Green Design
II. METHODOLOGY A. Data
The data used in this study was Landsat 8 Multispectral of Bandung acquired on 2019.In addition to satellite images, topograpchic maps also used in this study as complimentary data.
B. Pre-Processing
Geometric Correction
To remove geometric distortion that causes a mismatch between object imagery position and object actual position, it is necessary to have a geometric correction. It establishes the pixel position of imagery to the actual position. In this study, geometric correction is carried out by the image to image method where Landsat-8 become the base/reference to determine GCP on Landsat which will be corrected geometrically.
Conversion to TOA Radiance
The first thing to do is to convert the digital number on Landsat 8 becomes spectral radiance by using the following equation (USGS, 2015).
Lλ = ML.Qcal + AL (1)
Where :
Lλ = TOA spectral radiance (Watts/( m2 * srad * μm))
ML= Band-specific multiplicative rescaling factor from the metadata RADIANCE_MULT_BAND_x, where x is the band number)
AL = Band-specific additive rescaling factor from the metadata (RADIANCE_ADD_BAND_x, where x is the band number)
Qcal= Quantized and calibrated standard product pixel values (DN)
Conversion to TOA Reflectance
The following equation is used to convert DN values to TOA reflectance as follows (USGS,2015):
ρλ' = Mρ.Qcal + Aρ (2)
Where :
ρλ'= TOA planetary reflectance.
Mρ = Band-specific multiplicative rescaling factor from the metadata (REFLECTANCE_MULT_BAND_x, where x is the band number)
Aρ = Band-specific additive rescaling factor from the metadata (REFLECTANCE_ADD_BAND_x, where x is the band number)
Qcal = Quantized and calibrated standard product pixel values (DN).
C. Processing
Particulate Matter (PM10)
To determine PM10 an algorithm is used as specified below (Lim, 2004 dan Nadzri, 2010):
PM10 = ao Rλ1 +a1 Rλ2 + a2 Rλ3 (3) Where:
ao= Algorithm coefficient (Aerosol Optical Thickness).
Rλ1 = Band reflectance value used (corresponding to satellite bands).
The value of Aerosol thickness was obtained from
NASA's official website AERONET
(https://aeronet.gsfc.nasa.gov/). The reflectance values used are the reflectance values of the red, green, and blue (RGB) bands.
PM10 Ground Measurement
PM10 concentrations were collected simultaneously with the image acquisition date at several points of streets in Bandung. It is a small, handheld, mobile operated and completely portable unit. This unit named Pocket Sensor PM2.5 which provides both particle counts and mass PM measurements of PM2.5 and PM10 as stored data logged values, real-time networked data. The distribution of PM10 values based from ground measurement in Bandung can be seen in Fig 1.
Fig 1. Distribution of PM10 Values Based from Ground Measurement in Bandung
III. RESULTANDDISCUSSION
PM10 value is obtained by entering the PM10 algorithm in the band math using ENVI 5.1. The PM10 algorithm consists of reflectance values of red, green and blue bands, and AOT parameters obtained from AERONET. The AOT parameter used is obtained simultaneously with the image acquisition date. PM10 distribution map Bandung divided into five classes. According to ISPU the healthy class of PM10 has a range of 0-50 µg/m³ shown as green, the moderate class has a range of values of 51-100 µg/m³ as blue, the unhealthy class has a range of 101-199 µg/m³ as yellow, the very unhealthy class has a range of 200-300 µg/m³ as red and hazardous classes have values above 300 µg/m³ as black. Following distribution of PM10 based on Landsat satellite image processing in 2019 can be seen in Fig 2.
Based on the Fig 2, the results shown from the distribution of PM10 are grouped into five classes, including healthy visualized in green, medium in blue, unhealthy in yellow, very unhealthy in red, and dangerous in black.the healthy classes range from 0 – 50 µg/m3 are found in almost all over the cities of Bandung, there are only a few unhealthy class spots and there are no very unhealthy and dangerous classes. Which means Bandung is still healthy from the danger of PM10.
Fig 2. Distribution of PM10 in Bandung, May 2019 Around 29 sample points were taken randomly throughout the city of Bandung. PM10 values from ground
measurements and image processing can be seen in Table 1.
ground measurements are carried out by walking every 90 meters down several streets in Bandung, while the PM10 value in the image calculation is calculated using the PM10 algorithm consisting of AOT values obtained from AERONET and the reflectance values of the band used. The results of PM10 concentration values between field measurements and image processing in Table 1 show that the two approach each other.
