Prioritizing the Conversion of Heating Boilers in the Bronx Methodology

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Prioritizing the Conversion of Heating Boilers in the Bronx Methodology
Prioritizing the Conversion of Heating Boilers in the Bronx
Hea ng Boilers Burning #4 and #6 Fuel Oil Methodology (Bronx, n= 2,387) Priority Neighborhoods for Dirty Boiler Phase Out
This approach to priori zing the phase out of hea ng fuels #4 and #6 in the Bronx is influenced by GIS methodologies that consider mul ple environmental burdens and their health impacts on vulnerable popula ons (Maantay 2007; Pearce et al). Boiler loca ons were obtained from NYC Department of Buildings and geocoded, prior to crea ng a raster surface of boiler density. Along with considering the density of dirty boilers in the Bronx, in developing my model, I selected five addi onal factors relevant to the environmental health and vulnerability of Bronx residents: proximity of high traffic roads, proximity of parks and open space, poverty rate, concentra on of winter me par culate ma er (PM2.5), and proximity of elementary schools (a proxy for the presence of young children). Raster surfaces were created for each of these factors, reclassified and scored (high priority to low priority), and then used to generate a final map that combines these six factors, demonstra ng high priority loca ons for dirty boiler phase out. Dirty Boiler Phase Out
Results of Pilot Analysis Score
The scoring of mul ple environmental and health vulnerability factors resulted in a boiler conversion priori za on map of the Bronx that ranges from low priority sites (combined score = 3) to high priority sites (combined score = 24). The priori za on map produced shows that the phase out of boilers using #4 and #6 fuel oils should be priori zed in the Bronx neighborhoods of University Heights—
Fordham (top priority) and Highbridge‐Concourse Village. This pilot analysis could be expanded to include neighborhoods citywide. The methodology could be used to target funding, low/no‐
interest loans and other resources to the neighborhoods most in need of boiler conversion, reducing disparity in the transi on to cleaner hea ng fuels citywide. Further analysis could use addi onal data to strengthen and refine this methodology, for instance, incorpora ng data on relevant health outcomes linked to par culate ma er pollu on (respiratory and heart disease) or data on exis ng building condi ons (physical structure, building code viola ons, and owner financial stability). 3 (Low Priority)
Credit: Environmental Defense Fund Purpose 10
In New York City, there are almost 8,900 buildings that heat with fuel oils #4 and #6, heavy industrial grade hea ng fuels. New York is one of the few U.S. ci es that s ll allows use of these fuels in buildings. Found in mul ‐unit apartment buildings and public facili es across the five boroughs, “dirty” boilers contribute almost 90 percent of all hea ng related air pollu on in the city. Par culate ma er pollu on from hea ng oils #4 and #6 is contaminated with nickel and other heavy metals, and has been linked to respiratory and cardiovascular disease. New York City children, especially, exhibit high rates of asthma when compared to peers na onally. In 2010, the New York City Council passed legisla on manda ng the phase out of hea ng oils #4 and #6 by 2030. However, there are loopholes in the legisla on that could delay boiler conversion s ll further, waivers that can be nego ated between the New York City Department of Environmental Protec on and building owners based on financial hardship. In par cular, buildings with low‐income tenants have limited streams of opera ng funds, and o en do not have capital available for major repairs like boiler replacement. With dirty boilers already unevenly distributed across the city, and with the pollu on from these boilers compounded by other environmental burdens, how can New York more equitably pursue boiler phase out? This project pilots a methodology for priori zing the phase out of dirty boilers in the Bronx, where 2,387 boilers burning #4 and #6 fuel oils are s ll in opera on. 12
24 (High Priority)
Cartographer: Emily E. Earle May 9, 2012 ᐧ UEP 232 ᐧ Tu s University
Urban + Environmental Policy + Planning Projection: NAD 1983 State Plane New York Long Island (Ft)
Boilers Burning #4 or #6 Fuel Oils
Concentration of Particulate Matter
Distance to Parks
Families Living in Poverty
Distance to High Traffic Roadways
Distance to Elementary Schools
Data Sources: NYC Department of Buildings/
Environmental Defense Fund (2010); U.S. Census Bureau: 2010 Census, American Community Survey 2006‐2010; NYC Citywide IT Services (2007; 2012); NYC Department of City Planning (2007; 2012); NYC Community Air Survey (2009); NYC Department of Health (2009); ESRI Data ArcMap 10 (2009).
Maantay, J. (2007). Asthma and air pollu on in the Bronx: Methodological and data considera ons in using GIS for environmental jus ce and health research. Health & Place 13:32‐56. Proximity to Roads
in feet
Proximity to Schools
in feet
Dirty Boilers
per square mile
Proximity to Parks
in feet
0 - 84 (1)
0 - 400 (-3)
12.7 - 14.62 (1)
0 - 5 (1)
6,001 - 13,445 (1)
10,001 - 16,500 (1)
85 - 169 (2)
401 - 800 (-2)
13.67 - 14.62 (2)
6 - 14 (2)
1,601 - 6,000 (2)
1,601 - 10,000 (2)
170 - 253 (3)
801 - 1,600 (-1)
14.63 - 15.58 (3)
15 - 23 (3)
801 - 1,600 (3)
801 - 1,600 (3)
254 - 338 (4)
1,601 - 7,500 (0)
15.59 - 16.54 (4)
24 - 33 (4)
401 - 800 (4)
401 - 800 (4)
16.55 - 17.5 (5)
34 - 45 (5)
0 - 400 (5)
0 - 400 (5)
339 - 422 (5)
Mean Concentration of PM2.5
mcg per cubic meter
Family Poverty Rate
423 - 507 (6)
46 - 68 (6)
508 - 591 (7)
69 - 100 (7)
Office of the Manha an Borough President. (2011). Tenants and Toxins: conver ng dirty boilers in New York City’s affordable housing stock. Report. Available: h p://www.libertycontrol.net/uploads/
mbpo/TenantsToxinsFinal.pdf [accessed 14 July 2011]. Pearce, J.R., Richardson, E.A., et al. (2010). Environmental jus ce and health: the implica ons of the socio‐spa al distribu on of mul ple environmental depriva on for health inequali es in the United Kingdom. Transac ons of the Ins tute of Bri sh Geographers 35:522‐539. 
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