华北地区沙尘天气垂直气溶胶直接辐射强迫

利用CALIOP数据和SBDART（Santa Barbara DISORT Atmospheric Radiative Transfer）辐射传输模式研究了2013~2016年华北地区8d沙尘天气气溶胶及其直接辐射强迫垂直分布特征,分析了气溶胶垂直分布和光学特性对直接辐射强迫的影响.结果表明,气溶胶集中分布在地表及以上3km范围,其中纯净沙尘型和污染沙尘型气溶胶位于上层,污染大陆型气溶胶和烟雾位于下层.大气层顶、地表和大气层的日均气溶胶直接辐射强迫分别是-38.41~-88.44,-74.03~-225.86,9.06~137.42W/m².0~8km高度范围气溶胶直接辐射强迫是负值,且随着高度的增加绝对值逐渐减小.气溶胶垂直分布对大气层顶、地表和大气层的直接辐射强迫影响较小,但对直接辐射强迫垂直分布影响较大,由气溶胶廓线差异造成的同一高度层气溶胶直接辐射强迫最大差值能达到31.18W/m².气溶胶光学厚度和单次散射反照率对直接辐射强迫影响明显.消光能力相同时,吸收性气溶胶对短波太阳光的衰减作用大于散射性气溶胶,后向散射比例大的气溶胶大于后向散射比例小的气溶胶.     

亚铁添加条件下潜流湿地中氮和化学需氧量的去除效应

为了强化水平潜流人工湿地脱氮和去除化学需氧量的效果,文章利用模拟实验开展了不同浓度的亚铁(Fe~(2+))添加对其去除效果的影响研究,并初步探讨了其作用机理。研究结果表明,添加Fe~(2+)影响了水平潜流人工湿地脱氮和去除化学需氧量的能力,而且各形态的氮素和化学需氧量的去除率随水力停留时间的延长而增大。添加高浓度Fe~(2+)(≥50 mg/L)促进了湿地系统总氮和硝态氮的去除,其去除率分别超过67.7%和72.6%;而对铵态氮去除效果的影响不明显;添加低浓度Fe~(2+)(≤10 mg/L)限制了硝态氮和铵态氮的去除,而对总氮的去除效果的影响较弱。添加高浓度Fe~(2+)(≥50 mg/L)显著促进水平潜流人工湿地化学需氧量的去除,水力停留时间为24 h,其去除率均高于91%,而添加低浓度Fe~(2+)(≤10 mg/L)对促进化学需氧量的去除效果不明显,但其去除率略高于对照组,表明添加Fe~(2+)可以提高水中易降解有机物的去除能力。Fe~(2+)进入湿地系统后,水中可溶性总铁、Fe~(2+)和Fe3+浓度迅速下降,5 h后基本保持平稳,其中可溶性总铁浓度低于3.2 mg/L。添加Fe~(2+)明显降低了人工湿地出水的pH和氧化还原电位,并发生了显著变化。因此,添加适宜浓度Fe~(2+)(50 mg/L)可促进水平潜流湿地对氮素和化学需氧量的去除,可以为人工湿地强化技术研发提供理论依据。     

贵州农村污水典型抗生素污染水平及生态风险

抗生素残留物是一种新兴的微污染物,广泛存在于各种环境介质及生物样品中,长期暴露在抗生素环境会对人体健康、生态环境产生一定的潜在威胁。由于农村污水排放量逐渐增大、农村地区对抗生素的不合理使用以及受经济、地理地形的限制大部分农村地区都缺乏完整的污水收集管网和污水治理设施,进而导致抗生素通过多种途径进入农村环境,可能会对农村生态环境产生一定的环境风险。为了解贵州农村污水中抗生素的污染水平及处理后污水对环境的影响,作者采用固相萃取—液相色谱—串联质谱(SPE-LC-MS)联用技术,选取贵州省3处典型农村地区,对农村污水治理设施进出水中典型抗生素(包括6种磺胺类、3种四环素类、1种喹诺酮类抗生素)的浓度水平进行分析。结果表明,3处农村污水治理设施进水和出水中均检出不同程度的抗生素,浓度范围分别在ND～417.57 ng/L和ND～253.68 ng/L,其中土霉素浓度最高,进出水的浓度分别为417.57、253.68 ng/L;其次是氧氟沙星,在进出水中的最高浓度分别为278.75、183.73 ng/L。3处农村污水治理设施对抗生素的去除率在-58.32%～45.52%,对目标抗生素的去除率较低。与国内外其他地区农村污水相比,所选地区的进出水中抗生素的浓度较高。通过生态风险评估发现,经处理后的污水中均含有一种RQ≥1的抗生素物质,并且发现氧氟沙星为处理后污水中的高风险污染物,污水的排放会对环境造成一定的生态风险。     

Optimal range of plug-in electric vehicles in Beijing and Shanghai

Both China’s national subsidy policies for plug-in electric vehicles (PEVs) purchasers and passenger cars corporate average fuel consumption and new vehicle credit regulation (dual-credit policy) favor long-range 300+ km battery electric vehicles (BEVs) and 80+ km plug-in hybrid electric vehicles (PHEVs). However, these electric vehicles tend to have lower energy efficiency and higher purchase and operation costs. Vehicle with larger batteries can also be less equitable because the subsidies are often provided to more expensive vehicles and wealthier owners. This study takes advantage of a novel dataset of daily driving data from 39,854 conventional gasoline vehicles in Beijing and 4999 PHEVs in Shanghai to determine the optimal range of BEVs and PHEVs within their respective cities. We simulate a model to explore ranges with which PEVs emit less GHGs than that of a baseline hybrid and conventional gasoline vehicle while ensuring that all daily travel demands are met. Our findings indicate that in both cities, the optimal ranges to balance cost and travel demand for BEVs are 350 km or less and for PHEVs are 60 km or less in Beijing and 80 km or less in Shanghai. We also find that to minimize carbon dioxide (CO₂) emissions, the ranges are even lower 10 km in Beijing and 30 km in Shanghai. Our study suggests that instead of encouraging long-range PEVs, governments should subsidize PEV models with shorter ranges. Parallel efforts should also be made to both increase renewable energy over fossil fuels and expand charging facilities. Although individual mobility demand varies, the government could reduce occasional long-distance driving by subsidizing alternative transportation choices. Providing week-long driving trials to consumers before their purchases may help decrease the demand of very long range PEVs by alleviating the range anxiety through a learning process.

     

基于机器学习的细粒度空气质量时间预测器

针对空气质量状况的预防和治理,提出了基于线性回归的空气质量预测方法。作为时间预测器,更多的是考虑本地的历史数据、过去与未来的状况,挖掘空气质量随时间变化的关系以及变化趋势,为了选取最优的特征,对数据进行分析,找到与空气质量变化关系密切且存在线性关系的特征,以此特征来建立线性回归模型,并使用Cross-Validation方法进行评估验证。实验结果表明线性回归方法能够有效地预测到空气质量的变化趋势,效果良好,具有较强的实用性。