Air pollutant concentrations

About: Air pollutant concentrations is a research topic. Over the lifetime, 1652 publications have been published within this topic receiving 36138 citations.

Air quality index using machine learning – a jordan case study

Abstract: Predicting changes in air pollutant concentrations due to human and nature drivers are critical and challenging, particularly in areas with scant data inputs and high variability of parameters. This paper builds an Air Quality Index (AQI) model using Machine Learning algorithms and techniques. The paper employs Machine Learning Algorithms such as Decision Tree (DT), Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Random Forest (RF) and Logistic Regression. The model can predict the most pollutant factors from real readings published daily by the Jordan Ministry of Environment (MoEnv) for the period from January 2017 to April 2019. Jordan has prioritized air quality problems by establishing detection and monitoring stations in 12 positions across the country to measure Air Quality (AQ). Pollutant concentrations recorded by MoEnv use fully forewarn official organizations and individuals of daily air quality in the atmosphere over time and beneficially used by health and climate studies organizations. The study has detected the most contaminated sites and determined the pollutant concentrations. These estimates will indicate the most influenced pollutants and their behavior in the pollution process for further recommendations and actions to effects cardiopulmonary patients, environmental and climate researches, as well as to vulnerable ecosystems.

ANN and Fuzzy c-Means applied to environmental pollution prediction

Abstract: Salamanca, situated in center of Mexico is among the cities which suffer most from the air pollution in Mexico. The vehicular park and the industry, as well as orography and climatic characteristics have propitiated the increment in pollutant concentration of Sulphur Dioxide (SO 2 ). In this work, a Multilayer Perceptron Neural Network has been used to make the prediction of an hour ahead of pollutant concentration. A database used to train the Neural Network corresponds to historical time series of meteorological variables and air pollutant concentrations of SO 2 . Before the prediction, Fuzzy c-Means and K-means clustering algorithms have been implemented in order to find relationship among pollutant and meteorological variables. Our experiments with the proposed system show the importance of this set of meteorological variables on the prediction of SO 2 pollutant concentrations and the neural network efficiency. The performance estimation is determined using the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The results showed that the information obtained in the clustering step allows a prediction of an hour ahead, with data from past 2 hours.

Air Quality and Pollution Control

Abstract: The Engineer’s Joint Council on Air Pollution and Its Control defines air pollution as “the presence in the outdoor atmosphere of one or more contaminants, such as dust, fumes, gas, mist, odor, smoke or vapor in quantities, of characteristics, and of duration, such as to be injurious to human, plant, or property, or which unreasonably interferes with the comfortable enjoyment of life and property.”

Environmental impact assessment of petrol and gas filling stations on air quality in umuahia, nigeria

Abstract: This study investigated pollutants emissions from filling stations and their impact on the air quality. Gas monitors were employed to identify the different pollutants present in the ambient air of the study areas. The results showed that the most prominent pollutants present in the ambient air are the volatile organic compounds followed by methane, then carbon monoxide. Measurements were taken at the controls at distances between 20 to 200m.The pollutants concentration recorded at the study areas showed that the level of pollutants exceeded the FEPA air quality guidelines. There are few exceptions in pollutants like the particulate matter which was found to be at concentrations within the FEPA limits. Regression analysis of the pollutants at the controls showed that only the volatile organic compounds and methane are the only significant pollutants present in the ambient air primarily because of the presence of the filling stations. These two pollutants showed a strong negative correlation with distance from the study area. While the regression curve for the volatile organic compounds (VOCs) shows a nearly perfect curve with exponential functions as its regression equations, that of methane is linear. Specifically, both VOCs and methane have a correlation coefficient (R) that is above 0.9 for the study areas. Hence a conclusion was drawn from the findings that the primary pollutant to consider when building filling station are the VOCs and methane and that the minimum safe distance to site a filling station is a distance of 80m away from residential areas.

The Clean Air Act in a Nutshell: How It Works

Abstract: Congress designed the Clean Air Act to protect public health and welfare from different types of air pollution caused by a diverse array of pollution sources.