Contrasting structure and composition of the understory in species-rich tropical rain forests

In large samples of trees > or = 1 cm dbh (more than 1 million trees and 3000 species), in six lowland tropical forests on three continents, we assigned species with >30 individuals to one of six classes of stature at maturity (SAM). We then compared the proportional representation of understory trees (1-2 cm dbh) among these classes. The understory of the three Asian sites was predominantly composed of the saplings of large-canopy trees whereas the African and American sites were more richly stocked with trees of the smaller SAM classes. Differences in class representation were related to taxonomic families that were present exclusively in one continent or another. Families found in the Asian plots but not in the American plot (e.g., Dipterocarpaceae, Fagaceae) were predominantly species of the largest SAM classes, whereas families exclusive to the American plots (e.g., Melastomataceae sensu stricto, Piperaceae, and Malvaceae [Bombacacoidea]) were predominantly species of small classes. The African plot was similar to Asia in the absence of those American families rich in understory species, while similar to America in lacking the Asian families rich in canopy species. The numerous understory species of Africa were chiefly derived from families shared with Asia and/or America. The ratio of saplings (1-2 cm dbh) to conspecific canopy trees (>40 cm dbh) was lower in American plots than in the Asian plots. Possible explanations for these differences include phenology, moisture and soil fertility regimes, phyletic constraints, and the role of early successional plants in forest development. These results demonstrate that tropical forests that appear similar in tree number, basal area, and the family taxonomy of canopy trees nonetheless differ in ecological structure in ways that may impact the ecology of pollinators, dispersers, and herbivores and might reflect fundamental differences in canopy tree regeneration.     

Graphical estimation of the dual-enzyme kinetic parameters for Cr(VI) reduction

Chromium(VI) (Cr(VI)) contamination of soil and groundwater is a major environmental concern. Bioreduction of Cr(VI) by Shewanella oneidensis MR-1 (MR-1) can be considered a feasible option to reduce the toxic and mobile Cr(VI) to the less toxic and less mobile chromium(III) (Cr(III)). The reaction rate expression for Cr(VI) reduction is nonlinear and the rate constants are evaluated by employing nonlinear optimization techniques. The outcome of the optimization techniques, in general, depends on the initial estimate of the kinetic parameters which is not always available. A graphical approach based on sound mathematical reasoning has been developed which is accurate, simpler to use, and can provide the best initial estimate for nonlinear optimization.     

Nanocarrier-mediated co-delivery systems for lung cancer therapy: recent developments and prospects

Environmental Chemistry Letters - Research has recently focused on combinational therapy using nanocarriers to overcome the obstacles associated with conventional therapy of lung cancer. The...     

Climate change and soil salinity: The case of coastal Bangladesh

This paper estimates location-specific soil salinity in coastal Bangladesh for 2050. The analysis was conducted in two stages: First, changes in soil salinity for the period 2001–2009 were assessed using information recorded at 41 soil monitoring stations by the Soil Research Development Institute. Using these data, a spatial econometric model was estimated linking soil salinity with the salinity of nearby rivers, land elevation, temperature, and rainfall. Second, future soil salinity for 69 coastal sub-districts was projected from climate-induced changes in river salinity and projections of rainfall and temperature based on time trends for 20 Bangladesh Meteorological Department weather stations in the coastal region. The findings indicate that climate change poses a major soil salinization risk in coastal Bangladesh. Across 41 monitoring stations, the annual median projected change in soil salinity is 39 % by 2050. Above the median, 25 % of all stations have projected changes of 51 % or higher.     

