The metal concentrations and isotopic compositions (¹³C, ^207/206Pb) of urban dust, topsoil, and PM₁₀ samples were analyzed in a residential area near Donghae port, Korea, which is surrounded by various types of industrial factories and raw material stockpiled on empty land, to determine the contributions of the main pollution sources (i.e., Mn ore, Zn ore, cement, coal, coke, and topsoil). The metal concentrations of urban dust in the port and residential area were approximately 85~112 times higher (EF > 100) in comparison with the control area (EF < 2), especially the Mn and Zn ions, indicating they were mainly derived from anthropogenic source. These ions have been accumulating in urban dust for decades; furthermore, the concentration of PM₁₀ is seven times higher than that of the control area, which means that contamination is even present. The isotopic (¹³C, ^207/206Pb) values of the pollution sources were highly different, depending on the characteristics of each source: cement (−19.6‰, 0.8594‰), Zn ore (−24.3‰, 0.9175‰), coal (−23.6‰, 0.8369‰), coke (−27.0‰, 0.8739‰), Mn ore (−24.9‰, 0.9117‰), soil (−25.2‰, 0.7743‰). As a result of the evaluated contributions of pollution source on urban dust through the Iso-source and SIAR models using stable isotope ratios (¹³C, ^207/206Pb), we found that the largest contribution of Mn (20.4%) and Zn (20.3%) ions are derived from industrial factories and ore stockpiles on empty land (Mn and Zn). It is suggested that there is a significant influence of dust scattered by wind from raw material stockpiles, which are stacked near ports or factories. Therefore, there is evidence to support the idea that port activities affect the air quality of residence areas in a city. Our results may indicate that metal concentrations and their stable isotope compositions can predict environmental changes and act as a powerful tool to trace the past and present pollution history in complex contexts associated with peri-urban regions. Full article

Nature-based solutions can represent beneficial tools in the field of urban transformation for their contribution to important environmental services such as air quality improvement. To evaluate the impact on urban air pollution of a CityTree (CT), an innovative wall-type green infrastructure in passive (deposition) and active (filtration) modes of operation, a study was conducted in a real urban setting in Modena (Italy) during 2017 and 2018, combining experimental measurements with modelling system evaluations. In this work, relying on the computational resources of CRESCO (Computational Centre for Research on Complex Systems)/ENEAGRID High Performance Computing infrastructure, we used the air pollution microscale model PMSS (Parallel Micro-SWIFT-Micro SPRAY) to simulate air quality during the experimental campaigns. The spatial characteristics of the impact of the CT on local air pollutants concentrations, specifically nitrogen oxides (NOx) and particulate matter (PM₁₀), were assessed. In particular, we used prescribed bulk deposition velocities provided by the experimental campaigns, which tested the CT both in passive (deposition) and in active (filtration) mode of operation. Our results showed that the PM₁₀ and NOx concentration reductions reach from more than 0.1% up to about 0.8% within an area of 10 × 20 m² around the infrastructure, when the green infrastructure operates in passive mode. In filtration mode the CT exhibited higher performances in the abatement of PM₁₀ concentrations (between 1.5% and 15%), within approximately the same area. We conclude that CTs may find an application in air quality hotspots within specific urban settings (i.e., urban street canyons) where a very localized reduction of pollutants concentration during rush hours might be of interest to limit population exposure. The optimization of the spatial arrangement of CT modules to increment the “clean air zone” is a factor to be investigated in the ongoing development of the CT technology. Full article

The PannEx is a GEWEX-initiated, community driven research network in the Pannonian Basin. One of the main scientific issues to address in PannEx is the investigation of precipitation extremes. Meteorological Services in the PannEx area collected the hourly precipitation data and commonly used a computer program, which was developed in the INTENSE project, to produce a set of global hydro-climatic indices. Calculations are carried out on data aggregated 1-, 3- and 6-h intervals. Selected indices are analyzed in this paper to assess the general climatology of the short-term precipitation in the Pannonian basin. The following indices are illustrated on maps and graphs: the annual mean and maxima of 1-h, 3-h and 6-h sums, the count of 3-hr periods greater than 20 mm thresholds, the maximum length of wet hours, the timing of wettest hour and the 1-h precipitation intensity. The seasonal trends of the 1-h precipitation intensity were tested from 1998 to 2019. Analysis of sub-daily precipitation has been limited by the availability of data on a global or a regional scale. The international effort made in this work through collaboration in the PannEx initiative contributes to enlarging the data availability for regional and global analysis of sub-daily precipitation extremes. Full article

