Cryptosporidium and Giardia lamblia Epidemiology in Middle Eastern Countries: study of the proliferation problem in the aquatic environment

The aim is to present a summarized study of the available literature on Cryptosporidium and Giardia lamblia for Middle Eastern countries, in order to identify trends in human cryptosporidiosis and childhood morbidity, and to raise awareness among residents. This is necessary in order to address the gap in preventive measures required to mitigate the overall effect attributed to associated illness and its impact in the already water stressed Middle Eastern countries. To assess seroprevalence of Cryptosporidium and Giardia lamblia in Middle Eastern countries systematic review was carried out based on online articles published from 2010-2018. PubMed, Web of Science, Google Scholar, Science Direct, Scopus, World Bank and WHO report and scientific database were explored. The current study highlights the existing subsequent epidemiology, its seroprevalence distribution, genetic diversity across Middle Eastern countries since 2009. This study therefore will provide the platform for future research work and development in comprehending Cryptosporidium and Giardia lamblia epidemiology in Middle Eastern countries. It was found that lack of awareness, personal hygiene and sanitation facilities, poverty, indiscriminate eating habits are favourable infestation conditions for Cryptosporidium and Giardia lamblia infections. The prevalence for both Cryptosporidium and Giardia lamblia, is higher in developing countries as compared to developed countries. The originality is that it is the only study of its kind in the region, as such studies are still lacking in Middle East countries as compared to other European, Asian, American continents and countries

Study of Seasonal variation and Index Based Assessment of Water Quality and Pollution in Semi-Arid Region of Morocco

Water resources quality assessment a basic requirement for ensuring its sustainability. Groundwater resources being restricted under the earth crust are at high risk of being polluted as compared to rivers which flow continuously. This study evaluated groundwater quality in Mohammedia prefecture, Morocco in terms of physicochemical parameters and seasonal variation. The physicochemical parameters analysed were Temperature, pH, EC, TDS, Na+, Ca2+, K+, NH4+, NO2-, NO3-, PO43-, SO42. Seasonal variation was evaluated for winter and spring seasons. The water quality was assessed in terms of overall water and Pollution index. Cation/anion ratio to TDS revealed evaporation and rock weathering dominance. Based on Pollution index, water quality of 88% samples was in excellent to good category in winter season. The pollution index during winter season was <1 for all sample locations. In Spring PI was >1 only at Location P1 which was attributed to NO2-. In Spring season 78% water samples were in Good to excellent category. The decrease in concentration during spring season was attributed to lack of soil-water interaction with reduced infiltration rate. The increase in concentration of parameters was attributed to anthropogenic activities. Further studies are needed to establish relationship between infiltration rate and pollutants concentration with respect to precipitation during monsoon season. Even though water quality in majority areas was fit for consumption and domestic use still further analysis should be carried out in terms of heavy metals and other emerging pollutants

Rainfall Prediction using Artificial Neural Network in Semi-Arid mountainous region, Saudi Arabia

Rainfall prediction using Artificial Intelligence technique is gaining attention nowadays. Semi-arid region receives rainfall below potential evapotranspiration but more than arid region. However, in mountainous semi-arid region high rainfall intensity makes it highly variable. This renders rainfall prediction difficult by applying normal techniques and calls for data pre-processing. This study presents rainfall prediction in semi-arid mountainous region of Abha, KSA. The study adopted Moving Average (Method) for data pre-processing based on 2 years, 3 years, 4 years, 5 years and 10 years. The Artificial Neural Network (ANN) was trained for a period of 1978-2016 rainfall data. The neural network was validated against the existing data of period 1997-2006. The trained neural network was used to predict for period of 2017-2025. The performance of the model was evaluated against AAE, MAE, RMSE, MASE and PP. The mean absolute error was observed least in 2 years moving average model. However, the most accurate prediction models were obtained from 2 years moving average and 5 year moving average. The study concludes that ANN coupled with MA have potential of predicting rainfall in Semi-Arid mountainous region

Epidemiology study of Diarrhoea, Cholera, Typhoid, Hepatitis A and Hepatitis E in Middle East and North Africa Region

