Distance Weighting Research Articles

Spatiotemporal trends and variability of rainfall across agro-ecologies in East Guji Zone, Southeast Ethiopia

Distribution and trends of rainfall reveal spatial and temporal variability that have a paramount effect on the life and livelihood of small-holder farmers. This study aimed to analyze spatial variability and temporal trends of rainfall distribution across the three Agro-Ecological Zones (AEZs) of East Guji. Time series gridded daily rainfall data (1990–2020) were collected from the Ethiopian Meteorological Institution. Different descriptive statistics, trend tests: Man Kendal and Sen’s slope estimator, Inverse Distance Weighted Index and Precipitation Concentration Index (PCI) was used in the study. The finding demonstrated that altitude and rainfall decrease as one advances from the western (highland) to the eastern (lowland) direction in the study area where the highest rainfall was recorded in Solemo (highland) and the least in Negele (lowland).The study showed that as altitude increases annual rainfall also increases and rainfall variability decreases. Similarly the mean length of the growing season declines as one advance from the highlands to the lowlands. The PCI of the lowlands, midlands, and highlands AEZs was 19%, 17%, and 12% respectively. The PCI showed that those highlands had moderately concentrated rainfall but both lowlands, and midlands, had an irregular distribution of rainfall. The Coefficient of Variation (CV) indicated that highland areas had moderate variability in rainfall in all seasons except winter. In contrast, the low and midlands had shown high variability of rainfall (>30%) in all seasons. From a seasonal perspective, both CV and PCI revealed that the winter season showed more variability than others. Moreover, a significant increasing trend of annual rainfall was observed in the highlands AEZs (Bore 15.3mm/year and Solemo14.6mm/year), lowland AEZs (Chembe 10.9mm/year, Dawa 8mm/year and Bitata 7.8mm/year) as well as midland AEZs (Kercha 14.5mm/year) at a significant level of 5%. Therefore, strategies should be designed to use additional water resources for irrigation; and provide short-cycle grown and drought-resistant crops in the rest of the midlands and lowlands AEZs.

Prediction, mapping, and implication for better soil organic carbon management in Ethiopia

AbstractA precise soil organic carbon (SOC) content estimate is crucial soil quality parameter for agricultural produce and ecological safety. Moreover, geospatial modeling of SOC is critical when there are limited laboratory equipment and chemical reagents for soil analysis. This study used geostatistics—ordinary kriging (OK) and inverse distance weighting (IDW)—to map SOC in Libokemkem area, Northwest Ethiopia, for improved SOC management. About 107 soil samples were obtained from the plow layer at a 20‐cm depth and SOC was determined. Statistical Package for Social Sciences version 24.0 was used to generate descriptive statistics, and geostatistical analysis was also performed on the data using ArcGIS platform. The coefficient of determination (R2) and root mean square error (RMSE) derived from the validation of the predicted maps were used to assess the models. The results revealed homogeneity (coefficient of variation < 10%), low (0.12%–1.74%), and optimal (1.74%–4.06%) mean levels of SOC in study area. The OK showed an R2 of 0.74 and an RMSE of 13%, and the IDW revealed an R2 of 0.69 and an RMSE of 14%. The semivariogram results indicate a moderate dependence for SOC with stable, circular, spherical, exponential, and Gaussian models. We conclude that the sustainable monitoring of SOC is significant in enhancing soil quality. However, further study considering all drivers of spatial variability for SOC in the study and other soil sampling approaches improving performance of the prediction models is needed.

Superomedial Pedicle Technique and Management of Circulation Problems in Gigantomastia : Treatment of Gigantomastia.

