Overview

Dataset statistics

Number of variables9
Number of observations1188
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory90.6 KiB
Average record size in memory78.1 B

Variable types

Categorical3
Numeric6

Dataset

Description강원특별자치도 시군별 도시대기측정망 측정 결과 - 측정내역 및 단위 : 아황산가스(SO₂) (단위 : ppm/year), 일산화탄소(CO) (단위 : ppm/8hours), 이산화질소(NO₂) (단위 : ppm/year), 먼지(Dust) (단위 : ㎍/㎥/year), 초미세먼지 (단위 : ㎍/㎥/year), 오존(O₃) (단위 : ppm/8hours)
URLhttps://www.data.go.kr/data/15050447/fileData.do

Alerts

일산화탄소 is highly overall correlated with 이산화질소 and 1 other fieldsHigh correlation
이산화질소 is highly overall correlated with 일산화탄소 and 1 other fieldsHigh correlation
먼지 is highly overall correlated with 일산화탄소 and 1 other fieldsHigh correlation
먼지 has 16 (1.3%) zerosZeros
초미세먼지 has 490 (41.2%) zerosZeros

Reproduction

Analysis started2023-12-12 04:48:52.627575
Analysis finished2023-12-12 04:48:58.565193
Duration5.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군별
Categorical

Distinct18
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
춘천시
144 
동해시
144 
삼척시
144 
원주시
144 
강릉시
144 
Other values (13)
468 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
춘천시 144
12.1%
동해시 144
12.1%
삼척시 144
12.1%
원주시 144
12.1%
강릉시 144
12.1%
영월군 36
 
3.0%
태백시 36
 
3.0%
속초시 36
 
3.0%
홍천군 36
 
3.0%
횡성군 36
 
3.0%
Other values (8) 288
24.2%

Length

2023-12-12T13:48:58.670118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
춘천시 144
12.1%
삼척시 144
12.1%
원주시 144
12.1%
강릉시 144
12.1%
동해시 144
12.1%
화천군 36
 
3.0%
정선군 36
 
3.0%
고성군 36
 
3.0%
인제군 36
 
3.0%
양구군 36
 
3.0%
Other values (8) 288
24.2%

연도별
Categorical

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
2019년
216 
2020년
216 
2021년
216 
2010년
60 
2011년
60 
Other values (7)
420 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2010년
2nd row2010년
3rd row2010년
4th row2010년
5th row2010년

Common Values

ValueCountFrequency (%)
2019년 216
18.2%
2020년 216
18.2%
2021년 216
18.2%
2010년 60
 
5.1%
2011년 60
 
5.1%
2012년 60
 
5.1%
2013년 60
 
5.1%
2014년 60
 
5.1%
2015년 60
 
5.1%
2016년 60
 
5.1%
Other values (2) 120
10.1%

Length

2023-12-12T13:48:58.816499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019년 216
18.2%
2020년 216
18.2%
2021년 216
18.2%
2010년 60
 
5.1%
2011년 60
 
5.1%
2012년 60
 
5.1%
2013년 60
 
5.1%
2014년 60
 
5.1%
2015년 60
 
5.1%
2016년 60
 
5.1%
Other values (2) 120
10.1%

월별
Categorical

Distinct12
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size9.4 KiB
01월
99 
02월
99 
03월
99 
04월
99 
05월
99 
Other values (7)
693 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row01월
2nd row02월
3rd row03월
4th row04월
5th row05월

Common Values

ValueCountFrequency (%)
01월 99
8.3%
02월 99
8.3%
03월 99
8.3%
04월 99
8.3%
05월 99
8.3%
06월 99
8.3%
07월 99
8.3%
08월 99
8.3%
09월 99
8.3%
10월 99
8.3%
Other values (2) 198
16.7%

Length

2023-12-12T13:48:58.965795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
01월 99
8.3%
02월 99
8.3%
03월 99
8.3%
04월 99
8.3%
05월 99
8.3%
06월 99
8.3%
07월 99
8.3%
08월 99
8.3%
09월 99
8.3%
10월 99
8.3%
Other values (2) 198
16.7%

아황산가스
Real number (ℝ)

Distinct14
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.00325
Minimum0
Maximum0.014
Zeros7
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T13:48:59.072241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.001
Q10.002
median0.003
Q30.004
95-th percentile0.007
Maximum0.014
Range0.014
Interquartile range (IQR)0.002

