Meng Wang

State University of New York (SUNY) - School of Public Health and Health Professions

SCHOLARLY PAPERS

3

DOWNLOADS

72

TOTAL CITATIONS

1

Scholarly Papers (3)

1.

Generating High Spatial Resolution Exposure Estimates from Sparse Regulatory Monitoring Data

Number of pages: 31 Posted: 08 Mar 2023
Duke University - Nicholas School of the Environment, affiliation not provided to SSRN, Duke University, University of Rochester, Carnegie Mellon University, State University of New York (SUNY) - School of Public Health and Health Professions, University of Rochester - School of Medicine and Dentistry and Duke University - Duke Global Health Institute
Downloads 36 (982,770)

Abstract:

Loading...

Fine particle matter (PM2.5), Random Forest, Regression Enhanced Random Forest, Low-cost sensor

2.

Gestational Exposure to Wildfire Pm2.5 and its Specific Components and the Risk of Gestational Hypertension and Eclampsia in the Southwestern United States

Number of pages: 24 Posted: 24 May 2024
Zhejiang Chinese Medical University, University at Buffalo, Peking University, Baylor University, University at Buffalo, Zhejiang Chinese Medical University and State University of New York (SUNY) - School of Public Health and Health Professions
Downloads 20 (1,167,856)
Citation 1

Abstract:

Loading...

Wildfire PM2.5, Gestational hypertension, Eclampsia, pregnancy, Social vulnerability index, Hypertensive disorders

3.

National Pm2.5 Spatiotemporal Model Integrating Intensive Monitoring Data and Land Use Regression in a Likelihood-Based Universal Kriging Framework in the United States: 2000-2019

Number of pages: 25 Posted: 02 Aug 2024
State University of New York (SUNY) - School of Public Health and Health Professions, University of Washington, University of Washington, University of Washington, University of Washington, University of Washington and University of Washington - Department of Biostatistics
Downloads 16 (1,216,787)

Abstract:

Loading...

fine particulate matters, spatiotemporal model, exposure assessment, fine-scale monitors