Personal Exposure to Black Carbon at School and Levels of Fractional Exhaled Nitric Oxide in New York City

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.

Application of artificial intelligence in quantifying lung deposition dose of black carbon in people with exposure to ambient combustion particles

BACKGROUND

Understanding lung deposition dose of black carbon is critical to fully reconcile epidemiological evidence of combustion particles induced health effects and inform the development of air quality metrics concerning black carbon. Macrophage carbon load (MaCL) is a novel cytology method that quantifies lung deposition dose of black carbon, however it has limited feasibility in large-scale epidemiological study due to the labor-intensive manual counting.

OBJECTIVE

To assess the association between MaCL and episodic elevation of combustion particles; to develop artificial intelligence based counting algorithm for MaCL assay.

METHODS

Sputum slides were collected during episodic elevation of ambient PM2.5 (n = 49, daily PM2.5 > 10 µg/m3 for over 2 weeks due to wildfire smoke intrusion in summer and local wood burning in winter) and low PM2.5 period (n = 39, 30-day average PM2.5 < 4 µg/m3) from the Lovelace Smokers cohort.

RESULTS

Over 98% individual carbon particles in macrophages had diameter <1 µm. MaCL levels scored manually were highly responsive to episodic elevation of ambient PM2.5 and also correlated with lung injury biomarker, plasma CC16. The association with CC16 became more robust when the assessment focused on macrophages with higher carbon load. A Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP) was developed based on the Mask Region-based Convolutional Neural Network. MacLEAP algorithm yielded excellent correlations with manual counting for number and area of the particles. The algorithm produced associations with ambient PM2.5 and plasma CC16 that were nearly identical in magnitude to those obtained through manual counting.

IMPACT STATEMENT

Understanding lung black carbon deposition is crucial for comprehending health effects of combustion particles. We developed "Machine-Learning algorithm for Engulfed cArbon Particles (MacLEAP)", the first artificial intelligence algorithm for quantifying airway macrophage black carbon. Our study bolstered the algorithm with more training images and its first use in air pollution epidemiology. We revealed macrophage carbon load as a sensitive biomarker for heightened ambient combustion particles due to wildfires and residential wood burning.

Traffic-related air pollution, chronic stress, and changes in exhaled nitric oxide and lung function among a panel of children with asthma living in an underresourced community

We examined associations between short-term exposure to traffic-related air pollutants (TRAP) and airway inflammation and lung function in children with asthma, and whether these associations are modified by chronic psychological stress. Residents of underresourced port-adjacent communities in New Jersey were concerned about the cumulative impacts of exposure to TRAP, particularly diesel-engine truck emissions, and stress on exacerbation of asthma among children. Children with asthma aged 9-14 (n = 35) were recruited from non-smoking households. We measured each participant's (1) continuous personal exposure to black carbon (BC, a surrogate of TRAP) at 1-min intervals, (2) 24-h integrated personal exposure to nitrogen dioxide (NO2), (3) daily fractional exhaled nitric oxide (FeNO), and (4) lung function for up to 30 consecutive days. Personal BC was recorded by micro-aethalometers. We measured daily FeNO using the NIOX MINO, forced expiratory volume in one second (FEV1), and forced vital capacity (FVC) using Easy One Frontline spirometers. Chronic stress was measured with the UCLA Life Stress Interview for Children. The association was examined using linear mixed-effect models. In the fully adjusted model, an interquartile range (IQR) increase in BC at lag 0-6 h before the FeNO measurement was associated with 8 % (95 % CI: 3 % - 12 %) increase in FeNO, whereas an IQR increase in BC at lag 7-12 h and lag 0-24 h were associated with 6 % (95 % CI: 2 % - 11 %) and 7 % (2 % - 12 %) FeNO increases, respectively. There were no significant lung function changes per IQR increase in BC. No interactions were observed between chronic stress and BC on FeNO. Chronic stress was negatively associated with individual average FeNO levels. Our findings suggest that higher levels of BC exposure within the prior 24 h increased airway inflammation levels in children with asthma, with the strongest effect observed within the first 6 h.

Associations between mitochondrial biomarkers, urban residential exposures and childhood asthma outcomes over 6 months

Determining biomarkers of responses to environmental exposures and evaluating whether they predict respiratory outcomes may help optimize environmental and medical approaches to childhood asthma. Relative mitochondrial (mt) DNA abundance and other potential mitochondrial indicators of oxidative stress may provide a sensitive metric of the child's shifting molecular responses to its changing environment. We leveraged two urban childhood cohorts (Environmental Control as Add-on Therapy in Childhood Asthma (ECATCh); Columbia Center for Children's Environmental Health (CCCEH)) to ascertain whether biomarkers in buccal mtDNA associate with airway inflammation and altered lung function over 6 months of time and capture biologic responses to multiple external stressors such as indoor allergens and fine particulate matter (PM2.5). Relative mtDNA content was amplified by qPCR and methylation of transfer RNA phenylalanine/rRNA 12S (TF/RNR1), cytochrome c oxidase (CO1), and carboxypeptidase O (CPO) was measured by pyrosequencing. Data on residential exposures and respiratory outcomes were harmonized between the two cohorts. Repeated measures and multiple regression models were utilized to assess relationships between mitochondrial biomarkers, respiratory outcomes, and residential exposures (PM2.5, allergens), adjusted for potential confounders and time-varying asthma. We found across the 6 month visits, a 0.64 fold higher level of TF/RNR1 methylation was detected among those with asthma in comparison to those without asthma ((parameter estimate (PE) 0.64, standard error 0.28, p = 0.03). In prospective analyses, CPO methylation was associated with subsequent reduced forced vital capacity (FVC; PE -0.03, standard error 0.01, p = 0.02). Bedroom dust mouse allergen, but not indoor PM2.5, was associated with higher methylation of TF/RNR1 (PE 0.015, standard error 0.006, p = 0.01). Select mtDNA measures in buccal cells may indicate children's responses to toxic environmental exposures and associate selectively with asthma and lung function.

