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  1. 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.
  2. Obaid HA, Shahid S, Basim KN, Chelliapan S
    Water Sci Technol, 2015;72(6):1029-42.
    PMID: 26360765 DOI: 10.2166/wst.2015.297
    Water pollution during festival periods is a major problem in all festival cities across the world. Reliable prediction of water pollution is essential in festival cities for sewer and wastewater management in order to ensure public health and a clean environment. This article aims to model the biological oxygen demand (BOD(5)), and total suspended solids (TSS) parameters in wastewater in the sewer networks of Karbala city center during festival and rainy days using structural equation modeling and multiple linear regression analysis methods. For this purpose, 34 years (1980-2014) of rainfall, temperature and sewer flow data during festival periods in the study area were collected, processed, and employed. The results show that the TSS concentration increases by 26-46 mg/l while BOD(5) concentration rises by 9-19 mg/l for an increase of rainfall by 1 mm during festival periods. It was also found that BOD(5) concentration rises by 4-17 mg/l for each increase of 10,000 population.
  3. 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.
  4. 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.
  5. Nashwan MS, Shahid S, Chung ES
    Sci Data, 2019 07 31;6(1):138.
    PMID: 31366936 DOI: 10.1038/s41597-019-0144-0
    This study developed 0.05° × 0.05° land-only datasets of daily maximum and minimum temperatures in the densely populated Central North region of Egypt (CNE) for the period 1981-2017. Existing coarse-resolution datasets were evaluated to find the best dataset for the study area to use as a base of the new datasets. The Climate Prediction Centre (CPC) global temperature dataset was found to be the best. The CPC data were interpolated to a spatial resolution of 0.05° latitude/longitude using linear interpolation technique considering the flat topography of the study area. The robust kernel density distribution mapping method was used to correct the bias using observations, and WorldClim v.2 temperature climatology was used to adjust the spatial variability in temperature. The validation of CNE datasets using probability density function skill score and hot and cold extremes tail skill scores showed remarkable improvement in replicating the spatial and temporal variability in observed temperature. Because CNE datasets are the best available high-resolution estimate of daily temperatures, they will be beneficial for climatic and hydrological studies.
  6. 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.
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  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. Pham QB, Sammen SS, Abba SI, Mohammadi B, Shahid S, Abdulkadir RA
    PMID: 33625698 DOI: 10.1007/s11356-021-12792-2
    Precise monitoring of cyanobacteria concentration in water resources is a daunting task. The development of reliable tools to monitor this contamination is an important research topic in water resources management. Indirect methods such as chlorophyll-a determination, cell counting, and toxin measurement of the cyanobacteria are tedious, cumbersome, and often lead to inaccurate results. The quantity of phycocyanin (PC) pigment is considered more appropriate for cyanobacteria monitoring. Traditional approaches for PC estimation are time-consuming, expensive, and require high expertise. Recently, some studies have proposed the application of artificial intelligence (AI) techniques to predict the amount of PC concentration. Nonetheless, most of these researches are limited to standalone modeling schemas such as artificial neural network (ANN), multilayer perceptron (MLP), and support vector machine (SVM). The independent schema provides imprecise results when faced with highly nonlinear systems and data uncertainties resulting from environmental disturbances. To alleviate the limitations of the existing models, this study proposes the first application of a hybrid AI model that integrates the potentials of relevance vector machine (RVM) and flower pollination algorithm (RVM-FPA) to predict the PC concentration in water resources. The performance of the hybrid model is compared with the standalone RVM model. The prediction performance of the proposed models was evaluated at two stations (stations 508 and 478) using different statistical and graphical performance evaluation methods. The results showed that the hybrid models exhibited higher performance at both stations compared to the standalone RVM model. The proposed hybrid RVM-FPA can therefore serve as a reliable predictive tool for PC concentration in water resources.
  13. Shiru MS, Shahid S, Dewan A, Chung ES, Alias N, Ahmed K, et al.
    Sci Rep, 2020 06 22;10(1):10107.
    PMID: 32572138 DOI: 10.1038/s41598-020-67146-8
    Like many other African countries, incidence of drought is increasing in Nigeria. In this work, spatiotemporal changes in droughts under different representative concentration pathway (RCP) scenarios were assessed; considering their greatest impacts on life and livelihoods in Nigeria, especially when droughts coincide with the growing seasons. Three entropy-based methods, namely symmetrical uncertainty, gain ratio, and entropy gain were used in a multi-criteria decision-making framework to select the best performing General Circulation Models (GCMs) for the projection of rainfall and temperature. Performance of four widely used bias correction methods was compared to identify a suitable method for correcting bias in GCM projections for the period 2010-2099. A machine learning technique was then used to generate a multi-model ensemble (MME) of the bias-corrected GCM projection for different RCP scenarios. The standardized precipitation evapotranspiration index (SPEI) was subsequently computed to estimate droughts from the MME mean of GCM projected rainfall and temperature to predict possible spatiotemporal changes in meteorological droughts. Finally, trends in the SPEI, temperature and rainfall, and return period of droughts for different growing seasons were estimated using a 50-year moving window, with a 10-year interval, to understand driving factors accountable for future changes in droughts. The analysis revealed that MRI-CGCM3, HadGEM2-ES, CSIRO-Mk3-6-0, and CESM1-CAM5 are the most appropriate GCMs for projecting rainfall and temperature, and the linear scaling (SCL) is the best method for correcting bias. The MME mean of bias-corrected GCM projections revealed an increase in rainfall in the south-south, southwest, and parts of the northwest whilst a decrease in the southeast, northeast, and parts of central Nigeria. In contrast, rise in temperature for entire country during most of the cropping seasons was projected. The results further indicated that increase in temperature would decrease the SPEI across Nigeria, which will make droughts more frequent in most of the country under all the RCPs. However, increase in drought frequency would be less for higher RCPs due to increase in rainfall.
  14. 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.
  15. 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.
  16. 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.

