The cardiovascular risk associated with short-term ambient air pollution exposure is well-documented. However, recent advancements in geospatial techniques have provided new insights into this risk. This systematic review focuses on short-term exposure studies that applied advanced geospatial pollution modelling to estimate cardiovascular disease (CVD) risk and accounted for additional unconventional neighbourhood-level confounders to analyse their modifier effect on the risk. Four databases were investigated to select publications between 2018 and 2023 that met the inclusion criteria of studying the effect of particulate matter (PM2.5 and PM10), SO₂, NO_x, CO, and O₃ on CVD mortality or morbidity, utilizing pollution modelling techniques, and considering spatial and temporal confounders. Out of 3277 publications, 285 were identified for full-text review, of which 34 satisfied the inclusion criteria for qualitative analysis, and 12 of them were chosen for additional quantitative analysis. Quality assessment revealed that 28 out of 34 included articles scored 4 or above, indicating high quality. In 30 studies, advanced pollution modelling techniques were used, while in 4 only simpler methods were applied. The most pertinent confounders identified were socio-demographic variables (e.g., socio-economic status, population percentage by race or ethnicity) and neighbourhood-level built environment variables (e.g., urban/rural area, percentage of green space, proximity to healthcare), which exhibited varying modifier effects depending on the context. In the quantitative analysis, only PM 2.5 showed a significant positive association to all-cause CVD-related hospitalisation. Other pollutants did not show any significant effect, likely due to the high inter-study heterogeneity and a limited number of cases. The application of advanced geospatial measurement and modelling of air pollution exposure, as well as its risk, is increasing. This review underscores the importance of accounting for unconventional neighbourhood-level confounders to enhance the understanding of the CVD risk associated with short-term pollution exposure.

Understanding the spatial–temporal patterns of air pollution is crucial for mitigation strategies, a task fostered nowadays by the generation of continuous concentration maps by remote sensing technologies. We applied spatial modelling to analyze such spatial–temporal patterns in Lombardy, Italy, one of the most polluted regions in Europe. We conducted monthly spatial autocorrelation (global and local) of the daily average concentrations of PM_2.5, PM₁₀, O₃, NO₂, SO₂, and CO from 2016 to 2020, using 10 × 10 km satellite data from the Copernicus Atmosphere Monitoring Service (CAMS), aggregated on districts of approximately 100,000 population. Land-use classes were computed on identified clusters, and the significance of the differences was evaluated through the Wilcoxon rank-sum test with Bonferroni correction. The global Moran’s I autocorrelation was overall high (>0.6), indicating a strong clustering. The local autocorrelation revealed high–high clusters of PM_2.5 and PM₁₀ in the central urbanized zones in winter (January–December), and in the agrarian southern districts in summer and autumn (May–October). The temporal decomposition showed that values of PMs are particularly high in winter. Low–low clusters emerged in the northern districts for all the pollutants except O₃. Seasonal peaks for O₃ occurred in the summer months, with high–high clusters mostly in the hilly and mildly urban districts in the northwest. These findings elaborate the spatial patterns of air pollution concentration, providing insights for effective land-use-based pollution management strategies.

Background: Spatial granularity plays a central role in the analysis of environmental hazards, yet its influence on health impact assessment remains overlooked. This study explicitly treats spatial aggregation level as a methodological variable and examines how different spatial aggregation strategies affect the association between high temperature, ozone, and out-of-hospital cardiovascular emergencies recorded by emergency medical services. Methods: A distribution thresholding approach is applied to both the environmental hazard and the health outcome. The analysis is conducted at three spatial levels: a fully aggregated region-wide level, population-based districts, and a combined strategy that cumulates district level results. The model estimates the Odds Ratio for each configuration. Results: The combined district-based strategy provides the most robust association, with an Odds Ratio of 1.13 (95% confidence interval 1.10 to 1.17). The region-wide and single district approaches show weaker or inconsistent significance. The findings indicate that the spatial level of analysis heavily impacts both the significance and the interpretability of the statistical results. Conclusions: The study demonstrates that the spatial structure of data strongly influences the detection of short-term health effects linked to environmental stressors. This contributes to the geomatics field by explicitly isolating spatial aggregation as an analytical dimension, demonstrating how spatial aggregation choices and explicit consideration of the Modifiable Areal Unit Problem can enhance methodological accuracy, support clearer spatial reasoning, and guide the development of more reliable territorial health indicators.