Unveiling microplastics pollution in a subtropical rural recreational lake: A novel insight.

While global attention has been primarily focused on the occurrence and persistence of microplastics (MP) in urban lakes, relatively little attention has been paid to the problem of MP pollution in rural recreational lakes. This pioneering study aims to shed light on MP size, composition, abundance, spatial distribution, and contributing factors in a rural recreational lake, 'Nikli Lake' in Kishoreganj, Bangladesh. Using density separation, MPs were extracted from 30 water and 30 sediment samples taken from ten different locations in the lake. Subsequent characterization was carried out using a combination of techniques, including a stereomicroscope, Fourier transform infrared spectroscopy (FTIR) and field emission scanning electron microscopy (FE-SEM). The results showed a significant prevalence of MPs in all samples, with an average amount of 109.667 ± 10.892 pieces/kg3 (dw) in the sediment and 98.167 ± 12.849 pieces/m3 in the water. Small MPs (<0.5 mm), fragments and transparent colored particles formed the majority, accounting for 80.2%, 64.5% and 55.3% in water and 78.9%, 66.4% and 64.3% in sediment, respectively. In line with global trends, polypropylene (PP) (53%) and polyethylene (PE) (43%) emerged as the predominant polymers within the MPs. MP contents in water and sediment showed positive correlations with outflow, while they correlated negatively with inflow and lake depth (p > 0.05). Local activities such as the discharge of domestic sewage, fishing waste and agricultural runoff significantly influence the distribution of polypropylene. Assessment of pollution factor, pollution risk index and pollution load index values at the sampling sites confirmed the presence of MPs, with values above 1. This study is a baseline database that provides a comprehensive understanding of MP pollution in the freshwater ecosystem of Bangladesh, particularly in a rural recreational lake. A crucial next step is to explore ecotoxicological mechanisms, legislative measures and future research challenges triggered by MP pollution.

Flood hazard potential evaluation using decision tree state-of-the-art models.

Floods occur frequently in Romania and throughout the world and are one of the most devastating natural disasters that impact people's lives. Therefore, in order to reduce the potential damages, an accurate identification of surfaces susceptible to flood phenomena is mandatory. In this regard, the quantitative calculation of flood susceptibility has become a very popular practice in the scientific research. With the development of modern computerized methods such as geographic information system and machine learning models, and as a result of the possibility of combining them, the determination of areas susceptible to floods has become increasingly accurate, and the algorithms used are increasingly varied. Some of the most used and highly accurate machine learning algorithms are the decision tree models. Therefore, in the present study focusing on flood susceptibility zonation mapping in the Trotus River basin, the following algorithms were applied: forest by penalizing attribute-weights of evidence (forest-PA-WOE), best first decision tree-WOE, alternating decision tree-WOE, and logistic regression-WOE. The best performant, characterized by a maximum accuracy of 0.981, proved to be forest-PA-WOE, whereas in terms of flood exposure, an area of over 16.22% of the Trotus basin is exposed to high and very high floods susceptibility. The performances applied models in the present work are higher than the models applied in the previous studies in the same study area. Moreover, it should be noted that the accuracy of the models is similar with the accuracies of the decision tree models achieved in the studies focused on other areas across the world. Therefore, we can state that the models applied in the present research can be successfully used in by the researchers in other case studies. The findings of this research may substantially map the flood risk areas and further aid watershed managers in limiting and remediating flood damage in the data-scarce regions. Moreover, the results of this study can be a very useful for the hazard management and planning authorities.

Change in cyclone disaster vulnerability and response in coastal Bangladesh.

