (CNN) trained to predict UFP and BC concentrations using satellite and street-level images. The combined UFP model (R² = 0.54) outperformed the CNN (R² = 0.47) and land use regression (LUR) models (R² = 0.47) on their own. Similarly, the combined BC model also outperformed the CNN and LUR BC models (R² = 0.51 vs 0.43 and 0.45, respectively). Spatial variations in model performance were more stable for the CNN and combined models compared to the LUR models, suggesting that the combined approach may be less likely to contribute to differential exposure measurement error in epidemiological studies. In general, our findings demonstrated that satellite and street-level images can be combined with a traditional LUR modeling approach to improve predictions of within-city spatial variations in outdoor UFP and BC concentrations.

Effects of using different exposure data to estimate changes in premature mortality attributable to PM2.5 and O3 in China

The assessment of premature mortality associated with the dramatic changes in fine particulate matter (PM_2.5) and ozone (O₃) has important scientific significance and provides valuable information for future emission control strategies. Exposure data are particularly vital but may cause great uncertainty in health burden assessments. This study, for the first time, used six methods to generate the concentration data of PM_2.5 and O₃ in China between 2014 and 2018, and then quantified the changes in premature mortality due to PM_2.5 and O₃ using the Environmental Benefits Mapping and Analysis Program-Community Edition (BenMAP-CE) model. The results show that PM_2.5-related premature mortality in China decreases by 263 (95% confidence interval (CI95): 142-159) to 308 (CI95: 213-241) thousands from 2014 to 2018 by using different concentration data, while O₃-related premature mortality increases by 67 (CI95: 26-104) to 103 (CI95: 40-163) thousands. The estimated mean changes are up to 40% different for the PM_2.5-related mortality, and up to 30% for the O₃-related mortality if different exposure data are chosen. The most significant difference due to the exposure data is found in the areas with a population density of around 10³ people/km², mostly located in Central China, for both PM_2.5 and O₃. Our results demonstrate that the exposure data source significantly affects mortality estimations and should thus be carefully considered in health burden assessments.

High-Spatial-Resolution Estimates of Ultrafine Particle Concentrations across the Continental United States

There is growing evidence that ultrafine particles (UFP; particles smaller than 100 nm) are likely more toxic than larger particles. However, the health effects of UFP remain uncertain due in part to the lack of large-scale population-based exposure assessment. We develop a national-scale empirical model of particle number concentration (PNC; a measure of UFP) using data from mobile monitoring and fixed sites across the United States and a land-use regression (LUR) modeling framework. Traffic, commercial land use, and urbanicity-related variables explain much of the spatial variability of PNC (base model R² = 0.77, RMSE = 2400 cm^-3). Model predictions are robust across a diverse set of evaluations [random 10-fold holdout cross-validation (HCV): R² = 0.72, RMSE = 2700 cm^-3; spatially defined HCV: R² = 0.66, RMSE = 3000 cm^-3; evaluation against an independent data set: R² = 0.54, RMSE = 2600 cm^-3]. We apply our model to predict PNC at ∼6 million residential census blocks in the contiguous United States. Our estimates are annual average concentrations for 2016-2017. The predicted national census-block-level mean PNC ranges between 1800 and 26 600 cm^-3 (population-weighted average: 6500 cm^-3), with hotspots in cities and near highways. Our national PNC model predicts large urban-rural, intra-, and inter-city contrasts. PNC and PM_2.5 are moderately correlated at the city scale, but uncorrelated at the regional/national scale. Our high-spatial-resolution national PNC estimates are useful for analyzing population exposure (socioeconomic disparity, epidemiological health impact) and environmental policy and regulation.

AERMOD modeling of ambient manganese for residents living near a ferromanganese refinery in Marietta, OH, USA

Elevated exposure to ambient manganese (Mn) is associated with adverse health outcomes. In Marietta, Ohio, the primary source of ambient Mn exposure is from the longest operating ferromanganese refinery in North America. In this study, the US EPA air dispersion model, AERMOD, was used to estimate ambient air Mn levels near the refinery for the years 2008-2013. Modeled air Mn concentrations for 2009-2010 were compared to concentrations obtained from a stationary air sampler. Census block population data were used to estimate population sizes exposed to an annual average air Mn > 50 ng/m3, the US EPA guideline for chronic exposure, for each year. Associations between modeled air Mn, measured soil Mn, and measured indoor dust Mn in the modeled area were also examined. Median modeled air Mn concentrations ranged from 6.3 to 43 ng/m3 across the years. From 12,000-56,000 individuals, including over 2000 children aged 0-14 years, were exposed to respirable annual average ambient air Mn levels exceeding 50 ng/m3 in five of the six years. For 2009-2010, the median modeled air Mn concentration at the stationary site was 20 ng/m3, compared to 18 ng/m3 measured with the stationary air sampler. All model performance measures for monthly modeled concentrations compared to measured concentrations were within acceptable limits. The study shows that AERMOD modeling of ambient air Mn is a viable method for estimating exposure from refinery emissions and that the Marietta area population was at times exposed to Mn levels that exceeded US EPA guidelines.

