Evolution of Pollution Levels from COVID-19 Lockdown to Post-Lockdown over India

The spread of the COVID-19 pandemic forced the administration to lock down in many countries globally to stop the spread. As the lockdown phase had only the emergency use of transportation and most of the industries were shut down, there was an apparent reduction in pollution. With the end of the lockdown period, pollution is returning to its regular emission in most places. Though the background was abnormally low in emissions (during the lockdown phase) and the reduced pollution changed the radiation balance in the northern hemispheric summer period, a modified pollution pattern is possible during the unlock phases of 2020. The present study analysed the unlock 1 and 2 stages (June–July) of the COVID-19 lockdown over India. The rainfall, surface temperature and cloud cover anomalies of 2020 for understanding the differences in pollutants variation were also analysed. The unlock phases show remarkable differences in trends and mean variations of pollutants over the Indian region compared to climatological variations. The results indicated changing high-emission regions over India to climatological variations and identified an AOD dipole with future emissions over India

Above ground carbon stock mapping over Coimbatore and Nilgiris Biosphere: a key source to the C sink

Mapping and quantifying above ground carbon (AGC) Stocks reflect significant dynamics in the terrestrial carbon cycle and cascade climate change. Estimation of such key driver was performed for the dominant species (Bamboo, Eucalyptus and Teak) over Coimbatore and Nilgiris Biosphere (2006 − 2018 quadruple interval) of Tamilnadu, India with the developed global stepwise multiple linear regression (SMLR) and local geographically weighted regression (GWR) models using multi-dynamic variables. Evapotranspiration (ET) developed using Mapping Evapotranspiration at High Resolution with Internalized Calibration (METRIC) model for the region was analysed with the best-fitted AGC estimation model to understand AGC—ET synergistic pertinence dynamics. The study compared and validated the estimation by the models and indicated that AGC estimation using SMLR exhibiting a high degree of accuracy ( ) with nominal negative bias in the estimation ranging from with amplification of GWR prediction indicted positive bias with comparatively least mean accuracy ( ). The ET—AGC reciprocity for the dominant species resulted that, bamboo with lower AGC correlated with higher ET tailed by teak with higher AGC and ET and eucalyptus with relatively higher AGC and lower ET and respectively. The analysis resulted in minimal biasness in AGC mapping using SMLR, and both the model signifies that the region can potentially be considered a long-term carbon sink

Thermodynamical structure of atmosphere during pre-monsoon thunderstorm season over Kharagpur as revealed by STORM data

Variation of atmospheric thermodynamical structure parameters between days of thunderstorm occurrence and non-occurrence is presented based on data sets obtained during Severe Thunderstorm-Observations and Regional Modeling (STORM) experiments conducted over Kharagpur (22.3°N, 87.2°E) in pre-monsoon season of 2009 and 2010. Potential instability (stable to neutral) is noticed in the lower layers and enhanced (suppressed) convection in the middle troposphere during thunderstorm (non-thunderstorm) days. Low-level jets are observed during all days of the experimental period but with higher intensity on thunderstorm days. Convective available potential energy (CAPE) builds up until thunderstorm occurrence and becomes dissipated soon after, whereas convective inhibition (CIN) is greatly decreased prior to the event on thunderstorm days. In contrast, higher CAPE and CIN are noticed on non-thunderstorm days. Analysis of thermodynamic indices showed that indices including moisture [humidity index (HI) and dew point temperature at 850 hPa (DPT850)] are useful in differentiating thunderstorm from non-thunderstorm days. The present study reveals that significant moisture availability in the lower troposphere in the presence of convective instability conditions results in thunderstorm occurrence at Kharagpur

Reviewing the Crop Residual Burning and Aerosol Variations during the COVID-19 Pandemic Hit Year 2020 over North India

The north Indian states of Haryana and Punjab are believed to be the key sources of air pollution in the National Capital Region due to massive agricultural waste burning in crop harvesting seasons. However, with the pandemic COVID-19 hitting the country, the usual practices were disrupted. COVID-19 preventive lockdown led to restricted vehicular and industrial emissions and caused the labours to leave the agricultural business in Haryana and Punjab. With the changed scenario of 2020, the present study investigates the variations in air quality over the Haryana and Punjab, and their relative impact on the air quality of Delhi. The work attempts to understand the change in agricultural waste burning during 2020 and its implication on the local air quality over both the states and the transported pollution on the national capital Delhi. The study utilises in-situ data for the year 2019–2020 with satellite observations of MODIS aqua/terra for fire counts, aerosol optical depth (AOD) and back-trajectories run by the hybrid single-particle Lagrangian integrated trajectory model (HYSPLIT)

The Impact of El-Niño and La-Niña on the Pre-Monsoon Convective Systems over Eastern India

