Search Results (132)

Nephrogenic diabetes insipidus (NDI) is a rare hereditary disorder characterized by renal resistance to arginine vasopressin (AVP), resulting in the kidneys’ inability to concentrate urine. Approximately 90% of NDI cases follow an X-linked inheritance pattern and are associated with pathogenic variants in the AVPR2 gene, which encodes the vasopressin receptor type 2. The remaining 10% are attributed to mutations in the AQP2 gene, which encodes aquaporin-2, and may follow either autosomal dominant or recessive inheritance patterns. We present the case of a male infant, younger than nine months of age, who was clinically diagnosed with NDI at six months. The patient presented recurrent episodes of polydipsia, polyuria, dehydration, hypernatremia, and persistently low urine osmolality. Despite adjustments in pharmacologic treatment and strict monitoring of urinary output, the clinical response remained suboptimal. Given the lack of improvement and the radiological finding of an absent posterior pituitary (neurohypophysis), the possibility of coexistent central diabetes insipidus (CDI) was raised, prompting a therapeutic trial with desmopressin. Nevertheless, in the absence of clinical improvement, desmopressin was discontinued. The patient’s management was continued with hydrochlorothiazide, ibuprofen, and a high-calorie diet restricted in sodium and protein, resulting in progressive clinical stabilization. Whole-exome sequencing identified a novel homozygous missense variant in the AQP2 gene (c.398T > A; p.Val133Glu), classified as likely pathogenic according to the American College of Medical Genetics and Genomics (ACMG) criteria: PM2 (absent from population databases), PP2 (missense variant in a gene with a low rate of benign missense variation), and PP3 (multiple lines of computational evidence supporting a deleterious effect)]. NDI is typically diagnosed during early infancy due to the early onset of symptoms and the potential for severe complications if left untreated. In this case, although initial clinical suspicion included concomitant CDI, the timely initiation of supportive management and the subsequent incorporation of molecular diagnostics facilitated a definitive diagnosis. The identification of a previously unreported homozygous variant in AQP2 contributed to diagnostic confirmation and therapeutic decision-making. The diagnosis and comprehensive management of NDI within the context of polyuria-polydipsia syndrome necessitates a multidisciplinary approach, integrating clinical evaluation with advanced molecular diagnostics. The novel AQP2 c.398T > A (p.Val133Glu) variant described herein was associated with early and severe clinical manifestations, underscoring the importance of genetic testing in atypical or treatment-refractory presentations of diabetes insipidus. Full article

This study analyzes air pollution time-series big data to assess stationarity, seasonal patterns, and the performance of machine learning models in forecasting PM2.5 concentrations. Fifty-two low-cost sensors (LCS) were deployed across Krakow city and its surroundings (Poland), collecting hourly air quality data and generating nearly 20,000 observations per month. The network captured both spatial and temporal variability. The Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test confirmed trend-based non-stationarity, which was addressed through differencing, revealing distinct daily and 12 h cycles linked to traffic and temperature variations. Additive seasonal decomposition exhibited time-inconsistent residuals, leading to the adoption of multiplicative decomposition, which better captured pollution outliers associated with agricultural burning. Machine learning models—Ridge Regression, XGBoost, and LSTM (Long Short-Term Memory) neural networks—were evaluated under high spatial and temporal variability (winter) and low variability (summer) conditions. Ridge Regression showed the best performance, achieving the highest $R^2$ (0.97 in winter, 0.93 in summer) and the lowest mean squared errors. XGBoost showed strong predictive capabilities but tended to overestimate moderate pollution events, while LSTM systematically underestimated PM2.5 levels in December. The residual analysis confirmed that Ridge Regression provided the most stable predictions, capturing extreme pollution episodes effectively, whereas XGBoost exhibited larger outliers. The study proved the potential of low-cost sensor networks and machine learning in urban air quality forecasting focused on rare smog episodes (RSEs). Full article

