Search Results (147)

Ultrahigh resolution fine particulate matter (PM_2.5) mass concentration remote sensing products are crucial for atmospheric environmental monitoring, pollution source verification, health exposure risk assessment, and other fine-scale applications in urban environments. This study developed an ultrahigh resolution retrieval algorithm to estimate 30 m resolution PM_2.5 mass concentrations over urban areas from Landsat-8 and Sentinel-2A/B satellite measurements. The algorithm utilized aerosol optical depth (AOD) products retrieved from the Landsat-8 OLI and Sentinel-2 MSI measurements from 2017 to 2020, combined with multi-source auxiliary data to establish a PM_2.5-AOD relationship model across China. The results showed an overall high coefficient of determination (R²) of 0.82 and 0.76 for the model training accuracy based on samples and stations, respectively. The model prediction accuracy in Beijing and Wuhan reached R² values of 0.86 and 0.85. Applications in both cities demonstrated that ultrahigh resolution PM_2.5 has significant advantages in resolving fine-scale spatial patterns of urban air pollution and pinpointing pollution hotspots. Furthermore, an analysis of point source pollution at a typical heavy pollution emission enterprise confirmed that ultrahigh spatial resolution PM_2.5 can accurately identify the diffusion trend of point source pollution, providing fundamental data support for refined monitoring of urban air pollution and air pollution prevention and control. Full article

Aerosol optical depth (AOD) serves as a critical indicator for atmospheric aerosol monitoring and air quality assessment, and quantifies the radiative attenuation caused by airborne particulate matter. This study uses MODIS remote sensing imagery together with land use transition datasets (2000–2020) and road network density metrics (2014–2020), to investigate the spatiotemporal evolution of AOD in Zhejiang Province and its synergistic correlations with urbanization patterns and transportation infrastructure. By integrating MODIS_1KM AOD product, grid-based road network density mapping, land use dynamic degree modeling, and transfer matrix analysis, this study systematically evaluates the interdependencies among aerosol loading, impervious surface expansion, and transportation network intensification. The results indicate that during the study period (2000–2020), the provincial AOD level shows a significant declining trend, with obvious spatial heterogeneity: the AOD values in eastern coastal industrial zones and urban agglomerations continue to increase, with lower values dominating southwestern forested highlands. Meanwhile, statistical analyses confirm highly positive correlations between AOD, impervious surface coverage, and road network density, emphasizing the dominant role of anthropogenic activities in aerosol accumulation. These findings provide actionable insights for enhancing land-use zoning, minimizing vehicular emissions, and developing spatially targeted air quality management strategies in rapidly urbanizing regions. This study provides a solid scientific foundation for advancing environmental sustainability by supporting policy development that balances urban expansion and air quality. It contributes to building more sustainable and resilient cities in Zhejiang Province. Full article

Background: Delirium frequently manifests in hospitalized geriatric patients and is associated with negative health outcomes. Available large-scale data regarding its prevalence rate and impact on older Thai patients are limited. This study aimed to analyze trends in the prevalence rate, its consequences, and the factors contributing to death at discharge among this population. Methods: A retrospective study of inpatients over the age of 60 who received a diagnosis of delirium was conducted, utilizing inpatient medical expense documentation for the fiscal years 2019–2023. The identification of delirium was conducted by the National Health Security Office using the International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Thai Modification (ICD-10-TM) code F05. Results: The 5-year prevalence rate and mortality rate of delirium were 215.1 and 18.7/100,000 population, respectively, and tended to rise over the studied periods. The average hospitalization was 10 days, and the average healthcare expenditure was about 1470 USD/visit. Respiratory disease emerged as the most common primary diagnosis in delirious patients (23.5%). Factors associated with mortality were individuals aged >80 years when juxtaposed with the cohort aged 61–70 years (adjusted odds ratio [AOD] 1.07), being female (AOR 1.13), and a primary diagnosis of respiratory disease (AOR 2.72), cardiovascular disease (AOR 1.68), musculoskeletal disease (AOR 0.61), systemic infection/septicemia (AOR 2.08); or malignancy (AOR 2.97). Conclusions: There was an upward trend in rates of both prevalence and mortality associated with delirium among hospitalized geriatric patients. Advancing age, gender, and particular primary diagnoses were associated with mortality at hospital discharge. Full article

