Low-cost Sensors Research Articles

City-Scale Air Quality Network of Low-Cost Sensors

In this paper, we present a city-scale (possibly global) air pollution network made of low-cost sensors for particulate matter concentration in the air. The components of the presented system are based on our research and experience from previous studies focused on air quality instruments and sensors for addressing urban air pollution. Sensor nodes are produced locally and distributed over Sarajevo, Bosnia and Herzegovina. The system collects, calibrates, and displays particulate matter concentrations in real-time. In comparison to the available measurements from governmental institutions, our system demonstrated good agreement of measured parameters, with several advantages including, higher resolution in space and time, lower costs, both horizontal and vertical measurements. One of the capabilities of the system is the real-time air pollution city map with animations. By installing multiple sensor nodes over a slope, we receive vertical measurements of temperature, humidity, and particulate matter concentration in real time, which gives a valuable insight into the dynamics of temperature inversion episodes and air pollution below the inversion layer.

Source Tracing of PM2.5 in a Metropolitan Area Using a Low-Cost Air Quality Monitoring Network: Case Study of Denver, Colorado, USA

Air quality assessments often require source apportioning of the air pollutants observed at the receptor site. Conventional source apportionment models are subject to high uncertainties due to the lack of accurate emission profiles of all the contributing sources and a limited number of measurements at the receptor sites. Recent advances in the development and application of low-cost PM2.5 sensors have facilitated the formation of a more robust database with greater numbers of measurements per location and time. The main objective of this study is to combine a large database of PM2.5 concentration records to records from low-cost sensors in Denver, Colorado, during January 2021. Using wind speed and wind direction at the receptors, we developed a visualization tool for source tracing of PM2.5 with resulting statistical analyses and back-trajectory modeling. For this purpose, a combination of in-house and existing packages of R scripts along with National Oceanic and Atmospheric Administration (NOAA)’s trajectory model and climate and weather toolkits were used. In general, the results show that the PM2.5 measurements obtained from such a network of PM2.5 sensors incorporated with hourly wind field data, which are publicly available, can provide a powerful screening tool to discover the transport pathways of PM2.5 before requiring costly source apportionment approaches. The fraction of PM2.5 concentration detected by each sensor in regard to wind direction and speed bins were quantified using this method. The results of cluster analysis identified the area groups in respect to wind speed and wind direction bins, which shines a light on how far and in which direction polluting sources are. Finally, the back-trajectory modeling outputs illustrated the exact travel path of the PM2.5-laden air parcels of each day to each sensor.

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.

Convenient and Accurate Detection of Dopamine and Glucose by Modifying Carbon Fiber Electrodes

Fast, sensitive, and low-cost high-performance detectors have gradually become an indispensable tool for people to keep healthy, and sensors are the key devices of detection equipment. In this work, a ZnO nanocrystal modified carbon fiber electrode was prepared using a hydrothermal method, and on this basis, a galvanostatic point deposition method was used to load copper nanoparticles to prepare a sensor electrode. Scanning electron microscopy and X-ray diffraction were used to comprehensively analyze composition, morphology, and environmental adaptability of the prepared electrodes. The DPV test was used to verify the enhanced effect of ZnO nanorods on neurotransmitter detection. The ZnO/CF showed an obvious electrical signal (0.22 V, 4 × 10−5A) in the detection of dopamine (DA) solution, and the Cu-NPs/ZnO/CF also showed excellent detection results in the glucose detection experiment., providing two excellent examples for the development of low-cost electrochemical sensors. The electrodes can specifically detect DA in the presence of ascorbic acid and uric acid, and the detection limit of the electrode for detecting DA is about 0.4 μM. In addition, the Cu-NPs/ZnO/CF electrode successfully realized the enzyme-free detection of glucose, and the detection limit could reach 0.5 μM.

