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Half‐Century Ammonia Emissions From Agricultural Systems in Southern Asia: Magnitude, Spatiotemporal Patterns, and Implications for Human Health

Abstract Much concern has been raised about the increasing threat to air quality and human health due to ammonia (NH3) emissions from agricultural systems, which is associated with the enrichment of reactive nitrogen (N) in southern Asia (SA), home of more than 60% the world's population (i.e., the people of West, central, East, South, and Southeast Asia). Southern Asia consumed more than half of the global synthetic N fertilizer and was the dominant region for livestock waste production since 2004. Excessive N application could lead to a rapid increase of NH3 in the atmosphere, resulting in severe air and water pollution in this region. However, there is still a lack of accurate estimates of NH3 emissions from agricultural systems. In this study, we simulated the agricultural NH3 fluxes in SA by coupling the Bidirectional NH3 exchange module (Bi‐NH3) from the Community Multi‐scale Air Quality model with the Dynamic Land Ecosystem Model. Our results indicated that NH3 emissions were 21.3 ± 3.9 Tg N yr−1 from SA agricultural systems with a rapidly increasing rate of ~0.3 Tg N yr−2 during 1961−2014. Among the emission sources, 10.8 Tg N yr−1 was released from synthetic N fertilizer use, and 10.4 ± 3.9 Tg N yr−1 was released from manure production in 2014. Ammonia emissions from China and India together accounted for 64% of the total amount in SA during 2000−2014. Our results imply that the increased NH3 emissions associated with high N inputs to croplands would likely be a significant threat to the environment and human health unless mitigation efforts are applied to reduce these emissions.


