Agency for Toxic

Substances and

Disease Registry

Division of Health Studies

A Study of Ambient Air Contaminants and

Asthma in New York City

Part A and B

FINAL REPORT

July 2006

U.S. DEPARTMENT OF HEALTH

& HUMAN SERVICES

Public Health Service

Agency for Toxic Substances 

and Disease Registry

Atlanta, Georgia 30333

106152-8-06
Additional copies of this report are available from:

National Technical Information Service, Springfield, Virginia

(800) 553-6847 or (703) 605-6244

Request publication number PB2006-113523
A Study of Ambient Air Contaminants and

Asthma in New York City

Final Report

Part A:

A Comparison of Ambient Air Quality in

the Bronx and Manhattan

Prepared by:

New York State Department of Health

Center for Environmental Health

547 River St

Troy, NY 12180-2216

Prepared for:

The U.S. Department of Health and Human Services

Agency for Toxic Substances and Disease Registry

(Cooperative Agreement V50/ATV200002-11)

Project Manager – Mohammed Uddin, MD, MPH

The New York State Energy Research and Development Authority

(Grant 14-0034-01)

Project Manager – Ellen Burkhard, PhD

Final: 17 July, 2006 
NOTICE

This report was prepared by the New York State Department of Health’s Center for Environmental Health, 

the New York State Department of Environmental Conservation and Columbia University in the course of 

performing work contracted for and sponsored by the New York State Energy Research and Development 

Authority and the U.S. Agency for Toxic Substances and Disease Registry (hereafter “the Sponsors”). The 

opinions expressed in this report do not necessarily reflect those of the Sponsors or the State of New York, 

and reference to any specific product, service, process, or method does not constitute an implied or 

expressed recommendation or endorsement of it. Further, the Sponsors and the State of New York make no 

warranties or representations, expressed or implied, as to the fitness for particular purpose or 

merchantability of any product, apparatus, or service, or the usefulness, completeness, or accuracy of any 

processes, methods, or other information contained, described, disclosed, or referred to in this report. The 

Sponsors, the State of New York, and the contractors make no representation that the use of any product, 

apparatus, process, method, or other information will not infringe privately owned rights and will assume 

no liability for any loss, injury, or damage resulting from, or occurring in connection with, the use of 

information contained, described, disclosed, or referred to in this report. 

ii 
CONTENTS

NOTICE ......................................................................................................................................................ii

LIST OF TABLES .....................................................................................................................................iv

LIST OF FIGURES ..................................................................................................................................... v

SUMMARY ................................................................................................................................................ 9

INTRODUCTION ..................................................................................................................................... 13

OBJECTIVES ........................................................................................................................................... 15

BACKGROUND ....................................................................................................................................... 17

METHODS ................................................................................................................................................ 21

RESULTS .................................................................................................................................................. 29

DISCUSSION ........................................................................................................................................... 37

CONCLUSIONS AND RECOMMENDATIONS .................................................................................... 43

REFERENCES .......................................................................................................................................... 45

AUTHORS AND ACKNOWLEDGMENTS ............................................................................................ 49

TABLES .................................................................................................................................................... 51

FIGURES .................................................................................................................................................. 63

APPENDICES ......................................................................................................................................... 135

iii 
LIST OF TABLES

Table 1. U.S. Census and NYS motor vehicle registration data ................................................................. 52

Table 2. Business data ................................................................................................................................ 53

Table 3. Average particulate matter concentrations ................................................................................... 54

Table 4. Average particle count.................................................................................................................. 54

Table 5. Average pH, sulfate, elemental carbon and organic carbon concentrations................................. 54

Table 6. Average particulate metals concentrations ................................................................................... 55

Table 7. Average pollen concentrations ..................................................................................................... 55

Table 8. Average mold concentrations ....................................................................................................... 55

Table 9. Average acetone and aldehyde concentrations ............................................................................. 56

Table 10. Average acidic and basic gas concentrations.............................................................................. 56

Table 11. Average ozone, sulfur dioxide and nitrogen oxide concentrations............................................. 56

Table 12. Comparison of average particulate matter concentrations between two Bronx

sampling locations................................................................................................................. 57

Table 13. Comparison of average pH, sulfate, elemental carbon and organic carbon

concentrations between two Bronx sampling locations ........................................................ 57

Table 14. Comparison of average particulate metals concentrations between two Bronx

sampling locations................................................................................................................. 57

Table 15. Comparison of average pollen and mold concentrations between two Bronx

sampling locations................................................................................................................. 58

Table 16. Comparison of average acetone and aldehyde concentrations between two

Bronx sampling locations...................................................................................................... 58

Table 17. Comparison of average acidic and basic gas concentrations between two Bronx

sampling locations................................................................................................................. 59

Table 18. Comparison of average ozone, sulfur dioxide and nitrogen oxide concentrations

between two Bronx sampling locations ................................................................................ 59

Table 19. Daily maximum concentrations for analytes measured as one-hour or three-hour

time-weighted averages.........................................................................................................60

Table 20. Pearson correlation coefficients for corresponding observations at the Bronx

and Manhattan sampling locations........................................................................................ 61

Table 21. Pearson correlation coefficients within sampling location for corresponding

daily average and daily maximum observations ................................................................... 61

Table 22. Pearson correlation coefficients for corresponding observations at the Bronx

and Manhattan sampling locations, stratified by year........................................................... 62

iv 
LIST OF FIGURES

Figure 1. Bronx sampling locations............................................................................................................ 64

Figure 2. Manhattan sampling location ...................................................................................................... 65

Figure 3. Daily averages and daily average differences between Bronx and Manhattan:

PM2.5 (TEOM)....................................................................................................................... 66

Figure 4. Daily averages and daily average differences between Bronx and Manhattan:

PM10 (TEOM) ....................................................................................................................... 67

Figure 5. Daily averages and daily average differences between Bronx and Manhattan:

particle count......................................................................................................................... 68

Figure 6. Daily averages and daily average differences between Bronx and Manhattan:

pH.......................................................................................................................................... 69

Figure 7. Daily averages and daily average differences between Bronx and Manhattan:

sulfate.................................................................................................................................... 70

Figure 8. Daily averages and daily average differences between Bronx and Manhattan:

organic carbon....................................................................................................................... 71

Figure 9. Daily averages and daily average differences between Bronx and Manhattan:

elemental carbon ................................................................................................................... 72

Figure 10. Daily averages and daily average differences between Bronx and Manhattan:

iron ........................................................................................................................................ 73

Figure 11. Daily averages and daily average differences between Bronx and Manhattan:

nickel..................................................................................................................................... 74

Figure 12. Daily averages and daily average differences between Bronx and Manhattan:

total pollen ............................................................................................................................ 75

Figure 13. Daily averages and daily average differences between Bronx and Manhattan:

tree pollen.............................................................................................................................. 76

Figure 14. Daily averages and daily average differences between Bronx and Manhattan:

grass pollen ........................................................................................................................... 77

Figure 15. Daily averages and daily average differences between Bronx and Manhattan:

ragweed pollen ...................................................................................................................... 78

Figure 16. Daily averages and daily average differences between Bronx and Manhattan:

total mold .............................................................................................................................. 79

Figure 17. Daily averages and daily average differences between Bronx and Manhattan:

basidiospores......................................................................................................................... 80

Figure 18. Daily averages and daily average differences between Bronx and Manhattan:

ascospores ............................................................................................................................. 81

v 
Figure 19. Daily averages and daily average differences between Bronx and Manhattan: 

mitospores............................................................................................................................. 82

Figure 20. Daily averages and daily average differences between Bronx and Manhattan:

dark mitospores..................................................................................................................... 83

Figure 21. Daily averages and daily average differences between Bronx and Manhattan:

non-dark mitospores.............................................................................................................. 84

Figure 22. Daily averages and daily average differences between Bronx and Manhattan:

mold spores < 10 μm............................................................................................................. 85

Figure 23. Daily averages and daily average differences between Bronx and Manhattan:

mold spores > 10 μm............................................................................................................. 86

Figure 24. Daily averages and daily average differences between Bronx and Manhattan:

acetone .................................................................................................................................. 87

Figure 25. Daily averages and daily average differences between Bronx and Manhattan:

acetaldehyde.......................................................................................................................... 88

Figure 26. Daily averages and daily average differences between Bronx and Manhattan:

formaldehyde ........................................................................................................................ 89

Figure 27. Daily averages and daily average differences between Bronx and Manhattan:

hydrochloric acid................................................................................................................... 90

Figure 28. Daily averages and daily average differences between Bronx and Manhattan:

nitrous acid............................................................................................................................ 91

Figure 29. Daily averages and daily average differences between Bronx and Manhattan:

nitric acid .............................................................................................................................. 92

Figure 30. Daily averages and daily average differences between Bronx and Manhattan:

ammonia................................................................................................................................ 93

Figure 31. Daily averages and daily average differences between Bronx and Manhattan:

ozone..................................................................................................................................... 94

Figure 32. Daily averages and daily average differences between Bronx and Manhattan:

sulfur dioxide ........................................................................................................................ 95

Figure 33. Daily averages and daily average differences between Bronx and Manhattan:

nitrogen dioxide .................................................................................................................... 96

Figure 34. Daily averages and daily average differences between Bronx and Manhattan:

nitrogen oxide ....................................................................................................................... 97

Figure 35. Daily averages and daily average differences between Bronx and Manhattan:

total nitrogen oxides.............................................................................................................. 98

Figure 36. Multidimensional scaling results: all seasons combined........................................................... 99

Figure 37. Multidimensional scaling results: January–March.................................................................. 100

Figure 38. Multidimensional scaling results: April–June......................................................................... 101

vi 
Figure 39. Multidimensional scaling results: July–September................................................................. 102

Figure 40. Multidimensional scaling results: October–December............................................................ 103

Figure 41. Hierarchical clustering results: all seasons combined ............................................................. 104

Figure 42. Hierarchical clustering results: January–March ...................................................................... 105

Figure 43. Hierarchical clustering results: April–June ............................................................................. 106

Figure 44. Hierarchical clustering results: July–September ..................................................................... 107

Figure 45. Hierarchical clustering results: October–December................................................................ 108

Figure 46. Day of week averages: PM2.5 and PM10 (TEOM) ................................................................... 109

Figure 47. Hour of day averages: PM2.5 and PM10 (TEOM) .................................................................... 110

Figure 48. Day of week averages: particle count...................................................................................... 111

Figure 49. Hour of day averages: particle count....................................................................................... 112

Figure 50. Day of week averages: pH ...................................................................................................... 113

Figure 51. Day of week averages: sulfate................................................................................................. 114

Figure 52. Day of week averages: organic carbon and elemental carbon................................................. 115

Figure 53. Hour of day averages: organic carbon and elemental carbon.................................................. 116

Figure 54. Day of week averages: particulate metals ............................................................................... 117

Figure 55. Day of week averages: total pollen ......................................................................................... 118

Figure 56. Day of week averages: tree, grass and ragweed pollen ........................................................... 119

Figure 57. Day of week averages: total mold ........................................................................................... 120

Figure 58. Day of week averages: ascospores, basidiospores and mitospores ......................................... 121

Figure 59. Day of week averages: dark and non-dark mitospores............................................................ 122

Figure 60. Day of week averages: small and large mold spores............................................................... 123

Figure 61. Day of week averages: acetone, acetaldehyde and formaldehyde........................................... 124

Figure 62. Day of week averages: hydrochloric acid ............................................................................... 125

Figure 63. Day of week averages: nitrous acid and nitric acid................................................................. 126

Figure 64. Day of week averages: ammonia ............................................................................................ 127

Figure 65. Day of week averages: ozone.................................................................................................. 128

Figure 66. Hour of day averages: ozone................................................................................................... 129

Figure 67. Day of week averages: sulfur dioxide ..................................................................................... 130

Figure 68. Hour of day averages: sulfur dioxide ...................................................................................... 131

Figure 69. Day of week averages: nitric oxide, nitrogen dioxide and total nitrogen oxides..................... 132

Figure 70. Hour of day averages: nitric oxide, nitrogen dioxide and total nitrogen oxides...................... 133

vii 
viii
SUMMARY

This report compares ambient levels of certain hazardous air pollutants, criteria pollutants, and bioaerosols in 

two New York City neighborhoods that have different rates of hospital admissions for asthma and different 

socio-economic status characteristics. Chemical and biological analytes were chosen for this study based on 

existing information suggesting that exposure to these analytes may be related to acute asthma exacerbations. 

In addition to data on many commonly measured chemical air pollutants, information was collected on 

several components of airborne particulate matter that have not previously been assessed for their possible 

association with asthma exacerbations. The primary goal was to assess whether ambient air quality differed in 

two New York City locations. A separate report presents the results of the analysis evaluating the effects of 

various air contaminants on acute asthma exacerbations. 

The study measured 24-hour average ambient air concentrations of acetone, aldehydes, chromium, iron, 

nickel, manganese, hydrogen ion, sulfate, pollen and mold spores. One-hour average concentrations were 

measured for ozone, sulfur dioxide, nitrogen oxides, number of particles measuring 0.007 to 2.5 micrometers, 

particulate matter < 2.5 micrometers (PM2.5) and particulate matter < 10 micrometers (PM10). Three-hour 

average concentrations were measured for elemental and organic carbon. The hourly data were used for 

calculating daily averages, maximum concentrations and for ozone, eight-hour moving averages. 

Meteorological data (temperature, wind speed and direction, humidity) were also collected. Ambient air data 

were collected from one site in Manhattan from January 1999 through November 2000, from one site in the 

Bronx from January 1999 through August 1999 and from a second nearby site in the Bronx from September 

1999 through November 2000.

 Statistical analyses comparing ambient air concentrations between the Bronx and Manhattan sites were 

conducted using a paired t-test adjusted for autocorrelation. Comparisons were made on a seasonal basis 

(quarterly) and for the entire study period. Mean levels of fine particulate matter, particulate acidity, 

particulate sulfate, particulate nickel, acid gases, ammonia, sulfur dioxide and nitrogen oxides were 

significantly higher in Manhattan than in the Bronx over the entire study period. Mean levels of ozone, 

ragweed pollen and grass pollen were significantly higher in the Bronx. Statistical tests had power to detect 

small mean differences because of large sample sizes. Therefore, although several mean comparisons were 

significantly different, the absolute differences in analyte concentrations between the two sites were generally 

not large. For example, for most comparisons, the higher mean was no more than about 1.6-fold larger than 

the lower mean, and many of the significant mean differences were less than 1.2-fold. 

9
Most of the variables were correlated (Pearson r > 0.6) over the entire study period between the Manhattan 

and Bronx sites. In general, low correlations were due to a few outliers. Weak correlations between the two 

sites were found for particle count, iron, nickel, acetone and non-dark mitospores. 

Exploratory temporal analyses of certain air contaminants were conducted. PM10 and PM2.5, organic carbon 

and elemental carbon were evaluated by the hour and day of week. Both sites exhibited a daily temporal 

pattern in PM10 and PM2.5 levels. Lowest levels were seen in the middle of the night (2 A.M.). The highest 

levels were seen in the morning, with a smaller peak in the early evening. Particulate matter elemental carbon 

concentrations peaked at 9 A.M. at both sites. The particulate organic carbon fraction increased modestly in 

concentration from early in the morning to a high in the evening for Manhattan, whereas the Bronx organic 

carbon levels remained nearly constant throughout the day. Acetone, elemental carbon, nitrogen oxides, PM10 

and particulate Fe were the only variables showing a noticeable day-of-week trend, with somewhat lower 

daily means on Sundays, increasing through the week to Thursdays. 

Two multivariate statistical procedures (multidimensional scaling and hierarchical cluster analysis) were used 

in exploratory analyses of associations among chemical analytes within each sampling site. Very robust 

patterns of clustering among variables were not observed in these analyses, but some modest associations 

were found. Ozone tended to be negatively associated with all other analytes, particularly during the coldweather months. The strongest positive associations tended to be between or among variables measuring 

closely related chemical species. That is, all nitrogen oxide variables tended to cluster together, two different 

measures of sulfur dioxide were closely associated and particulate matter variables tended to be closely 

associated with each other. 

Larger clusters of analytes varied somewhat by season, but in general, nitrogen oxides, sulfur dioxide, 

elemental carbon and some metals tended to form a relatively consistent aggregation of variables. A second 

aggregation usually included the particulate matter variables, some aldehydes, organic carbon, sulfate and in 

some instances inorganic acid measures. Patterns of associations among analytes did not differ noticeably 

between the Bronx and Manhattan. 

CONCLUSIONS AND RECOMMENDATIONS 

Ambient air quality measured with rooftop monitors at two locations in New York City found that, for most 

analytes, either the two sites did not differ or mean air levels were higher at the Manhattan location than at the 

Bronx location. Analyte measurements from both locations were subject to large temporal variations on 

hourly, daily, and often seasonal time scales. When statistically different average pollutant levels were 

detected between the two locations, they differed by less than twofold. Average ozone and pollen levels 

tended to be higher in the Bronx, with mean differences of about 30% to 70% between the two sites. These 

results, representing approximately two years of hourly or daily observations on nearly three dozen analytes 

10
from two locations in New York City, provide a more detailed characterization of ambient air pollutants, 

especially particulate matter constituents, than has been previously reported for a large urban area. We 

recommend that future studies investigating ambient air pollutant exposures on an urban neighborhood scale 

collect additional data to better characterize spatial variability of ambient pollutants in urban areas, 

particularly for noncriteria pollutants. 

11
12
Section 1

INTRODUCTION

Asthma is a serious chronic disease that in 1999 affected roughly four percent of the U.S. population 

(approximately 11 million total cases of diagnosed asthma with an acute asthma episode in the previous 12 

months). Its prevalence has been increasing over the past few decades (Mannino et al. 1998, 2002; IOM 

2000). Lifetime prevalence (i.e., ever-diagnosed asthma) in the United States was approximately 10% in the 

1997–1999 National Health Interview Survey, which is consistent with the adult lifetime prevalence estimated 

in the 2000 Behavioral Risk Factor Surveillance System data (CDC 2006). Asthma disproportionately affects 

African American communities, with higher rates of asthma emergency department visits, asthma 

hospitalizations and asthma mortality (Mannino et al. 2002). 

The New York State Department of Health (NYSDOH) received many letters from students, teachers, 

community groups and environmental organizations requesting an environmental health investigation in the 

South Bronx. The South Bronx is a densely populated, inner-city area with high traffic volume, multifamily 

residential developments and a variety of industrial operations. The Bronx is the site of a city water pollution 

control plant, a sludge pelletization plant that handles over 70% of the city’s sewage sludge, a large wholesale 

food market and distribution center and many small industries. Bronx residents and elected officials raised 

concerns that high asthma rates in the borough were related to ambient air pollution exposures from these 

sources. 

As part of the response to these concerns, NYSDOH undertook to compare the air quality in the South Bronx 

with that of another area in New York City, and to evaluate potential associations between measured air 

pollutants and emergency department visits for asthma. The study involved continuous ambient air 

monitoring in the South Bronx and Manhattan for criteria air pollutants, pollutants categorized by the U.S. 

Environmental Protection Agency (EPA) as hazardous air pollutants (HAPs) and bioaerosols, including pollen 

and fungal spores. The chemical and biological analytes chosen for the study were selected based on existing 

information suggesting that exposure to these ambient air pollutants may be associated with acute asthma 

exacerbations. In addition to mass concentration, ambient particulate matter was chemically characterized in 

terms of elemental and organic carbon fractions, acidity and metals content. The study utilized centralized 

monitoring stations that were expected to be representative of air quality in the two communities. Attribution 

of measured pollutant concentrations to specific point sources was not a goal of the study, and this was not 

technically feasible with the type of data collected. 

A comparison of the ambient air monitoring results from the South Bronx and Manhattan monitoring sites is 

reported here. A separate study component investigated associations between ambient air monitoring results 

and asthma emergency-department visits in the two areas. Those results are presented in Part B of this report. 

13
14
Section 2

OBJECTIVES

The purpose of the study was to evaluate and compare ambient concentrations of several air pollutants in two 

areas of New York City and to evaluate temporal associations between these air pollutants and acute 

asthmatic symptoms as measured by emergency department visits for asthma by residents in parts of the 

Bronx and Manhattan. Ultimately, this study should contribute to the body of knowledge about the effects of 

components of ambient air on asthma in urban areas. 

Specific objectives were as follows: 

1. to evaluate whether ambient levels of certain hazardous air pollutants, criteria pollutants or 

bioaerosols differ in two New York City neighborhoods that have different rates of hospital admissions for 

asthma and different socio-economic status characteristics; 

2. to compute the overall rates of air-contaminant-attributable asthma emergency department 

visits among residents of the two communities over a one-year period, and test whether the magnitude of the 

air pollution effect differs in the two communities; and 

3. to investigate which air contaminants are most associated with acute asthma exacerbations 

in each community. 

This report focuses on the first objective—evaluating whether ambient levels of certain hazardous air 

pollutants, criteria pollutants or bioaerosols differ in two New York City neighborhoods. More specifically, 

this report compares air concentrations on a seasonal basis between sites and describes the correlation 

between the sites for the air contaminants, the correlations among contaminants within each site, and temporal 

contaminant patterns. 

15
16
Section 3

BACKGROUND

Asthma is a multi-factorial disease with a complicated and still not completely understood etiology and 

physiological basis. Genetic factors and environmental exposures are both thought to play a role in asthma 

development. However, it has been argued that the recent increase in asthma prevalence has occurred too 

rapidly to be the result of genetic changes and is therefore assumed to be largely due to changes in 

environmental exposures (e.g., Ronchetti et al. 2001). Laboratory studies and studies looking at human 

populations that have found associations between air quality and different asthma outcomes suggest that 

ambient air exposures may be one important factor influencing asthma morbidity. 

Ambient air contaminants, including ozone, sulfur dioxide, nitrogen dioxide, acid particulates (hydrogen ion), 

sulfates, PM2.5 and PM10, total particulates, wood smoke and bioaerosols (pollen and fungal spores), have all 

been associated with increased asthma symptoms (Boman et al. 2003; Brunekreef and Holgate 2002; Burnett 

et al. 1994; Committee of the Environmental and Occupational Health Assembly of the American Thoracic 

Society 1996; Dales et al. 2000; Delfino et al. 1996; Gavitt and Koren 2001; Peden 2002; Schwela 2000). 

Evaluating these associations for individual contaminants, however, is complicated by the temporal 

correlations among air contaminants and weather factors. A detailed review of the epidemiological literature 

on the relationship between ambient air pollution and asthma morbidity is beyond the scope of this report. 

However, brief examples of associations between ambient air contaminant exposures and asthma morbidity 

are discussed below. 

PARTICULATE MATTER AND OTHER AEROSOLS 

Many epidemiological studies have suggested that increases in particulate air contaminant levels can cause an 

increase in acute asthmatic episodes (see Dockery and Pope 1994 for review). Currently, there is no 

agreement among scientists as to whether a specific characteristic or component of PM is responsible for the 

observed health effects. Among the possibilities proposed are the physical characteristics of the particle or 

droplet (e.g., its size, shape or density), the number of particles present (i.e., particle number), its surface area, 

surface chemistry, surface charge or acidity. The specific chemical makeup of the particle or droplet is also 

thought to potentially contribute to health effects (e.g., elemental or organic carbon, volatile organic 

compounds, sulfates, nitrates, and metals such as iron, cadmium, cobalt, copper, manganese, nickel, lead, 

titanium, vanadium, zinc). Also of interest are particles of biological origin, such as fungal spores and pollen. 

The consistent finding of increased respiratory effects associated with increasing PM across areas with widely 

differing types of PM supports the hypothesis that more than one type of PM may be capable of producing the 

observed effects. Information about the potential for each of the various components of PM to worsen asthma 

or produce other respiratory symptoms is incomplete. 

17
Diesel exhaust particulates (DEP) make up a significant portion of the PM10 in New York City (NYSDEC 

1995). Diesel exhaust particles are generally composed of an elemental carbon core that may have a variety of 

organic compounds, metals, trace elements, sulfates and nitrates associated with its surface. Studies looking at 

DEP exposure and subsequent exposure to ragweed have associated increased allergic response with 

increased DEP exposure (Diaz-Sanchez et al. 1997). Studies in rodents have reported increases in airway 

hyper-responsiveness and inflammation following DEP and allergen challenge. These responses were 

reported to be greater than those observed with either DEP or antigen challenge alone (Takano et al.1998; 

Miyabara et al. 1998 as referenced in U.S. EPA 2002). 

Several metals that can be associated with particulate matter have been found to affect lung function, 

including chromium, manganese and nickel. Nickel compounds have been associated with occupational 

asthma and can also act as a primary irritant (Agency for Toxic Substances and Disease Registry 1995). 

Chromium compounds have been associated with occupational asthma and decreases in forced expiratory 

volume at 1 second (FEV1) and forced expiratory flow (Agency for Toxic Substances and Disease Registry 

1993). Manganese compounds have been reported to cause an inflammatory response in the lung and 

reductions in lung function, and there has been some evidence of respiratory effects in residential populations 

near ferromanganese factories (Agency for Toxic Substances and Disease Registry 1992). 

Both nitrous and sulfuric acids can be present in ambient air as acid aerosols, and strong acids such as these 

are known irritants. Nitrous acid is an irritant that is capable of producing symptoms in asthmatics (WHO 

2000). Sulfuric acid, although a recognized irritant and corrosive at high concentrations, has not, by itself, 

been found to significantly affect lung function at environmentally relevant concentrations. Naturally 

occurring ammonia in the respiratory system is able to neutralize some inhaled acids, reducing the 

opportunity for acidic particles to contact tissues. However, if acid aerosol concentrations are elevated, or if 

underlying respiratory conditions diminish the system’s ability to neutralize acids, the potential for respiratory 

irritation may be increased. 

Airborne biological particles, or bioaerosols, carry protein allergens and inflammatory agents (such as β-1,3­

glucans) that can contribute to asthma exacerbations in sensitized patients. The common allergen bioaerosols 

in ambient air are pollen and fungal spores. In a study of asthma symptoms and air quality in Southern 

California, Delfino et al. (1997) found that exposure to fungal spores adversely affected respiratory status as 

increased asthma symptoms, inhaler use, and reduced peak expiratory flow rate. An earlier study by Delfino 

et al. (1996) found that personal ozone and fungal exposures were associated with increased asthma 

symptoms and inhaler use. Higgins et al. (2000) reported that increasing spore counts were associated with a 

drop in mean peak expiratory flow and an increase in its variability. These effects were reportedly greater 

when ozone levels were elevated prior to the increase in the spore counts. Dales et al. (2000) reported that 

18
increases in ascomycete spores in air were associated with a 2.8% increase in pediatric emergency department 

visits for asthma. Dales et al. (2000) also reported that increases in basidiomycete spores in air were 

associated with a 4.1% increase in pediatric emergency department visits for asthma. Sensitization and 

exposure to grass pollen are risk factors for asthma prevalence and exacerbations (e.g., Schappi et al. 1999; 

Soriano et al. 1999; Basagana et al. 2001). 

GASES 

Short-term exposures to high concentrations of sulfur dioxide in laboratory settings have produced respiratory 

symptoms (decrease in mean FEV1, increase in specific airway resistance, wheezing and shortness of breath) 

in healthy and asthmatic subjects (e.g., Linn et al. 1984a, b; Horstman et al. 1986, 1988; Heath et al. 1994; 

Gong et al. 1996 ). Epidemiological studies looking at populations exposed to sulfur dioxide as part of the 

ambient pollutant mixture have reported mixed results, perhaps due to the presence of other pollutants having 

similar effects on health (Schwela 2000) . 

Results from health effect studies of exposure to nitrogen dioxide are not consistent. However, relatively high 

concentrations of NO2 have been shown to increase bronchial reactivity, and in several studies they have been 

shown to enhance the response to aeroallergens when exposures to the gas and the allergen occur within a 

short time frame (Schwela 2000; Jenkins et al. 1999; D’Amato et al. 2002; Brunekreef and Holgate 2002). 

In contrast to the other gaseous pollutants studied, laboratory and epidemiological studies of ozone exposure 

consistently show increases in respiratory symptoms and a variety of measures of asthma exacerbation as 

ozone concentrations increase (Schwela 2000; Peden 2002; Weisel et al. 1995). In addition, studies looking at 

combined or sequential exposures to ozone and allergens have noted an enhanced respiratory response 

compared with either exposure alone (D’Amato et al. 2002; Jenkins et al.1999). These studies may indicate 

that ozone exposures could create conditions within the respiratory system that might lower the threshold of 

effect for allergens or irritants. 

Aldehydes (e.g., acetaldehyde, acrolein, formaldehyde, propionaldehyde) represent a class of HAPs that could 

negatively affect asthmatics. Formaldehyde has been reported to induce asthma in some individuals exposed 

in occupational settings (e.g., Feinman 1988). Acute, small decreases in respiratory function (FEV1) have 

been reported after formaldehyde exposure in occupational settings (e.g., Alexandersson et al. 1982). Studies 

of asthmatics suggest that they may not be sensitive to formaldehyde at concentrations below those seen in 

occupational settings (e.g., Harving et al. 1986). Other aldehydes have not been as well studied, and potential 

interactions of aldehydes with other ambient contaminants have not been explored. Leikauf et al. (1995) point 

out that recent epidemiological studies suggest that pollutant interactions may potentiate respiratory 

responses. 

19
ASTHMA AND AIR POLLUTANTS IN NEW YORK CITY 

A limited number of studies have investigated the association of air contaminants with acute asthma attacks in 

New York City. Thurston et al. (1992) studied the relationship between hospital admissions for asthma (and 

all respiratory admissions) and ambient acidic particulate matter and ozone concentrations during the summer 

in three regions in New York State. The researchers did not have air contaminant data for New York City, and 

they used ambient air data from the less urbanized suburbs. They found that higher concentrations of ozone, 

aerosol strong acidity (hydrogen ion) and sulfate were associated with increases in asthma admissions in the 

summer in Buffalo and New York City. However, they found the associations were weaker in Albany and the 

less urbanized New York City suburbs. This may be due, in part, to some chemical or physical difference in 

the composition or mix of air contaminants in the more densely populated areas. 

In an older study conducted in New York, Greenburg et al. (1964) did not find an association between 

emergency clinic visits for asthma and sulfur dioxide, carbon monoxide, or coefficient of haze during 

September and October. Goldstein and Dulberg (1981) also found no significant relationship between hospital 

emergency department visits and sulfur dioxide or coefficient of haze measurements during the late summer 

and early fall. Jamason et al. (1997) found an association between asthma hospital admissions and air 

pollution in New York City during the spring and summer seasons but not during fall and winter. A recent 

study of asthma hospitalizations and ambient sulfur dioxide monitoring data in New York City found a 

consistent positive association between sulfur dioxide air levels and risk of asthma hospitalization in children, 

after adjusting for race, age and season (Lin et al. 2004). 

Considering the limited information available regarding ambient air pollutants and asthma in New York City, 

and considering the state of the science on specific air pollutants and asthma in general, a better 

characterization of those air contaminants that may be associated with acute asthma attacks is needed. This 

study selected a set of chemical and biological factors that have been shown or are thought to have the 

potential to aggravate asthma and are likely to be present in urban air. The types of factors assessed were 

gases and vapors (SO2, O3, NO2, NO, NOx and a limited range of volatile organic compounds), particulates, 

particulate components (including sulfate, metals, carbon and hydrogen ion) and bioaerosols (pollen and 

fungal spores). These chemical and biological agents were measured in ambient air in two New York City 

locations, the South Bronx and Manhattan, over a period of nearly two years. Average air levels of the 

measured pollutants and patterns of change in pollutant levels over time were compared between the two 

sites. 

20
Section 4

METHODS

SAMPLING LOCATIONS 

Two neighborhood sampling sites —I.S. 155 in the South Bronx and Mabel Dean Bacon School in Lower 

Manhattan—were selected for the study (Figures 1 and 2). These two monitoring sites were long-standing, 

EPA-approved air quality monitoring sites operated by New York State Department of Environmental 

Conservation (NYSDEC) for certain criteria air pollutants. They were located approximately 6.7 miles apart. 

The adequacy of these monitoring sites was evaluated by Lippman (1998). He concluded that both monitoring 

sites were “very well situated as regional urban sites.” He further stated, “In fact, as urban monitoring sites 

go, these two currently have fewer complicating factors related to topography, major thoroughfares, major 

construction or demolition sites, etc., than most sites.” 

Partway through the project, a change in sampling location in the Bronx was necessary due to a construction 

project at IS 155. Working with EPA, NYSDEC and the New York City School Construction Authority, a 

new site was established at M.S. 52 (681 Kelly Street) in the South Bronx. As with the other monitoring sites, 

this site was evaluated and approved by EPA as an acceptable site. M.S. 52 is approximately 0.5 miles 

northeast of I.S. 155. Sampling occurred at I.S. 155 from January 1999 through August 1999, and at M.S. 52 

from September 1999 through November 2000 (Figure 1). The Manhattan site at the Mabel Dean Bacon 

School (also known as Manhattan Comprehensive Night and Day High School), remained the same during the 

study period (January 1999 through November 2000). Sampling height in Manhattan was approximately 

seven stories and approximately four stories in the Bronx. 

Sampling equipment was set up both on rooftops and indoors. Some outdoor equipment had climatecontrolled housing units (described below). A glass manifold attached to the building’s exterior provided 

ambient air to equipment operating indoors. At the Bronx locations, the manifold’s inlet was situated at 

approximately the same sampling height as, and located within 15 feet of, the rooftop instruments. 

Manhattan’s manifold was located approximately 10 feet higher than, and 30 feet from, the outdoor sampling 

equipment. 

QUALITATIVE ENVIRONMENTAL AND ECONOMIC INFORMATION 

Information on the population size, housing stock, traffic characteristics and number and types of businesses 

was collected for the two communities. Information sources included the U.S. Census Bureau, NYC TransitMTA, New York State and New York City Departments of Motor Vehicles, NYSDEC permits and the EPA 

Toxic Release Inventory. The information was used only as part of a qualitative description and comparison 

21
of the two communities with respect to broad classes of potential air pollution sources. The study design did 

not include a detailed analysis of pollutant point sources, mobile sources or source apportionment. 

ANALYTICAL METHODS 

A brief description of the analytical methods for ambient air analytes follows. Details, including quality 

assurance and quality control protocol references, are provided in Appendix 1. 

PM10 and PM2.5 

Two TEOM ® Series 1400a Ambient Particulate Monitors (Rupprecht and Patashnick Co., Inc., Albany, NY) 

were deployed at each location, one measuring PM10 and the other measuring PM2.5. Hourly average data 

were logged by the instruments and downloaded weekly by project staff. A supplemental system was attached 

to the PM2.5 units at each location for the measurement of metals (described below). 

FRM PM10 and PM2.5 

Twenty-four-hour particulate samples were collected for gravimetric measure of PM10 and PM2.5 using 

Federal Reference Method (FRM) protocols. PM2.5 was collected using R&P 2025 sequential samplers with 

WINS impactors. PM10 samples were collected using Wedding high-volume samplers with 8- by 10-inch 

quartz filters. 

Particle Number 

A TSI Model 2022A condensation counter was used to measure the total number of airborne particles 

between 0.007 and 2.5 micrometers in diameter. The TSI instrument detects and counts particles using an 

optical detector. A computer linked to the counter logged data and data were downloaded once per week. 

Hourly and daily (24-hour) average values were calculated. 

Organic and Elemental Carbon 

A Series 5400 Ambient Carbon Particulate Monitor (Rupprecht & Patashnick Co., Inc., Albany, NY) was 

used for the measurement of organic and elemental carbon. The instrument uses a direct thermal-CO2 

measurement to provide an indirect measure of the amount of carbon in the collected PM2.5 sample. The 

fraction volatilized or oxidized to CO2 between 250°C and 340°C was considered the volatile organic 

fraction, and the amount oxidized to CO2 between 340°C and 750°C was considered the elemental carbon 

fraction. Samples analyzed by the instrument represented three-hour averages. The instrument reports data to 

0.1 μg/m3. The results were logged by the instrument and downloaded weekly. 

Metals 

An R&P AccuSystem was installed on the TEOM collecting PM2.5 and used to collect particulate on filters for 

24 hours each day (midnight to midnight) for metal analysis. The samples (filters) were gathered each week 

22
and brought to the laboratory. The samples were analyzed at the Wadsworth Laboratory using inductively 

conductive plasma/mass spectrometry (ICP/MS). The following metals thought to have a possible relationship 

with asthma exacerbation or respiratory irritancy, based on existing information, were included in the analysis 

(detection limits): Cr (5 nanograms/m3

), Fe (22 ng/m3), Pb (12 ng/m3), Mn (3 ng/m3), Ni (4 ng/m3) and Zn (77 

ng/m3

). 

Acid Aerosols, Ammonia, and Acid Gases 

Daily samples were collected on filters and denuders to characterize five reactive gases (NH3, HCl, HNO2, 

HNO3, and SO2), particulate (PM2.5) sulfate and pH (U.S. EPA Method IO-4.2). The five gases were not part 

of the original study plan and were analyzed for only approximately one year of the study. Samples represent 

24-hour averages. Samples were collected on a URG-2000-01J Weekly Air Particulate Sampler (URG, 

Chapel Hill, NC). The gases were collected on denuders and the aerosols on a Zeflour filter supported by a 

PTFE-coated stainless steel screen. Ion chromatography was used to measure concentrations. The detection 

limits for the various analytes were NH3 (0.19 micrograms/m3

), HCl (0.10 μg/m3) , HNO2(0.16 μg/m3) , 

HNO3 (0.10 μg/m3

), and SO2 (0.18 μg/m3). 

Particulate nitrate was originally included in the analyte list but was later dropped due to concerns about the 

accuracy of the reported concentrations. During the study, research was published that called into question 

particulate nitrate concentrations collected on Teflon filters, especially at higher temperatures (U.S. EPA 

1999). The particulate nitrate samples were collected on Teflon filters, and temperature measurements made 

inside the sampler enclosure for about one month showed a high reading of 108°F. Because the sampler was 

serviced only once per week, samples collected after servicing were potentially subject to more hightemperature periods than those collected just prior to servicing, likely increasing the potential for particulate 

nitrate volatilization. This information, along with inconsistencies found in the concentrations of some colocated samples, led to the removal of particulate nitrate from the analyte list. 

Bioaerosols 

Bioaerosol samples for enumeration of pollen and fungal spores were collected into the wind on adhesivecoated tape that was mounted on a clock-driven drum inside a low-volume sampler (Burkard seven-day 

recording spore trap). The clock allowed a seven-day, non-integrated, time-ordered sample to be collected. 

After removal of the drum, the tape was sectioned into seven equal parts, mounted on microscope slides, 

stained and viewed microscopically. Bioaerosol results were reported as daily (24-hour) averages. 

Pollen and fungal spores were categorized into several large (in some cases overlapping) groups for statistical 

analyses, based on taxonomic and/or morphologic similarities. For pollen, the categories were tree, grass, 

ragweed, and total pollen. For fungal spores, the categories were basidiospores, ascospores, dark color 

23
mitospores, non-dark mitospores, small spores (< 10 micrometers in the largest dimension), large spores (> 10 

micrometers in the largest dimension) and total spores (see Appendix 1, Table A1). 

Acetone and Aldehydes 

An automated sampler was used in the collection of daily (24-hour average) samples for acetone and aldehyde 

analysis, according to U.S. EPA Method TO-11. The analytes measured were acetone, acetaldehyde, acrolein, 

benzaldehyde, butyraldehyde, crotonaldehyde, 2,5-dimethylbenzaldehyde, formaldehyde, hexaldehyde, 

isovaleraldehyde, propionaldehyde, m-tolualdehyde, o-tolualdehyde, p-tolualdehyde and valeraldehyde. 

Detection limit for each was 1 μg/m3

. During the study, questions were raised about the validity of the 

acrolein data from this method due to poor recovery and possible dimerization of this analyte on sample 

cartridges. 

Criteria Pollutant Gases and Other Nitrogen Oxides 

SO2, NO, NO2, NOx and O3 were measured by EPA-approved methods (40 CFR Chapter I Part 50 and DEC 

web page, www.dec.state.ny.us/website/dar/reports/99annrpt/99ar_mtd.html). Data for all of these analytes 

were analyzed on an hourly and daily (24-hour) average basis. O3 was also analyzed on an eight-hour moving 

average basis, following the National Ambient Air Quality Standards calculation algorithm (40 CFR Chapter I 

Part 50; see below). 

Meteorological Data 

Temperature, relative humidity, wind speed and wind direction were logged from a roof-mounted 

meteorological station at each site. The unit logged the data from wind monitor Model 05305 and relative 

humidity and temperature probe Model 41372LC (R.M. Young Co., Traverse City MI). 

DATA QUALITY 

Data cleaning beyond the quality assurance and quality control protocols developed for the instruments was 

conducted to ensure that data importation had been correctly implemented. Any observations associated with 

known instrument malfunctions (e.g., power loss or incorrect airflow) were marked as rejected. To identify 

more subtle potential reporting problems with the pollutants, time series plots of some pollutants were 

examined for unusual observations or abnormal fluctuations. Differences between the two sites were 

calculated, and the data for time periods with large differences were further investigated. Screening criteria 

were developed to identify observations that required review. Observations were further examined for data 

quality if any of the following obtained: 

• a value was considered a statistical outlier (i.e., more than two standard deviations from the 

mean); 

• the data did not follow previous patterns often identified from inspection of graphs of the data; 

or 

24
• an unusual trend in the data was found (e.g., a low value every third day). 

Possible causes of such observations were explored. If instrument error (e.g., airflow or temperature outside 

specifications) was not determined to be the cause, the data were assumed to be accurate; otherwise, the result 

was marked as suspicious. Suspicious and rejected observations were removed from the dataset and not 

included in any descriptive statistics or analyses. 

STATISTICAL ANALYSIS 

Summary Statistics 

Summary statistics were compiled for each pollutant at each site. For sulfate, aldehydes, and metals, 

observations below the limit of detection were estimated at half the detection limit. No non-detects occurred 

for the other chemical analytes. Bioaerosol samples where non-detects occurred were entered as zeros. The 

summaries included mean, standard deviation, sample percentiles, sample size (N), number of suspicious 

results (SR), number of rejected results (RJ), number of observations below detection limit (LT), number of 

observations present but less than detection limit (PL) and number of missing observations. Detailed data 

summaries for all analytes are provided in Appendix 2. 

Analytical chemistry results were reported as one-hour, three-hour or 24-hour time-weighted averages 

(TWA), depending on the sampling methodology for each analyte. Therefore, summary statistics for each 

analyte could be calculated for up to three averaging times (24-hour, seasonal and the entire study period). In 

the presentation of results, daily mean refers to 24-hour averages from either 24-hour time-weighted-average 

sample results or from averaging hourly or three-hour TWA observations across 24-hour intervals. Seasonal 

mean is used to refer to observations averaged over three-month intervals (described below) and overall mean 

is used to refer to observations averaged over the entire study period. Seasonal and overall summary statistics 

were calculated from daily means. 

Exploratory analyses were conducted for all analyte data sets to evaluate whether the distribution shape for 

each was approximately normal. Distributions were characterized informally using histograms and normal 

probability plots. The Anderson-Darling goodness-of-fit test for the normal distribution was used to formally 

test distributions for their deviation from normality (D’Augostino and Stevens 1986). Since statistical 

comparisons were of meaningfully paired observations, the differences between paired observations were the 

data subjected to statistical analysis. Although differences between paired observations tended to deviate from 

normality, based on formal goodness-of-fit tests, their distributions deviated less from normality than did the 

original observations and were generally symmetric and bell-shaped, similar to a normal distribution. 

Therefore, it was felt that, since the t- and F-tests are robust to deviations from the normality assumption (e.g., 

Neter et al. 1990), these tests could be applied to non-transformed differences. 

25
Site Comparisons 

Analyte air concentrations in the two communities were compared using daily (24-hour) mean analyte levels 

and daily maximum analyte levels at the Manhattan and Bronx sampling sites. Hourly observations or threehour average observations (elemental and organic carbon variables) were averaged together on a 24-hour 

basis to obtain daily averages. A daily maximum value was identified from hourly and three-hour average 

observations if at least 75% of that day’s hourly (three-hour) observations were available. Daily maximum 

comparisons were not made for those variables collected only on a daily (24-hour) average basis. 

For ozone, moving eight-hour averages were calculated from the original hourly observations by applying the 

EPA National Ambient Air Quality Standards (NAAQS) guidelines for evaluating moving eight-hour 

averages against the eight-hour ambient air standard. Eight-hour moving averages for ozone were assigned to 

the first hour of the eight-hour window. If six or more hourly observations were valid for an eight-hour 

segment, the non-missing observations were averaged; if less than six but at least one hourly observation was 

valid for the eight-hour segment, missing values were estimated at half the detection limit (0.002/2 = 0.001) 

and all eight values were averaged; if none of the eight observations were valid, the eight-hour average is 

missing. Twenty-four-hour average ozone concentrations were calculated from the original hourly average 

data. Daily maximum hourly ozone observations were based on original hourly average data and on eighthour moving-average data. 

There was substantial seasonal variation for many analytes in the study, so seasonally stratified statistical 

analyses as well as unstratified analyses were performed. The data were divided into eight seasonal 

categories: 

• Winter 1999: January 1–March 20 

• Spring 1999: March 21–June 20 

• Summer 1999: June 21–September 22 

• Fall 1999: September 23–December 21 

• Winter 2000: December 22, 1999–March 19 

• Spring 2000: March 20–June 19 

• Summer 2000: June 20–September 21 

• Fall 2000: September 22–November 22 

The analytes were measured at the same times for the same duration at each site. For this reason, the pollutant 

data for the two sites were considered paired data. Daily differences were calculated and analyzed for each 

analyte. The mean differences were computed seasonally and for the entire study period. The analyses of the 

daily differences used paired t-tests with an autocorrelation adjustment. The variance of the differences is 

adjusted to account for the non-independence of autocorrelated time-series data. The adjustment given by 

Gilbert (1987), taking the sample variance as an estimator of the population variance, is as follows: 

26
2 sd 

2 ⎡ 2 n−1 ⎤

sˆd = ⎢1+ ∑(n −l)ρ l ⎥ n ⎣ n l=1 ⎦

where sd

2 is the original sample variance of the differences, sˆd 2

 is the adjusted sample variance of the 

differences, n is the sample size, l is the lag distance between two observations in the series and ρl is the 

autocorrelation coefficient for lag l. The adjustment was applied assuming that the only contribution to the 

sum comes from statistically significant autocorrelation coefficients. That is, if the first m autocorrelations are 

significant (and therefore n – m autocorrelations are not significant), then for l > m, ρl = 0. 

Daily differences were calculated for daily average and for daily maximum hour for those contaminants with 

hourly data and daily three-hour maximum for carbon measures. For pollutant data collected hourly, daily 

maximums were generated for days considered 75% complete. Daily differences of the maximums were 

analyzed seasonally in the same way as daily mean differences, using a paired t-tests adjusted for 

autocorrelation. Detailed results of all statistical comparisons, analyzed for the entire study period and by 

season, are presented in Appendices 3 and 4, respectively. 

The relocation of the Bronx monitoring site during the study brought into question whether the two Bronx 

sites were sufficiently similar in their representation of local air quality that their results could be combined. 

This question led to an additional analysis to evaluate the comparability of the two locations in terms of air 

quality. A direct comparison of Bronx Site A with Site B was not possible because data could not be collected 

at the two places simultaneously. Instead, data from each site were compared with data for the corresponding 

period at the Manhattan location using an adjusted paired t-test to try to control, at least to some extent, for 

temporal differences. By comparing the relationship between analyte levels at the Manhattan site and the two 

Bronx sites, a qualitative assessment could be made as to whether the two Bronx sites provided comparable 

results regarding the differences between pollutant levels in the Bronx and in Manhattan. However, if 

different trends were observed in results relating Manhattan and the two Bronx sites, it would not be possible 

to determine whether they were due to differences in the Bronx monitoring sites or to differences in the 

relationship between pollutants in the Bronx and pollutants in Manhattan over time. 

Correlation Between Monitoring Sites. 

The correlation between the two sampling sites for each analyte was estimated using the Pearson correlation 

coefficient. This statistic measures the degree to which the same variable at the two sites followed a similar 

pattern of fluctuations through time, whether or not the mean levels were different. 

27
Correlation Among Pollutant Variables at a Monitoring Site. 

Non-metric multidimensional scaling (MDS) analysis and complete-linkage hierarchical clustering (HC) were 

employed in an exploratory analysis to characterize associations among chemical analytes (Mardia et al. 

1979). Data from each sampling site were analyzed separately. In both analyses, correlation matrices for 21 

pollutant variables were summarized graphically to explore patterns of associations among variables. In both 

analyses, the pH variable was recoded as hydrogen ion concentration (by taking the anti-log of –pH), so that 

increasing hydrogen-ion values would indicate increasing concentration, similar to the other pollutant 

variables. Details of the implementation of these techniques are provided in Appendix 1. 

Pearson correlation estimates were also obtained for all pairwise analyte combinations within each sampling 

location as part of the initial exploratory analysis of the data. The detailed raw Pearson correlation matrices 

are presented in Appendix 5. 

Temporal Analyses 

To characterize the temporal patterns of the pollutants, data from the entire study for each pollutant were 

averaged on a day-of-week basis and, when applicable, on an hour-of-day basis. For pollutant concentrations 

collected more than once per day, daily averages were used for day-of-week trends. Daily averages were 

calculated for days in which at least 75% of the available data were collected. All available hourly data were 

included in the hour-of-day averages. Day-of-week and hour-of-day averages ± two standard errors were 

plotted and temporal patterns were inferred from these graphs. 

28
Section 5

RESULTS

QUALITATIVE ENVIRONMENTAL AND ECONOMIC INFORMATION 

The 2000 U.S. Census data show that about 100,000 more people live in the Manhattan study area than in the 

Bronx study area (Table 1A). The Manhattan study area also has about 120,000 more occupied housing units, 

so the average occupancy per housing unit in the Bronx study area is almost twice that in the Manhattan area 

(Table 1B). Renters in both communities occupy most of the housing units. 

The number of motor vehicles registered in 2001 with the New York State Department of Motor Vehicles is 

about equal between New York County (i.e., Manhattan) and Bronx County (Table 1C). An evaluation of axle 

counts on selected roads showed that the number of vehicles is about equal. Both communities are adjacent to 

major highways— FDR Drive for the Manhattan study area and the Major Deegan and Bruckner Boulevard 

for the Bronx community. Although the total amount of vehicle traffic on these highways is about the same, 

FDR Drive does not allow commercial traffic while the Major Deegan and Bruckner Boulevard are major 

commercial traffic routes. The number of MTA buses in the two communities is similar but the routes in 

Manhattan are traveled with greater frequency. 

Manhattan has one hazardous waste site on NYSDEC’s New York State Registry of Inactive Hazardous 

Waste Sites; the Bronx has three. No NYSDEC-permitted waste-handling facilities were located in Manhattan 

in 2000, but there were 15 in the Bronx. 

Both communities have industrial sources of urban air contaminants. The Toxic Release Inventory (TRI) 

program tracks some industrial chemical emissions to the environment. TRI facilities are manufacturing and 

other industrial operations required to report chemical emissions or transfers to air, water, soil and waste 

treatment facilities under Section 313 of the federal Emergency Planning and Community Right to Know Act. 

In 2000, two TRI facilities submitted reports in Manhattan compared with eight in the Bronx. However, the 

total quantity of air emissions reported under the TRI program in 2000 was greater in Manhattan than in the 

Bronx (approximately 30,000 pounds versus 15,500 pounds). All but about 6.5 pounds of the Manhattan TRI 

releases (i.e., 99.98%) were sulfuric acid. The remainder included less than 0.5 pound of dioxin and 

dioxinlike compounds and 6 pounds of polycyclic aromatic hydrocarbons. All the Manhattan releases were 

reported from a single facility (Consolidated Edison, East River Facility). The other Manhattan facility 

submitting a TRI report had no air releases in 2000. Almost 90% of the Bronx releases were trichloroethylene 

from a single facility (G.A.L. Manufacturing Corp.), with the remainder consisting of small amounts of 

toluene, xylene, zinc, glycol ethers and 1,2,4-trimethylbenzene. Three of the eight Bronx facilities submitting 

TRI reports had no air releases in 2000. 

29
A review of the 2000 U.S. Census Bureau data suggested that the Manhattan study area had more businesses 

and that the types of businesses differed between the two communities (Table 2). Information was not 

available to assess whether businesses enumerated in these data sources actually represent activities that 

would be associated with air emissions. For example, many businesses recorded as agricultural or 

manufacturing in the Census data may only represent corporate offices, without significant agricultural or 

manufacturing activity. 

Based on anecdotal NYSDOH staff observations, the Manhattan study area generally had taller buildings and 

more pedestrian and vehicular traffic than the Bronx study area. Prior to the study, Manhattan community 

members expressed concern about an electricity-generating plant as an air pollution point source. Members of 

the Bronx community expressed concerns about impacts on air quality from a large sewage treatment facility 

(Hunts Point), rotting produce at the Hunts Point markets, and a sewage sludge pelletization plant (New York 

Organic Fertilizer Co., NYOFCO). 

DATA COLLECTION AND LABORATORY ANALYSIS QUALITY CONTROL 

Data collection was generally successful, despite some intermittent equipment malfunctions. The equipment 

to count particle number was the most problematic and a large amount of data from both sites was dropped 

because it did not meet data quality standards. Intermittent equipment breakdowns also caused loss of 

nitrogen dioxide, nitric oxide and nitrogen oxides data from the Bronx (and to a lesser degree, Manhattan) for 

the winter of 1999. Details of data completeness are provided in Appendix 2. 

Some additional analytes (hydrochloric acid, nitrous acid, nitric acid and ammonia) were evaluated for a more 

limited time period (approximately a year, from June 23, 1999, to July 11, 2000). The period for ammonia 

samples was more limited, from June 23 to August 31, 1999 and from December 29, 1999, to May 16, 2000. 

These analytes were not included in the original study design and were added to the analysis as limited 

resources allowed. 

The laboratory analysis for acetone and aldehydes could have measured up to 14 compounds. However, most 

were generally below the detection limit of 1 microgram/meter

3

 and were therefore not included in the 

analyses comparing the ambient air levels in the Bronx and Manhattan. Acetone was detected in 99.2% of the 

samples in the Bronx and 97.2% in Manhattan. Acetaldehyde was detected in 98.8% of the samples in the 

Bronx and 98.2% in Manhattan. Formaldehyde was detected in 99.2% of the samples in the Bronx and 99.1% 

in Manhattan. The remaining aldehydes were detected in less than 35% of the samples. 

Four of the metals analyzed were only detected in a limited set of the samples. Chromium, manganese, lead 

and zinc were detected in less than 11% of the samples and were not analyzed further. Iron and nickel were 

30
detected in enough samples to allow comparison between the two sites. Iron was detected in 77.7% of the 

samples in the Bronx and 79.7% in Manhattan. Nickel was detected in 66.8% of the samples in the Bronx and 

74.1% in Manhattan. 

COMPARISON OF AMBIENT AIR QUALITY 

The comparisons detailed in this section consider the two Bronx sites as one; the appropriateness of this 

treatment is discussed in the next section. 

The daily average air concentration data are graphically summarized in Figures 3 to 35. The top panel in each 

figure shows the values for the Bronx and Manhattan monitoring sites and the lower panel shows the 

difference in concentration between the two sites (Manhattan – Bronx). A negative number in the lower panel 

indicates that the average concentration was greater in the Bronx on that day. Generally the data for the two 

sites look quite similar in most figures. Daily concentrations at both sites varied substantially, with ranges 

often varying by 10-fold or more. Some analytes (e.g., pollen, fungal spores, ozone, sulfur dioxide) showed 

marked seasonal variation. Many contaminants had no consistent trend showing higher levels in one sampling 

area or the other. For other compounds, however, the trend is consistently higher in one location. For instance, 

ozone was fairly consistently higher in the Bronx (Figure 31), whereas nitrogen dioxide was higher in 

Manhattan (Figure 33). 

The daily average results for particulate matter are presented in Table 3. Two size fractions (less than 2.5 

micrometers and less than 10 micrometers) were measured, each by two different methods. In all cases the 

overall mean concentration was higher at the Manhattan monitoring site than at the Bronx monitoring site. 

The differences in concentrations ranged from 3% to 11%. The differences in mean values using the two 

methods are due to several factors, including differences in how the mass is measured, missing data for one 

method but not the other and slight variations possibly due to differences in location of the air intakes. In most 

seasons, the concentration of PM2.5 was significantly greater in Manhattan. Similarly, significant differences 

in seasonal results were also generally observed for PM10 measured with the automated mass measurement 

method. However, this was not generally the case for measurements made using the FRM. The FRM PM10 

collected data only once every sixth day and so had less statistical power to discern a given difference 

between sites than the automated mass measurement method. 

The number of particles less than 2.5 micrometers was not significantly different over the study period at the 

two sites (Table 4). Because of technical problems, data were not collected for winter, spring and summer 

1999, limiting particle count data to only five seasons. 

Results for pH, sulfate and organic and elemental carbon constituents of PM2.5 are summarized in Table 5, 

and PM2.5 metals results are summarized in Table 6. Overall, the pH was slightly lower (more acidic) at the 

31
Manhattan monitoring site than at the Bronx monitoring site. In only three of the eight study seasons was the 

difference statistically significant, and the difference was never statistically significant in the winter. Overall, 

sulfate was higher at the Manhattan monitoring site; the differences were statistically different in four of the 

eight study seasons. Overall, organic carbon was not consistently different between the two sites. Average 

elemental carbon concentrations were slightly greater in Manhattan, although the differences were statistically 

different in only three of the eight study seasons. Overall, iron concentrations did not vary between the two 

sites. Although in some seasons there were significant differences, they were not consistently in one direction. 

Overall, nickel was higher at the Manhattan monitoring site. The differences were statistically different in 

four of the eight study seasons. 

Pollen counts tended to be higher at the Bronx monitoring sites than at the Manhattan monitoring site (Table 

7). For ragweed pollen and grass pollen, these differences were statistically significant over the entire study 

period, although seasonal differences were generally not significant. For tree pollen and total pollen, some 

seasonal mean comparisons were statistically significant, but the overall comparisons were not significant. 

Seasonal variability in tree pollen levels during the entire study period was large compared with variability 

between the study areas, such that overall study means were not significantly different. The variance estimate 

for the overall tree pollen comparison was also increased compared with the individual seasonal comparisons 

because more lag periods were included in the autocorrelation adjustment for the overall comparison. Total 

pollen levels were dominated by tree pollen levels, and thus site differences over the study period in total 

pollen were also not significant, despite significant seasonal differences. All statistically significant seasonal 

differences in tree pollen and total pollen were greater in the Bronx. 

Overall, mean fungal spore levels were not different between the two sites (Table 8). The only statistically 

significant difference between sites for the entire study period was for large spores. On a seasonal basis, most 

mean differences between the sites were not statistically significant, and one site did not have consistently 

higher mean levels among those seasonal comparisons where significant differences were observed. 

Over the entire study period, no statistically significant differences between the mean concentrations of 

acetone, formaldehyde or acetaldehyde were found at the two sites (Table 9). Slightly more seasonal 

differences were in the direction of higher levels in Manhattan than in the Bronx. 

Mean hydrochloric acid, nitrous acid, nitric acid, denuder sulfur dioxide and ammonia levels all were 

significantly higher over the entire study period at the Manhattan monitoring site compared with the Bronx 

site (Table 10). Most statistically significant seasonal mean differences were also in the direction of higher 

mean levels in Manhattan for these analytes, with the exception of one seasonal difference for hydrochloric 

acid. 

32
The daily average results for ozone, sulfur dioxide, nitric oxide, nitrogen dioxide and total nitrogen oxides are 

summarized in Table 11. Mean ozone concentrations were higher at the Bronx monitoring site. Mean 

concentrations for the other pollutant gases over the entire study period were all significantly higher in 

Manhattan. The same pattern of statistically significant differences between the two sites for these five 

analytes was seen on a seasonal basis. All significant seasonal ozone differences were in the direction of 

higher mean levels in the Bronx, while higher mean levels for the sulfur and nitrogen oxide variables were 

observed in Manhattan. 

COMPARISON OF THE TWO DIFFERENT MONITORING SITES IN THE BRONX TO 

MANHATTAN 

The results of the comparison of daily average concentrations for each Bronx site to the Manhattan site are 

summarized in Tables 12 to 18. For 24 of 34 analytes, the monitoring site with the higher mean was the same 

in 1999 and in 2000. In 10 cases, the direction of the mean difference reversed between 1999 and 2000, 

although only four of the 10 comparisons that reversed direction involved significant differences in at least 

one of the comparisons. Although some variation in the relative levels of air contaminants between Bronx and 

Manhattan was observed between the two Bronx sites, strong evidence indicating that it would be 

inappropriate to combine data from the two Bronx sites was not found. 

Correlations were also estimated for corresponding observations from each Bronx sampling location and the 

Manhattan location and were qualitatively compared (Table 22). Most correlations were of similar magnitude. 

A few pollutants (acetone, nitrogen oxides, PM2.5 FRM) had notably different correlation coefficients when 

comparing the two years. In all cases, a small number of unusually high or low observations at one site, not 

paralleled by similar extreme observations at the other site, substantially lowered the overall correlation 

coefficient. This correlational analysis also failed to provide strong evidence that it would be inappropriate to 

combine data from the two Bronx sites. 

