Future impacts of distributed power generation on ambient ozone and particulate matter concentrations in the San Joaquin Valley of California.

Distributed power generation-electricity generation that is produced by many small stationary power generators distributed throughout an urban air basin-has the potential to supply a significant portion of electricity in future years. As a result, distributed generation may lead to increased pollutant emissions within an urban air basin, which could adversely affect air quality. However, the use of combined heating and power with distributed generation may reduce the energy consumption for space heating and air conditioning, resulting in a net decrease of pollutant and greenhouse gas emissions. This work used a systematic approach based on land-use geographical information system data to determine the spatial and temporal distribution of distributed generation emissions in the San Joaquin Valley Air Basin of California and simulated the potential air quality impacts using state-of-the-art three-dimensional computer models. The evaluation of the potential market penetration of distributed generation focuses on the year 2023. In general, the air quality impacts of distributed generation were found to be small due to the restrictive 2007 California Air Resources Board air emission standards applied to all distributed generation units and due to the use of combined heating and power. Results suggest that if distributed generation units were allowed to emit at the current Best Available Control Technology standards (which are less restrictive than the 2007 California Air Resources Board standards), air quality impacts of distributed generation could compromise compliance with the federal 8-hr average ozone standard in the region.

Air quality impacts of distributed energy resources implemented in the northeastern United States.

Emissions from the potential installation of distributed energy resources (DER) in the place of current utility-scale power generators have been introduced into an emissions inventory of the northeastern United States. A methodology for predicting future market penetration of DER that considers economics and emission factors was used to estimate the most likely implementation of DER. The methodology results in spatially and temporally resolved emission profiles of criteria pollutants that are subsequently introduced into a detailed atmospheric chemistry and transport model of the region. The DER technology determined by the methodology includes 62% reciprocating engines, 34% gas turbines, and 4% fuel cells and other emerging technologies. The introduction of DER leads to retirement of 2625 MW of existing power plants for which emissions are removed from the inventory. The air quality model predicts maximum differences in air pollutant concentrations that are located downwind from the central power plants that were removed from the domain. Maximum decreases in hourly peak ozone concentrations due to DER use are 10 ppb and are located over the state of New Jersey. Maximum decreases in 24-hr average fine particulate matter (PM2.5) concentrations reach 3 microg/m3 and are located off the coast of New Jersey and New York. The main contribution to decreased PM2.5 is the reduction of sulfate levels due to significant reductions in direct emissions of sulfur oxides (SO(x)) from the DER compared with the central power plants removed. The scenario presented here represents an accelerated DER penetration case with aggressive emission reductions due to removal of highly emitting power plants. Such scenario provides an upper bound for air quality benefits of DER implementation scenarios.

Need for a marginal methodology in assessing natural gas system methane emissions in response to incremental consumption.

Accurate quantification of methane emissions from the natural gas system is important for establishing greenhouse gas inventories and understanding cause and effect for reducing emissions. Current carbon intensity methods generally assume methane emissions are proportional to gas throughput so that increases in gas consumption yield linear increases in emitted methane. However, emissions sources are diverse and many are not proportional to throughput. Insights into the causal drivers of system methane emissions, and how system-wide changes affect such drivers are required. The development of a novel cause-based methodology to assess marginal methane emissions per unit of fuel consumed is introduced. Implications: The carbon intensities of technologies consuming natural gas are critical metrics currently used in policy decisions for reaching environmental goals. For example, the low-carbon fuel standard in California uses carbon intensity to determine incentives provided. Current methods generally assume methane emissions from the natural gas system are completely proportional to throughput. The proposed cause-based marginal emissions method will provide a better understanding of the actual drivers of emissions to support development of more effective mitigation measures. Additionally, increasing the accuracy of carbon intensity calculations supports the development of policies that can maximize the environmental benefits of alternative fuels, including reducing greenhouse gas emissions.

Air quality modeling in the South Coast Air Basin of California: what do the numbers really mean?

This study evaluates air quality model sensitivity to input and to model components. Simulations are performed using the California Institute of Technology (CIT) airshed model. Results show the impacts on ozone (O3) concentration in the South Coast Air Basin (SCAB) of California because of changes in: (1) input data, including meteorological conditions (temperature, UV radiation, mixing height, and wind speed), boundary conditions, and initial conditions (ICs); and (2) model components, including advection solver and chemical mechanism. O3 concentrations are strongly affected by meteorological conditions and, in particular, by temperature. ICs also affect O3 concentrations, especially in the first 2 days of simulation. On the other hand, boundary conditions do not significantly affect the absolute peak O3 concentration, although they do affect concentrations near the inflow boundaries. Moreover, predicted O3 concentrations are impacted considerably by the chemical mechanism. In addition, dispersion of pollutants is affected by the advection routine used to calculate its transport. Comparison among CIT, California Photochemical Grid Model (CALGRID), and Urban Airshed Model air quality models suggests that differences in O3 predictions are mainly caused by the different chemical mechanisms used. Additionally, advection solvers contribute to the differences observed among model predictions. Uncertainty in predicted peak O3 concentration suggests that air quality evaluation should not be based solely on this single value but also on trends predicted by air quality models using a number of chemical mechanisms and with an advection solver that is mass conservative.

Determining air quality and greenhouse gas impacts of hydrogen infrastructure and fuel cell vehicles.

Adoption of hydrogen infrastructure and hydrogen fuel cell vehicles (HFCVs) to replace gasoline internal combustion engine (ICE) vehicles has been proposed as a strategy to reduce criteria pollutant and greenhouse gas (GHG) emissions from the transportation sector and transition to fuel independence. However, it is uncertain (1) to what degree the reduction in criteria pollutants will impact urban air quality, and (2) how the reductions in pollutant emissions and concomitant urban air quality impacts compare to ultralow emission gasoline-powered vehicles projected for a future year (e.g., 2060). To address these questions, the present study introduces a "spatially and temporally resolved energy and environment tool" (STREET) to characterize the pollutant and GHG emissions associated with a comprehensive hydrogen supply infrastructure and HFCVs at a high level of geographic and temporal resolution. To demonstrate the utility of STREET, two spatially and temporally resolved scenarios for hydrogen infrastructure are evaluated in a prototypical urban airshed (the South Coast Air Basin of California) using geographic information systems (GIS) data. The well-to-wheels (WTW) GHG emissions are quantified and the air quality is established using a detailed atmospheric chemistry and transport model followed by a comparison to a future gasoline scenario comprised of advanced ICE vehicles. One hydrogen scenario includes more renewable primary energy sources for hydrogen generation and the other includes more fossil fuel sources. The two scenarios encompass a variety of hydrogen generation, distribution, and fueling strategies. GHG emissions reductions range from 61 to 68% for both hydrogen scenarios in parallel with substantial improvements in urban air quality (e.g., reductions of 10 ppb in peak 8-h-averaged ozone and 6 mug/m(3) in 24-h-averaged particulate matter concentrations, particularly in regions of the airshed where concentrations are highest for the gasoline scenario).