Low-cost Air Research Articles

A Multi-Scale Convolutional Residual Time-Frequency Calibration Method for Low-Accuracy Air Pollution Data

Air pollution concerns have led to the widespread deployment of air quality monitoring stations. While high-cost government stations provide accurate data, their deployment is limited, whereas low-cost sensors offer widespread coverage but with lower accuracy. To enhance the accuracy of measurement data from low-cost air monitoring sensors, this study proposes a Multi-Scale Convolutional Residual Time-Frequency Calibration Method (MCRTF-CM), focusing on the PM2.5 sensor as an example. This method leverages multi-scale convolution in the feature extractor to capture diverse features at various scales using parallel convolutional kernels. Residual connections merge the original and multi-scale features, preserving the initial input for enhanced stability. The calibration module employs Gated Recurrent Units (GRUs) to capture long-term dependencies in time-series data through reset and update gates. Additionally, the Frequency Enhanced Channel Attention Mechanism (FECAM) uses Discrete Cosine Transform (DCT) to convert time-domain data to frequency-domain, assigning weights to different frequency components to enhance key features and suppress irrelevant ones. Experimental results demonstrate that MCRTF-CM outperforms optimal Long Short-Term Memory (LSTM) networks, reducing RMSE, MAE, MSE, and MAPE by 13.59%, 14.04%, 25.33%, and 8.22%, respectively, indicating its better performance.

The Air Transportation System as a Subsystem of Modern Communication Space: Analysis Based on Transfer Entropy Graphs

The processes of information exchange and the movement of material flows form a communication space that reflects the relationship of complex intersystem interactions in various spheres of our life within the framework of the concepts of information-theoretical theory. One of these concepts, reflecting the mutual influence between processes at a qualitative level, is the transfer of entropy. The direction and intensity of these flows reflect the main social and economic processes. As it is known, air transport is one of the most reliable and high-speed modes of transport, influencing the processes of socio-cultural interaction between different regions. This indirectly affects the development of industrial relations, the development of technology and intercultural exchange. New technologies in aviation improve the flight performance of airliners and reduce the costs of transporting passengers. The size and range of modern airliners are increasing, and ticket prices are being optimized. The processes of the liberalization of developing air transportation markets, the emergence of low-cost air carriers, open skies agreements, and the reduction in restrictions on the nomenclature of carriers and routes have led to the growth and diversity of air transport links. This article considers air transport as a complex system that takes into account the interconnectedness of the elements of the transportation system and the influence of some subsystems on others, which are not always obvious. The object of the study was the communication space formed on the basis of air transportation between regions of the world. To assess the dynamic properties of the world communication space, ICAO data for the period of 1970–2021 were used. The subject of the analysis was a time series reflecting the flows of passengers and cargo over the considered time horizon. The entropy transfer algorithm was used as an analysis tool. In the course of the research, the features of dynamic changes in the properties of the communication space were revealed. The analysis showed that the flows of entropy transfer between regions of the world change depending on political, economic, social, and technological factors. Examples of the application of the proposed approach are considered: an analysis of the cognitive model of the air transport flow structure, an analysis of the regional communication space, and an analysis of changes in the global communication field. The results of the analysis can be useful for assessing the development of the communication field of various regions, which will allow us to solve the problems of forming forecasts and effective scenarios for the development of transport flows at different hierarchical levels of economic management.

An Innovative Collaboration-Based Ozone and Aerosol Observation Network in Northeast Asia

Abstract Situated in close proximity to the Korea Peninsula and the Japan Islands, Shandong Province in China (SDP) has emerged as a focal point for global attention due to its significant surface ozone (O3) and aerosol pollution. Despite this attention, there are notable gaps in knowledge across various interconnected research domains, encompassing climate change, atmospheric circulation, anthropogenic emissions, and the chemistry of O3 and aerosols. The impact of frequent heat waves on regional O3 and aerosol pollution remains unclear, while our comprehension of atmospheric circulation dynamics and the chemistry involved with O3 and aerosols is still hampered by substantial limitations. The unique topography and geographical setting of SDP, with its complex interplay of factors like sea–land breezes, mountain–valley winds, and urban heat islands, make it an ideal location for investigating the dynamics of O3 and aerosol chemistry. Moreover, it is essential to explore the effects of transboundary transport on O3 and aerosol pollution and delve into the underlying mechanisms contributing to their combined pollution effects. To bridge these knowledge gaps, the Innovative Collaboration-Based Ozone and Aerosol Observation Network in Northeast Asia has been established through collaborative efforts involving the Bureaus of Ecology and Environment, Meteorology in SDP, and the Chinese Academy of Sciences. This network encompasses various components, including air quality and weather stations, a network of low-cost air quality sensors, monitoring stations focused on atmospheric photochemical smog and aerosol chemical speciation, and vertical measurement systems targeting O3 and its precursor gases (utilizing light detection and ranging, balloon sounding, and drone-based measurements), as well as measurements conducted via vehicles and ships. Additionally, preliminary findings from this comprehensive observational campaign will be shared.