Introduction
Anthropogenic perturbation of the global nitrogen (N) cycle has contributed approximately two thirds of the annual flux of reactive N (Nr) into the atmosphere in the early 21st century (Fowler et al., 2015;Galloway et al., 2004). This substantial enrichment of Nr is mainly caused by combustion, industrial ammonia (NH 3 ) production (by the Haber-Bosch process), and leguminous crop cultivation Fowler et al., 2015). Ammonia, as one of the major forms of Nr loss from agricultural soils, has produced adverse impacts on the environment and human health. High N fertilizer use and production of livestock wastes have resulted in a substantial increment of NH 3 in the atmosphere (Sutton et al., 2014;Yan et al., 2003). Ammonia is one of the key precursors for aerosol formation in the atmosphere and has caused severe air pollution in East and South Asia, which could have adverse effects on human respiratory and cardiovascular systems and might contribute to visibility reduction and regional haze (Pinder et al., 2007;Seinfeld & Pandis, 1998). For example, the NH 3 -induced secondary inorganic aerosol was found as a main contributing factor to haze days in China (Fu et al., 2015). Due to the significant increase of NH 3 in the atmosphere, more ammonium (NH 4 + ) is returned (Lü & Tian, 2007;Tian et al., 2003). To better assess the potential impact of NH 3 emissions on human health and the environment, it is critical to have robust estimates of the spatial and temporal patterns of NH 3 emissions from agricultural activities in the southern Asia (SA), home of more than 60% the world's population (i.e., the people of West, central, East, South, and Southeast Asia) (Food and Agriculture Organization of the United Nations, 2016).
The southern Asia region has experienced rapid expansion of agricultural activities Tian et al., 2014) and has become a hotspot for studies of N-related gas emissions (Tian et al., 2016). The Haber-Bosch process produced approximately 109 Tg N fertilizer per year globally in 2014, of which more than 50% was consumed in Asia since 2004 (FAOSTAT, 2016). Nitrogen fertilizer is applied to agricultural systems to increase crop yield to meet the rising demand for food. In addition, higher demand for livestock products has stimulated the rapid growth of livestock production in the past century L. Bouwman et al., 2013). Meat consumption in major countries of Asia has grown substantially since 1990 and this trend is expected to continue (Organisation for Economic Co-operation and Development/Food and Agriculture Organization, 2016). Consequently, annual livestock waste production has increased substantially and reached approximately 44.3 ± 0.7 Tg N in the 2010s . Increases in both N fertilizer use and livestock excreta have resulted in increased NH 3 emissions, but the magnitude and spatiotemporal pattern of NH 3 emissions from the agricultural sector in SA countries have not been thoroughly investigated.
Estimating NH 3 volatilization from N fertilizer application and livestock excreta has been a research topic of continuous interest for atmospheric and environmental scientists. Previous studies have used numerous approaches to achieve this research goal. The most common methodology to estimate NH 3 emissions was based on emission factors (EFs). Zhao and Wang (1994) generated an inventory of NH 3 emissions using source-based EFs with considerations of various emission sources from livestock, energy consumption, and N fertilizer use in Asia. Using two constant EFs in upland and rice paddy soils, Zheng et al. (2002) found that the amount of NH 3 volatilization was 13.8 Tg N yr À1 in 2000 and is expected to approach~18.9 Tg N yr À1 in 2030 in Asia, mainly from increased anthropogenic Nr. Streets et al. (2003) developed an emission inventory for Asia in 2000 using source-based EFs and provided emission estimates for each country in the region. In their study, NH 3 emission was estimated to be 27.5 Tg N yr À1 from all major anthropogenic sources in Asia, among which 45% and 38% were from fertilizer application and animal productions, respectively. Later, Kurokawa et al. (2013) updated the regional emission inventory using country/subregional-sourcebased EFs. They estimated NH 3 emissions to be 32.8 Tg N yr À1 in 2008, of which 57% was from fertilizer application, and 20% was from manure management.
A constant EF might not provide an accurate measure of NH 3 volatilization and its variability in relation to crops growth or N fertilizer application practices because the conversion of Nr to NH 3 involves several biological and chemical processes that are strongly sensitive to environmental conditions (Fowler et al., 2015;Sutton et al., 2014). For example, Gilliland et al. (2003) indicated that static emission factors are inadequate to reflect seasonal variability in NH 3 emissions. Fu et al. (2015) pointed out that environmental factors could strongly affect the NH 3 volatilization from N fertilizer application. Process-based modeling could therefore address this deficiency by considering climatic variables that are not explicitly considered in the EF approach (Fu et al., 2015;Sutton et al., 2014). The single-layer version of the bidirectional NH 3 exchange model (Bi-NH 3 ) was developed in the late 1990s, and only simulated gas exchange processes occurring on leaf surface (Sutton et al., 1995). Later, a two-layer model was developed by also considering NH 3 exchange between the soil surface and the atmosphere (Nemitz et al., 2001). Currently, the two-layer Bi-NH 3 model is capable of simulating NH 3 emissions caused by N fertilizer application in agricultural systems under various environmental conditions (Sutton et al., 2014). Further, the Bi-NH 3 model has been fully coupled with various ecosystem/agricultural models to simulate NH 3 emissions at the global, regional, and country scale (Bash et al., 2013;Fu et al., 2015;Riddick et al., 2016). For example, the Bi-NH 3 module in the Community Multi-scale Air Quality model (CMAQ, Foley et al., 2010) was coupled with the United States Department of Agriculture's Environmental Policy Integrated Climate (EPIC) agroecosystem model (Bash et al., 2013;Massad et al., 2010;Nemitz et al., 2000) to simulate NH 3 emissions from N fertilizer application in the United States and China. The simulated results showed that the Bi-NH 3 module can be applied to other regions and even at the global scale. However, previous studies using the CMAQ-EPIC approach have provided only 1 year estimates of NH 3 emissions.

