Displaying publications 1 - 20 of 32 in total

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  1. Bhagat SK, Pyrgaki K, Salih SQ, Tiyasha T, Beyaztas U, Shahid S, et al.
    Chemosphere, 2021 Aug;276:130162.
    PMID: 34088083 DOI: 10.1016/j.chemosphere.2021.130162
    Copper (Cu) ion in wastewater is considered as one of the crucial hazardous elements to be quantified. This research is established to predict copper ions adsorption (Ad) by Attapulgite clay from aqueous solutions using computer-aided models. Three artificial intelligent (AI) models are developed for this purpose including Grid optimization-based random forest (Grid-RF), artificial neural network (ANN) and support vector machine (SVM). Principal component analysis (PCA) is used to select model inputs from different variables including the initial concentration of Cu (IC), the dosage of Attapulgite clay (Dose), contact time (CT), pH, and addition of NaNO3 (SN). The ANN model is found to predict Ad with minimum root mean square error (RMSE = 0.9283) and maximum coefficient of determination (R2 = 0.9974) when all the variables (i.e., IC, Dose, CT, pH, SN) were considered as input. The prediction accuracy of Grid-RF model is found similar to ANN model when a few numbers of predictors are used. According to prediction accuracy, the models can be arranged as ANN-M5> Grid-RF-M5> Grid-RF-M4> ANN-M4> SVM-M4> SVM-M5. Overall, the applied statistical analysis of the results indicates that ANN and Grid-RF models can be employed as a computer-aided model for monitoring and simulating the adsorption from aqueous solutions by Attapulgite clay.
  2. Fu M, Le C, Fan T, Prakapovich R, Manko D, Dmytrenko O, et al.
    Environ Sci Pollut Res Int, 2021 Dec;28(45):64818-64829.
    PMID: 34318419 DOI: 10.1007/s11356-021-15574-y
    The atmospheric particulate matter (PM) with a diameter of 2.5 μm or less (PM2.5) is one of the key indicators of air pollutants. Accurate prediction of PM2.5 concentration is very important for air pollution monitoring and public health management. However, the presence of noise in PM2.5 data series is a major challenge of its accurate prediction. A novel hybrid PM2.5 concentration prediction model is proposed in this study by combining complete ensemble empirical mode decomposition (CEEMD) method, Pearson's correlation analysis, and a deep long short-term memory (LSTM) method. CEEMD was employed to decompose historical PM2.5 concentration data to different frequencies in order to enhance the timing characteristics of data. Pearson's correlation was used to screen the different frequency intrinsic-mode functions of decomposed data. Finally, the filtered enhancement data were inputted to a deep LSTM network with multiple hidden layers for training and prediction. The results evidenced the potential of the CEEMD-LSTM hybrid model with a prediction accuracy of approximately 80% and model convergence after 700 training epochs. The secondary screening of Pearson's correlation test improved the model (CEEMD-Pearson) accuracy up to 87% but model convergence after 800 epochs. The hybrid model combining CEEMD-Pearson with the deep LSTM neural network showed a prediction accuracy of nearly 90% and model convergence after 650 interactions. The results provide a clear indication of higher prediction accuracy of PM2.5 with less computation time through hybridization of CEEMD-Pearson with deep LSTM models and its potential to be employed for air pollution monitoring.
  3. Alavi J, Ewees AA, Ansari S, Shahid S, Yaseen ZM
    PMID: 34741267 DOI: 10.1007/s11356-021-17190-2
    Accurate prediction of inlet chemical oxygen demand (COD) is vital for better planning and management of wastewater treatment plants. The COD values at the inlet follow a complex nonstationary pattern, making its prediction challenging. This study compared the performance of several novel machine learning models developed through hybridizing kernel-based extreme learning machines (KELMs) with intelligent optimization algorithms for the reliable prediction of real-time COD values. The combined time-series learning method and consumer behaviours, estimated from water-use data (hour/day), were used as the supplementary inputs of the hybrid KELM models. Comparison of model performances for different input combinations revealed the best performance using up to 2-day lag values of COD with the other wastewater properties. The results also showed the best performance of the KELM-salp swarm algorithm (SSA) model among all the hybrid models with a minimum root mean square error of 0.058 and mean absolute error of 0.044.