Low-Cost Flexible Graphene Oxide Humidity Sensor Fabricated Using Inkjet Printing and Aerosol Deposition

Flexible humidity sensors play a critical role in medical diagnostics and industrial control systems. In this study, a low-cost flexible humidity sensor is presented. The humidity sensor is developed by printing silver interdigitated electrodes (IDE) on a polyethylene terephthalate (PET) substrate using an Epson Stylus C88+ inkjet printer. The sensing layer of the humidity sensor was fabricated using graphene oxide (GO) ink, which is deposited onto the electrodes using an aerosol deposition technique. The GO humidity sensor achieves excellent sensing performance over a wide range of humidity levels from 11% to 97% RH range, with a fast response time of 2 s and recovery time of 17 s. The sensor also exhibits ultra-high sensitivity (243 kΩ/%RH), low hysteresis (2.16%), excellent repeatability, long-term stability, and high flexibility (tested at bending radiuses of 4 cm, 3.5 cm, 3 cm, and 2.5 cm). The humidity sensing mechanism of the proposed GO humidity sensor was also discussed. Furthermore, the sensor exhibited excellent capabilities in monitoring human respiration, distinguishing between nose and mouth breathing, detecting finger movements without physical contact, and even recognising basic spoken words. These features of the sensor possess significant potential for various applications in human healthcare.

Predictive-Cognitive Maintenance for Advanced Integrated railway Management

Railway systems play a vital role in modern transportation and Predictive-Cognitive Maintenance (PCM) has emerged as a transformative approach in the context of Advanced Integrated Railway Management for ensuring the safety, reliability, and efficiency of these systems. PCM leverages data analytics and machine learning to optimize railway system maintenance. This requires effective structural health monitoring (SHM) using low-cost sensor devices. This paper presents a prototype solar-powered wireless sensor node with a 3-axis MEMS accelerometer and energy-harvesting features for monitoring rail track vibrations. The node contains a microcontroller that runs embedded machine-learning models to preprocess the vibration data after train crossing. Abnormal vibrations, indicative of defects, were detected in real time using the TinyML inference at the edge. Instead of raw data, only the model results were wirelessly transmitted to a digital twin in the cloud. The digital twin aggregates data across the rail network for system-level assessment of the RUL and maintenance planning. This edge computing approach minimizes wireless transmission and cloud storage compared to raw sensor streaming. Embedded ML enables real-time damage detection, whereas the cloud digital twin enables system-level prognosis insights. The solar-powered platform enables long-term remote monitoring at low cost without wiring or battery changes. A full-scale physical model was used to validate the edge node prototypes against calculation models and wired accelerometers for impulse loads. The results demonstrate that these nodes can provide a sensor layer for cost-effective PCM in railway systems. In summary, this work proposes an edge computing and embedded ML approach for SHM that integrates cloud-based digital twins to enable the predictive-cognitive maintenance of railway infrastructure. Wireless nodes demonstrate potential for low-cost, convenient, and automated rail health monitoring.

Integrating Mobile and Fixed-Site Black Carbon Measurements to Bridge Spatiotemporal Gaps in Urban Air Quality.

Urban air pollution can vary sharply in space and time. However, few monitoring strategies can concurrently resolve spatial and temporal variation at fine scales. Here, we present a new measurement-driven spatiotemporal modeling approach that transcends the individual limitations of two complementary sampling paradigms: mobile monitoring and fixed-site sensor networks. We develop, validate, and apply this model to predict black carbon (BC) using data from an intensive, 100-day field study in West Oakland, CA. Our spatiotemporal model exploits coherent spatial patterns derived from a multipollutant mobile monitoring campaign to fill spatial gaps in time-complete BC data from a low-cost sensor network. Our model performs well in reconstructing patterns at fine spatial and temporal resolution (30 m, 15 min), demonstrating strong out-of-sample correlations for both mobile (Pearson's R ∼ 0.77) and fixed-site measurements (R ∼ 0.95) while revealing features that are not effectively captured by a single monitoring approach in isolation. The model reveals sharp concentration gradients near major emission sources while capturing their temporal variability, offering valuable insights into pollution sources and dynamics.

Multi-Sensor-Assisted Low-Cost Indoor Non-Visual Semantic Map Construction and Localization for Modern Vehicles.

