Reviewing global estimates of surface reactive nitrogen concentration and deposition using satellite retrievals
- 1College of Earth and Environmental Sciences, Lanzhou University, Lanzhou, 730000, China
- 2International Institute for Earth System Science, Nanjing University, Nanjing, 210023, China
- 3College of Resources and Environmental Sciences, National Academy of Agriculture Green Development, China Agricultural University, Beijing, 100193, China
- 4State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China
- 5Department of Atmospheric and Oceanic Science, Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA
- 6Chief Technology Officer SailBri Cooper Inc., Beaverton, OR 97008, USA
- 7Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Correspondence: Lei Liu (firstname.lastname@example.org)
Since the industrial revolution, human activities have dramatically changed the nitrogen (N) cycle in natural systems. Anthropogenic emissions of reactive nitrogen (Nr) can return to the earth's surface through atmospheric Nr deposition. Increased Nr deposition may improve ecosystem productivity. However, excessive Nr deposition can cause a series of negative effects on ecosystem health, biodiversity, soil, and water. Thus, accurate estimations of Nr deposition are necessary for evaluating its environmental impacts. The United States, Canada and Europe have successively launched a number of satellites with sensors that allow retrieval of atmospheric NO2 and NH3 column density and therefore estimation of surface Nr concentration and deposition at an unprecedented spatiotemporal scale. Atmosphere NH3 column can be retrieved from atmospheric infra-red emission, while atmospheric NO2 column can be retrieved from reflected solar radiation. In recent years, scientists attempted to estimate surface Nr concentration and deposition using satellite retrieval of atmospheric NO2 and NH3 columns. In this study, we give a thorough review of recent advances of estimating surface Nr concentration and deposition using the satellite retrievals of NO2 and NH3, present a framework of using satellite data to estimate surface Nr concentration and deposition based on recent works, and summarize the existing challenges for estimating surface Nr concentration and deposition using the satellite-based methods. We believe that exploiting satellite data to estimate Nr deposition has a broad and promising prospect.
Nitrogen (N) exists in three forms in the environment, including reactive nitrogen (Nr), organic nitrogen (ON) and nitrogen gas (N2) (Canfield et al., 2010). N2 is the main component of air, accounting for 78 % of the total volume of air, but it cannot be directly used by most plants. Nr refers to the general term of N-containing substances in the atmosphere, plants, soils and fertilizers that are not combined with carbon. Nr (such as and ) is the main form of N that can be directly used by most plants, but the content of Nr in nature is much lower compared with ON and N2 (Vitousek et al., 1997; Nicolas and Galloway, 2008). The supply of Nr is essential for all life forms and contributes to the increase in agricultural production, thus providing sufficient food for the growing global population (Galloway et al., 2008, 2014b; David et al., 2013; Erisman et al., 2008). Before the industrial revolution, Nr mainly came from natural sources such as biological N fixation, lightning and volcanic eruption (Galloway et al., 2004a). Since the industrial revolution, human activities (e.g., agricultural development, combustion of fossil energy) have greatly perturbed the N cycle in natural systems (Canfield et al., 2010; Kim et al., 2014; Lamarque et al., 2005).
Nr (NOx and NH3) emitted to the atmosphere will return to the earth's surface through atmospheric deposition (Liu et al., 2011). Atmospheric Nr deposition refers to the process in which Nr is removed from the atmosphere, including wet (rain and snow) and dry (gravitational settling, atmospheric turbulence, etc.) deposition (Xu et al., 2015; Zhang et al., 2012; Pan et al., 2012). The input of Nr over terrestrial natural ecosystems primarily comes from the Nr deposition (Shen et al., 2013; Sutton et al., 2001; Larssen et al., 2011). In the short term, atmospheric Nr deposition can increase the Nr input to ecosystems, which promotes plant growth and enhances ecosystem productivity (Erisman et al., 2008). However, excessive atmospheric Nr deposition also causes a series of environmental problems (X. Liu et al., 2017). Due to the low efficiency of agricultural N application, plenty of Nr is lost through runoff, leaching and volatilization, causing serious environmental pollution. Excessive Nr deposition may aggravate the plant's susceptibility to drought or frost, reduce the resistance of the plant to pathogens or pests, and further affect the physiology and biomass distribution of vegetation (ratio of roots, stems and leaves) (Stevens et al., 2004; Nadelhoffer et al., 1999; Bobbink et al., 2010; Janssens et al., 2010). Excessive Nr leads to eutrophication and related algal blooms over aquatic ecosystems, reducing water biodiversity (Paerl et al., 2014), while excessive Nr in drinking water also poses a threat to human health (Zhao et al., 2013; Wei et al., 2019). Therefore, monitoring and estimation of surface Nr concentration and deposition on the global scale are of great importance and urgency.
The methods of estimating atmospheric Nr deposition can be divided into three categories: ground-based monitoring, atmospheric chemical transport modeling (ACTM) and satellite-based estimation. Ground-based monitoring is considered to be the most accurate and quantitative method, which can effectively reflect the Nr deposition in local areas. ACTM can simulate the processes of Nr chemical reaction, transport, and deposition, as well as the vertical distribution of Nr. Satellite-based estimation establishes empirical, physical or semi-empirical models by connecting the ground-based Nr concentrations and deposition with satellite-derived Nr concentration. This study focuses on reviewing the recent development of satellite-based methods to estimate Nr deposition. Since the estimation of Nr concentrations is just a part of the estimation of dry Nr depositions, we here mainly reviewed the progress of dry Nr depositions using the satellite observation. We firstly give a brief introduction to the progress of ground-based monitoring and ACTM-based methods and then present a detailed framework of using satellite observation to estimate dry and wet Nr deposition (including both oxidized and reduced Nr). Next, we review the recent advances of the satellite-based methods of estimating Nr deposition. Finally, we discuss the remaining challenges for estimating surface Nr concentration and deposition using satellite observation.
