Winthrop Community Health Survey

Winthrop Environmental Health Facts Subcommittee

(Winthrop Airport Hazards Committee)

Winthrop Board of Health

AIR

Brian Dumser, PhD, CIH

Chair of the Subcommittee

August 18, 2024

Summary

In many communities located close to major airports, power generation facilities, or other major industries, there is a strong perception that pollution generating activities at these facilities result in a direct negative impact on the health of residents. Statements to this effect have been repeatedly voiced by representatives of the communities surrounding Logan airport, but, absent hard data in the existing record, no action has been taken by responsible authorities to investigate further. Currently, plans are underway for the construction of additional facilities Logan airport which will markedly increase operational capacity and the generation of pollutants. While potent arguments in favor of this expansion are being presented from an economic standpoint, once again no consideration is being given to the possible public health impact.

In light of the failure to address this issue by Massport, or by Federal or State regulatory authorities, the Winthrop Environmental Health Facts Subcommittee, a voluntary group made up of residents of the Town of Winthrop Massachusetts, elected to address the question directly. A strong correlation is known to exist between exposure to petrochemical exhaust emissions and a variety of respiratory and cardiovascular diseases (1-10). Logan airport estimates its daily production of such pollutants at approximately 50,000 pounds per day (11). The Subcommittee undertook a survey to determine whether a correlation also exists between frequency and severity of respiratory disease and level of exposure to these pollutants as determined by location in Winthrop relative to the airport.

The results of this survey demonstrate that a clear increase in several respiratory diseases and disease symptoms exists between areas of the Town which are adjacent to the airport, and those more distantly located on Broad Sound. In fact, for the most common respiratory diseases, asthma and allergy, disease is twice as common in the most heavily exposed neighborhood as it is in the least exposed. Finding no other likely explanation for this effect, the Subcommittee proposes that airport activities, most likely the generation of airborne pollution from the combustion of gasoline and kerosene, are indeed negatively affecting the health of the residents of Winthrop.

The implications of these findings are serious. While the unique geography and demographics of Winthrop provided a situation where the effects of airport generated pollution could be studied in isolation from other pollutant sources, Winthrop is by no means the only community impacted, nor the community most highly impacted by airport activity-generated emissions. As sample size determines the sensitivity of the analysis, only the most frequently occurring respiratory diseases could be adequately tested. Thus, while the case can be made strongly for asthma and allergenic disease, effects on other less common serious or life-threatening respiratory and cardiopulmonary conditions which are also linked to fuel exhaust exposure remain an unexplored possibility. Finally,

while the study convincingly illustrates the difference in impact due to relative exposure level, it does not define a level of exposure where impact is minimal or tolerable. In brief, the study demonstrates that serious damage is being done to the health of the residents of Winthrop at current levels of airport activity, and this damage correlates with location, a measure of exposure to airport activity-generated pollution. The Subcommittee feels it is incumbent on State regulatory authorities responsible for the public health to further investigate this matter, to further define the scope and severity of the problem, and initiate processes which will return our community to the state of health enjoyed by the majority of Massachusetts citizens.

Introduction

Winthrop is a peninsula which extends from East Boston south by south east to form the division between Broad Sound, on its eastern shore, and Boston Harbor on its western shore. A portion of the western shore entirely encloses, and closely approaches Logan airport. Winthrop is subjected to a variety of disturbances from the airport, including excessive noise and odors from burned and unburned fuel. Although Logan carries out no air pollution monitoring in the surrounding communities, their published estimates from modeling studies indicate approximately 50,000 pounds of airborne pollutants are released daily, primarily from the combustion of Jet Fuel A. Elsewhere it has been shown that a strong correlation exists between exposure to such pollutants and a variety of respiratory and cardiovascular diseases including lung cancer, chronic obstructive pulmonary disease, asthma and allergic rhinitis (1-10). Individuals residing in communities surrounding Logan airport show a considerably higher incidence of these diseases compared to the statewide average (12-14). It has not been possible to determine whether Logan airport activities contribute substantially to this health burden however, since the urban location of these communities presents a complex picture of pollution sources, including petrochemical pollution from power plants, industries, and heavy road traffic.

Winthrop, by contrast, is a stable, mature residential community without significant pollution sources except for the airport. Despite this fact, asthma incidence in Winthrop closely mirrors that in the mainland communities which abut the airport, and lung cancer rates for females is 50% higher than the statewide average (14). Some neighborhoods in Winthrop are located within a few hundred feet of major airport runways, while others are located as much as a mile and a half away. Residents report a marked difference in perception of chemical odors from the airport in relation to location in the Town, indicating that different levels of exposure occur within the Town resulting from distance from the airport and wind direction. In consideration of these facts, this study was conducted to determine whether any correlation exists between the level of exposure to air pollutants generated by airport activity and the incidence of and frequency of symptoms to respiratory disease.

