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Artykuły w czasopismach na temat "United States. Environmental Protection Agency. National Library Network"
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Altendorf, Jereme M. "APPLYING THE NATIONAL ENVIRONMENTAL POLICY ACT'S (NEPA) FUNCTIONAL EQUIVALENCE DOCTRINE TO THE NATIONAL OIL AND HAZARDOUS SUBSTANCES POLLUTION CONTINGENCY PLAN (NCP)". International Oil Spill Conference Proceedings 2005, nr 1 (1.05.2005): 107–10. http://dx.doi.org/10.7901/2169-3358-2005-1-107.
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ABSTRACT NEPA is a policy and procedural statute that makes environmental protection a part of the mandate of every federal agency and department. NEPA was enacted to establish a framework for public review of the environmental impacts of actions carried out by the federal government. NEPA anticipates that most federal actions are planned in detail and are implemented over the course of months or years. This planning and implementation cycle, allows detailed analysis of specific project impacts. Environmental response actions taken by the United States Environmental Protection Agency (EPA) or the United States Coast Guard (CG) under the regulatory authorities established by the National Oil and Hazardous Substances Pollution Contingency Plan (NCP) are also considered specific federal actions. However, the nature of these specific actions varies greatly depending on the exact nature of each incident; therefore traditional NEPA planning is neither possible nor appropriate. The NCP establishes a mechanism of continuous environmental assessment and review through the network of Regional Response Teams (RRT), local emergency area planning committees, Area Contingency Planning (ACP) Committees, and the availability of local area contingency plans to the public on a contingency basis for review or comment. Federal courts have allowed functional equivalence doctrine to apply exclusively to EPA because of their adherence to “substantive and procedural standards ensuring full and adequate consideration of environmental issues.” These decisions have held up the interpretation that NEPA compliance is unnecessary where the agency is independently required to consider environmental issues. The EPA and the CG share the responsibility of protecting public health, welfare, and environment from discharges or threats of discharges of oil and/or releases or threats of a releases of hazardous substances, pollutants and/or contaminants under the planning, preparedness, and response scheme established by the NCP and carried out by those working within the National Response System (NRS). For this reason any planning, preparedness, and response activities undertaken by EPA and CG personnel to mitigate accidental or intentional discharges of oil or releases of hazardous substances, pollutants, and/or contaminants within the purview of the NCP should be interpreted as functionally equivalent to the requirements found within NEPA.
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Dewage, Prabuddha M. H., Lakitha O. H. Wijeratne, Xiaohe Yu, Mazhar Iqbal, Gokul Balagopal, John Waczak, Ashen Fernando, Matthew D. Lary, Shisir Ruwali i David J. Lary. "Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches". Remote Sensing 16, nr 13 (3.07.2024): 2454. http://dx.doi.org/10.3390/rs16132454.
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This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM 2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM 2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM 2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM 2.5 analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM 2.5 to 9 μg/m 3, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM 2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM 2.5 levels that exceeded 9 μg/m 3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m 3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM 2.5 concentrations below 9 μg/m 3.
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Hu, Lei, Stephen A. Montzka, Ben R. Miller, Arlyn E. Andrews, John B. Miller, Scott J. Lehman, Colm Sweeney i in. "Continued emissions of carbon tetrachloride from the United States nearly two decades after its phaseout for dispersive uses". Proceedings of the National Academy of Sciences 113, nr 11 (29.02.2016): 2880–85. http://dx.doi.org/10.1073/pnas.1522284113.
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National-scale emissions of carbon tetrachloride (CCl4) are derived based on inverse modeling of atmospheric observations at multiple sites across the United States from the National Oceanic and Atmospheric Administration’s flask air sampling network. We estimate an annual average US emission of 4.0 (2.0–6.5) Gg CCl4 y−1 during 2008–2012, which is almost two orders of magnitude larger than reported to the US Environmental Protection Agency (EPA) Toxics Release Inventory (TRI) (mean of 0.06 Gg y−1) but only 8% (3–22%) of global CCl4 emissions during these years. Emissive regions identified by the observations and consistently shown in all inversion results include the Gulf Coast states, the San Francisco Bay Area in California, and the Denver area in Colorado. Both the observation-derived emissions and the US EPA TRI identified Texas and Louisiana as the largest contributors, accounting for one- to two-thirds of the US national total CCl4 emission during 2008–2012. These results are qualitatively consistent with multiple aircraft and ship surveys conducted in earlier years, which suggested significant enhancements in atmospheric mole fractions measured near Houston and surrounding areas. Furthermore, the emission distribution derived for CCl4 throughout the United States is more consistent with the distribution of industrial activities included in the TRI than with the distribution of other potential CCl4 sources such as uncapped landfills or activities related to population density (e.g., use of chlorine-containing bleach).
