Climate change modeling based public health resource planning for Narmada basin, India

Transcription

1 Indian Journal of Geo-ine Sciences Vol. 45(5), 2016, pp Climate change modeling based public health resource planning for Narmada basin, India Rahi Jain 1 & Satanand Mishra 2 * 1 Centre for Technology Alternatives for Rural Areas (CTARA), Indian Institute of Technology Bombay (IITB), Mumbai , India 2 Advanced Materials and Processes Research Institute (AMPRI), Bhopal , India *[ snmishra07@gmail.com] Received 27 il 2015; revised 18 ust 2015 Present study is to determine the potential public health risk of climate change parameters (temperature and precipitation) based on its effect on surface drinking water quality of Narmada River Basin. This study was performed by past data collection and multiple linear regression based modeling. The inter-region and inter-district variation was observed in the study. The study found that climate change over time does have an impact on the local climate conditions for the lower regions of the basin. However, the public health risk was more prevalent for the upper region districts based on the linear regression model. Monsoon months especially y and ust are at much greater risk compared to other months. These two months accounted for over 80% of the total risk months in 102 years of data. The study is concluded by providing a framework for evidence-based decision making of resource allocation on the basis of climate parameters. [Keywords: Narmada Water quality; Water borne disease; Climate change; Total Coliform Count] Introduction In lesser developed world like India, communicable diseases have been the major cause of death and water borne diseases like diarrhea, cholera are some of the major diseases 1 5. These nations also suffer from resource limitations which makes resource allocation for different problems an important challenge. The use of evidence based decision making had been proposed 6. The studies are needed which could enable in prioritizing the problems for resource allocation. Climate change studies have been proposed to be important for geographical area planning 7. Climate parameters change have been proposed to have potential impact on the human health 7 10, these health impacts can occur through various routes like change in water quality and quantity, air quality, climatic variations and infection disease ecology However, different studies on different geographical regions have showcased different level of impact on the health of the regional human population 7 10, 12, 13. This makes it important to study the climate change impact on the region directly before making any recommendation for regional planning. In resource limited scenario, it is important to identify the months as well as area which are most prone to such diseases. A framework is needed to perform such a study and this paper proposes one such climate parameters based framework for identifying and prioritizing the geographical areas and months for resource allocation. In India, basin level study for estimating impact of different climate parameters on different geographical areas have been performed only for Ganga Basin 10. Multiple water basins and several agro-climactic conditions make it important to study different basins to understand, identify and prioritize the different geographical location and timing for resource allocation. This study uses Narmada River basin as a case study to develop framework for identifying and prioritizing the geographical areas and months with water borne diseases risk based on the climate parameters

2 622 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 through change in surface water quality. Materials and Methods Narmada is one of the major rivers of western India with 98, 796 sq. km of total basin in three Indian states namely Madhya Pradesh, Maharashtra and Gujarat with river length of 1312 Kms (Figure 1). Narmada basin is divided into five parts namely Upper hilly region, Upper plains, Middle Plains, Lower Hilly s and Lower Plains (Table 1) which encompass 22 districts with majority portion in Madhya Pradesh 23. Upper basins records lower temperature as compared to middle basin. Its origin is in Amarkantak in Shadol district, Madhya Pradesh, flowing through deccan trap in between Vindhya and Satpur hills and meets Arabian Sea at Gulf of Cambay about 10 km north of Bharuch district, Gujarat. Geographically, the basin lies between east longitudes 72 32' to 81 45' and north latitudes 21 20' to 23 45'. It flows through can trap in between Vindhya and Satpura ranges of hills before flowing into the Gulf of Cambay in the Arabian Sea. There are 41 important tributaries to the Narmada River 14, 15. S. No. 1 Table 1: Districts in Narmada River Basin Districts Name State Shahdol 2 Mandla Upper Hilly 3 Balaghat 4 Seoni 5 Jabalpur 6 Narsimhapur 7 Sagar 8 Damoh 9 Upper Chhindwara 10 Plains Hoshangabad 11 Betul 12 Raisen 13 Sehore 14 Middle Plains Khandwa Madhya Pradesh 15 Khargone 16 Dewas 17 Indore 18 Dhar 19 Lower Jhabua 20 Hilly Dhule Maharashtra 21 Lower Baroda 22 Plains Bharuch Gujarat Several major irrigation projects have been completed on this river. The basin contains many important minerals like bauxite, clay, coal, dolomite, graphite, iron ore, manganese, talc and limestone. The river is monitored for its water quality and sediment quality through its 18 and 11 stations respectively. Further, Government of Gujarat maintains 31 gauge and discharge sites in basin. Monthly multi-parametric data of all the 22 districts in Narmada Basin was collected for this study to understand climate parameter change impact on public health through change in water quality. The climate parameters namely average monthly air temperature and monthly precipitation was used for study 10 and water quality parameter namely total coliform count was used as it has been usually considered as the indicator for water borne infectious diseases 7, 10, 13, 16. The effect of various other climatic and non-climatic parameters on the water quality was not considered owing to lack of data availability The Graphical Flow Diagram of the data collection and analysis is shown in Figure 2 is similar to our previous analysis 20. The method used to collect the data for climate change parameters was by performing the secondary literature review and data collected was from 1901 to 2002 for each month 21. The multi-parametric climate change data collected was fitted on the modified form of Kay and McDonald multiple linear regression form 22. The model originally developed used around 20 parameters to develop the model. In this study, the coefficients of all parameters other than the water surface temperature and monthly rainfall was added to the

