2016 Progress Report: Project 1: Regional Air Pollution Mixtures: The Past and Future Impacts of Emission Controls and Climate Change on Air Quality and Health

EPA Grant Number: R835872C001
Subproject: this is subproject number 001 , 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 1: Regional Air Pollution Mixtures: The Past and Future Impacts of Emission Controls and Climate Change on Air Quality and Health
Investigators: Koutrakis, Petros , Coull, Brent , Jacob, Daniel J. , Mickley, Loretta J. , Schwartz, Joel
Institution: Harvard University
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 , Particulate Matter , Air Quality and Air Toxics

Objective:

The overall objective of Project 1 is to apply new approaches to characterize and analyze both historical and projected regional air pollution mixtures and emissions across the continental US. Project 1 characterizes temporal and spatial patterns of pollutant mixtures within and across regions. In addition, this project investigates factors influencing regional pollutant mixtures and predicts the impact of climate change on future air quality. Project 1 has four specific objectives.

Objective 1 is to compile comprehensive air pollution, weather, emissions, and GIS datasets for the entire continental US for the period 2000-2015. We will estimate gas and particle concentrations at a high spatial resolution by assimilating data from monitoring networks (compiled in collaboration with the Air Pollution Core), satellite platforms, air pollution models, and spatiotemporal statistical models. Objective 2 is to develop and make publically available a national PM2.5 emission inventory database of high spatial resolution (1 km) for 2000-2015. This will be achieved through the application of a novel methodology we developed that predicts point and area source emissions using aerosol optical thickness measured by satellite remote sensors. Objective 3 is to characterize spatial and temporal trends of pollutant mixtures. We will perform cluster analysis to group areas that exhibit distinct pollutant profiles or mixtures, referred to as “Air Pollution Regions,” then analyze their spatial patterns and temporal trends to investigate the impact of regulations, climate change, and modifiable factors on regional mixtures. Objective 4 is to forecast the impact of regional climate change on air quality for 2016-2040 using an ensemble of climate models. We will project the potential impact of climate change on regional pollutant mixtures and predict future regional air quality assuming no changes in anthropogenic emissions.

Progress Summary:

Objective 1, Compile comprehensive air pollution, weather, emissions, and GIS datasets for the entire continental US for the period 2000-2015. In conjunction with the Air Pollution Core, we have made significant progress on assembling our comprehensive national database. Our initial focus was on obtaining information for the northeastern USA for the years 2000-2015, including satellite remote sensing data and generating a complete database for the New England states. We have continued to expand our spatial and temporal data coverage area toward our continental US 2000-2015 target, and expect these efforts to be complete on schedule. Work on the national satellite data and modeling and GEOS-Chem modeling are still in relatively early stages, but are going to continue to be a main focus for Year 2. In addition, during Year 1 we worked with Project 3 as they developed a model to predict daily ozone (8-hour maximum) on a 1x1km grid for the entire United States (Di, 2017). This model had a cross-validated R2 of 0.76.

Objective 2, Develop and make publically available a national PM2.5 emission inventory database of high spatial resolution (1 km) for 2000-2015. Our efforts on this objective during Year 1 included using Particle Emission Inventory using Remote Sensing (PEIRS), a novel method to construct spatially- and temporally-resolved emission inventories for PM2.5. We applied PEIRS to predict emissions in the northeastern USA during the period 2002–2013 using high-resolution 1×1 km aerosol optical depth (AOD). In Tang et al. 2017a, we showed that PEIRS emission estimates moderately agreed with the EPA National Emission Inventory (R2=0.66–0.71, CV=17.7–20%). We also found that predicted emissions were correlated with land use parameters, suggesting that PEIRS can capture emissions from land-use-related sources. In addition, we distinguished small-scale intra-urban variation in emissions reflecting distribution of metropolitan sources. Therefore, we demonstrated the great potential of remote sensing data to predict particle source emissions cost-effectively. We are currently working on extending this work to cover more of the USA and the complete targeted time span.

In efforts that support both Objectives 2 (primarily) and 3, we have used the spatiotemporally resolved Northeastern PEIRS emission predictions described above to evaluate regional emission trends (2002-2013) using quantile regression, and model source-oriented trends with land use regression (Tang et al., 2017b). We found a regional decrease in PM2.5 emissions of 3.3 tons/yr/km2 (18%) over the 12-yr period. Furthermore, the rate of emission change at the extremes of the emission distribution was significantly different than the mean. Both quantile regression and spatial trends imply that the majority of the reduction in PM2.5 emissions was attributable to highly developed spaces such as metropolitan areas and important traffic corridors. Finally, emissions exhibited strong seasonal patterns apparent during the cold season.