With the transformation and development of the automotive industry, low-cost and seamless indoor and outdoor positioning has become a research hotspot for modern vehicles equipped with in-vehicle infotainment systems, Internet of Vehicles, or other intelligent systems (such as Telematics Box, Autopilot, etc.). This paper analyzes modern vehicles in different configurations and proposes a low-cost, versatile indoor non-visual semantic mapping and localization solution based on low-cost sensors. Firstly, the sliding window-based semantic landmark detection method is designed to identify non-visual semantic landmarks (e.g., entrance/exit, ramp entrance/exit, road node). Then, we construct an indoor non-visual semantic map that includes the vehicle trajectory waypoints, non-visual semantic landmarks, and Wi-Fi fingerprints of RSS features. Furthermore, to estimate the position of modern vehicles in the constructed semantic maps, we proposed a graph-optimized localization method based on landmark matching that exploits the correlation between non-visual semantic landmarks. Finally, field experiments are conducted in two shopping mall scenes with different underground parking layouts to verify the proposed non-visual semantic mapping and localization method. The results show that the proposed method achieves a high accuracy of 98.1% in non-visual semantic landmark detection and a low localization error of 1.31 m.

Low-Cost Sensor Monitoring of Air Quality Indicators during Outdoor Renovation Activities around a Dwelling House

A couple of air quality (AQ) parameters were monitored with two types of low-cost sensors (LCSs) before, during and after the garden fence rebuilding of a dwelling house, located at the junction of a main road and a side street in a suburban area of Budapest, Hungary. The AQ variables, recorded concurrently indoors and outdoors, were particulate matter (PM1, PM2.5, PM10) and some gaseous trace pollutants, such as CO2, formaldehyde (HCHO) and volatile organic compounds (VOCs). Medium-size aerosol (PM2.5-1), coarse particulate (PM10-2.5) and indoor-to-outdoor (I/O) ratios were calculated. The I/O ratios showed that indoor fine and medium-size PM was mostly of outdoor origin; its increased levels were observed during the renovation. The related pollution events were characterized by peaks as high as 100, 95 and 37 µg/m3 for PM1, PM2.5-1 and PM10-2.5, respectively. Besides the renovation, some indoor sources (e.g., gas-stove cooking) also contributed to the in-house PM1, PM2.5-1 and PM10-2.5 levels, which peaked as high as 160, 255 and 220 µg/m3, respectively. In addition, these sources enhanced the indoor levels of CO2, HCHO and, rarely, VOCs. Increased and highly fluctuating VOC levels were observed in the outdoor air (average: 0.012 mg/m3), mainly due to the use of paints and thinners during the reconstruction, though the use of a nearby wood stove for heating was an occasional contributing factor. The acquired results show the influence of the fence renovation-related activities on the indoor air quality in terms of aerosols and gaseous components, though to a low extent. The utilization of high-resolution LCS-assisted monitoring of gases and PMx helped to reveal the changes in several AQ parameters and to assign some dominant emission sources.

Smart environmental drone utilization for monitoring urban air quality

Urban air quality has significant and far-reaching impacts on both human health and the broader environment. Pollutants like particulate matter (PM2.5 and PM10), for instance, are associated with a range of health issues including respiratory conditions, asthma, heart diseases, and even contribute to low birth weight in newborns. These health implications extend to larger environmental concerns such as air pollution, greenhouse gas emissions, and global climate change. Recognizing the urgent need for effective and dynamic air quality monitoring solutions, this paper explores the use of smart environmental drones as a promising approach. Our drone is equipped with a state-of-the-art, low-cost particulate matter sensor that can accurately measure PM2.5 and PM10 concentrations. Operating at a flight speed of 10 m/s and capable of covering a range of 5 km, the drone executes a pre-programmed flight plan to autonomously map pollution levels across urban areas. With a 95% accuracy rate in sensor readings, our model significantly minimizes potential errors commonly associated with traditional air quality monitoring methods. Furthermore, it simplifies maintenance procedures, reducing both time and financial costs. By employing drone technology in this innovative manner, our model offers a cost-effective, reliable, and dynamic solution for monitoring urban air quality. It provides real-time, actionable pollution indices that can inform public health decisions, regulatory policies, and community awareness, thereby contributing to the broader goal of improving air quality and public health.

