微生物发酵床猪舍空气微生物种群结构的研究

为了研究微生物发酵床猪舍空气微生物的组成,采用自然沉降法对猪舍空气微生物进行收集,利用16S rRNA序列分析及形态观察确定微生物的分类,揭示猪舍空气微生物的多样性。从猪舍空气中共分离到细菌60余株,经过16S rRNA基因序列的同源性比对,最终确定27个代表菌株进行后续分析。分析结果表明,27株细菌中包含芽孢杆菌属14个种（51.9%）,假单胞菌5个种（18.5%）,葡萄球菌4个种（14.8%）,苍白杆菌属、类芽孢杆菌属、赖氨酸芽孢杆菌属及单胞菌属各1个种;27株细菌中包含5种条件致病菌,6种有机物降解菌。采用SPSS分析将猪舍空气微生物按采集地点明显聚成6类：仅分布于猪舍下风处的菌、仅分布于猪舍上风处的菌、仅分布于猪舍外的菌、猪舍上风和下风处共有菌、猪舍下风处和猪舍外共有菌及3处共有菌。对猪舍空气微生物空间分布的研究显示,猪舍下风处微生物种类较多,有20种,多于猪舍上风处和猪舍外部,其中15种为下风处特有种。由于微生物以气溶胶的形式存在,猪舍内相邻位置具有相似的微生物组成。以上研究结果表明,微生物发酵床猪舍空气微生物种类丰富,且微生物分布与风向相关。     

桑塔纳汽车空调系统故障的诊断与使用保养

夏天汽车空调的使用频率较高的,空调虽然给人们带来舒适,但出现故障也让人头疼,文章以桑塔纳汽车空调系统为例,对出现的常见故障及正确使用与保养知识进行了分析和介绍,希望能够为大家解决空调系统故障带来方便。     

Impacts of Molybdate Unreactive Phosphorus on Phosphate Release from Intensively Fertilized Soil

Total phosphorus (P) in soil is classified as molybdate-reactive P (MRP) and molybdate-unreactive P (MUP) based on bioavailability, and P tests are generally focused on MRP fraction of soil, despite the fact that MUP can contribute significantly to total extracted P. This survey study indicated that water-soluble P (WP) contributed as much as 9% to total P (TP) in two intensively fertilized (rice–onion cropping upland and greenhouse) soils, and most (ca. 80%) of WP was present in MUP form. The total P lost through runoff and leaching was high (>5 ppm), due to rotating greenhouse into submerged paddy soil, especially at the initial stage of rice cultivation, and MUP contributed 43% and 77% to total P lost through runoff and leaching, respectively. Therefore, it is necessary to monitor MUP to develop suitable soil-management strategies to reduce MUP release and P loss from high-P-containing soils.     

Structure and hydraulic properties of the boreal clay soil under differently managed buffer zones

Vegetated buffer zones (BZs) between arable fields and bodies of water are commonly established to reduce erosion and run‐off of particle‐bound nutrients. Functioning of a BZ depends on soil structure, as it is important for water infiltration. Therefore, it is vital to understand how varying management practices affect soils of BZs. We studied the structural and hydraulic properties of three differently managed BZs established in a boreal Vertic Stagnic Cambisol (clay, 51%). The three management practices for vegetation were as follows: natural with no treatment, harvested yearly and grazed by cattle. We used bulk density and macroporosity, together with a pore geometry index (air permeability per unit air‐filled porosity), to describe the soil structural properties. Hydraulic properties were measured at different length scales by means of an aggregate sorptivity test, saturated hydraulic conductivity of the core samples and field‐saturated hydraulic conductivity. Vegetation management markedly affected the physical properties in the top 5 cm of the soil. Properties were least favourable for infiltration at the grazed site, with the greatest bulk density, least macroporosity and hydraulic conductivity or greatest pore tortuosity. In general, spatial variation in zones with restricted and good hydraulic conductivity together with reduced aggregate sorptivity in the deeper horizons made the soil prone to preferential flow when initially dry. Prolonged wetness, on the other hand, reduced saturated hydraulic conductivity significantly, resulting in surface run‐off. Harvesting was considered the best management practice due to its inherent capacity for reducing the soil nutrient content and because it has minor implications for soil physical properties.     

基于C8051F020的智能新风控制系统设计

针对当前新风控制系统中存在的测量误差偏大、控制目标单一等问题，设计一种以二氧化碳、TVOC、温湿度为控制目标，以串口指令触摸屏为人机界面的智能控制系统。该系统以C8051F020单片机为核心，通过比较各控制目标实时值和设定值，作出相应的逻辑计算，确定各风机的档位及热交换阀门的开闭。通过试验证明，该系统能够在满足新风量的要求下大大降低能耗，具有良好的应用前景。     

南昌市大气污染因子浓度变化规律研究

指出了SO2、NO2、PM10和O3是大气的主要污染因子，大气中的SO2和NO2是形成酸雨的污染物质，PM10和O3浓度过高会对人体健康产生危害。针对大气中SO2、NO2、PM10和O3等污染因子的浓度变化规律，分析了2011年8月至12月南昌市空气质量的变化规律，探讨了其市区大气环境质量现状，大气污染特征及变化趋势。     

灰色系统理论在环境空气质量预测中的应用——以济南市为例

指出了灰色系统理论是由邓聚龙教授于1982年创立的,并在经济、气象、水利、交通运输、环境保护等众多领域得到了广泛应用。该理论体系中灰色预测GM(1,1)模型具有要求数据较少,原理简单有效,结果精度高等特点,适合对空气质量变化的预测。利用山东省济南市2001～2010年环境空气质量监测数据,分析了主要污染物(SO2、NO2和PM10)浓度的年际变化及其原因,并通过对其数据处理,建立了相应的GM(1,1)模型,对济南市未来五年空气质量做出预测。结果表明:在未来5年内,济南市SO2年平均浓度将呈现稳中有降的趋势,NO2和PM10则将呈现略微上升趋势。其中PM10仍是济南市最主要的污染物,是防治的重点。