Search Results (157)

Aerosols exert significant impacts on Earth’s radiation balance through direct and indirect effects, with the latter representing the largest uncertainty in current climate models. To clarify aerosol–cloud interactions over China, this study synergized MODIS satellite retrievals (2015–2020) with in-situ MA60 aircraft observations across six representative regions. Satellite data provided aerosol optical depth (AOD), cloud optical depth (COD), cloud effective radius (CER), and cloud phase, while aircraft measurements delivered vertical profiles and microphysical properties of aerosols and cloud droplets. Results show that AOD exhibits a “high east, low west” pattern, with hotspots in the North China Plain and Sichuan Basin, and a significant decreasing trend over polluted regions. Cloud phase is spatially heterogeneous, with water clouds dominating the southeast and ice clouds prevailing in the northwest (>85% over the Tibetan Plateau). Water cloud COD shows a southeast-high–northwest-low distribution, with a clear inverse CER-COD correlation. Aircraft data reveal that polluted northern sites have high near-surface aerosol concentrations with small effective diameters (~0.3 μm), while cloud droplet number concentration and size spectra vary markedly across regions. These findings provide a robust observational foundation for improving aerosol–cloud interaction parameterizations in climate models. Full article

Surface solar radiation (SSR) is a fundamental component of the Earth’s energy budget, driving climate dynamics, ecosystem stability, and solar energy potential. By integrating high-quality in situ observations from 87 ground stations with multi-source satellite products (CERES) and reanalysis data, this study systematically quantifies the spatiotemporal evolution and driving mechanisms of SSR across China from 1994 to 2022. Nationally, SSR exhibits a significant brightening trend of 72 MJ m⁻² per decade, despite pronounced regional and seasonal divergence. Annual SSR increases significantly in North, Central, Southwest, and South China—with North China experiencing the fastest growth (160 MJ m⁻² per decade). Strikingly, the Tibetan Plateau exhibits a persistent and significant “dimming” trend (−86 MJ m⁻² per decade). Furthermore, spatial analysis reveals that summer SSR trends show a unique meridional “+ − +” dipole structure that closely resembles the dominant mode of summer precipitation over eastern China. Quantitative attribution analysis indicates that declining aerosol optical depth (AOD) is the primary driver of brightening across most of China, whereas the dimming on the Tibetan Plateau is specifically triggered by increased atmospheric water vapor, cloud cover, and precipitation. These findings underscore that in sensitive high-altitude regions, regional climate dynamics and hydrological feedback can override the radiative effects of anthropogenic aerosol reductions, highlighting the necessity for localized solar resource assessments. Full article

The Taklamakan Desert, the primary source of sand and dust in China, has a significant impact on PM_2.5 concentrations in the urban belt around the Tarim Basin. In this study, moderate-resolution imaging spectroradiometer (MODIS) images and machine learning algorithms were used to simulate PM_2.5 concentrations and analyze deaths from ischemic heart disease (IHD), chronic obstructive pulmonary disease (COPD), lung cancer (LC), and stroke (STK) attributed to PM_2.5. From 2015 to 2024, the aerosol optical depth (AOD) showed a decreasing trend, and the spatial distribution pattern gradually increased from west to east. The counties and cities with higher average PM_2.5 concentrations during the 2024 dust-prone season were Awati County, Alar City, Moyu County, and Aksu City, with average concentrations of 89.7 μg/m³, 88.8 μg/m³, 86.0 μg/m³, and 84.1 μg/m³, respectively. PM_2.5 concentrations during the non-dust season were significantly lower (approximately 50%) than those during the dust-prone season. Among the four major fatal diseases, STK and IHD accounted for the majority of attributable deaths, with proportions exceeding 80% of the total. The results show that PM_2.5 concentrations can be accurately monitored over a large scale, addressing the shortage of fixed monitoring stations, and providing a theoretical basis for sustainable development of ecological environment, health-risk management and control in the urban belt around the Tarim Basin. Full article

