Overview

Dataset statistics

Number of variables8
Number of observations9125
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory623.9 KiB
Average record size in memory70.0 B

Variable types

Numeric6
Categorical2

Dataset

Description측정일자,도로변구분,측정소명,미세먼지(㎍/㎥),오존(ppm),이산화질소농도(ppm),일산화탄소농도(ppm),아황산가스농도(ppm)
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-2224/S/1/datasetView.do

Alerts

측정소명 is highly overall correlated with 도로변구분High correlation
도로변구분 is highly overall correlated with 측정소명High correlation
미세먼지(㎍/㎥) is highly overall correlated with 이산화질소농도(ppm)High correlation
이산화질소농도(ppm) is highly overall correlated with 미세먼지(㎍/㎥) and 1 other fieldsHigh correlation
일산화탄소농도(ppm) is highly overall correlated with 이산화질소농도(ppm)High correlation
일산화탄소농도(ppm) is highly skewed (γ1 = 48.41437964)Skewed

Reproduction

Analysis started2024-05-11 06:43:35.922082
Analysis finished2024-05-11 06:43:53.086704
Duration17.16 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

측정일자
Real number (ℝ)

Distinct365
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20234231
Minimum20230511
Maximum20240511
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-11T06:43:53.312396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20230511
5-th percentile20230529
Q120230810
median20231109
Q320240208
95-th percentile20240423
Maximum20240511
Range10000
Interquartile range (IQR)9398

Descriptive statistics

Standard deviation4508.3697
Coefficient of variation (CV)0.00022280905
Kurtosis-1.6361164
Mean20234231
Median Absolute Deviation (MAD)407
Skewness0.59684336
Sum1.8463736 × 1011
Variance20325397
MonotonicityDecreasing
2024-05-11T06:43:53.777546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240511 25
 
0.3%
20230901 25
 
0.3%
20230903 25
 
0.3%
20230904 25
 
0.3%
20230905 25
 
0.3%
20230906 25
 
0.3%
20230907 25
 
0.3%
20230908 25
 
0.3%
20230909 25
 
0.3%
20230910 25
 
0.3%
Other values (355) 8875
97.3%
ValueCountFrequency (%)
20230511 25
0.3%
20230512 25
0.3%
20230513 25
0.3%
20230514 25
0.3%
20230515 25
0.3%
20230516 25
0.3%
20230517 25
0.3%
20230518 25
0.3%
20230519 25
0.3%
20230520 25
0.3%
ValueCountFrequency (%)
20240511 25
0.3%
20240510 25
0.3%
20240509 25
0.3%
20240508 25
0.3%
20240507 25
0.3%
20240506 25
0.3%
20240505 25
0.3%
20240504 25
0.3%
20240503 25
0.3%
20240502 25
0.3%

도로변구분
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size71.4 KiB
일반도로
3650 
입체
1460 
경계
1095 
중앙차로
1095 
고공
1095 

Length

Max length4
Median length4
Mean length3.2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반도로
2nd row일반도로
3rd row입체
4th row경계
5th row중앙차로

Common Values

ValueCountFrequency (%)
일반도로 3650
40.0%
입체 1460
 
16.0%
경계 1095
 
12.0%
중앙차로 1095
 
12.0%
고공 1095
 
12.0%
전용차로 730
 
8.0%

Length

2024-05-11T06:43:54.250417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T06:43:54.595511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반도로 3650
40.0%
입체 1460
 
16.0%
경계 1095
 
12.0%
중앙차로 1095
 
12.0%
고공 1095
 
12.0%
전용차로 730
 
8.0%

측정소명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size71.4 KiB
영등포로
 
365
청계천로
 
365
마포아트센터
 
365
세곡
 
365
동작대로
 
365
Other values (20)
7300 

Length

Max length6
Median length5
Mean length3.52
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영등포로
2nd row청계천로
3rd row마포아트센터
4th row세곡
5th row동작대로

Common Values

ValueCountFrequency (%)
영등포로 365
 
4.0%
청계천로 365
 
4.0%
마포아트센터 365
 
4.0%
세곡 365
 
4.0%
동작대로 365
 
4.0%
정릉로 365
 
4.0%
시흥대로 365
 
4.0%
한강대로 365
 
4.0%
강변북로 365
 
4.0%
공항대로 365
 
4.0%
Other values (15) 5475
60.0%

Length

2024-05-11T06:43:55.051710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
영등포로 365
 
