Overview

Dataset statistics

Number of variables9
Number of observations63
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory79.1 B

Variable types

Text1
Numeric5
Categorical3

Dataset

Description대구교통공사 1, 2호선 역사별 공기질 측정에 대한 데이터로 권고기준에 따른 미세먼지, 이산화탄소, 일산화탄소 등 역별 공기질 측정결과 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15002094/fileData.do

Alerts

휘발성유기화합물 is highly overall correlated with 이산화탄소 and 3 other fieldsHigh correlation
라돈 is highly overall correlated with 이산화탄소 and 3 other fieldsHigh correlation
미세먼지(PM10) is highly overall correlated with 미세먼지(PM2.5)High correlation
미세먼지(PM2.5) is highly overall correlated with 미세먼지(PM10)High correlation
이산화탄소 is highly overall correlated with 라돈 and 1 other fieldsHigh correlation
일산화탄소 is highly overall correlated with 라돈 and 1 other fieldsHigh correlation
이산화질소 is highly overall correlated with 라돈 and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 16:31:46.742767
Analysis finished2023-12-12 16:31:49.359012
Duration2.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

역사명
Text

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size636.0 B
2023-12-13T01:31:49.595730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.5079365
Min length2

Characters and Unicode

Total characters221
Distinct characters91
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)96.8%

Sample

1st row1호선평균
2nd row설화명곡
3rd row화원
4th row대곡역
5th row진천역
ValueCountFrequency (%)
반월당역 2
 
3.2%
반고개역 1
 
1.6%
임당역 1
 
1.6%
문양역 1
 
1.6%
다사역 1
 
1.6%
대실역 1
 
1.6%
강창역 1
 
1.6%
계명대역 1
 
1.6%
성서산업단지역 1
 
1.6%
이곡역 1
 
1.6%
Other values (52) 52
82.5%
2023-12-13T01:31:49.978173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 59
26.7%
대 12
 
5.4%
월 6
 
2.7%
당 5
 
2.3%
구 5
 
2.3%
반 4
 
1.8%
촌 4
 
1.8%
산 4
 
1.8%
원 4
 
1.8%
신 4
 
1.8%
Other values (81) 114
51.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 219
99.1%
Decimal Number 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 59
26.9%
대 12
 
5.5%
월 6
 
2.7%
당 5
 
2.3%
구 5
 
2.3%
반 4
 
1.8%
촌 4
 
1.8%
산 4
 
1.8%
원 4
 
1.8%
신 4
 
1.8%
Other values (79) 112
51.1%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 219
99.1%
Common 2
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 59
26.9%
대 12
 
5.5%
월 6
 
2.7%
당 5
 
2.3%
구 5
 
2.3%
반 4
 
1.8%
촌 4
 
1.8%
산 4
 
1.8%
원 4
 
1.8%
신 4
 
1.8%
Other values (79) 112
51.1%
Common
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 219
99.1%
ASCII 2
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 59
26.9%
대 12
 
5.5%
월 6
 
2.7%
당 5
 
2.3%
구 5
 
2.3%
반 4
 
1.8%
촌 4
 
1.8%
산 4
 
1.8%
원 4
 
1.8%
신 4
 
1.8%
Other values (79) 112
51.1%
ASCII
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%

미세먼지(PM10)
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)84.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.606349
Minimum6.7
Maximum33.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-13T01:31:50.127895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.7
5-th percentile9.67
Q113.3
median18.3
Q323.85
95-th percentile28.73
Maximum33.4
Range26.7
Interquartile range (IQR)10.55

Descriptive statistics

Standard deviation6.256271
Coefficient of variation (CV)0.33624388
Kurtosis-0.62870247
Mean18.606349
Median Absolute Deviation (MAD)5.2
Skewness0.27885124
Sum1172.2
Variance39.140927
MonotonicityNot monotonic
2023-12-13T01:31:50.625794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.2 3
 
4.8%
12.9 3
 
4.8%
24.3 3
 
4.8%
13.1 2
 
3.2%
24.4 2
 
3.2%
11.1 2
 
3.2%
10.3 2
 
3.2%
24.6 1
 
1.6%
17.4 1
 
1.6%
20.0 1
 
1.6%
Other values (43) 43
68.3%
ValueCountFrequency (%)
6.7 1
 
1.6%
8.5 1
 
1.6%
9.3 1
 
1.6%
9.6 1
 
1.6%
10.3 2
3.2%
11.1 2
3.2%
11.2 1
 
1.6%
12.1 1
 
1.6%
12.9 3
4.8%
13.0 1
 
1.6%
ValueCountFrequency (%)
33.4 1
1.6%
32.3 1
1.6%
30.3 1
1.6%
28.8 1
1.6%
28.1 1
1.6%
26.9 1
1.6%
26.8 1
1.6%
25.4 1
1.6%
24.9 1
1.6%
24.6 1
1.6%

