Marc Carreras-Sospedra

Images reveal that atmospheric particles can undergo liquid–liquid phase separations

A large fraction of submicron atmospheric aerosol particles contains both organic material and inorganic salts. As the relative humidity cycles in the atmosphere and the water content of the particles correspondingly changes, these mixed particles can undergo a range of phase transitions, possibly including liquid–liquid phase separation. If liquid–liquid phase separation occurs, the gas-particle partitioning of atmospheric semivolatile organic compounds, the scattering and absorption of solar radiation, and the reactive uptake of gas species on atmospheric particles may be affected, with important implications for climate predictions. The actual occurrence of liquid–liquid phase separation within individual atmospheric particles has been considered uncertain, in large part because of the absence of observations for real-world samples. Here, using optical and fluorescence microscopy, we present images that show the coexistence of two noncrystalline phases for real-world samples collected on multiple days in Atlanta, GA as well as for laboratory-generated samples under simulated atmospheric conditions. These results reveal that atmospheric particles can undergo liquid–liquid phase separations. To explore the implications of these findings, we carried out simulations of the Atlanta urban environment and found that liquid–liquid phase separation can result in increased concentrations of gas-phase NO3 and N2O5 due to decreased particle uptake of N2O5.

A methodology for developing Distributed Generation scenarios in urban areas using geographical information systems

The implementation of Distributed Generation (DG) may lead to increased pollutant emissions that adversely affect air quality. This work presents a systematic methodology to characterise DG installation in urban basins. First, a set of parameters that characterise a DG implementation scenario is described. Second, a general approach using Geographic Information Systems (GIS) data is presented. Third, the methodology is demonstrated by application to the South Coast Air Basin (SoCAB) of California. Results show that realistic scenarios in the SoCAB concentrate DG technologies nearby industrial zones and introduce pollutant mass increments no larger than 0.43% with respect to baseline emissions.

Air Quality Modeling in the South Coast Air Basin of California: What Do the Numbers Really Mean?

Abstract 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.

Air Quality Impacts of Distributed Energy Resources Implemented in the Northeastern United States

Abstract 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 μg/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 (SOx) 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.

Future Impacts of Distributed Power Generation on Ambient Ozone and Particulate Matter Concentrations in the San Joaquin Valley of California

ABSTRACT 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. IMPLICATIONS The San Joaquin Valley is a fast growing region that demands increasing power generation to sustain the economic development, and at the same time it is one of the worst polluted areas in the United States. Hence, the region demands alternatives that minimize the air quality impacts of power generation. This paper addresses the air quality impacts of distributed generation of power, an alternative to central power generation that can potentially reduce greenhouse gas and pollutant emissions throughout the United States.

Assessment of the emissions and air quality impacts of biomass and biogas use in California

ABSTRACT It is estimated that there is sufficient in-state “technically” recoverable biomass to support nearly 4000 MW of bioelectricity generation capacity. This study assesses the emissions of greenhouse gases and air pollutants and resulting air quality impacts of new and existing bioenergy capacity throughout the state of California, focusing on feedstocks and advanced technologies utilizing biomass resources predominant in each region. The options for bioresources include the production of bioelectricity and renewable natural gas (NG). Emissions of criteria pollutants and greenhouse gases are quantified for a set of scenarios that span the emission factors for power generation and the use of renewable natural gas for vehicle fueling. Emissions are input to the Community Multiscale Air Quality (CMAQ) model to predict regional and statewide temporal air quality impacts from the biopower scenarios. With current technology and at the emission levels of current installations, maximum bioelectricity production could increase nitrogen oxide (NOx) emissions by 10% in 2020, which would cause increases in ozone and particulate matter concentrations in large areas of California. Technology upgrades would achieve the lowest criteria pollutant emissions. Conversion of biomass to compressed NG (CNG) for vehicles would achieve comparable emission reductions of criteria pollutants and minimize emissions of greenhouse gases (GHG). Air quality modeling of biomass scenarios suggest that applying technological changes and emission controls would minimize the air quality impacts of bioelectricity generation. And a shift from bioelectricity production to CNG production for vehicles would reduce air quality impacts further. From a co-benefits standpoint, CNG production for vehicles appears to provide the best benefits in terms of GHG emissions and air quality. Implications: This investigation provides a consistent analysis of air quality impacts and greenhouse gas emissions for scenarios examining increased biomass use. Further work involving economic assessment, seasonal or annual emissions and air quality modeling, and potential exposure analysis would help inform policy makers and industry with respect to further development and direction of biomass policy and bioenergy technology alternatives needed to meet energy and environmental goals in California.