Overview

Dataset statistics

Number of variables6
Number of observations140
Missing cells16
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory48.9 B

Variable types

Categorical2
Text4

Dataset

Description경상남도 양산시에 등록된 폐기물 처리 업체에 대한 정보(업체명, 소재지, 연락처, 폐기물 종류 등)를 제공합니다.
Author경상남도 양산시
URLhttps://www.data.go.kr/data/15103386/fileData.do

Alerts

처리업 구분 is highly overall correlated with 폐기물종류High correlation
폐기물종류 is highly overall correlated with 처리업 구분High correlation
연락처 has 16 (11.4%) missing valuesMissing

Reproduction

Analysis started2023-12-12 14:31:35.298969
Analysis finished2023-12-12 14:31:36.526284
Duration1.23 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

폐기물종류
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
사업장폐기물
121 
건설폐기물
19 

Length

Max length6
Median length6
Mean length5.8642857
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사업장폐기물 121
86.4%
건설폐기물 19
 
13.6%

Length

2023-12-12T23:31:36.603345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:36.721115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사업장폐기물 121
86.4%
건설폐기물 19
 
13.6%

업소명
Text

Distinct120
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T23:31:37.007521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.1428571
Min length2

Characters and Unicode

Total characters720
Distinct characters165
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique106 ?
Unique (%)75.7%

Sample

1st row㈜양산개발
2nd row㈜중앙에코텍
3rd row㈜청솔
4th row㈜대아
5th row㈜대성환경
ValueCountFrequency (%)
㈜카원 4
 
2.9%
㈜청솔 4
 
2.9%
㈜대성환경 3
 
2.1%
대신금속 3
 
2.1%
대진기업(주 2
 
1.4%
㈜다성 2
 
1.4%
㈜태영자원 2
 
1.4%
㈜좋은환경 2
 
1.4%
㈜양산개발 2
 
1.4%
㈜도호네트웍스 2
 
1.4%
Other values (110) 114
81.4%
2023-12-12T23:31:37.464534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 72
 
10.0%
업 31
 
4.3%
산 31
 
4.3%
이 30
 
4.2%
경 28
 
3.9%
대 22
 
3.1%
환 21
 
2.9%
원 17
 
2.4%
) 16
 
2.2%
( 16
 
2.2%
Other values (155) 436
60.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 593
82.4%
Other Symbol 72
 
10.0%
Uppercase Letter 21
 
2.9%
Close Punctuation 16
 
2.2%
Open Punctuation 16
 
2.2%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 31
 
5.2%
산 31
 
5.2%
이 30
 
5.1%
경 28
 
4.7%
대 22
 
3.7%
환 21
 
3.5%
원 17
 
2.9%
주 13
 
2.2%
진 13
 
2.2%
양 12
 
2.0%
Other values (142) 375
63.2%
Uppercase Letter
ValueCountFrequency (%)
S 4
19.0%
T 3
14.3%
D 3
14.3%
B 3
14.3%
E 2
9.5%
C 2
9.5%
K 2
9.5%
H 1
 
4.8%
N 1
 
4.8%
Other Symbol
ValueCountFrequency (%)
㈜ 72
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 665
92.4%
Common 34
 
4.7%
Latin 21
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 72
 
10.8%
업 31
 
4.7%
산 31
 
4.7%
이 30
 
4.5%
경 28
 
4.2%
대 22
 
3.3%
환 21
 
3.2%
원 17
 
2.6%
주 13
 
2.0%
진 13
 
2.0%
Other values (143) 387
58.2%
Latin
ValueCountFrequency (%)
S 4
19.0%
T 3
14.3%
D 3
14.3%
B 3
14.3%
E 2
9.5%
C 2
9.5%
K 2
9.5%
H 1
 
4.8%
N 1
 
4.8%
Common
ValueCountFrequency (%)
) 16
47.1%
( 16
47.1%
. 2
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 593
82.4%
None 72
 
10.0%
ASCII 55
 
7.6%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 72
100.0%
Hangul
ValueCountFrequency (%)
업 31
 
