Inverse Distance Weighted Interpolation Technique Research Articles

Water quality assessment of Bheemasandra Lake, South India: A blend of water quality indices, multivariate data mining techniques, and GIS.

An integrated approach combining water quality indices (WQIs), multivariate data mining, and geographic information system (GIS) was employed to examine the water quality of Bheemasandra Lake, located adjacent to a sewage treatment plant (STP) in Tumakuru city, India. The analysis of 22 lake water samples, examined before and after the monsoons, revealed that the physicochemical parameters namely - electrical conductivity, biochemical oxygen demand, turbidity, total dissolved solids, ammoniacal nitrogen, nitrates, phosphates, magnesium, total hardness, total alkalinity, and calcium - exceeded the acceptable limits stipulated by national and international standards. The Canadian Council of Ministers of the Environment WQI (pre-monsoon: 25.3; post-monsoon: 33.9) and weighted arithmetic WQI (pre-monsoon: 3398; post-monsoon: 2093) designated the water as unsafe for drinking. Irrigation WQIs (sodium adsorption ratio, sodium percentage, residual sodium carbonate, magnesium hazard, permeability index, and potential salinity) implied water's suitability for irrigation. However, electrical conductivity indicated otherwise. Industrial WQIs (Larson-Skold Index, Langelier Index, Aggressive Index, and Puckorius Scaling Index) illustrated scaling propensity and the chloride sulfate mass ratio alluded galvanic corrosion potential. Hierarchical cluster analysis gathered 22 sampling points into two clusters (cluster 1: relatively lower polluted regions; cluster 2: highly polluted regions) for each season based on similarities in water features. Principal component analysis extracted four (79.07% cumulative variance) and six (87.14% cumulative variance) principal components before and after the monsoons, respectively. These components identified the primary pollution sources as urban sewage and natural lithological processes. WQI maps, created using the inverse distance weighted interpolation technique, enhanced the visualization of spatial-temporal variations. This study highlights the dire consequences of urbanization, STP pollution, and sewage management failures, necessitating that concerned authorities should implement policies and measures to curb the negative impacts on the environment and public health.

Prioritization of Milli Watershed of Kodar River Catchment Based on Integrated Approach of Morphometric and Land Use Land Cover Analysis

Watershed prioritisation is now important for effective natural resource planning and management in sustainable development, however, their implementation necessitates a careful examination of the morphometric and hydrological characteristics existing in the watershed. In the present study an attempts has been made to prioritize the Kantori nala milli watersheds of the Kodar river catchment using integrated approach of morphometric features and land use/landcover. Study area watershed was automatically delineated and divided into eleven micro watersheds Micro Watershed-1 to Micro Watershed-11 (MWS-1 to MWS-11) on the basis of topography from the Depression less DEM) prepared by Inverse Distance Weighted (IDW) interpolation technique. Morphometric analysis was done using DEM and each parameter has been assigned their ranks according to their value. Thereafter, an average value of the rank score for each of the micro watershed is calculated. The micro watershed with the lowest compound factor (Cp) was given the highest prioritized rank out of the group of micro watersheds, and vice versa. Land Use Land Cover (LULC) analysis was also done using sentinel 2 satellite data. The result from the priority ranking of morphometric analysis shows that MWS 7 is having high priority ranking while MWS 8 is having low priority ranking, while the results of the LULC analysis-based prioritizing showed that the micro watersheds MWS 10 and MWS 5 micro watersheds are of are of highest and lowest priority respectively. It is observed that upon integration of morphometric and land use/ land cover compound factor values information, MWS 3 and MWS 10 found to receive common priority falls under the highest priority, though MWS 6 and MWS 8 falls under the lowest priority ranking. The results of the study can be employed for identifying the sub-watersheds that require immediate restoration and ultimately help in managing watershed resources for sustainable development.