2.1 Ground-based monitoring
Ground-based monitoring of Nr deposition can be divided into two parts: wet and dry Nr deposition monitoring. Since the 1970s, there have been large-scale monitoring networks focusing on the wet Nr deposition. The main large-scale regional monitoring networks include the Canadian Air and Precipitation Monitoring Network (CAPMoN), Acid Deposition Monitoring Network in East Asia (EANET), European Monitoring and Evaluation Program (EMEP), United States National Atmospheric Deposition Program (NADP), World Meteorological Organization Global Atmosphere Watch Precipitation Chemistry Program, and Nationwide Nitrogen Deposition Monitoring Network in China (NNDMN) (Tan et al., 2018; Vet et al., 2014). The detailed scientific objectives of the wet Nr deposition observation networks vary, but most of the observation networks mainly concentrate on the spatiotemporal variation of wet deposition of ions including Nr compounds, the long-term trends of ions in precipitation, and the evaluation of ACTMs.
Compared with wet Nr deposition monitoring, dry Nr deposition monitoring started late, due to the limitation of monitoring technology since it is more difficult to be quantified (affected greatly by surface roughness, air humidity, climate and other environmental factors) (Liu et al., 2017c). Dry Nr deposition observation networks include the US ammonia monitoring network (AMoN), CAPMoN, EANET and EMEP. The monitoring methods of dry Nr deposition are mainly divided into direct monitoring (such as dynamic chambers) and indirect monitoring (such as inferential methods). The inferential model is widely applied in ground-based monitoring networks (such as EANET and NNDMN), mainly because this method is more practical and simpler. In inferential models, dry deposition is divided into two parts: surface Nr concentrations and the deposition velocity (Vd) of Nr (Nowlan et al., 2014). Vd can be estimated by meteorology, land use types of the underlying surface as well as the characteristics of each Nr component itself using resistance models (Nemitz et al., 2001). Thus, dry Nr deposition monitoring networks only need to focus on the quantification of surface concentration of individual Nr components. The Nr components in the atmosphere are very complex, including N2O5, HONO, NH3, NO2, HNO3 and particulate and . Most monitoring networks include the major Nr species such as gaseous NH3, NO2, HNO3 and the particles of and .
Efforts of ground-based Nr deposition monitoring are mostly concentrated on wet Nr deposition, while observations of dry Nr deposition are relatively scarce, especially for surface HNO3 and and . Second, most observation networks focus on a few years or a certain period of time, leading to the lack of long-term continuously monitoring on both wet and dry Nr deposition. More importantly, the global Nr deposition monitoring network has not been established, and the sampling standards in different regions are not unified. These outline the potential room for improvement of ground-based Nr deposition monitoring.
2.2 Atmospheric Chemistry Transport Model (ACTM) simulation
An ACTM can simulate Nr deposition at regional or global scales by explicitly representing the physical and chemical processes of atmospheric Nr components (Zhao et al., 2017; Zhang et al., 2012). Wet Nr deposition flux is parameterized as in-cloud, under-cloud and precipitation scavenging (Amos et al., 2012; Levine and Schwartz, 1982; Liu et al., 2001; Mari et al., 2000), while dry deposition flux can be obtained as the product of surface Nr concentration and Vd, which is typically parameterized as a network of resistances (Wesely and Hicks, 1977). Based on the integrated results of 11 models of HTAP (hemispheric transport of air pollution), Tan et al. (2018) found that about 76 %–83 % of the ACTM's simulation results were ±50 % of the monitoring values, and the modeling results underestimated the wet deposition of and over Europe and East Asia and overestimated the wet deposition of over the eastern US (Tan et al., 2018). Though regional ACTMs can be configured at very high horizontal resolution (e.g., 1×1 km2) (Kuik et al., 2016), the horizontal resolutions of global ACTMs are relatively coarse (–) (Williams et al., 2017), which cannot indicate the local pattern of Nr deposition. On the other hand, the Nr emission inventory used to drive an ACTM is highly uncertain, with the uncertainty of the NOx emission at about ±30 %–40 % and that of NH3 emission at about ±30 %–80 % (Zhang et al., 2009; Cao et al., 2011).
2.3 Satellite-based estimation of surface Nr concentration and deposition
Satellite observation has wide spatial coverages and high resolution and is spatiotemporally continuous. Atmospheric NO2 and NH3 columns can be derived from satellite measurements with relatively high accuracy (Van Damme et al., 2015; Boersma et al., 2011), providing a new perspective about atmospheric Nr abundance.
Satellite instruments that can monitor NO2 in the atmosphere include GOME (Global Ozone Monitoring Experience), SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric ChartographY), OMI (Ozone Monitoring Instrument), and GOME-2 (Global Ozone Monitoring Experience-2). Some scholars applied satellite NO2 columns to estimate the surface NO2 concentration and then dry NO2 deposition by combining the surface NO2 concentration and modeled Vd. Cheng et al. (2013) established a statistical model to estimate the surface NO2 concentration based on the SCIAMACHY NO2 columns and then estimated the dry deposition of NO2 over eastern China (Cheng et al., 2013). This method used the simple linear model and did not consider the vertical profiles of NO2 (Cheng et al., 2013). Lu et al. (2013) established a multivariate linear regression model based on the SCIAMACHY and GOME NO2 columns, meteorological data and ground-based monitoring Nr deposition and then estimated the global total Nr deposition (Lu et al., 2013). Lu et al. (2013) could not distinguish the contribution of dry and wet Nr deposition using the multivariate linear regression model (Lu et al., 2013). Jia et al. (2016) established a simple linear regression model based on OMI tropospheric NO2 column and ground-based surface Nr concentration and then estimated the total amounts of dry Nr deposition (Jia et al., 2016). Jia et al. (2016) used the OMI tropospheric NO2 column to estimate the dry deposition of reduced Nr deposition (NH3 and ), which could also bring great errors since the OMI NO2 column could not indicate the NH3 emission. These studies highlight the problem of using only NO2 columns to derive total Nr deposition: that NO2 columns give us highly limited information about the abundance of reduced Nr (NH3 and ).
