Faculty Bibliography

In this study we investigated the concentration and composition of particulate matter (PM2.5) in the New York City subway system. Realtime measurements, at a 1-s cadence, and gravimetric measurements were performed inside train cars along 300 km of nine subway lines, as well as on 333 platforms on 287 subway stations. The mean (±SD) PM2.5 concentration on the underground platforms was 142 ± 69 μg/m³ versus 29 ± 20 μg/m³ for aboveground stations. The average concentrations inside train cars were 88 ± 14 μg/m³ when traveling through underground tunnels and platforms and 29 ± 31 μg/m³ while on aboveground tracks. The particle composition analysis of filtered samples was done using X-ray fluorescence (XRF), revealing that iron made up approximately 43% of the total PM2.5 mass on station platforms, approximately 126 times higher than the outdoor ambient iron concentration. Other trace elements include silicon, sulfur, copper, nickel, aluminum, calcium, barium, and manganese. Considering the very high iron content, the comparative analysis of the measured concentration versus the standards set by the Environmental Protection Agency (US EPA) is not appropriate since those limits are largely based on particulate matter from fossil fuel combustion. Health impact analysis of inhalation of iron-based particles is needed to contextualize the results presented here.

INTRODUCTION:The purpose of this study is to examine the associations between built environments and life expectancy across a gradient of urbanicity in the U.S. METHODS:Census tract‒level estimates of life expectancy between 2010 and 2015, except for Maine and Wisconsin, from the U.S. Small-Area Life Expectancy Estimates Project were analyzed in 2022. Tract-level measures of the built environment included: food, alcohol, and tobacco outlets; walkability; park and green space; housing characteristics; and air pollution. Multilevel linear models for each of the 4 urbanicity types were fitted to evaluate the associations, adjusting for population and social characteristics. RESULTS:Old housing (built before 1979) and air pollution were important built environment predictors of life expectancy disparities across all gradients of urbanicity. Convenience stores were negatively associated with life expectancy in all urbanicity types. Healthy food options were a positive predictor of life expectancy only in high-density urban areas. Park accessibility was associated with increased life expectancy in all areas, except rural areas. Green space in neighborhoods was positively associated with life expectancy in urban areas but showed an opposite association in rural areas. CONCLUSIONS:After adjusting for key social characteristics, several built environment characteristics were salient risk factors for decreased life expectancy in the U.S., with some measures showing differential effects by urbanicity. Planning and policy efforts should be tailored to local contexts.

PM2.5 is a frequently studied particulate matter metric, due to its wide range of identified overall adverse health effects, particularly cardiovascular health risks. However, there are no clear clinical practice guidelines for air pollution in regard to the prevention of cardiovascular health risks, since most of the current medical guidelines for CVD focus on metabolic risk factors such as hyperlipidemia or diabetes. We sought to determine the relationship between PM2.5 and cardiovascular disease, cardiovascular events, and all-cause mortality by performing a systematic review and meta-analysis. We searched Ovid MEDLINE, Ovid Embase, Ovid Cochrane Database of Systematic Reviews, Scopus, and Web of Science from the database inception to December 2022 for studies that reported an association between PM2.5 and cardiovascular disease, cardiovascular events, and all-cause mortality. We used the DerSimonian & Laird random-effects method to pool hazard ratios or risk ratios separately from the included studies. Of the total 18 prospective studies, 7,300,591 individuals were followed for a median follow-up of 9 years. Compared to low long-term exposure to PM 2.5 levels, an increase in exposure to PM 2.5 levels resulted in an increase in all-cause mortality (HR 1.08 95% CI of 1.05-1.11, P < 0.05). Similarly, when compared to a low long-term exposure to PM 2.5 levels, an increase in exposure to PM 2.5 levels resulted in an increase in cardiovascular disease (HR 1.09, 95% CI of 1.00-1.18, P < 0.05) and an increase in cardiovascular disease mortality (HR 1.12, 95% CI of 1.07-1.18, P < 0.05). Increased exposure to PM 2.5 levels is significantly associated with an increased risk of all-cause mortality, cardiovascular disease, and cardiovascular disease mortality. Although federal primary and secondary standards are in place, those standards are not low enough to prevent CVD health effects. Clinicians should emphasize PM2.5 as a modifiable CV risk factors for their patients to potentially reduce the development of CV complications. A clinical action guideline is needed specifically for air pollution effects on CVD, and how to mitigate them.

