Jerry M. Davis

A characterization of the spatiotemporal variability of non-urban ozone concentrations over the eastern United States

Abstract The spatial and temporal variability of the daily 1-h maximum O 3 concentrations over non-urban areas of the eastern United States of America was examined for the period 1985–1990 using principal component analysis. Utilization of Kaisers Varimax orthogonal rotation led to the delineation of six contiguous subregions or “influence regimes” which together accounted for 64.02% of the total variance. Each subregion displayed statistically unique O 3 characteristics and corresponded well with the path and frequency of anticyclones. When compared to the entire domain, the mid-Atlantic and south subregions observe higher mean daily 1-h maximum concentrations. Concentrations are near the domain average for the northeast and southwest subregions and are lowest in the Great Lakes and Florida subregions. The percentage of observations exceeding 120 ppb were greates in the mid-Atlantic and southwest subregions, near the domain average in the northeast and south subregions, and lowest in the Great Lakes and Florida subregions. Examination of the time series of the principal component scores associated with the subregions indicated that Great Lakes and mid-Atlantic subregions tend to observe a stronger seasonal cycle, with maximum concentrations occurring during the last week in June and first week in July, respectively. The strength of this seasonality is weakened for the northeast and south subregions and its timing delayed, until the end of July and the first of August, respectively. The southwest subregion experiences a greatly diminished seasonality, with maximum concentrations delayed until the middle of August. The seasonality found in the Florida subregion is unique in both its strength and timing, as the highest concentrations consistently occur during the months of April and May. The time series were then deseasonalized and autocorrelations and spectral density estimates calculated, revealing that persistence is much more prevalent in the Florida (autocorrelation significant to a lag of 4 days), south (3 days) and southwest (3 days) subregions. Conversely, autocorrelations are only significant to a lag of one day in the northeast and two days for the Great Lakes and mid-Atlantic subregions.

Regression models for air pollution and daily mortality : analysis of data from Birmingham, Alabama

In recent years, a very large literature has built up on the human health effects of air pollution. Many studies have been based on time series analyses in which daily mortality counts, or some other measure such as hospital admissions, have been decomposed through regression analysis into contributions based on long-term trend and seasonality, meteorological effects, and air pollution. There has been a particular focus on particulate air pollution represented by PM10 (particulate matter of aerodynamic diameter 10 µm or less), though in recent years more attention has been given to very small particles of diameter 2.5 µm or less. Most of the existing data studies, however, are based on PM10 because of the wide availability of monitoring data for this variable. The persistence of the resulting effects across many different studies is widely cited as evidence that this is not mere statistical association, but indeed establishes a causal relationship. These studies have been cited by the United States Environmental Protection Agency (USEPA) as justification for a tightening on particulate matter standards in the 1997 revision of the National Ambient Air Quality Standard (NAAQS), which is the basis for air pollution regulation in the United States. The purpose of the present paper is to propose a systematic approach to the regression analyses that are central to this kind of research. We argue that the results may depend on a number of ad hoc features of the analysis, including which meteorological variables to adjust for, and the manner in which different lagged values of particulate matter are combined into a single ‘exposure measure’. We also examine the question of whether the effects are linear or nonlinear, with particular attention to the possibility of a ‘threshold effect’, i.e. that significant effects occur only above some threshold. These points are illustrated with a data set from Birmingham, Alabama, first cited by Schwartz (1993, American Journal of Epidemiology137: 1136 – 1147) and since extensively re-analyzed. For this data set, we find that the results are sensitive to whether humidity is included along with temperature as a meteorological variable, and to the definition of the exposure measure. We also find evidence of a threshold effect, with the greatest increase in mortality occurring above 50 µg/m3, which is the long-term average level permitted by the current NAAQS. Thus, on the basis of this data set, the need for a tighter NAAQS is not established. Although this particular analysis is focussed just on one data set, the issues it raises are typical in this area of research. We do not dispute that there is a reasonable level of evidence linking atmospheric particulate matter with adverse health outcomes even within the levels permitted by current regulations. However, the impression has been created by some of the published literature that such associations are overwhelmingly supported by epidemiological research. Our viewpoint is that the statistical analyses allow different interpretations, and that the case for tighter regulations cannot be based solely on studies of this nature. Copyright

Spatio-Temporal Analysis of Total Nitrate Concentrations Using Dynamic Statistical Models

Atmospheric concentrations of total nitrate (TNO3), defined here as gas-phase nitric acid plus particle-phase nitrate, are difficult to simulate in numerical air quality models due to the presence of a variety of formation pathways and loss mechanisms, some of which are highly uncertain. The goal of this study is to estimate the relative importance of these different pathways across the Eastern United States by identifying empirical relationships that exist between TNO3 concentrations and a set of covariates (ammonium, sulfate, ozone, wind speed, relative humidity, and precipitation) measured from January 1997 to July 2004. We develop two dynamic statistical models to quantify these relationships. A major advantage of these models over typical linear regression models is that their regression coefficients can vary temporally. Results show that TNO3 is sensitive to ozone throughout the year, indicating an importance of daytime photochemical production of TNO3, especially in the Southeast. Sensitivity of TNO3 to residual ammonium (NH4+–2SO42−) is most pronounced during winter, indicating a seasonal importance of gas/particle partitioning that is accentuated in the Midwest. Using a number of physical and chemical explanations, confidence is established in the spatial and temporal patterns of several such empirical relationships. In the future, these relationships may be used quantitatively to improve our mechanistic understanding of TNO3 formation pathways and loss mechanisms in the atmosphere.

