## 3 Results  
* Air Quality Data  
All studied regions are urban environments that have available coverage of satellite imagery, ground monitoring sites, and LUR-modeled target data at high spatial resolution. LUR models require substantial time and effort, mainly in obtaining a large number of input datasets, and while they are used for planning and decision-making purposes, LUR-modeled data are not commonly available. The continuous LUR-modeled PM\({}_{2.5}\) concentrations in all our case study regions are more heterogeneously spread, while NO\({}_{2}\) concentrations demonstrate a clearer spatial trend, following roads as the main contributor (Figures 2 and 3). It is clear from the target data surfaces that PM\({}_{2.5}\) annual concentrations are much lower in Vancouver than in LA and London, especially related to the city centers' density (Details in Table 1). In particular, the spatial distribution of \(\mathrm{PM_{2.5}/NO_{2}}\) in LA is dominated by highways and roads. Particularly high \(\mathrm{PM_{2.5}/NO_{2}}\) levels are observed in the downtown area and at the border of Los Angeles and Orange counties. Low pollution concentrations are spotted at recreation areas such as Angeles National Forest in Northeastern LA. Vancouver demonstrates lower \(\mathrm{PM_{2.5}/NO_{2}}\) concentrations compared to LA and is mainly dominated by recreational areas with low pollution levels. Downtown Vancouver and areas close to the Vancouver International Airport are regions with relatively higher pollution levels. The \(\mathrm{PM_{2.5}/NO_{2}}\) concentrations over London are mostly moderate with distinct high-pollution areas close to roads. Low pollution levels are observed in the southern and western regions co-located with South Downs National Park and North Wessex Downs Area of Outstanding Natural Beauty, respectively. NYC has a high concentration of pollutants in the densely populated Manhattan region, as well as Queens and Brooklyn Heights. * Estimating \(\mathrm{PM_{2.5}}\) Concentrations  
Table 2 presents the model results for each individual city and for all cities combined. The model was trained for 100 iterations and showed a Pearson correlation between the target data and the model-predicted \(\mathrm{PM_{2.5}}\) concentrations in the testing subset of R = 0.93, and an RMSE of 1.64 \(\mathrm{\SIUnitSymbolMicro g/m^{3}}\), for all locations trained by the model. The model explained 87% of the variance in the multi-location testing dataset using solely images as input to the model, and the model outcomes for validation and testing phases result in a relatively low RMSE < 2 \(\mathrm{\SIUnitSymbolMicro g/m^{3}}\) for all study regions (Figure 2; Table 2). Figure 2: \(\mathrm{PM_{2.5}}\) target data, model estimates and errors. \(\mathrm{PM_{2.5}}\) target data (**a**), based on 2010 LUR data in Los Angeles, Vancouver, and London. Column (**b**) shows the 100 m model estimates of \(\mathrm{PM_{2.5}}\) annual concentrations using the testing dataset (10% of the available data for all 3 cities), and column (**c**) shows the model residuals calculated by the difference between the predicted concentrations and the target data. The residuals’ distributions are shown in column (**d**). The model captures most of the coarser spatial trends, such as greenery spaces and populated areas, with fairly small residuals spanning mostly from \(-4\) to \(+4\)\(\mathrm{\SIUnitSymbolMicro g/m^{3}}\). It also captures some of the spatial trends of larger road features in LA. However, it tends to underestimate pollution levels over roads (more dominant in LA) and overestimate over green areas (seen in all locations). All validation residuals are distributed fairly evenly around zero \(\mathrm{\SIUnitSymbolMicro g/m^{3}}\) for all locations (Figure 2). * Estimating NO2 Concentrations