Challenges and progresses in the detailed estimation of sediment export in agricultural watersheds in Navarra (Spain) after two decades of experience.

Soil erosion is a very serious environmental problem worldwide, with agriculture considered the main source of sediment in inland waters. In order to determine the extent and importance of soil erosion in the Spanish region of Navarra, in 1995 the Government of Navarra established the Network of Experimental Agricultural Watersheds (NEAWGN), which consists of five small watersheds representative of local conditions. In each watershed, key hydrometeorological variables, including turbidity, were recorded every 10 min, and daily samples were taken to determine suspended sediment concentration. In 2006, the frequency of suspended sediment sampling was increased during hydrologically relevant events. The main objective of this study is to explore the possibility of obtaining long and accurate time series of suspended sediment concentration in the NEAWGN. To this end, simple linear regressions between sediment concentration and turbidity are proposed. In addition, supervised learning models incorporating a larger number of predictive variables are used for the same purpose. A series of indicators are proposed to objectively characterize the intensity and timing of sampling. It was not possible to obtain a satisfactory model for estimating the concentration of suspended sediment. This would be mainly due to the large temporal variability found of the physical and mineralogical characteristics of the sediment, which would be affecting the turbidity value, independently of the sediment concentration, per se. This fact would be particularly important in small river watersheds such as those of this study, and especially if their physical conditions are spatially and temporally radically disturbed by agricultural tillage and by a constant modification of the vegetation cover, as is the case in cereal basins. Our findings suggest that better results could be obtained by including in the analysis variables such as soil texture and exported sediment texture, rainfall erosivity, and the state of vegetation cover and riparian vegetation.

Intelligent Localization Sampling System Based on Deep Learning and Image Processing Technology.

In this paper, deep learning and image processing technologies are combined, and an automatic sampling robot is proposed that can completely replace the manual method in the three-dimensional space when used for the autonomous location of sampling points. It can also achieve good localization accuracy, which solves the problems of the high labor intensity, low efficiency, and poor scientific accuracy of the manual sampling of mineral powder. To improve localization accuracy and eliminate non-linear image distortion due to wide-angle lenses, distortion correction was applied to the captured images. We solved the problem of low detection accuracy in some scenes of Single Shot MultiBox Detector (SSD) through data augmentation. A visual localization model has been established, and the image coordinates of the sampling point have been determined through color screening, image segmentation, and connected body feature screening, while coordinate conversion has been performed to complete the spatial localization of the sampling point, guiding the robot in performing accurate sampling. Field experiments were conducted to validate the intelligent sampling robot, which showed that the maximum visual positioning error of the robot is 36 mm in the x-direction and 24 mm in the y-direction, both of which meet the error range of less than or equal to 50 mm, and could meet the technical standards and requirements of industrial sampling localization accuracy.

Adaptive monitoring approach to assess dissolved organic matter dynamics during rainfall events.

Rainfall events induce water quality transformation in river systems influenced by the watershed land use and hydrology dynamics. In this context, an adaptive monitoring approach (AMA) is used to assess non-point sources (NPS) of pollution events, through dissolved organic matter (DOM) contribution. The case study is a monitoring site in a semi-urban watershed characterized by NPS contribution. An integrated quali-quantitative method for DOM based on dissolved organic carbon (DOC) content, spectroscopic techniques of excitation-emission fluorescence (EEF), and UV-visible absorbance is proposed. The results indicate a mix of allochthonous and autochthonous DOM characteristics from NPS sources associated to vegetation area influence (A285/DOC of 15.43 L (g cm)-1 and SUVA254 of 2.11 L (mg m)-1). The EEF signals showed more humic-like than protein-like characteristics with peaks A and C (approximately 5.72 r.u.) more intense than peaks B, T1, and T2 (approximately 4.33 r.u.), indicating NPS from the soil leachate. The absorbance ratio values indicate a mix of organic compounds with greater proportion of refractory characteristics with high aromaticity and molecular weight (approximately A300/A400 of 4.15 and A250/A365 of 4.48), associated with the surface wash-off of accumulated residual and subsurface soil erosion, which contribute to complex organic matter structures. The fluorescence indexes, overall, indicated allochthonous sources with intermediate humic characteristics (FI ≈ 1.43, BIX ≈ 0.65, and HIX ≈ 7.98). The proposed integrated optical property strategy represents an opportunity for better understanding of DOM dynamic assessment for identifying potential mitigation techniques for organic pollution control and improving water quality conditions.

Water quality dynamic during rainfall episodes: integrated approach to assess diffuse pollution using automatic sampling.

