Company engagement with supply chains to protect biodiversity and rare,threatened, and endangered species

Future global megatrends project a population increase of 2 billion people between 2019 and 2050 and at least 1–2 billion people added to the global middle class between 2016 and 2030. In addition, 68% of the world's population is projected to be living in urban areas by 2050. With these projected large population increases and shifts, demand for food, water, and energy is projected to grow by approximately 35, 40, and 50%, respectively, between 2010 and 2030. In addition, between 1970 and 2014 there was an estimated 60% reduction in the number of wildlife in the world and an estimated net loss of 2.9 billion birds, or 29%, in North America between 1970 and 2018. Loss of species populations and number of species is interconnected with reduced health of biodiversity and ecosystems. Human activity has been the main catalyst for these substantial declines primarily through impacts on habitats. These losses are accelerating. Since a company's supply chain environmental impacts are often as great or greater than its own direct environmental impacts, it may be prudent for companies to engage with their supply chains to protect and enhance habitats and biodiversity and protect rare, threatened, and endangered species. As one example, companies may have opportunities and strategic reasons to include requirements in their supplier codes of conduct and supplier standards for suppliers to protect biodiversity and rare, threatened, and endangered species, as well as additional requirements to expand or enhance habitats and ecosystems to increase biodiversity. This article follows one pathway that companies could pursue further and with greater speed—to engage with their supply chains to strengthen supplier codes of conduct to protect biodiversity and rare, threatened, and endangered species. The importance of forests, private land, and landscape partnerships is discussed as means to protect much more of the planet's biodiversity and rare, threatened, and endangered species. Lastly, the article identifies examples of opportunities for companies to more formally incorporate biodiversity into their business, supply chain, and sustainability strategies.     

What Matters Most: Are Future Stream Temperatures More Sensitive to Changing Air Temperatures,Discharge, or Riparian Vegetation?

Simulations of stream temperatures showed a wide range of future thermal regimes under a warming climate — from 2.9°C warmer to 7.6°C cooler than current conditions — depending primarily on shade from riparian vegetation. We used the stream temperature model, Heat Source, to analyze a 37‐km study segment of the upper Middle Fork John Day River, located in northeast Oregon, USA. We developed alternative future scenarios based on downscaled projections from climate change models and the composition and structure of native riparian forests. We examined 36 scenarios combining future changes in air temperature (ΔT_air = 0°C, +2°C, and +4°C), stream discharge (ΔQ = ?30%, 0%, and +30%), and riparian vegetation (post‐wildfire with 7% shade, current vegetation with 19% shade, a young‐open forest with 34% shade, and a mature riparian forest with 79% effective shade). Shade from riparian vegetation had the largest influence on stream temperatures, changing the seven‐day average daily maximum temperature (7DADM) from +1°C to ?7°C. In comparison, the 7DADM increased by 1.4°C with a 4°C increase in air temperature and by 0.7°C with a 30% change in discharge. Many streams throughout the interior western United States have been altered in ways that have substantially reduced shade. The effect of restoring shade could result in future stream temperatures that are colder than today, even under a warmer climate with substantially lower late‐summer streamflow.     

北沙河上游流域潜在非点源污染风险时空变化分析

非点源污染风险时空分布特征解析和等级分区是有效控制非点源污染的关键.为了弥补PNPI（potential non-point pollution index,潜在非点源污染指数）模型中专家评价法主观赋权的不足,引入均方差决策法,并采用改进后的PNPI模型分析1980-2017年北京市北沙河上游流域潜在非点源污染风险时空变化特征,划分潜在非点源污染风险等级.结果表明:①均方差决策法根据土地利用指标、径流指标和距离指标集的数值离散程度确定各指标权重,其结果可体现各指标权重随土地利用类型动态变化的特点.②1980-2017年土地利用指标、径流指标和距离指标的平均权重分别为0.49、0.18、0.33,说明土地利用类型对非点源污染风险相对影响较大.③受土地利用类型空间分布格局影响,北沙河上游流域潜在非点源污染风险呈西北部山区低、东南部平原区高的分布特征.④随着城镇化的推进,潜在非点源污染极高风险区主要土地利用类型由旱地和园地逐渐演变为城镇用地、农村居民地和建设用地.研究显示,非点源污染风险高低与土地利用类型密切相关,可通过土地利用类型的合理布局,降低流域非点源污染风险.      

