Search Results (969)

Introduction: International studies have demonstrated a positive impact on air quality associated with the presence of green areas in urban conglomerates. However, in Brazil, studies addressing the impacts of urban green areas on air quality are still incipient and are predominantly focused on large urban centers. The objective of this study was to investigate the relationship between urban green areas, surface temperature (LST), and air quality across 15 medium-sized Brazilian cities. Methods: Concentrations of particulate matter fractions (PM₁, PM_2.5, and PM₁₀) were monitored from January 2023 to May 2024 using second data from low-cost sensors. The NDVI and both daytime and nighttime LST profiles were extracted via Google Earth Engine within a 1 km buffer zone surrounding each station via the Sentinel-2 and MODIS 11A1 satellite data, respectively. Spatial–temporal co-variation patterns were explored using principal component analysis (PCA). To model these dynamics while controlling for spatial dependencies, a multi-criteria framework compared linear models (simple linear regression (LM) and linear mixed (LMM)) and generalized models (generalized additive (GAM) and generalized additive mixed (GAMM)). Results: The results revealed a positive relationship between NDVI and PM_2.5 and PM₁₀ fractions in specific regions, while surface temperatures showed a direct association with finer particles (PM₁ and PM_2.5). The regression coefficient showed the significant association of PM2.5 with NDVI and nighttime LST (β = 1.330; IC 95%: [0.397; 2.270]; p = 0.005). The GAMM was the best-fitting model for all particle fractions, demonstrating that incorporating monitoring stations as random intercepts successfully controls for unmeasured local heterogeneity, while penalized splines accurately capture non-linear environmental factors. Conclusions: Although many studies have shown that green areas in temperate regions typically act as consistent sinks for particulate matter, our study revealed localized and seasonal responses in tropical urban landscapes. It should be noted that our study is conducted on a national scale and that the use of low-cost sensors and remote sensing does not allow us to distinguish between the localized microclimatic benefits of vegetation and the long-range transport of regional pollutants. Full article

Air pollution is primarily caused by human activities such as industrial emissions, road traffic, waste incineration, and fossil fuel power plants. Pollution refers to the presence of harmful substances in the air, such as nitrogen dioxide (NO₂), sulfur dioxide (SO₂), ozone (O₃), carbon monoxide (CO), and other environmental pollutants. Some pollutants pose health risks even at low doses. Given the critical importance of air quality, monitoring air pollution has become an urgent and essential subject. Air quality monitoring relies on accurate data, so changeable environments and sensor issues make using interval diagnostic techniques for addressing uncertainty in systems interesting. In this article, we focus on three key aspects to achieve precise and efficient results: (1) the use of an accurate fault detection method that accounts for data uncertainty while maintaining model symmetry, (2) the implementation of a reliable detection index invariant to symmetric sensor behaviors, and (3) the combination of both to improve fault localization accuracy. This paper presented a fault detection and localization framework designed for uncertain and nonlinear monitoring environments. A novel fault-sensitive detection index was developed and integrated into an elimination-based localization strategy within a reduced-rank interval kernel PCA (RR-IKPCA) model. By exploiting information contained in modified residual subspaces and explicitly accounting for measurement uncertainty, the proposed approach enhances fault sensitivity while preserving robust localization capability, as validated on the AIRLOR air quality monitoring network. Full article

Rapid urbanization in semi-arid cities intensifies heat exposure, air pollution, and land-surface degradation, yet these stressors are often assessed separately. This study develops a scale-aware Urban Environmental Stress (UES) framework for Jaipur, India, using multi-sensor Earth observation data. The framework explicitly addresses indicator redundancy, weighting bias, short time-series interpretation, and temporal comparability. The final primary UES surface uses twelve retained stress-oriented indicators on a 500 m common analysis grid, excludes NDBI because it is algebraically redundant with NDMI when both are computed from the same NIR/SWIR bands, and applies equal weights so that built fraction does not dominate the composite. Entropy weighting is reported only as a sensitivity diagnostic. The resulting UES map identifies high relative stress in Jaipur’s dense urban core and transport-industrial corridors, with lower stress along the Aravalli flank and peri-urban green or water-adjacent areas. The framework is presented as a relative spatial prioritization tool rather than an absolute physical time series; temporal claims are limited to independently reported land-cover and individual-indicator trajectories unless fixed multi-year normalization and fixed weights are applied. Full article

