Download Using GIS to Identify Urban Non-point Water Pollution

Using GIS to Identify Urban Non‐point Water Pollution Shannon McManus Water pollution is a prominent problem in regions all around the world. The amount of pollutants increase as the population increases and land that was once uninhabited is changed to provide resources for humans. Rivers, watersheds, and groundwater are all impacted by pollutants that are generated from point sources (households and industrial discharges) as well as non point or diffuse sources (Naranjo, 1998). These non point sources can be attributed to urban runoff, agriculture, construction, mining, solid waste disposal and many other sources (Naranjo, 1998). Urbanizing areas has a sometimes overlooked, but prominent impact on water pollution. Changing land for urban use severely alters hydrologic characteristics such as water flow, water pathways, and rates of flow as well as degrading water quality by carrying urban pollutants such as oils, grease, sediment, heavy metals, pathogens, nutrients, and many others (Bhaduri, Harbor, Engel & Grove, 2000). For this reason it is necessary to study this phenomenon and analyze its spatial characteristics using a Geographic Information System (GIS). GIS is a tool that can analyze a great deal of spatial data at once. In the area of water quality it provides an approach to evaluating land use and other factors to help identify, analyze and predict the distribution of water pollution (Naranjo, 1998). Since more and more of the population lives in urban areas, it is important that we understand the impacts of urbanization on water quality, and utilize GIS to create risk and hazard maps that point us to severe problems that require further study or management. This task requires the use of certain data components as well as a variety of analysis functions provided by GIS software. In order to utilize GIS functions to their fullest extent, certain data must be obtained from state, federal or private agencies, or obtained through research. There are many pieces of information that impact the way in which water flows to a watershed, river, or basin including impervious surface area, soil permeability, topography, annual rainfall, catchment area (geographic area that drains into a river) and vegetation to name just a few (Mitchell, 2005). In order for studies to be cost effective and efficient in obtaining simplified results quickly, many turn to information that already exists at the state or federal level. For example, a great deal of information regarding topography can be obtained using a digital elevation model (DEM). In addition, land use data can be obtained from Landsat satellite imagery or USGS (U.S. Geologic Survey) and classified using the Anderson classification system. A great deal of soil information is available through SSURGO (the soil survey geographic database). (Wang & Yin, 1997) Another necessary set of information required in non point pollution models are the water quality factors. Water quality measures a wide range of factors such as pH, alkalinity, dissolved oxygen, turbidity, conductivity, total suspended solids, concentration of nutrients, metals and organics (Zandbergen, 1998). Different studies utilize different quality parameters depending on the availability and purpose. For example, a study done in British Columbia utilized a water quality index that factored in many parameters by calculating a value that was dependent on frequency a pollutant exceeded limits, amount by which the pollutant exceeded limit and percentage of pollutants that do not meet objectives (Zandbergen, 1998). Another study in Brazil focused on just four main pollution parameters: total nitrogen, total phosphorus, biological oxygen demand and fecal coliforms (Naranjo, 1998). While yet another model in the U.K. created a runoff model based on event mean concentrations (EMC) which is the total mass load of a chemical yielded from a storm divided by the total storm discharge. This value is then used to identify differences in EMC values based on land use and pollutant type. The pollutant load for that area can then be calculated by multiplying the typical runoff volume with the EMC value. (Mitchell, 2005) Still another method that a study in Ohio performed was to focus on one measurement, conductivity, to assess pollutants. This is a good indicator to use for analyzing urban non point pollution due to the fact that conductivity is used to identify dissolved solids, salts (many pollutants contain soluble salts), and heavy metals. (Wang & Yin, 1997) While another study done in Indianapolis utilized a runoff calculation in conjunction with a pollutant build up – wash off function (Bhaduri, Harbor, Engel & Grove, 2000). It is apparent that no one source of information is used to complete non‐point pollution analyses, however all of the information can be synthesized using GIS to develop meaningful and accurate risk and hazard maps. To begin analyzing spatial data, it is necessary to modify the information in a way that the software can produce meaningful results. For example, using Boolean operations, a ranking and weight can be placed on a water quality characteristic in relation to its pollution potential (Evans & Myers, 1990). In addition, multiple grid