In recent years, black-box machine learning approaches have become a dominant modeling
paradigm for knowledge extraction in remote sensing. Despite the potential benefits of …

With the increase in climate-change-related hazardous events alongside population
concentration in urban centres, it is important to provide resilient cities with tools for …

Remote sensing based on satellites can provide long-term, consistent, and global coverage
of NO 2 (an important atmospheric air pollutant) as well as other trace gases. However …

The major link between satellite-derived vertical column densities (VCDs) of nitrogen
dioxide (NO 2) and ground-level concentrations is theoretically the NO 2 mixing height …

Drought and heatwave are the primary climate extremes for vegetation productivity loss in
the global temperate semi-arid grassland, challenging the ecosystem productivity stability in …

Accurate estimation of nitrogen dioxide (NO2) levels at high spatio‐temporal resolution is
crucial for atmospheric research and public health assessments. This study introduces a …

Nitrogen dioxide (NO2) is a critical air pollutant that has significant health and environmental
impacts. Tropospheric NO2 refers specifically to the vertical column density of NO2, which is …

Nitrogen dioxide (NO 2) is a major air pollutant, and its concentration data are crucial for the
study of air pollution and its impact on the environment. Although satellite data provide an …

This paper forecasts the hourly AQI in Azamgarh, Uttar Pradesh, India, using deep learning
(DL) models. In order to measure hourly particulate matter (PM2. 5, PM10), gaseous …

Traditional NO< sub> 2 monitoring technique faces challenges such as delay in response
time. It is crucial to predict the atmospheric NO< sub> 2 levels for informing environmental …