4.0%
관악산 365
 
4.0%
북한산 365
 
4.0%
서울숲 365
 
4.0%
신촌로 365
 
4.0%
자연사박물관 365
 
4.0%
강남대로 365
 
4.0%
올림픽공원 365
 
4.0%
홍릉로 365
 
4.0%
도산대로 365
 
4.0%
Other values (15) 5475
60.0%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct172
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.510904
Minimum0
Maximum327
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-11T06:43:55.448637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9
Q120
median30
Q342
95-th percentile73
Maximum327
Range327
Interquartile range (IQR)22

Descriptive statistics

Standard deviation23.585843
Coefficient of variation (CV)0.68343162
Kurtosis23.311293
Mean34.510904
Median Absolute Deviation (MAD)11
Skewness3.3521636
Sum314912
Variance556.292
MonotonicityNot monotonic
2024-05-11T06:43:56.010461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 260
 
2.8%
26 254
 
2.8%
24 251
 
2.8%
22 241
 
2.6%
33 238
 
2.6%
27 238
 
2.6%
23 237
 
2.6%
20 233
 
2.6%
32 232
 
2.5%
21 229
 
2.5%
Other values (162) 6712
73.6%
ValueCountFrequency (%)
0 18
 
0.2%
3 24
 
0.3%
4 45
 
0.5%
5 54
0.6%
6 79
0.9%
7 86
0.9%
8 83
0.9%
9 81
0.9%
10 109
1.2%
11 132
1.4%
ValueCountFrequency (%)
327 1
< 0.1%
284 1
< 0.1%
283 1
< 0.1%
279 1
< 0.1%
276 2
< 0.1%
275 1
< 0.1%
268 1
< 0.1%
267 1
< 0.1%
264 1
< 0.1%
263 1
< 0.1%

오존(ppm)
Real number (ℝ)

Distinct701
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.028587956
Minimum0
Maximum0.3583
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-11T06:43:56.451330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0083
Q10.0185
median0.0274
Q30.037
95-th percentile0.05278
Maximum0.3583
Range0.3583
Interquartile range (IQR)0.0185

Descriptive statistics

Standard deviation0.014337906
Coefficient of variation (CV)0.50153657
Kurtosis33.1268
Mean0.028587956
Median Absolute Deviation (MAD)0.0092
Skewness2.0993605
Sum260.8651
Variance0.00020557554
MonotonicityNot monotonic
2024-05-11T06:43:56.946870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0259 41
 
0.4%
0.0288 36
 
0.4%
0.0233 35
 
0.4%
0.0196 35
 
0.4%
0.0281 35
 
0.4%
0.024 35
 
0.4%
0.018 34
 
0.4%
0.0284 34
 
0.4%
0.0325 34
 
0.4%
0.0274 33
 
0.4%
Other values (691) 8773
96.1%
ValueCountFrequency (%)
0.0 18
0.2%
0.0016 1
 
< 0.1%
0.0017 2
 
< 0.1%
0.002 3
 
< 0.1%
0.0021 1
 
< 0.1%
0.0022 2
 
< 0.1%
0.0023 3
 
< 0.1%
0.0024 3
 
< 0.1%
0.0025 4
 
< 0.1%
0.0026 3
 
< 0.1%
ValueCountFrequency (%)
0.3583 1
< 0.1%
0.1984 1
< 0.1%
0.1138 1
< 0.1%
0.107 1
< 0.1%
0.105 2
< 0.1%
0.1043 1
< 0.1%
0.0885 1
< 0.1%
0.0865 1
< 0.1%
0.085 1
< 0.1%
0.084 2
< 0.1%

이산화질소농도(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct535
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.022618575
Minimum0
Maximum0.0673
Zeros17
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-11T06:43:57.391424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0063
Q10.0139
median0.0217
Q30.0302
95-th percentile0.04268
Maximum0.0673
Range0.0673
Interquartile range (IQR)0.0163

Descriptive statistics

Standard deviation0.011152359
Coefficient of variation (CV)0.49306197
Kurtosis-0.33311339
Mean0.022618575
Median Absolute Deviation (MAD)0.0081
Skewness0.44481753
Sum206.3945
Variance0.00012437512
MonotonicityNot monotonic
2024-05-11T06:43:57.877142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.016 44
 
