A Spatio-Temporal Visualization Approach of PM10 Concentration Data in Metropolitan Lima

Abstract

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. The highest PM10 concentrations were registered in 2018, during the summer, highlighting the month of March with daily averages that reached 435 μμg/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 a 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.

Air Quality Assessment and Pollution Forecasting using Recurrent Artificial Neural Networks in Metropolitan Lima-Peru

“…The second peak involves the winter season and the beginning of spring, highlighting mainly October as part of the second semester of the year. Similar results were found by Encalada et al 30 .…”

“…The order of the stations from the lowest to the highest levels of the mean of PM 10 is as follows: CRB; CDM; SMP; ATE; HCH. Similar behaviour was found in other studies 30,48 . Encalada et al 30…”

“…When considering environmental data, such as PM 10 concentrations, from different locations, preliminary spatio-temporal visualization studies are of great use to better understand the behavior of the meteorological variables, the topography of the area, and pollutants 30 .…”

“…Contrary to the above, the stations located in the lowest area have a lower concentration of PM 10 . This trend is due to the entry direction of persistent local winds from the coast to the south-southwest, which causes that pollutants such as PM 10 are transferred to the northeast and east areas of the city, making them in critical places of contamination by particulate matter 27,30 .…”

Air quality assessment and pollution forecasting using artificial neural networks in Metropolitan Lima-Peru

“…When considering environmental data, such as concentrations, from different locations, preliminary spatio-temporal visualization studies are of great use to better understand the behavior of the meteorological variables, the topography of the area, and the pollutants 31 .…”