Search Results (506)

Battery-powered Internet of Things (IoT) sensor nodes for environmental monitoring face strict energy constraints, particularly when employing high-consumption sensors such as particulate matter sensors or gas analyzers. Extending operational lifetime without sacrificing measurement reliability remains a key challenge for large-scale air-quality monitoring deployments. This paper proposes a digital-twin-assisted adaptive sensing algorithm for reducing energy consumption by dynamically optimizing sensor usage for Indoor Air Quality (IAQ) monitoring system. The system consists of distributed sensing nodes and a central station that maintains digital twins to evaluate candidate sensing strategies based on historical data and environmental patterns. Strategies are assessed in terms of energy consumption and measurement fidelity and deployed only when a measurable improvement is achieved. The approach is evaluated across mobile and stationary sensor configurations used for monitoring indoor air quality in university laboratories while educational and research activities are carried out. For stationary nodes, clustering-based scheduling reduces the activation of high-power sensors, while for mobile nodes, variation-based triggering exploits correlations between equivalent and reference CO₂ measurements to limit energy-intensive sensing. Results demonstrate energy savings of up to approximately 70% while maintaining acceptable measurement fidelity. The findings show that reduced sensing can be used for system initialization, while digital twin evaluation enables reliable transition to adaptive sensing under suitable conditions. Full article

Particulate matter pollution in northern Utah comes from various sources, including industry, traffic and the western desert, plus dried shoreline sediments of the Great Salt Lake (GSL). Particulate matter air pollution, particularly that containing heavy metals, can have severe effects on human health. Since the high-water levels in the 1980s, the GSL has been drying and reached record low water levels in 2022. Accurate Environmental Protection Agency (EPA) PM_2.5 and PM₁₀ sensors within northern Utah are few. This makes the mapping of particulate matter air pollution difficult. We show spatial patterns in particulate matter air pollution using a combination of PM_2.5 and PM₁₀ levels from 7 years of Purple Air Network data (a network of inexpensive air quality sensors installed by private citizens or businesses) and atmospheric optical depth (AOD) data from Sentinel imagery. We also show that PM_2.5 and PM₁₀ levels are significantly higher on a regular basis within 10 km of the Great Salt Lake and close to Farmington and Bear River Bays, which are upwind of large population centers. The levels of heavy metals (arsenic, copper, lead and zinc) were particularly high for the Farmington Bay and Saltair study sites, and the percentage of silt-sized particles that are most susceptible to wind erosion was largest for Farmington Bay, which is upwind of large population centers. Links between heavy metal concentrations, particle size and PM air pollution and asthma outcomes are investigated. Closeness to the lake was a significant predictor of asthma emergency room visits in 2018–2022 but not in 2016. Full article

Urban construction activities are recognized as significant contributors to particulate matter (PM) emissions; however, the accurate real-time monitoring of size-resolved PM fractions presents a formidable challenge. Traditional low-cost PM sensors predominantly report cumulative concentrations, which obscures the distinct health and regulatory significance of PM1, PM2.5, and PM10. This study systematically evaluates the performance of two low-cost sensors—PMS5003 and Sniffer4D—utilizing non-cumulative measurements obtained under controlled laboratory conditions designed to simulate construction PM generated from concrete slab drilling. Sensor performance was rigorously analyzed using Pearson correlation coefficients, standard deviation, and mean percentage differences. Six correction models—linear regression, polynomial regression, Random Forest (RF), XGBoost, Artificial Neural Network (ANN), and Kalman filter—were independently developed for each PM size fraction to enhance measurement precision. Findings reveal that RF and ANN consistently provided the most accurate corrections, particularly for PM1 and PM2.5, with RF achieving a coefficient of determination (R²) > 0.89 for PM1 and R² > 0.87 for PM2.5 at the 50 s duration. This investigation introduces a size-resolved correction framework specifically designed for construction environments, thereby advancing the capability of low-cost sensors to enable accurate particle-specific exposure assessments. Full article

