Impacts of Vehicle Activity on Airborne
Particle Deposition to Lake Tahoe
May 26, 2011
PREPARED BY:
Dr. Dongzi (Davis) Zhu
Dr. Hampden Kuhns (Principal Investigator)
Dr. John Gillies
Dr. Alan Gertler
Ms. Jacqueline Mason
zhu@dri.edu
(775) 674 7086
Division of Atmospheric Sciences
Desert Research Institute
2215 Raggio Parkway, Reno, NV 89512
PREPARED FOR:
Jonathan W. Long
Tahoe SNPLMA Science Program Coordinator
USDA Forest Service
Pacific Southwest Research Station
Tahoe Environmental Research Center
This research was supported by an agreement with the USDA Forest Service Pacific Southwest
Research Station. This research was supported using funds provided by the Bureau of Land
Management through the sale of public lands as authorized by the Southern Nevada Public Land
Management Act. The findings and recommendations in this report are those of the authors.
i
“Down through the transparency of these great depths, the water was not merely transparent, but
dazzlingly, brilliantly so. All objects seen through it had a bright, strong vividness, not only of
outline, but of every minute detail, which they would not have had when seen simply through the
same depth of atmosphere. So empty and airy did all spaces seem below us, and so strong was
the sense of floating high aloft in mid-nothingness, that we called these boat-excursions ‘balloonvoyages.’”
– Mark Twain describing the clarity of Lake Tahoe in his 1872 book Roughing It
iii
Executive Summary
Lake Tahoe is famous for its water clarity, which was first measured as 102 feet deep in 1968.
Since then, however, it has lost almost a foot of clarity per year, dropping down to below 70 feet
in recent years. Three major culprits for the lake’s increasingly murky character are nitrogen and
phosphorous, which feed algal growth, and fine sediments that remain suspended in the water
instead of settling to the bottom.
Atmospheric deposition (transfer of pollutants from the air to the surface) accounts for
approximately 55% of the nitrogen and 15% of the phosphorous (Cliff & Cahill, 2000) as well as
15% of the fine sediments (CWB & NDEP, 2008) that enters the lake. Most of the atmospheric
deposition of particulate matter (fine sediment particles, FSP, particles diameter < 16 m) and
phosphorus to the lake comes from re-entrained road dust (Engelbrecht et al., 2009). Thus,
understanding how much road dust ends up in the lake and what factors affect this amount is
crucial for making informed decisions about which road dust mitigation strategies will
successfully preserve the lake’s clarity and also be cost-effective for the communities in the Lake
Tahoe Basin.
This study integrates the results of previous and ongoing research to quantitatively examine how
road dust emissions and atmospheric deposition are affected by several factors, including season,
local wind conditions, vehicle class, vehicle speed, vehicle kilometers driven, road type, road
maintenance practices, vegetative density, and proximity of the source road to the lake.
Observations of these factors and patterns from several different studies have been combined and
analyzed to create a basin-wide emission factor database based on road type and jurisdiction.
(Road jurisdiction is a reasonable indicator based on consistency of traction control application
and road maintenance practices.) This database was then linked to a traffic demand model to
create emission estimates for each of the 7000+ road segments around the basin.
Summary of major results:

Proximity to the lake, prevailing wind directions, and traffic patterns play a dominant role
in determining which roads have the greatest potential to deposit fine sediment into the
lake.

It appears that only roads close to the lake have a substantial impact on atmospheric
deposition of fine particles. Moreover, most areas around the lake benefit from onshore
wind directions during peak traffic times (i.e. daylight hours) that effectively push
emissions away from the lake. However, this is not the case in El Dorado County
(California) and Douglas County (Nevada), which are calculated to be responsible for
67% of the paved road dust deposited to the lake. More aggressive measures to reduce the
reservoir of suspendable material on roads in these areas will be more cost-effective than
applying a blanket policy to the entire road network.

Emissions vary both by season and by location. Wintertime Total Suspended Particle
(TSP) emissions are ~5 greater than summertime TSP emissions due to the application of
traction control material to the roads during the winter. Vehicle kilometers traveled
(VKT) are not evenly distributed in the basin’s urban area; in particular, the South Lake
Tahoe area has the highest VKT values in the basin.
iv

Although South Lake Tahoe (in El Dorado County, CA) already employs an aggressive
street sweeping program, its high VKT causes it to still be a major source of atmospheric
deposition of particles into the lake.

Approximately 98% of the vehicles in the basin are in the light-duty class. So, although
the wakes from mid- and heavy-duty trucks can entrain dust particles from the sides of
roadways, the number of such vehicles is insufficient to make a major impact.

Only ~2% of road emissions of PM10 (20 Mg/year) and ~1.5% of TSP (35 Mg/year) is
estimated to reach the lake. The vast majority of PMlarge emitted into the air is deposited
within minutes, especially in the presence of dense vegetation. An analysis of vegetative
density coverage was overlaid on the spatially resolved emission inventory so that each
road segment could be assessed based on the type of vegetation on the shortest path to the
lake.

Lake Tahoe Atmospheric Deposition Study (LTADS) referenced in the 2010 Total
Maximum Daily Load report estimated that dry atmospheric depositions to the lake are
230 Mg/year of PM10 and 590 Mg/year TSP. Including wet deposition, the total
atmospheric deposition to the lake are 375 Mg/year of PM10 and 755 Mg/year TSP. Our
results indicate that PMlarge and PMcoarse are rapidly depleted near their source and thus
the shoreline concentrations may only be representative of the first 1-to-3 kilometers
offshore. The results support much lower estimates of dry deposition to the lake than
calculated by LTADS. We estimate that from paved road travel, the atmospheric dry
deposition to the lake is approximately 6% of the total LTADS dry deposition. Other
sources of fine sediment that are not included in our estimates include unpaved road
travel that is relatively small (due to development around the lake and especially in the
wintertime when a snow pack is present) and located at greater distances from the shore
than paved roads. In addition, re-entrained windblown dust is not factored into our
estimates.
v
Table of Contents
Executive Summary .................................................................................................................. iv Table of Contents ...................................................................................................................... vi Table of Figures ....................................................................................................................... vii Table of Tables ....................................................................................................................... viii Acronyms ................................................................................................................................. ix 1 Introduction .........................................................................................................................1 2 Literature Review and Summary of Research to Date ..........................................................4 3 Temporal and Spatial Distribution of Mobile Source Emissions in the Basin .......................9 4 Vehicle Class Distribution Based on Roadway Types. ....................................................... 15 5 Seasonal Near Field Deposition ......................................................................................... 18 6 Relationship of Trip Location and Atmospheric Deposition to the Lake.............................20 7 Marginal Impacts of a Vehicle Trip on Re-entrained Dust .................................................33 8 Reconcile Atmospheric Deposition with Other Sources .....................................................35 9 Conclusions ....................................................................................................................... 37 10 Recommendations ............................................................................................................. 40 11 References .........................................................................................................................41 vi
Table of Figures
Figure 1. Images of processes linking traction control material and road dust to lake water
clarity. .............................................................................................................................2 Figure 2. PM10 road dust emission factors of Secondary Road Sections near Marlette Lake
SNOTEL Station. Upper panel shows TRAKER signal while lower panel shows nearby
snow accumulation. .........................................................................................................9 Figure 3. Emission rates (g/km/day) calculated by multiplying the two-way traffic flow for each
link with the emission factor measured by the TRAKER for the 41 sections of road
surveyed in the basin.. ................................................................................................... 11 Figure 4. Gridded annual PM10 emissions (kg/day) for all major sources including fires, boats,
paved road travel, and biogenic sources. ........................................................................14 Figure 5. Fleet distribution at two sites in Incline Village, NV. ..................................................16 Figure 6. Traffic volume variation measured at two primary roads and one secondary road in the
Lake Tahoe Basin. ......................................................................................................... 16 Figure 7. Tree coverage (percent) in the Lake Tahoe basin. .......................................................19 Figure 8. Sample vegetation coverage of the three vegetation type (Shrubs, Open Trees, and
Dense Trees, displayed as gray, green, and blue, respectively) and shortest paths (purple
lines in the figure) from traffic points in the Tahoma region to the Lake. The black dots
are 1/10th of the length and are used to calculate the length coverage fraction in each
vegetation category. ....................................................................................................... 21 Figure 9. Shortest paths to the lake from 7235 traffic points in the Tahoe roadway network. The
blue lines are roadway networks and the red lines are the shortest paths to the Lake. .....22 Figure 10. Exponential decay for PM10 at various distances downwind from the source. The
exponents are from field measurement of Cowherd et al. (2006) and Zhu et al. (2011)... 24 Figure 11. Meteorological stations set up around Lake Tahoe by the UC Davis REMOTE project.
