纳米ZnO/PS薄膜的制备及光降解性能

利用纳米ZnO与聚苯乙烯(PS)高速共混法制备了一种在紫外光下具有自降解性能的复合薄膜.分析表明,经过KH570处理的纳米ZnO与PS通过化学键的方式桥联在一起.TG分析表明,复合薄膜的耐热性较PS有所提高；SEM图像显示0.5 wt％ ZnO在PS薄膜表面分布均匀,经过UV照射后薄膜表面出现光腐蚀现象.复合薄膜紫外光自降解实验表明:0.5 wt％ ZnO/PS薄膜在15W的紫外灯照射下,15d的自降解率为3.715％.     

基于STIRPAT模型的环境压力空间差异分析——以能源消费为例

定量分析区域人文驱动力对环境的影响,对认识我国区域环境压力及其影响因素具有一定的科学意义.利用STIRPAT模型,以人口数量、富裕度为自变量,以能源消费总量为因变量,应用岭回归函数拟合得到环境压力模型.以研究结果为依据.利用Mapinfo7.0 软件分别绘制人口数量和富裕度对环境压力影响的空间差异图.结果显示,不同省份间人口数量和富裕度对环境压力影响存在显著差异.尽管各省份人口数量对环境压力都产生线性正效应影响,但影响强度有明显差异,最大者是最小者的8.079倍.富裕度对环境压力影响,有正效应或者负效应;有线性作用或者弹性作用.30个考察对象中,北京、上海、四川、新疆4个省市的富裕度二次项系数为负,这说明,在数据观测范围内以上4个省市的结果支持富裕度与环境压力间存在倒"U"型环境库兹涅茨曲线的观点;但这4个省市的环境压力并没有出现下降,这是由于各种人文驱动力影响的净效应决定了环境压力变化趋势.人口数量的增长对环境压力的上升存在较强正效应.因此,控制人口数量的速度对改善环境压力将起到关键作用.     

渭南主城区道路积尘负荷及交通扬尘颗粒物排放

对渭南主城区道路积尘负荷进行了实测,并计算了2018年不同道路类型和不同车型的交通扬尘颗粒物排放量。结果表明:渭南主城区支路积尘负荷最大,为1.79g/m~2,高速积尘负荷最小,为0.05g/m~2,洒水作业能有效降低积尘负荷;渭南主城区道路交通扬尘PM_(2.5)和PM_(10)的年排放量分别为1 149.65、4 751.88t;小型客车引起的交通扬尘颗粒物排放在城市道路(包括主干道、次干道、支路)和国省道(包括国道和省道)上的分担率最高,分别为59.49%、41.46%,重型货车在高速上的分担率最高,为63.35%;城市道路交通扬尘颗粒物排放有明显的双峰日变化规律,而国省道和高速不明显。     

施用生物炭对云南烟区红壤团聚体组成及有机碳分布的影响

生物炭是一种重要的土壤改良剂,为深入研究其对云南烟区红壤团聚体组成及有机碳分布的作用,开展了为期3年的生物炭田间定位试验.试验共设3个处理,分别为常规施肥(B0)、常规施肥配施生物炭15 t·hm-2(B15)、常规施肥配施生物炭30 t·hm-2(B30).结果表明:1随着生物炭施用年限和施用量的增加,土壤有机碳含量显著增加,B15和B30处理较对照(B0)分别增加了38.7%和60.1%;2施用生物炭显著提高了土壤各粒级团聚体有机碳含量,其中B30处理增幅最大.在不同粒级团聚体中0.25~2 mm团聚体有机碳含量增幅最大,与对照相比,B15和B30处理分别增加了24.9%和36.4%;3施炭处理(B15,B30)土壤团聚体平均重量直径(MWD)、几何平均直径(GMD)和大于0.25 mm团聚体数量(R0.25)也较对照显著增加,表明土壤团聚体稳定性显著提高;4连续施用生物炭3年后,大团聚体有机碳的贡献率明显升高,而微团聚体则相反.综上所述,生物炭对土壤团聚体和有机碳的作用过程是持续的,连续施用生物炭可显著提升土壤大团聚体含量、团聚体稳定性、土壤和各粒级团聚体的有机碳含量,在改善土壤物理性状的同时,有利于稳定增加土壤碳汇.     

渭南市道路移动源高分辨污染物排放清单及特征研究

应用MOVES-2014a模型并对其输入参数进行了本地化修正,计算了 2018年渭南市道路移动源污染物的排放因子和排放总量.基于渭南市路网分布和GIS信息及车流分布对污染物总排放量进行了空间和时间分配,建立了 1 km×1 km和l h分辨率的排放清单.结果表明,渭南市机动车排放CO、NMVOCs、NOx、NO2、NO...     

西安市典型道路扬尘排放清单及化学组分

道路扬尘是城市大气颗粒的主要来源之一,扬尘中含有的重金属、碳质组分和水溶性离子会危害人体健康 . 为研究西安市道路扬尘的排放量及颗粒物的化学组分,在西安市环路、主干路、次干路和支路设监测点,采集了 141个道路积尘样品,估算了不同类型道路的积尘负荷 . 采用 AP-42 模型估算了不同类型道路的扬尘排放因子,建立了 2018 年西安市道路扬尘 PM_2.5和 PM₁₀的排放清单,分析了道路扬尘颗粒物的化学组分 . 基于西安市路网分布、GIS信息和车流量对道路扬尘 PM_2.5和 PM₁₀的排放量进行了空间分配 . 结果表明,西安市机动车道、非机动车道和人行道的积尘负荷分别为（0.88±0.83）、（2.62±2.23）和（1.41±1.42）g·m^-2. 按道路长度加权平均的扬尘中 PM_2.5和 PM₁₀的排放因子分别为 0.22和0.93 g·km^-1·veh^-1. 2018 年西安市道路扬尘...     

