Institution
United States Environmental Protection Agency
Government•Washington D.C., District of Columbia, United States•
About: United States Environmental Protection Agency is a government organization based out in Washington D.C., District of Columbia, United States. It is known for research contribution in the topics: Population & Environmental exposure. The organization has 13873 authors who have published 26902 publications receiving 1191729 citations. The organization is also known as: EPA & Environmental Protection Agency.
Papers published on a yearly basis
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University College London1, University of London2, Johns Hopkins University3, Rockefeller Foundation4, United Nations University5, University of Washington6, Tsinghua University7, Harvard University8, Wildlife Conservation Society9, Duke University10, United States Environmental Protection Agency11, World Bank12
TL;DR: In this paper, the authors identify three categories of challenges that have to be addressed to maintain and enhance human health in the face of increasingly harmful environmental trends: conceptual and empathy failures (imagination challenges), such as an overreliance on gross domestic product as a measure of human progress, the failure to account for future health and environmental harms over present day gains, and the disproportionate eff ect of those harms on the poor and those in developing nations.
1,452 citations
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TL;DR: In this article, the authors present the technical basis for establishing sediment quality criteria using equilibrium partitioning (EqP), which is chosen because it addresses the two principal technical issues that must be resolved: the varying bioavailability of chemicals in sediments and the choice of the appropriate biological effects concentration.
Abstract: The purpose of this review paper is to present the technical basis for establishing sediment quality criteria using equilibrium partitioning (EqP). Equilibrium partitioning is chosen because it addresses the two principal technical issues that must be resolved: the varying bioavailability of chemicals in sediments and the choice of the appropriate biological effects concentration.
The data that are used to examine the question of varying bioavailability across sediments are from toxicity and bioaccumulation experiments utilizing the same chemical and test organism but different sediments. It has been found that if the different sediments in each experiment are compared, there is essentially no relationship between sediment chemical concentrations on a dry weight basis and biological effects. However, if the chemical concentrations in the pore water of the sediment are used (for chemicals that are not highly hydrophobic) or if the sediment chemical concentrations on an organic carbon basis are used, then the biological effects occur at similar concentrations (within a factor of two) for the different sediments. In addition, the effects concentrations are the same as, or they can be predicted from, the effects concentration determined in water- only exposures.
The EqP methodology rationalizes these results by assuming that the partitioning of the chemical between sediment organic carbon and pore water is at equilibrium. In each of these phases, the fugacity or activity of the chemical is the same at equilibrium. As a consequence, it is assumed that the organism receives an equivalent exposure from a water-only exposure or from any equilibrated phase, either from pore water via respiration, from sediment carbon via ingestion; or from a mixture of the routes. Thus, the pathway of exposure is not significant. The biological effect is produced by the chemical activity of the single phase or the equilibrated system.
Sediment quality criteria for nonionic organic chemicals are based on the chemical concentration in sediment organic carbon. For highly hydrophobic chemicals this is necessary because the pore water concentration is, for those chemicals, no longer a good estimate of the chemical activity. The pore water concentration is the sum of the free chemical concentration, which is bioavailable and represents the chemical activity, and the concentration of chemical complexed to dissolved organic carbon, which, as the data presented below illustrate, is not bioavailable. Using the chemical concentration in sediment organic carbon eliminates this ambiguity.
Sediment quality criteria also require that a chemical concentration be chosen that is sufficiently protective of benthic organisms. The final chronic value (FCV) from the U.S. Environmental Protection Agency (EPA) water quality criteria is proposed. An analysis of the data compiled in the water quality criteria documents demonstrates that benthic species, defined as either epibenthic or infaunal species, have a similar sensitivity to water column species. This is the case if the most sensitive species are compared and if all species are compared. The results of benthic colonization experiments also support the use of the FCV.
Equilibrium partitioning cannot remove all the variation in the experimentally observed sediment- effects concentration and the concentration predicted from water-only exposures. A variation of approximately a factor of two to three remains. Hence, it is recognized that a quantification of this uncertainty should accompany the sediment quality criteria.
The derivation of sediment quality criteria requires the octanol/water partition coefficient of the chemical. It should be measured with modern experimental techniques, which appear to remove the large variation in reported values. The derivation of the final chronic value should also be updated to include the most recent toxicological information.
1,369 citations
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TL;DR: In this article, the root biomass density is estimated based on existing data from the literature and linear regression analysis is used to determine if a reliable method to estimate root density for forests could be developed.
Abstract: Because the world's forests play a major role in regulating nutrient and carbon cycles, there is much interest in estimating their biomass. Estimates of aboveground biomass based on well-established methods are relatively abundant; estimates of root biomass based on standard methods are much less common. The goal of this work was to determine if a reliable method to estimate root biomass density for forests could be developed based on existing data from the literature. The forestry literature containing root biomass measurements was reviewed and summarized and relationships between both root biomass density (Mg ha−1) and root:shoot ratios (R/S) as dependent variables and various edaphic and climatic independent variables, singly and in combination, were statistically tested. None of the tested independent variables of aboveground biomass density, latitude, temperature, precipitation, temperature:precipitation ratios, tree type, soil texture, and age had important explanatory value for R/S. However, linear regression analysis showed that aboveground biomass density, age, and latitudinal category were the most important predictors of root biomass density, and together explained 84% of the variation. A comparison of root biomass density estimates based on our equations with those based on use of generalized R/S ratios for forests in the United States indicated that our method tended to produce estimates that were about 20% higher.
1,334 citations
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1,286 citations
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Health Canada1, United States Environmental Protection Agency2, Brigham Young University3, University of Texas at Austin4, University of British Columbia5, Health Effects Institute6, McGill University7, University of Minnesota8, Harvard University9, Utrecht University10, University of Washington11, Fudan University12, New York University13, University of California, Los Angeles14, University of Ottawa15, American Cancer Society16, University of California, Davis17, Cancer Prevention Institute of California18, University of New Brunswick19, Dalhousie University20, Carleton University21, Statistics Canada22, University of Toronto23, Chinese Center for Disease Control and Prevention24, St George's, University of London25, University of Hong Kong26, University of Ulm27, SERC Reliability Corporation28
TL;DR: PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.
Abstract: Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations.
1,283 citations
Authors
Showing all 13926 results
Name | H-index | Papers | Citations |
---|---|---|---|
Joel Schwartz | 183 | 1149 | 109985 |
Timothy A. Springer | 167 | 669 | 122421 |
Chien-Jen Chen | 128 | 655 | 66360 |
Matthew W. Gillman | 126 | 529 | 55835 |
J. D. Hansen | 122 | 975 | 76198 |
Dionysios D. Dionysiou | 116 | 675 | 48449 |
John P. Giesy | 114 | 1162 | 62790 |
Douglas W. Dockery | 105 | 244 | 57461 |
Charles P. Gerba | 102 | 692 | 35871 |
David A. Savitz | 99 | 572 | 32947 |
Stephen Polasky | 99 | 354 | 59148 |
Judith C. Chow | 96 | 427 | 32632 |
Diane R. Gold | 95 | 443 | 30717 |
Scott L. Zeger | 95 | 377 | 78179 |
Rajender S. Varma | 95 | 672 | 37083 |