...................................................................................................................................... 25 Figure 12. Wind rose map from 1-year (2006) monitoring data for the 6 meteorological stations
around Lake Tahoe. ....................................................................................................... 26 Figure 13. Cumulative Distribution of total PM deposition potential as a function of distance to
the lake. ......................................................................................................................... 29 Figure 14. Gridded wintertime total PM deposition potential for 7235 traffic segments around the
lake. Each colored group represents 20% of the TSP (Total Suspended Particulate)
deposited to the lake. ..................................................................................................... 30 Figure 15. Gridded summertime total PM deposition potential for 7235 traffic segments around
the lake. Each colored group represents 20% of the TSP (Total Suspended Particulate)
deposited to the lake. ..................................................................................................... 31 Figure 16. Gridded annual total PM deposition potential for 7235 traffic segments, taking into
consideration the vehicle kilometers traveled (VKT), seasonal emission factors (EFs),
wind speed and direction, distance to the lake, and vegetation barrier density. Each
colored group represents 20% of the TSP (Total Suspended Particulate) deposited to the
lake, with classification based on cumulative 20%, 40%, 60%, 80%, and 100% of
deposition potential........................................................................................................ 32 Figure 17. VKT (vehicle kilometers traveled) distribution and cumulative VKT distribution as a
function of distance from the lake in the Tahoe basin. .................................................... 34 vii
Table of Tables
Table 1. Basin-wide total paved road dust PM emissions (metric tons per year) compared with
other studies. .................................................................................................................13 Table 2. Example of vehicle classes reported by the TimeMark traffic counter on Highway 50
near Rabe Meadow on Tuesday, April 14, 2009. ............................................................17 Table 3. Road dust PM10 Emission Factor (EF) in terms of grams per vehicle kilometer traveled
(g/VKT) based on county and road type from TRAKER one-year measurement campaign
(Zhu et al., 2009). ..........................................................................................................21 Table 4. Total PM (fine, coarse, large) deposition contributions and vehicle kilometers traveled
(VKT) from different counties. ........................................ Error! Bookmark not defined. Table 5. Summary table of cost-effectiveness of storm water management (CWB & NDEP,
2008). ............................................................................................................................ 36 viii
Acronyms
AADT
BMP
EF
GID
LTADS
Mg
PCO
PM
PM2.5
PM10
PMfine
PMcoarse
PMlarge
TSP
Road RAM
SRP
TMDL
TRAKER
TransCAD
TSP
TWS
VKT
Average Annual Daily Travel
Best Management Practice
Emission Factor
General Improvement District
Lake Tahoe Atmospheric Deposition Study
Million grams (metric tons)
Pollutant Control Option
Particulate Matter
Particulate Matter with an aerodynamic diameter ≤ 2.5 μm
Particulate Matter with an aerodynamic diameter ≤ 10 μm
PM2.5
PM10 – PM2.5
Total Suspended Particulate – PM10
Total Suspended Particulate
Road Rapid Assessment Methodology
Soluble Reactive Phosphorus
Total Maximum Daily Load
Testing Re-entrained Aerosol Kinetic Emissions from Roads, a vehicle-based
mobile platform for measuring dust emissions from paved and unpaved roads
Transportation Computer Assisted Design
Total Suspended Particulate
Two-Week Sampler
Vehicle Kilometers Traveled
ix
1
Introduction
The Lake Tahoe Watershed Assessment provided an initial summary of the status of our
scientific knowledge regarding the factors leading to the observed decline in water quality
(Reuter and Miller, 2000) and steps that can be taken to restore the Lake Tahoe Basin ecosystem
(Murphy and Knopp, 2000). Among the factors contributing to the decline in water quality are
nitrogen, phosphorous, and sediment flow into Lake Tahoe. Cliff and Cahill (2000) reported that
atmospheric deposition accounts for approximately 55% of the nitrogen (N) and 15% of the
phosphorous (P) load into the lake. No estimate of atmospheric particulate input was presented.
These estimates were highly uncertain.
Using on-lake deposition buckets, Jassby et al. (1994), Reuter et al. (2003), and Hackley et al.
(2004, 2005) demonstrated that atmospheric inputs of nitrogen and phosphorus are a significant
source of the nutrients supporting algal growth. Recent studies of water clarity implicate
insoluble particles, largely soil derived, as important contributors to declining lake clarity
(Coker, 2000; Losada-Perez, 2002; Swift et al., 2006). Preliminary estimates suggest that on the
order of 15 percent of the fine, soil-derived particles enters directly into Lake Tahoe via
atmospheric deposition (LRWQCB and NDEP, 2008).
Recent studies have demonstrated that pollutants from the transportation system in the Lake
Tahoe basin adversely affect air quality (Gertler et al., 2006a). Airborne particles from reentrained road dust have the potential to deposit to the lake and decrease water clarity. In
addition, quantitative estimates of the atmospheric deposition of fine particles were rated as
having the lowest confidence due to high uncertainty and insufficient data (Roberts & Reuter,
2007).
To date, many basin-specific studies have addressed uncertainties related to the individual steps
linking activities to water clarity impact (as shown in Figure 1: vehicle activity → emissions →
transport → deposition → water clarity). These studies have been and will continue to be critical
to the understanding and mitigating high atmospheric PM concentrations, which contribute to the
number and mass of particles in the lake water.
Through the Lake Tahoe Total Maximum Daily Load (TMDL) Program (CWB and NDEP,
2010), fine sediment particles (“FSP”, < 16 μm in diameter) in the lake have been attributed to
four main sources in the Lake Tahoe Basin:
 72% from Urban Upland Loading (i.e., storm water runoff)
 15% from Atmospheric Deposition (both dry and wet)
 9% from Non-Urban Upland Loading
 4% from Stream Channel Erosion
At the time the empirical Lake Tahoe Clarity Model was assembled, the confidence in the
Atmospheric Deposition source component for fine sediment was determined to be "low"
whereas all other sources were rated as "medium" (CWB and NDEP, 2010). Improving the
confidence in this source of waterborne sediment derived from re-entrained road dust has been
the objective of numerous studies in the last several years.
1
Figure 1. Images of processes linking traction control material and road dust to lake water clarity.
2
The overall goal of this project was to integrate the results of previous studies and quantitatively
link vehicle kilometers traveled (VKT) and road location to lake particulate loading (e.g.,
atmospheric deposition, surface runoff). Establishing this relationship enables regional planners
to develop effective strategies to reduce the negative impact of the transportation system on lake
clarity.
The specific objectives of the are listed below along with the section that each objective is
addressed.
1) Assemble all relevant reports and data from prior studies (Section 2).
2) Summarize the temporal and spatial distribution of mobile sources of nitrogen (N),
phosphorous (P), and particulate emissions (PM) in the basin in order to temporally
integrate the source with subsequent pollutant transport (Section 3).
3) Summarize vehicle class (e.g., car, truck, tractor trailer, etc.) distribution on different
roadway types in order to account for changes in emission factors due to fleet variation
(Section 4).
4) Summarize the seasonal near-field deposition of sediment near the roadways in order to
assess how effectively airborne emissions travel from their source to the lake (Section 5).
5) Assess the relationship of trip location and atmospheric deposition to the lake (Section 6).
6) Assess the marginal impacts of a single vehicle trip on re-entrained dust to evaluation
traffic reduction strategies (Section 7).
7) Reconcile emissions and deposition rates with measured sediment loading to the lake
(Section 8).
3
2
Literature Review and Summary of Research to Date
Extensive information exists on the location, timing, and amounts of particulate matter and
gaseous emissions released in the Lake Tahoe basin as well as their fate through either
deposition or transport out of the basin. The information generated from these research projects
served as a foundation to consider the costs and benefits of control measures to preserve and
improve the water clarity of Lake Tahoe.
UC Davis Tahoe Loading Study (Jassby et al., 1994). Initial estimates of nutrient loading into
Lake Tahoe focused primarily on the nitrogen, ammonia, and soluble reactive phosphorus (SRP)
from streams, runoff, and atmospheric wet and dry deposition. These nutrients foster algal
growth that in turn impairs water clarity. Measurements attributed 80% of the dissolved
inorganic nitrogen (DIN) in the lake to out-of-basin sources delivered by atmospheric deposition.
The spatial heterogeneity of the SRP indicated that local sources were responsible, with nearly
equal contributions from watershed and atmospheric deposition sources. Algal growth was
determined to be phosphorus-limited, indicating that the algae can be controlled by focusing on
phosphorus sources. The analysis did not consider the sources and pathways of fine particles
loading into the lake.
Water Clarity Empirical Modeling in Lake Tahoe (Swift et al., 2006). A more recent analysis
of water quality in Lake Tahoe has focused on what pollutants in the lake water are responsible
for degradation of clarity. Analyzing the physical and chemical properties of particles in water
samples collected throughout the lake, Swift et al. (2006) concluded that 58% of clarity
degradation was associated with fine inorganic sediment and 25% with organic particles, which
would include algae. The remaining ~17% contributors to water clarity degradation are pure
water absorption and scattering as well as some additional absorption from other pollutants (e.g.,
black carbon).
The Lake Tahoe Atmospheric Deposition Study, LTADS (Dolislager et al., 2009). This multipronged assessment by the California Air Resources Board generated new information on the
contribution of atmospheric deposition to the declining clarity of Lake Tahoe. Subtopics
included ambient monitoring of aerosols into and out of the basin, emission inventories and
chemical characterization, physical processes of pollutant transport, and wet and dry deposition
to the lake.
