metadata: authors: 0: author: Dasgupta, Susmita
1: author: Meisner, Craig
2: author: Wheeler, David
count: Tunisia
docna: 0: docna: Stockpiles of obsolete pesticides and
            cleanup priorities : a methodology and application for Tunisia
docty: Policy Research Working Paper
subtopic: Health Care Services Industry,Pest Management,Food Security,Inequality,Global Environment
teratopic: Environment,Industry,Health, Nutrition and Population
abstracts: cdata!: Obsolete pesticides have accumulated in
            almost every developing country or economy in transition
            over the past several decades. Public health and
            environmental authorities are eager to reduce health threats
            by removing and decontaminating stockpile sites, but there
            are many sites, cleanup can be costly, and public resources
            are scarce. Under these conditions, it seems sensible to
            develop a methodology for prioritizing sites and treating
            them sequentially, as budgetary resources permit. This paper
            presents a methodology that develops cleanup priority
            indices for Tunisia. The approach integrates information on
            populations at risk, their proximity to stockpiles, and the
            relative toxic hazards of the stockpiles. The robustness of
            this approach is tested by varying model parameters widely
            and testing for stability in the rank-ordering of results.
geo_reg_key: 80475,107234,80464
repnme: repnme: Stockpiles of obsolete pesticides and
            cleanup priorities : a methodology and application for Tunisia
display_title: Stockpiles of obsolete pesticides
            and cleanup priorities : a methodology and application for Tunisia
listing_relative_url: /research/2009/04/10420725/stockpiles-obsolete-pesticides-cleanup-priorities-methodology-application-tunisia
topicv3: Agriculture and Food Security m1326205 435,Poverty m1326687 695,Environment, Natural Resources and Blue
            Economy m1327391 566,Pesticides m1504403 2914,Health m1504661 3480
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Environment,Industry,Health, Nutrition and Population
no_of_pages: 27
model_extractions: WPS4893 Policy ReseaRch WoRking PaPeR 4893 Stockpiles of Obsolete Pesticides and Cleanup Priorities A Methodology and Application for Tunisia _Susmita Dasgupta Craig Meisner David Wheeler_ The World Bank Development Research Group Environment and Energy Team April 2009
Policy ReseaRch WoRking PaPeR 4893

# **Abstract**

Obsolete pesticides have accumulated in almost every developing country or economy in transition over the past several decades. Public health and environmental authorities are eager to reduce health threats by removing and decontaminating stockpile sites, but there are many sites, cleanup can be costly, and public resources are scarce. Under these conditions, it seems sensible to develop a methodology for prioritizing sites and treating them sequentially, as budgetary resources permit. This paper presents a methodology that develops cleanup priority indices for Tunisia. The approach integrates information on populations at risk, their proximity to stockpiles, and the relative toxic hazards of the stockpiles. The robustness of this approach is tested by varying model parameters widely and testing for stability in the rank-ordering of results.

This paper—a product of the Environment and Energy Team, Development Research Group—is part of a larger effort in the department to understand implications of Toxic Pollution from Agriculture. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at sdasgupta@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Stockpiles of Obsolete Pesticides and Cleanup Priorities: A Methodology and Application for Tunisia

Susmita Dasgupta Craig Meisner David Wheeler

**DECRG-EE The World Bank**

We would like to extend our special thanks to Mr. Abdelaziz Lagnaoui for his valuable guidance. We are also thankful to Mr. Walid Dhouibi, National Coordinator, Africa Stockpiles Project and Mr. Hassine Bensalah, Consultant, Africa Stockpiles ProjectTunisia for their useful comments and suggestions. We are also grateful to Mr. Brian Blankespoor for his valuable help with the GIS and Ms. Yvonne Edwards for editorial help. The views expressed here are the authors’, and do not necessarily reflect those of the World Bank, its Executive Directors, or the countries they represent.

Corresponding author: Susmita Dasgupta, MC3-435, World Bank, 1818 H Street, N.W., Washington, DC 20433; E-mail: sdasgupta@worldbank.org; telephone: 202-473-2679; fax: 202-522-1151.
# **1. Introduction**

Mounting evidence of health and environmental damage has focused the attention of the international community on stockpiles of obsolete pesticides<sup>1</sup> . In the absence of a clear obsolete pesticides management strategy, over the years, significant amounts of obsolete pesticides have been stockpiled in developing countries. The FAO Obsolete Pesticides Program has defined six key factors that lead to the accumulation of obsolete pesticides in developing countries (FAO, 1995a): (i) product bans, (ii) inadequate storage and poor stock management, (iii) unsuitable products or packaging, (iv) donation or purchase in excess of requirements, (v) lack of coordination between donor agencies, and (vi) commercial interests of private sector and hidden factors.

In developing countries, often the warehouses are not secured, are dilapidated and packages have deteriorated with the passage of time. As pesticides have decompose they form by-products, and some by-products of decomposition are more toxic than the original poison. Toxic products often leak from corroded or otherwise damaged containers into the surrounding environment – the main pathway for contamination.<sup>2</sup> Frequently people, especially the poor and their livestock, are found to be living near the stock pile, edible crops are grown on contaminated land, and contaminated water is used for drinking and irrigation (World Bank 2002). The absence of secure storage also leads to vandalism, theft of products and access by children.

Leaving the current pesticide problem in developing countries in its current state is not an option.<sup>3</sup> Given the high cost involved in cleanup and safe disposal of obsolete pesticides,

> 1 Obsolete pesticides are pesticides that are unfit for further use or for re-conditioning (OECD, FAO, UNEP, 2000). The FAO defines obsolete pesticides as all pesticide products not in current use because they have banned, have deteriorated or are damaged, have passed their expiration date, cannot be used for any other reason, or are not wanted by the current owner (FAO, 1996).

> 2 Current environmental hazards from obsolete pesticide stocks include (but not limited to) leakage to soil and dispersal in soil through capillary action and soil microfauna; leaching to groundwater through contaminated soil; surface water contamination by surface runoff and wind dispersal; dispersal by air through volatilization or wind dispersal of pesticide dusts or pesticide contaminated soil particles; and widespread dispersal through natural disasters, such as hurricanes and floods.

> 3 It is often difficult to ascertain the ownership of old stockpiles as a result of changes in ownership and in the status of organizations or the disappearance of owners. For example, state enterprises that have since been privatized, or organizations that no longer exist, do not retain responsibility for previously accumulated stockpiles of obsolete pesticides.

2
interventions should be prioritized on basis of a detailed inventory of pesticide stockpiles and contaminated sites, determining the identity of the contaminant, its quantity and proximity to people and biodiversity. However, most developing countries lack adequate technical, institutional and financial capacity for reliable analysis to manage clean up of contaminated wastes/ sites and the safe destruction of obsolete pesticide stocks, and are therefore dependent on external assistance. In this paper, we develop and apply a methodology prioritizing 197 stockpile sites in Tunisia for clean up- treating them sequentially, as budgetary resources permit. Like many low- and middle-income countries, Tunisia has numerous sites of obsolete pesticide stockpiles, and we take Tunisia as an illustrative example since they are currently finalizing a detailed inventory of all the publicly-held pesticide stockpiles in the country under the Africa Stockpiles Program<sup>4</sup> .

Our model is based on the principle of welfare maximization subject to a budget constraint. We develop a composite measure of potential exposure risk for each site as a function of the volume of pesticides, their relative hazard, and the conditions of the containers in which they are stored. We convert this to per-capita exposure risk by introducing a risk-decay factor that is a function of inhabitants’ average distance from the site in each Tunisian delegation (our most spatially-disaggregated unit). Then we compute total exposure risks for proximate populations, taking into account the relative vulnerability of children and women of childbearing age. We introduce alternative vulnerability weights for population groups and hazard weights for pesticides, as well as alternative risk-decay parameters that govern the effect of distance on exposure. The result is a set of alternative priority indices for each site, whose variation across weightings provide a test of the robustness of our methodology. After an assessment of the results, we provide what we believe to be a defensible priority ranking for removal and cleanup at Tunisia’s 197 sites.

> 4 In order to eliminate obsolete pesticides from Africa, Africa Stockpiles Program (estimated duration 1015 years; funding: US $250 million) has been approved by the World Bank, the GEF and the FAO. In 2005, the First Phase of Africa Stockpiles Program, ASP-P1 (duration 4 years) with focus on 7 countries: Ethiopia, Mali, Morocco, Nigeria, Tanzania, Tunisia,  South Africa was initiated with US $60 million grant from the GEF, FAO, the African Development Bank, Belgium, Canada, Denmark, EU, Finland, France, Japan, Netherlands, Sweden, Switzerland and the World Bank Development Grant Facility. A description of the ASP program and its progress is available at: <u>www.africastockpiles.net.</u>

3
The remainder of the paper is organized as follows. In Section 2, we develop the theoretical model that underlies our priority index methodology. Section 3 introduces the Tunisian database, while Section 4 presents our priority index computations and discusses the results. We provide a summary and conclusions in Section 5.

