基于MF-DCCA 的呼吸道疾病与大气污染物相关性分析

针对呼吸道系统疾病与大气 PM2:5、 SO2 浓度序列的相关性特征, 应用多重分形消除趋势波动分析法 (MF-DCCA), 对张家界市永定区呼吸道系统疾病患病人数与大气 PM2:5、 SO2 浓度序列进行了研究。结果发现该地区 呼吸道系统疾病患病人数与大气 PM2:5、 SO2 浓度的相关性具有长期持续特征和多重分形特征。随后对它们相关性 多重分形特征的动力来源进行了分析, 通过随机重排和相位随机处理, 结果表明在不同时间尺度上的长期持续性影响 是其主要动力来源。进一步研究发现该地区呼吸道系统疾病与大气 PM2:5、 SO2 浓度序列的相关性在四个季节均具 有长期持续性的多重分形特征, 且夏季多重分形特征相对强于其他季节。     

基于纸喷雾离子化衍生质谱法快速检测PM2.5中的醌污染物

醌类化合物是PM2.5中的一类有害物质。本研究建立了纸喷雾离子化衍生质谱法快速测定PM2.5中的醌类污染物。通过衍生化反应在醌化合物中引入氨基，提高醌在纸喷雾中的离子化效率。随后对衍生化试剂种类、电压、喷雾溶剂种类等反应条件进行优化。在最优实验条件下，采用内标法定量分析1,4-苯醌、甲基对苯醌、1,4-萘醌和1,4-蒽醌，4种化合物均呈现较好的线性关系，其检出限分别为4.49、20.89、0.13、0.17 ng。利用该方法分析PM2.5实际样品中的萘醌和蒽醌，均获得了较好的定性和定量结果。     

基于VMD和改进TCN的短期光伏发电功率预测

光伏发电功率存在波动性,且光伏出力易受各种气象特征影响,传统TCN网络容易过度强化空间特性而弱化个体特性。针对上述问题,文中提出一种基于VMD和改进TCN的短期光伏发电功率预测模型。通过VMD将原始光伏发电功率时间序列分解为若干不同频率的模态分量,将各个模态分量以及相对应的气象数据输入至改进TCN网络进行建模学习。利用中心频率法确定VMD的最优分解模态分解个数。在传统TCN预测模型的基础上,使用DropBlock正则化取代Dropout正则化以达到抑制卷积层中信息协同的效果,并引入注意力机制自主挖掘并突出关键气象输入特征的影响,量化各气象因素对光伏发电的影响,从而提高预测精度。以江苏省某光伏电站真实数据为例进行仿真实验,结果表明所提预测方法的RMSE为0.62 MW,MAPE为2.03%。     

Braking behaviours of C/C–SiC mated with iron/copper-based PM in dry,wet and salt fog conditions

C/C–SiC composites have enormous potential as a new generation of brake materials. It is worth studying the friction and wear behaviours of these materials in special environments to ensure the safe and effective braking of trains in practical applications. In this study, the braking behaviours and wear mechanisms of C/C–SiC mating with iron/copper-based PM in dry, wet and salt fog conditions are compared in detail. The results show that the coefficient of friction (COF) in the wet condition is reduced by 14.13% compared with that under the dry condition. The COF value of the first braking under salt fog condition is increased by 12.27% and 30.75% compared to the dry and wet conditions, respectively. Additionally, the tail warping phenomenon of the braking curve disappears in wet condition, which is attributed to the weak adhesion of friction interfaces and the lubrication of the water film. The main wear mechanisms of C/C–SiC mating with iron/copper-based PM under dry condition are adhesive, fatigue and oxidation wear. However, the dominant wear in wet condition is abrasive wear. The cooling and lubrication of water reduce the tendency of thermal stress, and weaken adhesive and fatigue wear. Furthermore, salt fog can accelerate the corrosion of alloy friction film, leading to the damage of friction film. Meanwhile, the third body particles formed in salt fog condition participate in the braking process. The wear mechanisms in salt fog condition are dominated by abrasive and delamination wear.     

Estimating long-term time-resolved indoor PM2.5 of outdoor and indoor origin using easily obtainable inputs

To evaluate the separate impacts on human health and establish effective control strategies, it is crucial to estimate the contribution of outdoor infiltration and indoor emission to indoor PM_2.5 in buildings. This study used an algorithm to automatically estimate the long-term time-resolved indoor PM_2.5 of outdoor and indoor origin in real apartments with natural ventilation. The inputs for the algorithm were only the time-resolved indoor/outdoor PM_2.5 concentrations and occupants’ window actions, which were easily obtained from the low-cost sensors. This study first applied the algorithm in an apartment in Tianjin, China. The indoor/outdoor contribution to the gross indoor exposure and time-resolved infiltration factor were automatically estimated using the algorithm. The influence of outdoor PM_2.5 data source and algorithm parameters on the estimated results was analyzed. The algorithm was then applied in four other apartments located in Chongqing, Shenyang, Xi'an, and Urumqi to further demonstrate its feasibility. The results provided indirect evidence, such as the plausible explanations for seasonal and spatial variation, to partially support the success of the algorithm used in real apartments. Through the analysis, this study also identified several further development directions to facilitate the practical applications of the algorithm, such as robust long-term outdoor PM_2.5 monitoring using low-cost light-scattering sensors.     

