Grantee Research Project Results
2018 Progress Report: SEARCH: Solutions to Energy, AiR, Climate, and Health
EPA Grant Number: R835871Center: Solutions for Energy, AiR, Climate and Health Center (SEARCH)
Center Director: Bell, Michelle L.
Title: SEARCH: Solutions to Energy, AiR, Climate, and Health
Investigators: Bell, Michelle L. , Hobbs, Benjamin F. , Gentner, Drew R. , Gillingham, Kenneth , Koehler, Kirsten , Zhang, Yang
Current Investigators: Bell, Michelle L. , Hobbs, Benjamin F. , Peng, Roger D. , Esty, Daniel C.
Institution: Yale University , Stanford University , University of Chicago , Pacific Northwest National Laboratory , North Carolina State University , The Johns Hopkins University , Centers for Disease Control and Prevention , Northeastern University , University of Michigan
Current Institution: Yale University , Northeastern University , Stanford University , University of Chicago , University of Michigan , North Carolina State University , The Johns Hopkins University , Centers for Disease Control and Prevention , Pacific Northwest National Laboratory
EPA Project Officer: Callan, Richard
Project Period: October 1, 2015 through September 30, 2020 (Extended to September 30, 2022)
Project Period Covered by this Report: October 1, 2017 through September 30,2018
Project Amount: $9,999,990
RFA: Air, Climate And Energy (ACE) Centers: Science Supporting Solutions (2014) RFA Text | Recipients Lists
Research Category: Climate Change , Air Quality and Air Toxics , Airborne Particulate Matter Health Effects , Particulate Matter , Air
Objective:
The main objectives of the SEARCH Center are to investigate energy-related transitions underway across the U.S. by combining state-of-the-science modeling of energy systems, air quality, climate, and health. SEARCH aims to characterize factors contributing to emissions, air quality and health associated with key energy-related transitions in order to understand how these factors affect regional and local differences in air pollution and public health effects today and under a changing climate. In addition, we will identify key modifiable factors (e.g., transportation, land-use, power generation) and how those factors and their air pollution impacts are likely to change over time. SEARCH has four projects, 2 research support units (Environmental Data Science Support Unit and the Policy and Decision Making Support Unit), and an Administrative Unit. Below we summarize progress on each of the four key projects.
Project 1: Modeling Emissions from Energy Transitions
Co-PI: Ben Hobbs (Johns Hopkins University); Co-PI: Ken Gillingham (Yale University)
Objectives of Research:In Project 1, researchers are collaborating with the SEARCH Center Policy and Decision Making Support Unit and state air regulatory agencies to develop a suite of energy transition scenarios representing many drivers and shifts in the energy sector that could impact regional emissions and air quality. These transitions are being modeled using the National Energy Modeling System (NEMS). NEMS results will be downscaled, and combined with emissions from indirect energy use determined through lifecycle cost assessment (LCA), for input into air quality simulation models, performed by collaborators in Project 3.
Project 2: Assessment of Energy-Related Sources, Factors, and Transitions Using Novel High-
Resolution Ambient Air Monitoring Networks and Personal Monitors
Co-PI: Kirsten Koehler (Johns Hopkins University); Co-PI: Drew Gentner (Yale University)
Objectives of Research:The Objectives are: Objective 1) develop novel online multipollutant monitors to simultaneously measure air pollutants and GHGs (i.e., CO, CO2,CH4,PM2.5, NO2, O3,SO2, oxidative potential, VOCs); Objective 2) developing a network of sites for stationary monitors during field deployment and protocols for the personal sampling; and Objective 3) measure temporally resolved personal exposures with detailed time-activity information using novel personal multipollutant monitors.
Project 3: Improving Projections of the Spatial and Temporal Changes of Multi-Pollutants to
Enhance Assessment of Public Health in a Changing World
PI: Yang Zhang (North Carolina State University)
Objectives of Research:The main goal of Project 3 is to make critical improvements to online-coupled air quality models (AQMs) and their inputs and outputs, and apply the improved AQMs to estimate the concentrations resulting from energy and emission scenarios (Project 1) to be used in health risk assessments (Project 4). During this reporting period, our objectives are: 1) continuously improve the 4 regional online-coupled AQMs and perform the 5-yr (2008-2012) baseline simulations, and process and test the emission change factors under baseline energy use and energy transition scenario provided by Project 1; 2) understand impacts of wildfires on air quality by analyzing a set of global simulations with and without wildfire emissions and with and without wildfire induced ecosystem changes, which will lay the groundwork for a better representation of wildfire emissions for air quality simulations; and 3) develop methods to generate high resolution snapshots of several air pollutants (NO2, PM2.5)using WRF-Chem output, MAIAC aerosol optical depth, high resolution land-use type from the National Land Cover Database, and ERA-Interim re-analysis meteorology.
Project 4: Human Health Impacts of Energy Transitions: Today and under a Changing World
PI: Michelle Bell (Yale University)
Objectives of Research:Decision-makers who protect health from air pollution are faced with complex systems involving multiple emission sources, variation in health response by population and region, and temporal changes such as climate change and economic development. The overall goal of Project 4 is to provide scientific evidence to aid sound policy by investigating: 1) factors that could influence air pollution-health associations, including modifiable factors and factors that could account for regional variability in observed associations (e.g., urbanicity, land-use), for PM2.5 and O3 on risk ofcardiovascular and respiratory hospital admissions, including understudied rural populations; 2) health impacts from energy transitions using the most up-to-date scientific information on the multipollutant mixture, regional variation, and sensitive subpopulations; and 3) how climate change could affect health impacts of energy transitions and the co-benefits/costs of air quality policies by calculating their climate change impact.
Progress Summary:
Progress Summary/Accomplishment Highlights:
Project 1
Development of transition scenarios: The Project 1 and Policy and Decision Making Support Unit
teams collaborated on refining four of the five scenarios: the abundant natural gas scenario, the electric
vehicle scenario, the port electrification scenario, and the distributed generation/demand response
scenario.
