A cost-benefit analysis of a pellet boiler with electrostatic precipitator versus conventional biomass technology: A case study of an institutional boiler in Syracuse, New York.

BACKGROUND: Biomass facilities have received increasing attention as a strategy to increase the use of renewable fuels and decrease greenhouse gas emissions from the electric generation and heating sectors, but these facilities can potentially increase local air pollution and associated health effects. Comparing the economic costs and public health benefits of alternative biomass fuel, heating technology, and pollution control technology options provides decision-makers with the necessary information to make optimal choices in a given location. METHODS: For a case study of a combined heat and power biomass facility in Syracuse, New York, we used stack testing to estimate emissions of fine particulate matter (PM2.5) for both the deployed technology (staged combustion pellet boiler with an electrostatic precipitator) and a conventional alternative (wood chip stoker boiler with a multicyclone). We used the atmospheric dispersion model AERMOD to calculate the contribution of either fuel-technology configuration to ambient primary PM2.5 in a 10km×10km region surrounding the facility, and we quantified the incremental contribution to population mortality and morbidity. We assigned economic values to health outcomes and compared the health benefits of the lower-emitting technology with the incremental costs. RESULTS: In total, the incremental annualized cost of the lower-emitting pellet boiler was $190,000 greater, driven by a greater cost of the pellet fuel and pollution control technology, offset in part by reduced fuel storage costs. PM2.5 emissions were a factor of 23 lower with the pellet boiler with electrostatic precipitator, with corresponding differences in contributions to ambient primary PM2.5 concentrations. The monetary value of the public health benefits of selecting the pellet-fired boiler technology with electrostatic precipitator was $1.7 million annually, greatly exceeding the differential costs even when accounting for uncertainties. Our analyses also showed complex spatial patterns of health benefits given non-uniform age distributions and air pollution levels. CONCLUSIONS: The incremental investment in a lower-emitting staged combustion pellet boiler with an electrostatic precipitator was well justified by the population health improvements over the conventional wood chip technology with a multicyclone, even given the focus on only primary PM2.5 within a small spatial domain. Our analytical framework could be generalized to other settings to inform optimal strategies for proposed new facilities or populations.

Evaluation of relative response factor methodology for demonstrating attainment of ozone in Houston, Texas.

In 2007, the U.S. Environmental Protection Agency (EPA) released guidance on demonstrating attainment of the federal ozone (O3) standard. This guidance recommended a change in the use of air quality model (AQM) predictions from an absolute to a relative way. This was accomplished by using a ratio, and not the absolute difference of AQM O3 predictions from a historical year to an attainment year. This ratio of O3 concentrations, labeled the relative response factor (RRF), is multiplied by an average of observed concentrations at every monitor. In this analysis, whether the methodology used to calculate RRFs is severing the source-receptor relationship for a given monitor was investigated. Model predictions were generated with a regulatory AQM system used to support the 2004 Houston-Galveston-Brazoria State Implementation Plan. Following the procedures in the EPA guidance, an attainment demonstration was completed using regulatory AQM predictions and measurements from the Houston ground-monitoring network. Results show that the model predictions used for the RRF calculation were often based on model conditions that were geographically remote from observations and counter to wind flow. Many of the monitors used the same model predictions for an RRF, even if that O3 plume did not impact it. The RRF methodology resulted in severing the true source-receptor relationship for a monitor. This analysis also showed that model performance could influence RRF values, and values at monitoring sites appear to be sensitive to model bias. Results indicate an inverse linear correlation of RRFs with model bias at each monitor (R2 = 0.47), resulting in a change in future O3 design values up to 5 parts per billion (ppb). These results suggest that the application of RRF methodology in Houston, TX, should be changed from using all model predictions above 85 ppb to a method that removes any predictions that are not relevant to the observed source-receptor relationship.