Overview

Dataset statistics

Number of variables11
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory96.1 B

Variable types

Categorical6
Text2
Numeric2
DateTime1

Dataset

Description제주특별자치도 서귀포시 관내 환경오염물질배출사업에 관련한 데이터로 분류, 사업장명, 소재지, 위도, 경도, 업종, 대기종별(특정) 등의 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15034335/fileData.do

Alerts

분류 has constant value ""Constant
데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
업종 is highly overall correlated with 대기 등급 and 1 other fieldsHigh correlation
대기 등급 is highly overall correlated with 업종High correlation
수질 종별(특정) is highly overall correlated with 업종High correlation
사업장명 has unique valuesUnique
소재지 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 23:10:42.251310
Analysis finished2023-12-12 23:10:43.521796
Duration1.27 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
공통(대기+수질)
32 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공통(대기+수질)
2nd row공통(대기+수질)
3rd row공통(대기+수질)
4th row공통(대기+수질)
5th row공통(대기+수질)

Common Values

ValueCountFrequency (%)
공통(대기+수질) 32
100.0%

Length

2023-12-13T08:10:43.602401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:10:43.707835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공통(대기+수질 32
100.0%

사업장명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T08:10:43.882741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11.5
Mean length8.53125
Min length3

Characters and Unicode

Total characters273
Distinct characters103
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row낙원레미콘
2nd row남영산업㈜ 사이프러스골프장
3rd row레이크힐스 제주C.C
4th row㈜클린턴
5th row부영CC
ValueCountFrequency (%)
낙원레미콘 1
 
2.5%
남영산업㈜ 1
 
2.5%
케슬렉스제주(골프장 1
 
2.5%
사조레저 1
 
2.5%
한국공항(주 1
 
2.5%
한국관광공사 1
 
2.5%
중문골프장 1
 
2.5%
㈜한라 1
 
2.5%
한라콘크리트㈜ 1
 
2.5%
한송산업(레미콘 1
 
2.5%
Other values (30) 30
75.0%
2023-12-13T08:10:44.256342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 16
 
5.9%
제 11
 
4.0%
㈜ 10
 
3.7%
( 9
 
3.3%
) 9
 
3.3%
9
 
3.3%
스 8
 
2.9%
레 8
 
2.9%
콘 8
 
2.9%
리 8
 
2.9%
Other values (93) 177
64.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 231
84.6%
Other Symbol 10
 
3.7%
Open Punctuation 9
 
3.3%
Close Punctuation 9
 
3.3%
Space Separator 9
 
3.3%
Uppercase Letter 4
 
1.5%
Other Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 16
 
6.9%
제 11
 
4.8%
스 8
 
3.5%
레 8
 
3.5%
콘 8
 
3.5%
리 8
 
3.5%
한 7
 
3.0%
미 6
 
2.6%
사 6
 
2.6%
트 5
 
2.2%
Other values (87) 148
64.1%
Other Symbol
ValueCountFrequency (%)
㈜ 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Uppercase Letter
ValueCountFrequency (%)
C 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 241
88.3%
Common 28
 
10.3%
Latin 4
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 16
 
6.6%
제 11
 
4.6%
㈜ 10
 
4.1%
스 8
 
3.3%
레 8
 
3.3%
콘 8
 
3.3%
리 8
 
3.3%
한 7
 
2.9%
미 6
 
2.5%
사 6
 
2.5%
Other values (88) 153
63.5%
Common
ValueCountFrequency (%)
( 9
32.1%
) 9
32.1%
9
32.1%
. 1
 
3.6%
Latin
ValueCountFrequency (%)
C 4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 231
84.6%
ASCII 32
 
11.7%
None 10
 
3.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 16
 
6.9%
제 11
 
4.8%
스 8
 
3.5%
레 8
 
3.5%
콘 8
 
3.5%
리 8
 
3.5%
한 7
 
3.0%
미 6
 
2.6%
사 6
 
2.6%
트 5
 
2.2%
Other values (87) 148
64.1%
None
ValueCountFrequency (%)
㈜ 10
100.0%
ASCII
ValueCountFrequency (%)
( 9
28.1%
) 9
28.1%
9
28.1%
C 4
12.5%
. 1
 
3.1%

소재지
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-13T08:10:44.523170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length25.96875
Min length18

Characters and Unicode

Total characters831
Distinct characters79
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row제주특별자치도 서귀포시 표선면 세성로 114-26
2nd row제주특별자치도 서귀포시 번영로 2300
3rd row제주특별자치도 서귀포시 산록남로 1391
4th row제주특별자치도 서귀포시 토평공단로127번길 23
5th row제주특별자치도 서귀포시 남조로 960
ValueCountFrequency (%)
제주특별자치도 32
21.9%
서귀포시 32
21.9%
표선면 5
 