China’s accelerating industrialization has led to worsening air pollution, characterized by recurrent haze episodes. The accurate quantification of PM_2.5 distribution is crucial for air quality assessment and public health management. Although traditional prediction models can effectively identify PM_2.5 concentration fluctuations with moderate accuracy, their dependence relies heavily on extensive ground-based monitoring station data, limiting their applicability in areas with sparse monitoring coverage. To address this limitation, this study proposes a novel algorithm for high-precision PM_2.5 concentration prediction, termed VDMS (Vision Transformer with DLSTM Multi-Head Self-Attention and Self-supervision). Based on the traditional Vision Transformer (ViT) architecture, VDMS incorporates a Double-Layered Long Short-Term Memory (DLSTM) network and a Multi-Head Self-Attention mechanism to enhance the model’s capacity to capture temporal sequence features and global dependencies. These enhancements contribute to greater stability and robustness in feature representation, ultimately improving prediction performance. Cross-validation experimental results show that the VDMS model outperforms benchmark models in PM_2.5 concentration prediction tasks, achieving a coefficient of determination (R²) of 0.93, a root mean square error (RMSE) of 4.05 μg/m³, and a mean absolute error (MAE) of 3.23 μg/m³. Furthermore, experiments conducted in areas with sparse ground monitoring stations demonstrate that the model maintains high predictive accuracy, further validating its applicability and generalization capability in data-limited scenarios. Moreover, the VDMS model adopts a modular design, offering strong scalability that allows its architecture to be adjusted according to specific requirements. This adaptability renders it suitable for monitoring various atmospheric pollutants, providing essential technical support for precise environmental management and air quality forecasting. Full article

PM₁₀ samples were collected at an urban site of Zhuozhou, the southern gateway of Beijing, from 28 December 2021 to 29 January 2022, in order to explore the chemical composition, sources and physical and chemical formation processes of prominent components. The results showed that five trace elements (Mn, Cu, As, Zn and Pb) had high enrichment in PM₁₀ and were closely related with anthropogenic combustion and vehicle emissions; organic and element carbon had a high correlation due to the same primary sources and similar evolution; nitrate dominated SNA (sulfate, nitrate, ammonium) and nitrate/sulfate ratios reached 2.35 on the polluted days owing to the significant contribution of motor vehicle emissions. Positive matrix factorization analysis indicated that secondary source, traffic, biomass burning, industry, coal combustion and crustal dust were the main sources of PM₁₀, contributing 32.5%, 20.9%, 15.0%, 13.9%, 9.4% and 8.3%, respectively; backward trajectories and potential source contribution function analysis showed that short-distance airflow was the dominant cluster and accounted for nearly 50% of total trajectories. The Weather Research and Forecasting model with Chemistry, with integrated process rate analysis, showed that dominant gas-phase reactions (heterogeneous reaction) during daytime (nighttime) in presence of ammonia led to a significant enhancement of nitrate in Zhuozhou, contributing 12.6 μg/m³ in episode 1 and 22.9 μg/m³ in episode 2. Full article

To better inform the public about ambient air quality and associated health risks and prevent cardiovascular and chronic respiratory diseases in Macau, the local government authorities apply the Air Quality Index (AQI) for air quality management within its jurisdiction. The application of AQI requires first determining the sub-indices for several pollutants, including respirable suspended particulates (PM₁₀), fine suspended particulates (PM_2.5), nitrogen dioxide (NO₂), ozone (O₃), sulfur dioxide (SO₂), and carbon monoxide (CO). Accurate prediction of AQI is crucial in providing early warnings to the public before pollution episodes occur. To improve AQI prediction accuracy, deep learning methods such as artificial neural networks (ANNs) and long short-term memory (LSTM) models were applied to forecast the six pollutants commonly found in the AQI. The data for this study was accessed from the Macau High-Density Residential Air Quality Monitoring Station (AQMS), which is located in an area with high traffic and high population density near a 24 h land border-crossing facility connecting Zhuhai and Macau. The novelty of this work lies in its potential to enhance operational AQI forecasting for Macau. The ANN and LSTM models were run five times, with average pollutant forecasts obtained for each model. Results demonstrated that both models accurately predicted pollutant concentrations of the upcoming 24 h, with PM₁₀ and CO showing the highest predictive accuracy, reflected in high Pearson Correlation Coefficient (PCC) between 0.84 and 0.87 and Kendall’s Tau Coefficient (KTC) between 0.66 and 0.70 values and low Mean Bias (MB) between 0.06 and 0.10, Mean Fractional Bias (MFB) between 0.09 and 0.11, Root Mean Square Error (RMSE) between 0.14 and 0.21, and Mean Absolute Error (MAE) between 0.11 and 0.17. Overall, the LSTM model consistently delivered the highest PCC (0.87) and KTC (0.70) values and the lowest MB (0.06), MFB (0.09), RMSE (0.14), and MAE (0.11) across all six pollutants, with the lowest SD (0.01), indicating greater precision and reliability. As a result, the study concludes that the LSTM model outperforms the ANN model in forecasting air pollutants in Macau, offering a more accurate and consistent prediction tool for local air quality management. Full article

