The changes of groundwater chemistry of semi-confined buried valley aquifer during one decade of water exploitation

This article presents the analysis of the temporal changes in water chemistry in a semi-confined aquifer (Wielkopolska Buried Valley aquifer, Poland) during one decade of water exploitation. It is shown that the groundwater contamination, as documented in a previous work, still persists and has lead to steady groundwater quality deterioration. The most intensive changes in water chemistry due to contamination are observed in the regions recognized earlier as the most vulnerable parts of the aquifer. The influence of contamination is effective despite implementation of groundwater protection activities. The travails of groundwater quality protection of the confined or semi-confined aquifers were accented.     

Stable 18O and 13C isotope records of Viviparus diluvianus (Kunth, 1865) shells from Holsteinian (MIS 11) lakes of eastern Poland as palaeoenvironmental and palaeoclimatic proxies

The oxygen and carbon stable isotope composition of Viviparus diluvianus shells was determined in the palaeolakes of the Holsteinian interglacial (MIS 11) in eastern Poland: the Ortel Królewski, Hrud, Ossówka, Roskosz and Szymanowo lakes. The occurrence of V. diluvianus covers the Taxus zone, the so‐called intra‐interglacial cooling period (Pinus‐Larix zone), the climatic optimum (Carpinus‐Abies zone) and the post‐optimal period with an undefined pollen zone. The isotope record of V. diluvianus shells allowed palaeoclimate and palaeoenvironmental reconstructions. δ¹⁸O and δ¹³C vary from −8.7‰ in the post‐optimal period to −4.8‰ in the optimal period, and from −10.6‰ at the beginning of the Taxus zone to −4.2‰ at the climatic optimum, respectively. A positive correlation of the isotope curves indicates closed‐water bodies, and only at Roskosz was some occasional overflow inferred. Higher oxygen isotopes correspond to an increase in temperature and/or enhanced evaporation, which is well marked in the Carpinus‐Abies zone, whereas samples enriched in ¹³C are correlated with periods demonstrating a lower water level and higher productivity in the lakes. Episodes of lake shallowing and dense aquatic vegetation occurred in the Pinus‐Larix zone and during the climatic optimum. The isotope ratios for the climatic optimum significantly exceed those of the other periods, thus pointing to warmer conditions. An estimation of relative changes of average summer temperatures using the δ¹⁸O/temperature gradient yielded variation of 1–2 °C within the lakes. Absolute temperature reconstructions indicate the effects of specific local conditions. Hence, V. diluvianus shells offer a reliable proxy in qualitative studies of interglacial lake records.     

A generalization of the fractal/facies model

In order to generalize the fractal/facies concept, a new stochastic fractal model for ln(K) increment probability density functions (PDFs) is presented that produces non-Gaussian behavior at smaller lags and converges to Gaussian at larger lags. The model is based on the classical Laplace PDF. The new stochastic fractal family is called fractional Laplace motion (fLam) having stationary increments called fractional Laplace noise (fLan). This fractal is different from other fractals because the character of the underlying increment PDFs changes dramatically with lag size, which leads to lack of self-similarity. Data also appear to display this characteristic. In the larger lag size ranges, approximate self-affinity does hold. The basic field procedure for further testing of the fractional Laplace theory is to measure ln(K) increment distributions along transects, calculate frequency distributions from the data, and compare results to appropriate fLan family members. The variances of the frequency distributions should also change with lag size (scale) in a prescribed manner. There are mathematical reasons such as the geometric central limit theorem, for surmising that fLam/fLan may be more fundamental than other approaches that have been proposed for modeling ln(K) frequency distributions.     

Thylacocephalans

Thylacocephalans are an extinct group of arthropods of an uncertain systematic position. Originally considered phyllocarid crustaceans, they have since been classified within their own class, the Thylacocephala Pinna, Arduini, Pesarini and Teruzzi, 1982 on the basis of the exceptionally preserved Lower Jurassic (Sinemurian) species Ostenocaris cypriformis from Osteno, Italy. Since that time, the membership of the Thylacocephala has grown as a number of new species have been discovered, as well as previously known species moved into the group.     

Measurement of aerosol optical thickness over the Atlantic Ocean and in West Antarctica, 2006–2007

In this paper, optical measurements of aerosol properties made during a ship cruise from Poland to Antarctic Station in September and October 2006, and during the cruise back to Gdynia in April and May 2007 are described. A large gradient of pollution between the clear South Atlantic and the dusty North Atlantic was observed. The maximum of aerosol optical thickness at a wavelength of 500 nm reached 0.4 at 20°N in September 2006 and 0.3 at 40°N in May 2007, respectively. Strong Saharan dust transport is suggested as an explanation for the small values of Ångström exponent observed (values of 0.2 and 0.4 on these respective dates). On the Southern Hemisphere the aerosol optical thickness at 500 nm ranged from 0.05 to 0.2. Significant increases of the aerosol optical thickness were associated with strong wind and sea salt production. Good agreement was found when the in situ measurements of aerosol optical thickness were compared to satellite retrievals and modelling results.     

