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Semi-markovian approach to modelling air pollution

Bogalecka Magdalena

Safety and Reliability of Systems and Processes

2021

Summer Safety and Reliability Seminar 2021

31--44

The air pollution assessment based on concentration’s changes of sulphur dioxide, carbon monoxide, nitrogen dioxide, ozone, benzene, and particulate matter is discussed in the chapter. The semi-Markov model of the environmental pollution process is introduced and its characteristics are determined. Next the proposed model is practically applied to examine and characterized air pollution in Gdańsk (Poland) as the exemplary industrial agglomeration. The main parameters and characteristics of the air pollution process are determined, such as concentration states of particular kinds of air pollutants, the limit values of transient probabilities and the mean total sojourn times staying at the air pollutants’ concentration states, for the fixed time interval.

Extracting mass concentration time series features for classifcation of indoor and outdoor atmospheric particulates

Hussain Lal, Aziz Wajid, Saeed Sharjil, Rafque Muhammad, Nadeem Malik Sajjad Ahmed, Shim Seong-O, Aftar Sania, Pirzada Jawad-ur-Rehman

Acta Geophysica

2020

Vol. 68, no. 3

945--963

Particulate matters (PMs) are considered as one of the air pollutants generally associated with poor air quality in both outdoor and indoor environments. The composition, distribution and size of these particles hazardously afect the human health causing cardiovascular health problems, lung dysfunction, respiratory problems, chronic obstructive pulmonary disease and lungs cancer. Classifcation models developed by analyzing mass concentration time series data of atmospheric particulate matter can be used for the prediction of air quality and for issuing warnings to protect the health of the public. In this study, mass concentration time series data of both outdoor and indoor particulates matters PM2.5 (aerodynamics size up to 2.5 μ) and PM10.0 (aerodynamics size up to 10.0 μ) were acquired using Haz-Dust EPAM-5000 from six diferent locations of the Muzafarabad city, Azad Kashmir. The linear and nonlinear approaches were used to extract mass concentration time series features of the indoor and outdoor atmospheric particulates. These features were given as an input to the robust machine learning classifers. The support vector machine (SVM) kernels, ensemble classifers, decision tree and K-nearest neighbors (KNN) are used to classify the indoor and outdoor particulate matter time series. The performance was estimated in terms of area under the curve (AUC), accuracy, true negative rate, true positive rate, negative predictive value and positive predictive value. The highest accuracy (95.8%) was obtained using cubic and coarse Gaussian SVM along with the cosine and cubic KNN, while the highest AUC, i.e., 1.00, is obtained using fne Gaussian and cubic SVM as well as with the cubic and weighted KNN.