Breast reduction surgeries encompass a wide range of methods that are continuously evolving to discover more reliable and satisfactory techniques. This presentation aims to address the research gap by sharing outcomes and experiences using the superomedial pedicle in gigantomastia, as well as the implemented protocol for managing nipple-areola complex (NAC) ischemia. The Wise pattern and superomedial pedicle reduction mammaplasty method were utilized in treating 19 patients (38 breasts). The average age of the patients was 41.47 years, with a basal mass index (BMI) of 33.27 kg/m2. The mean sternal notch to nipple (SN-N) length for the entire population was found to be 40.97 cm. On both sides, this length was statistically similar at 41.11 cm on the right side and 40.84 cm on the left side. The average weight of resected tissue from all patients was calculated to be 1793.42 g, with slightly higher weight on the right side at 1800 g compared to the left side's weight of 1786.84 g. Postoperative NAC ischemia occurred in three patients, one bilateral case, and two unilateral cases. The study revealed that in both the groups with and without NAC ischemia, the average values were as follows: age, which ranged from 45.33 to 40.75 years; BMI, ranging from 35.01 kg/m2 to 32.95 kg/m2; SN-N distance, which varied from 40 cm to 41.09 cm; and excision material weights, ranging from 1650 g to 1810.29 g. The p-value in the comparisons was found to be greater than 0.05. These results indicate that age, BMI, SN-N distance, and excision material weight did not have an impact on NAC vascularity issues. All NACs were successfully saved through a protocol involving hyperbaric oxygen therapy (HOT) and vacuum-assisted therapy (VAT). The study suggests that utilizing a superomedial flap is a viable option for treating gigantomastia and highlights the effectiveness of their outlined protocol in managing postoperative complications. While acknowledging the need for comparative studies, the study proposes incorporating HOT and VAT into protocols aimed at saving NACs.Level of Evidence IV This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

A comparative evaluation of spatial interpolation techniques for maximum temperature series in the Montreal region, Canada

This study aims to provide a comparative analysis of two of the most used methods of spatial interpolation – Thiessen Polygons (TP) and Inverse Distance Weighting (IDW) with a spatio-temporal approach – Spatio-temporal kriging (STK) on a data series from Canada. The IDW parameter is optimized to obtain the best fitting for the studied series, based on the Root Mean Squared Errors (RMSE) and Mean Absolute Percentage Error (MAPE). The advantages and disadvantages of each algorithm are emphasized. Although TP registered the lowest RMSE and a MAPE, the analysis favors the STK use for modeling Montreal’s maximum temperature series.

Local physical gradient-based coupling information interpolate method and application on double-wall turbine blade multidisciplinary analysis

A novel generalization interpolate modification method based on local gradient is presented. The main idea was to remove the sample points located in the gradient direction away from the unknown points compared to the sample points located in the perpendicular direction. This method first uses the Taylor expansion to construct a set of equations and solves the local physical gradient. Subsequently, a scale vector is derived to modify the interpolate space. Three classical interpolation methods were modified: Inverse Distance Weighted (IDW), Radial Basis Function (RBF), and Kriging method. The results of the test functions prove that the proposed method can effectively decrease the interpolate error regardless of low or high dimensional interpolate problem and irrespective of the sample point size. Surface and volume interpolate validations demonstrate that the local gradient generates a large interpolate error, and the modified method can essentially diminish this error with a maximum reduction of 9.94 %. Subsequently, this method is applied to implement a multidisciplinary analysis of a practical double-wall cooling blade with a large physical gradient. The results showed that the pressure and temperature interpolate errors could be decreased by 5.18 % and 14.5 %, respectively, with only an additional 115 s, at the fillet and film hole area where the local gradient was high. When the strength analysis was implemented, the maximum stress error reduction was 25.04 MPa at the stress concentration region.

