Benchmark datasets and benchmark problems have been a key aspect for the success of
modern machine learning applications in many scientific domains. Consequently, an active …

Ozone is one of the most important air pollutants, with significant impacts on human health,
regional air quality and ecosystems. In this study, we use geographic information and …

Weather Normalized Models (WNMs) are modeling methods used for assessing air
contaminants under a business-as-usual (BAU) assumption. Therefore, WNMs are used to …

Gaps in the measurement series of atmospheric pollutants can impede the reliable
assessment of their impacts and trends. We propose a new method for missing data …

Tropospheric ozone is a toxic greenhouse gas with a highly variable spatial distribution
which is challenging to map on a global scale. Here, we present a data-driven ozone …

Abstract Machine learning (ML) has emerged as a powerful technique in the Earth system
science, nevertheless, its potential to model complex atmospheric chemistry remains largely …

Air quality is relevant to society because it poses environmental risks to humans and nature.
We use explainable machine learning in air quality research by analyzing model predictions …

Surface ozone (O 3) is primarily formed through complex photo-chemical reactions in the
atmosphere, which are non-linearly dependent on precursors. Even though, there have …

Abstract Indian Summer Monsoon (ISM) rainfall is largely contributed by synoptic scale low-
pressure systems over the Bay of Bengal and moves towards Indian landmass through …

Ozone pollution is harmful to human health and ecosystem, which occurs in ecosystems and
has occurred frequently in China in recent years, especially during the warm seasons …