Evans, John

2007
Ying Zhou, Jonathan I Levy, James K Hammitt, and John S Evans. 2007. “Population exposure to pollutants from the electric power sector using CALPUFF.” In Clearing the air: The health and economic damages of air pollution in China, edited by Mun S Ho and Chris P Nielsen. Cambridge, MA: MIT Press. Publisher's VersionAbstract

An interdisciplinary, quantitative assessment of the health and economic costs of air pollution in China, and of market-based policies to build environmental protection into economic development.

China's historic economic expansion is driven by fossil fuels, which increase its emissions of both local air pollutants and greenhouse gases dramatically. Clearing the Air is an innovative, quantitative examination of the national damage caused by China's degraded air quality, conducted in a pathbreaking, interdisciplinary U.S.-China collaboration. Its damage estimates are allocated by sector, making it possible for the first time to judge whether, for instance, power generation, transportation, or an unexpected source such as cement production causes the greatest environmental harm. Such objective analyses can reset policy priorities.

Clearing the Air uses this information to show how appropriate "green" taxes might not only reduce emissions and health damages but even enhance China's economic growth. It also shows to what extent these same policies could limit greenhouse gases, suggesting that wealthier nations have a responsibility to help China build environmental protection into its growth.

Clearing the Air is written for diverse readers, providing a bridge from underlying research to policy implications, with easily accessible overviews of issues and summaries of the findings for nonspecialists and policymakers followed by more specialized, interlinked studies of primary interest to scholars. Taken together, these analyses offer a uniquely integrated assessment that supports the book's economic and policy recommendations.

2006
Ying Zhou, Jonathan I Levy, John S Evans, and James K Hammitt. 2006. “The influence of geographic location on population exposure to emissions from power plants throughout China.” Environment International, 32, 3, Pp. 365-373. Publisher's VersionAbstract
This analysis seeks to evaluate the influence of emission source location on population exposure in China to fine particles and sulfur dioxide. We use the concept of intake fraction, defined as the fraction of material or its precursor released from a source that is eventually inhaled or ingested by a population. We select 29 power-plant sites throughout China and estimate annual average intake fractions at each site, using identical source characteristics to isolate the influence of geographic location. In addition, we develop regression models to interpret the intake fraction values and allow for extrapolation to other sites. To model the concentration increase due to emissions from selected power plants, we used a detailed long-range atmospheric dispersion model, CALPUFF. Primary fine particles have the highest average intake fraction (1 × 10− 5), followed by sulfur dioxide (5 × 10− 6), sulfate from sulfur dioxide (4 × 10− 6), and nitrate from nitrogen oxides (4 × 10− 6). For all pollutants, the intake fractions span approximately an order of magnitude across sites. In the regression analysis, the independent variables are meteorological proxies (such as climate region and precipitation) and population at various distances from the source. We find that population terms can explain a substantial percentage of variability in the intake fraction for all pollutants (R2 between 0.86 and 0.95 across pollutants), with a significant modifying influence of meteorological regime. Near-source population is more important for primary coarse particles while population at medium to long distance is more important for primary fine particles and secondary particles. A significant portion of intake fraction (especially for secondary particles and primary fine particles) occurs beyond 500 km of the source, emphasizing the need for detailed long-range dispersion modeling. These findings demonstrate that intake fractions for power plants in China can be estimated with reasonable precision and summarized using simple regression models. The results should be useful for informing future decisions about power-plant locations and controls.
2003
Y. Zhou, Jonathan I Levy, James K Hammitt, and John S Evans. 2003. “Estimating population exposure to power plant emissions using CALPUFF: A case study in Beijing, China.” Atmospheric Environment, 37, 6, Pp. 815-826. Publisher's VersionAbstract
Epidemiological studies have shown a significant association between ambient particulate matter (PM) exposures and increased mortality and morbidity risk. Power plants are significant emitters of precursor gases of fine particulate matter. To evaluate the public health risk posed by power plants, it is necessary to evaluate population exposure to different pollutants. The concept of intake fraction (the fraction of a pollutant emitted that is eventually inhaled or ingested by a population) has been proposed to provide a simple summary measure of the relationship between emissions and exposure. Currently available intake fraction estimates from developing countries used models that look only at the near field impacts, which may not capture the full impact of a pollution source. This case study demonstrated how the intake fraction of power plant emissions in China can be calculated using a detailed long-range atmospheric dispersion model—CALPUFF. We found that the intake fraction of primary fine particles is roughly on the order of 10−5, while the intake fractions of sulfur dioxide, sulfate and nitrate are on the order of 10−6. These estimates are an order of magnitude higher than the US estimates. We also tested how sensitive the results were to key assumptions within the model. The size distribution of primary particles has a large impact on the intake fraction for primary particles while the background ammonia concentration is an important factor influencing the intake fraction of nitrate. The background ozone concentration has a moderate impact on the intake fraction of sulfate and nitrate. Our analysis shows that this approach is applicable to a developing country and it provides reasonable population exposure estimates.
2002
Jonathan I Levy, Scott K. Wolff, and John S Evans. 2002. “A regression-based approach for estimating primary and secondary particulate matter intake fractions.” Risk Analysis, 22, 5, Pp. 893-901. Publisher's VersionAbstract
One of the common challenges for life cycle impact assessment and risk assessment is the need to estimate the population exposures associated with emissions. The concept of intake fraction (a unitless term representing the fraction of material or its precursor released from a source that is eventually inhaled or ingested) can be used when limited site data are available or the number of sources to model is large. Although studies have estimated intake fractions for some pollutant‐source combinations, there is a need to quickly and accurately estimate intake fractions for sources and settings not previously evaluated. It would be expected that limited source or site information could be used to yield intake fraction estimates with reasonable accuracy. To test this theory, we developed regression models to predict intake fractions previously estimated for primary fine particles (PM2.5) and secondary sulfate and nitrate particles from power plants and mobile sources in the United States. Our regression models were able to predict pollutant‐specific intake fractions with R2 between 0.53 and 0.86 and equations that reflected expected relationships (e.g., intake fraction increased with population density, stack height influenced the intake fraction of primary but not secondary particles). Further analysis would be needed to generalize beyond this case study and construct models applicable across source categories and settings, but our analysis demonstrates that inclusion of a limited number of parameters can significantly reduce the uncertainty in population‐average exposure estimates.