Inverse Distance Weighting Method Research Articles

A novel on-line dual sensing system for soil property measurement and mapping

Real-time assessment of within-field soil fertility variation is crucial for making informed and sustainable decisions, like crop nitrogen (N) fertilization. Two commonly used soil sensors i.e., visible-near-infrared (vis-NIR) spectroscopy and ion-selective electrodes (ISE) have been reported to successfully estimate various soil macronutrients and nitrate-N, respectively, however, their integrated use for mapping a full-scale soil fertility variation has not been taken into consideration to date. The aim of this study is to use an integrated on-line dual-sensor system of vis-NIRS and ISE to estimate and map within-field variation in multiple soil fertility parameters including pH, organic carbon (OC), extractable- phosphorus (P), potassium (K), calcium (Ca) and moisture content (MC) and nitrate-N. The sensing system was mounted to the three-point linkage of a tractor and surveyed two arable fields in Belgium. Partial least squares regression models for vis-NIRS sensor were calibrated and validated, while a linear regression model was established for validation of the ISE sensor. Geostatistical surface maps for on-line measured soil attributes were generated using the inverse distance weighting and ordinary kriging methods for ISE data and vis NIR, respectively. The linear correlation confirms a very high similarity between ISE-measured nitrate-N and laboratory-analyzed nitrate-N (coefficient of determination (R2) = 0.93). Besides, the vis-NIRS demonstrates very good prediction accuracies for all the fertility attributes with R2 = 0.70-0.78, root mean square error (RMSE) =0.52-2.46 %, residual of prediction deviation (RPD) = 1.89 – 2.13 and the ratio of the performance to interquartile distance (RPIQ) = 1.72 – 6.56. Validation between laboratory and on-line measured soil maps also shows quite comparable spatial distribution patterns. Therefore, the proposed dual on-line sensor system has a high potential to estimate and map within-field spatial fertility distributions including nitrate-N, offering a basis for sustainable management decisions for precision soil and crop production.

Comparison of Inverse Distance Weighted and Thin Plate Spline Interpolation Methods in Projecting the Strength of the West Sumatra Earthquake

The Indonesian archipelago is situated in a highly active geological zone, making it prone to frequent earthquakes. West Sumatra, located on the west coast of central Sumatra, comprises lowland coastal areas and volcanic plateaus formed by the Barisan Mountains, covering a land area of 42,297.30 km² (2.17% of Indonesia's territory). This research aims to determine which interpolation method—Inverse Distance Weighted (IDW) and Thin Plate Spline (TPS)—provides more accurate predictions of earthquake strength in West Sumatra. The dataset consists of 229 earthquake events, divided into 90% for training (206 points) and 10% for testing (23 points). The training data was further subdivided into 80% training data 2 (164 points) and 20% validation data (42 points). The interpolation processes using the IDW and TPS methods were repeated 100 times, with the training 2 and validation data randomly shuffled in each iteration. Visualization of the interpolation results indicated that the earthquake magnitudes ranged from 2.0 to 4.5. Although the Mean Absolute Percentage Error (MAPE) values for the TPS method on the test and validation datasets were 16.42 and 14.29, respectively—slightly lower than the MAPE values for the IDW method—the t-test results showed no statistically significant difference between the two methods. Statistically, there is no significant difference between IDW and TPS in terms of predictive accuracy. However, researchers prefer the IDW method due to its computational efficiency and simplicity. Therefore, IDW is considered the most suitable method for analyzing earthquake strength in the West Sumatra region

A Novel Interpolation Method for Soil Parameters Combining RBF Neural Network and IDW in the Pearl River Delta

