Low-cost Air Quality Sensors Research Articles

Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring

During the past decade, substantial efforts have been dedicated to advancing cost-effective sensor platforms for air quality monitoring. Calibration is a crucial step in ensuring that the data quality of low-cost air monitoring systems meets established standards. Recent research has extensively evaluated low-cost air monitoring platforms against the data quality objectives set by the European Directive. This paper introduces a novel calibration model for low-cost air quality sensors, significantly improving the accuracy of the measurement of nitric oxide (NO), sulfur dioxide (SO2), and particulate matter (PM1, PM2.5, and PM10), while promoting accessibility and adaptability in environmental monitoring technologies. This study extends the evaluation of a developed platform capable of integrating a diverse array of sensors to measure up to 12 parameters. Our proposed models demonstrate a significant improvement, achieving a 60% better accuracy for SO2. Additionally, these models deliver similar results for PMx and NO or exceed those of state of the art research. The calibration methodology meets the requirements of the Data Quality Objectives (DQO) for all monitored parameters and also achieves indicative levels for PM parameters.

Exploring spatiotemporal dynamics, seasonality, and time-of-day trends of PM2.5 pollution with a low-cost sensor network: Insights from classic and spatially explicit Markov chains

Fine particulate matter (PM2.5) is a major health and environmental concern, with significant spatiotemporal dynamics in urban areas. Low-cost air quality sensor (LCS) networks offer a paradigm-changing opportunity to acquire high spatiotemporal resolution data, revealing the urban pollution landscape with sufficient detail for effective policymaking and health assessment. This study advances geospatial air quality research by using classic and spatial Markov chains to analyze the seasonality and intra-daily variations of PM2.5 using LCS data. Results highlight distinctive PM2.5 seasonality, with the “Good” state predominating in summer and being least common in winter. Midday is the peak period for the “Good” state, while mornings and nights have poorer conditions, suggesting a need for stricter pollution control during evening traffic rush hours. Notably, the impact of temporal scale on spatial Markov analysis is substantial, showing a broader range of air pollution states, increased stability, and reduced variation between time intervals compared to daily assessments. Site-level analysis reveals that rural sites are more likely to maintain “Good” state and less likely to transition out of it. Overall, this study highlights the effectiveness of high spatiotemporal resolution data and demonstrates the capacity of Markov chains to reveal nuances in phenomena such as air pollution.

Unveiling the potential of a novel portable air quality platform for assessment of fine and coarse particulate matter: in-field testing, calibration, and machine learning insights.

Although low-cost air quality sensors facilitate the implementation of denser air quality monitoring networks, enabling a more realistic assessment of individual exposure to airborne pollutants, their sensitivity to multifaceted field conditions is often overlooked in laboratory testing. This gap was addressed by introducing an in-field calibration and validation of three PAQMON 1.0 mobile sensing low-cost platforms developed at the Mining and Metallurgy Institute in Bor, Republic of Serbia. A configuration tailored for monitoring PM2.5 and PM10 mass concentrations along with meteorological parameters was employed for outdoor measurement campaigns in Bor, spanning heating (HS) and non-heating (NHS) seasons. A statistically significant positive linear correlation between raw PM2.5 and PM10 measurements during both campaigns (R > 0.90, p ≤ 0.001) was observed. Measurements obtained from the uncalibrated NOVA SDS011 sensors integrated into the PAQMON 1.0 platforms exhibited a substantial and statistically significant correlation with the GRIMM EDM180 monitor (R > 0.60, p ≤ 0.001). The calibration models based on linear and Random Forest (RF) regression were compared. RF models provided more accurate descriptions of air quality, with average adjR2 values for air quality variables in the range of 0.70 to 0.80 and average NRMSE values between 0.35 and 0.77. RF-calibrated PAQMON 1.0 platforms displayed divergent levels of accuracy across different pollutant concentration ranges, achieving a data quality objective of 50% during both measurement campaigns. For PM2.5, uncertainty ( ) was below 50% for concentrations between 9.06 and 34.99μg/m3 in HS and 5.75 and 17.58μg/m3 in NHS, while for PM10, it stayed below 50% from 19.11 to 51.13μg/m3 in HS and 11.72 to 38.86μg/m3 in NHS.