Furthering a partnership: Air quality modeling and improving public health

Air pollution is one of the top five risk factors for population health globally. In recent years, advances in air pollution data and modeling have occurred simultaneously with advances in data and methods available for health studies. To realize the potential of such advances, the air quality modeling and public health communities should continue to strengthen their engagements and build effective interdisciplinary teams. These partnerships recognize the tight coupling between air quality and health data and methods and the value of expertise from multiple fields to ensure that this information is applied appropriately with a deep understanding of its capabilities and limitations. Building effective multidisciplinary teams takes a sustained commitment to engage with partners with different expertise to establish working partnerships and collaborations to better address public exposures to air pollution. Effective partnerships enable better targeting of research resources to answer important questions and provide essential information to protect public health.Implications: Air quality models are an effective tool that can be used to estimate air pollution exposure in epidemiologic studies and risk assessments. Working together in collaborative multidisciplinary teams will lead to greater advancements in understanding of air pollution impacts and in useful information informing actions to improve public health.

Effect of ambient fine particulates (PM2.5) on hospital admissions for respiratory and cardiovascular diseases in Wuhan, China

Background

Positive associations between ambient PM_2.5 and cardiorespiratory disease have been well demonstrated during the past decade. However, few studies have examined the adverse effects of PM_2.5 based on an entire population of a megalopolis. In addition, most studies in China have used averaged data, which results in variations between monitoring and personal exposure values, creating an inherent and unavoidable type of measurement error.

Methods

This study was conducted in Wuhan, a megacity in central China with about 10.9 million people. Daily hospital admission records, from October 2016 to December 2018, were obtained from the Wuhan Information center of Health and Family Planning, which administrates all hospitals in Wuhan. Daily air pollution concentrations and weather variables in Wuhan during the study period were collected. We developed a land use regression model (LUR) to assess individual PM_2.5 exposure. Time-stratified case-crossover design and conditional logistic regression models were adopted to estimate cardiorespiratory hospitalization risks associated with short-term exposure to PM_2.5. We also conducted stratification analyses by age, sex, and season.

Results

A total of 2,806,115 hospital admissions records were collected during the study period, from which we identified 332,090 cardiovascular disease admissions and 159,365 respiratory disease admissions. Short-term exposure to PM_2.5 was associated with an increased risk of a cardiorespiratory hospital admission. A 10 μg/m³ increase in PM_2.5 (lag0–2 days) was associated with an increase in hospital admissions of 1.23% (95% CI 1.01–1.45%) and 1.95% (95% CI 1.63–2.27%) for cardiovascular and respiratory diseases, respectively. The elderly were at higher PM-induced risk. The associations appeared to be more evident in the cold season than in the warm season.

Conclusions

This study contributes evidence of short-term effects of PM_2.5 on cardiorespiratory hospital admissions, which may be helpful for air pollution control and disease prevention in Wuhan.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12931-021-01731-x.

Traffic-related air pollution is associated with glucose dysregulation, blood pressure, and oxidative stress in children

BACKGROUND

Metabolic syndrome increases the risk of cardiovascular disease in adults. Antecedents likely begin in childhood and whether childhood exposure to air pollution plays a contributory role is not well understood.

OBJECTIVES

To assess whether children's exposure to air pollution is associated with markers of risk for metabolic syndrome and oxidative stress, a hypothesized mediator of air pollution-related health effects.

METHODS

We studied 299 children (ages 6-8) living in the Fresno, CA area. At a study center visit, questionnaire and biomarker data were collected. Outcomes included hemoglobin A1c (HbA1c), urinary 8-isoprostane, systolic blood pressure (SBP), and BMI. Individual-level exposure estimates for a set of four pollutants that are constituents of traffic-related air pollution (TRAP) - the sum of 4-, 5-, and 6-ring polycyclic aromatic hydrocarbon compounds (PAH456), NO₂, elemental carbon, and fine particulate matter (PM_2.5) - were modeled at the primary residential location for 1-day lag, and 1-week, 1-month, 3-month, 6-month, and 1-year averages prior to each participant's visit date. Generalized additive models were used to estimate associations between each air pollutant exposure and outcome.

RESULTS

The study population was 53% male, 80% Latinx, 11% Black and largely low-income (6% were White and 3% were Asian/Pacific Islander). HbA1c percentage was associated with longer-term increases in TRAP; for example a 4.42 ng/m³ increase in 6-month average PAH456 was associated with a 0.07% increase (95% CI: 0.01, 0.14) and a 3.62 μg/m³ increase in 6-month average PM_2.5 was associated with a 0.06% increase (95% CI: 0.01, 0.10). The influence of air pollutants on blood pressure was strongest at 3 months; for example, a 6.2 ppb increase in 3-month average NO₂ was associated with a 9.4 mmHg increase in SBP (95% CI: 2.8, 15.9). TRAP concentrations were not significantly associated with anthropometric or adipokine measures. Short-term TRAP exposure averages were significantly associated with creatinine-adjusted urinary 8-isoprostane.

DISCUSSION

Our results suggest that both short- and longer-term estimated individual-level outdoor residential exposures to several traffic-related air pollutants, including ambient PAHs, are associated with biomarkers of risk for metabolic syndrome and oxidative stress in children.