TABLE 1. PM10 Value Based On Landsat Imagery and Ground Measurements
No Koordinat PM10 Ground
Measurements (µg/m3)
PM10 Landsat Imagery (µg/m3)
X Y
1 789396.07 9236379.47 19,8 13,57
2 789400.44 9236381.90 25 13,57
3 789438.41 9236382.21 14,7 8,19
4 789453.34 9236544.46 13 6,7
5 789452.31 9236571,59 12.4 10,6
6 788727.95 9236660.03 15,2 21,08
7 788735.80 9236709.92 20,2 11,05
8 788737.68 9236687.20 16,9 21,51
9 788748.69 9236743.81 20,6 11,09
10 788742.56 9236779.06 13,4 18,29
11 788745.52 9236805.99 19,3 14,26
12 788745.45 9236815.36 22,6 17,45
13 788746.72 9236826.67 17,6 17,45
14 788750.99 9236871.47 22,2 22,43
15 788750.41 9236902.83 19,2 18,87
16 788327.94 9237226.36 13,6 10,98
17 788309.54 9237219.90 15,5 7,9
18 792089.15 9236517.28 20,9 15,23
19 792091.37 9236517.16 9,7 15,23
20 798516.19 9234905.95 23,4 12,27
21 798597.27 9234894.31 17,8 26,83
22 798579.57 9234897.23 26,5 30,76
23 798685.83 9234887.05 30,2 24,9
24 798695.60 9234880.09 27,5 24,9
25 798729.29 9234856.83 24,3 26,62
26 798716.10 9234868.78 40,9 34,82
27 798694.44 9234870.43 36,6 24,9
28 798488.62 9234887.11 19,3 23,87
29 798479.96 9234889.62 20 23,8
In this study the variables that will be measured for strength are PM10 values from ground measurements and
PM10 values from the processing of Landsat Satellite Imagery. To find out the correlation between these two variables, it used simple linear regression.
Fig 3. Linear Regression of PM10 from Landsat Imagery and PM10 from Ground Measurement.
Linear regression is performed on the results of Landsat satellite image processing and PM10 ground measurements to show the relationship between the two variables can be seen in Fig 3. The data used of ground measurements and Landsat was taking on same month of April 2019.
Based on the linear regression relationship above (Fig 3), the equation of the coefficient of determination on the regression relationship is 0.61, this means that the relationship between PM10 from ground measurements and Landsat image processing is 61,87%. According to Boediono and Koster [8], with a correlation value in 0.70 <r <0.90 or - 0.90 <r <- 0.70, it means a moderate relationship. This explains that the values generated by the calculation of the Landsat image algorithm and ground measurements using the Pocket Sensor PM 2.5 tool have concentration values that were close to each other.
IV. CONCLUSION
The results of the wide distribution of PM10 values in Bandung based on healthy classes in 2019 with an area of 161,296 km2. Whereas the medium class in 2019 only has an area of 5,87 km2. Based on the wide distribution of PM10 concentration values, it can be concluded that PM10 air pollutants in Bandung are still in the healthy category. Based on the linear regression coefficient equation of the relationship between PM10 from the results of ground measurements and Landsat image processing having a moderate relationship to 61.87%.
ACKNOWLEDGMENT
Thanks to LPPM/ Lembaga Penelitian dan Pengabdian Masyarakat (Research Institutions and Community Service) Institute of Technology Nasional Bandung for providing research funding and this research is still ongoing and develop, also thanks to Prof. Wataru Takeuchi from IIS- Tokyo University for research collaboration, and thanks to Aab, Anisa, Novita, Anis, Reza, Felita, Ichwan, and Derry.
REFERENCES
[1] Saleh, S. A., & Hasan, G. (2014). Estimation of PM10 Concentration using Ground Measurements and Landsat 8 OLI Satellite Image.
Journal of Remote Sensing & GIS, 3(2), 1.
[2] Mukhtar, R., Panjaitan, E. H., Wahyudi, H., Santoso, M., &
Kurniawati, S. (2013). Komponen Kimia PM2,5 dan PM10 di Udara Ambien di Serpong- Tangerang. Jurnal Ecolab, 7(1).
[3] Retalis, A., Cartalis, C., & Athanassious, E. (1999). Assesment of the Distribution of Aerosols in the Area of Athens with the Use of Landsat Thematic Mapper Data. Int J Remote Sensing, 20, 939-945.
[4] Wald, L., and Baleynaud, J. (1999). Observed Air Quality Over City of Nantes by Means of Landsat Thermal Infrared Data. International Journal of Remote Sensing, 20, 947-959.
