Assessing the environmental factors affecting the sustainability of Aini Falaj system.

This study investigates the spatial distribution patterns and environmental factors influencing the Aini Falaj system in a specific study area. The research findings are presented through the lens of the following four categories: collinearity diagnostics, spatial autocorrelation analysis, kernel density (KD) findings, and multivariate geographically weighted regression (MGWR) analysis. The collinearity diagnostics were applied to examine the interrelationships among 18 independent environmental variables. The results indicate the absence of significant multicollinearity concerns, with most variables showing values below the critical threshold of five for variance inflation factors (VIFs). The selected variables indicate minimal intercorrelation, suggesting that researchers should be confident utilizing them in subsequent modelling or regression analyses. A spatial autocorrelation analysis using Moran's Index revealed positive spatial autocorrelation and significant clustering patterns in the distribution of live and non-functional Aini Falajs. High concentrations of live or dead Falajs tended to be surrounded by neighbouring areas with similar characteristics. These findings provide insights into the ecological preferences and habitat associations of Aini Falajs, thereby aiding conservation strategies and targeted studies. The kernel density (KD) analysis depicted distribution patterns of live and dry Aini Falajs through hotspots and cold spots. Specific regions exhibited high-density areas of live Falajs, indicating favourable environmental conditions or historical factors contributing to their concentrated distribution. Identifying these high-density zones can enhance our understanding of the spatial patterns and potential factors influencing the prevalence and sustainability of Aini Falajs. The multivariate geographically weighted regression (MGWR) models revealed strong associations between the live or dead status of Aini Falajs and environmental factors. The precipitation, topographic wetness index (TWI), aspect and slope exerted positive impacts on the live status, while evaporation, solar radiation, distance to drains and drain density exerted negative influences. Similar associations were observed for the dead status, emphasising the importance of controlling evaporation, shading mechanisms, proper drainage planning and sustainable land-use practices. This study provides valuable insights into the spatial distributions and factors influencing the live and dead status of Aini Falajs, thereby contributing to our understanding of their ecological dynamics and guiding conservation efforts and management strategies.

The impact of COVID-19 lockdowns on surface urban heat island changes and air-quality improvements across 21 major cities in the Middle East.

This study investigates changes in air quality conditions during the restricted COVID-19 lockdown period in 2020 across 21 metropolitan areas in the Middle East and how these relate to surface urban heat island (SUHI) characteristics. Based on satellite observations of atmospheric gases from Sentinel-5, results indicate significant reductions in the levels of atmospheric pollutants, particularly nitrogen dioxide (NO2), sulfur dioxide (SO2), and carbon monoxide (CO). Air quality improved significantly during the middle phases of the lockdown (April and May), especially in small metropolitan cities like Amman, Beirut, and Jeddah, while it was less significant in "mega" cities like Cairo, Tehran, and Istanbul. For example, the concentrations of NO2 in Amman, Beirut, and Jeddah decreased by -56.6%, -43.4%, and -32.3%, respectively, during April 2020, compared to April 2019. Rather, there was a small decrease in NO2 levels in megacities like Tehran (-0.9%) and Cairo (-3.1%). Notably, during the lockdown period, there was a decrease in the mean intensity of nighttime SUHI, while the mean intensity of daytime SUHI experienced either an increase or a slight decrease across these locations. Together with the Gulf metropolitans (e.g. Kuwait, Dubai, and Muscat), the megacities (e.g. Tehran, Ankara, and Istanbul) exhibited anomalous increases in the intensity of daytime SUHI, which may exceed 2 °C. Statistical relationships were established to explore the association between changes in the mean intensity and the hotspot area in each metropolitan location during the lockdown. The findings indicate that the mean intensity of SUHI and the spatial extension of hotspot areas within each metropolitan had a statistically significant negative relationship, with Pearson's r values generally exceeding - 0.55, especially for daytime SUHI. This negative dependency was evident for both daytime and nighttime SUHI during all months of the lockdown. Our findings demonstrate that the decrease in primary pollutant levels during the lockdown contributed to the decrease in the intensity of nighttime SUHIs in the Middle East, especially in April and May. Changes in the characteristics of SUHIs during the lockdown period should be interpreted in the context of long-term climate change, rather than just the consequence of restrictive measures. This is simply because short-term air quality improvements were insufficient to generate meaningful changes in the region's urban climate.

Demographic and socioeconomic determinants of COVID-19 across Oman - A geospatial modelling approach.

