Trend analysis of ambient air quality of Chattogram city

Several air quality parameters such as particulate matter (PM), ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2), and carbon monoxide (CO) are considered as the major pollutants which can impose a significant threat to human health and surrounding environment. In this study, seasonal and temporal variations were analyzed for both gaseous air pollutants and particulate matter to investigate the trend analysis of ambient air quality of Chattogram city, a commercial hub of Bangladesh. Air quality data for six selected parameters (PM2.5, PM10, CO, SO2, NO2, and O3) were collected from Continuous Air Monitoring Stations (CAMS) during the period 2013 to 2021 for each pollutant. Air Quality Index (AQI) for each tested pollutant was determined as well as pollution level sharing among the pollutants was also investigated in this work. Results of this study showed that particulate matters (PM2.5 and PM10) were the most responsible pollutants that contributed significantly to air pollution levels in the city. The yearly average AQI was observed to be in the caution (unhealthy for sensitive groups) (100-150) category during the period from 2013 to 2021. Trend analysis showed that there is an ups and downs trend in the AQI level in the city that may be triggered by some interventions taken and Covid-19 pandemic situations. Overall, seasonal variation had a considerable effect on the concentration of pollutants. For each year, the highest concentration of PM2.5 and PM10 was recorded in winter season while the lowest was reported in monsoon season. This study will assist the researchers and policymakers in taking the required steps to take preventive measures in reducing air pollution levels for the studied area. © 2023 Author(s).

Statistical evaluation of selected air quality parameters influenced by COVID-19 lockdown. (Special Issue: Covid-19.)

Air pollution has become a serious concern for its potential health hazard, however, often got less attention in developing countries, like Bangladesh. It is expected that worldwide lockdown due to COVID-19 widespread cause reduction in environmental pollution in particularly the air pollution: however, such changes have been different in different places. In Chittagong, a city scale lockdown came in force on 26 March 2020, a week after when first three cases of COVID-19 have been reported in Bangladesh. This study aims to statistically evaluate the effects of COVID-19 lockdown (26 March to 26 April 2020) on selected air quality pollutants and air quality index s. The daily average concentrations of air pollutants PM10, PM2.5, NO2, SO2 and CO of Chittagong city during COVID-19 lockdown were statistically evaluated and were compared with dry season data averaging over previous 8 years (2012 to 2019). During lockdown, except NO2, all other pollutants studied showed statistically significant decreasing trend. During the COVID-19 shutdown notable reduction of 40%, 32% and 13% compared to the daily mean concentrations of these previous dry season were seen for PM2.5, PM10 and NO2, respectively. The improvement in air quality index value was found as 26% in comparison to the previous dry season due to less human activities in COVID-19 shutdown. The factor analysis showed that AQI in Chittagong city is largely influenced by PM10 and PM2.5 during COVID-19 shutdown. The lesson learnt in this forced measure of lockdown is not surprising and unexpected. It is rather thought provoking for the decision makers to tradeoff the tangible air quality benefits with ongoing development strategies' that was often overlooked directly or indirectly.

Association between Meteorological Parameters and SARS-CoV-2 Outbreak Trends in Major Divisions of Bangladesh

Severe Acute Respiratory Syndrome Coronavirus-2 (SARS-CoV-2) has caused the current pandemic situation worldwide. Studies based on different factors affecting the pandemic are useful in order to find effective measures against COVID-19. Therefore, this study aims to explore the association between meteorological parameters and COVID-19 positive cases, along with a number of fatalities. In this study, the number of identified COVID-19 cases along with death figures, daily records of rainfall, temperature, relative humidity, and wind speed were collected from April and May 2020 for eight major divisions in Bangladesh. Significant positive association (r = 0.24 to 0.58) between relative humidity and COVID-19 cases across the cities was found in this study, while for temperature both positive and negative associations (r = -0.23 to 0.72) were observed. Similarly, a positive association is found between humidity ((r = -0.012 to 0.384)) and numbers of COVID-19 death cases. Rainfall and wind speed exhibit positive correlations with COVID-19 positive cases and numbers of death cases. The findings of the study surely help the policymaker for further decision making, conducting new study and initiate the mitigation measures against COVID-19. [ABSTRACT FROM AUTHOR] Copyright of Journal of Scientific Research is the property of Rajshahi University, Faculty of Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

r-LSTM: Time Series Forecasting for COVID-19 Confirmed Cases with LSTM-based Framework

The coronavirus disease 2019 (COVID-19) caused a pandemic outbreak with affecting 213 nations worldwide. Global policymakers are imposing many measures to slow and reduce the rapid growth of the infections. On the other hand, the healthcare system is encountering significant challenges for a massive number of COVID-19 confirmed or suspected individuals seeking treatment. Therefore, estimating the number of confirmed cases is necessary to provide valuable insights into the growth of the outbreak and facilitate policy making process. In this study, we apply ARIMA models as well as LSTM-based recurrent neural network to forecast the daily cumulative confirmed cases. The LSTM architecture generates more precise forecasting by leveraging both short- and long-term temporal dependencies from the pandemic time series data. Due to the stochastic nature in optimization and random initialization of weights in neural network, the LSTM based model produce less reproducible outcome. In this paper, we propose a reproducible-LSTM (r-LSTM) framework that produces a reproducible and robust results leveraging z-score outlier detection method. We performed five round of nested cross validation to show the consistency in evaluating model performance. The experimental results demonstrate that r-LSTM outperformed the ARIMA model producing minimum MAPE, RMSE, and MAE.

Actionable Knowledge Extraction Framework for COVID-19

In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19) containing over 51,000 scholarly articles, including over 40,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. Medical professional including physicians frequently seek answers to specific questions to improve guidelines and decisions. The huge resource of medical literature is important sources to generate new insights that can help medical communities to provide relevant knowledge and overall fight against the infectious disease. There are ongoing attempts to develop intelligent systems to automatically extract relevant knowledge from many unstructured documents. In this paper, we propose an efficient question answering framework based on automatically analyzing thousands of articles to generate both long text answers (sections/ paragraphs) in response to the questions that are posed by medical communities. In the process of developing the framework, we explored natural language processing techniques like query expansion, data preprocessing, and vector space models early. We show the initial results of an example query answering for the incubation period. © 2020 IEEE.