Chemical characterisation of particulate matter in urban transport modes

Traffic is a main source of air pollutants in urban areas and consequently daily peak exposures tend to occur during commuting. Personal exposure to particulate matter (PM) was monitored while cycling and travelling by bus, car and metro along an assigned route in Lisbon (Portugal), focusing on PM2.5 and PM10 (PM with aerodynamic diameter <2.5 and 10 µm, respectively) mass concentrations and their chemical composition. In vehicles, the indoor-outdoor interplay was also evaluated. The PM2.5 mean concentrations were 28 ± 5, 31 ± 9, 34 ± 9 and 38 ± 21 µg/m³ for bus, bicycle, car and metro modes, respectively. Black carbon concentrations when travelling by car were 1.4 to 2.0 times higher than in the other transport modes due to the closer proximity to exhaust emissions. There are marked differences in PM chemical composition depending on transport mode. In particular, Fe was the most abundant component of metro PM, derived from abrasion of rail-wheel-brake interfaces. Enhanced concentrations of Zn and Cu in cars and buses were related with brake and tyre wear particles, which can penetrate into the vehicles. In the motorised transport modes, Fe, Zn, Cu, Ni and K were correlated, evidencing their common traffic-related source. On average, the highest inhaled dose of PM2.5 was observed while cycling (55 µg), and the lowest in car travels (17 µg). Cyclists inhaled higher doses of PM2.5 due to both higher inhalation rates and longer journey times, with a clear enrichment in mineral elements. The presented results evidence the importance of considering the transport mode in exposure assessment studies.

Novel metrics for relating personal heat exposure to social risk factors and outdoor ambient temperature

A more precise understanding of individual-level heat exposure may be helpful to advance knowledge about heat-health impacts and effective intervention strategies, especially in light of projected increases in the severity and frequency of extreme heat events. We developed and interrogated different metrics for quantifying personal heat exposure and explored their association with social risk factors. To do so, we collected simultaneous personal heat exposure data from 64 residents of metropolitan Phoenix, Arizona. From these data, we derived five exposure metrics: Mean Individually Experienced Temperature (IET), Maximum IET, Longest Exposure Period (LEP), Percentage Minutes Above Threshold (PMAT), and Degree Minutes Above Threshold (DMAT), and calculated each for Day Hours, Night Hours, and All Hours of the study period. We then calculated effect sizes for the associations between those metrics and four social risk factors: neighborhood vulnerability, income, home cooling type, and time spent outside. We also investigated exposure misclassification by constructing linear regression models of observations from a regional weather station and hourly IET for each participant. Our analysis revealed that metric choice and timeframe added depth and nuance to our understanding of differences in exposure within and between populations. We found that time spent outside and income were the two risk factors most strongly associated with personal heat exposure. We also found evidence that Mean IET is a good, but perhaps not optimal, measure for assessing group differences in exposure. Most participants' IETs were poorly correlated with regional weather station observations and the slope and correlation coefficient for linear regression models between regional weather station data and IETs varied widely among participants. We recommend continued efforts to investigate personal heat exposure, especially in combination with physiological indicators, to improve our understanding of links between ambient temperatures, social risk factors, and health outcomes.

Links between air pollution and COVID-19 in England

In December 2019, a novel disease, coronavirus disease 19 (COVID-19), emerged in Wuhan, People's Republic of China. COVID-19 is caused by a novel coronavirus (SARS-CoV-2) presumed to have jumped species from another mammal to humans. This virus has caused a rapidly spreading global pandemic. To date, over 300,000 cases of COVID-19 have been reported in England and over 40,000 patients have died. While progress has been achieved in managing this disease, the factors in addition to age that affect the severity and mortality of COVID-19 have not been clearly identified. Recent studies of COVID-19 in several countries identified links between air pollution and death rates. Here, we explored potential links between major fossil fuel-related air pollutants and SARS-CoV-2 mortality in England. We compared current SARS-CoV-2 cases and deaths from public databases to both regional and subregional air pollution data monitored at multiple sites across England. After controlling for population density, age and median income, we show positive relationships between air pollutant concentrations, particularly nitrogen oxides, and COVID-19 mortality and infectivity. Using detailed UK Biobank data, we further show that PM2.5 was a major contributor to COVID-19 cases in England, as an increase of 1 m3 in the long-term average of PM2.5 was associated with a 12% increase in COVID-19 cases. The relationship between air pollution and COVID-19 withstands variations in the temporal scale of assessments (single-year vs 5-year average) and remains significant after adjusting for socioeconomic, demographic and health-related variables. We conclude that a small increase in air pollution leads to a large increase in the COVID-19 infectivity and mortality rate in England. This study provides a framework to guide both health and emissions policies in countries affected by this pandemic.

Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors

Human exposure to air pollution is associated with increased risk of morbidity and mortality. However, personal air pollution exposures can vary substantially depending on an individual's daily activity patterns and air quality within their residence and workplace. This work developed and validated an adaptive buffer size (ABS) algorithm capable of dynamically classifying an individual's time spent in predefined microenvironments using data from global positioning systems (GPS), motion sensors, temperature sensors, and light sensors. Twenty-two participants in Fort Collins, CO were recruited to carry a personal air sampler for a 48-h period. The personal sampler was retrofitted with a GPS and a pushbutton to complement the existing sensor measurements (temperature, motion, light). The pushbutton was used in conjunction with a traditional time-activity diary to note when the participant was located at "home", "work", or within an "other" microenvironment. The ABS algorithm predicted the amount of time spent in each microenvironment with a median accuracy of 99.1%, 98.9%, and 97.5% for the "home", "work", and "other" microenvironments. The ability to classify microenvironments dynamically in real time can enable the development of new sampling and measurement technologies that classify personal exposure by microenvironment.

Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project

BACKGROUND

Air pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations.

METHODS

Taking advantage of recent advancements in sensor technologies and computational techniques, this paper presents a novel methodological approach that improves dose estimations of multiple air pollutants in large-scale health studies. We show the results of an intensive field campaign that measured personal exposures to gaseous pollutants and particulate matter of a health panel of 251 participants residing in urban and peri-urban Beijing with 60 personal air quality monitors (PAMs). Outdoor air pollution measurements were collected in monitoring stations close to the participants' residential addresses. Based on parameters collected with the PAMs, we developed an advanced computational model that automatically classified time-activity-location patterns of each individual during daily life at high spatial and temporal resolution.

RESULTS

Applying this methodological approach in two established cohorts, we found substantial differences between doses estimated from outdoor and personal air quality measurements. The PAM measurements also significantly reduced the correlation between pollutant species often observed in static outdoor measurements, reducing confounding effects.

CONCLUSIONS

Future work will utilise these improved dose estimations to investigate the underlying mechanisms of air pollution on cardio-pulmonary health outcomes using detailed medical biomarkers in a way that has not been possible before.

Measuring Environmental Exposure to Enteric Pathogens in Low-Income Settings: Review and Recommendations of an Interdisciplinary Working Group

Infections with enteric pathogens impose a heavy disease burden, especially among young children in low-income countries. Recent findings from randomized controlled trials of water, sanitation, and hygiene interventions have raised questions about current methods for assessing environmental exposure to enteric pathogens. Approaches for estimating sources and doses of exposure suffer from a number of shortcomings, including reliance on imperfect indicators of fecal contamination instead of actual pathogens and estimating exposure indirectly from imprecise measurements of pathogens in the environment and human interaction therewith. These shortcomings limit the potential for effective surveillance of exposures, identification of important sources and modes of transmission, and evaluation of the effectiveness of interventions. In this review, we summarize current and emerging approaches used to characterize enteric pathogen hazards in different environmental media as well as human interaction with those media (external measures of exposure), and review methods that measure human infection with enteric pathogens as a proxy for past exposure (internal measures of exposure). We draw from lessons learned in other areas of environmental health to highlight how external and internal measures of exposure can be used to more comprehensively assess exposure. We conclude by recommending strategies for advancing enteric pathogen exposure assessments.

Ambient particulate matter and biomass burning: an ecological time series study of respiratory and cardiovascular hospital visits in northern Thailand

BACKGROUND

Exposure to particulate matter (PM) emitted from biomass burning is an increasing concern, particularly in Southeast Asia. It is not yet clear how the source of PM influences the risk of an adverse health outcome. The objective of this study was to quantify and compare health risks of PM from biomass burning and non-biomass burning sources in northern Thailand.

METHODS

We collected ambient air pollutant data (PM with a diameter of < 10 μm [PM10], PM2.5, Carbon Monoxide [CO], Ozone [O3], and Nitrogen Dioxide [NO2]) from ground-based monitors and daily outpatient hospital visits in Thailand during 2014-2017. Outpatient data included chronic lower respiratory disease (CLRD), ischaemic heart disease (IHD), and cerebrovascular disease (CBVD). We performed an ecological time series analysis to evaluate the association between daily air pollutants and outpatient visits. We used the 90th and 95th percentiles of PM10 concentrations to determine days of exposure to PM predominantly from biomass burning.

RESULTS

There was significant intra annual variation in PM10 levels, with the highest concentrations occurring during March, coinciding with peak biomass burning. Incidence Rate Ratios (IRRs) between daily PM10 and outpatient visits were elevated most on the same day as exposure for CLRD = 1.020 (95% CI: 1.012 to 1.028) and CBVD = 1.020 (95% CI: 1.004 to 1.035), with no association with IHD = 0.994 (95% CI: 0.974 to 1.014). Adjusting for CO tended to increase effect estimates. We did not find evidence of an exposure response relationship with levels of PM10 on days of biomass burning.

CONCLUSIONS

We found same-day exposures of PM10 to be associated with certain respiratory and cardiovascular outpatient visits. We advise implementing measures to reduce population exposures to PM wherever possible, and to improve understanding of health effects associated with burning specific types of biomass in areas where such large-scale activities occur.

