Next Article in Journal
Air Pollution Tolerance Index and Heavy Metals Accumulation of Tree Species for Sustainable Environmental Management in Megacity of Lahore
Previous Article in Journal
Non-Road Mobile Machinery Emissions and Regulations: A Review
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:

Ammonia Cycling and Emerging Secondary Aerosols from Arable Agriculture: A European and Irish Perspective

Environmental Sustainability and Health Institute, Technological University Dublin, Grangegorman Lower, D07 EWV4 Dublin, Ireland
College of Engineering and Built Environment, Technological University Dublin, Bolton Street, D01 K822 Dublin, Ireland
School of Chemistry, University College Cork, T12 CY82 Cork, Ireland
School of Chemical and BioPharmaceutical Sciences, Technological University Dublin, Grangegorman Lower, D07 H6K8 Dublin, Ireland
School of Chemical Sciences, Dublin City University, Glasnevin, D09 E432 Dublin, Ireland
Author to whom correspondence should be addressed.
Air 2023, 1(1), 37-54;
Submission received: 10 October 2022 / Revised: 23 November 2022 / Accepted: 24 November 2022 / Published: 6 December 2022


Ammonia (NH3) is a naturally occurring, highly reactive and soluble alkaline trace gas, originating from both natural and anthropogenic sources. It is present throughout the biosphere, yet plays a complicated role in atmospheric acid–base reactions resulting in the formation of inorganic secondary inorganic aerosols (SIAs). While the general mechanisms are recognised, factors controlling the reactions leading to SIA formation are less explored. This review summarises the current knowledge of NH3 sources, emission and deposition processes and atmospheric reactions leading to the formation of SIA. Brief summaries of NH3 and SIA long-range transport and trans-boundary pollution, a discussion of precursor species to SIAs (other than NH3), abiotic and biotic controls and state-of-the-art methods of measurement and modelling of pollutants are also included. In Ireland, NH3 concentrations remained below National and European Union limits, until 2016 when a rise in emissions was seen due to agricultural expansion. However, due to a lack of continuous monitoring, source and receptor relationships are difficult to establish, including the appointment of precursor gases and aerosols to source regions and industries. Additionally, the lack of continuous monitoring leads to over- and underestimations of precursor gases present, resulting in inaccuracies of the estimated importance of NH3 as a precursor gas for SIA. These gaps in data can hinder the accuracy and precision of forecasting models. Deposition measurements and the modelling of NH3 present another challenge. Direct source measurements are required for the parameterization of bi-directional fluxes; however, high-quality data inputs can be limited by local micrometeorological conditions, or the types of instrumentation used. Long-term measurements remain challenging for both aerosols and precursor gases over larger areas or arduous terrains.

1. Introduction

Nitrogen (N2) is a vital element of life on Earth, constituting approximately 79% of the atmosphere. It can be found in major and minor pools throughout the biosphere in various forms. However, excessive anthropogenic contributions of various nitrogen (N) compounds have made it one of the four primary pollutants resulting in significant damage to both environmental and human health [1,2]. The other three main classes are sulphur compounds, volatile organic compounds (VOCs) and heavy metals [3]. Species of N can be present as gases and aerosols (solid or liquid) in the atmosphere or alternatively as part of water vapor [2,3]. Species include oxides (nitrogen dioxide (NO2) and nitric oxide (NO) collectively known as NOx), nitric acid (HNO3), nitrate (NO3), ammonia (NH3) and ammonium (NH4+) [4,5,6,7], as well as the highly reactive nitrate radicals (NO3) Among all the forms of N present in the atmosphere, NH3 plays a key role in atmospheric reactions resulting in the formation of secondary inorganic aerosols (SIAs) [8,9].
In the atmosphere, NH3 is a key alkaline constituent, which readily reacts with acidic species present forming ammonium salts, such as ammonium sulphate ((NH4)2SO4), ammonium bisulphate (NH4HSO4), ammonium nitrate (NH4NO3) and ammonium chloride (NH4Cl), collectively known as SIAs (secondary inorganic aerosols) [10,11,12,13]. SIAs are known to possess the ability to harm both human and environmental health, generating interest from the research community in recent years, especially in the area of air quality and models based around pollutant emissions and effects [14,15,16]. SIAs can remain in the atmosphere for several weeks, causing atmospheric haze (however, due to Ireland’s wet climate, haze formation is not as severe an issue compared with countries with drier climates) [17]. Due to SIA persistence in the atmosphere, it can be transported to much greater distances than NH3 gas.
This paper reviews the current knowledge base and state-of-the-art methodologies in use for both NH3 and SIA detection and modelling. Additionally, it is proposed that in order to design a model capable of accurately predicting SIA concentrations in the atmosphere, the precursor species’ dynamics should be included in the model construction. Current models in use require a set of basic parameters to be established, similar to those set forth by the US Environmental Protection Agency in order to obtain a structured, transparent approach, regulating models for specific uses [18]. This would harmonize current models and enhance accuracy and precision of forecasts of pollution events on both local and continental scales within Europe.

2. Precursor Species’ Dynamics and SIA Formation

2.1. Source Appointment and Emission

While a concentrated EU-wide attempt is being carried out for the reduction in N emissions through various legislative measures, such as the Gothenburg Protocol and the National Emission Ceilings (NEC) regulations, there has been little progress in controlling NH3 emissions resulting in increasing air pollution arising from sources such as agriculture within the European Union [12,13]. In Ireland, the Environmental Protection Agency (EPA) currently monitors atmospheric particulate NH4+ at three sites (Carnsore, County Wexford; Oak Park, County Carlow and Malin, County Donegal) in agreement with the European Monitoring and Evaluation Program (EMEP); however, there is no continuous monitoring network in place for ambient atmospheric NH3 gas concentrations at present [19]. Currently, NH3 is one of few pollutants not covered by the CAFE Directive under ambient air quality and does not fall under the National Ambient Air Quality Network, which is managed by the EPA [20]. This presents major difficulties in mapping NH3 concentrations both on localised and national scales.
Ireland has seen limited work undertaken with regard to NH3 concentrations; however, annual average concentration of NH3 were measured during a study by Kluizenaar et al. (2000) across 40 sites in Ireland and found to be 1.45 μg/m3 in 1999 [21]. Annual averages obtained for each site ranged between 0.14 and 7.24 μg/m3. From 2013 to 2014, another study performed Doyle et al. (2017) found the annual average concentration of NH3 to be 1.72 μg/m3 for 25 study sites in Ireland from June 2013 to July 2014 [19]. The minimum detectable concentration was 0.20 μg/m3 and the maximum concentration detected during the study was 10.51 μg/m3 over the study period. Measured concentrations from these studies demonstrate a strong correlation between regions of high NH3 concentrations and NH3 hotspots. Additionally, a general increase in average concentrations is also demonstrated both in minimum and maximum concentrations observed.
Among all N species emitted to the atmosphere, NH3 emissions arise from both natural and anthropogenic sources alike. Between all anthropogenic sources, the greatest contributor to atmospheric NH3 is agriculture, accounting for more than 90% of all emissions [10]. Other sources include sewage, biomass burning, fossil fuel combustion and catalytic converters used in cars [22,23,24]. Natural emissions of NH3 include sources such as the oceans, forest fires and vegetation. Sources of NH3 can be found throughout the three major reservoirs on Earth: atmosphere, soils/groundwater, and biomass. The terrestrial N cycle consists of soil, flora and fauna pools containing low quantities of N in various forms (in comparison to atmospheric and lithospheric reservoirs) yet still exerts a substantial impact on the natural dynamics of the biogeochemical N cycle [25]. This can result in N being a limiting agent in nutrient uptake.
Generally, plants and vegetation acquire N from the soil in much greater quantity than any other element; however, most plants are only able to utilise N in two of its solid forms: NH4+ and nitrate (NO3) [26,27]. The bioavailability of these inorganic forms of N and the natural dynamics within the cycle can be broken down into six main reactions: mineralisation and immobilisation, nitrification and denitrification, microbial N fixation and volatilization [28]. These reactions can be severely affected by anthropogenic additions of bioavailable N, such as NH3, NH4+ and NO3.
NH3 losses from arable agricultural systems primarily occur through volatilization after the application of organic (manure, slurry and/or urea) and synthetic fertilizers. Other emissions from agricultural systems include emissions related to animal husbandry, such as storage of manure for example. [29,30]. Of the N applied to land, more than 40% of the loss is recorded as NH3 and under specific environmental and edaphic conditions, an average of 10–14% is reported as lost through volatilization from synthetic fertilizer application [2,31,32,33]. Presently, approximately 100 million metric tonnes of N-based fertilizer is produced per annum globally, in comparison to 1 million metric tonnes 40 years ago [2,34]. Excessive NH3 emissions from anthropogenic sources such as agriculture can lead to biodiversity loss, eutrophication, air pollution and acidification of aquatic and terrestrial environments; and unbalancing N loads throughout the cycle [35,36,37,38]. An abbreviated N cycle indicating the role of NH3 in agricultural settings is given in Figure 1.
The susceptibility of NH3 to volatilize from fertilizer is largely driven by the alkalinity of the zone surrounding the granule or droplet of fertilizer as it reacts with the soil [39]. Other factors, such as nitrification, plant uptake, immobilization and exchange in the soil can also reduce volatilization potential [38]. NH3 emissions near intensive arable agricultural sources are known as “hot-spots”; however, generally, NH3 concentrations reduce to background levels as the ammonia is dispersed on the surrounding landscape as well as emitted to the atmosphere where it undergoes atmospheric reactions and transport [40].

