Search Results (63)

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

This study investigates the use of a Random Forest (RF), an artificial intelligence (AI) model, to estimate the planetary boundary layer height (PBLH) over Central Amazonia from climatic elements data collected during the GoAmazon experiment, held in 2014 and 2015, as it is a key metric for air quality, weather forecasting, and climate modeling. The novelty of this study lies in estimating PBLH using only surface-based meteorological observations. This approach is validated against remote sensing measurements (e.g., LIDAR, ceilometer, and wind profilers), which are seldom available in the Amazon region. The dataset includes various meteorological features, though substantial missing data for the latent heat flux (LE) and net radiation (Rn) measurements posed challenges. We addressed these gaps through different data-cleaning strategies, such as feature exclusion, row removal, and imputation techniques, assessing their impact on model performance using the Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and $r^2$ metrics. The best-performing strategy achieved an RMSE of 375.9 m. In addition to the RF model, we benchmarked its performance against Linear Regression, Support Vector Regression, LightGBM, XGBoost, and a Deep Neural Network. While all models showed moderate correlation with observed PBLH, the RF model outperformed all others with statistically significant differences confirmed by paired t-tests. SHAP (SHapley Additive exPlanations) values were used to enhance model interpretability, revealing hour of the day, air temperature, and relative humidity as the most influential predictors for PBLH, underscoring their critical role in atmospheric dynamics in Central Amazonia. Despite these optimizations, the model underestimates the PBLH values—by an average of 197 m, particularly in the spring and early summer austral seasons when atmospheric conditions are more variable. These findings emphasize the importance of robust data preprocessing and higtextight the potential of ML models for improving PBLH estimation in data-scarce tropical environments. Full article

PM_2.5 pollution poses significant risks to human health and the environment, underscoring the importance of accurate PM_2.5 simulation. This study simulated a representative PM_2.5 pollution event using the Weather Research and Forecasting model coupled with chemistry (WRF-Chem), incorporating the assimilation of infrared atmospheric motion vector (AMV) data from the Fengyun-4A (FY-4A) satellite. A comprehensive analysis was conducted to examine the meteorological characteristics of the event and their influence on PM_2.5 concentration simulations. The results demonstrate that the assimilation of FY-4A infrared AMV data significantly enhanced the simulation performance of meteorological variables, particularly improving the wind field and capturing local and small-scale wind variations. Moreover, PM_2.5 concentrations simulated with AMV assimilation showed improved spatial and temporal agreement with ground-based observations, reducing the root mean square error (RMSE) by 8.2% and the mean bias (MB) by 15.2 µg/m³ relative to the control (CTL) experiment. In addition to regional improvements, the assimilation notably enhanced PM_2.5 simulation accuracy in severely polluted cities, such as Tangshan and Tianjin. Mechanistic analysis revealed that low wind speeds and weak atmospheric divergence restricted pollutant dispersion, resulting in higher near-surface concentrations. This was exacerbated by cooler nighttime temperatures and a lower planetary boundary layer height (PBLH). These findings underscore the utility of assimilating satellite-derived wind products to enhance regional air quality modeling and forecasting accuracy. This study highlights the potential of FY-4A infrared AMV data in improving regional pollution simulations, offering scientific support for the application of next-generation Chinese geostationary satellite data in numerical air quality forecasting. Full article

Reducing anthropogenic fossil fuel CO₂ (FFCO₂) emissions in urban areas is key to mitigating climate change. To better understand the spatial characteristics and temporal variations in urban CO₂ levels in the Beijing (BJ) region, we conducted a long-term CO₂ simulation study by using the Weather Research and Forecasting WRF-Chem model and CO₂ observation data. To assess the model performance, three representative sites with high-precision CO₂ observation data were chosen in this study: the rural regional background Shangdianzi (SDZ) site, the suburban Xianghe (XH) site, and the urban BJ site. The simulation results generally captured the observed variations at these three sites, but the model performed much better at the SDZ and XH sites, with mean biases of −0.7 ppm and −2.3 ppm, respectively, and RMSE of 12.3 ppm and 21.4 ppm, respectively. The diurnal variations in the model results agreed well with those in the observed CO₂ concentrations at the SDZ and XH sites during all seasons. In the meanwhile, the diurnal variations in the modeled FFCO₂ were similar to those in the CO₂ observation with a positive bias at the BJ site, which may have been caused by higher emissions especially in winter. Moreover, both the modeled FFCO₂ and biospheric CO₂ (BIOCO₂) have positive correlations with the observed CO₂ concentration, whereas the planetary boundary layer height (PBLH) and observed CO₂ concentration exhibited negative correlations at all sites. In addition, the contributions of FFCO₂ and BIOCO₂ to CO₂ varies depending on the seasons and the location of sites. Full article

