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Search Results (440)

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Keywords = low-cost air quality sensors

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44 pages, 4243 KiB  
Review
AI-Powered Building Ecosystems: A Narrative Mapping Review on the Integration of Digital Twins and LLMs for Proactive Comfort, IEQ, and Energy Management
by Bibars Amangeldy, Nurdaulet Tasmurzayev, Timur Imankulov, Zhanel Baigarayeva, Nurdaulet Izmailov, Tolebi Riza, Abdulaziz Abdukarimov, Miras Mukazhan and Bakdaulet Zhumagulov
Sensors 2025, 25(17), 5265; https://doi.org/10.3390/s25175265 - 24 Aug 2025
Abstract
Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis [...] Read more.
Artificial intelligence (AI) is now the computational core of smart building automation, acting across the entire cyber–physical stack. This review surveys peer-reviewed work on the integration of AI with indoor environmental quality (IEQ) and energy performance, distinguishing itself by presenting a holistic synthesis of the complete technological evolution from IoT sensors to generative AI. We uniquely frame this progression within a human-centric architecture that integrates digital twins of both the building (DT-B) and its occupants (DT-H), providing a forward-looking perspective on occupant comfort and energy management. We find that deep reinforcement learning (DRL) agents, often developed within physics-calibrated digital twins, reduce annual HVAC demand by 10–35% while maintaining an operative temperature within ±0.5 °C and CO2 below 800 ppm. These comfort and IAQ targets are consistent with ASHRAE Standard 55 (thermal environmental conditions) and ASHRAE Standard 62.1 (ventilation for acceptable indoor air quality); keeping the operative temperature within ±0.5 °C of the setpoint and indoor CO2 near or below ~800 ppm reflects commonly adopted control tolerances and per-person outdoor air supply objectives. Regarding energy impacts, simulation studies commonly report higher double-digit reductions, whereas real building deployments typically achieve single- to low-double-digit savings; we therefore report simulation and field results separately. Supervised learners, including gradient boosting and various neural networks, achieve 87–97% accuracy for short-term load, comfort, and fault forecasting. Furthermore, unsupervised models successfully mine large-scale telemetry for anomalies and occupancy patterns, enabling adaptive ventilation that can cut sick building complaints by 40%. Despite these gains, deployment is hindered by fragmented datasets, interoperability issues between legacy BAS and modern IoT devices, and the computer energy and privacy–security costs of large models. The key research priorities include (1) open, high-fidelity IEQ benchmarks; (2) energy-aware, on-device learning architectures; (3) privacy-preserving federated frameworks; (4) hybrid, physics-informed models to win operator trust. Addressing these challenges is pivotal for scaling AI from isolated pilots to trustworthy, human-centric building ecosystems. Full article
(This article belongs to the Section Environmental Sensing)
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22 pages, 5242 KiB  
Article
Quantification of the Spatial Heterogeneity of PM2.5 to Support the Evaluation of Low-Cost Sensors: A Long-Term Urban Case Study
by Róbert Mészáros, Zoltán Barcza, Bushra Atfeh, Roland Hollós, Erzsébet Kristóf, Ágoston Vilmos Tordai and Veronika Groma
Atmosphere 2025, 16(9), 998; https://doi.org/10.3390/atmos16090998 - 23 Aug 2025
Abstract
During the last decades, development of novel low-cost sensors commercialized for indoor air quality measurements has gained interest. In this research, three AirVisual Pro air quality monitors were used to monitor PM2.5 and carbon dioxide concentrations in which two were installed indoors [...] Read more.
