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Dive into the research topics where Jeffrey A. Geddes is active.

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Featured researches published by Jeffrey A. Geddes.


Environmental Health Perspectives | 2015

Long-Term Trends Worldwide in Ambient NO2 Concentrations Inferred from Satellite Observations.

Jeffrey A. Geddes; Randall V. Martin; Brian L. Boys; Aaron van Donkelaar

Background Air pollution is associated with morbidity and premature mortality. Satellite remote sensing provides globally consistent decadal-scale observations of ambient nitrogen dioxide (NO2) pollution. Objective We determined global population-weighted annual mean NO2 concentrations from 1996 through 2012. Methods We used observations of NO2 tropospheric column densities from three satellite instruments in combination with chemical transport modeling to produce a global 17-year record of ground-level NO2 at 0.1° × 0.1° resolution. We calculated linear trends in population-weighted annual mean NO2 (PWMNO2) concentrations in different regions around the world. Results We found that PWMNO2 in high-income North America (Canada and the United States) decreased more steeply than in any other region, having declined at a rate of –4.7%/year [95% confidence interval (CI): –5.3, –4.1]. PWMNO2 decreased in western Europe at a rate of –2.5%/year (95% CI: –3.0, –2.1). The highest PWMNO2 occurred in high-income Asia Pacific (predominantly Japan and South Korea) in 1996, with a subsequent decrease of –2.1%/year (95% CI: –2.7, –1.5). In contrast, PWMNO2 almost tripled in East Asia (China, North Korea, and Taiwan) at a rate of 6.7%/year (95% CI: 6.0, 7.3). The satellite-derived estimates of trends in ground-level NO2 were consistent with regional trends inferred from data obtained from ground-station monitoring networks in North America (within 0.7%/year) and Europe (within 0.3%/year). Our rankings of regional average NO2 and long-term trends differed from the satellite-derived estimates of fine particulate matter reported elsewhere, demonstrating the utility of both indicators to describe changing pollutant mixtures. Conclusions Long-term trends in satellite-derived ambient NO2 provide new information about changing global exposure to ambient air pollution. Our estimates are publicly available at http://fizz.phys.dal.ca/~atmos/martin/?page_id=232. Citation Geddes JA, Martin RV, Boys BL, van Donkelaar A. 2016. Long-term trends worldwide in ambient NO2 concentrations inferred from satellite observations. Environ Health Perspect 124:281–289; http://dx.doi.org/10.1289/ehp.1409567


Environmental Science & Technology | 2017

Global Land Use Regression Model for Nitrogen Dioxide Air Pollution

Andrew Larkin; Jeffrey A. Geddes; Randall V. Martin; Qingyang Xiao; Yang Liu; Julian D. Marshall; Michael Brauer; Perry Hystad

Nitrogen dioxide is a common air pollutant with growing evidence of health impacts independent of other common pollutants such as ozone and particulate matter. However, the worldwide distribution of NO2 exposure and associated impacts on health is still largely uncertain. To advance global exposure estimates we created a global nitrogen dioxide (NO2) land use regression model for 2011 using annual measurements from 5,220 air monitors in 58 countries. The model captured 54% of global NO2 variation, with a mean absolute error of 3.7 ppb. Regional performance varied from R2 = 0.42 (Africa) to 0.67 (South America). Repeated 10% cross-validation using bootstrap sampling (n = 10,000) demonstrated a robust performance with respect to air monitor sampling in North America, Europe, and Asia (adjusted R2 within 2%) but not for Africa and Oceania (adjusted R2 within 11%) where NO2 monitoring data are sparse. The final model included 10 variables that captured both between and within-city spatial gradients in NO2 concentrations. Variable contributions differed between continental regions, but major roads within 100 m and satellite-derived NO2 were consistently the strongest predictors. The resulting model can be used for global risk assessments and health studies, particularly in countries without existing NO2 monitoring data or models.


Environmental Science & Technology | 2016

Relationships between Changes in Urban Characteristics and Air Quality in East Asia from 2000 to 2010

Andrew Larkin; Aaron van Donkelaar; Jeffrey A. Geddes; Randall V. Martin; Perry Hystad

Characteristics of urban areas, such as density and compactness, are associated with local air pollution concentrations. The potential for altering air pollution through changing urban characteristics, however, is less certain, especially for expanding cities within the developing world. We examined changes in urban characteristics from 2000 to 2010 for 830 cities in East Asia to evaluate associations with changes in nitrogen dioxide (NO2) and fine particulate matter (PM2.5) air pollution. Urban areas were stratified by population size into small (100 000-250 000), medium, (250 000-1 000 000), and large (>1 000 000). Multivariate regression models including urban baseline characteristics, meteorological variables, and change in urban characteristics explained 37%, 49%, and 54% of the change in NO2 and 29%, 34%, and 37% of the change in PM2.5 for small, medium and large cities, respectively. Change in lights at night strongly predicted change in NO2 and PM2.5, while urban area expansion was strongly associated with NO2 but not PM2.5. Important differences between changes in urban characteristics and pollutant levels were observed by city size, especially NO2. Overall, changes in urban characteristics had a greater impact on NO2 and PM2.5 change than baseline characteristics, suggesting urban design and land use policies can have substantial impacts on local air pollution levels.


