A comparison of population-level exposure and equity tradeoffs across strategies to reduce fine particulate matter emissions from transportation sources in the northeastern US.

BACKGROUND: Many climate mitigation policies to reduce transportation emissions have public health benefits related to ambient air pollution. However, few health analyses consider the equity implications of alternative policies. Equity can be conceptualized in many different ways that may be relevant to communities, decision-makers, and other stakeholders. OBJECTIVES: To evaluate alternative transportation emissions reduction scenarios across the northeastern United States considering population exposure reductions and multiple equity constructs. METHODS: We developed four quantitative indicators reflecting equity constructs that aligned with stakeholder perspectives, including racial/ethnic exposure inequities, proportion of benefits in environmental justice communities, distribution of benefits among participating states, and rural/urban share of benefits. We analyzed numerous transportation emissions reduction scenarios for directly emitted fine particulate matter (primary PM2.5) covering 12 Northeast states and the District of Columbia. We used the Community Multiscale Air Quality model with the decoupled direct method to estimate the reduction in population-weighted primary PM2.5 exposure and the impact on equity for each scenario. RESULTS: Scenarios that yielded greater reductions in population-weighted primary PM2.5 exposure generally emphasized emissions reductions in urban areas or states with large urban centers, with a more than threefold difference in benefits across scenarios. The higher exposure-benefit scenarios typically also had greater reductions in racial/ethnic exposure inequities but led to higher between-state or rural/urban inequality. Scenarios that targeted uniform percentage emission reductions from light or heavy-duty trucks best addressed rural/urban inequalities but led to the smallest reductions in racial/ethnic inequity. CONCLUSION: There are intrinsic tradeoffs among equity constructs, where focusing resources on distributing benefits across states or between urban and rural populations could come at the expense of less reduction in racial/ethnic exposure inequities or in environmental justice communities. Future health benefits analyses should incorporate multiple equity indicators that reflect different stakeholder perspectives and articulate the underlying constructs and tradeoffs.

ZIP Code-Level Estimation of Air Quality and Health Risk Due to Particulate Matter Pollution in New York City.

Exposure to PM2.5 is associated with hundreds of premature mortalities every year in New York City (NYC). Current air quality and health impact assessment tools provide county-wide estimates but are inadequate for assessing health benefits at neighborhood scales, especially for evaluating policy options related to energy efficiency or climate goals. We developed a new ZIP Code-Level Air Pollution Policy Assessment (ZAPPA) tool for NYC by integrating two reduced form models─Community Air Quality Tools (C-TOOLS) and the Co-Benefits Risk Assessment Health Impacts Screening and Mapping Tool (COBRA)─that propagate emissions changes to estimate air pollution exposures and health benefits. ZAPPA leverages custom higher resolution inputs for emissions, health incidences, and population. It, then, enables rapid policy evaluation with localized ZIP code tabulation area (ZCTA)-level analysis of potential health and monetary benefits stemming from air quality management decisions. We evaluated the modeled 2016 PM2.5 values against observed values at EPA and NYCCAS monitors, finding good model performance (FAC2, 1; NMSE, 0.05). We, then, applied ZAPPA to assess PM2.5 reduction-related health benefits from five illustrative policy scenarios in NYC focused on (1) commercial cooking, (2) residential and commercial building fuel regulations, (3) fleet electrification, (4) congestion pricing in Manhattan, and (5) these four combined as a "citywide sustainable policy implementation" scenario. The citywide scenario estimates an average reduction in PM2.5 of 0.9 µg/m3. This change translates to avoiding 210-475 deaths, 340 asthma emergency department visits, and monetized health benefits worth $2B to $5B annually, with significant variation across NYC's 192 ZCTAs. ZCTA-level assessments can help prioritize interventions in neighborhoods that would see the most health benefits from air pollution reduction. ZAPPA can provide quantitative insights on health and monetary benefits for future sustainability policy development in NYC.

Assessment of the health benefits to children of a transportation climate policy in New York City.