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

Quantifying impact of correlated predictors on low-cost sensor PM2.5 data using KZ filter

PM2.5, fine particulate matter with a diameter smaller than 2.5 μm, is associated with a range of health problems. Monitoring PM2.5 levels at the community scale is crucial for understanding personal exposure and implementing preventive measures. While monitoring agencies around the world, such as the U.S. Environmental Protection Agency (EPA), provide accurate data, the spatial coverage is limited due to a sparse monitoring network. Recently, the emergence of low-cost air quality sensor networks has enabled the availability of air quality data with higher spatiotemporal resolution, which is more representative of personal exposure. However, concerns persist regarding the sensitivity, noise, and reliability of data from these low-cost sensors. In this study, we analyzed PM2.5 data from both EPA and Purple Air (PA) sensors in Cook County, Illinois, with two primary goals: (1) understanding the differential impact of meteorological factors on PA and EPA sensor networks and (2) provide a mathematical approach to quantify the individual impact of correlated predictors on both short-term and baseline variations in noisy time series data. We used the Kolmogorov-Zurbenko (KZ) filter to separate the time series into short-term and baseline components, followed by fitting linear models to quantify the impact of meteorological predictors, including temperature, relative humidity (RH), wind speed (WS), and wind direction (WD). Furthermore, we applied the Lindeman, Merenda, and Gold (LMG) method to these linear models to quantify the individual contribution of each predictor in the presence of multicollinearity. Our results show that the PM2.5 data from PA sensors exhibit higher sensitivity to meteorological factors, particularly wind speed, in the short-term and RH in the baseline component. This method provides a structured approach for analyzing noisy sensor data under diverse environmental conditions.

Supporting knowledge justice through community science air quality monitoring and a reciprocal reporting process

In community science on air quality, low-cost air monitors have emerged as an opportunity to democratize data reporting and support knowledge justice by providing participants with instantaneous access to air quality data. In this study, we equipped residents in four environmental justice communities in North Denver with low-cost air monitors to collect real-time air quality data for four separate 30-day field deployments over two years. We conceptualize an improvement to conventional report-back processes by suggesting a 3-part approach – a reciprocal reporting process that includes 1) bidirectional open channels of communication with participants, 2) democratized data access via instant monitor data and written data summaries, and 3) responsive intervention opportunities to respond in real-time to participants air quality concerns. Through 120 interviews with 30 air quality monitor users after each of the four field deployments, we identify how this reciprocal reporting process increased Air Quality (AQ) awareness and supported distinct modes of environmental learning, which we term data-driven, health-conscious, and progressive logics of inquiry. In addition, this non-prescriptive approach centered participants’ alternative ways of knowing, such as sensory experiences to understand pollution exposure, and fostered collective learning that ultimately furthered knowledge justice. Our study highlights the potential for a more robust and holistic approach to report-back processes within community science projects to foster flexibility, reciprocity, and responsiveness.

Maximizing CdTe Nanowire Potential: Rapid Electrodeposition for High-Performance Photoanodes

Vertically aligned CdTe nanowire arrays mitigate performance losses from crystallographic defects through efficient strain relaxation and enhance light trapping, absorption, and charge extraction due to their radial geometry and architecture. This transformative nanostructured solution could address the long-standing defect and fault tolerance issues hindering the performance of polycrystalline CdTe thin-film solar cells. Crucially, our breakthrough modified electrodeposition approach enables unprecedently rapid growth of these nanowire arrays at astonishing rates up to 500 nm/min - a game-changing achievement for industrial-scale manufacturing and economic viability. Executed at room temperature and followed by a simple, low-cost air annealing step, this technique significantly reduces fabrication costs and complexity compared to vapor-based methods. The resulting nanowire arrays exhibit exceptional structural and optoelectronic properties: highly uniform 70 nm average diameter, lengths up to an impressive 1 μm, an ideal direct 1.5 eV bandgap, and outstanding 10 ns minority carrier lifetime. Furthermore, integrating a wider 1.65 eV bandgap CdSeTe ternary window layer synergistically leverages the favorable conduction band alignment to unlock impressive photovoltaic performance with 16 mA/cm² short-circuit current density and 0.6 V open-circuit voltage. While further optimizations are ongoing to boost performance even higher, our innovative approach already overcomes longstanding barriers by combining the inherent advantages of nanostructured geometries with optimized junction designs, enabling scalable and cost-effective manufacturing of high-efficiency CdTe nanowire-based photovoltaics with unparalleled conversion efficiencies.