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Lee, Pius, Daiwen Kang, Jeff McQueen, Marina Tsidulko, Mary Hart, Geoff DiMego, Nelson Seaman i Paula Davidson. "Impact of Domain Size on Modeled Ozone Forecast for the Northeastern United States". Journal of Applied Meteorology and Climatology 47, nr 2 (1.02.2008): 443–61. http://dx.doi.org/10.1175/2007jamc1408.1.
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Abstract This study investigates the impact of model domain extent and the specification of lateral boundary conditions on the forecast quality of air pollution constituents in a specific region of interest. A developmental version of the national Air Quality Forecast System (AQFS) has been used in this study. The AQFS is based on the NWS/NCEP Eta Model (recently renamed the North American Mesoscale Model) coupled with the U.S. Environmental Protection Agency Community Multiscale Air Quality (CMAQ) model. This coupled Eta–CMAQ modeling system provided experimental air quality forecasts for the northeastern region of the United States during the summers of 2003 and 2004. The initial forecast over the northeastern United States was approved for operational deployment in September 2004. The AQFS will provide forecast coverage for the entire United States in the near future. In a continuing program of phased development to extend the geographical coverage of the forecast, the developmental version of AQFS has undergone two domain expansions. Hereinafter, this “developmental” domain-expanded forecast system AQFS will be dubbed AQFS-β. The current study evaluates the performance of AQFS-β for the northeastern United States using three domain sizes. Quantitative comparisons of forecast results with compiled observation data from the U.S. Aerometric Information Retrieval Now (AIRNOW) network were performed for each model domain, and interdomain comparisons were made for the regions of overlap. Several forecast skill score measures have been employed. Based on the categorical statistical metric of the critical success index, the largest domain achieved the highest skill score. This conclusion should catapult the implementation of the largest domain to attain the best forecast performance whenever the operational resource and criteria permit.
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Travis, Katherine R., Daniel J. Jacob, Jenny A. Fisher, Patrick S. Kim, Eloise A. Marais, Lei Zhu, Karen Yu i in. "Why do models overestimate surface ozone in the Southeast United States?" Atmospheric Chemistry and Physics 16, nr 21 (1.11.2016): 13561–77. http://dx.doi.org/10.5194/acp-16-13561-2016.
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Abstract. Ozone pollution in the Southeast US involves complex chemistry driven by emissions of anthropogenic nitrogen oxide radicals (NOx ≡ NO + NO2) and biogenic isoprene. Model estimates of surface ozone concentrations tend to be biased high in the region and this is of concern for designing effective emission control strategies to meet air quality standards. We use detailed chemical observations from the SEAC4RS aircraft campaign in August and September 2013, interpreted with the GEOS-Chem chemical transport model at 0.25° × 0.3125° horizontal resolution, to better understand the factors controlling surface ozone in the Southeast US. We find that the National Emission Inventory (NEI) for NOx from the US Environmental Protection Agency (EPA) is too high. This finding is based on SEAC4RS observations of NOx and its oxidation products, surface network observations of nitrate wet deposition fluxes, and OMI satellite observations of tropospheric NO2 columns. Our results indicate that NEI NOx emissions from mobile and industrial sources must be reduced by 30–60 %, dependent on the assumption of the contribution by soil NOx emissions. Upper-tropospheric NO2 from lightning makes a large contribution to satellite observations of tropospheric NO2 that must be accounted for when using these data to estimate surface NOx emissions. We find that only half of isoprene oxidation proceeds by the high-NOx pathway to produce ozone; this fraction is only moderately sensitive to changes in NOx emissions because isoprene and NOx emissions are spatially segregated. GEOS-Chem with reduced NOx emissions provides an unbiased simulation of ozone observations from the aircraft and reproduces the observed ozone production efficiency in the boundary layer as derived from a regression of ozone and NOx oxidation products. However, the model is still biased high by 6 ± 14 ppb relative to observed surface ozone in the Southeast US. Ozonesondes launched during midday hours show a 7 ppb ozone decrease from 1.5 km to the surface that GEOS-Chem does not capture. This bias may reflect a combination of excessive vertical mixing and net ozone production in the model boundary layer.
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Adeniran, Jamiu Adetayo, Rafiu O. Yusuf, Michael O. Amole, Lukuman Adekilekun Jimoda i Jacob Ademola Sonibare. "Air quality impact of diesel back-up generators (BUGs) in Nigeria’s mobile telecommunication base transceiver stations (BTS)". Management of Environmental Quality: An International Journal 28, nr 5 (14.08.2017): 723–44. http://dx.doi.org/10.1108/meq-09-2015-0168.
