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. 2023 Nov 30;57(49):20718–20725. doi: 10.1021/acs.est.3c06947

Data Gap: Air Quality Networks Miss Air Pollution from Concentrated Animal Feeding Operations

Alyssa M Burns †, Gabriel Chandler ‡, Kira J Dunham §, Annmarie G Carlton †,*
PMCID: PMC10720380  PMID: 38032082

Abstract

graphic file with name es3c06947_0005.jpg

In the U.S., the agricultural sector is the largest controllable source of several air pollutants, including ammonia (NH3), which is a key precursor to PM2.5 formation. Livestock waste is the dominant contributor to ammonia emissions. In contrast to most controllable air pollutants, satellite records show ammonia mixing ratios are rising. The number of confined animal feeding operations (CAFOs) that generate considerable livestock waste is also increasing. Spatial and temporal trends in USDA-reported animal numbers normalized by county area at medium and large CAFOs provide plausible explanations for patterns in satellite-derived NH3 over the contiguous U.S. (CONUS). The correlation between summertime ammonia derived from the European Space Agency’s (ESA) Infrared Atmospheric Sounding Interferometer (IASI) and CAFO animal unit density in 2017 is positive and significant (r = 0.642; p ≈ 0). The temporal changes from 2002 to 2017 in animal unit density and NH3 derived from NASA’s Atmospheric Infrared Sounder (AIRS) are spatially similar. Trends and ambient concentrations of PM2.5 mass in agricultural regions are difficult to assess relative to those of urban population centers given the sparseness of rural monitors in regulatory surface networks. Results suggest that in agricultural areas where ammonia concentrations and animal density are highest, air quality improvement lags behind the national average.

Keywords: agriculture, ammonia, livestock waste, air quality

Short abstract

This is the first quantitative positive correlation between USDA Census of Agriculture animal units and remotely sensed ammonia.

1. Introduction

The U.S. Environmental Protection Agency (EPA) reports that agriculture is the largest source of anthropogenic emissions of ammonia (NH3), nitrous oxide (N2O), and methane (CH4).1,2 Emissions of nitric oxides (NOx; NOx = NO + NO2), a variety of organic species and reduced sulfur compounds, and primary PM2.5 from agriculture are also documented.3−6 These emissions adversely affect air quality and ecological welfare, destroy stratospheric ozone, and add to the global burden of greenhouse gases.2,7−17

Domestically, U.S. agricultural ammonia emissions are estimated to cause over 12,000 premature deaths and incur societal costs of roughly $160 billion each year, primarily through the formation of fine particulate matter (PM2.5).18,19 As the most abundant basic gas in the atmosphere, ammonia readily reacts with oxidation products of sulfur dioxide (SO2) and NOx to form ammonium sulfate and ammonium nitrate, which contribute substantially to ambient PM2.5, a criteria air pollutant (CAP). A 42% reduction in PM2.5 mass concentrations is observed over the contiguous U.S. (CONUS) from 2000 to 2022.20 The national average is weighted by monitoring site locations and driven largely by trends in the Northeast (73 sites, −48%), Ohio Valley (59 sites, −51%), and Southeast (63 sites, −48%). Decreased PM2.5 mass concentrations can be attributed to environmental policies, such as acid rain and ozone mitigation. For example, SO2 and NO2 are CAPs regulated through the National Ambient Air Quality Standards (NAAQS) framework and performance standards at controlled sources. Measurements at approximately 500 air quality monitoring locations21,22 document sharp reductions in ambient mixing ratios of SO2 and NO2 that contribute to declines in PM2.5 mass concentrations due to reduced formation of ammonium sulfate and ammonium nitrate, respectively.23−25 In sharp contrast and despite its similar contribution to PM2.5, there is no NAAQS for ammonia, and ambient concentrations are not as routinely monitored in regulatory networks. Multiple satellite products indicate that the atmospheric burden of NH3 is increasing over the U.S., most notably in agricultural regions.26,27

Atmospheric mixing ratios of ammonia are spatially heterogeneous and generally spike downwind in close proximity of sources due to the relatively short atmospheric lifetime (<1 day).28−31 Physical loss mechanisms such as wet and dry deposition dominate over chemical loss.32 Nitrogen deposition is increasing at National Atmospheric Deposition Program (NADP) monitoring locations influenced by agriculture.15,28,29 Similarly, since 2007, biweekly passive surface measurements from the Ammonia Monitoring Network (AMoN) document the highest ammonia concentrations are generally associated with agricultural locations.33 The number of agricultural operations with large numbers of feedlot-confined animals, commonly called concentrated animal feeding operations (CAFOs), is increasing. The Government Accountability Office (GAO) estimates that the number of CAFOs increased 230% from 1982 to 2002,34 in part due to economic factors that encourage increased agricultural intensity and higher animal density with the shift in market focus to exports.35 More recently, EPA data shows the number of CAFOs increased 16% from 2011 to 2022.36

Despite the growing number of CAFOs and their contribution to environmental pollution, accurate understanding of the number, size, and exact location of animals housed in CAFOs is difficult to acquire34 because consistent, public CAFO data is sparse. For example, some agricultural enterprises, including large-scale CAFOs, are not included in the USDA’s Census of Agriculture (hereafter, “AgCensus”) and other public data sets, due to USDA’s programmatic privacy concerns.37 CAFOs that meet certain definitions in the Clean Water Act are subject to the National Pollution Discharge Elimination System (NPDES) program, which regulates the discharge of pollutants from point sources to navigable waters of the U.S.36 EPA records NPDES permit data, but not all CAFOs discharge to navigable waters. The precise locations and animal populations of all CAFOs are not fully disclosed in any public inventory. Both the USDA and EPA data sets represent lower bounds for the actual number of CAFO-associated animals. CAFOs are more common in high-poverty and majority-non-White communities,38,39 and their pollution may represent an environmental justice issue.