To better capture ecological quality under aerosol pollution stress, an AOD-integrated Remote Sensing Ecological Index (ARSEI) was developed for the Shenyang Metropolitan Area (2000–2025). Using Google Earth Engine, multi-source MODIS products were compiled to generate an annual growing-season ARSEI through PCA, combining PC1 and PC2 by variance-weighted contributions. Long-term trends were assessed with Theil–Sen slope estimation and the Mann–Kendall test, future persistence with the Hurst index, and drivers with an optimal parameter geographical detector. ARSEI closely matched conventional RSEI in multi-year pixel means (R² = 0.98, p < 0.001) but identified larger “poor” (+0.4%) and “moderate” (+3.4%) areas from 2000 to 2025, indicating higher sensitivity to pollution-related stress. Ecological quality improved overall, with high grades in eastern mountainous forests and low grades in the central built-up core and surrounding croplands. Improvement was dominant (31.08% significant, 38.27% slight), while degradation was limited (4.27% significant, 13.92% slight) and concentrated in peri-urban expansion belts. Elevation was the strongest natural control, whereas land use and population were the leading socioeconomic drivers with increasing influence over time. Finally, we delineated differentiated management zones based on current status and projected trajectories to support targeted regional governance. Full article

Aerosol optical depth (AOD) and Ångström exponent (AE) are critical parameters for characterizing atmospheric aerosols, playing a pivotal role in atmospheric environmental monitoring and climate change studies. This study addressed the imperative need for a systematic evaluation of mainstream reanalysis products by conducting a comprehensive multi-scale assessment of the CAMS and MERRA-2 datasets (2003–2023), encompassing data quality verification, spatiotemporal pattern analysis, and trend evolution investigation. The following key findings emerge: (1) Both AOD data exhibited the best performance observed in low–mid latitudes. CAMS AOD (AODC) showed a slightly better correlation, while MERRA-2 AOD (AODM) demonstrated superior robustness. Both AE data performed similarly, and MERRA-2 AE (AEM) was superior. Both AE data performed better in low latitudes and near Europe. (2) CAMS and MERRA-2 showed good performance in annual and seasonal variations, with significant fluctuations and biases in the annual cycle. Both models achieved the highest AE performance in summer. MERRA-2 AOD demonstrated better hourly performance during daytime. The hourly stability of AE was slightly worse than AOD, with notably degraded performance during midday hours. (3) The distribution and trends of AOD over land showed spatial consistency. The distribution of AEM was generally lower than AEC’s. After ensemble empirical mode decomposition (EEMD), all datasets showed monotonically decreasing trends except for AEM. This study provides valuable insights into the strengths and limitations for CAMS and MERRA-2 and suggests possible areas for improvement in future data assimilation and parameterization. Full article

Studies have established that extreme air pollution is more prevalent and is responsible for more deaths and disability-adjusted life years (DALY) in urban cities, especially in developing economies. However, the paucity of ground-based observation has greatly hindered extensive and long-term monitoring and, as such, a good understanding of the trend and characteristics of air quality where it matters most. Aerosol optical depth (AOD) from satellites retrievals provides good spatial and temporal resolutions of atmospheric aerosols and could be a good proxy for ground-level PM_2.5 concentration. This study used a Bayesian regression model to determine the parameters of a PM_2.5 model at four monitoring stations using AOD and selected atmospheric variables (PBLH and RH) as input. The dry-air reference value (K) and the integrated humidity coefficient (γ) were used to delineate the effects of the aerosol characteristics. The values of K and γ, $0.02<K<0.07$ (${\mathrm{m}}^2{\mathrm{g}}^{\mathrm{-1}}$) and $0.54<\gamma <3.14$, respectively, are site-specific even within the same country as is the case for Lekki and Benin (both in Nigeria). The PM_2.5 estimates from the developed observation-based model were in good agreement with the ground-based observations ($0.55<r<0.77$). RH and a combination of PBLH-RH were the best performers in the development of the model. Firstly, this study identifies the unique range of values for K and $\gamma$ for site-classes in the sub-Saharan tropical climate. Secondly, PBLH adds more explanatory power to the PM_2.5 estimates in Benin and Douala (both non-coastal cities) while RH improves the performance of the model significantly in Lekki and Owendo (both coastal cities). For West Africa and similar data-sparse regions, the methodology presented here offers a practical pathway to enhance air quality monitoring capabilities. Full article