Estimating the spatial distribution of soil volumetric water content in an agricultural field employing remote sensing and other auxiliary data under different tillage management practices

AbstractKnowledge of soil volumetric water content (VWC) on agricultural soils as influenced by different soil management practices is important, but the measurement outputs of frequently used traditional sampling techniques are restricted to point‐based measurements with limited spatial coverage. Remote sensing (RS) techniques are therefore being explored because of their greater spatial and temporal availability as well as their ability to cover large‐scale areas. But a general limitation for RS data is the presence of vegetation cover, cloud cover and the effect of topography. To minimize the effects of these factors during field sampling, the use of spectral indices and terrain attributes have proven successful in the estimation of several soil properties; however, the impact of these approaches for the estimation and mapping of soil VWC, an important factor in crop growth and development, especially where different soil management practices are employed, remains limited. To contribute to the knowledge base of RS under varying soil systems, this study explores the possibility of combining in‐situ measurements with remotely sensed (explanatory variables) data obtained under four different tillage practices to produce an estimated soil VWC that represents the entire study field. The tillage practices used include reduced till (RT), strategic till (ST), no‐till (NT) and conventional till (CT). From these tillage plots, three explanatory datasets, namely Sentinel‐2 (S2), spectral indices (SI) and terrain attributes (TA), were collected as predictors. In addition, each of the explanatory variables was structured into four groups, representing each of the four tillage methods. The eXtreme Gradient Boosting (XGBoost) model was used, and the best results were selected based on the root mean squared error (RMSE), the coefficient of determination (R2) and the mean absolute error (MAE). Prior to soil VWC prediction, the Pearson correlation matrix was used to determine the linear relationship between each of the selected explanatory variables (S2 bands, SI and TA) and soil VWC. Furthermore, spatial distribution maps of soil VWC were constructed using the inverse distance weighting (IDW) interpolation technique. For soil VWC estimation, the TA outperformed the SI and S2 datasets (R2 = 0.84). Similarly, the spatial distribution maps obtained from the TA data show a study area with a large concentration of VWC compared with the other datasets. The study also identified CT as the tillage approach that most impacts soil VWC estimation because all predicted results were poor without the addition of data from the CT plot. According to our findings, using TA data collected from various tillage management systems to estimate soil VWC is very promising because they can be used as predictors to improve soil VWC estimation and mapping. This study demonstrates conclusively that remote sensing data collected from different tillage management systems can be used as predictors to enhance the estimation and mapping of soil VWC, complementing the basis for the development of reliable and consistent precision irrigation management systems.

Water quality monitoring and assessment in coastal lagoons: Developing a protocol using a case study in Sainthamaruthu lagoon, Sri Lanka

AbstractSustainable water resources management requires monitoring and assessment of water quality in addition to quantity. Coastal lagoons play a critical role for the people and the ecosystem at large. Water quality monitoring and assessment for lagoons in Sri Lanka is important considering its services to the country. However, due to a lack of resources, most of the lagoons are not monitored and reported properly for further action. This study aims to develop a protocol in terms of monitoring sites, parameters, and frequency for water quality monitoring using a case study in a coastal lagoon, in Sainthamaruthu, Sri Lanka. The physicochemical parameters were measured weekly at eighteen monitoring sites for 4 months during the dry season from June to September 2022. Cluster analysis (CA) along with Inverse Distance Weighted (IDW) interpolation technique revealed that a minimum of three sites out of 18 are sufficient for monitoring water quality in the lagoon. In light of Principal Component Analysis (PCA) and Discriminant Analysis (DA), it was decided to retain temperature, pH, turbidity, Total Dissolved solids (TDS), Electrical Conductivity (EC), and Dissolved Oxygen (DO) in monitoring programs, since they cumulatively account for 79% of the variation in water quality in the lagoon spatially and temporally. Monitoring turbidity, TDS, and EC at weekly interval, while monitoring temperature, pH, and DO at biweekly or monthly is recommended. A further indication of the lagoon's water quality is that the mean values for turbidity (27 NTU), EC (754 μS/cm), and DO (4.6 ppm) higher than ambient standards, while TDS (344 ppm) and pH (6.98) are within. Therefore, we conclude that the lagoon requires remediation measures to be suitable for environmental purposes during dry periods.

