Abstract
As ground-level O3 has become one of the major pollutants affecting air quality in recent years, monitoring and revealing the spatial distribution pattern of O3 are of great significance to study air-pollution characteristics. Based on the multilayer backpropagation neural network, one O3 estimation model is proposed to obtain the continuous spatial distribution of O3 concentrations, where the Landsat 8 images, meteorological parameters, and air quality data have been integrated together as the input for the model training and validation. In order to enhance the estimation accuracy, the proposed model has been optimized with respect to the influencing factors and spatial extent. In the test areas of Beijing, Tianjin, and Shijiazhuang of China, the proposed O3 estimation model has demonstrated quite satisfactory performance—with the average coefficient of determination ( R 2) larger than 0.90 and root-mean-square error smaller than 19.0 μ g/m3. It is worth mentioning that all the data employed in this research are freely available and can be applied nationwide in the mainland of China. Taking advantage of the generic nature and the positive O3 estimation results with high accuracy and spatial resolution, the proposed model can be expected to be a new way for studying air-pollution characteristics and, thus, support the decision making for environmental governance.
Highlights
WITH the rapid development of economy and the acceleration of urbanization and industrialization, the emissions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) in China have been dramatically increased in past years [1], [2]
Beijing, Tianjin, and Shijiazhuang in China have been selected as the three test areas in this research, where the Landsat 8 images together with the meteorological and air quality data in the period from May 1, 2014 to October 1, 2019 have been employed for the training and validation of the proposed O3 estimation model
In order to enhance the O3 estimation accuracy, the training set has been further employed to explore (a) the optimal combination of the input parameters and (b) the optimal spatial extent, while the validation set will be employed to evaluate the performance of the proposed O3 estimation model
Summary
WITH the rapid development of economy and the acceleration of urbanization and industrialization, the emissions of nitrogen oxides (NOx) and volatile organic compounds (VOCs) in China have been dramatically increased in past years [1], [2]. NOx and VOCs can generate ozone (O3) through complex photochemical reactions. As the main secondary pollutants in the atmosphere, long-term exposure to high concentrations of O3 can adversely affect the respiratory system and cardiovascular system of humans, which may pose a great threat to human health [3], [4]. A series of measures have been already taken to prevent and control O3 pollution precursors in China, the situation is still severe [6]. Monitoring and revealing the spatial and temporal distribution of O3 is of great significance
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