Identifying ammonia hotspots in China using a national observation

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Identifying ammonia hotspots in China using a national observation network Yuepeng Pan, Shili Tian, Yuanhong Zhao, Lin Zhang, Xiaying Zhu, Jian Gao, Wei Huang, Yanbo Zhou, Yu Song, Qiang Zhang, and Yuesi Wang Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b05235 • Publication Date (Web): 02 Mar 2018 Downloaded from http://pubs.acs.org on March 3, 2018

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Environmental Science & Technology

Pan Page 1

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Identifying ammonia hotspots in China using a national observation network

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Yuepeng Pan*†, Shili Tian†, Yuanhong Zhao‡, Lin Zhang‡, Xiaying Zhu§, Jian Gaoǁ,

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Wei Huang†, Yanbo Zhou†, Yu Song⊥, Qiang Zhang #, Yuesi Wang†

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† State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry

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(LAPC), Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029,

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China

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‡ Department of Atmospheric and Oceanic Sciences, School of Physics, Peking University,

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Beijing, 100871, China

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§ National Climate Center, China Meteorological Administration, Beijing 100081, China

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ǁ

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Academy of Environmental Sciences, Beijing 100012, China

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⊥ Department of Environmental Science, Peking University, Beijing 100871, China

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# Ministry of Education Key Laboratory for Earth System Modeling, Center for Earth System

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Science, Tsinghua University, Beijing 100084, China

State Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research

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Contact author: Yuepeng Pan ([email protected])

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TOC/Abstract art

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National observation of ammonia in China ACS Paragon Plus Environment


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ABSTRACT

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The limited availability of ammonia (NH3) measurements is currently a barrier to

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understanding the vital role of NH3 in secondary aerosol formation during haze

24

pollution events and prevents a full assessment of the atmospheric deposition of

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reactive nitrogen. The observational gaps motivated us to design this study to

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investigate the spatial distributions and seasonal variations in atmospheric NH3 on a

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national scale in China. Based on a 1-year round observation campaign at 53 sites

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with uniform protocols, we confirm that abundant concentrations of NH3 [1 to 23.9 µg

29

m−3] were spotted in typical agricultural regions, especially in the North China Plain

30

(NCP). The spatial pattern of the NH3 surface concentration was generally similar to

31

those of the IASI column concentrations as well as a bottom-up agriculture NH3

32

emission inventory. However, observed NH3 concentrations at urban and desert sites

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were comparable with those from agricultural sites and 2-3 times those of

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mountainous/forest/grassland/waterbody sites. We also found that NH3 deposition

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fluxes at urban sites account for only half of the emissions in the NCP, suggesting the

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transport of urban NH3 emissions to downwind areas. This finding provides policy

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makers with insights into the potential mitigation of non-agricultural NH3 sources in

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developed regions.

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1. INTRODUCTION

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The intensive human activities of the past decades have significantly affected the

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global nitrogen cycle by fixing N2, both deliberately for fertilizer production and

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inadvertently during fossil fuel combustion 1. Rapid increases in reactive nitrogen

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emissions to the atmosphere have resulted in serious reactive nitrogen pollution in the

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air and excessive nitrogen deposition in natural ecosystems worldwide 2. To reduce

47

these adverse impacts, previous efforts have been made to reduce oxidized nitrogen,

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such as NOx emissions, whereas a reduction in reduced nitrogen, especially in

49

ammonia (NH3) emissions, has not been fully implemented

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2013, NH3 levels over agricultural regions experienced significant increasing trends

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across the U.S. (2.6% yr−1), the European Union (1.8% yr−1), and China (2.3% yr−1),

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as observed from satellite 5. It is demonstrated that the deposition of reactive nitrogen

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in the U.S. has recently shifted from nitrate-dominated to ammonium-dominated

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conditions 6, while in China reduced nitrogen plays a key role in atmospheric nitrogen

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deposition, contributing from 71% to 88% of the total depositions in hotspot regions

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such as the North China Plain (NCP) 7.

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3, 4

. Between 2002 and

In addition, there is increasing evidence indicating the critical role of NH3 in the 3, 8

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formation of secondary aerosols

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and related sulfate and nitrate contribute 10% and 35% of the particulate masses

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during haze events 9. The profound role of NH3 on the haze pollution was also

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highlighted by recent studies that argued its capability to neutralize aerosol pH, which

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can strongly enhance the formation of sulfate through the heterogeneous oxidation of

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SO2 by NO2 10. All evidence leads to increasing concerns that future progress toward

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reducing the nitrogen-related impacts on aerosol pollution and nitrogen deposition

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will be increasingly difficult without a well-resolved spatiotemporal picture of NH3.

