Elsevier

Atmospheric Environment

Volume 43, Issue 20, June 2009, Pages 3155-3167
Atmospheric Environment

Outdoor air pollution in close proximity to a continuous point source

https://doi.org/10.1016/j.atmosenv.2009.03.056Get rights and content

Abstract

Data are lacking on human exposure to air pollutants occurring in ground-level outdoor environments within a few meters of point sources. To better understand outdoor exposure to tobacco smoke from cigarettes or cigars, and exposure to other types of outdoor point sources, we performed more than 100 controlled outdoor monitoring experiments on a backyard residential patio in which we released pure carbon monoxide (CO) as a tracer gas for continuous time periods lasting 0.5–2 h. The CO was emitted from a single outlet at a fixed per-experiment rate of 120–400 cc min−1 (∼140–450 mg min−1). We measured CO concentrations every 15 s at up to 36 points around the source along orthogonal axes. The CO sensors were positioned at standing or sitting breathing heights of 2–5 ft (up to 1.5 ft above and below the source) and at horizontal distances of 0.25–2 m. We simultaneously measured real-time air speed, wind direction, relative humidity, and temperature at single points on the patio. The ground-level air speeds on the patio were similar to those we measured during a survey of 26 outdoor patio locations in 5 nearby towns. The CO data exhibited a well-defined proximity effect similar to the indoor proximity effect reported in the literature. Average concentrations were approximately inversely proportional to distance. Average CO levels were approximately proportional to source strength, supporting generalization of our results to different source strengths. For example, we predict a cigarette smoker would cause average fine particle levels of approximately 70–110 μg m−3 at horizontal distances of 0.25–0.5 m. We also found that average CO concentrations rose significantly as average air speed decreased. We fit a multiplicative regression model to the empirical data that predicts outdoor concentrations as a function of source emission rate, source–receptor distance, air speed and wind direction. The model described the data reasonably well, accounting for ∼50% of the log-CO variability in 5-min CO concentrations.

Introduction

In studying human exposure to air pollutants, scientists seek to quantify levels of airborne species that occur in the breathing zones of subjects (Ott et al., 2007). Although a person can be exposed to air pollution from industrial sources such as power plants, much exposure, in general, has been attributed to local residential sources, including smoking, cooking, cleaning, and the use of various common household products (Wallace, 1991, Özkaynak et al., 1996). Relatively weak sources close to an individual are likely to contribute more to a person's exposure than stronger, but more distant sources.

A “personal cloud” phenomenon has been observed in personal monitoring surveys, characterized by significantly elevated airborne particle concentrations in subjects' breathing zones relative to concurrent samples taken at a fixed sampling location in a given home (Rodes et al., 1991, Özkaynak et al., 1996). Possible reasons for the personal cloud include the effect of multiple, distinct compartments due to separate rooms, and the unmixed streams of pollutant close to an active source. A time period of ∼5 min or more is required for fresh emissions to become mixed in a given room (Klepeis, 1999). Thus, being in close proximity to an active indoor air pollution source may cause one to inhale undiluted, and highly concentrated emissions. The indoor proximity effect has been measured and modeled for a home (McBride et al., 1999, McBride, 2002) with average levels within 3 m of a continuous point source found to be 2–3 times greater than well-mixed room concentrations at farther distances.

In a prior study of outdoor tobacco smoke concentrations near smokers (Klepeis et al., 2007), we began an initial investigation of the outdoor proximity effect. We measured fine particles during visits to 10 outdoor public places where active cigar and cigarette smokers were present, including parks, sidewalk cafés, and restaurant and pub patios. In these outdoor settings, where smokers can be very close to other people, we observed low wind speeds but with relatively large turbulent eddies that distorted the tobacco smoke plumes and dispersed them in unpredictable directions. Outdoor emissions in places where smokers and nonsmokers visit are subjected to more intense and highly variable turbulent mixing patterns than occur for indoor locations.

