Low-Cost Air Quality Monitoring Tools: From Research to Practice (A Workshop Summary)

In May 2017, a two-day workshop was held in Los Angeles (California, U.S.A.) to gather practitioners who work with low-cost sensors used to make air quality measurements. The community of practice included individuals from academia, industry, non-profit groups, community-based organizations, and regulatory agencies. The group gathered to share knowledge developed from a variety of pilot projects in hopes of advancing the collective knowledge about how best to use low-cost air quality sensors. Panel discussion topics included: (1) best practices for deployment and calibration of low-cost sensor systems, (2) data standardization efforts and database design, (3) advances in sensor calibration, data management, and data analysis and visualization, and (4) lessons learned from research/community partnerships to encourage purposeful use of sensors and create change/action. Panel discussions summarized knowledge advances and project successes while also highlighting the questions, unresolved issues, and technological limitations that still remain within the low-cost air quality sensor arena.

Assessing the Utility of Low-Cost Particulate Matter Sensors over a 12-Week Period in the Cuyama Valley of California

The use of low-cost air quality sensors has proliferated among non-profits and citizen scientists, due to their portability, affordability, and ease of use. Researchers are examining the sensors for their potential use in a wide range of applications, including the examination of the spatial and temporal variability of particulate matter (PM). However, few studies have quantified the performance (e.g., accuracy, precision, and reliability) of the sensors under real-world conditions. This study examined the performance of two models of PM sensors, the AirBeam and the Alphasense Optical Particle Counter (OPC-N2), over a 12-week period in the Cuyama Valley of California, where PM concentrations are impacted by wind-blown dust events and regional transport. The sensor measurements were compared with observations from two well-characterized instruments: the GRIMM 11-R optical particle counter, and the Met One beta attenuation monitor (BAM). Both sensor models demonstrated a high degree of collocated precision (R² = 0.8-0.99), and a moderate degree of correlation against the reference instruments (R² = 0.6-0.76). Sensor measurements were influenced by the meteorological environment and the aerosol size distribution. Quantifying the performance of sensors in real-world conditions is a requisite step to ensuring that sensors will be used in ways commensurate with their data quality.

Impact of Outdoor Air Pollution on Indoor Air Quality in Low-Income Homes during Wildfire Seasons

Indoor and outdoor number concentrations of fine particulate matter (PM2.5), black carbon (BC), carbon monoxide (CO), and nitrogen dioxide (NO2) were monitored continuously for two to seven days in 28 low-income homes in Denver, Colorado, during the 2016 and 2017 wildfire seasons. In the absence of indoor sources, all outdoor pollutant concentrations were higher than indoors except for CO. Results showed that long-range wildfire plumes elevated median indoor PM2.5 concentrations by up to 4.6 times higher than outdoors. BC, CO, and NO2 mass concentrations were higher indoors in homes closer to roadways compared to those further away. Four of the homes with mechanical ventilation systems had 18% higher indoor/outdoor (I/O) ratios of PM2.5 and 4% higher I/O ratios of BC compared to other homes. Homes with exhaust stove hoods had PM2.5 I/O ratios 49% less than the homes with recirculating hoods and 55% less than the homes with no stove hoods installed. Homes with windows open for more than 12 hours a day during sampling had indoor BC 2.4 times higher than homes with windows closed. This study provides evidence that long-range wildfire plumes, road proximity, and occupant behavior have a combined effect on indoor air quality in low-income homes.

A Survey and Comparison of Low-Cost Sensing Technologies for Road Traffic Monitoring

This paper reviews low-cost vehicle and pedestrian detection methods and compares their accuracy. The main goal of this survey is to summarize the progress achieved to date and to help identify the sensing technologies that provide high detection accuracy and meet requirements related to cost and ease of installation. Special attention is paid to wireless battery-powered detectors of small dimensions that can be quickly and effortlessly installed alongside traffic lanes (on the side of a road or on a curb) without any additional supporting structures. The comparison of detection methods presented in this paper is based on results of experiments that were conducted with a variety of sensors in a wide range of configurations. During experiments various sensor sets were analyzed. It was shown that the detection accuracy can be significantly improved by fusing data from appropriately selected set of sensors. The experimental results reveal that accurate vehicle detection can be achieved by using sets of passive sensors. Application of active sensors was necessary to obtain satisfactory results in case of pedestrian detection.

