Search Results (47)

Air pollution has become a significant concern for human health, especially in developing countries. Among Primary Pollutants, particulate matter 2.5 (${\mathrm{PM}}_{2.5}$), refers to airborne particles which have a diameter of 2.5 micrometres or less, and has become a widely used measure for monitoring air quality globally. The standard go-to method usually uses Federal Reference Grade sensors to understand air quality. But, they are quite cost-prohibitive, so the popular alternative is low-cost (LC) air quality sensors. Even LC air quality monitors do not cover many areas, especially across the global south. On the other hand, the ubiquitous use of online social media OSM has led to its evolution in participatory sensing. While it does not function as a physical sensor, it can be a proxy indicator of public perception on the topic under study. OSM platforms such as Twitter/X and Reddit have already demonstrated their value in understanding human perception across various domains, including air quality monitoring. This study focuses on understanding air pollution in a resource-constrained setting by examining how the community perception on social media can complement traditional monitoring. We leverage metadata readily available from social media user data to find patterns with air quality fluctuations before and during the pandemic. We use the US Embassy ${\mathrm{PM}}_{2.5}$ data for baseline measurement. In the study, we empirically analyse the variations in quantitative & intent-based community perception in seasonal & pandemic outbreaks with varying air quality. We compare the baseline against temporal & user-specific attributes of Twitter/X relating to tweets like daily frequency of tweets, tweet lags 1–5, user followers, user verified, and user lists memberships across two timelines: pre-COVID-19 (20 March 2019– 29 February 2020) & COVID-19 (1 March 2020–20 September 2020). Our analysis examines both the quantitative and the intent-based community engagement, highlighting the significance of features like user authenticity, tweet recurrence rates, and intensity of participation. Furthermore, we show how behavioural patterns in the online discussions diverged across the two periods, which reflected the broader shifts in the air pollution levels and the public attention. This study empirically demonstrates the significance of X/Twitter metadata, beyond standard tweet content, and provides additional features for modelling and understanding air quality in developing countries. Full article

Air pollution poses major risks to public health, driving the adoption of low-cost sensor (LCS) networks for fine-grained and real-time monitoring. However, the variable accuracy of LCS data compared with reference instruments necessitates robust quality control (QC) frameworks. Over the past decade, machine learning (ML) has emerged as a powerful tool to calibrate sensors, detect anomalies, and mitigate drift in large-scale deployment. This survey reviews advances in three methodological categories: traditional ML models, deep learning architectures, and hybrid or unsupervised methods. We also examine spatiotemporal QC frameworks that exploit redundancies across time and space, as well as real-time implementations based on edge–cloud architectures. Applications include personal exposure monitoring, integration with atmospheric simulations, and support for policy decision making. Despite these achievements, several challenges remain. Traditional models are lightweight but often fail to generalize across contexts, while deep learning models achieve higher accuracy but demand large datasets and remain difficult to interpret. Spatiotemporal approaches improve robustness but face scalability constraints, and real-time systems must balance computational efficiency with accuracy. Broader adoption will also require clear standards, reliable uncertainty quantification, and sustained trust in corrected data. In summary, ML-based QC shows strong potential but is still constrained by data quality, transferability, and governance gaps. Future work should integrate physical knowledge with ML, leverage federated learning for scalability, and establish regulatory benchmarks. Addressing these challenges will enable ML-driven QC to deliver reliable, high-resolution data that directly support science-based policy and public health. Full article

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

Tocopherols, major lipid-soluble components of vitamin E, are essential natural products with significant nutritional and pharmacological value. Their structural diversity and uneven distribution across vegetable oils require accurate analytical strategies for compositional profiling, quality control, and authenticity verification, amid concerns over food fraud and regulatory demands. Analytical challenges, such as matrix effects in complex oils and the cost trade-offs of green extraction methods, complicate these processes. This review examines recent advances in tocopherol analysis, focusing on extraction and detection techniques. Green methods like supercritical fluid extraction and deep eutectic solvents offer selectivity and sustainability, though they are costlier than traditional approaches. On the analytical side, hyphenated techniques such as supercritical fluid chromatography-mass spectrometry (SFC-MS) achieve detection limits as low as 0.05 ng/mL, improving sensitivity in complex matrices. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) provides robust analysis, while spectroscopic and electrochemical sensors offer rapid, cost-effective alternatives for high-throughput screening. The integration of chemometric tools and miniaturized systems supports scalable workflows. Looking ahead, the incorporation of Artificial Intelligence (AI) in oil authentication has the potential to enhance the accuracy and efficiency of future analyses. These innovations could improve our understanding of tocopherol compositions in vegetable oils, supporting more reliable assessments of nutritional value and product authenticity. Full article