5.2%
산 31
 
5.2%
이 30
 
5.1%
경 28
 
4.7%
대 22
 
3.7%
환 21
 
3.5%
원 17
 
2.9%
주 13
 
2.2%
진 13
 
2.2%
양 12
 
2.0%
Other values (142) 375
63.2%
ASCII
ValueCountFrequency (%)
) 16
29.1%
( 16
29.1%
S 4
 
7.3%
T 3
 
5.5%
D 3
 
5.5%
B 3
 
5.5%
E 2
 
3.6%
. 2
 
3.6%
C 2
 
3.6%
K 2
 
3.6%
Other values (2) 2
 
3.6%

소재지
Text

Distinct125
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T23:31:37.833032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length70
Median length42
Mean length23.435714
Min length15

Characters and Unicode

Total characters3281
Distinct characters119
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique113 ?
Unique (%)80.7%

Sample

1st row경상남도 양산시 유산공단10길 63
2nd row경상남도 양산시 동면 금오5길 46-20
3rd row경상남도 양산시 하북면 삼덕로 25
4th row경상남도 양산시 동면 석금산로 10, 103호
5th row경상남도 양산시 중앙로 232 (북부동, B동 15호)
ValueCountFrequency (%)
경상남도 140
20.5%
양산시 140
20.5%
상북면 43
 
6.3%
하북면 20
 
2.9%
양산대로 12
 
1.8%
동면 10
 
1.5%
유산공단10길 7
 
1.0%
어실로 7
 
1.0%
공원로 7
 
1.0%
물금읍 5
 
0.7%
Other values (205) 292
42.8%
2023-12-12T23:31:38.365050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
583
17.8%
산 206
 
6.3%
상 190
 
5.8%
양 156
 
4.8%
남 151
 
4.6%
시 145
 
4.4%
경 140
 
4.3%
도 140
 
4.3%
1 117
 
3.6%
북 94
 
2.9%
Other values (109) 1359
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1979
60.3%
Space Separator 583
 
17.8%
Decimal Number 565
 
17.2%
Dash Punctuation 43
 
1.3%
Close Punctuation 41
 
1.2%
Open Punctuation 41
 
1.2%
Other Punctuation 20
 
0.6%
Uppercase Letter 9
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 206
 
10.4%
상 190
 
9.6%
양 156
 
7.9%
남 151
 
7.6%
시 145
 
7.3%
경 140
 
7.1%
도 140
 
7.1%
북 94
 
4.7%
면 76
 
3.8%
로 74
 
3.7%
Other values (89) 607
30.7%
Decimal Number
ValueCountFrequency (%)
1 117
20.7%
3 80
14.2%
2 79
14.0%
0 57
10.1%
5 47
8.3%
4 46
 
8.1%
6 44
 
7.8%
7 38
 
6.7%
9 34
 
6.0%
8 23
 
4.1%
Uppercase Letter
ValueCountFrequency (%)
B 5
55.6%
A 2
 
22.2%
P 1
 
11.1%
T 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 19
95.0%
: 1
 
5.0%
Space Separator
ValueCountFrequency (%)
583
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1979
60.3%
Common 1293
39.4%
Latin 9
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 206
 
10.4%
상 190
 
9.6%
양 156
 
7.9%
남 151
 
7.6%
시 145
 
7.3%
경 140
 
7.1%
도 140
 
7.1%
북 94
 
4.7%
면 76
 
3.8%
로 74
 
3.7%
Other values (89) 607
30.7%
Common
ValueCountFrequency (%)
583
45.1%
1 117
 
9.0%
3 80
 
6.2%
2 79
 
6.1%
0 57
 
4.4%
5 47
 
3.6%
4 46
 
3.6%
6 44
 
3.4%
- 43
 
3.3%
) 41
 
3.2%
Other values (6) 156
 
12.1%
Latin
ValueCountFrequency (%)
B 5
55.6%
A 2
 
22.2%
P 1
 
11.1%
T 1
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1979
60.3%
ASCII 1302
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
583
44.8%
1 117
 