66

. Extensive observations reveal that ammonium

Compared with the increasing rich datasets of satellite observations of 5, 11, 12

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atmospheric NH3 concentration

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geographical extent are still lacking

, surface network datasets covering large

13, 14

, especially in China


15, 16

. To fill the


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observational data gaps, in this study, a year-round campaign was launched to

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measure monthly NH3 by using uniform protocols with a diffusive technique and

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other supporting data across China. The objectives of the present study are to (1)

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identify the hotspots of NH3 in China, (2) explore the variability of atmospheric NH3,

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and (3) present the implications for mitigating NH3 on a national scale. To our

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knowledge, this study represents the first national observations of NH3 in China,

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especially in background regions, setting a baseline against which concentration

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changes resulting from future emission control strategies can be assessed. The data

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collected here are unique and will advance our understanding of atmospheric

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chemistry and related processes. The results will also be valuable for scientists and

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policy makers to estimate excess nitrogen inputs into ecosystems, validate

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atmospheric chemistry and transport models including seasonal trends and regional

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variability.

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2. MATERIALS AND METHODS

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2.1 Ammonia sampling networks

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Accurately measuring NH3 concentrations in the air is not an easy task due to the

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interference of particle-borne ammonium 17. This problem can however be solved by

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utilizing the well-known fact that, when ambient air passes through a tube, gas

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molecules diffuse much more quickly than particles onto the tube wall 18. The main

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disadvantage of this manual sampling method (hereafter referred to as the diffusive

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sampling technique) is its low temporal-resolution when high frequency

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measurements (e.g., hourly) are needed. However, such a simple and cost effective

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technique can increase the spatial resolution of the measurement and to aid in

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screening studies to evaluate monitoring site locations

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measurements for trend analyses 13.

14

or in long-term

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For large-scale surveys of NH3 variability across China, starting in September

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2015, we implemented a passive NH3 monitoring network based on the diffusive


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technique with monthly integrated measurements at 53 sites. The current Ammonia

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Monitoring Network in China (AMoN-China) was established based on the Chinese

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Ecosystem Research Network (CERN, http://www.cern.ac.cn/0index/index.asp) and

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the Regional Atmospheric Deposition Observation Network in North China Plain

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(READ-NCP) 7. AMoN-China includes 13 mountain & forest sites, 5 water body sites,

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7 grassland, 4 desert, 11 farmland and 13 urban/suburban/industrial sites (Figure 1).

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All the sites are selected far away (> 1 km) from a known source of ammonia (e.g.,

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farmland and feedlot), considering that the ammonia concentrations decrease

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significantly away from the source (several hundred meters)

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site selection and siting protocols can be found in Supporting Information (SI, text

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and Table S1).

19

. More details on the

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Sites were assigned to regions to assess whether the seasonal variations and spatial

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distributions of NH3 concentrations show different patterns in different broad areas of

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China (Figure 2). The regions are defined as follows: the NCP (NC, 11 sites),

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northeast China (NE, 9 sites); northwest China (NW, 5 sites), southeast China (SE, 13

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sites), southwest China (SW, 9 sites), and Central China (Central, 6 sites). The regions

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were chosen based on the spatially different geographical, climate and availability

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characteristics of the sites.

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2.2 Chemical analysis and validation of ammonia samplers

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The year-round sampling campaign was carried out from September 2015 to

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August 2016. In total, 636 samples of NH3 were taken during this work by using

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diffusive samplers (Analysts®, CNR-Institute of Atmospheric Pollution, Roma, Italy).

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The

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phosphorous-acid-impregnated glass microfiber filter as an adsorption layer. The

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sampler is a robust and reliable tool for the measurement of atmospheric NH3 whose

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development, theory, laboratory validation, and field application have been fully

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described elsewhere 20.

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passive

sampler

is

made

of

polyethylene

and

employs

a

During sample collection, the passive samplers were exposed at a height of 2 m National observation of ammonia in China ACS Paragon Plus Environment


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with their open ends oriented downwards to exclude the dry deposition of particles. In

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addition, the sampler was protected from rain and direct sunlight by an inverted

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stainless-steel shield. After exposure, the passive samplers were returned to Beijing

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for analysis at the State Key Laboratory of Atmospheric Boundary Layer Physics and

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Atmospheric Chemistry, Institute of Atmospheric Physics, Chinese Academy of

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Sciences. There in the laboratory 5 ml of deionized water was used to extract the

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exposed samples, and the ammonium ion concentration in the extraction was

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determined via ion chromatography with a cation separator and conductivity detector

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(Dionex Corp., Sunnyvale, CA, USA).

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The ambient NH3 concentrations (cNH3 , µg m−3) were calculated based on the

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amount of ammonium (mNH+4 , µg) collected on the exposed filter and the sample

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collection time (t, hour) and can be expressed using the following equation.

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cNH3 =9.06×102 ×

mNH+4 t

137 138

where 9.06×102 is the conversion factor from the manufacturer’s description,

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which is a function of the parameters of the passive sampler. This formula assumes

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that the average temperature (T) during sampling is 20 °C. In the case that temperature

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is different, the correction coefficient ( 273+T )

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temperature effects is negligible, with the corrected NH3 concentrations less than 5%

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at each 5 °C. Most of the sampling sites belong to the CERN, where the temperature

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was measured at each site using an automatic meteorological observation instrument

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(Milos520, Vaisala, Finland). In the case that temperature is not measured at the site,

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the nearest meteorological observation stations available on the China Meteorological

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Data Sharing Services System website (http://cdc.cma.gov.cn/) was used in this study.

