Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.6 KiB
Average record size in memory88.3 B

Variable types

Numeric5
Categorical5

Alerts

측정일 has constant value ""Constant
시도명 is highly overall correlated with 기본키 and 2 other fieldsHigh correlation
지점 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
시가지 CO 량(g/km) is highly overall correlated with 기본키 and 4 other fieldsHigh correlation
시군구명 is highly overall correlated with 기본키 and 5 other fieldsHigh correlation
기본키 is highly overall correlated with 지점 and 3 other fieldsHigh correlation
도로인접 CO 량(g/km) is highly overall correlated with 도로인접 PM 량(g/km)High correlation
도로인접 PM 량(g/km) is highly overall correlated with 도로인접 CO 량(g/km)High correlation
시가지 PM 2.5 량(g/km) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
시가지 PM 10 량(g/km) is highly overall correlated with 지점 and 2 other fieldsHigh correlation
기본키 has unique valuesUnique
도로인접 CO 량(g/km) has unique valuesUnique
도로인접 PM 량(g/km) has unique valuesUnique
시가지 PM 2.5 량(g/km) has 4 (4.0%) zerosZeros
시가지 PM 10 량(g/km) has 4 (4.0%) zerosZeros

Reproduction

Analysis started2024-04-17 11:34:53.630889
Analysis finished2024-04-17 11:34:56.501064
Duration2.87 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기본키
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.5
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T20:34:56.567183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.95
Q125.75
median50.5
Q375.25
95-th percentile95.05
Maximum100
Range99
Interquartile range (IQR)49.5

Descriptive statistics

Standard deviation29.011492
Coefficient of variation (CV)0.57448499
Kurtosis-1.2
Mean50.5
Median Absolute Deviation (MAD)25
Skewness0
Sum5050
Variance841.66667
MonotonicityStrictly increasing
2024-04-17T20:34:56.689439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
65 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%

측정일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200401
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200401
2nd row20200401
3rd row20200401
4th row20200401
5th row20200401

Common Values

ValueCountFrequency (%)
20200401 100
100.0%

Length

2024-04-17T20:34:56.803534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:34:56.894877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200401 100
100.0%

지점
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)22.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A-0100-0698S-8
8 
A-0251-0721E-7
7 
A-5510-0116E-6
 
6
A-0010-0083E-6
 
6
A-0120-0062E-6
 
6
Other values (17)
67 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA-5510-0116E-6
2nd rowA-5510-0116E-6
3rd rowA-5510-0116E-6
4th rowA-5510-0116E-6
5th rowA-5510-0116E-6

Common Values

ValueCountFrequency (%)
A-0100-0698S-8 8
 
8.0%
A-0251-0721E-7 7
 
7.0%
A-5510-0116E-6 6
 
6.0%
A-0010-0083E-6 6
 
6.0%
A-0120-0062E-6 6
 
6.0%
A-0121-0066S-4 4
 
4.0%
A-6000-0265E-4 4
 
4.0%
A-0100-0407S-4 4
 
4.0%
A-0160-0058E-4 4
 
4.0%
A-0450-0480S-4 4
 
4.0%
Other values (12) 47
47.0%

Length

2024-04-17T20:34:56.983950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
a-0100-0698s-8 8
 
8.0%
a-0251-0721e-7 7
 
7.0%
a-5510-0116e-6 6
 
6.0%
a-0010-0083e-6 6
 
6.0%
a-0120-0062e-6 6
 
6.0%
a-0150-0290s-4 4
 
4.0%
a-0140-0273s-4 4
 
4.0%
a-0251-0652s-4 4
 
4.0%
a-0140-0388e-4 4
 
4.0%
a-0251-1042e-4 4
 
4.0%
Other values (12) 47
47.0%

시도명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
전남
53 
경남
32 
광주
7 
울산
 
4
대구
 
4

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전남 53
53.0%
경남 32
32.0%
광주 7
 
