Spatial Analysis of Air Pollution and Potential Environmental and Social Factors in Boston

Overview Methodology Conclusions

Sensor ID Address Town Average O3

(ppb) Average No2 (ppb) Average VOCs (ppb)

Average PM 10 (ppb)

Latitude Longitude Distance to Main Road

Distance to MBTA Bus Route (m)

Distance to MBTA Bus Stop (m)

Zoning Area(s) Other Geographic Landmarks Total Median Town Household An-

nual Income (2013 adjusted dol-

1354 41 Decatur Street Arlington 28.5 17.94 11.6 12.19 42.41412 -71.134233 65 468 548 Residential 285m from school 56028

1328 29 Florence Avenue Arlington 35.51 25.55 15.79 74.13 42.42147 -71.181707 103 103 103 Residential 56028

1317 46 Jeanette Avenue Belmont 10.75 6.87 9.61 51.56 42.38173 -71.1836 149 276 293 Residential 200m from school 72907

1326 1157 Harrison Ave- Boston 46.38 33.38 19.35 158.71 42.32953 -71.08273 26 22 102 Commercial 15 "main roads" within 200m 32140

1329 34 Shipway Place Boston 32.38 22.07 12.85 16.3 42.37609 -71.050841 309 92 121 Commercial 340m from Tobin Bridge 72907

1312 27 Sherman Street Cambridge 28.36 20.12 12.3 39.49 42.38515 -71.130536 277 277 302 Residential 34663

1313 9-2 Bellis Circle Cambridge 29.54 24.13 10.75 35.71 42.38974 -71.133023 607 415 427 Residential 34663

1322 4 Gerrish Avenue Chelsea 26.09 30.57 19.13 396.57 42.39446 -71.030426 34 34 46 Commercial 382m from Northeast Expressway 25874

1325 19 Vine Brook Road Lexington 26.14 28.26 17.55 30.35 42.44539 -71.226292 159 159 170 Residential; Com-

mercial 81443

1327 135 Laconia Street Lexington 34.22 28.99 15.37 105.55 42.45468 -71.20416 121 750 750 Residential 81443

1309 47 Sherwood Road Medford 16.5 10.67 4.18 114.73 42.42916 -71.106054 137 157 199 Residential 235m from I-93 39179

1315 309 High Street Medford 52.88 34.66 32.98 64.96 42.42178 -71.12409 0 0 56 Residential 39179

1330 94 Bayfield Street Quincy 30.44 21.12 15.15 91.57 42.28129 -71.019771 352 352 358 Residential 154m from ocean 60947

1336 71 Turner Street Quincy 31.33 16.45 12.67 3951.54 42.26648 -70.953704 569 233 229 Residential 229m from ocean 60947

1303 96 Line Street Somerville 28.5 25.88 22.64 132.33 42.37554 -71.104157 69 69 82 Residential 37308

1318 152 Greenwood Waltham 24.26 21.94 18.14 59.8 42.38867 -71.246819 555 353 372 Residential 36441

1355 940 Winter Street Waltham 8.32 2.36 4.3 3983.42 42.40481 -71.27427 1188 1059 1321 Commercial on roof of office building 36441

1320 197 Nahatan Street Westwood 31.41 28.17 16.29 184.75 42.21461 -71.215257 66 1619 1555 Residential 155m from school 120078

1311 41 Belcher Street Winthrop 17.04 22.58 25.78 73.08 42.37846 -70.983996 56 1870 1934 Residential 1200m from Logan Airport 60947

1321 24-46 Beacon Street Winthrop 27.25 37.26 18.89 141.96 42.36952 -70.969363 141 3421 3490 Residential 1800m from Logan Airport 61744

Standard Error Particulate Matter Nitrogen Dioxide

The rapid urbanization of our communities brings new importance to our ability to know and understand the quality of our air. The Elm air quality monitoring sen-

sor network provides a real-time tracking of air quality on a neighborhood-by-neighborhood, street-by-street basis, giving relevant, accurate information to citi-

zens about the air quality affecting themselves and their families. Elm’s pilot project, located in the greater Boston area, currently contains 20 Elm sensors that

track ozone, nitrogen dioxide, volatile organic chemicals, and particulate matter, along with temperature, humidity, and noise in 20 second intervals. Elm sensors

are compact, easy to install, and can be installed with more coverage than typical monitors from the Environmental Protec-

tion Agency (of which there are only five for the entire Boston area). The Elm network allows not only the ability to track air

quality most relevant to an individual, but to see more extensive data for the entire greater Boston area. Knowing the typical

amount of pollutants in the air provides citizens with the necessary information to understand the health effects of each pol-

lutant, and to make changes in their daily lives and activities to reduce their exposure to particulates. For more information

on Elm, check out their website at http://elm.perkinelmer.com/.