2.2. Atmospheric Chemistry of NH3 and SIA Formation

Agricultural systems are inclined to concentrate N, with the use of either organic or synthetic fertilizers, with subsequent emission of NH3 into the atmosphere. As NH3 enters the atmosphere, it generally moves laterally with a relatively short half-life, and can be deposited within a small radius (a few hundred meters) of the source clinging to nearby surfaces [41]. However, the residence time of NH3 is dependent on various factors, such as the conversion rate of NH3 to NH4+ and the rate of deposition or decomposition of each species [42]. A residence time of between 0.8 and 4 days for NH3 and between 5 and 19 days for NH4+ is generally accepted, after which they are deposited back to ground level [19,43].
Ambient atmospheric NH3 can undergo deposition in three major forms: NH3 gas is returned to the surface by dry deposition, it is deposited as an aerosol in submicron atmospheric water droplets forming a salt in association with other pollutants (this is not to be confused with SIA formation, where NH3 undergoes a neutralization reaction with oxides of sulphur and nitrogen), and as NH4+ in the form of wet deposition [44]. Dry deposition refers to ambient NH3 gas being directly deposited back to ground level [45]. This is owed to the translational kinetic energy of particles competing with gravitational forces. However, given the density of a gas such as NH3 it decreases with increasing altitude in the atmosphere [46]. The main driving force for dry deposition of NH3 gas from the atmosphere, therefore, is turbulent diffusion. This may be affected by near surface winds, atmospheric stability, surface roughness, density profile and spatial distribution of sources of NH3 [47].
Notably, NH3 flow is not unidirectional, but may flow in both directions (flux) between the atmosphere, vegetation and soils (both soils and vegetation may emit as well as absorb NH3 gas) [48,49,50]. As shown in Figure 2, there are compensation points in the leaf stomata and the soil which have their own concentration levels. Thus, the fluxes through these pathways are bidirectional; depositing if the air concentration exceeds the surface compensation concentration, and emitting if the surface concentration is in exceedance [51,52,53]. The surface compensation concentration in soil and leaves is dependent on the NH3 concentration in the soil pore (air space) or the stomatal cavity in leaves, being in equilibrium with aqueous NH4+ ions and hydrogen (H+) ions in solution in the soil water or the apoplast leaf tissue, respectively [52,53,54]. Sutton et al. (1992) found NH3 emission to be favoured during warm, dry conditions, and deposition to be favoured during cool, wet conditions [55]. This is due to the relationship between NH3 on leaf surfaces and the presence of water on the cuticle [2]. A study by Sutton et al. (1993) also noted similarities in patterns of deposition to melting snow and wet vegetation as those found over unfertilized vegetation with canopy resistance less than 30 sm−1 [56]. The reduced deposition velocities that occurred during some runs of this experiment were probably a consequence of the surface being frozen, although these might have resulted from an increase in either surface resistance or surface concentration.
Ambient atmospheric NH3 can also rapidly transform into NH4+ due to reactions with water present in the atmosphere. Normally, there is less NH3 present in the atmosphere compared with NH4+, except at localised hot spots, where large quantities are volatilized [57]. Wet deposition removes NH4+ from the atmosphere through two main processes, namely, nucleation scavenging and impact scavenging. Nucleation scavenging occurs when particles act as cloud condensation nuclei [58]. As water accumulates on the particle, the aerosol may increase in size until the plume (fog) droplets deposit on the Earth’s surface or fall from the air as precipitation. When the plume is combined with a cloud of water droplets, the NH4+ can be relocated into these droplets [59]. These aggregate, by various microphysical processes, to form raindrops or even snowflakes and are deposited from the atmosphere [2]. This is a more efficient deposition pathway for NH3; however, it differs from the in-cloud scavenging of NH4+ aerosols (SIAs), as measurements of NH4+ wet deposition are needed to interpret wet deposition data for NH4+. The deposition also depends on an accurate description of wet scavenging (both in-cloud and below-cloud) [41]. This occurs by physical contact or, in the case of NH3, through absorption due to its high solubility, with the much larger droplets of precipitation [58]. Impact scavenging is one of the atmosphere’s cleansing processes, and this removal process determines the chemical composition of precipitation [45]. While many studies have focused on the relationship between concentrations of gas and/or particles in the atmosphere measured at the surface and the corresponding concertation of ions in precipitation collected [60,61,62], few studies have investigated the changes which occur in gas and particle concentrations in the air during separate precipitation events. Mountains can also heighten SIA concen-trations by trapping pollution that may alternatively be advected away from a given area [63,64]. Precipitation as well as cloud water are naturally acidic [65,66,67]; thus, most of the NH3 scavenged by drops reacts with a hydrogen ion (H+) to form NH4+ [57]. Precipitation can, henceforth, be considered as a potential component of ‘acid rain’, using the term in its broadest sense [41].

2.3. Atmospheric Chemistry of PM Formation

In the atmosphere, NH3 gas can react with sulphur dioxide (SO2) and NOx to form aerosols. SO2 and NOx can also undergo oxidation in the atmosphere, forming sulfuric acid (H2SO4) and nitric acid (HNO3) which are neutralized in the atmosphere by NH3. These neutralization reactions result in ammonium sulphate ((NH4)2SO4), ammonium bisulphate ((NH4)HSO4) and ammonium nitrate (NH4NO3). These salts are commonly referred to as SIAs [66,67,68,69,70,71]. Aerosols, also known as particulate matter (PM), are generally broken down into two main groups for monitoring purposes: a coarse fraction (particle size of 2.5–10 µm,) and a fine fraction (particle sizes between 2.5 and 0.1 µm). More recently, the terms PM2.5 and PM10 have been used and denote particles less the 2.5 and 10 micron in size, respectively. In Europe, secondary PM, including SIAs and secondary organic aerosols (SOAs), contribute to an estimated 70% of the background concentrations of particulate matter in the 2.5 µm size fraction alone [72]. NH3 aerosols comprise a significant portion of SIAs present in the atmosphere, accounting for 30–50% of aerosol mass of PM2.5 and PM10 [2,40,72].
Atmospheric PM consists of inorganic and organic species such as sulfate, nitrate, chloride, water content, soil dust, elemental carbon, and organic carbon [73]. Primary sources of SIAs (emissions which do not undergo reactions in the atmosphere, but are directly emitted) include wildfires, geogenic (wind erosion) and biogenic sources [74]. Anthropogenic sources of primary SIA comprise industrial processes, combustion, electric utility (combustion sources), residential emissions (burning of coal and peat), construction (fugitive sources), and vehicular emissions [68,75,76,77].
Ambient atmospheric SIAs, such as NH3 and NH4+, can be removed from the atmosphere through wet and dry deposition [78]. The dry deposition of aerosol NH4+ is somewhat different to that of gaseous NH3 as atmospheric turbulence dominates the transport from the atmosphere to the laminar boundary layer. Mechanisms such as Brownian motion, inertial impaction, interception, phoretic forces, etc., also play key roles in the deposition of PM; however, they act differently depending on the size of PM (i.e., whether it is PM10 or PM2.5) [59,79,80]. The size of PM2.5 also ensures that re-emission into the atmosphere does not occur easily; hence, PM flux, unlike NH3 flux, is unidirectional [81]. Ambient atmospheric SIA wet deposition processes are collectively known as wet scavenging. Wet scavenging is an essential process for the maintenance of balance between sources and sinks of SIA [82]. Wet scavenging of atmospheric particulate matter (PM) occurs through two notable processes: below-cloud scavenging (washout) and in-cloud scavenging (rainout) [83].
In the process of wet deposition, particles are incorporated into hydrometeors before being brought back to the surface in aqueous form [84]. Similarly to dry deposition, both in-cloud and below-scavenging is highly dependent on the size of particulate matter, with rates of removal differing for each size fraction [2,85]. Consequently, coarse particles are deposited near source areas while fine and ultrafine fractions are transported away from sources prior to deposition [84,86,87]. Monitoring at both national and EMEP scale indicates that Ireland has a number of important transboundary pollution pathways, namely, from the United Kingdom, mainland Europe and North America, although pollution sources also arise from Africa, especially during springtime when elevated levels of Saharan dust can be detected in Ireland [88]. Atmospheric PM can also serve as cloud condensation nuclei (CCN) [89,90,91,92,93,94,95,96]. The condensation of nitric acid on aerosol particles may enhance aerosol activation to cloud droplets by providing additional soluble material to the particle surface, as well as elevat-ing the water uptake and growth of aerosol particles [97,98,99,100,101,102,103,104]. Under favourable meteorological conditions, hygroscopic water molecules are attracted to the particles present in the atmosphere, leading to a rapid increase in mass fraction [105]. The process of the hygroscopic growth process can be described by Köhler’s theory of water vapour condensation, forming liquid cloud drops based on equilibrium thermodynamics [106]. This process plays a key role in cloud physics, making atmospheric PM a vital element in understanding cloud formation, as well as the role it plays in the Earth’s climate systems.

2.4. Controlling Factors of Emission and Transport

Emissions for pollutants are controlled by various factors. Biotic factors represent all living things which affect emissions, such as the flora and fauna of environments. Abiotic factors refer to all the non-living factors which can affects emissions, for example meteorology and climate. Anthropogenic activities also affect the emission of pollutants through their interaction with controlling factors. It must be acknowledged that, while biotic and abiotic factors play major roles in emission control of SIA, the biggest control factor for the formation of secondary pollutants will be the availability of precursor gases. Therefore, it can be stipulated that anything which may control and/or affect NH3 emissions, will have an indirect effect on SIA formation.
Notably, anthropogenic activities such as agriculture have the ability to influence and even alter the N cycle between these three major pools by additional N loading through the use of synthetic fertilizers to soils and vegetation. The N cycling of these additions is affected by climate, soil properties to which the fertilizer is added, vegetation type covering the soil and the management of agricultural activities [107]. Soil texture, pH, carbon to nitrogen (C:N) ratio, soil organic matter (SOC) content and moisture content exhibit significant control on the soil’s ability to cycle various forms of N.
Climate (on both local and global scales) and pollutant emissions have a cyclic effect on one another. Global and local climate are affected by the emission of pollutants and the rate of emissions are affected by local meteorology and climate. The terrestrial N cycle has been drastically modified by global climate change as a result of increasing agricultural intensification and fossil fuels [4]. Microbial processes involved in denitrification and nitrification are affected by climate change at a local and global level, resulting in serious environmental issues, such as elevated NO3 leaching and NOx emissions [108].
Similarly, the formation and transport of SIA is also affected by local meteorology and climate change. Ambient relative humidity (RH) and temperature are key meteorological factors for the determination of the state of SIA. For example, reactions of NH3 with HNO3 at low tem-peratures will show a shift in the equilibrium of the system towards the aerosol phase. Low RH results in NH4NO3 to form as a solid particle [109,110]. As temperature increases, the air’s capacity to hold moisture also increases, resulting in RH decreasing [111]. These changes can affect SIA dynamics such as transport pathways, and formation on a localised basis [112,113]. The specifics of the sources and causes of locally high levels of PM are singular to each location. In Philadelphia, PA, Cheng et al. (1992) found that high pressure associated with maritime topical and non-polar continental air masses generated the highest total particulate matter concentration. These air masses were defined by high pressure and temperature values, high dew points, percent of clear sky and stability [46]. Usually, high-pressure systems develop after the passage of a cyclonic system [114]. Sporadically, low-pressure systems can develop as opposed to high-pressure systems with the passage of a storm, resulting in high PM levels if the winds associated with the storm stir up dust and/or other particulates [114,115,116]. Lower PM concentrations are generally correlated with polar and moderate air masses. Depending on weather conditions (precipitation, wind direction, wind speed, etc.), these air masses occur ahead of a low-pressure system [117]. These air masses are generally advected from the Atlantic Ocean in Ireland. Terrain also affects the specific weather patterns which influence SIA levels. Mountains and canyons can increase atmospheric stability, and thus increase SIA concentrations in the neighbouring valleys due to cold air drainage [118]. Augmented stability in valleys is most prevalent under synoptic high-pressure conditions [119].

3. Linking the Soil–Water–Atmosphere Nexus

The development, parameterization and validation of NH3 models over the years, has been based on steadily emerging data for NH3 concentrations in a broad range of ecosystems and the atmosphere and the associated flux values across all scales [120]. At sub-ecosystem scales (chamber, plot, field), this has stemmed from technological advances in NH3 measurement and analysis, both quantitatively and qualitatively.
The use of flux measurement instrumentation capable of lower detection limits than was available before, while also selectively quantifying gaseous NH3 from aerosol NH4+, enables more accurate measurements [121,122,123,124,125,126,127,128,129]. This is particularly valid on a field scale, using Bowen ratio techniques at remote background locations (i.e., where sub-parts per billion levels of NH3 are present) and for over-fertilized agricultural ecosystems, which has helped generate many exchange datasets [130,131].
The key mechanisms and controls of NH3 exchange have been determined at substrate, plant, and ecosystem level, although a substantial gap in knowledge remains regarding the complete NH3 cycle. This can partially be attributed to the lack of regulation of NH3 as a gaseous atmospheric pollutant. Compared to other atmospheric gaseous pollutants such SO2, NOx and volatile organic compounds (VOCs), no extensive control measures have been put in place for the control and mitigation of NH3 emissions [2]. Indeed, there are currently very few regulations in place, and incentive programmes to reduce emissions are highly lacking in many countries globally, including Ireland. This is all despite the contribution NH3 makes to the overall atmospheric particulate matter mass loading. In fact, Ireland has been implementing policies which are contrary to the reduction and mitigation strategies which should be in place, with schemes such as the Food Harvest 2020 [132] and Food Wise 2025 [132] which boast agricultural intensification. This has resulted in atmospheric NH3 concentrations exceeding the permitted levels from 2016 over the subsequent five years.