Unraveling the Relationship Between Stubble Burning and Air Quality Degradation in Punjab: A Temporal and Spatial Analysis (2019-2022)

Stubble burning in Punjab, India, poses significant environmental challenges, particularly impacting air quality. This study aims to examine the spatial and temporal patterns of stubble burning events and their potential effect on ambient air quality from 2019 to 2022. High-resolution Sentinel-2 satellite imagery was employed to delineate the spatial extent of stubble burning. Burnt areas were identified using the Normalised Burn Ratio (NBR). Air quality was evaluated based on PM2.5 and PM10 concentrations data obtained from the Punjab Pollution Control Board. The Inverse Distance Weighting (IDW) interpolation technique was used to estimate pollution values in areas lacking direct monitoring. The study revealed significant year-to-year variations in areas affected by stubble burning. The smallest burnt areas were recorded in October 2019 and 2021 (209 sq km), while the largest was in 2020 (755.38 sq km). In every year studied, the burnt area in November consistently exceeded that in October, with the largest area (10315 sq km) observed in 2021. PM2.5 and PM10 concentrations also showed annual fluctuations, with the highest recorded in 2020 and 2021. In particular, in October 2020, higher PM2.5 and PM10 levels were detected in the eastern region of Punjab. November consistently exhibited higher PM2.5 and PM10 concentrations than October for all years analysed, peaking in 2021. The spatial and temporal variations of stubble burning events and their relationship with air quality highlight the need for targeted interventions. Understanding these patterns is crucial for mitigating the adverse effects of stubble burning on air quality in Punjab, India. Future research should focus on evaluating the effectiveness of various mitigation strategies.

COVID‐19 in Chihuahua, Mexico: Assessing its spatial behaviour through the inverse distance weighted interpolation technique

AbstractThis study focused on the spatial evolution of COVID‐19 in the state of Chihuahua, Mexico. Data were retrieved from governmental databases and analyzed by means of GIS, applying the inverse distance weighted (IDW) method. The period of December 2019 through November 2021 was split into eight seasons. The root mean square error (RMSE) was used to assess the reliability of the interpolations, showing acceptable values (RSME < 25). During the period, the municipalities of Juarez and Chihuahua reached the highest number of confirmed cases and deaths, Juarez being the main hotspot of contagion (37.2% of confirmed cases; 46.9% of deaths). Four waves of contagion were identified during the evaluated period, with Fall 2020 being the strongest season. Since Fall 2020, the spread of the disease was more often observed in municipalities with the highest human mobility. Although the spread of COVID‐19 decreased after Spring 2021, in Fall 2021 records indicated a continuous increase in cases in the state. That could be due to a relaxation of the implementation of sanitary measures, as well as to the propagation of novel COVID‐19 variants having an elevated infectious level. Geospatial techniques allowed for an understanding of the spatial spread of COVID‐19 and could be useful for its control.

Fuzzy Inference System-based Geo Visualization Tool Development for Yamuna River Water Quality Analysis

An analysis of Yamuna River Water Quality through the use of a Fuzzy Inference System illustrates the capabilities of artificial intelligence in the development of novel Geovisualizer. Numerous Geovisualizer systems exist worldwide, but none can estimate water quality with artificial intelligence instead, relying on statistical data that vary with the scenario and have dubious accuracy. This logic has not been applied to the classification of Water Quality Standards by any designated government agency for water quality monitoring and management. A robust and integrated application program interface is developed by combining MongoDB and JavaScript library utilities Leaflet.js, Node.js, ArcGIS, ERDAS Imagine, and Fuzzy Algorithm; embellished with HTML and CSS. The Yamuna River is considered for the spatial-temporal aspect of the application. End users can authenticate with JSON web tokens. Spatial and non-spatial data are visualized by Inverse Distance Weighted Interpolation technique of ArcGIS. The Water Quality Index can be calculated using combinations of critically chosen input parameters integrated through frontend and backend functionality that incorporates fuzzy set theory with input from a Geovisualizer database and frontend platforms; not available on the existing geovisualization platform. With its scalability, extensibility, and execution speed, the resultant 121RRwebgis Geovisualizer outperforms conventional applications. Thus, a robust platform for global researchers is developed.