Soil fertility is a critical factor in agricultural production, directly impacting crop growth, yield, and quality. To achieve precise agricultural management, accurate spatial interpolation of soil parameters is essential. This study developed a new interpolation prediction framework that combines Radial Basis Function (RBF) neural networks with Inverse Distance Weighting (IDW), termed the IDW-RBFNN. This framework initially uses the IDW method to apply preliminary weights based on distance to the data points, which are then used as input for the RBF neural network to form a training dataset. Subsequently, the RBF neural network further trains on these data to refine the interpolation results, achieving more precise spatial data interpolation. We compared the interpolation prediction accuracy of the IDW-RBFNN framework with ordinary Kriging (OK) and RBF methods under three different parameter settings. Ultimately, the IDW-RBFNN demonstrated lower error rates in terms of RMSE and MRE compared to direct RBF interpolation methods when adjusting settings based on different power values, even with a fixed number of data samples. As the sample size decreases, the interpolation accuracy of OK and RBF methods is significantly affected, while the error of IDW-RBFNN remains relatively low. Considering both interpolation accuracy and resource limitations, we recommend using the IDW-RBFNN method (p = 2) with at least 60 samples as the minimum sampling density to ensure high interpolation accuracy under resource constraints. Our method overcomes limitations of existing approaches that use fixed steady-state distance decay parameters, providing an effective tool for soil fertility monitoring in delta regions.

Evaluating Spatial Interpolation Maps of the Age Structure of the Population of Thi Qar Governorate Using Geographic Information Systems (GIS) Technologies

The research aims to harness spatial interpolation techniques to produce maps with a high level of perceptual accuracy in representing the population data of the study area. This is achieved after exploring the statistical and spatial nature of the databases used, analyzing them, and determining their distribution using a variety of spatial data exploration tools available within the GIS (Geographic Information Systems) environment. These tools contribute to evaluating the characteristics, distribution, and analysis of data, including testing data distribution, identifying its direction, and uncovering its spatial correlations. This highlights the importance of the study and understanding the distribution pattern of the population data in the study area, thereby facilitating the preparation of future that serve spatial organization by building spatial models for the numerical distribution of the data for age groups: young, middle-aged, and elderly, at the level of administrative units of the study area by relying on the inductive approach and the quantitative analysis approach. This is done using Geostatistical Analysis methods within the concept of spatial interpolation in GIS. The research also seeks to predict the population maps for Thi Qar Governorate by comparing spatial interpolation methods, namely Inverse Distance Weighting (IDW) and Simple Kriging (SK). The study concluded, after verifying the accuracy of the results using the Cross-Validation curve, that the Simple Kriging (SK) method is the most accurate model in spatial prediction of population data for representing the data of the young age group in the study area, as it had the lowest Root Mean Square Error (RMSE) of 10.012. It was followed by the Inverse Distance Weighting (IDW) method for representing the data of the elderly age group with an RMSE of 350.48. This indicates that the use of statistical criteria improves the accuracy of spatial interpolation and that the spatial prediction of the population data for the study area using the Simple Kriging (SK) method was more accurate than the Inverse Distance Weighting (IDW) method based on statistical indicators.

P031 SPATIAL ASSOCIATION BETWEEN HYPERTENSION AND AIR POLLUTION IN ULAANBAATAR, MONGOLIA

Background and Objective: This study aims to assess the spatial association between hypertension prevalence and PM2.5 levels in Ulaanbaatar. Methods: To estimate the hypertension prevalence, we conducted a complex survey with a two-stage cluster sampling method in 2018. For PM2.5 level estimation, we used data collected during cold weather months (October to March) from 2015 to 2019. These estimates were used to predict PM2.5 levels across the entire city, using the Inverse Distance Weighted (IDW) interpolation method. The same approach was applied to predict the hypertension prevalence. We fitted a multiple linear regression model to estimate the association between PM2.5 and hypertension prevalence with adjustment for income, a proxy of socioeconomic factors to examine effect modification. In exploratory analysis, we estimated the association between PM2.5 level and income level. We used RStudio 1.3.1093 and ArcMap 10.8.2 for data analysis. Results: A total of 4515 participants were included in the sample. The hypertension prevalence was 46.5% with a threshold of 130/80 mm Hg. Adjusting for other covariates, a 10-unit increase in PM2.5 level is associated with a 6.66 (95% CIs;[10.23,10.23]) point increase in hypertension prevalence (p<0.001). With each 100,000 (MNT) increase in income level, the impact of 10-unit increases in PM2.5 level on hypertension decreases by 2.98 (95% CIs;[-5.48,-0.47]) points (p=0.02). In the partial F-test, an increase in income level was associated with a lower hypertension prevalence (p=0.002). In the exploratory analysis, a 100,000 (MNT) increase in income level was associated with a 0.23 (95% CIs;[-0.41,-0.05]) point decrease in PM2.5 level (p=0.011). Figure 1. Spatial association between hypertension prevalence. A).Hypertension prevalence (%); B).Income level (MNT) PM2.5 level; C).PM 2.5 (μg/m3); D).Linear regression model. Note: Inverse Distance Weighted method is used for predictions. Green points indicate the PM2.5-measuring site locations. Conclusions: The people who live in lower-income areas experience a stronger influence of air pollution on hypertension prevalence compared to others in Ulaanbaatar.

