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<p>These show directions from which the wind blew when industrial odors were detected at either station 13 or 14 during mornings in (A) winter, with the outermost ring indicating a 20% frequency, (B) spring, with the outermost ring representing a 10% frequency, (C) summer, with the outermost ring representing a 12% frequency, and (D) fall, with the outermost ring indicating a 24% frequency. These frequencies, and those stated in the text, include wind directions even when the measured wind speed was 0 m s^-1, except where specifically indicated.</p

Combining Ordinary Kriging with wind directions to identify sources of industrial odors in Portland, Oregon

<div><p>This study combines Ordinary Kriging, odor monitoring, and wind direction data to demonstrate how these elements can be applied to identify the source of an industrial odor. The specific case study used as an example of how to address this issue was the University Park neighborhood of Portland, Oregon (USA) where residents frequently complain about industrial odors, and suspect the main source to be a nearby Daimler Trucks North America LLC manufacturing plant. We collected 19,665 odor observations plus 105,120 wind measurements, using an automated weather station to measure winds in the area at five-minute intervals, logging continuously from December 2014 through November 2015, while we also measured odors at 19 locations, three times per day, using methods from the American Society of the International Association for Testing and Materials. Our results quantify how winds vary with season and time of day when industrial odors were observed versus when they were not observed, while also mapping spatiotemporal patterns in these odors using Ordinary Kriging. Our analyses show that industrial odors were detected most frequently to the northwest of the Daimler plant, mostly when winds blew from the southeast, suggesting Daimler’s facility is a likely source for much of this odor.</p></div

Winds measured between 4pm and 6pm local time when food preparation odors were detected.

<p>These show directions from which the wind blew when food preparation odors were detected at either station 13 or 14 during evenings in (A) winter, with the outermost ring indicating a 7% frequency, (B) spring, with the outermost ring representing a 19% frequency, (C) summer, when no food preparation odors were detected during the evening, and (D) fall, with the outermost ring indicating a 16% frequency. These frequencies, and those stated in the text, include wind directions even when the measured wind speed was 0 m s^-1, except where specifically indicated.</p

Frequencies of industrial odor detections (above) and standard error (below) during morning in fall.

<p>Odor detections generally decrease towards the northwest region of the map during this period. These frequencies of odor detections are all much larger than the standard errors from the interpolation shown here (below) indicating a high signal-to-noise ratio. As expected, standard errors generally increase with increasing distance from the stations used to produce the interpolation (the dots shown here). The bins here include all the values (no locations exceeded 20% of days with detected odors, or a standard error of 0.08%). Basemap from Esri’s ArcGIS.</p

Frequencies of industrial odor detections (above) and standard error (below) during morning in spring.

<p>The areas northwest of the Daimler plant experienced far more industrial odors during this period than any other areas in the map according to both raw station data and the spatial interpolation here (above). These frequencies of odor detections are all much larger than the standard errors from the interpolation shown here (below) indicating a high signal-to-noise ratio. As expected, standard errors generally increase with increasing distance from the stations used to produce the interpolation (the dots shown here). The bins here include all the values (no locations exceeded 20% of days with detected odors, or a standard error of 0.08%). Basemap from Esri’s ArcGIS.</p

Frequencies of industrial odor detections (above) and standard error (below) during evening in winter.

<p>The areas northwest of the Daimler plant experienced slightly more industrial odors during this period than any other areas in the map according to both raw station data and the spatial interpolation here (above). These frequencies of odor detections are all much larger than the standard errors from the interpolation shown here (below) indicating a high signal-to-noise ratio. As expected, standard errors generally increase with increasing distance from the stations used to produce the interpolation (the dots shown here). The bins here include all the values (no locations exceeded 20% of days with detected odors, or a standard error of 0.08%). Basemap from Esri’s ArcGIS.</p

Frequencies of industrial odor detections (above) and standard error (below) during midday in summer.

<p>The spatial distribution of odor detections shows no clear pattern during this period. The bins here include all the values (no locations exceeded 20% of days with detected odors, or a standard error of 0.08%). Basemap from Esri’s ArcGIS.</p

The study area in Portland, Oregon (USA).

<p>Orange numbers indicate locations of the 19 stations where this study collected odor data three times per day for one year. The blue label “Daimler” represents the location of the Daimler Trucks North America LLC plant this study hypothesized as producing industrial odors in the area. The yellow label “University Park” represents the location of a residential area on a bluff ~40 meters above the Daimler plant. The University of Portland campus is southwest of the University Park neighborhood. Odor detection stations were located subject to limited resources such as labor, and obstacles such as steep terrain and private property: all stations are either on public and easily accessible property, or on the University of Portland campus. The pink “X” shows the location of the automated weather station this study installed to measure wind speeds and directions in the area. Basemap from Esri’s ArcGIS.</p

Frequencies of industrial odor detections (above) and standard error (below) during midday in spring.

<p>The areas northwest of the Daimler plant experienced far more industrial odors during this period than any other areas in the map according to both raw station data and the spatial interpolation here (above). These frequencies of odor detections are all much larger than the standard errors from the interpolation shown here (below) indicating a high signal-to-noise ratio. As expected, standard errors generally increase with increasing distance from the stations used to produce the interpolation (the dots shown here). The bins here include all the values (no locations exceeded 20% of days with detected odors, or a standard error of 0.08%). Basemap from Esri’s ArcGIS.</p

Frequencies of industrial odor detections (above) and standard error (below) during morning in winter.

<p>The areas northwest of the Daimler plant experienced far more industrial odors during this period than any other areas in the map according to both raw station data and the spatial interpolation here (above). These frequencies of odor detections are all much larger than the standard errors from the interpolation shown here (below) indicating a high signal-to-noise ratio. As expected, standard errors generally increase with increasing distance from the stations used to produce the interpolation (the dots shown here). The bins here include all the values (no locations exceeded 20% of days with detected odors, or a standard error of 0.08%). Basemap from Esri’s ArcGIS.</p