DAILY MAXIMUM VALUES 

For PM2.5 and PM10 (by automated samplers), particle number, organic and elemental carbon, ozone, sulfur 

dioxide and nitrogen oxides, multiple measurements were made throughout the day, making possible a daily 

maximum observation (one-hour or three-hour, depending on analyte). Over the entire study period, most of 

the mean differences in daily maximum value were in the same direction as for the daily averages; however, 

fewer of the differences were statistically significant (Table 19). The only contaminant where the direction of 

the difference changed between the overall means and the daily maximum means was organic carbon. Mean 

daily maximum organic carbon was slightly higher in Manhattan for the entire study period, in contrast to the 

overall mean comparison for this analyte, which was slightly higher in the Bronx. Neither difference was 

statistically significant. 

33
CORRELATION BETWEEN THE BRONX AND MANHATTAN MONITORING SITES 

Although daily average concentrations may be statistically significantly different between Manhattan and the 

Bronx, the daily averages at the sites may tend to fluctuate in a similar pattern over time. This can be seen 

graphically in Figures 3–35. To evaluate this, correlations between the two monitoring sites were estimated 

for each analyte. Most between-site correlations were relatively strong, with correlation estimates falling 

below 0.6 for only five analytes (non-dark mitospores, formaldehyde, acetone, iron and nickel; Table 20). 

CORRELATION BETWEEN DIFFERENT AIR CONTAMINANTS WITHIN MONITORING SITES 

Daily Mean versus Daily Maximum 

For analytes where a daily maximum value could be obtained, correlations of daily maximum and daily mean 

values were estimated within each sampling location (Table 21). Not surprisingly, the correlations between 

daily maximums and daily average were fairly high. Pearson r values were ≥ 0.85 for all analytes except 

particle number. This is consistent with the strong influence of large values on the arithmetic daily mean. 

Multidimensional Scaling 

Special tests, referred to as diagnostics, were included in the MDS analyses to ensure that models of the 

associations among variables were not based on non-degenerate solutions (e.g., Wilkinson 1999; see 

Appendix 1). None of the MDS solutions produced diagnostics that would indicate a degenerate model 

solution. Similar patterns of associations among variables were observed from MDS results for the two 

sampling locations. 

Striking patterns of variables—with points very close together in the MDS plots and clearly separated from 

other distinct clusters—were generally not observed (Figures 36–40), although in most configurations the two 

measures of sulfur dioxide (SO2 and denuder-SO2) did appear closely associated and relatively isolated from 

all other variables. This indicates a strong positive correlation between these two variables and a tendency to 

weak or negative correlations of those two with most other variables. During the two seasonal periods 

spanning the fall and winter months (especially January–March), ozone (O3) tended to be widely separated 

from all other variables in the MDS plots (Figures 37, 40), indicating a strong negative correlation with most 

other pollutant variables during those periods. The large negative association between O3 and most other 

variables during these periods obscured any other patterns of association among the remaining variables. 

In the combined-seasons plots (Figure 36) and to a lesser degree in the spring and summer plots (Figures 38, 

39), two loose aggregations of variables appeared to fall on opposites sides of the first MDS dimension, 

although the resolution of these two aggregations as distinct clusters was not strong. One aggregation usually 

included all nitrogen oxide variables (NO, NO2, NOX), SO2, denuder-SO2, elemental carbon and nitrous acid 

(HNO2). The other aggregation generally included the two particulate-matter variables (PM25, PM10), sulfate 

34
(SO4

--), formaldehyde, acetaldehyde, acetone and organic carbon. Iron (Fe), nickel (Ni), hydrochloric (HCl) 

and nitric (HNO3) acids, hydrogen ion (H+), ammonia (NH3) and ozone (O3) tended to be less consistently 

associated with either of the two main aggregations. As noted above, these aggregations tended to be 

obscured during the fall and winter seasons, when O3 tended to be strongly negatively associated with all 

other variables. 

Hierarchical Clustering 

The HC results (Figures 41–45) were generally consistent with the MDS results. In most cases, the pairs of 

variables that clustered together with the lowest distances (highest correlations) were NOX/NO, PM25/PM10, 

SO2/denuder-SO2 and acetaldehyde/formaldehyde. SO2, elemental carbon, metals and NO2 or NOX were 

frequently clustered together at relatively low distances. SO4

-- (either alone or clustered with hydrogen ion 

concentration), aldehydes, acetone, organic carbon, inorganic acids and PM variables were closely associated 

in several trees. Especially in the fall and winter seasons, O3 tended to diverge from the other clusters 

containing all other variables at large distances—indicating strong negative associations—at both sampling 

locations. 

TEMPORAL ANALYSES 

Measurements for most variables did not vary noticeably by day of the week (Figures 46, 48, 50–52, 54–65, 

67, 69). PM10, acetone, elemental carbon, NO, NO2, NOx and particulate Fe were the only variables showing a 

noticeable day-of-week trend, with somewhat lower daily means on the weekends (especially Sundays) 

increasing during the week. Day-of-week variation was similar between the two monitoring areas. 

Time-of-day trends were more pronounced than day-of-week trends for many of the analytes where hourly or 

three-hour-average observations were available (Figures 47, 49, 53, 66, 68). SO2, NO, NO2, NOx, PM2.5, PM10 

(automated mass monitors) and, to a lesser degree, elemental carbon all showed daily peaks in the morning 

hours (approximately 6–8 A.m.). O3 showed a tendency toward daily minimum values at the same morning 

hours and a daily afternoon (2 P.M.) peak. These trends were consistent between the two monitoring areas. 

The time-of-day trends in hourly average particle number differed between Bronx and Manhattan, with 

somewhat elevated hourly averages in the Bronx from midnight to 4 A.m., whereas Manhattan particle counts 

during those hours were somewhat lower than during the rest of the day (Figure 49). Little time-of-day 

variation was observed in three-hour-average organic carbon levels at either site (Figure 53). 

Seasonally, the concentrations of nitric acid, hydrochloric acid, ammonia, and sulfate were higher during 

summer than winter. The summer-winter ratios for nitric acid, hydrochloric acid and sulfate in Manhattan 

were 3.9,3.1 and 1.9, respectively. The concentrations of nitrous acid and sulfur dioxide were higher during 

winter than summer; the summer-winter ratios in Manhattan were 0.48 and 0.44, respectively. Gaseous 

nitrous acid was the predominant form compared with nitric acid except in summer. The annual mean 

35
concentrations of PM2.5 were 15.2 and 15.5 μg/m3

 in the Bronx and in Manhattan, respectively. The monthly 

mean concentrations in Manhattan ranged from 13.2 to 21.7 μg/m3

; they were highest in June and July and 

lowest in March and April. The monthly mean fraction of PM2.5 as sulfate ranged from 0.17 to 0.31; the 

highest fraction values were observed during June–September. 

An analysis of the air monitoring data for sulfate, SO2, HCl, ammonia, nitric acid, nitrous acid and PM2.5 has 

been published (Bari et al. 2003b). 

WIND TRAJECTORY ANALYSES 

Although detailed source attribution was not a focus of the study design, the data were amenable to evaluating 

the relative contributions of long-distance pollutant transport versus local pollutant emissions by backtrajectory analysis. This was a secondary analysis that did not apply directly to the main objective of this 

report—that is, the air quality comparison between the two communities. 

Air trajectories were used to study the effect of upwind emissions on the observed concentrations in New 

York City. Episodes of high concentrations of chemical species were observed in both the Bronx and 

Manhattan throughout the year, although they were more prominent during summer. The highest 

concentrations were invariably associated with the air flow from southwest to west of New York City. 

Three-hour HYSPLIT4 air trajectories were used to apportion the daily measured concentrations of seven 

analytes—PM2.5, sulfate, SO2, HCl, nitric acid, nitrous acid and ammonia—and as a function of direction. 

Comparison of the air trajectories with the measured concentrations suggested that a fraction of sulfate, SO2, 

HCl, nitric acid, and PM2.5 is transported from west and southwest of New York. Nitrous acid and ammonia 

concentrations appeared unrelated to the air trajectories. Air trajectories were used to evaluate contributions 

from the regional emission sources to the observed levels of SO2, sulfate, PM2.5, nitric acid and HCl. On an 

annual basis, ~40% of sulfate was transported from the Midwest and ~60% from nearby (~150 km) sources. 

On the other hand, only ~14% of SO2, 30% of PM2.5, 27% of HCl and 24% of nitric acid were transported, 

with the remainder coming from the nearby sources. During the third quarter of 1999, about 26% and 40% of 

HCl and nitric acid, respectively, were transported from the distant sources. The modeled contributions from 

regional sources and transport were generally similar in Manhattan and the Bronx. The complete details are 

reported in Bari et al. (2003a). 

36
Section 6

DISCUSSION

Most analytes measured in the study either did not show a statistically significant difference between levels at 

the Manhattan site and the Bronx site (most mold categories, iron, aldehydes, elemental carbon and organic 

carbon) or had mean levels in Manhattan that were significantly higher than those in the Bronx (PM, 

particulate acidity and sulfate, nickel, nitric, nitrous and hydrochloric acids, ammonia, sulfur dioxide and 

nitrogen oxides). Mean levels for certain kinds of pollen and ozone were significantly higher in the Bronx 

than in Manhattan. 

The study’s large sample sizes resulted in statistical power to detect small mean differences as statistically 

significant, such that even some modest mean differences in analyte concentrations between the two sites 

were considered “significant.” The largest relative differences were for ozone and pollen, where Bronx means 

exceeded Manhattan means by 30% to 70%, depending on the analyte, and for ammonia, nitric oxide and 

nickel levels, where Manhattan means exceeded Bronx means by about 30% to 60%. For all other analytes, 

the relative mean differences over the entire study period (percentage increase of the higher over the lower 

mean) were about 25% or less between the two sites, and in most cases were less than 10%. Nearly half 

(10/21) of the statistically significant mean differences between the two sites over the entire study period were 

relative differences of about 10% or less. 

Even though this study was not designed to address whether or not these two communities were meeting 

federal National Ambient Air Quality Standards (NAAQS), comparisons can be made to provide an 

assessment on the overall air quality. For SO2, NO2, and PM10, the values were well below the corresponding 

NAAQS levels in both communities, as were the 24-hour average PM2.5 concentrations. However, the overall 

average PM2.5 measured concentrations—14.5 μg/m3

 at the Bronx site and 16.6 μg/m3 at the Manhattan site— 

were both near the annual NAAQS level of 15 μg/m3

. For ozone, the eight-hour moving average exceeded the 

NAAQS level of 0.08 ppm five times in Bronx and three times in Manhattan over the course of the study, or 

less than 1% of the study days. These results cannot be used to evaluate compliance with federal air quality 

standards, since non-attainment of the NAAQS involves consideration of a longer measurement period over a 

larger region not restricted to these two communities. The US EPA currently considers the entire New York 

City metropolitan region (including the five New York City boroughs, plus adjacent counties in Long Island, 

the lower Hudson Valley, Connecticut and New Jersey) to be in non-attainment status for the ozone and fine 

particle NAAQS. 

One possible source of the modest differences in air pollutant levels seen between the two sampling areas 

could be differences in the overall level of commercial and industrial activity. As an initial screening, we 

37
attempted to assess this by counting the numbers of certain business types in the Bronx and Manhattan as 

reported in U.S. Census data. However, we were not able to determine whether Census business listings 

represented activities that actually contributed to air pollutant emissions in either borough. These listings are 

based on mailing addresses and in many cases could represent corporate offices or post office boxes. Also, the 

number of industrial facilities in an area does not necessarily imply a particular level of environmental 

chemical emissions. For example, air emissions from a single facility in Manhattan during 2000, as reported 

under the federal Toxic Release Inventory program, exceeded the total air emissions reported from five TRI 

facilities in the Bronx. 

Other possible contributors to pollutant level differences in the two communities include traffic differences 

and the influence of more distant industrial emissions. Overall vehicle use does not appear to differ greatly in 

Manhattan and the Bronx, based on limited information regarding vehicle registrations and axle counts. 

However, local traffic patterns, such as commercial traffic and bus routes, could have a significant effect on 

pollutant differences between the two monitors. The industrial development in northern New Jersey, west of 

New York City, is substantial, and emissions related to those facilities could make different contributions to 

local air pollutant levels. However, data were not collected that allow those hypotheses to be evaluated. 

Two analyte categories, ozone and pollen, tended toward higher average levels in the Bronx. Ozone is formed 

when nitrogen oxides (related to fuel combustion, especially vehicle emissions) and volatile organic 

compounds (VOCs) react together in the presence of sunlight. Mean nitrogen oxide levels were higher in 

Manhattan than in the Bronx during the study period. Although nitrogen oxides contribute to daytime ozone 

production, they can reduce ozone levels at night because of scavenging of oxygen atoms from ozone by 

nitric oxide to form nitrogen dioxide. This phenomenon, NO titration, could have the effect of decreasing 

daily average ozone levels in Manhattan below those in the Bronx. If this were true, overnight ozone and 

nitric oxide levels would be expected to decrease more and nitrogen dioxide levels would be expected to be 

proportionately higher overnight in Manhattan compared with the Bronx. However, hour-of-day trends for 

ozone, nitric oxide and nitrogen dioxide do not differ between the two study locations. Steady or increasing 

ozone levels in urban areas on weekends, despite reduced nitrogen oxide emissions on weekends, have been 

hypothesized to occur because of increased VOC-to-NOx ratios in a VOC-limited regime (e.g., Fujita et al. 

2003). This is another mechanism that could be contributing to higher average ozone levels in the Bronx, 

where the reduced NOx levels could be causing increased VOC-NOx ratios. 

The higher pollen levels in the Bronx may be a reflection of that community’s larger areas of green space. 

They could also be an indication of sampling height differences or relative proximity of the samplers to 

wooded areas, giving wooded areas a stronger influence on the Bronx monitoring site than Central Park had 

on the Manhattan monitoring site. 

38
An important limitation of the air monitoring data is that only a single monitoring site was operated in each 

borough. The monitors were sited to be representative of general area air quality. However, because of this, 

they may not reflect the effects of particular emissions sources, such as the Hunts Point wastewater treatment 

plant, on air quality in localized areas of the Bronx or Manhattan. The degree to which this may have affected 

the monitoring results is uncertain. However, the hour-of-day analysis (discussed below) suggests that local, 

ground-level traffic emissions did appear to be reflected in the monitoring results. The Bronx monitoring sites 

were located closer to ground level than the Manhattan site, and so could have been somewhat more 

influenced by local, street-level emissions sources. 

The study was also limited to some degree by the choice of pollutants analyzed. Although the number of 

analytes was larger than in many previous studies, particular emissions sources may not have been reflected in 

the sampling results. For example, a very limited range of VOC pollutants was analyzed that may not have 

been particularly reflective of most industrial air emissions or odorous emissions from solid-waste or 

wastewater treatment facilities. 

The extensive longitudinal database allows characterization of temporal trends in air contaminants on hourly, 

daily and seasonal scales. Several analytes that were measured on an hourly basis showed marked variation by 

hour of day, including both PM size fractions, elemental carbon, sulfur dioxide and nitrogen oxides. All of 

these contaminants had peak hourly concentrations occurring at 7–9 A.M. and in some cases also had a less 

distinct peak around 7–8 P.M. One-hour time-weighted ozone averages showed a reversed trend, with a midafternoon hourly peak and low hourly means during the morning, consistent with many previous studies (U.S. 

EPA 1996). Hourly temporal patterns were generally similar at the two sampling sites and could be related to 

traffic-volume patterns, changes in vertical mixing of air due to daytime heating and/or changes during the 

day in demand for heat and electricity and corresponding changes in emissions from power sources. 

A tendency toward lower day-of-week means on Sundays, increasing through the week to Thursdays, was 

found for PM10, elemental carbon and NOX. Ozone showed a slight trend toward higher weekend levels, as 

has been found previously in some U.S. locations (e.g., Fujita et al. 2003; Pun et al. 2003; Heuss et al. 2003). 

Except for ozone, these results might be hypothesized to reflect a buildup of traffic and perhaps industrial 

emissions during the work week. In some locations, higher weekend peak levels of ozone have been 

correlated to reduced NOx levels, relative to VOC levels, in areas where tropospheric ozone production is 

VOC-limited (e.g., Pun et al. 2003; Huess et al., 2003). However, the significance of these apparent trends for 

all analytes is unclear because the variance estimates for the day-of-week means are large, at least in part due 

to substantial seasonal variation. PM2.5, organic carbon and SO2 did not show a tendency toward day-of-week 

differences. 

39
Many of the analytes (pollen, mold spores, ozone, SO2, nitrogen oxide, HNO2, HNO3, HCl, NH3, pH and 

SO4

2-) showed marked seasonal variations. For instance, the concentrations of HNO3, HCl, NH3 and SO42­

were higher during summer than in winter. The summer-winter ratios for HNO3, HCl, and SO4

2- in Manhattan 

were 3.9, 3.1 and 1.9, respectively. The concentrations of HNO2, and SO2 were higher during winter than in 

summer, with summer-winter ratios in Manhattan 0.48 and 0.44, respectively. Seasonal trends were similar at 

the Bronx sampling site. 

Another indication of the similarity in pollutant trends in the two monitoring areas is the consistency observed 

in descriptive multivariate statistical results between the Bronx and Manhattan. In both areas, ozone levels 

tended to be strongly negatively associated with most other analytes, especially during the fall and winter. 

Similar patterns of positive associations among analytes were also seen in the two monitoring areas, with PM 

usually associated with sulfate and organic carbon; SO2, nitrogen oxides and elemental carbon formed another 

cluster of associated analytes. 

Limited studies of urban air toxics have been conducted in some of the boroughs of New York City. The most 

extensive data have been collected on Staten Island. Ambient volatile organic compounds, benzo(a)pyrene, 

formaldehyde and metals were monitored in a joint EPA–New York–New Jersey study in 1987–1989. Nickel, 

manganese and iron were routinely detected in total suspended particulate samples and tended to range in 

concentration by approximately threefold between seasons and monitoring sites. Nickel was detected in more 

than 70% of the PM10 samples analyzed. The NYSDEC also conducted aldehyde sampling at a station in the 

North Bronx in summer 1995. Sampling duration of three hours in that study resulted in detectable levels of 

acetaldehyde, formaldehyde, and propionaldehyde in more than 99% of the samples collected. 

Since 1992, NYSDEC has analyzed every-sixth-day total suspended particulates samples for five trace 

metals—arsenic, cadmium, mercury, nickel, and vanadium—from one monitoring station each in Brooklyn 

and Manhattan, two stations in Staten Island and three stations upstate. The trace metals data show regional 

differences in concentrations, with nickel being elevated in Manhattan compared with the other sites. 

Similarly, in the current study, the overall mean PM2.5 nickel level from Manhattan was higher than the 

overall Bronx mean. This consistency could suggest that particulate nickel is largely associated with the fine 

fraction. Or, nickel levels could be higher in all particulate fractions from Manhattan, compared with the other 

boroughs. 

In conjunction with the implementation planning process for its mid-town Manhattan street-level PM10 site, 

which was classified moderate non-attainment in January 1994, NYSDEC has studied particulate 

characterization and PM10 emissions inventory data for this portion of Manhattan (NYSDEC 1995). 

Microscopic and chemical characterization of PM10 at the street-level Manhattan monitor indicated 53% from 

diesel emissions, 13% ammonium nitrate, and 9% ammonium sulfates, with smaller contributions from road 

40
dust, automobile emissions, sea salt, iron sources and residual fuel oil. The emissions inventory for the entire 

county indicates that 70% of PM10 emissions comes from area combustion sources, 19% from road dust, 6% 

from all vehicle emissions and smaller amounts from other sources. These results may indicate that streetlevel exposure to PM is more heavily influenced by vehicle emissions than emissions inventories would 

indicate. Although the current study results were obtained from rooftop monitors (four to seven stories above 

street level), the strong morning rush-hour peak in many of the analytes with hourly data suggests that vehicle 

emissions may be an important PM contributor up to at least 20 meters above ground level. 

In the current study, we measured several PM2.5 components (elemental and organic carbon, sulfate, hydrogen 

ion and metals) and found that, on average, about 60% of FRM PM2.5 measured at our sampling locations was 

accounted for by the simultaneously measured components. PM2.5 in our data set accounted for about 65% to 

85% of PM10, depending on the measurement method used and the sampling location. 

Data from previous studies suggest there are discernible differences in ambient concentrations of some air 

contaminants in urban areas, including New York City, for sites separated by as little as three to five miles. 

For example, Suh et al. (1995) collected 24-hour samples of sulfate, hydrogen ion and ammonia 

simultaneously at seven locations in Philadelphia and an upwind monitor during the summers of 1992 and 

1993. Based on their assessment of spatial variation, they concluded that a single monitoring station was 

adequate for sulfate (consistent with the assumption that long-range transport is the dominant source), but 

multiple sites were necessary to determine local outdoor hydrogen ion concentrations, although variation in 

hydrogen ion over time was highly correlated across sites. 

Goldstein and Landovitz (1977) found that for certain air contaminants (e.g., sulfur dioxide) there is a poor 

correlation among air monitoring sites within a metropolitan area. This suggests that the validity of exposure 

measures for certain contaminants can depend strongly on monitoring them within the community being 

studied. However, no study has determined precise limits on the area of validity of measurements for specific 

contaminants, and it is probably not possible to do so on a general basis. In the current study, and contrasting 

with Goldstein and Landovitz’s results, between-site correlations were high for many of the analytes, 

including PM2.5, PM10, sulfate, SO2, nitrogen oxides, ozone, inorganic acids, ammonia and most bioaerosols. 

Between-site correlations within a large metropolitan area may depend on several factors, such as local 

topography, canyon effects, monitor height, prevailing meteorology, seasonality and local source strength. 

Even when contaminant data are generally well correlated between monitoring sites, the strength of any 

correlation may not persist when monitored concentrations are at the high end of the range. The higher 

concentrations are those that are most likely to have health effects. For instance, an exploratory analysis of 

contemporaneous concentrations at pairs of NYSDEC ambient air monitoring sites in New York City, 

conducted prior to this study, found that temporal variation was strongly correlated among sites for ozone, 

41
sulfur dioxide, nitrogen oxides and PM10. However, the temporal correlations between high contaminant 

levels (defined as upper quartile observations) were weaker, especially for ozone (unpublished data). Greater 

spatial heterogeneity in temporal patterns of high excursions in contaminant concentrations might contribute 

to spatial differences in acute asthma exacerbations, even if temporal patterns for all contaminant levels 

appear very similar across locations. 

42
Section 7

CONCLUSIONS AND RECOMMENDATIONS

Ambient air quality measured with rooftop monitors at two locations in New York City found that, for most 

analytes, either the two sites did not differ or mean air levels were higher at the Manhattan location than at the 

Bronx location. Analyte measurements from both locations were subject to large temporal variations on 

hourly, daily and often seasonal time scales. When statistically different average pollutant levels were 

detected between the two locations, they differed by less than two fold. Average ozone and pollen levels 

tended to be higher in the Bronx, with mean differences of about 30% to 70% between the two sites. These 

results, representing approximately two years of hourly or daily observations on nearly three dozen analytes 

from two locations in New York City, provide a more detailed characterization of ambient air pollutants, 

especially particulate matter constituents, than has been previously reported for a large urban area. We 

recommend that future studies investigating ambient air pollutant exposures on an urban neighborhood scale 

collect additional data to better characterize spatial variability of ambient pollutants in urban areas, 

particularly for non-criteria pollutants. 

43
44
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48
AUTHORS AND ACKNOWLEDGEMENTS

Daniel Luttinger (NYSDOH) was the study’s principal investigator, and Lloyd Wilson, Edward Fitzgerald, 

Laiquat Husain, Kenneth Aldous (NYSDOH), Phillip Galvin (NYSDEC), Larry Syzdek (private aerobiology 

consultant) and Patrick Kinney (Columbia University) were co-investigators. Additional study-design and 

statistical-analysis consultation was provided by John Hawley (NYSDOH), Frank Buckman (NYSDEC), 

Tracey Holloway (University of Michigan) and Ken Demerjian (NYSDEC). The study report was authored by 

Daniel Luttinger, Lloyd Wilson, Gregg Recer and Kim Mazor (NYSDOH). Field sampling staff included Dan 

Lince, Pat Palmer, Mike Rivara, Lloyd Wilson, Stephanie Selmer, James Kamara, Ellen Fitzsimmons, Dan 

Sharron and Stan House (NYSDOH), and Ed Marion, Mike Christophersen, Bob Murway, Bob Elburn and 

Frank Buckman (NYSDEC). Laboratory staff included Abdul Bari, Vincent Ferraro and Amarjit Narang 

(NYSDOH). Data analysis support staff included Jeff Hughes, Ying Wang, Valerie Haley, James Kamara, 

Kim Mazor, Karen Nolan, Dan Luttinger, Gregg Recer, Lloyd Wilson and Laiquat Husain (NYSDOH), and 

Phil Galvin and Frank Buckman (NYSDEC). 

The study was funded with partial support from the Agency for Toxic Substances and Disease Registry 

(ATSDR) as part of Cooperative Agreement V50/ATV200002-11 and from the New York State Energy and 

Research Development Authority (NYSERDA). 

49
50
TABLES

51
Table 1A. Population Characteristics of the Bronx and Manhattan Study Areas 

Population Bronx Study Area Manhattan Study Area 

2000 254,167 355,655 

1990 234,478 343,006 

Percent Change + 8% + 4% 

Source: U.S. Bureau of Census 

Table 1B. Housing Characteristics of the Bronx and Manhattan Study Areas 

Housing, 2000 Bronx Study Area Manhattan Study Area 

Units 85,807 215,016 

Occupied 79,584 201,656 

Unoccupied 6223 13,360 

Owner Occupied 6750 42,532 

Renter Occupied 72,834 159,124 

Source: U.S. Bureau of Census. 

Table 1C. Motor Vehicle Registrations in the Bronx and Manhattan Study Areas 

Vehicle Registrations, 2001 Bronx County Manhattan County 

Total 269,577 257,531 

Standard Series 249,785 229,715 

Commercial 9340 13,655 

Taxi 5394 6722 

Bus 624 230 

Other 4434 7209 

Source: New York State Department of Motor Vehicles. 

52
Table 2. U.S. Census Bureau Zip Code Pattern 

Zip Code Business Patterns (1997 Sector Summary) 

Bronx Study Area Manhattan Study Area 

Total 3121 47,340 

Agricultural Services, Forestry, Fishing 1 62 

Construction 159 897 

Mining 1 16 

Manufacturing 219 4090 

Transportation and Public Utilities 185 1388 

Wholesale Trade 402 8789 

Retail Trade 876 7545 

Finance, Insurance, and Real Estate 443 5909 

Services 785 18,108 

Unclassified 50 536 

53
Table 3. Summary of Daily Average Concentrations for Particulate Matter 

Analyte Overall Meana 

Manhattan Bronx 

# of Seasons 

Statistically 

Greater M/Bb 

Range of Seasonal 

Differencesc 

PM2.5 (TEOM)* 16.2 15.3 

PM2.5 (FRM)* 16.6 14.5 

PM10 (TEOM) 23.1 22.3 

PM10 (FRM)* † 22.0 20.9 

*Significantly different over entire study period (P ≤ 0.05) a Units = micrograms per cubic meter (μg/m3

) b # Manhattan > Bronx / # Manhattan < Bronx c Difference = Manhattan – Bronx 

† PM10 (FRM) was collected every six days 

6 / 0 

5 / 0 

5 / 1 

1 / 0 

0.3 – 1.2 

0.8 – 2.0 

-6.3 – 3.4 

-0.2 – 3.0 

Table 4. Summary of Particle Counts in PM2.5 Fraction 

Analyte Overall Meana # of Seasons Range of Seasonal 

Manhattan Bronx 

Statistically 

Greater M/Bb 

Differencesc 

Particle Counts 1463152 1560780 1 / 1‡ -450936 - 221627 a Units = count b # Manhattan > Bronx / # Manhattan < Bronx c Difference = Manhattan – Bronx 

‡

 Total particle counts were not available for winter 1999, spring 1999, or summer 1999 

Table 5. Summary of Daily Averages for pH, Sulfate, and Carbon in Particulate Matter (PM2.5) 

Analyte Overall Meana 

Manhattan Bronx 

# of Seasons 

Statistically 

Greater M/Bb 

Range of Seasonal 

Differencesc 

pH* 

Sulfate* 

Organic Carbon 

Elemental Carbon 

5.04 

4.0 

3.09 

1.32 

5.15 

3.6 

3.17 

1.19 

0 / 3 

4 / 0 

2 / 3 

3 / 0 

-0.12 – -0.02 

0.0 – 0.3 

-0.57 – 0.94 

-0.06 – 0.25 

* Significantly different over entire study period (P ≤ 0.05) a Units = micrograms per cubic meter (μg/m3

) (except pH) b # Manhattan > Bronx / # Manhattan < Bronx c Difference = Manhattan – Bronx 

54
Table 6. Summary of Daily Averages for Selected Metals in Particulate Matter (PM2.5) 

Analyte Overall Meana 

Manhattan Bronx 

# of Seasons 

Statistically 

Greater M/Bb 

Range of Seasonal 

Differencesc 

Iron 72 75 

Nickel* 15 12 

* Significantly different over entire study period (P ≤ 0.05) a Units = nanograms per cubic meter (ng/m3

) b # Manhattan > Bronx / # Manhattan < Bronx c Difference = Manhattan – Bronx 

2 / 1 

4 / 0 

-21 – 14 

-1 – 11 

Table 7. Summary of Daily Averages for Pollen 

Analyte Overall Meana 

Manhattan Bronx 

# of Seasons 

Statistically 

Greater M/Bb 

Range of Seasonal 

Differencesc 

Total Pollen 13.17 22.32 

Tree 12.18 20.53 

Ragweed* 0.37 0.45 

Grasses* 0.38 0.59 

* Significantly different over entire study period (P ≤ 0.05) a Units = #/m3 

b # Manhattan > Bronx / # Manhattan < Bronx c Difference = Manhattan – Bronx 

0 / 4 

0 / 2 

0 / 1 

0 / 0 

-41.72 – 0.28 

-41.50 – 0.27 

-0.74 – 0.01 

-0.36 – 0.01 

Table 8. Summary of Daily Averages for Mold 

Analyte Overall Meana 

Manhattan Bronx 

# of Seasons 

Statistically 

Greater M/Bb 

Range of Seasonal 

Differencesc 

Total Mold 490.3 447.8 0 / 2 -208.8 – 112.3 

Basidiospores 186.0 184.0 1 / 2 -101.5 – 99.6 

Ascospores 39.0 43.2 0 / 1 -17.1 – 3.4 

Mitospores 259.9 212.5 1 / 2 -89.4 – 117.3 

Dark Mitospores 254.1 208.1 1 / 2 -83.7 – 108.0 

Non-Dark Mitospores 5.8 4.4 0 / 1 5.7 – 9.3 

Small Spores (< 10 μm) 470.4 427.8 0 / 2 -204.8 – 111.6 

Large Spores (> 10 μm)* 12.5 9.9 0 / 0 -17.7 – 0.4 

* Significantly different over entire study period (P ≤ 0.05) a Units = #/m3 

b # Manhattan > Bronx / # Manhattan < Bronx c Difference = Manhattan – Bronx 

55
Table 9. Summary of Daily Averages for Acetone and Selected Aldehydes 

Analyte Overall Meana # of Seasons Range of Seasonal 

Manhattan Bronx 

Statistically 

Greater M/Bb 

Differencesc 

Acetaldehyde 2.7 2.5 4 / 1 -1.0 – 0.5 

Acetone 6.9 6.8 3 / 2 -2.6 – 1.2 

Formaldehyde 4.4 4.2 3 / 1 -1.9 – 0.5 

* Significantly different over entire study period (P ≤ 0.05) a Units = micrograms per cubic meter (μg/m3

) b # Manhattan > Bronx / # Manhattan < Bronx c Difference = Manhattan – Bronx 

Table 10. Summary of Daily Averages for Acidic and Basic Gases 

Analyte Overall Meana # of Seasons Range of Seasonal 

Manhattan Bronx 

Statistically 

Greater M/Bb 

Differencesc 

Hydrochloric Acid (HCl)* 

Nitrous Acid (HONO)* 

Nitric Acid (HNO3)* 

Ammonia (NH3)* 

Sulfur Dioxide (SO2)*

0.51 

3.21 

1.74 

3.536 

 26.4 

0.47 

3.06 

1.11 

2.273 

25.8 

0 / 1† 

3 / 0† 

2 / 0† 

2 / 0‡ 

2 / 0† 

-0.16 – 0.09 

0.14 – 0.50 

0.02 – 0.50 

0.551 – 1.485 

1.0 – 3.8 

(~0.01 ppm) (~0.01 ppm) 

* Significantly different over entire study period (P ≤ 0.05) a Units = micrograms per cubic meter (μg/m3

) b # Manhattan > Bronx / # seasons Manhattan < Bronx c Difference = Manhattan – Bronx 

† Gases were collected from 6/23/99 to 7/11/00 

‡ Ammonia results were not available from 9/1/99 to 12/28/99 and from 5/17/00 to 7/11/00 

Table 11. Summary of Daily Average Concentrations for U.S. EPA Criteria Pollutant Gases and Other 

Nitrogen Oxides 

Analyte Overall Meana # of Seasons Range of Seasonal 

Manhattan Bronx 

Statistically 

Greater M/Bb 

Differencesc 

Ozone (O3)* 0.012 0.016 0 / 8 -0.011 – -0.002 

Sulfur Dioxide (SO2)* 0.012 0.011 5 / 0 0.000 – 0.006 

Nitrogen Dioxide (NO2)* 

Nitric Oxide (NO)* 

Nitrogen Oxides (NOX)* 

0.036 

0.031 

0.066 

0.031 

0.022 

0.053 

7 / 0† 

7 / 0† 

7 / 0† 

0.003 – 0.013 

0.004 – 0.011 

0.008 – 0.022 

* Significantly different over entire study period (P ≤ 0.05) a Units = parts per million (ppm) b # Manhattan > Bronx / # Manhattan < Bronx c Difference = Manhattan – Bronx 

† Nitrogen oxide results were not available for Bronx for winter 1999 

56
Table 12. Summary of Daily Averages Concentrations for Particulate Matter: Comparison of the Two Bronx 

Monitoring Sites 

Analyte

a Bronx Site A (1999) Bronx Site B (2000) 

Manhattan Mean Manhattan Mean 

Bronxb Differencec Bronxb Differencec 

PM2.5 (TEOM) 15.9 / 15.2 0.7* 15.5 / 14.8 0.7* 

PM2.5 (FRM) 15.2 /14.3 0.8 16.7 / 15.2 1.6* 

PM10 (TEOM) 21.3 / 22.3 -1.0 24.2 / 22.5 1.7* 

PM10 (FRM)

† 23.7 / 22.8 0.9 21.8 / 21.9 -0.1 

* Significantly different over entire study period (P ≤ 0.05) a Units = micrograms per cubic meter (μg/m3

) (except pH) b Means are from paired data c Difference = Manhattan – Bronx 

† PM10 (FRM) was collected every six days 

Table 13. Summary of Daily Averages for pH, Sulfate, and Carbon in Particulate Matter (PM2.5): Comparison 

of the Two Bronx Monitoring Sites 

Analyte

a Bronx Site A (1999) Bronx Site B (2000) 

Manhattan Mean Manhattan Mean 

Bronxb Differencec Bronxb Differencec 

pH 5.20 / 5.26 -0.06 5.05 / 5.13 -0.08* 

Sulfate 3.5 / 3.4 0.1* 3.9 / 3.7 0.2* 

Organic Carbon 2.84 / 2.97 -0.13 3.03 / 3.53 -0.51* 

Elemental Carbon 1.58 / 1.44 0.14 1.26 / 1.12 0.14* 

* Significantly different over entire study period (P ≤ 0.05) a Units = micrograms per cubic meter (μg/m3

) (except pH) b Means are from paired data c Difference = Manhattan – Bronx 

Table 14. Summary of Daily Averages for Selected Metals in Particulate Matter (PM2.5): Comparison of the 

Two Bronx Monitoring Sites 

Analyte

a Bronx Site A (1999) Bronx Site B (2000) 

Manhattan Mean Manhattan Mean 

Bronxb Differencec Bronxb Differencec 

Iron 86 / 86 0 51 / 64 -13 

Nickel 20 / 16 4 18 / 12 6* 

* Significantly different over entire study period (P ≤ 0.05) a Units = nanograms per cubic meter (ng/m3

) b Means are from paired data c Difference = Manhattan – Bronx 

57
Table 15. Summary of Daily Averages for Pollen and Mold: Comparison of the Two Bronx Monitoring Sites 

Analyte

a Bronx Site A (1999) Bronx Site B (2000) 

Manhattan Mean Manhattan Mean 

Bronxb Differencec Bronxb Differencec 

Total Pollen 11.8 / 16.7 -4.9* 32.6 / 52.7 -20.1 

Tree 11.4 / 16.0 -4.6* 32.2 / 52.1 -19.9 

Ragweed 0.0 / 0.0 0.0 0.0 / 0.0 0.0 

Grasses 0.4 / 0.6 -0.2 0.4 / 0.6 -0.2* 

Total Mold -0.5 336.8 / 47.2 307.6 / 308.1 289.6 

Basidiospores 36.2 / 39.3 -3.1 146.1 / 46.0 

100.1

Ascospores 36.8 / 38.7 -1.8 27.7 / 29.2 -1.5

Mitospores 2.5 161.0 / 2.6 230.6 / 228.1 158.5 

Dark Mitospores 228.2 / 225.5 2.7 156.3 / 1.9 

154.5

Non-ark Mitospores 2.5 / 2.6 -0.2 4.7 / 4.0 0.7

Small Spores (< 10 μg) 0.8 325.4 / 47.4 293.2 / 292.3 278.0 

Large Spores (> 10 μg) 9.9 / 12.9 -3.0 6.6 / 6.8 -0.2 

* Significantly different over entire study period (P ≤ 0.05) a Units = #/m3 

b Means are from paired data c Difference = Manhattan – Bronx 

Table 16. Summary of Daily Averages for Acetone and Selected Aldehydes: Comparison of the Two Bronx 

Monitoring Sites 

Analyte

a Bronx Site A (1999) Bronx Site B (2000) 

Manhattan Mean Manhattan Mean 

Bronxb Differencec Bronxb Differencec 

Acetaldehyde 2.4 / 2.2 0.2 2.7 / 3.0 -0.3 

Acetone 7.7 / 8.6 -0.9 6.5 / 6.3 0.2 

Formaldehyde 4.1 / 3.8 0.4* 4.1 / 4.8 -0.8 

* Significantly different over entire study period (P ≤ 0.05) a Units = micrograms per cubic meter (μg/m3

) b Means are from paired data c Difference = Manhattan – Bronx 

58
Table 17. Summary of Daily Averages for Acidic and Basic Gases: Comparison of the Two Bronx 

Monitoring Sites (June 23 to July 14) 

Analyte

a Bronx Site A (1999) Bronx Site B (2000) 

Manhattan 

Bronxb 

Mean 

Differencec 

Manhattan 

Bronxb 

Mean 

Differencec 

Hydrochloric Acid (HCl)

 † 

Nitrous Acid (HONO)

 † 

Nitric Acid (HNO3)

 † 

Ammonia (NH3)

 † ‡ 

Sulfur Dioxide (SO2) † 

1.30 / 1.21 

1.33 / 1.06 

3.91 / 3.53 

5.299 / 

4.748 

20.19 / 

15.47 

0.09 

0.27 

0.38* 

0.551 

4.7* 

0.83 / 1.02 

1.38 /1.15 

3.32 / 3.25 

NA 

17.81 / 

15.77 

-0.18* 

0.23 

0.07 

NA 

2.0* 

* Significantly different over entire study period (P ≤ 0.05) a Units = micrograms per cubic meter (μg/m3

) b Means are from paired data c Difference = Manhattan – Bronx

† Gases were collected from 6/23/99 to 7/11/00 

‡ Ammonia results were not available from 9/1/99 to 12/28/99 and from 5/17/00 to 7/11/00 

Table 18. Summary of Daily Averages Concentrations for U.S. EPA Criteria Pollutant Gases and Other 

Nitrogen Oxides: Comparison of the Two Bronx Monitoring Sites 

Analyte

a Bronx Site A (1999) 

Manhattan 

Bronxb 

Mean 

Differencec 

Bronx Site B (2000) 

Manhattan 

Bronxb 

Mean 

Differencec 

Ozone (O3) 0.016 / 0.022 -0.006* 0.012 / 

0.017 

-0.005* 

Sulfur Dioxide (SO2) 

Nitrogen Dioxide (NO2)

 † 

Nitric Oxide (NO) † 

Nitrogen Oxides (NOX)

 † 

0.014 / 0.010 

0.037 / 0.027 

0.017 / 0.009 

0.054 / 0.037 

0.004* 

0.010* 

0.008* 

0.017* 

0.013 / 

0.012 

0.038 / 

0.033 

0.030 / 

0.024 

0.067 / 

0.057 

0.001* 

0.005* 

0.007* 

0.010* 

* Significantly different over entire study period (P ≤ 0.05) a Units = parts per million (ppm) b Means are from paired data c Difference = Manhattan – Bronx

† Nitrogen oxides were not available for Bronx for winter 1999 

59
Table 19. Summary of Comparison of Daily Maximum Concentrations 

Analyte Overall Mean # of Seasons Range of Seasonal 

Statistically Differencesb 

Manhattan Bronx Greater M/Ba 

PM2.5 (TEOM) (μg/m3) 

PM10 (TEOM) (μg/m3) 

Total Particles (#)* 

Organic Carbon (μg/m3) 

Elemental Carbon (μg/m3) 

27.5 

38.4 

2294848 

3.71 

2.04 

27.3 

37.3 

2696751 

3.66 

1.94 

2/1 

2/2 

0/2‡ 

2/2 

1/1 

-1.47 – 2.25 

-10.72 – 6.32 

-937376 – -44048 

-0.378 – 0.944 

-0.254 – 0.354 

Ozone (O3) – 1 hour (ppm)* 0.028 0.033 0/8 0.016 – 0.005 

Ozone (O3) – 8 hour (ppm)* 0.021 0.027 0/8 0.012 – 0.004 

Sulfur Dioxide (SO2) (ppm) 

Nitrogen Dioxide (NO2) (ppm)* 

Nitric Oxide (NO) (ppm) 

Nitrogen Oxides (NOX) (ppm) 

0.024 

0.050 

0.083 

0.127 

0.023 

0.049 

0.075 

0.119 

2/0 

1/0† 

1/0† 

1/0† 

-0.002 – 0.004 

0.000 – 0.014 

-0.004 – 0.021 

0.004 – 0.032 

Elemental and organic carbon are based on 3-hour concentrations; the rest are based on 1-hour concentrations. 

* Significantly different over entire study period (P ≤ 0.05) a # Manhattan > Bronx / # Manhattan < Bronx b Difference = Manhattan – Bronx 

† Nitrogen oxide results were not available for Bronx for winter 1999 

‡

 Total particle counts were not available for winter 1999, spring 1999, or summer 1999 

60
Table 20. Correlations (Pearson r) between Bronx and Manhattan Monitoring Sites for the Same Air 

Contaminants at the Two Sites 

Particles Gases 

PM2.5 (TEOM) 0.97 Acetaldehyde* 0.81 

PM2.5 (FRM) 0.90 Acetone* 0.23 

PM10 (TEOM) 0.92 Formaldehyde* 0.80 

PM10 (FRM) 0.96 Ozone (O3) 0.92 

Particle Count 0.22 Nitrogen Oxides (NOX) 0.87 

pH 0.69 Nitric Oxide (NO) 0.88 

Sulfate 0.96 Nitrogen Dioxide (NO2) 0.77 

Organic Carbon 0.62 Sulfur Dioxide (SO2) 0.90 

Elemental Carbon 0.77 Hydrochloric Acid (HCl) 0.84 

Iron 0.37 Nitrous Acid (HONO) 0.84 

Nickel 0.38 Nitric Acid (HNO3) 0.93 

Total Pollen 0.98 Ammonia (NH3) 0.92 

Tree Pollen 0.98 Sulfur Dioxide (SO2) (denuder) 0.90 

Ragweed 0.86 

Grasses 0.75 Meteorological 

Total Mold 0.84 Temperature 1.00 

Basidiospores 0.71 Relative Humidity 0.98 

Ascospores 0.68 

Mitospores 0.87 

Mitospores (Dark) 0.88 

Mitospores (Non-Dark) 0.05 

Small Spores (< 10 um) 0.83 

Large Spores (> 10 um) 0.79 

*Correlations between sites were calculated excluding data from April 20 to April 30, 2000. If these dates are 

included, the correlations between sites for acetaldehyde, acetone, and formaldehyde would be 0.66, 0.21, and 

0.19, respectively. 

Table 21. –Correlations (Pearson r) between Daily Average and Daily Maximum 

Pollutant Bronx Manhattan 

Organic Carbon (ug/m3) 0.91 0.90 

Elemental Carbon (ug/m3) 0.93 0.93 

Ozone – (1 hr max) (ppm) 0.90 0.92 

Ozone – (8 hr max) (ppm) 0.94 0.95 

NOXx (ppm) 0.89 0.89 

NO (ppm) 0.89 0.88 

NO2 (ppm) 0.86 0.85 

SO2 (ppm) 0.88 0.85 

PM2.5 (TEOM) (ug/m3) 0.88 0.90 

PM10 (TEOM) (ug/m3) 0.90 0.81 

Total Particulates (#) 0.68 0.64 

Temperature (deg F) 0.98 0.99 

Relative Humidity (%) 0.89 0.89 

61
Table 22. Correlations (Pearson r) between Bronx and Manhattan Monitoring Sites for the Same Air 

Contaminants at the Two Sites, Stratified by Year, for Comparable Date Ranges between the Two Bronx sites 

(January 1–July 14) 

Pollutant 1999 Pearson r 2000 Pearson r 

pH 0.56 0.77 

Sulfate 0.96 0.98 

Formaldehyde 0.79 0.81 

Acetaldehyde 0.61 0.86 

Acetone 0.029 0.66 

Organic carbon 0.80 0.86 

Elemental carbon 0.74 0.76 

Nitric oxide (NO) 0.55 0.91 

Nitrogen oxides (NOx) 0.41 0.92 

Nitrogen dioxide (NO2) 0.40 0.88 

Ozone (O3) 0.85 0.93 

Sulfur dioxide (SO2) 0.87 0.94 

PM2.5 (TEOM) 0.96 0.96 

PM2.5 (FRM) 0.34 0.99 

PM10 (TEOM) 0.92 0.97 

PM10 (FRM) 0.99 0.95 

Hydrochloric acid (HCl) 0.95 0.79 

Nitrous acid (HONO) 0.92 0.79 

Nitric acid (HNO3) 0.98 0.91 

Sulfur dioxide (SO2) (denuder) 0.72 0.88 

Ammonia (NH3) 0.61 0.92 

Iron (Fe) 0.30 0.31 

Nickel (Ni) 0.29 0.58 

Total pollen 0.89 0.98 

Tree pollen 0.89 0.98 

Ragweed pollen 0.023 0.0075 

Grass pollen 0.81 0.85 

Total mold 0.92 0.73 

Basidiomycetes 0.74 0.49 

Ascomycetes 0.55 0.70 

Mitospores 0.94 0.82 

Dark mitospores 0.94 0.83 

Non-dark mMitospores 0.014 0.19 

Small spores 0.91 0.72 

Large spores 0.76 0.89 

Total particle number - 0.049 

Temperature 1.0 1.0 

Relative humidity 0.99 0.97 

62
FIGURES

63
Figure 

Figure 1. Bronx Sampling Locations 
Figure 2. Manhattan Sampling Location 

































Figure 36. Multidimensional scaling results for (A) Bronx and (B) 

air-monitoring data. Results for all seasons combined. 

Panel A Panel B 

1 1

-2 0 1 2

-2-1

0

DSO2

SO2

NOHNO2

NI

NOX

SOOT

FE

NO2

PM25

O3

HNO3

NH3

HION

HCL

FORM

ACTN

CARB

SULF

ACTL

PM10

-1 -2 -1 0 

-2-1

0

NO

HNO2

SO2

NI

DSO2

NOX NO2

SOOT

FE

ACTN

FO

CAR

SULF

PM10

PM25

ACTL

Key: ACTL = acetaldehyde 

ACTN = acetone 

CARB = organic carbon DSO2 = sulfur dioxid

FE = iron FORM = formaldehyde HCL = hydrochloric acid HION = hyrdogen ion HNO2 = nitrous acid

 HNO3 = nitric acid 

NI = nickel NO = nitrogen oxide 

NOX = nitrogen oxide

 NO2 = nitrogen dioxide 

PM25 = PM2.5 PM10 = PM10 

SOOT = elemental carbon SO2 = sulfur dioxide SULF = sulfate 
Figure 37. Multidimensional scaling results for (A) Bronx and (B) 

air-monitoring data. Results for January – March. 

-4 -3 -2 -1 0 1

-2-1

01

O3

HNO3

NI

HCL

FE

ACTN

NO2

DSO2

FORM

NOX

HION

CARB

SULF

HNO2

SOOT

NO

ACTL

PM25

SO2

NH3

PM10

Panel A Panel B 

2 2

2 -3 -2 -1 0

-2-1

01

O3

NI

FE

HCL

HNO

NO2

A

C

A

N

S

Key: ACTL = acetaldehyde 

ACTN = acetone 

CARB = organic carbon DSO2 = sulfur dioxid

FE = iron FORM = formaldehyde HCL = hydrochloric acid HION = hyrdogen ion HNO2 = nitrous acid

 HNO3 = nitric acid 

NI = nickel NO = nitrogen oxide 

NOX = nitrogen oxide

 NO2 = nitrogen dioxide 

PM25 = PM2.5 PM10 = PM10 

SOOT = elemental carbon SO2 = sulfur dioxide SULF = sulfate 
Figure 38. Multidimensional scaling results for (A) Bronx and (B) 

air-monitoring data. Results for April - June. 

Panel A Panel B 

1 1

-2 -1 0 1 2

-2-1

0

HNO2

NO

SO2

SOOT

NOX

FE

NO2

NI

DSO2

PM25

O3

FORM

HNO3

HION

ACTN

ACTL

HCL

CARB

NH3

SULF

PM10

-2 -1 0 1

-2-1

0

HNO2

NI

SO2

NO

DSO2

NOX

FE

NO2

SOOT

PM25

HC

NH3

CAR

SULF

FORM

ACTN

ACTL

PM10

Key: ACTL = acetaldehyde 

ACTN = acetone 

CARB = organic carbon DSO2 = sulfur dioxid

FE = iron FORM = formaldehyde HCL = hydrochloric acid HION = hyrdogen ion HNO2 = nitrous acid

 HNO3 = nitric acid 

NI = nickel NO = nitrogen oxide 

NOX = nitrogen oxide

 NO2 = nitrogen dioxide 

PM25 = PM2.5 PM10 = PM10 

SOOT = elemental carbon SO2 = sulfur dioxide SULF = sulfate 
Figure 39. Multidimensional scaling results for (A) Bronx and (B) 

air-monitoring data. Results for July – September. 

Panel A Panel B 

2 1

-2 -1 0 1 2

-2-1

01

NH3

NO

HNO2

NOX

NO2

SOOT

NI

FE

SO2

PM10

O3

HCL

CARB

HNO3

FORM

HION

SULF PM25

ACTL

ACTN

DSO2

-3 -2 -1 0 

-2-1

0

HNO2

NO

NOX

NH3

ACTN

ACTL

FORM

NO2

SOOT

SO2

SULF

C

DSO2

PM10

FE

PM2

Key: ACTL = acetaldehyde 

ACTN = acetone 

CARB = organic carbon DSO2 = sulfur dioxid

FE = iron FORM = formaldehyde HCL = hydrochloric acid HION = hyrdogen ion HNO2 = nitrous acid

 HNO3 = nitric acid 

NI = nickel NO = nitrogen oxide 

NOX = nitrogen oxide

 NO2 = nitrogen dioxide 

PM25 = PM2.5 PM10 = PM10 

SOOT = elemental carbon SO2 = sulfur dioxide SULF = sulfate 
Figure 40. Multidimensional scaling results for (A) Bronx and (B) 

air-monitoring data. Results for October - December. 

Panel A Panel B 

1

-3 -2 -1 0 1

-2-1

0

O3

HCL

NH3

HNO3

HION

DSO2

ACTN

SULF

PM10

SO2

CARB

NI

NO

HNO2

SOOT

FE

NOX

ACTL

PM25 FORM

NO2

2 -3 -2 -1 0 

-2-1

01

O3

HCL

HNO3

NH3

HION

ACTN

S

N

PM10

PM25

Key: ACTL = acetaldehyde 

ACTN = acetone 

CARB = organic carbon DSO2 = sulfur dioxid

FE = iron FORM = formaldehyde HCL = hydrochloric acid HION = hyrdogen ion HNO2 = nitrous acid

 HNO3 = nitric acid 

NI = nickel NO = nitrogen oxide 

NOX = nitrogen oxide

 NO2 = nitrogen dioxide 

PM25 = PM2.5 PM10 = PM10 

SOOT = elemental carbon SO2 = sulfur dioxide SULF = sulfate 
Figure 41. Hierarchical clustering results for (A) Bronx and (B) Manhattan 

air-monitoring data. Results for all seasons combined. 

Panel A

 0 distance (=1-r) 2

 O3 ————————————————————————————————┐

 HCL —————┐ ├|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

| 

———————————————————┐

|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

 HNO3 —————├┘——————————————┐|

├———————┐ HION ——————————————┐

├——┐

 | | | |

|

|

| 

SULF ——————┐ | | | ├———————┘ | | PM25 ——┐ | | | |

| | PM10 —— | ├┘———┘ ||

| ACTL ————┐ | | |

| 

├——┘

 FORM ————├┘—————— |┐ ||

| CARB ——————————— | ├

┘—————┘ ||

|

|

| NH3 ————————————┐ |├———┘├———————————————┘

ACTN ————————————┘ ||———————————— NO2 ———————————┐ ||

|

|

|

 SOOT ————————┐ |├—————|

|

|

| 

┐

 NOX —┐

├——————

├

|

┘

——┘

|

|

|

|

 NO —┘ |├———————————————————┐ HNO2 —————————————————┘ |

DSO2 —┐

├—————————————————————————————┐

 ||

|

├——————————————┘

 SO2 —┘ |├—————┘

NI ——————————————┐

├————————————————|┘

FE ——————————————┘

Panel B

 0 distance (=1-r) 2

 O3 ———————————┐

 HNO3 ————┐

├——————

├

|

┘

————————————————┐

|

| HCL ————┘ |

HION ——————┐ ||

|

|

|

|

| 

├——————————————————————|

|

|

|

|

┐

 SULF ——————├┘————— |┐

 PM25 ———┐

├————————|

├

┘

—————————|

|

| 

┐

|

|

|

|

 PM10 ———┘

Actl —————┐ ||

| 

├—————┘

 FORM —————├┘——————————— |┐ ||

|

|

| CARB ——————————┐ |├————┘├——————┘

NH3 ——————————┘ ||————————————— NI —————————————————————┐ || fe ————————————————————— | ├

┘——————————— |┐

├—————————————————┘

AcTN —————————————————┐

├—————————————┐

 || NO2 ——————————┐

├——————|┘ | | | | SOOT ——————————┘ | |

HNO2 ————————————————————┐ |├—┘

 NO ┐

├—————————┐

 ||

|

├——————————┘

 NOX ┘ |├—————————┘

SO2 ——┐

├———————|┘

DSO2 ——┘

Key: ACTL = acetaldehyde 

DSO2 = sulfur dioxide (denuder) 

HCL = hydrochloric acid 

HNO3 = nitric acid 

NOX = nitrogen oxide 

PM10 = PM10 

SULF = sulfate 

ACTN = acetone 

FE = iron 

HION = hyrdogen ion concentration 

NI = nickel 

NO2 = nitrogen dioxide 

SOOT = elemental carbon 

CARB = organic carbon 

FORM = formaldehyde 

HNO2 = nitrous acid 

NO = nitrogen oxide 

PM25 = PM2.5 

SO2 = sulfur dioxide 
Figure 42. Hierarchical clustering results for (A) Bronx and (B) Manhattan 

air-monitoring data. Results for January – March. 

Panel A

 0 distance (=1-r) 2

 O3 ——————————————————————————————————————————————————————————┐

|—————— HCL —————————————————————————————————┐ ||

|

|

|

 ACTN —————————┐

├———————————————————————|

├

┘

—————|

|

| 

┐

 NH3 —————————┘

HION ———————————————————┐

├—————┐

 ||

|

├——————————————————┘

 HNO2 ——————┐

├————————————|┘ || | | |

|

|

|

|

|

|

|

|

 CARB ——————┘

SULF —————┐ ||

|

|

| 

├————|

|

| 

┐

 PM10 ——┐ |├——————————|

|

| 

┐

 PM25 ——├┘——┘ ||

|

|

| SO2 ——┐ |├————————┘

 DSOW ——├┘—————————————┘|

├——————┐

|

|

| HNO3 ———————————————————————┐

├——————|┘ | | | | | | | | | | | | | | | | | |

 ACTL ———├┐——┐ ||

|

|

|

|

|

|

| 

FORM ———┘ |├————┐ NO ┐

├——┐

| | | |

|

|

| 

NOX ┘ | |

NO2 ———├┘——┘

├———————————┘

| | | | | | SOOT ———————————┘ | |

FE ————————————┐ |├—┘

├————————————————————————┘

NI ————————————┘

Panel B

 0 distance (=1-r) 2

 O3 ———————————————————————————————————————————————————————┐

|————————— HCL —————————————————————┐

├—┐

 ||

|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

|

 NO2 ————————————┐

├——┐

 | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | 

ACTLE ———————┐ | | ├—┐ | | PM10 ————┐ | | | |

PM5 ————├┘——┘ | | | | | |

NOX ┐ |├——┘

├————————┘

NO ┘

SOOT ——————┐

 CARB ———┐

├——|

├

┘

————|

|

| 

┐

 FORM ———┘

|

|

|

|

|

|

|

|

| 

├——|

|

| 

┐

|

|

|

| ├———┘HNO2 ———————————┘

SULF ——————┐

├———————————|

├

┘

——┘ ||

| |

|

|

|

|

|

 HION ——————┘ |├——————————————————————┐ NH3 ——————————————┐

├————————|┘ || ACTN ——————————————┘ |

FE ————————————————┐

├————————————————————┐

 ||

|

├————————┘

 NI ————————————————┘ |├————————┘

HNO3 ———————————┐

├—————————————————————————|┘

SO2 ———┐

├———————|┘

DSO2 ———┘

Key: ACTL = acetaldehyde 

DSO2 = sulfur dioxide (denuder) 

HCL = hydrochloric acid 

HNO3 = nitric acid 

NOX = nitrogen oxide 

PM10 = PM10 

SULF = sulfate 

ACTN = acetone 

FE = iron 

HION = hyrdogen ion concentration 

NI = nickel 

NO2 = nitrogen dioxide 

SOOT = elemental carbon 

CARB = organic carbon 

FORM = formaldehyde 

HNO2 = nitrous acid 

NO = nitrogen oxide 

PM25 = PM2.5 

SO2 = sulfur dioxide 
 

 

 

Figure 43. Hierarchical clustering results for (A) Bronx and (B) Manhattan 

air-monitoring data. Results for April – June. 

Panel A

 0 distance (=1-r) 2

 O3 ———————————————————————————————┐

 HION —————————————┐

├—————————————┐

├

|

|

|

|

|

|

| 

————————————————————┐

|

|

|

|

|

 FORM ———┐

├——————┐

| | | | | |

|

| 

ACTL ———┘ |

CARB ——————————├┘——┘ ||

|

|

|

|

|

|

|

|

 PM10 —┐ ||

|

|

|

|

|

|

| 

├———┘

 PM25 —├┘—————— |┐

 SULF —————┐ ├|

| 

————————|

|

|

|

| 

┐

├┐ HNO3 —————┘| |

HCL ——————├┘—┘ ||

|

|

| ACTN ———————————┐ |├—————————┘├—————┘

NH3 ———————————┘ ||———————————— HNO2 —————————┐ || NO —┐ | | ├————|

|

| 

┐

 NOX —├┘———————┘ ||

|

|

|

|

|

|

|

|

├┐ SOOT ——————————————┘|

├———————————┐ NO2 ———————————————┘ |├——┐ DSO2 ——┐

├————————————————————————|┘ || SO2 ——┘ |

FE ————————————————————┐

├—————————|

├

┘

—————————————————————┘

 NI ————————————————————┘

Panel B

 0 distance (=1-r) 2

 HION ———————┐

 SULF ———————├┘———————┐|

PM10 ———┐ ├|

|

| 

——|┐

 PM25 ———├┘— |┐ ||

|

|

| FORM ——┐ |├—————————┘

 ACTL ——├┘——┘ |

SOOT —————————┐ |├————————————|

|

|

|

┐

 HCL ———┐ |├————————┘

 HNO3 ———├┘—————┘ |

O3 ——————————————————————————┐ |├———————————————|

|

|

|

┐

 CARB ——————┐

├———————————————————|

├

┘

————┘

 NH3 ——————┘ ||————————————————— ACTN ——————————————————————————————┐

├———┐

 ||

|

|

|

|

|

|

|

|

|

|

|

 NO —┐

├————————┐

 | | | | |

|

|

|

|

|

| 

|

|

|

|

| 

NOX —┘ |├———┐ NO2 ——————————┘ |├———————————┐ DSO2 —————┐

├————————|┘ || | | |

|

| 

SO2 —————┘ ├———┘

HNO2 ——————————————————————————┘

NI ———————————————————————┐

├——————————|

├

┘

————————————┘

 FE ———————————————————————┘

Key: ACTL = acetaldehyde ACTN = acetone CARB = organic carbon

DSO2 = sulfur dioxide (denuder) FE = iron FORM = formaldehyde

HCL = hydrochloric acid HION = hyrdogen ion concentration HNO2 = nitrous acid

HNO3 = nitric acid NI = nickel NO = nitrogen oxide

NOX = nitrogen oxide NO2 = nitrogen dioxide PM25 = PM2.5

PM10 = PM10 SOOT = elemental carbon SO2 = sulfur dioxide

SULF = sulfate
 

 

Figure 44. Hierarchical clustering results for (A) Bronx and (B) Manhattan 

air-monitoring data. Results for July – September. 

