Impact of Air Pollutant on Human Health in Kushtia Sugar Mill, Bangladesh

Abstract. The study dealt with the concentration of air pollutants emitted from Kushtia sugar mills in Jagati region of Bangladesh in order to evaluate their impact on human health. The dispersion of air pollutants from sugar mill's chimney was obtained through point source Gaussian dispersion model. The air pollutants were monitored during winter season in 2011-2012. A questionnaire survey was randomly carried out in a small scale at the study area. The result showed that the maximum concentration of SO 2 , NO x and PM 10 were 28.837 µg/m 3 , 76.177 µg/m 3 and 380.339 µg/m 3 respectively. The particulate matter (PM 10 ) concentration was found to be very high whereas sulfur dioxide (SO 2 ) and nitrogen oxide (NO x) concentrations were low at the study area. The calculated value of air pollution index (API) was 88.18 which indicate that heavy air pollution can predispose individuals to heart and lung disease in the study area people. This study revealed that the concentration of particulate matter found in Kushtia sugar mill had exceeded the minimum level according to the WHO standards. The high concentration of PM 10 is suggested to affect human health and environmental conditions in the study area

Drought Hazard Evaluation in Boro Paddy Cultivated Areas of Western Bangladesh at Current and Future Climate Change Conditions

Drought hazard is one of the main hindrances for sustaining food security in Bangladesh, and climate change may exacerbate it in the next several decades. This study aims to evaluate drought hazard at current and future climate change conditions in the Boro paddy cultivated areas of western Bangladesh using simulated climate data from the outputs of three global climate models (GCMs) based on the SRES A1B scenario for the period between 2041 and 2070. The threshold level of Standardized Precipitation Evapotranspiration Index (SPEI) was employed to identify drought events and its probability distribution function (PDF) was applied to create the drought hazard index. The study demonstrates that enhancement of potential evapotranspiration (PET) will surpass that of precipitation, resulting in intensified drought events in future. In addition, the PDFs of drought events will move the upper tail in future period compared to the baseline. The results showed that the southwestern region was more severe to the drought hazard than the northwestern region during the period of 1984 to 2013. From the results of three GCMs, in the mid-century period, drought hazard will slightly increase in the northwestern region and flatten with a decrease in the southwestern region. The outcomes will help to allocate agricultural adaptation plans under climate change condition in Bangladesh

Evaluating Structural, Chlorophyll-Based and Photochemical Indices to Detect Summer Maize Responses to Continuous Water Stress

his study evaluates the performance of structural, chlorophyll-based, and photochemical indices to detect maize water status and to assess production based on five years of field experiments (2013–2017) during the primary growth stages. We employed three categories of indicators, including water condition and productive and thermal indicators, to quantify the responses of summer maize under continuous water stress from drought to waterlogging conditions. Furthermore, we adopted several spectral indices to assess their sensitivity to three categories of metrics. The results showed the association is the best between the treatment level and Leaf Water Content (LWC). The waterlogging treatment influenced Leaf Water Potential (LWP) in moderate drought stress. Severe drought stress caused the strongest reduction in productivity from both Leaf Area Index (LAI) and chlorophyll content. In terms of sensitivity of various indices, red-edge-position (REP) was sensitive to maize water conditions LWP, LAI and chlorophyll content. Photochemical Reflectance Index (PRI) and Normalized Difference Vegetation Index (NDVI) were the most and second most sensitive indices to productive indicators, respectively. The results also showed that no indices were capable of capturing the information of Crop Water Stress Index (CWSI)

Comparison of future changes in frequency of climate extremes between coastal and inland locations of Bengal delta based on CMIP6 climate models

Climate change is perceived to be the primary reason for the amplification of extreme climatic phenomena. Estimation of changes in extreme values under climate change thus plays an important role in disaster risk assessment and management. However, the different changes in extremes in two distinct regions: inland and coast under climate change are yet to be investigated meticulously. This study is intended to assess the changes in frequency of rainfall and temperature extremes under the impact of climate change in two distinct locations: coast and inland of Bengal delta, a region highly vulnerable to climate change. The multi-model ensemble (projections from CMIP6 framework) technique with the application of frequency analysis was employed to appraise the impact in two future time horizons. Results suggest that the inland estimate of extreme rainfall by the end of this century is barely able to exceed the coastal estimate of extreme rainfall in present conditions. The rate of increase of warm extremes is almost similar; however, with the cold extreme, the increase rate is a little higher inland than on the coast. In both regions, a greater rise in climate extremes is expected in the far future than in the near future. Overall, the coastal area is expected to be more vulnerable to flooding while the inland to drought under climate change in the Bengal delta region

How air quality and COVID-19 transmission change under different lockdown scenarios? A case from Dhaka city, Bangladesh

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

The optimal alternative for quantifying reference evapotranspiration in climatic sub-regions of Bangladesh