In this work, we investigate animal density at CAFOs in the CONUS over a 15-year period (2002–2017) in relation to satellite-detected ammonia and surface level PM2.5 mass. We examine animal density at CAFOs to the extent possible with public data sets through analyses of the USDA Agricultural Census and EPA NPDES permit filings. We assess remotely detected ammonia and the evolving chemical climatology of rural areas in the context of surface measurements of PM2.5 mass and its chemical constituents. We apply analyses according to the EPA and USDA’s regional categories of the CONUS to explore the ability of current governmental accounting systems to accurately describe ambient air quality where ammonia pollution is greatest.

2. Materials and Methods

For all analyses, we investigate trends in the U.S. using various public, government-hosted data sets, as identified below. R statistical software is used for all data processing.40

2.1. County-Level Animal Density Maps and CAFO Trends

Under the Clean Water Act (CWA), one animal unit (AU) is equivalent to 1000 lb of live weight, which is roughly equivalent to 1 individual cow or cattle, 2.5 swine, and/or 30–125 egg-laying or broiler chickens depending on the manure management system.41 We employ the USDA Census of Agriculture reports, available at the county level every five years from 2002 to 2017.37 We focus on medium- and large-scale operations that meet the following descriptions: 500+ cattle/cows, 1000+ hogs, 500,000+ broilers sold annually, or 100,000+ egg layers. To normalize emissions among species, AUs are used instead of individual head counts. In this work, we define AUs more consistent with the USDA’s Economic Research Service (ERS) definitions, which account for animal lifetimes by employing “animal unit months”. One AU is approximately equivalent to 1.14 beef cattle, 0.74 dairy cows, 9.09 hogs, 250 laying hens, or 455 broilers.42 Other animal species are also industrially farmed,43 but are not assessed here. USDA may not fully disclose animal counts at the county level due to facility privacy concerns. We use state-level animal totals to estimate the number of undisclosed CAFO animal units in the AgCensus and assign those animals to CAFO facilities located in omitted counties. Animal data from the AgCensus are linked to county-specific land areas from USDA’s ERS using the Census Service’s unique Geographic Identifier (GeoID) for each county. AU density is calculated as the number of animals in each category normalized to AU definitions and divided by the county land area. Ordinary least-squares linear regression is used to examine trends in individual counties over time. Trends are defined as the slope of the regression lines (AU km–2 year–1).

2.2. Satellite Retrievals

Two satellite products are used to examine the spatial distributions and trends in ambient NH3. We focus on summertime values (meteorological summer defined as June, July, and August), as higher temperatures favor gas-phase NH3. The European Space Agency’s (ESA) Infrared Atmospheric Sounding Interferometer (IASI) provides NH3 observations with pixel size of 12 km in diameter at nadir and was recently evaluated with aircraft measurements over the U.S.44 NH3 values are averages over the vertical extent. The National Aeronautics and Space Administration (NASA) Atmospheric Infrared Sounder (AIRS) provides observations with a spatial resolution of 13.5 km at nadir,45 and NH3 values are taken at 918 hPa.46 We employ version 3.1.0 of the level 2 IASI files to examine the spatial extent of NH3 in 2017, the latest Census of Agriculture. AIRS data are available earlier in the record for the AgCensus and we employ this product to examine the annual trends from 2002 to 2017. To evaluate trends, we examine every grid point in the AIRS data set for which data existed for a minimum of five years in the available time window of the USDA censuses. We employ Pearson correlation to link variability between AUs and remotely sensed NH3 in 2017. We use ordinary least-squares to calculate linear fits to temporal trends and report trends as the slope of the regression line (ppb year–1).

2.3. Surface Air Quality Data and Data Set Synthesis

We employ air quality concentration and emission data from EPA public repositories. Ammonia emissions estimates are derived from the National Emissions Inventory (NEI), available every 3 years.2 We focus on 2017 emissions estimates, as the methodology for NH3 emissions from livestock waste changed substantially between the 2014 and 2017 inventories.47 The NEI identifies more than 50 sectors that contribute to NH3 emissions. We consolidate these sectors into 7 categories and keep the agricultural subsectors of fertilizer application and livestock waste separate. Ambient concentrations of PM2.5 mass, chemical constituents (NH4, NO3, SO4, and OC), and precursor gases (SO2 and NO2) are retrieved from EPA’s Air Quality System (AQS) pregenerated data files for all site and monitor locations that operated continuously from 2002 to 2017.48 We use all reported values, including Federal Reference Methods (FRM) and Federal Equivalency Methods (FEM). Measurements flagged in the AQS as exceptional events are removed. Total organic carbon values are converted to organic matter (OM) using the standard multiplier of 1.8 to better compare to other species in terms of mass concentrations.49 Similar to trends in animal density and satellite NH3, a linear regression is used to examine trends at individual monitor locations. Trends are defined as the slope of the regression line divided by the median concentration of the study time period (2002–2017), multiplied by 100% (% year–1).

Air quality trends are examined for both individual monitor locations and, more broadly, in regional analyses. Regions are informed by the EPA and USDA definitions. EPA commonly uses 9 U.S. Climate regions to describe PM2.5 trends.50 USDA uses Farm Production Expenditure Regions to examine the costs incurred running a farm, and groups states with consideration of farm output and size.51,52 Any state listed as an “estimate” for the USDA regions was included in the respective region. Employing either the EPA or USDA definitions, each region has a minimum of 10 PM2.5 continuous sites reporting over the study period (2002–2017) and a minimum of three speciation sites. Monitor coverage is the most sparse for ammonium ion measurements. We have more confidence in trends of PM2.5 mass and chemical constituents for regions where monitor density is highest (Tables S1–S4).

To more closely assess trends where both AU density and ammonia mixing ratios are relatively high and rising in the Midwest, we created a “hotspot” region for the year 2017. CAFO information from AgCensus is only available for unevenly spaced county-level designations. Consequently, we associate each AU value with the corresponding county centroid. A kernel smoother is applied to this data to obtain animal density estimates over the entire CONUS. We perform an identical smoothing technique to the IASI NH3 measurements. Though higher resolutions are possible with the IASI data,53 we apply this estimate for a consistent comparison with the spatially limited animal data. A region of high AU density is identified via a regression level set estimate (i.e., the coordinates where the estimated AU density exceeds a given value). We represent this hotspot as the boundary of the level set corresponding to the 91st percentile of the AU density across the CONUS.