Air pollution monitoring in Korea has not yet been implemented in agricultural areas. Documenting air quality in purely agricultural areas is inherently valuable. This study compares agricultural air quality with urban air quality during two periods: (1) the entire measurement period and (2) high-PM episodes. To ensure broad spatial coverage, eight monitoring stations were installed in Yeoju, Nonsan, Naju, Gimhae, Hongcheon, Danyang, Muan, and Sangju. Real-time measurements of PM₁₀, PM_2.5, SO₂, and NOx were conducted continuously from March 2023 to December 2024. Over the entire measurement period, PM concentrations were similar in both agricultural and urban areas, but gaseous pollutants were lower in agricultural areas. PM levels were higher in agricultural areas during summer, whereas urban areas showed higher concentrations in other seasons. During high-PM episodes (29 days), all pollutants were significantly higher in urban areas, with PM_2.5 showing a greater difference than PM₁₀. Diurnal variations revealed that PM₁₀, PM_2.5, and NO₂ peaked in the morning and reached their lowest levels around 3 PM, with urban levels consistently higher than those in agricultural areas. SO₂ showed a different pattern, reaching its lowest concentration at 6 AM and peaking at noon in urban areas and at 6 PM in agricultural areas. This pattern closely followed temperature and wind speed variations. Full article

Air pollution, particularly fine particulate matter (PM_2.5), poses significant environmental and public health challenges in South Korea. The National Institute of Environmental Research (NIER) currently relies on numerical models such as the Community Multiscale Air Quality (CMAQ) model for PM_2.5 forecasting. However, these models exhibit inherent uncertainties due to limitations in emission inventories, meteorological inputs, and model frameworks. To address these challenges, this study evaluates and compares the forecasting performance of two alternative models: Long Short-Term Memory (LSTM), a deep learning model, and Seasonal Auto Regressive Integrated Moving Average with Exogenous Variables (SARIMAX), a statistical model. The performance evaluation was focused on Seoul, South Korea, and took place over different forecast lead times (D00–D02). The results indicate that for short-term forecasts (D00), SARIMAX outperformed LSTM in all statistical metrics, particularly in detecting high PM_2.5 concentrations, with a 19.43% higher Probability of Detection (POD). However, SARIMAX exhibited a sharp performance decline in extended forecasts (D01–D02). In contrast, LSTM demonstrated relatively stable accuracy over longer lead times, effectively capturing complex PM_2.5 concentration patterns, particularly during high-concentration episodes. These findings highlight the strengths and limitations of statistical and deep learning models. While SARIMAX excels in short-term forecasting with limited training data, LSTM proves advantageous for long-term forecasting, benefiting from its ability to learn complex temporal patterns from historical data. The results suggest that an integrated air quality forecasting system combining numerical, statistical, and machine learning approaches could enhance PM_2.5 forecasting accuracy. Full article

