Change in cyclone disaster vulnerability and response in coastal Bangladesh.

The number of deaths owing to tropical cyclones in Bangladesh has significantly reduced. Category 4 Cyclone Gorky in 1991 and Sidr in 2007 caused 147,000 and 4,500 deaths respectively, whereas Category 1 Cyclone Mora in 2017 resulted in six. Face-to-face interviews with 362 residents, participant observation, and focus-group discussions answer a research question about how change in coastal areas has contributed to this outcome. The study considered institutional approaches of disaster risk management through legal frameworks, administrative arrangements, cyclone preparedness activities, cyclone detection and early warning dissemination, construction of shelter centres, strengthening of various types of coastal embankments, paved roads, and pre-cyclone evacuation. The findings indicate significant improvement in house structures and design, income levels and diversification, education, awareness, individual capacity, poverty reduction, and lowering dependency on agriculture-based earning. Furthermore, the availability of mobile telephones, radio, television, and social media platforms enhanced social connectivity and greater gender equality and empowerment helped to facilitate disaster preparedness, evacuation, and response.

Limitations of COVID-19 testing and case data for evidence-informed health policy and practice.

BACKGROUND: Coronavirus disease 2019 (COVID-19) became a pandemic within a matter of months. Analysing the first year of the pandemic, data and surveillance gaps have subsequently surfaced. Yet, policy decisions and public trust in their country's strategies in combating COVID-19 rely on case numbers, death numbers and other unfamiliar metrics. There are many limitations on COVID-19 case counts internationally, which make cross-country comparisons of raw data and policy responses difficult. PURPOSE AND CONCLUSIONS: This paper presents and describes steps in the testing and reporting process, with examples from a number of countries of barriers encountered in each step, all of which create an undercount of COVID-19 cases. This work raises factors to consider in COVID-19 data and provides recommendations to inform the current situation with COVID-19 as well as issues to be aware of in future pandemics.

Hydro-chemical based assessment of groundwater vulnerability in the Holocene multi-aquifers of Ganges delta.

Determining the degree of high groundwater arsenic (As) and fluoride (F-) risk is crucial for successful groundwater management and protection of public health, as elevated contamination in groundwater poses a risk to the environment and human health. It is a fact that several non-point sources of pollutants contaminate the groundwater of the multi-aquifers of the Ganges delta. This study used logistic regression (LR), random forest (RF) and artificial neural network (ANN) machine learning algorithm to evaluate groundwater vulnerability in the Holocene multi-layered aquifers of Ganges delta, which is part of the Indo-Bangladesh region. Fifteen hydro-chemical data were used for modelling purposes and sophisticated statistical tests were carried out to check the dataset regarding their dependent relationships. ANN performed best with an AUC of 0.902 in the validation dataset and prepared a groundwater vulnerability map accordingly. The spatial distribution of the vulnerability map indicates that eastern and some isolated south-eastern and central middle portions are very vulnerable in terms of As and F- concentration. The overall prediction demonstrates that 29% of the areal coverage of the Ganges delta is very vulnerable to As and F- contents. Finally, this study discusses major contamination categories, rising security issues, and problems related to groundwater quality globally. Henceforth, groundwater quality monitoring must be significantly improved to successfully detect and reduce hazards to groundwater from past, present, and future contamination.

Future groundwater potential mapping using machine learning algorithms and climate change scenarios in Bangladesh.