Panel A

 0 distance (=1-r) 2

 ACTN ——————————————————————————┐

 O3 ——————————————————————————├┘———————————┐|

├———————————————┐ HION —————————┐

├———————————┐

 | | | | | | | | | | | | |

|

|

|

|

|

|

|

|

|

|

|

|

|

| 

|

|

|

|

|

|

|

|

|

|

|

|

|

 SULF ——┐ ||

| 

|

PM25 ——├┘—— |┐ ||

|

| ├———┘PM10 —————┘

SO2 —————┐

├—————————————┐

|

| 

├———|

|

|

|

| 

┐

 DSO2 —————┘ | |

ACTL ————┐ ||

| 

├—┘

 FORM ————├┘———————— |┐ ||

|

|

|

|

| 

CARB ————————┐ |├—————┘

 HNO3 ————┐ ├| ————┘

 HCL ————├┘———┘| ||

|

| ├————————————┘FE —————————————————————————┘

 |—————————— NI ———————————————————————————┐ ||

|

|

|

|

|

|

|

 NO2 ———┐

├———————┐

├

|

|

|

|

| 

——————|

|

| 

┐

 NOX ———┘ |├————┐ |

|

|

| 

SOOT ———————————┘ |├——————————┘

NO ————————————————┘

HNO2 ————————————┐

├—————————————————————|

├

┘

———————————————————┘

 NH3 ————————————┘

Panel B

 0 distance (=1-r) 2

 HNO2 ———————————————————————┐

 NO ————┐ ├| ——————|

|

|

┐

├——————————————————┘

NOX ————┘ ├———————————————————┐ NH3 ——————————————————————————————┘ ||—————————————— ACTN ————————————————————┐ ||

|

|

|

|

|

|

|

 NO2 —————————┐

├————————┐

|

| 

├———————————————|

|

|

|

|

| 

┐

 SOOT —————————┘ | |

FORM ——————┐ |├—┘

 ACTL ——————├┘———————————┘ |

CARB ————————┐ ||

|

|

|

├—————————————┘

├—┐ PM10 ———————┐| | ├┘ ||

| PM25 ———┐├———|┘ |

|

|

|

|

|

|

|

|

|

|

|

|

|

 SULF ———┘ |├—————┐ HION ——————————┘ |├—┐ O3 ————————————————┘ |├——————————┐ HNO3 —————┐

├———┐

| | | | | |

|

|

| 

HCL —————┘ |├————————┘

DSO2 ————┐

├————|┘

SO2 ————┘ |├——————┘

FE —————————————————————————┐

├———|┘

NI —————————————————————————┘

Key: ACTL = acetaldehyde ACTN = acetone CARB = organic carbon

DSO2 = sulfur dioxide (denuder) FE = iron FORM = formaldehyde

HCL = hydrochloric acid HION = hyrdogen ion concentration HNO2 = nitrous acid

HNO3 = nitric acid NI = nickel NO = nitrogen oxide

NOX = nitrogen oxide NO2 = nitrogen dioxide PM25 = PM2.5

PM10 = PM10 SOOT = elemental carbon SO2 = sulfur dioxide

SULF = sulfate
Figure 45. Hierarchical clustering results for (A) Bronx and (B) Manhattan 

air-monitoring data. Results for October – December. 

Panel A

 0 distance (=1-r) 2

 O3 ——————————————————————————————————————┐

├—————————┐ HCL ———————————————┐

├———————┐

| | | | | |

|

|

|

 HNO3 ———————————————┘ |├——————————————┘

SULF ———————┐

├———————————————|┘

HION ———————┘ ||———————————————— NO ┐ ||

|

|

|

|

|

|

|

|

|

|

|

|

|

 NOX ├┘————— |┐

 SOOT ——————├┘—————— |┐

 NO2 ———————┐ |├————————|

|

|

|

|

|

| 

┐

 ACTL —┐ ||

|

|

|

|

| 

├—————┘

 FORM —├┘—┐|

├┐ PM25 ——┐|| ├┘| | | |

|

| 

PM10 ——┘ ├——┘

ACTN ————┘ ||

|

|

|

|

|

|

 FE —————————————┐ ||

| 

├————|┐

 HNO2 —————————————├┘————┐|

├———┘

|

|

| CARB ——————————————————┘ |

NI —————————————————————┐

├—————|

├

┘

————————————————————┘

 SO2 ————┐

├————————————————|┘

DSO2 ————┘

Panel B

 0 distance (=1-r) 2

 O3 ————————————————————————————————┐

 HNO3 ——————————————┐ ├|

|

| 

————————————————┐

|

HCL ——————————————├┘———— |┐ ||

|

|

| SULF —————┐ |├————————————┘├—————————————┘

HION —————┘ ||——————————————— FE —————————————┐ ||

|

|

|

|

|

|

|

|

|

 ACTN ————————┐ |├———————|

|

|

|

|

|

| 

┐

 FORM —┐ ||

|

|

|

| 

├————┘

 ACTL —├┘—— |┐

├┐ PM10 ——┐

├—||┘| PM25 ——┘ | | |

|

| NO2 ————— | ├

┘——┘ ||

|

|

|

|

|

|

|

|

|

|

├———————————┐ CARB —————┐

├————————┐

| | | | | |

|

|

|

|

| 

HNO2 —————┘ |├——————┘

SOOT ————┐

├—————————|┘

NOX ┐

├———|┘

NO ┘ |├———————————————┘

DSO2 —┐

├——————————————————┐

 || SO2 —┘ | | ├————————————┘

NI ————————————————————┘

Key: ACTL = acetaldehyde ACTN = acetone CARB = organic carbon

DSO2 = sulfur dioxide (denuder) FE = iron FORM = formaldehyde

HCL = hydrochloric acid HION = hyrdogen ion concentration HNO2 = nitrous acid

HNO3 = nitric acid NI = nickel NO = nitrogen oxide

NOX = nitrogen oxide NO2 = nitrogen dioxide PM25 = PM2.5

PM10 = PM10 SOOT = elemental carbon SO2 = sulfur dioxide

SULF = sulfate

























134
APPENDICES

135
APPENDIX 1. DETAILS OF ANALYTICAL AND STATISTICAL METHODS 

QA/QC Protocols 

The quality assurance and quality control measures instituted for this sampling program followed standard 

laboratory and field practices for calibrations, running blanks, flow audits, servicing of equipment, etc. The schedule 

for performing the various QA/QC measures was at least as rigorous as that required in EPA protocols; where no 

EPA protocol existed, the schedule was as rigorous as the most widely accepted protocol. A list of the various 

approved methods and associated protocols used for each of the measurements is provided in Table A1. 

Table A1. Measurement Technologies and Associated Protocols 

Measurement Technology/Field Instrument EPA-Approved Method/Protocol 

Acid Aerosols, Ammonia and Acid Gases EPA Method IO-4.2 

Aldehydes EPA Method TO-11 

Elemental Carbon, Organic Carbon, Total Carbon Rupprecht and Patashnick 5400 Series Carbon analyzer 

FRM10 Wedding &Assoc PM10 High Vol Sampler RFPS-1087­

062 

FRM2.5 Rupprecht and Patashnick Partisol Plus Model 

2025 RFPS-0498-118 

Metals Inductively Couple Plasma/Mass Spectrometry/ 

Swami et al (2001) Journal of Analytical Chemistry (2001) 

369:63-70 

Molds and Pollen Burkard Bioaerosol Sampler/No EPA Protocol Issued 

NO/NO2/NOx Thermo Environmental Instruments Model 42 

EPA Equivalence Number (RFNA-1289-074) 

Ozone Thermo Environmental Instruments Model –49, EPA 

Equivalence Number (EQOA-0880-047) 

Particle Number TSI Inc. Model 1022 Condensation Particle Counter 

PM10 (particulate matter 10 microns or less) Rupprecht and Patashnick TEOM Particulate Analyzer 

EPA Equivalence Number (EQPM-10900079) 

PM2.5 (particulate matter 2.5 microns or less) Rupprecht and Patashnick TEOM Particulate Analyzer 

EPA Equivalence Number (EQPM-10900079) 

SO2 Thermo Environmental Instruments Model 43 C SO2 

Pulsed Fluoresence.Analyzer 

EPA Equivalence Number (EQSA-0486-060)_ 

136
Our study implementation required staff to travel every Wednesday from Albany to New York City to collect 

samples, download data, and service equipment. Every piece of equipment associated with the study was reviewed 

and serviced to make sure that it was performing to pre-established QA/QC standards. All of the self-diagnostics 

tools in the various pieces of equipment were reviewed. After being downloaded, the data were reviewed to see if 

any noticeable issues could be identified. All flow audits were performed at least as frequently as required by EPA 

protocols and manufacturers’ recommendations with a NIST traceable flow meter. All of the work required was 

documented on field forms as well as many of the parameters from the self-diagnostics. At the conclusion of each 

sampling event on Wednesday, a supervisor reviewed the work documented on each field form. 

Because the monitoring stations were also part of the DEC air monitoring network, DEC staff were on-site more 

frequently than once a week. They serviced the NOx, SO2 and ozone meters as required by EPA. DEC staff also 

reported to us any problems with the additional equipment, and staff were then deployed to make the appropriate 

corrections. 

More detail on the methodology used for each measurement appears in the narrative for each analyte. 

Analytical Methods 

PM10 and PM2.5 

Two TEOM® Series 1400a Ambient Particulate Monitors (Rupprecht & Patashnick Co., Inc., Albany, NY) were 

deployed at each location, with one unit measuring PM10 and the other measuring PM2.5. The TEOM® Series 1400a 

was used to measure particulate mass concentrations continuously. The instrument incorporates the patented tapered 

element oscillating microbalance (TEOM), a microweighing technology. Using a choice of sample inlets (either 

inertial or cyclonic), the same hardware can be configured to measure either PM10 or PM2.5. This microprocessorbased unit provides internal data storage and advanced analog and serial data input/output capabilities. The TEOM®

Series 1400a monitor has received the EPA PM10 equivalency approval EQPM-1090-079. PM2.5 measurements are 

within the context of a EPA-correlated acceptable continuous monitor (40 CFR 58). 

The Series 1400a monitor incorporates an inertial balance that directly measures the mass collected on an 

exchangeable Teflon®-coated borosilicate glass filter cartridge by monitoring the corresponding frequency changes 

of a tapered element. The sample flow passes through the filter, where particulate matter collects, and then continues 

through the hollow tapered element on its way to an active volumetric flow control system and vacuum pump. 

Active volumetric flow control is maintained by mass flow controllers whose set points are constantly adjusted in 

accordance with the measured ambient temperature and pressure. Both the mass and the flow rate measurements are 

verifiable using NIST-traceable standards. R&P PM10 and Teflon® coated PM2.5 size-selective inlets were used for 

particle cutoff. Sample inlet flow was 16.7 l/min, with the main flow rate through the sensor unit maintained at 3.0 

l/min. Sample stream temperature was heated to 50°C, and the filter unit was held at 50°C to prevent condensation. 

A measure of change in the mass concentration was made every two seconds and used to calculate hourly averages. 

137
Data were logged by the instruments and downloaded every Wednesday by project staff. Sample filters were 

exchanged when the filter’s percent loading (capacity) reached 75% or greater, which was about every three weeks. 

Approximately every two months, inlet heads were either cleaned on-site or replaced with clean heads. TEOM®

units were kept in temperature- controlled rooftop enclosures. A supplemental ACCU system was attached to the 

PM2.5 units at each location (described below). 

FRM 10 and 2.5 

Particle Number 

The Model 3022A Condensation Particle Counter (TSI Inc., Shoreview, MN) was used to measure the number of 

airborne particles between 0.007 and 2.5 micrometers in diameter. This instrument detects and counts particles with 

an optical detector. The butanol vapor is introduced into the air stream and condenses on particulates. This 

condensation enlarges the particle so that it can be measured with the optical detector. Approximate sampling flow 

was 300 cm3

/min. Data were logged by the instrument at one-minute intervals and downloaded once per week. 

Maintenance of the instrument included weekly draining of the interior butanol reservoir, as well as replacement of 

old butanol to prevent interference due to saturation of the reservoir wick by water. Wicks were replaced at sixmonth intervals. 

Organic and Elemental Carbon 

A Series 5400 Ambient Carbon Particulate Monitor (Rupprecht & Patashnick Co., Inc., Albany, NY) was used to 

measure organic and elemental carbon. The instrument uses a direct thermal-CO2 measurement to provide an 

indirect measure of the amount of carbon in the collected particulate. Outdoor air was drawn from the glass manifold 

(described earlier) at 16.7 lpm through a Teflon® coated, PM2.5 size-selective inlet. The particulate was collected for 

three hours on a filter, which was then heated. The instruments were programmed to heat the filter to 250, 340, 550 

and 750°C. The fraction volatilized or oxidized to CO2 at 250°C is considered the volatile organic fraction. The 

semi-volatile organic fraction is oxidized at 340°C, and the elemental carbon is the difference in the amount 

oxidized to CO2 at 750°C minus that oxidized to CO2 340°C. Data were logged by the instrument and downloaded 

weekly. 

EPA’s Environmental Technology Verification Program reviewed R&P’s 5400 Carbon Analyzer in 2000–2001 and 

issued a verification statement, which reads, in part, 

The program evaluates the performance of innovative technologies by developing test plans that 

are responsive to the needs of stakeholders, conducting field or laboratory tests (as appropriate), 

collecting and analyzing data, and preparing peer-reviewed reports. All evaluations are conducted 

in accordance with rigorous quality assurance protocols to ensure that data of known and adequate 

quality are generated and that the results are defensible. 

138
Field testing was conducted in two phases. The first took place at the DOE National Energy Technology Laboratory 

in Pittsburgh, from August 1 through September 1, 2000. The second phase was at the California Air Resources 

Board’s ambient air monitoring station in Fresno from December 18, 2000, through January 17, 2001. Performance 

characteristics verified included inter-unit precision, agreement with and correlation to time-integrated reference 

methods, effect of meteorological conditions, and influence of precursor gases. OC, EC, and TC results from the 

5400 were compared with laboratory thermal/optical reflectance (TOR) analysis of filter-based reference samples. 

Technological Description 

See report at http://www.epa.gov/etv/verifications/vcenter1-3.html. 

Verification of Performance 

Inter-unit precision 

PHASE I RESULTS 

Linear Regression Organic Carbon Elemental Carbon Total Carbon 

Hourly Average (OC) (EC) (TC) 

r2 0.94 0.93 0.95 

Slope (95% C.I.) 1.063 (0.021) 1.037 (0.022) 1.069 (0.020) 

Linear Regression 24-hr Organic Carbon Elemental Carbon Total Carbon 

Average (OC) (EC) (TC) 

r2 0.97 0.94 0.97 

Slope (95% C.I.) 1.094 (0.081) 1.038 (0.113) 1.098 (0.088) 

PHASE II RESULTS 

Linear Regression Hourly Organic Carbon Elemental Carbon Total Carbon 

Average (OC) (EC) (TC) 

r2 0.94 0.92 0.86 

Slope (95% C.I.) 0.971 (0.019) 1.029 (0.024) 1.074 (0.035) 

Linear Regression 24-hr Organic Carbon Elemental Carbon Total Carbon 

Average (OC) (EC) (TC) 

r2 > 0.97 > 0.97 > 0.97 

Slope (95% C.I.) 1.027 (0.072) 1.164 (0.083) 1.090 (0.070) 

Comparability and Predictability 

In both Phase I and Phase II, 24-hour averages from the 5400 showed a negative bias when compared with OC, 

EC, and TC reference measurements. Phase I regression slopes were below 0.4 for the OC, EC, and TC, and r2 

values were between 0.43 and 0.52. Phase II regression slopes fell between 0.2 and 0.7 and between 0.2 and 0.9 for 

monitors 1 and 2, respectively, for all three carbon fractions, and r2 values were between 0.65 and 0.90. 

139
Meteorological Effects 

For Phase I, the multivariable model ascribed a small but significant effect on the 5400’s readings relative to the 

reference for vertical and horizontal wind speed, wind direction, and ambient air temp at 2 and 10 meters. In 

general, the combined effect of these parameters was small. (For example, the model predicts a Phase I average 

OC value that differs from the linear regression model by about 5%.) For Phase II, small but significant effects 

were ascribed to wind speed, wind direction, standard deviation of wind direction, solar radiation, relative 

humidity, and barometric pressure. 

Influence of Precursor Gases 

For Phase I, the model ascribed statistical influence to O3, H2S, and NO2 on the readings of one or both 5400 

monitors relative to the reference results. For Phase II, NO and total NOx were ascribed a statistical influence to 

both monitors relative to the reference EC and TC, and to NO2 an influence on one monitor relative to the 

reference OC. The combined effect of the multiple parameters was typically a few percent, relative to the linear 

regression of the 5400 and reference results. 

Other Parameters 

In general, these monitors required little maintenance and could be largely operated unattended. Data recovery was 

about 90% over both phases of testing. 

Metals 

In conjunction with the TEOM® PM2.5 systems at each location, an Automatic Cartridge Collection Unit, or ACCU 

(Rupprecht & Patashnick Co., Inc., Albany, NY), was used to collect particulates for metals analysis. The ACCU 

attached to the 13.7 l/min bypass flow line of the TEOM® monitor and permitted filter-based sampling. The 

system’s eight internal flow channels allowed for daily collection of particulate samples through the use of a bank of 

solenoid valves. These valves were electronically controlled by the Series 1400a monitor. The airflow was directed 

through filter holders fitted with 47-mm, 2.0-μm pore size ZeflourTM supported PTFE filters (Pall Corp., Ann Arbor, 

MI). The following metals were included in the analysis (detection limits are in parentheses): Cr (5 ng/m3), Fe (22 

ng/m3), Pb (12 ng/m3), Mn (3 ng/m3), Ni (4 ng/m3), and Zn (77 ng/m3). 

Acid Aerosols, Ammonia, and Acid Gases 

The URG-2000-01J Weekly Air Particulate Sampler (URG, Chapel Hill, NC), an 8-channel annular denuder system, 

was used to characterize five reactive gases (NH3, HCl, HNO2, HNO3, and SO2), particulate sulfate, and aerosol pH 

(EPA Method IO-4.2). Each channel was fitted with two 120-mm glass heavy-wall annular denuders connected in 

series, followed by a 47-mm, 2.0-μm supported PTFE filter (Pall Corp., Ann Arbor, MI). The first annular denuder 

was coated with sodium carbonate to collect acid gases, and the second with citric acid to collect NH3. The flush end 

of the citric acid-coated denuder was attached directly to the filter module. The filters were positioned on the Teflon140
coated stainless steel screen such that the air stream particulates were trapped on the Teflon-coated side of the filter. 

The denuders were coated with appropriate coating solutions (citric acid: 1%weight/volume in methanol; sodium 

carbonate: 1% w/v, 1% w/v glycerol in a 1:1 methanol/water solution). The coated tubes were dried with “zero” air 

at a rate of 3 L/min. The denuder trains were assembled and leak-checked in clean laboratory conditions. A blank 

denuder assembly was included with each batch of seven denuder assemblies sent out in the field. It was left for 

seven days inside the sampler but was not connected to the airflow. 

Ambient air was drawn through aluminum, Teflon®-coated PM10 and PM2.5 size-selective inlet, then through the 

denuder and filter. Daily (24-hour) samples were collected beginning at midnight, at a flow rate of 10 L/min. Inlets 

were cleaned and replaced when necessary. After exchanging the denuders, leak checks were performed to assure 

system integrity. 

The coated annular denuders from the exposed assemblies and field blanks were extracted with 10 ml ultra-pure 

water (Millipore, Milli-Q UV Plus water systems), and stored at 4°C for analysis. The water extract from sodium 

carbonate-coated denuders was used for the determination of HONO, HNO3, and HCl. For SO2 analysis, 5 ml of the 

water extracts from the sodium carbonate-coated denuders were oxidized with 0.05 ml of 30% aqueous H202 

solution to completely oxidize the collected SO2 to SO4 before analysis. The water extract from citric acid-coated 

denuders was used to determine ammonia. The measurement of chloride, nitrite, nitrate, sulfate, and ammonium was 

made with a DIONEX 500 Ion Chromatography System. The results were calculated for gaseous HCl, HONO, 

HNO3, SO2, and NH3. The separation of chloride, nitrite, nitrate and sulfate was accomplished using an IonPac AS 

14 (4 x 250 mm) analytical column, AG 14 guard column, with a 10 μl sample loop, and an anion self-regenerating 

suppressor-ultra. A solution of 3.5 mM Na2CO3/1.0 mM NaHCO3 was used as eluent at a flow rate of 1 ml/min. The 

separation of ammonium was accomplished using an IonPac CS14 (4 x 250 mm) analytical column and a CG 14 

guard column with a 50 μl sample loop, and a cation self-regenerating suppressor-ultra. A solution of 10 mM 

methanesulfonic acid was used as eluent at a flow rate of 1ml/min. 

The Zefluor filters were ultrasonically extracted for one hour in 5 ml of ultra-pure water, the pH was measured, and 

the samples were stored at 4°C for analysis of particulate sulfate. The filter extracts were analyzed for particulate 

sulfate by ion chromatography using the DIONEX 100 Ion Chromatography System. Selenium was also determined 

in some of the filter extracts using inductively coupled plasma mass spectrometry (ICP-MS). Concentrations in the 

field blanks for the target species were subtracted on a batch-to-batch basis. Accuracy of calibration curves was 

checked by analyzing the quality control samples containing the analytes of interest at a concentration in the low and 

high concentration range provided by an independent QA/QC laboratory within the Wadsworth Center. For all the 

analytes, the controls were within ±10%. The percent standard deviation of measurements, evaluated on duplicate 

runs of several samples, was found to be better than ± 3.0 %. 

141
Particulate nitrate was originally included in the analyte list but was later dropped because of concerns about the 

accuracy of the reported concentrations. During the study, research was published that called into question 

particulate nitrate concentrations collected on Teflon filters. (The ADS used in the study collected samples on 

Teflon filters.) Higher temperatures experienced during the daytime in the summer months may lead to a loss of 

particulate nitrate from the sample. Temperatures inside the ADS enclosure on some days exceeded 108°F. Because 

the ADS was serviced only once per week, samples collected after servicing were subject to more high-temperature 

periods than those collected the day prior to servicing, likely increasing the potential for particulate nitrate 

volatilization. This information, along with inconsistencies found in the concentrations of some co-located samples, 

led to the removal of particulate/aerosol nitrate from the analyte list. 

Pollen and Mold 

Weekly pollen and mold samples were collected with a Burkard Recording Volumetric Spore Trap (Burkard 

Manufacturing Co., Ltd, Rickmansworth, England). Particles were impacted on adhesive-coated Melinex transparent 

plastic tape, supported on a clockwork-driven drum. After a thin film of 10% Gelvatol was applied to the tape and 

allowed to dry, the adhesive (Vaseline and 10% paraffin wax in toluene) was then applied. The clockwork drum 

allowed for a seven-day sample to be collected, with the sampling volume ranging between 9 and 12 lpm. After 

removal of the drum, the tape was sectioned and viewed as individual days. Each slide was mounted with glycerin 

jelly and phenosafranin stain. 

Individual bioaerosol categories were grouped into larger aggregations of pollen or mold types based on taxonomic 

or aerodynamic relationships. The pollen and spore aggregations used in statistical analyses are as follows: 

142
Table A2. Bioaerosol Aggregate Categories 

Pollen Mold 

Tree Pollens 

Abies, Acer, Alnus, Betula, Carya, 

Cupressa, Fagus, Fraxinus, Gingko, 

Juglans, Liquidaum, Morus, Olea, Picea, 

Pinus, Platanus, Populus, Quercus, Salix, 

Tilia, Tsuga, Ulmus 

Basidiospores 

Ganoderma, Coprinus, unidentified 

basidiospores 

Grass Pollens 

Graminea 

Ascospores 

Diatrype, Leptosphaeria, Sporormiella, 

unidentified ascospores 

Ragweed Pollen 

Ambrosia 

Dark Mitospores 

Alternaria, Arthrinium, Cladosporium, 

Curvularia, Epicoccum, Helminthosporium, 

Nigrospora, Periconium, Pithomyces, 

Torula, Stemphylium 

Total Pollens 

Tree pollen + Grass pollen + Ragweed pollen + 

Unidentified pollens 

Non-dark Mitospores 

Penicillium/Aspergillus, Botrytis, 

Cercospora, Fusarium, Oidium, 

Peronospora, Pestalotiopsis, Polythrincium 

Small spores 

all fungal spores < 10 μm 

Large spores 

all fungal spores > 10 μm 

Total Molds 

Basidiospores + Ascospores + Dark mitospores 

+ Non-dark mitospores + Unidentified mold 

spores 

Acetone and Aldehydes 

An ATEC Model 1600 automated multi-port sampler (Atmospheric Technology, Calabasas, CA) was used in the 

collection of samples for acetone and aldehyde analysis, according to EPA Method TO-11. The ATEC was 

programmed with a week-long run schedule to collect seven daily 24-hour samples. Channels ran consecutively 

from midnight to midnight. Air was drawn through cartridges containing 2,4-dinitrophenylhydrazine- (DNPH-) 

coated silica (Waters Corp., Milford, MA). Following collection, the samples were eluted from the cartridge as the 

DNPH derivative, then analyzed by HPLC with UV detection. Flows varied between 0.28 and 0.29 lpm, yielding 

143
approximate sample volumes of 403 to 417 liters. Actual sample volumes and run times were recorded by the 

instrument and were used for concentration calculations. After the installation of the new cartridges, and prior to 

resumption of the sampling run, all ports were checked for leaks. A denuder box was attached to the inlet port to 

remove ozone from the sample stream (using a potassium iodide-coated copper coil). These boxes were replaced at 

three- to four-week intervals. The analytes measured were acetaldehyde, acetone, acrolein, benzaldehyde, 

butyraldehyde, crotonaldehyde, 2,5-dimethylbenzaldehyde, formaldehyde, hexaldehyde, isovaleraldehyde, 

propionaldehyde, m-tolualdehyde, o-tolualdehyde, p-tolualdehyde and valeraldehyde. Detection limit was 1 μg/m3

. 

SO2 Determination 

The Thermo Environmental Instruments (TEI) Model 43C SO2 Pulsed Fluorescence Analyzer has been designated 

by EPA as Equivalent SO2 Analyzer (No. EQSA-0486-060). Pulsating UV light is focused through a narrow band 

pass of 190 nanometers that directs it into the fluorescence chamber. Sampled ambient air containing SO2 flows 

continuously through the chamber, where the UV light excites the SO2 molecules causing them to emit their 

characteristic decay radiation. This SO2-specific radiation passes through a second filter and onto a sensitive photomultiplier tube. Incoming light energy is transformed electronically into a 0-5VDC output signal that is directly 

proportional to the concentration of SO2 in the sample air. 

NO/NO2/NOx Determination 

The Thermo Environmental Instruments (TEI) Model 42 NO/NO2/NOx analyzers utilize the technique of 

photometric detection of chemiluminescent light resulting from the flameless reaction of nitric oxide (NO) with 

ozone (O3) for interference-free measurement of NO2. The analyzer includes a NOx-to-NO heated molybdenum 

converter to change NO2 into NO for subsequent measurement via the chemiluminescent detection method. The 

ambient air sample enters Model 42 through a single flow-control capillary and is directed to a solenoid valve. The 

solenoid valve routes the sample either through the NO2-to-NO converter (NOx mode) or around the converter (NO 

mode). When flowing through the converter, the chemiluminescence measured within the reaction chamber 

represents the NOx concentration. Bypassing the converter allows measurement of the NO level only. The signals 

generated in the two modes are stored and held in memory by the instrument’s microcomputer, where the difference 

between them is used to generate a NO2 signal. The digital-to-analog converter then converts the three stored values 

into analog signals that are output to the rear of the instrument. The NO and NOx concentrations calculated in the 

NO and NOx modes are stored in memory. The difference between the concentrations is used to calculate the NO2 

concentration. 

Ozone Determination 

The Thermo Environmental Instruments (TEI) Model 49-Ultraviolet Photometer ozone analyzer has been designated 

by U.S. EPA as an equivalent method for the measurement of ambient concentration of ozone pursuant to the 

requirements defined in 40 CFR Part 53. Its designated equivalence method number is EQQA-0880-047. The UV 

photometer determines ozone concentrations by measuring the attenuation of light due to ozone in the absorption 

144
cell, at a wavelength of 254 nanometers. The concentration of ozone is directly related to the magnitude of the 

attenuation. The reference ozone-free gas passes into the absorption cell to establish a “zero” light intensity reading 

(Io). The solenoid then switches, and the sample passes through the absorption cell to establish a “sample” light 

intensity reading (I). The ratio of these readings (I/Io) is a measure of the light absorbed by ozone in the sample at 

254nm. It is directly related to the concentration of ozone in the sample through the Beer-Lambert Law. A second 

detector is used to monitor the changes in light intensity and to correct for these changes. This system is basically 

two photometers utilizing two separate but similar absorption cells and detector systems. They share the same 

source. These two photometers operate 180 degrees out of phase but synchronously and integrate the signals 

simultaneously: thus I in cell B (I(B)) is determined at the identical time Io in cell A (Io(A)) is determined. The 

solenoids then switch, and after an appropriate flush time (approximately 7 seconds), Io (B) and I(A) are determined. 

Taking the average value of these two readings factors out the fluctuation in lamp intensity. The microcomputer in 

the TEI Model 49 solves the Beer-Lambert equation directly for each cell and outputs the average concentration in 

both the front panel digital display and the recorder analog output. 

Meteorological Data 

Temperature, relative humidity, and wind speed and direction were logged with a Young 27600 Programmable 

Translator (R.M. Young Co., Traverse City, MI). The unit logged the data from the roof-mounted wind monitor and 

relative humidity/temperature probe (Models 05305 and 41372LC, respectively, from R.M. Young Co.). 

Flow Rates 

Flow rates for the TSI, URG, and TEOM-ACCU were checked and calibrated with a DryCal DC-1 digital flow 

calibrator (BIOS International, Pompton Plains, NJ). The NIST-traceable DryCal DC-1 has an accuracy of ±1%, 

with a worst-case resolution of 0.2%. 

Statistical Methods 

Multivariate procedures 

Square Spearman correlation matrices were used as input to the MDS procedure implemented in SYSTAT v. 9 

(SPSS Inc.). The SYSTAT procedure creates dissimilarity matrices from correlation matrices by taking the negative 

of all correlation coefficients. MDS distances are then computed from dissimilarities. Two-dimensional MDS 

configurations were generated for each correlation matrix using SYSTAT defaults for number of iterations and for 

convergence criteria. Among the three possible loss functions (Kruskal, Guttman, Young) available in the SYSTAT 

procedure, the Guttman loss function (Wilkinson 1999) generally explained the greatest proportion of variance in 

preliminary analyses and therefore was used throughout. Shepard diagrams and output of the Guttman coefficient of 

alienation at each iteration step were used as diagnostics for degenerate solutions. 

MDS configuration plots were constructed for each correlation matrix. Non-metric MDS re-scales measures of 

dissimilarity between variables so that the rank order of distances between variables in the MDS plot correspond as 

145
closely as possible to the rank order of dissimilarities between variables in the original multi-dimensional space. 

When dissimilarities between variables are measured with correlation coefficients, the distance between variables in 

the MDS plot indicates the strength of their correlation. The plots were interpreted qualitatively by observing 

whether points representing the pollutant analytes clustered closely together (indicating strong positive correlation 

among variables) or whether points were far apart (indicating large negative correlations). Intermediate distances 

between variables were indicative of relatively weak associations. 

Rectangular data matrices were used as input to the HC procedure implemented in SYSTAT v. 9 (SPSS Inc.). 

Pearson correlations (r) were used to calculate the distance metric (d) between variables, where d = 1 – r. 

Complete-linkage hierarchical clustering was used to construct a tree diagram representing distances between 

clusters of variables. As in MDS, the tree diagrams were interpreted qualitatively by observing which variables 

tended to be strongly associated with each other and whether consistent clustering of variables could be 

observed. In the cluster trees, distances between variables or clusters near zero represent strong positive 

correlations, while distances near two represent strong negative correlations. Intermediate distances represent 

weak correlations between variables or clusters. 

146
Appendix 2 – Detailed Data Summary 

147
Appendix 2 - Summary of Data 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

pH

Winter99 Bronx --- --- --- 0 79 5.5 0.4 6.8 6.3 5.7 5.5 

Manhattan --- --- --- 0 79 5.5 0.6 10.0 6.0 5.6 5.4 

Spring99 Bronx --- --- --- 0 92 5.2 0.5 7.1 6.0 5.5 5.2 

Manhattan --- --- --- 0 92 5.1 0.4 5.9 5.7 5.3 5.1 

Summer99 Bronx --- --- --- 58 36 4.8 0.5 6.0 6.0 5.0 4.6 

Manhattan --- --- --- 3 91 4.6 0.4 5.6 5.5 4.9 4.6 

Fall99 Bronx --- --- --- 0 90 5.2 0.5 6.6 6.5 5.5 5.2 

Manhattan --- --- --- 0 90 5.1 0.4 5.9 5.7 5.4 5.1 

Winter00 Bronx --- --- --- 0 89 5.3 0.4 6.3 6.0 5.6 5.3 

Manhattan --- --- --- 0 89 5.2 0.3 5.8 5.7 5.5 5.3 

Spring00 Bronx --- --- --- 0 92 5.1 0.5 6.5 5.9 5.4 5.0 

Manhattan --- --- --- 0 92 5.0 0.4 5.6 5.5 5.2 5.0 

Summer00 Bronx --- --- --- 7 87 4.8 0.4 6.3 5.6 5.0 4.8 

Manhattan --- --- --- 7 87 4.8 0.4 5.7 5.5 5.0 4.7 

Fall00 Bronx --- --- --- 0 62 5.1 0.4 6.4 5.8 5.4 5.1 

Manhattan --- --- --- 2 60 5.1 0.3 5.8 5.6 5.3 5.1 

Sulfate 

(ug/m3 ) 

Winter99 Bronx --- --- --- 0 0 79 2.93 1.56 7.36 6.45 3.38 2.65 

Manhattan --- --- --- 0 0 79 3.01 1.63 8.84 6.56 3.54 2.78 

Spring99 Bronx --- --- --- 0 1 91 3.22 2.93 15.60 9.59 3.68 2.20 

Manhattan --- --- --- 0 0 92 3.35 2.87 14.43 10.15 3.92 2.38 

Summer99 Bronx --- --- --- 1 58 35 5.16 4.54 17.49 15.63 7.28 3.60 

Manhattan --- --- --- 0 5 89 6.32 5.66 23.88 17.65 8.63 4.07 

Fall99 Bronx --- --- --- 1 0 89 3.05 2.70 19.47 7.38 3.89 2.17 

Manhattan --- --- --- 0 2 88 3.16 2.21 9.61 8.18 4.18 2.40 

Winter00 Bronx --- --- --- 0 7 82 2.92 1.81 7.78 7.16 3.75 2.37 

Manhattan --- --- --- 0 1 88 3.14 1.93 9.44 7.46 4.07 2.46 

Spring00 Bronx --- --- --- 0 0 92 4.01 3.25 15.71 11.95 5.01 2.91 

Manhattan --- --- --- 0 1 91 4.10 3.10 15.23 11.11 5.12 2.99 

Summer00 Bronx --- --- --- 0 7 87 4.97 3.65 13.82 12.58 7.09 4.25 

Manhattan --- --- --- 0 7 87 5.05 3.52 13.50 12.16 6.94 4.32 

Fall00 Bronx --- --- --- 0 0 62 3.30 2.96 13.57 9.65 4.00 2.43 

Manhattan --- --- --- 0 2 60 3.37 2.86 12.17 9.70 4.08 2.62 

Carbon 250

(ug/m3 ) 

Winter99 Bronx --- 2 --- --- 96 534 2.478 0.791 6.958 3.988 2.852 2.285 

Manhattan 32 8 --- --- 67 525 2.736 0.795 7.282 4.095 3.127 2.665 

Spring99 Bronx --- 6 --- --- 7 723 2.975 0.918 7.640 4.931 3.307 2.726 

Manhattan 89 6 --- --- 16 625 2.738 1.074 6.489 4.779 3.303 2.650 

Summer99 Bronx 1 14 --- --- 467 270 3.710 0.935 5.884 5.389 4.419 3.650 

Manhattan 5 2 --- --- 3 742 3.471 1.050 9.856 5.332 3.987 3.299 

Fall99 Bronx 3 2 --- --- 119 596 3.637 0.672 9.486 4.757 3.898 3.480 

Manhattan 4 --- --- --- 14 702 2.959 0.955 8.680 4.541 3.439 2.769 

Winter00 Bronx 3 1 --- --- 15 693 3.206 0.763 6.276 4.844 3.515 3.043 

Manhattan --- 3 --- --- 18 691 2.631 1.045 8.626 4.552 3.021 2.399 

Spring00 Bronx 1 --- --- --- 65 670 3.697 0.470 5.670 4.661 3.848 3.642 

Manhattan --- --- --- --- 69 667 3.327 1.121 10.755 5.588 3.837 3.001 

Summer00 Bronx --- 4 --- --- 10 738 3.182 0.822 7.528 4.393 3.735 3.201 

Manhattan --- 5 --- --- 8 739 3.333 0.447 5.520 4.135 3.595 3.271 

Fall00 Bronx --- 11 --- --- 4 481 2.525 0.801 5.590 3.778 3.055 2.588 

Manhattan 4 13 --- --- 5 474 3.471 0.581 6.606 4.561 3.753 3.388 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Soot Carbon

(ug/m3 

) 

Winter99 Bronx --- 18 --- --- 98 516 1.592 1.185 9.528 3.943 1.970 1.263 

Manhattan 32 119 --- --- 67 414 1.689 1.019 9.646 3.395 2.061 1.438 

Spring99 Bronx 3 12 --- --- 7 714 1.146 0.824 6.494 2.774 1.298 0.904 

Manhattan 90 172 --- --- 17 457 1.477 1.151 10.200 3.412 1.777 1.159 

Summer99 Bronx --- 19 --- --- 467 266 1.069 0.613 3.913 2.232 1.352 0.959 

Manhattan 2 84 --- --- 4 662 1.413 0.974 9.993 3.031 1.761 1.197 

Fall99 Bronx 3 13 --- --- 120 584 1.338 0.995 7.157 3.577 1.529 1.021 

Manhattan 2 7 --- --- 14 697 1.427 0.974 8.703 3.273 1.724 1.141 

Winter00 Bronx --- 5 --- --- 15 692 1.401 1.047 6.890 3.689 1.629 1.014 

Manhattan --- 5 --- --- 18 689 1.353 0.860 9.080 3.144 1.597 1.111 

Spring00 Bronx 1 19 --- --- 66 650 0.924 0.715 5.873 2.268 1.079 0.690 

Manhattan --- 3 --- --- 69 664 1.228 0.837 5.662 2.812 1.533 0.971 

Summer00 Bronx --- 5 --- --- 13 734 1.027 0.690 6.900 2.154 1.250 0.859 

Manhattan --- 1 --- --- 13 738 0.974 0.446 2.867 1.859 1.212 0.874 

Fall00 Bronx --- --- --- --- 17 479 1.063 0.820 5.290 2.693 1.231 0.832 

Manhattan 6 --- --- --- 20 470 1.182 0.736 6.356 2.435 1.457 0.989 

O3

(ppm) 

Winter99 Bronx --- 0 --- --- 1289 607 0.017 0.011 0.046 0.033 0.026 0.017 

Manhattan --- 0 --- --- 882 1014 0.006 0.006 0.033 0.018 0.009 0.004 

Spring99 Bronx --- 0 --- --- 143 2065 0.021 0.015 0.105 0.045 0.030 0.020 

Manhattan --- 0 --- --- 84 2124 0.016 0.013 0.090 0.038 0.023 0.013 

Summer99 Bronx --- 0 --- --- 1975 281 0.034 0.020 0.104 0.066 0.046 0.030 

Manhattan --- 0 --- --- 49 2207 0.021 0.019 0.122 0.056 0.030 0.015 

Fall99 Bronx --- 0 --- --- 857 1303 0.007 0.007 0.036 0.022 0.011 0.004 

Manhattan --- 0 --- --- 81 2079 0.005 0.006 0.047 0.018 0.007 0.003 

Winter00 Bronx --- 34 --- --- 7 2095 0.010 0.009 0.037 0.026 0.016 0.007 

Manhattan --- 51 --- --- 2 2083 0.006 0.006 0.030 0.017 0.009 0.004 

Spring00 Bronx --- 51 --- --- 0 2157 0.020 0.016 0.110 0.049 0.028 0.017 

Manhattan --- 198 --- --- 0 2010 0.016 0.014 0.087 0.041 0.022 0.012 

Summer00 Bronx --- 46 --- --- 0 2210 0.021 0.017 0.080 0.056 0.031 0.017 

Manhattan --- 134 --- --- 0 2122 0.016 0.015 0.072 0.046 0.025 0.012 

Fall00 Bronx --- 35 --- --- 0 1453 0.009 0.009 0.061 0.027 0.014 0.005 

Manhattan --- 52 --- --- 0 1436 0.006 0.007 0.056 0.020 0.009 0.004 

NOX

(ppm) 

Winter99 Bronx --- 0 --- --- 1896 0 --- --- --- --- --- --- 

Manhattan --- 0 --- --- 79 1817 0.086 0.051 0.540 0.181 0.103 0.072 

Spring99 Bronx --- 0 --- --- 331 1877 0.044 0.042 0.351 0.120 0.056 0.033 

Manhattan --- 0 --- --- 744 1464 0.061 0.039 0.350 0.140 0.071 0.050 

Summer99 Bronx --- 0 --- --- 1711 545 0.038 0.025 0.196 0.088 0.046 0.033 

Manhattan --- 0 --- --- 58 2198 0.048 0.029 0.250 0.103 0.058 0.041 

Fall99 Bronx --- 0 --- --- 1231 929 0.061 0.056 0.477 0.161 0.071 0.045 

Manhattan --- 0 --- --- 76 2084 0.078 0.054 0.551 0.180 0.093 0.063 

Winter00 Bronx --- 275 --- --- 240 1621 0.072 0.062 0.563 0.194 0.083 0.052 

Manhattan --- 63 --- --- 5 2068 0.084 0.049 0.460 0.183 0.101 0.070 

Spring00 Bronx --- 58 --- --- 0 2150 0.051 0.046 0.473 0.139 0.057 0.038 

Manhattan --- 384 --- --- 0 1824 0.057 0.036 0.334 0.120 0.069 0.048 

Summer00 Bronx --- 749 --- --- 0 1507 0.037 0.022 0.159 0.080 0.046 0.032 

Manhattan --- 157 --- --- 0 2099 0.048 0.024 0.216 0.092 0.059 0.042 

Fall00 Bronx --- 45 --- --- 0 1443 0.062 0.057 0.467 0.167 0.072 0.045 

Manhattan --- 62 --- --- 0 1426 0.074 0.051 0.461 0.175 0.089 0.062 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

NO

(ppm) 

Winter99 Bronx --- 0 --- --- 1896 0 --- --- --- --- --- --- 

Manhattan --- 0 --- --- 79 1817 0.048 0.045 0.445 0.132 0.061 0.034 

Spring99 Bronx --- 0 --- --- 331 1877 0.015 0.030 0.271 0.062 0.015 0.004 

Manhattan --- 0 --- --- 745 1463 0.023 0.030 0.278 0.081 0.027 0.013 

Summer99 Bronx --- 0 --- --- 1711 545 0.008 0.017 0.153 0.037 0.008 0.002 

Manhattan --- 0 --- --- 58 2198 0.014 0.021 0.209 0.056 0.017 0.007 

Fall99 Bronx --- 0 --- --- 1231 929 0.030 0.049 0.415 0.116 0.032 0.012 

Manhattan --- 0 --- --- 88 2072 0.045 0.048 0.472 0.138 0.055 0.030 

Winter00 Bronx --- 275 --- --- 240 1621 0.036 0.051 0.452 0.138 0.039 0.017 

Manhattan --- 63 --- --- 5 2068 0.047 0.042 0.392 0.131 0.060 0.034 

Spring00 Bronx --- 60 --- --- 0 2148 0.018 0.035 0.398 0.082 0.016 0.006 

Manhattan --- 384 --- --- 0 1824 0.021 0.027 0.262 0.066 0.025 0.012 

Summer00 Bronx --- 749 --- --- 0 1507 0.009 0.013 0.100 0.037 0.011 0.003 

Manhattan --- 157 --- --- 0 2099 0.016 0.018 0.170 0.050 0.020 0.010 

Fall00 Bronx --- 45 --- --- 0 1443 0.032 0.048 0.391 0.121 0.034 0.017 

Manhattan --- 62 --- --- 0 1426 0.041 0.044 0.402 0.129 0.051 0.027 

NO2

(ppm) 

Winter99 Bronx --- 0 --- --- 1896 0 --- --- --- --- --- --- 

Manhattan --- 0 --- --- 79 1817 0.039 0.009 0.117 0.054 0.044 0.037 

Spring99 Bronx --- 0 --- --- 331 1877 0.029 0.017 0.112 0.059 0.040 0.029 

Manhattan --- 0 --- --- 744 1464 0.039 0.012 0.088 0.061 0.047 0.038 

Summer99 Bronx --- 0 --- --- 1711 545 0.031 0.013 0.094 0.054 0.039 0.030 

Manhattan --- 0 --- --- 59 2197 0.036 0.013 0.094 0.061 0.043 0.034 

Fall99 Bronx --- 0 --- --- 1231 929 0.031 0.012 0.085 0.052 0.039 0.031 

Manhattan --- 0 --- --- 74 2086 0.034 0.009 0.079 0.051 0.040 0.034 

Winter00 Bronx --- 274 --- --- 240 1622 0.036 0.014 0.114 0.060 0.045 0.034 

Manhattan --- 63 --- --- 5 2068 0.038 0.010 0.118 0.056 0.043 0.037 

Spring00 Bronx --- 58 --- --- 0 2150 0.033 0.015 0.098 0.060 0.041 0.030 

Manhattan --- 385 --- --- 0 1823 0.037 0.014 0.102 0.061 0.046 0.035 

Summer00 Bronx --- 749 --- --- 0 1507 0.028 0.013 0.082 0.052 0.035 0.027 

Manhattan --- 157 --- --- 0 2099 0.033 0.011 0.079 0.053 0.040 0.032 

Fall00 Bronx --- 45 --- --- 0 1443 0.030 0.014 0.101 0.058 0.038 0.026 

Manhattan --- 62 --- --- 0 1426 0.034 0.012 0.082 0.059 0.041 0.032 

SO2

(ppm) 

Winter99 Bronx --- 0 --- --- 42 1854 0.015 0.010 0.078 0.035 0.019 0.013 

Manhattan --- 0 --- --- 49 1847 0.020 0.010 0.096 0.038 0.025 0.018 

Spring99 Bronx --- 0 --- --- 49 2159 0.008 0.006 0.053 0.019 0.010 0.006 

Manhattan --- 0 --- --- 51 2157 0.010 0.006 0.058 0.023 0.012 0.008 

Summer99 Bronx --- 0 --- --- 1704 552 0.007 0.004 0.036 0.015 0.009 0.005 

Manhattan --- 0 --- --- 53 2203 0.008 0.006 0.092 0.017 0.010 0.006 

Fall99 Bronx --- 0 --- --- 333 1827 0.013 0.010 0.098 0.033 0.016 0.010 

Manhattan --- 0 --- --- 72 2088 0.013 0.009 0.082 0.030 0.016 0.010 

Winter00 Bronx --- 36 --- --- 5 2095 0.018 0.012 0.112 0.041 0.022 0.015 

Manhattan --- 54 --- --- 3 2079 0.020 0.012 0.097 0.043 0.025 0.017 

Spring00 Bronx --- 45 --- --- 0 2163 0.007 0.006 0.053 0.018 0.009 0.006 

Manhattan --- 365 --- --- 0 1843 0.008 0.006 0.056 0.019 0.011 0.006 

Summer00 Bronx --- 42 --- --- 0 2214 0.006 0.005 0.057 0.016 0.007 0.005 

Manhattan --- 492 --- --- 0 1764 0.006 0.005 0.088 0.016 0.008 0.005 

Fall00 Bronx --- 35 --- --- 0 1453 0.013 0.008 0.068 0.029 0.016 0.010 

Manhattan --- 43 --- --- 0 1445 0.012 0.009 0.053 0.028 0.017 0.010 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

PM2.5 (TEOM) 

(ug/m3 ) 

Winter99 Bronx --- --- --- --- 17 1879 14.98 9.27 75.60 31.94 20.21 13.35 

Manhattan --- --- --- --- 17 1879 15.29 7.63 75.91 28.78 19.31 13.64 

Spring99 Bronx --- --- --- --- 34 2174 14.02 9.16 68.13 32.29 17.34 12.10 

Manhattan --- --- --- --- 78 2130 14.91 9.27 68.80 33.71 18.22 12.76 

Summer99 Bronx --- --- --- --- 1701 555 21.17 12.85 61.37 45.27 29.79 20.33 

Manhattan --- 39 --- --- 2 2215 20.42 14.47 94.76 47.97 28.53 17.42 

Fall99 Bronx --- --- --- --- 792 1368 15.22 10.15 74.10 34.06 21.46 13.01 

Manhattan --- 407 --- --- 3 1750 15.54 9.73 76.92 33.71 21.20 13.40 

Winter00 Bronx --- --- --- 76 2060 14.45 9.61 77.98 33.17 18.73 12.35 

Manhattan --- 3 --- --- 20 2113 14.72 8.47 70.70 31.60 18.76 12.65 

Spring00 Bronx --- --- --- --- 92 2116 15.50 11.63 91.80 39.60 19.96 12.22 

Manhattan --- --- --- --- 397 1811 15.14 11.07 78.38 38.29 19.04 12.58 

Summer00 Bronx --- 192 --- --- 4 2060 16.71 10.83 86.43 37.40 23.55 14.14 

Manhattan --- 375 --- --- 2 1879 17.62 12.23 57.13 41.80 25.05 15.12 

Fall00 Bronx --- 229 --- --- 0 1259 14.44 10.17 52.60 37.04 18.33 11.72 

Manhattan --- 109 --- --- 0 1379 15.08 10.02 74.26 36.37 19.88 12.43 

PM10 (TEOM) 

(ug/m3 ) 

Winter99 Bronx --- --- --- --- 988 908 19.78 10.95 109.86 40.20 23.16 17.16 

Manhattan --- 20 --- --- 14 1862 19.55 8.77 95.92 35.64 23.91 17.88 

Spring99 Bronx --- --- --- --- 31 2177 22.35 11.75 101.43 45.71 27.55 19.50 

Manhattan --- --- --- --- 26 2182 21.63 10.37 71.50 42.32 26.81 19.31 

Summer99 Bronx --- 167 --- --- 1698 391 27.30 14.71 91.88 56.40 35.69 25.94 

Manhattan --- 30 --- --- 3 2223 26.11 14.45 93.84 53.63 34.84 23.14 

Fall99 Bronx --- 140 --- --- 756 1264 19.42 12.72 91.80 44.19 26.48 15.31 

Manhattan --- 1258 --- --- 0 902 22.12 13.19 111.30 47.66 26.46 18.56 

Winter00 Bronx --- --- --- --- 216 1920 20.56 13.39 150.76 45.17 24.58 16.90 

Manhattan --- 3 --- --- 25 2108 22.35 12.18 125.11 42.88 27.01 19.76 

Spring00 Bronx --- --- --- --- 570 1638 24.64 16.14 105.85 58.87 32.01 19.69 

Manhattan --- --- --- --- 476 1732 23.82 15.09 120.61 56.97 29.16 20.03 

Summer00 Bronx --- 13 --- --- 5 2238 23.50 12.48 98.03 47.62 31.50 20.44 

Manhattan --- 139 --- --- 2 2115 25.33 15.26 345.51 47.87 32.24 23.01 

Fall00 Bronx --- 10 --- --- 0 1478 21.78 13.60 79.74 50.02 28.14 17.70 

Manhattan --- 24 --- --- 0 1464 22.59 13.64 106.48 49.87 29.25 19.11 

Acetaldehyde 

(ug/m3 ) 

Winter99 Bronx --- --- 0 1 1 78 2.2 1.2 7.7 4.7 2.8 1.7 

Manhattan --- --- 0 0 0 79 2.1 0.9 5.5 4.0 2.6 2.0 

Spring99 Bronx --- --- 0 0 8 84 2.2 1.1 6.9 4.2 2.7 1.9 

Manhattan --- --- 0 0 0 92 2.7 1.0 5.9 4.8 3.2 2.5 

Summer99 Bronx --- --- 0 0 65 29 2.9 1.0 4.5 4.3 3.5 3.1 

Manhattan --- --- 5 1 8 86 3.8 2.7 13.6 9.5 5.2 3.0 

Fall99 Bronx --- --- 0 0 13 77 2.5 1.3 7.4 4.9 3.4 2.1 

Manhattan --- --- 0 0 1 89 2.7 1.2 7.1 5.2 3.2 2.3 

Winter00 Bronx --- --- 0 1 0 89 2.3 1.4 10.4 4.6 2.7 1.9 

Manhattan --- --- 0 0 0 89 2.6 1.4 10.7 4.6 2.9 2.2 

Spring00 Bronx --- --- 0 0 8 84 4.4 2.5 12.7 8.9 5.4 4.1 

Manhattan --- --- 0 0 0 92 2.6 1.3 6.2 5.4 3.1 2.3 

Summer00 Bronx --- --- 0 0 2 92 2.3 0.9 4.8 3.8 3.0 2.2 

Manhattan --- --- 2 0 1 93 2.4 0.9 4.8 3.9 2.9 2.3 

Fall00 Bronx --- --- 1 0 7 55 2.1 1.4 6.6 5.5 3.0 1.6 

Manhattan --- --- 0 0 8 54 2.4 1.5 7.0 5.8 3.2 1.9 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Acetone

(ug/m3 )

Winter99 Bronx --- --- 0 0 1 78 9.6 8.1 35.3 30.0 9.1 6.6 

Manhattan --- --- 0 0 0 79 7.0 3.7 22.0 15.0 7.8 6.2 

Spring99 Bronx --- --- 0 0 8 84 8.0 3.3 18.0 13.4 10.1 7.7 

Manhattan --- --- 0 0 0 92 8.8 11.6 116.0 13.3 9.0 7.2 

Summer99 Bronx --- --- 0 0 65 29 7.0 3.5 15.3 13.0 9.8 6.2 

Manhattan --- --- 2 11 8 86 7.6 6.9 40.4 22.2 9.9 6.4 

Fall99 Bronx --- --- 0 0 13 77 5.6 2.8 14.7 10.7 7.5 5.0 

Manhattan --- --- 0 0 1 89 6.8 2.7 16.1 12.6 8.0 6.5 

Winter00 Bronx --- --- 0 1 0 89 4.5 2.5 12.9 10.4 5.2 3.6 

Manhattan --- --- 0 0 0 89 5.4 2.5 15.8 10.8 6.2 4.8 

Spring00 Bronx --- --- 0 0 8 84 9.7 6.2 38.5 21.1 11.8 7.8 

Manhattan --- --- 0 0 0 92 6.6 2.3 13.3 10.9 7.8 6.5 

Summer00 Bronx --- --- 0 0 2 92 5.9 3.7 34.1 9.9 7.4 5.7 

Manhattan --- --- 2 0 1 93 6.1 2.8 15.3 10.8 7.9 5.8 

Fall00 Bronx --- --- 0 0 7 55 6.0 3.2 15.9 12.0 8.3 4.9 

Manhattan --- --- 0 0 8 54 7.0 3.6 21.2 13.0 9.4 5.9 

Acrolein

(ug/m3 )

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 0 84 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 92 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer99 Bronx --- --- 0 29 65 29 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 5 73 8 86 1.5 3.4 17.3 11.7 0.5 0.5 

Fall99 Bronx --- --- 0 77 13 77 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 89 1 89 0.5 0.0 0.5 0.5 0.5 0.5 

Winter00 Bronx --- --- 0 88 0 89 0.5 0.0 0.6 0.5 0.5 0.5 

Manhattan --- --- 0 89 0 89 0.5 0.0 0.5 0.5 0.5 0.5 

Spring00 Bronx --- --- 0 84 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 92 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer00 Bronx --- --- 5 87 2 92 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 7 86 1 93 0.5 0.0 0.5 0.5 0.5 0.5 

Fall00 Bronx --- --- 0 55 7 55 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 54 8 54 0.5 0.0 0.5 0.5 0.5 0.5 

Benzaldehyde 

(ug/m3 ) 

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 7 77 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 2 90 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer99 Bronx --- --- 12 17 65 29 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 40 34 8 86 0.7 0.5 2.3 2.0 0.5 0.5 

Fall99 Bronx --- --- 44 31 13 77 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 63 22 1 89 0.5 0.0 0.6 0.5 0.5 0.5 

Winter00 Bronx --- --- 33 53 0 89 0.5 0.0 0.7 0.5 0.5 0.5 

Manhattan --- --- 42 46 0 89 0.5 0.0 0.5 0.5 0.5 0.5 

Spring00 Bronx --- --- 20 11 8 84 0.7 0.3 2.2 1.3 0.7 0.5 

Manhattan --- --- 57 30 0 92 0.5 0.0 0.6 0.5 0.5 0.5 

Summer00 Bronx --- --- 88 3 2 92 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 89 3 1 93 0.5 0.0 0.5 0.5 0.5 0.5 

Fall00 Bronx --- --- 37 16 7 55 0.5 0.1 1.0 0.5 0.5 0.5 

Manhattan --- --- 34 19 8 54 0.5 0.1 1.0 0.5 0.5 0.5 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Butyraldehyde

(ug/m3 )

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 75 0 79 0.8 1.2 7.4 4.0 0.5 0.5 

Spring99 Bronx --- --- 8 42 8 84 0.8 0.4 2.4 1.4 1.1 0.5 

Manhattan --- --- 5 14 0 92 1.8 1.2 6.0 3.8 2.8 1.3 

Summer99 Bronx --- --- 13 7 65 29 0.7 0.4 1.5 1.4 1.1 0.5 

Manhattan --- --- 48 5 8 86 1.0 0.9 4.3 3.1 1.2 0.5 

Fall99 Bronx --- --- 41 36 13 77 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 62 24 1 89 0.5 0.0 0.6 0.5 0.5 0.5 

Winter00 Bronx --- --- 27 59 0 89 0.5 0.0 0.7 0.5 0.5 0.5 

Manhattan --- --- 27 28 0 89 0.6 0.3 1.5 1.3 0.6 0.5 

Spring00 Bronx --- --- 12 10 8 84 1.6 0.9 4.1 3.3 2.1 1.7 

Manhattan --- --- 59 22 0 92 0.5 0.0 0.7 0.6 0.5 0.5 

Summer00 Bronx --- --- 68 2 2 92 0.5 0.1 1.1 0.8 0.5 0.5 

Manhattan --- --- 53 4 1 93 0.6 0.2 1.5 1.2 0.6 0.5 

Fall00 Bronx --- --- 39 15 7 55 0.5 0.1 1.0 0.5 0.5 0.5 

Manhattan --- --- 36 15 8 54 0.5 0.1 1.0 1.0 0.5 0.5 

Crotonaldehyde

(ug/m3 )

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 0 61 8 84 1.0 0.9 3.3 2.9 1.3 0.5 

Manhattan --- --- 0 74 0 92 0.8 0.7 3.5 2.5 0.5 0.5 

Summer99 Bronx --- --- 3 18 65 29 0.9 0.8 3.3 3.0 1.1 0.5 

Manhattan --- --- 38 18 8 86 1.4 1.5 7.1 4.5 1.6 0.5 

Fall99 Bronx --- --- 21 20 13 77 0.9 0.6 2.6 2.1 1.2 0.5 

Manhattan --- --- 15 10 1 89 1.0 0.6 3.2 2.1 1.3 0.8 

Winter00 Bronx --- --- 19 1 0 89 0.8 0.4 2.1 1.6 1.0 0.7 

Manhattan --- --- 10 0 0 89 0.9 0.4 2.3 1.7 1.0 0.8 

Spring00 Bronx --- --- 0 82 8 84 0.5 0.0 0.6 0.5 0.5 0.5 

Manhattan --- --- 0 90 0 92 0.5 0.0 0.7 0.5 0.5 0.5 

Summer00 Bronx --- --- 0 92 2 92 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 93 1 93 0.5 0.0 0.5 0.5 0.5 0.5 

Fall00 Bronx --- --- 0 55 7 55 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 54 8 54 0.5 0.0 0.5 0.5 0.5 0.5 

Formaldehyde 

(ug/m3 ) 