# **2.  Optimal Allocation of Cleanup Investment**<sup>**5**</sup>

We model the welfare impact of pesticide stockpile cleanup activities as a function of their level and distribution across Tunisian sub-regions (delegations). Political considerations make it desirable to strike a balance between area representation and national welfare maximization in resource allocation decisions. We cannot realistically characterize Tunisia’s objective function as linear (infinite elasticity of substitution across sub-regions), because sole allocation of cleanup resources to one sub-region is highly unlikely, whatever the relative scale of its problem. At the same time, Tunisia’s objective function is not purely fixed-coefficient (zero elasticity of substitution across sub-regions), because nothing forces the government to maintain cross-subregion parity in per-capita resource allocation. This assertion is acceptable in the case of the pesticide cleanup program, since the distribution of obsolete pesticide stockpiles across subregions may not be highly correlated with the distribution of population.

We adopt an intermediate assumption: that the government’s objective function is characterized by unit-elastic substitution across subregions. A unit-elastic (CobbDouglas) welfare function permits tailoring of cleanup programs to conditions in each subregion, while encouraging some diversification through the operation of diminishing returns. We assign the same opportunity values to human life and health in all subregions.

We specify the objective function for pesticide cleanup as:

> 5 The allocation model developed in this section is a significant extension of the model developed by the authors in Bolt _et al_ . (2003).

4
where Ai = Activity in subregion i ωi = Poverty weight assigned to subregion i.

We assign poverty weights to incorporate the relative inability of the poor to protect themselves from pesticide exposure. For each subregion, we specify the relevant damage abatement function as: where Ci = Scale of cleanup activity in subregion i Di = Scale of potential damage in subregion i Equation (2) incorporates scale economies: The productivity of pesticide cleanup rises with the scale of potential exposure damage.

The government faces a fixed budget constraint and differential cleanup costs that reflect the mix of pesticide-related problems in each subregion.

where ci = Unit cost of cleanup in subregion i IT = Total cleanup budget Substitution from (2) into (1) yields the following welfare function: 5
Maximization of W subject to the budget constraint yields the following ratio of optimal allocations in subregions i and j: Since ω is a poverty weight, we can specify it as a function of income per capita: Substituting (6) into (5), we obtain: and From this, we can derive a relative priority index for subregion i: In (9), a subregion’s priority index is equal to the product of its exposure damage potential, the appropriate exponential of per-capita income and the inverse of its unit cleanup cost, divided by the sum of products for all subregions.

For a particular pesticide, exposure damage potential is determined by three factors: the pesticide’s risk, the number of exposed people (by weighted vulnerability class), and the degree of their exposure. An ideal model would incorporate a risk measure for each exposed individual, and specify risk as declining with distance from the pesticide stockpile. Potential damage from a stockpile would be a function of both total pesticide hazard and storage stability. For feasibility, we model at the subregion level. Defining the area around each stockpile as a subregion, we specify the potential damage function as: 6
_Di_   _Pi Ridi_  where Pi = Population at risk in subregion i Ri = Pesticide risk index for subregion i di = Average inhabitant distance from the site in subregion i where φl = Vulnerability weight for population group l Pil = Population in group l, subregion i where ρm = Hazard rating of pesticide m Qim = Quantity of pesticide m in subregion i Si = Share of pesticide stock in subregion i in degraded containers The pesticide risk index incorporates several factors. Potential pesticide risk for a subregion is indexed by pesticide volume (in kg) in each risk class, multiplied by the appropriate risk weighting. To compute the risk index, potential risk is multiplied by the share of the pesticide stock in containers that are broken or leaking, as well as the stock that has already contaminated the soil. Substituting (10), (11) and (12) into (9), we obtain the fully-dimensioned priority rating for subregion i: 7
# **3.  The Tunisian Pesticide Stockpile Database**

Like many low- and middle-income countries, Tunisia has hundreds of sites of obsolete pesticide stockpiles with potentially-serious health hazards to nearby populations. Product bans, outdated products, donations or purchases in access of requirement, poor stock management and inadequate storage are primary reasons behind this accumulation. Under the ongoing Africa Stockpiles Program, Project1 (ASP-P1), The Ministry of Environment and Sustainable Development’s waste management department, (Agence Nationale de Gestion des Déchets – ANGed) of the Government of Tunisia, has currently finalized and verified a detailed inventory of all the publicly-held pesticide stockpiles in the country, determining the geo-location of the storage sites, whether the storage sites are protected or not, identity of the contaminant, its quantity, and general condition of the stockpiles.

Overall, Tunisia has approximately 1,915 metric tons of obsolete pesticide _formulations_ in 197 storage sites. A total of 692 metric tons of _active ingredients_ were identifiable from the database, while 415 tons were not identifiable (or 22% of the total).<sup>6</sup> Preliminary investigation revealed only 11% of the stockpiles were contained in “undamaged” packages; 47% of the packages were either broken or showed surface damage, 8% indicated leakage, 34% were considered to be contaminating the soil and equipment.

At the storage site (stockpile) level, the database included pesticide volume, corresponding active ingredient and pesticide volume by damage class for containers (leaking, broken, contaminated soil, etc.). The Chemical Abstract Number (CAS) of each active ingredient was used to assign hazard indicators: the World Health Organisation toxicity class. The analysis revealed the presence of 13 metric tons of WHO-Ia (extremely hazardous), 2 metric tons of WHO-Ib (highly hazardous), 196 metric tons of

> 6 The chemical in the pesticide formulation that actually kills the pest(s) is termed the _active ingredient_ . The added chemical(s), those which make the product easy and safe to formulate or apply, are termed the inert ingredients or carriers. Each formulation has a specific percentage concentration of active ingredient usually measured as a percentage of the total formulation weight, in grams per kilogram or liter. In this study we are primarily interested in the toxicological properties of the active ingredient.

8
WHO-II (moderately hazardous) and 258 metric tons of WHO-III (slightly hazardous) active ingredients in the storage sites.<sup>7,8</sup> The currently-available information in Tunisia is sufficient to compute the index in (13) for the population and per-capita risk factors. A further avenue for future work could incorporate several of the other parameters such as per-capita income for governorates and poverty weights, as well as unit cleanup costs for pesticides with different hazard ratings and protective container conditions.

Population information was downloaded from the 2004 Demographic Census of Tunisia9 and two vulnerable population classes were constructed - children under the age of 5 and women of childbearing age 20-49. Land area was computed at the Delegation level from the GIS databases of the ANGed.

Table 1 provides distribution statistics for all the relevant variables in our model. It is clear that the distributions of all variables are highly skewed. Population and area follow their standard skewed (rank-size) distribution, while the pesticide variables display even more skewness. The two riskiest pesticide classes (WHO-Ia and Ib) are not stockpiled in 75% of the delegations, and large volumes only appear in delegations above the 90<sup>th</sup> percentile. Similarly-skewed patterns are evident for the other pesticide classes and for the three classes of container security, the latter indicating that containers are intact at most stockpile sites.

[Insert Table 1]

> 7 The remaining 26 metric tons were classified under WHO-Table 5 - unlikely to pose a health hazard; 1 metric ton under WHO - other, and 196 metric tons under WHO - not classified, respectively. 8 Although detailed data for each storage site was used for computation in this paper, the information is sensitive and classified by the government, hence could not be included in this paper. 9  Institut National de la Statistique.

9
# **4.  Estimation of Priority Indices**

# **4.1 Model Data**

Using the storage site-specific data, we construct variables for alternative forms of model (13) as follows: (1) Vulnerability-weighted population in the delegation (ΣΦiPi): We set φ=1 for less-vulnerable population groups. In the weighted-population version, we use weights of 2 and 1.5 for children younger than 5 and women aged 20-49, respectively. In the un-weighted version, we assign weights of 1 to all three groups.

(2) Average delegation inhabitant’s distance from the stockpile site (di): We base our distance proxy on simple geometry. Within a circular area around a stockpile, average distance from the stockpile is proportional to the radius. Accordingly, our average distance proxy is the square root of delegation area (this is a constant (π<sup>.5</sup> ) times the radius.<sup>10</sup> In our tests of model robustness we use three values of γ, the distance risk-decay parameter in equation (10): -1, -2 and 0.5.

(3) The proportion of pesticides that have container problems at the site (Si): We compute the share of total active-ingredient pesticides that are in broken or leaking containers, or have already leached into the soil.

(4) Hazard-weighted pesticide volume at the site (ΣρjQij): We use two radicallydifferent hazard ratings to test the robustness of our methodology. For the first set, we use exponential differences in hazard ratings: ρj = 100, 10, 1, 0.1 for WHO classes 1a, 1b, 2 and 3, respectively. For the second set, we set weights equal to 1 for all four hazard classes.

