Search Results (6,844)

Background/Objectives: This review describes the advantages of new photonic-based approaches for assessing the activity of caries lesions. Many lesions have been arrested or are non-carious developmental defects, such as fluorosis, which do not require intervention. New methods are needed to assess lesion activity and avoid unnecessary removal of the tooth structure. Methods: At present, there are no reliable methods for assessing lesion activity in vivo. Nondestructive optical monitoring of lesion structure and the changes in light scattering that occur during drying offer the potential for lesion activity assessment during a single examination. Since optical diagnostic instruments exploit changes in the porosity and the permeability of the lesion, they have the potential to assess whether lesions are active and expanding or arrested and undergoing remineralization. Optical coherence tomography (OCT), Raman imaging and fluorescence loss, thermal and short-wavelength infrared (SWIR) reflectance measurements during lesion dehydration with forced air are presented. Results: Clinical studies have shown that optical coherence tomography is capable of showing distinct structural differences between active and arrested lesions on coronal and root surfaces. Differences in the kinetics of dehydration measured using reflectance measurements at SWIR wavelengths coincident with water absorption bands also show great potential. Conclusions: OCT and dehydration imaging at SWIR wavelengths have great potential for assessing lesion activity since they can also be used for caries screening, are safe for frequent monitoring and do not require the application of external agents. Full article

Health impact assessments of ambient particulate matter remain far less developed in sub-Saharan African cities, despite fine particulate matter (PM_2.5) being a significant contributor to premature mortality globally. This study quantified the public health burdens of adult mortality associated with long-term PM_2.5 exposure in Addis Ababa, Ethiopia, under different counterfactual air quality scenarios. Hourly PM_2.5 data were collected across nine monitoring stations from 2022 to 2023. AirQ+ tool was utilized to estimate attributable natural-cause and cardiovascular disease (CVD) mortality among adults aged ≥ 30 years. Spatial analysis showed mean concentrations ranging from 15 µg/m³ to 33 µg/m³, with an overall mean of 26.74 µg/m³, exceeding the WHO annual guideline by more than fivefold. Seasonal peaks occurred from June to August and diurnal maxima at 7:00 AM. In 2022, attributable natural-cause deaths ranged from 1489 (6.16%) at the less stringent WHO Interim Target 3 (15 µg/m³) to 3169 (13.11%) at the WHO Air Quality Guidelines (5 µg/m³). In 2023, the range was 1544 (6.40%) to 3218 (13.33%). For specific chronic endpoints, PM_2.5 concentration level was responsible for between 509 and 1071 CVD deaths in 2022, and between 535 and 1126 CVD deaths in 2023 across the counterfactual scenario. These results highlight the substantial health burden posed by ambient PM₂.₅ in Addis Ababa and emphasize the urgent need for targeted interventions. Full article

Reliable air pollution monitoring remains a major challenge in Sub-Saharan Africa (SSA), limiting the assessment of population exposure and the development of effective mitigation strategies. Recent advances in low-cost (LC) sensors offer promising opportunities, but their deployment in low-infrastructure settings still faces significant technical and logistical challenges. This study presents the experience gained from deploying LC sensor networks in Burkina Faso, Niger, and the Republic of Guinea, focusing on the practical challenges of installing and maintaining these systems under demanding conditions. In Burkina Faso, an LC station was co-located with a reference-grade instrument, enabling field calibration. In Niger, factory-calibrated LC sensors were deployed across urban, semi-urban, and rural settings, while in Guinea they were installed in a remote area. Several practical issues and challenges emerged, including unstable power supplies, limited internet connectivity, safety, and logistical constraints. Careful planning and involvement of local expertise proved essential for the long-term sustainability of LC sensors. Knowledge transfer to local partners supported ongoing maintenance and strengthened data ownership. Overall, this study demonstrated that the reliability of LC air quality networks in SSA depends not only on technology, but also on adaptive strategies, robust calibration, and strong local engagement, offering practical guidance for future scalable and sustainable implementations in resource-limited settings. Full article