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No previous study has specifically assessed NH 3 fluxes on a decadal scale and captured the influence of reactive N sources (synthetic N fertilizer and manure) on regional NH 3 emissions. In this study, we have incorporated the Bi-NH 3 module into the process-based Dynamic Land Ecosystem Model (DLEM version 2.0) to estimate NH 3 emission from agricultural systems in SA. Our research objectives are to (1) quantify the magnitude of NH 3 emissions during the 1961À2014 period at the country level and across the SA subcontinent, (2) investigate spatial and temporal variations in NH 3 emissions from agricultural ecosystems during that period, (3) quantify the relative contribution of synthetic N fertilizer use and manure application on NH 3 emissions, and (4) discuss potential threats of increasing NH 3 emissions to air quality and human health.

. Overall N Flow Processes in DLEM
The DLEM is a highly integrated process-based ecosystem model that makes daily, spatially-explicit estimates of carbon, nitrogen, and water fluxes within both natural and human-dominated ecosystems (Pan et al., 2015;Tian et al., 2011). There are five key components in the DLEM: biophysical characteristics, plant physiological processes, biogeochemical cycles, vegetation dynamics, and land use. A detailed description of these processes can be found in Tian et al. (2010). The DLEM is capable of simulating the impacts of environmental changes (e.g., land use, climate, N deposition, atmospheric CO 2 , N fertilizer use, and manure application) on the full N cycling processes (e.g., volatilization, mineralization, immobilization, denitrification, nitrification, N leaching, and N export and uptake), and N fluxes (e.g., NH 3 , NO, N 2 O, and N 2 ) between terrestrial ecosystems and the atmosphere (Lu & Tian, 2013;Ren et al., 2011Ren et al., , 2012Tian et al., 2010Tian et al., , 2011Tian et al., , 2012Xu et al., 2012Xu et al., , 2017. The algorithms for NH 3 emissions in the previous version of DLEM can only simulate the unidirectional release of NH 3 from soils to the atmosphere but are unable to simulate canopy uptake and transport. Therefore, coupling the CMAQ Bi-NH 3 module with DLEM can greatly improve the simulation efficiency of NH 3 processes.

NH 3 Fluxes in the DLEM-Bi-NH 3
Ammonia production is from the transformation of organic compounds and various sources of N inputs. The ammonium (NH 4 + , g N m À2 ) dynamics in soils: where NH 4 + soil (g N m À2 ) is the soil NH 4 + pool involved in biogeochemical processes, NH 4 + input (g N m À2 ) is the input of NH 4 + from N deposition from the atmosphere, N fertilizer application by human, and crop N fixation ( Figure 1).
In the DLEM, NH 4 + inputs into soils experience the following: NH 3 volatilization, plant uptake, nitrification, or N leaching, resulting in variable amounts of NH 4 + over time. Soil NH 4 + decreases because of soil immobilization, nitrification, organic, and inorganic N leaching and runoff, while NH 4 + increases due to soil mineralization, crop N fixation, N deposition, and fertilizer/manure N application ( Figure 1). These processes are regulated by environmental factors (e.g., soil temperature, moisture, and pH) and vegetation (e.g., crop type and NH 4 + uptake).
The emission of NH 3 is calculated as follows: where C c is the canopy NH 3 compensation point, calculated in equation (3), R a is the aerodynamic resistance, and R inc is the aerodynamic resistance within the canopy. The unit of NH 3 fluxes is μg m À3 s À1 ; the unit of all compensation points is μg m À3 ; the unit of all the above resistances is s m À1 .
where R b is the quasi-laminar boundary layer resistance at the leaf surface, R st is the stomatal resistance, R bg is the quasi-laminar boundary layer resistance at the ground surface, R w is the cuticular resistance, R soil is the resistance to diffusion through the soil layer (

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The soil emission potential of NH 3 is regulated by the ratio of NH 4 + concentration ([NH 4 + ]) to H + concentration ([H + ]), which is defined as where N fer is the fertilizer application rate (g N m À2 ), d l is the depth of soil layer (m), pH is the pH of the soil water solution, M N is the molar mass of N (14 g mol À1 ), and θ s is the soil volumetric water content in m 3 m À3 .
The ground layer (C g ) compensation points are defined as follows: where M n = 1.7 × 10 7 μg mol À1 , V m = 1 × 10 À3 (convert L to m 3 ), and T s is soil temperature in K.
The stomatal compensation points are defined as follows: where T can is the canopy temperature in K, Γ s is the ratio of [NH 4 + ] to [H + ] in the apoplast. To execute this module on a large scale, all resistances (R a , R b , R inc , R bg , R st , R soil , and R w ) and Γ s were parameterized according to Massad et al. (2010).

