No abstract

The coronavirus disease 2019 (COVID-19), which became a global pandemic by March 2020, forced almost all countries over the world to impose the lockdown as a measure of social distancing to control the spread of infection. India also strictly implemented a countrywide lockdown, starting from 24 March to 12 May 2020. This measure resulted in the reduction of the sources of air pollution in general: industrial, commercial, and vehicular pollution in particular, with visible improvement in ambient air quality. In this study, the impact of COVID-19 lockdown on the ambient concentration of air pollutants over the city of Bangalore (India) is assessed using Continuous Ambient Air Quality Measurement (CAAQM) data from 10 monitoring stations spread across the city. The data was obtained from Central Pollution Control Board (CPCB) and Karnataka State Pollution Control Board (KSPCB). The analysis of the relative changes in the ambient concentration of six major air pollutants (NO, NO 2 , NO X , PM 2.5 , O 3 , and SO 2 ) has been carried out for two periods: March–May 2020 (COVID-19 lockdown) and the corresponding period of 2019 during when there was no lockdown. The analysis revealed significant reduction in the concentration of ambient air pollutants at both daily and monthly intervals. This can be attributed to the reduction in sources of emission; vehicular traffic, industrial, and other activities. The average reduction in the concentration of NO, NO 2 , NO X , PM 2.5 , and O 3 between 01 March and 12 May 2020 was found to be 63%, 48%, 48%, 18%, and 23% respectively when compared to the same period in 2019. Similarly, the comparative analysis of pollutant concentrations between pre-lockdown (01–23 March 2020) and lockdown (24 March–12 May 2020) periods has shown a huge reduction in the ambient concentration of air pollutants, 47.3% (NO), 49% (NO 2 ), 49% (NO X ), 10% (SO 2 ), 37.7% (PM 2.5 ), and 15.6% (O 3 ), resulting in improved air quality over Bangalore during the COVID-19 lockdown period. It is shown that the strict lockdown resulted in a significant reduction in the pollution levels. Such lockdowns may be useful as emergency intervention strategies to control air pollution in megacities when ambient air quality deteriorates dangerously.

No abstract

Southern peninsular India experiences widespread thunderstorm activities during the pre-monsoon season due to the presence of a north-south trough, an easterly trough, and low pressure areas over the surrounding Indian Seas. In March 2008, southern peninsular India received unusual heavy rains because of the interaction between a large amplitude easterly trough and a mid-latitude westerly trough during 12 to 14 March, and because of the formation of a low pressure system over the Arabian Sea during 21 to 24 March. Simulation of these two heavy rainfall events is attempted using the Weather Research and Forecasting (WRF) model. The model's performance is studied in terms of the wind, pressure, movement of these synoptic scale systems, moisture and rainfall fields. Results indicate that this model has the capability to predict the movement of both easterly and westerly troughs and to simulate low pressure very well. The model is able to capture the areas of precipitation maxima in both the cases. The bias score is found to be more than 1 in precipitation thresholds up to 35 mm and less than 1 in precipitation thresholds above 35 mm. The maximum values of critical success index (CSI) and equitable threat score (

Karnataka, a State in south peninsular India, receives 73% of its annual rainfall during the southwest monsoon season. Because of the complex physiographical features, the rainfall pattern over the State shows large spatial variation from 50 to 350 cm. The co-efficient of interannual variation of the monsoon rainfall is about 15% over coastal Karnataka (CK) and between 20 and 30% over interior Karnataka. It is, therefore, a difficult task to predict the location specific daily rainfall over Karnataka. In this study, an attempt has been made to develop an objective tool for forecasting the occurrence and non-occurrence of precipitation during a 24 h period for the 19 stations in Karnataka during the monsoon season. The probability of precipitation (POP) model is developed using forward stepwise regression with the available surface and upper air parameters from synoptic and radiosonde and radio wind stations in and around Karnataka as potential predictors. The POP model has been developed based on the data from 1981 to 1996 and verified with the data from 1997 to 2002. Different skill scores are computed using a yes/no contingency table. The POP model performs very well, with percentages of correct (PC) forecasts for occurrence/non-occurrence of precipitation being 57-91% for the independent data. Comparing the results of the POP model with that of the conventional method of forecast for Bangalore City, the PC forecasts improves from 44 to 56% with the use of the POP model.