Estimation of groundwater recharge variability using a GIS-based distributed water balance model in Makutupora basin, Tanzania

Groundwater recharge estimation in the Makutupora basin (1500 km2) is vital considering the potentiality of the basin to Dodoma city. This study aims to apply the WetSpass model to estimate the long-term average seasonal (dry and wet) and annual groundwater recharge for the Makutupora basin. Data required for this study were biophysical data (topography, land use, soil, slope, and depth to the groundwater) and long-term hydro-meteorological data (2000–2020). Data were collected by using both field visits and disk transfer from respective institutions and websites. Hydro-meteorological data were prepared for dry and wet seasons. Raster maps were prepared in ArcMap 10.4 using the Inverse Distance Weighting (IDW) interpolation technique followed by resampling into a 200 × 200 m grid size. Resampled raster maps were converted from raster to ASCII format suitable to input in the WetSpass model. The findings indicated that more recharge was dominating in the wet season ranging between 0 and 120 mm/year with a mean value of 24.65 mm (99%) while less recharge occurs in the dry season ranging between 0 and 4.35 mm/year with a mean value of 0.24 mm/year (1%) and annually recharge ranges between 0 and 120.88 mm/year with a mean value of 24.88 mm. Mean annual precipitation computed from data for twenty (20) years was 694 mm/year out of which recharge accounted for 3.6%, surface runoff 33.9% and evapotranspiration 62.5%. The groundwater table receives total average volumetric recharge of 37.3 million m3 annually from precipitation for the entire basin area. The model was employed to realize the area’s capacity for groundwater recharge to manage the water supply effectively, use it wisely, and plan for the future. Sustainable groundwater exploitation can be feasible only when there is knowledge of the rate at which groundwater is replenished annually. Therefore, the results of this study are useful in sustainable management plans, it may also be used as a benchmark for water supply authorities, policymakers and researchers to set proper protection measures and pumping policies.

Spatial and Temporal Solar Potential Variation Analysis in Uganda Using Measured Data

The paper presents an analysis of spatial and temporal solar potential variations in Uganda. The solar radiation potential distribution was investigated based on measured data from 56 ground meteorological stations across Uganda from January 2015 to February 2022. The data were recorded after every 5-minute interval. The monthly average daily Global Horizontal Irradiance (GHI) was analysed using the spatial inverse distance weighting (IDW) interpolation technique in ArcGIS 10.7.1. The GHI distribution was found to have maxima at equinox and minimum between June and July. The average global horizontal irradiation is found to be 1680 KWh per year, with a daily variability below 10%. This is above the threshold for solar energy applications and with such low variability, large scale solar PV plants can be installed with minimum backup requirements. Northern Uganda receives the highest average daily global horizontal irradiation of 5.38 Whm–2 day–1, while Western Uganda receives 4.16 Whm–2 day–1, the lowest. The average AM/PM ratio depicted a value above 2 for all regions in Uganda and therefore, optimal solar energy exploitation for fixed systems favours eastern orientation. The central, eastern and northern Uganda which is 69.3% of the country has relief favourable for large scale solar PV power plants and CSP facilities.
 Keywords: GHI, IDW, Global solar radiation, Solar insolation, PV power plants, Uganda

Analyzing physical and socio-economic factors for property crime incident in Addis Ababa, Ethiopia