Aerosols play a critical role in modulating the land–atmosphere energy balance, influencing regional climate dynamics, and affecting air quality. Xinjiang, a typical arid and semi-arid region in China, frequently experiences dust events and complex aerosol transport processes. This study provides a comprehensive analysis of the spatiotemporal evolution and potential source regions of aerosols in Xinjiang from 2005 to 2023, based on Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol products (MCD19A2), Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) vertical profiles, ground-based PM_2.5 and PM₁₀ concentrations, MERRA-2 and ERA5 reanalysis datasets, and HYSPLIT backward trajectory simulations. The results reveal pronounced spatial and temporal heterogeneity in aerosol optical depth (AOD). In Northern Xinjiang (NXJ), AOD exhibits relatively small seasonal variation with a wintertime peak, while Southern Xinjiang (SXJ) shows significant seasonal and interannual variability, characterized by high AOD in spring and a minimum in winter, without a clear long-term trend. Dust is the dominant aerosol type, accounting for 96.74% of total aerosol content, and AOD levels are consistently higher in SXJ than in NXJ. During winter, aerosols are primarily deposited in the near-surface layer as a result of local and short-range transport processes, whereas in spring, long-range transport at higher altitudes becomes more prominent. In NXJ, air masses are primarily sourced from local regions and Central Asia, with stronger pollution levels observed in winter. In contrast, springtime pollution in Kashgar is mainly influenced by dust emissions from the Taklamakan Desert, exceeding winter levels. These findings provide important scientific insights for atmospheric environment management and the development of targeted dust mitigation strategies in arid regions. Full article

This study addresses the challenges of characterizing aerosol optical depth (AOD) dynamics from satellite observations, which are often hindered by data gaps and variability. A long short-term memory (LSTM) network was trained on an extended AOD dataset from Sicily to capture temporal patterns. The trained model was then applied to AOD data from distinct geographical regions: Cluj-Napoca and the central Mediterranean Sea. While the LSTM effectively captured general seasonal trends, it tended to smooth extreme AOD events. To mitigate this, a post-processing algorithm was developed to enhance the representation of AOD peaks and valleys. This enhancement method refines the characterization of historical AOD, providing a more accurate representation of observed atmospheric variability, particularly in capturing high and low AOD episodes. The results demonstrate the efficacy of the hybrid approach in improving the characterization of AOD dynamics across different regions. Full article

This study analyzes the spatial and temporal variability of PM₁₀ and PM_2.5 concentrations in Santo Domingo, Dominican Republic, based on short-term sampling campaigns conducted in 2019 and 2022. In 2019, PM₁₀ levels averaged 38.14 µg/m³, while in 2022 they rose significantly to 62.18 µg/m³. PM_2.5 in 2022 averaged 30.37 µg/m³. These differences are likely influenced by meteorological variability, including increased transport of Saharan dust in mid-2022, and seasonal factors. Although local emission changes were not directly assessed, they may have also played a role in the observed trends. Statistical analyses revealed that aerosol optical depth (AOD), air pressure, and rainfall were significant predictors of PM₁₀ in 2022, explaining up to 75% of the variance. Correlations and regression models confirmed a robust association between AOD and PM levels on a weekly timescale. These findings highlight the importance of integrating remote sensing and meteorological data to improve air quality monitoring and inform environmental policy in Caribbean urban areas. Full article

Aerosols play a crucial role in air quality, climate regulation, and public health; their timely monitoring is hence fundamental. The aerosol optical depth (AOD) is the parameter used to investigate the spatial–temporal distribution of aerosols from space. Specifically, the AOD retrieved from the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm applied to a Moderate Resolution Imaging Spectroradiometer (MODIS) is suitable for aerosol investigation at a local scale by exploiting its high spatial resolution (1 km × 1 km). In this study, the MAIAC AOD retrieval over Rome (Italy) was validated for the first time, using ground-based data provided by an AERONET station operating in a semi-rural environment close to the city, over a time series from January 2001 to December 2022. Moreover, AOD trends were evaluated in a study area encompassing Rome and its surroundings, characterized by a transition zone between urban and rural environments. The results show a general underestimation of the MAIAC AOD; specifically, the validation process highlighted the less accurate performance of the algorithm under higher aerosol loading and with predominantly coarse mode aerosol. Interesting results were obtained concerning the influence of the geometrical configuration of satellite acquisition on the accuracy of the MAIAC product. In particular, the solar zenith angle, the relative azimuth and the scattering angle between the principal plane of the sun and satellite synergistically influence retrievals. Finally, the spatial distribution of the AOD shows a decreasing trend over the 2001–2022 period and a strong influence of the city of Rome over the whole study area. Full article

