Performance Assessment of Two Low-Cost PM2.5 and PM10 Monitoring Networks in the Padana Plain (Italy)

Sensors, Journal Year: 2024, Volume and Issue: 24(12), P. 3946 - 3946

Published: June 18, 2024

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in PM2.5 PM10 daily concentrations the Padana Plain (Northern Italy). A total of 19 LC stations for 20 compared vs. regulatory-grade during a full “heating season” (15 October 2022–15 April 2023). Both sensor networks showed higher accuracy fitting magnitude than reference observations, while lower was shown terms RMSE, MAE R2. under-estimated both (MB = −4.8 −2.9 μg/m3, respectively), over-estimated +5.4 μg/m3) slightly −0.4 μg/m3). finer at capturing time variation (R2 0.68–0.75 0.59–0.61). sensors from failed to capture dynamics PM2.5/PM10 ratio, confirming their well-known issues correctly discriminating size individual particles. These findings suggest need further efforts implementation mass conversion algorithms within units improve tuning outputs.

Language: Английский

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Field testing of low-cost particulate matter sensors for Digital Twin applications in nanomanufacturing processes

Journal of Physics Conference Series, Journal Year: 2024, Volume and Issue: 2695(1), P. 012002 - 012002

Published: Jan. 1, 2024

Abstract The EU-project ASINA is testing Low-Cost Particulate Matter Sensors (LCPMS) for industrial monitoring of the concentration airborne particles, with purpose integrating this sensor technology within data collection layer Digital Twins (DTs) manufacturing. This paper shows results field performance evaluations carried out five LCPMS from different manufacturers ( Alphasense OPC-N3, Plantower 9003, Sensirion SPS30, SEN55 and Tera Sensor NetxPM ), during several sampling campaigns, conducted in four pre-commercial commercial pilot lines (PLs) that manufacture nano-enabled products, belonging to OASIS H2020 EU-projects [2,28]. Field tests consisted deploying manufacturing process, measuring parallel collocated reference informative instruments (OPS TSI 3330/CPC 3007), enable intercomparison. show complexity differential response depending on characteristics monitored scenario (PL). Overall, they exhibit uneven precision linearity significant bias, so their use digital systems without proper calibration can lead uncertain biased measurements. In sense, simple linear models are not able capture problem (non-linear systems) advanced schemes (e.g. based machine learning), applied “scenario by scenario” operating conditions as close possible final application, suggested achieve reliable measurements LCPMS.

Language: Английский

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Evaluating Indoor Air Quality in Residential Environments: A Study of PM2.5 and CO2 Dynamics Using Low-Cost Sensors

Environments, Journal Year: 2024, Volume and Issue: 11(11), P. 237 - 237

Published: Oct. 28, 2024

Indoor air quality (IAQ) poses a significant public health concern, and exposures to high levels of fine particulate matter (PM2.5) carbon dioxide (CO2) could have detrimental impacts. This study focused on assessing the indoor pollutants in residential house located town Mission, Hidalgo County, South Texas, USA. The PM2.5 CO2 were monitored indoors: kitchen bedroom. investigation also aimed elucidate effects household activities such as cooking human occupancy these pollutants. Low-cost sensors (LCSs) from TSI AirAssure™ used this study. They deployed within breathing zone at approximately 1.5 m above ground. Calibration low-cost against Federal Equivalent Method (FEM) instruments was undertaken using multiple linear regression method (MLR) model improve data accuracy. significantly influenced by activities, with peak concentrations reaching up 118.45 μg/m3. bedroom increased during occupant’s sleeping period, 1149.73 ppm. risk assessment assessed through toxicity potential (TP) calculations for concentrations. TP values 0.21 0.20 obtained bedroom, respectively. below hazard threshold (i.e., < 1). These low be attributed use electric stoves efficient ventilation systems. research highlights effectiveness continuous IAQ monitoring helps promote better awareness necessary interventions salubrious microenvironments.

Language: Английский

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Precision and Accuracy Analysis of PM2.5 Light-Scattering Sensor: Field and Laboratory Experiments

Atmosphere, Journal Year: 2025, Volume and Issue: 16(1), P. 76 - 76

Published: Jan. 12, 2025

The widely used low-cost particulate matter (PM) sensors in Thailand, such as the DustBoy, require performance improvements to ensure their data align with established standards set by US Environmental Protection Agency (US EPA). This study evaluates accuracy and reliability of a commonly PM2.5 monitoring device Thailand. A comparative analysis was conducted between DustBoy EPA’s Federal Reference Method (FRM) Equivalent (FEM). research involved both laboratory field testing, where DustBoy’s analyzed at various concentration levels environmental conditions. demonstrated that readings diverged from those standard monitors higher concentrations; however, positive correlation devices remained evident. Below 100 µg/m3, overestimated PM concentrations compared FRM but underestimated them FEM devices. At concentrations, showed significant overestimation, although trends aligned sensor also affected factors age model. Corrections were developed adjust match reference more closely, enhancing post-adjustment. These corrections will refine public reporting serve guidelines for other