NH 3 Emission From Livestock Excreta
Emission factors for estimating NH 3 emissions from livestock excreta were adopted from previous studies. A. Bouwman et al. (2002) provided a median value of EF (23%) to estimate the global NH 3 loss from animal manure. The range of EF in A. Bouwman et al. (2002) was 19À29%. Based on the work by Beusen et al. (2008), Riddick et al. (2016) applied a revised EF (17%) to estimate NH 3 emissions from animal manure. In this study, we adopted the minimum EF (17%) from Riddick et al. (2016) and the maximum EF (29%) from A. Bouwman Figure 1. The framework of N biogeochemical processes and fluxes in the DLEM-Bi-NH 3 . R a , R soil , and R w : aerodynamic, soil, and cuticular resistance; R b : the quasi-laminar boundary layer resistance; R bg : the soil boundary layer resistance; R st : stomatal resistance; R inc : the aerodynamic resistance within the canopy; C a : the atmospheric NH 3 concentration; C c , C g , and C st : canopy, ground, and stomatal layer compensation point.
GeoHealth 10.100210. /2017GH000098 et al. (2002. Then, we calculated the mean value (23%), the same as the median value in A. Bouwman et al. (2002), to estimate NH 3 loss from livestock excreta. The uncertainty range of EFs is 17À29%.

Input Data and Simulation Setup
The input data sets for the DLEM-Bi-NH 3 simulation included griddedcrop fraction data, dynamic crop distribution maps, topography and soil properties, climate data, carbon dioxide (CO 2 ) concentration, and N fertilizer application. The procedure for developing time series data of crop distribution maps was described in detail by Ren et al. (2012). Cropland distribution data sets were developed by aggregating the 5 arc min resolution History Database of the Global Environment (HYDE v3.2) global cropland distribution data (Klein Goldewijk et al., 2017). Halfdegree daily climate data (including average, maximum, and minimum air temperature; precipitation; relative humidity; and shortwave radiation) were derived from Climatic Research Unit-National Centers for Environmental Prediction climate forcing data (Viovy, 2014). The monthly CO 2 concentration data set was obtained from NOAA GLOBALVIEW-CO 2 data set derived from atmospheric measurements. Spatially-explicit time series data set of agricultural N fertilizer use was developed through spatializing International Fertilizer Industry Association (IFA)-based country-level N fertilizer consumption data according to crop-specific N fertilizer application rates, distribution of crop types, and historical cropland distribution during the 1961À2013 period . All mentioned data sets were applied to drive model simulations during the 1961À2014 period and estimate NH 3 emissions from N fertilizer application in SA agricultural systems.
The manure production data set was derived from Food and Agriculture Organization of the United Nations statistics website (FAO, http://faostat.fao.org) and defaulted for N excretion rate as described in the Intergovernmental Panel on Climate Change (IPCC) 2006 Guidelines Tier1 . We used the gridded manure production data set to estimate NH 3 emissions from livestock excreta during 1961À2014.