The number of deaths owing to tropical cyclones in Bangladesh has significantly reduced. Category 4 Cyclone Gorky in 1991 and Sidr in 2007 caused 147,000 and 4,500 deaths respectively, whereas Category 1 Cyclone Mora in 2017 resulted in six. Face-to-face interviews with 362 residents, participant observation, and focus-group discussions answer a research question about how change in coastal areas has contributed to this outcome. The study considered institutional approaches of disaster risk management through legal frameworks, administrative arrangements, cyclone preparedness activities, cyclone detection and early warning dissemination, construction of shelter centres, strengthening of various types of coastal embankments, paved roads, and pre-cyclone evacuation. The findings indicate significant improvement in house structures and design, income levels and diversification, education, awareness, individual capacity, poverty reduction, and lowering dependency on agriculture-based earning. Furthermore, the availability of mobile telephones, radio, television, and social media platforms enhanced social connectivity and greater gender equality and empowerment helped to facilitate disaster preparedness, evacuation, and response.

A review of recent advances and future prospects in calculation of reference evapotranspiration in Bangladesh using soft computing models.

Evapotranspiration (ETo) is a complex and non-linear hydrological process with a significant impact on efficient water resource planning and long-term management. The Penman-Monteith (PM) equation method, developed by the Food and Agriculture Organization of the United Nations (FAO), represents an advancement over earlier approaches for estimating ETo. Eto though reliable, faces limitations due to the requirement for climatological data not always available at specific locations. To address this, researchers have explored soft computing (SC) models as alternatives to conventional methods, known for their exceptional accuracy across disciplines. This critical review aims to enhance understanding of cutting-edge SC frameworks for ETo estimation, highlighting advancements in evolutionary models, hybrid and ensemble approaches, and optimization strategies. Recent applications of SC in various climatic zones in Bangladesh are evaluated, with the order of preference being ANFIS > Bi-LSTM > RT > DENFIS > SVR-PSOGWO > PSO-HFS due to their consistently high accuracy (RMSE and R2). This review introduces a benchmark for incorporating evolutionary computation algorithms (EC) into ETo modeling. Each subsection addresses the strengths and weaknesses of known SC models, offering valuable insights. The review serves as a valuable resource for experienced water resource engineers and hydrologists, both domestically and internationally, providing comprehensive SC modeling studies for ETo forecasting. Furthermore, it provides an improved water resources monitoring and management plans.

Appraising water resources for irrigation and spatial analysis based on fuzzy logic model in the tribal-prone areas of Bangladesh.

The lack of quality water resources for irrigation is one of the main threats for sustainable farming. This pioneering study focused on finding the best area for farming by looking at irrigation water quality and analyzing its location using a fuzzy logic model on a Geographic Information System platform. In the tribal-prone areas of Khagrachhari Sadar Upazila, Bangladesh, 28 surface water and 39 groundwater samples were taken from shallow tube wells, rivers, canals, ponds, lakes, and waterfalls. The samples were then analyzed for irrigation water quality parameters like electrical conductivity (EC), total dissolved solids (TDS), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), residual sodium bicarbonate (RSBC), magnesium hazard ratio (MHR), Kelley's ratio (KR), and permeability index (PI). Fuzzy Irrigation Water Quality Index (FIWQI) was employed to determine the irrigation suitability of water resources. Spatial maps for parameters like EC, KR, MH, Na%, PI, SAR, and RSBC were developed using fuzzy membership values for groundwater and surface water. The FIWQI results indicate that 100% of the groundwater and 75% of the surface water samples range in the categories of excellent to good for irrigation uses. A new irrigation suitability map constructed by overlaying all parameters showed that surface water (75%) and some groundwater (100%) in the northern and southwestern portions are fit for agriculture. The western and central parts are unfit for irrigation due to higher bicarbonate and magnesium contents. The Piper and Gibbs diagram also indicated that the water in the study area is magnesium-bicarbonate type and the primary mechanism of water chemistry is controlled by the weathering of rocks, respectively. This research pinpoints the irrigation spatial pattern for regional water resource practices, identifies novel suitable areas, and improves sustainable agricultural uses in tribal-prone areas.

Natural and anthropogenic contributions to the elemental compositions and subsequent ecological consequences of a transboundary river's sediments (Punarbhaba, Bangladesh).

