Risk Assessment Presentation
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Transcript of Risk Assessment Presentation
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Bicyclist Health and Safety Issues on Four Alternative
Transportation Routes
Monroe County Alternative Transportation Plan Risk Assessment
Craig Harper · Zeynep AltinayCourtney Bonney ·Max Jie Cui
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Outline
Health
• Health Effects• Exposure• Modeling• Risk Characterization
Safety
• Causes of Accidents• Predictive Modeling
•Intro and Problem Formulation•Current Routes and Alternatives•Hazard ID
•Uncertainty•Recommendations•Conclusion
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Introduction
Problem Overview: When sharing roads with vehicles, bicyclists are exposed to health and safety risks from exhaust and accidents
Key Question of Project: How would risks to bicyclists change on 4 priority routes with alternatives considered by Monroe County?
Goal: Inform the Monroe County Council of the relative risks to both health and safety from current and alternative routes.
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Target population
• Current study:– Adult male
• 18-30 years old• 70 kg weight• Cycling at a moderate
pace (13 mph)
– Asthmatic adult male• Future Studies:– Adult female– Elderly– Children
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Hazard ID
• Criteria Pollutants– Sulfur Dioxide– Nitrogen Oxides– Particulate matter < 2.5 µm
• Hazardous Air Pollutants– VOCs (e.g. Benzene)
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Pollutants emitted from vehicles Function of: fleet makeup, traffic volume, fuel composition, season
Dispersal of PollutantsFunction of: wind velocity, mixing height, season, buffer width
Inhalation of Pollutants Function of: inhalation rate (varies with population)
Pollutant DoseFunction of: absorption
Health Response (acute or chronic)
Endpoints: Risk to bicyclists from particular pollutants (mg/kg/day) over the course of 30 years
Accident Rate
Road Characteristics(type, lane width, shoulder, sidewalk, signage, bike lanes, etc)
Traffic and Bicycle Volume (vary spatially and temporally)
Confounding FactorsWeather, distractions
Driver and Bicyclists Error
Endpoints: Predicted number of accidents on a given route (accidents/year)
Bicyclists’ Health and Safety: A Conceptual Site Model
Bicyclists share roads with vehicles
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ROUTES
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Route 1State Road 46Commuter Route with a possible greenway option that would encourage recreational users
Vehicle Traffic Volume:Current: 10704-19071 Avg: 15000Alternative 1: 10700Alternative 2: 4900-13000
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Route 2State Road 45Recreational Route from Lake Lemon into Bloomington
Vehicle Traffic Volume:Current: 3422-11491 Avg: 5225Typical Multiuse Volumes:
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Route 33rd to Ivy TechCommuter Route to Ivy Tech
Vehicle Traffic Volume:Current: 102 – 42803 Avg: 17100Alternative 1:Alternative 2:
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Route 4Fairfax RdRecreational Route from Clear Creek Trail head to Monroe Lake Beach and Four Winds Resort
Volume:Current: 49-6860 Avg: 2270
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OptionsCurrent
ConditionsSigned Bike Route Urban Bike Lane Multi-Use Path
Hazardous shoulders for road
bikes
Safe for use by both vehicles and
bicycles
Greater predictability of
bicyclists and cars
Dangerous at intersections if
unmarked
Suitable for the most experienced
bicyclists
Not suitable for street
w/inexperienced bicyclists
Greater confidence for inexperienced
bicyclists
Creates a feeling of “green space” for
riders and pedestrians
More dangerous at blind hills and curves and stops
Suitable for low speeds and traffic
volumes
Speeds >40mph, w/curb and/or