Development of Low-Cost Air Quality Stations for Next Generation Monitoring Networks: Calibration and Validation of PM2.5 and PM10 Sensors

A low-cost air quality station has been developed for real-time monitoring of main atmospheric pollutants. Sensors for CO, CO₂, NO₂, O₃, VOC, PM2.5 and PM10 were integrated on an Arduino Shield compatible board. As concerns PM2.5 and PM10 sensors, the station underwent a laboratory calibration and later a field validation. Laboratory calibration has been carried out at the headquarters of CNR-IBIMET in Florence (Italy) against a TSI DustTrak reference instrument. A MATLAB procedure, implementing advanced mathematical techniques to detect possible complex non-linear relationships between sensor signals and reference data, has been developed and implemented to accomplish the laboratory calibration. Field validation has been performed across a full "heating season" (1 November 2016 to 15 April 2017) by co-locating the station at a road site in Florence where an official fixed air quality station was in operation. Both calibration and validation processes returned fine scores, in most cases better than those achieved for similar systems in the literature. During field validation, in particular, for PM2.5 and PM10 mean biases of 0.036 and 0.598 µg/m³, RMSE of 4.056 and 6.084 µg/m³, and R² of 0.909 and 0.957 were achieved, respectively. Robustness of the developed station, seamless deployed through a five and a half month outdoor campaign without registering sensor failures or drifts, is a further key point.

A Framework to Automate Assessment of Upper-Limb Motor Function Impairment: A Feasibility Study

Standard upper-limb motor function impairment assessments, such as the Fugl-Meyer Assessment (FMA), are a critical aspect of rehabilitation after neurological disorders. These assessments typically take a long time (about 30 min for the FMA) for a clinician to perform on a patient, which is a severe burden in a clinical environment. In this paper, we propose a framework for automating upper-limb motor assessments that uses low-cost sensors to collect movement data. The sensor data is then processed through a machine learning algorithm to determine a score for a patient’s upper-limb functionality. To demonstrate the feasibility of the proposed approach, we implemented a system based on the proposed framework that can automate most of the FMA. Our experiment shows that the system provides similar FMA scores to clinician scores, and reduces the time spent evaluating each patient by 82%. Moreover, the proposed framework can be used to implement customized tests or tests specified in other existing standard assessment methods.

Performance Evaluation and Community Application of Low-Cost Sensors for Ozone and Nitrogen Dioxide

This study reports on the performance of electrochemical-based low-cost sensors and their use in a community application. CairClip sensors were collocated with federal reference and equivalent methods and operated in a network of sites by citizen scientists (community members) in Houston, Texas and Denver, Colorado, under the umbrella of the NASA-led DISCOVER-AQ Earth Venture Mission. Measurements were focused on ozone (O₃) and nitrogen dioxide (NO₂). The performance evaluation showed that the CairClip O₃/NO₂ sensor provided a consistent measurement response to that of reference monitors (r² = 0.79 in Houston; r² = 0.72 in Denver) whereas the CairClip NO₂ sensor measurements showed no agreement to reference measurements. The CairClip O₃/NO₂ sensor data from the citizen science sites compared favorably to measurements at nearby reference monitoring sites. This study provides important information on data quality from low-cost sensor technologies and is one of few studies that reports sensor data collected directly by citizen scientists.

Validation of a Low-Cost Electromyography (EMG) System via a Commercial and Accurate EMG Device: Pilot Study

Electromyography (EMG) devices are well-suited for measuring the behaviour of muscles during an exercise or a task, and are widely used in many different research areas. Their disadvantage is that commercial systems are expensive. We designed a low-cost EMG system with enough accuracy and reliability to be used in a wide range of possible ways. The present article focuses on the validation of the low-cost system we designed, which is compared with a commercially available, accurate device. The evaluation was done by means of a set of experiments, in which volunteers performed isometric and dynamic exercises while EMG signals from the rectus femoris muscle were registered by both the proposed low-cost system and a commercial system simultaneously. Analysis and assessment of three indicators to estimate the similarity between both signals were developed. These indicated a very good result, with spearman’s correlation averaging above 0.60, the energy ratio close to the 80% and the linear correlation coefficient approximating 100%. The agreement between both systems (custom and commercial) is excellent, although there are also some limitations, such as the delay of the signal (<1 s) and noise due to the hardware and assembly in the proposed system.

