Abstract
One of the main environmental problems that affects people’s health and quality of life is air pollution by particulate matter. Chile has nine of the ten most polluted cities in South America according to a report presented in 2019 by Greenpeace and AirVisual that measured the air quality index based on the levels of fine particles. Most Chilean cities are highly contaminated by particulate matter, especially during the months of April to August (the critical episode management period). The objective of this study is to predict particulate matter levels based on meteorological and climatic features, such as temperature, wind speed, wind direction, precipitation and relative air humidity in Talca, Chile, during the critical episode management periods between 2014 and 2018. Predictive models based on machine learning techniques were used, considering training datasets with meteorological and climatic data, and particulate matter levels from the three air quality monitoring stations in Talca, Chile. We carried out the training of 24 models to predict particulate matter levels considering the 24-h average and average between 05:00 to 11:00 p.m. For the model testing, data from the year 2018 during the critical episode management period were used. The obtained results indicate that our models are able to effectively predict levels of particulate matter, enabling correct management of critical episodes, especially for alert, pre-emergency and emergency conditions. We used the cross-platform and open-source programming language Python for the development and implementation of the proposed models and R-project for some visualizations.
Highlights
The increase in air pollution rates, the product of suspended particulate matter (PM), is a constant concern throughout the world that, year after year, impacts human health and quality of life [1]
Different graphs of the data ordered by year and type of PM for the different air quality monitoring stations (AQMS) were obtained
Based on the results obtained with the training dataset for all stations, it was observed that the best performance was obtained with the models that used the extended datasets, but limited to the hourly range from 05:00 to 11:00 p.m., both for PM10 and PM2.5 levels, as can be seen in Tables 5 and 6
Summary
The increase in air pollution rates, the product of suspended particulate matter (PM), is a constant concern throughout the world that, year after year, impacts human health and quality of life [1]. Diagnosis of emissions of atmospheric pollutants has been carried out in the main cities of the country, stimulating efforts to reduce the pollution generated by the combustion of residential firewood and industrial activity [2]. These efforts have not been sufficient to avoid high levels of environmental pollution. Based on the world air quality report, prepared by AirVisual and Greenpeace (https://www.iqair.com/worldmost-polluted-cities, accessed on 22 July 2020), Chile has nine of the ten most polluted cities in South America.
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