Air Quality: Two Week Samplers (TWS) for Total Suspended Particulates (TSP), PM10, and
PM2.5 were installed at five sites throughout the basin. In addition, MiniVol filter samplers were
used to acquire TSP samples at remote/satellite sites with various sampling durations. Collected
TSP, PM10, and PM2.5 samples were gravimetrically analyzed for total mass concentration
followed by detailed chemical speciation. A total of 127, 129, and 128 sets of TWS samples
were collected for TSP, PM10, and PM2.5, respectively; additionally, 36 sets of MiniVol TSP
samples from buoys on the lake, and 160 sets of non-buoy (e.g., piers) MiniVol TSP samples
were collected during the LTADS. Ambient measurements showed large spatial heterogeneity in
the TSP matter shifting to a near uniform distribution throughout the basin for particulate matter
less than 2.5 μm (PM2.5). This phenomenon is not unusual since large, mechanically generated
particles (e.g. road dust) greater than 10 μm have high settling velocities and are removed from
the air column faster than the smaller combustion particles that may stay aloft for weeks when
4
they do not agglomerate with larger particles or wash out as wet deposition. TSP concentrations
around the lake were observed to peak in winter and have crustal signatures (Dolislager et al.,
2009). Crustal species including iron, aluminum, calcium, and silcon are major components of
re-entrained emissions from the traction control material applied to the roads in the wintertime
(Kuhns et al., 2004; Gertler et al., 2006b).
Transport: Meteorology in the Lake Tahoe basin is governed by regional-scale (synoptic) flows
that generally create southerly winds coupled with mesoscale wind patterns that tend to blow
offshore (to the center of the lake) in the nighttime and onshore (upslope) in the daytime. Over
the lake, inversion conditions, which are strongest in the summer with air cooled by the lake,
tend to contain emissions in the basin and prevent downward mixing of pollutants that have
traveled from sources upwind of the basin. In the wintertime, the relatively warm lake helps to
mix pollutants into a deeper mixed layer.
VanCuren et al. (2010a) described PM measurements from boat cruises on the lake near the
shore line. Measurements showed that elevated PM concentrations were localized downwind of
populated areas and decreased substantially toward the middle of the lake and near lesspopulated shorelines. The authors concluded that deposition driven by high PM concentrations is
greatest in proximity to urban areas. These patterns are fortuitous for minimizing deposition to
the lake since road dust emissions tend to peak during the daytime when winds are onshore.
Deposition: By averaging the TWS’s readings for each season at four sites with each site
representing a quadrant of the lake, the annual average PM2.5, PM10, and TSP concentrations in
the basin were calculated. The annual deposition rate was modeled as the product of the seasonal
average ambient concentration and the hourly deposition velocity across the areaeach quadrant of
the lake surface. The LTADS estimated that approximately 160 Mg of PM2.5, 230 Mg of PM10,
and 590 Mg of TSP dry deposition were deposited into the lake per year. Including wet
deposition, the total atmospheric depositions to the lake are 235 Mg of PM2.5, 375 Mg/year of
PM10 and 755 Mg/year TSP. (Dolislager et al., 2009).
VanCuren (2010b) conducted high temporal resolution studies of particle size distributions
downwind of Highway 50 on the southeast shore of the lake. The authors found that PMlarge (>10
μm) fell out of the air column much faster than small particles, with substantial deposition of
these large particles occurring within 100 m of the source. These observations suggest that the
LTADS PM deposition estimates may be an upper bound, since at larger distances from the
lakeshore most of the large particles may have already deposited.
Wet deposition to the lake was contrasted with measurements collected at Sagehen Creek (~30
km northwest of the lake), which is part of the National Acid Deposition Monitoring Program
(NADPM). Approximately 20% of the estimated PM deposition to the lake is attributed to wet
deposition (snow and rain), which occurs primarily in the winter and spring.
DRI Lake Tahoe Source Characterization Study (Kuhns et al., 2004). This study investigated
the chemical composition and emission factors (EFs) of selected particulate matter (PM) sources
in the Lake Tahoe Basin. Sources sampled included residential wood combustion, motor vehicle
exhaust, and entrainment of road dust, traction control material, and road deicing material. In
addition, several new emission measurement technologies were applied during this study to
5
investigate residential wood combustion, motor vehicle exhaust, and re-entrained road dust. For
TSP, the combined emission inventory indicated that the largest sources were residential wood
combustion and campfires (726 Mg or 33%), unpaved road dust (1100 Mg or 31%), and paved
road dust (630 Mg or 14%). Residential wood smoke is predominantly in the fine (< 2.5 μm) size
mode and unlikely to be a major source of dry deposition to the lake due to low deposition
velocities. Fugitive road-dust emissions from vehicle travel on paved and unpaved roads was
estimated to account for ~45% of ambient PM10 (PM with aerodynamic diameter ≤10 μm) in the
Lake Tahoe basin. Paved road emissions were based on TRAKER measurements (Kuhns et al.,
2001). Unpaved road dust emissions were based on county-based inventories assembled by the
California Air Resources Board (CARB) and estimated based on the fraction of the county that
occupied the basin. These numbers are likely to be biased high since there are relatively few
unpaved public roads in the developed areas of the basin.
Impact of Winter Road Sand/Salt and Street Sweeping of Road Dust Re-Entrainment
(Gertler et al., 2006b). In this study, roadside measurements of PM flux and instrumented vehicle
PM measurements were performed to evaluate the effectiveness of street sweeping in reducing
dust re-entrainment and assess the impact of abrasives and deicers on ambient PM near the lake.
The results indicate that use of liquid deicers contributes less to road dust emissions than
abrasives do. Street sweeping was found to increase the PM10 re-entrainment rate of the
remaining road dust. This effect is believed to be due to sweeper brooms lifting material out of
pits in the road so that the material may then be entrained by vehicle tires (Kuhns et al., 2003).
Emission factors for roads in the Lake Tahoe Basin tend to decrease significantly from late
spring to early summer by as much as a factor of four.
Measurement and Modeling of Fugitive Dust Emissions from Paved Road Travel in the
Lake Tahoe Basin (Kuhns et al., 2007). Based on the results from the Lake Tahoe Source
Characterization study, which identified re-entrained road dust as a significant contributing
source to in-basin particulate matter levels, this project measured road dust emissions from a
network of road types in the basin over a one-year study period. Field data were collected using a
mobile sampling platform (TRAKER) from August 2006 to August 2007. The final report relates
road dust emissions with road type, seasonality, precipitation, and road management practices to
better quantify the sources of re-entrained road dust. Results of this study found that compared to
the summer season, road dust emissions increased by an average factor of 5 in winter when
traction control material was applied to the roads after snow events. The highest emission factors
were observed on very-low-traffic volume roads on the west side of the lake. These roads were
composed of either a 3/8-inch gravel material or degraded asphalt. The principle factors
influencing road dust emissions in the basin are season, vehicle speed (or road type), road
condition, road grade, and proximity to other high-emitting roads. Combined with the TransCAD
traffic volume model, an analysis of the total emissions from the road sections surveyed
indicated that urban areas (in particular South Lake Tahoe) had the highest emitting roads in the
basin.
Development of an Air Pollutant Emissions Inventory for the Lake Tahoe Basin (Gertler et
al., 2008). This project constructed a baseline emissions inventory to quantify and evaluate the
contribution of various sources to ambient pollutant concentrations in the Lake Tahoe Basin. In
addition to the usual pollutants included in most inventories (i.e., CO, NOx, VOCs, PM10, PM2.5,
and SO2), estimates of ammonia (NH3), phosphorous (P), and phosphate (PO4) were also
6
developed due to their contribution to the declining water clarity of the lake. The inventory
spatially allocated emissions based on vehicle kilometers traveled (VKT) per link, population
density, and land use. For the species linked to the decline in water clarity, the major contributors
to PM, P, and PO4 are area-wide sources, particularly residential fuel combustion and road dust
re-suspension, while mobile sources were the dominant source of NOx and NH3. Most of the
PM10 sources were located on the south side of the lake where VKT is greatest. Mobile sources
of nitrogen include tailpipe NOx and NH3. NOx transforms in the atmosphere to nitric acid that is
readily deposited to the lake and vegetation. Gertler (2006a) concluded that 90% of the nitric
acid originated from NOx emitted in the basin. Overall, these atmospheric sources have been
attributed to provide 55% of the nitrogen in the lake. Spatial and temporal analysis of NOx
emission sources suggests that controls are not likely to provide substantial benefit because nitric
acid tends to form hours or days after precursor emission, when the pollutants have generally
been well dispersed.
Receptor Modeling to Determine Sources of Observed Ambient Particulate Matter (PM) in
the Lake Tahoe Basin (Engelbrecht et al., 2009). This project analyzed the LTADS and DRI
PM filter data using multivariate statistical procedures, including Principal Components Analysis
(PCA) and Positive Matrix Factorization (PMF). These analytical tools identified factors and
groups of chemical species of relevance that (within the measured dataset) distinguish sub-sets of
site and seasonal data which enabled assessment of seasonal sources of the observed particulate
matter. The study indicated that re-suspended paved road dust was the major source of PM10 in
the basin. Wood burning was an important source of PM2.5 during the winter months. After wood
burning, motor vehicle tailpipe emissions are the major source of PM2.5 in the urban sites.