ADVANCEMENTS AND INNOVATIONS IN PM2.5 MONITORING: A COMPREHENSIVE REVIEW OF EMERGING TECHNOLOGIES

This comprehensive review examines the evolving landscape of PM2.5 monitoring, emphasizing its critical role in environmental chemistry, public health and electrical/electronic engineering. Traditional methods, including manual sampling, gravimetric analysis, and the Federal Reference Method (FRM), have long been relied upon for PM2.5 measurement but are hindered by limitations in spatial coverage, temporal resolution, and cost. In response, emerging technologies such as wireless sensor networks, low-cost sensor technologies, remote sensing techniques, and machine learning algorithms offer promising solutions to overcome these challenges. Through an analysis of case studies and applications in various environmental settings, including urban areas, industrial zones, and indoor environments, the review highlights the effectiveness of monitoring networks in enhancing spatial and temporal resolution, as well as the need for community engagement and real-time monitoring solutions. Furthermore, technological innovations such as sensor fusion, data analytics, and artificial intelligence hold great promise for improving the accuracy, reliability, and accessibility of PM2.5 monitoring data. Regulatory agencies and policymakers play a crucial role in advancing PM2.5 monitoring by harmonizing monitoring standards, strengthening quality assurance measures, and developing evidence-based regulations to mitigate air pollution and protect public health. In conclusion, international cooperation and collaboration are essential for addressing transboundary air pollution and global environmental challenges. Regional monitoring networks and international agreements provide frameworks for data sharing, standardization of monitoring practices, and collaborative research efforts. To this end, stakeholders can leverage PM2.5 monitoring by adopting new technologies, improving data quality, and supporting evidence-based actions to safeguard public health, the environment, and sustainability

Dynamic Position Accuracy of Low-Cost Global Navigation Satellite System Sensors Applied in Road Transport for Precision and Measurement Reliability

Low-cost Global Navigation Satellite System (GNSS) sensors have been successfully applied in commercial vehicles’ position monitoring, and they continually raise interest among research audiences both in theoretical and practical aspects. While numerous studies have applied simulations and numerical methods to evaluate the accuracy of the sensors, this paper presents an analysis, supported by actual measurements collected under diversified conditions. The measurements were collected under a variety of conditions, including urban and suburban routes of considerable length, and in accordance with the position in lane applied in most European countries, which is considerably related to the sustainability of road transport. The measurements were collected during driving of three different passenger vehicles, and the response of the measurements to correct, partially correct and incorrect vehicle positions was recorded. Differentiated kinematic conditions and actual dynamic performance during driving were analyzed. This research compared the position accuracy of a low-cost GNSS sensor and a dual-antenna GNSS/INS sensor for vehicle dynamics monitoring. Both types of sensors were operated on all the passenger vehicles and with the same measurement conditions. Statistical hypothesis tests have been considered to compare the results, in accordance with the latest guidelines for carrying out such tests. Studies have indicated that a low-cost GNSS sensor also has satisfactory accuracy. However, this paper points out additional considerations and conclusions. Both the positive and negative results are described and commented on in the paper, including research limitations and suggestions for future measurement and future research agendas, both by the authors and as an inspiration for other researchers.

Chronoamperometric Ammonium Ion Detection in Water via Conductive Polymers and Gold Nanoparticles.

Monitoring of ammonium ion levels in water is essential due to its significant impact on environmental and human health. This work aims to fabricate and characterize sensitive, real-time, low-cost, and portable amperometric sensors for low NH4+ concentrations in water. Two strategies were conducted by cyclic voltammetry (CV): electrodeposition of Au nanoparticles on a commercial polyaniline/C electrode (Au/PANI/C), and CV of electropolymerized polyaniline on a commercial carbon electrode (Au/PANIep/C). Au NPs increase the electrical conductivity of PANI and its ability to transfer charges during electrochemical reactions. The electrode performances were tested in a concentration range from 0.35 µM to 7 µM in NH4+ solution. The results show that the Au/PANI/C electrode performs well for high NH4+ concentrations (0.34 µM LoD) and worsens for low NH4+ concentrations (0.01 µM LoD). A reverse performance occurs for the electrode Au/PANIep/C, with a 0.03 µM LoD at low NH4+ concentration and 0.07 µM LoD at high NH4+ concentration. The electrodes exhibit a good reproducibility, with a maximum RSD of 3.68% for Au/PANI/C and 5.94% for Au/PANIep/C. In addition, the results of the repeatability tests show that the electrochemical reaction of sensing is fully reversible, leaving the electrode ready for a new detection event.