3 JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 623 constant. The final equation is as given below: = ( 6716 )+ (1088 ) (1) Where, Y stands for the Log10 value of total coliform count per 100 ml, ST stands surface water temperature in degree Celsius and R stands for the total monthly rainfall in mm. Surface water temperature was calculated from air temperature data using the average slope and y-intercept values i.e and 2.56 respectively obtained by Morill et al for 43 rivers across the globe 23. The public health risk to water borne disease is considered when the log10 value of total coliform count is found more than 3.3. This calculation for the Hoshangabad district was skipped was directly used from our other analysis 20. Fig. 1 Map showing Narmada River basin catchment

4 624 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 Results The study found tropical climate conditions with temperature and precipitation range for the entire basin was C and mm respectively (Table 2). In case of temperature parameter at regional level, it was observed that minimum and maximum reported temperature for a region increased and decreased respectively from Upper hills to Lower plains. Further, the variation in the monthly air and precipitation values for all the districts in the basin for all years had been found as shown in Table 3 and Table 4. Monsoon season had lower variation for both temperature and precipitation for all districts. The maximum variation in temperature was consistently for winter season namely ember, ember, uary and ruary. Further, districts in lower plains region have relatively lesser temperature variation of up to % as compared to other regions that have much higher variation. In case of precipitation, much higher variation was observed as compared to temperature with maximum variation being observed in the months of ember and ember. Further, an increasing trend in precipitation variation from higher region districts to lower region districts was observed. The correlation coefficients of different months over the years indicated that temperature has much stronger correlation as compared to precipitation (Table 5). In case of temperature, significant positive correlation with correlation coefficient in range of was observed for all districts in the month of ember and ember. This indicates higher susceptibility of winter months towards climate change and also explains the potential cause behind the higher temperature variation for the winter months. Further, slightly positive correlation with correlation coefficient in the range of -0.4 was observed for tember, ober and ruary for almost all districts. In addition, the total number of months with correlation with time was increasing from the districts of higher region to lower region districts. In case of precipitation, only lower regions namely Middle plains, Lower hilly and Lower plains seems to have some positive or negative correlation with correlation coefficient in the range -0.4 and - and -0.4 respectively. Further, only one month per district was found correlated with time. Monsoon season months namely y and ust showed correlation in most of the districts except in lower plains regions in which ruary was found to be negatively correlated. This indicates the climate change lower impact on the rainfall as compared to temperature. The study found variation in the total coliform count with change in temperature and precipitation value for all districts. The district wise variation was observed in total number of months under water borne disease risk out of 1224 months (102 years) as shown in Table 6. These districts were categorized into low risk, medium risk and highrisk districts based on the average number of public health risk months/year. The low risk districts are those with less than or equal to one public health risk month/year. Medium risk districts are those with greater than one public health risk month/year and less than or equal to two months/year. High-risk districts are those with greater than two public health risk months/year. The most of the high-risk districts are present in upper hilly and upper plains, while all the low risk districts are present in middle plains region. This indicates districts namely Shahdol, Mandla, Balaghat, Seoni, Jabalpur, Narsimhapur, Chhindwara, Raisen and Dhule require maximum focus to minimize the water borne disease health risk. Further, at the regional level, Upper hilly region is the most risk prone region that requires maximum focus for water borne disease risk minimization followed by the upper plains region and lower hilly region.

