Overview

Dataset statistics

Number of variables8
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory76.1 B

Variable types

Numeric7
Categorical1

Dataset

Description오염원별 해양오염사고 발생현황에 관한 데이터로 연도별(2010~2022년), 오염원별(화물선, 유조선, 어선, 기타선, 육상, 미상)로 해양오염 사고건수와 유출량을 정리한 데이터이다.
Author해양경찰청
URLhttps://www.data.go.kr/data/15088428/fileData.do

Alerts

유조선 is highly overall correlated with 구분High correlation
어선 is highly overall correlated with 구분High correlation
미상 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 유조선 and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 14:48:32.241210
Analysis finished2023-12-12 14:48:38.850976
Duration6.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016
Minimum2010
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T23:48:38.918041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010.25
Q12013
median2016
Q32019
95-th percentile2021.75
Maximum2022
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.8157568
Coefficient of variation (CV)0.0018927365
Kurtosis-1.2131211
Mean2016
Median Absolute Deviation (MAD)3
Skewness0
Sum52416
Variance14.56
MonotonicityIncreasing
2023-12-12T23:48:39.083516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2010 2
 
7.7%
2011 2
 
7.7%
2012 2
 
7.7%
2013 2
 
7.7%
2014 2
 
7.7%
2015 2
 
7.7%
2016 2
 
7.7%
2017 2
 
7.7%
2018 2
 
7.7%
2019 2
 
7.7%
Other values (3) 6
23.1%
ValueCountFrequency (%)
2010 2
7.7%
2011 2
7.7%
2012 2
7.7%
2013 2
7.7%
2014 2
7.7%
2015 2
7.7%
2016 2
7.7%
2017 2
7.7%
2018 2
7.7%
2019 2
7.7%
ValueCountFrequency (%)
2022 2
7.7%
2021 2
7.7%
2020 2
7.7%
2019 2
7.7%
2018 2
7.7%
2017 2
7.7%
2016 2
7.7%
2015 2
7.7%
2014 2
7.7%
2013 2
7.7%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
건 수
13 
유출량(㎘)
13 

Length

Max length6
Median length4.5
Mean length4.5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건 수
2nd row유출량(㎘)
3rd row건 수
4th row유출량(㎘)
5th row건 수

Common Values

ValueCountFrequency (%)
건 수 13
50.0%
유출량(㎘) 13
50.0%

Length

2023-12-12T23:48:39.237435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:48:39.392793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건 13
33.3%
수 13
33.3%
유출량(㎘ 13
33.3%

화물선
Real number (ℝ)

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean92.119231
Minimum2.3
Maximum785.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T23:48:39.541898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.3
5-th percentile5.825
Q125.25
median33.5
Q376.65
95-th percentile384.65
Maximum785.6
Range783.3
Interquartile range (IQR)51.4

Descriptive statistics

Standard deviation167.18012
Coefficient of variation (CV)1.8148233
Kurtosis12.650651
Mean92.119231
Median Absolute Deviation (MAD)13.5
Skewness3.4488463
Sum2395.1
Variance27949.194
MonotonicityNot monotonic
2023-12-12T23:48:39.719097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
26.0 2
 
7.7%
21.0 2
 
7.7%
33.0 1
 
3.8%
2.3 1
 
3.8%
96.3 1
 
3.8%
121.7 1
 
3.8%
28.0 1
 
3.8%
5.1 1
 
3.8%
23.0 1
 
3.8%
8.0 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
2.3 1
3.8%
5.1 1
3.8%
8.0 1
3.8%
21.0 2
7.7%
23.0 1
3.8%
25.0 1
3.8%
26.0 2
7.7%
27.0 1
3.8%
28.0 1
3.8%
30.0 1
3.8%
ValueCountFrequency (%)
785.6 1
3.8%
438.9 1
3.8%
221.9 1
3.8%
121.7 1
3.8%
96.8 1
3.8%
96.3 1
3.8%
82.1 1
3.8%
60.3 1
3.8%
49.1 1
3.8%
48.0 1
3.8%

유조선
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.657692
Minimum1
Maximum148.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T23:48:39.906253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.625
Q110.35
median24.5
Q331.75
95-th percentile75.975
Maximum148.6
Range147.6
Interquartile range (IQR)21.4

Descriptive statistics

Standard deviation30.514733
Coefficient of variation (CV)1.1033
Kurtosis10.111217
Mean27.657692
Median Absolute Deviation (MAD)10.9
Skewness2.8830541
Sum719.1
Variance931.14894
MonotonicityNot monotonic
2023-12-12T23:48:40.070837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
25.0 2
 