Inverse radius weighting and its python package “IRWPy”: A new topography-informed interpolation to enhance geological interpretations

The extent and volume of geochemical sampling are inherently constrained by numerous factors, including budgetary limitations, analytical costs, and access restrictions. These constraints result in sampling networks that vary in density and regularity, dividing regions into sampled and unsampled areas. The creation of continuous geochemical maps is essential for geochemical prospectivity, which aims to identify geochemical anomalies, distinguish between background levels and anomalies, and define mineralization patterns. Therefore, it is necessary to interpolate data from sampled areas to estimate values in unsampled regions. Although several interpolation models exist, including Inverse Distance Weighting and various kriging methods, Inverse Distance Weighting is often used in two-dimensional ground modeling because, unlike kriging methods, Inverse Distance Weighting has no smoothing effect on edges. Inverse Distance Weighting’s reliance solely on horizontal Euclidean distances between samples overlooks critical factors such as topography and the ensuing effects on dilution, transportation, and element mobility, rendering it less effective over varied elevations. This study introduces Inverse Radius Weighting, a new interpolation technique that incorporates both Euclidean and elevation fluctuations, therefore Pythagorean distance, to help with better geological interpretations. We assessed the efficacy of Inverse Radius Weighting compared to Inverse Distance Weighting across three elements with varying mobility (Arsenic − highly mobile, Copper − moderately mobile, and Barium − nearly immobile), using different numbers of neighbors and by comparing three different evaluation measures, namely R2, Mean Absolute Error and Mean Absolute Percentage Error. By evaluating the spatial uncertainty of the generated maps and selecting the configurations with the least uncertainty as the final maps, our analysis reveals an improvement in correlation between interpolated and actual values with Inverse Haversine Radius Weighting. With this advancement, Inverse Haversine Radius Weighting overcomes the limitations of traditional Inverse Distance Weighting by accounting for elevation and its associated effects, thereby paving the way for more accurate and geochemically meaningful interpolation in geochemical prospectivity mapping.

Optimal interpolation approach for groundwater depth estimation

In arid and semi-arid regions where surface water resources are scarce, groundwater is crucial. Accurate mapping of groundwater depth is vital for sustainable management practices. This study evaluated the performance of three spatial interpolation techniques – inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF) – in predicting groundwater depth distribution across Dire Dawa City, Ethiopia. The results demonstrated the superiority of the RBF method, exhibiting the lowest RMSE (3.21 m), MAE (0.16 m), and the highest R2 (0.99) compared to IDW and OK. The IDW method emerged as the next best performer (RMSE = 4.68 m, MAE = 0.16 m, R2= 0.97), followed by OK (RMSE = 5.32 m, MAE = 0.42 m, R2= 0.95). The RBF's superior accuracy aligns with findings from other semi-arid regions, underscoring its suitability for data-scarce areas like Dire Dawa. This comparative evaluation provides valuable insights for selecting the optimal interpolation method for groundwater depth mapping, supporting informed decision-making in local water resource management.The methodological approach comprised:•Implementation of three interpolation techniques, namely, inverse distance weighting (IDW), ordinary kriging (OK), and radial basis functions (RBF), utilizing 56 groundwater depth measurements from locations dispersed throughout the study area.•Cross-validation through randomly withholding 20 % of the data for validation purposes.•Comparison of the techniques based on statistical measures of accuracy, including root mean square error (RMSE), mean absolute error (MAE), and the coefficient of determination (R2).

