Potentially toxic elemental contamination in Wainivesi River, Fiji impacted by gold-mining activities using chemometric tools and SOM analysis.

Potentially toxic element (PTE) contamination in Wainivesi River, Fiji triggered by gold-mining activities is a major public health concern deserving attention. However, chemometric approaches and pattern recognition of PTEs in surface water and sediment are yet hardly studied in Pacific Island countries like Fijian urban River. In this study, twenty-four sediment and eight water sampling sites from the Wainivesi River, Fiji were explored to evaluate the spatial pattern, eco-environmental pollution, and source apportionment of PTEs. This analysis was done using an integrated approach of self-organizing map (SOM), principle component analysis (PCA), hierarchical cluster analysis (HCA), and indexical approaches. The PTE average concentration is decreasing in the order of Fe > Pb > Zn > Ni > Cr > Cu > Mn > Co > Cd for water and Fe > Zn > Pb > Mn > Cr > Ni > Cu > Co > Cd for sediment, respectively. Outcomes of eco-environmental indices including contamination and enrichment factors, and geo-accumulation index differed spatially indicated that majority of the sediment sites were highly polluted by Zn, Cd, and Ni. Cd and Ni contents can cause both ecological and human health risks. According to PCA, both mixed sources (geogenic and anthropogenic such as mine wastes discharge and farming activities) of PTEs for water and sediment were identified in the study area. The SOM analysis identified three spatial patterns, e.g., Cr-Co-Zn-Mn, Fe-Cd, and Ni-Pb-Cu in water and Zn-Cd-Cu-Mn, Cr-Ni and Fe, Co-Pb in sediment. Spatial distribution of entropy water quality index (EWQI) values depicted that northern and northwestern areas possess "poor" to "extremely poor" quality water. The entropy weights indicated Zn, Cd, and Cu as the major pollutants in deteriorating the water quality. This finding provides a baseline database with eco-environmental and health risk measures for the Wainivesi river contamination.

Application of novel framework approach for prediction of nitrate concentration susceptibility in coastal multi-aquifers, Bangladesh.

This study aims to construct a novel framework approach for predicting and mapping nitrate concentration susceptibility in the coastal multi-aquifers of Bangladesh by coupling the K-fold cross-validation method and novel ensemble learning algorithms, including Boosting, Bagging and Random Forest (RF). In total, 286 nitrate sampling sites were employed in the model work. The dataset was demarcated into a 75:25 ratio for model construction (75% 3-fold â‰… 214 sites) and (25% 1-fold â‰… 72 sites) for model validation using the 4-fold cross-validation schemes. A total of 14 groundwater causative factors including salinity, depth, pH, EC, As, HCO3-, F-, Cl-, SO42-, PO42-, Na+, K+, Mg2+, and Ca2+ were adopted for the construction of the proposed models. OneR relative importance model was employed to choose and rank critical factors for spatial nitrate modeling. The results showed that depth, pH and As are the most influential causative factors in the elevated nitrate concentration in groundwater. Based on the model assessment criteria such as receiver operating characteristic (ROC)'s AUC (area under curve), sensitivity, specificity, accuracy, precession, F score, and Kappa coefficient, the Boosting model outperforms others (r = 0.92, AUG ≥ 0.90) in mapping nitrate concentration susceptibility, followed by Bagging and RF models. The results of mapping nitrate concentration also demonstrated that the south-central and western regions had an elevated amount of nitrate content than other regions due to depth variation in the study area. During our sampling campaign, we observed hundreds of fish hatcheries operation, a fish landing center and aquaculture farms which are the reasons for overexploitation and excessive agrochemicals used in the study area. Thus, the dependability of ensemble learning modeling verifies the effectiveness and applicability of the proposed novel approach for decision-makers in groundwater pollution management at the local and regional levels.

Appraising the historical and projected spatiotemporal changes in the heat index in Bangladesh.

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.

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.

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.

How air quality and COVID-19 transmission change under different lockdown scenarios? A case from Dhaka city, Bangladesh.