  17. Rahman MB, Salam R, Islam ARMT, Tasnuva A, Haque U, Shahid S, et al.
    Theor Appl Climatol, 2021;146(1-2):125-138.
    PMID: 34334853 DOI: 10.1007/s00704-021-03705-x
    Climate change-derived extreme heat phenomena are one of the major concerns across the globe, including Bangladesh. The appraisal of historical spatiotemporal changes and possible future changes in heat index (HI) is essential for developing heat stress mitigation strategies. However, the climate-health nexus studies in Bangladesh are very limited. This study was intended to appraise the historical and projected changes in HI in Bangladesh. The HI was computed from daily dry bulb temperature and relative humidity. The modified Mann-Kendal (MMK) test and linear regression were used to detect trends in HI for the observed period (1985-2015). The future change in HI was projected for the mid-century (2041-2070) for three Representative Concentration Pathway (RCP) scenarios, RCP 2.6, 4.5, and 8.5 using the Canadian Earth System Model Second Generation (CanESM2). The results revealed a monotonic rise in the HI and extreme caution conditions, especially in the humid summer season for most parts of Bangladesh for the observed period (1985-2015). Future projections revealed a continuous rise in HI in the forthcoming period (2041-2070). A higher and remarkable increase in the HI was projected in the northern, northeastern, and south-central regions. Among the three scenarios, the RCP 8.5 showed a higher projection of HI both in hot and humid summer compared to the other scenarios. Therefore, Bangladesh should take region-specific adaptation strategies to mitigate the impacts of HI.

    Supplementary Information: The online version contains supplementary material available at 10.1007/s00704-021-03705-x.

  18. Islam ARMT, Islam HMT, Shahid S, Khatun MK, Ali MM, Rahman MS, et al.
    J Environ Manage, 2021 Jul 01;289:112505.
    PMID: 33819656 DOI: 10.1016/j.jenvman.2021.112505
    Climate extremes have a significant impact on vegetation. However, little is known about vegetation response to climatic extremes in Bangladesh. The association of Normalized Difference Vegetation Index (NDVI) with nine extreme precipitation and temperature indices was evaluated to identify the nexus between vegetation and climatic extremes and their associations in Bangladesh for the period 1986-2017. Moreover, detrended fluctuation analysis (DFA) and Morlet wavelet analysis (MWA) were employed to evaluate the possible future trends and decipher the existing periodic cycles, respectively in the time series of NDVI and climate extremes. Besides, atmospheric variables of ECMWF ERA5 were used to examine the casual circulation mechanism responsible for climatic extremes of Bangladesh. The results revealed that the monthly NDVI is positively associated with extreme rainfall with spatiotemporal heterogeneity. Warm temperature indices showed a significant negative association with NDVI on the seasonal scale, while precipitation and cold temperature extremes showed a positive association with yearly NDVI. The DEA revealed a continuous increase in temperature extreme in the future, while no change in precipitation extremes. NDVI also revealed a significant association with extreme temperature indices with a time lag of one month and with precipitation extreme without time lag. Spatial analysis indicated insensitivity of marshy vegetation type to climate extremes in winter. The study revealed that elevated summer geopotential height, no visible anticyclonic center, reduced high cloud cover, and low solar radiation with higher humidity contributed to climatic extremes in Bangladesh. The nexus between NDVI and climatic extremes established in this study indicated that increasing warm temperature extremes due to global warming might have severe implications on Bangladesh's ecology and the environment in the future.
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