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Yamuna Foundation for Blue Water

Note: All Blue Yamuna team members are participating in the Yamuna Cleanup activities on their personal time and contributing their personal funds. Not a single volunteer of the Yamuna Foundation is receiving any compensation, rather they are paying towards the cause. They are conducting the activities in compliance with applicable regulations (US 18 U.S.C. ? 207 , 5 CFR ?2635, and others; India - IPC for Ethics) in their respective countries. They are dedicated To Turn the Yamuna Water Blue for the poor and needy who have no access to municipal or bottled water.

The Role of Environmental Indicators in Mining Operations

Subijoy Dutta, PE
Institute for Urban Environmental Research, GWU
1496 Harwell Ave., Crofton, MD 21114, Phone: 202-994-2781; Email: subijoy at msn dot com

Elizabeth F. Wells, PhD
Dept. of Biological Sciences, George Washington University, Lisner Hall, Rm. 348, 2023 G. Street NW, Washington, DC 20052, Ph: 202-994-6970; e-mail: efwekks at gwu dot edu


Watersheds and airsheds are the major focus of various environmental programs in federal, state, and local agencies today. A significant part of mining regulations are designed for protection of the environment. Environmental management thus takes up a significant fraction of the resources. Regulatory requirements of sampling and analysis of mine drainage, spoil piles, and emissions are on the rise with the advancement of technology. These requirements are often hard to comply with, especially for the small miners and quarry operators. A healthy environment is often reflected by a few common indicators, which could be easily observed and monitored at a much lower cost and in a more definitive manner.

This paper examines the possibility of meeting the ultimate objective of minimizing the impact of mining on the environment by monitoring the total health of the watershed and its habitat by use of environmental indicators.


As the urban sprawl continues its stride towards suburban areas, health of local watersheds is becoming increasingly important to the resident population because of the dearth of clean and pollution-free environment. In response to this and other water quality concerns, the U.S. Environmental Protection Agency (EPA) has stepped forward with a revision to the regulatory requirement of the Total Maximum Daily Loads (TMDL) under section 303(d) of Clean Water Act (CWA). Amendments to regulations 40 CFR part 122.4(i) and 40 CFR part 130 were made to provide states, territories, and authorized tribes with the necessary information to identify impaired water bodies and to establish TMDLs to restore water quality. States, territories, and authorized tribes establish the section 303(d) lists of impaired waters and submit to EPA for approval of the lists or to add waters to the submitted lists, if EPA determines that the list is not complete. TMDLs have been applied to several mining sites because of the causal link between the impaired water body and the source of pollution. The most common examples of mining sites where TMDLs have been applied include the following sites from EPA Regions 8 and 10 (USEPA 2000):

Animis River,    
Coeur d'Alene site,
South Dakota Black Hills area  (e.g., Whitewood Creek)
Colorado's Cripple Creek, and
West Fork of Clear Creek.

    The top three causes of impairment of water bodies nationally include sediments, nutrients, and pathogens (USEPA 2000). Mine runoffs are oftentimes linked with high sediment concentrations. As a result, the surface water sampling and monitoring requirements in mining operations have been increased in certain areas. For example, in case of greater water quality concerns, the State of Minnesota sometimes requires the sand and gravel operators, and rock quarries to submit water quality analyses including pH, maximum turbidity, total suspended solids (TSS), total phosphorus, and other potential pollutants, at certain times during their operation, in addition to their initial permit application and renewal. The cost of these additional sampling and monitoring could be formidable for small operators. Also, some operators are unable to perceive the broad picture involving the local watershed and fail to employ the most effective control for silts and sediments in the mine runoff.
    This paper examines the use of various environmental indicators to provide a more comprehensive picture to the mine operators and communities in a watershed concerning the health of their watershed and possible signs of ecological "brown spots". By using these indicators, all of the stakeholders from the watershed can develop an integrated approach to protecting the health of their watershed. This should lead to a more sustainable growth and development in that watershed as compared to the standard regulatory mandates.
Use of Indicators by the Loudoun County Environmental Indicator Project (LEIP)
    To study the environmental impact of proliferating growth and development in Loudoun County, Virginia, various indicators of change are studied by LEIP, under the auspices of the Geography Department at the George Washington University. There are fifteen different indicators used by the LEIP. They are Roadside imagery, Aerial imagery, Digitized imagery, Conventional map imagery, Forested areas, Agricultural lands, Wetlands, Riparian areas, Impervious surfaces, Urbanized areas, Listed plant species, Key soil types, Water quality, Air quality and Historic and cultural sites. These indicators are monitored at various sites in the county and the changes studied and analyzed. Seventeen sites throughout the county were selected for long term monitoring. These sites represent a wide variety of environmental conditions ranging from forest areas and riparian zones to shopping centers and residential areas of varying densities. Amongst the findings on various indicators as (LEIP 2000) the following few sample observations from a few indicators are summarized here.
Roadside Imagery
    Continuous changes to familiar landmark or its setting to a new form have been routinely observed in LEIP's roadside image logs. Almost daily changes were apparent along Route 50. The concentration and extension of commercial facilities along Route 28 have placed a deeply engraved rubber stamp of urbanism in this suburban setting by placement of several multistory buildings along the Route 28 corridor. The inventory of images captured by LEIP in 2000 have been marked with both significant and subtle changes when compared to the previous years.
Remote Sensing Technologies for Mapping Changes in Forest Cover
    A series of landscape metrics was calculated and presented by LEIP to show how patterns of forest fragmentation have changed in the county during the past three decades. The results revealed that there were increases in forest fragmentation in most watersheds of the county, especially between 1987 and 1999. Later, LEIP acquired a new digital land cover map of the Washington-Baltimore metropolitan region which was used as a basis for analyzing the relationship between the size of the forest fragments and the percent of urbanized area per major watershed. For the eleven major watersheds the calculated mean forest fragment size is plotted against the percent urbanized land as shown in the figure below. Each of the data points in the figure represent one major watershed in Loudoun County.
    Figure 1. Mean forest fragment size (in km2) versus percent urbanized land in major watersheds, Loudoun County, VA (Fuller 2000)