During the last decades, development of novel low-cost sensors commercialized for indoor air quality measurements has gained interest. In this research, three AirVisual Pro air quality monitors were used to monitor PM2.5 and carbon dioxide concentrations in which two were installed indoors and one outdoors at two residential apartments in Central Europe (Budapest, Hungary). In our research, we present a methodology to support the evaluation of indoor sensors by utilizing official outdoor monitoring data, leveraging the fact that indoor spaces are frequently ventilated and thus influenced by outdoor conditions. We compared six-year measurement data (01.2017–12.2022) with outdoor concentrations provided by the Hungarian Air Quality Monitoring Network (HAQM). However, the well-known low spatial representativeness and high spatio-temporal variability of PM2.5 in city environments made this evaluation problematic, which needed to be addressed before comparison. Here we quantify the spatial heterogeneity of the HAQM PM2.5 data for a maximum of eight stations. Then, based on the carbon dioxide readings of the AirVisual Pro units, data filtering was performed for the AirVisual 1 and AirVisual 2 sensors located in indoor environments to identify ventilated periods (nearly 10,000 ventilated events) for the AirVisual 1 and AirVisual 2 sensors, respectively, for the comparison of indoor and outdoor PM2.5 concentrations. The AirVisual 3 sensor was placed in a garden storage, and the measurements taken there were considered outdoor values throughout. Finally, four heterogeneity criteria were set for the HAQM data to filter conditions that were assumed to be comparable with the indoor sensor data. The results indicate that the spatial heterogeneity was indeed detectable, and in approximately 50–60% of the cases, the readings could be considered as non-representative to single location comparison, but the results depend on the selected homogeneity criteria. The AirVisual and HAQM comparison indicated relatively low sensitivity to heterogeneity criteria, which is a promising result that can be exploited. AirVisual sensors generally overestimated PM2.5, but this bias could be corrected with a simple linear adjustment. Slopes changed across sensors (0.83–0.85 for AirVisual 1, 0.48–0.53 for AirVisual 2, and 0.70–0.73 for AirVisual 3), indicating general overestimation and correlations from moderate to high (R2 = 0.45–0.89) depending on the device. In contrast, when we compared the measurements only with data from the nearest reference station, we obtained a weaker match and slopes that did not match those calculated by taking into account homogeneity criteria. This research contributes to the proliferation of citizen science and supports the application of LCSs in indoor conditions. Full article
(This article belongs to the Section Atmospheric Techniques, Instruments, and Modeling)
28 pages, 3464 KiB  
Article
Real-Time Intelligent Monitoring of Outdoor Air Quality in an Urban Environment Using IoT and Machine Learning Algorithms
by Osama Alsamrai, Maria D. Redel-Macias and M. P. Dorado
Appl. Sci. 2025, 15(16), 9088; https://doi.org/10.3390/app15169088 - 18 Aug 2025
Viewed by 252
Abstract
The monitoring and prediction of air quality (AQ) is key to minimizing the negative impact of air pollution, as it enables the implementation of corrective measures. An IoT-based multi-purpose monitoring system has therefore been designed. To develop a reliable remote system, this study [...] Read more.
The monitoring and prediction of air quality (AQ) is key to minimizing the negative impact of air pollution, as it enables the implementation of corrective measures. An IoT-based multi-purpose monitoring system has therefore been designed. To develop a reliable remote system, this study addresses three challenges: (1) design of a low-cost compact, robust, multi-sensor system, (2) model validation over several months to ensure accurate detection, and (3) the application of machine learning (ML) techniques to classify and predict AQ. The developed system demonstrates a significant cost reduction for regular monitoring, including effective data management under harsh environmental conditions. The prototype integrates pollutant sensors, as well as the detection of liquified petroleum gas, humidity, and temperature. A dataset with more than 30,000 entries per month (data recorded approximately every minute) was saved on the platform. Results identified the three highest pollution categories, highlighting the urgency of addressing AQ in densely populated regions. The ML algorithms allowed us to predict AQ trends with 99.97% accuracy. To summarize, by reducing monitoring costs and enabling large-scale data management, this system offers an effective solution for real-time environmental monitoring. It also highlights the potential of artificial intelligence-based AQ predictions in supporting public health initiatives. This is particularly interesting for developing countries, where pollution control is limited. Future research will develop the models to include data from different environments and seasons, exploring its integration into mobile apps and cloud platforms for real-time monitoring. Full article
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16 pages, 5778 KiB  
Article
A Living Lab for Indoor Air Quality Monitoring in an Architecture School: A Low-Cost, Student-Led Approach
by Robiel Manzueta, César Martín-Gómez, Leire Gómez-Olagüe, Amaia Zuazua-Ros, Sara Dorregaray-Oyaregui and Arturo H. Ariño
Buildings 2025, 15(16), 2873; https://doi.org/10.3390/buildings15162873 - 14 Aug 2025
Viewed by 281
Abstract
Students and educators spend considerable time in indoor learning spaces on university campuses, where indoor air quality (IAQ), of which particulate matter (PM) is an important component, is a critical concern that architecture students must address. However, IAQ is seldom monitored and very [...] Read more.