Reference evapotranspiration (ETo) is a basic element for hydrological designing and agricultural water resources management. The FAO56 recommended Penman–Monteith (FAO56-PM) formula recognized worldwide as the robust and standard model for calculating ETo. However, the use of the FAO56-PM model is restricted in some data-scarce regions like Bangladesh. Therefore, it is imperative to find an optimal alternative for estimating ETo against FAO56-PM model. This study comprehensively compared the performance of 13 empirical models (Hargreaves–Samani, HargreavesM1, Hargreaves M2, Berti, WMO, Abtew, Irmak 1, Irmak 2, Makkink, Priestley-Taylor, Jensen–Haise, Tabari and Turc) by using statistical criteria for 38-years dataset from 1980 to 2017 in Bangladesh. The radiation-based model proposed by Abtew (ETo,6) was selected as an optimal alternative in all the sub-regions and whole Bangladesh against FAO56-PM model owing to its high accuracy, reliability in outlining substantial spatiotemporal variations of ETo, with very well linearly correlation with the FAO56-PM and the least errors. The importance degree analysis of 13 models based on the random forest (RF) also depicted that Abtew (ETo,6) is the most reliable and robust model for ETo computation in different sub-regions. Validation of the optimal alternative produced the largest correlation coefficient of 0.989 between ETo,s and ETo,6 and confirmed that Abtew (ETo,6) is the best suitable method for ETo calculation in Bangladesh

Comparison of CMIP6 and CMIP5 model performance in simulating historical precipitation and temperature in Bangladesh: a preliminary study

The relative performance of global climate models (GCMs) of phases 5 and 6 of the coupled model intercomparison project (CMIP5 and CMIP6, respectively) was assessed in this study based on their ability to simulate annual and seasonal mean rainfall and temperature over Bangladesh for the period 1977–2005. Multiple statistical metrics were used to measure the performance of the GCMs at 30 meteorological observation stations. Two robust multi-criteria decision analysis methods were used to integrate the results obtained using different metrics for an unbiased ranking of the GCMs. The results revealed MIROC5 as the most skillful among CMIP5 GCMs and ACCESS-CM2 among CMIP6 GCMs. Overall, CMIP6 MME showed a significant improvement in simulating rainfall and temperature over Bangladesh compared to CMIP5 MME. The highest improvements were found in simulating cold season (winter and post-monsoon) rainfall and temperature in higher elevated areas. The improvement was relatively more for rainfall than for temperature. The models could capture the interannual variability of annual and seasonal rainfall and temperature reliably, except for the winter rainfall. However, systematic wet and cold/warm biases still exist in CMIP6 models for Bangladesh. CMIP6 GCMs showed higher spatial correlations with observed data, but the higher difference in standard deviations and centered root mean square errors compared to CMIP5 GCMs indicates better performance in simulating geographical distribution but lower performance in simulating spatial variability of most of the climate variables except for minimum temperature at different timescales. In terms of Taylor skill score, the CMIP6 MME showed higher performance in simulating rainfall but lower performance in simulating temperature than CMIP5 MME for most of the timeframes. The findings of this study suggest that the added value of rainfall and temperature simulations in CMIP6 models is not consistent among the climate models used in this research. However, it sets a precedent for future research on climate change risk assessment for the scientific community

Seribu islands in the megacities of Jakarta on the frontlines of the climate crisis

Jakarta, the biggest city in Indonesia, has one district that consists of hundreds of islands that face severe climate hazards called the Seribu Islands complex. This study explores the evidence of local climate trends, the potential impact, and its policy intervention on Seribu Islands, which are classified as small island states and widely recognized as being especially at risk from climate change, threatening their economic and social growth. Long-term in-situ climate data, satellite data, interviews with local stakeholders, and literature reviews were utilized to conduct an exploratory descriptive analysis. The result revealed that Seribu Island experienced a 2.2°C increase in minimum temperature from 1980 until 2021, 3.5-fold of the frequency of extreme temperature and precipitation, 4.17 mm/year of sea level rise, and 10.8 ha land expansion in the densest island. Moreover, about 67% of the inhabitant’s islands were occupied by built-up areas that cover more than 50% of the region. Further, under the worst-case SLR scenario, about 58.4% of the area will be affected, and about 29 islands will disappear. This evidence was also reinforced by every single local respondent’s viewpoint who felt that climate change is occurring in the region. Even though the region faces a severe threat of climate change, the issue of climate change adaptation has not been mainstreamed yet into their local policy. Therefore, the urgency of a real-time climate ground station, a real-time early warning system, and establishing a Regional Disaster Management Agency (BPBD) at the district level have yet to be addressed. Furthermore, the knowledge gained from such case studies is outlined, along with some scientific evidence that may assist small island states in better fostering the opportunities provided by climate change adaptation

Mental Health Condition among University Students of Bangladesh during the Critical COVID-19 Period

Bangladesh’s education sector has been in a state of flux since COVID-19. During the pandemic, all university campuses were closed. There was a mental health issue among the students. This study aims to examine the mental health condition and the determinants that contribute to adverse mental health conditions among university students of Bangladesh. A survey was performed online among university students in Bangladesh, in mid-June 2020 when averaging 3345 affected cases of the population daily. The convenience sampling technique was used and the survey gathered data from 365 university students. The relationship between general information and Depression, Anxiety, and Stress Scale 21 (DASS-21) subscales of university students was determined. The questionnaire was administered to respondents during the pandemic, which ensured fast replies. Linear regression models were used for statistical analysis. University students indicated normal levels of depression (30.41%), anxiety (43.29%), and stress (47.40%). However, a disproportionate number of extremely depressed, anxious, and stressed university students suggested a mental health status of concern. There were significant connections between the individual’s opinion of social satisfaction, mental health concerns, and the present location’s safety with an undesirable mental health condition. Female students were shown to be much more anxious and stressed than male students. Capital Dhaka city students were more depressed and anxious than students outside of Dhaka. Financial and psychological support for students may help mitigate the psychological impact. Authorities should make effective efforts to reduce mental health problems among these students. This research may aid organizations, health care providers, and social workers in their attempts to prepare for and respond to pandemics