In megacities, where conventional mitigation strategies exhibit variable and environment-dependent performance, urban air pollution continues to be a significant public health concern. To methodically assess the operational reliability of urban smog mitigation systems under dynamic atmospheric conditions, this study proposes a data-driven failure analysis approach. A machine learning architecture based on Random Forest and XGBoost algorithms is developed using integrated meteorological and air quality metrics from Lahore, Pakistan, such as temperature, wind speed, and relative humidity. AQI is used as an integrated pollution indicator alongside meteorological variables to enhance the model’s ability to capture overall atmospheric pollution impact and improve the accuracy of smog mitigation failure prediction. This study presents a data-driven framework for predicting the failure of smog mitigation methods based on meteorological conditions. Unlike existing approaches that primarily focus only on air quality prediction, this work identifies specific environmental conditions, along with AQI as an input feature, to determine when mitigation strategies become ineffective. This enables proactive decision-making to maintain healthy indoor air quality. A threshold-controlled indoor air purification system that self-activates when the model predicts mitigation failure using real-time sensor inputs is introduced to address outdoor mitigation restrictions. PM_2.5 reduction efficiency, clean air delivery rate, and energy consumption indicators are used to evaluate the purifier’s optimized performance. Predicting mitigation failure rather than just pollution levels and connecting it with an intelligent interior reaction mechanism is what makes this research novel. In a comparative analysis, Random Forest outperforms XGBoost with an accuracy of 95.5% as opposed to 94.5%, as well as higher precision (96.9%), recall (96.1%), and F1-score (96.5%). The purifier lowered indoor AQI from dangerous to safe levels within 30–40 min. 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

Low-cost IoT-based air quality sensors enable dense monitoring networks but suffer from significant measurement noise and instability particularly in dynamic environments. Conventional fixed-window smoothing reduces noise but introduces a trade-off between signal stability and temporal responsiveness, often attenuating short-term pollution events. This paper proposes an adaptive filtering algorithm that dynamically adjusts the averaging window size based on short-term signal variability. The method relies on real-time variance estimation to balance noise suppression and sensitivity to rapid changes without increasing computational complexity. The approach is implemented within an IoT-based monitoring framework and evaluated using parallel measurements with a certified reference device. Comparative analysis against a certified reference device demonstrates strong agreement, with Pearson correlation coefficients reaching r = 0.88 for PM_2.5 and r = 0.86 for PM₁₀, and low error levels (RMSE ≈ 2.1–2.2 µg/m³). The proposed adaptive filtering approach preserves temporal dynamics while improving signal stability and robustness compared to raw and fixed-window filtering. In addition, this method improves event detection stability, achieving low false alarm rates and near real-time response (latency < 1 sampling interval), supporting RPA-based workflow triggering. The results show that the proposed adaptive filtering provides an efficient and lightweight solution for real-time signal processing on resource-constrained devices, making it suitable for large-scale deployment in environmental monitoring systems. Full article

Air quality affects large urban areas, where rapid urban development and human activities place constant pressure on ecosystems and public health. In this context, large-scale air quality assessment, supported by short-term forecasts, can provide useful information for environmental management and decision-making in urban areas, thus supporting evidence-based urban environmental management. The aim of this work is to design an affordable, smart real-time air pollution monitoring and prediction system for urban planning in overpopulated locations, which is deeply related to community health. The system focuses on real-time monitoring and forecasting of air quality. Prediction tasks were limited to gaseous pollutants CO and CO₂. Measurements were obtained over four months from a low-cost sensor platform installed in a highly populated neighborhood district in Baghdad, Iraq. Air quality prediction of gas concentrations was done using three types of time-series algorithms: Long Short-Term Memory, or LSTM; Gated Recurrent Unit, or GRU; and Temporal Convolutional Network, or TCN, models. Among these, the LSTM architecture showed more stable behavior and a higher predictive ${\mathrm{R}}^2$, ranging from 98.2% to 98.9%. Generally, the findings suggest that combining low-cost sensing technologies with artificial intelligence can offer a feasible and scalable solution for urban air quality monitoring. This approach may support cost-effective strategies for monitoring air quality in resource-constrained urban environments. Full article