Modeling transition scenarios in NEMS: In Year 3, we have entirely completed three of the five
scenarios and a fourth is nearly complete. The "abundant natural gas scenario", "electric vehicle" and
"port electrification" scenarios have been completed. We already have the modelling results from
NEMS and have passed on the results to be downscaled. NEMS modeling results for these scenarios
have been passed on to the JHU team for downscaling. The team has also generated NEMS results for
the life-cycle cost analysis. Regarding the fourth scenario, the specification and incorporation into NEMS of the "increased
distributed generation and demand response" energy transition scenario is underway, emphasizing the
exploration of impacts of expanded electricity from distributed, renewable sources in the commercial
and residential sectors. Our initial findings suggest that a "Low DG" variant of this scenario could
result in 14% less distributed generation among end use sectors relative to baseline, while a
"Breakthrough" variant yield more than 40% additional distributed generation relative to baseline.
Downscaling: We built software and processes to downscale NEMS results, using standard and nonstandard
results from the Yale-NEMS model produced by our colleagues. This downscaling method
produces a set of emission change rates differentiated by location, sector, and emission species that
will be used by our partners in Project 3 for processing and air quality simulation. Initial results from
the base case and four scenarios (high natural gas, high electric vehicle, ports and an optimistic port
case) have been produced. The change factors have been tested and we are continuing to debug the
modeling of area sources using a "grow-in-place" assumption. We have also made progress on
downscaling point sources and transportation. We began implementing innovative techniques for
downscaling new point sources that include a GIS screening step and generation placement based on
capacity expansion modeling, to improve on current technique of "grow-in-place". We developed
sophisticated modeling techniques for allocating new generator capacity to modeling subregions,
results from which were presented at two conferences this year. We have developed emission change
factors to transportation sources and are working closely with Project 3 to use these factors in the
SMOKE transportation emissions model.
Life Cycle Assessment: We have successfully developed a post-processing module to the National
Energy Modeling System (NEMS) that outputs physical material flow data from all industrial sectors,
by year, based on data from the UN COMTRADE database. We have also successfully appended air
pollutant emissions to NEMS, based on data from the National Emissions Inventory, as implemented
in the US Environmentally-Extended Input-Output (USEEIO) model developed by USEPA.
Project 2
The third year of Project 2 has focused on the completion of the stationary and portable multipollutant monitor design and assembly (Obj. 1), infield/lab testing, and preparations for the field measurements of Project 2, which began piloting at the
end of Year 2 (Obj. 2-3) and have had month-long winter and summer tests. The first monitor was
deployed in October 2018 and we expect all monitors will be deployed by January 31, 2019.
Objective 1: The activities of Year 3 on Obj. 1 "Develop novel online multipollutant monitors to
simultaneously measure air pollutants and GHGs (i.e., CO, CO2, CH4, PM2.5, NO2, O3, SO2, oxidative
potential, VOCs)" have included monitor development and building, sensor calibration, design and
assembly of an on-line calibration system, and improvements to the data assimilation platform and
cyber infrastructure.
All electrical systems for the multipollutant monitor were designed, assembled, and tested. The ozone
sensor was extensively tested in New Haven, CT in March and April 2017. We found that at low ozone
concentrations (< 10 ppb), there was big measurement discrepancy between the two devices. This is
not unexpected, as the sensor manufacturer rated the device’s measurement range to 10-1000 ppb. For
ozone concentrations higher than 10 ppb, 67% of the data points reached agreement within +/-10%,
and 99% within +/-30%.
A manuscript describing the detailed laboratory and field evaluation of the Plantower PM sensor is
currently under revision at Environmental Science & Technology. In brief, we evaluated three
Plantower PMS A003 sensors when exposed to 8 different particulate matter (PM) sources including
residential air over several days and ambient outdoor air in Baltimore, MD over a 1-month period. The
PM2.5 sensors exhibited a high degree of precision and R2 values greater than 0.86 for all sources, but
the accuracy ranged from 13% for talcum powder to >90% for cooking and residential air when
compared with the reference instrumentation.
Substantial testing of the full monitor was done in Year 3. We partnered with the NASA study Ozone
Water-Land Environmental Transition 2018 Study (OWLETS-2), to co-locate our monitors with high
quality instrumentation on the University of Maryland Baltimore County campus. Good agreement
was found with the monitor PM sensor, after correction for impacts of temperature and humidity.
We successfully collected personal exposure data using our portable monitor without a backpack.
These tests occurred in Manhattan, New York City and New Haven, CT. In New York City, PM2.5
concentrations reached a maximum of 235 ug m-3, which occurred inside a restaurant where the
average was 38 ug m-3 and PM1 average was 26 ug m-3. Enhancements in CO concentrations were
observed with motor vehicle traffic, food carts/track, the indoor restaurant, and cigarette plumes. CO2
concentrations were dynamic throughout the 6-hour period and were highest while on the subway. This
data demonstrates the ability to capture very-high time resolution events, events at 5 s or better
resolution.
Objective 2: Developing a network of sites for stationary monitors during field deployment and
protocols for the personal sampling: Site selection has been handled by co-PI Prof. Kirsten Kohler and
has focused on a set of initial sites that co-locate with other existing instrumentation at existing
sampling sites. In Year 3 we have taken the identified priority sampling locations and confirmed sites
with power requirements. The first monitor was deployed in October 2018. The next wave of monitors,
including co-locations at the MDE sites will begin on December 4, 2018. We expect all monitors will
be deployed by January 31, 2019. Fifty stationary units were produced in the Gentner Lab with another
8 portable monitors. This is behind our anticipated start date, mostly reflecting issues with
communications between the monitors and the database. However, we feel that we will still be able to
meet the study objectives in the shortened timeframe of deployment.
Objective 3: Plans and materials for the personal exposure study have been developed by postdoc Misti
Zamora and Kirsten Kohler at JHU. This has included developing participant recruitment materials,
selection strategy/protocol, survey materials, and sampling plan. We have updated our sampling plans
to include the use of electric scooters. Lime, a new company that has rental scooters, is now very
popular in Baltimore and around the country. We think this will add a very novel aspect of this work.
