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<p>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&#xa0;µg/m<sup>3</sup> for PM<sub>2.5</sub> and 39 ± 6.7&#xa0;µg/m<sup>3</sup> for PM<sub>10</sub>. The corresponding concentrations were 67 ± 15.9&#xa0;µg/m<sup>3</sup> and 119 ± 43.6&#xa0;µg/m<sup>3</sup>, 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&#xa0;μm) and PM-mass fractions (PM<sub>1</sub>, PM<sub>2.5</sub>, PM<sub>10</sub>, 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 &lt; 30% for PM-mass and smaller PNC-bins (≤ 1.0–2.5&#xa0;μm), while &gt; 43% for larger-bins, indicating poor reproducibility. Accuracy metrics were highest for PM<sub>1</sub> (R<sup>2</sup> ~ 0.90–0.95) and decreased with increasing particle-size (PM<sub>10</sub>-R<sup>2</sup> ~ 0.28–0.68). Strong PNC-correlations were observed for 0.3–0.5&#xa0;μm particles and 0.5–1.0&#xa0;μm (R<sup>2</sup> ~ 0.72) but decreased substantially for &gt; 1.0&#xa0;μm particles (R<sup>2</sup> &lt; 0.36). The evaluated sensors did not capture the dominant contribution of particles &lt; 0.30&#xa0;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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月8日

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  • Mudassir Ali,
  • Ahmed Sameer,
  • Saifi Izhar

摘要

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 PM2.5 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, PM2.5, 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.