The DOI matches its Crossref record.
Estimating ground‐level PM2.5 using aerosol optical depth determined from satellite remote sensing
Open accessAbstract
We assess the relationship of ground‐level fine particulate matter (PM 2.5) concentrations for 2000–2001 measured as part of the Canadian National Air Pollution Surveillance (NAPS) network and the U.S. Air Quality System (AQS), versus remote‐sensed PM 2.5 determined from aerosol optical depths (AOD) measured by the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multiangle Imaging Spectroradiometer (MISR) satellite instruments. A global chemical transport model (GEOS‐CHEM) is used to simulate the factors affecting the relation between AOD and PM 2.5. AERONET AOD is used to evaluate the method (r = 0.71, N = 48, slope = 0.69). We find significant spatial variation of the annual mean ground‐based measurements with PM 2.5 determined from MODIS (r = 0.69, N = 199, slope = 0.82) and MISR (r = 0.58, N = 199, slope = 0.57). Excluding California significantly increases the respective slopes and correlations. The relative vertical profile of aerosol extinction is the most important factor affecting the spatial relationship between satellite and surface measurements of PM 2.5; neglecting this parameter would reduce the spatial correlation to 0.36. In contrast, temporal variation in AOD is the most influential parameter affecting the temporal relationship between satellite and surface measurements of PM 2.5; neglecting daily variation in this parameter would decrease the correlation in eastern North America from 0.5–0.8 to less than 0.2. Other simulated aerosol properties, such as effective radius and extinction efficiency have a minor role temporally, but do influence the spatial correlation. Global mapping of PM 2.5 from both MODIS and MISR reveals annual mean concentrations of 40–50 ug/m 3 over northern India and China.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Safe and just Earth system boundaries
Rockström, J., Gupta, J., Qin, D., Lade, S. J., Abrams, J. F., Andersen, L. S., Armstrong McKay, D. I., Bai, X., Bala, G., Bunn, S. E., et al. (2023) · Nature · doi:10.1038/s41586-023-06083-8 · Cited by 1,400
U-Air: when urban air quality inference meets big data
Zheng, Y., Liu, F., & Hsieh, H.-P. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2487575.2488188 · Cited by 916
van Donkelaar, A., Martin, R. V., Brauer, M., & Boys, B. L. (2015) · Environmental Health Perspectives · doi:10.1289/ehp.1408646 · Cited by 894
Self-citationAssessing PM 2.5 Exposures with High Spatiotemporal Resolution across the Continental United States
Di, Q., Kloog, I., Koutrakis, P., Lyapustin, A., Wang, Y., & Schwartz, J. (2016) · Environmental Science & Technology · doi:10.1021/acs.est.5b06121 · Cited by 510
Metadata
- Authors
- van Donkelaar, A., Martin, R. V., & Park, R. J.
- Venue
- Journal of Geophysical Research: Atmospheres
- Publisher
- American Geophysical Union (AGU)
- Published
- 2006-11-02
- Volume
- 111
- Issue
- D21
- ISSN
- 0148-0227 (ISSN-L)
- OpenAlex
- W1987098847
- Cited by
- 537
- References
- 0
- FWCI
- 9.74
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Estimating ground‐level PM2.5 using aerosol optical depth determined from satellite remote sensing. https://app.cochat.ai/papers/10.1029/2005jd006996
- The paper
- van Donkelaar, A., Martin, R. V., & Park, R. J. (2006). Estimating ground‐level PM2.5 using aerosol optical depth determined from satellite remote sensing. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2005jd006996
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.