Salinity-Based Spatial Evaluation of Groundwater Quality for Agricultural Use

The Miandoab Plain has the largest water reserve in the province of West Azerbaijan, northwest Iran. Groundwater resources along with surface-water meet the needs of urban, industrial, and agricultural sectors, and therefore, their quality should be examined. Water quality indices are useful tools for aquifer management. In this research, the groundwater quality of the Miandoab Plain for agricultural purposes was investigated. For this purpose, the concentrations of the ions Mg2+, Ca2+, Na+, Hco3-, So42-, Cl- and the pH level were measured. The indices effective salinity and potential salinity as well as sodium adsorption ratio and electrical conductivity were analyzed to evaluate the salinity. The geostatistical analysis was performed using the GS+ software, and the zoning maps of salinity hazard were prepared using ArcGIS. To prepare the maps, EC, ES, PS, and SAR as well as Mg2+, Ca2+, Na+, Hco3-, So42, and Cl- were selected based on the semi-variogram values ​​and cross-validation technique. The Cl- map was considered as the basis for preparing the groundwater quality maps of the region. The results showed that the groundwater quality in the east of the plain is suitable, in the central part can be recommended under constant supervision, and in the west is unsuitable for agriculture. In other words, according to the geography of the plain, the recharge area is the low-risk part of the plain and the salinity hazard increases toward the discharge area. The results can pave the way for the relevant organizations to plan for the agricultural and environmental sectors.

Supervised Learning With Perceptual Similarity for Multimodal Gene Expression Registration of a Mouse Brain Atlas

The acquisition of high quality maps of gene expression in the rodent brain is of fundamental importance to the neuroscience community. The generation of such datasets relies on registering individual gene expression images to a reference volume, a task encumbered by the diversity of staining techniques employed, and by deformations and artifacts in the soft tissue. Recently, deep learning models have garnered particular interest as a viable alternative to traditional intensity-based algorithms for image registration. In this work, we propose a supervised learning model for general multimodal 2D registration tasks, trained with a perceptual similarity loss on a dataset labeled by a human expert and augmented by synthetic local deformations. We demonstrate the results of our approach on the Allen Mouse Brain Atlas (AMBA), comprising whole brain Nissl and gene expression stains. We show that our framework and design of the loss function result in accurate and smooth predictions. Our model is able to generalize to unseen gene expressions and coronal sections, outperforming traditional intensity-based approaches in aligning complex brain structures.

Maps of a Nation? The Digitized Ordnance Survey for New Historical Research

Although the Ordnance Survey has itself been the subject of historical research, scholars have not systematically used its maps as primary sources of information. This is partly for disciplinary reasons and partly for the technical reason that high-quality maps have not until recently been available digitally, geo-referenced, and in color. A final, and crucial, addition has been the creation of item-level metadata which allows map collections to become corpora which can for the first time be interrogated en masse as source material. By applying new Computer Vision methods leveraging machine learning, we outline a research pipeline for working with thousands (rather than a handful) of maps at once, which enables new forms of historical inquiry based on spatial analysis. Our ‘patchwork method’ draws on the longstanding desire to adopt an overall or ‘complete’ view of a territory, and in so doing highlights certain parallels between the situation faced by today’s users of digitized maps, and a similar inflexion point faced by their predecessors in the nineteenth century, as the project to map the nation approached a form of completion.

Addressing Missing Environmental Data via a Machine Learning Scheme

An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air-quality-monitoring networks that operate in urban areas provide data on the most important pollutants, which, via environmental statistics, can be used for the development of continuous surfaces of pollutants’ concentrations. Generating ambient air-quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air-quality distributions. This study used point-monitoring data from the network of stations that operates in Athens, Greece. A machine-learning scheme was applied as a method to spatially estimate pollutants’ concentrations, and the results can be effectively used to implement missing values and provide representative data for statistical analyses purposes.

Mapping surface water quality in Myanmar using aquatic drones

<p>Water resources in Myanmar are increasingly affected by anthropogenic pressure and climate change related impacts. At the Inle Lake a unique village is located on the water in close proximity to intense fishing/farming activities. The nearby floating gardens provide invaluable resources for local communities, who are highly vulnerable to changes to water quality in the lake. Diversely, within the city of Yangon, the Kandawgyi lake is a popular recreational area which has become heavily affected by excessive algae proliferation. The deterioration of water quality Is likely caused by uncontrolled untreated wastewater, and poses a risk to the citizens. Finally, rivers such as the Pan Hlaing River, flow through industrial zones and collect waste water discharges.</p><p>Monitoring in these regions is scarce and limited to a few point-sampling locations. Local stakeholders lack adequate tools to monitor the needed parameters and are in need of reliable and updated baseline water quality data to support them in setting-up sustainable water management strategies. Tools such as aquatic drones and in-situ sensors are innovative ways of monitoring water quality and ecology that could contribute for effectively gathering valuable environmental data.</p><p>In this project, aquatic drones (both underwater and surface) were equipped with water quality sensors and cameras for low-cost and rapid assessment of surface water quality at high spatial resolution. The drones are able to navigate autonomously through way-points while collecting geo-referenced data. This study aims at field-testing of two affordable aquatic drones with sensors to map water quality parameters in different types of water systems (large lake, urban lake, river). This study reports the challenges encountered, and evaluates the resulting dataset/maps are in relation to the cost and value for the local stakeholders (ongoing research).</p><p>At the Inle Lake, results show varying concentrations of the different parameters that were measured. Low dissolved oxygen levels were found within the villages and underneath floating gardens, while chlorophyll-a and cyanobacteria levels were low across the whole lake. Underwater images show the presence of fish and provide insights into the aquatic ecosystems. At the Kandawgyi Lake, the generated water quality maps illustrate the spatial distribution of the different parameters, and two main areas of contamination could be identified (high algae content, low dissolved oxygen, high E-coli concentrations). At the Pan Hlaing river, the plotted data show degrading levels of dissolved oxygen concentrations, indicating potential effects caused by industry outlets.</p><p>The water quality maps that were generated with this data are very illustrative of the condition of the water bodies and the location of contaminations hotspots. The measurement process was accompanied by stakeholders and local universities, which contributed to stimulate capacity building and to create awareness for water quality related problems. As follow-up activities, these results will be used to draft a long-term water quality monitoring plan for local Myanmar students to continue collecting water quality data at these lakes. The detected issues are being discussed with local stakeholders, as well as the possibilities for establishing a larger scale monitoring campaign using this type of monitoring tools.</p>