Spatial Modeling of Daily PM2.5, NO2, and CO Concentrations Measured by a Low-Cost Sensor Network: Comparison of Linear, Machine Learning, and Hybrid Land Use Models

Previous studies have characterized spatial patterns of pollution with land use regression (LUR) models from distributed passive or filter samplers at low temporal resolution. Large-scale deployment of low-cost sensors (LCS), which typically sample in real time, may enable time-resolved or real-time modeling of concentration surfaces. The aim of this study was to develop spatiotemporal models of PM_2.5, NO₂, and CO using an LCS network in Pittsburgh, Pennsylvania. We modeled daily average concentrations in August 2016-December 2017 across 50 sites. Land use variables included 13 time-independent (e.g., elevation) and time-dependent (e.g., temperature) predictors. We examined two models: LUR and a machine-learning-enabled land use model (land use random forest, LURF). The LURF models outperformed LUR models, with increase in the average externally cross-validated R² of 0.10-0.19. Using wavelet decomposition to separate short-lived events from the regional background, we also created time-decomposed LUR and LURF models. Compared to the standard model, this resulted in improvement in R² of up to 0.14. The time-decomposed models were more influenced by spatial parameters. Mapping our models across Allegheny County, we observed that time-decomposed LURF models created robust PM_2.5 predictions, suggesting that this approach may improve our ability to map air pollutants at high spatiotemporal resolution.

On the Feasibility of Low-Cost Wearable Sensors for Multi-Modal Biometric Verification

Biometric systems designed on wearable technology have substantial differences from traditional biometric systems. Due to their wearable nature, they generally capture noisier signals and can only be trained with signals belonging to the device user (biometric verification). In this article, we assess the feasibility of using low-cost wearable sensors—photoplethysmogram (PPG), electrocardiogram (ECG), accelerometer (ACC), and galvanic skin response (GSR)—for biometric verification. We present a prototype, built with low-cost wearable sensors, that was used to capture data from 25 subjects while seated (at resting state), walking, and seated (after a gentle stroll). We used this data to evaluate how the different combinations of signals affected the biometric verification process. Our results showed that the low-cost sensors currently being embedded in many fitness bands and smart-watches can be combined to enable biometric verification. We report and compare the results obtained by all tested configurations. Our best configuration, which uses ECG, PPG and GSR, obtained 0.99 area under the curve and 0.02 equal error rate with only 60 s of training data. We have made our dataset public so that our work can be compared with proposals developed by other researchers.

Low Cost Sensor Networks: How Do We Know the Data Are Reliable?

The plausibility of data from networks of low-cost measurement devices is a growing and important contentious issue. Informal networks of low-cost devices have particularly come to prominence for air quality monitoring. The contentious point is the believability of data without regular on-site calibration, since this is a specialist task and the costs very quickly become much larger than the cost of installation in the first place. This Sensor Issues suggests that approaches to the problem that involve appropriate use of independent information have the potential to resolve the contention. Ideas are illustrated particularly with reference to low-cost sensor networks for air quality measurement.

Establishing A Sustainable Low-Cost Air Quality Monitoring Setup: A Survey of the State-of-the-Art

Low-cost sensors (LCS) are becoming popular for air quality monitoring (AQM). They promise high spatial and temporal resolutions at low-cost. In addition, citizen science applications such as personal exposure monitoring can be implemented effortlessly. However, the reliability of the data is questionable due to various error sources involved in the LCS measurement. Furthermore, sensor performance drift over time is another issue. Hence, the adoption of LCS by regulatory agencies is still evolving. Several studies have been conducted to improve the performance of low-cost sensors. This article summarizes the existing studies on the state-of-the-art of LCS for AQM. We conceptualize a step by step procedure to establish a sustainable AQM setup with LCS that can produce reliable data. The selection of sensors, calibration and evaluation, hardware setup, evaluation metrics and inferences, and end user-specific applications are various stages in the LCS-based AQM setup we propose. We present a critical analysis at every step of the AQM setup to obtain reliable data from the low-cost measurement. Finally, we conclude this study with future scope to improve the availability of air quality data.