美国对外农业投资格局演变及其影响因素——兼论“一带一路”农业合作

对外农业投资是“一带一路”倡议的重要内容,研究美国对外农业投资特征,既可总结先行之国的发展经验,也可响应并适应主要竞争对手的投资行为,为充分利用两个市场、两种资源提供科学依据。本文立足对外直接投资理论,采用Logistic模型与面板数据分析方法,研究了2000-2018年美国对外农业投资的时空格局、影响因素及其对“一带一路”农业合作的启示。结果表明:（1）美国对外农业投资以食品加工等产前产后环节为主,主要分布于西欧等发达国家以及墨西哥、巴西等地理临近的发展中国家。（2）美国对外农业投资呈现显著的市场导向特征,同时也受到地理与文化距离、国家治理等东道国因素的影响。（3）对比中美对外农业投资特征,结合当前国际经贸形势与中国农业国际合作目标,建议中国进一步优化农业产业链布局,在促进实现联合国可持续发展目标的同时,提高中国在全球粮安领域的定价权与渠道把控力;进一步深耕既有对外农业投资市场,在降低地缘竞争压力的同时,充分挖掘潜在市场机会;关注“一带一路”沿线国家的农业技术需求,保证投资目标与东道国的投资需求相协调;尤需解决中国对外农业投资面临的文化与体制差异较大等现实问题,提高“一带一路”农业合作项目的可持续性。     

合成树脂行业挥发性有机物排放成分谱及影响

使用SUMMA罐采集华东地区5类典型合成树脂企业有组织排口样品,通过气相色质联用技术（GC-MS）定量分析106种VOCs,计算了合成树脂行业排放量、排放系数和不确定性,分析了VOCs的排放特征和臭氧生成潜势,建立了5类合成树脂VOCs排放成分谱.结果表明：合成树脂企业VOCs排放量为346~3467kg/a,5类合成树脂排放系数为0.06~1.24g/kg,其中涂料树脂（CR）类企业排放量和排放系数均最大.芳香烃、含氧烃（OVOCs）和卤代烃是合成树脂行业VOCs排放基本组分,累计占比范围是73.2%~98.3%.涂料树脂、酚醛树脂（PF）、聚氨酯（PU）、共聚物树脂（ABS）和聚碳酸酯（PC）特征污染物分别为：甲基异丁基酮、苯、甲苯、苯乙烯和二氯甲烷.合成树脂企业臭氧生成潜势（OFP）为22.7~202.5mg/m³,源反应性（SR）为0.3~4.6g/g,CR类企业OFP和SR均最大.合成树脂行业SR处于各行业平均水平.芳香烃、OVOCs和烯炔烃是合成树脂行业的主要光化学活性组分,累计OFP贡献率为64.1%~100.0%,苯、甲苯、甲基异丁基酮、乙烯、苯乙烯是合成树脂行业关键活性物种.研究显示,合成树脂行业VOCs治理应管控芳香烃和OVOCs的排放,重视污染物恶臭问题和卤代烃溶剂的危害,减排VOCs排放量大、臭氧生成能力强的CR类企业.     

Carbon reduction potential and cost evaluation of different mitigation approaches in China's coal to olefin Industry

Coal-based olefin (CTO) industry as a complement of traditional petrochemical industry plays vital role in China's national economic development. However, high CO₂ emission in CTO industry is one of the fatal problems to hinder its development. In this work, the carbon emission and mitigation potentials by different reduction pathways are evaluated. The economic cost is analyzed and compared as well. According to the industry development plan, the carbon emissions from China's CTO industry will attain 189.43 million ton CO₂ (MtCO₂) and 314.11 MtCO₂ in 2020 and 2030, respectively. With the advanced technology level, the maximal carbon mitigation potential could be attained to 15.3% and 21.9% in 2020 and 2030. If the other optional mitigation ways are combined together, the carbon emission could further reduce to some extent. In general, the order of mitigation potential is followed as: feedstock alteration by natural gas > CO₂ hydrogenation with renewable electricity applied > CCS technology. The mitigation cost analysis indicates that on the basis of 2015 situation, the economic penalty for feedstock alteration is the lowest, ranged between 186 and 451 CNY/tCO₂, and the cost from CCS technology is ranged between 404 and 562 CNY/tCO₂, which is acceptable if the CO₂ enhanced oil recovery and carbon tax are considered. However, for the CO₂ hydrogenation technology, the cost is extremely high and there is almost no application possibility at present.     

