Assessing the Groundwater Quality and Related Hazards of - - PowerPoint PPT Presentation

assessing the groundwater quality and related hazards of
SMART_READER_LITE
LIVE PREVIEW

Assessing the Groundwater Quality and Related Hazards of - - PowerPoint PPT Presentation

Assessing the Groundwater Quality and Related Hazards of Neighbourhood in Roorkee, Uttarakhand, India Ignite Stage Presentation Presented by: Vickyson Naorem PhD Research Scholar IIT Roorkee, India Introduction (1/2) Informal


slide-1
SLIDE 1

“Assessing the Groundwater Quality and Related Hazards of Neighbourhood in Roorkee, Uttarakhand, India”

Presented by: Vickyson Naorem

PhD Research Scholar IIT Roorkee, India

Ignite Stage Presentation

slide-2
SLIDE 2

Introduction (1/2)

  • Informal settlements living in the urban periphery of both

metro cities and developing urban regions of India are constantly exposed to a variety of hazards.

  • One such community in Roorkee, Uttarakhand, India was

selected i.e. Bharatnagar, Mahigran Ward as part of case study on social vulnerability and disaster risk reduction.

  • Moreover, a canal is passing through the study area is also a

flood prone area.

  • The purpose of the canal was for the drainage but now it

becomes contaminated due to human activities.

  • So many human activities happened in the neighbourhood like

majority of buildings are of concrete cement, is located near the canal.

slide-3
SLIDE 3

Introduction (2/2)

Key Map India Map Location Map Uttarakhand State

slide-4
SLIDE 4

Purpose

The aim is to assess the water quality and related hazards of Bharatnagar, Mahigran Ward in Roorkee, Uttarakhand, India. The objectives are as follows:

  • To identify the location of highly contaminated groundwater that has

potential to cause adverse health effects.

  • To analyse and evaluate the risk associated with that hazard

qualitatively. Limitations of the case study

  • The case study has taken only within the buffer of 150 metres from

the canal.

  • Only

10 parameters have considered for groundwater quality measurements.

  • Temporal variation is limited to one time due to academic work.
slide-5
SLIDE 5

Methodology

  • Attributes of settlements and groundwater are mapped by using mobile apps.
  • Collected data are interpolated to visualise the spatial variation of each parameter.
  • (Bianchi & Harter, 2002 – NSP; Gulgundi & Shetty, 2018 – Case study; Harter, 2003 – GW

Quality and Pollutant; Jie, Hanting, Hui, Jianhua, & Xuedi, 2013 – Selection of interpolation)

Figure: Flowchart of the case study

slide-6
SLIDE 6

Results (1/6)

Maximum, minimum and average concentrations of critical parameters and BIS permissible limits

500 1000 1500 2000 2500 pH Chlorides (Cl) TDS Total Hardness Calcium (Ca) Magnesium (Mg) Nitrate (NO3) Sulphate (SO4) Fluoride (F) Iron (Fe) Maximum Minimum Average BIS limits

slide-7
SLIDE 7

Results (2/6)

Spatial variation of pH

Low High

slide-8
SLIDE 8

Results (3/6)

Hardness Chloride (Cl) Magnesium (Mg)

Low High

slide-9
SLIDE 9

Results (4/6)

Calcium (Ca) Iron (Fe) Nitrate (NO3)

Low High

slide-10
SLIDE 10

Results (5/6)

Fluoride Total Dissolved Solids (TDS) Sulphate (SO4)

Low High

slide-11
SLIDE 11

Results (6/6)

  • Groundwater quality was qualitatively analysed with respect to

standards.

  • Vulnerabilities and their extents were identified with regards to

groundwater quality available to the people in the study area.

  • Spatial relation between the settlements and the groundwater

quality was qualitatively analysed thereby showing risks and infrastructural lacunae in the area.

slide-12
SLIDE 12

Conclusions

  • This assessment emphasizes mainly to consider the extension of stations

more especially in the informal settlements.

  • Assessing groundwater can provide better quality of life.
  • Highly overlapped settlements with highly affected ground water must be

considered for restriction so as to reduce hazard exposure and vulnerability to hazards.

  • The spatial analysis of groundwater can provide in aiding further local

development plans which can integrate multi-hazard management by continuous collection of data with temporal variation.