Laboratory Evaluation of Low-Cost Optical Particle Counters for Environmental and Occupational Exposures

Low-cost optical particle counters effectively measure particulate matter (PM) mass concentrations once calibrated. Sensor calibration can be established by deriving a linear regression model by performing side-by-side measurements with a reference instrument. However, calibration differences between environmental and occupational settings have not been demonstrated. This study evaluated four commercially available, low-cost PM sensors (OPC-N3, SPS30, AirBeam2, and PMS A003) in both settings. The mass concentrations of three aerosols (salt, Arizona road dust, and Poly-alpha-olefin-4 oil) were measured and compared with a reference instrument. OPC-N3 and SPS30 were highly correlated (r = 0.99) with the reference instrument for all aerosol types in environmental settings. In occupational settings, SPS30, AirBeam2, and PMS A003 exhibited high correlation (>0.96), but the OPC-N3 correlation varied (r = 0.88–1.00). Response significantly (p < 0.001) varied between environmental and occupational settings for most particle sizes and aerosol types. Biases varied by particle size and aerosol type. SPS30 and OPC-N3 exhibited low bias for environmental settings, but all of the sensors showed a high bias for occupational settings. For intra-instrumental precision, SPS30 exhibited high precision for salt for both settings compared to the other low-cost sensors and aerosol types. These findings suggest that SPS30 and OPC-N3 can provide a reasonable estimate of PM mass concentrations if calibrated differently for environmental and occupational settings using site-specific calibration factors.

Youth Engaged Participatory Air Monitoring: A 'Day in the Life' in Urban Environmental Justice Communities

Air pollution in Southern California does not impact all communities equally; communities of color are disproportionately burdened by poor air quality and more likely to live near industrial facilities and freeways. Government regulatory monitors do not have the spatial resolution to provide air quality information at the neighborhood or personal scale. We describe the A Day in the Life program, an approach to participatory air monitoring that engages youth in collecting data that they can then analyze and use to take action. Academics partnered with Los Angeles-based youth environmental justice organizations to combine personal air monitoring, participatory science, and digital storytelling to build capacity to address local air quality issues. Eighteen youth participants from four different neighborhoods wore portable personal PM2.5 (fine particles <2.5 µm in diameter) monitors for a day in each of their respective communities, documenting and mapping their exposure to PM2.5 during their daily routine. Air monitoring was coupled with photography and videos to document what they experienced over the course of their day. The PM2.5 exposure during the day for participants averaged 10.7 µg/m3, although the range stretched from <1 to 180 µg/m3. One-third of all measurements were taken <300 m from a freeway. Overall, we demonstrate a method to increase local youth-centered understanding of personal exposures, pollution sources, and vulnerability to air quality.

A Low-Cost, Multi-Sensor System to Monitor Temporary Stream Dynamics in Mountainous Headwater Catchments

While temporary streams account for more than half of the global discharge, high spatiotemporal resolution data on the three main hydrological states (dry streambed, standing water, and flowing water) of temporary stream remains sparse. This study presents a low-cost, multi-sensor system to monitor the hydrological state of temporary streams in mountainous headwaters. The monitoring system consists of an Arduino microcontroller board combined with an SD-card data logger shield, and four sensors: an electrical resistance (ER) sensor, temperature sensor, float switch sensor, and flow sensor. The monitoring system was tested in a small mountainous headwater catchment, where it was installed on multiple locations in the stream network, during two field seasons (2016 and 2017). Time-lapse cameras were installed at all monitoring system locations to evaluate the sensor performance. The field tests showed that the monitoring system was power efficient (running for nine months on four AA batteries at a five-minute logging interval) and able to reliably log data (<1% failed data logs). Of the sensors, the ER sensor (99.9% correct state data and 90.9% correctly timed state changes) and flow sensor (99.9% correct state data and 90.5% correctly timed state changes) performed best (2017 performance results). A setup of the monitoring system with these sensors can provide long-term, high spatiotemporal resolution data on the hydrological state of temporary streams, which will help to improve our understanding of the hydrological functioning of these important systems.

