January 2024

Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, v1.10 (2000 – 2016)


PURPOSE

To provide daily and annual Nitrogen Dioxide (NO2) concentration data in the U.S. at a resolution of 1-km (about 30 arc-seconds) for public health research to respectively estimate short- and long-term effects on human health, and for other related research.


DESCRIPTION

The Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016) data set contains daily predictions of Nitrogen Dioxide (NO2) concentrations at a high resolution (1-km grid cells) for the years 2000 to 2016. An ensemble modeling framework was used to assess NO2 levels with high accuracy, which combined estimates from three machine learning models (neural network, random forest, and gradient boosting), with a generalized additive model. Predictor variables included NO2 column concentrations from satellites, land-use variables, meteorological variables, predictions from two chemical transport models, GEOS-Chem and the U.S. Environmental Protection Agency (EPA) Community Multiscale Air Quality Modeling System (CMAQ), along with other ancillary variables. The annual predictions were calculated by averaging the daily predictions for each year in each grid cell. The ensemble produced a cross-validated R-squared value of 0.79 overall, a spatial R-squared value of 0.84, and a temporal R-squared value of 0.73. In version 1.10, the completeness of daily NO2 predictions have been enhanced by employing linear interpolation to impute missing values. Specifically, for days with small spatial patches of missing data with less than 100 grid cells, inverse distance weighting interpolation was used to fill the missing grid cells. Other missing daily NO2 predictions were interpolated from the nearest days with available data. Annual predictions were updated by averaging the imputed daily predictions for each year in each grid cell. These daily and annual NO2 predictions allow public health researchers to respectively estimate the short- and long-term effects of NO2 exposures on human health, supporting the U.S. EPA for the revision of the National Ambient Air Quality Standards for daily average and annual average concentrations of NO2. The data are available in RDS and GeoTIFF formats for statistical research and geospatial analysis.

The RDS files, containing a matrix of data values corresponding to geographic coordinates, are native to the R statistical computing environment that is widely used in public health research and applications. The GeoTIFF data format is widely used in earth science data and GIS communities.


ACCESSING THE DATA

The data may be downloaded at https://sedac.ciesin.columbia.edu/data/set/aqdh-no2-concentrations-contiguous-us-1-km-v1-10-2000-2016/data-download


DATA FORMAT

This archive contains data in RDS and GeoTIFF formats.

The data files are compressed zipfiles. Downloaded files need to be uncompressed in a single folder using either WinZip (Windows file compression utility) or similar application. Users should expect an increase in the size of downloaded data after decompression. 


DATA UNIT

The unit for NO2 is parts per billion (ppb).


SPATIAL EXTENT

Contiguous United States (1-km grids, about 30 arc-seconds).


DISCLAIMER

CIESIN follows procedures designed to ensure that data disseminated by CIESIN are of reasonable quality. If, despite these procedures, users encounter apparent errors or misstatements in the data, they should contact SEDAC User Services at ciesin.info@ciesin.columbia.edu. Neither CIESIN nor NASA verifies or guarantees the accuracy, reliability, or completeness of any data provided. CIESIN provides this data without warranty of any kind whatsoever, either expressed or implied. CIESIN shall not be liable for incidental, consequential, or special damages arising out of the use of any data provided by CIESIN.


USE CONSTRAINTS

This work is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0). Users are free to use, copy, distribute, transmit, and adapt the work for commercial and non-commercial purposes, without restriction, as long as clear attribution of the source is provided.


CITATION(S)

Data Set:

Qian Di1,3, Yaguang Wei1, Alexandra Shtein1, Xiaoshi Xing2, Edgar Castro1, Heresh Amini4, Carolynne Hultquist2, Liuhua Shi5, Itai Kloog4,6, Rachel Silvern7, James T. Kelly8, M. Benjamin Sabath9, Christine Choirat9, Petros Koutrakis1, Alexei Lyapustin10, Yujie Wang11, Loretta J. Mickley12, Yasmine Daouk2, and Joel Schwartz1,13. 2024. Daily and Annual NO2 Concentrations for the Contiguous United States, 1-km Grids, Version 1.10 (2000-2016). Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/rz28-p167. Accessed DAY MONTH YEAR.

