Issues in the analysis of air quality modeling data in attainment demonstrations of the 8-hr ozone standard

Attainment demonstrations of the ozone standard serve as the basis for regional air quality planning, but do the recommended analytical techniques establish control strategies that will be effective? The relative interpretation of model data was introduced by the Environmental Protection Agency (EPA) to compensate for poor model performance, and use of 8-hr averages was introduced to mirror the standard. The sensitivity of the relative reduction factor (RRF) is tested against model bias, using a Houston, Texas regulatory model. There is an inverse linear correlation with model bias at each site (R2 = 0.47), changing future design values by 0-5 ppb. Ninety percent of cell selections for the RRF calculation in these episodes are 14-19 km from the monitor. The RRF approach is recommended only when modeled ozone response is linear. Model error statistics using 8-hr concentration averages overestimate performance compared to those using 1-hr values (11.7% versus 14.8%, median)

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 10 km x 10 km 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.This research was supported by the New York State Energy Research and Development Authority (NYSERDA), via an award to the Northeast States for Coordinated Air Use Management (Agreement #92229). The SCICHEM work of KMZ was supported by the Electric Power Research Institute (EPRI)