operations can be performed to modify data. For example, if one wanted to show a map of variations in groundwater level, they could subtract depth to groundwater from elevation to create a new data field (Evans & Myers, 1990). From there, multiple layers of information that have specific values in each grid cell can be layered in the software and summed together. Even some of the more complex calculations like runoff volume and EMC value described above can be assigned to specific cells so that pollutant loads are assessed for every location in the map. Arc View also has a Hydrologic Modeling program that can use digital elevation data to determine flow paths. (Mitchell, 2005) The data layers can then overlay each other such as land use, impervious areas, streams, vegetation cover, contaminant loadings, road network, sediment quality and water quality (Zandbergen, 1998). With all of the pieces put in place, extremely informational maps can be produced and environmentally sensitive areas be identified. Pollutant “hot spot” maps can be created that identify areas that have extreme pollutant sources as well as factors that allow for that contaminated water to wash into rivers or watershed, or factors that allow for contaminated water to seep into groundwater. Hazard maps for each pollutant can also be created and used to identify where specific environmental standards are not being met. (Mitchell, 2005) In addition, using land use data from past and present, it is possible to analyze how urbanization has impacted water quality and predict how it may change in the future (Bhaduri, Harbor, Engel, & Grove, 2000). Overall, GIS is an extremely powerful tool that can provide a great deal of information to assist communities in managing urban non point water pollution. It is evident that up to now GIS has been a useful tool in managing non point water quality hazards. Ideally, it will have even more powerful functions to help monitor and analyze water pollution by integrating hydrologic characteristics of land, with the movement of specific pollutants. As of now, many studies have to create their own method of analysis in terms of pollutant loads and runoff calculations. In the future, it would be beneficial to have a standard method of non point pollution quantification as well as universal data that is recorded on an annual basis for water systems and the impact of land uses nearby. It is in our best interest to accumulate and spatially represent this information so that it can be accessed easily and utilized on a regular basis for land use planning and water quality management. Annotated Bibliography: Bhaduri, Budhendra; Harbor, Jon; Engel, Bernie and Grove, Matt. 2000. Assessing Watershed‐Scale, Long‐Term Hydrologic Impacts of Land‐Use Change Using a GIS‐NPS Model. Environmental Management. Volume 26, No. 6. This study used past and present land use data to show the significance that land use change has on the hydrologic system. Changing land to agriculture, mining, industry, or residential areas can change water pathways and flow rates, increase downstream flooding, and decrease long‐term groundwater supply. This is the focus of concern for the study and it focused on Little Eagle Creek in Indianapolis, Indiana, an area of major change in the past 40 years. A long term hydrologic impact assessment model was designed to measure the impact on land use change incorporated runoff calculations, build – up/wash – off calculations, pollutant sources, transport routes and soil moisture conditions. This information, along with land use data from Landsat in 1973, 1984, and 1991, was used with GIS to analyze how land use change impacts the hydrologic system. A great deal of information resulted from this study including runoff volume changes, and changes in individual pollutant concentrations. The results of this study are extremely useful in analyzing land use changes in urban areas and its effect on water quality. Evans, Barry M. and Myers, Wayne L. 1990. A GIS – based approach to evaluating regional groundwater pollution potential with DRASTIC. Journal of Soil and Water Conservation. This paper focused on the use of GIS to assess pollution potential for groundwater in a southeastern region of Delaware. The focus of the study was to create a database that would be able to produce high risk and hazard maps using data that was already available from state or federal agencies. Some of the information inputted followed the typical DRASTIC (Depth to groundwater, net Recharge, Aquifer media, Soil media, Topography, Impact of vadose or unsaturated zone, Conductivity) methodology of groundwater factors as well as land use/land cover, and septic system density. The information was digitized using a heads up method of manually digitizing lines and assigning x, y coordinates. Each characteristic was then assigned a weight based on pollution potential as well as its relative importance to impacting groundwater. Each data layer then utilized the additive overlay function so as to create maps representing high and low risks. This study was useful for establishing basic parameters and procedures in analyzing groundwater pollution potential for any area. Mitchell, Gordon. 