4.1. Spatial Analysis Using GIS
GIS is an appropriate tool to deduce spatial relationships and to facilitate the proper understanding and resolving of related complex issues. GIS can be used to characterize the sources of pollution and analyze its impact on human health as revealed by numbers of asthma reported to hospital or caused by elevated air pollution. By analyzing the spatial pattern of asthma hospitalization and air pollutants, major threat areas can be visualized in the form of maps with the help of GIS. The use of GIS techniques for statistical analysis and modelling (e.g., pollutants, and diseases) is rarely done in previous studies. However, the past studies have not been fully explored and applied in analyzing the association between exposure of air pollution and human health. During last decade, GIS-based pollution mapping using interpolation techniques such as inverse distance weighting, Kriging and land use regression modeling [
33] was explored by many researchers for epidemiological studies. The outbreak of asthma has drawn much attention in the past two decades since data all around the world have shown a high rate of asthma morbidity and mortality despite the availability of effective symptomatic treatment.
Measurements of air pollutants were based on data routinely collected at 72 (twenty two for SO
2, twenty five for PM
2.5 and ozone) USEPA administered monitoring stations distributed in different counties as represented in
Figure 4. All point data were entered into a Geographic Information System using Arcview software from Environmental Systems Research Inc. (ESRI). The first stage involved determining the location (latitude and longitude) of air pollution monitoring stations. The spatial location (latitude and longitude) of air pollution monitoring stations were also obtained from the same source of air pollution monitoring website. The concentrations of SO
2, O
3, and PM
2.5 are reported as daily maximum 1 hour average concentrations, daily maximum 8 h average concentrations and daily 24 h average concentrations respectively. Annual average concentrations were calculated using the daily average value in a particular year for each of the monitoring station. The spatial locations of each of the selected monitoring stations along with the pollutant concentrations were fed into the GIS system. With the point data from each monitoring station, the Ordinary Kriging (OK) method was used to estimate the spatial distribution of pollutant levels in each county from 2005 to 2007 for each of the three pollutants: SO
2, O
3, and PM
2.5. Analyses were carried out by Ordinary Kriging method using the Geostatistical Analyst Extension of ArcGIS. In Kriging, a smooth surface is estimated from irregularly spaced data points based on the assumptions that the spatial variation in the feature (O
3, PM
2.5, and SO
2) is homogeneous over the domain depends only on the distance between sites.
Figure 4.
Air Pollution Monitoring Station in New York State.
Figure 4.
Air Pollution Monitoring Station in New York State.
In general, the Kriging method [
39,
47] was used as a statistical mapping technique for using data collected at each point location, to predict concentration in each grid cell over a spatial domain. This paper used stable kind of semivariogram model for prediction of pollution concentration. Mean standardized error (MSE) and root mean square standardized (RMSSE) were used to select accuracy of the model fit which estimate the distribution of air pollutants. Partial sill, range and nugget values of the selected error were determined to represent the semivariogram model characteristics. The parameters of semivariogram model along with the errors outcome of each individual model are listed in
Table 2. The cross-validation of the four air pollutants was done manually by ArcGIS Geostatistical extension. The criteria for a good fitting Kriging model used in this study were an average MSE near 0 and RMSS near 1. According to the cross validation results, the variance of PM
2.5, SO
2 and O
3 was little bit overestimated as the RMSS value was less than 1 [
48]. Since, we are more emphasized on the relative concentration level and its association with asthma rate; it doesn’t influence much on the final outcome.
Table 2.
Parameters of semivariogram model.
Table 2.
Parameters of semivariogram model.
