Atmospheric deposition of inorganic nitrogen is critical to the function of ecosystems and elemental cycles. During the industrial period, humans have doubled the amount of inorganic nitrogen in the ...biosphere and radically altered rates of atmospheric nitrogen deposition. Despite this rapid change, estimates of global nitrogen deposition patterns generally have low, centennial‐scale temporal resolution. Lack of information on annual‐ to decadal‐scale changes in global nitrogen deposition makes it difficult for scientists researching questions on these finer timescales to contextualize their work within the global nitrogen cycle. Here we use the GEOS‐Chem Chemical Transport Model to estimate wet and dry deposition of inorganic nitrogen globally at a spatial resolution of 2° × 2.5° for 12 individual years in the period from 1984 to 2016. During this time, we found an 8% increase in global inorganic nitrogen deposition from 86.6 to 93.6 TgN/year, a trend that comprised a balance of variable regional patterns. For example, inorganic nitrogen deposition increased in areas including East Asia and Southern Brazil, while inorganic nitrogen deposition declined in areas including Europe. Further, we found a global increase in the percentage of inorganic nitrogen deposited in chemically reduced forms from 30% to 35%, and this trend was largely driven by strong regional increases in the proportion of chemically reduced nitrogen deposited over the United States. This study provides spatially explicit estimates of inorganic nitrogen deposition over the last four decades and improves our understanding of short‐term human impacts on the global nitrogen cycle.
Key Points
We simulated global wet and dry deposition of inorganic nitrogen for 12 individual years in the period from 1984 to 2016
Global inorganic nitrogen deposition increased by 8% during the period simulated, but trends varied regionally
The proportion of inorganic nitrogen deposited in chemically reduced forms also increased during the simulated period
We describe spatial patterns in environmental injustice and inequality for residential outdoor nitrogen dioxide (NO2) concentrations in the contiguous United States. Our approach employs Census ...demographic data and a recently published high-resolution dataset of outdoor NO2 concentrations. Nationally, population-weighted mean NO2 concentrations are 4.6 ppb (38%, p<0.01) higher for nonwhites than for whites. The environmental health implications of that concentration disparity are compelling. For example, we estimate that reducing nonwhites' NO2 concentrations to levels experienced by whites would reduce Ischemic Heart Disease (IHD) mortality by ∼7,000 deaths per year, which is equivalent to 16 million people increasing their physical activity level from inactive (0 hours/week of physical activity) to sufficiently active (>2.5 hours/week of physical activity). Inequality for NO2 concentration is greater than inequality for income (Atkinson Index: 0.11 versus 0.08). Low-income nonwhite young children and elderly people are disproportionately exposed to residential outdoor NO2. Our findings establish a national context for previous work that has documented air pollution environmental injustice and inequality within individual US metropolitan areas and regions. Results given here can aid policy-makers in identifying locations with high environmental injustice and inequality. For example, states with both high injustice and high inequality (top quintile) for outdoor residential NO2 include New York, Michigan, and Wisconsin.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Managing excess nutrients remains a major obstacle to improving ecosystem service benefits of urban waters. To inform more ecologically based landscape nutrient management, we compared watershed ...inputs, outputs, and retention for nitrogen (N) and phosphorus (P) in seven subwatersheds of the Mississippi River in St. Paul, Minnesota. Lawn fertilizer and pet waste dominated N and P inputs, respectively, underscoring the importance of household actions in influencing urban watershed nutrient budgets. Watersheds retained only 22% of net P inputs versus 80% of net N inputs (watershed area-weighted averages, where net inputs equal inputs minus biomass removal) despite relatively low P inputs. In contrast to many nonurban watersheds that exhibit high P retention, these urban watersheds have high street density that enhanced transport of P-rich materials from landscapes to stormwater. High P exports in storm drainage networks and yard waste resulted in net P losses in some watersheds. Comparisons of the N/P stoichiometry of net inputs versus storm drain exports implicated denitrification or leaching to groundwater as a likely fate for retained N. Thus, these urban watersheds exported high quantities of N and P, but via contrasting pathways: P was exported primarily via stormwater runoff, contributing to surface water degradation, whereas N losses additionally contribute to groundwater pollution. Consequently, N management and P management require different strategies, with N management focusing on reducing watershed inputs and P management also focusing on reducing P movement from vegetated landscapes to streets and storm drains.
Disparities in exposure to air pollution by race-ethnicity and by socioeconomic status have been documented in the United States, but the impacts of declining transportation-related air pollutant ...emissions on disparities in exposure have not been studied in detail.
This study was designed to estimate changes over time (2000 to 2010) in disparities in exposure to outdoor concentrations of a transportation-related air pollutant, nitrogen dioxide (NO2), in the United States.
We combined annual average NO2 concentration estimates from a temporal land use regression model with Census demographic data to estimate outdoor exposures by race-ethnicity, socioeconomic characteristics (income, age, education), and by location (region, state, county, urban area) for the contiguous United States in 2000 and 2010.
