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A method for predicting the accumulative load of benzene series on urban road surface

A road surface, benzene series technology, applied in prediction, neural learning method, biological neural network model, etc., can solve the problems of difficult identification, high cost, long time, etc., to achieve low difficulty in data acquisition and ensure safe reuse , the effect of high reliability

Active Publication Date: 2021-09-24
SHENZHEN UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0008] The present invention provides a method for predicting the cumulative load of benzene series on the surface of urban roads in order to solve the problems in the prior art that the monitoring of the cumulative load of benzene series on urban roads and the identification of its spatial distribution law are difficult, time-consuming and costly

Method used

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  • A method for predicting the accumulative load of benzene series on urban road surface
  • A method for predicting the accumulative load of benzene series on urban road surface
  • A method for predicting the accumulative load of benzene series on urban road surface

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Embodiment 1

[0036] The invention provides a method for predicting the cumulative load of benzene series on the surface of urban roads, which is characterized in that it comprises the following steps:

[0037] 1. Collect the multidimensional data used to construct the prediction model in the research area, the multidimensional data includes the area percentage of the functional area around the road in the research area; determine the sample collection area and the benzene series in the research area The accumulative load on the particles of different particle sizes on the road surface in the above sample collection area; the functional area includes residential area, commercial area and industrial area;

[0038] The specific method includes the following steps:

[0039] 11. Divide the research area into square grids with the same size of 50m-100m, and the land around the road in each square grid is defined as residential area, industrial area or commercial area in the functional area accor...

Embodiment 2

[0068] The implementation case area is located in Shenzhen City, Guangdong Province, and 16 roads including typical commercial, industrial and residential lands are selected. All road surfaces are relatively flat and paved with asphalt. Land use characteristics and sampling points such as image 3 shown. The study area (5km×3.5km) is divided into grids with a resolution of 50m×50m. The percentage of land use area of ​​residential area, industrial area and commercial area around the road in the grid is defined as the residential area, Industrial areas and commercial areas each account for a percentage of the total area. At the same time, the particles of different particle sizes and dissolved pollutants on the road surface were collected. In order to ensure that the accumulation of pollutants on the road surface had reached an equilibrium state, all samples in the study were collected when the last rainfall ended more than 7 days ago. Sampling point collection 4m 2For road...

Embodiment 3

[0076] Simulation and prediction of cumulative load of benzene series on roads with low grid resolution

[0077] Similar to Implementation Case 1, this implementation case is located in Shenzhen City, Guangdong Province. In this implementation case, the research area (5km×3.5km) is divided into grids with a resolution of 100m×100m. The area percentage of the residential area, industrial area and commercial area around the road in the grid is defined as the residential area and industrial area in the square area (7 grids × 7 grids, 700m × 700m) composed of all the grids within 3 grids from the grid and commercial areas as a percentage of the total area respectively. At the same time, the particles of various particle sizes and dissolved pollutants on the road surface are collected. The collected samples are divided into 5 grades of >300μm, 300-150μm, 150-100μm, 100-75μm and <7μm through a standard sieve through a wet sieving method The particle size is used to detect the benz...

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Abstract

The invention provides a method for predicting the cumulative load of benzene series on the surface of urban roads, comprising the following steps: collecting multidimensional data for constructing a prediction model in a research area; constructing a prediction model according to an artificial neural network, and setting the multidimensional data as an input layer , the obtained output layer is the cumulative load of the simulated road benzene series on particles of various particle sizes; according to the cumulative load of road benzene series on particles of various particle sizes or Carry out reliability evaluation and feedback debugging on the total cumulative load on the built prediction model; according to the prediction model after reliability evaluation and feedback debugging, the cumulative load of road benzene series in the whole city is predicted. The established prediction model has high reliability and accurate prediction results; it will help to estimate the cumulative load of road benzene series, thereby ensuring the safety of road rainwater reuse; at the same time, it can provide important guidance for urban planning decisions related to rainwater management and water environmental protection .

Description

technical field [0001] The invention relates to the field of prediction of urban road surface source pollution, in particular to a method for predicting the cumulative load of benzene series on the surface of urban roads. Background technique [0002] Rainwater reuse has received more and more attention worldwide. In order to alleviate the problem of water shortage, rainwater has been regarded as an important alternative water resource. In a typical urban environment, urban roads can generate a large amount of stormwater runoff during rainfall, so road stormwater is often reused. However, road rainwater usually contains many pollutants, which mainly come from urban traffic and have high toxicity. BTEX (mainly including benzene, ethylbenzene, toluene and xylene) is one of the more toxic high levels of pollutants. These pollutants accumulate on urban roads during non-rainy seasons. When rainfall occurs, they will be washed into stormwater runoff, seriously threatening the sa...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06N3/08
CPCG06N3/084G06Q10/04Y02A10/40
Inventor 刘安洪念朱攀峰
Owner SHENZHEN UNIV
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