GIS AND STATISTICAL EVALUATION OF SOIL QUALITY OF SIDI YAHYA, GHARBPLAIN (MOROCCO)

The study of soil quality in irrigated areas is necessary to evaluate the sustainability of the agricultural production system. Indeed, the assessment of this quality is based on the physicochemical and biological characterization of soil parameters, as well as the knowledge of their spatial distribution and their evolution over time. This work aims to make a diagnosis of the current situation of soil quality of SidiYahya in the Gharb plain, Morocco. For this, sampling was carried out from 33 sites distributed over the studied plain during 2019. In this study, different soil properties including specifically texture, pH, electrical conductivity (EC), organic matter (OM), phosphorus (P2O5), and potassium (K2O) were measured while exchangeable sodium percentage (ESP) was calculated using the standard formula. Based on the observed soil properties a map was prepared by using a geographic information system (GIS), which was based specifically on the inverse distance weighted (IDW) spatial interpolation method. Data were processed using different statistical tools like descriptive statistics, correlation, and principal component analysis (PCA). Results of the study revealed that 70% of the soils have a heavy clayey texture with a predominance of vertisols (55%). Further, the study area soil is mainly alkaline (70%), poor in organic matter (61%) and phosphorus (52%), while very rich in potassium (70%), and non-saline (88%) contents. Soil pH was reported to be the least variable whereas sand, phosphorus, and salinity were the highest variable. IDW allowed mapping the soil properties by moving from punctual information to whole extent information. Furthermore, correlations were found between various soil properties by using PCA, 3 principal components (PCs) were able to extract 76% of the information from the 9 initial soil properties. Collected soil samples were grouped into 3 groups, based on their scores on the 3 PCs. Based on these two kinds of information, delineation of management zones can be established for a site-specific supply of agricultural inputs leading to better management of soil and water resources for securing their sustainable use.

Identifying the best spatial interpolation method for estimating spatial distribution of PM2.5 in Jakarta

Nowadays, many researchers are focused on analyzing the association between PM2.5 concentration and respiratory diseases. PM2.5 is one of the most threatening air pollutant for human health in cities and causes an increasing number of deaths. However, obtaining detailed PM2.5 concentration data constitutes one of the problems in analyzing its relationship with the human health effect. This study aims to select the best model for predicting PM2.5, spatially explicit in Jakarta, and estimate its spatial distribution in this region over the 2019-2020 period. The observation data of PM2.5 measurement results were in eight points spread across Jakarta. Furthermore, the data is a two-year daily time series from 2019-2020, which was then be processed into annual average data. Seven spatial interpolations of different methods were selected to identify which is most realistic in generating the estimated concentration value of PM2.5. From the results, we conclude that the Spline with Tension was the best interpolation method based on 2D visualization and model evaluation. Based on the model evaluation, the Spline with Tension method generated the best model with minimum error, where RMSE, MSE, MAE, and MAP had values of 0.0533,0.0028, 0.0400, 0.0008, respectively. Meanwhile, Ordinary Kriging with spherical had the most significant.

Spatial Evaluation of Groundwater Quality Using GIS and WQI in Gadilam River Basin, Tamil Nadu, India

The aims of the current research are to assess the drinking water quality of the groundwater in the Gadilam River Basin, which is located in the northern part of Tamil Nadu, by identifying the groundwater quality index and examine its suitability for drinking. The current work determines the levels of groundwater quality parameters based on 120 groundwater samples; 50 samples from Archaean formation, 34 samples from Quaternary formation, 35 samples from Tertiary formation and the remaining sample from Cretaceous formation. Additionally, this research compares the determined levels with the various standards for drinking. Furthermore, the variability of parameters of the groundwater quality is explored in this paper by using the spatial interpolation method. The conclusion of this research reveals that the groundwater quality parameters such as Calcium (Ca2+), Magnesium (Mg2+), Nitrate (NO32-), Fluoride (F-), Sulphate (SO42-), Bi-carbonate (HCO3-) and Percentage of Hydrogen (pH) values are observed to be within the limiting value for WHO 2017 in all the formations during the seasons in which they were taken. The water quality index (WQI) values of the Archaean, Quaternary and Tertiary formations are found to be less than 100 meq/L in all stations in both seasons. In order of WQI, these stations come under the category of “Excellent” and “Good”. The Piper trilinear classification of groundwater samples fall in the field of mixed Ca-Mg-Cl, and No dominance, some of the samples represent Na-K, Cl types of water.

