| 长三角农田轮作系统氨排放特征、转化机制和减排潜力 |
| 摘要点击 2400 全文点击 623 投稿时间：2021-05-30 修订日期：2021-07-31 |
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| 中文关键词 氨排放 转化机制 氨排放转化率 排放系数 排放清单 减排路径 减排潜力 稻麦轮作 稻虾共作 长三角 |
| 英文关键词 ammonia emission transformation mechanism ammonia conversion rate emission factor emission inventory emission reduction path reduction potential rice/winter wheat rotation rice shrimp cultivation Yangtze River Delta |
| 作者 | 单位 | E-mail | | 徐昶 | 上海市环境科学研究院, 国家环境保护新型污染物环境健康影响评价重点实验室, 国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | xuc@saes.sh.cn | | 苗文亮 | 上海市环境科学研究院, 国家环境保护新型污染物环境健康影响评价重点实验室, 国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233
华东理工大学资源与环境学院, 上海 200237 | | | 倪远之 | 上海市环境科学研究院, 国家环境保护新型污染物环境健康影响评价重点实验室, 国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | | | 沈根祥 | 上海市环境科学研究院, 国家环境保护新型污染物环境健康影响评价重点实验室, 国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233
华东理工大学资源与环境学院, 上海 200237 | | | 钱晓雍 | 上海市环境科学研究院, 国家环境保护新型污染物环境健康影响评价重点实验室, 国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | | | 付侃 | 上海市环境科学研究院, 国家环境保护新型污染物环境健康影响评价重点实验室, 国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | | | 高宗源 | 上海市环境科学研究院, 国家环境保护新型污染物环境健康影响评价重点实验室, 国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233
华东理工大学资源与环境学院, 上海 200237 | | | 王振旗 | 上海市环境科学研究院, 国家环境保护新型污染物环境健康影响评价重点实验室, 国家环境保护城市大气复合污染成因与防治重点实验室, 上海 200233 | |
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| 中文摘要 |
| 为评估长三角农田轮作系统氨排放特征和减排潜力，通过密闭室间歇通气法对典型农田轮作系统的氨排放水平进行同步对比观测，探讨不同条件下的氨排放影响因素和转化机制；通过整理近10年长三角地区农田氨排放实测系数，建立基于本地因子的长三角农田轮作系统氨排放时空分布清单，并获取了不同氨减排路径下的减排效果.结果表明，常规稻麦轮作模式（CR-W）和稻虾-紫云英轮作模式（RS-C）的氨排放累积量分别为65.95 kg·hm-2和20.31 kg·hm-2，氨排放损失率分别为10.86%和9.20%.田面水NH4+-N、田面水pH和表层土NH4+-N是影响稻田氨排放通量的主要内在因素，而在麦季表层土NH4+-N和大气温度则对氨排放有着重要影响.通过定义氨排放转化率（ARN）来定量表征田面水和表层土NH4+-N向气态NH3的转化机制.稻季CR和RS模式下的田面水ARN分别达到了0.35±0.27和0.14±0.19，达到麦季表层土ARN的10~25倍，是造成稻季氨排放通量显著高于麦季的主要驱动因素；较高的田面水pH（8.0~9.0）、大气温度（>28℃）和风速（>5.0 m·s-1）条件下，ARN均呈显著上升趋势，是较低条件下的1.6~4.6倍，表明三者是影响田面水NH4+-N向气态NH3转化的主要因素；施肥类型对ARN也有显著影响，不同条件下，尿素的ARN达到有机肥的1.5~5.5倍.长三角地区常规种植模式下水稻和小麦氨排放通量分别达到了（49.2±17.6）kg·hm-2和（16.0±13.5）kg·hm-2，排放损失率则分别为（20.1±5.7）%和（5.9±3.6）%，前者达到后者的3倍左右.基于各地区本地因子的氨排放清单表明：2019年长三角农田轮作系统的氨排放总量达到（400.3±206.4）kt，平均氨排放强度为（1.33±1.39）t·km-2，氨排放主要集中在安徽省和江苏省的中北部地区；不同系数选取对清单结果的变化范围影响相对较大，达到了-51.6%~51.6%.通过梳理分析6种主要的农田氨减排路径发现，采用氮肥增效的氨减排效果最佳，达到了（30.9±51.4）%，但籽粒增产率为（-4.2±17.4）%，不确定性较大；添加土壤添加剂的氨减排效果总体较差，仅为（-5.4±45.1）%，但籽粒增产率最高，达到了（6.8±23.9）%；相对而言，通过生态种养模式的氨减排效果和籽粒增产率分别达到了（22.3±15.1）%和（5.6±3.8）%，兼具氨减排和作物增产的两大优势. |
| 英文摘要 |
| To study the characteristics and reduction potential of the ammonia emissions of a crop rotation system in the Yangtze River Delta, we monitored and compared the ammonia fluxes from two rotation systems:a conventional rice/winter wheat rotation system and a rice-shrimp cultivation/Chinese milk vetch rotation system. This study was conducted through closing chamber methods to investigate the influencing factors and transformation mechanism of ammonium emissions between the two studied cultivation patterns. Additionally, we established the temporal-spatial emission inventory by sorting out the local ammonia emission factors of farmland in the Yangtze River Delta in the last ten years. The emission reduction effects under different