Descriptive statistics

Standard deviation0.0019459309
Coefficient of variation (CV)0.59874796
Kurtosis3.4530179
Mean0.00325
Median Absolute Deviation (MAD)0.001
Skewness1.6625966
Sum3.861
Variance3.786647 × 10-6
MonotonicityNot monotonic
2023-12-12T13:48:59.229890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.002 468
39.4%
0.003 285
24.0%
0.004 117
 
9.8%
0.006 106
 
8.9%
0.001 81
 
6.8%
0.005 44
 
3.7%
0.007 31
 
2.6%
0.008 25
 
2.1%
0.009 10
 
0.8%
0.0 7
 
0.6%
Other values (4) 14
 
1.2%
ValueCountFrequency (%)
0.0 7
 
0.6%
0.001 81
 
6.8%
0.002 468
39.4%
0.003 285
24.0%
0.004 117
 
9.8%
0.005 44
 
3.7%
0.006 106
 
8.9%
0.007 31
 
2.6%
0.008 25
 
2.1%
0.009 10
 
0.8%
ValueCountFrequency (%)
0.014 2
 
0.2%
0.012 3
 
0.3%
0.011 4
 
0.3%
0.01 5
 
0.4%
0.009 10
 
0.8%
0.008 25
 
2.1%
0.007 31
 
2.6%
0.006 106
8.9%
0.005 44
 
3.7%
0.004 117
9.8%

일산화탄소
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.45686869
Minimum0
Maximum1.6
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T13:48:59.411292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.006
Q10.375
median0.4
Q30.5
95-th percentile0.8
Maximum1.6
Range1.6
Interquartile range (IQR)0.125

Descriptive statistics

Standard deviation0.21610029
Coefficient of variation (CV)0.47300307
Kurtosis4.1155721
Mean0.45686869
Median Absolute Deviation (MAD)0.1
Skewness1.0449835
Sum542.76
Variance0.046699336
MonotonicityNot monotonic
2023-12-12T13:48:59.539901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.4 371
31.2%
0.5 227
19.1%
0.3 205
17.3%
0.6 144
 
12.1%
0.7 61
 
5.1%
0.006 60
 
5.1%
0.8 38
 
3.2%
0.2 24
 
2.0%
0.9 15
 
1.3%
1.0 11
 
0.9%
Other values (6) 32
 
2.7%
ValueCountFrequency (%)
0.0 8
 
0.7%
0.006 60
 
5.1%
0.2 24
 
2.0%
0.3 205
17.3%
0.4 371
31.2%
0.5 227
19.1%
0.6 144
 
12.1%
0.7 61
 
5.1%
0.8 38
 
3.2%
0.9 15
 
1.3%
ValueCountFrequency (%)
1.6 3
 
0.3%
1.5 2
 
0.2%
1.3 3
 
0.3%
1.2 9
 
0.8%
1.1 7
 
0.6%
1.0 11
 
0.9%
0.9 15
 
1.3%
0.8 38
 
3.2%
0.7 61
5.1%
0.6 144
12.1%

이산화질소
Real number (ℝ)

HIGH CORRELATION 

Distinct37
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.012449495
Minimum0
Maximum0.039
Zeros8
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T13:48:59.715480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004
Q10.007
median0.012
Q30.016
95-th percentile0.025
Maximum0.039
Range0.039
Interquartile range (IQR)0.009

Descriptive statistics

Standard deviation0.0065460568
Coefficient of variation (CV)0.52580902
Kurtosis0.60026876
Mean0.012449495
Median Absolute Deviation (MAD)0.005
Skewness0.81512193
Sum14.79
Variance4.2850859 × 10-5
MonotonicityNot monotonic
2023-12-12T13:48:59.863976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0.006 124
 
10.4%
0.011 73
 
6.1%
0.01 73
 
6.1%
0.013 68
 
5.7%
0.012 68
 
5.7%
0.008 64
 
5.4%
0.005 63
 
5.3%
0.009 62
 
5.2%
0.015 61
 
5.1%
0.014 60
 
5.1%
Other values (27) 472
39.7%
ValueCountFrequency (%)
0.0 8
 
0.7%
0.001 1
 
0.1%
0.002 5
 
0.4%
0.003 29
 
2.4%
0.004 35
 
2.9%
0.005 63
5.3%
0.006 124
10.4%
0.007 52
4.4%
0.008 64
5.4%
0.009 62
5.2%
ValueCountFrequency (%)
0.039 1
 
