我国主要粮产区PM2.5、PM10时空分布特征及影响因素——以河南省为例

利用2018年河南省PM_2.5、PM₁₀监测数据,结合统计学方法及克里格插值技术,分析河南省PM_2.5、PM₁₀的时空分布特征及影响因素,结果表明:（1）PM_2.5、PM₁₀日均、月均浓度均呈现出“U”型变化特征,PM_2.5/PM₁₀月均值呈现出“W”型变化特征,PM_2.5、PM₁₀季均浓度及其比值均呈现出冬季>秋季>春季>夏季的规律;（2）PM_2.5、PM₁₀月均浓度的空间分布差异较大,而年均浓度则呈现出相似的分布规律,PM_2.5/PM₁₀季均值呈现出不同的空间分布规律,总体上东部及东南部较高,中西部区域较低;（3）PM_2.5、PM₁₀与NDVI、年降水量呈显著负相关,与人口密度、第二产业占比呈显著正相关。研究结论可为粮产区大气污染防治及粮食安全生产提供重要的科学依据。     

京津冀“以电代煤”替代大气污染物排放清单

本研究建立了基于人工神经网络的民用散煤燃用量估算模型,得到了京津冀地区“以电代煤”替代民用散煤大气污染物排放清单.结果表明：截止到2018年,京津冀地区“以电代煤”替代散煤用户约259万户,每年可减少散煤燃烧约706.6万t,减少PM_2.5、NO_x、SO₂的排放量分别约为2.81,0.76,2.17万t.其中,北京市和天津市实施效果较为明显,占京津冀地区“以电代煤”散煤替代总量的62.01%和22.82%.基于调研数据得到京津冀各市燃煤量月分布系数,1月份分布系数最大,燃煤量占比为27%~40%.     

DBD低温等离子体气化杨木及模型化合物

本文利用低温等离子体技术,使用介质阻挡放电反应器气化杨木屑、纤维素、木聚糖和木质素,用控制变量法研究了频率、载气对反应系统产气能力的影响以及生物质化学组成的气化特性.结果表明,频率越小、载气电离能越低,气化率越高,杨木的最佳气化率为64.11%;木聚糖和纤维素气化产物相似,较纤维素和木质素产气率更高并且能够产生更多CO;木质素主要生成气化炭.同时,利用SEM、BET对原料及气化炭进行了表征研究得出碱金属在高温下发生结晶;木聚糖在气化过程中会发生熔融;等离子体的高功率以及更多的氧化性物质能够使气化炭比表面积显著增加,达到150.71m²/g.     

我国林地土壤有效态镉的影响因素及拟合模型

选取全国13个具有代表性的不同气候带和植被类型的森林土壤,通过外源添加重金属镉（Cd）,比较分析土壤Cd的固-液分配系数（K_d）和有效态Cd的分布特征,探讨了土壤/土壤溶液性质对K_d及土壤有效态Cd的影响,并建立了土壤K_d及有效态Cd的拟合模型.结果表明,在不同浓度Cd处理的土壤中,其K_d值的变化范围为0.91~623.66L/kg,平均值为53.11L/kg,最大值和最小值的差异达到684.37倍;土壤孔隙水中Cd浓度（PW-Cd）的变化范围为0.309~104.450mg/L,其最大值与最小值的差异为338;DTPA提取态Cd含量（DTPA-Cd）从未添加Cd处理的本底土壤C0至最大添加量128mg/kg,其最大值与最小值的差异分别为9和1.4倍.土壤溶液pH值与lgK_d呈显著正相关（R²=0.49,P<0.001）,其与PW-Cd含量呈显著负相关（R²=0.41,P<0.05）;单一的土壤溶液Mg²⁺可解释46%的DTPA-Cd的变异.在对土壤性质的回归分析中,并未发现有单一的主控土壤性质影响Cd的固液分配,当回归方程中加入其它土壤或溶液性质时,可在一定程度上提高拟合模型的预测能力.总之,土壤溶液pH值和Mg²⁺对K_d和有效态Cd含量的影响比较显著.     