Numerous machine learning models have been widely used for the spatial prediction of PM_2.5 mass concentrations in the field of remote sensing, but most studies rely on single models, limiting their ability to capture complex nonlinear relationships. Furthermore, traditional Aerosol Optical Depth (AOD) methods suffer from extensive missing values due to algorithmic limitations, hindering daily PM_2.5 mass concentration retrieval. This study first developed a hybrid random forest and extreme gradient boosting model (RF-XGBoost) to overcome single-model accuracy constraints. Subsequently, Top-of-Atmosphere (TOA) reflectance replaced conventional AOD as the hybrid model’s input. Finally, we integrated four-year (2020–2023) TOA reflectance, normalized difference vegetation index (NDVI) data, meteorological data, digital elevation model (DEM) data, and day-of-year data to develop a high-precision hybrid model specifically optimized for Jiangxi Province. The simulation results demonstrated that the hybrid RF-XGBoost model (test-R² = 0.82, RMSE = 7.25 μg/m³, MAE = 4.90 μg/m³) outperformed the single Random Forest Model by 25% and 26% in terms of the root mean square error (RMSE) and mean absolute error (MAE), respectively. The high predictive accuracy of our method confirms its effectiveness in generating reliable PM_2.5 estimates. The resulting four-year dataset also successfully delineated the characteristic seasonal PM_2.5 pattern in the region, with the highest levels in winter and the lowest in summer, alongside a clear decreasing annual trend, signifying gradual atmospheric improvement. Full article

Yazd province in central Iran is highly prone to dust and sand storms, causing significant environmental, economic, and health impacts. This study investigates the spatiotemporal dynamics of dust storms in Yazd over 2003–2022 using ground-based meteorological station records and satellite-derived aerosol optical depth (AOD) data from MODIS (MYD08_D3 v6.1) at monthly, seasonal, and annual scales. Analysis of ten synoptic stations data revealed an increasing trend of ~0.5 dusty days/year, with the highest frequency in spring and winter, particularly from March to May. MODIS AOD data confirmed these patterns and showed a rising annual aerosol load, peaking in May. Spatial analysis indicated that central and northern regions are most affected, consistent across datasets. The increasing frequency and intensity of dust storms are driven by natural and anthropogenic factors, including regional drought, desertification, drying wetlands, land use changes, and transboundary dust transport (from Iraq, Syria, Saudi Arabia). These findings underscore the value of integrating in situ and remote sensing observations to monitor dust events. To mitigate impacts, policymakers should prioritize long-term environmental monitoring and interventions addressing both natural and human factors influencing dust emissions. This study provides actionable insights for decision-makers to enhance environmental resilience and protect public health in arid regions. Full article

