- Email: [email protected]
Prediction and mitigation potential of anthropogenic ammonia emissions within the Beijing–Tianjin–Hebei region, China
Journal Pre-proof Prediction and mitigation potential of anthropogenic ammonia emissions within the Beijing–Tianjin–Hebei region, China Xiurui Guo, Zhilan Ye, Dongsheng Chen, Hongkan Wu, Yaqian Shen, Junfang Liu, Shuiyuan Cheng PII:
S0269-7491(19)34104-1
DOI:
https://doi.org/10.1016/j.envpol.2019.113863
Reference:
ENPO 113863
To appear in:
Environmental Pollution
Received Date: 26 July 2019 Revised Date:
19 December 2019
Accepted Date: 19 December 2019
Please cite this article as: Guo, X., Ye, Z., Chen, D., Wu, H., Shen, Y., Liu, J., Cheng, S., Prediction and mitigation potential of anthropogenic ammonia emissions within the Beijing–Tianjin–Hebei region, China, Environmental Pollution (2020), doi: https://doi.org/10.1016/j.envpol.2019.113863. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Graphical Abstract:
1 2
Prediction and mitigation potential of anthropogenic ammonia emissions within the Beijing–Tianjin–Hebei region, China
3 4 5
Xiurui Guo∗, Zhilan Ye, Dongsheng Chen, Hongkan Wu, Yaqian Shen, Junfang Liu, Shuiyuan Cheng College of Environmental & Energy Engineering, Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
6
Abstract: Large ammonia (NH3) emissions contribute approximately 8 30% to the fine particle
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
pollution in China and highlight the need for understanding the emission trends and mitigation effects of NH3 in the future. The purpose of this study is to predict the NH3 emissions and analyze the mitigation potential up to year 2040 by scenario analysis based on the established new NH3 emission inventory from anthropogenic sources for the Beijing–Tianjin–Hebei (BTH) region. The results showed that the total NH3 emission in the BTH region was estimated at 966.14 Gg in 2016. Under the Business-as-Usual (BAU) scenario, the total NH3 emissions in 2030 and 2040 would increase by 13% and 26% compared with 2016 levels, with average annual growth rates of 0.9% and 1.0%, respectively. Livestock will continue to dominate NH3 emissions in the future, with the proportions of total emissions increasing from 57% in 2016 to 64% in 2030 and 68% in 2040. The share of the second-largest NH3 emission source, synthetic fertilizer application, will decrease from 36% in 2016 to 31% in 2030 and 27% in 2040. Among five other sources, the largest change occurred in waste disposal, increasing notably by 3.31 times from 2016 to 2040 owing to rapid urbanization. Under the Combined Options (CO) scenario, the total NH3 emissions could be reduced by as much as 34% by 2030 and 50% by 2040 compared with the BAU scenario, which is attributed to livestock (24% in 2030, 37% in 2040) and synthetic fertilizer application (10% in 2030, 13% in 2040), respectively. This study can give a reliable estimation of anthropogenic NH3 emission in the BTH region during 2020–2040 and provide a valuable reference for effective mitigation measures and control strategies for policy makers. Capsule: The total NH3 emissions would increase in the future and livestock source had largest mitigation potential. Key words: Ammonia emission inventory; Prediction; Reduction potential
28 29 30 31 32 33 34 35 36
1. Introduction Ammonia (NH3) is the primary alkaline gas present in the atmosphere. NH3 plays an important role in forming secondary inorganic aerosols with diameters equal to or less than 2.5 µm (PM2.5) by neutralizing both sulfuric acid and nitric acid in the atmosphere (Backes et al., 2016; Luo et al., 2015). Large amounts of this fine particulate matter cause environmental effects (such as low visibility) and threaten human health (Beelen et al., 2014; Langridge et al., 2012; Lelieveld et al., 2015; Li et al., 2017). Moreover, when NH3 is deposited onto soil, it can cause environmental issues such as soil acidification, water eutrophication, and rapid declines in species richness directly or indirectly (Krupa, 2003; Southon et al., 2013; Ye et al., 2011).
∗
Corresponding author. E-mail address: [email protected] (X. Guo).
37
Previous studies confirmed that China produced approximately 5.9 11.1 Tg NH3 emissions
38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
annually during 1980-2012 (Huang et al., 2012; Kang et al., 2016; Paulot et al., 2015). In recent years, domestic scholars have developed NH3 emission inventories in China (Zhao et al., 2012; Zheng et al., 2012; Zhou et al., 2015). Most of these emission inventories were developed with average emission factors (EFs) cited from the literature or foreign values, which introduced significant inaccuracies in emission estimations because ammonia EFs are highly sensitive to fertilizer type, local soil, and meteorological properties (Zhang et al., 2018). Moreover, several NH3 emission inventories have been developed by considering available domestic experiment results or parameterizing the EFs by the environmental factors (Huang et al., 2012; Kang et al., 2016; Wang et al., 2018; Wu et al., 2017; Xu et al., 2015, 2016; Zhang et al., 2010, 2011). However, several points still need improvement. First, most of the NH3 emissions from synthetic fertilizer applications were estimated by using the National Ammonia Reduction Strategy Evaluation System (NARSES) model; the maximum potential EFs for each kind of fertilizer obtained abroad were not suitable for domestic situations. The NH3 emissions from livestock were estimated by using a top-down Regional Air Pollution Information and Simulation (RAINS) model, and the NH3–N volatilization proportion of each emission stage was obtained referring to other research without considering the differences of NH3 emissions in different stages under different conditions. In addition, not all NH3 emission sources were estimated in majority of these studies. Although fertilizer application and livestock manure are the dominant sources of NH3 emissions, the contributions of non-agricultural sources cannot be ignored, since their emission maybe increase in the future. Abundant evidence indicates that poor agricultural production management and low efficiency of N use are the main causes of the large NH3 emissions over China (Liu et al., 2019). Some scholars have studied the effects of mitigation measures on NH3 emissions produced by synthetic fertilizer application, pigs, and dairy cattle (Van der Heyden et al., 2015; Chai et al., 2015; Hagenkamp-Korth et al., 2015; Liu et al., 2017; Mendes et al., 2017; Pereira et al., 2011; Philippe et al., 2011; Wang et al., 2017) due to their large NH3 emissions. In recent years, some scholars conducted meta-analysis to evaluate the effectiveness of mitigation strategies for NH3 volatilization from cropping systems and livestock production (Hou et al., 2015; Huang et al., 2016; Pan et al., 2016; Ti et al., 2019; Wei et al., 2017). The results showed strong mitigation potential for agricultural NH3 emissions. Hence, it is important to provide comprehensive and reliable prediction and mitigation measures considering the potential of the NH3 emissions. For example, Xu et al. (2017) found that the NH3 emissions from livestock manure in China in 2030 were expected to increase by 90.41% compared with 2008 levels under the Business-as-Usual (BAU) scenario. In addition, the predicted emissions could be cut by 18.9–37.3% under the scenario of mitigating technologies during four livestock rearing stages compared with 2030 BAU levels. Zhao et al (2017) found that agricultural NH3 emissions in the Hai River Basin in 2030 would further increase by 33% compared with 2012 levels under BAU. In addition, the predicted emissions could be reduced by 60% compared with 2030 BAU levels under the scenario of improved technology and management combined with human diet optimization. However, the prediction of the activity level was based on the modified average annual growth rate (AAGR) approach in the study of Xu et al (2017) without considering the impact of Chinese policies on agriculture development. The activity levels were forecast on the basis of the empirical estimation
80 81 82 83 84 85 86 87 88 89 90 91
by Zhao et al (2017), which created large uncertainties. Moreover, the emission reduction potential from different sources remains unclear. This study attempted to predict the emission and mitigation potential of anthropogenic NH3 under different mitigation scenarios based on the new established NH3 emission inventory for the Beijing–Tianjin–Hebei (BTH) region in 2016. First, we developed a comprehensive emission inventory of the anthropogenic NH3 for the BTH region for 2016 using the authoritative method recommended by MEE (2014). NH3 emissions during 2020–2040 were predicted based on the forecast activity data. Four different mitigation scenarios for NH3 emissions were designed considering the control technologies for both synthetic fertilizer application and livestock manure volatilization, reflecting the different NH3 control options. Finally, the mitigation potential of agricultural NH3 from the implementation of feasible abatement measures of agricultural activities up to year 2040 was estimated.
92
2. Methodology
93
2.1. Study area
94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117
As the economic hub of China, the BTH region encompasses two municipalities including Beijing and Tianjin as well as 11 cities in the Hebei Province (Fig.1). This region covered 2.3% of the overall Chinese land, fed 8.0% of the population, and generated 10.4% of the National Gross Domestic Product in 2016. Moreover, the BTH region provided 6% of the nation’s total meat production and 6% of total grain output (NBSC, 2017b). Intensive agricultural production along with fertilizer use to improve the production of farmlands for meeting the increasing human food demands have led to high NH3 emissions in this region (Qi et al., 2017; Zhao et al., 2012; Zhang et al., 2010; Zhou et al., 2015) and thus contributed over 40% to PM2.5 through the formation of secondary inorganic aerosols (Peng, 2013). To continuously improve the air quality, it is necessary to understand the emission trends and mitigation effects of NH3 in the future. 2.2. Methods of estimating ammonia emissions We used the EF method mentioned in MEE (2014) to calculate the NH3 emissions from seven sectors: (1) synthetic fertilizer application, (2) livestock, (3) biomass burning, (4) chemical industry, (5) human beings, (6) waste disposal, and (7) traffic source. In addition, fuel combustion source was not included during estimation, because the emission factor was not available in MEE (2014) and the emission was small. Specific activity data used for NH3 emission estimate is listed in Table S1 (Supplementary Material). NH3 emission was calculated based on activity data (NBSC, 2017a, 2017b, 2017c, 2017d; NDRC, 2017b) and the corresponding EFs (Table S2, Table S3, and Table S4 in the Supplementary Material). The formula can be expressed as ,
(2-1)
where i represents the area, j represents the emission source, E is the total emission, γ is the conversion coefficient of N–NH3 emissions which is 1.214 for livestock and 1.0 for other sources, Ai,j is the activity level of source j in i area, EFi,j is the EF of source j in i area. An effort was made to reduce the inaccuracy of the NH3 emission inventory in this study. For
118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156
example, the estimated NH3 emission from synthetic fertilizer application was more accurate using the application amount of five N fertilizers multiplied by the corresponding EFs, respectively, rather than the synthetic EFs of N fertilizer. In addition, the livestock NH3 emission was estimated by mass-flow approach considering different management stages of livestock outdoor, livestock housing, manure storage, and land spreading, rather than using the average emission factor per capita. 2.3. Methods of uncertainty analysis There are two key steps in quantitative approach of NH3 emission inventory uncertainty analysis. First, the mathematical distribution and the coefficient variation (CV) of activity level and EFs for each emission source was determined referring to the previous researches (Zhao et al., 2011; Huang et al., 2012; Zheng et al., 2012). Then, the uncertainties in the activity data and EFs were transmitted to the emission inventory through the Monte Carlo simulation, based on Crystal ball software (Liu et al., 2018; Zheng et al., 2012). To ensure the accuracy of the simulation results, 10,000 Monte Carlo simulations were performed to estimate the range of the NH3 emissions for each source with a 95% confidence interval. 2.4. Method of predicting ammonia emissions This study forecasted anthropogenic NH3 emissions up to 2040 on the basis of 2000-2016 emission inventories. The EFs for anthropogenic NH3 emissions were considered unchanged in the future owing to the stability of the environmental conditions influencing ammonia volatilization such as soil pH values and ambient temperature. The activity data for anthropogenic NH3 emissions were predicted on the historical trend of the values during 2000-2016. The prediction methods and prediction indicators of activity data for anthropogenic NH3 emissions are summarized in Table 1. The predicted values were adjusted according to government policies (BMBARA, 2016; CPG, 2016; MARA, 2017; TPGHP, 2014, 2016; TMPG, 2016). Specifically, there were some indicators of activity level (such as the populations of dairy cow, beef cattle, etc) in government policies, once our predicted value was higher or lower than that in government policies, the predicted value was replaced by the indicator of activity data in government policies. In addition, the intensive farming rates for different livestock in the BTH region in 2020, 2030, and 2040 were listed in Table S5 of the Supplementary Material. 2.5. Abatement scenarios of future NH3 emissions This study focuses on NH3 mitigation from agricultural source because synthetic fertilizer application and livestock contributed to more than 80% of the total emissions (Huang et al., 2012; Xu et al., 2015, 2016; Zhang et al., 2018). Control technologies with high efficiency were selected based on field research, telephone consultation, and literature research combined with the emission characteristics of agricultural NH3 in the BTH region. The definitions of the emission scenarios are summarized in Table 2. The BAU scenario assumes that the fertilizer type and field management of the fertilizer application source, feed composition, housing system, manure storage management, and manure application techniques of the livestock source are identical to those used in 2016. No additional abatement technique will be
157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172
implemented in the future. To compare and analyze the control effects of NH3 from different agricultural sources, we defined two single control scenarios, including lowering emission from fertilizer application (LEF scenario) and lowering emission from livestock (LEL scenario). The LEF scenario, which specifies low emission from fertilizer application, assumes that relative to 2016 BAU, coated urea fertilizer will be widely used. This measure could alleviate NH3 emissions by substituting urea with fertilizers of enhanced efficiency characterized by reducing N losses after application to farmland. Moreover, deep placement of synthetic fertilizers was selected, which reduced NH3 volatilization when compared with the surface broadcast of urea on soils. The LEL scenario assumes that relative to 2016 BAU, the lower crude protein content in livestock diets could decrease the N content and PH of livestock manure to ultimately decrease NH3 emissions through manure management. Farmers install air scrubbers as part of an intensive rearing system to alleviate NH3 emissions from housing stage. Manure acidification can reduce NH3 emissions during manure storage stage. Deep placement of manure into soil can obviously reduce the manure surface exposure. The combined options of LEF and LEL in the CO scenario assumes that the NH3-removal techniques of fertilizer application and the livestock rearing system mentioned above are combined and applied to complement each other.