Air pollution remains a major challenge for sustainable development because of its impacts on human health, ecosystems, and climate. At the same time, the rapid growth of environmental data and advances in artificial intelligence (AI) have created new opportunities for atmospheric composition research and air-quality management. This review examines AI applications in atmospheric composition studies, focusing on two related but distinct tasks: (i) spatiotemporal forecasting of pollutant concentrations and (ii) emission inference from mobile and point sources. It emphasizes the fundamental differences between these tasks in terms of data requirements, model design, and physical interpretability. A synthesis of representative studies published between 2018 and 2025 is provided, covering machine learning and deep learning approaches for air-quality prediction and emission characterization. Recent foundation-style architectures and global AI weather models introduced in late 2025 and early 2026 further demonstrate the growing role of large-scale spatiotemporal learning in atmospheric and environmental prediction. Particular attention is given to hybrid and physics-informed models that aim to connect data-driven methods with atmospheric processes. The review also discusses major methodological challenges, including data representativeness, sensor uncertainty, spatial transferability, and model generalization under nonstationary conditions. It highlights the importance of leakage-resistant evaluation, appropriate temporal and spatial splitting strategies, and the roles of interpretability and uncertainty quantification in physically meaningful atmospheric modelling. From a sustainability perspective, these AI approaches can support more reliable monitoring, improved emission assessment, and better-informed strategies for air-pollution mitigation. Full article

Post-monsoon open-field stubble burning in northwestern (NW) India—a key agricultural region known as the “breadbasket”—is a longstanding practice used to clear fields. Satellite observations spanning over two decades have revealed significant upward trends in crop production, vegetative greenness, and the frequency of post-harvest fires, with this last contributing to hazardous air quality during the peak burning season (mid-October to mid-November). Since 2022, thermal anomaly data from Aqua-MODIS and SNPP-VIIRS sensors have shown a sharp decline in reported fire events—an observation that contrasts starkly with the concurrent rise in regional aerosol loading detected from space. This apparent discrepancy became particularly pronounced in 2024–2025, prompting a closer examination using high-temporal-resolution imagery from the Advanced Meteorological Imager (AMI) on the geostationary satellite GEO-KOMPSAT-2A. These observations revealed a clear spike in fire-related signals occurring around and after 4:00 p.m. local time, i.e., outside the typical noon to 2:00 p.m. detection window of the MODIS and VIIRS. A fire detection algorithm exploiting the fire-sensitive shortwave-infrared 3.8 μm signal and its contrast to 11.2 μm infrared observations is designed to adopt AMI observations and applied to its multi-year observations (2019–2025). The resulting fire dataset unambiguously shows a gradual shift in stubble burning activity toward the late afternoon hours beginning in 2022 which is underreported by polar-orbiting satellites. The orbital drift of NASA’s MODIS sensor on the Aqua platform allows detection of some of the gradually shifting fires during afternoon hours, but the MODIS still misses a large number of fires occurring around and after 4 p.m. The AMI’s relatively coarse spatial resolution (~4 km), a consequence of its slant viewing geometry over NW India, imposes inherent limitations on quantifying the full extent of fire occurrences. The operational air quality forecasting models currently assimilate satellite fire detections predominantly captured during early afternoon overpasses of the MODIS and VIIRS. The temporal shift in fire activity complicates such forecast, leading to a substantial underestimation of emissions. Intense stubble burning and the resulting air pollution highlight the need for effective crop residue management practices for mitigating the frequency of open biomass burning and thereby reducing episodic degradation of air quality and its associated public health and economic impacts. 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

Volatile Organic Compounds (VOCs) are important indicators of environmental pollution and metabolic activity, making their sensitive and selective detection highly relevant for applications in health monitoring and air quality assessment. Graphene, owing to its exceptional charge transport properties, large surface area, and tunable surface chemistry, is a promising candidate for advanced gas and VOCs sensing. Here we report chemoresistive sensors based on pristine graphene and graphene decorated with platinum (Pt), palladium (Pd), and gold (Au) nanoparticles toward both aromatic (benzene, toluene, and xylene) and non-aromatic (ethanol, methanol, and acetone) vapor compound detection. The detection is achieved at room temperature, and the results demonstrate that graphene functionalized with noble metal nanoparticles shows significant enhancements in sensitivity compared to pristine graphene, mainly against ethanol, toluene and xylene vapors for the Au–graphene sensors. A comparative study with Multi-Walled Carbon Nanotube (MWCNT) sensors decorated with the same type of nanoparticles revealed clear advantages of graphene, attributed to the microstructure and porous structure of graphene powders, which facilitate efficient charge transfer upon vapor adsorption. 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

