Key role for nuclear energy in global biodiversity conservation

Modern society uses massive amounts of energy. Usage rises as population and affluence increase, and energy production and use often have an impact on biodiversity or natural areas. To avoid a business‐as‐usual dependence on coal, oil, and gas over the coming decades, society must map out a future energy mix that incorporates alternative sources. This exercise can lead to radically different opinions on what a sustainable energy portfolio might entail, so an objective assessment of the relative costs and benefits of different energy sources is required. We evaluated the land use, emissions, climate, and cost implications of 3 published but divergent storylines for future energy production, none of which was optimal for all environmental and economic indicators. Using multicriteria decision‐making analysis, we ranked 7 major electricity‐generation sources (coal, gas, nuclear, biomass, hydro, wind, and solar) based on costs and benefits and tested the sensitivity of the rankings to biases stemming from contrasting philosophical ideals. Irrespective of weightings, nuclear and wind energy had the highest benefit‐to‐cost ratio. Although the environmental movement has historically rejected the nuclear energy option, new‐generation reactor technologies that fully recycle waste and incorporate passive safety systems might resolve their concerns and ought to be more widely understood. Because there is no perfect energy source however, conservation professionals ultimately need to take an evidence‐based approach to consider carefully the integrated effects of energy mixes on biodiversity conservation. Trade‐offs and compromises are inevitable and require advocating energy mixes that minimize net environmental damage. Society cannot afford to risk wholesale failure to address energy‐related biodiversity impacts because of preconceived notions and ideals.     

新型缓释尿素对削减温室气体、NH3排放和淋溶作用的研究

温室气体(CO₂、CH₄、N₂O)和NH₃的排放及水体污染带来的环境问题日益突出,其中,化肥的施用等农业排放是重要来源之一.本实验评价了新型缓释化肥对温室气体排放和水体污染的影响,采用密闭箱法,在饱和田间持水量(WFPS) 60%的条件下,检测恒温(23℃)环境中土壤温室气体(CO₂、CH₄、N₂O)和NH₃挥发.结果表明,30 d内缓释尿素组比普通尿素组释放的CO₂、CH₄、N₂O平均排放通量分别降低24.69%、3.01%、26.75%,NH₃排放量减少24.36%;模拟降雨条件的淋溶实验(15 d)显示,缓释尿素组的淋失率明显低于普通尿素组,总氮减少6.97%,尿素氮减少4.75%,缓释尿素和普通尿素组淋出水样中尿素氮和总氮量所占比重均在淋溶第1 d最大,缓释尿素组尿素氮和总氮量分别为36.1%和41.23%,普通尿素组尿素氮和总氮量分别为48.7%和72.4%.结果表明,缓释尿素较普通尿素在消减温室气体排放和氨排放有明显的效果.     

典型农药制造企业废气污染物排放特征及风险评估

选取2家典型农药制造企业为研究对象,探究农药制造行业废气排放特征、环境影响和人体健康风险.结果表明,不同企业由于产品、生产环节的不同产生的污染物存在一定差异,A企业污染物以氨、含氧有机物和卤代烃为主,B企业污染物以卤代烃为主.臭氧生成潜势(OFP)范围在1.96～107.24 mg·m^-3之间,二次有机气溶胶生成潜势(SOAFP)范围为0.94～74.72 mg·m^-3,对OFP和SOAFP贡献较大为含氧有机物、芳香烃和卤代烃.农药企业恶臭物质较为复杂,主要恶臭物质涵盖了硫化物、含氧有机物、含氮化合物和芳香烃.农药企业所有排气筒废气的LCR均高于10^-6,存在一定的致癌风险,A企业LCR范围为4.10×10^-6～5.34×10^-3之间,B企业LCR范围在1.23×10^-3～4.35×10^-1之间,卤代烃,特别是1,2-二氯乙烷是农药企业主要的致癌风险物质,需要企业加以重视.     

