Factors Contributing to the Containment of the COVID-19 in Kurdistan Region of Iraq

A highly contagious coronavirus disease 2019 (COVID-19) is caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), which was first identified in Wuhan, China in December 2019. The virus primarily affects the respiratory system of human beings and results in the symptoms of headache, fever, dry cough, sore throat, shortness of breath and fatigue with abnormal chest computed tomography (CT) scan. In some cases, nasal sputum discharge and diarrhea have been also reported. Up to the 26th of April 2020, more than three million laboratory confirmed cases of COVID-19 have been recorded worldwide with more than 220,000 confirmed deaths. In the Kurdistan region of Iraq, the first case of laboratory confirmed COVID-19 was recorded in March 1st, 2020 in Sulaymaniyah province.&nbsp

Predicting and mapping land cover/land use changes in Erbil /Iraq using CA-Markov synergy model

One of the most dynamic components of the environment is land use land cover (LULC), which have been changing remarkably since after the industrial revolution at various scales. Frequent monitoring and quantifying LULC change dynamics provide a better understanding of the function and health of ecosystems. This study aimed at modelling the future changes of LULC for the Erbil governorate in the Kurdistan region of Iraq (KRI) using the synergy Cellular Automata (CA)-Markov model. For this aim, three consecutive-year Landsat imagery (i.e., 1988, 2002, and 2017) were classified using the Maximum Likelihood Classifier. From the classification, three LULC maps with several class categories were generated, and then change-detection analysis was executed. Using the classified (1988–2002) and (2002–2017) LULC maps in the hybrid model, LULC maps for 2017 and 2050 were modelled respectively. The model output (modelled 2017) was validated with the classified 2017 LULC map. The accuracy of agreements between the classified and the modelled maps were Kno = 0.8339, Klocation = 0.8222, Kstandard = 0.7491, respectively. Future predictions demonstrate between 2017 and 2050, built-up land, agricultural land, plantation, dense vegetation and water body will increase by 173.7% (from 424.1 to 1160.8 km2), 79.5% (from 230 to 412.9 km2), 70.2% (from 70.2 to 119.5 km2), 48.9% (from 367.2 to 546.9 km2) and 132.7% (from 10.7 to 24.9 km2), respectively. In contrast, sparse vegetation, barren land will decrease by 9.7% (2274.6 to 2052.8 km2), 18.4% (from 9463.9-7721 km2), respectively. The output of this study is invaluable for environmental scientists, conservation biologists, nature-related NGOs, decision-makers, and urban planners

Mapping the Birch and Grass Pollen Seasons in the UK Using Satellite Sensor Time-series

Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15-20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to produce detailed maps for the flowering of birch and grass across the UK for each of the years from 2003 to 2010. The results demonstrate that the remote sensing-based maps of onset flowering of birch and grass for the UK together with the pollen forecast from the Meteorology Office and National Pollen and Aerobiology Research Unit (NPARU) can potentially provide more accurate information to pollen allergy sufferers in the UK

Predicting the Spatial Distribution of <i>Hyalomma</i> ssp., Vector Ticks of Crimean–Congo Haemorrhagic Fever in Iraq

Crimean–Congo hemorrhagic fever (CCHF) typically spreads through ticks and is categorized as a viral hemorrhagic fever. CCHF is a fatal endemic disease in Iraq, and it has been reported sporadically since its first report in 1979. Recent outbreaks during 2021–2023 and their fatal consequences captured the interest of this study. CCHF is a tick-borne disease that represents a major challenge to the public health, social, and economic sectors. The geographical distribution of CCHF is closely linked with Hyalomma vector tick distribution. Therefore, predicting and mapping the spatial distribution of the disease vector in relation to relevant environmental factors provides invaluable information for establishing an early warning system based on which preventive measures can be taken to minimize the spread and, hence, the fatal consequences of CCHF. To achieve this, this study incorporates geospatial techniques and maximum entropy modeling (Maxent) to assess the habitat suitability of the Hyalomma vector and to identify the key environmental drivers contributing to its spatial distribution in Iraq. Utilizing the area under the ROC curve (AUC) as the performance metric, the model evaluation yielded successful results in predicting habitat suitability for Hyalomma vector ticks in Iraq. The AUC attained an average score of 0.885 with a regularization multiplier (β) set at 1. The Hyalomma ticks’ suitable habitat distribution within the study area covers a fraction of the total land, at approximately 51% (225,665 km2) of the entire 441,724 km2 region. Among these suitable areas, 41.57% (183,631 km2) were classified as lowly suitable, 8.61% (38,039 km2) as moderately suitable, and 0.9% (3994 km2) as highly suitable. Several factors have significantly influenced Hyalomma vector tick distribution in Iraq. These include land cover (accounting for 50.8%), elevation (contributing 30.4%), NDVI (5.7%), temperature seasonality (4.7%), precipitation seasonality (3.3%), sheep density (2.3%), goat density (2.2%), and the mean diurnal range (0.5%). The findings of this study could have significant implications for establishing a strategic early warning system and taking preventive measures beforehand to minimize and control Crimean–Congo haemorrhagic fever in Iraq and similar ecoregions in the Middle East. As a primary precaution, this study recommends focusing on highly suitable areas (3994 km2) in the southern part of Iraq for management and preventive actions

An Integrated Approach to Map the Impact of Climate Change on the Distributions of <i>Crataegus azarolus</i> and <i>Crataegus monogyna</i> in Kurdistan Region, Iraq