0.1%
0.036 4
 
0.3%
0.034 1
 
0.1%
0.033 3
 
0.3%
0.032 1
 
0.1%
0.031 6
0.5%
0.03 4
 
0.3%
0.029 6
0.5%
0.028 10
0.8%
0.027 10
0.8%

먼지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct85
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.408552
Minimum0
Maximum109
Zeros16
Zeros (%)1.3%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T13:49:00.070647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.006
Q125
median34
Q345
95-th percentile67
Maximum109
Range109
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.734291
Coefficient of variation (CV)0.50084766
Kurtosis0.59721467
Mean35.408552
Median Absolute Deviation (MAD)10
Skewness0.34717769
Sum42065.36
Variance314.50506
MonotonicityNot monotonic
2023-12-12T13:49:00.235136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.006 60
 
5.1%
32.0 48
 
4.0%
41.0 39
 
3.3%
38.0 37
 
3.1%
29.0 34
 
2.9%
28.0 34
 
2.9%
25.0 34
 
2.9%
35.0 33
 
2.8%
30.0 32
 
2.7%
43.0 30
 
2.5%
Other values (75) 807
67.9%
ValueCountFrequency (%)
0.0 16
 
1.3%
0.006 60
5.1%
8.0 1
 
0.1%
10.0 1
 
0.1%
11.0 1
 
0.1%
12.0 8
 
0.7%
13.0 10
 
0.8%
14.0 15
 
1.3%
15.0 14
 
1.2%
16.0 7
 
0.6%
ValueCountFrequency (%)
109.0 1
0.1%
99.0 1
0.1%
98.0 1
0.1%
95.0 1
0.1%
94.0 1
0.1%
90.0 1
0.1%
89.0 1
0.1%
87.0 1
0.1%
85.0 1
0.1%
83.0 1
0.1%

초미세먼지
Real number (ℝ)

ZEROS 

Distinct45
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.220539
Minimum0
Maximum50
Zeros490
Zeros (%)41.2%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T13:49:00.378771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10
Q317
95-th percentile28.65
Maximum50
Range50
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.460429
Coefficient of variation (CV)1.0234714
Kurtosis-0.036976013
Mean10.220539
Median Absolute Deviation (MAD)10
Skewness0.7412564
Sum12142
Variance109.42057
MonotonicityNot monotonic
2023-12-12T13:49:00.540974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0 490
41.2%
17 46
 
3.9%
14 45
 
3.8%
15 43
 
3.6%
16 39
 
3.3%
13 38
 
3.2%
11 35
 
2.9%
10 34
 
2.9%
8 34
 
2.9%
12 34
 
2.9%
Other values (35) 350
29.5%
ValueCountFrequency (%)
0 490
41.2%
2 2
 
0.2%
4 1
 
0.1%
5 6
 
0.5%
6 12
 
1.0%
7 12
 
1.0%
8 34
 
2.9%
9 31
 
2.6%
10 34
 
2.9%
11 35
 
2.9%
ValueCountFrequency (%)
50 1
 
0.1%
48 1
 
0.1%
45 3
0.3%
44 1
 
0.1%
43 2
0.2%
42 4
0.3%
40 1
 
0.1%
39 1
 
0.1%
38 2
0.2%
37 2
0.2%

오존
Real number (ℝ)

Distinct58
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028405724
Minimum0
Maximum0.064
Zeros7
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size10.6 KiB
2023-12-12T13:49:00.724481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.006
Q10.022
median0.028
Q30.036
95-th percentile0.047
Maximum0.064
Range0.064
Interquartile range (IQR)0.014

Descriptive statistics

Standard deviation0.011259011
Coefficient of variation (CV)0.39636416
Kurtosis-0.081905332
Mean0.028405724
Median Absolute Deviation (MAD)0.007
Skewness0.039045964
Sum33.746
Variance0.00012676533
MonotonicityNot monotonic
2023-12-12T13:49:00.898718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.026 63
 
