Inequity in consumption of goods and services adds to racial–ethnic disparities in air pollution exposure

To relate final economic demand for commodities to economic activity or production in individual industries, we use the following US Bureau of Economic Analysis (BEA) Input–Output Accounts Data (https://www.bea.gov/industry/io_annual.htm):

where $d_f$ and $d_{f,i}$ are vectors with one entry for each of 389 commodity sectors, and $R_t$, $R_{t,d}$, and $R_{t,i}$ are matrices with one row for each of 389 industry sectors and one column for each of 389 commodity sectors.

We calculate economic production, $\rho ,$ caused by final demand as in Eq. 2:

where $R$ is one of $R_t$, $R_{t,d}$, or $R_{t,i}$ depending on whether total, domestic, or international economic production is desired. For imports, $d_f$ is replaced with $d_{fi}$. $\rho$ is a vector with one entry for each industry sector.

BEA input–output data are disaggregated to the detailed level of 389 industries and 389 commodities for year 2007, and to the summary level of 71 industries and 73 commodities for years 1997–2015. To perform calculations for years other than 2007, we scale the detailed 2007 data as in Eq. 3:

where $v_{d,i,c,y}$ is a value at the detailed level of aggregation for industry i and commodity c for the year of interest, $v_{d,i,c,2007}$ is the corresponding value at the detailed level of aggregation for year 2007, and $v_{s,i,c,y}$ and $v_{s,i,c,2007}$ are values for the corresponding summary level of aggregation for the year of interest and 2007, respectively.

Some negative values for final demand exist in the BEA input–output data tables. These typically relate to divestments or reductions in amounts of stocks. Because our objective is to use economic relationships to model air pollution emissions and impacts, and divestments or stock reductions do not cause negative emissions in the same way that investments and increases in stocks can be said to cause positive emissions, we set all negative final demand values to zero.

BEA input–output data report final demand from personal expenditures, but the data do not disaggregate consumption by racial or ethnic groups. To calculate demographic-specific consumption, we match categories in the US Bureau of Labor Statistics Consumer Expenditure Survey (CES) (https://www.bls.gov/cex/) to the BEA input–output sectors, then use the demographic information in the CES data to allocate BEA personal expenditures among demographic groups. The CES data report expenditures separately for the following: Hispanics or Latinos; Not Hispanic or Latino: whites and all other races; and Not Hispanic or Latino: blacks or African-Americans.

As of this writing, CES data are available for the years 2003–2015. EIEIO does not account for geographic variation in consumption amounts or in the proportions of goods and services consumed.

In addition to personal consumption (causing 46,000 premature deaths from PM_2.5), we also attribute BEA private expenditure final demand categories to individual end users and allocate the expenditures among demographic groups. We do this by directly adding final demand for “Residential private fixed investment” (16,000 premature deaths from PM_2.5) to personal consumption, as individuals are the ultimate end users of residential buildings. The remaining private expenditure categories include expenditures on nonresidential structures (9,400 deaths), nonresidential equipment (9,400 deaths), and intellectual property (500 deaths), as well as changes in inventory (1,700 deaths). Because consumption activities provide the revenue streams that organizations use to make capital investments and to generate inventory, albeit with time lags that we do not account for here, we consider these expenditures—and the resulting air pollution—to be caused by personal consumption. Therefore, we attribute these additional categories of demand to demographic groups proportionate to each group’s overall fraction of combined personal consumption and residential investments. Although government expenditures are also ultimately funded by individuals, the taxes that fund the government are compulsory, and relationships between individual tax contributions and government spending decisions are uncertain. Therefore, we do not attribute government expenditures to individuals, but instead track and display them as their own category.

We create spatially explicit emissions factors—in units of mass per time of emissions of primary PM_2.5 and secondary PM_2.5 precursors [oxides of nitrogen (NO_x), oxides of sulfur (SO_x), ammonia (NH₃), and volatile organic compounds (VOCs)] per dollar—for each of the 5,434 EPA source classification codes (SCCs) in the year 2014 US National Emissions Inventory (NEI), version 1 (4). Each emissions record in the NEI contains an SCC that specifies the type of source creating the emissions. First, we match each SCC to one or more of the 389 BEA industries. Some sources of emissions cannot be directly matched to BEA industries because they do not result from economic transactions. We match these sources to the BEA industry to which it is most closely related. The largest source of these nontransactional emissions is the personal use of light-duty vehicles, which we match to the “automobile manufacturing” industry based on the assumption that the individuals and entities that drive light-duty vehicles and create the resulting emissions are the same as the individuals and entities that purchase automobiles. Other nontransactional sources of emissions include leisure activities such as barbecuing and operating recreational vehicles, which we attribute to relevant residential or recreational industries. The cross-walk between SCCs and BEA industries can be found in Tessum et al. (24). We use this cross-walk to map the economic production vector, $\rho ,$ which has one element for each BEA industry, to vector $\hat{\rho }$, which has one element for each SCC equal to the sum of economic production in the BEA industry or industries that the SCC is matched to. $\hat{\rho }$ double counts economic production in some cases, but is used in a way that ensures emissions are not double counted.