mining-fig2.gif (2010 bytes)

   With the progress of urbanization the forest patches are getting smaller and smaller. These patches are less likely to support an ecologically diverse wildlife and plant species. A diverse wildlife population normally requires a large continuum of forest cover without fragmentation. For example, many songbird species found in the mid-Atlantic region usually occur in forest patches of 1 Km2 or greater in area. Using the relationship shown in the above figure, the amount of urbanized area likely to result in a mean forest patch size of 1 Km2 for maintaining viable population of songbirds is 52.58 percent (Fuller 2000). This could be an approximate value of maximum urbanization potential in a watershed while maintaining the minimum forest patch size requirement for a viable songbird population. Similarly, the forest fragmentation could be tied to other ecological factors identified in a watershed.

Environmental Indicators for Monitoring Impacts of Mining Operations
    Impacts of mining operations on the watershed and sensitive waterbodies are generally monitored by regulatory agencies a little more carefully by requiring the operators to submit water quality data at certain times during the operation. For small scale mining operations this requirement becomes too cost intensive. The validity of such data are also questionable because of the possibility of bias exercised in the sampling time and location. In addition, the reported results are subject to sampling and analytical errors and oftentimes do not provide a clear and comprehensive picture of the actual damage done to the local environment due to a mining operation. Effective use of environmental indicators in such operations could possibly resolve these problems. Amongst many different indicators, such as aquatic life, vegetational stress, satellite imagery, aerial imagery, roadside imagery, forested areas, water quality, air quality, wildlife species, and genetic biomarkers, only the following three different indicators will be covered briefly in this paper.
Aquatic Life
    Aquatic environments reflect the effects of acid mine drainage (AMD) most clearly.
    Most lakes and streams have a pH between 6 and 8 (USDI 2000). A pH between 6.5 and 9.0 is harmless to most aquatic species. However, near an acid mine spoil site, AMD water flows to streams, lakes, and ponds which may greatly acidify the water. Lakes and streams become acidic when the water itself and its surrounding soil cannot buffer the acidic water enough to neutralize it. The pH of many AMD waters fall below 5.0. Generally, the young of most species are more sensitive to changes in acidity than adults. When the acidity of a lake or stream drops the pH to less than 6.0, there are decreases in the reproductive success in many aquatic species. As the acidity increases to a pH level below 5.0, the number of aquatic species that live in lakes and streams decreases. Some species like frogs are able to tolerate acidic waters but eventually disappear due to low prey (mayfly) populations. At a pH of less than 4.5, most fish species can not survive. Figure 2 shows that not all adult fish, shellfish, or their food insects can tolerate the same amount of acid (top wide bars). It also shows the levels of acidity that are harmful to reproduction for each species listed in the chart (narrow bars below the top wide bars).
    Figure 2. Degree of acidity endurable by different aquatic species

mining-fig1.gif (7123 bytes)
    A small water body (part of a stream or a small baffle part of a stream or lake), downstream of the active mine boundary, could be stocked with some of the sensitive aquatic species and used as a control area. This water body could be periodically checked to evaluate the impact of mining operations.
    Biomonitoring methods using macrophytes, phytoplankton, and periphyton also have the potential to be useful tools for monitoring impacts of mine effluents on the aquatic environment. All of these methods are undergoing further testing and evaluation by various agencies. A good example is EPA's Mid-Atlantic Integrated Assessment (MAIA) project, where the evaluation of anthropogenic activities on aquatic ecosystems and integration of watershed studies at different scales are being conducted to develop a landscape assessment on the environmental condition of the Mid-Atlantic region.
Vegetational Stress
    Some terrestrial plant species have better survivability under acidic condition in the mine area. Examples are Pines (Pinus spp.), Black Locust (Robinia pseudoacacia), Broomsedge (Andropogon virginicus), Common cattail (Typha latifolia), Common reed (Phragmites australis), Flowering dogwood (Cornus florida), River birch (Betula nigra), Sericea lespedeza (Lespedeza cuneata), Sugar maple (Acer saccharum), and American sycamore (Platanus occidentalis)(USDI 2000)