3.1. Direct Source Measurement: State-of-the-Art Techniques Currently in Use

Measurement techniques of atmospheric NH3 have improved over the last two decades. One major difficulty when developing measurement techniques for atmospheric NH3 arises from the simultaneous presence of NH3 gas and NH4+ in the form of PM (liquid and solid state) [123]. Additionally, variations in ambient atmospheric NH3 concentrations and the ability of NH3 gas to interact with surfaces [123,125] present further difficulties when developing techniques for the measurement of atmospheric NH3.
The most widely used techniques for NH3 measurement are denuder sampling techniques and diffusive samplers. A definition given by Doyle et al. (2013) describes diffusive samplers as devices which are capable of taking samples of gas or vapor pollutants from the atmosphere at a rate controlled by a physical process, such as diffusion through a static air layer or permeation through a membrane of the air through the sampler [19]. Diffusive sampling relies on the mass flux of substances from regions of high concentration to regions of low concentration. Denuder sampling techniques such as the Annual Denuder Method (ADM) have proven to be successful for NH3 gas sampling. Denuders work based on a laminar airstream passing through a suitably long tubular enclosure whose walls are coated in the appropriate sorbent for a given acidic or basic gas present in the atmosphere [133]. The sampler also has a capacity to differentiate between NH3 as part of SIA and NH3 gas [134,135]. Despite widespread use, both denuder techniques and diffusive sampling come with limitations such as relatively low time resolution, labour-intensive sampling procedures and post-sampling wet chemistry analysis being required, which can introduce contaminants to the samples during periods of storage and/or analysis [123]. Despite these limitations, denuders and diffusive samplers remain the most cost-efficient sampling techniques for atmospheric sampling of NH3 and SIA.
Other methods for measuring ambient atmospheric NH3 are spectroscopic techniques such as photoacoustic spectroscopy (PAS) [136,137,138], differential optical absorption spectroscopy (DOAS) [126,129], tuneable diode laser absorption spectroscopy (TD-LAS) [129,139] and chemical ionization mass spectroscopy (CIMS) [121]. These techniques rely on infrared (IR) or laser-based detection such as laser diode detectors which can single out NH3 gas. Differences in accuracy and precision of the instrumentation used for the measurement of ambient atmospheric NH3 arise from differences in inlet length, the calibration frequency of each instrument and the frequency of changes in collection vessels such as filters or diffusion tubes [122].
A comparison of diffusive samplers and denuders performed by Sutton et al. (2001) found that passive diffusion tubes were imprecise for the measurement of NH3, several more complex methods for sampling ammonia are available, including automatic batch denuders, continuous denuders and diffusion scrubbers. However, each of these are highly expensive and would be inappropriate for monthly sampling at many sites [140]. They also found the most precise method to be sampling using the denuder technique with a time resolution of two weeks and ambient concentrations of >2 µg m−3 NH3.
Another comparative study conducted by Von Bobrutzki et al. (2010) explored an array of techniques from spectroscopic to wet chemistry methods [125]. While differences were found in the concentrations measured, an overall high correlation of R2 > 0.84 was found compared with the average of all instruments used. Correlation worsens when concentrations <10 ppb NH3, due to differences in inlet length of samplers and time–response.
The Environmental Protection Agency (EPA) in Ireland currently monitors atmospheric NH4+ at three representative sites (Carnsore, County Wexford; Oak Park, County Carlow and Malin, County Donegal) in agreement with the European Monitoring and Evaluation Programme (EMEP); however, there is currently no continuous monitoring network in place for ambient atmospheric NH3 gas concentration [141].
The UK National Ammonia Monitoring Network (NAMN), Northern Ireland, has three continuous monitoring sites for NH3 gas in the atmosphere. NH3 is also a pollutant which is currently not covered by the CAFE Directive under ambient air quality [20] and does not fall under the national ambient air quality network, which is managed by the EPA under a policy-driven programme for the Convention on Long-range Transboundary Air Pollution (CLRTAP) for international co-operation to solve transboundary air pollution problems within the EMEP [19].

3.2. Modelling of NH3 and SIA

Various numerical models have been generated for the implementation of modified gradient techniques to infer the surface flux of NH3 (and chemically reactive species) from field measurements, while also accounting for gas-to-particle interconversion (GPIC) and its effects on vertical flux divergence [52,53,142,143,144,145,146,147,148]. Modelling results presented in literature showed that atmospheric reactions could theoretically change NH3 fluxes as much as 40% [128] or even cause flux reversal [142].
While most emission model studies focus on the influence of precursor species (e.g., NH3) on aerosol concentrations; a study by Zöll et al. (2016) has shown a novel approach to NH3 emission modelling, with an overall aim of creating a better understanding of geological and temporal aspects of emissions [144]. Models focusing on the prior, result in the models being too simplistic, with inaccuracies in estimating emissions, by not accounting for environmental factors affecting emissions overall. More precise model constructs can be achieved to include the evaporation process as a mechanism of action to improve performance where models with a forecasting element are the focus. This improved understanding has allowed for a more complicated model construct having higher accuracy and precision than other models of this type.
Many localised field experiments based around NH3 deposition measure concentrations decreasing as a function of distance from the source. Dry deposition processes control the transfer of pollutants from the atmosphere to the surface [46]. Studies conducted in Denmark have made critical improvements in atmospheric models of NH3, specifically in the development of a regional N deposition assessment model. The model was built by replacing static seasonal variations with dynamic applications accounting for physical processes (e.g., volatilization) and agricultural management practices such as seasonal timing of fertilization [149]. However, the data required for such a model to be constructed are insufficient in most European countries, as such inventories are poorly managed or do not exist on a nation-wide scale. The state of the art of NH3 surface–atmosphere exchange, in terms of measurement and modelling, has been investigated in a number for reviews [150,151,152]. Existing models of surface exchange are reviewed at different scales from leaf to the global level, with a focus on the development of canopy-scale models and their application at larger scales (regional). A large number of models have been generated to simulate NH3 exchange fluxes for various ecosystem components (soil, leaf, plant, plant canopy, litter) or processes (heterogenous phase chemical reactions) [52,123,153,154]. These system dynamics have been modelled either individually or at a canopy-scale soil–vegetation–atmosphere basis. Larger scale (landscape, regional or global) models are 2D or even 3D, usually including simplified versions of canopy-scale models simulating 1D surface exchange, as part of the wider context including chemistry, emissions, dispersion, and deposition [53,123,155].
Canopy-scale models incorporate individual component processes and the interactions they undergo within the soil-vegetation-atmosphere framework [123,155,156,157,158,159]. The objective of this type of modelling is to determine the net ecosystem NH3 flux from the following inputs: (i) ambient NH3 levels; (ii) meteorology, or alternatively micrometeorological factors; (iii) ecosystem characteristics such as canopy height and leaf area index (LAI) [123,159]) There are many ways in which models have been developed to address system dynamics, some more mechanistic than others.
Several experimental campaigns have been carried in order to supply data for the dry deposition velocities for different types of pollutants (e.g., SIA) and deposition surfaces [81,160,161,162,163,164,165]. However, due to the issues associated with influencing factors which play a part in deposition velocity, differences between the data lead to a difficulty in generalising this phenomenon [81]. Due to this controversy, the dry deposition process cannot be studied using a single modelling approach. Indeed, the models proposed in the literature have limited ability of representing dry deposition phenomena as a whole for several categories of pollutants such as SIAs and its deposition surfaces (plant canopy, water surfaces, etc.) as their applications are only valid for a definitive set of conditions (certain types of climates, meteorology, topography, etc.) [81,161].
Most models are based on the resistance analogy, in which the flux (Fx) between two potentials, A and B, is equal to the potential difference divided by the resistance, with the atmosphere–soil being represented as a network of potentials connected by resistances in series for different layers and in parallel for different pathways [81]. Kinetics for the chemical source and/or sink associated with the NH3–HNO3–NH4NO3 triad are described either using chemical timescales, reaction rate coefficients or as a full model of size-resolved chemistry with the addition of microphysics [166,167]. The model developed by van Oss et al. (1998) described the above-mentioned reaction’s shift towards equilibrium as a relaxation-type equation for the flux divergence. Atmospheric forecasting of pollution events is a recent development with a large research focus involving research institutes globally in model development [168]. While these types of models are still in the development stage, some of the first systems have appeared as “operational” systems. However, due to large disagreements between parameterization, these models are largely experimental and until a unified set of parameters are established on an EU-wide scale, these models will remain so.
The difficulties of unified parameters for modelling in the EU mainly arise from the differences of environmental factors between countries such as temperature effects of local meteorology, climate and geographical features which all affect emission and deposition processes. As a result, temperature effect is not taken into account in current European models according to Menut and Bessagne’s (2010) review on Chemistry Transport Models (CTMs) [168]. A study by Skjøth et al. (2013) found that this is also the case for Chemistry Climate Models (CCMs) [40]. These studies are in agreement with the proposed theory of Undine Zöll et al. (2016) of improving models by applying the dynamic processes which result in spatio-temporal variations in emissions of pollutants such as NH3 [153].
Most EU models receive data from the EMEP system, which is a gridded emission inventory. These inventories are constructed based on national emission factors integrated with gridded activity data such as number of animals on a national basis [40]. However, the data represented by the EMEP campaign are too generalised for accurate and precise model constructs to be achieved, especially for models with a forecasting element of total nitrogen load. This can currently be seen, as of the 27 air pollution prediction models in use in the EU with a temporal profile element used for the forecasting of NH3 pollution, none have sufficient accuracy or precision, but in fact most either over or under-estimate ambient atmospheric concentrations [149].
As there is currently no continuous monitoring of ambient NH3 concentrations in Ireland, most European-scale models exclude Ireland as there are insufficient data supplied for inclusion. In order for Ireland to be included in modelling campaigns, highly detailed data are required. A monitoring campaign based on the dynamics of NH3 emission and deposition processes can provide such data, as well as providing clarification of SIA dynamics and transportation.

4. Concluding Remarks

Ambient atmospheric NH3 is an important pollutant contributing significantly to SIA generation, a contribution which is often under-estimated due to the short-range transport of NH3 from hot-spots and its short half-life in the atmosphere. Studies focusing on NH3 measurement are often based on distance, meaning the distance NH3 is transported from an area of interest, especially in deposition study models. The dynamics of NH3 through the environment are poorly defined; thus, source and receptor interactions and effects are crudely understood at best. While a cause-and-effect relationship has been established between NH3 and SIAs, mitigation strategies have started to recognise that the reduction in precursor gases inevitably serves the reduction in SIAs. There is a lack of understanding of system dynamics the precise nature of how to efficiently mitigate both species of pollutants is still not clearly understood.
Accurate data on the spatial and temporal distribution of NH3 and SIA emissions are crucial input to models of atmospheric transport and deposition. This is particularly important when the resulting deposition maps are utilised to establish suitable mitigation strategies regarding ecosystem decline as a result of pollution. The accuracy and precision of prediction models is dependent on the quality of the input data; hence, there is a need for high-quality emission and deposition inventories for both species of pollutants. This would require analysis and monitoring of the dynamics of both pollutants rather than studies based around only precursor gases from which concentrations of secondary pollutants are extrapolated. However, long-term campaigns of direct measurements on which these inventories and models are based remain difficult to conduct.
All techniques of measurement are affected by the built-in bias of the design chosen whether spectroscopic or techniques based on wet chemistry methods. To these potential errors, additional errors arise from geographical features of the terrain where measurements are carried out as well as local meteorological conditions which can affect measurement. Choosing the most suitable method of measurement can minimise these errors, improving data quality by precise and accurate measurements, making it a crucial step in any research study. Therefore, all studies based around NH3 pollution and SIAs arising from NH3 should take these factors into consideration. Furthermore, studies with the aim of contributing towards a continuous monitoring system for Ireland have to consider the quality of data that the monitoring network would provide, as it would have to be sufficient to contribute not only at a national, but at a European-wide scale.

