This session will showcase best practices in using LCS for AQ monitoring. We will explore case studies where LCS enhance remote sensing, improve forecasting and modeling, and support public health and community monitoring initiatives, particularly, but not limited, to resource-limited settings. This session promotes practical sensor applications that enhance data capabilities and foster strong local and international collaboration. It will also explore strategies for sustainable practices that empower communities and ensure equitable partnerships.
We welcome, but not limit, contributions that aim at:
- Proposing innovative solutions to address QA/QC in sensors.
- Characterizing sensor uncertainty and evaluating sensor performance across various locations and/or climatic conditions.
- Addressing calibration and maintenance challenges to ensure long-term accuracy and reliability of sensors.
- Enhancing local technical skills and computational resources to manage, integrate, and analyze large volumes of data.
- Highlighting the integration of sensor data with other data sources such as satellite imagery, models, and higher-end measurements.
- Designing effective sensor networks to optimize coverage and data reliability.
- Tackling technical and logistical challenges in sensor use within financially and technically constrained environments.
- Assessing the role of sensors in public health and personal exposure studies.
- Evaluating the role of policy and regulation in promoting and overseeing the effective use of sensors.
- Fostering citizen involvement in AQ monitoring to enhance community engagement and data collection.
- Building frameworks for enduring local and international partnerships that support comprehensive AQ initiatives.
Low-cost air quality sensing holds the promise to transform how we address air pollution by providing more information and more science that can drive public interest, political will, and policy toward cleaner, healthier air. Crucially, through a combination of its price point and transportability, the technology also makes it feasible for more people to do this work in more places.
However, technology alone cannot fulfill this promise. Realizing its potential requires the low-cost sensing community of people who build sensors, measure data, generate analyses, and use insights from those analyses to actively and strategically work together toward shared goals.
This presentation will explore what could be possible in the coming decade for the low-cost sensing community to accomplish, in terms of influencing policy and public engagement — and ultimately, cleaner healthier air.
The presentation will also define three actionable opportunities for the low-cost sensing community to shape itself toward that greater impact:
- Advocate for greater software, hardware, and data transparency policies, along with stronger consumer ownership rights from air quality sensing companies to better support the community — particularly those in low-resource settings
- Collaborate on open-source quality assurance/quality control (QA/QC) technical and community frameworks that minimize inefficient duplication of effort and allow for adaptability across a range of sensor array sizes
- Attract and allocate financial resources to expand accessibility and utility of low-cost sensing, especially in places with high air pollution levels yet low existing air quality management infrastructure and resources
How to cite: Hasenkopf, C.: How can the low-cost air quality sensor community maximize its positive impact? , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-6193, https://doi.org/10.5194/egusphere-egu25-6193, 2025.
Particulate matter (PM₂.₅ and PM₁₀) and black carbon (BC) significantly affect climate and public health, especially in rapidly urbanizing regions. This study examines the spatial and temporal variations of PM₂.₅, PM₁₀, and BC in the Greater Accra Metropolitan Area (GAMA), Ghana, from January to December 2021. Measurements were conducted at a busy, high-density residential site and a low-density residential background site using two federal equivalent monitors, alongside meteorological parameters (relative humidity, temperature, wind speed, and wind direction).
Annual mean concentrations of PM₂.₅, PM₁₀, and BC were significantly higher at the high-density site characterized by heavy traffic and residential congestion than at the predominantly residential background site. These discrepancies underscore the influence of distinct land-use patterns, local emissions, and site-specific activities on air quality. Seasonal differences were also evident, particularly during the Harmattan—a dry, dusty trade wind unique to sub-Saharan Africa that substantially degrades air quality. During this period, both sites experienced elevated pollutant levels, with consistently higher measurements at the urban site, highlighting the marked increase in PM₂.₅ and BC concentrations in busy urban areas.
Analysis of the PM₂.₅/PM₁₀ ratio showed lower values during the Harmattan, reflecting the predominance of coarse dust particles from natural sources, whereas higher ratios during the wet season indicated greater contributions from fine particles generated by anthropogenic activities such as traffic and industrial processes. Conditional Bivariate Polar Plot analysis further revealed that pollutant levels at the urban site were more strongly driven by wind speed, indicating substantial local emissions and particle resuspension due to heightened human activity. In contrast, concentrations at the background site remained relatively uniform, indicating minimal local emissions and a stronger influence of regional background levels.
Overall, this study illustrates the significant spatial and temporal variability of air pollution in GAMA, with important implications for public health and climate change. The elevated levels of PM₂.₅ and BC during the Harmattan period underscore the need for targeted air quality management strategies to mitigate health risks and environmental impacts in sub-Saharan Africa’s rapidly urbanizing environments
How to cite: Nimo, J., Yussif, I.-A., Borketey, M. A., Appoh, E. K.-E., Essien, B., Amoah, S., Modupeh Hodasi, J., and Hughes, A. F.: Space-Time Air Quality Disparities in Sub-Saharan Africa: PM₂.₅, PM₁₀, and Black Carbon Trends in the Greater Accra Metropolitan Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9303, https://doi.org/10.5194/egusphere-egu25-9303, 2025.
The use of low-cost sensors is continuously increasing as more evidence appears of their benefits: from the wide range of applications to the ease of use and accessible costs. Still, they are extensively studied to rightfully understand their range of use and the right way to handle their data. The first part of this work focuses on one month of simultaneous measurements of temperature, relative humidity, pressure, CO, NO, NO2, O3, OX, VOCs, PM2.5 and PM10 by three different low-cost sensors and a Pollution PyxisGC BTEX. Dataset acquired by ARTA (Agenzia Regionale per la Tutela dell’Ambiente) and ITAF (Italian Air Force) have been used as reference for the comparison. Vaisala AQT530, while also being part of the characterized instruments, has been used as reference for those quantities that were not present in the mentioned dataset and a 2B Ozone Monitor has also been used for O3 characterization.
Aside from a discrepancy in temperature and RH for AirSensEUR, the meteorological quantities for all the sensors show high correlation values (R ∼ 0.9). NO, NO2 and VOCs have high correlation with the same compounds observed by ARTA instruments (R ≥ 0.8), except for Libelium Smart Environment PRO NO2 that has a lower value (R = 0.348). Pollution PyxisGC BTEX VOCs comparison shows low slope values (∼ 0.16 against 1 hoped). CO measurements have a high similarity between the three low-cost sensors, differently OX values show a generally lower similarity with the reference instruments (with R ∼ 0.8 for Vaisala while being R ∼ 0.5 for the others). Libelium Smart Environment PRO measurement of NO, NO2 and O3 are affected by an extremely high bias (with values ≥ 100), a peculiar result considering how the sensors mounted where all factory-new and already calibrated by the manufacturer. PM values have only been compared averaging over the entire day (due to the kind of reference data available), showing a general matching with ARTA measurements only for AirSensEUR, which, at the same time, has the highest standard deviation.
Moreover, the dataset has also been used as a case study to investigate the ability of these instruments to catch signals from different near sources: 1) the local regional airport, 2) heavily used highway and roads. Analysis during rush hours, weekdays vs weekends, during the Christmas Holiday and with the help of wind data have been conducted. The results prove the ability of these low-cost sensors to detect rush hours measurements as well as the contribution to CO, NO, NO2, VOCs and PM emission due to the presence of the near highway.
How to cite: Gentile, L., Aruffo, E., Mascitelli, A., Chiacchieretta, P., and Di Carlo, P.: Characterization of low-cost sensors via simultaneous field measurements: a case study, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12322, https://doi.org/10.5194/egusphere-egu25-12322, 2025.
We reviewed 60 sensor networks and 17 related efforts (sensor review papers and data accessibility projects) to better understand the landscape of stationary low-cost gas-phase sensor networks deployed in outdoor environments worldwide. We found that particulate matter (PM) is more commonly studied globally than gas-phase compounds, and coverage gaps are most severe in the Global South and rural areas. Data quality and availability were also found to be barriers to access, with the highest quality data typically emanating from research institutions, which also tend to have the least straightforward data access for the public. In response, we aim to harmonize sensor networks by amalgamating measurements from a multitude of networks into one open access database hosted by NASA’s Atmospheric Science Data Center. As of early 2025, data from 12 unique US-based sensor networks have been collected for redistribution in the archive, and we are currently recruiting global network participants. Data from each network will be reformatted in a common data format with metadata embedded to streamline data processing. A key feature is a tier system in which we critically review each sensor network by their calibration efforts and subsequent data quality, providing data quality flags to help end users determine which sensor data best meets their target application. The accessible and inclusive open science platform will allow users to search based on calibration criteria in addition to location, date range, and pollutant of interest. The sensor database is aimed at scientific end users seeking ground-based validation data for satellites and models alike, but is also accessible to community scientists. Future iterations will include data from global sensor networks, and assimilation with satellite and ground-based remote sensing data.
How to cite: Okorn, K. and Iraci, L.: Development of an Open-Source Harmonized Low-Cost Sensor Data Archive to Maximize Scientific Return from Existing Networks, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7017, https://doi.org/10.5194/egusphere-egu25-7017, 2025.
Air pollution continues to be a major global health concern, with nitrogen dioxide (NO2) significantly contributing to negative health impacts. Low-cost sensors (LCS) present promising opportunities for accessible, high-resolution air quality monitoring but are often questioned for their accuracy and reliability. This study assesses the performance of electrochemical LCS for NO2 measurements compared to high-precision reference instruments—cavity attenuated phase shift (CAPS) and chemiluminescence NO2 monitors—across eleven temporal resolutions (ranging from 10 seconds to 6 hours). Data were collected over six months at an urban-traffic air quality monitoring site in Berlin using three EarthSense Zephyr sensor systems equipped with electrochemical sensors. Statistical metrics, including R², relative error (%), and mean bias error (MBE), were used to evaluate sensor performance. The results indicate that LCS demonstrate strong agreement with reference instruments at coarse time resolutions (≥1-hour averages, R² > 0.8), but their accuracy declines considerably at higher resolutions (<1-minute, R² < 0.5). Performance improves when sensors are calibrated against CAPS monitors compared to chemiluminescence monitors. Factors such as chemistry and emissions play a significant role, with poorer performance during the day than at night, a discrepancy that is further amplified at finer temporal resolutions. CAPS-calibrated predictive models also excel in capturing short-term concentration peaks compared to those calibrated with chemiluminescence monitors. These findings highlight that while LCS are effective for coarse-resolution NO2 monitoring, their limitations in dynamic environments at high temporal resolutions pose challenges for use in exposure studies and mobile applications. The study recommends careful calibration, strategic experimental design, and a focus on lower time-resolution applications to enhance LCS deployment.
How to cite: Schmitz, S., Caseiro, A., and von Schneidemesser, E.: Assessing low-cost sensor performance at varying temporal resolution against reference instruments for the measurement of NO2, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11078, https://doi.org/10.5194/egusphere-egu25-11078, 2025.
Exposure to impaired ambient air quality, including fine particulate matter (PM2.5), poses significant risks to human health. Monitoring air pollutants is essential for understanding pollution trends, assessing exposure risks, and informing mitigation strategies. However, traditional regulatory-grade air quality monitoring networks are often sparse and costly to operate, limiting their ability to provide data at high spatial resolution. Low-cost sensors offer an alternative by enabling the deployment of localized monitoring stations with broader coverage, but their accuracy can be compromised under variable environmental conditions. To address this, data fusion techniques can be used to integrate data from multiple sensors and improve air quality predictions. However, integrating multimodal data presents challenges, including incompatible measurement units, spatial and temporal resolutions, and inherent uncertainties.
Here, we propose a probabilistic spatiotemporal model based on the stochastic advection-diffusion (SAD) equation for data fusion in air quality monitoring. The SAD model offers computational efficiency and flexibility, allowing it to handle large datasets while accounting for prediction uncertainties in air quality data. This probabilistic approach is well-suited for air quality managers and policymakers, as it not only predicts air quality with high accuracy but also provides interpretable model parameters that offer insights into the underlying processes driving air pollution. The approach is then evaluated using PM2.5 data from the Coastal Bend Region of Texas, an area facing growing environmental concerns due to expanding industrial development. When the spatiotemporal model is integrated with data from both regulatory-grade stations and low-cost sensors, error is reduced by 40% compared to the nearest regulatory-grade monitor located 20 km away and 11% compared to the nearest low-cost sensor located 1 km away. The model captures 78% of observed data within a 75% confidence interval, demonstrating its ability to accurately represent uncertainty. This method provides a promising framework for integrating diverse air quality data sources, addressing uncertainties, and enhancing community-engaged pollution monitoring efforts.
How to cite: Hummel, M. and Choi, B.: Enhancing Low-Cost Sensor Networks through Multimodal Data Fusion: Application of a Probabilistic Spatiotemporal Air Quality Model, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10518, https://doi.org/10.5194/egusphere-egu25-10518, 2025.
Increased industrialization has led to heightened exposure to poor air quality, raising the risk of cardiovascular diseases. Forest environments are increasingly valued for their ability to improve human mental and physical health by releasing phytoncide and reducing air pollutants. Xitou Experimental Forest of National Taiwan University (23.40°N, 120.47°E, 1178 m a.s.l.), a cloud forest in central Taiwan, located in a valley linked to industrial and metropolitan areas to the northwest, experiences air pollutant transport influenced by land-sea and mountain-valley breezes. These local circulation patterns bring urban air pollutants inland during the day, causing higher daytime concentrations compared to nighttime levels. To evaluate the contributions of physical transport and chemical reactions across different seasons, we applied five home-built Air Quality Box (AQB) systems along the valley. Each AQB integrates low-cost sensors to monitor ambient gaseous pollutants (CO, NO, NO2, O3, SO2, CO2, and non-methane hydrocarbon), ambient particle number size distribution (0.38-17 μm diameter), and meteorological parameters (temperature, relative humidity, and pressure). The particle size distribution shifts toward larger sizes with elevation, driven by hygroscopic growth as relative humidity increases during parcel ascent. Aerosols might act as cloud condensation nuclei, forming fog droplets predominantly around 5 μm in diameter in the Xitou area. With Mie scattering calculations, the extinction effect of aerosols and visibility in the study area can be estimated. CO concentrations, a marker of local pollutant transport, increase with the development of sea breeze and valley wind but decrease as the cleaner mountain wind prevails. Seasonal variations show that the mountain wind develops earlier in winter than in summer, leading to earlier pollutant reductions. Furthermore, agglomerative hierarchical clustering of diurnal CO patterns shows how pollutant concentrations rise with the development of valley winds and decrease with mountain wind onset around 17:00. These results demonstrate the utility of AQBs in providing high temporal-spatial resolution data to analyze complex transport dynamics and fog formation processes in mountain environments.