Implementation of an IoT architecture for promoting healthy air quality in 84 homes of families with children

IoT systems incorporating low-cost air quality sensors (LCS) have emerged as valuable tools for empowering citizens to take action to improve indoor air quality (IAQ). This work aimed to assess the effectiveness of a newly developed IoT system in promoting behavioural changes and improving IAQ in 84 homes of families with children. To accomplish this aim, a modular IoT architecture using calibrated LCS was developed to collect real-time data on CO2, temperature, relative humidity and particulate matter (PM2.5 and PM10) during a randomised cross-over trial (November 16, 2022–January 24, 2023). The intervention under study consisted of providing access to real-time IAQ data to participants and alerting them via a smartphone app when CO2, PM2.5, and/or PM10 concentrations were higher than the recommended limits. The findings showed that LCS readings were strongly correlated with those obtained from reference equipment. Nevertheless, long-term assessments revealed a signal degradation effect for the readings obtained from the LCS PM sensor. The comparison of IAQ data from control and intervention periods showed a significant alleviation of CO2 concentrations (mean reduction of 10.3 % in 52 out of 84 participant homes). Notably, about 70 % of participants recognized that data presented through the app motivated them to take valuable actions to enhance IAQ. Overall, this study constitutes a step forward to provide valuable field evidence on the strengths and limitations of using IoT systems based on LCS to empower citizens on the factors that may influence exposure to air pollution at home.

Transferability of machine-learning-based global calibration models for NO2 and NO low-cost sensors

Abstract. It is essential to accurately assess and verify the effects of air pollution on human health and the environment in order to develop effective mitigation strategies. More accurate analysis of air pollution can be achieved by utilizing a higher-density sensor network. In recent studies, the implementation of low-cost sensors has demonstrated their capability to quantify air pollution at a high spatial resolution, alleviating the problem of coarse spatial measurements associated with conventional monitoring stations. However, the reliability of such sensors is in question due to concerns about the quality and accuracy of their data. In response to these concerns, active research efforts have focused on leveraging machine learning (ML) techniques in the calibration process of low-cost sensors. These efforts demonstrate promising results for automatic calibration, which would significantly reduce the efforts and costs of traditional calibration methods and boost the low-cost sensors' performance. As a contribution to this promising research field, this study aims to investigate the calibration transferability between identical low-cost sensor units (SUs) for NO2 and NO using ML-based global models. Global models would further reduce calibration efforts and costs by eliminating the need for individual calibrations, especially when utilizing networks of tens or hundreds of low-cost sensors. This study employed a dataset acquired from four SUs that were located across three distinct locations within Switzerland. We also propose utilizing O3 measurements obtained from available nearby reference stations to address the cross-sensitivity effect. This strategy aims to enhance model accuracy as most electrochemical NO2 and NO sensors are extremely cross-sensitive to O3. The results of this study show excellent calibration transferability between SUs located at the same site (Case A), with the average model performance being R2 = 0.90 ± 0.05 and root mean square error (RMSE) = 3.4 ± 0.9 ppb for NO2 and R2 = 0.97 ± 0.02 and RMSE = 3.1 ± 0.8 ppb for NO. There is also relatively good transferability between SUs deployed at different sites (Case B), with the average performance being R2 = 0.65 ± 0.08 and RMSE = 5.5 ± 0.4 ppb for NO2 and R2 = 0.82 ± 0.05 and RMSE = 5.8 ± 0.8 ppb for NO. Interestingly, the results illustrate a substantial improvement in the calibration models when integrating O3 measurements, which is more pronounced when SUs are situated in regions characterized by elevated O3 concentrations. Although the findings of this study are based on a specific type of sensor and sensor model, the methodology is flexible and can be applied to other low-cost sensors with different target pollutants and sensing technologies. Furthermore, this study highlights the significance of leveraging publicly available data sources to promote the reliability of low-cost air quality sensors.