Environmental monitoring systems are often constrained by high cost, limited portability, restricted pollutant coverage, and dependence on fixed infrastructure, which can limit their suitability for distributed real-time sensing. This study presents FRESH, an autonomous Internet of Things (IoT)-based platform for multi-parameter environmental monitoring and short-term forecasting. The system integrates sensors for air quality, thermal conditions, light, acoustics, and weather, together with GSM-based remote data transmission, onboard data logging, and hybrid battery–solar power management. FRESH was deployed across multiple indoor and outdoor locations in Coventry and at the University of Warwick, UK, and operated over a 10-month period to assess practical performance under varied environmental conditions. In addition to continuous environmental sensing, machine learning models were developed to predict short-term changes in selected environmental variables. Across the tested models, the best predictive performance was obtained for several key parameters, including particulate matter (R² = 0.93), volatile organic compounds (R² = 0.92), and ozone (R² = 0.98). The results suggest that FRESH has potential to support portable, multi-parameter environmental monitoring with integrated short-horizon forecasting, providing a basis for further development of distributed sensing and localised early-warning applications. Full article

Farmland particulate pollution seriously affects regional atmospheric quality, and exploring efficient field dust control strategies is an urgent need for agricultural ecological protection. This study employed a wind tunnel and online dust monitoring system to investigate the dust reduction effect of straw return in conservation tillage in Beijing farmland under varying wind speeds and precipitation levels, providing theoretical and technical support for straw coverage configuration and dust pollution control. Given the insufficient understanding of the combined impacts of straw coverage, wind speed and precipitation on farmland particulate emissions, this study examined how these key factors jointly affect fine particulate matter (PM_2.5), inhalable particulate matter (PM₁₀), and total suspended particulate (TSP) emissions. A three-factor, three-level response surface experiment modeled these relationships and identified optimal conditions for suppressing PM emissions—51.35% straw coverage, 3.96 m·s⁻¹ wind speed, and 32.36 mm precipitation—yielding average PM_2.5, PM₁₀, and TSP concentrations of 26.31, 31.71, and 42.43 μg·m⁻³, respectively. Field data showed that the mean absolute errors (MAEs) between predicted and measured concentrations were 0.52–5.80, 0.46–3.93, and 1.83–5.68 μg·m⁻³ for PM_2.5, PM₁₀, and TSP, respectively, corresponding to relative prediction accuracies of 90.42–97.95%, 95.03–98.52%, and 93.10–97.21%—indicating strong model accuracy. This approach enhances dynamic monitoring of straw return practices and guides rational field management. By integrating meteorological conditions and particulate emission characteristics, the model can quantitatively assess regional straw coverage and screen optimal straw mulching rates. It provides a clear data reference for decision-makers to formulate targeted dust prevention policies, standardize straw return regulation, and advance eco-friendly and sustainable agricultural production. Full article

Air pollution associated with public transport systems constitutes a critical yet highly heterogeneous component of urban exposure and represents an important challenge for sustainable urban mobility and environmental health governance. Commuters and transport workers are frequently subjected to pollutant concentrations that exceed those reported by ambient background monitoring networks. This review provides a comprehensive synthesis of the global scientific literature on air quality in public transport microenvironments—including buses, bus stops, terminals, and underground stations—through a multidimensional analytical framework that considers climatic classification, socio-economic context, meteorological drivers, transport microenvironment typology, sampling strategies, analytical techniques, and exposure metrics. A large body of peer-reviewed studies published worldwide was examined to identify dominant patterns, methodological trends, and persistent knowledge gaps. Across regions, the evidence consistently reports elevated concentrations of particulate matter (PM_2.5, PM₁₀, and ultrafine particles) and traffic-related gaseous pollutants, particularly within confined or poorly ventilated environments and during peak traffic periods. Marked geographical, climatic, and socio-economic imbalances are evident, with most studies conducted in temperate and tropical climates and in countries with very high or high Human Development Index, whereas arid, continental, and low-HDI regions remain substantially underrepresented. From a methodological perspective, the literature is dominated by short- to intermediate-term monitoring campaigns relying on active sampling, mobile measurements, and increasingly calibrated low-cost sensors, while long-term stationary observations and standardized integrative monitoring frameworks remain scarce. Although advanced analytical approaches—such as chemical characterization, environmental magnetism, receptor modeling, computational fluid dynamics, and inhaled dose assessment—are increasingly applied, their systematic integration remains limited. Overall, this review reveals persistent methodological, geographical, and conceptual gaps and highlights the urgent need for standardized, interdisciplinary, and long-term monitoring strategies to improve exposure assessment and support evidence-based mitigation policies and sustainable urban transport planning aimed at reducing health risks associated with public transport-related air pollution. Full article