7.7%
32.0 2
 
7.7%
24.0 2
 
7.7%
37.0 1
 
3.8%
88.5 1
 
3.8%
2.4 1
 
3.8%
26.0 1
 
3.8%
29.7 1
 
3.8%
35.0 1
 
3.8%
18.4 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
1.0 1
3.8%
1.4 1
3.8%
2.3 1
3.8%
2.4 1
3.8%
2.9 1
3.8%
7.5 1
3.8%
9.8 1
3.8%
12.0 1
3.8%
13.2 1
3.8%
18.4 1
3.8%
ValueCountFrequency (%)
148.6 1
3.8%
88.5 1
3.8%
38.4 1
3.8%
37.0 1
3.8%
35.0 1
3.8%
32.0 2
7.7%
31.0 1
3.8%
30.0 1
3.8%
29.7 1
3.8%
26.0 1
3.8%

어선
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.830769
Minimum5.6
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T23:48:40.562039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.6
5-th percentile18.8
Q143.425
median81
Q3107.5
95-th percentile139
Maximum190
Range184.4
Interquartile range (IQR)64.075

Descriptive statistics

Standard deviation45.334285
Coefficient of variation (CV)0.57508363
Kurtosis-0.16162026
Mean78.830769
Median Absolute Deviation (MAD)32.6
Skewness0.38896572
Sum2049.6
Variance2055.1974
MonotonicityNot monotonic
2023-12-12T23:48:40.735562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
91.0 2
 
7.7%
140.0 1
 
3.8%
115.0 1
 
3.8%
190.0 1
 
3.8%
85.0 1
 
3.8%
88.9 1
 
3.8%
25.2 1
 
3.8%
103.0 1
 
3.8%
49.8 1
 
3.8%
136.0 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
5.6 1
3.8%
17.8 1
3.8%
21.8 1
3.8%
25.2 1
3.8%
25.6 1
3.8%
37.4 1
3.8%
41.3 1
3.8%
49.8 1
3.8%
52.0 1
3.8%
54.0 1
3.8%
ValueCountFrequency (%)
190.0 1
3.8%
140.0 1
3.8%
136.0 1
3.8%
130.5 1
3.8%
126.0 1
3.8%
115.0 1
3.8%
109.0 1
3.8%
103.0 1
3.8%
97.0 1
3.8%
91.0 2
7.7%

기타선
Real number (ℝ)

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean114.22692
Minimum14.7
Maximum692.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T23:48:40.884477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14.7
5-th percentile24.25
Q157.075
median75.5
Q388.275
95-th percentile286.65
Maximum692.4
Range677.7
Interquartile range (IQR)31.2

Descriptive statistics

Standard deviation135.80514
Coefficient of variation (CV)1.1889066
Kurtosis13.614073
Mean114.22692
Median Absolute Deviation (MAD)17.45
Skewness3.4411966
Sum2969.9
Variance18443.037
MonotonicityNot monotonic
2023-12-12T23:48:41.019282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
66.0 2
 
7.7%
76.0 2
 
7.7%
77.0 2
 
7.7%
120.7 1
 
3.8%
23.9 1
 
3.8%
43.0 1
 
3.8%
25.3 1
 
3.8%
54.0 1
 
3.8%
692.4 1
 
3.8%
53.6 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
14.7 1
3.8%
23.9 1
3.8%
25.3 1
3.8%
43.0 1
3.8%
53.6 1
3.8%
54.0 1
3.8%
56.1 1
3.8%
60.0 1
3.8%
66.0 2
7.7%
68.0 1
3.8%
ValueCountFrequency (%)
692.4 1
3.8%
305.4 1
3.8%
230.4 1
3.8%
224.4 1
3.8%
154.8 1
3.8%
120.7 1
3.8%
88.7 1
3.8%
87.0 1
3.8%
84.5 1
3.8%
77.0 2
7.7%

육상
Real number (ℝ)

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.588462
Minimum1.1
Maximum904.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T23:48:41.174677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1
5-th percentile7.075
Q123.425
median31.5
Q344.25
95-th percentile164.8
Maximum904.8
Range903.7
Interquartile range (IQR)20.825

Descriptive statistics

Standard deviation173.77476
Coefficient of variation (CV)2.3297807
Kurtosis23.119551
Mean74.588462
Median Absolute Deviation (MAD)10
Skewness4.7153295
Sum1939.3
Variance30197.667
MonotonicityNot monotonic
2023-12-12T23:48:41.336085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
33.0 2
 
7.7%
19.0 2
 
7.7%
40.0 1
 
3.8%
25.0 1
 
3.8%
1.1 1
 
3.8%
47.0 1
 
3.8%
30.0 1
 
3.8%
28.3 1
 
3.8%
22.0 1
 
3.8%
22.9 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
1.1 1
3.8%
4.7 1
3.8%
14.2 1
3.8%
19.0 2
7.7%
22.0 1
3.8%
22.9 1
3.8%
25.0 1
3.8%
26.0 1
3.8%
27.0 1
3.8%
28.3 1
3.8%
ValueCountFrequency (%)
904.8 1
3.8%
166.2 1
3.8%
160.6 1
3.8%
79.8 1
3.8%
47.0 1
3.8%
46.0 1
3.8%
45.0 1
3.8%
42.0 1
3.8%
40.0 1
3.8%
39.1 1
3.8%

