Significance of sources and size distribution on calibration of low-cost particle sensors: Evidence from a field sampling campaign

Malyan, Vasudev; Kumar, Vikas; Sahu, Manoranjan

doi:10.1016/j.jaerosci.2022.106114

Cited by 11 publications

(5 citation statements)

References 46 publications

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“…Figure S1 shows that the ALT to CF1 ratio is approximately 0.15 : 1. Although the CF1 and ATM data have dominated most calibration efforts (Malyan et al, 2023;Puttaswamy et al, 2022;Barkjohn et al, 2021;McFarlane et al, 2021;Magi et al, 2020;Malings et al, 2019), the usage of ALT data continues to propagate in peer-reviewed literature (Wallace and Zhao, 2023;Wallace and Ott, 2023). Therefore we use CF1, ATM, and ALT in our study to work towards harmonizing a calibration approach for PA-II in India.…”

Section: Low-cost Sensorsmentioning

confidence: 99%

“…Recently there has been increased interest in understanding low-cost sensor performance in the Global South to fill major monitoring gaps (Bai et al, 2020;Jha et al, 2021;Malyan et al, 2023;McFarlane et al, 2021;Puttaswamy et al, 2022;Sreekanth et al, 2022;Zheng et al, 2018Zheng et al, , 2019. In north India, Zheng et al (2018) deployed Plantower models in Kanpur, Uttar Pradesh, for 90 d and found that multilinear regression improved Plantower performance, albeit with significant error for hourly data.…”

Section: Introductionmentioning

confidence: 99%

“…In south India, Puttaswamy et al (2022) calibrated Plantower units for 68 d in Chennai and found a multilinear regression approach that reduced uncertainty to within 15 % and 18 % for PM 2.5 and PM 10 , respectively. Low-cost sensor studies in India report the importance of climate and emissions variability on aerosol characteristics and advise future deployments to test calibration algorithms across longer timelines (Malyan et al, 2023;Puttaswamy et al, 2022;Sreekanth et al, 2022;Zheng et al, 2018Zheng et al, , 2019.…”

Section: Introductionmentioning

confidence: 99%

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Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India

Campmier,

Gingrich,

Singh

et al. 2023

Atmos. Meas. Tech.

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Abstract. Lower-cost air pollution sensors can fill critical air quality data gaps in India, which experiences very high fine particulate matter (PM2.5) air pollution but has sparse regulatory air monitoring. Challenges for low-cost PM2.5 sensors in India include high-aerosol mass concentrations and pronounced regional and seasonal gradients in aerosol composition. Here, we report on a detailed long-time performance evaluation of a popular sensor, the Purple Air PA-II, at multiple sites in India. We established three distinct sites in India across land use categories and population density extremes (in urban Delhi and rural Hamirpur in north India and urban Bengaluru in south India), where we collocated the PA-II model with reference beta attenuation monitors. We evaluated the performance of uncalibrated sensor data, and then developed, optimized, and evaluated calibration models using a comprehensive feature selection process with a view to reproducibility in the Indian context. We assessed the seasonal and spatial transferability of sensor calibration schemes, which is especially important in India because of the paucity of reference instrumentation. Without calibration, the PA-II was moderately correlated with the reference signal (R2 = 0.55–0.74) but was inaccurate (NRMSE ≥ 40 %). Relative to uncalibrated data, parsimonious annual calibration models improved the PurpleAir (PA) model performance at all sites (cross-validated NRMSE 20 %–30 %; R2 = 0.82–0.95), and greatly reduced seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed among our sites, suggesting that local calibrations are desirable when possible. Using a moving-window calibration, we found that using seasonally specific information improves performance relative to a static annual calibration model, while a short-term calibration model generally does not transfer reliably to other seasons. Overall, we find that the PA-II model can provide reliable PM2.5 data with better than ±25 % precision and accuracy when paired with a rigorous calibration scheme that accounts for seasonality and local aerosol composition.

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Section: Low-cost Sensorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India

Campmier,

Gingrich,

Singh

et al. 2023

Atmos. Meas. Tech.

View full text Add to dashboard Cite

show abstract

“…Recently there is increased interest in understanding LCS performance in the Global South to fill major monitoring gaps (Bai et al, 2020;Jha et al, 2021;Malyan et al, 2023;McFarlane et al, 2021;Puttaswamy et al, 2022;Sreekanth et al, 2022;Zheng et al, 2018Zheng et al, , 2019. In North India, Zheng et al (2018) deployed Plantower models in Kanpur, Uttar Pradesh, for 90 days and found multilinear regression improved Plantower performance, albeit with significant error for hourly data.…”

Section: Introductionmentioning

confidence: 99%

“…In South India, Puttaswamy et al (2022) calibrated Plantower units for 68-days in Chennai and found a multilinear regression approach reduced uncertainty to within 15% and 18% for PM 2.5 and PM 10 respectively. LCS studies in India report the importance of climate and emissions variability on aerosol characteristics and advise future deployments to test calibration algorithms across longer timelines (Malyan et al, 2023;Puttaswamy et al, 2022;Sreekanth et al, 2022;Zheng et al, 2018Zheng et al, , 2019.…”

Section: Introductionmentioning

confidence: 99%

Site and Season Specific Calibrations Improve Low-cost Sensor Performance: Long-term Field Evaluation of PurpleAir Sensors in Urban and Rural India

Campmier

Gingrich

Singh

et al. 2023

Preprint

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Abstract. We report on the long-term performance of a popular low-cost PM2.5 sensor, the PurpleAir PA-II, at multiple sites in India, with the aim of identifying robust calibration protocols. We established 3 distinct sites in India (North India: Delhi, Hamirpur; South India: Bangalore), where we collocated PA-II with reference beta-attenuation monitors to characterize sensor performance and to model calibration relationships between PA-IIs and reference monitors for hourly data. Our sites remained in operation across all major seasons of India. Without calibration, the PA-IIs had high precision (Normalized Root Mean Square Error [NRMSE] among replicate sensors ≤ 15 %) and tracked the overall seasonal and diurnal signals from the reference instruments well (Pearson’s r ≥ 0.9) but were inaccurate (NRMSE ≥ 40 %). We used a comprehensive feature selection process to create optimized site-specific calibrations. Relative to the uncalibrated data, parsimonious least-squares long-term calibration models improved PA-II performance at all sites (cross-validated NRMSE: 20–30 %, R2: 0.82–0.95), particularly by reducing seasonal and diurnal biases. Because aerosol properties and meteorology vary regionally, the form of these long-term models differed by site. Likewise, using a moving-window calibration, we find a calibration scheme using seasonally specific information somewhat improves performance relative to a static long-term calibration model. In contrast, we demonstrate that a successful short-term calibration exercise for one season may not transfer reliably to other seasons. Overall, we demonstrate how the PA-II, when paired with a careful calibration scheme, can provide actionable information on PM2.5 in India with only modest irreducible uncertainty.

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Impact assessment of spatial–temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling

Chen,

Lin

2024

Environ Sci Pollut Res

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Significance of sources and size distribution on calibration of low-cost particle sensors: Evidence from a field sampling campaign

Cited by 11 publications

References 46 publications

Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India

Seasonally optimized calibrations improve low-cost sensor performance: long-term field evaluation of PurpleAir sensors in urban and rural India

Site and Season Specific Calibrations Improve Low-cost Sensor Performance: Long-term Field Evaluation of PurpleAir Sensors in Urban and Rural India

Impact assessment of spatial–temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling

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