The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing challenges for the long-term analysis of atmospheric pollution trends, responses to sudden ecological events, and disaster management. This study aims to develop a high-precision method to fill spatial AOD missing values and generate daily full-coverage AOD products for the Beijing–Tianjin–Hebei region in 2021 by integrating multi-dimensional data, including meteorological models, multi-source remote sensing, surface conditions, and nighttime light parameters, and applying machine learning methods. A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R²) of 0.93. Seasonal evaluation further indicated that the model’s simulation was best in winter. Variable importance analysis identified relative humidity (RH) as the most critical factor influencing model results. The reconstructed full-coverage AOD product exhibited a spatial distribution trend of significantly higher values in the southern plain areas compared to mountainous regions, consistent with the actual aerosol distribution patterns in the Beijing–Tianjin–Hebei area. Moreover, the product demonstrated overall smoothness and high accuracy. This research lays the foundation for establishing a long-term, 1 km resolution, daily spatially continuous AOD product for the Beijing–Tianjin–Hebei region and beyond, providing more robust data support for addressing regional and larger-scale environmental challenges. Full article

Long-term investigations of Aerosol Optical Depth (AOD) across Asia are crucial for understanding its regional impacts on the global climate system. However, satellite-derived AOD datasets frequently suffer from missing values due to factors such as cloud cover, algorithmic limitations, and various atmospheric conditions. To overcome these challenges, this study employs the deep learning model TabNet, incorporating Digital Elevation Model (DEM) data and ERA5 meteorological variables, to fuse MERRA-2 AOD with MODIS MAIAC AOD observations. The resulting integration yields a high-resolution, seamless daily AOD dataset for Asia spanning the period from 2001 to 2024. The fused dataset demonstrates significant improvements over the original MERRA-2 AOD, with an increase in the coefficient of determination (R²) by 0.1065 and a reduction in root mean square error (RMSE) by 0.0369. Spatio-temporal analysis, conducted using Empirical Orthogonal Function (EOF) decomposition, reveals that AOD concentrations across Asia are strongly influenced by anthropogenic factors, including industrial activities, transportation emissions, and biomass burning. The results indicate a generally increasing trend in AOD from 2001 to 2014, followed by a declining trend from 2015 to 2024. Notably, EOF results show a marked rise in AOD levels in Mongolia after 2020, likely attributable to an uptick in dust storm activity. This research offers valuable insights into the spatiotemporal trends of aerosols across Asia, underscoring the need for sustained air quality measures to mitigate pollution and protect public health. Full article

This study presents the longest time series of aerosol optical properties and Precipitable Water Vapor (PW) from two AERONET sites in the Indo-Gangetic Plains (IGP). Analyzing 22 years of data (2001–2022) from Kanpur and 16 years (2007–2023) from Gandhi College, the study focuses on Aerosol Optical Depth (AOD), Ångström Exponent (α), Single Scattering Albedo (SSA), and Precipitable Water Vapor (PW). Significant variability in aerosol properties is observed across monthly, seasonal, and annual scales. The highest mean AOD₅₀₀ values, coupled with higher α_440–870 during post-monsoon and winter, indicate the dominance of fine-mode aerosols. A decrease in SSA with wavelength during these seasons further highlights the absorbing nature of these fine-mode aerosols, driven by fossil fuels and biomass burning. In contrast, summer and pre-monsoon have relatively lower mean AOD₅₀₀, lowest α_440–870, and increased SSA with wavelength, suggesting the dominance of coarse-mode scattering dust aerosols. PW exhibits a seasonal cycle, reaching its peak during the monsoon due to moisture transport from the Arabian Sea and Bay of Bengal, then decreasing post-monsoon as drier conditions prevail. Long-term annual trends reveal increasing aerosol concentrations, with AOD₅₀₀ rising by 18% at Kanpur and 29% at Gandhi College, suggesting faster aerosol loading at the latter. Sub-period analysis indicates a slowdown in AOD₅₀₀ increase during 2012–2023 at Kanpur, indicating potential stabilization post-industrialization, while Gandhi College’s more pronounced AOD₅₀₀ and α_440–870 increase underscores the growing impact of fine aerosols in rural IGP areas. Kanpur shows a sustained SSA increase, though at a slower rate in recent years, indicating dominant scattering aerosols. In contrast, Gandhi College has transitioned from moderate SSA increases to declines at longer wavelengths, suggesting enhanced fine-mode absorbing aerosols. At Gandhi College, the decline in PW reduces atmospheric moisture, limiting wet scavenging and likely contributing to the rise in fine-mode aerosols, especially during the monsoon and post-monsoon seasons. Our findings highlight the evolving aerosol sources in the IGP, with Kanpur stabilizing and rural areas like Gandhi College seeing continued increases in pollution. Full article