The aim of the study was to estimate future groundwater potential zones based on machine learning algorithms and climate change scenarios. Fourteen parameters (i.e., curvature, drainage density, slope, roughness, rainfall, temperature, relative humidity, lineament density, land use and land cover, general soil types, geology, geomorphology, topographic position index (TPI), topographic wetness index (TWI)) were used in developing machine learning algorithms. Three machine learning algorithms (i.e., artificial neural network (ANN), logistic model tree (LMT), and logistic regression (LR)) were applied to identify groundwater potential zones. The best-fit model was selected based on the ROC curve. Representative concentration pathways (RCP) of 2.5, 4.5, 6.0, and 8.5 climate scenarios of precipitation were used for modeling future climate change. Finally, future groundwater potential zones were identified for 2025, 2030, 2035, and 2040 based on the best machine learning model and future RCP models. According to findings, ANN shows better accuracy than the other two models (AUC: 0.875). The ANN model predicted that 23.10 percent of the land was in very high groundwater potential zones, whereas 33.50 percent was in extremely high groundwater potential zones. The study forecasts precipitation values under different climate change scenarios (RCP2.6, RCP4.5, RCP6, and RCP8.5) for 2025, 2030, 2035, and 2040 using an ANN model and shows spatial distribution maps for each scenario. Finally, sixteen scenarios were generated for future groundwater potential zones. Government officials may utilize the study's results to inform evidence-based choices on water management and planning at the national level.

Bioaccumulation and sources of metal(loid)s in fish species from a subtropical river in Bangladesh: a public health concern.

Toxic metals and freshwater fish's metalloid contamination are significant environmental concerns for overall public health. However, the bioaccumulation and sources of metal(loids) in freshwater fishes from Bangladesh still remain unknown. Thus, the As, Pb, Cd, and Cr concentrations in various freshwater fish species from the Rupsha River basin were measured, including Tenualosa ilisha, Gudusia chapra, Otolithoides pama, Setipinna phasa, Mystus vittatus, Glossogobius giuris, and Pseudeutropius atherinoides. An atomic absorption spectrophotometer was used to determine metal concentrations. The mean concentrations of metal(loids) in the fish muscle (mg/kg) were found to be As (1.53) > Pb (1.25) > Cr (0.51) > Cd (0.39) in summer and As (1.72) > Pb (1.51) > Cr (0.65) > Cd (0.49) in winter. The analyzed fish species had considerably different metal(loid) concentrations with seasonal variation, and the distribution of the metals (loids) was consistent with the normal distribution. The demersal species, M. vittatus, displayed the highest bio-accumulative value over the summer. However, in both seasons, none of the species were bio-accumulative. According to multivariate statistical findings, the research area's potential sources of metal(loid) were anthropogenic activities linked to geogenic processes. Estimated daily intake, target hazard quotient (THQ), and carcinogenic risk (CR) were used to assess the influence of the risk on human health. The consumers' THQs values were < 1, indicating that there were no non-carcinogenic concerns for local consumers. Both categories of customers had CRs that fell below the permissible range of 1E - 6 to 1E - 4, meaning they were not at any increased risk of developing cancer. The children's group was more vulnerable to both carcinogenic and non-carcinogenic hazards. Therefore, the entry of metal(loids) must be regulated, and appropriate laws must be used by policymakers.

Are hotspots and frequencies of heat waves changing over time? Exploring causes of heat waves in a tropical country.

Heat waves significantly impact people's lives and livelihoods and are becoming very alarming and recognized as hot topics worldwide, including in Bangladesh. However, much less is understood regarding recent hotspots, the frequency of heat waves over time, and their underlying causes in Bangladesh. The objective of the study is to explore the current scenario and frequency of heat waves and their possible causes across Bangladesh. The Mann-Kendall and Sen's slope techniques were used to determine seasonal and annual temperature trend patterns of heat wave frequencies. Daily maximum temperature datasets collected from the Bangladesh Meteorological Department (BMD) during 1991-2021 are applied. The frequency of days with Tmax≥ 36°C as the threshold was used to compute different types of heat waves based on the BMD's operational definition. The results show that the mild heat wave (MHW) days followed the subsequent hotspot order: Rajshahi (103) > Chuadanga (79), Ishurdi (60), and Jessore (58), respectively. The frequency of days with Tmax≥36°C was persistence for many days in 2014, especially in the western part of Bangladesh compared to other parts. Similarly, the heat waves condition shown its deadliest event by increasing more days in 2021. The highest increasing trend was identified at the Patuakhali site, with a rate of 0.516 days/year, while the highest decreasing trend was noticed at the Chuadanga site, with a rate of -0.588 days/year. The frequency of days (Tmax≥36°C) is an increasing trend in the south-western part of Bangladesh. The synoptic condition in and around Bangladesh demonstrates that the entrance of heat waves in Bangladesh is due to the advection of higher temperatures from the south/southwest of the Bay of Bengal. The outcomes will guide the national appraisal of heatwave effects, shedding light on the primary causes of definite heatwave phenomena, which are crucial for developing practical adaptation tools.