The planetary boundary layer height (PBLH) in coastal Arctic regions is influenced by sea breeze circulation. However, the specific mechanisms through which sea breeze affects PBLH evolution remain insufficiently explored. This study uses meteorological data, micro-pulse lidar (MPL) data, and sounding profiles from 2014 to 2021 to investigate the annual and polar day PBLH evolution driven by sea breezes in the Barrow region of Alaska, as well as the specific mechanisms. The results show that sea breeze events significantly suppress PBLH, especially during the polar day, when prolonged solar radiation intensifies the thermal contrast between land and ocean. The cold, moist sea breeze stabilizes the atmospheric conditions, reducing net radiation and sensible heat flux. All these factors inhibit turbulent mixing and PBLH development. Lidar and sounding analyses further reveal that PBLH is lower during sea breeze events compared to non-sea-breeze conditions, with the peak of its probability density distribution occurring at a lower PBLH range. The variable importance in projection (VIP) analysis identifies relative humidity (VIP = 1.95) and temperature (VIP = 1.1) as the primary factors controlling PBLH, highlighting the influence of atmospheric stability in regulating PBLH. These findings emphasize the crucial role of sea breeze in modulating PBL dynamics in the Arctic, with significant implications for improving climate models and studies on pollutant dispersion in polar regions. Full article

PM_2.5 levels increase in cities during the first hours of the year due to combustion activities and the use of fireworks. In Quito (2800 masl), the capital of Ecuador, air quality records at the beginning of 2020 to 2025 (6 years) ranged between 13.4 and 217.8 µg m⁻³ (maximum mean levels for 24 h), most of them being higher than 15.0 µg m⁻³, the current recommended concentration by the World Health Organization (WHO), highlighting the need to decrease these emissions and promote actions to reduce the exposure to these extreme events. Air pollution forecasting as a preventive warning system could help achieve this objective. Therefore, the primary aim of this research was to analyze the variation in PM_2.5 levels in this city during the initial hours of the year to define, through numerical experiments, the spatiotemporal configuration of PM_2.5 emissions to reproduce the observed PM_2.5 levels and obtain insights to build an emission-based forecasting tool. For this purpose, we modeled atmospheric variables and the PM_2.5 levels using the Weather Research and Forecasting with Chemistry (WRF-Chem) model. Consistent with the behavior suggested by records of associated meteorological variables, the modeled planetary boundary layer height (PBLH) was generally lower in the city’s south compared with the center and the north. The records and modeled results indicated that in the south, the higher PM_2.5 levels were produced by higher emissions and lower values of the PBLH compared with the center and north, highlighting the importance of reducing the PM_2.5 emissions. The emission maps used for modeling the dispersion at the beginning of 2024 and 2025 are proposed as inputs for the future forecasting of the PM_2.5 levels at the start of the year, as preventive information for the public, to discourage, in advance, both combustion activities and the use of fireworks and to take action to avoid exposure. Full article

This study investigates the estimation of the boundary layer height (PBLH) in Huelva, Spain, in November 2023, using different methods: Richardson number, humidity gradient and refractivity gradient. From the virtual potential profiles of temperature and specific humidity, in the case of daytime PBLH, which method works best in some situations when there are discrepancies between results is discussed. The results are then compared with the PBLH values obtained from the ERA-5 reanalysis. The synoptic analysis shows that the decrease in PBLH in the central weeks of the month is compatible with a thermal inversion by subsidence due to a persistent anticyclonic situation. Regarding air quality, the NO₂ concentrations in the air quality station of Matalascañas, which is a background station, show negative correlations with the PBLH. Full article

As an important means to monitor atmospheric vertical temperature and humidity, the ground-based microwave radiometer has been widely used in environmental monitoring, climate prediction, and other fields, but its application in desert areas is particularly limited. At Minfeng Station on the southern edge of the Taklimakan Desert, Global Telecommunications System (GTS) detection technology was used to evaluate the microwave radiometer observations under different weather conditions and at different altitudes. The planetary boundary layer height (PBLH) was calculated using the potential temperature gradient method, and the planetary boundary layer results were calculated by analyzing dust and rainfall events. The results show that the determination coefficients (R²) of the overall observed temperature (T), specific humidity (q), and water vapor density (${\rho }_v$) of the microwave radiometer are all above 0.8 under different weather conditions. When the relative humidity is 0–10%, the temperature is the best, and the R² is 0.9819. When the relative humidity is 70–80%, the R² of q and ${\rho }_v$ is the best, and the R² is 0.9630 and 0.9777, respectively. This is in good agreement with the temperature observed by the FY–4A satellite; the observation effect is the best in May, and its R² is 0.9142. Under the conditions of clear sky, precipitation day, and dusty weather, the R² of the atmospheric boundary layer height calculated by the microwave radiometer is greater than 0.7 compared to the GTS sounding calculation results. These results demonstrate the reliability of microwave radiometry in extremely arid environments, providing valuable insights for boundary layer studies in desert regions. Full article