Particulate matter (PM) pollution is a significant concern in public health, yet children's exposure is not adequately characterized. This study evaluated PM exposures among primary school-aged children in NYS across different microenvironments. This study helps fill existing knowledge gaps by characterizing PM exposure among this population across seasons and microenvironments. Sixty students were recruited from randomly selected public primary schools representing various socioeconomic statuses. Individual real-time exposure to PM_2.5 was measured continuously using AirBeam personal monitors for 48 h. Children were consistently exposed to higher PM_2.5 concentrations in the fall (median: fall = 2.84, spring = 2.31, winter = 0.90 µg/m³). At school, 2.19% of PM_2.5 measurements exceeded the EPA annual fine particle standard, 12 µg/m³ (winter = 7.38%, fall = 2.39%, spring = 1.38%). In classrooms, PM_1-4 concentrations were higher in spring and overnight, while PM_7-10 concentrations were higher in fall and school hours. At home, 37.2% of fall measurements exceeded EPA standards (spring = 10.39%, winter = 4.37%). Overall, PM_2.5 levels in classrooms and during transportation never rose above the EPA standard for any significant length of time. However, PM_2.5 levels routinely exceeded these standards at home, in the fall, and the evening. More extensive studies are needed to confirm these results.

We assessed mortality risks associated with source-specific fine particles (PM_2.5) in a pooled European cohort of 323,782 participants. Cox proportional hazard models were applied to estimate mortality hazard ratios (HRs) for source-specific PM_2.5 identified through a source apportionment analysis. Exposure to 2010 annual average concentrations of source-specific PM_2.5 components was assessed at baseline residential addresses. The source apportionment resulted in the identification of five sources: traffic, residual oil combustion, soil, biomass and agriculture, and industry. In single-source analysis, all identified sources were significantly positively associated with increased natural mortality risks. In multisource analysis, associations with all sources attenuated but remained statistically significant with traffic, oil, and biomass and agriculture. The highest association per interquartile increase was observed for the traffic component (HR: 1.06; 95% CI: 1.04 and 1.08 per 2.86 μg/m³ increase) across five identified sources. On a 1 μg/m³ basis, the residual oil-related PM_2.5 had the strongest association (HR: 1.13; 95% CI: 1.05 and 1.22), which was substantially higher than that for generic PM_2.5 mass, suggesting that past estimates using the generic PM_2.5 exposure response function have underestimated the potential clean air health benefits of reducing fossil-fuel combustion. Source-specific associations with cause-specific mortality were in general consistent with findings of natural mortality.

Fine particulate matter air pollution (PM2.5) is a major contributor to cardiovascular morbidity and mortality, potentially via increased inflammation. PM2.5 exposure increases inflammatory biomarkers linked to cardiovascular disease, including CRP, IL-6 and TNFα. Portable air cleaners (PACs) reduce individual PM2.5 exposure but evidence is limited regarding whether PACs also reduce inflammatory biomarkers. We performed a systematic review and meta-analysis of trials evaluating the use of PACs to reduce PM2.5 exposure and inflammatory biomarker concentrations. We identified English-language articles of randomized sham-controlled trials evaluating high efficiency particulate air filters in non-smoking, residential settings measuring serum CRP, IL-6 and TNFα before and after active versus sham filtration, and performed meta-analysis on the extracted modeled percent change in biomarker concentration across studies. Of 487 articles identified, we analyzed 14 studies enrolling 778 participants that met inclusion criteria. These studies showed PACs reduced PM2.5 by 61.5 % on average. Of the 14 included studies, 10 reported CRP concentrations in 570 participants; these showed active PAC use was associated with 7 % lower CRP (95 % CI: −14 % to 0.0 %, p = 0.05). Nine studies of IL-6, with 379 participants, showed active PAC use was associated with 13 % lower IL-6 (95 % CI: [−23 %, −3 %], p = 0.009). Six studies, with 269 participants, reported TNF-α and demonstrated no statistical evidence of difference between active and sham PAC use. Portable air cleaners that reduce PM2.5 exposure can decrease concentrations of inflammatory biomarkers associated with cardiovascular disease. Additional studies are needed to evaluate clinical outcomes and other biomarkers.

