Search Results (12,700)

Fine particulate matter with a diameter ≤2.5 μm (PM_2.5) pollution poses a global public health crisis, demonstrating significant threats to human health. This study focused on the strategically important Chengdu-Chongqing Economic Circle in western China, systematically comparing the toxic effects of urban and rural PM_2.5 across five levels. PMF and regression analysis were used to identify source contributions, dual-omics to pinpoint key molecules, and epidemiological data with a GAM model to assess health risks. Findings demonstrate that rural PM_2.5 possesses greater biotoxicity than its urban counterpart. Cytotoxicity in urban and rural PM_2.5 originated from road dust/vehicle emissions and biomass burning, respectively. Subsequently, integrated omics and molecular biology analyses identify kinesin family member 20A (KIF20A) as a shared key target, which mediates toxicity induced by both urban and rural PM_2.5. Finally, epidemiological analysis reveals that females and ≥65 years old exhibit relatively high sensitivity to urban PM_2.5 exposure trends, with rhinitis showing a comparatively higher impact among various related diseases. The novelty of this work lies in its pioneering application of a multi-tiered investigative approach. This approach spans “environmental samples-cellular mechanisms-population health” within the Chengdu-Chongqing economic circle context, systematically elucidating common and distinct respiratory health risk of urban and rural PM_2.5. This work offers a vital scientific foundation for advancing region-specific, precise air pollution prevention and control measures. Full article

Road tunnel ventilation systems have traditionally been designed to dilute vehicle-generated pollutants and control smoke during fires. However, the thermal environment, including temperature and humidity, is not the variable taken into consideration. Despite the operation of its ventilation system, Boryeong Subsea Tunnel (6.9 km), the longest subsea road tunnel in Korea, has experienced severe condensation since its opening in December 2021. As hot, humid ambient air enters the tunnel and meets wall surfaces cooled by seawater and the surrounding ground, condensation and fog may form, reducing visibility. To investigate the causes of condensation and develop a decision-making tool for prediction, a variety of tasks were carried out: (1) field measurements of temperature, humidity, tunnel wall temperature, and tunnel air velocity; (2) development of a 1D model for condensation rate quantification; and (3) 3D CFD simulations. Condensation occurred mainly from June to September, with the most severe conditions in July and August. Both the 1D model analysis and the CFD simulations showed good agreement with field measurement data, with wall temperature errors within 7.3%. Under current traffic conditions (with a peak of approximately 250 veh/h), the annual condensation volume was estimated at approximately 12,415 ton/year. Under the design traffic volume (1550 veh/h), heat from vehicles was found to effectively suppress condensation. The Condensation Contour Map (CCM) was developed as a decision support tool to predict the likelihood and amount of condensation based on the tunnel air temperature and humidity conditions. The results of this study clearly indicate that condensation should be explicitly considered in the design and operation of long subsea road tunnels. Full article

The particle emissions from diesel engines are a major environmental problem due to their harmful effects on air quality and human health. This article investigates the underlying acoustic parameters that determine the efficiency of reducing the fine particles through agglomeration. The impact of a change in frequency and voltage on the acoustic waves through the excitation source is researched and analyzed. Three blends of rapeseed methyl ester and isopropanol (RME95I5, RME90I10, and RME80I20) are used for experiments to study the combined benefit of oxygen-rich blends. The exhaust particles are measured before and after the exposure to acoustic waves operated at a varying voltage and frequency ranges. The fine particle reduction with a simultaneous increase in 5–10 µm particles is found to be better at the lower frequency due to the severe acoustic attenuation at the higher frequency. With the increase in voltage to 200 V, the reduction in fine particles and the simultaneous increase in coarse particles are comparatively less. The change in voltage induces an increase in sound intensity, which slows down the growth of agglomerates. The study presents the critical information necessary to use acoustic waves to reduce particle pollution using conventional filters, including the mechanisms by which sound intensity affects particle size distribution and the effectiveness of this method in various environmental conditions. 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

