Air Quality Emission Control Effect Evaluation Method
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods struggle to accurately quantify the impact of emission control factors on air quality changes, as they often mix meteorological and emission factors, leading to model errors and uncertainties in evaluating air quality changes.
Innovation Solution
A method involving meteorological condition frequency statistics, pollutant concentration distribution analysis, and decomposition of meteorological and non-meteorological factors using Taylor expansion and Monte Carlo methods to separate their contributions, constructing a source emission control effect evaluation data set.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If emission-based numerical models are used to evaluate air quality changes, then the evaluation can be performed, but model errors and uncertainties of model input increase the accuracy problem
Solution Approach 1:
The patent segments the air quality change evaluation into two distinct components: meteorological factor effects and emission factor effects. By using a Taylor expansion approach, the total air quality change is decomposed into partial derivatives with respect to meteorological parameters and emission parameters, allowing separate quantification of each factor's contribution and reducing the mixing of effects that causes model errors
Solution Approach 2:
The patent introduces an intermediary statistical framework that uses observed air quality data and meteorological data to calculate the effects of emission factors without relying on complex numerical models. This intermediary approach uses regression analysis and statistical decomposition to isolate emission effects from meteorological effects, thereby reducing model input uncertainties
2Measurement precision
If observation-based regression statistical methods are used, then model input uncertainties are reduced, but it is hard to reconstruct a factor reasonably representing emission variation
Solution Approach 1:
The patent changes the parameters used to represent emission variations from complex reconstructed emission factors to simpler, more reliable proxies such as production activity data, fuel consumption data, and meteorological correction factors. By using these alternative parameters that are easier to obtain and more reliably represent emission variations, the patent reduces the complexity of emission factor reconstruction while maintaining measurement precision
3Loss of information
If numerical models are used to separate meteorological and emission factor effects, then evaluation can be performed, but model errors increase the complexity of the system
Solution Approach 1:
The patent replaces the mechanical numerical modeling system with a statistical analysis system based on observed data. Instead of using complex atmospheric transport models to separate meteorological and emission effects, the patent uses statistical decomposition methods including Taylor expansion and regression analysis applied to observed air quality and meteorological data, thereby reducing system complexity while minimizing information loss
Data Source
AI summary
An atmospheric pollution emission control effect evaluation method, device and storage medium, including: carrying out meteorological condition frequency statistics of data collected from meteorological station, obtaining meteorological condition frequency distribution information; obtaining pollutant concentration information, performing pollutant concentration distribution statistics to obtain pollution concentration distribution information and pollution concentration variation information; decomposing effects of meteorological factors and non-meteorological factors according to meteorological condition frequency distribution information, pollution concentration distribution information and pollution concentration variation information, to obtain meteorological and non-meteorological contribution information; constructing source emission control effect evaluation data set according to meteorological condition frequency distribution information, pollution concentration distribution information, meteorological and non-meteorological contribution information. Accordingly, emission control effect can be quantitatively evaluated based on observation data. Emission increasing effect and contribution of meteorological changes to variation of average pollution level can be quantified.