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

Hydroxychloroquine (HCQ), a cornerstone therapeutic agent for autoimmune diseases, requires precise serum concentration monitoring due to its narrow therapeutic window. Current HCQ monitoring methods such as HPLC and LC-MS/MS are sensitive but costly and complex. While electrochemical sensors offer rapid, cost-effective detection, their large chambers and high sample consumption hinder point-of-care use. To address these challenges, we developed a microfluidic electrochemical sensing platform based on a screen-printed carbon electrode (SPCE) modified with a hierarchical nanocomposite of gold nanoparticles (AuNPs), copper-based metal–organic frameworks (Cu-MOFs), and multi-walled carbon nanotubes (MWCNTs). The Cu-MOF provided high porosity and analyte enrichment, MWCNTs established a 3D conductive network to enhance electron transfer, and AuNPs further optimized catalytic activity through localized plasmonic effects. Structural characterization (SEM, XRD, FT-IR) confirmed the successful integration of these components via π-π stacking and metal–carboxylate coordination. Electrochemical analyses (CV, EIS, DPV) revealed exceptional performance, with a wide linear range (0.05–50 μM), a low detection limit (19 nM, S/N = 3), and a rapid response time (<5 min). The sensor exhibited outstanding selectivity against common interferents, high reproducibility (RSD = 3.15%), and long-term stability (98% signal retention after 15 days). By integrating the nanocomposite-modified SPCE into a microfluidic chip, we achieved accurate HCQ detection in 50 μL of serum, with recovery rates of 95.0–103.0%, meeting FDA validation criteria. This portable platform combines the synergistic advantages of nanomaterials with microfluidic miniaturization, offering a robust and practical tool for real-time therapeutic drug monitoring in clinical settings. Full article

This study presents a novel low-cost and low-power prepaid water meter system that combines tokenization and LC sensors to monitor water consumption accurately with mobile application via Bluetooth Low Energy (BLE) connectivity compared to conventional meters. Water meters play a vital role in monitoring water usage in Indonesia. Postpaid billing methods that rely on manual data recording are a source of concern due to potential inaccuracies caused by human error. This study presents the development of a prepaid water meter system that integrates LC sensors, BLE connectivity, a tokenization mechanism, and a mobile application to address this issue. The system offers a cost-effective solution by utilizing BLE + Global System for Mobile (GSM) from the user’s mobile phone. Using the design thinking methodology, the mobile application for the prepaid water meter achieved a usability testing score of 80. The load testing results for the back-end server, conducted with a sample size of 515 users, revealed a back-end latency of 1.973 milliseconds and an error rate of 8.74%. Furthermore, the LC sensors integrated into the PWM device showed an average error rate of 1.33%. The power consumption during each work cycle was measured at 129 mA and each battery is expected to last six years. Overall, with simple LC sensors, this system can precisely measure water usage. Full article

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

Assessing air quality in urban areas is vital for protecting public health, and low-cost sensor networks help quantify the population’s exposure to harmful pollutants effectively. This paper introduces an innovative method to calibrate air-quality sensor networks by combining CFD modeling with dependable AQ measurements. The developed CFD model is used to simulate traffic-related PM₁₀ dispersion in a 1.6 × 2 km² urban area. Hourly simulations are conducted, and the resulting concentrations are cross-validated against high-quality measurements. By offering detailed 3D information at a micro-scale, the CFD model enables the creation of concentration maps at sensor locations. Through regression analysis, relationships between low-cost sensor (LCS) readings and modeled outcomes are established and used for network calibration. The study demonstrates the methodology’s capability to provide aid to low-cost devices during a representative 24 h period. The precision of a CFD model can also guide optimal sensor placement based on prevailing meteorological and emission scenarios and refine existing networks for more accurate urban air quality representation. The usage of cost-effective air quality networks, high-quality monitoring stations, and high-resolution air quality modeling combines the strengths of both top-down and bottom-up approaches for air quality assessment. Therefore, the work demonstrated plays a significant role in providing reliable pollutant monitoring and supporting the assessment of environmental policies, aiming to address health issues related to urban air pollution. Full article