1 Department of Environmental Health, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
2 Center for International Earth Science Information Network (CIESIN), Columbia Climate School, Columbia University, Palisades, NY, United States
3 Vanke School of Public Health, Tsinghua University, Beijing, China
4 Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, NY, United States
5 Gangarosa Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA, United States
6 Department of Geography and Environmental Development, Ben-Gurion University of the Negev, Beer Sheva, Israel
7 Department of Earth and Planetary Sciences, Harvard University, Cambridge, MA, United States
8 U.S. Environmental Protection Agency (EPA), Office of Air Quality Planning and Standards (OAQPS), Research Triangle Park, NC, United States
9 Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States
10 National Aeronautics and Space Administration (NASA), Goddard Space Flight Center (GSFC), Greenbelt, MD, United States
11 University of Maryland, Baltimore County, Baltimore, MD, United States
12 John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, United States
13 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA, United States

Scientific Publication:

Di, Q., H. Amini, L. Shi, I. Kloog, R. Silvern, J. T. Kelly, M. B. Sabath, C. Choirat, P. Koutrakis, A. Lyapustin, Y. Wang, L. J. Mickley, and J. Schwartz. 2020. Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging. Environmental Science & Technology 2020 54 (3): 1372-1384. https://doi.org/10.1021/acs.est.9b03358.

Two R code files are provided as a part of the data set dissemination, under the same DOI (https://doi.org/10.7927/f8eh-5864) and open access license: Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0).


ACKNOWLEDGEMENTS

This work was supported by U.S. EPA grants RD-834798, RD-835872, and 83587201, and Health Effects Institute (HEI) grant 4953-RFA14-3/16-4 and assistance award CR-83467701. The HEI is an organization jointly funded by the U.S. EPA and certain motor vehicle and engine manufacturers. The computations were run on the Odyssey cluster supported by the Faculty of Arts & Sciences (FAS) Division of Science, Research Computing Group at Harvard University. The data conversion work from RDS to GeoTIFF with QA/QC was supported by the National Institutes of Health, National Institute of Environmental Health Sciences (NIH/NIEHS) grant R01ES032418. The contents are solely the responsibility of the grantees and do not necessarily represent the official views of the U.S. EPA. Further, the U.S. EPA does not endorse the purchase of any commercial products or services mentioned in the data or documents. The authors also thank Gregory Yetman (CIESIN) for his help with the data conversion process.


POTENTIAL USE CASES

It is anticipated for this work to be used for conducting new studies on individual and combined health risks of total NO2 concentration, environmental justice analysis, or understanding fine-scale spatiotemporal variabilities of NO2. 

This high-resolution daily NO2 estimation, along with predicted uncertainty, would help to better assess both long-term and short-term exposures for studies of large cohorts with residents in locations far from or without monitors. Epidemiological studies may also use this data to guide public health decisions, health impact assessments, and understand the effects of localized NO2 pollutant exposure, particularly for small areas, such as over census tracts or ZIP Codes.


REFERENCES

Public Health (55), Environmental Justice and Disparities (5), Air Quality Standards (6), Epidemiologic Risks and Studies (4), Urban Studies (17)

Akushevich, I., Yashkin, A., Ukraintseva, S., Yashin, A. I., and Kravchenko, J. (2023). The construction of a multidomain risk model of Alzheimer’s disease and related dementias. Journal of Alzheimer's Disease, 96(2), 535-550. https://doi.org/10.3233/JAD-221292.

Bai, J., Pugh, S. L., Eldridge, R., Yeager, K. A., Zhang, Q., Lee, W. R., Shah, A. B., Dayes, I. S., D'Souza, D. P., Michalski, J. M., Efstathiou, J. A., Longo, J. M., Pisansky, T. M., Maier, J. M., Faria, S. L., Desai, A. B., Seaward, S. A., Sandler, H. M., Cooley, M. E., and Bruner, D. W. (2023). Neighborhood deprivation and rurality associated with patient-reported outcomes and survival in men with prostate cancer in NRG RTOG 0415. International Journal of Radiation Oncology Biology Physics, 116(1), 39-49. https://doi.org/10.1016/j.ijrobp.2023.01.035.

Bravo, M. A., Zephyr, D., Fiffer, M. R., and Miranda, M. L. (2024). Weekly prenatal PM2.5 and NO2 exposures in preterm, early term, and full term infants: Decrements in birth weight and critical windows of susceptibility. Environmental Research, 240(Part 1), 117509. https://doi.org/10.1016/j.envres.2023.117509.