7.0%
울산 4
 
4.0%
대구 4
 
4.0%

Length

2024-04-17T20:34:57.080222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:34:57.180503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전남 53
53.0%
경남 32
32.0%
광주 7
 
7.0%
울산 4
 
4.0%
대구 4
 
4.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
담양군
22 
양산시
12 
김해시
8 
진주시
8 
무안군
8 
Other values (9)
42 

Length

Max length3
Median length3
Mean length2.93
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
담양군 22
22.0%
양산시 12
12.0%
김해시 8
 
8.0%
진주시 8
 
8.0%
무안군 8
 
8.0%
순천시 7
 
7.0%
북구 7
 
7.0%
하동군 4
 
4.0%
울주군 4
 
4.0%
달성군 4
 
4.0%
Other values (4) 16
16.0%

Length

2024-04-17T20:34:57.275089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
담양군 22
22.0%
양산시 12
12.0%
김해시 8
 
8.0%
진주시 8
 
8.0%
무안군 8
 
8.0%
순천시 7
 
7.0%
북구 7
 
7.0%
하동군 4
 
4.0%
울주군 4
 
4.0%
달성군 4
 
4.0%
Other values (4) 16
16.0%

도로인접 CO 량(g/km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3679.4173
Minimum35.76
Maximum13894.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T20:34:57.382142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.76
5-th percentile163.818
Q11642.7275
median2831.865
Q35014.3425
95-th percentile9686.8345
Maximum13894.57
Range13858.81
Interquartile range (IQR)3371.615

Descriptive statistics

Standard deviation2996.0964
Coefficient of variation (CV)0.81428558
Kurtosis1.2468691
Mean3679.4173
Median Absolute Deviation (MAD)1791.85
Skewness1.200356
Sum367941.73
Variance8976593.9
MonotonicityNot monotonic
2024-04-17T20:34:57.507944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11111.0 1
 
1.0%
476.26 1
 
1.0%
2380.22 1
 
1.0%
5168.55 1
 
1.0%
2178.7 1
 
1.0%
4592.61 1
 
1.0%
2115.1 1
 
1.0%
2695.27 1
 
1.0%
442.62 1
 
1.0%
2679.45 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
35.76 1
1.0%
42.89 1
1.0%
104.01 1
1.0%
132.21 1
1.0%
153.52 1
1.0%
164.36 1
1.0%
230.25 1
1.0%
341.88 1
1.0%
407.42 1
1.0%
442.62 1
1.0%
ValueCountFrequency (%)
13894.57 1
1.0%
12334.51 1
1.0%
11169.54 1
1.0%
11111.0 1
1.0%
9826.57 1
1.0%
9679.48 1
1.0%
9649.81 1
1.0%
9301.11 1
1.0%
8691.35 1
1.0%
8689.11 1
1.0%

도로인접 PM 량(g/km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean575.212
Minimum1.61
Maximum3513.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T20:34:57.621832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.61
5-th percentile13.1135
Q185.95
median304.205
Q3883.355
95-th percentile1979.2995
Maximum3513.51
Range3511.9
Interquartile range (IQR)797.405

Descriptive statistics

Standard deviation680.81893
Coefficient of variation (CV)1.1835965
Kurtosis3.6145104
Mean575.212
Median Absolute Deviation (MAD)268.735
Skewness1.7935112
Sum57521.2
Variance463514.41
MonotonicityNot monotonic
2024-04-17T20:34:57.738396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2091.58 1
 
1.0%
24.96 1
 
1.0%
158.29 1
 
1.0%
1197.96 1
 
1.0%
143.57 1
 
1.0%
947.63 1
 
1.0%
165.57 1
 
1.0%
455.61 1
 
1.0%
33.02 1
 
1.0%
390.67 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.61 1
1.0%
2.61 1
1.0%
4.49 1
1.0%
10.82 1
1.0%
12.8 1
1.0%
13.13 1
1.0%
14.81 1
1.0%
17.39 1
1.0%
24.55 1
1.0%
24.96 1
1.0%
ValueCountFrequency (%)
3513.51 1
1.0%
2810.55 1
1.0%
2273.96 1
1.0%
2104.42 1
1.0%
2091.58 1
1.0%
1973.39 1
1.0%
1756.34 1
1.0%
1689.24 1
1.0%
1653.78 1
1.0%
1591.95 1
1.0%