Secondary pollutants from outside the basin are minor sources of PM2.5 and PM10. Phosphorous
concentrations in the coarse fraction are elevated and are therefore indicative of mechanically
resuspended soil. Secondary Ammonium Nitrate accounted less than 5% of the coarse and fine
PM fraction at the near shore sites. Hence, compared to the gaseous N (e.g., NH3, HNO3) the PM
contribution to the atmospheric deposition of N is small.
Assessing the Impact of Best Management Practices (BMPs) Designed to Reduce the
Contribution from Resuspended Road Rust to Lake Tahoe (Kuhns et al., 2010b). The study
quantified the cost effectiveness of different road dust control strategies applied in the Lake
Tahoe Basin for reducing airborne emissions. By reanalyzing the results from Kuhns et al.
(2007), reservoirs of resuspendable road dust were found to be anti-correlated with street
sweeping frequency and correlated with poor pavement condition and proximity to unswept
neighborhoods. Costs to reduce airborne PM10 emissions by sweeping were calculated to be $600
per Mg PM10/year reduced versus costs to pave roads in poor condition which were calculated to
be $700,000 per Mg PM10/year. Roadside measurements of airborne road dust indicated that the
ratio of TSP to PM10 was ~2.4, resulting in TSP sweeping costs of $1400 per Mg TSP/year and
repaving costs of $3.4M per Mg/year. It should be emphasized that these costs are associated
with reduced airborne emissions and not with subsequent lake loading. This study focused on
PM emission reductions in terms of mass although many TMDL agencies prefer to consider
particle counts as a better indicator of factors affecting lake water clarity.
This study (Zhu et al., 2011) also quantified the effect that landscape has on the deposition of
particulate matter as it is transported towards the lake. The PM10 mass concentrations decreased
exponentially with downwind distance from the highway. With aspen and pine trees as a
7
vegetation barrier, at 30 m and 100 m downwind the PM10 mass concentrations decreased to 51%
and 11%, respectively, of the observed concentrations at 5 m downwind of the roadway.
Consistent with the findings of Dolislager et al. (2009) and VanCuren et al. (2010a), offshore
winds typically blew traffic-induced PM towards the lake between late afternoon and early
morning.
Size-resolved chemical analyses of roadside filter and DRUM samples showed that phosphorus
concentrations were associated with combustion-sized aerosol (PM2.5), with the motor oil
additive zinc dialkyldithiophosphates (ZDDP) being the likely source.
Tahoe TMDL [Total Maximum Daily Load] Pollutant Reduction Opportunity Report
(CWB & NDEP, 2008). This analysis synthesized studies concerning atmospheric, urban upland
and groundwater, forested upland, and stream channel sources to evaluate the costs and benefits
of a range of mitigating options. This integrated approach concluded that control measures
targeting urban runoff sources would have the greatest impact on reducing lake loading, followed
by atmospheric controls. The report did not attempt to include entrained road dust deposited to
the lake from mobile sources because lake loading changes from this source did not appear to be
proportional to estimates of VKT reduction. The authors noted that linkages between airborne
emissions of fine particles and lake loading were not well-characterized. Using largely
independent sources and methods for emission control effectiveness from Kuhns et al. (2010b),
the CWB and NDEP authors estimated that every-other-week street sweeping would have an
annual cost of $380,000 and reduce airborne PM10 emissions by 25%. Weekly sweeping would
cost $760,000 and reduce emissions by 41%. Based on their adjusted emissions estimate of
paved road dust TSP emissions using a multiplier of PM10 (2300 Mg/year), the airborne control
measure cost would be $660 per Mg/year for every-other-week sweeping and $1320 per Mg/year
for weekly sweeping. These results are very similar to those from the prior study, providing
reassurance of the estimate’s accuracy.
Road Rapid Assessment Methodology (Road RAM) (2NDNature, LLC et al., 2010). The
current study (described below) synthesizes numerous spatial and temporal patterns derived from
research-grade instrumentation. With mitigating regulations implemented, the challenge will be
to ensure that loading into the lake is reduced. Proxies of the road dust reservoir are needed to
ensure that control measures are effective. Dr. Nicole Beck of 2ND Nature developed a simple
and repeatable field observation tool to assess the transportable particulate load of
impervious/paved road surfaces.
The project involved field measurements using inexpensive equipment and visual observations to
characterize the conditions of road surfaces. In a demonstration of the technology with several
hundred observations, road dust loading patterns were observed that were very consistent with
the seasonal and spatial patterns measured by the TRAKER vehicle as described by Zhu et al
2009. The Road RAM system provides a sustainable monitoring system that can be implemented
across the basin to confirm that road dust emissions do indeed decrease as Best Management
Practices are added and refined.
8
3
Temporal and Spatial Distribution of Mobile Source Emissions in the Basin
Emissions from mobile sources are a significant contributor of pollutants to the Lake Tahoe
basin. As summarized in the literature review (Section 2), vehicle re-entrained road dust is a
significant part of PM10 emissions in the basin. Motor vehicle tailpipe emissions are the
dominant contributors to NOx and NH3, and wood smoke and tailpipe exhaust are the major
sources of PM2.5 in the basin (Gertler et al., 2008). Aerosols in the PM2.5 size fraction have
deposition velocities that are generally orders of magnitude slower than PMcoarse and PMlarge
aerosols. Phosphorus emissions are predominantly associated with crustal material such as road
dust that are predominantly associated with PMcoarse and PMlarge.
Kuhns et al. (2007) measured seasonal and spatial variations in road dust emissions using the
TRAKER vehicle, a mobile platform that measures the dust suspended behind the vehicle’s tire.
Measurements took place on a 75-mile route around the lake on a biweekly (every other week)
basis between August 2006 and September 2007. Examples of the measured emission factors are
shown in Figure 2 and Error! Reference source not found..
Figure 2. PM10 road dust emission factors of Secondary Road Sections near Marlette Lake SNOTEL Station.
Upper panel shows TRAKER signal while lower panel shows nearby snow accumulation.
In terms of re-entrained road dust PM10 emissions, wintertime emission factors were on average
5 times greater than summertime emission factors due to the application of traction control
material. Wintertime emissions increased with the development of snowpack at nearby
9
meteorological stations (mid-November) and returned to lower and more constant summer levels
by the beginning of May when the snowpack had melted (Figure 2).
The researchers also found that the vehicle kilometers travelled (VKT) were not evenly
distributed in the basin; in particular, the South Lake Tahoe area had the highest VKT values in
the basin. Although the highest emission factors were observed on very low traffic volume roads
on the west side of the lake, when emission factors were combined with the high VKT in urban
areas, total emissions (Error! Reference source not found.) from the road sections surveyed
indicated that urban areas (in particular South Lake Tahoe) had the highest particle emissions
from roads in the basin (Zhu et al., 2009).
10
Figure 3. Emission rates (g/km/day) calculated by multiplying the two-way traffic flow for each link with the
emission factor measured by the TRAKER for the 41 sections of road surveyed in the basin..
11
These results link road type, vehicle speed, vehicle class, season, and (General Improvement
District) GID maintenance practices (e.g. sweeping frequency, sanding material, road resurfacing
schedule) to road dust emission factors. The emission factors were compiled and assembled into
a lookup table to extrapolate road dust emissions factors for each road link described in the
Tahoe Regional Planning Agency (TRPA’s) TransCAD traffic demand model by season.
The link level table of road dust emissions was created using the ArcGIS gridding algorithm
used by Gertler et al. (2008) for the seasonal area source emissions. An example emissions
inventory image for all PM10 sources is shown in Figure 4.
Through the Lake Tahoe Maximum Daily Load (TMDL) Program, atmospheric deposition was
estimated to account for 55% of total nitrogen and 15% of total phosphorous inputs to the lake.
Nitrate (NO3-) and ammonium (NH4+) are considered particulate (i.e. aerosol) forms of nitrogen.
(Englebrecht, 2009) analyzed the chemical components on the filters collected in the Lake Tahoe
Atmospheric Deposition Study (LTADS) and found that, on average, nitrate accounts for 2.6%
of the PM10 mass and 3.2% of the PM2.5 mass collected by Two Week Samplers (TWS) at the 5
sites in the Lake Tahoe Basin. NH4+ accounts for 3.0% of PM10 mass and 4.5% of PM2.5 mass.
Dolislager et al. (2009) estimated that, not including organic nitrogen (which was not measured
in the LTADS), on average the particle nitrate (NO3-) accounts for 15.4% of total nitrogen [NH4+
(p)+ NO3-(p)+HNO3(g) +NH3(g)] airborne concentrations.
Phosphorus from motor vehicle emissions as measured in PM2.5 was very low for all cases. UC
Davis’ researcher measured the phosphorus concentrations in roadside resuspended dust using
the DRUM sampler on the east side of the lake in 2009. Compared with the 3 collocated PM10
filters, the phosphorous (P) concentration (ng/m3) recorded by the DRUM sampler was ~0.15%
of filter PM10 mass (Kuhns et al., 2010b).
