We investigated the relationships among air quality indexes, such as the total number of bacteria "BAC", concentration of carbon dioxide "CO2", relative humidity "HUM", concentration of negative ion "NI" and opening time "TIM" in the environment of indoor public place. The data were divided into 3 levels based on its percentile and analyzed by correspondence analysis (ANACOR) and homogeneity analysis (HOMALS) in SPSS 8.0/10.0. Using these methods, we analyzed among 2 variables and their 3 levels, there were the relationships between BAC and CO2, CO2 and TIM, and BAC and TIM. When analyzing by HOMALS, among 3 variables and their 3 levels, there were some similar relationships: BAC, CO2 and TIM; BAC, CO2 and NI. The increase of CO2 and SAC were changed with TIM. The higher CO2 tended to increase BAC and to decrease NI. These results indicated under general conditions, CO2 can show the status of the air BAC. Increasing the ventilation and the relative humidity suitably may raise NI concentration and improve the air quality. Although the analytical results of the air quality by ANACOR and HOMALS are similar to that of chi-square test, ANACOR and HOMALS can be clearly shown in the plots with 2 or 3 axes. These two methods (ANACOR and HOMALS) may be useful to show the relationships among indoor air quality indexes more sensitively and directly

Aoyagi Kiyoshi

Pan Baojun

Saito Hiroshi

Takemoto Taiichiro

Wu Zhiren

Xu Feng

Nagasaki University's Academic Output SITE: NAOSITE

Acta Med. Nagasaki 46 : 33-37Study on the Relationship of Indoor Air Quality Indexes by Correspondence Analysis and Homogeneity AnalysisBaoj un PAN 1), Feng XU 1), Zhiren Wu 2), Kiyoshi AOYAGI 4), Hiroshi SAITO 3), Taiichiro TAKEMOTO 4)1) Fujian Centers for Disease Control, Fuzhow, China 2) Okusa Clinic, Nagasaki Yurino Hospital, Nagasaki 3) Department of Preventive Medicine and Health Promotion, Nagasaki University School of Medicine 4) Department of Public Health, Nagasaki University School of Medicine We investigated the relationships among air quality in-dexes, such as the total number of bacteria "BAC", concen-tration of carbon dioxide "CO2", relative humidity "HUM", concentration of negative ion "NI" and opening time "TIM" in the environment of indoor public place. The data were divided into 3 levels based on its percentile and analyzed by correspondence analysis (ANACOR) and homogeneity analysis (HOMALS) in SPSS 8.0/10.0. Using these methods, we analyzed among 2 variables and their 3 levels, there were the relationships between BAC and CO2, CO2 and TIM, and BAC and TIM. When analyzing by HOMALS, among 3 variables and their 3 levels, there were some simi-lar relationships: BAC, CO2 and TIM; BAC, CO2 and NI. The increase of CO2 and SAC were changed with TIM. The higher CO2 tended to increase BAC and to decrease NI. These results indicated under general conditions, CO2 can show the status of the air BAC. Increasing the ventilation and the relative humidity suitably may raise NI concen-tration and improve the air quality.  Although the analytical results of the air quality by ANACOR and HOMALS are similar to that of chi-square test, ANACOR and HOMALS can be clearly shown in the plots with 2 or 3 axes. These two methods (ANACOR and HOMALS) may be useful to show the relationships among indoor air quality indexes more sensitively and directly.      ACTA MEDICA NAGASAKIENSIA 46 : 33-37, 2001Key Words: indoor public places, air quality indexes, corre-           spondence analysis (ANACOR), homogeneity           analysis (HOMALS)Address Correspondence: Zhiren Wu, M.D. Okusa Clinic, Nagasaki Yurino Hospital No.302 Maeda Bldg. Namesi 1-29-3, Nagasaki 852-8061 Japan TEL: +81-95-856-5798 FAX: +81-95-856-6663 E-mail: hitoshi-go@h8.dion.jpIntroduction For analyzing the qualitative or quantitative data of indoor air, t-test, ANOVA, correlation, regression and Chi-square test etc. have been used. The authors intro-duce two new methods for processing the data: corre-spondence analysis (ANACOR) and homogeneity analysis (HOMALS) in Statistical Package for the Social Science (SPSS) 8.0/10.0 for Windows. These