0.5%
0.02 44
 
0.5%
0.017 42
 
0.5%
0.023 41
 
0.4%
0.026 41
 
0.4%
0.019 40
 
0.4%
0.0185 39
 
0.4%
0.028 38
 
0.4%
0.0232 38
 
0.4%
0.0221 37
 
0.4%
Other values (525) 8721
95.6%
ValueCountFrequency (%)
0.0 17
0.2%
0.0008 1
 
< 0.1%
0.0013 1
 
< 0.1%
0.0024 1
 
< 0.1%
0.0027 1
 
< 0.1%
0.0028 4
 
< 0.1%
0.0029 1
 
< 0.1%
0.003 4
 
< 0.1%
0.0031 3
 
< 0.1%
0.0032 2
 
< 0.1%
ValueCountFrequency (%)
0.0673 1
< 0.1%
0.066 1
< 0.1%
0.0606 1
< 0.1%
0.0605 1
< 0.1%
0.0604 1
< 0.1%
0.06 1
< 0.1%
0.0599 1
< 0.1%
0.0582 1
< 0.1%
0.058 2
< 0.1%
0.0573 1
< 0.1%

일산화탄소농도(ppm)
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct136
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59964603
Minimum0
Maximum280.82
Zeros28
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-11T06:43:58.369548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.26
Q10.39
median0.48
Q30.59
95-th percentile0.8
Maximum280.82
Range280.82
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation5.0372066
Coefficient of variation (CV)8.4003002
Kurtosis2371.8308
Mean0.59964603
Median Absolute Deviation (MAD)0.1
Skewness48.41438
Sum5471.77
Variance25.373451
MonotonicityNot monotonic
2024-05-11T06:43:58.769033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4 354
 
3.9%
0.5 323
 
3.5%
0.45 269
 
2.9%
0.46 268
 
2.9%
0.42 265
 
2.9%
0.49 255
 
2.8%
0.43 248
 
2.7%
0.44 245
 
2.7%
0.47 231
 
2.5%
0.48 224
 
2.5%
Other values (126) 6443
70.6%
ValueCountFrequency (%)
0.0 28
0.3%
0.01 5
 
0.1%
0.02 1
 
< 0.1%
0.03 1
 
< 0.1%
0.04 2
 
< 0.1%
0.05 2
 
< 0.1%
0.06 1
 
< 0.1%
0.07 5
 
0.1%
0.08 2
 
< 0.1%
0.09 4
 
< 0.1%
ValueCountFrequency (%)
280.82 1
< 0.1%
229.97 1
< 0.1%
227.97 1
< 0.1%
220.41 1
< 0.1%
1.99 1
< 0.1%
1.63 1
< 0.1%
1.5 1
< 0.1%
1.4 1
< 0.1%
1.38 1
< 0.1%
1.35 1
< 0.1%

아황산가스농도(ppm)
Real number (ℝ)

Distinct53
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0028273753
Minimum0
Maximum0.0066
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size80.3 KiB
2024-05-11T06:43:59.470215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.002
Q10.0025
median0.0028
Q30.0031
95-th percentile0.0038
Maximum0.0066
Range0.0066
Interquartile range (IQR)0.0006

Descriptive statistics

Standard deviation0.00054872432
Coefficient of variation (CV)0.19407551
Kurtosis2.9631622
Mean0.0028273753
Median Absolute Deviation (MAD)0.0003
Skewness0.52063511
Sum25.7998
Variance3.0109838 × 10-7
MonotonicityNot monotonic
2024-05-11T06:43:59.966533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 768
 