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Before and during the study period, comparisons with automatic reference

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methods were performed during two campaigns. During 2013, the Analysts passive

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sampler samples were compared to the continuously active analyzers of MARGA (a

293

1.8

was applied to cNH3 . Such a


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model ADI 2080 online analyzer for the Monitoring of Aerosols and Gases, Applikon

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Analytical B.V. Corp., the Netherlands, aggregated to monthly data points), showing a

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linear regression slope of 1.10 ± 0.14 and R2 of 0.94 (Figure 3a). This strong linear

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relationship indicates that the Analyst passive sampler is reliable for such a study,

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assuming that the NH3 concentration values measured by the wet chemistry

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instruments are more accurate

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passive samplers to DELTA (DEnuder for Long-Term Atmospheric sampling, Centre

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for Ecology and Hydrology, UK) at a monthly resolution. This comparison shows a

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linear regression slope close to unity (1.04 ± 0.17) and an intercept of 2.06 ± 2.23 µg

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m−3 (Figure 3b), the bias appears to be systematic so that it does not impact the

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patterns of the spatial distributions or seasonal variations.

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2.3 Dry deposition velocity simulation

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The inferential technique 7, which combines the measured NH3 concentration and a

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modeled dry deposition velocity (Vd) by the Goddard Earth Observing System-Chem

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(GEOS-Chem; http://geos-chem.org) chemical transport model, was used to estimate

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the dry deposition fluxes of NH3. The GEOS-Chem simulation of nitrogen dry

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deposition has been described by Zhao et al.

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version of GEOS-FP assimilated meteorological fields from the NASA Global

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Modeling and Assimilation Office (GMAO), which has been applied to analyze

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particle pollution over North China

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resolution of 0.25° latitude × 0.3125° longitude over East Asia (70°E–140°E, 15°N–

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55°N) and a coarse resolution of 2° latitude × 2.5° longitude over other place of the

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world.

17

. During this study, we also compared the Analysts

21

Here the model is driven by the latest

22

. Our simulation used the native GEOC-FP

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Follow the standard big-leaf resistance-in-series model 23, Vd in GEOS-Chem was

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calculated by considering the aerodynamic resistance, the boundary layer resistance,

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and the surface resistance. Here we have not considered air–surface bi-directional

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exchange of NH3 24, and treat the NH3 fluxes as uncoupled emission and deposition

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processes. We run the model from 2014 to 2016 and applied the monthly dry National observation of ammonia in China ACS Paragon Plus Environment


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deposition velocities at reference height of 2m to observed NH3 concentrations to get

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monthly NH3 dry deposition. We find in the model that the NH3 monthly dry

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deposition fluxes as calculated by the monthly mean concentration and Vd are about 7%

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higher than the hourly integrated values, reflecting some small covariance between

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NH3 concentrations and Vd.

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3. RESULTS AND DISCUSSION

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3.1 Spatial distribution of ammonia in China

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Large spatial differences in NH3 concentrations were found at the 53 sites in the

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sampling network, with annual mean NH3 concentrations during the 1-yr period

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ranging from 1 to 23.9 µg m−3, as illustrated in Table 1 and Figure 1. The upper range

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is higher than concentrations observed in China around 2012 (0.3−13.1 µg m−3) 16 and

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Asia around 2000 (< 0.7~ 13.9 µg m−3)

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this study reached 7.0 ± 5.4 µg m−3, which is much higher than the values observed in

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the US AMoN network using a similar diffusive sampling technique 13, although the

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AMoN sites are mainly located outside the intensive source areas of the US. At the

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Colorado near-agricultural sites, the NH3 concentrations reach 42.7 µg m−3 25.

14

. The overall mean NH3 concentrations in

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The sites were assigned to six regions to assess the regional variations among

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them. The highest regional averaged ambient NH3 concentrations were found at the

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NCP (13.4 µg m−3), followed by those at NW (10.0 µg m−3), Central (5.4 µg m−3), SE

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(5.1 µg m−3), NE (4.4 µg m−3), and SW (3.8 µg m−3). The spatial distributions of the

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surface NH3 concentrations were consistent with the top-down IASI satellite NH3

200

columns

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inventory from agriculture sources in China, as included in Figure 4. NCP is then

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confirmed to be the largest region with high surface concentrations and highest

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emissions. This region as a whole accounts for 43% of the NH3 emitted from

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fertilization in China 26.

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12

, as shown in Figure 1, and similar to the bottom-up NH3 emissions

In addition, several smaller hotspots were observed in China, e.g., in Dzungaria


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and surrounding the Tarim basin (NW), Chengdu Plain (SW), and Guanzhong Plain

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(Central). These hotspots coincided with intensive agricultural activities, suggesting

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the major contribution of volatilized fertilizer and livestock waste to atmospheric NH3

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26

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the Inner Mongolia, NH3 levels were low and can be treated as the background values.