3. Results and Discussion

Spatial patterns in animal unit density at medium and large CAFOs are consistent with satellite-detected NH3 concentrations over the CONUS (Figure 1). Ammonia emission rates from livestock vary with the animal type, population size, and farm management. Practices that mitigate air pollution emissions at farms with lower animal densities, such as sustainable application of livestock waste to fields, are less feasible at the industrial scale. For example, feedlot ammonia emissions are much higher than those for pasture-raised animals due, in part, to greater reliance on manure storage and silage.54 Livestock waste is the predominant source of controllable ammonia in the U.S., a factor of 3 higher than all other sources combined, including fertilizer use (Figure S1), and some fraction of fertilizer is animal manure. Summertime ammonia from IASI for 2017 indicates the highest values are over the Midwest, Central Valley of California, Arizona, and locations in the Northwest, where 2017 county-level animal unit density derived from the AgCensus is also high (Figure 1a,b and Movie S1). The ammonia spatial pattern is consistent with the animal unit density of feedlot cattle and hogs in the Midwest, dairy cows in the CA Central Valley, and cattle and dairy cows in Arizona and the Northwest (Movies S2–S4). The spatial patterns for the AU density level set derived from USDA’s 2017 AgCensus and IASI-detected ammonia data for 2017 are remarkably similar (Figure 2). Across the CONUS for 2017, there is a significant positive correlation between remotely detected NH3 and area-normalized AU density (r = 0.642). Ammonia to the east of the level set for animal unit density is consistent with prevailing easterly winds over the Midwest Plains, and suggests application of wind adjustments53 at the surface and aloft could improve correlation.

Figure 1.

Figure 1

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Spatial distributions and trends of animal unit density and ammonia over the CONUS. (A) Animal units (1000 lb. of live weight) at medium- and large-sized CAFOs normalized by county area for the year 2017. (B) Average summertime 2017 NH3 mixing ratios from the ESA IASI product. (C) Change in county-level AU density at medium- and large-sized CAFOs from 2002 to 2017. (D) Change in summertime NH3 mixing ratio from NASA AIRS product from 2002 to 2017.

Figure 2.

Figure 2

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Relationship between remotely detected NH3 and EPA surface PM2.5 monitor coverage within the “hotspot” region of highest animal unit density.

Rising emissions as a consequence of increasing animal consolidation are a plausible explanation for the temporal trends in rising ammonia mixing ratios. Over the past two decades, raw animal numbers rose approximately 6%, while the number of CAFOs rose more quickly, approximately 16% over the last 10 years (Figure S2). Farm consolidation and greater county-level animal density are found for all analyzed animal categories, including cattle, dairy cows, hogs, broiler chickens, and egg-laying hens from 2002 to 2017 (Movies S1–S6). AIRS ammonia data from 2002 to 2017 indicates an increasing atmospheric NH3 burden across the CONUS, with higher rates of increase in the Midwest and CA Central Valley (Figure 1d). This pattern is similar to the growth in the number of CAFOs and increasing deposition of reduced nitrogen in agricultural areas.15,17,55 Evaluation of EPA emission estimates during intensive observation periods (IOPs) and with IASI data generally find a low bias for ambient ammonia in rural Midwest regions.56−58 This is consistent with the low bias in estimates of CAFO-associated animals in publicly available Federal archives. Recent efforts by environmental interest groups (EIGs) and some States have improved CAFO accounting. However, recent application of a deep-learning algorithm to satellite data detects 15% more poultry CAFOs in North Carolina than recorded by EIGs.59

Accounting for animal numbers normalized by county area implicitly accounts for some degree of industrial-scale management practices that affect ammonia emissions. For example, CAFO management relies on increased use of silage and storage that results in higher emissions of air pollutants.60,61 Measured ammonia emission rates from anaerobic lagoons are substantial,61,62 and large dairy and swine CAFOs preferentially employ anaerobic lagoons for sanitary waste management, particularly in the Midwest Plains61 where satellites detect ammonia hotspots. In air quality simulations, Hu et al. find that summertime ammonia observations over the Midwest Plains region are up to a factor of 4 higher than model predictions.56 The livestock ammonia emissions employed in those air quality simulations are developed from EPA’s NEI.47 Evaluation of NEI agricultural emissions finds the highest predictive error for ammonia (90%) is for anaerobic waste lagoons.62 In general, ambient NH3 is most abundant over rural agricultural land that includes both cropland and CAFOs (Figure S3), and where surface air quality monitoring locations are thinly distributed (Figure 2).

Surface monitoring sites are sparsely located in rural agricultural regions, and air quality is more difficult to quantitatively assess. In and near the geographic areas with the largest increases in NH3 and highest animal unit density as identified above, trends in PM2.5 mass appear to lag behind the national average (Figure 3). Approximately one-third of all EPA air quality sites are located in rural settings, and ∼10% are in agricultural locations (Figure S4). The strongest declines in PM2.5 mass are generally observed east of the Mississippi River, particularly in urban areas and downwind of large industrial sources of the Southeast and Ohio Valley, in addition to localized monitoring sites in California (Figure 3). In areas where ammonia mixing ratios are rising and animal unit density is high, other air quality metrics are stagnant or also getting worse. Some specific Midwest surface sites document rising mixing ratios of SO2 and NO2 (Figures S5 and S6). Nonlinearities in the gas-phase oxidation of SO2 and NO2, the subsequent gas-to-particle partitioning, aerosol pH, and aerosol liquid water content complicate relationships between PM2.5 and precursor emissions.63 Within the boundaries of the animal unit level set (Figure 2), there are 23 surface sites active during the study period. Only three sites are classified as rural location settings. The available monitors are primarily located in the northeast corner of the geographic area, far away from satellite-detected ammonia hotspots, and are insufficient to properly assess air quality trends fully representative of the entire region.

Figure 3.