    However, there are a few sensitive plant species that will exhibit stress and show decline in population and growth when impacted by acidic drainage or high metals or other contaminants from the mining operation. Amongst a wide variety of species from the plant database provided by Natural Resources Conservation Service of the US Department of Agriculture (USDA 2002), the following few species are presented as samples of sensitive vegetation which could reveal adverse impacts of mining operations because of their high sensitivity to low pH and low tolerance to other adverse impacts of mining.
    1. Purple crowberry (Empetrum nigrum, pH 4.3-7.8); 2. Kura clover (Trifolium ambiguum, pH 6.0-7.4); 3. Redosier dogwood (Cornus sericea L., pH 4.8-7.5), 4. Dotted blazingstar(Liatris punctata, pH 6.0-7.8).
    Similar to the aquatic indicator, a control area with some of the sensitive plant species could be monitored using photographic imagery and reported to the regulatory agency. Proper selection of the control area will be a critical factor for its effective use as an indicator.
Remote Sensing Methodologies
    Aerial photography and Satellite imagery could also be used to monitor the periodic changes at a mine site. The data from a remote sensor is digitally stored as a matrix of numbers. A picture element (pixel) is the smallest element of a digital image representing the reflectance from one specific location (i,j) of row and column out of a matrix comprising of the whole picture. A variety of multispectral and hyperspectral remote sensing systems are being used today. A few commonly used remote sensing technologies categorized by the type of technology are furnished below (Jensen 2000):

Multispectral Imaging using Discrete Detectors and Scanning Mirrors
Landsat Multispectral Scanner (MSS)
Landsat 7 Enhanced Thematic Mapper Plus (ETM+)
NOAA Geostationary Operational Environmental Satellite (GOES)
NOAA Advanced Very High Resolution Radiometer (AVHRR)

Multispectral Imaging using Linear Arrays
SPOT 4 High Resolution Visible Infrared (HRVIR)and Vegetation Sensor
Indian Remote Sensing System (IRS)
Space Imaging (IKONOS)
Earthwatch (Quickbird)

Imaging Spectrometry Using Linear and Area Arrays
Airborne Visible-Infrared Imaging Spectrometer (AVIRIS)
NASA Terra Moderate Resolution Imaging Spectrometer (MODIS) Digital Frame Cameras
Litton Emerge Spatial
Positive Systems

Satellite Photographic System
Russian KVR-1000
NASA Space Shuttle Photography

    Changes in disturbed area, spoil pile accumulation, and watercourse diversions at a mine site can be easily mapped by using remote sensing technologies. By reviewing temporal changes in satellite imagery of an area, the forest canopy cover, the forest types (deciduous vs. evergreen), and the vegetative cover ( forest vs. savanna) of the area could be determined (Brakken 2002). Determining water quality using hyperspectral data is also possible with new high resolution imagery provided by such satellites as IKONOS and Quickbird. Using high resolution and clear distinction between the reflected wavelengths in the blue (0.45 ?m), and yellow (0.8 ?m) region, the sediment load in a surface water near a mine site can be estimated. Remote sensing can be used to periodically monitor the changes in permit boundary, forest cover, site runoff, drainage ditches, slope erosion, and spoil piles by the regulatory agencies and other stakeholders. One similar program is being used for enforcement by the Technology Information Processing System (TIPS) program of the Office of Surface Mining (OSM). The remote sensing technique would allow an early warning and identification of environmental problems at a mine site. By proper use of the remote sensing imagery, a timely alert could be relayed to the mine operator to resolve the problem before it grows too large, difficult and expensive to handle.
1.    USDA 2002, Online Plants Database ,, Natural Resources Conservation Service. April.
2.    Brakken, K.T 2002, Personal Communication, U.S. DOE, Rocky Flats Environmental Technology Site, Golden, CO, April.
3.    LEIP 2000, Annual Report , Published by the George Washington University, Washington, DC.
4.    Fuller, D.O. 2000, The Effect of Urbanization on Forest Fragmentation in Loudoun County, LEIP Annual Report 2000, The George Washington University, Washington, DC.
5.    Jensen, J.R. 2000, Remote Sensing of the Environment: an earth resource perspective, Prentice-Hall, New Jersey.
6.    USDI 2000, Old Ben Scout Reservation Natural Resources and Mining Handbook, Office of Surface Mining, Alton, IL, May.
7.    US EPA 2000, Total Maximum Daily Load (TMDL) FAQs, Office of Water, Mining Waste Scientist to Scientist Meeting, EPA/ORD National Exposure Research Laboratory, Las Vegas, NV, June.

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