Students and educators spend considerable time in indoor learning spaces on university campuses, where indoor air quality (IAQ), of which particulate matter (PM) is an important component, is a critical concern that architecture students must address. However, IAQ is seldom monitored and very rarely, if at all, reported in these spaces. We used a novel living lab approach to provide third-year students of building services with a hands-on learning activity. During a two-week monitoring period, students designed, assembled, and operated low-cost PM sensors using Arduino platforms. The data analysis showed hotspots where the IAQ was consistently compromised and showed repetitive patterns in time. Workshop and laboratory areas repeatedly recorded the highest PM levels in 15 min sampling events distributed over daily two-hour segments, averaging 43.3 and 47.9 μg/m3 PM10, respectively, with maxima of 118.6 and 119.9 μg/m3 PM10. These measurements would have qualified as ‘moderate’ IAQ if sustained over a full day. A distinct weekly pattern was discovered, with Mondays being worse. The results demonstrated a new practical approach to monitoring the building’s IAQ at minimal cost while obtaining reproducible data. This tool provided educators with a valuable teaching tool that provided students with a deeper understanding of indoor air pollution. Full article
(This article belongs to the Special Issue Indoor Air Quality and Ventilation in the Era of Smart Buildings)
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19 pages, 2197 KiB  
Article
In-Field Performance Evaluation of an IoT Monitoring System for Fine Particulate Matter in Livestock Buildings
by Provvidenza Rita D’Urso, Alice Finocchiaro, Grazia Cinardi and Claudia Arcidiacono
Sensors 2025, 25(16), 4987; https://doi.org/10.3390/s25164987 - 12 Aug 2025
Viewed by 332
Abstract
The livestock sector significantly contributes to atmospheric emissions of various pollutants, such as ammonia (NH3) and particulate matter of diameter under 2.5 µm (PM2.5) from activity and barn management. The objective of this study was to evaluate the reliability of low-cost [...] Read more.
The livestock sector significantly contributes to atmospheric emissions of various pollutants, such as ammonia (NH3) and particulate matter of diameter under 2.5 µm (PM2.5) from activity and barn management. The objective of this study was to evaluate the reliability of low-cost sensors integrated with an IoT system for monitoring PM2.5 concentrations in a dairy barn. To this end, data acquired by a PM2.5 measurement device has been validated by using a high-precision one. Results demonstrated that the performances of low-cost sensors were highly correlated with temperature and humidity parameters recorded in its own IoT platform. Therefore, a parameter-based adjustment methodology is proposed. As a result of the statistical assessments conducted on this data, it has been demonstrated that the analysed sensor, when corrected using the proposed correction model, is an effective device for the purpose of monitoring the mean daily levels of PM2.5 within the barn. Although the model was developed and validated by using data collected from a dairy barn, the proposed methodology can be applied to these sensors in similar environments. Implementing reliable and affordable monitoring systems for key pollutants is crucial to enable effective mitigation strategies. Due to their low cost, ease of transport, and straightforward installation, these sensors can be used in multiple locations within a barn or moved between different barns for flexible and widespread air quality monitoring applications in livestock barns. Full article
(This article belongs to the Section Internet of Things)
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21 pages, 4772 KiB  
Article
Integrating Environmental Sensing into Cargo Bikes for Pollution-Aware Logistics in Last-Mile Deliveries
by Leonardo Cameli, Margherita Pazzini, Riccardo Ceriani, Valeria Vignali, Andrea Simone and Claudio Lantieri
Sensors 2025, 25(15), 4874; https://doi.org/10.3390/s25154874 - 7 Aug 2025
Viewed by 350
Abstract
Cycling represents a significant share of urban transportation, especially in terms of last-mile delivery. It has clear benefits for delivery times, as well as for environmental issues related to freight distribution. Furthermore, the increasing accessibility of low-cost environmental sensors (LCSs) provides an opportunity [...] Read more.