Source identification and potential health risks from elevated groundwater nitrate contamination in Sundarbans coastal aquifers, India.

In recent years groundwater contamination through nitrate contamination has increased rapidly in the managementof water research. In our study, fourteen nitrate conditioning factors were used, and multi-collinearity analysis is done. Among all variables, pH is crucial and ranked one, with a value of 0.77, which controls the nitrate concentration in the coastal aquifer in South 24 Parganas. The second important factor is Cl-, the value of which is 0.71. Other factors like-As, F-, EC and Mg2+ ranked third, fourth and fifth position, and their value are 0.69, 0.69, 0.67 and 0.55, respectively. Due to contaminated water, people of this district are suffering from several diseases like kidney damage (around 60%), liver (about 40%), low pressure due to salinity, fever, and headache. The applied method is for other regions to determine the nitrate concentration predictions and for the justifiable alterationof some management strategies.

Hydro-chemical characterization and irrigation suitability assessment of a tropical decaying river in India.

Water pollution is a major concern for a decaying river. Polluted water reduces ecosystem services and human use of rivers. Therefore, the present study aims to assess the irrigation suitability of the Jalangi River water. A total of 34 pre-selected water samples were gathered from the source to the sink of the Jalangi River with an interval of 10 km and one secondary station's data from February 2012 to January 2022 were used for this purpose. The Piper diagram exhibits that the Jalangi River water is Na+-HCO3- types, and the alkaline earth (Ca2+ + Mg2+) outperforms alkalises (Na+ + K+) and weak acids (HCO3- + CO32-) outperform strong acids (Cl- + SO42-). SAR values ranging from 0.35 to 0.64 show that water is suitable for irrigation and poses no sodicity risks. The %Na results show that 91.18% of water samples are good and acceptable for irrigation. RSC levels indicate a significant alkalinity hazard, with 94.12% of samples considered inappropriate for irrigation. PI findings show that 91.18% of water samples are suitable for irrigation. Apart from the spatial water samples, seasonal water samples exhibit a wide variations as per the nature of irrigation hazards. Gibbs plot demonstrates that the weathering of rocks determined the hydro-chemical evolution of Jalangi River water. This study identifies very little evaporation dominance for pre- and post-monsoon water. The analysis of variance (ANOVA) test illustrates that there are no spatial variations in water quality while seasonal variations are widely noted (p < 0.05). The results also revealed that river water for irrigation during monsoon is suitable compared to the pre-monsoon season. Anthropogenic interventions including riverbed agriculture, and the discharge of untreated sewage from urban areas are playing a crucial role in deteriorating the water quality of the river, which needs substantial attention from the various stakeholders in a participatory, and sustainable manner.

Factors of cyclone disaster deaths in coastal Bangladesh.

Bangladesh's success in disaster risk management is often evidenced by referencing the reduction of deaths caused by tropical cyclones - the Cyclone Gorky 1991 caused 147,000 deaths, the Cyclone Sidr 2007 caused 4500 deaths and only 6 deaths by the Cyclone Mora in 2017. This raises questions of how deaths occurred by tropical cyclones in the past and what factors still might contribute towards deaths from cyclone hazards? This study answers these questions through face-to-face interviews with 362 residents, field visits and observations across coastal Bangladesh. The findings indicate that there have been improvements in house structures and design, warning responses and evacuation processes to public cyclone shelters and informal cyclone shelter centres. In the past, due to a lack of built infrastructure, strong residential houses and public cyclone shelters, deaths occurred whilst living in fragile houses; attempting to survive through holding trees and floating in storm surges. The top ten factors that may still cause deaths by tropical cyclones include: (1) Living adjacent to the coast without an embankment or lack of embankment, or the failure of an established embankment; (2) the repeat of a 1991-like cyclone; (3) non-evacuation following early warning; (4) poor roads in remote areas to facilitate mass movement; (5) distance to and insufficient number of public cyclone shelters; (6) lack of protective measures for the rising number of elderly and disabled people; (7) community's unawareness; (8) communication failure during the emergency period; (9) failure to evacuate people from remote locations; and (10) Poor radio signal and mobile network issues resulting in no warning information being effectively and timely communicated. This study provides several key recommendations addressing these factors of deaths, to be implemented by individual, community, private sectors, non-government organisations (NGOs) and public sectors across coastal Bangladesh.

