Inverse Distance Weighting Technique Research Articles

Integrative Geospatial Analysis: Unveiling Insights through GIS Modeling and Statistical Evaluation of SPT‐N and Soil Types Data of New Kabul City, Afghanistan

The precise evaluation of subsurface soil information is paramount for effective infrastructure design and planning. Geotechnical soil maps (GSMs) play a pivotal role in estimating subsurface properties. As Kabul City, Afghanistan’s largest metropolitan area, undergoes rapid expansion and the demand for safer infrastructure rises, the necessity for precise geotechnical information becomes increasingly urgent. Nevertheless, there is a lack of comprehensive studies on urban geotechnical zoning maps in Afghanistan. Therefore, to this end, this study utilizes inverse distance weighting (IDW) interpolation approaches for the construction and characterization of GSMs using a traditional geographic information system (GIS). Data about soil types and standard penetration (N‐value) up to 30 m depth were extracted from 130 locations in New Kabul City, Afghanistan. GSMs incorporating N‐value and soil types were constructed utilizing the IDW technique within the ArcGIS platform. The outcomes reveal that the subjected region was dominated by cohesive soil with N‐values varying from 7 to 45 in three different sections. The findings exhibit strong empirical correlations between N‐values and depth, with an R2 value of 0.95 and an RMSD value of 0.71. Moreover, the correlation coefficient to predict soil type classification is 97%, and it stands at 95% for N‐value prediction. These results facilitate the rapid evaluation of subsoil strength and stiffness, offering valuable insights for project planners and feasibility researchers.

Quantification of rainfall, temperature, and reference evapotranspiration trend and their interrelationship in sub-climatic zones of Bangladesh

Rainfall, temperature, and reference evapotranspiration (ET0) have a significant influence on irrigation, aridity, flooding, and crop water requirements. The primary aims of this study were to analyze the trends in rainfall, temperature, and ET0 in seven sub-climatic zones of Bangladesh from 1989 to 2020, as well as examine their interrelationships. The Modified Mann-Kendall method was employed to assess trends, while linear regression was used for trend validation. ET0 was calculated using the FAO-56 Penman-Monteith method, and Sen’s slope was utilized to quantify the magnitude. Spatial analysis was conducted using Inverse Distance Weighting techniques. The findings revealed that annual rainfall increased only in the south-eastern zone, while the other zones experienced a decline. No significant changes were observed in annual maximum temperature, except in the south-eastern, north-eastern, and south-central zones, which showed variations ranging from 0.02 to 0.05 (°C/year). However, the yearly minimum temperature increased in all zones. Additionally, negative changes were observed in the annual magnitude of ET0 for all zones and seasons, except for the south-eastern and north-eastern zones, with a range of 0.01–0.02 mm/year. It was also noted that rainfall and ET0 displayed a strong decreasing relationship, except during the pre-monsoon season. Regarding regional variation, the northern regions exhibited a significant decreasing trend in both rainfall and ET0. The study identified key challenges, including water scarcity and irrigation difficulties due to declining rainfall and evapotranspiration, increased aridity, changing flood patterns, temperature-related impacts on crop growth, regional disparities in climate trends, and the need for effective climate change adaptation measures. Therefore, the study’s findings can contribute to knowledge in areas such as irrigation scheduling, promoting climate-smart agricultural practices, encouraging crop diversification to reduce dependence on water-intensive crops cultivation, and planning resilient water resource management to minimize the effects of environmental shifts, regulate human operations, and implement disaster remedial actions in Bangladesh.

Wastewater irrigation and its impact on crops in major cultivated belt of Rechna Doab, Pakistan

The purpose of this study was to evaluate the toxicity of wastewater that is used for irrigation in study area. This research focused on the wastewater irrigation in suburban areas of Faisalabad district. The study involved collection of wastewater samples from ten different locations from Faisalabad, Jaranwala and Saddar districts to assess its quality at different sites. Areas covered by wastewater irrigation ranged from 100 to 2000 acres and major crops were cultivated using this contaminated water, including wheat, cauliflower, radish, spinach, and fenugreek. Physiochemical characteristics of wastewater, including the presence of heavy metals, were measured. The ionic compositions and heavy metal toxicity of wastewater was determined to fall at poisonous levels. Physiochemical properties and quality indices used in the current study also depict the unsuitable nature of the wastewater used in the area. The sources of wastewater varied from drains, sewage water, and the Chakkera Disposal system. Maps generated in Arc Map 10.5 show the spatial distribution of constituents via the geostatistical IDW (Inverse Distance Weighted) technique. The Piper and Durov diagrams highlighted the wastewater chemistry and showed that the dominant water type obtained was NaCl. Results were statistically analyzed by a correlation matrix. Dominant ions, pH levels, electric conductivity (EC), and heavy metals were detected by lab analysis and results indicated variability in toxic levels of these properties within the site and the area using the wastewater irrigation system. Consequently, this indicated the unsuitable nature of using wastewater as an irrigation source. The study concludes that it is necessary to conduct proper treatments to irrigation water before it is used on crops.

