Grey Clustering Algorithm for Urban Air Quality Classification: A Case Study in Lima, Peru

This study introduces a grey clustering algorithm based on the Central Triangular Whitenization Weight Function (CTWF), designed to classify urban air quality under conditions of limited or uncertain data. Based on Grey Systems The-ory (GST), the proposed algorithm facilitates structured multi-criteria assessments using sparse or irregular datasets—a condition frequently encountered in urban environmental monitoring. The algorithm stands out for its low computational complexity, interpretability, and ability to integrate multiple pollutants into a single qualitative classification, making it particularly suitable for smart city applications and real-time decision support systems. To evaluate its performance, the grey clustering algorithm (CTWF) was applied to a case study in Northern Lima, Peru, covering eight semesters between 2011 and 2019 and including four key pollutants: PM10, SO2, NO2, and CO. Although all periods were classified as “Good” under national standards, the disaggregated analysis revealed PM10 as the most persistent concern, while CO levels remained consistently low, and SO2 and NO2 showed moderate fluctuations. These findings validate the algorithm’s capacity to extract pollutant-specific insights and spatiotemporal trends even in data-scarce environments. Future enhancements may include meteorological integration, broader pollutant sets (e.g., PM2.5, ozone), and satellite data to extend forecasting capabilities and spatial resolution.

@article{Delgado2025,
  title     = {Grey Clustering Algorithm for Urban Air Quality Classification: A Case Study in Lima, Peru},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Alexi Delgado and Katherine Paredes Guerrero and Anderson Carrillo},
  doi       = {10.14569/IJACSA.2025.01608101},
  url       = {https://doi.org/10.14569/IJACSA.2025.01608101}
}