Analysis of the Trend and Seasonal Cycle of Carbon Monoxide Concentrations in an Urban Area (4 pp)

Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the time series analysed, due to their strong masking effects. Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February 2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied. One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter. The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O₃ and PM to examine underlying trends. Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal variations. Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions. There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions which is a direct consequence of the holiday period. Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions. Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability     

Trends in on-road vehicle emissions and ambient air quality in Atlanta,Georgia, USA,from the late 1990s through 2009

On-road vehicle emissions of carbon monoxide (CO), nitrogen oxides (NO_x), and volatile organic compounds (VOCs) during 1995–2009 in the Atlanta Metropolitan Statistical Area were estimated using the Motor Vehicle Emission Simulator (MOVES) model and data from the National Emissions Inventories and the State of Georgia. Statistically significant downward trends (computed using the nonparametric Theil-Sen method) in annual on-road CO, NO_x, and VOC emissions of 6.1%, 3.3%, and 6.0% per year, respectively, are noted during the 1995–2009 period despite an increase in total vehicle distance traveled. The CO and NO_x emission trends are correlated with statistically significant downward trends in ambient air concentrations of CO and NO_x in Atlanta ranging from 8.0% to 11.8% per year and from 5.8% to 8.7% per year, respectively, during similar time periods. Weather-adjusted summertime ozone concentrations in Atlanta exhibited a statistically significant declining trend of 2.3% per year during 2001–2009. Although this trend coexists with the declining trends in on-road NO_x, VOC, and CO emissions, identifying the cause of the downward trend in ozone is complicated by reductions in multiple precursors from different source sectors.

Implications:Large reductions in on-road vehicle emissions of CO and NO_x in Atlanta from the late 1990s to 2009, despite an increase in total vehicle distance traveled, contributed to a significant improvement in air quality through decreases in ambient air concentrations of CO and NO_x during this time period. Emissions reductions in motor vehicles and other source sectors resulted in these improvements and the observed declining trend in ozone concentrations over the past decade. Although these historical trends cannot be extrapolated to the future because pollutant concentration contributions due to on-road vehicle emissions will likely become an increasingly smaller fraction of the atmospheric total, they provide an indication of the benefits of past control measures.     

Assessment of vehicular pollution in China

As the motor vehicle population in China continues to increase at an annual rate of approximately 15%, air pollution related to vehicular emissions has become the focus of attention, especially in large cities. There is an urgent need to identify the severity of this pollution in China. Based on an investigation into vehicle service characteristics, this study used a series of driving cycle tests of in-use Chinese motor vehicles for their emission factors in laboratories, which indicated that CO and HC emission factors are 5-10 times higher, and NOx 2-5 times higher, than levels in developed countries. The MOBILE5 model was adapted to the Chinese situation and used to calculate the emission of pollutants from motor vehicles. Results show that vehicle emission is concentrated in major cities, such as Beijing, Guangzhou, Shanghai, and Tianjin. Motor vehicle emissions contribute a significant proportion of pollutants in those cities, with contribution rates of CO and NOx greater than 80% and 40%, respectively, in Beijing and Guangzhou. Urban air quality is far worse than the national ambient air quality standard. In conclusion, although China has a relatively small number of motor vehicles, most of them are concentrated within metropolitan areas, and their emissions are closely related to urban air pollution problems in large cities.     

Comparison of air pollutant emissions among mega-cities

Ambient measurements of hydrocarbons, carbon monoxide and nitrogen oxides from three mega-cities (Beijing, Mexico City, Tokyo) are compared with similar measurements from US cities in the mid-1980s and the early 2000s. The common hydrocarbon pattern seen in all data sets suggests that emissions associated with gasoline-fueled vehicles dominate in all of these cities. This commonality suggests that it will be efficient and, ultimately, cost effective to proceed with vehicular emission controls in most emerging mega-cities, while proceeding with development of more locally appropriate air quality control strategies through emissions inventory development and ambient air monitoring. Over the three decades covered by the US data sets, the hydrocarbon emissions decreased by a significant factor (something like an order of magnitude), which is greater than suggested by emission inventories, particularly the EDGAR international inventory. The ambient hydrocarbon and CO concentrations reported for the three non-US mega-cities are higher than present US ambient concentrations, but lower than those observed in the 1980s in the US. The one exception to the preceding statement is the high concentrations of CO observed in Beijing, which apparently have a large regional contribution.     