Property crime has become a challenge in major cities of developing countries including Addis Ababa, Ethiopia. However, factors contributing to property crime have not been carefully examined. Therefore, this paper presents the physical and socio-economic factors that clearly have a substantial impact on property crime. In this study, both primary and secondary data were used. Recorded property crime was collected from Addis Ababa police commission. Property crime incidence locations were georefrenced using Google Earth as well as handheld Global Positioning System. Then, a property crime surface map and pattern were created using point pattern analysis and inverse distance weighted interpolation technique. The study area was then classified into low crime and hotspot areas based on the frequency of crime events as well as the created crime surface map. A total of 200 respondents (100 in high crime area and 100 in low crime area) were selected using purposive sampling techniques. Five key informants were selected purposely from senior police officers. Data were collected using questionnaires and in-depth interview. A binary logistic regression model was employed to analyze the collected data. The data analysis showed that physical features mainly commercial center, road with no street light and river courses were positively correlated at significant level with the committing of property crime. On the other hand, public participation in crime prevention, witness and crime events reporting influenced negatively the committing of property crime. In addition, unemployment, family background, education level, age, time, season and police patrolling activities determine the occurrence of property crime events. The finding depicts that the identified physical and socio-economic factors can influence the patterns and rate of crime incidents in the study area. Therefore, the law enforcement officers should consider these influential factors to deploy police officers and to reduce property crime problems.

Vulnerability of groundwater to pollution at the Dabiss bauxite mining area, Boké Prefecture, Republic of Guinea

ABSTRACT Boké Prefecture has become a popular mining location, with various extractive corporations competing for bauxite extraction. Water samples were obtained from eighteen water wells across the Dabiss study area. All Physico-chemical parameters and major ions of groundwater were compared with World Health Organization (WHO) standard. Furthermore, using Excel and the Inverse Distance Weighted (IDW) interpolation technique in Arc GIS software, bi-plots of Physico-chemical parameters and spatial variations of water quality maps were generated respectively. A vulnerability study of groundwater pollution by mining activities was conducted by applying the standard DRASTIC method. It was found that the drinking water samples of the study areas were usually within the water quality standards of WHO. According to physicochemical analysis, most of the groundwater in the study area appears to be acidic. An equilibrium was set up in the aquifer between the chemical composition of the water and that of the rocks. The DRASTIC method enabled us to show a low vulnerability index to groundwater pollution. The results of this study could prevent diseases linked to the consumption of water contaminated and improve the quality of life of the population and will serve as a reference document in other mining areas.

Novel application of Google earth engine interpolation algorithm for the development of geotechnical soil maps: a case study of mega-district

This article explores the novel application of the Google Earth Engine (GEE) platform using the inverse distance weighting (IDW) interpolation technique based on an improved version of the modified Shephard method for the development of specialized geotechnical soil maps (GSMs) for mega-district Lahore. The study area was assessed at multiple depths in terms of key geotechnical properties. The result depicts a predominance of fine-grained soil at a shallow depth of 3 m exhibiting low to moderate soil stiffness, consistency and activity along with a few zones of problematic soil with adverse geotechnical characteristics which require due attention. Whereas beyond 3 m the study area exhibits higher bearing values, low plasticity and non-active soil. Besides, the chemical content level was within permissible limits except for a few spots. The comprehensive statistical evaluation based on key performance indices indicates the efficient performance of the novel GEE-based IDW algorithm to yield significantly accurate maps.