Diffuse pollution caused by rainfall events potentially affects water quality in rivers and, therefore, must be investigated in order to improve water quality planning and management recovery strategies. For these, a quali-quantitative approach was used to monitor the water quality parameters in a river located in a semi-urban watershed area based upon automatic sampling. Thirteen water quality parameters were measured during five rainfall events. Events ranged from 2.3 to 56.8 mm and water peak flows from 3.3 to 4.5 m3/s. The pollutographs measured showed a standard pattern for total suspended solids (TSS). However, for the other chemical parameters, as total phosphorous (TP) and dissolved organic carbon (DOC), the dilution effects were more evident. It was possible to observe the rainfall influence mainly for physical parameters indicating a mass transport pattern for diffuse pollutants, which increased, for example, the amount of TSS in the river. Furthermore, hydrological characteristics were relevant considering the pollutant behavior. Antecedent dry periods, ranging from 1.3 days to 21.4 days, and critical time, from 2.0 to 10.4 h, are determinants to evaluate non-traditional water quality impacts in the river. In general, each rainfall episode has its own characteristics, which produces distinct mass contribution and temporal behavior, being challenging in making generalization. Therefore, the results indicate that diffuse pollution has to be considered to establish future decision-making strategies to water resources management.

Microplastics in urban water cycles: Looking for a more scientific approach for sampling and characterization in wastewater and drinking water treatment plants.

Specific campaigns to detect microplastics (MPs) in the urban water cycle were carried out in three drinking water plants and two wastewater treatment plants. A self-designed sampler for MPs detection in water matrices was in this study preliminary validated and then tested in long term campaigns sampling up to 1000 L. Raw drinking water and wastewater show microplastics (MPs) concentrations of 2-11 and of 480-801 MPs/m3, respectively, and MPs removals of 47-78 % and of 84-98 %, correspondingly. Specific roles of chemical and physical conventional processes in microplastics removals were investigated. Solid-liquid separation, flotation and filtration are the main processes for achieving high microplastics removal. Regarding concentrated matrices, MPs concentrations in sludge samples varied in the range of 5000-500,000 MPs/m3. Finally, shapes, size classes and polymers' typologies were investigated in the extracted MPs. The detected sizes are mainly 0.5-0.1 mm in drinking waters while 5-1 mm in wastewaters. Wastewaters were predominated by synthetic fibers (polyester type), while drinking waters were mainly characterized by fragments and the fibers were mostly of natural origin. Finally, the results of this study supported best practices and guidelines for a representative assessment of MPs in water (sampling methods, extraction procedures, characterization and quantification).

Novel combined crystallization plate for high-throughput crystal screening and in situ data collection at a crystallography beamline.

In situ microplates are small in size, crystal cultivation and operation are difficult, and the efficiency of crystal screening is relatively low. To solve this problem, a novel combined crystallization plate was designed for high-throughput crystal cultivation and in situ data collection. A frame was used to hold 48 in situ microplates, and the in situ microplates were sealed on one side with an ultralow background-scattering Kapton film. An automatic liquid handler (Mosquito) was used to add a liquid drop to the in situ microplates in the frame, and CrystalClear HD tape was used to seal the frame. A sealed frame holding 48 microplates was developed as a novel combined crystallization plate and was used for crystal cultivation under different conditions and in situ data collection at the synchrotron beamline. Moreover, individual microplates can be separated from the combined crystal plate and then fixed on a magnetic base or loaded onto a UniPuck for in situ data collection. Automatic grid scanning was used to locate crystals. The efficiency of the combined crystallization plate for crystal screening was verified. This method avoids the manual manipulation of crystals during crystal screening and diffraction data collection; therefore, the combined crystallization plate is suitable for large-scale screening of microcrystals.

BoSL FAL pump: A small, low-cost, easily constructed, 3D-printed peristaltic pump for sampling of waters.

Water sampling is an essential undertaking for water utilities and agencies to protect and enhance our natural resources. The high variability in water quality, however, often necessitates a spatially distributed sampling program which is impeded by high-cost and large sampling devices. This paper presents the BoSL FAL Pump - a low-cost, easily constructed, 3D-printed peristaltic pump which can be made from commonly available components and is sized to suit even the most space constrained installations. The pump is 38 mm in height and 28 mm in diameter, its components cost $19 AUD and the construction time is just 12 min (excluding 3D printing times). The pump is driven by a direct current motor which is commonly available, cheap and allows for flexibility in the energy supply (5-12 V). Optionally, the pump has a Hall effect sensor and magnet to detect rotation rates and pumping volumes to improve the accuracy of pumping rates/volumes. The pump can be easily controlled by commonly available microcontrollers, as demonstrated by this paper which implements the ATmega328P on the Arduino Uno R3. This paper validates the pump for long-term deployments at flow rates of up to 13 mL per minute in 0.14 mL volume increments at accuracy levels of greater than 99%. The pump itself is scalable, allowing for a wider range of pumping rates when, for example, large volume samples are required for pathogen and micropollutant detection.