Chemical content and source apportionment of 36 heavy metal analysis and health risk assessment in aerosol of Beijing

The concentration levels of 36 airborne heavy metals and atmospheric radioactivity in total suspended particulate (TSP) samples were measured to investigate the chemical characteristics, potential sources of aerosols, and health risk in Beijing, China, from September 2016 to September 2017. The TSP concentrations varied from 6.93 to 469.18 μg/m³, with a median of 133.97 μg/m³. The order for the mean concentrations of heavy metals, known as hazardous air pollutants (HAPs), was as follows: Mn > Pb > As > Cr > Ni > Se > Cd > Co > Sb > Hg > Be; Non-Designated HAPs Metals: Ca > Fe > Mg > Al > K > Na > Zn > P > Ba > Ti > Cu > Sr > B > Sn > I > V > Rb > Ce > Mo > Cs > Th > Ag > U > Pt. The median concentration of As was higher than China air quality standard (6 ng/m³). The gross α and β concentration levels in aerosols were (1.84?±?1.59) mBg/m³ and (1.15?±?0.85) mBg/m³, respectively. The enrichment factor values of Cu, Ba, B, Ce, Tl, Cs, Pb, As, Cd, Sb, Hg, Fe, Zn, Sn, I, Mo, and Ag were higher than 10, which indicated enriched results from anthropogenic sources. Pb, As, and Cd are considered to originate from multiple sources; fireworks released Ba during China spring festival; Fe, Ce, and Cs may come from stable emissions such as industrial gases. The health risks from anthropogenic metals via inhalation, ingestion, and dermal pathway were estimated on the basis of health quotient as well as the results indicated that children faced the higher risk than adults during the research period. For adults, the health risk posed by heavy metals in atmospheric particles was below the acceptable level.

     

西安市机动车污染物排放清单与空间分布特征

基于机动车排放因子(MOVES)模型和地理信息系统(ArcGIS)技术,建立了西安市2017年分辨率为1km×1km的机动车污染物排放清单。结果显示:2017年西安市机动车污染物PM_(2.5)、PM_(10)、NO_x(NO+NO_2)、NO、NO_2、N_2O和挥发性有机物(VOCs)的年排放总量分别为126.1×10~4、138.2×10~4、2 884.2×10~4、2 577.8×10~4、306.4×10~4、27.9×10~4、1 281.2×10~4 kg;柴油车是PM_(2.5)、PM_(10)和NO_x排放的主要来源,贡献率分别为80.2%、79.5%和75.8%;VOCs和N_2O则主要来自汽油车,贡献率分别为74.2%、89.7%;总体看来,研究区域内不同污染物的空间分布规律相似,这与西安市公路分布有关,PM_(2.5)和NO_x的排放主要集中在主城区及周边县区的高速路和国道,而VOCs的排放主要集中在主城区二环及环内。     

最近2000年江苏沿海风暴潮灾害的特征

根据地方志和文献记录对江苏沿海最近2000a风暴潮灾害进行了初步分析，结果表明，风暴潮灾害在时间和空间上均发生着变化。最近600a风暴潮灾害存在21a、36a和116a三个显著的周期，并且不同时间段内发生的风暴潮灾害受不同的因素控制。.     

Effect of acetate and propionate on the production and characterization of soluble microbial products (SMP) in aerobic granular sludge system

It is well known that soluble microbial products (SMP) derived from biological systems found in wastewater treatment plant effluent are responsible for chemical oxygen demand (COD). Aerobic granular sludge (AGS) is recognized as an efficient and innovative approach for wastewater treatment. Acetate and propionate as two dominant organic substances in wastewater need to be effectively removed before discharge. The aim of this study was to investigate the effectiveness of acetate and propionate to interfere with the accumulation, molecular weight (MW) distribution and composition of SMP in aerobic granular sludge (AGS) system using two identical sequencing batch reactors (SBR) named R1 and R2, respectively, thus reducing the COD. The results demonstrated that more SMP accumulated in R1 in presence of acetate compared to R2 treated with propionate. A positive correlation for SMP formation was detected as evidenced by aeration rates, but a negative correlation with hydraulic retention time (HRT). The MW distribution analysis suggested that small molecules (MW <3?kDa) were the dominant fraction of SMP in R1 and R2, accounting for 57–79% and 39–61%, respectively. In addition, gas chromatography-mass spectrometry (GC-MS) demonstrated that esters, alkanes, alkenes and alcohols were the predominant low-MW SMP in R1 and R2. More peaks were present in R2 (61) than R1 (46). Esters were the predominant SMP (39%) in R1, while alkanes were the predominant SMP (31%) in R2. Further, microbial community analysis indicated that more α- and γ-Proteobacteria groups which readily utilize low-MW SMP were found in R2 compared to R1-granules which may have contributed to less SMP accumulated in R2. The abundant genera in the granules were Zoogloea and Azoarcus, in particular, a greater quantity of Azoarcus was detected in R2 than R1-granules, which may be associated with higher degradation of aromatics in SMP.