VTechWorks staff will be away for the Thanksgiving holiday beginning at noon on Wednesday, November 27, through Friday, November 29. We will resume normal operations on Monday, December 2. Thank you for your patience.

Browsing by Author "Islam, Md Sariful"

Now showing 1 - 7 of 7
  • Thumbnail Image
    Assessing the Relationship between COVID-19, Air Quality, and Meteorological Variables: A Case Study of Dhaka City in Bangladesh
    Islam, Md Sariful; Rahman, Mizanur; Tusher, Tanmoy Roy; Roy, Shimul; Razi, Mohammad Arfar (Taiwan Association for Aerosol Research, 2021-01-15)
    The novel coronavirus disease 2019 (COVID-19) has become a serious health concern worldwide for almost a year. This study investigated the effects of selected air pollutants and meteorological variables on daily COVID-19 cases in Dhaka city, Bangladesh. Air pollutants and meteorological data for Dhaka city were collected from 8 April to 16 June 2020 from multiple sources. This study implied spearman’s correlation to see the correlation between daily COVID-19 cases and different air pollutants and meteorological variables. Besides, multiple linear regression and the Generalized Additive Model (GAM) were used to investigate the association between COVID-19 cases and other variables used in this study. Due to lockdown measures, significant differences between PM₂.₅, SO₂, NO₂, CO, and O₃ in 2019 and 2020 were observed in Dhaka city. We used lag-0, lag-7, lag-14, and lag-21 days on daily COVID-19 cases to look at the lag effect of different air pollutants and meteorology. The LRM results showed that the daily COVID-19 cases are significantly correlated with relative humidity (lag-0 days) and pressure (lag-14 days) (p < 0.05). Additionally, the GAM model results showed a significant nonlinear association among daily COVID-19 cases and meteorology and air quality variables on different lag days. Therefore, our results suggest that an effective public health intervention measures should be implemented to slowdown the spreading of COVID-19.
  • Thumbnail Image
    Assessment of Spatio-Temporal Empirical Forecasting Performance of Future Shoreline Positions
    Islam, Md Sariful; Crawford, Thomas W. (MDPI, 2022-12-16)
    Coasts and coastlines in many parts of the world are highly dynamic in nature, where large changes in the shoreline position can occur due to natural and anthropogenic influences. The prediction of future shoreline positions is of great importance in the better planning and management of coastal areas. With an aim to assess the different methods of prediction, this study investigates the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Multi-temporal Landsat imagery, from 1988 to 2021, was used to quantify the rates of shoreline movement for different time period. Predictions using the simple extrapolation of the end point rate (EPR), linear regression rate (LRR), weighted linear regression rate (WLR), and the Kalman filter method were used to predict future shoreline positions. Root mean square error (RMSE) was used to assess prediction accuracies. For time depth, our results revealed that the higher the number of shorelines used in calculating and predicting shoreline change rates the better predictive performance was yielded. For the time horizon, prediction accuracies were substantially higher for the immediate future years (138 m/year) compared to the more distant future (152 m/year). Our results also demonstrated that the forecast performance varied temporally and spatially by time period and region. Though the study area is located in coastal Bangladesh, this study has the potential for forecasting applications to other deltas and vulnerable shorelines globally.
  • Thumbnail Image
    Coastal Erosion and Human Perceptions of Revetment Protection in the Lower Meghna Estuary of Bangladesh
    Crawford, Thomas W.; Islam, Md Sariful; Rahman, Munshi Khaledur; Paul, Bimal Kanti; Curtis, Scott; Miah, Md. Giashuddin; Islam, Mohammad Rafiqul (MDPI, 2020-09-22)
    This study investigates coastal erosion, revetment as a shoreline protection strategy, and human perceptions of revetments in the Lower Meghna estuary of the Bangladesh where new revetments were recently constructed. Questions addressed were: (1) How do rates of shoreline change vary over the period 2011–2019? (2) Did new revetments effectively halt erosion and what were the magnitudes of erosion change? (3) How have erosion rates changed for shorelines within 1 km of revetments, and (4) How do households perceive revetments? High-resolution Planet Lab imagery was used to quantify shoreline change rates. Analysis of household survey data assessed human perceptions of the revetment’s desirability and efficacy. Results revealed high rates of erosion for 2011–2019 with declining erosion after 2013. New revetments effectively halted erosion for protected shorelines. Significant spatial trends for erosion rates existed for shorelines adjacent to revetments. Survey respondents overwhelmingly had positive attitudes about a desire for revetment protection; however, upstream respondents expressed a strong majority perception that revetment acts to make erosion worse. Highlights of the research include integration of remote sensing with social science methods, the timing of the social survey shortly after revetment construction, and results showing significant erosion change upstream and downstream of new revetments.