Winter99 Bronx --- --- 0 0 1 78 3.0 1.3 7.8 6.2 3.5 2.7 

Manhattan --- --- 0 0 0 79 3.3 1.5 10.0 6.7 3.9 3.0 

Spring99 Bronx --- --- 0 0 8 84 3.9 2.0 12.7 8.3 4.7 3.3 

Manhattan --- --- 0 0 0 92 4.5 1.7 9.7 7.8 5.1 4.2 

Summer99 Bronx --- --- 0 0 65 29 6.4 2.3 10.9 10.8 7.7 5.9 

Manhattan --- --- 0 1 8 86 7.5 3.0 15.4 12.6 9.9 7.2 

Fall99 Bronx --- --- 0 0 13 77 3.9 1.7 9.9 6.8 4.9 3.6 

Manhattan --- --- 0 0 1 89 4.0 1.5 8.9 6.7 4.9 3.9 

Winter00 Bronx --- --- 0 0 0 89 3.0 1.7 11.5 6.4 3.7 2.4 

Manhattan --- --- 0 0 0 89 3.2 1.6 9.8 5.8 3.7 2.7 

Spring00 Bronx --- --- 0 1 8 84 11.8 15.0 63.2 53.9 10.2 6.5 

Manhattan --- --- 0 1 0 92 4.1 2.2 10.5 9.3 4.7 3.4 

Summer00 Bronx --- --- 0 0 2 92 4.8 1.5 8.5 7.5 6.1 4.6 

Manhattan --- --- 0 0 1 93 4.6 1.5 8.0 7.2 5.6 4.5 

Fall00 Bronx --- --- 0 0 7 55 3.3 1.8 8.8 7.0 4.6 2.6 

Manhattan --- --- 0 0 8 54 3.5 1.8 8.9 7.0 4.4 3.0 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Hexaldehyde

(ug/m3 )

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 8 64 8 84 0.6 0.3 2.8 0.8 0.5 0.5 

Manhattan --- --- 6 63 0 92 0.5 0.2 1.3 1.0 0.5 0.5 

Summer99 Bronx --- --- 13 15 65 29 0.5 0.1 1.0 0.5 0.5 0.5 

Manhattan --- --- 61 9 8 86 2.3 4.4 23.3 11.6 0.5 0.5 

Fall99 Bronx --- --- 47 25 13 77 0.5 0.0 0.8 0.5 0.5 0.5 

Manhattan --- --- 68 12 1 89 0.5 0.1 0.9 0.7 0.5 0.5 

Winter00 Bronx --- --- 13 74 0 89 0.5 0.0 0.6 0.5 0.5 0.5 

Manhattan --- --- 25 59 0 89 0.5 0.0 0.7 0.5 0.5 0.5 

Spring00 Bronx --- --- 8 11 8 84 2.4 3.5 22.0 9.9 2.0 1.2 

Manhattan --- --- 52 17 0 92 0.6 0.2 2.2 1.0 0.5 0.5 

Summer00 Bronx --- --- 74 4 2 92 0.5 0.2 1.7 0.9 0.5 0.5 

Manhattan --- --- 55 4 1 93 0.6 0.4 2.5 1.6 0.5 0.5 

Fall00 Bronx --- --- 38 15 7 55 0.5 0.1 1.0 0.5 0.5 0.5 

Manhattan --- --- 43 8 8 54 0.5 0.1 1.0 0.5 0.5 0.5 

Isovaleraldehyde

(ug/m3 )

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 0 84 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 92 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer99 Bronx --- --- 1 24 65 29 0.7 0.6 3.3 1.7 0.5 0.5 

Manhattan --- --- 29 45 8 86 0.8 1.1 6.4 3.4 0.5 0.5 

Fall99 Bronx --- --- 18 58 13 77 0.5 0.1 1.0 0.5 0.5 0.5 

Manhattan --- --- 22 66 1 89 0.5 0.0 0.6 0.5 0.5 0.5 

Winter00 Bronx --- --- 16 70 0 89 0.5 0.0 0.7 0.5 0.5 0.5 

Manhattan --- --- 21 62 0 89 0.5 0.1 0.9 0.6 0.5 0.5 

Spring00 Bronx --- --- 12 50 8 84 0.7 0.4 2.2 1.6 0.5 0.5 

Manhattan --- --- 16 64 0 92 0.5 0.1 1.2 0.9 0.5 0.5 

Summer00 Bronx --- --- 68 11 2 92 0.5 0.0 0.6 0.5 0.5 0.5 

Manhattan --- --- 65 11 1 93 0.5 0.1 0.8 0.6 0.5 0.5 

Fall00 Bronx --- --- 30 23 7 55 0.5 0.1 1.0 0.5 0.5 0.5 

Manhattan --- --- 22 32 8 54 0.5 0.0 0.5 0.5 0.5 0.5 

m-Tolualdehyde 

(ug/m3 ) 

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 1 83 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 92 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer99 Bronx --- --- 4 25 65 29 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 3 83 8 86 0.5 0.0 0.5 0.5 0.5 0.5 

Fall99 Bronx --- --- 2 75 13 77 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 2 87 1 89 0.5 0.0 0.5 0.5 0.5 0.5 

Winter00 Bronx --- --- 0 89 0 89 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 89 0 89 0.5 0.0 0.5 0.5 0.5 0.5 

Spring00 Bronx --- --- 1 83 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 3 89 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer00 Bronx --- --- 0 92 2 92 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 93 1 93 0.5 0.0 0.5 0.5 0.5 0.5 

Fall00 Bronx --- --- 3 52 7 55 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 1 53 8 54 0.5 0.0 0.5 0.5 0.5 0.5 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

o-Tolualdehyde

(ug/m3 )

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 1 83 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 3 89 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer99 Bronx --- --- 2 23 65 29 0.7 0.4 2.1 2.0 0.5 0.5 

Manhattan --- --- 29 55 8 86 0.5 0.2 2.0 0.5 0.5 0.5 

Fall99 Bronx --- --- 8 69 13 77 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 8 80 1 89 0.5 0.0 0.9 0.5 0.5 0.5 

Winter00 Bronx --- --- 0 89 0 89 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 89 0 89 0.5 0.0 0.5 0.5 0.5 0.5 

Spring00 Bronx --- --- 6 77 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 3 89 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer00 Bronx --- --- 4 88 2 92 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 7 85 1 93 0.5 0.0 0.6 0.5 0.5 0.5 

Fall00 Bronx --- --- 0 55 7 55 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 1 53 8 54 0.5 0.0 0.5 0.5 0.5 0.5 

p-Tolualdehyde 

(ug/m3 ) 

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 2 82 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 92 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer99 Bronx --- --- 7 22 65 29 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 24 58 8 86 0.5 0.2 1.7 0.5 0.5 0.5 

Fall99 Bronx --- --- 16 59 13 77 0.5 0.1 1.1 0.5 0.5 0.5 

Manhattan --- --- 22 67 1 89 0.5 0.0 0.5 0.5 0.5 0.5 

Winter00 Bronx --- --- 10 69 0 89 0.5 0.1 1.3 0.6 0.5 0.5 

Manhattan --- --- 16 63 0 89 0.5 0.1 1.5 0.8 0.5 0.5 

Spring00 Bronx --- --- 20 18 8 84 0.8 0.5 2.8 1.8 0.9 0.5 

Manhattan --- --- 34 37 0 92 0.7 0.5 3.4 1.9 0.5 0.5 

Summer00 Bronx --- --- 55 12 2 92 0.6 0.2 1.4 1.1 0.5 0.5 

Manhattan --- --- 56 8 1 93 0.6 0.1 1.3 0.9 0.6 0.5 

Fall00 Bronx --- --- 43 3 7 55 0.6 0.3 1.7 1.3 0.5 0.5 

Manhattan --- --- 46 1 8 54 0.6 0.2 1.4 1.2 0.5 0.5 

Propionaldehyde 

(ug/m3 ) 

Winter99 Bronx --- --- 0 69 1 78 0.8 1.1 6.9 3.8 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 8 37 8 84 1.6 2.3 14.8 5.7 1.6 0.5 

Manhattan --- --- 9 45 0 92 1.5 2.5 16.3 6.9 1.3 0.5 

Summer99 Bronx --- --- 10 0 65 29 2.3 2.8 12.1 9.0 2.0 1.5 

Manhattan --- --- 34 3 8 86 2.2 2.1 8.7 6.6 3.1 1.5 

Fall99 Bronx --- --- 51 5 13 77 0.6 0.2 1.4 1.0 0.5 0.5 

Manhattan --- --- 61 4 1 89 0.6 0.2 1.8 1.1 0.5 0.5 

Winter00 Bronx --- --- 69 1 0 89 0.6 0.2 1.3 1.0 0.5 0.5 

Manhattan --- --- 69 0 0 89 0.5 0.1 1.4 0.8 0.5 0.5 

Spring00 Bronx --- --- 25 1 8 84 1.0 0.8 6.2 2.2 1.1 0.7 

Manhattan --- --- 58 1 0 92 0.6 0.2 1.3 1.1 0.6 0.5 

Summer00 Bronx --- --- 57 2 2 92 0.6 0.1 1.4 0.8 0.6 0.5 

Manhattan --- --- 53 2 1 93 0.6 0.1 1.1 0.9 0.6 0.5 

Fall00 Bronx --- --- 42 6 7 55 0.6 0.2 1.4 1.1 0.5 0.5 

Manhattan --- --- 41 5 8 54 0.6 0.2 1.5 1.1 0.5 0.5 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Valeraldehyde

(ug/m3 )

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 2 78 8 84 0.5 0.0 0.6 0.5 0.5 0.5 

Manhattan --- --- 2 85 0 92 0.5 0.2 2.5 0.5 0.5 0.5 

Summer99 Bronx --- --- 7 22 65 29 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 16 62 8 86 0.7 0.6 3.9 2.1 0.5 0.5 

Fall99 Bronx --- --- 20 57 13 77 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 44 45 1 89 0.5 0.0 0.5 0.5 0.5 0.5 

Winter00 Bronx --- --- 2 86 0 89 0.5 0.0 0.6 0.5 0.5 0.5 

Manhattan --- --- 13 76 0 89 0.5 0.0 0.5 0.5 0.5 0.5 

Spring00 Bronx --- --- 28 19 8 84 0.9 0.8 4.2 3.2 0.7 0.5 

Manhattan --- --- 35 57 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer00 Bronx --- --- 49 43 2 92 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 56 36 1 93 0.5 0.1 1.3 0.5 0.5 0.5 

Fall00 Bronx --- --- 11 44 7 55 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 12 42 8 54 0.5 0.0 0.5 0.5 0.5 0.5 

2,5-Dimethylbenzaldehyde

(ug/m3 )

Winter99 Bronx --- --- 0 78 1 78 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 79 0 79 0.5 0.0 0.5 0.5 0.5 0.5 

Spring99 Bronx --- --- 0 84 8 84 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 92 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer99 Bronx --- --- 0 29 65 29 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 86 8 86 0.5 0.0 0.5 0.5 0.5 0.5 

Fall99 Bronx --- --- 5 72 13 77 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 5 84 1 89 0.5 0.0 0.5 0.5 0.5 0.5 

Winter00 Bronx --- --- 0 89 0 89 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 89 0 89 0.5 0.0 0.5 0.5 0.5 0.5 

Spring00 Bronx --- --- 2 77 8 84 0.5 0.1 0.9 0.5 0.5 0.5 

Manhattan --- --- 0 92 0 92 0.5 0.0 0.5 0.5 0.5 0.5 

Summer00 Bronx --- --- 2 90 2 92 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 4 88 1 93 0.5 0.0 0.5 0.5 0.5 0.5 

Fall00 Bronx --- --- 0 55 7 55 0.5 0.0 0.5 0.5 0.5 0.5 

Manhattan --- --- 0 54 8 54 0.5 0.0 0.5 0.5 0.5 0.5 

Total Aldehydes 

(ug/m3 ) 

Winter99 Bronx --- --- --- --- 1 78 15.1 10.1 53.6 38.7 17.5 10.6 

Manhattan --- --- --- --- 0 79 12.7 5.8 27.5 27.3 15.5 11.5 

Spring99 Bronx --- --- --- --- 8 84 16.7 8.0 43.8 32.0 21.2 15.0 

Manhattan --- --- --- --- 0 92 19.6 12.8 122.9 34.8 22.1 17.1 

Summer99 Bronx --- --- --- --- 65 29 19.9 6.1 30.0 29.5 25.2 20.1 

Manhattan --- --- --- --- 9 85 26.7 18.9 93.9 66.4 32.9 20.2 

Fall99 Bronx --- --- --- --- 13 77 12.9 6.3 33.4 24.6 17.0 10.5 

Manhattan --- --- --- --- 1 89 14.7 6.1 38.6 27.7 17.0 13.7 

Winter00 Bronx --- --- --- --- 0 89 10.9 6.6 40.3 24.5 12.9 8.7 

Manhattan --- --- --- --- 0 89 12.7 6.0 37.8 23.5 13.3 11.2 

Spring00 Bronx --- --- --- --- 8 84 32.4 29.8 134.1 104.0 32.6 22.7 

Manhattan --- --- --- --- 0 92 14.3 6.7 34.9 29.9 16.8 12.4 

Summer00 Bronx --- --- --- --- 2 92 13.8 5.4 39.1 22.8 17.7 13.0 

Manhattan --- --- --- --- 1 93 14.3 5.1 27.0 22.6 18.1 13.8 

Fall00 Bronx --- --- --- --- 7 55 11.8 7.4 36.8 25.1 16.2 9.0 

Manhattan --- --- --- --- 8 54 13.4 7.4 39.9 25.8 16.9 10.9 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Chromium

(ng/m3 

)

Winter99 Bronx --- --- --- 77 0 79 5.1 16.5 107.6 2.5 2.5 2.5 

Manhattan --- --- --- 77 0 79 2.7 1.3 13.7 2.5 2.5 2.5 

Spring99 Bronx --- --- --- 91 0 92 2.5 0.4 6.5 2.5 2.5 2.5 

Manhattan --- --- --- 91 1 91 2.5 0.0 2.5 2.5 2.5 2.5 

Summer99 Bronx --- --- --- 35 59 35 2.5 0.0 2.5 2.5 2.5 2.5 

Manhattan --- --- --- 84 8 86 2.6 0.5 5.9 2.5 2.5 2.5 

Fall99 Bronx --- --- --- 86 2 88 2.6 0.5 6.0 2.5 2.5 2.5 

Manhattan --- --- --- 89 1 89 2.5 0.0 2.5 2.5 2.5 2.5 

Winter00 Bronx --- --- --- 77 9 80 2.7 0.9 9.0 2.5 2.5 2.5 

Manhattan --- --- --- 84 4 85 2.6 1.2 13.5 2.5 2.5 2.5 

Spring00 Bronx --- --- --- 82 7 85 2.6 0.7 7.5 2.5 2.5 2.5 

Manhattan --- --- --- 89 3 89 2.5 0.0 2.5 2.5 2.5 2.5 

Summer00 Bronx --- --- --- 91 2 92 2.6 0.8 10.0 2.5 2.5 2.5 

Manhattan --- --- --- 81 11 83 2.9 3.1 30.0 2.5 2.5 2.5 

Fall00 Bronx --- --- --- 49 11 51 3.0 3.0 23.1 2.5 2.5 2.5 

Manhattan --- --- --- 55 3 59 3.2 4.0 32.9 7.2 2.5 2.5 

Iron

(ng/m3 )

Winter99 Bronx --- --- --- 6 0 79 107.9 141.2 885.8 357.9 116.0 79.2 

Manhattan --- --- --- 4 0 79 97.6 68.9 408.6 236.0 127.9 76.4 

Spring99 Bronx --- --- --- 10 0 92 67.4 49.2 302.0 176.9 87.8 55.8 

Manhattan --- --- --- 4 1 91 77.0 40.9 180.2 156.0 99.7 67.5 

Summer99 Bronx --- --- --- 2 59 35 93.0 62.3 261.4 196.4 133.8 70.8 

Manhattan --- --- --- 3 8 86 88.4 63.1 423.9 190.9 119.3 74.0 

Fall99 Bronx --- --- --- 4 2 88 93.3 68.4 332.5 258.9 115.1 71.5 

Manhattan --- --- --- 7 1 89 72.5 54.0 302.0 191.1 89.8 56.3 

Winter00 Bronx --- --- --- 25 9 80 80.3 195.4 1720.0 172.4 82.2 37.7 

Manhattan --- --- --- 21 4 85 65.0 55.3 290.6 176.1 83.2 58.9 

Spring00 Bronx --- --- --- 26 7 85 60.2 51.6 253.6 159.4 85.0 55.8 

Manhattan --- --- --- 32 3 89 57.0 52.1 230.9 171.3 87.0 38.7 

Summer00 Bronx --- --- --- 50 2 92 43.6 45.2 236.5 130.6 67.5 11.0 

Manhattan --- --- --- 47 11 83 49.4 56.5 293.1 161.4 81.8 11.0 

Fall00 Bronx --- --- --- 15 11 51 64.2 58.6 272.1 166.9 94.7 43.3 

Manhattan --- --- --- 20 3 59 69.1 70.5 356.4 187.9 119.6 41.1 

Lead

(ng/m3 ) 

Winter99 Bronx --- --- --- 73 0 79 6.7 2.9 25.9 13.6 6.0 6.0 

Manhattan --- --- --- 74 0 79 6.7 2.9 23.7 13.6 6.0 6.0 

Spring99 Bronx --- --- --- 92 0 92 6.0 0.0 6.0 6.0 6.0 6.0 

Manhattan --- --- --- 87 1 91 6.4 2.1 21.5 6.0 6.0 6.0 

Summer99 Bronx --- --- --- 35 59 35 6.0 0.0 6.0 6.0 6.0 6.0 

Manhattan --- --- --- 83 8 86 6.3 1.6 16.3 6.0 6.0 6.0 

Fall99 Bronx --- --- --- 79 2 88 7.2 3.8 23.1 18.7 6.0 6.0 

Manhattan --- --- --- 82 1 89 7.1 4.1 26.8 19.8 6.0 6.0 

Winter00 Bronx --- --- --- 68 9 80 9.7 19.2 175.0 21.0 6.0 6.0 

Manhattan --- --- --- 77 4 85 6.9 3.0 21.0 14.5 6.0 6.0 

Spring00 Bronx --- --- --- 82 7 85 6.3 1.5 15.0 6.0 6.0 6.0 

Manhattan --- --- --- 87 3 89 6.2 1.3 15.2 6.0 6.0 6.0 

Summer00 Bronx --- --- --- 85 2 92 6.7 2.6 18.3 14.6 6.0 6.0 

Manhattan --- --- --- 80 11 83 6.5 2.7 26.1 6.0 6.0 6.0 

Fall00 Bronx --- --- --- 51 11 51 6.0 0.0 6.0 6.0 6.0 6.0 

Manhattan --- --- --- 57 3 59 6.3 1.6 16.8 6.0 6.0 6.0 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Manganese

(ng/m3 )

Winter99 Bronx --- --- --- 73 0 79 2.6 5.5 35.5 4.7 1.5 1.5 

Manhattan --- --- --- 74 0 79 1.7 1.0 8.1 4.3 1.5 1.5 

Spring99 Bronx --- --- --- 91 0 92 1.6 0.5 6.3 1.5 1.5 1.5 

Manhattan --- --- --- 90 1 91 1.5 0.3 4.0 1.5 1.5 1.5 

Summer99 Bronx --- --- --- 34 59 35 1.6 0.4 3.8 1.5 1.5 1.5 

Manhattan --- --- --- 83 8 86 1.6 0.4 3.8 1.5 1.5 1.5 

Fall99 Bronx --- --- --- 80 2 88 1.8 1.1 6.7 4.8 1.5 1.5 

Manhattan --- --- --- 83 1 89 1.8 1.2 7.6 4.3 1.5 1.5 

Winter00 Bronx --- --- --- 75 9 80 1.7 0.6 5.3 3.4 1.5 1.5 

Manhattan --- --- --- 76 4 85 1.8 1.1 7.1 4.4 1.5 1.5 

Spring00 Bronx --- --- --- 83 7 85 1.5 0.2 3.0 1.5 1.5 1.5 

Manhattan --- --- --- 83 3 89 1.7 0.6 4.5 3.4 1.5 1.5 

Summer00 Bronx --- --- --- 80 2 92 1.8 1.0 8.1 3.7 1.5 1.5 

Manhattan --- --- --- 53 11 83 2.7 2.5 19.5 6.1 3.4 1.5 

Fall00 Bronx --- --- --- 48 11 51 1.6 0.4 3.7 3.0 1.5 1.5 

Manhattan --- --- --- 49 3 59 2.0 1.2 6.4 4.8 1.5 1.5 

Nickel

(ng/m3 ) 

Winter99 Bronx --- --- --- 6 0 79 30.7 36.1 190.6 85.3 34.0 22.6 

Manhattan --- --- --- 1 0 79 35.1 26.6 119.9 83.5 51.5 30.5 

Spring99 Bronx --- --- --- 45 0 92 5.5 6.8 37.6 20.4 5.2 4.0 

Manhattan --- --- --- 28 1 91 10.3 20.0 140.4 35.3 8.9 5.5 

Summer99 Bronx --- --- --- 20 59 35 4.7 4.4 22.0 14.8 6.2 2.0 

Manhattan --- --- --- 47 8 86 5.5 5.1 20.2 17.1 7.9 2.0 

Fall99 Bronx --- --- --- 25 2 88 10.7 10.5 67.4 26.6 14.9 7.7 

Manhattan --- --- --- 28 1 89 11.8 12.8 63.2 35.3 18.9 5.5 

Winter00 Bronx --- --- --- 14 9 80 16.4 16.8 103.9 40.0 21.4 12.4 

Manhattan --- --- --- 7 4 85 27.0 21.3 118.3 56.1 34.1 25.6 

Spring00 Bronx --- --- --- 31 7 85 8.6 8.4 49.5 22.4 12.0 6.7 

Manhattan --- --- --- 25 3 89 11.7 9.2 42.0 26.2 17.3 10.2 

Summer00 Bronx --- --- --- 14 29 65 8.9 6.7 29.1 22.2 12.9 7.0 

Manhattan --- --- --- 6 32 62 12.0 7.1 41.3 22.1 16.1 12.1 

Fall00 Bronx --- --- --- 24 11 51 6.1 10.9 78.9 11.4 6.4 4.3 

Manhattan --- --- --- 13 3 59 8.9 8.2 41.3 30.3 12.3 5.9 

Zinc 

(ng/m3 ) 

Winter99 Bronx --- --- --- 76 0 79 41.0 12.9 118.8 38.5 38.5 38.5 

Manhattan --- --- --- 77 0 79 39.8 8.3 92.9 38.5 38.5 38.5 

Spring99 Bronx --- --- --- 92 0 92 38.5 0.0 38.5 38.5 38.5 38.5 

Manhattan --- --- --- 91 1 91 38.5 0.0 38.5 38.5 38.5 38.5 

Summer99 Bronx --- --- --- 35 59 35 38.5 0.0 38.5 38.5 38.5 38.5 

Manhattan --- --- --- 84 8 86 40.4 12.8 134.0 38.5 38.5 38.5 

Fall99 Bronx --- --- --- 83 2 88 41.9 14.3 113.8 78.3 38.5 38.5 

Manhattan --- --- --- 86 1 89 41.5 17.5 169.1 38.5 38.5 38.5 

Winter00 Bronx --- --- --- 77 9 80 40.8 12.0 118.0 38.5 38.5 38.5 

Manhattan --- --- --- 81 4 85 42.0 19.0 196.0 38.5 38.5 38.5 

Spring00 Bronx --- --- --- 85 7 85 38.5 0.0 38.5 38.5 38.5 38.5 

Manhattan --- --- --- 88 3 89 39.1 5.9 94.0 38.5 38.5 38.5 

Summer00 Bronx --- --- --- 86 2 92 46.3 31.2 209.7 122.1 38.5 38.5 

Manhattan --- --- --- 82 11 83 39.0 4.5 79.5 38.5 38.5 38.5 

Fall00 Bronx --- --- --- 50 11 51 39.6 7.7 93.5 38.5 38.5 38.5 

Manhattan --- --- --- 59 3 59 38.5 0.0 38.5 38.5 38.5 38.5 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Total Metals

(ng/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 194.1 194.7 1264.0 480.9 200.5 152.1 

Manhattan --- --- --- --- 0 79 183.6 91.5 543.8 397.7 233.8 166.5 

Spring99 Bronx --- --- --- --- 0 92 121.4 53.2 389.6 230.5 141.6 108.4 

Manhattan --- --- --- --- 1 91 136.2 51.7 333.0 234.2 161.6 120.6 

Summer99 Bronx --- --- --- --- 59 35 146.3 64.6 313.9 255.9 191.6 121.3 

Manhattan --- --- --- --- 8 86 144.8 67.6 474.4 258.0 171.7 127.0 

Fall99 Bronx --- --- --- --- 2 88 157.5 86.6 504.5 348.0 173.1 132.7 

Manhattan --- --- --- --- 1 89 137.2 75.3 544.3 271.4 156.1 114.2 

Winter00 Bronx --- --- --- --- 9 80 151.5 234.5 2105.0 260.4 150.1 109.0 

Manhattan --- --- --- --- 4 85 145.3 73.8 390.7 302.7 167.5 137.9 

Spring00 Bronx --- --- --- --- 7 85 117.7 55.4 337.6 223.9 143.7 107.4 

Manhattan --- --- --- --- 3 89 118.2 57.7 297.5 235.7 155.2 101.5 

Summer00 Bronx --- --- --- --- 2 92 107.3 53.1 314.9 198.7 142.9 82.9 

Manhattan --- --- --- --- 11 83 109.5 63.5 363.2 220.6 143.2 76.0 

Fall00 Bronx --- --- --- --- 11 51 120.5 65.3 333.2 275.9 171.8 94.3 

Manhattan --- --- --- --- 3 59 128.0 75.8 457.4 253.2 172.8 99.5 

SO2 (Denuder)

(ug/m3 )

Winter99 Bronx --- --- --- --- 79 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 79 0 --- --- --- --- --- --- 

Spring99 Bronx --- --- --- --- 92 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 92 0 --- --- --- --- --- --- 

Summer99 Bronx --- --- --- --- 60 34 14.0 9.4 30.2 29.6 23.7 10.9 

Manhattan --- --- --- --- 7 87 17.9 10.1 45.5 38.3 22.2 15.1 

Fall99 Bronx --- --- --- --- 2 88 27.4 16.5 94.1 56.5 32.9 23.6 

Manhattan --- --- --- --- 2 88 28.6 17.5 99.2 58.3 35.6 24.1 

Winter00 Bronx --- --- --- --- 5 84 41.6 21.2 132.7 78.5 54.0 35.1 

Manhattan --- --- --- --- 4 85 43.7 22.8 156.8 77.1 55.7 39.5 

Spring00 Bronx --- --- --- --- 0 92 16.5 9.1 50.7 30.8 22.0 14.6 

Manhattan --- --- --- --- 0 92 18.2 9.8 47.7 37.5 25.3 15.7 

Summer00 Bronx --- --- --- --- 72 22 16.8 10.3 43.9 35.8 19.5 12.7 

Manhattan --- --- --- --- 72 22 17.8 7.3 34.4 29.1 23.0 15.9 

Fall00 Bronx --- --- --- --- 62 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 62 0 --- --- --- --- --- --- 

HCl

(ug/m3 ) 

Winter99 Bronx --- --- --- --- 79 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 79 0 --- --- --- --- --- --- 

Spring99 Bronx --- --- --- --- 92 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 92 0 --- --- --- --- --- --- 

Summer99 Bronx --- --- --- --- 60 34 0.9 0.7 2.5 2.4 1.2 0.6 

Manhattan --- --- --- --- 7 87 1.0 0.7 3.0 2.4 1.4 0.9 

Fall99 Bronx --- --- --- --- 2 88 0.3 0.2 1.1 0.7 0.4 0.3 

Manhattan --- --- --- --- 1 89 0.3 0.2 1.2 0.7 0.3 0.2 

Winter00 Bronx --- --- --- --- 5 84 0.4 0.3 1.4 0.9 0.5 0.3 

Manhattan --- --- --- --- 4 85 0.3 0.2 0.9 0.6 0.4 0.3 

Spring00 Bronx --- --- --- --- 0 92 0.4 0.5 2.7 1.6 0.4 0.3 

Manhattan --- --- --- --- 0 92 0.4 0.5 2.0 1.6 0.4 0.3 

Summer00 Bronx --- --- --- --- 72 22 1.0 0.3 1.6 1.6 1.2 0.9 

Manhattan --- --- --- --- 72 22 0.8 0.3 1.4 1.3 1.0 0.8 

Fall00 Bronx --- --- --- --- 62 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 62 0 --- --- --- --- --- --- 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

HNO2

(ug/m3 )

Winter99 Bronx --- --- --- --- 79 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 79 0 --- --- --- --- --- --- 

Spring99 Bronx --- --- --- --- 92 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 92 0 --- --- --- --- --- --- 

Summer99 Bronx --- --- --- --- 60 34 1.9 1.6 5.3 5.0 2.8 1.4 

Manhattan --- --- --- --- 8 86 1.9 1.5 6.4 4.8 3.0 1.6 

Fall99 Bronx --- --- --- --- 2 88 4.0 2.5 12.7 9.0 5.2 3.4 

Manhattan --- --- --- --- 1 89 4.4 2.2 10.4 8.3 5.7 4.1 

Winter00 Bronx --- --- --- --- 5 84 3.7 2.6 12.5 8.6 5.1 2.9 

Manhattan --- --- --- --- 4 85 4.0 2.7 16.8 8.8 4.6 3.2 

Spring00 Bronx --- --- --- --- 0 92 2.4 1.9 11.8 6.4 2.9 2.1 

Manhattan --- --- --- --- 0 92 2.9 2.0 11.8 7.3 3.7 2.7 

Summer00 Bronx --- --- --- --- 72 22 1.4 1.2 5.0 2.8 2.2 0.9 

Manhattan --- --- --- --- 72 22 1.5 1.1 3.9 3.5 2.4 1.1 

Fall00 Bronx --- --- --- --- 62 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 62 0 --- --- --- --- --- --- 

HNO3

(ug/m3 ) 

Winter99 Bronx --- --- --- --- 79 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 79 0 --- --- --- --- --- --- 

Spring99 Bronx --- --- --- --- 92 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 92 0 --- --- --- --- --- --- 

Summer99 Bronx --- --- --- --- 60 34 2.5 2.0 6.4 6.4 3.7 1.8 

Manhattan --- --- --- --- 9 85 3.9 3.1 14.7 10.9 5.6 2.9 

Fall99 Bronx --- --- --- --- 2 88 0.6 0.6 3.1 1.6 0.6 0.4 

Manhattan --- --- --- --- 1 89 0.6 0.5 2.5 1.8 0.8 0.5 

Winter00 Bronx --- --- --- --- 5 84 0.5 0.3 1.8 1.0 0.6 0.5 

Manhattan --- --- --- --- 4 85 1.0 0.7 3.0 2.3 1.5 0.8 

Spring00 Bronx --- --- --- --- 0 92 1.2 1.4 7.2 4.4 1.1 0.7 

Manhattan --- --- --- --- 0 92 1.2 1.4 6.9 4.7 1.2 0.8 

Summer00 Bronx --- --- --- --- 72 22 3.1 1.1 5.0 4.9 3.9 3.2 

Manhattan --- --- --- --- 72 22 3.2 1.0 5.3 4.9 4.0 3.1 

Fall00 Bronx --- --- --- --- 62 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 62 0 --- --- --- --- --- --- 

NH3

(ug/m3 ) 

Winter99 Bronx --- --- --- --- 79 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 79 0 --- --- --- --- --- --- 

Spring99 Bronx --- --- --- --- 92 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 92 0 --- --- --- --- --- --- 

Summer99 Bronx --- --- --- --- 74 20 4.5 1.2 6.7 6.4 5.4 4.6 

Manhattan --- --- --- --- 43 51 4.3 0.9 6.0 5.5 5.0 4.3 

Fall99 Bronx --- --- --- --- 90 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 90 0 --- --- --- --- --- --- 

Winter00 Bronx --- --- --- --- 11 78 1.3 1.4 6.7 4.8 1.7 0.9 

Manhattan --- --- --- --- 9 80 2.8 1.8 8.3 7.0 3.7 2.3 

Spring00 Bronx --- --- --- --- 34 58 2.8 1.7 6.9 6.3 3.8 2.3 

Manhattan --- --- --- --- 37 55 4.0 2.2 10.8 7.2 5.3 3.4 

Summer00 Bronx --- --- --- --- 94 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 94 0 --- --- --- --- --- --- 

Fall00 Bronx --- --- --- --- 62 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 62 0 --- --- --- --- --- --- 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Total Pollen

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 0.9 2.0 11.0 5.7 0.4 0.2 

Manhattan --- --- --- --- 0 79 0.4 0.9 4.4 2.6 0.4 0.1 

Spring99 Bronx --- --- --- --- 0 92 33.4 49.5 233.2 192.5 44.4 15.8 

Manhattan --- --- --- --- 0 92 23.5 31.3 131.2 105.3 27.0 10.4 

Summer99 Bronx --- --- --- --- 53 41 8.2 10.7 44.0 33.2 8.0 4.6 

Manhattan --- --- --- --- 0 94 4.3 4.9 26.0 16.7 5.1 2.5 

Fall99 Bronx --- --- --- --- 0 90 0.7 1.5 7.8 3.5 0.6 0.2 

Manhattan --- --- --- --- 0 90 0.3 0.8 4.2 2.2 0.2 0.0 

Winter00 Bronx --- --- --- --- 6 83 2.0 9.1 77.5 8.9 0.2 0.0 

Manhattan --- --- --- --- 0 89 2.2 9.6 83.4 8.0 0.4 0.0 

Spring00 Bronx --- --- --- --- 0 92 106.5 358.2 2212.9 930.2 28.9 10.6 

Manhattan --- --- --- --- 0 92 64.7 222.8 1558.3 375.2 27.7 8.8 

Summer00 Bronx --- --- --- --- 0 94 6.2 11.3 65.9 25.8 5.8 2.2 

Manhattan --- --- --- --- 0 94 3.3 4.9 32.7 13.4 3.8 1.6 

Fall00 Bronx --- --- --- --- 1 61 0.4 0.7 3.4 1.3 0.5 0.2 

Manhattan --- --- --- --- 1 61 0.2 0.4 2.0 0.9 0.2 0.0 

Pollen - Trees

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 0.9 2.0 11.2 5.9 0.4 0.2 

Manhattan --- --- --- --- 0 79 0.4 0.9 4.4 2.6 0.4 0.1 

Spring99 Bronx --- --- --- --- 0 92 32.6 48.4 232.0 165.7 42.7 15.8 

Manhattan --- --- --- --- 0 92 23.1 30.5 131.2 105.3 27.0 10.0 

Summer99 Bronx --- --- --- --- 48 46 1 2 7 5 2 1 

Manhattan --- --- --- --- 0 94 1 2 12 6 2 1 

Fall99 Bronx --- --- --- --- 0 90 0 0 1 0 0 0 

Manhattan --- --- --- --- 0 90 0 0 0 0 0 0 

Winter00 Bronx --- --- --- --- 6 83 2 9 78 9 0 0 

Manhattan --- --- --- --- 0 89 2 10 83 8 0 0 

Spring00 Bronx --- --- --- --- 0 92 106 358 2213 930 29 10 

Manhattan --- --- --- --- 0 92 64 223 1558 373 28 9 

Summer00 Bronx --- --- --- --- 0 94 1 3 25 2 0 0 

Manhattan --- --- --- --- 0 94 0 1 9 2 0 0 

Fall00 Bronx --- --- --- --- 1 61 0 0 1 0 0 0 

Manhattan --- --- --- --- 1 61 0 0 1 0 0 0 

Pollen - Ragweed

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 0.0 0.0 0.2 0.0 0.0 0.0 

Manhattan --- --- --- --- 0 79 0.0 0.0 0.2 0.2 0.0 0.0 

Spring99 Bronx --- --- --- --- 0 92 0.0 0.0 0.2 0.0 0.0 0.0 

Manhattan --- --- --- --- 0 92 0.0 0.0 0.3 0.0 0.0 0.0 

Summer99 Bronx --- --- --- --- 48 46 2 4 25 9 1 0 

Manhattan --- --- --- --- 0 94 1 3 24 7 1 0 

Fall99 Bronx --- --- --- --- 0 90 0 1 6 1 0 0 

Manhattan --- --- --- --- 0 90 0 1 3 1 0 0 

Winter00 Bronx --- --- --- --- 6 83 0 0 0 0 0 0 

Manhattan --- --- --- --- 0 89 0 0 0 0 0 0 

Spring00 Bronx --- --- --- --- 0 92 0 0 0 0 0 0 

Manhattan --- --- --- --- 0 92 0 0 0 0 0 0 

Summer00 Bronx --- --- --- --- 0 94 2 3 15 9 3 0 

Manhattan --- --- --- --- 0 94 1 2 8 5 2 0 

Fall00 Bronx --- --- --- --- 1 61 0 0 1 0 0 0 

Manhattan --- --- --- --- 1 61 0 0 1 0 0 0 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Pollen - Grasses

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 0.0 0.1 0.4 0.2 0.0 0.0 

Manhattan --- --- --- --- 0 79 0.0 0.0 0.2 0.0 0.0 0.0 

Spring99 Bronx --- --- --- --- 0 92 0.7 3.1 26.9 2.9 0.0 0.0 

Manhattan --- --- --- --- 0 92 0.4 1.5 12.4 3.2 0.0 0.0 

Summer99 Bronx --- --- --- --- 48 46 2 3 12 7 2 1 

Manhattan --- --- --- --- 0 94 1 1 6 4 1 1 

Fall99 Bronx --- --- --- --- 0 90 0 0 0 0 0 0 

Manhattan --- --- --- --- 0 90 0 0 0 0 0 0 

Winter00 Bronx --- --- --- --- 6 83 0 0 0 0 0 0 

Manhattan --- --- --- --- 0 89 0 0 0 0 0 0 

Spring00 Bronx --- --- --- --- 0 92 1 2 12 6 0 0 

Manhattan --- --- --- --- 0 92 1 2 9 3 0 0 

Summer00 Bronx --- --- --- --- 0 94 1 2 9 5 1 0 

Manhattan --- --- --- --- 0 94 1 2 17 3 1 0 

Fall00 Bronx --- --- --- --- 1 61 0 0 0 0 0 0 

Manhattan --- --- --- --- 1 61 0 0 0 0 0 0 

Total Mold

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 9.0 34.6 293.2 42.6 6.5 0.0 

Manhattan --- --- --- --- 0 79 5.7 13.7 69.4 47.6 3.5 0.0 

Spring99 Bronx --- --- --- --- 0 92 265.4 521.3 3539.0 1246.5 247.1 46.7 

Manhattan --- --- --- --- 0 92 231.7 414.4 2200.2 1244.2 253.9 34.7 

Summer99 Bronx --- --- --- --- 53 41 1335.8 1006.6 3652.3 3030.9 1914.6 1284.7 

Manhattan --- --- --- --- 0 94 1424.6 1123.6 5357.6 3704.9 2109.8 1145.8 

Fall99 Bronx --- --- --- --- 0 90 449.5 828.3 6171.4 1520.8 502.6 219.4 

Manhattan --- --- --- --- 0 90 372.4 529.6 2843.9 1299.6 493.3 132.7 

Winter00 Bronx --- --- --- --- 6 83 10.7 20.6 119.4 48.9 13.2 0.0 

Manhattan --- --- --- --- 0 89 3.6 6.7 31.3 21.9 6.0 0.0 

Spring00 Bronx --- --- --- --- 0 92 363.4 604.3 2450.4 2027.1 335.6 102.3 

Manhattan --- --- --- --- 0 92 475.7 831.8 4089.9 2228.0 470.0 93.0 

Summer00 Bronx --- --- --- --- 0 94 1041.2 881.4 6553.4 2452.5 1476.6 822.4 

Manhattan --- --- --- --- 0 94 832.3 730.2 5486.1 2052.1 1052.1 652.2 

Fall00 Bronx --- --- --- --- 1 61 499.7 550.4 2226.1 1655.7 771.2 335.6 

Manhattan --- --- --- --- 1 61 446.9 515.9 2860.4 1329.4 739.6 309.0 

Basidospores

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 0.9 4.3 27.5 6.6 0.0 0.0 

Manhattan --- --- --- --- 0 79 1.1 6.3 54.3 3.6 0.0 0.0 

Spring99 Bronx --- --- --- --- 0 92 37.3 64.9 368.1 201.4 45.4 9.7 

Manhattan --- --- --- --- 0 92 31.7 63.8 447.7 143.2 34.7 6.0 

Summer99 Bronx --- --- --- --- 48 46 346 504 2488 1444 415 149 

Manhattan --- --- --- --- 0 94 372 350 1485 1214 522 259 

Fall99 Bronx --- --- --- --- 0 90 249 547 4694 959 291 100 

Manhattan --- --- --- --- 0 90 195 304 1605 853 259 72 

Winter00 Bronx --- --- --- --- 6 83 1 4 24 11 0 0 

Manhattan --- --- --- --- 0 89 1 2 21 3 0 0 

Spring00 Bronx --- --- --- --- 0 92 106 249 1665 581 86 21 

Manhattan --- --- --- --- 0 92 206 435 2775 1170 186 24 

Summer00 Bronx --- --- --- --- 0 94 554 440 2545 1367 757 446 

Manhattan --- --- --- --- 0 94 453 355 2316 1068 638 383 

Fall00 Bronx --- --- --- --- 1 61 220 274 1096 822 310 124 

Manhattan --- --- --- --- 1 61 188 224 819 669 227 117 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Ascospores

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 4.7 31.5 280.1 9.9 0.0 0.0 

Manhattan --- --- --- --- 0 79 1.2 3.9 25.1 9.1 0.0 0.0 

Spring99 Bronx --- --- --- --- 0 92 49.6 116.0 891.4 277.8 39.1 12.0 

Manhattan --- --- --- --- 0 92 53.0 117.4 776.7 277.2 39.6 9.1 

Summer99 Bronx --- --- --- --- 48 46 82 93 380 267 115 49 

Manhattan --- --- --- --- 0 94 61 80 467 195 65 39 

Fall99 Bronx --- --- --- --- 0 90 24 29 149 84 31 16 

Manhattan --- --- --- --- 0 90 17 29 205 65 19 6 

Winter00 Bronx --- --- --- --- 6 83 2 4 27 7 0 0 

Manhattan --- --- --- --- 0 89 1 2 14 4 0 0 

Spring00 Bronx --- --- --- --- 0 92 39 64 478 123 51 16 

Manhattan --- --- --- --- 0 92 39 75 431 231 31 8 

Summer00 Bronx --- --- --- --- 0 94 111 151 925 413 135 66 

Manhattan --- --- --- --- 0 94 94 115 590 354 115 57 

Fall00 Bronx --- --- --- --- 1 61 42 64 300 154 58 20 

Manhattan --- --- --- --- 1 61 39 48 201 142 59 18 

Mitospores

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 3.4 9.0 43.8 26.1 3.3 0.0 

Manhattan --- --- --- --- 0 79 3.1 9.8 52.5 35.5 0.0 0.0 

Spring99 Bronx --- --- --- --- 0 92 176.8 389.3 2407.2 1006.9 146.5 15.4 

Manhattan --- --- --- --- 0 92 143.2 302.5 1791.3 803.8 105.5 12.1 

Summer99 Bronx --- --- --- --- 48 46 752 786 2676 2359 1209 483 

Manhattan --- --- --- --- 0 94 980 963 4837 2858 1392 713 

Fall99 Bronx --- --- --- --- 0 90 173 360 2474 677 179 49 

Manhattan --- --- --- --- 0 90 155 301 2133 521 179 41 

Winter00 Bronx --- --- --- --- 6 83 6 15 109 35 7 0 

Manhattan --- --- --- --- 0 89 2 5 26 15 0 0 

Spring00 Bronx --- --- --- --- 0 92 216 468 2136 1656 108 33 

Manhattan --- --- --- --- 0 92 228 503 2431 1636 197 21 

Summer00 Bronx --- --- --- --- 0 94 365 558 4675 1093 453 204 

Manhattan --- --- --- --- 0 94 276 383 2611 1094 313 142 

Fall00 Bronx --- --- --- --- 1 61 229 327 1543 881 340 66 

Manhattan --- --- --- --- 1 61 212 370 2477 681 262 87 

Mitospores - Dark Color

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 2.1 6.2 42.6 9.8 0.0 0.0 

Manhattan --- --- --- --- 0 79 3.0 9.4 52.5 35.5 0.0 0.0 

Spring99 Bronx --- --- --- --- 0 92 173.4 379.9 2320.4 1006.9 146.5 15.0 

Manhattan --- --- --- --- 0 92 142.5 302.8 1791.3 803.8 105.5 10.9 

Summer99 Bronx --- --- --- --- 48 46 749 784 2666 2356 1177 483 

Manhattan --- --- --- --- 0 94 968 953 4837 2858 1360 713 

Fall99 Bronx --- --- --- --- 0 90 168 360 2474 677 179 44 

Manhattan --- --- --- --- 0 90 142 279 1931 443 174 41 

Winter00 Bronx --- --- --- --- 6 83 5 13 87 35 3 0 

Manhattan --- --- --- --- 0 89 2 5 26 15 0 0 

Spring00 Bronx --- --- --- --- 0 92 211 466 2136 1550 98 27 

Manhattan --- --- --- --- 0 92 220 501 2431 1636 153 17 

Summer00 Bronx --- --- --- --- 0 94 353 554 4664 1089 446 199 

Manhattan --- --- --- --- 0 94 270 382 2611 1094 313 138 

Fall00 Bronx --- --- --- --- 1 61 227 328 1543 881 340 62 

Manhattan --- --- --- --- 1 61 208 362 2391 681 242 85 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Mitospores - Non-Dark Color

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 1.3 6.9 43.8 6.3 0.0 0.0 

Manhattan --- --- --- --- 0 79 0.1 0.8 6.5 0.0 0.0 0.0 

Spring99 Bronx --- --- --- --- 0 92 3.5 15.0 86.8 13.9 0.0 0.0 

Manhattan --- --- --- --- 0 92 0.7 3.2 21.1 9.1 0.0 0.0 

Summer99 Bronx --- --- --- --- 48 46 3 10 55 16 3 0 

Manhattan --- --- --- --- 0 94 12 31 178 81 3 0 

Fall99 Bronx --- --- --- --- 0 90 5 16 102 21 3 0 

Manhattan --- --- --- --- 0 90 13 50 354 120 0 0 

Winter00 Bronx --- --- --- --- 6 83 1 5 25 3 0 0 

Manhattan --- --- --- --- 0 89 0 0 3 0 0 0 

Spring00 Bronx --- --- --- --- 0 92 5 15 106 30 0 0 

Manhattan --- --- --- --- 0 92 9 27 156 47 0 0 

Summer00 Bronx --- --- --- --- 0 94 12 45 351 108 0 0 

Manhattan --- --- --- --- 0 94 6 17 129 36 3 0 

Fall00 Bronx --- --- --- --- 1 61 2 7 48 11 0 0 

Manhattan --- --- --- --- 1 61 4 18 111 20 0 0 

Small (<10um) Spores

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 8.5 34.4 293.2 42.6 6.3 0.0 

Manhattan --- --- --- --- 0 79 5.3 13.2 66.4 44.8 3.5 0.0 

Spring99 Bronx --- --- --- --- 0 92 261.3 517.2 3519.0 1236.1 236.1 45.7 

Manhattan --- --- --- --- 0 92 225.3 404.2 2180.7 1173.5 247.5 34.7 

Summer99 Bronx --- --- --- --- 48 46 1108 969 3585 2781 1756 877 

Manhattan --- --- --- --- 0 94 1348 1041 4722 3417 2026 1109 

Fall99 Bronx --- --- --- --- 0 90 440 821 6148 1510 493 213 

Manhattan --- --- --- --- 0 90 361 518 2771 1280 474 133 

Winter00 Bronx --- --- --- --- 6 83 9 17 101 42 11 0 

Manhattan --- --- --- --- 0 89 3 6 28 22 6 0 

Spring00 Bronx --- --- --- --- 0 92 349 584 2391 1901 313 93 

Manhattan --- --- --- --- 0 92 460 798 3863 2209 465 88 

Summer00 Bronx --- --- --- --- 0 94 1010 861 6374 2401 1458 800 

Manhattan --- --- --- --- 0 94 805 709 5378 1931 1042 645 

Fall00 Bronx --- --- --- --- 1 61 484 534 2141 1623 753 332 

Manhattan --- --- --- --- 1 61 433 502 2817 1304 733 299 

Large (>10um) Spores

(#/m3 

) 

Winter99 Bronx --- --- --- --- 0 79 0.5 1.7 9.8 6.5 0.0 0.0 

Manhattan --- --- --- --- 0 79 0.1 0.6 3.5 0.0 0.0 0.0 

Spring99 Bronx --- --- --- --- 0 92 2.1 4.3 20.8 13.4 3.3 0.0 

Manhattan --- --- --- --- 0 92 2.6 6.9 38.2 19.3 3.0 0.0 

Summer99 Bronx --- --- --- --- 48 46 68 87 373 262 94 40 

Manhattan --- --- --- --- 0 94 61 99 635 183 65 25 

Fall99 Bronx --- --- --- --- 0 90 5 9 53 23 7 0 

Manhattan --- --- --- --- 0 90 5 9 46 24 7 0 

Winter00 Bronx --- --- --- --- 6 83 0 1 7 0 0 0 

Manhattan --- --- --- --- 0 89 0 0 3 0 0 0 

Spring00 Bronx --- --- --- --- 0 92 8 25 158 46 0 0 

Manhattan --- --- --- --- 0 92 8 34 227 21 3 0 

Summer00 Bronx --- --- --- --- 0 94 15 18 80 52 26 7 

Manhattan --- --- --- --- 0 94 13 19 97 63 13 7 

Fall00 Bronx --- --- --- --- 1 61 6 10 42 26 8 3 

Manhattan --- --- --- --- 1 61 5 9 56 20 7 0 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Total Particles

(#) 

Winter99 Bronx --- --- --- --- 1896 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 1896 0 --- --- --- --- --- --- 

Spring99 Bronx --- --- --- --- 2208 0 --- --- --- --- --- --- 

Manhattan --- --- --- --- 2208 0 --- --- --- --- --- --- 

Summer99 Bronx --- --- --- --- 2247 9 2511592 511147 3293187 3293187 2978565 2426573 

Manhattan --- --- --- --- 2252 4 786065 41310 828917 828917 815119 792214 

Fall99 Bronx --- --- --- --- 0 2160 1278806 837520 3664530 3138862 1655154 913698 

Manhattan --- --- --- --- 1 2159 1252193 514714 3486562 2303163 1510293 1027820 

Winter00 Bronx --- --- --- --- 279 1857 1285863 532023 3468775 2409326 1542867 1080677 

Manhattan --- --- --- --- 446 1690 1523313 420305 2932349 2276606 1826742 1438253 

Spring00 Bronx --- --- --- --- 361 1847 1943252 844710 3535831 3129558 2703701 2049893 

Manhattan --- --- --- --- 374 1834 1441250 360839 3332480 2134929 1524628 1406442 

Summer00 Bronx --- --- --- --- 1285 971 1524672 738377 3371389 2838222 2044459 1425871 

Manhattan --- --- --- --- 1441 815 1448814 508487 3446219 2768514 1566482 1354807 

Fall00 Bronx --- --- --- --- 179 1309 1934484 848884 3545731 3183346 2718196 1963012 

Manhattan --- --- --- --- 311 1177 1806120 793697 3378155 3103462 2656988 1469457 

PM2.5 (FRM)

(ug/m3 

) 

Winter99 Bronx --- --- --- --- 56 23 15.27 7.19 26.38 25.83 22.00 15.00 

Manhattan --- --- --- --- 14 65 17.70 8.57 41.46 32.00 21.04 16.50 

Spring99 Bronx --- --- --- --- 5 87 13.65 8.06 39.18 30.71 16.04 11.00 

Manhattan --- --- --- --- 9 83 13.08 8.62 39.08 30.92 16.83 11.50 

Summer99 Bronx --- --- --- --- 63 31 16.65 10.08 39.00 36.68 23.88 16.27 

Manhattan --- --- --- --- 4 90 15.59 11.17 50.85 36.23 20.95 13.33 

Fall99 Bronx --- --- --- --- 37 53 12.01 6.79 33.80 26.40 14.60 9.79 

Manhattan --- --- --- --- 36 54 15.94 8.89 38.40 34.10 21.65 13.73 

Winter00 Bronx --- --- --- --- 24 65 17.17 11.14 47.30 40.50 21.90 13.20 

Manhattan --- --- --- --- 27 62 19.13 11.23 48.20 41.20 25.10 15.30 

Spring00 Bronx --- --- --- --- 8 84 13.82 8.60 40.60 31.50 17.90 11.00 

Manhattan --- --- --- --- 4 88 15.34 8.53 44.10 33.50 17.80 12.75 

Summer00 Bronx --- --- --- --- 5 89 15.03 7.92 37.30 30.40 20.50 12.90 

Manhattan --- --- --- --- 7 87 16.90 7.93 37.70 31.30 21.40 15.20 

Fall00 Bronx --- --- --- --- 5 57 13.99 8.51 53.00 29.00 17.50 11.80 

Manhattan --- --- --- --- 0 62 16.18 11.31 63.80 31.30 19.40 13.65 

PM10 (FRM)

(ug/m3 

) 

Winter99 Bronx --- --- --- --- 6 7 12 4 19 19 18 11 

Manhattan --- --- --- --- 10 3 12 1 13 13 13 12 

Spring99 Bronx --- --- --- --- 2 13 22 8 35 35 27 21 

Manhattan --- --- --- --- 1 14 22 7 36 36 27 22 

Summer99 Bronx --- --- --- --- 13 3 38 8 46 46 46 38 

Manhattan --- --- --- --- 1 15 27 14 46 46 40 25 

Fall99 Bronx --- --- --- --- 4 11 16 6 27 27 22 15 

Manhattan --- --- --- --- 0 15 17 6 30 30 21 18 

Winter00 Bronx --- --- --- --- 3 12 20 11 50 50 23 17 

Manhattan --- --- --- --- 0 15 19 9 43 43 22 16 

Spring00 Bronx --- --- --- --- 3 12 24 11 45 45 33 21 

Manhattan --- --- --- --- 0 15 22 11 49 49 28 17 

Summer00 Bronx --- --- --- --- 0 16 21 7 37 37 23 18 

Manhattan --- --- --- --- 0 16 22 7 39 39 25 23 

Fall00 Bronx --- --- --- --- 4 6 23 10 37 37 29 24 

Manhattan --- --- --- --- 1 9 28 17 61 61 34 30 
Appendix 2 - Summary of Data (continued) 

Descriptive Statistics 

Season Site SR's RJ's PL's LT's Missing N Mean Std. Dev. Max 95th 75th Median 

Temperature

(deg F) 

Winter99 Bronx 5 2 --- --- 0 1889 36.0 9.5 68.4 51.1 42.2 36.7 

Manhattan 0 3 --- --- 0 1893 36.9 9.5 68.3 51.7 43.4 37.6 

Spring99 Bronx 2 6 --- --- 110 2090 59.2 11.4 94.8 79.8 66.6 58.4 

Manhattan 11 4 --- --- 106 2087 59.8 11.2 95.4 80.2 67.0 58.8 

Summer99 Bronx 31 7 --- --- 1409 809 75.7 8.6 100.4 90.8 81.1 75.1 

Manhattan 13 13 --- --- 443 1787 76.7 8.0 100.1 90.4 81.7 76.5 

Fall99 Bronx 13 1 --- --- 0 2146 52.3 10.5 77.7 69.2 60.2 52.7 

Manhattan 34 52 --- --- 2 2072 53.4 10.1 76.9 69.3 61.3 53.9 

Winter00 Bronx 6 1 --- --- 0 2129 35.8 11.7 70.8 55.0 43.9 35.7 

Manhattan 8 2 --- --- 170 1956 37.8 11.4 69.9 57.1 45.7 37.7 

Spring00 Bronx 2 3 --- --- 0 2203 58.1 12.4 93.6 82.5 64.7 56.6 

Manhattan 2 0 --- --- 0 2206 58.8 12.1 92.9 83.1 65.4 57.6 

Summer00 Bronx 53 0 --- --- 65 2138 72.6 6.6 90.3 83.9 77.0 72.4 

Manhattan 82 19 --- --- 1 2154 72.6 6.0 90.6 83.0 76.5 72.5 

Fall00 Bronx 146 1 --- --- 10 1331 53.8 9.7 78.6 69.0 60.3 54.4 

Manhattan 4 2 --- --- 7 1475 54.8 9.7 80.4 69.6 61.5 55.7 

Relative Humidity 

(%) 

Winter99 Bronx 5 3 --- --- 0 1888 66 23 108 104 86 61 

Manhattan 2 3 --- --- 0 1891 60 23 107 102 78 55 

Spring99 Bronx 90 5 --- --- 110 2003 61 25 108 104 81 58 

Manhattan 11 5 --- --- 106 2086 55 24 106 98 73 52 

Summer99 Bronx 3 30 --- --- 1409 814 68 21 107 102 85 69 

Manhattan 20 10 --- --- 443 1783 62 20 104 95 78 63 

Fall99 Bronx 14 1 --- --- 0 2145 71 21 110 106 89 69 

Manhattan 7 4 --- --- 2 2147 65 20 106 100 81 63 

Winter00 Bronx 6 1 --- --- 0 2129 67 22 114 108 82 62 

Manhattan 5 1 --- --- 170 1960 61 21 109 101 76 57 

Spring00 Bronx 2 6 --- --- 0 2200 73 25 115 110 97 73 

Manhattan 1 0 --- --- 0 2207 66 22 106 100 86 65 

Summer00 Bronx 75 0 --- --- 65 2116 78 21 117 111 97 78 

Manhattan 4 3 --- --- 1 2248 70 20 106 100 87 69 

Fall00 Bronx 102 23 --- --- 10 1353 75 20 117 110 89 74 

Manhattan 3 2 --- --- 7 1476 65 18 107 96 77 63 
Appendix 3 – Detailed Statistical Results, Entire Study Period 

167
Appendix 3 - Statistical Analyses - Entire Study Period 

Statistics and Analyses - Daily Averagesa 

Analyte 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx D

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects (%) Meanc N Missing (%) Meanc Me

pH 0.6853 --- 680 1.7% --- 5.04 627 9.4% --- 5.15 622 10.1% -0.07 -

Sulfate 0.9647

 0.24 

674 2.6% 0.0% 4.0 617 10.8% 0.3% 3.6 607 12.3% 0.1 

Carb250 0.6155 --- 650 6.1% --- 3.09 590 14.7% --- 3.17 556 19.7% -0.15 -

Soot 0.7671 --- 592 14.5% --- 1.32 582 15.9% --- 1.19 498 28.0% 0.08 

Ozone 0.9235 --- 630 9.0% --- 0.012 518 25.1% --- 0.016 482 30.3% -0.004 -3

NOX 0.8652 --- 625 9.7% --- 0.066 425 38.6% --- 0.053 367 47.0% 0.012 1

NO 0.8794 --- 625 9.7% --- 0.031 425 38.6% --- 0.022 367 47.0% 0.008 2

NO2 0.7704 --- 625 9.7% --- 0.036 425 38.6% --- 0.031 367 47.0% 0.005 1

SO2 0.8967 --- 648 6.4% --- 0.012 608 12.1% --- 0.011 566 18.2% 0.002 1

PM2.5 (TEOM) 0.9659 --- 631 8.8% --- 16.2 567 18.1% --- 15.3 517 25.3% 0.8 

PM10 (TEOM) 0.9185 --- 609 12.0% --- 23.1 497 28.2% --- 22.3 444 35.8% 0.9 

Acetaldehyde* 0.8086 1.0 674 2.6% 1.2% 2.7 577 16.6% 0.4% 2.5 568 17.9% 0.0 Acetone* 0.2342

 1.0 

674 2.6% 2.2% 6.9 577 16.6% 0.1% 6.8 568 17.9% 0.0 -

Acrolein* ---

1.0 

674 2.6% 96.2% 0.6 577 16.6% 83.2% 0.5 568 17.9% 0.0 

Benzaldehyde* 0.4232

 1.0 

674 2.6% 93.9% 0.5 577 16.6% 76.2% 0.5 568 17.9% 0.0 -

Butyraldehyde* -0.0285

 1.0 

674 2.6% 68.9% 0.8 577 16.6% 66.0% 0.7 568 17.9% 0.1 1

Crotonaldehyde* 0.5875

 1.0 

674 2.6% 69.5% 0.8 577 16.6% 63.4% 0.7 568 17.9% 0.0 

Formaldehyde* 0.7982

 1.0 

674 2.6% 0.3% 4.4 577 16.6% 0.1% 4.2 568 17.9% -0.1 -

Hexaldehyde* 0.3813

 1.0 

674 2.6% 81.1% 0.8 577 16.6% 70.4% 0.6 568 17.9% -0.1 -1

Isovaleraldehyde* 0.0614

 1.0 

674 2.6% 90.5% 0.6 577 16.6% 78.5% 0.5 568 17.9% 0.0 -

m-Tolualdehyde* ---

1.0 

674 2.6% 97.4% 0.5 577 16.6% 83.4% 0.5 568 17.9% 0.0 

o-Tolualdehyde* -0.0038

 1.0 

674 2.6% 96.8% 0.5 577 16.6% 82.7% 0.5 568 17.9% 0.0 -

p-Tolualdehyde* 0.6907

 1.0 

674 2.6% 87.1% 0.5 577 16.6% 71.1% 0.6 568 17.9% 0.0 -

Propionaldehyde* 0.4719

 1.0 

674 2.6% 67.1% 0.9 577 16.6% 55.3% 0.8 568 17.9% -0.1 -

Valeraldehyde* 0.0271

 1.0 

674 2.6% 95.4% 0.5 577 16.6% 78.9% 0.5 568 17.9% 0.0 -

2,5-dimethylbenzaldehyde* ---

1.0 

674 2.6% 97.3% 0.5 577 16.6% 83.4% 0.5 568 17.9% 0.0 

Total Aldehydes* 0.5322 --- 673 2.7% --- 16.2 588 16.6% --- 16.6 567 18.1% -0.1 -