Future Low-Cost Urban Air Quality Monitoring Networks: Insights from the EU’s AirHeritage Project

The last decade has seen a significant growth in the adoption of low-cost air quality monitoring systems (LCAQMSs), mostly driven by the need to overcome the spatial density limitations of traditional regulatory grade networks. However, urban air quality monitoring scenarios have proved extremely challenging for their operative deployment. In fact, these scenarios need pervasive, accurate, personalized monitoring solutions along with powerful data management technologies and targeted communications tools; otherwise, these scenarios can lead to a lack of stakeholder trust, awareness, and, consequently, environmental inequalities. The AirHeritage project, funded by the EU’s Urban Innovative Action (UIA) program, addressed these issues by integrating intelligent LCAQMSs with conventional monitoring systems and engaging the local community in multi-year measurement strategies. Its implementation allowed us to explore the benefits and limitations of citizen science approaches, the logistic and functional impacts of IoT infrastructures and calibration methodologies, and the integration of AI and geostatistical sensor fusion algorithms for mobile and opportunistic air quality measurements and reporting. Similar research or operative projects have been implemented in the recent past, often focusing on a limited set of the involved challenges. Unfortunately, detailed reports as well as recorded and/or cured data are often not publicly available, thus limiting the development of the field. This work openly reports on the lessons learned and experiences from the AirHeritage project, including device accuracy variance, field recording assessments, and high-resolution mapping outcomes, aiming to guide future implementations in similar contexts and support repeatability as well as further research by delivering an open datalake. By sharing these insights along with the gathered datalake, we aim to inform stakeholders, including researchers, citizens, public authorities, and agencies, about effective strategies for deploying and utilizing LCAQMSs to enhance air quality monitoring and public awareness on this challenging urban environment issue.

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.

Feasibility and Affordability of Low-Cost Air Sensors with Internet of Things for Indoor Air Quality Monitoring in Residential Buildings: Systematic Review on Sensor Information and Residential Applications, with Experience-Based Discussions

In residential buildings that are private, autonomous, and occupied spaces for most of the time, it is necessary to maintain good indoor air quality (IAQ), especially when there are children, elderly, or other vulnerable users. Within the development of sensors, their low-cost features with adequate accuracy and reliability, as well as Internet of Things applications, make them affordable, flexible, and feasible even for ordinary occupants to guarantee IAQ monitoring in their homes. This systematic review searched papers based on Scopus and Web of Science databases about the Low-Cost Sensors (LCS) and IoT applications in residential IAQ research, and 23 studies were included with targeted research contents. The review highlights several aspects of the active monitoring strategies in residential buildings, including the following: (1) Applying existing appropriate sensors and their target pollutants; (2) Applying micro-controller unit selection; (3) Sensors and devices’ costs and their monitoring applications; (4) Data collection and storage methods; (5) LCS calibration methods in applications. In addition, the review also discussed some possible solutions and limitations of LCS applications in residential buildings based on the applications from the included works and past device development experiences.

The sphere of exposure: centering user experience in community science air monitoring

Community science has increased in popularity in communities where residents hope to investigate the relationship between environmental issues and personal health. This study partnered with neighborhoods in the most polluted residential zip code in the US to conduct community science air quality monitoring. We conducted 60 semi-structured interviews after two monitoring deployments to understand participants’ subjective experiences of pollution exposure, their engagement with low-cost air quality monitors, and their data interpretation. We utilize the environmental health concept ‘exposure experience’ to analyze how participants use personal monitors, understand their data, and reinterpret their pollution exposure as a result. We further explore how participants’ understandings are circumscribed by the technological features of low-cost monitors. We find that participants adopt both protective and mitigating behavioral changes based on information gained from personal experiments and hypothesis testing while using the monitors. Of their own accord, 40% of participants in this study adopted mitigation behaviors after identifying sources that impacted their personal air quality. Our analysis reveals that real-time data accessibility through low-cost monitors builds exposure awareness and enables residents of environmental justice communities to test, validate, or invalidate sensory experiences and challenge existing assumptions. These findings point to specific pathways for using low-cost monitors to support individual decision-making and contribute to behavioral change. Findings also identify some limitations of low-cost monitors; designers of low-cost monitors should consider how composite Air Quality Scores may encourage community scientists to equally value scientifically-established pollutants (e.g., PM) with less scientifically-established pollutants (e.g., TVOCs), without additional scientific training and health-related information.

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.