Aerobic stability is a key indicator of silage quality, reflecting microbial activity through increases in pH and temperature during exposure to oxygen. However, fluctuations in ambient temperature may compromise the accuracy of this assessment. This study evaluated the aerobic stability of Sudan grass silage subjected to different particle sizes (PS) and inoculation with homofermentative microorganisms, as well as the use of inert materials as thermal references. Twenty-four experimental PVC silos were used in a randomized block design with a 2 × 2 factorial arrangement, including two PS (small or large) and the presence or absence of a homofermentative inoculant (Lentilactobacillus plantarum and Pediococcus acidilactici). Additional silos containing inert materials (sand, water, sawdust, hay, expanded polystyrene, and air) were used to monitor environmental thermal variation. Smaller particles resulted in lower pH values throughout the aerobic exposure period, while larger particles showed higher pH and greater temperature increases, indicating lower stability. Microbial inoculation did not affect pH or temperature. Among the tested materials, sand most effectively buffered ambient temperature fluctuations, enabling more accurate detection of biologically driven heating. Thus, small particles enhance aerobic stability, and the use of sand as a thermal reference enhances the reliability of measurements under variable environmental conditions, offering a practical approach for silage evaluation outside controlled settings. Full article

Air quality forecasting and environmental health research at urban and regional scales depend on the combination of measurements from heterogeneous sensor networks, yet the construction of integrated multi-source datasets is rarely described or released as a self-contained deliverable. This paper presents an open dataset that combines four sensor-derived sources covering the whole of Spain over the period from 2022 to 2024: hourly air quality observations from the 588 stations of the national network operated by the Ministerio para la Transición Ecológica y el Reto Demográfico (MITECO), daily meteorological records from the Agencia Estatal de Meteorología (AEMET), daily mobility indicators derived from anonymised mobile telephony events published by the Ministerio de Transportes y Movilidad Sostenible (MITMA) at the municipality level, and a calendar of national and Autonomous Community public holidays. The processing pipeline harmonises sources that differ in temporal resolution, spatial codification and quality regime into a tidy hourly table indexed by station and timestamp, with a fixed feature schema of 56 variables per record. Air quality stations are paired with their nearest AEMET station through a three-tier distance rule, and the daily exogenous features are aligned to the air quality time axis through a two-variant temporal-alignment scheme (lag-and-expand to the hourly grid for the hourly release, same-calendar-day join for the daily release). A complementary daily resolution variant of the dataset is also released, with 72 columns and the same feature schema except for the air quality block, which is aggregated to daily mean, minimum and maximum. The integrated dataset contains approximately 15 million hourly records across the 588 stations and is released on Zenodo (DOI 10.5281/zenodo.20196221) under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. It is intended as a substrate for research on air quality forecasting, environmental epidemiology and multi-source data fusion at the nationwide scale. Full article

Urban Park vegetation plays a crucial role in mitigating air pollution by serving as a natural sink for gaseous and particulate pollutants, thereby enhancing the ecological sustainability of cities. Identifying tree species with high tolerance to air pollution is therefore essential for effective urban park planning and management in highly polluted urban environments. This study evaluated the air pollution tolerance of selected tree species commonly found in urban parks of Kandy City, Sri Lanka, using the Air Pollution Tolerance Index (APTI). Five tree species—Terminalia catappa (Indian almond), Cassia fistula (golden shower tree), Pongamia pinnata (Indian beech), Madhuca longifolia (butter tree), and Tabebuia rosea (pink poui)—were assessed at two urban park locations representing contrasting pollution levels, identified based on ambient SO₂, NO₂, and PM_2.5 concentrations. APTI was calculated using four leaf biochemical parameters: pH, ascorbic acid content, relative water content, and total chlorophyll content. Leaf samples were collected from ten replicates of each species at both sites. Madhuca longifolia exhibited the highest APTI values (17.06 at the HP site and 25.17 at the LP site), followed by Cassia fistula, Terminalia catappa, Tabebuia rosea, and Pongamia pinnata. These findings suggest that the identified species, particularly Madhuca longifolia and Cassia fistula, are well-suited for urban greening and can contribute to mitigating air pollution impacts. However, these findings are constrained by a single cross-sectional sampling term, limited species screening, sequential data collection variances, and fixed mathematical equations. Consequently, future research should implement continuous multi-station monitoring arrays, expand species diversity, establish localized biochemical weightings, and initiate long-term multi-seasonal tracking to resolve temporal dynamics in tropical urban ecosystems. Full article