173
3. Results and discussion
174
3.1. Ammonia emission inventory in 2016
175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196
The total amount of NH3 emitted in the BTH region in 2016 was estimated to be approximately 966.14 Gg, of which synthetic fertilizer application and livestock manure spreading accounted for 36% and 57%, respectively. Hebei contributed to an overwhelming majority (91%) of the total NH3 emissions in the BTH region, amounting to 876.44 Gg, owing to the large cultivated land areas and high degree of free-range livestock rearing in that area. 3.1.1. Contributions by source The contributions of main sources to the total NH3 emissions as well as the contributions of four livestock manure management stages in the entire BTH region, Beijing, Tianjin, and Hebei are depicted in Fig. 2. Livestock were the largest contributors to the NH3 emissions in the entire BTH region. Livestock NH3 emissions in 2016 were estimated as 548.90 Gg, accounting for approximately 56.81% of the total emissions in the BTH region. For the contribution of NH3 emissions from the four livestock stages in the BTH region, livestock manure field application dominated the NH3 emission, followed by housing, outdoor, and manure storage. As the second-largest NH3 contributor, synthetic fertilizer application had NH3 emissions of 350.04 Gg, accounting for 36.23% of the total emissions in the BTH region in 2016. The non-agricultural sources (such as biomass burning, chemical industry, human beings, waste disposal, and traffic sources) were not as notable as agricultural sources for the entire BTH region, contributing approximately 6.96% to the total NH3 emissions in the BTH region. In detail, the NH3 emission from livestock in Beijing, Tianjin, and Hebei accounted for about half of the total emissions. The NH3 emission from synthetic fertilizer application in Beijing was relatively low, accounting for below 30% of the total emissions, owing to its smaller cultivated
197 198 199
land area. Obviously, the contribution of non-agricultural sources in Beijing was over twice those in Tianjin and Hebei, owing to the relatively high urbanization rate and vehicle ownership rate in that area.
200
3.1.2. Contributions by city
201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217
The contributions of NH3 emissions from different cities for different sources and the total NH3 emissions in different cities in 2016 are shown in Fig. 3. The largest NH3 emission occurred in Shijiazhuang, contributing 14.92% to the total emissions. Followed by Handan and Baoding, the emissions contributed 12.59% and 11.37% to total emissions, respectively. Synthetic fertilizer application and livestock were the two dominated NH3 sources in different cities, accounting for 16.53–44.23% and 47.54–78.96% of the total emissions, respectively. The distinctive emission characteristics of 13 BTH cities were attributed to the different levels of farming practice. The percentage of synthetic fertilizer application was significantly low in Zhangjiakou, Chengde, and Beijing compared with that in Cangzhou, owing to the smaller proportion of cultivated land areas in the three aforementioned cities. In contrast, the percentage of livestock was relatively higher in Zhangjiakou and Chengde than that in Cangzhou. The non-agricultural sources in Beijing contributed relatively large proportion of 18.86% to the total NH3 emissions among the 13 BTH cities, attributed to waste disposal and traffic source. In contrast, the proportion was below 9% in other 12 cities. Biomass burning was another important source of NH3 emissions in the BTH region, with contributions ranging from 1.70% to 3.57% among the cities. Human beings were responsible for larger portions in Hebei Province owing to the larger rural populations and the lower access rate to sanitary toilets in that area.
218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
3.1.3. Uncertainty analysis Uncertainties exist unavoidably during the estimation of NH3 emissions, generated from both the activity data and EFs. The uncertainties of BTH’s NH3 emissions were quantified by Monte Carlo simulation assuming that both activity data and EFs obeyed lognormal distributions. Generally, for the activity levels acquired from the governmental yearbooks and EFs summarized from official guidelines, the coefficient variation (CV) were 5-10% and 20-50%, respectively (Huang et al., 2012; Zhao et al., 2011; Zheng et al., 2012). The uncertainty ranges of the NH3 emissions from different sources with a 95% confidence interval are ranked in the Supplementary Material (Table S4). It was found that the uncertainty ranges from all sectors were between -43% and 74%. Relatively high uncertainties typically existed for the chemical industry and human beings, ranging from -43% to 74%. This could be attributed to the low accuracy of the source level classification in comparison with the sources of livestock or biomass burning, and few measured EFs were available for NH3 estimation. Relatively moderate uncertainties typically existed for livestock and biomass burning, owing to the consideration of three different livestock rearing systems and four stages of manure management for livestock, as well as numerous parameters including the residue-to-crop yield ratio, domestic or in-field straw burning percentage, and burning efficiency for biomass burning.
235
3.1.4. Comparison with other studies
236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260
The anthropogenic NH3 estimations from prior studies over the past years in the BTH region are summarized in Fig. 4. The total and source-specific NH3 emissions in this study were between other researchers’ estimations. Specifically, the total NH3 emission in this study was about 15.84%-55.15% larger than that of Zhang et al. (2010), Cai et al. (2017), and Qi et al. (2017), approximately 18.95%-38.61% lower than Zhao et al.’s (2012) and Zhou et al.’s (2015) results. The disparity is caused mainly by the following reasons. Firstly, the interannual variation of activity data created some variety. The base year of other studies was 3–12 years earlier than that of this study. Additionally, the selection of EFs and relative parameters is important. For agricultural sources, the NH3 emission from synthetic fertilizer application was estimated using the application amounts of five types of N fertilizers calculated based on NDRC (2017a) with corresponding EFs, rather than the total consumption of chemical fertilizers and synthetic EF. The NH3 emission from livestock was estimated using mass-flow methodology considering different management stages of livestock outdoor, livestock housing, manure storage, and land spreading, rather than using the average emission factor per capita. For non-agricultural sources, the cited EFs selected from literature or foreign values were used widely. In addition, we compared the relative weight of the contributions for different categories and different cities with Zhou et al.’s (2015) results. It showed that the proportion of NH3 emissions from synthetic fertilizer application in each city of this study was about 8% larger than Zhou et al.’s (2015) result, owing to the smaller NH3 emissions in this study without estimating fuel combustion source and natural sources. What’s more, the proportion of NH3 emissions from non-agricultural sources of this study was smaller than Zhou et al.’s (2015) result. For example, the proportion of NH3 emissions from human beings in Beijing of this study was about approximately 17% lower than that of Zhou et al (2015), since NH3 emission from urban population was merged to waste disposal source in this study. There was no NH3 emission from chemical industry in Beijing in this study, owing to the fewer chemical plants in Beijing.
261 262
3.2. Future emission trends under the BAU scenario and mitigation potential of ammonia
263 264 265 266 267 268 269 270 271 272 273 274 275
3.2.1. Future emission trends by four mitigation scenarios The trends of NH3 emission and their sectoral distributions during 2016–2040 under different scenarios are given in Fig.5. Under the BAU scenario, the predicted total NH3 emissions in the BTH region during 2020–2040 would increase with an average annual growth rate of 0.99%. The predicted total NH3 emissions in 2020, 2030, and 2040 under the BAU scenario were 999.28 Gg, 1087.08 Gg, and 1216.11 Gg, and 3.43%, 12.52%, and 25.87% higher than that in 2016, respectively. Livestock production, one of the most important emission sources in the BTH region, contributed largely to this increasing trend. Moreover, the NH3 emissions in the BTH region would continue to increase during 2020–2040 under the LEF scenario, with a significantly lower growth rate (0.58%) than that in the BAU scenario. However, the total NH3 emissions in the BTH region would decrease during 2020–2040 under the LEL and CO scenarios, owing to the NH3 emission reduction from livestock source. This trend was consistent with the NH3 emission in the Hebei Province. In contrast, the NH3
276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314
emission in Beijing experienced consistent negative growth during 2020–2040 under the four scenarios, owing to decreases in fertilizer application and the livestock population. For the NH3 emission in Tianjin under the LEF scenario, it showed a steadily decreasing trend before it bottomed at 47.91 Gg in 2030 and then slightly increased to 53.58 Gg in 2040. This trend occurred because the NH3 mitigation from synthetic fertilizer application in Tianjin was not enough to offset the growth of NH3 emission from the livestock source since 2030. 3.2.2. Mitigation effects of different measures The total NH3 emission reduction ratios by different control technologies under different scenarios in the BTH region during 2020–2040 compared with the BAU scenario are shown in Fig 6. Large differences were noted in the amount of NH3 emission mitigation among synthetic fertilizer application and livestock sources. Obviously, livestock had largest mitigation potential, at approximately 60–74% of the total emission reduction during 2020–2040. Among which the installation of air scrubber in the housing stage could alleviate NH3 emission by more than 25% owing to large NH3 emission in the housing stage and the high removal efficiency. In 2040, deep manure placement and the reduction of crude protein content in the feeding stage could alleviate NH3 emission by more than 20%. In contrast, the livestock emission reduction was lowest in the manure acidification stage because ammonia emission in the storage stage was the lowest. For synthetic fertilizer application, lowering the fertilization was the most effective measure for NH3 emission mitigation, accounting for 11–23% of the total emission reduction during 2020–2040, followed by deep placement of fertilizers and coated urea fertilizers. 3.3. Recommendations of NH3 control measures The estimated results show that the total anthropogenic NH3 emissions in the BTH region would increase by 25.87% from 2016 to 2040 without ammonia-reduction measures. The overwhelming majority of NH3 emissions would be contributed from livestock source, so it would have larger mitigation potential from livestock source than from synthetic fertilizer application. Overall, NH3 control measures such as installing air scrubbers in the housing stage and lowering fertilization are recommended during 2016-2020, with each measure accounting for approximately more than 22% of the total emission reduction. More attention should be paid to reducing the NH3 emissions from livestock sources during 2020-2040, such as to promote the penetration rate of control technologies in the future, owing to the dramatically growing trends of livestock emissions. Installing air scrubbers in the housing stage would still be the most effective control measures, accounting for approximately more than 25% of the total emission reductions. Followed by deep manure placement and crude protein content reduction, showing better emission reduction effect than other technologies. In contrast, NH3 control measures from synthetic fertilizer application would become less dominant in the future years. 4. Conclusions This study attempted to predict the emissions and mitigation potential of anthropogenic NH3
315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347
under four mitigation scenarios based on the newly established NH3 emission inventory in the BTH region in 2016. We intended to provide optimized strategies for the BTH’s NH3 emission abatement policy in the near future. The total anthropogenic NH3 emission amount was valued at 966.14 Gg, with approximately 57% and 36% originating from livestock and synthetic fertilizer application and relatively low proportions of 7% from the sum of biomass burning, human beings, traffic source, chemical industry, and waste disposal. Hebei contributed to an overwhelming majority (91%) of total NH3 emissions in the BTH region. Under the BAU scenario, the total NH3 emission in 2020, 2030 and 2040 would increase by 3%, 13% and 26% compared to 2016, respectively. Livestock would continue to dominate the NH3 emission in the future and the proportion would increase from 57% in 2016 to 68% in 2040. The share of the second-largest NH3 emission source, synthetic fertilizer application, would decrease from 36% in 2016 to 27% in 2040, respectively, resulting from the government’s plan to achieve negative growth in fertilizer application in the BTH region by 2020. The total NH3 emissions from other five non-agricultural sources were 73.39 Gg in 2040. Compared with the BAU scenario, the total emission reduction of NH3 during 2020–2040 was estimated as 54.50–155.93 Gg, 82.99–453.61 Gg, and 137.49–609.54 Gg under the LEF, LEL, and CO scenarios, respectively, with reduction ratios of 5.42–12.64%, 8.30–37.30%, and 13.76–50.12%, respectively. Overall, the livestock source had the biggest mitigation potential; the most effective methods were the installation of air scrubbers in the housing stage. Lowering the fertilization amount was the most efficient measure for reducing NH3 emission in cropland. This study used the EF method to establish the anthropogenic NH3 emission inventory in the BTH region, 2016. Owing to a lack of comprehensive measured EFs for NH3 emission in the BTH region, errors were present in the results even though the authoritative EFs provided by the guidelines were used, bringing higher reliability to this study. Therefore, strengthening of more localized EFs is needed in future research work. Acknowledgements The authors would like to acknowledge the anonymous reviewers for their valuable comments. This study was supported by the Natural Key R&D Programs of China (No. 2018YFC0213202), Heavy Air Pollution Origin and Tackling Project (No. DQGG0201), and Natural Sciences Foundation of China (No. 51638001). The assessments in this paper are provided by only the authors and do not necessarily represent the official views of the sponsors.