We also incorporated information from the SAC in our questionnaires, including the value to
participants from having exposure information. Portable units will be deployed with study participants
starting February 2019, with recruitment and subject survey/interviews starting January 2019.
Project 3:
We continued to improve model performance for the 4 regional models. Using the same emissions
with the improved models, we performed simulations for a 5-year period (2008-2012). The NMBs for
simulated Max 8-h O3 concentrations are all within ±15%, which are considered to be a very good
performance. While WRF-CAM5 and WRF/CMAQ tend to overpredict O3 concentrations at AQS
sites, WRF/Chem and WRF/Chem-ROMS tend to underpredict them at AQS and CASTNET sites.
Large NMBs exit for simulated PM10 concentrations for all models, ranging from -30.2% to -45.6%,
which may be caused by underestimated dust emissions. The NMBs for simulated 24-hr average PM2.5
concentrations are all within ±16% (which are considered to be an excellent performance) except for
WRF/CAM5 whose NMB is within ±27% (which is considered to be an acceptable performance).
These models tend to underpredict PM2.5. One possible reason is the underestimation of wildfire
emissions. We are setting up the final production simulations for the current period of 2008-2012 with
the FINN fire emissions and several additional model improvements. To further improve the model
predictions, we explored two ensemble methods: a simple ensemble method with an equal weighting
factor for all four models, and a multi-linear ensemble method with unequal weighting factors for all
four models. Results show that the multi-linear ensemble method gives the overall best performance
among the six sets of model predictions.
We analyzed a set of global simulations with and without wildfire emissions and with and without
wildfire induced ecosystem changes performed by unfunded collaborators as part of their research
funded by NSF. Comparisons are made between the various simulations for a historical period (2001-
2010) and a future period (2051-2060) following the RCP4.5 mitigation scenario. Despite a reduction
in anthropogenic emissions in the future that reduces aerosol optical depth (AOD) in northern midlatitude
regions, our analysis shows that a fraction of the projected reduction in AOD is compensated
by an increase in AOD due to wildfire emissions. Wildfires also increase particles at the surface in the
future, with obvious signals in North America, South America, Southeast Oceania and Siberia. In
southern hemisphere, increase in wildfire-induced PM at the surface will contribute to > 50% of the
total increase in PM in the future decades. We found that wildfire-induced increase in PM
concentration changes at the surface is larger in the future than the present over the Great Plains,
Canadian Prairie, the Southeast U.S. and parts of the western U.S. Surface PM in Canada and Alaska
will be largely dominated by wildfire emissions and about 40 U.S. states may see adverse health
effects of wildfire-induced emissions in the future. The increase in wildfire-induced surface PM in
North America is partly related to the increase in wildfires in Siberia, which increase the long-range
transport of PM to North America in the future.
We developed a daily PM2.5 product at 1 × 1 km2 spatial resolution across the eastern United States
(east of 90° W) with the aid of 36 × 36 km2 WRF-Chem output for the year of 2008, 1 × 1 km2
MAIAC AOD data, 1 × 1 km2 land-use type from the National Land Cover Database, and 0.125° ×
0.125° ERA-Interim re-analysis meteorology. A gap-filling technique is applied to MAIAC AOD data
to construct robust daily estimates of AOD when the satellite data are missing (e.g., areas obstructed
by clouds or snow cover). The input data are incorporated into a multiple-linear regression model
trained to surface observations of PM2.5 from the EPA Air Quality System monitoring network. The
model developed herein generates a high-fidelity estimate (r2 = 0.75 using a 10-fold cross-validation)
of daily PM2.5 over a large area at a high spatial resolution. Our study yields comparable statistics to
other studies producing similar PM2.5 products (between r2=0.67 and r2=0.88). Information from WRFChem
is a substantial contributor to the skill of our model. Gap-filled satellite AOD is also an
important contributor to the performance of the model. Meteorological information and land-use data
are secondary contributors to model skill. The statistical model performs better than WRF-Chem as a
stand-alone product. By downscaling chemical transport model output with satellite data and other
spatiotemporal predictors, we can better fit the needs of those linking PM2.5 to health outcomes. The
enhanced daily PM2.5 data for 2008 has been provided to Project 4 for their epidemiological studies.
Project 4:
We have accomplished several key tasks related to Project 4. We are investigating factors that could
influence air pollution-health associations, such as long-term temporal trends in the short-term effects
of PM2.5 of hospital admissions in the United States. Due to the complex nature of policy
implementation, accountability research is hindered by difficulty in classifying pre and post policy
exposure and health outcome, excluding transboundary pollution from outside the study area, and
distinguishing health impact change from multiple pollutants. For PM2.5, understating how the health
effects may have changed over time is further complicated by its nature as a complex mixture with
varying chemical components by season, location and time, which could affect its toxicity. Changes in
health effect estimates over time could result from policy or technological changes that affect the
chemical compositions of PM2.5 during this period. Other modifiable factors could also play a role such
as changes in central air conditioning. We are examining the temporal trend of the association between
short-term exposure (i.e., a day or a few days) to PM2.5 total mass and risk of hospital admissions for
those 65 years and older using Medicare billing records, to examine if this association changes over
time. In particular, we examine the relationship of this trend with observed change in particulate matter
composition and changes in population and community characteristics. Representative projects include
epidemiological analysis of the temporal trends of PM2.5. We are investigating the temporal trend of
short-term association between hospital admission rates and PM2.5 level of previous days across more
than 200 counties for over a decade U.S. Specifically, the subaims of this work are: a) to explore the
temporal trend of association between short-term change in PM2.5 mass concentrations and hospital
admissions among older Americans (>65 years); b) to examine the relationship of this trend with
observed changes in particulate matter composition and behavioral changes in population, which could
enlighten us on possible causes of the trend; and c) to investigate possible connections between the
trend and existing policies during that period, which will represent joint effects of multiple polices in
previous decades, and aid the design of future air pollution control policies. We have made substantial
progress in our estimation of the long-term trends of risk estimates of PM2.5 and hospitalizations for the
Medicare population. As an example of progress since last year, we have begun a collaborative project
with the Harvard/MIT EPA Center to combine estimates of PM2.5 that provide better spatial coverage
than monitor-based estimates, and we will compare these estimates to those generated with the
monitor.
