Non-sample point monitoring method aiming at AQI (air quality index) sparse monitoring

Abstract

The invention discloses a non-sample point monitoring method aiming at AQI (air quality index) sparse monitoring. The method comprises the following steps: building an extension field intensity single-parameter model, and introducing a parameter as shown in the specification to control the field intensity attenuation degree; obtaining a relational graph between the parameter and a mean-square error on the basis of sample data; solving the optimal value as shown in the specification by virtue of a binary search algorithm; building an extension field intensity dual-parameter model, adding a parameter as shown in the specification to adjust the field intensity influence range, and obtaining a relational graph between the parameters and the mean-square error on the basis of the sample data; solving the optimal combination of the parameters by virtue of an iterative bilinear interpolation algorithm; and carrying out spatial interpolation calculation of AQI spare monitoring data by virtue of a single-parameter or dual-parameter extension field intensity model aiming at the region corresponding to the sample data. The method disclosed by the invention has the advantages of simple model and high interpolation precision, and is easily popularized and applied to different regions and other types of spatial data except for AQI.

Description

technical field [0001] The invention relates to the field of air pollution monitoring related to the air quality index (AQI), in particular to a technical scheme for acquiring non-sample point monitoring values ​​for AQI sparse monitoring data. Background technique [0002] In recent years, with the deterioration of air quality, my country's air pollution has increased and has become more harmful, such as smog. The main components of smog include sulfur dioxide, nitrogen oxides, and inhalable particles. The formation of smog is closely related to the emission of pollutants such as coal combustion, vehicle exhaust, fuel oil, and waste combustion. In January 2013, an area of ​​about 1.43 million square kilometers across the country fell into smog, of which Beijing was smoggy for as many as 25 days. In February 2014, smog appeared in an area of ​​about 810,000 square kilometers in the central and eastern parts of my country, of which more than 20 cities were heavily polluted. ...

AI Technical Summary

Problems solved by technology

However, the direct use of the field strength model for spatial interpolation has shortcomings: the distance in the formula has a single effect on the field strength, and different types of interpolation are often affected by distance in different ways

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