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Low-cost gas and particulate matter 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 colocation data collected at a regulatory site in North Carolina, USA. We compare the performance of five computational modeling approaches to calibrate the electrochemical gas sensors: k -nearest neighbors (k NN) hybrid, random forest (RF) hybrid, high-dimensional model representation (HDMR), multilinear regression (MLR), and quadratic regression (QR). For the CO, O x , NO, and NO 2 sensors, we found that k NN hybrid models returned the highest coefficients of determination and lowest error metrics when validated. Hybrid models were also the most transferable approach when applied to deployment data collected in Malawi. We compared k NN hybrid calibrated CO observations from two regions in Malawi to remote sensing data and found qualitative agreement in spatial and annual trends. However, ARISense monthly mean surface observations were 2 to 4 times higher than the remote sensing data, partly due to proximity to residential biomass 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 colocation 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 residential biomass combustion. We did not find obvious evidence of systematic sensor performance decay after the 1-year deployment to Malawi. Data recovery (30 %–80 %) varied by sensor and season and was limited by insufficient power and access to resources at the remote deployment sites. Future low-cost sensor deployments to rural, low-income settings would benefit from adaptable power systems, standardized sensor calibration methodologies, and increased regional regulatory-grade monitoring infrastructure. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Low-cost particulate mass sensors provide opportunities to assess air quality at unprecedented spatial and temporal resolutions. Established traditional monitoring networks have limited spatial resolution and are simply absent in many major cities across sub-Saharan Africa (SSA). Satellites provide snapshots of regional air pollution but require ground-truthing. Low-cost monitors can supplement and extend data coverage from these sources worldwide, providing a better overall air quality picture. We investigate the utility of such a multi-source data integration approach using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system, and a linear conversion factor is developed to convert AOD to surface fine particulate matter mass concentration (as PM 2.5). With 10 or more ground monitors in Pittsburgh, there is a 2-fold reduction in surface PM 2.5 estimation mean absolute error compared to using only a single ground monitor. Second, we assess the ability of combined regional-scale satellite retrievals and local-scale low-cost sensor measurements to improve surface PM 2.5 estimation at several urban sites in SSA. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement in surface PM 2.5 estimation accuracy with respect to using low-cost ground monitoring data alone. A linear AOD-to-surface-PM 2.5 conversion factor developed in Kigali, Rwanda, did not generalize well to other parts of SSA and varied seasonally for the same location, emphasizing the need for ongoing and localized ground-based monitoring, which can be facilitated by low-cost sensors. Overall, we find that combining ground-based low-cost sensor and satellite data, even without including additional meteorological or land use information, can improve and expand spatiotemporal air quality data coverage, especially in data-sparse regions. [ABSTRACT FROM AUTHOR]

Low-cost particulate mass sensors provide opportunities to assess air quality at unprecedented spatial and temporal resolutions. Established traditional monitoring networks have limited spatial resolution and are frequently absent in less-developed countries (e.g. in sub-Saharan Africa). Satellites provide snapshots of regional air pollution, but require ground-truthing. Low-cost monitors can supplement and extend data coverage from these sources worldwide, providing a better overall air quality picture. We demonstrate such a multi-source data integration using two case studies. First, in Pittsburgh, Pennsylvania, both traditional monitoring and dense low-cost sensor networks are present, and are compared with satellite aerosol optical depth (AOD) data from NASA's MODIS system. We assess the performance of linear conversion factors for AOD to surface PM2.5 using both networks, and identify relative benefits provided by the denser low-cost sensor network. In particular, with 10 or more ground monitors in the city, there is a two-fold reduction in worst-case surface PM2.5 estimation mean absolute error compared to using only a single ground monitor. Second, in Rwanda, Malawi, and the Democratic Republic of the Congo, traditional ground-based monitoring is lacking and must be substituted with low-cost sensor data. Here, we assess the ability of regional-scale satellite retrievals and local-scale low-cost sensor measurements to complement each other. In Rwanda, we find that combining local ground monitoring information with satellite data provides a 40 % improvement (in terms of surface PM2.5 estimation accuracy) with respect to using ground monitoring data alone. Overall, we find that combining ground-based low-cost sensor and satellite data can improve and expand spatio-temporal air quality data coverage in both well-monitored and data-sparse regions. [ABSTRACT FROM AUTHOR]

Low-cost sensors for the measurement of fine particulate matter mass (PM2.5) enable dense networks to increase the spatial resolution of air quality monitoring. However, these sensors are affected by environmental factors such as temperature and humidity and their effects on ambient aerosol, which must be accounted for to improve the in-field accuracy of these sensors. We conducted long-term tests of two low-cost PM2.5 sensors: Met-One NPM and PurpleAir PA-II units. We found a high level of self-consistency within each sensor type after testing 25 NPM and 9 PurpleAir units. We developed two types of corrections for the low-cost sensor measurements to better match regulatory-grade data. The first correction accounts for aerosol hygroscopic growth using particle composition and corrects for particle mass below the optical sensor size cut-point by collocation with reference Beta Attenuation Monitors (BAM). A second, fully-empirical correction uses linear or quadratic functions of environmental variables based on the same collocation dataset. The two models yielded comparable improvements over raw measurements. Sensor performance was assessed for two use cases: improving community awareness of air quality with short-term semi-quantitative comparisons of sites and providing long-term reasonably quantitative information for health impact studies. For the short-term case, both sensors provided reasonably accurate concentration information (mean absolute error of ∼4 µg/m3) in near-real time. For the long-term case, tested using year-long collocations at one urban background and one near-source site, error in the annual average was reduced below 1 µg/m3. Hence, these sensors can supplement sparse networks of regulatory-grade instruments, perform high-density neighborhood-scale monitoring, and be used to better understand spatial patterns and temporal air quality trends across urban areas. Copyright © 2019 American Association for Aerosol Research [ABSTRACT FROM AUTHOR]

