Search Results (250)

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 $R^2$ (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

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 PM_2.5 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, PM_2.5, 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

Low-cost air temperature sensors are an emerging theme in environmental monitoring. These sensors offer the advantage of making microclimate monitoring feasible due to their affordability. However, they are limited by the quality of the data they provide; in many cases, they have been reported to have presented errors in the sensor readings. These errors have been shown to improve after calibration was applied. The lack of a comprehensive understanding of the available calibration techniques, models, and sensor types has led to studies presenting heterogeneity in models and techniques alongside different performance metrics. To address this gap, this study conducted a systematic review following the PRISMA guidelines, reviewing studies from 2015 to 2024 across the databases Web of Science and Scopus, alongside the search engine Google Scholar. The aim was to identify the calibration techniques and models, the commercially available low-cost air temperature sensors used, the performance metrics utilised, and the calibration settings. The findings presented three main categories of calibration models utilised in the collected studies: linear, polynomial, and machine learning. Twenty-two commercially available low-cost sensors were identified, with the DHT22 sensor being the most utilised. Indoor settings were identified as the most preferred for conducting calibrations. Key challenges included limitations in reported results for calibration by the studies, the use of different performance metrics across studies, insufficient studies conducting calibration, and the diversity in sensor types utilised. Full article

In addition to the use of reference instruments, low-cost sensors (LCSs) are becoming increasingly popular for air quality monitoring both indoors and outdoors. These sensors provide real-time measurements of pollutants and facilitate better spatial and temporal coverage. However, these simpler devices are typically characterised by lower accuracy and precision and can be more sensitive to the environmental conditions than the reference instruments. It is therefore crucial to characterise the applicability and limitations of these instruments, for which a possible solution is their comparison with reference measurements in real-world conditions. To this end, a measurement campaign has been carried out to evaluate the PM_2.5 readings of several low- and medium-cost air quality instruments of different types and categories (IQAir AirVisual Pro, TSI DustTrak™ II Aerosol Monitor 8532, Xiaomi Mijia Air Detector, and Xiaomi Smartmi PM_2.5 Air Detector). A GRIMM EDM180 instrument was used as the reference. This campaign took place in Budapest, Hungary, from 12 November to 15 December 2020, during typically humid and foggy weather conditions, when the air pollution level was high due to the increased anthropogenic emissions, including wood burning for heating purposes. The results indicate that the individual sensors tracked the dynamics of PM_2.5 concentration changes well (in a linear fashion), but the readings deviated from the reference measurements to varying degrees. Even though the AirVisual sensors performed generally well (0.85 < R² < 0.93), the accuracy of the units showed inconsistency (13–93%) with typical overestimation, and their readings were significantly affected by elevated relative humidity levels and by temperature. Despite the overall overestimation of PM_2.5 by the Xiaomi sensors, they also exhibited strong correlation coefficients with the reference, with R² values of 0.88 and 0.94. TSI sensors exhibited slight underestimations with high explained variance (R² = 0.93–0.94) and good accuracy. The results indicated that despite the inherent bias, the low-cost sensors are capable of capturing the temporal variability of PM_2.5, thus providing relevant information. After simple and multiple linear regression-based correction, the low-cost sensors provided acceptable results. The results indicate that sensor data correction is a necessary prerequisite for the usability of the instruments. The ensemble method is a reasonable alternative for more accurate estimations of PM_2.5. Full article

Air pollution is a major environmental and public health challenge, especially in urban areas where fine-grained air quality data are essential to effective interventions. Traditional monitoring networks, while accurate, often lack spatial resolution and public engagement. This study presents a novel wearable wireless sensor node (WSN) that was developed within the Horizon Europe SOCIO-BEE project to support air quality monitoring through citizen science (CS). The low-cost, body-mounted WSN measures NO₂, O₃, and PM_2.5. Three pilot campaigns were conducted in Ancona (Italy), Maroussi (Greece), and Zaragoza (Spain), and involved diverse user groups—seniors, commuters, and students, respectively. PM_2.5 sensor data were validated through two approaches: direct comparison with reference stations and spatial clustering analysis using K-means. The results show strong correlation with official PM_2.5 data (R² = 0.75), with an average absolute error of 0.54 µg/m³ and a statistical confidence interval of ±3.3 µg/m³. In Maroussi and Zaragoza, where no reference stations were available, the clustering approach yielded low intra-cluster coefficients of variation (CV = 0.50 ± 0.40 in Maroussi, CV = 0.28 ± 0.30 in Zaragoza), indicating that the measurements had high internal consistency and spatial homogeneity. Beyond technical validation, user engagement and perceptions were evaluated through pre-/post-campaign surveys. Across all pilots, over 70% of participants reported satisfaction with the system’s usability and inclusiveness. The findings demonstrate that wearable low-cost sensors, when supported by a structured engagement and data validation framework, can provide reliable, actionable air quality data, empowering citizens and informing evidence-based environmental policy. Full article