slide-13
SLIDE 13

Appendix 1

Sample no pH Turbidity NTU Total Hardness mg/L as CaCO3 Ca, mg/L Mg, mg/L Na, mg/L K, mg/L Fe, mg/L HCO3 CO3, mg/L Cl, mg/L NO3, mg/L SO4, mg/L PO4, mg/L TDS, mg/L EC umhos/ F, mg/L Cu, mg/L Pb, mg/L Cr, mg/L 1 7.42 0.1 190 52 14 26 2 0.16 204 nil 100 18 10 0.3 328 520 1.3 nil nil nil 2 7.6 nil 210 56 18 30 1.4 0.04 275 nil 90 32 40 0.8 390 620 1.2 nil nil nil 3 7.5 5.1 210 50 16 32 0.6 1.24 200 nil 82 54 16 1.9 340 540 1 0.1 nil nil 4 7.84 nil 688 130 69 55 1.1 0.2 544 8 210 52 42 0.1 840 976 1.3 nil nil nil 5 7.15 0.4 492 128 41 76 3 0.1 309 nil 137 157 84 0.8 806 1210 0.8 nil nil nil 6 7.26 nil 416 102 38 56 2 nil 270 nil 120 109 56 2.8 646 1050 0.9 nil nil nil 7 7.62 0.7 584 108 76 54 1.3 0.12 458 6 220 10 44 6 628 1010 1.3 nil nil nil 8 7.48 0.7 480 64 72 48 1.2 nil ·471 nil 205 34 45 0.5 744 1174 1 nil nil nil 9 7.42 0.1 430 108 40 54 1 0.2 427 nil 235 10 56 1.1 714 1100 0.9 nil nil nil 10 7.22 nil 380 88 39 44 1.4 0.1 280 nil 160 36 29 1 530 850 0.7 nil nil nil 11 7.6 0.2 360 56 54 48 2.2 nil 442 nil 130 9 31 0.4 550 830 1.4 nil nil nil 12 7.6 0.5 392 94 38 35 2.6 nil 300 10 130 12 25 0.8 490 780 1.9 nil nil nil 13 8.21 nil 280 55 35 48 3 nil 402 12 164 12 14 0.6 510 810 0.4 nil nil nil 14 7.8 nil 626 202 30 52 3.4 0.1 549 nil 265 70 30 1 930 1350 nil nil nil 0.1 15 7.55 nil 560 118 65 46 3.2 1.08 442 nil 350 11 31 0.4 840 1330 nil nil nil nil 16 6.92 nil 388 106 31 203 20 0.1 630 nil 212 20 56 2 990 1520 0.5 nil nil nil 17 6.55 0.4 432 110 40 200 14 nil 580 nil 150 5 80 2.6 1000 1520 nil nil nil nil 18 7.2 nil 612 150 58 40 1 0.08 225 nil 300 54 28 1 730 1120 1.4 nil nil nil 19 7.32 1.2 608 144 60 120 3 0.1 328 12 306 52 30 1.4 860 1340 0.5 nil nil nil 20 7.82 nil 672 202 40 40 1.4 0.12 321 nil 275 20 34 1 764 1240 nil nil nil nil 21 8.42 0.5 618 204 26 72 2 nil 336 nil 285 60 51 1.2 880 1450 0.1 nil nil nil 22 6.79 1.4 1960 386 249 202 10 0.8 286 nil 1338 84 216 6.2 2850 4270 0.8 nil nil nil 23 6.76 0.8 604 155 36 140 5.2 0.4 494 nil 232 153 51 4 1045 1712 0.6 nil nil nil 24 7.7 nil 620 142 64 220 30 0.09 540 nil 400 10 150 6 1310 2010 0.6 nil nil nil 25 6.6 nil 70 15 8 14 0.4 0.12 140 nil 60 38 18 nil 200 310 nil nil nil nil 26 6.8 0.8 570 108 73 36 1.1 nil 407 nil 305 20 18 1.8 746 1220 0.6 nil nil nil 27 7.12 nil 416 100 40 48 2.2 0.05 332 nil 320 24 40 1.2 742 1260 0.7 nil nil nil 28 7.4 12 960 300 50 86 3.1 1.04 608 nil 1012 80 120 2.6 2042 3370 1.8 nil nil nil 29 7.62 26 1062 330 58 78 3.6 1.02 570 nil 1044 84 90 2 2008 3270 2.3 nil nil nil 30 7.8 14 1095 270 101 106 4 0.28 700 80 780 98 90 4.3 2200 4010 2.5 nil nil 0.2

Table: Results of physio-chemical analysis of groundwater samples

slide-14
SLIDE 14

Appendix 2

Table: Maximum, minimum and average concentrations of critical parameters and BIS permissible limits

No. Parameter Maximum Minimum Average BIS limits 1 pH 8.42 6.55 7.46.5 to 8.5 2 Chlorides (Cl) 1338 60 320.63 1000 3 TDS (Total Dissolved Solids) 2850 200 921.77 2000 4 Total Hardness 1960 70 563.67 600 5 Calcium (Ca) 386 15 137.83 200 6 Magnesium (Mg) 249 8 52.63 100 7 Nitrate (NO3) 157 5 47.6 50 8 Sulphate (SO4) 216 10 54.17 400 9 Fluoride (F) 2.5Nil 0.87 1.5 10 Iron (Fe) 1.24Nil 0.225 1

slide-15
SLIDE 15

Thank you

  • ir. Vickyson Naorem

Research Scholar, Centre of Excellence in Disaster Mitigation and Management (CoEDMM), Indian Institute of Technology (IIT) Roorkee, India (vnaorem@dm.iitr.ac.in) +91-7011636099

Reference:

Bianchi, M., & Harter, T. (2002). Nonpoint sources of pollution in irrigated agriculture. University of

  • California. California. Retrieved from

http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Nonpoint+Sources+of+Pollution+in+irrigat ed+agriculture#0 Gulgundi, M. S., & Shetty, A. (2018). Groundwater quality assessment of urban Bengaluru using multivariate statistical techniques. Applied Water Science, 8(1), 43. http://doi.org/10.1007/s13201-018-0684-z Harter, T. (2003). Groundwater Quality and Groundwater Pollution. Agricultural and Natural Resources (Vol. 8084). California. Retrieved from http://www.nrcs.usda.gov/ Jie, C., Hanting, Z., Hui, Q., Jianhua, W., & Xuedi, Z. (2013). Selecting Proper Method for Groundwater Interpolation Based on Spatial Correlation. In 2013 Fourth International Conference on Digital Manufacturing & Automation (pp. 1192–1195). IEEE. http://doi.org/10.1109/ICDMA.2013.282

Collaborators:

  • Dr. Mahua Mukherjee, Head, CoEDMM, IIT Roorkee, Arjun Satheesh

(Researcher), Siddharth Jena (Masters student), Tanu Gupta (Masters student), Mohana Manna (Masters student), Shalu Mathuria (Masters student)