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In addition to the limited NH3 sources in these background regions, cold weather or

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acidic soil is unfavorable for NH3 emissions. Heavy rainfall may also contribute to the

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scavenging of atmospheric NH3 21, resulting in the lower concentrations observed in

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south China.

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3.2 Differences among land use types

. In vast regions surrounding these hotspots, e.g., the Tibetan plain, south China, and

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From the aspect of land use types, the highest values were observed at

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urban/suburban/industrial sites (10.8 µg m−3), followed by those at farmland (10.2 µg

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m−3), desert (7.8 µg m−3), mountain & forest (3.6 µg m−3), water body (3.6 µg m−3)

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and grassland (3.4 µg m−3) sites. Given the influences of volatilized fertilizer, the

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mean NH3 concentrations at the agricultural sites WNA, HCA, FQA, LCA and YCA

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reached 12.4, 15.1, 16.8, 19.3 and 22.3 µg m−3, respectively (Table 1, where

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abbreviations of site names are defined, the same below). These values are much

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higher than the results from the other farmland sites, i.e., AKA, SYA, LZA, LSA,

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YTA, and ASA observed in this study. The difference is likely attributed to the

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different fertilizer inputs, climate zones and soil pH values in these regions.

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As shown in Table 1, the results also demonstrated relatively high NH3 values at

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urban sites in the NCP, e.g., TGI (10.2 µg m−3), TJU (11.3 µg m−3), BJU (13.7 µg m−3),

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BDI (15.3 µg m−3) and CZS (23.9 µg m−3). Although agricultural activities are

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intensive in this region, non-agricultural emissions are found to be an important

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contributor to atmospheric NH3 in the region, as evidenced by the isotopic signatures

231

27

.

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In addition, relatively high concentrations of NH3 were also observed at urban

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sites in south China, e.g., at NJU (10.8 µg m−3), MMU (9.8 µg m−3), CDU (8.4 µg National observation of ammonia in China ACS Paragon Plus Environment


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m−3), THL (6.3 µg m−3), GZU (5.8 µg m−3). These values were higher than or

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comparable to those of nearby farmlands. For example, the average NH3

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concentration measured at the urban site CDU was twice that of the agricultural site

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YTA (4.4 µg m−3). Such high values observed in urban areas are one of the distinct

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features in this study. Non-agricultural sources are therefore suggested to be important

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contributors to atmospheric NH3 in developed regions across China. An improved

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global NH3 emission inventory for combustion and industrial sources provided

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distinct evidence that the emissions density of NH3 in urban areas is an order of

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magnitude higher than in rural areas 8.

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Another important finding is the higher NH3 concentrations observed at the desert

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sites of SPT, NMD, CLD and FKD, which have values of 5.1, 5.3, 6.1 and 14.4 µg

245

m−3, respectively. These values are higher than those observed in background regions,

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including mountain & forest, water body and grassland sites, averaging 3.5 µg m−3.

247

This is the first observational evidence showing high NH3 values over NW dry lands,

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in addition to over farmlands. This finding indicated an important regional source of

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NH3 from dried saline soils nearby

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non-sea-salt crustal sulfate has been observed from deserts and Gobi region in the

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north China 29. The unexpected high values at desert sites of FKD (60 km to the NE of

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the Urumqi city) may be explained by the transport of nearby industrial and/or urban

253

sources.

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3.3 Seasonal variations in ammonia concentration in China

28

; considering that a certain amount of

255

When the data were aggregated at seasonal levels, the seasonal maximum NH3

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concentrations were observed in the summer, whereas the minimum values occurred

257

in the winter at most sites (Figure 2). Note that pulse peaks were also observed during

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August at GGM, in May at ERG, in Dec at ASA and in July at HJK. At the

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mountainous/forest/grassland sites, the NH3 concentrations have weaker seasonal

260

variations during the observational periods, with nearly constant values of less than

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3.5 µg m−3, reflecting the remoteness of the monitoring site, and can serve as the National observation of ammonia in China ACS Paragon Plus Environment

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background value in China.

263

In contrast, the seasonal variations in NH3 at agricultural sites are evident, with

264

low values in the winter time, elevated values in the late spring, peaks in the summer,

265

and decreased values in the autumn. The relatively high values of the warmer seasons

266

correspond with peak emissions from agricultural activities and high temperatures. A

267

similar seasonal pattern can be also found at urban sites, e.g., BDI and NJU, although

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the agricultural activities in urban regions are less evident.

269

Although the volatilization of NH3 in agricultural regions was sufficient to justify

270

high NH3 that observed in developed regions during the warm season, the

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contribution of non-agricultural sources of NH3 can not be neglected in urban areas 30.

