Revisiting urban air quality forecasting: a regression approach

Kostas Karatzas,Nikos Katsifarakis,Cezary Orlowski,Arkadiusz Sarzyński

doi:10.1007/s40595-018-0113-0

Abstract

We address air quality (AQ) forecasting as a regression problem employing computational intelligence (CI) methods for the Gdańsk Metropolitan Area (GMA) in Poland and the Thessaloniki Metropolitan Area (TMA) in Greece. Linear Regression as well as Artificial Neural Network models are developed, accompanied by Random Forest models, for five locations per study area and for a dataset of limited feature dimensionality. An ensemble approach is also used for generating and testing AQ forecasting models. Results indicate good model performance with a correlation coefficient between forecasts and measurements for the daily mean hbox {PM}_{10} concentration one day in advance reaching 0.765 for one of the TMA locations and 0.64 for one of the GMA locations. Overall results suggest that the specific modelling approach can support the provision of air quality forecasts on the basis of limited feature space dimensionality and by employing simple linear regression models.

Highlights

In a recently published paper [1] we underlined the importance of air quality (AQ) forecasting in urban environmental management as well as in contemporary smart city development [2,3]
We address the problem of air quality forecasting for two different geographical areas of interest, the Gdansk Metropolitan Area (GMA) and the Thessaloniki Metropolitan Area (TMA), by employing a regression approach, making use of a limited dimension feature space, and targeting at the forecast of the mean daily PM10 concentration of the day
We initially develop location specific models by employing Artificial Neural Networks (ANNs), Linear Regression (LR) and Random Forests (RF), and achieving correlation coefficients between 0.406 and 0.641 for the GMA stations, and between 0.693 and 0.742 for the TMA stations

Summary

Introduction

In a recently published paper [1] we underlined the importance of air quality (AQ) forecasting in urban environmental management as well as in contemporary smart city development [2,3]. The nature of function f is dictated by the model type employed: if f reconstructs the physical and chemical relationships between the parameters p(t, x) and values c(t, x), where x addresses the whole area of interest in a 3-D gridded manner, models are said to follow an analytic-deterministic approach [7], while if f is a statistical or data-mining oriented function, models are said to follow a data-driven approach (as reported in [8] and in references therein). X refers to specific areas within the studied area, which usually

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Methods

Conclusion