Atmospheric reduced nitrogen: Sources, transformations, effects, and management

Human activities have increased atmospheric emissions and deposition of oxidized and reduced forms of nitrogen, but emission control programs have largely focused on oxidized nitrogen. As a result, in many regions of the world emissions of oxidized nitrogen are decreasing while emissions of reduced nitrogen are increasing. Emissions of reduced nitrogen largely originate from livestock waste and fertilizer application, with contributions from transportation sources in urban areas. Observations suggest a discrepancy between trends in emissions and deposition of reduced nitrogen in the U.S., likely due to an underestimate in emissions. In the atmosphere, ammonia reacts with oxides of sulfur and nitrogen to form fine particulate matter that impairs health and visibility and affects climate forcings. Recent reductions in emissions of sulfur and nitrogen oxides have limited partitioning with ammonia, decreasing long-range transport. Continuing research is needed to improve understanding of how shifting emissions alter formation of secondary particulates and patterns of transport and deposition of reactive nitrogen. Satellite remote sensing has potential for monitoring atmospheric concentrations and emissions of ammonia, but there remains a need to maintain and strengthen ground-based measurements and continue development of chemical transport models. Elevated nitrogen deposition has decreased plant and soil microbial biodiversity and altered the biogeochemical function of terrestrial, freshwater, and coastal ecosystems. Further study is needed on differential effects of oxidized versus reduced nitrogen and pathways and timescales of ecosystem recovery from elevated nitrogen deposition. Decreases in deposition of reduced nitrogen could alleviate exceedances of critical loads for terrestrial and freshwater indicators in many U.S. areas. The U.S. Environmental Protection Agency should consider using critical loads as a basis for setting standards to protect public welfare and ecosystems. The U.S. and other countries might look to European experience for approaches to control emissions of reduced nitrogen from agricultural and transportation sectors.Implications: In this Critical Review we synthesize research on effects, air emissions, environmental transformations, and management of reduced forms of nitrogen. Emissions of reduced nitrogen affect human health, the structure and function of ecosystems, and climatic forcings. While emissions of oxidized forms of nitrogen are regulated in the U.S., controls on reduced forms are largely absent. Decreases in emissions of sulfur and nitrogen oxides coupled with increases in ammonia are shifting the gas-particle partitioning of ammonia and decreasing long-range atmospheric transport of reduced nitrogen. Effort is needed to understand, monitor, and manage emissions of reduced nitrogen in a changing environment.

Chemical characterization and speciation of the soluble fraction of Arctic PM10

The chemical composition of the soluble fraction of atmospheric particulate matter (PM) and how these components can combine with each other to form different species affect the chemistry of the aqueous phase dispersed in the atmosphere: raindrops, clouds, fog, and ice particles. The study was focused on the analysis of the soluble fraction of Arctic PM10 samples collected at Ny-Ålesund (Svalbard Islands, Norwegian Arctic) during the year 2012. The concentration values of Na+, K+, NH4+, Ca2+, Mg2+, Mn2+, Cu2+, Zn2+, Fe3+, Al3+, Cl-, NO2-, NO3-, SO42-, PO43-, formate, acetate, malonate, and oxalate in the water-soluble fraction of PM10 were determined by atomic spectroscopy and ion chromatography. Speciation models were applied to define the major species that would occur in aqueous solution as a function of pH (2-10). The model highlights that (i) the main cations such as Na+, K+, Mg2+, and Ca2+ occur in the form of aquoions in the whole investigated pH range; (ii) Cu2+, Zn2+, and, in particular, Fe3+ and Al3+ are mostly present in their hydrolytic forms; and (iii) Al3+, Fe3+, and Cu2+ form solid hydrolytic species that precipitate at pH values slightly higher than neutrality. These latter metals show interesting interactions with oxalate and sulfate ions, too. The speciation models were also calculated considering the seasonal variability of the concentration of the components and at higher concentration levels than those found in water PM extracts, to better simulate concentrations actually found in the atmospheric aqueous phase. The results highlight the role of oxalate as the main organic ligand in solution.