Campbell, C. E., Cotter, D. L., Bottenhorn, K. L., Burnor, E., Ahmadi, H., Gauderman, W. J., Cardenas-Iniguez, C., Hackman, D., McConnell, R., Berhane, K., Schwartz, J., Chen, J. C., and Herting, M. M. (2023). Air pollution and emotional behavior in adolescents across the U.S. Environmental Research. https://doi.org/10.1016/j.envres.2023.117390.

Cardenas-Iniguez, C., Schachner, J., Ip, K. I., Schertz, K. E., Gonzalez, M. R., Abad, S., and Herting, M. (2024). Building Towards an Adolescent Neural Urbanome: Expanding Environmental Measures using Linked External Data (LED) in the ABCD Study. Developmental Cognitive Neuroscience, 101338, ISSN 1878-9293. https://doi.org/10.1016/j.dcn.2023.101338.

Chu, L., Chen, K., Di, Q., Crowley, S., and Dubrow, R. (2023). Associations between short-term exposure to PM2.5, NO2 and O3 pollution and kidney-related conditions and the role of temperature-adjustment specification: A case-crossover study in New York State. Environmental Pollution, 328, 121629. https://doi.org/10.1016/j.envpol.2023.121629.

Cotter, D. L., Campbell, C. E., Sukumaran, K., McConnell, R., Berhane, K., Schwartz, J., Hackman, D. A., Ahmadi, H., Chen, J., and Herting, M. M. (2023). Effects of ambient fine particulates, nitrogen dioxide, and ozone on maturation of functional brain networks across early adolescence. Environment International, 177, 108001. https://doi.org/10.1016/j.envint.2023.108001.

Danesh-Yazdi, M. (2020). Air pollution and morbidity: A comprehensive assessment [Doctoral dissertation, Harvard University Graduate School of Arts and Sciences]. Harvard University Repository. https://nrs.harvard.edu/URN-3:HUL.INSTREPOS:37368930.

Danesh-Yazdi, M., Nassan, F. L., Kosheleva, A., Wang, C., Xu, Z., Di, Q., Requia, W. J., Comfort, N. T., Wu, H., Laurent, L. C., DeHoff, P., Vokonas, P., Baccarelli, A. A., and Schwartz, J. D. (2023). Short-term air pollution and temperature exposure and changes in the extracellular microRNA profile of Normative Aging Study (NAS) participants. Environment International, 171, 107735. https://doi.org/10.1016/j.envint.2023.107735.

Danesh-Yazdi, M., Nassan, F. L., Kosheleva, A., Wang, C., Xu, Z., Di, Q., Requia, W. J., Comfort, N. T., Wu, H., Laurent, L. C., DeHoff, P., Vokonas, P., Baccarelli, A. A., and Schwartz, J. D. (2023). Intermediate and long-term exposure to air pollution and temperature and the extracellular microRNA profile of participants in the normative aging study (NAS). Environmental Research, 229, 115949. https://doi.org/10.1016/j.envres.2023.115949.

Danesh-Yazdi, M., Wang, Y., Di, Q., Wei, Y., Requia, W. J., Shi, L., Sabath, M. B., Dominici, F., Coull, B. A., Evans, J. S., Koutrakis, P., and Schwartz, J. D. (2021). Long-Term Association of Air Pollution and Hospital Admissions Among Medicare Participants Using a Doubly Robust Additive Model. Circulation, 143, 1584–1596. https://doi.org/10.1161/CIRCULATIONAHA.120.050252.

Danesh-Yazdi, M., Wei, Y., Di, Q., Requia, W. J., Shi, L., Sabath, M. B., Dominici, F., and Schwartz, J. (2022). The effect of long-term exposure to air pollution and seasonal temperature on hospital admissions with cardiovascular and respiratory disease in the United States: A difference-in-differences analysis. Science of The Total Environment, 843, 156855. https://doi.org/10.1016/j.scitotenv.2022.156855.

Dominici, F., Zanobetti, A., Schwartz, J., Braun, D., Sabath, B., and Wu, X. (2022). Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods. Res Rep Health Eff Inst, 2022(211), 1-56. PMID: 36193708; PMCID: PMC9530797.

