The NSAPH Subcollection, part of the Climate-Health CAFÉ Dataverse, features data contributions from the National Studies on Air Pollution and Health (NSAPH) group based at the Harvard T.H. Chan School of Public Health.

This subcollection is focused on providing datasets related to air pollution, climate change, and public health. These datasets result from NSAPH's work in studying the environmental impacts on health outcomes and regulatory policy.

Instructions

The NSAPH Subcollection is open for reuse of the general public, but contributions are restricted to NSAPH collaborators. Instructions for NSAPH collaborators that want to upload datasets are offered in the CAFÉ Dataverse upload instructions.

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1,051 to 1,060 of 1,072 Results
Gzip Archive - 6.8 MB - MD5: 3a4a54a0189342190a97156b3d28bc77
Vector of AQI classifications of daily PM2.5 (from the Di et al. estimates)
Tabular Data - 2.8 MB - 44 Variables, 12851 Observations - UNF:6:xR+XaZ3qdLwX5mmz1q6AKw==
Locations and IDs of outdoor PurpleAir LCS
Gzip Archive - 62.6 MB - MD5: b4541732492bd4e3d1962481cd42f1b9
Vector of deciles of daily PM2.5 (from the Di et al. estimates), used for empirical LCS measurement error sampling
Comma Separated Values - 140.6 MB - MD5: 9c50d2abfbcb337b4f220e36a062bbe6
Tabular Data - 21.6 MB - 24 Variables, 74001 Observations - UNF:6:g0+chDrn/3LDh2B0Y9y+ow==
census-tract level demographics, including population
Tabular Data - 2.3 MB - 3 Variables, 72538 Observations - UNF:6:wzapA1RYn0R1o35XHTQysw==
Census-tract level PM2.5 estimates. We thank Randall Martin and the members of the Atmospheric Composition Analysis Group at Dalhousie University for providing access to their open-source datasets. Their data (V4.NA.02.MAPLE) that we used can be found here: https://sites.wustl.edu/acag/datasets/surface-pm2-5/. Citation: van Donkelaar, A., R. V. Mar...
Tabular Data - 104.4 KB - 4 Variables, 3082 Observations - UNF:6:JRO0TFYZOj2Ow9im043PKQ==
the county-level NO2 and O3 exposure data averaged across the period 2000-2016, averaged across grid cells within a zip code and then averaging across zip codes within a county. See Q. Di, H. Amini, L. Shi, I. Kloog, R. Silvern, J. Kelly, M. B. Sabath, C. Choirat,P. Koutrakis, A. Lyapustin,et al., Assessing no2 concentration and model uncertainty w...
R Data - 17.1 MB - MD5: 3d69104d6eec9c632662820883892dc8
Stanfit object for additional analysis 2: main analysis with data up to June 18, 2020
R Data - 5.4 MB - MD5: 5791b5b2ed095f8cf77be77394e6fb4e
Stanfit object for additional analysis 3: ecological regression without individual-level data up to June 18, 2020
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