Linking energy sector and air quality models through downscaling: Long-run siting of electricity generators to account for spatial variability and technological innovation.

Modeling the air pollution implications of long-term energy transitions requires a downscaling process as an intermediate step between national-scale energy models and fine-scaled air quality models. Traditional "Grow-in-Place" (GIP) downscaling methods assume that future patterns of generator siting and emissions will be similar to those in the past. However, rapid technological change and shifting policy might yield very different future spatial patterns of power emissions. Here, we propose a "Site-and-Grow" (SAG) downscaling framework to couple the Electricity Market Module (EMM) of the National Energy Modeling System (NEMS) with the Community Multi-scale Air Quality (CMAQ) model to simulate future changes in emissions from power sector. The SAG framework consists of two steps. First, we downscale regional energy information to subregions using a modified generation expansion model under the assumption that economic fundamentals drive decisions at that scale. Second, we use GIS-based screening to locate potential sites for new power plants, and specify the final county-level placement using a multicriteria value function, assuming that land use and environmental constraints are most influential. The method is implemented in one EMM region (Carolinas and Virginia) as a case study. We compare spatial and temporal variability of downscaled emissions using both GIP and SAG methods, as well as emissions differences among four NEMS scenarios (base case, high natural gas consumption, high penetration of electric vehicles, and marine vessel electrification in ports). The results indicate that coal power plant emissions such as SO2 and NOx continue to dominate emissions from all other traditional power plants even in 2040, which suggests that emission changes will mainly be determined by where old coal plants are retired. An ANOVA (analysis of variance) comparison of four energy scenarios with two downscaling methods shows that the choice of downscaling method can contribute as much to emissions patterns as much as the choice of scenario.

Exposure Assessment Using Secondary Data Sources in Unconventional Natural Gas Development and Health Studies.

Studies of unconventional natural gas development (UNGD) and health have ranked participants along a gradient of geographic information system (GIS)-based activity that incorporated the distance between participants' home addresses and unconventional natural gas wells. However, studies have used different activity metrics, making result comparisons across the studies difficult. The existing studies have only incorporated wells, without accounting for other components of development (e.g., compressors, impoundments, and flaring events), for which it is often difficult to obtain reliable data but may have relevance to health. Our aims were to (1) describe, in space and time, UNGD-related compressors, impoundments, and flaring events; (2) evaluate whether and how to incorporate these into UNGD activity assessment; and (3) evaluate associations of these different approaches with mild asthma exacerbations. We identified 361 compressor stations, 1218 impoundments, and 216 locations with flaring events. A principal component analysis identified a single component that was approximately an equal mix of the metrics for compressors, impoundments, and four phases of well development (pad preparation, drilling, stimulation, and production). However, temporal coverage for impoundments and flaring data was sparse. Ultimately, we evaluated three UNGD activity metrics, including two based on the existing studies and a novel metric that included well pad development, drilling, stimulation, production, and compressor engine aspects of UNGD. The three metrics had varying magnitudes of association with mild asthma exacerbations, although the highest category of each metric (vs the lowest) was associated with the outcome.

Assessing the impact of airline travel on the geographic spread of pandemic influenza.

The objective of this research is to explore what would happen if the Hong Kong influenza pandemic strain of 1968-1969 returned in 2000. We report the results of a series of simulations of an SEIR epidemic model coupled with air transportation data for 52 global cities. Preliminary results suggest that if the 1968-1969 pandemic strain returned, it would spread concurrently to cities in both the northern and southern hemispheres thereby exhibiting less of the characteristic seasonal swing. In addition, after recognition of pandemic onset in the focal city, the time lag for public health intervention is very short. These findings highlight the importance of coordinated global surveillance and pandemic planning.