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Halogen (F, Cl, Br, and I) concentrations for 129 loess samples from worldwide localities yield geometric means of 517 ± 53 μg/g F, 150 ± 20 μg/g Cl, 1.58 ± 0.16 μg/g Br, 1.16 ± 0.11 μg/g I (2 standard errors). These concentrations, notably for Br and I, are substantially higher than previous estimates for the average upper continental crystalline bedrocks, with enrichment factors of 1.3 +0.7/−0.4 (F), 1.8 +2.4/−0.8 (Cl), 3.8 +1.3/−1.0 (Br), and 39 +71/−16 (I) (95%confidence), documenting enrichment of halogens on the continental surface. These surface halogens are likely sourced from the oceans and may be influenced by climate fluctuations. Halogen ratios (Br/Cl, I/Cl, and Br/I) in loess are similar to those of organic-rich soils/sediments from both terrigenous and marine settings, suggesting that terrigenous and marine organic matter have indistinguishable halogen ratios. The Br/I ratios differ from those in the fine grained matrix of glacial diamictites, indicating that another process (beyond biological influence) is responsible for fractionating halogens in the upper continental crust. Using a mixing model, we calculate that over 80–90 % of loess originates from crystalline bedrocks, while the remainder (<10–20 %) derives from the halogen- and organic-rich sedimentary cover or other sources (e.g., marine aerosols). 

An integrated urban transportation system usually consists of multiple transport modes that have complementary characteristics of capacities, speeds, and costs, facilitating smooth passenger transfers according to planned schedules. However, such an integration is not designed to operate under disruptive events, e.g., a signal failure at a subway station or a breakdown of a bus, which have rippling effects on passenger demand and significantly increase delays. To address these disruptive events, current solutions mainly rely on a substitute service to transport passengers from and to affected areas using adhoc schedules. To fully utilize heterogeneous transportation systems under disruptive events, we design a service called eRoute based on a hierarchical receding horizon control framework to automatically reroute, reschedule, and reallocate multi-mode transportation systems based on real-time and predicted demand and supply. Focusing on an integration of subway and bus, we implement and evaluate eRoute with large datasets including (i) a bus system with 13,000 buses, (ii) a subway system with 127 subway stations, (iii) an automatic fare collection system with a total of 16,840 readers and 8 million card users from a metropolitan city. The data-driven evaluation results show that our solution improves the ratio of served passengers (RSP) by up to 11.5 times and reduces the average traveling time by up to 82.1% compared with existing solutions.