348
Reference
349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390
Backes, A. M., Aulinger, A., Bieser, J., Matthias, V., Quante, M., 2016. Ammonia emissions in europe, part II: how ammonia emission abatement strategies affect secondary aerosols. Atmos. Environ. 126, 153-161. https://doi.org/10.1016/j.atmosenv.2015.11.039. Beelen, R., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z.J., Weinmayr, G., Hoffmann, B., Wolf, K., Samoli, E., Fischer, P., Nieuwenhuijsen, M., Vineis, P., Xun, W.W., Katsouyanni, K., Dimakopoulou, K., Oudin, A., Forsberg, B., Modig, L., Havulinna, A.S., Lanki, T., Turunen, A., Oftedal, B., Nystad, W., Nafstad, P., Faire, U.D., Pedersen, N.L., Östenson, C.-G., Fratiglioni, L., Penell, J., Korek, M., Pershagen, G., Eriksen, K.T., Overvad, K., Ellermann, T., Eeftens, M., Peeters, P.H., Meliefste, K., Wang, M., Buenode-Mesquita, B., Sugiri, D., Krämer, U., Heinrich, J., Hoogh, K.d., Key, T., Peters, A., Hampel, R., Concin, H., Nagel, G., Ineichen, A., Schaffner, E., Probst-Hensch, N., Künzli, N., Schindler, C., Schikowski, T., Adam, M., Phuleria, H., Vilier, A., ClavelChapelon, F., Declercq, C., Grioni, S., Krogh, V., Tsai, M.-Y., Ricceri, F., Sacerdote, C., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Tamayo, I., Amiano, P., Dorronsoro, M., Katsoulis, M., Trichopoulou, A., Brunekreef, B., Hoek, G., 2014. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet. 383, 785-95. https://doi.org/10.1016/S0140-6736(13)62158-3. Beijing Municipal Bureau of Agriculture and Rural Affairs (BMBARA), 2016. The 13th Five-Year Plan for the Development of Urban Modern Agriculture in Beijing. http://nw.beijing.gov.cn/zfxxgk/fgwj/zcxwj/201612/t20161208_379050.html (in Chinese). Cai, S. Y., Wang, Y. J ., Zhao, B., Wang, S. X., Chang, X., Hao, J. M., 2017. The impact of the “ Air Pollution Prevention and Control Action Plan ” on PM2.5 concentrations in Jing-Jin-Ji region during 2012-2020. Sci. Total Environ. 580, 197-209. https://doi.org/10.1016/j.scitotenv.2016.11.188. Central People's Government of the People's Republic of China (CPG), 2016. National Agricultural Modernization Plan (2016-2020) http://www.gov.cn/zhengce/content/2016-10/20/content_5122217.htm (in Chinese). Chai, L., Roland Kröbel, Macdonald, D., Bittman, S., Vanderzaag, A., 2015. An ecoregion-specific ammonia emissions inventory of Ontario dairy farming: Mitigation potential of diet and manure management practices. Atmos. Environ. 126, 1-14. https://doi.org/10.1016/j.atmosenv.2015.11.030. Deng, M. J., Luo, W. B., 2018. Space-time evolution of China's agricultural ammonia emission and emission reduction potential. Chin. J. Eco-Agric. 26(9): 1257-1268. https://doi.org/10.13930/j.cnki.cjea.180016 (in Chinese). Hagenkamp-Korth, F., Haeussermann, A., Hartung, E., Reinhardt-Hanisch, A., 2015. Reduction of ammonia emissions from dairy manure using novel urease inhibitor formulations under laboratory conditions. Biosyst. Eng. 130, 43-51. https://doi.org/10.1016/j.biosystemseng.2014.12.002. Hou, Y., Velthof, G. L., Oenema, O., 2015. Mitigation of ammonia, nitrous oxide and methane emissions from manure management chains: a meta‐analysis and integrated assessment. Glob. Change Biol. 21(3): 1293-312. https://doi.org/10.1111/gcb.12767.
391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434
Huang, S., Lv, W., Bloszies, S., Shi, Q., Pan, X., Zeng, Y., 2016. Effects of fertilizer management practices on yield-scaled ammonia emissions from croplands in China: a meta-analysis. Field Crop. Res. 192, 118-125. https://doi.org/10.1016/j.fcr.2016.04.023. Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., Zhang, H., 2012. A high-resolution ammonia emission inventory in China. Glob. Biogeochem. Cycles 26, 2-14. http://dx.doi.org/10.1029/2011gb004161. Kang, Y. N., Liu, M. X., Song, Y., Huang, X., Yao, H., Cai, X. H., Zhang, H. S., Kang, L., Liu, X. J., Yan, X. Y., He, H., Shao, M., Zhu, T., 2016. High-resolution ammonia emissions inventories in China from 1980-2012. Atmos. Chem. Phys. 15(19), 26959-26995. http://dx.doi.org/10.5194/acp-16-2043-2016. Keshavarzian, M., Anaraki, S. K., Zamani, M., Erfanifard, A., 2012. Projections of oil demand in road transportation sector on the basis of vehicle ownership projections, worldwide: 1972–2020. Econ. Model. 29(5), 1979-1985, https://doi.org/10.1016/j.econmod.2012.06.009. Krupa, S. V., 2003. Effects of atmospheric ammonia (NH3) on terrestrial vegetation: a review. Environ. Pollut. 124, 179-221. https://doi.org/10.1016/S0269-7491(02)00434-7. Langridge, J. M., Lack, D. A., Brock, C. A., Bahreini, R., Middlebrook, A. M., Neuman, J. A., Nowak, J. B., Perring, A. E., Schwarz, J. P., Spackman, J. R., Holloway, J., Pollack, I. B., Ryerson, T. B., Roberts, J. M., Warneke,. C., Gouw, J. D., Trainer, M. K., Murphy, D. M., 2012. Evolution of aerosol properties impacting visibility and direct climate forcing in an ammonia-rich urban environment. J. Geophys. Res. -Atmos. 117(D21), 2240-2260. https://doi.org/10.1029/2011JD017116. Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., Pozzer, A., 2015. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569), 367-371. https://doi.org/10.1038/nature15371. Liu, H. J., Wu, B. B., Liu, S. H., Shao, P. Y., Liu, X. Y., Zhu, C. Y., Wang, Y., Wu, Y. M., Xue, Y. F., Gao, J. J., Hao, Y., Tian, H. Z., 2018. A regional high-resolution emission inventory of primary air pollutants in 2012 for Beijing and the surrounding five provinces of North China, Atmos. Environ. 81, 0-33, ttps://doi.org/10.1016/j.atmosenv.2018.03.013. Liu, R. M., Xu, F., Liu, Y. Y., Wang, J. W., Yu, W. W., 2016. Spatio-temporal characteristics of livestock and their effects on pollution in China based on geographic information system. Environ. Sci. Pollut. R. 23, 14183-14195. Liu, S. L., Ni, J. Q., Radcliffe, J. S., Vonderohe, C. E., 2017. Mitigation of ammonia emissions from pig production using reduced dietary crude protein with amino acid supplementation. Bioresour. Technol. 233, 200-208. https://doi.org/10.1016/j.biortech.2017.02.082. Liu, M. X., Huang, X., Song, Y., Tang, J., Cao, J. J., Zhang, X. Y., Zhang, Q., Wang, S. X., Xu, T. T., Kang , L., Cai, X. H., Zhang, H. S., Yang , F. M., Wang, H. B., Yu, J. Z., Lau, A. K. H., He, L. Y., Huang, X. F., Duan, L., Ding, A. J., Xue, L. K., Gao, J., Liu, B., Zhu, T., 2019. Ammonia emission control in China would mitigate haze pollution and nitrogen deposition, but worsen acid rain. Proc. Natl. Acad. Sci. U. S. A. 116(16), 7760-7765. https://doi.org/10.1073/pnas.1814880116. Li, Y. C., Shu, M., Ho, S. S. H., Yu, J. Z., Yuan, Z. B., Wang, X. X., Zhao, X. Q., 2017. Effects of chemical composition of PM2.5 on visibility in a semi-rural city of Sichuan Basin. Aerosol Air Qual. Res. 18(4). https://doi.org/10.4209/aaqr.2017.08.0264. Luo, M., Shephard, M. W., Cady-Pereira, K. E., Henze, D. K., Zhu, L. Y., Bash, J. O., Pinder, R.
