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Fort Union Environmental Monitoring

Evan in the fileld
A state of the art weather station was installed on site which digitally records data at 15 minute intervals. This highly localized data can be used to develop environmental simulations that will provide a more accurate representation of the microclimate of Fort Union.
Fort Union has a long legacy of weather monitoring and recording, dating to 1851. By 1870 army surgeons had a state-of-the-art weather station, with thermometers, hygrometer, wind vane, anemometer, and rain gauge.

Environmental Simulation
Following the wall collapse of the Summer of 2015, but  prior to the first CAC site visit, the research team developed preliminary environmental simulations based on available data with the intent to identify, categorize, and prioritize the complex environmental factors contributing to the behavior of the adobe ruins. Spatial information from the Historic American Buildings Survey (HABS) drawings and environmental data from the nearby Las Vegas Municipal Airport in Las Vegas, NM were used in the simulations.  This airport weather station is located approximately 20 miles away from the park, but at the time was the closest, most complete and properly formatted weather data for use with the simulations.  The results of this series of simulations were used to propose a risk hierarchy based on environmental exposure for six buildings at FOUN.
While atmospheric conditions such as temperature, relative humidity, and solar radiation exposure vary little at this distance, phenomena including precipitation, wind speed, and wind direction may differ due to the variation in local geography and boundary conditions. Following the initial simulations, as part of the first phase of site work, an Onset weather station was installed approximately 30 meters to the northeast of the Mechanics Corral to provide a record of the micro-climatic variables affecting the park.

Environmental Monitoring
Environmental simulations 
In-wall Sensors
To complement the weather station data, the research at the fort included a pilot study to evaluate the various methods for measuring and monitoring environmental conditions of the adobe walls in an attempt to identify location for potential wall collapse. Embedded monitoring and time-lapse photography were employed, similar to the approach implemented by Crosby at Mission San José de Tumacácori and elsewhere. Resistance moisture sensors were placed within and around one wall located in the Mechanics Corral. This wall was selected based on available data, preliminary simulation, and observable conditions, to be the most environmentally exposed and therefore represented a worst-case scenario for weather vulnerability. Ambient temperature and relative humidity sensors were also installed within the wall to determine the advantages of different sensor types.

In wall sensors
Installation of wall sensors required removal of the protective shelter coat which the Park Service applies to the wall surfaces.Drilling holes into the adobe were necessary to install the sensors which is inherently invasive.
Self evaluation of the work  resulted in concerns for the embedded method implemented, using conventional means, which is inherently invasive and destructive. While feasible for single installations, this approach is not practical when implemented at the scale of the entire site, which is ultimately the goal. The pilot monitoring program initially proposed drilling nine cores to receive the sensors, instead of three, for the single test wall. The cost of the equipment alone would have rendered the method impractical.
    The second criticism of the monitoring program refers to the use of ambient relative humidity sensors to measure moisture movement in an adobe wall. It was difficult to ensure that the cores in which the sensors were placed were effectively isolated from the exterior environmental conditions. Furthermore, the data collected poses additional limitations in its analysis. Relative humidity is, by definition, affected by temperature, thus this measurement cannot be isolated to provide an accurate reading of moisture content because of this influence. The temperature of the adobe wall can affect the temperature of the air within the core by convection and radiation. In other words, the thermal inertia of the wall, or the lag in thermal response to exterior conditions, influences the ambient hygric conditions within the cores. Thus, the use of resistance moisture sensors is recommended, which provide a direct, analog measurement of moisture, rather than a proxy measurement.

Weather Station Monitoring
  Since 2016, the ACL has collected detailed, local weather information for the site using an Onsite weather station with six individual sensors collecting the following data.

  • Temperature
  • Relative Humidity
  • Wind Direction
  • Wind Speed
  • Solar Gain
  • Moisture

The initial assumption in 2015 was that climate (long-term trend and seasonal trends) and environmental exposure (specifically rapidly fluctuating micro-climate) were the primary contributing factors to the recent deterioration and vulnerability of the adobe ruins at FOUN. To understand these factors, a state-of-the-art weather station was installed adjacent to the Mechanics Corral. The excellent data collected during the project suggests that the station remain indefinitely. In establishing guidelines for identifying, understanding, and addressing various risks at sites like Fort Union, particularly those relating to or exacerbated by environmental exposure and changing climate, this approach is critical.

Weather Station
Weather station 
Weather data from the onsite weather station can be analyzed statistically to objectively present risk attributed to certain factors. Exceedance probability (EP) curves have become one popular method for risk evaluation. EP curves present the probability that a risk event will be exceeded. As a low-cost alternative to simulation and computational analysis, this statistical method can be performed without additional software or expertise to quickly identify the risk associated with a particular factor. For example, wind speed and direction are unique, because they can affect instantaneous, catastrophic failure of walls.  Thus, wall segment orientation is immediately a significant contributing risk factor. With only the onsite weather data, one could determine the probability that wind speed exceeds a threshold value (perhaps determined by a structural engineer) for each orientation, thereby prioritizing risk-prone wall segments by orientation.
Further Research
Recent research into the use of passive radio frequency identification tags (RFID) for embedded moisture monitoring can address these factors as a low-cost, passive, non-destructive alternative. Passive technology, such as the RFID tags, requires no power to operate. This eliminates embedding restrictions that would be otherwise imposed by the need to power, offload data, and troubleshoot equipment. An RFID reader is used to measure the change in resonant frequency of the tag's antenna, which is caused by a corresponding change in the moisture content of the surrounding medium. Because the technology leverages the near-field of resonating antenna, the tags do not need to be physically in contact with the wet media, only near it. The limitation of the installed resistance moisture sensors, that they must be in near perfect contact with the adobe, is resolved. The tags also surmount the installation and serviceability limitations of conventional methods, as they lend themselves to being easily integrated with ongoing maintenance cycles of the shelter coats and wall-caps.
Radio Frequency Identification (RFID) tags are passive sensors which are embedded into the adobe walls to allow internal moisture data to be collected.
Traditional monitoring methods, such as active embedded moisture monitoring currently implemented, are inherently flawed when considering the underlying question they are trying to resolve: can we predict risk using the monitoring methods described? The dependent variable in this risk 'equation' is wall collapse. Even if the data sampling rate is high, it is the occurrence frequency of dependent variable events that drives the usefulness of the entire data set. In other words, if several test walls are monitored for the necessary variables (e.g., temperature and moisture,) at 15-minute intervals, and 10 years of data is collected the data is not useful in predicting risk of collapse unless that particular wall collapses. Using data collected from the embedded sensors and the weather station at FOUN, a regression model  was developed to predict the relative humidity (as a proxy for moisture content) based on exterior environmental factors. The model can accurately account for 98% of the observed conditions by correlating wind speed and direction, exterior temperature and relative humidity, and precipitation with interior relative humidity. Unless this wall collapses, however, this data and the predictive model are no more accurate at predicting risk than the EP curve method described earlier. The particular regression model used, however, does provide the relative contribution of each of the variables correlated, which can be helpful in prioritizing the weight of various environmental exposure factors.