Effects of anthropogenic precursor emissions and meteorological conditions on PM2.5 concentrations over the "2+26" cities of northern China

Elucidating the characteristics and influencing mechanisms of PM_2.5 concentrations is the premise and key to the precise prevention and control of air pollution. However, the temporal and spatial heterogeneity of PM_2.5 concentrations and its driving mechanism are complex and need to be further analyzed. We analyzed the temporal and spatial variations of PM_2.5 concentrations in the "2 + 26" cities from 2015 to 2021, and quantified the influence of meteorological factors and anthropogenic emissions and their interactions on PM_2.5 concentrations based on geographic detector model. We find the inter-annual and inter-season PM_2.5 concentrations show downward trend from 2015 to 2021, and the inter-month PM_2.5 concentrations present a U-shaped distribution. The PM_2.5 concentrations in the "2 + 26" cities manifest a spatial distribution pattern of high in the south and low in the north, and high in the middle and low in the surroundings. Meteorological conditions have stronger effects on PM_2.5 concentrations than anthropogenic emissions, and planetary boundary layer height and temperature are the two main driving factors at the annual scale. On the seasonal scale, sunshine duration is the dominant factor of PM_2.5 concentrations in summer and autumn, and planetary boundary layer height is the dominant factor of PM_2.5 concentrations in winter. The effect of anthropogenic emissions on PM_2.5 concentration is higher in winter and spring than in summer and autumn, and ammonia and ozone have stronger effects on PM_2.5 concentrations than other anthropogenic emissions. Interactions between the factors significantly enhance the PM_2.5 concentrations. The interactions between planetary boundary layer height and other impacting factors play dominant roles on PM_2.5 concentrations at annual scale and in winter. Our results not only provide crucial information for further developing air quality policies of the "2 + 26" cities, but also bear out several important implications for clean air policies in China and other regions of the world.

Multi-Scale Effects of Meteorological Conditions and Anthropogenic Emissions on PM2.5 Concentrations over Major Cities of the Yellow River Basin

The mechanism behind PM2.5 pollution is complex, and its performance at multi-scales is still unclear. Based on PM2.5 monitoring data collected from 2015 to 2021, we used the GeoDetector model to assess the multi-scale effects of meteorological conditions and anthropogenic emissions, as well as their interactions with PM2.5 concentrations in major cities in the Yellow River Basin (YRB). Our study confirms that PM2.5 concentrations in the YRB from 2015 to 2021 show an inter-annual and inter-season decreasing trend and that PM2.5 concentrations varied more significantly in winter. The inter-month variation of PM2.5 concentrations shows a sinusoidal pattern from 2015 to 2021, with the highest concentrations in January and December and the lowest from June to August. The PM2.5 concentrations for major cities in the middle and downstream regions of the YRB are higher than in the upper areas, with high spatial distribution in the east and low spatial distribution in the west. Anthropogenic emissions and meteorological conditions have similar inter-annual effects, while air pressure and temperature are the two main drivers across the whole basin. At the sub-basin scale, meteorological conditions have stronger inter-annual effects on PM2.5 concentrations, of which temperature is the dominant impact factor. Wind speed has a significant effect on PM2.5 concentrations across the four seasons in the downstream region and has the strongest effect in winter. Primary PM2.5 and ammonia are the two main emission factors. Interactions between the factors significantly enhanced the PM2.5 concentrations. The interaction between ammonia and other emissions plays a dominant role at the whole and sub-basin scales in summer, while the interaction between meteorological factors plays a dominant role at the whole-basin scale in winter. Our study not only provides cases and references for the development of PM2.5 pollution prevention and control policies in YRB but can also shed light on similar regions in China as well as in other regions of the world.

Rapid sulfuric acid-dimethylamine nucleation enhanced by nitric acid in polluted regions

Recent research [Wang et al., Nature 581, 184-189 (2020)] indicates nitric acid (NA) can participate in sulfuric acid (SA)-ammonia (NH₃) nucleation in the clean and cold upper free troposphere, whereas NA exhibits no obvious effects at the boundary layer with relatively high temperatures. Herein, considering that an SA-dimethylamine (DMA) nucleation mechanism was detected in megacities [Yao et al., Science 361, 278-281 (2018)], the roles of NA in SA-DMA nucleation are investigated. Different from SA-NH₃ nucleation, we found that NA can enhance SA-DMA-based particle formation rates in the polluted atmospheric boundary layer, such as Beijing in winter, with the enhancement up to 80-fold. Moreover, we found that NA can promote the number concentrations of nucleation clusters (up to 27-fold) and contribute 76% of cluster formation pathways at 280 K. The enhancements on particle formation by NA are critical for particulate pollution in the polluted boundary layer with relatively high NA and DMA concentrations.