Short-term exposure to gaseous ambient pollution and hospital admissions for mental health among children and adolescents: A time-stratified case-crossover study

Little evidence has demonstrated the linkage of gaseous air pollution and hospitalization rates for mental diseases among children and adolescents in China. Based on a time-stratified case-crossover design among children and adolescents in nine cities, Sichuan, a conditional logistic regression and a concentration-response (C–R) curve model were applied to investigate mental disorders in relation to gaseous air pollutants exposure at lag 0-lag 7, and lag 01-lag 07. Hospitalization costs were calculated through the attributable risk method. With daily data from official environmental monitoring centers, individual daily mean ambient pollutants estimates were evaluated via Inverse Distance Weighted method. Daily hospitalized records for mental illness were collected from medical organization or/and institutions from January 2016 to December 2019. There were 11479 inpatients suffering depression, anxiety, and/or other mental disorders. In single- and cumulative-day-lag analyses, with each 10 μg/m3 increment of NO2, SO2, and O3, the greatest odds ratio (OR) for all-cause mental disorders were 1.114 (95% confidence interval [CI]: 1.067–1.164) (lag 0), 1.219 (95% CI: 1.040–1.430) (lag 4), and 1.039 (95% CI: 1.009–1.069) (lag 7), separately. Stronger associations were found in inpatients hospitalized in warm days in SO2 analysis. C–R curve showed that all-cause mental disorders hospitalizations were positively related to SO2 and O3 exposure at relative high levels. During study period, the total economic cost of hospitalization for all-cause mental disorders caused by NO2 pollution was 94.71 million CNY. These findings indicated that gaseous air pollutants exposure may increase the risk and economic burdens of mental disorders among children and adolescents.

Cost-effectiveness of robot-assisted total hysterectomy for benign pathologies compared to laparoscopic surgery: A retrospective study with propensity score

BackgroundHysterectomy for benign pathologies is one of the most common gynecological surgeries. In recent years, robotic surgery has become an alternative to traditional surgery, but at a higher cost. ObjectiveEstimate the cost of benign robot-assisted hysterectomy for the purpose of supporting public decision-making, as well as the additional cost per major postoperative complication (ClavienDindo score ≥ 3) avoided one month after surgery robotic versus traditional laparoscopic. MethodsSingle-center retrospective study including patients operated on for benign hysterectomy at La Pitié Salpêtrière hospital between January 2016 and December 2019: 99 by robotic approach, and 86 by laparoscopic approach. Comparison of robotic surgery to laparoscopy. Calculation of a cost-effectiveness ratio (ICER). Use of the propensity score inverse weighting method to ensure comparability of groups. ResultsRobotic surgery has a total cost of € 6,615 at 1 month per patient compared to € 3,859 for laparoscopic surgery with an additional cost of € 377,534 per major postoperative complication avoided, longer operating time and an absence of significant difference in terms of complications and length of hospitalization. ConclusionIn terms of cost-effectiveness, according to this study, the robot does not appear to be better than laparoscopy. In the years to come, we can expect a development of robotic surgery with rationalization of the practice, with appropriate selection of patients for robotic surgery, development of outpatient surgery and a reduction in the cost of the equipment.

PEMETAAN JUMLAH KRIMINALITAS DI KALIMANTAN SELATAN MENGGUNAKAN METODE CO-KRIGING DAN INVERSE DISTANCE WEIGHTING

AbstractCriminality is any form of action that harms economically and psychologically and violates the law, social norms and religion. South Kalimantan is in 21st position with an average of 3,282 criminal cases per year in the period 2018 to 2020. One method to interpolate data in unsampled locations is the co-kriging and inverse distance weighting (IDW) method. This study aims to estimate the number of crimes in South Kalimantan with the co-kriging and IDW methods, compare the results to determine the best method, and present a thematic map of the distribution of crime in South Kalimantan. In the co-kriging method, the number of crimes is the primary variable and population density is the secondary variable. The result of this research is that between the two methods, IDW is better for interpolation in estimating the crime value than the co-kriging method. The IDW method provides good interpolation results whose interpolation value is close to the value of the real data. Although co-kriging has the potential to provide accurate results if the cross-variogram model can be formed properly, the constraints faced in this study indicate that IDW remains a more stable and reliable choice in estimating crime in South Kalimantan. Keywords: Crime, Co-kriging, Inverse Distance Weighting