Air pollution in Chiang Mai during the dry winter season is extremely severe. During this period, high levels of fine particles are primarily generated by open biomass burning in Thailand and neighboring countries. In this study, ambient VOC(Volatile Organic Compounds) samples were collected using an adsorbent tube from 13 March to 26 March 2024, with careful consideration of sampling uncertainties to ensure data reliability. Furthermore, while interannual variability exists, the findings reflect atmospheric conditions during this specific period, allowing for an in-depth VOC assessment. A comprehensive approach to VOCs was undertaken, including benzene, toluene, ethylbenzene, m,p-xylene (BTEX); biogenic volatile organic compounds (BVOCs); and carbonyl compounds. Regression analysis was performed to analyze the correlation between isoprene concentrations and wind direction. The results showed a significant variation in isoprene levels, indicating their high concentrations due to biomass burning originating from northern areas of Chiang Mai. The emission sources of BTEX and carbonyl compounds were inferred through their ratio analysis. Additionally, correlation analyses between PM_2.5, BTEX, and carbonyl compounds were conducted to identify common emission pathways. The ratio of BTEX among compounds suggested that long-range pollutant transport contributed more significantly than local traffic emissions. Carbonyl compounds were higher during the episode period, which was likely due to local photochemical reactions and biological contributions. Previous studies in Chiang Mai have primarily focused on PM_2.5, whereas this study examined individual VOC species, their temporal trends, and their interrelationships to identify emission sources. Full article

This study aims to assess the short-term effects of extreme heat, cold, and air pollution episodes on excess mortality from natural causes in Luxembourg over 1998–2023. Using a high-resolution dataset from downscaled and bias-corrected temperature (ERA5) and air pollutant concentrations (EMEP MSC-W), weekly mortality p-scores were linked to environmental episodes. A distributional regression approach using a logistic distribution was applied to model the influence of environmental risks, capturing both central trends and extreme values of excess mortality. Results indicate that extreme heat, cold, and fine particulate matter (PM_2.5) episodes significantly drive excess mortality. The estimated attributable age-standardized mortality rates are 2.8 deaths per 100,000/year for extreme heat (95% CI: [1.8, 3.8]), 1.1 for extreme cold (95% CI: [0.4, 1.8]), and 6.3 for PM_2.5 episodes (95% CI: [2.3, 10.3]). PM_2.5-related deaths have declined over time due to the reduced frequency of pollution episodes. The odds of extreme excess mortality increase by 1.93 times (95% CI: [1.52, 2.66]) per extreme heat day, 3.49 times (95% CI: [1.77, 7.56]) per extreme cold day, and 1.11 times (95% CI: [1.04, 1.19]) per PM_2.5 episode day. Indicators such as return levels and periods contextualize extreme mortality events, such as the p-scores observed during the 2003 heatwave and COVID-19 pandemic. These findings can guide public health emergency preparedness and underscore the potential of distributional modeling in assessing mortality risks associated with environmental exposures. Full article

The paper analyzes the processes of emission and dispersion of particulate contaminants from a large point source emitter: a hard coal-fired power plant. Reference is made to the European Green Deal and its main objective of reducing anthropogenic particulate and greenhouse gas emissions. CHPP, Krakow Combined Heat and Power Plant, Poland, as described in the article, has a strong impact on the mechanisms that shape the microclimatic factors of the Krakow agglomeration. This combined heat and power plant provides heat and electricity for the city, while simultaneously emitting significant amounts of suspended particulate matter into the atmosphere. Due to the adverse impact of non-conventional energy sources on the natural environment and the increasing effects of climate warming, radical changes need to be implemented. The HYSPLIT (Hybrid Single-Particles Lagrangian Integrated Trajectories) model was used to track the movement of contaminated air masses. A 5-day episode of increased hourly concentrations of PM2.5 particulate matter contamination was selected to analyze the backward trajectories of air mass displacement. From 15 August 2022 to 19 August 2022, high 24-h particulate matter concentrations were recorded, measuring around 20 µg/m³. The HYSPLIT model, a unique tool in the precise identification of point sources of pollution and their impact on the air quality of the region, was used to analyze the influx of polluted air masses. A 5-day episode of increased hourly concentrations of PM2.5 pollutants was selected for the study, with values of approximately 20 µg/m³. It was found that low-pressure systems over the North Atlantic brought wet and variable weather conditions, while high-pressure systems in southern and eastern Europe, including Poland, provided stable and dry weather conditions. The simulation results were verified by analyzing synoptic maps of the study area. The image of the displacement of contaminated air masses obtained from the HYSPLIT model was found to be consistent with the synoptic maps, confirming the accuracy of the applied model. This means that the HYSPLIT model can be used to create maps of contaminant dispersion directions. Consequently, it was confirmed that modeling using the HYSPLIT model is an effective method for predicting the displacement directions of particulate contamination originating from coal combustion in a combined heat and power plant. Identifying circulation patterns and front zones during episodes of increased contaminant concentrations is strategic for effective weather monitoring, air quality management, and alerting the public to episodes of increased health risk in a large agglomeration. Full article