[5] Ung, A., Wald, L., Ranchin, T., Weber, C., Hirsch, J., & dkk. (2001).
Satellite data for Air Pollution Mapping over a City. Virtual Stations.
[6] Akimoto, H. (2003). Global Air Quality and Pollution. Science, 1716- 1719.
[7] Nugroho, D. S., and Syaohid, E. (2015). Strategi Peningkatan Kualitas Empat Atribut Green City di Kecamatan Bandung Wetan Kota Bandung. Jurnal Perencanaan Wilayah dan Kota.
[8] Boediono, and Koster, W. (2001). Theory and Application Statistic and Probabilty. Bandung: PT. Remaja Rosdakarya.
Human Error Contributions to Potential Incident in Laboratories at Institut Teknologi Nasional
Caecilia Sri Wahyuning Industrial Engineering Institut Teknologi Nasional
Bandung, Indonesia [email protected]
Abstract—Itenas has 43 laboratories with sufficient means and infrastructures for practice session. However, observation results showed unsafe conditions and near-miss or accidents often occurred. Practice session is a man-machine interface system, with practicioners often incapable or not qualified to operate the machinery or tools required. This research identifies contributing factors that triggers incidents in laboratory around Itenas. According investigation using Human Factor Analysis Classification System (HFACS), it is then identified that level 1 (organizational factors) plays big role on work incident. However, environmental factors contributes the most in the occurrence of unsafe act preceding to an incident. Therefore, a system is required to comprehensively manage safety and health around the precinct of the campus to minimize potential dangers presented by environmental factors.
Keywords—safety, accident, unsafe acts, human error, human factor
I. INTRODUCTION
Institut Teknologi Nasional (Itenas) was established on 14th of December, 1972, currently operating 13 majors managed by 3 faculties (FTI, FTSP, dan FSRD). Itenas is located on a 52,954 m2 area with 41,205m2 building area.
One of the mission of Itenas is ‘To develop infrastructure and scientific- and technology-based management system to create conducive academic situation’. Therefore Itenas is equipped with various facilities to support learning process, including lecture rooms spread across 21 3-to-4-story buildings, 43 laboratories, and 14 studios spread across 13 majors.
Since 2003 Itenas received plenty of competition grant, both for the development of each majors as well as development of educations in various forms of academic activities. As a result, Itenas has managed to improve the quality of means and infrastructures supporting the educational process. The quality of means and infrastructures of laboratories and studios is referred to the development of necessities in learning process and technologies one wish to acquire. This condition caused a high mobility in the campus vicinity, day-in and day-out, be it indoor or outdoor activity.
For example, mobilities in laboratory, interaction between practitioners/assistant/lecturer/technician with tools/machinery, even material, regardless of the goal for every activities in laboratory.
On Man-Machine Interface/ MMI, response and stimuli occurred between man and machine. Technology allowed
MMI to become a very complex system where failure becomes a constant consequence from man-machine system.
This condition can happen because of the condition of the system itself, be it institutional ignorance, conditions of machine, man, and environment. Failure can occur because of human error, errors in design of the machine, or even the system itself. An error is defined as a failure to achieve desired goal. Unsafe act often occurred during direct contacts with system, where this act is a mistake caused by lapses in action, triggered by conditions encouraging one to act in an unsafe manner. This condition is caused by unsafe supervision caused by terrible resource management influenced by the organization [1]. Unsafe act triggered by conditions are classified into 3 main categories, which are environmental factor, individual condition, and individual/resource management.
According to observations, there are unsafe conditions on several facilities available in Itenas. This caused near-miss or even incidents in the vicinity of Itenas, be it indoor or outdoor. For the time being, health and safety system in Itenas is still not managed comprehensively, and is still being revamped from time to time. There are still unmanaged areas and work systems necessary to support conducive environment in campus. This is indicated by near-misses and small incidents. However, in accordance to the attempt from Itenas to overhaul the health and safety management system, human factors as a contributing factor of incidents should be studied in a more detailed way.
This research is done to observe how much human contributes to a potential incidents, especially in laboratories around Itenas. Swiss Cheese model of human error is a special approach visualizing the relation of human error in between 4 levels in Swiss Cheese model [2]. This approach is used because in laboratories, all personnel involved did mental activity at a higher level than physical activity. This happened because most practicioners worked in laboratory for no more than 8 hours (with the exception for electric vehicle design studio). On several laboratories, activities are not directly practiced by practicioners.