Water-quality assessment and spatial distribution of water-quality parameters of Dodoma Urban, Tanzania

The study was conducted in 2022 in shallow wells and deep boreholes owned by residents, to investigate the water quality status in the emerging developing area of Dodoma Urban municipality in central Tanzania. Eighteen boreholes and five shallow wells were assessed using the water quality index (WQI), the inverse distance weighting (IDW) method under the ArcGIS environment, and the Euclidean similarity index. The WQI classified the water samples into a good category class with a covered area of 42.5% and an excellent category class with a covered area of 57.5%, indicating that most of the deep boreholes and shallow wells were suitable for residential purposes. The IDW revealed the spatial distribution of the physico-chemical parameters and WQI, indicating that water quality improved from west to east within the study area. Yet, the Euclidean similarity index was dominated by nitrates, pH and heavy metals, with the highest correlation coefficients. Despite the WQI being above the average class, water treatment technologies should be practiced to lower those values of physico-chemical parameters existing above the threshold limits.

Modeling and Data Mining Analysis for Long-Term Temperature-Stress-Strain Monitoring Data of a Concrete Gravity Dam

The safety condition of concrete gravity dams is influenced by multiple factors, and assessing their safety solely based on a single factor is difficult to comprehensively evaluate. Therefore, this paper proposes a comprehensive modeling and analysis approach to assess dam safety by considering long-term temperature, stress, and strain monitoring data of actual concrete gravity dams. Firstly, the K-means clustering algorithm is utilized to classify the data. Then, the study area of the dam is meshed and three indicator evaluation values for all the elements are calculated. The other elements’ evaluation values can be obtained by the Inverse Distance Weighting (IDW) method. Finally, the analytic hierarchy process extended by the D numbers preference relation (D-AHP) method is applied to compute the weights of temperature, stress, and strain and evaluate the dam’s safety comprehensively. The effectiveness of this method is validated through application to specific engineering cases. The results demonstrate that compared to assessing methods considering only single factors, the comprehensive evaluation method proposed in this paper can more comprehensively and accurately reflect the actual safety condition of concrete gravity dams, providing important references for engineering decision-making.

Selection of the Value of the Power Distance Exponent for Mapping with the Inverse Distance Weighting Method—Application in Subsurface Porosity Mapping, Northern Croatia Neogene

Selection of the Value of the Power Distance Exponent for Mapping with the Inverse Distance Weighting Method—Application in Subsurface Porosity Mapping, Northern Croatia Neogene

Association of ambient air pollutant mixtures with IVF/ICSI-ET clinical pregnancy rates during critical exposure periods

