Dynamic Modeling of Space-Time Variation of Black Smoke in an Industrial Urban Setting

Abstract

ISEE-511 Abstract: This paper presents novel techniques of exposure estimation developed for a large (100,000+ births) study of associations between black smoke (BS) and perinatal health outcomes in an industrial urban setting (population 260,000) for the period 1961 to 1992. An average of 20 monitoring stations recorded BS at a given point in time, but collection periods varied. Objective: Space-time modeling aimed to create estimates of the exposure surface across the city at a resolution of 1 week (for analysis by trimester of pregnancy) and 100 m for all residential areas where births occurred including predictions of error. Material and Methods: We abstracted daily BS readings for the study period (25,000+ weekly observations). We digitized all domestic and industrial chimneys (90,000+) and georeferenced information on slum clearances, phasing in of smokeless zones and industry closures. We created variables for “area of industry within 500 m,” “distance to nearest industry” and “chimney density” for 26,000+ pixels of the map of the study area. Using GIS and R software we developed a 2-stage approach: (1) Modeling temporal variation in spatially-averaged BS levels, and (2) modeling residual spatiotemporal variation about temporal averages. Results: A dynamic harmonic regression model incorporating weekly minimum temperature improved upon a simpler static model in capturing long-term decreasing trends and seasonal variation in spatially-averaged log-BS levels. Spatiotemporal regression modeling, using chimney density and distance to nearest industry as explanatory variables, reduced the space-time dependence of residuals at individual monitoring stations and allowed predictions of BS levels to be made throughout the region of interest. Conclusions: Our dynamic spatiotemporal model, which incorporates monitored pollutant data and detailed georeferenced land-use information, allows exposure estimates for environmental epidemiology studies to gain tremendously both in precision and clarity of error structure.