Effects of Meteorological Factors and Anthropogenic Precursors on PM2.5 Concentrations in Cities in China

Fine particulate matter smaller than 2.5 μm (PM2.5) in size can significantly affect human health, atmospheric visibility, climate, and ecosystems. PM2.5 has become the major air pollutant in most cities of China. However, influencing factors and their interactive effects on PM2.5 concentrations remain unclear. This study used a geographic detector method to quantify the effects of anthropogenic precursors (AP) and meteorological factors on PM2.5 concentrations in cities of China. Results showed that impacts of meteorological conditions and AP on PM2.5 have significant spatio-temporal disparities. Temperature was the main influencing factor throughout the whole year, which can explain 27% of PM2.5 concentrations. Precipitation and temperature were primary impacting factors in southern and northern China, respectively, at the annual time scale. In winter, AP had stronger impacts on PM2.5 in northern China than in other seasons. Ammonia had stronger impacts on PM2.5 than other anthropogenic precursors in winter. The interaction between all factors enhanced the formation of PM2.5 concentrations. The interaction between ammonia and temperature had strongest impacts at the national scale, explaining 46% (q = 0.46) of PM2.5 concentrations. The findings comprehensively elucidated the relative importance of driving factors in PM2.5 formation, which can provide basic foundations for understanding the meteorological and anthropogenic influences on the concentration patterns of PM2.5.

Current and Future Responses of Aerosol pH and Composition in the U.S. to Declining SO2 Emissions and Increasing NH3 Emissions

Aerosol pH can affect gas-particle partitioning of semivolatile species, secondary aerosol formation, aerosol water uptake and growth, acid deposition, and, potentially, aerosol toxicity. Despite its importance, aerosol pH projected in the near future has not been addressed explicitly while investigating the response of aerosol concentrations to emission regulations. In this study, we apply CMAQ to simulate aerosol pH in 2011 and 2050 across the continental U.S. We also assess the influence of two major emission trends, declining SO₂ emissions and rising NH₃ emissions, with a set of sensitivity simulations. Our results show that the aerosols will remain acidic with average pH typically ranging from 0.5 to 3.5 in 2050. Further reducing domestic SO₂ emissions does not significantly decrease aerosol acidity, even if SO₂ emissions were reduced to preindustrial levels because of the nonlinear response of SO₄^2- concentration to SO₂ emissions, and the semivolatile NH₃-NH₄⁺ buffering effect. Aerosol pH response to NH₃ emission increase will remain minor. Consequently, future fine particulate matter control efficiency will not be undercut by additional nitrate aerosol formation even if SO₂ emissions from industry and electricity generation are aggressively controlled, although areas will see some substitution leading to nitrate increases if NO_x emissions are not reduced.

The role of nitric acid in atmospheric new particle formation

Nitric acid, an air pollutant with strong acidity and oxidizability, can be found in considerable quantities in the gas and aerosol phase. Understanding the role of nitric acid in atmospheric new particle formation is essential to study the complicated nucleation mechanism. Using density functional theory combined with the Atmospheric Clusters Dynamic Code (ACDC), the role of nitric acid in the formation of new particles has been investigated under different atmospheric conditions (different precursor concentrations and temperatures). The results show that nitric acid can form clusters with sulfuric acid and ammonia by hydrogen bond or even proton-transfer interactions. The concentrations of clusters involving nitric acid can be comparable with those of sulfuric acid-ammonia-based clusters, considering the thermodynamic stability combined with the realistic atmospheric concentrations of precursors. Within the atmospheric concentration range, nitric acid can enhance the formation rates of sulfuric acid-ammonia clusters, especially at low temperature, low sulfuric acid concentration and high ammonia concentration. In addition, the new particle formation mechanism indicates that nitric acid can contribute to the cluster formation and the role of nitric acid in the cluster formation pathway is as a "bridge" connecting the smaller and larger clusters.

