Assessing the Potential of PlanetScope Satellite Imagery to Estimate Particulate Matter Oxidative Potential
<|reference_start|>Assessing the Potential of PlanetScope Satellite Imagery to Estimate Particulate Matter Oxidative Potential: Oxidative potential (OP), which measures particulate matter's (PM) capacity to induce oxidative stress in the lungs, is increasingly recognized as an indicator of PM toxicity. Since OP is not routinely monitored, it can be challenging to estimate exposure and health impacts. Remote sensing data are commonly used to estimate PM mass concentration, but have never been used to estimate OP. In this study, we evaluate the potential of satellite images to estimate OP as measured by acellular ascorbic acid (OP AA) and dithiothreitol (OP DTT) assays of 24-hour PM10 sampled periodically over five years at three locations around Grenoble, France. We use a deep convolutional neural network to extract features of daily 3 m/pixel PlanetScope satellite images and train a multilayer perceptron to estimate OP at a 1 km spatial resolution based on the image features and common meteorological variables. The model captures more than half of the variation in OP AA and almost half of the variation in OP DTT (test set R2 = 0.62 and 0.48, respectively), with relative mean absolute error (MAE) of about 32%. Using only satellite images, the model still captures about half of the variation in OP AA and one third of the variation in OP DTT (test set R2 = 0.49 and 0.36, respectively) with relative MAE of about 37%. If confirmed in other areas, our approach could represent a low-cost method for expanding the temporal or spatial coverage of OP estimates.<|reference_end|>
@article{hough2024assessing,
    title={Assessing the Potential of PlanetScope Satellite Imagery to Estimate
  Particulate Matter Oxidative Potential},
    author={Ian Hough, Lo"ic Argentier, Ziyang Jiang, Tongshu Zheng, Mike Bergin,
  David Carlson, Jean-Luc Jaffrezo, Jocelyn Chanussot, Ga"elle Uzu},
    journal={arXiv preprint arXiv:2407.13778},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.13778},
    primaryClass={cs.CV cs.LG}
}