Air pollution is a major problem in the modern world; although it particularly impacts developing countries, which are experiencing fast and often uncontrolled industrialization, its effects constitute a global burden on the environment and health. At the same time, the costs of effective air quality monitoring programs are prohibitive for emerging economies, thus making any correction difficult to assess. Emerging technologies, such as distributed networks of sensors organized in the Internet of Things, are under the lens of scientific and industrial communities as a valuable, low-cost alternative to standard techniques. In this paper, we report a review of current approaches to distributed air quality monitoring. Specifically, we (1) emphasize the role of crowdsensing in leveraging sensor-enabled mobile devices for large-scale environmental data collection and (2) discuss criticalities, open challenges, and future perspectives in enforcing data security when such approaches are deployed in real application scenarios. Full article

Air quality monitoring (AQM) is key to maintaining healthy air in cities. This is crucial in low- and middle-income countries due to increasing evidence of poor air quality but lack of monitors to consistently collect evaluate air quality data and effect policy changes, mainly because of the costs of monitoring devices. In participating in a challenge for the development of low-cost AQM devices in low-resource regions, an Arduino-based device with sensors for particulate matter size, temperature, and humidity data acquisition was developed for deployment in Port Harcourt, a city in Nigeria’s Niger Delta region, exposed to poor air quality partly due to gas and oil production activities. During the project, challenges to AQM were encountered, including inadequate awareness of air quality issues, lack of necessary AQM device components, unavailability of trained manpower and partnerships, and lack of funding. However, lack of a means of calibrating the device was a major hindrance, as no reference AQM instrument was available, rendering the data acquired largely qualitative, educational, and useless for regulatory purposes. There is an urgent need for AQM in such cities. However, a robust AQM strategy must be designed and used to address these constraints, especially whilst using low-cost devices, for significant progress in acquiring robust air quality data in such low-resource regions to be made. Full article

This paper presents a new model for low-cost solar-powered Automatic Weather Stations based on the ESP-32 microcontroller, modern sensors, and intelligent forecasts for Brasilia. The proposed system relies on compact, multifunctional sensors and features an open-source firmware project and open-circuit board design. It includes a BME688, AS7331, VEML7700, AS3935 for thermo-hygro-barometry (plus air quality), ultraviolet irradiance, luximetry, and fulminology, besides having a rainfall gauge and an anemometer. Powered by photovoltaic panels and batteries, it operates uninterruptedly under variable weather conditions, with data collected being sent via WiFi to a Web API that adapts the MZDN-HF (Meteorological Zone Delimited Neural Network–Hourly Forecaster) model compilation for Brasilia to produce accurate 24 h multivariate forecasts, which were evaluated through MAE, RMSE, and R² metrics. Installed at the University of Brasilia, it demonstrates robust hardware performance and strong correlation with INMET’s A001 data, suitable for climate monitoring, precision agriculture, and environmental research. Full article

In this research, we present the development and validation of a compact, resource-efficient (low-cost, low-energy), distributed, real-time traffic and air quality monitoring system. Deployed since November 2023 in a small town that relies on burning various fuels and waste for winter heating, the system comprises three IoT units that integrate image processing and environmental sensing for sustainable urban and environmental management. Each unit uses an embedded camera and sensors to process live data locally, which are then transmitted to a central database. The image processing algorithm counts vehicles by type with over 95% daylight accuracy, while air quality sensors measure pollutants including particulate matter (PM), equivalent carbon dioxide (eCO₂), and total volatile organic compounds (TVOCs). Data analysis revealed fluctuations in pollutant concentrations across monitored areas, correlating with traffic variations and enabling the identification of pollution sources and their relative impacts. Recorded PM10 daily average levels even reached eight times above the safe 24 h limits in winter, when traffic values were low, indicating a strong link to household heating. This work provides a scalable, cost-effective approach to traffic and air quality monitoring, offering actionable insights for urban planning and sustainable development. Full article