Nowadays, indoor air quality monitoring has become an issue of great importance, especially in industrial spaces and laboratories where materials are handled that may release particles into the air that are harmful to health. This study focuses on the monitoring of air quality and particle concentration using low-cost sensors (LCSs). To carry out this work, particulate matter (PM) monitoring sensors were used, in controlled conditions, specifically focusing on particle classifications with PM2.5 and PM10 diameters: the Nova SDS011, the Sensirion SEN54, the DFRobot SEN0460, and the Sensirion SPS30, for which an adapted environmental chamber was built, and gaged using the Temtop M2000 2nd as a reference sensor (SRef). The main objective was to preliminarily assess the performance of the sensors, to select the most suitable ones for future research and their possible use in different work environments. The monitoring of PM2.5 and PM10 particles is essential to ensure the health of workers and avoid possible illnesses. This study is based on the comparison of the selected LCS with the SRef and the results of the comparison based on statistics. The results showed variations in the precision and accuracy of the LCS as opposed to the SRef. Additionally, it was found that the Sensirion SEN54 was the most suitable and valuable tool to be used to maintain a safe working environment and would contribute significantly to the protection of the workers’ health. Full article

A couple of air quality (AQ) parameters were monitored with two types of low-cost sensors (LCSs) before, during and after the garden fence rebuilding of a dwelling house, located at the junction of a main road and a side street in a suburban area of Budapest, Hungary. The AQ variables, recorded concurrently indoors and outdoors, were particulate matter (PM₁, PM_2.5, PM₁₀) and some gaseous trace pollutants, such as CO₂, formaldehyde (HCHO) and volatile organic compounds (VOCs). Medium-size aerosol (PM_2.5-1), coarse particulate (PM_10-2.5) and indoor-to-outdoor (I/O) ratios were calculated. The I/O ratios showed that indoor fine and medium-size PM was mostly of outdoor origin; its increased levels were observed during the renovation. The related pollution events were characterized by peaks as high as 100, 95 and 37 µg/m³ for PM₁, PM_2.5-1 and PM_10-2.5, respectively. Besides the renovation, some indoor sources (e.g., gas-stove cooking) also contributed to the in-house PM₁, PM_2.5-1 and PM_10-2.5 levels, which peaked as high as 160, 255 and 220 µg/m³, respectively. In addition, these sources enhanced the indoor levels of CO₂, HCHO and, rarely, VOCs. Increased and highly fluctuating VOC levels were observed in the outdoor air (average: 0.012 mg/m³), mainly due to the use of paints and thinners during the reconstruction, though the use of a nearby wood stove for heating was an occasional contributing factor. The acquired results show the influence of the fence renovation-related activities on the indoor air quality in terms of aerosols and gaseous components, though to a low extent. The utilization of high-resolution LCS-assisted monitoring of gases and PM_x helped to reveal the changes in several AQ parameters and to assign some dominant emission sources. Full article

Two low-cost (LC) monitoring networks, PurpleAir (instrumented by Plantower PMS5003 sensors) and AirQino (Novasense SDS011), were assessed in monitoring PM_2.5 and PM₁₀ daily concentrations in the Padana Plain (Northern Italy). A total of 19 LC stations for PM_2.5 and 20 for PM₁₀ concentrations were compared vs. regulatory-grade stations during a full “heating season” (15 October 2022–15 April 2023). Both LC sensor networks showed higher accuracy in fitting the magnitude of PM₁₀ than PM_2.5 reference observations, while lower accuracy was shown in terms of RMSE, MAE and R². AirQino stations under-estimated both PM_2.5 and PM₁₀ reference concentrations (MB = −4.8 and −2.9 μg/m³, respectively), while PurpleAir stations over-estimated PM_2.5 concentrations (MB = +5.4 μg/m³) and slightly under-estimated PM₁₀ concentrations (MB = −0.4 μg/m³). PurpleAir stations were finer than AirQino at capturing the time variation of both PM_2.5 and PM₁₀ daily concentrations (R² = 0.68–0.75 vs. 0.59–0.61). LC sensors from both monitoring networks failed to capture the magnitude and dynamics of the PM_2.5/PM₁₀ ratio, confirming their well-known issues in correctly discriminating the size of individual particles. These findings suggest the need for further efforts in the implementation of mass conversion algorithms within LC units to improve the tuning of PM_2.5 vs. PM₁₀ outputs. Full article