How to cite: Huang, W.-C. and Hung, H.-M.: Monitoring Fog Evolution of Air Quality in Central Taiwan Mountain Area Using Air Quality Boxes, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-12373, https://doi.org/10.5194/egusphere-egu25-12373, 2025.
Low-cost air quality sensor systems (LCS) represent a transformative tool in modern air quality management strategies, offering unprecedented opportunities to complement traditional monitoring approaches. The integration of LCS data with established monitoring systems, including satellite observations and reference-grade instrumentation, has the potential to significantly enhance the reliability and applicability of air quality data. In regions lacking comprehensive monitoring networks, LCS can bridge critical gaps by identifying local factors affecting air quality, thus guiding targeted monitoring efforts and informing policy development.
A key advantage of LCS lies in their capacity to extend the spatial and temporal coverage of existing monitoring networks. By deploying LCS in underserved areas, policymakers can gain actionable insights into localized pollution patterns, which are essential for designing effective mitigation strategies. Furthermore, the use of LCS promotes community engagement by empowering local stakeholders to participate in air quality monitoring and advocacy.
Despite their potential, the application of LCS data must account for inherent limitations in accuracy and precision. Co-locating LCS with reference-grade monitors is a critical step to quantify measurement uncertainties and ensure data quality. This approach facilitates the calibration of LCS, enabling their use in advanced applications such as air quality forecasting, source impact analysis, and public health assessments.
The World Meteorological Organization (WMO) has played a pivotal role in coordinating global efforts to standardize and optimize the deployment of LCS technologies. Through the development of guidelines, best practices, and frameworks for integration, the WMO has provided critical support for national and regional initiatives aimed at improving air quality management. Recent developments, overviewed in the WMO’s 2024 report (WMO, 2024), highlight the organization’s leadership in promoting the use of LCS as integral components of comprehensive air quality management frameworks. This report underscores the importance of integrating LCS with traditional and emerging data sources, offering practical guidance on network design, calibration protocols, performance evaluation, and data communication. These insights align with previous WMO publications that establish foundational principles for LCS operation and deployment.
The continued refinement of LCS technologies, alongside efforts to standardize their use within monitoring networks—coordinated by institutions such as the WMO—will be pivotal in unlocking their full potential and fostering a more equitable approach to air quality management worldwide.
The present contribution will overview the main outcomes of the WMO’s 2024 report on the use of LCS for different air quality applications from supporting air quality management strategies to promoting social awareness of air pollution issues.
How to cite: Subramanian, R. S., Basart, S., Malings, C., Amegah, K., Diez, S., Rosales, C., and Zimmerman, N.: Integrating Low-cost Sensor Systems and Networks to Enhance Air Quality Applications, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21629, https://doi.org/10.5194/egusphere-egu25-21629, 2025.
Air quality monitoring networks in the Global South often face challenges due to data scarcity and limited resources, which can hinder effective air quality management. This study presents a methodology for optimizing the spatial distribution of air quality monitoring stations in urban areas of the Global South, aiming to improve data representativeness for population exposure and inform evidence-based policies.
Building upon existing monitoring network design literature (Kanaroglou et al., 2005; Gupta et al., 2018), our approach integrates high-resolution population data with a modified K-means clustering algorithm. We combine the center of gravity concept with standard K-means to determine monitor locations, prioritizing areas of high population density. The methodology incorporates a two-tier approach, categorizing areas into low and high population density zones and applying weighted K-means clustering separately to each category. To enhance applicability across diverse urban landscapes, we implement geospatial considerations in distance calculations, addressing limitations of standard Euclidean distance-based methods in geographic coordinate systems.
We applied this methodology to rapidly growing urban centers including Lahore (Pakistan), Lagos (Nigeria), and Dhaka (Bangladesh). Results suggest potential improvements in representing population exposure compared to current monitoring configurations.
Limitations of our approach include its reliance on population data, which may overlook other important air quality determinants. The current method also does not account for land use patterns, emission sources, or meteorology. However, the proposed methodology provides a foundation for further development of air quality monitoring network design, potentially enhancing urban air quality management by optimizing air quality monitor placement in data-sparse regions of the Global South.
How to cite: Omar, A. and Naveed, M.: Population-Centric Optimization of Air Quality Monitoring Networks in Data-Sparse Urban Regions: A Weighted K-Means Approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4723, https://doi.org/10.5194/egusphere-egu25-4723, 2025.
Over the past decade, I have had the privilege of collaborating with multiple teams in the USA, Europe, Africa, and Asia on projects to develop, characterise, and use low-cost sensors for air quality studies. These experiences and lessons for the future will be summarised in this talk.
In 2015, we partnered with SenSevere (now part of Sensit) to develop the Real-time Affordable Multi-Pollutant (RAMP) monitor, showing that startups can quickly enter this field with innovative ideas. However, extensive field testing by an academic group was required to make the devices usable, following which a network of 50 RAMPs was deployed across Pittsburgh, Pennsylvania. The high spatial density, high time resolution monitoring enabled by the sensor network revealed the impact of a large point source outside the city, while also highlighting hyperlocal pollution and street canyon effects. The extended deployment also showed that electrochemical sensors for nitrogen dioxide have a relatively high detection limit (~15 ppb) at odds with laboratory data and need to be replaced annually.
Hurricane Maria critically impaired traditional monitoring in San Juan, Puerto Rico, but the portability and low power requirements of sensor-based devices enabled rapid, solar-powered deployments of RAMPs that found significant sulfur dioxide pollution from generator usage across the city.
Sensing with RAMPs and Modulair-PM nodes and on-site observations identified hyperlocal sources of pollution at stadiums in Qatar and justified mitigation actions to ensure that football fans breathed cleaner air. Multi-year sensor-based monitoring in Kigali, Rwanda; Nairobi, Kenya; Abidjan, Côte d'Ivoire; and Accra, Ghana (coupled with CHIMERE air quality modelling over East Africa and 3D satellite data over West Africa) identified key sources of local air pollution, the influence of regional transport including Saharan dust in West Africa, and the need to develop local emission inventories.
The West African and Middle-Eastern experiences also showed the inability of low-cost PM sensors to detect supermicron dust. Evaluations at the first India Sensor Evaluation and Training (Indi-SET) facility in Bengaluru, India over 2024 showed that more expensive OPCs can better detect supermicron construction dust, but using similar internal sensors does not guarantee similar performance across device integrators.
Field collocation with reference monitors is ideal to improve sensor data quality, but this is not always possible especially in the Global South. Global reanalysis data sets can help reduce known artifacts in PM sensors. Establishing more facilities like Afri-SET (Accra, Ghana) and Indi-SET can also help improve sensor data quality.
This work would not be possible without international collaborations and networks like AfriqAir, CAMS-Net, ASIC, IGAC/Allin Wayra, and WMO/GAFIS. Collaborators, especially early-career researchers, will be acknowledged on the respective slides.
I will conclude with key learnings and prospects for the use of low-cost air quality sensors to address the grand challenge of clean air for all.
(Note: I have no financial interest in any sensor company.)
How to cite: Subramanian, R. S.: A decade of research with low-cost air quality sensors: air pollution insights, key learnings, and the road ahead, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14577, https://doi.org/10.5194/egusphere-egu25-14577, 2025.
Low-cost sensor can be key additional tools to fixed air quality monitoring stations (AQMS)for more extended AQ assessment. Sensors can be deployed at a higher density due to their lower cost. However, the data quality of sensors is still unknown and can be function of location and meteorological conditions.
One of the issues that PM sensors are dealing with is the ability to measure coarse fractions of PM. It is known that some low-cost sensors calculate PM10 concentrations based on the measured concentrations of PM2.5. The main issue with the evaluation of PM sensors for PM10 in the field, is that PM10-2.5 fractions at most AQMS are relatively small, and relying on only field test for PM10 would not identify the problem, whereas the sensor would largely underestimate the PM10 concentrations when deployed at areas or specific events with high coarse fractions.
Coarse particles are defined here as PM10-2.5 = PM10 – PM2.5. A laboratory test was developed for PM sensors (as part of the CEN/TS 17660-2:2024) and will evaluate the potential of the PM10 sensor system to correctly measure the coarse fraction. This presentation presents the lab test to evaluate if sensor systems can measure also coarse PM fractions and can measure PM10 rather than calculating it from the PM2.5 signal.
The sensor systems under test are placed in the test chamber in close vicinity of the optical monitor (as equivalent method) and particles are generated and mixed so that sensor systems and optical monitor are exposed to the same PM concentrations and fractions.
The sensor systems are exposed to two different PM fractions over two tests (‘Coarse test’ and ‘Fine test’) to evaluate their response to PM. The size fractions generated inside the test chamber will fulfil the following requirements:
- Coarse test: >80% PM10-2.5 in PM10
- Fine test: <20% PM10-2.5 in PM10
For each test, a monodisperse aerosol with respectively coarse and fine size restrictions (and with the same composition) is used to generate these conditions. Based on these tests, the sensor response (ratio of the output of the sensor versus the equivalent method) is calculated for PM10-2.5 (coarse test) and PM2,5 (fine test). When the sensor measures the PM10 concentration it is assumed that the sensor response will not change significantly between the two conditions. An example to illustrate this approach will be given using the AirsensEUR (version 3.0) sensor system. The AirsensEUR sensor system has two sensors included. Laboratory test results will also be compared to field observations for this sensor system; data collected as part of the sensEURcity project.
How to cite: Van Poppel, M., Hofman, J., Peters, J., Van Laer, J., Lazarov, B., Gerboles, M., and Yatkin, S.: Performance of low-cost sensors to measure PM10: do they also measure coarse particles?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20256, https://doi.org/10.5194/egusphere-egu25-20256, 2025.
Over the last decade, low-cost sensors (LCSs) have improved air quality monitoring by enabling widespread, community-driven data collection, particularly in regions with limited resources. Although these LCSs have increased public engagement and enriched datasets for understanding pollution dynamics, challenges related to data quality, standardization, and interoperability have hindered their full integration into regulatory frameworks and large-scale environmental monitoring (Barkjohn et al., 2024; Carotenuto et al., 2023). The lack of consistent Quality Control (QC) processes and correction methodologies limits the reliability of LCS-derived data for applications such as public health assessments, modeling, and policymaking.
To address these issues, we introduce FILTER (Framework for Improving Low-cost Technology Effectiveness and Reliability), a scalable, multi-level QC, and correction framework designed to improve the reliability of PM2.5 data from citizen-operated LCS networks. FILTER employs spatial “correlation” and “similarity” QC tests, allowing for in-situ correction of LCS data in different environments. The FILTER framework’s effectiveness is validated using large-scale European data from the sensor.community and PurpleAir networks, two of Europe’s largest citizen-driven air quality networks, covering the period 2018 – 2023. The final dataset, including 521,115,762 hourly PM2.5 measurements from 37,085 locations, was categorized into “high quality,” “good quality,” and “other quality” groups. At the raw data stage, applying QC steps through the spatial similarity level results in a ~50.3% decrease in median RMSE (from 7.61 to 3.78 µg m⁻³) across 483 LCSs. For corrected data, applying the same QC steps reduces the median RMSE by ~49.5% (from 7.59 to 3.83 µg m⁻³) across 456 LCSs. These enhancements enable LCS data to be more effectively integrated into scientific research, regulatory datasets, and policy development.
FILTER demonstrates several key advantages: independence from sensor-specific designs, geospatial scalability, adaptability to real-time processing, and applicability to other pollutants with spatial patterns similar to PM2.5. However, its utility is constrained for pollutants like NO₂, which exhibit hyper-local variability, or scenarios requiring sub-hour temporal resolution. FILTER also demonstrates the potential of on-site calibration techniques that do not require co-location to generate accurate data from LCS networks. This is particularly important for developing large LCS networks that can contribute to both science and policy, especially in light of the new European Air Quality Directive.
References
Barkjohn, K. K., Clements, A., Mocka, C., Barrette, C., Bittner, A., Champion, W., et al. (2024). Air Quality Sensor Experts Convene: Current Quality Assurance Considerations for Credible Data. ACS ES&T Air.
Carotenuto, F., Bisignano, A., Brilli, L., Gualtieri, G., & Giovannini, L. (2023). Low‐cost air quality monitoring networks for long‐term field campaigns: A review. Meteorological Applications, 30(6), e2161.
We acknowledge funding for CitiObs project from the European Union’s Horizon Europe research and innovation programme under grant agreement No.101086421. We also acknowledge the contributions of sensor.community (https://sensor.community/en/, accessed October 2024) and PurpleAir (https://www2.purpleair.com/, accessed October 2024) sensor networks where the original sensor data come from, as well as the citizens who provided the low-cost sensor data.
How to cite: Hassani, A., Salamalikis, V., Schneider, P., Stebel, K., and Castell, N.: FILTER: Framework for Improving Low-Cost Sensor Network Data for Air Quality Monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5634, https://doi.org/10.5194/egusphere-egu25-5634, 2025.
This study investigates in-cabin air quality on Philippine public utility buses (PUBs) by measuring particulate matter (PM) and carbon dioxide (CO2) concentrations to assess potential exposure levels among daily commuters. Using compact air quality monitors with small-sensor technology, measurements were taken inside two types of PUBs: regular buses in the EDSA carousel and the smaller modern jeepneys currently being deployed across different routes in the metro. Data was collected across different routes with varying traffic and occupancy conditions to evaluate how these factors influence the variability and range of PM and CO2 concentrations. The air quality aboard the EDSA Carousel bus was assessed along its bus rapid transit route on EDSA, extending to the Parañaque Integrated Terminal Exchange (PITX). Meanwhile, the modern jeepney route began at its terminal near EDSA-Shaw Boulevard and ended in Antipolo City. The bus route covered approximately 28 kilometers (~2 hrs), while the modern jeepney route spanned 14 kilometers (~1 hr). Elevated CO2 levels were observed in both types of PUBs during rush hours increasing from 1039.51 ppm to 2284.03 ppm and 3806.9 ppm to 6150.44 ppm for the carousel bus and modern jeepney, respectively. This effect can be attributed to higher passenger occupancy and is more pronounced in the modern jeepney, which has a smaller cabin and often experiences passenger overloading, compared to the EDSA carousel bus, which features a larger cabin and enforces stricter passenger limits. CO2 concentrations also read higher at the rear of the buses, farther from the bus doors. Additionally, PM2.5 levels were elevated during periods of heavier road traffic, with levels climbing from 7.12 µg/m3 to 10.39 µg/m3 and 6.66 µg/m3 to 18.79 µg/m3 for the carousel bus and modern jeepney, respectively. The observed increase suggests that traffic conditions contribute considerably to indoor particulate matter exposure within the PUBs, which is likely due to the diffusion of outdoor air pollution when the doors open during stops.