Estimated annual average NO2 concentrations decreased from 2000 to 2010 for all of the race-ethnicity and socioeconomic status groups, including a decrease from 17.6 ppb to 10.7 ppb (-6.9 ppb) in nonwhite non-(white alone, non-Hispanic) populations, and 12.6 ppb to 7.8 ppb (-4.7 ppb) in white (white alone, non-Hispanic) populations. In 2000 and 2010, disparities in NO2 concentrations were larger by race-ethnicity than by income. Although the national nonwhite-white mean NO2 concentration disparity decreased from a difference of 5.0 ppb in 2000 to 2.9 ppb in 2010, estimated mean NO2 concentrations remained 37% higher for nonwhites than whites in 2010 (40% higher in 2000), and nonwhites were 2.5 times more likely than whites to live in a block group with an average NO2 concentration above the WHO annual guideline in 2010 (3.0 times more likely in 2000).
Findings suggest that absolute NO2 exposure disparities by race-ethnicity decreased from 2000 to 2010, but relative NO2 exposure disparities persisted, with higher NO2 concentrations for nonwhites than whites in 2010. https://doi.org/10.1289/EHP959.
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CEKLJ, DOBA, IZUM, KILJ, NUK, OILJ, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK, VSZLJ
Land-use regression (LUR) is widely used for estimating within-urban variability in air pollution. While LUR has recently been extended to national and continental scales, these models are typically ...for long-term averages. Here we present NO2 surfaces for the continental United States with excellent spatial resolution (∼100 m) and monthly average concentrations for one decade. We investigate multiple potential data sources (e.g., satellite column and surface estimates, high- and standard-resolution satellite data, and a mechanistic model WRF-Chem), approaches to model building (e.g., one model for the whole country versus having separate models for urban and rural areas, monthly LURs versus temporal scaling of a spatial LUR), and spatial interpolation methods for temporal scaling factors (e.g., kriging versus inverse distance weighted). Our core approach uses NO2 measurements from U.S. EPA monitors (2000–2010) to build a spatial LUR and to calculate spatially varying temporal scaling factors. The model captures 82% of the spatial and 76% of the temporal variability (population-weighted average) of monthly mean NO2 concentrations from U.S. EPA monitors with low average bias (21%) and error (2.4 ppb). Model performance in absolute terms is similar near versus far from monitors, and in urban, suburban, and rural locations (mean absolute error 2–3 ppb); since low-density locations generally experience lower concentrations, model performance in relative terms is better near monitors than far from monitors (mean bias 3% versus 40%) and is better for urban and suburban locations (1–6%) than for rural locations (78%, reflecting the relatively clean conditions in many rural areas). During 2000–2010, population-weighted mean NO2 exposure decreased 42% (1.0 ppb ∼5.2% per year), from 23.2 ppb (year 2000) to 13.5 ppb (year 2010). We apply our approach to all U.S. Census blocks in the contiguous United States to provide 132 months of publicly available, high-resolution NO2 concentration estimates.
Nitrous oxide (N₂O) has a global warming potential that is 300 times that of carbon dioxide on a 100-y timescale, and is of major importance for stratospheric ozone depletion. The climate sensitivity ...of N₂O emissions is poorly known, which makes it difficult to project how changing fertilizer use and climate will impact radiative forcing and the ozone layer. Analysis of 6 y of hourly N₂O mixing ratios from a very tall tower within the US Corn Belt—one of the most intensive agricultural regions of the world—combined with inverse modeling, shows large interannual variability in N₂O emissions (316 Gg N₂O-N·y−1 to 585 Gg N₂O-N·y−1). This implies that the regional emission factor is highly sensitive to climate. In the warmest year and spring (2012) of the observational period, the emission factor was 7.5%, nearly double that of previous reports. Indirect emissions associated with runoff and leaching dominated the interannual variability of total emissions. Under current trends in climate and anthropogenic N use, we project a strong positive feedback to warmer and wetter conditions and unabated growth of regional N₂O emissions that will exceed 600 Gg N₂O-N·y−1, on average, by 2050. This increasing emission trend in the US Corn Belt may represent a harbinger of intensifying N₂O emissions from other agricultural regions. Such feedbacks will pose a major challenge to the Paris Agreement, which requires large N₂O emission mitigation efforts to achieve its goals.