Dong, S., Abu-Awad, Y., Kosheleva, A., Fong, K. C., Koutrakis, P., and Schwartz, J. D. (2022). Maternal exposure to black carbon and nitrogen dioxide during pregnancy and birth weight: Using machine-learning methods to achieve balance in inverse-probability weights. Environmental Research, 211, 112978. https://doi.org/10.1016/j.envres.2022.112978.

Fan, C. C., Marshall, A., Smolker, H., Gonzalez, M. R., Tapert, S. F., Barch, D. M., Sowell, E., Dowling, G.  J., Cardenas-Iniguez, C., Ross, J., Thompson, W. K., and Herting, M. M. (2021). Adolescent Brain Cognitive Development (ABCD) study Linked External Data (LED): Protocol and practices for geocoding and assignment of environmental data. Developmental Cognitive Neuroscience, 52, 101030. https://doi.org/10.1016/j.dcn.2021.101030.

Feng, Y., Wei, Y., Coull, B. A., and Schwartz, J. D. (2023). Measurement error correction for ambient PM2.5 exposure using stratified regression calibration: Effects on all-cause mortality. Environmental Research, 216(Part 4), 114792. https://doi.org/10.1016/j.envres.2022.114792.

Herting, M., Cotter, D., Ahmadi, H., et al. (2023). Sex-specific effects in how childhood exposures to multiple ambient air pollutants affect white matter microstructure development across early adolescence. Research Square. Version 1. https://doi.org/10.21203/rs.3.rs-3213618/v1.

Iyanna, N., Yolton, K., LeMasters, G., Lanphear, B. P., Cecil, K. M., Schwartz, J., Brokamp, C., Rasnick, E., Xu, Y., MacDougall, M. C., and Ryan, P. H. (2023). Air pollution exposure and social responsiveness in childhood: The Cincinnati combined childhood cohorts. International Journal of Hygiene and Environmental Health, 251, 114172. https://doi.org/10.1016/j.ijheh.2023.114172.

Jin, T., Di, Q., Réquia, W. J., Danesh-Yazdi, M., Castro, E., Ma, T., Wang, Y., Zhang, H., Shi, L., and Schwartz, J. (2022). Associations between long-term air pollution exposure and the incidence of cardiovascular diseases among American older adults. Environment International, 170, 107594. https://doi.org/10.1016/j.envint.2022.107594.

Kelp, M. M., Fargiano, T. C., Lin, S., Liu, T., Turner, J. R., Kutz, N., and Mickley, L. J. (2023). Data-driven placement of PM2.5 air quality sensors in the United States: An approach to target urban environmental injustice. GeoHealth, 7, e2023GH000834. https://doi.org/10.1029/2023GH000834.

Klompmaker, J. O., Laden, F., Browning, M. H.E.M., Dominici, F., Ogletree, S. S., Rigolon, A., Hart, J. E., and James, P. (2022). Associations of parks, greenness, and blue space with cardiovascular and respiratory disease hospitalization in the US Medicare cohort. Environmental Pollution, 312, 120046. https://doi.org/10.1016/j.envpol.2022.120046.

Klompmaker, J. O., Hart, J. E., James, P., Sabath, M. B., Wu, X., Zanobetti, A., Dominici, F., and Laden, F. (2021). Air pollution and cardiovascular disease hospitalization – Are associations modified by greenness, temperature and humidity? Environment International, 156, 106715. https://doi.org/10.1016/j.envint.2021.106715.

Lee, W., Wu, X., Heo, S., Fong, K. C., Son, J. Y., Sabath, M. B., Braun, D., Park, J. Y., Kim, Y. C., Lee, J. P., Schwartz, J., Kim, H., Dominici, F., and Bell, M. (2022). Associations between long term air pollution exposure and first hospital admission for kidney and total urinary system diseases in the US Medicare population: Nationwide longitudinal cohort study. BMJ Medicine, 1(1), e000009. https://doi.org/10.1136/bmjmed-2021-000009.

Lee, W., Wu, X., Heo, S., Kim, J. M., Fong, K. C., Son, J.-Y., Sabath, M. B., Bell, M. L., et al. (2023). Air pollution and acute kidney injury in the U.S. Medicare population: A longitudinal cohort study. Environmental Health Perspectives, 131(4), 047008. https://doi.org/10.1289/EHP10729.

