scholarly journals A Framework for Untangling Transient Groundwater Mixing and Travel Times

2021 ◽  
Vol 57 (4) ◽  
Author(s):  
Andrea L. Popp ◽  
Álvaro Pardo‐Álvarez ◽  
Oliver S. Schilling ◽  
Andreas Scheidegger ◽  
Stéphanie Musy ◽  
...  
Keyword(s):  
Travel Times ◽  
Groundwater Mixing

Untangling transient groundwater mixing and travel times with noble gas time series and numerical modeling

2020 ◽  
Author(s):  
Andrea L. Popp ◽  
Álvaro Pardo-Álvarez ◽  
Oliver S. Schilling ◽  
Stéphanie Musy ◽  
Andreas Scheidegger ◽  
...  
Keyword(s):  
Surface Water ◽  
Environmental Changes ◽  
Noble Gas ◽  
Gas Analysis ◽  
Marginal Effect ◽  
Travel Times ◽  
Groundwater Pumping ◽  
Mountainous Regions ◽  
Mixing Ratios ◽  
Groundwater Mixing

<p class="western"><span lang="en-US">The quality and quantity of alluvial groundwater in mountainous areas are particularly susceptible to the effects of climate change, as well as increasing pollution from agriculture and urbanization. Understanding mixing between surface water and groundwater as well as groundwater travel times in such systems is thus crucial to sustain a safe and sufficient water supply. We used a novel combination of real-time, in-situ noble gas analysis to quantify groundwater mixing of recently infiltrated river water (<em>F<sub>rw</sub></em><!-- Please note that everything in “$$” will look differently once submitted -->) and regional groundwater, as well as travel times of <em>F<sub>rw</sub></em> during a two-month groundwater pumping test carried out at a drinking water wellfield in a prealpine valley in Switzerland. Transient groundwater mixing ratios were calculated using helium-4 concentrations combined with a Bayesian end-member mixing model. Having identified the groundwater fraction of <em>F<sub>rw</sub></em> consequently allowed us to infer the travel times from the stream to the wellfield, estimated based on radon-222 activities of <em>F<sub>rw</sub></em>. Additionally, we compared and validated our tracer-based estimates of <em>F<sub>rw</sub></em> using a calibrated surface water-groundwater model. Our findings show that (i) mean travel times of <em>F<sub>rw</sub></em> are in the order of two weeks, (ii) during most of the experiment, <em>F<sub>rw</sub></em> is substantially high (~70\%), and (iii) increased groundwater pumping only has a marginal effect on groundwater mixing ratios and travel times. The high fraction of <em>F<sub>rw</sub></em> in the abstracted groundwater and its short travel times emphasize the vulnerability of mountainous regions to present and predicted environmental changes.</span></p>

HETEROGENEITY IN RIVER-GROUNDWATER MIXING, MICROBIOLOGY, AND GEOCHEMISTRY IN AN ALPINE RIVERBED DURING BASEFLOW

2018 ◽  
Author(s):  
Amelia R. Nelson ◽  
◽  
Michael J. Wilkins ◽  
Casey M. Saup ◽  
Savannah R. Bryant ◽  
...  
Keyword(s):  
Groundwater Mixing

scholarly journals “In cities, it’s not far, but it takes long”: comparing estimated and replicated travel times to reach life-saving obstetric care in Lagos, Nigeria

2021 ◽  
Vol 6 (1) ◽  
pp. e004318
Author(s):  
Aduragbemi Banke-Thomas ◽  
Kerry L M Wong ◽  
Francis Ifeanyi Ayomoh ◽  
Rokibat Olabisi Giwa-Ayedun ◽  
Lenka Benova
Keyword(s):  
Travel Time ◽  
Friction Surface ◽  
Obstetric Care ◽  
Travel Times ◽  
Night Time ◽  
Google Maps ◽  
Surface Approach ◽  
Low Resource ◽  
Life Saving ◽  
The Cost

BackgroundTravel time to comprehensive emergency obstetric care (CEmOC) facilities in low-resource settings is commonly estimated using modelling approaches. Our objective was to derive and compare estimates of travel time to reach CEmOC in an African megacity using models and web-based platforms against actual replication of travel.MethodsWe extracted data from patient files of all 732 pregnant women who presented in emergency in the four publicly owned tertiary CEmOC facilities in Lagos, Nigeria, between August 2018 and August 2019. For a systematically selected subsample of 385, we estimated travel time from their homes to the facility using the cost-friction surface approach, Open Source Routing Machine (OSRM) and Google Maps, and compared them to travel time by two independent drivers replicating women’s journeys. We estimated the percentage of women who reached the facilities within 60 and 120 min.ResultsThe median travel time for 385 women from the cost-friction surface approach, OSRM and Google Maps was 5, 11 and 40 min, respectively. The median actual drive time was 50–52 min. The mean errors were >45 min for the cost-friction surface approach and OSRM, and 14 min for Google Maps. The smallest differences between replicated and estimated travel times were seen for night-time journeys at weekends; largest errors were found for night-time journeys at weekdays and journeys above 120 min. Modelled estimates indicated that all participants were within 60 min of the destination CEmOC facility, yet journey replication showed that only 57% were, and 92% were within 120 min.ConclusionsExisting modelling methods underestimate actual travel time in low-resource megacities. Significant gaps in geographical access to life-saving health services like CEmOC must be urgently addressed, including in urban areas. Leveraging tools that generate ‘closer-to-reality’ estimates will be vital for service planning if universal health coverage targets are to be realised by 2030.

Estimation of Path Travel Time Distributions in Stochastic Time-Varying Networks with Correlations

2021 ◽  
pp. 036119812110184
Author(s):  
Monika Filipovska ◽  
Hani S. Mahmassani ◽  
Archak Mittal
Keyword(s):  
Time Dependence ◽  
Travel Time ◽  
Correlation Structure ◽  
Time Varying ◽  
Travel Times ◽  
Trajectory Data ◽  
Time Varying Networks ◽  
Transportation Research ◽  
Search Approach ◽  
Stochastic Time

Transportation research has increasingly focused on the modeling of travel time uncertainty in transportation networks. From a user’s perspective, the performance of the network is experienced at the level of a path, and, as such, knowledge of variability of travel times along paths contemplated by the user is necessary. This paper focuses on developing approaches for the estimation of path travel time distributions in stochastic time-varying networks so as to capture generalized correlations between link travel times. Specifically, the goal is to develop methods to estimate path travel time distributions for any path in the networks by synthesizing available trajectory data from various portions of the path, and this paper addresses that problem in a two-fold manner. Firstly, a Monte Carlo simulation (MCS)-based approach is presented for the convolution of time-varying random variables with general correlation structures and distribution shapes. Secondly, a combinatorial data-mining approach is developed, which aims to utilize sparse trajectory data for the estimation of path travel time distributions by implicitly capturing the complex correlation structure in the network travel times. Numerical results indicate that the MCS approach allowing for time-dependence and a time-varying correlation structure outperforms other approaches, and that its performance is robust with respect to different path travel time distributions. Additionally, using the path segmentations from the segment search approach with a MCS approach with time-dependence also produces accurate and robust estimates of the path travel time distributions with the added benefit of shorter computation times.

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