The characteristics of low-level turbulence at Boseong, located on the southern coast of South Korea, were investigated in terms of eddy dissipation rate (EDR) using 1-year (2018) of wind data obtained from the Boseong Meteorological Observatory (BMO), a World Meteorological Organization testbed. At BMO, a 307 m tall tower is installed on which four high-frequency (20 Hz) sonic anemometers are mounted at 60, 140, and 300 m above ground level (AGL). In addition, a sonic anemometer at 2.5 m AGL is located to the south of the tower. EDRs are estimated from the wind measurements based on three different EDR estimation methods. The first two methods use the inertial dissipation method derived from Kolmogorov turbulence theory, and the third uses a maximum likelihood estimation assuming a von Kármán spectral model. Reasonable agreement was obtained between the three methods with various fluctuations, including diurnal variations for all seasons, while the EDR calculated from the third method displayed slightly higher EDR values than the other two methods. The result of the analysis showed that the mean (standard deviations) of logarithms of EDR had larger values as height decreased (increased), and the means were higher in the unstable planetary boundary layer (PBL) than in the stable PBL for this heterogeneous location adjacent to the coastlines. The probability density functions (PDFs) of the EDRs showed that the distribution was well-represented by a lognormal distribution in both the stable and unstable PBL, although the PDFs at the lowest level (2.5 m) deviated from those at other levels due to surface effects. Seasonal variations in the PDFs showed that there was less difference in the shape of the PDFs depending on atmospheric stability in the wintertime. Finally, we calculate the 1-yr statistics of the observed EDR, which will be used for future LLT forecast systems in Korea. Full article

During recent decades, stable oxygen isotopes derived from tree-ring cellulose (δ¹⁸O_TRC) have been frequently utilised as the baseline for palaeoclimatic reconstructions. In this context, numerous studies take advantage of the high sensitivity of trees close to their ecological distribution limit (high elevation or high latitudes). However, this increases the chance that indirect climatic forces such as cold ground induced by permafrost can distort the climate-proxy relationship. In this study, a tree stand of sub-alpine larch trees (Larix decidua Mill.) located in an inner alpine dry valley (Val Bever), Switzerland, was analysed for its δ¹⁸O_TRC variations during the last 180 years. A total of eight L. decidua trees were analysed on an individual base, half of which are located on verified sporadic permafrost lenses approximately 500 m below the expected lower limit of discontinuous permafrost. The derived isotope time series are strongly dependent on variations in summer temperature, precipitation and large-scale circulation patterns (geopotential height fields). The results demonstrate that trees growing outside of the permafrost distribution provide a significantly stronger and more consistent climate-proxy relationship over time than permafrost-affected tree stands. The climate sensitivity of permafrost-affected trees is analogical to the permafrost-free tree stands (positive and negative correlations with temperature and precipitation, respectively) but attenuated partly leading to a complete loss of significance. In particular, decadal summer temperature variations are well reflected in δ¹⁸O_TRC from permafrost-free sites (r = 0.62, p < 0.01), while permafrost-affected sites demonstrate a full lack of this dependency (r = 0.30, p > 0.05). Since both tree stands are located just a few meters away from one another and are subject to the same climatic influences, discrepancies in the isotope time series can only be attributed to variations in the trees’ source water that constraints the climatic fingerprints on δ¹⁸O_TRC. If the two individual time series are merged to one local mean chronology, the climatic sensitivity reflects an intermediate between the permafrost-free and –affected δ¹⁸O_TRC time series. It can be deduced, that a significant loss of information on past climate variations arises by simply averaging both tree stands without prior knowledge of differing subsurface conditions. Full article