Figure 3

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Trends in EPA measured PM2.5. Each point represents an EPA PM2.5 monitor active over the study period (2002–2017), and is color-coded according to the quartiles of the overall trend distribution, where black points represent trends within the interquartile range, blue points represent trends above the third quartile (less decrease than average), and green points represent trends below the first quartile (greater decrease than average).

Conventional federal-level frameworks and modeling tools, such as air quality network sites, their regional definitions, and emission estimates used in assessments, are not ideal to quantify air pollution amount or chemical composition in areas where CAFO emissions are abundant and the air pollution burden is rising. For example, in national and regional assessments, the EPA documents improving surface air quality. However, those assessments have an implicit bias that largely excludes agricultural locations, even for predominantly rural areas. For example, using EPA-defined climate regions, the largest decreases in PM2.5 mass from 2002 to 2017 are due to sulfate and occur in the Ohio Valley (66 sites, −44%), Upper Midwest (48 sites, −39%), Southeast (60 sites, −39%), and Northeast (83 sites, −35%) (Figure 4a, Tables S1 and S3). Employing USDA Farm Production Expenditure regions, the largest PM2.5 mass concentration decreases (94 sites, −43%) occur in the Midwest region (Figure 4b, Tables S2 and S4) and are due to sulfate. This finding reflects many earlier findings that note the success of acid rain rules that reduce SO2 emissions and sulfate formation and is consistent with a primarily urban distribution of Eastern and Midwest monitoring sites (e.g., Detroit, Minneapolis). Such distribution hinders accurate assessment of air quality for the Midwest and other regions that are largely rural and agricultural.

Figure 4.

Figure 4

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Trends in PM2.5 mass and speciation across U.S. EPA climate regions (A) and USDA Farm production regions (B). OV refers to the Ohio Valley, UM refers to the Upper Midwest, SE refers to the Southeast, NE refers to the Northeast, W refers to the West, NW refers to the Northwest, S refers to the South, NR+P refers to the Northern Rockies and Plains, and SW refers to the Southwest. Each column represents a different EPA- or USDA-defined region, mapped in row 1. Row 2 represents average concentrations for each region in 2002, and row 3 shows the same for 2017. Purple circles display the average PM2.5 mass for each year and are sized accordingly. Bar charts show annual average concentrations of ammonium (orange), nitrate (blue), organic matter (green), and sulfate (red), measured through EPA CSN.

Air pollution policy in the U.S. is primarily focused on large point sources, mobile emissions, and sources of toxic pollutants in highly populated areas. Declines in anthropogenic emissions and improved urban air quality demonstrate the successful implementation of this policy. Yet, most of the U.S. land area is not urban and approximately half is agricultural.64 Air quality studies related to health and environmental justice typically focus on urban and suburban areas.65−67 Rural PM2.5 is toxic68 and impacts human and livestock health.69 Neglect of agricultural regions in air quality analyses may be inconsistent with national goals for environmental equity.70 The agricultural sector relies heavily on a vulnerable immigrant workforce.71 In one study, dairy workers in California’s Central Valley, who identify primarily as Hispanic/Latino, experience decreased lung function due to particulate matter exposure in relation to work shift.72 As air quality generally improves across the U.S., frontline communities in agricultural areas may be left behind, and a relative dearth of data prevents accurate assessment.

Strategies to reduce agricultural air pollution and its impacts are widely discussed and debated. Because a significant proportion of U.S. meat production is for export, mitigation through changes in trade strategies and production-based abatement (e.g., more efficient manure management and fertilizer use, increasing free-range farming practices) are proposed.6,13,73 Consumer-side actions are also predicted to have a substantial influence over agricultural ammonia. Reduction of animal product consumption, red meat especially, and utilization of more nitrogen-efficient protein sources reduce predictions of food-production-related PM2.5 and subsequent associated mortality and societal costs.13,18,73 Research suggests controls on agricultural ammonia emissions could be a cost-effective strategy to improve air quality and safeguard human health.9,13,74 For example, wintertime NH3 control strategies that focus on improved farm animal housing and manure management are predicted to be more cost-effective for reducing PM2.5 mass than existing control strategies for SO2 or NOx.9 However, quantitative assessment of the impacts of such strategies would be difficult given the poor spatiotemporal coverage in air quality networks at rural locations in addition to the lack of full transparency in animal data. There is precedent for satellite tools to correct ammonia seasonality in inventory estimates from the land sector within EPA.75 Air quality policies reliant on regulation-defined surface measurements may need to evolve to incorporate advanced tools.

Acknowledgments

The authors thank Dr. Amy Christiansen for helpful conversations regarding Midwest air quality. This research was funded, in part, by NASA grant no. 80NSSC19K0987 and NSF-AGS grant no. 1719245.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.est.3c06947.

  • Trend in county-level animal unit density across all animal types (Movie S1) (AVI)

  • Trend in county-level feedlot cattle animal unit density (Movie S2) (AVI)

  • Trend in county-level dairy cow animal unity density (Movie S3) (AVI)

  • Trend in county-level hog animal unit density (Movie S4) (AVI)

  • Trend in county-level broiler chicken animal unit density (Movie S5) (AVI)

  • Trend in county-level layer chicken animal unit density (Movie S6) (AVI)

  • Information on NH3 emissions by sector, trends in animal inventory numbers and CAFO practices, analysis of land use (cropland and CAFO spatial distribution) and EPA AQS monitor location settings, trends in NO2 and SO2 ambient concentrations (Figures S1–S6), and numeric data for Figure 4 (Tables S1–S4) (PDF)

Author Present Address

∥ Environmental Integrity Project, Washington, District of Columbia 20005, United States

The authors declare no competing financial interest.