BACKGROUND: Assessments of health and environmental effects of clean air and climate policies have revealed substantial health benefits due to reductions in air pollution, but have included few pediatric outcomes or assessed benefits at the neighborhood level. OBJECTIVES: We estimated benefits across a suite of child health outcomes in 42 New York City (NYC) neighborhoods under the proposed regional Transportation and Climate Initiative. We also estimated their distribution across racial/ethnic and socioeconomic groups. METHODS: We estimated changes in ambient fine particulate matter (PM2.5) and nitrogen dioxide (NO2) concentrations associated with on-road emissions under nine different predefined cap-and-invest scenarios. Health outcomes, including selected adverse birth, respiratory, and neurodevelopmental outcomes, were estimated using a program similar to the U.S. EPA BenMAP program. We stratified the associated monetized benefits across racial/ethnic and socioeconomic groups. RESULTS: The benefits varied widely over the different cap-and-investment scenarios. For a 25% reduction in carbon emissions from 2022 to 2032 and a strategy prioritizing public transit investments, NYC would have an estimated 48 fewer medical visits for childhood asthma, 13,000 avoided asthma exacerbations not requiring medical visits, 640 fewer respiratory illnesses unrelated to asthma, and 9 avoided adverse birth outcomes (infant mortality, preterm birth, and term low birth weight) annually, starting in 2032. The total estimated annual avoided costs are $22 million. City-wide, Black and Hispanic children would experience 1.7 times the health benefits per capita than White and Non-Hispanic White children, respectively. Under the same scenario, neighborhoods experiencing the highest poverty rates in NYC would experience about 2.5 times the health benefits per capita than the lowest poverty neighborhoods. CONCLUSION: A cap-and-invest strategy to reduce carbon emissions from the transportation sector could provide substantial health and monetized benefits to children in NYC through reductions in criteria pollutant concentrations, with greater benefits among Black and Hispanic children.

Integrating Personal Air Sensor and GPS to Determine Microenvironment-Specific Exposures to Volatile Organic Compounds.

Personal exposure to volatile organic compounds (VOCs) from indoor sources including consumer products is an understudied public health concern. To develop and evaluate methods for monitoring personal VOC exposures, we performed a pilot study and examined time-resolved sensor-based measurements of geocoded total VOC (TVOC) exposures across individuals and microenvironments (MEs). We integrated continuous (1 min) data from a personal TVOC sensor and a global positioning system (GPS) logger, with a GPS-based ME classification model, to determine TVOC exposures in four MEs, including indoors at home (Home-In), indoors at other buildings (Other-In), inside vehicles (In-Vehicle), and outdoors (Out), across 45 participant-days for five participants. To help identify places with large emission sources, we identified high-exposure events (HEEs; TVOC > 500 ppb) using geocoded TVOC time-course data overlaid on Google Earth maps. Across the 45 participant-days, the MEs ranked from highest to lowest median TVOC were: Home-In (165 ppb), Other-In (86 ppb), In-Vehicle (52 ppb), and Out (46 ppb). For the two participants living in single-family houses with attached garages, the median exposures for Home-In were substantially higher (209, 416 ppb) than the three participant homes without attached garages: one living in a single-family house (129 ppb), and two living in apartments (38, 60 ppb). The daily average Home-In exposures exceeded the estimated Leadership in Energy and Environmental Design (LEED) building guideline of 108 ppb for 60% of the participant-days. We identified 94 HEEs across all participant-days, and 67% of the corresponding peak levels exceeded 1000 ppb. The MEs ranked from the highest to the lowest number of HEEs were: Home-In (60), Other-In (13), In-Vehicle (12), and Out (9). For Other-In and Out, most HEEs occurred indoors at fast food restaurants and retail stores, and outdoors in parking lots, respectively. For Home-In HEEs, the median TVOC emission and removal rates were 5.4 g h-1 and 1.1 h-1, respectively. Our study demonstrates the ability to determine individual sensor-based time-resolved TVOC exposures in different MEs, in support of identifying potential sources and exposure factors that can inform exposure mitigation strategies.

Impact of School Location on Children's Air Pollution Exposure.