How to cite: Pineda, R. E., Galido, A. C., Eslit, J. J., Agub, U. R., Ganhinhin, J., Menguito, M. C., Magpantay, P., Rosales, M., Austria, I., de Guzman, J., de Leon, M. T., Cajote, R., Co, P. J., and Hizon, J. R.: Investigating In-Cabin Air Quality in Public Utility Buses in the Philippines Using Small-Sensors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18970, https://doi.org/10.5194/egusphere-egu25-18970, 2025.
With the decrease of electronic component prices, powerful yet low-cost optical particle counters (OPCs) gain in popularity and are frequently used in citizen science as well as classical science projects. The application of OPCs in large numbers can yield higher spatial resolution and, thus, offers great opportunities for studies of spatial distribution and development, e.g. of dust or air pollution. As a consequence, sensor performance and long-term accuracy must be evaluated in order maintain data quality.
During the HiLDA campaign, a measurement campaign focused on Arctic dust emission, we deployed seven measurement stations at Arctic locations (Jan Mayen, Northern and Southern Iceland, Southern Svalbard, North West Norway, South East Greenland, Faroe Islands). Each station was equipped with a compact weather station and four Alphasense OPC-N3 low-cost OPCs, among others. Data was collected for different periods of one to over three years between 2020 and 2025.
During the deployment under occasionally severe weather conditions, most of the sensors age significantly, which was to be expected at the time of deployment. Therefore, of the four instruments at each station, only one system was operated permanently, while a second one was switched on every week for a short time period to allow for a detection of this aging. The other two served as spare, which were used when the continuously running system was deemed to be degraded. The findings from a total of 28 low-cost OPCs are presented. We observed a combination of continuous aging due to soiling and sudden degradations, probably linked to single extreme events. We present a correction scheme for the continuous aging and point out quality markers for the degradation, as well as observed instrument variation. This information can be used to develop adapted measurement strategies and yield an overall increased data quality.
How to cite: Schneiders, K., Moormann, L., Dupont, S., Koenen, D., Rabe, J., Dagsson Waldhauserová, P., Schepanski, K., Panta, A., Klose, M., Meyer, H., González-Flórez, C., González-Romero, A., Querol, X., Alastuey, A., Yus-Díez, J., Pérez García-Pando, C., and Kandler, K.: Long-term aerosol measurements of the Alphasense OPC-N3 in arctic regions: Sensor performance and corrections, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19762, https://doi.org/10.5194/egusphere-egu25-19762, 2025.
The rapid urbanization and industrialization in many parts of the world have made air pollution a global public health problem. Exposure to air pollutants has both acute and chronic impacts on health. In low and middle income countries like Tanzania have experienced accelerated population growth and urbanization in which air quality is generally poor and there is a lack of long-term reliable air quality monitoring.
Among the major air pollutants, particulate matter 2.5 (PM2.5) is the most harmful, and its long-term exposure can impair lung functions. Low-cost sensors (LCS) are becoming increasingly popular for measuring the level of particulate matter (PM) in the air. However, issues with reliability require calibrations before the sensors can be used in regulatory settings. The aim of this paper was to develop a statistical model for determining the accuracy of low-cost sensor network data. Considering PM2.5 adverse health impacts, especially on people’s respiratory systems. We therefore, developed a low-cost PM2.5 sensor calibration model for measuring PM2.5 concentrations using maximum likelihood method. Moreover, we used the model to predict the PM2.5 with its driving forces that is temperature and humidity, and estimated its parameters. The PM2.5 data used for the developed model were collected from LCS network of five stations from Dar es Salaam City including Kigamboni, Vingunguti primary, Jangwani, Ubungo, and Buza recorded from April 2022 to May 2023. The data was fitted to a regression model using Maximum-Likelihood (MLR). Descriptive and trend analysis was also performed using Mann-Kendall Trend analysis to describe the pollutant characteristics and identify significant trends in the selected stations in Dar es Salaam. The model performed well with high accuracy and performance with root mean of 3.58 and mean squared errors of 12.846, a coefficient of determination of 0.967, and mean absolute errors of 2.8.The results for MLR showed a high value of coefficient determination (R2=0.82) and low error measure.
Our results will aid in improving the accuracy of low-cost sensors for measuring PM2.5 concentrations, thereby providing cost-effective solutions for enhancing people’s health and well-being in Tanzania.
How to cite: Jacob Ngailo, T.: Statistical Modelling of low cost PM2.5 sensor data in Dar es Salaam City , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-788, https://doi.org/10.5194/egusphere-egu25-788, 2025.
NH3 is the major alkaline gas in the atmosphere and the third most abundant N-containing species, after N2 and N2O. It is an important target pollutant due to its role in N deposition processes impacting over ecosystems, and it is also a precursor of fine particulate matter (PM), known to cause several impacts on human health. Being able to detect and quantify NH3 is essential for determining the best mitigation policies to reduce these impacts, yet this is challenging given the high spatial and temporal variabilities of this pollutant.
Miniaturized sensors in theory combine the high time resolution with the flexibility in size and cost, however they have associated challenges including high or undefined LODs, variable response times, unspecified cross–interferences with other pollutants and degradation with usage time. The NH3 sensor market is less developed than some pollutants for ambient air applications, with most suppliers offering indoor applications at ppm level concentrations, while ambient NH3 concentrations are in the ppb range.
The results of a campaign comparing five NH3 sensors (four electrochemical and one chemiresistive) against a Picarro G2103 are reported. The campaign was carried out over 1 month at the Whim Bog, Scotland, which releases NH3 if specific wind speed and direction conditions are fulfilled simulating a small chicken farm. Local ambient concentrations are ~1-2 ppb. The commercial suppliers do not provide practical guidelines on how to properly use, maintain and clean the data for these sensors however the sensors were set-up accordingly to their technical capabilities, either sampling from a weatherproof enclosure or directly outdoor air. NH3 concentrations were <10 ppb up to 3 ppm from the Picarro data. The sensors had variable responses, with only two correlating with the NH3 release and the Picarro data (R2= 0.59 and 0.70). However, they underestimated the concentration levels (slopes = 0.26 and 0.6) and response times are still not satisfactory.
The study shows that only two out of five sensors were fit for measuring NH3 in the ambient air. Nonetheless, these two sensors data are to be used only as qualitative information and would need significant improvement before use in situations which require quantitative data. Specific technical challenges need to be addressed, including sensor orientation, housing, and the airflow inside of and quantification of the concentration range.
How to cite: Espina-Martin, P., Leeson, S., Nicoll, R., Yeung, K., Mullinger, N., Redon, N., Spelman, G., Costello, H., and Braban, C. F.: Ammonia miniaturized sensors: Are they ready to be used in outdoor environments?, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-298, https://doi.org/10.5194/egusphere-egu25-298, 2025.
Hybrid air quality monitoring integrating reference-grade instruments, low-cost sensors (LCS) and satellite data is revolutionising the air quality monitoring standards globally. The current study is from Bengaluru, a metropolitan city in southern India, where a multipollutant (particulate and gaseous) sensor network comprising 60 LCS (5-25 nodes each from five different Indian integrators) has been deployed across the city. Before deployment, these sensor nodes were initially collocated with reference-grade instruments at the India Sensor Evaluation and Training (Indi-SET) centre in Bengaluru. Correction models for PM2.5, PM10, NO2, and O3 were developed using regression and machine learning methods to improve sensor data reliability. The sensors used include electrochemical sensors (Alphasense (A4 or B4 series) or EC Sense TB600 series) for the gases and optical sensors (Plantower (PMS7003 or PMS5003), Tera Sense New Gen OEM, Sensirion SPS30 and/or Alphasense (OPC-R2 or OPC-N3)) for particulate matter (PM). One node from each integrator was collocated for a year to assess the long-term performance of these multipollutant sensors. During this period, an Aerodyne Time-of-Flight Aerosol Chemical Speciation Monitor (ToF-ACSM) is operated to evaluate the real-time performance of PM sensors with varying aerosol chemical composition. The localised correction of sensors reduced errors to 10-40 %, achieving a correlation with reference instruments greater than 0.7 and ensuring uniform performance across different integrators. Initial analysis of the deployed sensors indicated that PM2.5 levels exhibit significant monthly temporal variation but minimal spatial variation. In contrast, NO2 showed both spatial and diurnal variations across different nodes, with peaks in the morning and evening traffic rush hours. Additionally, spatial maps of various particulate and gaseous pollutants are being developed using the Land-Use Regression (LUR) model to estimate population exposure. The presentation will cover the long-term performance of multipollutant sensors, the performance of PM sensors with varying aerosol chemical compositions, the most effective long-term correction model, and the high-resolution mapping of pollutants using low-cost sensors and reference-grade instruments.
How to cite: Varghese, E., Lekinwala, N., N Kalkura, K., Malik, N., Shekar, V., Sridharan, S., Pratap S.Y., Y., Dey, S., and Ramachandran, S.: A Multipollutant Low-Cost Sensor Network in Bengaluru, India: Long-Term Performance Evaluation and High-Resolution Mapping of Particulate and Gaseous Pollutants, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-881, https://doi.org/10.5194/egusphere-egu25-881, 2025.
The advancement of low-cost sensors provides new opportunities in aerosol research. After calibrating the low-cost sensors in the laboratory and in the fields with research-grade instruments, the accuracy concern of the data quality is resolved. With these research-grade low-cost sensors, PM2.5 and PM1 in a time-resolution of minutes can be obtained. This presentation demonstrates the application of research-grade low-cost sensors in source evaluation for community and indoor PM sources, personal PM exposure assessment, and panel-type epidemiological studies which investigates the associations of peak PM exposure and heart rate variability (HRV). HRV is a marker of cardiac autonomic balance; the reduced HRV indicators were found to be associated with an increased risk of myocardial infarction.
Cases studies conducted in Asia will be presented. The sensor application on evaluating the contribution of community PM sources was conducted in the Central Taiwan in 2017. The sensor application on assessing indoor PM sources was conducted in the Taipei metropolitan area in the northern Taiwan in 2018. The PM exposure assessment and panel-type of epidemiological studies were conducted in the southern Taiwan and Indonesia in 2018 to 2020. Research-grade low-cost sensors, namely AS-LUNG-O, AS-LUNG-I, and AS-LUNG-P, were used for outdoor, indoor, and personal monitoring in these studies, respectively. The medical-certified RootiRx® sensors were used for HRV monitoring.
The results showed that incremental contribution from the stop-and-go traffic, market, temple, and fried chicken vendor to PM2.5 levels at 3–5m away were 4.38, 3.90, 2.72, and 1.80 μg/m3, respectively. Significant PM spatial variations observed further emphasized the importance of conducting community air quality assessment. For indoor sources, cooking occurred most frequently; cooking with and without solid fuel contributed to high PM2.5 increments of 76.5 and 183.8 μg/m3 (1 min), respectively. Incense burning had the highest mean PM2.5 indoor/outdoor (1.44 ± 1.44) ratios at home and on average the highest 5-min PM2.5 increments (15.0 μg/m3) to indoor levels, among all single sources. In exposure assessment and epidemiological studies, it was found that for a 10 μg/m3 increase in PM2.5, HRV indicators were reduced 1.3-4.0% in Taiwan subjects in summer and 1.8 -5.7% in Indonesia subjects in dry season. The low-cost sensors used and methodology demonstrated in this presentation can be applied in resource-limited countries to conduct PM and health research.
How to cite: Lung, S.-C. C., Liu, C.-H., Wen, T.-Y. J., Shui, C.-K., and Tsou, M.-C. M.: Evaluating contributions of community and indoor PM sources, assessing personal PM exposure, and conducting panel-type epidemiological studies in Asia with research-grade low-cost sensors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9426, https://doi.org/10.5194/egusphere-egu25-9426, 2025.
Air pollution poses a significant global health concern, particularly for children, who are especially vulnerable due to their developing bodies1. This research examines particulate matter (PM) levels and their sources at three schools in Port Harcourt, the largest and capital city of Rivers State in Nigeria. It is the fifth most populous city in Nigeria with air quality highly impacted by the oil and gas industry. Low-cost source apportionment techniques were employed that use the size distribution of PM to fingerprint air pollution sources2. The approach allows for a highly detailed understanding of the risks of exposure faced by both students and staff across rainy and dry seasons. By employing affordable sensors, PM1, PM2.5, and PM10 levels were measured, revealing frequent exceedances of WHO air quality standards. During school hours, indoor PM concentrations were found to surpass outdoor levels, influenced by internal sources and the penetration of outdoor pollutants. Seasonal differences were evident, with elevated PM levels during the dry season largely attributed to Harmattan desert dust, while anthropogenic emissions were the primary contributors during the rainy season. These findings highlight the pressing need for interventions to reduce PM exposure in schools and comparable urban environments affected by natural and anthropogenic pollution. This study offers practical recommendations to minimize exposure risks and enhance air quality in educational spaces globally.
1 Rose, O.G., D. Bousiotis, C. Rathbone and F.D. Pope (2024) Investigating Indoor Air Pollution Sources and Student’s Exposure Within School Classrooms: Using a Low-Cost Sensor and Source Apportionment Approach, Indoor Air, vol. 2024, Article ID 5544298, https://doi.org/10.1155/2024/5544298
2 Bousiotis, D., Allison, G., Beddows, D.C., Harrison, R.M. and Pope, F.D., 2023. Towards comprehensive air quality management using low-cost sensors for pollution source apportionment. npj Climate and Atmospheric Science, 6(1), p.122. https://doi.org/10.1038/s41612-023-00424-0
How to cite: Pope, F., Nwokorie, V., and Bousiotis, D.: Investigating the influence natural and anthropogenic air pollution sources upon school environments in a Global South metropolis using a low-cost source apportionment approach, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8927, https://doi.org/10.5194/egusphere-egu25-8927, 2025.
