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 Table of Contents  
ORIGINAL ARTICLE
Year : 2022  |  Volume : 13  |  Issue : 2  |  Page : 81-84

Field test and reporting of disaster waste mapping in flood-affected areas of Kodagu district


Department of Community Medicine, Kodagu Institute of Medical Sciences, Government of Karnataka, Madikeri, Karnataka, India

Date of Submission20-Jul-2022
Date of Acceptance07-Oct-2022
Date of Web Publication10-Jan-2023

Correspondence Address:
Dr. Ashwini Madeshan
Department of Community Medicine, Kodagu Institute of Medical Sciences, Madikeri, Karnataka
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/mjmsr.mjmsr_42_22

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  Abstract 


Introduction: Floods are the most commonly occurring hydrological disasters in India. The disaster wastes (DWs) produced in floods involve various departments to clear the waste and consume more resources. Thus, the study was conducted to assess the type and visual quantity of DW during the initial recovery phase in flood-affected areas of the Kodagu district. Materials and Methods: A qualitative study was conducted in randomly selected 10 flood-affected areas in September 2019. Data were collected by a structured questionnaire and analyzed using Epi Info version 7.2. Results: The DWs were present in all 10 areas and it was classified into seven types. Out of 10 flood-affected areas, the majority of the areas had packaging wastes (nine areas) and bedding/furniture/cloth/textile waste (nine areas), followed by rubble/building material (six areas), electrical/electronic waste (five areas), food waste (three areas), and toxic/harmful/hazardous waste and biomedical waste/hospital waste (two areas). The total DW visual quantity was 6220 kg and was highest for rubble/building material DW. Conclusions: The DWs were containing both biomedical and nonbiomedical wastes. The wastes clogged the canals and polluted mainly water and soil. The visual quantity of DW was more and clearing the waste was difficult without the vehicles.

Keywords: Disaster waste, flood-affected areas, the initial recovery phase


How to cite this article:
Narasimha B C, Kamath R, Udayar SE, Madeshan A. Field test and reporting of disaster waste mapping in flood-affected areas of Kodagu district. Muller J Med Sci Res 2022;13:81-4

How to cite this URL:
Narasimha B C, Kamath R, Udayar SE, Madeshan A. Field test and reporting of disaster waste mapping in flood-affected areas of Kodagu district. Muller J Med Sci Res [serial online] 2022 [cited 2023 Jan 31];13:81-4. Available from: https://www.mjmsr.net/text.asp?2022/13/2/81/367407




  Introduction Top


Floods are one of the most common hydrological disasters occurring in India.[1],[2] Flood is caused when water overflows from its natural course and onto the adjoining areas. It is also caused when the upper catchment reservoir capacity is saturated and results in severe damage to life, crops, property, and infrastructures.[3]

Karnataka has witnessed various disasters in the past two decades such as drought, floods, landslides, and hailstorms. In 2018, also Malnad and Coastal Karnataka of southern India had experienced floods and landslides due to heavy rainfall. The Malnad region is formed by the Sahyadri mountain range of Western Ghats and distributed in the districts of Shivamogga, Hassan, Chikkamagaluru, and Kodagu (Madikeri). All these areas had suffered damage to agriculture, horticulture, plantation crops, public infrastructure, and houses and made many homeless.[3],[4],[5] These severe damages produce a lot of disaster wastes (DWs) and it includes rubble/building material, packaging waste, toxic/harmful waste, electrical waste, bedding/furniture/cloth, food waste, biomedical waste, etc.[6],[7]

The DW produced during floods and landslides contaminates water bodies with biological organisms such as bacteria, viruses, and parasites, and chemical agents such as pesticides, insecticides, and heavy metals. Microbiological organisms may cause various infections in humans and animals. The heavy metals may cause cancer of various organs and affects other vital systems of our body. All these hazards may result in deterioration of the public health.[7],[8]

The DW management during the recovery phase involves various departments' to clear the waste. It also consumes more resources such as workforce and vehicles to clear DW. Thus, the study was conducted to assess the type and visual quantity of DW during the initial recovery phase in flood-affected areas of Kodagu district.