Chromium -0.0068 5 661 4.5% 93.9% 3 602 13.0% 85.0% 3 575 16.9% 0 -1

Iron 0.3656 22 661 4.5% 19.9% 72 602 13.0% 19.9% 75 575 16.9% -4 -

Lead 0.0541 12 661 4.5% 90.6% 7 602 13.0% 81.6% 7 575 16.9% 0 -

Manganese 0.1166 3 661 4.5% 85.4% 2 602 13.0% 81.5% 2 575 16.9% 0 

Nickel 0.3835 4 640 7.5% 22.4% 15 575 16.9% 25.9% 12 548 20.8% 4 2

Zinc 0.1283 77 661 4.5% 93.6% 40 602 13.0% 84.4% 41 575 16.9% -2 -

Total Metals 0.3283 --- 615 11.1% --- 94 532 23.1% --- 101 489 29.3% -4 -

SO2 0.8973 --- 374 46.0% --- 26.4 320 53.8% --- 25.8 310 55.2% 1.9 

HCl 0.8364 --- 375 45.8% --- 0.51 320 53.8% --- 0.48 311 55.1% -0.05 -1

HONO 0.8350 --- 374 46.0% --- 3.21 320 53.8% --- 3.07 311 55.1% 0.38 1

HNO3 0.9293 --- 373 46.1% --- 1.75 320 53.8% --- 1.11 310 55.2% 0.19 1

NH3 0.9150 --- 186 73.1% --- 3.536 156 77.5% --- 2.274 135 80.5% 1.331 3

Total Pollen 0.9792 --- 691 0.1% --- 13.2 632 8.7% --- 22.3 632 8.7% -8.2 -5

Tree Pollen 0.9795 --- 691 0.1% --- 12.2 637 7.9% --- 20.5 637 7.9% -7.4 -5

Ragweed 0.8619 --- 691 0.1% --- 0.4 637 7.9% --- 0.4 637 7.9% -0.1 -4

Grasses 0.7479 --- 691 0.1% --- 0.4 637 7.9% --- 0.5 637 7.9% -0.1 -3

Total Mold 0.8381 --- 691 0.1% --- 490.3 632 8.7% --- 447.8 632 8.7% -35.0 -

Basidospores 0.7090 --- 691 0.1% --- 186.0 637 7.9% --- 184.0 637 7.9% -16.7 -1

Ascospores 0.6789 --- 691 0.1% --- 39.1 637 7.9% --- 43.2 637 7.9% -4.1 -1

Mitospores 0.8709 --- 691 0.1% --- 259.9 637 7.9% --- 212.5 637 7.9% -12.5 -

Mitospores (Dark) 0.8753 --- 691 0.1% --- 254.1 637 7.9% --- 208.1 637 7.9% -13.5 -

Mitospores (Non-Dark) 0.0538 --- 691 0.1% --- 5.8 637 7.9% --- 4.4 637 7.9% 1.1 1

Small Spores (<10 um) 0.8304 --- 691 0.1% --- 470.4 637 7.9% --- 427.8 637 7.9% -31.4 -

Large Spores (>10 um) 0.7879 --- 691 0.1% --- 12.5 637 7.9% --- 9.9 637 7.9% -1.7 -2

Particle Count 0.2231 --- 308 55.5% --- 1463152 329 52.5% --- 1560780 288 58.4% -100940 -

PM2.5 (FRM) 0.8979 --- 571 17.5% --- 16.6 489 29.3% --- 14.5 413 40.3% 1.5 

PM10 (FRM) 0.9559 --- 102 11.3% --- 22.0 80 30.4% --- 20.9 74 35.7% 0.8 

Average Temperature 0.9989 --- 649 6.2% --- 56.5 610 11.8% --- 53.9 593 14.3% 0.7 

Average Relative Humidity 0.9773 --- 658 4.9% --- 63 603 12.9% --- 70 596 13.9% -7 -1

a Difference=Manhattan - Bronx d For analytes collected on an hourly basis, daily averages were calculated for b Non-detects were given values of 1/2 the detection limit for statistical calculations * Data for April 20-30, 2000 at the Bronx site has been excluded from these anc Mean Difference (%) =Mean Difference / Manhattan (using only days with daily averages available for both sites) 
Appendix 4 – Detailed Statistical Results by Season 

169
Appendix 4 - Statistical Analyses - By Season 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

pH

Winter99 0.0927 --- 79 0.0% --- 5.47 79 0.0% --- 5.51 79 0.0% -0.04 -0.7% Spring99 0.7454 --- 92 0.0% --- 5.09 92 0.0% --- 5.18 92 0.0% -0.09 -1.8% Summer99 0.9502 --- 91 3.2% --- 4.62 36 61.7% --- 4.77 33 64.9% -0.07 -1.5% Fall99 0.7365 --- 90 0.0% --- 5.10 90 0.0% --- 5.23 90 0.0% -0.12 -2.4% Winter00 0.5726 --- 89 0.0% --- 5.25 89 0.0% --- 5.31 89 0.0% -0.06 -1.1% Spring00 0.7658 --- 92 0.0% --- 4.98 92 0.0% --- 5.07 92 0.0% -0.09 -1.8% Summer00 0.8376 --- 87 7.4% --- 4.79 87 7.4% --- 4.84 87 7.4% -0.05 -1.0% Fall00 0.6964 --- 60 3.2% --- 5.10 62 0.0% --- 5.12 60 3.2% -0.02 -0.4% 

Sulfate 

(ug/m3 )

Winter99 0.7968 0.24 79 0.0% 0.0% 3.01 79 0.0% 0.0% 2.93 79 0.0% 0.08 2.7% Spring99 0.9769 0.24 92 0.0% 0.0% 3.35 91 1.1% 0.0% 3.22 91 1.1% 0.15 4.4% Summer99 0.9954 0.24 89 5.3% 0.0% 6.32 35 62.8% 2.9% 5.16 31 67.0% 0.26 5.1% Fall99 0.8813 0.24 88 2.2% 0.0% 3.16 89 1.1% 1.1% 3.05 87 3.3% 0.11 3.3% Winter00 0.9547 0.24 88 1.1% 0.0% 3.14 82 7.9% 0.0% 2.92 81 9.0% 0.14 4.5% Spring00 0.9857 0.24 91 1.1% 0.0% 4.10 92 0.0% 0.0% 4.02 91 1.1% 0.19 4.7% Summer00 0.9823 0.24 87 7.4% 0.0% 5.05 87 7.4% 0.0% 4.97 87 7.4% 0.09 1.7% Fall00 0.9903 0.24 60 3.2% 0.0% 3.37 62 0.0% 0.0% 3.30 60 3.2% 0.03 0.8% 

Carbon 250 

(ug/m3 )

Winter99 0.9275 --- 65 17.7% --- 2.746 67 15.2% --- 2.48 59 25.3% 0.277 10.0% Spring99 0.7737 --- 79 14.1% --- 2.743 91 1.1% --- 2.977 78 15.2% -0.308 -11.3% Summer99 0.7342 --- 93 1.1% --- 3.461 32 66.0% --- 3.751 31 67.0% -0.309 -9.1% Fall99 0.7887 --- 90 0.0% --- 2.952 74 17.8% --- 3.628 74 17.8% -0.639 -21.4% Winter00 0.9133 --- 86 3.4% --- 2.618 88 1.1% --- 3.207 85 4.5% -0.570 -21.7% Spring00 0.7888 --- 84 8.7% --- 3.323 83 9.8% --- 3.698 76 17.4% -0.508 -15.8% Summer00 0.7466 --- 93 1.1% --- 3.337 94 0.0% --- 3.183 93 1.1% 0.141 4.2% Fall00 0.6602 --- 60 3.2% --- 3.487 61 1.6% --- 2.524 60 3.2% 0.942 27.0% 

Soot Carbon (ug/m3 )

Winter99 0.8184 --- 50 36.7% --- 1.667 65 17.7% --- 1.587 44 44.3% -0.031 -1.8% Spring99 0.7222 --- 52 43.5% --- 1.482 91 1.1% --- 1.140 51 44.6% 0.249 17.0% Summer99 0.8417 --- 80 14.9% --- 1.403 30 68.1% --- 1.021 21 77.7% 0.199 15.3% Fall99 0.8494 --- 90 0.0% --- 1.421 73 18.9% --- 1.334 73 18.9% 0.137 9.3% Winter00 0.8669 --- 85 4.5% --- 1.351 87 2.2% --- 1.392 84 5.6% -0.041 -3.1% Spring00 0.6654 --- 84 8.7% --- 1.234 81 12.0% --- 0.930 74 19.6% 0.248 21.2% Summer00 0.3886 --- 93 1.1% --- 0.976 94 0.0% --- 1.029 93 1.1% -0.059 -6.0% Fall00 0.8370 --- 58 6.5% --- 1.190 61 1.6% --- 1.070 58 6.5% 0.120 10.1% 

O

3

(ppm)

Winter99 0.9462 --- 41 48.1% --- 0.006 26 67.1% --- 0.017 9 88.6% -0.008 -79.2% Spring99 0.8300 --- 88 4.3% --- 0.016 88 4.3% --- 0.021 84 8.7% -0.005 -32.2% Summer99 0.8632 --- 93 1.1% --- 0.021 11 88.3% --- 0.035 11 88.3% -0.011 -44.2% Fall99 0.9271 --- 86 4.4% --- 0.005 56 37.8% --- 0.007 56 37.8% -0.002 -56.2% Winter00 0.9282 --- 88 1.1% --- 0.006 89 0.0% --- 0.010 88 1.1% -0.004 -76.7% Spring00 0.9130 --- 85 7.6% --- 0.016 92 0.0% --- 0.020 85 7.6% -0.005 -30.8% Summer00 0.8536 --- 89 5.3% --- 0.016 94 0.0% --- 0.021 89 5.3% -0.004 -26.2% Fall00 0.9095 --- 60 3.2% --- 0.006 62 0.0% --- 0.009 60 3.2% -0.003 -42.4% 

NO

X

(ppm) 

Winter99 --- --- 76 3.8% --- 0.085 0 100.0% --- --- 0 100.0% --- -Spring99 0.3756 --- 60 34.8% --- 0.061 79 14.1% --- 0.044 48 47.8% 0.022 37.7% Summer99 0.7734 --- 93 1.1% --- 0.049 23 75.5% --- 0.038 23 75.5% 0.008 17.5% Fall99 0.8597 --- 87 3.3% --- 0.078 38 57.8% --- 0.061 38 57.8% 0.013 17.5% Winter00 0.9279 --- 88 1.1% --- 0.084 69 22.5% --- 0.073 68 23.6% 0.011 13.0% Spring00 0.8730 --- 74 19.6% --- 0.057 91 1.1% --- 0.051 73 20.7% 0.008 13.8% Summer00 0.7474 --- 88 6.4% --- 0.047 63 33.0% --- 0.037 58 38.3% 0.009 20.2% Fall00 0.9009 --- 59 4.8% --- 0.074 62 0.0% --- 0.062 59 4.8% 0.012 15.9% 
Appendix 4 - Statistical Analyses - By Season (continued) 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

NO

(ppm)

Winter99 --- --- 76 3.8% --- 0.047 0 100.0% --- --- 0 100.0% --- -Spring99 0.4929 --- 60 34.8% --- 0.022 79 14.1% --- 0.015 48 47.8% 0.010 48.4% Summer99 0.8271 --- 93 1.1% --- 0.014 23 75.5% --- 0.007 23 75.5% 0.004 37.3% Fall99 0.8262 --- 87 3.3% --- 0.045 38 57.8% --- 0.030 38 57.8% 0.011 26.6% Winter00 0.9049 --- 88 1.1% --- 0.047 69 22.5% --- 0.037 68 23.6% 0.009 19.3% Spring00 0.8729 --- 74 19.6% --- 0.021 91 1.1% --- 0.018 73 20.7% 0.004 21.3% Summer00 0.7089 --- 88 6.4% --- 0.016 63 33.0% --- 0.008 58 38.3% 0.006 42.1% Fall00 0.8717 --- 59 4.8% --- 0.040 62 0.0% --- 0.033 59 4.8% 0.009 21.1% 

NO

2

(ppm)

Winter99 --- --- 76 3.8% --- 0.038 0 100.0% --- --- 0 100.0% --- -Spring99 0.3619 --- 60 34.8% --- 0.039 79 14.1% --- 0.030 48 47.8% 0.013 32.7% Summer99 0.7272 --- 93 1.1% --- 0.036 23 75.5% --- 0.031 23 75.5% 0.005 14.0% Fall99 0.9442 --- 87 3.3% --- 0.034 38 57.8% --- 0.031 38 57.8% 0.003 9.2% Winter00 0.9535 --- 88 1.1% --- 0.038 69 22.5% --- 0.036 68 23.6% 0.004 9.5% Spring00 0.8365 --- 74 19.6% --- 0.037 91 1.1% --- 0.033 73 20.7% 0.005 13.2% Summer00 0.8091 --- 88 6.4% --- 0.033 63 33.0% --- 0.028 58 38.3% 0.005 14.1% Fall00 0.9623 --- 59 4.8% --- 0.034 62 0.0% --- 0.030 59 4.8% 0.004 12.8% 

SO

2

(ppm)

Winter99 0.7954 --- 77 2.5% --- 0.020 79 0.0% --- 0.015 77 2.5% 0.006 27.4% Spring99 0.8495 --- 91 1.1% --- 0.010 92 0.0% --- 0.008 91 1.1% 0.002 23.7% Summer99 0.5679 --- 93 1.1% --- 0.008 23 75.5% --- 0.006 23 75.5% 0.002 20.9% Fall99 0.9365 --- 87 3.3% --- 0.013 77 14.4% --- 0.013 75 16.7% 0.000 2.1% Winter00 0.9182 --- 88 1.1% --- 0.020 89 0.0% --- 0.018 88 1.1% 0.002 8.4% Spring00 0.8168 --- 78 15.2% --- 0.008 92 0.0% --- 0.007 78 15.2% 0.001 10.8% Summer00 0.7620 --- 74 21.3% --- 0.006 94 0.0% --- 0.006 74 21.3% 0.000 1.1% Fall00 0.8508 --- 60 3.2% --- 0.012 62 0.0% --- 0.013 60 3.2% 0.000 -3.8% 

PM2.

5 (TEOM) 

(ug/m3 

)

Winter99 0.9365 --- 78 1.3% --- 15.27 79 0.0% --- 15.00 78 1.3% 0.33 2.2% Spring99 0.9756 --- 89 3.3% --- 14.83 92 0.0% --- 14.03 89 3.3% 0.92 6.2% Summer99 0.9899 --- 93 1.1% --- 20.40 23 75.5% --- 21.22 23 75.5% 0.71 3.2% Fall99 0.9627 --- 71 21.1% --- 15.53 57 36.7% --- 15.01 48 46.7% 1.04 6.6% Winter00 0.9610 --- 89 0.0% --- 14.73 88 1.1% --- 14.34 88 1.1% 0.49 3.3% Spring00 0.9705 --- 76 17.4% --- 15.15 88 4.3% --- 15.60 72 21.7% 0.85 5.5% Summer00 0.9750 --- 78 17.0% --- 17.50 87 7.4% --- 16.58 71 24.5% 1.20 6.8% Fall00 0.9768 --- 57 8.1% --- 15.21 53 14.5% --- 14.38 48 22.6% 0.66 4.3% 

PM10 (TEOM) (ug/m3 )

Winter99 0.9061 --- 77 2.5% --- 19.54 38 51.9% --- 19.83 38 51.9% 0.19 1.0% Spring99 0.9538 --- 92 0.0% --- 21.66 92 0.0% --- 22.37 92 0.0% -0.71 -3.3% Summer99 0.8854 --- 93 1.1% --- 26.15 15 84.0% --- 28.14 15 84.0% -6.25 -28.6% Fall99 0.9771 --- 37 58.9% --- 21.82 52 42.2% --- 19.34 21 76.7% 3.42 15.2% Winter00 0.9638 --- 89 0.0% --- 22.38 79 11.2% --- 20.64 79 11.2% 1.98 8.8% Spring00 0.9826 --- 72 21.7% --- 23.91 65 29.3% --- 25.24 50 45.7% 1.60 6.2% Summer00 0.8329 --- 88 6.4% --- 25.36 94 0.0% --- 23.47 88 6.4% 2.38 9.4% Fall00 0.9740 --- 61 1.6% --- 22.68 62 0.0% --- 21.83 61 1.6% 0.82 3.6% 

Acetaldehyde 

(ug/m3 ) 

Winter99 0.4465 1.0 79 0.0% 0.0% 2.1 78 1.3% 1.3% 2.2 78 1.3% -0.1 -3.0% Spring99 0.7969 1.0 92 0.0% 0.0% 2.7 84 8.7% 0.0% 2.2 84 8.7% 0.5 18.2% Summer99 0.8311 1.0 86 8.5% 7.0% 3.8 29 69.1% 0.0% 2.9 22 76.6% -0.1 -3.9% Fall99 0.9337 1.0 89 1.1% 0.0% 2.7 77 14.4% 0.0% 2.5 76 15.6% 0.1 5.2% Winter00 0.9453 1.0 89 0.0% 0.0% 2.6 89 0.0% 1.1% 2.3 89 0.0% 0.3 10.7% Spring00* 0.8892 1.0 92 0.0% 0.0% 2.6 73 20.7% 0.0% 3.7 73 20.7% -1.0 -35.9% Summer00 0.8098 1.0 93 1.1% 2.2% 2.4 92 2.1% 0.0% 2.3 92 2.1% 0.1 3.3% Fall00 0.9734 1.0 54 12.9% 0.0% 2.4 55 11.3% 1.8% 2.1 54 12.9% 0.3 10.5% 
Appendix 4 - Statistical Analyses - By Season (continued) 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

Acetone

(ug/m3 )

Winter99 0.0058 1.0 79 0.0% 0.0% 7.0 78 1.3% 0.0% 9.6 78 1.3% -2.6 -38.1% Spring99 0.0700 1.0 92 0.0% 0.0% 8.8 84 8.7% 0.0% 8.0 84 8.7% 1.0 11.3% Summer99 0.6469 1.0 86 8.5% 15.1% 7.6 29 69.1% 0.0% 7.0 22 76.6% -2.1 -43.8% Fall99 0.7769 1.0 89 1.1% 0.0% 6.8 77 14.4% 0.0% 5.6 76 15.6% 1.2 17.4% Winter00 0.6935 1.0 89 0.0% 0.0% 5.4 89 0.0% 1.1% 4.5 89 0.0% 0.9 16.7% Spring00* 0.8417 1.0 92 0.0% 0.0% 6.6 73 20.7% 0.0% 7.9 73 20.7% -1.1 -16.4% Summer00 0.4152 1.0 93 1.1% 2.2% 6.1 92 2.1% 0.0% 5.9 92 2.1% 0.2 2.8% Fall00 0.9198 1.0 54 12.9% 0.0% 7.0 55 11.3% 0.0% 6.0 54 12.9% 1.0 14.6% 

Acrolein

(ug/m3 )

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 --- 1.0 92 0.0% 100.0% 0.5 84 8.7% 100.0% 0.5 84 8.7% 0.0 0.0% Summer99 --- 1.0 86 8.5% 90.7% 1.5 29 69.1% 100.0% 0.5 22 76.6% 0.0 0.0% Fall99 --- 1.0 89 1.1% 100.0% 0.5 77 14.4% 100.0% 0.5 76 15.6% 0.0 0.0% Winter00 --- 1.0 89 0.0% 100.0% 0.5 89 0.0% 98.9% 0.5 89 0.0% 0.0 -0.2% Spring00* --- 1.0 92 0.0% 94.6% 0.5 73 20.7% 100.0% 0.5 73 20.7% 0.0 0.0% Summer00 --- 1.0 93 1.1% 100.0% 0.5 92 2.1% 100.0% 0.5 92 2.1% 0.0 0.0% Fall00 --- 1.0 54 12.9% 100.0% 0.5 55 11.3% 100.0% 0.5 54 12.9% 0.0 0.0% 

Benzaldehyde

(ug/m3 ) 

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 --- 1.0 92 0.0% 100.0% 0.5 84 8.7% 100.0% 0.5 84 8.7% 0.0 0.0% Summer99 --- 1.0 86 8.5% 86.0% 0.7 29 69.1% 100.0% 0.5 22 76.6% 0.0 0.0% Fall99 --- 1.0 89 1.1% 95.5% 0.5 77 14.4% 97.4% 0.5 76 15.6% 0.0 0.5% Winter00 --- 1.0 89 0.0% 98.9% 0.5 89 0.0% 96.6% 0.5 89 0.0% 0.0 -0.5% Spring00* 0.3435 1.0 92 0.0% 94.6% 0.5 73 20.7% 42.5% 0.6 73 20.7% -0.1 -12.3% Summer00 --- 1.0 93 1.1% 98.9% 0.5 92 2.1% 98.9% 0.5 92 2.1% 0.0 0.0% Fall00 0.7004 1.0 54 12.9% 98.1% 0.5 55 11.3% 96.4% 0.5 54 12.9% 0.0 -1.8% 

Butyraldehyde 

(ug/m3 )

Winter99 --- 1.0 79 0.0% 94.9% 0.8 78 1.3% 100.0% 0.5 78 1.3% 0.3 36.0% Spring99 -0.3374 1.0 92 0.0% 20.7% 1.8 84 8.7% 59.5% 0.8 84 8.7% 1.0 55.2% Summer99 0.4501 1.0 86 8.5% 61.6% 1.0 29 69.1% 69.0% 0.7 22 76.6% 0.2 21.2% Fall99 --- 1.0 89 1.1% 96.6% 0.5 77 14.4% 100.0% 0.5 76 15.6% 0.0 0.3% Winter00 -0.0276 1.0 89 0.0% 61.8% 0.6 89 0.0% 96.6% 0.5 89 0.0% 0.1 21.4% Spring00* 0.3511 1.0 92 0.0% 88.0% 0.5 73 20.7% 30.1% 1.3 73 20.7% -0.8 -160.7% Summer00 0.0649 1.0 93 1.1% 61.3% 0.6 92 2.1% 76.1% 0.5 92 2.1% 0.1 11.0% Fall00 0.5664 1.0 54 12.9% 94.4% 0.5 55 11.3% 98.2% 0.5 54 12.9% 0.0 3.5% 

Crotonaldehyde

(ug/m3 

) 

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 0.4884 1.0 92 0.0% 80.4% 0.8 84 8.7% 72.6% 1.0 84 8.7% -0.2 -18.0% Summer99 0.3924 1.0 86 8.5% 65.1% 1.4 29 69.1% 72.4% 0.9 22 76.6% 0.1 15.9% Fall99 0.4708 1.0 89 1.1% 28.1% 1.0 77 14.4% 53.2% 0.9 76 15.6% 0.2 17.6% Winter00 0.9072 1.0 89 0.0% 11.2% 0.9 89 0.0% 22.5% 0.8 89 0.0% 0.0 5.5% Spring00* 0.9473 1.0 92 0.0% 97.8% 0.5 73 20.7% 97.3% 0.5 73 20.7% 0.0 0.2% Summer00 --- 1.0 93 1.1% 100.0% 0.5 92 2.1% 100.0% 0.5 92 2.1% 0.0 0.0% Fall00 --- 1.0 54 12.9% 100.0% 0.5 55 11.3% 100.0% 0.5 54 12.9% 0.0 0.0% 

Formaldehyde 

(ug/m3 ) 

Winter99 0.5596 1.0 79 0.0% 0.0% 3.3 78 1.3% 0.0% 3.0 78 1.3% 0.3 8.7% Spring99 0.8336 1.0 92 0.0% 0.0% 4.5 84 8.7% 0.0% 3.9 84 8.7% 0.5 11.9% Summer99 0.7641 1.0 86 8.5% 1.2% 7.5 29 69.1% 0.0% 6.4 22 76.6% -0.1 -2.0% Fall99 0.9342 1.0 89 1.1% 0.0% 4.0 77 14.4% 0.0% 3.9 76 15.6% 0.1 2.0% Winter00 0.9425 1.0 89 0.0% 0.0% 3.2 89 0.0% 0.0% 3.0 89 0.0% 0.2 5.2% Spring00* 0.7740 1.0 92 0.0% 1.1% 4.1 73 20.7% 1.4% 6.3 73 20.7% -1.9 -44.0% Summer00 0.8001 1.0 93 1.1% 0.0% 4.6 92 2.1% 0.0% 4.8 92 2.1% -0.2 -4.9% Fall00 0.9589 1.0 54 12.9% 0.0% 3.5 55 11.3% 0.0% 3.3 54 12.9% 0.2 7.0% 
Appendix 4 - Statistical Analyses - By Season (continued) 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

Hexaldehyde

(ug/m3 

)

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 0.1970 1.0 92 0.0% 75.0% 0.5 84 8.7% 85.7% 0.6 84 8.7% 0.0 -1.3% Summer99 --- 1.0 86 8.5% 81.4% 2.3 29 69.1% 96.6% 0.5 22 76.6% 0.0 0.0% Fall99 -0.0540 1.0 89 1.1% 89.9% 0.5 77 14.4% 93.5% 0.5 76 15.6% 0.0 2.0% Winter00 0.6620 1.0 89 0.0% 94.4% 0.5 89 0.0% 97.8% 0.5 89 0.0% 0.0 0.9% Spring00* 0.5842 1.0 92 0.0% 75.0% 0.6 73 20.7% 26.0% 1.2 73 20.7% -0.6 -106.8% Summer00 0.6739 1.0 93 1.1% 63.4% 0.6 92 2.1% 84.8% 0.5 92 2.1% 0.1 10.6% Fall00 0.7004 1.0 54 12.9% 94.4% 0.5 55 11.3% 96.4% 0.5 54 12.9% 0.0 -1.8% 

Isovaleraldhyde 

(ug/m3 )

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 --- 1.0 92 0.0% 100.0% 0.5 84 8.7% 100.0% 0.5 84 8.7% 0.0 0.0% Summer99 -0.0907 1.0 86 8.5% 86.0% 0.8 29 69.1% 86.2% 0.7 22 76.6% -0.2 -44.9% Fall99 -0.0133 1.0 89 1.1% 98.9% 0.5 77 14.4% 98.7% 0.5 76 15.6% 0.0 -1.0% Winter00 0.0578 1.0 89 0.0% 93.3% 0.5 89 0.0% 96.6% 0.5 89 0.0% 0.0 1.5% Spring00* 0.4054 1.0 92 0.0% 87.0% 0.5 73 20.7% 84.9% 0.5 73 20.7% 0.0 4.0% Summer00 -0.0385 1.0 93 1.1% 81.7% 0.5 92 2.1% 85.9% 0.5 92 2.1% 0.0 2.5% Fall00 --- 1.0 54 12.9% 100.0% 0.5 55 11.3% 96.4% 0.5 54 12.9% 0.0 -3.7% 

m-Tolualdehyde

(ug/m3 )

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 --- 1.0 92 0.0% 100.0% 0.5 84 8.7% 100.0% 0.5 84 8.7% 0.0 0.0% Summer99 --- 1.0 86 8.5% 100.0% 0.5 29 69.1% 100.0% 0.5 22 76.6% 0.0 0.0% Fall99 --- 1.0 89 1.1% 100.0% 0.5 77 14.4% 100.0% 0.5 76 15.6% 0.0 0.0% Winter00 --- 1.0 89 0.0% 100.0% 0.5 89 0.0% 100.0% 0.5 89 0.0% 0.0 0.0% Spring00* --- 1.0 92 0.0% 100.0% 0.5 73 20.7% 100.0% 0.5 73 20.7% 0.0 0.0% Summer00 --- 1.0 93 1.1% 100.0% 0.5 92 2.1% 100.0% 0.5 92 2.1% 0.0 0.0% Fall00 --- 1.0 54 12.9% 100.0% 0.5 55 11.3% 100.0% 0.5 54 12.9% 0.0 0.0% 

o-Tolualdehyde

(ug/m3 )

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 --- 1.0 92 0.0% 100.0% 0.5 84 8.7% 100.0% 0.5 84 8.7% 0.0 0.0% Summer99 -0.0841 1.0 86 8.5% 97.7% 0.5 29 69.1% 86.2% 0.7 22 76.6% -0.1 -20.0% Fall99 --- 1.0 89 1.1% 98.9% 0.5 77 14.4% 100.0% 0.5 76 15.6% 0.0 1.0% Winter00 --- 1.0 89 0.0% 100.0% 0.5 89 0.0% 100.0% 0.5 89 0.0% 0.0 0.0% Spring00* --- 1.0 92 0.0% 100.0% 0.5 73 20.7% 98.6% 0.5 73 20.7% 0.0 0.0% Summer00 --- 1.0 93 1.1% 98.9% 0.5 92 2.1% 100.0% 0.5 92 2.1% 0.0 0.2% Fall00 --- 1.0 54 12.9% 100.0% 0.5 55 11.3% 100.0% 0.5 54 12.9% 0.0 0.0% 

p-Tolualdehyde 

(ug/m3 )

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 --- 1.0 92 0.0% 100.0% 0.5 84 8.7% 100.0% 0.5 84 8.7% 0.0 0.0% Summer99 --- 1.0 86 8.5% 95.3% 0.5 29 69.1% 100.0% 0.5 22 76.6% 0.1 9.8% Fall99 --- 1.0 89 1.1% 100.0% 0.5 77 14.4% 97.4% 0.5 76 15.6% 0.0 -1.8% Winter00 0.5277 1.0 89 0.0% 88.8% 0.5 89 0.0% 88.8% 0.5 89 0.0% 0.0 1.7% Spring00* 0.7408 1.0 92 0.0% 77.2% 0.7 73 20.7% 46.6% 0.7 73 20.7% 0.0 -5.2% Summer00 0.4577 1.0 93 1.1% 68.8% 0.6 92 2.1% 72.8% 0.6 92 2.1% 0.0 -2.7% Fall00 0.8107 1.0 54 12.9% 87.0% 0.6 55 11.3% 83.6% 0.6 54 12.9% 0.0 -7.3% 

Propionaldehyde 

(ug/m3 ) 

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 88.5% 0.8 78 1.3% -0.3 -60.3% Spring99 0.5269 1.0 92 0.0% 58.7% 1.5 84 8.7% 53.6% 1.6 84 8.7% 0.1 4.0% Summer99 0.1063 1.0 86 8.5% 43.0% 2.2 29 69.1% 34.5% 2.3 22 76.6% -0.8 -47.8% Fall99 0.6081 1.0 89 1.1% 73.0% 0.6 77 14.4% 72.7% 0.6 76 15.6% 0.0 3.8% Winter00 0.7297 1.0 89 0.0% 77.5% 0.5 89 0.0% 78.7% 0.6 89 0.0% 0.0 -2.9% Spring00* 0.8591 1.0 92 0.0% 64.1% 0.6 73 20.7% 35.6% 0.7 73 20.7% -0.1 -20.5% Summer00 0.6082 1.0 93 1.1% 59.1% 0.6 92 2.1% 64.1% 0.6 92 2.1% 0.0 3.0% Fall00 0.9396 1.0 54 12.9% 85.2% 0.6 55 11.3% 87.3% 0.6 54 12.9% 0.0 2.6% 
Appendix 4 - Statistical Analyses - By Season (continued) 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

Valeraldehyde

(ug/m3 )

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 0.1539 1.0 92 0.0% 94.6% 0.5 84 8.7% 95.2% 0.5 84 8.7% 0.0 4.4% Summer99 --- 1.0 86 8.5% 90.7% 0.7 29 69.1% 100.0% 0.5 22 76.6% 0.0 0.0% Fall99 --- 1.0 89 1.1% 100.0% 0.5 77 14.4% 100.0% 0.5 76 15.6% 0.0 0.0% Winter00 --- 1.0 89 0.0% 100.0% 0.5 89 0.0% 98.9% 0.5 89 0.0% 0.0 -0.2% Spring00* --- 1.0 92 0.0% 100.0% 0.5 73 20.7% 64.4% 0.6 73 20.7% -0.1 -11.8% Summer00 --- 1.0 93 1.1% 98.9% 0.5 92 2.1% 100.0% 0.5 92 2.1% 0.0 1.7% Fall00 --- 1.0 54 12.9% 100.0% 0.5 55 11.3% 100.0% 0.5 54 12.9% 0.0 0.0% 

2,5-Dimethylbenzaldehyde

(ug/m3 )

Winter99 --- 1.0 79 0.0% 100.0% 0.5 78 1.3% 100.0% 0.5 78 1.3% 0.0 0.0% Spring99 --- 1.0 92 0.0% 100.0% 0.5 84 8.7% 100.0% 0.5 84 8.7% 0.0 0.0% Summer99 --- 1.0 86 8.5% 100.0% 0.5 29 69.1% 100.0% 0.5 22 76.6% 0.0 0.0% Fall99 --- 1.0 89 1.1% 100.0% 0.5 77 14.4% 100.0% 0.5 76 15.6% 0.0 0.0% Winter00 --- 1.0 89 0.0% 100.0% 0.5 89 0.0% 100.0% 0.5 89 0.0% 0.0 0.0% Spring00* --- 1.0 92 0.0% 100.0% 0.5 73 20.7% 100.0% 0.5 73 20.7% 0.0 0.0% Summer00 --- 1.0 93 1.1% 98.9% 0.5 92 2.1% 100.0% 0.5 92 2.1% 0.0 0.0% Fall00 --- 1.0 54 12.9% 100.0% 0.5 55 11.3% 100.0% 0.5 54 12.9% 0.0 0.0% 

Total Aldehydes 

(ug/m3 )

Winter99 --- #DIV/0! --- 12.7 78 1.3% --- 15.1 78 1.3% -2.5 -19.5% Spring99 --- #DIV/0! --- 19.6 84 8.7% --- 16.7 84 8.7% 3.2 15.9% Summer99 --- 100.0% --- 26.7 29 69.1% --- 19.9 21 77.7% -2.3 -14.1% Fall99 --- 100.0% --- 14.7 77 14.4% --- 12.9 76 15.6% 1.8 12.5% Winter00 --- #DIV/0! --- 12.7 89 0.0% --- 10.9 89 0.0% 1.8 14.2% Spring00* --- 0.0% --- 14.3 73 20.7% --- 22.0 73 20.7% -6.9 -45.8% Summer00 --- 100.0% --- 14.3 92 2.1% --- 13.8 92 2.1% 0.4 3.0% Fall00 --- 100.0% --- 13.4 55 11.3% --- 11.8 54 12.9% 1.5 11.1% 

Chromium 

(ng/m3 ) 

Winter99 -0.0226 5 79 0.0% 97.5% 2.7 79 0.0% 97.5% 5.1 79 0.0% -2.5 -91.8% Spring99 --- 5 91 1.1% 100.0% 2.5 92 0.0% 98.9% 2.5 91 1.1% 0.0 -1.8% Summer99 --- 5 86 8.5% 97.7% 2.6 35 62.8% 100.0% 2.5 28 70.2% 0.0 0.0% Fall99 --- 5 89 1.1% 100.0% 2.5 88 2.2% 97.7% 2.6 87 3.3% -0.1 -2.9% Winter00 -0.0215 5 85 4.5% 98.8% 2.6 80 10.1% 96.3% 2.7 77 13.5% 0.0 -0.8% Spring00 --- 5 89 3.3% 100.0% 2.5 85 7.6% 96.5% 2.6 82 10.9% -0.1 -5.6% Summer00 -0.0142 5 83 11.7% 97.6% 2.9 92 2.1% 98.9% 2.6 82 12.8% 0.3 10.4% Fall00 -0.0442 5 59 4.8% 93.2% 3.2 51 17.7% 96.1% 3.0 49 21.0% -0.3 -10.8% 

Iron

(ng/m3 

)

Winter99 0.1843 22 79 0.0% 5.1% 97.6 79 0.0% 7.6% 107.9 79 0.0% -10.4 -10.6% Spring99 0.5778 22 91 1.1% 4.4% 77.0 92 0.0% 10.9% 67.4 91 1.1% 9.4 12.2% Summer99 0.4647 22 86 8.5% 3.5% 88.4 35 62.8% 5.7% 93.0 28 70.2% 7.3 8.9% Fall99 0.6004 22 89 1.1% 7.9% 72.5 88 2.2% 4.5% 93.3 87 3.3% -20.7 -28.4% Winter00 0.2527 22 85 4.5% 24.7% 65.0 80 10.1% 31.3% 80.3 77 13.5% -21.2 -34.4% Spring00 0.5387 22 89 3.3% 36.0% 57.0 85 7.6% 30.6% 60.2 82 10.9% -4.6 -8.5% Summer00 0.7977 22 83 11.7% 56.6% 49.4 92 2.1% 54.3% 43.6 82 12.8% 5.3 11.5% Fall00 0.8833 22 59 4.8% 33.9% 69.1 51 17.7% 29.4% 64.2 49 21.0% 14.4 19.2% 

Lead 

(ng/m3 ) 

Winter99 0.1948 12 79 0.0% 93.7% 6.7 79 0.0% 92.4% 6.7 79 0.0% 0.0 -0.4% Spring99 --- 12 91 1.1% 95.6% 6.4 92 0.0% 100.0% 6.0 91 1.1% 0.4 6.4% Summer99 --- 12 86 8.5% 96.5% 6.3 35 62.8% 100.0% 6.0 28 70.2% 0.0 0.0% Fall99 0.3442 12 89 1.1% 92.1% 7.1 88 2.2% 89.8% 7.2 87 3.3% -0.1 -1.2% Winter00 -0.0115 12 85 4.5% 90.6% 6.9 80 10.1% 85.0% 9.7 77 13.5% -3.0 -44.2% Spring00 -0.0306 12 89 3.3% 97.8% 6.2 85 7.6% 96.5% 6.3 82 10.9% -0.1 -1.4% Summer00 -0.0551 12 83 11.7% 96.4% 6.5 92 2.1% 92.4% 6.7 82 12.8% -0.3 -4.7% Fall00 --- 12 59 4.8% 96.6% 6.3 51 17.7% 100.0% 6.0 49 21.0% 0.3 5.5% 
Appendix 4 - Statistical Analyses - By Season (continued) 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

Manganese

(ng/m3 

)

Winter99 0.0774 3 79 0.0% 93.7% 1.7 79 0.0% 92.4% 2.6 79 0.0% -0.9 -50.6% Spring99 1.0000 3 91 1.1% 98.9% 1.5 92 0.0% 98.9% 1.6 91 1.1% 0.0 -1.6% Summer99 -0.0370 3 86 8.5% 96.5% 1.6 35 62.8% 97.1% 1.6 28 70.2% 0.0 -1.9% Fall99 0.7584 3 89 1.1% 93.3% 1.8 88 2.2% 90.9% 1.8 87 3.3% 0.0 -0.8% Winter00 0.6654 3 85 4.5% 89.4% 1.8 80 10.1% 93.8% 1.7 77 13.5% 0.1 5.6% Spring00 0.4113 3 89 3.3% 93.3% 1.7 85 7.6% 97.6% 1.5 82 10.9% 0.1 4.8% Summer00 0.0238 3 83 11.7% 63.9% 2.7 92 2.1% 87.0% 1.8 82 12.8% 0.8 30.7% Fall00 0.3602 3 59 4.8% 83.1% 2.0 51 17.7% 94.1% 1.6 49 21.0% 0.4 21.7% 

Nickel

(ng/m3 )

Winter99 0.0729 4 79 0.0% 1.3% 35.1 79 0.0% 7.6% 30.7 79 0.0% 4.4 12.6% Spring99 0.1674 4 91 1.1% 30.8% 10.3 92 0.0% 48.9% 5.5 91 1.1% 4.8 46.5% Summer99 0.5196 4 86 8.5% 54.7% 5.5 35 62.8% 57.1% 4.7 28 70.2% -0.8 -19.9% Fall99 0.6102 4 89 1.1% 31.5% 11.8 88 2.2% 28.4% 10.7 87 3.3% 1.0 8.4% Winter00 0.5528 4 85 4.5% 8.2% 27.0 80 10.1% 17.5% 16.4 77 13.5% 10.5 38.2% Spring00 0.4047 4 89 3.3% 28.1% 11.7 85 7.6% 36.5% 8.6 82 10.9% 2.8 24.6% Summer00 0.2769 4 62 34.0% 9.7% 12.0 65 30.9% 21.5% 8.9 55 41.5% 3.2 24.5% Fall00 -0.0325 4 59 4.8% 22.0% 8.9 51 17.7% 47.1% 6.1 49 21.0% 2.8 31.8% 

Zinc

(ng/m3 )

Winter99 0.3183 77 79 0.0% 97.5% 39.8 79 0.0% 96.2% 41.0 79 0.0% -1.2 -3.0% Spring99 --- 77 91 1.1% 100.0% 38.5 92 0.0% 100.0% 38.5 91 1.1% 0.0 0.0% Summer99 --- 77 86 8.5% 97.7% 40.4 35 62.8% 100.0% 38.5 28 70.2% 0.0 0.0% Fall99 0.3357 77 89 1.1% 96.6% 41.5 88 2.2% 94.3% 41.9 87 3.3% -0.3 -0.8% Winter00 -0.0313 77 85 4.5% 95.3% 42.0 80 10.1% 96.3% 40.8 77 13.5% -1.1 -2.9% Spring00 --- 77 89 3.3% 98.9% 39.1 85 7.6% 100.0% 38.5 82 10.9% 0.7 1.7% Summer00 -0.0295 77 83 11.7% 98.8% 39.0 92 2.1% 93.5% 46.3 82 12.8% -8.2 -21.0% Fall00 --- 77 59 4.8% 100.0% 38.5 51 17.7% 98.0% 39.6 49 21.0% -1.1 -2.9% 

Total Metals 

(ng/m3 ) 

Winter99 0.1580 --- 79 0.0% --- 136.0 76 3.8% --- 152.5 76 3.8% -14.1 -10.2% Spring99 0.4753 --- 88 4.3% --- 89.9 84 8.7% --- 77.5 81 12.0% 14.6 15.6% Summer99 0.4136 --- 83 11.7% --- 99.5 33 64.9% --- 101.9 25 73.4% 6.3 6.8% Fall99 0.6825 --- 83 7.8% --- 95.5 84 6.7% --- 116.2 79 12.2% -24.3 -25.5% Winter00 0.1771 --- 79 11.2% --- 103.9 68 23.6% --- 119.7 63 29.2% -19.5 -18.5% Spring00 0.6010 --- 79 14.1% --- 74.2 72 21.7% --- 77.3 64 30.4% -1.0 -1.3% Summer00 0.6707 --- 68 27.7% --- 68.2 73 22.3% --- 70.1 62 34.0% -1.1 -1.5% Fall00 0.7310 --- 56 9.7% --- 80.1 42 32.3% --- 83.5 39 37.1% 20.9 20.6% 

SO

2 (Denuder) (ug/m3 )

Winter99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Spring99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Summer99 0.8138 --- 87 7.4% --- 17.9 34 63.8% --- 14.0 29 69.1% 3.8 23.2% Fall99 0.9337 --- 88 2.2% --- 28.6 88 2.2% --- 27.4 87 3.3% 1.3 4.5% Winter00 0.8137 --- 85 4.5% --- 43.7 84 5.6% --- 41.6 80 10.1% 2.4 5.4% Spring00 0.8190 --- 92 0.0% --- 18.2 92 0.0% --- 16.5 92 0.0% 1.7 9.3% Summer00 0.7395 --- 22 76.6% --- 17.8 22 76.6% --- 16.8 22 76.6% 1.0 5.6% Fall00 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -

HCl

(ug/m3 ) 

Winter99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Spring99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Summer99 0.9228 --- 87 7.4% --- 1.00 34 63.8% --- 0.92 29 69.1% 0.09 9.3% Fall99 0.6053 --- 89 1.1% --- 0.27 88 2.2% --- 0.31 88 2.2% -0.04 -16.3% Winter00 0.1119 --- 85 4.5% --- 0.30 84 5.6% --- 0.40 80 10.1% -0.10 -34.0% Spring00 0.8742 --- 92 0.0% --- 0.41 92 0.0% --- 0.42 92 0.0% -0.02 -3.9% Summer00 0.8952 --- 22 76.6% --- 0.81 22 76.6% --- 0.97 22 76.6% -0.16 -19.9% Fall00 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -
Appendix 4 - Statistical Analyses - By Season (continued) 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

HNO2

(ug/m3 )

Winter99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Spring99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Summer99 0.9135 --- 86 8.5% --- 1.92 34 63.8% --- 1.92 29 69.1% 0.28 14.1% Fall99 0.8774 --- 89 1.1% --- 4.42 88 2.2% --- 4.04 88 2.2% 0.39 8.7% Winter00 0.7815 --- 85 4.5% --- 4.00 84 5.6% --- 3.66 80 10.1% 0.32 8.0% Spring00 0.7409 --- 92 0.0% --- 2.92 92 0.0% --- 2.42 92 0.0% 0.50 17.2% Summer00 0.7559 --- 22 76.6% --- 1.52 22 76.6% --- 1.37 22 76.6% 0.14 9.5% Fall00 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -

HNO3

(ug/m3 )

Winter99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Spring99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Summer99 0.9793 --- 85 9.6% --- 3.85 34 63.8% --- 2.52 28 70.2% 0.33 11.5% Fall99 0.7941 --- 89 1.1% --- 0.62 88 2.2% --- 0.55 88 2.2% 0.07 11.9% Winter00 0.5801 --- 85 4.5% --- 1.00 84 5.6% --- 0.50 80 10.1% 0.50 50.1% Spring00 0.9494 --- 92 0.0% --- 1.22 92 0.0% --- 1.20 92 0.0% 0.02 1.5% Summer00 0.8754 --- 22 76.6% --- 3.23 22 76.6% --- 3.13 22 76.6% 0.09 2.9% Fall00 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -

NH3

(ug/m3 ) 

Winter99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Spring99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Summer99 0.6133 --- 51 45.7% --- 4.316 20 78.7% --- 4.514 4 95.7% 0.551 10.4% Fall99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Winter00 0.9282 --- 80 10.1% --- 2.753 78 12.4% --- 1.329 76 14.6% 1.485 53.1% Spring00 0.9120 --- 55 40.2% --- 3.953 58 37.0% --- 2.771 55 40.2% 1.174 29.7% Summer00 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Fall00 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -

Total Pollen

(#/m3 

)

Winter99 0.5555 --- 79 0.0% --- 0.4 79 0.0% --- 0.9 79 0.0% -0.5 -116.8% Spring99 0.8690 --- 92 0.0% --- 23.5 92 0.0% --- 33.4 92 0.0% -9.9 -42.3% Summer99 0.5541 --- 94 0.0% --- 4.3 41 56.4% --- 8.2 41 56.4% -2.5 -43.6% Fall99 0.8574 --- 90 0.0% --- 0.3 90 0.0% --- 0.7 90 0.0% -0.3 -106.6% Winter00 0.9770 --- 89 0.0% --- 2.2 83 6.7% --- 2.0 83 6.7% 0.3 12.0% Spring00 0.9806 --- 92 0.0% --- 64.7 92 0.0% --- 106.5 92 0.0% -41.7 -64.5% Summer00 0.6997 --- 94 0.0% --- 3.3 94 0.0% --- 6.2 94 0.0% -2.9 -87.3% Fall00 0.7618 --- 61 1.6% --- 0.2 61 1.6% --- 0.4 61 1.6% -0.2 -80.9% 

Pollen - Trees 

(#/m3 ) 

Winter99 0.5304 --- 79 0.0% --- 0.4 79 0.0% --- 0.9 79 0.0% -0.5 -117.9% Spring99 0.8670 --- 92 0.0% --- 23.1 92 0.0% --- 32.6 92 0.0% -9.5 -41.3% Summer99 0.2263 --- 94 0.0% --- 1.3 46 51.1% --- 1.2 46 51.1% 0.3 19.9% Fall99 -0.0391 --- 90 0.0% --- 0.0 90 0.0% --- 0.1 90 0.0% 0.0 -63.1% Winter00 0.9770 --- 89 0.0% --- 2.2 83 6.7% --- 2.0 83 6.7% 0.3 11.9% Spring00 0.9805 --- 92 0.0% --- 64.2 92 0.0% --- 105.7 92 0.0% -41.5 -64.7% Summer00 0.0719 --- 94 0.0% --- 0.4 94 0.0% --- 0.6 94 0.0% -0.2 -52.1% Fall00 -0.0723 --- 61 1.6% --- 0.0 61 1.6% --- 0.0 61 1.6% 0.0 5.9% 

Pollen - Ragweed

(#/m3 

) 

Winter99 -0.0297 --- 79 0.0% --- 0.0 79 0.0% --- 0.0 79 0.0% 0.0 79.7% Spring99 -0.0137 --- 92 0.0% --- 0.0 92 0.0% --- 0.0 92 0.0% 0.0 1.0% Summer99 0.8813 --- 94 0.0% --- 1.4 46 51.1% --- 1.7 46 51.1% -0.3 -20.5% Fall99 0.8181 --- 90 0.0% --- 0.2 90 0.0% --- 0.3 90 0.0% -0.1 -49.5% Winter00 -0.0122 --- 89 0.0% --- 0.0 83 6.7% --- 0.0 83 6.7% 0.0 5.0% Spring00 --- --- 92 0.0% --- 0.0 92 0.0% --- 0.0 92 0.0% 0.0 -Summer00 0.7962 --- 94 0.0% --- 1.1 94 0.0% --- 1.8 94 0.0% -0.7 -67.5% Fall00 0.5930 --- 61 1.6% --- 0.1 61 1.6% --- 0.1 61 1.6% 0.0 -60.0% 
Appendix 4 - Statistical Analyses - By Season (continued) 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

Pollen - Grasses

(#/m3 )

Winter99 0.1743 --- 79 0.0% --- 0.0 79 0.0% --- 0.0 79 0.0% 0.0 -126.4% Spring99 0.8412 --- 92 0.0% --- 0.4 92 0.0% --- 0.7 92 0.0% -0.4 -100.2% Summer99 0.4835 --- 94 0.0% --- 1.0 46 51.1% --- 1.6 46 51.1% -0.1 -7.8% Fall99 0.1981 --- 90 0.0% --- 0.0 90 0.0% --- 0.0 90 0.0% 0.0 27.2% Winter00 --- --- 89 0.0% --- 0.0 83 6.7% --- 0.0 83 6.7% 0.0 100.0% Spring00 0.8609 --- 92 0.0% --- 0.6 92 0.0% --- 0.8 92 0.0% -0.2 -38.5% Summer00 0.7369 --- 94 0.0% --- 0.9 94 0.0% --- 1.0 94 0.0% -0.2 -18.1% Fall00 -0.0517 --- 61 1.6% --- 0.0 61 1.6% --- 0.0 61 1.6% 0.0 -210.3% 

Total Mold 

(#/m3 ) 

Winter99 0.1840 --- 79 0.0% --- 5.7 79 0.0% --- 9.0 79 0.0% -3.3 -57.9% Spring99 0.7962 --- 92 0.0% --- 231.7 92 0.0% --- 265.4 92 0.0% -33.8 -14.6% Summer99 0.8773 --- 94 0.0% --- 1424.6 41 56.4% --- 1335.8 41 56.4% 31.9 2.3% Fall99 0.8325 --- 90 0.0% --- 372.4 90 0.0% --- 449.5 90 0.0% -77.1 -20.7% Winter00 0.2690 --- 89 0.0% --- 3.6 83 6.7% --- 10.7 83 6.7% -7.2 -203.6% Spring00 0.6553 --- 92 0.0% --- 475.7 92 0.0% --- 363.4 92 0.0% 112.3 23.6% Summer00 0.8520 --- 94 0.0% --- 832.3 94 0.0% --- 1041.2 94 0.0% -208.8 -25.1% Fall00 0.8593 --- 61 1.6% --- 446.9 61 1.6% --- 499.7 61 1.6% -52.8 -11.8% 

Basidospores

(#/m3 )

Winter99 0.6239 --- 79 0.0% --- 1.1 79 0.0% --- 0.9 79 0.0% 0.1 14.0% Spring99 0.4571 --- 92 0.0% --- 31.7 92 0.0% --- 37.3 92 0.0% -5.5 -17.5% Summer99 0.7346 --- 94 0.0% --- 372.0 46 51.1% --- 345.8 46 51.1% -63.2 -22.4% Fall99 0.8073 --- 90 0.0% --- 194.6 90 0.0% --- 248.5 90 0.0% -53.9 -27.7% Winter00 0.5790 --- 89 0.0% --- 0.6 83 6.7% --- 1.4 83 6.7% -0.8 -130.6% Spring00 0.3739 --- 92 0.0% --- 205.8 92 0.0% --- 106.2 92 0.0% 99.6 48.4% Summer00 0.6968 --- 94 0.0% --- 452.8 94 0.0% --- 554.3 94 0.0% -101.5 -22.4% Fall00 0.8097 --- 61 1.6% --- 187.8 61 1.6% --- 220.0 61 1.6% -32.2 -17.2% 

Ascospores

(#/m3 

)

Winter99 0.0403 --- 79 0.0% --- 1.2 79 0.0% --- 4.7 79 0.0% -3.4 -276.6% Spring99 0.5139 --- 92 0.0% --- 53.0 92 0.0% --- 49.6 92 0.0% 3.4 6.5% Summer99 0.4360 --- 94 0.0% --- 60.5 46 51.1% --- 81.8 46 51.1% -2.3 -3.0% Fall99 0.6262 --- 90 0.0% --- 16.5 90 0.0% --- 24.1 90 0.0% -7.6 -45.7% Winter00 0.1396 --- 89 0.0% --- 0.7 83 6.7% --- 1.6 83 6.7% -0.8 -111.2% Spring00 0.6419 --- 92 0.0% --- 39.0 92 0.0% --- 38.6 92 0.0% 0.4 1.0% Summer00 0.8050 --- 94 0.0% --- 93.8 94 0.0% --- 110.9 94 0.0% -17.1 -18.2% Fall00 0.5409 --- 61 1.6% --- 38.9 61 1.6% --- 42.1 61 1.6% -3.2 -8.3% 

Mitospores 

(#/m3 ) 

Winter99 0.3687 --- 79 0.0% --- 3.1 79 0.0% --- 3.4 79 0.0% -0.3 -9.8% Spring99 0.8438 --- 92 0.0% --- 143.2 92 0.0% --- 176.8 92 0.0% -33.6 -23.4% Summer99 0.9454 --- 94 0.0% --- 980.0 46 51.1% --- 752.2 46 51.1% 117.3 13.5% Fall99 0.8535 --- 90 0.0% --- 155.2 90 0.0% --- 172.7 90 0.0% -17.5 -11.2% Winter00 0.2297 --- 89 0.0% --- 2.2 83 6.7% --- 6.2 83 6.7% -4.2 -205.4% Spring00 0.8058 --- 92 0.0% --- 228.2 92 0.0% --- 215.9 92 0.0% 12.4 5.4% Summer00 0.8470 --- 94 0.0% --- 275.8 94 0.0% --- 365.2 94 0.0% -89.4 -32.4% Fall00 0.7759 --- 61 1.6% --- 212.0 61 1.6% --- 229.1 61 1.6% -17.1 -8.0% 

(#/m3 )

Mitospores - Dark 

Winter99 0.5358 --- 79 0.0% --- 3.0 79 0.0% --- 2.1 79 0.0% 0.9 29.1% Spring99 0.8496 --- 92 0.0% --- 142.5 92 0.0% --- 173.4 92 0.0% -30.8 -21.6% Summer99 0.9449 --- 94 0.0% --- 968.3 46 51.1% --- 748.7 46 51.1% 108.0 12.6% Fall99 0.8637 --- 90 0.0% --- 142.3 90 0.0% --- 167.8 90 0.0% -25.5 -17.9% Winter00 0.1807 --- 89 0.0% --- 2.1 83 6.7% --- 5.2 83 6.7% -3.2 -159.4% Spring00 0.8123 --- 92 0.0% --- 219.5 92 0.0% --- 211.0 92 0.0% 8.5 3.9% Summer00 0.8544 --- 94 0.0% --- 269.6 94 0.0% --- 353.3 94 0.0% -83.7 -31.1% Fall00 0.7807 --- 61 1.6% --- 207.9 61 1.6% --- 227.2 61 1.6% -19.3 -9.3% 
Appendix 4 - Statistical Analyses - By Season (continued) 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

Mitospores - Non-Dark

(#/m3 ) 

Winter99 -0.0287 --- 79 0.0% --- 0.1 79 0.0% --- 1.3 79 0.0% -1.2 -959.5% Spring99 -0.0517 --- 92 0.0% --- 0.7 92 0.0% --- 3.5 92 0.0% -2.8 -392.5% Summer99 0.0435 --- 94 0.0% --- 11.7 46 51.1% --- 3.5 46 51.1% 9.3 72.8% Fall99 -0.0365 --- 90 0.0% --- 12.9 90 0.0% --- 4.9 90 0.0% 8.0 62.0% Winter00 0.5327 --- 89 0.0% --- 0.0 83 6.7% --- 1.0 83 6.7% -1.0 -2545.3% Spring00 0.1554 --- 92 0.0% --- 8.7 92 0.0% --- 4.9 92 0.0% 3.9 44.3% Summer00 0.0740 --- 94 0.0% --- 6.2 94 0.0% --- 11.9 94 0.0% -5.7 -92.5% Fall00 0.6535 --- 61 1.6% --- 4.1 61 1.6% --- 1.9 61 1.6% 2.2 53.6% 

Small Spores (<10um)

(#/m3 ) 

Winter99 0.1706 --- 79 0.0% --- 5.3 79 0.0% --- 8.5 79 0.0% -3.2 -60.7% Spring99 0.7984 --- 92 0.0% --- 225.3 92 0.0% --- 261.3 92 0.0% -36.0 -16.0% Summer99 0.8814 --- 94 0.0% --- 1348.5 46 51.1% --- 1108.4 46 51.1% 70.3 6.0% Fall99 0.8293 --- 90 0.0% --- 360.6 90 0.0% --- 439.7 90 0.0% -79.1 -21.9% Winter00 0.3441 --- 89 0.0% --- 3.5 83 6.7% --- 9.1 83 6.7% -5.7 -169.8% Spring00 0.6442 --- 92 0.0% --- 460.2 92 0.0% --- 348.6 92 0.0% 111.6 24.2% Summer00 0.8486 --- 94 0.0% --- 804.8 94 0.0% --- 1009.7 94 0.0% -204.8 -25.4% Fall00 0.8489 --- 61 1.6% --- 433.2 61 1.6% --- 483.7 61 1.6% -50.5 -11.7% 

Large Spores (>10um) 

(#/m3 ) 

Winter99 0.1772 --- 79 0.0% --- 0.1 79 0.0% --- 0.5 79 0.0% -0.3 -301.1% Spring99 0.4358 --- 92 0.0% --- 2.6 92 0.0% --- 2.1 92 0.0% 0.4 17.3% Summer99 0.7055 --- 94 0.0% --- 60.6 46 51.1% --- 67.6 46 51.1% -17.7 -35.5% Fall99 0.7024 --- 90 0.0% --- 4.7 90 0.0% --- 5.0 90 0.0% -0.3 -5.9% Winter00 -0.0199 --- 89 0.0% --- 0.0 83 6.7% --- 0.2 83 6.7% -0.1 -303.6% Spring00 0.9167 --- 92 0.0% --- 8.1 92 0.0% --- 7.7 92 0.0% 0.4 4.6% Summer00 0.6972 --- 94 0.0% --- 13.3 94 0.0% --- 15.0 94 0.0% -1.7 -12.4% Fall00 0.5883 --- 61 1.6% --- 4.8 61 1.6% --- 6.4 61 1.6% -1.6 -32.6% 

Particle Count

(#)

Winter99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Spring99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Summer99 --- --- 0 100.0% --- --- 0 100.0% --- --- 0 100.0% --- -Fall99 0.0058 --- 89 1.1% --- 1255412 90 0.0% --- 1278806 89 1.1% -30518 -2.4% Winter00 0.1092 --- 66 25.8% --- 1515846 74 16.9% --- 1277994 65 27.0% 221627 14.7% Spring00 0.0761 --- 75 18.5% --- 1442280 74 19.6% --- 1952556 66 28.3% -450936 -31.1% Summer00 -0.0999 --- 31 67.0% --- 1451768 38 59.6% --- 1493997 29 69.1% -140774 -9.8% Fall00 0.2066 --- 47 24.2% --- 1823350 53 14.5% --- 1935310 39 37.1% -177335 -10.0% 

PM2.5 (FRM) 

(ug/m3 ) 

