Jay R. Turner

The greater New York City area has failed to attain the National Ambient Air Quality Standard for ozone for more than 30 yr. Ozone is produced in the atmosphere by photochemical reactions involving volatile organic compounds (VOCs) and nitrogen oxides (NO_x). Analyses suggest that summertime ozone production in the urban core of the nonattainment area is often limited by VOCs or is in the transition regime between NO_x and VOC limitation. VOC monitoring at hourly time resolution was conducted for more than 1 yr on Staten Island, NY, to gain insights into VOC emission sources upwind of the New York City area. These data were analyzed using a suite of source apportionment tools. A petroleum refinery was identified as a major propylene source, while a spatial zone with numerous hydrocarbon storage tank farms and terminal operations was identified as a major source for several additional VOCs previously deemed influential in ozone production in the New York City urban core. VOC source regions along Newark Bay were also identified. This work demonstrates the use of highly time-resolved VOC data and source apportionment tools to support air quality management.Implications: This study developed a refined understanding of volatile organic compounds (VOCs) emission sources through hourly VOC monitoring on Staten Island, NY, and demonstrated the value of highly time-resolved VOC data to support air quality management. The study's results collectively suggest that targeted VOC control strategies within and immediately upwind of the dense NOx emissions area within the New York City urban core can help reduce ozone in the New York City urban region as a complement to regional NOx control strategies addressing high ozone formed farther downwind of the city.

Trichloroethylene (TCE) is an important environmental contaminant in the United States due to widespread use industrially. Epidemiologic studies suggest that occupational exposure to TCE and TCE-contaminated drinking water may increase the risk of Parkinson disease (PD). The aim of this study was to investigate the nationwide relationship between ambient TCE and PD risk. We performed a nationwide, population-based, case-control study to investigate the association between incident PD in US Medicare beneficiaries aged 67 years and older in 2016–2018 and their residential exposure to ambient (outdoor) TCE in 2002. We assigned residence based on the latitude and longitude of Medicare beneficiaries' zip + 4 center 2 years before diagnosis/reference. We assigned TCE exposure based on census tract–level data from the US Environmental Protection Agency's (EPA's) National Air Toxics Assessment program. We used logistic regression to estimate relative risk (RR) adjusted for age, sex, race, smoking, health care utilization, rural-urban commuting area (RUCA), and PM_2.5. We also mapped the nationwide geospatial pattern of ambient TCE and then explored high-resolution local PD risk patterns using MapGAM for the 10-mile radius around the top 3 TCE-emitting facilities from the EPA's Toxic Release Inventory in 2002. We identified 221,789 incident PD cases (median age 78.8 years; 45% female) and 1,132,765 matched controls (median age 75.7 years; 57% female). We found a dose-dependent positive association between ambient TCE concentrations and PD risk, wherein beneficiaries exposed to the top decile of ambient TCE levels (0.14–8.66 μg/m³) had an RR for PD of 1.10 (95% CI 1.08–1.13) compared with those exposed to the lowest decile of TCE (0.005–0.01 μg/m³). We observed high levels of ambient TCE in the rust belt region of the United States and several smaller areas throughout the nation. Our MapGAM results suggested greater PD risk in the area surrounding 2 of the 3 highest ambient TCE-emitting facilities, one of which demonstrated a marked decreasing gradient of risk with increasing distance from the facility. We identified a positive association between ambient TCE and PD risk, suggesting that TCE may contribute to PD. We identified relatively higher risk of PD near 2 TCE-emitting facilities. This study was limited to Medicare-aged individuals.

The Green Heart Project is a controlled, community-based clinical trial to evaluate the effects of increasing greenery on community health. The study was initiated in 2018 in a low- to middle-income residential area of nearly 30 000 racially diverse residents in Louisville, KY. Community engagement was maintained throughout the project, with feedback integrated into its design and implementation. Based on land use, demographics, and greenness in the area, we designated 8 paired clusters of demographically and environmentally matched target (T) and adjacent control (C) areas (total of 16 clusters). Levels of ultrafine particles, ozone, and nitrogen oxides were measured in each cluster. In-person exams were conducted for 735 participants in wave 1 (2018-2019) and 545 participants in wave 2 (2021). Blood, urine, nail, and hair samples were collected to evaluate cardiovascular risk, inflammation, stress, and pollutant exposure. Demographic and psychosocial data were collected as well. Cardiovascular function was assessed by measuring arterial stiffness and flow-mediated dilation. After wave 2, more than 8000 mature, mostly evergreen, trees and shrubs were planted in the T clusters. After planting, data were collected during wave 3 (2022) from 561 participants. We plan to follow changes in area characteristics and participant health to evaluate the long-term impact of the greening intervention. Trial registration: ClinicalTrials.gov. Identifier: NCT03670524.