Modelling spatiotemporal tendencies of climate types by Markov chain approach : A case study in Sanliurfa province in the south-eastern of Turkey

Identification of spatiotemporal tendencies of climate types may help water managers mitigate the negative impacts of droughts on water-demanding sectors. The primary objective of this study was to figure out the spatiotemporal tendencies of climatetypes in Sanliurfa province by using Erinc’s aridity index (EDI). To that end, long-term (1965-2018) annual precipitation and average annual maximum temperature series of meteorological stations were obtained and utilized to calculate the EDI series on a yearly basis. The EDI series of each station was divided into three periods, non-overlapping and successive, i.e., P1 (1965-1981), P2 (1982-1999) and P3 (2000-2018). Outliers were detected, andremoved from the EDI series; missing data were completed by regression analysis. The Markov transition probability matrix of the climate classes for the three periods was estimated for each station. Maps of the initial probability vectors and steady-state probabilities for the three periods of each climate class were generated by the inverse distance-weighted technique. Hypsometric curves for each climate class, as well as period, were developed and areal coverage of occurrence probabilities (OP) was determined. Results indicated that, as time progressed, the areal extent of severe-arid and arid climatic classes continued consistently to spread from the south to the north. Areas of semi-arid climate type showed a slight tendency towards the arid-climate type. Construction of large dams in the region could not prevent the shifts in the climate in favour of developing arid zones. The humid climate class is likely to vanish away in the future. Research led us to conclude that the expansion of the aridzone from south to northhas been alarming in terms of the adequacy of water resources. It is strongly recommended that spatiotemporal climate change studies should be periodically conducted in tandem with forest management practices for the region.

Groundwater chemistry, pollution and health risk assessment for nitrogen compounds. A case study in a suburban region of Romania

As groundwater is used as a source of drinking water, monitoring its quality is essential due to possible adverse effects on human health. Nitrogen compounds (nitrates, nitrites, ammonia) within certain concentration limits are natural components of the nitrogen cycle. Due to anthropogenic activities, high concentrations of nitrogen compounds are released into groundwater. The aim of this study was to evaluate the chemistry of groundwater in a suburban area in northeastern Romania, the sources of pollution with nitrogen compounds (nitrates, nitrites, ammonium), and the non-carcinogenic risk to human health associated with consumption in different groups age (women, men, and children) in the investigated region. The results showed that the concentration of nitrogen compounds varies from 5.12 to 98.3 mg/L for nitrates, from 0.008 to 85.2 mg/L for ammonium, and from 0.001 to 1.12 mg/L for nitrites. The maximum admissible concentrations have been exceeding 25%, 40% and respectively 10% of the total analyzed samples Bivariate graphs and Principal Component Analysis (PCA) were used to identify potential sources of nitrate, nitrites, and ammonium pollution of groundwater in the study area. The non-carcinogenic risk assessment for water consumption showed a hazard index (HItotal) for nitrogen compounds in groundwater in the investigated region, which ranged from 0.037 to 2.856 for men, between 0.054 and 3.427 for women, respectively between 0.080 and 6.145, for children. Spatial distribution maps using the Inverse Distance Weighting technique presented the geographical areas with the probability of groundwater contamination with nitrate, nitrite, and ammonium and the areas that pose a risk to human health by consuming groundwater in the study area for the three groups: men, women and children.

THE PERFORMANCE OF ESTIMATION TECHNIQUES FOR NICKEL LATERITE RESOURCE MODELING

The choice of estimation technique according to geological conditions and mineralization character is the main problem in estimating block grade of nickel laterite. CV (coefficient of variance) and variogram determine the choice of estimation technique for nickel laterite resource classification. This study aims to evaluate various techniques for estimating block grades and to select the appropriate method for the classification of nickel laterite resources. The basic statistical analysis is to find out the description of the data, while the variography is to find out the spatial correlation between the data. Nickel grade estimation results are based on Near Neighbor Polygon (NNP), Inverse Distance Weighting (IDW), and Ordinary Kriging (OK) techniques to determine the classification of nickel resources. Accuracy levels are based on cross-sectional visualization comparisons, plan views, probability plots and linear regression analysis. The OK technique were not superior in grade estimation, especially in nickel laterite deposits. The results showed that the IDW technique was suitable to be applied to the limonite zone, while the NNP technique was suitable to be applied to the saprolite zone. Based on the performance of the estimation technique, the weighted average method can be applied for the classification of inferred, indicated, and measurable resources. The grade-tonnage curve shows the nickel laterite resource potential in the study area.