Winter99 0.3269 --- 65 17.7% --- 17.70 23 70.9% --- 15.28 16 79.7% 2.02 13.1% Spring99 0.7468 --- 75 18.5% --- 14.44 87 5.4% --- 13.65 70 23.9% 0.81 5.6% Summer99 0.4443 --- 78 17.0% --- 17.88 31 67.0% --- 16.65 21 77.7% 1.01 6.4% Fall99 0.9319 --- 54 40.0% --- 15.94 53 41.1% --- 12.01 37 58.9% 1.61 11.5% Winter00 0.9812 --- 62 30.3% --- 19.13 65 27.0% --- 17.17 49 44.9% 1.59 8.2% Spring00 0.9898 --- 88 4.3% --- 15.34 84 8.7% --- 13.82 81 12.0% 1.67 11.0% Summer00 0.9751 --- 87 7.4% --- 16.90 89 5.3% --- 15.03 82 12.8% 1.70 10.0% Fall00 0.9861 --- 62 0.0% --- 16.18 57 8.1% --- 13.99 57 8.1% 2.02 12.6% 

PM10 (FRM) 

(ug/m3 ) 

Winter99 -1.0000 --- 3 76.9% --- 12.3 7 46.2% --- 12.4 3 76.9% 3.0 24.3% Spring99 0.9734 --- 14 6.7% --- 22.4 13 13.3% --- 22.2 12 20.0% 0.7 2.9% Summer99 0.9512 --- 15 6.3% --- 27.3 3 81.3% --- 38.3 3 81.3% 0.0 0.0% Fall99 0.8903 --- 15 0.0% --- 17.2 11 26.7% --- 16.2 11 26.7% 2.4 12.7% Winter00 0.9785 --- 15 0.0% --- 19.5 12 20.0% --- 20.3 12 20.0% -0.2 -0.8% Spring00 0.9614 --- 15 0.0% --- 22.3 12 20.0% --- 24.2 12 20.0% -0.2 -0.7% Summer00 0.9178 --- 16 0.0% --- 22.0 16 0.0% --- 20.7 16 0.0% 1.3 6.0% Fall00 0.9661 --- 9 10.0% --- 27.7 6 40.0% --- 23.0 5 50.0% 0.4 1.8% 
Appendix 4 - Statistical Analyses - By Season (continued) 

Seasonal Statistics and Analyses - Daily Averagesa

Analyte

(units)

Season 

Pearson 

Correlation 

Detection 

Limit 

Manhattan Bronx Differen

N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) 

NonDetects 

(%) Meanc N 

Missing 

(%) Meanc Mean (%)

Temperature

(deg F) 

Winter99 0.9973 --- 79 0.0% --- 37.0 79 0.0% --- 36.0 79 0.0% 1.0 2.6% Spring99 0.9984 --- 87 5.4% --- 59.8 87 5.4% --- 59.2 87 5.4% 0.6 1.0% Summer99 0.9904 --- 73 22.3% --- 76.8 32 66.0% --- 76.1 31 67.0% 0.4 0.5% Fall99 0.9983 --- 86 4.4% --- 53.5 90 0.0% --- 52.4 86 4.4% 0.7 1.3% Winter00 0.9977 --- 82 7.9% --- 37.8 89 0.0% --- 35.8 82 7.9% 1.1 2.8% Spring00 0.9978 --- 92 0.0% --- 58.8 92 0.0% --- 58.1 92 0.0% 0.7 1.3% Summer00 0.9819 --- 89 5.3% --- 72.6 87 7.4% --- 72.6 82 12.8% 0.1 0.2% Fall00 0.9970 --- 61 1.6% --- 55.1 54 12.9% --- 53.8 54 12.9% 0.8 1.4% 

(%)

Relative Humidity Winter99 0.9925 --- 79 0.0% --- 60 79 0.0% --- 66 79 0.0% -5 -8.7% 

Spring99 0.9934 --- 87 5.4% --- 55 82 10.9% --- 61 82 10.9% -6 -10.3% Summer99 0.9025 --- 73 22.3% --- 62 31 67.0% --- 67 31 67.0% -5 -8.7% Fall99 0.9956 --- 90 0.0% --- 65 90 0.0% --- 71 90 0.0% -6 -9.0% Winter00 0.9870 --- 82 7.9% --- 61 89 0.0% --- 67 82 7.9% -6 -9.7% Spring00 0.9482 --- 92 0.0% --- 66 92 0.0% --- 73 92 0.0% -7 -11.2% Summer00 0.9903 --- 94 0.0% --- 70 84 10.6% --- 78 84 10.6% -9 -13.7% Fall00 0.9806 --- 61 1.6% --- 65 56 9.7% --- 75 56 9.7% -9 -14.1% 

a For analytes collected on an hourly basis, daily averages were calculated for days with at least 75% valid data b Difference=Manhattan - Bronx c Non-detects were given values of 1/2 the detection limit for statistical calculations d Mean Difference (%) =Mean Difference / Manhattan (using only days with daily averages available for both sites) 

*Data for April 20-30, 2000 at the Bronx site has been excluded from these analyses 
180
Appendix 5 – Pearson Correlations Among All Analytes Within Sampling Location 

181
Appendix 5 - Correlation Matrix 

Bronx

phb sulfateb carb250b sootb 

o3b noxb nob no2b so2b pm2_5_30b pm10_30b acetaldeb acetoneb acroleinb benzaldeb butyraldb crotonalb formaldeb hexaldehb isovalerb m_tolualb o_tolualb p_tolualb propionab valeraldb _2_5_dimb total_aldehydesb metal_crb metal_feb metal_pbb metal_mnb metal_nib metal_znb total_metalsb denu_so2b hclb hno2b hno3b nh3b totalpolb tree_pollenb ragweedb grassesb totalmolb basidosporesb ascosporesb mitosporesb dark_mitob non_dark_mitob small_sporesb large_sporesb parttotb 

frm2_5b frm10b temp_aveb rh_aveb max_250b max_sootb max_o3b max_8hrb max_noxb max_nob max_no2b max_so2b max_pm2_5b max_pm10b max_parttotb temp_maxb rh_maxb 

phb sulfateb carb250sootb o3b noxb nob no2b so2b pm2_5_pm10_3acetaldeacetone acrolein benzald butyrald crotonal formaldehexalde isovaler m_tolua o_tolual p_tolual propionavalerald _2_5_di total_aldmetal_

1 -0.661 -0.495 -0.174 -0.255 -0.147 -0.071 -0.288 0.064 -0.584 -0.601 -0.460 -0.149 0.018 -0.227 -0.308 -0.140 -0.558 -0.240 -0.109 -0.058 -0.280 -0.191 -0.143 -0.415 0.05

-0.661 1 0.559 0.343 0.294 0.234 0.134 0.410 0.133 0.861 0.809 0.576 0.225 -0.020 0.355 0.263 0.088 0.641 0.277 0.072 -0.017 0.418 0.272 0.245 0.506 -0.03-0.495 0.559 1 0.430 0.121 0.322 0.243 0.430 0.146 0.642 0.628 0.679 0.318 -0.019 0.238 0.362 0.225 0.686 0.296 0.028 -0.009 0.296 0.228 0.077 0.604 -0.06-0.174 0.343 0.430 1 -0.345 0.796 0.761 0.694 0.585 0.637 0.655 0.537 0.405 -0.032 0.174 -0.084 0.172 0.304 -0.083 0.003 -0.064 0.184 0.125 -0.069 0.420 -0.00

-0.255 0.294 0.121 -0.345 1 -0.497 -0.544 -0.271 -0.488 0.118 0.139 0.095 0.182 -0.003 0.102 0.335 -0.135 0.356 0.278 -0.003 0.206 0.165 0.203 0.266 -0.01-0.147 0.234 0.322 0.796 -0.497 1 0.969 0.825 0.654 0.527 0.585 0.543 0.335 -0.013 0.268 -0.028 0.086 0.273 -0.002 0.253 -0.025 0.285 0.044 0.036 0.352 0.06-0.071 0.134 0.243 0.761 -0.544 0.969 1 0.662 0.650 0.420 0.468 0.440 0.254 -0.020 0.226 -0.110 0.065 0.175 -0.062 0.248 -0.032 0.255 0.007 -0.013 0.249 0.07-0.288 0.410 0.430 0.694 -0.271 0.825 0.662 1 0.502 0.649 0.707 0.644 0.441 0.004 0.300 0.167 0.115 0.430 0.133 0.203 0.000 0.284 0.123 0.140 0.503 0.03

0.064 0.133 0.146 0.585 -0.488 0.654 0.650 0.502 1 0.389 0.335 0.272 0.179 -0.011 -0.007 -0.246 0.150 -0.008 -0.153 0.000 -0.012 0.022 -0.059 -0.045 0.113 -0.01

-0.584 0.861 0.642 0.637 0.118 0.527 0.420 0.649 0.389 1 0.949 0.743 0.387 -0.014 0.368 0.221 0.148 0.695 0.236 0.198 0.040 0.450 0.247 0.238 0.634 -0.03

-0.601 0.809 0.628 0.655 0.139 0.585 0.468 0.707 0.335 0.949 1 0.811 0.577 -0.019 0.401 0.342 0.197 0.755 0.305 0.216 0.043 0.500 0.218 0.259 0.738 -0.04-0.460 0.576 0.679 0.537 0.095 0.543 0.440 0.644 0.272 0.743 0.811 1 0.582 -0.061 0.528 0.519 0.132 0.850 0.507 0.065 -0.034 0.536 0.230 0.407 0.871 -0.03-0.149 0.225 0.318 0.405 0.182 0.335 0.254 0.441 0.179 0.387 0.577 0.582 1 -0.058 0.226 0.275 0.093 0.437 0.185 -0.032 -0.073 0.225 0.187 0.163 0.824 -0.01

0.018 -0.020 -0.019 -0.032 -0.003 -0.013 -0.020 0.004 -0.011 -0.014 -0.019 -0.061 -0.058 1 -0.008 -0.017 -0.016 -0.045 -0.012 -0.005 -0.003 -0.012 -0.012 -0.006 -0.062 0.01

-0.227 0.355 0.238 0.174 0.102 0.268 0.226 0.300 -0.007 0.368 0.401 0.528 0.226 -0.008 1 0.486 -0.063 0.477 0.602 0.153 -0.014 0.667 0.056 0.572 0.466 -0.00

-0.308 0.263 0.362 -0.084 0.335 -0.028 -0.110 0.167 -0.246 0.221 0.342 0.519 0.275 -0.017 0.486 1 0.054 0.563 0.734 0.024 -0.019 0.416 0.252 0.503 0.591 -0.02-0.140 0.088 0.225 0.172 -0.135 0.086 0.065 0.115 0.150 0.148 0.197 0.132 0.093 -0.016 -0.063 0.054 1 0.108 -0.086 -0.026 0.001 -0.063 0.198 -0.049 0.206 -0.025 -0.558 0.641 0.686 0.304 0.356 0.273 0.175 0.430 -0.008 0.695 0.755 0.850 0.437 -0.045 0.477 0.563 0.108 1 0.578 0.095 0.002 0.540 0.260 0.393 0.821 -0.044 

-0.240 0.277 0.296 -0.083 0.278 -0.002 -0.062 0.133 -0.153 0.236 0.305 0.507 0.185 -0.012 0.602 0.734 -0.086 0.578 1 0.117 -0.011 0.523 0.067 0.706 0.509 -0.015 

-0.109 0.072 0.028 0.003 -0.003 0.253 0.248 0.203 0.000 0.198 0.216 0.065 -0.032 -0.005 0.153 0.024 -0.026 0.095 0.117 1 0.506 0.112 -0.010 0.074 0.063 -0.01-0.058 -0.017 -0.009 -0.064 -0.025 -0.032 0.000 -0.012 0.040 0.043 -0.034 -0.073 -0.003 -0.014 -0.019 0.001 0.002 -0.011 0.506 1 -0.022 -0.013 -0.010 -0.021 -0.00-0.280 0.418 0.296 0.184 0.206 0.285 0.255 0.284 0.022 0.450 0.500 0.536 0.225 -0.012 0.667 0.416 -0.063 0.540 0.523 0.112 -0.022 1 0.025 0.454 0.485 -0.01

-0.191 0.272 0.228 0.125 0.165 0.044 0.007 0.123 -0.059 0.247 0.218 0.230 0.187 -0.012 0.056 0.252 0.198 0.260 0.067 -0.010 -0.013 0.025 1 0.015 0.401 -0.02

-0.143 0.245 0.077 -0.069 0.203 0.036 -0.013 0.140 -0.045 0.238 0.259 0.407 0.163 -0.006 0.572 0.503 -0.049 0.393 0.706 0.074 -0.010 0.454 0.015 1 0.374 0.00

-0.415 0.506 0.604 0.420 0.266 0.352 0.249 0.503 0.113 0.634 0.738 0.871 0.824 -0.062 0.466 0.591 0.206 0.821 0.509 0.063 -0.021 0.485 0.401 0.374 1 -0.034 

0.053 -0.030 -0.067 -0.007 -0.011 0.068 0.078 0.031 -0.011 -0.039 -0.040 -0.039 -0.010 0.017 -0.006 -0.026 -0.025 -0.044 -0.015 -0.011 -0.003 -0.010 -0.020 0.000 -0.034 

-0.034 0.154 0.222 0.400 -0.129 0.486 0.464 0.417 0.266 0.324 0.369 0.282 0.201 -0.001 0.060 -0.058 0.111 0.203 -0.054 0.178 0.039 0.150 0.077 -0.057 0.234 0.45

0.019 0.025 0.116 0.174 -0.101 0.291 0.294 0.210 0.189 0.143 0.212 0.146 0.087 -0.005 -0.010 -0.049 0.068 0.080 -0.028 0.136 -0.005 0.088 -0.011 -0.017 0.101 -0.004 

-0.002 0.055 0.025 0.154 -0.046 0.371 0.364 0.293 0.090 0.091 0.083 0.071 0.062 -0.006 0.008 -0.046 -0.006 0.034 -0.022 0.004 -0.006 0.033 -0.025 -0.010 0.044 0.90

0.094 0.026 -0.004 0.328 -0.155 0.475 0.452 0.406 0.422 0.156 0.101 0.101 0.217 -0.004 -0.040 -0.141 -0.009 -0.029 -0.075 0.013 0.005 -0.017 -0.044 -0.026 0.095 0.57

-0.078 0.126 0.122 0.250 -0.021 0.319 0.322 0.233 0.171 0.202 0.175 0.129 0.072 -0.007 -0.014 -0.036 0.008 0.093 -0.049 0.013 -0.007 0.059 -0.036 -0.022 0.075 -0.00

0.003 0.110 0.180 0.418 -0.149 0.491 0.474 0.409 0.309 0.285 0.299 0.250 0.201 -0.004 0.035 -0.079 0.072 0.154 -0.067 0.144 0.027 0.117 0.029 -0.059 0.200 0.500.018 0.132 -0.001 0.568 -0.445 0.586 0.579 0.500 0.957 0.307 0.290 0.124 -0.022 -0.010 -0.039 -0.266 0.383 -0.100 -0.187 -0.142 -0.118 -0.023 -0.049 -0.042 -0.038 0.07

-0.462 0.646 0.400 -0.017 0.560 -0.070 -0.145 0.142 -0.038 0.542 0.518 0.376 0.301 -0.027 0.276 0.267 0.049 0.516 0.256 -0.057 -0.023 0.365 0.489 0.318 0.444 -0.04-0.168 0.136 0.376 0.752 -0.661 0.778 0.783 0.627 0.510 0.376 0.370 0.316 0.192 -0.036 -0.110 -0.231 0.430 0.078 -0.201 -0.013 -0.036 -0.068 -0.069 -0.165 0.141 0.08-0.581 0.726 0.507 -0.061 0.740 -0.141 -0.218 0.088 -0.174 0.606 0.584 0.433 0.358 -0.020 0.354 0.332 -0.013 0.651 0.362 -0.002 -0.012 0.458 0.530 0.325 0.537 -0.04-0.560 0.394 0.631 0.176 0.175 0.173 0.089 0.386 -0.337 0.521 0.601 0.597 0.634 -0.065 0.339 0.409 0.221 0.656 0.331 0.241 0.274 0.395 0.278 0.303 0.686 0.095 -0.154 0.190 0.182 -0.019 0.164 0.066 0.012 0.173 -0.060 0.189 0.226 0.343 0.166 0.457 0.474 -0.025 0.263 0.509 0.140 -0.006 0.275 0.063 0.701 0.315 -0.01

-0.144 0.178 0.174 -0.015 0.157 0.069 0.016 0.173 -0.055 0.181 0.218 0.340 0.166 0.456 0.473 -0.024 0.256 0.508 0.037 -0.012 0.270 0.062 0.700 0.312 -0.01

-0.203 0.226 -0.023 0.021 0.063 -0.033 -0.061 0.042 -0.101 0.152 0.154 0.023 -0.063 -0.041 -0.090 -0.083 0.095 -0.063 -0.017 -0.018 0.162 -0.049 -0.031 -0.016 -0.00-0.286 0.390 0.280 -0.038 0.451 -0.124 -0.158 -0.013 -0.139 0.274 0.298 0.239 0.123 0.148 0.297 0.036 0.427 0.233 0.001 -0.016 0.271 0.222 0.143 0.307 -0.02-0.438 0.413 0.219 -0.095 0.422 -0.131 -0.175 0.009 -0.230 0.319 0.327 0.207 0.031 0.151 0.149 -0.077 0.419 0.142 0.058 0.085 0.233 0.123 0.094 0.214 -0.03

-0.305 0.228 0.053 -0.091 0.203 -0.101 -0.106 -0.063 -0.236 0.153 0.143 0.046 -0.072 0.053 -0.008 -0.137 0.191 0.026 -0.021 -0.035 0.112 -0.025 -0.024 0.015 -0.03

-0.269 0.204 0.093 -0.105 0.224 -0.173 -0.187 -0.096 -0.245 0.067 0.090 0.055 -0.037 -0.003 0.118 -0.041 0.192 0.097 -0.032 -0.001 0.028 0.065 0.023 0.070 -0.03-0.390 0.431 0.288 -0.052 0.463 -0.092 -0.152 0.073 -0.130 0.361 0.382 0.277 0.114 0.197 0.218 -0.009 0.465 0.185 -0.011 -0.008 0.274 0.200 0.161 0.308 -0.02-0.392 0.433 0.288 -0.052 0.467 -0.090 -0.151 0.075 -0.127 0.363 0.386 0.279 0.116 0.195 0.217 -0.009 0.465 0.185 -0.010 -0.008 0.275 0.202 0.162 0.309 -0.02

-0.025 0.028 0.042 -0.015 -0.002 -0.065 -0.067 -0.045 -0.083 0.002 0.005 0.016 -0.020 0.068 0.041 -0.010 0.067 0.022 -0.012 -0.015 0.019 -0.010 -0.002 0.016 -0.01-0.426 0.403 0.211 -0.092 0.415 -0.128 -0.172 0.009 -0.230 0.312 0.322 0.203 0.033 0.152 0.149 -0.083 0.407 0.141 -0.022 -0.023 0.232 0.117 0.094 0.208 -0.03-0.352 0.441 0.332 -0.018 0.413 -0.105 -0.146 0.018 -0.103 0.355 0.371 0.211 0.019 0.065 0.113 0.071 0.432 0.114 0.018 0.006 0.174 0.236 0.061 0.230 -0.02

-0.198 0.149 -0.115 -0.287 0.369 -0.257 -0.273 -0.156 -0.316 0.003 0.061 0.139 0.175 0.206 0.482 -0.368 0.206 0.414 0.005 0.255 0.137 0.205 0.231 0.01

-0.572 0.838 0.624 0.678 0.030 0.575 0.478 0.633 0.501 0.920 0.868 0.678 0.361 -0.026 0.315 0.163 0.171 0.582 0.168 0.163 0.038 0.385 0.244 0.174 0.564 0.03-0.633 0.813 0.675 0.512 0.187 0.481 0.409 0.510 0.165 0.932 0.950 0.748 0.496 0.491 0.420 0.193 0.768 0.305 0.054 0.434 0.518 0.239 0.755 0.316 -0.574 0.466 0.443 -0.091 0.518 -0.154 -0.222 0.048 -0.474 0.351 0.420 0.369 0.146 -0.049 0.187 0.354 0.070 0.623 0.283 0.112 0.072 0.289 0.218 0.154 0.415 -0.052 -0.292 0.257 0.200 0.155 -0.341 0.124 0.138 0.061 -0.139 0.165 0.108 0.064 -0.046 -0.005 0.058 0.005 0.040 0.069 0.043 0.110 0.010 0.073 0.086 -0.038 0.036 0.014 -0.475 0.534 0.907 0.558 0.099 0.416 0.346 0.480 0.190 0.671 0.657 0.666 0.407 -0.029 0.196 0.283 0.221 0.635 0.203 0.023 -0.017 0.269 0.249 0.014 0.618 -0.07-0.161 0.278 0.403 0.928 -0.277 0.713 0.691 0.600 0.484 0.563 0.592 0.467 0.386 -0.026 0.105 -0.060 0.143 0.257 -0.083 -0.004 -0.052 0.141 0.135 -0.091 0.382 -0.00-0.403 0.454 0.262 -0.165 0.901 -0.327 -0.418 -0.041 -0.421 0.320 0.354 0.273 0.291 -0.005 0.174 0.375 -0.122 0.506 0.317 0.048 0.292 0.200 0.220 0.407 -0.015 -0.367 0.407 0.230 -0.219 0.936 -0.366 -0.442 -0.103 -0.430 0.264 0.295 0.234 0.259 0.004 0.161 0.375 -0.136 0.468 0.323 0.040 0.282 0.172 0.240 0.370 -0.01-0.144 0.180 0.318 0.704 -0.363 0.891 0.873 0.713 0.492 0.474 0.534 0.487 0.336 -0.023 0.183 0.021 0.072 0.265 0.016 0.148 -0.018 0.249 0.051 0.066 0.338 0.042 -0.112 0.139 0.278 0.697 -0.408 0.880 0.887 0.650 0.500 0.434 0.489 0.444 0.296 -0.030 0.169 -0.019 0.059 0.221 -0.010 0.142 -0.023 0.231 0.033 0.046 0.289 0.03-0.353 0.409 0.459 0.559 0.001 0.662 0.504 0.864 0.267 0.618 0.676 0.650 0.520 0.005 0.292 0.276 0.104 0.495 0.184 0.192 0.006 0.316 0.157 0.180 0.575 0.02

0.007 0.167 0.195 0.518 -0.353 0.591 0.574 0.485 0.875 0.401 0.380 0.282 0.160 -0.018 -0.008 -0.186 0.149 0.062 -0.111 -0.005 0.020 0.046 -0.038 -0.036 0.136 -0.014 

-0.507 0.714 0.596 0.629 0.094 0.551 0.471 0.605 0.315 0.876 0.856 0.680 0.373 -0.019 0.305 0.208 0.146 0.623 0.198 0.160 0.020 0.433 0.222 0.202 0.585 -0.04-0.529 0.677 0.587 0.664 0.066 0.600 0.512 0.651 0.303 0.839 0.901 0.730 0.542 -0.025 0.312 0.293 0.242 0.662 0.221 0.158 0.022 0.434 0.221 0.182 0.671 -0.05-0.219 0.158 0.103 0.014 0.139 0.016 -0.005 0.072 -0.197 0.157 0.202 0.269 0.271 0.207 0.324 -0.180 0.255 0.287 0.122 0.253 0.237 0.169 0.296 0.052 -0.560 0.462 0.444 -0.071 0.537 -0.130 -0.203 0.074 -0.449 0.368 0.453 0.401 0.158 -0.048 0.210 0.373 0.072 0.642 0.304 0.122 0.071 0.309 0.218 0.168 0.436 -0.05-0.298 0.254 0.211 0.151 -0.213 0.113 0.129 0.048 -0.164 0.177 0.149 0.108 -0.014 -0.021 0.088 0.072 0.014 0.120 0.108 0.116 0.030 0.121 0.055 0.007 0.080 0.04
Appendix 5 - Correlation Matrix (continued)

Bronx

hno2b hno3b nh3b totalpolbtree_polragweedgrasses totalmol basidos ascospomitospo dark_mi non_darsmall_splarge_spparttotb frm2_5bfrm10b temp_avrh_avebmax_25 max_so max_o3 max_8h max_no max_no max_no max_s

phb sulfateb carb250b sootb o3b noxb nob no2b so2b pm2_5_30b pm10_30b acetaldeb acetoneb acroleinb benzaldeb butyraldb crotonalb formaldeb hexaldehb isovalerb m_tolualb o_tolualb p_tolualb propionab valeraldb _2_5_dimb 

-0.168 -0.581 -0.560 -0.154 -0.144 

0.136 0.726 0.394 0.190 0.178 0.376 0.507 0.631 0.182 0.174 0.752 -0.061 0.176 -0.019 -0.015 

-0.661 0.740 0.175 0.164 0.157 

0.778 -0.141 0.173 0.066 0.069 0.783 -0.218 0.089 0.012 0.016 0.627 0.088 0.386 0.173 0.173 0.510 -0.174 -0.337 -0.060 -0.055 0.376 0.606 0.521 0.189 0.181 0.370 0.584 0.601 0.226 0.218 0.316 0.433 0.597 0.343 0.340 0.192 0.358 0.634 0.166 0.166 

-0.036 -0.020 -0.065 -0.110 0.354 0.339 0.457 0.456 -0.231 0.332 0.409 0.474 0.473 

0.430 -0.013 0.221 -0.025 -0.024 0.078 0.651 0.656 0.263 0.256 

-0.201 0.362 0.331 0.509 0.508 -0.013 -0.002 0.241 0.140 0.037 -0.036 -0.012 0.274 -0.006 -0.012 -0.068 0.458 0.395 0.275 0.270 -0.069 0.530 0.278 0.063 0.062 -0.165 0.325 0.303 0.701 0.700 

-0.203 -0.286 -0.438 -0.305 -0.269 -0.390 -0.392 -0.025 -0.426 

0.226 0.390 0.413 0.228 0.204 0.431 0.433 0.028 0.403 

-0.023 0.280 0.219 0.053 0.093 0.288 0.288 0.042 0.211 

0.021 -0.038 -0.095 -0.091 -0.105 -0.052 -0.052 -0.015 -0.092 

0.063 0.451 0.422 0.203 0.224 0.463 0.467 -0.002 0.415 

-0.033 -0.124 -0.131 -0.101 -0.173 -0.092 -0.090 -0.065 -0.128 -0.061 -0.158 -0.175 -0.106 -0.187 -0.152 -0.151 -0.067 -0.172 

0.042 -0.013 0.009 -0.063 -0.096 0.073 0.075 -0.045 0.009 

-0.101 -0.139 -0.230 -0.236 -0.245 -0.130 -0.127 -0.083 -0.230 

0.152 0.274 0.319 0.153 0.067 0.361 0.363 0.002 0.312 0.154 0.298 0.327 0.143 0.090 0.382 0.386 0.005 0.322 0.023 0.239 0.207 0.046 0.055 0.277 0.279 0.016 0.203 

-0.063 0.123 0.031 -0.072 -0.037 0.114 0.116 -0.020 0.033 

-0.041 0.148 0.151 0.053 -0.003 0.197 0.195 0.068 0.152 -0.090 0.297 0.149 -0.008 0.118 0.218 0.217 0.041 0.149 

-0.083 0.036 -0.077 -0.137 -0.041 -0.009 -0.009 -0.010 -0.083 

0.095 0.427 0.419 0.191 0.192 0.465 0.465 0.067 0.407 

-0.063 0.233 0.142 0.026 0.097 0.185 0.185 0.022 0.141 -0.017 0.001 0.058 -0.021 -0.032 -0.011 -0.010 -0.012 -0.022 -0.018 -0.016 0.085 -0.035 -0.001 -0.008 -0.008 -0.015 -0.023 

0.162 0.271 0.233 0.112 0.028 0.274 0.275 0.019 0.232 

-0.049 0.222 0.123 -0.025 0.065 0.200 0.202 -0.010 0.117 -0.031 0.143 0.094 -0.024 0.023 0.161 0.162 -0.002 0.094 

-0.016 0.307 0.214 0.015 0.070 0.308 0.309 0.016 0.208 -0.008 -0.021 -0.037 -0.030 -0.033 -0.028 -0.027 -0.018 -0.036 

0.017 0.016 0.015 -0.034 -0.035 0.059 0.060 -0.011 0.013 

-0.018 -0.022 -0.044 -0.028 -0.026 -0.042 -0.042 -0.015 -0.043 

0.009 -0.026 -0.003 0.000 -0.013 -0.003 -0.003 -0.003 -0.003 

-0.055 -0.081 -0.171 -0.144 -0.126 -0.135 -0.133 -0.067 -0.170 -0.027 -0.014 0.093 0.196 0.088 -0.025 -0.029 0.109 0.095 -0.012 -0.020 -0.031 -0.039 -0.059 -0.007 -0.006 -0.017 -0.032 -0.044 -0.115 -0.217 -0.211 -0.207 -0.128 -0.128 -0.015 -0.207 

0.022 0.510 0.556 0.138 0.304 0.687 0.690 -0.009 0.541 

0.011 -0.275 -0.250 -0.112 -0.173 -0.267 -0.268 0.009 -0.246 

0.050 0.637 0.639 0.194 0.402 0.755 0.755 0.072 0.621 

0.169 0.204 0.396 0.198 0.273 0.333 0.333 0.127 0.354 

-0.005 0.066 0.047 -0.037 0.047 0.098 0.099 -0.024 0.048 

-0.035 0.047 0.032 -0.050 0.034 0.087 0.089 -0.026 0.034 

1 0.211 0.376 0.396 0.284 0.224 0.222 0.079 0.371 

0.211 1 0.333 0.082 0.242 0.424 0.423 0.068 0.325 0.376 0.333 1 0.779 0.630 0.855 0.851 0.201 0.999 0.396 0.082 0.779 1 0.427 0.357 0.353 0.132 0.787 0.284 0.242 0.630 0.427 1 0.471 0.462 0.269 0.635 0.224 0.424 0.855 0.357 0.471 1 0.999 0.167 0.847 0.222 0.423 0.851 0.353 0.462 0.999 1 0.122 0.843 

0.079 0.068 0.201 0.132 0.269 0.167 0.122 1 0.204 

0.371 0.325 0.999 0.787 0.635 0.847 0.843 0.204 1 0.258 0.364 0.555 0.249 0.228 0.651 0.654 0.018 0.525 

-0.005 0.113 0.204 0.097 0.095 0.243 0.244 0.006 0.203 

0.124 0.255 0.222 0.061 0.059 0.287 0.288 0.004 0.215 0.190 0.376 0.399 0.132 0.181 0.482 0.484 0.035 0.391 

0.259 0.400 0.603 0.444 0.460 0.529 0.528 0.112 0.598 

0.113 -0.066 0.042 0.142 0.133 -0.072 -0.075 0.063 0.042 

-0.009 0.241 0.187 0.046 0.069 0.244 0.245 0.017 0.179 -0.021 -0.044 -0.109 -0.091 -0.116 -0.074 -0.073 -0.037 -0.106 

0.180 0.465 0.505 0.314 0.293 0.497 0.499 0.036 0.499 0.138 0.452 0.490 0.283 0.272 0.502 0.505 0.021 0.484 

-0.063 -0.096 -0.120 -0.102 -0.157 -0.080 -0.077 -0.079 -0.118 -0.063 -0.115 -0.132 -0.096 -0.166 -0.100 -0.097 -0.071 -0.129 

0.044 0.049 0.126 -0.001 0.014 0.185 0.187 -0.020 0.127 

-0.092 -0.093 -0.172 -0.183 -0.181 -0.092 -0.089 -0.074 -0.172 

0.111 0.262 0.268 0.144 0.063 0.293 0.296 -0.016 0.264 0.097 0.259 0.255 0.105 0.082 0.299 0.301 0.013 0.250 

-0.024 0.026 0.131 0.050 -0.027 0.185 0.184 0.043 0.132 

0.252 0.401 0.592 0.421 0.443 0.532 0.530 0.109 0.586 

0.127 -0.032 0.098 0.170 0.170 -0.010 -0.012 0.044 0.100 

-0.352 -0.198 -0.572 -0.633 -0.574 -0.292 -0.475 -0.161 -0.403 -0.367 -0.144 -0.112 -0.353 0.00

0.441 0.149 0.838 0.813 0.466 0.257 0.534 0.278 0.454 0.407 0.180 0.139 0.409 0.160.332 -0.115 0.624 0.675 0.443 0.200 0.907 0.403 0.262 0.230 0.318 0.278 0.459 0.195 

-0.018 -0.287 0.678 0.512 -0.091 0.155 0.558 0.928 -0.165 -0.219 0.704 0.697 0.559 0.51

0.413 0.369 0.030 0.187 0.518 -0.341 0.099 -0.277 0.901 0.936 -0.363 -0.408 0.001 -0.35

-0.105 -0.257 0.575 0.481 -0.154 0.124 0.416 0.713 -0.327 -0.366 0.891 0.880 0.662 0.59-0.146 -0.273 0.478 0.409 -0.222 0.138 0.346 0.691 -0.418 -0.442 0.873 0.887 0.504 0.574 

0.018 -0.156 0.633 0.510 0.048 0.061 0.480 0.600 -0.041 -0.103 0.713 0.650 0.864 0.485 

-0.103 -0.316 0.501 0.165 -0.474 -0.139 0.190 0.484 -0.421 -0.430 0.492 0.500 0.267 0.875 

0.355 0.003 0.920 0.932 0.351 0.165 0.671 0.563 0.320 0.264 0.474 0.434 0.618 0.400.371 0.061 0.868 0.950 0.420 0.108 0.657 0.592 0.354 0.295 0.534 0.489 0.676 0.380.211 0.139 0.678 0.748 0.369 0.064 0.666 0.467 0.273 0.234 0.487 0.444 0.650 0.282 0.019 0.175 0.361 0.496 0.146 -0.046 0.407 0.386 0.291 0.259 0.336 0.296 0.520 0.16

-0.026 -0.049 -0.005 -0.029 -0.026 -0.005 0.004 -0.023 -0.030 0.005 -0.01

0.065 0.206 0.315 0.491 0.187 0.058 0.196 0.105 0.174 0.161 0.183 0.169 0.292 -0.000.113 0.482 0.163 0.420 0.354 0.005 0.283 -0.060 0.375 0.375 0.021 -0.019 0.276 -0.18

0.071 -0.368 0.171 0.193 0.070 0.040 0.221 0.143 -0.122 -0.136 0.072 0.059 0.104 0.140.432 0.206 0.582 0.768 0.623 0.069 0.635 0.257 0.506 0.468 0.265 0.221 0.495 0.062 

0.114 0.414 0.168 0.305 0.283 0.043 0.203 -0.083 0.317 0.323 0.016 -0.010 0.184 -0.110.018 0.005 0.163 0.054 0.112 0.110 0.023 -0.004 0.048 0.040 0.148 0.142 0.192 -0.005 0.006 0.038 0.072 0.010 -0.017 -0.052 -0.018 -0.023 0.006 0.020.174 0.255 0.385 0.434 0.289 0.073 0.269 0.141 0.292 0.282 0.249 0.231 0.316 0.040.236 0.137 0.244 0.518 0.218 0.086 0.249 0.135 0.200 0.172 0.051 0.033 0.157 -0.030.061 0.205 0.174 0.239 0.154 -0.038 0.014 -0.091 0.220 0.240 0.066 0.046 0.180 -0.03

0.230 0.231 0.564 0.755 0.415 0.036 0.618 0.382 0.407 0.370 0.338 0.289 0.575 0.13

-0.021 0.016 0.039 0.316 -0.052 0.014 -0.073 -0.009 -0.015 -0.017 0.042 0.039 0.021 -0.014 

0.079 -0.173 0.491 0.626 0.004 0.034 0.293 0.403 -0.069 -0.095 0.495 0.486 0.388 0.25

-0.025 -0.092 0.249 0.255 -0.048 0.019 0.165 0.223 -0.084 -0.086 0.324 0.315 0.224 0.155 -0.008 -0.144 0.270 0.407 -0.031 0.021 0.049 0.145 -0.024 -0.033 0.313 0.316 0.248 0.08-0.084 -0.224 0.268 0.085 -0.297 -0.005 0.053 0.301 -0.132 -0.138 0.407 0.385 0.339 0.36

0.008 -0.071 0.156 0.096 0.015 0.014 0.149 0.235 0.035 0.019 0.266 0.254 0.217 0.160.029 -0.144 0.474 0.551 -0.060 0.036 0.254 0.423 -0.098 -0.122 0.484 0.475 0.374 0.28

-0.082 -0.326 0.549 0.207 -0.450 -0.230 0.066 0.456 -0.400 -0.404 0.409 0.407 0.281 0.85

0.636 0.143 0.480 0.562 0.534 -0.187 0.329 -0.043 0.632 0.635 -0.039 -0.080 0.266 -0.00

-0.200 -0.409 0.502 0.167 -0.162 0.448 0.482 0.703 -0.550 -0.579 0.681 0.692 0.434 0.49

0.701 0.276 0.504 0.646 0.701 -0.149 0.400 -0.079 0.837 0.820 -0.106 -0.145 0.243 -0.102 

0.227 0.310 0.373 0.764 0.786 0.232 0.603 0.177 0.344 0.338 0.224 0.193 0.497 -0.20

0.000 0.143 0.156 0.240 0.174 -0.054 0.195 0.012 0.225 0.237 0.106 0.082 0.238 -0.05

-0.013 0.145 0.150 0.232 0.162 -0.056 0.187 0.022 0.215 0.228 0.109 0.085 0.237 -0.04

0.258 -0.005 0.124 0.190 0.259 0.113 -0.009 -0.021 0.180 0.138 -0.063 -0.063 0.044 -0.092 

0.364 0.113 0.255 0.376 0.400 -0.066 0.241 -0.044 0.465 0.452 -0.096 -0.115 0.049 -0.090.555 0.204 0.222 0.399 0.603 0.042 0.187 -0.109 0.505 0.490 -0.120 -0.132 0.126 -0.172 0.249 0.097 0.061 0.132 0.444 0.142 0.046 -0.091 0.314 0.283 -0.102 -0.096 -0.001 -0.180.228 0.095 0.059 0.181 0.460 0.133 0.069 -0.116 0.293 0.272 -0.157 -0.166 0.014 -0.180.651 0.243 0.287 0.482 0.529 -0.072 0.244 -0.074 0.497 0.502 -0.080 -0.100 0.185 -0.092 0.654 0.244 0.288 0.484 0.528 -0.075 0.245 -0.073 0.499 0.505 -0.077 -0.097 0.187 -0.08

0.018 0.006 0.004 0.035 0.112 0.063 0.017 -0.037 0.036 0.021 -0.079 -0.071 -0.020 -0.074 

0.525 0.203 0.215 0.391 0.598 0.042 0.179 -0.106 0.499 0.484 -0.118 -0.129 0.127 -0.172 

1 0.187 0.302 0.462 0.426 -0.023 0.275 -0.037 0.428 0.429 -0.103 -0.122 0.073 -0.07

0.187 1 -0.081 0.131 0.310 0.065 -0.118 -0.238 0.369 0.367 -0.240 -0.254 -0.029 -0.292 

0.302 -0.081 1 0.886 0.225 0.188 0.635 0.571 0.210 0.170 0.462 0.424 0.554 0.485 0.462 0.131 0.886 1 0.498 0.164 0.744 0.391 0.301 0.257 0.433 0.398 0.475 0.092 

0.426 0.310 0.225 0.498 1 0.150 0.404 -0.065 0.658 0.615 -0.075 -0.107 0.223 -0.312 

-0.023 0.065 0.188 0.164 0.150 1 0.177 0.135 -0.246 -0.279 0.027 0.046 -0.060 -0.15

0.275 -0.118 0.635 0.744 0.404 0.177 1 0.607 0.256 0.221 0.475 0.436 0.562 0.245 

-0.037 -0.238 0.571 0.391 -0.065 0.135 0.607 1 -0.105 -0.151 0.738 0.733 0.536 0.47

0.428 0.369 0.210 0.301 0.658 -0.246 0.256 -0.105 1 0.972 -0.192 -0.243 0.207 -0.262 0.429 0.367 0.170 0.257 0.615 -0.279 0.221 -0.151 0.972 1 -0.221 -0.272 0.165 -0.27

-0.103 -0.240 0.462 0.433 -0.075 0.027 0.475 0.738 -0.192 -0.221 1 0.991 0.679 0.50-0.122 -0.254 0.424 0.398 -0.107 0.046 0.436 0.733 -0.243 -0.272 0.991 1 0.592 0.512 

0.073 -0.029 0.554 0.475 0.223 -0.060 0.562 0.536 0.207 0.165 0.679 0.592 1 0.29

-0.076 -0.292 0.485 0.092 -0.312 -0.153 0.245 0.473 -0.262 -0.279 0.507 0.512 0.296 

0.271 -0.050 0.822 0.867 0.333 0.151 0.690 0.645 0.287 0.245 0.587 0.550 0.653 0.360.296 0.019 0.778 0.856 0.360 0.123 0.678 0.667 0.267 0.222 0.612 0.572 0.671 0.37

0.099 0.682 0.051 0.371 0.326 0.129 0.082 0.005 0.163 0.162 -0.004 -0.016 0.119 -0.160.425 0.303 0.236 0.525 0.985 0.097 0.416 -0.043 0.683 0.639 -0.043 -0.077 0.266 -0.28

-0.011 0.125 0.192 0.149 0.190 0.889 0.196 0.148 -0.118 -0.148 0.044 0.061 -0.031 -0.14

total_aldehydesb metal_crb metal_feb metal_pbb metal_mnb metal_nib metal_znb total_metalsb denu_so2b hclb hno2b hno3b nh3b totalpolb tree_pollenb ragweedb grassesb totalmolb basidosporesb ascosporesb mitosporesb dark_mitob non_dark_mitob small_sporesb large_sporesb parttotb frm2_5b frm10b temp_aveb rh_aveb max_250b max_sootb max_o3b max_8hrb max_noxb max_nob max_no2b max_so2b max_pm2_5b max_pm10b max_parttotb temp_maxb rh_maxb 

0.141 0.087 0.266 0.093 0.329 0.319 0.194 0.271 0.410 

-0.175 

1 

-0.303 

0.188 

-0.106 -0.104 

0.011 

-0.275 -0.250 -0.112 -0.173 -0.267 -0.268 

0.009 

-0.246 -0.200 -0.409 

0.502 0.167 

-0.162 

0.448 0.482 0.703 

-0.550 -0.579 

0.681 0.692 0.434 0.491 0.407 0.443 

-0.111 -0.158 

0.379 

0.537 

-0.041 

0.033 

-0.052 

0.025 

-0.095 -0.052 -0.023 -0.047 

0.821 

-0.303 

1 

0.413 

0.209 0.204 

0.050 

0.637 0.639 0.194 0.402 0.755 0.755 

0.072 

0.621 0.701 0.276 

0.504 0.646 

0.701 

-0.149 

0.400 

-0.079 

0.837 0.820 

-0.106 -0.145 

0.243 

-0.102 

0.485 0.456 

0.125 0.703 

-0.070 

0.686 0.095 0.127 

-0.029 

0.160 

-0.202 

0.005 0.069 

-0.364 

0.315 

0.188 

0.413 

1 

0.357 0.324 

0.169 

0.204 0.396 0.198 0.273 0.333 0.333 

0.127 

0.354 0.227 0.310 

0.373 0.764 

0.786 

0.232 0.603 0.177 

0.344 0.338 

0.224 0.193 0.497 

-0.206 

0.496 0.570 

0.535 0.789 

0.245 

0.315 

-0.011 -0.060 -0.019 -0.002 -0.034 -0.025 -0.062 -0.025 

0.228 

-0.106 

0.209 

0.357 

1 

0.999 

-0.005 

0.066 0.047 

-0.037 

0.047 0.098 0.099 

-0.024 

0.048 0.000 0.143 

0.156 0.240 

0.174 

-0.054 

0.195 0.012 

0.225 0.237 

0.106 0.082 0.238 

-0.051 

0.179 0.170 

0.125 0.199 

-0.014 

0.312 

-0.010 -0.061 -0.018 -0.002 -0.031 -0.023 -0.061 -0.021 

0.224 

-0.104 

0.204 

0.324 

0.999 

1 

-0.035 

0.047 0.032 

-0.050 

0.034 0.087 0.089 

-0.026 

0.034 

-0.013 

0.145 

0.150 0.232 

0.162 

-0.056 

0.187 0.022 

0.215 0.228 

0.109 0.085 0.237 

-0.047 

0.173 0.164 

0.128 0.188 

-0.017 
Appendix 5 - Correlation Matrix (continued)

Manhattan

phm sulfatem carb250m sootm o3m noxm 

nom no2m so2m pm2_5_30m pm10_30m acetaldem acetonem acroleinm benzaldem butyraldm crotonalm formaldem hexaldehm isovalerm m_tolualm o_tolualm p_tolualm propionam valeraldm _2_5_dimm total_aldehydesm metal_crm metal_fem metal_pbm metal_mnm metal_nim metal_znm total_metalsm denu_so2m hclm hno2m hno3m nh3m 

totalpolm tree_pollenm ragweedm grassesm totalmolm basidosporesm ascosporesm mitosporesm dark_mitom non_dark_mitom small_sporesm large_sporesm parttotm 

frm2_5m frm10m temp_avem rh_avem max_250m max_sootm max_o3m max_8hrm max_noxm max_nom max_no2m max_so2m max_pm2_5m max_pm10m max_parttotm temp_maxm rh_maxm 

phm sulfatemcarb250sootm o3m noxm nom no2m so2m pm2_5_pm10_3acetaldeacetone acrolein benzald butyrald crotonal formaldehexalde isovaler m_tolua o_tolual p_tolual propionavalerald _2_5_di total_aldmetal_

1 -0.707 -0.671 -0.295 -0.488 0.042 0.124 -0.247 0.181 -0.627 -0.585 -0.432 -0.124 -0.053 -0.070 0.066 -0.143 -0.591 -0.109 -0.113 -0.036 -0.224 -0.234 -0.062 -0.350 0.05

-0.707 1 0.642 0.421 0.421 0.100 -0.010 0.410 0.078 0.880 0.774 0.484 0.119 -0.033 0.004 0.005 0.090 0.617 0.100 0.151 -0.013 0.276 0.211 -0.028 0.340 -0.02-0.671 0.642 1 0.614 0.277 0.304 0.220 0.452 0.047 0.728 0.716 0.576 0.227 -0.013 0.019 -0.089 0.122 0.669 0.080 0.112 -0.011 0.329 0.149 -0.010 0.416 -0.02-0.295 0.421 0.614 1 -0.057 0.758 0.669 0.720 0.504 0.651 0.642 0.575 0.473 -0.033 -0.003 0.137 0.216 0.502 0.036 0.074 -0.025 0.232 0.112 -0.050 0.449 -0.04-0.488 0.421 0.277 -0.057 1 -0.486 -0.586 0.026 -0.404 0.267 0.247 0.126 0.086 0.010 0.036 0.081 -0.069 0.426 0.051 0.065 0.025 0.193 0.225 0.036 0.209 -0.03

0.042 0.100 0.304 0.758 -0.486 1 0.970 0.673 0.767 0.397 0.437 0.403 0.361 -0.061 -0.051 0.009 0.167 0.173 -0.058 -0.027 -0.040 0.112 -0.088 -0.080 0.233 0.002 

0.124 -0.010 0.220 0.669 -0.586 0.970 1 0.474 0.757 0.267 0.300 0.296 0.268 -0.046 -0.052 -0.041 0.165 0.054 -0.068 -0.055 -0.041 0.043 -0.124 -0.065 0.138 0.01

-0.247 0.410 0.452 0.720 0.026 0.673 0.474 1 0.473 0.636 0.682 0.572 0.499 -0.080 -0.023 0.144 0.112 0.473 0.001 0.071 -0.019 0.282 0.063 -0.088 0.432 -0.04

0.181 0.078 0.047 0.504 -0.404 0.767 0.757 0.473 1 0.255 0.240 0.180 0.049 -0.096 -0.119 -0.004 0.012 -0.004 -0.119 -0.077 -0.046 0.002 -0.122 -0.103 0.000 0.03

-0.627 0.880 0.728 0.651 0.267 0.397 0.267 0.636 0.255 1 0.907 0.607 0.200 -0.044 -0.011 0.025 0.135 0.654 0.068 0.120 -0.009 0.289 0.192 -0.043 0.404 -0.01-0.585 0.774 0.716 0.642 0.247 0.437 0.300 0.682 0.240 0.907 1 0.592 0.223 -0.064 -0.022 0.020 0.114 0.625 0.045 0.108 -0.033 0.363 0.153 -0.060 0.396 -0.01-0.432 0.484 0.576 0.575 0.126 0.403 0.296 0.572 0.180 0.607 0.592 1 0.344 0.317 0.536 0.336 0.599 0.827 0.603 0.513 0.016 0.302 0.214 0.306 0.811 -0.03-0.124 0.119 0.227 0.473 0.086 0.361 0.268 0.499 0.049 0.200 0.223 0.344 1 -0.114 0.097 0.088 0.235 0.301 0.148 0.217 -0.023 0.100 0.126 -0.103 0.708 -0.02-0.053 -0.033 -0.013 -0.033 0.010 -0.061 -0.046 -0.080 -0.096 -0.044 -0.064 0.317 -0.114 1 0.640 0.177 0.390 0.282 0.444 -0.013 -0.006 -0.021 0.095 0.873 0.335 -0.01-0.070 0.004 0.019 -0.003 0.036 -0.051 -0.052 -0.023 -0.119 -0.011 -0.022 0.536 0.097 0.640 1 0.308 0.651 0.407 0.808 0.559 -0.008 0.115 0.171 0.617 0.590 -0.01

0.066 0.005 -0.089 0.137 0.081 0.009 -0.041 0.144 -0.004 0.025 0.020 0.336 0.088 0.177 0.308 1 0.242 0.290 0.349 0.288 0.012 -0.008 0.118 0.176 0.375 -0.034 

-0.143 0.090 0.122 0.216 -0.069 0.167 0.165 0.112 0.012 0.135 0.114 0.599 0.235 0.390 0.651 0.242 1 0.417 0.753 0.622 0.152 0.041 0.293 0.377 0.666 -0.025 -0.591 0.617 0.669 0.502 0.426 0.173 0.054 0.473 -0.004 0.654 0.625 0.827 0.301 0.282 0.407 0.290 0.417 1 0.409 0.315 0.011 0.301 0.359 0.289 0.752 -0.03-0.109 0.100 0.080 0.036 0.051 -0.058 -0.068 0.001 -0.119 0.068 0.045 0.603 0.148 0.444 0.808 0.349 0.753 0.409 1 0.855 -0.003 0.128 0.207 0.440 0.653 -0.014 -0.113 0.151 0.112 0.074 0.065 -0.027 -0.055 0.071 -0.077 0.120 0.108 0.513 0.217 -0.013 0.559 0.288 0.622 0.315 0.855 1 0.065 0.218 0.170 -0.010 0.549 -0.012 -0.036 -0.013 -0.011 -0.025 0.025 -0.040 -0.041 -0.019 -0.046 -0.009 -0.033 0.016 -0.023 -0.006 -0.008 0.012 0.152 0.011 -0.003 0.065 1 -0.012 0.005 -0.006 0.019 0.07-0.224 0.276 0.329 0.232 0.193 0.112 0.043 0.282 0.002 0.289 0.363 0.302 0.100 -0.021 0.115 -0.008 0.041 0.301 0.128 0.218 -0.012 1 0.019 -0.023 0.242 -0.02-0.234 0.211 0.149 0.112 0.225 -0.088 -0.124 0.063 -0.122 0.192 0.153 0.214 0.126 0.095 0.171 0.118 0.293 0.359 0.207 0.170 0.005 0.019 1 0.116 0.403 -0.014 -0.062 -0.028 -0.010 -0.050 0.036 -0.080 -0.065 -0.088 -0.103 -0.043 -0.060 0.306 -0.103 0.873 0.617 0.176 0.377 0.289 0.440 -0.010 -0.006 -0.023 0.116 1 0.330 -0.01

-0.350 0.340 0.416 0.449 0.209 0.233 0.138 0.432 0.000 0.404 0.396 0.811 0.708 0.335 0.590 0.375 0.666 0.752 0.653 0.549 0.019 0.242 0.403 0.330 1 -0.03

0.056 -0.029 -0.025 -0.046 -0.038 0.002 0.018 -0.049 0.036 -0.018 -0.019 -0.039 -0.020 -0.010 -0.013 -0.034 -0.025 -0.037 -0.014 -0.012 0.070 -0.020 -0.014 -0.010 -0.038 

-0.146 0.244 0.327 0.506 0.007 0.452 0.384 0.474 0.264 0.403 0.437 0.338 0.200 0.043 0.028 0.061 0.121 0.312 -0.001 -0.025 0.007 0.059 0.084 0.032 0.256 0.05

0.007 0.042 0.098 0.255 -0.097 0.280 0.281 0.162 0.170 0.133 0.205 0.173 0.103 -0.020 -0.010 -0.010 0.053 0.116 -0.027 -0.019 -0.010 0.059 -0.005 -0.028 0.108 -0.02

-0.121 0.136 0.183 0.177 -0.091 0.210 0.199 0.164 0.101 0.207 0.260 0.122 0.039 -0.027 -0.024 -0.040 0.010 0.097 -0.026 -0.022 -0.013 0.088 -0.033 -0.030 0.055 0.155 

0.186 -0.018 -0.082 0.251 -0.235 0.428 0.425 0.249 0.555 0.071 0.070 -0.008 -0.016 -0.066 -0.092 -0.031 -0.056 -0.142 -0.098 -0.077 -0.017 -0.045 -0.103 -0.068 -0.092 0.072 

-0.046 0.057 0.067 0.194 -0.019 0.207 0.211 0.111 0.145 0.108 0.156 0.061 0.078 -0.013 -0.018 -0.035 0.052 0.053 -0.018 -0.015 -0.006 -0.009 0.043 -0.014 0.064 -0.012 -0.077 0.182 0.246 0.491 -0.058 0.512 0.465 0.438 0.372 0.343 0.382 0.267 0.167 0.006 -0.016 0.021 0.081 0.212 -0.044 -0.054 0.007 0.019 0.044 -0.005 0.179 0.08

0.097 0.139 0.076 0.447 -0.330 0.729 0.707 0.472 0.943 0.289 0.347 0.166 0.081 -0.110 -0.137 -0.038 0.107 0.003 -0.133 -0.081 -0.061 0.005 -0.108 -0.109 0.011 0.00

-0.660 0.694 0.569 0.339 0.670 -0.086 -0.227 0.409 -0.049 0.613 0.588 0.412 0.307 0.035 0.061 0.116 0.039 0.730 0.067 0.089 0.028 0.265 0.354 0.055 0.421 -0.01

0.034 0.019 0.276 0.468 -0.588 0.661 0.678 0.309 0.403 0.232 0.227 0.248 0.124 -0.008 -0.031 -0.049 0.240 -0.026 -0.014 0.000 -0.012 -0.019 -0.139 -0.034 0.087 -0.00

-0.669 0.704 0.511 0.342 0.734 -0.120 -0.249 0.354 -0.005 0.588 0.532 0.317 0.258 -0.028 -0.008 0.080 -0.027 0.645 0.013 0.046 -0.002 0.176 0.285 -0.006 0.325 -0.00-0.527 0.323 0.737 0.427 0.272 0.077 -0.045 0.404 -0.278 0.459 0.473 0.516 0.452 0.055 0.309 -0.133 0.088 0.573 0.478 0.273 0.009 0.404 0.187 0.577 -0.04

-0.135 0.132 0.209 0.123 0.120 0.053 -0.001 0.196 -0.033 0.169 0.215 0.163 0.089 -0.012 0.001 -0.011 -0.025 0.157 0.018 0.068 -0.008 0.390 0.056 0.000 0.138 -0.014 

-0.125 0.121 0.203 0.124 0.113 0.058 0.006 0.195 -0.027 0.161 0.208 0.154 0.087 -0.014 -0.009 -0.015 -0.034 0.146 0.004 0.054 -0.008 0.384 0.051 -0.002 0.128 -0.01

-0.193 0.210 0.083 -0.061 0.116 -0.131 -0.144 -0.033 -0.139 0.146 0.148 0.193 0.054 0.040 0.200 0.091 0.268 0.214 0.364 0.382 -0.002 0.046 0.076 0.037 0.226 -0.002 -0.297 0.347 0.257 0.057 0.314 -0.151 -0.200 0.068 -0.132 0.219 0.215 0.129 0.001 -0.009 0.100 0.042 0.058 0.307 0.154 0.191 0.008 0.302 0.167 0.022 0.160 -0.02-0.463 0.413 0.363 0.036 0.443 -0.242 -0.300 0.049 -0.270 0.313 0.292 0.251 0.056 0.026 0.104 -0.032 0.110 0.497 0.170 0.185 0.073 0.127 0.237 0.036 0.260 0.00

-0.318 0.163 0.200 -0.125 0.222 -0.254 -0.266 -0.100 -0.337 0.091 0.112 0.134 0.023 0.051 0.122 -0.054 0.114 0.281 0.197 0.184 0.033 0.055 0.112 0.049 0.166 0.01

-0.264 0.188 0.199 -0.048 0.231 -0.239 -0.257 -0.074 -0.271 0.143 0.137 0.066 0.012 -0.013 0.008 -0.002 0.005 0.178 0.101 0.108 0.037 0.037 0.236 0.045 0.115 -0.00-0.435 0.464 0.367 0.132 0.462 -0.165 -0.237 0.134 -0.149 0.357 0.325 0.270 0.063 0.009 0.076 -0.012 0.089 0.520 0.113 0.140 0.079 0.146 0.238 0.016 0.257 0.00-0.437 0.467 0.367 0.134 0.464 -0.164 -0.237 0.135 -0.146 0.358 0.327 0.269 0.064 0.009 0.077 -0.010 0.090 0.520 0.113 0.142 0.080 0.149 0.240 0.016 0.258 0.00

-0.075 0.063 0.071 -0.016 0.061 -0.062 -0.079 0.019 -0.094 0.069 0.025 0.070 0.004 0.010 -0.003 -0.052 -0.002 0.123 0.008 -0.004 -0.012 -0.024 0.020 0.012 0.034 0.00-0.462 0.404 0.359 0.031 0.440 -0.245 -0.302 0.044 -0.277 0.304 0.287 0.246 0.054 0.028 0.104 -0.033 0.108 0.491 0.169 0.182 0.076 0.128 0.236 0.038 0.257 0.004 -0.344 0.442 0.294 0.105 0.343 -0.118 -0.176 0.114 -0.064 0.350 0.281 0.253 0.054 -0.015 0.074 0.015 0.100 0.440 0.135 0.169 -0.003 0.093 0.167 -0.005 0.225 -0.01

0.080 -0.003 0.085 0.014 -0.026 0.112 0.131 0.004 0.182 -0.030 0.005 -0.057 -0.004 0.013 0.182 -0.131 -0.082 0.006 0.003 -0.072 0.049 -0.015 -0.023 -0.042 -0.005 

-0.467 0.705 0.607 0.680 0.050 0.549 0.460 0.598 0.457 0.801 0.737 0.521 0.179 -0.057 -0.022 0.000 0.126 0.470 0.049 0.109 0.030 0.327 0.079 -0.064 0.321 0.022 -0.667 0.751 0.741 0.702 0.179 0.428 0.306 0.598 0.221 0.827 0.832 0.597 0.429 -0.069 -0.084 0.054 0.180 0.588 0.221 0.285 -0.006 0.540 0.194 -0.087 0.503 0.05-0.657 0.479 0.567 0.067 0.614 -0.301 -0.382 0.089 -0.557 0.405 0.411 0.334 0.144 0.063 0.110 0.027 0.115 0.605 0.139 0.142 0.061 0.215 0.300 0.076 0.374 -0.034 -0.210 0.174 0.189 0.000 -0.287 -0.033 0.021 -0.180 -0.203 0.123 0.013 0.013 -0.077 0.034 0.064 -0.107 0.077 -0.031 0.088 0.077 0.021 0.019 0.038 0.033 -0.013 -0.032 -0.555 0.533 0.901 0.649 0.194 0.375 0.301 0.459 0.113 0.640 0.637 0.555 0.213 -0.004 0.021 -0.058 0.157 0.599 0.069 0.090 -0.016 0.262 0.159 -0.010 0.392 -0.035 -0.254 0.350 0.542 0.934 -0.039 0.675 0.601 0.626 0.396 0.550 0.537 0.520 0.437 -0.007 0.021 0.124 0.227 0.448 0.056 0.078 -0.016 0.180 0.122 -0.028 0.420 -0.05-0.584 0.533 0.421 0.077 0.921 -0.358 -0.492 0.204 -0.340 0.432 0.405 0.265 0.137 0.013 0.047 0.080 -0.016 0.556 0.086 0.102 0.006 0.212 0.259 0.026 0.303 -0.03-0.541 0.483 0.382 0.046 0.947 -0.381 -0.505 0.164 -0.349 0.386 0.361 0.232 0.130 0.021 0.052 0.073 -0.032 0.515 0.074 0.083 0.010 0.222 0.238 0.048 0.280 -0.03-0.015 0.110 0.350 0.755 -0.346 0.892 0.854 0.634 0.582 0.387 0.427 0.422 0.376 -0.007 -0.007 0.064 0.198 0.241 -0.019 -0.015 -0.019 0.096 -0.035 -0.028 0.288 0.004 

0.032 0.043 0.301 0.710 -0.428 0.887 0.879 0.544 0.586 0.322 0.360 0.365 0.330 -0.004 -0.017 0.032 0.192 0.174 -0.033 -0.036 -0.014 0.064 -0.068 -0.027 0.233 0.01

-0.379 0.480 0.533 0.639 0.303 0.464 0.276 0.849 0.225 0.631 0.669 0.600 0.477 -0.051 0.012 0.164 0.130 0.591 0.046 0.110 0.008 0.303 0.193 -0.050 0.492 -0.04