EGSB-CANON工艺启动及动力学特性

采用EGSB（Expanded Granular Sludge Bed）反应器,在启动全程自养脱氮（CANON）工艺中通过改变曝气方式以及优化曝气/非曝气逐步实现CANON工艺的稳定运行.通过研究不同曝气/非曝气条件下CANON工艺的脱氮性能的变化,探究系统内不同污泥粒径中功能菌的活性,揭示EGSB反应器不同的运行条件以及系统内不同粒径的微生物聚集体在CANON工艺启动过程中的影响机制与菌群结构特性.结果表明,当恒定曝气量为0.5L/min,曝气/非曝气为2：1（60min：30min）时,实现了AerAOB（Aerobic Ammonia-Oxidizing Bacteria）与AnAOB（Anaerobic Ammonium-Oxidation Bacteria）协同脱氮的目的,并成功将NOB（Nitrite-Oxidizing Bacteria）的活性由3.41mgN/（h·gVSS）抑制到0.75mgN/（h·gVSS）.在对AnAOB的双底物（NO₂^--N和NH₄⁺-N）抑制动力学结果进行Haldane拟合时,得到NH₄⁺-N和NO₂^--N的半饱和常数（k_s）、抑制动力学常数（k_h）分别为106.8,331.9mg/L,272.4,66.61mg/L,相关性系数（R²）分别为0.98133和0.99142.在CANON污泥颗粒化形成过程中,污泥粒径在0.154~0.335mm范围内主要以AerAOB为主,污泥粒径在1mm以上主要以AnAOB为主.不同粒径下微生物聚集体的协同作用以及稳定的群落结构实现了CANON工艺的稳定运行.     

BPA和EE2在PA微塑料上竞争吸附的位点能量

为研究微塑料上雌激素共存的吸附行为,以17α-乙炔基雌二醇（EE2）和双酚A （BPA）为目标污染物,微塑料聚酰胺（PA）为吸附剂,通过等温吸附实验研究二者在单溶质和双溶质体系下的吸附性能,基于位点能量分布理论进一步剖析二者在PA微塑料上的的吸附特性.同时,采用X射线光谱（XPS）以及傅里叶变换红外光谱（FTIR）对吸附前后的PA微塑料进行表征,探究其可能存在的吸附机理.等温吸附拟合结果及XPS、FTIR的表征结果表明BPA和EE2及二者混合溶液在PA上的吸附属于非均质吸附,疏水分配及氢键作用为主要的吸附机制.位点能量分布分析结果表明,相同浓度（1～4mg/L）条件下,BPA吸附位点主要分布于高能量区;单溶质体系的EE2吸附位点主要集中于低能量区.双溶质体系下,相同浓度的两种物质位点分布函数均随着位点能量的增大而呈指数降低,BPA下降趋势较为平缓,吸附位点分布更集中.两种体系相比较,BPA平均位点能量和位点能量非均质性分别增加了0.749%和2.483%,吸附位点数量减少了10.852%;双溶质体系下EE2平均位点能量降低0.813%,位点能量非均质性增加1.870%,吸附位点数量增加42.429%.双酚A和EE2在PA微塑料上的竞争吸附中,EE2占优势.     

Distribution and chemical speciation of arsenic in different sized atmospheric particulate matters

The distribution and chemical speciation of arsenic (As) in different sized atmospheric particulate matters (PMs), including total suspended particles (TSP), PM₁₀, and PM_2.5, collected from Baoding, China were analyzed. The average total mass concentrations of As in TSP, PM₁₀, and PM_2.5 were 31.5, 35.3, and 54.1 µg/g, respectively, with an order of PM_2.5 >PM ₁₀ > TSP, revealing that As is prone to accumulate on fine particles. Due to the divergent toxicities of different As species, speciation analysis of As in PMs is further conducted. Most of previous studies mainly focused on inorganic arsenite (iAs^III), inorganic arsenate (iAs^V), monomethylarsonate (MMA), and dimethylarsinate (DMA) in PMs, while the identification and sensitive quantification of trimethylarsine oxide (TMAO) were rarely reported. In this study, a high-performance liquid chromatography coupled to inductively coupled plasma mass spectrometry system was optimized for As speciation including TMAO in PMs. An anion exchange column was used to separate MMA, DMA and iAs^V, while a cation exchange column to separate TMAO and iAs^III. Results showed that iAs^V was the dominate component in all the samples, corresponding to a portion of 79.2% ± 9.3% of the total extractable species, while iAs^III, TMAO and DMA made up the remaining 21%. Our study demonstrated that iAs^III accounted for about 14.4% ± 11.4% of the total extracted species, with an average concentration of 1.7 ± 1.6 ng/m³. It is worth noting that TMAO was widely present in the samples (84 out of 97 samples), which supported the assumption that TMAO was ubiquitous in atmospheric particles.     