RASPOTION—A New Global PET Dataset by Means of Remote Monthly Temperature Data and Parametric Modelling

Regional estimations of Potential Evapotranspiration (PET) are of key interest for a number of geosciences, particularly those that are water-related (hydrology, agrometeorology). Therefore, several models have been developed for the consistent quantification of different time scales (hourly, daily, monthly, annual). During the last few decades, remote sensing techniques have continued to grow rapidly with the simultaneous development of new local and regional evapotranspiration datasets. Here, we develop a novel set T maps over the globe, namely RASPOTION, for the period 2003 to 2016, by integrating: (a) mean climatic data at 4088 stations, extracted by the FAO-CLIMWAT database; (b) mean monthly PET estimates by the Penman–Monteith method, at the aforementioned locations; (c) mean monthly PET estimates by a recently proposed parametric model, calibrated against local Penman–Monteith data; (d) spatially interpolated parameters of the Parametric PET model over the globe, using the Inverse Distance Weighting technique; and (e) remote sensing mean monthly air temperature data. The RASPOTION dataset was validated with in situ samples (USA, Germany, Spain, Ireland, Greece, Australia, China) and by using a spatial Penman–Monteith estimates in England. The results in both cases are satisfactory. The main objective is to demonstrate the practical usefulness of these PET map products across different research disciplines and spatiotemporal scales, towards assisting decision making for both short- and long-term hydro-climatic policy actions.

Classification Maps for TDS Concentrations in the GIS Along Euphrates River, Iraq

Iraq currently undergoing the problem of water shortage, although Iraq has two Rivers (Euphrates and Tigris) pass throughout most of its areas, and they have represented a major source of water supply. In the current research, to evaluate the quality of the Euphrates river in Iraq based on the values of total dissolved salts (TDS), the TDS concentrations were collected from sixteen sections along the river in the three succeeding years (2011, 2012, and 2013). The evaluation of the river was done depending on the classification of (W.H.O. (World Health Organization). (2003). Total Dissolved Salts in Drinking-water: Background document for development of W.H.O. Guidelines for Drinking-water Quality. World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland). of rivers for drinking uses. Inverse Distance Weighting Technique (IDWT) as a tool in the GIS was employed to establish the maps of the river that using interpolation/prediction for the TDS concentrations to each selected year and the average values of TDS for these 3 years. Based on the five categories of rivers’ classification of the TDS concentrations according to the (W.H.O. (World Health Organization). (2003). Total Dissolved Salts in Drinking-water: Background document for development of W.H.O. Guidelines for Drinking-water Quality. World Health Organization, 20 Avenue Appia, 1211 Geneva 27, Switzerland), the Euphrates river was classified, and the maps of classification for the years 2011, 2012 and 2013 and the average values for 3 years were created. The average values for 3 years of TDS along the Euphrates river indicated that the sections from SC-1 to SC-4 as moderate-water-quality-Category-3, the sections from SC-5 to SC-10 as poor-water-quality-Category-4, while the sections between SC-11 to SC-16 as very poor-water-quality-Category-5. The interpolation maps showed that the Euphrates river in Iraq was ranged from moderate water quality (Category-3) to very poor water quality (Category-5).

Spatial-Temporal Analysis of Air Pollutant Concentrations in Relation to Land Use and School Environment