Middle eastern countries are among one of the highly water stressed region in the world. Which renders it highly susceptible to water borne diseases. Water borne diseases epidemiology in Middle eastern countries were investigated in this research to determine existing health security in Middle eastern countries. Recent conflicts in the region, deteriorating water supply and infrastructure has led to major outbreaks of diarrhoea and cholera in Syria, Iraq, and Yemen. The water borne disease investigated are; diarrhoea, cholera, hepatitis A, hepatitis E and typhoid to present an overall scenario in the region. Despite proper infrastructure and water supply, stability (social, political and economy) of each country is vital to contain and curb water borne diseases and its outbreak in Middle eastern countries. According to the research results, it can be assumed that there is a high need for an elaborate study to come up with a comprehensive plan to mitigate and control water borne diseases in Middle eastern countries in terms of present and future perspective

Geospatial distribution and machine learning algorithms for assessing water quality in surface water bodies of Morocco

Abstract Surface waterbodies being primary source of water for human consumption are being investigated for its quality globally. This study evaluated water quality in three rivers (River Nfifikh, Hassar and El Maleh) of Mohammedia prefecture, Morocco in terms of heavy metals occurrence during two seasons of winter and spring. The heavy metals analyzed were cadmium, iron, copper, zinc, and lead. Heavy metal pollution index was derived to quantify water quality and pollution. Hazard quotient and carcinogenic risk were calculated to determine possible health risk. Modelling and prediction were performed using random forest, support vector machine and artificial neural network. The heavy metal concentration was lower in the winter season than in the spring season. Heavy metal pollution index (H.P.I.) was in the range of 1.5–2 during the winter season and 2–3 during the spring season. In the Nfifikh river, Cd2+ and Fe were the main polluting heavy metal. H.Q. was < 1 in all three rivers, which signified no adverse health effect from exposure to heavy metals. However, carcinogenic risk assessment revealed that 1 in every 100 people was susceptible to cancer during the life span of 70 years. Based on the control point reference, it was found that Mohammedia prefecture as river water was already contaminated before it entered the prefecture boundary. This was again validated with the water lagoon Douar El Marja which is located near the industrial zones of Mohammedia prefecture. Future studies are required to investigate pollution of rivers prior to their entry in Mohammedia prefecture to identify potential source and adopt mitigation measures accordingly

Changing urban dynamics: Empty building spaces

The Netherlands are facing a problem of vacant buildings or building spaces. The present study focuses on vacant office building spaces and their possible solutions. The transformation or reuse of the building has not been very successful as the available space is way too large or does not meet the requirement fully. The study focuses on the possible transformation based on area, location and feasibility. The study has analysed and suggested multiple feasible solutions to the empty spaces in Amsterdam as per the current scenario. The environmental impact by these transformations has been calculated in terms of Carbon Equivalent, making it a sustainable approach towards development of future. Then the Carbon Equivalent has been converted to carbon credits to evaluate the benefits of the transformation in financial terms. Last but not the least, the study has analysed the current scenario in developing countries like Saudi Arabia and India and suggested to take required steps at present to avoid problems in future

Water quality, heavy metal contamination and health risk assessment of surface water bodies of Mohammedia prefecture, Morocco

Research work on surface water bodies in Morocco has been in majority restricted to physicochemical and biological concentration. Hence, this study was conducted to address the existing research gap by evaluating heavy metal contamination and its associated risk assessment in surface water bodies, in Mohammedia prefecture, Morocco. A total of 22 water samples were evaluated regarding physicochemical factors and heavy metals. The parameters analyzed are pH, temperature (T), electrical conductivity (EC), total dissolved solids (TDS), calcium (Ca2+), Sodium (Na+), Potassium (K+), Ammonia (NH+), dissolved oxygen (DO), Sulphate (SO42−), nitrite (NO2−), nitrate (NO3−), phosphate (PO43−), total phosphate (TP), total kjeldahl nitrogen (TKN), cadmium (Cd), copper (Cu), Iron (Fe), lead (Pb), and zinc (Zn). Overall water quality (Ow) and status of contamination presented the water quality and pollution quantitatively. Carcinogenic and noncarcinogenic risks were estimated for health risk assessment. Ca2+ was the most abundant cation, and SO43− was the most abundant anion. Heavy metal concentrations were within permissible limits. Ow was good in terms of being suitable for parameters, i.e., 2.5. Pollution index (PI) indicated high pollution (14–74) at S3, S4, S5, and S6 sample points. In addition, Pb was a significant contributor to deteriorating water quality, with individual contributions ranging from 1 to 12 at sites S1, S2, and S3. For heavy metal hazards, i.e., adverse health effects, Hazard Quotient (HQ) (0.0002–0.01) was <1 for sample points, and Hazard Index (HI) (0.007–0.01) had a similar trend. They were inferring no significant non-carcinogenic health impact on its consumers. Carcinogenic risk (CR) was found to be within acceptable limits for CD, Cu, Pb, and Zn, i.e., 10−4 to 10−6. At points S2, S3, and S4, the carcinogenic index (CI) was above the acceptable limit, with values ranging from 1 × 10−3 to 7.9 × 10−3 attributed to Fe. Therefore, according to the findings of this research, the water quality is not suitable for direct consumption in its current state. Hence the study recommends treatment of surface water bodies prior to consumption

