There are 3 main databases. #1 is the VOC concentrations of soil gas and indoor air samples collected over the site. #2 is the pressure readings used to monitor the pressure differential between subslab and indoor air. #3 is the vacuum reading used to monitor effectiveness, strength, and reach of vacuum created during the SVE operation. This dataset is associated with the following publication: Stewart, L., C. Lutes, R. Truesdale, B. Schumacher, J. Zimmerman, and R. Connell. Field Study of Soil Vapor Extraction for Reducing Off‐Site Vapor Intrusion. Groundwater Monitoring & Remediation. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 40(1): 74-85, (2020).

There are 3 main databases. #1 is the VOC concentrations of soil gas and indoor air samples collected over the site. #2 is the pressure readings used to monitor the pressure differential between subslab and indoor air. #3 is the vacuum reading used to monitor effectiveness, strength, and reach of vacuum created during the SVE operation. This dataset is associated with the following publication: Schumacher, B., J. Zimmerman, C. Lutes, R. Truesdale, and C.W. Holton. Key Design Elements of Building Pressure Cycling for Evaluating Vapor Intrusion—A Literature Review. Groundwater Monitoring & Remediation. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 39(1): 66-72, (2019).

There are 3 main databases. #1 is the VOC concentrations of soil gas and indoor air samples collected over the site. #2 is the pressure readings used to monitor the pressure differential between subslab and indoor air. #3 is the vacuum reading used to monitor effectiveness, strength, and reach of vacuum created during the SVE operation. This dataset is associated with the following publication: Schumacher, B., J. Zimmerman, C. Lutes, R. Truesdale, and C.W. Holton. Key Design Elements of Building Pressure Cycling for Evaluating Vapor Intrusion—A Literature Review. Groundwater Monitoring & Remediation. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 39(1): 66-72, (2019).

There are 3 main databases. #1 is the VOC concentrations of soil gas and indoor air samples collected over the site. #2 is the pressure readings used to monitor the pressure differential between subslab and indoor air. #3 is the vacuum reading used to monitor effectiveness, strength, and reach of vacuum created during the SVE operation. This dataset is associated with the following publication: Lutes, C., L. Stewart, R. Truesdale, J. De Loeora, J. Zimmerman, and B. Schumacher. Cost Comparison of Soil Vapor Extraction and Subslab Depressurization for Vapor Intrusion Mitigation. GROUNDWATER MONITORING AND REMEDIATION. National Ground Water Association, Westerville, OH, USA, 42(4): 43-53, (2022).

The dataset is comprised of: 1)VOC concentrations of soil gas and indoor air samples collected over the site; 2) the pressure readings used to monitor the pressure differential between subslab and indoor air.

The dataset is comprised of: 1)VOC concentrations of soil gas and indoor air samples collected over the site; 2) the pressure readings used to monitor the pressure differential between subslab and indoor air.

The dataset is comprised of: 1)VOC concentrations of soil gas and indoor air samples collected over the site; 2) the pressure readings used to monitor the pressure differential between subslab and indoor air.

Dataset includes VOC concentrations in ambient air, soil gas, subslab gas, and indoor air; radon measurements from the same locations as the VOC concentrations; weather data; groundwater levels and VOC concentrations in the groundwater; and sampling times and dates. Predominant VOCs are PCE, TCE, and chloroform. This dataset is associated with the following publication: Lutes, C., C. Holton, B. Schumacher, J. Zimmerman, A. Kondash, and R. Truesdale. Observation of Conditions Preceding Peak Indoor Air Volatile Org Compound Concentrations in Vapor Intrusion Studies. Groundwater Monitoring & Remediation. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 41(2): 99-111, (2021).

This data set provides an estimate of the vapor pressure deficit. It infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight.The Spatial Statistical Data Fusion (SSDF) surface continental United States (CONUS) products, fuse data from the Atmospheric InfraRed Sounder (AIRS) instrument on the EOS-Aqua spacecraft with data from the Cross-track Infrared and Microwave Sounding Suite (CrIMSS) instruments on the Suomi-NPP spacecraft. The CrIMSS instrument suite consists of the Cross-track Infrared Sounder (CrIS) infrared sounder and the Advanced Technology Microwave Sounder (ATMS) microwave sounder. These are all daily products on a ¼ x ¼ degree latitude/longitude grid covering the continental United States (CONUS).The SSDF algorithm infers a value for each grid point based on nearby and distant values of the input Level-2 datasets and estimates of the variance of those values, with lower variances given higher weight. Performing the data fusion of two (or more) remote sensing datasets that estimate the same physical state involves four major steps: (1) Filtering input data; (2) Matching the remote sensing datasets to an in situ dataset, taken as a truth estimate; (3) Using these matchups to characterize the input datasets via estimation of their bias and variance relative to the truth estimate; (4) Performing the spatial statistical data fusion. We note that SSDF can also be performed on a single remote sensing input dataset. The SSDF algorithm only ingests the bias-corrected estimates, their latitudes and longitudes, and their estimated variances; the algorithm is agnostic as to which dataset or datasets those estimates, latitudes, longitudes, and variances originated from.

There is no dataset. This dataset is associated with the following publication: Lutes, C., C. Holton, R. Truesdale, J. Zimmerman, and B. Schumacher. Key Design Elements of Building Pressure Cycling for Evaluating Vapor Intrusion—A Literature Review.. Groundwater Monitoring & Remediation. Wiley-Blackwell Publishing, Hoboken, NJ, USA, 39(1): 66-72, (2019).