Supplementary Material

es3c06947_si_001.avi (208.7KB, avi)
es3c06947_si_002.avi (192.1KB, avi)
es3c06947_si_003.avi (165KB, avi)
es3c06947_si_004.avi (159.4KB, avi)
es3c06947_si_005.avi (130.1KB, avi)
es3c06947_si_006.avi (126KB, avi)
es3c06947_si_007.pdf (955.5KB, pdf)

References

  1. US EPA . Overview of Greenhouse Gases. https://www.epa.gov/ghgemissions/overview-greenhouse-gases (accessed Sept 23, 2022).
  2. US EPA . National Emissions Inventory (NEI) Data. https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-data (accessed April 9, 2021).
  3. Almaraz M.; Bai E.; Wang C.; Trousdell J.; Conley S.; Faloona I.; Houlton B. Z. Agriculture Is a Major Source of NOx Pollution in California. Sci. Adv. 2018, 4 (1), eaao3477 10.1126/sciadv.aao3477. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Aneja V. P.; Schlesinger W. H.; Erisman J. W. Effects of Agriculture upon the Air Quality and Climate: Research, Policy, and Regulations. Environ. Sci. Technol. 2009, 43 (12), 4234–4240. 10.1021/es8024403. [DOI] [PubMed] [Google Scholar]
  5. Hafner S. D.; Howard C.; Muck R. E.; Franco R. B.; Montes F.; Green P. G.; Mitloehner F.; Trabue S. L.; Rotz C. A. Emission of Volatile Organic Compounds from Silage: Compounds, Sources, and Implications. Atmos. Environ. 2013, 77, 827–839. 10.1016/j.atmosenv.2013.04.076. [DOI] [Google Scholar]
  6. Chamanara S.; Goldstein B.; Newell J. P. Where’s the Beef? Costco’s Meat Supply Chain and Environmental Justice in California. J. Cleaner Prod. 2021, 278, 123744 10.1016/j.jclepro.2020.123744. [DOI] [Google Scholar]
  7. EIA . Greenhouse Gas Emissions - Nitrous Oxide Emissions. https://www.eia.gov/environment/emissions/ghg_report/ghg_nitrous.php (accessed May 2, 2021).
  8. Portmann R. W.; Daniel J. S.; Ravishankara A. R. Stratospheric Ozone Depletion Due to Nitrous Oxide: Influences of Other Gases. Philos. Trans. R. Soc., B 2012, 367 (1593), 1256–1264. 10.1098/rstb.2011.0377. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Pinder R. W.; Adams P. J.; Pandis S. N. Ammonia Emission Controls as a Cost-Effective Strategy for Reducing Atmospheric Particulate Matter in the Eastern United States. Environ. Sci. Technol. 2007, 41 (2), 380–386. 10.1021/es060379a. [DOI] [PubMed] [Google Scholar]
  10. Bouwman A. F.; Lee D. S.; Asman W. a. H.; Dentener F. J.; Hoek K. W. V. D.; Olivier J. G. J. A Global High-Resolution Emission Inventory for Ammonia. Global Biogeochem. Cycles 1997, 11 (4), 561–587. 10.1029/97GB02266. [DOI] [Google Scholar]
  11. Meng W.; Zhong Q.; Yun X.; Zhu X.; Huang T.; Shen H.; Chen Y.; Chen H.; Zhou F.; Liu J.; Wang X.; Zeng E. Y.; Tao S. Improvement of a Global High-Resolution Ammonia Emission Inventory for Combustion and Industrial Sources with New Data from the Residential and Transportation Sectors. Environ. Sci. Technol. 2017, 51 (5), 2821–2829. 10.1021/acs.est.6b03694. [DOI] [PubMed] [Google Scholar]
  12. Pozzer A.; Tsimpidi A. P.; Karydis V. A.; de Meij A.; Lelieveld J. Impact of Agricultural Emission Reductions on Fine-Particulate Matter and Public Health. Atmos. Chem. Phys. 2017, 17 (20), 12813–12826. 10.5194/acp-17-12813-2017. [DOI] [Google Scholar]
  13. Paulot F.; Jacob D. J. Hidden Cost of U.S. Agricultural Exports: Particulate Matter from Ammonia Emissions. Environ. Sci. Technol. 2014, 48 (2), 903–908. 10.1021/es4034793. [DOI] [PubMed] [Google Scholar]
  14. Bauer S. E.; Tsigaridis K.; Miller R. Significant Atmospheric Aerosol Pollution Caused by World Food Cultivation. Geophys. Res. Lett. 2016, 43 (10), 5394–5400. 10.1002/2016GL068354. [DOI] [Google Scholar]
  15. Beachley G. M.; Rogers C. M.; Lavery T. F.; Walker J. T.; Puchalski M. A.. Long-Term Trends in Reactive Nitrogen Deposition in the United States. EM Pittsburgh Pa 2019; Vol. 1http://nadp.slh.wisc.edu/committees/tdep/reports/EMissue2019/Long-term%20Trends%20in%20N%20Dep.pdf. [PMC free article] [PubMed]
  16. Behera S. N.; Sharma M.; Aneja V. P.; Balasubramanian R. Ammonia in the Atmosphere: A Review on Emission Sources, Atmospheric Chemistry and Deposition on Terrestrial Bodies. Environ. Sci. Pollut. Res. 2013, 20 (11), 8092–8131. 10.1007/s11356-013-2051-9. [DOI] [PubMed] [Google Scholar]
  17. Li Y.; Schichtel B. A.; Walker J. T.; Schwede D. B.; Chen X.; Lehmann C. M. B.; Puchalski M. A.; Gay D. A.; Collett J. L. Increasing Importance of Deposition of Reduced Nitrogen in the United States. Proc. Natl. Acad. Sci. U.S.A. 2016, 113 (21), 5874–5879. 10.1073/pnas.1525736113. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Domingo N. G. G.; Balasubramanian S.; Thakrar S. K.; Clark M. A.; Adams P. J.; Marshall J. D.; Muller N. Z.; Pandis S. N.; Polasky S.; Robinson A. L.; Tessum C. W.; Tilman D.; Tschofen P.; Hill J. D. Air Quality–Related Health Damages of Food. Proc. Natl. Acad. Sci. U.S.A. 2021, 118 (20), e2013637118 10.1073/pnas.2013637118. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Heo J.; Adams P. J.; Gao H. O. Public Health Costs of Primary PM2.5 and Inorganic PM2.5 Precursor Emissions in the United States. Environ. Sci. Technol. 2016, 50 (11), 6061–6070. 10.1021/acs.est.5b06125. [DOI] [PubMed] [Google Scholar]