Citizen Observatories (COs) empower residents to become active contributors to the observation and management of their local environments. Instrumented with low-cost sensors, communities can monitor the environment. However, the data and insights from COs do not always contribute to policy development. To address this challenge, the CitiObs project (www.citiobs.eu) is developing tools, methodologies and approaches to enhance COs in observing, monitoring, and protecting the urban environment, with a focus on air quality.
CitiObs employs a co-creation framework, working closely with 85 COs to develop, test, and scale its tools and methodologies. Activities such as needs assessment workshops, capacity-building sessions, and strategic roadshows ensure the broad participation of stakeholders, from local citizens to EU policymakers.
We follow a socio-technical approach, aiming to drive societal transformation by integrating technical innovations with participatory governance, enabling localized citizen-led actions. CitiObs technical approach focuses on advancing the use of sensors, generating Analysis Ready Data (ARD), and creating Decision Ready Information (DRI) to support environmental monitoring and policymaking. To make data FAIR the project uses Sensor Thing API (and the Citizen Science extension: STAplus). To create ARD, the project has developed automated calibration methods to improve sensor accuracy and has proposed a unified terminology for data quality levels to ensure consistency and interoperability across datasets. The validated data is then used to generate DRI, as, for example, high-resolution maps of air pollution and thermal stress (e.g., integrating sensor data with satellite observations and CAMS models). The ARD and DRI can then be used for customized analytics, and it facilitates the uptake of policy makers and scientists as it ensures that citizen-collected data is reliable, or of known-quality. CitiObs social approach emphasizes inclusive citizen engagement, participatory governance, and citizen-led actions to foster sustainable urban environments. The project is developing four toolkits.
The Leaving No One Behind Toolkit helps COs enhance diversity and inclusion with practical guidance and resources for more inclusive initiatives. The Participation Dynamics Toolkit supports COs in fostering strong stakeholder relationships, addressing conflicts, and building trust. The Citizen Led Action Toolkit empowers communities to drive environmental protection through co-creation tools for planning impactful actions, often with artists and creatives. Lastly, the Environmental Monitoring Toolkit (https://github.com/citiobs) provides resources for affordable, open environmental monitoring, covering sensor selection, data quality, and interoperability.
The project’s outcomes include the development of a comprehensive Knowledge Platform and the CitiObs Cookbook, which will serve as resources for communities and practitioners in Europe and worldwide to establish or enhance their own Citizen Observatories. We are currently collaborating with the Citizen Science Global Partnership to transfer the know-how generated in the project to other countries in Africa, Latin America and Asia.
We acknowledge funding for CitiObs project from the European Union’s Horizon Europe research and innovation programme under grant agreement No.101086421.
How to cite: Castell, N., Hassani, A., Wehn, U., Maso, J., Tavares, J., and Chtzigoulas, A.: Enhancing Citizen Observatories for healthy, sustainable, resilient and inclusive cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15040, https://doi.org/10.5194/egusphere-egu25-15040, 2025.
Personal exposure to fine particulate matter (i.e., PM2.5) istypically measured for 24 or 48 hours in health effects research. Real-time, low-cost sensors (LCS) offer long-term PM monitoring solutions with potentially high spatiotemporal resolution that can facilitate better exposure – response analysis. We assessed long-term exposures to PM2.5 among pregnant women in the Tamil Nadu Air Pollution and Health Effects-II (TAPHE-II) and Reproductive effects from Exposure to Airborne Chemicals in urban Homes (REACH) cohorts using such real-time LCS.
Battery-operated real-time PM sensors equipped with PMS7003™ (Plantower Inc., China) were used to monitor living-room PM2.5 levels for a period of 21-days and 7-months in the TAPHE-II (n=80) and REACH (n=15) cohort homes, respectively. Further, pregnant women wore a portable ultrasonic personal air sampler (UPAS™ v2.1) for 24 hours, equipped with a 37-mm PTFE filter, to measure PM mass concentration. The LCS recorded PM, temperature, and relative humidity at 1-minute time intervals and transmitted data in real-time to the cloud. Sensors were collocated with gravimetric samplers for a period of 24-h on three consecutive days in 25 homes to develop indoor-specific calibration equations. In addition, all sensors were collocated with a reference-grade beta attenuation monitor pre- and post-monitoring period; linear models were used to derive ambient calibration coefficients.
Continuous PM data was monitored on average 21 (SD 3) days; data availability ranged between 97 to 100% across rural and urban homes in the TAPHE-II cohort. Precision across all sensors was satisfactory, with a standard deviation of 2.6 µg/m3 and a coefficient of variation of 15.6%. The normalized root mean square error (NRMSE) for indoor and ambient collocation was 31.6% (r=0.86) and 43.2% (r=0.80), respectively. Correlation (NRMSE) between measured personal daily exposures and 21-day real-time PM2.5 measures was 0.62 (47.9%). Long-term averages (min–max) of indoor PM2.5 levels were high among biomass users (n=20, 49.8 µg/m3 (35.8 – 80.6)), followed by mixed-fuel (n=14, 28.7 µg/m3 (29.4 – 61.9)), and liquefied petroleum gas (LPG) (n=46, 25.7 µg/m3 (21.3 – 39.5)) users.
We demonstrate the applicability of LCS for long-term indoor PM monitoring to assess health risks associated with indoor air pollution. Indoor-specific calibrations capture the true range of PM exposures and temporal variability, minimizing uncertainty in exposure – response relationships in health effects research. To more accurately assess the exposure of urban pregnant women, we are using LCS to measure indoor PM throughout most of the gestational period (i.e., up to 7 months). This data will be used to evaluate the representativeness of the 24-hour and 21-day average PM2.5 levels as a proxy of gestational exposure.
How to cite: Puttaswamy, N., Vackacherla, S., Ghosh, S., Saidam, S., Sendhil, S., Jayakumar, D., Patil, S., Pillarisetti, A., and Balakrishnan, K.: Quantifying long-term exposures to fine particulate matter (PM2.5) using real-time, low-cost sensors to assess the impact of household air pollution on birthweight in two cohort studies in southern India , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-3891, https://doi.org/10.5194/egusphere-egu25-3891, 2025.
Low-cost sensors are revolutionizing air quality monitoring, especially in under-resourced regions like Kenya. These devices enable affordable, localized data collection, which allows communities to identify pollution hotspots, raise awareness, and advocate for action. However, their integration into formal regulatory frameworks remains limited due to concerns over data reliability and perceived shortcomings compared to traditional systems (Lewis et al., 2016).
Many African cities face mounting air pollution challenges but lack consistent urban air quality monitoring. Although Kenya enacted Air Quality Regulations in 2014, data on particulate pollutants in Nairobi remains scarce. This gap is common across many African nations, hindering efforts to assess pollution impacts, inform policy, and respond effectively to deteriorating air quality. The global Air Quality Community of Practice (CoP) of the Citizen Science Global Partnership (CSGP) actively addresses these challenges by working to scale up air quality monitoring in under-resourced regions and demonstrating evidence-based policymaking through citizen science.
This presentation proposes actionable strategies to enhance the credibility and impact of low-cost sensors in policymaking and regulatory contexts in such regions. First, establishing universal calibration and validation protocols in collaboration with academic and industry stakeholders can significantly bolster the credibility of sensor data by ensuring alignment with regulatory standards (Crilley et al., 2018). The Air Quality CoP is collaborating with the WorldFAIR+ Project and the CitiObs project to create interoperability frameworks based on FAIR principles for citizen science air quality monitoring. Second, creating effective data communication strategies can maximize the visibility and impact of sensor-derived insights. Platforms that transform complex datasets into accessible visualizations and narratives can engage policymakers and the public, fostering broader support (Kumar et al., 2022).
Integrating citizen science into policy through multi-stakeholder collaborations institutionalizes community-driven data collection. Open-access platforms, such as OpenAQ, bridge local monitoring efforts with policy-level interventions, building stakeholder trust and cooperation. Finally, advocating for adaptive regulatory systems that position low-cost sensors as complementary tools to traditional monitoring methods and not replacements can drive innovation and amplify impact.
Drawing from case studies within the CoP and successful implementations, this session explores how these solutions can bridge the gap between citizen-driven data and institutional action. By tackling technical, communication, and policy challenges, low-cost sensors can be repositioned as essential tools for community agencies, filling data gaps, raising awareness, and impactful policy change.
We acknowledge funding for the CitiObs project from the European Union’s Horizon Europe research and innovation programme under grant agreement No.101086421.
References:
Lewis, A., et al. (2016). Evaluating low-cost sensors for air quality monitoring. Faraday Discussions. https://doi.org/10.1039/C5FD00201J
Crilley, L., et al. (2018). Calibration of low-cost air quality sensors for urban environments. Atmospheric Measurement Techniques. https://doi.org/10.5194/amt117092018
Kumar, P., et al. (2022). Bridging the gap between citizen science and policymaking. Environmental Monitoring and Assessment. https://doi.org/10.xxxxxx
Code for Africa. (2019, October 21). Measuring Nairobi’s air quality using locally assembled low-cost sensors. Medium. https://medium.com/code-for-africa/measuring-nairobis-air-quality-using-locally-assembled-low-cost-sensors-94a356885120
Hasenkopf C. et al (2024) Energy Policy Institute at Chicago. The Case for Filling Air Quality Data Gaps with Local Actors: A Golden Opportunity for the Philanthropic Community
How to cite: Nangila Wafula, F., Castell, N., and Hepburn, L.: Beyond Data: Leveraging Low-Cost Sensors for Policy Impact and Regulatory Acceptance , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20418, https://doi.org/10.5194/egusphere-egu25-20418, 2025.
Low-cost sensors (LCS) have emerged as versatile tools for air quality monitoring across diverse applications. This presentation synthesizes some of our work and findings from multiple projects, demonstrating the adaptability and effectiveness of LCS in various monitoring scenarios.
We have investigated the usefulness of low-cost sensors for continuous monitoring of particle exposure as a tool for workplace interventions. Through a systematic study across four companies, we have benchmarked the performance of LCS against reference-grade equipment in realistic work environment settings. LCS with sufficient accuracy potentially enables organizations to perform their own air quality management. We have provided such a system to the companies and are currently assessing how access to real-time LCS data has changed the safety cultures in the workplaces. Moreover, we have utilized LCS sensors to validate the performance of novel air cleaning technology in diverse environments such as garbage truck cabins and elder care facilities.
Another initiative aimed to develop an early warning system for fungal spores in greenhouse environments, to reduce the use of preventive fungicide. The system achieved an accuracy of 83% with less than 5% false positives in identification of “high” greymold spore-counts. This system demonstrated the potential for LCS in preventing crop diseases while reducing fungicide use.
In another project, we utilized CO2 sensors to study user behavior modifications in office environments, while also looking at its effects on the indoor climate. Revealing a simple case of how data analysis of simple measurements and the implementation of LCS itself provided insights into nudging humans to improve their working environment while determining the potential effects. Lastly, we have utilized LCS in schools and daycares to evaluate indoor air quality (IAQ) and correlate IAQ to the transmission of airborne patogens and thereby spread of disease.
These diverse applications highlight a small part of the versatility and strengths of LCS, while demonstrating practical solutions for data quality challenges, calibration procedures, and long-term reliability. Our experiences provide insights for implementing LCS across various scenarios, particularly in resource-constrained environments.
How to cite: Koust, S., Stoltenberg, M., Ottosen, T.-B., Møller, S., Rasmussen, F., Andersen, J., Rasmussen, J., Clausen, H., Ørby, P. V., and Gosewinkel, U.: From Workplace Safety to Behavioral Change: Diverse Applications of Low-Cost Sensors in Indoor Environments, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15372, https://doi.org/10.5194/egusphere-egu25-15372, 2025.
In the US, approximately 200,000 nail salon workers face chronic exposure to airborne chemicals. Health effects among this sector have been well documented and public health laws aimed at exposure reduction have been implemented across the US. In this study, we evaluated the accuracy and feasibility of using commercially available low-cost sensors as tools for workers to monitor and reduce their daily exposures. We compared the performance and utility of six commercially available low-cost total volatile organic compound (TVOC) sensors (Awair Omni, Kaiterra Sensedge, UHoo Smart Air Monitor, Airthings View Plus, Atmotube, and Atmocube) to validated reference instruments. Sensors were collocated in at least 4 different salons for 7 consecutive days during an initial baseline measurement period. Salons then received an intervention on methods to reduce exposure by utilizing existing controls and another 7 days of exposure measurements were collected. TVOC measurements from low-cost sensors exhibited moderate to strong correlations (rs ~ 0.54 - 0.88) with readings from validated reference instruments. Accuracy of the low-cost sensors varied, especially at higher TVOC concentrations and after repeated days of use. Low-cost sensors on average underestimated TVOC concentrations at the highest quartile of exposure (Mean Absolute Error (MAE) Q4: 16.19 – 28.49 ppm) but performed more similarly to reference instruments at lower quartiles of exposure (MAE Q1: 0.69 – 2.53 ppm, Q2: 1.60 – 2.75 ppm, Q3: 5.55 – 6.85 ppm). Despite some limitations, these sensors can be valuable tools for exposure assessment, including monitoring nail salon workers' daily exposures and studying the health effects of chemical exposures in longitudinal epidemiologic studies among this group.
How to cite: Thompson, R., Fisher, S., Ierardi, A. M., and Pavilonis, B.: Validating low-cost indoor air quality monitors to improve exposure monitoring in nail salons, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-7223, https://doi.org/10.5194/egusphere-egu25-7223, 2025.
Poor air quality disproportionately affects children's health, causing both clinical and subclinical effects including respiratory infections, asthma, allergies, absenteeism, and cognitive impairment. This challenge is particularly acute in developing countries and urban areas, where air pollution frequently exceeds the World Health Organization's global air quality guidelines. Leveraging innovative low-cost sensor technology, we designed a cluster randomized control trial to evaluate the effectiveness and economic feasibility of classroom portable air purification systems, while simultaneously developing a framework for continuous air quality monitoring in educational settings. We randomly assigned 95 classrooms (~2000 students) across five primary schools in Milan, Italy, to treatment and control groups, implementing a comprehensive monitoring system that integrated indoor and outdoor air quality sensors with health outcome data. Our sensor network collected continuous measurements of air quality parameters while enabling real-time data analysis and integration with survey data on health symptoms and air quality perception. Results demonstrate that air purifiers reduced indoor air pollution by 28%, corresponding to an 11% reduction in student absences. The impact was most pronounced among vulnerable students with higher pre-treatment absences and those of non-Italian nationality. Notably, the purifiers' effectiveness showed an inverse relationship with outdoor pollution levels, suggesting limitations in their ability to maintain healthy indoor air quality during severe pollution events. Our intervention also revealed improved self-reported respiratory health, enhanced awareness of air quality issues, and increased support for urban air quality policies among treated students. This study not only demonstrates the cost-effectiveness of school-based air purifiers but also establishes a replicable framework for implementing and evaluating air quality interventions in resource-constrained educational settings using affordable sensor technologies.