郑州市典型工业企业VOCs排放特征及风险评估

为了解郑州市不同行业企业挥发性有机物(VOCs)污染特征及潜在危害,通过Summa罐和气袋采集样品,之后采用GC-MS/FID对样品进行测定分析.结果表明:郑州市包装印刷企业排放VOCs组分主要为含氧VOCs(OVOCs),包括乙酸乙酯和异丙醇,占比93.1%以上;汽车制造、家具制造和涂料生产企业VOCs组分主要为芳香烃,占比为33.5%～90.0%,以间/对-二甲苯、邻-二甲苯和乙苯为主;食品加工企业VOCs中卤代烃占比较高,为52.3%;橡胶企业VOCs主要为卤代烃和烷烃,占比分别为25.5%和28.8%;石墨碳素企业VOCs主要为芳香烃和OVOCs,占比分别为28.5%和24.1%.对比以往研究发现,郑州市与其他城市溶剂使用企业VOCs排放特征较为类似,但由于生产工艺及原辅料使用不同,相同行业企业中不同环节VOCs组分存在差异.风险评估结果显示,各企业排放的VOCs组分对臭氧生成潜势(OFP)和危害指数(HI)贡献具有一定正关联性,尤其苯、甲苯、乙苯及二甲苯等C_6～C_8芳香烃对OFP和HI贡献较大.汽车制造企业3号和家具企业5号HI值分别为1.18和2.74,均高于EPA规定限值,主要与该企业生产过程中使用溶剂型原辅料有关,且排放的主要以芳香烃为主,尤其苯、甲苯、乙苯、二甲苯和三甲基苯等C_6～C_9苯系物对HI和OFP的贡献较高,今后需重点管控溶剂型企业VOCs的排放.     

秸秆与化肥减量配施对菜地土壤温室气体排放的影响

采用静态箱/气相色谱法,2016年11月至2017年9月通过田间原位试验,设置了无物料还田(CK)、常规化肥(F)、秸秆还田配施100%化肥(100FS)、秸秆还田配施70%化肥(70FS)、秸秆还田配施60%化肥(60FS)、秸秆还田配施50%化肥(50FS),对比分析了在化肥减量的基础上,配施秸秆处理的菜地(莴笋-卷心菜-辣椒轮作)土壤CO_2、CH_4、N_2O动态变化特征及温室效应,研究秸秆与化肥减量配施对菜地温室气体排放的影响.结果表明,土壤CO_2、CH_4、N_2O排放具有一定的季节变化规律,排放高峰主要集中在4~8月,且在施肥灌水后均会出现气体的排放峰.秸秆与化肥配施较常规施肥(F)处理提高了土壤N_2O排放量,累积排放量及其排放系数,其中100FS处理的效果最为明显,辣椒季的累积排放通量明显高于莴笋季和卷心菜季,高达60.76 kg·hm~(-2)(P0.05),N_2O的排放系数(以N_2O-N/N计)为0.138 kg·kg-1,而秸秆与化肥减量配施较100FS处理可以降低氮肥的N_2O排放系数.与对照CK和F处理相比,70FS处理降低了土壤CO_2排放量和累积排放量,分别为55.28~1 831.62 mg·(m2·h)-1和7 502.13~25 988.55 kg·hm~(-2),而其他秸秆与化肥配施处理均增加了CO_2累积排放通量,尤其是60FS和50FS处理.对土壤CH_4排放而言,辣椒季的排放波动较大,除CK外,各处理的土壤CH_4累积排放量多为负值,表现为大气中CH_4汇;秸秆与化肥减量30%~50%配施处理均降低了辣椒季的土壤CH_4排放量和累积排放通量,而100FS处理提高了CH_4排放量和累积排放通量.与CK和F处理相比,除70FS外,100FS、60FS和50FS均显著提高了GWP.总体上,从温室气体排放角度,在常规化肥施用的基础上减量30%再与秸秆配施可以降低土壤CO_2和CH_4排放,缓解温室气体的增温潜势,而对土壤N_2O减排效果不显著.     