An anthropogenically less affected transboundary river (Punarbhaba, Bangladesh) was studied to detect associated risks from the combined origin(s) of geochemically and toxicologically significant elements in benthic sediments. A total of 30 river bed sediments were analyzed by instrumental neutron activation analysis targeting the 15 chemical elements viz., Na, Al, K, Ti, Cr, Mn, Co, Zn, As, Rb, Sb, Cs, Ba, Th, and U. Among the estimated elements, the mean abundances (µg/g) of Rb (136), Sb (0.66), Cs (6.66), Th (14.6), and U (3.92) were 1.4-1.7 times higher than the crustal origin. These elements are primarily responsible for the contaminated state of the Punarbhaba River. The studied area is 'moderately polluted' (Igeo: 2.01 to 0.02) and possesses 'minor enrichment' (EF: 1.98 to 0.48) in terms of the measured elements. The output of statistical analyses projected that the studied elements are geochemically fractionated in an oxidizing environment (U/Th = 0.44) and mostly originated from felsic sources, thus confirming the mineral is comprised of aluminosilicates and alkali feldspar. However, SQGs-based and ecological risk indices invoked minor (Cr: 6.67%) to no potential ecotoxicological threats for Cr, Mn, Co, Zn, As, and Sb. Nonetheless, altered distribution patterns caused by geogenic activities increased Cr and Zn in the environment which may cause toxicity (Cr: 22-53%, Zn: 35-70%), and pose potential ecological risks, specifically in upstream locations (P-2, P-3, P-5). Further, this study broadened the perspective of sediment deposition from fractionation, fluvial transportation, and weathering events beyond the industrial disintegration of elements, which will aid researchers and policymakers to comprehend combined risks from suspended sediments.

Identifying factors affecting irrigation metrics in the Haor basin using integrated Shannon's entropy, fuzzy logic and automatic linear model.

The sustainability of agricultural practices is seriously threatened by the quality of water used for irrigation. This paper aims to evaluate the suitability of irrigation water and identify the region suitable for agricultural use in the Haor basin of Bangladesh using conventional irrigation indices such as sodium adsorption ratio (SAR), percent sodium (Na%), magnesium hazard ratio (MHR), permeability index (PI), and Kelly's ratio (KR), as well as novel irrigation indices such as, Shannon's entropy index for irrigation water quality (EWQ) and fuzzy logic index for irrigation water quality (FIWQI). The main influences of groundwater and surface water parameters on irrigation indices were predicted using automatic linear modeling (ALM). Forty water samples were collected from shallow tube wells, rivers, canals, ponds, and drainage systems within agricultural land sampled and analyzed for cations and anions. SAR and KR show that 52.5% and 60% of the samples exceeded the allowable level, respectively, indicating that they were unsuitable for irrigation. According to EWQI, about 55% of the analyzed samples were of good quality, while 45% were of medium quality. ALM predicted that KR (0.98), Na% (0.87), and MHR (0.14) were the main significant factors affecting SAR and KR. ALM shows that elevated sodium, magnesium, and calcium are the most important factors affecting irrigation water suitability. The EWQI and FIWQI integrated models showed that water from nearly 30% of the sampling sites would need treatment before use. A new suitability map created by overlaying all parameters showed that surface water and some groundwater in the western and southwestern portions are suitable for agriculture. The north-central part is unsuitable for irrigation due to excessive sodium and magnesium levels. This paper will highlight the irrigation pattern for regional water resource use, identify new suitable regions, and improve sustainable agricultural practices in the Haor basin.

Fuzzy logic, geostatistics, and multiple linear models to evaluate irrigation metrics and their influencing factors in a drought-prone agricultural region.

