@article{4743,
  author = {Hsing-Cheng Liu},
  title = {Missing-Value Behavior and Sensor Drift Analysis in Distributed PM2.5 Microclimate Sensing Networks for Trustworthy Environmental Intelligence},
  journal = {Journal of Electronic Systems},
  year = {2026},
  volume = {16},
  number = {2},
  doi = {https://doi.org/10.6025/jes/2026/16/2/49-69},
  url = {https://www.dline.info/jes/fulltext/v16n2/jesv16n2_1.pdf},
  abstract = {Reliable air-quality monitoring is fundamental to environmental sustainability, public-health surveillance,
climate resilience, and smart city governance. Among atmospheric pollutants, fine particulate matter with
an aerodynamic diameter of <2.5 μm (PM2.5) is particularly hazardous because prolonged exposure is
strongly associated with respiratory illness, cardiovascular disease, and premature mortality. Although
distributed low-cost environmental sensing systems have emerged as scalable alternatives to traditional
regulatory-grade monitoring stations, their operational reliability is frequently compromised by missing
observations, communication instability, calibration degradation, and long-term sensor drift. These issues
significantly affect data quality and may reduce the reliability of downstream predictive environmental
analytics.
This study presents a comprehensive analytical framework for evaluating missing-value behavior and sensor
drift within a large-scale PM2.5 microclimate sensing network. The analysis utilizes 897,085 environmental
observations collected from 12 distributed sensing devices between May 2022 and May 2026. The proposed
framework integrates device level and feature level missingness analysis, temporal missingness evaluation,
rolling statistical drift analysis, baseline deviation monitoring, Kolmogorov Smirnov distributional drift
testing, and z-score anomaly detection.
The findings reveal substantial heterogeneity in sensing reliability across devices and environmental
variables. Device-level analysis demonstrates severe acquisition instability in selected sensing nodes, while
feature level evaluation indicates that wind related variables exhibit the highest missingness behavior.
Sensor drift analysis further identifies significant long term deviations in PM2.5 sensing behavior, suggestingprogressive calibration instability and operational degradation in multiple sensing devices. Statistical drift
testing confirms substantial distributional evolution across sensing periods.
The proposed framework contributes toward trustworthy environmental intelligence by establishing a
scalable methodology for evaluating sensing reliability in distributed smart city infrastructures. The findings
additionally emphasize the importance of continuous quality assurance, adaptive recalibration, and
automated drift monitoring for maintaining robust environmental sensing ecosystems},
}