Suitability of using carbon dioxide as a tracer gas for studying vehicle emission dispersion in a real street canyon

High-rise buildings form deep urban street canyons and restrict the dispersion of vehicle emissions, posing severe health risks to the public by aggravating roadside air quality. Field measurements are important for understanding the dispersion process of tailpipe emissions in street canyons, while a major challenge is the lack of a suitable tracer gas. Carbon dioxide (CO2), which is safe to the public and inexpensive to obtain, can be reliably measured by existing gas analysers. This study investigated the suitability of using CO2 as a tracer gas for characterising vehicle emission dispersion in a real-world street canyon. The tracer gas was released via a line or point source, whose dispersion was characterised by a sensors network comprising low-cost air quality sensors. The results showed that the CO2 contained in the exhaust gas of a test vehicle itself had unmeasurable effect at roadsides. Both the line and point sources produced obvious CO2 level elevations at approximately 30 s after the test vehicle passed by. In addition, for both line and point sources, the CO2 elevations were much more distinct at the roadside next to tailpipe exit than the opposite side, and were higher at 0.8 m than 1.6 m above the ground. The present study demonstrated that using CO2 as a tracer gas is feasible for investigating vehicle emission dispersion in real-world street canyons. Future studies are needed to improve the gas release rate of the developed tracer gas systems for more reliable measurements and larger street canyons.

The present state of low-cost air quality sensors in Japan and their accuracy

ABSTRACT The Sustainable Development Goals (SDGs) emphasise the importance of clean air (SDG 3 and SDG 11.6). Japan faces challenges in air quality prediction, especially in rural areas, because of the absence of a climate-specific calibration model and limited deployment of low-cost sensors. Our study compared three sensor types: low-cost pre-assembled and pre-calibrated sensors (PurpleAir); self-assembled low-cost sensors (Citizen Science Project); and highly accurate weather stations, focusing on PM2.5 values. Linear and generalised linear mixed models show potential for low-cost sensor calibration.

A Place-Based Teacher Engagement Program Using Low-Cost Air Quality and Weather Sensors in Hawaiʻi After the 2018 Kīlauea Eruption

A Place-Based Teacher Engagement Program Using Low-Cost Air Quality and Weather Sensors in Hawaiʻi After the 2018 Kīlauea Eruption

Air Quality Monitoring Using Low-Cost Sensors in Urban Areas of Jodhpur, Rajasthan.

Air pollution poses a significant health hazard in urban areas across the globe, with India being one of the most affected countries. This paper presents environmental monitoring study conducted in Jodhpur, Rajasthan, India, to assess air quality in diverse urban environments. The study involved continuous indoor and outdoor air quality monitoring, focusing on particulate matter (PM2.5) levels, bioaerosols, and associated meteorological parameters. Laser sensor-based low-cost air quality monitors were utilized to monitor air quality and Anderson 6-stage Cascade Impactor & Petri Dish methods for bioaerosol monitoring. The study revealed that PM2.5 levels were consistently high throughout the year, highlighting the severity of air pollution in the region. Notably, indoor PM2.5 levels were often higher than outdoor levels, challenging the common notion of staying indoors during peak pollution. The study explored the spatial and temporal diversity of air pollution across various land-use patterns within the city, emphasizing the need for tailored interventions in different urban areas. Additionally, bioaerosol assessments unveiled the presence of pathogenic organisms in indoor and outdoor environments, posing health risks to residents. These findings underscore the importance of addressing particulate matter and bioaerosols in air quality management strategies. Despite the study's valuable insights, limitations, such as using low-cost air quality sensors and the need for long-term data collection, are acknowledged. Nevertheless, this research contributes to a better understanding of urban air quality dynamics and the importance of public awareness in mitigating the adverse effects of air pollution. In conclusion, this study underscores the urgent need for effective air quality management strategies in urban areas. The findings provide valuable insights for policymakers and researchers striving to address air pollution in rapidly urbanizing regions.