Next, we process the NEI emissions (excluding emissions occurring in Canada and Mexico, which are tracked separately) using the InMAP Air Emissions Preprocessor program, also included in Tessum et al. (24). We assign each emissions record to the BEA industry or industries it belongs to and allocate the emissions to a spatial grid with cell edge lengths varying between 1 and 48 km, depending on population density and emission density. [The grid employed by InMAP is described further by Tessum et al. (5).] We allocate county-specific emissions to grid cells within counties using spatial surrogates, as described by the US EPA (4).

Finally, we calculate spatially explicit emissions factors by dividing the emissions from each SCC by the total domestic economic production in the matched industry or industries (i.e., $\hat{\rho }$) resulting from domestic and export final demand. The result is a series of emissions factor matrices, $E_p$, where $p$ is one of the pollutants in (primary PM_2.5, NO_x, SO_x, NH₃, VOC). Each emissions factor matrix has one row for each spatial grid cell, one column for each SCC, and dimensions of [mass⋅time⁻¹⋅$⁻¹].

For analysis years other than 2014, we adjust the 2014 NEI emissions according to state- and source-group-specific annual trends in emissions published by the US EPA (https://www.epa.gov/air-emissions-inventories/air-pollutant-emissions-trends-data). To quantify health impacts from non–human-related emissions sources, we also include combined biogenic and wildfire emissions from year 2005, as processed by Tessum et al. (26). Further information is in SI Appendix. We calculate spatially explicit emissions of a pollutant $p$ $\left(e_p\right)$ induced by human activity (using economic final demand as a surrogate for human activity) as shown in Eq. 4:

where $e_p$ is a vector with length equaling the number of spatial grid cells and dimensions of [mass⋅time⁻¹].

Primary PM_2.5 and secondary PM_2.5 precursors are emitted into the atmosphere where they are transported by wind, transformed by chemistry, and ultimately inhaled by humans or otherwise removed. We account for these phenomena using InMAP, version 1.2.1 (5); InMAP creates spatially explicit estimates of ambient PM_2.5 concentrations caused by the emissions estimated by EIEIO. For computational expedience, we use InMAP to create a set of source–receptor matrices, which describe linear relationships between (i) emissions in each of many source locations and (ii) concentrations in each of many receptor locations. We create the InMAP source–receptor matrix (ISRM) by running separate InMAP simulations that estimate the ground-level changes in PM_2.5 concentrations of emissions of SO_x, NO_x, VOCs, NH₃, and primary PM_2.5 in each of ∼50,000 InMAP grid cells. This is repeated three times to consider emissions plume height ranges of 0–57, 240–380, and 760–1,000 m, for a total of ∼150,000 simulations. The result can be represented as a rank-four tensor describing independent linear relationships between emissions and PM_2.5 concentrations for discrete combinations of pollutant emitted, emissions source location, emissions plume height, and concentration receptor location. By using linear interpolation to calculate impacts for sources with plume heights that do not fall within the modeled height ranges, ISRM can quickly calculate PM_2.5 concentrations resulting from arbitrary combinations of emissions sources and locations. ISRM model performance evaluation is in SI Appendix.

Ground-level concentrations of PM_2.5 depend on the height and location of emissions; therefore, instead of directly using the $E_p$ matrices to calculate concentration impacts, we create a separate series of matrices for the concentration factor, $C_p$, for each emitted pollutant, $p,$ by using the ISRM to calculate total concentrations from the NEI emissions records associated with each SCC—while accounting for individual plume heights from each emissions record—and dividing the result by the total transformed domestic economic production, $\hat{\rho }$. The resulting matrices, $C_p,$ have one row for each spatial grid cell, one column for each SCC, and units of micrograms per cubic meter per dollar. Total PM_2.5 concentration impacts $\left(c\right)$ of economic final demand are calculated by summing impacts from each emitted pollutant as in Eq. 5:

where $c$ is a vector with length equaling the number of spatial grid cells and units of micrograms per cubic meter.

Air pollution-related health impacts from economic final demand are a function of population counts, underlying incidence rates, and concentration–response relationships, in addition to the PM_2.5 concentrations themselves.