Contributors: Hichem Omrani, Bilel Omrani, Benoit Parmentier, Marco Helbich
... Monitoring of air pollution is an important task in public health. Availability of data is often hindered by the paucity of the ground monitoring station network. We present here a new spatio-temporal dataset collected and processed from the Sentinel-5P remote sensing platform aiming at the monitoring of air pollution for public institutions. As an example application, we applied the full workflow to process measurements of Nitrogen dioxide (NO2) collected over the territory of mainland France from May 2018 to June 2019. The data stack generated is daily measurements at a 4×7km spatial resolution. The supplementary code package used to collect and process the data is made publicly available to ease the access and processing for any location and product. The dataset provided in this article is of value for policy-makers and health assessment. Please find the full dataset in a Dropbox shared repository using this link: https://drive.google.com/drive/folders/1t5vbQq1g0LtJa37Sc6NYoq45bkLP2EWp?usp=sharing The raw data file is zipped to save disk space. The original raw data have a size of 60 Gigabyte
Utilizing Indicator Kriging to Identify Suitable Zones for Manual Drilling in Weathered Crystalline Basement Aquifers
Contributors: Philip Deal
... Manual drilling offers a practical and affordable method of increasing access to groundwater supply in regions struggling with economic water scarcity. However, manual techniques are limited to specific hydrogeological contexts and must be sited appropriately. Indicator kriging is proposed as an interpolation method that builds upon previous efforts to identify suitable zones for manual drilling, particularly in weathered crystalline basement aquifers. This approach allows for heterogeneity within weathering profiles and provides probability mapping of success for regional planning. Modeling was conducted in the Upper East Region of Ghana using available borehole-log data, including: transmissivity, static water depth, laterite thickness, depth to hard rock, water quality parameters, and the degree of weathering. Indicator kriging interpolations predicted binary variables with over 90% accuracy. The model predicts that drilling into highly weathered layers will be common, and percussion techniques will be necessary to reach sufficient depths. The results suggest that suitable zones occur near Bolgatanga, Bawku, and Zebila, which coincide with historical drilling efforts in the central and eastern portions of the region. The original dataset was derived from the Hydrogeological Assessment of the Northern Regions of Ghana Project (HAP) implemented by SNC-Lavalin, Institut national de Recherche Scientifique (INRS) and the Water Resources Comission (WRC) of Ghana, and was supported by the Canadian International Development Agency. Hydrogeological data was collected and aggregated for the Voltaian Sedimentary Basin and Precambrian Basement complexes in Ghana from numerous sources. The data was compiled into a GIS databased for further study and analysis of the groundwater resources in Ghana. For this study, the dataset was obtained from the University of Ghana upon request with a focus on manual drilling feasibility. Borehole records were manipulated with various interpolation methods within the Upper East Region in ArcGIS, as described within the journal article.
Contributors: Tetsuji Okada
... DSA files of human (N to Z, by gene name) : UniProt ID is used for a protein to which no gene name is assigned.
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Contributors: Tetsuji Okada
... DSA files of human (A to M, by gene name) : UniProt ID is used for a protein to which no gene name is assigned.
Contributors: Tetsuji Okada
... DSA files of E. coli (A to M, by gene name) UniProt ID is used for a protein to which no gene name is assigned.
Contributors: Chunli Dai
... Here are the results in a paper entitled "Characterization of the 2008 phreatomagmatic eruption of Okmok from ArcticDEM and InSAR: deposition, erosion, and deformation" submitted to JGR Solid Earth in 2019. It includes the 2-m resolution surface elevation change of the 2008 Okmok eruption (Fig. 2a in the paper) and the 2-m resolution post-eruptive elevation change rate map (Fig. 3), as well as the corresponding uncertainties (Fig. S3). It also includes the boundary of the proximal deposit field classified using a minimum elevation increase of 2 m, the boundary of large slope failure, and the shorelines of two lakes (Figs. 2a, S5, and S6) at different acquisition times. The GeoTIFF files can be viewed in free and open-source software QGIS, in Google Earth, or by Matlab using code https://github.com/ihowat/setsm_postprocessing/blob/master/readGeotiff.m. The shapefiles can be viewed in QGIS and Google Earth.
