Short-term relation between air pollutants and hospitalizations for respiratory diseases: analysis by temporal association rules.

This study investigates the relation between exposure to critical air pollution events with multipollutant (CO, PM10, PM2.5, NO2, O3, and SO2) and hospitalizations for respiratory diseases in the metropolitan area of São Paulo (RMSP) and in the countryside and coastline, from 2017 to 2021. Data mining analysis by temporal association rules searched for frequent patterns of respiratory diseases and multipollutants associated with time intervals. In the results, pollutants PM10, PM2.5, and O3 showed high concentration values in the three regions, SO2 on the coast, and NO2 in the RMSP. Seasonality was similar between pollutants and between cities and concentrations significantly higher in winter, except for O3, which was present in warm seasons. Hospitalizations were recurrent during the transition from summer to colder periods. In approximately 35% of the total days with hospitalization greater than the annual average, one or more pollutants had a high concentration. The rules showed that PM2.5, PM10, and O3 pollutants are strongly associated with increased hospitalizations in the RMSP (PM2.5 and PM10 with 38.5% support and 77% confidence) and in Campinas (PM2.5 with 66.1% support and 94% confidence) and the pollutant O3 with maximum support of 17.5%. On the coast, SO2 was related to high hospitalizations (43.85% support and 80% confidence). The pollutants CO and NO2 were not associated with the increase in hospitalizations. The ratio delay indicates the pollutants that were associated with hospitalizations, having concentration remained above the limit for three days, oscillating in smaller hospitalizations on the 1st day and again higher on the 2nd and 3rd days of delay, in a decreasing way. In conclusion, high pollutant exposure is significantly associated with daily hospitalization for respiratory problems. The cumulative effect of air pollutants increased hospitalization in the following days, in addition to identifying the pollutants and which pollutant combinations are most harmful to health in each region.

Spatial patterns and temporal variations of pollutants at 56 air quality monitoring stations in the state of São Paulo, Brazil.

This study applied two data mining tasks: clustering and association rules to a dataset of pollutants in the state of São Paulo. The clustering task was applied to temporal patterns and geospatial distributions of pollutants, and the association rules were used to identify prevailing meteorological conditions when there were high concentrations of pollutants from 2017 to 2019. The results indicated good adequacy of the cluster, indicating different pollution levels per group, with a silhouette coefficient from 0.26 to 0.72. In the spatial evaluation, the groups severely polluted were located in the metropolitan region, on the coast and, some inland cities, by industrial, vehicular, burning, agriculture, and other emissions. The cluster identified a strong presence of O3 and PM2.5 in 65% and 72% of the monitored stations in several areas of the state. As for the distance between the sources of pollution, the groups of PM10 and NO2 were geographically distant, while PM2.5, CO, SO2, and O3 were closer, suggesting a spatial relationship of exposure. Seasonality was similar between groups, with significantly higher concentrations in winter, except for O3, for which higher concentrations occurred in summer. Meteorological conditions contributed to critical episodes of pollution (support and confidence greater than 80%), with low temperature and humidity, low rainfall, and milder wind associated with increased pollutants. In conclusion, investigating spatial representativeness allows revealing spatial and temporal patterns of pollutants and unfavorable meteorological conditions to diffusion. Thus, ideal and effective measures can be taken to avoid critical periods of exposure based on the behavior of pollutants in different regions and related climate changes.