Modeling Emissions and Environmental Impacts of Transportation Activities Associated With High Volume Horizontal Hydraulic Fracturing Operations the Marcellus Shale Formation

2015Final reportPDFTech ReportUTRC/RF Grant No: 49997-29-26Environmental impactsHealthHydraulic fracturingMethodologyParticulatesPollutantsTruck trafficEnergyEnvironmentFreight transportationHighwaysGeographic information systemsTruckingAERMOD (computer model)hydraulic fracturingMarcellus Shalenetwork analystGIFT Modeltruck transportationemissionsResearch HubEast North Central StatesEastern StatesNew YorkPennsylvaniaOhioWest VirginiaVirginiaUnited StatesCity University of New York. University Transportation Research CenterKorfmacher, KarlMathews, AlyssaVan Munster, KatelijnRochester Institute of Technology. Laboratory for Environmental Computing and Decision-MakingUnited States. Dept. of Transportation. Office of the Assistant Secretary for Research and TechnologyUniversity Transportation Centers Program (U.S.)United States. Federal Highway AdministrationUS Transportation CollectionThe researchers' initial University Transportation Research Center (UTRC) research project identified routes and road segments with predicted high volumes of truck traffic related to natural gas extraction in the Marcellus Shale region. Results also generated annual estimates of pollution emissions per route and road segment related to these transportation activities. The road segment results identified areas of potentially elevated pollution emissions due to incremental truck traffic resulting from natural gas development. The current UTRC project uses these \u201chot spot\u201d data as source inputs for the AERMOD pollution dispersion model to begin the assessment of potential health impacts on local populations due to increased truck traffic. Total truck traffic estimates from the Pennsylvania Department of Transportation (PADOT) were also used to generate emission estimates and also input into AERMOD as a comparison dispersion analysis to help assess the relative impact of the natural gas truck activity. This report outlines the developed pollution dispersion methodology and provides results for 2011. Results indicate that the maximum average daily truck induced emission concentrations for PM10 are 2.845 \u3bcg/m\ub3 for the PADOT roads and 0.082 \u3bcg/m\ub3 for the Geospatial Intermodal Freight Transport (GIFT) roads, the differences primarily due to overall truck counts. Subtracting the output files from the 2007 rate simulations from the 2008 rate simulations, if MY 2007 emission standards had been in place, pollution concentrations would have been reduced anywhere from 0.0625 - 0.00002 \u3bcg/m\ub3 in the GIFT network analysis, and PADOT network reductions would have ranged from 2.1917 \u2013 0.1165 \u3bcg/m\ub3. Overall, however, model results suggest truck emissions are a small part of the average daily particulate matter (PM) pollution, as determined by limited Pennsylvania Department of Environmental Protection (PADEP) monitoring station data. In 2011, the average daily PM10 emission concentrations ranged from 7.37 to 25.12 \u3bcg/m\ub3, and 5.97 to 14.50 \u3bcg/m\ub3 for PM2.5. Results are considered to underestimate the true emission concentrations, however, due to temporal limitations of the truck count and total PM10 emissions (both are annual values distributed evenly throughout the year as a model requirement). Despite the lower than expected concentrations, model results do highlight areas within the Marcellus Shale region that are predicted to have higher relative emission concentrations as a result of increased truck traffic related to hydraulic fracturing activities. These show where roads with higher truck counts may be impacting environmental and human health and may be used to help site monitoring stations. Using the AERMOD emission contour intervals to select 2010 US Census block data, model results suggest that over 1.2 million people are exposed at some level to elevated PM pollution due to trucks supporting gas extraction, although the vast majority of the population experiences low level exposure