Measurements and validated models are currently lacking on potential human exposure to ground-level, outdoor air pollutant concentrations at very short distances from the source. Deterministic atmospheric diffusion models have traditionally been applied to outdoor air pollutant emissions from continuous point sources, such as industrial smokestacks (Seinfeld and Pandis, 1998), at distances greater than 500 m. Recently, investigators have monitored or modeled pollutant dispersion in urban canyons (e.g., Baik et al., 2007, Eliasson et al., 2006), and the U.S. Department of Homeland Security has initiated studies into contaminant transport along urban canyons, which include wind measurements, tracer gas sampling, and computational fluid dynamics (CFD) modeling (Kalb et al., 2005). However these efforts have not focused on air flow and pollutant dispersion patterns over small distance scales, which are relevant for human exposure occurring in outdoor residential, recreational, or hospitality settings. In addition, it is not likely that the random dispersion patterns of ground-level plumes are easily predicted by traditional deterministic models.

The purpose of the present study is to collect data and develop models to quantify the outdoor proximity effect over short distances in everyday outdoor locations, following an approach similar to that taken by McBride et al. To provide empirical data on elevated human exposures that may occur over distances on the order of meters or fractions of a meter, we performed a large number of controlled tracer gas experiments using a grid of gas sensors on an outdoor patio, which was located in a residential neighborhood and characterized by closely spaced homes and substantial vegetation. To support generalization of the results to different locations and source magnitudes, we performed controlled tracer gas releases and measured concurrent air speed and wind direction. Because of the random nature of the air flow paths, we devised and applied a statistical regression model to encapsulate the experimental data.

Section snippets

Methods

We performed 103 separate outdoor monitoring experiments in which we released a tracer gas at a controlled emission rate on a residential backyard patio with similar dimensions as patio cafés in nearby towns. During the source emission period, we continuously measured wind speed and wind direction, and we used continuous sensors on a multi-point grid to measure the tracer gas concentrations (see Fig. 1 and Table 1). We compared air speeds measured on the test patio with those we measured at

Results and discussion

We chose 5 min as the fundamental averaging time for presenting results for both the CO concentrations and air speeds, which allowed us to analyze for the effects of rapidly changing wind patterns and also minimized time-response error. We calculated 5-min average CO concentrations from the 15-s CO concentration readings (n = 20 per 5-min period) and 5-min average air speeds from the 1-min VelociCalc™ readings (n = 5 per 5-min period). Using the 1-s WindSonic™ anemometer readings, we calculated the

Summary and conclusions

Although wind and open space in outdoor microenvironments reduce the persistence of locally generated outdoor air pollutants, persons very close to active outdoor sources may still receive substantial exposure. Our experimental investigation of the outdoor proximity effect measured carbon monoxide (CO) tracer gas concentrations at up to 36 fixed points surrounding a continuous mass-flow-controlled source in both vertical and horizontal directions.

Based on approximately 104 h of continuous

Acknowledgments

The research described in this article was supported by a grant from the Flight Attendant Medical Research Institute (FAMRI, Miami, FL) to Stanford University. We made use of equipment, including stands, sensors, and data loggers from a previous study at Stanford University performed by Dr. Sandra McBride during her doctoral research. We appreciate the advice of Professor Timothy Larson, of the University of Washington, with regard to the selection of an ultrasonic anemometer used to measure

References (19)

  • J. Baik et al.

    Modeling reactive pollutant dispersion in an urban street canyon

    Atmospheric Environment

    (2007)
  • I. Eliasson et al.

    Wind fields and turbulence statistics in an urban street canyon

    Atmospheric Environment

    (2006)
  • G. Hoek et al.

    Spatial variability of fine particle concentrations in three European areas

    Atmospheric Environment

    (2002)
  • D.W. Dockery et al.

    An association between air pollution and mortality in six U.S. Cities

    New England Journal of Medicine

    (1993)
  • P. Kalb et al.

    Urban dispersion program: towards a better understanding of contaminant transport in urban canyon environments

  • N.E. Klepeis

    Validity of the uniform mixing assumption: determining human exposure to environmental tobacco smoke

    Environmental Health Perspectives

    (1999)
  • N.E. Klepeis et al.

    A multiple-smoker model for predicting indoor air quality in public lounges

    Environmental Science & Technology

    (1996)
  • N.E. Klepeis et al.

    Real-time measurement of outdoor tobacco smoke particles

    Journal of the Air and Waste Management Association

    (2007)
  • H.R. Künsch

    The jackknife and the bootstrap for general stationary observations

    Annals of Statistics

    (1989)
There are more references available in the full text version of this article.

Cited by (0)

View full text