3.4%
안덕면 4
 
2.7%
성산읍 4
 
2.7%
중문관광로72번길 3
 
2.1%
토평동 2
 
1.4%
토평공단로107번길 2
 
1.4%
1 2
 
1.4%
산록남로 2
 
1.4%
Other values (58) 58
39.7%
2023-12-13T08:10:44.892838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
119
 
14.3%
서 35
 
4.2%
주 34
 
4.1%
시 32
 
3.9%
포 32
 
3.9%
귀 32
 
3.9%
제 32
 
3.9%
도 32
 
3.9%
치 32
 
3.9%
자 32
 
3.9%
Other values (69) 419
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 563
67.7%
Decimal Number 137
 
16.5%
Space Separator 119
 
14.3%
Dash Punctuation 8
 
1.0%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 35
 
6.2%
주 34
 
6.0%
시 32
 
5.7%
포 32
 
5.7%
귀 32
 
5.7%
제 32
 
5.7%
도 32
 
5.7%
치 32
 
5.7%
자 32
 
5.7%
별 32
 
5.7%
Other values (55) 238
42.3%
Decimal Number
ValueCountFrequency (%)
1 26
19.0%
2 19
13.9%
3 16
11.7%
5 15
10.9%
0 14
10.2%
7 12
8.8%
6 12
8.8%
4 11
8.0%
9 7
 
5.1%
8 5
 
3.6%
Space Separator
ValueCountFrequency (%)
119
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 563
67.7%
Common 268
32.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 35
 
6.2%
주 34
 
6.0%
시 32
 
5.7%
포 32
 
5.7%
귀 32
 
5.7%
제 32
 
5.7%
도 32
 
5.7%
치 32
 
5.7%
자 32
 
5.7%
별 32
 
5.7%
Other values (55) 238
42.3%
Common
ValueCountFrequency (%)
119
44.4%
1 26
 
9.7%
2 19
 
7.1%
3 16
 
6.0%
5 15
 
5.6%
0 14
 
5.2%
7 12
 
4.5%
6 12
 
4.5%
4 11
 
4.1%
- 8
 
3.0%
Other values (4) 16
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 563
67.7%
ASCII 268
32.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
119
44.4%
1 26
 
9.7%
2 19
 
7.1%
3 16
 
6.0%
5 15
 
5.6%
0 14
 
5.2%
7 12
 
4.5%
6 12
 
4.5%
4 11
 
4.1%
- 8
 
3.0%
Other values (4) 16
 
6.0%
Hangul
ValueCountFrequency (%)
서 35
 
6.2%
주 34
 
6.0%
시 32
 
5.7%
포 32
 
5.7%
귀 32
 
5.7%
제 32
 
5.7%
도 32
 
5.7%
치 32
 
5.7%
자 32
 
5.7%
별 32
 
5.7%
Other values (55) 238
42.3%

위도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.313776
Minimum33.248022
Maximum33.432847
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:10:45.036326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.248022
5-th percentile33.250955
Q133.285518
median33.293225
Q333.331298
95-th percentile33.426056
Maximum33.432847
Range0.18482462
Interquartile range (IQR)0.04578025

Descriptive statistics

Standard deviation0.054721286
Coefficient of variation (CV)0.0016426023
Kurtosis0.031425033
Mean33.313776
Median Absolute Deviation (MAD)0.028085615
Skewness0.98689625
Sum1066.0408
Variance0.0029944191
MonotonicityNot monotonic
2023-12-13T08:10:45.156230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
33.31832741 1
 
3.1%
33.38252926 1
 
3.1%
33.29206715 1
 
3.1%
33.43284666 1
 
3.1%
33.29338226 1
 
3.1%
33.25830869 1
 
3.1%
33.24802204 1
 
3.1%
33.24859961 1
 
3.1%
33.3214847 1
 
3.1%
33.290689 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
33.24802204 1
3.1%
33.24859961 1
3.1%
33.25288139 1
3.1%
33.25421861 1
3.1%
33.25598099 1
3.1%
33.25830869 1
3.1%
33.26965338 1
3.1%
33.27514076 1
3.1%
33.2889768 1
3.1%
33.29051593 1
3.1%
ValueCountFrequency (%)
33.43284666 1
3.1%
33.43249141 1
3.1%
33.42079006 1
3.1%
33.4111181 1
3.1%
33.38540681 1
3.1%
33.38252926 1
3.1%
33.3592183 1
3.1%
33.33834117 1
3.1%
33.32895033 1
3.1%
33.3214847 1
3.1%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.58614
Minimum126.22911
Maximum126.9278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-13T08:10:45.283403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.22911
5-th percentile126.33646
Q1126.40992
median126.58172
Q3126.76523
95-th percentile126.861
Maximum126.9278
Range0.698692
Interquartile range (IQR)0.35530817