Influence of particle size on the aggregation behavior of nanoparticles: Role of structural hydration layer

More and more attention has been paid to the aggregation behavior of nanoparticles, but little research has been done on the effect of particle size. Therefore, this study systematically evaluated the aggregation behavior of nano-silica particles with diameter 130–480 nm at different initial particle concentration, pH, ionic strength, and ionic valence of electrolytes. The modified Smoluchowski theory failed to describe the aggregation kinetics for nano-silica particles with diameters less than 190 nm. Besides, ionic strength, cation species and pH all affected fast aggregation rate coefficients of 130 nm nanoparticles. Through incorporating structural hydration force into the modified Smoluchowski theory, it is found that the reason for all the anomalous aggregation behavior was the different structural hydration layer thickness of nanoparticles with various sizes. The thickness decreased with increasing of particle size, and remained basically unchanged for particles larger than 190 nm. Only when the distance at primary minimum was twice the thickness of structural hydration layer, the structural hydration force dominated, leading to the higher stability of nanoparticles. This study clearly clarified the unique aggregation mechanism of nanoparticles with smaller size, which provided reference for predicting transport and fate of nanoparticles and could help facilitate the evaluation of their environment risks.     

Distribution and source of microplastics in China's second largest reservoir - Danjiangkou Reservoir

Fresh water microplastic pollution is of pressing concern globally, but its distribution and sources in reservoirs are poorly documented. Danjiangkou Reservoir is the second largest reservoir in China and is divided into the Han Reservoir and Dan Reservoir. In this work, microplastic abundances and morphological characteristics of the reservoir were investigated. The microplastic abundance of 15 main tributaries of the reservoir was also measured. The vertical distribution (in water column and sediment), horizontal distribution (in Han Reservoir and Dan Reservoir) and source of microplastics were analyzed. Microplastics accumulated in the middle layer of the reservoir, and the size and color of the microplastic particles changed from the surface to the bottom, which implies that surveys of surface water are not enough to determine the microplastic contamination for deep water reservoirs. In the surface water, the microplastic abundance in the Han Reservoir was lower than that in the Dan Reservoir (p < 0.05), but microplastic abundance did not differ significantly in the intermediate and bottom water. Tributaries were one of the main sources of microplastics for Han Reservoir but not for Dan Reservoir. Agricultural cultivation in the hydro-fluctuation belt might be an important source of microplastics in the Dan Reservoir, which should be given additional attention. The results of this study can provide valuable information for developing microplastic sampling strategies in deep water reservoirs. Further studies are recommended to investigate the process through which microplastics in the hydro-fluctuation belt enter the reservoir and the sinking behavior of microplastics in the reservoir.     

基于移动监测的城市街谷交通相关污染物浓度时空变化研究

为更好认识城市街谷内的大气污染特征和提供城市街谷优化设计的实证参考,本文以干旱区绿洲城市乌鲁木齐市北京南路为例,采用移动监测技术,分析了城市街谷大气污染物(CO、PM2.5)的时空分布,并识别其主要影响因素。结果表明:(1)早高峰空气质量优于晚高峰;交叉路口处污染物浓度普遍较低,但苏州路立交桥下污染物浓度较高;(2)两种污染物同源,其浓度与固定站点监测数据高度相关,风向与街谷成锐角时污染物浓度较低,风速较大时污染物浓度较高,污染物浓度与车流量相关程度较低;(3)街谷两侧建筑物高度比在[1.5,2)之间,污染物浓度较低,在[1,1.5)之间,污染物浓度较高;路网密度在[12,14)之间,污染物浓度较低,[12,14)之间的道路密度能够最大程度的降低街谷内污染物浓度。