Groundwater Arsenic Contamination in Bangladesh and West Bengal, India.

Transcription

1 Groundwater Arsenic Contamination in Bangladesh and West Bengal, India. Professor Bimal K. Roy Ph. D. Director Indian Statistical Institute (ISI) India (Based on work by my student Dr. Amir Hossain, Dhaka University)

2 Treating arsenic contaminated groundwater Community based Arsenic Removal Plants (ARPs) attached with contaminated tubewell Household based ARPs

3 Arsenic Removal Processes/Mechanisms Developed to Treat Arsenic Contaminated Groundwater Precipitation processes Adsorption processes Ion exchange processes Separation (membrane) processes. Alternative technologies

4 Ineffectiveness and poor reliability of arsenic removal plants (ARPs) A cross-sectional study on 577 ARPs installed in arsenic affected areas of West Bengal India A follow-up study (two years) on 18 ARPs instaled in North-24 Parganas under India Canada Environment Facilities (ICEF) project.

5 Evaluation of ARP (Cross Section Study) Summary of the Study Criteria No. % ARPs investigated 577 Defunct ARPs ARPs in operation (good or bad) Yellow/red/reddish brown water ARPs with regular backwashing Raw water analyzed for arsenic Raw water arsenic <50μg/L Filtered water arsenic >50μg/L ARPs found useful

6 Evaluation of ARP (Follow-up Study) Arsenic removal efficiency (Summary statistics) ARP ID N Max Mean SD

7 Evaluation of ARP (Follow-up Study) Arsenic removal efficiency None of the ARPs could maintain WHO guideline value of arsenic in drinking water (As 10µg/L) Only 3 ARPs achieve the Indian standard throughout the project period (As 50µg/L).

8 Evaluation of ARP (Follow-up Study) Arsenic removal efficiency based on a Composite index (CI) ARP ID CI Rank Performance Categories GOOD (CI value: ) MODERATE (CI value: ) BAD (CI value: )

9 Indexing and modeling arsenic contamination data in Bangladesh

10 Based on mean concentration, districts were categorized into four categories Arsenic Safe : Mean 10μg/L Mildly Arsenic Affected : Mean between 11 and 50μg/L Moderately Arsenic Affected : Mean between 51 and 300μg/L Severely Arsenic Affected : Mean >300μg/L On the basis of Proportions, districts were also classified into four categories Arsenic Safe : Proportion of samples exceeding 10μg/L of arsenic is zero Mildly Arsenic Affected : Proportion of samples exceeding 10μg/L of arsenic is positive but exceeding 50μg/L is zero. Moderately Arsenic Affected : Proportion of samples exceeding 50μg/L of arsenic is positive but exceeding 300μg/L is zero Severely Arsenic Affected : Proportion of samples exceeding 300μg/L is positive

11 Severity Index An attempt has been made to define an index for a specific geographic region (here thana and district) so that on the basis of this indices one have a quick check about the concentration level of the region. Computation of severity index is a pivotal step in the analysis procedure The available arsenic data is spread over the region (thana/district) so, a single latitude, longitude value for each region was considered to find out a representative value of arsenic concentration for the region is of primary concern This is by far the first attempt of indexing the arsenic contamination data.

12 Severity Index: Aim and Definition The severity index was defined keeping in mind two aims The high concentration values should be given special weight The representative value should not be far apart from individual data values. Keeping these two aims in mind, the defined as: representative value was

13 Severity Index: Loss Function Now the problem is to find required a and b values satisfying certain optimality conditions conforming to aims. Hence, the assumed loss function is:

14 Severity Index: Revised Definition The representative value defined earlier has range [0 ), which is difficult to handle for classification purposes. So, the representative values were transformed with the help of CDFs on positive support to [0 1] range. This transformed value is now defined as the Severity Index. A Beta (m,n) distribution was fitted to the transformed data and based on quantiles of that fitted distribution the regions of interest was classified into: arsenic safe, mildly arsenic affected, moderately arsenic affected, and severely arsenic affected regions.

15 Arsenic Safe: having severity index between 0 and Mildly Affected: having severity index between and Moderately Affected: having severity index between and Severely Affected: having severity index between and 1.

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17 ARSENIC CONTAMINATION STATUS IN SELECTED DISTRICTS OF BANGLADESH SL.NO. DISTRICT MEAN PROPORTION INDEX 1. Khagrachari Safe Safe Safe 2. Rangamati Safe Safe Safe 3. Joypurhat Safe Mildly Affected Safe 4. MymanSingh Safe Moderately Affected Mildly Affected 5. Barguna Safe Mildly Affected Mildly Affected 6. Gaibandha Mildly Affected Severely Affected Mildly Affected 7. Jhalakati Mildly Affected Moderately Affected Moderately Affected 8. Meherpur Mildly Affected Severely Affected Moderately Affected 9. Manik Ganj Moderately Affected Severely Affected Moderately Affected 10. Satkhira Moderately Affected Severely Affected Severely Affected 11. Chandpur Moderately Affected Severely Affected Severely Affected 12. Noakhali Severely Affected Severely Affected Severely Affected