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

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

A multi-city air pollution population exposure study: Combined use of chemical-transport and random-Forest models with dynamic population data

Cities are severely affected by air pollution. Local emissions and urban structures can produce large spatial heterogeneities. We aim to improve the estimation of NO₂, O₃, PM_2.5 and PM₁₀ concentrations in 6 Italian metropolitan areas, using chemical-transport and machine learning models, and to assess the effect on population exposure by using information on urban population mobility. Three years (2013-2015) of simulations were performed by the Chemical-Transport Model (CTM) FARM, at 1 km resolution, fed by boundary conditions provided by national-scale simulations, local emission inventories and meteorological fields. A downscaling of daily air pollutants at higher resolution (200 m) was then carried out by means of a machine learning Random-Forest (RF) model, considering CTM and spatial-temporal predictors, such as population, land-use, surface greenness and vehicular traffic, as input. RF achieved mean cross-validation (CV) R² of 0.59, 0.72, 0.76 and 0.75 for NO₂, PM₁₀, PM_2.5 and O₃, respectively, improving results from CTM alone. Mean concentration fields exhibited clear geographical gradients caused by climate conditions, local emission sources and photochemical processes. Time series of population weighted exposure (PWE) were estimated for two months of the year 2015 and for five cities, by combining population mobility data (derived from mobile phone traffic volumes data), and concentration levels from the RF model. PWE_RF metric better approximated the observed concentrations compared with the predictions from either CTM alone or CTM and RF combined, especially for pollutants exhibiting strong spatial gradients, such as NO₂. 50% of the population was estimated to be exposed to NO₂ concentrations between 12 and 38 μg/m³ and PM₁₀ between 20 and 35 μg/m³. This work supports the potential of machine learning methods in predicting air pollutant levels in urban areas at high spatial and temporal resolutions.

Integrating Modes of Transport in a Dynamic Modelling Approach to Evaluate Population Exposure to Ambient NO2 and PM2.5 Pollution in Urban Areas

To evaluate the effectiveness of alternative policies and measures to reduce air pollution effects on urban citizen's health, population exposure assessments are needed. Due to road traffic emissions being a major source of emissions and exposure in European cities, it is necessary to account for differentiated transport environments in population dynamics for exposure studies. In this study, we applied a modelling system to evaluate population exposure in the urban area of Hamburg in 2016. The modeling system consists of an urban-scale chemistry transport model to account for ambient air pollutant concentrations and a dynamic time-microenvironment-activity (TMA) approach, which accounts for population dynamics in different environments as well as for infiltration of outdoor to indoor air pollution. We integrated different modes of transport in the TMA approach to improve population exposure assessments in transport environments. The newly developed approach reports 12% more total exposure to NO₂ and 19% more to PM_2.5 compared with exposure estimates based on residential addresses. During the time people spend in different transport environments, the in-car environment contributes with 40% and 33% to the annual sum of exposure to NO₂ and PM_2.5, in the walking environment with 26% and 30%, in the cycling environment with 15% and 17% and other environments (buses, subway, suburban, and regional trains) with less than 10% respectively. The relative contribution of road traffic emissions to population exposure is highest in the in-car environment (57% for NO₂ and 15% for PM_2.5). Results for population-weighted exposure revealed exposure to PM_2.5 concentrations above the WHO AQG limit value in the cycling environment. Uncertainties for the exposure contributions arising from emissions and infiltration from outdoor to indoor pollutant concentrations range from -12% to +7% for NO₂ and PM_2.5. The developed "dynamic transport approach" is integrated in a computationally efficient exposure model, which is generally applicable in European urban areas. The presented methodology is promoted for use in urban mobility planning, e.g., to investigate on policy-driven changes in modal split and their combined effect on emissions, population activity and population exposure.

Multivariate Air Pollution Prediction Modeling with partial Missingness

Missing observations from air pollution monitoring networks have posed a longstanding problem for health investigators of air pollution. Growing interest in mixtures of air pollutants has further complicated this problem, as many new challenges have arisen that require development of novel methods. The objective of this study is to develop a methodology for multivariate prediction of air pollution. We focus specifically on tackling different forms of missing data, such as: spatial (sparse sites), outcome (pollutants not measured at some sites), and temporal (varieties of interrupted time series). To address these challenges, we develop a novel multivariate fusion framework, which leverages the observed inter-pollutant correlation structure to reduce error in the simultaneous prediction of multiple air pollutants. Our joint fusion model employs predictions from the Environmental Protection Agency's Community Multiscale Air Quality (CMAQ) model along with spatio-temporal error terms. We have implemented our models on both simulated data and a case study in South Carolina for 8 pollutants over a 28-day period in June 2006. We found that our model, which uses a multivariate correlated error in a Bayesian framework, showed promising predictive accuracy particularly for gaseous pollutants.

Top 10 Research Priorities in Spatial Lifecourse Epidemiology

The International Initiative on Spatial Lifecourse Epidemiology (ISLE) convened its first International Symposium on Lifecourse Epidemiology and Spatial Science at the Lorentz Center in Leiden, Netherlands, 16–20 July 2018. Its aim was to further an emerging transdisciplinary field: Spatial Lifecourse Epidemiology. This field draws from a broad perspective of scientific disciplines including lifecourse epidemiology, environmental epidemiology, community health, spatial science, health geography, biostatistics, spatial statistics, environmental science, climate change, exposure science, health economics, evidence-based public health, and landscape ecology. The participants, spanning 30 institutions in 10 countries, sought to identify the key issues and research priorities in spatial lifecourse epidemiology. The results published here are a synthesis of the top 10 list that emerged out of the discussion by a panel of leading experts, reflecting a set of grand challenges for spatial lifecourse epidemiology in the coming years. https://doi.org/10.1289/EHP4868.