Tropospheric aerosols play an important role in the notable warming phenomenon and climate change occurring in the Arctic. The accuracy of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol optical depth (AOD) and the distribution of Arctic AOD based on the CALIOP Level 2 aerosol products and the Aerosol Robotic Network (AERONET) AOD data during 2006–2021 were analyzed. The distributions, trends, and three-dimensional (3D) structures of the frequency of occurrences (FoOs) of different aerosol subtypes during 2006–2021 are also discussed. We found that the CALIOP AOD exhibited a high level of agreement with AERONET AOD, with a correlation coefficient of approximately 0.67 and an RMSE of less than 0.1. However, CALIOP usually underestimated AOD over the Arctic, especially in wet conditions during the late spring and early summer. Moreover, the Arctic AOD was typically higher in winter than in autumn, summer, and spring. Specifically, polluted dust (PD), dust, and clean marine (CM) were the dominant aerosol types in spring, autumn, and winter, while in summer, ES (elevated smoke) from frequent wildfires reached the highest FoOs. There were increasing trends in the FoOs of CM and dust, with decreasing trends in the FoOs of PD, PC (polluted continental), and DM (dusty marine) due to Arctic amplification. In general, the vertical distribution patterns of different aerosol types showed little seasonal variation, but their horizontal distribution patterns at various altitudes varied by season. Furthermore, locally sourced aerosols such as dust in Greenland, PD in eastern Siberia, and ES in middle Siberia can spread to surrounding areas and accumulate further north, affecting a broader region in the Arctic. Full article

This study examined spatial–temporal variations in air pollutant levels across four provinces on Sumatra Island, Indonesia, utilizing data from the Sentinel-5P satellite equipped with TROPOMI and MODIS aboard NASA’s Terra and Aqua satellites from 2019 to 2021. Sentinel-5P data, with a spatial resolution of 3.5 × 5.5 km² and near-daily temporal coverage, were used to analyze the nitrogen dioxide (NO²), carbon monoxide (CO), and Aerosol Optical Depth (AOD) in North Sumatra, West Sumatra, Jambi, and Riau—regions selected for their distinct industrial, agricultural, and urban characteristics. The purpose of this study was to investigate seasonal trends, regional differences, and the impact of the COVID-19 pandemic on air pollution, aiming to provide insights for improved air quality management and policy development. The satellite data were validated using zonal statistics to ensure consistency and reliability. The findings revealed significant seasonal fluctuations in pollution, with elevated levels during the dry season, primarily due to land clearing and forest fires. Urban and industrial areas such as Medan, Pekanbaru, Jambi, and Padang consistently exhibited high levels of NO₂, primarily due to vehicular and industrial emissions. The regions affected by biomass burning and agriculture, particularly Jambi and Riau, displayed notably higher CO and AOD levels during the dry season. The COVID-19 pandemic provided a unique opportunity to observe potential improvements in air quality, with significant reductions in NO₂, CO, and AOD levels during the 2020 lockdowns. The NO₂ levels in urban centers decreased by over 20%, while the reductions in CO and AOD reached up to 29% and 64%, respectively, reflecting diminished human activities and biomass burning. This study underscores the need for enhanced air quality monitoring and targeted management strategies in Sumatra, Indonesia. Future research should aim to improve the resolution and validation of data with ground-based measurements and broaden the number of pollutants studied to better understand air quality dynamics and support effective policy development. Full article