9.0%
3 80
 
6.1%
2 79
 
6.1%
0 57
 
4.4%
5 47
 
3.6%
4 46
 
3.5%
6 44
 
3.4%
- 43
 
3.3%
) 41
 
3.1%
Other values (10) 165
 
12.7%
Hangul
ValueCountFrequency (%)
산 206
 
10.4%
상 190
 
9.6%
양 156
 
7.9%
남 151
 
7.6%
시 145
 
7.3%
경 140
 
7.1%
도 140
 
7.1%
북 94
 
4.7%
면 76
 
3.8%
로 74
 
3.7%
Other values (89) 607
30.7%

연락처
Text

MISSING 

Distinct100
Distinct (%)80.6%
Missing16
Missing (%)11.4%
Memory size1.2 KiB
2023-12-12T23:31:38.566193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length12
Mean length12.774194
Min length12

Characters and Unicode

Total characters1584
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique86 ?
Unique (%)69.4%

Sample

1st row055-387-6115
2nd row055-367-9890
3rd row055-374-0007
4th row055-384-8822
5th row055-367-8415
ValueCountFrequency (%)
055-367-8415 4
 
3.2%
055-374-0007 4
 
3.2%
055-375-5800 4
 
3.2%
055-374-2528 3
 
2.4%
055-385-5863 3
 
2.4%
055-365-6807 3
 
2.4%
055-381-0703 3
 
2.4%
055-387-2277 2
 
1.6%
055-387-6115 2
 
1.6%
055-362-1834 2
 
1.6%
Other values (90) 94
75.8%
2023-12-12T23:31:38.976070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 356
22.5%
- 260
16.4%
0 220
13.9%
3 174
11.0%
8 127
 
8.0%
7 117
 
7.4%
2 74
 
4.7%
1 71
 
4.5%
4 68
 
4.3%
6 64
 
4.0%
Other values (4) 53
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1302
82.2%
Dash Punctuation 260
 
16.4%
Space Separator 10
 
0.6%
Open Punctuation 6
 
0.4%
Close Punctuation 6
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 356
27.3%
0 220
16.9%
3 174
13.4%
8 127
 
9.8%
7 117
 
9.0%
2 74
 
5.7%
1 71
 
5.5%
4 68
 
5.2%
6 64
 
4.9%
9 31
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 260
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1584
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 356
22.5%
- 260
16.4%
0 220
13.9%
3 174
11.0%
8 127
 
8.0%
7 117
 
7.4%
2 74
 
4.7%
1 71
 
4.5%
4 68
 
4.3%
6 64
 
4.0%
Other values (4) 53
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1584
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 356
22.5%
- 260
16.4%
0 220
13.9%
3 174
11.0%
8 127
 
8.0%
7 117
 
7.4%
2 74
 
4.7%
1 71
 
4.5%
4 68
 
4.3%
6 64
 
4.0%
Other values (4) 53
 
3.3%

처리대상 폐기물
Text

Distinct71
Distinct (%)50.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T23:31:39.267037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length163
Median length58
Mean length13.771429
Min length3

Characters and Unicode

Total characters1928
Distinct characters167
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique61 ?
Unique (%)43.6%

Sample

1st row건설폐기물
2nd row건설폐기물
3rd row건설폐기물
4th row건설폐기물
5th row건설폐기물
ValueCountFrequency (%)
사업장배출시설계 33
 
17.7%
건설폐기물 16
 
8.6%
폐합성수지 10
 
5.4%
사업장생활계 7
 
3.8%
폐합성수지류 5
 
2.7%
사업장배출시설계(고상 4
 
2.2%
폐전선 4
 
2.2%
밖의 3
 
1.6%
외 3
 
1.6%
폐합성섬유 3
 
1.6%
Other values (93) 98
52.7%
2023-12-12T23:31:39.750619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
폐 144
 
7.5%
- 71
 
3.7%
설 67
 
3.5%
) 65
 
3.4%
( 65
 
3.4%
성 57
 
3.0%
사 56
 
2.9%
합 55
 
2.9%
업 54
 
2.8%
장 53
 
2.7%
Other values (157) 1241
64.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1412
73.2%
Decimal Number 200
 
