# Machine Learning Approaches for Outdoor Air Quality Modelling: A Systematic Review
## 1 Introduction  
Worsening air quality is one of the major global causes of premature mortality and is the main environmental risk claiming seven million deaths every year [(1)]. Nearly all urban areas do not comply with air quality guidelines of the World Health Organization (WHO) [(2; 3)]. The risk populations that suffer from the negative effects of air pollution the most are children, elderly, and people with respiratory and cardiovascular problems. These health complications can be avoided or diminished through raising the awareness of air quality conditions in urban areas, which could allow citizens to limit their daily activities in the cases of elevated pollution episodes, by using models to forecast or estimate air quality in regions lacking monitoring data. Air pollution modelling is based on a comprehensive understanding of interactions between emissions, deposition, atmospheric concentrations and characteristics, meteorology, among others; and is an indispensable tool in regulatory, research, and forensic applications [(4)]. These models calculate and predict physical processes and the transport within the atmosphere [(5)]. Therefore, they are widely used in estimating and forecasting the levels of atmospheric pollution and assessing its impact on human and environmental health and economy [(6; 7; 8; 9)]. In addition, air pollutionmodelling is used in science to help understand the relevant processes between emissions and concentrations, and understand the interaction of air pollutants with each other and with weather [10] and terrain [11; 12] conditions. Modelling is not only important in helping to detect the causes of air pollution but also the consequences of past and future mitigation scenarios and the determination of their effectiveness [4]. There are a few main approaches to air pollution modelling--atmospheric chemistry, dispersion (chemically inert species), and machine learning. Different complexity Gaussian models (e.g., AERMOD, PLUME) are widely used by authorities, industries, and environmental protection organizations for impact studies and health risk investigations for emissions dispersion from a single or multiple point sources (also line and area sources, in some applications) [13; 14]. These models are based on assumptions of continuous emission, steady-state conditions and conservation of mass. Lagrangian models study a trajectory of an air parcel, the position and properties of which are calculated according to the mean wind data over time (e.g., NAME) [5; 14]. On the other hand, Eulerian models use a gridded system that monitors atmospheric properties (e.g., concentration of chemical tracers, temperature and pressure) in specific points of interest over time (e.g., Unified Model). Chemical Transport Models (CTMs) (e.g., air-quality, air-pollution, emission-based, source-based, source, etc.) are prognostic models that process emission, transport, mixing, and chemical transformation of trace gases and aerosols simultaneously with meteorology [15]. Complex and computationally costly CTMs can be of a global (e.g., online: Fim-Chem, AM3, MPAS, CAM-Chem, GEM-MACH, etc. ; and offline: GEOS-Chem, MOZART, TM5) and regional (e.g., online: MM5-Chem, WRF-Chem, BRAMS; and offline: CMAQ, CAMx, CHIMERE) scale [16; 17]. These models combine atmospheric science and multi-processor computing techniques, highly relying on considerable resources like real-time meteorological data and an updated detailed emission sources inventory [18]. Unfortunately, emission inventory inputs for boundary layers and initial conditions may be lacking in some regions, while geophysical characteristics (terrain and land use) might further complicate the implementation of these models [19]. To deal with complex structure of air flows and turbulence in urban areas Computer Fluid Dynamics (CFD) methods are used [20].