New England Air Quality

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New England Air Quality

• A multi-level approach to measuring the health
  and economic costs of poor air quality

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Research Team

• Ross Gittell
• James R. Carter Professor, Whittemore School of
  Business and Economics
• Ann McAdam Griffin
• Research Associate, Whittemore School of Business
  and Economics
• Aicha Hassane
• Ph.D. Student, Economics
• Thomas D. Lambert
• Graduate Student, Natural Resources
• Jason Rudokas
• Mathematics/Economics student
• Cameron P. Wake
• Research Associate Professor, Climate Change
  Research Center
• Robert Woodward
• Professor of Health Economics

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Potential Health Effects of Climate Variability
and Climate Change
Population Standard of living Access to health
care Public health infrastructure
Vaccination programs Disease surveillance Protecti
ve technologies Weather/climate
forecasts Emergency managment
From Climate Change Impacts on the United
States, 2000. http//www.usgcrp.gov/usgcrp/nacc/

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Conceptual Framework
Env Health Tracking
Vulnerability Sensitivity - Adaptation
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Economic and Public Health Research

• Quantitative assessment of the economic costs
  associated with poor air quality in New England
• Examination of the potential uses of air quality
  information and forecasts

Research Objectives

• Improve understanding of relationship between air
  quality and public health and worker productivity
• Identify beneficial applications of air quality
  information and forecasts by New England based
  employers working in partnership with business
  organizations

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Air Quality Economic Impact Pyramid
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The Research Region, Institutional Partners and
Monitoring Sites
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Daily Air Quality Data

• Chemical
• Ozone, sulfur dioxide, carbon monoxide, fine
  particles, acidic particles, hydrocarbons, etc.
• Biological
• Pollen (trees, grasses, weeds)
• Molds/fungal spores
• Physical
• Temperature, humidity, precipitation

The ICARTT project will provide us with the most
complete air quality data for New England ever
collected
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August 10-12th Large fine particle event
Fine Particles in Portsmouth (ug/m3)
EPA Site Map
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EPA Survey

• Summer 2003

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EPA Survey Individual Responses to Air Quality
Alert, Summer 2003
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EPA Survey Organizational Responses to Air
Quality AlertBut small number of commercial
organizations on the EPA response list
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EPA Survey What is most important in Air Quality
Forecasts?
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Starting the analysis at the base of pyramid and
moving up to hospitalizations Air Quality and
Indoor Worker Productivity

• Survey results from Cisco employees and other
  participating employers

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Employee Survey

• Summer 2004

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Survey Demographics

• Average Number of Respondents 321
• Participant workers were from Cisco Systems,UNH
  and Exeter, W-D and Portsmouth Hospitals
• Highest Response 340 7/30/04 Week 2
• Lowest Response 302 8/26/04 Week 6
• Ratio of Men to Women remained almost constant
• 30 Men to 70 Women
• Average Age between 42 and 43
• 50 of respondents between 33-50

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Regression model results Particulate Matter and
Ozone significantly and positively correlated to
Productivity Decline (but not tree pollen)
Odds Ratio A unit increase in Pm25 corresponds
to a 2.5 increase in the odds of a worker
feeling less productive. 2.2 for O3
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The estimated probability of a worker feeling
less productive at low concentrations of Pm25 and
O3 is approximately (8,25) and (7,20).
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Poor air quality days can impact the economy
through declines in worker productivity.

• Exposure to poor air quality while outdoors
  ..including commuting to and from work.. and
  while indoors can have an impact on productivity
  even if work is indoors  
• In the survey about 1 of 10 workers noted that
  that their productivity at work was lowered
  during bad air quality days compared to normal
  days

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How does this relate to the economy and labor
force in New England during a typical year? 10
poor air quality days a summer

• Assume 10 percent of New Englands labor force
  experienced reduced productivity of a modest 10
  percent during poor air quality days in summer.
• Assumer that if workers and their employers had
  an air quality forecast the day before and made
  arrangements to adjust work schedule and
  practices worker productivity could decline by
  1/10th of what it typically did on a normal
  workday on a bad air day.  
• For example, employers allow some workers to work
  at home during the bad air day or during the
  periods of the day when air quality is at its
  worst
• The net annual benefit to the region in avoided
  loss in short-term productivity in a typical
  summer (with 10 poor air quality) would be about
  20 million.  

