Surface Ozone Retrieval Using UV Irradiance and Deep Learning
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Solution Overview
Problem
Current satellite remote sensing methods for surface ozone monitoring suffer from low accuracy due to a lack of understanding of ozone formation mechanisms and complex relationships between surface ozone and its relative factors, such as photochemical, transport, and sink factors, which are not effectively captured by existing machine learning models.
Innovation Solution
The use of the Stacking ensemble machine learning algorithm combined with deep learning models that incorporate atmospheric photochemistry mechanisms, specifically utilizing surface ultraviolet irradiance at 380 nm as a key input feature, to establish a precise relationship between surface ozone and its relative factors, thereby enhancing the accuracy of surface ozone retrieval.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If conventional satellite remote sensing methods based on ozone column are used, then wide coverage monitoring is achieved, but retrieval accuracy is low due to low correlation between ozone column and surface ozone
Solution Approach 1:
The patent changes the retrieval parameter from ozone column (total vertical integration) to surface ultraviolet irradiance (surface-level photochemical indicator). This parameter change directly addresses the low correlation issue by using a parameter that actually drives surface ozone formation through photochemical reactions, thereby improving retrieval accuracy while maintaining satellite remote sensing coverage
Solution Approach 2:
The patent introduces surface ultraviolet irradiance as an intermediary parameter that mediates the relationship between satellite observations and surface ozone concentration. Instead of directly retrieving surface ozone from ozone column, the method uses UV irradiance as a proxy that captures the photochemical formation process, serving as a bridge between remote sensing data and surface ozone levels
2Device complexity
If simple empirical equations are used to model the relationship between surface ozone and relative factors, then model complexity is reduced, but the complex non-linear relationships cannot be captured
Solution Approach 1:
The patent replaces simple empirical mathematical equations with a machine learning model (Random Forest algorithm). This substitution allows the system to automatically capture complex non-linear relationships between surface ozone and its influencing factors (UV irradiance, meteorological conditions, transport, and sink factors) without requiring explicit formulation of complex physical equations, thus improving relationship characterization while keeping the model computationally efficient
3Loss of information
If conventional machine learning models are used to capture surface ozone characteristics, then data complexity is resolved, but the complex non-linear statistical relationships cannot be effectively captured
Solution Approach 1:
The patent employs a composite approach by combining multiple input features (surface UV irradiance, meteorological parameters, transport factors, and sink factors) into an ensemble machine learning model. This composite model integrates diverse data types and relationships, allowing it to capture the complex non-linear statistical patterns in surface ozone data more effectively than conventional single-model approaches, thereby improving both information utilization and relationship capture
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables highly accurate and spatially continuous retrieval of surface ozone concentrations, breaking through the limitations of conventional methods by effectively integrating photochemical processes and utilizing deep learning models to capture the intrinsic characteristics of surface ozone, resulting in improved monitoring capabilities and wide coverage.
Implementation Method 1
the effect of ozone photochemical formation is fundamental to the surface ozone level
Implementation Method 2
the rate-determining step of the photochemical ozone formation is the equation (1), in which the surface ultraviolet irradiance provides the activation energy of the reaction
Data Source
AI summary
A retrieval method for surface ozone based on surface ultraviolet radiation irradiance includes: establishing a deep learning model; establishing a statistical relationship between input variables including surface UV irradiance, column ozone, elevation of a geolocation, year/month/date, latitude and longitude, and surface ozone concentrations at monitoring sites; matching a site-monitored surface ozone concentration with the surface UV irradiance and column ozone; training the deep learning model; estimating surface ozone concentrations in regions with available satellite observations based on the trained deep learning model; and inputting surface UV irradiance, column ozone, elevation of a geolocation, year/month/date, latitude and longitude into the trained deep learning model to estimate surface ozone concentration; evaluating an air quality based on the surface ozone concentration of the geolocation.