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In a case study, the NH3 emissions from vehicles in urban Beijing may have

273

contributed to the observed summer maximum 31. However, such sources tend not to

274

have obvious seasonal changes. The recycling of predeposited NHx offers an

275

alternative explanation for these seasonal changes, which has been touched on by a

276

recent study 32. This speculation was partially supported by a good correlation of NH3

277

concentrations and ambient temperature at urban sites (e.g., BJU; SI, Fig. S1). Note

278

that changes in emissions don’t necessarily relate to changes in concentrations, and

279

vice-versa. Low NH3 concentrations in winter may also be attributable to

280

gas-to-particle conversion under cold weather conditions. So, non-agricultural

281

emissions in urban areas may still be quite high in winter, for example. To fill the gap,

282

concurrent measurements of NH3 and NH4+ concentrations and fluxes covering

283

different seasons are needed.

284

3.4 Ammonia dry deposition vs. emissions in China

285

We estimated the dry deposition flux of NH3 to assess the bulk input to

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ecosystems by combining the monthly observed concentrations with a modeled Vd.

287

The mean monthly Vd values of NH3 modeled at most of sites during this study period

288

ranged from 0.20 to 0.55 cm s−1, with the exception of one coastal site (MMA,

289

1.02−1.42 cm s−1) and an island site (YXI, 0.74−1.73 cm s−1). These ranges agree well National observation of ammonia in China ACS Paragon Plus Environment


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with the previous estimations in the target region

291

variations in Vd (data not shown) were weaker than seasonal variations of the

292

concentrations, implying that the ambient concentration plays a more important role

293

in determining the dry deposition flux of NH3. As a consequence, the spatial pattern of

294

the NH3 dry deposition (Figure 4) was similar to that of the concentrations in China

295

(Figure 1), as mentioned in Sect. 3.1.

. We find that the seasonal

296

The annual dry deposition of NH3 estimated in this study falls within the range of

297

0.8-30.3 kg N ha−1 yr−1, with a national mean of 7.3 ± 6.1 kg N ha−1 yr−1, which is

298

slightly lower than the estimation (8.2 kg N ha−1 yr−1) produced a few years ago

299

Due to the relatively high ambient concentrations, the dry depositions of more than 10

300

kg N ha−1 yr−1 of NH3 were mostly estimated at those sites located in agricultural

301

areas (e.g., YCA, LCA, FQA, HCA and WNA) and in or near developed regions (e.g.,

302

CZS, BDI, BJU, NJU, TJU and FKD). Note that the highest deposition was estimated

303

for MMU due to both the high local concentrations and Vd.

16

.

304

Figure 5 compares the dry depositions estimated at the site with the gridded

305

agriculture emission, showing that the sites in NW have depositions that are a factor

306

of 2 (or more) greater than their emissions. Since dry deposition was the major sink of

307

ammonia in this region, the unexpected high deposition in NW indicated the missing

308

sources in the current inventory. In contrast, the sites in SE have higher emissions

309

than depositions, highlighting the significant removal of NH3 via precipitation (wet

310

deposition) in south China

311

accounted for 25%-75% of the emissions of NH3. With wet/dry deposition ratio

312

considered 7, the total NHx depositions at sites YCA, LCA, YFS and CZU would be

313

much closer to those of emissions, whereas the NHx depositions at BDI, BJU, TJU

314

and FQA would be 50% of the emissions, suggesting the possible regional transport of

315

NH3 to the surrounding regions.

21

. At the NCP sites, estimated dry deposition fluxes

316

Uncertainties in both the emissions and depositions may also contribute to their

317

discrepancies. For example, emission uncertainty is associated with activity data and

318

emission factors (EFs)

319

and were subject to systemic errors rather than the regional inconsistencies. However,

26

. The activity data were obtained via statistical information


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the EFs that are parameterized by the ambient temperature, soil property, fertilizer

321

types and other factors may vary regionally,

322

validated with future work. Uncertainties also exist in dry deposition estimations, in

323

particular, NH3 fluxes over vegetated land are bi-directional 34 and the net direction of

324

this flux is often uncertain. A so-called canopy compensation point was used in

325

previous studies to determine the direction of the NH3 flux 24. Since the principle of

326

bi-directional NH3 exchange was not employed in this study, the flux calculated here

327

represents a rather non-conservative deposition estimate (upper boundary). In this

328

study, NH3 deposition may be overestimated at vegetated sites with relatively high

329

canopy compensation points (e.g., up to 5 µgN m-3) due to fertilized croplands

330

vegetation

331

compensation point on NH3 deposition in agricultural sites due to the counterbalance

332

between deposition and emission.

333

ASSOCIATED CONTENT

334

Supporting Information

33

and thus, these speculations should be

7

or

35

. We recommend future research to evaluate the effects of the stomatal

335

Figure S1 illustrates ammonia concentration and temperature correlation, Table S1

336

summarizes site information, and text details the site selection and siting protocols,

337

with accompanying references. (PDF)

338

AUTHOR INFORMATION

339

Corresponding Author

340

*

341

[email protected].