435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478
W., Capps, S. L., Walker, J. T., Jones, M. R., 2015. Satellite observations of tropospheric ammonia and carbon monoxide: global distributions, regional correlations and comparisons to model simulations. Atmos. Environ. 106, 262-277. https://doi.org/10.1016/j.atmosenv.2015.02.007. Ministry of Agriculture and Rural Affairs of the People’s Republic of China (MARA), 2017. National Pig Production Development Plan (2016-2020). http://www.moa.gov.cn/nybgb/2016/diwuqi/201711/t20171127_5920859.htm (in Chinese). Ministry of Agriculture and Rural Affairs of the People’s Republic of China (MARA), 2015. Zero Growth in Synthetic Fertilizer Use from 2020 Onwards. http://www.moa.gov.cn/ztzl/mywrfz/gzgh/201509/t20150914_4827907.htm (in Chinese). Mendes, L. B., Pieters, J. G., Snoek, D., Ogink, N. W. M., Brusselman, E., Demeyer, P., 2017. Reduction of ammonia emissions from dairy cattle cubicle houses via improved management or design-based strategies: a modeling approach. Sci. Total Environ. 574, 520-531. https://doi.org/10.1016/j.scitotenv.2016.09.079. Ministry of Ecology and Environment of the People’s Republic of China (MEE), 2014. Technical Guidelines for the Compilation of Atmospheric Ammonia Source Emission Inventories (for Trial Implementation). http://www.mee.gov.cn/gkml/hbb/bgg/201408/W020140828351293771578.pdf (in Chinese). National Bureau of Statistics of China (NBSC), 2017a. China Industry Statistical Yearbook, China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). National Bureau of Statistics of China (NBSC), 2017b. China Statistical Yearbook. China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). National Bureau of Statistics of China (NBSC), 2017c. China Statistical Yearbook on Environment. China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). National Bureau of Statistics of China (NBSC), 2017d. China Urban Construction Statistical Yearbook. China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). National Development and Reform Commission (NDRC), 2017a. Cost and Income of Chinese Farm Produce. China Statistics Press, Beijing. http://tongji.cnki.net/kns55/Navi/YearBook.aspx?id=N2017120269&floor=1 (in Chinese). National Development and Reform Commission (NDRC), 2017b. Year Book of China Transportation and Communications. China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). Pan, B., Lam, S. K., Mosier, A., Luo, Y., Chen, D., 2016. Ammonia volatilization from synthetic fertilizers and its mitigation strategies: a global synthesis. Agric. Ecosyst. Environ. 232, 283-289. https://doi.org/10.1016/j.agee.2016.08.019. Paulot, F., Jacob, D. J., Pinder, R. W., Bash, J. O., Travis, K., Henze, D. K., 2015. Ammonia emissions in the United States, European Union, and China derived by high‐resolution inversion of ammonium wet deposition data: interpretation with a new agricultural emissions inventory (MASAGE NH3). J. Geophys. Res. - Atmos. 119(7), 4343-4364. https://doi.org/10.1002/2013JD021130. Peng, 2013. Analysis on the causes of haze pollution in Beijing in the recent period and its control measures. J. Eng. Stud. 5(3), 233-239. https://doi.org/10.3724/SP.J.1224.2013.00233 (in Chinese). Pereira, J., Fangueiro, D., Misselbrook, T. H., Chadwick, D. R., Trindade, H., 2011. Ammonia and
479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522
greenhouse gas emissions from slatted and solid floors in dairy cattle houses: a scale model study. Biosyst. Eng. 109(2), 148-157. https://doi.org/10.1016/j.biosystemseng.2011.02.011. Philippe, François Xavier, Cabaraux, Jean François, Nicks, B., 2011. Ammonia emissions from pig houses: influencing factors and mitigation techniques. Agric. Ecosyst. Environ. 141(3), 245-260. https://doi.org/10.1016/j.agee.2011.03.012. Qi J, Zheng B, Li M, Yu, F., Chen, C. C., Liu, F., Zhou, X. F., Yuan, J., Zhang, Q., He, K. B., 2017. A high-resolution air pollutants emission inventory in 2013 for the Beijing-Tianjin-Hebei region, China. Atmos. Environ. 170, 156-168. https://doi.org/10.1016/j.atmosenv.2017.09.039. Southon, G.E., Field, C., Caporn, S.J.M., Britton, A.J., Power, S.A., 2013. Nitrogen deposition reduces plant diversity and alters ecosystem functioning: field-scale evidence from a Nationwide Survey of UK Heathlands. PLoS One. 8. https://doi.org/10.1371/journal.pone.0059031. Ti, C. P., Xia, L. L., Chang, S. X., Yan, X. Y., 2019. Potential for mitigating global agricultural ammonia emission: A meta-analysis. Environ. Pollut. 245, 141-148. https://doi.org/10.1016/j.envpol.2018.10.124. The People’s Government of Hebei Province (TPGHP), 2014. Development Plan of Beef and Mutton Production in Hebei Province (2014-2020). http://info.hebei.gov.cn/hbszfxxgk/329975/329988/330035/6387662/index.html (in Chinese). The People’s Government of Hebei Province (TPGHP), 2016. The 13th Five-Year Plan for the Development of Modern Agriculture in Hebei Province. http://www.hebei.gov.cn/hebei/10731222/10751796/10758975/13998862/201710101630226 9788.pdf (in Chinese). Tianjin Municipal People’s Government (TMPG), 2016. The 13th Five-Year Plan for the Development of Agricultural and Rural Economic in Tianjin. http://fzgg.tj.gov.cn/zwgk/fzgh/zxgh/201706/t20170612_65831.shtml (in Chinese). Van der Heyden, C., Demeyer, P., Volcke, E. I. P., 2015. Mitigating emissions from pig and poultry housing facilities through air scrubbers and biofilters: State-of-the-art and perspectives, Biosyst. Eng. 134, 74-93. https://doi.org/10.1016/j.biosystemseng.2015.04.002. Wang, C., Yin, S. S., Bai, L., Zhang, X. C., Gu, X. K., Zhang, H., Lu, Q., Zhang, R. Q., 2018. High-resolution ammonia emission inventories with comprehensive analysis and evaluation in Henan, China, 2006-2016. Atmos. Environ. 193, 11-23. https://doi.org/10.1016/j.atmosenv.2018.08.063. Wang, Y., Dong, H., Zhu, Z., Gerber, P. J., Xin, H., Smith, P., Opio, C., Steinfeld, H., Chadwick, D., 2017. Mitigating greenhouse gas and ammonia emissions from swine manure management: a system analysis. Environ. Sci. Technol. 51(8), 4503-4511. https://doi.org/10.1021/acs.est.6b06430. Wei, S., Bai, Z.H., Chadwick, D., Hou, Y., Qin, W., Zhao, Z.Q., Jiang, R. F., Ma, L., 2017. Greenhouse gas and ammonia emissions and mitigation options from livestock production in peri-urban agriculture: Beijing - a case study. J. Clean Prod. 178. https://doi.org/10.1016/j.jclepro.2017.12.257. Wu, S. P., Zhang, Y. J., Schwab, J. J., Li, Y. F., Liu, Y. L., Yuan, C. S., 2017. High-resolution ammonia emissions inventories in Fujian, China, 2009-2015. Atmos. Environ. 162, 100-114. https://doi.org/10.1016/j.atmosenv.2017.04.027.
523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563
Xu, P., Koloutsou-Vakakis, S., Rood, M. J., Luan, S., 2017. Projections of NH3 emissions from manure generated by livestock production in china to 2030 under six mitigation scenarios. Sci. Total Environ. 607-608, 78-86. https://doi.org/10.1016/j.scitotenv.2017.06.258. Xu, P., Liao, Y. J., Lin, Y. H., Zhao, C. X., Yan, C. H., Cao, M. N., Wang, G. S., Luan, S. J., 2016. High-resolution inventory of ammonia emissions from agricultural fertilizer in China from 1978 to 2008. Atmos. Chem. Phys. 16, 1207-1218. https://doi.org/10.5194/acp-16-1207-2016. Xu, P., Zhang, Y. S., Gong, W. W., Hou, X. K., Kroeze, C., Gao, W., Luan, S. J., 2015. An inventory of the emission of ammonia from agricultural fertilizer application in China for 2010 and its high-resolution spatial distribution. Atmos. Environ. 115, 141-148. https://doi.org/10.1016/j.atmosenv.2015.05.020. Ye, X. N., Ma, Z., Zhang, J. C., Du, H. H., Chen, J. M., Chen, H., Yang, X., Gao, W., Geng, F. H., 2011. Important role of ammonia on haze formation in Shanghai. Environ. Res. Lett. 6(2), 024019. https://doi.org/10.1088/1748-9326/6/2/024019. Zhang, L., Chen, Y. F., Zhao. Y. H., Henze, D. K., Zhu, L. Y., Song, Y., Paulot, F., Liu, X. J., Pan, Y. P., Lin, Y., Huang, B. X., 2018. Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates. Atmos. Chem. Phys. 18(1), 1-36. https://doi.org/10.5194/acp-2017-749. Zhang, Y., Dore, A. J., Ma, L., Liu, X. J., Ma, W. Q., Cape, J. N., Zhang, F. S., 2010. Agricultural ammonia emissions inventory and spatial distribution in the North China Plain. Environ. Pollut. 158(2), 490-501. https://doi.org/10.1016/j.envpol.2009.08.033. Zhang, Y., Luan, S., Chen, L., Shao, M., 2011. Estimating the volatilization of ammonia from synthetic nitrogenous fertilizers used in China. J. Environ. Manage. 92(3), 480-493. https://doi.org/10.1016/j.jenvman.2010.09.018. Zhao, B., Wang, P., Ma, J. Z., Zhu, S., Pozzer, A., Li, W., 2012. A high-resolution emission inventory of primary pollutants for the Huabei region, China. Atmos. Chem. Phys. 11(1), 20331-20374. https://doi.org/10.5194/acpd-11-20331-2011. Zhao, Y., Nielsen, C. P., Lei, Y., 2011. Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China. Atmos. Chem. Phys. 11, 2295-2308. https://doi.org/10.5194/acp-11-2295-2011, 2011. Zhao, Z. Q., Bai, Z. H., Winiwarter, W., Kiesewetter, G., Heyes, C., Ma, L., 2017. Mitigating ammonia emission from agriculture reduces PM2.5, pollution in the Hai River Basin in China. Sci. Total Environ. 609, 1152-1160. https://doi.org/10.1016/j.scitotenv.2017.07.240. Zheng, J. Y., Yin, S. S., Kang, D. W., Che, W. W., Zhong, L. J., 2012. Development and uncertainty analysis of a high-resolution NH3 emissions inventory and its implications with precipitation over the Pearl River Delta region, China. Atmos. Chem. Phys. 12(15), 7041-7058. https://doi.org/10.5194/acp-12-7041-2012. Zhou, Y., Cheng, S. Y., Lang, J. L., Chen, D. S., Zhao, B. B., Liu, C., Xu, R., Li, T. T., 2015. A comprehensive ammonia emission inventory with high-resolution and its evaluation in the Beijing-Tianjin-Hebei (BTH) region, China. Atmos. Environ. 106(9), 305-317. http://dx.doi.org/10.1016/j.atmosenv.2015.01.069.
564
Table and Figure captions
565
Table 1 Prediction method and relevant indicators of activity data for anthropogenic NH3 emission
566
Table 2 Definition of agricultural ammonia mitigation scenarios in the BTH region.
567 568
Fig. 1. Study domain of the BTH region and its location.
569 570
Fig. 2. Contributions of main sources to the total NH3 emissions and contributions of four livestock manure management stages in 2016 for (a) BTH, (b) Beijing, (c) Tianjin, and (d) Hebei.
571 572
Fig. 3. Contributions of NH3 emissions from different cities for different sources in the BTH region in 2016
573 574
Fig. 4. Comparison of anthropogenic ammonia emissions in this study with other published results for Beijing, Tianjin, and Hebei.
575 576
Fig. 5. Prediction of NH3 emission and their sectoral distributions under different scenarios for (a) BTH, (b) Beijing, (c) Tianjin, and (d) Hebei.
577 578
Fig. 6. The NH3 emission reduction ratio by different control technologies under control scenarios in the BTH region during 2020–2040 compared to the BAU scenario.
579
580
Table 1 Prediction method and relevant indicators of activity data for anthropogenic NH3 emission. Category
Prediction method of activity data
Prediction indicators
Synthetic fertilizer application
Regression functions
Fertilizer consumption a
Livestock
Modified AAGR approach
Animal population
Biomass burning
Regression functions, Linear function
Yield of major farm crops, Fuel wood consumption, Area of fire damage
Chemical industry
Regression functions
Production
Human beings
Regression functions
Rural population, The access rate to harmless sanitary toilets in rural areas
Waste disposal
Regression functions
Domestic sewage treated capacity, Volume of garbage disposal
Traffic source
Regression functions, Gompertz model
b
Vehicle population, Annual average vehicle kilometers travelled
581 582 583
a
AAGR: Average Annual Growth Rate. For details, please refer to Liu et al. (2016) and Xu et al. (2017).
b
Gompertz model: For details, please refer to Keshavarzian et al. (2012).
584
Table 2 Definition of agricultural ammonia mitigation scenarios in the BTH region. Synthetic fertilizer application
Scenario
Lowering
Coated
Deep
Crude protein
Air
Manure
Deep
fertilization
urea
placement of
content
scrubbers
acidification
manure
fertilizers
fertilizers
reduction
√
√
placement
BAU LEF
√
LEL
√
√
√
√
CO
√
√
√
√
√
√
√
Penetrations of
2020
9%
3%
10%
5%
30%
10%
10%
major
2030
15%
9%
30%
15%
70%
30%
30%
2040
21%
15%
50%
25%
90%
50%
50%
b
/
68.0%
54.7%
65.0%
73.6%
92.5%
98.7%
control
technologies
a
Removal efficiency
585 586 587
Livestock
a.
From Deng et al. (2018), MARA (2015), and Xu et al. (2017).
b
From Hou et al. (2015), Pan et al. (2016), and Ti et al. (2019).
588 589
Fig. 1. Study domain of the BTH region and its location.
590
591 592 593 594
Fig. 2. Contributions of main sources to the total NH3 emissions and contributions of four livestock manure management stages in 2016 for (a) BTH, (b) Beijing, (c) Tianjin, and (d) Hebei.
595 596
Fig. 3. Contributions of NH3 emissions from different cities for different sources in the BTH region in 2016
597
598 599 600 601
Fig. 4. Comparison of anthropogenic ammonia emissions in this study with other published results for Beijing, Tianjin, and Hebei.
602 603
604 605
(a) BTH
(b) Beijing
(c) Tianjin
(d) Hebei
606 607 608 609
Fig. 5. Prediction of NH3 emission and their sectoral distributions under different scenarios for (a) BTH, (b) Beijing, (c) Tianjin, and (d) Hebei.
610 611 612
Fig. 6. The NH3 emission reduction ratio by different control technologies under control scenarios in the BTH region during 2020–2040 compared to the BAU scenario.
Highlights:
The NH3 emission trends from seven sectors were predicted during 2020-2040. More efforts should be directed to reduce NH3 emission from livestock source. The NH3 mitigation effects from various control measures till 2040 were compared.
Author Statement
Xiurui Guo: Supervision and Manuscript Revision. Zhilan Ye: Methodology, Writing, and Editing. Dongsheng: Chen: Manuscript Revision and Project Administration. Hongkan Wu: Data Collection and Investigation. Yaqian Shen: Data Collection and Investigation. Junfang Liu: Data Collection and Investigation. Shuiyuan Cheng: Project Administration.