> 10 The square root of the area is the best approximation we can provide for average distance in non-circular cases as well.

10
# **4.2 Results**

Tables 2, 3 and 4 summarize our results for computation of six priority indices with equation (13). For the first three indices, we use weighted populations and pesticide volumes and vary the distance risk-decay parameter across -2, -1 and -0.5. For the second set of three indices, we use un-weighted population and pesticide volume and vary the distance risk-decay parameter across -2, -1 and -0.5. Taken together, these variations test the robustness of our methodology by assigning very different values to key model parameters.

As Table 2 shows, our approach is quite robust. For the three weighted indices, large changes in the distance risk-decay parameter have almost no effect on the results. In all cases, the rank correlation coefficients (in bold for Rank 1, 2, 3) are 0.96 or higher. The same is true for the three distance risk-decay parameter values in the case of the unweighted set (in bold for Rank 4, 5, 6). Cross-correlations between sets (Ranks 1, 2, 3 vs. Ranks 4, 5, 6) are also quite high, varying between 0.84 and 0.89.

# [Insert Table 2]

Tables 3 and 4 provide detailed results for all 197 stockpile sites and all six variations in model parameter sets.<sup>11</sup> The first column of Table 3 provides the priority ranking. All index values are normalized to a total value of 1000 for all delegations. This allows for easy interpretation of the relative magnitudes in each index. In Table 3, the next three columns display the highest, average and lowest index values for each site across the six variations, while the next three columns provide the equivalent information in the form of ranks. The following columns present index values for weighted and un-weighted population, weighted and un-weighted pesticide volume, and the proportion of pesticides in seriously-damaged containers or contaminated soil. Table 4 provides complete information on index values for the six model variations.

> 11 As stated earlier, the stockpile database is still considered confidential – thus we have preserved this confidentiality by replacing the specific location names with generic names.

11
These results have several striking features. First, variation in population weighting makes little difference. Visual inspection of the weighted and un-weighted population indices in Table 3 confirms that the numbers are practically identical in all cases.

Second, variations in pesticide hazard ratings do make a substantial difference for index values within delegations. Clear examples are provided by sites A and B, respectively. These two sites have the highest overall index values in the sixth column, which displays the highest of the six index values for each stockpile site. Site A’s un-weighted pesticide index is far higher than its weighted index, while the converse is true for the site B. Inspection of raw data shows that this site B is a very large repository of WHO - Ia pesticides, which have a hazard weight of 100 in the weighted versions of the model (vs. a weight of 1 for class 3 pesticides). Conversely, site A is a huge repository of WHO-III pesticides, which assume much more significance in the un-weighted versions of the model.

Third, in all variations of the model, priority index values are heavily concentrated at the top of the list. The results for Index 1 in Table 4 provide a good illustration. Here it is useful to recall that index values are normalized to a total of 1000 in each column. For Index 1, site B accounts for about 56% of the total for all 197 sites. The top four sites account for 83% of total value for Index 1. Similar concentration is observable for the other five indices.

Fourth, variations of results within the un-weighted and weighted versions show that the treatment of risk-decay with distance does make a substantial difference. A good example is provided by site D, the second row in Table 4. The surrounding delegation has a fairly large area, so our proxy for average inhabitant distance from the site is also relatively large (40.64). For a risk-decay parameter of -1, the index value is 187.5. Doubling the parameter to -2 reduces the index value to 63.6, while halving it increases the value to 272.4. Similar patterns are apparent elsewhere in the results.

12
In the face of such variation, it might seem plausible to posit substantial variation in the rank-ordering of sites across the six model versions. If this were the case, developing a stable priority list for cleanup would require the arbitrary imposition of one parameter set on the model. Fortunately, this is not the case. As Table 2 shows, the rank correlations are quite high across all versions of the model. This fortuitous result owes something to self-canceling random variation across model variables. However, the largest part seems due to the correlation of weighted and un-weighted pesticide indices across sites. Those which have big volumes of hazardous pesticides also have big total volumes, so they rank high whether or not highly-varied hazard ratings are applied.

The ultimate implications of this study are most clearly visible in the sixth column of Table 3 (Top Rank), which displays the highest index value across all six models for each site. It is immediately clear that the results are heavily dominated by the top-5 sites, and the index values tail off very quickly. All things considered, we believe that column 6 of Table 3 provides the best guide for cleanup action, because it highlights sites that achieve a very high priority index value for some combination of the hazard, population vulnerability and risk-decay parameters that drive the model.

# **5.  Summary and Conclusions**

Virtually every developing country or economy in transition has stockpiles of obsolete pesticides that have accumulated over the past several decades.<sup>12</sup> Public health and environmental authorities strongly recommend removal of stockpiles and site decontamination, but there are many sites, cleanup can be costly, and public resources are scarce. Under these conditions, it seems sensible to develop a methodology for prioritizing sites and treating them sequentially, as budgetary resources permit.

> 12 The FAO has completed data gathering for Africa and the Near East. A total of 51,794 tons of obsolete pesticides have been identified in 53 countries in this region (FAO, 1995b). The FAO program on data gathering was expanded to Latin America in 1998 and to Asia in 2001. In total, it is estimated that global obsolete pesticides stockpiles in developing countries and economies in transition amount to approximately 440,800 – 551,000 tons.

13
In this paper, we have constructed and tested a methodology that computes cleanup priority indices for stockpiles of obsolete pesticides in Tunisia. Our approach integrates information on populations at risk, their proximity to stockpiles, and the relative toxic hazards of the stockpiles themselves. We have tested the robustness of our approach by varying model parameters widely and testing for stability in the rank-ordering of results. Our results indicate that the results are quite stable and robust, and we conclude that it is feasible to divide Tunisian stockpile sets into relatively unambiguous priority groups for cleanup operations. Our results also reveal a spectacular degree of clustering among the top-10 sites in Tunisia. We conclude that a sequenced strategy that follows our summary priority ordering in column 3, Table 3 can rapidly and cost-effectively decrease potential health damage from obsolete pesticide stockpiles in Tunisia.

# **References:**

- Africa Stockpiles Programme - Annual Report, July 2007 – June 2008. Available at: <u>http://www.africastockpiles.net/.</u>

- Bolt, Katharine, Piet Buys, Susmita Dasgupta, Kirk Hamilton, Craig Meisner, Kiran Pandey, Limin Wang and David Wheeler. 2003. Measuring Up: New Directions for Environmental Programs at the World Bank. World Bank Policy Research Working Paper Number 3097.

- FAO, 1995a. _Prevention of accumulation of obsolete pesticide stocks_ , Provisional Guidelines. No. 2., Food and Agriculture Organisation, Rome, Italy.  Available at:

- <u>www.fao.org/WAICENT/FAOINFO/AGRICULT/AGP/AGPP/Pesticid/Disposal/default. htm</u>

- FAO, 1995b. _Prevention and disposal of obsolete and unwanted pesticide stocks in Africa and the Near East_ , The First FAO Consultation Meeting. No. 1, Food and Agriculture Organisation, Rome, Italy. Available at:

- <u>www.fao.org/WAICENT/FAOINFO/AGRICULT/AGP/AGPP/Pesticid/Disposal/default. htm</u>

14
FAO, 1996. _Disposal of bulk quantities of obsolete pesticide in developing countries_ . Provisional technical guidelines No. 4., Food and Agriculture Organisation, Rome, Italy. Available at: <u>www.fao.org/WAICENT/FAOINFO/AGRICULT/AGP/AGPP/Pesticid/Disposal/default. htm.</u> OECD, FAO, UNEP, 2000. Report of the OECD-FAO-UNEP Workshop on Obsolete Pesticides, Alexandria, Virginia, 13-15 September.

World Bank, 2002. “Toxics and Poverty: The Impact of Toxic Substances on the Poor in Developing Countries”, World Bank: Washington: DC.