Remote Sensing & GIS Integration Bases Evaluation of Ground Water Quality in Telangana State, India

About 85% of drinking water needs in rural and 40% drinking water needs in urban areas are met from groundwater resources in Telangana state. There is a huge demand of ground water for different uses like agriculture, drinking and industrial purposes as the command area is very low in the state and it is evidenced by the fact that around 70% irrigation is dependent on ground water sources as compared to other surface sources like Tanks/Canals/Reservoirs etc. Climate change has affected the hydrological cycle with uncertain and increased variability in rainfall followed by with high rainfall in a short span of time resulting more surface runoff which ultimately decreases there charge to groundwater. Decreased recharge to the groundwater and excess withdrawal of groundwater has accelerated the depletion of groundwater resources there by resulting in stress on groundwater regime in terms of quality as well as quantity. The groundwater quality evaluation of Telangana state is carried out by using legacy quality data (2012-14) collected from Rural Water Supply & Sanitation Department, Government of Telangana. Contamination spread of groundwater is one of the most important concerns that have received attention at regional, local and global levels because of their importance on public health and its further impact in ecosystems. The spatial distribution of chemical parameters like pH, Total Dissolved Solids (TDS), Total Hardness (TH), Total Alkalinity (TA), Fluoride (F), Chloride (Cl), Iron (Fe), Nitrate (NO3) and Sulfate (SO4) are examined with respect to its contamination level. The spatial distribution and concentration of chemical elements is carried out by using spatial interpolation technique namely Inverse Distance Weightage (IDW) method from the point source data. The distribution maps reveal important information to understand the hot spot areas of the groundwater systems and for identification of potential areas for providing safe drinking water supply. Site specific water conservation and artificial recharge measures are needed to be taken up with scientific lines by using Remote Sensing & GIS techniques, for enhancing the groundwater storage and also improving its quality.

Hazard-mapping and health risk analysis of iron and arsenic contamination in the groundwater of Sylhet district.

This study investigates groundwater contamination by arsenic and iron and its health implications within the Sylhet district in Bangladesh. Utilizing geographic information system (GIS) and inverse distance weighting (IDW) methods, hazard maps have been developed to evaluate contamination risk across various upazilas. The findings show significant arsenic and iron pollution, particularly in the northwestern part of the district. In about 50% of the area, especially in Jaintiapur, Zakiganj, Companiganj, and Kanaighat where arsenic levels surpass 0.05 mg/L which is the standard limit of Bangladesh. Iron levels peak at 13.83 mg/L, severely impacting 45% of the region, especially in Gowainghat, northeastern Jaintiapur, Zakigonj, and Golabganj. The study employs USEPA health risk assessment methods to calculate the hazard quotient (HQ) and hazard index (HI) for both elements via oral and dermal exposure. Results indicate that children face greater noncarcinogenic and carcinogenic risks than adults, with oral HI showing significant risk in Balagonj and Bishwanath. Dermal adsorption pathways exhibit comparatively lower risks. Cancer risk assessments demonstrate high carcinogenic risks from oral arsenic intake in all areas. This comprehensive analysis highlights the urgent need for effective groundwater management and policy interventions in the Sylhet district to mitigate these health risks and ensure safe drinking water.

Experimental and Theoretical Investigation of External Electric-Field-Induced Crystallization of TKX-50 from Solution by Finite-Temperature String with Order Parameters as Collective Variables for Ionic Crystals.

External electric fields are an effective tool to induce phase transformations. The crystallization of ionic crystals from solution is a common phase transformation. However, understanding of mechanisms is poor at the molecular level. In this work, we carried out an experimental and theoretical investigation of the external electric-field-induced crystallization of TKX-50 from saturated formic acid solution by finite-temperature string (FTS) with order parameters (OPs) as collective variables for ionic crystals. The minimum-free-energy path was sketched by the string method in collective variables. The results show that the K-means clustering algorithm based on Euclidean distance and density weights can be used for enhanced sampling of the OPs in external electric-field-induced crystallization of ionic crystal from solution, which improves the conventional FTS. The crystallization from solution is a process of surface-mediated nucleation. The external electric field can accelerate the evolution of the string and decrease the difference in the potential of mean forces between the crystal and the transition state. Due to the significant change in OPs induced by the external electric field in nucleation, the crystalline quality was enhanced, which explains the experimental results that the external electric field enhanced the density, detonation velocity, and detonation pressure of TKX-50. This work provides an effective way to explore the crystallization of ionic crystals from solution at the molecular level, and it is useful for improving the properties of ionic crystal explosives by using external electric fields.

