Search Results (2,949)

Particulate matter is a significant component of air pollutants, especially PM_2.5-bound polycyclic aromatic hydrocarbons (PAHs), due to multiple toxicological effects on organisms. In this study, the concentrations of PM_2.5-bound PAHs at the two most important ports in Mexico (Veracruz and Manzanillo) were determined to identify emission sources and evaluate potential health impacts. Average PM_2.5 concentrations were higher in Veracruz (12.90 ± 4.77 μg/m³) than in Manzanillo (10.96 ± 3.99 μg/m³), although both were below Mexico’s current air quality standards. Total PAH concentrations were also higher in Veracruz (22.14 ± 16.76 ng/m³) compared to Manzanillo (11.65 ± 9.04 ng/m³). The identified PAHs and diagnostic ratios indicated different emissions patterns: in Manzanillo, concentrations were associated with high-temperature pyrogenic sources, while in Veracruz, greater contributions from mixed sources were observed. The ILCR assessment was 4.61 × 10⁻⁷ for Manzanillo and 8.77 × 10⁻⁷ for Veracruz, both below the accepted risk threshold. Despite relatively low health risk estimates, the presence of carcinogenic PAHs, such as benzo[a]pyrene, highlights the need for continuous monitoring and mitigation strategies in port environments. These results provide pioneering, highly valuable insights into the dynamics of air pollution in these Mexican ports and their potential health implications. 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

Cooking-related emissions represent a major contributor to indoor air pollution in residential kitchens, producing complex mixtures of volatile organic compounds (VOCs), odor-causing gases, oil vapors, particulate matter (PM_2.5), and combustion-related pollutants (CO and NO_x). In this study, a controlled ozone-assisted oxidation approach was integrated into a recirculating (ductless) domestic kitchen hood equipped with a confined reaction chamber and experimentally evaluated under closed-loop operating conditions where treated air was returned to the indoor environment after post-treatment. A multivariate Response Surface Methodology (RSM) framework based on the Box–Behnken design was employed to quantify and optimize the coupled effects of temperature (20–30 °C), relative humidity (40–60%), ozone dosage (1–3 ppm within the confined reaction zone), and airflow rate (150–250 m³/h) on multi-pollutant removal performance. The results demonstrate that ozone assistance substantially improves the abatement of oxidation-sensitive pollutants, particularly VOCs and odor, while airflow rate strongly governs transport-dominated pollutants such as PM_2.5 and oil vapors. In contrast, CO and NO_x exhibited limited improvement, indicating that ozone-assisted oxidation alone is insufficient for comprehensive control of combustion-related gases under short-residence-time recirculating hood conditions. The main contribution of this work is the implementation of a demand-driven ozone management strategy, supported by dual ozone sensing for reaction-zone control and outlet safety verification, where ozone generation is activated only in the presence of reactive gaseous pollutants and automatically reduced or terminated once pollutant concentrations fall below predefined thresholds, minimizing unnecessary oxidant release. Residual ozone downstream of the reaction stage was continuously monitored to prevent excess ozone return to the occupied zone. Overall, the proposed closed-loop, feedback-controlled ozone-assisted recirculating range hood concept demonstrated device-level reductions in measured VOC/odor signals under controlled conditions, while also highlighting the need for complementary post-treatment components for particle- and combustion-related pollutants. However, the potential formation of secondary oxidation byproducts was not characterized in this study, and therefore the results should be interpreted with respect to device-level pollutant removal rather than comprehensive indoor air quality improvement. Full article

Lake Erhai is an important plateau freshwater lake in China. It serves not only as a crucial drinking water source for the local region but also as the core area of the Cangshan Erhai National Nature Reserve. Consequently, Lake Erhai plays an extremely significant role in the local economy, society, and ecology. Since 2000, the water quality of Lake Erhai has continuously deteriorated, showing a eutrophic trend. To identify the primary driving forces behind these water quality changes, this study employed stepwise regression analysis. Climate conditions, socio-economic development within the basin, and implementation of environmental protection measures (IEPMs) were considered influencing factors for a comprehensive and systematic analysis of Lake Erhai’s water quality. The results indicate that rising air temperature may increase total phosphorus (TP) concentration, while rainfall may elevate both TP and total nitrogen (TN) levels. In contrast, higher wind speed may reduce chemical oxygen demand (COD_Mn), TP, and TN concentrations. Socio-economic development, meanwhile, may contribute to increased COD_Mn concentration. Based on these findings, this paper proposes recommendations focusing on formulating more effective non-point source pollution control measures and strengthening water quality monitoring in Lake Erhai during summer. By identifying the key natural and anthropogenic drivers of water quality changes in Lake Erhai, this study provides a scientific basis for the development of targeted pollution control strategies and directly contributes to the protection of clean water sources. Moreover, its revelation of the coupled impacts of climate change and socio-economic activities enhances understanding of plateau lake ecosystem resilience. This insight is critical for ensuring regional ecological security and serves as a model for advancing sustainable development goals in similar lake systems worldwide. Full article

