Satellite-based estimation of full-coverage ozone (O3) concentration and health effect assessment across Hainan Island-Reference-Cited by-同舟云学术

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Satellite-based estimation of full-coverage ozone (O3) concentration and health effect assessment across Hainan Island

Published:2020-01 Issue: Volume:244 Page:118773
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Li Rui,Cui Lulu,Hongbo Fu,Li Junlin,Zhao Yilong,Chen Jianmin

Funder

National Natural Science Foundation of China

National Key R&D Program of China

Shanghai Tongji Gao Tingyao Environmental Science & Technology Development Foundation

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment

Reference53 articles.

1. Growing season extension affects ozone uptake by European forests;Anav;Sci. Total Environ.,2019

2. Identifying establishment year and pre-conversion land cover of rubber plantations on Hainan Island, China using landsat data during 1987–2015;Chen;Remote Sens.,2018

3. Estimating spatiotemporal distribution of PM1 concentrations in China with satellite remote sensing, meteorology, and land use information;Chen;Environ. Pollut.,2018

4. XGBoost: a scalable tree boosting system;Chen,2016

5. Estimating PM2.5 concentrations based on non-linear exposure-lag-response associations with aerosol optical depth and meteorological measures;Chen;Atmos. Environ.,2018

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2. A Deep Forest Algorithm Based on TropOMI Satellite Data to Estimate Near-Ground Ozone Concentration;Atmosphere;2024-08-23

3. Improvement of the ozone forecast over Beijing through combining the chemical transport model with multiple machine learning methods;Atmospheric Pollution Research;2024-08

4. A machine learning model for estimating daily maximum 8-hour average ozone concentrations using OMI and MODIS products;Atmospheric Environment;2024-08

5. Ozone Pollution in China Affected by Climate Change in a Carbon Neutral Future as Predicted by a Process‐Based Interpretable Machine Learning Method;Geophysical Research Letters;2024-07-06

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