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

Indoor 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 healthThis 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

Hazard IdentificationPollutant Source Effect

Suspended Particulate MatterPM10PM2.5

•sheddingmechanical abrasionnatural anthropogenic

•Aggravated asthmaDecreased lung functioningRespiratory irritation

Volatile Organic Compounds

•automobile emissionscleaning materials

•Sensory irritationheadachesnauseaallergic skin reactions

Bio-aerosols •organic dust microbes

•living organisms •allergiesrespiratory irritation

Carbon dioxide •occupants •Indicator of poor ventilation

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.

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.

Exposure Assessment Tasks

Pathway

Amount

Duration

Pathway: Source Receptor Model

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

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

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.

Dose-Response: Pollutants and SBS Unfortunately, a dose-response relationship could not be

established due to data gapsSBS scoring is a valuable epidemiologic too that can provide prevalence data and elucidate associations between pollutant levels and symptoms

NeedsWe need to establish a temporal relationship between pollutant concentrations and symptoms (SBS scores) A complete data set is neededLarger number of observations are needed across all demographic categoriesGather post-shift questionnaires to assess any reduction in symptoms record time of interview

We need more data points for pollutant concentrations and environment characteristics such as relative humidity and air-exchange rates

personal monitoring devices The time of each measurement

SBS Questionnaire Data

Sometimes

Always

0.5 1Male (12) Female (10) Male (23) Female (15) Male (9) Female (3) Male (1)

Female (0)

Irritation in the eyes (%) 19% 24% 14% 25% 52% 55% -

Irritation in the nose (%) 31% 23% 21% 43% 27% 52% 100%

Dryness in mucous (%) 16% 18% 41% 53% 61% 75% -

Lethargy/drowsiness/tiredness (%) 43% 29% 49% 58% 72% 81% 100%

Dryness on the face/hands (%) 23% 14% 63% 37% 27% 42% 100%

Headache (%) 37% 25% 49% 65% 56% 78% -

12 10 23 15 9 3 1

0.10 0.24 0.07 0.13 0.52 0.550.16 0.23 0.11 0.22 0.14 0.26 0.500.16 0.18 0.21 0.27 0.31 0.380.22 0.29 0.49 0.58 0.72 0.81 1.000.23 0.14 0.32 0.37 0.14 0.21 1.000.19 0.13 0.49 0.65 0.28 0.39

Total 1.04 1.21 1.68 2.21 2.10 2.60 2.50

Rank 6 5 4 2 3 1 -

Age under 20 Age between 20- 39 Age between 40-59 Age above 59

SBS Questionnaire Data continued

Hazard Identification (Microbial)

Days Concentration (cfu/m3) Bacterial types

Average S.D. E. coli Bacillus Staphylococcus

01 1586 93.599 32% 40% 15%

02 962 75.139 28% 36% 10%

03 1103 84.602 19% 35% 29%

04 990 88.682 20% 26% 20%

05 810 55.643 30% 38% 15%

06 1025 141.860 13% 50% 18%Data gaps1.Concentration of microorganisms of each monitoring cycleAmbiguity of identification of bacterial type (species and strains)Exposure parameters for lung infection - Exposure rate - Exposure frequency - Exposure duration

Data given

Escherichia coli (E. coli)

A large and diverse group of bacteriaGram-negative, facultative anaerobic, and rod-shaped Commonly found in the lower intestine of warm-blooded organismsUsed as markers for water contaminationMost strains of E. coli are harmless

Centers for Disease Control and Prevention

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

Exposure Assessment

Concentrations of E.coli (cfu/m3): 50% of microbes inhaled will be ingested1 in 100,000 of E. coli inhaled are pathogenicInhalation rates (u=5.0E-03 m3/min) *multiplied by 480min/shift

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

- Agent strain: EIEC 1624

- Route: Oral (in milk)

Pearson-Tukey Method

�Decision Tree model basedAllows analysis of three different scenarios;

�BestWorstAverage

Best

Average

Worst

μ+1.

64Ϭ

μ-1.64Ϭ

μ

Tukey TestDay 1 Day 2 Day 3 Day 4 Day 5 Day 6

High 594.8751008 334.1625188 270.540551 263.0420656 292.9821591 216.8045679Medium 508 269 210 198 243 133Low 421.1248992 182.1248992 123.1248992 111.1248992 156.1248992 46.1248992

1/100,000 chance of pathogic e coli cfu/m3Day 1 Day 2 Day 3 Day 4 Day 5 Day 6

High 0.005948751 0.003341625 0.002705406 0.002630421 0.002929822 0.002168046Medium 0.00508 0.00269 0.0021 0.00198 0.00243 0.00133Low 0.004211249 0.001821249 0.001231249 0.001111249 0.001561249 0.000461249

50% of microbes inhaled will be ingested cfu/m3Day 1 Day 2 Day 3 Day 4 Day 5 Day 6

High 0.002974376 0.001670813 0.001352703 0.00131521 0.001464911 0.001084023Medium 0.00254 0.001345 0.00105 0.00099 0.001215 0.000665Low 0.002105624 0.000910624 0.000615624 0.000555624 0.000780624 0.000230624

Taking into account breathing rate of 2.4 m3/8hrs shift = 8 hoursDay 1 Day 2 Day 3 Day 4 Day 5 Day 6

High 0.007138501 0.00400995 0.003246487 0.003156505 0.003515786 0.002601655Medium 0.006096 0.003228 0.00252 0.002376 0.002916 0.001596Low 0.005053499 0.002185499 0.001477499 0.001333499 0.001873499 0.000553499

Systematic Sampling Method

Pearson-Tukey Method was usedThe beta-Poisson model was usedEach of the six days of data given was assessed for riskData in table is probability of one person getting ill out of the number given

1 out of how many will get sickDay 1 Day 2 Day 3 Day 4 Day 5 Day 6

High 22039682.64 39234973.26 48461708.4 49843196.09 44749677.99 60473158.43 44133732.8Medium 25808775.77 48739246.11 62432652.65 66216449.28 53954144.54 98577869.81 59288189.69Low 31132943.74 71988272.21 106484201.2 117983069 83976719.55 284246827.4 115968672.2

Risk Management

�ASHRAE Ventilation standards

�Between 15 and 60 cubic ft./m of outdoor air per personFiltration devices; increased air exchangeInstallation of monitoring systems Conducting emission inventory Cost benefit analysis: compare productivity lost to sick days and the cost of improvements to station

Risk Communication

Employer

Regulatory agencies

Employer

Employee

References

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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.

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Norbèack, Dan, and Klas Nordstrèom. "Sick Building Syndrome in Relation to Air Exchange Rate, Co₂, 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.

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Chart Reference