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Purpose The introduction of mobile telecommunication services in Nigeria led to the development of base transceiver stations (BTS) across the country. Inadequate power supply from the national grid has led to massive use of diesel-fueled back-up generators (BUGs). The purpose of this paper is to attempt to quantify and inform relevant stakeholders about air quality implications of BTS BUGs. Design/methodology/approach Seven major telecommunication network operators were identified. Emission factor approach was used to estimate the quantity of important air pollutants such as NOx, CO, SO2, PM10, PM2.5, PAH and TVOC that are emitted from the use of the BUGs based on fuel consumption rate and generators’ capacity. Fuel-based emission inventory and emission factor from the United States Environmental Protection Agency AP-42 and National Pollution Inventory were used to estimate pollutants emission from diesel-powered generators used in the BTS sites and amount of diesel consumed. Land distribution and per capita dose of the estimated pollutants load were calculated. Findings The study showed that the deployment of BUGs will lead to increase emissions of these air pollutants. The states that are most affected are Lagos, Kano and Oyo, Katsina and Akwa Ibom states with respective total air pollutants contribution of 9,539.61, 9,445.34, 8,276.46, 7,805.14 and 7,220.70 tonnes/yr. Originality/value This study has estimated pollutant emissions from the use of diesel-fueled BUGs in mobile telecommunications BTS sites in Nigeria. The data obtained could assist in policy making.
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Basu, Sourish, Scott J. Lehman, John B. Miller, Arlyn E. Andrews, Colm Sweeney, Kevin R. Gurney, Xiaomei Xu, John Southon i Pieter P. Tans. "Estimating US fossil fuel CO2emissions from measurements of14C in atmospheric CO2". Proceedings of the National Academy of Sciences 117, nr 24 (1.06.2020): 13300–13307. http://dx.doi.org/10.1073/pnas.1919032117.
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We report national scale estimates of CO2emissions from fossil-fuel combustion and cement production in the United States based directly on atmospheric observations, using a dual-tracer inverse modeling framework and CO2andΔ14CO2measurements obtained primarily from the North American portion of the National Oceanic and Atmospheric Administration’s Global Greenhouse Gas Reference Network. The derived US national total for 2010 is 1,653 ± 30 TgC yr−1with an uncertainty (1σ) that takes into account random errors associated with atmospheric transport, atmospheric measurements, and specified prior CO2and14C fluxes. The atmosphere-derived estimate is significantly larger (>3σ) than US national emissions for 2010 from three global inventories widely used for CO2accounting, even after adjustments for emissions that might be sensed by the atmospheric network, but which are not included in inventory totals. It is also larger (>2σ) than a similarly adjusted total from the US Environmental Protection Agency (EPA), but overlaps EPA’s reported upper 95% confidence limit. In contrast, the atmosphere-derived estimate is within1σof the adjusted 2010 annual total and nine of 12 adjusted monthly totals aggregated from the latest version of the high-resolution, US-specific “Vulcan” emission data product. Derived emissions appear to be robust to a range of assumed prior emissions and other parameters of the inversion framework. While we cannot rule out a possible bias from assumed prior Net Ecosystem Exchange over North America, we show that this can be overcome with additionalΔ14CO2measurements. These results indicate the strong potential for quantification of US emissions and their multiyear trends from atmospheric observations.
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Brunner, J., R. B. Pierce i A. Lenzen. "Development and validation of satellite-based estimates of surface visibility". Atmospheric Measurement Techniques 9, nr 2 (11.02.2016): 409–22. http://dx.doi.org/10.5194/amt-9-409-2016.
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Abstract. A satellite-based surface visibility retrieval has been developed using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as a proxy for Advanced Baseline Imager (ABI) data from the next generation of Geostationary Operational Environmental Satellites (GOES-R). The retrieval uses a multiple linear regression approach to relate satellite aerosol optical depth, fog/low cloud probability and thickness retrievals, and meteorological variables from numerical weather prediction forecasts to National Weather Service Automated Surface Observing System (ASOS) surface visibility measurements. Validation using independent ASOS measurements shows that the GOES-R ABI surface visibility retrieval (V) has an overall success rate of 64.5 % for classifying clear (V ≥ 30 km), moderate (10 km ≤ V < 30 km), low (2 km ≤ V < 10 km), and poor (V < 2 km) visibilities and shows the most skill during June through September, when Heidke skill scores are between 0.2 and 0.4. We demonstrate that the aerosol (clear-sky) component of the GOES-R ABI visibility retrieval can be used to augment measurements from the United States Environmental Protection Agency (EPA) and National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) network and provide useful information to the regional planning offices responsible for developing mitigation strategies required under the EPA's Regional Haze Rule, particularly during regional haze events associated with smoke from wildfires.
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Brunner, J., R. B. Pierce i A. Lenzen. "Development and validation of satellite based estimates of surface visibility". Atmospheric Measurement Techniques Discussions 8, nr 10 (29.10.2015): 11255–84. http://dx.doi.org/10.5194/amtd-8-11255-2015.