Language: Английский

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Long-Term Evaluation of Mid-Cost Optical Particle Counters for PM2.5 Monitoring in an Underground Subway Station: Insights from a 15-Month Study

Chemosensors, Journal Year: 2025, Volume and Issue: 13(1), P. 25 - 25

Published: Jan. 20, 2025

A beta-ray attenuation monitor (BAM) is preferred as a reference instrument for monitoring particulate matter in the air due to its accuracy. However, BAM cannot be used large numbers spatial distribution because of high investment cost. Thus, mid-cost optical particle counter (OPC) an alternative solution widespread use. long-term performance with respect various environments should taken into account. In this study, six OPCs were measure PM2.5 concentrations at underground subway station and compared over 15 months. placed waiting space platforms compare determine PM2.5/PM10 ratios. The was installed on platform. Error analysis revealed significant discrepancy, normalized errors exceeding 30%, between 1-h average recorded by same location. contrast, 24-h measured location showed similar patterns, stronger correlations (r2 = 0.80–0.93) averages 0.63–0.83). ranged from 13.9% 21.2%, depending seasonal variations. These findings suggest that can effectively year without additional calibration, making them cost-effective option. addition, varied across different sampling spaces influenced Hence, when measuring mass concentration PM2.5, it essential consider PM characteristics seasons.

Language: Английский

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A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors

Sensors, Journal Year: 2025, Volume and Issue: 25(4), P. 1028 - 1028

Published: Feb. 9, 2025

Accurate air pollution monitoring is critical to understand and mitigate the impacts of on human health ecosystems. Due limited number geographical coverage advanced, highly accurate sensors pollutants, many low-cost low-accuracy have been deployed. Calibrating essential fill gap in sensor coverage. We systematically examined how different machine learning (ML) models open-source packages could help improve accuracy particulate matter (PM) 2.5 data collected by Purple Air sensors. Eleven ML five were examined. This systematic study found that both impacted accuracy, while random training/testing split ratio (e.g., 80/20 vs. 70/30) had minimal impact (0.745% difference for R2). Long Short-Term Memory (LSTM) trained RStudio TensorFlow excelled, with high R2 scores 0.856 0.857 low Root Mean Squared Errors (RMSEs) 4.25 µg/m3 4.26 µg/m3, respectively. However, LSTM may be too slow (1.5 h) or computation-intensive applications fast response requirements. Tree-boosted including XGBoost (0.7612, 5.377 µg/m3) Random Forest (RF) (0.7632, 5.366 offered good performance shorter training times (<1 min) suitable such applications. These findings suggest AI/ML models, particularly can effectively calibrate produce precise, localized quality data. research among most comprehensive studies pollutant calibration. also discussed limitations, applicability other sensors, explanations model performances. adapted enhance public risk assessments, support broader environmental initiatives, inform policy decisions.

Language: Английский

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Household PM2.5 in a South African urban and rural setting: a comparative analysis using low-cost sensors

Atmospheric Pollution Research, Journal Year: 2025, Volume and Issue: unknown, P. 102459 - 102459

Published: Feb. 1, 2025

Language: Английский

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Advancing Air Quality Monitoring Systems Towards Sustainable Green Development: Insight for Metropolitan Cities in Indonesia

Environmental and Sustainability Indicators, Journal Year: 2025, Volume and Issue: unknown, P. 100649 - 100649

Published: Feb. 1, 2025

Language: Английский

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Low-Cost Sensors for Atmospheric NO2 Measurement: A review

Environmental Pollution, Journal Year: 2025, Volume and Issue: unknown, P. 126418 - 126418

Published: May 1, 2025

Language: Английский

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A paradigm shift: Low-cost sensors for effective air quality monitoring and management in developing countries

Environment International, Journal Year: 2025, Volume and Issue: 200, P. 109521 - 109521

Published: May 10, 2025

This study gives an overview of the air quality monitoring challenges faced by South Asian countries, with a specific focus on Pakistan, and explores potential application low-cost sensors (LCS) to address these issues. Currently, 89% 4.2 million premature global deaths attributable ambient pollution occur in low- middle-income underscoring urgent need for improved abatement measures. In result significant public health economic consequences due institutional financial constraints, limited data availability, transboundary pollution. The situation is exacerbated absence effective regulatory network. proposes shift establishing hybrid network that integrates conventional instruments LCS. current PM2.5 scenario major Pakistani cities analyzed using from 61 LCSs installed nationwide, one TSI BlueSky sensor validated against BAM (Beta Attenuation Monitor) Chakwal. Results show unadjusted values demonstrate strong correlation (Pearson 0.85) reference instrument, acceptable error margins (NRMSE NMAE 8% 6%, respectively), indicating sensor's reliable monitoring. Though adjusted aligned better, analysis focuses broader applicability. These findings suggest combining advanced analytics can serve as technically economically viable solution accurate management Pakistan other developing countries facing similar high concentrations. paradigm enhances capabilities supports informed policy-making initiatives.

Language: Английский

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