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Short-term air pollution forecasting based on environmental factors and deep learning models

Arsov Mirche, Zdravevski Eftim, Lameski Petre, Corizzo Roberto, Koteli Nikola, Mitreski Kosta, Trajkovik Vladimir

Annals of Computer Science and Information Systems

2020

Vol. 21

15--22

The effects of air pollution on people, the environment, and the global economy are profound - and often under-recognized. Air pollution is becoming a global problem. Urban areas have dense populations and a high concentration of emission sources: vehicles, buildings, industrial activity, waste, and wastewater. Tackling air pollution is an immediate problem in developing countries, such as North Macedonia, especially in larger urban areas. This paper exploits Recurrent Neural Network (RNN) models with Long Short-Term Memory units to predict the level of PM10 particles in the near future (+3 hours), measured with sensors deployed in different locations in the city of Skopje. Historical air quality measurements data were used to train the models. In order to capture the relation of air pollution and seasonal changes in meteorological conditions, we introduced temperature and humidity data to improve the performance. The accuracy of the models is compared to PM10 concentration forecast using an Autoregressive Integrated Moving Average (ARIMA) model. The obtained results show that specific deep learning models consistently outperform the ARIMA model, particularly when combining meteorological and air pollution historical data. The benefit of the proposed models for reliable predictions of only 0.01 MSE could facilitate preemptive actions to reduce air pollution, such as temporarily shutting main polluters, or issuing warnings so the citizens can go to a safer environment and minimize exposure.

Design and development of original WSN sensor for suspended particulate matter measurements

Makowski L., Dziadak Bogdan, Suproniuk M.

Opto-Electronics Review

2019

Vol. 27, No. 4

363--368

This paper comprehensively presents key issues in design of an original optoelectronic measurement device built to assess amount of suspended particulate matter. The paper is introduced with a short explanation of concerns with a suspended particulate matter, what role it has in the air quality and how it affects health of human population. Then, problems of construction of the measurement device supported by a theoretical explanation on the basis of Mie theory are discussed. Subsequently, it is followed by an analysis of the device operation both in laboratory and in real conditions. Results obtained with the presented device are compared with the professional measurement equipment and an expensive, outdoor measurement station. Paper is concluded with observations of differences in spatio-temporal PM change at very close but significantly different city locations.