Although progress has been made in controlling CO emissions from vehicles, some problems remain. One concern is that failures in the operation of emission-control systems typically default to fuel-rich conditions that produce higher CO emissions while allowing engine performance to be maintained. For example, when O 2 or temperature sensors are defective, engine computers may default to fuel-rich conditions. Similarly, defective fuel injectors may result in higher CO emissions.
A weak spark ignition can cause hard starting, misfires, and poor performance and result in increased CO emissions. Because engine failures are unavoidable and many such failures cause higher CO emissions, a strategy is needed to identify vehicles with unacceptably high emissions. High emitters exhibit increased emissions under almost all onroad operating conditions because of failure in emissions-control or fuel-control systems.
Not surprisingly, cold-start emissions from high-emitting vehicles are typically also substantially increased. Thus, identification of vehicles as high emitters during the summer months and their repair before winter can yield significant reductions in cold-start emissions.
Placing a vehicle under a driving load, such as by testing it on a dynamometer, as is done in the IM test, has a higher probability of revealing an O 2 -sensor malfunction and other defects.
Remote-sensing systems, which determine CO emissions by measuring absorption of infrared radiation from a beam directed across the roadway, can also be used to identify high emitters NRC Air quality modeling is an essential element of air quality management. Models can be used to demonstrate attainment of the NAAQS, evaluate the effects of new construction projects, and conduct further research into what causes pollution episodes and how to predict them.
A number of modeling techniques—requiring various levels of scientific expertise, input data, and computing resources—are available for those purposes. The simplest models assume a direct correlation between emissions and ambient pollutant concentrations; the most complicated models resolve temporal and spatial variations in pollutant concentrations and include the effects of meteorology, emissions, chemistry, and topography. Models are also characterized by the size of the problem they address: microscale models simulate pollution from an intersection or point source, mesoscale models simulate metropolitan or multistate pollution, and large-scale models simulate continental or global pollution.
In the attainment demonstration presented in their SIPs, states are required by EPA to model how emissions reductions will lead to the desired air quality.
Three types of models have been used to demonstrate attainment of the CO NAAQS: statistical rollback, Gaussian dispersion, and numerical predictive models.
The simplest is a statistical rollback model in which the needed reduction in emissions is assumed to be proportional to the required reduction in ambient CO concentrations ADEC a :. Because no information is needed about meteorology or the spatial distribution of emissions in a nonattainment area, EPA has allowed states to use rollback models to demonstrate attainment in smaller cities, rather than the more resource-intensive dispersion and urban-airshed models described below.
Although easy to implement, rollback models do not explicitly consider the role of meteorology or the spatial heterogeneity of CO emissions and concentrations. A second type of model that has been used for CO-attainment demonstrations is a Gaussian dispersion model, which is typically used to simulate CO concentrations for microscale analysis in the vicinity of intersections or along major traffic corridors EPA These models simulate how a pollutant is dispersed into the immediately surrounding atmosphere.
They assume that the atmospheric concentration of the pollutant is proportional to its emissions and inversely proportional to windspeed and that the resulting spatial distribution of the pollutant is Gaussian Wayson Inputs for dispersion models include meteorological data, such as windspeed and inversion strength in the vicinity of the pollutant source, and temporally resolved emissions.
Because predicted concentrations are directly proportional to emissions, the accuracy of emissions measurements is crucial to the modeling process. In the case of modeling of intersections, the emissions inventory may be derived from information about traffic patterns, mean speeds, and vehicle-fleet composition. Larger cities have also used Gaussian dispersion models to evaluate the air quality effects of increasing road capacity or other large construction projects EPA The air in the box is assumed to be well mixed, so spatial variations in emissions or pollutant concentrations on scales smaller than the box model are not resolved.
Box models are particularly useful for understanding how various emissions scenarios and meteorological conditions affect pollutant concentrations. For example, a box model for CO in Anchorage, Alaska, has been used to quantify how mechanical turbulence from roadway traffic might increase the mixing height and reduce CO concentrations on severe-stagnation days Morris Limitations of the box-model approach include an inability to include spatial variations and a dependence on assumptions to represent meteorological parameters.
Chapter 2 presents a simple box model developed for the Fairbanks nonattainment area. The most complicated models used for attainment demonstrations simulate how a pollutant concentration varies with time and space over an entire urban area. These numerical predictive models, generally intended for mesoscale analysis, can simulate emissions from multiple sources and the dispersion, advection, and photochemical reactions of gaseous pollutants in the atmosphere.