Dear Editor, We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled. Xiurui Guo (Ph.D.) Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental & Energy Engineering Beijing University of Technology Beijing 100124, P.R. China Email: [email protected]
S0269-7491(19)34104-1
DOI:
https://doi.org/10.1016/j.envpol.2019.113863
Reference:
ENPO 113863
To appear in:
Environmental Pollution
Received Date: 26 July 2019 Revised Date:
19 December 2019
Accepted Date: 19 December 2019
Please cite this article as: Guo, X., Ye, Z., Chen, D., Wu, H., Shen, Y., Liu, J., Cheng, S., Prediction and mitigation potential of anthropogenic ammonia emissions within the Beijing–Tianjin–Hebei region, China, Environmental Pollution (2020), doi: https://doi.org/10.1016/j.envpol.2019.113863. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Graphical Abstract:
1 2
Prediction and mitigation potential of anthropogenic ammonia emissions within the Beijing–Tianjin–Hebei region, China
3 4 5
Xiurui Guo∗, Zhilan Ye, Dongsheng Chen, Hongkan Wu, Yaqian Shen, Junfang Liu, Shuiyuan Cheng College of Environmental & Energy Engineering, Key Laboratory of Beijing on Regional Air Pollution Control, Beijing University of Technology, Beijing 100124, China
6
Abstract: Large ammonia (NH3) emissions contribute approximately 8 30% to the fine particle
7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27
pollution in China and highlight the need for understanding the emission trends and mitigation effects of NH3 in the future. The purpose of this study is to predict the NH3 emissions and analyze the mitigation potential up to year 2040 by scenario analysis based on the established new NH3 emission inventory from anthropogenic sources for the Beijing–Tianjin–Hebei (BTH) region. The results showed that the total NH3 emission in the BTH region was estimated at 966.14 Gg in 2016. Under the Business-as-Usual (BAU) scenario, the total NH3 emissions in 2030 and 2040 would increase by 13% and 26% compared with 2016 levels, with average annual growth rates of 0.9% and 1.0%, respectively. Livestock will continue to dominate NH3 emissions in the future, with the proportions of total emissions increasing from 57% in 2016 to 64% in 2030 and 68% in 2040. The share of the second-largest NH3 emission source, synthetic fertilizer application, will decrease from 36% in 2016 to 31% in 2030 and 27% in 2040. Among five other sources, the largest change occurred in waste disposal, increasing notably by 3.31 times from 2016 to 2040 owing to rapid urbanization. Under the Combined Options (CO) scenario, the total NH3 emissions could be reduced by as much as 34% by 2030 and 50% by 2040 compared with the BAU scenario, which is attributed to livestock (24% in 2030, 37% in 2040) and synthetic fertilizer application (10% in 2030, 13% in 2040), respectively. This study can give a reliable estimation of anthropogenic NH3 emission in the BTH region during 2020–2040 and provide a valuable reference for effective mitigation measures and control strategies for policy makers. Capsule: The total NH3 emissions would increase in the future and livestock source had largest mitigation potential. Key words: Ammonia emission inventory; Prediction; Reduction potential
28 29 30 31 32 33 34 35 36
1. Introduction Ammonia (NH3) is the primary alkaline gas present in the atmosphere. NH3 plays an important role in forming secondary inorganic aerosols with diameters equal to or less than 2.5 µm (PM2.5) by neutralizing both sulfuric acid and nitric acid in the atmosphere (Backes et al., 2016; Luo et al., 2015). Large amounts of this fine particulate matter cause environmental effects (such as low visibility) and threaten human health (Beelen et al., 2014; Langridge et al., 2012; Lelieveld et al., 2015; Li et al., 2017). Moreover, when NH3 is deposited onto soil, it can cause environmental issues such as soil acidification, water eutrophication, and rapid declines in species richness directly or indirectly (Krupa, 2003; Southon et al., 2013; Ye et al., 2011).
∗
Corresponding author. E-mail address: [email protected] (X. Guo).
37
Previous studies confirmed that China produced approximately 5.9 11.1 Tg NH3 emissions
38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79
annually during 1980-2012 (Huang et al., 2012; Kang et al., 2016; Paulot et al., 2015). In recent years, domestic scholars have developed NH3 emission inventories in China (Zhao et al., 2012; Zheng et al., 2012; Zhou et al., 2015). Most of these emission inventories were developed with average emission factors (EFs) cited from the literature or foreign values, which introduced significant inaccuracies in emission estimations because ammonia EFs are highly sensitive to fertilizer type, local soil, and meteorological properties (Zhang et al., 2018). Moreover, several NH3 emission inventories have been developed by considering available domestic experiment results or parameterizing the EFs by the environmental factors (Huang et al., 2012; Kang et al., 2016; Wang et al., 2018; Wu et al., 2017; Xu et al., 2015, 2016; Zhang et al., 2010, 2011). However, several points still need improvement. First, most of the NH3 emissions from synthetic fertilizer applications were estimated by using the National Ammonia Reduction Strategy Evaluation System (NARSES) model; the maximum potential EFs for each kind of fertilizer obtained abroad were not suitable for domestic situations. The NH3 emissions from livestock were estimated by using a top-down Regional Air Pollution Information and Simulation (RAINS) model, and the NH3–N volatilization proportion of each emission stage was obtained referring to other research without considering the differences of NH3 emissions in different stages under different conditions. In addition, not all NH3 emission sources were estimated in majority of these studies. Although fertilizer application and livestock manure are the dominant sources of NH3 emissions, the contributions of non-agricultural sources cannot be ignored, since their emission maybe increase in the future. Abundant evidence indicates that poor agricultural production management and low efficiency of N use are the main causes of the large NH3 emissions over China (Liu et al., 2019). Some scholars have studied the effects of mitigation measures on NH3 emissions produced by synthetic fertilizer application, pigs, and dairy cattle (Van der Heyden et al., 2015; Chai et al., 2015; Hagenkamp-Korth et al., 2015; Liu et al., 2017; Mendes et al., 2017; Pereira et al., 2011; Philippe et al., 2011; Wang et al., 2017) due to their large NH3 emissions. In recent years, some scholars conducted meta-analysis to evaluate the effectiveness of mitigation strategies for NH3 volatilization from cropping systems and livestock production (Hou et al., 2015; Huang et al., 2016; Pan et al., 2016; Ti et al., 2019; Wei et al., 2017). The results showed strong mitigation potential for agricultural NH3 emissions. Hence, it is important to provide comprehensive and reliable prediction and mitigation measures considering the potential of the NH3 emissions. For example, Xu et al. (2017) found that the NH3 emissions from livestock manure in China in 2030 were expected to increase by 90.41% compared with 2008 levels under the Business-as-Usual (BAU) scenario. In addition, the predicted emissions could be cut by 18.9–37.3% under the scenario of mitigating technologies during four livestock rearing stages compared with 2030 BAU levels. Zhao et al (2017) found that agricultural NH3 emissions in the Hai River Basin in 2030 would further increase by 33% compared with 2012 levels under BAU. In addition, the predicted emissions could be reduced by 60% compared with 2030 BAU levels under the scenario of improved technology and management combined with human diet optimization. However, the prediction of the activity level was based on the modified average annual growth rate (AAGR) approach in the study of Xu et al (2017) without considering the impact of Chinese policies on agriculture development. The activity levels were forecast on the basis of the empirical estimation
80 81 82 83 84 85 86 87 88 89 90 91
by Zhao et al (2017), which created large uncertainties. Moreover, the emission reduction potential from different sources remains unclear. This study attempted to predict the emission and mitigation potential of anthropogenic NH3 under different mitigation scenarios based on the new established NH3 emission inventory for the Beijing–Tianjin–Hebei (BTH) region in 2016. First, we developed a comprehensive emission inventory of the anthropogenic NH3 for the BTH region for 2016 using the authoritative method recommended by MEE (2014). NH3 emissions during 2020–2040 were predicted based on the forecast activity data. Four different mitigation scenarios for NH3 emissions were designed considering the control technologies for both synthetic fertilizer application and livestock manure volatilization, reflecting the different NH3 control options. Finally, the mitigation potential of agricultural NH3 from the implementation of feasible abatement measures of agricultural activities up to year 2040 was estimated.
92
2. Methodology
93
2.1. Study area
94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117
As the economic hub of China, the BTH region encompasses two municipalities including Beijing and Tianjin as well as 11 cities in the Hebei Province (Fig.1). This region covered 2.3% of the overall Chinese land, fed 8.0% of the population, and generated 10.4% of the National Gross Domestic Product in 2016. Moreover, the BTH region provided 6% of the nation’s total meat production and 6% of total grain output (NBSC, 2017b). Intensive agricultural production along with fertilizer use to improve the production of farmlands for meeting the increasing human food demands have led to high NH3 emissions in this region (Qi et al., 2017; Zhao et al., 2012; Zhang et al., 2010; Zhou et al., 2015) and thus contributed over 40% to PM2.5 through the formation of secondary inorganic aerosols (Peng, 2013). To continuously improve the air quality, it is necessary to understand the emission trends and mitigation effects of NH3 in the future. 2.2. Methods of estimating ammonia emissions We used the EF method mentioned in MEE (2014) to calculate the NH3 emissions from seven sectors: (1) synthetic fertilizer application, (2) livestock, (3) biomass burning, (4) chemical industry, (5) human beings, (6) waste disposal, and (7) traffic source. In addition, fuel combustion source was not included during estimation, because the emission factor was not available in MEE (2014) and the emission was small. Specific activity data used for NH3 emission estimate is listed in Table S1 (Supplementary Material). NH3 emission was calculated based on activity data (NBSC, 2017a, 2017b, 2017c, 2017d; NDRC, 2017b) and the corresponding EFs (Table S2, Table S3, and Table S4 in the Supplementary Material). The formula can be expressed as ,
(2-1)
where i represents the area, j represents the emission source, E is the total emission, γ is the conversion coefficient of N–NH3 emissions which is 1.214 for livestock and 1.0 for other sources, Ai,j is the activity level of source j in i area, EFi,j is the EF of source j in i area. An effort was made to reduce the inaccuracy of the NH3 emission inventory in this study. For
118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156
example, the estimated NH3 emission from synthetic fertilizer application was more accurate using the application amount of five N fertilizers multiplied by the corresponding EFs, respectively, rather than the synthetic EFs of N fertilizer. In addition, the livestock NH3 emission was estimated by mass-flow approach considering different management stages of livestock outdoor, livestock housing, manure storage, and land spreading, rather than using the average emission factor per capita. 2.3. Methods of uncertainty analysis There are two key steps in quantitative approach of NH3 emission inventory uncertainty analysis. First, the mathematical distribution and the coefficient variation (CV) of activity level and EFs for each emission source was determined referring to the previous researches (Zhao et al., 2011; Huang et al., 2012; Zheng et al., 2012). Then, the uncertainties in the activity data and EFs were transmitted to the emission inventory through the Monte Carlo simulation, based on Crystal ball software (Liu et al., 2018; Zheng et al., 2012). To ensure the accuracy of the simulation results, 10,000 Monte Carlo simulations were performed to estimate the range of the NH3 emissions for each source with a 95% confidence interval. 2.4. Method of predicting ammonia emissions This study forecasted anthropogenic NH3 emissions up to 2040 on the basis of 2000-2016 emission inventories. The EFs for anthropogenic NH3 emissions were considered unchanged in the future owing to the stability of the environmental conditions influencing ammonia volatilization such as soil pH values and ambient temperature. The activity data for anthropogenic NH3 emissions were predicted on the historical trend of the values during 2000-2016. The prediction methods and prediction indicators of activity data for anthropogenic NH3 emissions are summarized in Table 1. The predicted values were adjusted according to government policies (BMBARA, 2016; CPG, 2016; MARA, 2017; TPGHP, 2014, 2016; TMPG, 2016). Specifically, there were some indicators of activity level (such as the populations of dairy cow, beef cattle, etc) in government policies, once our predicted value was higher or lower than that in government policies, the predicted value was replaced by the indicator of activity data in government policies. In addition, the intensive farming rates for different livestock in the BTH region in 2020, 2030, and 2040 were listed in Table S5 of the Supplementary Material. 2.5. Abatement scenarios of future NH3 emissions This study focuses on NH3 mitigation from agricultural source because synthetic fertilizer application and livestock contributed to more than 80% of the total emissions (Huang et al., 2012; Xu et al., 2015, 2016; Zhang et al., 2018). Control technologies with high efficiency were selected based on field research, telephone consultation, and literature research combined with the emission characteristics of agricultural NH3 in the BTH region. The definitions of the emission scenarios are summarized in Table 2. The BAU scenario assumes that the fertilizer type and field management of the fertilizer application source, feed composition, housing system, manure storage management, and manure application techniques of the livestock source are identical to those used in 2016. No additional abatement technique will be
157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172
implemented in the future. To compare and analyze the control effects of NH3 from different agricultural sources, we defined two single control scenarios, including lowering emission from fertilizer application (LEF scenario) and lowering emission from livestock (LEL scenario). The LEF scenario, which specifies low emission from fertilizer application, assumes that relative to 2016 BAU, coated urea fertilizer will be widely used. This measure could alleviate NH3 emissions by substituting urea with fertilizers of enhanced efficiency characterized by reducing N losses after application to farmland. Moreover, deep placement of synthetic fertilizers was selected, which reduced NH3 volatilization when compared with the surface broadcast of urea on soils. The LEL scenario assumes that relative to 2016 BAU, the lower crude protein content in livestock diets could decrease the N content and PH of livestock manure to ultimately decrease NH3 emissions through manure management. Farmers install air scrubbers as part of an intensive rearing system to alleviate NH3 emissions from housing stage. Manure acidification can reduce NH3 emissions during manure storage stage. Deep placement of manure into soil can obviously reduce the manure surface exposure. The combined options of LEF and LEL in the CO scenario assumes that the NH3-removal techniques of fertilizer application and the livestock rearing system mentioned above are combined and applied to complement each other.