Upper Northern Thailand continues to face a protracted structural crisis from fine-particulate matter (PM_2.5), primarily driven by biomass burning and wildfires. Conventional mechanical capture systems, such as cyclones, often suffer a drastic efficiency drop when treating sub-micron particles. This study introduces an innovative Evaporative Condensation Growth Scrubber (ECGS) designed to bridge this technological gap by promoting the growth of fine particles through heterogeneous nucleation. Experimental testing across 10 different nozzle configurations was conducted to optimize the system’s performance. The results revealed that the ECGS system significantly outperformed the dry cyclone (Baseline) across all nine testing configurations. While the Baseline showed inherent limitations in capturing sub-micron particles, the ECGS demonstrated relative efficiency improvements ranging from 39.53% to 83.23% for PM_2.5, and 26.10% to 61.50% for PM₁₀ compared to the baseline. Optimal performance was achieved using a 90-degree injection angle and a 10 cm distance, which created a complete spray curtain and maximized collision probability. Under these conditions, the outlet PM_2.5 concentration stabilized at 11.81 µg/m³ within 180 s of water injection. Crucially, despite sensor interference caused by high relative humidity, the system’s effectiveness was confirmed by a significant difference in performance in PM₁₀ and PM_2.5 removal. The PM₁₀ collection efficiency outperformed that of PM_2.5 by 28.82%, providing empirical evidence that PM_2.5 particles successfully acted as nuclei for condensation and grew into the larger PM₁₀ size range. This particle growth enabled more effective centrifugal separation, demonstrating that the ECGS system offers a viable and efficient solution for fine particle removal in highly polluted environments. Full article

Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM_2.5 and PM₁₀) data at one-minute intervals over an 18-month period, where PM_2.5 and PM₁₀ refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤10 µm, respectively, reported in micrograms per cubic metre (µg/m³. Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM_2.5 and PM₁₀ excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Full article

This two-week pilot study within the Horizon Europe EDIAQI project evaluated the real-life performance of portable air filtration units in two office environments (a small office and a shared kitchen) under continuous device operation and daily filter replacement. Indoor particle concentrations were monitored continuously using low-cost sensors (LCS) from three providers and supported by gravimetric measurements, while daily activity logs documented occupancy patterns, printing, cooking, and other source events together with purifier ON/OFF status. Particulate matter (PM) mass concentrations showed no systematic improvement during purifier ON periods; instead, temporal variability was dominated by indoor activities and episodic emissions, with occasional short-term peaks around filter replacement suggestive of minor resuspension. Chemical analysis provided a clearer picture: polycyclic aromatic hydrocarbons (PAHs) responded differently across fractions and compositions. Across monitored locations, high-molecular-weight PAHs in the PM₁ fraction decreased during purifier ON periods (approximately 30% lower on average), whereas low-molecular-weight PAHs measured in total suspended particles (TSP) were higher during ON periods, indicating that semi-volatile fractions and activity/ventilation dynamics can outweigh simple filtration effects. Overall, the findings highlight a gap between laboratory-derived filtration performance metrics and outcomes in occupied, mixed-source indoor environments and emphasise the importance of device sizing, placement, airflow mixing, and complementary source control and ventilation strategies when deploying filtration-based IAQ interventions. Full article