Numerical predictive models, such as the Urban Airshed Model UAM , have been used for many years to simulate O 3 , which is an areawide or mesoscale pollutant. Because of the local nature of high-CO episodes, extensive modeling of the entire urban airshed may be unnecessary for CO-attainment demonstrations. Highly trained personnel are needed to conduct the simulations. However, a simplified approach of this method may be appropriate in some cases.
More complicated models are not always appropriate for attainment demonstrations, but they can be valuable in improving our understanding of the interactions among atmospheric processes.
Even better research tools than the numerical predictive models describe above such as the UAM are process numerical models, which allow coupling between processes specific to air quality modeling and meteorology. Process numerical models typically are formulated by adding pollutant emissions, chemistry, and transport into an existing meteorological model rather than simply using the meteorological. The relatively nonreactive behavior of CO makes it an ideal chemical species for simulation in a weather model.
Despite advances in air quality modeling capabilities over the last 30 y, many improvements are still possible and needed, particularly in the numerical predictive models, which are used more widely than process numerical models. One problem is that the vertical and horizontal resolution of both types of models is too coarse to capture the variability in pollutant concentrations, which is necessary to identify local hotspots. Most numerical predictive and process numerical models are based on statistical representations of atmospheric motion on scales smaller than the spatial resolution of the models.
When unusual meteorological conditions occur, the validity of these representations becomes questionable and could lead to errors in the prediction Pielke Models used for regulatory purposes can suffer the loss of realism as a result of such shortcomings. Various models have been applied to predict future pollutant concentrations, particularly with the goal of identifying conditions that might create an episode. Numerical predictive models can be used, as can simpler empirical models, which attempt to identify statistically significant relationships between specific air quality variables and a set of predictors.
Empirical models typically use regression or neural-network techniques to develop a relationship based on observations of meteorological variables and pollutant concentrations. Future air quality can be predicted by using the output of weather-forecast models as values for the predictors. CO is a pollutant that impairs the ability of blood to carry O 2 to body tissues. Exposure to CO at sufficiently high concentrations can cause headaches, exacerbate heart problems, lengthen reaction times, and affect fetal development.
On the basis of a compilation of scientific knowledge about the relationship between various concentrations of ambient CO and their adverse health effects, EPA set CO standards of a maximum 1-h average concentration of 35 ppm and a maximum 8-h average concentration of 9 ppm.
Even though. CO remains in the outdoor air long enough to penetrate the indoor environment and is not removed by filtration. Because the measures taken to reduce CO emissions typically result in reduced emissions of copollutants, such as PM 2. Controls on CO emissions, particularly in the form of improved vehicle technology, have led to significant reductions in ambient CO concentrations throughout the United States. However, a few locations still experience concentrations that approach or exceed the CO 8-h health standard.
Most of those areas have meteorological conditions such as frequent inversion or stagnation conditions or topography such as being situated in a mountain valley that inhibit ventilation and allow CO to accumulate at high concentrations near the surface. One such location is Fairbanks, Alaska. The task of the Committee on Carbon Monoxide Episodes in Meteorological and Topographical Problem Areas was to assess approaches for predicting, assessing, and managing episodes of high CO concentrations in meteorological or topographical problem areas.
The rest of this report describes the CO problem there, including its physical characteristics, the emissions-control strategies used there, and the prospects for the area to remain in attainment with the NAAQS for CO. Carbon monoxide CO is a toxic air pollutant produced largely from vehicle emissions. Breathing CO at high concentrations leads to reduced oxygen transport by hemoglobin, which has health effects that include impaired reaction timing, headaches, lightheadedness, nausea, vomiting, weakness, clouding of consciousness, coma, and, at high enough concentrations and long enough exposure, death.
In recognition of those health effects, the U. Most areas that were previously designated as "nonattainment" areas have come into compliance with the NAAQS for CO, but some locations still have difficulty in attaining the CO standards.
Those locations tend to have topographical or meteorological characteristics that exacerbate pollution. In view of the challenges posed for some areas to attain compliance with the NAAQS for CO, congress asked the National Research Council to investigate the problem of CO in areas with meteorological and topographical problems. This interim report deals specifically with Fairbanks, Alaska. Fairbanks was chosen as a case study because its meteorological and topographical characteristics make it susceptible to severe winter inversions that trap CO and other pollutants at ground level.