Mapping Modeled Exposure of Wildland Fire Smoke for Human Health Studies in California

Wildland fire smoke exposure affects a broad proportion of the U.S. population and is increasing due to climate change, settlement patterns and fire seclusion. Significant public health questions surrounding its effects remain, including the impact on cardiovascular disease and maternal health. Using atmospheric chemical transport modeling, we examined general air quality with and without wildland fire smoke PM2.5. The 24-h average concentration of PM2.5 from all sources in 12-km gridded output from all sources in California (2007-2013) was 4.91 μg/m3. The average concentration of fire-PM2.5 in California by year was 1.22 μg/m3 (~25% of total PM2.5). The fire-PM2.5 daily mean was estimated at 4.40 μg/m3 in a high fire year (2008). Based on the model-derived fire-PM2.5 data, 97.4% of California's population lived in a county that experienced at least one episode of high smoke exposure ("smokewave") from 2007-2013. Photochemical model predictions of wildfire impacts on daily average PM2.5 carbon (organic and elemental) compared to rural monitors in California compared well for most years but tended to over-estimate wildfire impacts for 2008 (2.0 μg/m3 bias) and 2013 (1.6 μg/m3 bias) while underestimating for 2009 (-2.1 μg/m3 bias). The modeling system isolated wildfire and PM2.5 from other sources at monitored and unmonitored locations, which is important for understanding population exposure in health studies. Further work is needed to refine model predictions of wildland fire impacts on air quality in order to increase confidence in the model for future assessments. Atmospheric modeling can be a useful tool to assess broad geographic scale exposure for epidemiologic studies and to examine scenario-based health impacts.

The impact of air exchange rate on ambient air pollution exposure and inequalities across all residential parcels in Massachusetts

Individual housing characteristics can modify outdoor ambient air pollution infiltration through air exchange rate (AER). Time and labor-intensive methods needed to measure AER has hindered characterization of AER distributions across large geographic areas. Using publicly-available data and regression models associating AER with housing characteristics, we estimated AER for all Massachusetts residential parcels. We conducted an exposure disparities analysis, considering ambient PM_2.5 concentrations and residential AERs. Median AERs (h^-1) with closed windows for winter and summer were 0.74 (IQR: 0.47-1.09) and 0.36 (IQR: 0.23-0.57), respectively, with lower AERs for single family homes. Across residential parcels, variability of indoor PM_2.5 concentrations of ambient origin was twice that of ambient PM_2.5 concentrations. Housing parcels above the 90th percentile of both AER and ambient PM_2.5 (i.e., the leakiest homes in areas of highest ambient PM_2.5)-vs. below the 10 percentile-were located in neighborhoods with higher proportions of Hispanics (20.0% vs. 2.0%), households with an annual income of less than $20,000 (26.0% vs. 7.5%), and individuals with less than a high school degree (23.2% vs. 5.8%). Our approach can be applied in epidemiological studies to estimate exposure modifiers or to characterize exposure disparities that are not solely based on ambient concentrations.

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

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

A systematic literature review and critical appraisal of epidemiological studies on outdoor air pollution and tuberculosis outcomes

Ambient air pollution is the leading environmental risk factor for disease globally. Air pollutants can increase the risk of some respiratory infections, but their effects on tuberculosis (TB) are unclear. In this systematic literature review, we aimed to assess epidemiological studies on the association between outdoor air pollutants and TB incidence, hospital admissions and death (collectively referred to here as 'TB outcomes'). We sought to consolidate available evidence on this topic and propose recommendations for future studies. Following PRISMA guidelines, we searched PubMed, Web of Science, Google Scholar, and Scopus with no restrictions imposed on year of publication. A total of 11 epidemiological studies, performed in Asia, Europe and North America, met our inclusion criteria (combined sample size: 215,337 people). We extracted key study characteristics from each eligible publication, including design, exposure assessment, analytical approaches and effect estimates. The studies were assessed for overall quality and risk of bias using standard criteria. The pollutant most frequently associated with statistically significant effects on TB outcomes was fine particulate matter ( < 2.5 µm; PM_2.5); 6/11 studies assessed PM_2.5, of which 4/6 demonstrated a significant association). There was some evidence of significant associations between PM₁₀ ( < 10 µm), nitrogen dioxide (NO₂) and sulfur dioxide (SO₂) and TB outcomes, but these associations were inconsistent. The existing epidemiological evidence is limited and shows mixed results. However, it is plausible that exposure to air pollutants, particularly PM_2.5, may suppress important immune defence mechanisms, increasing an individual's susceptibility to development of active TB and TB-related mortality. Considering the small number of studies relative to the demonstrably large global health burdens of air pollution and TB, further research is required to corroborate the findings in the current literature. Based on a critical assessment of existing evidence, we conclude with methodological suggestions for future studies.

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

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

Outdoor air pollution and mosaic loss of chromosome Y in older men from the Cardiovascular Health Study

BACKGROUND

Mosaic loss of chromosome Y (mLOY) can occur in a fraction of cells as men age, which is potentially linked to increased mortality risk. Smoking is related to mLOY; however, the contribution of air pollution is unclear.

OBJECTIVE

We investigated whether exposure to outdoor air pollution, age, and smoking were associated with mLOY.

