The coronavirus disease 2019 (COVID-19), the pandemic is an unprecedented health emergency never seen in the recorded history of humankind due to its sheer scale, rapid spread, and subsequent shock to the global economy. The past respiratory viral pandemics of the 21st century (SARS-CoV-2 in 2003, Influenza AH1N1 in 2009) have revealed seasonality in environmental factors to play a role in the dynamics of their spread. Here, we report the observed state-level relationship between environmental factors such as temperature (T), relative humidity (RH), specific humidity (SH), and solar radiation (SR) on the COVID-19 spread over the Indian region. The results show that T and RH have a significant impact on the disease growth rate and doubling time. Every degree rise in temperature corresponds to a 0.99 % decrease in the number of cases and an increase in doubling time by ~ 1.13 days implying a slowing down of spread. A similar analysis for RH reveals that more moisture leads to a higher growth rate and reduced doubling time. Lower SH and higher surface-reaching SR are found to reduce the spread and increase the doubling time similar to that of temperature. The range of average state-level T (RH) encountered during this period was between 24 and 35oC (30 and 87%) which implies that environmental impact is still effective at all these T (RH) and is not limited to specific T (RH) ranges. The progression of the season towards monsoon, post-monsoon, and thereafter winter with a continuous reduction in temperature will prove a major challenge for health workers and policymakers attempting to enforce mitigation and control measures.

The intensity of Indian summer monsoon rainfall (ISMR) over Central India (CI) is known to be positively correlated with the dust aerosol loading over the Arabian Sea (AS) on short time scales of about a week. However, global oscillations such as the El-Nino Southern Oscillation (ENSO) modulate both the rainfall over India and aerosol loading over the AS. This study uses long-term satellite-based aerosol and gridded rainfall datasets to explore the correlation between AS aerosol and CI rainfall and their relationship to ENSO. It is found that the highest correlation is during El-Nino (0.53), followed by Normal (0.44) and La-Nina (0.34) years, closely following the overall dust aerosol loading over the AS. Spatially, irrespective of the phase of ENSO, the high aerosol loading conditions are associated with increased winds over the AS, shifting eastward towards the Indian mainland and enhancing rainfall over CI and elsewhere across the Indian landmass. In contrast, the low aerosol loading conditions over the AS are associated with reduced winds, shifting westward away from the Indian mainland, suppressing rainfall over CI. In response to anthropogenic climate change, the El-Nino-like conditions are likely to increase in the future, making the dust aerosol-induced monsoon rainfall enhancement/modulation significant.

Climate change and sustainability are among the most widely used terms among policymakers and the scientific community in recent times. However, climate action or steps to sustainable growth in cities in the global south are mostly borrowed from general studies at a few large urban agglomerations in the developed world. There are very few modeling studies over south Asia to understand and quantify the impact of climate change and urbanization on even the most primary meteorological variable, such as temperature. Such quantifications are difficult to estimate due to the non-availability of relevant long-term observational datasets. In this modeling study, an attempt is made to understand the urban heat island (UHI), its transition, and the segregation of regional climate change effects and urbanization over the rapidly growing tier 2 tropical smart city Bhubaneswar in India. The model is able to simulate the UHI for both land surface temperature, called the SUHI, and 2-m air temperature, called UHI, reasonably well. Their magnitudes were ~ 5 and 2.5°C, respectively. It is estimated that nearly 60–70% of the overall air and 70–80% of the land surface temperature increase during nighttime over the city between the period 2004 and 2015 is due to urbanization, with the remaining due to the regional/non-local effects.