Based on feedback from you, our users, we've made some improvements that make it easier than ever to read thousands of publications on our website. Jump up to the previous page or down to the next one. Also, you can type in a page number and press Enter to go directly to that page in the book. Switch between the Original Pages , where you can read the report as it appeared in print, and Text Pages for the web version, where you can highlight and search the text.
To search the entire text of this book, type in your search term here and press Enter. Ready to take your reading offline? Click here to buy this book in print or download it as a free PDF, if available. Do you enjoy reading reports from the Academies online for free? Sign up for email notifications and we'll let you know about new publications in your areas of interest when they're released.
Get This Book. Visit NAP. Looking for other ways to read this? No thanks. Page 20 Share Cite. Page 21 Share Cite. The committee will address the following specific issues: Types of emissions sources and operating conditions that contribute most to episodes of high ambient CO.
The public-health impact of such episodes. Page 22 Share Cite. Page 23 Share Cite. Page 24 Share Cite. Page 25 Share Cite. CO Exposure. Page 26 Share Cite. The relationship between indoor and outdoor CO concentrations can be evaluated with a simple differential mass-balance model Shair and Heitner that has the following steady-state solution when we combine active ventilation and passive infiltration into a single air-exchange term: 1. Page 27 Share Cite. Therefore, the solution is 2.
Page 28 Share Cite. Page 29 Share Cite. Page 30 Share Cite. Areas in Nonattainment for CO. Page 31 Share Cite. Page 32 Share Cite. Page 33 Share Cite. Page 34 Share Cite. Page 35 Share Cite. Page 36 Share Cite. Page 37 Share Cite. Page 38 Share Cite. Page 39 Share Cite. TABLE 1—2 National CO Emissions Inventory Estimates for Source Category Thousands of Short Tons Point- or Area-source fuel combustion 5, Electric utilities Industry 1, Residential wood burning 3, Other Industrial processes 7, Chemical and allied product manufacturing 1, Metals processing 1, Petroleum and related industries Waste disposal and recycling 3, Other industrial processes Onroad vehicles 49, Light-duty gas vehicles and motorcycles 27, Light-duty gas trucks 16, Heavy-duty gas vehicles 4, Diesels 2, Nonroad engines and vehicles 25, Recreational 3, Lawn and garden 11, Aircraft 1, Light commercial 4, Other 5, Miscellaneous 9, Slash or prescribed burning 6, Forest wildfires 2, Other Total 97, Source: EPA a.
Page 40 Share Cite. Page 41 Share Cite. Cold-Start Emissions. Page 42 Share Cite. New-Vehicle Certification Programs. Page 43 Share Cite. Strategies to Address Emissions-Control Failures. Page 44 Share Cite. Page 45 Share Cite. Page 46 Share Cite. Page 47 Share Cite. Page 48 Share Cite. Page 19 Share Cite. These people already have a reduced ability for getting oxygenated blood to their hearts in situations where the heart needs more oxygen than usual.
They are especially vulnerable to the effects of CO when exercising or under increased stress. In these situations, short-term exposure to elevated CO may result in reduced oxygen to the heart accompanied by chest pain also known as angina.
Skip to main content. Contact Us. EPA standards and data help state, tribal and local agencies to make sure that CO is kept at a safe level. Contact Us. Sources of Carbon Monoxide Sources of CO include: unvented kerosene and gas space heaters leaking chimneys and furnaces back-drafting from furnaces, gas water heaters, wood stoves and fireplaces gas stoves generators and other gasoline powered equipment automobile exhaust from attached garages tobacco smoke auto, truck, or bus exhaust from attached garages, nearby roads, or parking areas incomplete oxidation during combustion in gas ranges, and unvented gas or kerosene heaters worn or poorly adjusted and maintained combustion devices e.
Levels in Homes Average levels in homes without gas stoves vary from 0. Steps to Reduce Exposure to Carbon Monoxide It is most important to be sure combustion equipment is maintained and properly adjusted. Keep gas appliances properly adjusted.
Consider purchasing a vented space heater when replacing an unvented one. Use proper fuel in kerosene space heaters. Install and use an exhaust fan vented to outdoors over gas stoves. Open flues when fireplaces are in use. Choose properly sized wood stoves that are certified to meet EPA emission standards. Make certain that doors on all wood stoves fit tightly.
Have a trained professional inspect, clean and tune-up central heating system furnaces, flues and chimneys annually. Repair any leaks promptly. Do not idle the car inside garage. Measurement Methods Some relatively high-cost infrared radiation adsorption and electrochemical instruments do exist.
Available in Other Languages.
0コメント