Cycling represents a significant share of urban transportation, especially in terms of last-mile delivery. It has clear benefits for delivery times, as well as for environmental issues related to freight distribution. Furthermore, the increasing accessibility of low-cost environmental sensors (LCSs) provides an opportunity for urban monitoring in any situation. Moving in this direction, this research aims to investigate the use of LCSs to monitor particulate PM2.5 and PM10 levels and map them over delivery ride paths. The calibration process took 49 days of measurements into account, spanning different seasonal conditions (from May 2024 to November 2024). The employment of multiple linear regression and robust regression revealed a strong correlation between pollutant levels from two sources and other factors such as temperature and humidity. Subsequently, a one-month trial was carried out in the city of Faenza (Italy). In this study, a commercially available LCS was mounted on a cargo bike for measurement during delivery processes. This approach was adopted to reveal biker exposure to air pollutants. In this way, the operator’s route would be designed to select the best route in terms of delivery timing and polluting factors in the future. Furthermore, the integration of environmental monitoring to map urban environments has the potential to enhance the accuracy of local pollution mapping, thereby supporting municipal efforts to inform citizens and develop targeted air quality strategies. Full article
(This article belongs to the Section Environmental Sensing)
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7 pages, 337 KiB  
Proceeding Paper
Exposure to PM2.5 While Walking in the City Center
by Anna Mainka, Witold Nocoń, Aleksandra Malinowska, Julia Pfajfer, Edyta Komisarczyk and Pawel Wargocki
Environ. Earth Sci. Proc. 2025, 34(1), 2; https://doi.org/10.3390/eesp2025034002 - 6 Aug 2025
Viewed by 221
Abstract
This study investigates personal exposure to fine particulate matter (PM2.5) during walking commutes in Gliwice, Poland—a city characterized by elevated levels of air pollution. Data from a low-cost air quality sensor were compared with a municipal monitoring station and the Silesian [...] Read more.
This study investigates personal exposure to fine particulate matter (PM2.5) during walking commutes in Gliwice, Poland—a city characterized by elevated levels of air pollution. Data from a low-cost air quality sensor were compared with a municipal monitoring station and the Silesian University of Technology laboratory. PM2.5 concentrations recorded by the low-cost sensor (7.3 µg/m3) were lower than those reported by the stationary monitoring sites. The findings suggest that low-cost sensors may offer valuable insights into short-term peaks in PM2.5 exposure to serve as a practical tool for increasing public awareness of personal exposure risks to protect respiratory health. Full article
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36 pages, 5003 KiB  
Article
Towards Smart Wildfire Prevention: Development of a LoRa-Based IoT Node for Environmental Hazard Detection
by Luis Miguel Pires, Vitor Fialho, Tiago Pécurto and André Madeira
Designs 2025, 9(4), 91; https://doi.org/10.3390/designs9040091 - 5 Aug 2025
Viewed by 417
Abstract
The increase in the number of wildfires in recent years in different parts of the world has caused growing concern among the population, since the consequences of these fires go beyond the destruction of the ecosystem. With the growing relevance of the Internet [...] Read more.
The increase in the number of wildfires in recent years in different parts of the world has caused growing concern among the population, since the consequences of these fires go beyond the destruction of the ecosystem. With the growing relevance of the Internet of Things (IoT) industry, developing solutions for the early detection of fires is of critical importance. This paper proposes a low-cost network based on Long-Range (LoRa) technology to autonomously assess the level of fire risk and the presence of a fire in rural areas. The system consists of several LoRa nodes with sensors to measure environmental variables such as temperature, humidity, carbon monoxide, air quality, and wind speed. The data collected is sent to a central gateway, where it is stored, processed, and later sent to a website for graphical visualization of the results. In this paper, a survey of the requirements of the devices and sensors that compose the system was made. After this survey, a market study of the available sensors was carried out, ending with a comparison between the sensors to determine which ones met the objectives. Using the chosen sensors, a study was made of possible power solutions for this prototype, considering the expected conditions of use. The system was tested in a real environment, and the results demonstrate that it is possible to cover a circular area with a radius of 2 km using a single gateway. Our system is prepared to trigger fire hazard alarms when, for example, the signals for relative humidity, ambient temperature, and wind speed are below or equal to 30%, above or equal to 30 °C, and above or equal to 30 m/s, respectively (commonly known as the 30-30-30 rule). Full article
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30 pages, 9610 KiB  
Article
Can the Building Make a Difference to User’s Health in Indoor Environments? The Influence of PM2.5 Vertical Distribution on the IAQ of a Student House over Two Periods in Milan in 2024
by Yong Yu, Marco Gola, Gaetano Settimo and Stefano Capolongo
Atmosphere 2025, 16(8), 936; https://doi.org/10.3390/atmos16080936 - 4 Aug 2025
Viewed by 328
Abstract
This study investigates indoor and outdoor air quality monitoring in a student dormitory located in northern Milan (Italy) using low-cost sensors. This research compares two monitoring periods in June and October 2024 to examine common PM2.5 vertical patterns and differences at the [...] Read more.