18 SL.NO. DISTRICT MEAN PROPORTION INDEX 1. Khagrachari Safe Safe Safe 2. Rangamati Safe Safe Safe 3. Bandarban Safe Safe Safe 4. Panchagar Safe Mildly Affected Safe 5. Coxs Bazar Safe Safe Safe 6. Dinajpur Safe Mildly Affected Safe 7. Naogaon Safe Mildly Affected Safe 8. Joypurhat Safe Mildly Affected Safe 9. Nilphamari Safe Mildly Affected Safe 10. Patuakhali Safe Safe Safe 11. Lalmonirhat Safe Mildly Affected Safe 12. MymanSingh Safe Moderately Affected Mildly Affected 13. Barguna Safe Mildly Affected Mildly Affected 14. Kurigram Safe Moderately Affected Mildly Affected 15. Thakurgaon Safe Moderately Affected Mildly Affected 16. Bhola Safe Mildly Affected Mildly Affected 17. Gazipur Safe Severely Affected Mildly Affected 18. Sylhet Safe Moderately Affected Mildly Affected 19. Natore Safe Moderately Affected Mildly Affected 20. Tangail Mildly Affected Moderately Affected Mildly Affected 21. Chittagong Mildly Affected Moderately Affected Mildly Affected 22. Bogra Mildly Affected Severely Affected Mildly Affected 23. Siraijganj Mildly Affected Moderately Affected Mildly Affected 24. Sherpur Mildly Affected Moderately Affected Mildly Affected 25. Gaibandha Mildly Affected Severely Affected Mildly Affected 26. Jamalpur Mildly Affected Severely Affected Moderately Affected 27. Rajshahi Mildly Affected Severely Affected Mildly Affected 28. Habiganj Mildly Affected Moderately Affected Moderately Affected 29. Moulavibazar Mildly Affected Moderately Affected Moderately Affected 30. Dhaka Mildly Affected Severely Affected Moderately Affected 31. Rajbari Mildly Affected Severely Affected Moderately Affected 32. Magura Mildly Affected Severely Affected Moderately Affected 33. Jhenaidaha Mildly Affected Severely Affected Moderately Affected 34. Kishoreganj Mildly Affected Severely Affected Moderately Affected 35. Jhalakati Mildly Affected Moderately Affected Moderately Affected 36. Jessore Mildly Affected Severely Affected Moderately Affected 37. Rangpur Mildly Affected Severely Affected Moderately Affected 38. Meherpur Mildly Affected Severely Affected Moderately Affected 39. Netrokona Moderately Affected Severely Affected Moderately Affected 40. Kushtia Moderately Affected Severely Affected Severely Affected 41. Manik Ganj Moderately Affected Severely Affected Moderately Affected 42. Pirojpur Moderately Affected Severely Affected Moderately Affected 43. Chuadanga Moderately Affected Severely Affected Moderately Affected 44. Brahmanbaria Moderately Affected Moderately Affected Moderately Affected 45. Feni Moderately Affected Severely Affected Moderately Affected 46. Khulna Moderately Affected Severely Affected Severely Affected 47. Pabna Moderately Affected Severely Affected Severely Affected 48. Narsingdi Moderately Affected Severely Affected Severely Affected 49. Chapainawabganj Moderately Affected Severely Affected Severely Affected 50. Sunamganj Moderately Affected Severely Affected Moderately Affected 51. Shariatpur Moderately Affected Severely Affected Severely Affected 52. Munshi Ganj Moderately Affected Severely Affected Severely Affected 53. Gopalganj Moderately Affected Severely Affected Severely Affected 54. Narail Moderately Affected Severely Affected Severely Affected 55. Faridpur Moderately Affected Severely Affected Severely Affected 56. Madaripur Moderately Affected Severely Affected Severely Affected 57. Bagerhat Moderately Affected Severely Affected Severely Affected 58. Satkhira Moderately Affected Severely Affected Severely Affected 59. Narayan Ganj Moderately Affected Severely Affected Severely Affected 60. Barisal Moderately Affected Severely Affected Severely Affected 61. Chandpur Moderately Affected Severely Affected Severely Affected 62. Lakshmipur Moderately Affected Severely Affected Severely Affected

19 Linear modeling based approach Since the available arsenic data have several explanatory variables and a response variable arsenic concentration, so a linear model based approach on this point referenced data will be the most natural approach. The explanatory variables are: Longitude Number of user of the tubewell Depth of the tubewell Age of the tubewell Latitude

20 Interpretation of the results of Statistical Analysis From severity index: Out of 64 districts in Bangladesh, arsenic safe 11 districts; mildly affected 15 districts; 19 moderately affected 19 districts; and severely affected 19 districts. From Linear model fitting: Linear models approach however fails to deliver any insight into the effects of the number of users, depth and age of tubewells on the arsenic contamination. The reason may be the presence of spatial correlation. Variogram Analysis: The directional semivariogram shows different correlation in four different directions. The highest is (135 0, ), which is the direction of Padma river flow indicating high arsenic in Padma river basin.

21 Thanks