The role of school location in children's air pollution exposure and ability to actively commute is a growing policy issue. Well-documented health impacts associated with near-roadway exposures have led school districts to consider school sites in cleaner air quality environments requiring school bus transportation. We analyze children's traffic-related air pollution exposure across an average Detroit school day to assess whether the benefits of reduced air pollution exposure at cleaner school sites are eroded by the need to transport students by bus or private vehicle. We simulated two school attendance scenarios using modeled hourly pollutant concentrations over the school day to understand how air pollution exposure may vary by school location and commute mode. We found that busing children from a high-traffic neighborhood to a school 19 km away in a low-traffic environment resulted in average daily exposures 2 to 3 times higher than children walking to a local school. Health benefits of siting schools away from high-volume roadways may be diminished by pollution exposure during bus commutes. School districts cannot simply select sites with low levels of air pollution, but must carefully analyze tradeoffs between location, transportation, and pollution exposure.

Sex, obesity, diabetes, and exposure to particulate matter among patients with severe asthma: Scientific insights from a comparative analysis of open clinical data sources during a five-day hackathon.

This special communication describes activities, products, and lessons learned from a recent hackathon that was funded by the National Center for Advancing Translational Sciences via the Biomedical Data Translator program ('Translator'). Specifically, Translator team members self-organized and worked together to conceptualize and execute, over a five-day period, a multi-institutional clinical research study that aimed to examine, using open clinical data sources, relationships between sex, obesity, diabetes, and exposure to airborne fine particulate matter among patients with severe asthma. The goal was to develop a proof of concept that this new model of collaboration and data sharing could effectively produce meaningful scientific results and generate new scientific hypotheses. Three Translator Clinical Knowledge Sources, each of which provides open access (via Application Programming Interfaces) to data derived from the electronic health record systems of major academic institutions, served as the source of study data. Jupyter Python notebooks, shared in GitHub repositories, were used to call the knowledge sources and analyze and integrate the results. The results replicated established or suspected relationships between sex, obesity, diabetes, exposure to airborne fine particulate matter, and severe asthma. In addition, the results demonstrated specific differences across the three Translator Clinical Knowledge Sources, suggesting cohort- and/or environment-specific factors related to the services themselves or the catchment area from which each service derives patient data. Collectively, this special communication demonstrates the power and utility of intense, team-oriented hackathons and offers general technical, organizational, and scientific lessons learned.

Development and Evaluation of the R-LINE Model Algorithms to Account for Chemical Transformation in the Near-road Environment.

With increased urbanization, there is increased mobility leading to higher amount of traffic-related activity on a global scale. Most NOx from combustion sources (about 90-95%) are emitted as NO, which is then readily converted to NO2 in the ambient air, while the remainder is emitted largely as NO2. Thus, the bulk of ambient NO2 is formed due to secondary production in the atmosphere, and which R-LINE cannot predict given that it can only model the dispersion of primary air pollutants. NO2 concentrations near major roads are appreciably higher than those measured at monitors in existing networks in urban areas, motivating a need to incorporate a mechanism in R-LINE to account for NO2 formation. To address this, we implemented three different approaches in order of increasing degrees of complexity and barrier to implementation from simplest to more complex. The first is an empirical approach based upon fitting a 4th order polynomial to existing near-road observations across the continental U.S., the second involves a simplified two-reaction chemical scheme, and the third involves a more detailed set of chemical reactions based upon the Generic Reaction Set (GRS) mechanism. All models were able to estimate more than 75% of concentrations within a factor of two of the near-road monitoring data and produced comparable performance statistics. These results indicate that the performance of the new R-LINE chemistry algorithms for predicting NO2 is comparable to other models (i.e. ADMS-Roads with GRS), both showing less than ±15% fractional bias and less than 45% normalized mean square error.

Finely Resolved On-Road PM2.5 and Estimated Premature Mortality in Central North Carolina.