Air pollution is one of the most important problems in big cities, resulting in adverse health effects. The aim of the present study was to characterize the personal exposure to indoor and outdoor pollution in the Greater Athens Area in Greece by taking measurements during a journey from suburban to mixed industrial–urban areas, encompassing walking, waiting, bus travel, and metro travel at various depths. For this reason, low-cost (LC) sensors were used, and the inhaled dose of particulate matter with an aerodynamic diameter of less than or equal to 2.5 μm (PM_2.5) in different age groups of passengers was calculated. Specific bus routes and the Athens metro network were monitored throughout different hours of the day. Then, the average particulate matter (PM_2.5) exposure for a metro passenger was calculated and evaluated. By considering the ventilation rate of a passenger, an estimation of the total PM_2.5 inhaled dose for males and females as well as for different age groups was made. The results showed that the highest PM_2.5 concentrations were observed inside the wagons with significant increases during rush hours or after rush hours. Furthermore, there should be a concern regarding older individuals using the subway network in Athens during rush hours and in general for sensitive groups (people with asthma, respiratory and cardiovascular problems, etc.). Full article

Growth in population and increased environmental awareness demand the emergence of new energy sources with low environmental impact. Lignocellulosic biomass is mainly composed of cellulose, lignin, and hemicellulose. These materials have been used in the energy industry for the production of biofuels as an eco-friendly alternative to fossil fuels. However, their use in the fabrication of small electronic devices is still under development. Lignocellulose-based triboelectric nanogenerators (LC-TENGs) have emerged as an eco-friendly alternative to conventional batteries, which are mainly composed of harmful and non-degradable materials. These LC-TENGs use lignocellulose-based components, which serve as electrodes or triboelectric active materials. These materials can be derived from bulk materials such as wood, seeds, or leaves, or they can be derived from waste materials from the timber industry, agriculture, or recycled urban materials. LC-TENG devices represent an eco-friendly, low-cost, and effective mechanism for harvesting environmental mechanical energy to generate electricity, enabling the development of self-powered devices and sensors. In this study, a comprehensive review of lignocellulosic-based materials was conducted to highlight their use as both electrodes and triboelectric active surfaces in the development of novel eco-friendly triboelectric nano-generators (LC-TENGs). The composition of lignocellulose and the classification and applications of LC-TENGs are discussed. Full article

Air quality has important climate and health effects. There is a need, therefore, to monitor air quality both indoors and outdoors. Methods of measuring air quality should be cost-effective if they are to be used widely, and one such method is low-cost sensors (LCS). This study reports on the use of LCSs in Ulaanbataar, Mongolia to measure PM_2.5 concentrations inside yurts or “gers”. Some of these gers were part of a non-government agency (NGO) initiative to improve insulating properties of these housing structures. The goal of the NGO was to decrease particulate emissions inside the gers; a secondary result was to lower the use of coal and other biomass material. LCSs were installed in gers heated primarily by coal, and interior air quality was measured. Gers that were modified by increasing their insulating capacities showed a 17.5% reduction in PM_2.5 concentrations, but this is still higher than recommended by health organizations. Gers that were insulated and used a combination of both coal and electricity showed a 19.1% reduction in PM_2.5 concentrations. Insulated gers that used electricity for both heating and cooking showed a 48% reduction in PM_2.5 but still had higher concentrations of PM_2.5 that were 6.4 times higher than recommended by the World Health Organization (WHO). Nighttime and daytime trends followed similar patterns and trends in PM_2.5 concentrations with slight variations. It was found that at nighttime the outside PM_2.5 concentrations were generally higher than the inside concentrations of the gers in this study, meaning that PM_2.5 would flow into the ger whenever the doors were opened, causing spikes in PM_2.5 concentrations. Full article