Stable control of the main combustion chamber temperature is critical for pollutant emission compliance, energy recovery, and equipment longevity in municipal solid waste incineration (MSWI). However, the response delays from manipulated variables such as primary air, secondary air, and feed rate to the furnace temperature span from seconds to tens of minutes, and a uniform-delay assumption is inadequate to characterize the true response lag. Moreover, without an action-smoothing constraint, optimizers tend to produce abrupt control commands that destabilize the temperature trajectory. Using real industrial distributed control system (DCS) data from a full-scale grate furnace, this paper develops a prediction–decision collaborative control framework. In the prediction module, mutual information (MI) is used to identify the optimal delay of each manipulated variable separately, and the time-aligned manipulated variables together with a low-order autoregressive component serve as input to XGBoost and yield a prediction RMSE of 6.85 °C with an $R^2$ of 0.9845. In the decision module, a normalized smoothing penalty is incorporated into the fitness function of particle swarm optimization (PSO) to constrain the step-to-step variation in manipulated variables. Offline predictor-in-the-loop simulation on the test set shows that, compared with a multi-loop PID controller, the proposed method reduces the standard deviation of the furnace temperature tracking error by about 35% (from 5.80 °C to 3.80 °C), and lowers the mean tracking error to 3.65 °C while improving actuator smoothness over both unconstrained PSO and a genetic algorithm. The framework provides a collaborative-control design for pre-deployment evaluation of data-driven controllers in MSWI operation. Full article

Accurate air pollutant concentration prediction is crucial for public health and sustainable urban development. Existing methods predominantly rely on single-modal data, resulting in inadequate representation of pollutant spatiotemporal evolution, poor prediction accuracy, and limited generalization capabilities. To address these challenges, this research proposes a novel PM${}_{2.5}$ prediction framework termed MFD-DF that integrates ground-station time series and satellite remote sensing images. In feature extraction, learnable decomposition and deformable convolution are introduced, and a Cross-Modal Slot Attention module explicitly decomposes features to resolve information blurring. Subsequently, a dynamic cross-modal alignment mechanism is designed alongside a learnable Time-Expansion Network (TEN) to ensure fine-grained interaction. Furthermore, a local-global attention feature fusion mechanism is proposed to optimize data integration efficacy. Experimental results demonstrate that in single-step PM${}_{2.5}$ prediction tasks, the proposed MFD-DF achieves significant improvements of approximately 10–20% in MAE, RMSE, and MAPE compared to state-of-the-art baselines. In multi-step PM${}_{2.5}$ prediction, it effectively alleviates the error accumulation problem in long-sequence forecasting, demonstrating superior robustness and accuracy. 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

Type 2 diabetes mellitus and arterial hypertension are some of the most common socially significant diseases worldwide. The reasons for this are complex and in recent years there has been increasing evidence of associations between the diseases and various air pollutants. Studies show varying degrees of influence of particulate matter (PM) and atmospheric gases. The present study focuses on the long-term exposure of PM₁₀ and Ozone O₃ on the incidence of type 2 diabetes and arterial hypertension. The studied period is 7 years from 2018 to 2024. The region in which the cohort study was conducted is the city of Sofia, the capital of the Republic of Bulgaria. Logistic regression models, with Odds Ratios (ORs) and 95% Confidence Intervals (CIs) to establish the associations with levels of air pollutions, were used. Relationships of diseases with various socio-demographic factors were also established. The models for the arterial hypertension have strongly positive relationships with O₃ with values OR 1.023 (95% CI: 1.001 ÷ 1.045) for each increase of 10 µg/m³ O₃. The model for PM₁₀ and arterial hypertension showed OR of 1.014 (95% CI: 0.927 ÷ 1.12). For type 2 diabetes and PM₁₀ and O₃ the obtained results are OR 1.007 (95% CI: 0.923 ÷ 1.109) and OR 1.008 (95% CI: 0.98 ÷ 1.033). The combined model of two pollutants was also examined, for which a positive influence was obtained with an OR of 1.024 (95% CI: 1.001 ÷ 1.046). Full article