미세먼지(PM2.5)
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.8603175
Minimum3.2
Maximum20.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-13T01:31:50.773003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.2
5-th percentile4.34
Q17
median9
Q311.8
95-th percentile18.24
Maximum20.7
Range17.5
Interquartile range (IQR)4.8

Descriptive statistics

Standard deviation4.1660897
Coefficient of variation (CV)0.4225107
Kurtosis-0.055831937
Mean9.8603175
Median Absolute Deviation (MAD)2.5
Skewness0.73377274
Sum621.2
Variance17.356303
MonotonicityNot monotonic
2023-12-13T01:31:50.929760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11.8 2
 
3.2%
7.0 2
 
3.2%
8.0 2
 
3.2%
8.9 2
 
3.2%
8.6 2
 
3.2%
10.7 2
 
3.2%
10.2 2
 
3.2%
8.7 1
 
1.6%
14.3 1
 
1.6%
5.6 1
 
1.6%
Other values (46) 46
73.0%
ValueCountFrequency (%)
3.2 1
1.6%
3.9 1
1.6%
4.0 1
1.6%
4.3 1
1.6%
4.7 1
1.6%
4.8 1
1.6%
5.2 1
1.6%
5.3 1
1.6%
5.4 1
1.6%
5.5 1
1.6%
ValueCountFrequency (%)
20.7 1
1.6%
18.7 1
1.6%
18.5 1
1.6%
18.3 1
1.6%
17.7 1
1.6%
16.9 1
1.6%
16.6 1
1.6%
15.8 1
1.6%
15.6 1
1.6%
14.9 1
1.6%

이산화탄소
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean533.73016
Minimum455
Maximum832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-13T01:31:51.074658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum455
5-th percentile465
Q1474.5
median497
Q3520
95-th percentile740.7
Maximum832
Range377
Interquartile range (IQR)45.5

Descriptive statistics

Standard deviation99.512393
Coefficient of variation (CV)0.18644701
Kurtosis2.5259187
Mean533.73016
Median Absolute Deviation (MAD)23
Skewness1.9368098
Sum33625
Variance9902.7163
MonotonicityNot monotonic
2023-12-13T01:31:51.258015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
474 5
 
7.9%
465 3
 
4.8%
517 3
 
4.8%
469 2
 
3.2%
473 2
 
3.2%
503 2
 
3.2%
534 2
 
3.2%
480 2
 
3.2%
518 2
 
3.2%
499 2
 
3.2%
Other values (38) 38
60.3%
ValueCountFrequency (%)
455 1
 
1.6%
464 1
 
1.6%
465 3
4.8%
469 2
 
3.2%
470 1
 
1.6%
471 1
 
1.6%
473 2
 
3.2%
474 5
7.9%
475 1
 
1.6%
476 1
 
1.6%
ValueCountFrequency (%)
832 1
1.6%
829 1
1.6%
814 1
1.6%
741 1
1.6%
738 1
1.6%
737 1
1.6%
734 1
1.6%
724 1
1.6%
711 1
1.6%
614 1
1.6%

폼알데하이드
Real number (ℝ)

Distinct43
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4428571
Minimum2.7
Maximum27.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-13T01:31:51.419225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.7
5-th percentile3.34
Q14.5
median6
Q37.4
95-th percentile9.87
Maximum27.2
Range24.5
Interquartile range (IQR)2.9

Descriptive statistics

Standard deviation3.3290856
Coefficient of variation (CV)0.51670952
Kurtosis24.21188
Mean6.4428571
Median Absolute Deviation (MAD)1.5
Skewness4.1129115
Sum405.9
Variance11.082811
MonotonicityNot monotonic
2023-12-13T01:31:51.569258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
5.0 4
 
6.3%
6.3 3
 
4.8%
5.4 3
 
4.8%
7.7 3
 
4.8%
5.9 3
 
4.8%
3.8 2
 
3.2%
4.3 2
 
3.2%
6.6 2
 
3.2%
4.0 2
 
3.2%
6.4 2
 
3.2%
Other values (33) 37
58.7%
ValueCountFrequency (%)
2.7 1
1.6%
3.0 1
1.6%
3.2 1
1.6%
3.3 1
1.6%
3.7 1
1.6%
3.8 2
3.2%
3.9 1
1.6%
4.0 2
3.2%
4.1 1
1.6%
4.2 1
1.6%
ValueCountFrequency (%)
27.2 1
1.6%
12.4 1
1.6%
11.9 1
1.6%
9.9 1
1.6%
9.6 1
1.6%
9.0 1
1.6%
8.8 1
1.6%
8.7 1
1.6%
8.6 1
1.6%
8.2 1
1.6%