analyses can show the relationships among variables visually in the plots with two or three dimensions. To study the relationships among air quality indexes, such as the total number of bacteria "BAC", concentra-tion of carbon dioxide "C02", relative humidity "HUM", concentration of negative ion "NI" and opening time "TIM", we evaluated the environment of indoor place (Karaoke, dancing halls etc, with central air condition-ing) using ANACOR and HOMALS.Data and MethodsStatistical methods  ANACOR analyzes the correspondence relationship between row and column variables (2 dimensions). Five normalization methods can be used: CANONICAL, PRINCIPAL, RPRINCIPAL, CPRINCIPAL, value -2 to +2. The running output contents are: the tables (TABLES) (Table 1), the row profiles, the column profiles (PROFILE), inertia, the singular value (SINGULAR)/ Eigen values (EIGEN) of each dimensions, proportion explained, cumulative proportion, row scores, column scores (SCORES), contribution of row points of each dimension (CONTRIBUTIONS) and 5 plots (TRROWS, TRCOLUMNS. ROWS, COLUMNS and JOINT). The most important plot is JOINT (Row and Column Scores Chart). The distances (Euclidean distances) be-tween categories of the row variable and betweenTable 1. The contingency tables and its Chi-square values for 5 air indexes (variables)Air index (Var.) LB MB HB x z test ----------------------------------------------------------------      LC 17 28 9 X'=27. 035      MC 35 79 26       HC 8 24 32 P=0.000 ----------------------------------------------------------------                   LC MC HC       TO 31 38 11 X 2=28. 253      T1 15 52 21       T2 8 50 32 P=0.000 ----------------------------------------------------------------                   LB MB HB       TO 29 36 15 x =13.436      T1 18 48 22       T2 13 47 30 P=0.009 ----------------------------------------------------------------                  LN MN HN       LH 27 4 1 X z=13. 078      MH 19 8 2       HH 1 2 2 P=0.011dioxide (LC), middle carbon oxide (MC) and high car-bon oxide (HC), HUM into low humidity (LH), middle humidity (MH) and high humidity (HH), NI into low negative ion (LN), middle negative ion (MN) and high negative ion (HN), TIM into TO (before opening), T 1 (1 hours later) and T2 (2 hours later) (Table 1).The main commands used:  When using ANACOR, DIMENSION=2; HOMALS, DIMENSION=2 or 3 (Table 2). This example uses CANONICAL (default) for normalization.The main running resultsTable 2. The main commands used in ANACOR and HOMALS for 5 variablesGET FILE = 'C:\SPSS\SAV\Air.sav'. VALUE LABELS BAC 1 'LB' 2 'MB' 3 'HB'               /CO2 1 'LC' 2 'MC' 3 'HC'              /HUM 1 'LH' 2 'MH' 3 'HH'              /NI 1 'LN' 2 'MN' 3 'HN'               /TIM 1 'TO' 2 'T1' 3 'T2'. ANACOR TABLE = C02(1,3) BY BAC(1,3)       /DIMENSION = 2 /NORMALIZATION = CANONICAL       /PRINT = TABLE SCORES PROFILE CONTRIBUTIONS       /PLOT = JOINT. HOMALS VARIABLES = BAC(3) C02(3) TIM(3)       /DIMENSION = 2 /PRINT = FREQ EIGEN DISCRIM QUANT       /PLOT = QUANT.The results by ANACOR:  The main running results are shown in Table 3-5, and the most important outputs are Fig 1-4 (The JOINT plot is introduced only in this paper). The val-ues of the coordinate points (LC, MC, HC, and LB, MB, and HB) in Fig 1 ( the same as in Fig 2-4) can find from Table 4-5. For example:         LC (-0.408, 0.419);         LB (-0.484, 0.372);         HB (0.942, 0.062), and so on.Table 3. 2 dimensions, Inertia and Proportion Explainedcategories of the column variable are approximations of Chi-square distances (Table 2).  HOMALS analyzes the homogeneity relationship among row and column variables (2 or 3 dimensions), plotting charts with 2 or 3 dimensions. The most im-portant chart is QUANT (Category Quantification) (Table 2).Data source and processing:  Data on the air index was collected in Fuzhou, China (1989-1998, 258 points), and a database "Air.sav" was made, using SPSS.             