Excessive exposure schoolchildren to air pollution can lead to long-lasting health problems, allergies and respiratory disease. It is well known that the major factors contributing to increase of air pollution are motor vehicles and industries. Thus, it is important to analyze the spatial temporal air pollutant concentrations and its relation with school location as the location of schools and its surrounding can increase their exposure. In this study, six schools in Johor were selected and the land use surrounding the schools were updated using ArcGIS. The Inverse Distance Weighting (IDW) interpolation technique was used to identify which schools’ area in Johor has a higher range of air pollutant concentration. There are four air pollution parameters obtained from the Department of Environment (DOE) which are PM2.5, CO, O3 and SO2. Hourly air pollutant concentration reading was obtained from the DOE in order to analyze air pollutant concentration during school period. The results obtained from the IDW technique showed that Sekolah Menengah Pasir Gudang (2) located in Pasir Gudang, Malaysia has reached a very unhealthy and hazardous level as compared to other schools in Johor. On the other hand, Sekolah Menengah Kebangsaan Tanjung Pengelih, Pengerang, Malaysia showed good to unhealthy range as compared to other schools in Johor. The spatial autocorrelation tool was used to analyze the relationship between the air pollution concentration and the school’s location in Johor. The results showed that the Moran’s Indices is positive showing a strong relationship that is clustering. It can be stated that there is a relationship between air pollutant concentrations with the school locations.

GIS-based assessment of water quality for Chambal River, Kota City

The prevailing research focuses on physico-chemical attributes of Chambal river, a perennial and major water source for Kota city (The commercial zone of Rajasthan, is ensconced on the shore of Chambal river) with various socioeconomic fields marked it’s uses in industries, in thermal power station, annual irrigation of 5668.01sq km from Kota barrage, domestic uses, etc. and estimated via geographical information system and inverse distance weighting (IDW) techniques. Hydrological module of Arc-GIS mapping utilized to prepare maps of every single parameter and interpolated through IDW technique. The study was carried out in the Kota city for 4 stations, targeted water samples collected through National water monitoring program (NWMP) and analyzed. The selected water quality parameters are pH, total dissolved solids (TDS), electric conductivity (EC), nitrate content, fluoride (F), BOD, hardness. Seasonal variation in these parameters were observed throughout the monthly study period and parameters like Electric conductivity (EC) and hardness shown fluctuations in concentration during Monsoon period. Overall tested water quality parameters are in permissible limit.

A monitoring of the spatial and temporal evolutions of aridity in northern Algeria

In order to enhance the understanding of the potential impact of climate change on water availability in the North of Algeria, the spatial and temporal evolutions of the De Martonne aridity index and the Pinna combinative index during the 1980–2018 period were investigated in the present study. The temporal trends in the aridity index time series were examined using the Mann–Kendall and Sen’s slope estimator tests, while the spatial interpolation of the aridity indices and their trends were conducted in a geographic information system environment using the Inverse Distance Weighting technique. The most important outcomes of this study are that the De Martonne aridity index is more appropriate for climate classification in the North of Algeria and that there were no statistically significant aridity changes in most regions during the studied period even though two regional behaviors with respect to aridity index trends were identified in the study area.

Improvement of daily precipitation estimations using PRISM with inverse-distance weighting

Improved daily precipitation estimations were attempted using the parameter-elevation regressions on a parameter-elevation regression on independent slopes model (PRISM) with inverse-distance weighting (IDW) and a precipitation-masking algorithm for precipitation areas. The PRISM (PRISM_ORG) suffers two overestimation problems when the daily precipitation is estimated: overestimation of the precipitation intensity in mountainous regions and overestimation of the local precipitation areas. In order to solve the problem of overestimating the precipitation intensity, we used the IDW technique that employs the same input stations as those used in the PRISM regression (PRISM_IDW). A precipitation-masking algorithm that selectively masks the precipitation estimation grid points was additionally applied to the PRISM_IDW results (PRISM_MSK). For 6 months from March to August 2012, daily precipitation data were produced in a horizontal resolution of 1 km based on the above two experiments and PRISM_ORG. Afterwards, each experiment was evaluated for improvements. The monthly root mean squared errors (RMSEs) of PRISM_IDW and PRISM_MSK were reduced by 0.83 mm/day and 0.86 mm/day, respectively, compared to PRISM_ORG.

Subsurface 3D analysis, modeling, and presentation of pollutant nitrate in semi-arid region.

The work incorporates the field data collection, assessment, and use of interpolation and extrapolation methods in modeling the existence of pollutant in groundwater, namely nitrate, and future prediction. To view the variation of concentration of pollutants in any form, 3D representation is required. However, not many techniques are available to achieve the objective accurately. Many modeling studies are done but very little is known about the variation of a group of chemical parameters at vertical scale in any of the climatic zone or habitable region. This kind of study is needed to help stakeholders for better planning. Earlier studies do not show the variation of chemical parameters (contaminants) at vertical scale in the climatic zone, and modellings are very objective specific. Present work presents 3D models using Inverse Distance Weighting technique in Matlab. The concentration pattern of nitrate is studied in 3D and presented in lucid manner at regional scale. The 3D block presentation demonstrates its affiliation and dispersion. The relationship from these models between parameter fixation and profundity shows the presence of distinct layers up to desired depth. The relationship plots are developed to extract the information how the groundwater quality is being transmitted beneath the surface. The projection is verified with the real field data, which will help in future resource management actions and minimize the pollution risks to mankind and the environment. The modeling helps in selecting the danger zones for ground water recharge and discharge for natural cause of elevated concentration of nitrate in groundwater. This study opens up the methodology for finding the variation of other contaminants against depth and that of total water quality.