Li, H., Deng, W., Small, R., Schwartz, J., Liu, J., and Shi, L. (2022). Health effects of air pollutant mixtures on overall mortality among the elderly population using Bayesian kernel machine regression (BKMR). Chemosphere, 286(Part 1), 131566. https://doi.org/10.1016/j.chemosphere.2021.131566.

Lu, W., Hackman D. A., and Schwartz J. (2021). Ambient air pollution associated with lower academic achievement among US children: A nationwide panel study of school districts. Environ Epidemiol. Nov 3;5(6):e174. PMID: 34909554; PMCID: PMC8663889. https://doi.org/10.1097/EE9.0000000000000174.

Ma, T., Danesh-Yazdi, M., Schwartz, J., Réquia, W. J., Di, Q., Wei, Y., Chang, H. H., Vaccarino, V., Liu, P., and Shi, L. (2022). Long-term air pollution exposure and incident stroke in American older adults: A national cohort study. Global Epidemiology, 4, 100073. https://doi.org/10.1016/j.gloepi.2022.100073.

Ma, Y., Zang, E., Liu, Y., Lu, Y., Krumholz, H. M., Bell, M. L., and Chen, K. (2023). Wildfire smoke PM2.5 and mortality in the contiguous United States. medRxiv. https://doi.org/10.1101/2023.01.31.23285059.

Ma, Y., Zang, E., Opara, I. et al. (2023). Racial/ethnic disparities in PM2.5-attributable cardiovascular mortality burden in the United States. Nature Human Behaviour. https://doi.org/10.1038/s41562-023-01694-7.

Mohegh, A., Goldberg, D., Achakulwisut, P., and Anenberg, S. C. (2021). Sensitivity of estimated NO2-attributable pediatric asthma incidence to grid resolution and urbanicity. Environmental Research Letters, 16(1), 014019. https://doi.org/10.1088/1748-9326/abce25.

Mork, D., Braun, D., and Zanobetti, A. (2023). Time-lagged relationships between a decade of air pollution exposure and first hospitalization with Alzheimer's disease and related dementias. Environment International, 171, 107694. https://doi.org/10.1016/j.envint.2022.107694.

Nassan, F. L., Kelly, R. S., Koutrakis, P., Vokonas, P. S., Lasky-Su, J. A., and Schwartz, J. D. (2021). Metabolomic signatures of the short-term exposure to air pollution and temperature. Environmental Research, 201, 111553. https://doi.org/10.1016/j.envres.2021.111553.

Peralta, A. A., Gold, D. R., Danesh-Yazdi, M., Wei, Y., and Schwartz, J. (2023). The role of short-term air pollution and temperature on arterial stiffness in a longitudinal closed cohort of elderly individuals. Environmental Research, 216(Part 2), 114597. https://doi.org/10.1016/j.envres.2022.114597.

Qiu, X., Danesh-Yazdi, M., Wei, Y., Di, Q., Just, A., Zanobetti, A., Weisskopf, M., Dominici, F., and Schwartz, J. (2022). Associations of short-term exposure to air pollution and increased ambient temperature with psychiatric hospital admissions in older adults in the USA: A case–crossover study. The Lancet Planetary Health, 6(4), e331-e341. https://doi.org/10.1016/S2542-5196(22)00017-1.

Qiu, X., Danesh-Yazdi, M., Weisskopf, M., Kosheleva, A., Spiro, A., Wang, C., Coull, B. A., Koutrakis, P., and Schwartz, J. D. (2022). Associations between air pollution and psychiatric symptoms in the Normative Aging Study. Environmental Research Letters, 17(3), 034004. https://doi.org/10.1088/1748-9326/ac47c5.

Qiu, X., Shi, L., Kubzansky, L. D., Wei, Y., Castro, E., Li, H., Weisskopf, M. G., and Schwartz, J. D. (2023). Association of long-term exposure to air pollution with late-life depression in older adults in the US. JAMA Network Open, 6(2), e2253668-e2253668. https://doi.org/10.1001/jamanetworkopen.2022.53668.

Qiu, X., Wei, Y., Weisskopf, M., et al. (2023). Air pollution, climate conditions and risk of hospital admissions for psychotic disorders in U.S. residents. Environmental Research, 216(Part 2), 114636. https://doi.org/10.1016/j.envres.2022.114636.

