Urban fine-scale day-by-day atmospheric pollutant concentration estimation method

Through the combination of multi-scale space-time matching and global estimation models, the problem that the distribution of atmospheric pollutants within cities is difficult to reflect in detail, and high-precision daily-period atmospheric pollutant concentration estimation is achieved, and precise prevention and control of urban air pollution is supported.

CN119740205AActive Publication Date: 2025-04-01NINGBO UNIV
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Patent Information

Application Number
CN202411908590.8
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2024-12-24
Publication Date
2025-04-01
Estimated Expiration
2044-12-24

AI Technical Summary

Technical Problem

The existing technology is difficult to reflect the distribution of atmospheric pollutants on a fine scale within the city, making it difficult to achieve precise prevention and control of urban air pollution.

Method used

By acquiring multi-source data in urban areas, performing multi-scale spatiotemporal matching processing, a global estimation model of atmospheric pollutants is constructed, and a daily atmospheric pollutant concentration is calculated using global trend weights and local residuals for correction.

Benefits of technology

It has realized a high-precision 100-meter-level daily air pollutant data product within the city, which can more accurately locate polluted areas, thereby guiding the precise prevention and control measures for urban air pollution more scientifically.

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Abstract

The invention relates to an urban fine-scale day-by-day atmospheric pollutant concentration estimation method, which comprises the following steps: acquiring urban region multi-source data, and performing multi-scale space-time matching processing; constructing a global estimation model of the atmospheric pollutants and training the global estimation model; obtaining the annual global atmospheric pollutant concentration under the fine scale of the urban area, and converting the annual global atmospheric pollutant concentration into a global atmospheric pollutant concentration weight; calculating a day-by-day local atmospheric pollutant concentration estimation residual error; and estimating to obtain the urban fine-scale day-by-day atmospheric pollutant concentration. The method has the beneficial effects that global features and local changes are comprehensively considered, the spatial resolution of an existing data product in a city is effectively improved, a high-precision hectometer-level day-by-day atmospheric pollutant data product is obtained, a polluted area can be positioned more accurately, and the method has a wide application prospect. Therefore, formulation and implementation of accurate prevention and control measures of urban atmospheric pollution are more scientifically guided, and the method has important application value.
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