Monitoring Spatial and Temporal Variability of Air Quality Using Satellite Observation Data: a Case Study of MODIS-Observed Aerosols in Southern Ontario, Canada
Bibliographic record
Abstract
Air Quality 66MODIS-derived AOD is subject to weather condition and has lower accuracy and lower temporal frequency (once a day) than the sunphotometer measurements.A number of studies have been conducted to address the spatial and temporal variability of aerosols.Early research mainly focused on a group of cities that hold an Aerosol Robotic Network (AERONET) site (equipped with a sunphotometer).For instance, Masmoudi et al. (2003) found higher spatial variability of AOD and AE for the central African sites than the Mediterranean ones.The central African sites showed a lower variation with the smallest values of their measured AE due to the presence of very large dust particles.Remotelysensed data have also been used in the analysis of aerosol loading distribution in recent years.By mapping AOD over Europe at a continental scale, Koelemeijer et al. (2006) clearly identified Northern Italy, Southern Poland, and the Belgium/Netherlands/Ruhr area as major aerosol source regions.Frank et al. (2007) used the data from MISR for an inter-annual analysis of AOD variation over the Mojave desert of southern California.The authors suggested that AOD varies significantly across the desert and therefore the AERONET site at Rogers Dry Lake (within the desert) cannot be used to represent the aerosol conditions over the entire study area.However, the relationship between aerosol distribution and land use structure/topography has not been examined in previous studies.This chapter reviews the algorithms used to extract AOD from MODIS data and presents a case study of using MODIS AOD data to investigate the spatial-temporal distribution patterns of aerosols in southern Ontario, Canada.The relationship between land-use structure and AOD has been analyzed through a correlation analysis and discuss the impacts of topography on the aerosol distribution. How to referenceIn order to correctly reference this scholarly work, feel free to copy and paste the following: DongMei Chen and Jie Tian (2010).Monitoring Spatial and Temporal Variability of Air Quality Using Satellite Observation Data: a Case Study of MODIS-Observed Aerosols in Southern Ontario, Canada, Air Quality, Ashok Kumar (Ed.),
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".