A POD‐selective inverse distance weighting method for fast parametrized shape morphing

SummaryEfficient shape morphing techniques play a crucial role in the approximation of partial differential equations defined in parametrized domains, such as for fluid‐structure interaction or shape optimization problems. In this paper, we focus on inverse distance weighting (IDW) interpolation techniques, where a reference domain is morphed into a deformed one via the displacement of a set of control points. We aim at reducing the computational burden characterizing a standard IDW approach without significantly compromising the accuracy. To this aim, first we propose an improvement of IDW based on a geometric criterion that automatically selects a subset of the original set of control points. Then, we combine this new approach with a dimensionality reduction technique based on a proper orthogonal decomposition of the set of admissible displacements. This choice further reduces computational costs. We verify the performances of the new IDW techniques on several tests by investigating the trade‐off reached in terms of accuracy and efficiency.

APPLICABILITY OF VARIOUS INTERPOLATION APPROACHES FOR HIGH RESOLUTION SPATIAL MAPPING OF CLIMATE DATA IN KOREA

Abstract. The purpose of this study is to create a new dataset of spatially interpolated monthly climate data for South Korea at high spatial resolution (approximately 30m) by performing various spatio-statistical interpolation and comparing with forecast LDAPS gridded climate data provided from Korea Meterological Administration (KMA). Automatic Weather System (AWS) and Automated Synoptic Observing System (ASOS) data in 2017 obtained from KMA were included for the spatial mapping of temperature and rainfall; instantaneous temperature and 1-hour accumulated precipitation at 09:00 am on 31th March, 21th June, 23th September, and 24th December. Among observation data, 80 percent of the total point (478) and remaining 120 points were used for interpolations and for quantification, respectively. With the training data and digital elevation model (DEM) with 30 m resolution, inverse distance weighting (IDW), co-kriging, and kriging were performed by using ArcGIS10.3.1 software and Python 3.6.4. Bias and root mean square were computed to compare prediction performance quantitatively. When statistical analysis was performed for each cluster using 20 % validation data, co kriging was more suitable for spatialization of instantaneous temperature than other interpolation method. On the other hand, IDW technique was appropriate for spatialization of precipitation.

Producing Erosion Risk Map Using Remote Sensing Techniques and RUSLE Method in Halfalı Stream Watershed of Kahramanmaras

In this study, it is aimed to determine the potential erosion risk condition of the area using remote sensing techniques and RUSLE method in Halfalı Stream Watershed, which is 25 km away from Kahramanmaras province. With the purpose of applying this method, rainfall erosivitiy index (R) has been generated by means of modified fournier index (MFI).Soil erodibility factor (K) has been produced using Inverse Distance Weighted technique and soil sample obtained from study area. Slope length and slope steepness factor (LS) has been created by the help of digital terrain elevation model. Vegetation management factor (C) has been generated using remote sensing techniques. Whole factors obtained have been overlapped in Geographic Information System (GIS) and soil loss belonging to study area has been determined using raster calculator command within map algebra module. It has been divided into 5 classes and erosion risk map has been produced. According to potential erosion risk map obtained, 60.35 % of area has been subject to very low erosion risk, while 2.60% , 6.08 %, 10.37 % and 20.60 % has been subject to low, medium, high and very high erosion risk, respectively.

Spatiotemporal Interpolation for Environmental Modelling.

A variation of the reduction-based approach to spatiotemporal interpolation (STI), in which time is treated independently from the spatial dimensions, is proposed in this paper. We reviewed and compared three widely-used spatial interpolation techniques: ordinary kriging, inverse distance weighting and the triangular irregular network. We also proposed a new distribution-based distance weighting (DDW) spatial interpolation method. In this study, we utilised one year of Tasmania’s South Esk Hydrology model developed by CSIRO. Root mean squared error statistical methods were performed for performance evaluations. Our results show that the proposed reduction approach is superior to the extension approach to STI. However, the proposed DDW provides little benefit compared to the conventional inverse distance weighting (IDW) method. We suggest that the improved IDW technique, with the reduction approach used for the temporal dimension, is the optimal combination for large-scale spatiotemporal interpolation within environmental modelling applications.