일산화탄소
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)25.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.92380952
Minimum0.3
Maximum3.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2023-12-13T01:31:51.691815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.3
5-th percentile0.4
Q10.6
median0.7
Q31
95-th percentile2.58
Maximum3.4
Range3.1
Interquartile range (IQR)0.4

Descriptive statistics

Standard deviation0.61374427
Coefficient of variation (CV)0.66436235
Kurtosis5.8486607
Mean0.92380952
Median Absolute Deviation (MAD)0.2
Skewness2.3831052
Sum58.2
Variance0.37668203
MonotonicityNot monotonic
2023-12-13T01:31:51.842074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.7 10
15.9%
0.6 9
14.3%
0.8 8
12.7%
0.5 6
9.5%
0.4 6
9.5%
1.1 5
7.9%
1.0 5
7.9%
0.9 3
 
4.8%
1.2 2
 
3.2%
2.6 2
 
3.2%
Other values (6) 7
11.1%
ValueCountFrequency (%)
0.3 1
 
1.6%
0.4 6
9.5%
0.5 6
9.5%
0.6 9
14.3%
0.7 10
15.9%
0.8 8
12.7%
0.9 3
 
4.8%
1.0 5
7.9%
1.1 5
7.9%
1.2 2
 
3.2%
ValueCountFrequency (%)
3.4 1
 
1.6%
2.8 1
 
1.6%
2.6 2
 
3.2%
2.4 1
 
1.6%
1.7 1
 
1.6%
1.3 2
 
3.2%
1.2 2
 
3.2%
1.1 5
7.9%
1.0 5
7.9%
0.9 3
4.8%

이산화질소
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
미측정
33 
0.037
6 
0.015
 
3
0.02
 
3
0.026
 
2
Other values (12)
16 

Length

Max length5
Median length3
Mean length3.9047619
Min length3

Unique

Unique8 ?
Unique (%)12.7%

Sample

1st row미측정
2nd row미측정
3rd row미측정
4th row미측정
5th row미측정

Common Values

ValueCountFrequency (%)
미측정 33
52.4%
0.037 6
 
9.5%
0.015 3
 
4.8%
0.02 3
 
4.8%
0.026 2
 
3.2%
0.035 2
 
3.2%
0.023 2
 
3.2%
0.005 2
 
3.2%
0.013 2
 
3.2%
0.036 1
 
1.6%
Other values (7) 7
 
11.1%

Length

2023-12-13T01:31:52.008546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미측정 33
52.4%
0.037 6
 
9.5%
0.015 3
 
4.8%
0.02 3
 
4.8%
0.023 2
 
3.2%
0.005 2
 
3.2%
0.013 2
 
3.2%
0.035 2
 
3.2%
0.026 2
 
3.2%
0.036 1
 
1.6%
Other values (7) 7
 
11.1%

라돈
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Memory size636.0 B
미측정
33 
23
 
2
31
 
2
17
 
2
27.5
 
1
Other values (23)
23 

Length

Max length6
Median length3
Mean length3.1746032
Min length1

Unique

Unique24 ?
Unique (%)38.1%

Sample

1st row미측정
2nd row미측정
3rd row미측정
4th row미측정
5th row미측정

Common Values

ValueCountFrequency (%)
미측정 33
52.4%
23 2
 
3.2%
31 2
 
3.2%
17 2
 
3.2%
27.5 1
 
1.6%
29 1
 
1.6%
24.333 1
 
1.6%
28.333 1
 
1.6%
32.333 1
 
1.6%
33 1
 
1.6%
Other values (18) 18
28.6%

Length

2023-12-13T01:31:52.149249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미측정 33
52.4%
31 2
 
3.2%
17 2
 
3.2%
23 2
 
3.2%
33.667 1
 
1.6%
20.333 1
 
1.6%
21.5 1
 
1.6%
17.5 1
 
1.6%
44.5 1
 
1.6%
45 1
 
1.6%
Other values (18) 18
28.6%

휘발성유기화합물
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)49.2%
Missing0
Missing (%)0.0%
Memory size636.0 B
미측정
33 
66
 