Robot joint space grid error compensation based on three-dimensional discrete point space circular fitting

The poor absolute positioning accuracy of industrial robots has limited their application in fields such as aerospace manufacturing. To address this issue, the spatial grid compensation method has been proposed as an effective solution. In this paper, we propose a sampling method based on three-dimensional discrete point space circular fitting for grid points to significantly reduce the sampling workload and improve compensation accuracy compared to traditional joint space grid compensation methods. Additionally, we use the Kriging interpolation algorithm instead of the inverse distance weighting (IDW) algorithm for spatial interpolation prediction of pose error. Based on this, the proposed sampling and interpolation prediction method in this paper was verified on a Comau NJ500–2.7 manipulator equipped with a fiber-laying end effector. The experimental results demonstrate that using our proposed sampling method yields pose data of grid points that have only a small deviation from directly sampled results and are only slightly higher than the robot's repeat positioning accuracy. Moreover, our proposed method can significantly reduce the sampling workload by 60% under the experimental conditions of this study (sampling 10 groups of data within 90 degrees range), with increasing sampling range leading to more obvious efficiency improvements. Finally, we show that compared to the IDW algorithm, the Kriging interpolation algorithm yields better results and improves the mean absolute positioning accuracy of robots after compensation by 30%.

Effects of selective laser melting process parameters on 3D-printing of Ti6Al4V/ST316L composite material and their optimization using response surface methodology

The mechanical properties of Ti6Al4V and ST316L bone implants make them superior to natural bone, which results in fewer contact points with the bone. Different manufacturing processes, such as selective laser melting (SLM), may result in different mechanical properties between Ti-6Al-4V and ST316L implants. Sustainable development for the composite implants was optimized (SLM) in this study to minimize their compressive strength and Young's modulus. A three-dimensional printing process using SLM was optimized based on laser power, hatch distance, laser velocity, and ST316L weight percentage. Composite implants made from titanium alloys and steel alloys were evaluated using response surface methodology (RSM). ST316L composition has been found to influence the mechanical properties of composites in a significant manner, based on the results of parameter optimization. Using Ti6Al4V/ST316L as a biomaterial in knee joint prostheses is possible.

Improving MODIS-IR precipitable water vapor based on the FIDWFT model

The monitoring of precipitable water vapor (PWV) is of significant importance for meteorological research and rainstorm prediction. The moderate resolution imaging spectroradiometer (MODIS) is one of the most widely used remote sensing PWV instruments aboard the Terra and Aqua satellites. The main objective of this study is to develop a fused inverse distance weighting and the Fourier transform model (FIDWFT) to calibrate MODIS Infrared (IR) PWV. In this research, one year of ground observation data from 17 GPS stations were utilized to evaluate the accuracy of MODIS IR PWV in Hong Kong. Initially, the PWV obtained by one radiosonde was used to verify the GPS PWV. Comparison between GPS and radiosonde showed good agreement with an R-squared of 0.97. After the estimation of GPS PWV in the study area was evaluated, MODIS IR products in the study area were extracted for evaluation. The comparison between MODIS and GPS showed that the R-squared is 0.81 and the mean bias (MB) is −1.60 mm. Considering the spatial interpolation and calibration problems of MODIS products, new model used an inverse distance weighting method to make MODIS IR PWV and GPS PWV completely coincide in space, and fifteen MODIS pixel points were selected for interpolation. Then the MODIS IR PWV was calibrated by GPS PWV using Fourier transform model. Results showed that compared with the MODIS IR PWV before calibration, the R-squared increases from 0.81 to 0.94, the root mean square error decreases from 8.72 mm to 4.98 mm, and the MB decreases from −1.60 mm to −0.83 mm. Therefore, the method is feasible to estimate MODIS IR water vapor, and it achieves better results.

