RESUMEN
Severe air pollution and foggy conditions during winter are persistent challenges, pose significant health hazards, and disrupt daily routines worldwide. In this study, we have investigated the conditions favoring the prolonged fog events in Delhi during January 2024 using observations, back trajectories, and reanalysis datasets. Analysis of visibility observations reveals that foggy (54, 121, 139, and 372 half-hours of very dense, dense, moderate, and shallow fog, respectively) conditions persisted in Delhi for 46 % of the time during the study period. The existence of 3-4 days of cold wave to severe cold wave conditions and the lack of passage of strong western disturbances across north and northwest India have also favored the prolonged fog formation. In addition, high relative humidity (>80 %), shallow boundary layer (216 m), stable weather conditions such as the absence of significant surface winds, the existence of cold wave to severe cold wave, temperature inversion (up to 4 °C), poor ventilation, and presence of high particulate matter (PM10: 298 µg/m3 and PM2.5: 182 µg/m3) facilitated the fog formation. Further, analyses reveal a spurt in daily particulate matter (PM10: 603 µg/m3 and PM2.5: 420 µg/m3; 13.4 and 28 times, respectively, exceeded the WHO air quality guideline levels) along with 4.5 h of zero visibility on 14th January. The analysis of particulate matter reveals the dominance of fine particles from nearby regions, which could have originated from the large-scale anthropogenic open biomass burning used for heating activities. The results derived from this study indicate the need for an accurate representation of the local anthropogenic emissions in the atmospheric models to improve the predictability of air quality and fog and provide insights into the need for their control, particularly during such extreme events.
RESUMEN
In this research, we study a new metaheuristic algorithm called Moth-Flame Optimization (MFO) for hyperspectral band selection. With the hundreds of highly correlated narrow spectral bands, the number of training samples required to train a statistical classifier is high. Thus, the problem is to select a subset of bands without compromising the classification accuracy. One of the ways to solve this problem is to model an objective function that measures class separability and utilize it to arrive at a subset of bands. In this research, we studied MFO to select optimal spectral bands for classification. MFO is inspired by the behavior of moths with respect to flames, which is the navigation method of moths in nature called transverse orientation. In MFO, a moth navigates the search space through a process called transverse orientation by keeping a constant angle with the Moon, which is a compelling strategy for traveling long distances in a straight line, considering that the Moon's distance from the moth is considerably long. Our research tested MFO on three benchmark hyperspectral datasets-Indian Pines, University of Pavia, and Salinas. MFO produced an Overall Accuracy (OA) of 88.98%, 94.85%, and 97.17%, respectively, on the three datasets. Our experimental results indicate that MFO produces better OA and Kappa when compared to state-of-the-art band selection algorithms such as particle swarm optimization, grey wolf, cuckoo search, and genetic algorithms. The analysis results prove that the proposed approach effectively addresses the spectral band selection problem and provides a high classification accuracy.