[en] Highlights: • The lockdown policy triggered a sudden reduction of air pollution in Dhaka city. • The containment policy did not play a crucial role to modulate COVID-19 infection. • The air quality parameters can't be explained as COVID-19 transmission modulator. 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.

[en] Highlights: • The spatiotemporal patterns of E_pan were investigated in the HRB. • The performance of PenPan model was evaluated in the HRB. • Wind speed (u₂) played a critical dominant role in E_pan trends. • Significance of climate variables is proportional to their dominance in E_pan trends. Pan evaporation (E_pan), which we examine in this study to better understand atmospheric evaporation demand, represents a pivotal indicator of the terrestrial ecosystem and hydrological cycle, particularly in the Huai River Basin (HRB) in eastern China, where high potential risks of drought and flooding are commonly observed. In this study, we examine the spatiotemporal trend patterns of climatic factors and E_pan by using the Mann-Kendall test and the Theil-Sen estimator based on a daily meteorological dataset from 89 weather stations during 1965–2013 in the HRB. Furthermore, the PenPan model is employed to estimate E_pan at a monthly time scale, and a differential equation method is applied to quantify contributions from four meteorological variables to E_pan trends. The results show that E_pan significantly decreased (P < 0.001) at an average rate of − 8.119 mm·a^{− 2} at annual time scale in the whole HRB, with approximately 90% of stations occupied. Meanwhile, the generally higher E_pan values were detected in the northern HRB. The values of the aerodynamic components in the PenPan model were much greater than those of the radiative components, which were responsible for the variations in the E_pan trend. The significantly decreasing wind speed (u₂) was the most dominant factor that controlled the decreasing E_pan trend at each time scale, followed by the notable decreasing net radiation (R_n) at the annual time scale also in growing season and summer. However, the second dominant factor shifted to the mean temperature (T_a) during the spring and winter and the vapor pressure deficit (vpd) during the autumn. These phenomena demonstrated a positive link between the significance of climate variables and their control over the E_pan trend.

[en] Highlights: • Novel framework approach for nitrate concentration susceptibility mapping in coastal regions • South-central and western regions are found highly susceptible for nitrate pollution. • Depth, pH and arsenic are the influential factors for groundwater nitrate level. • Boosting was the best model for high prediction precision achievement. 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 ≅ 214 sites) and (25% 1-fold ≅ 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.

[en] Highlights: • Sand fraction was the major proportion of the studied mangrove sediment. • Insignificant variability of physicochemical properties in sediment was observed. • Zn was the highest concentrated metal among the studied metals in sediment samples. • The study area was low contaminated & no adverse impact in bottom dwelling organisms. • A. ilicifolius can be utilized for phytoremediation and phytoextraction procedures. The focus of this study was to determine the depth-wise variability of physicochemical properties (i.e., pH, TOC, TN, and EC), and heavy metals (i.e., Pb, Cu, Zn, As, and Cr) concentration, and the associated biological and ecological risks of the mangrove sediment. The accumulation of metal contents and the phytoremediation and phytoextraction were also investigated in a mangrove species, Acanthus ilicifolius. The mangrove sediment consists of a higher proportion of sand fraction (56.6–74.7%) followed by clay (10–28%) and silt (10.1–15. 7%) fractions. The concentrations (mg/kg) of Pb, Cu, Zn, As, and Cr were ranged from 22.05–34.3, 8.58–22.77, 85.07–114, 5.56–12.91, and 0.98–5.12 in all the sediment layers. The hierarchy of the mean metal concentration in sediment was Zn (102 mg/kg) > Pb (25.6 mg/kg) > Cu (14.8 mg/kg) > As (8.79 mg/kg) > Cr (2.74 mg/kg) respectively. The examined metal concentrations were below the respective average shale values (ASVs). The degree of environmental, ecological, and biological risks was minimal according to various pollution indices like geoaccumulation index (I_geo), contamination factor (CF), and pollution load index (PLI). According to sediment quality guidelines (SQGs), the adverse biological risk effect was not likely to occur. The result of the potential ecological risk index (PERI) demonstrated that the study area was in the low-risk condition as the corresponded RI value < 100. A combined influence of geogenic and anthropogenic factors was identified as the metal sources by multivariate analysis. The study found that the accumulation rate of the metal contents was higher in leaves than that of roots. The mean descending metal concentration values were Zn (107) > Pb (28. 7) > Cu (16.9) > As (11.2) > Cr (4.99) in leaves and Zn (104.32) > Pb (27.02) > Cu (15.29) > As (10.39) > Cr (3.80) in roots. The translocation and bioaccumulation factors of heavy metals suggested that the mangrove plant species, A. ilicifolius can be used for phytoremediation and phytoextraction since the bio-concentration factor and translocation factor > 1. The studied species exhibited the metal tolerance associated with two following strategies, metal exclusion, and metal accumulation. However, excess metal tolerance can impact the surrounding marine environment.