Comparative analysis of long-term precipitation trends and its implication in the Modjo catchment, central Ethiopia

Abstract Understanding trends and variability of precipitation is essential to improve water resources utilization as well as agricultural activities. This study aims to investigate the spatiotemporal trends and variability of rainfall in the Modjo watershed, central Ethiopia. The Mann-Kendall trend (M–K) test, innovative trend analysis (ITA) and Sen's slope estimator were used to determine temporal trends, while the inverse distance weighted interpolation technique was adopted to visualize the spatial trends in time series. The result showed that complex patterns of rainfall variability that range from 16 to 59%, 18 to 63%, and 50 to 90% for the annual, summer, and spring seasons, respectively was observed over the study watershed. The result also indicated that significant trend (p < 0.05) in annual rainfall was detected only in 28.6% and 42.9% of the stations under the M-K test and ITA method, respectively, which indicates relatively more significant trends are displayed by the ITA method than the M–K test. At the seasonal scale, positive trends have been more dominant in the summer season (Z > 0, SITA > 0), whereas negative trends (Z < 0, SITA < 0) were detected in the spring season. Comparatively, the ITA method is found to be robust and allows more detailed trend analysis results using graphical illustrations for extreme events. The study concludes that the increasing and decreasing trends in summer and spring rainfall patterns could have implications leading to an increase in extreme events and lower agricultural productivity, respectively. The result suggests the need for planning effective adaptation strategies at the regional and local scales.

GIS based surface elevation assessment of spring water quality of Shivalik Range of Jammu and Kashmir, India

Spring water is known as the prominent source to meet human's daily needs for water, particularly in Himalayan regions. This study is carried out in the sub-tropical, Shivalik range of the lower Himalayas of the Jammu division of the state of Jammu and Kashmir, India. The main objectives of the study are to find out the water quality of the study area based on the Geographic Information System (GIS) and find out the most sensitive parameter in the Water Quality Index (WQI) computation. The inverse distance weighted interpolation technique is used to generate spatial variation maps of the study area with the help of the data obtained. WQI is computed by using the Provencher and Lamontagne (1977) WQI equation. The results show that the water quality of the study area is excellent for the drinking purpose with respect to 11 parameters. The results also show that pH, Ca2+, and Na+are need to be monitored regularly with high accuracy as pH and ions of Ca2+ and Na+ are the most sensitive water quality parameters in the study area. This study finds a positive correlation between WQI and Surface elevation.

Assessment of Boro Rice Areas of Meghalaya Using Geospatial Technology

Boro rice areas were delineated from Sentinel 2B images of March, 2021 by using standard visual interpretation technique. Soil fertility status of Boro rice feilds was evaluated by using soil sample analysis data collected from Soil Health Card dashboard and soil sampled collected from different rice fields in GIS environment. Soil sample location map was prepared from latitude and longitude information and soil analysis data was linked to it. The soil fertility maps were generated through Inverse Distance Weighted (IDW) interpolation technique using Spatial Analyst tools of Arc Toolbox. The study revealed that Boro rice is cultivated over 7647.9ha which is 15% of the total geographical area of the Selsella block. The soils of Boro rice fields are non-saline, slightly acidic, high to medium in organic carbon and sufficient in iron, manganese, zinc and copper content. The availability of phosphorus and potassium varies from medium to low and nitrogen availability is high to medium.

Retrospective study on dengue transmission

Weather variables, mainly temperature and humidity, influence vectors, viruses, human biology, ecology and consequently the intensity and distribution of vector-borne diseases . The climatic factors connected with Dengue fever happening in Chennai were investigated. The factors accountable for Dengue fever have been recognized and mapped to dengue cases in Chennai city. This work studies the association between yearly climate patterns between 2016 and 2017 and the spread of Dengue fever in Chennai. Monthly rainfall and temperature is taken as the factors of study. The study develops a statistical analysis to quantitatively assess the relationship between climate and Dengue fever. The correlation technique was used to fit the statistical model. The Inverse Distance Weighted (IDW) interpolation and Geographic Information System (GIS) techniques were used in mapping the spatial diffusion of Dengue fever risk zones. The results show that there is a significant correlation between Dengue fever and climate factors for the majority of the study area. Statistical analysis was conducted for comparison of model outputs and predictions.