  Materials and Methods Top


A qualitative study was conducted during the initial recovery phase in randomly selected 10 flood-affected areas of Kodagu district in September 2019. It was a part of a pilot project conducted by the State Emergency Operation Center, Revenue Department (disaster management) Government of Karnataka. A mobile-based application (APK file) was developed by the state emergency operation center which was used to identify the disaster-affected areas precisely using Global Positioning System (GPS) location and to collect the data regarding the type and quantity of DW. The same data were accessed by the state emergency operation center to compile the data and percolate the information to the concerned authority to take action. Data were analyzed using Epi Info version 7.2.1, (Centre for Disease Control and Prevention, Atlanta, Georgia, United States).

The DW categories and their description given by the State Emergency Operation Center, Revenue Department (disaster management) Government of Karnataka in the APK file were as follows:

  1. Rubble/building material – bricks, mud, steel rods, windows, doors, glass panes, and household utensils
  2. Packaging waste – plastic covers, tetra packs, plastic bottles, paper, broken buckets, mugs, cardboard packaging, etc.,
  3. Toxic/harmful/hazardous waste – leftover pesticide in containers, kerosene contaminated with water, and hospital waste
  4. Electrical waste or electronic waste – cables/wires, switchboards, household items such as broken TVs, mixers, refrigerators, iron boxes, bulbs, and other electric fixtures
  5. Textile waste – beddings, furniture, cloth, and other textile materials
  6. Food waste – food waste or any other organic waste which is in a state of decay
  7. Biomedical waste – hospital waste, discarded bandage materials, etc.



  Results Top


Among the 10 randomly selected flood-affected areas in our study, the majority of 6 (60%) areas were under Virajpet taluka, followed by 4 (40%) Madikeri taluka [Figure 1].
Figure 1: Distribution of flood-affected areas based on the Taluka in Kodagu district

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During DW mapping, we found that the DW was present in all 10 areas. It was classified into seven types as shown in [Figure 2] and they were, nonhazardous waste such as (i) rubble/building material, (ii) packaging waste, (iii) bedding/furniture/cloth/other textile material, (iv) food waste/any other organic waste which is in the state of decay, and hazardous waste (v) toxic/harmful/hazardous waste, (vi) electrical/electronic waste, and (vii) biomedical waste/hospital waste/discarded bandage materials.
Figure 2: Distribution of flood-affected areas based on types of disaster waste

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Out of 10 flood-affected areas, the majority (nine areas) of the areas had packaging waste and bedding/furniture/cloth and other textile materials waste followed by rubble/building material (six areas), electrical/electronic waste (five areas), food waste/any other organic waste which is in the state of decay (three areas), and toxic/harmful/hazardous waste and biomedical waste/hospital waste/discarded bandage materials (two areas).

The DW visual quantity estimation was made with the approximate perception of weight. The total DW visual quantity was 6220 kg. Out of the 6220 kg, the visual quantity was highest for rubble/building materials (3200 kg) followed by packaging waste (1570 kg), bedding/furniture/cloth/other textile material (670 kg), electrical/electronic waste (570 kg), food waste/any other organic waste which is in the state of decay (170 kg), toxic/harmful/hazardous waste (30 kg), and lowest for biomedical waste/hospital waste/discarded bandage materials (10 kg) [Figure 3].
Figure 3: Distribution of flood-affected areas based on the visual quantity of disaster waste

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The wastes were scattered all over the place in six areas, whereas it was collected in heaps in four areas. The wastes from the relief center were also found in two flood-affected areas.

Based on the visual quantity estimation described above, we estimated that the resources required to clear DW were 34 personnel and 36 vehicles.


  Discussion Top


The present study was conducted to identify the disaster-affected areas precisely using a mobile-based application having GPS location developed by the State Emergency Operation Center, Revenue Department (disaster management) Government of Karnataka and to collect the data regarding the type and visual quantity of DW. The results were used to assess the resources such as workforce and vehicles required to clear the DW. Whereas in the study carried out by Yoo et al.,[6] they used 3D spatial information to identify the amount and location of waste, rubble, and debris generated by the disaster. They also reported that their result calculation can contribute to decision-making on the distribution of vehicles and laborers for the transport of DW from the disaster fields to the staging site. A study was done by Shirai et al.[9] used RapidEye multi-spectral data to select the area for identifying land cover changes before and after the earthquake, and to estimate the amount of DW generated.

In our study, we found both nonhazardous and hazardous waste. Hazardous wastes such as toxic/harmful/hazardous waste; electrical/electronic waste and may cause various cancers, affect the nervous system, cardiovascular system, etc. Biomedical wastes contaminate water bodies and have an adverse impact on public health.