Ren, B., Wu, X., Braun, D., Pillai, N., and Dominici, F. (2023). A Bayesian Gaussian Process for Estimating a Causal Exposure Response Curve in Environmental Epidemiology (Version 3). arXiv preprint  arXiv:2105.03454v. https://doi.org/10.48550/arXiv.2105.03454.

Ren, X., Mi, Z. and Georgopoulos, P.G. (2023). Socioexposomics of COVID-19 across New Jersey: a comparison of geostatistical and machine learning approaches. J Expo Sci Environ Epidemiol. https://doi.org/10.1038/s41370-023-00518-0.

Schwartz, J. D., Di, Q., Requia, W. J., Dominici, F., and Zanobetti, A. (2021). A direct estimate of the impact of PM2.5, NO2, and O3 exposure on life expectancy using propensity scores. Epidemiology, 32(4), 469-476. https://doi.org/10.1097/EDE.0000000000001354.

Schwartz, J. D., Yitshak-Sade, M., Zanobetti, A., Di, Q., Requia, W. J., Dominici, F., and Mittleman, M. A. (2021). A self-controlled approach to survival analysis, with application to air pollution and mortality. Environment International, 157, 106861. https://doi.org/10.1016/j.envint.2021.106861.

Schwartz, J., Wei, Y., Dominici, F., and Danesh-Yazdi, M. (2023). Effects of low-level air pollution exposures on hospital admission for myocardial infarction using multiple causal models. Environmental Research, p.116203. https://doi.org/10.1016/j.envres.2023.116203.

Schwartz, J., Wei, Y., Yitshak-Sade, M., Di, Q., Dominici, F., and Zanobetti, A. (2021). A national difference in differences analysis of the effect of PM2.5 on annual death rates. Environmental Research, 194, 110649. https://doi.org/10.1016/j.envres.2020.110649.

Shi, L., Rosenberg, A., Wang, Y., Liu, P., Danesh-Yazdi, M., Réquia, W., Steenland, K., Chang, H., Sarnat, J. A., Wang, W., Zhang, K., Zhao, J. and Schwartz, J. (2022). Low-Concentration Air Pollution and Mortality in American Older Adults: A National Cohort Analysis (2001–2017). Environmental Science & Technology, 56(11), 7194-7202. https://doi.org/10.1021/acs.est.1c03653. 

Sukumaran, K., Cardenas-Iniguez, C., Burnor, E., Bottenhorn, K. L., Hackman, D. A., McConnell, R., Berhane, K., Schwartz, J., Chen, J. C. and Herting, M. M. (2023). Ambient fine particulate exposure and subcortical gray matter microarchitecture in 9-and 10-year-old children across the United States. Iscience, 26(3). https://doi.org/10.1016/j.isci. 2023.106087.

Sukumaran, K., Cardenas-Iniguez, C., Burnor, E., Bottenhorn, K. L., Hackman, D. A., McConnell, R., Berhane, K., Schwartz, J., Chen, J. C. and Herting, M. M. (2023). Ambient fine particulate exposure and subcortical gray matter microarchitecture in 9-and 10-year-old children across the United States. Iscience, 26(3). https://doi.org/10.1016/j.isci. 2023.106087.

Wang, X., Ding, N., Harlow, S. D., Randolph, J. F., Gold, E. B., Derby, C., Kravitz, H. M., Greendale, G., Wu, X., Ebisu, K., Schwartz, J., and Park, S. K. (2024). Associations between exposure to air pollution and sex hormones during the menopausal transition. Science of The Total Environment, 908, 168317. https://doi.org/10.1016/j.scitotenv.2023.168317.

Wang, X., Karvonen-Gutierrez, C. A., Gold, E. B., Derby, C., Greendale, G., Wu, X., Schwartz, J. and Park, S. K. (2022). Longitudinal Associations of Air Pollution With Body Size and Composition in Midlife Women: The Study of Women’s Health Across the Nation. Diabetes Care, 45(11), pp.2577-2584. https://doi.org/10.2337/dc22-0963.  

Wang, Y., Liu, P., Schwartz, J., Castro, E., Wang, W., Chang, H., Scovronick, N., and Shi, L. (2023). Disparities in ambient nitrogen dioxide pollution in the United States. Proceedings of the National Academy of Sciences, 120(16), e2208450120. https://doi.org/10.1073/pnas.2208450120. 