Spatial and Temporal Characteristics of PM2.5 Sources and Pollution Events in a Low Industrialized City

In recent years, cities in southern China have experienced severe air pollution, despite having few sources of pollutants. To study the pollution characteristics of PM2.5 in these “low industrialized” cities, a numerical method based on the HYSPLIT4 Model and Kriging Spatial Interpolation Technology was established. Simulation results showed that the PM2.5 pollution in Guilin was affected by both internal and external sources. The backward air mass trajectory from July 2017 to June 2018 was simulated using the HYSPLIT model. The cluster analysis results indicated that the direction of trajectory ? accounted for 63.09% of the air pollution in the city. The average concentration of PM2.5 pollution was 45.94 ?g.m-3. The pollutant originated from the “Xiang-Gui Corridor.” The location of the sources was collocated with high industry regions. The spatial characteristics of the four pollution processes in the winter of 2017 were analyzed using a spatial interpolation method. The results showed that the transport of air masses in the direction of trajectory ? was obstructed by a mountain system in the northeast. Therefore, two air pollution accumulation centers and a topographic weakening zone dominated by internal and external sources were formed. It can be inferred that the air pollution in Guilin is affected by both internal and external factors. These results provide important theoretical and technical support for regional air pollution control and environmental protection.

Spatio-temporal variation of reference evapotranspiration from empirical methods in Chihuahua, Mexico

<strong>Introduction:</strong> Evapotranspiration is key in the management of arid agricultural areas. In Chihuahua, the volume of irrigation water is based on reference evapotranspiration (ET_o) calculated with empirical methods and extrapolated to the cropped area, which is inaccurate. The alternative is to calculate ET_o variation by spatial interpolation.</br> <strong>Objective:</strong> To analyze the spatio-temporal variation of ET_o using empirical methods and spatial interpolation in Chihuahua, Mexico.</br> <strong>Methodology:</strong> Records from 33 meteorological stations from 1960-2013 and seven ET_o estimation methods were used. The results were compared with the Penman-Monteith method, modified by FAO (PMMF), ANOVA analysis (P ≤ 0.05), and homogeneous ET_o surfaces built from the point values by spatial interpolation.</br> <strong>Results:</strong> The Hargreaves method (R² = 0.91, RMSE = 1.16 and ME = -0.69 mm-day^-1) had a smaller bias with respect to PMMF. ET_o values ranged from 2.5 to 7.1 mm-day^-1 in a west-east direction, with maximum values at low elevations and minimum values at high elevations, which showed the influence of the Sierra Madre Occidental on ET_o. This characteristic was most noticeable in the warm months (June to September).</br> <strong>Limitations of the study:</strong> The use of estimated data needs field validation.</br> <strong>Originality:</strong> The ET_o estimation with seven empirical methods and one spatial interpolation method to extrapolate values to areas with scarce meteorological data.</br> <strong>Conclusions:</strong> The Hargreaves method allows estimating the spatio-temporal variation of ET_o in large extensions and areas with limited meteorological information.</br>

AN IMPROVED TEMPERATURE SPATIAL INTERPOLATION METHOD FOR SPACEBORNE LIDAR ATMOSPHERIC CORRECTION

As one of the most important meteorological elements, temperature is an indispensable meteorological parameter for the atmospheric correction of spaceborne LiDAR ranging. Given a limited number of surface meteorological observation stations, the temperature values for all region of LiDAR observation need to be interpolated using appropriate spatial interpolation methods. In this paper, based on the monthly surface observation values in individual years (1981–2010) of Sichuan province observation stations, we firstly analyze the effects of three common interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). To solve the problem of low interpolation accuracy in severely undulating terrain area, an improved gradient distance inverse square method based on the adiabatic lapse rate (GIDS-ALR) is proposed. The experimental results show that the GIDS-ALR has an obvious improvement in the effect of severely undulating terrain, where the absolute error has been improved by more than 43% in average. Additionally, the temperature-interpolated MAE is reduced by more than 30%. The effectiveness and applicability of the proposed method is verified in this paper.

An enhanced dual IDW method for high-quality geospatial interpolation

Many geoscience problems involve predicting attributes of interest at un-sampled locations. Inverse distance weighting (IDW) is a standard solution to such problems. However, IDW is generally not able to produce favorable results in the presence of clustered data, which is commonly used in the geospatial data process. To address this concern, this paper presents a novel interpolation approach (DIDW) that integrates data-to-data correlation with the conventional IDW and reformulates it within the geostatistical framework considering locally varying exponents. Traditional IDW, DIDW, and ordinary kriging are employed to evaluate the interpolation performance of the proposed method. This evaluation is based on a case study using the public Walker Lake dataset, and the associated interpolations are performed in various contexts, such as different sample data sizes and variogram parameters. The results demonstrate that DIDW with locally varying exponents stably produces more accurate and reliable estimates than the conventional IDW and DIDW. Besides, it yields more robust estimates than ordinary kriging in the face of varying variogram parameters. Thus, the proposed method can be applied as a preferred spatial interpolation method for most applications regarding its stability and accuracy.