To quantify the on-road PM2.5 -related premature mortality at a national scale, previous approaches to estimate concentrations at a 12-km × 12-km or larger grid cell resolution may not fully characterize concentration hotspots that occur near roadways and thus the areas of highest risk. Spatially resolved concentration estimates from on-road emissions to capture these hotspots may improve characterization of the associated risk, but are rarely used for estimating premature mortality. In this study, we compared the on-road PM2.5 -related premature mortality in central North Carolina with two different concentration estimation approaches-(i) using the Community Multiscale Air Quality (CMAQ) model to model concentration at a coarser resolution of a 36-km × 36-km grid resolution, and (ii) using a hybrid of a Gaussian dispersion model, CMAQ, and a space-time interpolation technique to provide annual average PM2.5 concentrations at a Census-block level (∼105,000 Census blocks). The hybrid modeling approach estimated 24% more on-road PM2.5 -related premature mortality than CMAQ. The major difference is from the primary on-road PM2.5 where the hybrid approach estimated 2.5 times more primary on-road PM2.5 -related premature mortality than CMAQ due to predicted exposure hotspots near roadways that coincide with high population areas. The results show that 72% of primary on-road PM2.5 premature mortality occurs within 1,000 m from roadways where 50% of the total population resides, highlighting the importance to characterize near-road primary PM2.5 and suggesting that previous studies may have underestimated premature mortality due to PM2.5 from traffic-related emissions.

A web-based screening tool for near-port air quality assessments.

The Community model for near-PORT applications (C-PORT) is a screening tool with an intended purpose of calculating differences in annual averaged concentration patterns and relative contributions of various source categories over the spatial domain within about 10 km of the port. C-PORT can inform decision-makers and concerned citizens about local air quality due to mobile source emissions related to commercial port activities. It allows users to visualize and evaluate different planning scenarios, helping them identify the best alternatives for making long-term decisions that protect community health and sustainability. The web-based, easy-to-use interface currently includes data from 21 seaports primarily in the Southeastern U.S., and has a map-based interface based on Google Maps. The tool was developed to visualize and assess changes in air quality due to changes in emissions and/or meteorology in order to analyze development scenarios, and is not intended to support or replace any regulatory models or programs.

Dispersion Modeling of Traffic-Related Air Pollutant Exposures and Health Effects Among Children with Asthma in Detroit, Michigan.

Vehicular traffic is a major source of ambient air pollution in urban areas. Traffic-related air pollutants, including carbon monoxide, nitrogen oxides, particulate matter less than 2.5 µm in diameter, and diesel exhaust emissions, have been associated with adverse human health effects, especially in areas near major roads. In addition to emissions from vehicles, ambient concentrations of air pollutants include contributions from stationary sources and background (or regional) sources. Although dispersion models have been widely used to evaluate air quality strategies and policies and can represent the spatial and temporal variation in environments near roads, the use of these models in health studies to estimate air pollutant exposures has been relatively limited. This paper summarizes the modeling system used to estimate exposures in the Near-Roadway Exposure and Urban Air Pollutant Study, an epidemiological study that examined 139 children with asthma or symptoms consistent with asthma, most of whom lived near major roads in Detroit, Michigan. Air pollutant concentrations were estimated with a hybrid modeling framework that included detailed inventories of mobile and stationary sources on local and regional scales; the RLINE, AERMOD, and CMAQ dispersion models; and monitored observations of pollutant concentrations. The temporal and spatial variability in emissions and exposures over the 2.5-year study period and at more than 300 home and school locations was characterized. The paper highlights issues with the development and understanding of the significance of traffic-related exposures through the use of dispersion models in urban-scale exposure assessments and epidemiology studies.

Air pollution benefits from reduced on-road activity due to COVID-19 in the United States.

