Retrieval and Evaluation of NO X Emissions Based on a Machine Learning Model in Shandong

Tongqiang Liu, Jinghao Zhao, Rumei Li and Yajun Tian ()
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Tongqiang Liu: Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China
Jinghao Zhao: Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China
Rumei Li: Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China
Yajun Tian: Key Laboratory of Photoelectric Conversion and Utilization of Solar Energy, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China

Sustainability, 2025, vol. 17, issue 13, 1-19

Abstract: Nitrogen oxides (NO X ) are important precursors of ozone and secondary aerosols. Accurate and timely NO X emission estimates are essential for formulating measures to mitigate haze and ozone pollution. Bottom–up and satellite–constrained top–down methods are commonly used for emission inventory compilation; however, they have limitations of time lag and high computational demands. Here, we propose a machine learning model, WOA-XGBoost (Whale Optimization Algorithm–Extreme Gradient Boosting), to retrieve NO X emissions. We constructed a dataset incorporating satellite observations and conducted model training and validation in the Shandong region with severe NO X pollution to retrieve high spatiotemporal resolution of NO X emission rates. The 10–fold cross–validation coefficient of determination ( R 2 ) for the NO X emission retrieval model was 0.99, indicating that WOA-XGBoost has high accuracy. Validation of the model for the other year (2019) showed high agreement with MEIC (Multi–resolution Emission Inventory for China), confirming its strong robustness and good temporal transferability. The retrieved NO X emissions for 2021–2022 revealed that emission rate hotspots were located in areas with heavy traffic flow. Among 16 prefecture–level cities in Shandong, Zibo exhibited the highest NO X rate (>1 μg/m 2 /s), explaining its high NO 2 pollution levels. In the future, priority areas for emission reduction should focus on heavy industry clusters such as Zibo and high traffic urban centers.

Keywords: NO X; TROPOMI; WOA-XGBoost; emission rate (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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