Dual integration design of approximate random weight generator and computation-in-memory for event-based neuromorphic computing

This paper comprehensively analyses dual integration of approximate random weight generator (ARWG) and computation-in-memory for event-based neuromorphic computing. ARWG can generate approximate random weights and perform multiply-accumulate (MAC) operation for reservoir computing (RC) and random weight spiking neural network (SNN). Because of using device variation to generate random weights, ARWG does not require any random number generators (RNGs). Because RC and random weight SNN allow approximate randomness, ARWG only needs to generate approximate random weights, which does not require error-correcting code to correct weights to make the randomness accurate. Moreover, ARWG has a read port for MAC operation. In this paper, the randomness of random weights generated by the proposed ARWG is evaluated by Hamming distance and Hamming weight. As a result, this paper reveals that the randomness required for ARWG is much lower than that for physically unclonable functions and RNGs, and thus the proposed ARWG achieves high recognition accuracy.

ANALYSIS OF EXISTING METHODS FOR DETERMINING INTERMEDIATE GEOTECHNICAL CHARACTERISTICS OF SOILS

This article presents an overview of the main methods for determining the intermediate geotechnical characteristics of soils, emphasizing the importance of strict adherence to the testing regime. Deterministic and stochastic interpolation methods such as Thiessen polygons, inverse distance weighting (IDW), Spline and Kriging are discussed. Special attention is given to the problem of determining strength properties at various points, where only certain areas are to be investigated and intermediate values are obtained by interpolation methods. The analysis of geotechnical reliability using Kriging numerical models and methods of using geographic information systems for processing and presenting geotechnical data are also discussed.

Land suitability studies for paddy cultivation in Dadahup Swampy Irrigation Area

The Dadahup Swampy Irrigation Area is crucial in the government’s Food Estate initiative to enhance food security in Indonesia. Following extensive infrastructure improvements, including network rehabilitation and gate construction between 2020 and 2022, a pilot project was initiated in block A5. However, rice yields in this area still need to improve, primarily due to inadequate land management practices. This study evaluates and maps land suitability for paddy cultivation in the Dadahup Swamp Irrigation Area, employing a combination of laboratory data and field research integrated with geographic information system (GIS) technology. Various parameters were considered, such as climate factors (temperature and water availability), soil characteristics (drainage, texture, depth), peatland attributes (thickness, maturity), nutrient content (CEC, base saturation, pH, C-organic), available nutrients (nitrogen, phosphorus, potassium), salinity, alkalinity, pyrite depth, and inundation levels during planting. Then, thematic raster mapping was conducted using the Inverse Distance Weighted (IDW) method, followed by reclassification based on Food and Agriculture Organization (FAO) standards for land evaluation. The result categorized Block A5 into three suitability classes: S1 (highly suitable), S2 (moderately suitable), and S3 (marginally suitable). Approximately 22.5% of Block A5 is highly suitable (S1), 39.5% is moderately suitable (S2), and around 38% is marginally suitable (S3).

Analysis of determining the location of potential flood evacuation shelter in Butuh District, Purworejo Regency

Map of flood events in Purworejo Regency, on January 16 2019 there were 7 flood-affected subdistricts, namely Butuh District, Bayan District, Banyuurip District, Pituruh District, Purwodadi District, Grabag District, and Ngombol District. Butuh District is the area most affected by flooding in Purworejo Regency. There are 9 villages out of 41 villages in Butuh District that are not affected. The flooding in Butuh District was caused by the overflow of several rivers, such as tributaries of the Wawar River, Gebang Besar River, and Kedunggupit River. This study aims to determine the distribution of flood vulnerability and potential shelter flood disaster evacuation in Butuh District, Purworejo Regency. The water level is estimated to reach 1 meter and makes all access closed. According to the Commander of Kodim 0708 Purworejo, there were still some residents who did not want to be taken to a safe location and with a good approach, the residents were eventually evacuated. Losses caused by floods in Butuh District were only damage to household appliances and there were no casualties. Another problem caused by the flooding in Butuh District is itching of the skin. There must be efforts to prevent floods in the Butuh District so that they do not happen again and cause the same losses. Flood disaster mitigation is an action that must be taken. Interpolation IDW (Inverse Distance Weight) and scoring to get flood hazard results and digitization to get evacuation routes. The research results show that the area is prone to flooding. Interviews showed that the southern region experienced the worst flooding because the further south you go, the lower the slope and several villages in the southern region are affected by the discharge of water into rivers from other areas. Meanwhile, secondary data shows that the northeast to south and northwest regions of Butuh District experienced the worst flooding due to high rainfall and a soil profile that was saturated with water so that several areas that had flat and sloping slopes were affected by flooding. Interviews and potential secondary evacuation shelters obtained 18 evacuation shelters and 37 evacuation shelters.