Air pollution monitoring in industrial regions like Moravia-Silesia faces challenges due to complex environmental conditions. Low-cost sensors offer a promising, cost-effective alternative for supplementing data from regulatory-grade air quality monitoring stations. This study evaluates the accuracy and reliability of a prototype node containing low-cost sensors for carbon monoxide (CO) and particulate matter (PM), specifically tailored for the local conditions of the Moravian-Silesian Region during winter and spring periods. An analysis of the reference data observed during the winter evaluation period showed a strong positive correlation between PM, CO, and ${\mathrm{NO}}_2$ concentrations, attributable to common pollution sources under low ambient temperature conditions and increased local heating activity. The Sensirion SPS30 sensor exhibited high linearity during the winter period but showed a systematic positive bias in ${\mathrm{PM}}_{10}$ readings during Polish smog episodes, likely due to fine particles from domestic heating. Conversely, during Saharan dust storm episodes, the sensor showed a negative bias, underestimating ${\mathrm{PM}}_{10}$ levels due to the prevalence of coarse particles. Calibration adjustments, based on the ${\mathrm{PM}}_1{\mathrm{/PM}}_{10}$ ratio derived from Alphasense OPC-N3 data, were initially explored to reduce these biases. For the first time, this study quantifies the influence of particle size distribution on the SPS30 sensor’s response during smog episodes of varying origin, under the given local and seasonal conditions. In addition to sensor evaluation, we analyzed the potential use of data from the Copernicus Atmospheric Monitoring Service (CAMS) as an alternative to increasing sensor complexity. Our findings suggest that, with appropriate calibration, selected low-cost sensors can provide reliable data for monitoring air pollution episodes in the Moravian-Silesian Region and may also be used for future adjustments of CAMS model predictions. Full article

The episodes of elevated concentrations of different gaseous pollutants and particulate matter (PM) are of major concern worldwide, especially in city agglomerations. Krakow is an example of an urban–industrial agglomeration with constantly occurring PM₁₀ air limit value exceedances. In recent years, a number of legislative actions have been undertaken to improve air quality in this area. The multitude of factors affecting the emergence of cases of very high air pollutant concentrations makes it difficult to analyze them using simple statistical methods. Machine learning (ML) methods can be an adequate option, especially when proper amounts of credible data are available. The main aim of this paper was to examine the influence of various factors (including main gaseous pollutant concentrations and some meteorological factors) on the effect of high PM₁₀ concentration episodes in the ambient air in Krakow (Poland) using the random forest algorithm. The original methodology based on the PM₁₀ limit and binary classification of cases with and without the occurrence of high concentration episodes was developed. The data used were derived from routine public air quality monitoring and a local meteorological station. A range of random forest classification models with various predictor sets and for different subsets of the observations coupled with variable importance analysis were performed. The performance of the algorithm was assessed using confusion matrices. The variable importance rankings revealed, among other things, the dominant impact of the mixing layer height on elevated PM₁₀ concentration episode formation. This research work showed the usefulness of the random forest algorithm in identifying factors contributing to poor air quality, even in the absence of reliable emission data. Full article