Methods

The Town was divided into 10 neighborhoods, primarily on the basis of natural topography, containing between 1,000 and 2,500 residents each. Two neighborhoods were selected as likely representing areas of highest (#1, Court Road, and #2, Cottage Park), and lowest (#5, Winthrop Beach, and #6, Winthrop Highlands) exposure. A questionnaire was devised, consisting of 30 questions to obtain information on the incidence of diagnosed asthma, allergies, chronic bronchitis, chronic sinusitis, and emphysema, and on the frequency of symptoms experienced. Standard demographic information was also obtained on gender, age, and the duration of residence in the neighborhood. A smoking history was obtained, and information on the frequency of perception of odors caused by airport-related activities. Responses to questions on diagnosed disease incidence were yes/no, followed by a question on time since onset. Responses to questions on symptom frequency included none and either 4 or 5 frequency ranges.

Interviews were conducted by volunteers from the community who were trained in requirements for objective data collection, chain-of-custody, and anonymity requirements. Interviews were conducted 4 weekday evenings per week, between the hours of 6:30 and 8:30 PM. Team leaders assigned streets to the interviewers. Every residence in the neighborhood was approached, one time only, until the entire neighborhood was canvassed. All residences, single and multiple family dwellings and apartment complexes were sampled, with the exception of mechanically ventilated buildings. No commercial establishments were encountered in the zones polled. In this manner, a random sample of residents was polled which averaged approximately 18% of the population of the selected neighborhood. The only exception to this was neighborhood 5, the last area sampled. Activity was continued in this area, progressing from north to south, until the desired quota of 500 interviews each in low and high exposure areas was obtained. Each questionnaire was identified only by neighborhood, and no names or addresses were collected. The questionnaires were collected each evening and held centrally.

Following data entry, the database was screened to exclude unsuitable responses. Corrections were made to the database where possible, for example intelligible but non-numerical responses. Questionnaires with critical data missing or internally contradictory responses were excluded. Data was also discarded for individuals residing in the identified zone for less than one year, or who were not in residence for at least four days per week. All such changes were recorded. Of the 1000 questionnaires obtained, 838 were admissible, 430 from the high-exposure zone (Area 1 - 172; Area 2 - 258) and 408 from the low-exposure zone (Area 5 - 197; Area 6 - 211).

In light of the seriousness of the effects on human health, and the truncated timetable presented by airport expansion activities, simplified exploratory statistical analyses were first carried out by excluding from the data all individuals not smoke-free for the past five years. Data from high exposure (areas 1 and 2) and low exposure (areas 5 and 6) zones were pooled, and symptom frequency compared by chi-squared contingency analysis. The results of this analysis formed the basis for an earlier report which was presented by the Caucus on Air Transportation to representatives of the state government July 1, 1999.

While that approach provided a convincing and statistically significant demonstration of the differential effect of location on disease incidence, the dataset contains more information which can be accessed by more sophisticated analyses. To this end, the Subcommittee contracted the services of an epidemiological analytical firm, John Snow Inc., to further analyze the data. SAS software was employed to re-incorporate smokers into the study, correcting for smoking history, age and sex by means of the Mantel-Haenszel Test. Additional statistical analyses were performed with Epi Info V6 (15). Further, it was noted that while low-exposure zones 5 and 6 were essentially equivalent, high exposure zones 1 and 2 showed a differential from one another which was consistent with position relative to the airport. Contingency analysis was thus carried out for each of these zones separately, compared to the joined low-exposure population 5 and 6. The complete set of statistical analyses, identification and criteria for data exclusion, complete and amended datasets, and original survey questionnaires are on file with the Winthrop Board of Health.

Results

Table 1.

Frequency of Odor Perception

% Response on Scale 0 - 100 (Days/Year)

Area	0	1	12	25	50	100	Median
1	12.8 %	1.2 %	4.7 %	8.7 %	14.5 %	58.1 %	100 %
2	13.6 %	3.9 %	8.9 %	8.6 %	16.7 %	48.2 %	50 %
5	61.4 %	2.5 %	1.5 %	4.6 %	7.1 %	15.2 %	0 %
6	37.9 %	3.3 %	9.5 %	12.3 %	9.0 %	28.0 %	12 %

Table 2.

Relative Risk

High Exposure Area 1 vs Pooled Low Exposure Zone (Areas 5 + 6)

Total Sample Size - 580

Disease	Total Cases	Relative Risk*	p value**
Allergy	202	2.18	0.001
Asthma	95	1.97	0.004
Chronic Sinusitis	110	1.41	0.085
Chronic Bronchitis	45	1.25	0.5
Emphysema	14	1.18	0.76

Table 3.

