Un enfoque de visualización espacio-temporal para la exploración de datos de concentración de PM10 en Lima Metropolitana
Lima is considered one of the cities with the highest air pollution in Latin America. Institutions such as DIGESA, PROTRANSPORTE and SENAMHI are in charge of permanently monitoring air quality; therefore, the air quality visualization system must manage large amounts of data of different concentr...
Guardado en:
| Autores principales: | , |
|---|---|
| Otros Autores: | |
| Formato: | Tesis de pregrado |
| Lenguaje: | inglés |
| Publicado: |
Universidad Peruana Unión
2021
|
| Materias: | |
| Acceso en línea: | Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| Sumario: | Lima is considered one of the cities with the highest air pollution in Latin America. Institutions such as DIGESA, PROTRANSPORTE and SENAMHI are in charge of permanently monitoring air quality; therefore, the air quality visualization system must manage large amounts of data of different concentrations. In this study, a spatio-temporal visualization approach was developed for the exploration of data of the PM10 concentration in Metropolitan Lima, where the spatial behavior, at different time scales, of hourly concentrations of PM10 are analyzed using basic and specialized charts. The results show that the stations located to the east side of the metropolitan area had the highest concentrations, in contrast to the stations located in the center and north that reported better air quality. According to the temporal variation, the station with the highest average of biannual and annual PM10 was the HCH station; for this season, the highest PM10 concentrations were registered in 2018, during the summer, highlighting the month of March with daily averages that reached 435 μg/m3. During the study period, the CRB was the station that recorded the lowest concentrations and the only one that met the Environmental Quality Standard for air quality. The proposed approach exposes a sequence of steps for the elaboration of charts with increasingly specific time periods according to their relevance, and statistical analysis, such as the dynamic temporal correlation, that allows to obtain a detailed visualization of the spatio- temporal variations of PM10 concentrations; furthermore, it was concluded that the meteorological variables do not indicate a causal relationship with respect to PM10 levels, but rather that the concentrations of particulate material are related to the urban characteristics of each district. |
|---|