Previous conventional mist devices can induce a detrimental effect of leaf burn by intense, focused sunlight in summer. A mist generator is designed to prevent particulate matter (PM) damage to trees by combining mist with PM during high PM episodes. We measured changes in microclimate conditions and the concentration of PM before, during, and after mist spraying in urban parks (Yangjae Citizen Forest, YCF; Cheongdam Road Park, CRP) from May 6 to 8, 2020. PM changes in YCF and CRP were observed immediately after mist spraying and were found to return to the previous concentrations. Mist spraying had no significant effects on the meteorological traits of air temperature, humidity, and wind speed but had significant effects on the concentration of PMx and the ratio of PM during a short time. Also, the ratio of PMx was partially affected by mist spraying. During the morning rush hour and lunch, mist, high wind speed, and low relative humidity conditions were related to the increase in mist movement, resulting in increasing PM (2.5–10 μm) and the deposition of these PM. During the evening rush hour, high relative humidity and low wind speed affected PM concentrations more than mist. This prototype of mist spraying could effectively condense and deposit the PM during high PM episodes. Full article

Haze refers to an atmospheric phenomenon with extremely low visibility, which has significant impacts on human health and safety. Water vapor alters the scattering properties of atmospheric particulate matter, thus affecting visibility. A comprehensive analysis of the role of water vapor in haze formation is of great scientific significance for forecasting severe pollution weather events. This study investigates the distribution characteristics and variations of water vapor during haze weather in Changchun City (44°N, 125.5°E) in autumn and winter seasons, aiming to reveal the relationship between haze and atmospheric water vapor content. Analysis of observational results for a period of two months (October to November 2023) from a three-wavelength Raman lidar deployed at the site reveals that atmospheric water vapor content is mainly concentrated below 5 km, accounting for 64% to 99% of the total water vapor below 10 km. Furthermore, water vapor content in air pollution exhibits distinct stratification characteristics with altitude, especially within the height range of 1–3 km, where significant water vapor variation layers exist, showing spatial consistency with inversion layers. Statistical analysis of haze events at the site indicates a high correlation between the concentration variations of PM_2.5 and PM₁₀ and the variations in average water vapor mixing ratio (WVMR). During haze episodes, the average WVMR within 3 km altitude is 3–4 times higher than that during clear weather. Analysis of spatiotemporal height maps of aerosols and water vapor during a typical haze event suggests that the relative stability of the atmospheric boundary layer may hinder the vertical transport and diffusion of aerosols. This, in turn, could lead to a sharp increase in aerosol extinction coefficients through hygroscopic growth, thereby possibly exacerbating haze processes. These observational findings indicate that water vapor might play a significant role in haze formation, emphasizing the potential importance of observing the vertical distribution of water vapor for better simulation and prediction of haze events. Full article

Tangshan, a major industrial and agricultural center in northern China, frequently experiences significant PM_2.5 pollution events during winter, impacting its large population. These pollution episodes are influenced by multi–scale meteorological processes, though the complex mechanisms remain not fully understood. This study integrates surface PM_2.5 concentration data, ground-based and upper–air meteorological observations, and ERA5 reanalysis data from 2015 to 2019 to explore the interactions between local planetary boundary layer (PBL) structures and large-scale atmospheric processes driving PM_2.5 pollution in Tangshan. The results indicate that seasonal variations in PM_2.5 pollution levels are closely linked to changes in PBL thermal stability. During winter, day–to–day increases in PM_2.5 concentrations are often tied to atmospheric warming above 1500 m, as enhanced thermal inversions and reduced PBL heights lead to pollutant accumulation. Regionally, this aloft warming is driven by a high-pressure system at 850 hPa over the southern North China Plain, accompanied by prevailing southwesterly winds. Additionally, southwesterly winds within the PBL can transport pollutants from the adjacent Beijing–Tianjin–Hebei region to Tangshan, worsening pollution. Simulations from the chemical transport model indicate that regional pollutant transport can contribute to approximately half of the near-surface PM_2.5 concentration under the unfavorable synoptic conditions. These findings underscore the importance of multi-scale meteorology in predicting and mitigating severe wintertime PM_2.5 pollution in Tangshan and surrounding regions. Full article