Local, bivariate relationships between coronavirus 2019 (COVID-19) infection rates and a set of demographic and socioeconomic variables were explored at the district level in Oman. To limit multicollinearity a principal component analysis was conducted, the results of which showed that three components together could explain 65% of the total variance that were therefore subjected to further study. Comparison of a generalized linear model (GLM) and geographically weighted regression (GWR) indicated an improvement in model performance using GWR (goodness of fit=93%) compared to GLM (goodness of fit=86%). The local coefficient of determination (R2) showed a significant influence of specific demographic and socioeconomic factors on COVID-19, including percentages of Omani and non-Omani population at various age levels; spatial interaction; population density; number of hospital beds; total number of households; purchasing power; and purchasing power per km2. No direct correlation between COVID- 19 rates and health facilities distribution or tobacco usage. This study suggests that Poisson regression using GWR and GLM can address unobserved spatial non-stationary relationships. Findings of this study can promote current understanding of the demographic and socioeconomic variables impacting the spatial patterns of COVID-19 in Oman, allowing local and national authorities to adopt more appropriate strategies to cope with this pandemic in the future and also to allocate more effective prevention resources.

Spatiotemporal Assessment of COVID-19 Spread over Oman Using GIS Techniques.

Coronavirus disease (COVID-19), caused by acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a worldwide challenge effecting millions of people in more than 210 countries, including the Sultanate of Oman (Oman). Spatiotemporal analysis was adopted to explore the spatial patterns of the spread of COVID-19 during the period from 29th April to 30th June 2020. Our assessment was made using five geospatial techniques within a Geographical Information System (GIS) context, including a weighted mean centre (WMC), standard deviational ellipses, Moran's I autocorrelation coefficient, Getis-Ord General-G high/low clustering, and Getis-Ord G i ∗ statistic. The Moran's I-/G- statistics proved that COVID-19 cases in datasets (numbers of cases) were clustered throughout the study period. The Moran's I and Z scores were above the 2.25 threshold (a confidence level above 95%), ranging from 2274 cases on 29th April to 40,070 cases on 30th June 2020. The results of G i ∗ showed varying rates of infections, with a large spatial variability between the different wilayats (district). The epidemic situation in some wilayats, such as Mutrah, As-Seeb, and Bowsher in the Muscat Governorate, was more severe, with Z score higher than 5, and the current transmission still presents an increasing trend. This study indicated that the directional pattern of COVID-19 cases has moved from northeast to northwest and southwest, with the total impacted region increasing over time. Also, the results indicate that the rate of COVID-19 infections is higher in the most populated areas. The findings of this paper provide a solid basis for future study by investigating the most resolute hotspots in more detail and may help decision-makers identify targeted zones for alleviation plans. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s41748-020-00194-2.

Modelling the potential effects of climate factors on Dubas bug (Ommatissus lybicus) presence/absence and its infestation rate: A case study from Oman.

BACKGROUND: Ommatissus lybicus de Bergevin (Hemiptera: Tropiduchidae) (Dubas Bug, DB) is an insect pest attacking date palms. It occurs in Arab countries including Oman. In this paper, the logistic, ordinary least square, and geographical weighted regressions were applied to model the absence/presence and density of DB against climate factors. A method is proposed for modelling spatially correlated prorations annually over the study period, based on annual and seasonal outbreaks. The historical 2006-2015 climate data were obtained from weather stations located in nine governorates in northern Oman, while dataloggers collected the 2017 microclimate data in eight of these nine governorates. RESULTS: Logistic regression model showed the percentages of correctly predicted values using a cut-off point of 0.5 were 90%, 88% and 84%, indicating good classification accuracy. OLS and GWR models showed an overall trend of strong linear correlation between DB infestation levels and short- and long-term climate factors. The three models suggested that precipitation, elevation, temperature, humidity, wind direction and wind speed are important in influencing the spatial distribution and the presence/absence of dense DB populations. CONCLUSION: The results provide an improved understanding of climate factors that impact DB's spread and is considered useful for managing DB infestations in date palm plantations. © 2019 Society of Chemical Industry.

Predicting the potential geographical distribution of parasitic natural enemies of the Dubas bug (Ommatissus lybicus de Bergevin) using geographic information systems.

The Dubas bug (Ommatissus lybicus de Bergevin) is a pest species whose entire life cycle occurs on date palms, Phoenix dactylifera L, causing serious damage and reducing date palm growth and yield. Pseudoligosita babylonica Viggiani, Aprostocetus nr. Beatus, and Bocchus hyalinus Olmi are very important parasitic natural enemies of Ommatissus lybicus in northern Oman. In this study, random farms were selected to (a) model the link between occurrences of the Pseudoligosita babylonica, Aprostocetus nr beatus, and Bocchus hyalinus (dependent variables) with environmental, climatological, and Dubas bug infestation levels (the independent variables), and (b) produce distribution and predictive maps of these natural enemies in northern Oman. The multiple R2 values showed the model explained 63%, 89%, and 94% of the presence of P. babylonica, A. nr beatus, and Bocchus hyalinus, respectively. However, the distribution of each species appears to be influenced by distinct and geographically associated climatological and environmental factors, as well as habitat characteristics. This study reveals that spatial analysis and modeling can be highly useful for studying the distribution, the presence or absence of Dubas bugs, and their natural enemies. It is anticipated to help contribute to the reduction in the extent and costs of aerial and ground insecticidal spraying needed in date palm plantations.