In the Lake Tahoe Basin emissions inventory report (Gertler et al., 2008), mobile sources were
the dominant contributor of NOx. It is estimated that 6.2 metric tons (Mg) of NOx is emitted from
mobile sources per year in the basin. Table 1 displays the basin-wide total paved road dust PM
emissions compared with other studies. The Kuhns 2004 EI was substantially lower than that
reported in this study due to a different calibration of the TRAKER vehicle with respect to
downwind emission fluxes based on unpaved road measurements that were orders of magnitude
higher than paved road on a per kilometer basis. The revised calibration conducted by
Etyemezian et al. 2005 and 2007 on paved roads greatly improved the calibration of TRAKER
Emission Factor for paved roads. The TSP emissions from this study are consistent with those of
NDEP and CWB (2010) since they are based on the same set of year round TRAKER
measurements but calculated by inferring silt loading for the basin and using equations published
in EPA’s Emission Factor database, AP-42.
12
Table 1. Basin-wide total paved road dust PM emissions (metric tons per year) compared with other studies.
Winter
PM10
Emissions
This study
Kuhns et al.,
(2004)c
CWB &
NDEP (2008)d
Summer
PM10
emissions
Annual PM10
Emissions
647
393
1040
140
148
288
Annual PMlarge
Emissionsa
1425
Annual PM2.5
Emissions
Mina
Maxb
14.56
187.2
Annual
TSPe
emissions
2465
2334
a. The PMlarge, PM2.5 emissions are calculated from the PMlarge/PM10, PM2.5/PM10 ratio
values from roadside measurement in Bourne Meadow (eastern lake) in Tahoe area
(Kuhns et al., 2010b).
b. Roadside filter PM2.5/PM10 ratio (Gertler et al., 2006b).
c. Wintertime and summertime emission factors measured from April to July in Tahoe
region.
d. The authors use silt loading values inferred from basin wide TRAKER values (Kuhns
2004) to estimate fugitive dust emissions from paved roads.
e. TSP stands for Total Suspended Particulate.
13
Figure 4. Gridded annual PM10 emissions (kg/day) for all major sources including fires, boats, paved road
travel, and biogenic sources.
14
4
Vehicle Class Distribution Based on Roadway Types.
Road dust emissions are also affected by vehicle type through the process of entrainment by the
aerodynamic forces present in vehicle wakes. . Primary roads sections have two-way vehicle
flow that range from 5,000 to ~40,000 vehicles per day and posted speed limits that range from
15 mph (in urban areas) to 55 mph. Secondary roads have AADT (annual average daily travel) of
2000-to-10,000 with speed limits between 35 mph and 45 mph. Tertiary roads are typically
neighborhood roads but can have AADTs up to 5000. Speed limits on tertiary roads range from
20 mph to 40 mph.
Vehicle class distributions were determined using video transcription in two locations in Incline
Village (near point 3 in Figure 3)and also using road tube counters on primary roads near Tahoe
City and South Lake Tahoe. Using video transcription, motorcycles, passenger cars, pickup
trucks, vehicles with trailers, and various classes of trucks were classified (Kuhns et al., 2004).
Vehicle class data were monitored for Lakeshore and Village (secondary roads) and Southwood
and Mays (tertiary roads) in Incline Village, NV. Figure 5 shows the distribution of different
vehicle types in the fleet passing the sampling sites.
Road tube counters that measure the axle spacing of vehicles were also used on to obtain a
coarse separation between light and heavy duty vehicles based on axle spacing. Measurements
were conducted at Bourne Meadow (US50) and Rabe Meadow (US50) (both nearby eastern
Lake Roundhill area (point 13 on Figure 3), Incline Village (secondary roads; point 2 on on
Figure 3), Tahoe City (SR28 point 27 on Figure 3). An example data report from the road tube
counters is show in Table 2 where vehicle volume and vehicle class data are presented from a
24-hour period on Highway 50 near Rabe Meadow (Round Hill area) in April 2009.
As seen in Table 2, the heavy duty trucks (>5 axle) accounted for 2.4% of the fleet; this
percentage is almost the same (~2%) for the vehicle class data obtained from the secondary and
tertiary roads in the Incline Village, NV. Cars and SUVs dominate, comprising ~80% of the
fleet. Consequently, to simplify the analysis, the PM emission factor for light-duty vehicles was
assumed for all vehicle types when calculating the PM emissions in the basin.
Traffic volumes peak in the morning and midday and are lowest at night. Vehicle speed and
counts were also measured using radar technology on SR-28 near Tahoe City in May 2010.
Traffic data were also collected using traffic counters on the secondary roads in the Incline
Village area during June 2010. The diurnal traffic patterns at these three sites are shown in
Figure 6.
All sites exhibited similar variation in hourly traffic volume. The traffic volume from 7:00 to
15:00 accounted for ~70% of daily total volume and the traffic volume late at night – from 22:00
to 4:00 – only accounted for ~4% of daily traffic volume. Although the TransCAD model
assigned 4 time periods in a day corresponding to the vehicle volume diurnal variation: A.M.,
Mid-Day, P.M., and Late Night, we updated the diurnal variation of traffic flow in the
calculation with hourly field measurements as shown in Figure 6. With these data we are able to
update and describe the magnitude of vehicle emission factors for the range of vehicles used in
the Lake Tahoe Basin and relate these to diurnal wind patterns.
15
Figure 5. Fleet distribution at two sites in Incline Village, NV.
Rabe Meadow Hwy50 04‐2009
Tahoe City SR28 05‐2010
Incline Village Secondary road 06‐2010
80
70
500
60
400
50
40
300
30
200
20
100
Secondary road vehicle volume
Primary road vehicle volume
600
10
0
0
Time Figure 6. Traffic volume variation measured at two primary roads and one secondary road in the Lake Tahoe
Basin.
16
Table 2. Example of vehicle classes reported by the TimeMark traffic counter on Highway 50 near Rabe
Meadow on Tuesday, April 14, 2009.
17
5
Seasonal Near Field Deposition
Trip location is a critical parameter affecting the deposition potential of transportation-produced
PM to the lake surface. Landscapes (i.e., density of trees and buildings) can play a large role in
attenuating concentrations of air pollutants before they reach the lake. To investigate the
vegetation density in the Lake Tahoe Basin, the Tahoe Basin Existing Vegetation Map V4.1
(Dobrowski et al., 2005) was imported into ArcGIS mapping software to run the analysis. The
map was created through the use of digital remote sensing analysis that produced a raster image
containing a cover type for each 1-meter pixel in the image. The vector file provided with this
document is the result of querying polygons overlaying the raster map. It provides the percentage
coverage by trees. Dominant tree species in the Lake Tahoe Basin are coniferous trees including
Jeffrey pine, lodgepole pine and western white pine, ponderosa pine, white fir, and red fir;
however, deciduous trees such as aspen may form stands close to the lake. Based on the different
tree coverage ratios, we divided the vegetation into 3 categories: Shrubs, Open Trees, and Dense
Trees, with the median tree coverage in each category as 10%, 35%, and 75%, respectively
(Figure 7). The TransCAD model assigned central points to each of the 7235 road sections in the
basin roadway network. ArcGIS queries were performed to find the shortest distance to the lake
for each of the 7235 traffic points. For every shortest path of 7235 traffic segments to the lake,
the fraction of each type of vegetation coverage was estimated using ArcGIS analysis tools.
18
Figure 7. Tree coverage (percent) in the Lake Tahoe basin.
19
6
Relationship of Trip Location and Atmospheric Deposition to the Lake
Based on the TransCAD model output for AADT (annual average daily travel), the roads in the
basin were grouped as primary, secondary, or tertiary roads. In the TRAKER one-year yearround survey around Lake Tahoe, the 41-section route was a combination of different road types
(primary, secondary, tertiary) and covered all road maintenance organizations, including Washoe
County, Carson City, and Douglas County in Nevada, and Placer and El Dorado counties in
California (Zhu et al., 2009). TRAKER measurements of PM10 were calibrated with the flux of
PM10 measured downwind of a paved road using flux towers. Using this calibration, TRAKER
measurements were converted into emission factors (EFs) in units of grams of PM10 per vehicle
kilometer traveled (g/VKT). The road dust PM10 emission factors in 41 sections were grouped
into two seasons (December to April as winter and rest of the year as summer) and five counties
and three road types (Error! Reference source not found.). Thus all the traffic sections from
the Tahoe TransCAD model output were assigned with emission factors from Table 3 based on
the jurisdiction and road type.
PMlarge emission factors calculations were applied a multiplier of 1.38 of PM10 EF based on
roadside mass size fraction as measured at the Bourne Meadow site in 2009 (Kuhns et al.,
2010b). In that study, the roadside PM2.5 was found to only accounts for 1.4% of the PM10 mass
inferred from optical particle measurements. Gertler et al. (2006b) also measured the downwind
roadside PM2.5 and PM10 on filters as part of a study conducted near Sand Harbor, NV. In that
study, the PM2.5 mass concentration was found to be up to 18% of the filter PM10 concentration,
thus the PM2.5 EF calculation has a multiplier range of 0.014-to-0.18 of the PM10 EF. The
studies represent emissions measured downwind of a road. For longer term ambient monitoring
such as that used in LTADS, the two week long PM2.5 to PM10 ratios were higher (0.31 to 0.53 at
SLT) since for approximately half of the day, the road was downwind of the monitor. During
these periods, the coarse and large particles are depleted from the air mass.
