Mapping spatial distribution of traffic induced criteria pollutants and associated health risks using kriging interpolation tool in Delhi

2020 ◽  
Vol 18 ◽  
pp. 100879 ◽  
Author(s):  
Amrit Kumar ◽  
Rajeev Kumar Mishra ◽  
Kiranmay Sarma
2011 ◽  
Vol 6 (1) ◽  
pp. 91
Author(s):  
Andi Indrajaya Asaad ◽  
Akhmad Mustafa

Spatial distribution of brackishwater pond soil has a vital role in the system of bioenvironment including brackishwater pond environment. This research was aimed to determine the spatial distribution of brackishwater pond soil characteristics in Pekalongan City, Central Java Province. A total of 59 sampling points each with two different soil depth samplings were determined by simple random method. A total of 21 soil characteristics were measured in the field and analyzed further in the laboratory. Geostatistic with Kriging Interpolation method in the ArcGIS 9.3 software were used to depict the distribution of the data across the landscape. Furthermore, the spatial distribution was presented by using ALOS AVNIR-2 image. Research result indicates that in general, pond soil in Pekalongan City can be classified as soil with high variability or relatively heterogenic with the value of variation coefficient more than 36%. Soil characteristics which have similar pattern of spatial distribution are acid sulfate soil and soil nutrient content. High value of pH, organic matter, and total-N of soil, and on the other hand, low value of PO4 were generally found in the pond area of Krapyak Lor Village, while in Pekalongan City, it was found high clayish soil content but relatively homogenous. It is recommended that pond management must be based on soil characteristics which are different from one area to another. The soil characteristics itself can be drawn and assessed through spatial distribution.


2021 ◽  
pp. 141-149
Author(s):  
Hui Li ◽  
Fengna Liang ◽  
Zijun Qin ◽  
Jinxin Zhang ◽  
Ku Wang ◽  
...  

CATENA ◽  
2020 ◽  
Vol 190 ◽  
pp. 104540 ◽  
Author(s):  
Fabiola S. Sosa-Rodríguez ◽  
Jorge Vazquez-Arenas ◽  
Patricia Ponce Peña ◽  
Miguel A. Escobedo-Bretado ◽  
Francisco X. Castellanos-Juárez ◽  
...  

2016 ◽  
Vol 216 ◽  
pp. 538-547 ◽  
Author(s):  
Yanyan Fang ◽  
Zhiqiang Nie ◽  
Qingqi Die ◽  
Yajun Tian ◽  
Feng Liu ◽  
...  

2021 ◽  
Vol 14 (1) ◽  
Author(s):  
Antonio Bosco ◽  
Leucio Camara Alves ◽  
Paola Cociancic ◽  
Alessandra Amadesi ◽  
Paola Pepe ◽  
...  

Abstract Background Cystic echinococcosis (CE) is a worldwide parasitic zoonosis caused by the larval stage of Echinococcus granulosus sensu lato affecting livestock, particularly sheep and goats. However, often this parasitosis is underestimated. For this reason, this study aimed to evaluate the epidemiological features and spatial distribution of CE in sheep and goats slaughtered in a hyperendemic Mediterranean area. Methods A survey was conducted in the Basilicata region (southern Italy) from 2014 to 2019. A total of 1454 animals (1265 sheep and 189 goats) from 824 farms were examined for hydatid cyst detection by visual inspection, palpation and incision of target organs. All the CE cysts were counted and classified into five morphostructural types (unilocular, multiseptate, calcified, caseous and hyperlaminated). Molecular analysis was performed on 353 cysts. For spatial analysis, a kriging interpolation method was used to create risk maps, while clustering was assessed by Moran’s I test. Results CE prevalence of 72.2% (595/824) and 58.4% (849/1454) was observed at the farm and animal levels, respectively, with higher values in sheep (62.9%) than goats (28.0%). The liver and lungs were the most frequently infected organs in both sheep and goats. Most of recovered cysts were of the calcified and multiseptate morphotypes. All the isolates were identified as E. granulosus sensu stricto (genotypes G1–G3). Spatial distribution showed a moderate clustering of positive animals. Conclusion The findings of this study can be used to better understand the eco-epidemiology of echinococcosis and to improve CE surveillance and prevention programs in regions highly endemic for CE. Graphical abstract


2020 ◽  
Vol 179 ◽  
pp. 223-241
Author(s):  
Kamaladdin Karimyan ◽  
Mahmood Alimohammadi ◽  
Afshin Maleki ◽  
Masud Yunesian ◽  
Ramin Nabizadeh Nodehi ◽  
...  

Chemosphere ◽  
2021 ◽  
Vol 266 ◽  
pp. 129019
Author(s):  
Paula Renata Muniz Araújo ◽  
Caroline Miranda Biondi ◽  
Clístenes Williams Araújo do Nascimento ◽  
Fernando Bruno Vieira da Silva ◽  
William Ramos da Silva ◽  
...  

2020 ◽  
Vol 980 ◽  
pp. 437-448
Author(s):  
Hui Juan Zhang ◽  
Shou Chen Ma ◽  
Wen Kai Liu ◽  
He Bing Zhang ◽  
Song He Yuan

Underground mining has caused drastic disturbances to regional ecosystems and soil nutrients. Understanding the 3D spatial distribution of soil organic matter in coal arable land is crucial for agricultural production and environmental management. However, little research has been done on the three-dimensional modeling of soil organic matter. In this study, 3D kriging interpolation method and 3D stochastic simulation method were used to develop the 3D model of soil organic matter , and the root-mean-square error (RMSE) and mean error (ME) were used as evaluation indexes to compare the simulation accuracy of the two methods. Results showed that the spatial distribution of soil organic matter obtained by using 3D kriging interpolation method is relatively smooth, which reduce the difference of spatial data; while the spatial distribution of soil organic matter obtained by using 3D stochastic simulation method is relatively discrete and highlights the volatility of spatial distribution of raw data, the RMSE obtained by 3D kriging interpolation method and 3D stochastic simulation method respectively is 2.7711 g/kg and 1.8369 g/kg. The prediction accuracy of organic matter interpolation obtained by 3D stochastic simulation method is higher than that by 3D kriging interpolation method; so the 3D stochastic simulation method can reflect the spatial distribution characteristics of soil organic matter more realistically, and more suitable for 3D modeling of soil organic matter. According to the 3D modeling of soil organic matter, the content of soil organic matter has obvious spatial difference in different soil depth(0-20 cm、20-40 cm、40-60 cm) and decreases with the increase of soil depth; The result also showed that the content of soil organic matter decreased rapidly from the upper slope to the middle slope, and gradually increased from the middle slope to the bottom, so the soil organic matter content was obviously lost in the middle slope. This result may provide useful data for land reclamation and ecological reconstruction in coal mining subsidence area.


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