This study investigates indoor and outdoor air quality monitoring in a student dormitory located in northern Milan (Italy) using low-cost sensors. This research compares two monitoring periods in June and October 2024 to examine common PM2.5 vertical patterns and differences at the building level, as well as their influence on the indoor spaces at the corresponding positions. In each period, around 30 sensors were installed at various heights and orientations across indoor and outdoor spots for 2 weeks to capture spatial variations around the building. Meanwhile, qualitative surveys on occupation presence, satisfaction, and well-being were distributed in selected rooms. The analysis of PM2.5 data reveals that the building’s lower floors tended to have slightly higher outdoor PM2.5 concentrations, while the upper floors generally had lower PM2.5 indoor/outdoor (I/O) ratios, with the top-floor rooms often below 1. High outdoor humidity reduced PM infiltration, but when outdoor PM fell below 20 µg/m3 in these two periods, indoor sources became dominant, especially on the lower floors. Air pressure I/O differences had minimal impact on PM2.5 I/O ratios, though slightly positive indoor pressure might help prevent indoor PM infiltration. Lower ventilation in Period-2 possibly contributed to more reported symptoms, especially in rooms with higher PM from shared kitchens. While outdoor air quality affects IAQ, occupant behavior—especially window opening and ventilation management—remains crucial in minimizing indoor pollutants. Users can also manage exposure by ventilating at night based on comfort and avoiding periods of high outdoor PM. Full article
(This article belongs to the Special Issue Air Quality in Metropolitan Areas and Megacities (Second Edition))
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21 pages, 2103 KiB  
Article
Air-STORM: Informed Decision Making to Improve the Success of Solar-Powered Air Quality Samplers in Challenging Environments
by Kyan Kuo Shlipak, Julian Probsdorfer and Christian L’Orange
Sensors 2025, 25(15), 4798; https://doi.org/10.3390/s25154798 - 4 Aug 2025
Viewed by 404
Abstract
Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to [...] Read more.
Outdoor air pollution poses a major global health risk, yet monitoring remains insufficient, especially in regions with limited infrastructure. Solar-powered monitors could allow for increased coverage in regions lacking robust connectivity. However, reliable sample collection can be challenging with these systems due to extreme temperatures and insufficient solar energy. Proper planning can help overcome these challenges. Air Sampler Solar and Thermal Optimization for Reliable Monitoring (Air-STORM) is an open-source tool that uses meteorological and solar radiation data to identify temperature and solar charging risks for air pollution monitors based on the target deployment area. The model was validated experimentally, and its utility was demonstrated through illustrative case studies. Air-STORM simulations can be customized for specific locations, seasons, and monitor configurations. This capability enables the early detection of potential sampling risks and provides opportunities to optimize monitor design, proactively mitigate temperature and power failures, and increase the likelihood of successful sample collection. Ultimately, improving sampling success will help increase the availability of high-quality outdoor air pollution data necessary to reduce global air pollution exposure. Full article
(This article belongs to the Special Issue Recent Trends in Air Quality Sensing)
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37 pages, 3005 KiB  
Review
Printed Sensors for Environmental Monitoring: Advancements, Challenges, and Future Directions
by Amal M. Al-Amri
Chemosensors 2025, 13(8), 285; https://doi.org/10.3390/chemosensors13080285 - 4 Aug 2025
Viewed by 701
Abstract
Environmental monitoring plays a key role in understanding and mitigating the effects of climate change, pollution, and resource mismanagement. The growth of printed sensor technologies offers an innovative approach to addressing these challenges due to their low cost, flexibility, and scalability. Printed sensors [...] Read more.