173
3. Results and discussion
174
3.1. Ammonia emission inventory in 2016
175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196
The total amount of NH3 emitted in the BTH region in 2016 was estimated to be approximately 966.14 Gg, of which synthetic fertilizer application and livestock manure spreading accounted for 36% and 57%, respectively. Hebei contributed to an overwhelming majority (91%) of the total NH3 emissions in the BTH region, amounting to 876.44 Gg, owing to the large cultivated land areas and high degree of free-range livestock rearing in that area. 3.1.1. Contributions by source The contributions of main sources to the total NH3 emissions as well as the contributions of four livestock manure management stages in the entire BTH region, Beijing, Tianjin, and Hebei are depicted in Fig. 2. Livestock were the largest contributors to the NH3 emissions in the entire BTH region. Livestock NH3 emissions in 2016 were estimated as 548.90 Gg, accounting for approximately 56.81% of the total emissions in the BTH region. For the contribution of NH3 emissions from the four livestock stages in the BTH region, livestock manure field application dominated the NH3 emission, followed by housing, outdoor, and manure storage. As the second-largest NH3 contributor, synthetic fertilizer application had NH3 emissions of 350.04 Gg, accounting for 36.23% of the total emissions in the BTH region in 2016. The non-agricultural sources (such as biomass burning, chemical industry, human beings, waste disposal, and traffic sources) were not as notable as agricultural sources for the entire BTH region, contributing approximately 6.96% to the total NH3 emissions in the BTH region. In detail, the NH3 emission from livestock in Beijing, Tianjin, and Hebei accounted for about half of the total emissions. The NH3 emission from synthetic fertilizer application in Beijing was relatively low, accounting for below 30% of the total emissions, owing to its smaller cultivated
197 198 199
land area. Obviously, the contribution of non-agricultural sources in Beijing was over twice those in Tianjin and Hebei, owing to the relatively high urbanization rate and vehicle ownership rate in that area.
200
3.1.2. Contributions by city
201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217
The contributions of NH3 emissions from different cities for different sources and the total NH3 emissions in different cities in 2016 are shown in Fig. 3. The largest NH3 emission occurred in Shijiazhuang, contributing 14.92% to the total emissions. Followed by Handan and Baoding, the emissions contributed 12.59% and 11.37% to total emissions, respectively. Synthetic fertilizer application and livestock were the two dominated NH3 sources in different cities, accounting for 16.53–44.23% and 47.54–78.96% of the total emissions, respectively. The distinctive emission characteristics of 13 BTH cities were attributed to the different levels of farming practice. The percentage of synthetic fertilizer application was significantly low in Zhangjiakou, Chengde, and Beijing compared with that in Cangzhou, owing to the smaller proportion of cultivated land areas in the three aforementioned cities. In contrast, the percentage of livestock was relatively higher in Zhangjiakou and Chengde than that in Cangzhou. The non-agricultural sources in Beijing contributed relatively large proportion of 18.86% to the total NH3 emissions among the 13 BTH cities, attributed to waste disposal and traffic source. In contrast, the proportion was below 9% in other 12 cities. Biomass burning was another important source of NH3 emissions in the BTH region, with contributions ranging from 1.70% to 3.57% among the cities. Human beings were responsible for larger portions in Hebei Province owing to the larger rural populations and the lower access rate to sanitary toilets in that area.
218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234
3.1.3. Uncertainty analysis Uncertainties exist unavoidably during the estimation of NH3 emissions, generated from both the activity data and EFs. The uncertainties of BTH’s NH3 emissions were quantified by Monte Carlo simulation assuming that both activity data and EFs obeyed lognormal distributions. Generally, for the activity levels acquired from the governmental yearbooks and EFs summarized from official guidelines, the coefficient variation (CV) were 5-10% and 20-50%, respectively (Huang et al., 2012; Zhao et al., 2011; Zheng et al., 2012). The uncertainty ranges of the NH3 emissions from different sources with a 95% confidence interval are ranked in the Supplementary Material (Table S4). It was found that the uncertainty ranges from all sectors were between -43% and 74%. Relatively high uncertainties typically existed for the chemical industry and human beings, ranging from -43% to 74%. This could be attributed to the low accuracy of the source level classification in comparison with the sources of livestock or biomass burning, and few measured EFs were available for NH3 estimation. Relatively moderate uncertainties typically existed for livestock and biomass burning, owing to the consideration of three different livestock rearing systems and four stages of manure management for livestock, as well as numerous parameters including the residue-to-crop yield ratio, domestic or in-field straw burning percentage, and burning efficiency for biomass burning.
235
3.1.4. Comparison with other studies
236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260
The anthropogenic NH3 estimations from prior studies over the past years in the BTH region are summarized in Fig. 4. The total and source-specific NH3 emissions in this study were between other researchers’ estimations. Specifically, the total NH3 emission in this study was about 15.84%-55.15% larger than that of Zhang et al. (2010), Cai et al. (2017), and Qi et al. (2017), approximately 18.95%-38.61% lower than Zhao et al.’s (2012) and Zhou et al.’s (2015) results. The disparity is caused mainly by the following reasons. Firstly, the interannual variation of activity data created some variety. The base year of other studies was 3–12 years earlier than that of this study. Additionally, the selection of EFs and relative parameters is important. For agricultural sources, the NH3 emission from synthetic fertilizer application was estimated using the application amounts of five types of N fertilizers calculated based on NDRC (2017a) with corresponding EFs, rather than the total consumption of chemical fertilizers and synthetic EF. The NH3 emission from livestock was estimated using mass-flow methodology considering different management stages of livestock outdoor, livestock housing, manure storage, and land spreading, rather than using the average emission factor per capita. For non-agricultural sources, the cited EFs selected from literature or foreign values were used widely. In addition, we compared the relative weight of the contributions for different categories and different cities with Zhou et al.’s (2015) results. It showed that the proportion of NH3 emissions from synthetic fertilizer application in each city of this study was about 8% larger than Zhou et al.’s (2015) result, owing to the smaller NH3 emissions in this study without estimating fuel combustion source and natural sources. What’s more, the proportion of NH3 emissions from non-agricultural sources of this study was smaller than Zhou et al.’s (2015) result. For example, the proportion of NH3 emissions from human beings in Beijing of this study was about approximately 17% lower than that of Zhou et al (2015), since NH3 emission from urban population was merged to waste disposal source in this study. There was no NH3 emission from chemical industry in Beijing in this study, owing to the fewer chemical plants in Beijing.
261 262
3.2. Future emission trends under the BAU scenario and mitigation potential of ammonia
263 264 265 266 267 268 269 270 271 272 273 274 275
3.2.1. Future emission trends by four mitigation scenarios The trends of NH3 emission and their sectoral distributions during 2016–2040 under different scenarios are given in Fig.5. Under the BAU scenario, the predicted total NH3 emissions in the BTH region during 2020–2040 would increase with an average annual growth rate of 0.99%. The predicted total NH3 emissions in 2020, 2030, and 2040 under the BAU scenario were 999.28 Gg, 1087.08 Gg, and 1216.11 Gg, and 3.43%, 12.52%, and 25.87% higher than that in 2016, respectively. Livestock production, one of the most important emission sources in the BTH region, contributed largely to this increasing trend. Moreover, the NH3 emissions in the BTH region would continue to increase during 2020–2040 under the LEF scenario, with a significantly lower growth rate (0.58%) than that in the BAU scenario. However, the total NH3 emissions in the BTH region would decrease during 2020–2040 under the LEL and CO scenarios, owing to the NH3 emission reduction from livestock source. This trend was consistent with the NH3 emission in the Hebei Province. In contrast, the NH3
276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314
emission in Beijing experienced consistent negative growth during 2020–2040 under the four scenarios, owing to decreases in fertilizer application and the livestock population. For the NH3 emission in Tianjin under the LEF scenario, it showed a steadily decreasing trend before it bottomed at 47.91 Gg in 2030 and then slightly increased to 53.58 Gg in 2040. This trend occurred because the NH3 mitigation from synthetic fertilizer application in Tianjin was not enough to offset the growth of NH3 emission from the livestock source since 2030. 3.2.2. Mitigation effects of different measures The total NH3 emission reduction ratios by different control technologies under different scenarios in the BTH region during 2020–2040 compared with the BAU scenario are shown in Fig 6. Large differences were noted in the amount of NH3 emission mitigation among synthetic fertilizer application and livestock sources. Obviously, livestock had largest mitigation potential, at approximately 60–74% of the total emission reduction during 2020–2040. Among which the installation of air scrubber in the housing stage could alleviate NH3 emission by more than 25% owing to large NH3 emission in the housing stage and the high removal efficiency. In 2040, deep manure placement and the reduction of crude protein content in the feeding stage could alleviate NH3 emission by more than 20%. In contrast, the livestock emission reduction was lowest in the manure acidification stage because ammonia emission in the storage stage was the lowest. For synthetic fertilizer application, lowering the fertilization was the most effective measure for NH3 emission mitigation, accounting for 11–23% of the total emission reduction during 2020–2040, followed by deep placement of fertilizers and coated urea fertilizers. 3.3. Recommendations of NH3 control measures The estimated results show that the total anthropogenic NH3 emissions in the BTH region would increase by 25.87% from 2016 to 2040 without ammonia-reduction measures. The overwhelming majority of NH3 emissions would be contributed from livestock source, so it would have larger mitigation potential from livestock source than from synthetic fertilizer application. Overall, NH3 control measures such as installing air scrubbers in the housing stage and lowering fertilization are recommended during 2016-2020, with each measure accounting for approximately more than 22% of the total emission reduction. More attention should be paid to reducing the NH3 emissions from livestock sources during 2020-2040, such as to promote the penetration rate of control technologies in the future, owing to the dramatically growing trends of livestock emissions. Installing air scrubbers in the housing stage would still be the most effective control measures, accounting for approximately more than 25% of the total emission reductions. Followed by deep manure placement and crude protein content reduction, showing better emission reduction effect than other technologies. In contrast, NH3 control measures from synthetic fertilizer application would become less dominant in the future years. 4. Conclusions This study attempted to predict the emissions and mitigation potential of anthropogenic NH3
315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347
under four mitigation scenarios based on the newly established NH3 emission inventory in the BTH region in 2016. We intended to provide optimized strategies for the BTH’s NH3 emission abatement policy in the near future. The total anthropogenic NH3 emission amount was valued at 966.14 Gg, with approximately 57% and 36% originating from livestock and synthetic fertilizer application and relatively low proportions of 7% from the sum of biomass burning, human beings, traffic source, chemical industry, and waste disposal. Hebei contributed to an overwhelming majority (91%) of total NH3 emissions in the BTH region. Under the BAU scenario, the total NH3 emission in 2020, 2030 and 2040 would increase by 3%, 13% and 26% compared to 2016, respectively. Livestock would continue to dominate the NH3 emission in the future and the proportion would increase from 57% in 2016 to 68% in 2040. The share of the second-largest NH3 emission source, synthetic fertilizer application, would decrease from 36% in 2016 to 27% in 2040, respectively, resulting from the government’s plan to achieve negative growth in fertilizer application in the BTH region by 2020. The total NH3 emissions from other five non-agricultural sources were 73.39 Gg in 2040. Compared with the BAU scenario, the total emission reduction of NH3 during 2020–2040 was estimated as 54.50–155.93 Gg, 82.99–453.61 Gg, and 137.49–609.54 Gg under the LEF, LEL, and CO scenarios, respectively, with reduction ratios of 5.42–12.64%, 8.30–37.30%, and 13.76–50.12%, respectively. Overall, the livestock source had the biggest mitigation potential; the most effective methods were the installation of air scrubbers in the housing stage. Lowering the fertilization amount was the most efficient measure for reducing NH3 emission in cropland. This study used the EF method to establish the anthropogenic NH3 emission inventory in the BTH region, 2016. Owing to a lack of comprehensive measured EFs for NH3 emission in the BTH region, errors were present in the results even though the authoritative EFs provided by the guidelines were used, bringing higher reliability to this study. Therefore, strengthening of more localized EFs is needed in future research work. Acknowledgements The authors would like to acknowledge the anonymous reviewers for their valuable comments. This study was supported by the Natural Key R&D Programs of China (No. 2018YFC0213202), Heavy Air Pollution Origin and Tackling Project (No. DQGG0201), and Natural Sciences Foundation of China (No. 51638001). The assessments in this paper are provided by only the authors and do not necessarily represent the official views of the sponsors.