  4. Hashim BM, Al-Naseri SK, Hamadi AM, Mahmood TA, Halder B, Shahid S, et al.
    Int J Disaster Risk Reduct, 2023 Aug;94:103799.
    PMID: 37360250 DOI: 10.1016/j.ijdrr.2023.103799
    The COVID-19 pandemic was a serious global health emergency in 2020 and 2021. This study analyzed the seasonal association of weekly averages of meteorological parameters, such as wind speed, solar radiation, temperature, relative humidity, and air pollutant PM2.5, with confirmed COVID-19 cases and deaths in Baghdad, Iraq, a major megacity of the Middle East, for the period June 2020 to August 2021. Spearman and Kendall correlation coefficients were used to investigate the association. The results showed that wind speed, air temperature, and solar radiation have positive and strong correlations with the confirmed cases and deaths in the cold season (autumn and winter 2020-2021). The total COVID-19 cases negatively correlated with relative humidity but were not significant in all seasons. Besides, PM2.5 strongly correlated with COVID-19 confirmed cases for the summer of 2020. The death distribution by age group showed the highest deaths for those aged 60-69. The highest number of deaths was 41% in the summer of 2020. The study provided useful information about the COVID-19 health emergency and meteorological parameters, which can be used for future health disaster planning, adopting prevention strategies and providing healthcare procedures to protect against future infraction transmission.
  5. Tao H, Jawad AH, Shather AH, Al-Khafaji Z, Rashid TA, Ali M, et al.
    Environ Int, 2023 May;175:107931.
    PMID: 37119651 DOI: 10.1016/j.envint.2023.107931
    This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
  6. Salehie O, Ismail TB, Shahid S, Sammen SS, Malik A, Wang X
    PMID: 35075345 DOI: 10.1007/s00477-022-02172-8
    Assessment of the thermal bioclimatic environmental changes is important to understand ongoing climate change implications on agriculture, ecology, and human health. This is particularly important for the climatologically diverse transboundary Amy Darya River basin, a major source of water and livelihood for millions in Central Asia. However, the absence of longer period observed temperature data is a major obstacle for such analysis. This study employed a novel approach by integrating compromise programming and multicriteria group decision-making methods to evaluate the efficiency of four global gridded temperature datasets based on observation data at 44 stations. The performance of the proposed method was evaluated by comparing the results obtained using symmetrical uncertainty, a machine learning similarity assessment method. The most reliable gridded data was used to assess the spatial distribution of global warming-induced unidirectional trends in thermal bioclimatic indicators (TBI) using a modified Mann-Kendall test. Ranking of the products revealed Climate Prediction Center (CPC) temperature as most efficient in reconstruction observed temperature, followed by TerraClimate and Climate Research Unit. The ranking of the product was consistent with that obtained using SU. Assessment of TBI trends using CPC data revealed an increase in the Tmin in the coldest month over the whole basin at a rate of 0.03-0.08 °C per decade, except in the east. Besides, an increase in diurnal temperature range and isothermally increased in the east up to 0.2 °C and 0.6% per decade, respectively. The results revealed negative implications of thermal bioclimatic change on water, ecology, and public health in the eastern mountainous region and positive impacts on vegetation in the west and northwest.

    Supplementary Information: The online version contains supplementary material available at 10.1007/s00477-022-02172-8.

  7. Kamruzzaman M, Wahid S, Shahid S, Alam E, Mainuddin M, Islam HMT, et al.
    Heliyon, 2023 May;9(5):e16274.