On-road transportation is one of the largest contributors to air pollution in the United States. The COVID-19 pandemic provided the unintended experiment of reduced on-road emissions' impacts on air pollution due to lockdowns across the United States. Studies have quantified on-road transportation's impact on fine particulate matter (PM2.5)-attributable and ozone (O3)-attributable adverse health outcomes in the United States, and other studies have quantified air pollution-attributable health outcome reductions due to COVID-19-related lockdowns. We aim to quantify the PM2.5-attributable, O3-attributable, and nitrogen dioxide (NO2)-attributable adverse health outcomes from traffic emissions as well as the air pollution benefits due to reduced on-road activity during the pandemic in 2020. We estimate 79,400 (95% CI 46,100-121,000) premature mortalities each year due to on-road-attributable PM2.5, O3, and NO2. We further break down the impacts by pollutant and vehicle types (passenger [PAS] vs. freight [FRT] vehicles). We estimate PAS vehicles to be responsible for 63% of total impacts and FRT vehicles 37%. Nitrogen oxide (NOX) emissions from these vehicles are responsible for 78% of total impacts as it is a precursor for PM2.5 and O3. Utilizing annual vehicle miles traveled reductions in 2020, we estimate that 9,300 (5,500-14,000) deaths from air pollution were avoided in 2020 due to the state-specific reductions in on-road activity across the continental United States. By quantifying the air pollution public health benefits from lockdown-related reductions in on-road emissions, the results from this study stress the need for continued emission mitigation policies, like the U.S. Environmental Protection Agency's (EPA) recently proposed NOX standards for heavy-duty vehicles, to mitigate on-road transportation's public health impact.

Air Quality and Health Impacts of Onshore Oil and Gas Flaring and Venting Activities Estimated Using Refined Satellite-Based Emissions.

Emissions from flaring and venting (FV) in oil and gas (O&G) production are difficult to quantify due to their intermittent activities and lack of adequate monitoring and reporting. Given their potentially significant contribution to total emissions from the O&G sector in the United States, we estimate emissions from FV using Visible Infrared Imaging Radiometer Suite satellite observations and state/local reported data on flared gas volume. These refined estimates are higher than those reported in the National Emission Inventory: by up to 15 times for fine particulate matter (PM2.5), two times for sulfur dioxides, and 22% higher for nitrogen oxides (NOx). Annual average contributions of FV to ozone (O3), NO2, and PM2.5 in the conterminous U.S. (CONUS) are less than 0.15%, but significant contributions of up to 60% are found in O&G fields with FV. FV contributions are higher in winter than in summer months for O3 and PM2.5; an inverse behavior is found for NO2. Nitrate aerosol contributions to PM2.5 are highest in the Denver basin whereas in the Permian and Bakken basins, sulfate and elemental carbon aerosols are the major contributors. Over four simulated months in 2016 for the entire CONUS, FV contributes 210 additional instances of exceedances to the daily maximum 8-hr average O3 and has negligible contributions to exceedance of NO2 and PM2.5, given the current form of the national ambient air quality standards. FV emissions are found to cause over $7.4 billion in health damages, 710 premature deaths, and 73,000 asthma exacerbations among children annually.

A hyperlocal hybrid data fusion near-road PM2.5 and NO2 annual risk and environmental justice assessment across the United States.

Exposure to traffic-related air pollutants (TRAPs) has been associated with numerous adverse health effects. TRAP concentrations are highest meters away from major roads, and disproportionately affect minority (i.e., non-white) populations often considered the most vulnerable to TRAP exposure. To demonstrate an improved assessment of on-road emissions and to quantify exposure inequity in this population, we develop and apply a hybrid data fusion approach that utilizes the combined strength of air quality observations and regional/local scale models to estimate air pollution exposures at census block resolution for the entire U.S. We use the regional photochemical grid model CMAQ (Community Multiscale Air Quality) to predict the spatiotemporal impacts at local/regional scales, and the local scale dispersion model, R-LINE (Research LINE source) to estimate concentrations that capture the sharp TRAP gradients from roads. We further apply the Regionalized Air quality Model Performance (RAMP) Hybrid data fusion technique to consider the model's nonhomogeneous, nonlinear performance to not only improve exposure estimates, but also achieve significant model performance improvement. With a R2 of 0.51 for PM2.5 and 0.81 for NO2, the RAMP hybrid method improved R2 by ~0.2 for both pollutants (an increase of up to ~70% for PM2.5 and ~31% NO2). Using the RAMP Hybrid method, we estimate 264,516 [95% confidence interval [CI], 223,506-307,577] premature deaths attributable to PM2.5 from all sources, a ~1% overall decrease in CMAQ-estimated premature mortality compared to RAMP Hybrid, despite increases and decreases in some locations. For NO2, RAMP Hybrid estimates 138,550 [69,275-207,826] premature deaths, a ~19% increase (22,576 [11,288 - 33,864]) compared to CMAQ. Finally, using our RAMP hybrid method to estimate exposure inequity across the U.S., we estimate that Minorities within 100 m from major roads are exposed to up to 15% more PM2.5 and up to 35% more NO2 than their White counterparts.