Chromatography related performance of the Monitor for AeRosols and GAses in ambient air (MARGA): laboratory and field-based evaluation

Evaluation of the semi-continuous Monitor for AeRosols and GAses in ambient air (MARGA, Metrohm Ap-plikon B.V.) was conducted with an emphasis on examination of accuracy and precision associated with processing of chromatograms. Using laboratory standards and atmospheric measurements, analytical accuracy, precision and method detection limits derived using the commercial MARGA software were compared to an alternative chromatography procedure consisting of a custom Java script to reformat raw MARGA conductivity data and Chromeleon (Thermo Scientific Dionex) software for peak integration. Our analysis revealed issues with accuracy and precision resulting from misidentification and misintegration of chromatograph peaks by the MARGA automated software as well as a systematic bias at low concentrations for anions. Reprocessing and calibration of raw MARGA data using the alternative chromatography method lowered method detection limits and re-duced variability (precision) between parallel sampler boxes. Instrument performance was further evaluated during a 1-month intensive field campaign in the fall of 2014, including analysis of diurnal patterns of gaseous and particulate water-soluble species (NH₃, SO₂, HNO₃, N H 4 + , S O 4 2 - and N O 3 - ^, gas-to-particle partitioning and particle neutralization state. At ambient concentrations below ~ 1 µg m^-3, concentrations determined using the MARGA software are biased +30 and +10 % for N O 3 - and S O 4 2 - , respectively, compared to concentrations determined using the alternative chromatography procedure. Differences between the two methods increase at lower concentrations. We demonstrate that positively biased N O 3 - and S O 4 2 - measurements result in overestimation of aerosol acidity and introduce nontrivial errors to ion balances of inorganic aerosol. Though the source of the bias is uncertain, it is not corrected by the MARGA online single-point internal LiBr standard. Our results show that calibra-tion and verification of instrument accuracy by multilevel external standards is required to adequately control analytical accuracy. During the field intensive, the MARGA was able to capture rapid compositional changes in PM_2.5 due to changes in meteorology and air mass history relative to known source regions of PM precursors, including a fine N O 3 - aerosol event associated with intrusion of Arctic air into the southeastern US.

Scale- and Region-Dependence in Landscape-PM2.5 Correlation: Implications for Urban Planning

Under rapid urbanization, many cities in China suffer from serious fine particulate matter (PM2.5) pollution. As the emission sources or adsorption sinks, land use and the corresponding landscape pattern unavoidably affect the concentration. However, the correlation varies with different regions and scales, leaving a significant gap for urban planning. This study clarifies the correlation with the aid of in situ and satellite-based spatial datasets over six urban agglomerations in China. Two coverage and four landscape indices are adopted to represent land use and landscape pattern. Specifically, the coverage indices include the area ratios of forest (F_PLAND) and built-up areas (C_PLAND). The landscape indices refer to the perimeter-area fractal dimension index (PAFRAC), interspersion and juxtaposition index (IJI), aggregation index (AI), Shannon’s diversity index (SHDI). Then, the correlation between PM2.5 concentration with the selected indices are evaluated from supporting the potential urban planning. Results show that the correlations are weak with the in situ PM2.5 concentration, which are significant with the regional value. It means that land use coverage and landscape pattern affect PM2.5 at a relatively large scale. Furthermore, regional PM2.5 concentration negatively correlate to F_PLAND and positively to C_PLAND (significance at p < 0.05), indicating that forest helps to improve air quality, while built-up areas worsen the pollution. Finally, the heterogeneous landscape presents positive correlation to the regional PM2.5 concentration in most regions, except for the urban agglomeration with highly-developed urban (i.e., the Jing-Jin-Ji and Chengdu-Chongqing urban agglomerations). It suggests that centralized urbanization would be helpful for PM2.5 pollution controlling by reducing the emission sources in most regions. Based on the results, the potential urban planning is proposed for controlling PM2.5 pollution for each urban agglomeration.

Organic aerosol spatial/temporal patterns: perspectives of measurements and model

Ambient measurements from SEARCH and model results from CMAQ-MADRID are analyzed side by side for the southeastern United States to understand the strengths and weaknesses of an air quality model in reproducing key spatial and temporal patterns related to organic aerosol (OA), with inferences regarding secondary organic aerosol (SOA). The model predicts a larger difference in OA concentrations between an urban (JST) and a rural site (YRK) than indicated by measurements. Modeled OA concentrations at JST and YRK are more strongly correlated than measurements. On average, models may understate urban OA emissions, while overstating urban SOA production; measurements indicate that SOA production takes place on the regional scale. Modeled diurnal fluctuations for OA are stronger than measured, due partially to overestimations of the temperature dependence parameters (deltaH(vap)) for SOA in the model. Urban-rural differences in the composition of SOA, inferred from the variations of estimated deltaH(vap), are not properly captured by the model, which does not represent multiple generations of SOA or varied reaction pathways as a function of chemical regimes. Model results are hampered by day-of-the-week and diurnal allocation issues related to EC and OA emissions. Top quintile (20%) afternoon OA concentrations are observed in both warm and cold seasons at the urban site. The frequency of high OA in the cold season is overstated in the model. The model predicts the warm vs cold season frequency of elevated OA episodes better at YRK than at JST, suggesting that regional emissions, chemistry, and transport are better simulated than urban processes.

