Background: Automobile exhaust contains precursors to ozone and fine particulate matter (PM ≤ 2.5 µm in aerodynamic diameter; PM2.5), posing health risks. Dependency on car commuting also reduces physical fitness opportunities.Objective: In this study we sought to quantify benefits from reducing automobile usage for short urban and suburban trips.Methods: We simulated census-tract level changes in hourly pollutant concentrations from the elimination of automobile round trips ≤ 8 km in 11 metropolitan areas in the upper midwestern United States using the Community Multiscale Air Quality (CMAQ) model. Next, we estimated annual changes in health outcomes and monetary costs expected from pollution changes using the U.S. Environmental Protection Agency Benefits Mapping Analysis Program (BenMAP). In addition, we used the World Health Organization Health Economic Assessment Tool (HEAT) to calculate benefits of increased physical activity if 50% of short trips were made by bicycle.Results: We estimate that, by eliminating these short automobile trips, annual average urban PM2.5 would decline by 0.1 µg/m3 and that summer ozone (O3) would increase slightly in cities but decline regionally, resulting in net health bene-fits of $4.94 billion/year [95% confidence interval (CI): $0.2 billion, $13.5 billion), with 25% of PM2.5 and most O3 bene-fits to populations outside metropolitan areas. Across the study region of approximately 31.3 million people and 37,000 total square miles, mortality would decline by approximately 1,295 deaths/year (95% CI: 912, 1,636) because of improved air quality and increased exercise. Making 50% of short trips by bicycle would yield savings of approximately $3.8 billion/year from avoided mortality and reduced health care costs (95% CI: $2.7 billion, $5.0 billion]. We estimate that the combined benefits of improved air quality and physical fitness would exceed $8 billion/year.Conclusion: Our findings suggest that significant health and economic benefits are possible if bicycling replaces short car trips. Less dependence on automobiles in urban areas would also improve health in downwind rural settings.
This paper presents the results of a study on the effectiveness of smart growth development patterns and vehicle fleet hybridization in reducing mobile source emissions of carbon dioxide (CO 2 ) across 11 major metropolitan regions of the Midwestern U.S. over a 50-year period. Through the integration of a vehicle travel activity modeling framework developed by researchers at the Oak Ridge National Laboratory with small area population projections, we model mobile source emissions of CO 2 associated with alternative land development and technology change scenarios between 2000 and 2050. Our findings suggest that under an aggressive smart growth scenario, growth in emissions expected to occur under a business as usual scenario is reduced by 34%, while the full dissemination of hybrid-electric vehicles throughout the light vehicle fleet is found to offset the expected growth in emissions by 97%. Our results further suggest that high levels of urban densification could achieve reductions in 2050 CO 2 emissions equivalent to those attainable through the full dissemination of hybrid-electric vehicle technologies.
Developers and users of watershed modeling systems face a tradeoff between increased spatial detail and the amount of time and computing resources needed to build, calibrate, and run models. A number of systems have been developed that can estimate or predict surface water runoff and nonpoint source (NPS) pollution at different scales, under variable soil, land use, climate, and topographic conditions. With advances in data processing and network storage capacity, public data on these variables are increasingly available Does soil data resolution matter? State Soil Geographic database versus Soil Survey Geographic database in rainfall-runoff modeling across Wisconsin A.C. Mednick Abstract: Whether or not the use of generalized, State Soil Geographic (STATSGO) data in place of higher resolution Soil Survey Geographic (SSURGO) data reduces the accuracy of hydrologic and nonpoint source pollution models has thus far been an open question. Comparative studies have yet to reveal a systematic bias in STATSGO-based model outputs on account of their small sample sizes and differences in the models employed. In an effort to determine whether a bias exists, direct runoff was modeled for a hypothetical 24-hour rainfall event, using STATSGO and SSURGO as alternative inputs to a series of standard rainfall-runoff models in nearly 300 contiguous watersheds, spanning most of the state of Wisconsin. The Long-Term Hydrologic Impact Assessment (L-THIA) modeling tool was used for this analysis. Results indicate that there is a negative bias in STATSGO-based runoff over the large majority of the study area and that the degree of underprediction is highest for spatially disaggregated (distributed parameter) models. Runoff was also modeled for daily precipitation in six gauged watersheds and was compared to observed runoff, with SSURGO-based, distributed models typically producing the most accurate outputs. In addition, a series of regression analyses was conducted to determine whether, and in what direction, the STATSGO bias is affected by the percent coverage of land uses that discourage infiltration. The results of these analyses suggest that STATSGO-based, lumped, and partially distributed models, on average, underpredict the relative impact of increasing land-use intensity. These findings indicate that two of the most common approaches to improving the computational efficiency of watershed modeling systems: the use of lower resolution soils data and the lumping of model parameters to larger spatial units of analysis, combine to reduce the accuracy of modeled runoff under current conditions, while simultaneously underestimating the impact of potential future land-use change.Key words: hydrologic group-land use-rainfall-runoff modeling-Soil Survey Geographic (SSURGO) database-State Soil Geographic (STATSGO) database-Wisconsin at higher resolutions. Spatially disaggregated Soil Survey Geographic (SSURGO) data are now available for the vast majority of US counties (for the current status of available SSURGO data across the United...
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.