342

Author Contributions

343

Y.P. and Y.W. conceived and designed the project, Y.P., Y. Z. and S.T. conducted the

344

field work, Y.Z. and L.Z. performed the dry deposition modeling experiments, J. G.

(Y.P.)

Phone:

+86

01062022285;

fax:

+86


01062362389;

e-mail:


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performed the MARGA measurements, Y.S. and Q.Z. prepared the ammonia emission

346

inventory, Y.P., X.Z. and W. H. analyzed the data and drew figures, and Y.P. wrote the

347

paper with comments from the coauthors.

348

Notes

349

The authors declare no competing financial interest.

350

ACKNOWLEDGEMENTS

351

This work was supported by the National Key Research and Development Program of

352

China (Grant 2017YFC0210100) and the National Natural Science Foundation of

353

China (Grant 41405144). We are indebted to the staff who collected the samples at the

354

sites (listed in Table 1) during the study period.

355

Data availability

356

All data sets related to this paper can be requested by contacting the principal

357

investigator, Yuepeng Pan ([email protected]).

358

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359

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488

Figure captions

489

Fig. 1 Spatial distribution of ammonia concentrations observed from surface network (site) versus IASI satellite column data (grid) in China. The detailed surface observation site information can be found in Table 1 and SI. Satellite NH3 total column distributions are derived from the Infrared Atmospheric Sounding Interferometer (IASI) aboard MetOp-A for the year 2015. We collect the observations from morning overpass time (9:30 LTC) and filter the columns with relative error above 100% following procedures presented in Van Damme et al 12. The filtered IASI satellite columns are then mapping to 0.25°×0.25° horizontal resolution by averaging available observations within each grid cell. The provincial boundary layer with a scale of 1:4,000,000 was obtained from the National Geomatics Center of China (http://ngcc.sbsm.gov.cn/). Maps were generated based upon a geospatial analysis using ESRI ArcGIS software (version 10.1: http://www.esri.com/software/arcgis/arcgis-for-desktop).

490 491 492 493 494 495 496 497 498 499 500 501 502

Fig. 2 Seasonal variations of ammonia concentrations observed from surface network (site) in China.

503 504 505 506

Fig. 3 Comparisons of passive diffusion sampler to the continuously active analyzers of MARGA and DELTA. Ammonia concentrations are aggregated to monthly data points.

507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522

Fig. 4 Spatial distribution of site-based dry deposition flux versus the gridded agriculture emission inventory of ammonia in China. The legend for gridded ammonia inventory was also shown in unit of kg N ha−1 yr−1 (red numbers in the left corner), in addition to t yr−1 per 0.25o by 0.25o. The NH3 inventory used in this study is from the Multi-Resolution Emission Inventory of China (MEIC, http://meicmodel.org) 36, and are originally developed and described by Huang et al. 26 The MEIC inventory is provided with monthly gridded emissions of NH3 at 0.25°×0.25° by five sectors, i.e., power generation, industry, residential, transportation, and agriculture. The agriculture sector is a dominant source of NH3 emissions on national scale, mainly contributed by fertilizer applications and manure managements. We choose the year of 2012 to conduct the spatial comparison because emissions after 2012 are not available at present. The provincial boundary layer with a scale of 1:4,000,000 was obtained from the National Geomatics Center of China (http://ngcc.sbsm.gov.cn/). Maps were generated based upon a geospatial analysis using ESRI ArcGIS software (version 10.1: http://www.esri.com/software/arcgis/arcgis-for-desktop).

523 524 525 526 527

Fig. 5 Comparison of site-based dry deposition flux versus the gridded agriculture emission inventory of ammonia in China (1o by 1o). The sites are colored by regions. The unit of emission data is converted to kg N ha−1 yr−1 for comparison with that of ammonia deposition. 1:1 line represents the same value of ammonia dry deposition to emissions.

528


Page 21 of 26


Pan Page 21 529

530

Tables

531

Table 1 Seasonal and annual concentrations of ammonia (µg m−3) observed at the 53

532

sites in China during this study period (SON=Sept-Oct-Nov, DJF=Dec-Jan-Feb,

533

MAM=Mar-Apr-May, JJA=Jun-Jul-Aug)

534 Code

Location

Lat

Lon

FQA

Fengqiu

35.0

114.6

LCA

Luancheng

37.9

YCA

Yucheng

TSM

Elv. (m)