Statistical Modelling of CO and NO2 Concentrations in the Athens Area - Evaluation of Emission Abatement Policies (7 pp)

- DOI: http://dx.doi.org/10.1065/espr2006.04.299 Goal, Scope and Background This paper describes a statistical modelling approach, suggested as a policy tool in the Athens area for the assessment of the emissions reduction level required to meet the air quality standards for two criteria air pollutants, namely CO and NO2. Methods. More than ten years of hourly CO and NOx-NO2 concentration data measured by the monitoring network of the Hellenic Ministry for the Environment, Physical Planning and Public Works were analyzed and the original dataset has been reduced using a data evaluation procedure. Results and Discussion Seasonal pollutant concentration trends suggested that the reduction of CO and NOx concentrations observed in the beginning of the '90s is almost entirely attributed to the increase of the catalyst-equipped cars during this period. The numerical parameters of an empirical model relating EU standard exceedances with mean annual concentrations were defined and the model was validated using datasets from years that were not used for the estimation of these parameters. This model was used in conjunction with a roll-back equation as a policy tool for the assessment of the effect of different CO and NOx emissions reduction scenarios on air quality standard compliance for CO and NO2. Results predicted with this empirical modelling approach were assessed with monitored data averaged over a 3-year period, giving satisfactory results. Conclusion A methodology suggested for assessing the effects of different emissions reduction scenarios on air quality standard attainment was successfully applied for CO and NO2 in the Athens area. Recommendation and Perspective The proposed methodology can provide a useful tool for the evaluation of policies already in progress as well as the development of future policies for emissions reduction in urban areas with similar characteristics, aiming at air quality standard compliance on a timely manner. Such a methodology could be applied in other urban areas of Greece characterized by dense traffic, therefore assisting the development of national policies in relation to air pollutants for which standard exceedances occur.     

Diurnal and seasonal variations of carbon monoxide and nitrogen dioxide in Delhi city

Motor vehicle exhaust emissions are one of the major causes of air quality deterioration in most of the cities of the developing world. Carbon monoxide (CO) and nitrogen dioxide (NO₂) are significant contributors to this adverse effect on the environment. This study analyses air quality data for three years from 1997 to 1999, at two air quality control regions in Delhi city. The regions are a major traffic intersection and the moderately busy straight Khelgaon Marg road. The data were obtained from the Central Pollution Control Board (CPCB), Delhi. The results show that the highest ground-level concentrations of CO and NO₂ occurred during winter (November to March) and the lowest during the tropical monsoon period (July to September) at both regions. Typical average monthly, weekly and diurnal cycles of CO at both regions have also been analysed, and show that CO concentrations are higher at the intersection than along the road. Further, the monthly average NO₂ concentrations were also found to be higher at the intersection.     

Measurement of Hydrocarbon Emissions Fluxes from Refinery Wastewater Impoundments Using Atmospheric Tracer Techniques

Sulfur hexafluoride (SF6) tracer was used in a series of the experiments to simulate emissions of benzene, toluene, ethyl-benzene, and xylenes (BTEX) from a refinery wastewater basin. The ratio of the measured tracer release to the ambient tracer concentration established a dilution factor which was then used to calculate the mass flux of BTEX from the wastewater basin using the ambient BTEX concentration data. Measured fluxes of BTEX varied from 7 g/min to 70 g/min.

The CHEMDAT7 air emissions model was then used to predict emissions for comparison with the emissions measured using the tracer flux simulation. CHEMDAT7 typically overpredicted total measured BTEX emissions by factors of twelve to seventeen. The degree of overprediction varied both by the individual compound and the module of CHEMDAT7 used to predict emission fluxes.     

Ambient Air Quality and Automotive Emission Control

This paper is concerned with uncertainties involved in projecting ambient air quality. Ambient air quality was projected by assuming a linear dependence on estimated future emissions. Future automotive emissions were estimated by a method recommended by EPA. Projections were made for the locations reported to have the highest ambient air concentrations of each pollutant; Chicago for carbon monoxide and the California South Coast Air Basin for hydrocarbon and oxidant. The sensitivity of the projections to several input parameters was determined.

The uncertainty in projection of air quality due to the use of a maximum, once-per-year concentration is large. For example, the reduction in total CO emissions in Chicago in 1975, necessary to meet the air quality standard, was as high as 68% or as low as 26%, depending on whether the historic high, 8 hr average concentration of 44 ppm or the 1970 maximum of 21 ppm was used. The effects of uncertainties in growth rates and fraction of emissions attributed to the automobile were also sizeable. Differences in automotive growth rate had a large near-term effect on projected concentrations, while differences in nonautomotive growth rate or fraction of emissions attributed to the automobile had a large long-term effect. The effect of 1975 interim automotive emission standards on projected air quality was negligible when compared with projected air quality based on the previous Federal automotive emission standards for 1975.     