Environmental monitoring plays a key role in understanding and mitigating the effects of climate change, pollution, and resource mismanagement. The growth of printed sensor technologies offers an innovative approach to addressing these challenges due to their low cost, flexibility, and scalability. Printed sensors enable the real-time monitoring of air, water, soil, and climate, providing significant data for data-driven decision-making technologies and policy development to improve the quality of the environment. The development of new materials, such as graphene, conductive polymers, and biodegradable substrates, has significantly enhanced the environmental applications of printed sensors by improving sensitivity, enabling flexible designs, and supporting eco-friendly and disposable solutions. The development of inkjet, screen, and roll-to-roll printing technologies has also contributed to the achievement of mass production without sacrificing quality or performance. This review presents the current progress in printed sensors for environmental applications, with a focus on technological advances, challenges, applications, and future directions. Moreover, the paper also discusses the challenges that still exist due to several issues, e.g., sensitivity, stability, power supply, and environmental sustainability. Printed sensors have the potential to revolutionize ecological monitoring, as evidenced by recent innovations such as Internet of Things (IoT) integration, self-powered designs, and AI-enhanced data analytics. By addressing these issues, printed sensors can develop a better understanding of environmental systems and help promote the UN sustainable development goals. Full article
(This article belongs to the Section Electrochemical Devices and Sensors)
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11 pages, 3192 KiB  
Data Descriptor
Carbon Monoxide (CO) and Ozone (O3) Concentrations in an Industrial Area: A Dataset at the Neighborhood Level
by Jailene Marlen Jaramillo-Perez, Bárbara A. Macías-Hernández, Edgar Tello-Leal and René Ventura-Houle
Data 2025, 10(8), 125; https://doi.org/10.3390/data10080125 - 1 Aug 2025
Viewed by 380
Abstract
The growth of urban and industrial areas is accompanied by an increase in vehicle traffic, resulting in rising concentrations of various air pollutants. This is a global issue that causes environmental damage and risks to human health. The dataset presented in this research [...] Read more.
The growth of urban and industrial areas is accompanied by an increase in vehicle traffic, resulting in rising concentrations of various air pollutants. This is a global issue that causes environmental damage and risks to human health. The dataset presented in this research contains records with measurements of the air pollutants ozone (O3) and carbon monoxide (CO), as well as meteorological parameters such as temperature (T), relative humidity (RH), and barometric pressure (BP). This dataset was collected using a set of low-cost sensors over a four-month study period (March to June) in 2024. The monitoring of air pollutants and meteorological parameters was conducted in a city with high industrial activity, heavy traffic, and close proximity to a petrochemical refinery plant. The data were subjected to a series of statistical analyses for visualization using plots that allow for the identification of their behavior. Finally, the dataset can be utilized for air quality studies, public health research, and the development of prediction models based on mathematical approaches or artificial intelligence algorithms. Full article
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11 pages, 1161 KiB  
Proceeding Paper
Spatio-Temporal PM2.5 Forecasting Using Machine Learning and Low-Cost Sensors: An Urban Perspective
by Mateusz Zareba, Szymon Cogiel and Tomasz Danek
Eng. Proc. 2025, 101(1), 6; https://doi.org/10.3390/engproc2025101006 - 25 Jul 2025
Viewed by 331
Abstract
This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and [...] Read more.