Singular Proportion Cumulative Dimension Value Inertia Explained proportion --------------------------------------------------------------    1 0.31916 0.10187 0.972 0.972     2 0.05403 0.00292 0.028 1.000     3 - 0.00145 1.000 1.000Table 4. Row Scores and Contribution of Row points to each dimension                                   Row Scores Contribution Carbon dioxide Marginal ---------------------------    ( % ) Profile Dim 1 Dim 2 Dim 1 Dim 2 -----------------------------------------------------------------------  LC (<O.05) 0.209 -0.408 0.419 0. 109 0.682   MC (0.05=0.0711) 0.543 -0.291 -0.177 0.144 0.314   HC (0.0712) 0.248 0.980 0.033 0.747 0.005       Total 1.000 - - 1.000 1.000Air index classification:  We measured the total number of bacteria (BAC), concentration of carbon dioxide (C02), relative humid-ity (HUM), concentration of negative ion (NI) and opening time (TIM). Based on the percentile (P) of air index value (<P25, P25-P75 and >=P75), the values were divided into 3 levels (low (L), middle (M) or high (H)). BAC divided into low bacteria (LB), middle bacteria (MB) and high bacteria (HB), C02 into low carbonTable 5. Column Scores and Contribution of Column points to each dimension                              Column Scores Contribution Total Number Marginal -----------------------------------------Bacteria Profile Dim 1 Dim 2 Dim 1 Dim 2 ---- ------------------------------------------------------------------- LB (<7) 0.233 -0.484 0.372 0.171 0.597  MB (7=17.99) 0.508 -0.260 -0.202 0.107 0.385  HB (>- 18) 0.260 0.942 0.062 0.722 0.019     Total 1.000 1.000 1.000 Table 3-5 and Fig 1-4 showed that the higher car-bon dioxide value in the environment was associated with higher total number of bacteria. The increases of carbon dioxide and the total number of bacteria in the air were associated with the opening time of indoor to public places. The higher humidity environment tended to be higher negative ion concentration.Row and Column ScoresRow and Column ScoresDimension 1 Normalization: CanonicalFigure 4. A correspondence relationship (combined plot) be-tween NI and HUM for row and column points (JOINT chart)Dimension 1 Normalization: CanonicalFigure 1. A correspondence relationship (combined plot) be-tween BAC and C02 for row and column points (JOINT chart)Row and Column ScoresDimension 1 Normalization: CanonicalFigure 2. A correspondence relationship (combined plot) be-tween C02 and TIM for row and column points (JOINT chart)Row and Column ScoresThe results by HOMALS:  The main running results are shown in Table 6-8. The following results can be printed out: Marginal Frequencies (FREQ, Table 6), the. Eigen value of each dimension (EIGEN), Discrimination measures per vari-able per dimension, Category Quantifications of each Dimensions (Table 2, Table 7-8). The most important charts are showen in Fig 5-7 (The QUANT plot is in-troduced only in this paper, plotting Category Quantifications).  Fig 5-7 showed that under general conditions, the increases of C02 density and BAC were associated with opening time, and with the decrease of NI. C02Table 6. The ranking classification of 4 variables, frequency and its codeAir Index (Var. ) Classification Freq. Ranking Class Labels ------------------------------------------- --------------------------Carbon Dioxide <O.050 54 1 LC     (C02) 0.050- 140 2 MC                         0.071- 64 3 HC Total Number <7 60 1 LB  Bacteria (BAC) 7- 131 2 MB                        18- 67 3 HB Negative Ion <10 47 1 LN     (NI) 10- 14 2 MN                         35- 5 3 HN Opening Time Before open. 80 1 TO    (TIM) After open. 1 hr 88 2 T1                   After open. 2 hrs 90 3 T2Dimension 1 Normalization: CanonicalFigure 3. A correspondence relationship (combined plot) be-tween BAC and TIM for row and column points (JOINT chart)Table 7. Discrimination measures for 3 variables and 3 di-mensions     Air Index (Var. ) Dim. 1 Dim. 2 Dim. 3 ----------------------------------------------------------------Total Number Bacteria (BAC) 0.452 0.426 0.042 Carbon Dioxide (C02) 0.578 0.530 0.090 Opening Time (TIM) 0.495 0.244 0.843Table 8. Category quantifications of 2 dimensions for 3 vari-ablesAir Index (Var. ) Categories Dim. 1 Dim. 2 --------------------------- --------------------------------------Carbon