Expected Decline in Carboxyhemogldbin Levels As Related to Automobile Carbon Monoxide Emission Standards

Carboxyhemoglobin (COHb) levels in blood are principally due to inhalation of carbon monoxide, although a low level (approximately 0.3%) of COHb is endogenous. A carboxyhemoglobin level above 1.5 % in non-smokers indicates exposure to CO in excess of the 10 mg/m³ air quality standard established under the Clean Air Act Amendments of 1970.

In most major U.S. cities, automobile emissions constitute the principal source of CO; in Chicago, according to EPA estimates,¹ light duty vehicles are responsible for 69.3% of all CO emissions. Thus as new automobiles incorporating emission controls enter the automotive fleet and older, emission-uncontrolled automobiles are phased out, ambient CO concentrations should decline and corresponding reductions in blood carboxyhemoglobin levels of nonsmokers can be expected.     

The Need for Representative Ambient Air Carbon Monoxide Sampling

Recent investigations have indicated that ambient air CO measurements may not reflect population exposure to CO. The lack of correlation may be due to improper siting of CO instruments, improper interpretation of air quality data, or both. Studies of population carboxy-hemoglobin levels are evaluated and compared with ambient air data.,

No significant correlation was found between median population COHb levels and reductions in CO concentrations required to meet ambient air standards when calculations used to estimate reductions were based on the second highest 8 hour average. However, calculated reductions based on annual average concentrations and a trend analysis technique correlated significantly with COHb levels in five cities from which both CAMP and COHb data were available.

Studies to determine the nature of the relationship between ambient air CO concentrations and population COHb levels are needed. The differences between the Occupational Safety and Health Act Regulations and the National Ambient Air Standards for carbon monoxide should be scrutinized to determine if a redefinition of the standards or their applicability is warranted. A reevaluation of the controls necessary to make reductions in population COHb burden may be necessary.     

Sources of Atmospheric Carbonaceous Particulate Matter in Pittsburgh,Pennsylvania

Abstract

The organic carbon (OC)/elemental carbon (EC) tracer method is applied to the Pittsburgh, PA, area to estimate the contribution of secondary organic aerosol (SOA) to the monthly average concentration of organic particu-late matter (PM) during 1995. An emissions inventory is constructed for the primary emissions of OC and EC in the area of interest. The ratio of primary emissions of OC to those of EC ranges between 2.4 in the winter months and 1.0 in the summer months. A mass balance model and ambient measurements were used to assess the accuracy of the emissions inventory. It is estimated to be accurate to within 50%. The results from this analysis show a strong monthly dependence on SOA contribution to the total organic PM concentration, varying from near zero during winter months to 50% or more of the total OC concentration in the summer.     

Estimating Emissions from Air Concentration Measurements

A one-year-long experiment in which two different tracers were simultaneously released from two different locations was used to test various hybrid receptor modeling techniques to estimate the tracer emissions using the measured air concentrations and a meteorological model. Air concentrations were measured over an 8-hour averaging time at three sites 14 to 40 km downwind. When the model was used to estimate emissions at only one tracer source, 6 percent of the short-term (8-h) emission estimates were within a factor of 2 of the actual emissions. Temporal averaging of the 8-h data enhanced the precision of the estimate such that after 10 days 42 percent of the estimates were within a factor of 2 and after six months all of them (each source-receptor pair) were within a factor of 2. To test the ability of the model to separate two sources, both tracer sources were combined, and a multiple linear regression technique was used to determine the emissions from each source from a time series of air concentration measurements representing the sum of both tracers. In general, 50 percent of the short-term estimates were within a factor of 10, 25 percent were biased low, and in another 25 percent the regression technique failed. The bias and failures are attributed to low or no correlation between measured air concentrations and model calculated dispersion factors. In the regression method increased temporal averaging did not consistently improve the emission estimate since the ability of the model to distinguish emissions between sources was diminished with increased averaging time. However, including progressively longer time periods (more data) into the regression or spatially averaging the data over all the receptors was found to be the most effective method to improve the estimated emissions. At best about 75 percent of the estimated monthly emission data were within a factor of 10 of the measured values. This suggests that the usefulness of meteorological models and statistical methods to address questions of source attribution requires many data points to reduce the uncertainty in the emission estimates.     