A recurrent neural network using historical data to predict time series indoor PM2.5 concentrations for residential buildings

Due to the severe outdoor PM2.5 pollution in China, many people have installed air-cleaning systems in homes. To make the systems run automatically and intelligently, we developed a recurrent neural network (RNN) that uses historical data to predict the future indoor PM2.5 concentration. The RNN architecture includes an autoencoder and a recurrent part. We used data measured in an apartment over the course of an entire year to train and test the RNN. The data include indoor/outdoor PM2.5 concentration, environmental parameters and time of day. By comparing three different input strategies, we found that a strategy employing historical PM2.5 and time of day as inputs performed best. With this strategy, the model can be applied to predict the relatively stable trend of indoor PM2.5 concentration in advance. When the input length is 2 h and the prediction horizon is 30 min, the median prediction error is 8.3 µg/m³ for the whole test set. For times with indoor PM2.5 concentrations between (20,50] µg/m³ and (50,100] µg/m³, the median prediction error is 8.3 and 9.2 µg/m³, respectively. The low prediction error between the ground-truth and predicted values shows that the RNN can predict indoor PM2.5 concentrations with satisfactory performance.     

樟树叶精油抗PM2.5致肺上皮细胞损伤的研究

以肺细胞A549为模型,研究樟树叶精油对PM2.5所致细胞损伤的拮抗活性。研究结果显示:樟树叶精油1.0 mg/mL以内对A549细胞增殖无影响,0.05～1.00mg/mL樟树叶精油对PM2.5所致的细胞毒性作用有显著拮抗作用(P0.05);樟树叶精油可显著降低染毒细胞中丙二醛(MDA)含量,增加超氧化物歧化酶(SOD)、过氧化氢酶(CAT)活性和总抗氧化物质(T-AOC)含量(P0.05),表明樟树叶精油可通过抗氧化应激拮抗PM2.5致细胞毒性;樟树叶精油可显著降低染毒细胞中炎性因子TNF-α、IL-6的含量(P0.05),表明樟树叶精油可通过抑制炎症反应拮抗PM2.5致细胞毒性。     

Effectiveness of portable HEPA air cleaners on reducing indoor PM2.5 and NH3 in an agricultural cohort of children with asthma: A randomized intervention trial

We conducted a randomized trial of portable HEPA air cleaners with pre-filters designed to also reduce NH₃ in non-smoking homes of children age 6-12 with asthma in Yakima Valley (Washington, USA). Participants were recruited through the Yakima Valley Farm Workers Clinic asthma education program. All participants received education on home triggers while intervention families additionally received two HEPA cleaners (child's sleeping area, main living area). Fourteen-day integrated samples of PM_2.5 and NH₃ were measured at baseline and one-year follow-up. We fit ANCOVA models to compare follow-up concentrations in HEPA vs control homes, adjusting for baseline concentrations. Seventy-one households (36 HEPA, 35 control) completed the study. Most were single-family homes, with electric heat and stove, A/C, dogs/cats, and mean (SD) 5.3 (1.8) occupants. In the sleeping area, baseline geometric mean (GSD) PM_2.5 was 10.7 (2.3) μg/m³ (HEPA) vs 11.2 (1.9) μg/m³ (control); in the living area, it was 12.5 (2.3) μg/m³ (HEPA) vs 13.6 (1.9) μg/m³ (control). Baseline sleeping area NH₃ was 62.4 (1.6) μg/m³ (HEPA) vs 65.2 (1.8) μg/m³ (control). At follow-up, HEPA families had 60% (95% CI, 41%-72%; p < .0001) and 42% (19%-58%; p = .002) lower sleeping and living area PM_2.5, respectively, consistent with prior studies. NH₃ reductions were not observed.     

哈尔滨典型街谷空间PM2.5分布场与绿色界面指数相关性 研究

在城市化快速进程的背景下，城市 街区PM2.5污染日益严重，本文选取哈尔滨 在不同季节的典型街谷空间，对以叶面积密 度（LA D）、叶面积指数（LAI）为实测要素 的绿色界面指数以及PM2.5浓度进行实测对 比研究。通过对实测数据的分析和挖掘，最 终得出如下结论：首先，典型街谷空间PM2.5 时段浓度呈现上午比下午平均高37.75%，冬 季比夏季高4.7倍的特征；其次，街谷空间的 灌木界面对PM2.5浓度场平均积极贡献率为 18.62%；最后，对PM2.5的衰减率与实测街 谷绿色界面的叶面积密度（LAD）与叶面积 指数（LAI）进行相关性分析，结果显示街谷 绿色界面对PM2.5浓度的衰减作用与叶面积 密度（LAD）呈显著负相关关系，与叶面积指 数（LAI）的相关性程度较弱。     

时态神经网络模型及其在股票分类预测上应用

对具有时间属性的数据进行数据挖掘称为时态数据挖掘，用以发现数据在时间上的知识，当数据变化不规律时，如股票交易数据,就很难发现有价值的规律与规则。而神经网络具有并行、容错、可以硬件实现以及自我学习的优点,可作为股票分类预测应用的一种方法。通过将股票数据与时态型相结合，将股票数据转换成时态型股票数据，提出时态神经网络模型的分类方法，对收集的若干上市公司十年内的股票数据进行分析，构建了时态股票数据神经网络分类器对股票进行分类预测。经过实验验证，相比改进前的神经网络和支持向量机方法，该分类器具有更高的分类准确率。结果证明，这种时态数据神经网络模型对于多只股票的分类预测是非常有效的，可以很好地运用到股票市场的分类预测中。