潘家口水库温室气体溶存、排放特征及影响因素

以大型深水水电类水库潘家口水库为例,于2020年春季（5月）、夏季（8月）在研究区设置33个采样点,采用顶空平衡-气相色谱法和经验模型法对水柱温室气体浓度和水-气界面扩散通量进行了观测及估算,并分析了潘家口水库温室气体浓度及通量的主要影响因素.结果表明：春季潘家口水库水-气界面CH₄、CO₂、N₂O平均通量分别为（1.11±1.60）μmol/（m²·h）,（1333.31±546.43）μmol/（m²·h）,（76.65±19.54）nmol/（m²·h）.夏季潘家口水库水-气界面CH₄、CO₂、N₂O平均通量分别为（0.62±1.13）μmol/（m²·h）,（746.08±1152.44）μmol/（m²·h）,（141.18±256.02）nmol/（m²·h）.潘家口水库温室气体排放呈现出大的时空异质性,空间上春季和夏季各温室气体通量均表现为干流大于支流;季节上CH₄与CO₂扩散通量表现为春季大于夏季,而N₂O扩散通量夏季大于春季.统计分析表明CH₄扩散通量主要受电导率、风速等环境因子影响,CO₂扩散通量受风速、pH及DOC影响,N₂O扩散通量主要受水柱NO₃^--N、NO₂^--N的影响.     

2009~2019年中国人为CH4排放量的估算及时空格局

以中国340个地级及以上城市为研究对象,采用自下而上的核算方法,针对农业活动、能源活动和废物处理3个一级CH₄排放源,核算2009年和2019年中国人为CH₄排放量并分析其时空格局.结果表明：2009年中国人为CH₄总排放40.71Tg,2019年为42.89Tg,CH₄排放总量在增加,排放强度和人均排放量基本保持不变.一级排放源中,农业活动、能源活动和废物处理在2009年的CH₄排放量分别为24.88Tg、12.06Tg和3.76Tg,农业活动为主要排放源,2019年三者排放量分别为16.99Tg、21.62Tg和4.28Tg,能源活动成为主要排放源;二级排放源中煤炭开采的CH₄排放最多;不同城市中各排放源的占比存在较大差异,淮安、扬州和南通农业活动排放最多,晋城、大同和太原能源活动排放最多,而北京、上海和广州以废物处理排放为主.城市人为CH₄排放存在显著的空间正相关,高排放—高聚集的城市数量减少,分布集中;低排放—低聚集的城市数量增加,分布重心向东部地区转移;局部城市空间关联类型呈现出较强的空间锁定效应和迁移惰性特征.CH₄排放强度与人均CH₄排放的区域总体差异较大;CH₄排放强度地区间Theil指数差异较大,而地区内差异较小;人均CH₄排放地区间与地区内的Theil指数差异均较小.     

基于STIRPAT模型天津减污降碳协同效应多维度分析

基于STIRPAT模型,从排放总量、减排量和协同效应系数这3个维度定量分析了天津市减污降碳协同效应.结果表明,天津市大气污染物和温室气体的主要排放源均为工业源,大气污染物和温室气体的Pearson相关系数为0.984;人口总数、城镇化率、地区生产总值、能源强度和二氧化碳排放强度是影响天津市减污降碳协同效应的重要因素;天津市2011年和2012年大气污染物和温室气体协同增排,协同效应系数分别为0.18和0.17;2013~2014年和2018~2023年大气污染物减排且温室气体增排,协同效应系数均小于0,减污降碳不具有协同效应;2015~2017年和2024~2060年大气污染物和温室气体同时减排,协同效应系数范围为2.74~8.76.天津市具备在2024年进入减污降碳协同增效阶段的条件,天津市推动减污降碳协同增效最关键的是严格控制温室气体排放总量,持续推动能源强度和二氧化碳排放强度的下降,合理控制人口总数、城镇化率和地区生产总值.     

