Influence of spatial distribution pattern of buildings on the distribution of urban gaseous pollutants.

Buildings are the main component of urban, and their three-dimensional spatial patterns affect meteorological conditions and consequently, the spatial distribution of gaseous pollutants (CO, NO, NO2, and SO2). This study uses the Jinan Central District as the study area and constructs a building spatial distribution index system based on DEM, urban road network, and building big data. ANOVA and spatial regression models were used to study the effects of building spatial distribution indicators on the distribution of gaseous pollutants along with their spatial heterogeneity. The results showed that (1) the effects of most of spatial distribution indexes of building on the concentration distribution of the four gaseous pollutants were significant, with one-way ANOVA outcomes reaching a significance level of 0.01 or more. The DEM mean, building altitude, and their interaction with other building spatial distribution indicators are important factors affecting the distribution of gaseous pollutants; The interaction of other three-factor indicators did not have a significant effect on the distribution of gaseous pollutant concentrations. (2) The spatial distribution of CO and NO2 is mainly influenced by the indicators of the spatial distribution of buildings in this study unit, and the effects of CO and NO2 concentrations in adjacent study units are the result of the action of stochastic factors. The NO and SO2 concentrations are influenced by the spatial distribution index of buildings in this study unit, the neighborhood homogeneity index, and NO and SO2 concentrations. (3) Spatial heterogeneity was observed in the effects of building spatial distribution indicators on the concentrations of different pollutants. The GWR models constructed using CO and NO concentrations and building spatial distribution indicators were well fitted globally and locally. The CO and NO concentrations were negatively correlated with the mean topographic elevation and NO concentrations were correlated with building density.

A Stabilized Kriging Method for Mapping Disease Rates.

BACKGROUND: Mapping disease rates is an important aspect of epidemiological research because it helps inform public health policy. Disease maps are often drawn according to local administrative areas (LAAs), such as counties, cities, or towns. In LAAs with small populations, disease rates are unstable and are prone to appear extremely high or low. The empirical Bayes methods consider variance differences among different LAAs, thereby stabilizing the disease rates. The methods of kriging break the constraints of geopolitical boundaries and produce a smooth curved surface in the form of contour lines, but the methods lack the stabilizing effect of the empirical Bayes methods. METHODS: An easy-to-implement stabilized kriging method is proposed to map disease rates, which allows different errors in different LAAs. RESULTS: Monte Carlo simulations revealed that the stabilized kriging method had smaller symmetric mean absolute percentage error than three other types of methods (the original LAA-based method, empirical Bayes methods, and traditional kriging methods) in nearly all scenarios considered. Real-world data analysis of oral cancer incidence rates in men from Taiwan demonstrated that the age-standardized rates in the central mountainous sparsely-populated region of Taiwan were stabilized using our proposed method, with no more large differences in numerical values, whereas the rates in other populous regions were not over-smoothed. Additionally, the stabilized kriging map had improved resolution and helped locate several hot and cold spots in the incidence rates of oral cancer. CONCLUSION: We recommend the use of the stabilized kriging method for mapping disease rates.

Groundwater quality evaluation based on water quality indices (WQI) using GIS: Maadher plain of Hodna, Northern Algeria.

In a semi-arid region of Maadher, central Hodna (Algeria), groundwater is the main source for agricultural and domestic purposes. Anthropogenic activities and the presence of climate change's effects have a significant impact on the region's groundwater quality. This study's goals were to use water quality indices to evaluate the groundwater's quality and its suitability for drinking and irrigation, as well as to identify contaminated wells using a geographic information system (GIS) and the spatial interpolation techniques of ordinary kriging and inverse distance weighting (IDW). The results reveal that all water samples exceeded the World Health Organization's standards for nitrate ions and had alarming concentrations of calcium, chlorine, and sulfate (WHO). According to Piper's diagram, the groundwater hydrochemical facies is composed of the elements sulfate-chloride-nitrate-calcium (SO42--Cl-NO3--Ca2+ water type). The majority of samples fall into the poor water category, slightly more than 10% fall into the very poor water category, and less than 10% fall into the good to the excellent quality category, per the water quality indices, which classify samples in a similar manner. According to irrigation water indices, every sample is suitable for irrigation. Depending on the direction of groundwater flow, the spatial distributions of Ca2+, Na+, Mg2+, SO42-, and Cl- show that their concentrations are high north of the area and relatively low south of Maadher village (Fig. 3). Nitrate concentrations are high in the majority of samples, particularly those close to the Bousaada wadi. In most samples, particularly those close to the Bousaada wadi, nitrate levels are high. Various water quality models were described, and GIS spatial distribution maps were created using standard kriging and inverse distance weighting (IDW) techniques through selected semi-variograms predicted against measurements. To determine the origin of mineralization and the chemical processes that take place in the aquifer-which include the precipitation and dissolution of dolomite, calcite, aragonite, gypsum, anhydrite, and halite-the groundwater saturation index was calculated.