This study presents a seasonal synoptic climatology of cut-off lows (COLs) that produced extreme precipitation in the Valencia region of Spain during 1998–2018 and uses simulations with the Weather Research and Forecasting (WRF) model to study how extreme COL precipitation may change in a future warmer climate. COLs were shown to be the main producer of extreme precipitation in the Valencia region, especially during the transition seasons. The strongest raining COL events occurred during September–November. Six-day composites of thermodynamic and dynamic fields and precipitation show that COLs that produce extreme precipitation in this region remain stationary over Spain for 2–3 days and tend to produce precipitation over the Valencia region for at least two consecutive days. In the low levels these COLs are characterized by low pressure over the Mediterranean sea and winds with an easterly, onshore component thus fueling precipitation. Comparison of current and future climate ensembles of WRF simulations of 14 September–November extreme precipitation producing COL events suggest that in a warmer climate extreme COL precipitation may increase by as much as 88% in northeastern Spain and 61% in the adjoining Mediterranean Sea. These projected increases in extreme COL precipitation in the northeast of Spain present additional challenges to a region where COL flooding already has significant socio-economic impacts. Additionally, about half of the future climate COL event simulations showed increases in precipitation in the Valencian region of eastern Spain. These results provide important nuance to projections of a decreasing trend of total precipitation in the Iberian Peninsula as the climate warms. Full article

The machine learning algorithms application in atmospheric sciences along the Earth System Models has the potential of improving prediction, forecast, and reconstruction of missing data. In the current study, a combination of two machine learning techniques namely K-means, and decision tree (C4.5) algorithms, are used to separate observed precipitation into clusters and classified the associated large-scale circulation indices. Observed precipitation from the Chinese Meteorological Agency (CMA) during 1961–2016 for 83 stations in the Poyang Lake basin (PLB) is used. The results from K-Means clusters show two precipitation clusters splitting the PLB precipitation into a northern and southern cluster, with a silhouette coefficient ~0.5. The PLB precipitation leading cluster (C1) contains 48 stations accounting for 58% of the regional station density, while Cluster 2 (C2) covers 35, accounting for 42% of the stations. The interannual variability in precipitation exhibited significant differences for both clusters. The decision tree (C4.5) is employed to explore the large-scale atmospheric indices from National Climate Center (NCC) associated with each cluster during the preceding spring season as a predictor. The C1 precipitation was linked with the location and intensity of subtropical ridgeline position over Northern Africa, whereas the C2 precipitation was suggested to be associated with the Atlantic-European Polar Vortex Area Index. The precipitation anomalies further validated the results of both algorithms. The findings are in accordance with previous studies conducted globally and hence recommend the applications of machine learning techniques in atmospheric science on a sub-regional and sub-seasonal scale. Future studies should explore the dynamics of the K-Means, and C4.5 derived indicators for a better assessment on a regional scale. This research based on machine learning methods may bring a new solution to climate forecast. Full article

Mesocale atmospheric flows that develop in the boundary layer or microscale flows that develop in urban areas are challenging to predict, especially due to multiscale interactions, multiphysical couplings, land and urban surface thermal and geometrical properties and turbulence. However, these different flows can indirectly and directly affect the exposure of people to deteriorated air quality or thermal environment, as well as the structural and energy loads of buildings. Therefore, the ability to accurately predict the different interacting physical processes determining these flows is of primary importance. To this end, alternative approaches based on the lattice Boltzmann method (LBM) wall model large eddy simulations (WMLESs) appear particularly interesting as they provide a suitable framework to develop efficient numerical methods for the prediction of complex large or smaller scale atmospheric flows. In particular, this article summarizes recent developments and studies performed using the hybrid recursive regularized collision model for the simulation of complex or/and coupled turbulent flows. Different applications to the prediction of meteorological humid flows, urban pollutant dispersion, pedestrian wind comfort and pressure distribution on urban buildings including uncertainty quantification are especially reviewed. For these different applications, the accuracy of the developed approach was assessed by comparison with experimental and/or numerical reference data, showing a state of the art performance. Ongoing developments focus now on the validation and prediction of indoor environmental conditions including thermal mixing and pollutant dispersion in different types of rooms equipped with heat, ventilation and air conditioning systems. Full article