Spatial Interpolation of the Concentrations of Particulate Matter and Carbon Dioxide of Some Selected Tourist Sites in Srinagar City, Jammu and Kashmir, India

This investigation was carried out to monitor and prepare thematic maps of the monthly spatial variability of the ambient concentrations of particulate matter (PM1, PM2.5, PM4, PM10, and TSP) and carbon dioxide (CO2) of some selected tourist sites (viz. Shalimar Garden, Chesmashahi Botanical Garden, Harwan Garden, Nishat Garden, Naseem Bagh, Lal Chowk, and Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir (SKUAST-K) Shalimar campus of Srinagar city, Kashmir valley from November 2019 to April 2020 using the Inverse Distance Weighting (IDW) interpolation technique in Quantum Geographical Information System (QGIS). Considering Srinagar city as a growing city in terms of population, construction, vehicles, etc., the Jammu and Kashmir State Pollution Control Board in a report on managing air quality in Srinagar city identified the pollution sources in the city with their estimated source proportion as follows: vehicular emission (65-75 %), dust from bad roads (10-15 %), biomass and garbage burning (10-20 %), construction and demolition emissions (5-8 %), minor industrial activities (7-8 %) and other sources (3 %). Thus, Srinagar city’s air quality is deteriorated by these sources. Therefore, this research attempts to reveal the monthly variation and spatial interpolation of particulate matter and CO2¬. The Aerocet 831-Aerosol Mass Monitor and CDM 901-CO2 Monitor were used fortnightly in each month to monitor the ambient concentration of particulate matter and CO2 in the morning, afternoon, and evening and the sampling was carried out by taking three replications. From the data, the average mean morning, afternoon and evening concentrations of each pollutant at all the monitoring sites were as follows respectively: PM1 (67.07, 55.87, 57.62 µg/m3); PM2.5 (129.49, 95.52, 112.50 µg/m3); PM4 (170.44, 121.09, 161.81 µg/m3); PM10 (315.49, 203.09, 383.97 µg/m3); TSP (376.43, 240.49,496.55 µg/m3); and CO2 (595.33, 557.94, 601.07 ppm) showing that the morning and evening concentrations of pollutants were highest in Srinagar city. Also, the data and IDW maps make it clear that there was a statistically significant (p ≤ 0.05) variation of the monthly mean and the average six months concentrations of the monitored pollutants between most of the monitoring sites. This informs us that the concentration of particulate matter and carbon dioxide varies on monthly basis with distance from one location to another in Srinagar city. The correlation of the monthly average of most locations was non-significantly positive between most parameters but significantly strongly positive between PM1, PM2.5, and PM4 at p ≤ 0.01 with each other. Also, there was a significantly strong positive correlation (p ≤ 0.05) between PM4 and PM10, and likewise PM10 and TSP. Thus, indicating that all the monitored parameters increase or decrease with each other simultaneously. It is therefore concluded that the poor air quality of Srinagar city varies with distance as depicted by the data and IDW maps with respect to the monitoring sites and the monitored pollutants. Thus, giving an idea of the pollutants blanket over the city.

Landmark-guided region-based spatial normalization for functional magnetic resonance imaging.

As the size of the neuroimaging cohorts being increased to address key questions in the field of cognitive neuroscience, cognitive aging, and neurodegenerative diseases, the accuracy of the spatial normalization as an essential preprocessing step becomes extremely important. Existing spatial normalization methods have poor accuracy particularly when dealing with the highly convoluted human cerebral cortex and when brain morphology is severely altered (e.g., aging populations). To address this shortcoming, we propose a novel spatial normalization technique that takes advantage of the existing surface‐based human brain parcellation to automatically identify and match regional landmarks. To simplify the nonlinear whole brain registration, the identified landmarks of each region and its counterpart are registered independently with topology‐preserving deformation. Next, the regional warping fields are combined by an inverse distance weighted interpolation technique to have a global warping field for the whole brain. To ensure that the final warping field is topology‐preserving, we used simultaneously forward and reverse maps with certain symmetric constraints to yield bijectivity. We have evaluated our proposed solution using both simulated and real (structural and functional) human brain images. Our evaluation shows that our solution can enhance structural correspondence compared to the existing methods. Such improvement also increases the sensitivity and specificity of the functional imaging studies, reducing the required number of subjects and subsequent study costs. We conclude that our proposed solution can effectively substitute existing substandard spatial normalization methods to deal with the demand of large cohorts which is now common in clinical and aging studies.