Public health, climate, and economic impacts of desulfurizing jet fuel.

In jurisdictions including the US and the EU ground transportation and marine fuels have recently been required to contain lower concentrations of sulfur, which has resulted in reduced atmospheric SO(x) emissions. In contrast, the maximum sulfur content of aviation fuel has remained unchanged at 3000 ppm (although sulfur levels average 600 ppm in practice). We assess the costs and benefits of a potential ultra-low sulfur (15 ppm) jet fuel standard ("ULSJ"). We estimate that global implementation of ULSJ will cost US$1-4bn per year and prevent 900-4000 air quality-related premature mortalities per year. Radiative forcing associated with reduction in atmospheric sulfate, nitrate, and ammonium loading is estimated at +3.4 mW/m(2) (equivalent to about 1/10th of the warming due to CO(2) emissions from aviation) and ULSJ increases life cycle CO(2) emissions by approximately 2%. The public health benefits are dominated by the reduction in cruise SO(x) emissions, so a key uncertainty is the atmospheric modeling of vertical transport of pollution from cruise altitudes to the ground. Comparisons of modeled and measured vertical profiles of CO, PAN, O(3), and (7)Be indicate that this uncertainty is low relative to uncertainties regarding the value of statistical life and the toxicity of fine particulate matter.

Current and future particulate-matter-related mortality risks in the United States from aviation emissions during landing and takeoff.

Demand for air travel is projected to increase in the upcoming years, with a corresponding influence on emissions, air quality, and public health. The trajectory of health impacts would be influenced by not just emissions growth, but also changes in nonaviation ambient concentrations that influence secondary fine particulate matter (PM(2.5) ) formation, population growth and aging, and potential shifts in PM(2.5) concentration-response functions (CRFs). However, studies to date have not systematically evaluated the individual and joint contributions of these factors to health risk trajectories. In this study, we simulated emissions during landing and takeoff from aircraft at 99 airports across the United States for 2005 and for a 2025 flight activity projection scenario. We applied the Community Multiscale Air Quality (CMAQ) model with the Speciated Modeled Attainment Test (SMAT) to determine the contributions of these emissions to ambient concentrations, including scenarios with 2025 aircraft emissions and 2005 nonaviation air quality. We combined CMAQ outputs with PM(2.5) mortality CRFs and population projections, and evaluated the influence of changing emissions, nonaviation concentrations, and population factors. Given these scenarios, aviation-related health impacts would increase by a factor of 6.1 from 2005 to 2025, with a factor of 2.1 attributable to emissions, a factor of 1.3 attributable to population factors, and a factor of 2.3 attributable to changing nonaviation concentrations which enhance secondary PM(2.5) formation. Our study emphasizes that the public health burden of aviation emissions would be significantly influenced by the joint effects of flight activity increases, nonaviation concentration changes, and population growth and aging.

Dry Deposition Methods Based on Turbulence Kinetic Energy: Part 2. Extension to Particle Deposition Using a Single-Point Model.