BACKGROUND:Greenhouse gas emissions are changing the Earth's climate, most directly by modifying temperatures and temperature variability (TV). Residents of low- and middle-income countries (LMICs) are likely more adversely affected, due to lack of air conditioning to compensate. To date, there is no local epidemiological evidence documenting the cardio-respiratory health effects of TV in Dhaka, Bangladesh, one of the most climate change vulnerable cities in the world. OBJECTIVES/OBJECTIVE:We assessed short-term TV associations with daily cardiovascular disease (CVD) and respiratory emergency department (ED) visits, as well as effect modification by age and season. METHODS:TV was calculated from the standard deviations of the daily minimum and maximum temperatures over exposure days. Time-series regression modeling was applied to daily ED visits for respiratory and CVD from January 2014 through December 2017. TV effect sizes were estimated after controlling for long-term trends and seasonality, day-of-week, holidays, and daily mean relative humidity and ambient temperature. RESULTS:) was associated with a 7.45% (95 %CI: 2.33%, 12.57%) increase in respiratory ED visits among patients above 50 years of age. CONCLUSION/CONCLUSIONS:This study provided novel and important evidence that cardio-pulmonary health in Dhaka is adversely affected year-round by day-to-day increases in TV, especially among older adults. TV is a key factor that should be considered in evaluating the potential human health impacts of climate change induced temperature changes.

Although PM_2.5 mass varies in source and composition over time and space, most health effects assessment have made the inherent assumption that all PM_2.5 mass has the same health implications, irrespective of composition. Nationwide estimates of source-specific PM_2.5 mass and constituents at local-scale would allow for epidemiological studies and health effects assessments that consider the variability in PM_2.5 characteristics in their health impact assessments. In response, we developed US models of annual exposures at the census tract level for five major PM_2.5 sources (traffic, soil, coal, oil, and biomass combustion) and six trace elements (elemental carbon, sulfur, silicon, selenium, nickel, and non-soil potassium) for 2001 through 2014. We employed Absolute Factor Analysis (APCA) to derive the source-specific PM_2.5 impacts at monitoring stations. Random forest algorithms that incorporated predictors derived from satellite, chemical transport model, and census tract resolution land-use data on traffic, meteorology, and emissions, which were rigorously tested by 10-fold cross-validation (CV), were then employed to estimate elemental and source-specific PM_2.5 levels at non-monitoring site census-tracts over the study years. Model performances were moderate to good, with CV R² ranging from 0.41 to 0.95. For PM_2.5 sources, the highest CV R² was attained for traffic PM_2.5 (CV R²Â = 0.73), followed by coal (CV R²Â = 0.65), oil (CV R²Â = 0.62), soil (CV R²Â = 0.60), and biomass (CV R²Â = 0.41). Among constituents, the CV was highest for sulfur (CV R²Â = 0.95). Our analyses provided highly resolved spatial estimates of annual elemental and source-specific PM_2.5 concentrations at the census-tract level, for 2001 through 2014. This dataset offers exposure estimates in support of future nationwide long-term health effects studies of source-specific PM_2.5 mass and constituents, enabling epidemiological research that addresses the fact that not all particles are the same.

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