  • Thumbnail Image
    Coastal Erosion Hazard in Bangladesh: Space-time pattern analysis and empirical forecasting, impacts on land use/cover, and human risk perception
    Islam, Md Sariful (Virginia Tech, 2023-06-27)
    Coastal areas are vulnerable to different natural hazards, including hurricanes, cyclones, tsunami, floods, coastal erosion, and saltwater intrusion. These hazards cause extensive social, ecological, economic, and human losses. Continued climate change and sea-level rise is expected to substantially impact the people living in coastal areas. Sea level rise poses serious threats for the people living in the coastal zone, which leads to coastal erosion, inundations in the low-lying areas, tidal water encroachment and subsequent salt-water intrusion, as well as the displacement of the people living along the coast. Coastal erosion is one of the biggest environmental threats in the coastal areas globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. For a lower middle-class country, such as Bangladesh, with limited internal resources, it is hard to cope with catastrophic natural hazards, such as coastal erosion and its related consequences. This research aims to advance the scientific understanding of past and future coastal erosion risk and associated changes in land change and land cover using geospatial analysis techniques. It also aims to understand the patterns and drivers of human perception of coastal erosion risk. To place the research questions and objectives in content, Chapter 1 includes a brief introduction and literature review of the coastal erosion context in Bangladesh. Chapter 2 assesses different methods of prediction to investigate the performance of future shoreline position predictions by quantifying how prediction performance varies depending on the time depths of input historical shoreline data and the time horizons of predicted shorelines. Chapter 3 evaluates historical land loss and how well predicted shorelines predict amounts of succeeding LULC resources lost to erosion. Chapter 4 focuses on the patterns and drivers of erosion risk perception using data from spatially explicit measures of coastal erosion risk derived from satellite imagery and a random sample survey of residents living in the coastal communities. In summary, this research advances our scientific understanding of past and future coastal erosion risk and associated changes in land change and land cover using geospatial analysis techniques. It also enhances the understanding of the patterns and drivers of human perception of coastal erosion risk by combining satellite imagery and social survey data. Compared to much of the coastal erosion literature, this work draws from a 35-year time series of satellite-derived shorelines at annual temporal resolution. This time depth enables us to employ a temporal design strategy expected to yield a robust characterization of space-time erosion patterns. This study also enabled us to assess how well predicted shorelines predict amounts of succeeding LULC resources lost to erosion by using long-term historical data. The innovative we use has potential applications to other deltas and vulnerable shorelines globally. While empirical results are specific to the project's study area, results can inform the region's shoreline forecasting ability and associated mitigation and adaptation strategies.
  • Thumbnail Image
    Evaluation of predicted loss of different land use and land cover (LULC) due to coastal erosion in Bangladesh
    Islam, Md Sariful; Crawford, Thomas W.; Shao, Yang (Frontiers, 2023-04)
    Coastal erosion is one of the most significant environmental threats to coastal communities globally. In Bangladesh, coastal erosion is a regularly occurring and major destructive process, impacting both human and ecological systems at sea level. The Lower Meghna estuary, located in southern Bangladesh, is among the most vulnerable landscapes in the world to the impacts of coastal erosion. Erosion causes population displacement, loss of productive land area, loss of infrastructure and communication systems, and, most importantly, household livelihoods. With an aim to assess the impacts of historical and predicted shoreline change on different land use and land cover, this study estimated historical shoreline movement, predicted shoreline positions based on historical data, and quantified and assessed past land use and land cover change. Multi-temporal Landsat images from 1988-2021 were used to quantify historical shoreline movement and past land use and land cover. A time-series classification of historical land use and land cover (LULC) were produced to both quantify LULC change and to evaluate the utility of the future shoreline predictions for calculating amounts of lost or newly added land resources by LULC type. Our results suggest that the agricultural land is the most dominant land cover/use (76.04% of the total land loss) lost over the studied period. Our results concluded that the best performed model for predicting land loss was the 10-year time depth and 20-year time horizon model. The 10-year time depth and 20-year time horizon model was also most accurate for agricultural, forested, and inland waterbody land use/covers loss prediction. We strongly believe that our results will build a foundation for future research studying the dynamics of coastal and deltaic environments.
