Discovering Hidden Pollution Hotspots Using Sparse Sensor Measurements
<|reference_start|>Discovering Hidden Pollution Hotspots Using Sparse Sensor Measurements: Effective air pollution management in urban areas relies on both monitoring and mitigation strategies, yet high costs often limit sensor networks to a few key pollution hotspots. In this paper, we show that New Delhi's public sensor network is insufficient for identifying all pollution hotspots. To address this, we augmented the city's network with 28 low-cost sensors, monitoring PM 2.5 concentrations over 30 months (May 2018 to November 2020). Our analysis uncovered 189 additional hotspots, supplementing the 660 already detected by the government network. We observed that Space-Time Kriging with limited but accurate sensor data provides a more robust and generalizable approach for identifying these hotspots, as compared to deep learning models that require large amounts of fine-grained multi-modal data (emissions inventory, meteorology, etc.) which was not reliably, frequently and accurately available in the New Delhi context. Using Space-Time Kriging, we achieved 98% precision and 95.4% recall in detecting hotspots with 50% sensor failure. Furthermore, this method proved effective in predicting hotspots in areas without sensors, achieving 95.3% precision and 88.5% recall in the case of 50% missing sensors. Our findings revealed that a significant portion of New Delhi's population, around 23 million people, was exposed to pollution hotspots for at least half of the study period. We also identified areas beyond the reach of the public sensor network that should be prioritized for pollution control. These results highlight the need for more comprehensive monitoring networks and suggest Space-Time Kriging as a viable solution for cities facing similar resource constraints.<|reference_end|>
@article{bhardwaj2024discovering,
    title={Discovering Hidden Pollution Hotspots Using Sparse Sensor Measurements},
    author={Ankit Bhardwaj, Ananth Balashankar, Shiva Iyer, Nita Soans, Anant
  Sudarshan, Rohini Pande, Lakshminarayanan Subramanian},
    journal={arXiv preprint arXiv:2410.04309},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04309},
    primaryClass={cs.CY cs.LG}
}