** Relative Risk is the proportionate increase (or decrease) in disease incidence in the high exposure area compared to the low exposure area, adjusted for influences due to the age, sex and smoking history as estimated by the Mantel-Haenszel procedure.

** p value is the likelihood that the values obtained in the high and low exposure zones come from the same population and differences are due simply to random variation.

The results clearly show that a differential increase in respiratory disease occurs from the low exposure zones (area 5 and 6) through the moderately exposed area 2 to the highly exposed Court Road area 1. The statistical significance is absent for the infrequent conditions chronic bronchitis and emphysema, though a positive trend is still evident. Chronic sinusitis shows a strong correlation with the most highly exposed area. For the more common diseases, allergies and asthma, statistical significance of the correlation with location is extremely strong for the most highly exposed area 1; while less strong for the more moderately exposed area 2, the trend is well maintained.

Table 4.

Disease Incidence; Clinically Diagnosed, Self-Reported

Most Likely Estimate, 95% Confidence Limits

Area

Allergies

Asthma

Chronic Sinusitis

45.9 %

(38.3 % - 53.7 %)

22.7 %

(16.6 % - 29.7 %)

23.3 %

(17.2 % - 30.3 %)

33.1 %

(27.4 % - 39.2%)

16.3 %

(12.0 % - 21.4 %)

18.7 %

(14.1 % - 24.0 %)

27.4 %

(21.3 % - 34.2 %)

13.7 %

(9.2 % - 19.3 %)

18.8 %

(13.6 % - 24.9 %)

32.2 %

(26.0 % - 39.0 %)

13.7 %

(9.4 % - 19.1 %)

15.6 %

(11.0 % - 21.3 %)

Table 5.

Predicted Excess Disease in High Exposure Areas

Area	Disease	Pop. Size	% Disease Incidence	Relative Risk	Projected Cases	Expected Cases	Excess Cases
1	Allergy	1283	45.9	2.18	589	270	319
1	Asthma	1283	22.7	1.97	291	148	143
1	Sinusitis	1283	23.3	1.46	299	205	94
2	Allergy	1940	33.1	1.22	642	526	116
2	Asthma	1940	16.3	1.32	316	240	77
2	Sinusitis	1940	18.7	1.08	363	336	27

Table 6.

Frequency of Respiratory Symptoms

% Response in Scale 0 - 100

Area	Inhaler Use	Asthma Attack	Wheezing	Coughing	Rhinitis
1	1.90	0.59	2.79	10.39	7.79
2	1.59	0.44	1.89	10.44	16.83
5	2.26	0.59	2.10	5.59	10.34
6	1.74	0.26	1.17	4.37	10.80

Table 7.

Percent of Respondents Symptomatic At Any Level

Restricted Lung Function (Inhaler Use, Asthma Attack, Wheezing) and

Bronchonasal Irritation (Cough, Rhinitis)

Area	Restricted Lung Function	Bronchonasal Irritation
1	14.1 %	29.2 %
2	14.4 %	35.8 %
5	10.0 %	17.8 %
6	9.5 %	23.2 %

Discussion

The primary goal of this study was to determine whether spatial location relative to Logan airport, as a determinant of chemical exposure, has an influence on respiratory disease in the Town of Winthrop. While the exact component or mixture of components responsible for the effect is as yet unclear, it has been well established in the literature that exposure to pyrolysis products of fossil fuels correlates strongly with both incidence of and symptomatic response for several important respiratory diseases. In the majority of urban settings, multiple sources of such pollutants make it difficult or impossible to identify the impact of individual polluters. Winthrop, a residential community occupying a peninsula in Massachusetts Bay, has no major local petrochemical pollution sources with the exception of Logan airport. While generalized airborne pollution from nearby Boston and its suburbs no doubt contributes to the burden, such effects are sufficiently distant as to be well-mixed, affecting the Town equally. Logan airport by contrast approaches within a few hundred yards of portions of the Town. Residents report a very distinct geographical pattern of odor perception of burned and unburned kerosene (Jet Fuel A) and burning rubber from airplane tires. Other neighborhoods within the Town are more remote and less plagued by this problem. We thus conducted a survey to determine if there existed a correlation between spatial location and odor perception, as an index of chemical exposure, and both frequency of diagnosed respiratory disease, and prevalence of symptoms to that disease as an indicator of negative health impact.