Simultaneous identification of contaminant sources and hydraulic conductivity field by combining geostatistics method with self-organizing maps algorithm.

In the contaminant remediation of groundwater, the release history of contaminant sources and hydraulic conductivity field are two key parameters that need to know, but their actual values are difficult to obtain and can only be inversely identified by limited measured data. However, the process of solving the inverse problem needs to repeatedly call the forward model of contaminant transport, which is very time-consuming, especially for the high-dimensional inverse problems. In this study, based on the training data generated from a prior range of parameters (the release strength of contaminant sources and hydraulic conductivity at pilot points), the self-organizing maps (SOM) algorithm was employed to construct the surrogate model for the numerical model of contaminant transport in a simplified hypothetical aquifer, then the surrogate model was used to retrieve jointly the contaminant strength of sources and the hydraulic conductivity at pilot points, and the Kriging method of geostatistics was further used to process the estimated K-values at pilot points to obtain the hydraulic conductivity field. Also, to investigate the ability of the SOM-based surrogate model for retrieving both contaminant source strengths and hydraulic conductivity, we gradually expanded the prior range and increased the number of inversion terms in each prior range. Moreover, the robustness of the SOM-based surrogate model for inversion was illustrated by proposing the scarcity of data and different degrees of measurement error in the limited actual observation data. When the actual observation data is reduced by 2/3, the Root Mean Square Error (RMSE) of retrieving source strengths and hydraulic conductivity at pilot points are 1.07 and 0.09, respectively. The results indicated the SOM-based surrogate model shows remarkable inversion precision and robustness, and an accurate estimation of the actual hydraulic conductivity field could be obtained by the Kriging method based on that.

Three-dimensional distribution and oxidation degree analysis of coal gangue dump fire area: A case study.

Spontaneous combustion of coal gangue dumps poses a significant threat to the health and safety of nearby residents and has adverse effects on the environment. The establishment of measures to extinguish these fires requires information on the three-dimensional characteristics and oxidation degree of the dumps. An acquisition method for the index data was proposed. The temperature and the radon concentration were used as the principal indicators, and the gas concentration was a secondary index for verifying the results. Kriging interpolation was applied to predict the value of the unsampled points. Additionally, the three-dimensional characteristics of the temperature and radon anomalies were determined, thresholds were set, and the changes in the temperature and radon migration were considered to estimate the extent and depth of the fire in the coal gangue dumps. The oxidation degree of the anomalous area was identified according to the critical value of the temperature and radon anomalies. The application of this method in the gangue dump of the Tashan coal mine showed the existence of 17 oxidation areas, covering an area of 31,433 m2, including 4 shallow oxidation areas, 4 deep oxidation areas in coal waste dumps, and 9 medium-deep oxidation areas. According to the decision criterion, 4 areas with relatively high oxidation degree were identified, whereas the remaining sites were low-oxidation areas. Additionally, surface fires and internal fires can be transformed into each other, posing a significant threat. The results obtained from the various data sources were consistent and in agreement with the ground survey results, indicating that the proposed method is effective for the detection of fires in coal gangue dumps.

Associations between ambient air pollution and cancer incidence in Taiwan: an ecological study of geographical variations.

BACKGROUND: Air pollution is a global public health concern. The World Health Organization has recently set up a goal of saving 7 million people globally by 2030 from air pollution related death. We conducted an ecological study of geographical variation to explore the association between air pollution (specifically, particulate matter <2.5 µm in aerodynamic diameter [PM2.5], particulate matter <10 µm in aerodynamic diameter, sulfur dioxide, nitrogen dioxide, nitric oxide, and ozone) and cancer incidence in Taiwan, from 2012 to 2016. METHODS: In this study, the yearly average concentrations of each air pollutant at 75 air quality monitoring stations were calculated, and using the kriging method, the concentrations were extrapolated to each and every geographical central point of 349 local administrative areas of Taiwan. Spearman rank correlation coefficients between the age-adjusted cancer incidence rates and various air pollutants were calculated by stratifying genders and urbanization degrees of the local administrative areas. A total of 70 correlation coefficients were calculated. RESULTS: In total, 17 correlation coefficients were significantly positive at an alpha level of 0.05. Among these, four correlation coefficients between the age-adjusted cancer incidence rates and PM2.5 levels remained significant after Bonferroni correction. For men in developing towns, general towns, and aged towns and for women in aged towns, the age-adjusted cancer incidence rates increased 13.1 (95% confidence interval [CI], 8.8-17.6), 11 (95% CI, 5.6-16.4), 16.7 (95% CI, 6.9-26.4), and 11.9 (95% CI, 5.6-18.2) per 100,000 populations, respectively, for every 1 µg/m3 increment in PM2.5 concentrations. CONCLUSIONS: A significantly positive correlation was observed between the PM2.5 level and cancer incidence rate after multiple testing correction.