The quality of water used for irrigation is one of the major threats to maintaining the long-term sustainability of agricultural practices. Although some studies have addressed the suitability of irrigation water in different parts of Bangladesh, the irrigation water quality in the drought-prone region has yet to be thoroughly studied using integrated novel approaches. This study aims to assess the suitability of irrigation water in the drought-prone agricultural region of Bangladesh using traditional irrigation metrics such as sodium percentage (NA%), magnesium adsorption ratio (MAR), Kelley's ratio (KR), sodium adsorption ratio (SAR), total hardness (TH), permeability index (PI), and soluble sodium percentage (SSP), along with novel irrigation indices such as irrigation water quality index (IWQI) and fuzzy irrigation water quality index (FIWQI). Thirty-eight water samples were taken from tube wells, river systems, streamlets, and canals in agricultural areas, then analyzed for cations and anions. The multiple linear regression model predicted that SAR (0.66), KR (0.74), and PI (0.84) were the primary important elements influencing electrical conductivity (EC). Based on the IWQI, all water samples fall into the "suitable" category for irrigation. The FIWQI suggests that 75% of the groundwater and 100% of the surface water samples are excellent for irrigation. The semivariogram model indicates that most irrigation metrics have moderate to low spatial dependence, suggesting strong agricultural and rural influence. Redundancy analysis shows that Na+, Ca2+, Cl-, K+, and HCO3- in water increase with decreasing temperature. Surface water and some groundwater in the southwestern and southeastern parts are suitable for irrigation. The northern and central parts are less suitable for agriculture because of elevated K+ and Mg2+ levels. This study determines irrigation metrics for regional water management and pinpoints suitable areas in the drought-prone region, which provides a comprehensive understanding of sustainable water management and actionable steps for stakeholders and decision-makers.

Receptor model-based source-specific health risks of toxic metal(loid)s in coal basin-induced agricultural soil in northwest Bangladesh.

Toxic metal(loid)s (TMLs) in agricultural soils cause detrimental effects on ecosystem and human health. Therefore, source-specific health risk apportionment is very crucial for the prevention and control of TMLs in agricultural soils. In this study, 149 surface soil samples were taken from a coal mining region in northwest Bangladesh and analyzed for 12 TMLs (Pb, Cd, Ni, Cr, Mn, Fe, Co, Zn, Cu, As, Se, and Hg). Positive matrix factorization (PMF) and absolute principal component score-multiple linear regression (APCS-MLR) receptor models were employed to quantify the pollution sources of soil TMLs. Both models identified five possible sources of pollution: agrochemical practice, industrial emissions, coal-power-plant, geogenic source, and atmospheric deposition, while the contribution rates of each source were calculated as 28.2%, 17.2%, 19.3%, 19% and 16.3% in APCS-MLR, 22.2%, 13.4%, 24.3%, 15.1% and 25.1% in PMF, respectively. Agrochemical practice was the major source of non-carcinogenic risk (NCR) (adults: 32.37%, children: 31.54%), while atmospheric deposition was the highest source of carcinogenic risk (CR) (adults: 48.83%, children: 50.11%). NCR and CR values for adults were slightly higher than for children. However, the trends in NCR and CR between children and adults were similar. As a result, among the sources of pollution, agrochemical practices and atmospheric deposition have been identified as the primary sources of soil TMLs, so prevention and control strategies should be applied primarily for these pollution sources in order to protect human health.

Perception and legacy of soil chromium and lead contamination in an operational small-scale coal mining community.