“Human elephant conflict in changing land-use land-cover scenario in and adjoining region of Buxa tiger reserve, India”

The land use land cover changes (LULCC) in and around wildlife habitat are one of the major driving forces behind frequent human elephant conflict (HEC). The present study aims to determine the spatiotemporal LULC changes and its impact on Human – Elephant conflict in and adjoining Buxa Tiger Reserve (BTR). Landsat TM and OLI images of 1990, 2000, 2010, and 2019 were used and supervised classification using maximum likelihood classification algorithm applied to classify the LULC attributes. Each sign of elephant presence was recorded using GPS used for corridor mapping. The recorded sign of conflict was collected, used for settlement based human elephant conflict probability zone identification mapping by areal interpolation method. We found that (1990 - 2019) dense forest, surface water bodies, scrub land and agricultural fallow land decreased by 184.04 sq km, 21.94 sq km, 142.48 sq km, and 130.21 sq km respectively. On the other hand crop land, sparse vegetation, tea garden, open forest, degraded forest and built up areas increased by 119.74 sq km, 90.96 sq km, 87.05 sq km, 57.02 sq km, 19.07 sq km and 32.21 sq km respectively. The visual overlay method was performed between LULC map, and settlement based HEC probability map, therefore impact of spatiotemporal LULC change on HEC was determined. It finds out moderate to very high HE conflict settlement zone lies where nearby forest tract either degraded or large areas of open forest, scrub land replaced into crop fields and settlements. In addition, elephant sign along the broad gauge rail line were collected on foot, recorded using GPS used to prepared accident prone susceptibility map by Inverse Distance Weighting (IDW) interpolation technique to estimate where accidents were more likely to occur due to the concentration of elephant activity. It finds out high to very high accident prone susceptible zone lies in between Rajabhatkhawa station (st) – Damanpur st, Gorapara st- Rajabhatkhawa st, and railway line within Hamiltonganj range. The study wants to draw attention to the concerned authority and policy makers on that there is a need of long term land use planning to save BTR forest and its biodiversity as well as need for proper mitigate method for HE conflict for peaceful coexistences of both the species.

Climate resilience of farming systems in steep mountain terrain of selected regions in South Asia

This study assessed the climate resilience and characterized the existing farming systems in steep terrain in the hilly regions in South Asia. The farming systems considered were at an elevation ≥300 m in the mountain regions of two sites from Sri Lanka (Hatton and Welimada) and one site each from Bangladesh (Chittagong) and Nepal (Jhikhu Khola). A Climate Resilience Index (CRIi) score, varying from 0 (negligible resilience) and 1 (very high resilience), was calculated for each household using 31 parameters under Adaptive Capacity (ADC), Absorptive Capacity (ABC) and Transformative Capacity (TC). To spatially represent the CRIi, the four study locations were mapped using Inverse Distance Weighted (IDW) interpolation technique of GIS. All 424 households in the study sites scored a CRIi between 0.36 and 0.76, while the average CRI was the highest in Hatton (0.67), followed by Welimada (0.60), Jhikhu Khola (0.59) and Chittagong (0.48). Different demographic, socioeconomic and environmental parameters have contributed to the level of climate resilience of farming system units. Identification of good management practices of the climate-resilient farming systems and implementing those practices in vulnerable systems would increase the resilience and well-being of farming communities in steep terrain of mountain regions in South Asia.