The most widely used radiance sensor for monitoring Night Sky Brightness (NSB) is the Sky Quality Meter (SQM), making its measurement stability fundamental. A method using the Sun as a calibrator was applied to analyse the quality of the measures recorded in the Veneto Region (Italy) and at La Silla (Chile). The analysis mainly revealed a tendency toward reductions in measured NSB due to both instrument ageing and atmospheric variations. This work compared the component due to instrumental ageing with the contribution of atmospheric conditions. The spectral responsivity of two SQMs working outdoors were analysed in a laboratory after several years of operation, revealing a significant decay, but not enough to justify the measured long-term trends. The contribution of atmospheric variations was studied through the analysis of solar irradiance at the ground, considering it as an indicator of air transparency, and values of the aerosol optical depth obtained from satellite measurements. The long-term trends measured by weather stations at different altitudes and conditions indicated an increase in solar irradiance in the Italian study sites. The comparison among the daily irradiance increase, the reduction in the aerosol optical depth, and the NSB measurements highlighted a darker sky for sites contaminated by light pollution (LP) and a brighter sky for sites not affected by LP, showing a significant and predominant role of atmospheric conditions in relation to NSB change. In the most significant case, the fraction of the variation in NSB explained by AOD changes exceeded 75%. Full article

It is assumed that climate change (global warming) worsens the living conditions for conifers and at the same time favours the cultivation of deciduous trees, including oaks. In fact, in Poland, for example, many more oaks are now being planted as forest-forming tree species than in the 1980s and 1990s. However, the monitoring of the health status of European forests (according to the International Co-operation Project) does not confirm these optimistic assumptions, and oak has been cited as one of the most damaged tree species in terms of defoliation in recent decades. The prospects for oak cultivation in European forestry are therefore a combination of abiotic conditions and biotic damage factors. This review article focuses in particular on the new threats posed by pathogenic organisms causing emerging diseases. These include newly identified bacteria responsible for the so-called Acute Oak Decline (AOD), oomycetes (especially those specialised in damaging fine roots, such as Phytophthora quercina T.Jung) and semi-parasites of the genus Loranthus. At the same time, the pressure from commonly observed insects and fungi described in connection with the complex syndrome of oak decline, which is divided into predisposing, inciting, and contributing factors (according to Manion’s disease spiral), has not abated. Therefore, international, interdisciplinary research (such as that proposed in Oakland) is needed, using modern technologies (RS remote sensing) based on the comparison of satellite images (from different years), not only to inventory the most valuable oak stands in Europe (microrefugia) but also to identify trends in changes in their condition and biodiversity. As RS has its limitations (e.g., resolution), aerial monitoring should be complemented by quantitative and qualitative inventory from the ground, e.g., monitoring of the presence of soil microorganisms using effective molecular biological methods (e.g., Next-Generation Sequencing NGS). Full article

In this study, the spatial–temporal characteristics of AOD in the Yellow River Basin (YRB) and urban agglomerations within the basin were analyzed at a 1 km scale from 2011 to 2020 based on the MCD19A2 AOD dataset. This study shows the following: (1) From 2011 to 2020, the AOD value of the YRB showed a declining trend, with 96.011% of the zones experiencing a decrease in AOD. The spatial distribution of AOD displayed a pattern of high in the east, low in the west, high in the south, and low in the north. The rate of decline showed a distribution pattern of fast in the southeast and slow in the northwest. (2) The AOD in the YRB showed similar characteristics in different seasons: the south and east were consistently higher than the north and west. The seasonal AOD values in the YRB showed the following pattern: summer > spring > autumn > winter. The AOD values of urban agglomeration were basically larger in spring and summer. (3) The SDE and mean center of the yearly AOD were located in the southeast and Shanxi Province, with the movement from southeast to northwest. It can be divided into three stages based on the movement trajectory: northeast–southwest round-trip movement (2011–2014), one-way movement to the northwest (2014–2018), and southeast–northwest round-trip movement (2018–2020). Full article