Atmospheric Measurement Techniques Discussions | 2018

Stratosphere-troposphere separation of nitrogen dioxide columns from the TEMPO geostationary satellite instrument

Jeffrey A. Geddes; Randall V. Martin; Eric John Bucsela; Chris A. McLinden; Daniel J. M. Cunningham

Separating the stratospheric and tropospheric contributions in satellite retrievals of atmospheric NO2 column abundance is a crucial step in the interpretation and application of the satellite observations. A variety of stratosphere– troposphere separation algorithms have been developed for sun-synchronous instruments in low Earth orbit (LEO) that benefit from global coverage, including broad clean regions with negligible tropospheric NO2 compared to stratospheric NO2. These global sun-synchronous algorithms need to be evaluated and refined for forthcoming geostationary instruments focused on continental regions, which lack this global context and require hourly estimates of the stratospheric column. Here we develop and assess a spatial filtering algorithm for the upcoming TEMPO geostationary instrument that will target North America. Developments include using independent satellite observations to identify likely locations of tropospheric enhancements, using independent LEO observations for spatial context, consideration of diurnally varying partial fields of regard, and a filter based on stratospheric to tropospheric air mass factor ratios. We test the algorithm with LEO observations from the OMI instrument with an afternoon overpass, and from the GOME-2 instrument with a morning overpass. We compare our TEMPO field of regard algorithm against an identical global algorithm to investigate the penalty resulting from the limited spatial coverage in geostationary orbit, and find excellent agreement in the estimated mean daily tropospheric NO2 column densities (R2 = 0.999, slope= 1.009 for July and R2 = 0.998, slope= 0.999 for January). The algorithm performs well even when only small parts of the continent are observed by TEMPO. The algorithm is challenged the most by east coast morning retrievals in the wintertime (e.g., R2 = 0.995, slope= 1.038 at 14:00 UTC). We find independent global LEO observations (corrected for time of day) provide important context near the field-of-regard edges. We also test the performance of the TEMPO algorithm without these supporting global observations. Most of the continent is unaffected (R2 = 0.924 and slope= 0.973 for July and R2 = 0.996 and slope= 1.008 for January), with 90 % of the pixels having differences of less than±0.2×1015 moleculescm−2 between the TEMPO tropospheric NO2 column density and the global algorithm. For near-real-time retrieval, even a climatological estimate of the stratospheric NO2 surrounding the field of regard would improve this agreement. In general, the additional penalty of a limited field of regard from TEMPO introduces no more error than normally expected in most global stratosphere–troposphere separation algorithms. Overall, we conclude that hourly near-real-time stratosphere–troposphere separation for the retrieval of NO2 tropospheric column densities by the TEMPO geostationary instrument is both feasible and robust, regardless of the diurnally varying limited field of regard. Published by Copernicus Publications on behalf of the European Geosciences Union. 6272 J. A. Geddes et al.: Stratosphere–troposphere separation of nitrogen dioxide columns


Atmospheric Environment | 2009

Long term changes in nitrogen oxides and volatile organic compounds in Toronto and the challenges facing local ozone control

Jeffrey A. Geddes; Jennifer G. Murphy; D. Wang


Biogeosciences | 2012

Methane fluxes measured by eddy covariance and static chamber techniques at a temperate forest in central Ontario, Canada

J. M. Wang; Jennifer G. Murphy; Jeffrey A. Geddes; C. L. Winsborough; N. Basiliko; Sean C. Thomas


Atmospheric Chemistry and Physics | 2014

The impacts of precursor reduction and meteorology on ground-level ozone in the Greater Toronto Area

Stephanie C. Pugliese; Jennifer G. Murphy; Jeffrey A. Geddes; Jon M. Wang


Atmospheric Chemistry and Physics | 2013

Observations of reactive nitrogen oxide fluxes by eddy covariance above two midlatitude North American mixed hardwood forests

Jeffrey A. Geddes; Jennifer G. Murphy


Agricultural and Forest Meteorology | 2014

Net ecosystem exchange of an uneven-aged managed forest in central Ontario, and the impact of a spring heat wave event

Jeffrey A. Geddes; Jennifer G. Murphy; Jon Schurman; Alexandre Petroff; Sean C. Thomas


Remote Sensing of Environment | 2012

Biases in long-term NO2 averages inferred from satellite observations due to cloud selection criteria

Jeffrey A. Geddes; Jennifer G. Murphy; Jason M. O'Brien; Edward Celarier

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Colette L. Heald

Massachusetts Institute of Technology

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Perry Hystad

Oregon State University

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