Evaluating the Impact of the Clean Heat Program on Air Pollution Levels in New York City

Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, New York, USA Department of Environmental Medicine and Public Health, Icahn School of Medicine at Mount Sinai, New York, New York, USA Department of Epidemiology and Biostatistics, Drexel University Dornsife School of Public Health, Philadelphia, Pennsylvania, USA Urban Health Collaborative, Drexel University Dornsife School of Public Health, Philadelphia, Pennsylvania, USA Department of Environmental and Occupational Health, Drexel University Dornsife School of Public Health, Philadelphia, Pennsylvania, USA


Introduction
Residual heating oil is a class of heavy oil that remains after the lighter components are distilled away from crude oil in the refining process (EIA 2020) and has been linked to adverse health outcomes (Bell et al. 2009). In New York City (NYC), residual heating oil has been identified as a major source of multiple air pollutants, including fine particulate matter [PM ≤2:5 lm in aerodynamic diameter (PM 2:5 )] (Clougherty et al. 2010;Kheirbek et al. 2014), sulfur dioxide (SO 2 ), nitrogen oxides (NO x ) (U.S. EPA 1998), and black carbon (Cornell et al. 2012). Prior to policy implementation, three types of heating oil were used in NYC: heating oil #4, #6, and ultra-low sulfur oil #2. Both #6 and #4 are referred to as residual heating oils, and oil #2, which is the lightest of the three, has been considered a cleaner alternative (Kheirbek et al. 2014). In 2012, NYC established the Clean Heat Program (CHP) to eliminate the use of residual heating oil and move toward cleaner energy forms (Hernández 2016). Here, we have evaluated the CHP outcomes, quantified the CHPattributable air pollution reductions between 2012 and 2016, and assessed if and how these reductions vary by neighborhood socioeconomic status (SES). We aim to contribute to the knowledge of CHP effects since its implementation, assess relevant equity issues, and inform future policy improvements.

Methods
We conducted analyses at the census-tract level based on the 2010 U.S. Census (N = 2,151 tracts). Air pollution data were obtained from the New York City Air Community Survey (NYCCAS), which is a large urban air monitoring program that measures levels of numerous air pollutants across NYC. NYCCAS sampling is conducted through various monitoring units placed throughout the city; these data are subsequently included in a land-use regression model to estimate air pollution levels across the city, including locations where no measurements were directly taken (New York City Department of Health 2018). As our pollutants of interest, we selected winter average SO 2 and annual average PM 2:5 and NO 2 because these pollutants are sensitive to changes to heating oil combustion. Because building fuel conversion began in 2012, we selected 2011 and 2016 to estimate the pre-vs. postpolicy difference in pollutant concentrations. Fuel (heating oils #2, #4, and #6; natural gas; and diesel #2) conversion was quantified by the change of the number of buildings that used a certain fuel type in each census tract. Data were obtained from a) Spot the Soot from the NYC CHP and b) Benchmark Data provided by the NYC Mayor's Office of Long-Term Planning and Sustainability. We aggregated individual building records by census tract for each fuel type, then calculated the difference in the buildings using each fuel type by census tract between 2012 and 2016. We also considered vehicle miles traveled by buses, cars, and heavy-and medium-duty trucks (from the NYC Department of Transportation) as separate covariates, the average year that the buildings were built in the tract (from the NYC Department of City Planning Property Land Use Tax Lot Output), and median household income (from the U.S. Census Bureau's American Community Survey) as an SES surrogate to account for potential confounding by other policies with similar spatial patterns as CHP.
We used linear regression models and Lagrange multiplier tests to assess spatial autocorrelation and select the appropriate spatial autoregressive model. We used spatial lag models at the census-tract level to investigate the association between fuel conversion and changes in SO 2 , PM 2:5 , and NO 2 concentrations while adjusting for covariates. As a sensitivity analysis, we reran models without including the year that the buildings were built or median household income, repeated analyses restricted to those tracts that had at least one building burning fuel #6 in 2012, and additionally adjusted for the change in median household income over the study period.
To examine how SES modified the relationship between fuel conversion and air pollution, we included interaction terms between median household income (quartiles) and fuel conversion away from heating oil #6. All statistical analyses were performed using the R (version 3.5.1; R Development Core Team).
We observed that the heating oil #6 ban was associated with reductions in air pollution. Conversion away from heating oil #2 was associated with a slight reduction in PM 2:5 levels but not with any other pollutants. We observed decreases in SO 2 levels associated with heating oil #4, which comprises a mix of oils #2 and #6 combustion and emits 70% of the soot of oil #6 combustion (Urban Green Council 2017). Instead of converting to cleaner fuels, some buildings that burned fuel oil #6 kept their boilers and only switched to fuel oil #4. Based on our results, this intermediate transition step is also partially responsible for reducing air pollution, likely in part due to the architecture of the CHP to also reduce allowable sulfur content for fuel oil #4 (Carrión et al. 2018).
Our study has taken advantage of multiple data sources and provided a framework to evaluate the CHP impact since the time of implementation. By rigorous model diagnostics and selection, we identified and controlled for spatial autocorrelation in the data and adequately accounted for spatial dependence. Our study is limited by the quality of the Benchmark data set, which contained incomplete information. However, we also conducted analyses using information from the Spot the Soot data set, which provides a much more comprehensive coverage of building records for burning and converting from oils #6 and #4. Furthermore, it is particularly promising to see that, regardless of the data set used for analyses, our results are consistent, both in the main and sensitivity analyses. We also acknowledge that although we attempted to account for the influence of factors other than this policy intervention, there may be additional confounders at the census-tract level, and such variables may be partly responsible for the air pollution reductions observed in our analysis.
The CHP has achieved overall success, and it is particularly encouraging to see that the policy was effective for both low-and high-income neighborhoods. However, the heating oil conversion policies were noted to "be designed to reduce emission from a specific sector, not to target sensitive populations" (Kheirbek et al. 2014), and, in fact, low-income communities encountered more barriers in the process of transition, such as lack of knowledge, financial hardship, and uncertainty of the clean fuel market (Carrión et al. 2018). Given the well-established associations of SO 2 , PM 2:5 , and NO 2 with numerous adverse health outcomes, the reductions in these air pollutants are likely to result in numerous potential health benefits and improve population health outcomes in NYC.