Monitoring and Quantitative Human Risk Assessment of Municipal Solid Waste Landfill Using Integrated Satellite–UAV–Ground Survey Approach
Abstract
:1. Introduction
2. Study Area
3. Materials and Methods
3.1. Ground Measurements
3.2. InSAR Technique
3.3. UAV Photogrammetry
3.4. Quantitative Risk Assessment
4. Results
4.1. InSAR Technique
4.2. UAV Photogrammetry
4.3. Analysis of Deformation Mechanism
4.4. Quantitative Risk Assessment of Potential Landfill Failure
4.4.1. Determination of Failure Probability, Pf
4.4.2. Determination of Temporal Probability (PT:L) and Spatial Probability (PS:T)
4.4.3. Consequence Analysis
4.4.4. Risk Calculation
4.4.5. Risk Management
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Year | Region and References | Elevation (m) | Distance of Flow (m) | Volume of Waste Flow (m3) | Cause of Failure | Loss of Life |
---|---|---|---|---|---|---|
2017 | Meethotamualla (Sri Lanka) Karunawardena et al. (2019) | 50 | 280 | 72,342 | Rainfall induced landfill failure | 32 |
2015 | Shenzhen (China) Xu et al. (2017) | 111 | 1203 | 2.5 million | Inadequate compaction, insufficient dewatering systems, and increased steepening of the landfill slopes | 82 |
2013 | Shiraz City (Iran) (Falamaki et al., 2021) | 42 | 134 | 10,000 | Pressurised water infiltrating through the slope | 7 |
2010 | Patras (Greece) G. et al. (2013) | 30 | 50 | 12,000 | The failure was attributed to poor landfill practices, the steep inclination of the waste mass, and the increased percolation of rainfall water in the waste mass (and associated gas pressure generation) due to the absence of daily soil cover and surface water management system | 0 |
2005 | Badung (Indonesia) Lavigne et al. (2014) | 100 | 1000 | 2.7 million | Rainfall induced landfill failure | 147 |
2000 | Quezon City (Philippines) Huvaj-Sarihan and Stark (2008) | 33 | 122 | 1.2 million | Shear failure followed heavy typhoon rains | 330 |
1997 | Bogota (Columbia) Blight (2008) Caicedo et al. (2002) | 80 | 500 | 1.5 million | The pore pressure was the main factor responsible for the instability of the landfill | 0 |
1997 | Durban (South Africa) Blight (2008) | 45 | 80 | 180,000 | The liquids were obviously accumulating in the waste body and building up a head of pore pressure | 0 |
1993 | Istanbul (Turkey) Kocasoy and Curi (1995) | 40 | 60 | 15,000 | Suddenly applied additional disturbing force and the winter rain triggered the failure | 39 |
1977 | Sarajevo (former Yugoslavia) Blight (2008) | 130 | 1000 | 200,000 | Probably resulting from large quantities of winter rain infiltration into the uncompacted and uncovered waste | 0 |
Monitoring Method | Observable Deformation Direction | Observable Deformation Range | Advantages | Disadvantages |
---|---|---|---|---|
InSAR technique | LOS (Line Of Sight) | ≤2.8 cm | High-precision, large coverage, time-efficient. labor-saving | Decorrelations caused by spatial distribution, temporal intervals, single-look directions, and atmospheric delay; difficulties in solving deformation direction |
UAV photogrammetry | Vertical | ≥5.7 cm | Flexible, macro-scale coverage, labor-saving | Weather-sensitive, relatively low accuracy, deformation monitoring direction limitations |
Ground measurements | Vertical and horizontal | 1–2 mm | High-precision and stable | High cost, small coverage, labor-intensive |
Radar Satellite | Sentinel-1 |
---|---|
Time | November 2019–November 2020 |
Orbital direction | Ascending |
Band | C |
Radar wavelength | 5.6 cm |
Spatial resolution | 5 × 20 m |
Revisit period | 12 days |
Polarization mode | VV |
Incidence angle | 39.3° |
Weight | 1380 g |
Maximum horizontal flight speed | 72 km/h |
Maximum allowable wind speed | 10 m/s |
Maximum flight time | 28~30 min |
Satellite positioning module | GPS / GLONASS dual mode |
Controllable rotation range | Pitch: −90° to +30° |
Image sensor | 1/2.3 inch CMOS 12.4 million effective pixels |
Precipitation Scenario | Return Period | Rainfall Intensity | Additional Condition |
---|---|---|---|
Scenario 1 | 50 years | ≥194 mm/d | Effective drainage facilities |
Scenario 2 | 100 years | ≥207 mm/d | Failure of drainage facilities |
Partition | Depth (m) | Unit Weight (kN/m3) | Cohesion (kPa) | Friction Angle (°) | ||
---|---|---|---|---|---|---|
Mean | COV | Mean | COV | |||
Shallow | 0–10 | 11.3 | 20 | 0.15 | 12 | 0.3 |
Middle | 10–30 | 12.5 | 15 | 0.15 | 18 | 0.3 |
Deep | Over 30 | 13.9 | 10 | 0.15 | 25 | 0.3 |
Precipitation Scenario | RDL (Individual Risk) | RLOL (Societal Risk) | |||
---|---|---|---|---|---|
Daytime | Nighttime | Daytime | Nighttime | ||
T = 50a | Min | 2.66 × 10−3 | 2.75 × 10−4 | 2.20 | 0.22 |
Max | 3.37 × 10−3 | 3.39 × 10−4 | 2.79 | 0.28 | |
Mean | 3.06 × 10−3 | 3.05 × 10−4 | 2.50 | 0.25 | |
T = 100a | Min | 0.015 | 1.52 × 10−3 | 12.47 | 1.25 |
Max | 0.019 | 1.91 × 10−3 | 16.01 | 1.58 | |
Mean | 0.017 | 1.72 × 10−3 | 14.12 | 1.41 |
Year | Fatalities | Registered Residents/×104 | Probability of Death/×10−5 |
---|---|---|---|
2013 | 89 | 707 | 1.26 |
2014 | 72 | 716 | 1.01 |
2015 | 75 | 724 | 1.04 |
2016 | 107 | 736 | 1.45 |
2017 | 85 | 754 | 1.13 |
2018 | 54 | 774 | 0.70 |
2019 | 30 | 795 | 0.38 |
2020 | 33 | 823 | 0.40 |
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Zhang, S.; Lv, Y.; Yang, H.; Han, Y.; Peng, J.; Lan, J.; Zhan, L.; Chen, Y.; Bate, B. Monitoring and Quantitative Human Risk Assessment of Municipal Solid Waste Landfill Using Integrated Satellite–UAV–Ground Survey Approach. Remote Sens. 2021, 13, 4496. https://doi.org/10.3390/rs13224496
Zhang S, Lv Y, Yang H, Han Y, Peng J, Lan J, Zhan L, Chen Y, Bate B. Monitoring and Quantitative Human Risk Assessment of Municipal Solid Waste Landfill Using Integrated Satellite–UAV–Ground Survey Approach. Remote Sensing. 2021; 13(22):4496. https://doi.org/10.3390/rs13224496
Chicago/Turabian StyleZhang, Shuai, Yunhong Lv, Haiben Yang, Yingyue Han, Jingyu Peng, Jiwu Lan, Liangtong Zhan, Yunmin Chen, and Bate Bate. 2021. "Monitoring and Quantitative Human Risk Assessment of Municipal Solid Waste Landfill Using Integrated Satellite–UAV–Ground Survey Approach" Remote Sensing 13, no. 22: 4496. https://doi.org/10.3390/rs13224496