Further, we are investigating the change in health impact of PM2.5 and ozone under different climate
change scenarios, in close coordination with Project 3. While our overall SEARCH Center objectives
involve many scenarios, here we describe our current work which we have begun with Project 3.
Besides, we will evaluate and compare multiple health linkage functions used in projecting air
pollution related health impact under climate change, isolating different features of the system (e.g.
population growth vs. climate change). Further, we are refining the methods to estimate human health
outcomes with careful consideration of what assumptions and factors can be considered quantitatively
versus qualitatively. Progress in the last year in this part of the project includes advances in the
methods to link climate change estimates of exposure to the health portion of the project. Another key
feature of Project 4 is review and meta-analysis. We continue our work on systematic reviews and
meta-analyses to investigate which subpopulations are most vulnerable or susceptible to
environmental conditions including air pollution and temperature. Weather impacts on human
mortality are a critical public health concern with respect to climate change. We are assessing the
sensitivity of subpopulations to weather mortality associations in previously published literature, and
have completed a systematic search using a MEDLINE/PubMed database for population-based
studies of exposure to heat or high temperature, cold, and heat waves. As an example of progress in
submitted our review of temperature-health studies, with a focus on effect modification. The search
identified over 50,000 articles, with over 200 articles for analysis.
To study the impact of green space on the association between air pollution and health, we are
examining Normalized Difference Vegetation Index (NDVI) derived from the Moderate Resolution
Imaging Spectroradiometer NASA’s Earth Observing System, along with demographic, weather, and
socio-economic variables in relation to air pollution’s association with hospital admissions. As an
example of progress on this front, we have submitted our work on the influence of NDVI on the
association between air pollution and health for publication.
Future Activities:
Project 1:
Development of transition scenarios: Final transition scenarios will be developed with input from
internal teams and external stakeholders.
Modeling transition scenarios in NEMS: Our next steps include completing the final two transition
scenarios on distributed generation + demand response and large-scale building energy efficiency. The
results from these scenarios will be passed on to be downscaled.
Downscaling: We will continue to refine and streamline the basic growth factor calculation
methodology and continue to coordinate with Project 3 on procedures for sharing downscaled change
factors. We will also continue to implement the point source and transportation downscaling
techniques and to ensure the new downscaling methods can be implemented by the air quality
simulation team. The debugging and quality control on the point source downscaling will be completed
soon. This method is well documented and streamlined so it is straightforward to reapply it to the
remaining transition scenarios. Anticipating that we will have more NEMS transition runs than will
ultimately be analyzed for air quality impacts, our team will develop a method to prioritize which
transitions are most significant from a public health perspective. These will likely be scenarios that
show relatively larger growth or more significant changes in spatial distribution for ozone precursors
and particulate matter.
Life Cycle Assessment: In the coming year, life-cycle emissions will be estimated and passed on for
downscaling. These will be based on several specific future energy/transportation scenarios to
determine physical material flows and emissions of air toxics, and compare with the baseline scenario
already run.
Project 2:
Year 4 of the project will primarily include the deployment of the fixed stationary
network (Objective 2) and the start of personal exposure monitoring (Objective 3). This will include
the completion of our detailed assessment of monitor performance in both laboratory controlled
conditions and field environments, along with evaluation of monitors co-located with reference
instrumentation. Objectives 2 and 3's data analysis sub-objectives will then begin with incoming data
from the networks. All IRB approvals will be renewed, as required.
Project 3:
We plan to finalize model configurations for the 4 regional models and perform
final production simulations for the current 5-yr period to obtain the best possible performance. We
will complete processing and testing of ECFs for energy-related sectors and mobile sectors, generate
model-ready emissions using these ECFs and SMOKE for future period simulations, and conduct
simulations for a future 5-yr period using projected emissions under future energy transition scenarios.
For wildfire emissions, we plan to develop empirical relationships between wildfire emissions and
plume rise with hydroclimate factors for use in regional climate-air-quality models to evaluate the
impacts of climate and land use land cover change on air quality in the U.S. Finally, we plan to extend
the enhanced daily PM2.5 product to 2009-2012 using the same method that we have developed.
Additional work will also focus on developing a statistical model that can simulate speciated PM2.5.
We also intend to update this model as future AOD products are publicly released. In particular, AOD
data from a geostationary satellite such as GOES-16 could be especially helpful in improving daily
PM2.5 estimation. The enhanced daily PM2.5 data for 2009-2012 will be provided to Project 4 for their
epidemiological studies. We will also compare the spatial variability's of enhanced satellite-derived
NO2 and PM2.5 products with the high density NO2 and PM2.5 measurements from Project 2.
Project 4:
Future Activities: In the upcoming year, we plan to complete several meta-analysis projects,
including work on birth outcomes and another piece on NO2. Currently, we have one meta-analysis
submitted and plan to submit two more within the next year. This will involve completing the review
of roughly hundreds of articles for inclusion in the analysis. Work will continue on the coordination
with Project 3 to link estimates of air pollution under a changing climate, generated through Project 3’s
air quality and climate change modeling systems as described above, to epidemiological assessment to
estimate the health impacts of increased (or decreased) levels of air pollution. Work is moving forward
on the collaborative project, which is a joint effort between Project 4 and the Harvard/MIT Center.
This work involves joining estimated PM2.5 concentrations for the continental United States with our
models of the long-term temporal change of the association of PM2.5 with risk of hospital admissions
for the Medicare population. This collaborative project builds on our original aims, but adds benefit to
the Centers' work by allowing inclusion of more suburban and rural areas, as compared to the more
urban centers that are more likely to have U.S. government monitors.