Operational small-scale coal mining (OSCM) is one of the most significant sources of chromium (Cr) and lead (Pb) pollution in Bangladesh. Attempts to minimize or lessen the use of Cr and Pb in OSCM have shown unsatisfactory results, mainly because they need to address the sociotechnical complexity of pollution concerns in OSCM. This research adopts a multidisciplinary, sociotechnical approach to addressing Cr and Pb problems, coupling soil sampling for Cr and Pb with questionnaires of miners' and inhabitants' perceptions of pollution and its distribution. The study was undertaken in the Barapukuria coal basin in northwest Bangladesh. Except for mining areas (average of 49.80 ± 27.25 mg/kg), Cr levels in soils exceeded the world average in the periphery (73.34 ± 24.39 mg/kg, ~ 1.2 times) and residential areas (88.85 ± 35.87 mg/kg, 1.5 times the world standard of 59.5 mg/kg). Pb levels in soils exceeded national and global averages in mining (53.56 ± 37.62 mg/kg, ~ 1.9 times), periphery (35.05 ± 21.77 mg/kg, ~ 1.3 times), and residential areas (32.14 ± 26.59 mg/kg, ~ 1.2 times) when compared to Bangladesh and global standards of 20 and 27 mg/kg. Pb levels were highest in mining areas, while Cr concentrations were highest in residential areas. The questionnaire findings indicated that miners and inhabitants did not correctly assume that the highest levels of Cr and Pb pollution would be found in these areas. Among all respondents, 54% are unaware of the health impacts of prolonged Cr and Pb exposure. They face respiratory problems (38.6%), skin diseases (32.7%), and other health issues. A large number of people (66.6%) agreed with the fact that Cr and Pb contamination has an impact on drinking water. Cr and Pb pollution has caused 40% crop loss and a 36% decrease in productivity in the agricultural sector. However, respondents underestimated the level of Cr pollution in mining areas, and most assumed that only individuals working directly with mines were impacted by the Cr and Pb content. Participants also rated the reduction of Cr and Pb contamination as of low importance. There is less awareness of Cr and Pb pollution among miners and inhabitants. Sincere efforts to reduce Cr and Pb pollution will likely be met with extra attention and hostility.

Spatial-temporal distributions, probable health risks, and source identification of organic pollutants in surface waters of an extremely hypoxic river basin in Türkiye.

This study was carried out to determine the spatiotemporal distributions of organic pollution parameters in the Meriç-Ergene River Basin subjected to intensive agricultural and industrial pressure. A total of 5 basin components, including Anadere (A), Çorlu (Ç), Tunca (T), Meriç (M), and Ergene (E) rivers, and 9 stations (A1, Ç1, T1, M1-M2, and E1-E4) were identified in the watershed, and surface water samples were collected in the dry (end of summer) and wet (end of winter) seasons of 2021-2022. The Water Quality Index (WQI) and Nutrient Pollution Index (NPI) were applied to the data to evaluate the overall water quality characteristics. The Chronic Daily Index (CDI), Hazard Quotient (HQ), and Hazard Index (HI) were applied to the data to reveal the probable noncarcinogenic health risks of organic contaminants. Cluster Analysis (CA) and Principal Component Analysis (PCA) were applied to the data to classify the sampling sites and identify the source apportionment of organic pollution parameters. The recorded spatiotemporal averages of the investigated parameters in the basin are as follows: 6.26 mg/L for DO, 9 for pH, 1626 µS/cm for EC, 985 mg/L for TDS, 1 ‰ for salinity, 6.88 mg/L for nitrate, 0.1 mg/L for nitrite, 1.8 mg/L for phosphate, 81 mg/L for sulfate, and 473 mg/L for chloride. The results indicate that the most contaminated components of the basin are Çorlu Stream and Ergene River, and in addition to their quite high salt and nutrient content, they have extreme hypoxic conditions to the extent that it is impossible for many aquatic organisms to live. The contamination degrees of the investigated basin components were determined using the organic pollution risk assessment indices as follows: Çorlu Stream > Ergene River > Anadere Stream > Tunca River > Meriç River.

Modelling the reference crop evapotranspiration in the Beas-Sutlej basin (India): an artificial neural network approach based on different combinations of meteorological data.

Accurate prediction of the reference evapotranspiration (ET0) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35 years (1984-2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6-2-4-1), (6-5-4-1), (6-5-4-1), and (6-4-6-1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET0 in data-scarce locations.

Monitoring drought pattern for pre- and post-monsoon seasons in a semi-arid region of western part of India.