Large-Area Empirically Based Visual Landscape Quality Assessment for Spatial Planning—A Validation Approach by Method Triangulation

Large area visual landscape quality assessment, especially at the national level is needed to answer the demand from strategic planning. In our paper, we describe and compare two recent modelling approaches for this task regarding their theoretical and empirical basis, resolution, model configuration and results. To compare the outcomes of the two methods, both correlation measures and a visual overlay analysing the inversions are used. The results show, that despite the different methodological approaches, in over 90% of the area of Germany there are only minor deviations between the resulting scenic quality maps (less or equal one step on a five-step scale). The main differences occur due to a different relative weight given to terrain and water indicators in the respective methods. We conclude that a methodologically valid scenic quality evaluation using geodata of homogenous quality is possible also at the national level. By triangulating between different methods, for both, the validity could be proven. The datasets elaborated can also be used as a benchmark for regional landscape assessments and for an upcoming monitoring of changes in visual landscape quality.

Application of a Machine Learning Methodology for Data Implementation

An important aspect in environmental sciences is the study of air quality, using statistical methods (environmental statistics) which utilize large datasets of climatic parameters. The air quality monitoring networks that operate in urban areas provide data on the most important pollutants, which via environmental statistics can be used for the development of continuous surfaces of pollutants’ concentrations. Generating ambient air quality maps can help guide policy makers and researchers to formulate measures to minimize the adverse effects. The information needed for a mapping application can be obtained by employing spatial interpolation methods to the available data, for generating estimations of air quality distributions. This study used point monitoring data from the network of stations that operates in Athens. A machine learning scheme was applied as a method to spatially estimate pollutants’ concentrations and the results could be effectively used to implement missing values and provide representative data for statistical analyses purposes.

Microbiological and Physicochemical Assessment of Artisanally Produced “Alheira” Fermented Sausages in Northern Portugal

Alheira is a traditional non-ready-to-eat sausage produced mainly in northern Portugal. The objective of this study was to evaluate the associations between some relevant physicochemical and microbiological attributes of alheiras produced by different regional producers. Finished products from 8 regional factories amounting to 40 samples were analyzed. Counts of mesophiles, lactic acid bacteria, Staphylococcus aureus, presumptive Clostridium perfringens, and Salmonella spp., as well as pH, water activity (aW), and proximate analysis were determined. Principal component analysis (PCA) of these variables was conducted to construct quality maps. Three meaningful components were extracted, accounting for 63% of data variability. PC1 (26% data variability) was positively associated with LAB, mesophiles, and S. aureus, characterizing therefore longer fermentation. PC2 (22% data variability) correlated negatively with moisture, aW and positively with C. perfringens, and thus has been linked to greater dehydration of sausages. PC3 (15% data variability) correlated positively with pH and protein content, implying the use of more meat in the formulation. This preliminary work has identified three quality factors underpinning the variability in artisanal alheiras; and has also highlighted the need to implement better microbiological control and process standardization during the production of artisanal alheiras.

ASSESSMENT AND MAPPING OF DESERTIFICATION USING SOIL QUALITY INDICATORS FOR SOME PARTS OF IRAQ

This study was conducted to assess desertification for dry lands in some parts of Iraq. The study area located between longitudes 43025- 41" - 460 28- 01" E and latitudes 340 18- 35" - 360 20- 56" N with an area of 26500Km2which include some parts of the governorates of Sulaimani, Diyala, Kirkuk, and Erbil in Iraq. Eighty nine surface soil samples were taken, air dried, sieved through a 2 mm sieve and then analyzed for some physical and chemical properties. Desertification is assessed according to Mediterranean Desertification and Land Use model (MEDALUS). ArcGIS 10.2 was used to analyze and prepare the layers of soil quality maps. In turn the geometric mean of all six quality maps was used to generate a single desertification status map .In calculating the weight of the soil quality indicator SQI it seems that it was divided into two classes, firstly, class 2(moderate quality) with an area of 25147 km2, which occupied 95% of the study area and the rest is class3 (low quality) with an area of ​​1309 km 2 which equal to 5% of the total area.