Background: Pancreatic cancer is characterized by increasing incidence and high mortality in Poland and worldwide. The aim of this study was to assess the relationship between selected risk factors and the age-standardized incidence rate of pancreatic cancer at the voivodeship level in Poland, and to evaluate the accuracy of a prediction model. Methods: Age-standardized incidence rate data for 16 Polish voivodeships in 2011–2023 were obtained from the Polish National Cancer Registry. The risk factor burden for 2011–2019, expressed as disability-adjusted life years (DALYs) per 100,000 population, was obtained from the System Analysis and Implementation Database of the Polish Ministry of Health. A generalized estimating equation model was constructed to predict the age-standardized incidence rate, with multicollinearity addressed using variance inflation factor analysis. Predictions for 2020–2023 were validated against observed data, and forecasts for 2024–2030 were subsequently calculated. Results: The number of new pancreatic cancer cases in Poland increased in eight out of 16 voivodeships. The highest burden was recorded in the Masovian, Subcarpathian, Świętokrzyskie and Greater Poland voivodeships. Air pollution was positively associated with pancreatic cancer incidence. Predictions for 2020–2023 showed satisfactory agreement with observed data, with the largest discrepancy being equal to 4.1 in terms of the age-standardized incidence rate. Based on the models, the incidence of pancreatic cancer was projected for all of 16 voivodeships through to 2030. Conclusions: Air pollution is associated with the regional burden of pancreatic cancer in Poland. The generalized estimating equation prediction approach demonstrated acceptable accuracy and can support monitoring and public health planning at the voivodeship level. Full article

With increasing constraints on extensive farming—including soil degradation, salinisation and more frequent climatic anomalies—the development of ‘smart’ agriculture requires the integration of affordable, non-invasive methods for monitoring the physiological state of plants. A key indicator for assessing productivity and the early detection of stress is the rate of photosynthetic CO₂ assimilation (A); however, widely available commercial gas analysers are characterised by high cost, technical complexity and considerable weight, which limits their use in large-scale field studies. Here, a new handheld system for measuring assimilation was developed and tested, based on the accumulative principle of recording changes in CO₂ concentration using simple infrared sensors and without maintaining a constant air flow around the leaf. A comparison was carried out between a prototype of the developed system and a commercial gas analyser when measuring leaf assimilation under irrigation and simulated drought conditions. The results demonstrated the consistency of the readings from the two systems. The developed system is characterised by its compact size, low cost, and the absence of moving parts and consumables. The proposed system has the potential to be effective for large-scale screening tasks and rapid diagnosis of stress-induced changes; it represents a promising, affordable tool for addressing applied tasks in precision agriculture, environmental monitoring and physiological research. Full article

Monitoring urban biodiversity is essential for designing resilient and sustainable cities. Urban trees provide a wide range of ecosystem services (ESs), including air pollution reduction, urban heat island mitigation, and psychological benefits for citizens. Accurate and updated tree inventories are therefore essential tools for urban environmental monitoring. However, existing urban tree inventories are often incomplete or outdated, especially in private areas, limiting accurate ES assessment and urban planning. Earth observation satellite missions, particularly very-high-resolution multispectral (VHR-MS) imagery, offer a valuable alternative to field surveys for gathering information on urban environments. This work proposes a deep learning (DL) framework based on VHR-MS satellite imagery for the automatic generation of accurate urban tree inventories. DL models reduce human effort and save operational time by automatically learning complex representations and patterns from satellite imagery. The proposed encoder–decoder architecture extends prior point-based detection approaches by integrating a ResNet-50 backbone and a percentile-based threshold calibration procedure. Given the lack of suitable training data covering heterogeneous and densely vegetated urban environments, a dedicated dataset was constructed from VHR-MS satellite imagery acquired over the Lombardy region (Italy). The dataset encompasses a wide range of land uses and land covers, including residential and industrial zones, public parks, private gardens, and agricultural areas. Through the photointerpretation of more than 2800 images, precise coordinates for more than 50,000 manually annotated trees were obtained. The DL model is trained with point-level annotations, enabling precise localization of individual trees while reducing annotation ambiguity in dense urban contexts. On the Lombardy dataset at 30 cm/px resolution, the proposed framework achieves 86.72% Precision, 66.92% Recall, an F1-score of 75.54%, and a localization error of 1.473 m. Full article

Air temperature measurements in atmospheric environmental monitoring are susceptible to radiation-induced bias under natural ventilation. This study develops a low-power naturally ventilated air temperature sensor and a correction method combining computational fluid dynamics (CFD) with machine learning. The sensor integrates a Pt100 thin-film platinum resistance probe (Heraeus Holding GmbH, Hanau, Germany), symmetric guide plates, and a dual aluminum-plate radiation shield to reduce radiative heating while improving airflow around the probe. A three-dimensional fluid–solid coupled heat-transfer model was established in ANSYS FLUENT 15.0 to optimize guide-plate spacing and inclination angle and quantify the effects of solar radiation, long-wave radiation, scattered radiation, air density, wind speed, solar elevation angle, and surface albedo on radiation error. CFD results identified a guide-plate spacing of 24 mm and an inclination angle of 45° as the preferred parameters. A multilayer perceptron (MLP) model trained with CFD-derived data was validated in field experiments using a Model 076B aspirated radiation shield (Met One Instruments, Inc., Grants Pass, OR, USA) as the reference. The model predicted radiation error with a root mean square error (RMSE) of 0.052 °C, a mean absolute error (MAE) of 0.042 °C, and a correlation coefficient of 0.92. The proposed sensor and correction method provide a low-power and easy-to-maintain approach for reducing radiation-induced bias in naturally ventilated air-temperature measurements, with potential applications in meteorological observation, air-quality monitoring, and agricultural microclimate assessment. Full article