Drought has become a regular phenomenon in the western semi-arid regions of India, where severe drought occurs once in 8-9 years. Therefore, two drought indices, namely temperature condition index (TCI) and vegetation condition index (VCI), were prepared from using Landsat datasets to appraise and monitor of drought pattern for the pre- and post-monsoon seasons between 1996 and 2016 in the Latur district, the north-western part of India. Additionally, the average frequency layers (AFL) of all drought and land use indices were prepared to analyse the correlation between them. The results show a substantial increase in the area under high, very high and severe drought classes both pre- and post-monsoon seasons during the study period. The highest increase was noticed from the high drought class from 2532.45 to 4792.49 sq. km and 1559.84 to 3342.32 sq. km for pre- and post-monsoon season, respectively, based on the TCI and 1269.81 to 1787.77 sq. km in very high drought class for the post-monsoon season using the VCI. The correlation analysis showed that there exists a significant relationship between the land use indices and drought indices. However, the spatial pattern of correlation was heterogeneous for both pre- and post-monsoon seasons. The results of this research can help in the drought management and mitigation planning in the study area. In addition, a similar approach may be applied to analyse drought patterns in other places with similar geographic characteristics as both VCI and TCI are cost-effective and less time-consuming methods and produce reliable outcomes.

Preliminary assessment of heavy metals in surface water and sediment in Nakuvadra-Rakiraki River, Fiji using indexical and chemometric approaches.

River water and sediment embody environmental characteristics that give valuable environmental management information. However, indexical and chemometric appraisal of heavy metals (HMs) in river water and sediment is very scarce in Island countries including Fiji. In this research, forty-five sediment and fifteen water samples from the Nakuvadra-Rakiraki River, Fiji were analyzed for appraising spatial distribution, pollution, and source identification of selected heavy metals (HMs) using the coupling tools of self-organizing map (SOM), compositional data analysis (CDA), and sediment and water quality indices. The mean concentration of HMs increased in the order of Cd < Co < Pb < Cu < Zn < Ni < Cr < Mn < Fe for sediment and Cd < Pb < Cu < Ni < Zn < Co < Cr < Fe < Mn for water, respectively. The outcomes of the enrichment factor, geo-accumulation index and contamination factor index varied spatially and most of the sediment samples were polluted by Pb, Mn, and Cu. The potential ecological risk recognized Cd, and Pb as ecological and public health risks to the surrounding communities. Based on SOM and CDA, three potential sources (e.g., point, nonpoint and lithological sources) of HMs for sediment and two sources (e.g., geogenic and human-induced sources) of HMs for water were identified. The spatial patterns of EWQI values revealed that the northern and northeast zones of the studied area possess a high degree of water pollution. The entropy weight indicated Ni and Cd as the main pollutants degrading the water quality. This study gives a baseline dataset for combined eco-environmental measures for the river's water and sediment pollution as well as contributes to an inclusive appraisal of HMs contamination in global rivers.

Water resources pollution associated with risks of heavy metals from Vatukoula Goldmine region, Fiji.

Although mining is essential for human economic development, is amongst the most polluting anthropogenic sources that influence seriously in water resources. Thus, understanding the presence and concentration of heavy metals in water and sediment in the vicinity of mines is important for the sustainability of the ecosystem. In this work, a multidisciplinary approach was developed to characterize the contamination level, source apportionment, co-existence, and degree of ecological and human health risks of HMs on water resources in the Vatukoula Goldmine region (VGR), Fiji. The outcomes suggested significant contamination by Cd (range: 0.01-0.95 g/L), Pb (range: 0.03-0.53 g/L), and Mn (range: 0.01-3.66 g/L) in water samples surpassed the level set by Fiji and international laws, whereas higher concentration of Cd (range: 2.60-23.16 mg/kg), Pb (range: 28.50-200.90 mg/kg) and Zn (range: 36.50-196.66 mg/kg) were detected in sediment samples. Lead demonstrated a strong significant co-existence network with other metals (e.g., Mn, Ni). Source apportionment recognized four source patterns (Cd, Pb, Ni, and Mn) for water and (Cr, Cd-Pb, Mn, and Zn) for sediment which was further confirmed by principal component analysis. The mine inputs source mainly contributed to Cd (66.07%) for water, while mineral processing mostly contributed to Zn (76.10%) for sediment. High non-carcinogenic (>1) and carcinogenic (>10-4) health risks, particularly in children, are related to the elevated Cd, Pb and Cr contents from the VGR. Uncertainty analysis demonstrates that the 90th quantile of Cd led to higher carcinogenic risk. Pollution indices disclosed a moderate to extremely contamination status mainly along the Toko dam which poses high ecological risks identified by index calculation. However, sediment quality indicators based on probable effect levels showed that there was a 75% of likelihood that the concentrations of Cd and Pb adjacent to the VGR have a severe toxic impact on aquatic lives.