Abstract STUDY QUESTION Does exposure to a mixture of ambient air pollutants during specific exposure periods influence clinical pregnancy rates in women undergoing IVF/ICSI-embryo transfer (ET) cycles? SUMMARY ANSWER The specific exposure period from ET to the serum hCG test was identified as a critical exposure window as exposure to sulfur dioxide (SO2) or a combination of air pollutants was associated with a decreased likelihood of clinical pregnancy. WHAT IS KNOWN ALREADY Exposure to a single pollutant may impact pregnancy outcomes in women undergoing ART. However, in daily life, individuals often encounter mixed pollution, and limited research exists on the effects of mixed air pollutants and the specific exposure periods. STUDY DESIGN, SIZE, DURATION This retrospective cohort study involved infertile patients who underwent their initial IVF/ICSI-ET cycle at an assisted reproduction center between January 2020 and January 2023. Exclusions were applied for patients meeting specific criteria, such as no fresh ET, incomplete clinical and address information, residency outside the 17 cities in the Sichuan Basin, age over 45 years, use of donor semen, thin endometrium (<8 mm) and infertility factors unrelated to tubal or ovulation issues. In total, 5208 individuals were included in the study. PARTICIPANTS/MATERIALS, SETTING, METHODS Daily average levels of six air pollutants (fine particulate matter (PM2.5), inhalable particulate matter (PM10), SO2, nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3)) were acquired from air quality monitoring stations. The cumulative average levels of various pollutants were determined using the inverse distance weighting (IDW) method across four distinct exposure periods (Period 1: 90 days before oocyte retrieval; Period 2: oocyte retrieval to ET; Period 3: ET to serum hCG test; Period 4: 90 days before oocyte retrieval to serum hCG test). Single-pollutant logistic regression, two-pollutant logistic regression, Quantile g-computation (QG-C) regression, and Bayesian kernel machine regression (BKMR) were employed to evaluate the influence of pollutants on clinical pregnancy rates. Stratified analyses were executed to discern potentially vulnerable populations. MAIN RESULTS AND THE ROLE OF CHANCE The clinical pregnancy rate for participants during the study period was 54.53%. Single-pollutant logistic models indicated that for PM2.5 during specific exposure Period 1 (adjusted odds ratio [aOR] = 0.83, 95% CI: 0.70–0.99) and specific exposure Period 4 (aOR = 0.83, 95% CI: 0.69–0.98), and SO2 in specific exposure Period 3 (aOR = 0.92, 95% CI: 0.86–0.99), each interquartile range (IQR) increment exhibited an association with a decreased probability of clinical pregnancy. Consistent results were observed with dual air pollution models. In the multi-pollution analysis, QG-C indicated a 12% reduction in clinical pregnancy rates per IQR increment of mixed pollutants during specific exposure Period 3 (aOR = 0.89, 95% CI: 0.79–0.99). Among these pollutants, SO2 (33.40%) and NO2 (33.40%) contributed the most to the negative effects. The results from BKMR and QG-C were consistent. Stratified analysis revealed increased susceptibility to ambient air pollution among individuals who underwent transfer of two embryos, those with BMI ≥ 24 kg/m2 and those under 35 years old. LIMITATIONS, REASONS FOR CAUTION Caution was advised in interpreting the results due to the retrospective nature of the study, which was prone to selection bias from non-random sampling. Smoking and alcohol, known confounding factors in IVF/ICSI-ET, were not accounted for. Only successful cycles that reached the hCG test were included, excluding a few patients who did not reach the ET stage. While IDW was used to estimate pollutant concentrations at residential addresses, data on participants’ work locations and activity patterns were not collected, potentially affecting the accuracy of exposure prediction. WIDER IMPLICATIONS OF THE FINDINGS Exposure to a mixture of pollutants, spanning from ET to the serum hCG test (Period 3), appeared to be correlated with a diminished probability of achieving clinical pregnancy. This association suggested a potential impact of mixed pollutants on the interaction between embryos and the endometrium, as well as embryo implantation during this critical stage, potentially contributing to clinical pregnancy failure. This underscored the importance of providing women undergoing ART with comprehensive information to comprehend the potential environmental influences and motivating them to adopt suitable protective measures when feasible, thereby mitigating potential adverse effects of contaminants on reproductive health. STUDY FUNDING/COMPETING INTEREST(S) This work received support from the National Key Research and Development Program of China (No. 2023YFC2705900), the National Natural Science Foundation of China (Nos. 82171664, 81971391, 82171668), the Natural Science Foundation of Chongqing Municipality of China (Nos. CSTB2022NSCQ-LZX0062, CSTB2023TIAD-KPX0052) and the Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (No. 2021KFKT013). The authors report no conflicts of interest. TRIAL REGISTRATION NUMBER N/A.