Community-Based Health and Exposure Study around Urban Oil Developments in South Los Angeles

Oilfield-adjacent communities often report symptoms such as headaches and/or asthma. Yet, little data exists on health experiences and exposures in urban environments with oil and gas development. In partnership with Promotoras de Salud (community health workers), we gathered household surveys nearby two oil production sites in Los Angeles. We tested the capacity of low-cost sensors for localized exposure estimates. Bilingual surveys of 205 randomly sampled residences were collected within two 1500 ft. buffer areas (West Adams and University Park) surrounding oil development sites. We used a one-sample proportion test, comparing overall rates from the California Health Interview Survey (CHIS) of Service Planning Area 6 (SPA6) and Los Angeles County for variables of interest such as asthma. Field calibrated low-cost sensors recorded methane emissions. Physician diagnosed asthma rates were reported to be higher within both buffers than in SPA6 or LA County. Asthma prevalence in West Adams but not University Park was significantly higher than in Los Angeles County. Respondents with diagnosed asthma reported rates of emergency room visits in the previous 12 months similar to SPA6. 45% of respondents were unaware of oil development; 63% of residents would not know how to contact local regulatory authorities. Residents often seek information about their health and site-related activities. Low-cost sensors may be useful in highlighting differences between sites or recording larger emission events and can provide localized data alongside resident-reported symptoms. Regulatory officials should help clarify information to the community on methods for reporting health symptoms. Our community-based participatory research (CBPR) partnership supports efforts to answer community questions as residents seek a safety buffer between sensitive land uses and active oil development.

Disposable and Low-Cost Colorimetric Sensors for Environmental Analysis

Environmental contamination affects human health and reduces the quality of life. Therefore, the monitoring of water and air quality is important, ensuring that all areas are acquiescent with the current legislation. Colorimetric sensors deliver quick, naked-eye detection, low-cost, and adequate determination of environmental analytes. In particular, disposable sensors are cheap and easy-to-use devices for single-shot measurements. Due to increasing requests for in situ analysis or resource-limited zones, disposable sensors’ development has increased. This review provides a brief insight into low-cost and disposable colorimetric sensors currently used for environmental analysis. The advantages and disadvantages of different colorimetric devices for environmental analysis are discussed.

Quantifying Neighborhood-Scale Spatial Variations of Ozone at Open Space and Urban Sites in Boulder, Colorado Using Low-Cost Sensor Technology

Recent advances in air pollution sensors have led to a new wave of low-cost measurement systems that can be deployed in dense networks to capture small-scale spatio-temporal variations in ozone, a pollutant known to cause negative human health impacts. This study deployed a network of seven low-cost ozone metal oxide sensor systems (UPods) in both an open space and an urban location in Boulder, Colorado during June and July of 2015, to quantify ozone variations on spatial scales ranging from 12 m between UPods to 6.7 km between open space and urban measurement sites with a measurement uncertainty of ~5 ppb. The results showed spatial variability of ozone at both deployment sites, with the largest differences between UPod measurements occurring during the afternoons. The peak median hourly difference between UPods was 6 ppb at 1:00 p.m. at the open space site, and 11 ppb at 4:00 p.m. at the urban site. Overall, the urban ozone measurements were higher than in the open space measurements. This study evaluates the effectiveness of using low-cost sensors to capture microscale spatial and temporal variation of ozone; additionally, it highlights the importance of field calibrations and measurement uncertainty quantification when deploying low-cost sensors.

Calibration Method for Particulate Matter Low-Cost Sensors Used in Ambient Air Quality Monitoring and Research

Over the last decade, manufacturers have come forth with cost-effective sensors for measuring ambient and indoor particulate matter concentration. What these sensors make up for in cost efficiency, they lack in reliability of the measured data due to their sensitivities to temperature and relative humidity. These weaknesses are especially evident when it comes to portable or mobile measurement setups. In recent years many studies have been conducted to assess the possibilities and limitations of these sensors, however mostly restricted to stationary measurements. This study reviews the published literature until 2020 on cost-effective sensors, summarizes the recommendations of experts in the field based on their experiences, and outlines the quantile-mapping methodology to calibrate low-cost sensors in mobile applications. Compared to the commonly used linear regression method, quantile mapping retains the spatial characteristics of the measurements, although a common correction factor cannot be determined. We conclude that quantile mapping can be a useful calibration methodology for mobile measurements given a well-elaborated measurement plan assures providing the necessary data.