Existing Environmental, Social, and Governance (ESG) monitoring approaches rely primarily on static reports and dashboard-based interfaces, limiting real-time analysis and interactive exploration of sustainability data in complex built environments. In addition, current digital twin systems often lack integration with IoT-based sensing or depend on cloud-based rendering infrastructures, increasing deployment complexity and restricting accessibility. This study proposes a lightweight Web3D-based digital twin framework for real-time ESG monitoring in smart buildings. The system integrates an independently developed IoT sensor network with a browser-native 3D visualization platform, enabling real-time monitoring of ESG indicators—including electricity consumption—without requiring proprietary software or dedicated rendering hardware. ESG indicators are derived using a rule-based classification aligned with the WELL Building Standard v1. The framework was validated through a 12-month real-world deployment involving 60 IoT sensors. Results demonstrate stable performance, achieving 66 FPS rendering, 78 ms system latency, and 98% sensor data consistency based on cross-sensor agreement. The system also enabled timely detection of environmental anomalies, leading to measurable improvements in air quality and lighting conditions. Unlike prior digital twin systems, the proposed framework delivers a fully browser-native, lightweight architecture that integrates real-time IoT sensing, adaptive Web3D visualization, and structured ESG monitoring within a single deployable system. This approach provides a practical solution with potential for broader deployment in real-time sustainability monitoring for smart buildings. Full article

Air pollution caused by fine particulate matter (PM_2.5) poses a serious public health threat in many South Asian megacities where monitoring networks remain limited. Lahore, Pakistan—frequently ranked among the world’s most polluted cities—still lacks reliable short-term PM_2.5 forecasting systems. This study develops a performance-weighted ensemble machine learning framework that integrates satellite observations, meteorological reanalysis data, and ground monitoring measurements to improve daily PM_2.5 prediction. Eleven predictor variables were processed using a unified Google Earth Engine pipeline, including MODIS aerosol optical depth, Sentinel-5P trace gases (CO, NO₂, SO₂), and ERA5 meteorological parameters. Four tree-based machine learning algorithms—Random Forest, XGBoost, LightGBM, and CatBoost—were trained using daily observations from 2019 to 2023. Model evaluation using an independent 2024 dataset showed strong predictive capability, with Random Forest achieving R² = 0.77 (RMSE = 24.75 µg m⁻³), XGBoost R² = 0.76 (RMSE = 26.32 µg m⁻³), CatBoost R² = 0.73 (RMSE = 30.39 µg m⁻³), and LightGBM R² = 0.70 (RMSE = 32.75 µg m⁻³). To further enhance performance, the best models were combined into a weighted ensemble (RF 0.5, XGBoost 0.3, and CatBoost 0.2), which produced the highest validation accuracy (R² = 0.77; RMSE = 23.37 µg m⁻³). Statistical testing using paired t-tests and Diebold–Mariano tests confirmed that the ensemble significantly reduced forecast errors compared with individual models. Feature importance analysis revealed that surface pressure, temperature, CO, and NO₂ were the most influential predictors of PM_2.5 variability. The proposed framework demonstrates that combining satellite data, reanalysis meteorology, and ground observations through ensemble learning can provide accurate and scalable air quality forecasting for data-limited urban environments. Full article

Air quality management in zoological gardens plays a crucial role in their sustainable development. However, air quality in these settings remains understudied. In addition, previous research has largely focused on airborne microbial contamination merely in animal enclosures. This exploratory study provides preliminary insights into airborne fungal contamination alongside microclimate conditions in the visitor and worker areas of animal premises in the Zagreb Zoo. The study was performed in the Monkey House, Tropical House, Rainy Africa, and Bird House, as well as outdoors in fall. Fungi were identified based on macroscopic and microscopic examinations. Total culturable fungal concentration in indoor air ranged between 50 and 4.25 × 10³ CFU/m³, and in outdoor air between 1.00 × 10² and 1.50 × 10³ CFU/m³. Molds of eight genera and yeasts were isolated from the air. Both indoors and outdoors, the predominant genera were Cladosporium and Penicillium, and also genus Aspergillus indoors. Cladosporium spp. and Penicillium spp. concentrations, as well as total fungal concentration in the air, were on average, highest in Rainy Africa and Bird House, while the highest average Aspergillus spp. concentration was found in the Tropical House. Levels of Cladosporium spp., Penicillium spp., and Aspergillus spp. concentrations were associated with microclimate conditions. Study results suggest that the airborne fungal contamination may depend on the animals housed in the premises, and the design and management of the premises. Although total fungal concentration determined may not necessarily pose a health risk for exposed people, the qualitative composition of fungi signifies the importance of implementing good practices in zoo premises, including optimal microclimate conditions and effective ventilation. The results obtained also indicate the need for air quality monitoring, which concurs with zoo sustainability goals. Full article