시가지 CO 량(g/km)
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0.3
35 
0.2
26 
0.5
19 
0.4
16 
0.0
4 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.4
2nd row0.4
3rd row0.4
4th row0.4
5th row0.4

Common Values

ValueCountFrequency (%)
0.3 35
35.0%
0.2 26
26.0%
0.5 19
19.0%
0.4 16
16.0%
0.0 4
 
4.0%

Length

2024-04-17T20:34:57.865525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-17T20:34:57.950310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.3 35
35.0%
0.2 26
26.0%
0.5 19
19.0%
0.4 16
16.0%
0.0 4
 
4.0%

시가지 PM 2.5 량(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.1
Minimum0
Maximum31
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T20:34:58.034570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22
Q123
median25
Q329
95-th percentile31
Maximum31
Range31
Interquartile range (IQR)6

Descriptive statistics

Standard deviation6.0058894
Coefficient of variation (CV)0.23927846
Kurtosis10.198486
Mean25.1
Median Absolute Deviation (MAD)2.5
Skewness-2.8272502
Sum2510
Variance36.070707
MonotonicityNot monotonic
2024-04-17T20:34:58.123128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
23 26
26.0%
25 20
20.0%
30 12
12.0%
31 11
11.0%
22 8
 
8.0%
28 8
 
8.0%
29 7
 
7.0%
0 4
 
4.0%
27 4
 
4.0%
ValueCountFrequency (%)
0 4
 
4.0%
22 8
 
8.0%
23 26
26.0%
25 20
20.0%
27 4
 
4.0%
28 8
 
8.0%
29 7
 
7.0%
30 12
12.0%
31 11
11.0%
ValueCountFrequency (%)
31 11
11.0%
30 12
12.0%
29 7
 
7.0%
28 8
 
8.0%
27 4
 
4.0%
25 20
20.0%
23 26
26.0%
22 8
 
8.0%
0 4
 
4.0%

시가지 PM 10 량(g/km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.16
Minimum0
Maximum20
Zeros4
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2024-04-17T20:34:58.218002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q16
median13
Q316
95-th percentile19
Maximum20
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.279501
Coefficient of variation (CV)0.43416949
Kurtosis-0.66514116
Mean12.16
Median Absolute Deviation (MAD)3
Skewness-0.40182824
Sum1216
Variance27.873131
MonotonicityNot monotonic
2024-04-17T20:34:58.320312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6 26
26.0%
12 16
16.0%
19 15
15.0%
13 15
15.0%
16 12
12.0%
0 4
 