Background and Objectives: Numerous studies suggest that environmental exposures play a critical role in Parkinson disease (PD) pathogenesis, and large, population-based studies have the potential to advance substantially the identification of novel PD risk factors. We sought to study the nationwide geographic relationship between PD and air pollution, specifically PM_2.5 (particulate matter with a diameter 2.5 as the primary exposure of interest. We also performed an individual-level analysis using logistic regression with cases and controls with zip+4 centroid PM_2.5. We adjusted a priori for the same covariates and verified no confounding by indicators of socioeconomic status or neurologist density. Results: Among 21,639,190 Medicare beneficiaries, 89,390 had incident PD in 2009. There was a nationwide association between average annual PM_2.5 and PD risk whereby the RR for PD was 56% (95% CI 47%-66%) greater for those exposed to the median level of PM_2.5 compared to those with the lowest level of PM_2.5. This association was linear up to 13 μg/m³ corresponding to a 4.2% (95% CI 3.7%-4.8%) greater risk of PD for each additional μg/m³ of PM_2.5 (p_trend2.5 and PD was found in a region with low PD risk in the Rocky Mountains. PM_2.5 was also associated with PD in the Mississippi-Ohio River Valley where the association was relatively weaker, due to a possible ceiling effect at average annual PM_2.5 levels of ∼13 μg/m³. Discussion: State-of-the-art geographic analytic techniques revealed an association between PM_2.5 and PD that varied in strength by region. A deeper investigation into the specific subfractions of PM_2.5 may provide additional insight into regional variability in the PM_2.5-PD association.

In the United States, citizens and policymakers heavily rely upon Environmental Protection Agency mandated regulatory networks to monitor air pollution; increasingly they also depend on low‐cost sensor networks to supplement spatial gaps in regulatory monitor networks coverage. Although these regulatory and low‐cost networks in tandem provide enhanced spatiotemporal coverage in urban areas, low‐cost sensors are located often in higher income, predominantly White areas. Such disparity in coverage may exacerbate existing inequalities and impact the ability of different communities to respond to the threat of air pollution. Here we present a study using cost‐constrained multiresolution dynamic mode decomposition (mrDMDcc) to identify the optimal and equitable placement of fine particulate matter (PM_2.5) sensors in four U.S. cities with histories of racial or income segregation: St. Louis, Houston, Boston, and Buffalo. This novel approach incorporates the variation of PM_2.5 on timescales ranging from 1 day to over a decade to capture air pollution variability. We also introduce a cost function into the sensor placement optimization that represents the balance between our objectives of capturing PM_2.5 extremes and increasing pollution monitoring in low‐income and nonwhite areas. We find that the mrDMDcc algorithm places a greater number of sensors in historically low‐income and nonwhite neighborhoods with known environmental pollution problems compared to networks using PM_2.5 information alone. Our work provides a roadmap for the creation of equitable sensor networks in U.S. cities and offers a guide for democratizing air pollution data through increasing spatial coverage of low‐cost sensors in less privileged communities.

Exposure to urban air pollution is linked to increased mortality from cardiopulmonary causes. Urban areas juxtapose large numbers of residences and workplaces with near-road environments, exacerbating traffic-related air pollution (TRAP) exposure. TRAP is the primary source of variability in intraurban air quality, but continuous regulatory monitoring stations lack the spatial resolution to detect fine-scale pollutant patterns that recent studies using long-term, resource-intensive mobile measurements have established as persistent and associated with higher risk of cardiovascular events. This work evaluates a low-cost, fixed-site approach to characterizinglong-term, hyperlocal exposure to oxides of nitrogen (including NO ₂ , a common surrogate for TRAP) as part of Green Heart Louisville, a prospective cohort study examining linkages between urban vegetation, local air quality, and cardiovascular health. We used a fixed 60-site network of Ogawa passive samplers in a 12 km ² section of Louisville, KY, to measure two-week integrated NO ₂ , NO _x (NO + NO ₂ ), and O ₃ mixing ratios nominally every two months between May 2018-March 2021. Seasonal NO _x averages were 2.5-fold higher during winter than in summer, and annual average NO (calculated by difference in NO _x and NO ₂ ) and NO ₂ ranged from 4-21 ppb and 5-12 ppb, respectively. NO increased 3-to-5-fold within 150 m of highways or major arterial roads and 2-to-3-fold near parking lots. While both NO and NO ₂ were elevated in near-road environments, the corresponding O ₃ was depressed, consistent with titration by NO. We developed land-use regression models for annual average NO, NO ₂ , and NO _x using parameters of proximity (distance to nearest road type, restaurant, traffic signal), cumulative occurrence (length of roads, number of restaurants and traffic lights, all in buffers of up to 500 m in 50-m increments), and greenness (normalized difference vegetative index (NDVI)). Adjusted spatial variability explained by the models were 70% (p2 , and NO _x , respectively. Common predictors were distances to the nearest restaurant and road as well as total length of roads within 350 m. Only one greenness metric was significant: mean NDVI within 50 m was negatively associated (p=0.02) with NO ₂ . We plan to use these hyperlocal models to estimate residential-level exposures of the clinical study participants.