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# **Table 1:  Distribution of Model Variables**

|Stats|Pop<5|Women<br>20-49|Other<br>Pop.|WHO-Ia<br>(kg)|WHO-<br>Ib<br>(kg)|WHO-II<br>(kg)|WHO-III<br>(kg)|Leakage<br>(kg)|Broken<br>(kg)|Soil<br>Contam.<br>(kg)|Area<br>(sq km)|
|---|---|---|---|---|---|---|---|---|---|---|---|
|Min|474|1,636|5,243|0.00|0.00|0.00|0.00|0.00|0.00|0.00|2.84|
|p10|1,680|4,845|14,199|0.00|0.00|0.00|0.00|0.00|0.00|0.00|49.49|
|p25|2,276|6,377|19,698|0.00|0.00|0.00|0.00|0.00|0.00|0.00|195.54|
|p50|3,124|9,346|26,797|0.00|0.00|4.50|0.35|0.00|0.00|0.00|406.13|
|p75|4,666|13,851|39,246|0.00|0.00|187.35|28.13|59.74|0.00|0.00|740.95|
|p90|5,678|16,769|51,102|6.75|7.30|1,800.00|300.00|1,620.00|250.00|2,112.86|1,114.78|
|Max|7,989|25,132|73,197|10,614.68|1,320.00|22,500.00|108,075.00|84,214.28|7,977.27|117,465.50|2,779.05|

Note: p – stands for percentile

# **Table 2:  Rank Correlations:** **<u>Six Models</u>**

||||Rank 1|Rank 2|Rank3|Rank 4|Rank5|Rank6|
|---|---|---|---|---|---|---|---|---|
|Model|Distance<br>Exponent||||||||
|Weighted|-1|Rank 1|1||||||
||-2|Rank 2|**0.9818**|1|||||
||-.5|Rank3|**0.9946**|**0.9600**|1||||
|Unweighted|-1|Rank 4|0.8797|0.8709|0.8742|1|||
||-2|Rank5|0.8612|0.8853|0.8398|**0.9823**|1||
||-.5|Rank6|0.8781|0.8537|0.8810|**0.9951 **|**0.9620**|1|

16
# **Table 3: Cleanup Priority Ranking**

|Priority<br>Site ID|<br>Highest<br>Index<br>|Average<br>Index|Lowest<br>Index<br>|Top<br>Rank|Average<br>Rank|Lowest<br>Rank<br>|Weighted<br>Population<br>U<br>P|nweighted<br>opulation|Risk-Weighted<br>Pesticides|Unweighted<br>Pesticides|Containers<br>Proportion<br>Damaged|<br>Exposure<br>Distance<br>Index|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|1 A|618.36|263.24|17.03|1|2.5|5|2.7|2.7|7.09|217.39|1.00|3.59|
|2 B|561.09|287.89|19.55|1|2.5|5|11.42|11.46|136.17|43.23|0.82|16.74|
|3 C|342.44|106.14|10.06|1|4.2|8|10.27|10.2|5.81|178.08|0.98|24.41|
|4 D|272.38|93.58|1.38|2|5.3|11|3.86|3.84|711.85|117.07|0.38|40.64|
|5 E|226.26|69.79|2.30|2|8.3|25|4.37|4.47|4.28|131.29|0.10|1.88|
|6 F|113.95|53.53|23.70|3|3.5|5|8.78|8.76|3.73|11.42|1.00|5.41|
|7 G|31.61|20.30|12.84|5|6.2|7|5.3|5.29|4.23|12.96|1.00|8.5|
|8 H|16.94|4.99|0.61|7|24.3|43|4.04|4.07|1.17|3.55|0.10|1.69|
|9 I|14.99|6.31|0.25|6|19.3|38|5.42|5.44|8.67|2.66|1.00|27.9|
|10 J|11.99|4.89|0.03|8|36.7|73|4.03|4.05|9.38|0.39|0.92|24.1|
|11 K|9.54|4.48|1.98|8|16.2|27|8.78|8.76|0.31|0.96|1.00|5.41|
|12 L|9.41|4.43|0.75|8|15|27|3.13|3.15|5.82|17.83|1.00|31.9|
|13 M|9.33|4.21|1.11|8|15.2|24|10.27|10.2|1.63|7.09|0.67|24.41|
|14 N|8.60|3.54|0.05|9|33.7|64|10.27|10.2|2.71|0.24|0.91|24.41|
|15 O|8.47|3.99|0.67|10|17.5|29|3.13|3.15|5.24|16.05|1.00|31.9|
|16 P|8.45|4.22|0.86|10|14|21|3.9|3.88|3.91|11.98|1.00|27.12|
|17 Q|6.74|1.74|0.06|12|45.3|86|6.1|6.11|0.28|8.47|1.00|52.72|
|18 R|6.28|3.75|1.24|11|14.7|19|5.39|5.41|1.67|5.13|1.00|17.43|
|19 S|6.20|3.19|0.72|14|19.7|28|4.92|4.92|2.17|6.64|1.00|24.88|
|20 T|6.05|3.53|0.46|10|17.7|26|10.27|10.2|2.14|2.47|0.81|24.41|
|21 AA|5.29|2.94|0.82|15|18.7|22|7.42|7.39|1.12|3.42|1.00|20.5|
|22 AB|5.26|2.93|0.81|16|19.7|23|7.42|7.39|1.11|3.40|1.00|20.5|
|23 AC|5.19|2.14|0.19|17|29.7|46|6.1|6.11|2.13|6.52|1.00|52.72|
|24 AD|5.03|3.22|0.55|12|19|25|5.24|5.2|2.71|2.95|0.87|18.21|
|25 AE|4.84|2.71|0.79|18|21.5|25|3.12|3.14|2.37|7.30|0.99|19.79|
|26 AF|4.81|3.49|1.94|9|16.2|24|3.14|3.12|14.77|45.26|0.10|9.92|
|27 AG|3.77|1.25|0.05|15|49|79|9.19|9.14|2.53|0.23|0.10|7.93|
|28 AH|3.72|1.77|0.02|16|45.8|77|3.38|3.37|7.44|0.40|0.29|14.75|
|29 AI|3.67|1.84|0.38|21|28|37|5.42|5.41|1.21|3.71|1.00|26.79|
|30 AJ|3.49|1.06|0.12|22|44.5|76|6.1|6.11|0.60|5.04|0.87|52.72|
|31 AK|3.46|1.35|0.03|18|43.5|74|7.55|7.57|1.66|0.33|0.90|30.09|
|32 AL|3.14|0.93|0.11|12|46.8|79|4.04|4.07|0.22|0.66|0.10|1.69|
|33 AM|3.13|1.60|0.35|24|29.8|38|1.06|1.06|5.20|15.93|0.99|25.59|
|34 AN|2.94|1.14|0.33|13|35.3|56|2.7|2.7|0.14|0.47|0.95|3.59|
|35 AO|2.59|1.07|0.10|26|41|63|6.1|6.11|1.06|3.26|1.00|52.72|
|36 AP|2.47|0.83|0.11|27|51.2|77|3.24|3.21|0.12|3.54|1.00|18.96|
|37 AQ|2.41|1.19|0.23|29|35.7|44|4.52|4.48|0.98|3.02|1.00|28.14|
|38 AR|2.14|1.11|0.19|10|39.3|68|3.13|3.08|0.93|17.03|0.10|6.87|
|39 AS|1.99|1.19|0.39|28|31.8|35|5.39|5.41|0.53|1.64|0.99|17.43|
|40 AT|1.97|1.17|0.39|29|32.8|36|5.39|5.41|0.52|1.60|1.00|17.43|
|41 AU|1.88|0.71|0.21|21|44.5|66|2.7|2.7|0.09|0.29|0.94|3.59|
|42 AV|1.86|0.87|0.01|28|56.2|84|6.68|6.68|0.70|0.04|0.93|17.39|
|43 AW|1.66|1.17|0.24|30|35|39|4.89|4.88|1.25|1.83|0.68|18.4|
|44 AX|1.64|0.93|0.27|33|36.2|40|3.87|3.86|0.65|2.00|1.00|19.73|
|45 AY|1.64|0.72|0.09|34|46|67|2.33|2.31|1.58|4.83|1.00|41.32|
|46 AZ|1.52|0.76|0.15|35|41.2|50|4.15|4.17|0.66|2.01|1.00|27.24|
|47 BA|1.42|0.76|0.15|31|41.3|52|3.13|3.13|0.63|0.49|1.00|11.92|
|48BB|1.40|0.64|0.32|32|45.3|59|9.49|9.42|0.10|0.75|0.83|15.82|
|<br>49 BC|1.39|0.71|0.14|19|47.3|76|5.3|5.29|0.07|1.19|0.67|8.5|

