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Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
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Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
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A potential controlling approach on surface ozone pollution based upon power big data.

Xin Wang1, Weihua Gu2, Feng Wang3

  • 1State Grid Anhui Electric Power Research Institute, Hefei, 230026 Anhui China.

SN Applied Sciences
|May 16, 2022
PubMed
Summary

Power big data effectively controls surface ozone pollution. Reducing electricity use in volatile organic compound (VOC) emitting industries by 10-20% significantly lowers ozone concentrations, offering a new pollution control strategy.

Keywords:
Generalized additive models (GAMs)Power big dataSurface ozone pollutionVolatile organic compounds(VOCs)

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Area of Science:

  • Environmental Science
  • Atmospheric Chemistry
  • Data Science

Background:

  • Surface ozone pollution is a growing concern, particularly in China, with challenges in timely control despite research on its mechanisms.
  • Traditional methods struggle with efficient and immediate ozone pollution management.

Purpose of the Study:

  • To develop and validate a method for effective surface ozone pollution control using power big data.
  • To quantify the influence of various factors on ozone distribution and identify key contributors.

Main Methods:

  • Utilized a generalized additive model (GAM) integrating surface ozone, NO2, meteorological data, and hourly electricity consumption from VOC-emitting companies.
  • Analyzed data from 2020-2021 in Anhui province, China, covering 16 cities.

Main Results:

  • The GAM model achieved an average R² of 0.82, demonstrating its effectiveness in capturing ozone pollution characteristics.
  • Nitrogen dioxide (NO2) and industrial volatile organic compounds (VOCs) were identified as major contributors to ozone formation (33.72% and 21.12%, respectively).
  • A 10% and 20% reduction in electricity consumption in sensitive industries led to a 9.7% and 19.1% decrease in ozone concentrations during pollution periods.

Conclusions:

  • Power big data offers a powerful tool for implementing effective ozone pollution control strategies.
  • Targeted reductions in electricity use within VOC-intensive industries present a viable and significant pathway for mitigating ozone pollution.