Hydro-Geochemical Assessment of Groundwater Quality in Aseer Region, Saudi Arabia

Saudi Arabia is an arid country with very limited water resources. The absence of surface water bodies along with erratic rainfall renders groundwater as the most reliable source of potable water in arid and semi-arid regions globally. Groundwater quality is determined by aquifer characteristics regional geology and it is extensively influenced by both natural and anthropogenic activities. In the recent past, several methodologies have been adopted to analyze the quality of groundwater and associated hydro-geochemical process i.e., multivariate statistical analysis, geochemical modelling, stable isotopes, a redox indicator, structural equation modelling. In the current study, statistical methods combined with geochemical modelling and conventional plots have been used to investigate groundwater and related geochemical processes in the Aseer region of Saudi Arabia. A total of 62 groundwater samples has been collected and analyzed in laboratory for major cations and anions. Groundwater in the study region is mostly alkaline with electrical conductivity ranging from 285&#8315;3796 &#956;S/cm. The hydro-geochemical characteristics of groundwater are highly influenced by extreme evaporation. Climatic conditions combined with low rainfall and high temperature have resulted in a highly alkaline aquifer environment. Principal component analysis (PCA) yielded principal components explaining 79.9% of the variance in the dataset. PCA indicates ion exchange, soil mineralization, dissolution of carbonates and halite are the major processes governing the groundwater geochemistry. Groundwater in this region is oversaturated with calcite and dolomite while undersaturated with gypsum and halite which suggests dissolution of gypsum and halite as major process resulting into high chloride in groundwater. The study concludes that the combined approach of a multivariate statistical technique, conventional plots and geochemical modelling is effective in determining the factors controlling the groundwater quality

Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping

Landslides and other catastrophic environmental disasters pose a significant danger to environmental, infrastructure, and people's lives. This research aimed to construct four optimized ensemble machine learning algorithms for landslide susceptibility (LS) mapping, namely particle swarm optimization (PSO) based artificial neural network (ANN), random forest, M5P, and support vector machine. The logistic regression (LR) model was then applied to the four-ensemble machine learning model and generated a hybrid optimized machine learning model. The receiver operating characteristics (ROC) curve was then used to validate LS map. The best model of four LS models depending on ROC's area under curve (AUC) is PSO-ANN (AUC-0.958) model. Also, LR model-based hybrid ensemble machine learning model achieved better accuracy (AUC: 0.962) than PSO-ANN model. Various resources, viz. grassland, built-up, and scarce foliage, are declared as landslide risk zones. Finally, elevation, soil-texture, slope, rainfall, and road distance are considered the most sensitive parameters for landslide occurrences

Spatio-temporal analysis and simulation of land cover changes and their impacts on land surface temperature in urban agglomeration of Bisha Watershed, Saudi Arabia

The present study investigates the spatiotemporal pattern of Land Use land cover (LULC) and land surface temperature (LST) in Abha for the years 1990, 2000, and . This research also forecasts the future LULC and LST for the year 2028. The support vector machine (SVM) was utilised to classify the LULC for the periods 1990-. The LST for the same period was derived using the mono window algorithm. The artificial neural network-cellular automata model (ANN-CA) was employed to forecast LULC and LST for the year 2028. The results indicated that urban areas rose by 434.6% between 1990 and 2018, while the LST soared to 50 °C in 2018, covering half of the study area. The built-up area, as well as the high LST zone, will be expanded in 2028. As a result, sustainable management strategies should be implemented to limit uncontrolled urban sprawl and LST