How to cite: Renna, S., Bonan, J., Granella, F., and Sarmiento, L.: Improving indoor air quality in schools: Evidence from an air purifier intervention with low-cost sensors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20302, https://doi.org/10.5194/egusphere-egu25-20302, 2025.
Air pollution constitutes a significant environmental justice challenge, particularly affecting vulnerable communities in low- and middle-income countries (LMICs). Urban mobility plays a substantial role in personal exposure to pollutants, notably PM2.5, exacerbating health disparities among transport users. Effective monitoring of these exposures is essential for understanding and addressing these inequities; however, traditional air quality measurement infrastructures are often inadequate in LMIC contexts. The growing availability of low-cost sensors (LCS) presents a promising avenue for bridging data gaps in urban air quality monitoring. Nonetheless, the reliability and applicability of these sensors in dynamic urban transport environments require thorough evaluation. This study, executed as part of an interdisciplinary collaboration among experts in urban policy, air quality, and transport studies, investigates the deployment of an LCS for PM2.5 monitoring in Soledad, Colombia. The research aims to assess the potential of LCS to capture exposure disparities among various modes of transport while addressing associated technical and logistical challenges. The primary focus is to evaluate the feasibility of utilizing LCS to measure personal exposure to PM2.5 in urban transport microenvironments, emphasizing calibration accuracy, adaptability to local conditions, and the potential to inform equitable transport policies. The AirBeam3 sensor was employed across motorized three-wheelers, buses, and private cars during predetermined urban routes. A rigorous 15-day calibration process against a reference-grade station was conducted to ensure data accuracy, achieving a correlation coefficient of R² = 0.87. Data collection strategies were tailored to account for transport-specific dynamics, including variations in ventilation, proximity to emission sources, and traffic conditions. The study encountered several challenges, including adaptation to high humidity, protection of equipment in high-risk environments, and correction of measurement biases. Notably, the sensor identified significant PM2.5 exposure disparities among transport modes, with motorized three-wheeler users exhibiting the highest exposure levels. Adjusted data indicated that environmental conditions, traffic density, and vehicle type emerged as critical determinants of exposure. Despite certain limitations, the LCS provided robust, high-resolution exposure data, demonstrating its suitability for capturing real-world variability in LMIC contexts. This research underscores the challenges and opportunities presented by the deployment of LCS for air quality monitoring in resource-constrained urban settings. While technical hurdles, such as calibration and environmental sensitivity, persist, the affordability and accessibility of LCS render them invaluable tools for addressing environmental justice issues. The findings emphasize the potential of LCS to enhance local air quality initiatives, inform sustainable transport policies, and promote equitable health outcomes through data-driven interventions.
Keywords: low-cost sensors, air quality, PM2.5, urban mobility, personal exposure, environmental justice, LMICs.
How to cite: Maldonado, S., Nieto-Combariza, M. J., Arellana, J., Dávila, J., Oviedo, D., Torreglosa, S., Parody, A., and Agudelo-Castañeda, D.: Challenges and opportunities in using low-cost sensors for PM2.5 monitoring in urban transport microenvironments: a study case in Barranquilla Metropolitan Region, Colombia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1084, https://doi.org/10.5194/egusphere-egu25-1084, 2025.
Air pollution is the second cause of death from non-communicable diseases. In Guanajuato, Mexico, the brick industry is the main means of life and source of polluting emissions, with health impacts (short stature, neurological deterioration, cardiorespiratory diseases and asthma). This sector has initiated regulatory changes but now there is no monitoring and its impact on health. As a first pilot phase, the objective was to measure the main air pollutants in a rural community in Guanajuato, Mexico, using a low-cost ATMOTUBE® monitor and describe the area and population group at greatest risk of exposure. Analytical and longitudinal design from September 2023 to February 2024, with the ATMOTUBE® measurement parameters, VOC, PM1, PM2.5, PM10, temperature, humidity and pressure. During the six months of measurement, the results were VOC 4.15±11.79 ppm, AQS 65.17±30.11, PM1 4.90±18.43 ug/m3. From January to February 2024 was the period of highest concentration of pollutants with a maximum concentration of PM2.5 of 664±12.5μg/m3, PM10 of 650±14.8μg/m3 and low humidity value (34.1 ± 5.2) where they are near two schools. The first inventory of the main air pollutants in a rural community is presented, with children and women being the population at greatest risk. With this data from this pilot phase, it is recommended to begin with surveillance measures as well as health and nutrition indicators.
How to cite: Monroy-Torres, R.: Pilot Study About Inventory of Air Pollutants in a Rural Community of Guanajuato, Mexico using a Low-Cost ATMOTUBE® Monitor, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-127, https://doi.org/10.5194/egusphere-egu25-127, 2025.
Personal exposure to PM2.5 poses a significant health risk, necessitating assessments at very high spatial and temporal resolutions. However, existing monitoring techniques, like Continuous Ambient Air Quality Monitoring Systems (CAAQMS), provide highly accurate data but are expensive and limited by their sparse distribution. Low-cost sensors (LCS) offer dense spatial data but often encounter reliability challenges and need extensive calibration. These limitations prevent the precise tracking of PM2.5 exposure at the personal level. To overcome these challenges, we have developed a hybrid framework integrating calibrated LCS data with CAAQMS observations. This approach aims to generate a unified, high-resolution spatiotemporal PM2.5 database, bridging existing gaps and significantly improving exposure assessments at the personal scale.
This study developed a high spatial (1 km × 1 km) and temporal (1 h) scale PM2.5 estimates by integrating calibrated static low-cost sensor (LCS) data with hourly ground-based PM2.5 measurements from CAAQMS across Kolkata, India, for the winter season (1st December 2023–31st January 2024). To harmonize these datasets and create a spatiotemporal database of PM2.5 estimates, we utilized hourly PM2.5 data from seven CAAQMS stations and calibrated data from 22 static LCS stations. The LCS calibration incorporated meteorological data, precisely temperature (T) and relative humidity (RH), sourced from the nearest CAAQMS stations.
We employed a Random Forest machine learning model, an ensemble algorithm that effectively captures complex non-linear relationships in the data and improves accuracy by combining multiple decision trees. Our model achieved an approximately 24% reduction in RMSE and an R² of 0.90, validated using an 80:20 train-test split, ensuring robust evaluation of its accuracy. This reduction demonstrates the efficacy of the integrated approach for high-resolution air quality mapping. On 4th December 2023, PM2.5 exposure estimates for a common grid point (88.34°E, 22.54°N) were derived using two approaches: one with only CAAQMS data and another with a hybrid of CAAQMS and LCS data. Without LCS, the exposure range at this grid point was 51.34 µg/m³, with an average exposure of 89.40 µg/m³. By integrating LCS data with CAAQMS, the exposure range was reduced to 30.02 µg/m³, and the average exposure increased to 103.39 µg/m³. This increase suggested that LCS might have captured more localized variations, contributing to the higher average exposure value. The reduction in the exposure range indicated a more consistent exposure pattern, highlighting the importance of integrating sparse, accurate CAAQMS data with spatially dense LCS data. This integration enhanced the spatial variability of PM2.5 and provided a more accurate estimate for personal exposure assessments.
How to cite: Sharma, K., Dey, S., Sen, R., and Chauhan, S.: Improving PM2.5 Exposure Modeling by Hyperlocal Monitoring Using Low-Cost Sensors in the Kolkata Metropolitan Area, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-829, https://doi.org/10.5194/egusphere-egu25-829, 2025.
Background: Air pollution is a major global health risk, causing seven million deaths each year. In The Gambia, the use of firewood and charcoal for household energy, along with harmattan dust, significantly increases exposure to harmful particulate matter (PM2.5). Women and children are particularly vulnerable to these health risks.
To address the air quality data gaps in The Gambia, we launched the Clean Air Initiative in 2023. Our goal was to set up a nationwide ambient air quality monitoring network using low-cost sensors. Led by the Permian Health Lung Institute (PHLI) and the National Environment Agency (NEA) in collaboration with various stakeholders, the initiative aimed to systematically tackle air pollution challenges.
Methodology: We began with stakeholder mapping to identify the multifaceted challenges of air pollution. Key participants included government ministries of health and environment, private construction companies, and academic institutions. The process involved installing air quality sensors, conducting education workshops, and integrating scientific data with actionable plans.
Results: Over 12 months, we deployed 17 low-cost air quality sensors (IQAir AVO) across all seven regions of The Gambia. This network helped identify air quality trends and assess their health impacts. Challenges such as sensor security and cellular network reliability were addressed through community engagement and solar panel installations. Our focus on the densely populated Greater Banjul Area enabled crucial observations and set the stage for nationwide expansion.
Data from our initial four sensors showed a 12-month average PM2.5 concentration of 36.9 µg/m³ (95% Confidence Interval: 31.9 - 42.0 µg/m³), which is seven times higher than the World Health Organization (WHO) guideline of 5 µg/m³. Seasonal variations were significant, with dry season levels (November to May) averaging 44.9 µg/m³ (95% CI: 39.7 - 50.2 µg/m³), compared to 25.8 µg/m³ (95% CI: 18.6 - 32.9 µg/m³) during the rainy season.
Discussion: A key outcome of the initiative was The Gambia's first Air Quality State Report, bolstered by continuous PM2.5 monitoring and capacity building at the National Environment Agency through international partnerships. Funding from the University of Chicago Energy Policy Institute was crucial for our success. Collaborations with regional entities like Makerere University are improving data accessibility across Africa. Despite these advances, challenges such as connectivity issues with remote sensors and environmental impacts on equipment remain, especially during seasonal transitions.
Conclusion: The Clean Air Initiative marks a significant leap in managing air quality in The Gambia. By establishing a comprehensive monitoring network and creating foundational policies, we are paving the way for improved public health and governance. Continued collaboration and overcoming operational hurdles are essential for sustained progress. Future plans for expansion and regional data integration offer promising pathways for effective air quality management in the region.
Keywords: Air pollution, PM2.5, The Gambia
How to cite: David, J., Badgie, D., Dumbuya, M., Sabally Touray, A., Touray, I., Camara, S., Manjang, B., and Touray, S.: Establishing a Nationwide Ambient Air Quality Monitoring Network: The Clean Air Initiative and PM2.5 Monitoring in The Gambia, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1232, https://doi.org/10.5194/egusphere-egu25-1232, 2025.
The growing availability of open geospatial data presents significant opportunities to address spatial and temporal gaps in air quality (AQ) monitoring. This is particularly crucial in resource-limited and underserved regions. In this study, we introduce a novel framework that combines low-cost sensor (LCS) measurements with Land Use Regression (LUR) models, utilising publicly accessible datasets to improve the spatial and temporal resolution of AQ estimates. By integrating empirical sensor data with open, openly available model predictors, the framework enhances the accuracy of air quality predictions, particularly in areas with limited high-density monitoring infrastructure.
The open LUR model uses freely available data, such as traffic density, land cover, population distribution, and meteorological information, from OpenStreet Map and OpenWeatherMap, to predict local pollutant concentrations. These predictions are dynamically calibrated with real-time LCS measurements through machine learning regression techniques, which adjust for sensor biases, reduce noise, and quantify uncertainties. This integration allows for a more accurate, real-time representation of air pollution levels, especially in urban areas where traditional monitoring is often inadequate or nonexistent.
To demonstrate our framework's capability in refining Nitrogen Dioxide (NO₂) and Particulate Matter (PM₂.₅) estimates, we conduct a study in a dense population area in Nairobi, Kenya. Our result achieves a significant improvement in alignment with regulatory-grade measurements. By relying on open data and open-source LUR models, this approach is scalable, adaptable, and transferable, making it a cost-effective solution for AQ monitoring in diverse geographic and socio-economic settings.
This work emphasises the transformative potential of open data and open LUR models in democratising access to high-resolution, real-time air quality monitoring tools. By combining low-cost sensors with these open-source data and models, the framework offers actionable insights for urban planning, public health initiatives, and environmental policy. It underscores the broad applicability of this solution in addressing global air pollution challenges, providing scalable tools for effective air quality management and policy-making across various regions.
Keywords:
Low-cost sensors (LCS), open data, open Land Use Regression (LUR) models, air quality monitoring, Nitrogen Dioxide (NO₂), Particulate Matter (PM₂.₅), urban planning, public health, environmental policy, data fusion, scalability, global applicability, open-source models.
How to cite: Oduori, G., Cocco, C., Sajadi, P., and Pilla, F.: Leveraging Open Data and Land Use Regression Models for Scalable Air Quality Monitoring: Integrating Low-Cost Sensors for Global Applicability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1095, https://doi.org/10.5194/egusphere-egu25-1095, 2025.
Outdoor air pollution is responsible for more than 4 million premature deaths annually, with disproportionate impacts in low- and middle-income countries. Despite the human and economic cost of air pollution, many cities worldwide lack reliable air pollution data, even in regions suspected of having the highest levels of pollution. The publication of air quality information has been widely advocated as a critical step towards reducing pollution and improving public health, as it can raise awareness, drive regulatory action, and empower citizens to demand effective policies.
In practice, China’s extensive Air Quality Monitoring (AQM) campaigns have successfully raised public attention and awareness while reducing pollution levels. Similarly, it is documented that reference-grade monitors at U.S. embassies have led to significant pollution reductions. Despite these examples, we lack a systematic understanding of how AQM campaigns translate into pollution reductions. Notably, the role of ‘low-cost’ sensors -that are cheaper, easier to deploy and maintain and show increasing accuracy to measure air pollution levels- is not yet broadly documented.
This research is a quantitative evaluation of the extent to which reference-grade monitors and low-cost sensors might be associated with reductions in pollution levels. We also explore how socio-economic and political contexts may mediate the effectiveness of AQM, particularly in developing regions where regulatory enforcement and public responsiveness vary. Understanding how AQM in different developing contexts may or may not lead to improved air quality could prove invaluable in designing new successful campaigns without hindering economic development.