Parameter | Fitted Model | Nugget | Partial Sill | Lag Size (in degree) | Range(in degree) | MSE | RMSSE |
---|
Ozone | |
2005 | Stable | 0 | 29.78 | 0.32 | 3.79 | −0.03 | 0.80 |
2006 | Stable | 0 | 30.11 | 0.08 | 0.66 | −0.006 | 0.83 |
2007 | Stable | 9.66 | 16.72 | 0.08 | 0.67 | 0.001 | 0.77 |
PM2.5 | |
2005 | Stable | 0.85 | 6.52 | 0.30 | 2.46 | 0.09 | 0.76 |
2006 | Stable | 0.72 | 7.68 | 0.34 | 2.70 | 0.07 | 0.68 |
2007 | Stable | 0.01 | 8.13 | 0.30 | 2.41 | 0.08 | 0.69 |
SO2 | |
2005 | Stable | 0 | 31.79 | 0.17 | 1.38 | 0.00 | 0.83 |
2006 | Stable | 0 | 24.14 | 0.17 | 1.42 | 0.02 | 0.77 |
2007 | Stable | 21.49 | 13.22 | 0.26 | 2.14 | −0.01 | 0.85 |
In this study, ambient O
3, PM
2.5, and SO
2 levels for each county within the New York State were estimated because these values could be linked to the residences of asthma rate obtained from recorded and interpolated data. The spatial distribution maps for the O
3 obtained from Kriging method are represented in
Figure 5a–c respectively for 2005, 2006 and 2007.
Figure 5a–c clearly indicate that the concentrations were maximum in the counties situated in north-east part (Clinton, Franklin, St. Lawrence and Essex) and southern part counties (Chautauqua) of the New York State, USA. The minimum concentrations were observed in some parts of the central region (Monroe) and south-east corner counties (Bronx, and Queens) of the New York State, USA.
Figure 5.
(a–c) Spatial distribution of annual average maximum daily eight hours ozone concentrations during 2005 to 2007.
Figure 5.
(a–c) Spatial distribution of annual average maximum daily eight hours ozone concentrations during 2005 to 2007.
Similarly, the spatial distribution maps for SO
2 obtained from Kriging method are represented in
Figure 6a–c respectively for 2005, 2006 and 2007.
Spatial distribution of annual average concentration represented in
Figure 2a–c clearly indicates that the concentration values were maximal in the counties situated in western part (Erie, Niagara, and Chautauqua) and south-eastern part counties (Bronx, and Queens) of New York State, USA. The minimum concentrations were observed in north-eastern part (Clinton, Franklin, Hamilton and Essex) and extended to central region of New York State, USA.
The spatial distribution maps for PM
2.5 obtained from Kriging method are represented in
Figure 7a–c, respectively, for 2005, 2006 and 2007. Spatial distribution of annual average concentrations represented in
Figure 7a–c indicates more or less similar distribution as that of the SO
2 distribution but reverse spatial trend from the O
3 distribution. That is the concentration values were maximum in the counties situated in western part (Erie) and south-eastern part (Bronx, and Queens) of New York State, USA. The minimum concentrations were observed in north-eastern part (Clinton, Essex and Franklin) of New York State, USA. The spatial distributions of each of the individual pollutants were more or less same in each of the three years from 2005 to 2007.
For the distribution analysis of asthma rate (asthma discharge rate and emergency department visit rate), we have designed a points shape file by considering a location at the centroid of each county of New York State. The attributes entered to particular centroid point were the asthma rate calculated for same county. Subsequently, the raster images for asthma discharge rate and emergency department visit rate were created by interpolation using the Kriging method. The spatial distributions of asthma discharge rate obtained from Kriging analysis are represented in
Figure 8a–c for the year 2005, 2006 and 2007 respectively. Spatial distribution of asthma discharge rate represented in
Figure 8a–c clearly indicates that rate was maximum in south-eastern part of the state and minimum western part of the state. Similar spatial trend was observed in each of the three years from 2005 to 2007. Similarly, the spatial distributions of asthma emergency visit department rate obtained from Kriging analysis are represented in
Figure 9a–c, respectively, for the year 2005, 2006 and 2007. Spatial distribution of asthma discharge rate represented in
Figure 9a–c clearly indicates that the maximum rate was found in the south-eastern part of the State and the minimum in the central region.
Figure 6.
(a–c) Spatial distribution of annual average maximum daily one hour SO2 concentrations during 2005 to 2007.
Figure 6.
(a–c) Spatial distribution of annual average maximum daily one hour SO2 concentrations during 2005 to 2007.
Figure 7.