ADCN: An anisotropic density‐based clustering algorithm for discovering spatial point patterns with noise

Density‐based clustering algorithms such as DBSCAN have been widely used for spatial knowledge discovery as they offer several key advantages compared with other clustering algorithms. They can discover clusters with arbitrary shapes, are robust to noise, and do not require prior knowledge (or estimation) of the number of clusters. The idea of using a scan circle centered at each point with a search radius Eps to find at least MinPts points as a criterion for deriving local density is easily understandable and sufficient for exploring isotropic spatial point patterns. However, there are many cases that cannot be adequately captured this way, particularly if they involve linear features or shapes with a continuously changing density, such as a spiral. In such cases, DBSCAN tends to either create an increasing number of small clusters or add noise points into large clusters. Therefore, in this article, we propose a novel anisotropic density‐based clustering algorithm (ADCN). To motivate our work, we introduce synthetic and real‐world cases that cannot be handled sufficiently by DBSCAN (or OPTICS). We then present our clustering algorithm and test it with a wide range of cases. We demonstrate that our algorithm can perform equally as well as DBSCAN in cases that do not benefit explicitly from an anisotropic perspective, and that it outperforms DBSCAN in cases that do. Finally, we show that our approach has the same time complexity as DBSCAN and OPTICS, namely O(n log n) when using a spatial index and O(n²) otherwise. We provide an implementation and test the runtime over multiple cases.     

Making direction a first‐class citizen of Tobler's first law of geography

Waldo Tobler frequently reminded us that the law named after him was nothing more than calling for exceptions. This article discusses one of these exceptions. Spatial relations between points are frequently modeled as vectors in which both distance and direction are of equal prominence. However, in Tobler's first law of geography, such a relation is described only from the perspective of distance by relating the decreasing similarity of observations in some attribute space to their increasing distance in geographic space. Although anisotropic versions of many geographic analysis techniques, such as directional semivariograms, anisotropy clustering, and anisotropic point pattern analysis, have been developed over the years, direction remains on the level of an afterthought. We argue that, compared to distance, directional information is still under‐explored and anisotropic techniques are substantially less frequently applied in everyday GIS analysis. Commonly, when classical spatial autocorrelation indicators, such as Moran's I, are used to understand a spatial pattern, the weight matrix is only built from distance, without direction being considered. Similarly, GIS operations, such as buffering, do not take direction into account either, with distance in all directions being treated equally. In reality, meanwhile, particularly in urban structures and when processes are driven by the underlying physical geography, direction plays an essential role. In this article we ask whether the development of early GIS, data (sample) sparsity, and Tobler's law lead to a theory‐induced blindness for the role of direction. If so, is it possible to envision direction becoming a first‐class citizen of equal importance to distance instead of being an afterthought only considered when the deviation from a perfect circle becomes too obvious to be ignored?     

Traffic transformer: Capturing the continuity and periodicity of time series for traffic forecasting

Traffic forecasting is a challenging problem due to the complexity of jointly modeling spatio‐temporal dependencies at different scales. Recently, several hybrid deep learning models have been developed to capture such dependencies. These approaches typically utilize convolutional neural networks or graph neural networks (GNNs) to model spatial dependency and leverage recurrent neural networks (RNNs) to learn temporal dependency. However, RNNs are only able to capture sequential information in the time series, while being incapable of modeling their periodicity (e.g., weekly patterns). Moreover, RNNs are difficult to parallelize, making training and prediction less efficient. In this work we propose a novel deep learning architecture called Traffic Transformer to capture the continuity and periodicity of time series and to model spatial dependency. Our work takes inspiration from Google’s Transformer framework for machine translation. We conduct extensive experiments on two real‐world traffic data sets, and the results demonstrate that our model outperforms baseline models by a substantial margin.     

Validation of Galileo orbits using SLR with a focus on satellites launched into incorrect orbital planes

The space segment of the European Global Navigation Satellite System (GNSS) Galileo consists of In-Orbit Validation (IOV) and Full Operational Capability (FOC) spacecraft. The first pair of FOC satellites was launched into an incorrect, highly eccentric orbital plane with a lower than nominal inclination angle. All Galileo satellites are equipped with satellite laser ranging (SLR) retroreflectors which allow, for example, for the assessment of the orbit quality or for the SLR–GNSS co-location in space. The number of SLR observations to Galileo satellites has been continuously increasing thanks to a series of intensive campaigns devoted to SLR tracking of GNSS satellites initiated by the International Laser Ranging Service. This paper assesses systematic effects and quality of Galileo orbits using SLR data with a main focus on Galileo satellites launched into incorrect orbits. We compare the SLR observations with respect to microwave-based Galileo orbits generated by the Center for Orbit Determination in Europe (CODE) in the framework of the International GNSS Service Multi-GNSS Experiment for the period 2014.0–2016.5. We analyze the SLR signature effect, which is characterized by the dependency of SLR residuals with respect to various incidence angles of laser beams for stations equipped with single-photon and multi-photon detectors. Surprisingly, the CODE orbit quality of satellites in the incorrect orbital planes is not worse than that of nominal FOC and IOV orbits. The RMS of SLR residuals is even lower by 5.0 and 1.5 mm for satellites in the incorrect orbital planes than for FOC and IOV satellites, respectively. The mean SLR offsets equal \(-44.9, -35.0\), and \(-22.4\) mm for IOV, FOC, and satellites in the incorrect orbital plane. Finally, we found that the empirical orbit models, which were originally designed for precise orbit determination of GNSS satellites in circular orbits, provide fully appropriate results also for highly eccentric orbits with variable linear and angular velocities.