Search Results (69)

Low-cost sensors (LCSs) have rapidly expanded in urban air quality monitoring but still suffer from limited data accuracy and vulnerability to environmental interference compared with regulatory monitoring stations. To improve their reliability, we proposed a machine learning (ML)-based framework for LCS correction that integrates various meteorological factors at observation sites. Taking Tongshan District of Xuzhou City as an example, this study carried out continuous co-location data collection of hourly PM_2.5 measurements by placing our LCS (American Temtop M10+ series) close to a regular fixed monitoring station. A mathematical model was developed to regress the PM_2.5 deviations (PM_2.5 concentrations at the fixed station—PM_2.5 concentrations at the LCS) and the most important predictor variables. The data calibration was carried out based on six kinds of ML algorithms: random forest (RF), support vector regression (SVR), long short-term memory network (LSTM), decision tree regression (DTR), Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), and the final model was selected from them with the optimal performance. The performance of calibration was then evaluated by a testing dataset generated in a bootstrap fashion with ten time repetitions. The results show that RF achieved the best overall accuracy, with R² of 0.99 (training), 0.94 (validation), and 0.94 (testing), followed by DTR, BiLSTM, and GRU, which also showed strong predictive capabilities. In contrast, LSTM and SVR produced lower accuracy with larger errors under the limited data conditions. The results demonstrate that tree-based and advanced deep learning models can effectively capture the complex nonlinear relationships influencing LCS performance. The proposed framework exhibits high scalability and transferability, allowing its application to different LCS types and regions. This study advances the development of innovative techniques that enhance air quality assessment and support environmental research. Full article

Background: Urban air pollution, particularly fine particulate matter (PM2.5 and PM10), poses health risks, including damage to the ocular surface. This pilot study (BIKE-EYE) aimed to assess ocular exposure to airborne pollutants during bicycle commuting and to evaluate particle presence in human tear fluid. Methods: Fifteen healthy volunteers wore portable sensors measuring PM2.5 and PM10 during daily bike commutes over six months. Exposure was calculated as time-weighted integrals over the ten days preceding an ophthalmologic exam assessing conjunctival hyperemia, epithelial damage, tear film quality, and meibomian gland function. Ocular symptoms were assessed via the Ocular Surface Disease Index (OSDI). Tear samples were analyzed using scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS). Results: Higher pollutant exposure was significantly associated with conjunctival hyperemia and corneal epithelial damage, while temperature and humidity showed no effect. OSDI scores moderately correlated with PM levels. SEM/EDS analysis confirmed airborne particles in post-exposure tear samples, including carbonaceous material, aluminosilicates, iron, and sulfur compounds. Conclusions: Ocular surface alterations and conjunctival hyperemia were significantly associated with air pollution exposure, while subjective symptoms showed weaker trends. The detection of particulate matter in human tear fluid supports the use of the ocular surface as a sensitive, non-invasive tool for biomonitoring. These findings highlight its potential role in early warning systems for pollution-related health effects, with implications for public health surveillance and urban planning. Full article

Fine particulate matter (PM_2.5) poses substantial urban health risks that vary across space, time, and population vulnerability. We integrate a spatio-temporal INLA–SPDE PM_2.5 field with an agent-based model (ABM) of 10,000 daily home–work commuters in Indianapolis’s Pleasant Run airshed (50 weeks; 250 m grid). The PM_2.5 surface fuses 23 corrected PurpleAir PA-II-SD sensors with meteorology, land use, road proximity, and MODIS AOD. Validation indicated strong agreement (leave-one-out R² = 0.79, RMSE = 3.5 μg/m³; EPA monitor comparison R² = 0.81, RMSE = 3.1 μg/m³). We model a spatial-equity counterfactual by assigning susceptibility independently of residence and workplace, isolating vulnerability from residential segregation. Under this design, annual PM_2.5 exposure was statistically indistinguishable across groups (16.22–16.29 μg/m³; max difference 0.07 μg/m³, <0.5%), yet VWDI differed by ~10× (High vs. Very Low). Route-level maps reveal recurrent micro-corridors (>20 μg/m³) near industrial zones and arterials that increase within-group variability without creating between-group exposure gaps. These findings quantify a policy-relevant “floor effect” in environmental justice: even with perfect spatial equity, substantial health disparities remain driven by susceptibility. Effective mitigation, therefore, requires dual strategies—place-based emissions and mobility interventions to reduce exposure for all, paired with vulnerability-targeted health supports (screening, access to care, indoor air quality) to address irreducible risk. The data and code framework provides a reproducible baseline against which real-world segregation and mobility constraints can be assessed in future, stratified scenarios. Full article

