Estimating daily PM2.5 concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements

Keyong Huang; Jianzhao Bi; Xia Meng; Guannan Geng; Alexei Lyapustin; Kevin J Lane; Dongfeng Gu; Patrick L Kinney; Yang Liu

doi:10.1016/j.scitotenv.2019.134094

Estimating daily PM_2.5 concentrations in New York City at the neighborhood-scale: Implications for integrating non-regulatory measurements

Sci Total Environ. 2019 Dec 20:697:134094. doi: 10.1016/j.scitotenv.2019.134094. Epub 2019 Aug 27.

Authors

Keyong Huang¹, Jianzhao Bi², Xia Meng², Guannan Geng², Alexei Lyapustin³, Kevin J Lane⁴, Dongfeng Gu⁵, Patrick L Kinney⁶, Yang Liu⁷

Affiliations

¹ Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China; Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
² Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA.
³ NASA Goddard Space Flight Center, Greenbelt, MD, USA.
⁴ Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA.
⁵ Department of Epidemiology, State Key Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.
⁶ Department of Environmental Health, Boston University School of Public Health, Boston, MA, USA. Electronic address: pkinney@bu.edu.
⁷ Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322, USA. Electronic address: yang.liu@emory.edu.

PMID: 32380602
DOI: 10.1016/j.scitotenv.2019.134094

Abstract

Previous PM_2.5 related epidemiological studies mainly relied on data from sparse regulatory monitors to assess exposure. The introduction of non-regulatory PM_2.5 monitors presents both opportunities and challenges to researchers and air quality managers. In this study, we evaluated the advantages and limitations of integrating non-regulatory PM_2.5 measurements into a satellite-based daily PM_2.5 model at 100 m resolution in New York City in 2015. Two separate machine learning models were developed, one using only PM_2.5 data from the US Environmental Protection Agency (EPA), and the other with measurements from both EPA and the New York City Community Air Survey (NYCCAS). The EPA-only model obtained a cross-validation (CV) R² of 0.85 while the EPA + NYCCAS model obtained a CV R² of 0.73. With the help of the NYCCAS measurements, the EPA + NYCCAS model predicted distinctly different PM_2.5 spatial patterns and more pollution hotspots compared with the EPA model, and its predictions were >15% higher than the EPA model along major roads and in densely populated areas. Our results indicated that satellite AOD and non-regulatory PM_2.5 measurements can be fused together to capture neighborhood-scale PM_2.5 levels and previous studies may have underestimated the disease burden due to PM_2.5 in densely populated areas.

Keywords: AOD; Environmental justice; Neighborhood-scale; PM(2.5).