Enhancing landslide risk reduction strategies in Southeast Bangladesh.

Since 2000, landslides in southeast Bangladesh have resulted in over 700 deaths, most of which were in informal settlements. For instance, the 2007 landslides in informal settlements in Chittagong and the 2017 landslides in Rangamati took 127 and 150 lives, respectively. Although the government of Bangladesh claims to implement risk reduction, the number of deaths associated with landslides is increasing. Hence, this study investigated the ways to enhance landslide risk reduction interventions by addressing several scientific and technical issues related to disaster risk reduction. This study conducted focus group discussions, key informant interviews and expert interviews with key stakeholders in landslide risk management to collect qualitative data. Moreover, the historical accounts of settlements, media reporting, institutional action plans and policies were reviewed. Contribution: By integrating primary and secondary data, this study found that several political-economic aspects are major anthropogenic contributors to the recent increase in landslides in the region. The contributing factors included the following: policy and action plans to raise regional population; land management; illegal deforestation; plans to establish hydroelectricity in hilly areas; ownership of settlements; manipulation of water, gas and electricity supply to illegal settlements; commercial plantations; lack of risk governance; unplanned development activities; natural population rise; increased settlement along hill slopes. This study identified and discussed lessons learned from previous landslide disasters, the weakness of early warning systems and their dissemination and ways to improve evacuation, rescue, relief and risk reduction. Finally, this study formulated recommendations for the effective implementation of landslide risk reduction in southeast Bangladesh.

Non-state initiatives on enhancing counter-trafficking of Rohingya influx in Cox's Bazar of Bangladesh.

Human trafficking is the third most lucrative form of trafficking in the world (following drugs and counterfeit goods). Multiple outbreaks of unrest between October 2016 and August 2017 in the Rakhine State of Myanmar triggered ~745,000 influxes of Rohingyas crossing into Bangladesh through the border boundaries at Teknaf and Ukhiya sub-districts of Cox's Bazar. In this regard, the media confirmed that over a thousand Rohingya people, particularly women and girls, were victims of human trafficking. This research aims to explore the underlying causes of human trafficking (HT) during emergency responses and seeks to understand how the knowledge and capacity of the refugee, local administration, and law enforcement agencies in Bangladesh can be improved in promoting counter-trafficking (CT) and safe migration processes. In order to achieve the objectives, this study reviews acts, rules, policies, and action plans of the Government of Bangladesh on the HT, CT, and safe migration processes. Then, a case study has been applied to present the ongoing CT and safe migration programs of an NGO called Young Power in Social Action (YPSA), which received funding and technical support from the International Organization of Migration (IOM) for this purpose. This study also evaluates the effectiveness of the program through conducting key informant interviews (KIIs) and focus group discussions (FGDs) with the beneficiary and non-beneficiary participants including refugees, law-enforcing agencies (LEAs), and NGOs in Teknaf and Ukhyia. Thus, this study identifies program-level strengths and weaknesses in relation to the CT and safe migration process and provides key directions on how they can be improved. It concludes that non-state actors have a significant role in preventing HT and promoting CT and safe migration for Rohingyas in Bangladesh.

Spatiotemporal analysis and predicting rainfall trends in a tropical monsoon-dominated country using MAKESENS and machine learning techniques.