Long-term evaluation of commercial air quality sensors: an overview from the QUANT (Quantification of Utility of Atmospheric Network Technologies) study

Abstract. In times of growing concern about the impacts of air pollution across the globe, lower-cost sensor technology is giving the first steps in helping to enhance our understanding and ability to manage air quality issues, particularly in regions without established monitoring networks. While the benefits of greater spatial coverage and real-time measurements that these systems offer are evident, challenges still need to be addressed regarding sensor reliability and data quality. Given the limitations imposed by intellectual property, commercial implementations are often “black boxes”, which represents an extra challenge as it limits end users' understanding of the data production process. In this paper we present an overview of the QUANT (Quantification of Utility of Atmospheric Network Technologies) study, a comprehensive 3-year assessment across a range of urban environments in the United Kingdom, evaluating 43 sensor devices, including 119 gas sensors and 118 particulate matter (PM) sensors, from multiple companies. QUANT stands out as one of the most comprehensive studies of commercial air quality sensor systems carried out to date, encompassing a wide variety of companies in a single evaluation and including two generations of sensor technologies. Integrated into an extensive dataset open to the public, it was designed to provide a long-term evaluation of the precision, accuracy and stability of commercially available sensor systems. To attain a nuanced understanding of sensor performance, we have complemented commonly used single-value metrics (e.g. coefficient of determination, R2; root mean square error, RMSE; mean absolute error, MAE) with visual tools. These include regression plots, relative expanded uncertainty (REU) plots and target plots, enhancing our analysis beyond traditional metrics. This overview discusses the assessment methodology and key findings showcasing the significance of the study. While more comprehensive analyses are reserved for future detailed publications, the results shown here highlight the significant variation between systems, the incidence of corrections made by manufacturers, the effects of relocation to different environments and the long-term behaviour of the systems. Additionally, the importance of accounting for uncertainties associated with reference instruments in sensor evaluations is emphasised. Practical considerations in the application of these sensors in real-world scenarios are also discussed, and potential solutions to end-user data challenges are presented. Offering key information about the sensor systems' capabilities, the QUANT study will serve as a valuable resource for those seeking to implement commercial solutions as complementary tools to tackle air pollution.

Calibration and Inter-Unit Consistency Assessment of an Electrochemical Sensor System Using Machine Learning.

This paper addresses the challenges of calibrating low-cost electrochemical sensor systems for air quality monitoring. The proliferation of pollutants in the atmosphere necessitates efficient monitoring systems, and low-cost sensors offer a promising solution. However, issues such as drift, cross-sensitivity, and inter-unit consistency have raised concerns about their accuracy and reliability. The study explores the following three calibration methods for converting sensor signals to concentration measurements: utilizing manufacturer-provided equations, incorporating machine learning (ML) algorithms, and directly applying ML to voltage signals. Experiments were performed in three urban sites in Greece. High-end instrumentation provided the reference concentrations for training and evaluation of the model. The results reveal that utilizing voltage signals instead of the manufacturer's calibration equations diminishes variability among identical sensors. Moreover, the latter approach enhances calibration efficiency for CO, NO, NO2, and O3 sensors while incorporating voltage signals from all sensors in the ML algorithm, taking advantage of cross-sensitivity to improve calibration performance. The Random Forest ML algorithm is a promising solution for calibrating similar devices for use in urban areas.

High-Precision Low-Cost Mid-Infrared Photoacoustic Gas Sensor Using Aspherical Beam Shaping for Rapidly Measuring Greenhouse Gases

A high-precision low-cost mid-infrared photoacoustic sensor for greenhouse composite gases based on aspherical beam shaping is proposed and demonstrated. The assembled optical source module and luminous characteristics of infrared source are innovatively investigated and analyzed with aspherical beam shaping. The proposed aspherical-beam-shaping-technique could effectively reduce optical loss and enhance system sensitivity, achieving an effective power utilization ratio of a radiation source of 91% and sidewall noise ratio of 8.9%. Experiments verify the 1.7 times improvement in responsivity and 50% enhancement in minimum detection limit (MDL) on average. In terms of comprehensive greenhouse gas composites and with short integration time of 1 s, MDLs of CO2, CH4, N2O, NF3, SF6, PFC-14, and HFC-134a are 73 ppb, 267 ppb, 72 ppb, 81 ppb, 14 ppb, 9 ppb and 115 ppb, respectively. Furthermore, a 48 h continuous monitoring of H2O, CO2 and CH4 in the atmosphere is conducted and verifies the performance of the gas sensor. The developed sensor allows for the rapid route of low-cost and high-precision detection of multiple greenhouse gases.

