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The Athabasca River watershed plays a dominant role in both the economy and the environment in Alberta, Canada. Natural and anthropogenic factors rapidly changed the landscape of the watershed in recent decades. The dynamic of such changes in the landscape characteristics of the watershed calls for a comprehensive and up-to-date land-use and land-cover (LULC) map, which could serve different user-groups and purposes. The aim of the study herein was to delineate a 2016 LULC map of the Athabasca River watershed using Landsat-8 Operational Land Imager (OLI) images, Moderate Resolution Imaging Spectroradiometer (MODIS)-derived enhanced vegetation index (EVI) images, and other ancillary data. In order to achieve this, firstly, a preliminary LULC map was developed through applying the iterative self-organizing data analysis (ISODATA) clustering technique on 24 scenes of Landsat-8 OLI. Secondly, a Terra MODIS-derived 250-m 16-day composite of 30 EVI images over the growing season was employed to enhance the vegetation classes. Thirdly, several geospatial ancillary datasets were used in the post-classification improvement processes to generate a final 2016 LULC map of the study area, exhibiting 14 LULC classes. Fourthly, an accuracy assessment was carried out to ensure the reliability of the generated final LULC classes. The results, with an overall accuracy and Cohen’s kappa of 74.95% and 68.34%, respectively, showed that coniferous forest (47.30%), deciduous forest (16.76%), mixed forest (6.65%), agriculture (6.37%), water (6.10%), and developed land (3.78%) were the major LULC classes of the watershed. Fifthly, to support the data needs of scientists across various disciplines, data fusion techniques into the LULC map were performed using the Alberta merged wetland inventory 2017 data. The results generated two useful maps applicable for hydro-ecological applications. Such maps depicted two specific categories including different types of burned (approximately 6%) and wetland (approximately 30%) classes. In fact, these maps could serve as important decision support tools for policy-makers and local regulatory authorities in the sustainable management of the Athabasca River watershed.

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Background The population's mental and physical health worldwide are currently at risk due to the coronavirus pandemic. We evaluated the mental health status of the adolescents trapped indoors because of the precautionary restrictions and prolonged closure of the educational institutions. Method A cross-sectional study was conducted on adolescents from multiple urban and semi-urban areas of Bangladesh from 22 January to 3 February 2021. A self-reported online questionnaire containing questions regarding sociodemographic factors, home quarantine-related factors and mental health symptoms was distributed to collect data. Descriptive analysis, bivariate and multivariable logistic regressions were performed to measure the association of the variables. Cronbach's alpha was estimated to present the internal consistency of the scales. Results A total of 322 adolescents (aged 12–19) with a mean age of 16.00 years (SD = 1.84) responded to the invitation. 54.97% (n = 177) of them were male, and the participants were predominantly urban residents (87.27%, n = 281). We observed varying degrees of depression in 67.08%, anxiety in 49.38% and stress in 40.68% of the participants according to DASS-21. Age, sex, education, mother's occupation, total monthly income, playing sports, doing household chores, going out of home, watching television, using the internet, attending online classes, changing food habits, and communicating with friends had a positive significant association with mental health burdens. Conclusion Home quarantine has a noticeable adverse impact on the mental health of teenagers. Psychological evaluations and counselling via online and offline programs are essential to improve adolescents' declining mental health conditions.

Abstract-Bangladesh is one of the most vulnerable countries of the world to climate change. The magnitude and frequency of extreme events such as high intensity rainfall, flash flooding, severe droughts, etc. are expected to be altered in future as a consequence of this change. This can introduce an element of uncertainty in the design of hydraulic structures, urban drainage systems, and other water-sensitive structures, if the variability is not taken into consideration. This study aims at developing a regional Intensity-Duration-Frequency (IDF) relationship for Dhaka city for present as well as future climatic scenarios. The scaling properties of extreme rainfall are examined to establish scaling relationship behavior of statistical moments over different durations. The results show that a rainfall property in time does follow a simple scaling process. A scale invariance concept is explored for disaggregation (or downscaling) of rainfall intensity from low to high resolution and is applied to the derivation of scaling IDF curves. These curves are developed based on scaling of the generalized extreme value (GEV) and Gumbel probability distributions. It is seen that scaled estimates are relatively close to observed estimates.

Dhaka, the capital of Bangladesh, is one of the megacities in the world with the worst air quality. In this study, we develop statistical models for predicting particulate matter (PM) concentration in ambient air of Dhaka using meteorological and air quality data from 2002 to 2004 of a continuous air quality monitoring station (CAMS). Model for finer fraction of PM (PM2.5) explains up to 57% variability of daily PM2.5 concentration, whereas model for coarser fraction (PM2.5-10) explains up to 35% of its variability, indicating that PM2.5 is influenced more by meteorology than PM2.5-10. Temperature, wind speed, and wind direction account for 94% of total PM2.5 variability explained by the model, while relative humidity contributes to 75% of total PM2.5-10 variability. Inclusion of PM lag effect increases models' predictive power by 4-16%. In general, our developed models show promising performance in capturing the seasonal variability of Dhaka's PM concentration, although overestimate the low concentrations during wet season (April to September). We validate these models using a recent dataset (2013-2017) from the same monitoring site, in which modeled PM show strong positive correlations with observed concentrations (r = 0.81 and 0.76 for PM2.5 and PM2.5-10 respectively). Models also exhibit strong predictive power in forecasting PM levels of two other CAMSs in Dhaka. Thus, the developed models have potentials to explain the temporal and spatial variability of daily PM within Dhaka. These models can be helpful to policymakers as they can predict daily PM at any location of Dhaka with reasonable accuracy if daily meteorological data and previous day's PM concentration are available. The effect of climate change scenarios on air pollution dynamics of Dhaka can also be assessed using these models.