Contemporary and Historical Patterns of NH 3 Emissions
In this study, we estimated NH 3 emissions from N fertilizer application and livestock excreta in SA between 2000 and 2014. Our results indicated that the mean NH 3 emission was 19.1 ± 3.5 Tg N yr À1 . Ammonia emissions from N fertilizer application and livestock excreta were 9.7 and 9.4 ± 3.5 Tg N yr À1 , respectively, during that period.
In SA, our results indicated that NH 3 emissions were 21.3 ± 3.9 Tg N yr À1 in 2014 with a rapidly increasing rate of~0.3 Tg N yr À2 during 1961À2014 (Figure 2). Nitrogen fertilizer application increased from 2.6 to 71 Tg N yr À1 at a rate of~1.4 Tg N yr À2 during 1961À2013. Consequently, NH 3 emissions from synthetic N fertilizer increased from 0.4 to 10.8 Tg N yr À1 in SA. Livestock excreta increased from 19 to 45 Tg N yr À1 at a rate of~0.5 Tg N yr À2 during 1961À2014. The NH 3 emission from manure increased from 4.3 ± 1.6 to 10.4 ± 3.9 Tg N yr À1 in SA.
Ammonia emissions from N fertilizer application during 2000À2014 showed a substantial seasonal variation (Figure 3), which was highly correlated with monthly emission variations. On average, the summer season accounted for 59.8%, while the winter season accounted for 5.6% of annual total emissions. The highest amount of NH 3 emission was in July, followed by June and August (Figure 3). The lowest amount of NH 3 emission was found in January. Spring and autumn accounted for 22.5% and 12% of annual total emissions, respectively. Both seasons had an increase of NH 3 emissions during 2010À2014   The increase in emission between these two periods was of a lesser magnitude when one considers the spring and autumn months (Figure 3).

Spatial Pattern of NH 3 Emissions
Ammonia emissions from croplands varied significantly across the SA region during the past half century (Figure 4). In this study, SA was divided into five regions: Southeast, East, South, West, and central Asia. As shown in Figure 5, East and South Asia were two regions exhibiting the largest emissions and experiencing the most rapid increase in NH 3 emissions. In 1961, as a small amount of N fertilizer was applied to croplands in East Asia (e.g., China, South Korea, and Japan), NH 3 emissions were concentrated in the north and northeast regions of China, but uniform across Japan and South Korea. A substantial amount of N fertilizer was applied in East and South Asia since the 1980s. There was a clear trend of increased NH 3 emissions in most grids of Pakistan and India especially in the northern regions, and China, especially in the North China Plain during 1980À2000. After 2000, more intense NH 3 emissions entirely covered India and a majority of arable land in China. A significant increase of NH 3 emissions was detected in West Asia especially in Turkey and western Iran, and Southeast Asia especially in Thailand during 1961À2014.
In the 1960s, East Asia emitted more than 50% of the SA's total emissions, while South Asia accounted approximately for 25%. However, a faster rate of NH 3 emission increases was found in South Asia during the past half century. During 2010À2014, South Asia accounted for more than 40% of the total emission. In Southeast Asia, NH 3 emissions increased from 35 Gg N yr À1 to 1.3 Tg N yr À1 during 1961À2014 (1 Gg = 10 À3 Tg). Compared to South and East Asia, there was a slight increase in NH 3 emission in Central and West Asia during the observation period (1961À2014). NH 3 emissions increased sharply from 25 Gg N yr À1 to 0.4 Tg N yr À1 in West Asia during the 1961À2000 period, but only moderately during the last decade (2001À2014). In central Asia, NH 3 emission was estimated to be 0.2 Tg N yr À1 in 2014, which was approximately sevenfold higher than the emission amount in 1961 ( Figure 5).
Similar to N fertilizer-induced NH 3 emissions, South and East Asia were two dominant regions that accounted for more than 50% of the total NH 3 emission from manure during 1961À2014. Ammonia emission from South Asia grew from 1.2 to 2.12 Tg N yr À1 , while emission from East Asia increased from 1.4 to 4.0 Tg N yr À1 during that period ( Figure 5). West Asia was the third largest source of manure-induced NH 3 emission and showed an increasing trend ( Figure 5). A moderate trend of increased NH 3 emission was found in Southeast Asia. There was no significant increase in manure-related NH 3 emission in central Asia, a subregion that contributed to less than 10% of the total emission from manure in SA.