Chuncheon, a medium-sized city in South Korea, frequently shows high PM_2.5 concentrations despite scarce anthropogenic emission sources. To identify factors increasing PM_2.5 concentrations, PM_2.5 and its major chemical components were concurrently measured at two different sites, namely, downtown and suburban areas. The average PM_2.5 concentrations at the two sites were similar, but the daily and monthly variations in PM_2.5 and its components were significantly larger at the suburban site. NH₄⁺ was significantly higher at the suburban site than at the downtown site, whereas organic carbon (OC) showed the opposite trend. Several PM_2.5 samples showed an abrupt increase during winter at the suburban site, along with an increase in the amount of OC, NH₄⁺, and K⁺, and the correlations between water-soluble OC, K⁺, and NH₄⁺ were considerably strong, implying that local biomass burning in the suburban site was an important source of high PM_2.5 episodes. Secondary OC (SOC) concentration was generally lower at the suburban site than at the downtown site, but its contribution to OC increased during winter with an increase in relative humidity, indicating the significance of heterogeneous SOC formation reactions at the suburban site. These results indicate that relevant local measures can be put into place to alleviate the occurrence of high PM_2.5 concentration episodes even in medium-sized residential cities where medium-and long-range transport is anticipated to be significant. Full article

The capability to estimate monsoon rainfall is investigated by using S-band polarimetric radar (S-POL) and two-dimensional Video Disdrometer (2DVD) during 2017–2018 in South China. Based on 2 years of 2DVD raindrop size distribution (DSD) observations of monsoon precipitation systems, four different quantitative precipitation estimation (QPE) algorithms were obtained, including R(Z_H), R(Z_H, Z_DR), R(K_DP), and R(K_DP, Z_DR). In order to clearly demarcate the optimal ranges of the four QPE algorithms by considering the impact of the monsoon precipitation system of South China, the optimal ranges of the four QPE algorithms were integrated together according to the characteristics of different QPE algorithms in the reflectivity-differential reflectivity (Z_H-Z_DR) space distribution by reference to 8 monsoon rainfall events from 2016 to 2020 observed in Guangzhou and Yangjiang S-POL. Then, an optimal algorithm was proposed for the quantitative estimation of monsoon precipitation in South China (2DVD-SCM) using S-POL. The 2DVD-SCM was tested by comparing it with a traditional radar QPE algorithm PPS (WSR-88D Precipitation Processing System); a classical QPE algorithm CSU-HIDRO (Colorado State University-Hydrometeor Identification Rainfall Optimization) for the polarimetric radar; a piecewise fitting algorithm LPA-PFM (Piecewise Fitting Method) based on laser raindrop spectrum. The rainfall event one-by-one test results show that the 2DVD-SCM algorithm performs obviously better than the other three algorithms in most of the rainfall events. The hourly accumulated rainfalls estimated by the 2DVD-SCM algorithm are agreed well with rain gauge observations. The normalized errors (NE) and the root mean square errors (RMSE) values of 2DVD-SCM are remarkably less than the other three algorithms, and the correlation coefficient (CC) values are higher. The results of the classified rain rate test show that the NE and RMSE values of the 2DVD-SCM algorithm are the lowest in all classified rain rates. The overall evaluation results show that the 2DVD-SCM algorithm performs obviously better than the existing three algorithms and have the potential to apply in S-band polarimetric radar monsoon rainfall estimation operational system in South China. Full article

Accurate, reliable estimates of tropical cyclone (TC) intensity are a crucial element in the warning and forecast process worldwide, and for the better part of 50 years, estimates made from geostationary satellite observations have been indispensable to forecasters for this purpose. One such method, the Advanced Dvorak Technique (ADT), was used to develop analog ensemble (AnEn) techniques that provide more precise estimates of TC intensity with instant access to information on the reliability of the estimate. The resulting methods, ADT-AnEn and ADT-based Error Analog Ensemble (ADTE-AnEn), were trained and tested using seventeen years of historical ADT intensity estimates using k-fold cross-validation with 10 folds. Using only two predictors, ADT-estimated current intensity (maximum wind speed) and TC center latitude, both AnEn techniques produced significant reductions in mean absolute error and bias for all TC intensity classes in the North Atlantic and for most intensity classes in the Eastern Pacific. The ADTE-AnEn performed better for extreme intensities in both basins (significantly so in the Eastern Pacific) and will be incorporated in the University of Wisconsin’s Cooperative Institute for Meteorological Satellite Studies (UW-CIMSS) workflow for further testing during operations in 2021. Full article

