2016 Progress Report: Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate and Particle-induced MortalityEPA Grant Number: R835872C002
Subproject: this is subproject number 002 , established and managed by the Center Director under grant R835872
(EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
Center: Regional Air Pollution Mixtures
Center Director: Koutrakis, Petros
Title: Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate and Particle-induced Mortality
Investigators: Coull, Brent , Koutrakis, Petros , Schwartz, Joel
Institution: Harvard University , Massachusetts Institute of Technology
EPA Project Officer: Keating, Terry
Project Period: December 1, 2015 through November 30, 2020
Project Period Covered by this Report: December 1, 2015 through November 30,2016
RFA: Air, Climate And Energy (ACE) Centers: Science Supporting Solutions (2014) RFA Text | Recipients Lists
Research Category: Air , Climate Change , Global Climate Change , Air Quality and Air Toxics , Airborne Particulate Matter Health Effects
The objective of Project 2 is to characterize historical air pollution in Eastern Massachusetts at a high spatial resolution and identify modifiable factors responsible for observed changes in PM2.5 mass, emissions, elemental profiles, and ground air temperature. Project 2 investigates within-region variability of pollutant mixtures; examines the impact of modifiable factors on air quality; and evaluates the effectiveness of source control policies. Project 2 has four specific objectives.
Objective 1 is to use a novel, multi-resolution spatial analysis based on wavelet decomposition of high-resolution (1x1 km) remote sensing data on PM2.5 mass and ground air temperature to identify daily regional, sub-regional (urban background) and locally generated variation in these fields. Objective 2 is to develop and apply spatiotemporal regression models to (a) quantify the impact of modifiable factors, including transportation, heating fuel use, energy, urban planning, PM2.5 emissions, population statistics, and policy interventions, on (i) sub-regional and local variation in PM2.5 mass and ground air temperature and (ii) high resolution local estimates of PM2.5 emissions; (b) identify locations in which these impacts are greatest; and (c) identify lag times between implementation of a given control strategy and decreases in PM2.5 emissions and mass. Objective 3 is to implement a novel multi-resolution correlation analysis to identify PM2.5 elemental profiles that vary at regional, sub-regional, and local scales, and apply spatiotemporal regression models to these profiles to identify modifiable factors driving urban background and local variability in PM2.5 composition. And Objective 4 is to use the spatial scale-specific (regional, sub-regional, and local) temporal variability in PM2.5 mass and the PM2.5 elemental profiles to identify source types (regional, urban background, or local) and the composition of their emissions driving pollution-induced mortality in Eastern Massachusetts. This project relies on existing remote-sensing satellite data, ambient monitoring data collected from numerous sampling campaigns (including the HSPH Boston Supersite daily samples collected since 1998 and samples from 600 locations), as well as new data collected from 2015-2018 in Eastern Massachusetts.
During Year 1, Project 2 investigators (Antonelli et al., in press) developed and applied the proposed multi-resolution approach to decompose 1x1 km remote sensing data on PM2.5 mass in Massachusetts into estimated contributions from long-range transport and local traffic contributions. These two components were then used in a health effects analyses that estimated the association between birthweights in Massachusetts and difference sources of variability in PM2.5 mass: purely temporal variation (which is susceptible to temporal confounding), spatial variation in smoothly varying pollution exposures, and high spatial frequency contributions representing pollution from local sources. Results suggested that standard effect estimation that does not apply the decomposition and using overall PM2.5 mass can yield attenuated estimates of associations due to temporal confounding.
We had previously fit a model estimating BC concentrations in the greater Boston area (Gryparis et al. 2007); however this model was built using limited monitoring data and could not capture the complex spatio-temporal patterns of ambient BC. In order to improve our predictive ability, we (Abu Awad et al., under revision) obtained more data for a total of 24,004 measurements from 402 monitors over a 12-year period in Massachusetts, Rhode Island and New Hampshire. We also used Nu-Support Vector Regression - a machine learning technique which incorporates nonlinear terms and higher order interactions, with appropriate regularization of parameter estimates. Both spatial and temporal predictors were included in the model which allowed us to capture the change in spatial patterns of BC over time. The 10-fold cross-validated R2 of the model was good in both cold (10-fold CV R2 = 0.73) and warm seasons (CV R2 = 0.75). We have successfully built a model that can be used to estimate short and long-term exposures to BC and will be useful for studies looking at various health outcomes in MA, RI and Southern NH.
During Year 2, we are developing spatial-temporal models for element data collected by XRF in the Eastern Massachusetts region, for use in further epidemiologic study.
Second, we are developing refined multi-resolution analyses that will capture local traffic contributions within a single level of the multi-resolution analyses, making the decomposition as interpretable as possible.
Journal Articles on this Report : 1 Displayed | Download in RIS Format
|Other subproject views:||All 2 publications||1 publications in selected types||All 1 journal articles|
|Other center views:||All 60 publications||51 publications in selected types||All 51 journal articles|
||Antonelli, J., Schwartz, J., Kloog, I., & Coull, B. A. (2017). Spatial multiresolution analysis of the effect of PM2.5 on birth weights. The Annals of Applied Statistics, 11(2), 792-807.||
Supplemental Keywords:climate change, regional pollution, multi-resolution spatial analysis, source emissions, local pollution control strategies, wavelet analysis
Progress and Final Reports:Original Abstract
Main Center Abstract and Reports:R835872 Regional Air Pollution Mixtures
Subprojects under this Center: (EPA does not fund or establish subprojects; EPA awards and manages the overall grant for this center).
R835872C001 Project 1: Regional Air Pollution Mixtures: The Past and Future Impacts of Emission Controls and Climate Change on Air Quality and Health
R835872C002 Project 2: Air Pollutant Mixtures in Eastern Massachusetts: Spatial Multi-resolution Analysis of Trends, Effects of Modifiable Factors, Climate and Particle-induced Mortality
R835872C003 Project 3: Causal Estimates of Effects of Regional and National Pollution Mixtures on Health: Providing Tools for Policy Makers
R835872C004 A Causal Inference Framework to Support Policy Decisions by Evaluating the Effectiveness of Past Air Pollution Control Strategies for the Entire United States
R835872C005 Project 5: Projecting and Quantifying Future Changes in Socioeconomic Drivers of Air Pollution and its Health-Related Impacts