Spatiotemporal nexus between vegetation change and extreme climatic indices and their possible causes of change.

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.

Strategic assessment of COVID-19 pandemic in Bangladesh: comparative lockdown scenario analysis, public perception, and management for sustainability.

ABSTRACT: Community transmission of COVID-19 is happening in Bangladesh-the country which did not have a noteworthy health policy and legislative structures to combat a pandemic like COVID-19. Early strategic planning and groundwork for evolving and established challenges are crucial to assemble resources and react in an appropriate timely manner. This article, therefore, focuses on the public perception of comparative lockdown scenario analysis and how they may affect the sustainable development goals (SDGs) and the strategic management regime of COVID-19 pandemic in Bangladesh socio-economically as well as the implications of the withdrawal of partial lockdown plan. Scenario-based public perceptions were collected via a purposive sampling survey method through a questionnaire. Datasets were analysed through a set of statistical techniques including classical test theory, principal component analysis, hierarchical cluster analysis, Pearson's correlation matrix and linear regression analysis. There were good associations among the lockdown scenarios and response strategies to be formulated. Scenario 1 describes how the death and infection rate will increase if the Bangladesh Government withdraws the existing partial lockdown. Scenario 2 outlines that limited people's movement will enable low-level community transmission of COVID-19 with the infection and death rate will increase slowly (r = 0.540, p < 0.01). Moreover, there will be less supply of necessities of daily use with a price hike (r = 0.680, p < 0.01). In scenario 3, full lockdown will reduce community transmission and death from COVID-19 (r = 0.545, p < 0.01). However, along with the other problems gender discrimination and gender-based violence will increase rapidly (r = 0.661, p < 0.01). Due to full lockdown, the formal and informal business, economy, and education sector will be hampered severely (R = 0.695). Subsequently, there was a strong association between the loss of livelihood and the unemployment rate which will increase due to business shutdown (p < 0.01). This will lead to the severe sufferings of poor and vulnerable communities in both urban and rural areas (p < 0.01). All these will further aggravate the humanitarian needs of the most vulnerable groups in the country in the coming months to be followed which will undoubtedly affect the Bangladesh targets to achieve the SDGs of 2030 and other development plans that need to be adjusted. From our analysis, it was apparent that maintaining partial lockdown with business and economic activities with social distancing and public health guidelines is the best strategy to maintain. However, as the government withdrew the partial lockdown, inclusive and transparent risk communication towards the public should be followed. Recovery and strengthening of the health sector, economy, industry, agriculture, and food security should be focused on under the "new normal standard of life" following health guidelines and social distancing. Proper response plans and strategic management are necessary for the sustainability of the nation.

Effect of meteorological factors on COVID-19 cases in Bangladesh.