    PMID: 37234666 DOI: 10.1016/j.heliyon.2023.e16274
    Understanding spatiotemporal variability in precipitation and temperature and their future projections is critical for assessing environmental hazards and planning long-term mitigation and adaptation. In this study, 18 Global Climate Models (GCMs) from the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) were employed to project the mean annual, seasonal, and monthly precipitation, maximum air temperature (Tmax), and minimum air temperature (Tmin) in Bangladesh. The GCM projections were bias-corrected using the Simple Quantile Mapping (SQM) technique. Using the Multi-Model Ensemble (MME) mean of the bias-corrected dataset, the expected changes for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were evaluated for the near (2015-2044), mid (2045-2074), and far (2075-2100) futures in comparison to the historical period (1985-2014). In the far future, the anticipated average annual precipitation increased by 9.48%, 13.63%, 21.07%, and 30.90%, while the average Tmax (Tmin) rose by 1.09 (1.17), 1.60 (1.91), 2.12 (2.80), and 2.99 (3.69) °C for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. According to predictions for the SSP5-8.5 scenario in the distant future, there is expected to be a substantial rise in precipitation (41.98%) during the post-monsoon season. In contrast, winter precipitation was predicted to decrease most (11.12%) in the mid-future for SSP3-7.0, while to increase most (15.62%) in the far-future for SSP1-2.6. Tmax (Tmin) was predicted to rise most in the winter and least in the monsoon for all periods and scenarios. Tmin increased more rapidly than Tmax in all seasons for all SSPs. The projected changes could lead to more frequent and severe flooding, landslides, and negative impacts on human health, agriculture, and ecosystems. The study highlights the need for localized and context-specific adaptation strategies as different regions of Bangladesh will be affected differently by these changes.
  8. Shahid SK
    Ann Trop Med Parasitol, 2008 Jan;102(1):63-71.
    PMID: 18186979 DOI: 10.1179/136485908X252151
    Multidrug-resistant organisms cause late-onset ventilator-associated pneumonia (VAP). In a pilot, randomized and controlled study, the efficacy and safety of cefepime, in late-onset VAP in infants, have now been evaluated in Malaysia. Thirty children aged <1 year with late-onset VAP (i.e. VAP occurring 5 or more days after intubation) were randomized to receive cefepime or, as a control, ceftazidime. The clinical responses and the microbiological clearance of tracheal aspirates were evaluated in each arm. Adverse events, if any, were monitored clinically and by blood tests. Ten of the 15 children given cefepime and five of the 15 given ceftazidime showed a satisfactory clinical response (P<0.1). Cefepime appeared significantly better at clearing polymicrobial infections from tracheal aspirates. There were no fatalities in the cefepime arm but three in ceftazidime (P<0.1). The mean (S.E.) durations of antibiotic use were 9.4 (1.5) days for cefepime and 7.6 (1.0) days for ceftazidime (P>0.05). No serious adverse effects were observed in either arm. In conclusion, in late-onset VAP in infants, cefepime monotherapy appears to be at least as effective and safe as ceftazidime monotherapy, with better microbiological clearance.
  9. Rad S, Shamsudin S, Taha MR, Shahid S
    Water Sci Technol, 2016;73(2):405-13.
    PMID: 26819397 DOI: 10.2166/wst.2015.465
    The photo-degradation of nutrients in stormwater in photocatalytic reactor wet detention pond using nano titanium dioxide (TiO2) in concrete was investigated in a scale model as a new stormwater treatment method. Degradation of phosphate and nitrate in the presence of nano-TiO2 under natural ultra violet (UV) from tropical sunlight was monitored for 3 weeks compared with normal ponds. Two types of cement, including ordinary Portland and white cement mixed with TiO2 nano powder, were used as a thin cover to surround the body of the pond. Experiments with and without the catalyst were carried out for comparison and control. Average Anatase diameter of 25 nm and Rutile 100 nm nano particles were applied at three different mixtures of 3, 10 and 30% weight. The amounts of algae available orthophosphate and nitrate, which cause eutrophication in the ponds, were measured during the tests. Results revealed that the utilization of 3% up to 30% weight nano-TiO2 can improve stormwater outflow quality by up to 25% after 48 h and 57% after 3 weeks compared with the control sample in normal conditions with average nutrient (phosphate and nitrate) removal of 4% after 48 h and 10% after 3 weeks.
  10. Hussein H, Mustafa R, Quek KF, Hassanudin NS, Shahid S
    Int J Rheum Dis, 2008;11(3):237-240.