  20. US EPA . Particulate Matter (PM2.5) Trends. https://www.epa.gov/air-trends/particulate-matter-pm25-trends (accessed July 5, 2023).
  21. US EPA. . Overview of Sulfur Dioxide (SO2) Air Quality in the United States. https://www.epa.gov/system/files/documents/2022-08/SO2_2021.pdf. (accessed July 3, 2023).
  22. US EPA . Overview of Nitrogen Dioxide (NO2) Air Quality in the United States https://www.epa.gov/system/files/documents/2022-08/NO2_2021.pdf (accessed July 3, 2023).
  23. Hand J. L.; Schichtel B. A.; Malm W. C.; Pitchford M. L. Particulate Sulfate Ion Concentration and SO2 Emission Trends in the United States from the Early 1990s through 2010. Atmos. Chem. Phys. 2012, 12 (21), 10353–10365. 10.5194/acp-12-10353-2012. [DOI] [Google Scholar]
  24. Blanchard C. L.; Hidy G. M.; Tanenbaum S.; Edgerton E. S.; Hartsell B. E. The Southeastern Aerosol Research and Characterization (SEARCH) Study: Temporal Trends in Gas and PM Concentrations and Composition, 1999–2010. J. Air Waste Manage. Assoc. 2013, 63 (3), 247–259. 10.1080/10962247.2012.748523. [DOI] [PubMed] [Google Scholar]
  25. Butler T. J.; Vermeylen F. M.; Rury M.; Likens G. E.; Lee B.; Bowker G. E.; McCluney L. Response of Ozone and Nitrate to Stationary Source NOx Emission Reductions in the Eastern USA. Atmos. Environ. 2011, 45 (5), 1084–1094. 10.1016/j.atmosenv.2010.11.040. [DOI] [Google Scholar]
  26. Warner J. X.; Dickerson R. R.; Wei Z.; Strow L. L.; Wang Y.; Liang Q. Increased Atmospheric Ammonia over the World’s Major Agricultural Areas Detected from Space. Geophys. Res. Lett. 2017, 44 (6), 2875–2884. 10.1002/2016GL072305. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Van Damme M.; Clarisse L.; Franco B.; Sutton M. A.; Erisman J. W.; Kruit R. W.; Zanten M. van.; Whitburn S.; Hadji-Lazaro J.; Hurtmans D.; Clerbaux C.; Coheur P.-F. Global, Regional and National Trends of Atmospheric Ammonia Derived from a Decadal (2008–2018) Satellite Record. Environ. Res. Lett. 2021, 16 (5), 055017 10.1088/1748-9326/abd5e0. [DOI] [Google Scholar]
  28. Hiranuma N.; Brooks S. D.; Thornton D. C. O.; Auvermann B. W. Atmospheric Ammonia Mixing Ratios at an Open-Air Cattle Feeding Facility. J. Air Waste Manage. Assoc. 2010, 60 (2), 210–218. 10.3155/1047-3289.60.2.210. [DOI] [PubMed] [Google Scholar]
  29. Miller D. J.; Sun K.; Tao L.; Pan D.; Zondlo M. A.; Nowak J. B.; Liu Z.; Diskin G.; Sachse G.; Beyersdorf A.; Ferrare R.; Scarino A. J. Ammonia and Methane Dairy Emission Plumes in the San Joaquin Valley of California from Individual Feedlot to Regional Scales. J. Geophys. Res.: Atmos. 2015, 120 (18), 9718–9738. 10.1002/2015JD023241. [DOI] [Google Scholar]
  30. Xu L.; Penner J. E. Global Simulations of Nitrate and Ammonium Aerosols and Their Radiative Effects. Atmos. Chem. Phys. 2012, 12 (20), 9479–9504. 10.5194/acp-12-9479-2012. [DOI] [Google Scholar]
  31. Dennis R. L.; Mathur R.; Pleim J. E.; Walker J. T. Fate of Ammonia Emissions at the Local to Regional Scale as Simulated by the Community Multiscale Air Quality Model. Atmos. Pollut. Res. 2010, 1 (4), 207–214. 10.5094/APR.2010.027. [DOI] [Google Scholar]
  32. Dentener F. J.; Crutzen P. J. A Three-Dimensional Model of the Global Ammonia Cycle. J. Atmos. Chem. 1994, 19 (4), 331–369. 10.1007/BF00694492. [DOI] [Google Scholar]
  33. Ammonia Monitoring Network (AMoN). National Atmospheric Deposition Program (NRSP-3). 2022. NADP Program Office, Wisconsin State Laboratory of Hygiene, 465 Henry Mall, Madison, WI 53706.
  34. Government Accountability Office . Concentrated Animal Feeding Operations: EPA Needs More Information and a Clearly Defined Strategy to Protect Air and Water Quality from Pollutants of Concern, 2008, Retrieved from http://www.gao.gov/new.items/d08944.pdf.
  35. Lencucha R.; Pal N. E.; Appau A.; Thow A.-M.; Drope J. Government Policy and Agricultural Production: A Scoping Review to Inform Research and Policy on Healthy Agricultural Commodities. Global Health 2020, 16 (1), 11 10.1186/s12992-020-0542-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. US EPA . National Pollutant Discharge El imination System (NPDES). https://www.epa.gov/npdes (accessed May 29, 2023).
  37. USDA . National Agricultural Statistics Service - Census of Agriculture. https://www.nass.usda.gov/AgCensus/ (accessed May 31, 2023).