The impact of air pollution on human health has been widely studied in recent decades. Recent findings show that even low levels of air pollution can be harmful to our health, causing disease and early death. However, these studies are very limited in the central region of Türkiye. Therefore, this study focused on the association between the daily variations in air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂) and hospital admissions due to respiratory, cardiovascular, and total (non-accidental) causes in the Sivas province. Daily average concentrations of air pollutants were obtained from two air quality (AQ) monitoring stations, and daily meteorological (air temperature and relative humidity) data were obtained from one meteorological station in Sivas province to determine the effects of air pollution on hospital admissions. It was found to be a significant relationship between air pollution and respiratory hospital admissions in the province. The results of the study showed the relative magnitudes of the risks of cardiovascular diseases and hospital admissions related to air pollutants were as follows: The highest association of each pollutant with cardiovascular diseases was observed for PM₁₀ at lag 4 (ER = 1.74%; 95% CI = 0.95–3.19%), PM_2.5 at lag 2 (ER = 5.12%; 95% CI = 1.39–19.0%), NO₂ at lag 8 (ER = 4.89%; 95% CI = 0.08–288.8%) and SO₂ at lag 5 (ER = 1.21%; 95% CI = 1.10–1.32%). It was seen that short-term exposure to air pollution in Sivas between 2016 and 2019 was positively associated with increasing respiratory hospital admissions. As the first air pollution study to use the generalized linear model (GLM) method in hospital admissions in Sivas, these findings may have implications for local environmental policies and help to combat air pollution. Full article

The aim of this study was to determine the effects of PM10 and PM2.5 particulate matter pollution from the coal mining sector in the three municipalities of Cesar, La Guajira, and El Magdalena on respiratory morbidity in children under 5 years of age and adults over 60 years of age residing in these municipalities. This descriptive time series study included three municipalities in three departments: Algarrobo, Albania, and La Jagua de Ibirico. The SEVCA (Seasonal Environmental Monitoring System) was used to collect PM10 and PM2.5 pollutants. Data on secondary source air quality (RIPS) were collected from the public health services (ESE) in each municipality. The daily average concentration of μg/m³ was used for the statistical analysis of the pollutants. A time series statistical model was applied to compare the temporal variations in exposure levels and the event itself. The air quality data databases were analyzed using descriptive statistics. A logistic regression model was used to assess the association between pollutants and air quality. To account for the effects of time lags in air quality data, moving averages with lags of 0 to 3 days were used. Statistical analyses were performed using R version 4.5.1. We found daily averages of ARI in children under 5 years of age and adults over 60 years of age in the three municipalities of (1.35) admissions per day. The average daily concentrations of μg/m3 for Algarrobo were (29.79 μg/m³) for PM10 and (12.68 μg/m³) for PM2.5, for Albania (33.49 μg/m³) for PM10 and (13.23 μg/m³) for PM2.5, and for La Jagua (41.42 μg/m³) for PM10 and (15.18 μg/m³) for PM2.5. Significant positive associations greater than 1 were obtained between ARI admissions and PM10 and PM2.5 pollutants, with an RR of 1.105, 1.106, 1.125, 1.124, 1.157, and 1.155 95% CI, when PM10 and PM2.5 increase by 10 μg/m³ and for delays of 1 and 1–3 days. In conclusion, we observed significant positive associations between hospital admissions for ARI in children under 5 years of age and adults over 60 years of age for the three municipalities and the pollutants PM10 and PM2.5, which leads us to conclude that there is an epidemiological association and that the change in μg/m³ levels represents a change in the risk of hospital admission for ARI for children under 5 years of age and older adults in this coal corridor of the Colombian Caribbean. Full article

