Quantifying Disparities in Air Pollution Exposures across the United States Using Home and Work Addresses

Burden and predictors of chronic obstructive pulmonary disease occurrence and severity among an occupational cohort of United States Department of Energy former workers

BACKGROUND

Chronic Obstructive Pulmonary Disease (COPD) is a chronic inflammatory lung disease that reduces lung function and primarily affects older adults. Evidence suggests that occupational exposures like diesel exhaust, cadmium, welding fumes, and silica increase the risk of COPD. Some United States Department of Energy (DOE) workers may be exposed to these noxious substances as they execute their job responsibilities. Assessment of the burden of COPD among these workers and identification of the potential associations between the condition and the above occupational exposures is important for guiding screening, prevention, and control programs. Therefore, the objectives of this study are to: (a) estimate the burden of COPD among former workers of the DOE in the United States and (b) investigate the association between occupational exposures and COPD occurrence and severity among these workers while controlling for environmental, behavioral, and socio-demographic factors.

METHODS

Retrospective data containing health screening records of former DOE workers, covering the time period 2006-2019, were obtained from the National Supplemental Screening Program. Multivariate imputation by chained equation was used to impute missing values. Binary and multinomial logistic regression models were used to investigate predictors of COPD occurrence and severity, respectively.

RESULTS

Of the 17,376 participants included in the study, 20.8% had COPD. History of asthma, age at exam, body mass index, and smoking were significant predictors of both COPD occurrence and severity. Individuals exposed to silica had higher odds of COPD compared to those that were not exposed to silica. Similarly, diesel exhaust exposure was significantly associated with risk of more severe COPD.

CONCLUSIONS

The findings of this study demonstrate the importance of considering occupational experience in the assessment of both COPD occurrence and severity. This information may be important for occupational screening programs as well as aiding in identifying modifiable risk factors to guide prevention and control efforts.

Taiwan population-based epigenetic clocks and their application to long-term air pollution exposure

Most epigenetic clocks have been developed in populations of European or Hispanic descent; therefore, population-specific models are needed for Asian cohorts to enhance predictive accuracy and generalizability. This study aims to develop epigenetic clocks in a Taiwanese cohort and examine the association between long-term air pollution exposure and epigenetic age acceleration (EAA). The Taiwan Biobank (TWB) has been recruiting community-based adults aged 30-70 years since 2012, enrolling 173,806 participants by the end of 2022. Among them, 2,469 participants were selected for serum DNA methylation (DNAm) analysis. Epigenetic ages were estimated using penalized elastic net regression, with residuals defined as TWB-based epigenetic age acceleration (TWBEAA) and healthy-subset-based acceleration (TWBhEAA). Additionally, four previously established EAAs were obtained using Horvath's online DNA Methylation Age Calculator: DNAmEAA, DNAmSBEAA, PhenoEAA, and GrimEAA. Air pollution exposure levels at participants' residential townships were estimated from pre-1 day to pre-1 year using a kriging-based spatial interpolation method. Associations were assessed using multiple linear regression models, with robustness verified through Bayesian Kernel Machine Regression (BKMR). The TWBAge (325 CpG sites) and TWBhAge (179 CpG sites) prediction models demonstrated high accuracy (R2 = 0.95) in predicting chronological age. In the single-pollutant model, pre-1 year PM2.5 exposure was significantly associated with TWBhEAA (β = 0.67 [0.14-1.19], year) and DNAmEAA (β = 0.93 [0.03-1.83], year), while O3 exposure showed a positive association with DNAmSBEAA (β = 0.53 [0.29-0.77], year) and a negative association with GrimEAA (β = -0.44 [-0.70 to -0.17], year). BKMR analysis confirmed these findings. This study is among the first attempts to develop epigenetic clocks tailored for Asian population, providing evidence of air pollution's role in accelerating biological aging. Our findings highlight PM2.5 and O3 exposure as major contributors to EAA, emphasizing the need for air pollution mitigation strategies to promote healthier aging.

A Scalable Calibration Method for Enhanced Accuracy in Dense Air Quality Monitoring Networks

Deployment of large numbers of low capital cost sensors to increase the spatial density of air quality measurements enables applications that build on mapping air at neighborhood scales. Effective deployment requires not only low capital costs for observations but also a simultaneous reduction in labor costs. The Berkeley Environmental Air Quality and CO2 Network (BEACO2N) is a sensor network measuring O3, CO, NO, and NO2, particulate matter (PM2.5), and CO2 at dozens of locations in cities where it is deployed. Here, we describe a low labor cost in situ field calibration for the BEACO2N O3, CO, NO, and NO2 sensors. This method identifies and leverages uniform periods in concentrations across the network for calibration. The calibration achieves high accuracy and low biases with respect to temperature, humidity, and concentration, with coefficients of determination and root mean square errors of 0.88 and 3.70 ppb for O3, 0.66 and 3.16 ppb for NO2, and 0.79 and 1.58 ppb for NO. Performance of the CO sensor is 0.90 and 33.3 ppb at a site colocated with reference measurements. The method is a crucial step toward lowering operational costs of delivering accurate measurements in dense networks employing large numbers of inexpensive air quality sensors.

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

BACKGROUND

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

MAIN BODY

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

CONCLUSIONS

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

PM2.5 exposure disparities persist despite strict vehicle emissions controls in California

As policymakers increasingly focus on environmental justice, a key question is whether emissions reductions aimed at addressing air quality or climate change can also ameliorate persistent air pollution exposure disparities. We examine evidence from California's aggressive vehicle emissions control policy from 2000 to 2019. We find a 65% reduction in modeled statewide average exposure to PM2.5 from on-road vehicles, yet for people of color and overburdened community residents, relative exposure disparities increased. Light-duty vehicle emissions are the main driver of the exposure and exposure disparity, although smaller contributions from heavy-duty vehicles especially affect some overburdened groups. Our findings suggest that a continued trend of emissions reductions will likely reduce concentrations and absolute disparity but may not reduce relative disparities without greater attention to the systemic factors leading to this disparity.

Evaluating the sensitivity of mortality attributable to pollution to modeling Choices: A case study for Colorado

We evaluated the sensitivity of estimated PM2.5 and NO2 health impacts to varying key input parameters and assumptions including: 1) the spatial scale at which impacts are estimated, 2) using either a single concentration-response function (CRF) or using racial/ethnic group specific CRFs from the same epidemiologic study, 3) assigning exposure to residents based on home, instead of home and work locations for the state of Colorado. We found that the spatial scale of the analysis influences the magnitude of NO2, but not PM2.5, attributable deaths. Using county-level predictions instead of 1 km2 predictions of NO2 resulted in a lower estimate of mortality attributable to NO2 by ∼ 50 % for all of Colorado for each year between 2000 and 2020. Using an all-population CRF instead of racial/ethnic group specific CRFs results in a 130 % higher estimate of annual mortality attributable for the white population and a 40 % and 80 % lower estimate of mortality attributable to PM2.5 for Black and Hispanic residents, respectively. Using racial/ethnic group specific CRFs did not result in a different estimation of NO2 attributable mortality for white residents, but led to ∼ 50 % lower estimates of mortality for Black residents, and 290 % lower estimate for Hispanic residents. Using NO2 based on home instead of home and workplace locations results in a smaller estimate of annual mortality attributable to NO2 for all of Colorado by 2 % each year and 0.3 % for PM2.5. Our results should be interpreted as an exercise to make methodological recommendations for future health impact assessments of pollution.