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Record W2328137915 · doi:10.1289/ehp.123-a43

The View from Afar: Satellite-Derived Estimates of Global PM <sub>2.5</sub>

2015· letter· en· W2328137915 on OpenAlexaboutno aff
Lindsey Konkel

Bibliographic record

VenueEnvironmental Health Perspectives · 2015
Typeletter
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsnot available
Fundersnot available
KeywordsSatelliteAir pollutionParticulatesEnvironmental scienceNova scotiaGeographyMeteorologyClimatologyGeologyEngineering

Abstract

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Satellite-Derived Estimates of Global PM 2.5More than 3 million people died prematurely in 2010 due to ambient exposure to fine particulate matter (PM 2.5 ), according to estimates from the Global Burden of Disease Study. 1 Although air pollution measurements taken from ground-level monitors can help inform such estimates, a paucity of monitoring stations outside of North America and Western Europe make it difficult to compare levels and trends in PM 2.5 and their health effects around the world. 2 Fortunately, satellite data provide a way of filling in data gaps for areas with no ground-based monitoring.In this issue of EHP, a team of researchers report their satellite-derived estimates of global exposure trends to PM 2.5 over 15 years.3 "We found notable trends of increasing PM 2.5 in South and East Asia, where billions of people live.Meanwhile, parts of North America are getting cleaner," says study author Randall Martin, an atmospheric scientist at Dalhousie University in Halifax, Nova Scotia.Satellite sensors don't measure PM 2.5 directly.Instead, they assess how particles in the air, including PM 2.5 , scatter sunlight as it passes through the atmosphere."In a sense, the satellites we use are little more than extremely well calibrated cameras that take pictures of the earth below.When aerosol particles are present, these pictures begin to look a little hazy," explains first author Aaron van Donkelaar, also an atmospheric scientist at Dalhousie University.The extent to which aerosols scatter the light is called the aerosol optical depth (AOD).The researchers used AOD data from the National Aeronautics and Space Administration to estimate ground-level PM 2.5 at a spatial resolution of approximately 10 km × 10 km.Although some regions experienced a decrease in PM 2.5 over the period 1998-2012, the global population-weighted average increased by an estimated 2.1% per year.Rising levels of air pollution in developing regions in South and East Asia largely drove the upward trend.3 After adjusting for population changes, the researchers esti mated that the proportion of people in South and East Asia exposed to PM 2.5 at levels exceeding the World Health Organization (WHO) interim target of 35 µg/m 3 rose from 51% in 1998-2000 to 70% in 2010-2012.In contrast, the proportion of North Americans exposed to PM 2.5 at levels above the WHO air quality guideline of 10 µg/ m 3 fell from 62% in 1998-2000 to 19% in 2010-2012.4 Where ground-level PM 2.5 data were available, the researchers found a significant association with satellite-based estimates, though satellitederived PM 2.5 estimates tended to be slightly lower than ground-level readings.3 "Satellite-based estimates reported here will enable researchers to design and conduct large epidemiological studies in low-and middle-income countries that lack the extensive ground monitoring networks found in higher income countries," says Aaron Cohen, an epidemiologist at the Health Effects Institute in Boston.He was not involved in the current study.As satellite-derived PM 2.5 estimates have become available, groups such as the Global Burden of Disease Study 1 and the WHO 5 have begun to use them as the basis for estimates of the global burden of disease.The current study builds upon a previous analysis by these authors, which estimated global PM 2.5 levels from 2001 through 2006

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.029
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0030.002
Scholarly communication0.0030.004
Open science0.0010.001
Research integrity0.0290.030
Insufficient payload (model declined to judge)0.0070.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.031
GPT teacher head0.306
Teacher spread0.275 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2015
Admission routes1
Has abstractyes

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