Ambient NO2 Air Pollution and Public Schools in the United States: Relationships with Urbanicity, Race-Ethnicity, and Income

Schools may have important impacts on children's exposure to ambient air pollution, yet ambient air quality at schools is not consistently tracked. We characterize ambient air quality at home and school locations in the United States using satellite-based empirical model (i.e., land use regression) estimates of outdoor annual nitrogen dioxide (NO2). We report disparities by race-ethnicity and impoverishment status, and investigate differences by level of urbanicity. Average NO2 levels at home and school for racial-ethnic minoritized students are 18-22% higher than average (and 37-39% higher than for non-Hispanic, white students). Minoritized students are less likely than their white peers to live (0.55 times) and attend school (0.58 times) in areas below the World Health Organization's NO2 guideline. Predominantly minoritized schools (i.e., >50% minoritized students) are less likely than predominantly white schools (0.43 times) to be in locations below the guideline. Income and race-ethnicity impacts are intertwined, yet in large cities, racial disparities persist after controlling for income.

Structural and Social Determinants of Inequitable Environmental Exposures in the United States

American Indian (AI)/Alaskan Natives, African Americans, and Latino Americans have disproportionally high exposure to harmful environmental conditions as a consequence of unjust laws and policies, systemic racism, residential segregation, and discrimination. In this review, we draw connections between historical policies and social movements in the United States' history that have been rooted in racism and classism, leading to social isolation and marginalization of AIs, African Americans, and Latino Americans. We then discuss the structural factors that stem from the aforementioned inequities and that contribute to the inequitable distribution of environmental hazards.

Characteristics of peak exposure to black carbon pollution in school, commute and home environments among school children in an urban community

Although real-time personal exposure monitoring devices have the ability to capture a wealth of data regarding fluctuations in pollutant levels, only a few studies have defined 'peaks' in black carbon (BC) exposure utilizing high-resolution data. Furthermore, studies to assess and characterize various features of peak exposure are very limited especially among children. A better understanding of characteristics of BC peak exposure would improve our understanding of health risks associated with BC. By capturing personal BC exposure at 5-min intervals using a real-time monitor during 24-hr monitoring periods among children in New York City (NYC), we defined 'peak characteristics' in 4 different ways across three major microenvironments (school vs. commute vs. home): 1) mean concentrations of BC across the 3 microenvironments, 2) 'peak duration' or time spent above the peak threshold (i.e., ≥1.5 μg/m3), 3) 'peak intensity' or the rate of exposure, defined as time spent above the threshold within each microenvironment divided by the total time spent in the microenvironment and 4) a novel metric of 'peak variability', defined as frequency of peaks (i.e., data points with +50% and -50% changes compared to the preceding and the subsequent data points), divided by the total time spent in the microenvironment. While peak duration was greatest at home, the intensity of peak exposure was greatest during commute hours, despite the short time spent in commute (p < 0.05). Peak variability was highest during commute, yet lowest in home environments (p < 0.05), particularly during non-sleeping hours. Children residing in a high-density urban setting spent on average, 5.4 hr per day above our peak threshold (≥1.5 μg/m3) in their everyday environments. Policies that limit children's exposure during high traffic periods and improved efforts to increase the number of vehicles using clean air technology could reduce the intensity of peaks and peak variability in children's BC exposure.

The effects of the historical practice of residential redlining in the United States on recent temporal trends of air pollution near New York City schools

BACKGROUND

In the 1930's the United States (US) sponsored Home Owners' Loan Corporation (HOLC) created maps that determined risk for mortgage lending based on the racial and ethnic composition of neighborhoods leading to disinvestment in "redlined" or highest risk neighborhoods. This historical practice has perpetuated racial and economic segregation, and health disparities, that persist today. Interventions near schools where children spend large portions of the day, could impact large groups of children but schools are an often-overlooked environment for exposure. Despite a declining trend of ambient pollution in New York City (NYC) between 1998 and 2012, little is known about differences in air quality improvement near schools by historical redlining neighborhood status. Our objective was to examine if recent temporal trends of air pollution near NYC public schools differed in historically redlined neighborhoods.

METHODS

We examined annual average street-level concentrations of combustion-related air pollutants (black carbon (BC), particulate matter (PM_2.5), nitrogen dioxide (NO₂), and nitric oxide (NO)), within a 250-m radius around schools using NYC Community Air Survey land-use regression models (n = 1,462). Year of monitoring, historical redlining (binary), and summer ozone were included in multivariable linear regression using generalized estimating equation models. Average annual percent change (APC) in pollutant concentration was calculated. Models were further stratified by historical redlining and a multiplicative interaction term (year of monitoring × historical redlining) was used to assess effect modification.

RESULTS

Overall, there was a decreasing trend of BC (APC = -4.40%), PM_2.5 (-3.92%), NO₂ (-2.76%), and NO (-6.20%) during the 10-year period. A smaller reduction of BC, PM_2.5 and NO was observed in redlined neighborhoods (n = 722), compared to others (n = 740): BC (APC: -4.11% vs -4.69%; P_interaction < 0.01), PM_2.5 (-3.82% vs -4.11%; P_interaction < 0.01), and NO (-5.73% vs -6.67%; P_interaction < 0.01). Temporal trends of NO₂ did not differ by historical redlining (P_interaction = 0.60).

CONCLUSIONS

Despite significant reductions in annual average pollution concentrations across NYC, schools in historically redlined neighborhoods, compared to others, experienced smaller decrease in pollution, highlighting a potential ongoing ramification of the discriminatory practice.