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Background: COVID-19 pandemic outbreak is an unprecedented shock throughout the world, which has generated a massive social, human, and economic crisis. Identification of risk factors is crucial to prevent the COVID-19 spread by taking appropriate countermeasures effectively. Therefore, this study aimed to identify the potential risk factors contributing to the COVID-19 incidence rates at the district-level in Bangladesh. Method: Spatial regression methods were applied in this study to fulfill the aim. Data related to 28 demographic, economic, built environment, health, and facilities related factors were collected from secondary sources and analyzed to explain the spatial variability of this disease incidence. Three global (ordinary least squares (OLS), spatial lag model (SLM), and spatial error model (SEM)) and one local (geographically weighted regression (GWR)) regression models were developed in this study. Results: The results of the models identified four factors: percentage of the urban population, monthly consumption, number of health workers, and distance from the capital city, as significant risk factors affecting the COVID-19 incidence rates in Bangladesh. Among the four developed models, the GWR model performed the best in explaining the variation of COVID-19 incidence rates across Bangladesh, with an R 2 value of 78.6%. Conclusion: Findings and discussions from this research offer a better insight into the COVID-19 situation, which helped discuss policy implications to negotiate the future epidemic crisis. The primary policy response would be to decentralize the urban population and economic activities from and around the capital city, Dhaka, to create self-sufficient regions throughout the country, especially in the northwestern region.

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Air pollution caused by vehicle emissions has raised serious public health concerns. Vehicle emissions generally depend on many factors, such as the nature of the vehicle, driving style, traffic conditions, emission control technologies, and operational conditions. Concerns about the certification cycles used by various regulatory authorities are growing due to the difference in emission during certification procedure and Real Driving Emissions (RDE). Under laboratory conditions, certification tests are performed in a ‘chassis dynamometer’ for light-duty vehicles (LDVs) and an ‘engine dynamometer’ for heavy-duty vehicles (HDVs). As a result, the test drive cycles used to measure the automotive emissions do not correctly reflect the vehicle’s real-world driving pattern. Consequently, the RDE regulation is being phased in to reduce the disparity between type approval and vehicle’s real-world emissions. According to this review, different variables such as traffic signals, driving dynamics, congestions, altitude, ambient temperature, and so on have a major influence on actual driving pollution. Aside from that, cold-start and hot-start have been shown to have an effect on on-road pollution. Contrary to common opinion, new technology such as start-stop systems boost automotive emissions rather than decreasing them owing to unfavourable conditions from the point of view of exhaust emissions and exhaust after-treatment systems. In addition, the driving dynamics are not represented in the current laboratory-based test procedures. As a result, it is critical to establish an on-road testing protocol to obtain a true representation of vehicular emissions and reduce emissions to a standard level. The incorporation of RDE clauses into certification procedures would have a positive impact on global air quality.