Lamsal et al. (2008) first used the relationship between the NO2 column and surface NO2 concentration at the bottom layer simulated by an ACTM to convert the OMI NO2 column to surface NO2 concentration (Lamsal et al., 2008). A series of works (Lamsal et al., 2013; Nowlan et al., 2014; Kharol et al., 2018) have effectively estimated the regional and global surface NO2 concentration using the satellite NO2 column combined with the ACTM-derived relationship between the NO2 column and surface NO2 concentration simulated. It is worth mentioning that Nowlan et al. (2014) applied the OMI NO2 column to obtain the global dry NO2 deposition during 2005–2007 for the first time (Nowlan et al., 2014). However, using the satellite NO2 column and ACTM-derived relationship between the NO2 column and surface NO2 concentration may lead to an underestimation of surface NO2 concentration. Kharol et al. (2015) found that the satellite-derived surface NO2 concentration using the above method is only half of the observed values (Kharol et al., 2015). To resolve such potential underestimation, Larkin et al. (2017) established a statistical relationship between the satellite-derived and ground-measured surface NO2 concentration and then calibrated the satellite-derived surface NO2 concentration using the established relationship (Larkin et al., 2017).
Some researchers also estimated other Nr components (such as particulate ) based on the satellite NO2 column. Based on the linear model between NO2, , and HNO3 obtained by ground-based measurements, Jia et al. (2016) calculated the surface and HNO3 concentration using satellite-derived surface NO2 concentration and their relationship (Jia et al., 2016). Geddes et al. (2016) reconstructed the NOx emission data by using the satellite NO2 column and then estimated the global NOx deposition by an ACTM, but the spatial resolution of global NOx deposition remains low (), failing to exploit the higher resolution of satellite observation (Geddes and Martin, 2017).
Compared with NO2, the development of satellite NH3 monitoring is relatively late. Atmospheric NH3 was first detected by the TES in Beijing and Los Angeles (Beer et al., 2008). The IASI sensor also detected atmospheric NH3 from a biomass burning event in Greece (Coheur et al., 2009). Subsequently, many scholars began to develop more reliable satellite NH3 column retrievals (Whitburn et al., 2016; Van Damme et al., 2015), validate the satellite-retrieved NH3 column with the ground-based observation (Van Damme et al., 2015; Dammers et al., 2016; Li et al., 2017), and compare the satellite NH3 column with the aircraft-measured NH3 column (Van Damme et al., 2014; Whitburn et al., 2016). In recent years, some scholars have carried out the works of estimating surface NH3 concentration based on the satellite NH3 column. Liu et al. (2017) obtained the satellite-derived surface NH3 concentration in China based on the IASI NH3 column coupled with an ACTM and deepened the understanding of the spatial pattern of surface NH3 concentration in China (Liu et al., 2017b). Similarly, Van der Graaf et al. (2018) carried out the relevant work in Europe based on the IASI NH3 column coupled with an ACTM and estimated the dry NH3 deposition in western Europe (Van der Graaf et al., 2018). Jia et al. (2016) first constructed the linear model between surface NO2 and concentration based on ground monitoring data and then calculated the concentration using the satellite-derived surface NO2 concentration and their relationship (Jia et al., 2016). However, as the emission sources of NOx (mainly from the transportation and energy sectors) and NH3 (mainly from the agricultural sector) are different (Hoesly et al., 2018), the linear model between surface NO2 and concentration may lead to large uncertainties in estimating the global concentration. There is still no report about the satellite-derived dry and wet-reduced Nr deposition using the satellite NH3 column at a global scale. As reduced Nr plays an important role in total Nr deposition, satellite NH3 should be better utilized to help estimate reduced Nr deposition.
2.4 Problems in estimating global Nr deposition
The spatial coverage of ground monitoring sites focusing on Nr deposition is still not adequate, and the monitoring standards and specifications in different regions of the world are not consistent, presenting a barrier to integrating different regional monitoring data. Large uncertainties exist in the Nr emission inventory used to drive the ACTMs, and the spatial resolution of the modeled Nr deposition by ACTMs is coarse. Using satellite monitoring data to estimate surface Nr concentration and deposition is still in its infancy, especially for reduced Nr.
Some scholars tried to use the satellite NO2 and NH3 column to estimate the surface Nr concentration and dry Nr deposition. However, there are relatively few studies on estimating wet Nr deposition. In addition, the development of satellite monitoring for NH3 in the atmosphere is relatively late (compared with NO2). At present, IASI NH3 data have been widely used, while the effective measurements of TES are less than IASI; CrIS and AIRS NH3 column products are still under development. There are three main concerns in high-resolution estimation of surface Nr concentration and deposition based on satellite Nr observation. (1) How to effectively couple the satellite high-resolution NO2 and NH3 column data with the vertical profiles simulated by an ACTM and then estimate the surface Nr concentrations? This step is the key to simulating the dry Nr deposition. (2) How to construct a model for estimating dry Nr deposition including all major Nr species based on the satellite NO2 and NH3 column and then for estimating the dry Nr deposition at a high spatial resolution? (3) How to combine the high-resolution satellite NO2 and NH3 column data and ground-based monitoring data to construct wet Nr deposition models and then estimate the wet Nr deposition at a high spatial resolution?