[en] Coal-fly-ash is one of the major byproducts of coal-based power plant in which naturally occurring radioactive materials (NORMs) are drastically enriched compared to those of feed coals. Thus, improper management of fly-ash may introduce additional radioactivity to the surrounding environment and cause radiological risk. So, in order to study the distribution of radionuclides in soil around a coal-based power plant and to evaluate their radiological risk, soil, coal and fly-ash samples were analyzed by using a HPGe detector for U-238, Ra-226, Th-232 and K-40 radioactivity concentrations. Furthermore, soil minerals were also studied by X-ray diffractometer to assess the mineralogical provenance of the radionuclides. Mean radioactivity concentrations (in Bq $\cdot <!--\; ???\; -->$ kg${}^{\text{â<!-- ?? -->}\text{ˆ<!-- ?? -->}\text{’<!-- ??? -->}1}$) of U-238, Ra-226, Th-232 and K-40 in soil samples are 102.9$\pm <!--\; ??\; -->$41.4, 63.6$\pm <!--\; ??\; -->$7.4, 103.4$\pm <!--\; ??\; -->$13.9 and 494.2$\pm <!--\; ??\; -->$107.5, respectively which are comparatively higher than the typical world mean value. Elevated levels of radioactivity are likely due to the presence of illite, kaolinite, monazite, rutile and zircon minerals in the soil samples rather than technogenic contributions from the power plant. Furthermore, mean soil contamination factor (CF) are close to unity and mean pollution load index (PLI) is below unity while the average radium equivalent activity (Ra${}_{eq}$ in Bq $\cdot <!--\; ???\; -->$ kg${}^{\text{â<!-- ?? -->}\text{ˆ<!-- ?? -->}\text{’<!-- ??? -->}1}$), external hazard index (H${}_{ex}$), absorbed $\mathrm{\gamma <!--\; ??\; -->}$ dose rate (D in nGyh${}^{\text{â<!-- ?? -->}\text{ˆ<!-- ?? -->}\text{’<!-- ??? -->}1}$), annual effective dose rate (E in mSv $\cdot <!--\; ???\; -->$ y${}^{\text{â<!-- ?? -->}\text{ˆ<!-- ?? -->}\text{’<!-- ??? -->}1}$) and excess lifetime cancer risk (ELCR in Sv${}^{\text{â<!-- ?? -->}\text{ˆ<!-- ?? -->}\text{’<!-- ??? -->}1}$) are 249.5$\pm <!--\; ??\; -->$21.7, 0.67$\pm <!--\; ??\; -->$0.06, 114.2$\pm <!--\; ??\; -->$9.4, 0.20$\pm <!--\; ??\; -->$0.02, 4.9$\times <!--\; ??\; -->$10${}^{\text{â<!-- ?? -->}\text{ˆ<!-- ?? -->}\text{’<!-- ??? -->}4}$$\pm <!--\; ??\; -->$0.4$\times <!--\; ??\; -->$10${}^{\text{â<!-- ?? -->}\text{ˆ<!-- ?? -->}\text{’<!-- ??? -->}4}$, respectively, which are within the permissible limit. Thus, in terms of radioactivity concentrations and associated environmental and radiological indices, the effect of the power plant is insignificant.