  38. Son J.-Y.; Muenich R. L.; Schaffer-Smith D.; Miranda M. L.; Bell M. L. Distribution of Environmental Justice Metrics for Exposure to CAFOs in North Carolina, USA. Environ. Res. 2021, 195, 110862 10.1016/j.envres.2021.110862. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Casey J. A.; Cushing L.; Depsky N.; Morello-Frosch R. Climate Justice and California’s Methane Superemitters: Environmental Equity Assessment of Community Proximity and Exposure Intensity. Environ. Sci. Technol. 2021, 55 (21), 14746–14757. 10.1021/acs.est.1c04328. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. R Core Team . R: A Language and Environment for Statistical Computing, 2020.
  41. 40 CFR 122.23 . Concentrated animal feeding operations (applicable to State NPDES programs, see § 123.25) https://www.ecfr.gov/current/title-40/part-122/section-122.23 (accessed July 28, 2023).
  42. Gollehon N.; Caswell M.; Ribaudo M.; Kellogg R.; Lander C.; Letson D.. Confined Animal Production and Manure Nutrients..
  43. Liu L.; Xu W.; Lu X.; Zhong B.; Guo Y.; Lu X.; Zhao Y.; He W.; Wang S.; Zhang X.; Liu X.; Vitousek P. Exploring Global Changes in Agricultural Ammonia Emissions and Their Contribution to Nitrogen Deposition since 1980. Proc. Natl. Acad. Sci. U.S.A. 2022, 119 (14), e2121998119 10.1073/pnas.2121998119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Guo X.; Wang R.; Pan D.; Zondlo M. A.; Clarisse L.; Van Damme M.; Whitburn S.; Coheur P.-F.; Clerbaux C.; Franco B.; Golston L. M.; Wendt L.; Sun K.; Tao L.; Miller D.; Mikoviny T.; Müller M.; Wisthaler A.; Tevlin A. G.; Murphy J. G.; Nowak J. B.; Roscioli J. R.; Volkamer R.; Kille N.; Neuman J. A.; Eilerman S. J.; Crawford J. H.; Yacovitch T. I.; Barrick J. D.; Scarino A. J. Validation of IASI Satellite Ammonia Observations at the Pixel Scale Using In Situ Vertical Profiles. J. Geophys. Res.: Atmos. 2021, 126 (9), e2020JD033475 10.1029/2020JD033475. [DOI] [Google Scholar]
  45. Bowman K. W.TROPESS AIRS-Aqua L2 Ammonia for Forward Stream, Standard Product V1 10.5067/EYXLPVGTSWFF. [DOI]
  46. Warner J. X.; Wei Z.; Strow L. L.; Dickerson R. R.; Nowak J. B. The Global Tropospheric Ammonia Distribution as Seen in the 13-Year AIRS Measurement Record. Atmos. Chem. Phys. 2016, 16 (8), 5467–5479. 10.5194/acp-16-5467-2016. [DOI] [Google Scholar]
  47. US EPA . 2017 National Emissions Inventory (NEI) Technical Support Document (TSD). https://www.epa.gov/air-emissions-inventories/2017-national-emissions-inventory-nei-technical-support-document-tsd (accessed Oct 11, 2021).
  48. US EPA . AirData website File Download page. https://aqs.epa.gov/aqsweb/airdata/download_files.html (accessed June 28, 2023).
  49. Hand J. L.; Copeland S. A.; Day D. E.; Dilner A. M.; Indresand H.; Malm W. C.; McDade C. E.; Moore C. T.; Pitchford M. L.; Schichtel B. A.; Watson J. G.. Spatial and Seasonal Patterns and Temporal Variability of Haze and Its Constituents in the United States: Report V, 2011. http://vista.cira.colostate.edu/Improve/spatial-and-seasonal-patterns-and-temporal-variability-of-haze-and-its-constituents-in-the-united-states-report-v-june-2011/ (accessed July 10, 2023).
  50. Karl T.; Koss W. J., 1931-. Regional and National Monthly, Seasonal, and Annual Temperature Weighted by Area, 1895–1983, 1984.
  51. ARMS III Farm Production Regions Map . USDA National Agricultural Statistics Service. https://www.nass.usda.gov/Charts_and_Maps/Farm_Production_Expenditures/reg_map_c.php (accessed July 17, 2023).
  52. USDA ERS Documentation https://www.ers.usda.gov/data-products/arms-farm-financial-and-crop-production-practices/documentation/#about (accessed July 17, 2023).
  53. Clarisse L.; Van Damme M.; Clerbaux C.; Coheur P.-F. Tracking down Global NH3 Point Sources with Wind-Adjusted Superresolution. Atmos. Meas. Tech. 2019, 12 (10), 5457–5473. 10.5194/amt-12-5457-2019. [DOI] [Google Scholar]
  54. McQuilling A. M.; Adams P. J. Semi-Empirical Process-Based Models for Ammonia Emissions from Beef, Swine, and Poultry Operations in the United States. Atmos. Environ. 2015, 120, 127–136. 10.1016/j.atmosenv.2015.08.084. [DOI] [Google Scholar]
  55. Schwede D. B.; Lear G. G. A Novel Hybrid Approach for Estimating Total Deposition in the United States. Atmos. Environ. 2014, 92, 207–220. 10.1016/j.atmosenv.2014.04.008. [DOI] [Google Scholar]
  56. Hu C.; Griffis T. J.; Frie A.; Baker J. M.; Wood J. D.; Millet D. B.; Yu Z.; Yu X.; Czarnetzki A. C. A Multiyear Constraint on Ammonia Emissions and Deposition Within the US Corn Belt. Geophys. Res. Lett. 2021, 48 (6), e2020GL090865 10.1029/2020GL090865. [DOI] [Google Scholar]
  57. Chen Y.; Shen H.; Kaiser J.; Hu Y.; Capps S. L.; Zhao S.; Hakami A.; Shih J.-S.; Pavur G. K.; Turner M. D.; Henze D. K.; Resler J.; Nenes A.; Napelenok S. L.; Bash J. O.; Fahey K. M.; Carmichael G. R.; Chai T.; Clarisse L.; Coheur P.-F.; Van Damme M.; Russell A. G. High-Resolution Hybrid Inversion of IASI Ammonia Columns to Constrain US Ammonia Emissions Using the CMAQ Adjoint Model. Atmos. Chem. Phys. 2021, 21 (3), 2067–2082. 10.5194/acp-21-2067-2021. [DOI] [Google Scholar]