The TransCAD model output provided the AADT and the link length of each of the 7235 traffic
sections in the basin. The TransCAD-modeled roadway segments and traffic points were
imported into ArcGIS and queries were performed to find the shortest distance to the lake for
each of the 7235 traffic points and the (true-north based) azimuth angle of each shortest path.
The tool used for this analysis was the ArcGIS ArcView Desktop plus three additional scripts:
the Add Line script, the XTools script, and the Grips and Shapes script.
Gertler et al. (2008) produced data layer of emissions inventories using the TransCAD output.
When merged with our land cover and lake boundary coverages, we found the spatial features
were shifted by ~150 m due to an arbitrary choice of geographic projections. Thus, spatial
adjustment was conducted on these two layers with 13 control point pairs. After spatial
adjustment, these two layers can approximately match other layers and were incorporated into
this analysis. That was an important adjustment for this study since distance to the lake is a
critical factor to determine how much PM is deposited prior to reaching the Lake boundary.
Our analysis indicates that for the 7235 traffic points, the average distance to the lake is 2100 m,
with a median distance to the lake of 890 m and a range from 4 m to 18,069 m. Figure 8 shows a
sample GIS overlay with shortest paths and the vegetation coverage en route in the Tahoma area
and Figure 9 shows the shortest paths of the 7235 traffic points in the basin.
20
Table 3. Road dust PM10 Emission Factor (EF) in terms of grams per vehicle kilometer traveled (g/VKT)
based on county and road type from TRAKER one-year measurement campaign (Zhu et al., 2009).
County
Road
Type
Washoe
Washoe
Washoe
Carson
Douglas
Douglas
Douglas
El Dorado
El Dorado
El Dorado
Placer
Placer
Placer
Primary
Secondary
Tertiary
Primary
Primary
Secondary
Tertiary
Primary
Secondary
Tertiary
Primary
Secondary
Tertiary
Winter Daily
Average EF
(g/VKT)
Winter EF
Standard
Deviation
(g/VKT)
Summer
Daily
Average EF
(g/VKT)
Summer EF
Standard
Deviation
(g/VKT)
0.30
0.62
1.55
0.24
0.27
1.00
1.89
0.74
2.02
1.38
0.61
1.74
3.70
0.09
0.16
0.12
0.11
0.07
0.68
1.99
0.25
1.83
0.16
0.15
1.53
3.70
0.05
0.15
0.59
0.04
0.04
0.27
0.50
0.16
0.55
1.17
0.15
0.65
1.65
0.01
0.06
0.34
0.02
0.03
0.16
0.31
0.14
0.57
0.48
0.04
0.28
1.65
Figure 8. Sample vegetation coverage of the three vegetation type (Shrubs, Open Trees, and Dense Trees,
displayed as gray, green, and blue, respectively) and shortest paths (purple lines in the figure) from traffic
points in the Tahoma region to the Lake. The black dots are 1/10th of the length and are used to calculate the
length coverage fraction in each vegetation category.
21
Figure 9. Shortest paths to the lake from 7235 traffic points in the Tahoe roadway network. The blue lines are
roadway networks and the red lines are the shortest paths to the Lake.
22
Based on the work of Noll and Aluko (2006), we have empirically calculated parameters for a
first-order near-source deposition model that accounts for the large particle deposition that
occurs within the first kilometer of emissions. At a constant height above the ground, downwind
concentrations are assumed to follow an exponential decay:
C ( x )  C0 e

Vd X
UH
(1)
where C(x) is the particle concentration at x meters of horizontal distance from source
(counts/cm3 or ug/m3)
C0 is the particle concentration at source (same units as C(x)
Vd is the deposition velocity, cm/s
X is the meters of horizontal distance from the source
U is the horizontal wind velocity, m/s
H is the injection height of resuspended particle source and was assumed to be 2 m
In the Lake Tahoe Atmospheric Deposition Study (LTADS), R. VanCuren et al. (2011a) fitted
the near-source mass concentration decrease into an exponential function C = Co e–Kx, with K as
the depletion coefficient that incorporates vertical diffusion and deposition. Cowherd et al.
(2006) studied the fugitive dust generated by military training exercises conducted on an
unpaved road. He reported the removal rates of fugitive dust by different vegetation
characteristics. In that study, with light winds (0.9 to 1.8 m/s), the PM10 mass loss rate over 20 m
travel distance was 45 to 67% for tall cedar trees, 41 to 50% for oak trees, 29% for short cedar
trees, and <10% without trees. Cowherd et al. (2006) reported the exponential K as follows:
0.035 m-1 for dense trees, 0.0175 m-1 for long grass, and 0.0035 m-1 for short grass in exponential
function C = Co e–Kx. Zhu et al. (2011) reported PM exponential decay with distance from the
road from a study conducted in Bourne Meadow of Lake Tahoe with leafless aspen trees as a
barrier. PM concentrations decreased significantly with downwind distance. Regression
exponents (K) decreased by 0.024 m-1 for the largest measured particles greater than 15 μm,
0.021 m-1 for coarse particles 10 μm to 2.5 μm, down to 0.017 m-1 for particles in the 0.5 μm to
0.7 μm size range under an average wind speed of 1.1 m/s. Detailed plots and descriptions of
these decays are presented in Kuhns et al., 2010b.
To summarize the mentioned studies, the exponent K in the decay equation is a function of Vd,
U, and H, as K = Vd/UH. Prior studies (Moller & Schumann, 1970; Sehmel, 1980) have shown
that Vd values increase with particle size and increase with higher friction velocities U*
(equivalent to roughness length Z0). Thus we chose three different K values for defined
vegetation types in the Lake Tahoe Basin to simulate the exponential decay of traffic-caused
PM10: K as 0.035 m-1 for Dense Trees, 0.021 m-1 for Open Trees, and 0.0035 m-1 for Shrubs
(Figure 10).
The selection of K values also estimated the corresponding Vd values under the three types of
vegetation for PM10 deposition. Selection of the K and Vd values for PMfine and PMlarge are based
on near-source PM deposition measurement done in Tahoe area (Zhu et al., 2011) and Ft. Riley,
KS (Gillies et al., 2010). After estimated Vd values were selected in the exponent decay Equation
1, the exponential decay equation was applied to the Tahoe area under certain wind speeds (U)
23
and wind directions. The angle theta (θ) measured from the perpendicular to the road between
the wind direction and the azimuth angle of the shortest path is related with X (downwind
distance) by the equation X= L cos θ, where L is the shortest distance to the lake for each traffic
points and is a sum of L1, L2, L3, which are the corresponding distances covering the Shrubs,
Open Trees, and Dense Trees. Only an angle theta (θ) between -90o and 90o would be treated as a
favorable wind direction for the specific traffic point. Five year (2005-2009) hourly wind speed
and wind direction data around Lake Tahoe were obtained from UC Davis’s REMOTE project.
The REMOTE project set up 6 meteorological stations around lake: Cave Rock, Timber Cove,
Rubicon, Sunnyside, USCG, and Tahoe Vista (
Figure 11). The yearly wind rose maps for all stations are shown in Figure 12.
Normalized PM10 Concentration (mg/m3)
1
0.9
0.8
0.7
Dense Trees
y= e(‐0.035x)
Open Trees
y= e(‐0.021x)
Shrubs
y= e(‐0.0035x)
0.6
0.5
0.4
0.3
0.2
0.1
0
0
50
100
150
200
250
300
350
Distance from source (m)
Figure 10. Exponential decay for PM10 at various distances downwind from the source. The exponents are
from field measurement of Cowherd et al. (2006) and Zhu et al. (2011).
24
400
Figure 11. Meteorological stations set up around Lake Tahoe by the UC Davis REMOTE project.
25
Figure 12. Wind rose map from 1-year (2006) monitoring data for the 6 meteorological stations around Lake
Tahoe.
26
All the 7235 traffic sections were assigned with the nearest met station. Thus the PM mass
reaching the lake as a result of vehicle travel in 7235 road segments in the basin after the
vegetation attenuation is estimated as:
n
1
PM   EFi * TrafficVolume * LinkLenth* exp(
(Vd1 L1  Vd 2 L2  Vd3 L3 ))
(2)
UH cos
i 1
where n is the number of traffic segments
U is the horizontal wind velocity, m/s
H is the injection height of resuspended particle source and was assumed to be 2 m
Vd1 is the PM deposition velocity under Shrubs, cm/s
Vd2 is the PM deposition velocity under Open Trees, cm/s
Vd3 is the PM deposition velocities under Dense Trees, cm/s
As mentioned above, EF has a winter vs. summer factor, traffic volume is variable throughout
the day, and the wind speed U and angle θ measured from the perpendicular to the road all
changes hourly from the nearby met stations. Thus, the five-year average (2005-2009) PMfine,
PM10, PM large reaching the lake can be calculated integrating Equation 2 over time.