FEATURES OF THE DISTRIBUTION AND COMPOSITION OF SNOW COVER WITHIN THE LANDSCAPES OF CHASHNIKOVO

For landscape conditions in the upper reaches of the river Klyazma, Solnechnogorsk district, Moscow region, the height and reserves of snow cover were investigated, and the chemical composition of the snow was determined. The basis for considering the component composition of snow cover was the geochemical taxonomy of chemical elements based on the characteristics of water migration and abundance. Data from 23 snow sampling points were interpolated in SAGA GIS using the inverse distance weighting (IDW) method. On this basis, zones differing in the chemical composition of snow are identified. One of the zones is confined to the M-10 Moscow-St. Petersburg highway, while the second borders on populated areas. The area close to the highway is characterized by increased levels of calcium, sodium, aluminum, and chloride ions in the snow cover. The second zone, bordering populated areas, is characterized by a high content of calcium, copper, and manganese in the snow. For the third zone, low concentrations of components in the snow were observed which are characteristic of a superaquatic landscape due to the distance from sources of pollution. The studied composition of snow waters belongs to the bicarbonate-sodium-calcium-chloride class. It has been shown that the height and reserves of snow cover are partially controlled by two factors: the type of elementary landscape and the type of ecosystem. Against this background, the spatial distribution of concentrations of elements and anions in snow is predominantly controlled by the anthropogenic factor.

Spatio-Temporal Trend Analysis of Annual and Seasonal Rainfall in Ho Chi Minh City

Spatio-temporal variations and trends in climatic variables play an essential role in the adaptation and mitigation of climate change policy. This study analyzed spatial and temporal variability of annual and seasonal rainfall in Ho Chi Minh City based on non-parametric statistical trend tests and spatial interpolation techniques. In particular, Sen’s Slope Estimator and Inverse Distance Weighting (IDW) methods were applied to construct the spatial distribution of annual and seasonal rainfall over the city. The outcomes show that annual and seasonal rainfall experienced significant increasing trends. Generally, these findings draw out an overall picture of rainfall variability in terms of space and time, which makes it possible for local decision-makers to promote more promising action plans in order to respond to natural disasters in the context of climate change.

A spatiotemporal comprehensive constraint interpolation method for missing values in monthly composite Suomi-NPP VIIRS Cloud Mask images

ABSTRACT The Earth’s nighttime light imaging data collected by the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS DNB) are severely lost in summer, hindering the further application of VIIRS DNB data. In this article, a spatiotemporal comprehensive constraint interpolation method (SCIM) that accounts for the relative stationarity of temporal nighttime light, spatial correlations and heterogeneity among pixels is designed. Meanwhile, the method considers the seasonal fluctuations in nighttime light radiation, introduces spatial autocorrelation coefficients and spatial heterogeneity coefficients, and solves the problem of missing VIIRS DNB data. First, 8 widely used temporal interpolation methods (TIM) are selected to perform temporal interpolation for missing image data. Second, the existing VIIRS monthly images are used to construct the constraint relationship between time and space, and the pixels that do not meet the constraint conditions in the TIM simulation image are removed. The inverse distance weight (IDW) method is applied to the removed null pixels to obtain the image simulated by the SCIM. Finally, the accuracy of the TIM and SCIM is evaluated. The results show that the accuracy of the SCIM-simulated image is better than that of the TIM-simulated image. The proposed approach improves the quality of the VIIRS DNB dataset.

Kajian Kualitas Air Laut dan Sebaran Indeks Pencemaran di Pesisir Utara Kecamatan Palang-Tuban pada Musim Kemarau

The northern coastal area of ​​Palang Sub-District is a densely populated area in the Tuban district with high differentiation of economic activities. The large population and heterogeneous community activities on the northern coastal of Palang Sub-District have the potential to become a source of pollution which results in a decrease in seawater quality. The research aim is to analyze seawater quality on the north coast of Palang Sub-District based on seawater quality parameters and the spatial distribution of the pollution index. It is hoped that this research can help local governments to monitor and control water pollution in the area. Sea water sampling was carried out in the dry season for 2 days at high and low tide conditions. This research analyzes the concentration values ​​of BOD, COD, NO3-, TSS, detergent, oil & grease, pH, temperature, and Total coliform compared to seawater quality standards of PP RI No. 22 of 2021 Appendix VIII on Marine Biota. The spatial distribution of the water quality pollution index is the result of interpolation using the Inverse Distance Weighted (IDW) method from the results of measuring test parameters. The Pearson Correlation statistical test was carried out to determine the level of correlation between the parameters selected in determining water quality. The results of the research show a decline in the quality of the northern coastal waters of Palang-Tuban District with several parameter concentration average values ​​exceeding the quality standards, namely pH = 6.9 (QS = 7.0-8.5); TSS = 29.2 ppm (QS= 20 ppm); Nitrate = 0.4 ppm (QS = 0.06 ppm), BOD5 = 54.8 ppm (QS= 20 ppm), COD = 112.5 ppm (QS= 100 ppm). The water quality status shows that the water conditions are moderately polluted (ST1A), lightly polluted (ST1B-ST5B), and good conditions (ST4C & ST5C). Controlling and reducing the pollution load entering coastal waters needs to be carried out to prevent the quality of coastal waters from decreasing further. One of them is by creating wastewater treatment at every fishing port and fish auction.