gutters
Could cause inverse effect due to higher pedestrian volumes
Minimum 10.5’ Vehicle Travel
Lane (VTL)
Minimum 14’ VTL Minimum 10.5’ VTL plus 5’ marked
bike lane
Minimum 10.5’ VTL, 3-6’ buffer, and 5-10’ paved
trail
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HEALTH
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Exposure: Methods
• EPA ‘s Mobile 6.2 Emissions Modeling Software
• Estimates emissions (g/s or g/day)• Assumes average fleet makeup, traffic
volumes, seasonal variations, fuel composition, average speed
http://elseware.univ-pau.fr/MAINPAGEPUB/carpollu/pol1.gif
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(Schnelle and Dey, 2000)
Dispersion Box ModelConcentration (C)
Where ,The emission rate per unit area
Assumptions of the box model:1. Concentrations are homogenous
within the box.2. Sources distribute uniformly.3. Emitted pollutants instantaneously
and uniformly mix.4. A wind of constant speed flows
across the cells cross-sectional area
uH
qLC
LWQq
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Calculation of Intakes
Where:
• I ≡ intake (mg/kg bodyweight/day)• C ≡ chemical concentration (mg/s)• CR ≡ contact rate (m3/hr)• EFD ≡ exposure frequency and duration
– EFD = EF*ED• EF ≡ exposure frequency (days/year)• ED ≡ exposure duration (years)
• BW ≡ bodyweight; the average bodyweight over the exposure period (kg)• AT ≡ averaging time; time over which exposure is averaged (days)
ATBW
EFDCRCI
1
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Combined Health Effects• Respiratory
– Inflammation– Reduced Lung Function (FEV1/FVC)– Increased Upper Respiratory
Infections• Bronchitis• Pneumonia
– Allergic Reactions– Exacerbation of COPD, Asthma,
and Emphysema
• Central Nervous System– Headaches/Dizziness/Vomiting– Brain damage
• asphyxiation
– Stroke– Coma (VOCs)
• Cardiovascular– Increased myocardial
ischemia• Pro-inflammatory mediators
– Atherosclerosis• Leukocyte and platelet
activation
– Arrhythmia– Increased risk of diabetes
and hypertension
• Cancer– Lung Cancer– Leukemia
• Premature Death
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Non-Cancer
• Reference dose=Threshold Dose/U.F.• U.F.s depend on the type of study• Large RfCs indicate weaker pollutants
Pollutant EffectLOAEL (ug/m^3)
NOAEL (ug/m^3)
U.F. Total M.F. U.F. * M.F.
RfC (mg/m^3) Study
Benzene
Decreased lymphocyte count 17100 30 9 270 6.33E-02 Ward et al. 1985
PM2.5 11 10 3 30 3.67E-04Harvard Six Cities, ACS, Dockery et al.
SO2 FEV decline 1144 10 3 30 3.81E-02 WHO 2006
NOx
decline in pulmonary function 1880 10 3 30 6.27E-02
EPA 1993, Berglund M. et al. 1993
U.F. Modifying FactorThreshold Dose Reference ConcentrationPollutant and Effect
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Cancer
• VOCs (Benzene as an example) and PM have the ability to cause cancer
• Risk measured as Unit Risk: risk per µg/m3 breathed
• Benzene – Leukemia (EPA, 1998)– Unit Risk = 7.8E-03 (mg/m3)-1
– Slope Factor = 2.73E-02 (mg/kg-day)
33 1020701
daymkgSFRisk
lyIntakeChronicDairSlopeFactoRisk
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Modeling Uncertainties
• Calculated RfC from threshold doses – corrected for uncertainty (see table)
• Utilized @Risk to run 5000 iterations – 5 frequency durations ranging from 50-250 days
• Used @Risk to place uncertainty values around:– wind speed
– mixing height
– width of box
ATBW
EFDCRCI
1uH
qLC
LW
RfC
IHQ
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Non-Cancer Output from @risk
• Calculated a HQ with nested uncertainties for the longest route in 4 seasons
• NOx: HQ>1
• All other pollutants HQ<1
• Relative Hazard Index, sum of the HQs, calculated for varying proposed alternatives
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Results: Non-Cancer
EF (days) HI (current) HI (signage) HI (bike lane) HI (multiuse)
50 0.945 0.385 0.348 0.230
100 1.891 0.771 0.697 0.460
150 2.837 1.155 1.046 0.691
200 3.783 1.541 1.395 0.921