Author Contributions

Conceptualization, V.P. and D.J.O.; methodology, A.G., E.M., E.J.M., J.C. and V.B.; formal analysis, V.P. and D.J.O.; investigation, V.P. and F.N.; resources, V.P.; data curation, V.P.; writing—original draft preparation, V.P.; writing—review and editing, F.N.; visualization, V.P. and A.G.; supervision, D.J.O. and S.H.; project administration, D.J.O. and S.H.; funding acquisition, A.G. All authors have read and agreed to the published version of the manuscript.


This research was funded by EPA Research and the Department of Agriculture, Food and Marine (DAFM), grant number 2021-HE-1052.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.


  1. Morán, M.; Ferreira, J.; Martins, H.; Monteiro, A.; Borrego, C.; González, J.A. Ammonia Agriculture Emissions: From EMEP to a High Resolution Inventory. Atmos. Pollut. Res. 2016, 7, 786–798. [Google Scholar] [CrossRef] [Green Version]
  2. 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, 8092–8131. [Google Scholar] [CrossRef] [PubMed]
  3. Krupa, S.V. Effects of Atmospheric Ammonia (NH3) on Terrestrial Vegetation: A Review. Environ. Pollut. 2003, 124, 179–221. [Google Scholar] [CrossRef] [PubMed]
  4. Galloway, J.N.; Dentener, F.J.; Capone, D.G.; Boyer, E.W.; Howarth, R.W.; Seitzinger, S.P.; Asner, G.P.; Cleveland, C.C.; Green, P.A.; Holland, E.A.; et al. Nitrogen Cycles: Past, Present, and Future. Biogeochemistry 2004, 70, 153–226. [Google Scholar] [CrossRef]
  5. Fowler, D.; Coyle, M.; Skiba, U.; Sutton, M.A.; Cape, J.N.; Reis, S.; Sheppard, L.J.; Jenkins, A.; Grizzetti, B.; Galloway, J.N.; et al. The Global Nitrogen Cycle in the Twentyfirst Century. Philos. Trans. R. Soc. B Biol. Sci. 2013, 368, 20130165. [Google Scholar] [CrossRef] [PubMed]
  6. Galloway, J. The Global Nitrogen Cycle: Past, Present and Future. Sci. China. Ser. C Life Sci./Chin. Acad. Sci. 2005, 48 (Suppl. 2), 669–678. [Google Scholar] [CrossRef]
  7. Galloway, J.N.; Townsend, A.R.; Erisman, J.W.; Bekunda, M.; Cai, Z.; Freney, J.R.; Martinelli, L.A.; Seitzinger, S.P.; Sutton, M.A. Transformation of the Nitrogen Cycle. Science 2008, 320, 889–892. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Gong, L.; Lewicki, R.; Griffin, R.J.; Tittel, F.K.; Lonsdale, C.R.; Stevens, R.G.; Pierce, J.R.; Malloy, Q.G.J.; Travis, S.A.; Bobmanuel, L.M.; et al. Role of Atmospheric Ammonia in Particulate Matter Formation in Houston during Summertime. Atmos. Environ. 2013, 77, 893–900. [Google Scholar] [CrossRef]
  9. Petetin, H.; Sciare, J.; Bressi, M.; Gros, V.; Rosso, A.; Sanchez, O.; Sarda-Estève, R.; Petit, J.E.; Beekmann, M. Assessing the Ammonium Nitrate Formation Regime in the Paris Megacity and Its Representation in the CHIMERE Model. Atmos. Chem. Phys. 2016, 16, 10419–10440. [Google Scholar] [CrossRef] [Green Version]
  10. Ferrara, R.M.; Loubet, B.; Di Tommasi, P.; Bertolini, T.; Magliulo, V.; Cellier, P.; Eugster, W.; Rana, G. Eddy Covariance Measurement of Ammonia Fluxes: Comparison of High Frequency Correction Methodologies. Agric. For. Meteorol. 2012, 158–159, 30–42. [Google Scholar] [CrossRef]
  11. Saylor, R.; Myles, L.; Sibble, D.; Caldwell, J.; Xing, J. Recent Trends in Gas-Phase Ammonia and PM2.5 Ammonium in the Southeast United States. J. Air Waste Manag. Assoc. 2015, 65, 347–357. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Backes, A.M.; Aulinger, A.; Bieser, J.; Matthias, V.; Quante, M. Ammonia Emissions in Europe, Part II: How Ammonia Emission Abatement Strategies Affect Secondary Aerosols. Atmos. Environ. 2016, 126, 153–161. [Google Scholar] [CrossRef] [Green Version]
  13. Backes, A.; Aulinger, A.; Bieser, J.; Matthias, V.; Quante, M. Ammonia Emissions in Europe, Part I: Development of a Dynamical Ammonia Emission Inventory. Atmos. Environ. 2016, 131, 55–66. [Google Scholar] [CrossRef] [Green Version]
  14. Pope, C.A.; Brook, R.D.; Burnett, R.T.; Dockery, D.W. How Is Cardiovascular Disease Mortality Risk Affected by Duration and Intensity of Fine Particulate Matter Exposure? An Integration of the Epidemiologic Evidence. Air Qual. Atmos. Health 2011, 4, 5–14. [Google Scholar] [CrossRef]
  15. Priewus, H.; Schutte-Postma, E. Notes on the Particulate Matter Standards in the European Union and the Netherlands. Int. J. Environ. Res. Public Health 2009, 6, 1155–1173. [Google Scholar] [CrossRef] [Green Version]
  16. Fine, P.M.; Sioutas, C.; Solomon, P.A. Secondary Particulate Matter in the United States: Insights from the Particulate Matter Supersites Program and Related Studies. J. Air Waste Manag. Assoc. 2008, 58, 234–253. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Cai, G. Ammonia Volatilization. Dev. Plant Soil Sci. 1997, 77, 193–213. [Google Scholar] [CrossRef]
  18. Donnelly, A.; Misstear, B.; Broderick, B. Air Quality Modelling for Ireland; EPA: Wexford, Ireland, 2019.
  19. Doyle, B.; Cummins, T.; Augustenborg, C.; Aherne, J. Ambient Atmospheric Ammonia in Ireland, 2013–2014; EPA: Wexford, Ireland, 2017.
  20. Directive 2008/50/EC. Available online: (accessed on 5 February 2019).
  21. De Kluizenaar, Y.; Farrell, E.P. Ammonia Monitoring in Ireland—A Full Year of Monitoring; EPA: Wexford, Ireland, 2000.
  22. Schiferl, L.D.; Heald, C.L.; Nowak, J.B.; Holloway, J.S.; Neuman, J.A.; Bahreini, R.; Pollack, I.B.; Ryerson, T.B.; Wiedinmyer, C.; Murphy, J.G. An Investigation of Ammonia and Inorganic Particulate Matter in California during the CalNex Campaign. J. Geophys. Res. Atmos. 2014, 119, 1883–1902. [Google Scholar] [CrossRef]
  23. Olivier, J.G.J.; Bouwman, A.F.; Van der Hoek, K.W.; Berdowski, J.J.M. Global Air Emission Inventories for Anthropogenic Sources of NOx, NH3 and N2O in 1990. Environ. Pollut. 1998, 102, 135–148. [Google Scholar] [CrossRef]
  24. Aneja, V.P.; Schlesinger, W.H.; Erisman, J.W.; Behera, S.N.; Sharma, M.; Battye, W. Reactive Nitrogen Emissions from Crop and Livestock Farming in India. Atmos. Environ. 2012, 47, 92–103. [Google Scholar] [CrossRef]
  25. Rosswall, T. The Biogeochemical Nitrogen Cycle. In Some Perspectives of the Major Biogeochemical Cycles; Linkens, G.E., Ed.; John Wiley & Sons: Chichester, UK; New York, NY, USA; Brisbane, Australia; Toronto, ON, Canada, 1981; pp. 25–50. ISBN 0471279897. [Google Scholar]
  26. Kern, M.; Simon, J. Chapter Nineteen—Production of Recombinant Multiheme Cytochromes c in Wolinella Succinogenes. In Research on Nitrification and Related Processes, Part A; Klotz, M.G., Ed.; Academic Press: Cambridge, MA, USA, 2011; Volume 486, pp. 429–446. ISBN 0076-6879. [Google Scholar]
  27. Widdison, P.E.; Burt, T.P. Nitrogen Cycle. In Encyclopedia of Ecology; Jørgensen, S.E., Fath, B.D., Eds.; Academic Press: Oxford, UK, 2008; pp. 2526–2533. ISBN 978-0-08-045405-4. [Google Scholar]
  28. Cabello, P.; Roldán, M.D.; Castillo, F.; Moreno-Vivián, C. Nitrogen Cycle. In Encyclopedia of Microbiology; Schaechter, M., Ed.; Academic Press: Oxford, UK, 2009; pp. 299–321. ISBN 978-0-12-373944-5. [Google Scholar]
  29. Faria, L.D.; do Nascimento, C.A.C.; Vitti, G.C.; Luz, P.H.D.; Guedes, E.M.S. Loss of Ammonia from Nitrogen Fertilizers Applied to Maize and Soybean Straw. Rev. Bras. Cienc. Solo 2013, 37, 969–975. [Google Scholar] [CrossRef] [Green Version]
  30. Leip, A.; Billen, G.; Garnier, J.; Grizzetti, B.; Lassaletta, L.; Reis, S.; Simpson, D.; Sutton, M.A.; De Vries, W.; Weiss, F.; et al. Impacts of European Livestock Production: Nitrogen, Sulphur, Phosphorus and Greenhouse Gas Emissions, Land-Use, Water Eutrophication and Biodiversity. Environ. Res. Lett. 2015, 10, 115004. [Google Scholar] [CrossRef]
  31. Basosi, R.; Spinelli, D.; Fierro, A.; Jez, S. Mineral Nitrogen Fertilizers: Environmental Impact of Production and Use. In Fertilizers: Components, Uses in Agriculture and Environmental Impacts; Nova Publishers: Hauppauge, NY, USA, 2014; pp. 3–43. ISBN 9781633210585. [Google Scholar]
  32. Bouwman, A.F.; Boumans, L.J.M.; Batjes, N.H. Estimation of Global NH3 Volatilization Loss from Synthetic Fertilizers and Animal Manure Applied to Arable Lands and Grasslands. Glob. Biogeochem. Cycles 2002, 16, 8-1–8-14. [Google Scholar] [CrossRef]
  33. De Klein, C.; Novoa, R.S.; Ogle, S.; Smith, K.; Rochette, P.; Wirth, T.; McConkey, B.; Mosier, A.; Rypdal, K.; Walsh, M. N2O Emissions from Managed Soils, and CO2 Emissions from Lime and Urea Application. In IPCC Guidelines for National Greenhouse Gas Inventories; IPCC: Hayama, Japan, 2006; Volume 4, pp. 1–54. [Google Scholar]
