Name
Abstract | ||
|---|---|---|
In this article, we apply signal processing and data science methodologies to study the environmental impact of burning different types of heating oil in New York City, where currently the burning of heavy fuel oil in buildings produces more annual black carbon, a key component of PM2.5, emissions, than all cars and trucks combined. The data utilized in this article are collected through New York City's Local Law 84 (LL84) energy disclosure mandate. The mandate requires annual energy consumption reporting for large buildings (i.e., approximately greater than 50,000 gross feet) of all use types. This analysis utilizes actual heating oil consumption data for calendar year 2012. The LL84 data set was merged with land use and geographic data at the tax lot level from the Primary Land Use Tax Lot Output (PLUTO) data set from the New York City Department of City Planning. The PLUTO data set provides building and tax lot characteristics, as well as their geographic location. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/MSP.2014.2330357 | Signal Processing Magazine, IEEE |
Keywords | DocType | Volume |
Big Data,aerosols,air pollution,data mining,energy consumption,environmental science computing,fossil fuels,signal processing,AD 2012,Big Data,New York City Department of City Planning,New York LL84 energy disclosure mandate,PLUTO data set,PM2.5, emissions,Primary Land Use Tax Lot Output,USA,Urban,annual energy consumption reporting,data science methodologies,environmental impacts,geographic data,heating oil burning,heating oil consumption data,heavy fuel oil burning,land use data,signal processing methodologies,urban air pollution | Journal | 31 |
Issue | ISSN | Citations |
5 | 1053-5888 | 6 |
PageRank | References | Authors |
0.46 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Rishee K. Jain | 1 | 7 | 3.58 |
| José M. F. Moura | 2 | 5137 | 426.14 |
| Constantine E. Kontokosta | 3 | 25 | 6.81 |