Previous studies using satellite observation to estimate surface Nr concentration and deposition only focused on one or several Nr components, but did not include all Nr components, which were decentralized, unsystematic and incomplete. Here we give a framework of using satellite observation to estimate surface Nr concentration and deposition as shown in Fig. 1 based on recent advances.
3.1 Conversion of the satellite NO2 and NH3 column to surface Nr concentration
An ACTM can simulate the vertical profiles of NO2 and NH3 with multiple layers from the surface to the troposphere. For example, the GEOS-Chem ACTM includes 47 vertical layers from the earth's surface to the top of the stratosphere. Most previous studies estimated the ratio of surface Nr concentration (at the first layer) to total columns by an ACTM and then multiply the ratio by satellite columns to estimate satellite-derived surface concentration (Geddes et al., 2016; Van der Graaf et al., 2018; Nowlan et al., 2014).
Another approach tries to fit general vertical profiles of NO2 and NH3 (Zhang et al., 2017; Liu et al., 2017b, c) and then estimate the ratio of Nr concentration at any height to total Nr columns and finally multiply the ratio by satellite NO2 and NH3 columns. This approach has an advantage compared with the previous one because NO2 and NH3 concentration at all altitudes included in ACTM simulations can be estimated. Satellite NO2 and NH3 column data had no vertical profiles. Surface NO2 and NH3 concentration was estimated by modeled NO2 and NH3 vertical profiles from the CTM. The Gaussian model was constructed to fit the multiple layers' NO2 and NH3 concentrations with the altitude. The constructed Gaussian model has general rules, appropriate for converting satellite columns to surface concentration simply.
Taking the estimation of surface NO2 concentration using the latter approach as an example, the methods and steps are introduced in the following.
Step 1: calculate the monthly mean NO2 concentrations at all layers simulated by an ACTM.
Step 2: construct the vertical profile function of NO2. Multiple Gaussian functions are used to fit the vertical distribution of NO2 based on the monthly NO2 concentrations at all layers calculated in Step 1, in which the independent variable is the height (altitude) and the dependent variable is NO2 concentration at a certain height.
The basic form of the single Gaussian function is (Zhang et al., 2017; Liu et al., 2017b, c; Whitburn et al., 2016)
where Z is the height of a layer in the ACTM; ρmax, Z0 and σ are the maximum NO2 concentration, the corresponding height with the maximum NO2 concentration and the thickness of the NO2 concentration layer (1 standard error of the Gaussian function).
There are two basic forms of profile shapes of NO2: (1) NO2 concentration reaches the maximum concentration when reaching a certain height (Zo≠0). As the height increases, the NO2 concentration begins to decline; (2) NO2 concentration is basically concentrated on the earth's surface (Zo=0). These two cases are the ideal state of the vertical distribution of NO2 concentration. In reality, single Gaussian fitting may not capture the vertical distribution of NO2 well. To improve the accuracy of fitting, the sum of multiple Gaussian functions can be used (Liu et al., 2019):
Step 3: calculate the ratio of NO2 concentration at the height of hG to total columns () and then multiply the ratio by the satellite column (Strop). The satellite-derived Nr concentration at the height of hG can be calculated as
Step 4: convert the instantaneous satellite-derived surface NO2 concentration (SG_NO2) to the daily average () using the ratio of average surface NO2 concentration () to that at satellite overpass time () by an ACTM (Liu et al., 2020):
The method for estimating the surface NH3 concentration () is similar to that for estimating the surface NO2 concentration.
3.2 Estimating surface concentrations of other Nr species
At present, only the NO2 and NH3 column can be retrieved reliably, and there are no reliable satellite retrievals of HNO3, and . For example, the IASI HNO3 product is still in the stage of data development and verification (Ronsmans et al., 2016). Previous studies firstly derive the relationship between Nr species by an ACTM or by ground-based measurements and then use the relationship to convert satellite-derived surface NO2 and NH3 concentration () to HNO3, and concentrations:
, , and are the estimated ratios between NO2 and , NO2 and HNO3, and NH3 and .
3.3 Dry deposition of Nr
The resistance of dry Nr deposition mainly comes from three aspects: aerodynamic resistance (Ra), quasi laminar sub-layer resistance (Rb) and canopy resistance (Rc). The Vd can be expressed as
Vg is gravitational settling velocity. For gases, the Vg is negligible (Vg=0).
Dry NO2, , HNO3, and deposition can be calculated by
Unlike the above species, NH3 is bi-directional, presenting both upward and downward fluxes. There is a so-called “canopy compensation point” (Co) controlling dry NH3 deposition. Dry NH3 deposition can be calculated by
The calculation of Co is very complex, including the leaf stomatal and soil emission potentials related to the meteorological factors, the plant growth stage and the canopy type. The satellite-based methods usually neglected this complex process and set Co as zero (Van der Graaf et al., 2018; Kharol et al., 2018) or set fixed values in each land use type based on ground-based measurements (Jia et al., 2016).
3.4 Wet deposition of Nr
The satellite-based estimation of wet Nr deposition can be simplified as the product of the concentration of Nr (C), precipitation (P) and scavenging coefficient (w) (Pan et al., 2012). Satellite NO2 and NH3 can be used to indicate the oxidized Nr and reduced Nr; precipitation (P) can be obtained from ground monitoring data or reanalysis data (such as NCEP). However, the scavenging coefficient (w) is usually highly uncertain. To improve the accuracy of estimation, a mixed-effects model (Liu et al., 2017a; Zhang et al., 2018) is proposed to build the relationship between satellite NO2 and NH3, precipitation and ground monitoring wet Nr deposition:
WetNij is wet N or –N deposition at month i and site j; is the atmospheric boundary layer (ABL) NO2 or NH3 columns at month i and site j; Pij is precipitation at month i and site j; βi and αj are the slope and intercept of random effects, representing seasonal variability and spatial effects, and εij represents the random error at month i and site j. The mixed-effects models were appropriate for estimating both wet and deposition using the satellite observations.