The individual and cumulative effect of brominated flame retardant and polyvinylchloride (PVC) on thermal degradation of acrylonitrile-butadiene-styrene (ABS) copolymer

Acrylonitrile-butadiene-styrene (ABS) copolymers without and with a polybrominated epoxy type flame retardant were thermally degraded at 450 degrees C alone (10 g) and mixed with polyvinylchloride (PVC) (8 g/2 g). Gaseous and liquid products of degradation were analysed by various gas chromatographic methods (GC with TCD, FID, AED, MSD) in order to determine the individual and cumulative effect of bromine and chlorine on the quality and quantity of degradation compounds. It was found that nitrogen, chlorine, bromine and oxygen are present as organic compounds in liquid products, their quantity depends on the pyrolysed polymer or polymer mixture. Bromophenol and dibromophenols were the main brominated compounds that come from the flame retardant. 1-Chloroethylbenzene was the main chlorine compound observed in liquid products. It was also determined that interactions appear at high temperatures during decomposition between the flame retardant, PVC and the ABS copolymer.     

Recent spatial gradients and time trends in Dhaka,Bangladesh, air pollution and their human health implications

Dhaka, the capital of Bangladesh, is among the most polluted cities in the world. This research evaluates seasonal patterns, day-of-week patterns, spatial gradients, and trends in PM_2.5 (<2.5 µm in aerodynamic diameter), PM₁₀ (<10 µm in aerodynamic diameter), and gaseous pollutants concentrations (SO₂, NO₂, CO, and O₃) monitored in Dhaka from 2013 to 2017. It expands on past work by considering multiple monitoring sites and air pollutants. Except for ozone, the average concentrations of these pollutants showed strong seasonal variation, with maximum during winter and minimum during monsoon, with the pollution concentration of PM_2.5 and PM₁₀ being roughly five- to sixfold higher during winter versus monsoon. Our comparisons of the pollutant concentrations with Bangladesh NAAQS and U.S. NAAQS limits analysis indicate particulate matter (PM_2.5 and PM₁₀) as the air pollutants of greatest concern, as they frequently exceeded the Bangladesh NAAQS and U.S. NAAQS, especially during nonmonsoon time. In contrast, gaseous pollutants reported far fewer exceedances throughout the study period. During the study period, the highest number of exceedances of NAAQS limits in Dhaka City (Darus-Salam site) were found for PM_2.5 (72% of total study days), followed by PM₁₀ (40% of total study days), O₃ (1.7% of total study days), SO₂ (0.38% of total study days), and CO (0.25% of total study days). The trend analyses results showed statistically significant positive slopes over time for SO₂ (5.6 ppb yr^?1, 95% confidence interval [CI]: 0.7, 10.5) and CO (0.32 ppm yr^?1, 95% CI: 0.01, 0.56), which suggest increase in brick kilns operation and high-sulfur diesel use. Though statistically nonsignificant annual decreasing slopes for PM_2.5 (?4.6 µg/m³ yr^?1, 95% CI: ?12.7, 3.6) and PM₁₀ (?2.7 µg/m³ yr^?1, 95% CI: ?7.9, 2.5) were observed during this study period, the PM_2.5 concentration is still too high (~ 82.0 µg/m³) and can cause severe impact on human health.

Implications: This study revealed key insights into air quality challenges across Dhaka, Bangladesh, indicating particulate matter (PM) as Dhaka’s most serious air pollutant threat to human health. The results of these analyses indicate that there is a need for immediate further investigations, and action based on those investigations, including the conduct local epidemiological PM exposure-human health effects studies for this city, in order to determine the most public health effective interventions.     

Changes in Hydrology of the Ganges Delta of Bangladesh and Corresponding Impacts on Water Resources

The Ganges Delta in Bangladesh is an example of water‐related catastrophes in a major rural river basin where limitations in quantity, quality, and timing of available water are producing disastrous conditions. Water availability limitations are modifying the hydrologic characteristics especially when water allocation is controlled from the upstream Farakka Barrage. This study presents the changes and consequences in the hydrologic regime due to climate‐ and human‐induced stresses. Flow duration curves (FDCs), rainfall elasticity, and temperature sensitivity were used to assess the pre‐ and post‐barrage water flow patterns. Hydrologic and climate indices were computed to provide insight on hydro‐climatic variability and trend. Significant increases in temperature, evapotranspiration, hot days, heating, and cooling degree days indicate the region is heading toward a warmer climate. Moreover, increase in high‐intensity rainfall of short duration is making the region prone to extreme floods. FDCs depict a large reduction in river flows between pre‐ and post‐barrage periods, resulting in lower water storage capacity. The reduction in freshwater flow increased the extent and intensity of salinity intrusion. This freshwater scarcity is reducing livelihood options considerably and indirectly forcing population migration from the delta region. Understanding the causes and directions of hydrologic changes is essential to formulate improve water resources management in the region.     