1
60.45
 
1
23.25
 
1
77.4
 
1
Other values (26)
26 

Length

Max length7
Median length3
Mean length3.5714286
Min length2

Unique

Unique30 ?
Unique (%)47.6%

Sample

1st row미측정
2nd row미측정
3rd row미측정
4th row미측정
5th row미측정

Common Values

ValueCountFrequency (%)
미측정 33
52.4%
66 1
 
1.6%
60.45 1
 
1.6%
23.25 1
 
1.6%
77.4 1
 
1.6%
30 1
 
1.6%
56 1
 
1.6%
80.867 1
 
1.6%
48.5 1
 
1.6%
77.733 1
 
1.6%
Other values (21) 21
33.3%

Length

2023-12-13T01:31:52.294451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미측정 33
52.4%
134.9 1
 
1.6%
29 1
 
1.6%
127.05 1
 
1.6%
100.05 1
 
1.6%
61.85 1
 
1.6%
63 1
 
1.6%
60.767 1
 
1.6%
81.35 1
 
1.6%
80.5 1
 
1.6%
Other values (21) 21
33.3%

Interactions

2023-12-13T01:31:48.703916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.127151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.450533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.894490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.332157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.786678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.188801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.518473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.970158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.401066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.867414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.253504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.593299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.062631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.478745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.968606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.322984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.689682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.146643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.559779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:49.051990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.382377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:47.794678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.241829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:31:48.628476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:31:52.397668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
역사명미세먼지(PM10)미세먼지(PM2.5)이산화탄소폼알데하이드일산화탄소이산화질소라돈휘발성유기화합물
역사명1.0000.9550.8780.0000.9030.0000.9670.9740.989
미세먼지(PM10)0.9551.0000.8070.0000.5590.0000.4250.0000.000
미세먼지(PM2.5)0.8780.8071.0000.0000.0000.3190.5060.7870.780
이산화탄소0.0000.0000.0001.0000.3820.7630.8010.9510.958
폼알데하이드0.9030.5590.0000.3821.0000.0000.1550.5850.462
일산화탄소0.0000.0000.3190.7630.0001.0000.8260.9500.944
이산화질소0.9670.4250.5060.8010.1550.8261.0000.9941.000
라돈0.9740.0000.7870.9510.5850.9500.9941.0001.000
휘발성유기화합물0.9890.0000.7800.9580.4620.9441.0001.0001.000
2023-12-13T01:31:52.541832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이산화질소휘발성유기화합물라돈
이산화질소1.0000.8340.810
휘발성유기화합물0.8341.0000.956
라돈0.8100.9561.000
2023-12-13T01:31:52.657604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
미세먼지(PM10)미세먼지(PM2.5)이산화탄소폼알데하이드일산화탄소이산화질소라돈휘발성유기화합물
미세먼지(PM10)1.0000.807-0.0840.0170.0240.1530.0000.000
미세먼지(PM2.5)0.8071.000-0.0910.1040.0650.2150.3520.325
이산화탄소-0.084-0.0911.0000.0610.3580.4540.6030.601
폼알데하이드0.0170.1040.0611.000-0.0450.0790.2610.185
일산화탄소0.0240.0650.358-0.0451.0000.4710.6030.563
이산화질소0.1530.2150.4540.0790.4711.0000.8100.834
라돈0.0000.3520.6030.2610.6030.8101.0000.956
휘발성유기화합물0.0000.3250.6010.1850.5630.8340.9561.000

Missing values

2023-12-13T01:31:49.172765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:31:49.303717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

역사명미세먼지(PM10)미세먼지(PM2.5)이산화탄소폼알데하이드일산화탄소이산화질소라돈휘발성유기화합물
01호선평균17.38.75006.20.7미측정미측정미측정
1설화명곡14.27.07378.63.4미측정미측정미측정
2화원22.917.74654.10.5미측정미측정미측정
3대곡역14.89.45138.80.6미측정미측정미측정
4진천역19.49.05175.50.9미측정미측정미측정
5월배역33.414.94657.60.3미측정미측정미측정
6상인역30.316.94977.50.4미측정미측정미측정
7월촌역16.912.445512.40.7미측정미측정미측정
8송현역23.410.74649.90.5미측정미측정미측정
9성당못역21.310.75046.30.4미측정미측정미측정
역사명미세먼지(PM10)미세먼지(PM2.5)이산화탄소폼알데하이드일산화탄소이산화질소라돈휘발성유기화합물
53만촌역24.49.94896.41.00.03711.519.2
54담티역23.211.85186.30.70.0373180.5
55연호역14.29.54844.40.60.0355681.35
56대공원역24.38.94737.10.60.0354560.767
57고산역25.416.64883.30.80.02344.563
58신매역24.315.84794.40.60.03617.561.85
59사월역28.820.74716.11.00.02621.5100.05
60정평역13.14.84775.00.80.02523127.05
61임당역22.38.04913.81.20.013629
62영남대역24.38.94744.31.00.022310.4