This paper proposes a data-driven framework for simulating turbocharger (TC) failure scenarios and modelling specific fuel oil consumption (SFOC) degradation in two-stroke low-speed marine diesel engines. A GaussianCopula model was fitted to the joint distribution of fifteen variables, using approximately eleven months of operational sensor data (n = 480 clean records, 4 h interval, January–December 2014) taken from a container ship. Three physically motivated failure scenarios were produced: turbine blade fouling, bearing wear and compressor surge. Predictive models trained on the real dataset achieved R² = 0.9998 for TC RPM and R² = 0.984 for fuel flow when using Gradient Boosting with 5-fold cross-validation. Feature importance analysis showed that the dominant determinants of TC speed were scavenging air intake pressure (35.3%) and engine power (MCR, 31.3%). Shaft power (45.5%) and TC RPM (19.3%) together explained most of the fuel consumption variance. Simulated failure scenarios produced SFOC increases of +6.6% (fouling), +9.6% (surge), and +13.3% (bearing wear) when compared to a normal operating baseline of 202 g/kWh, which is in line with published empirical data from MAN B&W engine performance curves. An IsolationForest anomaly detector trained only on normal operating samples flagged failure scenario records at a rate of 17.5–23.7%, which demonstrates that moderate-sensitivity early warning detection is feasible from routine sensor streams. The results show that TC condition monitoring could serve as a leading indicator of fuel-efficiency degradation. This has significant implications for condition-based maintenance planning and CII (Carbon Intensity Indicator) compliance. Full article

Aerobiology, the science that studies atmospheric biological particles (including pollen, fungal spores, bacteria, and viruses), has undergone a profound transformation from a descriptive, observational discipline into a predictive, data-driven field, thanks to advances in statistical methods and environmental sensing technologies. Early research, based on classical statistical methods such as descriptive analysis, correlation analysis, and linear regression, established a fundamental understanding of seasonal dynamics and environmental relationships. However, the inherent complexity of aerosol biological systems—characterized by nonlinear interactions, spatiotemporal variability, and multiscale processes—has spurred the adoption of modern statistical techniques. These techniques include time-series analysis, generalized linear and additive models, spatial statistics, Bayesian inference, machine learning, and data assimilation, often combined with high-resolution environmental monitoring and sensor networks. In recent years, hybrid modeling approaches have emerged, combining mechanistic understanding of atmospheric transport and biological emissions processes with data-driven learning to improve the accuracy, robustness, and interpretability of predictions. This review comprehensively compares classical, modern, and hybrid statistical methods in air biology, exploring their theoretical foundations, practical applications, and inherent limitations. Furthermore, this review highlights emerging paradigms such as uncertainty quantification, causal inference, digital twins, and AI-driven real-time prediction systems. It also discusses challenges, including data heterogeneity, model interpretability, and cross-regional portability. By treating aerobiology as a complex adaptive environmental–biological system, this study highlights statistical methods that link observations to mechanisms and advance scalable, reliable, systems-oriented prediction frameworks for future research and applications. Full article

This study presents the development and field testing of a Pressurized Driving-Down Air Multilevel Sampler (PDA-MLS), an integrated groundwater sampling device designed for depth-discrete sampling in boreholes affected by floating non-aqueous phase liquids (NAPLs). Conventional sampling methods—such as low-flow pumps, bailers, and packer-isolated systems—often fail under these conditions due to limited accessibility, cross-contamination, or disturbance of the water column. The proposed system addresses these limitations through a controlled pressurized-gas actuation mechanism that transfers groundwater from multiple PTFE-membrane chambers installed at discrete depths. This configuration enables low-disturbance sampling below floating contaminant layers. The use of chemically inert materials (stainless steel and PTFE) minimizes sampling artifacts and ensures compatibility with volatile organic compound (VOC) analyses. A simplified hydraulic conceptual framework describing inflow, outflow, and pressure-driven displacement was developed to support purge-duration estimation and operational parameter definition. The device was tested in a 90 m deep fractured limestone aquifer contaminated by tetrachloroethylene (PCE), where floating hydrocarbons limited the applicability of conventional sampling techniques. Field testing showed stable discharge conditions (~145–160 mL/min), repeatable sampling cycles, and successful collection of depth-discrete groundwater samples under the investigated site conditions. No evidence of sampler-related hydrocarbon entrainment was observed in the collected samples within the analytical detection limits of the adopted laboratory methods. To the authors’ knowledge, the PDA-MLS represents one of the few groundwater sampling systems specifically designed to combine low-disturbance multilevel sampling with operation in wells affected by floating NAPL. These features make it a promising tool for environmental monitoring, high-resolution characterization of fractured aquifers, and long-term assessment of contaminated sites. Full article