This project evaluates the spatial distribution of air pollutant values in the greater Boston area, as well as the spatial distribu-

tion of environmental and social factors that may affect air quality over time, in order to determine potential relationships

between environmental/social factors and pollutant data. The towns with Elm sensors that are included in this analysis are:

Arlington, Belmont, Boston, Cambridge, Chelsea, Lexington, Medford, Quincy, Somerville, Waltham, Westwood, and Winthrop. All of the air quality data included

in this analysis is aggregated for a single average value for data from 15th May to 22nd November 2014.

Created by: Heidi Schillinger, December 11th, 2014

Data Sources: Elm Network Air Quality Data, PerkinElmer Inc.; MassGIS (2010);

American Community Survey 2013, 5 Year Estimates

Map Projection: NAD_1983_StatePlane_Massachusetts_Mainland_FIPS_2001

Elm air pollutant data was first aggregated to determine a single

average value per particulate for the time period from May 15th

to November 22nd, 2014. Each sensor location was geocoded us-

ing the XY latitude-longitude location into ArcMap to be com-

pared with environmental and social data by town. Using the

kriging interpolation method a prediction map was created for

each of the four pollutants based on the available Elm pollutant

data to give a prediction of pollutant values throughout the 12

towns included in this analysis. Elm locations are represented by a

point in greyscale, the darkness of the point represents the real

raw number in order to compare these values with each predic-

tion map. Standard error maps for each pollutant were also pro-

duced. Comparing to environmental and social factors involved a

series of maps and use of ArcMap tools to arrive at the values in-

cluded in the table below. Pollutant data was compared to the fol-

lowing environmental and social factors:

Air Pollutant

MBTA Bus Stops

Other landmarks

Main Roads

Zoning Areas

MBTA Bus Routes

Household Income

The spatial distribution of air pollutants shown in the prediction maps provide an interesting way of examining patterns of pol-

lutants in the long term. Each pollutant has a very distinct pattern and distribution in each prediction map. At the same time, it

is clear to see that (at least for No2, O3, and VOCs) that there tends to be a prediction of a higher level of pollutants towards

the northeastern part of the map. It is difficult to account for why this might be without examining the environmental factors as

well, but one possible explanation is that there are a higher density of sensors in this area than the other towns.

Relationships with Environmental and Social Factors:

Household Income: Comparing between pollutants and household median town income showed that as median income in-

creased, average pollutant count did decrease for all pollutants except nitrogen dioxide. However, the strength of this trend was

somewhat limited, with r^2 values ranging from 0.03 to 0.024. The strongest trend was

between VOCs and median household income.

Distance to Main Road: All pollutants except particulate matter showed stronger regres-

sion trends that as distance to a main road increased, pollutant count decreased. Particu-

late matter showed the exact opposite trend, however it is impossible to say if this is due

to traffic or road proximity or another variable.

Distance to MBTA Bus Routes and Stops: Both distance to bus routes and bus stops had

little correlational relationship to any of the pollutant counts. Many of the main roads overlapped the closest bus route or stop.

In the future, examining the number of stops and routes in close proximity might be a better indicator. Results: Measured Environmental and Social Factors Zoning Areas: Most of the pollutants showed little meaningful relationship between pollutant counts and zoning. However, particulate matter showed that

there was a much higher average particulate matter count in commercial zones than residential.

Sensor with Lowest Air Quality: The sensor with the lowest overall air quality (meaning highest pollutant count for majority of pollutants) was sensor 1315 l o-

cated in Medford. There were no particular landmarks nearby that might cause higher than normal pollutant counts, but the sensor is located directly on a main

road and extremely close to an MBTA Bus stop and route (closer than GIS was able to determine, so it is likely on someone’s porch just outside the bus stop). This

shows how influential traffic patterns and proximity to areas with a lot of exhaust pollutants is on air quality both in short term trends and in the long term.

Limitations

Though the variables compared to air pollutants and air quality in this analysis likely have somewhat of a causative relationship, each of the relationships and spatial distributions examined here pro-

vide correlational data only. This does not mean the correlational relationships lack significance, they simply must be treated in a very different way than a causative relationship, which would require

much more rigor in its scientific testing.

Secondly, the limited amount of data points given by the Elm sensors (with only 20 locations) also puts some limitations on the reliability of the prediction maps. Maps of the standard error of each

prediction map are shown below, illustrating that there is a limited amount of standard error (around 1) for each pollutant at its most reliable prediction, and that standard error increases the farther

outside the center of the map one looks. With more Elm sensors giving more data points from a more extensive network of locations, the capacity for prediction of the kriging interpolation should in-

crease. In the future, once the Elm network has expanded, using this kriging method one would be able to find a predictive pollutant value within the prediction map at any specified point. For exam-

ple, one could find predictive value for every K-12 school in the Boston area, or at every public green space. This would be highly valuable for an expansion of the network if resources for installing

sensors are limited.

Finally, one of the major benefits of the Elm network is its ability to track patterns in daily air quality and pollutant levels, producing a pattern for pollutant levels at a given location in a regular 24

hour period. Since the pollutant values used here are aggregated for months at a time, changes in pollutants by hour or season requires an additional analysis, but could be done using this method.

Ozone Volatile Organic Chemicals