  58. Wang R.; Guo X.; Pan D.; Kelly J. T.; Bash J. O.; Sun K.; Paulot F.; Clarisse L.; Van Damme M.; Whitburn S.; Coheur P.-F.; Clerbaux C.; Zondlo M. A. Monthly Patterns of Ammonia Over the Contiguous United States at 2-Km Resolution. Geophys. Res. Lett. 2021, 48 (5), e2020GL090579 10.1029/2020GL090579. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Handan-Nader C.; Ho D. E. Deep Learning to Map Concentrated Animal Feeding Operations. Nat. Sustainability 2019, 2 (4), 298–306. 10.1038/s41893-019-0246-x. [DOI] [Google Scholar]
  60. Kupper T.; Häni C.; Neftel A.; Kincaid C.; Bühler M.; Amon B.; VanderZaag A. Ammonia and Greenhouse Gas Emissions from Slurry Storage - A Review. Agric., Ecosyst. Environ. 2020, 300, 106963 10.1016/j.agee.2020.106963. [DOI] [Google Scholar]
  61. ICF International . Managing Agricultural Land for Greenhouse Gas Mitigation within the United States, 2016 https://www.usda.gov/sites/default/files/documents/White_Paper_WEB71816.pdf (accessed July 3, 2023).
  62. McQuiling A.Ammonia Emissions from Livestock in the United States: From Farm-Level Models to a New National Inventory. Dissertation. Carneige Mellon University: Pittsburgh, PA, 2016. [Google Scholar]
  63. Nenes A.; Pandis S. N.; Kanakidou M.; Russell A. G.; Song S.; Vasilakos P.; Weber R. J. Aerosol Acidity and Liquid Water Content Regulate the Dry Deposition of Inorganic Reactive Nitrogen. Atmos. Chem. Phys. 2021, 21 (8), 6023–6033. 10.5194/acp-21-6023-2021. [DOI] [Google Scholar]
  64. USDA ERS . Land and Natural Resources https://www.ers.usda.gov/data-products/ag-and-food-statistics-charting-the-essentials/land-and-natural-resources/?topicId=a7a658d4-f209-4641-9172-066ca0896abe (accessed Nov 6, 2023).
  65. Sicard P.; Agathokleous E.; Anenberg S. C.; De Marco A.; Paoletti E.; Calatayud V. Trends in Urban Air Pollution over the Last Two Decades: A Global Perspective. Sci. Total Environ. 2023, 858, 160064 10.1016/j.scitotenv.2022.160064. [DOI] [PubMed] [Google Scholar]
  66. Farkas C. M.; Moeller M. D.; Felder F. A.; Henderson B. H.; Carlton A. G. High Electricity Demand in the Northeast U.S.: PJM Reliability Network and Peaking Unit Impacts on Air Quality. Environ. Sci. Technol. 2016, 50 (15), 8375–8384. 10.1021/acs.est.6b01697. [DOI] [PubMed] [Google Scholar]
  67. Southerland V. A.; Brauer M.; Mohegh A.; Hammer M. S.; Donkelaar A. van.; Martin R. V.; Apte J. S.; Anenberg S. C. Global Urban Temporal Trends in Fine Particulate Matter (PM2·5) and Attributable Health Burdens: Estimates from Global Datasets. Lancet Planet. Health 2022, 6 (2), e139–e146. 10.1016/S2542-5196(21)00350-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Wang Y.; Puthussery J. V.; Yu H.; Liu Y.; Salana S.; Verma V. Sources of Cellular Oxidative Potential of Water-Soluble Fine Ambient Particulate Matter in the Midwestern United States. J. Hazard. Mater. 2022, 425, 127777 10.1016/j.jhazmat.2021.127777. [DOI] [PubMed] [Google Scholar]
  69. Beaupied B. L.; Martinez H.; Martenies S.; McConnel C. S.; Pollack I. B.; Giardina D.; Fischer E. V.; Jathar S.; Duncan C. G.; Magzamen S. Cows as Canaries: The Effects of Ambient Air Pollution Exposure on Milk Production and Somatic Cell Count in Dairy Cows. Environ. Res. 2022, 207, 112197 10.1016/j.envres.2021.112197. [DOI] [PubMed] [Google Scholar]
  70. Rural Playbook | Build.gov. The White House. https://www.whitehouse.gov/build/resources/rural/ (accessed Aug 9, 2023).
  71. USDA ERS . Farm Labor. https://www.ers.usda.gov/topics/farm-economy/farm-labor/ (accessed July 5, 2023).
  72. Mitchell D. C.; Armitage T. L.; Schenker M. B.; Bennett D. H.; Tancredi D. J.; Langer C. E.; Reynolds S. J.; Dooley G.; Mehaffy J.; Mitloehner F. M. Particulate Matter, Endotoxin, and Worker Respiratory Health on Large Californian Dairies. J. Occup. Environ. Med. 2015, 57 (1), 79–87. 10.1097/JOM.0000000000000304. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Ma R.; Li K.; Guo Y.; Zhang B.; Zhao X.; Linder S.; Guan C.; Chen G.; Gan Y.; Meng J. Mitigation Potential of Global Ammonia Emissions and Related Health Impacts in the Trade Network. Nat. Commun. 2021, 12 (1), 6308 10.1038/s41467-021-25854-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Gu B.; Zhang L.; Van Dingenen R.; Vieno M.; Van Grinsven H. J.; Zhang X.; Zhang S.; Chen Y.; Wang S.; Ren C.; Rao S.; Holland M.; Winiwarter W.; Chen D.; Xu J.; Sutton M. A. Abating Ammonia Is More Cost-Effective than Nitrogen Oxides for Mitigating PM2.5 Air Pollution. Science 2021, 374 (6568), 758–762. 10.1126/science.abf8623. [DOI] [PubMed] [Google Scholar]
  75. Gilliland A. B.; Dennis R. L.; Roselle S. J.; Pierce T. E. Seasonal NH3 Emission Estimates for the Eastern United States Based on Ammonium Wet Concentrations and an Inverse Modeling Method. J. Geophys. Res.: Atmos. 2003, 108 (D15), 4477 10.1029/2002JD003063. [DOI] [Google Scholar]

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