(a–c) Spatial distribution of annual average 24 h PM2.5 concentrations during 2005 to 2007.
Figure 7.
(a–c) Spatial distribution of annual average 24 h PM2.5 concentrations during 2005 to 2007.
Figure 8.
(a–c) Spatial distribution of asthma discharge rate (ADR) during 2005 to 2007.
Figure 8.
(a–c) Spatial distribution of asthma discharge rate (ADR) during 2005 to 2007.
Figure 9.
(a–c). Spatial distribution of asthma emergency department visit rate (AEVR) during 2005 to 2007.
Figure 9.
(a–c). Spatial distribution of asthma emergency department visit rate (AEVR) during 2005 to 2007.
4.2. Statistical Analyses
The study sought to investigate the spatio-temporal association between air pollutants and asthma rate. To understand the inter-relationships among predictor variables, both GIS based correlation analysis (map correlation analysis) and point data correlation analyses were carried out. We conducted Pearson Correlation analysis among the pollutant variables and asthma rates.
4.2.1. Map Correlation Analysis
Spatial distribution maps of ambient air pollution (SO
2, O
3, and PM
2.5) and asthma rates (AEVR and ADR) were used for correlation analysis in GIS on year wise basis. The map correlation results for three years (2005, 2006, and 2007) are reported in
Table 3. Map correlation results clearly indicate that PM
2.5 and SO
2 are positively correlated with asthma rate whereas ozone has a negative correlation with asthma rate.
Table 3.
Map Correlation Statistics.
Table 3.
Map Correlation Statistics.
2005 |
---|
| AEVR | ADR | O3 | PM2.5 | SO2 |
---|
AEVR * | 1.00 | | | | |
ADR ** | 0.84 | 1.00 | | | |
O3 | −0.54 | −0.55 | 1.00 | | |
PM2.5 | 0.29 | 0.39 | −0.56 | 1.00 | 0.82 |
SO2 | 0.46 | 0.52 | −0.59 | 0.82 | 1.00 |
2006 |
| AEVR | ADR | O3 | PM2.5 | SO2 |
AEVR | 1.00 | | | | |
ADR | 0.54 | 1.00 | | | |
O3 | −0.25 | −0.25 | 1.00 | | |
PM2.5 | 0.39 | 0.46 | −0.41 | 1.00 | |
SO2 | 0.31 | 0.38 | −0.30 | 0.86 | 1.00 |
2007 |
| AEVR | ADR | O3 | PM2.5 | SO2 |
AEVR | 1.00 | | | | |
ADR | 0.86 | 1.00 | | | |
O3 | −0.42 | −0.49 | 1.00 | | |
PM2.5 | 0.25 | 0.53 | −0.48 | 1.00 | |
SO2 | 0.13 | 0.41 | −0.33 | 0.73 | 1.00 |
Results of the map correlation analyses reveal that AEVR and ADR are negatively correlated with the ground level ozone (O3) concentration. All the three years showed the same trend. The correlation values of ozone concentration and the AEVR are −0.54, −0.25 and −0.25 in 2005, 2006 and 2007 respectively. Similarly, the correlation values between ADR and ozone concentration are −0.55, −0.25, and −0.49 in 2005, 2006 and 2007 respectively. But the reverse trend was observed with PM2.5 and SO2 concentrations in each of the three years from 2005 to 2007. PM2.5 is significantly correlated with both the factors AEVR and ADR. The correlation values between PM2.5 and AEVR are 0.29, 0.39, and 0.25 in 2005, 2006, and 2007 respectively. Similarly, the correlation values between PM2.5 and ADR are 0.39, 0.46, and 0.53 in 2005, 2006, and 2007 respectively. Map layers correlation results also indicate that the spatial distribution of SO2 concentrations is positively correlated with both the factor AEVR and ADR. The correlation values between spatial distribution of SO2 and ADR are 0.52, 0.38, and 0.41 in 2005, 2006, and 2007 respectively. Similarly, the correlation values between spatial distribution of SO2 and AEVR are 0.46, 0.31, and 0.13 in 2005, 2006, and 2007 respectively.
