(561g) Mercury Trading for Sustainable Industrial Waste Management

1
Introduction

Pollutant
trading in a watershed is a market based strategy to economically achieve
environmental waste management. Trading programs allow facilities facing higher
pollution control costs to meet their regulatory obligations by purchasing environmentally
equivalent (or superior) pollution reductions from another source at lower cost
[1]. But the added flexibility due to trading also complicates decision making
for polluters such as process industries. In the presence of multiple
polluters, multiple waste treatment technologies and option of trading,
decisions such as if and how much to trade become difficult for the polluters.
It is also important to understand the effect of trading on the final emissions
and their environmental and health impacts. Finally, information about of the
watershed and trading, if available while finalizing the regulation, might
influence the regulation development itself.

This work uses optimization techniques to develop a decision making
framework that will guide the industries in taking optimal decisions in wake of
the added flexibility due to trading. The model will also guide regulators in
developing optimal regulations in different situations. The proposed model is
applied on a Savannah River watershed mercury waste management case study as
mercury is an important concern for environmentalists.

2
Methodology and Results

2.1 Basic
Problem Formulation

The
formulation considers that TMDL (Total Maximum Daily Load) regulation has
already been developed translating into specific load allocations for each
point source. Consider a set of point sources (PS_i), i=1,?N,
disposing pollutant containing waste water to a common water body or watershed.
Here D_i, L_i andred_i
are the volumetric discharge quantity, waste load allocation and desired
pollutant quantity reduction for PS_i. P_iis
the treatment cost incurred by PS_ito reduce pollution when
trading is not possible. Let j = 1,?,M  be the set of reduction
technologies available to the point sources for implementation. TC_j is
the total treatment plant cost [$/Volume] and q_jis the pollution
reduction possible from the process [Mass/Volume]. Trading is possible between
all point sources and a single trading policy exists between all possible pairs
of point sources. Let r be the trading ratio and F be the trading
transaction cost in $/Mass. The objective of the model is to achieve the
desired TMDL goal at minimum overall cost. Let b_ijbe
the binary variables representing the point source-technology correlation. The
variable is 1 when PS_iinstalls technology j, and 0
otherwise. Let t_ik(mass/year) be the amount of pollutant
traded by PS_iwith PS_k. All the parameters
are on an annual basis.

The objective in the problem is to reduce the overall waste reduction
cost, a function of TC_j, D_i and b_ij,
by all point sources. The constraints ensure that all the targeted reduction red_i
are achieved and no industry spends more while trading as compared to when not
trading. The problem is a mixed integer linear programming problem (MILP). The
decision variables in the problem are the binary variables b_ijand
the continuous variables t_ik. The problem is quite general,
applicable to any watershed and any pollutant.

For the considered case study, discharge of any mercury to the watershed
is associated with some health care cost. This cost is calculated as a function
of the mercury LC50 value and final total mercury discharge. It estimates the
long term effects of mercury exposure on the population consuming fishes from
the watershed.

2.2 Savannah River basin case study results

TMDL of 32.8
Kg/year of total mercury has been established for five contiguous segments of
the Savannah River in Georgia, U.S. which corresponds to a water quality standard
(WQS) of 2.8 ng/liter [2]. Based on the current volumetric discharge of each of
the point sources, waste load allocation is carried out. In all, there are 29
significant point sources discharging mercury in the Savannah River watershed. The
combined targeted reduction for point sources is taken to be 40%.

Three treatment technologies, coagulation and filtration, activated
carbon adsorption and ion exchange process are considered. The cost data for
the treatments is reported in [3]. The trading ratio r is 1.1 and trading
transaction fee, based on the average treatment costs of the processes, is
around 4x10⁸ $/Kg.

Results at 32 Kg/year TMDL indicate about 18% annual reduction (27
Million $) in the treatment cost when trading is implemented. However total
mercury discharge for the trading option is higher by about 17% which results
in 20% less health care costs for the technology only option. The results show
a trend towards avoiding expensive technology implementations and satisfying
part of the pollutant reduction through trading.

To compare the trading and technology options with respect to TMDL
regulation, the problem is solved for various TMDL values between 23 Kg/year
and 40 Kg/year. While reduction and total (reduction and health care) costs for
trading option are consistently lower than for technology option, the variation
is not always linear and appears to depend on the TMDL regulation value. For this
problem trading becomes economically attractive at lower TMDL values. Also the
difference between the discharge reductions for technology and trading option
varies, depending on TMDL value.

2.3 Health
care cost as objective

Health care
cost is an important parameter affecting the regulatory decision. Hence
optimization problem is modified to include it in the objective of
minimization. This cost, previously estimated for a particular solution, is added
to the existing objective function of minimization.

The modified problem is solved for Savannah River basin at various TMDL
values between 23 Kg/year and 40 Kg/year. The results depend on the significance
of the health care cost in the overall objective. For small compensation
values, solutions do not change as compared to the original formulation. At
higher values of compensation, the health care cost becomes significant and industries
are made to reduce more than their load allocations by using more efficient and
expensive technologies. The increased treatment costs are compensated for by
reduced health care costs leading to reduced total costs.

2.4 Chance
constrained formulation

The previous
formulations assumed the data to be known deterministically which is not always
true. In this work, the current discharge of mercury by each industry is
considered to be uncertain, normally distributed around a mean value. This
makes the parameter red_i in the problem to be normally
distributed around a mean and converts the problem into a stochastic
programming one. The formulation does not consider health care costs in the
objective function. Equations 1-4 constitute the stochastic problem, the difference
being that in stochastic case the parameter red_iis
uncertain. The problem is solved by the chance constrained programming method
[4]. Two different degrees of uncertainty ±15% and ±50% and various constraint
satisfaction probabilities (50%, 90% and 99%) are modeled. The problem is
solved for TMDL between 23 Kg/year to 40 Kg/year.

The results show that larger uncertainty and higher constraint
satisfaction probability result in higher reduction but lower health care cost
and the variations are not linear. The impact of uncertainty becomes more
prominent at stricter regulations, sometimes resulting in infeasible problems.

3 Results
and Conclusion

The results
indicate that the advantages offered by trading in the form of reduced
treatment costs should be carefully weighed against increased risk of adverse
health effects of mercury. The extent of this tradeoff depends on the watershed
and industry specific parameters and the regulation itself. Stochastic analysis
shows that uncertainty is more pronounced as the regulations become stricter.
Nonlinear dependence of the solutions on TMDL regulation indicates that
consideration of trading and watershed specific details during regulation
development might help develop better regulations, achieving an effective
tradeoff between cost and reductions. It is important to consider all these
aspects for industrial symbiosis and sustainability. The framework can thus be
effectively used for sustainable industrial management.

References

[1] USEPA.
Draft framework for watershed-based trading. Technical report, EPA
800-R-96-001. Washington, DC: United States Environmental Protection Agency,
Office of Water, 1996.

[2] USEPA.
Total maximum daily load (TMDL) for total mercury in fish tissue residue in the
middle and lower savannah River watershed. Report, United States Environmental
Protection Agency, Region 4, 2001.

[3] USDOI.
Total plant costs: For contaminant fact sheets. Technical report, U.S.

Department of
Interior, Bureau of Reclamation, Water treatment engineering and research
group, Denver CO 80225, 2001.

[4] U.M.
Diwekar. Introduction to Applied Optimization. Kluwer Academic
Publishers, Dordrecht, 2003.