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EXETER HOSPITAL DATA ANALYSISUpdate

• 3/28/05

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Goal and Objectives

• Analyze Exeter Hospital respiratory and
  cardiovascular services for
• Seasonal patterns
• Variations in seasonal patterns between years
• Merge Exeter Hospital data with Air Quality and
  Pollen data to identify
• Possible asthma triggers
• Possible link between air pollution and human
  health

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Exeter Hospital Data

• Cardiovascular and Respiratory Services
  (inpatient admissions, emergency department
  visits, outpatient visits)
• 126,294 services (17978 IP ,37363 ED, 70953 OP)
• 38,436 services with primary diagnosis
  cardiovascular disease
• (6077 IP, 4402 ED, 27957 OP)
• 37,450 services with primary diagnosis
  respiratory disease
• (2937 IP, 18647 ED, 15860 OP)
• 3,813 services with principal diagnosis asthma
• (239 IP, 639 ED, 2935 OP)
• 51 of patients were female.
• 94 of patients live in the areas surrounding the
  hospital

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Results from Objective 1(as previously reported)

• Seasonal pattern exist
• Summer decrease and fall increase in respiratory
  services (asthma) but not in cardiovascular
  services
• Variations from year to year are different
• Individuals age 0-4 and 18-24 are more affected
  by asthma

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Air Quality Data and Pollen Data

• Pollen Data from Salem, MA
• 5 highly allergenic pollen (morus, ragweed, sage,
  cedar, oak)
• 2 Years (2002 and 2003)
• Air pollutants from local EPA and AIRMAP sites
• Ozone
• Carbon Monoxide
• Sulfur Dioxide
• Particulate Matter size 2.5

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Methodology for Objective 2

• Focus on asthma and the fall peak
• Graph of daily cumulative asthma
• From January 1 to December 31 each year
• Identify changes in the slope of the graph
• Look for variations in air pollutants
  concentration and pollen count

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Preliminary Results from Objective 2

• Air pollutants
• Preliminary analysis shows no link between asthma
  and pollutants (O3 ,SO2 ,CO)
• Variations in PM2.5 similar to that of asthma but
  more analysis needed to confirm
• Pollen
• Ragweed and Sage explain the fall peak

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Discussion

• Ragweed and Sage pollen trigger asthma in the
  fall (mid to end of September)
• More analysis needed for PM2.5
• Better data needed for air pollutants such as O3
  ,SO2 ,CO and NO

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Air Quality and Pulmonary Function in New England
During Summer 2004
Rationale To improve our understanding of
pulmonary function, symptoms, and broad measures
of air quality
Health Data
Twice daily pulmonary function (peak flow, fev1,
fev6) from 300 people during July and August
Daily respiratory symptom data
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Air quality, Climate, Health
8-hour ozone (ppbv)
Seacoast average
Average Daily Temp. (celcius)
Seacoast average
Average FEV1 from Seacoast Residents
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AIRMAP Streaming Real Time Air Quality Data
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Northeast Indicators of Climate Change - 2005
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Spatial Variation of Annual Temperature Trend
1899-2000
Linear trend in annual temperature (oF) from
1899-2000 for the Northeast. The change was
estimated from a linear regression of annual
average temperature for each station.
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Continuing Research at Exeter Health Services and
with others

• Data collection employee surveys and
  hospitalization data through 2005
• Further exploration and analysis of data
  health, productivity and air quality
• Real Time Monitoring Station on site
• Indoor Ozone Sensor on site