English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

. Fine particulate matter with a diameter of less than 2.5 µm (PM2.5) has been used as an important atmosphericenvironmental parameter mainly because of its impact on human health. PM2.5 is affected by both natural and anthropogenic factors thatusually have strong diurnal variations. Such information helps toward understanding the causes of air pollution, as well as our adaptation toit. Most existing PM2.5 products have been derived from polar-orbiting satellites. This study exploits the use of the next-generationgeostationary meteorological satellite Himawari-8/AHI (Advanced Himawari Imager) to document the diurnal variation in PM2.5. Given the huge volume of satellite data,based on the idea of gradient boosting, a highly efficient tree-based Light Gradient Boosting Machine (LightGBM) method by involving thespatiotemporal characteristics of air pollution, namely the space-time LightGBM (STLG) model, is developed. An hourly PM2.5 dataset forChina (i.e., ChinaHighPM2.5) at a 5 km spatial resolution is derived based on Himawari-8/AHI aerosol products with additionalenvironmental variables. Hourly PM2.5 estimates (number of data samples = 1 415 188) are well correlated with ground measurements inChina (cross-validation coefficient of determination, CV-R2 = 0.85), with a root-mean-square error (RMSE) and mean absolute error (MAE) of13.62 and 8.49 µg m−3, respectively. Our model captures well the PM2.5 diurnal variations showing that pollution increasesgradually in the morning, reaching a peak at about 10:00 LT (GMT+8), then decreases steadily until sunset. The proposed approach outperforms mosttraditional statistical regression and tree-based machine-learning models with a much lower computational burden in terms of speed and memory, makingit most suitable for routine pollution monitoring.

atmospheric chemistry and physics

Himawari-8-derived diurnal variations in ground-level PM&amp;lt;sub&amp;gt;2.5&amp;lt;/sub&amp;gt; pollution across China using the fast space-time Light Gradient Boosting Machine (LightGBM)