Annual average PM10 deposition to the lake is estimated at 20 ± 10 Mg per year, PMlarge
(particles > 10 μm) deposition to the lake is estimated at 15 ± 7 Mg per year, and PMfine (PM2.5)
deposition to the lake is estimated to range from 0.23 ± 0.12 to 3.0 ± 1.5Mg per year. PMlarge and
PM2.5 EF in Equation 2 are based on the roadside PMlarge concentration, which is 137% of PM10
and the PM2.5 concentration is 1.4% to 18% of the PM10 concentration as measured in Tahoe area
by Kuhns et al. (2010b) and Gertler et al. (2006a) respectively. The annual total PM deposition
to lake from the vehicle travel in the Tahoe basin is 36 ± 12 Mg per year. The annual PM10
deposition (to the lake) ratio is approximately 2% of the ~1040 Mg PM10 resuspended by the
vehicles traveling on ~812 miles of roadway network in the basin, with an annual daily ~2.3
million vehicle kilometers traveled (VKT). The annual total PM deposition (to the lake) ratio is
approximately 1.4% of ~2465 Mg total PM resuspended by vehicles traveled on paved roads in
the basin. Table 4 summarizes the total PM (fine, coarse, large) deposition to the lake with
contribution from different counties in the basin.
The result indicates that 99% of resuspended road dust is captured by the vegetation or
deposited on the pathway (roadway) before it reaches the lakeshore; however, some of that dust
could still be transported into the lake via water runoff, thus street sweeping or other collection
efforts should be made to reduce the total PM load to the lake.
Figure 13 shows the cumulative distribution of the total PM deposition potential as a function of
distance to the lake. It was found that 90% of total PM deposition occurred within 500 meters of
the lake. Thus, mitigation efforts (e.g., street sweeping) should ensure that these roads are kept as
clean as possible.
27
Table 4. Total PM (fine, coarse, large) deposition contributions and vehicle kilometers traveled (VKT) from
different counties
County
El Dorado, CA
Douglas County, NV
Placer County, CA
Washoe County, NV
Carson City, NV
Total
Total PM deposition to lake (Mg/year)
Annual
Winter
Annual Ratio
21
12
61%
7.2
5.9
20%
5.7
4.0
16%
0.91
0.62
2.6%
0.005
0.004
0.0%
36
22
VKT
VKT ratio
1,264,703
345,531
455,463
141,913
11,137
2,218,750
57%
16%
21%
6.4%
0.5%
For the winter period from December to April in the basin, the PM10 resuspended and deposited
to the lake averages 14 ± 7 Mg/year, with around 66% of the deposition in the winter period due
to the traction control material applied on the road (and some track-out soil brought onto
secondary roads by vehicles) and resuspended and deposited to the lake. These contributions are
reflected in the results since the TRAKER system captures instantaneous measures of
suspendable road dust during operation. Analysis indicates that El Dorado County in California
(which includes the City of South Lake Tahoe) is the largest contributor (57%) to the PM
deposition to the lake due to its dominant VKT ratio (51%) in the basin and its proximity to the
lake. Douglas County in Nevada contributed 21% of the PM deposition and Placer County, CA,
contributed 18% of PM deposition, with VKT ratios of 13% and 25%, respectively. That El
Dorado County (including South Lake Tahoe) is the dominant contributor of total PM deposition
to the lake is also a result of the prevailing south/southwest wind in the region (the wind rose for
Timber Cove station in Figure 12). Figure 16 displays the annual PM10 deposition potential in the
basin for 7235 traffic segments, taking into consideration the VKT, seasonal emission factors,
wind speed and direction, distance to the lake, and vegetation barrier density. Figure 14Figure 14
and Figure 15 display the winter and summer deposition potential of total PM around the lake.
As can be seen in the annual summary, Figure 16, the traffic links with highest PM deposition
potential are located at the near-shore zones. This indicates that the distance from the road to the
lake is a significant factor impacting the amount of PM deposition to the lake.
28
Total PM Deposition Potential Cumulative Distribution 1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1
10
100
1000
Distance to the lake (m)
Figure 13. Cumulative Distribution of total PM deposition potential as a function of distance to the lake.
29
Winter Total PM Deposition Potential
kg/winter
0 - 14
15 - 51
52 - 119
120 - 375
376 - 1575
Figure 14. Gridded winter time total PM deposition potential for 7235 traffic segments, taking into
consideration the vehicle kilometers traveled (VKT), seasonal emission factors (EFs), wind speed and
direction, distance to the lake, and vegetation barrier density. Each colored group represents 20% of the TSP
(Total Suspended Particulate) deposited to the lake, with classification based on cumulative 20%, 40%, 60%,
80%, and 100% of deposition potential.
30
Summer Total PM Deposition Potential
kg/summer
0 - 10
11 - 44
45 - 126
127 - 423
424 - 1150
Figure 15. Gridded summertime total PM deposition potential for 7235 traffic segments around the lake.
Each colored group represents 20% of the TSP (Total Suspended Particulate) deposited to the lake.
31
Annual Total PM Deposition Potential
kg/year
0 - 23
24 - 87
88 - 246
247- 596
597 - 2725
Figure 16. Gridded annual total PM deposition potential for 7235 traffic segments around the lake. Each
colored group represents 20% of the TSP (Total Suspended Particulate) deposited to the lake.
32
7
Marginal Impacts of a Vehicle Trip on Re-entrained Dust
Vehicle trips simulated by TransCAD translate into vehicle kilometers traveled (VKT) on
specific roadway links. As discussed above, the impact of a vehicle trip on lake water clarity
depends on seasonality, vehicle type, speed, local Best Management Practices (BMPs),
proximity to the lake, and wind direction. The differences between the winter and summer
seasons not only impact the road dust PM emission factor but also impact the traffic volume. The
estimated year-round population of the Lake Tahoe Basin is approximately 56,000 residents with
peaks exceeding 100,000 during summer months. Generally the highest annual daily traffic
volumes in the basin occur in August.
In analysis by Zhu et al. (2009), the wintertime road dust PM10 emission factor is an average of 5
times that of the summer emission factors (EF); despite the higher summer seasonal traffic
volume. Lower traffic counts in wintertime help to reduce the overall impact of resuspended road
dust to the lake.
Also, as we saw in Figure 6, Mid-Day (7:00-15:00) accounts for ~70% of daily traffic flow on
primary roads and secondary roads. As Kuhns et al. (2010b) and Dolislager et al. (2006) reported
and meteorological data from UC Davis REMOTE sites indicate, in the absence of strong
synoptic systems (like winter storms), the wind near shore has a consistent diurnal pattern.
During daytime, beginning with the sunrise, the on-shore winds dominate pushing peak
emissions away from the lake, then after sunset, until the next morning, the off-shore winds
dominate. Combined with reduced night vehicle volume, the diurnal wind direction and traffic
volume fortuitously reduces the direct airborne PM deposition to the lake.
However, the high-volume vehicle-resuspended road dust from the daytime still deposited onto
the road surfaces, curbs, shoulders, and nearby vegetation and soils; these dust deposits may still
enter Lake Tahoe via water runoff or fugitive wind erosion processes, especially if the dust is
deposited back onto road surfaces where it will be re-entrained again. This track-out effect helps
replenish the silt loadings of the re-suspendable road dust (Kuhns et al., 2010b).
Figure 17 displays the VKT distribution and cumulative VKT distribution as a function of
distance from the lake in the Tahoe basin. 80% of the VKT is within 3000 meters of the lake and
70% of the VKT is within 2000 meters of the lake. As to the 7235 traffic segments, the average
distance to the lake is 2100 meters with a median distance to the lake of 890 meters. Thus to
mitigate PM10 deposition, street sweeping should be concentrated on roads closest to the lake.
33
Figure 17. VKT (vehicle kilometers traveled) distribution and cumulative VKT distribution as a function of
distance from the lake in the Tahoe basin.
34
8
Reconcile Atmospheric Deposition with Other Sources
As we summarized in Section 6, only 35 metric tons (Mg) of total PM would deposit to the lake
from the 2465 Mg of total PM emissions generated every year from vehicles traveling on paved
roads in the Lake Tahoe Basin. As Kuhns et al. (2010b) reported, cost-effectiveness for road dust
control is $0.6 per kg of PM10 emission reduced for street sweeping versus $700 per kg of PM10
emission reduced for pavement improvement. With the basin average PM10/TSP ratio at 1/1.37,
combined with 1.4% total PM deposition rate to the lake, the control cost for every Mg of
deposited total PM is $98,434 for street sweeping and 114 million for pavement inprovement
(Table ). Compared with storm water management (Table 5 and Error! Reference source not
found.), the cost-effectiveness of street sweeping is comparable to storm water treatment
whereas pavement improvement is nearly 1000 times higher than the storm water management.
However, if the street sweeping just concentrated on the dirtiest roads with the highest (for
example, 20%) deposition potential that are very close to the lake (Figure 16), the costeffectiveness would be greatly increased.
Table 5. Comparison of road dust control costs with storm water management.
