Characterizing the spatiotemporal variability of PM 2.5 in Cusco, Peru using kriging with external drift

Abstract

Advancing the understanding of spatiotemporal aspects of air pollution in the urban environment is an area where improved methods can be of great benefit to exposure assessment and policy support. This paper explores the potential of a technique known as kriging with external drift (KED) to provide high resolution maps of fine particulate matter for a downtown region of Cusco, Peru. There were three stages in this research. The first was to conduct a pilot level monitoring campaign to investigate ambient, regional, and street-level air pollutant concentrations for particulate matter (PM 2.5, PM 10) and carbon monoxide (CO) in the Province of Cusco. The second was to compile observations within a geographic information system (GIS) in order to characterize the proximal effect of the local transportation network, elevation, and land use classifications on PM 2.5. Third, regression, ordinary kriging and kriging with external drift were used to model PM 2.5 for three select time periods during a 24-h day. Statistical evaluations indicate kriging with external drift resulted in the strongest models explaining 64% of variability seen with morning particle concentrations, 25% for afternoon particles, and 53% in evening particles. These models capture spatial and temporal variability for air pollution in Cusco. These variations seem to be influenced, to varying degrees, by elevation, meteorological conditions, spatial location, and transportation characteristics. In conclusion, combining GIS, meteorological data and geostatistics proved to be a complementary suite of tools for incorporating spatiotemporal analysis into the air quality assessment.