4.2.2. Point Correlation Analysis
The asthma rates (asthma discharge rate and asthma emergency department visit rate) were compared with the extracted values of pollutant concentrations (SO
2, PM
2.5 and O
3) for understanding the association between asthma and air pollution. Asthma rate at the specified centroid position of each county was considered the same as that of the corresponding county value. Since the monitoring values of pollutant concentrations at the same location were not available, they were extracted from the interpolated map using GIS. The extracted values of pollutant concentrations (O
3, PM
2.5, and SO
2) at the centroid point of each of the county are represented in
Table 4. The data represented in
Table 4 for three years (2005, 2006, and 2007) were used for correlation analyses to determine the correlation coefficients values. The correlation analyses were carried out using Pearson two tailed correlation analysis using SPSS Statistics software version 21. Correlation analyses results are represented in
Table 5.
The results represented in
Table 5 clearly indicate that the associations or relations of the asthma rates (ADR and AEVR) with pollutant concentrations (PM
2.5, O
3, and SO
2) are similar to those obtained from map correlation analyses. That is ozone concentration showed a negative correlation with both asthma discharge rate and asthma emergency visit rate while the other two pollutants (SO
2 and PM
2.5) showed a positive correlation.
The correlation coefficients for ozone concentration and the AEVR are −0.638, −0.479 and −0.400 for 2005, 2006 and 2007 respectively. These correlation coefficients are significant at 1% level. Similarly, the correlation coefficients between ADR and ozone concentration are −0.575, −0.490, and −0.455 for 2005, 2006 and 2007 respectively. But the reverse trend was observed with PM2.5 and SO2 concentration in each of the three years from 2005 to 2007. PM2.5 is significantly correlated with both AEVR and ADR. The correlation coefficients between PM2.5 and AEVR are 0.395, 0.511, and 0.343 for 2005, 2006, and 2007 respectively. Similarly, the correlation coefficients for PM2.5 and ADR are 0.371, 0.505, and 0.431 in 2005, 2006, and 2007 respectively. Correlation data also indicate that the SO2 concentration is positively correlated with both the factor AEVR and ADR. The correlation coefficients for SO2 and ADR are 0.449, 0.470, and 0.509 in 2005, 2006, and 2007 respectively. Similarly, the correlation coefficients between SO2 and AEVR are 0.448, 0.446, and 0.469 in 2005, 2006, and 2007 respectively. Year-wise variation in correlation coefficients between asthma rate (ADR and AEVR) and annual average pollutant concentration (PM2.5, SO2, and Ozone) indicates a consistent trend.
According to the past research, the majority of researchers considered air pollutants a risk factor for asthma, although the roles of specific air pollutants on various respiratory illnesses remain unclear [
49,
50]. That is, the general effect of air pollution leads to adverse respiratory events. But, the effect of specific air pollutants on asthma rate is yet to be examined for a plausible conclusion. According to Chan
et al. [
51] PM
10 might have a positive impact on asthma outpatient and emergency settings. They further suggested that future research is required to validate robust spatiotemporal patterns and trends. A study conducted by Nawahda [
32] suggested that the association between elevated concentrations of PM
2.5 and surface ozone and asthma prevalence among school children in Japan is not strong enough to assume concretely a plausible and significant association. On the other hand, a controlled laboratory study conducted by Koenig [
52] revealed that ozone aggravates asthma. Thus, more specific studies are needed for understanding the role of specific air pollutants on asthma rate. These studies should also consider the socioeconomic factors and climatic factors for better reflections.
Table 4.
Asthma rate at the centroid of each county along with extracted pollutant concentration from interpolated maps.
Table 4.
Asthma rate at the centroid of each county along with extracted pollutant concentration from interpolated maps.