How to cite: Fournier de Lauriere, C., Henninger, E., Smith, E. K., Kouby, V., and Bernauer, T.: From monitoring to mitigating: the role of data in reducing air pollution levels in low- and middle-income countries, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19203, https://doi.org/10.5194/egusphere-egu25-19203, 2025.
Air pollution is a critical global challenge, impacting quality of life and public health of vulnerable communities in low- and middle-income countries (LMICs), due to absence of monitoring devices, weak policies and fragmented institutions for effective air quality (AQ) management. Carbon monoxide (CO), nitrogen dioxide (NO2) and ammonia (NH3), are among the gaseous pollutants found in urban cities with potential to cause respiratory illnesses or cardiovascular diseases. The metal oxide low cost sensors are among the emerging air pollutants measuring devices for indicative measurements in urban areas, because they provide fast cheap results and allow to attain good spatial coverage at a low cost even in areas with no reference monitors. The low cost of these sensors comes with low performances and low qualities of data recorded as compared to reference grade sensors and thus with requirements for frequent calibrations. Frequent calibration necessitates availability of computational resources with skills for data analysis and modeling. The low-cost sensor systems (LCS) are solution for LMICs in building local capacities for addressing these challenges but strong local and international collaboration to improve AQ management is required.
The study focuses on calibrations and performance evaluations of MICS 6814 based gaseous low cost sensors using datasets collected in laboratory and field settings. The calibration is based on testing the performances of model equation for transforming sensor responses into air pollutants measurements in laboratory and field deployments. The results of linear calibration procedures in laboratory showed the transformation of sensor responses into air pollutant concentration to be significantly good with coefficient of determination R2 ranging from 0.55 – 1, for all gaseous pollutants. The performance evaluation results for sensor deployments in field across the city showed varying results depending on site categories and time of day such that the coefficient of determination R2 for CO, ranged from 0.82 – 1; for NH3, R2 ranging from 0.23 - 0.98, and for NO2 , R2 ranged from 0.52 – 0.9. Variations of coefficient of determination R2 during sensor calibrations in field, poses challenges in developing network wide calibration models for real time air pollutants monitoring. Further challenge, comes from the fact that the trends for individual field observations datasets, showed varying picking levels of pollutions during morning and evening rush hours for all categories of sensor location. Over all, the observed indicative measure of air pollutants across the city were sufficient for public awareness and policy making purposes.
How to cite: manyele, A. and Mkiramweni, M.: Calibrations and Performance Evaluations of Metal Oxide Low- Cost Air Sensor for NH3, CO and NO2 Detections: Case study for Laboratory and Field Calibration of across Dar es Salaam City, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-778, https://doi.org/10.5194/egusphere-egu25-778, 2025.
Zimbabwe is one of the African countries severely affected by high air pollution levels with significant impacts on human health and the environment. To develop mitigation strategies and sustainable policies, monitoring of air quality (AQ) is essential. However, Zimbabwe has few air quality monitoring stations, and these are concentrated in the densely populated industrial area of Harare. This results in significant data gaps, limited assessment of air quality, ineffective health and environmental policies. We present the progress made within the “AQ4Zim” project, funded by ESA within the EOAFRICA framework, of which the objectives are: 1) expand the AQ monitoring network through installation of low-cost sensors; 2) explore the spatial and temporal variations in Aerosol Optical Thickness (AOT) patterns across Zimbabwe using TROPOMI on the Sentinel-5p platform; and 3) develop and evaluate a smoke-dust discriminator for TROPOMI. The satellite data will be compared with ground-based data from the low-cost sensors as they become available. Relating satellite measurements to the measurements at ground level, ensures that they can be used to routinely monitor air quality efficiently. The data will form a basis for the calculation of the UN Sustainable Development Goals (SDG) target indicators 11.6.2 and 3.9.1.
How to cite: Penning de Vries, M., de Graaf, M., Shekede, M. D., and Nondo, N.: Improving Zimbabwe’s capacity for air quality monitoring from the ground and by TROPOMI , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5687, https://doi.org/10.5194/egusphere-egu25-5687, 2025.
Air pollution has become a critical public health issue in recent years, contributing significantly to the rise of respiratory diseases across large segments of the population and reducing average life expectancy. Although regulations exist to limit the emission of gases and particulate matter, there is a pressing need for more effective and comprehensive monitoring strategies. These strategies should enable real-time and large-scale detection of pollutant levels with greater accuracy. While monitoring stations are present in both developed and developing regions, more frequent and reliable data collection is essential to improve coverage and enhance data reliability.
This study focuses on pollutant monitoring and the development of innovative networks using miniaturized, low-cost sensor systems (LCS). It emphasizes research grounded in basic science, particularly the use of nanomaterials as sensors to selectively detect harmful gases and particles at low concentrations [1]. The study also explores the influence of local electric fields on the photophysical behavior of molecules near miniaturized systems [2], enabling the measurement of radiative and non-radiative processes triggered by device perturbations. This research aims to establish the foundation for building miniaturized sensors by integrating nanometer-scale devices into lab-on-a-chip systems [3], marking a significant step toward advancing low-cost, portable sensor technology.
The author are grateful to SECIHTI (CONAHCYT) for funding through grant CBF2023-2024-3073. The authors gratefully acknowledge the computing time granted by LANCAD and CONAHCYT on the supercomputers Yoltla (grant 21-2025) at LSVP UAM-Iztapalapa and UNAM.
[1] Experimental and computational investigation on the surface plasmon resonance of copper thin-films produced via pulsed laser deposition. Luis Mendoza-Luna, Cesar A. Guarin, Estefania Castañeda, Felipe Navarro Sánchez, Emmanuel Haro-Poniatowski, José L. Hernández-Pozos. Results in Optics. In review.
[2]Li, J.-F.; Li, C.-Y.; Aroca, R. F., Plasmon-enhanced fluorescence spectroscopy. Chem. Soc. Rev. 2017,46(13), 3962-3979
[3] Anderrsson, Helene; Van den berg, Albert. Microfluidic devices for cellomics: a review. Sensors and actuators B: Chemical, 2003, vol. 92, no 3, p. 315-325.
How to cite: Guarin Duran, C. A., Navarro Sanchez, F., Galicia López, J., Hernández Pozos, J. L., Mendoza Luna, L. G., and Haro Poniatowski, E.: Optical Signals From Confined Materials At Nanometer Scales: Miniaturization As The Future Of Contaminant Detection, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14757, https://doi.org/10.5194/egusphere-egu25-14757, 2025.
Low-cost sensor networks (LCSNs) have attracted widespread attention as valuable monitoring tools for environment science and air quality management, revolutionizing traditional air monitoring systems. However, previous studies concentrated on the urban pollution hotspots monitoring, leaving a gap in understanding the performance and application of LCSN for long-term regional monitoring in extreme environments.
This study presents a novel and challenging application of LCSN to investigate the causes and sources of ozone pollution in the unique and remote Tibetan Plateau, characterized by rapid temperature and humidity fluctuations and significant diurnal variations. Since 2023, a real-time and high-density LCSN with 40 sites has been gradually established in Tibet, covering 6 cities including Nagri and Lhasa. The sensors are designed to mitigate the influence of temperature and humidity from both hardware and algorithm adjustments. Calibration and side-by-side tests against reference-grade instruments are conducted to characterize the sensitivity and baseline of the sensors based on the variation in pollutant concentrations in the real-world atmosphere. The spatial-temporal characteristics, production and transport patterns of ozone were preliminarily interpreted in combination with meteorological data and the HYSPLIT model.
The results show that the optimized and calibrated low-cost sensors were consistent with standard equipment in long-term monitoring, indicating the accuracy and stability of sensors in the extreme plateau environment. LCSN can provide reliable real-time data with high temporal and spatial resolution to support the exploration of regional ozone and its precursor production and transport patterns. Tibet ozone pollution is dominated by regional contributions with favorable weather conditions, while locally generated pollution has little impact. Different cities and regions show variability in ozone pollution pattern. For instance, in Nagri, located near the western border, approximately 70% of ozone pollution is attributed to regional transport, which is affected by the cross-border transport accompanied by westerly winds; while Lhasa, with a larger population and more transportation and industrial activities, has an increased proportion of local contributions influenced by precursors. These findings reveal the robustness and applicability of LCSN in extreme environments, showcasing its potential to provide actionable insights into dynamic air pollution. This study highlights the opportunities and challenges of utilizing LCSN for air quality monitoring in remote regions and extreme environments, providing scalable solutions for global air quality management and sustainable development strategies.
How to cite: Chen, W.: Application of Low-cost Sensor Network in Extreme Environment: A Case Study of Ozone Pollution in Tibetan Plateau, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-5007, https://doi.org/10.5194/egusphere-egu25-5007, 2025.
Monitoring urban microenvironments using low-cost sensors effectively addresses the spatiotemporal limitations of conventional monitoring networks. However, their widespread adoption is hindered by concerns regarding data quality. Calibrating these sensors is crucial for enabling large-scale deployment and increasing confidence among researchers and users. This study focuses on an Internet of Things (IoT) application in Wuhan, China, aiming to enhance the quality of long-term hourly air quality and air temperature data collected by low-cost sensors through on-site calibration.
Standard weather stations operated by meteorological regulatory agencies at various locations served as reference points for calibrating the sensors. Multiple linear regression (MLR) and machine learning (ML) algorithms were employed for calibration, with leave-one-out cross-validation (LOOCV) used for model evaluation. Factors, such as environmental conditions, spatial distances, and seasonal variations were also examined for their influence on long-term data calibration. Experimental findings revealed that the random forest (RF) model consistently outperformed other methods. Calibration using this approach markedly improved sensor data quality, with the R-squared value of a sensor with the poorest raw data increasing from 0.416 to 0.980, mean absolute error (MAE) decreasing from 6.255 to 1.002, and root mean square error (RMSE) reducing from 7.881 to 1.447. The study also investigated the effects of multiple scenes and distances on calibration through three experimental scenarios. Results indicated that calibrated models using training sites with the same surface type as testing sites performed better when distances were similar. However, due to a limited number of sites with similar surface types, there are insufficient experiments on the impact of distance when the surface types are similar.
The ML-based calibration model developed in this study has the potential to enhance the utility of low-cost sensors for urban environmental monitoring. It enables real-time monitoring, cost-effective and reliable data collection, supporting research on urban regional environments and urban residents’ health.
How to cite: Zeng, C., Nan, F., Li, H., Li, J., and Guan, X.: Calibration of integrated low-cost environmental sensors based on machine learning, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-15277, https://doi.org/10.5194/egusphere-egu25-15277, 2025.
Many cities in Low and Middle Income Countries (LMICs), particularly in Africa lack access to timely data from continuous networks due to the prohibitive costs of setting up conventional networks. Low-cost sensors (LCS) costing less than $2,000 have the potential to close the data gaps in data-hungry cities because of the advances in sensing technology, computational capability and affordability. Application of LCS has gained traction over the years, increasingly recognised by regulatory and international agencies including the World Meteorological Organisation, US EPA, and the recent UNEA-6/10 resolution on regional cooperation for improved air quality globally recognises the need to leverage LCS and digital platforms. However, there are contextual bottlenecks including data reliability and availability, limited internet and electricity, and local capacity for network management that hinder successful deployments of large-scale networks. These challenges are intricately linked to local environmental conditions and logistical circumstances in African settings. The current work based on AirQo’s experience across Africa seeks to unlock the barriers to the adoption of low-cost sensors for continuous monitoring through contextualization, firstly, by untangling the requirements for custom-sensor applications in an African context that transcends beyond the choice of technology. We demonstrate from ongoing case studies in major African cities including Kampala, Nairobi, Kisumu, Yaounde, and Lagos, that achieving a robust LCS network requires integration of four key pillars; (i) custom technology for autonomous portable air quality sensors, (ii) decentralised sensing network (iii) data management platform and (iv) community ownership. The current work advances the case for replicating real-life case studies across diverse settings in different data-hungry cities across Africa.
How to cite: Okure, D., Sserunjogi, R., Ssematimba, J., Abdul-Ganiy, U., Brown, J., and Bainomugisha, E.: Optimal data framework for large-scale application of low-cost sensor networks in African cities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19153, https://doi.org/10.5194/egusphere-egu25-19153, 2025.
Resource-constrained regions often face the highest levels of air pollution yet lack the infrastructure to monitor and mitigate its impacts effectively. Reliable air quality (AQ) data is critical for understanding and addressing the adverse effects of air pollution. While low-cost monitors can reduce equipment and maintenance costs, ensuring the accuracy of the collected data presents challenges, often requiring more expertise and effort than conventional reference equipment.
This presentation introduces open-source tools designed to address these challenges, democratizing access to accurate AQ monitoring. Focusing on outdoor PM2.5 sensors, our work highlights three key innovations that enhance the reliability and accessibility of low-cost air quality monitoring systems.
Calibration Strategies Without a Reference Instrument
The accuracy of low-cost sensors can be significantly enhanced by calibrating them against a local reference instrument. However, many regions lack access to such equipment. To address this gap, AirGradient has developed multiple calibration methods, including:
- In-house calibration using a Federal Equivalent Method during monitor assembly.
- Calibration via background extraction from sensor networks (see Point 2).
- Application of a generalized correction formula, which is the focus of this discussion:
The U.S. Environmental Protection Agency has devised a comprehensive correction formula for Plantower PM2.5 sensors. This algorithm accounts for relative humidity effects and the sensors’ non-linear response at high concentrations. When applied to AirGradient sensors deployed in nine global locations, the formula delivered significant improvements in accuracy. Average R2 values increased from 0.899 (raw) to 0.923 (corrected), while the normalized root mean square error (nRMSE) was reduced from 86% to 34%.
Sensor Networks for Enhanced Calibration and Maintenance
Beyond granular pollution mapping, AQ sensor networks offer advanced capabilities for calibration and maintenance. A network of 16 AirGradient monitors deployed in Pai, Thailand, was used to extract regional background PM2.5 concentrations. With no local reference instruments available, calibration data from Mae Hong Son (a city 50 km away) was leveraged to adjust sensor readings, demonstrating the potential for regional calibration strategies.
Automatic Detection of Sensor Failures
A robust AQ dataset requires the detection and removal of anomalous data. Using a combination of AirGradient-owned co-location datasets and user-contributed public data, we identified various artefacts, including outliers, missing data, extended periods of high-concentration readings, and physically implausible values. An automated data cleaning algorithm was developed to identify and flag these anomalies effectively. While the system reliably enhances data quality, challenges remain in distinguishing short-term real emission events from artefacts. The integration of duplicate PM sensors into individual monitors could help address this issue, providing further reliability.