NH 3 Emissions From Major Countries
Eleven countries from SA (Table 1) contributed to~65% of global total N application. China and India together contributed~77% of total N application among these 11 countries, as shown in Table 1. In  We identified 11 countries that showed large manure production amounts during 1961À2014 (Table 1). China and India were the two top producing countries and accounted for approximately 67% of the total manure production during 2000À2014. Ammonia emissions from Chinese livestock wastes increased from 1.0 ± 0.4 in 1961 to 4.1 ± 1.5 Tg N yr À1 in 2014. In contrast, NH 3 emissions did not show a rapid increase in India. In the remaining countries, Pakistan produced the highest amount of manure, followed by Turkey and Iran. West Asian countries (Turkey, Iran, Syria, and Jordan) and Southeast Asian countries (Indonesia, Vietnam, and Myanmar) were identified as hotspots for NH 3 emissions from manure.

The Importance of Asia's Contribution to Global NH 3 Emissions
Previous studies have presented global estimates of NH 3 emissions from agricultural systems (Beusen et al., 2008;Bouwman et al., 1997;Dentener & Crutzen, 1994;Riddick et al., 2016;Sutton et al., 2013;Van Aardenne et al., 2001). In 2000, the mean estimate of global NH 3 emission was 29.9 ± 5.2 Tg N yr À1 . In this study, the estimated NH 3 emission from southern Asian agricultural systems was 16.0 ± 3.0 Tg N yr À1 , which accounted for~55% of the total global emissions. Although gaseous NH 3 has a short lifetime and low emission height (Clarisse et al., 2009), the transport of deposited NH 4 + from one ecosystem to another could lead to widespread environmental pollution (Liu et al., 2013).

Comparison With Other Studies in SA
There are few regional-scale reports on NH 3 emissions in SA, and large uncertainties exist in the regional estimates of NH 3 emissions ( Table 2). The research on NH 3 emissions from Asia conducted by Zhao and Wang (1994) and Streets et al. (2003) provided comprehensive estimates in different countries in Asia. Their estimates of total NH 3 emission from N fertilizer in 1990 and 2000 were 8.3 and 12.4 Tg N yr À1 , which were 32% and 57%, respectively, higher than our estimates (Figure 2). Yan et al. (2003) provided a total estimate of NH 3 emissions in South, Southeast, and East Asia in 1995 from N fertilizer use, which was 8% lower than our estimate (6.3 Tg N yr À1 ). There are two primary reasons why our estimates differ from previous estimates. First, we applied a process-based model to simulate NH 3 emissions that considered environmental factors,

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while the previous studies mentioned above applied an empirical EF. Second, the N fertilizer application data used in this study and previous studies were quite different. For example, the data set of N fertilizer application rates used in our estimate was developed by Lu and Tian (2017), while the data set used in Streets et al. (2003) was adopted from International Fertilizer Industry Association (IFA, 1998). The comparisons of NH 3 emission from N fertilizer with other studies in China and India are listed in Table 2.
For NH 3 emission from livestock excreta, the estimate by Yan et al. (2003) for 1995 was 4.7 Tg N yr À1 , which was 17% lower than our estimate (5.6 Tg N yr À1 , Figure 2). However, the estimate (10.5 Tg N yr À1 ) from Streets et al. (2003) was 29% higher than our estimate (8.1 Tg N yr À1 ) for 2000. The differences could be associated with the application of different EFs and different livestock excreta amount. In this study, we adopted a mean EF of 23% (17~29%) to estimate NH 3 emissions from livestock excreta, while Yan et al. (2003) adopted the European Environment Agency (EEA) EFs based on the animal types with an average EF less than 20%. Streets et al. (2003) conceded that the use of European-based EFs in their study might have introduced uncertainty in their estimates of NH 3 emission from Asian countries. Thus, future work is needed to better constrain the uncertainties associated with NH 3 emission from livestock excreta. The comparisons of NH 3 emission from manure with other studies in China and India are listed in Table 2.