AI-based runoff simulation based on remote sensing observations: A case study of two river basins in the United States and Canada

Data-driven techniques are used extensively for hydrologic time-series prediction. We created various data-driven models (DDMs) based on machine learning: long short-term memory (LSTM), support vector regression (SVR), extreme learning machines, and an artificial neural network with backpropagation, to define the optimal approach to predicting streamflow time series in the Carson River (California, USA) and Montmorency (Canada) catchments. The moderate resolution imaging spectroradiometer (MODIS) snow-coverage dataset was applied to improve the streamflow estimate. In addition to the DDMs, the conceptual snowmelt runoff model was applied to simulate and forecast daily streamflow. The four main predictor variables, namely snow-coverage (S-C), precipitation (P), maximum temperature (T_max), and minimum temperature (T_min), and their corresponding values for each river basin, were obtained from National Climatic Data Center and National Snow and Ice Data Center to develop the model. The most relevant predictor variable was chosen using the support vector machine-recursive feature elimination feature selection approach. The results show that incorporating the MODIS snow-coverage dataset improves the models' prediction accuracies in the snowmelt-dominated basin. SVR and LSTM exhibited the best performances (root mean square error = 8.63 and 9.80) using monthly and daily snowmelt time series, respectively. In summary, machine learning is a reliable method to forecast runoff as it can be employed in global climate forecasts that require high-volume data processing.     

Cadmium contamination in agricultural soils of Bangladesh and management by application of organic amendments: evaluation of field assessment and pot experiments

Environmental Geochemistry and Health - In recent years, cadmium (Cd) contamination in agricultural soils and its subsequent transfer to crops is one of the high-priority environmental and public...     

Analyses on phenological and morphological variations of mangrove forests along the southwest coast of Bangladesh

Drastic changes in river discharge and salinity levels are threatening the phenology and morphology of the coastal mangrove forests of the Sundarbans of Bangladesh. We have used AVHRR GIMMS (1985–2006) and MODIS (2005–2010) satellite Normalized Difference Vegetation Index (NDVI) data to identify the temporal variation of the phenology of the mangroves. Linear interpolation and Fourier-based adjustment were applied to remove noise from the NDVI time series. Then linear regression analysis on a single area (8 km ? 8 km) and a composite of 36 areas for three NDVI statistics the annual minimum, annual average, and annual maximum were performed--over the time periods 1985–1990, 1990–2000, 2000–2006 and 2005–2010 to identify possible functional changes in NDVI time series around the Sundarbans. Furthermore, we used fourteen LANDSAT images spanning the period 1989–2010 to estimate the spatiotemporal rate of shoreline changes over the three time periods 1989–2000, 2000–2006, and 2006–2010. A decreasing trend in the annual minimum NDVI was observed in most of the areas of the Sundarbans for the period 1990–2000. During the years 2000–2006, the trends of the three NDVI statistics became significantly positive, indicating an improvement of the mangrove phenology. In the period 2005–2010, a decreasing trend in all the NDVI variables was again dominant. The coast underwent rapid erosion from 1989–2000 and 2006–2010. However, the rate substantially declined between 2000 and 2006, when accretion was dominant. The advent of the upstream Farakka barrage caused a significant reduction in the Ganges-Gorai River discharge and increased the salinity in and around the Sundarbans. Our study concludes that this may be responsible for the degradation of mangrove phenology and accelerated erosion in the earlier and recent periods. In the interim, 2000–2006, improved river discharge and salinity levels due to the Ganges water sharing agreement (1996) and dredging of the Gorai River bed (1998–1999) enhanced the mangrove phenology and helped the coast to gain land.