County Name | ADR | AEVR | Ozone Concentration (ppb) | PM2.5 Concentration (µg/m3) | SO2 Concentration (ppb) |
---|
2005 | 2006 | 2007 | 2005 | 2006 | 2007 | 2005 | 2006 | 2007 | 2005 | 2006 | 2007 | 2005 | 2006 | 2007 |
---|
Clinton | 21.04 | 12.36 | 14.66 | 105.55 | 103.94 | 108.77 | 42.38 | 39.91 | 41.97 | 8.45 | 6.04 | 6.94 | 3.20 | 2.40 | 4.18 |
Franklin | 8.97 | 7.96 | 8.88 | 69.84 | 56.49 | 60.45 | 41.54 | 38.86 | 40.49 | 8.63 | 6.09 | 7.04 | 3.67 | 2.82 | 4.88 |
Essex | 6.10 | 7.34 | 6.35 | 38.66 | 54.19 | 59.69 | 42.02 | 38.08 | 39.41 | 8.56 | 6.21 | 7.13 | 3.66 | 3.05 | 3.21 |
Hamilton | 9.82 | 8.02 | 8.05 | 5.89 | 10.03 | 12.07 | 41.04 | 37.67 | 39.62 | 9.97 | 7.24 | 8.14 | 4.03 | 3.05 | 3.06 |
Herkimer | 9.64 | 10.78 | 7.77 | 33.44 | 29.05 | 32.48 | 40.87 | 36.37 | 38.18 | 10.50 | 7.75 | 8.70 | 4.00 | 3.40 | 3.25 |
Washington | 7.04 | 9.56 | 9.36 | 37.14 | 34.25 | 32.83 | 41.15 | 38.31 | 40.23 | 10.46 | 8.06 | 8.72 | 4.16 | 3.64 | 3.68 |
Warren | 11.96 | 14.34 | 11.41 | 41.56 | 50.95 | 50.05 | 41.28 | 37.37 | 39.31 | 9.80 | 7.40 | 8.07 | 4.01 | 3.45 | 3.14 |
Saratoga | 17.38 | 16.58 | 12.10 | 74.10 | 67.59 | 61.48 | 41.12 | 36.43 | 39.01 | 10.96 | 8.31 | 9.13 | 4.28 | 4.07 | 3.57 |
Fulton | 19.35 | 20.97 | 10.06 | 20.98 | 19.16 | 70.46 | 40.67 | 36.06 | 38.10 | 10.76 | 8.16 | 8.98 | 4.50 | 3.66 | 3.22 |
Montgomery | 12.32 | 12.87 | 11.45 | 66.86 | 67.37 | 75.51 | 40.53 | 35.97 | 38.09 | 10.99 | 8.43 | 9.17 | 4.83 | 4.15 | 3.66 |
Rensselaer | 16.21 | 15.89 | 13.21 | 55.22 | 59.82 | 58.71 | 40.64 | 36.38 | 38.81 | 11.63 | 8.91 | 9.66 | 6.32 | 4.90 | 4.67 |
Schenectady | 7.56 | 8.11 | 7.37 | 31.41 | 29.07 | 27.53 | 40.30 | 35.67 | 39.10 | 11.35 | 8.69 | 9.41 | 5.02 | 4.99 | 4.03 |
Otsego | 11.42 | 11.58 | 10.97 | 28.70 | 31.57 | 31.31 | 40.13 | 36.36 | 35.98 | 11.00 | 8.57 | 9.28 | 7.37 | 5.92 | 4.08 |
Schoharie | 16.58 | 13.84 | 11.69 | 72.84 | 68.13 | 62.52 | 40.61 | 36.10 | 38.10 | 11.25 | 8.76 | 9.42 | 4.89 | 4.22 | 4.14 |
Albany | 13.28 | 12.63 | 11.95 | 65.72 | 68.16 | 64.60 | 39.90 | 35.72 | 38.23 | 11.70 | 8.98 | 9.69 | 6.91 | 6.04 | 4.39 |
Delaware | 11.78 | 11.39 | 10.53 | 32.87 | 32.32 | 27.86 | 40.14 | 35.54 | 34.66 | 11.19 | 8.91 | 9.50 | 7.58 | 6.15 | 5.09 |