Dioxide 1 1.107 0.815    (C02) 2 0.090 -0.673                              3 -1.130 0.785Total Number 1 0.927 0.407 Bacteria (BAC) 2 0.076 -0.624                            3 -0.980 0.855Opening Time 1 0.958 0.651     (TIM) 2 -0.079 -0.554                                3 -0.774 -0.037to show the relationship between ranking categories and the air quality indexes by plotting with 2 or 3 axes. It may be a more sensitive and direct method than the analytical method of x 2 test.Quantificationsin the air showed the status of BAC and the level of NI. Increasing the ventilation decreased the accumula-tion of C02 and BAC in the air, raised the concentra-tion of NI, and improved the indoor air quality.  The HOMALS method could analyze three or more variables (indexes) of the air quality. It may be usefulCategory QuantificationsFigure 6 - 2. Homogeneity relationship ( combined plot ) among C02, BAC and TIM for row and column points ( 3 Dim. Chart in SPSS 10.0 )Category QuantificationsFigure 5. A homogeneity relationship (combined plot) among C02, BAC and TIM for row and column points (QUANT chart)Category QuantificationsDimension 1Figure 7. A homogeneity relationship within C02, BAC and NI for the ranking classifications (2 Dim. chart)DiscussionFigure 6 - 1. Homogeneity relationship (combined plot) among C02, BAC and TIM for row and column points (3 Dim. Chart in SPSS 8.0) The ANACOR & HOMALS are more novel and im-agery than classical methods. Three kinds of data may be collected in survey (quantitative, qualitative and ranked data). The methods, such as t-test, ANOVA and x 2 test etc, have been used as classical statisticalanalysis. However, the ANACOR and HOMALS in SPSS 8.0/10.0 are used as new analytical methods re-cently. When the data are transformed into rank (for example in this paper, the air quality indexes were transformed into 3 ranks ("L", "M" and "H"), the ANACOR can analyze the relationships between differ-ent classifications for 2 variables, and can imaginably show the 2 dimensions charts additionally. Furthermore, the HOMALS can show 3 dimensions charts, and these charts are more easily understood and imagin-able in SPSS 10.0 edition than in 8.0 edition.  There are more complex mathematical and statisti-cal concepts involved in these two methods, for exam-ple, canonical correlation, principal component analy-sis, discriminatory analysis, eigenvalue, score, contribution and Euclidean distance etc. These con-cepts are worth studying by researchers. These two methods may be useful in order to obtain the charts of 2 or 3 dimensions more imaginably.Conclusions1. The relationships among 5 air quality indexes at  indoor places (total number bacteria, carbon dioxide,  humidity, negative ion and opening time) were ana- lyzed by two new methods (ANACOR and HOMALS) in SPSS 8.0/10.0. 2. These two methods mentioned above could analyze  the relationships between different classifications for  2 variables, and imaginably show 2 or 3 dimensions  charts (HOMALS method). These charts were more  novel, more easily understood and imaginable, but it  is necessary to understand some complex statistical  concepts involved in these analysis and plotting.Acknowledgement Mr. Chen Ming-Jun and Ms. Zheng Yan participated in this investigation.References1) SPSS Inc. SPSS Categories, Chicago: Marketing Department of   SPSS Inc, B49-B62, 1990 2) Pan B, Que S, You M, et al: General Packages for Scientific    Research (SPSS), Fuzhou: Fujian Science & Tech. Publish. House,   755-756, 1995 3) Pan B, Liu S, Chen Z, et al: Study on Birth Weight, Fetal Position    and the Mother's Age by Homogeneity Analysis. Journal of   Shanghai Medical University, 26(5): 385, 1999 4) Pan B, Que S, You M, et al: A Training Material on SPSS    8.0/9.0/10.0 for Windows, Jan.1999/2000

Study on the Relationship of Indoor Air Quality Indexes by Correspondence Analysis and Homogeneity Analysis

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Study on the Relationship of Indoor Air Quality Indexes by Correspondence Analysis and Homogeneity Analysis

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