Indoor air pollution, including fine particulate matter (PM_2.5), poses a severe threat to human health. Due to the diverse sources of indoor PM_2.5 and its high spatial heterogeneity in distribution, traditional single-point fixed monitoring fails to accurately reflect the actual human exposure level. In recent years, the development of high spatiotemporal resolution monitoring technologies has provided a new perspective for revealing the dynamic distribution patterns of indoor PM_2.5. This study discusses two cutting-edge monitoring strategies: (1) mobile monitoring technology based on Indoor Positioning Systems (IPS) and portable sensors, which maps 2D exposure trajectories and concentration fields by having personnel carry sensors while moving; and (2) 3D dynamic monitoring technology based on in situ Lateral Scattering LiDAR (I-LiDAR), which non-intrusively reconstructs the 3D dynamic distribution of PM_2.5 concentrations using laser arrays. This study elaborates on the principles, calibration methods, application cases, advantages, and disadvantages of the two technologies, compares their applicable scenarios, and outlines future research directions in multi-technology integration, intelligent calibration, and public health applications. It aims to provide a theoretical basis and technical reference for the accurate assessment of indoor air quality and the prevention and control of health risks. Full article

Indoor sports facilities present unique challenges for air quality management due to high crowd densities and limited ventilation. This study investigated air quality in a municipal athletic facility in Komotini, Greece, focusing on concentrations of airborne particulate matter (PM_1.0, PM_2.5, PM₁₀), humidity, and temperature across spectator zones, under varying mask scenarios. Sensing devices were installed in the stands to collect high-frequency environmental data. The system, based on optical particle counters and cloud-enabled analytics, enabled real-time data capture and retrospective analysis. The main experiment investigated the impact of spectators wearing medical masks during two basketball games. The results show consistently elevated PM levels during games, often exceeding recommended international thresholds in the spectator area. Notably, the use of masks by spectators led to measurable reductions in PM_1.0 and PM_2.5 concentrations, because they seem to have limited the release of human-generated aerosols as well as the amount of movement among spectators, supporting their effectiveness in limiting fine particulate exposure in inadequately ventilated environments. Humidity emerged as a reliable indicator of occupancy and potential high-risk periods, making it a valuable parameter for real-time monitoring. The findings underscore the urgent need for improved ventilation strategies in small to medium-sized indoor sports facilities and support the deployment of low-cost sensor networks for actionable environmental health management. Full article

This study investigates personal exposure to fine particulate matter (PM_2.5) 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. PM_2.5 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 PM_2.5 exposure to serve as a practical tool for increasing public awareness of personal exposure risks to protect respiratory health. 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

Low-cost particulate matter sensors have seen increased use for monitoring at personal and local levels due to their affordability, ease of operation, and high time resolution. However, the quality of data reported by these sensors can be questionable, and a thorough evaluation of their performance is necessary. This study evaluated the particle sizing accuracy of several commonly used optical sensors, including the Alphasense optical particle counter (OPC), TSI DustTrak DRX aerosol monitor, Plantower PMS5003 sensor, and Sensirion SPS30 sensor, using laboratory-generated monodisperse particles. The OPC and DRX agreed partially with reference instruments and showed promise in detecting coarse-size particles. However, the PMS5003 and SPS30 did not correctly size fine and coarse particles. Furthermore, their reported mass distributions do not directly correspond to their number distribution. Despite these limitations, field measurements involving a dust storm period showed that the SPS30 correlated reasonably well with reference instruments for both PM_2.5 and PM₁₀, though the regression slopes differed significantly. These findings underscore the need for caution when interpreting data from low-cost optical sensors, particularly for coarse particles. Recommendations for improving the performance of these sensors are also provided. Full article