5 JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 625 Fig. 2 Graphical Flow Diagram of data collection and analysis In all the districts, it was observed that the monsoon season months namely e, y, ust and tember showed consistently high coliform risk, but one month of ober in Seoni District in year 1985 is an exception (Table 6). This unexpected risk in Seoni district could be attributed to the unexpectedly very heavy rainfall of 2 mm mean precipitation; it was also the highest recorded mean precipitation for ober month for Seoni District. The study results are consistent with the findings of Moors et al regarding monsoon season to be water disease prone owing to high precipitation which lead to surface run-off from nearby areas into surface water bodies 10. These surface run-off causes water quality deterioration of surface water bodies. The years ( ) for each district were segregated based on the number of months/ per year in which public health risk was found (Table 7). The years with zero or one month with public health risk were designated low risk years. The years with two months of public health risk were designated medium risk years. The years with 3 or more months of public health risk were designated high-risk years. The segregations showed that maximum four months/year have been found to be of public health risk. Further, a strong positive correlation of 0.95 was found between total number of months under water borne disease risk out of 1224 months (102 years) and high-risk years. This further indicates that the identified risk prone districts and region based on total number of months under water borne disease risk out of 1224 months (102 years) will also be the areas with greater incidence of high public health risk years. The further analysis of the monsoon months showed that among the four months namely e, y, ust and tember, y and ust are most risky months as they together account for around 80% of the months with water borne disease risk (Table 6). Further, most of districts have y as the most risky month, but 50% of districts in Upper regions (Hills and Plains) namely Shahdol, Seoni, Jabalpur Narsimhapur, Sagar, Damoh and Betul that have ust with either higher or equal risk as compared to y. This indicates the resources spent on controlling water borne diseases should focus on the monsoon months especially y and ust.

6 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY Table 2: imum, imum and Average surface air temperature and monthly rainfall for all the regions over 102 years Parameter Monthly Mean Surface Air Temperature ( C) Monthly Rainfall (mm) Upper Hills Upper Plains Middle Plains Lower Hills Lower Plains

7 JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 627 Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years District Parameter Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) Upper Hilly Shahdol Mandla Balaghat Seoni Upper Plains Jabalpur

8 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years District Parameter Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) Narsimhapur Sagar Damoh Chhindwara Hoshangabad

9 JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 629 Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years District Parameter Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) Betul Raisen Sehore Middle Plains Khandwa Khargone

10 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years District Parameter Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) Dewas Indore Dhar Lower Hilly Jhabua Dhule

11 JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 631 Table 3: imum, imum and Average surface air temperature and monthly rainfall for all the districts over 102 years Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) Bharuch Lower Plains District Parameter Baroda Table 4: Percentage Variation of Temperature and Precipitation with respect to change in year for each month over 102 years (Darker shade of green represents low variation and higher shades of red represents high variation) Monthly Mean Surface Air Temperature ( C) Monthly Mean Rainfall (mm) District Seoni Upper Hilly Balaghat Mandla Shahdol

12 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY Upper Plains Jabalpur Narsimhapur Sagar Damoh Chhindwara Hoshangabad Betul Raisen Sehore Middle Plains Khandwa Khargone Dewas Indore

13 JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 633 Lower Plains Bharuch Baroda District Lower Hilly Dhule Jhabua Dhar Table 5: Correlation coefficient of Temperature and Precipitation with respect to change in year for each month over 102 years Monthly Mean Surface Air Temperature Monthly Mean Rainfall Upper Plains Narsimhapur Jabalpur Seoni Upper Hilly Balaghat Mandla Shahdol

14 634 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY 2016 Table 5: Correlation coefficient of Temperature and Precipitation with respect to change in year for each month over 102 years Monthly Mean Surface Air Temperature Monthly Mean Rainfall Indore Middle Plains Dewas Khargone Khandwa Sehore District Raisen Betul Hoshangaba d Chhindwara Damoh Sagar

15 JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 635 Table 5: Correlation coefficient of Temperature and Precipitation with respect to change in year for each month over 102 years Monthly Mean Surface Air Temperature Monthly Mean Rainfall Lower Plains Bharuch Baroda District Lower Hilly Dhule Jhabua Dhar Table 6: Total number of months under water borne disease risk out of 1224 months (102 years) # Districts Name Months with Coliform Risk % contribution of each month to total risk Total 1 Shahdol Mandla Upper Hilly 3 Balaghat Seoni Jabalpur Narsimhapur Sagar Damoh Upper Plains Chhindwara Hoshangabad Betul Raisen Sehore Khandwa Middle Plains Khargone Dewas