17
# **Table 3:  Cleanup Priority Ranking (cont.)**

|Priority<br>Site ID|Highest<br>Index<br>|Average<br>Index|Lowest<br>Index<br>|Top<br>Rank<br>|Average<br>Rank<br>|Lowest<br>Rank<br>|Weighted<br>Population<br>|Unweighted<br>Population|Risk-Weighted<br>Pesticides<br>Unweighted<br>Pesticides|Containers<br>Proportion<br>Damaged|<br>Exposure<br>Distance<br>Index|
|---|---|---|---|---|---|---|---|---|---|---|---|
|50 BD|1.37|0.39|0.03|37|66|97|7.85|7.82|0.04<br>1.20|0.90|33.39|
|51 BE|1.19|0.46|0.17|34|49.3|64|6.63|6.58|0.87<br>0.92|0.10|7.03|
|52 BF|1.11|0.53|0.10|40|48.8|64|7.55|7.57|0.28<br>0.85|1.00|30.09|
|53 BG|1.08|0.33|0.04|41|64.5|88|4.52|4.48|0.33<br>6.66|0.20|28.14|
|54 BH<br>|1.02<br>|0.52<br>|0.11<br>|43<br>|48.3<br>|59<br>|1.06<br>|1.06<br>|1.68<br>5.15<br><br>|1.00<br>|25.59<br>|
|55 BI|0.89|0.57|0.18|42|43.3|45|6.26|6.24|0.29<br>0.77|0.81|17.24|
|56 BJ|0.78|0.29|0.00|40|81.3|121|8.24|8.27|0.31<br>0.01|1.00|29.75|
|57 BK|0.72|0.53|0.30|34|43.5|53|3.14|3.12|2.20<br>6.91|0.10|9.92|
|58 BL|0.64|0.50|0.26|36|45.3|52|6.14|6.16|1.18<br>3.62|0.10|10.79|
|59 BM|0.59|0.27|0.01|43|69.3|96|8.1|8.06|0.13<br>0.01|0.94|12.93|
|60 BN|0.58|0.27|0.00|45|73.2|102|2.28|2.27|0.76<br>0.06|0.76|16.89|
|61 BO|0.55|0.23|0.00|46|78.3|107|5.32|5.35|1.60<br>0.11|0.19|23.7|
|62 BP|0.54|0.31|0.09|48|57.2|65|4.28|4.32|1.87<br>5.76|0.10|19.15|
|63 BQ|0.49|0.17|0.03|49|72|93|5.39|5.41|0.17<br>3.97|0.10|17.43|
|64 BR|0.48|0.23|0.04|50|65.2|81|7.85|7.82|0.12<br>0.38|1.00|33.39|
|65 BS|0.47|0.23|0.03|48|65.3|76|3.83|3.81|0.35<br>0.17|0.46|11.59|
|66 BT|0.45|0.31|0.11|49|54.2|59|6.3|6.21|1.02<br>1.83|0.10|11.97|
|67 BU|0.44|0.21|0.00|49|75.3|100|4.28|4.32|1.59<br>0.17|0.17|19.15|
|68 BV|0.42|0.26|0.05|50|58.2|65|2.28|2.27|0.46<br>0.47|0.92|16.89|
|69 BW|0.38|0.23|0.05|33|63|89|2.9|2.89|0.26<br>2.89|0.10|6.11|
|70 BX|0.34|0.12|0.05|53|72.8|85|11.86|11.91|0.01<br>0.01|1.00|4.75|
|71 BY|0.33|0.21|0.04|58|63.2|68|2.28|2.27|0.36<br>0.39|0.92|16.89|
|72 BZ|0.32|0.24|0.05|55|60|66|6.1|6.04|0.23<br>0.40|0.62|19.95|
|73 CA|0.30|0.13|0.02|58|72.7|87|3.86|3.84|0.17<br>0.53|1.00|40.64|
|74 CB|0.30|0.14|0.00|56|92.8|128|2.13|2.14|2.66<br>0.08|0.10|13.98|
|75 CC|0.29|0.14|0.00|62|88.7|114|4.28|4.32|1.75<br>0.14|0.10|19.15|
|76 CD|0.27|0.14|0.04|60|70.3|82|7.63|7.64|0.58<br>1.78|0.10|22.68|
|77 CE|0.26|0.14|0.01|65|76.5|87|6.26|6.24|0.13<br>0.05|0.75|17.24|
|78 CF|0.25|0.14|0.04|61|70.5|80|1.64|1.65|0.24<br>0.74|1.00|21.06|
|79 CG|0.25|0.19|0.10|52|63|75|6.14|6.16|0.46<br>1.41|0.10|10.79|
|80 CH|0.24|0.11|0.04|47|71.2|90|11.86|11.91|0.01<br>0.04|0.41|4.75|
|81 CI|0.20|0.08|0.00|71|96|124|4.73|4.7|0.07<br>0.01|0.78|11.24|
|82 CJ|0.20|0.09|0.01|65|79.5|95|2.33|2.31|0.19<br>0.58|1.00|41.32|
|83 CK|0.19|0.09|0.01|67|79|91|8.24|8.27|0.09<br>0.06|0.89|29.75|
|84 CL|0.19|0.08|0.03|58|77.8|94|11.86|11.91|0.04<br>0.11|0.10|4.75|
|85 CM|0.18|0.06|0.01|68|88.2|109|2.33|2.31|0.50<br>5.34|0.10|41.32|
|86 CN|0.17|0.07|0.00|70|87.8|103|8.24|8.27|0.16<br>0.06|0.42|29.75|
|87 CO|0.16|0.08|0.00|71|87.5|104|4.28|4.32|0.99<br>0.22|0.10|19.15|
|88 CP|0.16|0.10|0.06|61|73.2|84|5.3|5.29|0.21<br>0.64|0.10|8.5|
|89 CQ|0.15|0.10|0.04|67|73.7|81|8.61|8.53|0.22<br>0.68|0.10|13.61|
|90 CR|0.14|0.04|0.00|70|96.7|121|3.13|3.15|0.11<br>2.59|0.10|31.9|
|91 CS|0.12|0.04|0.01|63|90.2|112|3.17|3.19|0.03<br>0.10|0.10|2.99|
|92 CT|0.09|0.07|0.04|69|77.5|87|6.14|6.16|0.17<br>0.53|0.10|10.79|
|93 CU|0.09|0.03|0.00|74|100.7|124|7.63|7.64|0.02<br>0.60|0.10|22.68|
|94 CV|0.08|0.02|0.00|75|103.7|128|3.86|3.84|0.01<br>0.21|0.71|40.64|
|95 CW|0.08|0.05|0.01|80|87.3|94|8.24|8.27|0.20<br>0.34|0.15|29.75|
|96 CX|0.07|0.03|0.00|60|97.8|129|2.7|2.7|0.00<br>0.03|0.97|3.59|
|97 CY|0.06|0.05|0.03|76|84.2|92|3.14|3.12|0.20<br>0.61|0.10|9.92|
|98 CZ|0.06|0.03|0.00|86|103.2|118|2.66|2.66|0.60<br>0.22|0.10|23.58|