Measurements of the Limit of Detection for Electrochemical Gas Sensors

Abstract Electrochemical amperometric gas cells are becoming the sensor of choice when measuring polluting gases using low-cost air quality networks. A number of technical issues remain to be resolved to deliver fit-for-purpose monitoring systems: humidity corrections are needed but not well understood, interfering gases such as ozone can have variable cross-sensitivity and calibration intervals, and procedures are still being investigated. Another unanswered question is the limit of detection (LOD) for electrochemical gas sensors. Estimates range from hundreds of equivalent parts per billion (ppbv) to single-digit ppbv concentrations. We discuss the LOD for nitrogen dioxide (NO2), an important gas when monitoring air quality. Multiple NO2 sensor systems were tested in an environmental chamber to determine, among other parameters, the LOD for NO2 electrochemical gas sensors. Low-noise electronics and battery powering further reduced electronic noise, allowing the intrinsic LOD of the electrochemical cell to be determined. Noise, quantified as the standard deviation in zero air in a very stable temperature and relative humidity–controlled chamber was <500 pA, which translated into 1.6 ppbv, so the LOD, 3 × standard deviation, was 4.8 ppb. Interestingly, the LOD calculated with 300 ppbv NO2 test gas was the same (±0.1 ppbv). Further tests with a higher resolution analog-to-digital converter resulted in the same LOD, further leading to the conclusion that for the Alphasense NO2-A43F NO2 sensor, the limiting value for LOD is 4.8 ppbv.

Evaluating the Performance of Low-Cost Air Quality Monitors in Lima, Peru

Evaluating the Performance of Low-Cost Air Quality Monitors in Lima, Peru

Household air pollution exposure assessment using low-cost air monitors: results from a large prospective cohort (HEALS-AIR) study in Bangladesh.

Household air pollution exposure assessment using low-cost air monitors: results from a large prospective cohort (HEALS-AIR) study in Bangladesh.

Design and Enhancement of a Fog-Enabled Air Quality Monitoring and Prediction System: An Optimized Lightweight Deep Learning Model for a Smart Fog Environmental Gateway.

Effective air quality monitoring and forecasting are essential for safeguarding public health, protecting the environment, and promoting sustainable development in smart cities. Conventional systems are cloud-based, incur high costs, lack accurate Deep Learning (DL)models for multi-step forecasting, and fail to optimize DL models for fog nodes. To address these challenges, this paper proposes a Fog-enabled Air Quality Monitoring and Prediction (FAQMP) system by integrating the Internet of Things (IoT), Fog Computing (FC), Low-Power Wide-Area Networks (LPWANs), and Deep Learning (DL) for improved accuracy and efficiency in monitoring and forecasting air quality levels. The three-layered FAQMP system includes a low-cost Air Quality Monitoring (AQM) node transmitting data via LoRa to the Fog Computing layer and then the cloud layer for complex processing. The Smart Fog Environmental Gateway (SFEG) in the FC layer introduces efficient Fog Intelligence by employing an optimized lightweight DL-based Sequence-to-Sequence (Seq2Seq) Gated Recurrent Unit (GRU) attention model, enabling real-time processing, accurate forecasting, and timely warnings of dangerous AQI levels while optimizing fog resource usage. Initially, the Seq2Seq GRU Attention model, validated for multi-step forecasting, outperformed the state-of-the-art DL methods with an average RMSE of 5.5576, MAE of 3.4975, MAPE of 19.1991%, R2 of 0.6926, and Theil's U1 of 0.1325. This model is then made lightweight and optimized using post-training quantization (PTQ), specifically dynamic range quantization, which reduced the model size to less than a quarter of the original, improved execution time by 81.53% while maintaining forecast accuracy. This optimization enables efficient deployment on resource-constrained fog nodes like SFEG by balancing performance and computational efficiency, thereby enhancing the effectiveness of the FAQMP system through efficient Fog Intelligence. The FAQMP system, supported by the EnviroWeb application, provides real-time AQI updates, forecasts, and alerts, aiding the government in proactively addressing pollution concerns, maintaining air quality standards, and fostering a healthier and more sustainable environment.

Developing and testing low-cost air cleaners for safer spaces during wildfires

Air cleaning reduces indoor exposure to fine particulate matter (PM2.5) during wildfire smoke events. However, resource and cost constraints may limit access to air cleaning during such an event, as both commercial devices and the higher-rated MERV filters that do-it-yourself (DIY) assemblies typically rely upon tend to be expensive and in short supply. With these constraints in mind, we developed and evaluated several configurations of a novel, DIY air cleaner that uses common household fabrics as filtration media. Clean air delivery rates (CADRs) of the devices were experimentally evaluated in two ways: first, with independent measurements of flowrates and single pass removal efficiencies, and second, via pull-down testing in a large chamber. With two layers of cotton batting fabric and a flowrate-increasing cardboard shroud attached, the device achieved particulate matter CADRs of 162, 134, and 206 m3/h in 0.02–0.3, 0.3–1, and 1–2.5 µm particle diameter bins, respectively, during chamber testing. Results indicate that these simple, inexpensive, fabric configurations can meaningfully reduce PM2.5 levels in smaller zones of a home, and thus represent a viable option for improving indoor air quality during rapid-onset air pollution events, such as wildfires.

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.