Ambient air quality levels in Tehran, Iran, from 1988 to 1993

The objectives of this study were to describe trends in ambient air quality in Tehran between 1988 and 1993, to determine if these levels exceeded the World Health Organization (WHO) guidelines, and to discuss possible health effects related to exposure for these particular pollutants. Data were acquired from Iran's Environmental Protection Agency (IEPA) and the Ministry of Health (MH). These agencies operate five automated ambient air monitoring stations located in areas with heavy traffic. Daily samples of SO₂, NO₂, CO, total suspended particulate matter (TSM), and hydrocarbons (HC) were collected to provide 24 hour averages for each pollutant. Every three months, mean concentrations were reported to IEPA. Composite samples from all five stations were stored in a databank operated by IEPA. The ambient air quality guidelines were obtained from WHO reports. Statistical analysis was carried out using a regression model, which was designed to fit the air pollution data and take into account missing data. The results showed that there was a statistically significant upward trend in air pollution levels for all of the measured pollutants, except NO2, during the years 1988 to 1993. WHO guidelines were routinely and substantially exceeded by all pollutants except TSM. These findings suggest that as the population continues to grow, and increasing numbers of motor vehicles are driven in Tehran, there is concern for the health effects that may result from exposure to these pollutants.     

Develop dynamic model for predicting traffic CO emissions in urban areas

The greater the use of energy in the transportation sectors, the higher the emission of carbon monoxide (CO), and hence inevitable harm to environment and human health. In this concern, measuring and predicting of CO emission from transportation sector—especially large cities—is important as it constitute 90 % of all CO emission. Many urban cities in developing world have not properly experienced such measurements or predictions. In this paper, for the first time, field measurements of traffic characteristics data and corresponding CO concentration have been performed for developing a model for predicting CO emissions from transportation sector for New Borg El Arab (NBC), Egypt. The performance of Swiss-German Handbook Emission Factors for Road Transport (HBEFA v3.1) model has been assessed for predicting the CO concentration at roadside in the study area. Results indicated that HBEFA v3.1 underestimate emission figures. The developed CO dynamic emission model involves the traffic flow characteristics with roadside CO concentrations. Acceptable representation of measured CO concentration has been shown by the developed dynamic CO emission model which introduces R ²?=?0.77, mean biases and frictional biases of ?0.27 mg m^?3 and 0.09, respectively. A comparison between predicted CO concentrations using HBEFA v3.1 and the promoted dynamic model indicate that HBEFA v3.1 estimates CO emission concentrations in the study area with a mean error and frictional biases 159.26 and 233.33 %, respectively, higher than those of the developed model.     

Seasonal variation of air pollution index: Hong Kong case study

Air pollution is an important and popular topic in Hong Kong as concerns have been raised about the health impacts caused by vehicle exhausts in recent years. In Hong Kong, sulphur dioxide SO2, nitrogen dioxide (NO2), nitric oxide (NO), carbon monoxide (CO), and respirable suspended particulates (RSP) are major air pollutants caused by the dominant usage of diesel fuel by goods vehicles and buses. These major pollutants and the related secondary pollutant, e.g., ozone (O3), become and impose harmful impact on human health in Hong Kong area after the northern shifting of major industries to Mainland China. The air pollution index (API), a referential parameter describing air pollution levels, provides information to enhance the public awareness of air pollutions in time series since 1995. In this study, the varying trends of API and the levels of related air pollutants are analyzed based on the database monitored at a selected roadside air quality monitoring station, i.e., Causeway Bay, during 1999-2003. Firstly, the original measured pollutant data and the resultant APIs are analyzed statistically in different time series including daily, monthly, seasonal patterns. It is found that the daily mean APIs in seasonal period can be regarded as stationary time series. Secondly, the auto-regressive moving average (ARMA) method, implemented by Box-Jenkins model, is used to forecast the API time series in different seasonal specifications. The performance evaluations of the adopted models are also carried out and discussed according to Bayesian information criteria (BIC) and root mean square error (RMSE). The results indicate that the ARMA model can provide reliable, satisfactory predictions for the problem interested and is expecting to be an alternative tool for practical assessment and justification.     

Trends and Relationships of O3, NOx and HC in the South Coast Air Basin of California

A basin-wide air quality trend analysis for the South Coast Air Basin of California is conducted for hydrocarbons (HC), NO_x, O₃ and CO using multi-station composite daily maximum-hour average ambient concentrations for the third quarter (July, August and September) from 1968 to 1985. Emissions and air quality trends are compared for the period 1968-1984. Ambient HC and NOX trends are somewhat different from estimated emission trends of HC and NO_x, while a definite, downward trend of ambient CO is consistent with vehicular emission control measures. Basin-wide ambient HC, NO_x and O₃ appear to show downward trends for the period 1970-1985, but because of high fluctuations it is difficult to delineate trends for shorter periods. The meteorology (850 mb temperature)-adjusted O₃ shows a more consistent downward trend than does unadjusted O₃. Polynomial and multiplicative regression models for basin-wide empirical O₃-HC-NO_x relationships Indicate that the O₃ variation is explained largely by the meteorological variable (850 mb temperature) although model estimations are improved by adding HC and NO_x concentration terms.     