Columbia | 7.53 | 5.83 | 6.15 | 44.89 | 43.20 | 45.72 | 39.36 | 36.12 | 38.20 | 11.82 | 9.20 | 10.01 | 7.60 | 6.24 | 5.39 |
Greene | 10.38 | 9.49 | 7.47 | 41.92 | 46.45 | 42.80 | 40.22 | 36.39 | 38.22 | 11.65 | 9.11 | 9.81 | 6.82 | 5.60 | 5.11 |
Ulster | 11.07 | 11.05 | 10.67 | 51.52 | 53.76 | 47.31 | 39.92 | 36.78 | 36.60 | 11.83 | 9.40 | 10.19 | 9.71 | 7.09 | 6.81 |
Dutchess | 11.11 | 10.89 | 12.33 | 46.41 | 54.32 | 54.69 | 37.66 | 35.17 | 39.37 | 11.95 | 9.47 | 10.37 | 10.05 | 7.50 | 7.79 |
Sullivan | 23.31 | 14.63 | 13.72 | 73.59 | 72.12 | 61.55 | 39.19 | 35.33 | 36.09 | 11.96 | 9.58 | 10.35 | 10.63 | 8.21 | 7.27 |
Orange | 15.55 | 15.70 | 13.54 | 64.58 | 72.01 | 68.04 | 38.90 | 36.30 | 35.64 | 12.42 | 10.47 | 10.96 | 11.23 | 8.73 | 9.74 |
Putnam | 8.84 | 7.45 | 8.35 | 32.44 | 21.17 | 25.94 | 41.49 | 37.18 | 36.30 | 12.24 | 10.01 | 10.84 | 10.79 | 7.09 | 9.52 |
Westchester | 13.15 | 15.04 | 14.23 | 59.00 | 63.31 | 59.89 | 38.22 | 36.12 | 35.29 | 12.73 | 10.74 | 11.50 | 12.26 | 9.70 | 10.97 |
Rockland | 9.21 | 11.69 | 9.12 | 37.69 | 36.17 | 34.01 | 37.53 | 35.78 | 35.04 | 13.29 | 11.35 | 12.03 | 12.23 | 10.16 | 11.37 |
Bronx | 62.64 | 64.16 | 64.16 | 237.17 | 267.27 | 262.73 | 31.92 | 33.21 | 33.73 | 13.94 | 12.20 | 12.91 | 16.04 | 14.65 | 12.51 |
Nassau | 14.92 | 14.26 | 13.57 | 38.35 | 38.41 | 41.19 | 36.11 | 36.16 | 33.35 | 12.35 | 10.79 | 11.18 | 13.09 | 10.21 | 12.11 |
New York | 26.09 | 27.25 | 25.27 | 135.63 | 130.72 | 132.69 | 32.14 | 32.45 | 32.40 | 15.41 | 13.15 | 14.55 | 16.35 | 16.25 | 12.88 |
Queens | 20.95 | 21.32 | 20.67 | 72.92 | 81.13 | 73.35 | 33.48 | 35.39 | 33.17 | 13.04 | 11.81 | 11.67 | 15.18 | 12.78 | 12.83 |
Suffolk | 13.61 | 13.91 | 12.76 | 53.52 | 53.06 | 51.12 | 39.01 | 37.46 | 39.76 | 11.43 | 9.64 | 10.15 | 11.74 | 9.00 | 9.23 |
Kings | 33.11 | 32.98 | 30.37 | 123.53 | 121.08 | 124.33 | 33.82 | 34.46 | 33.50 | 14.42 | 12.28 | 13.29 | 15.28 | 13.93 | 13.27 |
Richmond | 16.67 | 16.98 | 16.82 | 70.52 | 72.36 | 66.42 | 35.49 | 33.62 | 32.13 | 13.37 | 11.37 | 11.93 | 14.58 | 12.68 | 13.40 |
St. Lawrence | 6.54 | 6.25 | 5.36 | 33.54 | 38.61 | 27.91 | 41.04 | 38.27 | 40.22 | 9.37 | 6.74 | 7.22 | 5.05 | 4.11 | 5.90 |