Odor Perception / Exposure Level

A central component of the argument put forward in this report is that spatial location within the Town of Winthrop relative to the airport is an adequate determinant of exposure to airport-activity generated pollutants. While anecdotal reports regarding the perception of fuel and burnt rubber odors from residents support the contention, and epicenters of the sampled neighborhoods are approximately 0.4 miles (area 1), 0.8 miles (area 2) and 1.5 miles (areas 5 and 6) from runways, direct correlation of location/exposure level is lacking. Actual pollutant concentration in these areas is unknown, as no monitoring is carried out. In lieu of direct measurement, Massport carries out mathematical dispersion modeling of several important components of fuel and fuel exhaust (Carbon Monoxide, Nitrogen Dioxide, Volatile Organic Compounds, and Particles of diameter 10 :m. or less). Three sites in the Massport projection grid correspond very closely to the areas sampled in this study. Exact matches are found for area 1 (Court Road) and area 2 (Cottage Park), areas in close proximity to the airport. In addition, area 6 forms its northern border with the Massport projection area Revere Beach. While such models are useful tools, they are at best approximations of real conditions and subject to considerable error (16). Massport’s model predicts uniform particulate concentrations at all three sites, and an increase in combustion gases of approximately 10% at the Court Road site, with equivalent concentrations at both Cottage Park and Revere Beach. Concentrations of Volatile Organic Compounds, which comprise the fraction responsible for the noticeable odor, show a wider latitude of dispersion. Concentrations at Court Road are approximately double that predicted at Revere Beach. The difference in concentration between Court Road (area 1) and Cottage Park (area 2) varies from about 20% (highest peak value in 1 hour) to about 90% (highest peak value in 24 hours).

Direct evidence of this differential local concentration was sought in the survey. Frequency of perception of fuel and rubber odors was sampled in each neighborhood, and the responses converted to an approximately linear scale from 0 (never) to 100 (two or more times per week). Results (Table 1) were consistent with spatial location, with mean scores ranging from approximately 30 in zones 5 and 6 to 60 and 69 in zones 2 and 1. Median scores were 0 (never) in zone 5, 12 (once per month) in zone 6, 50 (once per week) in zone 2 and 100 (two or more times per week) in zone 1. While it is clear that only direct monitoring can establish actual and relative concentrations of these pollutants, sufficient information has been presented here to justify the classifications of low (areas 5 and 6), moderate (area 2) and high exposure (area 1).

Disease Incidence.

Ten questions were posed regarding the presence of each of five respiratory diseases which have been correlated with exposure to fossil-fuel exhaust , and the date of onset of the disease. The wording of the questions stressed that the diagnosis had to have been made by a physician ( “Have you ever been told by a Doctor that you have...”), and this fact of clinical diagnosis reinforced with an approximate date of diagnosis. Thus, while the replies to these questions are self-reported diagnoses, and actual incident rates derived from them should be viewed with that qualification, they are presmed to be reasonably truthful and at least should not be affected by reporting bias between different areas sampled. Bias on the part of the interviewer is also controlled in part by the binary response (yes/no) recorded. It should be further noted that the initial sampling strategy presented to the interviewers who were also members of the Subcommittee which defined the study was a sampling of highest expected and lowest expected exposure zones. The initial report presented by the committee was analyzed within that paradigm, and only further analyzed by individual zones following recognition of real response difference between zones 1 and 2. It is very unlikely that the interviewers regarded these two contiguous neighborhoods as different in terms of exposure level during the course of the survey, and the existence of substantial difference in response indicates an absence of interviewer bias.

Tables 3 and 4 show that a very clear increase in diagnosed disease exists in the neighborhoods in close proximity to the airport relative to the more remote locations. Further, while areas 1 and 2 are contiguous, the epicenter of area 2 is approximately twice the distance from the airport as that of area 1. Relative risks were calculated, controlling for possible confounding variables of sex, age and smoking history. In fact, all four neighborhoods are demographically very similar, and little effect of these variables was noted. Estimates of the reliability of the predicted relative risks, as indicated by the p values, are influenced both by the magnitude of the difference and the frequency of the disease in the population. For the three most prevalent conditions, allergy, asthma, and chronic sinusitis, the existence of a clear increase in frequency with position closer to the airport is striking. Further, the size of the difference is also impressive. For allergy and asthma, the most highly exposed population experiences a two-fold increase in disease incidence compared to the least exposed neighborhoods.

As mentioned above, these incident rates (Table 4) are a reasonable estimate of the level of diagnosed disease in the sample group, although they should not be compared to other studies which are primarily based on hospitalization rate or mortality. The rates presented here are consistent throughout the population under study, and appropriate for analysis of spatially-located differences in disease rate among the subgroups of that population. They do however include historical cases, and well-controlled or other asymptomatic conditions which would not appear for example in the Massachusetts Disease Registry. However, they do represent negative impacts on the health of the community, and to place these figures in a more human context, predictions on the effects of this differential are presented in Table 4. This estimates that, in areas 1 and 2, contiguous neighborhoods with a combined population of about 3200 people, there are 220 individuals with asthma, 435 with allergies, and 131 with chronic sinusitis whose condition is correlated with their location relative to Logan airport.