  34. Aneja, V.P.; Roelle, P.A.; Murray, G.C.; Southerland, J.; Erisman, J.W.; Fowler, D.; Asman, W.A.H.; Patni, N. Atmospheric Nitrogen Compounds. II: Emissions, Transport, Transformation, Deposition and Assessment. Atmos. Environ. 2001, 35, 1903–1911. [Google Scholar] [CrossRef]
  35. Erisman, J.W.; Bleeker, A.; Galloway, J.; Sutton, M.S. Reduced Nitrogen in Ecology and the Environment. Environ. Pollut. 2007, 150, 140–149. [Google Scholar] [CrossRef] [Green Version]
  36. Kelleghan, D.B.; Hayes, E.T.; Everard, M.; Curran, T.P. Mapping Ammonia Risk on Sensitive Habitats in Ireland. Sci. Total Environ. 2019, 649, 1580–1589. [Google Scholar] [CrossRef]
  37. Galloway, J.N.; Schlesinger, W.H.; Clark, C.M.; Grimm, N.B.; Jackson, R.B.; Law, B.E.; Thornton, P.E.; Townsend, A.R.; Martin, R. Biogeochemical Cycles; American Geophysical Union (AGU): Washington, DC, USA, 2014. [Google Scholar]
  38. Pan, B.; Lam, S.K.; Mosier, A.; Luo, Y.; Chen, D. Ammonia Volatilization from Synthetic Fertilizers and Its Mitigation Strategies: A Global Synthesis. Agric. Ecosyst. Environ. 2016, 232, 283–289. [Google Scholar] [CrossRef]
  39. van Grinsven, H.J.M.; Bouwman, L.; Cassman, K.G.; van Es, H.M.; McCrackin, M.L.; Beusen, A.H.W. Losses of Ammonia and Nitrate from Agriculture and Their Effect on Nitrogen Recovery in the European Union and the United States between 1900 and 2050. J. Environ. Qual. 2015, 44, 356. [Google Scholar] [CrossRef] [Green Version]
  40. Skjøth, C.A.; Geels, C. The Effect of Climate and Climate Change on Ammonia Emissions in Europe. Atmos. Chem. Phys. 2013, 13, 117–128. [Google Scholar] [CrossRef] [Green Version]
  41. Bouwman, A.F.; Lee, D.S.; Asman, W.A.H.; Dentener, F.J.; Van Der Hoek, K.W.; Olivier, J.G.J. A Global High-Resolution Emission Inventory for Ammonia. Glob. Biogeochem. Cycles 1997, 11, 561–587. [Google Scholar] [CrossRef]
  42. Fangmeier, A.; Hadwiger-Fangmeier, A.; Van der Eerden, L.; Jäger, H.-J. Effects of Atmospheric Ammonia on Vegetation—A Review. Environ. Pollut. 1994, 86, 43–82. [Google Scholar] [CrossRef] [PubMed]
  43. Sutton, M.A.; Pitcairn, C.E.R.; Fowler, D. The Exchange of Ammonia Between the Atmosphere and Plant Communities. Adv. Ecol. Res. 1993, 24, 301–393. [Google Scholar] [CrossRef]
  44. Hanson, P.J.; Lindberg, S.E. Dry Deposition of Reactive Nitrogen Compounds: A Review of Leaf, Canopy and Non-Foliar Measurements. Atmos. Environ. Part A Gen. Top. 1991, 25, 1615–1634. [Google Scholar] [CrossRef]
  45. Lovett, G.M. Atmospheric Deposition of Nutrients and Pollutants in North America. Ecol. Appl. 1994, 4, 629–650. [Google Scholar] [CrossRef]
  46. Wayne, R.P. Chemistry of Atmospheres, 3rd ed.; Oxford University Press: New York, NY, USA, 2000; ISBN 0-19-850375-X. [Google Scholar]
  47. Phillips, S.B.; Arya, S.P.; Aneja, V.P. Ammonia Flux and Dry Deposition Velocity from Near-Surface Concentration Gradient Measurements over a Grass Surface in North Carolina. Atmos. Environ. 2004, 38, 3469–3480. [Google Scholar] [CrossRef]
  48. Delon, C.; Galy-Lacaux, C.; Serça, D.; Loubet, B.; Camara, N.; Gardrat, E.; Saneh, I.; Fensholt, R.; Tagesson, T.; Le Dantec, V.; et al. Soil and Vegetation-Atmosphere Exchange of NO, NH3, and N2O from Field Measurements in a Semi Arid Grazed Ecosystem in Senegal. Atmos. Environ. 2017, 156, 36–51. [Google Scholar] [CrossRef] [Green Version]
  49. Pearson, J.; Stewart, G.R. The Deposition of Atmospheric Ammonia and Its Effects on Plants. New Phytol. 1993, 125, 283–305. [Google Scholar] [CrossRef]
  50. 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, 207–214. [Google Scholar] [CrossRef] [Green Version]
  51. Loubet, B.; Sutton, M.A.; Milford, C.; Cellier, P. Investigation of the Interaction between Sources and Sinks of Atmospheric Ammonia in an Upland Landscape Using a Simplified Dispersion-Exchange Model. J. Geophys. Res. 2001, 106, 183–195. [Google Scholar] [CrossRef]
  52. Pleim, J.E.; Bash, J.O.; Walker, J.T.; Cooter, E.J. Development and Evaluation of an Ammonia Bidirectional Flux Parameterization for Air Quality Models. J. Geophys. Res. Atmos. 2013, 118, 3794–3806. [Google Scholar] [CrossRef]
  53. Pleim, J.E.; Ran, L.; Appel, W.; Shephard, M.W.; Cady-Pereira, K. New Bidirectional Ammonia Flux Model in an Air Quality Model Coupled With an Agricultural Model. J. Adv. Modeling Earth Syst. 2019, 11, 2934–2957. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Sutton, M.A.; Schjorring, J.K.; Wyers, G.P. Plant-Atmosphere Exchange of Ammonia. Philos. Trans.-R. Soc. Lond. A 1995, 351, 261–278. [Google Scholar] [CrossRef]
  55. Sutton, M.A.; Moncrieff, J.B.; Fowler, D. Deposition of Atmospheric Ammonia to Moorlands. Environ. Pollut. 1992, 75, 15–24. [Google Scholar] [CrossRef]
  56. Sutton, M.A.; Fowler, D.; Moncrieft, J.B.; Storeton-West, R.L. The Exchange of Atmospheric Ammonia with Vegetated Surfaces. II: Fertilized Vegetation. Q. J. R. Meteorol. Soc. 1993, 119, 1047–1070. [Google Scholar] [CrossRef]
  57. Warneck, P. Chemistry of the Natural Atmosphere; Academic Press: San Diego, CA, USA, 1988. [Google Scholar]
  58. Kane, M.M.; Rendell, A.R.; Jickells, T.D. Atmospheric Scavenging Processes over the North Sea. Atmos. Environ. 1994, 28, 2523–2530. [Google Scholar] [CrossRef]
  59. Pacyna, J.M. Atmospheric Deposition. In Encyclopedia of Ecology; Academic Press: Cambridge, MA, USA, 2008; pp. 275–285. ISBN 9780080914565. [Google Scholar]
  60. Scott, B.C. Sulfate Washout Ratios in Winter Storms. J. Appl. Meteorol. 1981, 20, 619–625. [Google Scholar] [CrossRef]
  61. Duce, R.A.; LaRoche, J.; Altieri, K.; Arrigo, K.R.; Baker, A.R.; Capone, D.G.; Cornell, S.; Dentener, F.; Galloway, J.; Ganeshram, R.S.; et al. Impacts of Atmospheric Anthropogenic Nitrogen on the Open Ocean. Science 2008, 320, 893–897. [Google Scholar] [CrossRef] [Green Version]
  62. Misra, P.K.; Chan, W.H.; Chung, D.; Tang, A.J.S. Scavenging Ratios of Acidic Pollutants and Their Use in Long-Range Transport Models. Atmos. Environ. 1985, 19, 1471–1475. [Google Scholar] [CrossRef]
  63. Granat, L. On the Relation between PH and the Chemical Composition in Atmospheric Precipitation. Tellus 1972, 24, 550–560. [Google Scholar] [CrossRef] [Green Version]
  64. Hontoria, C.; Saa, A.; Almorox, J.; Cuadra, L.; Sánchez, A.; Gascó, J.M. The Chemical Composition of Precipitation in Madrid. Water Air Soil Pollut. 2003, 146, 35–54. [Google Scholar] [CrossRef]
  65. Moody, J.L.; Galloway, J.N. Quantifying the Relationship between Atmospheric Transport and the Chemical Composition of Precipitation on Bermuda. Tellus B 1988, 40, 463–479. [Google Scholar] [CrossRef] [Green Version]
  66. Fuzzi, S.; Baltensperger, U.; Carslaw, K.; Decesari, S.; Van Der Gon, H.D.; Facchini, M.C.; Fowler, D.; Nazionale, C. Particulate Matter, Air Quality and Climate: Lessons Learned and Furture Needs. Atmos. Chem. Phys. 2015, 15, 8217–8299. [Google Scholar] [CrossRef] [Green Version]
  67. Pathak, R.K.; Wu, W.S.; Wang, T. Summertime PM2.5 Ionic Species in Four Major Cities of China: Nitrate Formation in an Ammonia-Deficient Atmosphere. Atmos. Chem. Phys. 2009, 9, 1711–1722. [Google Scholar] [CrossRef] [Green Version]
  68. Tucker, W.G. Overview of PM2.5 Sources and Control Strategies. Fuel Process. Technol. 2000, 65, 379–392. [Google Scholar] [CrossRef]
  69. Snider, G.; Weagle, C.L.; Murdymootoo, K.K.; Ring, A.; Ritchie, Y.; Stone, E.; Walsh, A.; Akoshile, C.; Anh, N.X.; Balasubramanian, R.; et al. Variation in Global Chemical Composition of PM2.5: Emerging Results from SPARTAN. Atmos. Chem. Phys. 2016, 16, 9629–9653. [Google Scholar] [CrossRef] [Green Version]
  70. Vayenas, D.V.; Takahama, S.; Davidson, C.I.; Pandis, S.N. Simulation of the Thermodynamics and Removal Processes in the Sulfate-Ammonia-Nitric Acid System during Winter: Implications for PM2.5 Control Strategies. J. Geophys. Res. Atmos. 2005, 110, 1–11. [Google Scholar] [CrossRef]
  71. Martins, H.; Monteiro, A.; Ferreira, J.; Gama, C.; Ribeiro, I.; Borrego, C.; Miranda, A.I. The Role of Ammonia on Particulate Matter Pollution over Portugal. Int. J. Environ. Pollut. 2015, 57, 215–226. [Google Scholar] [CrossRef]
  72. Putaud, J.-P.; Van Dingenen, R.; Alastuey, A.; Bauer, H.; Birmili, W.; Cyrys, J.; Flentje, H.; Fuzzi, S.; Gehrig, R.; Hansson, H.C.; et al. A European Aerosol Phenomenology—3: Physical and Chemical Characteristics of Particulate Matter from 60 Rural, Urban, and Kerbside Sites across Europe. Atmos. Environ. 2010, 44, 1308–1320. [Google Scholar] [CrossRef]
  73. Anderson, N.; Strader, R.; Davidson, C. Airborne Reduced Nitrogen: Ammonia Emissions from Agriculture and Other Sources. Environ. Int. 2003, 29, 277–286. [Google Scholar] [CrossRef]