미상
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1423077
Minimum0.1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T23:48:41.469700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1
5-th percentile0.125
Q10.65
median1.6
Q36.5
95-th percentile15.25
Maximum17
Range16.9
Interquartile range (IQR)5.85

Descriptive statistics

Standard deviation4.891149
Coefficient of variation (CV)1.1807788
Kurtosis1.8577293
Mean4.1423077
Median Absolute Deviation (MAD)1.5
Skewness1.5173558
Sum107.7
Variance23.923338
MonotonicityNot monotonic
2023-12-12T23:48:41.646708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
4.0 3
11.5%
8.0 2
 
7.7%
0.2 2
 
7.7%
17.0 2
 
7.7%
0.1 2
 
7.7%
0.9 2
 
7.7%
0.8 2
 
7.7%
5.0 2
 
7.7%
0.5 1
 
3.8%
1.2 1
 
3.8%
Other values (7) 7
26.9%
ValueCountFrequency (%)
0.1 2
7.7%
0.2 2
7.7%
0.4 1
3.8%
0.5 1
3.8%
0.6 1
3.8%
0.8 2
7.7%
0.9 2
7.7%
1.0 1
3.8%
1.2 1
3.8%
2.0 1
3.8%
ValueCountFrequency (%)
17.0 2
7.7%
10.0 1
 
3.8%
9.0 1
 
3.8%
8.0 2
7.7%
7.0 1
 
3.8%
5.0 2
7.7%
4.0 3
11.5%
2.0 1
 
3.8%
1.2 1
 
3.8%
1.0 1
 
3.8%

Interactions

2023-12-12T23:48:37.772822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:32.511389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:33.782932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:34.634695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:35.386354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:36.224385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:37.038343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:37.885534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:32.619171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:33.908509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:34.734061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:35.484989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:36.331137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:37.140628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:38.025958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:32.744804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:34.044804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:34.840333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:35.570563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:36.444594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:37.253132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:38.148458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:32.874142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:34.176714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:34.946680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:35.713898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:36.572298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:37.372182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:38.265679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:33.447055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:34.296156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:35.070975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:35.851547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:36.721369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:37.490781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:38.374150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:33.575412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:34.415457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:35.211146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:35.993573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:36.832496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:37.586255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:38.461862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:33.669006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:34.513352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:35.300351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:36.110101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:36.916807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:48:37.669297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:48:41.774535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분화물선유조선어선기타선육상미상
연도1.0000.0000.6240.1690.3280.2630.5340.000
구분0.0001.0000.4140.5240.6380.5920.1461.000
화물선0.6240.4141.0000.0000.2970.7150.8100.000
유조선0.1690.5240.0001.0000.6630.4670.0000.230
어선0.3280.6380.2970.6631.0000.2160.0000.614
기타선0.2630.5920.7150.4670.2161.0000.6340.000
육상0.5340.1460.8100.0000.0000.6341.0000.000
미상0.0001.0000.0000.2300.6140.0000.0001.000
2023-12-12T23:48:41.918247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도화물선유조선어선기타선육상미상구분
연도1.000-0.326-0.0380.152-0.428-0.341-0.0940.000
화물선-0.3261.000-0.232-0.1490.1400.296-0.4560.466
유조선-0.038-0.2321.0000.401-0.151-0.0450.4010.591
어선0.152-0.1490.4011.000-0.289-0.3680.4340.535
기타선-0.4280.140-0.151-0.2891.0000.029-0.0070.384
육상-0.3410.296-0.045-0.3680.0291.000-0.3560.229
미상-0.094-0.4560.4010.434-0.007-0.3561.0000.913
구분0.0000.4660.5910.5350.3840.2290.9131.000

Missing values

2023-12-12T23:48:38.632037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:48:38.794766image/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

연도구분화물선유조선어선기타선육상미상
02010건 수33.037.0140.075.040.04.0
12010유출량(㎘)82.1148.6130.5224.414.21.2
22011건 수39.025.097.070.046.010.0
32011유출량(㎘)60.31.452.088.7166.20.5
42012건 수48.032.066.076.026.05.0
52012유출량(㎘)221.91.05.6154.834.60.8
62013건 수26.012.077.087.042.08.0
72013유출량(㎘)438.92.917.814.7160.60.1
82014건 수21.030.054.060.033.017.0
92014유출량(㎘)785.638.441.3230.4904.80.9
연도구분화물선유조선어선기타선육상미상
162018건 수30.031.0126.066.033.02.0
172018유출량(㎘)46.07.573.784.539.10.1
182019건 수27.032.0136.068.029.04.0
192019유출량(㎘)8.013.249.853.622.90.4
202020건 수23.024.0103.077.022.05.0
212020유출량(㎘)5.118.425.2692.428.30.9
222021건 수28.035.091.054.030.09.0
232021유출량(㎘)121.729.788.925.347.00.2
242022건 수26.026.085.043.019.07.0
252022유출량(㎘)96.32.4190.023.91.10.8