Symptom frequency

In contrast to the clear differences demonstrated for disease incidence, symptom frequency presents a much more complex picture. Table 6 illustrates symptom frequency for the five diseases sampled in each zone, as mean values within an approximately linear scale from 0 to 100. Results are highly variable, and overall scores low due to the high percentage in each group of asymptomatic respondents. It is probable that the sample size employed is insufficient to adequately characterize differences in the much smaller symptomatic subset, and the results should be regarded as inconclusive. The results reinforce rather than contradict data presented on disease incidence distribution however. If the responses are recast as binary elements (Table 7. Symptomatic vs Asymptomatic, grouped by functional pathology) a differential of approximately 50% again emerges between the pooled high exposure and low exposure zones.

References:

1 Abbey DE, Ostro BE, Petersen F, Burchette RJ. Chronic respiratory symptoms associated with estimated long-term ambient concentrations of fine particulates less than 2.5 microns in aerodynamic diameter (PM2.5) and other air pollutants. J Expo Anal Environ Epidemiol 1995 5: (2) 137-159

2 Bhatia R, Lopipero P, SmithAH Diesel Exhaust Exposure and Lung Cancer. Epidemiology 1997: 8 : 364

3 Brunekreef B, Janssen NAH, de Hartog J, Harssema H, Knape M, van Vliet P Air Pollution from Truck Traffic and Lung Function in Children Living near Motorways. Epidemiology 1997; 8 : 298

4 Dockery DW, Pope CA III, Xiphing X, Spengler JD, Ware JH, Fay ME, Ferris BG, Speizer FE. An association between air pollution and mortality in six US cities. New England Journal of Medicine, 1993 329: (24) 1753-1760

5 Duhme H, Weiland SK, Keil U, Kraemer K, Schmid M, Stender M, Chambless L The Association between Self-Reported Symptoms of Asthma and Allergic Rhinitis and Self-Reported Traffic Density on Street of Residence in Adolescents. Epidemiology 1996;7:578582

6 LoomisD, Castillejos M, Gold DR, McDonnell W, Borja-Aburto VH Air Pollution and Infant Mortality in Mexico City. Epidemiology 1999; 10: 118

7 Moolgavkar SH, Luebeck EG, Anderson EL Air Pollution and Hospital Admissions for Respiratory Causes in Minneapolis-St. Paul and Birmingham Epidemiology 1997: 8: 364

8 Schwartz J Air Pollution and Hospital Admissions for Cardiovascular Disease in Tucson. Epidemiology 1997; 8 : 371

9 Sheppard L, Levy D, Norris G, Larson TV, Koenig JQ Effects of Ambient Air Pollution on Nonelderly Asthma Hospital Admissions in Seattle, Washington, 1987-1994 Epidemiology 1999; 10: 225

10 Verhoeff A, Hoek G, Schwartz J, van Wijnen JH Air Pollution and Daily Mortality in Amsterdam Epidemiology 1996; 7: 225 230

11 Logan Airside Improvements Planning Project Volume IV 1999 EOEA #10458

12 Boston Neighborhood Health Status Report: The Health of South Boston. Boston Department of Health and Hospitals, Division of Public Health, Office of Research and Health Statistics. November 1994

13 The Health of Boston 1998. Boston Public Health Commission, Office of Research, Health Assessment and Data Systems, Boston Massachusetts 1998

14 Massachusetts Community Information Health Profile, Massachusetts Department of Public Health, Bureau of Health Statistics and Evaluation, Boston Masachusetts.

15 Dean AG, Dean JA, Coulombier D, Brendel KA, Smith DC, Burton AH, Dicker, RC, Sullivan K, Fagan, RF, Arner, TG. Epi Info Version 6: a word processing, database and statistics program for public health on IBM-compatible microcomputers. Centers for Disease Control and Prevention, Atlanta, Georgia, USA, 1996

16 Beychok MR. Error Propagation in Air Dispersion Modleing. Newport Beach CA

Winthrop Environmental Health Facts Subcommittee

Members of the Subcommittee Conducting the Survey

Barbara Bishop

Madeline Burke

Eleanor Casey

Greg Curci

John Dowd

Brian Dumser

Arthur Flavin, Sr.

Barbara Corbett Flavin

Connie Mara

John Marcy

Harvey Maibor

Bob Massa

Kathleen Mccauley

Ellie Olivolo

Judith Silck

Claire Sweeney

Winthrop Air Activist Community Health Survey

With the Winthrop Board of Health

This questionnaire is intended to establish relevant information regarding the incidence and severity of respiratory disease in the Town of Winthrop. Make sure that the respondent understands that nowhere on the questionnaire is there any information that identifies him or her in any way.