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 their 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 temperature data collected by low-cost sensors through on-site calibration. Multiple linear regression (MLR) and light gradient boosting machine (LightGBM) algorithms were employed for calibration, with leave-one-out cross-validation (LOOCV) being used for model evaluation. Factors, such as multiple scenarios, spatial distances, and seasonal variations, were also examined for their influence on long-term data calibration. The experimental findings revealed that the LightGBM method consistently outperformed MLR. Calibration using this approach markedly improved the sensor data quality, with the R-squared (R²) value of the sensor with the poorest raw data increasing from 0.416 to 0.957, its mean absolute error (MAE) decreasing from 6.255 to 1.680, and its root mean square error (RMSE) being reduced from 7.881 to 2.148. This study demonstrates the application potential of using LightGBM as an advanced machine learning (ML) method in innovative low-cost sensors, thereby providing a method of obtaining high-quality and real-time information for urban environmental and public health research. Full article

In the past decades, both low-cost gas sensors for air quality monitoring and smartphone devices have experienced a remarkable spread in the worldwide market. Smartphone devices have become a unique tool in everyday life, whilst the use of low-cost gas sensors in air quality monitors has allowed for a better understanding of the personal exposure to air pollutants. The traditional technologies for measuring air pollutant concentrations, even though they provide accurate data, cannot assure the necessary spatio-temporal resolution for assessing personal exposure to the various air pollutants. In this respect, one of the most promising solutions appears to be the use of smartphones together with the low-cost miniaturized gas sensors, because it allows for the monitoring of the air quality characterizing the different environments frequented in everyday life by leveraging the capability to perform mobile measurements. In this research, a handheld air quality monitor based on low-cost gas sensors capable of connecting to smartphone devices via Bluetooth link has been designed and implemented to explore the different ways of its use for assessing the personal exposure to air pollutants. For this purpose, two experiments were carried out: the first one was indoor monitoring of CO and NO₂ concentrations performed in an apartment occupied by four individuals and the second one was mobile monitoring of CO and NO₂ performed in a car cabin. During the indoor measurements, the maximum value for the CO concentrations was equal to 12.3 ppm, whilst the maximum value for NO₂ concentrations was equal to 64 ppb. As concerns the mobile measurements, the maximum concentration of CO was equal to 8.3 ppm, whilst the maximum concentration of NO₂ was equal to 38 ppb. This preliminary study has shown that this system can be potentially used in all those situations where the use of traditional chemical analyzers for measuring gas concentrations in everyday life environments is hardly feasible, but also has highlighted some limits concerning the performance of such systems. Full article

Low-cost air quality sensors (LCSs) are increasingly being used in environmental monitoring due to their affordability and portability. However, their sensitivity to environmental factors can lead to measurement inaccuracies, necessitating effective calibration methods to enhance their reliability. In this study, an Internet of Things (IoT)-based air quality monitoring system was developed and tested using the most commonly preferred sensor types for air quality measurement: fine particulate matter (PM_2.5), carbon dioxide (CO₂), temperature, and humidity sensors. To improve sensor accuracy, eight different machine learning (ML) algorithms were applied: Decision Tree (DT), Linear Regression (LR), Random Forest (RF), k-Nearest Neighbors (kNN), AdaBoost (AB), Gradient Boosting (GB), Support Vector Machines (SVM), and Stochastic Gradient Descent (SGD). Sensor performance was evaluated by comparing measurements with a reference device, and the best-performing ML model was determined for each sensor. The results indicate that GB and kNN achieved the highest accuracy. For CO₂ sensor calibration, GB achieved R² = 0.970, RMSE = 0.442, and MAE = 0.282, providing the lowest error rates. For the PM_2.5 sensor, kNN delivered the most successful results, with R² = 0.970, RMSE = 2.123, and MAE = 0.842. Additionally, for temperature and humidity sensors, GB demonstrated the highest accuracy with the lowest error values (R² = 0.976, RMSE = 2.284). These findings demonstrate that, by identifying suitable ML methods, ML-based calibration techniques can significantly enhance the accuracy of LCSs. Consequently, they offer a viable and cost-effective alternative to traditional high-cost air quality monitoring systems. Future studies should focus on long-term data collection, testing under diverse environmental conditions, and integrating additional sensor types to further advance this field. Full article