Building on prior evidence that biomass cooking drives personal air pollution in rural and peri-urban Bangladesh, we measured kitchen pollution alongside personal exposure and examined the influence of outdoor industrial and traffic emissions on personal and indoor air quality. In an mHealth based-behavior change communication (BCC) intervention study (NCT05570552), 400 women were enrolled from rural Matlab and peri-urban Araihazar in Bangladesh. We measured 24 h personal exposure to fine particulate matter 2.5 (PM_2.5) and black carbon (BC) using personal monitors (UPAS V2), and 72–120 h PM_2.5 in 200 kitchens and outdoors of households using air quality sensors (PurpleAir Flex). Compared to clean fuel users, biomass users showed greater personal and kitchen exposure to PM_2.5, showing good correlation between personal and indoor PM_2.5 measurements (R² = 0.722). Daily average personal PM_2.5 and kitchen PM_2.5 during both cooking and non-cooking periods were higher in rural than peri-urban areas. Geographic information system mapping revealed that personal PM_2.5 was inversely related to the distance of factories from households when below <300 m in both rural and urban areas. Only in Araihazar, personal BC was higher in households located near factories or roads (<200–300 m) compared to those situated further away. Higher personal BC exposure was found in peri-urban women than rural women (p < 0.001). Higher levels of PM_2.5 and increased BC were found in rural and peri-urban households, respectively, which were located in close proximities to formal/informal factories and main roads. These findings highlight the need for sustainable household energy transitions and improved air quality management to reduce air pollution exposure in Bangladesh. Full article

Indoor exposure to particulate matter (PM) depends on ventilation-driven transport, yet sensor placement in real rooms is often based on limited point data. This study develops and experimentally validates a transient CFD framework, using RANS airflow coupled with Lagrangian discrete phase tracking, to map PM_2.5 and PM₁₀ in a full-scale 2.0 × 3.0 × 2.5 m bedroom with a fixed, non-oscillating pedestal fan and an open window. Airflow was verified by grid independence and validated against 10-point velocity measurements (RMSE = 0.108 m·s⁻¹). Incense experiments (≈31 min burn) provided PM time series over the first 60 min at 16 locations on two heights; emission rate, burning time, and air-change rate (1.96–5.39 ACH) were calibrated so that accepted models achieved aggregate R² > 0.90. Spatial mapping on a 0.5 m grid shows that PM behavior is governed primarily by airflow-defined accumulation pockets rather than by source proximity alone. A near-source region consistently captured strong early-time peaks, whereas remote low-exchange pockets remained elevated during the decay phase. For PM_2.5, the most persistent hotspot is a ceiling-adjacent recirculation pocket, while for PM₁₀, gravitational settling shifted the dominant hotspots toward floor-layer, low-velocity regions. An exposure score combining normalized peak and time-averaged concentrations, interpreted together with particle-track persistence metrics, distinguished transiently traversed regions from true retention pockets. The results show that sensor placement should follow the monitoring objective: near-source regions are more responsive to peak events, ceiling pockets are more suitable for persistent PM_2.5 monitoring, and floor hotspots are more critical for PM₁₀. No single fixed sensor location adequately represents both particle sizes in the present bedroom and ventilation configuration. Full article

Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the operational reality for most network sensors—lacks systematic methodology. We address this gap using 24 months of hourly data (August 2023–July 2025) from Sofia, Bulgaria, where five official reference stations (Executive Environmental Agency) operate alongside 22 AirThings low-cost sensors, four of which are co-located. Random Forest models achieved $R^2\in (0.53,0.75)$ across PM_2.5, PM₁₀, NO₂, and O₃, representing from 40% (for O₃) to 408% (for PM_2.5) improvement over Multiple Linear Regression baselines. Using leave-one-station-out spatial cross-validation, we derived pollutant-specific uncertainty growth rates ($\alpha$) from 3.84% to 5.62% per km, characterizing how calibration uncertainty increases with distance from reference stations (statistically significant for PM₁₀ and O₃, $p<0.05$). Applied to 18 non-co-located sensors, the framework generated 1.2 million calibrated hourly measurements with 95% prediction intervals over the study period. Co-location sites spaced 6 km apart achieve a less than 30% uncertainty increase at network midpoints, within EU Air Quality Directive thresholds for indicative monitoring. These empirically derived $\alpha$ parameters enable network planners to predict measurement reliability at arbitrary sensor locations without ground-truth validation, providing evidence-based guidance for cost-effective hybrid monitoring network design. Full article