Air pollution remains a critical environmental and public health threat, particularly in highly populated urban areas such as the Lisbon Metropolitan Area (LMA). This study provides a refined and detailed assessment of the spatial distribution of air pollution and associated attributable mortality across the LMA. High-resolution (1 km²) annual mean concentrations of key pollutants (PM_2.5, PM₁₀ and NO₂) for 2022 and 2023 were estimated by integrating outputs from the URBAIR dispersion model with ground-based monitoring observations using advanced geostatistical data-fusion techniques. Air pollutant concentrations were combined with gridded population data and age-stratified baseline mortality rates within a Geographic Information System framework to quantify spatial variations in health impacts. Using the World Health Organization AirQ+ framework and established concentration–response functions, we estimated a total of 3195 air-pollution-attributable deaths across the Lisbon Metropolitan Area (LMA) in 2022, increasing to 4010 deaths in 2023. Fine particulate matter (PM_2.5) was identified as the dominant contributor, accounting for more than 40% of the total health burden. At a high spatial resolution (1 km² grid), estimated mortality exhibited substantial variability, ranging from 0 to 29 deaths per cell in 2022 and from 0 to 36 deaths per cell in 2023. These results highlight the importance of fine-scale spatial analysis, revealing intra-urban disparities that are not captured by aggregated estimates of total attributable mortality. The proposed methodological framework, integrating dispersion modelling, data fusion, and spatially explicit health impact assessment at fine spatial scales, provides a robust and transferable approach to support evidence-based air quality management and urban health policy development in European metropolitan contexts. This integrated approach enhances comparability, improves exposure assessment accuracy, and strengthens the scientific basis for designing targeted mitigation strategies that could prevent hundreds of premature deaths annually while addressing documented spatial inequalities in pollution exposure. Full article

Urban–suburban PM_2.5 differences are widely used to characterize spatial disparities in air pollution, yet their long-term trends may depend on urban definitions. For China during 2013–2020, this study used nationwide ground PM_2.5 monitoring data and 1 km × 1 km gridded population density data to analyze the sensitivity of urban–suburban PM_2.5 trends to spatial structure-based and population-density-based classification (300, 1500, 2200, 2500 people km⁻²) at national, Eastern and Western China scales. Results showed significant national PM_2.5 decline, with urban reduction rates of −3.1 to −3.3 µg m⁻³ yr⁻¹ in summer and −6.0 to −6.3 µg m⁻³ yr⁻¹ in winter, and faster air quality improvement in winter. Urban–suburban PM_2.5 differences were highly sensitive to classification methods: the spatial structure-based framework showed minimal differences (0.09 µg m⁻³ in summer, 5 µg m⁻³ in winter), while the 300 people km⁻² threshold yielded much larger ones (11 µg m⁻³ in summer, 29 µg m⁻³ in winter) with faster urban declines. Higher population density thresholds narrowed such differences and converged trends with the spatial structure-based results. Pronounced spatial heterogeneity existed: Eastern China had larger PM_2.5 declines with consistent response patterns to national trends, while Western China showed weaker declines, with urban–suburban differences highly sensitive to classification methods and opposite temporal evolution trends. This study confirms that urban definition is a critical methodological factor for interpreting China’s long-term urban–suburban PM_2.5 trends, as different methods cause notable inferential deviations. Future air pollution spatial heterogeneity studies should carefully select and specify urban classification methods to ensure comparable, scientifically rigorous findings. Full article

As a core resource of traditional Chinese medicine (TCM), medicinal plants are conventionally monitored and assessed using high-cost, low-efficiency methods. Remote sensing offers an efficient technical alternative for large-scale and dynamic evaluation. This study systematically reviewed the literature from 2005 to 2025, summarized remote sensing platforms, sensors, and data analytical methods, and specifically analyzed their applications in medicinal plant resource investigation, planting monitoring, stress monitoring, and TCM quality assessment. These studies mainly focus on resource surveys and quality analysis, targeting root and rhizome herbs. Integrated satellite-, UAV-, and ground-based remote sensing enables distribution mapping, growth retrieval, stress monitoring, and non-destructive quality evaluation in medicinal plants, achieving overall accuracies ranging from 80% to 100%. Currently, remote sensing applications in medicinal plants are evolving toward space–air–ground integration, multi-source data fusion, artificial intelligence empowerment, and multi-omics integration. However, they are constrained by complex wild habitats, difficulties in monitoring root herbs, spectral confusion, and limited model generalization. Future efforts should focus on establishing an integrated monitoring network, developing full-chain quality inversion models for geo-authentic herbs, building climate-adaptive cultivation systems, creating early pest–disease warning technologies, and deepening the integration of remote sensing and multi-omics to support the sustainable utilization and high-quality development of medicinal plant resources. 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

