Evaluation of low-cost sensors for size-resolved indoor particle monitoring

Low-cost sensors (LCS) are increasingly used to enhance air quality monitoring by enabling dense measurement networks; however, their performance across particle size ranges and under realistic indoor-emission conditions remains uncertain, particularly in low-and middle-income settings. This study evaluated the size-resolved performance of two widely used LCS (Plantower-PMS7003 and Sensirion-SPS30) in contrasting low- and high-PM indoor environments representative of real-world conditions in India. The study's low-PM environment exhibited 24-hour averaged concentrations of 37 ± 6.2 µg/m³ for PM_2.5 and 39 ± 6.7 µg/m³ for PM₁₀. The corresponding concentrations were 67 ± 15.9 µg/m³ and 119 ± 43.6 µg/m³, respectively, in a high-PM environment. Triplicate-LCS were collocated with a reference optical particle counter (Grimm-1.109). Analyses were done for particle number concentrations (PNC) across size bins (0.3-10.0 μm) and PM-mass fractions (PM₁, PM_2.5, PM₁₀, intermediate fractions). Intra-sensor correlation remained high, whereas in high-PM conditions inter-sensor correlation declined but improved with hourly-averaging. Coefficient of Variation was < 30% for PM-mass and smaller PNC-bins (≤ 1.0-2.5 μm), while > 43% for larger-bins, indicating poor reproducibility. Accuracy metrics were highest for PM₁ (R² ~ 0.90-0.95) and decreased with increasing particle-size (PM₁₀-R² ~ 0.28-0.68). Strong PNC-correlations were observed for 0.3-0.5 μm particles and 0.5-1.0 μm (R² ~ 0.72) but decreased substantially for > 1.0 μm particles (R² < 0.36). The evaluated sensors did not capture the dominant contribution of particles < 0.30 μm under either low- or high-PM conditions. These findings demonstrate that LCS effectively capture fine particles but shows limitations for coarse-intermediate fractions, constraining size-resolved assessments and cumulative-PM estimates based on proprietary conversions. This study provides insights to improve LCS deployment in monitoring networks and supports high-resolution exposure-assessment in resource-constrained environments.