Your SlideShare is downloading. ×
  • Like
Sbs
Upcoming SlideShare
Loading in...5
×

Thanks for flagging this SlideShare!

Oops! An error has occurred.

×

Now you can save presentations on your phone or tablet

Available for both IPhone and Android

Text the download link to your phone

Standard text messaging rates apply
Published

 

Published in Health & Medicine , Technology
  • Full Name Full Name Comment goes here.
    Are you sure you want to
    Your message goes here
    Be the first to comment
    Be the first to like this
No Downloads

Views

Total Views
302
On SlideShare
0
From Embeds
0
Number of Embeds
0

Actions

Shares
Downloads
0
Comments
0
Likes
0

Embeds 0

No embeds

Report content

Flagged as inappropriate Flag as inappropriate
Flag as inappropriate

Select your reason for flagging this presentation as inappropriate.

Cancel
    No notes for slide
  • Why are we investigating? Air quality is a major concern due to the conditions in subways (enclosed space, relies on ventilation systems to provide fresh air, a large number of occupants)
    From an occupational health POV , there is a vulnerable population (employees working in the station)
    Literature suggests that poor ventilation and suspended particulate matter is responsible for health problems
    Studying SBS are challenging: Sick building syndrome is a very unique health outcome that has no clinical diagnosis; symptoms only present themselves during the exposure; difficult to quantify
    <number>
  • Air supply intake and exhaust systems are in close proximity to one another
    Parking lot could be a source for combustion byproducts
    21
  • Every risk assessment starts with hazard ID
    All of these are a form of particulate matter (commonly cited) but it is important to enumerate the types unique to our case study situation
    PM2.5 can travel deeper into lungs and can stay suspended for longer periods of time
    These pollutants would be of particular concern in our case study
    <number>
  • 12/19/13
    <number>
  • 12/19/13
    <number>
  • 12/19/13
    <number>
  • 12/19/13
    <number>
  • 12/19/13
    <number>
  • 12/19/13
    <number>
  • 12/19/13
    21
  • Our case study can generate hypotheses that can direct future chemical D-R analysis
    Can identify susceptible/vulnerable groups
    Desirable response qualities: a measurable outcome, a clear outcome (detecting actual stressor in the body, biomarkers, death)
    Needs
    Individual SBS scores over different time frames were unknown; we weren’t given individual pollutant readings over time
    Some of the strata had very low numbers of observations- effects power of study
    Bias and validity issues
    21
  • In lieu of dose-response
    Each age strata is divided into male and female; values are weighted and a score for each gender in each age group is generated; higher values mean sicker
    Older groups report more symptom; females report more symptoms
    <number>
  • Percentage cells were multiplied against the n in each category; totals represent number of people experiencing each symptom based on the data given to us
    Lethargy and headaches are experienced most
    In conclusion we could not determine a dose response relationship between stressor and end point but..
    SBS scoring and pollutant monitoring can be used together to establish associations and bolster causal link that is currently missing in SBS.
    It gives direction to future investigations
    <number>
  • 21