The urbanization effect (UE) on local or regional climate is a prominent research topic in the research field of urban climates. However, there is little research on the UE of Urumqi, a typical arid region city, concerning various climatic factors and their spatio–temporal characteristics. This study quantitatively investigates the UE of Urumqi on multiple climatic factors in summer based on a decade-long period of WRF–UCM (Weather Research and Forecasting model coupled with the Urban Canopy Model) simulation data. The findings reveal that the UE of Urumqi has resulted in a reduction in the diurnal temperature range (DTR) within the urban area by causing an increase in night-time minimum temperatures, with the maximum decrease reaching −2.5 °C. Additionally, the UE has also led to a decrease in the water vapor mixing ratio (WVMR) and relative humidity (RH) at 2 m, with the maximum reductions being 0.45 g kg⁻¹ and −6.5%, respectively. Furthermore, the UE of Urumqi has led to an increase in planetary boundary layer height (PBLH), with a more pronounced effect in the central part of the city than in its surroundings, reaching a maximum increase of over 750 m at 19:00 Local Solar Time (LST, i.e., UTC + 6). The UE has also resulted in an increase in precipitation in the northern part of the city by up to 7.5 mm while inhibiting precipitation in the southern part by more than 6 mm. Moreover, the UE of Urumqi has enhanced precipitation both upstream and downstream of the city, with a maximum increase of 7.9 mm. The UE of Urumqi has also suppressed precipitation during summer mornings while enhancing it in summer afternoons. The UE has exerted certain influences on the aforementioned climatic factors, with the UE varying across different directions for each factor. Except for precipitation and PBLH, the UE on the remaining factors exhibit a greater magnitude in the northern region compared to the southern region of Urumqi. Full article

Currently, large-scale burning is an important straw disposal method in most developing countries. To execute prescribed burning while mitigating air pollution, it is crucial to explore the maximum possible range of meteorological changes. This study conducted a three-year monitoring program in Changchun, a core agricultural area in Northeast China severely affected by straw burning. The data included ground-level pollutant monitoring, ground-based polarized LiDAR observations, and ground meteorological factors such as planetary boundary layer height (PBLH), relative humidity (RH), and wind speed (WS). Using response surface methodology (RSM), this study analyzed key weather parameters to predict the optimal range for emission reduction effects. The results revealed that PM_2.5 was the primary pollutant during the study period, particularly in the lower atmosphere from March to April, with PM_2.5 rising sharply in April due to the exponential increase in fire points. Furthermore, during this phase, the average WS and PBLH increased, whereas the RH decreased. Univariate analysis confirmed that these three factors significantly impacted the PM_2.5 concentration. The RSM relevance prediction model (MET-PM_2.5) established a correlation equation between meteorological factors and PM_2.5 levels and identified the optimal combination of meteorological indices: WS (3.00–5.03 m/s), RH (30.00–38.30%), and PBLH (0.90–1.45 km). Notably, RH (33.1%) emerged as the most significant influencing factor, while the PM_2.5 value remained below 75 μg/m³ when all weather indicators varied by less than 20%. In conclusion, these findings could provide valuable meteorological screening schemes to improve planned agricultural residue burning policies, with the aim of minimizing pollution from such activities. Full article

We analyzed the planetary boundary layer (PBL) characteristics in Warsaw, Poland for a day of summer, autumn, winter, and spring of 2021 by integrating and comparing measured and simulated data. Using remote sensing lidar sensor data, the PBLH was calculated using wavelet covariance transform (WCT) and the gradient method (GM). Also, simulations of turbulent fluxes were performed utilizing the large eddy simulation (LES) from the Parallel Large Eddy Simulation Model (PALM) to better understand how turbulence and convection behave across different seasons in Warsaw. The PBLH diurnal cycles showed pronounced changes in their vertical structure as a function of the season: the winter heights were shallow (~0.7 km), while summer heights were deeper (~1.7 km). The spring and autumn presented transient characteristics of PBLH around 1.0 km. This study is crucial for enhancing urban air quality and climate modeling. The PBLH simulations from PALM showed agreement with the measured data, with an underestimation of approximately 10% in both methods. Through PALM, it was possible to observe that summer exhibited increased convection, enhanced mixing efficiency, and a deeper boundary layer compared to other seasons throughout the daily cycle. Winter has a lower sensible heat flux and little convection throughout the day. Spring and autumn showed intermediate characteristics. In this way, the effectiveness of the applicability of the PALM model to obtain flows within the PBL and their heights is highlighted, because correlations ranged from strong to very strong (r ≥ 0.70). Full article