The nonhazardous wastes such as food waste and vegetation (attract rodents and contaminate the water which may lead to leptospirosis),[10] rubble/building materials, packaging waste, and bedding/furniture/cloth/other textile material will block the water canals which make the area vulnerable for floods in subsequent years. It also poses threat to aquatic animals.

In the present study, the approximate estimation of DW visual quantity was made by visual inspection. The total DW visual quantity was 6220 kg. Whereas in the study conducted by Yoo et al.,[6] they reported that they could not estimate the volume of DW. If the quantity of DW is reduced by planning disaster-proof cities/buildings and proper planning of the city in the disaster-prone area, the high resource consumption can be reduced and used for other sustainable developmental programs.


  Conclusion Top


The findings of our study conclude that the DW contained both nonhazardous and hazardous wastes such as biomedical wastes. All the DW were scattered or collected in heaps. The wastes clogged the canals and polluted mainly water and soil. The visual quantity of DW was excess and clearing the waste was difficult without the vehicles.

Recommendations

There is a need for the development of an instrument/device to estimate the approximate quantity of DW. Solid Waste Management (Swachh Bharat Mission-2) should be implemented effectively to reduce the quantity of waste produced during the disaster. The collection of DW can be merged with Mahatma Gandhi National Rural Employment Guarantee Act 2005 for optimal use of resources.

Limitations

Results cannot be generalized to the whole population since it was a part of the pilot project. The visual quantity estimation may not correlate with the actual size.

Acknowledgment

We would like to sincerely thank the State Emergency Operation Center, Revenue Department (disaster management) Government of Karnataka, for giving us the opportunity to participate in the study and for letting to use the data collection tool.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
  References Top

1.
National Disaster Management Authority. Floods. New Delhi: National Disaster Management Authority; 2022. Available from: https://ndma.gov.in/Natural-Hazards/Floods. [Last accessed on 2022 Jan 12, Last updated on 2022 Jan 05].  Back to cited text no. 1
    
2.
International Federation of Red Cross. What is a Disaster? Geneva: International Federation of Red Cross and Red Crescent Societies; 2022. Available from: https://www.ifrc.org/ what-disaster. [Last accessed on 2022 Jan 12].  Back to cited text no. 2
    
3.
Government of Karnataka. Memorandum Presented To Government of India Seeking Central Assistance For Relief And AQ4 AQ4 Emergency Works Due To Flood And Landslides In Karnataka During August 2019. Karnataka: Department of Revenue (Disaster Management); 2019. Available from: https://ksdma.karnataka. gov.in/storage/pdf-files/Flood%202019.pdf. [Last accessed on 2022 Jan 12].  Back to cited text no. 3
    
4.
World Meteorological Organization. Natural Hazards and Disaster Risk Reduction. Geneva: World Meteorological Organization; 2022. Available from: https://public.wmo.int/en/our-mandate/ focus-areas/natural-hazards-and-disaster-risk-reduction. [Last accessed on 2022 Jan 12].  Back to cited text no. 4
    
5.
United Nations Office for Disaster Risk Reduction. Disaster. Geneva: United Nations Office for Disaster Risk Reduction; 2022. Available from: https://www.undrr.org/terminology/disaster. [Last accessed on 2022 Jan 12].  Back to cited text no. 5
    
6.
Yoo HT, Lee H, Chi S, Hwang BG, Kim J. A preliminary study on disaster waste detection and volume estimation based on 3D spatial information. Comput Civ Eng 2017;1:428-35.  Back to cited text no. 6
    
7.
Tsuchimura M, Asari M, Tsukiji M, Kirikawa T. Development of disaster waste management guideline in Asia and the pacific. Japan Soc Mater Cycles Waste Manag 2017;1:1-22.  Back to cited text no. 7
    
8.
Mahurpawar M. Effects of heavy metals on human health. Int J Res Granthaalayah 2015;1:1-7.  Back to cited text no. 8
    
9.
Shirai H, Kageyama Y, Ohuchi A, Nishida M. Baseline study to estimate the amount of disaster waste using RapidEye Data. J Ins Ind Appl Eng 2016;4:184-91.  Back to cited text no. 9
    
10.
Diaz JH. Rodent-borne infectious disease outbreaks after flooding disasters: Epidemiology, management, and prevention. Am J Disaster Med 2015;10:259-67.  Back to cited text no. 10
    


    Figures

  [Figure 1], [Figure 2], [Figure 3]



 

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