%0 Dataset
%T Tracking County-level Cooking Emissions and Their Drivers in China from 1990 to 2021 by Ensemble Machine Learning
%J National Cryosphere Desert Data Center
%I National Cryosphere Desert Data Center(www.ncdc.ac.cn)
%U http://www.ncdc.ac.cn/portal/metadata/d79638b3-72ca-4fba-9614-4e3108e5d33c
%W NCDC
%R 10.6084/m9.figshare.26085487.v2
%A None
%K Cooking emissions;PM2.5;UFP;PAH
%X Cooking emissions are a significant source of PM2.5, posing considerable public health risks due to their high toxicity and proximity to densely populated areas. Despite their importance, there is currently a lack of an accurate, long-term, high-resolution national cooking emission inventory in China, primarily due to the challenges in obtaining high-quality activity level data over extended periods and at fine spatial scales. Here, we address these limitations by leveraging advanced machine learning techniques to predict activity levels and further estimate emissions.