Storm water fine sediment reduction
Road dust control cost for every MT of deposited total PM*
$ (Million)/MT
Street Sweeping Pavement improvement
(million $/MT Total PM)
Urban ‐Ground water Tier 1
0.08
Tier 2
0.1
Tier 3
0.05
Forest Uplands
Tier 1
Tier 2
Tier 3
0.03
0.03
0.04
Stream Channel
Tier 1
Tier 2
Tier 3
0.01
0
0
0.10
Total Tier 1
Tier 2
Tier 3
115
Ratio compared Storm water management 0.82
957
0.76
883
1.09
1276
0.12
0.13
0.09
*The road dust control costs are from Kuhns et al. (2010b) and are calculated for each metric ton
of total PM deposited to the lake.
35
Table 5. Summary table of cost-effectiveness of storm water management (CWB & NDEP, 2008).
36
9
Conclusions
Below, we summarize of how each project objective was met, describe the implications of the
findings, and make recommendations based on these results.
Assemble all relevant reports and data from prior studies:
We have conducted a literature review of the relevant studies pertaining to the assessment of
pollutant loading in the lake, with an emphasis on dry deposition of airborne particulate matter.
These reports and published papers have been assembled and will be submitted on a CD as a
digital appendix to the report.
Summarize the temporal and spatial distribution of mobile sources of nitrogen (N),
phosphorous (P), and particulate emissions (PM) in the basin
The mobile nitrogen depositing to the lake is in the form of secondary nitric acid that forms from
NOx tailpipe emissions. As new tailpipe emission standards permeate the on-road vehicle fleet,
NOx emissions will decline as the onroad vehicle fleet is replaced with newer cars. Local
regulations, short of reducing vehicle kilometers traveled (VKT), will have little effect on
already decreasing NOx emissions (Gertler et al., 2006a). The major source (90+%) of
atmospheric deposition of fine sediment and phosphorus to the lake is re-entrained road dust
(CWB & NDEP, 2008; Dolislager et al., 2009). Kuhns et al. (2007) and Zhu et al. (2009) showed
that TSP (Total Suspended Particulate) emissions in wintertime (when traction control material is
applied) are ~5 times greater than TSP emissions in the summer. In addition, road maintenance
jurisdictions that sweep once per year in the springtime serve as a source of suspendable material
for jurisdictions with more frequent (e.g., as soon as the roads are dry) sweeping programs
(Kuhns et al., 2010a). Year-round measurements with the TRAKER vehicle showed that the
highest emitting roads were in Placer County and associated with poor pavement conditions (e.g.
cracked/crumbled asphalt surface, potholes). These spatial patterns observed throughout the year
were used to create a basin-wide emission factor database based on road type and jurisdiction. In
turn, this database was linked to the traffic demand model output from TransCAD to create
emission estimates for each of the 7000+ road segments around the basin. Hourly traffic patterns
were used to scale emissions by time of day such that 70% of emissions occur between 7 A.M.
and 4 P.M.
Summarize vehicle class (e.g., car, truck, tractor trailer) distribution on different roadway
types
The vehicle fleet in the Lake Tahoe Basin is composed of ~98% light duty (< 8500 lbs) vehicles.
Wakes from mid- and heavy-duty trucks can entrain sediment from the sides of roads, but these
impacts are assumed to have a small overall effect since these vehicles account for less than 2%
of the total on-road fleet.
Summarize the seasonal near-field deposition of sediment near the roadways
Results from VanCuren (2010a) and Kuhns et al. (2010b) clearly showed that the vast majority
of PMlarge deposits from the air column within minutes of emission. This effect is accelerated in
the presence of dense vegetation. An analysis of vegetative density coverage was overlaid with
the spatially resolved emission inventory so that each road segment could be assessed based on
37
the type of vegetation on the shortest path to the lake. Five years of wind measurements from six
stations around the basin were linked to each road segment to account for local transport of
resuspended road dust. Diurnal wind patterns had the fortuitous tendency of blowing road
emissions away from the lake during peak traffic periods. Overall, only ~2% of emitted PM10
and 1.5% of TSP (Total Suspended Particulate) was estimated to directly reach the lake via
atmospheric deposition.
Assess the relationship of trip location and atmospheric deposition to the lake
An integrated assessment of the potential for emissions to reach the lake was conducted using the
information assembled above. Proximity to the lake, prevailing wind directions, and traffic
patterns played dominant roles in determining which roads had the greatest potential to deposit
fine particles to the lake. Overall, roads in El Dorado County (and in particular the City of South
Lake Tahoe) had the highest potential (67%) to deposit sediment to the lake. South Lake Tahoe
already employs an aggressive street-sweeping program but its high VKT causes it to be a major
source of airborne-derived particulate matter in the lake. Incline Village and Tahoe City made
very minor contributions to lake loading.
Assess the marginal impacts of a single vehicle trip on re-entrained dust
This project has shown that an assessment of marginal impacts of road traffic in the basin is
remarkably complex due to the variation in the potential for road emissions to deliver particles to
the lake. On average, a nighttime trip in the City of South Lake Tahoe (with offshore winds) will
deposit more PM to the lake than a daytime trip in Incline Village (with onshore winds). The
converse is also true that emissions from daytime trips are typically blown away from the lake.
The analysis indicates that targeted mitigation in areas with high potential to impact the lake
(e.g., El Dorado County, CA, and Douglas County, NV) will be more effective than general
reduction in basin-wide VKT.
Reconcile emissions and deposition rates with measured sediment loading in the lake
The TMDL (Total Maximum Daily Load) (CWB & NDEP, 2010) estimated that atmosphericdeposited particles accounted for 15% of the lake loading of 75 x 1018 particles (1136 Mg based
on 66 * 1015 particles per Mg) (CWB & NDEP, 2008). In this study, we estimates that the mass
of sediment reaching the air column over the lake from paved road dust sources to be only 35 Mg
due to near-field deposition and the predominance of onshore winds during peak travel times.
This is a substantially different estimate and emphasizes the uncertainty in the estimation
process. There are several possibilities that may explain these differences.
The TMDL estimates are measured deposition fluxes that represent all sources rather than just
paved road dust, which is a subset of all sources. Although not likely to be a factor in the
wintertime, the magnitude of unpaved road emissions is on the same order as paved road dust
emissions as presented by CWB & NDEP (2008). These estimates originally derived from
Kuhns et al. (2004) who interpolated these emissions from whole county estimates. Lake Tahoe
is highly developed and has very few publicly accessible unpaved roads. Based on this
observation, we speculate that the true emissions from unpaved roads in the basis is less than
10% of those reported by Kuhns et al., 2004.
38
Another source of differentiation may be due to the method of the Lake Tahoe Atmospheric
Deposition Study (LTADS) deposition flux calculation. Their model assumed a uniform aerosol
concentration based on shoreline measurements across each of the four lake quadrants. Our
results indicate that PMlarge and PMcoarse are rapidly depleted near their source and that the
shoreline concentration may only be representative of the first 1-to-3 km offshore. In effect, this
greatly reduces the deposition area of the lake and may account for a large part of the decrease.
Other factors explaining this difference may be associated with the location of the shoreline
concentration measurements. The LTADS measured PM with Two-Week Samplers in areas that
included both urban and rural settings. The higher concentrations observed at urban sites may
cause deposition rates to be biased high if this distribution is not representative of the total
shoreline.
This discrepancy significantly affects the cost/benefits of emission control strategies since there
will be less value in reducing a source responsible for 0.8% (this study) of the fine lake loading
versus 8% of the fine sediment attributable to dry deposition lake loading (TMDL, 2010).
39
10 Recommendations
Kuhns et al. (2010) recommended adopting regular basin-wide street sweeping practices based
on the fact that well-maintained roads adjacent to unswept neighborhoods were significantly
dirtier than those adjacent to frequently swept roads. The simple premise was that by collecting
this reservoir of suspendable material, it could not be transported to higher speed roads where it
would then be re-entrained.
Based on the current study, it appears that only roads close to the lake have a substantial impact
on atmospheric fine particle deposition. Moreover, most areas around the lake benefit by onshore
winds during peak traffic times effectively pushing emissions away from the lake. However, this
is not the case in El Dorado County and Douglas County, which are calculated to be responsible
for 67% of the paved road dust deposited to the lake. More aggressive measures to reduce the
reservoir of suspendable material on roads in these areas are likely to be more cost effective than
applying a blanket policy to the entire road network.
This study has illuminated new details that will help focus emission controls (e.g. street
sweeping, anti-icing, reduced VKT) on the part of the basin where they will be most effective
(i.e. near shore roadways in Douglas and El Dorado Counties). Substantial uncertainties exist on
how controls will improve lake clarity since revised estimates of the contribution of road dust to
the lake are orders of magnitude smaller than the total sediment dry deposition loading used in
the TMDL.
Long-term monitoring of road dust emission potential (or road surface conditions) is essential to
ensure that current plans meet their targets. The Road RAM (Road Rapid Assessment
Methodology) system provides an excellent opportunity to evaluate whether controls are
effectively reducing the suspendable reservoir of material on paved road surfaces in the basin.
40
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43
APPENDIX
Table A-1. Definition of treatment tiers (CWB & NDEP, 2008).
44