Journal Articles: 74 Displayed | Download in RIS Format
| Other center views: | All 119 publications | 74 publications in selected types | All 74 journal articles |
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Armstrong B, Bell ML, de Sousa Zanotti Stagliorio Coelho M, Leon Guo YL, Guo Y, Goodman P, Hashizume M, Honda Y, Kim H, Lavigne E, Michelozzi P. Longer-term impact of high and low temperature on mortality:an international study to clarify length of mortality displacement. Environmental Health Perspectives.2017 Oct 27;125(10):107009 |
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Bell M, Banerjee G, Pereira G. Residential mobility of pregnant women and implications for assessment of spatially-varying environmental exposures. JOURNAL OF EXPOSURE SCIENCE & ENVIRONMENTAL EPIDEMIOLOGY 2018;28(5):470-480. |
R835871 (2021) |
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Bravo MA, Anthopolos R, Bell ML, Miranda ML. Racial isolation and exposure to airborne particulate matter and ozone in understudied US populations: environmental justice applications of downscaled numerical model output. Environment International 2016;92-93:247-255. |
R835871 (2016) R835871 (2017) R835871 (2020) R835871C004 (2016) |
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Bravo MA, Ebisu K, Dominici F, Wang Y, Peng RD, Bell ML. Airborne fine particles and risk of hospital admissions for understudied populations: effects by urbanicity and short-term cumulative exposures in 708 U.S. counties. Environmental Health Perspectives 2017;125(4):594-601. |
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Gentner DR, Xiong F. Tracking pollutant emissions. Nature Geoscience 2017;10(12):883-884. |
R835871 (2018) R835871 (2019) R835871 (2020) R835871C002 (2017) |
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Glotfelty T, He J, Zhang Y. Improving organic aerosol treatments in CESM/CAM5: development, application, and evaluation. Journal of Advances in Modeling Earth Systems 2017;9(2):1506-1539. |
R835871 (2017) R835871 (2020) |
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Goldberg DL, Lamsal LN, Loughner CP, Swartz WH, Lu Z, Streets DG. A high-resolution and observationally constrained OMI NO2 satellite retrieval. Atmospheric Chemistry & Physics 2017;17(18):11403-11421. |
R835871 (2017) R835871 (2020) |
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Goldberg DL, Lu Z, Oda T, Lamsal LN, Liu F, Griffin D, McLinden CA, Krotkov NA, Duncan BN, Streets DG. Exploiting OMI NO2 satellite observations to infer fossil-fuel CO2 emissions from US megacities. Science of the Total Environment 2019;695:133805. |
R835871 (2019) R835871 (2020) |
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Guo Y, Gasparrini A, Li S, Sera F, Vicedo-Cabrera AM, Coelho MD, Saldiva PH, Lavigne E, Tawatsupa B, Punnasiri K, Overcenco A. Quantifying excess deaths related to heatwaves under climate change scenarios: a multicountry time series modelling study. PLoS Medicine 2018;15(7). |
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He J, He R, Zhang Y. Impacts of Air–sea Interactions on Regional Air Quality Predictions Using a Coupled Atmosphere-Ocean Model in Southeastern US. Aerosol and Air Quality Research. 2018 Apr 1;18:1044-67. |
R835871 (2018) R835871 (2020) |
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Heo S, Li L, Son J, Koutrakis P, Bell M. Associations Between Gestational Residential Radon Exposure and Term Low Birthweight in Connecticut, USA. EPIDEMIOLOGY 2024;35(6):834-843 |
R835871 (Final) |
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Humes M, Wang M, Kim S, Machesky J, Gentner D, Robinson A, Donahue N, Presto A. Limited Secondary Organic Aerosol Production from Acyclic Oxygenated Volatile Chemical Products. ENVIRONMENTAL SCIENCE TECHNOLOGY 2022;56(8):4806-4815. |
R835871 (2021) R835873 (2020) |
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Jin L, Berman JD, Warren JL, Levy JI, Thurston G, Zhang Y, Xu X, Wang S, Zhang Y, Bell ML. A land use regression model of nitrogen dioxide and fine particulate matter in a complex urban core in Lanzhou, China. Environmental Research 2019;177:108597. |
R835871 (2019) R835871 (2020) |
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Keet CA, Keller JP, Peng RD. Long-term coarse particulate matter exposure is associated with asthma among children in Medicaid. American Journal of Respiratory & Critical Care Medicine 2018;197(6):737-746. |
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Keller JP, Peng RD. Error in estimating area‐level air pollution exposures for epidemiology. Environmetrics 2019;30(8):e2573. |
R835871 (2019) R835871 (2020) |
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Khare P, Gentner DR. Considering the future of anthropogenic gas-phase organic compound emissions and the increasing influence of non-combustion sources on urban air quality. Atmospheric Chemistry and Physics 2018;18(8):5391-5413. |
R835871 (2018) R835871 (2019) R835871 (2020) |
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Krall JR, Hackstadt AJ, Peng RD. A hierarchical modeling approach to estimate regional acute health effects of particulate matter sources. Statistics in Medicine 2017;36(9):1461-1475. |
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Li H, Dailey J, Kale T, Besar K, Koehler K, Katz HE. Sensitive and selective NO2 sensing based on alkyl- and alkylthio-thiophene polymer conductance and conductance ratio changes from differential chemical doping. ACS Applied Materials & Interfaces 2017;9(24):20501-20507. |
R835871 (2017) R835871 (2018) R835871 (2019) R835871 (2020) R835871C002 (2017) |
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Li L, Blomberg A, Lawrence J, Requia W, Wei Y, Liu M, Peralta A, Koutrakis P. A spatiotemporal ensemble model to predict gross beta particulate radioactivity across the contiguos United States. ENVIRONMENTAL INTERNATIONAL 2021;456(106643). |
R835871 (2021) |
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Lim CC, Hayes RB, Ahn J, Shao Y, Silverman DT, Jones RR, Garcia C, Bell ML, Thurston GD. Long-term exposure to ozone and cause-specific mortality risk in the United States. American Journal of Respiratory and Critical Care Medicine 2019;200(8):1022-1031. |