Silica graphite as an ink additive for stencil printed electrodes: A novel approach for electroanalytical determination of sulfanilamide

Silica-graphite composites present a high surface area, mechanical and chemical stability, and high functionality to be applied in electrochemical sensing. Indeed, those materials have been successfully employed to build composite electrodes such as carbon paste electrodes for a wide range of applications. Despite excellent performance, this kind of electrodes have been replaced by stencil printed electrodes (SPEs), emerging as a scalable alternative for manufacture offering sensors disposables, low-cost, customizable, and miniaturized. Based on this, herein a novel silica-graphite (SiG) composite is described as an ink additive aiming scalable production of SPEs with high functionality and surface area. The SPE as well as SiG were characterized by electrochemical impedance spectroscopy, scanning electron microscopy, thermogravimetric analysis, and contact angle. After an accumulation step, SiG-SPE pretreated has shown a significant improvement on oxidation of sulfanilamide compared with unmodified SPE. Electrochemical surface activation and pre-concentration conditions were investigated to enhance sensitivity and selectivity. Under optimized conditions, silica-graphite stencil printed electrode (SiG-SPE) was employed for the electrochemical determination of sulfanilamide by using square wave-adsorptive stripping voltammetry (SWAdSV). It was possible to achieve a linear dynamic range (LDR) from 2 to 20 mmol L−1 with a limit of detection (LOD) and quantification (LOQ) of 0.34 and 1.13 mmol L−1, respectively. The stability of the sensors was evaluated over five weeks, and a decrease of around 5.5 % in response was observed compared to fresh electrodes. SiG-SPE also presented high selectivity and low matrix interference for sulfanilamide determination with a recovery range of 96.2–100.1 % for urine and 98.2–106.1 % for commercial fat milk. Those results suggest that SiG is a promising ink additive for the production of low-cost and high-performance sensors, showing enhanced analytical properties, mechanical resistance, and chemical stability.

High Power Pulsed LED Driver for Vibration Measurements.

Vibration measurements pose specific experimental challenges to be faced. In particular, optical methods can be used to obtain full-field vibration information. In this scenario, stereo-camera systems can be developed to obtain 3D displacement measurements. As vibration frequency increases, the common approach is to reduce camera exposure time to avoid blurred images, which can lead to under-exposed images and data loss, as well as issues with the synchronization of the stereo pair. Both of these problems can be solved by using high-intensity light pulses, which can produce high-quality images and guarantee camera synchronization since data is saved by both cameras only during the short-time light pulse. To this extent, high-power Light-Emitting Diodes (LEDs) can be used, but even if the LED itself can have a fast response time, specific electronic drivers are needed to ensure the desired timing of the light pulse. In this paper, a circuit is specifically designed to achieve high-intensity short-time light pulses in the range of 1 µs. A prototype of the designed board was assembled and tested to check its capability to respect the specification. Three different measurement methods are proposed and validated to achieve short-time light pulse measurements: shunt voltage measurement, direct photodiode measurement with a low-cost sensor, and indirect pulse measurement through a low-frame-rate digital camera.

Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia.

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

Enhanced drift self-calibration of low-cost sensor networks based on cluster and advanced statistical tools

The escalating use of low-cost sensors in environmental monitoring demands effective management of sensor drift and errors. To tackle this challenge, lightweight calibration techniques are essential for accurate sensor readings. This paper presents a novel calibration method for low-cost sensors, prioritizing the detection and correction of sensor drifts. The approach incorporates clustering for efficient task distribution and leverages Inverse Distance Weighting (IDW) for calibration precision. Also, advanced statistical tools, including the Two-Sample Kolmogorov–Smirnov test (TSKS test) and Exponential Moving Average (EMA), assess and enhance sensor data stability based on historical measurements. Additionally, the Root Update Estimator (RUE) is utilized to adjust predicted values and improve the model’s adaptability to changes in the underlying data distribution. Extensive simulation experiments using an Intel Berkeley Research Laboratory (IBRL) dataset affirm the method’s effectiveness in swiftly addressing sensor drifts. These findings advance low-cost sensor calibration, boosting reliability and precision in environmental monitoring.