    DOI: 10.1111/j.1756-185X.2008.00384.x
    Objective: To validate the Malay version of the Health Assessment Questionnaire (Malay-HAQ) for use in Malay-speaking rheumatoid arthritis (RA) patients in the Malaysian setting. The HAQ - Disability Index has been validated in several languages, but not in Malay.Methods: The original HAQ was modified and translated into Malay by two translators, one of whom was aware of the objectives of the Questionnaire and the other as a lay translator. Two sets of Malay-HAQ were distributed to RA patients during their routine follow-up visits; one set to be completed immediately and another set to be completed 2 weeks later. A total of 61 patients completed the two sets of Malay-HAQ. The data collected was analysed using SPSS V. 11.0. Reliability of the data was evaluated using the test-retest method and internal consistency was assessed by Cronbach's alpha.Results: The study showed that the Malay-HAQ is feasible and reliable. The Spearman's correlation coefficient ranged from 0.65 to 0.82, while the internal consistency was 0.88-0.92.Conclusion: The Malay-HAQ is a sensitive, reliable and valid instrument for the measurement of functional status in RA patients in a Malay setting. © 2008 Asia Pacific League of Associations for Rheumatology.
  11. Islam R, Nazifa TH, Yuniarto A, Shanawaz Uddin ASM, Salmiati S, Shahid S
    Waste Manag, 2019 Jul 15;95:10-21.
    PMID: 31351595 DOI: 10.1016/j.wasman.2019.05.049
    Associated with the continuing increase of construction activities such as infrastructure projects, commercial buildings and housing programs, Bangladesh has been experiencing a rapid increase of construction and demolition (C&D) waste. Till now, the generation rate of C&D waste has not been well understood or not explicitly documented in Bangladesh. This study aims to provide an approach to estimate C&D waste generation using waste generation rates (WGR) through regression analysis. Furthermore, analyses the economic benefit of recycling C&D waste. The results revealed that WGR 63.74 kg/m2 and 1615 kg/m2 for construction and demolition activities respectively. Approximately, in financial year (FY) 2016, 1.28 million tons (0.149 construction and 1.139 demolition) waste were generated in Dhaka city, of which the three largest proportions were concrete (60%), brick/block (21%) and mortar (9%). After collection they were dumped in either landfills or unauthorized places. Therefore, it can be summarized as: waste is a resource in wrong place. The results of this study indicate that rapid urbanization of Dhaka city would likely experience the peak in the generation of C&D waste. This paper thus designates that C&D waste recycling is an entrepreneurial activity worth venturing into and an opportunity for extracting economic and environmental benefits from waste. The research findings also show that recycling of concrete and brick waste can add economic value of around 44.96 million USD. In addition, recycling of C&D waste leads to important reductions in CO2 emissions, energy use, natural resources and illegal landfills. Therefore, the findings of WGR and economic values provide valuable quantitative information for the future C&D waste management exercises of various stakeholders such as government, industry and academy.
  12. Yaseen ZM, Ali M, Sharafati A, Al-Ansari N, Shahid S
    Sci Rep, 2021 Feb 09;11(1):3435.
    PMID: 33564055 DOI: 10.1038/s41598-021-82977-9
    A noticeable increase in drought frequency and severity has been observed across the globe due to climate change, which attracted scientists in development of drought prediction models for mitigation of impacts. Droughts are usually monitored using drought indices (DIs), most of which are probabilistic and therefore, highly stochastic and non-linear. The current research investigated the capability of different versions of relatively well-explored machine learning (ML) models including random forest (RF), minimum probability machine regression (MPMR), M5 Tree (M5tree), extreme learning machine (ELM) and online sequential-ELM (OSELM) in predicting the most widely used DI known as standardized precipitation index (SPI) at multiple month horizons (i.e., 1, 3, 6 and 12). Models were developed using monthly rainfall data for the period of 1949-2013 at four meteorological stations namely, Barisal, Bogra, Faridpur and Mymensingh, each representing a geographical region of Bangladesh which frequently experiences droughts. The model inputs were decided based on correlation statistics and the prediction capability was evaluated using several statistical metrics including mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), correlation coefficient (R), Willmott's Index of agreement (WI), Nash Sutcliffe efficiency (NSE), and Legates and McCabe Index (LM). The results revealed that the proposed models are reliable and robust in predicting droughts in the region. Comparison of the models revealed ELM as the best model in forecasting droughts with minimal RMSE in the range of 0.07-0.85, 0.08-0.76, 0.062-0.80 and 0.042-0.605 for Barisal, Bogra, Faridpur and Mymensingh, respectively for all the SPI scales except one-month SPI for which the RF showed the best performance with minimal RMSE of 0.57, 0.45, 0.59 and 0.42, respectively.
