A new spatially explicit model of population risk level grid identification for children and adults to urban soil PAHs. (August 2020) - Li, Fufu; Wu, Shaohua; Wang, Yuanmin; Yan, Daohao; Qiu, Lefeng; Xu, Zhenci
Abstract: The traditional incremental lifetime cancer risk (ILCR) model of urban soil polycyclic aromatic hydrocarbon (PAH) health risk assessment has a large spatial scale and commonly calculates relevant statistics by regarding the whole area as a geographic unit but fails to consider the high heterogeneity of the PAH distribution and differences in population susceptibility and density in an area. Therefore, the risk assessment spatial performance is insufficient and does not reflect the characteristics of cities, which are centered on human activities and serve the needs of humans, thus making it difficult to effectively support PAH prevention and treatment measures in cities. Here, the random forest model combined with the kriging residual model (RFerr-K) is used to estimate high-precision PAH distributions, separately considering the exposure characteristics of children and adults with different susceptibilities, and kindergarten point-of-interest (POI) and population density index (PDI) data were used to estimate the distributions of the kindergarten children and adults in the study area. Through the refined expression of these three dimensions, a new spatially explicit model of the incremental lifetime cancer-causing population distribution (MapPILCR) was constructed, and the risk threshold range delineation method was proposed to accurately identify regional risk levels. The results showed that the RFerr-K model significantly improves the accuracy of PAH prediction. The susceptibility index (SI) of children is 45% higher than that of adults, and POI and PDI data can be used effectively in population distribution estimation. The MapPILCR model provides a useful method for the spatially explicit assessment of the cancer risk of urban populations to inspire urban pollution grid management. Graphical abstract: Image 1 Highlights: The model (MapPILCR) was established considering demographic characteristics to assess PAHs risk more effective. Soil PAHs distribution mapping was more precise and accurate based on machine learning and big data. Fine-grained distribution of risk for exposed groups of adults and children was displayed. The grid identification system for PAHs risk inspire urban pollution precise management. Abstract : A new PAHs risk assessment model and risk levels gridded identification system were established using machine learning and big data methods to inspire urban pollution precise management.