Dynamic Air Quality Forecasting via Emission Inventory Adaptation
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Solution Overview
Problem
Current air pollution forecast methods face challenges in accurately predicting air pollutant density due to fluctuating weather and pollutant emission factors, which are not effectively addressed by static emission inventory and limited sensor deployment, leading to inaccurate forecasts.
Innovation Solution
A multilayer-nested closed-loop control process is implemented to monitor deviations in air pollution emission by classifying differences in weather and terrain, and dynamically adapting the pollution emission inventory using real-time monitoring data and air quality forecast data, allowing for continuous refinement of emission estimates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If static pollutant emission inventory is used for air quality forecast, then the forecast model can be simplified and easier to operate, but the forecast accuracy deteriorates due to inability to capture dynamic emission changes
Solution Approach 1:
The patent transforms the static emission inventory into a dynamic system by continuously updating emission estimates using real-time monitoring data from sensors. The system adapts emission factors based on observed pollutant concentrations, weather conditions, and traffic patterns, allowing the forecast model to capture temporal variations in emissions while maintaining operational simplicity through automated adjustments.
Solution Approach 2:
The patent implements a feedback mechanism where real-time air quality monitoring data is fed back into the emission inventory system. The difference between monitored and predicted concentrations is used to iteratively adjust emission factors, creating a closed-loop system that continuously improves forecast accuracy without requiring complex manual interventions.
2Measurement precision
If more sensors are deployed to capture real-time emission data, then the measurement precision improves, but the device complexity and cost increase
Solution Approach 1:
The patent makes existing multi-purpose sensors (designed for general air quality monitoring) perform the additional function of inferring emission rates through data processing algorithms. By analyzing pollutant concentration gradients, weather conditions, and traffic data, the system extracts emission information from sensors originally designed only for concentration measurement, eliminating the need for dedicated emission sensors.
Solution Approach 2:
The patent introduces computational algorithms as an intermediary that transforms data from existing sensors into emission rate estimates. Rather than directly measuring emissions with specialized sensors, the system uses mathematical models and data fusion techniques to infer emission rates from readily available monitoring data, reducing hardware complexity while improving measurement precision.
3Productivity
If static emission inventory is used, then the data processing workload is reduced, but the adaptability to dynamic emission changes deteriorates
Solution Approach 1:
The patent implements periodic updates of the emission inventory at predetermined time intervals (e.g., hourly or daily) rather than requiring continuous real-time processing. This approach maintains adaptability to emission changes by regularly incorporating new monitoring data while avoiding the computational burden of continuous processing, thus balancing productivity and adaptability.
Data Source
AI summary
Disclosed is a novel system, computer program product, and method to compute an air quality forecast. An air quality forecast model, air quality real-time monitoring data, and air quality forecast data is accessed. A deviation in air pollution emission is monitored by classifying a difference between the air quality monitoring data and the air quality forecast data. This monitoring includes classifying any weather differences which are caused by weather, classifying any terrain differences which are caused by a geographic terrain; and, filtering the difference caused by inaccurate pollution emission inventory. The monitoring may repeat in response to a given time period elapsing or a chance in air quality forecast data received.