  13. Al-Abadi AM, Pradhan B, Shahid S
    Environ Monit Assess, 2015 Oct;188(10):549.
    PMID: 27600115 DOI: 10.1007/s10661-016-5564-0
    The objective of this study is to delineate groundwater flowing well zone potential in An-Najif Province of Iraq in a data-driven evidential belief function model developed in a geographical information system (GIS) environment. An inventory map of 68 groundwater flowing wells was prepared through field survey. Seventy percent or 43 wells were used for training the evidential belief functions model and the reset 30 % or 19 wells were used for validation of the model. Seven groundwater conditioning factors mostly derived from RS were used, namely elevation, slope angle, curvature, topographic wetness index, stream power index, lithological units, and distance to the Euphrates River in this study. The relationship between training flowing well locations and the conditioning factors were investigated using evidential belief functions technique in a GIS environment. The integrated belief values were classified into five categories using natural break classification scheme to predict spatial zoning of groundwater flowing well, namely very low (0.17-0.34), low (0.34-0.46), moderate (0.46-0.58), high (0.58-0.80), and very high (0.80-0.99). The results show that very low and low zones cover 72 % (19,282 km(2)) of the study area mostly clustered in the central part, the moderate zone concentrated in the west part covers 13 % (3481 km(2)), and the high and very high zones extended over the northern part cover 15 % (3977 km(2)) of the study area. The vast spatial extension of very low and low zones indicates that groundwater flowing wells potential in the study area is low. The performance of the evidential belief functions spatial model was validated using the receiver operating characteristic curve. A success rate of 0.95 and a prediction rate of 0.94 were estimated from the area under relative operating characteristics curves, which indicate that the developed model has excellent capability to predict groundwater flowing well zones. The produced map of groundwater flowing well zones could be used to identify new wells and manage groundwater storage in a sustainable manner.
  14. Tao H, Bobaker AM, Ramal MM, Yaseen ZM, Hossain MS, Shahid S
    Environ Sci Pollut Res Int, 2019 Jan;26(1):923-937.
    PMID: 30421367 DOI: 10.1007/s11356-018-3663-x
    Surface and ground water resources are highly sensitive aquatic systems to contaminants due to their accessibility to multiple-point and non-point sources of pollutions. Determination of water quality variables using mathematical models instead of laboratory experiments can have venerable significance in term of the environmental prospective. In this research, application of a new developed hybrid response surface method (HRSM) which is a modified model of the existing response surface model (RSM) is proposed for the first time to predict biochemical oxygen demand (BOD) and dissolved oxygen (DO) in Euphrates River, Iraq. The model was constructed using various physical and chemical variables including water temperature (T), turbidity, power of hydrogen (pH), electrical conductivity (EC), alkalinity, calcium (Ca), chemical oxygen demand (COD), sulfate (SO4), total dissolved solids (TDS), and total suspended solids (TSS) as input attributes. The monthly water quality sampling data for the period 2004-2013 was considered for structuring the input-output pattern required for the development of the models. An advance analysis was conducted to comprehend the correlation between the predictors and predictand. The prediction performances of HRSM were compared with that of support vector regression (SVR) model which is one of the most predominate applied machine learning approaches of the state-of-the-art for water quality prediction. The results indicated a very optimistic modeling accuracy of the proposed HRSM model to predict BOD and DO. Furthermore, the results showed a robust alternative mathematical model for determining water quality particularly in a data scarce region like Iraq.
