FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2744057969> ?p ?o ?g. }

• W2744057969 abstract "Abstract. Low-cost sensing strategies hold the promise of denser air quality monitoring networks, which could significantly improve our understanding of personal air pollution exposure. Additionally, low-cost air quality sensors could be deployed to areas where limited monitoring exists. However, low-cost sensors are frequently sensitive to environmental conditions and pollutant cross-sensitivities, which have historically been poorly addressed by laboratory calibrations, limiting their utility for monitoring. In this study, we investigated different calibration models for the Real-time Affordable Multi-Pollutant (RAMP) sensor package, which measures CO, NO2, O3, and CO2. We explored three methods: 1) laboratory univariate linear regression, 2) empirical multivariate linear regression and 3) machine-learning based calibration models using random forests (RF). Calibration models were developed for 19 RAMP monitors using training and testing windows spanning August 2016 through February 2017 in Pittsburgh, PA. The random forest models matched (CO) or significantly outperformed (NO2, CO2, O3) the other calibration models, and their accuracy and precision was robust over time for testing windows of up to 16 weeks. Following calibration, average mean absolute error on the testing dataset from the random forest models was 38 ppb for CO (14 % relative error), 10 ppm for CO2 (2 % relative error), 3.5 ppb for NO2 (29 % relative error) and 3.4 ppb for O3 (15 % relative error), and Pearson r versus the reference monitors exceeded 0.8 for most units. Model performance is explored in detail, including a quantification of model variable importance, accuracy across different concentration ranges, and performance in a range of monitoring contexts including the National Ambient Air Quality Standards (NAAQS), and the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. A key strength of the RF approach is that it accounts for pollutant cross sensitivities. This highlights the importance of developing multipollutant sensor packages (as opposed to single pollutant monitors); we determined this is especially critical for NO2 and CO2. The evaluation reveals that only the RF-calibrated sensors meet the US EPA Air Sensors Guidebook recommendations of minimum data quality for personal exposure measurement. We also demonstrate that the RF model calibrated sensors could detect differences in NO2 concentrations between a near-road site and a suburban site less than 1.5 km away. From this study, we conclude that combining RF models with the RAMP monitors appears to be a very promising approach to address the poor performance that has plagued low cost air quality sensors." @default.
• W2744057969 created "2017-08-17" @default.
• W2744057969 creator A5001316968 @default.
• W2744057969 creator A5010340252 @default.
• W2744057969 creator A5023524664 @default.
• W2744057969 creator A5026917260 @default.
• W2744057969 creator A5039246952 @default.
• W2744057969 creator A5052142335 @default.
• W2744057969 creator A5055528670 @default.
• W2744057969 creator A5065573669 @default.
• W2744057969 date "2017-08-09" @default.
• W2744057969 modified "2023-09-24" @default.
• W2744057969 title "Closing the gap on lower cost air quality monitoring: machine learning calibration models to improve low-cost sensor performance" @default.
• W2744057969 doi "https://doi.org/10.5194/amt-2017-260" @default.
• W2744057969 hasPublicationYear "2017" @default.
• W2744057969 type Work @default.
• W2744057969 sameAs 2744057969 @default.
• W2744057969 citedByCount "16" @default.
• W2744057969 countsByYear W27440579692017 @default.
• W2744057969 countsByYear W27440579692018 @default.
• W2744057969 countsByYear W27440579692019 @default.
• W2744057969 countsByYear W27440579692021 @default.
• W2744057969 countsByYear W27440579692022 @default.
• W2744057969 countsByYear W27440579692023 @default.
• W2744057969 crossrefType "posted-content" @default.
• W2744057969 hasAuthorship W2744057969A5001316968 @default.
• W2744057969 hasAuthorship W2744057969A5010340252 @default.
• W2744057969 hasAuthorship W2744057969A5023524664 @default.
• W2744057969 hasAuthorship W2744057969A5026917260 @default.
• W2744057969 hasAuthorship W2744057969A5039246952 @default.
• W2744057969 hasAuthorship W2744057969A5052142335 @default.
• W2744057969 hasAuthorship W2744057969A5055528670 @default.
• W2744057969 hasAuthorship W2744057969A5065573669 @default.
• W2744057969 hasBestOaLocation W27440579692 @default.
• W2744057969 hasConcept C105795698 @default.
• W2744057969 hasConcept C119857082 @default.
• W2744057969 hasConcept C121332964 @default.
• W2744057969 hasConcept C122383733 @default.
• W2744057969 hasConcept C126314574 @default.
• W2744057969 hasConcept C153294291 @default.
• W2744057969 hasConcept C161584116 @default.
• W2744057969 hasConcept C165838908 @default.
• W2744057969 hasConcept C169258074 @default.
• W2744057969 hasConcept C199163554 @default.
• W2744057969 hasConcept C33923547 @default.
• W2744057969 hasConcept C39432304 @default.
• W2744057969 hasConcept C41008148 @default.
• W2744057969 hasConcept C48921125 @default.
• W2744057969 hasConceptScore W2744057969C105795698 @default.
• W2744057969 hasConceptScore W2744057969C119857082 @default.
• W2744057969 hasConceptScore W2744057969C121332964 @default.
• W2744057969 hasConceptScore W2744057969C122383733 @default.
• W2744057969 hasConceptScore W2744057969C126314574 @default.
• W2744057969 hasConceptScore W2744057969C153294291 @default.
• W2744057969 hasConceptScore W2744057969C161584116 @default.
• W2744057969 hasConceptScore W2744057969C165838908 @default.
• W2744057969 hasConceptScore W2744057969C169258074 @default.
• W2744057969 hasConceptScore W2744057969C199163554 @default.
• W2744057969 hasConceptScore W2744057969C33923547 @default.
• W2744057969 hasConceptScore W2744057969C39432304 @default.
• W2744057969 hasConceptScore W2744057969C41008148 @default.
• W2744057969 hasConceptScore W2744057969C48921125 @default.
• W2744057969 hasFunder F4320306107 @default.
• W2744057969 hasFunder F4320306200 @default.
• W2744057969 hasFunder F4320334593 @default.
• W2744057969 hasLocation W27440579691 @default.
• W2744057969 hasLocation W27440579692 @default.
• W2744057969 hasOpenAccess W2744057969 @default.
• W2744057969 hasPrimaryLocation W27440579691 @default.
• W2744057969 hasRelatedWork W1975123081 @default.
• W2744057969 hasRelatedWork W2071841037 @default.
• W2744057969 hasRelatedWork W2089610442 @default.
• W2744057969 hasRelatedWork W2267219236 @default.
• W2744057969 hasRelatedWork W2295903912 @default.
• W2744057969 hasRelatedWork W2744057969 @default.
• W2744057969 hasRelatedWork W2784031884 @default.
• W2744057969 hasRelatedWork W4220961233 @default.
• W2744057969 hasRelatedWork W4255876030 @default.
• W2744057969 hasRelatedWork W4310513977 @default.
• W2744057969 isParatext "false" @default.
• W2744057969 isRetracted "false" @default.
• W2744057969 magId "2744057969" @default.
• W2744057969 workType "article" @default.