Leveraging Temporal Information to Improve Machine Learning-Based Calibration Techniques for Low-Cost Air Quality Sensors.

Low-cost ambient sensors have been identified as a promising technology for monitoring air pollution at a high spatio-temporal resolution. However, the pollutant data captured by these cost-effective sensors are less accurate than their conventional counterparts and require careful calibration to improve their accuracy and reliability. In this paper, we propose to leverage temporal information, such as the duration of time a sensor has been deployed and the time of day the reading was taken, in order to improve the calibration of low-cost sensors. This information is readily available and has so far not been utilized in the reported literature for the calibration of cost-effective ambient gas pollutant sensors. We make use of three data sets collected by research groups around the world, who gathered the data from field-deployed low-cost CO and NO2 sensors co-located with accurate reference sensors. Our investigation shows that using the temporal information as a co-variate can significantly improve the accuracy of common machine learning-based calibration techniques, such as Random Forest and Long Short-Term Memory.

Effect of Three-Dimensional-Printed Thermoplastics Used in Sensor Housings on Common Atmospheric Trace Gasses.

Low-cost air quality sensors (LCSs) are becoming more ubiquitous as individuals and communities seek to reduce their exposure to poor air quality. Compact, efficient, and aesthetically designed sensor housings that do not interfere with the target air quality measurements are a necessary component of a low-cost sensing system. The selection of appropriate housing material can be an important factor in air quality applications employing LCSs. Three-dimensional printing, specifically fused deposition modeling (FDM), is a standard for prototyping and small-scale custom plastics production because of its low cost and ability for rapid iteration. However, little information exists about whether FDM-printed thermoplastics affect measurements of trace atmospheric gasses. This study investigates how five different FDM-printed thermoplastics (ABS, PETG, PLA, PC, and PVDF) affect the concentration of five common atmospheric trace gasses (CO, CO2, NO, NO2, and VOCs). The laboratory results show that the thermoplastics, except for PVDF, exhibit VOC off-gassing. The results also indicate no to limited interaction between all of the thermoplastics and CO and CO2 and a small interaction between all of the thermoplastics and NO and NO2.

State-of-the-Art Low-Cost Air Quality Sensors, Assemblies, Calibration and Evaluation for Respiration-Associated Diseases: A Systematic Review

Indoor air quality and respiratory health have always been an area of prime interest across the globe. The significance of low-cost air quality sensing and indoor public health practices spiked during the pandemic when indoor air pollution became a threat to living beings, especially human beings. Problem Definition: Indoor respiration-associated diseases are hard to diagnose if they are due to indoor environmental conditions. A major challenge was observed in establishing a baseline between indoor air quality sensors and associated respiratory diseases. Methods: In this work, 10,000+ articles from top literature databases were reviewed using six bibliometric analysis methods (Lorenz Curve of Citations, Hirch’s H-Index, Kosmulski’s H2-Index, Harzing’s Hl-Norm-Index, Sidoropolous’s HC-Index, and Schrieber’s HM-index) to formulate indoor air quality sensor and disease correlation publication rubrics to critically review 482 articles. Results: A set of 152 articles was found based on systematic review parameters in six bibliometric indices for publications that used WHO, NIH, US EPA, CDC, and FDA-defined principles. Five major respiratory diseases were found to be causing major death toll (up to 32%) due to five key pollutants, measured by 30+ low-cost sensors and further optimized by seven calibration systems for seven practical parameters tailored to respiratory disease baselines evaluated through 10 cost parameters. Impact: This review was conducted to assist end-users, public health facilities, state agencies, researchers, scientists, and air quality protection agencies.

