Functional Kriging for Spatiotemporal Modeling of Nitrogen Dioxide in a Middle Eastern Megacity

Abstract

Long-term hour-specific air pollution exposure estimates have rarely been of interest in epidemiological research. However, this can be relevant for studies that aim to estimate the residential exposure for the hours that subjects mostly spend time there, or for those hours that they may work in another location. Here, we developed a model by spatially predicting the long-term diurnal curves of nitrogen dioxide (NO2) in Tehran, Iran, one of the most polluted and populated megacities in the Middle East. We used the statistical framework of functional data analysis (FDA) including ordinary kriging for functional data (OKFD) and functional analysis of variance (fANOVA) for modeling. The long-term NO2 diurnal curves had two distinct maxima and minima. The absolute minimum value of the city average was 40.6 ppb (around 4:00 p.m.) and the absolute maximum value was 52.0 ppb (around 10:00 p.m.). The OKFD showed the concentrations, the diurnal maximum/minimum values, and their corresponding occurring times varied across the city. The fANOVA highlighted that the effect of population density on the NO2 concentrations is not constant and depends on time within the diurnal period. The provided estimation of long-term hour-specific maps can inform future epidemiological studies to use the long-term mean for specific hour(s) of the day. Moreover, the demonstrated FDA framework can be used as a set of flexible statistical methods.

Application of Functional Principal Component Analysis in the Spatiotemporal Land-Use Regression Modeling of PM2.5

“…On top of that, we showed that the spatial pattern of PM 2.5 is not constant during the day, so hour-specific exposure is not necessarily proportional to the corresponding daily average. This observed pattern is also in line with a recently published study of nitrogen dioxide modeling in Tehran [10].…”

“…The trend of PM 2.5 concentrations changes during the day at the monitoring stations and at the estimated maps, as depicted in Figures 2 and 5; they are tied with traffic regulations that restrict the commuting of heavy supply trucks in the megacity of Tehran to the hours between 10 p.m. and 6 a.m. [34,35]. A similar pattern for long-term diurnal changes in nitrogen dioxides was reported in a recently published paper, where the authors employed a functional extension of ordinary kriging [10]. Furthermore, this is in line with an emission inventory approach which showed that 85% of particle matter emissions in Tehran were sourced from heavy-duty vehicles [36].…”

“…Moreover, researchers tried to recognize and use the best set of predicting information based on their available resources. Examples of such studies are the spatiotemporal realtime prediction of hourly PM 2.5 concentration to build a novel short-distance route finder using the back-propagation neural network as the modeling technique [5]; the modeling of several air pollutants to build an early warning system based on the technique of fuzzy time series, where the goal was to temporally predict prospective air-pollutant concentrations [6]; the spatial prediction of the annual average of several air pollutants to be used in epidemiological studies of long-term exposure to air pollutants, where the goal was to spatially estimate retrospective concentrations of air pollutants in the residential addresses of citizens using data from around 30 fixed monitoring stations by applying the ordinary land-use regression (LUR) model [7,8]; the spatiotemporal modeling of the daily averages of PM 2.5 concentrations using a set of land-use predictors that provided high-resolution daily-exposure estimation maps for epidemiological studies of long-term air pollutant health effects [9]; and the spatial modeling of the annually averaged diurnal curve of nitrogen dioxides using an extension of the technique of ordinary kriging for functional data, which provided retrospective long-term estimation which is suitable for subsequent environmental epidemiological studies [10].…”

“…The only similar study that was recently published modeled the long-term diurnal curves of nitrogen dioxides using the functional extension of the ordinary kriging technique. Despite its novelty in terms of modeling continuous diurnal pollutant curves, it suffered from a lack of providing any land-use predictor and relied on kriging, which resulted in low-resolution and smoothed long-term-exposure estimation maps [10].…”

“…Therefore, using estimated exposures at merely the geolocation of each person's residential address could result in exposure misclassification. Even using a ratio of the estimated daily average of a pollutant concentration to the time spent by a person in the geographic location of his/her home and workplace maybe not be justifiable because, as highlighted in a recently published study, the pattern of changes in air-pollutant concentrations during the daily timeframe may vary in the study area [10]. Hence, we adopted the framework of the LUR model and extended it to FLUR to provide high-resolution spatial estimation for the annually averaged diurnal curves of PM 2.5 and not just the estimation of spatial annual averages without considering intra-day changes.…”