Spatiotemporal rainfall trend analysis as an indicator of climatic change provides critical information for improved water resource planning. However, the spatiotemporal changing behavior of rainfall is much less understood in a tropical monsoon-dominated country like Bangladesh. To this end, this research aims to analyze spatiotemporal variations in rainfall for the period 1980-2020 over Bangladesh at seasonal and monthly scales using MAKESENS, the Pettitt test, and innovative trend analysis. Multilayer Perception (MLP) neural network was used to predict the next 8 years' rainfall changes nationally in Bangladesh. To investigate the spatial pattern of rainfall trends, the inverse distance weighting model was adopted within the ArcGIS environment. Results show that mean annual rainfall is 2432.6 mm, of which 57.6% was recorded from July to August. The Mann-Kendall trend test reveals that 77% of stations are declining, and 23% have a rising trend in the monthly rainfall. More than 80% of stations face a declining trend from November to March and August. There is a declining trend for seasonal rainfall at 82% of stations during the pre-monsoon, 75% during the monsoon, and 100% during the post-monsoon. A significant decline trend was identified in the north-center during the pre-monsoon, the northern part during the monsoon, and the southern and northwestern portions during the post-monsoon season. Predicted rainfall by MLP till 2030 suggests that there will be little rain from November to February, and the maximum fluctuating rainfall will occur in 2025 and 2027-2029. The ECMWF ERA5 reanalysis data findings suggested that changing rainfall patterns in Bangladesh may have been driven by rising or reducing convective precipitation rates, low cloud cover, and inadequate vertically integrated moisture divergence. Given the shortage of water resources and the anticipated rise in water demand, the study's findings have some implications for managing water resources in Bangladesh.

Climate change in Bangladesh: Temperature and rainfall climatology of Bangladesh for 1949-2013 and its implication on rice yield.

Bangladesh has been ranked as one of the world's top countries affected by climate change, particularly in terms of agricultural crop sector. The purpose of this study is to identify spatial and temporal changes and trends in long-term climate at local and national scales, as well as their implications for rice yield. In this study, Modified Mann-Kendall and Sen's slope tests were used to detect significant trends and the magnitude of changes in temperature and rainfall. The temperature and rainfall data observed and recorded at 35 meteorological stations in Bangladesh over 65-years in the time span between the years 1949 and 2013 have been used to detect these changes and trends of variation. The results show that mean annual Tmean, Tmin, and Tmax have increased significantly by 0.13°C, 0.13°C, and 0.13°C/decade, respectively. The most significant increasing trend in seasonal temperatures for the respective Tmean, Tmin, and Tmax was 0.18°C per decade (post-monsoon), 0.18°C/decade (winter), and 0.23°C/decade (post-monsoon), respectively. Furthermore, the mean annual and pre-monsoon rainfall showed a significant increasing trend at a rate of 4.20 mm and 1.35 mm/year, respectively. This paper also evaluates climate variability impacts on three major rice crops, Aus, Aman, and Boro during 1970-2013. The results suggest that crop yield variability can be explained by climate variability during Aus, Aman, and Boro seasons by 33, 25, and 16%, respectively. Maximum temperature significantly affected the Aus and Aman crop yield, whereas rainfall significantly affected all rice crops' yield. This study sheds light on sustainable agriculture in the context of climate change, which all relevant authorities should investigate in order to examine climate-resilient, high-yield crop cultivation.

Correction: Climate change in Bangladesh: Temperature and rainfall climatology of Bangladesh for 1949-2013 and its implication on rice yield.

[This corrects the article DOI: 10.1371/journal.pone.0292668.].

Assessment of soil heavy metal pollution and associated ecological risk of agriculture dominated mid-channel bars in a subtropical river basin.