Comparison of Monthly NH 3 Emissions With Other Studies in China
The uncertainty on monthly NH 3 emissions was associated with the input climate data sets, the cropland area, and N fertilizer application dates and ratios. Large uncertainties appeared in the estimate of monthly NH 3 emissions from N fertilizer application ( Figure S1). Similar to the result of Xu et al. (2016), NH 3 emission peaked in July and was followed by secondary peaks in June and August. Our estimates of NH 3 emission in September and October were different from Xu et al. (2016). Ammonia emissions in summer contributed nearly 60% of the annual total, which was consistent with the results in Zhang et al. (2011). As shown in Fu et al. (2015), NH 3 emission in September was low because of the lower amount of N fertilizer application during that time of the year.

Uncertainties
Uncertainties in modeled NH 3 emission are due to parameters and input data sets. Fertilizer application rates are a major factor that may have affected the accuracy of NH 3 emission estimates. The input data sets of soil properties and climate are two other important factors that might affect the spatial pattern and total amount of NH 3 emissions in SA. In the DLEM, we assumed that the applied N fertilizer could be used, stored, or lost within 30 days, and these assumptions could introduce uncertainties in the simulations. In addition, DLEM used empirical methods to calculate model parameters Γ s and Γ g , which might result in some uncertainties due to a lack of observational data to evaluate the validity of this approach (Bash et al., 2013;Fu et al., 2015). Different N fertilizer types have different EFs and thus could result in different rates of NH 3 emissions. For example, urea, a major form of N fertilizer applied to croplands in developing countries, contributed more than 50% of the total NH 3 emitted from N fertilizer (Zhang et al., 2011). The EF of urea was estimated as 0.2À0.25 (Bouwman et al., 1997;Matthews, 1994). In this study, we did not differentiate between N fertilizer types. Instead, all types of N fertilizer were considered as total N inputs for model simulation and that may have reduced the accuracy of the NH 3 estimates. A constant EF for NH 3 emissions from manure was used in this study, which might introduce more uncertainty in our estimate compared to other studies that have used region-based or animal species-based EFs. In addition, although NH 3 emissions from livestock excreta are affected by climate, soil properties, and plant phenology (Hansen et al., 2017;Massad et al., 2010), the timing and rates of animal manure application were not considered in this study. This may also have added to the uncertainty of our estimates.

