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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.

Modeling of ozone immission in the lowest layer of atmosphere using box-model

Bogacki M.

Archiwum Ochrony Środowiska

2006

Vol. 32, no. 2

3-20

In the article, results of the air-quality experiment in a Nowy Sącz area have been presented. The experiment combining both calculations and measurements was done in July 1993. Its goal was to assess the capability of the "box-model" method for simulation time-series of ozone and other pollutants in the lowest layer of the atmosphere. The numeric calculations' results were verified by the measurements from the air-quality monitoring network. The model's prognostic capacity was assessed by the qualitative and quantitative data analysis. For analyzed episode, the error of calculated maximum ozone concentrations did not exceed š22% of measured maximum values. The calculated daily-average ozone concentrations were 29% lower comparing to measured values. The errors of calculations were most probably due to the errors in distribution of depth of the mixing layer, assumed for the calculations.

W artykule przedstawiono wyniki eksperymentu obliczeniowo-pomiarowego, jaki przeprowadzono na obszarze Nowego Sącza w lipcu 1993 r. Celem eksperymentu była ocena przydatności "box-modelu" do symulacji przebiegów czasowych stężeń ozonu i innych zanieczyszczeń w przyziemnej warstwie atmosfery. Przeprowadzone obliczenia numeryczne weryfikowano danymi pomiarowymi pochodzącymi z naziemnych stacji monitoringu jakości powietrza. Ocenę skuteczności prognostycznej modelu dokonano w oparciu o analizę jakościową i ilościową zebranych danych pomiarowych oraz wyników obliczeń. Dla analizowanego epizodu błąd obliczonych wartości maksymalnych stężeń ozonu nic przekroczył š22% w stosunku do maksymalnych wartości pomiarowych. W przypadku stężeń średniodobowych ozonu wartości obliczone były zaniżone w stosunku do wartości pomiarowych średnio o 29%. Błąd obliczeń w dużej mierze zdeterminowany był niepewnością wprowadzonego do obliczeń przebiegu głębokości warstwy mieszania.