The transmission of novel coronavirus (COVID-19) can be reduced by implementing a lockdown policy, which has also been proven as an effective control measure for air pollution in the urban cities. In this study, we applied ground- and satellite-based data of five criteria air pollutants (PM2.5, NO2, SO2, O3, and CO) and meteorological factors from March 8 to May 15, 2020 (before, partial-, and full-lockdown). The generalized additive models (GAMs), wavelet coherence, and random forest (RF) model were employed to explore the relationship between air quality indicators and COVID-19 transmission in Dhaka city. Results show that overall, 26, 20.4, 17.5, 9.7 and 8.8% declined in PM 2.5, NO2, SO2, O3, and CO concentrations, respectively, in Dhaka City during the partial and full lockdown compared to the period before the lockdown. The implementation of lockdown policy for containing COVID-19 transmission played a crucial role in reducing air pollution. The findings of wavelet coherence and partial wavelet coherence demonstrate no standalone coherence, but interestingly, multiple wavelet coherence indicated a strong short-term coherence among air pollutants and meteorological factors with the COVID-19 outbreak. Outcomes of GAMs indicated that an increase of 1-unit in long-term exposure to O3 and CO (lag1) was associated with a 2.9% (95% CI: -0.3%, -5.6%), and 53.9% (95% CI: 0.2%, -107.9%) decreased risk of COVID-19 infection rate during the full-lockdown period. Whereas, COVID-19 infection and MT (mean temperature) are modulated by a peak during full-lockdown, which is mostly attributed to contact transmission in Dhaka city. RF model revealed among the parameters being studied, MT, RH (relative humidity), and O3 were the dominant factors that could be associated with COVID-19 cases during the study period. The outcomes reported here could elucidate the effectiveness of lockdown scenarios for COVID-19 containment and air pollution control in Dhaka city.

Are meteorological factors enhancing COVID-19 transmission in Bangladesh? Novel findings from a compound Poisson generalized linear modeling approach.

Novel coronavirus (SARS-CoV-2) causing COVID-19 disease has arisen to be a pandemic. Since there is a close association between other viral infection cases by epidemics and environmental factors, this study intends to unveil meteorological effects on the outbreak of COVID-19 across eight divisions of Bangladesh from March to April 2020. A compound Poisson generalized linear modeling (CPGLM), along with a Monte-Carlo method and random forest (RF) model, was employed to explore how meteorological factors affecting the COVID-19 transmission in Bangladesh. Results showed that subtropical climate (mean temperature about 26.6 °C, mean relative humidity (MRH) 64%, and rainfall approximately 3 mm) enhanced COVD-19 onset. The CPGLM model revealed that every 1 mm increase in rainfall elevated by 30.99% (95% CI 77.18%, - 15.20%) COVID-19 cases, while an increase of 1 °C of diurnal temperature (TDN) declined the confirmed cases by - 14.2% (95% CI 9.73%, - 38.13%) on the lag 1 and lag 2, respectively. In addition, NRH and MRH had the highest increase (17.98% (95% CI 22.5%, 13.42%) and 19.92% (95% CI: 25.71%, 14.13%)) of COVID-19 cased in lag 4. The results of the RF model indicated that TDN and AH (absolute humidity) influence the COVID-19 cases most. In the Dhaka division, MRH is the most vital meteorological factor that affects COVID-19 deaths. This study indicates the humidity and rainfall are crucial factors affecting the COVID-19 case, which is contrary to many previous studies in other countries. These outcomes can have policy formulation for the suppression of the COVID-19 outbreak in Bangladesh.

The optimal alternative for quantifying reference evapotranspiration in climatic sub-regions of Bangladesh.

Reference evapotranspiration (ETo) is a basic element for hydrological designing and agricultural water resources management. The FAO56 recommended Penman-Monteith (FAO56-PM) formula recognized worldwide as the robust and standard model for calculating ETo. However, the use of the FAO56-PM model is restricted in some data-scarce regions like Bangladesh. Therefore, it is imperative to find an optimal alternative for estimating ETo against FAO56-PM model. This study comprehensively compared the performance of 13 empirical models (Hargreaves-Samani, HargreavesM1, Hargreaves M2, Berti, WMO, Abtew, Irmak 1, Irmak 2, Makkink, Priestley-Taylor, Jensen-Haise, Tabari and Turc) by using statistical criteria for 38-years dataset from 1980 to 2017 in Bangladesh. The radiation-based model proposed by Abtew (ETo,6) was selected as an optimal alternative in all the sub-regions and whole Bangladesh against FAO56-PM model owing to its high accuracy, reliability in outlining substantial spatiotemporal variations of ETo, with very well linearly correlation with the FAO56-PM and the least errors. The importance degree analysis of 13 models based on the random forest (RF) also depicted that Abtew (ETo,6) is the most reliable and robust model for ETo computation in different sub-regions. Validation of the optimal alternative produced the largest correlation coefficient of 0.989 between ETo,s and ETo,6 and confirmed that Abtew (ETo,6) is the best suitable method for ETo calculation 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.