Utilizing a Low-Cost Air Quality Sensor: Assessing Air Pollutant Concentrations and Risks Using Low-Cost Sensors in Selangor, Malaysia

Utilizing a Low-Cost Air Quality Sensor: Assessing Air Pollutant Concentrations and Risks Using Low-Cost Sensors in Selangor, Malaysia

RPCA-based techniques for pattern extraction, hotspot identification and signal correction using data from a dense network of low-cost NO2 sensors in London

High-density low-cost air quality sensor networks are a promising technology to monitor air quality at high temporal and spatial resolution. However the collected data is high-dimensional and it is not always clear how to best leverage this information, particularly given the lower data quality coming from the sensors. Here we report on the use of robust Principal Component Analysis (RPCA) using nitrogen dioxide data obtained from a recently deployed dense network of 225 air pollution monitoring nodes based on low-cost sensors in the Borough of Camden in London. RPCA addresses the brittleness of singular value decomposition towards outliers by using a decomposition of the data into low-rank and sparse contributions, with the latter containing outliers. The modal decomposition enabled by RPCA identifies major periodic patterns including spatial and temporal bias, dominant spatial variance, and north-south bias. The five most descriptive components capture 98 % of the data's variance, achieving a compression by a factor of 1500. We present a new technique that uses the sparse part of the data to identify hotspots. The data indicates that at the locations of the top 15 % most susceptible nodes in the network, the model identifies 23 % more hotspots than in all other locations combined. Moreover, the median hotspot event at these at-risk locations exceeds the mean NO2concentration by 33μg/m3. We show the potential of RPCA for signal correction; it corrects random errors yielding a reference signal with R2>0.8. Moreover, RPCA successfully reconstructs missing data from a sensor with R2=0.72 from the rest of the sensor network, an improvement upon PCA of around 50 %, allowing air quality estimations even if a sensor is out of use temporarily.

Investigation of LASSO Regression Method as a Correction Measurements’ Factor for Low-Cost Air Quality Sensors

Air quality is a subject of study, particularly in densely populated areas, as it has been shown to affect human health and the local ecosystem. In recent years, with the rapid development of technology, low-cost sensors have emerged, with many people interested in the quality of the air in their area turning to the procurement of such sensors as they are affordable. The reliability of measurements from low-cost sensors remains a question in the research community. In this paper, the determination of the correction factor of low-cost sensor measurements by applying the least absolute shrinkage and selection operator (LASSO) regression method is investigated. The results are promising, as following the application of the correction factor determined through LASSO regression the adjusted measurements exhibit a closer alignment with the reference measurements. This approach ensures that the measurements from low-cost sensors become more reliable and trustworthy.

Using low-cost air quality sensors to estimate wildfire smoke infiltration into childcare facilities in British Columbia, Canada

The health risks associated with wildfires are expected to increase due to climate change. Children are susceptible to wildfire smoke, but little is known about indoor smoke exposure at childcare facilities. The objective of this analysis was to estimate the effects of outdoor PM2.5 and wildfire smoke episodes on indoor PM2.5 at childcare facilities across British Columbia, Canada. We installed low-cost air-quality sensors inside and outside 45 childcare facilities and focused our analysis on operational hours (Monday–Friday, 08:00–18:00) during the 2022 wildfire season (01 August–31 October). Using random-slope random-intercept linear mixed effects regression, we estimated the overall and facility-specific effects of outdoor PM2.5 on indoor PM2.5, while accounting for covariates. We examined how wildfire smoke affected this relationship by separately analyzing days with and without wildfire smoke. Average indoor PM2.5 increased by 235% on wildfire days across facilities. There was a positive relationship between outdoor and indoor PM2.5 that was not strongly influenced by linear adjustment for meteorological and area-based socio-economic factors. A 1.0 μg m−3 increase in outdoor PM2.5 was associated with a 0.55 μg m−3 [95% CI: 0.47, 0.63] increase indoors on non-wildfire smoke days and 0.51 μg m−3 [95% CI: 0.44, 0.58] on wildfire-smoke days. Facility-specific regression coefficients of the effect of outdoor PM2.5 on indoor PM2.5 was variable between facilities on wildfire (0.18–0.79 μg m−3) and non-wildfire days (0.11–1.03 μg m−3). Indoor PM2.5 responded almost immediately to increased outdoor PM2.5 concentrations. Across facilities, 89% and 93% of the total PM2.5 infiltration over 60 min occurred within the first 10 min following an increase in outdoor PM2.5 on non-wildfire and wildfire days, respectively. We found that indoor PM2.5 in childcare facilities increased with outdoor PM2.5. This effect varied between facilities and between wildfire-smoke and non-wildfire smoke days. These findings highlight the importance of air quality monitoring at childcare facilities for informed decision-making.

