Selection Of Monitoring Locations Research Articles

A hydrodynamic model–based approach to assess sampling approaches for dissolved oxygen criteria in the Chesapeake Bay

Technological advances in water quality measurement systems have provided the potential to expand high-frequency observations into coastal monitoring programs. However, with limited resources for monitoring budgets in natural waters that exhibit high temporal and spatial variability in water quality, there is a need to identify the locations and time periods where these new technologies can be deployed for maximum efficacy. To advance the capacity to make quantitative and objective decisions on the selection of monitoring locations and sampling frequency, we combined high-resolution numerical model simulations and multi-frequency water quality measurements to conduct a power analysis comparing alternative sampling designs in the assessment of water quality in the Chesapeake Bay. Specifically, we evaluated candidate monitoring networks that deployed both conventional long-term fixed station monitoring in deep channel areas and short-term continuous monitoring technologies in near-shore, shallow areas to assess 30-day dissolved oxygen criteria in two Bay tributaries. We conducted a cumulative frequency diagrams analysis to quantify the accuracy of each monitoring scheme in evaluating compliance with respect to the model. We used a Monte Carlo simulation to incorporate the spatial and temporal uncertainty of criteria failure. We found that additional long-term biweekly channel and short-term continuous shallow sampling efforts can lead to statistically unbiased and improved assessments at local spatial extents (less than 0.2 proportion of the assessed water body), especially when additional sampling is added at stations representing hypoxic water areas. Stations that represented seaward regions of the tributaries were more valuable in maintaining unbiased assessments of dissolved oxygen criteria attainment. This analysis highlights the importance of statistical evaluation of ongoing monitoring programs and suggests an approach to identify efficient deployments of monitoring resources and to improve assessment of other water quality metrics in estuarine ecosystems.

064 The building of the bavarian electronic pollen information network - ePIN

Pollen monitoring is mostly performed manually. There is, however, a need for rapid reporting of pollen counts in addition to the alleviation of the workload of manual operation. We investigated the different steps needed for building an automatic pollen monitoring network in Bavaria, Germany. First, we tested the performance of the automatic pollen monitor BAA500, by comparing its data with data from a Hirst-type pollen trap at the same location. Next, during 2015, we built a new manual Bavarian pollen monitoring network with 27 Hirst-type pollen traps, including all closed but reopened operating traps from the German Polleninformation Network (PID). The selection of monitoring locations was based on: 1. Coverage of Bavarian land surface and population, but also 2. trying to represent the different environmental and climatic areas and 3. selecting the best positions to increase the predictability of pollen amounts based on forecasting models. Traps were located under optimal local monitoring conditions. The most informative locations were selected by applying redundancy and clustering analysis. Only 8 pollen traps are needed to cover most of variability in pollen dynamics. We found that three large groups of stations could be differentiated: 1. An extensive central cluster agglomerating most of the stations 2. a cluster agglomerating the stations located at the coldest areas of Bavaria and 3. a cluster of the Northern part of Bavaria. At the same time, we found the existence of eight sub-clusters related with different environmental conditions. We are now replacing these 8 manual stations by automatic pollen monitors and incorporating our network into local law to guarantee continuity.

Structural Monitoring Techniques for the Largest Excavation Section Subsea Tunnel: Xiamen Xiang’an Subsea Tunnel

AbstractBecause of the particular and complex engineering environment of subsea tunnels, effective monitoring techniques should be adopted to ensure the operation security of subsea tunnels. Also, monitoring the condition of steel corrosion in reinforced concrete (RC) of subsea tunnels is especially imperative for ensuring structural serviceability and durability of RC structures. In this paper, monitoring the largest excavation section subsea tunnel, Xiamen Xiang’an Subsea Tunnel, is studied. It includes the layout of monitoring sections and monitoring contents, selection of monitoring locations, and the monitoring instruments. The design of corrosion monitoring system is especially focused on because of its complexity. Both commercial corrosion sensors and developed corrosion sensors based on electrochemical and fiber Bragg grating techniques, respectively, are adopted. The arrangement of corrosion monitoring section and locations as well as the installations of sensors and the establishment of corrosio...

Controls of surface soil moisture spatial patterns and their temporal stability in a semi‐arid steppe

AbstractTemporal stability of soil moisture spatial patterns has important implications for optimal soil and water management and effective field monitoring. The aim of this study was to investigate the temporal stability of soil moisture spatial patterns over four plots of 105 m × 135 m in grid size with different grazing intensities in a semi‐arid steppe in China. We also examined whether a time‐stable location can be identified from causative factors (i.e. soil, vegetation, and topography). At each plot, surface soil moisture (0–6 cm) was measured about biweekly from 2004 to 2006 using 100 points in each grid. Possible controls of soil moisture, including soil texture, organic carbon, bulk density, vegetation coverage, and topographic indices, were determined at the same grid points. The results showed that the spatial patterns of soil moisture were considerably stable over the 3‐y monitoring period. Soil moisture under wet conditions (averaged volumetric moisture contents > 20%) was more stable than that under dry ($\bar{\theta}_{j, t} < 10\%$) or moist ($\def\endash{\hbox{--}} \bar{\theta}_{j, t} = 10\endash 20\%$) conditions. The best representative point for the whole field identified in each plot was accurate in representing the field mean moisture over time (R2 ≥ 0·97; p < 0·0001). The degree of temporal persistence varied with grazing intensity, which was partly related to grazing‐induced differences in soil and vegetation properties. The correlation analysis showed that soil properties, and to a lesser extent vegetation and topographic properties, were important in controlling the temporal stability of soil moisture spatial patterns in this relatively flat grassland. Response surface regression analysis was used to quantitatively identify representative monitoring locations a priori from available soil‐plant parameters. This allows appropriate selection of monitoring locations and enhances efficiency in managing soil and water resources in semi‐arid environments. Copyright © 2010 John Wiley & Sons, Ltd.

Association between residual chlorine loss and HAA reduction in distribution systems

A goal of the proposed Stage 2 Microbial/Disinfection Byproducts Rules are to reduce peak concentrations of disinfection by‐products (DBPs) in distribution systems. Water utilities will be required to conduct an initial distribution system evaluation to select sampling locations that capture peak DBP concentrations. The selection of monitoring locations is difficult because trihalomethane (THM) and haloacetic acid (HAA) concentrations can vary over time and space in a distribution system. Maximum concentrations may not occur at the same location over time, and maximum THM and maximum HAA concentrations may not occur simultaneously at the same site. Although the maximum THM concentration often corresponds to the location with the maximum residence time, this does not hold true for HAA concentrations, which tend to reach a maximum value and then decline with increased residence time. Occurrence data for HAAs from several distribution system studies were compiled and analyzed to demonstrate variability in HAA concentrations. In these cases, under conditions of low residual chlorine or chloramine concentrations, HAA concentrations were low, even at locations with long residence times. In contrast, THM concentrations were generally high at these same sampling locations. HAA concentrations were often observed to decrease in storage tanks.