  • Thumbnail Image
    A Hydroclimatological Analysis of Precipitation in the Ganges–Brahmaputra–Meghna River Basin
    Curtis, Scott; Crawford, Thomas W.; Rahman, Munshi Khaledur; Paul, Bimal Kanti; Miah, Md. Giashuddin; Islam, Md Sariful; Patel, Mohin (MDPI, 2018-09-29)
    Understanding seasonal precipitation input into river basins is important for linking large-scale climate drivers with societal water resources and the occurrence of hydrologic hazards such as floods and riverbank erosion. Using satellite data at 0.25-degree resolution, spatial patterns of monsoon (June-July-August-September) precipitation variability between 1983 and 2015 within the Ganges–Brahmaputra–Meghna (GBM) river basin are analyzed with Principal Component (PC) analysis and the first three modes (PC1, PC2 and PC3) are related to global atmospheric-oceanic fields. PC1 explains 88.7% of the variance in monsoonal precipitation and resembles climatology with the center of action over Bangladesh. The eigenvector coefficients show a downward trend consistent with studies reporting a recent decline in monsoon rainfall, but little interannual variability. PC2 explains 2.9% of the variance and shows rainfall maxima to the far western and eastern portions of the basin. PC2 has an apparent decadal cycle and surface and upper-air atmospheric height fields suggest the pattern could be forced by tropical South Atlantic heating and a Rossby wave train stemming from the North Atlantic, consistent with previous studies. Finally, PC3 explains 1.5% of the variance and has high spatial variability. The distribution of precipitation is somewhat zonal, with highest values at the southern border and at the Himalayan ridge. There is strong interannual variability associated with PC3, related to the El Nino/Southern Oscillation (ENSO). Next, we perform a hydroclimatological downscaling, as precipitation attributed to the three PCs was averaged over the Pfafstetter level-04 sub-basins obtained from the World Wildlife Fund (Gland, Switzerland). While PC1 was the principal contributor of rainfall for all sub-basins, PC2 contributed the most to rainfall in the western Ganges sub-basin (4524) and PC3 contributed the most to the rainfall in the northern Brahmaputra (4529). Monsoon rainfall within these two sub-basins were the only ones to show a significant relationship (negative) with ENSO, whereas four of the eight sub-basins had a significant relationship (positive) with sea surface temperature (SST) anomalies in the tropical South Atlantic. This work demonstrates a geographic dependence on climate teleconnections in the GBM that deserves further study.
  • Thumbnail Image
    Impacts of Nationwide Lockdown due to COVID 19 Outbreak on Air Quality in Bangladesh
    Islam, Md Sariful (Virginia Tech, 2021-04-30)
    In Bangladesh, a nationwide lockdown was imposed on 26th March 2020. Due to restricted emissions, it was hypothesized that the air quality has been improved during lockdown throughout the country. The study is intended to assess the impact of nationwide lockdown measures on air quality in Bangladesh. Satellite data from different sources were analyzed for four different air pollutants (NO2, SO2, CO, and O3 to assess the changes in the atmospheric concentrations of pollutants across the country. In this study, the concentrations of NO2, SO2, CO, and O3 from 1st February to 30th May of the year 2019 and 2020 were analyzed. The average SO2 and NO2 concentrations were decreased by 43% and 40% respectively, while tropospheric O3 were found to be increased with a maximum of 7%. This analysis reveals that NO2 concentrations are highly correlated with the regional COVID-19 cases (r=0.74) in the country.