The scavenging process of wet Nr deposition usually starts from the height of rainfall rather than the top of the troposphere, so it is more reasonable to use the NO2 and NH3 column below the height of rainfall to build the wet Nr deposition model. The NO2 and NH3 column within the ABL is used to build the wet deposition model since precipitation height is close to the height of the ABL (generally less than 2–3 km).
4.1 Surface NO2 concentration and oxidized Nr deposition
The spatial resolutions of global ACTMs and therefore modeled surface Nr concentration are very coarse (for example, the spatial resolution of the global version of GEOS-Chem is ). Thus it can be hard to estimate surface Nr concentration and deposition at a fine resolution at a global scale by ACTMs alone. Instead, the satellite Nr retrievals have a high spatial resolution and can reveal more spatial details than ACTM simulations.
Cheng et al. (2013) and Jia et al. (2016) established a linear model between the surface NO2 concentration and NO2 column by assuming the ratio of the surface NO2 concentration to the tropospheric NO2 column to be fixed, then used the linear model to convert satellite NO2 columns to surface NO2 concentration, and finally estimated dry NO2 deposition using the inferential method (Cheng et al., 2013; Jia et al., 2016). However, these statistical methods are highly dependent on the ground-based measurements, and the established linear models may be ineffective over regions with few monitoring sites.
A comprehensive study (Nowlan et al., 2014) estimated global surface NO2 concentration during 2005–2007 by multiplying OMI tropospheric NO2 columns by the ACTM-modeled ratio between the surface NO2 concentration and tropospheric column (Fig. 2). Nowlan et al. (2014) also estimated dry NO2 deposition using the OMI-derived surface NO2 concentration by combining the modeled Vd during 2005–2007 (Nowlan et al., 2014). This approach followed an earlier study (Lamsal et al., 2008) that focused on North America. As reported by Lamsal et al., the satellite-derived surface NO2 concentration was generally lower than ground-based NO2 observations, ranging from −17 % to −36 % in North America (Lamsal et al., 2008). Kharol et al. (2015) used a similar method and found the satellite-derived surface NO2 concentration was only half of the ground-measured values in North America (Kharol et al., 2015).
Geddes et al. (2016) followed previous studies and used the NO2 column from GOME, SCIAMACHY, and GOME-2 to estimate surface NO2 concentration (Geddes et al., 2016). Although Geddes et al. (2016) did not evaluate their results with ground-based observation (Geddes et al., 2016), it is obvious that their surface NO2 estimates were higher than Nowlan's estimates based on OMI (Nowlan et al., 2014) (Fig. 2). This may be because the OMI-derived NO2 column is much lower than that derived by GOME, SCIAMACHY, and GOME-2, especially over polluted regions. For example, in China, the OMI NO2 column is about 30 % lower than that of SCIAMACHY and GOME-2 consistently (Fig. 3).
Larkin et al. (2017) established a land use regression model to estimate global surface NO2 concentration by combining satellite-derived surface NO2 concentration by Geddes et al. (2016) and ground-based annual NO2 measurements (Geddes et al., 2016; Larkin et al., 2017). The study by Larkin et al. (2017) can be considered to use the ground-based annual measurements to adjust the satellite-derived surface NO2 concentration by Geddes et al. (2016), which helped reduce the discrepancy between satellite-derived and ground-measured NO2 concentration. The regression model captured 54 % of global NO2 variation, with an absolute error of 2.32 µg N m−3.
Zhang et al. (2017) followed the framework in Sect. 3 to estimate the OMI-derived surface NO2 concentration (at ∼50 m) in China and found good agreement with ground-based surface NO2 concentration from the NNDMN at a yearly scale (slope =1.00, R2=0.89) (Zhang et al., 2017). The methods by Zhang et al. (2017) can also generate OMI-derived NO2 concentration at any height by the constructed NO2 vertical profile (Zhang et al., 2017). Zhang et al. (2017) also estimated dry NO2 deposition using the OMI-derived surface NO2 concentration by combining the modeled Vd during 2005–2016 (Zhang et al., 2017). Based on Zhang's estimates, the Gaussian function can well simulate the vertical distribution of NO2 from an ACTM (MOZART) (Emmons et al., 2010), with 99.64 % of the grids having R2 values higher than 0.99. This suggests that the ACTM-simulated vertical distribution of NO2 has a general pattern, which can be emulated by Gaussian functions. Once a vertical profile has been constructed, it can be easily used to estimate NO2 concentration at any height.
In this study, we used the framework in Sect. 3 to estimate the OMI-derived surface NO2 concentration globally. To validate the OMI-derived surface NO2 concentrations, ground-measured surface NO2 concentration in China, the US and Europe in 2014 was collected (Fig. 4). The total number of NO2 observations in China, the US and Europe are 43, 373 and 88, respectively. The OMI-derived annual average for all sites was 3.74 µg N m−3, which was close to the measured average (3.06 µg N m−3). The R2 between OMI-derived surface NO2 concentrations and ground-based NO2 measurements was 0.75 and the RMSE was 1.23 µg N m−3 (Fig. 5), which is better than the modeling results by the GEOS-Chem ACTM (R2=0.43, RMSE = 1.93 µg N m−3). We did not simply use the relationship between the NO2 column and surface NO2 concentration from the CTM. As presented in the methods, we can estimate surface NO2 concentration at any height by using the Gaussian function. We used the surface NO2 concentration at a certain height (∼60 m) which best matched with the ground-based measurements. Satellite-based methods have the advantages of spatiotemporally continuous monitoring Nr at a higher resolution, which helps alleviate the problem of the coarse resolution of ACTMs in estimating Nr concentration and deposition. The readers can use any satellite data (GOME, SCIAMACHY, GOME2 or OMI) combining the Gaussian function to estimate surface NO2 concentrations. They can use surface NO2 concentrations at a certain height which best matched with the ground-based measurements. The key is not selecting which satellite data we should use, but determining which height of surface NO2 concentrations better matched with the ground-based measurements by a Gaussian function.