Descriptive statistics

Standard deviation0.19335093
Coefficient of variation (CV)0.0015274258
Kurtosis-1.1209386
Mean126.58614
Median Absolute Deviation (MAD)0.1722158
Skewness0.074669951
Sum4050.7563
Variance0.037384583
MonotonicityNot monotonic
2023-12-13T08:10:45.414242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
126.8045281 1
 
3.1%
126.8350143 1
 
3.1%
126.5758316 1
 
3.1%
126.9277977 1
 
3.1%
126.5820371 1
 
3.1%
126.2291057 1
 
3.1%
126.4086786 1
 
3.1%
126.4103301 1
 
3.1%
126.8447337 1
 
3.1%
126.3379786 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
126.2291057 1
3.1%
126.3345944 1
3.1%
126.3379786 1
3.1%
126.3487166 1
3.1%
126.3514203 1
3.1%
126.3825152 1
3.1%
126.3936854 1
3.1%
126.4086786 1
3.1%
126.4103301 1
3.1%
126.4114343 1
3.1%
ValueCountFrequency (%)
126.9277977 1
3.1%
126.8808898 1
3.1%
126.8447337 1
3.1%
126.8350143 1
3.1%
126.8286763 1
3.1%
126.8102503 1
3.1%
126.8093615 1
3.1%
126.8045281 1
3.1%
126.7521245 1
3.1%
126.7167995 1
3.1%

업종
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
산업용
10 
레미콘제조
9 
세탁업
3 
겸용
2 
난방
2 
Other values (6)
6 

Length

Max length10
Median length9.5
Mean length4
Min length2

Unique

Unique6 ?
Unique (%)18.8%

Sample

1st row레미콘제조
2nd row산업용
3rd row산업용
4th row세탁업
5th row겸용

Common Values

ValueCountFrequency (%)
산업용 10
31.2%
레미콘제조 9
28.1%
세탁업 3
 
9.4%
겸용 2
 
6.2%
난방 2
 
6.2%
제조업(혼화제) 1
 
3.1%
업무용 1
 
3.1%
제조업(정수음료) 1
 
3.1%
숙박업 1
 
3.1%
호텔업 1
 
3.1%

Length

2023-12-13T08:10:45.556020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
산업용 10
31.2%
레미콘제조 9
28.1%
세탁업 3
 
9.4%
겸용 2
 
6.2%
난방 2
 
6.2%
제조업(혼화제 1
 
3.1%
업무용 1
 
3.1%
제조업(정수음료 1
 
3.1%
숙박업 1
 
3.1%
호텔업 1
 
3.1%

대기종별(특정)
Categorical

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
5
26 
4
6 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 26
81.2%
4 6
 
18.8%

Length

2023-12-13T08:10:45.682844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:10:45.782498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 26
81.2%
4 6
 
18.8%

대기 등급
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
우수
26 
일반
6 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우수
2nd row일반
3rd row우수
4th row일반
5th row우수

Common Values

ValueCountFrequency (%)
우수 26
81.2%
일반 6
 
18.8%

Length

2023-12-13T08:10:45.883299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:10:46.011613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우수 26
81.2%
일반 6
 
18.8%

수질 종별(특정)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
4
16 
5
16 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row5
3rd row5
4th row4
5th row5

Common Values

ValueCountFrequency (%)
4 16
50.0%
5 16
50.0%

Length

2023-12-13T08:10:46.176447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:10:46.280877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 16
50.0%
5 16
50.0%

수질 등급
Categorical

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
우수
24 
일반
8 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우수
2nd row일반
3rd row우수
4th row우수
5th row우수

Common Values

ValueCountFrequency (%)
우수 24
75.0%
일반 8
 
25.0%

Length

2023-12-13T08:10:46.409903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:10:46.508599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우수 24
75.0%
일반 8
 
25.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum2023-04-30 00:00:00
Maximum2023-04-30 00:00:00
2023-12-13T08:10:46.603013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:10:46.715942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T08:10:43.009197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:10:42.774737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:10:43.112444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:10:42.895601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:10:46.789343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장명소재지위도경도업종대기종별(특정)대기 등급수질 종별(특정)수질 등급
사업장명1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.000
위도1.0001.0001.0000.8910.0000.0740.4690.2130.000
경도1.0001.0000.8911.0000.7180.3240.5060.0000.356
업종1.0001.0000.0000.7181.0000.3430.7370.7440.000
대기종별(특정)1.0001.0000.0740.3240.3431.0000.0000.0000.000
대기 등급1.0001.0000.4690.5060.7370.0001.0000.0000.000
수질 종별(특정)1.0001.0000.2130.0000.7440.0000.0001.0000.000
수질 등급1.0001.0000.0000.3560.0000.0000.0000.0001.000
2023-12-13T08:10:46.902523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수질 종별(특정)대기종별(특정)업종대기 등급수질 등급
수질 종별(특정)1.0000.0000.6070.0000.000
대기종별(특정)0.0001.0000.2580.0000.000
업종0.6070.2581.0000.6010.000
대기 등급0.0000.0000.6011.0000.000
수질 등급0.0000.0000.0000.0001.000
2023-12-13T08:10:47.018679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도업종대기종별(특정)대기 등급수질 종별(특정)수질 등급
위도1.0000.7320.0000.0000.4040.1640.000
경도0.7321.0000.3970.2700.4370.0000.300
업종0.0000.3971.0000.2580.6010.6070.000
대기종별(특정)0.0000.2700.2581.0000.0000.0000.000
대기 등급0.4040.4370.6010.0001.0000.0000.000
수질 종별(특정)0.1640.0000.6070.0000.0001.0000.000
수질 등급0.0000.3000.0000.0000.0000.0001.000