Spatial distribution of soil erodibility factors in erosion-prone areas in Umuahia, Southeast, Nigeria

This study evaluates the spatial distribution of soil erodibility factor (K) in Umuahia, Abia State, Nigeria using the Universal Soil Loss Equation (USLE) nomograph. The soils used for the study was sampled from 14 erosion prone areas in Umuahia, Southeast, Nigeria. The topsoil samples collected at depths of 0–10 cm, 10–20 cm, and 20–30 cm around the middle of each location identified with the aid of a GPS. The percentages of sand, silt, clay, moisture content, saturated hydraulic conductivity, and organic matter (OM) were all examined. The Gaussian ordinary kriging model for the determination of K-factor was compared with the Inverse Distance Weighting method. The K-factor’s coefficient of variation (CV) was 0.29 and the K-factor value of the nuggets to sill ratio (0.44) indicates a moderate spatial distribution. The Gaussian semi-variogram approach yielded the best estimate accuracy and model fitting effects, meaning that the Gaussian ordinary Kriging model is better for K-factor estimation. The root mean squared error (RMSE) was 0.0079 and the mean squared deviation ratio (MSDR) was 0.89, implying that the Gaussian model was unbiased and adequately captured the experimental variation. The K-factor values were lower in the north of the research region ranging from 0.0250 to 0.0197 Mg h MJ−1 mm−1 compared to east with the K-factor ranging from 0.0399 to 0.0423 Mg h MJ−1 mm−1. The estimated K-factor was relatively unbiased since the root mean square error was extremely small and the mean error was nearly equal to 0. The determination of soil erodibility (k-factor) influenced by factors such as soil texture, structure, and organic matter is crucial in assessing the vulnerability of land area to soil erosion. The spatial distribution of these factors affects the k-factor at different locations in a landscape, which enables accurate estimation of k-factor. Nutrient levels impact soil erodibility and distribution. Low nitrogen limits growth, favoring erosion. Phosphorus aids stability. Optimal potassium benefits growth and erosion control. Spatial distribution is vital, emphasizing precise nutrient management for effective soil health.

Spatial Distribution of Seawater Intrusion Based on Various Lithologies in Pesanggaran Subdistrict, Banyuwangi, Indonesia

Seawater intrusion often occurs in shoreline areas closest to alluvium rock lithology and is caused by excessive groundwater exploration. In Pesanggaran Banyuwangi, saline groundwater was found at approximately 8000 m from the shoreline. Therefore, this study aimed to analyse the spatial distribution of seawater intrusion based on rock lithology, Electrical Conductivity (EC), and Total Dissolved Solids (TDS) in Pesanggaran, Banyuwangi. A descriptive-quantitative survey method was used to assess groundwater quality based on EC, TDS, and spatial distribution parameters. Interpolation with Inverse Distance Weighting (IDW) method was used to map seawater intrusion and its relationship to each parameter. The results showed that moderate-saline groundwater was found in several areas far from the coast with volcanic rock lithology, namely Kalibaru formations (Qpvk) and limestone lithology at Batuampar formation. Groundwater in shoreline areas (Alluvium) was dominated by freshwater due to the deposition process found in alluvium lithology, which could form an aquifer with high quantity. Saline groundwater in Batuampar and Kalibaru formations showed salt minerals flushing related to shallow marine deposition since the tertiary period. In conclusion, Pesanggaran Subdistrict had uneven spatial distribution, and no significant relationship with distance from the shoreline, but it was related to the deformation process for each lithology.