By improving the accuracy, reliability, and usability of low-cost AQ sensors, these open-source tools empower a wide range of users —such as schools, environmental organizations, and local governments— to generate actionable AQ data. This democratization of air quality monitoring fosters local engagement and equips resource-constrained regions with the tools needed to combat air pollution effectively.
How to cite: Krause, A. and Saladin, S.: Democratizing Access to Accurate Air Quality Measurements with Open-Source Tools, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-8701, https://doi.org/10.5194/egusphere-egu25-8701, 2025.
Overview
The Operational Network of Air Quality Impact Resources (OPENAIR) project set out to address a common issue for local governments concerned about poor air quality in their communities.
Local governments and community organisations are becoming aware of the availability of affordable air quality sensors and are keen to use them to support evidence based policy and interventions. To date however they have lacked the skills and capability to design, procure and operate air quality monitoring systems.
OPENAIR set out to address this through four goals, all of which have been achieved:
- Develop air quality sensing best practise guidance materials. Over 50 resources have been published covering topics including business case development, sensing system design and implementation, data interpretation and stakeholder engagement. These are freely available for all to use.
- Have participating local governments use affordable air quality sensors to address local community air quality issues. With expert support they each led and undertook projects focused on bushfire smoke, wood fire heater smoke, transportation pollution, coal dust, heat and building community STEM and technology literacy.
- Develop an online collaboration hub (openair.org.au) to host best practise resources and foster ongoing collaboration.
- Develop a “harmonised” data feed that ingests air quality measurements from a range of commercially available sensing devices and makes that data available via a single, open API or use by researchers, policy makers and the general public
The project was led by the New South Wales Smart Sensing Network (NSSN) - a research innovation network sponsored by the NSW Office of the Chief Scientist and Engineer. It involved experts from the NSW Department of Climate Change, Energy and the Environment and Water (DCCEEW), nine local governments, five universities and three small businesses.
Impact
This initiative has enabled more evidence based policy making in local government. It has led to ongoing collaboration between local governments, university and government researchers. The harmonised data feed has provided air quality and climate researchers with a great deal more data than they previous had access to. It has raised awareness of low cost environmental sensing more generally.
OPENAIR has been recognised for its innovation and impact by winning a number of national and state awards in Australia related to air quality, sustainability, environment outcomes and research innovation. See https://www.nssn.org.au/awards for more details.
A project overview is available at https://www.nssn.org.au/openairproject.
Future
The project team published a discussion paper describing 35 specific recommendations in four categories:
- Promoting and supporting the use of smart, low-cost air quality sensing
- Enhancing state government air quality information products and services
- Enabling improved data sharing
- Application to other environmental measurements
Several of these have commenced, including initiatives to:
- include other types of environmental measurements relating to heat, water and wildfires (soil and fuel moisture, fire ignitions and monitoring).
- explore international research and government collaboration focused on best practises, data sharing, data quality and international standards development.
How to cite: Runcie, P.: OPENAIR - Low Cost Air Quality Sensing Best Practises and Open Data Platform, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1550, https://doi.org/10.5194/egusphere-egu25-1550, 2025.
Investigating the Awareness of Risks of Exposure to Atmospheric Aerosol and the Potentials of Low-Cost Air Quality Sensors at Quarry Site in Ebonyi State-Nigeria
1Ibeh, Gabriel Friday, 2Lawrence Ibeh, 3Vwavware, O.J., 4Akande-Roland, P.I., 5Edebeatu C. C, and 6Njoku E. I
Corresponding Author: gabriel.ibeh@gmail.com
Abstract
The study is aimed at investigating the awareness of risks associated with the exposure to atmospheric aerosol at quarry site and its health implication in Ebonyi state, and to examines how low-cost air quality sensors can enhance monitoring and management efforts. The questionnaire used covers the demographic information, awareness of occupational health hazards, use of personal protective equipment, health effects experienced by workers, and suggestions for improvement. A total of three hundred and fifty (350) questionnaire were distributed to respondents. a sample of one hundred and eighty-five (185) for quarry workers, quarry owners/managers, community members living near quarry sites, sixty-five (65) healthcare providers, fifty-five (55) environmental protection agencies and forty-five (45) policymakers was selected through random sampling. the data collected was statistically analyzed using frequency counts and mean. a total of three hundred and forty-seven (348) were returned, two (2) were torn and five (5) were wrongly filed. a total of three hundred and forty-three (343) were accepted and assembled for analysis. The findings on the awareness of occupational health hazards among quarry workers indicate a concerning lack of knowledge and training in this field. The findings from assessing the use of personal protective equipment (PPE) among workers indicate varying levels of compliance with safety measures. The findings from investigating the health effects of workers' exposure to aerosols at a quarry site reveal significant impacts on their well-being. The findings from the investigation aimed at identifying suggestions for improving a conducive work environment at the quarry indicate enhancement of health care. The responses from questionnaire provide valuable insights into the current state of occupational health and safety at quarry sites in Ebonyi State and help identify areas for improvement. The research reviewed that lack of low-cost air quality sensors for monitoring of aerosol from quarry station is hindering the awareness of risk of exposure. Low-cost air quality sensors offer a practical solution for monitoring these risks, enabling real-time data collection that informs both operational practices and community engagement efforts. The integration of low-cost air quality sensors into the environmental management framework at quarry sites in Ebonyi State can significantly enhance the understanding and control of air pollution. By providing real-time data and fostering community involvement, these sensors can play a pivotal role in mitigating the adverse effects of quarrying on air quality and public health. Therefore, collaborative approaches to help in having access to low-cost air quality sensors in Nigeria, research grants and sponsorship for training are the panacea for clean air quarry sites of Ebonyi State.
How to cite: Ibeh, Dr. G. F.: Investigating the Awareness of Risks of Exposure to Atmospheric Aerosol and the Potentials of Low-Cost Air Quality Sensors at Quarry Site in Ebonyi State-Nigeria, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-279, https://doi.org/10.5194/egusphere-egu25-279, 2025.
Recent research from across the globe has unveiled a growing trend towards more frequent and intense climate events, marked by increasingly higher temperature peaks. Additionally, escalating human activities in urban areas are amplifying air pollution levels, notably particulates such as PM10 and PM2.5, which are closely linked to significant health problems and subsequent hospitalizations. These environmental concerns — heightened levels of particulates and more extreme climate events — have been definitively associated with adverse health outcomes, leading to an uptick in hospital admissions. This study seeks to explore the interconnections between these environmental factors and public health, assessing how human-induced climate change impacts healthcare systems. By integrating meteorological data, public health records, and readings from particulate matter sensors in Curitiba, Southern Brazil, we have constructed a comprehensive dataset that spans these three domains. Our statistical analysis reveals that average monthly concentrations of PM10 and PM2.5 exhibit a slight negative correlation with hospital admissions in the same month. Intriguingly, this relationship turns positive when analyzed with a one-month delay. Furthermore, we found that hospitalization rates increase in months when pollution levels exceed the World Health Organization's Air Quality Guidelines. Similarly, we observed that maximum temperature values show a negative correlation with hospital admissions initially but correlate positively when delayed by a month. These correlations suggest a synergistic effect between rising temperatures and increased levels of particulate matter, underscoring a direct link to hospital admissions with a one-month lag.
How to cite: godoi, R. H., Baglioli, F., Carpenedo, C. B., Silva, I. O., and Godoi, A. F. L.: The climate-health cascade: Assessing the impact of rising temperature and particulate pollution on hospital admissions, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20628, https://doi.org/10.5194/egusphere-egu25-20628, 2025.
Exposure to high levels of particulate matter (PM) could result to adverse health effects.
The use of fireworks during the New Year celebration result to the rapid increase in
ground-level air pollution which could linger for a few hours or longer depending on the
prevailing meteorological conditions. The rapid increase in the air pollution levels caused
by this event could affect vulnerable groups with underlying health conditions. This
research explores the variations of air pollution levels in select residential areas in Iloilo
City, Philippines during the 2025 New Year celebration. The sensors used are
AirGradient outdoor monitors managed by the Urban Air Quality Monitoring Group at
Central Philippine University. The observation period spanned from December 10, 2024
and January 10, 2025 and is focused on PM2.5 levels to give insight on the overall air
pollution condition. The data shows a total increase in pollution levels throughout all the
stations during the event followed by the subsequent reduction in air pollution levels
hours after the event. The observed PM2.5 concentration ranged from below 35 μg/m 3
prior to the event and up to greater than 500 μg/m 3 in some stations immediately after the
event. At the highest peak, some stations register values more than 40 times greater than
the WHO guideline value for 24-hr exposure. The research offers valuable information
on the air pollution levels in Iloilo City particularly during the New Year celebration. The
use of a network of low-cost sensors gives valuable insight on the characteristic
variations and trends on the air pollution in low-income urban communities and cities in
the Philippines and beyond.
How to cite: Magtulis, V. J.: PM2.5 level observations during New Year Celebration inIloilo City, Philippines using low-cost sensors, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20880, https://doi.org/10.5194/egusphere-egu25-20880, 2025.
Particulate matter (PM), particularly PM2.5 and PM10, poses significant risks to human health, with concentrations varying across time and space. While conventional sources of PM are well-known, domestic wood burning may also substantially influence air quality, particularly in residential areas. In this study, we use publicly available citizen-owned low-cost air quality sensor data (e.g., PM2.5 and PM10) and a web-scraping dataset on woodburning devices to investigate the effect of domestic wood burning on air quality in The Netherlands. We retrieved and analyzed large volumes of sensor data, which we compared with data from calibrated sensors. By examining the temporal and spatial patterns of PM data, we assessed the potential link between domestic wood burning and air quality. We will discuss specific results but also the opportunities and challenges of the use of citizen-owned sensors, including for the integration of environmental and health research.
How to cite: Veldhuijzen, P., Schultner, J., and Hein, L.: Using citizen-owned sensors for integrating environmental and health research: an exploration of domestic wood burning and air quality in The Netherlands, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-17566, https://doi.org/10.5194/egusphere-egu25-17566, 2025.
A CALM:ER approach to air quality education: low-cost sensors as a tool to increase awareness and invite behavioural change.
The Clean Air Living Matters – Exploring Reading collaborative programme aims to drive behavioural change and deliver air quality education to local schools and communities in Reading Borough Council UK.
The programme provides day-to-day solutions to pupils, parents and the wider community to improve air quality and reduce exposure to polluted air. The CALM:ER team utilise various approaches to engage with pupils, including exploring air quality using handheld low-cost devices. Low-cost sensors have also been used to map pollution in and around schools, all of which supports parts of the curriculum and encourages the development of various STEM skills, critical thinking and data analysis.
How to cite: O'Brien, M. and the CALM:ER Project team: A CALM:ER approach to air quality education: use of low-cost sensors to increase awareness and invite behavioural change., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-542, https://doi.org/10.5194/egusphere-egu25-542, 2025.
Low-cost air quality sensors offer promising, cost-effective solutions for monitoring indoor air quality (IAQ). However, their utility is often constrained by challenges in accuracy and data reliability, including those related to inadequate calibration. This is particularly important considering the large variabilities in indoor environmental conditions, in terms of temperature, humidity, and PM2.5 levels. To address this, we developed an experimental chamber using a container to calibrate two types of low-cost particulate matter (PM) sensors against a reference-grade instrument (Fidas 200) using machine learning methods. Sensors from two brands were tested in controlled conditions simulating common indoor pollutant sources such as cooking, smoking, and incense burning. Calibration revealed clear performance variability between sensor brands, with sensors underestimating or overestimating pollutant concentrations at different levels. Sensor correction using machine learning greatly improved sensor accuracy and data reliability. Calibrated sensors will be deployed to monitor PM concentrations (PM2.5 and PM10), temperature, relative humidity, and carbon dioxide (CO2) levels continuously over two years in retrofitted and control homes, capturing pre- and post-retrofit IAQ changes. Future recalibration is planned to evaluate long-term sensor drift. Our preliminary findings highlight the critical role of rigorous calibration in ensuring reliable IAQ monitoring using low-cost sensors. This study provides valuable insights into the practical applications and limitations of such sensors in retrofitted environments.
How to cite: Qian, J., Dai, Y., Liu, B., and Shi, Z.: Challenges and Opportunities in Monitoring Indoor Air Quality with Low-Cost Sensors , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4367, https://doi.org/10.5194/egusphere-egu25-4367, 2025.
Indoor air quality (IAQ) plays a vital role in providing healthier indoor environments, especially considering that most human activities occur indoors. Since indoor and outdoor air pollutants are closely interrelated, monitoring both can provide insights into IAQ dynamics.
The IDEAL (Indoor Air Quality Health) Cluster comprises seven Horizon-Europe funded projects (InChildHealth, INQUIRE, LEARN, K-HealthinAIR, SynAir-G, TWINAIR, and EDIAQI) and the Working Group (WG) on Sensors aims to enhance understanding of knowledge gaps in IAQ, identifying IAQ determinants and to assess their health impacts using various sensor technologies. The goal of the WG on Sensors is to develop common documentation on sensor types, operation modes, characterization, calibration, performance, assessment and validation methods for indoor air quality monitoring and health impact assessment.
The documentation is informed by the different technologies and methodologies used across the seven EU-projects. We have found that the most commonly measured parameters include particulate matter, total volatile organic compounds (TVOC), CO2 and comfort parameters (temperature and relative humidity) although other parameters are also monitored based on the specific needs of each project. Low-cost sensors for indoor air quality monitoring are used across all the IDEAL cluster’s projects, although they come from different manufacturers. For example, VOCs are monitored using metal oxide sensors from Sensirion, Alphasense, and Figaro, with non-distinction of the VOC species to be the common challenge across all low-cost sensors. All the projects have planned for co-location to understand the data quality. Sensor-measured particulate matter is mainly validated against reference measurements in the field, and in two out of seven projects co-location campaigns conducted in various European countries to assess how the sensors respond in different indoor environments.
In this transfer learning approach, all projects share their experiences, highlighting the advantages, limitations, and challenges associated with using different sensor technologies to measure air pollutants. All information gathered is mapped to identify possible similarities and challenges in measuring common parameters across the seven IDEAL Cluster’s projects. This information can be proven useful also to projects pertaining to other citizen science initiatives that are interested in monitoring IAQ.
Acknowledgments: We acknowledge funding for INQUIRE project from the European Union’s Horizon Europe Research and Innovation programme under grant agreement No.1011057499.