Magnitude of atmospheric turbulence, a key driver of several processes that contribute to aerosol (i.e., particle) deposition, is underrepresented in current models. Various formulations have been developed to model particle dry deposition; all these formulations typically rely on friction velocity and some use additional ad hoc factors to represent enhanced impacts of turbulence. However, none were formally linked with the three-dimensional (3-D) turbulence. Here, we propose a set of 3-D turbulence-dependent resistance formulations for particle dry deposition simulation and intercompare the performance of new resistance formulations with that obtained from using the existing formulations and measured dry deposition velocity. Turbulence parameters such as turbulence velocity scale, turbulence factor, intensity of turbulence, effective sedimentation velocity, and effective Stokes number are newly introduced into two different particle deposition schemes to improve turbulence strength representation. For an assumed particle size distribution, the newly proposed schemes predict stronger diurnal variation of particle dry deposition velocity and are comparable to corresponding measurements while existing formulations indicate large underpredictions. We also find that the incorporation of new turbulence parameters either introduced or added stronger diurnal variability to sedimentation velocity and collection efficiencies values, making the new schemes predict higher deposition values during daytime and nighttime when compared to existing schemes. The findings from this research may help improve the capability of dry deposition schemes in regional and global models.

Dry Deposition Methods Based on Turbulence Kinetic Energy: Part 1. Evaluation of Various Resistances and Sensitivity Studies Using a Single-Point Model.

Different functions are used to account for turbulence strength in the atmospheric boundary layer for different stability regimes. These functions are one of the sources for differences among different atmospheric models' predictions and associated biases. Also, turbulence strength is underrepresented in some of the resistance formulations. To address these issues with dry deposition, firstly we take advantage of three-dimensional (3-D) turbulence information in estimating resistances by proposing and validating a 3-D turbulence velocity scale that is relevant for different stability regimes of boundary layer. Secondly, we hypothesize and validate that friction velocity measured by 3-D sonic anemometer can be effectively replaced by the new turbulence velocity scale multiplied by the von Karman constant. Finally, we (1) present a set of resistance formulations for ozone (O3) based on the 3-D turbulence velocity scale; (2) intercompare estimations of such resistances with those obtained using existing formulations; and, (3) evaluate simulated O3 fluxes using a single-point dry deposition model against long-term observations of O3 fluxes at the Harvard Forest (MA) site. Results indicate that the new resistance formulations work very well in simulating surface latent heat and O3 fluxes when compared to respective existing formulations and measurements at a decadal time scale. Findings from this research may help to improve the capability of dry deposition schemes for better estimation of dry deposition fluxes and create opportunities for the development of a community dry deposition model for use in regional/global air quality models.

Air quality and health-related impacts of traditional and alternate jet fuels from airport aircraft operations in the U.S.

Aviation emissions from landing and takeoff operations (LTO) can degrade local and regional air quality leading to adverse health outcomes in populations near airports and downwind. In this study we aim to quantify the air quality and health-related impacts from commercial LTO emissions in the continental U.S. for two recent years' inventories, 2011 and 2016. We quantify the LTO-attributable PM2.5, O3, and NO2 concentrations and health outcomes for mortality and multiple morbidity health endpoints. We also quantify the impacts from two scenarios representing a nation-wide implementation of 5% or 50% blends of sustainable alternative jet fuels. We estimate 80 (68-93) and 88 (75-100) PM2.5-attributable and 610 (310-920) and 1,100 (570-1,700) NO2-attributable premature mortalities in 2011 and 2016, respectively. We estimate a net decrease of 28 (14-56) and 54 (27-110) in O3-attributable premature mortalities across the U.S. in 2011 and 2016, respectively due to the large O3 titration effects near the airports. We also find that the asthma exacerbations due to NO2 exposures from LTO emissions increase from 100,000 (2,500-200,000) in 2011 to 170,000 (4,400-340,000) in 2016. Implementing a 5% or 50% blend of sustainable alternative jet fuel in 2016 results in a 1% or 18% reduction, respectively in PM2.5-attributable premature mortalities. Monetizing the value of avoided total premature mortalities, we find that a 50%-blended sustainable alternative jet fuel results in a 19% decrease in PM2.5 damages per ton of fuel burned and a 2% decrease in total damages per ton of fuel burned as compared to damages from traditional jet fuel. We also quantify health impacts by state and find California to be the most impacted by LTO emissions. We find that LTO-attributable PM2.5 and NO2 premature mortalities increase by 10% and 80%, respectively from 2011 to 2016 and that NO2-attributable premature mortalities are responsible for 91% of total LTO-attributable premature mortalities in both 2011 and 2016. And since we find LTO-attributable NO2 to be unaffected by the implementation of sustainable alternative jet fuels, additional approaches focused on NOX reductions in the combustor are needed to mitigate the air quality-related health impacts from LTO emissions.

