Trends in Earth's Atmospheric Makeup: Pollution of the Air - Chemistry - Climate Connections
We aim to quantify the sensitivity of ground-level ozone and particle pollution to precursor emissions and chemistry. Ozone and particulates in surface air in many locations reflect a balance of production from local-to-regional anthropogenic and natural sources and of transport. Knowing the ‘break-down’ of sources contributing to air pollution is needed to set attainable standards and to implement effective pollution control policies. Much of our work has focused on U.S. or global air pollution, with more recent applications over India.
Air quality and health applications of satellite data
Cleaner air over New York State
We have examined uncertainties in using satellite aerosol optical depth products to estimate surface fine particulate matter (PM 2.5 ) over the Northeastern U.S.A. ( Jin et al., 2019a ) and compared multiple publicly available PM 2.5 products to document public health benefits associated with cleaner air across New York State ( Jin et al., 2019b ). We also contributed to technical guidance documents for incorporating satellite data into State Implementation Plans, which are part of the process by which non-attainment areas demonstrate how they will achieve compliance with the National Ambient Air Quality Standards. [NYSERDA, NASA HAQAST]
Surface ozone sensitivity to precursor emissions
While NO x reductions generally lower the overall ozone produced regionally or globally (a ‘NO x -sensitive’ ozone formation regime), urban areas with high NO x levels may benefit from VOC emission control (NO x -saturated regime). We build on prior work using satellite observations of the tropospheric column ratio of HCHO (an intermediate product of VOC oxidation which can serve as a proxy for these emissions) to NO 2 (a proxy for NO x ; NO x = NO + NO 2 ) as an indicator for ozone-forming chemistry.
We conducted a model-centric evaluation of uncertainty in using this column-based satellite indicator for surface ozone formation regimes in northern mid-latitude source regions and demonstrated a transition to increasing NO x -sensitivity during the warm season over several northern mid-latitude urban areas as controls on anthropogenic NO x emission controls were implemented from 2005 to 2015 ( Jin et al., 2017 ; see also 11/7/17 NASA image of the day). A new approach to harmonize multi-satellite retrievals (using the consistently retrieved European QA4ECV products) produced a two-decade record of this HCHO/NO 2 satellite indicator.
We found that trends in this satellite indicator correspond to patterns of ground-level ozone changes known to be associated with transitions from NO x -saturated to NO x - sensitive ozone formation ( Jin et al., 2020 ). Ongoing work examines transitions in ozone-forming chemistry on days when the ozone NAAQS is exceeded over the New York City and Baltimore/Washington DC areas using observations from two field campaigns during summer 2018 alongside satellite data plus high-resolution CMAQ simulations (Tao et al., in prep)
[NASA fellowship to Xiaomeng Jin, NASA ACMA & HAQAST].
Climate-air quality-health nexus
In a new collaboration with colleagues at the Mailman School of Public Health and the Center for International Earth Science Information Network, we are optimizing high-resolution exposure datasets for the Northeast U.S.A. We are investigating how pollutant exposures, and their uncertainty, co-vary with each other, and with meteorology, emissions, and demographic and socioeconomic indicators. We also seek to advance knowledge of short-term health responses to coincident exposure to multiple pollutants and heat. [NASA HAQAST]
Air pollution distributions and trends
Background ozone over the U.S.A.
We have completed several studies quantifying different aspects of “background” ozone in surface air. We are particularly interested in variability on daily to decadal time scales in the individual components contributing to background (i.e., ozone produced from global methane, from international anthropogenic emissions, from natural biogenic and soil emissions or lightning NO x , and transported from the stratosphere). For example, check out Guo et al. (2018) to see how we used a suite of sensitivity simulations in the GEOS-Chem global chemistry transport model to estimate the influence from individual background sources versus U.S. anthropogenic sources on total surface ozone over 10 continental US regions from 2004 to 2012. We found that the model attributes interannual variability in U.S. background ozone on days when the highest ground-level ozone concentrations were measured to natural sources, not
international pollution transport. [EPA, NASA AQAST]
Changing Air Pollution Extremes
Changes in regional NO x emissions as well as regional climate are expected to alter the frequency of high-ozone episodes. Rieder et al. (2013) used methods from extreme value
theory statistics to characterize changes over the eastern United States. Rieder et al. (2015) applied a statistical bias correction to 21 st century simulations with the GFDL CM3 model to project future changes. They found a simple relationship between simulated changes in 1 year return levels and regional NO x emission changes, implying that findings can be generalized to estimate changes in the frequency of eastern U.S. pollution events under different regional NO x emission scenarios. An additional application of probabilistic return levels demonstrates that continued increases in global methane abundances can offset benefits otherwise attainable by
controlling non-methane ozone precursors (Rieder et al., 2018). [EPA]
Air pollution over India
Karambelas et al. (2018) used high-resolution CMAQ simulations to demonstrate that the health burden borne by populations in rural northern India from exposure to fine particles and ozone is higher than for urban populations. A high-resolution nested configuration of GEOS-Chem was developed over India to examine the chemical composition and radiative implications of fine particle pollution episodes (Karambelas et al., in review). We are using these same model simulations as a testbed to develop machine learning approaches to deriving surface particulate distributions from publicly available satellite, emission, and meteorological datasets (Zheng et al., in prep). We are also using an initial condition chemistry-climate model ensemble with the CESM2-WACCM6 model to corroborate that concentrations of fine particles have increased over the 20th century due to emissions, not meteorological or climate changes (Hancock et al., in prep). [Earth Institute Postdoctoral Fellowship to Alexandra Karambelas; Columbia University]