Special Instructions: However, since in the future we may want to more quickly and efficiently reassess this information, it is desirable that we establish a means a communicating with environmentally sensitive individuals (those answering 'yes' to questions 17,19, or 21). Ask such individuals if they would be willing to participate further (essentially answer the same questions again at different times of the year), and if so may we identify them by address only. If you obtain their verbal permission, write the full address in your notebook, along with the number of the question answered in the affirmative.

Enter one answer for each question on the Survey Response Form (“SRF”).

Complete an SRF for EACH member of a household. ________________________________________________

1. Sex of respondent.

0=Male If the sex of the respondent is Male, enter a ZERO (0) on the SRF Q1.

1=Female If the respondent is Female, enter a ONE (1) on the SRF Q1.

2. Age. How old are you (is the respondent)?

0=0-5 If the respondent is between 0 and 5 (including 5), enter a ZERO (0) on the SRF Q2.

1=6-12 If the respondent is between 6 and 12 (including 6 and 12), enter a ONE (1) on the SRF Q2.

2=13-20 If the respondent is between 13 and 20 (including 13 and 20), enter a TWO (2) on the SRF Q2.

3=21-40 If the respondent is between 21 and 40 (including 21 and 40), enter a THREE (3) on the SRF Q2.

4=41-60 If the respondent is between 41 and 60 (including 41 and 60), enter a FOUR (4) on the SRF Q2.

5=61 and above If the respondent is 61 or over, enter FIVE (5) on the SRF Q2.

3. Current location. This is your (the respondent’s) current residence?

0-9 Enter the grid number, from the map supplied, of the current location (0 through 9) on SRF Q3.

4. Duration at Current Location. How long have you (the respondent) been living at this location?

0=Less than 1 If response is less than 1 year, enter a ZERO on the SRF Q4.

1-8 = 1-8 If the response is between (and including) 1 thru 8 years, enter the number (1-8) on the SRF Q4.

9= =>9 years If the Response is 9 years or more, enter a 9 in the SRF Q4.

5. Previous location. Where did you (the respondent) live just prior to moving to this location?

0-9 Enter the grid number, from the map supplied, of the current location (0 through 9) on SRF Q5.

Blank If the Previous Location is outside Winthrop, leave SRF Q5 blank.

6. Duration at Previous location. How long did you (the respondent) live at the previous location?

0=Less than 1 If response is less than 1 year, enter a ZERO on the SRF Q6.

1-8=1-8 If the response is between (and including) 1 thru 8 years, enter the number (1-8) on the SRF Q6.

9= =>9 years If the Response is 9 years or more, enter a 9 in the SRF Q6.

Blank If the Previous Location is outside Winthrop (Q5 answer is blank), leave SRF Q6 blank.

7. Previous-previous location. Where did you (the respondent) live two locations ago?

0-9 Enter the grid number, from the map supplied, of the Pre/Prev location (0 through 9) on SRF Q7.

Blank If the Previous-Previous location is outside Winthrop, leave SRF Q7 blank

8. Duration at Previous-Previous location. How long did you (the respondent) live at the previous/previous location?

0=Less than 1 If response is less than 1 year, enter a ZERO on the SRF Q8.

1-8=1-8 If the response is between (and including) 1 thru 8 years, enter the number (1-8) on the SRF Q8.

9= =>9 years If the Response is 9 years or more, enter a 9 in the SRF Q8.

Blank If the Previous-Previous Location is outside Winthrop (Q7 answer is blank), leave SRF Q8 blank.

9. Smoking Does the respondent currently smoke cigarettes?

0=Yes If the respondent currently smokes, enter a ZERO (0) on the SRF Q9.

1=No If the respondent does not smoke currently, enter a ONE (1) on the SRF Q9.

10. How Much How many packs a day does the respondent smoke?.

0=< 1 If the respondent smokes less than one pack per day, enter a ZERO (0) on the SRF Q10.

1=1 If the respondent smokes one pack per day, enter a ONE (1) on the SRF Q10.

2=1½ If the respondent smokes one and 1/2 packs per day, enter a TWO (2) on the SRF Q10.

3 = 2 If the respondent smokes two packs per day, enter a THREE (3) on the SRF Q10.

4 =>2 If the respondent smokes more than two packs per day, enter a FOUR (4) on the SRF Q10.

Blank If the Respondent does not smoke currently (Q9 = No), leave a blank in SRF Q10.

11. Quit Did the respondent formerly smoke (and has quit)?

0=Yes If the respondent has quit smoking, enter a ZERO (0) on the SRF Q11.

1=No If the respondent has NEVER SMOKED, enter a ONE (1) on the SRF Q11.

12. Smoke-Free How many years since the respondent quit?

0=<1 If the respondent quit less than a year ago, enter a ZERO (0) on the SRF Q12.