Remote sensing and spatial statistical techniques for modelling Ommatissus lybicus (Hemiptera: Tropiduchidae) habitat and population densities.

In order to understand the distribution and prevalence of Ommatissus lybicus (Hemiptera: Tropiduchidae) as well as analyse their current biographical patterns and predict their future spread, comprehensive and detailed information on the environmental, climatic, and agricultural practices are essential. The spatial analytical techniques such as Remote Sensing and Spatial Statistics Tools, can help detect and model spatial links and correlations between the presence, absence and density of O. lybicus in response to climatic, environmental, and human factors. The main objective of this paper is to review remote sensing and relevant analytical techniques that can be applied in mapping and modelling the habitat and population density of O. lybicus. An exhaustive search of related literature revealed that there are very limited studies linking location-based infestation levels of pests like the O. lybicus with climatic, environmental, and human practice related variables. This review also highlights the accumulated knowledge and addresses the gaps in this area of research. Furthermore, it makes recommendations for future studies, and gives suggestions on monitoring and surveillance methods in designing both local and regional level integrated pest management strategies of palm tree and other affected cultivated crops.

Impact of environmental variables on Dubas bug infestation rate: A case study from the Sultanate of Oman.

Date palm cultivation is economically important in the Sultanate of Oman, with significant financial investment coming from both the government and from private individuals. However, a global infestation of Dubas bug (Ommatissus lybicus Bergevin) has impacted the Middle East region, and infestations of date palms have been widespread. In this study, spatial analysis and geostatistical techniques were used to model the spatial distribution of Dubas bug infestations to (a) identify correlations between Dubas bug densities and different environmental variables, and (b) predict the locations of future Dubas bug infestations in Oman. Firstly, we considered individual environmental variables and their correlations with infestation locations. Then, we applied more complex predictive models and regression analysis techniques to investigate the combinations of environmental factors most conducive to the survival and spread of the Dubas bug. Environmental variables including elevation, geology, and distance to drainage pathways were found to significantly affect Dubas bug infestations. In contrast, aspect and hillshade did not significantly impact on Dubas bug infestations. Understanding their distribution and therefore applying targeted controls on their spread is important for effective mapping, control and management (e.g., resource allocation) of Dubas bug infestations.

Impacts of human-related practices on Ommatissus lybicus infestations of date palm in Oman.

Date palm cultivation is economically important in the Sultanate of Oman, with significant financial investments coming from both the government and private individuals. However, a widespread Dubas bug (DB) (Ommatissus lybicus Bergevin) infestation has impacted regions including the Middle East, North Africa, Southeast Russia, and Spain, resulting in widespread damages to date palms. In this study, techniques in spatial statistics including ordinary least squares (OLS), geographically weighted regression (GRW), and exploratory regression (ER) were applied to (a) model the correlation between DB infestations and human-related practices that include irrigation methods, row spacing, palm tree density, and management of undercover and intercropped vegetation, and (b) predict the locations of future DB infestations in northern Oman. Firstly, we extracted row spacing and palm tree density information from remote sensed satellite images. Secondly, we collected data on irrigation practices and management by using a simple questionnaire, augmented with spatial data. Thirdly, we conducted our statistical analyses using all possible combinations of values over a given set of candidate variables using the chosen predictive modelling and regression techniques. Lastly, we identified the combination of human-related practices that are most conducive to the survival and spread of DB. Our results show that there was a strong correlation between DB infestations and several human-related practices parameters (R2 = 0.70). Variables including palm tree density, spacing between trees (less than 5 x 5 m), insecticide application, date palm and farm service (pruning, dethroning, remove weeds, and thinning), irrigation systems, offshoots removal, fertilisation and labour (non-educated) issues, were all found to significantly influence the degree of DB infestations. This study is expected to help reduce the extent and cost of aerial and ground sprayings, while facilitating the allocation of date palm plantations. An integrated pest management (IPM) system monitoring DB infestations, driven by GIS and remote sensed data collections and spatial statistical models, will allow for an effective DB management program in Oman. This will in turn ensure the competitiveness of Oman in the global date fruits market and help preserve national yields.