  74. Okubo, M.; Kuwahara, T. Chapter 2—Emission Regulations. In Design for Additive Manufacturing; Okubo, M., Kuwahara, T., Eds.; Butterworth-Heinemann: Oxford, UK, 2020; pp. 25–51. ISBN 978-0-12-812307-2. [Google Scholar]
  75. Miller, B.; Miller, B. Sulfur Oxides Formation and Control. In Fossil Fuel Emissions Control Technologies; Butterworth-Heinemann: Oxford, UK, 2015; pp. 197–242. [Google Scholar] [CrossRef]
  76. Geddes, J.A.; Murphy, J.G. 10—The Science of Smog: A Chemical Understanding of Ground Level Ozone and Fine Particulate Matter. In Woodhead Publishing Series in Energy; Zeman, F.B.T.-M.S., Ed.; Woodhead Publishing: Sawston, UK, 2012; pp. 205–230. ISBN 978-0-85709-046-1. [Google Scholar]
  77. Acharya, B. Chapter 10—Cleaning of Product Gas of Gasification. In Basu Pyrolysis and Torrefaction, 3rd ed.; Basu, P., Ed.; Academic Press: Cambridge, MA, USA, 2018; pp. 373–391. ISBN 978-0-12-812992-0. [Google Scholar]
  78. Wu, Y.; Liu, J.; Zhai, J.; Cong, L.; Wang, Y.; Ma, W.; Zhang, Z.; Li, C. Comparison of Dry and Wet Deposition of Particulate Matter in Near-Surface Waters during Summer. PLoS ONE 2018, 13, e0199241. [Google Scholar] [CrossRef]
  79. Loosmore, G.A.; Cederwall, R.T. Precipitation Scavenging of Atmospheric Aerosols for Emergency Response Applications: Testing an Updated Model with New Real-Time Data. Atmos. Environ. 2004, 38, 993–1003. [Google Scholar] [CrossRef]
  80. Kulshrestha, U. Assessment of Atmospheric Emissions and Depositions of Major Nr Species in Indian Region. In The Indian Nitrogen Assessment: Sources of Reactive Nitrogen, Environmental and Climate Effects, Management Options, and Policies; Elsevier: Amsterdam, The Netherlands, 2017; pp. 427–444. ISBN 9780128119044. [Google Scholar]
  81. Giardina, M.; Buffa, P. A New Approach for Modeling Dry Deposition Velocity of Particles. Atmos. Environ. 2018, 180, 11–22. [Google Scholar] [CrossRef]
  82. Yang, Q.; Easter, R.C.; Campuzano-Jost, P.; Jimenez, J.L.; Fast, J.D.; Ghan, S.J.; Wang, H.; Berg, L.K.; Barth, M.C.; Liu, Y.; et al. Aerosol Transport and Wet Scavenging in Deep Convective Clouds: A Case Study and Model Evaluation Using a Multiple Passive Tracer Analysis Approach. J. Geophys. Res. Biogeosci. 2015, 120, 693–706. [Google Scholar] [CrossRef] [Green Version]
  83. Roy, A.; Chatterjee, A.; Ghosh, A.; Das, S.K.; Ghosh, S.K.; Raha, S. Below-Cloud Scavenging of Size-Segregated Aerosols and Its Effect on Rainwater Acidity and Nutrient Deposition: A Long-Term (2009–2018) and Real-Time Observation over Eastern Himalaya. Sci. Total Environ. 2019, 674, 223–233. [Google Scholar] [CrossRef] [PubMed]
  84. Santachiara, G.; Prodi, F.; Belosi, F. Atmospheric Aerosol Scavenging Processes and the Role of Thermo- and Diffusio-Phoretic Forces. Atmos. Res. 2013, 128, 46–56. [Google Scholar] [CrossRef]
  85. Tobias, C.; Neubauer, S.C. Chapter 16-Salt Marsh Biogeochemistry—An Overview. In Coastal Wetlands; Perillo, G.M.E., Wolanski, E., Cahoon, D.R., Hopkinson, C.S.B.T.-C.W., Eds.; Elsevier: Amsterdam, The Netherlands, 2019; pp. 539–596. ISBN 978-0-444-63893-9. [Google Scholar]
  86. Censi, P.; Darrah, T.H.; Erel, Y. Medical Geochemistry: Geological Materials and Health; Springer: Berlin/Heidelberg, Germany, 2012; pp. 1–194. ISBN ISBN 978-94-007-4372-4. [Google Scholar] [CrossRef]
  87. Baker, J. A Cluster Analysis of Long Range Air Transport Pathways and Associated Pollutant Concentrations within the UK. Atmos. Environ. 2010, 44, 563–571. [Google Scholar] [CrossRef]
  88. Environmental Protection Agency (EPA). Chapter 2-Air; EPA: Wexford, Ireland, 2016.
  89. Rosenfeld, D. Cloud-Aerosol-Precipitation Interactions Based of Satellite Retrieved Vertical Profiles of Cloud Microstructure. In Remote Sensing of Aerosols, Clouds and Precipitation; Islam, T., Hu, Y., Kokhanovsky, A., Wang, J., Eds.; Elsevier: Amsterdam, The Netherlands, 2018; pp. 129–152. ISBN 978-0-12-810437-8. [Google Scholar]
  90. Randall, D.A.; Albrecht, B.; Cox, S.; Johnson, D.; Minnis, P.; Rossow, W.; Starr, D.O. On Fire at Ten. In Advances in Geophysics; Dmowska, R., Saltzman, B., Eds.; Elsevier: Amsterdam, The Netherlands, 1996; Volume 38, pp. 37–177. ISBN 0065-2687. [Google Scholar]
  91. Wang, P.K. Scavenging and Transportation of Aerosol Particles by Ice Crystals in Clouds. In Ice Microdynamics; Academic Press: San Diego, CA, USA, 2002; pp. 152–196. ISBN 978-0-12-734603-8. [Google Scholar]
  92. Herckes, P.; Collett, J.L. TROPOSPHERIC CHEMISTRY & COMPOSITION | Cloud Chemistry. In Encyclopedia of Atmospheric Sciences, 2nd ed.; North, G.R., Pyle, J., Zhang, F., Eds.; Academic Press: Oxford, UK, 2015; pp. 218–225. ISBN 978-0-12-382225-3. [Google Scholar]
  93. Dickinson, R.E. Interaction Between Future Climate and Terrestrial Carbon and Nitrogen. In The Future of the World’s Climate, 2nd ed.; Henderson-Sellers, A., McGuffie, K.B., Eds.; Elsevier: Boston, MA, USA, 2012; pp. 289–308. ISBN 978-0-12-386917-3. [Google Scholar]
  94. Lohmann, U. AEROSOLS | Aerosol–Cloud Interactions and Their Radiative Forcing. In Encyclopedia of Atmospheric Sciences, 2nd ed.; North, G.R., Pyle, J., Zhang, F., Eds.; Academic Press: Oxford, UK, 2015; pp. 17–22. ISBN 978-0-12-382225-3. [Google Scholar]
  95. Deshler, T. CHEMISTRY OF THE ATMOSPHERE | Observations for Chemistry (In Situ): Particles. In Encyclopedia of Atmospheric Sciences Science; North, G.R., Pyle, J., Zhang, F., Eds.; Academic Press: Oxford, UK, 2015; pp. 379–386. ISBN 978-0-12-382225-3. [Google Scholar]
  96. Tao, W.-K.; Matsui, T. NUMERICAL MODELS | Cloud-System Resolving Modeling and Aerosols. In Encyclopedia of Atmospheric Sciences, 2nd ed.; DitionNorth, G.R., Pyle, J., Zhang, F., Eds.; Academic Press: Oxford, UK, 2015; pp. 222–231. ISBN 978-0-12-382225-3. [Google Scholar]
  97. Goodman, A.L.; Underwood, G.M.; Grassian, V.H. A Laboratory Study of the Heterogeneous Reaction of Nitric Acid on Calcium Carbonate Particles. J. Geophys. Res.-Atmos. 2000, 105, 29053–29064. [Google Scholar] [CrossRef]
  98. Squizzato, S.; Masiol, M.; Brunelli, A.; Pistollato, S.; Tarabotti, E.; Rampazzo, G.; Pavoni, B. Factors Determining the Formation of Secondary Inorganic Aerosol: A Case Study in the Po Valley (Italy). Atmos. Chem. Phys. 2013, 13, 1927–1939. [Google Scholar] [CrossRef]
  99. Silvern, R.F.; Jacob, D.J.; Kim, P.S.; Marais, E.A.; Turner, J.R.; Campuzano-Jost, P.; Jimenez, J.L. Inconsistency of Ammonium-Sulfate Aerosol Ratios with Thermodynamic Models in the Eastern US: A Possible Role of Organic Aerosol. Atmos. Chem. Phys. 2017, 17, 5107–5118. [Google Scholar] [CrossRef] [Green Version]
  100. Yi, Y.; Cao, Z.; Zhou, X.; Xue, L.; Wang, W. Formation of Aqueous-Phase Secondary Organic Aerosols from Glycolaldehyde and Ammonium Sulfate/Amines: A Kinetic and Mechanistic Study. Atmos. Environ. 2018, 181, 117–125. [Google Scholar] [CrossRef]
  101. Hämeri, K.; Väkevä, M.; Hansson, H.C.; Laaksonen, A. Hygroscopic Growth of Ultrafine Ammonium Sulphate Aerosol Measured Using an Ultrafine Tandem Differential Mobility Analyzer. J. Geophys. Res. Atmos. 2000, 105, 22231–22242. [Google Scholar] [CrossRef]
  102. Adams, P.J.; Seinfeld, J.H.; Koch, D.M. Global Concentrations of Tropospheric Sulfate, Nitrate, and Ammonium Aerosol Simulated in a General Circulation Model. J. Geophys. Res. Atmos. 1999, 104, 13791–13823. [Google Scholar] [CrossRef]
  103. Cai, Z.; Li, F.; Rong, M.; Lin, L.; Yao, Q.; Huang, Y. Introduction. In Novel Nanomaterials for Biomedical, Environmental and Energy Applications; Elsevier: Amsterdam, The Netherlands, 2019; pp. 1–36. ISBN 9780128144978. [Google Scholar]
  104. Forbes, P.B.C.; Garland, R.M. Outdoor Air Pollution. In Comprehensive Analytical Chemistry; Elsevier: Amsterdam, The Netherlands, 2016; Volume 73, pp. 73–96. ISBN 9780444636058. [Google Scholar]
  105. Andreae, M.O. Correlation between Cloud Condensation Nuclei Concentration and Aerosol Optical Thickness in Remote and Polluted Regions. Atmos. Chem. Phys. 2009, 9, 543–556. [Google Scholar] [CrossRef] [Green Version]
  106. Lohmann, U.; Luond, F.; Mahrt, F.; Lohmann, U.; Luond, F.; Mahrt, F. Cloud Droplet Formation and Köhler Theory. In An Introduction to Clouds; Cambridge University Press: Cambridge, UK; Cambridge, MA, USA, 2016; pp. 155–185. ISBN 9781139087513. [Google Scholar]