Jefferson | 8.64 | 7.57 | 6.08 | 49.79 | 46.46 | 39.54 | 40.80 | 37.12 | 38.92 | 10.37 | 7.86 | 8.35 | 5.75 | 5.15 | 5.56 |
Lewis | 6.72 | 7.78 | 5.91 | 53.41 | 51.48 | 45.78 | 40.84 | 37.13 | 38.92 | 10.25 | 7.61 | 8.33 | 5.24 | 4.28 | 4.46 |
Oswego | 8.89 | 8.25 | 7.20 | 36.20 | 39.07 | 35.10 | 40.99 | 37.14 | 38.93 | 11.07 | 8.25 | 9.09 | 5.89 | 5.08 | 5.62 |
Oneida | 16.65 | 16.78 | 14.07 | 50.45 | 51.23 | 48.23 | 41.20 | 38.09 | 39.52 | 10.81 | 8.07 | 9.02 | 4.26 | 3.71 | 3.99 |
Cayuga | 12.33 | 8.90 | 7.57 | 43.77 | 39.81 | 39.32 | 40.24 | 36.69 | 39.09 | 11.47 | 8.51 | 9.37 | 7.28 | 6.34 | 6.72 |
Niagara | 12.96 | 13.65 | 11.54 | 55.12 | 54.92 | 55.38 | 40.49 | 37.25 | 39.25 | 13.55 | 10.41 | 10.76 | 11.75 | 9.58 | 9.90 |
Orleans | 8.74 | 9.21 | 9.00 | 40.25 | 43.76 | 33.22 | 40.28 | 36.99 | 39.23 | 12.82 | 10.02 | 10.29 | 11.68 | 9.67 | 10.25 |
Monroe | 11.36 | 11.09 | 10.55 | 56.10 | 56.76 | 55.12 | 38.66 | 36.49 | 39.08 | 12.42 | 9.57 | 9.95 | 10.21 | 9.28 | 9.68 |
Wayne | 7.68 | 8.12 | 5.99 | 45.88 | 41.67 | 39.45 | 39.43 | 36.53 | 39.07 | 11.66 | 8.85 | 9.37 | 7.71 | 7.31 | 8.11 |
Onondaga | 8.79 | 9.18 | 7.24 | 47.06 | 46.08 | 42.73 | 40.97 | 36.87 | 39.10 | 11.55 | 8.41 | 9.75 | 8.37 | 6.43 | 6.14 |
Madison | 8.40 | 9.72 | 7.84 | 49.39 | 46.22 | 40.99 | 40.60 | 37.27 | 36.38 | 11.18 | 8.32 | 9.39 | 5.51 | 4.71 | 4.84 |
Genesee | 14.98 | 12.85 | 10.18 | 53.27 | 43.23 | 35.04 | 40.26 | 36.99 | 39.23 | 12.81 | 10.03 | 10.30 | 11.68 | 9.66 | 9.96 |
Erie | 13.92 | 13.46 | 11.75 | 57.63 | 56.42 | 53.08 | 40.59 | 38.06 | 40.10 | 13.86 | 10.54 | 11.13 | 11.70 | 9.27 | 9.80 |
Ontario | 8.44 | 5.83 | 7.41 | 54.29 | 48.07 | 43.81 | 39.34 | 37.05 | 39.50 | 11.64 | 9.01 | 9.39 | 9.97 | 8.66 | 8.92 |
Seneca | 8.47 | 13.33 | 6.95 | 76.27 | 61.86 | 53.46 | 39.96 | 36.61 | 39.08 | 11.44 | 8.60 | 9.31 | 7.40 | 6.59 | 7.81 |
Livingston | 7.50 | 7.65 | 7.79 | 40.72 | 40.55 | 41.09 | 39.76 | 36.92 | 39.12 | 11.94 | 9.39 | 9.58 | 10.13 | 8.68 | 9.48 |
Wyoming | 14.26 | 12.65 | 8.94 | 40.91 | 37.49 | 33.87 | 40.41 | 37.04 | 39.23 | 12.71 | 9.93 | 10.26 | 11.67 | 9.54 | 9.99 |
Cortland | 8.11 | 7.48 | 6.65 | 7.50 | 10.52 | 33.05 | 40.67 | 36.68 | 38.75 | 11.35 | 8.48 | 9.50 | 5.57 | 5.19 | 5.12 |