Implications
More than half of the N added to croplands can be lost in various ways (Lassaletta et al., 2014;Tian et al., 2012). This study indicated that NH 3 volatilization was one of the major outlets for Nr losses (Figure 1). As a significant source of NH 3 and other precursor gases that lead to the generation of PM 2.5 , agriculture not only affects the Earth's radiation budget but causes adverse health effects (Aneja et al., 2008;Cao et al., 2009;de Leeuw et al., 2003;Fowler et al., 1998;Seinfeld & Pandis, 1998). First, NH 3 can react with other air pollutants and form aerosols (e.g., PM 2.5 ) in the atmosphere, thus lead to the reduction of visibility (Cheng et al., 2014). Second, the increasing concentration of PM 2.5 is positively related to the rates of premature mortality, which increased by 21% and 85%, respectively, in East Asia and South Asia from 1990 to 2010 (Wang et al., 2017). Last, the dry and wet deposition of NH 4 + from NH 3 emissions may alter soil and water chemistry (e.g., eutrophication) and reduce biological diversity (Clark & Tilman, 2008;Vitousek & Farrington, 1997).
Using a global atmospheric chemistry model (Lelieveld et al., 2015), a recent study reported that outdoor air pollution, mainly by PM 2.5 , could lead to 3.3 (1.61À4.81) million premature deaths per year worldwide, predominantly in Asia. That study considered agriculture as the largest contributor to global premature deaths due to the releases of NH 3 from livestock wastes and N fertilizer. Although Jerrett (2015) argued that Lelieveld et al. (2015) may have overestimated the toxicity of PM 2.5 from agricultural sources, he suggested that scientists and policymakers should pay much more attention to PM 2.5 formed by agricultural sources and its adverse effects on human health. Recently, Wang et al. (2017) indicated that reduction of NH 3 emissions could maximize the effectiveness of nitrogen oxides emission controls and potentially provided some health benefits. Thus, effective farming strategies should be adopted by farmers in order to minimize NH 3 loss from N fertilizer or manure application and mitigate associated threats to human health.
How can we reduce N losses from Nr? One effective strategy is to reduce the application of N fertilizer and to rotate legume crops (e.g., soybean and alfalfa) with other cereal crops (e.g., wheat and maize). Nitrogen use efficiency (NUE) was found to be generally higher in agricultural systems with a higher proportion of N inputs from symbiotic N fixation (Lassaletta et al., 2014). The second strategy is to identify the optimal crop-specific N needs to avoid excessive N application to soils . Mueller et al. (2017) applied N input-yield response functions to investigate regional-or country-scale NUE based on the historical N budget data (1961À2009). Their studies provided an overview of the optimal N input rates in major agricultural countries and showed that East, South, and Southeast Asia received excessive N inputs up to 10 g N m 2 greater than needed for optimal crop yield. The third strategy is to minimize the loss of N through improved management techniques (Bouwman et al., 1997(Bouwman et al., , 2002Chen et al., 2011Chen et al., , 2014Fowler et al., 2015). In addition, some strategies have been approved to be effective for reducing N losses from animal manure in SA. For example, modifying feeding (diet with optimized N content) is an effective strategy to reduce gaseous N losses from animal manure (Chadwick et al., 2011;Hou et al., 2015). In comparison with composting, solid manure storages via compaction and covering could substantially decline NH 3 emissions (Chadwick, 2005;Hou GeoHealth 10.1002/2017GH000098 et al., 2015. Compared to surface application, manure application through band spreading, incorporation, and injection could significantly reduce NH 3 emissions . Asia, whose population could increase by 0.9 billion by 2050, has been projected to be the second largest contributor to future population growth (United Nations, Department of Economic and Social Affairs, World Population Prospects, 2015). Valin et al. (2014) reported that food demands would increase by 59À98% between 2005 and 2050 in the reference scenario (the GDP and population pathways of the "middle of the road" Shared Socio-economic Pathway (SSP2) developed by the climate change impacts research community). In the projection of FAO and agriculture models, South and East Asia are expected to experience a rapid increase in crop foods and livestock products during the first half of the 21st century (Alexandratos & Bruinsma, 2012;Valin et al., 2014). Under this scenario, N fertilizer application to global croplands would double by 2100 (Stocker et al., 2013), while manure production may increase to 50% by 2050 compared to 2000 (Bouwman et al., 2013). China and India are the top two countries with the largest N fertilizer consumption and are expected to remain so in the foreseeable future. Unfortunately, NUE has rapidly decreased in these countries Zhang et al., 2015). The decrease in NUE and increase in N fertilizer use would translate into greater emission of fertilized N as NH 3 and other N gases . Thus, farmers as well as policymakers in Asian countries ought to consider using N fertilizer more efficiently for the benefits of environmental quality and human health (Tian et al., 2016;Zhang, 2017). Moreover, livestock excreta in SA increased from 19 to 45 Tg N yr À1 during 1961À2014 . In 2000, Southeast, East, and South Asia contribute~30% of global GHG emissions from ruminants (Herrero et al., 2013). In order to reduce both GHG and NH 3 emissions from livestock systems, it is important to implement an effective strategy for managing animal manure (Bouwman et al., 2013;Fowler et al., 2015).