  15. Malik A, Tikhamarine Y, Sammen SS, Abba SI, Shahid S
    PMID: 33751346 DOI: 10.1007/s11356-021-13445-0
    Drought is considered one of the costliest natural disasters that result in water scarcity and crop damage almost every year. Drought monitoring and forecasting are essential for the efficient management of water resources and sustainability in agriculture. However, the design of a consistent drought prediction model based on the dynamic relationship of the drought index with its antecedent values remains a challenging task. In the present research, the SVR (support vector regression) model was hybridized with two different optimization algorithms namely; Particle Swarm Optimization (PSO) and Harris Hawks Optimization (HHO) for reliable prediction of effective drought index (EDI) 1 month ahead, at different locations of Uttarakhand State of India. The inputs of the models were selected through partial autocorrelation function (PACF) analysis. The output produced by the SVR-HHO and SVR-PSO models was compared with the EDI estimated from observed data using five statistical indicators, i.e., RMSE (Root Mean Square Error), MAE (Mean Absolute Error), COC (Coefficient of Correlation), NSE (Nash-Sutcliffe Efficiency), WI (Willmott Index), and graphical inspection of radar-chart, time-variation plot, box-whisker plot, and Taylor diagram. Appraisal of results indicates that the SVR-HHO model (RMSE = 0.535-0.965, MAE = 0.363-0.622, NSE = 0.558-0.860, COC = 0.760-0.930, and WI = 0.862-0.959) outperformed the SVR-PSO model (RMSE = 0.546-0.967, MAE = 0.372-0.625, NSE = 0.556-0.855, COC = 0.758-0.929, and WI = 0.861-0.956) in predicting EDI. Visual inspection of model performances also showed a better performance of SVR-HHO compared to SVR-PSO in replicating the median, inter-quartile range, spread, and pattern of the EDI estimated from observed rainfall. The results indicate that the hybrid SVR-HHO approach can be utilized for reliable EDI predictions in the study area.
  16. Hamed MM, Salehie O, Nashwan MS, Shahid S
    Environ Sci Pollut Res Int, 2023 Mar;30(13):38063-38075.
    PMID: 36576621 DOI: 10.1007/s11356-022-24985-4
    Global warming has amplified the frequency of temperature extremes, especially in hot dry countries, which could have serious consequences for the natural and built environments. Egypt is one of the hot desert climate regions that are more susceptible to climate change and associated hazards. This study attempted to project the changes in temperature extremes for three Shared Socioeconomic Pathways (SSPs), namely, SSP1-2.6, SSP2-4.5, and SSP5-8.5 and two future periods (early future: 2020-2059 and late future: 2060-2099) by using daily maximum (Tmax) and minimum temperature (Tmin) of general circulation model (GCMs) of Coupled Model Inter-comparison Project phase 6 (CMIP6). The findings showed that most temperature extreme indices would increase especially by the end of the century. In the late future, the change in the mean Tmin (4.3 °C) was projected to be higher than the mean Tmax (3.7 °C). Annual maximum Tmax, temperature above 95th percentile of Tmax, and the number of hot days above 40 °C and 45 °C were projected to increase in the range 3.0‒5.4 °C, 1.5‒4.8 °C, 20‒95 days, and 10‒52 days, respectively. In contrast, the annual minimum of Tmin, temperature below the 5th percentile, and the annual percentage of cold nights were projected to change in the range of 2.95‒5.0 °C, 1.4‒3.6 °C, and - 0.1‒0.1%, respectively. In all the cases, the lowest changes would be for SSP1-2.6 in the early period and the greatest changes for SSP5-8.5 in the late period. The study indicates that the country is likely to experience a rise in hot extremes and a decline in cold extremes. Therefore, Egypt should take long-term adaptation plans to build social resiliency to rising hot extremes.
  17. Tanimu B, Hamed MM, Bello AD, Abdullahi SA, Ajibike MA, Shahid S
    Environ Sci Pollut Res Int, 2024 Feb;31(10):15986-16010.