Liquefaction potential analysis based on standard penetration test in coastal area (Case study: Loh Buaya, Rinca Island, Indonesia)

An area has liquefaction potential when it has a shallow groundwater level, loose sandy soil, and is prone to earthquakes. There are several areas with such criteria that have not been analysed for liquefaction potential. This study aims to analyse and plot the liquefaction potential in the coastal area of Loh Buaya, Rinca Island, East Nusa Tenggara Province. Soil investigation data, such as SPT, sieve analysis, and groundwater level, as well as earthquake history that occurred from 1922-2022, served as the main data for liquefaction potential analyses. The methods used were Ground Motion Equation Prediction (GMPE) to calculate Peak Ground Acceleration (PGA), Simplified Procedure, and Liquefaction Severity Index (LSI) to make a liquefaction hazard assessment. LSI scores were used to provide micro-zonation of liquefaction potential with Inverse Distance Weighted (IDW) interpolation in QGIS. The result obtained is very dense gravel has no liquefaction potential whereas loose sandy soil has very high in LSI classification because loose sandy soil has liquefaction potential up to 20 meters of depth. The applying of micro-zonation LSI by IDW interpolation method can estimate the potential level of liquefaction hazard on Loh Buaya, Rinca Island with limited soil investigation data.

Hybrid Multi-Infeed Receiver Line Longitudinal Protection Scheme Based on Voltage Waveform Comprehensive Distance Similarity.

The coupling of AC and DC power will impact the protective actions on the AC side and pose a threat to the stable operation of the interconnection system. Therefore, a new longitudinal protection method is proposed based on the comprehensive distance similarity of voltage waveforms. Initially, the measured voltage and current data are extracted to calculate the reference voltage, and the voltage waveform fitting is optimized. Subsequently, the Euclidean dynamic time warp (DTW) distance and entropy weight method are utilized to process the voltage waveform, enabling the calculation of its comprehensive distance similarity. This similarity is adopted to determine fault location. A hybrid DC multi-feed AC/DC interconnection system, incorporating a line commutated converter-voltage source converter (LCC-VSC) and a line commutated converter-modular multilevel converter (LCC-MMC), was established in PSCAD, and fault data were simulated and output. The effectiveness of the protection scheme was validated using MATLAB. Simulation results demonstrate that the proposed method can accurately distinguish between faults inside and outside a region. When compared to existing protection methods, it demonstrates superior performance in resisting transition resistance and noise interference, while also mitigating the impact of data asynchronicity. The speed and reliability of the method are further enhanced.

Study on the Relationship between Hotspots and Air Pollution Standard Index Based on Carbon Monoxide Concentration in West Kalimantan

