15 Jun Environmental Equity Study
When we think about where to site green stormwater infrastructure (GSI), we far too often look only at places that receive the most runoff or regularly experience flooding. While these factors are important from a stormwater management perspective, they do not tell us anything about how that community may be uniquely impacted by flooding. For example, neighborhoods with high rates of asthma or low rates of health insurance coverage are especially impacted by mold that results from flooding, and communities that faced redlining and disinvestment struggle to find resources to deal with stormwater management.
Our small, seven square mile Nine Mile Run watershed is a microcosm of Pittsburgh and most urban areas across the country, showing stark differences in prosperity, opportunity, health, and safety for residents only a few blocks from each other. This is a result of issues that continue to surface across economic, social, and environmental systems. As we look to the future, we know we need to understand better how to allocate scarce resources so that we are doing the most good for the people and communities that are most in need.
We developed the Nine Mile Run Environmental Equity Study as a tool to find areas within the Nine Mile Run sewershed that are the most vulnerable to environmental issues based on public health, social vulnerability, environmental quality, and the urban landscape. Each of these four categories contain multiple datasets that were analyzed individually and the raw data values were reclassified on a scale from 1 to 5. For example, areas within 50 feet of a bus stop were given a score of 5. After ranking each individual dataset, a weighted overlay was created for each category showing the areas that are most vulnerable based on multiple datasets. This data dictionary shows the data sources and how they were reclassified.
Increasing green spaces can lead to increased outdoor physical activity in communities. According to the EPA, an increase in outdoor activities can positively impact people suffering from chronic diseases such as obesity, heart disease, type II diabetes, and high blood pressure. In addition, several studies have found links between exposure to natural landscapes and reduced stress levels. We chose data for the public health category based on the potential positive impact that GSI could have on people who suffer from childhood asthma, type II diabetes, hypertension, obesity, anxiety, and depression. This data was limited by the fact that it only represents people with health insurance plans. For this reason, we included data on the number of people without health insurance as well.
The CDC’s Social Vulnerability Index (SVI) was included in this study to provide greater insight into which areas of our community are the most vulnerable to natural disasters based on 15 factors.
The annual rainfall in Pittsburgh reached record levels in the past few years and will likely continue to increase. We must consider which communities are the most vulnerable to natural disasters to inform our decisions on where to build GSI.
In addition to increasing rainfall, Pittsburgh has the 8th highest levels in the US of the air pollutant PM2.5 which is known to cause serious health problems. Environmental quality data was included in this study to determine the areas that have the greatest amount of air pollution, highest surface temperature, and the most impervious surfaces within the sewershed. Several studies state that increasing urban tree canopy cover through the use of street trees and green spaces can help to decrease pollution and surface temperature.
While the first three categories in this study looked at which areas are most vulnerable, we also had to consider what areas are feasible for siting GSI. The urban landscape category included data on bus stops, schools, vacant parcels, parks, and plantable area. After running a weighted overlay for this category we were ready to create the overall GSI Suitability Index.
The overall GSI Suitability Index was created by running a weighted overlay on all four of the categories giving equal weighting to each category. This result was then used to determine areas where we would focus on building GSI. We used the GSI Suitability Index in conjunction with CivicMapper’s Watershed Hydrology Tool to generate catchment areas in places that scored highest on the suitability index. We found 10 areas within the Nine Mile Run sewershed where we should focus our efforts to build GSI.
Moving forward, we plan to use this project to guide our decision making on where to build GSI in a data driven way, based on equity and environmental justice. One of the strengths of this project is the ability to adapt it to incorporate updated datasets for our watershed as well as others.