Anomaly detection in geostatistical models with application to groundwater level data in the Gaza Coastal Aquifer

In geostatistics, the detection of anomalous observations has a particular importance because of the changes they can create in environmental and geological patterns. Few methods for detecting such observations in univariate data have been proposed for the spatial case, namely sample influence function (SIF), kriging, Intrinsic Random Functions (IRF), and geostatistical functional data. This article reviews the main outlier detection procedures in the context of geostatistics, and due to the absence of a numerical comparison between them, this article obtained the cut-off points of these methods for three different variogram models, and evaluated their performance via a simulation study. The results show that for all detection methods and the three considered models, there is an inverse relationship between the level of contamination and power of performance. In addition, the SIF for the cubic variogram model outperforms the exponential and Matérn. Because of the peculiarities of the Gaza Strip, as regards Palestine water condition, and for illustration purposes, we consider real groundwater level data in the Gaza Coastal Aquifer, where a set of possible outliers were identified

Environmental pollution and COVID-19 outbreak : insights from Germany

The impact of environmental pollutants and climate indicators on the outbreak of COVID-19 has gained considerable attention in the recent literature. However, specific investigation of industrial economies like Germany is not available. This provides us motivation to examine the association between environmental pollutants, climate indicators and the COVID-19 cases, recoveries, and deaths in Germany using daily data from February 24, 2020, to July 02, 2020. The correlation analysis and wavelet transform coherence (WTC) approach are the analytical tools, which are used to explore the association between variables included in the study. Our findings indicate that PM2.5, O3, and NO2 have a significant relationship with the outbreak of COVID-19. In addition, temperature is the only significant climate indicator which has significant correlation with the spread of COVID-19. Finally, PM10, humidity, and environmental quality index have a significant relationship only with the active cases from COVID-19 pandemic. Our findings conclude that Germany’s successful response to COVID-19 is attributed to environmental legislation and the medical care system, which oversaw significant overhaul after the SARS and MERS outbreaks. The current study implicates that other industrial economies, especially European economies, that are still facing COVID-19 outbreak can follow the German model for pandemic response