Sensor data are increasingly used in monitoring spatiotemporal phenomena for diverse applications such as flood management, urban traffic, air quality control, forest fire management, etc. Real-time modelling and representation of such evolving phenomena is fundamental for efficient and near-real-time decision-making processes. In addition to simple and local alerts about occurring changes over time at a given location, as is the case in Sensor Event Service (SES), the decision-making process may require more global spatial information, such as knowing if the monitored phenomenon is expanding or contracting around a given spot or if it is moving from one spot to another, especially for non-punctual spatial features. For such cases, spatiotemporal information should be computed over the whole set of distributed data from which the geometry of monitored phenomena can be assessed. This paper proposes an event-driven fuzzy rule-based decentralized spatial reasoning approach to compute spatiotemporal changes occurring in vague shape phenomena from distributed sensor data streams. Inferring local and partial spatial changes from individual nodes over the sensor network is prior to the computation of developing changes that the monitored phenomenon undergoes over the whole area covered by the sensor network. In this approach, we suggest a Fuzzy-Extended Spatiotemporal Change Pattern (FESTCP) to compute spatiotemporal changes about fuzzy regions. To evaluate our method, simulated case studies of ambient air pollution in Quebec City are carried out. The results reveal that the proposed method could provide satisfactory information about spatiotemporal changes in real-world phenomena monitored by a sensor network for a real-time decision-making process. Full article

Accurate air quality forecasting requires the effective integration of heterogeneous data sources that vary in spatial coverage, temporal resolution, and sensing reliability. This paper presents a unified spatio-temporal data processing framework designed to support multi-source air quality forecasting by jointly leveraging regulatory monitoring stations of the Central Pollution Control Board (CPCB) as reference-grade anchors and community-driven Internet of Things (IoT) sensing platforms for spatial densification. The proposed end-to-end workflow addresses key challenges associated with heterogeneity, data quality, and interoperability through systematic schema harmonization, multi-stage data cleaning, and robust missing data imputation using a Robocentric Iterated Extended Kalman Filter (RIEKF). The processed data are temporally aligned to a uniform sampling grid and enriched with spatial descriptors, including geospatial coordinates, administrative boundaries, and proximity-based emission features. These enriched observations are subsequently fused into a unified spatio-temporal representation that captures both spatial dependencies and temporal dynamics across the sensor network. Dynamic graphs constructed from this representation are processed using a Mobility-Aware Peripheral-Enhanced Graph Neural Network to forecast pollutant concentrations and generate categorical air quality indices. The framework is evaluated using regression metrics reported as RMSE/MAE in µg/m³ and MAPE in %, together with standard AQI classification metrics, demonstrating its effectiveness in improving predictive accuracy and robustness for real-world air quality forecasting applications. Full article

The increasing risk of air pollution in closed areas like passenger vehicles requires smart and real-time air quality reading solutions. Gases such as carbon monoxide (CO)—which is colorless and odorless and is produced by exhaust systems—air conditioners, and combustion sources are very dangerous to health because they can cause respiratory distress and poisoning at high levels. Traditional in-vehicle CO monitoring systems use a single-point sensor and a fixed threshold, which are insufficient in a dynamic cabin environment subject to factors such as vehicle size, ventilation rate, number of occupants, and incoming traffic. To address these drawbacks, this paper proposes a new E-Nose system with Virtual Sensor-Augmented Embedded Intelligence to estimate the CO concentration in vehicle cabins in real time. The system combines data from cheap gas sensors and improves it using virtual sensor machine learning models trained to predict or enhance sensor responses in real time. Embedded intelligence, deployed locally on edge hardware, supports low-latency processing, dynamic calibration, and noise filtering to respond to fluctuating environmental conditions adaptively. This architecture enables more accurate, robust, and context-aware estimation of CO levels compared to traditional threshold-based methods. Experimental validation across varied vehicular scenarios demonstrates superior precision and responsiveness, providing timely warnings even under complex dispersion patterns. Classifier Gradient Boosting, which builds an ensemble of weak learners sequentially, matched the Random Forest with 99.94% training and 98.59% model accuracy, confirming its strong predictive capability. The system is designed to be cost-effective, scalable, and easily integrable into modern automotive platforms. This study also contributes to the field of smart ecological recording and demonstrates the effectiveness of the virtual sensor-enhanced embedded system as an effective way to improve passenger safety by providing pre-emptive on-board air quality monitoring. Full article