R835871 (2019) R835871 (2020) R831697 (Final) R838300 (2020) |
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Liu JC, Wilson A, Mickley LJ, Dominici F, Ebisu K, Wang Y, Sulprizio MP, Peng RD, Yue X, Son JY, Anderson GB, Bell ML. Wildfire-specific fine particulate matter and risk of hospital admissions in urban and rural counties. Epidemiology 2017;28(1):77-85. |
R835871 (2017) R835871 (2018) R835871 (2020) R834798 (Final) |
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Liu JC, Wilson A, Mickley LJ, Ebisu K, Sulprizio MP, Wang Y, Peng RD, Yue X, Dominici F, Bell ML. Who among the elderly is most vulnerable to exposure to and health risks of fine particulate matter from wildfire smoke? American Journal of Epidemiology 2017;186(6):730-735. |
R835871 (2017) R835871 (2018) R835871 (2020) R834798 (Final) R835875 (2017) R835875 (2018) R835875 (2019) |
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Liu JC, Peng RD. The impact of wildfire smoke on compositions of fine particulate matter by ecoregion in the Western US. Journal of exposure science & environmental epidemiology. 2018 Sep 5:1. |
R835871 (2018) R835871 (2020) |
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Shi W, Zheng Y, Taylor AD, Yu J, Katz HE. Increased mobility and on/off ratio in organic field-effect transistors using low-cost guanine-pentacene multilayers. Applied Physics Letters 2017;111(4):043301. |
R835871 (2017) R835871 (2020) |
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Shi W, Yu J, Katz HE. Sensitive and selective pentacene-guanine field-effect transistor sensing of nitrogen dioxide and interferent vapor analytes. Sensors and Actuators B: Chemical 2018;254:940-948. |
R835871 (2017) R835871 (2020) |
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Silva GS, Warren JL, Deziel NC. Spatial modeling to identify sociodemographic predictors of hydraulic fracturing wastewater injection wells in Ohio census block groups. Environmental Health Perspectives 2018;126(6):067008 (8 pp.). |
R835871 (2018) R835871 (2020) CR839249 (2018) CR839249 (2019) CR839249 (Final) |
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Son JY, Liu JC, Bell ML. Temperature-related mortality:a systematic review and investigation of effect modifiers. Environmental Research Letters 2019;14(7):073004. |
R835871 (2019) R835871 (2020) R835871 (2021) |
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Son J-Y, Lane KJ, Lee J-T, Bell ML. Urban vegetation and heat-related mortality in Seoul, Korea. Environmental Research 2016;151:728-733. |
R835871 (2017) R835871 (2020) |
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Son J-Y, Lee HJ, Koutrakis P, Bell ML. Pregnancy and lifetime exposure to fine particulate matter and infant mortality in Massachusetts, 2001–2007. American Journal of Epidemiology 2017;186(11):1268-1276. |
R835871 (2017) R835871 (2018) R835871 (2020) |
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Son J-Y, Lee J-T, Bell ML. Is ambient temperature associated with risk of infant mortality? A multi-city study in Korea. Environmental Research 2017;158:748-752. |
R835871 (2017) R835871 (2020) |
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Vicedo-Cabrera A, Guo Y, Sera F, Huber V, Schlesner C, Mitchell D, Tong S, Coelho M, Saldiva P, Lavigne E, Correa P, Ortega N, Kan H, Osorio S, Kysely J, Urban A, Jaakkola J, Ryti N, Pascal M, Goodman PG, Zeka A, Michelozzi P, Scortichini M, Hashizume M, Honda Y, Hurtado-Diaz M, Cruz J, Seposo X, Kim H, Tobias A, Iniguez C, Forsberg B, Astrom DO, Ragettli MS, Roosli M, Guo YL, Wu CF, Zanobetti A, Schwartz J, Bell ML, Dang TN, Van DD, Heaviside C, Vardoulakis S, Hajat S, Haines A, Armstrong B, Ebi KL, Gasparrini A. Temperature-related mortality impacts under and beyond Paris Agreement climate change scenarios. CLIMATIC CHANGE 2018;150(3-4):391-402. |
R835871 (2021) |
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Warren J, Son JY, Leaderer BP, Bell ML. Investigating the impact of maternal residential mobility on identifying critical windows of susceptibility to ambient air pollution during pregnancy. American Journal of Epidemiology 2017; 187(5):992-1000. |
R835871 (2018) R835871 (2019) R835871 (2020) |
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Yahya K, Glotfelty T, Wang K, Zhang Y, Nenes A. Modeling regional air quality and climate: improving organic aerosol and aerosol activation processes in WRF/Chem version 3.7.1. Geoscientific Model Development 2017;10(6):2333-2363. |
R835871 (2017) R835871 (2020) |
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Zhang J, Gao Y, Luo K, Leung LR, Zhang Y, Wang K, Fan J. Impacts of compound extreme weather events on ozone in the present and future. Atmospheric Chemistry and Physics (Online). 2018 Jul 13;18(PNNL-SA-135886). |
R835871 (2018) R835871 (2020) |
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Esty DC, ML Bell. Business Leadership in Global Climate Change Responses . American Journal of Public Health2018;108(S2):S80-S84. |
R835871 (2018) R835871 (2020) |
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Levy Zamora M, Xiong F, Gentner D, Kerkez B, Kohrman-Glaser J, Koehler K. Field and laboratory evaluations of the low-cost plantower particulate matter sensor. Environmental Science & Technology 2018;53(2):838-849. |
R835871 (2018) R835871 (2019) R835871 (2020) |
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Keet CA, Keller JP, Peng RD. Long-term coarse particulate matter exposure is associated with asthma among children in Medicaid. American Journal of Respiratory and Critical Care Medicine. 2018 Mar 15;197(6):737-46. |
R835871 (2018) R835871 (2020) |
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Gong X, Lin Y, Bell ML, Zhan FB. Associations between maternal residential proximity to air emissions from industrial facilities and low birth weight in Texas, USA. Environment International 2018;120:181-198. |
R835871 (2019) R835871 (2020) |