Assessing the intracity spatial distribution and temporal variability in air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and hybrid random forest–linear regression models. Using data collected by almost 70 RAMP monitors over periods ranging up to 18 months, we recommend the use of limited quadratic regression calibration models for CO, neural network models for NO, and hybrid models for NO2 and O3 for any low-cost monitor using electrochemical sensors similar to those of the RAMP. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. Generalized models also transfer better when the RAMP is deployed to other locations. For long-term deployments, it is recommended that model performance be re-evaluated and new models developed periodically, due to the noticeable change in performance over periods of a year or more. This makes generalized calibration models even more useful since only a subset of deployed monitors are needed to build these new models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide. [ABSTRACT FROM AUTHOR]

Assessing the intra-city spatial distribution and temporal variability of air quality can be facilitated by a dense network of monitoring stations. However, the cost of implementing such a network can be prohibitive if traditional high-quality, expensive monitoring systems are used. To this end, the Real-time Affordable Multi-Pollutant (RAMP) monitor has been developed, which can measure up to five gases including the criteria pollutant gases carbon monoxide (CO), nitrogen dioxide (NO2), and ozone (O3), along with temperature and relative humidity. This study compares various algorithms to calibrate the RAMP measurements including linear and quadratic regression, clustering, neural networks, Gaussian processes, and random forests. Using data collected by more than sixty RAMP monitors over periods ranging up to eighteen months, it was found that quadratic regression models or a hybrid of random forest and linear models tend to be the most effective calibration models overall. In specific cases, other types of models can have comparable or even superior performance. Furthermore, generalized calibration models may be used instead of individual models with only a small reduction in overall performance. For long-term deployments, it is recommended that new models be developed each year, due to the noticeable change in performance when models for one year were used for processing data collected in the subsequent year. This makes annually-developed generalized calibration models even more useful since only a subset of deployed monitors are needed to build these models. These results will help guide future efforts in the calibration and use of low-cost sensor systems worldwide. [ABSTRACT FROM AUTHOR]

Low-cost sensors for major air pollutants like carbon monoxide (CO), nitrogen dioxide(NO2), ozone (O3), sulfur dioxide (SO2), and fine particulate mass (PM2.5) can be used tomap air pollution across a city with high spatial and temporal density. One such low-costsensor package is the Real-time Affordable Multi-Pollutant (RAMP) monitor, whichincorporates Alphasense electrochemical sensors for gases and a nephelometer forPM. Machine learning algorithms applied to the gas sensors have resulted in dataquality that matches US EPA guidelines for hotspot identification (precision andbias errors less than 30%) and supplemental monitoring (precision and bias errorsunder 20%) for NO2, CO, and O3. The optical nephelometers were collocated withregulatory-grade beta attenuation monitors (BAMs) and corrected to BAM-equivalentPM2.5 measurements using either a physical model that uses information aboutaerosol chemical composition or empirical fits; both methods were found to performsimilarly. The optical nephelometers also show promising performance for PM10in ongoing testing next to a TEOM in Paris. About 50 RAMPs were deployed inPittsburgh, Pennsylvania, USA for over a year and show that industrial facilities 5-20 kmaway from Pittsburgh can increase average PM2.5 by as much as 40% in some cityneighborhoods, while other parts of the city are relatively unaffected. RAMPs deployedin Kigali, Rwanda for over 16 months were used to determine the split betweenregional and local contributions to PM2.5. Local sources including traffic-related airpollution (TRAP) and domestic biofuel use could contribute as much as 50% ofPM2.5 in the wet seasons but less than 30% during the dry seasons. Kigali has alsoimplemented a "car-free Sunday" policy, which the RAMPs show reduces peak PM2.5concentrations by an average of 20%. We shall also present plans for high spatialdensity air quality monitoring in Paris starting in 2019 using stationary RAMPs andcomparisons with high-resolution chemical transport models through the "Make AirQuality Great Again" project funded by the "Make Our Planet Great Again" program. [ABSTRACT FROM AUTHOR]

Low-cost NO 2 sensors have been widely deployed for atmospheric sampling. While their initial performance has been characterized, few studies have examined their long-term degradation. This study focused on the performance of Alphasense low-cost NO 2 sensors (NO2-B42F and NO2-B43F) over 4 years (2016-2020). A total of 29 NO 2 sensors from 10 batches were collocated 78 times at two sites with reference instruments. Raw signals from "functional" NO 2 sensors correlated linearly with reference NO 2 concentrations. After long-term deployment, sensor raw signals started to deviate from reference NO 2 concentrations due to sensor aging, an accumulated effect after sensor unpacking. Several sensors eventually became "non-functional" as sensor raw signals showed no correlation with reference NO 2 concentrations. Sensor aging and non-functionality may be primarily caused by expiration of the ozone (O 3 ) scrubber built into these sensors so that sensors responded to both ambient NO 2 and O 3 . The influence of O 3 on sensor response is quantified through the permutation importance method. Most of the sensors are non-functional after approximately 200-400 days of deployment, and no sensor was functional after 400 days of deployment. This result agrees well with the estimated lifetime of the built-in ozone scrubbers considering the ambient ozone concentration in the Pittsburgh area where these sensors were deployed. To ensure reliable data quality in long-term field deployments, we recommend collocating NO 2 sensors with reference instruments regularly after 200-400 days of deployment to identify and replace non-functional sensors in a timely manner.