Air pollution represents a critical environmental challenge in stressed riverine cities, particularly in regions experiencing rapid urbanization and inadequate emission management infrastructure. This study investigates the spatio-temporal dynamics of atmospheric pollution in Baghdad, Iraq, during 2012–2023, analyzing seven key pollutants (CO, CO₂, SO₂, SO₄, O₃, CH₄, and AOD) using NASA’s Giovanni platform coupled with Google Earth Engine analytics. Monthly time-series data were processed through advanced statistical techniques, including Seasonal Autoregressive Integrated Moving Average (SARIMA) modeling and correlation analysis with meteorological parameters, to identify temporal trends, seasonal variations, and driving mechanisms. The analysis revealed three distinct pollutant trajectory categories reflecting complex emission–atmosphere interactions. Carbon monoxide exhibited dramatic decline (60–70% reduction from 2021), attributed to COVID-19 pandemic restrictions and demonstrating rapid responsiveness to activity modifications. Conversely, greenhouse gases showed persistent accumulation, with CO₂ increasing from 400.5 to 417.5 ppm and CH₄ rising 5.9% over the study period, indicating insufficient mitigation efforts. Sulfur compounds and ozone displayed stable concentrations with pronounced seasonal oscillations (winter peaks 2–3 times summer levels), while aerosol optical depth showed high temporal variability linked to dust storm events. Spatial analysis identified pronounced urban–rural concentration gradients, with central Baghdad CO levels exceeding 0.40 ppm compared to peripheral regions below 0.20 ppm. Linear concentration patterns along transportation corridors and industrial zones confirmed anthropogenic source dominance. Correlation analysis revealed strong relationships between meteorological factors and pollutant concentrations (atmospheric pressure: r = 0.62–0.70 with NO₂), providing insights for integrated climate–air quality management strategies. The study demonstrates substantial contributions to Sustainable Development Goals across four dimensions (Environmental Health 30%, Sustainable Cities and Climate Action 25%, Economic Development 25%, and Institutional Development 20%) while providing transferable methodological frameworks for evidence-based policy interventions and environmental monitoring in similar stressed urban environments globally. Full article

Atmospheric pollution constitutes one of the key environmental challenges hindering Atmospheric pollution is a key environmental challenge constraining the sustainable development of Gansu Province’s land-based Belt and Road corridor and its regional ecological barrier function. The spatiotemporal heterogeneity of aerosol optical depth (AOD) profoundly impacts regional environmental quality. Based on MODIS AOD, NCEP reanalysis, and emission data, this study employed trend analysis (Mann–Kendall test) and attribution analysis (multiple linear regression combined with LMG and Spearman correlation) to investigate the spatiotemporal evolution of AOD over Gansu Province during 2009–2019 and its meteorological and emission drivers. Key findings include the following: (1) AOD exhibited significant spatial heterogeneity, with high values concentrated in the Hexi Corridor and central regions; monthly variation showed a unimodal pattern (peak value of 0.293 in April); and AOD generally declined slowly province-wide during 2009–2019 (52.8% of the area showed significant decreases). (2) Following the implementation of the Air Pollution Prevention and Control Action Plan in 2013 (2014–2019), AOD trends stabilized or declined in 99.8% of the area, indicating significant improvement. (3) Meteorological influences displayed distinct regional-seasonal specificity—the Hexi Corridor (arid zone) was characterized by strong negative correlations with relative humidity (RH2) and wind speed (WS) year-round, and positive correlations with temperature (T2) in spring but negative in summer in the north; the Hedong region (industrial zone) featured strong positive correlations with planetary boundary layer height (PBLH) in summer (r > 0.6) and with T2 in spring/summer; and the Gannan Plateau (alpine zone) showed positive WS correlations in spring and weak positive RH2 correlations in spring/autumn, highlighting the decisive regulatory role of underlying surface properties. (4) Emission factors (PM_2.5, ${\mathrm{SO}}_4^{2-}$, ${\mathrm{NO}}_3^{-}$, ${\mathrm{NH}}_4^{+}$, OM, and BC) dominated (>50% relative contribution) in 80% of seasonal scenarios, prevailing in most regions (Hexi: 71–95% year-round; Hedong: 68–80% year-round; and Gannan: 69–72% in spring/summer). Key components included BC (contributing > 30% in 11 seasons, e.g., 52.5% in Hedong summer), ${\mathrm{NO}}_3^{-}$ + ${\mathrm{NH}}_4^{+}$ (>57% in Hexi summer/autumn), and OM (20.3% in Gannan summer, 19.0% province-wide spring). Meteorological factors were the primary driver exclusively in Gannan winter (82%, T2-dominated) and province-wide summer (67%, RH2 + WS-dominated). In conclusion, Gansu’s AOD evolution is co-driven by emission factors (dominant province-wide) and meteorological factors (regionally and seasonally specific). Post-2013 environmental policies effectively promoted regional air quality improvement, providing a scientific basis for differentiated aerosol pollution control in arid, industrial, and alpine zones. Full article

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