8.4%
0.0027 723
 
7.9%
0.0026 707
 
7.7%
0.0028 697
 
7.6%
0.0029 642
 
7.0%
0.0025 637
 
7.0%
0.0024 610
 
6.7%
0.0031 528
 
5.8%
0.0032 495
 
5.4%
0.0023 492
 
5.4%
Other values (43) 2826
31.0%
ValueCountFrequency (%)
0.0 16
0.2%
0.0008 1
 
< 0.1%
0.0009 1
 
< 0.1%
0.001 1
 
< 0.1%
0.0012 1
 
< 0.1%
0.0013 1
 
< 0.1%
0.0014 4
 
< 0.1%
0.0015 5
 
0.1%
0.0016 21
0.2%
0.0017 35
0.4%
ValueCountFrequency (%)
0.0066 1
 
< 0.1%
0.0062 1
 
< 0.1%
0.0061 1
 
< 0.1%
0.006 5
0.1%
0.0059 1
 
< 0.1%
0.0056 1
 
< 0.1%
0.0054 1
 
< 0.1%
0.0053 1
 
< 0.1%
0.0052 3
< 0.1%
0.0051 3
< 0.1%

Interactions

2024-05-11T06:43:50.491929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:39.003729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:40.964284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:43.222065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:46.297527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:48.618948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:50.835286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:39.383229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:41.289081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:43.584033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:46.703518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:48.910027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:51.176765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:39.681543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:41.580260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:44.366766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:47.166597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:49.209547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:51.501246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:40.018033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:41.933784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:44.846762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:47.493365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:49.520710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:51.818276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:40.373455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:42.311542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:45.312849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:47.775817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:49.895841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:52.116170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:40.671330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:42.822803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:45.902766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:48.213960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T06:43:50.177674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T06:44:00.479878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일자도로변구분측정소명미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
측정일자1.0000.0000.0000.3560.0220.2250.0000.117
도로변구분0.0001.0001.0000.1000.3400.5220.0350.191
측정소명0.0001.0001.0000.1140.3550.6130.0560.401
미세먼지(㎍/㎥)0.3560.1000.1141.0000.2060.4690.0000.342
오존(ppm)0.0220.3400.3550.2061.0000.2400.0000.054
이산화질소농도(ppm)0.2250.5220.6130.4690.2401.0000.0000.455
일산화탄소농도(ppm)0.0000.0350.0560.0000.0000.0001.0000.000
아황산가스농도(ppm)0.1170.1910.4010.3420.0540.4550.0001.000
2024-05-11T06:44:01.086844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정소명도로변구분
측정소명1.0000.999
도로변구분0.9991.000
2024-05-11T06:44:01.432877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
측정일자미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)도로변구분측정소명
측정일자1.0000.250-0.1420.2030.2040.0640.0000.000
미세먼지(㎍/㎥)0.2501.0000.0890.5240.4950.4130.0530.040
오존(ppm)-0.1420.0891.000-0.487-0.357-0.1080.1290.167
이산화질소농도(ppm)0.2030.524-0.4871.0000.6070.4160.3070.265
일산화탄소농도(ppm)0.2040.495-0.3570.6071.0000.4120.0230.030
아황산가스농도(ppm)0.0640.413-0.1080.4160.4121.0000.1000.150
도로변구분0.0000.0530.1290.3070.0230.1001.0000.999
측정소명0.0000.0400.1670.2650.0300.1500.9991.000

Missing values

2024-05-11T06:43:52.430547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T06:43:52.919436image/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

측정일자도로변구분측정소명미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
020240511일반도로영등포로280.04050.0180.330.0028
120240511일반도로청계천로370.03910.01560.350.0027
220240511입체마포아트센터260.0490.00880.290.0028
320240511경계세곡330.02910.0170.350.0025
420240511중앙차로동작대로300.03740.02380.340.0028
520240511전용차로정릉로270.03590.02620.470.0026
620240511일반도로시흥대로280.03570.01810.310.0028
720240511일반도로한강대로380.03990.01590.40.0029
820240511전용차로강변북로270.0320.01950.40.0028
920240511중앙차로공항대로260.04580.01120.260.0025
측정일자도로변구분측정소명미세먼지(㎍/㎥)오존(ppm)이산화질소농도(ppm)일산화탄소농도(ppm)아황산가스농도(ppm)
911520230511경계항동430.0570.0170.60.004
911620230511전용차로정릉로530.0470.0350.50.004
911720230511일반도로청계천로520.040.0310.60.005
911820230511일반도로시흥대로530.0330.050.60.005
911920230511중앙차로공항대로580.0450.0370.50.004
912020230511고공남산480.0640.0170.80.005
912120230511입체서울숲530.0460.0290.60.004
912220230511고공북한산410.0660.010.40.004
912320230511입체올림픽공원480.050.0260.40.003
912420230511일반도로홍릉로480.0510.0320.50.005