Contributors: Raúl Roberto poppiel, Marilusa Pinto Coelho Lacerda, José Lucas Safanelli, Rodnei Rizzo, Manuel Pereira de Oliveira junior, Jean Jesus Novais, Jose Alexandre Dematte
... Maps of clay, silt and sand contents (g kg-1) were predicted at 0-20 cm, 20-60 cm and 60-100 cm depths intervals by random forest regression in Google Earth Engine. Gridded soil information covers a part of the Midwest Brazil, from 12° S to 20° S and from 45° W to 54° W, and is available with 250m resolution. The maps were cross-validated and had Coefficient of Determination ranging from 0.64 to 0.85 at all depth intervals.
Contributors: Qiankun Liu, Jingang Jiang, Changwei Jing, Zhong Liu, Jiaguo Qi
... In this paper, a new, alternative, multi-scale, multi-pollution source waste load allocation (WLA) system was developed, with a goal to produce optimal, fair quota allocations at multiple scales. The new WLA system integrates multi-constrained environmental Gini coefficients (EGCs) and Delphi-analytic hierarchy process (Delphi-AHP) optimization models to achieve the stated goal. This dataset consists of the raw data and the source code of models (The multi-constrained environmental Gini coefficients and Delphi-analytic hierarchy process optimization models). The source code of the multi-constrained EGCs and Delphi-AHP models was used to run the program in MATLAB environment to allocate waste load reduction quotas at both the regional scale and the site-specific scale with multiple pollution sources. The raw data mainly consists of the following two parts: (1) The shp files of various geographic information data, which was used to depicture the administrative divisions, pollution source distribution, geographical characteristics and patterns of Xian-jiang watershed; (2) The basic data includes the statistical yearbook data of villages and towns in Ningbo city, the various indicator data using to calculate the weights at criteria level and decision-making level, the contribution coefficients, and the EGC values of the three pollutants. On the basis of these data, a new, alternative, multi-scale, multi-sector optimal WLA framework was developed. The new scheme provides decision-makers critical information (i.e., the best compromise solutions of WLA) and practical guidance as they address the related water pollution control. The results, in comparison with existing practices by the local governments, suggested that the pollution discharge quota at regional scale is much fairer than the existing WLA and, even have some environmental economic benefits at pollutant source scale after optimal WLA. Some important conclusions had been found: 1) Reductions and proportions of pollutants at regional scale are significantly associated with the region’s actual socioeconomic development modes. 2）There are certain characteristics that high-reduced pollution sources tend to share (which are listed in the article). The sources with the above features should be the top priorities in the reduction of removals. 3）Most previous studies reported primarily on the WLA of removals among point sources pollution. Conversely, we found that the industrial pollution source should be the last option for reduction from an environmental-economic benefit perspective. Instead, the often overlooked types, such as agricultural non-point source and domestic sources, deserve more attention, especially in extensive rural areas.
Contributors: Tongtong Wang, Yuankun Luo, Zhilin Tao, Weijie Chen, Xin Gu
... The zip file contains project files, screenshots of research results, chart data, experimental data, simulation data, and grid independence verification data.
Contributors: Olanrewaju Lawal
... Exposure capture factors which could be manifested in the magnitude and intensity of long-term changes in climate (Intergovernmental Panel on Climate Change, 2007) and in this context factors with impact on agricultural production. Temperature and rainfall were used to capture the extent to which Maize is exposed to climate change. Data was sourced from the Centre for Environmental Data Analysis (CRU TS release 4), with data extracted for 1941 - 2015. The data were processed within R (Version 3.4.2), within this environment the mean (temperature and rainfall) for northern and southern parts of the country were computed. The growing season for Maize in the north spans from May to September while in the south it starts from March and ends in August (FAO, 2018). Furthermore, long (1941 – 2015) and short (1961 – 2015) term averages for the respective growing season were computed for each of the regions. Following the computation of the long and short-term averages, exposure was computed as the ratio of the long-term to the short-term averages. With exposure index for rainfall and temperature computed separately, the two were added to get the combined exposure index. A high value indicates high exposure to climate variability. In this dataset, the exposure index is presented in raster format (Geotiff) to allow for easy processing across GIS software. In addition, the boundaries of the northern and the southern regions were also included as shapefiles.