The hawthorns Crataegus azarolus L. and Crataegus monogyna Jacq are two ecologically and medicinally important endemic tree species that occur only in forests of oak in the mountain ranges of the Kurdistan region of Iraq (KRI). These species have been degrading across the mountain ranges at an alarming pace due to climate-related factors (e.g., wildfire events and drought) and anthropogenic drivers. Nevertheless, there is a gap in understanding their distributions today and in the future under a changing climate in Iraq. To address the species’ knowledge gap and thus establish a baseline for a future management and conservation strategy, this study used field observation records, species distribution modeling integrated with GIS techniques, and relevant environmental predictors to (i) estimate the species’ potential distributions and map their current known distributions across unsurveyed areas; (ii) model the species’ possible response under several scenarios for a weather change in the future; (iii) map the species’ overlap ranges and the direction of the distributions. Results suggest that under two global climatic models (GCMs), BCC-CSM2-MR and CNRM-CM6-1, the overall habitat expansion magnitude for the two species would be less than the overall habitat reduction magnitude. For C. azarolus, the habitat range would contract by 3714.64 km2 (7.20%) and 3550.47 km2 (6.89%), whereas it would expand by 2415.90 km2 (4.68%) and 1627.76 km2 (3.16%) for the GCMs, respectively. Modeling also demonstrated a similar pattern for C. monogyna. The species overlap by 7626.53 km2 (14.80%) and 7351.45 km2 (14.27%) for the two GCMs. The two species’ habitat ranges would contract significantly due to the changing climate. The direction of the species’ potential distribution would be mostly toward the KRI’s east and southeast mountain forests. Our results, for the first time, provide new data on the species’ present and future distributions and outline the advantages of distribution modeling combined with geospatial techniques in areas where species data are limited, such as Iraq

Modeling the distribution of the Near Eastern fire salamander (Salamandra infraimmaculata) and Kurdistan newt (Neurergus derjugini) under current and future climate conditions in Iraq

Among the amphibians, the most sensitive group to climate change are salamanders (e.g., Salamandra infraimmaculata and Neurergus derjugini). In Iraq, these species are considered threatened by the International Union for Conservation of Nature (IUCN) RED List (2020). Apart from their important role in forest ecosystems stability and integrity, they are useful indicators for ecosystems functions. These species occur only in the mountain forests of the northeast, the Kurdistan region of Iraq (KRI), and information on their distributions is limited and poorly understood. Using the maximum entropy modeling and geospatial techniques, we aimed to: (i) map current distributions of the two species, and predict potential habitat distributions; (ii) model impact of the future climate change on their distributions; (iii) map overlapping habitat range for the species; and (iv) determine the main environmental variables shaping their distributions. Under the Representative Concentration Pathway (RCP) 2.62070 and RCP8.5 2070 climate change scenarios, the overall expansion magnitude of the habitat for the species would be smaller than the contraction magnitude. For S. infraimmaculata and N. derjugini, the habitat would contract by 1751.58 km2 (3.42%) and 2127.22 km2 (4.16%), whereas expand only 226.77 km2 (0.44%) and 1877.49 km2 (3.67%), respectively. Climate change would significantly reduce the habitat ranges of the two species in Iraq. Habitat reduction for S. infraimmaculata would be more than N. derjugini. The potential distribution of the species would be toward the mountain forests of the east mainly and southeast of the KRI. Conservation actions should concentrate on the mountain forests (mixed oak) by establishing national parks, protected areas, and developing forest management policy. Current emphasis for conservation priority should focus specifically on areas where the species overlap by 1583.71 km2 (3.09%). Our study provides baseline information for further investigation of the mountain forest ecosystems, and biodiversity conservation actions in Iraq.</p

Past and future prediction of land cover land use change based on earth observation data by the CA–Markov model: a case study from Duhok governorate, Iraq

Understanding land use land cover change (LULCC) dynamics is crucial for sustaining the integrity of structure and function of ecosystems. As such, frequent measuring and monitoring of LULCC are necessary. Over the last four decades, Duhok governorate in the north of Iraq has undergone sweeping changes caused mainly by anthropogenic factors (e.g. population growth). This study used geospatial techniques and the synergy Cellular Automata (CA)–Markov approach to quantify past, current and model the future changes of LULC. The maximum likelihood classifier (MLC) was employed to conduct classification for three consecutive-year Landsat imagery (i.e. 1988, 2008 and 2017). From the classified imageries, three LULC maps with several classes were created and then, change detection analysis was implied. The classified (1988–2008) and (2008–2017) LULC maps were incorporated into the hybrid model to predict LULC maps for 2017 and 2060, respectively. The classified 2017 LULC maps were used as a reference to validate the model output for 2017. Relatively high accuracy agreements were achieved between the classified and the modelled maps (Kno= 0.8315, Klocation= 0.8267, Kstandard = 0.7978). The model classes estimated for 2060 compared to the classified 2017 LULC classes revealed that dense forest, sparse forest, agricultural land and barren area would decrease by −26.26% (from 327.08 to 241.08 km 2), −0.76% (from 2372.29 to 2355.82 km 2), −5.86% (from 973.21 to 916.27 km 2) and −10.03% (from 2918.9–2626.19 km 2), respectively. In contrast, the urban area would significantly increase by 271.19%, (from 161.99 to 602.19.8 km 2). Dense forest in Duhok governorate has seen remarkable decline from 1988 to 2017, and future predictions demonstrated that the declining trend would continue. Dense forest would predominantly convert to sparse forest and barren areas, suggesting forest thinning and clearing. Urban areas were the most dynamic cover types that increased significantly between 1998 and 2017. This trend would continue to increase from 2.36% (2017) to 8.76% (2060). Urbanization would be predominantly at the cost of agricultural land and barren area. Information on spatiotemporal dynamics of LULCC has been proved as an effective measure for maintaining the integrity of the ecosystem components through sustainable planning and management actions. </p