Monitoring avocado (Persea americana Mill., cv. Semil-34) in tropical mountain landscapes of Cambita, San Cristóbal, Dominican Republic is inherently complex due to the pronounced topographical and climatic heterogeneity that modulates the crop’s ecophysiological responses, specifically vegetative vigor, carbon allocation, and the synchronization of reproductive flushes. This study integrates 5-year (2020–2025) Sentinel-2 time series, ERA5-Land climatic variables (air temperature, total precipitation, and radiation), and geomorphometric covariates to explain variability in yield and fruit quality. Multispectral indices, including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Normalized Difference Red Edge (NDRE), and Normalized Difference Moisture Index (NDMI), were analyzed using Partial Least Squares Regression (PLSR) to characterize phenological dynamics and rank dominant predictors. The results revealed coherent spectral phenological trajectories; however, a significant inverse relationship was detected between canopy vigor and yield during reproductive phases. High vegetation index values were significantly and negatively associated with lower production (r = −0.58, p < 0.0021), reflecting a potential source–sink imbalance. Topography functioned as a structural filter, regulating root drainage and productive stability across the landscape. While yield variability was partially explainable (R² = 0.38), internal fruit quality, measured as dry matter content, exhibited comparatively high environmental stability. A central contribution of this research lies in identifying the “vigor paradox” in cv. Semil-34 and the suggestion that topography may exert a stronger influence than direct spectral signals under tropical hillside conditions. These findings provide an exploratory framework for anticipating yield and fruit quality through satellite remote sensing or UAVs, supporting site-specific management decisions in mountain agricultural systems. Full article

Municipal solid waste disposed of in open dumpsites and unlined landfills contaminates groundwater, soils, and air across urban areas of low- and middle-income countries. Nevertheless, impacts across all three environmental media have not been systematically assessed together. We conducted a PRISMA 2020-compliant systematic review of 286 peer-reviewed studies from PubMed, Dimensions, and OpenAlex, applying structured eligibility screening and quality appraisal using an adapted JBI checklist. Heavy metals—lead, cadmium, chromium, and zinc—were the most frequently detected contaminants in leachate and groundwater, commonly exceeding WHO drinking water guidelines by one to three orders of magnitude. Soil contamination by potentially toxic elements was documented at virtually all open dumpsites studied, persisting for decades after site closure. Particulate matter at South Asian MSW sites reached up to 41 times the WHO 2021 annual guideline. Microplastics acting as heavy metal carriers and dumpsite leachate as a source of antimicrobial resistance genes were identified as emerging risks outside standard monitoring frameworks. Non-carcinogenic hazard indices exceeded acceptable thresholds in the majority of health risk studies reviewed. Engineered containment was the strongest predictor of contamination severity across all sites. Phytoremediation, constructed wetlands, and biofiltration showed promise as mitigation approaches. Critical evidence gaps remain for Central Asia, harmonized reporting standards, and longitudinal monitoring data. 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 PM₂.₅ 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

The high installation and operational costs of reference-grade air quality monitoring systems have accelerated the widespread adoption of low-cost sensors (LCS). However, their susceptibility to environmental influences, temporal drift, and measurement uncertainty necessitates robust calibration approaches to ensure reliable measurements. Although machine learning (ML)-based calibration methods have been widely investigated, most existing implementations rely on static analytical workflows and require programming expertise, which limits their accessibility for many domain specialists. To simplify and standardize the calibration process for low-cost air quality sensors, this study presents Air Quality Multi-Model Calibration (AQ-MultiCal), an interactive, no-code platform. The platform provides a unified environment for evaluating 14 regression models, performing automated hyperparameter optimization, and conducting comparative performance analysis through an intuitive graphical interface supported by interactive visualization tools. The platform is validated using CO₂ measurements collected from January and February 2025. Experimental results indicate that the optimized k-nearest neighbors (kNN) model achieved the best performance, with a coefficient of determination of R² = 0.990 with low prediction error. These results demonstrate that AQ-MultiCal enables accurate sensor calibration and systematic comparison of ML models while improving the accessibility of ML-based calibration through an open-source platform designed for domain experts without programming expertise. Full article