348
Reference
349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390
Backes, A. M., Aulinger, A., Bieser, J., Matthias, V., Quante, M., 2016. Ammonia emissions in europe, part II: how ammonia emission abatement strategies affect secondary aerosols. Atmos. Environ. 126, 153-161. https://doi.org/10.1016/j.atmosenv.2015.11.039. Beelen, R., Raaschou-Nielsen, O., Stafoggia, M., Andersen, Z.J., Weinmayr, G., Hoffmann, B., Wolf, K., Samoli, E., Fischer, P., Nieuwenhuijsen, M., Vineis, P., Xun, W.W., Katsouyanni, K., Dimakopoulou, K., Oudin, A., Forsberg, B., Modig, L., Havulinna, A.S., Lanki, T., Turunen, A., Oftedal, B., Nystad, W., Nafstad, P., Faire, U.D., Pedersen, N.L., Östenson, C.-G., Fratiglioni, L., Penell, J., Korek, M., Pershagen, G., Eriksen, K.T., Overvad, K., Ellermann, T., Eeftens, M., Peeters, P.H., Meliefste, K., Wang, M., Buenode-Mesquita, B., Sugiri, D., Krämer, U., Heinrich, J., Hoogh, K.d., Key, T., Peters, A., Hampel, R., Concin, H., Nagel, G., Ineichen, A., Schaffner, E., Probst-Hensch, N., Künzli, N., Schindler, C., Schikowski, T., Adam, M., Phuleria, H., Vilier, A., ClavelChapelon, F., Declercq, C., Grioni, S., Krogh, V., Tsai, M.-Y., Ricceri, F., Sacerdote, C., Galassi, C., Migliore, E., Ranzi, A., Cesaroni, G., Badaloni, C., Forastiere, F., Tamayo, I., Amiano, P., Dorronsoro, M., Katsoulis, M., Trichopoulou, A., Brunekreef, B., Hoek, G., 2014. Effects of long-term exposure to air pollution on natural-cause mortality: an analysis of 22 European cohorts within the multicentre ESCAPE project. Lancet. 383, 785-95. https://doi.org/10.1016/S0140-6736(13)62158-3. Beijing Municipal Bureau of Agriculture and Rural Affairs (BMBARA), 2016. The 13th Five-Year Plan for the Development of Urban Modern Agriculture in Beijing. http://nw.beijing.gov.cn/zfxxgk/fgwj/zcxwj/201612/t20161208_379050.html (in Chinese). Cai, S. Y., Wang, Y. J ., Zhao, B., Wang, S. X., Chang, X., Hao, J. M., 2017. The impact of the “ Air Pollution Prevention and Control Action Plan ” on PM2.5 concentrations in Jing-Jin-Ji region during 2012-2020. Sci. Total Environ. 580, 197-209. https://doi.org/10.1016/j.scitotenv.2016.11.188. Central People's Government of the People's Republic of China (CPG), 2016. National Agricultural Modernization Plan (2016-2020) http://www.gov.cn/zhengce/content/2016-10/20/content_5122217.htm (in Chinese). Chai, L., Roland Kröbel, Macdonald, D., Bittman, S., Vanderzaag, A., 2015. An ecoregion-specific ammonia emissions inventory of Ontario dairy farming: Mitigation potential of diet and manure management practices. Atmos. Environ. 126, 1-14. https://doi.org/10.1016/j.atmosenv.2015.11.030. Deng, M. J., Luo, W. B., 2018. Space-time evolution of China's agricultural ammonia emission and emission reduction potential. Chin. J. Eco-Agric. 26(9): 1257-1268. https://doi.org/10.13930/j.cnki.cjea.180016 (in Chinese). Hagenkamp-Korth, F., Haeussermann, A., Hartung, E., Reinhardt-Hanisch, A., 2015. Reduction of ammonia emissions from dairy manure using novel urease inhibitor formulations under laboratory conditions. Biosyst. Eng. 130, 43-51. https://doi.org/10.1016/j.biosystemseng.2014.12.002. Hou, Y., Velthof, G. L., Oenema, O., 2015. Mitigation of ammonia, nitrous oxide and methane emissions from manure management chains: a meta‐analysis and integrated assessment. Glob. Change Biol. 21(3): 1293-312. https://doi.org/10.1111/gcb.12767.
391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434
Huang, S., Lv, W., Bloszies, S., Shi, Q., Pan, X., Zeng, Y., 2016. Effects of fertilizer management practices on yield-scaled ammonia emissions from croplands in China: a meta-analysis. Field Crop. Res. 192, 118-125. https://doi.org/10.1016/j.fcr.2016.04.023. Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., Zhang, H., 2012. A high-resolution ammonia emission inventory in China. Glob. Biogeochem. Cycles 26, 2-14. http://dx.doi.org/10.1029/2011gb004161. Kang, Y. N., Liu, M. X., Song, Y., Huang, X., Yao, H., Cai, X. H., Zhang, H. S., Kang, L., Liu, X. J., Yan, X. Y., He, H., Shao, M., Zhu, T., 2016. High-resolution ammonia emissions inventories in China from 1980-2012. Atmos. Chem. Phys. 15(19), 26959-26995. http://dx.doi.org/10.5194/acp-16-2043-2016. Keshavarzian, M., Anaraki, S. K., Zamani, M., Erfanifard, A., 2012. Projections of oil demand in road transportation sector on the basis of vehicle ownership projections, worldwide: 1972–2020. Econ. Model. 29(5), 1979-1985, https://doi.org/10.1016/j.econmod.2012.06.009. Krupa, S. V., 2003. Effects of atmospheric ammonia (NH3) on terrestrial vegetation: a review. Environ. Pollut. 124, 179-221. https://doi.org/10.1016/S0269-7491(02)00434-7. Langridge, J. M., Lack, D. A., Brock, C. A., Bahreini, R., Middlebrook, A. M., Neuman, J. A., Nowak, J. B., Perring, A. E., Schwarz, J. P., Spackman, J. R., Holloway, J., Pollack, I. B., Ryerson, T. B., Roberts, J. M., Warneke,. C., Gouw, J. D., Trainer, M. K., Murphy, D. M., 2012. Evolution of aerosol properties impacting visibility and direct climate forcing in an ammonia-rich urban environment. J. Geophys. Res. -Atmos. 117(D21), 2240-2260. https://doi.org/10.1029/2011JD017116. Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., Pozzer, A., 2015. The contribution of outdoor air pollution sources to premature mortality on a global scale. Nature, 525(7569), 367-371. https://doi.org/10.1038/nature15371. Liu, H. J., Wu, B. B., Liu, S. H., Shao, P. Y., Liu, X. Y., Zhu, C. Y., Wang, Y., Wu, Y. M., Xue, Y. F., Gao, J. J., Hao, Y., Tian, H. Z., 2018. A regional high-resolution emission inventory of primary air pollutants in 2012 for Beijing and the surrounding five provinces of North China, Atmos. Environ. 81, 0-33, ttps://doi.org/10.1016/j.atmosenv.2018.03.013. Liu, R. M., Xu, F., Liu, Y. Y., Wang, J. W., Yu, W. W., 2016. Spatio-temporal characteristics of livestock and their effects on pollution in China based on geographic information system. Environ. Sci. Pollut. R. 23, 14183-14195. Liu, S. L., Ni, J. Q., Radcliffe, J. S., Vonderohe, C. E., 2017. Mitigation of ammonia emissions from pig production using reduced dietary crude protein with amino acid supplementation. Bioresour. Technol. 233, 200-208. https://doi.org/10.1016/j.biortech.2017.02.082. Liu, M. X., Huang, X., Song, Y., Tang, J., Cao, J. J., Zhang, X. Y., Zhang, Q., Wang, S. X., Xu, T. T., Kang , L., Cai, X. H., Zhang, H. S., Yang , F. M., Wang, H. B., Yu, J. Z., Lau, A. K. H., He, L. Y., Huang, X. F., Duan, L., Ding, A. J., Xue, L. K., Gao, J., Liu, B., Zhu, T., 2019. Ammonia emission control in China would mitigate haze pollution and nitrogen deposition, but worsen acid rain. Proc. Natl. Acad. Sci. U. S. A. 116(16), 7760-7765. https://doi.org/10.1073/pnas.1814880116. Li, Y. C., Shu, M., Ho, S. S. H., Yu, J. Z., Yuan, Z. B., Wang, X. X., Zhao, X. Q., 2017. Effects of chemical composition of PM2.5 on visibility in a semi-rural city of Sichuan Basin. Aerosol Air Qual. Res. 18(4). https://doi.org/10.4209/aaqr.2017.08.0264. Luo, M., Shephard, M. W., Cady-Pereira, K. E., Henze, D. K., Zhu, L. Y., Bash, J. O., Pinder, R.
