Urban air quality concentration monitoring missing data recovering method

Abstract

The invention discloses an urban air quality concentration monitoring missing data recovering method. The method comprises the steps of acquiring general environmental factors affecting the air quality concentration of each monitoring station within a city, and predicting the air quality concentration at a future moment of each monitoring station (including normal stations and fault stations) according to general environmental factor historical data and historical air quality concentration monitoring data; comparing monitoring data at the future moment of the normal working monitoring stations with the predicted concentration based on the historical data to obtain the difference between the monitoring concentration at the future moment and the predicted concentration, and estimating the difference between air quality monitoring concentration at the future moment of the fault monitoring stations and the prediction concentration based on the historical data by means of the spatial correlation theory; amending air quality concentration results, at the future moment, predicted based on the historical data, of the fault stations by means of the fault monitoring station difference obtained from the last step to generate recovered air quality concentration monitoring data at the future moment of the fault monitoring stations.

Description

technical field [0001] The present invention relates to the field of environmental monitoring and risk assessment, and in particular to an air quality concentration that uses air quality concentration monitoring data adjacent to normal monitoring stations to repair missing data of air quality concentration monitoring caused by daily maintenance, emergencies, etc. Monitor missing data repair methods. Background technique [0002] In the routine air quality concentration monitoring process, it is often necessary to carry out daily maintenance such as zero calibration and calibration of the monitoring instruments at the location of the monitoring site. The lack of air quality concentration monitoring data in a certain period of time will affect the public’s timely acquisition of air quality concentration in relevant areas and reduce the accuracy of air quality concentration monitoring data released to the public. Therefore, there is a need for an effective repair method for th...

AI Technical Summary

Problems solved by technology

Therefore, when a monitoring station undergoes routine maintenance or emergencies, the air quality concentration monitoring data will be missing, and the historical air quality concentration monitoring data of the faulty monitoring station can be used to monitor the missing air quality concentration monitoring data of the monitoring station in the future. predict

[0004] However, there are still differences between the regional environmental factors at the future moment and the regional environmental factors at the historical moment. Only using the historical air quality concentration monitoring data of the fault monitoring station to predict the missing air quality concentration monitoring data at the future time will lead to the predicted results and the real monitoring results. There is a certain deviation; however, the historical and future environmental factors are spatially correlated, so that the deviation between the historical air quality concentration prediction value of the fault monitoring site and the actual air quality monitoring concentration can be measured by the same deviation at the adjacent normal working monitoring site. estimate it

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