Development and Application of an Open Tool for Sharing and Analyzing Integrated Clinical and Environmental Exposures Data: Asthma Use Case.

BACKGROUND: The Integrated Clinical and Environmental Exposures Service (ICEES) serves as an open-source, disease-agnostic, regulatory-compliant framework and approach for openly exposing and exploring clinical data that have been integrated at the patient level with a variety of environmental exposures data. ICEES is equipped with tools to support basic statistical exploration of the integrated data in a completely open manner. OBJECTIVE: This study aims to further develop and apply ICEES as a novel tool for openly exposing and exploring integrated clinical and environmental data. We focus on an asthma use case. METHODS: We queried the ICEES open application programming interface (OpenAPI) using a functionality that supports chi-square tests between feature variables and a primary outcome measure, with a Bonferroni correction for multiple comparisons (α=.001). We focused on 2 primary outcomes that are indicative of asthma exacerbations: annual emergency department (ED) or inpatient visits for respiratory issues; and annual prescriptions for prednisone. RESULTS: Of the 157,410 patients within the asthma cohort, 26,332 (16.73%) had 1 or more annual ED or inpatient visits for respiratory issues, and 17,056 (10.84%) had 1 or more annual prescriptions for prednisone. We found that close proximity to a major roadway or highway, exposure to high levels of particulate matter ≤2.5 µm (PM2.5) or ozone, female sex, Caucasian race, low residential density, lack of health insurance, and low household income were significantly associated with asthma exacerbations (P<.001). Asthma exacerbations did not vary by rural versus urban residence. Moreover, the results were largely consistent across outcome measures. CONCLUSIONS: Our results demonstrate that the open-source ICEES can be used to replicate and extend published findings on factors that influence asthma exacerbations. As a disease-agnostic, open-source approach for integrating, exposing, and exploring patient-level clinical and environmental exposures data, we believe that ICEES will have broad adoption by other institutions and application in environmental health and other biomedical fields.

Response surface model based emission source contribution and meteorological pattern analysis in ozone polluted days.

Urban and regional ozone (O3) pollution is a public health concern and causes damage to ecosystems. Due to the diverse emission sources of O3 precursors and the complex interactions of air dispersion and chemistry, identifying the contributing sources of O3 pollution requires integrated analysis to guide emission reduction plans. In this study, the meteorological characteristics leading to O3 polluted days (in which the maximum daily 8-h average O3 concentration is higher than the China Class II National O3 Standard (160 µg/m3)) in Guangzhou (GZ, China) were analyzed based on data from 2019. The O3 formation regimes and source apportionments under various prevailing wind directions were evaluated using a Response Surface Modeling (RSM) approach. The results showed that O3 polluted days in 2019 could be classified into four types of synoptic patterns (i.e., cyclone, anticyclone, trough, and high pressure approaching to sea) and were strongly correlated with high ambient temperature, low relative humidity, low wind speed, variable prevailing wind directions. Additionally, the cyclone pattern strongly promoted O3 formation due to its peripheral subsidence. The O3 formation was nitrogen oxides (NOx)-limited under the northerly wind, while volatile organic compounds (VOC)-limited under other prevailing wind directions. Anthropogenic emissions contributed largely to the O3 formation (54-78%) under the westerly, southwesterly, easterly, southeasterly, or southerly wind, but only moderately (35-47%) under the northerly or northeasterly wind. Furthermore, as for anthropogenic contributions, local emission contributions were the largest (39-60%) regardless of prevailing wind directions, especially the local NOx contributions (19-43%); the dominant upwind regional emissions contributed 12-46% (e.g., contributions from Dongguan were 12-20% under the southeasterly wind). The emission control strategies for O3 polluted days should focus on local emission sources in conjunction with the emission reduction of upwind regional sources.