Missing values

2023-12-13T08:10:43.281225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:10:43.458138image/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

분류사업장명소재지위도경도업종대기종별(특정)대기 등급수질 종별(특정)수질 등급데이터기준일자
0공통(대기+수질)낙원레미콘제주특별자치도 서귀포시 표선면 세성로 114-2633.318327126.804528레미콘제조5우수4우수2023-04-30
1공통(대기+수질)남영산업㈜ 사이프러스골프장제주특별자치도 서귀포시 번영로 230033.411118126.752124산업용5일반5일반2023-04-30
2공통(대기+수질)레이크힐스 제주C.C제주특별자치도 서귀포시 산록남로 139133.297118126.435386산업용5우수5우수2023-04-30
3공통(대기+수질)㈜클린턴제주특별자치도 서귀포시 토평공단로127번길 2333.291546126.581403세탁업5일반4우수2023-04-30
4공통(대기+수질)부영CC제주특별자치도 서귀포시 남조로 96033.359218126.712464겸용5우수5우수2023-04-30
5공통(대기+수질)삼한서비스제주특별자치도 서귀포시 토평공단로 107번길 16-433.291316126.583069세탁업5일반5일반2023-04-30
6공통(대기+수질)서귀포의료원(탐라사랑의료원㈜)제주특별자치도 서귀포시 장수로 4733.255981126.562949겸용5우수4우수2023-04-30
7공통(대기+수질)서일레미콘제주특별자치도 서귀포시 표선면 돈오름로 130-5233.32895126.809361레미콘제조5우수4우수2023-04-30
8공통(대기+수질)성수레미콘제주특별자치도 서귀포시 성산읍 금백조로 43933.432491126.828676레미콘제조5우수4우수2023-04-30
9공통(대기+수질)성일레미콘제주특별자치도 서귀포시 안덕면 화순서동로56번길 258-333.275141126.334594레미콘제조5우수4일반2023-04-30
분류사업장명소재지위도경도업종대기종별(특정)대기 등급수질 종별(특정)수질 등급데이터기준일자
22공통(대기+수질)㈜한라제주특별자치도 서귀포시 토평공단로107번길 133.290854126.583864레미콘제조5우수4일반2023-04-30
23공통(대기+수질)한라콘크리트㈜제주특별자치도 서귀포시 표선면 세성로212번길 13433.321136126.81025레미콘제조5우수4우수2023-04-30
24공통(대기+수질)한송산업(레미콘)제주특별자치도 서귀포시 안덕면 서광동로34번길 82-633.290689126.337979레미콘제조5우수4우수2023-04-30
25공통(대기+수질)해비치호텔앤리조트(주)제주특별자치도 서귀포시 표선면 민속해안로 53733.321485126.844734숙박업5우수5일반2023-04-30
26공통(대기+수질)호텔롯데 롯데호텔제주제주특별자치도 서귀포시 중문관광로72번길 3533.2486126.41033호텔업5우수5우수2023-04-30
27공통(대기+수질)호텔신라 (제주)제주특별자치도 서귀포시 중문관광로72번길 7533.248022126.408679난방5우수4우수2023-04-30
28공통(대기+수질)화송산업제주특별자치도 서귀포시 대정읍 일주서로3000번길 155-1333.258309126.229106제조업(동식물사료)4우수4일반2023-04-30
29공통(대기+수질)㈜제주런드리제주특별자치도 서귀포시 토평공단로139번길 5 (토평동)33.293382126.582037세탁업5일반4우수2023-04-30
30공통(대기+수질)㈜제주해양과학관제주특별자치도 서귀포시 성산읍 섭지코지로 9533.432847126.927798산업용4일반4우수2023-04-30
31공통(대기+수질)한라환경산업 주식회사제주특별자치도 서귀포시 인정오름로 86번길 63 (토평동)33.292067126.575832산업용4우수5일반2023-04-30