Exposure to ultrafine particles (UFP) is increasingly linked to adverse health outcomes. While nation-wide air monitoring networks in the United States do not measure UFP, small-scale measurements have revealed persistent patterns in urban UFP. This project maps hyperlocal UFP in a 12 km ² study area of a health effects study in Louisville, KY, through mobile measurements to elucidate the relationship between the urban landscape and UFP exposures. We measured UFP number concentration along all drivable streets (∼340 km) during daytime and nighttime on both weekdays and weekends. After deconvoluting UFP levels to isolate local signals from neighborhood and urban signals, we fitted a land-use regression (LUR) model to explain differences in local UFP as a function of characteristics of the built and natural environment. Median UFP in the study domain was 6,850 #/cm ³ , which is comparable to urban background measured or estimated for other U.S. cities. UFP was higher during the weekend than on weekdays, potentially due to changes in local activity (e.g. increased restaurant hours) apparent at fine spatial scales. The final LUR model explained 61% of the spatial heterogeneity in log(UFP). Leave-one-area-out cross validation revealed overprediction in regions farther from highways and underprediction in regions with dense food service locations and major roads. This suggests that additional mobile measurements to capture longer-term, robust UFP may yield improved models.

A robust method to estimate mineral dust mass in ambient particulate matter (PM) is essential, as the dust fraction cannot be directly measured but is needed to understand dust impacts on the environment and human health. In this study, a global‐scale dust equation is developed that builds on the widely used Interagency Monitoring of Protected Visual Environments (IMPROVE) network's “soil” formula that is based on five measured elements (Al, Si, Ca, Fe, and Ti). We incorporate K, Mg, and Na into the equation using the mineral‐to‐aluminum (MAL) mass ratio of (K₂O + MgO + Na₂O)/Al₂O₃ and apply a correction factor (CF) to account for other missing compounds. We obtain region‐specific MAL ratios and CFs by investigating the variation in dust composition across desert regions. To calculate reference dust mass for equation evaluation, we use total‐mineral‐mass (summing all oxides of crustal elements) and residual‐mass (subtracting non‐dust species from total PM) approaches. For desert dust in source regions, the normalized mean bias (NMB) of the global equation (within ±1%) is significantly smaller than the NMB of the IMPROVE equation (−6% to 10%). For PM_2.5 with high dust content measured by the IMPROVE network, the global equation estimates dust mass well (NMB within ±5%) at most sites. For desert dust transported to non‐source regions, the global equation still performs well (NMB within ±2%). The global equation can also represent paved road, unpaved road, and agricultural soil dust (NMB within ±5%). This global equation provides a promising approach for calculating dust mass based on elemental analysis of dust.

Ultrafine particles (UFP) contribute to adverse health outcomes such as asthma, obstructive pulmonary disease, cardiovascular disease, and lung cancer. Recent research draws attention to elevated ambient UFP number concentrations near airports. In this study, high time-resolution UFP measurements were conducted along public roads near Mohammad Ali International Airport (SDF; Louisville, KY) which is a commercial passenger airport and a major air cargo hub. Short-duration (∼3 h) measurements with two instrumented vehicles were designed and executed to capitalize on the distinct features of the air cargo hub including periods of high flight activity (and either all landings or all take-offs) at night and early morning when the atmospheric mixing layer depth is shallow. We present preliminary measurements for quantifying individual aircraft contributions and showcase the complexities involved in interpreting these data. For example, during periods with high arrivals frequency, UFP plumes from multiple aircraft on approach are superposed and it is challenging to apportion impacts to individual aircraft. Ground-level impacts for individual aircraft on climb-out are difficult to discern because the planes rapidly ascend above the atmospheric mixed layer height and take different flight paths soon after take-off. Elevated UFP concentrations are observed downwind of the airport, in some cases admixed with approach/climb-out emissions. Although from these data UFP concentrations are difficult to associate with specific aircraft characteristics, UFP concentrations are elevated downwind of the airport. These impacts decrease with increasing distance from the airport yet are clearly discernible at least 3 km downwind.