Spatiotemporal interpolation of air pollutants in the Greater Cairo and the Delta, Egypt

Ahmed, Sara Osama; Mazloum, Reda; Abou-Ali, Hala

doi:10.1016/j.envres.2017.09.005

Cited by 28 publications

(15 citation statements)

References 10 publications

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“…Finally, this study showed that urban Kafr El-Zayat had the highest incidence rate ratios of both IBC and non-IBC, suggesting a possible association between these two subtypes of breast cancer and high exposure levels of heavy metals and inorganic pollutants in Kafr El-Zayat district [22][23][24][25] . While Kafr El-Zayat is not the most populated and urbanized city of the province, it is home to pesticide factories, agricultural activities, soap, sewage disposal sites, industrial-related wastewater management, and textiles.…”

Section: Discussionmentioning

confidence: 59%

Geographic Distribution of Inflammatory Breast Cancer and Non-Inflammatory Breast Cancer in Gharbiah, Egypt

Nguyen¹,

Soliman²,

Hablas³

et al. 2020

J Rare Dis Res Treat

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BACKGROUND: Inflammatory Breast Cancer (IBC) is a rare but aggressive form of breast cancer, constituting about 1-5% of breast cancers in the U.S. but about 11% in Egypt. The etiology of IBC is unknown, but the distribution of residence of IBC and non-IBC patients may provide clues to the disease risk factors. Therefore, we investigated the geographic distribution of IBC and non-IBC in the province of Gharbiah, Egypt. METHODS:All breast cancers diagnosed during the period of January 2009 to December 2010 were retrieved from the Gharbiah Population-based registry. IBC cases were obtained from a case-control study conducted in the same region and period. Incidence rates were calculated for IBC and non-IBC for each of the eight districts and their rural/urban regions. RESULTS:The incidence of IBC was higher in rural than urban areas, in contrast to the incidence of non-IBC. Clustering of IBC and non-IBC was observed more in urban than in rural areas. IBC clusters were detected in 7 of the 8 urban areas of Gharbiah, with the highest incidence identified in urban Kafr El-Zayat. Non-IBC clusters were distributed in all 8 urban areas of Gharbiah without clustering, with highest incidence is identified in urban Tanta. CONCLUSION:Our results showed different geospatial distributions of IBC and non-IBC relative to the regional environmental exposures. Future studies should explore specific environmental exposures that may contribute to the geographic distribution of IBC and non-IBC in the region to further explore the etiology of IBC and non-IBC.

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Section: Discussionmentioning

confidence: 59%

Geographic Distribution of Inflammatory Breast Cancer and Non-Inflammatory Breast Cancer in Gharbiah, Egypt

Nguyen¹,

Soliman²,

Hablas³

et al. 2020

J Rare Dis Res Treat

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“…The spatio-temporal Kriging interpolation [40], [41] extends data from spatial to spatio-temporal domain, fully taking into account spatial and temporal relevance among monitoring points [42]. The spatio-temporal position function is:…”

Section: B the Principle Of The Spatio-temporal Kriging Interpolationmentioning

confidence: 99%

An Improved Spatio-Temporal Kriging Interpolation Algorithm and Its Application in Slope

et al. 2020

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Slope stability analysis based on the deformation monitoring data has been commonly used to predict and warn slope disasters. However, due to breakdown of the monitoring equipment or restrictions and interferences of internal and external factors in the area, the loss of data is unavoidable during the process of the slope monitoring. This problem can be solved by the spatio-temporal Kriging interpolation algorithm. However, the subjective factors and theoretical semi-variogram of variogram models, and many parameters estimation may lead to low interpolation precision and poor calculation efficiency. In this paper, a hybrid spatio-temporal interpolation algorithm was presented by combining the improved adaptive genetic algorithm (IAGA) with the spatio-temporal Kriging interpolation method, and it was applied to monitor the deformation of HP1 slope in Yuebao open-pit mine. The results showed that the interpolation precision of the proposed method was about 1 times higher than that of the traditional spatio-temporal Kriging method and the spatio-temporal method of Gaussian process regression method. In addition, after the interpolation analysis of the missing part of the monitoring data, the maximum cumulative displacement of all monitoring points was around 35.8 mm, and the rate of the displacement deformation didn't exceed 2 mm/d for three consecutive days. The deformation trend was basically consistent with the actual slope. It shows that the improved spatio-temporal Kriging interpolation algorithm (ISTKIA) has certain feasibility and reliability, and could provide a new idea for the research of related problems in related fields. INDEX TERMS Slope deformation monitoring, spatio-temporal Kriging interpolation, ISTKIA, stability analysis.

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“…Þ. The equation for the metric variogram can be written as As several previous studies attested to the superior performance of the sum-metric model in fitting the spatiotemporal variogram using environmental parameters (i.e., smallest mean squared errors), this model was selected and used in this analysis [12,14].…”

Section: Product-summentioning

confidence: 99%

“…Previous research reveals that spatiotemporal analysis is more accurate than spatial analysis as both time and space are included in spatiotemporal analysis [10]. The underlying techniques have been previously used to model air pollutants by quantifying the space-time variability of certain particles' concentrations [10][11][12][13]. Spatiotemporal regression kriging has been used to predict precipitation using Moderate-Resolution Imaging Spectroradiometer (MODIS) and Normalized Difference Vegetation Index (NDVI) data [14].…”

Section: Introductionmentioning

confidence: 99%

Developing Statewide Optimal RWIS Density Guidelines Using Space-Time Semivariogram Models

Biswas

Kwon

2020

Journal of Sensors

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Preventing weather-related crashes is a significant part of maintaining the safety and mobility of the travelling public during winter months. To help mitigate detrimental effects of winter road conditions, transportation authorities rely on real-time and near-future road weather and surface condition information disseminated by road weather information systems (RWIS) to make more timely and accurate winter road maintenance-related decisions. However, the significant costs of these systems motivate governments to develop a framework for determining a region-specific optimal RWIS density. Building on our previous study to facilitate regional network optimization, this study is aimed at considering the nature of spatiotemporally varying RWIS measurements and integrating larger case studies comprising eight different US states. Space-time semivariogram models were developed to quantify the representativeness of RWIS measurements and examine their effects on regional topography and weather severity for improved generalization. The optimal RWIS density for different topographic and weather severity regions was then determined via one of the most successful combinatorial optimization techniques—particle swarm optimization. The findings of this study revealed a strong dependency of optimal RWIS density on varying environmental characteristics of the region under investigation. It is anticipated that the RWIS density guidelines developed in this study will provide decision makers with a tool they need to help design a long-term RWIS implementation plan.

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Spatiotemporal interpolation of air pollutants in the Greater Cairo and the Delta, Egypt

Cited by 28 publications

References 10 publications

Geographic Distribution of Inflammatory Breast Cancer and Non-Inflammatory Breast Cancer in Gharbiah, Egypt

Geographic Distribution of Inflammatory Breast Cancer and Non-Inflammatory Breast Cancer in Gharbiah, Egypt

An Improved Spatio-Temporal Kriging Interpolation Algorithm and Its Application in Slope

Developing Statewide Optimal RWIS Density Guidelines Using Space-Time Semivariogram Models

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