How to Get the Best from Low-Cost Particulate Matter Sensors: Guidelines and Practical Recommendations

Low-cost sensors based on the optical particle counter (OPC) are increasingly being used to collect particulate matter (PM) data at high space and time resolution. In spite of their huge explorative potential, practical guidelines and recommendations for their use are still limited. In this work, we outline a few best practices for the optimal use of PM low-cost sensors based on the results of an intensive field campaign performed in Bologna (44°30' N, 11°21' E; Italy) under different weather conditions. Briefly, the performances of a series of sensors were evaluated against a calibrated mainstream OPC with a heated inlet, using a robust approach based on a suite of statistical indexes capable of evaluating both correlations and biases in respect to the reference sensor. Our results show that the sensor performance is sensibly affected by both time resolution and weather with biases maximized at high time resolution and high relative humidity. Optimization of PM data obtained is therefore achievable by lowering time resolution and applying suitable correction factors for hygroscopic growth based on the inherent particle size distribution.

Low-Cost Sensors for Urban Noise Monitoring Networks-A Literature Review

Noise pollution reduction in the environment is a major challenge from a societal and health point of view. To implement strategies to improve sound environments, experts need information on existing noise. The first source of information is based on the elaboration of noise maps using software, but with limitations on the realism of the maps obtained, due to numerous calculation assumptions. The second is based on the use of measured data, in particular through professional measurement observatories, but in limited numbers for practical and financial reasons. More recently, numerous technical developments, such as the miniaturization of electronic components, the accessibility of low-cost computing processors and the improved performance of electric batteries, have opened up new prospects for the deployment of low-cost sensor networks for the assessment of sound environments. Over the past fifteen years, the literature has presented numerous experiments in this field, ranging from proof of concept to operational implementation. The purpose of this article is firstly to review the literature, and secondly, to identify the expected technical characteristics of the sensors to address the problem of noise pollution assessment. Lastly, the article will also put forward the challenges that are needed to respond to a massive deployment of low-cost noise sensors.

Features and Practicability of the Next-Generation Sensors and Monitors for Exposure Assessment to Airborne Pollutants: A Systematic Review

In the last years, the issue of exposure assessment of airborne pollutants has been on the rise, both in the environmental and occupational fields. Increasingly severe national and international air quality standards, indoor air guidance values, and exposure limit values have been developed to protect the health of the general population and workers; this issue required a significant and continuous improvement in monitoring technologies to allow the execution of proper exposure assessment studies. One of the most interesting aspects in this field is the development of the "next-generation" of airborne pollutants monitors and sensors (NGMS). The principal aim of this review is to analyze and characterize the state of the art and of NGMS and their practical applications in exposure assessment studies. A systematic review of the literature was performed analyzing outcomes from three different databases (Scopus, PubMed, Isi Web of Knowledge); a total of 67 scientific papers were analyzed. The reviewing process was conducting systematically with the aim to extrapolate information about the specifications, technologies, and applicability of NGMSs in both environmental and occupational exposure assessment. The principal results of this review show that the use of NGMSs is becoming increasingly common in the scientific community for both environmental and occupational exposure assessment. The available studies outlined that NGMSs cannot be used as reference instrumentation in air monitoring for regulatory purposes, but at the same time, they can be easily adapted to more specific applications, improving exposure assessment studies in terms of spatiotemporal resolution, wearability, and adaptability to different types of projects and applications. Nevertheless, improvements needed to further enhance NGMSs performances and allow their wider use in the field of exposure assessment are also discussed.