This work is intended to examine the effects of Bangladesh's subtropical climate on coronavirus diseases 2019 (COVID-19) transmission. Secondary data for daily meteorological variables and COVID-19 cases from March 8 to May 31, 2020, were collected from the Bangladesh Meteorological Department (BMD) and Institute of Epidemiology, Disease Control and Research (IEDCR). Distributed lag nonlinear models, Pearson's correlation coefficient and wavelet transform coherence were employed to appraise the relationship between meteorological factors and COVID-19 cases. Significant coherence between meteorological variables and COVID-19 at various time-frequency bands has been identified in this work. The results showed that the minimum (MinT) and mean temperature, wind speed (WS), relative humidity (RH) and absolute humidity (AH) had a significant positive correlation while contact transmission had no direct association with the number of COVID-19 confirmed cases. When the MinT was 18 °C, the relative risk (RR) was the highest as 1.04 (95%CI 1.01-1.06) at lag day 11. For the WS, the highest RR was 1.03 (95% CI 1.00-1.07) at lag day 0, when the WS was 21 km/h. When RH was 46%, the highest RR was 1.00 (95% CI 0.98-1.01) at lag day 14. When AH was 23 g/m3, the highest RR was 1.05 (95% CI 1.01-1.09) at lag day 14. We found a profound effect of meteorological factors on SARS-CoV-2 transmission. These results will assist policymakers to know the behavioral pattern of the SARS-CoV-2 virus against meteorological indicators and thus assist to devise an effective policy to fight against COVID-19 in Bangladesh.

Appraising drought hazard during Boro rice growing period in western Bangladesh.

Drought is one of the critical agro-meteorological hazards in the world including Bangladesh. Rice is the major food grain in Bangladesh, and drought has threatened the food grain and the country's food security. However, drought hazard (DH) in the Boro rice growing period and their associations are less investigated in Bangladesh. Hence, we intend to appraise the DH in the vegetative, reproductive, and ripening phases and the whole growing season based on the daily temperature, precipitation data, and yearly rice phenology dataset from 8 meteorological stations in western Bangladesh from 1980 to 2013. The Mann-Kendall (M-K) test was employed to identify the trend in phenology dates and drought hazard. The standardized precipitation evapotranspiration index (SPEI) was adopted to compute the DH. The results show that moderate to severe drought events occurred in western Bangladesh during Boro rice growing period. The transplanting dates were delayed in the southwestern part by a rate of 1.31 day/year along with a trend towards a shorter and more humid ripening phase (p < 0.05). By contrast, transplanting dates were started earlier in the northwestern part with a rate of - 0.48 day/year along with the enhanced length of the reproductive and the ripening phases (p < 0.10). We appraised the DH during each Boro rice growing season, where the ripening phase faced a more severe DH than other phases that were most susceptible to water stress conditions. There was a strong association between DH and crop yield loss during the whole growth period in the study area.

Sustainable wind energy potential in Sandwip and Kalapara coastal regions of Bangladesh: A way of reducing carbon dioxide emissions.

An enormous amount of power is required in a rising nation like Bangladesh, where achieving economic growth without endangering the environment is a burning issue. The majority of people who live in coastal areas of Bangladesh do not have sufficient access to electricity. There are almost 40 million people living along Bangladesh's 724-km shoreline. Furthermore, it is remarkable that coastal regions have year-round winds, strong enough to generate enormous amounts of power. The viability and promise of wind energy in Bangladesh's southern regions are highlighted in this study. The places demonstrate the possibility for cheaper power production at 30 m-40 m altitudes. The rate of electricity does, however, rise with height. The main objective of this study is to analyze the prospect of wind energy in Sandwip and Kalapara coastal areas of Bangladesh. The data from 1990 to 2020 was taken from the database from the Bangladesh Meteorological Department (BMD) and NASA's NREL (National Renewable Energy Laboratory). These data sources were used to determine the wind power density, wind power output, energy yield, and finally estimate the CO2 emission reduction. In this paper, a novel approach to the wind energy on selected coastal area is presented and realistic calculation of energy output is carried out of the planned wind system. Finally calculated the realistic CO2 emission reduction by using this approach for a sustainable future. Estimation reveals that about 162.43 GWh of electricity can be generated annually by installing 684 wind towers on southern Kalapara (Khepupara) area and about 257.25 GWh of electricity can be generated annually by installing 1024 wind tower on the periphery of Sandwip area. So, if 1,768 wind turbines are installed on the Sandwip and Kalapara coastal region instead of burning fossil fuels, about 1,11,373.29 tons of CO2 will be prevented from being emitted annually.