Effects of groundwater metal contaminant spatial distribution on overlaying kriged maps.

Groundwater is a major source of drinking water for many Canadians, and contamination by heavy metals poses a significant risk to people and the environment. In this study, three water quality indices are studied in the vicinity of an unlined landfill in a semiarid climate. The study investigates indices using geostatistical analysis and ordinary kriging. This study employs a novel coupling technique in order to compare the index-based maps to a groundwater quality map from overlapping heavy metal kriged maps. A total of 11 heavy metals were evaluated in preliminary analysis, but only four (Mn, As, Fe, and U) had higher concentrations than allowable limits in some or all of the monitoring wells at the site. Results from mean-based classification of indices suggest the aquifer in proximity to the landfill has been impacted by metal contaminants. Kriged maps show that the spatial variations of Mn and U are similar, while results of Fe and As are also similar. However, the two sets of maps have distinctly different patterns. Maps for indices show an elevated plateau extending from the unlined landfill to the southeast corner, implying that the landfill may have negatively impacted groundwater quality. A groundwater quality map is developed by overlaying the heavy metal maps. The resulting map shows that the north and west parts of the study have lower groundwater pollution with respect to metal contaminants. The groundwater quality map may be more applicable for practitioners who need comprehensive water quality measurement.

3D space-dependent models for stochastic dosimetry applied to exposure to low frequency magnetic fields.

In this study, an innovative approach that combines Principal Component Analysis (PCA) and Gaussian process regression (Kriging method), never used before in the assessment of human exposure to electromagnetic fields (EMF), was applied to build space-dependent surrogate models of the 3D spatial distribution of the electric field induced in central nervous system (CNS) of children of different ages exposed to uniform magnetic field at 50 Hz of 200 µT of amplitude with uncertain orientation. The 3D surrogate models showed very low normalized percentage mean square error (MSE) values, always lower than 0.16%, confirming the feasibility and accuracy of the approach in estimating the 3D spatial distribution of E with a low number of components. Results showed that the electric field values induced in CNS tissues of children were within the ICNIRP basic restrictions for general public, with 99th percentiles of the E values obtained for each orientation showing median values in the range 1.9-2.1 mV/m. Similar 3D spatial distributions of the electric fields were found to be induced in CNS tissues of children of different ages. Bioelectromagnetics. 9999:1-10, 2018. © 2019 Bioelectromagnetics Society.

Using a cost function based on kinematics and electromyographic data to quantify muscle forces.

A reliable evaluation of muscle forces in the human body is highly desirable for several applications in both clinical and research contexts. Several models of muscle force distribution based on non-invasive measurements have been proposed since 1836 (Weber and Weber, 1836), amongst which Crowninshield's model (Crowninshield and Brand, 1981), which maximizes a cost-function representing the muscle fiber endurance, is the most popular. It is worth noting that Crowninshield's model is the most widely adopted notwithstanding its major limitations of physiological coherence. Forster et al. (2004) pointed out that "these (conventional) criteria however do not predict co-contraction adequately". Besides, electromyographic (EMG)-driven models have been proposed to assess individual muscle forces, which have not been broadly adopted due to their complexity and the need for a calibration before each test. In this context, a cost function based on kinematic and electromyographic data could provide the advantage of being physiologically more coherent with muscle activations compared to conventional cost-functions based on kinematics solely, and easier to use than the EMG-driven models. The objective of this study is to propose the first cost-function based on kinematics and electromyographic data to quantify muscle forces. When applying this new cost-function on a database of upper limb motions data of 17 subjects, healthy or with cerebral palsy, the muscle force prediction of the proposed model was 17.74% more coherent with the EMG pattern than the prediction of Crowninshield's model. And on average, these results were more consistent whether the subjects were healthy or with cerebral palsy. In conclusion, we propose this cost-function for the quantification of muscle forces.

A kriging and entropy-based approach to raingauge network design.