ammonia emission reduction paths were also obtained. The results showed that, the cumulative amount of ammonia emissions throughout the whole monitoring year for the conventional rice/winter wheat rotation system (CR-W) and the rice-shrimp cultivation/Chinese milk vetch rotation system (RS-C) were 65.95 and 20.31 kg·hm-2, respectively, whereas the ammonia loss rates of CR-W and RS-C were 10.86% and 9.20%, respectively. Field surface water NH4+-N, field surface water pH, and topsoil NH4+-N were the major internal factors of ammonia emissions from paddy fields, whereas topsoil NH4+-N and atmospheric temperature had an important impact on ammonia emissions in the wheat season. The ammonia flux/field NH4+-N ratio (ARN) of field surface water under the CR and RS modes in the rice season reached 0.35±0.27 and 0.14±0.19, respectively, which was 10-25 times that of topsoil in the wheat season, such that the ammonia emission flux in the rice season was significantly higher than that in the wheat season. Under the conditions of high field water pH (8.0-9.0), atmospheric temperature (>28℃), and wind speed (>5.0 m·s-1), the ammonia flux/field NH4+-N ratios (ARN) were around 1.6-4.6 times that under low pH, temperature, and wind speed conditions, indicating that those three factors were the main factors affecting the conversion of NH4+-N from farmland to atmospheric NH3. Fertilization types also had significant effects on ARN; under different conditions, the ARN of urea was 1.5-5.5 times that of organic fertilizer. In 2019, the ammonia emission flux of rice and wheat under a conventional planting pattern in the Yangtze River Delta were (49.2±17.6) kg·hm-2 and (16.0±13.5) kg·hm-2, respectively, whereas the ammonia loss rates of rice and wheat were (20.1±5.7)% and (5.9±3.6)%, respectively. The ammonia emission loss rate of the former was about three times that of the latter. The ammonia emission inventory built by local factors shows that the total ammonia emissions of the farmland rotation system in the Yangtze River Delta reached (400.3±206.4) kt in 2019, which was mainly concentrated in the central and northern regions of Anhui province and Jiangsu province, and the ammonia emission intensity reached (1.33±1.39) t·km-2. The selection of different emission factors had a relatively large impact on the change range of the inventory results, reaching the standard of -51.6%~51.6%. Through combing and analyzing the six main paths of ammonia emission reduction in farmland, it was found that nitrogen fertilizer synergism was the best way to reduce ammonia emissions, with the efficiency of (30.9±51.4)%; however, the grain yield increase rate was (-4.2±17.4)%, with great uncertainty. The ammonia emission reduction effect of adding soil additives was relatively poor (-5.4±45.1)%; however, the grain yield increase rate was the highest among those of the six emission reduction paths, reaching (6.8±23.9)%. The ammonia emission reduction effect and grain yield increase rate of the ecological planting and breeding mode were (22.3±15.1)% and (5.6±3.8)%, respectively, which had the advantages of reducing ammonia emissions and increasing crop yield. |
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