In this study, the results from the analysis of annual ring widths (‘Dm’) time series of two “very sensitive” to the climate and solar–climate relationships of long lived European beech (Fagus sylvatica) samples (on age of 209 ± 1 and 245 ± 5 years correspondingly) are discussed. Both series are characterized by very good expressed and relating to the solar magnetic Hale cycle 20–22-year oscillations. A good coincidence between the changes of ‘Dm’ and the growth or fading of the solar magnetic cycle is found. The transition effects at the beginning and ending of the grand Dalton (1793–1833) and Gleissberg minima (1898–1933) are very clearly visible in the annual tree ring width data for the one of beech samples. Some of these effects are also detected in the second sample. The problem for the possible “lost” sunspot cycle at the end of 18th century is also discussed. A prediction for a possible “phase catastrophe” during the future Zurich sunspot cycles 26 and 27 between 2035–2040 AD as well as for general precipitation upward and temperature fall tendencies in Central Bulgaria, more essential after 2030 AD, are brought forth. Full article

Estimation of Meteorological visibility from image characteristics is a challenging problem in the research of meteorological parameters estimation. Meteorological visibility can be used to indicate the weather transparency and this indicator is important for transport safety. This paper summarizes the outcomes of the experimental evaluation of a Particle Swarm Optimization (PSO) based transfer learning method for meteorological visibility estimation method. This paper proposes a modified approach of the transfer learning method for visibility estimation by using PSO feature selection. Image data are collected at fixed location with fixed viewing angle. The database images were gone through a pre-processing step of gray-averaging so as to provide information of static landmark objects for automatic extraction of effective regions from images. Effective regions are then extracted from image database and the image features are then extracted from the Neural Network. Subset of Image features are selected based on the Particle Swarming Optimization (PSO) methods to obtain the image feature vectors for each effective sub-region. The image feature vectors are then used to estimate the visibilities of the images by using the Multiple Support Vector Regression (SVR) models. Experimental results show that the proposed method can give an accuracy more than 90% for visibility estimation and the proposed method is effective and robust. Full article

Understanding the effects of weather and topography on fire spread in specific contexts, such as oceanic islands, is critical for supporting fire prevention and suppression strategies. In this study, we analyse the atmospheric conditions associated with historical forest fires that have occurred over complex terrain in Madeira Island, Portugal. The atmospheric Meso-NH model was used to identify the mesoscale environment during three forest fires events. The model was configured into two nested horizontal domains, the outer domain at 2.5 km resolution and the inner domain at 500 m. The paper brings a comprehensive analysis on the factors favouring the evolution of significant large fires occurring in Madeira Island in August 2010, July 2012 and August 2016. These fire events were selected because they are characterized by their large size (between 324.99 ha and 7691.67 ha) that expanded in a short-time period, threatening people and property in the wildland-urban interfaces. The study highlights that local terrain produce orographic effects that enhance the fire danger over the southern slope during typical summer atmospheric conditions. Full article

Antibiotic resistance in fungal isolates is increasing on a global scale. Despite knowledge that pig farmers are occupationally exposed to infectious species of fungi, such as Aspergillus spp., little is known regarding their potential exposure to antifungal-resistant Aspergillus spp. The aim of this study is to obtain knowledge regarding the antifungal resistance profiles of isolates of Aspergillus species taken from different source materials—including airborne dust, surface dust, faeces, and straw—within a pig farm. The EUCAST broth microdilution method was used for testing antifungal resistance from 43 isolates of Aspergillus sampled in 3 periods inside a Danish pig farm. Seven species of Aspergillus were obtained, including A. candidus (n = 5), A. fumigatus (n = 5), A. glaucus (n = 13), A. nidulans (n = 2), A. niger (n = 15), A. terreus (n = 1), and A. versicolor (n = 2). Overall, 27.9% of the Aspergillus isolates displayed resistance against at least one antifungal, and 11.6% of Aspergillus isolates displayed resistance against multiple antifungals. The most abundant group exhibiting antifungal resistance was affiliated with the species A. niger, with isolates exhibiting resistance to itraconazole, voriconazole, and caspofungin. One isolate of A. glaucus and two isolates of A. versicolor were resistant to amphotericin B (MIC ≥ 2 mg/L amphotericin B). Antibiotic-resistant fungi were found on all three sampling days. Full article