    PMID: 38308777 DOI: 10.1007/s11356-024-32128-0
    Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.
  18. Mahesar RA, Shahid S, Asif S, Khoso AK, Kar SK, Shabbir T
    CNS Spectr, 2023 Oct 20.
    PMID: 37861078 DOI: 10.1017/S1092852923006351
    Numerous studies have been conducted globally to assess the compliance level of newspapers with the World Health Organization's media guidelines for responsible suicide reporting. To identify and review such studies conducted in Muslim-majority countries between 2014 and 2022, we searched PubMed and Google Scholar databases. We identified 12 eligible studies from Pakistan (n = 4), Bangladesh (n = 2), Malaysia (n = 1), Indonesia (n = 1), Iraq (n = 1), Iran (n = 1), Nigeria (n = 1), and Egypt (n = 1). These studies indicated an overall lack of adherence to the guidelines. However, the level of nonadherence was particularly high in Pakistan. Effective suicide prevention programs may help in promoting responsible reporting of suicide.
  19. Muhammad MKI, Hamed MM, Harun S, Sa'adi Z, Sammen SS, Al-Ansari N, et al.
    Sci Rep, 2024 Feb 21;14(1):4255.
    PMID: 38383678 DOI: 10.1038/s41598-024-53960-x
    One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950-2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the LandScan gridded population data from 2000 to 2020 was used to calculate the affected population to the heatwaves. This study also utilized Sen's slope for trend analysis of heatwave characteristics, which separates multi-decadal oscillatory fluctuations from secular trends. The findings demonstrated that the geographical pattern of heatwaves in PM could be reconstructed if daily Tmax is more than the 95th percentile for 3 or more days. The data indicated that the southwest was more prone to severe heatwaves. The PM experienced more heatwaves after 2000 than before. Overall, the heatwave-affected area in PM has increased by 8.98 km2/decade and its duration by 1.54 days/decade. The highest population affected was located in the central south region of PM. These findings provide valuable insights into the heatwaves pattern and impact.
  20. Salaudeen A, Shahid S, Ismail A, Adeogun BK, Ajibike MA, Bello AD, et al.
    Sci Total Environ, 2023 Feb 01;858(Pt 2):159874.
    PMID: 36334669 DOI: 10.1016/j.scitotenv.2022.159874
    Recently, there is an upsurge in flood emergencies in Nigeria, in which their frequencies and impacts are expected to exacerbate in the future due to land-use/land cover (LULC) and climate change stressors. The separate and combined forces of these stressors on the Gongola river basin is feebly understood and the probable future impacts are not clear. Accordingly, this study uses a process-based watershed modelling approach - the Hydrological Simulation Program FORTRAN (HSPF) (i) to understand the basin's current and future hydrological fluxes and (ii) to quantify the effectiveness of five management options as adaptation measures for the impacts of the stressors. The ensemble means of the three models derived from the Coupled Model Intercomparison Project Phase 5 (CMIP5) are employed for generating future climate scenarios, considering three distinct radiative forcing peculiar to the study area. Also, the historical and future LULC (developed from the hybrid of Cellular Automata and Markov Chain model) are used to produce the LULC scenarios for the basin. The effective calibration, uncertainty and sensitivity analyses are used for optimising the parameters of the model and the validated result implies a plausible model with efficiency of up to 75 %. Consequently, the results of individual impacts of the stressors yield amplification of the peak flows, with more profound impacts from climate stressor than the LULC. Therefore, the climate impact may trigger a marked peak discharge that is 48 % higher as compared to the historical peak flows which are equivalent to 10,000-year flood event. Whilst the combine impacts may further amplify this value by 27 % depending on the scenario. The proposed management interventions such as planned reforestation and reservoir at Dindima should attenuate the disastrous peak discharges by almost 36 %. Furthermore, the land management option should promote the carbon-sequestering project of the Paris agreement ratified by Nigeria. While the reservoir would serve secondary functions of energy production; employment opportunities, aside other social aspects. These measures are therefore expected to mitigate feasibly the negative impacts anticipated from the stressors and the approach can be employed in other river basins in Africa confronted with similar challenges.
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