1-8=1-8 If the respondent quit between one and eight years ago, enter the number (1-8) on the SRF Q12

9=9 or more If the respondent quit 9 or more years ago, enter a NINE (9) on the SRF Q12.

Blank If the Respondent never smoked (Q11= No), leave SRF Q12 blank.

The next two questions concern the amount of time the respondent actually spends in Winthrop.

13. Days/Week Away How many days per week does the respondent typically spend away from Winthrop, either all the time or during the summer months?

0-7=0-7 If the respondent spends “X” days per week away from Winthrop, enter the number (0-7) on SRF Q13.

14. Hours/Day Does the respondent work in the Town of Winthrop?

0=Yes If the respondent works in the Town of Winthrop, enter a ZERO (0) on the SRF Q14.

1=No If the respondent does not work in the Town of Winthrop, enter a ONE (1) on the SRF Q14.

Questions 15 - 24 deal with an existing condition which has been clinically diagnosed.

15. Allergies Have you ever been told by a Doctor that you have Allergies?

0 - Yes If the answer is “Yes”, enter a ZERO (0) on the SRF Q15.

1 - No If the answer is “No”, enter a ONE (1) on the SRF Q15

16. Allergies History How long ago were you diagnosed with this condition?

0=<1 Year If the answer is “Less than one year ago”, enter a ZERO (0) on the SRF Q16.

1=1-5 years If the answer is “Between one and five years ago”, enter a ONE (1) on the SRF Q16.

2=6-10 years If the answer is “Between six and ten years ago”, enter a TWO (2) on the SRF Q16.

3 =>10 years If the answer is “More than ten years ago”, enter a THREE (3) on the SRF Q16.

Blank If the respondent has never been diagnosed with allergies (Q15=No), leave SRF Q16 blank.

17. Asthma Have you ever been told by a Doctor that you have Asthma?

0 - Yes If the answer is “Yes”, enter a ZERO (0) on the SRF Q17. ***Special Instructions***

1 - No If the answer is “No”, enter a ONE (1) on the SRF Q17.

18. Asthma History “How long ago were you diagnosed with this condition?”

0=<1 Year If the answer is “Less than one year ago”, enter a ZERO (0) on the SRF Q18.

1=1-5 years If the answer is “Between one and five years ago”, enter a ONE (1) on the SRF Q18.

2=6-10 years If the answer is “Between six and ten years ago”, enter a TWO (2) on the SRF Q18.

3 =>10 years If the answer is “More than ten years ago”, enter a THREE (3) on the SRF Q18.

Blank If the respondent has never been diagnosed with asthma (Q17=No), leave SRF Q18 blank.

19. Chronic Bronchitis Have you ever been told by a Doctor that you have Chronic Bronchitis?

0 - Yes If the answer is “Yes”, enter a ZERO (0) on the SRF Q19. ***Special Instructions***

1 - No If the answer is “No”, enter a ONE (1) on the SRF Q19

20. Chronic Bronchitis History “How long ago were you diagnosed with this condition?”

0=<1 Year If the answer is “Less than one year ago”, enter a ZERO (0) on the SRF Q20.

1=1-5 years If the answer is “Between one and five years ago”, enter a ONE (1) on the SRF Q20.

2=6-10 years If the answer is “Between six and ten years ago”, enter a TWO (2) on the SRF Q20.

3 =>10 years If the answer is “More than ten years ago”, enter a THREE (3) on the SRF Q20.

Blank If the respondent has never been diagnosed with chronic bronchitis (Q19=No), leave Q20 blank.

21. Emphysema Have you ever been told by a Doctor that you have Emphysema?

0 - Yes If the answer is “Yes”, enter a ZERO (0) on the SRF Q21. ***Special Instructions***

1 - No If the answer is “No”, enter a ONE (1) on the SRF Q21.

22. Emphysema History “How long ago were you diagnosed with this condition?”

0=<1 Year If the answer is “Less than one year ago”, enter a ZERO (0) on the SRF Q22.

1=1-5 years If the answer is “Between one and five years ago”, enter a ONE (1) on the SRF Q22.

2=6-10 years If the answer is “Between six and ten years ago”, enter a TWO (2) on the SRF Q22.

3 =>10 years If the answer is “More than ten years ago”, enter a THREE (3) on the SRF Q22.

Blank If the respondent has never been diagnosed with emphysema (Q21=No), leave Q22 blank.

23. Sinusitis Have you ever been told by a Doctor that you have Sinusitis?

0 - Yes If the answer is “Yes”, enter a ZERO (0) on the SRF Q23.

1 - No If the answer is “No”, enter a ONE (1) on the SRF Q23.

24. Sinusitis History “How long ago were you diagnosed with this condition?”

0=<1 Year If the answer is “Less than one year ago”, enter a ZERO (0) on the SRF Q24.

