Intense urbanisation, combined with climate change impacts such as increased rainfall intensity, is overloading conventional drainage systems, increasing the number of combined sewer overflow events and making treatment plants outdated. There is a need for better urban planning, incorporating stormwater and flood management design in order to accurately design urban drainage networks. Geographic Information System (GIS) tools are capable of identifying and delineating the runoff flow direction, as well as accurately defining small-sized urban catchments using geospatial data. This study explores the synergies between GIS and stormwater management design tools for better land-use planning, providing a new methodology which has the potential to incorporate hydraulic and hydrological calculations into the design of urban areas. From data collection to final results, only freely available software and open platforms have been used: the U.S. EPA Storm Water Management Model (SWMM), QGis, PostgreSQL, PostGIS, SagaGIS, and GrassGIS. Each of these tools alone cannot provide all the necessary functionalities for large-scale projects, but once linked to GISWATER, a unique, fast, efficient, and accurate work methodology results. A case study of a newly urbanised area in the city of Gijón (northern Spain) has been utilised to apply this new methodology.

This study addresses the growing interest in utilizing remote sensing tools for locating sustainable drainage systems (SuDS) in urban environments. SuDS, recognized as Nature-based Solutions (NbS), play a crucial role in enhancing urban resilience against climate change. This study focuses on the calibration process required to establish a correlation between the Topographic Wetness Index (TWI), derived from high-precision digital elevation models (DEMs), and soil moisture (SM) data obtained from satellite imaging. This calibration serves as a method to optimize the placement of sustainable urban drainage system vegetated techniques in urban areas. This study leveraged the exceptional resolution of PAZ satellite radar data to effectively detect variations in SM, particularly in grass-type vegetated land. The sensitivity of the X-band radar signal to moisture levels and changes in ground roughness proved valuable in tracking SM dynamics. The core of the study involved deriving the TWI from a high-resolution digital terrain model (DTM). The correlation between the TWI and SM values demonstrates robustness, with an R² value of 0.77. These findings significantly advance the calibration of TWI values with SM measurements, enhancing their practicality in identifying areas prone to water accumulation. The study’s outcomes provide valuable insights for guiding the strategic placement of SuDS in urban environments, contributing to the effective management of water-related challenges in the face of urbanization and climate change.

This article discusses the use of Near Infrared spectroscopy (NIR) combined with multivariate classification methods for detecting bacterial contamination in milk in the dairy industry. In a first experiment, the study found that Nir was accurate and reliable in detecting the presence of biofilms in milk. Our results showed that the technology was effective in distinguishing between contaminated and uncontaminated samples with an area under the ROC curve (AUC) greater than 99%. It was also effective in classifying the samples belonging to different strains. In a second experiment, we used the same methodology to assess their effectiveness in detecting bacterial contamination proportions in milk. Our results showed that the technology was effective in classifying milk samples contaminated with four different bacteria and uncontaminated controls with an AUC greater than 97%. Moreover, results were still good when data from all bacteria were analyzed together, even at low bacterial concentrations, obtaining an average precision of 70%. These results demonstrate the potential of this technology to be used as a rapid and accurate method for identifying bacterial contamination in the dairy sector.

Spain has been pinpointed as one of the European countries at major risk of extreme urban events. Thus, Spanish cities pursue new urban plans to increase their resilience. In this scenario, experiences in the implementation of Sustainable Urban Drainage Systems (SUDS) have increased substantially. Nevertheless, few cities have developed a global urban strategy for SUDS, lacking, in many cases, a method to identify strategic areas to maximize their synergetic benefits. Furthermore, there is still a need for a holistic Multicriteria Decision Analysis (MCDA) framework that considers the four pillars of SUDS design. The city of Gijón, NW Spain, has been selected as a case study due to its environmental and climatic stresses. This research presents the methodology developed for this city, which aims to analyze the need for SUDS implementation throughout the identification of strategic areas. With this aim, a combination of Geographic Information System (GIS) software and the MCDA Analytical Hierarchical Process (AHP) were proposed. The results show the potential for SUDS’ implementation, according to nine criteria related to the SUDS’ design pillars. We found that the areas where the implementation of SUDS would bring the greatest functional, environmental and social benefits are mainly located in consolidated urban areas.

Bacterial biofilms play an important role in the pathogenesis of infectious diseases but are also very relevant in other fields such as the food industry. This fact has led to an increased focus on the early identification of these structures as prophylaxes to prevent biofilm-related contaminations or infections. One of the objectives of the present study was to assess the effectiveness of NIR (Near Infrared) spectroscopy in the detection and differentiation of biofilms from different bacterial species, namely Staphylococcus epidermidis, Staphylococcus aureus, Enterococcus faecium, Salmonella Typhymurium, Escherichia coli, Listeria monocytogenes, and Lactiplantibacillus plantarum. Additionally, we aimed to examine the capability of this technology to specifically identify S. aureus biofilms on glass surfaces commonly used as storage containers and processing equipment. We developed a detailed methodology for data acquisition and processing that takes into consideration the biochemical composition of these biofilms. To improve the quality of the spectral data, SNV (Standard Normal Variate) and Savitzky–Golay filters were applied, which correct systematic variations and eliminate random noise, followed by an exploratory analysis that revealed significant spectral differences in the NIR range. Then, we performed principal component analysis (PCA) to reduce data dimensionality and, subsequently, a Random Forest discriminant statistical analysis was used to classify biofilms accurately and reliably. The samples were organized into two groups, a control set and a test set, for the purpose of performing a comparative analysis. Model validation yielded an accuracy of 80.00% in the first analysis (detection and differentiation of biofilm) and 93.75% in the second (identification of biofilm on glass surfaces), thus demonstrating the efficacy of the proposed method. These results demonstrate that this technique is effective and reliable, indicating great potential for its application in the field of biofilm detection.