## 3 Results
### Air Pollution Effects on Hospital Emergency Admissions  
Considering all regression models obtained, about 62.3% showed a statistically significant (\(p<0.05\)) association with air pollution (see Figure 10 for an example of the 4 temporal models for the cardiac diseases and the senior group). The diseases with stronger associations between air pollution and emergency hospital admissions were all respiratory diseases, followed by cardiac diseases, chronic lower respiratory diseases, and acute upper respiratory infections, so these are tendentially higher in respiratory diseases, as only cardiac diseases are of the circulatory sort. The highest average effects of air pollution in admissions are mainly felt in acute upper respiratory infections (2.93% per 10 \(\upmu\)g m\({}^{-3}\) increase in air pollution), followed by ischemic heart disease (2.76% per 10 \(\upmu\)g m\({}^{-3}\) increase in air pollution). In terms of all circulatory diseases and all respiratory diseases, the latter has the biggest increase due to a 10 \(\upmu\)g m\({}^{-3}\) rise in air pollution: 2.2% against 1.45%. Many models with no statistically significant association between air pollution and emergency hospital admissions were found in the temporal modeling strategy D; also, the municipalities of Amadora and Odivelas were the ones which counted more of these situations and Lisbon counted the opposite. The age group of \(<\)15 years old was more prone to present in these models, which is consistent with the observations, as it usually counts with the least hospital admissions of all age groups (in the diseases selected), as shown in Figure 3. On the other side, for total number of associations and highest effects in emergency hospital admissions, the models W and WW have the biggest impact across all municipalities and age groups. Figure 10: Relationship between the slopes of cardiac diseases (senior age group) (y axis) and the standardized predicted values (x axis), including the slopes and R\({}^{2}\) values. (**A**)—temporal model W; (**B**)—temporal model D; (**C**)—temporal model WD; (**D**)—temporal model WW. Results show positive correlations between air pollution and emergency hospital admissions in all municipalities and across all temporal modeling strategies (Table 11). Nonetheless, the municipality of Amadora had the least diversity of air pollutants associations, as over 72% was with CO, Lisbon being the one with highest diversity. In general, CO is the pollutant that counts the most associations with emergency hospital admissions across all models (constituting 46.8% of associations), as is shown in Table 8 (Table 11). However, it normally yields the lowest increase per 10 \(\upmu\)g m\({}^{-3}\)--especially in the \(<\)15 age group, very rarely having a rise higher than 1%, happening only with acute upper respiratory infections in Lisbon and cerebrovascular disease in Odivelas. The other substantial pollutant is O\({}_{3}\), appearing consistently in 24.2% of models and showing important effects in emergency hospital admissions. Nevertheless, its impact is most significant in Lisbon, as the bulk of its associations and greater effects are felt in this municipality. Also, this air pollutant shows no correlations with the under-15 age group. Its biggest impacts are felt in respiratory diseases, namely acute upper respiratory infections, and chronic lower respiratory diseases, increasing admissions an average of 4.1% and 4.8% per 10 \(\upmu\)g m\({}^{-3}\) (the smallest values in temporal modeling strategy D and highest in W). The biggest average effect in emergency hospital admissions across all models, municipalities and age groups comes from NO\({}_{2}\), about 4.4% increase per 10 \(\upmu\)g m\({}^{-3}\). This pollutant shows greater impact in the Odivelas municipality, with little to no associations in Lisbon and Amadora. As for the diseases, its highest values appear in chronic lower respiratory diseases (\(>\)64) and acute upper respiratory infections (15-64).