Response of atmospheric particulate matter to changes in precursor emissions: a comparison of three air quality models

Three mathematical models of air quality (CMAQ, CMAQ-MADRID, and REMSAD) are applied to simulate the response of atmospheric fine particulate matter (PM2.5) concentrations to reductions in the emissions of gaseous precursors for a 10 day period of the July 1999 Southern Oxidants Study (SOS) in Nashville. The models are shown to predict similar directions of the changes in PM2.5 mass and component (sulfate, nitrate, ammonium, and organic compounds) concentrations in response to changes in emissions of sulfur dioxide (SO2), nitrogen oxides (NO(x)), and volatile organic compounds (VOC), except for the effect of SO2 reduction on nitrate and the effect of VOC reduction on PM2.5 mass. Furthermore, in many cases where the directional changes are consistent, the magnitude of the changes are significantly different among models. Examples are the effects of SO2 and NO(x) reductions on nitrate and PM2.5 mass and the effects of VOC reduction on organic compounds, sulfate and nitrate. The spatial resolution significantly influences the results in some cases. Operational model performance for a PM2.5 component appears to provide some useful indication on the reliability of the relative response factors (RRFs) for a change in emissions of a direct precursor, as well as for a change in emissions of a compound that affects this component in an indirect manner, such as via oxidant formation. However, these results need to be confirmed for other conditions and caution is still needed when applying air quality models for the design of emission control strategies. It is advisable to use more than one air quality model (or more than one configuration of a single air quality model) to span the full range of plausible scientific representations of atmospheric processes when investigating future air quality scenarios.

Source apportionment: findings from the U.S. Supersites Program

Receptor models are used to identify and quantify source contributions to particulate matter and volatile organic compounds based on measurements of many chemical components at receptor sites. These components are selected based on their consistent appearance in some source types and their absence in others. UNMIX, positive matrix factorization (PMF), and effective variance are different solutions to the chemical mass balance (CMB) receptor model equations and are implemented on available software. In their more general form, the CMB equations allow spatial, temporal, transport, and particle size profiles to be combined with chemical source profiles for improved source resolution. Although UNMIX and PMF do not use source profiles explicitly as input data, they still require measured profiles to justify their derived source factors. The U.S. Supersites Program provided advanced datasets to apply these CMB solutions in different urban areas. Still lacking are better characterization of source emissions, new methods to estimate profile changes between source and receptor, and systematic sensitivity tests of deviations from receptor model assumptions.

Ammonia emission controls as a cost-effective strategy for reducing atmospheric particulate matter in the Eastern United States

Current regulation aimed at reducing inorganic atmospheric fine particulate matter (PM2.5) is focused on reductions in sulfur dioxide (SO2) and oxides of nitrogen (NO(x) = NO + NO2); however, controls on these pollutants are likely to increase in cost and decrease in effectiveness in the future. A supplementary strategy is reduction in ammonia (NH3) emissions, yet an evaluation of controls on ammonia has been limited by uncertainties in emission levels and in the cost of control technologies. We use state of the science emission inventories, an emission-based regional air quality model, and an explicit treatment of uncertainty to estimate the cost-effectiveness and uncertainty of ammonia emission reductions on inorganic particulate matter in the Eastern United States. Since a paucity of data on agricultural operations precludes a direct calculation of the costs of ammonia control, we calculate the "ammonia savings potential", defined as the minimum cost of applying SO2 and NO(x) emission controls in order to achieve the same reduction in ambient inorganic PM2.5 concentration as obtained from a 1 ton decrease in ammonia emissions. Using 250 scenarios of NH3, SO2, and NO(x) emission reductions, we calculate the least-cost SO2 and NO(x) control scenarios that achieve the same reduction in ambient inorganic PM2.5 concentration as a decrease in ammonia emissions. We find that the lower-bound ammonia savings potential in the winter is $8,000 per ton NH3; therefore, many currently available ammonia control technologies are cost-effective compared to current controls on SO2 and NO(x) sources. Larger reductions in winter inorganic particulate matter are available at lower cost through controls on ammonia emissions.