For and HNO3, previous studies firstly constructed the relationship between NO2, and HNO3 and found a relatively high linear relationship between NO2, , and HNO3 at a monthly or yearly scale. For example, Jia et al. (2016) found a linear relationship between NO2, , and HNO3 concentration at an annual scale (R2=0.70) (Jia et al., 2016). Similarly, based on the ground-based measurements in the NNDMN, a high correlation was found between surface NO2 and concentration at monthly or annual timescales (Fig. 6) (Liu et al., 2017c). Using these linear relationships and satellite-derived surface NO2 concentration, the annual mean surface and HNO3 can be estimated. Alternatively, the relationship of NO2, and HNO3 can also be modeled by an ACTM. For example, a strong relationship of the tropospheric NO2, and HNO3 column was simulated over all months by an ACTM, with the correlation ranging from 0.69 to 0.91 (Liu et al., 2017a). But, over shorter timescales, the relationship between NO2, and HNO3 may be nonlinear, which we should be cautious about when estimating surface and HNO3 concentration from NO2 concentration.
For the wet Nr deposition, Liu et al. (2017a) followed the framework in Sect. 3 to estimate wet nitrate deposition using ABL NO2 columns derived from an OMI NO2 column and NO2 vertical profile from an ACTM (MOZART), and precipitation by a mixed-effects model showing the proposed model can achieve high predictive power for monthly wet nitrate deposition over China (R=0.83, RMSE = 0.72) (Liu et al., 2017a).
4.2 Surface NH3 concentration and reduced Nr deposition
With the development of atmospheric remote sensing of NH3, some scholars have estimated surface NH3 concentration and dry NH3 deposition based on the satellite NH3 column data. Assuming the ratio between the surface NH3 concentration to the NH3 column was fixed, Yu et al. (2019) applied a linear model to convert satellite NH3 columns to surface NH3 concentration and estimated dry NH3 deposition in China using the inferential method (Yu et al., 2019). But Yu et al. (2019) did not consider the spatial variability of the vertical profiles of NH3 (Yu et al., 2019), which may cause a large uncertainty in estimating surface NH3 concentration.
In western Europe, Van der Graaf et al. (2018) used the ratio of the surface NH3 concentration (in the bottom layer) to total NH3 column from an ACTM to convert the IASI NH3 column to surface NH3 concentration and then estimated dry NH3 deposition by combining the modeled deposition velocity and IASI-derived surface NH3 concentration (Van der Graaf et al., 2018). Similarly, in North America, Kharol et al. (2018) estimated the dry NH3 deposition by the CrIS-derived surface NH3 concentration and deposition velocity of NH3 (Kharol et al., 2018). They found a relatively high correlation (R=0.76) between the CrIS-derived surface NH3 concentration and AMoN measurements during warm seasons (from April to September) in 2013 (Fig. 7). Over China, Liu et al. (2017b) found a higher correlation (R=0.81) between IASI-derived surface NH3 concentrations and the measured surface NH3 concentrations than those from an ACTM (R=0.57, Fig. 8) (Liu et al., 2017b).
Liu et al. (2019) followed the framework in Sect. 3 to estimate the IASI-derived surface NH3 concentration (at the middle height of the first layer by an ACTM) (Fig. 9) and found a good agreement with ground-based surface NH3 concentration (Liu et al., 2019). The correlation between the measured and satellite-derived annual mean surface NH3 concentrations over all sites was 0.87 as shown in Fig. 10, while the average satellite-derived and ground-measured surface NH3 concentrations were 2.52 and 2.51 µg N m−3 in 2014 at the monitoring sites, respectively. The satellite-derived estimates achieved a better accuracy (R2=0.76, RMSE = 1.50 µg N m−3) than an ACTM (GEOS-Chem, R2=0.54, RMSE = 2.14 µg N m−3). The satellite NH3 retrievals were affected by the detection limits of the satellite instruments and thermal contrast. Higher correlation over China than other regions for the satellite estimates was linked to the detection limits by the instruments and thermal contrast (Liu et al., 2019). Higher accuracy could be gained with higher thermal contrast and NH3 abundance. Instead, the uncertainties of NH3 retrievals would be higher with lower thermal contrast and NH3 abundance.
The proposed methods (Liu et al., 2019) can also estimate NH3 concentration at any height using the constructed vertical profile function of NH3. The Gaussian function can well emulate the vertical distribution of NH3 from an ACTM output, with 99 % of the grids having R2 values higher than 0.90 (Fig. 11). This means, for regional and global estimation, the vertical distribution of NH3 concentration has a general pattern, which can be mostly emulated by the Gaussian function. Once a global NH3 vertical profile was simulated, it can be easily used to estimate satellite-derived NH3 concentration at any height. We can also estimate dry NH3 deposition using the IASI-derived surface NH3 concentration combining the modeled Vd. For the dry deposition, the uncertainty mainly came from the satellite-derived estimates using the modeled vertical profiles. The uncertainty of vertical profiles modeled by the ACTM mainly resulted from the chemical and transport mechanisms. We recommend using the Gaussian function to determine the height of surface NO2 and NH3 concentrations that best matched with the ground-based measurements. There may exist systematic biases by simply using the relationship of NO2 columns and surface concentration to estimate satellite surface NO2 concentrations. To date, there are still no studies developing satellite-based methods to estimate the wet reduced Nr deposition on a regional scale.