Transcript

  • 1. Air Pollutants and Health Risks: A Case Study of Sick Building Syndrome (SBS) in an Underground Metro Station Platform Area in Tropical Region Lee Voth-Gaeddert Yiseul Kim David Melton Stephanie Stumpos Mentors: Dr. Mukesh Khare & Dr. Hernando Perez
  • 2. Indo or Air Pollution and Sick Building Syndrome — The Chandi Chowk “Moonlit Market” Metro, built in 2005, is one of 35 underground stations that serve the National Capital Region of India — Due to building characteristics and a large number of daily commuters, there is concern for workers who spend their entire shifts working in the underground station and their health — This case study will focus on the quantification of exposures to a unique set of stressors and the mitigation of these events which are associated with SBS and microbial infection
  • 3. Hazard Identification Pollutant Suspended Particulate Matter PM10 PM2.5 Source • • • • Volatile Organic Compounds • • shedding mechanical abrasion natural anthropogenic automobile emissions cleaning materials Effect • • • • • • • Bio-aerosols • organic dust • microbes Carbon dioxide • living organisms • • • occupants • Aggravated asthma Decreased lung functioning Respiratory irritation Sensory irritation headaches nausea allergic skin reactions allergies respiratory irritation Indicator of poor ventilation
  • 4. Exposure Assessment: Pollutants Sick Building Syndrome is a discomfort caused by poor air quality, and only exists while the sufferer inhabits the building or container in question.
  • 5. Exposure Assessment: Pollutants In contrast to microbial infection, sick building syndrome exists only while the sufferer inhabits the container of interest. This suggests that the substance of interest is not a microbial but rather a chemical hazard.
  • 6. Exposure Assessment Tasks Pathway Amount Duration
  • 7. Pathway: Source Receptor Model
  • 8. Pathway • • • • A source of carbon dioxide is biological activities of humans Airspeed is unknown but average is 0.3 m/s. Contact with human is through inhalation Particulate matter generated by processes within the station or flowing in from external source Bio-aerosol emissions are not well documented and further testing is necessary to identify precisely the source
  • 9. Amount • • • Concentrations of each pollutant were recorded over eight hour monitoring cycles. Disturbances that may decrease/elevate the volumes of suspended particulate matter were not provided in the data Activity changes throughout the course of the day that may affect the concentration levels should be taken into consideration Acceptable levels
  • 10. Duration • • If concentration is assumed to be uniform, the duration of exposure is the length of time the person inhabits the building. If concentrations of the pollutants fluctuate throughout the day due to external disturbances, the duration of exposure becomes difficult to quantify, as the contact with the substance could be sporadic.
  • 11. Dose-Response: Pollutants and SBS — Unfortunately, a dose-response relationship could not be established due to data gaps — SBS scoring is a valuable epidemiologic too that can provide prevalence data and elucidate associations between pollutant levels and symptoms Needs — We need to establish a temporal relationship between pollutant concentrations and symptoms (SBS scores) — A complete data set is needed — Larger number of observations are needed across all demographic categories — Gather post-shift questionnaires to assess any reduction in symptoms — record time of interview
  • 12. SBS Questionnaire Data Sometimes 0.5 Always 1 Age under 20 Age between 20- 39 Age between 40-59 Male (12) Female (10) Male (23) Female (15) Male (9) Age above 59 Female (3) Female (0) Male (1) 19% 31% 16% 43% 23% 37% 24% 23% 18% 29% 14% 25% 14% 21% 41% 49% 63% 49% 25% 43% 53% 58% 37% 65% 52% 27% 61% 72% 27% 56% 55% 52% 75% 81% 42% 78% 100% 100% 100% - 12 10 23 15 9 3 1 0.10 0.16 0.16 0.22 0.23 0.19 0.24 0.23 0.18 0.29 0.14 0.13 0.07 0.11 0.21 0.49 0.32 0.49 0.13 0.22 0.27 0.58 0.37 0.65 0.52 0.14 0.31 0.72 0.14 0.28 0.55 0.26 0.38 0.81 0.21 0.39 Total 1.04 1.21 1.68 2.21 2.10 2.60 2.50 Rank 6 5 4 2 3 1 - Irritation in the eyes (%) Irritation in the nose (%) Dryness in mucous (%) Lethargy/drowsiness/tiredness (%) Dryness on the face/hands (%) Headache (%) 0.50 1.00 1.00
  • 13. SBS Questionnaire Data continued
  • 14. Hazard Identification (Microbial) Data given Days Concentration (cfu/m3) Average 01 02 03 04 05 06 1. 2. 3. 1586 962 1103 990 810 1025 S.D. 93.599 75.139 84.602 88.682 55.643 141.860 E. coli Bacterial types Bacillus Staphylococcus 32% 28% 19% 20% 30% 13% 40% 36% 35% 26% 38% 50% 15% 10% 29% 20% 15% 18% Data gaps Concentration of microorganisms of each monitoring cycle Ambiguity of identification of bacterial type (species and strains) Exposure parameters for lung infection - Exposure rate - Exposure frequency - Exposure duration