This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and generating nearly 20,000 observations per month. The network captured both spatial and temporal variability. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test confirmed trend-based non-stationarity, which was addressed through differencing, revealing distinct daily and 12 h cycles linked to traffic and temperature variations. Additive seasonal decomposition exhibited time-inconsistent residuals, leading to the adoption of multiplicative decomposition, which better captured pollution outliers associated with agricultural burning. Machine learning models—Ridge Regression, XGBoost, and LSTM (Long Short-Term Memory) neural networks—were evaluated under high spatial and temporal variability (winter) and low variability (summer) conditions. Ridge Regression showed the best performance, achieving the highest R2 (0.97 in winter, 0.93 in summer) and the lowest mean squared errors. XGBoost showed strong predictive capabilities but tended to overestimate moderate pollution events, while LSTM systematically underestimated PM2.5 levels in December. The residual analysis confirmed that Ridge Regression provided the most stable predictions, capturing extreme pollution episodes effectively, whereas XGBoost exhibited larger outliers. The study proved the potential of low-cost sensor networks and machine learning in urban air quality forecasting focused on rare smog episodes (RSEs). Full article
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17 pages, 4705 KiB  
Article
Impact of Teachers’ Decisions and Other Factors on Air Quality in Classrooms: A Case Study Using Low-Cost Air Quality Sensors
by Zhong-Min Wang, Wenhao Chen, David Putney, Jeff Wagner and Kazukiyo Kumagai
Environments 2025, 12(8), 253; https://doi.org/10.3390/environments12080253 - 24 Jul 2025
Viewed by 984
Abstract
This study investigates the impact of teacher decisions and other contextual factors on indoor air quality (IAQ) in mechanically ventilated elementary school classrooms using low-cost air quality sensors. Four classrooms at a K–8 school in San Jose, California, were monitored for airborne particulate [...] Read more.
This study investigates the impact of teacher decisions and other contextual factors on indoor air quality (IAQ) in mechanically ventilated elementary school classrooms using low-cost air quality sensors. Four classrooms at a K–8 school in San Jose, California, were monitored for airborne particulate matter (PM), carbon dioxide (CO2), temperature, and humidity over seven weeks. Each classroom was equipped with an HVAC system and a portable air cleaner (PAC), with teachers having full autonomy over PAC usage and ventilation practices. Results revealed that teacher behaviors, such as the frequency of door/window opening and PAC operation, significantly influenced both PM and CO2 levels. Classrooms with more active ventilation had lower CO2 but occasionally higher PM2.5 due to outdoor air exchange, while classrooms with minimal ventilation showed the opposite pattern. An analysis of PAC filter material and PM morphology indicated distinct differences between indoor and outdoor particle sources, with indoor air showing higher fiber content from clothing and carpets. This study highlights the critical role of teacher behavior in shaping IAQ, even in mechanically ventilated environments, and underscores the potential of low-cost sensors to support informed decision-making for healthier classroom environments. Full article
(This article belongs to the Special Issue Air Pollution in Urban and Industrial Areas III)
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31 pages, 4435 KiB  
Article
A Low-Cost IoT Sensor and Preliminary Machine-Learning Feasibility Study for Monitoring In-Cabin Air Quality: A Pilot Case from Almaty
by Nurdaulet Tasmurzayev, Bibars Amangeldy, Gaukhar Smagulova, Zhanel Baigarayeva and Aigerim Imash
Sensors 2025, 25(14), 4521; https://doi.org/10.3390/s25144521 - 21 Jul 2025
Viewed by 689
Abstract
The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO2 and PM2.5 often accumulate, elevating the risk of respiratory and cardiovascular [...] Read more.
The air quality within urban public transport is a critical determinant of passenger health. In the crowded and poorly ventilated cabins of Almaty’s metro, buses, and trolleybuses, concentrations of CO2 and PM2.5 often accumulate, elevating the risk of respiratory and cardiovascular diseases. This study investigates the air quality along three of the city’s busiest transport corridors, analyzing how the concentrations of CO2, PM2.5, and PM10, as well as the temperature and relative humidity, fluctuate with the passenger density and time of day. Continuous measurements were collected using the Tynys mobile IoT device, which was bench-calibrated against a commercial reference sensor. Several machine learning models (logistic regression, decision tree, XGBoost, and random forest) were trained on synchronized environmental and occupancy data, with the XGBoost model achieving the highest predictive accuracy at 91.25%. Our analysis confirms that passenger occupancy is the primary driver of in-cabin pollution and that these machine learning models effectively capture the nonlinear relationships among environmental variables. Since the surveyed routes serve Almaty’s most densely populated districts, improving the ventilation on these lines is of immediate importance to public health. Furthermore, the high-temporal-resolution data revealed short-term pollution spikes that correspond with peak ridership, advancing the current understanding of exposure risks in transit. These findings highlight the urgent need to combine real-time monitoring with ventilation upgrades. They also demonstrate the practical value of using low-cost IoT technologies and data-driven analytics to safeguard public health in urban mobility systems. Full article
(This article belongs to the Special Issue IoT-Based Sensing Systems for Urban Air Quality Forecasting)
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