Air quality significantly impacts the environment and human living conditions, with direct and indirect effects on the economy. Precise and prompt detection of air pollutants is crucial for mitigating risks and implementing strategies to control pollution within acceptable thresholds. One of the common pollutants is nitrogen dioxide (NO₂), high concentrations of which are detrimental to the human respiratory system and may lead to serious lung diseases. Unfortunately, reliable NO₂ detection requires sophisticated and expensive apparatus. Although cheap sensors are now widespread, they lack accuracy and stability and are highly sensitive to environmental conditions. The purpose of this study is to propose a novel approach to precise calibration of the low-cost NO₂ sensors. It is illustrated using a custom-developed autonomous platform for cost-efficient NO₂ monitoring. The platform utilizes various sensors alongside electronic circuitry, control and communication units, and drivers. The calibration strategy leverages comprehensive data from multiple reference stations, employing neural network (NN) and kriging interpolation metamodels. These models are built using diverse environmental parameters (temperature, pressure, humidity) and cross-referenced data gathered by surplus NO₂ sensors. Instead of providing direct outputs of the calibrated sensor, our approach relies on predicting affine correction coefficients, which increase the flexibility of the correction process. Additionally, a calibration stage incorporating global correlation enhancement is developed and applied. Demonstrative experiments extensively validate this approach, affirming the platform and calibration methodology’s practicality for reliable and cost-effective NO₂ monitoring, especially keeping in mind that the predictive power of the enhanced sensor (correlation coefficient nearing 0.9 against reference data, RMSE < 3.5 µg/m³) is close to that of expensive reference equipment. Full article

Private, non-intrusive presence detection methods contribute to various applications, from occupancy monitoring to energy optimization and security. This study presents a deep learning approach for predicting occupancy patterns using CO₂ sensor data and temporal features, derived from a year-long dataset (18 September 2023–21 November 2024) collected via the Smart Indoor Air Quality Monitor. We created a dataset of 19,189 samples of CO₂ levels (0–5000 ppm) with timestamps. A sequential neural network with three fully connected layers was implemented in TensorFlow. The developed model demonstrated the feasibility of predicting occupancy based on CO₂ data and temporal features with an accuracy of 0.97 and an F1-score of 0.92. Model visualization was performed using heatmaps. Its advantages include low computational requirements, cost-effective sensors, an IoT-enabled interface, and scalability. However, the study is limited to a university laboratory with a capacity of 1–16 occupants, which may impact its generalizability to other settings. These findings highlight the utility of CO₂ levels and temporal features for occupancy estimation in laboratory conditions and contribute a unique, long-term multimodal dataset to the research community. Full article

This review explores advanced sensing technologies and deep learning (DL) methodologies for monitoring airborne particulate matter (PM), which is critical for environmental health assessments. It begins with discussing the significance of PM monitoring and introduces surface plasmon resonance (SPR) as a promising technique in environmental applications, alongside the role of DL neural networks in enhancing these technologies. This review analyzes advancements in airborne PM sensing technologies and the integration of DL methodologies for environmental monitoring. This review emphasizes the importance of PM monitoring for public health, environmental policy, and scientific research. Traditional PM sensing methods, including their principles, advantages, and limitations, are discussed, covering gravimetric techniques, continuous monitoring, optical and electrical methods, and microscopy. The integration of DL with PM sensing offers potential for enhancing monitoring accuracy, efficiency, and data interpretation. DL techniques, such as convolutional neural networks (CNNs), autoencoders, recurrent neural networks (RNNs), and their variants, are examined for applications like PM estimation from satellite data, air quality prediction, and sensor calibration. This review highlights the data acquisition and quality challenges in developing effective DL models for air quality monitoring. Techniques for handling large and noisy datasets are explored, emphasizing the importance of data quality for model performance, generalizability, and interpretability. The emergence of low-cost sensor technologies and hybrid systems for PM monitoring is discussed, acknowledging their promise while recognizing the need for addressing data quality, standardization, and integration issues. This review identifies areas for future research, including the development of robust DL models, advanced data fusion techniques, applications of deep reinforcement learning, and considerations of ethical implications. Full article