  • 15. Escherichia coli (E. coli) — A large and diverse group of bacteria — Gram-negative, facultative anaerobic, and rod-shaped — Commonly found in the lower intestine of warm-blooded organisms — Used as markers for water contamination — Most strains of E. coli are harmless Centers for Disease Control and Prevention
  • 16. Escherichia coli (E. coli) — At present, 190 serogroups are known. — Six pathotypes are associated with diarrhea. - Shiga toxin-producing E. coli (STEC) - Enterotoxigenic E. coli (ETEC) - Enteropathogenic E. coli (EPEC) - Enteroaggregative E. coli (EAEC) - Enteroinvasive E. coli (EIEC) - Diffusely adherent E. coli (DAEC) Centers for Disease Control and Prevention
  • 17. Exposure Assessment • Concentrations of E.coli (cfu/m3): • 50% of microbes inhaled will be ingested • 1 in 100,000 of E. coli inhaled are pathogenic • Inhalation rates (u=5.0E m3/min) *multiplied by -03 480min/shift
  • 18. Dose-Response —Exposure parameters: Apply available dose response model from QMRA wiki. - Best fit model: beta-Poisson - Optimized parameters: α = 1.55E -01, N50 = 2.11E+06 - LD50/ID50: 2.11E+06 - Host type: Human
  • 19. Pearson-Tukey Method — Decision Tree model based — Allows analysis of three different scenarios; — Best — Worst — Average μ+ 1 .6 4Ϭ Best μ-1 . 64 μ Ϭ Average Worst
  • 20. Tukey Test Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 High 594.8751008 334.1625188 270.540551 263.0420656 292.9821591 216.8045679 Medium 508 269 210 198 243 133 Low 421.1248992 182.1248992 123.1248992 111.1248992 156.1248992 46.1248992 1/100,000 chance of pathogic e coli cfu/m3 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 High 0.005948751 0.003341625 0.002705406 0.002630421 0.002929822 0.002168046 Medium 0.00508 0.00269 0.0021 0.00198 0.00243 0.00133 Low 0.004211249 0.001821249 0.001231249 0.001111249 0.001561249 0.000461249 50% of microbes inhaled will be ingested cfu/m3 Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 High 0.002974376 0.001670813 0.001352703 0.00131521 0.001464911 0.001084023 Medium 0.00254 0.001345 0.00105 0.00099 0.001215 0.000665 Low 0.002105624 0.000910624 0.000615624 0.000555624 0.000780624 0.000230624 Taking into account breathing rate of 2.4 m3/8hrs shift = 8 hours Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 High 0.007138501 0.00400995 0.003246487 0.003156505 0.003515786 0.002601655 Medium 0.006096 0.003228 0.00252 0.002376 0.002916 0.001596 Low 0.005053499 0.002185499 0.001477499 0.001333499 0.001873499 0.000553499
  • 21. Systematic Sampling Method — Pearson-Tukey Method was used — The beta-Poisson model was used — Each of the six days of data given was assessed for risk — Data in table is probability of one person getting ill out of the number given 1 out of how many will get sick Day 1 Day 2 Day 3 Day 4 Day 5 Day 6 High 22039682.64 39234973.26 48461708.4 49843196.09 44749677.99 60473158.43 44133732.8 Medium 25808775.77 48739246.11 62432652.65 66216449.28 53954144.54 98577869.81 59288189.69 Low 31132943.74 71988272.21 106484201.2 117983069 83976719.55 284246827.4 115968672.2
  • 22. Risk Management — ASHRAE Ventilation standards — Between 15 and 60 cubic ft./m of outdoor air per person — Filtration devices; increased air exchange — Installation of monitoring systems — Conducting emission inventory — Cost benefit analysis: compare productivity lost to sick days and the cost of improvements to station
  • 23. Risk Communication Employer Regulatory agencies Employer Employee
  • 24. References Abdul-Wahab, Sabah A. Sick Building Syndrome: In Public Buildings and Workplaces. Berlin: Springer, 2011. Internet resource. Apte, Michael G, William J. Fisk, and Joan M. Daisey. Associations between Indoor Co2 Concentrations and Sick Building Syndrome Symptoms in Us Office Buildings: An Analysis of the 1994-1996 Base Study Data. Berkeley, CA: Lawrence Berkeley National Laboratory, 2000. Print. Dybwad, Marius, Gunnar Skogan, and Janet Martha Blatny. ''Temporal Variability of the Bioaerosol Background at a Subway Station: Concentration 2 Level, Size Distribution and Diversity of Airborne Bacteria. American Society for Microbiology, 2013. Exposure Factors Handbook. Washington, DC: Exposure Assessment Group, Office of Health and Environmental Assessment, U.S. Environmental Protection Agency, 1989. Print. Gupta, S, M Khare, and R Goyal. "Sick Building Syndrome-a Case Study in a Multistory Centrally Air-Conditioned Building in the Delhi City. Building and Environment. 42.8 (2007): 2797-2809. Print. " Indoor Air Facts, No. 4: Sick Building Syndrome. Washington, D.C: U.S. Environmental Protection Agency, Office of Air and Radiation, 1991. Print. Norbèack, Dan, and Klas Nordstrèom. "Sick Building Syndrome in Relation to Air Exchange Rate, Co<sub>2</sub>, Room Temperature and Relative Air Humidity in University Computer Classrooms: an Experimental Study. International " Archives of Occupational and Environmental Health. 82.1 (2008): 21-30. Print. Seedorf, Jens. "An Emission Inventory of Livestock-Related Bioaerosols for Lower Saxony, Germany. Atmospheric "
  • 25. Chart Reference