The Nr concentration and deposition modeled by ACTMs are highly dependent on the accuracy of input Nr emissions. The methods commonly used to estimate anthropogenic Nr emissions are based on the data of human activities and emission factors, which can be highly uncertain. The ACTM methods driven by the Nr emission inventory have relatively poor timeliness and have limitations in monitoring the recent trends of Nr deposition.
Satellite-based methods provide a simple, fast and relatively objective way to monitor Nr deposition at a high resolution and are less susceptible to the errors in the assumptions that emission inventories are based on, particularly the lack of reliable data on developing countries (Crippa et al., 2018). With such advantages, researchers developed the satellite-based methods to estimate surface Nr concentration, deposition and even emissions. Satellite-based methods have advantages in monitoring the recent trends of Nr deposition. Geddes et al. (2016) used the NO2 column from GOME, SCIAMACHY, and GOME-2 to estimate satellite-derived NOx emissions and then used the calibrated NOx emission inventory to drive an ACTM to simulate the long-term oxidized Nr deposition globally (Geddes and Martin, 2017). They found oxidized Nr deposition from 1996 to 2014 decreased by 60 % in the eastern US, doubled in eastern China, and declined by 20 % in western Europe (Fig. 12). We use the datasets by Geddes et al. (2016) to calculate the trends of total oxidized Nr deposition during 1996–2014 (Geddes and Martin, 2017). It is obvious that two completely opposite trends exist: (1) in eastern China with a steep increase of higher than 0.5 kg N ha−1 yr−1 and (2) in eastern US with a steep decrease of lower than −0.5 kg N ha−1 yr−1. Although it is not a direct way to use satellite Nr observation to estimate Nr deposition, the method of estimating trends of Nr deposition by Geddes et al. (2016) can be considered effective since it took account of the changes in both NOx emission and climate by an ACTM (Geddes and Martin, 2017).
Some researchers developed a more direct way to infer the trends of surface Nr concentration and deposition. Geddes et al. (2016) presented a comprehensive long-term global surface NO2 concentration estimate (at 0.1∘ resolution using an oversampling approach) between 1996 and 2012 by using the NO2 column from GOME, SCIAMACHY, and GOME-2 (Geddes et al., 2016). The surface NO2 concentration in North America (the US and Canada) decreased steeply, followed by western Europe, Japan and South Korea, but approximately tripled in China and North Korea (Geddes et al., 2016). Jia et al. (2016) established a simple linear regression model based on the OMI NO2 column and ground-based surface Nr concentration and then estimated the trends of dry Nr deposition globally between 2005 and 2014 (Jia et al., 2016). They found that dry Nr deposition in eastern China increased rapidly, while in the eastern US, western Europe, and Japan dry Nr deposition has decreased in recent decades.
We used the proposed framework to estimate the long-term surface NO2 concentrations by OMI during 2005–2016. Note that the simulated profile function has a general rule, which can be well simulated by a Gaussian function for any year (for our case during 2005–2016). The emission inventories should not affect the vertical profile shapes using a Gaussian function, but the transport and chemical mechanism in the CTM may affect the accuracy of the vertical profile distribution. The satellite-based methods did not need to rely on the accuracy of the statistical emission data. We split the time span of 2005–2016 into two periods, 2005–2011 and 2011–2016, as surface NO2 concentration shows the opposite trend in China in these two periods. The magnitudes of both growth and decline in surface NO2 concentration in China are most pronounced worldwide in the two periods (Fig. 13). During 2005–2011, apart from eastern China with the largest increase in surface NO2 concentration, there are also several areas with increasing trends, such as northwestern and eastern India (New Delhi and Orissa), western Russia, eastern Europe (northern Italy), western US (Colorado and Utah), northwestern US (Seattle and Portland), southwestern Canada (Vancouver, Edmonton, Calgary), northeastern Pakistan and northwestern Xinjiang (Urumqi). Notably, the biggest decreases in surface NO2 concentration during 2005–2011 occurred in the eastern US and western EU (North France, southern England, and western Germany). During 2011–2016, due to the strict control of NOx emissions, eastern China had the largest decrease in surface NO2 concentration than elsewhere worldwide, followed by western Xinjiang, western Europe and some areas in western Russia.
Liu et al. (2019) estimated surface NH3 concentration globally during 2008–2016 using satellite NH3 retrievals by IASI (Liu et al., 2019). A large increase in surface NH3 concentrations was found in eastern China, followed by northern Xinjiang Province in China during 2008–2016 (Fig. 14). Satellite-based methods have been proven as an effective and unique way to monitor the trends of global Nr concentration and deposition. To date, there are still few studies reporting the satellite-derived trends of reduced Nr deposition on a global scale.
First, the reduced Nr deposition makes an important contribution to total Nr deposition. NH3 exhibits bi-directional air–surface exchanges. The NH3 compensation point (Farquhar et al., 1980) is also an important and highly variable factor controlling dry NH3 deposition (Schrader et al., 2016; Zhang et al., 2010). However, the current existing satellite-based methods did not consider this bi-directional air–surface exchange. It is important to better parameterize the NH3 compensation point and assess the effects of bi-directional air–surface exchanges on estimating the dry NH3 deposition.
