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Improvements in short-term forecasting of air pollution levels

Date:
February 13, 2017
Source:
Universidad Politécnica de Madrid
Summary:
A new research project has successfully predicted the daily maximum ozone threshold exceedances in the Hong Kong area. The results show that an accurate and prompt prediction of tropospheric ozone concentrations is of great importance to the management of the public pollution warning system.
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The research project carried out by a researcher at UPM has successfully predicted the daily maximum ozone threshold exceedances in the Hong Kong area. The results show that an accurate and prompt prediction of tropospheric ozone concentrations is of great importance to the management of the public pollution warning system.

In recent years the air pollution has caught the public attention since it can cause health problems. The United States Environmental Protection Agency (EPA), the European Union and other countries have established different thresholds for both warning and risk to vegetation or human health taking into account the pollutant and the context.

The goal of governments is to guarantee that these thresholds do not exceed the limits, but when this occurs, the prediction of the evolution of values in the following hours becomes an essential element, at least for the corrective measures.

The problem arises because computational models have severe limitations in terms of accuracy as they are sensitive to the boundary conditions at certain points thus uncertainty grows rapidly. Besides, the techniques based on regression models often undervalue the pollutant peaks since they tend to minimize the errors made in the dataset. Precisely these peak values are indeed the most interesting to predict since their values will usually mark the measures to be adopted in each case.

In order to face this difficulty of predicting the highest values, a researcher from Projects & Quality group from Universidad Politécnica de Madrid has developed a methodology that combines the pre-processing of the datasets to learn the behavior of physical phenomena with regression and artificial intelligence techniques, using a voting technique among different models to improve predictive capacity. Bing Gong, the main author of the study says "the obtained data have good properties of sensitivity and stability and the results improve the traditional techniques between 30% and 80%."

This study was carried out in Hong Kong, however other members of the research group are conducting similar projects in other cities such as Marrakech or Mexico.

In addition, the team of researchers is currently working on the estimation of human exposure to pollutants, based on the immission values and the geographical location of the person. Joaquín Ordieres, the leader of the research group, says "we aim to add additional elements such as the consideration of the quality of home or office air." It is understood the adding of these elements is the next logical step in order to provide people and health systems with more conclusive evidences of exposure than the registered generic immission values."


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Materials provided by Universidad Politécnica de Madrid. Note: Content may be edited for style and length.


Journal Reference:

  1. Bing Gong, Joaquín Ordieres-Meré. Prediction of daily maximum ozone threshold exceedances by preprocessing and ensemble artificial intelligence techniques: Case study of Hong Kong. Environmental Modelling & Software, 2016; 84: 290 DOI: 10.1016/j.envsoft.2016.06.020

Cite This Page:

Universidad Politécnica de Madrid. "Improvements in short-term forecasting of air pollution levels." ScienceDaily. ScienceDaily, 13 February 2017. <www.sciencedaily.com/releases/2017/02/170213091014.htm>.
Universidad Politécnica de Madrid. (2017, February 13). Improvements in short-term forecasting of air pollution levels. ScienceDaily. Retrieved April 25, 2017 from www.sciencedaily.com/releases/2017/02/170213091014.htm
Universidad Politécnica de Madrid. "Improvements in short-term forecasting of air pollution levels." ScienceDaily. www.sciencedaily.com/releases/2017/02/170213091014.htm (accessed April 25, 2017).