Sources of atmospheric carbonaceous particulate matter in Pittsburgh,Pennsylvania

The organic carbon (OC)/elemental carbon (EC) tracer method is applied to the Pittsburgh, PA, area to estimate the contribution of secondary organic aerosol (SOA) to the monthly average concentration of organic particulate matter (PM) during 1995. An emissions inventory is constructed for the primary emissions of OC and EC in the area of interest. The ratio of primary emissions of OC to those of EC ranges between 2.4 in the winter months and 1.0 in the summer months. A mass balance model and ambient measurements were used to assess the accuracy of the emissions inventory. It is estimated to be accurate to within 50%. The results from this analysis show a strong monthly dependence of the SOA contribution to the total organic PM concentration, varying from near zero during winter months to as much as 50% of the total OC concentration in the summer.     

Predicting Particulate (PM10) Personal Exposure Distributions Using a Random Component Superposition Statistical Model

ABSTRACT

This paper presents a new statistical model designed to extend our understanding from prior personal exposure field measurements of urban populations to other cities where ambient monitoring data, but no personal exposure measurements, exist. The model partitions personal exposure into two distinct components: ambient concentration and nonambient concentration. It is assumed the ambient and nonambient concentration components are uncorrelated and add together; therefore, the model is called a random component superposition (RCS) model. The 24-hr ambient outdoor concentration is multiplied by a dimensionless “attenuation factor” between 0 and 1 to account for deposition of particles as the ambient air infiltrates indoors. The RCS model is applied to field PM₁₀ measurement data from three large-scale personal exposure field studies: THEES (Total Human Environmental Exposure Study) in Phillipsburg, NJ; PTEAM (Particle Total Exposure Assessment Methodology) in Riverside, CA; and the Ethyl Corporation study in Toronto, Canada. Because indoor sources and activities (smoking, cooking, cleaning, the personal cloud, etc.) may be similar in similar populations, it was hypothesized that the statistical distribution of nonambient personal exposure is invariant across cities.     

Detecting and Tracking Changes in Ozone Air Quality

This paper presents a statistical method for filtering out or moderating the influence of meteorological fluctuations on ozone concentrations. Use of this technique in examining trends in ambient ozone air quality is demonstrated with ozone data from a monitoring location in New Jersey. The results indicate that this method can detect changes in ozone air quality due to changes in emissions in the presence of meteorological fluctuations. This method can be useful in examining the effectiveness of regulatory initiatives in improving ozone air quality.     

Shock Wave Cleaning of Air Filters

The trends in and relationships between ambient air concentrations of sulfur dioxide and sulfate aerosols at 48 urban sites and 27 nonurban sites throughout the U.S. between 1963 and 1972 have been analyzed. The substantial decreases in ambient SO₂ concentrations measured at urban sites in the eastern and midwestern U.S. are consistent with the corresponding reductions in local SO₂ emissions, but these decreases have been accompanied by only modest decreases in ambient sulfate concentrations. Large differences in the amounts of SO₂ emitted within individual air quality control regions are associated with much smaller differences in the corresponding ambient sulfate concentrations. Substantial changes in the patterns of SO₂ emissions between air quality regions result in essentially no differences between ambient sulfate concentrations in those air quality regions. Comparisons of several air quality regions in the eastern and western U.S. with similar SO₂ emission levels and patterns of emissions clearly demonstrates the higher ambient sulfate concentration levels in eastern air quality control regions. Relationships between SO₂, sulfates, and vanadium concentrations at eastern nonurban U.S. sites cannot be explained by local emission sources. These various observed results can be best explained by long distance sulfur oxide transport with chemical conversion of SO₂ to sulfates occurring over ranges of hundreds of kilometers. This conclusion has been suggested earlier and the present analysis strongly supports previous discussions. An impact of long range transport of sulfates is to emphasize the need for Consistent strategies for reduction of sulfur oxides throughout large geographical regions. Additions of large capacities involving elevated sources in mid-continental or western regions could result in significant increases in sulfate concentrations well downwind of such sources. Some of the types of research activities required to quantitate crucial experimental parameters are discussed.