10.4%
Dash Punctuation 71
 
3.7%
Close Punctuation 65
 
3.4%
Open Punctuation 65
 
3.4%
Space Separator 49
 
2.5%
Other Punctuation 47
 
2.4%
Uppercase Letter 19
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
폐 144
 
10.2%
설 67
 
4.7%
성 57
 
4.0%
사 56
 
4.0%
합 55
 
3.9%
업 54
 
3.8%
장 53
 
3.8%
계 52
 
3.7%
시 46
 
3.3%
지 44
 
3.1%
Other values (133) 784
55.5%
Decimal Number
ValueCountFrequency (%)
0 53
26.5%
1 46
23.0%
5 39
19.5%
3 18
 
9.0%
9 17
 
8.5%
2 14
 
7.0%
8 4
 
2.0%
7 4
 
2.0%
4 3
 
1.5%
6 2
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
P 7
36.8%
T 3
15.8%
E 3
15.8%
V 1
 
5.3%
C 1
 
5.3%
B 1
 
5.3%
S 1
 
5.3%
A 1
 
5.3%
L 1
 
5.3%
Dash Punctuation
ValueCountFrequency (%)
- 71
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%
Open Punctuation
ValueCountFrequency (%)
( 65
100.0%
Space Separator
ValueCountFrequency (%)
49
100.0%
Other Punctuation
ValueCountFrequency (%)
, 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1412
73.2%
Common 497
 
25.8%
Latin 19
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
폐 144
 
10.2%
설 67
 
4.7%
성 57
 
4.0%
사 56
 
4.0%
합 55
 
3.9%
업 54
 
3.8%
장 53
 
3.8%
계 52
 
3.7%
시 46
 
3.3%
지 44
 
3.1%
Other values (133) 784
55.5%
Common
ValueCountFrequency (%)
- 71
14.3%
) 65
13.1%
( 65
13.1%
0 53
10.7%
49
9.9%
, 47
9.5%
1 46
9.3%
5 39
7.8%
3 18
 
3.6%
9 17
 
3.4%
Other values (5) 27
 
5.4%
Latin
ValueCountFrequency (%)
P 7
36.8%
T 3
15.8%
E 3
15.8%
V 1
 
5.3%
C 1
 
5.3%
B 1
 
5.3%
S 1
 
5.3%
A 1
 
5.3%
L 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1412
73.2%
ASCII 516
 
26.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
폐 144
 
10.2%
설 67
 
4.7%
성 57
 
4.0%
사 56
 
4.0%
합 55
 
3.9%
업 54
 
3.8%
장 53
 
3.8%
계 52
 
3.7%
시 46
 
3.3%
지 44
 
3.1%
Other values (133) 784
55.5%
ASCII
ValueCountFrequency (%)
- 71
13.8%
) 65
12.6%
( 65
12.6%
0 53
10.3%
49
9.5%
, 47
9.1%
1 46
8.9%
5 39
7.6%
3 18
 
3.5%
9 17
 
3.3%
Other values (14) 46
8.9%

처리업 구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
수집운반업
54 
종합재활용업소
44 
중간재활용업소
20 
건설폐기물 수집운반업소
16 
건설폐기물 중간처리업소
 
3
Other values (2)
 
3

Length

Max length12
Median length7
Mean length6.9
Min length5

Unique

Unique1 ?
Unique (%)0.7%

Sample

1st row건설폐기물 수집운반업소
2nd row건설폐기물 수집운반업소
3rd row건설폐기물 수집운반업소
4th row건설폐기물 수집운반업소
5th row건설폐기물 수집운반업소

Common Values

ValueCountFrequency (%)
수집운반업 54
38.6%
종합재활용업소 44
31.4%
중간재활용업소 20
 
14.3%
건설폐기물 수집운반업소 16
 
11.4%
건설폐기물 중간처리업소 3
 
2.1%
최종재활용업소 2
 
1.4%
중간처분업소 1
 
0.7%

Length

2023-12-12T23:31:39.920949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:31:40.074192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수집운반업 54
34.0%
종합재활용업소 44
27.7%
중간재활용업소 20
 