Hydrological data, such as precipitation, is fundamental for planning, designing, developing, and managing water resource projects as well as for hydrologic research. An optimal raingauge network leads to more accurate estimates of mean or point precipitation at any site over the watershed. Some studies in the past have suggested increasing gauge network density for reducing the estimation error. However, more stations mean more cost of installation and monitoring. This study proposes an approach on the basis of kriging and entropy theory to determine an optimal network design in the city of Shanghai, China. Unlike the past studies using kriging interpolation and entropy theory for network design, the approach developed in the current study not only used the kriging method as an interpolator to determine rainfall data at ungauged locations but also incorporated the minimum kriging standard error (KSE) and maximum net information (NI) content. The approach would thus lead to an optimal network and would enable the reduction of kriging standard error of precipitation estimates throughout the watershed and achieve an optimum rainfall information. This study also proposed an NI-KSE-based criterion which is dependent on a single-objective optimization. To evaluate the final optimal gauge network, areal average rainfall was estimated and its accuracy was compared with that obtained with the existing rain gauge network.

Spatial Analysis of Human Health Risk Due to Arsenic Exposure through Drinking Groundwater in Taiwan's Pingtung Plain.

Chronic arsenic (As) exposure continues to be a public health problem of major concern worldwide, affecting hundreds of millions of people. A long-term groundwater quality survey has revealed that 20% of the groundwater in southern Taiwan's Pingtung Plain is clearly contaminated with a measured As concentration in excess of the maximum level of 10 µg/L recommended by the World Health Organization. The situation is further complicated by the fact that more than half of the inhabitants in this area continue to use groundwater for drinking. Efforts to assess the health risk associated with the ingestion of As from the contaminated drinking water are required in order to determine the priorities for health risk management. The conventional approach to conducting a human health risk assessment may be insufficient for this purpose, so this study adopts a geostatistical Kriging method to perform a spatial analysis of the health risk associated with ingesting As through drinking groundwater in the Pingtung Plain. The health risk is assessed based on the hazard quotient (HQ) and target cancer risk (TR) established by the U.S. Environmental Protection Agency. The results show that most areas where the HQ exceeds 1 are in the southwestern part of the study area. In addition, the high-population density townships of Daliao, Linyuan, Donggang, Linbian, Jiadong, and Fangliao presently have exceedingly high TR values that are two orders of magnitude higher than the acceptable standard. Thus, the use of groundwater for drinking in these townships should be strictly avoided. A map that delineates areas with high TR values and high population densities is provided. The findings broaden the scope of the spatial analysis of human health risk and provide a basis for improving the decision-making process.

Surrogate Model Application to the Identification of Optimal Groundwater Exploitation Scheme Based on Regression Kriging Method-A Case Study of Western Jilin Province.

This paper introduces a surrogate model to identify an optimal exploitation scheme, while the western Jilin province was selected as the study area. A numerical simulation model of groundwater flow was established first, and four exploitation wells were set in the Tongyu county and Qian Gorlos county respectively so as to supply water to Daan county. Second, the Latin Hypercube Sampling (LHS) method was used to collect data in the feasible region for input variables. A surrogate model of the numerical simulation model of groundwater flow was developed using the regression kriging method. An optimization model was established to search an optimal groundwater exploitation scheme using the minimum average drawdown of groundwater table and the minimum cost of groundwater exploitation as multi-objective functions. Finally, the surrogate model was invoked by the optimization model in the process of solving the optimization problem. Results show that the relative error and root mean square error of the groundwater table drawdown between the simulation model and the surrogate model for 10 validation samples are both lower than 5%, which is a high approximation accuracy. The contrast between the surrogate-based simulation optimization model and the conventional simulation optimization model for solving the same optimization problem, shows the former only needs 5.5 hours, and the latter needs 25 days. The above results indicate that the surrogate model developed in this study could not only considerably reduce the computational burden of the simulation optimization process, but also maintain high computational accuracy. This can thus provide an effective method for identifying an optimal groundwater exploitation scheme quickly and accurately.

The spatial diffusion of norovirus epidemics over three seasons in Tokyo.

We studied the spatial trend of norovirus (NoV) epidemics using sentinel gastroenteritis surveillance data for patients aged <15 years (n = 140) in the Tokyo area for the 2006-2007 to 2008-2009 seasons utilizing the kriging method of geographical information system (GIS). This is the first study of the spreading pattern of NoV epidemics using sentinel surveillance data. Correlations of sentinel cases between the seasons and with demographic data were examined to identify the trend and related factors. A similar pattern of diffusion was observed over the seasons, and its mean correlation between seasons was significantly high. A higher number of cases were found in the peripheral area, which surrounds the most populated central area, and showed a correlation with the ratio of the children population (r = 0·321, P < 0·01) and the ratio of residents in larger families (r = 0·263, P < 0·01). While NoV susceptibility remained, the results suggest a transmission route in the local community as a possible epidemic factor. Prevention with focus on the peripheral area is desirable.