单次降水对高寒草地温室气体通量昼夜变化的影响研究

在全球变化背景下,青藏高原降水格局发生改变,并影响高寒草地温室气体排放.为了更好地认识降水变化与高寒草地温室气体排放的关系,在2015年7月24日,通过人工降水6.7 mm,研究了单次降水对高寒草地温室气体昼夜变化的影响.表明:(1)单次降水没有改变土壤温度,但显著增加了土壤湿度;(2)单次降水后24小时内,高寒草地CH4吸收量降低了2.46倍,CO2和N2 O排放量分别提高15.3%和98.9%;(3)单次降水弱化了高寒草地CH4和N2 O排放量与土壤温度的关系.     

Fluxes of CH4 and N2O from soil under a tropical seasonal rain forest in Xishuangbanna, Southwest China

CH4 and N2O fluxes from soil under a tropical seasonal rain forest in Xishuangbanna, Southwest China were measured for one year using closed static chamber technique and gas chromatography method. Three treatments were set in the studied field: (A) litter-free,(B) with litter, and (C) with litter and seedling. The results showed that the soil in our study was a sink of atmospheric CH4 and source of atmospheric N2O. The observed mean CH4 fluxes from treatments A, B, and C were -50.0±4.0, -35.9±2.8,-31.6±2.8 μgC/(m2·h),respectively,and calculated annual fluxes in2003 were -4.1,-3.1,and -2.9kgC/hm2,respectively.The observed mean N2O fluxes from treatments A,B,and C were 30.9±3.1,28.2±3.5,50.2±3.7μgN/(m2·h),respectively,and calculated annual fluxes in 2003 were 2.8, 2.6, and 3.7 kgN/hm2, respectively. Seasonal variations in CH4 and N2O fluxes were significant among all the three treatments. The presence of litter decreased CH4 uptake during wet season (P ＜ 0.05), but not during dry season. There was a similar increase in seedlings-mediated N2O emissions during wet and dry seasons, indicating that seedlings increased N2O emission in both seasons. A strong positive relationship existed between CH4 fluxes and soil moisture for all the three treatments, and weak relationship between CH4 fluxes and soil temperature for treatment B and treatment C. The N2O fluxes correlated with soil temperature for all the three treatments.     

Modeling effects of temperature and precipitation on carbon characteristics and GHGs emissions in Abies fabric forest of subalpine

Abies fabric forest in the eastern slope of Gongga mountain is one type of subalpine dark coniferous forests of southwestern China. It is located on the southeastern edge of the Qinghai-Tibet plateau and is sensitive to climatic changes. A process-oriented biogeochemical model, Forest-DNDC, was applied to simulate the e ects of climatic factors, temperature and precipitation changes on carbon characteristics, and greenhouse gases (GHGs) emissions in A. fabric forest. Validation indicated that the Forest-DNDC could be used to predict carbon characteristics and GHGs emissions with reasonable accuracy. The model simulated carbon fluxes, soil carbon dynamics, soil CO2, N2O, and NO emissions with the changes of temperature and precipitation conditions. The results showed that with variation in the baseline temperature from –2℃ to +2℃, the gross primary production (GPP) and soil organic carbon (SOC) increased, and the net primary production (NPP) and net ecosystem production (NEP) decreased because of higher respiration rate. With increasing baseline precipitation the GPP and NPP increased slightly, and the NEP and SOC showed decreasing trend. Soil CO2 emissions increased with the increase of temperature, and CO2 emissions changed little with increased baseline precipitation. With increased temperature and decreased baseline temperature, the total annual soil N2O emissions increased.With the variation of baseline temperature from –2℃ to +2℃, the total annual soil NO emissions increased. The total annual N2O and NO emissions showed increasing trends with the increase of precipitation. The biogeochemical simulation of the typical forest indicated that temperature changes strongly a ected carbon fluxes, soil carbon dynamics, and soil GHGs emissions. The precipitation was not a principal factor a ecting carbon fluxes, soil carbon dynamics, and soil CO2 emissions, but changes in precipitation could exert strong e ect on soil N2O and NO emissions.