250 4.728 1.926 1.744 1.152
Using the Mean
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Results: Cancer
BenzeneIntake = 0.14 µg/m3
Risk Level Concentration
E-4 (1 in 10,000) 13.0 to 45.0 µg/m3
E-5 (1 in 100,000) 1.3 to 4.5 µg/m3
E-6 (1 in 1,000,000) 0.13 to 0.45 µg/m3
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Data gaps/uncertainty
• Mobile 6.2– default traffic volume assumption– no account of road dust– exposure from ingestion– mixing height assumptions– interactive effects of pollutants
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SAFETY
3 June 2008, US-Mexico Border
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Bicycle Accident Rates: Contributing Factors
• Road Characteristics
• Traffic and Bicycle Volume
• Confounding Factors
• Driver and Bicyclist Error
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Predictive Modeling of Accidents:Data Sources
Bloomington Police Department
Bike and Pedestrian accident of 2008
DOT (Department of Transportation)
Average Traffic flow data of a day
Griffy Weather Station Precipitation data of 2008
Self-collection On-site risk factors
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Assumption: Bicycle/pedestrian volume
Months of CyclingMichael Steinhoff and Julie Harpring. (2008). Transportation and Sustainability
on the Indiana University, Bloomington Campus.
For the pedestrian, we assume the flow is relatively the same in spring, fall and winter. We dropped the number by 70% for summer because most of the
students will go home. We also assumed that the behavior of residents remains constant throughout the year.
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Variables for 2 Types of Model
Model 1 Variables Model 2 Variables
Bicycle Flow Lane Width
Pedestrian Flow Bike Lane Width
Weekday/Weekend Traffic Flow
Precipitation Intersections
Bicycle flow2 Crosswalk, Curb
Pedestrian flow2 Commercial vs. Residential
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Model Type I – Bicycle
abm = Hourly Bike flow adjusted by month; t=7.07
aps = Hourly pedestrian flow adjusted by season; t=5.5
week = (Weekend=1, weekday=0); t=4.4
Y = − 0.00308 + 0.70576abm – 0.00513aps – 0.25012week + 0.00014143B2 + ut
R2= 16.36% F=17.5 P=0.0001
Y = Number of accident(s) on each day of 2008
B2=Abm2 ; t=0.88
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Model Type II
Y = 0.61697 +0.00005965TF +0.06912LW2 +0.19403BLW -0.84127Int -0.28712Curb -0.21508SD+ 0.34976 CR
R2=96.63% F =36.81 P=0.0001
TF = Average Traffic Flow per day (2008) t=7.39
LW = Lane Width t=24.99
Intersection (INT) = (Yes=1, No=0) t=-3.55
Y = # of Accidents on each selected road in 2008
Curb (CB) = (Yes=1, No=0) t=1.88
CR = (Commercial =1,Residential=0) t=2.27
Sidewalk (SD) = (Yes=1, No=0) t=1.45
BLW = Bike Lane Width t=3.87
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Limitations
Data is very limited in this
area.
Cannot account for human behavior
Mixed-Poisson Distribution
Model
Specification Error
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Next steps to improve accident modeling
• Collect more data of risk characteristics on our primary routes (accidents!)
• Adjust the model by adopting Mixed Poisson Distribution and take human behavior into consideration
• Improve the assumptions by getting more official data
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Conclusions
• Little evidence of serious risk due to air pollutants on current routes
• Cannot make predictions of accidents on rural routes based on our model
• Cannot make generalizations about effects of multi-use path with our model
• Traffic calming measures (reduction of volume) seems to be more effective at reducing accidents than adding bike lanes
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Further Considerations
• Value of increasing perceived safety• Produce a map of county bike routes with
safety rating based on road characteristics to inform bicyclists of options