武汉市2014-2017年大气污染物分布特征及其潜在来源分析

利用武汉市2014—2017年大气污染物（SO₂、NO₂、CO、O₃、PM_2.5和PM₁₀）和气象要素的观测数据,分析了大气污染物的变化特征及其影响因素.使用HYSPLIT模式计算了影响武汉市的主要气团类型,并利用潜在源区贡献（PSCF）和浓度权重轨迹（CWT）分析方法,揭示了研究期内武汉市不同大气污染物的潜在源区分布及其贡献特性.结果表明,武汉市2014—2017年空气质量逐年好转,SO₂、O₃、PM_2.5和PM₁₀的浓度呈逐年下降的趋势,但NO₂和CO的浓度先下降后上升.2017年SO₂、O₃、PM_2.5、PM₁₀、NO₂和CO的浓度分别为9.6、50.8、52.7、89.2、47.5 μg·m^-3和1.1 mg·m^-3,分别比2014年降低了64.3%、23.0%、24.7%、18.8%、3.5%和5.9%.大气污染物存在显著的季节变化和月变化.大气污染物在四个季节中日变化类似,SO₂和O₃均为单峰型分布,NO₂、CO、PM_2.5和PM₁₀均为双峰型分布.武汉市空气污染以PM_2.5为主,随着污染程度的加剧PM_2.5/PM₁₀的值逐渐增大,在空气质量为严重污染时,PM_2.5/PM₁₀高达90%,比空气质量为优时高了31.34%.局地气团（45%）和来自山西、陕西和河南一带的西北气团（12.1%）下大气污染物浓度较高.大气污染物的潜在源区贡献（WPSCF）和浓度权重轨迹（WCWT）的较大值主要集中在武汉市本地及其周边地区,局地污染对武汉市大气污染物的贡献较大,但不同大气污染物受到排放源分布和停留时间等影响其WPSCF和WCWT的分布范围不同.     

1951—2014年中国北方地区季节气温突变与变暖停滞年份的时空变异性

利用中国北方357个气象站1951—2014年的季（月）平均最低气温,平均气温和平均最高气温数据,应用Mann-Kendall检验等方法,分析了中国北方地区三类气温季节突变与变暖停滞年份时空变异性。结果表明：平均最低气温、平均气温和平均最高气温各季节整体随纬度降低突变和变暖停滞年份变晚,突变至变暖停滞周期缩短。东北春、冬季突变和变暖停滞整体最早（20世纪70年代至80年代、1993—2002年）,华北次之,西北最晚（20世纪80年代至21世纪前10 a、1996—2010年）;夏、秋季突变华北最早（20世纪70年代和90年代）,东北次之,西北最晚（20世纪90年代至21世纪前10 a）,变暖停滞年份地区差异较小。平均最高气温未突变和平均最低气温未停滞站点较多,均主要分布在山地、高纬度地区和华北平原南部,其周边区域突变及停滞年份相对偏晚。同类气温突变和变暖停滞年份整体上分别按冬（1981—1990年）、春、秋、夏季（1994—2008年）和冬（1995—2008年）、秋、夏、春季（1998—2010年）顺序依次变晚,冬→春→秋→夏季突变至变暖停滞周期依次缩短。春、夏和冬季均为平均最低气温整体突变最早（1972—1999、1987—1999、1971—2000年）,平均气温次之,平均最高气温最晚（1975—2008、1994—2008、1972—2006年）,秋季与之不同。春、夏季整体按平均最低气温（1994—2008、1997—2008年）、平均气温、平均最高气温（均为1997—2010年）停滞依次变晚,秋、冬季与之相反。各季节突变至变暖停滞周期整体按平均最低气温（9~18 a）、平均气温和平均最高气温（5~12 a）依次缩短。夏季三类气温均在华北南部（低纬度）突变最早,与研究区整体规律相悖,该地区大部分站点未停滞,亦与突变早停滞也早的整体规律不同。