4.0%
20 4
 
4.0%
15 4
 
4.0%
14 4
 
4.0%
ValueCountFrequency (%)
0 4
 
4.0%
6 26
26.0%
12 16
16.0%
13 15
15.0%
14 4
 
4.0%
15 4
 
4.0%
16 12
12.0%
19 15
15.0%
20 4
 
4.0%
ValueCountFrequency (%)
20 4
 
4.0%
19 15
15.0%
16 12
12.0%
15 4
 
4.0%
14 4
 
4.0%
13 15
15.0%
12 16
16.0%
6 26
26.0%
0 4
 
4.0%

Interactions

2024-04-17T20:34:55.871297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:53.962552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:54.334903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.039388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.471635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.948304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:54.027926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:54.408259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.114856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.546218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:56.040797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:54.101620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:54.491339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.205332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.624054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:56.122550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:54.172548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:54.580687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.294755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.703511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:56.206316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:54.248197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:54.665218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.387702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T20:34:55.777116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T20:34:58.403994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키지점시도명시군구명도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)
기본키1.0000.9860.9230.9240.7360.4180.8910.7550.816
지점0.9861.0001.0001.0000.6970.5161.0001.0001.000
시도명0.9231.0001.0001.0000.7780.5730.8410.4220.603
시군구명0.9241.0001.0001.0000.6700.6181.0001.0001.000
도로인접 CO 량(g/km)0.7360.6970.7780.6701.0000.8640.5810.5170.519
도로인접 PM 량(g/km)0.4180.5160.5730.6180.8641.0000.3880.4620.450
시가지 CO 량(g/km)0.8911.0000.8411.0000.5810.3881.0000.8840.866
시가지 PM 2.5 량(g/km)0.7551.0000.4221.0000.5170.4620.8841.0000.921
시가지 PM 10 량(g/km)0.8161.0000.6031.0000.5190.4500.8660.9211.000
2024-04-17T20:34:58.515110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명지점시가지 CO 량(g/km)시군구명
시도명1.0000.9060.4740.951
지점0.9061.0000.9060.952
시가지 CO 량(g/km)0.4740.9061.0000.951
시군구명0.9510.9520.9511.000
2024-04-17T20:34:58.600486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기본키도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)지점시도명시군구명시가지 CO 량(g/km)
기본키1.000-0.454-0.2760.281-0.2600.8560.6140.7010.558
도로인접 CO 량(g/km)-0.4541.0000.921-0.0590.3510.3160.4220.3370.270
도로인접 PM 량(g/km)-0.2760.9211.000-0.0480.2040.2060.3680.3070.229
시가지 PM 2.5 량(g/km)0.281-0.059-0.0481.0000.2880.9010.3540.9460.874
시가지 PM 10 량(g/km)-0.2600.3510.2040.2881.0000.9110.4900.9570.765
지점0.8560.3160.2060.9010.9111.0000.9060.9520.906
시도명0.6140.4220.3680.3540.4900.9061.0000.9510.474
시군구명0.7010.3370.3070.9460.9570.9520.9511.0000.951
시가지 CO 량(g/km)0.5580.2700.2290.8740.7650.9060.4740.9511.000

Missing values

2024-04-17T20:34:56.320975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T20:34:56.448515image/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

기본키측정일지점시도명시군구명도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)
0120200401A-5510-0116E-6경남양산시11111.02091.580.42516
1220200401A-5510-0116E-6경남양산시4792.5386.370.42516
2320200401A-5510-0116E-6경남양산시9826.571306.860.42516
3420200401A-5510-0116E-6경남양산시6049.75236.780.42516
4520200401A-5510-0116E-6경남양산시11169.541466.40.42516
5620200401A-5510-0116E-6경남양산시9649.811591.950.42516
6720200401A-6000-0062E-4경남김해시2659.5888.150.32219
7820200401A-6000-0062E-4경남김해시6837.471164.90.32219
8920200401A-6000-0062E-4경남김해시2909.31205.360.32219
91020200401A-6000-0062E-4경남김해시6901.791502.70.32219
기본키측정일지점시도명시군구명도로인접 CO 량(g/km)도로인접 PM 량(g/km)시가지 CO 량(g/km)시가지 PM 2.5 량(g/km)시가지 PM 10 량(g/km)
909120200401A-0140-0388E-4전남담양군2225.85601.660.2236
919220200401A-0140-0388E-4전남담양군514.4332.030.2236
929320200401A-0140-0388E-4전남담양군2403.94572.610.2236
939420200401A-0251-0652S-4전남담양군2044.34100.060.2236
949520200401A-0251-0652S-4전남담양군3870.03629.720.2236
959620200401A-0251-0652S-4전남담양군2092.51110.890.2236
969720200401A-0251-0652S-4전남담양군3980.64618.490.2236
979820200401A-0270-0044S-4전남순천시692.5763.730.33119
989920200401A-0270-0044S-4전남순천시3032.89544.260.33119
9910020200401A-0270-0044S-4전남순천시922.8452.270.33119