The widespread and rapid social and economic changes from Covid-19 response might be expected to dramatically improve air quality. However, national monitoring data from the US Environmental ...Protection Agency for criteria pollutants (PM2.5, ozone, NO2, CO, PM10) provide inconsistent support for that expectation. Specifically, during stay-at-home orders, average PM2.5 levels were slightly higher (~10% of its multi-year interquartile range IQR) than expected; average ozone, NO2, CO, and PM10 levels were slightly lower (~30%, ~20%, ~27%, and ~1% of their IQR, respectively) than expected. The timing of peak anomaly, relative to the stay-at-home orders, varied by pollutant (ozone: 2 weeks before; NO2, CO: 3 weeks after; PM10: 2 weeks after); but, by 5–6 weeks after stay-at-home orders, the concentration anomalies appear to have ended. For PM2.5, ozone, CO, and PM10, no US state had lower-than-expected pollution levels for all weeks during stay-at-home-orders; for NO2, only Arizona had lower-than-expected levels for all weeks during stay-at-home orders. Our findings show that the enormous changes from the Covid-19 response have not lowered PM2.5 levels across the US beyond their normal range of variability; for ozone, NO2, CO, and PM10 concentrations were lowered but the reduction was modest and transient.
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•Impacts of stay-at-home orders on air pollution were evaluated using EPA monitoring data from 100s of stations across the US.•During stay-at-home orders, ozone, NO2, CO and PM10 were lower and PM2.5 were higher than expected levels by 1%-30% of their IQR.•Concentration anomalies ended only 5-6 weeks after stay-at-home orders were issued.•Ozone, NO2, and CO concentrations returned to expected levels and PM2.5 and PM10 levels were higher than expected.•Reductions in ozone, NO2, and CO levels were modest and short-lived. PM10 levels did not change and PM2.5 levels increased.
Land use regression (LUR) models typically investigate within-urban variability in air pollution. Recent improvements in data quality and availability, including satellite-derived pollutant ...measurements, support fine-scale LUR modeling for larger areas. Here, we describe NO2 and PM10 LUR models for Western Europe (years: 2005–2007) based on >1500 EuroAirnet monitoring sites covering background, industrial, and traffic environments. Predictor variables include land use characteristics, population density, and length of major and minor roads in zones from 0.1 km to 10 km, altitude, and distance to sea. We explore models with and without satellite-based NO2 and PM2.5 as predictor variables, and we compare two available land cover data sets (global; European). Model performance (adjusted R 2) is 0.48–0.58 for NO2 and 0.22–0.50 for PM10. Inclusion of satellite data improved model performance (adjusted R 2) by, on average, 0.05 for NO2 and 0.11 for PM10. Models were applied on a 100 m grid across Western Europe; to support future research, these data sets are publicly available.
Each year, millions of premature deaths worldwide are caused by exposure to outdoor air pollution, especially fine particulate matter (PM2.5). Designing policies to reduce these deaths relies on air ...quality modeling for estimating changes in PM2.5 concentrations from many scenarios at high spatial resolution. However, air quality modeling typically has substantial requirements for computation and expertise, which limits policy design, especially in countries where most PM2.5-related deaths occur. Lower requirement reduced-complexity models exist but are generally unavailable worldwide. Here, we adapt InMAP, a reduced-complexity model originally developed for the United States, to simulate annual-average primary and secondary PM2.5 concentrations across a global-through-urban spatial domain: "Global InMAP". Global InMAP uses a variable resolution grid, with horizontal grid cell widths ranging from 500 km in remote locations to 4km in urban locations. We evaluate Global InMAP performance against both measurements and a state-of-the-science chemical transport model, GEOS-Chem. Against measurements, InMAP predicts total PM2.5 concentrations with a normalized mean error of 62%, compared to 41% for GEOS-Chem. For the emission scenarios considered, Global InMAP reproduced GEOS-Chem pollutant concentrations with a normalized mean bias of 59%-121%, which is sufficient for initial policy assessment and scoping. Global InMAP can be run on a desktop computer; simulations here took 2.6-8.4 hours. This work presents a global, open-source, reduced-complexity air quality model to facilitate policy assessment worldwide, providing a screening tool for reducing air pollution-related deaths where they occur most.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Land-use regression models (LUR) estimate outdoor air pollution at high spatial resolution. Previous LURs have generally focused on individual cities. Here, we present an LUR for year-2006 ...ground-level NO2 concentrations throughout the contiguous United States. Our approach employs ground- and satellite-based NO2 measurements, and geographic characteristics such as population density, land-use (based on satellite data), and distance to major and minor roads. The results provide reliable estimates of ambient NO2 air pollution as measured by the U.S. EPA (R 2 = 0.78; bias = 22%) at a spatial resolution (∼30 m) that is capable of capturing within-urban and near-roadway gradients in NO2. We explore several aspects of temporal (time-of-day; day-of-week; season) and spatial (urban versus rural; U.S. region) variability in the model. Results are robust to spatial autocorrelation, to selection of an alternative input data set, and to minor perturbations in input data (using 90% of the data to predict the remaining 10%). The modeled population-weighted (unweighted) mean outdoor concentration in the United States is 10.7 (4.8) ppb. Our approach could be implemented in other areas of the world given sufficient road network and pollutant monitoring data. To facilitate future use and evaluation of the results, concentration estimates for the ∼8 million U.S. Census blocks in the contiguous United States are publicly available via the Supporting Information.
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