Hotspots, indicating active fires and potential sources of air pollution, are a major concern due to their impact on air quality in West Kalimantan. In this study, an analysis was carried out regarding the relationship of hotspots to the Air Pollution Standard Index (ISPU) based on the carbon monoxide (CO) parameter. This study aimed to determine the relationship between forest fires and air pollution levels. The data used in this study is in the form of monthly average CO concentration data and coordinates of hotspots in 2017-2021. To determine the effect of CO concentration, CO concentration was converted into ISPU and then mapped using the Inverse Distance Weighting (IDW) interpolation method. In addition, the overlay technique is also applied to the map and the correlation coefficient calculation between ISPU data and hotspot data. The study results show that the annual average number of hotspots ranges from 769 to 3,612, while the average ISPU ranges from 22.21 to 59.03. The highest average number of hotspots occurred in 2019, with 3,612 hotspots and the highest average ISPU value of 59.03, categorized as moderate. However, when examined monthly, the highest average number of hotspots is observed in August and September, with 8,505 and 8,321 hotspots, respectively, and average ISPU values of 55.36 and 88.32, categorized as moderate. Furthermore, the correlation coefficient between the average number of hotspots and the average ISPU per month is 0.91, and per year is 0.98, indicating a very high relationship. Spatially, clustered hotspot locations correspond to higher ISPU values. This implies that as the number of hotspots increases, the ISPU value based on CO concentration increases.

Long-term ozone exposure and mortality in patients with chronic kidney disease: a large cohort study

BackgroundEpidemiologic studies on the effects of long-term exposure to ozone (O3) have shown inconclusive results. It is unclear whether to O3 has an effect on chronic kidney disease (CKD). We investigated the effects of O3 on mortality and renal outcome in CKD.MethodsWe included 61,073 participants and applied Cox proportional hazards models to examine the effects of ozone on the risk of end-stage renal disease (ESRD) and mortality in a two-pollutants model adjusted for socioeconomic status. We calculated the concentration of ozone exposure one year before enrollment and used inverse distance weighting (IDW) for interpolation, where the exposure was evenly distributed.ResultsIn the single pollutant model, O3 was significantly associated with an increased risk of ESRD and all-cause mortality. Based on the O3 concentration from IDW interpolation, this moving O3 average was significantly associated with an increased risk of ESRD and all-cause mortality. In a two-pollutants model, even after we adjusted for other measured pollutants, nitrogen dioxide did not attenuate the result for O3. The hazard ratio (HR) value for the district-level assessment is 1.025 with a 95% confidence interval (CI) of 1.014–1.035, while for the point-level assessment, the HR value is 1.04 with a 95% CI of 1.035–1.045. The impact of ozone on ESRD, hazard ratio (HR) values are, 1.049(95%CI: 1.044–1.054) at the district unit and 1.04 (95%CI: 1.031–1.05) at the individual address of the exposure assessment. The ozone hazard ratio for all-cause mortality was 1.012 (95% confidence interval: 1.008–1.017) for administrative districts and 1.04 (95% confidence interval: 1.031–1.05) for individual addresses.ConclusionsThis study suggests that long-term ambient O3 increases the risk of ESRD and mortality in CKD. The strategy to decrease O3 emissions will substantially benefit health and the environment.

Air Pollution PM10 Forecasting Maps in the Maritime Area of the Bay of Algeciras (Spain)

Predicting the levels of a pollutant in a given area is an open problem, mainly because historical data are typically available at certain locations, where monitoring stations are located, but not at all locations in the area. This work presents an approach based on developing predictions at each of the points where an immission station is available; in this case, based on shallow Artificial Neural Networks, ANNs, and then using a simple geostatistical interpolation algorithm (Inverse Distance Weighted, IDW), a pollutant map is constructed over the entire study area, thus providing predictions at each point in the plane. The ANN models are designed to make 1 h ahead and 4 h ahead predictions, using an autoregressive scheme as inputs (in the case of 4 h ahead as a jumping strategy). The results are then compared using the Friedman and Bonferroni tests to select the best model at each location, and predictions are made with all the best models. In general, to the 1 h ahead prediction models, the optimal models typically have fewer neurons and require minimal historical data. For instance, the best model in Algeciras has an R of almost 0.89 and consists of 1 hidden neuron and 3 to 5 lags, similar to Colegio Los Barrios. In the case of 4h ahead prediction, Colegio Carteya station shows the best model, with an R of almost 0.89 and a MSE of less than 240, including 5 hidden neurons and different lags from the past. The results are sufficiently adequate, especially in the case of predictions 4 h into the future. The aim is to integrate the models into a tool for citizens and administrations to make decisions.