Modelling of Surface Air Temperature Element in Malaysia

In spatial interpolation approaches, attribute data assumes continuous over space and spatially dependence. The surface air temperature is a collection of data, which are observed at discrete locations. Normally, spatial interpolation analysis applies to produce a continuous surface of this discrete data. There are a number of spatial interpolation techniques available to create continuous distribution of surface air temperature. To generate map of surface air temperature, this study examined the interpolation technique of inverse distance weighting (IDW). In applying the IDW technique, two different types of main data were assessed, i.e. mean monthly temperature data of T and estimation error of T – T’, where T was observed mean monthly surface air temperature and T’ was estimated mean monthly mean surface air temperature from a multiple regression. The multiple regression model was developed based on eight independent variables of elevation, location of latitude and longitude, distances of a station to nearest coastline, four types of land use, which included water bodies, forest, agriculture and build up areas. Cross validation analysis was conducted to calculate five different measured of errors. Inverse distance weighting (IDW) spatial interpolation of T ‐ Tʹ main data was produced acceptable errors and reliable map for mean monthly mean surface air temperature element in Peninsular Malaysia.

Geospatial modelling for groundwater quality mapping: a case study of Rupnagar district, Punjab, India

Abstract. Over period of time, the water usage and management is under stress for various reasons including pollution in both surface and subsurface. The groundwater quality decreases due to the solid waste from urban and industrial nodes, rapid use of insecticides and pesticides in agricultural practices. In this study, ground water quality maps for Rupnagar district of Punjab has been prepared using geospatial interpolation technique through Inverse Distance Weighted (IDW) approach. IDW technique has been used for major ground water quality parameters observed from the field samples like Arsenic, Hardness, pH, Iron, Fluoride, TDS, and Sulphate. To assess the ground water quality of the Rupnagar district, total 280 numbers of samples from various sources of tubewells for both pre and post monsoon have collected. Out of which, 80 to 113 samples found Iron with non potable limits ranging 0.3–1.1mg/l and 0.3–1.02mg/l according to BIS standard for both the seasons respectively. Chamkaur Sahib, Rupnagar, Morinda blocks have been found non potable limit of iron in both pre & post-monsoon. 11 to 52 samples in this region have sulphate with permissible limits in both the season ranging 200–400mg/l and 201–400mg/l. But arsenic had acceptable limit in both the season. Various parameters-wise ground water quality map is generated using the range values of drinking water quality to know the distribution of different parameters and diversification in the concentration of different elements. These maps are very much needful for human being to expand awareness among the people to maintain the Cleanness of water at their highest quality and purity levels to achieve a healthy life.

Methods for Characterizing Fine Particulate Matter Using Ground Observations and Remotely Sensed Data: Potential Use for Environmental Public Health Surveillance

This study describes and demonstrates different techniques for surface fitting daily environmental hazards data of particulate matter with aerodynamic diameter less than or equal to 2.5 μm (PM2.5) for the purpose of inte grating respiratory health and environmental data for the Centers for Disease Control and Prevention (CDC) pilot study of Health and Environment Linked for Information Exchange (HELIX)–Atlanta. It presents a methodology for estimating daily spatial surfaces of ground-level PM2.5 concentrations using the B-Spline and inverse distance weighting (IDW) surface-fitting techniques, leveraging National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectrometer (MODIS) data to complement U.S. Environmental Protection Agency (EPA) ground observation data. The study used measurements of ambient PM2.5 from the EPA database for the year 2003 as well as PM2.5 estimates derived from NASA’s satellite data. Hazard data have been processed to derive the surrogate PM2.5 exposure estimates. This paper shows that merging MODIS remote sensing data with surface observations of PM2.5 not only provides a more complete daily representation of PM2.5 than either dataset alone would allow, but it also reduces the errors in the PM2.5- estimated surfaces. The results of this study also show that although the IDW technique can introduce some numerical artifacts that could be due to its interpolating nature, which assumes that the maxima and minima can occur only at the observation points, the daily IDW PM2.5 surfaces had smaller errors in general, with respect to observations, than those of the B-Spline surfaces. Finally, the methods discussed in this paper establish a foundation for environmental public health linkage and association studies for which determining the concentrations of an environmental hazard such as PM2.5 with high accuracy is critical.