18
# **Table 3:  Cleanup Priority Ranking (cont.)**

|Priority<br>Site ID|<br>Highest<br>Index<br>|Average<br>Index|Lowest<br>Index<br>|Top<br>Rank|Average<br>Rank|Lowest<br>Rank<br>|Weighted<br>Population<br>|Unweighted<br>Population|Risk-Weighted<br>Pesticides|Unweighted<br>Pesticides|Containers<br>Proportion<br>Damaged|<br>Exposure<br>Distance<br>Index|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|99 DA|0.05|0.03|0.00|88|103|118|3.87|3.86|0.37|0.12|0.10|19.73|
|100 DB|0.05|0.02|0.01|85|94.3|104|4.28|4.32|0.02|0.12|0.46|19.15|
|101 DC|0.05|0.02|0.00|86|105.7|123|3.42|3.46|0.05|0.75|0.10|22.97|
|102 DD|0.05|0.02|0.00|88|100.8|115|3.86|3.84|0.03|0.10|0.85|40.64|
|103 DE|0.05|0.03|0.01|89|93.5|98|5.93|5.92|0.12|0.37|0.10|20.13|
|104 DF|0.05|0.01|0.00|90|113.5|131|6.1|6.11|0.04|0.57|0.10|52.72|
|105 DG|0.04|0.02|0.00|91|97.7|105|8.24|8.27|0.09|0.29|0.10|29.75|
|106 DH|0.04|0.02|0.00|90|115.5|141|5.54|5.49|0.13|0.01|0.10|14.97|
|107 DI|0.03|0.02|0.01|92|100.5|108|7.42|7.39|0.04|0.22|0.10|20.5|
|108 DJ|0.03|0.02|0.00|93|109|124|2.34|2.33|0.36|0.11|0.10|18.14|
|109 DK|0.03|0.02|0.01|81|101.2|116|6.63|6.58|0.02|0.07|0.10|7.03|
|110 DL|0.02|0.01|0.00|97|109.8|119|3.86|3.84|0.15|0.42|0.10|40.64|
|111 DM|0.02|0.02|0.01|93|99.3|106|7.33|7.3|0.04|0.13|0.10|13.79|
|112 DN|0.02|0.01|0.00|87|111.5|131|6.14|6.16|0.00|0.06|0.24|10.79|
|113 DO|0.02|0.01|0.00|99|131.5|161|2.97|3.01|0.33|0.02|0.10|51.99|
|114 DP|0.02|0.01|0.00|99|115.3|132|2.28|2.31|0.03|0.01|1.00|27.02|
|115 DQ|0.02|0.01|0.00|101|107|112|1.06|1.06|0.04|0.11|1.00|25.59|
|116 DR|0.02|0.01|0.00|78|110.5|133|4.12|4.14|0.00|0.07|0.10|4.36|
|117 DS|0.02|0.01|0.00|96|105|114|5.69|5.67|0.04|0.14|0.10|14.94|
|118 DT|0.02|0.01|0.00|102|116.2|129|2.4|2.42|0.02|0.39|0.10|20.5|
|119 DU|0.02|0.01|0.00|102|119.7|138|3.3|3.31|0.19|0.06|0.10|28.92|
|120 DV|0.02|0.01|0.00|106|112.8|120|7.85|7.82|0.05|0.15|0.10|33.39|
|121 DW|0.02|0.01|0.00|102|114.5|127|4.37|4.38|0.12|0.05|0.10|21.76|
|122 DX|0.02|0.01|0.00|107|122|136|3.87|3.86|0.00|0.02|1.00|19.73|
|123 DY|0.01|0.01|0.00|110|114.2|119|7.42|7.39|0.00|0.01|0.88|20.5|
|124 DZ|0.01|0.01|0.00|104|110.2|115|6.26|6.24|0.04|0.10|0.10|17.24|
|125 EA|0.01|0.00|0.00|112|126|140|7.55|7.58|0.00|0.09|0.10|20.84|
|126 EB|0.01|0.01|0.00|109|126|142|4.03|4.05|0.08|0.03|0.10|24.1|
|127 EC|0.01|0.01|0.00|110|117|125|7.55|7.58|0.04|0.04|0.10|20.84|
|128 ED|0.01|0.00|0.00|117|129.5|143|1.83|1.85|0.00|0.12|0.25|22.47|
|129 EE|0.01|0.00|0.00|117|132.5|148|3.28|3.29|0.07|0.02|0.10|22.28|
|130 EF|0.01|0.00|0.00|120|134|150|5.42|5.41|0.05|0.01|0.10|26.79|
|131 EG|0.01|0.00|0.00|119|122.7|125|4.37|4.38|0.01|0.02|0.36|21.76|
|132 EH|0.01|0.00|0.00|114|121.8|126|6.68|6.68|0.01|0.01|0.34|17.39|
|133 EI|0.01|0.00|0.00|120|133.7|146|7.55|7.58|0.00|0.04|0.10|20.84|
|134 EJ|0.01|0.00|0.00|113|130.7|145|2.13|2.14|0.00|0.12|0.10|13.98|
|135 EK|0.00|0.00|0.00|127|135.8|145|6.86|6.9|0.00|0.00|0.94|30.72|
|136 EL|0.00|0.00|0.00|126|131.8|144|3.86|3.84|0.04|0.07|0.10|40.64|
|137 EM|0.00|0.00|0.00|128|139.7|150|2.94|2.93|0.00|0.06|0.10|19.17|
|138 EN|0.00|0.00|0.00|128|131.3|134|4.03|4.05|0.02|0.04|0.10|24.1|
|139 EO|0.00|0.00|0.00|134|142.5|151|5.42|5.44|0.00|0.03|0.10|27.9|
|140 EP|0.00|0.00|0.00|131|134|137|7.55|7.58|0.00|0.02|0.10|20.84|
|141 EQ|0.00|0.00|0.00|135|138.2|142|3.66|3.68|0.01|0.04|0.10|25.41|
|142 ER|0.00|0.00|0.00|129|136.5|142|2.52|2.53|0.01|0.04|0.10|15.76|
|143 ES|0.00|0.00|0.00|127|137.3|150|1.17|1.18|0.02|0.02|0.10|10.57|
|144 ET|0.00|0.00|0.00|130|135.3|139|2.9|2.91|0.01|0.04|0.10|20.98|
|145 EU|0.00|0.00|0.00|130|143.2|153|0.81|0.83|0.00|0.09|0.10|14.1|
|146 EV|0.00|0.00|0.00|142|145.7|148|4.05|4.03|0.00|0.02|0.10|26.47|
|147 EW|0.00|0.00|0.00|138|141|146|1.64|1.65|0.01|0.04|0.10|21.06|

19
# **Table 3:  Cleanup Priority Ranking (cont.)**

||<br>||||||||||Containers<br>|<br>Exposure<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|
|Priority<br>Site ID|Highest<br>Index<br>|verage<br>Index|Lowest<br>Index<br>|Top<br>Rank|Average<br>Rank|Lowest<br>Rank<br>|Weighted<br>Population|Unweighted<br>Population|Risk-Weighted<br>Pesticides|Unweighted<br>Pesticides|Proportion<br>Damaged|Distance<br>Index|
|148 EX|0.00|0.00|0.00|144|154|161|3.86|3.84|0.00|0.03|0.10|40.64|
|149 EY|0.00|0.00|0.00|145|152.5|159|3.9|3.88|0.00|0.00|0.85|27.12|
|150 EZ|0.00|0.00|0.00|147|149.3|152|8.24|8.27|0.00|0.01|0.10|29.75|
|151 FA|0.00|0.00|0.00|132|139.7|149|7.67|7.71|0.00|0.01|0.10|13.74|
|152 FB|0.00|0.00|0.00|143|153.8|161|2.2|2.18|0.00|0.01|0.10|11.53|
|153 FC|0.00|0.00|0.00|153|157.5|162|4.03|4.05|0.00|0.01|0.10|24.1|
|154 FD|0.00|0.00|0.00|138|147.2|154|2.72|2.72|0.00|0.01|0.10|10.16|
|155 FE|0.00|0.00|0.00|143|153.2|163|3.87|3.86|0.00|0.00|0.20|19.73|
|156 FF|0.00|0.00|0.00|154|158.8|163|2.91|2.91|0.00|0.01|0.10|18.08|
|157 FG|0.00|0.00|0.00|154|155.5|157|2.33|2.35|0.00|0.01|0.10|27.22|
|158 FH|0.00|0.00|0.00|149|153.2|157|3.24|3.21|0.00|0.01|0.10|18.96|
|159 FI|0.00|0.00|0.00|155|157.3|159|2.33|2.35|0.00|0.01|0.10|27.22|
|160 FJ|0.00|0.00|0.00|152|155.2|159|5.32|5.35|0.00|0.00|0.32|23.7|
|161 FK|0.00|0.00|0.00|160|160.2|161|3.42|3.46|0.00|0.00|0.10|22.97|
|162 FL|0.00|0.00|0.00|154|157.5|161|5.39|5.41|0.00|0.00|0.10|17.43|
|163 FM|0.00|0.00|0.00|157|159.7|162|2.4|2.38|0.00|0.00|0.10|29.91|
|164 FN|0.00|0.00|0.00|164|165|166|5.1|5.11|0.00|0.00|0.10|19.18|
|165 FO|0.00|0.00|0.00|164|164.5|165|3.66|3.68|0.00|0.00|0.10|25.41|
|166 FP|0.00|0.00|0.00|165|165.5|166|1.83|1.84|0.00|0.00|0.12|20.15|
|167 FQ|0.00|0.00|0.00|167|167|167|4.12|4.14|0.00|0.00|0.10|4.36|
|167 FR|0.00|0.00|0.00|167|167|167|2.54|2.54|0.00|0.00|0.10|24.46|
|167 FS|0.00|0.00|0.00|167|167|167|3.3|3.31|0.00|0.00|0.10|28.92|
|167 FT|0.00|0.00|0.00|167|167|167|3.87|3.86|0.00|0.00|0.10|19.73|
|167 FU|0.00|0.00|0.00|167|167|167|11.86|11.91|0.00|0.00|0.10|4.75|
|167 FV|0.00|0.00|0.00|167|167|167|6.63|6.58|0.00|0.00|1.00|7.03|
|167 FW|0.00|0.00|0.00|167|167|167|7.67|7.71|0.00|0.00|0.10|13.74|
|167 FX|0.00|0.00|0.00|167|167|167|4.52|4.48|0.00|0.00|0.10|28.14|
|167 FY|0.00|0.00|0.00|167|167|167|8.24|8.27|0.00|0.00|1.00|29.75|
|167 FZ|0.00|0.00|0.00|167|167|167|4.28|4.32|0.00|0.00|1.00|19.15|
|167 GA|0.00|0.00|0.00|167|167|167|5.32|5.35|0.00|0.00|1.00|23.7|
|167 GB|0.00|0.00|0.00|167|167|167|6.1|6.11|0.00|0.00|1.00|52.72|
|167 GC|0.00|0.00|0.00|167|167|167|6.84|6.81|0.00|0.00|0.98|14.62|
|167 GD|0.00|0.00|0.00|167|167|167|6.63|6.58|0.00|0.00|1.00|7.03|
|167 GE|0.00|0.00|0.00|167|167|167|9.19|9.14|0.00|0.00|0.10|7.93|
|167 GF|0.00|0.00|0.00|167|167|167|6.63|6.58|0.00|0.00|0.89|7.03|
|167 GG|0.00|0.00|0.00|167|167|167|6.1|6.11|0.00|0.00|1.00|52.72|
|167 GH|0.00|0.00|0.00|167|167|167|6.68|6.68|0.00|0.00|1.00|17.39|
|167 GI|0.00|0.00|0.00|167|167|167|3.3|3.31|0.00|0.00|0.10|28.92|
|167 GJ|0.00|0.00|0.00|167|167|167|3.86|3.84|0.00|0.00|0.10|40.64|
|167 GK|0.00|0.00|0.00|167|167|167|8.24|8.27|0.00|0.00|1.00|29.75|
|167 GL|0.00|0.00|0.00|167|167|167|5.42|5.44|0.00|0.00|0.10|27.9|
|167 GM|0.00|0.00|0.00|167|167|167|6.86|6.9|0.00|0.00|1.00|30.72|
|167 GN|0.00|0.00|0.00|167|167|167|4.37|4.38|0.00|0.00|0.10|21.76|
|167 GO|0.00|0.00|0.00|167|167|167|3.3|3.31|0.00|0.00|1.00|28.92|
|167 GP|0.00|0.00|0.00|167|167|167|4.28|4.32|0.00|0.00|0.10|19.15|
|167 GQ|0.00|0.00|0.00|167|167|167|2.91|2.91|0.00|0.00|0.10|18.08|
|167 GR|0.00|0.00|0.00|167|167|167|2.34|2.33|0.00|0.00|0.10|18.14|
|167 GS|0.00|0.00|0.00|167|167|167|5.3|5.29|0.00|0.00|0.10|8.5|
|167GT|0.00|0.00|0.00|167|167|167|4.03|4.05|0.00|0.00|0.10|24.1|
|<br>167 GU|0.00|0.00|0.00|167|167|167|3.13|3.13|0.00|0.00|0.10|11.92|