The elevated concentrations of heavy metals in soil considerably threaten ecological and human health. To this end, the present study assesses metals pollution and its threat to ecology from the mid-channel bar's (char) agricultural soil in the Damodar River basin, India. For this, the contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), pollution index, and ecological risk index (RI) were measured on 60 soil samples at 30 stations (2 from each station, i.e., surface and sub-surface) in different parts of the mid-channel bar. The CF and EF indicate that both levels of char soil have low contamination and hence portray a higher potential for future enrichment by heavy metals. Moreover, Igeo portrays that soil samples are uncontaminated to moderately contaminated. Further, pollution indices indicate that all the samples (both levels) are unpolluted with a mean of 0.062 for surface soils and 0.048 for sub-surface soils. Both levels of the char have a low potentiality for ecological risk with an average RI of 0.20 for the surface soils and 0.19 for the sub-surface soils. Moreover, Technique for order preference by similarity to ideal solution (TOPSIS) indicates that the sub-surface soils have lower pollution than the surface soils. The geostatistical modeling reveals that the simple kriging technique was estimated as the most appropriate interpolation model. The present investigation exhibits that reduced heavy metal pollution is due to the sandy nature of soils and frequent flooding. However, the limited pollution is revealed due to the intensive agricultural practices on riverine chars. Therefore, this would be helpful to regional planners, agricultural engineers, and stakeholders in a basin area.

Coupling of machine learning and remote sensing for soil salinity mapping in coastal area of Bangladesh.

Soil salinity is a pressing issue for sustainable food security in coastal regions. However, the coupling of machine learning and remote sensing was seldom employed for soil salinity mapping in the coastal areas of Bangladesh. The research aims to estimate the soil salinity level in a southwestern coastal region of Bangladesh. Using the Landsat OLI images, 13 soil salinity indicators were calculated, and 241 samples of soil salinity data were collected from a secondary source. This study applied three distinct machine learning models (namely, random forest, bagging with random forest, and artificial neural network) to estimate soil salinity. The best model was subsequently used to categorize soil salinity zones into five distinct groups. According to the findings, the artificial neural network model has the highest area under the curve (0.921), indicating that it has the most potential to predict and detect soil salinity zones. The high soil salinity zone covers an area of 977.94 km2 or roughly 413.51% of the total study area. According to additional data, a moderate soil salinity zone (686.92 km2) covers 30.56% of Satkhira, while a low soil salinity zone (582.73 km2) covers 25.93% of the area. Since increased soil salinity adversely affects human health, agricultural production, etc., the study's findings will be an effective tool for policymakers in integrated coastal zone management in the southwestern coastal area of Bangladesh.

Predicted changes in future precipitation and air temperature across Bangladesh using CMIP6 GCMs.

Understanding spatiotemporal variability in precipitation and temperature and their future projections is critical for assessing environmental hazards and planning long-term mitigation and adaptation. In this study, 18 Global Climate Models (GCMs) from the most recent Coupled Model Intercomparison Project phase 6 (CMIP6) were employed to project the mean annual, seasonal, and monthly precipitation, maximum air temperature (Tmax), and minimum air temperature (Tmin) in Bangladesh. The GCM projections were bias-corrected using the Simple Quantile Mapping (SQM) technique. Using the Multi-Model Ensemble (MME) mean of the bias-corrected dataset, the expected changes for the four Shared Socioeconomic Pathways (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) were evaluated for the near (2015-2044), mid (2045-2074), and far (2075-2100) futures in comparison to the historical period (1985-2014). In the far future, the anticipated average annual precipitation increased by 9.48%, 13.63%, 21.07%, and 30.90%, while the average Tmax (Tmin) rose by 1.09 (1.17), 1.60 (1.91), 2.12 (2.80), and 2.99 (3.69) °C for SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5, respectively. According to predictions for the SSP5-8.5 scenario in the distant future, there is expected to be a substantial rise in precipitation (41.98%) during the post-monsoon season. In contrast, winter precipitation was predicted to decrease most (11.12%) in the mid-future for SSP3-7.0, while to increase most (15.62%) in the far-future for SSP1-2.6. Tmax (Tmin) was predicted to rise most in the winter and least in the monsoon for all periods and scenarios. Tmin increased more rapidly than Tmax in all seasons for all SSPs. The projected changes could lead to more frequent and severe flooding, landslides, and negative impacts on human health, agriculture, and ecosystems. The study highlights the need for localized and context-specific adaptation strategies as different regions of Bangladesh will be affected differently by these changes.

Knowledge, Attitude and Practices Towards Dengue Fever Among Slum Dwellers: A Case Study in Dhaka City, Bangladesh.