This study focused on the spatial and temporal changes in photosynthetic pigment concentrations in the leaves of Ginkgo biloba and their integration into a new bioindicator index, the Pigment Integrity-to-Dust Ratio (PIDR), to assess urban air pollution stress on trees in Budapest, Hungary. High levels of chlorophyll and carotenoids in early summer indicated greater pigment integrity at the moderate-traffic site, whereas there were clear indications of reductions in the high-traffic area. The control site represented a low-traffic, pollution-free baseline. Chlorophyll concentrations dropped in the traffic-exposed leaves, and there were increased levels in the formation of pheophytin. It is thought that these reductions were caused by city stress. Responses of pigments were also variable at the moderate site, perhaps due to some form of recovery or adjustment in the study’s time frame. The observed negative relationships between selected pollutants and PIDR suggested that pollutant exposure was associated with pigment degradation and foliar dust deposition, although these associations should be interpreted as exploratory. The Air Pollution Tolerance Index (APTI) was significantly different between the pollution-exposed sites and the control, reflecting physiological tolerance in chronically exposed trees rather than directly measuring pigment damage. Therefore, the APTI and PIDR provide complementary information. Overall, the PIDR appears to be a promising exploratory bioindicator of physiological stress response, based on pigment concentration changes and dust deposition. Full article

Air pollution is a major environmental determinant of premature mortality and population health burden. Liguria represents a vulnerable Mediterranean region due to intense urbanisation, port-related emissions, complex topography and an ageing population. This study quantified the mortality burden and Years of Life Lost (YLL) attributable to long-term exposure to PM_2.5, NO₂ and O₃ in Liguria (Italy), and estimated the potentially avoidable burden under WHO guideline scenarios. A Health Impact Assessment (HIA) was conducted using ARPAL air quality data and ISTAT mortality data for the population aged ≥30 years during 2022–2024. Relative risks were derived from the European ELAPSE project and WHO meta-analyses. Attributable mortality was estimated using a log-linear Health Impact Function, while YLL were calculated using regional life tables and normalised per 100,000 inhabitants. PM_2.5 was the main contributor to air pollution-related mortality, accounting for 1333 attributable deaths in 2022. Corresponding YLL ranged from 755 to 1012 per 100,000 inhabitants over the study period. NO₂ showed a relevant but secondary contribution, while O₃ effects were smaller and more uncertain. WHO guideline scenarios indicated a substantial potentially avoidable burden of deaths and YLL. These findings support targeted environmental and public health interventions in highly urbanised coastal regions. 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

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

Indoor air quality (IAQ) plays a critical role in the health and cognitive performance of students, making its assessment essential for sustainable building design in educational environments. This study evaluates whether the ventilation flow rates prescribed by the Spanish Regulation for Thermal Installations in Buildings (RTIB), together with the occupancy densities defined by the Technical Building Code (TBC), are sufficient to maintain CO₂ concentrations within regulatory limits in classrooms and library reading rooms. A validated three-dimensional CFD model was developed to simulate airflow patterns and CO₂ distribution under typical operating conditions. The model was experimentally validated using measurements from a dedicated test room in the KUBIK experimental building of Tecnalia, demonstrating high predictive accuracy with average relative errors between 14% and 20%. Results indicate that, under current RTIB and TBC design criteria, (modelled for a 36 m² classroom with 24 occupants and a fresh air supply of 1080 m³/h), CO₂ levels frequently exceed the 910 ppm regulatory thresholds established by the RTIB’s direct method, highlighting potential shortcomings in existing standards for educational spaces. Additionally, two mechanical ventilation configurations were analyzed, revealing that floor-supply ventilation promotes more homogeneous pollutant dispersion and lower concentration peaks compared with ceiling-mounted systems. These findings underline the need to reconsider ventilation design strategies in educational buildings and demonstrate the value of CFD modelling as a tool to support evidence-based decisions toward healthier and more sustainable indoor environments. Full article