The widely used low-cost particulate matter (PM) sensors in Thailand, such as the DustBoy, require performance improvements to ensure their data align with the established standards set by the US Environmental Protection Agency (US EPA). This study evaluates the accuracy and reliability of the DustBoy, a commonly used PM_2.5 monitoring device in Thailand. A comparative analysis was conducted between the DustBoy and the US EPA’s Federal Reference Method (FRM) and Federal Equivalent Method (FEM). The research involved both laboratory and field testing, where the DustBoy’s performance was analyzed at various particulate matter concentration levels and environmental conditions. The study demonstrated that the DustBoy readings diverged from those of standard monitors at higher PM_2.5 concentrations; however, a positive correlation between the devices remained evident. Below 100 µg/m³, the DustBoy overestimated PM concentrations compared to the FRM devices but underestimated them compared to the FEM devices. At higher concentrations, the DustBoy showed a significant overestimation, although the data trends aligned with those of standard devices. The sensor performance was also affected by factors such as the sensor age and device model. Corrections were developed to adjust the DustBoy readings to match the reference devices more closely, enhancing the accuracy post-adjustment. These corrections will refine the DustBoy’s public data reporting and serve as guidelines for other low-cost sensors in Thailand. Full article

Air pollution remains a critical global health concern, with 91% of the world’s population exposed to air quality exceeding World Health Organization (WHO) standards and indoor pollution causing approximately 3.8 million deaths annually due to incomplete fuel combustion. Subways, as major public transportation modes in densely populated cities, can exhibit fine particulate matter (PM) levels that surpass safety limits, even in developed countries. Contributing factors include station location, ambient air quality, train frequency, ventilation efficiency, braking systems, tunnel structure, and electrical components. While elevated PM levels in underground platforms are recognized, the vertical and horizontal variations within stations are not well understood. This study examines the vertical and horizontal distribution of PM2.5 and PM10 levels at Shibuya Station, a structurally complex hub in the Tokyo Subway System. Portable sensors were employed to measure PM concentrations across different platform levels—both above and underground—and at various locations along the platforms. The results indicate that above-ground platforms have significantly lower PM2.5 and PM10 levels compared to underground platforms (17.09 μg/m³ vs. 22.73 μg/m³ for PM2.5; 39.54 μg/m³ vs. 56.98 μg/m³ for PM10). Notably, the highest pollution levels were found not at the deepest platform but at the one with the least effective ventilation. On the same platform, PM levels varied by up to 63.72% for PM2.5 and 120.23% for PM10, with elevated concentrations near the platform extremities compared to central areas. These findings suggest that ventilation efficiency plays a more significant role than elevation in vertical PM variation, while horizontal differences are likely influenced by piston effects from moving trains. This study underscores the risk of exposure to unsafe PM2.5 levels in underground platforms, particularly at platform extremities, highlighting the need for improved ventilation strategies to enhance air quality in subway environments. Full article

As the wealth of evidence grows as to the negative impact of indoor air quality on human health, it has become increasingly urgent to investigate and characterise sources of air pollution within the home. Fine particulate matter with a diameter of 2.5 µm or less (PM_2.5) is a key cause for concern, and cooking is known to be one of the most significant sources of domestic PM_2.5. In this study, the aim was to demonstrate the efficacy of combining experimental techniques and cutting-edge High-Performance Computing (HPC) to characterise the dispersion of PM_2.5 during stir-frying within a kitchen laboratory. This was carried out using both experimental measurement with low-cost sensors and high-fidelity Computational Fluid Dynamics (CFD) modelling, in which Lagrangian Stochastic Methods were used to model particle dispersion. Experimental results showed considerable spatio-temporal variation across the kitchen, with PM_2.5 mass concentrations in some regions elevated over 1000 μg ${\mathrm{m}}^{-3}$ above the baseline. This demonstrated both the impact that even a short-term cooking event can have on indoor air quality and the need to factor in such strong spatio-temporal variations when assessing exposure risk in such settings. The computational results were promising, with a reasonable approximation of the experimental data shown at the majority of monitoring points, and future improvements to and applications of the model are suggested. Full article