Objectives: This study intends to evaluate Dhaka city slum dwellers' responses to Dengue fever (DF). Methods: 745 individuals participated in a KAP survey that was pre-tested. Face-to-face interviews were performed to obtain data. Python with RStudio was used for data management and analysis. The multiple regression models were applied when applicable. Results: 50% of respondents were aware of the deadly effects of DF, its common symptoms, and its infectious nature. However, many were unaware that DF could be asymptomatic, a previously infected person could have DF again, and the virus could be passed to a fetus. Individuals agreed that their families, communities, and authorities should monitor and maintain their environment to prevent Aedes mosquito breeding. However, overall 60% of the study group had inadequate preventative measures. Many participants lacked necessary practices such as taking additional measures (cleaning and covering the water storage) and monitoring potential breeding places. Education and types of media for DF information were shown to promote DF prevention practices. Conclusion: Slum dwellers lack awareness and preventative activities that put them at risk for DF. Authorities must improve dengue surveillance. The findings suggest efficient knowledge distribution, community stimulation, and ongoing monitoring of preventative efforts to reduce DF. A multidisciplinary approach is needed to alter dwellers' behavior since DF control can be done by raising the population's level of life. People and communities must perform competently to eliminate vector breeding sites.

Strengthening the Trialability for the Intention to Use of mHealth Apps Amidst Pandemic: A Cross-Sectional Study.

Recent advancements in mHealth apps and services have played a vital role in strengthening healthcare services and enabling their accessibility to marginalized people. With the alarming rise in COVID-19 infection rates around the world, there appears to be an urgent call to modernize traditional medical practices to combat the pandemic. This study aims to investigate the key factors influencing the trialability of mHealth apps/services and behavioral intention to adopt mobile health applications. The study also examines the moderating effects of self-discipline motivation, knowledge, and attitude on the relationship between trialability and behavioral intention to use. The deductive reasoning approach was followed in a positivism paradigm. The study used convenience sampling and collected responses from 280 Generation Y participants in Bangladesh. Partial least square-based structural equation modeling was employed. The results revealed that relative advantage (ß = 0.229, p < 0.05), compatibility (ß = 0.232, p < 0.05), complexity (ß = −0.411, p < 0.05), and observability (ß = 0.235, p < 0.05) of mHealth apps influence the trialability of mHealth apps and services among users. Trialability compatibility (ß = 0.425, p < 0.05) of mHealth was positively related to the behavioral intention to use these mobile apps. The study found no moderating effects of attitude (ß = 0.043, p > 0.05) or self-discipline motivation (ß = −0.007, p > 0.05) on the hypothesized relationships. The empirical findings of this study may facilitate the development, design process, and implementation of mHealth applications with improved features that can lead to high user acceptance among Generation Y during future health crises.

Perceived Noise Pollution and Self-Reported Health Status among Adult Population of Bangladesh.

Despite the public health concern, there is a dearth of research regarding perceived noise pollution and noise-related health status in Bangladesh. This study was carried out to evaluate the noise-related health status among Bangladesh's adult population. 1386 adult Bangladeshis participated in an online survey. A linear regression model was used to evaluate overall noise-related health status determinants. 91% of the survey population reported noisy environments in their neighborhood, with the majority reporting two types (34%) of noise pollution sources. Road vehicles (38%) and construction activities (24%) were identified as significant source of noise pollution. The Bangladeshis are primarily exposed to noise during school and office hours. Socio-demographic information, perceived noise pollution and individual views towards noise pollution were examined as determinants of noise-related health problems. Females were found to be more impacted than males, and young people also expressed concern about noise pollution's influence. Residents in mixed-unit buildings exhibited a significant level of noise-related health problems such as deafness, insomnia, heart disease, headache, stress, poor concentration, production loss, fatigue, irritability, heartburn, indigestion, ulcers, and high blood pressure. Noise pollution from road vehicles and industry has been shown to have a negative effect on people's health. Individuals affected by noise were interested in noise reduction efforts. The findings of this research may aid in the improvement of international, national, and local noise control efforts.