Percentile-based slope-constrained linear interpolation for robust imputation of highly volatile PM2.5 time series
Sawet Somnugpong, Narut Butploy, Kanokwan Khiewwan
TL;DR
This paper introduces a new interpolation method for PM2.5 data that improves accuracy by constraining unrealistic slope changes.
Contribution
A novel percentile-based slope-constrained linear interpolation method is proposed for robust PM2.5 time series imputation.
Findings
A data-driven slope threshold is estimated from historical first-order differences.
Sequential slope constraints prevent unrealistic gradient transitions during interpolation.
The method maintains linear computational complexity while improving reconstruction accuracy.
Abstract
Reliable reconstruction of missing observations is essential for environmental time-series analysis, particularly for highly volatile air-quality indicators such as PM2.5. Although linear interpolation is widely used for short-gap imputation due to its simplicity and computational efficiency, it does not explicitly regulate slope dynamics and may produce physically implausible transitions in rapidly fluctuating data. This study proposes a percentile-based slope-constrained linear interpolation method that estimates a slope threshold from the empirical distribution of historical first-order differences and applies a sequential constraint during interpolation to prevent unrealistic gradient changes. The approach requires only a single data-driven parameter and maintains linear computational complexity.•Data-driven slope threshold estimated from the percentile distribution of historical…
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Taxonomy
TopicsAtmospheric chemistry and aerosols · Air Quality Monitoring and Forecasting · Air Quality and Health Impacts