This study explores the application of low-cost sensor networks for air quality monitoring in Cabo Verde islands, utilizing Clarity Node-S sensors to measure fine particulate matter with diameters equal to or smaller than 10 µm (PM10) and 2.5 µm (PM2.5) and nitrogen dioxide (NO₂) gasses, across various locations. The sensors were strategically placed and calibrated to ensure coverage of the whole archipelago and accurate data collection. The results consistently revealed seasonal patterns of dust variation across the archipelago, with concentrations of particulate matter exceeding World Health Organization (WHO) limits in all regions. However, Praia frequently exhibits the highest levels of air pollution, exceeding a 200 µg/m³ daily average, particularly during the dry season. Seasonal variations indicated that pollutants are significantly higher from November to March due to Saharan dust flux (a phenomenon locally know as Bruma Seca). Other cities showed more stable and lower pollutant concentrations. This study highlights the potential of low-cost sensors to provide extensive and real-time air quality data, enabling better environmental assessment and policy formulation. However, the variability in equipment accuracy and the limited geographical coverage remain the main limitations to be overcome. Future research should focus on these issues, and a sensor network integrated with reference methods could be a great asset to enhance data accuracy and improve outcomes of air quality monitoring in the country. Full article

Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and ${\mathrm{NO}}_2$ concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in ${\mathrm{PM}}_{10}$ readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating ${\mathrm{PM}}_{10}$ levels due to the prevalence of coarse particles. Calibration adjustments, based on the ${\mathrm{PM}}_1{\mathrm{/PM}}_{10}$ ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions. Full article

Indoor air quality (IAQ) poses a significant public health concern, and exposures to high levels of fine particulate matter (PM_2.5) and carbon dioxide (CO₂) could have detrimental health impacts. This study focused on assessing the indoor air pollutants in a residential house located in the town of Mission, Hidalgo County, South Texas, USA. The PM_2.5 and CO₂ were monitored indoors: the kitchen and the bedroom. This investigation also aimed to elucidate the effects of household activities such as cooking and human occupancy on these pollutants. Low-cost sensors (LCSs) from TSI AirAssure™ were used in this study. They were deployed within the breathing zone at approximately 1.5 m above the ground. Calibration of the low-cost sensors against Federal Equivalent Method (FEM) instruments was undertaken using a multiple linear regression method (MLR) model to improve the data accuracy. The indoor PM_2.5 levels were significantly influenced by cooking activities, with the peak PM_2.5 concentrations reaching up to 118.45 μg/m³. The CO₂ levels in the bedroom increased during the occupant’s sleeping period, reaching as high as 1149.73 ppm. The health risk assessment was assessed through toxicity potential (TP) calculations for the PM_2.5 concentrations. TP values of 0.21 and 0.20 were obtained in the kitchen and bedroom, respectively. The TP values were below the health hazard threshold (i.e., TP < 1). These low TP values could be attributed to the use of electric stoves and efficient ventilation systems. This research highlights the effectiveness of low-cost sensors for continuous IAQ monitoring and helps promote better awareness of and necessary interventions for salubrious indoor microenvironments. Full article

The spatial–temporal variations of fine particulate matter (PM_2.5) in Akure, a city in southwestern Nigeria, are examined based on multi-year continuous observations using low-cost PM_2.5 sensors at six different sites. The average annual concentration of PM_2.5 across these sites was measured at 41.0 µg/m³, which surpassed both the Nigerian national air quality standard and the World Health Organization air quality guideline level. PM_2.5 levels were significantly higher during the dry season (November–March), often exceeding hazardous levels (over 350 µg/m³), than during the wet season. The analyses of trends in air mass trajectories and satellite data on fire occurrences imply that the transport of dust and accumulation of PM_2.5 originating from local/regional open burning activities played crucial roles in increased PM_2.5 concentrations during the dry season. Further, site-to-site variations in the PM_2.5 levels were observed, with relatively high concentrations at less urbanized sites, likely due to high local emissions from solid fuel combustion, waste burning, and unpaved road dust. Diurnal patterns showed morning and evening peaks at less urbanized sites, accounting for an estimated 51–77% of local emissions. These results highlight the importance of local emission sources in driving spatial–temporal PM_2.5 variations within the city and the need for targeted mitigation strategies to address the significant air pollution challenges in Akure and similar regional cities in West Africa. Full article