Figure 1
The study location is marked with a red dot
Source: Esri, DeLorme, HERE, TomTom, Intermap, increment P Corp., GEBCO, USGS, FAO, NPS, NRCAN, GeoBase, IGN, Kadaster NL, Ordnance Survey, Esri Japan, METI, Esri China (Hong Kong), swisstopo, MapmyIndia, and the GIS User Community
Figure 2.
Energy transformation and air pollution in Kraków: a) Yearly average PM2.5 level in Bujaka reference station; b) Coal furnaces and boilers removed as an outcome of PONE programme; c) Number of new renewable energy source installations Source: own study
Figure 3.
PM2.5 prediction error distribution on smog day (30 December at 23:00) for DLinear, XGBoost, and ARIMA models
Source: own study
Figure 4.
PM2.5 prediction error distribution on steady low pollution day (December 12th 10:00) for DLinear, XGBoost, and ARIMA models
Source: own study
Figure 5.
Map of DLinear model prediction errors for the smog event day on 30 December at 23:00 and the steady low pollution day of 12 December at 10:00 for DLinear, XGBoost, and ARIMA models with LCS locations (grey rectangle with number)
Source: own study
Figure 6.
The relative importance of meteorological factors across sensors with the highest errors – LCS 10; LCS 19, and LCS 37
Source: own study
Figure 7.
Hot-spot and cold-spot maps for PM2.5 error predictions using Getis-Ord Gi* for different models during smog event and low pollution days
Source: own study