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Tang Z, Zhang H, Bai H, Chen Y, Zhao N, Zhou M, Cui H, Lerro C, Lin X, Lv L, Zhang C. Residential mobility during pregnancy in Urban Gansu, China. Health & Place 2018;53:258-263. |
R835871 (2019) R835871 (2020) |
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Heo S, Bell ML, Lee JT. Comparison of health risks by heat wave definition: applicability of wet-bulb globe temperature for heat wave criteria. Environmental Research 2019;168:158-170. |
R835871 (2018) R835871 (2019) R835871 (2020) |
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Gillingham K, Huang P. Is abundant natural gas a bridge to a low-carbon future or a dead-end?. The Energy Journal 2019;40(2). |
R835871 (2018) R835871 (2019) R835871 (2020) |
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Nori-Sarma A, Benmarhnia T, Rajiva A, Azhar GS, Gupta P, Pednekar MS, Bell ML. Advancing our understanding of heat wave criteria and associated health impacts to improve heat wave alerts in developing country settings. International Journal of Environmental Research and Public Health 2019;16(12):2089. |
R835871 (2019) R835871 (2020) |
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Berman JD, Jin L, Bell ML, Curriero FC. Developing a geostatistical simulation method to inform the quantity and placement of new monitors for a follow-up air sampling campaign. Journal of exposure science & environmental epidemiology. 2019 Mar;29(2):248. |
R835871 (2018) R835871 (2020) |
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Berman JD, Jin L, Bell ML, Curriero FC. Developing a geostatistical simulation method to inform the quantity and placement of new monitors for a follow-up air sampling campaign. Journal of Exposure Science & Environmental Epidemiology 2019;29(2):248-257. |
R835871 (2019) |
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Goldberg DL, Lu Z, Streets DG, de Foy B, Griffin D, McLinden CA, Lamsal LN, Krotkov NA, Eskes H. Enhanced capabilities of TROPOMI NO2:estimating NOx from North American cities and power plants. Environmental Science & Technology 2019;53(21):12594-12601. |
R835871 (2019) R835871 (2020) |
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Heo S, Bell ML. Heat waves in South Korea:differences of heat wave characteristics by thermal indices. Journal of Exposure Science & Environmental Epidemiology 2019;29(6):790-805. |
R835871 (2019) R835871 (2020) |
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Sera F, Armstrong B, Tobias A, Vicedo-Cabrera AM, Åström C, Bell ML, Chen BY, de Sousa Zanotti Stagliorio Coelho M, Matus Correa P, Cruz JC, Dang TN. How urban characteristics affect vulnerability to heat and cold:a multi-country analysis. International Journal of Epidemiology 2019;48(4):1101-1112. |
R835871 (2019) R835871 (2020) |
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Son JY, Lee JT, Lane KJ, Bell ML. Impacts of high temperature on adverse birth outcomes in Seoul, Korea:disparities by individual-and community-level characteristics. Environmental Research 2019;168:460-466. |
R835871 (2019) R835871 (2020) |
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Chen G, Wang A, Li S, Zhao X, Wang Y, Li H, Meng X, Knibbs LD, Bell ML, Abramson MJ, Wang Y. Long-term exposure to air pollution and survival after ischemic stroke:the China national stroke registry cohort. Stroke 2019;50(3):563-570. |
R835871 (2019) R835871 (2020) |
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Heo S, Fong KC, Bell ML. Risk of particulate matter on birth outcomes in relation to maternal socio-economic factors:a systematic review. Environmental Research Letters 2019;14(12):123004. |
R835871 (2019) R835871 (2020) |
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Anderson GB, Barnes EA, Bell ML, Dominici F. The future of climate epidemiology:opportunities for advancing health research in the context of climate change. American Journal of Epidemiology 2019;188(5):866-872. |
R835871 (2019) R835871 (2020) R835872 (2019) R835872C004 (Final) |
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Nori-Sarma A, Anderson GB, Rajiva A, ShahAzhar G, Gupta P, Pednekar MS, Son JY, Peng RD, Bell ML. The impact of heat waves on mortality in Northwest India. Environmental Research 2019;176:108546. |
R835871 (2019) R835871 (2020) |
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Heo S, Bell ML. The influence of green space on the short-term effects of particulate matter on hospitalization in the US for 2000–2013. Environmental Research 2019;174:61-68. |
R835871 (2019) R835871 (2020) |
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Yan M, Wilson A, Bell ML, Peng RD, Sun Q, Pu W, Yin X, Li T, Anderson GB. The shape of the concentration-response association between fine particulate matter pollution and human mortality in Beijing, China, and its implications for health impact assessment. Environmental Health Perspectives 2019;127(6):067007. |
R835871 (2019) R835871 (2020) |
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Heo S, Nori-Sarma A, Lee K, Benmarhnia T, Dominici F, Bell ML. The use of a quasi-experimental study on the mortality effect of a heat wave warning system in Korea. International Journal of Environmental Research and Public Health 2019;16(12):2245. |
R835871 (2019) R835871 (2020) |
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Goldberg DL, Gupta P, Wang K, Jena C, Zhang Y, Lu Z, Streets DG. Using gap-filled MAIAC AOD and WRF-Chem to estimate daily PM2.5 concentrations at 1 km resolution in the Eastern United States. Atmospheric Environment 2019;199:443-452. |
R835871 (2019) R835871 (2020) |
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Baklanov A, Zhang Y. Advances in air quality modeling and forecasting. Global Transitions 2020;2:261-70. |
R835871 (2020) |
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Woo SH, Liu JC, Yue X, Mickley LJ, Bell ML. Air pollution from wildfires and human health vulnerability in Alaskan communities under climate change. Environmental Research Letters 2020;15(9):094019. |
R835871 (2020) |
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Khare P, Machesky J, Soto R, He M, Presto AA, Gentner DR. Asphalt-related emissions are a major missing nontraditional source of secondary organic aerosol precursors. Science advances 2020;6(36):eabb9785. |