Multivariate spatial-temporal modeling and prediction of speciated fine particles

Fine particulate matter (PM2.5) is an atmospheric pollutant that has been linked to serious health problems, including mortality. PM2.5 has five main components: sulfate, nitrate, total carbonaceous mass, ammonium, and crustal material. These components have complex spatial-temporal dependency and cross dependency structures. It is important to gain better understanding about the spatial-temporal distribution of each component of the total PM2.5 mass, and also to estimate how the composition of PM2.5 changes with space and time to conduct spatial-temporal epidemiological studies of the association of these pollutants and adverse health effects. We introduce a multivariate spatial-temporal model for speciated PM2.5. Our hierarchical framework combines different sources of data and accounts for bias and measurement error in each data source. In addition, a spatiotemporal extension of the linear model of coregionalization is developed to account for spatial and temporal dependency structures for each component as well as the associations among the components. We apply our framework to speciated PM2.5 data in the United States for the year 2004.

A numerical model of the transport and diffusion of Peronospora tabacina spores in the evolving atmospheric boundary layer

Abstract Numerical solutions of the diffusion equation of Peronospora tabacina spores from a finite-area source over flat terrain in the evolving convective boundary layer are presented. Temporal variations in the release of spores, atmospheric stability, wind speed, and eddy diffusivity are considered. The model also includes the vertical variations of wind and eddy diffusivity. The model results indicate that ground level concentrations decrease with time as wind speed and eddy diffusivity increase in the evolving convective boundary layer. The loss of P. tabacina spores due to deposition at the surface also decrease with increasing instability and wind speed. Deposition is found to be particularly important close to the source area.

Estimating urban temperature bias using polar-orbiting satellite data

Abstract Urban temperature bias, defined to be the difference between a shelter temperature reading of unknown but suspected urban influence and some appropriate rural reference temperature, is estimated through the use of polar-orbiting satellite data. Predicted rural temperatures, based on a method developed using sounding data, are shown to be of reasonable accuracy in many cases for urban bias assessments using minimum temperature data from selected urban regions in the United States in July 1989. Assessments of predicted urban bias were based on comparisons with observed bias, as well as independent measures of urban heat island influence, such as population statistics and urban-rural differences in a vegetation index. This technique provides a means of determining urban bias in regions where few if any rural reference stations are available, or where inhomogeneities exist in land surface characteristics or rural station locations.

Modeling Ozone in the Chicago Urban Area

Ozone (O3) is a ubiquitous trace gas in the atmosphere. Its highest concentration is in the stratosphere, where it shields the earth’s surface from harmful ultraviolet radiation. At the surface, however, ozone is itself harmful, with destructive impacts on materials, crops, and health. Its levels have been high enough in certain areas to be of concern for several decades.

The use of polar-orbiting satellite sounding data to estimate rural maximum and minimum temperatures

Abstract Atmospheric sounding products from NOAAs polar-orbiting satellites were used to derive and test predictive equations of rural shelter-level maximum and minimum temperatures. Sounding data from both winter and summer months were combined with surface data from over 5300 cooperative weather stations in the continental United States to develop multiple linear regression equations. Separate equations were developed for both maximum and minimum temperature, using the three types of sounding retrievals (clear, partly cloudy, and cloudy). Clear retrieval models outperformed others, and maximum temperatures were more accurately predicted than minimums. Average standard deviations of observed rural shelter temperatures within sounding search areas were of similar magnitude to root-mean-square errors from satellite estimates for most clear and partly cloudy cases, but were significantly less for cloudy retrieval cases. Model validation for surrogate polar and tropical climatic regions showed success in ap...

Airborne Particles and Mortality

In London in December 1952, a combination of adverse weather conditions and soot in the atmosphere produced one of the most lethal smogs in history, resulting in thousands of deaths. Similar events had occurred earlier in the Meuse Valley in Belgium in 1930, and in Donora, Pennsylvania in 1948. They focused attention on the need to avoid excessive levels of soot in the atmosphere. In Britain, the Clean Air Act of 1956, which, among other things, placed severe restrictions on the use of coal for home heating, resulted in a tenfold reduction in soot levels in London by the late 1960s. In the United States, the creation of the Environmental Protection Agency (EPA) and the passing of successive Clean Air Acts by Congress led to the enforcement of air pollution standards which have laid down strict controls on levels of ozone and particulate matter in the atmosphere. Nevertheless, a series of studies since the late 1980s have suggested that current standards are by no means strict enough. The New York Times (July 19, 1993) reported that up to 60,000 people a year are dying prematurely in the United States as a result of particulate matter pollution which for the most part lies within current EPA standards. A similar calculation in the British science magazine The New Scientist (March 12, 1994) concluded that 10,000 people die prematurely each year in England and Wales, as a result of atmospheric particulates. Such reports, backed up by many papers in the scientific literature, naturally led to calls for action, and in November 1996, the EPA issued new draft standards for ozone and particulate matter.

Regional and Temporal Models for Ozone Along the Gulf Coast

The studies described in the previous chapter focused on estimating trends in a daily ozone summary having adjusted for the relationship of surface ozone concentrations to meteorology. Moreover, the analysis was largely restricted to the Chicago urban area. This chapter contrasts this narrow scope by studies that: model the entire day’s ozone profile rather than its maximum; relate ozone levels to specific weather patterns; characterize the ozone field at different spatial scales from urban to regional.