Using synthetic data to benchmark correction methods for low-cost air quality sensor networks

A benchmark was performed, comparing the results of three different methodologies proposed by three institutions to calibrate a network of low-cost PM2.5 sensors, on an hourly basis, using synthetically generated real concentrations and sensor measurements. The objective of the network calibrations was to correct the 2000+ sensor measurements in the Netherlands for the sensitivity to (local) environmental conditions. The option to use real measurements was dropped because the number of low-cost sensors sufficiently close to the 40 reference measurement locations was assessed to be spatially insufficient to benchmark the proposed approaches. Instead, synthetic real concentrations were generated to enable validation at all sensor locations. Hourly actual sensor and actual fixed concentrations, as well as interpolated concentration maps, were used as underlying data to generate the synthetic data sets for the period of 1 month. The synthetic sensor measurement errors were constructed by sampling from a collection of differences between actual sensor values and actual measurements. Of the three tested calibration methods, two follow a similar approach, although having differences in, e.g., outlier analyses and method of grouping sensors, leading also to comparable corrections to the raw sensor measurements. A third method uses significantly stricter rules in outlier selection, discarding considerably more sensors because of insufficient quality. Differences between the methods become most apparent when analyzing data at a smaller time scale. It is shown that two network calibration methods are better at correcting the hourly/daily bias.

Design and Development of a University Outdoor Air Quality Monitoring System

An increase in air quality monitoring research using low-cost air quality sensors has been observed. Existing air quality monitoring systems provide real-time data on at most five primary pollutants, cover a short area, and could be made expandable. An air quality monitoring system utilizing a scalable, low power wide area network, centralized system, and low cost sensors is proposed. The nodes gather data on air pollutants and environmental factors. Data conversion, storage, and calculation of air quality indices were processed on the central subsystem and visualization through IoT cloud. The results suggest improvement in the design and integration of prediction models, battery, and power management techniques.

Leveraging Unsupervised Data and Domain Adaptation for Deep Regression in Low-Cost Sensor Calibration.

Air quality monitoring is becoming an essential task with rising awareness about air quality. Low-cost air quality sensors are easy to deploy but are not as reliable as the costly and bulky reference monitors. The low-quality sensors can be calibrated against the reference monitors with the help of deep learning. In this article, we translate the task of sensor calibration into a semi-supervised domain adaptation problem and propose a novel solution for the same. The problem is challenging, because it is a regression problem with a covariate shift and label gap. We use histogram loss instead of mean-squared or mean absolute error (MAE), which is commonly used for regression, and find it useful against covariate shift. To handle the label gap, we propose the weighting of samples for adversarial entropy optimization. In experimental evaluations, the proposed scheme outperforms many competitive baselines, which are based on semi-supervised and supervised domain adaptation, in terms of R2 score and MAE. Ablation studies show the relevance of each proposed component in the entire scheme.

A Dual-Path Deep Learning Model for Low-Cost Air Quality Sensor Calibration

A Dual-Path Deep Learning Model for Low-Cost Air Quality Sensor Calibration