20
# **Table 4: Index Values**

||γ=-1<br>W ei|γ=-2<br>ghted Resul|γ=-0.5<br>ts|γ=-1<br>Unw|γ=-2<br>eighted Res|γ=-0.5<br>ults|
|---|---|---|---|---|---|---|
|Site Id|Index 1|Index 2|Index 3|Index 4|Index 5|Index 6|
|B|561.09|462.16|523.21|67.85|19.55|93.49|
|D|187.48|63.62|272.38|11.64|1.38|24.99|
|F|44.71|113.95|23.70|51.91|46.28|40.66|
|A|39.46|151.73|17.03|459.71|618.36|293.17|
|G|19.50|31.61|12.96|22.64|12.84|22.24|
|C|17.81|10.06|20.06|205.79|40.66|342.44|
|I|12.45|6.15|14.99|1.45|0.25|2.59|
|J|10.72|6.13|11.99|0.17|0.03|0.28|
|N|7.63|4.31|8.60|0.25|0.05|0.42|
|E|7.38|54.24|2.30|87.99|226.26|40.58|
|T|5.37|3.03|6.05|2.34|0.46|3.90|
|AD|5.03|3.81|4.89|2.06|0.55|2.97|
|L|4.22|1.82|5.43|4.95|0.75|9.41|
|P|4.16|2.11|4.93|4.82|0.86|8.45|
|R|3.83|3.03|3.64|4.46|1.24|6.28|
|O|3.80|1.64|4.89|4.45|0.67|8.47|
|K|3.74|9.54|1.98|4.34|3.87|3.40|
|AH|3.72|3.47|3.25|0.08|0.02|0.10|
|AF|3.46|4.81|2.49|3.99|1.94|4.24|
|M|3.43|1.94|3.86|5.61|1.11|9.33|
|S|3.17|1.76|3.60|3.69|0.72|6.20|
|AA|2.99|2.01|3.09|3.47|0.82|5.29|
|AB|2.97|2.00|3.07|3.45|0.81|5.26|
|AK|2.76|1.27|3.46|0.21|0.03|0.39|
|AE|2.74|1.91|2.77|3.23|0.79|4.84|
|AG|2.17|3.77|1.39|0.07|0.05|0.07|
|H|2.07|16.94|0.61|2.40|6.87|1.05|
|AV|1.86|1.47|1.77|0.04|0.01|0.05|
|AC|1.82|0.48|3.01|2.12|0.19|5.19|
|AI|1.81|0.93|2.14|2.11|0.38|3.67|
|AW|1.66|1.25|1.63|0.92|0.24|1.33|
|AM|1.58|0.85|1.83|1.84|0.35|3.13|
|BA|1.23|1.42|0.97|0.36|0.15|0.42|
|AS|1.22|0.96|1.16|1.42|0.39|1.99|
|AT|1.20|0.95|1.14|1.40|0.39|1.97|
|AQ|1.17|0.57|1.41|1.35|0.23|2.41|
|AX|0.94|0.66|0.96|1.10|0.27|1.64|
|AO|0.91|0.24|1.51|1.06|0.10|2.59|
|AN|0.77|2.94|0.33|0.95|1.27|0.60|
|AZ|0.74|0.37|0.88|0.87|0.15|1.52|
|AY|0.66|0.22|0.96|0.76|0.09|1.64|
|BJ|0.63|0.29|0.78|0.01|0.00|0.01|
|BI|0.63|0.50|0.60|0.63|0.18|0.89|
|BE|0.60|1.19|0.37|0.24|0.17|0.22|
|BN|0.58|0.47|0.54|0.02|0.00|0.02|
|BM|0.55|0.59|0.45|0.02|0.01|0.03|
|BK|0.52|0.72|0.37|0.61|0.30|0.65|

21
# **Table 4:  Index Values (cont.)**

||γ=-1<br>W ei|γ=-2<br>ghted Resul|γ=-0.5<br>ts|γ=-1<br>Unw|γ=-2<br>eighted Res|γ=-0.5<br>ults|
|---|---|---|---|---|---|---|
|Site Id|Index 1|Index 2|Index 3|Index 4|Index 5|Index 6|
|BF|0.52|0.24|0.64|0.60|0.10|1.11|
|BH|0.52|0.28|0.59|0.60|0.11|1.02|
|BL|0.50|0.64|0.37|0.58|0.26|0.64|
|BO|0.49|0.29|0.55|0.01|0.00|0.02|
|AU|0.49|1.88|0.21|0.57|0.76|0.36|
|AJ|0.45|0.12|0.74|1.43|0.13|3.49|
|BU|0.44|0.32|0.44|0.02|0.00|0.03|
|BV|0.42|0.34|0.39|0.16|0.05|0.22|
|BS|0.40|0.47|0.31|0.07|0.03|0.08|
|BT|0.40|0.45|0.31|0.27|0.11|0.31|
|AL|0.38|3.14|0.11|0.45|1.29|0.20|
|BB|0.38|0.33|0.34|1.04|0.32|1.40|
|BY|0.33|0.27|0.31|0.14|0.04|0.19|
|BZ|0.32|0.22|0.32|0.21|0.05|0.32|
|AR|0.31|0.63|0.19|2.14|1.51|1.89|
|BP|0.31|0.22|0.31|0.36|0.09|0.54|
|CB|0.30|0.29|0.25|0.00|0.00|0.00|
|CC|0.29|0.21|0.29|0.01|0.00|0.01|
|CE|0.26|0.21|0.25|0.04|0.01|0.05|
|Q|0.24|0.06|0.39|2.76|0.25|6.74|
|BC|0.22|0.35|0.14|1.39|0.79|1.36|
|BR|0.21|0.09|0.28|0.25|0.04|0.48|
|CG|0.19|0.25|0.14|0.23|0.10|0.25|
|CO|0.16|0.12|0.16|0.01|0.00|0.02|
|CI|0.16|0.20|0.12|0.00|0.00|0.01|
|CK|0.16|0.07|0.19|0.04|0.01|0.08|
|AP|0.15|0.11|0.14|1.68|0.43|2.47|
|CD|0.14|0.09|0.16|0.17|0.04|0.27|
|CF|0.14|0.09|0.15|0.16|0.04|0.25|
|CN|0.14|0.06|0.17|0.02|0.00|0.03|
|CA|0.12|0.04|0.18|0.14|0.02|0.30|
|BX|0.12|0.34|0.06|0.07|0.07|0.05|
|CQ|0.10|0.11|0.09|0.12|0.04|0.15|
|CP|0.10|0.16|0.06|0.11|0.06|0.11|
|BW|0.09|0.21|0.05|0.38|0.30|0.32|
|CH|0.08|0.24|0.04|0.11|0.12|0.08|
|BG|0.08|0.04|0.10|0.60|0.10|1.08|
|CJ|0.08|0.03|0.12|0.09|0.01|0.20|
|CT|0.07|0.09|0.05|0.08|0.04|0.09|
|CL|0.06|0.19|0.03|0.08|0.08|0.06|
|BD|0.06|0.03|0.08|0.71|0.10|1.37|
|CW|0.06|0.03|0.08|0.04|0.01|0.07|
|DA|0.05|0.04|0.05|0.01|0.00|0.01|
|CZ|0.05|0.03|0.06|0.01|0.00|0.01|