Deployment, Calibration, and Cross-Validation of Low-Cost Electrochemical Sensors for Carbon Monoxide, Nitrogen Oxides, and Ozone for an Epidemiological Study

We designed and built a network of monitors for ambient air pollution equipped with low-cost gas sensors to be used to supplement regulatory agency monitoring for exposure assessment within a large epidemiological study. This paper describes the development of a series of hourly and daily field calibration models for Alphasense sensors for carbon monoxide (CO; CO-B4), nitric oxide (NO; NO-B4), nitrogen dioxide (NO₂; NO2-B43F), and oxidizing gases (OX-B431)—which refers to ozone (O₃) and NO₂. The monitor network was deployed in the Puget Sound region of Washington, USA, from May 2017 to March 2019. Monitors were rotated throughout the region, including at two Puget Sound Clean Air Agency monitoring sites for calibration purposes, and over 100 residences, including the homes of epidemiological study participants, with the goal of improving long-term pollutant exposure predictions at participant locations. Calibration models improved when accounting for individual sensor performance, ambient temperature and humidity, and concentrations of co-pollutants as measured by other low-cost sensors in the monitors. Predictions from the final daily models for CO and NO performed the best considering agreement with regulatory monitors in cross-validated root-mean-square error (RMSE) and R² measures (CO: RMSE = 18 ppb, R² = 0.97; NO: RMSE = 2 ppb, R² = 0.97). Performance measures for NO₂ and O₃ were somewhat lower (NO₂: RMSE = 3 ppb, R² = 0.79; O₃: RMSE = 4 ppb, R² = 0.81). These high levels of calibration performance add confidence that low-cost sensor measurements collected at the homes of epidemiological study participants can be integrated into spatiotemporal models of pollutant concentrations, improving exposure assessment for epidemiological inference.

Evaluation of Two Low-Cost Optical Particle Counters for the Measurement of Ambient Aerosol Scattering Coefficient and Ångström Exponent

The aerosol scattering coefficient and Ångström exponent (AE) are important parameters in the understanding of aerosol optical properties and aerosol direct effect. These parameters are usually measured by a nephelometer network which is under-represented geographically; however, a rapid growth of air-pollution monitoring, using low-cost particle sensors, may extend observation networks. This paper presents the results of co-located measurements of aerosol optical properties, such as the aerosol scattering coefficient and the scattering AE, using low-cost sensors and using a scientific-grade polar Aurora 4000 nephelometer. A high Pearson correlation coefficient (0.94-0.96) between the low-cost particulate matter (PM) mass concentration and the aerosol scattering coefficient was found. For the PM10 mass concentration, the aerosol scattering coefficient relation is linear for the Dfrobot SEN0177 sensor and non-linear for the Alphasense OPC-N2 device. After regression analyses, both low-cost instruments provided the aerosol scattering coefficient with a similar mean square error difference (RMSE) of about 20 Mm-1, which corresponds to about 27% of the mean aerosol scattering coefficient. The relative uncertainty is independent of the pollution level. In addition, the ratio of aerosol number concentration between different bins showed a significant statistical (95% of confidence level) correlation with the scattering AE. For the SEN0177, the ratio of the particle number in bin 1 (radius of 0.15-0.25 µm) to bin 4 (radius of 1.25-2.5 µm) was a linear function of the scattering AE, with a Pearson correlation coefficient of 0.74. In the case of OPC-N2, the best correlation (r = 0.66) was found for the ratio between bin 1 (radius of 0.19-0.27 µm) and bin 2 (radius of 0.27-0.39 µm). Comparisons of an estimated scattering AE from a low-cost sensor with Aurora 4000 are given with the RMSE of 0.23-0.24, which corresponds to 16-19%. In addition, a three-year (2016-2019) observation by SEN0177 indicates that this sensor can be used to determine an annual cycle as well as a short-term variability.

Development of a Low-Cost System for the Accurate Measurement of Structural Vibrations

Nowadays, engineers are widely using accelerometers to record the vibration of structures for structural verification purposes. The main obstacle for using these data acquisition systems is their high cost, which limits its use to unique structures with a relatively high structural health monitoring budget. In this paper, a Cost Hyper-Efficient Arduino Product (CHEAP) has been developed to accurately measure structural accelerations. CHEAP is a system that is composed of five low-cost accelerometers that are connected to an Arduino microcontroller as their data acquisition system. Test results show that CHEAP not only has a significantly lower price (14 times cheaper in the worst-case scenario) compared with other systems used for comparison but also shows better accuracy on low frequencies for low acceleration amplitudes. Moreover, the final output results of Fast Fourier Transformation (FFT) assessments showed a better observable resolution for CHEAP than the studied control systems.