5.3%
0.025 63
 
5.3%
0.006 60
 
5.1%
0.023 47
 
4.0%
0.028 46
 
3.9%
0.029 45
 
3.8%
0.022 45
 
3.8%
0.027 45
 
3.8%
0.024 39
 
3.3%
0.034 36
 
3.0%
Other values (48) 699
58.8%
ValueCountFrequency (%)
0.0 7
 
0.6%
0.006 60
5.1%
0.007 2
 
0.2%
0.009 4
 
0.3%
0.01 5
 
0.4%
0.011 10
 
0.8%
0.012 16
 
1.3%
0.013 16
 
1.3%
0.014 8
 
0.7%
0.015 19
 
1.6%
ValueCountFrequency (%)
0.064 1
 
0.1%
0.063 1
 
0.1%
0.062 1
 
0.1%
0.06 1
 
0.1%
0.059 1
 
0.1%
0.058 1
 
0.1%
0.057 2
0.2%
0.056 1
 
0.1%
0.055 1
 
0.1%
0.054 3
0.3%

Interactions

2023-12-12T13:48:57.515151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:53.287718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:54.187088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:55.270939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:56.030852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:56.757553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:57.631483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:53.426157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:54.346568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:55.384296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:56.141966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:56.900944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:57.764912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:53.585747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:54.460618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:55.519211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:56.292947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:57.043868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:57.915601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:53.750021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:54.898644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:55.669747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:56.415826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:57.152579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:58.044145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:53.888263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:55.002711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:55.787132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:56.524179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:57.276533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:58.156495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:54.048043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:55.143471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:55.909021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:56.646782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:48:57.383384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:49:01.028478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군별연도별월별아황산가스일산화탄소이산화질소먼지초미세먼지오존
시군별1.0000.4940.0000.4640.3920.6050.3680.5340.420
연도별0.4941.0000.0000.5940.6630.5290.6480.6440.630
월별0.0000.0001.0000.4120.4090.3770.5180.4680.612
아황산가스0.4640.5940.4121.0000.8680.8280.8090.5020.680
일산화탄소0.3920.6630.4090.8681.0000.8810.8680.5470.829
이산화질소0.6050.5290.3770.8280.8811.0000.7900.5890.670
먼지0.3680.6480.5180.8090.8680.7901.0000.7520.792
초미세먼지0.5340.6440.4680.5020.5470.5890.7521.0000.519
오존0.4200.6300.6120.6800.8290.6700.7920.5191.000
2023-12-12T13:49:01.500605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
월별연도별시군별
월별1.0000.0000.000
연도별0.0001.0000.188
시군별0.0000.1881.000
2023-12-12T13:49:01.611633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아황산가스일산화탄소이산화질소먼지초미세먼지오존시군별연도별월별
아황산가스1.0000.3810.4660.349-0.325-0.2540.1960.2710.185
일산화탄소0.3811.0000.6340.557-0.067-0.1570.1590.3520.184
이산화질소0.4660.6341.0000.678-0.190-0.2060.2800.2540.168
먼지0.3490.5570.6781.0000.0240.2270.1490.3390.247
초미세먼지-0.325-0.067-0.1900.0241.0000.2310.2320.3340.208
오존-0.254-0.157-0.2060.2270.2311.0000.1720.3250.311
시군별0.1960.1590.2800.1490.2320.1721.0000.1880.000
연도별0.2710.3520.2540.3390.3340.3250.1881.0000.000
월별0.1850.1840.1680.2470.2080.3110.0000.0001.000

Missing values

2023-12-12T13:48:58.301085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:48:58.490584image/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

시군별연도별월별아황산가스일산화탄소이산화질소먼지초미세먼지오존
0춘천시2010년01월0.0081.00.02966.000.015
1춘천시2010년02월0.0060.80.02252.000.02
2춘천시2010년03월0.0040.50.01668.000.029
3춘천시2010년04월0.0040.40.01658.000.035
4춘천시2010년05월0.0030.40.01260.000.043
5춘천시2010년06월0.0020.40.01149.000.04
6춘천시2010년07월0.0020.30.01134.000.026
7춘천시2010년08월0.0010.40.0132.000.021
8춘천시2010년09월0.0020.30.01124.000.018
9춘천시2010년10월0.0030.50.01741.000.016
시군별연도별월별아황산가스일산화탄소이산화질소먼지초미세먼지오존
1178양양군2021년03월0.0020.50.00746.0160.039
1179양양군2021년04월0.0020.40.00635.0120.043
1180양양군2021년05월0.0010.50.00538.0100.046
1181양양군2021년06월0.0010.50.00517.0100.038
1182양양군2021년07월0.0010.60.00414.080.034
1183양양군2021년08월0.0020.60.00615.080.04
1184양양군2021년09월0.0020.50.00410.040.035
1185양양군2021년10월0.0020.40.00615.070.041
1186양양군2021년11월0.0020.50.00726.0130.042
1187양양군2021년12월0.0020.40.00722.0130.035