This paper presents the design of a sensor system for remote measurements of exhaust emissions from automotive combustion engines. The system’s purpose is to determine the likelihood of a given vehicle’s potential harmfulness to the environment. This system, if implemented, could detect vehicles posing a threat to the environment in road traffic. A remote measurement system can be installed in the front of a measuring vehicle driving behind the vehicle being diagnosed. This approach allows for rapid road testing of multiple vehicles while they are operating in real-world conditions where engines can emit the highest levels of undesirable pollutants. Exceeding emission standards may be related to modifications made to the vehicle’s exhaust gas aftertreatment systems, engine wear, or malfunctions of engine-related systems such as the diesel particulate filter (DPF) or catalytic converter. Toxic and undesirable substances include carbon monoxide (CO), hydrocarbons (HC), nitrogen oxides (NO_x), carbon dioxide (${\mathrm{C}\mathrm{O}}_2$), and particulate matter (PM) particles. The main goal of the measurements is to identify vehicles that potentially pose a threat to the environment during normal operation. The sensor system consists of several types of sensors utilizing various physical and chemical phenomena, with particular emphasis on their low cost and easy availability. The measurement unit utilizes MEMS technology, photoacoustic spectroscopy, electrochemical methods, light absorption and scattering, spectrophotometry, and electro-optical detection. Full article

The growth of human activity in cities is a key factor in the degradation of air quality. Numerous studies have demonstrated the link between air quality and the existence of dangerous and chronic diseases that are extremely costly for individuals and society. This study presents an analytical framework that compares fixed and mobile air-quality monitoring approaches in cities with limited resources, using Mohammedia city, Morocco, as an example. The framework centers on mobile monitoring units mounted on vehicles and equipped with affordable sensors, GPS technology, and wireless communication systems to track important pollutants, including fine particulate matter (PM_2.5 and PM₁₀) and harmful gaseous compounds (NO₂, SO₂, CO, O₃). The evaluation relies on scenario-based modeling, performance data from existing literature, and calculations of costs throughout the system’s lifetime. To enhance measurement reliability, the researchers developed a correction system that addresses measurement errors caused by temperature, humidity, vehicle speed, vibrations, traffic-related interference, operational interruptions, and communication limitations. The findings indicate that fixed monitoring stations deliver superior measurement precision, with estimated uncertainty ranging from ±1.2–2.5%, though their coverage area is restricted to 0.534 km² (representing 1.6% of Mohammedia). In comparison, the suggested mobile setup could potentially monitor 9.8 km², covering approximately 30% of the city, while decreasing infrastructure needs and setup time (2–4 h compared to 2–4 weeks). Over 10 years, the total cost is EUR 252,000 for mobile monitoring, compared with EUR 3.6 million for a network of 20 fixed stations. These results demonstrate that corrected mobile monitoring systems offer significant promise as an economical and sustainable approach for managing urban environmental conditions. Full article

Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal burning of materials like plastic or waste oil. This study introduces a mobile air pollution monitoring system using compact sensor modules installed on vehicles and drones. These autonomous modules are equipped with gas, particulate matter, and environmental sensors, along with Global Positioning System (GPS) tracking to record pollutant concentrations in real time and associate them with specific geographic locations. Field experiments conducted in Hungary and Uzbekistan demonstrated the system’s effectiveness in detecting elevated pollutant levels in rural areas with solid fuel heating and in urban zones affected by industrial activity and traffic. For instance, PM2.5 concentrations ranged from 15 μg/m³ in forested areas to as high as 160 μg/m³ in industrial zones, while CO₂ levels near chimneys exceeded background values by 15–25 ppm. Drone-based measurements enabled vertical profiling and direct analysis of emissions from individual chimneys, providing detailed spatial distribution data. The proposed mobile sensing approach allows for the accurate localization of pollution sources and the assessment of air quality variations within small-scale environments. This method overcomes limitations of stationary or pre-announced inspections and supports proactive environmental monitoring and enforcement. Full article