## 4 Discussion  
The temporal models used in this paper (D, W, WD, WW) accounted for various temporal relations between air pollution and emergency hospital admissions, from the smallest same-day exposure and hospital care relationship, to the week before exposure and week after hospital care. All models construct information and relate it at different times, so each one has its own validity; nonetheless, the biggest coefficients of determination are present in the W and WW data models (as the example in Figure 10 shows), especially in the respiratory diseases, reaching values as high as 0.69 (acute upper respiratory infections in the \(<\)15 age group in Lisbon). This shows the goodness-of-fit of the said models, concluding that these explain (depending on the disease and age group) a considerable amount of variation in this type of hospital admissions. So, air pollution can be reiterated as an important health determinant, especially in urban areas such as LMA. In this study, as opposed to many others (not only in the LMA region), the statistical significance was not a problem due to the analyzed period of 11 years--this translates to the existence of considerable amounts of data that ultimately sustained the findings (models and associations). Therefore, even temporal model W manages to have not only widespread statistically significant values but also many statistically significant associations between air pollution and emergency hospital admissions. The discrepancy between municipalities is a fact. The population of Amadora seems to be less prone to air pollution effects on health (with exception to PM\({}_{10}\)), as it generally denotes the lowest percentage increase in emergency hospital admissions due to air pollution growth. On the other side, Odivelas and Lisbon seem to be affected by the inverse situation, as consistently reveal the biggest increases in emergency hospital admissions due to rises in air pollution. The distinction of values in quantity and impact of effects is somewhat understandable, as the greater influences are felt in the municipality with the highest vulnerability. Lisbon municipality is the focus of daily commuting in LMA, being where most of employment (besides population) is located, and, consequently where the traffic is more intense [103]. Therefore, the air pollution concentration is generally higher for Lisbon, but, more importantly, this municipality registers the highest number of limits exceedances. Thus, the relation between air pollution and emergency hospital admissions is different, suggesting an urban/suburban areas effect [74], where the core urban region has a greater number of associations and many of the top effects, as is indicated to happen with Lisbon in the LMA region. Following many other international findings, this also suggests that higher exposure to traffic is closely related to higher numbers of admissions in the observed diseases [28; 109; 110]. Ultimately, the vulnerability to air pollution accessed (see the synthesis presented in Table 3) is coherent with the findings, as greater vulnerability conduces to more and higher impacts on population health. The considerable amount of associations between CO and emergency hospital admissions in all study areas brings a renovated interest for this pollutant. Although counting no exceedances in the 2005-2015 period, the fact is that its effect is still noticeable. [PERSON] et al. also found associations with CO and hospital admissions in LMA, however in much smaller numbers and impacts [41]. This makes the \"silent killer\" an actually \"silent killer\", as it poses--supposedly--minor threats due to the compliance with legal limits, yet the health effects seem to still be there and in great number. In this study we assessed that CO's greatest effects are felt by the 15-64 and \(>\)64 age groups in cerebrovasculardiseases and acute upper respiratory infections, reaching values over 2% increase in emergency hospital admissions per 10 \(\upmu\)g m\({}^{-3}\) increase.