435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478
W., Capps, S. L., Walker, J. T., Jones, M. R., 2015. Satellite observations of tropospheric ammonia and carbon monoxide: global distributions, regional correlations and comparisons to model simulations. Atmos. Environ. 106, 262-277. https://doi.org/10.1016/j.atmosenv.2015.02.007. Ministry of Agriculture and Rural Affairs of the People’s Republic of China (MARA), 2017. National Pig Production Development Plan (2016-2020). http://www.moa.gov.cn/nybgb/2016/diwuqi/201711/t20171127_5920859.htm (in Chinese). Ministry of Agriculture and Rural Affairs of the People’s Republic of China (MARA), 2015. Zero Growth in Synthetic Fertilizer Use from 2020 Onwards. http://www.moa.gov.cn/ztzl/mywrfz/gzgh/201509/t20150914_4827907.htm (in Chinese). Mendes, L. B., Pieters, J. G., Snoek, D., Ogink, N. W. M., Brusselman, E., Demeyer, P., 2017. Reduction of ammonia emissions from dairy cattle cubicle houses via improved management or design-based strategies: a modeling approach. Sci. Total Environ. 574, 520-531. https://doi.org/10.1016/j.scitotenv.2016.09.079. Ministry of Ecology and Environment of the People’s Republic of China (MEE), 2014. Technical Guidelines for the Compilation of Atmospheric Ammonia Source Emission Inventories (for Trial Implementation). http://www.mee.gov.cn/gkml/hbb/bgg/201408/W020140828351293771578.pdf (in Chinese). National Bureau of Statistics of China (NBSC), 2017a. China Industry Statistical Yearbook, China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). National Bureau of Statistics of China (NBSC), 2017b. China Statistical Yearbook. China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). National Bureau of Statistics of China (NBSC), 2017c. China Statistical Yearbook on Environment. China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). National Bureau of Statistics of China (NBSC), 2017d. China Urban Construction Statistical Yearbook. China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). National Development and Reform Commission (NDRC), 2017a. Cost and Income of Chinese Farm Produce. China Statistics Press, Beijing. http://tongji.cnki.net/kns55/Navi/YearBook.aspx?id=N2017120269&floor=1 (in Chinese). National Development and Reform Commission (NDRC), 2017b. Year Book of China Transportation and Communications. China Statistics Press, Beijing. http://www.stats.gov.cn/ (in Chinese). Pan, B., Lam, S. K., Mosier, A., Luo, Y., Chen, D., 2016. Ammonia volatilization from synthetic fertilizers and its mitigation strategies: a global synthesis. Agric. Ecosyst. Environ. 232, 283-289. https://doi.org/10.1016/j.agee.2016.08.019. Paulot, F., Jacob, D. J., Pinder, R. W., Bash, J. O., Travis, K., Henze, D. K., 2015. Ammonia emissions in the United States, European Union, and China derived by high‐resolution inversion of ammonium wet deposition data: interpretation with a new agricultural emissions inventory (MASAGE NH3). J. Geophys. Res. - Atmos. 119(7), 4343-4364. https://doi.org/10.1002/2013JD021130. Peng, 2013. Analysis on the causes of haze pollution in Beijing in the recent period and its control measures. J. Eng. Stud. 5(3), 233-239. https://doi.org/10.3724/SP.J.1224.2013.00233 (in Chinese). Pereira, J., Fangueiro, D., Misselbrook, T. H., Chadwick, D. R., Trindade, H., 2011. Ammonia and
479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522
greenhouse gas emissions from slatted and solid floors in dairy cattle houses: a scale model study. Biosyst. Eng. 109(2), 148-157. https://doi.org/10.1016/j.biosystemseng.2011.02.011. Philippe, François Xavier, Cabaraux, Jean François, Nicks, B., 2011. Ammonia emissions from pig houses: influencing factors and mitigation techniques. Agric. Ecosyst. Environ. 141(3), 245-260. https://doi.org/10.1016/j.agee.2011.03.012. Qi J, Zheng B, Li M, Yu, F., Chen, C. C., Liu, F., Zhou, X. F., Yuan, J., Zhang, Q., He, K. B., 2017. A high-resolution air pollutants emission inventory in 2013 for the Beijing-Tianjin-Hebei region, China. Atmos. Environ. 170, 156-168. https://doi.org/10.1016/j.atmosenv.2017.09.039. Southon, G.E., Field, C., Caporn, S.J.M., Britton, A.J., Power, S.A., 2013. Nitrogen deposition reduces plant diversity and alters ecosystem functioning: field-scale evidence from a Nationwide Survey of UK Heathlands. PLoS One. 8. https://doi.org/10.1371/journal.pone.0059031. Ti, C. P., Xia, L. L., Chang, S. X., Yan, X. Y., 2019. Potential for mitigating global agricultural ammonia emission: A meta-analysis. Environ. Pollut. 245, 141-148. https://doi.org/10.1016/j.envpol.2018.10.124. The People’s Government of Hebei Province (TPGHP), 2014. Development Plan of Beef and Mutton Production in Hebei Province (2014-2020). http://info.hebei.gov.cn/hbszfxxgk/329975/329988/330035/6387662/index.html (in Chinese). The People’s Government of Hebei Province (TPGHP), 2016. The 13th Five-Year Plan for the Development of Modern Agriculture in Hebei Province. http://www.hebei.gov.cn/hebei/10731222/10751796/10758975/13998862/201710101630226 9788.pdf (in Chinese). Tianjin Municipal People’s Government (TMPG), 2016. The 13th Five-Year Plan for the Development of Agricultural and Rural Economic in Tianjin. http://fzgg.tj.gov.cn/zwgk/fzgh/zxgh/201706/t20170612_65831.shtml (in Chinese). Van der Heyden, C., Demeyer, P., Volcke, E. I. P., 2015. Mitigating emissions from pig and poultry housing facilities through air scrubbers and biofilters: State-of-the-art and perspectives, Biosyst. Eng. 134, 74-93. https://doi.org/10.1016/j.biosystemseng.2015.04.002. Wang, C., Yin, S. S., Bai, L., Zhang, X. C., Gu, X. K., Zhang, H., Lu, Q., Zhang, R. Q., 2018. High-resolution ammonia emission inventories with comprehensive analysis and evaluation in Henan, China, 2006-2016. Atmos. Environ. 193, 11-23. https://doi.org/10.1016/j.atmosenv.2018.08.063. Wang, Y., Dong, H., Zhu, Z., Gerber, P. J., Xin, H., Smith, P., Opio, C., Steinfeld, H., Chadwick, D., 2017. Mitigating greenhouse gas and ammonia emissions from swine manure management: a system analysis. Environ. Sci. Technol. 51(8), 4503-4511. https://doi.org/10.1021/acs.est.6b06430. Wei, S., Bai, Z.H., Chadwick, D., Hou, Y., Qin, W., Zhao, Z.Q., Jiang, R. F., Ma, L., 2017. Greenhouse gas and ammonia emissions and mitigation options from livestock production in peri-urban agriculture: Beijing - a case study. J. Clean Prod. 178. https://doi.org/10.1016/j.jclepro.2017.12.257. Wu, S. P., Zhang, Y. J., Schwab, J. J., Li, Y. F., Liu, Y. L., Yuan, C. S., 2017. High-resolution ammonia emissions inventories in Fujian, China, 2009-2015. Atmos. Environ. 162, 100-114. https://doi.org/10.1016/j.atmosenv.2017.04.027.
523 524 525 526 527 528 529 530 531 532 533 534 535 536 537 538 539 540 541 542 543 544 545 546 547 548 549 550 551 552 553 554 555 556 557 558 559 560 561 562 563
Xu, P., Koloutsou-Vakakis, S., Rood, M. J., Luan, S., 2017. Projections of NH3 emissions from manure generated by livestock production in china to 2030 under six mitigation scenarios. Sci. Total Environ. 607-608, 78-86. https://doi.org/10.1016/j.scitotenv.2017.06.258. Xu, P., Liao, Y. J., Lin, Y. H., Zhao, C. X., Yan, C. H., Cao, M. N., Wang, G. S., Luan, S. J., 2016. High-resolution inventory of ammonia emissions from agricultural fertilizer in China from 1978 to 2008. Atmos. Chem. Phys. 16, 1207-1218. https://doi.org/10.5194/acp-16-1207-2016. Xu, P., Zhang, Y. S., Gong, W. W., Hou, X. K., Kroeze, C., Gao, W., Luan, S. J., 2015. An inventory of the emission of ammonia from agricultural fertilizer application in China for 2010 and its high-resolution spatial distribution. Atmos. Environ. 115, 141-148. https://doi.org/10.1016/j.atmosenv.2015.05.020. Ye, X. N., Ma, Z., Zhang, J. C., Du, H. H., Chen, J. M., Chen, H., Yang, X., Gao, W., Geng, F. H., 2011. Important role of ammonia on haze formation in Shanghai. Environ. Res. Lett. 6(2), 024019. https://doi.org/10.1088/1748-9326/6/2/024019. Zhang, L., Chen, Y. F., Zhao. Y. H., Henze, D. K., Zhu, L. Y., Song, Y., Paulot, F., Liu, X. J., Pan, Y. P., Lin, Y., Huang, B. X., 2018. Agricultural ammonia emissions in China: reconciling bottom-up and top-down estimates. Atmos. Chem. Phys. 18(1), 1-36. https://doi.org/10.5194/acp-2017-749. Zhang, Y., Dore, A. J., Ma, L., Liu, X. J., Ma, W. Q., Cape, J. N., Zhang, F. S., 2010. Agricultural ammonia emissions inventory and spatial distribution in the North China Plain. Environ. Pollut. 158(2), 490-501. https://doi.org/10.1016/j.envpol.2009.08.033. Zhang, Y., Luan, S., Chen, L., Shao, M., 2011. Estimating the volatilization of ammonia from synthetic nitrogenous fertilizers used in China. J. Environ. Manage. 92(3), 480-493. https://doi.org/10.1016/j.jenvman.2010.09.018. Zhao, B., Wang, P., Ma, J. Z., Zhu, S., Pozzer, A., Li, W., 2012. A high-resolution emission inventory of primary pollutants for the Huabei region, China. Atmos. Chem. Phys. 11(1), 20331-20374. https://doi.org/10.5194/acpd-11-20331-2011. Zhao, Y., Nielsen, C. P., Lei, Y., 2011. Quantifying the uncertainties of a bottom-up emission inventory of anthropogenic atmospheric pollutants in China. Atmos. Chem. Phys. 11, 2295-2308. https://doi.org/10.5194/acp-11-2295-2011, 2011. Zhao, Z. Q., Bai, Z. H., Winiwarter, W., Kiesewetter, G., Heyes, C., Ma, L., 2017. Mitigating ammonia emission from agriculture reduces PM2.5, pollution in the Hai River Basin in China. Sci. Total Environ. 609, 1152-1160. https://doi.org/10.1016/j.scitotenv.2017.07.240. Zheng, J. Y., Yin, S. S., Kang, D. W., Che, W. W., Zhong, L. J., 2012. Development and uncertainty analysis of a high-resolution NH3 emissions inventory and its implications with precipitation over the Pearl River Delta region, China. Atmos. Chem. Phys. 12(15), 7041-7058. https://doi.org/10.5194/acp-12-7041-2012. Zhou, Y., Cheng, S. Y., Lang, J. L., Chen, D. S., Zhao, B. B., Liu, C., Xu, R., Li, T. T., 2015. A comprehensive ammonia emission inventory with high-resolution and its evaluation in the Beijing-Tianjin-Hebei (BTH) region, China. Atmos. Environ. 106(9), 305-317. http://dx.doi.org/10.1016/j.atmosenv.2015.01.069.
564
Table and Figure captions
565
Table 1 Prediction method and relevant indicators of activity data for anthropogenic NH3 emission
566
Table 2 Definition of agricultural ammonia mitigation scenarios in the BTH region.
567 568
Fig. 1. Study domain of the BTH region and its location.
569 570
Fig. 2. Contributions of main sources to the total NH3 emissions and contributions of four livestock manure management stages in 2016 for (a) BTH, (b) Beijing, (c) Tianjin, and (d) Hebei.
571 572
Fig. 3. Contributions of NH3 emissions from different cities for different sources in the BTH region in 2016
573 574
Fig. 4. Comparison of anthropogenic ammonia emissions in this study with other published results for Beijing, Tianjin, and Hebei.
575 576
Fig. 5. Prediction of NH3 emission and their sectoral distributions under different scenarios for (a) BTH, (b) Beijing, (c) Tianjin, and (d) Hebei.
577 578
Fig. 6. The NH3 emission reduction ratio by different control technologies under control scenarios in the BTH region during 2020–2040 compared to the BAU scenario.
579
580
Table 1 Prediction method and relevant indicators of activity data for anthropogenic NH3 emission. Category
Prediction method of activity data
Prediction indicators
Synthetic fertilizer application
Regression functions
Fertilizer consumption a
Livestock
Modified AAGR approach
Animal population
Biomass burning
Regression functions, Linear function
Yield of major farm crops, Fuel wood consumption, Area of fire damage
Chemical industry
Regression functions
Production
Human beings
Regression functions
Rural population, The access rate to harmless sanitary toilets in rural areas
Waste disposal
Regression functions
Domestic sewage treated capacity, Volume of garbage disposal
Traffic source
Regression functions, Gompertz model
b
Vehicle population, Annual average vehicle kilometers travelled
581 582 583
a
AAGR: Average Annual Growth Rate. For details, please refer to Liu et al. (2016) and Xu et al. (2017).
b
Gompertz model: For details, please refer to Keshavarzian et al. (2012).
584
Table 2 Definition of agricultural ammonia mitigation scenarios in the BTH region. Synthetic fertilizer application
Scenario
Lowering
Coated
Deep
Crude protein
Air
Manure
Deep
fertilization
urea
placement of
content
scrubbers
acidification
manure
fertilizers
fertilizers
reduction
√
√
placement
BAU LEF
√
LEL
√
√
√
√
CO
√
√
√
√
√
√
√
Penetrations of
2020
9%
3%
10%
5%
30%
10%
10%
major
2030
15%
9%
30%
15%
70%
30%
30%
2040
21%
15%
50%
25%
90%
50%
50%
b
/
68.0%
54.7%
65.0%
73.6%
92.5%
98.7%
control
technologies
a
Removal efficiency
585 586 587
Livestock
a.
From Deng et al. (2018), MARA (2015), and Xu et al. (2017).
b
From Hou et al. (2015), Pan et al. (2016), and Ti et al. (2019).
588 589
Fig. 1. Study domain of the BTH region and its location.
590
591 592 593 594
Fig. 2. Contributions of main sources to the total NH3 emissions and contributions of four livestock manure management stages in 2016 for (a) BTH, (b) Beijing, (c) Tianjin, and (d) Hebei.
595 596
Fig. 3. Contributions of NH3 emissions from different cities for different sources in the BTH region in 2016
597
598 599 600 601
Fig. 4. Comparison of anthropogenic ammonia emissions in this study with other published results for Beijing, Tianjin, and Hebei.
602 603
604 605
(a) BTH
(b) Beijing
(c) Tianjin
(d) Hebei
606 607 608 609
Fig. 5. Prediction of NH3 emission and their sectoral distributions under different scenarios for (a) BTH, (b) Beijing, (c) Tianjin, and (d) Hebei.
610 611 612
Fig. 6. The NH3 emission reduction ratio by different control technologies under control scenarios in the BTH region during 2020–2040 compared to the BAU scenario.
Highlights:
The NH3 emission trends from seven sectors were predicted during 2020-2040. More efforts should be directed to reduce NH3 emission from livestock source. The NH3 mitigation effects from various control measures till 2040 were compared.
Author Statement
Xiurui Guo: Supervision and Manuscript Revision. Zhilan Ye: Methodology, Writing, and Editing. Dongsheng: Chen: Manuscript Revision and Project Administration. Hongkan Wu: Data Collection and Investigation. Yaqian Shen: Data Collection and Investigation. Junfang Liu: Data Collection and Investigation. Shuiyuan Cheng: Project Administration.
Dear Editor, We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled. Xiurui Guo (Ph.D.) Key Laboratory of Beijing on Regional Air Pollution Control College of Environmental & Energy Engineering Beijing University of Technology Beijing 100124, P.R. China Email: [email protected]