METHODS

We analyzed baseline (1989-1993) blood samples from 933 men ≥65 years of age from the prospective Cardiovascular Health Study. Particulate matter ≤10 μm (PM₁₀), carbon monoxide, nitrogen dioxide, sulfur dioxide, and ozone data were obtained from the U.S. EPA Aerometric Information Retrieval System for the year prior to baseline. Inverse-distance weighted air monitor data were used to estimate each participants' monthly residential exposure. mLOY was detected with standard methods using signal intensity (median log-R ratio (mLRR)) of the male-specific chromosome Y regions from Illumina array data. Linear regression models were used to evaluate relations between mean exposure in the prior year, age, smoking and continuous mLRR.

RESULTS

Increased PM₁₀ was associated with mLOY, namely decreased mLRR (p-trend = 0.03). Compared with the lowest tertile (≤28.5 μg/m³), the middle (28.5-31.0 μg/m³; β = -0.0044, p = 0.09) and highest (≥31 μg/m³; β = -0.0054, p = 0.04) tertiles had decreased mLRR, adjusted for age, clinic, race/cohort, smoking status and pack-years. Additionally, increasing age (β = -0.00035, p = 0.06) and smoking pack-years (β = -0.00011, p = 1.4E-3) were associated with decreased mLRR, adjusted for each other and race/cohort. No significant associations were found for other pollutants.

CONCLUSIONS

PM₁₀ may increase leukocyte mLOY, a marker of genomic instability. The sample size was modest and replication is warranted.

Prenatal air pollution exposure, smoking, and uterine vascular resistance

Supplemental Digital Content is available in the text.

Background:

Prenatal exposure to air pollution and smoking increases the risk of pregnancy complications and adverse birth outcomes, but pathophysiologic mechanisms are still debated. Few studies to date have examined the influence of air pollution on uterine vascular resistance, and no studies have examined the independent impact of these exposures. We aimed to assess the impact of prenatal exposure to traffic-related air pollution and smoking on uterine vascular resistance.

Methods:

Our study included 566 pregnant women recruited between 1993 and 1996 in Los Angeles who completed visits at three gestational ages. Information on smoking was collected, and uterine vascular resistance was measured at each visit by Doppler ultrasound. We calculated three resistance indices: the resistance index, the pulsatility index, and the systolic/diastolic ratio. We estimated exposure to NO₂ at the home address of the mother using a land use regression model and to nitrogen oxides using CALINE4 air dispersion modeling. We used generalized linear mixed models to estimate the effects of air pollution and smoking on uterine vascular resistance indices.

Results:

Land use regression–derived NO₂ and CALINE4-derived nitrogen oxides exposure increased the risk of high uterine artery resistance in late pregnancy. Smoking during pregnancy also increased the risk of higher uterine resistance and contributed to bilateral notching in mid-pregnancy.

Conclusion:

Our results suggest that uterine vascular resistance is a mechanism underlying the association between smoking and air pollution and adverse birth outcomes.

The importance of spatial resolutions of Community Multiscale Air Quality (CMAQ) models on health impact assessment

We used the Community Multiscale Air Quality (CMAQ) simulation model to predict daily average of fine particulate matter (PM_2.5) concentrations. The primary focus of our study was to investigate the sensitivity of CMAQ prediction accuracy associated with the horizontal grid resolutions and assess its impact on human health studies. To illustrate our point we ran CMAQ model at 4 km and 12 km resolutions over New York State for the year 2011, and systematically assessed the differences between two modeled PM_2.5 concentrations. Model performance was evaluated against PM_2.5 measured values at monitoring stations. The results indicated that simulations at both 4 km and 12 km resolutions reproduced measured PM_2.5 values with fractional error (54.41% for 4 km and 52.28% for 12 km) that are within the recommend performance criteria except for summer seasons and rural areas. Additionally, model results at 12 km compared to 4 km resolution generally performed better and had substantially lower computational burden. In our health impact assessment study, we found that estimated adverse health outcomes associated with PM_2.5 exposure derived from the two CMAQ models were compatible, especially in rural areas. Based on our findings, we conclude that the CMAQ simulation at 12 km resolution with further calibration and/or downscaling is a viable option than 4 km simulation to estimate small-scale within-city variations of air pollution concentrations.

Spatial and Temporal Dynamics in Air Pollution Exposure Assessment

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

Evaluation of daily time spent in transportation and traffic-influenced microenvironments by urban Canadians

Exposure to traffic and traffic-related air pollution is associated with a wide array of health effects. Time spent in a vehicle, in active transportation, along roadsides, and in close proximity to traffic can substantially contribute to daily exposure to air pollutants. For this study, we evaluated daily time spent in transportation and traffic-influenced microenvironments by urban Canadians using the Canadian Human Activity Pattern Survey (CHAPS) 2 results. Approximately 4-7% of daily time was spent in on- or near-road locations, mainly associated with being in a vehicle and smaller contributions from active transportation. Indoor microenvironments can be impacted by traffic emissions, especially when located near major roadways. Over 60% of the target population reported living within one block of a roadway with moderate to heavy traffic, which was variable with income level and city, and confirmed based on elevated NO₂ exposure estimated using land use regression. Furthermore, over 55% of the target population ≤ 18 years reported attending a school or daycare in close proximity to moderate to heavy traffic, and little variation was observed based on income or city. The results underline the importance of traffic emissions as a major source of exposure in Canadian urban centers, given the time spent in traffic-influenced microenvironments.