0.077 0.108 0.151 0.505 -0.289 0.709 0.681 0.497 0.851 0.275 0.283 0.227 0.100 -0.093 -0.117 0.005 0.021 0.098 -0.109 -0.071 0.011 0.002 -0.079 -0.104 0.067 0.032 

-0.615 0.790 0.750 0.672 0.282 0.389 0.274 0.587 0.179 0.898 0.841 0.604 0.215 -0.033 0.021 0.012 0.173 0.674 0.088 0.130 0.025 0.262 0.210 -0.041 0.425 -0.024 -0.421 0.511 0.543 0.526 0.145 0.375 0.278 0.523 0.164 0.658 0.813 0.436 0.175 -0.051 -0.014 0.011 0.113 0.442 0.042 0.087 -0.023 0.268 0.102 -0.056 0.297 -0.01-0.034 0.042 0.136 0.041 -0.106 0.095 0.114 -0.007 0.064 0.076 0.140 0.020 0.121 0.088 0.063 -0.058 0.053 0.028 -0.023 -0.020 -0.010 0.006 -0.036 0.064 -0.03-0.647 0.480 0.573 0.093 0.625 -0.275 -0.364 0.132 -0.525 0.415 0.436 0.353 0.156 0.059 0.101 0.040 0.108 0.620 0.129 0.131 0.052 0.221 0.293 0.071 0.384 -0.035 -0.193 0.163 0.209 0.058 -0.205 -0.018 0.018 -0.117 -0.203 0.114 0.023 0.038 -0.053 0.039 0.057 -0.068 0.051 -0.007 0.072 0.059 0.018 0.038 0.012 0.035 0.004 -0.01
Appendix 5 - Correlation Matrix (continued)

Manhattan

hno2m hno3m nh3m totalpolmtree_polragweedgrasses totalmol basidos ascospomitospo dark_mi non_darsmall_splarge_spparttotmfrm2_5mfrm10m temp_avrh_avemmax_25 max_so max_o3 max_8h max_no max_no max_no max_s

phm 0.034 -0.669 -0.527 -0.135 -0.125 -0.193 -0.297 -0.463 -0.318 -0.264 -0.435 -0.437 -0.075 -0.462 -0.344 0.080 -0.467 -0.667 -0.657 -0.210 -0.555 -0.254 -0.584 -0.541 -0.015 0.032 -0.379 0.07sulfatem 0.019 0.704 0.323 0.132 0.121 0.210 0.347 0.413 0.163 0.188 0.464 0.467 0.063 0.404 0.442 -0.003 0.705 0.751 0.479 0.174 0.533 0.350 0.533 0.483 0.110 0.043 0.480 0.10carb250m 0.276 0.511 0.737 0.209 0.203 0.083 0.257 0.363 0.200 0.199 0.367 0.367 0.071 0.359 0.294 0.085 0.607 0.741 0.567 0.189 0.901 0.542 0.421 0.382 0.350 0.301 0.533 0.15sootm 0.468 0.342 0.427 0.123 0.124 -0.061 0.057 0.036 -0.125 -0.048 0.132 0.134 -0.016 0.031 0.105 0.014 0.680 0.702 0.067 0.000 0.649 0.934 0.077 0.046 0.755 0.710 0.639 0.505 o3m -0.588 0.734 0.272 0.120 0.113 0.116 0.314 0.443 0.222 0.231 0.462 0.464 0.061 0.440 0.343 -0.026 0.050 0.179 0.614 -0.287 0.194 -0.039 0.921 0.947 -0.346 -0.428 0.303 -0.28noxm 0.661 -0.120 0.077 0.053 0.058 -0.131 -0.151 -0.242 -0.254 -0.239 -0.165 -0.164 -0.062 -0.245 -0.118 0.112 0.549 0.428 -0.301 -0.033 0.375 0.675 -0.358 -0.381 0.892 0.887 0.464 0.70nom 0.678 -0.249 -0.045 -0.001 0.006 -0.144 -0.200 -0.300 -0.266 -0.257 -0.237 -0.237 -0.079 -0.302 -0.176 0.131 0.460 0.306 -0.382 0.021 0.301 0.601 -0.492 -0.505 0.854 0.879 0.276 0.68no2m 0.309 0.354 0.404 0.196 0.195 -0.033 0.068 0.049 -0.100 -0.074 0.134 0.135 0.019 0.044 0.114 0.004 0.598 0.598 0.089 -0.180 0.459 0.626 0.204 0.164 0.634 0.544 0.849 0.49so2m 0.403 -0.005 -0.278 -0.033 -0.027 -0.139 -0.132 -0.270 -0.337 -0.271 -0.149 -0.146 -0.094 -0.277 -0.064 0.182 0.457 0.221 -0.557 -0.203 0.113 0.396 -0.340 -0.349 0.582 0.586 0.225 0.85pm2_5_30m 0.232 0.588 0.459 0.169 0.161 0.146 0.219 0.313 0.091 0.143 0.357 0.358 0.069 0.304 0.350 -0.030 0.801 0.827 0.405 0.123 0.640 0.550 0.432 0.386 0.387 0.322 0.631 0.275 pm10_30m 0.227 0.532 0.473 0.215 0.208 0.148 0.215 0.292 0.112 0.137 0.325 0.327 0.025 0.287 0.281 0.005 0.737 0.832 0.411 0.013 0.637 0.537 0.405 0.361 0.427 0.360 0.669 0.28acetaldem 0.248 0.317 0.516 0.163 0.154 0.193 0.129 0.251 0.134 0.066 0.270 0.269 0.070 0.246 0.253 -0.057 0.521 0.597 0.334 0.013 0.555 0.520 0.265 0.232 0.422 0.365 0.600 0.22acetonem 0.124 0.258 0.452 0.089 0.087 0.054 0.001 0.056 0.023 0.012 0.063 0.064 0.004 0.054 0.054 -0.004 0.179 0.429 0.144 -0.077 0.213 0.437 0.137 0.130 0.376 0.330 0.477 0.10acroleinm -0.008 -0.028 0.055 -0.012 -0.014 0.040 -0.009 0.026 0.051 -0.013 0.009 0.009 0.010 0.028 -0.015 -0.057 -0.069 0.063 0.034 -0.004 -0.007 0.013 0.021 -0.007 -0.004 -0.051 -0.09benzaldem -0.031 -0.008 0.309 0.001 -0.009 0.200 0.100 0.104 0.122 0.008 0.076 0.077 -0.003 0.104 0.074 0.013 -0.022 -0.084 0.110 0.064 0.021 0.021 0.047 0.052 -0.007 -0.017 0.012 -0.11butyraldm -0.049 0.080 -0.133 -0.011 -0.015 0.091 0.042 -0.032 -0.054 -0.002 -0.012 -0.010 -0.052 -0.033 0.015 0.182 0.000 0.054 0.027 -0.107 -0.058 0.124 0.080 0.073 0.064 0.032 0.164 0.005 crotonalm 0.240 -0.027 0.088 -0.025 -0.034 0.268 0.058 0.110 0.114 0.005 0.089 0.090 -0.002 0.108 0.100 -0.131 0.126 0.180 0.115 0.077 0.157 0.227 -0.016 -0.032 0.198 0.192 0.130 0.02formaldem -0.026 0.645 0.573 0.157 0.146 0.214 0.307 0.497 0.281 0.178 0.520 0.520 0.123 0.491 0.440 -0.082 0.470 0.588 0.605 -0.031 0.599 0.448 0.556 0.515 0.241 0.174 0.591 0.09hexaldehm -0.014 0.013 0.478 0.018 0.004 0.364 0.154 0.170 0.197 0.101 0.113 0.113 0.008 0.169 0.135 0.006 0.049 0.221 0.139 0.088 0.069 0.056 0.086 0.074 -0.019 -0.033 0.046 -0.10isovalerm 0.000 0.046 0.273 0.068 0.054 0.382 0.191 0.185 0.184 0.108 0.140 0.142 -0.004 0.182 0.169 0.003 0.109 0.285 0.142 0.077 0.090 0.078 0.102 0.083 -0.015 -0.036 0.110 -0.07m_tolualm o_tolualm -0.012 -0.002 0.009 -0.008 -0.008 -0.002 0.008 0.073 0.033 0.037 0.079 0.080 -0.012 0.076 -0.003 -0.072 0.030 -0.006 0.061 0.021 -0.016 -0.016 0.006 0.010 -0.019 -0.014 0.008 0.01p_tolualm -0.019 0.176 0.404 0.390 0.384 0.046 0.302 0.127 0.055 0.037 0.146 0.149 -0.024 0.128 0.093 0.049 0.327 0.540 0.215 0.019 0.262 0.180 0.212 0.222 0.096 0.064 0.303 0.002 propionam -0.139 0.285 0.187 0.056 0.051 0.076 0.167 0.237 0.112 0.236 0.238 0.240 0.020 0.236 0.167 -0.015 0.079 0.194 0.300 0.038 0.159 0.122 0.259 0.238 -0.035 -0.068 0.193 -0.07valeraldm -0.034 -0.006 0.000 -0.002 0.037 0.022 0.036 0.049 0.045 0.016 0.016 0.012 0.038 -0.005 -0.023 -0.064 -0.087 0.076 0.033 -0.010 -0.028 0.026 0.048 -0.028 -0.027 -0.050 -0.104 _2_5_dimm total_aldehydesm 0.087 0.325 0.577 0.138 0.128 0.226 0.160 0.260 0.166 0.115 0.257 0.258 0.034 0.257 0.225 -0.042 0.321 0.503 0.374 -0.013 0.392 0.420 0.303 0.280 0.288 0.233 0.492 0.06metal_crm -0.003 -0.005 -0.049 -0.014 -0.013 -0.002 -0.021 0.003 0.010 -0.006 0.000 0.000 0.006 0.004 -0.010 -0.005 0.022 0.057 -0.034 -0.032 -0.035 -0.051 -0.038 -0.031 0.004 0.018 -0.041 0.032 metal_fem 0.242 0.197 0.340 -0.068 -0.070 0.012 0.055 0.070 -0.020 0.021 0.106 0.106 0.017 0.068 0.080 -0.002 0.361 0.515 0.079 -0.134 0.351 0.458 0.099 0.072 0.450 0.419 0.437 0.294 metal_pbm 0.177 -0.035 0.035 -0.021 -0.019 -0.033 -0.055 -0.025 -0.020 -0.041 -0.017 -0.017 -0.009 -0.027 0.022 0.052 0.181 0.260 -0.043 0.045 0.124 0.227 -0.094 -0.097 0.242 0.244 0.155 0.16metal_mnm 0.246 0.008 0.252 -0.026 -0.027 0.011 0.063 0.059 0.100 0.039 0.018 0.016 0.039 0.058 0.034 0.072 0.196 0.365 0.103 0.081 0.164 0.149 -0.050 -0.065 0.191 0.195 0.153 0.134 metal_nim 0.166 -0.057 -0.319 -0.054 -0.049 -0.121 -0.128 -0.259 -0.238 -0.188 -0.198 -0.196 -0.088 -0.262 -0.106 0.115 0.153 0.046 -0.438 -0.054 -0.025 0.213 -0.225 -0.222 0.317 0.318 0.105 0.43metal_znm 0.068 0.033 -0.001 -0.012 -0.011 -0.016 -0.028 -0.023 -0.035 -0.027 -0.008 -0.007 -0.028 -0.023 -0.006 0.027 0.108 -0.078 -0.035 -0.019 0.088 0.205 -0.009 0.008 0.206 0.200 0.125 0.172 total_metalsm 0.293 0.114 0.193 -0.085 -0.084 -0.037 -0.006 -0.023 -0.075 -0.043 0.013 0.014 -0.013 -0.026 0.021 0.032 0.341 0.418 -0.041 -0.097 0.282 0.446 0.012 -0.004 0.471 0.449 0.388 0.372 denu_so2m 0.376 0.064 -0.237 0.006 0.009 -0.081 -0.100 -0.210 -0.303 -0.236 -0.107 -0.105 -0.060 -0.219 -0.024 0.451 0.511 0.251 -0.403 -0.259 0.104 0.331 -0.250 -0.251 0.570 0.568 0.246 0.83hclm -0.323 0.856 0.392 0.177 0.167 0.178 0.521 0.581 0.202 0.202 0.658 0.660 0.113 0.575 0.521 -0.006 0.418 0.540 0.634 -0.244 0.456 0.283 0.780 0.747 -0.017 -0.105 0.561 0.04hno2m 1 -0.401 0.327 -0.057 -0.052 -0.106 -0.300 -0.357 -0.235 -0.198 -0.340 -0.341 -0.066 -0.360 -0.232 -0.012 0.379 0.034 -0.245 0.435 0.298 0.430 -0.491 -0.518 0.601 0.624 0.130 0.394 hno3m -0.401 1 0.216 0.092 0.085 0.108 0.424 0.530 0.203 0.207 0.588 0.590 0.089 0.522 0.491 0.121 0.396 0.426 0.578 -0.255 0.416 0.309 0.823 0.787 -0.068 -0.153 0.497 0.05nh3m 0.327 0.216 1 0.292 0.290 0.077 0.262 0.251 0.181 0.258 0.239 0.238 0.150 0.253 0.151 -0.337 0.305 0.506 0.648 0.072 0.661 0.399 0.373 0.346 0.209 0.147 0.543 -0.10totalpolm -0.057 0.092 0.292 1 0.999 -0.008 0.056 0.007 -0.027 0.046 0.018 0.019 -0.009 0.007 -0.010 0.001 0.098 0.191 0.153 -0.060 0.193 0.118 0.171 0.177 0.077 0.051 0.214 -0.02tree_pollenm -0.052 0.085 0.290 0.999 1 -0.034 0.034 -0.009 -0.039 0.036 0.005 0.005 -0.011 -0.008 -0.023 0.002 0.093 0.181 0.142 -0.062 0.189 0.120 0.161 0.168 0.082 0.056 0.210 -0.01ragweedm -0.106 0.108 0.077 -0.008 -0.034 1 0.193 0.400 0.357 0.170 0.337 0.340 0.028 0.395 0.381 -0.167 0.113 0.339 0.257 0.112 0.040 -0.053 0.151 0.134 -0.103 -0.120 0.052 -0.07grassesm -0.300 0.424 0.262 0.056 0.034 0.193 1 0.363 0.135 0.281 0.394 0.395 0.070 0.362 0.265 -0.008 0.129 0.338 0.385 0.010 0.181 0.026 0.362 0.358 -0.126 -0.159 0.151 -0.09totalmolm -0.357 0.530 0.251 0.007 -0.009 0.400 0.363 1 0.745 0.529 0.907 0.903 0.310 0.999 0.690 -0.119 0.142 0.503 0.621 0.006 0.259 0.007 0.488 0.481 -0.170 -0.208 0.232 -0.18basidosporesm -0.235 0.203 0.181 -0.027 -0.039 0.357 0.135 0.745 1 0.473 0.408 0.400 0.267 0.758 0.255 -0.100 -0.002 0.257 0.476 0.135 0.110 -0.122 0.262 0.246 -0.185 -0.194 0.065 -0.224 ascosporesm -0.198 0.207 0.258 0.046 0.036 0.170 0.281 0.529 0.473 1 0.326 0.325 0.102 0.535 0.195 0.033 0.024 0.316 0.433 0.092 0.122 -0.060 0.275 0.257 -0.210 -0.222 0.060 -0.22mitosporesm -0.340 0.588 0.239 0.018 0.005 0.337 0.394 0.907 0.408 0.326 1 0.999 0.269 0.897 0.802 -0.110 0.198 0.505 0.539 -0.086 0.285 0.091 0.496 0.498 -0.106 -0.154 0.281 -0.09dark_mitom -0.341 0.590 0.238 0.019 0.005 0.340 0.395 0.903 0.400 0.325 0.999 1 0.226 0.893 0.805 -0.101 0.202 0.505 0.538 -0.086 0.285 0.092 0.499 0.500 -0.106 -0.154 0.283 -0.092 non_dark_mitom -0.066 0.089 0.150 -0.009 -0.011 0.028 0.070 0.310 0.267 0.102 0.269 0.226 1 0.312 0.138 -0.120 -0.035 0.211 0.160 -0.035 0.043 -0.011 0.074 0.072 -0.030 -0.042 0.025 -0.05small_sporesm -0.360 0.522 0.253 0.007 -0.008 0.395 0.362 0.999 0.758 0.535 0.897 0.893 0.312 1 0.661 -0.118 0.137 0.501 0.622 0.007 0.256 0.003 0.485 0.478 -0.171 -0.209 0.230 -0.184 large_sporesm -0.232 0.491 0.151 -0.010 -0.023 0.381 0.265 0.690 0.255 0.195 0.802 0.805 0.138 0.661 1 -0.064 0.180 0.379 0.404 -0.028 0.222 0.063 0.377 0.371 -0.093 -0.134 0.205 -0.042 parttotm -0.012 0.121 -0.337 0.001 0.002 -0.167 -0.008 -0.119 -0.100 0.033 -0.110 -0.101 -0.120 -0.118 -0.064 1 0.011 0.140 -0.084 -0.051 0.044 -0.018 -0.046 -0.024 0.066 0.071 -0.007 0.12frm2_5m 0.379 0.396 0.305 0.098 0.093 0.113 0.129 0.142 -0.002 0.024 0.198 0.202 -0.035 0.137 0.180 0.011 1 0.791 0.168 0.179 0.551 0.581 0.171 0.135 0.456 0.417 0.533 0.42frm10m 0.034 0.426 0.506 0.191 0.181 0.339 0.338 0.503 0.257 0.316 0.505 0.505 0.211 0.501 0.379 0.140 0.791 1 0.483 0.135 0.707 0.607 0.272 0.247 0.360 0.282 0.617 0.212 temp_avem -0.245 0.578 0.648 0.153 0.142 0.257 0.385 0.621 0.476 0.433 0.539 0.538 0.160 0.622 0.404 -0.084 0.168 0.483 1 0.100 0.413 0.062 0.683 0.653 -0.167 -0.217 0.323 -0.345 rh_avem 0.435 -0.255 0.072 -0.060 -0.062 0.112 0.010 0.006 0.135 0.092 -0.086 -0.086 -0.035 0.007 -0.028 -0.051 0.179 0.135 0.100 1 0.120 0.007 -0.222 -0.249 -0.050 -0.014 -0.185 -0.22max_250m 0.298 0.416 0.661 0.193 0.189 0.040 0.181 0.259 0.110 0.122 0.285 0.285 0.043 0.256 0.222 0.044 0.551 0.707 0.413 0.120 1 0.644 0.320 0.289 0.436 0.386 0.552 0.19max_sootm 0.430 0.309 0.399 0.118 0.120 -0.053 0.026 0.007 -0.122 -0.060 0.091 0.092 -0.011 0.003 0.063 -0.018 0.581 0.607 0.062 0.007 0.644 1 0.083 0.053 0.763 0.722 0.608 0.44max_o3m -0.491 0.823 0.373 0.171 0.161 0.151 0.362 0.488 0.262 0.275 0.496 0.499 0.074 0.485 0.377 -0.046 0.171 0.272 0.683 -0.222 0.320 0.083 1 0.977 -0.210 -0.300 0.462 -0.202 max_8hrm -0.518 0.787 0.346 0.177 0.168 0.134 0.358 0.481 0.246 0.257 0.498 0.500 0.072 0.478 0.371 -0.024 0.135 0.247 0.653 -0.249 0.289 0.053 0.977 1 -0.232 -0.321 0.430 -0.21max_noxm 0.601 -0.068 0.209 0.077 0.082 -0.103 -0.126 -0.170 -0.185 -0.210 -0.106 -0.106 -0.030 -0.171 -0.093 0.066 0.456 0.360 -0.167 -0.050 0.436 0.763 -0.210 -0.232 1 0.988 0.539 0.63max_nom 0.624 -0.153 0.147 0.051 0.056 -0.120 -0.159 -0.208 -0.194 -0.222 -0.154 -0.154 -0.042 -0.209 -0.134 0.071 0.417 0.282 -0.217 -0.014 0.386 0.722 -0.300 -0.321 0.988 1 0.428 0.63max_no2m 0.130 0.497 0.543 0.214 0.210 0.052 0.151 0.232 0.065 0.060 0.281 0.283 0.025 0.230 0.205 -0.007 0.533 0.617 0.323 -0.185 0.552 0.608 0.462 0.430 0.539 0.428 1 0.322 max_so2m 0.394 0.053 -0.107 -0.021 -0.016 -0.077 -0.097 -0.180 -0.224 -0.221 -0.093 -0.092 -0.051 -0.184 -0.042 0.129 0.421 0.212 -0.345 -0.223 0.191 0.441 -0.202 -0.217 0.636 0.633 0.322 max_pm2_5m 0.231 0.551 0.513 0.142 0.134 0.163 0.267 0.376 0.180 0.192 0.389 0.390 0.094 0.370 0.342 -0.031 0.724 0.767 0.459 0.128 0.714 0.636 0.435 0.402 0.462 0.397 0.663 0.26max_pm10m 0.274 0.382 0.457 0.130 0.125 0.116 0.161 0.206 0.094 0.082 0.223 0.221 0.095 0.204 0.183 -0.023 0.527 0.751 0.310 0.036 0.516 0.489 0.263 0.234 0.421 0.369 0.530 0.23max_parttotm 0.069 0.003 0.003 -0.072 -0.071 -0.110 -0.010 0.010 0.045 0.097 -0.039 -0.027 -0.131 0.012 -0.028 0.637 0.036 0.068 0.081 0.027 0.086 0.032 -0.080 -0.088 0.074 0.084 -0.004 0.05temp_maxm -0.236 0.584 0.665 0.173 0.161 0.246 0.388 0.618 0.461 0.423 0.544 0.543 0.165 0.618 0.405 -0.081 0.182 0.503 0.987 0.049 0.435 0.087 0.699 0.667 -0.143 -0.197 0.367 -0.314 rh_maxm 0.364 -0.212 0.095 -0.036 -0.038 0.098 0.009 0.049 0.156 0.141 -0.043 -0.042 -0.028 0.052 -0.013 -0.045 0.182 0.158 0.111 0.892 0.158 0.070 -0.149 -0.171 0.001 0.026 -0.093 -0.192 

A Study of Ambient Air Contaminants and

Asthma in New York City

Final Report

Part B:

Air Contaminants and Emergency

Department Visits for Asthma in the Bronx

and Manhattan

Prepared by:

New York State Department of Health

Center for Environmental Health

547 River St

Troy, NY 12180-2216

Prepared for:

The U.S. Department of Health and Human Services

Agency for Toxic Substances and Disease Registry

(Cooperative Agreement V50/ATV200002-11)

Project Manager – Mohammed Uddin, MD, MPH

The New York State Energy Research and Development Authority

(Grant 14-0034-01)

Project Manager – Ellen Burkhard, PhD

Final: 17 July, 2006 
NOTICE 

This report was prepared by the New York State Department of Health’s Center for Environmental Health, 

the New York State Department of Environmental Conservation and Columbia University in the course of 

performing work contracted for and sponsored by the New York State Energy Research and Development 

Authority and the U.S. Agency for Toxic Substances and Disease Registry (hereafter “the Sponsors”). The 

opinions expressed in this report do not necessarily reflect those of the Sponsors or the State of New York, 

and reference to any specific product, service, process, or method does not constitute an implied or expressed 

recommendation or endorsement of it. Further, the Sponsors and the State of New York make no warranties 

or representations, expressed or implied, as to the fitness for particular purpose or merchantability of any 

product, apparatus, or service, or the usefulness, completeness, or accuracy of any processes, methods, or 

other information contained, described, disclosed, or referred to in this report. The Sponsors, the State of New 

York, and the contractors make no representation that the use of any product, apparatus, process, method, or 

other information will not infringe privately owned rights and will assume no liability for any loss, injury, or 

damage resulting from, or occurring in connection with, the use of information contained, described, 

disclosed, or referred to in this report. 

ii 
CONTENTS

NOTICE ............................................................................................................................................................. ii

LIST OF TABLES ............................................................................................................................................ iv

LIST OF FIGURES............................................................................................................................................ v

SUMMARY ....................................................................................................................................................... 7

INTRODUCTION............................................................................................................................................ 11

METHODS....................................................................................................................................................... 13

RESULTS......................................................................................................................................................... 21

DISCUSSION .................................................................................................................................................. 27

CONCLUSIONS AND RECOMMENDATIONS........................................................................................... 37

REFERENCES................................................................................................................................................. 39

AUTHORS AND ACKNOWLEDGEMENTS ................................................................................................ 45

TABLES........................................................................................................................................................... 47

FIGURES ......................................................................................................................................................... 63

iii 
LIST OF TABLES

Table 1. Hospital Emergency Department Visits by Residents of Bronx and Manhattan Study Areas ........48

Table 2. Mean daily emergency department visits for asthma and control conditions..................................49

Table 3. Mean concentrations of air pollutants and bioaerosols measured in the Bronx and Manhattan......50

Table 4. Relative risks for asthma ED visits as a function of 5-day mean air pollution and bioaerosols from

single-pollutant models .........................................................................................................................51

Table 5. Relative risks from regressions based on daily maximum hourly exposures ..................................53

Table 6. Relative variance in asthma ED visits explained by variables included in the model for daily 8-hour

maximum O3..........................................................................................................................................54

Table 7. Relative risks for mean change in contaminant concentrations for models that did not include

temperature as a covariate. ....................................................................................................................55

Table 8. Results from Poisson regressions of asthma ED visits on pollens and molds. ................................56

Table 9. Relative risks for asthma ED visits as a function of 5-day mean air pollution from two-pollutant

models ...................................................................................................................................................57

Table 10. Correlations among key air pollutants in the Bronx study community. ........................................58

Table 11. Relative risks for control ED visits in relation to the five pollutants that showed significant

associations with asthma ED visits in the Bronx...................................................................................59

Table 12. Relative risks for asthma ED visits from single-pollutant models, stratified by gender................60

Table 13. Relative risks for asthma ED visits from single-pollutant models, stratified by age.....................61

iv 
LIST OF FIGURES

Figure 1. Air Monitoring Locations in Manhattan and Bronx ........................................................................ 64

Figures 3. Seasonal Patterns of Hospital ED Admissions for Asthma Fitted with 18 DF Natural Spline, for (a)

Figure 4. Day-of-Week Patterns Plotted for Hospital ED Admissions for Asthma for (a) Bronx and (b)

Figure 7. Relative Risk for Asthma ED Visits in Bronx and Manhattan for 14 key Contaminants for Primary

Figure 8. Lag Dependency of Relative Risk for Asthma in Bronx and Manhattan for Example Pollutants

Figure 2. Map of Study Areas and Hospitals Contributing Emergency Department Data ............................... 65

Bronx and (b) Manhattan.......................................................................................................................... 66

Manhattan................................................................................................................................................. 67

Figure 5. Asthma ED Visits Plotted against Temperature for (a) Bronx and (b) Manhattan............................ 68

Figure 6. Age Distributions of Study Communities (U.S. Census 2000) ......................................................... 69

Analysis with Base-Case Model............................................................................................................... 70

(PM2.5, SO2 and O3).................................................................................................................................. 71

v 
vi
SUMMARY

Most previous studies of acute asthma exacerbations and ambient air pollution have examined effects of 

only a few of the many contaminants that are found in urban air, making it difficult to determine which 

specific air pollutant or group of pollutants is most important in triggering hospital visits. In addition, 

whereas numerous studies have reported associations between daily air pollution concentrations and counts 

of hospital visits for asthma or other respiratory diseases, few studies have evaluated whether risks for air 

pollution-related hospital visits vary across communities that differ in their baseline health status. 

Mid-town Manhattan and the South Bronx are separated by less than 5 miles. However, the two regions of 

New York City differ greatly in levels of asthma morbidity. Although these differences are likely to be 

caused by multiple factors, including differential access to primary care for asthma, the present study was 

not designed to investigate these differences. Rather, we investigated whether day-to-day variations in air 

pollution were associated with asthma emergency department (ED) visits in each community and compared 

the magnitude of the air pollution effect between the two communities. To investigate this question, we 

analyzed daily counts of emergency asthma visits to hospitals serving two distinct communities, one in 

Manhattan and the other in the South Bronx, along with daily enhanced air monitoring in each community. 

We analyzed air quality and weather data collected over approximately a two year period, from January 

1999 through November 2000, at two centrally located measurement stations sampling a broad range of 

contaminants. Emergency department data on asthma visits for the corresponding dates were collected from 

the 22 hospitals throughout New York that served the communities surrounding the air monitoring stations. 

Data for hospital patients who lived in zip code areas within approximately 1.5 miles of either measurement 

site were extracted. Figure 1 depicts the location of the monitoring stations and adjacent areas for health 

data. (Note that in the Bronx, the measurement site was moved during the study period; Figure 1 shows 

both sites.) 

Using these data, we compared the magnitude of the relationships between daily asthma ED visits and air 

pollution and bioaerosol concentrations across the two communities, and examined relative impacts of 

multiple pollutants. In addition, we explored the lag-dependency of the asthma response, age and sex 

stratification, and whether effects were evident for control outcomes (i.e., ED visits for causes not likely to 

be related to air quality). We used Poisson regression to test for effects of 14 key air contaminants on daily 

ED visits, with control for temporal cycles, temperature, and day-of-week effects. The core analysis 

utilized the average exposure for the zero- to four-day lags. Sensitivity analyses examined individual lag 

effects. 

7
Mean daily crude rates of asthma ED visits were over eight fold higher in the Bronx study area (16.9 per 

100,000 persons) than in the Manhattan area (2.02 per 100,000 persons; Table 2). Exploring reasons for 

these differences was beyond the scope of the present study. Concentrations of air contaminants were 

generally similar in the two communities (Table 3), with mean levels tending to be slightly higher in 

Manhattan in most cases. Mean ozone and total pollen levels were significantly higher in the Bronx. 

Among 14 key pollutants examined individually in regression analyses, five had statistically significant 

effects on asthma ED visits in the Bronx, including daily eight-hour maximum ozone (O3), mean daily 

nitrogen dioxide (NO2), sulfur dioxide (SO2), particulate matter with aerodynamic diameter less than 2.5 

micrometers (PM2.5) and maximum one-hour PM2.5 (Table 4). No statistically significant pollution effects 

were observed in the Manhattan community. 

Our findings of more significant air pollution effects in the Bronx are likely to relate in part to greater 

statistical power for identifying effects in the Bronx where baseline ED visits were greater, but they may 

also reflect greater sensitivity to air pollution effects in the Bronx. 

In analyses restricted to the warm season (April through October), O3 effects in the Bronx were larger and 

more significant than in the full-year analysis, and they were approximately double those seen in 

Manhattan, suggesting greater susceptibility and/or exposure to this airway irritant and pro-inflammatory 

agent in the Bronx. Analyses by sex suggested that the air pollution effects in the Bronx were greater 

among females than males (Table 12). No strong differences in effects were observed with age strata, 

though there was some indication of larger effects in older adults (Table 13). 

In two-pollutant and three-pollutant regression models, O3 and SO2, and to a lesser extent maximum onehour PM2.5, were the most robust pollutants (Table 9). In other words, these pollutants exhibited less 

change in their effect estimates as additional pollutants were added to the models. It is of particular interest 

that we observed more robust health impacts of the daily maximum PM2.5 concentration than for the 24-

hour mean, suggesting that peak exposures may have larger health impacts. 

Analysis of ED visits for control outcomes (largely for digestive diseases) revealed positive or zero effects 

for all five of the pollutants that had been shown to be associated with asthma visits. In one case, 24-hour 

mean PM2.5, the control effect was statistically significant. The analysis of ED visits for control outcomes 

may suggest the possibility of overestimates of the observed associations. 

CONCLUSIONS AND RECOMMENDATIONS 

The results suggest that the criteria pollutants PM2.5, SO2, O3 and NO2 had a statistically detectable impact 

on acute asthma ED visits in a community with a relatively high baseline rate of acute asthma 

exacerbations. In two-pollutant and three-pollutant regression models, O3 and SO2, and to a lesser extent 

8
maximum one-hour PM2.5, were the most robust pollutants. Robust effects of O3 have been seen in previous 

ED asthma studies (Stieb et al. 1996; Martins et al. 2002) and in hospital admissions studies of asthma and 

other respiratory diseases (Burnett et al. 1997). It is of particular interest that we observed more robust 

health impacts of daily maximum PM2.5 concentration than of the 24-hour mean, suggesting that peak 

exposures may have larger health impacts. These associations with health effects in the Bronx occurred at 

ambient air levels that are below the current short-term National Ambient Air Quality Standards (NAAQS). 

The following recommendations are suggested based on the study results: 

1. EPA should consider the findings in this study and others identifying respiratory health effects 

associated with SO2 concentrations below current standards during their review of the SO2 NAAQS. 

2. Future time-series studies examining associations between ambient air pollutants and health 

outcomes would benefit from direct evaluation of the relationship between personal exposure and regional 

monitoring data. 

3. More research should be conducted to try to determine if peak, short-term (e.g. hourly) 

elevated concentrations of PM2.5 are more strongly associated than daily average concentrations with 

asthma and other health endpoints. If the science is sufficiently strong, consideration should be given to the 

effects of short-term PM2.5 excursions in future reviews of the particulate matter NAAQS. 

4. The high correlations between pollutants (including components of PM2.5) make it difficult in 

these epidemiologic studies to confidently identify critical compounds. Alternative strategies to address this 

question should be considered in the future. 

5. Further evaluation of the statistical methods employed in time-series epidemiological studies 

is warranted, based on the suggestion of possible model bias indicated by our analysis of control outcomes. 

6. To the extent that targeted community based asthma interventions are planned with respect to 

air pollution messages, higher priority should be given to communities with larger asthma burdens. 

9
10
Section 1

INTRODUCTION

Asthma is a serious and growing health problem. An estimated 14.9 million persons in the United States 

have asthma (NHLBI 1999). The number of people with asthma increased by 102% between 1979–80 and 

1993–94 (NCHS 1997). The greatest increase in prevalence and severity has been among children and 

young adults living in poor inner-city neighborhoods (Eggleston et al. 1999). The U.S. Department of 

Health and Human Services has acknowledged the seriousness of the problem by declaring asthma and 

environmental pollution as two of the Healthy People 2010 focus areas. 

Past studies have found discernible differences in ambient concentrations of some but not all air 

contaminants in urban areas for sites as close as three to five miles apart. Suh et al. (1995) collected 24-

hour samples of sulfate, hydrogen ion, and ammonia simultaneously at seven locations in Philadelphia and 

an upwind monitor during the summers of 1992 and 1993. Based on an assessment of spatial variation, they 

concluded that a single monitoring station was adequate for sulfate (consistent with long-range transport 

being the dominant source); however, multiple sites were necessary to determine local outdoor hydrogen 

ion concentrations. Goldstein and Landovitz (1977) found a poor correlation among air monitoring sites 

within a metropolitan area for certain air contaminants (e.g., sulfur dioxide). Recent work by Kinney and 

colleagues indicates that elemental carbon particle concentrations exhibit marked spatial variations within 

New York City as a function of local traffic density (Kinney et al. 2000; Lena et al. 2002). These studies 

suggest that for certain air contaminants it is very important to measure the air contaminants within the 

community being studied. In the present study, all subjects resided within approximately 1.5 miles of the 

monitoring sites used to characterize community air quality. 

Both particulate matter and O3 have been associated with respiratory impacts among asthmatics. For 

example, a study conducted in Seattle found a correlation between hospital emergency room visits for 

asthma and particulate (PM10) air concentrations (Schwartz et al. 1993). This effect was noted even though 

daily PM10 concentrations never exceeded current U.S. ambient air quality standards. Among 15 studies of 

asthma ED visits that incorporated adequate controls for seasonal patterns, all reported at least one 

significant positive association involving O3 or particulate matter (Cassino et al. 1999; Delfino et al. 1996, 

1998; Hernandez-Garduno et al. 1997; Jaffe et al. 2003; Jones et al. 1995; Martins et al. 2002; Stieb et al. 

1996; Tenias et al. 1998; Tobias et al. 1999; Tolbert et al. 2000). 

Few previous studies have investigated the association of air contaminants with acute asthma attacks in 

New York City. Thurston et al. (1992) studied the relationship between hospital admissions for asthma (and 

all respiratory admissions) and ambient acidic particulate matter and O3 concentrations during the summer 

in several regions in New York State. The researchers did not have air contaminant data for New York 

11
City, but rather used data from the nearby and less urbanized city of White Plains. They found that 

elevation of O3, aerosol strong acidity (hydrogen ion) and sulfate were associated with increases in asthma 

admissions in the summer in Buffalo and New York City. However, they found the associations were 

weaker in Albany and the less urbanized New York City suburbs. Potential reasons for this difference may 

be some chemical or physical difference in the composition or mix of air contaminants in the more densely 

populated areas or differences in susceptibility of the populations studied. Other older studies conducted in 

New York City did not report an air contaminant effect on hospital visits for asthma. Greenburg et al. 

(1964) did not find an association between sulfur dioxide, carbon monoxide, or particulate coefficient of 

haze and emergency clinic visits during September and October. Goldstein and Dulberg (1981) also found 

no significant relationship between hospital emergency department visits for asthma and sulfur dioxide or 

coefficient of haze measurements during the late summer and early fall. Many studies have evaluated the 

correlations between asthma attacks and ambient air contaminants during only one season, which may not 

be representative of the impact of various air contaminants throughout the year. In addition, studies may 

have had limited power to detect effects. 

One important factor in identifying a causal association between air contaminants and asthma is biological 

plausibility, such as that exhibited by contaminants known to irritate the respiratory tract. Aldehydes (e.g., 

acetaldehyde, acrolein, formaldehyde and propionaldehyde) represent an important class of hazardous air 

pollutants (HAPs) that could negatively affect asthmatics. Formaldehyde has been reported to induce 

asthma in some individuals exposed in occupational settings (e.g., Feinman 1988). Acute, small decreases 

in respiratory function (forced expiratory volume at 1 second, FEV1) have been reported after exposure in 

occupational settings (e.g., Alexandersson et al. 1982). Studies of asthmatics suggest that they may not be 

sensitive to formaldehyde at concentrations below those seen in occupational exposures (e.g., Harving et al. 

1986). Other aldehydes, and the potential interactions of aldehydes with other ambient contaminants, have 

not been as well studied. 

The health study presented here was designed to address two overall objectives. First, we sought to 

examine whether the magnitude of acute air pollution effects on acute asthma ED visits differed in two 

communities that had different baseline ED rates for asthma. Second, we wanted to investigate which air 

contaminants or mix of air contaminants was most associated with acute asthma exacerbations in each 

community. The study design focuses on acute exacerbations of existing asthma and does not address 

factors influencing asthma prevalence or development of newly-diagnosed asthma. Part A of this report 

presents the results of the ambient air sampling study that were used to explore the association between 

asthma ED visits and air pollutant concentrations. Part A compares air pollutant concentrations on a 

seasonal basis between sites and describes the correlation between the sites for the air contaminants, the 

correlations among contaminants within each site, and temporal contaminant patterns. 

12
Section 2

METHODS

To address the study objectives, we developed and analyzed an approximately two-year record of daily 

observations on emergency department visits and air contaminant measurements in two areas of New York 

City. The study design was a time-series analysis of air pollutant concentrations and acute asthma 

exacerbations (as assessed by asthma emergency department visits). The primary hypothesis was that 

temporal changes in individual ambient air pollutant concentrations were associated with temporal 

variation in asthma emergency department visits. These associations were tested separately in each study 

area. A secondary hypothesis was that the nature and/or strength of associations between ambient air 

pollutant patterns and asthma emergency department visits differed between the two study communities. 

A brief summary of the methods used for collection of air quality data and details on the methods used to 

collect and analyze the health data are presented in this section. A complete description of the methods 

used to collect and analyze ambient air contaminants is given in Part A. 

COLLECTION OF AIR QUALITY DATA 

Multiple air contaminants were monitored at a centrally located site in each community. Monitored air 

contaminants included real-time one-hour particulate matter (PM) less than 10 micrometers (μm) in 

aerodynamic diameter (PM10) and PM2.5 by tapered element oscillating microbalance (TEOM); daily 24-

hour average PM2.5 on filters using the Federal Reference Method (FRM); particle number concentrations 

from 0.007 to 2.5 μm aerodynamic diameter using a condensation particle counter; three-hour average 

organic and elemental (i.e., soot) carbon by thermal analysis; PM2.5 metals (Cr, Fe, Pb, Mn, Ni, and Zn); 

aerosol pH (expressed as H+ concentration, i.e., [H+] = 10-pH); aerosol sulfate; the criteria gases ozone 

(O3), nitrogen dioxide (NO2) and sulfur dioxide (SO2) using standard real-time methods; and bioaerosols 

including pollen and fungal spores. Pollen and fungal spores were categorized into several large (in some 

cases overlapping) groups for statistical analyses, based on taxonomic and/or morphologic similarities. For 

pollen, the categories were tree, grass, ragweed and total pollen. For fungal spores, the categories were 

basidiospores, ascospores, dark mitospores, non-dark mitospores, small spores (< 10 μm in the largest 

dimension), large spores (> 10 μm in the largest dimension) and total spores. 

Figure 1 shows a map of the study areas and air monitoring sites. The air sampling locations were US 

Environmental Protection Agency (EPA) approved air monitoring stations operated by the New York State 

Department of Environmental Conservation (NYSDEC), augmented by additional sampling equipment 

operated by the New York State Department of Health (NYS DOH). In the Bronx, two sites were used in a 

sequential fashion. The initial Bronx sampling site was at Intermediate School (IS) 155, located at 470 

13
Jackson Avenue. This site operated from January through July 1999, after which a construction project was 

initiated at the school. Accordingly, the Bronx study site was moved to Middle School (MS) 52, located at 

681 Kelly Street, which provided study data from September 1999 through November 2000. The MS 52 

site was approximately 0.5 miles northeast of the original IS 155 site. A comparison of results from both 

sites with corresponding Manhattan data (for January–July 1999 for the initial site and January–July 2000 

for the new site) suggested that the results from the two sites were comparable. In Manhattan, monitoring 

was carried out from January 1999 through November 2000 at the Manhattan Comprehensive Night and 

Day High School (also known as the Mabel Dean Bacon High School), located at 240 Second Avenue. 

Instruments in Manhattan sampled from a rooftop approximately seven stories high; those in the Bronx 

sampled from a rooftop approximately four stories above the ground. For further details on data collection 

methods and findings, see Part A. 

To perform the health analyses, it was necessary to replace missing values in the air data with estimates. 

Values were estimated by regression on a seasonal basis, first on the same analyte at the other site, then on 

correlated analytes (from either same site or other site, in order of decreasing strength of correlation). 

The first regression performed was across sites. For example, sulfate values at the Manhattan site were used 

to predict values for missing sulfate data at the Bronx site, and vice versa. To fill in remaining missing 

values, predictor variables were selected by ranking the variables from strongest to weakest correlation. For 

ranking purposes the correlation over the entire study period was used; the correlation was not compared on 

a seasonal basis. Regression on a seasonal basis was performed on the original data using the different 

predictor variables until all missing values had been replaced. For example, 67 of the 75 missing values for 

sulfate at the Bronx site were estimated by regression on sulfate at the Manhattan site; the eight remaining 

missing values were estimated by additional regression models to fill in the remaining missing values. 

Correlation coefficients utilized for filling in missing values were generally greater than 0.5. Aside from 

filling in the summer Bronx 1999 data, when the site was shut down and relocated, only a relatively few 

values had to be filled in; they generally changed the mean concentration estimates by less than 10%. 

COLLECTION OF HEALTH DATA 

As noted on Figure 1, the two study areas comprised six zip codes in the Bronx (10451, 10454, 10455, 

10456, 10459, and 10474) and 12 zip codes in Manhattan (10001, 10003, 10009, 10010, 10011, 10012, 

10014, 10016, 10017, 10018, 10020, and 10036). To select hospitals from which to extract ED data, we 

first identified hospitals that served residents living in the zip codes. During the planning phase of the 

study, data on asthma hospital admissions from the Statewide Planning and Research Cooperative System 

(SPARCS) were used to identify potential study hospitals. We used SPARCS data from 1996 and 1997 to 

determine the number of hospital admissions for asthma at each hospital that would service the study areas. 

We identified 24 hospitals throughout NYC that recorded an average of at least 10 asthma admissions per 

14
year by residents from the study area zip codes. One eligible facility (Union Hospital) was excluded 

because it had closed in early 1998, and another (St. Clare’s Hospital and Health Center) was excluded 

because we were unable to obtain the necessary data from this facility. Therefore, 22 hospitals were 

included in the study (Figure 2). 

Eight of the 22 hospitals were located in the Bronx and 14 were located in Manhattan (Figure 2). Sixteen 

hospitals were privately owned and operated; six were public hospitals (three in the Bronx and three in 

Manhattan) administered by the New York City Health and Hospitals Corporation (HHC). When several 

hospitals were jointly owned or merged during the course of the study, data were collected from a single 

source rather than from each hospital individually. The study was originally designed to collect data on all 

emergency department visits during one year, from January 1, 1999, through December 31, 1999, but to 

enhance study power and to capture data from a summer season in the Bronx, this time frame was extended 

to include additional data through November 2000. 

The study was given 206(1)(j) designation by the New York State Commissioner of Health in late 1996. 

This designation allowed NYSDOH to collect the data needed for the study and facilitated the cooperation 

of the study hospitals. This designation confers protection on the information and reports collected, 

maintains confidentiality, and guarantees that the data will be used solely for the purposes of scientific 

research with respect to this study. By February 2002, data had been received from all 22 hospitals. 

The data elements requested from the hospitals included medical record number; patient’s name, date of 

birth, sex, race, social security number and residential street address (including zip code); source of 

payment; emergency department visit date; principal diagnosis code; additional diagnosis codes and 

hospital admission and discharge dates (if applicable). The data essential for the study were medical record 

number, residential street address (including zip code), emergency department visit date and principal 

diagnosis code. In some cases additional diagnosis codes were also provided. 

SAS statistical software and SQL (Structured Query Language) were used to process the data into a 

consistent format. The datasets were concatenated into a master SAS dataset containing 629,227 

observations and 19 data fields. Twelve fields were from the ED data provided by the hospitals (including 

hospital identification number, sex, ED visit date, admission and discharge dates, principal diagnosis and 

secondary diagnoses). Seven fields were created, including the patient’s age (from the date of birth and ED 

visit date) and fields to identify the study areas, asthma cases and controls. The dataset used for statistical 

analysis did not contain any personal identifying information. 

Asthma cases were obtained from ED records with a principal diagnosis ICD-9 code of 493 and, in 

addition, for children less than one year old, codes 466.1 (acute bronchiolitis) and 786.09 (other dyspnea 

15
and respiratory abnormalities, including wheezing and shortness of breath). The latter were included 

because of the difficulty of diagnosing asthma in infants. 

We also counted ED visits for a set of “control” health conditions assumed a priori to be unrelated to air 

pollution. By analyzing these data in relation to air pollution, we hoped to verify the absence of significant 

associations, thereby inferring a lack of bias in our asthma analyses. These included cases with principal 

diagnosis ICD-9 codes 365 (glaucoma), 366.0-366.3 (cataract), 531.0-531.3 (acute gastric ulcer), 532.0-

532.3 (acute duodenal ulcer), 533.0-533.3 (acute peptic ulcer), 534.0-534.3 (acute gastrojejunal ulcer), 535 

(gastritis and duodenitis), 537 (disorders of stomach and duodenum), 540-543 (appendicitis or diseases of 

the appendix), 558 (non-infectious gastroenteritis and colitis), 574-575 (cholelithiasis), 590 (infections of 

the kidney) and 599 (other disorders of urethra and urinary tract). 

Secondary diagnoses were not used to identify asthma cases and controls for two reasons. First, New York 

City HHC could only provide the primary diagnosis and the number of secondary diagnoses varied among 

the remaining hospitals. Second, these diagnoses could be co-existing conditions but not necessarily acute 

conditions related to the primary diagnosis. 

STATISTICAL ANALYSIS 

S-Plus software was used to analyze associations between daily air quality and asthma ED visit counts, 

controlling for season, day-of-week and temperature (“confounding variables”). Although humidity was 

another potential confounder, it did not appear to be necessary to control separately for humidity, since it is 

highly correlated with season and temperature. We assessed the associations between the asthma 

admissions data and air pollutants both individually and in multi-pollutant models. Appropriately 

controlling for important confounding variables is critical to isolate the influence of air contaminants on 

asthma response. We used the general linear model (GLM) to perform Poisson regression. We used natural 

splines to control for date and temperature; we also controlled for day-of-week effects. This approach fits 

smooth functions (natural splines) of the asthma counts as a function of each confounding variable, which 

in effect should leave intact the shorter-term fluctuations in asthma counts that may be explainable in part 

by the air quality parameters. The GLM approach using spline smoothing has been recommended by 

Dominici et al. (2002) as an alternative to LOESS smoothing using generalized additive models (GAM). 

Although we did not constrain the shape of the spline fitted by S-plus, we selected the number of degrees of 

freedom (DF) for the curve. The choice of DF affects the final result, and we made efforts to test the 

sensitivity of results to a range of DF choices. An appropriate choice of DF captures the variability of the 

response variable with regard to the confounding variable but does not “over-fit” the data at risk of 

erroneously attributing too much variability to the confounding variable and underestimating the risk due to 

air pollution. 

16
Hospital visits for asthma vary over the year. Although some of this variation may be due to air quality 

variations (the subject of the present study), behavioral, physiological and other causes are thought to play 

a dominant role in driving seasonal patterns. For instance, there is an increase in the asthma attack rate in 

the fall from unknown factors (Blaisdell et al. 2002), although there is some suggestion that viral infections 

play an important role (e.g., Johnston et al. 1996, 2005). Thus, fitting the seasonal variability with natural 

spline functions is aimed at removing temporal correlations between exposure and outcome that are most 

likely not related to any causal relationship involving air pollution. Figures 3a and 3b show the relationship 

of asthma to day of year for the two study sites and illustrates the natural spline fit to the data using 18 

degrees of freedom, which was deemed adequate to capture the observed seasonal variability in asthma. 

Hospital utilization, including ED visits, is known to vary with day of week. To ensure that weekly patterns 

in hospital ED visits were not erroneously attributed to air pollutants (some of which also exhibit day-ofweek patterns), day-of-week effects were controlled as a class variable in GLM. Figures 4a and 4b show 

the relationship of asthma to day of week for the two study sites. Note that peak visits occurred on Monday 

at both sites. 

Temperature may also influence asthma exacerbations. Scatterplots of the raw asthma and temperature data 

are shown in Figures 5a and 5b. We see that asthma visits tended to be highest at lower temperatures, and 

lower as the temperature rose. Some or most of this relationship may reflect the same seasonal factors 

already controlled by the spline on date. However, because O3 and other pollutants are correlated with 

temperature, we included temperature as a confounding variable, smoothed with a natural spline using 3 

DF, which was adequate to capture the smooth curve. 

A Poisson regression model was selected to quantify the relationships between asthma and air quality. 

Poisson regression is a standard model for dealing with a dependent variable of counts. The Poisson 

regression assumes a log-linear response between the dependent variable and the linear predictor. In this 

case, the dependent variable is asthma ED counts, and the linear predictor is the sum of air quality and 

confounding variables included in the model. 

For a simple Poisson model with outcome Y regressed on one pollutant X, the assumption of a log-linear 

response implies that 

Log(Y) = α + β*X 

or 

17
Y = EXP(α)*EXP(β*X) = C * eβx 

RR = EXP(β*X) 

Y = C*RR 

where 

Y is the outcome variable (e.g., daily asthma ED counts); 

X is the level of the air pollution variable; 

α is the intercept term; 

β is the slope relating changes in asthma ED counts to changes in pollutant concentration; 

C is the baseline level of daily asthma ED counts in the absence of air pollution; and 

RR is relative risk, or the proportional increase in daily asthma ED counts for an increase of X in 

pollutant concentration. 

So, for an increase in pollutant concentration of a value X, the ED count increases by a factor of RR. Thus, 

the model assumes a constant proportional increase in asthma counts per unit increase in pollution. 

ANALYSIS STRATEGY 

Although a large number of air quality parameters were measured in the study, we chose to examine 14 key 

parameters or groups of parameters that, by consensus among the co-investigators, were considered a priori 

to carry the greatest potential risk with respect to asthma exacerbations (Table 3). To minimize multicolinearity as well as excessive statistical testing, we kept the list as short as possible. We included daily 

maximum eight-hour moving average O3, daily mean NO2 and SO2, daily 24-hour average FRM PM2.5, 

daily one-hour maximum PM2.5, daily 24-hour average PM10-2.5 (i.e., coarse PM, the particulate matter 

fraction between PM10 and PM2.5), PM2.5 sulfate, PM2.5, PM2.5 acidity (H+

), PM2.5 elemental carbon 

(“soot”), PM2.5 organic carbon, total PM2.5 metals (predominately nickel and iron), total carbonyl 

compounds (predominately formaldehyde, acetaldehyde and acetone), total pollen and total mold spores. 

Each “pollutant” was tested individually in the Poisson regression model to assess the independent health 

impacts of each air quality parameter. 

At issue early on was whether separate models would be fit for the two study sites, or whether a consistent 

model form (e.g., the choices of degrees of freedom for splines on confounding variables) should be 

applied to both sites. For instance, in comparing the response of pollutants between Los Angeles and New 

York City, it would be expected that seasonal patterns and temperature dependencies would differ and 

could thus require separate models. In contrast, for two communities within New York City, it is not 

obvious why separate models would be required. Further, the goal of comparing air pollution effects across 

18
the two communities argued for using a consistent model. Accordingly, for the main analyses, an identical 

model form was used in both communities, with confounding variables handled as noted above, and with 

the air pollutant expressed as the mean of lags zero through four. In other words, we expressed air pollution 

exposures as the five-day mean ending on the corresponding day of asthma data. We chose to use the mean 

of lags zero through four based in part on previous studies that suggested that most asthma ED visits occur 

24 to 72 hours following the onset of symptoms (Canny et al. 1989). In addition, exploratory analyses 

indicated that positive associations tended to exist within this lag range but the pattern of lags differed 

somewhat across locations. By averaging across relevant lags, we sought to smooth out these patterns and 

thereby provide a consistent basis for comparison across locations. Details on the exploratory analyses of 

lag dependency are presented below, under Results. 

19
20
Section 3

RESULTS

The number of asthma and control ED visits and the total number of visits by hospital are enumerated in 

Table 1. The hospital-specific ED data presented here may include data for residents from both study areas, 

since residents from the Bronx may have visited a Manhattan hospital and vice versa. In constructing 

analytical datasets, these data were separated by residential location into two separate ED data files. 

Average daily asthma ED visits differed substantially for residents of the two study communities (Table 2). 

Overall, daily asthma ED visits were six times higher in the Bronx study area (43 per day) than in the 

Manhattan study area (7.2 per day). To put these numbers in perspective, Table 2 also gives the Census 

2000 population counts in the two study areas. By dividing the daily asthma counts by the population, we 

can estimate crude daily rates of asthma ED visits overall and by sex and age for each community. The 

crude daily asthma ED rates for all ages were 16.9 per 100,000 persons for the Bronx and 2.02 per 100,000 

persons for Manhattan. Population age structures were quite different in the two communities, with larger 

proportions of younger persons in the Bronx versus Manhattan (Figure 6). 

Means and standard deviations for the 14 key air contaminants are given in Table 3. In general, mean 

concentrations were fairly similar across the two communities. Exceptions included maximum eight-hour 

O3, which was 33% higher in the Bronx (28 parts per billion, ppb) than in Manhattan (21 ppb), and total 

pollen, which was almost 60% higher in the Bronx (20.8 grains/m3

) than in Manhattan (13.1 grains/m3). 

The distributions of pollen and mold concentrations were highly skewed (data not shown), with many days 

of zeros and brief periods of very high levels. More detailed analysis of the air quality data and the 

differences across communities is presented in Part A. 

SINGLE-POLLUTANT MODELS 

Table 4 and Figure 7 present relative risks (RRs) and 95% confidence intervals (CIs) for Manhattan and the 

Bronx for the 14 air contaminants. Relative risks are computed relative to a fixed “increment” in 

contaminant concentration. The CIs on the RRs were computed based on taking plus or minus 

1.96×SD(regression slope) and then re-computing RRs at each of the CI bounds. For the results presented 

in Table 4a, we have used the two-community mean concentrations given in Table 3 as the exposure 

increment. It should be noted that the choice of concentration increments used to compute the RRs is an 

arbitrary one. The mean is a common choice. However, RRs based on variability metrics, such as the 

standard deviation of daily pollution concentrations, may be more appropriate for expressing health impacts 

associated with typical day-to-day changes in contaminant concentrations and for comparing the strength of 

effects among pollutants whose absolute air concentrations differ. To illustrate this, we re-computed the 

21
RRs and 95% confidence intervals for the five pollutants with significant RRs in the Bronx based on the 

two-community average standard deviation of the respective air pollutant concentration (Table 4b). 

Changing the scaling increment in this way does not affect the statistical significance of the RRs. 

The results in Table 4a indicate that the individual contaminants with statistically significant effects (based 

on the 95% CI excluding RR=1.00) in the Bronx were Max 8hr O3 (RR 1.06; 95% CI 1.01–1.10), NO2 (RR 

1.10; 95% CI 1.01–1.18), SO2 (RR 1.11; 95% CI 1.06–1.17), FRM PM2.5 (RR 1.05; 95% CI 1.01–1.10), 

and Max PM2.5 (RR 1.09; 95% CI 1.03–1.15). Although the magnitudes of the RR estimates in Manhattan 

were often similar to those observed in the Bronx, no statistically significant air pollution effects were 

observed for Manhattan. 

When the standard deviation increment was used for the five pollutants with significant RRs in Table 4a, 

the relative magnitudes of the pollutant-specific RRs decreased compared with the RRs based on the mean 

increment (Table 4b). When standard deviation increments were used, the SO2 effect stands out as the 

largest of those pollutants that were statistically significant in the Bronx. 

In additional exploratory analyses, we examined whether maximum hourly concentrations of NO2 or SO2 

or maximum three-hour elemental (soot) carbon or organic carbon (again, averaged over lag zero to four 

days) yielded substantially different results than were observed above for 24-hour mean concentrations. 

Table 5 shows these results. Slightly stronger associations were observed for these daily maximum results 

for NO2 and elemental carbon in the Bronx than were observed using the 24 hour means (Table 4a). In 

contrast with the daily-mean elemental carbon effect, the maximum three-hour elemental carbon 

association attained statistical significance. 

CONFOUNDER EFFECTS 

As noted above, the basic Poisson regression model included a single pollutant along with three 

“confounder” variables: a natural spline function of date with 18 degrees of freedom, a natural spline of 

temperature with 3 degrees of freedom and a weekday term. To assess the importance of these confounder 

variables, we examined the contribution of each variable to the model in terms of its ability to explain 

variations in ED visits. As an example, we present these results for the O3 model in the Bronx in Table 6. In 

the generalized linear modeling framework of S-Plus, the variance explained by a variable is characterized 

by the deviance divided by the degrees of freedom (Kaz Ito, personal communication). As seen in Table 6, 

for the single-pollutant model including O3, the date and weekday variables were the strongest predictors of 

asthma ED visits, followed by O3 itself. 

Temperature had a very low explanatory power in the O3 model, implying that it was probably not 

necessary to be included as a covariate. To examine what influence the inclusion of temperature had on key 

22
air pollution regression results, we re-ran the regressions of asthma ED visits on SO2, maximum PM2.5 and 

O3 without temperature in the models. There were no important changes in the RR estimates for these 

pollutants without temperature in the models (Table 7). However, in the interest of being conservative, 

temperature was retained as a covariate in all other results presented here. 

SEASONAL AND THRESHOLD ANALYSES 

Because bioaerosols, SO2 and O3 are seasonal contaminants that reach high airborne concentrations 

primarily in the warm (bioaerosols and O3) or cold (SO2) season in New York City, we re-estimated these 

effects in a data subset restricted to the relevant season. In addition to eliminating some statistical noise that 

may be introduced by including non-peak season data, seasonal restriction also can help reduce residual 

confounding by seasonal patterns (Burnett et al. 1994). For SO2, there was no change in results when we reran the regression within a winter data subset (data not shown), and these results are not discussed further. 

In the case of O3, the basic regression model was re-run using data for the seven-month period April 1 

through October 31, which yielded a larger and more significant RR in the Bronx (1.08; 95% CI 1.03–1.12) 

but a smaller RR in Manhattan (1.04; 95% CI 0.91–1.19) for a 24 ppb change in O3 concentration (Table 

4a). These results are contrasted to those obtained for O3 in the full-year analysis of the Bronx (1.06; 95% 

CI 1.01–1.10) and Manhattan (1.06; 95% CI 0.93–1.20) for a 24 ppb change in O3 concentration. Based on 

these results, it would appear that the warm-season effect of O3 on ED visits for asthma is about twice as 

high in the Bronx as it is in Manhattan, although the Bronx CI includes the Manhattan RR estimate. 

Further, because the RR represents the proportional increase in asthma ED visits associated with a fixed 

increase in O3 (here, 24 ppb), and because average asthma ED visits were six times higher in the Bronx 

study area than in the Manhattan area, the number of O3-related ED visits in the Bronx would be estimated 

to be about 12 times higher than in Manhattan. 