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Abstract. A satellite based surface visibility retrieval has been developed using Moderate Resolution Imaging Spectroradiometer (MODIS) measurements as a proxy for Advanced Baseline Imager (ABI) data from the next generation of Geostationary Operational Environmental Satellites (GOES-R). The retrieval uses a multiple linear regression approach to relate satellite aerosol optical depth, fog/low cloud probability and thickness retrievals, and meteorological variables from numerical weather prediction forecasts to National Weather Service Automated Surface Observing System (ASOS) surface visibility measurements. Validation using independent ASOS measurements shows that the GOES-R ABI surface visibility retrieval (V) has an overall success rate of 64.5% for classifying Clear (V ≥ 30 km), Moderate (10 km ≤ V < 30 km), Low (2 km ≤ V < 10 km) and Poor (V < 2 km) visibilities and shows the most skill during June through September, when Heidke skill scores are between 0.2 and 0.4. We demonstrate that the aerosol (clear sky) component of the GOES-R ABI visibility retrieval can be used to augment measurements from the United States Environmental Protection Agency (EPA) and National Park Service (NPS) Interagency Monitoring of Protected Visual Environments (IMPROVE) network, and provide useful information to the regional planning offices responsible for developing mitigation strategies required under the EPA's Regional Haze Rule, particularly during regional haze events associated with smoke from wildfires.
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Gantt, Brett, Kelsey McDonald, Barron Henderson i Elizabeth Mannshardt. "Incorporation of Remote PM2.5 Concentrations into the Downscaler Model for Spatially Fused Air Quality Surfaces". Atmosphere 11, nr 1 (15.01.2020): 103. http://dx.doi.org/10.3390/atmos11010103.
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The United States Environmental Protection Agency (EPA) has implemented a Bayesian spatial data fusion model called the Downscaler (DS) model to generate daily air quality surfaces for PM2.5 across the contiguous U.S. Previous implementations of DS relied on monitoring data from EPA’s Air Quality System (AQS) network, which is largely concentrated in urban areas. In this work, we introduce to the DS modeling framework an additional PM2.5 input dataset from the Interagency Monitoring of Protected Visual Environments (IMPROVE) network located mainly in remote sites. In the western U.S. where IMPROVE sites are relatively dense (compared to the eastern U.S.), the inclusion of IMPROVE PM2.5 data to the DS model runs reduces predicted annual averages and 98th percentile concentrations by as much as 1.0 and 4 μg m−3, respectively. Some urban areas in the western U.S., such as Denver, Colorado, had moderate increases in the predicted annual average concentrations, which led to a sharpening of the gradient between urban and remote areas. Comparison of observed and DS-predicted concentrations for the grid cells containing IMPROVE and AQS sites revealed consistent improvement at the IMPROVE sites but some degradation at the AQS sites. Cross-validation results of common site-days withheld in both simulations show a slight reduction in the mean bias but a slight increase in the mean square error when the IMPROVE data is included. These results indicate that the output of the DS model (and presumably other Bayesian data fusion models) is sensitive to the addition of geographically distinct input data, and that the application of such models should consider the prediction domain (national or urban focused) when deciding to include new input data.
Książki na temat "United States. Environmental Protection Agency. National Library Network"
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Vellios, Bernard T. Environmental Protection Agency library network. Hauppauge, NY: Nova Science Publishers, 2009.
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Bearden, David M. Restructuring EPA's Libraries: Background and issues for Congress. Washington, D.C: Congressional Research Service, Library of Congress, 2007.
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Office, General Accounting. Air pollution: National air monitoring network is inadequate : report to congressional requesters. Washington, D.C: The Office, 1989.
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Vellios, Bernard T. Environmental Protection Agency Library Network. Nova Science Publishers, Incorporated, 2009.
Znajdź pełny tekst źródłaCzęści książek na temat "United States. Environmental Protection Agency. National Library Network"
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Snyder, Amanda. "Environmental Protection Agency". W Cases on Electronic Records and Resource Management Implementation in Diverse Environments, 363–77. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4466-3.ch022.
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The Environmental Protection Agency (EPA) provides access to information on a variety of topics related to the environment and strives to inform citizens of health risks. The EPA also has an extensive library network that consists of 26 libraries throughout the United States, which provide access to a plethora of information to EPA employees, scientists, and researchers. The EPA implemented a reorganization project to digitize their materials so they would be more accessible to a wider range of users, but this plan was drastically accelerated when the EPA was threatened with a budget cut. It chose to close and reduce the hours and services of some of their libraries. As a result, the agency was accused of denying users the “right to know” by making information unavailable, not providing an adequate strategic plan, and discarding vital materials. This case study explores the background of the digitization project, the practices implemented, and the critiques of the project.