  107. Butterbach-Bahl, K.; Gundersen, P.; Ambus, P.; Augustin, J.; Beier, C.; Boeckx, P.; Dannenmann, M.; Sanchez Gimeno, B.; Ibrom, A.; Kiese, R.; et al. Nitrogen Processes in Terrestrial Ecosystems. In The European Nitrogen Assessment; Cambridge University Press: Cambridge, UK; Cambridge, MA, USA, 2011; pp. 99–125. ISBN 9780511976988. [Google Scholar]
  108. Davidson, E.A.; De Carvalho, C.J.R.; Figueira, A.M.; Ishida, F.Y.; Ometto, J.P.H.B.; Nardoto, G.B.; Sabá, R.T.; Hayashi, S.N.; Leal, E.C.; Vieira, I.C.G.; et al. Recuperation of Nitrogen Cycling in Amazonian Forests Following Agricultural Abandonment. Nature 2007, 447, 995–998. [Google Scholar] [CrossRef] [PubMed]
  109. Bauer, S.E.; Koch, D.; Unger, N.; Metzger, S.M.; Shindell, D.T.; Streets, D.G. Nitrate Aerosols Today and in 2030: A Global Simulation Including Aerosols and Tropospheric Ozone. Atmos. Chem. Phys. 2007, 7, 5043–5059. [Google Scholar] [CrossRef] [Green Version]
  110. Seigneur, C. Air Pollution: Concept, Theory and Application; Belin/Humensis, Ed.; Cambridge University Press: Cambridge, UK; Cambridge, MA, USA, 2019; ISBN 9781108481632. [Google Scholar]
  111. Hernandez, G.; Berry, T.-A.; Wallis, S.; Poyner, D. Temperature and Humidity Effects on Particulate Matter Concentrations in a Sub-Tropical Climate During Winter. Int. Proc. Chem. Biol. Environ. Eng. 2017, 102, 41–49. [Google Scholar] [CrossRef]
  112. McMurry, P.H.; Wilson, J.C. Droplet Phase (Heterogeneous) and Gas Phase (Homogeneous) Contributions to Secondary Ambient Aerosol Formation as Functions of Relative Humidity. J. Geophys. Res. 1983, 88, 5101–5108. [Google Scholar] [CrossRef]
  113. Zang, L.; Wang, Z.; Zhu, B.; Zhang, Y. Roles of Relative Humidity in Aerosol Pollution Aggravation over Central China during Wintertime. Int. J. Environ. Res. Public Health 2019, 16, 4422. [Google Scholar] [CrossRef]
  114. Wang, X.; Wang, Z.; Yu, T.; Gong, Y. Foreshowing of the Western Pacific Tropical Cyclone Track to PM10 Air Pollution Episode in the Beijing Area. Chin. Sci. Bull. 2009, 54, 830–835. [Google Scholar] [CrossRef]
  115. Wallace, J.; Kanaroglou, P. The Effect of Temperature Inversions on Ground-Level Nitrogen Dioxide (NO2) and Fine Particulate Matter (PM2.5) Using Temperature Profiles from the Atmospheric Infrared Sounder (AIRS). Sci. Total Environ. 2009, 407, 5085–5095. [Google Scholar] [CrossRef]
  116. Trinh, T.T.; Trinh, T.T.; Le, T.T.; Nguyen, T.D.H.; Tu, B.M. Temperature Inversion and Air Pollution Relationship, and Its Effects on Human Health in Hanoi City, Vietnam. Environ. Geochem. Health 2019, 41, 929–937. [Google Scholar] [CrossRef] [PubMed]
  117. Hawkins, T.W.; Holland, L.A. Synoptic and Local Weather Conditions Associated With PM2.5 Concentration in Carlisle, Pennsylvania. Middle States Geogr. 2010, 43, 72–84. [Google Scholar]
  118. Yao, T.; Fung, J.C.H.; Ma, H.; Lau, A.K.H.; Chan, P.W.; Yu, J.Z.; Xue, J. Enhancement in Secondary Particulate Matter Production Due to Mountain Trapping. Atmos. Res. 2014, 147–148, 227–236. [Google Scholar] [CrossRef]
  119. Beaver, S.; Palazoglu, A.; Singh, A.; Soong, S.-T.; Tanrikulu, S. Identification of Weather Patterns Impacting 24-h Average Fine Particulate Matter Pollution. Atmos. Environ. 2010, 44, 1761–1771. [Google Scholar] [CrossRef]
  120. Flechard, C.; Massad, R.S.; Loubet, B.; Personne, E. Advances in Undertsanding Models and Parameterisations of Biosphere-Atmosphere Ammonia Exchange. Biogeosci. Discuss. 2013, 10, 5385–5497. [Google Scholar] [CrossRef]
  121. Schwab, J.J.; Li, Y.; Bae, M.S.; Demerjian, K.L.; Hou, J.; Zhou, X.; Jensen, B.; Pryor, S.C. A Laboratory Intercomparison of Real-Time Gaseous Ammonia Measurement Methods. Environ. Sci. Technol. 2007, 41, 8412–8419. [Google Scholar] [CrossRef]
  122. Sutton, M.A.; Tang, Y.S.; Miners, B.; Fowler, D. A New Diffusion Denuder System for Long-Term, Regional Monitoring of Atmospheric Ammonia and Ammonium. Water Air Soil Pollut. Focus 2001, 1, 145–156. [Google Scholar] [CrossRef]
  123. Norman, M.; Spirig, C.; Wolff, V.; Trebs, I.; Flechard, C.; Wisthaler, A.; Schnitzhofer, R.; Hansel, A.; Neftel, A. Intercomparison of Ammonia Measurement Techniques at an Intensively Managed Grassland Site (Oensingen, Switzerland). Atmos. Chem. Phys. 2009, 9, 2635–2645. [Google Scholar] [CrossRef] [Green Version]
  124. Butler, T.; Marino, R.; Schwede, D.; Howarth, R.; Sparks, J.; Sparks, K. Atmospheric Ammonia Measurements at Low Concentration Sites in the Northeastern USA: Implications for Total Nitrogen Deposition and Comparison with CMAQ Estimates. Biogeochemistry 2015, 122, 191–210. [Google Scholar] [CrossRef]
  125. Von Bobrutzki, K.; Braban, C.F.; Famulari, D.; Jones, S.K.; Blackall, T.; Smith, T.E.L.; Blom, M.; Coe, H.; Gallagher, M.; Ghalaieny, M.; et al. Field Inter-Comparison of Eleven Atmospheric Ammonia Measurement Techniques. Atmos. Meas. Tech. 2010, 3, 91–112. [Google Scholar] [CrossRef] [Green Version]
  126. Dammers, E.; Schaap, M.; Haaima, M.; Palm, M.; Wichink Kruit, R.J.; Volten, H.; Hensen, A.; Swart, D.; Erisman, J.W. Measuring Atmospheric Ammonia with Remote Sensing Campaign: Part 1—Characterisation of Vertical Ammonia Concentration Profile in the Centre of The Netherlands. Atmos. Environ. 2017, 169, 97–112. [Google Scholar] [CrossRef]
  127. Singh, S.P.; Satsangi, G.S.; Khare, P.; Lakhani, A.; Maharaj Kumari, K.; Srivastava, S.S. Multiphase Measurement of Atmospheric Ammonia. Chemosphere Glob. Chang. Sci. 2001, 3, 107–116. [Google Scholar] [CrossRef]
  128. Aneja, V.P.; Bunton, B.; Walker, J.T.; Malik, B.P. Measurement and Analysis of Atmospheric Ammonia Emissions from Anaerobic Lagoons. Atmos. Environ. 2001, 35, 1949–1958. [Google Scholar] [CrossRef]
  129. Mount, G.H.; Rumburg, B.; Havig, J.; Lamb, B.; Westberg, H.; Yonge, D.; Johnson, K.; Kincaid, R. Measurement of Atmospheric Ammonia at a Dairy Using Differential Optical Absorption Spectroscopy in the Mid-Ultraviolet. Atmos. Environ. 2002, 36, 1799–1810. [Google Scholar] [CrossRef]
  130. Milford, C.; Theobald, M.; Nemitz, E.; Sutton, M.A. Dynamics of Ammonia Exchange in Response to Cutting and Fertilising in an Intensively-Managed Grassland. Water Air Soil Pollut. 2001, 5, 167–176. [Google Scholar] [CrossRef]
  131. Nemitz, E.; Sutton, M.A.; Gut, A.; San, R.; Husted, S.; Schjoerring, J.K. Sources and Sinks of Ammonia within an Oilseed Rape Canopy. Agric. For. Meteorol. 2000, 105, 385–404. [Google Scholar] [CrossRef]
  132. Department of Agriculture, Food and the Marine. Local Roots a Vision for Growth for the Irish Agricultural Economy for the Next 10 Years. Terms of Reference for the 2025 Agri-Food Strategy Committee; Department of Agriculture, Food and the Marine: Dublin, Ireland, 2015.
  133. Allegrini, I.; De Santis, F.; Di Palo, V.; Febo, A.; Perrino, C.; Possanzini, M.; Liberti, A. Annular Denuder Method for Sampling Reactive Gases and Aerosols in the Atmosphere. Sci. Total Environ. 1987, 67, 1–16. [Google Scholar] [CrossRef]
  134. Sim Tang, Y.; Braban, C.F.; Dragosits, U.; Simmons, I.; Leaver, D.; Van Dijk, N.; Poskitt, J.; Thacker, S.; Patel, M.; Carter, H.; et al. Acid Gases and Aerosol Measurements in the UK (1999–2015): Regional Distributions and Trends. Atmos. Chem. Phys. 2018, 18, 16293–16324. [Google Scholar] [CrossRef] [Green Version]
  135. Tang, Y.S.; Braban, C.F.; Dragosits, U.; Dore, A.J.; Simmons, I.; Van Dijk, N.; Poskitt, J.; Dos Santos Pereira, G.; Keenan, P.O.; Conolly, C.; et al. Drivers for Spatial, Temporal and Long-Term Trends in Atmospheric Ammonia and Ammonium in the UK. Atmos. Chem. Phys. 2018, 18, 705–733. [Google Scholar] [CrossRef]
  136. Besson, J.P.; Schilt, S.; Rochat, E.; Thévenaz, L. Ammonia Trace Measurements at Ppb Level Based on Near-IR Photoacoustic Spectroscopy. Appl. Phys. B Lasers Opt. 2006, 85, 323–328. [Google Scholar] [CrossRef] [Green Version]
  137. Schmohl, A.; Miklos, A.; Hess, P. Detection of Ammonia by Photoacoustic Spectroscopy with Semiconductor Lasers. Opt. Soc. Am. 2020, 41, 1815–1823. [Google Scholar] [CrossRef] [PubMed]
  138. Huszár, H.; Pogány, A.; Bozóki, Z.; Mohácsi, Á.; Horváth, L.; Szabó, G. Ammonia Monitoring at Ppb Level Using Photoacoustic Spectroscopy for Environmental Application. Sens. Actuators B Chem. 2008, 134, 1027–1033. [Google Scholar] [CrossRef]
  139. Yao, Z.; Zhang, W.; Wang, M.; Chen, J.; Shen, Y.; Wei, Y.; Yu, X.; Li, F.; Zeng, H. Tunable Diode Laser Absorption Spectroscopy Measurements of High-Pressure Ammonium Dinitramide Combustion. Aerosp. Sci. Technol. 2015, 45, 140–149. [Google Scholar] [CrossRef]
  140. Sutton, M.A.; Miners, B.; Tang, Y.S.; Milford, C.; Wyers, G.P.; Duyzer, J.H.; Fowler, D. Comparison of Low Cost Measurement Techniques for Long-Term Monitoring of Atmospheric Ammonia. J. Environ. Monit. 2001, 3, 446–453. [Google Scholar] [CrossRef]