22
# **Table 4: Index Values (cont.)**

||W ei|ghted Resul|ts|Unw|eighted Res|ults|
|---|---|---|---|---|---|---|
||γ=-1|γ=-2|γ=-0.5|γ=-1|γ=-2|γ=-0.5|
|Site Id|Index 1|Index 2|Index 3|Index 4|Index 5|Index 6|
|CY|0.05|0.06|0.03|0.05|0.03|0.06|
|BQ|0.04|0.03|0.04|0.35|0.10|0.49|
|DH|0.04|0.03|0.03|0.00|0.00|0.00|
|DJ|0.03|0.03|0.03|0.00|0.00|0.01|
|CS|0.03|0.12|0.01|0.03|0.05|0.02|
|DE|0.03|0.02|0.03|0.03|0.01|0.05|
|DG|0.02|0.01|0.02|0.02|0.00|0.04|
|DB|0.02|0.01|0.02|0.04|0.01|0.05|
|DW|0.02|0.01|0.02|0.00|0.00|0.00|
|DM|0.02|0.02|0.01|0.02|0.01|0.02|
|DU|0.02|0.01|0.02|0.00|0.00|0.00|
|DD|0.02|0.01|0.02|0.02|0.00|0.05|
|DO|0.01|0.00|0.02|0.00|0.00|0.00|
|DK|0.01|0.03|0.01|0.02|0.01|0.02|
|DI|0.01|0.01|0.01|0.02|0.01|0.03|
|DQ|0.01|0.01|0.01|0.01|0.00|0.02|
|DS|0.01|0.01|0.01|0.02|0.00|0.02|
|DL|0.01|0.00|0.02|0.01|0.00|0.02|
|EC|0.01|0.01|0.01|0.00|0.00|0.01|
|EB|0.01|0.01|0.01|0.00|0.00|0.00|
|DZ|0.01|0.01|0.01|0.01|0.00|0.01|
|DV|0.01|0.00|0.01|0.01|0.00|0.02|
|CR|0.01|0.00|0.01|0.07|0.01|0.14|
|EE|0.01|0.00|0.01|0.00|0.00|0.00|
|DY|0.01|0.01|0.01|0.01|0.00|0.01|
|EH|0.01|0.01|0.01|0.00|0.00|0.00|
|EF|0.01|0.00|0.01|0.00|0.00|0.00|
|CV|0.01|0.00|0.01|0.04|0.00|0.08|
|DC|0.01|0.00|0.01|0.03|0.01|0.05|
|CU|0.00|0.00|0.01|0.06|0.01|0.09|
|CX|0.00|0.02|0.00|0.05|0.07|0.03|
|EG|0.00|0.00|0.00|0.00|0.00|0.01|
|DF|0.00|0.00|0.01|0.02|0.00|0.05|
|EL|0.00|0.00|0.00|0.00|0.00|0.00|
|DT|0.00|0.00|0.00|0.01|0.00|0.02|
|DR|0.00|0.01|0.00|0.02|0.02|0.01|
|EN|0.00|0.00|0.00|0.00|0.00|0.00|
|DN|0.00|0.00|0.00|0.02|0.01|0.02|
|ES|0.00|0.00|0.00|0.00|0.00|0.00|
|ET|0.00|0.00|0.00|0.00|0.00|0.00|
|FA|0.00|0.00|0.00|0.00|0.00|0.00|
|EP|0.00|0.00|0.00|0.00|0.00|0.00|
|DX|0.00|0.00|0.00|0.01|0.00|0.02|
|EQ|0.00|0.00|0.00|0.00|0.00|0.00|
|EW|0.00|0.00|0.00|0.00|0.00|0.00|
|EA|0.00|0.00|0.00|0.01|0.00|0.01|
|ER|0.00|0.00|0.00|0.00|0.00|0.00|

23
# **Table 4:  Index Values (cont.)**

||W ei|ghted Resul|ts|Unw|eighted Res|ults|
|---|---|---|---|---|---|---|
||γ=-1|γ=-2|γ=-0.5|γ=-1|γ=-2|γ=-0.5|
|Site Id|Index 1|Index 2|Index 3|Index 4|Index 5|Index 6|
|EK|0.00|0.00|0.00|0.00|0.00|0.00|
|ED|0.00|0.00|0.00|0.01|0.00|0.01|
|FE|0.00|0.00|0.00|0.00|0.00|0.00|
|EJ|0.00|0.00|0.00|0.01|0.00|0.01|
|FD|0.00|0.00|0.00|0.00|0.00|0.00|
|EI|0.00|0.00|0.00|0.00|0.00|0.01|
|EV|0.00|0.00|0.00|0.00|0.00|0.00|
|EO|0.00|0.00|0.00|0.00|0.00|0.00|
|EZ|0.00|0.00|0.00|0.00|0.00|0.00|
|EM|0.00|0.00|0.00|0.00|0.00|0.00|
|FH|0.00|0.00|0.00|0.00|0.00|0.00|
|EU|0.00|0.00|0.00|0.00|0.00|0.00|
|FJ|0.00|0.00|0.00|0.00|0.00|0.00|
|FG|0.00|0.00|0.00|0.00|0.00|0.00|
|FL|0.00|0.00|0.00|0.00|0.00|0.00|
|FI|0.00|0.00|0.00|0.00|0.00|0.00|
|FM|0.00|0.00|0.00|0.00|0.00|0.00|
|EY|0.00|0.00|0.00|0.00|0.00|0.00|
|EX|0.00|0.00|0.00|0.00|0.00|0.00|
|FB|0.00|0.00|0.00|0.00|0.00|0.00|
|FK|0.00|0.00|0.00|0.00|0.00|0.00|
|FC|0.00|0.00|0.00|0.00|0.00|0.00|
|FF|0.00|0.00|0.00|0.00|0.00|0.00|
|FO|0.00|0.00|0.00|0.00|0.00|0.00|
|FP|0.00|0.00|0.00|0.00|0.00|0.00|
|FN|0.00|0.00|0.00|0.00|0.00|0.00|
|GR|0.00|0.00|0.00|0.00|0.00|0.00|
|FT|0.00|0.00|0.00|0.00|0.00|0.00|
|GE|0.00|0.00|0.00|0.00|0.00|0.00|
|FX|0.00|0.00|0.00|0.00|0.00|0.00|
|GT|0.00|0.00|0.00|0.00|0.00|0.00|
|GF|0.00|0.00|0.00|0.00|0.00|0.00|
|FV|0.00|0.00|0.00|0.00|0.00|0.00|
|GD|0.00|0.00|0.00|0.00|0.00|0.00|
|GN|0.00|0.00|0.00|0.00|0.00|0.00|
|FQ|0.00|0.00|0.00|0.00|0.00|0.00|
|FR|0.00|0.00|0.00|0.00|0.00|0.00|
|GL|0.00|0.00|0.00|0.00|0.00|0.00|
|FU|0.00|0.00|0.00|0.00|0.00|0.00|
|GM|0.00|0.00|0.00|0.00|0.00|0.00|
|FZ|0.00|0.00|0.00|0.00|0.00|0.00|
|GP|0.00|0.00|0.00|0.00|0.00|0.00|
|GS|0.00|0.00|0.00|0.00|0.00|0.00|
|GQ|0.00|0.00|0.00|0.00|0.00|0.00|
|FW|0.00|0.00|0.00|0.00|0.00|0.00|

24
# **Table 4:  Index Values (cont.)**

|Site Id|γ=-1<br>Index 1<br>W ei|γ=-2<br>Index 2<br>ghted Resu|γ=-0.5<br>Index 3<br>lts|γ=-1<br>Index 4<br>Unw|γ=-2<br>Index 5<br>eighted Res|γ=-0.5<br>Index 6<br>ults|
|---|---|---|---|---|---|---|
|GO|0.00|0.00|0.00|0.00|0.00|0.00|
|FS|0.00|0.00|0.00|0.00|0.00|0.00|
|GI|0.00|0.00|0.00|0.00|0.00|0.00|
|GB|0.00|0.00|0.00|0.00|0.00|0.00|
|GG|0.00|0.00|0.00|0.00|0.00|0.00|
|FY|0.00|0.00|0.00|0.00|0.00|0.00|
|GK|0.00|0.00|0.00|0.00|0.00|0.00|
|GJ|0.00|0.00|0.00|0.00|0.00|0.00|
|GA|0.00|0.00|0.00|0.00|0.00|0.00|
|GC|0.00|0.00|0.00|0.00|0.00|0.00|
|GU|0.00|0.00|0.00|0.00|0.00|0.00|
|GH|0.00|0.00|0.00|0.00|0.00|0.00|

25