Yates | 50.14 | 49.55 | 40.42 | 50.14 | 49.55 | 40.42 | 39.71 | 37.18 | 39.51 | 11.30 | 8.67 | 9.15 | 9.79 | 8.26 | 8.21 |
Chenango | 6.65 | 8.37 | 8.16 | 43.59 | 46.90 | 49.94 | 40.63 | 37.72 | 39.20 | 11.65 | 8.53 | 9.84 | 6.90 | 6.10 | 4.94 |
Tompkins | 4.53 | 4.32 | 5.10 | 28.26 | 27.60 | 24.92 | 40.14 | 36.66 | 38.94 | 11.31 | 8.44 | 9.33 | 6.61 | 5.84 | 6.36 |
Steuben | 13.15 | 13.71 | 8.73 | 52.80 | 51.99 | 47.59 | 40.00 | 36.99 | 39.12 | 10.97 | 8.57 | 9.02 | 10.11 | 8.16 | 8.80 |
Chautauqua | 12.93 | 11.21 | 9.74 | 71.54 | 62.22 | 55.65 | 41.34 | 38.64 | 40.16 | 11.94 | 8.81 | 10.01 | 11.48 | 9.24 | 9.26 |
Schuyler | 6.76 | 6.74 | 7.90 | 46.11 | 41.95 | 19.46 | 39.74 | 37.16 | 39.52 | 11.06 | 8.43 | 9.10 | 9.12 | 7.40 | 7.93 |
Cattaraugus | 11.82 | 10.70 | 7.77 | 43.88 | 46.35 | 55.39 | 40.32 | 38.05 | 40.10 | 12.62 | 9.66 | 10.26 | 11.46 | 9.30 | 9.53 |
Allegany | 13.06 | 8.91 | 10.80 | 34.36 | 28.36 | 36.68 | 40.40 | 37.24 | 39.29 | 12.04 | 9.28 | 9.84 | 10.64 | 8.80 | 9.23 |
Broome | 7.48 | 9.16 | 8.91 | 47.59 | 47.98 | 45.25 | 40.03 | 37.29 | 38.94 | 11.60 | 8.67 | 9.79 | 5.48 | 5.20 | 4.85 |
Tioga | 2.52 | 2.72 | 3.68 | 15.50 | 15.91 | 9.50 | 39.93 | 37.48 | 39.18 | 10.99 | 8.58 | 9.07 | 5.60 | 5.44 | 5.48 |
Chemung | 15.19 | 13.64 | 13.20 | 88.23 | 83.40 | 75.70 | 38.97 | 36.97 | 39.43 | 11.46 | 8.52 | 9.64 | 7.08 | 6.45 | 6.55 |
Table 5.
Correlation matrix of asthma rate and air pollutants.
Table 5.
Correlation matrix of asthma rate and air pollutants.
2005 | |
---|
| ADR | AEVR | Ozone | SO2 | PM2.5 |
---|
ADR | 1 | | | | |
AEVR | 749 ** | 1 | | | |
Ozone | −0.575 ** | −0.638 ** | 1 | | |
SO2 | 0.449 ** | 0.448 ** | −0.759 ** | 1 | |
PM2.5 | 0.371 ** | 0.395 ** | −0.709 ** | 0.868 ** | 1 |
2006 | |
| ADR | AEVR | Ozone | SO2 | PM2.5 |
ADR | 1 | | | | |
AEVR | 0.765 ** | 1 | | | |
Ozone | −0.490 ** | −0.479 ** | 1 | | |
SO2 | 0.470 ** | 0.446 ** | −0.716 ** | 1 | |
PM2.5 | 0.505 ** | 0.511 ** | −0.608 ** | 0.922 ** | 1 |
2007 | |
| ADR | AEVR | Ozone | SO2 | PM2.5 |
ADR | 1 | | | | |
AEVR | 0.822 ** | 1 | | | |
Ozone | −0.455 ** | −0.400 ** | 1 | | |
SO2 | 0.509 ** | 0.469 ** | −0.741 ** | 1 | |
PM2.5 | 0.431 ** | 0.343 ** | −0.535 ** | 0.794 ** | 1 |