  141. Leinert, S.; McGovern, F.; Jennings, G. New Transboundary Air Pollution Monitoring Capacity for Ireland; Johnstown Castle: Wexford, Ireland, 2008. [Google Scholar]
  142. Van Oss, R.; Duyzer, J.; Wyers, P. The Influence of Gas-to-Particle Conversion on Measurements of Ammonia Exchange over Forest. Atmos. Environ. 1998, 32, 465–471. [Google Scholar] [CrossRef]
  143. Wichink Kruit, R.J.; Aben, J.; de Vries, W.; Sauter, F.; van der Swaluw, E.; van Zanten, M.C.; van Pul, W.A.J. Modelling Trends in Ammonia in the Netherlands over the Period 1990–2014. Atmos. Environ. 2017, 154, 20–30. [Google Scholar] [CrossRef]
  144. Zöll, U.; Brümmer, C.; Schrader, F.; Ammann, C.; Ibrom, A.; Flechard, C.R.; Nelson, D.D.; Zahniser, M.; Kutsch, W.L. Surface-Atmosphere Exchange of Ammonia over Peatland Using QCL-Based Eddy-Covariance Measurements and Inferential Modeling. Atmos. Chem. Phys. 2016, 16, 11283–11299. [Google Scholar] [CrossRef] [Green Version]
  145. Schrader, F.; Brümmer, C.; Flechard, C.R.; Kruit, R.J.W.; Van Zanten, M.C.; Zöll, U.; Hensen, A.; Erisman, J.W. Non-Stomatal Exchange in Ammonia Dry Deposition Models: Comparison of Two State-of-the-Art Approaches. Atmos. Chem. Phys. 2016, 16, 13417–13430. [Google Scholar] [CrossRef] [Green Version]
  146. Kruit, R.J.W.; van Pul, W.A.J.; Sauter, F.J.; van den Broek, M.; Nemitz, E.; Sutton, M.A.; Krol, M.; Holtslag, A.A.M. Modeling the Surface-Atmosphere Exchange of Ammonia. Atmos. Environ. 2010, 44, 945–957. [Google Scholar] [CrossRef]
  147. Skiba, U.; Drewer, J.; Tang, Y.S.; van Dijk, N.; Helfter, C.; Nemitz, E.; Famulari, D.; Cape, J.N.; Jones, S.K.; Twigg, M.; et al. Biosphere-Atmosphere Exchange of Reactive Nitrogen and Greenhouse Gases at the NitroEurope Core Flux Measurement Sites: Measurement Strategy and First Data Sets. Agric. Ecosyst. Environ. 2009, 133, 139–149. [Google Scholar] [CrossRef]
  148. Bajwa, K.S.; Arya, S.P.; Aneja, V.P. Modeling Studies of Ammonia Dispersion and Dry Deposition at Some Hog Farms in North Carolina. J. Air Waste Manag. Assoc. 2008, 58, 1198–1207. [Google Scholar] [CrossRef] [PubMed]
  149. Geels, C.; Andersen, H.V.; Ambelas Skjøth, C.; Christensen, J.H.; Ellermann, T.; Løfstrøm, P.; Gyldenkærne, S.; Brandt, J.; Hansen, K.M.; Frohn, L.M.; et al. Improved Modelling of Atmospheric Ammonia over Denmark Using the Coupled Modelling System DAMOS. Biogeosciences 2012, 9, 2625–2647. [Google Scholar] [CrossRef] [Green Version]
  150. Zhang, L.; Wright, L.P.; Asman, W.A.H. Bi-Directional Air-Surface Exchange of Atmospheric Ammonia: A Review of Measurements and a Development of a Big-Leaf Model for Applications in Regional-Scale Air-Quality Models. J. Geophys. Res. Atmos. 2010, 115, D20310. [Google Scholar] [CrossRef]
  151. Sutton, M.A.; Nemitz, E.; Erisman, J.W.; Beier, C.; Bahl, K.B.; Cellier, P.; de Vries, W.; Cotrufo, F.; Skiba, U.; Di Marco, C.; et al. Challenges in Quantifying Biosphere–Atmosphere Exchange of Nitrogen Species. Environ. Pollut. 2007, 150, 125–139. [Google Scholar] [CrossRef] [Green Version]
  152. Asman, W.A.H.; Sutton, M.A.; Schjørring, J.K. Ammonia: Emission, Atmospheric Transport and Deposition. New Phytol. 1998, 139, 27–48. [Google Scholar] [CrossRef]
  153. Hansen, K.; Sørensen, L.L.; Hertel, O.; Geels, C.; Skjøth, C.A.; Jensen, B.; Boegh, E. Ammonia Emissions from Deciduous Forest after Leaf Fall. Biogeosciences 2013, 10, 4577–4589. [Google Scholar] [CrossRef] [Green Version]
  154. Asaadi, A.; Arora, V.K.; Melton, J.R.; Bartlett, P. An Improved Parameterization of Leaf Area Index (LAI) Seasonality in the Canadian Land Surface Scheme (CLASS) and Canadian Terrestrial Ecosystem Model (CTEM) Modelling Framework. Biogeosciences 2018, 15, 6885–6907. [Google Scholar] [CrossRef] [Green Version]
  155. Wu, Y.; Walker, J.; Schwede, D.; Peters-Lidard, C.; Dennis, R.; Robarge, W. A New Model of Bi-Directional Ammonia Exchange between the Atmosphere and Biosphere: Ammonia Stomatal Compensation Point. Agric. For. Meteorol. 2009, 149, 263–280. [Google Scholar] [CrossRef]
  156. Walker, J.; Spence, P.; Kimbrough, S.; Robarge, W. Inferential Model Estimates of Ammonia Dry Deposition in the Vicinity of a Swine Production Facility. Atmos. Environ. 2008, 42, 3407–3418. [Google Scholar] [CrossRef]
  157. Sutton, M.A.; Reis, S.; Riddick, S.N.; Dragosits, U.; Nemitz, E.; Theobald, M.R.; Tang, Y.S.; Braban, C.F.; Vieno, M.; Dore, A.J.; et al. Towards a Climate-Dependent Paradigm of Ammonia Emission and Deposition. Philos. Trans. R. Soc. B Biol. Sci. 2013, 368, 20130166. [Google Scholar] [CrossRef] [PubMed]
  158. Massad, R.S.; Loubet, B.; Tuzet, A.; Cellier, P. Relationship between Ammonia Stomatal Compensation Point and Nitrogen Metabolism in Arable Crops: Current Status of Knowledge and Potential Modelling Approaches. Environ. Pollut. 2008, 154, 390–403. [Google Scholar] [CrossRef] [PubMed]
  159. Nikolov, N.; Zeller, K. Efficient Retrieval of Vegetation Leaf Area Index and Canopy Clumping Factor from Satellite Data to Support Pollutant Deposition Assessments. Environ. Pollut. 2006, 141, 539–549. [Google Scholar] [CrossRef] [PubMed]
  160. Janhäll, S. Review on Urban Vegetation and Particle Air Pollution—Deposition and Dispersion. Atmos. Environ. 2015, 105, 130–137. [Google Scholar] [CrossRef]
  161. Giardina, M.; Buffa, P.; Cervone, A.; Lombardo, C. Dry Deposition of Particle on Urban Areas. J. Phys. Conf. Ser. 2019, 1224, 012050. [Google Scholar] [CrossRef]
  162. Mariraj Mohan, S. An Overview of Particulate Dry Deposition: Measuring Methods, Deposition Velocity and Controlling Factors. Int. J. Environ. Sci. Technol. 2016, 13, 387–402. [Google Scholar] [CrossRef] [Green Version]
  163. Kouznetsov, R.; Sofiev, M. A Methodology for Evaluation of Vertical Dispersion and Dry Deposition of Atmospheric Aerosols. J. Geophys. Res. Atmos. 2012, 117, D01202. [Google Scholar] [CrossRef] [Green Version]
  164. Le Roux, G.; Hansson, S.V.; Claustres, A. Chapter 3—Inorganic Chemistry in the Mountain Critical Zone: Are the Mountain Water Towers of Contemporary Society Under Threat by Trace Contaminants? In Mountain Ice and Water; Greenwood, G.B., Shroder, J.F., Eds.; Elsevier: Amsterdam, The Netherlands, 2016; Volume 21, pp. 131–154. ISBN 0928-2025. [Google Scholar]
  165. Hansen, K.; Thimonier, A.; Clarke, N.; Staelens, J.; Žlindra, D.; Waldner, P.; Marchetto, A. Chapter 18—Atmospheric Deposition to Forest Ecosystems. In Forest Monitoring; Ferretti, M., Fischer, R., Eds.; Elsevier: Amsterdam, The Netherlands, 2013; Volume 12, pp. 337–374. ISBN 1474-8177. [Google Scholar]
  166. Baek, B.H.; Aneja, V.P.; Tong, Q. Chemical Coupling between Ammonia, Acid Gases, and Fine Particles. Environ. Pollut. 2004, 129, 89–98. [Google Scholar] [CrossRef]
  167. Massad, R.-S.; Loubet, B. (Eds.) Review and Integration of Biosphere-Atmosphere Modelling of Reactive Trace Gases and Volatile Aerosols; Springer: Versailles, France, 2013; ISBN 978-94-017-7284-6. [Google Scholar]
  168. Menut, L.; Bessagnet, B. Atmospheric Composition Forecasting in Europe. Ann. Geophys. 2010, 28, 61–74. [Google Scholar] [CrossRef]
Figure 1. Abbreviated N cycle showing the role of NH3 adapted from Doyle et al. [19].
Figure 1. Abbreviated N cycle showing the role of NH3 adapted from Doyle et al. [19].
Air 01 00003 g001
Figure 2. Resistance model schematic for bi-directional NH3 flux adapted from Pleim et al. (2013) [52].
Figure 2. Resistance model schematic for bi-directional NH3 flux adapted from Pleim et al. (2013) [52].
Air 01 00003 g002
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Pohl, V.; Gilmer, A.; Hellebust, S.; McGovern, E.; Cassidy, J.; Byers, V.; McGillicuddy, E.J.; Neeson, F.; O’Connor, D.J. Ammonia Cycling and Emerging Secondary Aerosols from Arable Agriculture: A European and Irish Perspective. Air 2023, 1, 37-54.

AMA Style

Pohl V, Gilmer A, Hellebust S, McGovern E, Cassidy J, Byers V, McGillicuddy EJ, Neeson F, O’Connor DJ. Ammonia Cycling and Emerging Secondary Aerosols from Arable Agriculture: A European and Irish Perspective. Air. 2023; 1(1):37-54.

Chicago/Turabian Style

Pohl, Vivien, Alan Gilmer, Stig Hellebust, Eugene McGovern, John Cassidy, Vivienne Byers, Eoin J. McGillicuddy, Finnian Neeson, and David J. O’Connor. 2023. "Ammonia Cycling and Emerging Secondary Aerosols from Arable Agriculture: A European and Irish Perspective" Air 1, no. 1: 37-54.

Article Metrics

Back to TopTop