R835871 (2020) |
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Fong KC, Mehta NK, Bell ML. Disparities in exposure to surrounding greenness related to proportion of the population that were immigrants to the United States. International Journal of Hygiene and Environmental Health 2020;224:113434. |
R835871 (2019) R835871 (2020) |
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Rogers HM, Ditto JC, Gentner DR. Evidence for impacts on surface-level air quality in the northeastern US from long-distance transport of smoke from North American fires during the Long Island Sound Tropospheric Ozone Study (LISTOS) 2018. Atmospheric Chemistry and Physics 2020;20(2):671-82. |
R835871 (2020) |
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Zhang Y, Yang P, Gao Y, Leung RL, Bell ML. Health and economic impacts of air pollution induced by weather extremes over the continental US. Environment International 2020;143:105921. |
R835871 (2020) |
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Sheu R, Stonner C, Ditto JC, Klüpfel T, Williams J, Gentner DR. Human transport of thirdhand tobacco smoke:a prominent source of hazardous air pollutants into indoor nonsmoking environments. Science Advances 2020;6(10):eaay4109. |
R835871 (2019) R835871 (2020) |
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Gillingham KT, Huang P. Long-Run Environmental and Economic Impacts of Electrifying Waterborne Shipping in the United States. Environmental Science & Technology 2020;54(16):9824-33. |
R835871 (2020) |
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Nori-Sarma A, Thimmulappa RK, Venkataramana GV, Fauzie AK, Dey SK, Venkareddy LK, Berman JD, Lane KJ, Fong KC, Warren JL, Bell ML. Low-cost NO2 monitoring and predictions of urban exposure using universal kriging and land-use regression modelling in Mysore, India. Atmospheric Environment 2020;226:117395. |
R835871 (2020) |
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Shinozuka Y, Saide PE, Ferrada GA, Burton SP, Ferrare R, Doherty SJ, Gordon H, Longo K, Mallet M, Feng Y, Wang Q. Modeling the smoky troposphere of the southeast Atlantic:a comparison to ORACLES airborne observations from September of 2016. Atmospheric Chemistry and Physics 2020;20(19):11491-526. |
R835871 (2020) |
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Gao Y, Zhang J, Yan F, Leung LR, Luo K, Zhang Y, Bell ML. Nonlinear effect of compound extreme weather events on ozone formation over the United States. Weather and Climate Extremes 2020;30:100285. |
R835871 (2020) |
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Heo S, Lim CC, Bell ML. Relationships between Local Green Space and Human Mobility Patterns during COVID-19 for Maryland and California, USA. Sustainability 2020;12(22):9401. |
R835871 (2020) |
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Datta A, Saha A, Zamora ML, Buehler C, Hao L, Xiong F, Gentner DR, Koehler K. Statistical field calibration of a low-cost PM2. 5 monitoring network in Baltimore. Atmospheric Environment 2020;242:117761. |
R835871 (2020) |
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Schilling K, Gentner DR, Wilen L, Medina A, Buehler C, Perez-Lorenzo LJ, Pollitt KJ, Bergemann R, Bernardo N, Peccia J, Wilczynski V. An accessible method for screening aerosol filtration identifies poor-performing commercial masks and respirators. Journal of exposure science & environmental epidemiology 2020:1-0. |
R835871 (2020) |
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Berman JD, Ebisu K, Peng RD, Dominici F, Bell ML. Drought and the risk of hospital admissions and mortality in older adults in western USA from 2000 to 2013:a retrospective study. The Lancet Planetary Health. 2017 Apr 1;1(1):e17-25. |
R835871 (2018) R835871 (2020) |
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Son JY, Lane KJ, Miranda ML, Bell ML. Health disparities attributable to air pollutant exposure in North Carolina:Influence of residential environmental and social factors. Health & Place 2020;62:102287. |
R835871 (2020) |
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Gasparrini A, Guo Y, Sera F, Vicedo-Cabrera AM, Huber V, Tong S, Coelho MD, Saldiva PH, Lavigne E, Correa PM, Ortega NV. Projections of temperature-related excess mortality under climate change scenarios. The Lancet Planetary Health. 2017 Dec 1;1(9):e360-7. |
R835871 (2018) R835871 (2020) |
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Armstrong B, Sera F, Vicedo-Cabrera AM, Abrutzky R, Åström DO, Bell ML, Chen BY, de Sousa Zanotti Stagliorio Coelho M, Correa PM, Dang TN, Diaz MH. The role of humidity in associations of high temperature with mortality:a multicountry, multicity study. Environmental Health Perspectives 2019;127(9):097007. |
R835871 (2019) R835871 (2020) |
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Supplemental Keywords:
energy systems modeling, transportation, life cycle assessment, personal exposure monitoring, personal exposure, air monitoring, regional modeling, air quality, O3, PM2.5, NO2, model improvement, sensitivity simulation, climate extremes, wildfire emissions, gap-filling technique, health analysis, air pollution, temperatureRelevant Websites:
Progress and Final Reports:
Original Abstract Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R835871C001 Project 1: Modeling Emissions from Energy Transitions
R835871C002 Project 2: Assessment of Energy-Related Sources, Factors and Transitions Using Novel High-Resolution Ambient Air Monitoring Networks and Personal Monitors
R835871C003 Project 3: Air Quality and Climate Change Modeling: Improving Projections of the Spatial and Temporal Changes of Multipollutants to Enhance Assessment of Public Health in a Changing World
R835871C004 Project 4: Human Health Impacts of Energy Transitions: Today and Under a Changing World
The perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.
Project Research Results
- Final Report
- 2021 Progress Report
- 2020 Progress Report
- 2019 Progress Report
- 2017 Progress Report
- 2016 Progress Report
- Original Abstract
74 journal articles for this center