One of the keys towards sustainable policies and advanced air quality monitoring is the detailed assessment of all factors that affect the surface concentrations of greenhouse gases (GHGs) and aerosols. While the development of new atmospheric tracers can pinpoint emission sources, the atmosphere itself plays a relevant role even at local scales: Its dynamics can increase, or reduce, surface concentrations of pollutants harmful to human health and the environment. PBL (planetary boundary layer), or peplospheric, variability is known to affect such concentrations. In this study, an unprecedented characterization of PBL cycles and patterns is performed at the WMO/GAW regional coastal site of Lamezia Terme (code: LMT) in Calabria, Southern Italy, in conjunction with the analysis of key GHGs and aerosols. The analysis, accounting for five months of 2024 data, indicates that peplospheric variability and wind regimes influence the concentrations of key GHGs and aerosols. In particular, PBLH (PBL height) patterns have been tested to further influence the surface concentrations of carbon monoxide (CO), black carbon (BC), and particulate matter (PM). This research introduces four distinct wind regimes at LMT: breeze, not complete breeze, eastern synoptic, and western synoptic, each with its peculiar influences on the local transport of gases and aerosols. This research demonstrates that peplosphere monitoring needs to be considered when ensuring optimal air quality in urban and rural areas. Full article

Self-healing, stretchable, and moldable hydrogels have a great potential application in tissue engineering and soft robotics. Despite great success in reported hydrogels, it is still a great challenge to construct the moldable hydrogels with an ultrafast self-healing performance. Herein, the composite hydrogels (PBLH) with ultrafast self-healing, stretchable, and moldable properties were successfully constructed by poly (vinyl alcohol) (PVA), borate (B), ε-poly-l-lysine (EPL), and hyaluronic acid (HA) based on an efficient one-pot method. Fourier transform infrared spectroscopy, X-ray diffraction, and rheological measurements confirmed the formation of a dynamic network among PVA, B, EPL, and HA through the cross-linking of dynamic borate bonds, electrostatic interaction, and hydrogen bonding. Having fabricated the dynamic network structure, the damage gap of the composite hydrogels can heal within 1 min, presenting an excellent self-healing ability. Simultaneously, the composite hydrogels can be molded into various shapes, and the length of the composite hydrogels can be stretched to 15 times their original length. In addition, the composite hydrogels exhibited an excellent antibacterial property against Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli). Our results illustrated that the composite hydrogels not only retain the advantages of traditional hydrogels but also possess ultrafast self-healing, outstanding stretchable and antibacterial properties, presenting a prospective candidate for constructing biomedical materials. Full article

In this study, we investigate the planetary boundary layer height (PBLH) using micro-pulse lidar (MPL) and microwave radiometer (MWR) methods, examining its relationship with the mass concentration of particles less than 2.5 µm in aerodynamic diameter (PM_2.5) and its chemical compositions. Long-term PBLH retrieval results are presented derived from the MPL and the MWR, including its seasonal and diurnal variations, showing a superior performance regarding the MPL in terms of reliability and consistency with PM_2.5. Also examined are the relationships between the two types of PBLHs and PM_2.5. Unlike the PBLH derived from the MPL, the PBLH derived from the MWR does not have a negative correlation under severe pollution conditions. Furthermore, this study explores the effects of the PBLH on different aerosol chemical compositions, with the most pronounced impact observed on primary aerosols and relatively minimal influence on secondary aerosols, especially secondary organics during spring. This study underscores disparities in PBLH retrievals by different instruments during long-term observations and unveils distinct relationships between the PBLH and aerosol chemical compositions. Moreover, it highlights the greater influence of the PBLH on primary pollutants, laying the groundwork for future research in this field. Full article

The Planetary Boundary Layer Height (PBLH) significantly impacts weather, climate, and air quality. Understanding the global diurnal variation of the PBLH is particularly challenging due to the necessity of extensive observations and suitable retrieval algorithms that can adapt to diverse thermodynamic and dynamic conditions. This study utilized data from the Cloud-Aerosol Transport System (CATS) to analyze the diurnal variation of PBLH in both continental and marine regions. By leveraging CATS data and a modified version of the Different Thermo-Dynamics Stability (DTDS) algorithm, along with machine learning denoising, the study determined the diurnal variation of the PBLH in continental mid-latitude and marine regions. The CATS DTDS-PBLH closely matches ground-based lidar and radiosonde measurements at the continental sites, with correlation coefficients above 0.6 and well-aligned diurnal variability, although slightly overestimated at nighttime. In contrast, PBLH at the marine site was consistently overestimated due to the viewing geometry of CATS and complex cloud structures. The study emphasizes the importance of integrating meteorological data with lidar signals for accurate and robust PBLH estimations, which are essential for effective boundary layer assessment from satellite observations. Full article