Quantitative atmospheric remote sensing in action: operational air quality mapping across China

Practical application remains a critical challenge in advancing quantitative remote sensing technologies. Focusing on atmospheric environmental remote sensing, this study explores practical methodologies for quantitative atmospheric remote sensing using the ChinaHighAirPollutants (CHAP) dataset generated through remote sensing and big data technologies. The mass concentration data for six air pollutants (including inhalable particles, fine particulate matter, ozone, nitrogen dioxide, sulfur dioxide, and carbon monoxide) from 2019 to 2023 were collected and derived publicaccessible air quality metrics, including the air quality index (AQI), blue-sky days, and primary pollutants, to enhance public comprehension. Thematic maps and statistical charts were designed and produced to visualize these metrics. The findings indicate that the calculated indicators and visualizations effectively reveal air quality trends at both national and local scales, offering actionable insights for pollution mitigation and public education, thereby enhancing the practical application level of remote sensing.