Objective 4, Forecast weather changes in each region for the period of 2015-2040 using archived results from an ensemble of climate models. In particular, we developed statistical models to investigate the meteorological drivers of interannual to multidecadal variability of air quality, including ozone and fine particulate matter (PM2.5), in the United States. We examined processes involving climate patterns at different spatial scales, including that of local weather (~100 km), synoptic circulation (~1,000 km) and large-scale climate patterns (~10,000 km). Using our statistical models, we predicted summertime (June-July-August, JJA) ozone in the eastern United States one season in advance, quantified the multidecadal variability of air quality in the eastern United States, and predicted changes of air quality across the United States by 2050s. For future projections, we applied our statistical models to meteorological fields archived by the IPCC Coupled Model Intercomparision Project Phase 5 (CMIP5). These projects build on Shen et al. (2015, 2016), funded by NASA. A summary of Year 1 results for Objective 4 is presented below.

Shen and Mickley (2017), accepted for publication in PNAS, is the first study to identify the relationship between summertime ozone air quality in the eastern United States and large-scale meteorological patterns, including SST patterns and teleconnections, as they evolve over the preceding months. The paper shows that this relationship can be used in spring to predict ozone for the following summer. Results of our work imply that large-scale phenomena such as the Atlantic Multidecadal Oscillation (AMO) drive multidecadal variability in U.S. ozone air quality. Shen et al. (2017), in review at Atmospheric Chemistry and Physics, introduces a new method to characterize the influence of atmospheric circulation on surface PM2.5 concentrations across the eastern United States. The method uses singular value decomposition of the spatial correlations between local PM2.5 and meteorological variables across the surrounding region. Results show that current atmospheric chemistry models may underestimate or even fail to capture the strongly positive sensitivity of monthly mean PM2.5 to surface temperature in the East in summer, and so may underestimate PM2.5 changes in a warmer climate. Applying our statistical model to climate projections from an ensemble of 17 CMIP5 models reveals a strong influence of 2000-2050 climate change on PM2.5 air quality in the eastern United States, with potential increases of JJA PM2.5 of 3 mg∙m-3. Finally, we are currently preparing a manuscript in which we more closely examine the influence of the AMO on U.S. ozone in the past, and investigate the implications of this influence in future decades.

Future Activities:

In Year 2, we will wrap up the statistical studies of Year 1, Shen et al. (2017) and Shen (in preparation). We will also carry out the 16-year (2000-2015) GEOS-Chem simulation over North America with 0.5o x 0.625o horizontal resolution (approximately 50 km2). This simulation will be nested within a global simulation with 2o x 2.5o resolution providing dynamic boundary conditions. The simulation is one of the tasks listed in Objective 1. We expect the long-term trends over the 2000-2015 timeframe to be mainly driven by changes in anthropogenic emissions. As a result of declining emissions, air quality has improved substantially across much of the US since 2000, and we will examine whether the model captures these trends and their regional variations. Output from GEOS-Chem will be provided to the EPA-ACE team for use in data fusion.


Journal Articles on this Report : 4 Displayed | Download in RIS Format

Other subproject views: All 4 publications 4 publications in selected types All 4 journal articles
Other center views: All 60 publications 51 publications in selected types All 51 journal articles
Type Citation Sub Project Document Sources
Journal Article Tang C, Coull B, Schwartz J, Lyapustin A, Di Q, Koutrakis P (2016) Trends and Spatial Patterns of Fine Resolution AOD-Derived PM2.5 Emissions in the Northeast United States from 2002 to 2013. Journal of the Air Waste & Management Association. In press. http://dx.doi.org.ezp-prod1.hul.harvard.edu/10.1080/10962247.2016.1218393. R835872C001 (2016)
not available
Journal Article Di Q, Kloog I, Koutrakis P, Lyapustin A, Wang Y, Schwartz J (2016) Assessing PM2.5 exposures with high spatiotemporal resolution across the continental United States. Environmental Science & Technology, 50(9):4712-4721. R835872C001 (2016)
not available
Journal Article Di Q, Rowland S, Koutrakis P, Schwartz J (2017) A hybrid model for spatially and temporally resolved ozone exposures in the continental United States. Journal of the Air & Waste Management Association, 67(1):39-52. R835872C001 (2016)
not available
Journal Article Tang C, Coull B, Schwartz J, Lyapustin A, Di Q, Koutrakis P (2016) Developing Particle Emission Inventories Using Remote Sensing (PEIRS). Journal of the Air Waste & Management Association. In press. http://dx.doi.org.ezp-prod1.hul.harvard.edu/10.1080/10962247.2016.1214630. R835872C001 (2016)
not available

Supplemental Keywords:

particles, pollutant mixtures, pollution trends, regional pollution, public policy, data fusion

Relevant Websites:

Harvard/MIT ACE Center Exit

Progress and Final Reports:

Original Abstract
  • 2017

  • 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