Land use types

Region

SON

DJF

MAM

JJA

Mean

67

Farmland

NCP

19.2

14.3

12.8

21.1

16.8

114.7

57

Farmland

NCP

21.9

17.2

17.5

20.7

19.3

37.0

116.6

23

Farmland

NCP

21.3

12.8

10.0

45.2

22.3

Tainshan

36.3

117.1

1506

Mountain & Shrubbery

NCP

3.3

2.8

5.6

3.8

3.9

XLM

Xinglong

40.4

117.6

872


NCP

3.6

0.9

6.1

5.1

3.9

YFS

Yangfang

40.2

116.1

73

Suburban

NCP

6.0

4.1

6.0

11.9

7.0

CZS

Cangzhou

38.3

116.9

10

Suburban

NCP

22.0

22.2

25.6

26.0

23.9

TJU

Tianjin

39.1

117.2

6

Urban

NCP

12.3

7.2

11.0

14.5

11.3

BJU

Beijing

40.0

116.4

57

Urban

NCP

16.6

7.2

14.9

16.3

13.7

BDI

Baoding

38.9

115.5

21

Urban

NCP

12.7

10.5

12.4

25.7

15.3

TGI

Tanggu

39.0

117.7

0

Urban & Coastal

NCP

8.4

8.0

11.9

12.7

10.2

CLD

Cele

37.0

80.7

1319

Desert

NW

7.3

3.1

5.1

9.1

6.1

FKD

Fukang

43.3

87.9

475

Desert & Suburban

NW

8.3

14.7

17.2

17.4

14.4

AKA

Akesu

40.6

80.8

1031

Farmland

NW

3.8

3.2

10.9

17.6

8.9

HCA

Huocheng

44.0

80.7

590

Farmland

NW

9.0

11.2

26.6

13.4

15.1

ALT

Altai Mountains

47.6

86.0

847


NW

2.4

/

9.3

6.2

5.5

SPT

Shapotou

37.5

105.0

1258

Desert

Central

4.2

2.3

5.1

9.0

5.1

ASA

Anshai

36.9

109.4

1207

Farmland

Central

1.6

3.8

5.4

6.7

4.3

LZA

Linze

39.4

100.1

1385

Farmland

Central

3.5

2.3

3.7

10.4

5.0

WNA

Weinan

34.7

109.3

411

Farmland

Central

8.3

7.7

13.1

20.3

12.4

WLG

Waliguan

36.3

100.9

3772

Grassland

Central

2.0

2.0

2.2

2.3

2.1

HBG

Haibei

37.6

101.3

3198

Grassland

Central

2.1

2.0

3.2

7.2

3.6

HTF

Huitong

26.9

109.6

524

Forest

SE

2.1

1.9

0.8

1.8

1.7

QYF

Qianyanzhou

26.4

115.0

74

Mountain & Forest

SE

2.5

1.9

2.4

3.2

2.5

DHM

Dinghushan

23.2

112.6

44

Mountain & Forest

SE

3.8

2.6

3.2

1.7

2.8

HJK

Huanjiang

24.7

108.3

293

Mountain & Karst

SE

2.5

1.0

3.6

10.1

4.3

XMU

Xiamen

24.7

118.1

2

Urban

SE

6.0

4.0

5.7

5.0

5.2

GZU

Guangzhou

23.1

113.3

14

Urban

SE

4.9

4.4

6.9

6.9

5.8



Page 22 of 26

Pan Page 22 THL

Taihu

31.6

120.3

7

Urban

SE

3.4

5.7

6.0

10.3

6.3

MMU

Maoming

21.6

110.7

16

Urban

SE

10.1

5.9

8.0

15.1

9.8

NJU

Nanjing

32.1

118.4

15

Urban

SE

11.6

7.5

9.5

14.6

10.8

YXI

Yongxing island

16.8

112.3

10

Waterbody & Island

SE

3.2

1.9

2.0

3.7

2.7

PYL

Poyang lake

29.4

116.1

24

Waterbody & Lake

SE

1.6

2.5

2.0

4.4

2.6

DTL

Dongting lake

29.5

112.8

28

Waterbody & Lake

SE

3.7

4.1

6.0

9.0

5.7

DHL

Donghu lake

30.5

114.4

20

Waterbody & Lake

SE

3.8

3.8

7.3

10.4

6.3

NMD

Naiman

42.9

120.7

362

Desert

NE

5.6

2.9

3.4

9.4

5.3

SYA

Shenyang

41.5

123.4

38

Farmland

NE

5.1

2.3

6.7

12.6

6.7

MHF

Mohe

52.9

122.8

467

Forest

NE

1.3

1.1

0.7

1.0

1.0

ERG

Ergun

50.2

119.4

525

Grassland

NE

1.5

0.3

8.9

1.3

3.0

ING

Inner Mongolia

43.6

116.7

1187

Grassland

NE

2.2

2.0

3.2

7.2

3.6

DAG

Daan

45.6

123.8

1299

Grassland

NE

2.7

1.6

5.1

9.9

4.8

CCU

Changchun

44.0

125.4

195

Grassland & Suburban

NE

2.9

0.7

3.4

6.4

3.4

CBM

Changbaishan

42.4

128.1

736

Mountain & Forest

NE

0.7

0.7

8.3

23.4

8.3

SJW

Sanjiang

47.6

133.5

55

Waterbody & Wetland

NE

3.2

1.2

3.2

4.9

3.1

YTA

Yanting

31.3

105.5

437

Farmland

SW

3.6

1.6

2.8

9.6

4.4

LSA

Lhasa

29.6

91.0

3640

Farmland

SW

6.0

2.9

4.4

6.1

4.8

BNF

Xishuangbanna

22.0

100.8

648

Forest

SW

4.9

5.5

6.4

4.2

5.3

ALD

Ali

33.4

79.7

4256

Grassland

SW

1.3

0.9

0.7

6.3

1.7

ALM

Ailaoshan

24.3

101.0

2483

Mountain & Forest

SW

1.1

1.1

0.6

2.8

1.4

GGM

Gonggashan

29.6

102.0

2977

Mountain & Forest

SW

0.7

0.9

0.5

5.0

1.8

MXF

Maoxian

31.7

103.9

1826


SW

2.3

1.4

3.9

2.9

2.6

GZA

Guizhou

26.3

105.9

1468

Urban

SW

3.4

2.3

4.7

5.3

3.9

CDU

Chengdu

30.6

104.0

490

Urban

SW

7.9

5.5

9.6

10.5

8.4