1=1-5 years If the answer is “Between one and five years ago”, enter a ONE (1) on the SRF Q24.

2=6-10 years If the answer is “Between six and ten years ago”, enter a TWO (2) on the SRF Q24.

3 =>10 years If the answer is “More than ten years ago”, enter a THREE (3) on the SRF Q24.

Blank If the respondent has never been diagnosed with sinusitis (Q23=No), leave SRF Q24 blank.

Questions 25 through 30 deal with the frequency of symptoms to these conditions (How often do you have/use...?)

25. Inhaler How often do you use an inhaler?

0=once per month If the response is “Once per month”, enter a ZERO (0) on SRF Q25.

1=once per week If the response is “Once a week”, enter a ONE (1) on SRF Q25.

2=2 or more times per week If the response is “Two ormore times per week”, enter a TWO (2) on SRF Q25.

3=Once a day If the response is “Once per day”, enter a THREE (3) on SRF Q25.

4=2 or 3 times a day If the response is “Two or three times a day”, enter a FOUR (4) on SRF Q25.

5=More than 3 times a day If the response is “More than three times a day”, enter a FIVE (5) on SRF Q25.

Blank = Never If the response is “Never”, leave SRF Q25 blank.

26. Asthma Atttack How often do youexperience an asthma attack?

0=once per month If the response is “Once per month”, enter a ZERO (0) on SRF Q26.

1=once per week If the response is “Once a week”, enter a ONE (1) on SRF Q26.

2=2 or more times per week If the response is “Two or more times per week”, enter a TWO (2) on Q26.

3=Once a day If the response is “Once per day”, enter a THREE (3) on SRF Q26.

4=2 or 3 times a day If the response is “Two or three times a day”, enter a FOUR (4) on SRF Q26.

5=More than 3 times a day If the response is “More than three times a day”, enter a FIVE (5) on SRF Q26

Blank = Never If the response is “Never”, leave SRF Q26 blank.

27. Wheezing How often do you experience wheezing or shortness of breath?

0=once per month If the response is “Once per month”, enter a ZERO (0) on SRF Q27.

1=once per week If the response is “Once a week”, enter a ONE (1) on SRF Q26.

2=2 or more times per week If the response is “Two ormore times per week”, enter a TWO (2) on SRF Q27.

3=Once a day If the response is “Once per day”, enter a THREE (3) on SRF Q27.

4=2 or 3 times a day If the response is “Two or three times a day”, enter a FOUR (4) on SRF Q27.

5=More than 3 times a day If the response is “More than three times a day”, enter a FIVE (5) on SRF Q27.

Blank = Never If the response is “Never”, leave SRF Q27 blank.

28. Bronchitis How often do you experience coughing spells?

0=Once a year If the response is “Once a year”, enter a ZERO (0) on SRF Q28.

1=Once a month If the response is ”Once a month”, enter a ONE (1) on SRF Q28.

2=2 or more times per month If the response is “Two or more time a month”, enter a TWO (2) on SRF Q28.

3=Once a week If the response is “Once a week”, enter a THREE (3) on the SRF Q28.

4=More than once a week If the response is “More than once a week”, enter a FOUR (4) on SRF Q28.

Blank = Never If the response is “Never”, leave SRF Q28 blank.

29. Sinusitis How often do you experience sinusitis (Runny nose, tearing eyes, sinus headache)?

0=Once a year If the response is “Once a year”, enter a ZERO (0) on SRF Q29.

1=Once a month If the response is ”Once a month”, enter a ONE (1) on SRF Q29.

2=2 or more times per month If the response is “Two or more time a month”, enter a TWO (2) on SRF Q29.

3=Once a week If the response is “Once a week”, enter a THREE (3) on the SRF Q29.

4=More than once a week If the response is “More than once a week”, enter a FOUR (4) on SRF Q29.

Blank = Never If the response is “Never”, leave SRF Q29 blank.

30. Odors How often do you notice exhaust, chemical or fuel odors that you believe to be airport related?

0=Once a year If the response is “Once a year”, enter a ZERO (0) on SRF Q30.

1=Once a month If the response is ”Once a month”, enter a ONE (1) on SRF Q30.

2=2 or more times per month If the response is “Two or more time a month”, enter a TWO (2) on SRF Q30.

3=Once a week If the response is “Once a week”, enter a THREE (3) on the SRF Q30.

4=More than once a week If the response is “More than once a week”, enter a FOUR (4) on SRF Q30.

Blank = Never If the response is “Never”, leave SRF Q30 blank.

Note to Surveyor: Don’t forget to sign and date the Survey Response Form.

Review answers to Questions 17, 19 and 21. If “Yes”, follow Special Instructions on Page 1.