2005. Mapping hazard from urban non‐point pollution; a screening model to support sustainable urban drainage planning. Journal of Environmental Management Vol. 74, Issue 1. This paper described a study and model developed in the UK that analyzed and mapped the pollutant loads in a small water basin so as to identify areas to create sustainable drainage systems (SuDS). The study described the problem of non‐point urban pollution sources being a major factor in the poor quality of water. A volume concentration model was developed to calculate the site mean event concentration (EMC) values, the total mass load of a chemical yielding from a storm, divided by the total storm discharge, for each pollutants in each land use type. GIS software with grid algebra was then used to calculate flow paths for rainfall (using a digital elevation model), runoff volume per grid, and EMC values for each land use type. A “hot spot” map was created to identify extreme sites where SuDS should be implemented. In addition, hazard maps of each could be produced to identify risk areas for specific pollutants. The study was extremely useful in the UK because it allowed them to identify EMC values for their region, and study the concentration of many pollutants in urban storm water. Naranjo, Eugenia. 1998. A GIS based nonpoint pollution simulation model. VKI, Institute for the Water Environment. Denmark. This paper describes the use of GIS as a tool for mapping and evaluating characteristics that contribute to nonpoint sources of water contamination. The study focused on four water contaminants: Total Nitrogen, Total Phosphorus, Biological Oxygen Demand and Fecal Coliforms. A wide extent of data is required to get a complete analysis of pollution load such as the extent of the watershed basin, topology, type of population, industries, land uses, climate, vegetation, soil types, and sewage information. GIS is a powerful tool in this kind of environmental modeling and the paper describes how it can spatially represent data on a land surface and use grid representations to calculate the estimated pollutant loads for a given region. A detailed case study was described to explain how GIS was utilized for monitoring the Iguacu river basin in Brazil. A land use and pollution concentration database was created for the region assigning event mean concentrations to each land type; then using a digital elevation model, an accumulation command was used to find the total annual pollution generated by the land. This study was a good example of how to utilize GIS tools to assess the impact of long term diffuse pollution sources in watersheds. Wang, Xinhao and Yin, Zhi‐Yong. 1997. Using GIS to Assess the Relationship between Land Use and Water Quality at a Watershed Level. Environmental International. Vol. 23 No. 1. This study utilized water conductivity data to study the spatial correlation between land use and water quality in the Great Miami River in Ohio. The reason for this study was to perform cost effective research using readily available data so as to be able to consider water quality in land use planning. Using GIS as a spatial analysis tool, three major overlays were used to study the area. The first was land use data from USGS that was classified using the Anderson Classification system. The second was elevation from a Digital Elevation Model that assisted in delineating streams and catchment areas. The last was water quality information from the USGS. The results of the study showed that water conductivity is a good indicator of certain pollutants, such as certain metals that contain soluble salts, dissolved solids, and alkalinity; all major urban pollutants. It is not, however, a good indicator of some nutrients, fecal and bacterial matter, or suspended materials; major agricultural pollutants. Therefore, this study was able to show a direct correlation between urban land use and poor water quality. Zandbergen, Paul A. 1998. Urban watershed ecological risk assessment using GIS: a case study of the Brunette River watershed in British Columbia, Canada. Journal of Hazardous Materials. Volume 61, Issues 1 – 3. This study describes the use of GIS in identifying areas of concern in terms of watershed health that would require further study. The focus of the study was on the Brunette River, a small urban watershed near Vancouver, because of its ecological stressors such as increasing impervious surfaces, multiple sources of pollutant loadings, and decreasing water quality. The two major characteristics of watershed risk that were focused on in this study were impervious surfaces and water quality. Impervious areas were analyzed to determine how much water can infiltrate the soil and therefore, how that impacts the health of the receiving streams. In addition, water quality was calculated using a water quality index that incorporates many pollutants, their frequency of occurrence, and amount by which they exceed environmental standards. This information was used in a GIS database to produce a map of 25 drainage areas in the basin. Many data layers were then used including water quality, impervious areas, land use, streams, vegetation, transportation density, pollutant loadings, and others. This allowed for a good overview of the areas of concern for the basin, and a tool to identify regions to continue studying. Shannon McManus