How to cite: Salamalikis, V., Hasani, A., Castell, N., Kephalopoulos, S., González, Ó., Nenes, T., Figols, M., Eleftheriadis, K., Lovrić, M., Battaglia, A., De Beule, P., and Brongersma, S. and the IDEAL CLUSTER - WG5 Memebers: Opportunities and challenges of sensor technology for indoor air quality monitoring, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19930, https://doi.org/10.5194/egusphere-egu25-19930, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 5
EGU25-11635 | ECS | Posters virtual | VPS2
Spatio-Temporal Distribution of PM 2.5 and its Association with Agricultural Fires in Northern Argentina.Tue, 29 Apr, 14:00–15:45 (CEST) | vP5.22
Agricultural burning in Tucumán, Argentina, has been a major contributor to air pollution, particularly during the dry season (April to September). This environmental issue is mainly due to the limited availability of modern machinery for sustainable harvesting, leading to heavy reliance on traditional biomass burning for crop residue management. The combustion process generates large amounts of fine particulate matter (PM2.5), which severely affects air quality and public health. To address this challenge, an inter-institutional collaboration under the Networking Initiative Breathe2Change.org, supported by the Alexander von Humboldt Foundation, facilitated the creation of the first air quality monitoring network in Tucumán. This initiative aimed to raise awareness and provide actionable data to local communities and scientists.
A custom sensor module was designed, integrating an OPC Plantower PMS5003 sensor for real-time PM2.5 detection, CO2 sensors using NDIR technology, as well as humidity and temperature sensors. A forced ventilation system was also incorporated to ensure representative air circulation inside the module without affecting airflow into the OPC sensor. The network, consisting of 25 sensor modules deployed throughout the 22,500 square kilometers of Tucumán, provided continuous data collection for 12 months in 2023. The data were shared on a publicly accessible data platform, developed as part of the Breathe2Change Initiative, which facilitated both citizen consultation and analysis by the scientists involved in the project.
During an initial 3-week intercomparison phase, 10 sensor modules were assessed for consistency, yielding a high correlation (R² > 0.9), confirming the reliability of the modules. Afterward, 23 of the 25 sensors were deployed across urban, suburban, and rural areas, including regions directly affected by agricultural fires. High- and low-flow reference samplers were used to collect daily PM2.5 concentrations from August to December, coinciding with the peak biomass burning period. During this period, two of the sensor modules were co-located with the reference samplers to allow for direct comparison. This phase was essential for deriving a local correction factor for the sensors.
Results showed considerably high PM2.5 concentrations, with monthly averages exceeding 60 µg/m³ in fire-impacted areas, well above the daily limits set by the World Health Organization (WHO). Even urban areas recorded average levels of 30 µg/m³, surpassing WHO guidelines. The region’s mountainous terrain and climate further exacerbated the pollution, triggering thermal inversion phenomena that trapped pollutants near ground level. Using the corrected sensor network, spatial distribution maps of PM2.5 were generated through Kriging interpolation, revealing a strong correlation between elevated pollutant levels and fire activity. Higher PM2.5 concentrations were observed in the central-eastern part of the province, likely linked to sugarcane production areas, and possibly influenced by rural traffic and biomass burning. Kriging analysis confirmed this spatial trend, with a marked reduction in localized concentrations after September, likely due to rainfall events.
This study underscores the degradation of air quality during biomass burning events and the need for regulatory measures and sustainable agricultural practices to mitigate environmental and health impacts. It also highlights the potential of low-cost sensors as effective tools for monitoring air pollution in resource-limited regions.
How to cite: Gibilisco, R. G., Aguilera Sammaritano, M., Reynoso Posse, F., Huber, K., Elizondo, J., Torkar, S., Saez, M. M., Scaglioti, A., Tames, F., Puliafito, E., Castellano, M. J., Diaz, M., Parellada, N., Ciancaglini, G., Schillman, B., Kurtenbach, R., Wiesen, P., Caggiano, A., Ben Altabef, A., and Teruel, M.: Spatio-Temporal Distribution of PM 2.5 and its Association with Agricultural Fires in Northern Argentina., EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11635, https://doi.org/10.5194/egusphere-egu25-11635, 2025.
EGU25-20267 | ECS | Posters virtual | VPS2
Air Quality Monitoring in Nairobi City, Kenya: Role of Collocation in Low Cost Sensor DeploymentTue, 29 Apr, 14:00–15:45 (CEST) | vP5.23
Background: The increasing availability and usage of low-cost air quality sensors (LCS) presents both opportunities and challenges in terms of data accuracy, reliability, precision and interpretation. Various low cost sensors types differ in the degree of accuracy reliability and precision They can also be influenced by environmental conditions like temperatures and humidity. This study assesses three LCS, E-Samplers, ModulairTM and AirQO, deployed alongside a reference-grade Beta Attenuation Monitor (BAM-1022) in Nairobi, Kenya, to upraise their performance under varying conditions and explore the strategies for calibration and integration into the monitoring networks.
Methods: The study used BAM-1022 data to validate and calibrate the LCS installed at the University of Nairobi’s Parklands Campus (27 February 2024 to 26 December 2024). We analyzed sensor accuracy, precision and response to pollution across wet and dry seasons and varying temperature and humidity levels. We aligned the LCS data with BAM-1022 measurements using tailored correction factors and multiple linear regression (MLR) models. We used the coefficient of determination, represented by R-squared (R2), a statistical measure of how close the data from the LCS are from the data from the BAM and the Pearson correlation, r to show the strength of the linear relationship between the sensor measurements and reference measurements. Additionally, we conducted paired t-tests to determine whether statistically significant differences existed between the BAM-1022 and each LCS, and one-sample t-tests to find out if there was a statistically significant difference in the values recorded by low-cost sensors themselves. The study also explored the potential of LCS to improve spatial coverage and resolution while addressing challenges like sensor drift and environmental interference.
Results: The ModulairTM sensor showed closer measurements in reference to BAM-1022 measurements (R2= 0.82, r =0.9458) followed by AirQO (R2=0.54, r =0.8933) and E-Sampler (R2=0.36, r =0.7166). During wet season, ModulairTM maintained the closer measurements (R2=0.73, r =0.9123) with AirQO (R2=0.36, r =0.7219) and E-Sampler (R2=0.21, r =0.7812) showing lower alignment. Similar trend was observed in dry season with ModulairTM (R2=0.8, r=0.8124) followed by AirQO (R2=0.51, r=0.7001) and E-Sampler (R2=0.28, r=0.6996). During high PM2.5 concentration periods (July to December), ModulairTM reported higher values than the BAM on certain days. AirQO generally recorded lower values except during these high concentration periods while the E-Samplers fluctuated between higher or lower values across the collocation period. Consequently, correction factors of -12.5, 31.55 and 29.65 were derived for ModulairTM,AirQO and E-Samplers respectively. Statistical analysis revealed a significant difference between the BAM measurements and LCS (p-value < 0.001). However, no significant differences were observed between the measurements of each of the low-cost sensors.
Conclusion: The LCS can enhance air quality monitoring networks when collocated appropriately and, consistently and carefully calibrated. The readings should be corrected against reference sensor for accurate and reliable data. Collocation with reference monitors or among the LCS units for regions with limited access to high-end monitoring infrastructure such as Nairobi is key before deployment. Air quality modeling can create a comprehensive monitoring networks hence improved spatial resolution and public health insights.
How to cite: Nyamondo, J., Oguge, N., Anyango, S., Afulloh, A., Adera, N., and Okoth, B.: Air Quality Monitoring in Nairobi City, Kenya: Role of Collocation in Low Cost Sensor Deployment , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20267, https://doi.org/10.5194/egusphere-egu25-20267, 2025.
EGU25-10631 | ECS | Posters virtual | VPS2
Harmonizing Low-Cost Air Quality Sensors for a Hybrid Monitoring NetworkTue, 29 Apr, 14:00–15:45 (CEST) | vP5.24
Air quality monitoring is crucial for assessing environmental health and supporting mitigation strategies. This research focuses on the co-location of various low-cost particulate matter (PM) sensors—uRADMonitor, AirGradient, PurpleAir, Clarity, and sensors from community initiatives—alongside a mobile laboratory equipped with a reference-grade GRIM EDM 180 analyzer. The primary goal is to identify and quantify bias among these low-cost sensors for PM2.5 and PM10 measurements at the same location.
By systematically analyzing the measurement discrepancies, a generalized correction formula is derived, enabling the harmonization of readings across different sensor types. The corrected data will form the basis of a hybrid air quality monitoring network, which standardizes PM2.5 and PM10 concentrations regardless of the sensor manufacturer. This approach leverages the affordability and scalability of low-cost sensors while ensuring data quality comparable to reference instruments.
The results aim to address limitations in the current low-cost sensor ecosystem, enhance interoperability, and provide communities and policymakers with reliable, high-resolution air quality data. Ultimately, this study supports the development of inclusive and sustainable monitoring frameworks that empower both urban and rural regions with actionable environmental insights, using all kinds of sensors.
How to cite: Luchiian, A.: Harmonizing Low-Cost Air Quality Sensors for a Hybrid Monitoring Network, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10631, https://doi.org/10.5194/egusphere-egu25-10631, 2025.
EGU25-10268 | Posters virtual | VPS2
Bridging the Gap: Smart Benches for Accessible Urban Air Quality Monitoring and Public Engagement in the Region of AtticaTue, 29 Apr, 14:00–15:45 (CEST) | vP5.25
The rapid urbanization of modern cities presents significant challenges, with air pollution emerging as a critical concern for public health and environmental sustainability. In Greece, while the government collects extensive air quality data as mandated by the EU Directive 2881/2024 (recast of 2008/50, 2004/107), limited efforts are made to communicate this data to the public. The existing network of large monitoring stations is often inaccessible to the pubic and primarily serving scientists and policymakers.
Addressing this gap, the FAIRCITY (ATTP4-0360457) project—a collaboration between the National Observatory of Athens, the National and Kapodistrian University of Athens and the Greek Innovation Company Energy4Smart—introduces the “Smart Stations” an innovative solution incorporating public benches powered by photovoltaics, equipped with free charging sockets for people with electrical wheelchairs as well as other smart city sevices, with embedded low cost air quality sensors, designed to make air quality data accessible, timely, and engaging. This initiative not only aligns with global sustainability goals but also serves as a model for other cities seeking to improve urban liveability. The low-cost sensors embedded within the bench at a height of approximately 3 meteres above ground, were selected based on size, technology and price criteria to continuously monitor eight key pollutants: three fractions of Particulate Matters (PM1, PM2.5, PM10), carbon monoxide (CO), carbon dioxide (CO2), nitrogen dioxide (NO2), ozone (O3) and sulfur dioxide (SO2).
The Smart Stations are deployed in open, public spaces (e.g., commercial areas, residential zones, parks), at a distance from major pollutant sources and in collaboration with interested municipalities of the Attica Region. Their aim is to record the local air quality and pollutant diurnal variations in order to highlight the sources responsible (i.e., Korydallos high NO2, NO, PM concentrations from traffic) and estimate the population exposure. Citizens can walk up to these stations, sit down and instantly access critical information about their local air quality from digital displays that provide in near real-time the simplified Air Quality Index (AQI) along with health protection and other environmental infomation.
Preliminary results indicate that the diurnal variations of the monitored pollutants follow closely the local anthropogenic activities (traffic by passing the area, central heating, cooking). The pollutant levels are similar across the different municipalities, presenting peaks at different times depending on the type of area. The hourly AQI is mainly affected by larger scale events such as an extensive air pollution episode or dust intrusion event.
How to cite: Assimakopoulos, V., Fameli, K.-M., Kladakis, A., Efthymiou, C., Charalampidou, C., Sotiropoulou, M., Antoniou, I. –. M., Kytrilaki, A., Massas, A., and Assimakopoulos, M.-N.: Bridging the Gap: Smart Benches for Accessible Urban Air Quality Monitoring and Public Engagement in the Region of Attica, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10268, https://doi.org/10.5194/egusphere-egu25-10268, 2025.
EGU25-1073 | ECS | Posters virtual | VPS2
Building Equitable Air Quality Networks: Low-Cost Sensors and Community-Led Monitoring in DublinTue, 29 Apr, 14:00–15:45 (CEST) | vP5.26
Air quality monitoring remains a significant challenge in urban areas, particularly where high-cost infrastructure is unavailable or difficult to maintain. Traditional monitoring systems are often limited in scope due to expense and logistical constraints, leading to data gaps, especially in resource-constrained environments. Low-cost air quality sensors have the potential to transform environmental monitoring by providing accessible, affordable tools for collecting air quality data, especially in urban settings. As part of the SCORE project, a low-cost sensor system was developed to support real-time air quality monitoring across European cities. These sensors provide a more granular understanding of air pollution trends, making air quality data collection both scalable and accessible to a wider range of stakeholders, including local communities. This presentation will highlight the deployment of these sensors in Dublin, Ireland, where they have been successfully integrated into citizen science initiatives, enabling communities to actively participate in environmental data collection and contribute to air quality management.
Ensuring data accuracy and reliability is a key challenge in the use of low-cost sensors. We will examine the technical challenges of deploying low-cost sensors, such as calibration, accuracy, and long-term reliability in small-scale urban environments. The presentation will also discuss strategies for integrating sensor data into authoritative air quality monitoring networks to enhance overall data quality and spatial coverage.
In Dublin, the citizen science air quality initiative has built strong connections between local communities, researchers and policymakers. This collaboration exemplifies how co-created initiatives, backed by accessible technology, can empower citizens and bridge the gap between public engagement and formal policy processes. The outcomes of the Dublin case study suggest broader applicability for the SCORE model in other cities facing similar air quality challenges. By offering a replicable and scalable solution, low-cost sensors provide an affordable alternative to high-end monitoring stations, enabling resource-limited municipalities to expand their air quality infrastructure. The project demonstrates how engaging local communities in the data collection process can foster long-term, sustainable environmental stewardship. These insights underscore the importance of equitable partnerships between citizens, researchers, and governments in tackling air pollution, particularly in cities where financial or technical constraints have traditionally limited comprehensive air quality monitoring.
How to cite: Daruari, H., Crowley, S., Cocco, C., and Barrón, J. P. G.: Building Equitable Air Quality Networks: Low-Cost Sensors and Community-Led Monitoring in Dublin, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-1073, https://doi.org/10.5194/egusphere-egu25-1073, 2025.