To investigate whether there was evidence for O3 effects below a daily maximum eight-hour moving 

average of 80 ppb—the National Ambient Air Quality Standard—we repeated the summer-season 

regression after eliminating days with concentrations above this level. There were only five such days in 

the Bronx during the study period (fewer than 1% of all days). The O3 RR from this regression model (RR 

1.09; 95% CI 1.03–1.15) was similar to that obtained above for the summer-season regression over the full 

concentration range (RR.1.08; 95% CI 1.03–1.12). 

We also re-ran regressions for selected pollen and mold categories within warm-season data subsets (Table 

8). The results from the seasonal analysis did not differ from the results observed in the full annual 

analysis, with RR estimates generally close to 1.00. Because of the highly episodic temporal patterns of 

pollen and mold concentrations observed in this study, regression modeling may not represent the optimal 

23
analytical strategy for analyzing health effects of these contaminants. However, because the focus of the 

present report is on air pollutants and their effects on asthma, we did not pursue this issue further. 

LAG DEPENDENCY OF SINGLE-POLLUTANT MODELS 

Exploratory regression analysis of the Bronx and Manhattan data revealed distinct differences between the 

two regions in asthma effects as a function of lag. This observation prompted a more thorough investigation 

of the lag structures in each community, with testing of lags from zero to four days. This approach is 

consistent with past studies indicating that ED hospital visits for asthma peak for patients with symptoms 

beginning 24 to 72 hours prior to arrival (Canny et al. 1989). 

To illustrate the observed lag structures, Figure 8 plots the single-lag relative risks in the Bronx and 

Manhattan for three pollutants (PM2.5, SO2 and O3), with error bars representing 95% CIs. As before, the 

RR for each contaminant was calculated for a concentration increment corresponding to the mean 

contaminant concentration in Manhattan and the Bronx, given in Table 3, with the same concentration used 

for both community RRs (thus, all differences in the RRs between the two areas arise from differences in 

the calculated regression slopes). The data suggest differences exist between the two areas and among 

contaminants in the lag-dependencies of the responses. PM2.5 produced a maximum response in the Bronx 

at a zero-day lag, but in Manhattan at a one-day lag. SO2 produced a maximum response in the Bronx at a 

two-day lag, and in Manhattan at a three-day lag. O3 maximum Bronx response occurred at a one-day lag, 

whereas in Manhattan the response decreased sharply from zero- to four-day lags. 

For the 14 contaminants listed in Table 3, the analysis of lags zero to four yielded three statistically 

significant RRs for a specific-day lag in the Bronx, and one in Manhattan. In the Bronx, NO2 was 

significant at a zero-day lag, maximum PM2.5 was significant at a four-day lag and SO2 was significant at a 

two-day lag. In Manhattan, daily average PM2.5 was significant at a one-day lag (Figure 8, not all data 

shown). 

Because the patterns of lag-dependency differed among pollutants and locations, choosing a single-day lag 

to apply uniformly to both communities would have a profound impact on the conclusions regarding the 

differences in air pollution effects across the two communities. In an effort to mitigate this problem, 

comparisons across communities presented in this report were based on a model that regressed ED visits on 

the multi-day average concentrations computed over lags zero to four for each air quality parameter (i.e., a 

five-day distributed lag giving equal weight to the day of the ED visit and the preceding four days). 

MULTI-POLLUTANT MODELS 

As noted above, significant single-pollutant results were seen for PM2.5, O3, NO2 and SO2 in the Bronx 

dataset. We sought to investigate whether these individual pollutant effects were independent of one 

24
another, or on the other hand, whether results for individual pollutants were confounded by omission of 

other pollutants. This issue can be addressed by including two or more pollutants simultaneously into the 

regression model and examining whether the pollutant-specific effects change compared with the singlepollutant models presented above. 

Pairs of contaminants with significant effects in the single-pollutant models were tested simultaneously in 

the basic model that included controls for date, temperature and day of week. We report in Table 9 the 

relative risk and 95% CI for these results for the Bronx and Manhattan. To assist in interpretation of these 

results, Table 10 gives the correlations among the individual pollutant concentrations. 

For the Bronx, co-pollutant regression results for O3 and SO2 were robust to all other pollutants considered 

(Table 9). The univariate O3 RR was 1.06 (Table 4); with co-pollutants, the RR ranged from 1.04 to 1.06. 

The univariate SO2 RR was 1.11 (Table 4); with co-pollutants, the RR ranged from 1.09 to 1.11. FRM 

PM2.5 was robust to O3 but not to other co-pollutants tested (RR 1.05 in both univariate and bivariate O3 

models; RR reduced to approximately 1.00 with other co-pollutant models). The high correlation between 

FRM PM2.5 and maximum PM2.5 made it difficult to assess their relative importance. Still, the results in 

Table 9 do suggest that maximum PM2.5 was the stronger predictor of asthma ED visits in this study. 

Compared with the effects of co-pollutants on RRs for FRM PM2.5, the maximum PM2.5 RRs did not 

diminish to the same extent when co-pollutants were factored in. This robustness argues for a greater 

independent impact of maximum PM2.5 concentrations compared with 24-hour average PM2.5. NO2 effects 

were robust to O3 but were not robust to the other pollutants. As was seen for the single-pollutant results 

presented earlier, none of the Manhattan results were statistically significant. 

To summarize the results presented in Table 9, the O3 effect on daily asthma ED visits was robust to 

inclusion of the other pollutants into the model. The SO2 effect was also robust. PM2.5 exhibited somewhat 

less robustness. Of the two PM2.5 metrics, maximum hourly PM2.5 was the more robust. Note that in threepollutant models including O3, SO2 and maximum PM2.5, the RRs for O3, maximum PM2.5 and SO2 effects 

remained virtually unchanged from their univariate magnitudes. NO2 effects were robust only to inclusion 

of O3 in the model. 

ANALYSIS OF CONTROL VARIABLES 

To evaluate the specificity of the air pollution effects observed for asthma visits, we repeated the analysis 

of five key air pollutants with control-cause ED visits as the outcome variable. If there was no association 

with air pollution, one would expect non-significant RRs centered at 1.00 for the control outcome. Results 

for the Bronx and Manhattan are presented in Table 11. Of the five pollutants that had significant univariate 

effects on asthma in the Bronx, one, FRM PM2.5, had significant effects on the control outcome in the 

Bronx. Positive but non-significant effects were seen for the remaining pollutants, except O3. There was no 

25
evidence of any effect of O3 on control ED counts. Analysis of Manhattan control outcome data showed a 

similar but somewhat weaker positive bias for the same five pollutants. 

To determine whether patients diagnosed with one of the study control conditions also had a secondary 

diagnosis of asthma, additional diagnosis codes were examined for the nine hospitals from which the data 

were available. The nine hospitals had reported a total of 193,300 emergency department visits, including 

11,451 asthma visits (i.e., asthma as principal diagnosis) and 11,087 control visits (i.e., one of the control 

conditions as principal diagnosis). A total of 49 ED visits were made by patients with a control condition as 

the principal diagnosis and a secondary diagnosis of asthma, accounting for 0.4% of patients diagnosed 

with one of the control conditions. The control conditions for which this was most frequently the case were 

non-infectious gastroenteritis (ICD9 = 558; N = 13), urinary tract infection (ICD9 = 599; N = 11), and 

ulcers (ICD9 = 531–535; N = 7). Secondary asthma diagnoses do not appear to be a likely contributor to 

the observed trend of control-outcome RRs > 1 for the five air pollutants investigated in the Bronx. 

ANALYSIS OF SEX-SPECIFIC RESPONSES 

To examine whether males and females responded differently to pollution, we repeated the basic general 

linear modeling for five key pollutants in data subsets stratified by sex. Larger and/or more significant RRs 

in one sex or the other would be taken as evidence for differential responses. Results of the stratified 

analysis are presented in Tables 12a (the Bronx) and 12b (Manhattan). In the Bronx, the RRs were larger 

and more significant for females than for males for all pollutants except O3. Results in Manhattan were 

generally similar, with higher RRs for females (except for O3), though none of the RRs were statistically 

significant. These results suggest that female asthmatics may be more susceptible than males to the acute 

effects of air pollution. 

ANALYSIS OF AGE-SPECIFIC RESPONSES 

Health data were broken down by age group for regression against each of five key pollutants (Tables 13a 

and 13b). Age was split into five strata, 0–4, 5–18, 19–34, 35–64, and over 65. Because the numbers of 

cases in each age stratum were relatively small for these analyses (refer to Table 2), there was considerable 

variability in results across ages. Although some of the largest RRs occurred in the very young and very 

old, for most pollutants it was the older adult age group (35–64) that appeared to have larger and more 

significant effects. These findings should be taken as only suggestive, however, since study power was 

limited for testing effects within age strata. Larger studies would be needed to derive firm conclusions 

about age-specific effects. 

26
Section 4

DISCUSSION

This study evaluated daily asthma emergency department visits in relation to a range of air contaminants 

over a two-year period in two communities that differed substantially in baseline asthma morbidity – Lower 

Manhattan and the South Bronx. Primary objectives were identifying which air pollutants were most 

consistently associated with asthma ED visits and comparing the magnitude of air pollution effects across 

the two communities. The study design did not address factors influencing asthma prevalence or 

development of newly diagnosed asthma. 

In Poisson regression models that included controls for longer-term and day-of-week temporal cycles and 

temperature, five of 14 key air contaminants were significantly associated with daily asthma ED visits at 

the P < 0.05 level in the Bronx community only. Significant pollutants included daily eight-hour maximum 

O3, mean daily NO2, SO2, PM2.5 and maximum one-hour PM2.5, all expressed as the mean of lags zero to 

four. In secondary analyses of effects for peak hourly SO2 and NO2 concentrations or peak three-hour 

elemental (soot) carbon and organic carbon, all but organic carbon were significantly associated with 

asthma visits. 

In two- and three-pollutant regression models, O3, SO2, and to a lesser extent, maximum one-hour PM2.5 

were the most robust pollutants. In other words, these pollutants exhibited less change in their effect 

estimates as additional pollutants were added to the models. The relative risk for O3 did not change 

appreciably when we repeated the analysis after eliminating all days with maximum eight-hour moving 

average O3 above 80 ppb, the National Ambient Air Quality Standard. Robust effects of O3 have been seen 

in previous ED asthma studies (Stieb et al. 1996; Martins et al. 2002) and in hospital admissions studies of 

asthma and other respiratory diseases (Burnett et al. 1997). It is of particular interest that we observed more 

robust health impacts of the daily maximum PM2.5 concentration compared with the 24-hour mean, 

suggesting that peak exposures may have larger health impacts. Prior studies have also suggested that 

stronger associations between particulate matter exposure and asthma morbidity are observed with shorter 

particulate matter averaging times (e.g., Delfino et al. 1998; Michaels and Kleinman 2000). 

When the Bronx relative risks and 95% confidence intervals were re-computed based on the pollutant 

standard deviations rather than on means, SO2 effects appeared more prominent than the other pollutants. 

RRs calculated using the standard deviation normalize all pollutant concentration increments relative to 

their observed variability and convey a better sense of the health effects associated with typical day-to-day 

variations in concentrations. 

27
Although concentrations of air contaminants were generally similar in the two communities, health impacts 

of air pollution were more apparent in the Bronx than in Manhattan. Among the 14 pollutants examined 

individually in regression analyses, five had statistically significant effects on asthma ED visits in the 

Bronx. Although the magnitudes of the RR estimates in Manhattan were often similar to those observed in 

the Bronx, no statistically significant air contaminant effects were observed for Manhattan. 

The more prominent effects in the Bronx at least partially reflect greater statistical power for identifying 

effects there. Because asthma ED visits follow a Poisson distribution, the greater mean daily asthma ED 

counts in the Bronx would lead to reduced relative uncertainty around the effect estimates. This effect can 

be illustrated using the relative uncertainty of the estimate of the mean of a Poisson distribution. For a 

Poisson variable, the variance is equal to the mean and is referred to as lambda. Therefore, by the Central 

Limit Theorem, the sampling distribution of the mean lambda will have variance equal to lambda/n, or a 

standard error of the mean equal to sqrt(lambda/n). Expressed as the ratio of the standard error of the mean 

to the mean, this becomes 

Sqrt(lambda/n)/lambda = 1/sqrt(lambda*n) 

Thus, for a fixed sample size n, as lambda (the mean) increases, the uncertainty around the mean estimate 

relative to the mean diminishes as 1/sqrt(lambda). In the present study, the relative uncertainty of mean (or 

effect) estimates in Manhattan is about 2.5 times greater than in the Bronx. This translates into greater 

uncertainty around effect estimates and reduced power to detect effects. However, in addition to wider error 

bars relative to the mean, the RRs in Manhattan were also closer to 1.0 than those in the Bronx for the five 

pollutants that were significantly related to asthma visits, which does support the idea that effects might be 

larger in the Bronx. 

In analyses restricted to the warm season (April through October), the O3 RR in the Bronx was 

approximately double that of Manhattan (although the CIs overlap), suggesting greater susceptibility to this 

airway irritant and pro-inflammatory agent in the Bronx. Because the RR represents the proportional 

increase in asthma ED visits associated with a fixed increase in O3 (here, 24 ppb), and because average 

asthma ED visits were six times higher in the Bronx study area than in the Manhattan area, the number of 

O3-related ED visits in the Bronx would be estimated to be about 12 times higher than in Manhattan. 

A variety of factors could contribute to differences in susceptibility to air pollution effects as measured by 

asthma emergency department visits across the two study communities, if such differences exist. Factors 

that might play a role include differential access to primary asthma care, nutritional differences, co-morbid 

conditions or other factors related to general socio-economic status. Lack of adequate primary asthma care 

may lead to higher baseline asthma morbidity and to greater use of the ED as the first line of care during a 

28
severe exacerbation. Along with other community-level factors, such as nutritional status and comorbidities, this could manifest as a greater proportional response to a given increase in air pollutant levels. 

Data were not available to evaluate these hypotheses in this report. 

Variation in effects of unmeasured co-pollutants, such as indoor allergens, environmental tobacco smoke or 

local traffic and industrial emissions, might also influence the apparent differences in acute asthma ED 

responses to ambient air pollution observed in the two communities. Increased exposure to such local 

measured pollutants could directly increase baseline asthma morbidity and might also indirectly increase 

the response to changes in ambient air pollutants by increasing airway inflammation and hyperresponsiveness to acute airway irritants. Data were not available to address these possible effects in this 

report. 

Analyses by sex suggested that the air pollution effects in the Bronx were greater among females than 

males. Medical utilization for acute asthma exacerbations has been observed to be greater for females 

among adults, and greater for males among children (e.g., Schatz and Camargo 2003; Schatz et al. 2004). 

Schatz et al. (2004) concluded that increased asthma hospitalization among boys was a reflection of 

prevalence rather than increased asthma severity in boys versus girls. However, the larger relative increase 

in acute ED visits observed in females in this study with fixed incremental increases in air pollutant 

concentrations suggests possible sex differences due to factors other than prevalence, such as differences in 

severity, disease management or access to care. Data were not available to further evaluate this hypothesis 

in this report. 

No strong differences in effects were observed with age strata, though there was some indication of larger 

effects in older adults but not the elderly. Differences in the response of adults and children with asthma to 

different air pollution exposures have been observed in studies designed to investigate age-related effects 

(e.g., Atkinson et al. 1999; Sinclair and Tolsma 2004). In the present study, our ability to resolve agerelated differences in asthma response to air pollution exposure may have been too limited by the required 

stratified sub-analyses. 

To evaluate the specificity of the air pollution effects observed for asthma visits, we analyzed the 

relationships between air pollutants and control-cause ED visits. Of the five pollutants that had significant 

univariate effects on asthma in the Bronx, one, FRM PM2.5, had significant effects on the control outcome. 

Positive but non-significant effects were seen for the remaining pollutants, except O3. There was no 

evidence of any effect of O3 on control ED counts. These results could suggest some degree of 

overestimating risk in the analysis. 

29
We explored this apparent risk overestimation effect with additional analyses. For those hospitals where a 

secondary diagnosis was available, there was no indication that a diagnosis of asthma secondary to one of 

the control conditions contributed to the tendency toward positive associations with control outcomes. 

When control conditions were stratified by organ system, there was a similar tendency toward positive 

associations between the same five pollutant variables and control conditions grouped as gastrointestinal or 

urinary tract. Based on these follow-up analyses, we were not able to discern a clear explanation for the 

apparent positive model bias suggested by the analysis of control outcome variables. 

In the current study, significant associations were observed between asthma ED visits and four criteria air 

pollutants—O3, SO2, FRM PM2.5 and NO2. The results for O3 and SO2 remained significant in models 

considering the simultaneous effects of two and three pollutants. Other recent studies have found similar 

associations using time-series methods similar to those used here. However, finding associations between 

any of these pollutants and acute asthma ED visits varies among studies, as does the degree to which 

associations are robust to inclusion of additional pollutants in the models. 

Ozone has been associated with acute asthma ED visits in several recent studies (Fauroux et al. 2000; 

Galan et al. 2003; Cassino et al. 1999; Stieb et al. 2000; Jaffe et al. 2003), but it was not significantly 

associated with ED visits in single-pollutant models in a similar number of studies (Lierl and Hornung 

2003; Atkinson et al. 1999; Tolbert et al. 2000; Thompson et al. 2001; Jalaludin et al. 2004; Sinclair and 

Tolsma 2004). The association observed in Galan et al. (2003) remained significant for O3 after the 

inclusion of NO2 and pollen. Stieb et al. (2000) found that an association between O3 and all respiratory ED 

visits persisted in a multi-pollutant model with NO2 and SO2, but a separate multi-pollutant model for 

asthma ED visits was not reported. Conversely, inclusion of O3 in multi-pollutant models did not modify 

the significant effect of SO2, NO2, PM10 or CO (Atkinson et al. 1999) or grass pollen (Lewis et al. 2000), 

suggesting that any association between O3 and acute asthma ED visits was small compared with the other 

pollutants in these studies. 

In a majority of recent studies, SO2 has been significantly associated with acute asthma ED visits in singlepollutant models (Michaud et al. 2004; Atkinson et al. 1999; Stieb et al. 2000; Tolbert et al. 2000; Chew et 

al. 1999; Thompson et al. 2001; Jaffe et al. 2003; Norris et al. 1999), although several studies failed to 

observe a significant association (Fauroux et al. 2000; Galan et al. 2003; Cassino et al. 1999; Donoghue 

and Thomas 1999; Sinclair and Tolsma 2004). The SO2 association persisted in two-pollutant models 

including NO2, O3, CO, PM10 and black smoke in one study (Atkinson et al. 1999) and was not modified by 

the inclusion of PM1 (i.e., ultrafine PM) in another study (Michaud et al. 2004), but the association was not 

robust to inclusion of PM10 (Galan et al. 2003) or benzene (Thompson et al. 2001) in other studies. 

30
Compared with other criteria pollutants, NO2 and other nitrogen oxides have been included in fewer recent 

time-series analyses of acute asthma ED visits, but they tend to show mixed results in single-pollutant 

models, similar to the overall results observed for O3. Significant associations have been reported in several 

studies (Galan et al. 2003; Atkinson et al. 1999; Tobert et al. 2000; Thompson et al. 2001) but have not 

been found in others (Fauroux et al. 2000; Cassino et al. 1999; Jaffe et al. 2003; Jalaludin et al. 2004; 

Sinclair and Tolsma 2004; Norris et al. 1999). When NO2 was significantly associated with acute asthma 

ED visits in single-pollutant models and was included in multi-pollutant models in these studies, its 

association with asthma ED visits (Galan et al. 2003; Atkinson et al. 1999) or all respiratory ED visits 

(Stieb et al. 2000) has generally persisted, although the association did not persist in one study after 

inclusion of benzene in the model (Thompson et al. 2001). 

The relationship observed in recent time-series studies between changes in ambient particulate matter and 

acute asthma ED visits is complicated by the diversity of exposure indicators representing airborne 

particulates. Ambient particulate matter has most often been assessed as PM10, but other metrics have been 

used, including PM10-2.5 (coarse fraction), PM2.5, PM1, total suspended particulates, black smoke and ultra 

fines assessed on a particle count, surface area or light scatter basis. Collectively, ambient particulate 

matter has been significantly associated with acute asthma ED visits in a majority of recent studies (Galan 

et al. 2003; Atkinson et al. 1999; Stieb et al. 2000; Chew et al. 1999; Thompson et al. 2001; Jaffe et al. 

2003, Jalaludin et al. 2004; Sinclair and Tolsma 2004; Norris et al. 1999). A few studies that included 

particulate matter in models of acute asthma ED visits have not observed a significant association 

(Slaughter et al. 2004; Michaud et al. 2004; Lierl and Hornung 2003; Tolbert et al. 2000), although Lierl 

and Hornung (2003) did report that the association of acute asthma ED visits with pollen levels was 

stronger on high PM10 days than on low PM10 days, suggesting an enhancement of the pollen effect by 

PM10. The association between PM10 and acute asthma ED visits has generally persisted in the few studies 

that included other criteria pollutants in the model (Galan et al. 2003; Atkinson et al. 1999), although the 

association was not robust to inclusion of benzene in the model in one study (Thompson et al. 2001). 

The studies mentioned above give no clear indication that the association of ambient particulate matter and 

acute asthma exacerbations can be attributed to a specific size fraction. However, there has been relatively 

little previous investigation of the association between acute asthma ED visits and fine-fraction (PM2.5) 

particulate matter components, as was done in this study. In the current study, the association observed with 

acute asthma exacerbations in the Bronx was stronger for the PM2.5 fraction than for PM10. Tolbert et al. 

(2000) reported preliminary findings using one year of data from the Aerosol Research and Inhalation 

Epidemiology Study (ARIES) “supersite” air monitoring station in Atlanta. They found no significant 

associations between asthma ED visits and 10 particulate matter parameters—PM10, PM2.5, PM10-2.5, 

ultrafine particle number, ultrafine particle surface area and five PM2.5 constituents (metals, acidity, 

sulfates, organic carbon and elemental carbon). Sinclair and Tolsma (2004), investigated acute asthma 

31
visits to ambulatory care clinics in a private health-care network in relation to two years of data from the 

Atlanta ARIES supersite. Among all the same particulate matter variables, they reported significant 

associations between ultrafine particle surface area and adult asthma, and between child asthma and four 

particulate matter variables (PM10-2.5, PM10, PM2.5 elemental carbon, PM2.5 organic carbon). Fine fraction 

acidity and sulfate were significantly associated with acute asthma ED visits in single-pollutant models in 

another study (Stieb et al. 2000). 

In the current study, changes in bioaerosol levels and acute asthma ED visits were generally unrelated or 

only weakly associated. This contrasts somewhat with several recent studies showing significant 

associations between temporal patterns of ambient pollens or fungal spores and asthma ED visits (Lierl and 

Hornung 2003; Stieb et al. 2000; Lewis et al. 2000; Tobias et al. 2003, 2004; Dales et al. 2000, 2003). 

Average daily pollen and fungal-spore counts in these studies were generally several-fold higher than levels 

observed in the current study. Population prevalence of allergen sensitization varies geographically, 

depending on the geographic distribution of plants, animals and fungi that produce allergens (e.g., Arruda 

et al. 1991; Call et al. 1992; Gelber et al. 1993; Platts-Mills et al. 1995). For pollen and mold exposure to 

potentially have an effect on acute asthma exacerbations, individuals must be both sensitized and exposed 

to the relevant allergens. Some evidence indicates that prevalence of sensitization to pollen and mold 

allergens is relatively low in inner-city children with asthma (Kattan et al. 1997; Crain et al. 2002), which 

could limit study power to detect bioaerosol effects in urban environments. Our study design may not have 

been optimal to investigate direct associations between acute asthma exacerbations and ambient bioaerosol 

levels or potential effects modification between bioaerosols and ambient chemical pollutants, due to the 

relatively low bioaerosol levels we observed and their highly skewed temporal distribution. 

Associations were observed in this study between acute asthma ED visits and changes in daily air pollutant 

levels. For criteria air pollutants, these associations were found for levels that were generally near or below 

the National Ambient Air Quality Standards (NAAQS; see Part A). The annual average NO2 and SO2 

standards were not exceeded during the study at either site, and neither was the 24-hour SO2 standard. The 

maximum 24-hour FRM PM2.5 observation during the study did not exceed the 24-hour standard, but the 

annual averages at each site were approximately equal to or slightly above the standard (15–16 μg/m3 

versus the NAAQS of 15). More than 95% of all eight-hour O3 observations fell below the 80 ppb standard, 

and removing the eight-hour moving average O3 observations that exceeded 80 ppb did not alter the 

association between incremental O3 exposure and asthma ED visits. This is consistent with the results of 

other recent studies that observed significant associations between acute asthma ED visits and increments 

in ambient air pollutant concentrations at absolute concentrations at or below the NAAQS (e.g., Fauroux et 

al. 2000; Michaud et al. 2004; Galan et al. 2003; Atkinson et al. 1999; Jaffe et al. 2003). 

32
Five-day mean contaminant concentrations were used for assessing associations of pollutants and asthma 

emergency department visits. This was done to provide a consistent model for all pollutants at both study 

areas. Using five days has biological plausibility, based on both disease mechanisms and reports on when 

symptoms start versus visits to the ED. For instance, exposure to pollutants capable of inducing airway 

inflammation, such as ozone and fine particulates (Peden 2002), may promote underlying airway 

inflammation or hyper-responsiveness to an extent requiring medical treatment. Rodrigo (2004) reported 

that when airway inflammation was predominant in the progression of an asthma attack in adults, 

deterioration of lung function and clinical status usually occurred over a period of days or weeks prior to 

presenting to the emergency department. This type of asthma progression was noted in 80% to 90% of 

adults presenting to the emergency department with acute asthma. Canny et al. (1989) investigated the time 

between when asthma symptoms were first noted in pediatric patients and when they went to the ED. The 

average duration of symptoms before the ED visit was 41 hours; 84% went to the hospital within 72 hours 

and 97% within 168 hours. Sinclair and Tolsma (2004) investigated associations of various lags between 

pollutants and children’s ambulatory care visits for asthma and noted that most of the statistically 

significant associations occurred with lags of three to five days, compared with zero- to two-day lags and 

six- to eight-day lags. 

STUDY STRENGTHS AND LIMITATIONS 

The relatively high population density of the Bronx and Manhattan allowed for the central monitors to be 

used as an indicator for exposure for a relatively small area (i.e., the population residing within 

approximately 1.5 miles of the monitoring site). Furthermore, the correlation between the two monitoring 

sites was relatively high (i.e., greater than 0.6) and mean levels were very similar for most analytes, 

perhaps partially mitigating against exposure misclassification biases that might occur because of 

movement of residents throughout the greater New York City area. Nevertheless, using a central 

monitoring site to estimate exposure still adds some uncertainty to exposure estimates compared with 

personal monitoring. Personal exposure to air pollutants can be influenced not only by ambient 

concentrations but also by individual activity and other indoor and microenvironmental exposures (e.g., 

exposure to VOCs from consumer products, smoke from tobacco, candles or cooking). For pollutants such 

as particulate matter, these other sources can exert significant influence on personal exposure. However, 

ambient PM2.5 measured at central monitoring sites has been found to be correlated with average personal 

exposures to PM2.5 (e.g., Liu et al. 2003; Sarnat et al. 2001, 2005). 

Combining data across a five-day lag window when estimating associations between changes in pollutant 

concentrations and acute asthma ED visits represents a trade-off between the sensitivity of the analysis to 

detect effects in short time intervals versus obtaining a consistent understanding of the relationship between 

air pollutant changes and ED visits, given lag structures that differed among air contaminants as well as 

between the two communities. The five-day exposure window could capture ED visits for asthma attacks 

33
with either a slow or a sudden progression. However, using five days could also have potentially weakened 

or masked associations if the pollutant has a rapid onset, short lasting effect. Since a rolling five-day lag 

window was employed in the analysis, effects of multi-day pollution events would be captured by 

accumulating cases during the duration of the pollution event and during the following four days. 

The lack of consistency in statistically significant effects in the two study areas adds some uncertainty to 

the generality of the findings. However, as discussed above, differences in the statistical power in the two 

study areas may have contributed to this. Similarly, the tendency for the control conditions to have odds 

ratios greater than 1 adds some uncertainty to the robustness of the findings. Several of the findings, 

particularly O3 and SO2, are strengthened by the robustness of the findings when adding in the other 

pollutants. 

The observed associations between specific pollutants and asthma ED visits do not necessarily indicate 

cause and effect. One possible reason is that the association may be due to an unmeasured pollutant that covaries with the measured pollutant. For instance, Thompson et al. (2001) observed associations between 

PM10, SO2, NO, NO2, NOx, CO and benzene and acute asthma exacerbations in children. When adjusting 

for benzene, none of the other pollutants were associated with a significant effect. In addition, many other 

variables that can trigger an asthma attack were not controlled for in the study. It is also possible that 

unmeasured confounders related to indoor environmental exposures or socio-economic status variables 

might be contributing to variability in acute asthma exacerbations. However, within each study area, the 

time-series design at least partially controls for unmeasured confounders because each case acts essentially 

as its own control. The analysis detects marginal changes in the outcome variable relative to the baseline 

rate that are associated with the measured exposure variables, and the baseline rate would include effects 

due to unmeasured variables, such as local or indoor exposures. 

Our results suggest that increases in several ambient pollutants may be associated with increased acute 

asthma exacerbations in a community. Because of the community-based design used in the study, 

uncertainty exists regarding the precise pattern of exposure to the study analytes experienced by each 

asthma case and the extent to which individual exposure closely matches ambient pollutant patterns. Recent 

data suggest that there is variation in the degree to which personal monitoring reflects concomitant ambient 

pollutant patterns. In studies from Baltimore and Boston comparing urban ambient air monitoring data with 

personal monitoring data, ambient PM2.5 data correlated well with personal PM2.5 data, but ambient gaseous 

criteria pollutants did not correlate well with their corresponding personal data (Sarnat et al. 2001, 2005). 

Interestingly, ambient gaseous pollutants (particularly SO2, O3 and CO) were correlated with personal 

PM2.5 data, particularly the personal monitoring data for PM2.5 components associated with ambient sources 

(e.g., sulfate). The authors suggest that some respiratory effects associated with ambient variation in 

gaseous criteria pollutants in time-series studies might actually be detecting effects of personal PM2.5 

34
exposure, with the ambient gaseous concentrations acting as PM2.5 surrogates. Obtaining acute asthma cases 

through ED utilization made it impractical to consider personal exposure monitoring in this study, so we 

cannot investigate this potential surrogate effect further. Study designs that retain the power of communitybased time-series analyses but incorporate personal air monitoring to complement ambient monitoring data 

would be desirable. 

Some missing data were estimated by extrapolation from the same analyte at the other monitoring site or 

another analyte that was correlated with the analyte for the missing data at the same monitoring site. This 

adds some additional uncertainty to the measurements, but the effect on the mean exposure estimates 

appeared to be small and therefore unlikely to change the conclusions. 

35
36
Section 5

CONCLUSIONS AND RECOMMENDATIONS

The results suggest that the criteria pollutants PM2.5, SO2, O3 and NO2 had a statistically detectable impact 

on acute asthma ED visits in a community with a relatively high baseline rate of acute asthma 

exacerbations. In two-pollutant and three-pollutant regression models, O3 and SO2, and to a lesser extent 

maximum one-hour PM2.5, were the most robust pollutants. In other words, these pollutants exhibited less 

change in their effect estimates as additional pollutants were added to the models. Robust effects of O3 have 

been seen in previous ED asthma studies (Stieb et al. 1996; Martins et al. 2002) and in hospital admissions 

studies of asthma and other respiratory diseases (Burnett et al. 1997). It is of particular interest that we 

observed more robust health impacts of daily maximum PM2.5 concentration than of the 24-hour mean, 

suggesting that peak exposures may have larger health impacts. These associations with health effects in 

the Bronx occurred at ambient air levels that are below the current short-term National Ambient Air 

Quality Standards. 

The following recommendations are suggested based on the study results: 

1. EPA should consider the findings in this study and others identifying respiratory health effects 

associated with SO2 concentrations below current standards during their review of the SO2 NAAQS. 

2. Future time-series studies examining associations between ambient air pollutants and health 

outcomes would benefit from direct evaluation of the relationship between personal exposure and regional 

monitoring data. 

3. More research should be conducted to try to determine if peak, short-term (e.g. hourly) 

elevated concentrations of PM2.5 are more strongly associated than daily average concentrations with 

asthma and other health endpoints. If the science is sufficiently strong, consideration should be given to the 

effects of short-term PM2.5 excursions in future reviews of the particulate matter NAAQS. 

4. The high correlations between pollutants (including components of PM2.5) make it difficult in 

these epidemiologic studies to confidently identify critical compounds. Alternative strategies to address this 

question should be considered in the future. 

5. Further evaluation of the statistical methods employed in time-series epidemiological studies 

is warranted, based on the suggestion of possible model bias indicated by our analysis of control outcomes. 

6. To the extent that targeted community based asthma interventions are planned with respect to 

air pollution messages, higher priority should be given to communities with larger asthma burdens. 

37
38
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43
44
AUTHORS AND ACKNOWLEDGEMENTS

Daniel Luttinger (NYSDOH) was the study’s principal investigator, and Lloyd Wilson, Edward Fitzgerald, 

Laiquat Husain, Kenneth Aldous (NYSDOH), Phillip Galvin (NYSDEC), Larry Syzdek (private 

aerobiology consultant) and Patrick Kinney (Columbia University) were co-investigators. Additional studydesign and statistical-analysis consultation was provided by John Hawley and Syni-An Hwang (NYSDOH), 

Frank Buckman (NYSDEC), Tracey Holloway (University of Michigan) and Ken Demerjian (NYSDEC). 

The study report was authored by Daniel Luttinger and Gregg Recer (NYSDOH) and Patrick Kinney 

(Columbia University). Field sampling staff included Dan Lince, Pat Palmer, Mike Rivara, Lloyd Wilson, 

Stephanie Selmer, James Kamara, Ellen Fitzsimmons, Dan Sharron and Stan House (NYSDOH) and Ed 

Marion, Mike Christophersen, Bob Murway, Bob Elburn and Frank Buckman (NYSDEC). Laboratory staff 

included Abdul Bari, Vincent Ferraro and Amarjit Narang (NYSDOH). Data analysis support staff included 

Jeff Hughes, Ying Wang, Valerie Haley, James Kamara, Kim Mazor, Karen Nolan, Dan Luttinger, Gregg 

Recer, Syni-An Hwang, Lloyd Wilson and Laiquat Husain (NYSDOH) and Phil Galvin and Frank 

Buckman (NYSDEC). 

The study was funded with partial support from the Agency for Toxic Substances and Disease Registry 

(ATSDR) as part of Cooperative Agreement V50/ATV200002-11 and from the New York State Energy 

and Research Development Authority (NYSERDA). 

45
46
TABLES

47
Table 1. Hospital Emergency Department Visits by Residents of Bronx and Manhattan Study Areas 

Asthma Control All-Cause 

Hospital 

Visits* Visits** Visits 

Bellevue Hospital Center† 1658 1875 65,465 

Beth Israel Medical Center 1808 1728 44,441 

Bronx Lebanon Hospital, Concourse 7111 5280 85,316 

Cabrini Medical Center 135 224 5237 

Harlem Hospital Center† 548 259 8748 

Jacobi Medical Center (formerly Bronx Municipal Hospital 

Center)† 991 251 16,399 

Lenox Hill Hospital 143 324 5202 

Lincoln Medical and Mental Health Center† 16,754 9164 220,470 

Metropolitan Hospital Center† 403 341 9703 

Montefiore–Jack D. Weiler–Albert Einstein 119 177 4225 

Montefiore Medical Center 782 775 16,617 

Mount Sinai Hospital 912 691 12,109 

New York Hospital (Cornell) 236 518 8991 

New York–Presbyterian Hospital 302 292 6397 

New York University Medical Center 195 908 14,022 

North Central Bronx Hospital† 759 213 12,474 

Our Lady of Mercy Medical Center 265 266 5759 

St. Barnabas Hospital 848 841 15,256 

Presbyterian Hospital-Allen Pavilion 25 39 1158 

St. Luke’s–Roosevelt Medical Center 114 152 3980 

St. Luke’s–Roosevelt–St. Luke’s Division 238 459 11,316 

St. Vincent’s Hospital and Medical Center 655 1111 27,970 

TOTAL IN BRONX 29,987 18,974 422,849 

TOTAL IN MANHATTAN 5014 6914 178,406 

*Asthma case defined as primary diagnosis ICD-9 codes 493 and, for children less than one year of age, 

466.1 and 786.09.

**Control defined as primary diagnosis ICD-9 codes 365, 366.0–366.3, 531.0–531.3, 532.0–532.3, 533.0–

533.3, 534.0–534.3, 535, 537, 540–543, 558, 574–575, 590, 599.

†Managed by the New York City Health and Hospitals Corporation.

48
Table 2. Mean Daily Emergency Department Visits for Asthma and Control Conditions(U.S. Census 2000) 

Outcome Subgroup 

Bronx Manhattan 

Mean 

Daily 

Visits 

Population 

Crude 

Daily Rate 

per 105 

Mean 

Daily 

Visits 

Population 

Crude Daily 

Rate per 105 

All 43 254,167 16.9 7.2 355,655 2.02 

Male 20 122,686 16.3 3.6 174,051 2.07 

Female 23 131,481 17.5 3.7 181,604 2.04 

Asthma 

Ages 0–4 9.6 22,015 43.6 0.90 10,661 8.44 

Ages 5–18 9.8 71,314* 13.7 1.3 30,361* 4.28 

Ages 19–34 7.5 60,199* 12.5 1.4 127,771* 1.10 

Ages 35–64 14 81,841 17.1 3.1 146,960 2.11 

Ages 65+ 2.2 18,798 11.7 0.54 39,902 1.35 

Control All 27 254,167 10.6 10 355,655 2.81 

*Census age ranges were 5–19 and 20–35 for these categories, resulting in a slight underestimate of the 

crude rate in the 5–18 category and a slight overestimate of the crude rate in the 19–34 category. 

49
Table 3. Mean (SD) Concentrations of Air Pollutants and Bioaerosols Measured in Bronx and Manhattan,

with Two-Community Average 

Note: The two-community average concentrations were used to calculate relative risks. The values

represent summary statistics of all daily observations from January 1999 through November 2000.

Air Contaminant Bronx Manhattan 

Two-Community 

Average (SD)* 

Max 8-hour O3 (ppm) 0.028 (0.018) 0.021 (0.016) 0.024 (0.017) 

NO2 (ppm) 0.031 (0.010) 0.037 (0.008) 0.034 (0.0091) 

SO2 (ppm) 0.010 (0.007) 0.012 (0.008) 0.011 (0.0072) 

FRM PM2.5 (μg/m3) 15.0 (8.35) 16.7 (9.08) 15.85 (8.719) 

Max PM2.5 (μg/m3) 27.6 (13.5) 27.6 (13.5) 27.62 (13.52) 

Coarse PM (μg/m3) 7.69 (4.84) 7.10 (4.08) 7.394 (4.459) 

Sulfate (μg/m3) 3.85 (3.43) 4.00 (3.42) 3.924 (3.423) 

pH 5.10 (0.54) 5.03 (0.47) 5.066 (0.5074) 

Elemental (Soot) Carbon 

(μg/m3) 

1.19 (0.64) 1.31 (0.64) 1.252 (0.645) 

Organic Carbon (μg/m3) 3.23 (0.81) 3.06 (0.83) 3.144 (0.822) 

Total Metals (ng/m3) 95.9 (121.1) 91.0 (75.1) 93.45 (98.10) 

Total Aldehydes (μg/m3) 15.92 (8.82) 16.20 (10.62) 16.06 (9.717) 

Total Pollen (#/m3) 20.81 (135.0) 13.15 (84.53) 16.98 (110.26) 

Total Mold (#/m3) 518.8 (814.9) 489.9 (786.0) 504.3 (800.4) 

*The two-community averages are computed as the average of the two community-specific means (or 

standard deviations). 

50
Table 4a. Relative Risks and 95% Confidence Intervals for Asthma ED Visits as Function of 5-Day Mean 

Air Pollution and Bioaerosols from Single-Pollutant Models 

Note: Exposure increments used to compute RRs were the two-community average concentrations (Table 

3). Bold text indicates statistical significance at the 0.05 level. 

Air Contaminant Bronx Manhattan 

Max 8-hour O3 1.06 (1.01, 1.10) 1.06 (0.94, 1.19) 

Max 8-hour O3 (warm season) 1.08 (1.03, 1.12) 1.04 (0.91, 1.19) 

NO2 1.10 (1.01, 1.18) 0.95 (0.72, 1.25) 

SO2 1.11 (1.06, 1.17) 0.99 (0.88, 1.12) 

FRM PM2.5 1.05 (1.01, 1.10) 1.04 (0.94, 1.15) 

Max PM2.5 1.09 (1.03, 1.15) 1.04 (0.91, 1.18) 

Coarse PM 1.02 (1.00, 1.04) 1.02 (0.98, 1.07) 

Sulfate 1.03 (1.00, 1.06) 1.05 (0.98, 1.13) 

pH 0.99 (0.98, 1.00) 0.99 (0.95, 1.02) 

Elemental (Soot) Carbon 1.04 (0.99, 1.09) 1.06 (0.94, 1.19) 

Organic Carbon 1.05 (0.93, 1.17) 1.20 (0.96, 1.49) 

Total Metals 1.02 (0.99, 1.05) 1.02 (0.91, 1.15) 

Total Aldehydes 1.02 (1.00, 1.04) 1.03 (0.96, 1.10) 

Total Pollen 1.00 (1.00, 1.00)* 1.01 (1.00, 1.02) 

Total Mold 1.01 (0.99, 1.03) 1.01 (0.97, 1.06) 

*When RR and CI bounds appear equal, it is due to rounding. 

51
Table 4b. Comparison of Relative Risks (95% Confidence Intervals) Computed Using Alternative 

Concentration Increments 

Note: The following air pollutants were significant in the Bronx regression models (Table 4a). In the first 

column of results, we use the mean pollutant concentration as the RR increment (as in Table 4a). In the 

second column of results, we use the standard deviation pollution concentration as the RR increment. Note 

the change in relative size of the five RRs. Bold text indicates statistical significance at the 0.05 level. 

Air Contaminant Mean Increments SD Increments 

Max 8-hour O3 1.06 (1.01, 1.10) 1.04 (1.01, 1.07) 

FRM PM2.5 1.05 (1.01, 1.10) 1.03 (1.00, 1.05)

* 

Max PM2.5 1.09 (1.03, 1.15) 1.04 (1.02, 1.07) 

NO2 1.10 (1.01, 1.18) 1.02 (1.00, 1.05)* 

SO2 1.11 (1.06, 1.17) 1.07 (1.04, 1.11) 

*Choice of increment does not alter statistical significance at the α = 0.05 level; the appearance of 95% CI 

including 1 is due to rounding differences between the two increments. 

52
Table 5. Relative Risks from Regressions Based on Daily Maximum Hourly (SO2 and NO2) or Daily 

Maximum 3-Hour (Elemental and Organic Carbon) Exposures 

Note: Bold text indicates statistical significance at the 0.05 level. 

Contaminant 

Increment used to 

calculate RR 

Bronx Manhattan 

NO2 (ppm) 0.0492 1.12 (1.04, 1.20) 0.97 (0.75, 1.25) 

SO2 (ppm) 0.0227 1.07 (1.03, 1.12) 0.96 (0.86, 1.07) 

Elemental (Soot) 

Carbon (μg/m3) 

1.9787 1.05 (1.01, 1.09) 1.05 (0.95, 1.16) 

Organic Carbon 

(μg/m3) 

3.7014 1.05 (0.95, 1.16) 1.10 (0.92, 1.32) 

53
Table 6. Relative Variance in Asthma ED Visits Explained by Variables Included in Model for Daily 

Maximum 8-Hour O3 

Note: DEV/DF represents an estimate of variance explained. 

O3 

Date 

(natural spline 

18 degrees of 

freedom) 

Temperature 

(natural spline 

3 degrees of 

freedom) 

Day of Week 

Model DEV 6.3 810.1 3.1 310.5 

Model DF 1 18 3 6 

DEV/DF 6.3 45.0 1.0 51.8 

54
Table 7. Relative Risks (95% Confidence Intervals) for Mean Change in Contaminant Concentrations for 

Models Excluding Temperature as Covariate 

Note: Bold text indicates statistical significance at the 0.05 level. 

Contaminant Bronx Manhattan 

SO2 1.11 (1.06, 1.17) 0.99 (0.88, 1.11) 

Max PM2.5 1.08 (1.03, 1.13) 1.00 (0.90, 1.13) 

Max 8-hour O3 1.06 (1.02, 1.10) 1.04 (0.93, 1.16) 

55
Table 8. Relative Risks (95% Confidence Intervals) from Poisson Regressions of Asthma ED Visits on Pollen and 

Mold Categories 

 Variable 

Increment used to 

calculate RRs 

(#/m3)* 

Season 

RR (95% CI)

Bronx Manhattan 

Pollen 

Annual Total Pollen 

Seasonal Total Pollen 

Seasonal Grass Pollen 

Seasonal Tree Pollen 

Seasonal Ragweed 

Pollen 

16.98 

16.98 

0.43 

15.6 

0.44 

Jan–Dec 

Apr 1–Nov 1 

May 1–Oct 1 

Apr 1–Jul 1 

Aug 1–Nov 1 

1.00 (1.00, 1.00)** 

1.00 (1.00, 1.00) 

1.00 (0.99, 1.00) 

1.00 (1.00, 1.00) 

0.99 (0.99, 1.00) 

1.01 (1.00, 1.02) 

1.01 (1.00, 1.02) 

1.00 (0.98, 1.02) 

1.01 (1.00, 1.02) 

1.00 (0.97, 1.01) 

Mold 

Annual Total Mold 

Seasonal Total Mold 

Seasonal Basidospores 

Seasonal Ascospores 

Seasonal Mitospores 

Seasonal Small Spores 

Seasonal Large Spores 

Seasonal Alternaria 

Seasonal 

Aspergillus/Penicillium 

Seasonal Cladosporium 

504.33 

504.33 

193.68 

40.92 

265.61 

484.9 

13.3 

12.11 

4.17 

246.15 

Jan–Dec 

Apr 1–Dec 1 

Apr 1–Dec 1 

Apr 1–Dec 1 

Apr 1–Dec 1 

Apr 1–Dec 1 

Apr 1–Dec 1 

Apr 1–Dec 1 

Apr 1–Dec 1 

Apr 1–Dec 1 

1.01 (0.99, 1.03) 

1.01 (0.99, 1.03) 

1.02 (1.00, 1.03) 

0.99 (0.98, 1.01) 

1.01 (1.00, 1.03) 

1.02 (1.00, 1.04) 

1.01 (1.00, 1.02) 

1.01 (1.00, 1.02) 

1.00 (0.99, 1.03) 

1.01 (1.00, 1.03) 

1.01 (0.97, 1.06) 

1.01 (0.96, 1.06) 

1.03 (0.98, 1.07) 

1.01 (0.97, 1.05) 

1.00 (0.97, 1.03) 

1.01 (0.97, 1.06) 

1.00 (0.98, 1.02) 

1.00 (0.98, 1.02) 

1.00 (0.98, 1.01) 

1.00 (0.97, 1.03) 

*Annual mean for each pollen or mold category was used for the increment to calculate the RR 

**When RR and CI bounds appear equal, it is due to rounding 

56
Table 9. Relative Risks (95% Confidence Intervals) for Asthma ED Visits as Function of 5-Day Mean Air Pollution 

from Two-Pollutant Models 

Note: Pollutants included here were those that were significant predictors of ED visits in single-pollutant models. 

Exposure increments used to compute RRs were the two-community average concentrations (Table 3). Bold text 

indicates statistical significance at the 0.05 level. 

Contaminant Controlled with RR, Bronx RR, Manhattan 

Max 8-hour O3 FRM PM2.5 1.06 (1.01, 1.10) 1.05 (0.93, 1.19) 

Max PM2.5 1.04 (1.00, 1.09) 1.05 (0.93, 1.19) 

NO2 1.05 (1.01, 1.10) 1.07 (0.94, 1.21) 

SO2 1.05 (1.01, 1.10) 1.06 (0.93, 1.20) 

FRM PM2.5 Max 8-hour O3 1.05 (1.01, 1.10) 1.03 (0.94, 1.14) 

Max PM2.5 0.99 (0.92, 1.06) 1.04 (0.89, 1.23) 

NO2 1.03 (0.98, 1.09) 1.08 (0.95, 1.23) 

SO2 1.01 (0.96, 1.06) 1.05 (0.94, 1.17) 

Max PM2.5 Max 8-hour O3 1.07 (1.02, 1.13) 1.02 (0.89, 1.17) 

FRM PM2.5 1.09 (1.00, 1.20) 0.99 (0.79, 1.23) 

NO2 1.07 (1.01, 1.14) 1.10 (0.92, 1.31) 

SO2 1.05 (0.99, 1.11) 1.05 (0.90, 1.21) 

NO2 Max 8-hour O3 1.08 (1.00, 1.17) 0.91 (0.68, 1.21) 

FRM PM2.5 1.06 (0.97, 1.16) 0.83 (0.59, 1.17) 

Max PM2.5 1.04 (0.96, 1.14) 0.84 (0.59, 1.20) 

SO2 1.02 (0.94, 1.12) 0.95 (0.69, 1.30) 

SO2 Max 8-hour O3 1.11(1.05, 1.17) 0.99 (0.88, 1.12) 

FRM PM2.5 1.11 (1.04, 1.18) 0.97 (0.85, 1.11) 

Max PM2.5 1.09 (1.03, 1.16) 0.98 (0.85, 1.12) 

NO2 1.11 (1.04, 1.17) 1.01 (0.87, 1.16) 

57
Table 10. Correlations among Key Air Pollutants in Bronx Study Community 

Max 8-hour O3 NO2 SO2 FRM PM2.5 Max PM2.5 

Max 8-hour O3 1.00 . . . . 

NO2 0.03 1.00 . . . 

SO2 -0.35 0.47 1.00 . . 

FRM PM2.5 0.19 0.61 0.45 1.00 . 

Max PM2.5 0.35 0.55 0.28 0.78 1.00 

58
Table 11. Relative Risks (95% Confidence Intervals) for Control ED Visits in Relation to Five Pollutants Showing 

Significant Associations with Asthma ED Visits in Bronx 

Note: Exposure increments used to compute RRs were the two-community average concentrations (Table 3). Bold 

text indicates statistical significance at the 0.05 level. 

Bronx Manhattan 

Air Contaminant Asthma RRs Control RRs Asthma RRs Control RRs 

Max 8-hour O3 1.06 (1.01, 1.10) 1.00 (0.95, 1.05) 1.06 (0.94, 1.19) 1.01 (0.92, 1.11) 

FRM PM2.5 1.05 (1.01, 1.10) 1.08 (1.02, 1.14) 1.04 (0.94, 1.15) 1.00 (0.92, 1.08) 

Max PM2.5 1.09 (1.03, 1.15) 1.04 (0.97, 1.11) 1.04 (0.91, 1.18) 1.07 (0.96, 1.19) 

NO2 1.10 (1.01, 1.18) 1.07 (0.98, 1.18) 0.95 (0.72, 1.25) 1.03 (0.84, 1.28) 

SO2 1.11 (1.06, 1.17) 1.02 (0.96, 1.10) 0.99 (0.88, 1.12) 1.01 (0.91, 1.12) 

59
Table 12. Relative Risks (95% Confidence Intervals) for Asthma ED Visits from Single-Pollutant Models, Stratified 

by Sex 

Note: Exposure increments used to compute RRs were the two-community average concentrations (Table 3). Bold 

text indicates statistical significance at the 0.05 level. 

(a) Bronx 

Contaminant Male Female All 

Max 8-hour O3 1.06 (0.99, 1.13) 1.06 (1.00, 1.12) 1.06 (1.01, 1.10) 

FRM PM2.5 1.01 (0.95, 1.08) 1.08 (1.02, 1.15) 1.05 (1.01, 1.10) 

Max PM2.5 1.06 (0.98, 1.15) 1.13 (1.05, 1.21) 1.09 (1.03, 1.15) 

NO2 1.07 (0.95, 1.19) 1.13 (1.01, 1.26) 1.10 (1.01, 1.18) 

SO2 1.08 (1.00, 1.17) 1.14 (1.06, 1.23) 1.11 (1.06, 1.17) 

(b) Manhattan 

Contaminant Male Female All 

Max 8-hour O3 1.13 (0.95, 1.35) 0.99 (0.83, 1.17) 1.06 (0.93, 1.20) 

FRM PM2.5 0.95 (0.82, 1.10) 1.12 (0.98, 1.29) 1.04 (0.94, 1.15) 

Max PM2.5 1.01 (0.83, 1.27) 1.06 (0.88, 1.28) 1.04 (0.90, 1.18) 

NO2 0.75 (0.51, 1.11) 1.16 (0.80, 1.69) 0.95 (0.72, 1.25) 

SO2 0.90 (0.75, 1.07) 1.08 (0.91, 1.29) 0.99 (0.88, 1.12) 

60
Table 13. Relative Risks (95% Confidence Intervals) for Asthma ED Visits from Single-Pollutant Models, Stratified 

by Age 

Note: Exposure increments used to compute RRs were the two-community average concentrations (Table 3). Bold 

text indicates statistical significance at the 0.05 level. 

(a) Bronx 

Age Category (years) 

Contaminant 0–4 5–18 19–34 35–64 65–up 

Max 8-hour O3 1.08 (0.98, 1.19) 0.94 (0.85, 1.03) 1.11 (1.01, 1.23) 1.05 (0.98, 1.13) 1.29 (1.08, 1.53) 

FRM PM2.5 1.00 (0.92, 1.10) 0.99 (0.91, 1.08) 1.05 (0.95, 1.16) 1.14 (1.06, 1.23) 1.01 (0.84, 1.22) 

Max PM2.5 1.04 (0.93, 1.17) 1.03 (0.92, 1.15) 1.12 (0.99, 1.27) 1.14 (1.04, 1.25) 1.07 (0.86, 1.36) 

NO2 1.13 (0.96, 1.33) 1.14 (0.97, 1.34) 0.99 (0.82, 1.19) 1.13 (0.99, 1.30) 0.85 (0.61, 1.20) 

SO2 1.13 (1.01, 1.26) 1.03 (0.92, 1.16) 1.06 (0.93, 1.21) 1.18 (1.07, 1.30) 1.12 (0.88, 1.42) 

(b) Manhattan 

Age Category (years) 

Contaminant 0–4 5–18 19–34 35–64 65–up 

Max 8-hour O3 1.24 (0.84, 1.82) 1.11 (0.83, 1.49) 0.90 (0.68, 1.18) 1.09 (0.90, 1.30) 0.96 (0.61, 1.53) 

FRM PM2.5 0.96 (0.73, 1.27) 0.88 (0.70, 1.11) 1.25 (1.01, 1.55) 1.06 (0.91, 1.23) 0.91 (0.63, 1.33) 

Max PM2.5 0.99 (0.67, 1.44) 0.82 (0.59, 1.12) 1.31 (0.98, 1.78) 1.05 (0.86, 1.29) 0.94 (0.57, 1.55) 

NO2 0.99 (0.44, 2.19) 0.54 (0.28, 1.02) 1.40 (0.76, 2.58) 0.96 (0.64, 1.45) 0.99 (0.35, 2.77) 

SO2 0.82 (0.59, 1.15) 1.03 (0.77, 1.37) 1.01 (0.76, 1.35) 1.04 (0.86, 1.25) 0.88 (0.57, 1.37) 

61
62
FIGURES

63
Figure 1. Air Monitoring Locations in Manhattan and Bronx (squares). Shaded zip code areas 

indicate communities where emergency department cases resided. 

miles 

0 1.5 3 

10474 

10459 

10018

10001 

10017 

10036 

10020 

10016 

10455 

10454 

10451 

10456 

10012 

10014 

10011 

10003 

10009 

10010 

IS 155 MS 52 

South Bronx Study Area 

Mabel Dean Bacon HS 

Manhattan Study Area 

Air Monitoring Location 

Hudson

River

64
Figure 2. Map of Study Areas and Hospitals Contributing Emergency Department Data 

0 2.5 5 

miles 

M7 

M9 M8 

M6 

M5 

M4 

M3 

M2 

B4 

B5 

B6 

B7 

B2 

B3 

M1 

B1 

M13 

M12 M14 

M11 

M10 

B8 

Map Legend 

Key to Hospital Codes 

Bronx 

B1: Our Lady of Mercy Medical Center 

B2: North Central Bronx Hospital 

B3: Montefiore Medical Center 

B4: Jacobi Medical Center 

B5: Montefiore Medical Center -Weiler Hospital 

B6: St. Barnabas Hospital 

B7: Bronx-Lebanon Hospital Center -Concourse Div 

B8: Lincoln Medical Center 

Manhattan 

M1: Presbyterian Hospital - Allen Pavilion 

M2: New York - Presbyterian Hospital

M3: Harlem Hospital 

M4: St. Luke's Roosevelt Hospital 

M5: Mount Sinai Hospital 

M6: Metropolitan Hospital Center 

M7: Lenox Hill Hospital 

M8: New York Hospital 

M9: St. Luke's Roosevelt Hospital - Roosevelt Div 

M10: New York University Medical Center 

M11: Bellevue Hospital Center 

M12: Beth Israel Medical Center 

M13: Cabrini Medical Center 

M14: St. Vincent's Hospital 

Manhattan Study Area 

Bronx Study Area 

Study Hospitals 

65
100 

Figures 3. Seasonal Patterns of Hospital ED Admissions for Asthma Fitted with 18 DF Natural Spline, for 

(a) Bronx and (b) Manhattan Daily asthma E

D visits -- Manhattan 

Daily asthma ED visits -- Bronx 

(a) 

80

60

40

20

J F M A M J J A S O N D J F M A M J J A S O N 

0 200 400 600

Day of Study 

20

(b) 

15

10

J F M A M J J A S O N D J F M A M J J A S O N 

0 200 400 600

Day of Study 

66

5 
Figure 4. Day-of-Week Patterns Plotted for Hospital ED Admissions for Asthma for (a) Bronx and (b) 

Manhattan Note: Central line in box = median. Upper and lower lines of box = 75th and 25th percentiles, 

respectively. Ends of whiskers represent ±1.5 × interquartile range. Lines outside of whiskers represent 

outlying observations. 

20 

40 

60 

80 

100 

Daily asthma ED visits -- Bronx 

(a) 

Sun Mon Tue Wed Thu Fri Sat 

Day of Week 

5 

10 

15 

20 

Daily asth

ma ED visits -- Manhattan 

(b) 

Sun Mon Tue Wed Thu Fri Sat 

Day of Week 

67
Figure 5. Asthma ED Visits Plotted against Temperature for (a) Bronx and (b) Manhattan 

100

(a) 

80

60

40

20

20 40 60 80

Average daily temperature 

20

(b) 

15

10

5

20 40 60 80

Average daily temperature 

Daily asthma E

D visits -- Manhattan 

Daily asthma ED visits -- Bronx 

68
Figure 6. Age Distributions of Study Communities (U.S. Census 2000) 

0 

20,000 

40,000 

60,000 

80,000 

100,000 

120,000 

140,000 

160,000 

Ages 0-4 Ages 5-19 Ages 20-34 Ages 35-64 Ages 65+ 

Popul

ation

Bronx 

Manhattan 

69
Figure 7. Relative Risk for Asthma ED Visits in Bronx and Manhattan for 14 key Contaminants for Primary 

Analysis with Base-Case Model. Note: Error bars represent 95% confidence intervals on the risk. RRs 

calculated for mean increase in contaminant concentration (from Table 3, last column). The RRs and 

confidence intervals presented here are the same as those presented numerically in Table 4a. 

Bronx

0. 6 

0. 7 

0. 8 

0. 9 

1 

1. 1 

1. 2 

1. 3 

1. 4 

Max 8 hr O3 

NO2 

SO2 

FRM 2.5 

Max PM 2.5 

Coarse PM

Sulfate 

pH 

Elemental Carbon 

Organic Carbon 

Tot. Metals 

Tot. Aldehydes 

Tot. Pollen 

Tot. Mold 

Relat i ve Risk 

(a) 

Manhattan

0. 6 

0. 7 

0. 8 

0. 9 

1 

1. 1 

1. 2 

1. 3 

1. 4 

Max 8 hr O3 

NO2 

SO2 

FRM 2.5 

Max PM 2.5 

Coarse PM

Sulfate 

pH 

Elemental Carbon 

Organic Carbon 

Tot. Metals 

Tot. Aldehydes 

Tot. Pollen 

Tot. Mold 

Relat i ve Risk 

(b) 

70
Relative Risk 

Relative Risk Relative Risk 

0.90 0.95 1.00 1.05 1.10 0.90 0.95 1.00 1.05 1.10 0.90 0.95 1.00 1.05 1.10 Figure 8. Lag Dependency of Relative Risk for Asthma in Bronx and Manhattan for Example Pollutants 

(PM2.5, SO2 and O3). Note: Error bars represent 95% confidence intervals on the risk. 

Bronx FRM PM2.5 Manhattan FRM PM2.5 Relative Risk Relative Risk Relative Risk 0.90 0.95 1.00 1.05 1.10 0.90 0.95 1.00 1.05 1.10 0.90 0.95 1.00 1.05 1.10

0 1 2 3 4 0 1 2 3 4 

lags lags 

Bronx O3 Manhattan O3 

0 1 2 3 4 0 1 2 3 4 

lags lags 

Bronx SO2 Manhattan SO2 

0 1 2 3 4 0 1 2 3 4 

lags lags 

71