12.6%
건설폐기물 19
 
11.9%
수집운반업소 16
 
10.1%
중간처리업소 3
 
1.9%
최종재활용업소 2
 
1.3%
중간처분업소 1
 
0.6%

Correlations

2023-12-12T23:31:40.162957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐기물종류연락처처리대상 폐기물처리업 구분
폐기물종류1.0000.0001.0001.000
연락처0.0001.0000.9850.857
처리대상 폐기물1.0000.9851.0000.997
처리업 구분1.0000.8570.9971.000
2023-12-12T23:31:40.254168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리업 구분폐기물종류
처리업 구분1.0000.982
폐기물종류0.9821.000
2023-12-12T23:31:40.349744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐기물종류처리업 구분
폐기물종류1.0000.982
처리업 구분0.9821.000

Missing values

2023-12-12T23:31:36.362180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:31:36.484469image/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

폐기물종류업소명소재지연락처처리대상 폐기물처리업 구분
0건설폐기물㈜양산개발경상남도 양산시 유산공단10길 63055-387-6115건설폐기물건설폐기물 수집운반업소
1건설폐기물㈜중앙에코텍경상남도 양산시 동면 금오5길 46-20055-367-9890건설폐기물건설폐기물 수집운반업소
2건설폐기물㈜청솔경상남도 양산시 하북면 삼덕로 25055-374-0007건설폐기물건설폐기물 수집운반업소
3건설폐기물㈜대아경상남도 양산시 동면 석금산로 10, 103호055-384-8822건설폐기물건설폐기물 수집운반업소
4건설폐기물㈜대성환경경상남도 양산시 중앙로 232 (북부동, B동 15호)055-367-8415건설폐기물건설폐기물 수집운반업소
5건설폐기물강진건설㈜경상남도 양산시 상북면 충렬로 955055-382-9588건설폐기물건설폐기물 수집운반업소
6건설폐기물㈜도호네트웍스경상남도 양산시 하북면 신평남부길 109055-372-5545건설폐기물건설폐기물 수집운반업소
7건설폐기물㈜양주경상남도 양산시 상북면 상북중앙로 437-2, 301호055-364-1117건설폐기물건설폐기물 수집운반업소
8건설폐기물그린텍환경산업경상남도 양산시 동면 금오7길 65-12, 102호김해시 번화2로 90, 101동604호김해시 월산로 120, 604동501호055-362-8488건설폐기물건설폐기물 수집운반업소
9건설폐기물BK환경경상남도 양산시 신기서길14, 제상가 109동 2층 207-1055-913-0885건설폐기물건설폐기물 수집운반업소
폐기물종류업소명소재지연락처처리대상 폐기물처리업 구분
130사업장폐기물(복지)한국사회복지협의회경상남도 양산시 하북면 용연로 23055-375-5800폐전선중간재활용업소
131사업장폐기물㈜광원산업경상남도 양산시 하북면 용연로 10055-387-0001폐합성수지중간재활용업소
132사업장폐기물대신금속경상남도 양산시 북정공단1길 17055-375-5800폐합성수지-폐전선중간재활용업소
133사업장폐기물재권자원경상남도 양산시 유산공단8기리 67-1<NA>폐스티로폼중간재활용업소
134사업장폐기물㈜청솔경상남도 양산시 하북면 삼덕로 25055-374-0007사업장일반(폐합성수지류임목폐목재)건설폐기물(폐합성수지,폐목재)중간재활용업소
135사업장폐기물청운산업경상남도 양산시 하북면 삼동로 56-74<NA>사업장일반(폐합성고분자화합물, 폐전선)중간재활용업소
136사업장폐기물㈜코어환경경상남도 양산시 상북면 오룡길 205-39055-374-2537폐합성수지류,임목폐목재중간재활용업소
137사업장폐기물금영리싸이클링경상남도 양산시 어실로 344-16<NA>폐발포합성수지(51-03-06)중간재활용업소
138사업장폐기물(주)대성환경경상남도 양산시 산막공단남12길 107055-367-8415폐합성수지류 외 3종중간재활용업소
139사업장폐기물NC양산㈜경상남도 양산시 산막공단북5길 29(산막동)055-367-1515지정 외중간처분업소