Wang, Y., Qiu, X., Wei, Y., and Schwartz, J. D. (2023). Long-term exposure to ambient PM2.5 and hospitalizations for myocardial infarction among US residents: A difference-in-differences analysis. Journal of the American Heart Association, 12, e029428. https://doi.org/10.1161/JAHA.123.029428.

Wei, Y., Coull, B., Koutrakis, P. et al. (2021). Assessing additive effects of air pollutants on mortality rate in Massachusetts. Environmental Health 20, 19. https://doi.org/10.1186/s12940-021-00704-3.

Wei, Y., Danesh-Yazdi, M., Ma, T., Castro, E., Liu, C. S., Qiu, X., Healy, J., Vu, B. N., Wang, C., Shi, L., and Schwartz, J. (2023). Additive effects of 10-year exposures to PM2.5 and NO2 and primary cancer incidence in American older adults. Environmental Epidemiology, 7(4). https://doi.org/10.1097/EE9.0000000000000265.

Wei, Y., Qiu, X., Sabath, M. B., Danesh-Yazdi, M., Yin, K., Li, L., Peralta, A. A., Wang, C., Koutrakis, P., Zanobetti, A., Dominici, F., and Schwartz, J. (2022). Air Pollutants and Asthma Hospitalization in the Medicaid Population. American Journal of Respiratory and Critical Care Medicine, 205(9). https://doi.org/10.1164/rccm.202107-1596OC.

Wei, Y., Qiu, X., Danesh-Yazdi, M., Shtein, A., Shi, L., Yang, J., Peralta, A. A., Coull, B. A., and Schwartz, J. D. (2022). The impact of exposure measurement error on the estimated concentration–response relationship between long-term exposure to PM2.5 and mortality. Environmental Health Perspectives, 130(7), 077006. https://doi.org/10.1289/EHP10389.

Wei, Y., Danesh-Yazdi, M., Di, Q., et al. (2021). Emulating causal dose-response relations between air pollutants and mortality in the Medicare population. Environmental Health, 20, 53. https://doi.org/10.1186/s12940-021-00742-x.

Woodward S. M., Mork, D., Wu, X., Hou, Z., Braun, D., and Dominici, F. (2023) Combining aggregate and individual-level data to estimate individual-level associations between air pollution and COVID-19 mortality in the United States. PLOS Global Public Health 3(8): e0002178. https://doi.org/10.1371/journal.pgph.0002178.

Wright, R. J., Hsu, H.-H. L., Chiu, Y.-H. M., Coull, B. A., Simon, M. C., Hudda, N., Schwartz, J., Kloog, I., and Durant, J. L. (2021). Prenatal ambient ultrafine particle exposure and childhood asthma in the northeastern United States. American Journal of Respiratory and Critical Care Medicine, 204(6), 788-796. https://doi.org/10.1164/rccm.202010-3743OC.

Yitshak-Sade, M., Shi, L., Colicino, E., Amini, H., Schwartz, J. D., Di, Q., and Wright, R. O. (2023). Long-term air pollution exposure and diabetes risk in American older adults: A national secondary data-based cohort study. Environmental Pollution, 320, 121056. https://doi.org/10.1016/j.envpol.2023.121056.

Yitshak-Sade, M., Shi, L., Colicino, E., Amini, H., Schwartz, J. D., Di, Q., and Wright, R. O. (2021). Long-term air pollution exposure and diabetes risk in the elderly population. medRxiv. https://doi.org/10.1101/2021.09.09.21263282.

Yoo, E.-H., Cooke, A., and Eum, Y. (2023). Examining the geographical distribution of air pollution disparities across different racial and ethnic groups: Incorporating workplace addresses. Health & Place, 84, p.103112. https://doi.org/10.1016/j.healthplace.2023.103112.

Zhang, H., Shi, L., Ebelt, S. T., D’Souza, R. R., Schwartz, J. D., Scovronick, N., and Chang, H. H. (2023). Short-term associations between ambient air pollution and emergency department visits for Alzheimer’s disease and related dementias. Environmental Epidemiology, 7(1), e237. https://doi.org/10.1097/EE9.0000000000000237.

Zheng, Y., McElrath, T., Cantonwine, D., and Hu, H. (2023). Longitudinal associations between ambient air pollution and angiogenic biomarkers among pregnant women in the LIFECODES study, 2006–2008. Environmental Health Perspectives, 131(8), 087005. https://doi.org/10.1289/ehp11909.