Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O&amp;lt;sub&amp;gt;3&amp;lt;/sub&amp;gt; and NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; concentrations in diverse environments

Sahu, Ravi; Nagal, Ayush; Dixit, Kuldeep Kumar; Unnibhavi, Harshavardhan; Mantravadi, Srikanth; Nair, Srijith K.; Simmhan, Yogesh; Mishra, Brijesh Kumar; Zele, Rajesh; Sutaria, Ronak; Motghare, Vidyanand Motiram; Kar, Purushottam; Tripathi, Sachchida Nand

doi:10.5194/amt-14-37-2021

Cited by 36 publications

(25 citation statements)

References 41 publications

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“…A regional, shared facility would enable periodic, regionally representative collocations without requiring every country to establish its own regulatory network. Recent research has improved our ability to synthesize data from networks of LCS through computational calibration solutions which minimize the need to transport and collocate each individual monitor separately and increase the spatiotemporal resolution beyond that of reference networks (Buehler et al, 2021;Malings et al, 2019a;Kelly et al, 2021;Considine et al, 2021;Sahu et al, 2021). Concurrently, policy-focused researchers are helping to bridge the gap between governments and AQ scientists by creating comprehensive frameworks which provide systematic procedures to establish AQ monitoring networks in low and middle income countries (Gulia et al, 2020;Pinder et al, 2019).…”

Section: Discussionmentioning

confidence: 99%

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Bittner

Cross²,

Hagan³

et al. 2021

Preprint

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Abstract. Low-cost gas and particulate sensor packages offer a compact, lightweight, and easily transportable solution to address global gaps in air quality (AQ) observations. However, regions that would benefit most from widespread deployment of low-cost AQ monitors often lack the reference grade equipment required to reliably calibrate and validate them. In this study, we explore approaches to calibrating and validating three integrated sensor packages before a 1-year deployment to rural Malawi using collocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modelling approaches to calibrate the electrochemical gas sensors: k-Nearest Neighbor (kNN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, Ox, NO, and NO2 sensors, we found that kNN hybrid models returned the highest coefficients of determination and lowest error metrics when validated; they also appeared to be the most transferable approach when applied to field data collected in Malawi. We compared calibrated CO observations to remote sensing data in two regions in Malawi and found qualitative agreement in spatial and annual trends. However, the monthly mean surface observations were 2 to 4 times higher than the remote sensing data, possibly due to proximity to small-scale combustion activity not resolved by satellite imaging. We also compared the performance of the integrated Alphasense OPC-N2 optical particle counter to a filter-corrected nephelometer using collocation data collected at one of our deployment sites in Malawi. We found the performance of the OPC-N2 varied widely with environmental conditions, with the worst performance associated with high relative humidity (RH > 70 %) conditions and influence from emissions from nearby biomass cookstoves. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi; however, overall data recovery was limited by insufficient power and access to technical resources at deployment sites. Future low-cost sensor deployments to rural Sub-Saharan Africa would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regulatory grade regional infrastructure.

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Section: Discussionmentioning

confidence: 99%

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Bittner

Cross²,

Hagan³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In contrast, statistical approaches generally apply data and use regression methods and time series theory to explain the correlation between historical and future data. These methods are also considered simpler and more efficient than knowledge-based deterministic methods [7].…”

Section: Introductionmentioning

confidence: 99%

A Simple Dendritic Neural Network Model-Based Approach for Daily PM2.5 Concentration Prediction

Song

Tang

et al. 2021

Electronics

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Air pollution in cities has a massive impact on human health, and an increase in fine particulate matter (PM2.5) concentrations is the main reason for air pollution. Due to the chaotic and intrinsic complexities of PM2.5 concentration time series, it is difficult to utilize traditional approaches to extract useful information from these data. Therefore, a neural model with a dendritic mechanism trained via the states of matter search algorithm (SDNN) is employed to conduct daily PM2.5 concentration forecasting. Primarily, the time delay and embedding dimensions are calculated via the mutual information-based method and false nearest neighbours approach to train the data, respectively. Then, the phase space reconstruction is performed to map the PM2.5 concentration time series into a high-dimensional space based on the obtained time delay and embedding dimensions. Finally, the SDNN is employed to forecast the PM2.5 concentration. The effectiveness of this approach is verified through extensive experimental evaluations, which collect six real-world datasets from recent years. To the best of our knowledge, this study is the first attempt to utilize a dendritic neural model to perform real-world air quality forecasting. The extensive experimental results demonstrate that the SDNN offers very competitive performance relative to the latest prediction techniques.

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“…In recent years, various start-ups have emerged, providing compact and affordable wireless PM 2.5 sensors. However, the measured data using the low cost sensors is less reliable than that of the CAAQMS [10], [11]. Therefore, it is an essential requirement to calibrate the LCSDs against the CAAQMS.…”

Section: Introductionmentioning

confidence: 99%

Domain adaptation based deep calibration of low-cost PM2.5 sensors

Kumar¹,

Arora²,

Tripathi³

et al. 2021

Preprint

Self Cite

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<div>Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.</div>

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Robust statistical calibration and characterization of portable low-cost air quality monitoring sensors to quantify real-time O<sub>3</sub> and NO<sub>2</sub> concentrations in diverse environments

Cited by 36 publications

References 41 publications

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

Performance Characterization of Low-cost Air Quality Sensors for Off-grid Deployment in Rural Malawi

A Simple Dendritic Neural Network Model-Based Approach for Daily PM2.5 Concentration Prediction

Domain adaptation based deep calibration of low-cost PM2.5 sensors

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