Towards Personal Exposures: How Technology Is Changing Air Pollution and Health Research

PURPOSE OF REVIEW

We present a review of emerging technologies and how these can transform personal air pollution exposure assessment and subsequent health research.

RECENT FINDINGS

Estimating personal air pollution exposures is currently split broadly into methods for modeling exposures for large populations versus measuring exposures for small populations. Air pollution sensors, smartphones, and air pollution models capitalizing on big/new data sources offer tremendous opportunity for unifying these approaches and improving long-term personal exposure prediction at scales needed for population-based research. A multi-disciplinary approach is needed to combine these technologies to not only estimate personal exposures for epidemiological research but also determine drivers of these exposures and new prevention opportunities. While available technologies can revolutionize air pollution exposure research, ethical, privacy, logistical, and data science challenges must be met before widespread implementations occur. Available technologies and related advances in data science can improve long-term personal air pollution exposure estimates at scales needed for population-based research. This will advance our ability to evaluate the impacts of air pollution on human health and develop effective prevention strategies.

Application of an Original Wildfire Smoke Health Cost Benefits Transfer Protocol to the Western US, 2005-2015

Recent growth in the frequency and severity of US wildfires has led to more wildfire smoke and increased public exposure to harmful air pollutants. Populations exposed to wildfire smoke experience a variety of negative health impacts, imposing economic costs on society. However, few estimates of smoke health costs exist and none for the entire Western US, in particular, which experiences some of the largest and most intense wildfires in the US. The lack of cost estimates is troublesome because smoke health impacts are an important consideration of the overall costs of wildfire. To address this gap, this study provides the first time series estimates of PM2.5 smoke costs across mortality and several morbidity measures for the Western US over 2005-2015. This time period includes smoke from several megafires and includes years of record-breaking acres burned. Smoke costs are estimated using a benefits transfer protocol developed for contexts when original health data are not available. The novelty of our protocol is that it synthesizes the literature on choices faced by researchers when conducting a smoke cost benefit transfer. On average, wildfire smoke in the Western US creates $165 million in annual morbidity and mortality health costs.

Supporting sustainability initiatives through biometeorology education and training

The International Society of Biometeorology (ISB) has covered significant breadth and depth addressing fundamental and applied societal and environmental challenges in the last 60 years. Biometeorology is an interdisciplinary science connecting living organisms to their environment, but there is very little understanding of the existence and placement of this discipline within formal educational systems and institutions. It is thus difficult to project the ability of members of the biometeorological community-especially the biometeorologists of the future-to help solve global challenges. In this paper, we ask: At present, how we are training people to understand and think about biometeorology? We also ask: What are the current tools and opportunities in which biometeorologists might address future challenges? Finally, we connect these two questions by asking: What type of new training and skill development is needed to better educate "biometeorologists of the future" to more effectively address the future challenges? To answer these questions, we provide quantitative and qualitative evidence from an educationally focused workshop attended by new professionals in biometeorology. We identify four common themes (thermal comfort and exposures, agricultural productivity, air quality, and urbanization) that biometeorologists are currently studying and that we expect to be important in the future based on their alignment with the United Nations Sustainable Development Goals. Review of recent literature within each of these thematic areas highlights a wide array of skill sets and perspectives that biometeorologists are already using. Current and new professionals within the ISB have noted highly varying and largely improvised educational pathways into the field. While variability and improvisation may be assets in promoting flexibility, adaptation, and interdisciplinarity, the lack of formal training in biometeorology raises concerns about the extent to which continuing generations of scholars will identify and engage with the community of scholarship that the ISB has developed over its 60-year history.

Opportunities and Challenges for Personal Heat Exposure Research

BACKGROUND

Environmental heat exposure is a public health concern. The impacts of environmental heat on mortality and morbidity at the population scale are well documented, but little is known about specific exposures that individuals experience.

OBJECTIVES

The first objective of this work was to catalyze discussion of the role of personal heat exposure information in research and risk assessment. The second objective was to provide guidance regarding the operationalization of personal heat exposure research methods.

DISCUSSION

We define personal heat exposure as realized contact between a person and an indoor or outdoor environment that poses a risk of increases in body core temperature and/or perceived discomfort. Personal heat exposure can be measured directly with wearable monitors or estimated indirectly through the combination of time-activity and meteorological data sets. Complementary information to understand individual-scale drivers of behavior, susceptibility, and health and comfort outcomes can be collected from additional monitors, surveys, interviews, ethnographic approaches, and additional social and health data sets. Personal exposure research can help reveal the extent of exposure misclassification that occurs when individual exposure to heat is estimated using ambient temperature measured at fixed sites and can provide insights for epidemiological risk assessment concerning extreme heat.

CONCLUSIONS

Personal heat exposure research provides more valid and precise insights into how often people encounter heat conditions and when, where, to whom, and why these encounters occur. Published literature on personal heat exposure is limited to date, but existing studies point to opportunities to inform public health practice regarding extreme heat, particularly where fine-scale precision is needed to reduce health consequences of heat exposure. https://doi.org/10.1289/EHP556.