16 636 INDIAN J. MAR. SCI., VOL. 45, NO. 5 MAY Indore Dhar Jhabua Lower Hilly 20 Dhule Baroda Lower Plains 22 Bharuch Overall ~ S.No. Regio n Table 7: The number of months/ per year with public health risk for all districts # of years with different risk potential from (# of risk months/yr) Districts Name Low Risk Medium Risk High Risk 0/yr 1/yr 2/yr 3/yr 4/yr High Risk Year s Total Total Numbe r of Risk Months 1 Shahdol Upper Mandla Hilly Balaghat Seoni Jabalpur Narsimhapur Sagar Damoh Upper Plains Chhindwara Hoshangabad Betul Raisen Sehore Khandwa Middl Khargone e Dewas Plains Indore Dhar Lower Jhabua Hilly Dhule Lower Baroda Plains Bharuch

17 JAIN et al.: CLIMATE CHANGE MODELING BASED PUBLIC HEALTH RESOURCE PLANNING 637 Conclusion A The predictive modeling based framework had been proposed to identify and prioritize the regions, districts and months for the limited resource allocation to maximize the reduction in the public health risk due to the water borne disease. The study was performed using the Narmada river basin as a case study for the framework development. It was found that lower regions of the basin are more susceptible to climate change. However, it was identified from the study that the Upper regions of the Narmada basin require more focus. District wise, Shahdol, Mandla, Balaghat, Seoni, Jabalpur, Narsimhapur, Chhindwara, Raisen and Dhule have higher water borne disease based public health risk as compared to other 13 districts in the Narmada Basin. Monsoon months require more focus especially y and ust months. In addition, this study shows that despite the higher susceptibility of lower regions to climate change, it is the upper regions that are at greater public health risk. This indicates the possibility of lesser relevance of global climate change in certain geographical areas for certain human disease especially for planning. Finally, this study stresses upon the relevance of the local level modeling as it showed inter-district variation in the disease risk susceptibility. The major limitation of the study is the limited data availability. This prevented study validation, which could be done by comparing with the number of patients reported with water-borne diseases in hospitals of the different districts. Further, the coefficients used for predictive modeling in the study were developed for the other system as in this system lack of data on the total coliform count in surface waters prevented development the more customized model. References 1 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in World Health Organization, [Online]. Available: hdalyestimates.xls. [Accessed: ]. 2 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in World Health Organization, [Online]. Available: ase/global_burden_disease_death_estimates_sex_20 08.xls. [Accessed: ]. 3 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in World Health Organization, [Online].Available: info/global_burden_disease/ghe_deaths_2012_co untry.xls?ua=1. [Accessed: ]. 4 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in World Health Organization, [Online]. Available: al_burden_disease/ghe_deaths_2000_country.xls? ua=1. [Accessed: ]. 5 Department of Measurement and Health Information, Mortality and Burden of Disease estimates for WHO member states in World Health Organization, [Online]. Available: isease/gbddeathdalycountryestimates2004.xls. [Accessed: ]. 6 World Health Organization, Changing dsets: Strategy on Health Policy and Systems Research. Geneva, El-Fadel, M., Ghanimeh, S., oun, R., & Alameddine, I., Climate change and temperature rise: implications on food- and water-borne diseases. Sci. Total Environ, 437(2012), Funari, E., Manganelli, M. & Sinisi, L., Impact of climate change on waterborne diseases. Ann Ist Super Sanita, 48(4) (2012), Harper, S. L., Edge, V. L., Schuster-Wallace, C. J., Berke, O., & McEwen, S., Weather, water quality and infectious gastrointestinal illness in two Inuit communities in Nunatsiavut, Canada: potential implications for climate change. Ecohealth,. 8(2011), Moors, E., Singh, T., Siderius, C., Balakrishnan, S., & Mishra, A., Climate change and waterborne diarrhoea in northern India: impacts and adaptation strategies. Sci. Total Environ., (2013), Haines, A., Kovats, R. S., Campbell-Lendrum, D., & Corvalan, C., Climate change and human health: impacts, vulnerability and public health. Public Health, 120(2006), Patz, J. A., Campbell-Lendrum D., Holloway T., & a Foley J., Impact of regional climate change on human health. Nature, 438(2005), Hofstra, N., Quantifying the impact of climate change on enteric waterborne pathogen concentrations in surface water. Curr. Opin. Environ. Sustain, 3(6), Malviya, A., Diwakar, S. K. & Choubey, O. N., Chemical assessment of narmada river water at

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