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.
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.
Mapping potential changes in bioclimatic characteristics are critical for planning mitigation goals and climate change adaptation. Assessment of such changes is particularly important for Southeast Asia (SEA) - home to global largest ecological diversity. Twenty-three global climate models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) were used in this study to evaluate changes in 11 thermal bioclimatic indicators over SEA for two shared socioeconomic pathways (SSPs), 2-4.5 and 5-8.5. Spatial changes in the ensemble mean, 5th, and 95th percentile of each indicator for near (2020-2059) and far (2060-2099) periods were examined in order to understand temporal changes and associated uncertainty. The results indicated large spatial heterogeneity and temporal variability in projected changes of bioclimatic indicators. A higher change was projected for mainland SEA in the far future and less in maritime region during the near future. At the same time, uncertainty in the projected bioclimatic indices was higher for mainland than maritime SEA. Analysis of mean multi-model ensemble revealed a change in mean temperature ranged from - 0.71 to 3.23 °C in near and from 0.00 to 4.07 °C in far futures. The diurnal temperature range was projected to reduce over most of SEA (ranging from - 1.1 to - 2.0 °C), while isothermality is likely to decrease from - 1.1 to - 4.6%. A decrease in isothermality along with narrowing of seasonality indicated a possible shift in climate, particularly in the north of mainland SEA. Maximum temperature in the warmest month/quarter was projected to increase a little more than the coldest month/quarter and the mean temperature in the driest month to increase more than the wettest month. This would cause an increase in the annual temperature range in the future.
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.
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.
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.
Marine debris is often detected everywhere in the oceans after it enters the marine ecosystems from various sources. Marine litter pollution is a major threat to the marine ecosystem in Bangladesh. A preliminary study was conducted to identify the sources of marine litter (plastics, foamed plastic, clothes, glass, ceramic, metals, paper, and cardboard) along the Bay of Bengal coast. From the observations, the range of abundance of the collected marine litter was 0.14-0.58 items/m2. From the ten sampling sites, the highest amount of marine litter was observed for aluminium cans (3500), followed by plastic bottles (3200). The spatial distribution pattern indicated that all the study areas had beach litter of all types of materials. The present investigation showed that plastics were the dominating pollutants in the marine ecosystem in Bangladesh. The clean-coast index (CCI) value indicated that the Cox's Bazar coast was clean to dirty class. The abundance, distribution, and pollution of marine litter along the coastal belts pose a potential threat to the entire ecosystem. This study will help come up with ways to manage and get rid of marine litter along the coast in an effective way.
The spreading of sewage sludge from wastewater treatment plants and various industries arouses the growing interest due to the contamination by trace elements. Sludges were collected from one sewage treatment plant and two industries in Dhaka City, Bangladesh to assess physicochemical parameters and total and fraction content of trace elements like Cr, Ni, Cu, As, Cd, Pb, Fe, Mn and Zn in sludges. We evaluated the bioavailability of theses metals by determining their speciation by sequential extraction, each metal being distributed among five fractions: exchangeable fraction, bound to carbonate fraction, Fe-Mn oxide bound fraction, organic matter bound fraction and residual fractions. We found that all the analyzed sludges had satisfactory properties from an agronomic quality point of view. The average concentration (mg/kg) of trace metals in sludge samples were in the following decreasing order Fe (12807) > Cr (200) > Mn (158) > Zn (132) > Cu (68.2) > Ni (42.5) > Pb (36.4) > As (35.1) > Cd (3.7). The results of the sequential extraction showed that Cr, Ni, Cu, Fe and Mn were largely associated with the residual fraction where As, Cd and Pb was dominantly associated with the exchangeable and carbonate bound fractions and Zn showed a considerable proportion in carbonate bound fraction. These results showed that regulations must take into account the bioavailability with regard to the characteristics of the agricultural soils on which sludge will be spread.
Safe levels of heavy metals in the surface water and sediment of the eastern Bay of Bengal coast have not been universally established. Current study characterized heavy metals such as arsenic (As), chromium (Cr), cadmium (Cd) and lead (Pb) in surface water and sediments of the most important fishing resource at the eastern Bay of Bengal coast, Bangladesh. Both water and sediment samples were analyzed using inductively coupled plasma mass spectrometer. Considering both of the seasons, the mean concentrations of Cr, As, Cd, and Pb in water samples were 33.25, 8.14, 0.48, and 21.14 μg/L, respectively and in sediment were 30.47, 4.48, 0.20, and 19.98 mg/kg, respectively. Heavy metals concentration in water samples surpassed the acceptable limits of usable water quality, indicating that water from this water resource is not safe for drinking, cooking, bathing, and any other uses. Enrichment factors also directed minor enrichment of heavy metals in sediment of the coast. Other indexes for ecological risk assessment such as pollution load index (PLI), contamination factor (CF), geoaccumulation index (Igeo), modified contamination degree (mCd), and potential ecological risk index (PERI) also indicated that sediment of the coastal watershed was low contamination. In-depth inventorying of heavy metals in both water and sediment of the study area are required to determine ecosystem health for holistic risk assessment and management.
Reliable projection of evapotranspiration (ET) is important for planning sustainable water management for the agriculture field in the context of climate change. A global dataset of monthly climate variables was generated to estimate potential ET (PET) using 14 General Circulation Models (GCMs) for four main shared socioeconomic pathways (SSPs). The generated dataset has a spatial resolution of 0.5° × 0.5° and a period ranging from 1950 to 2100 and can estimate historical and future PET using the Penman-Monteith method. Furthermore, this dataset can be applied to various PET estimation methods based on climate variables. This paper presents that the dataset generated to estimate future PET could reflect the greenhouse gas concentration level of the SSP scenarios in latitude bands. Therefore, this dataset can provide vital information for users to select appropriate GCMs for estimating reasonable PETs and help determine bias correction methods to reduce between observation and model based on the scale of climate variables in each GCM.
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.
Interleukin 17 F is a member of IL-17 cytokine family with a 50% structural homology to IL-17A and plays a significant role either alone or in combination with IL-17A towards inflammation in Rheumatoid arthritis (RA). A growing number of drugs targeting IL-17 pathway are being tested against population specific disease markers. The major objective of this research was to investigate the anti-inflammatory effect of Anakinra (an IL-1 R1 inhibitor) and Ustekinumab (an IL-12 and IL-23 inhibitor) by targeting IL17F. The three dimensional structures of IL17F was taken from PDB while structures of drugs were taken from PubChem database. Docking was performed using MOE and Schrodinger ligand docking software and binding energies, including s-score using London-dG fitness function and glide score using glide internal energy function, between drug and targets were compared. Furthermore, Protein-Drug complex were subjected to 150 ns Molecular Dynamics (MD) Simulations using Schrodinger's Desmond Module. Docking and MD simulation results suggest anakinra as a more potent IL17F inhibitor and forming a more structurally stable complex.Communicated by Ramaswamy H. Sarma.
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.
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.
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.
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.
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.
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.
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.