El-Niño and La-Niña are believed to change the intensity and frequencies of extreme weather events globally. The present study aims to analyse the impact of El-Niño and La-Niña on the lightning activities of cloud systems and their associated precipitation and thermodynamic indices over the Eastern India regions (Odisha, Jharkhand, and West Bengal) during the pre-monsoon season (March–May). Eastern India receives catastrophic thunderstorm events during the pre-monsoon season. The results suggest that the number of lightning flashes was higher in the El-Niño years than in the La-Niña periods, which helps convective activities to be developed over the study region. The precipitation variations showed similar patterns during El-Niño and La-Niña periods, but the magnitudes were higher in the latter. Results from the analysis of thermodynamic indices show that, during the La-Niña phase, the convective available potential energy (CAPE), convective inhibition (CIN), severe weather threat index (SWEAT), humidity index (HI), and total totals index (TTI) values increased, while the cross total index (CTI) and K index (KI) decreased. In contrast, the vertical total index (VTI) and Boyden index (BI) values showed less significant changes in both El-Niño and La-Niña periods. The anomalies of flash rate densities over most parts of our domain were positive during the El-Niño years and negative during the La-Niña years. Precipitation anomalies had a higher positive magnitude during the La-Niña phase, but had spatial variability similar to the El-Niño phase. The anomalies of most of the thermodynamic indices also showed noticeable differences between El-Niño and La-Niña periods, except for the HI index. El-Niño periods showed higher lightning and increased values of associated thermodynamic indices over eastern India, indicating more pronounced convective systems

Analysis of Large-Scale Environmental Features during Maximum Intensity of Tropical Cyclones Using Reanalysis Data

The present study investigates the variation in large-scale environments during the maximum intensity of tropical cyclones (TCs) formed in the Bay of Bengal. TC tracks are classified into four groups based on their direction of movement using the k-means clustering technique. Results from the pressure level and azimuthal-averaged radial-height wind fields near the vortex centre show weak deep layer wind shear (WS) and abundant moisture in all clusters. However, large-scale environmental differences in the northwest quadrant are identified with a contrasting combination of WS and humid environment between clusters. The composites of OLR are also analyzed during maximum intensities of TCs. Results show that anomalous high OLR in the west–northwest direction from the vortex centre, along with the low OLR around the vortex centre, signify the formation of a strong OLR dipole during TC peak intensity. Furthermore, OLR dipole metrics, such as magnitude, orientation, and distance, are observed by having mean of 235 Wm−2, 147, and 1782 km along with standard deviation of 14 Wm−2, 34°, and 492 km, respectively. The identified large-scale environmental fields from this study could provide valuable insights for predicting the intensity and movement of TCs

Identification and Quantification of Emission Hotspots of Air Pollutants over Bhubaneswar: A Smart City in Eastern India

Considering the need to identify the sources of air pollution in smart city Bhubaneswar, a non-attainment city in India, a high-resolution comprehensive gridded emission inventory (EI) of eight primary air pollutants has been developed for the base year 2018. The inventory involves detailed activity data with ~0.4 km × ~0.4 km resolution covering the city using a Geographical Information System (GIS) based statistical approach. In total, ~17 major and minor sectors are responsible for the city’s air pollution crisis. Windblown road dust, transport sector, and residential cooking activities emerged as the dominating sources. Emissions of CO, NOx, SO2, VOC, PM2.5, PM10, BC, and OC are estimated to be 112 Gg yr–1, 44 Gg yr–1, 11 Gg yr–1, 73 Gg yr–1, 9 Gg yr–1, 17 Gg yr–1, 5 Gg yr–1 and 0.8 Gg yr–1 respectively. Nearly 14% and 12% area of the entire study domain are found to be responsible for almost half of PM10 and NOx emissions respectively. The central region of the city with the presence of national highways, major roads, and nearby industrial belts, experiences maximum emission of pollutants. The present gridded surface emission dataset is an essential requirement in framing new mitigation strategies to combat ongoing and future air pollution crises and achieve better air quality

Performance analysis of planetary boundary layer parameterization schemes in WRF modeling set up over Southern Italy

Predictions of boundary layer meteorological parameters with accuracy are essential for achieving good weather and air quality regional forecast. In the present work, we have analyzed seven planetary boundary layer (PBL) parameterization schemes in aWeather Research and Forecasting (WRF) model over the Naples-Caserta region of Southern Italy. WRF model simulations were performed with 1-km horizontal resolution, and the results were compared against data collected by the small aircraft Sky Arrow Environmental Research Aircraft (ERA) during 7-9 October 2014. The selected PBL schemes include three first-order closure PBL schemes (ACM2, MRF, YSU) and four turbulent kinetic energy (TKE) closure schemes (MYJ, UW, MYNN2, and BouLac). A performance analysis of these PBL schemes has been investigated by validating them with aircraft measurements of meteorological parameters profiles (air temperature, specific humidity, wind speed, wind direction) and PBL height to assess their efficiency in terms of the reproduction of observed weather conditions. Results suggested that the TKE closure schemes perform better than first-order closure schemes, and theMYNN2 closure scheme is close to observed values most of the time. It is observed that the inland locations are better simulated than sea locations, and themorning periods are better simulated than those in the afternoon. The results are emphasizing that meteorology-induced variability is larger than the variability in PBL schemes