535 536 537


Page 23 of 26


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538

Figures

539 540 541 542 543 544 545 546 547 548 549 550

Fig. 1 Spatial distribution of ammonia concentrations observed from surface network (site) versus satellite column data (grid) in China. The detailed surface observation site information can be found in Table 1 and SI. Satellite NH3 total column distributions are derived from the Infrared Atmospheric Sounding Interferometer (IASI) aboard MetOp-A for the year 2015. We collect the observations from morning overpass time (9:30 LTC) and filter the columns with relative error above 100% following procedures presented in Van Damme et al 12. The filtered IASI satellite columns are then mapping to 0.25°×0.25° horizontal resolution by averaging available observations within each grid cell. The provincial boundary layer with a scale of 1:4,000,000 was obtained from the National Geomatics Center of China (http://ngcc.sbsm.gov.cn/). Maps were generated based upon a geospatial analysis using ESRI ArcGIS software (version 10.1: http://www.esri.com/software/arcgis/arcgis-for-desktop).

551



Page 24 of 26

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552

553

554

555 556 557

Fig. 2 Seasonal variations of ammonia concentrations observed from surface network (site) in China.


Page 25 of 26


Pan Page 25 558 559

560 561 562

Fig. 3 Comparisons of passive diffusion sampler to the continuously active analyzers of

563

MARGA and DELTA. Ammonia concentrations are aggregated to monthly data points. MHF ERG

SJW

ALT DAG CCU

HCA FKD

LZA

CLD

HBG SPT WLG

ALD

BJU YFSXLM BDI TSM LCA YCA ASA

MXF LSA

564

kg N ha yr 0-3.3 3.3-6.6 6.6-13.2 13.2-26.4 26.4-39.5 39.5-52.7 52.7-65.9 65.9-125.6

o

NJU THL

CDU YTA

DHL DTL PYL

GGM

Agriculture inventory −1

HTF GZA HJK

ALM

250-500 500-1000 1000-2000 2000-3000 3000-4000

BNF −1

kg N ha yr 0-5 5-15 15-30

−1

MMU

YXI

Yellow River Yangtze River

4000-5000 5000-9530

QYF DHM XMU GZU

−1

0.25 grid [t yr ] 0 - 250

SYA

FQA

WNA

−1

CBM

ING NMD

AKA

0 250 500

km 1,000

565

Fig. 4 Spatial distribution of site-based dry deposition flux versus the gridded agriculture

566

emission inventory of ammonia in China. The legend for gridded ammonia inventory was also

567

shown in unit of kg N ha−1 yr−1 (red numbers in the left corner), in addition to t yr−1 per 0.25o

568

by 0.25o. The NH3 inventory used in this study is from the Multi-Resolution Emission



Page 26 of 26

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569

Inventory of China (MEIC, http://meicmodel.org)

570

described by Huang et al. 26 The MEIC inventory is provided with monthly gridded emissions

571

of NH3 at 0.25°×0.25° by five sectors, i.e., power generation, industry, residential,

572

transportation, and agriculture. The agriculture sector is a dominant source of NH3 emissions

573

on national scale, mainly contributed by fertilizer applications and manure managements. We

574

choose the year of 2012 to conduct the spatial comparison because emissions after 2012 are

575

not available at present. The provincial boundary layer with a scale of 1:4,000,000 was

576

obtained from the National Geomatics Center of China (http://ngcc.sbsm.gov.cn/). Maps were

577

generated based upon a geospatial analysis using ESRI ArcGIS software (version 10.1:

578

http://www.esri.com/software/arcgis/arcgis-for-desktop).

, and are originally developed and

579 580

581 582

Fig. 5 Comparison of site-based dry deposition flux versus the gridded agriculture emission

583

inventory of ammonia in China (1o by 1o). The sites are colored by regions. The unit of

584

emission data is converted to kg N ha−1 yr−1 for comparison with that of ammonia deposition.

585

1:1 line represents the same value of ammonia dry deposition to emissions.

586


Identifying ammonia hotspots in China using a national observation

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