Second, the existing satellite-based methods to estimate Nr deposition used the ratio of the surface Nr concentration to the Nr column by an ACTM to convert satellite Nr column to surface Nr concentration. However, the calculated ratio (by an ACTM) and the satellite Nr column have different spatial resolutions, and previous studies usually applied the modeled ratio directly or interpolated the ratio into the resolution of the satellite Nr column. This method assumes the relationship at coarse resolution by an ACTM can also be effective at fine resolution, as the satellite indicated. When regional studies are conducted, regional ACTMs coupled with another meteorological model (e.g., WRF-Chem, WRF-CMAQ) (Grell et al., 2005; Wong et al., 2012) can be configured to match the spatial resolution of satellite observation, but this is not as viable for global ACTMs (e.g., MOZART, GEOS-Chem) due to differences in model structures and computational cost. The modeled ratio of surface Nr concentration to the Nr column may have variability at spatial scales finer than the horizontal resolution of global ACTMs. The impact of such a scale effect (at different spatial scales) on estimated surface Nr concentration should be further studied.
Third, the satellite observation can only obtain a reliable NO2 and NH3 column presently, and there are no available high-resolution and reliable direct HNO3, , and retrievals. For HNO3, , and concentrations, the satellite-based methods often applied the satellite-derived NO2 and NH3 concentration and the relationship between Nr species from an ACTM (or ground-based measurements) to estimate surface HNO3, , and concentration. With the development of satellite technology, more and more Nr species can be detected, such as HNO3. However, at present, satellite HNO3 products are not mature, and the spatial resolution is low. Direct, high-resolution and reliable satellite monitoring of more Nr species is critical to further developing the use of atmospheric remote sensing to estimate Nr deposition at global and regional scales.
Fourth, estimating wet Nr deposition using the satellite NO2 and NH3 column remains relatively uncommon. Further studies should focus on how to combine the high-resolution satellite NO2 and NH3 column and the ground-based monitoring data to build wet Nr deposition models to estimate wet Nr deposition at a higher spatiotemporal resolution. The proposed scheme to estimate the wet Nr deposition in Sect. 3 is statistical. As far as we know, previous studies using the satellite NO2 and NH3 column to estimate wet Nr deposition used a statistical way, and no studies were done from a mechanism perspective. The wet Nr deposition includes the scavenging processes of in-cloud, under-cloud and precipitation. Processed-level knowledge and models can benefit the estimation of wet Nr deposition using the satellite NO2 and NH3 column.
The recent advances of satellite-based methods for estimating surface Nr concentration and deposition have been reviewed. Previous studies have focused on using the satellite NO2 column to estimate surface NO2 concentrations and dry NO2 deposition both regionally and globally. The research on calculating surface NH3 concentration and reduced Nr deposition by satellite NH3 data is just beginning, and some scholars have carried out estimations of surface NH3 concentration and dry NH3 deposition on different spatial and temporal scales, but the research degree is still relatively low. We present a framework of using the satellite NO2 and NH3 column to estimate Nr deposition based on recent advances. The proposed framework of using a Gaussian function to model vertical NO2 and NH3 profiles can be used to convert the satellite NO2 and NH3 column to surface NO2 and NH3 concentration at any height simply and quickly. The proposed framework of using the satellite NO2 and NH3 column to estimate wet Nr deposition is a statistical way, and further studies should be done from a mechanism perspective. Finally, we summarized current challenges of using the satellite NO2 and NH3 column to estimate surface Nr concentration and deposition, including a lack of considering NH3 bidirectional air–surface exchanges and the problem of different spatial scales between an ACTM and satellite observation.
OMI NO2 datasets are available at http://www.temis.nl/airpollution/no2.html, last access: 17 July 2020. IASI NH3 datasets are available at https://cds-espri.ipsl.upmc.fr/etherTypo/index.php?id=1700&L=1, last access: 17 July 2020. Surface NO2 concentration during 2005–2007 obtained by Nowlan et al. (2014) and long-term estimates (1996–2012) by Geddes et al. (2016) are available at http://fizz.phys.dal.ca/~atmos/martin/?page_id=232, last access: 17 July 2020. Total oxidized Nr deposition simulated by GEOS-Chem constrained with GOME, SCIAMACHY, and GOME-2 NO2 retrievals during 1996–2014 (Geddes and Martin, 2017) is available at http://fizz.phys.dal.ca/~atmos/martin/?page_id=1520, last access: 17 July 2020. A database of atmospheric Nr concentration and deposition from the nationwide monitoring network in China is available at https://www.nature.com/articles/s41597-019-0061-2, last access: 17 July 2020. Measured Nr concentration and deposition datasets in the United States are available on the website: https://www.epa.gov/outdoor-air-quality-data, last access: 17 July 2020. Measured surface NO2 and NH3 concentration datasets in Europe are available at https://www.nilu.no/projects/ccc/emepdata.html, last access: 17 July 2020. Global surface NO2 and NH3 concentration data used to calculate the long-term trends in Fig. 13 and Fig. 14 have been released on the website: https://zenodo.org/record/3546517#.Xj6I4GgzY2w, last access: 17 July 2020.
LL designed this study. LL, YYY and WX conducted the data analysis. All the co-authors contributed to the revision of the paper.
The authors declare that they have no conflict of interest.
This study is supported by the National Natural Science Foundation of China (nos. 41471343, 41425007, and 41101315) and the Chinese National Programs on Heavy Air Pollution Mechanisms and Enhanced Prevention Measures (Project no. 8 in the 2nd Special Program). The analysis in this study is supported by the Supercomputing Center of Lanzhou University.
This research has been supported by the National Natural Science Foundation of China (grant nos. 41471343, 41425007, and 41101315).
This paper was edited by Eliza Harris and reviewed by two anonymous referees.
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