某钢铁企业颗粒物无组织排放核算与监测对比分析

以济南东部老工业区一家大型钢铁企业为例,通过前期调研和排放系数核算出颗粒物(TSP)排放总量,再运用公式计算出厂区周边不同距离颗粒物(TSP)的落地浓度。将该落地浓度与厂区周边颗粒物(TSP)的监测值进行对比分析,充分印证了连续点源扩散的高斯模式的准确性和可行性。随着气流经过该厂区,对比上下风向监测点位颗粒物(TSP)浓度迅速升高(增大1.04倍),下风向颗粒物(TSP)浓度超出二级标准1.32倍;颗粒物(TSP)的落地浓度在厂区不同距离上的浓度变化明显,从0.1到10 km其落地浓度稀释了约3 500倍,距离>4 km时,无组织排放的颗粒物(TSP)对周围空气质量直接影响有限。     

Multiple copper adsorption and regeneration by zeolite 4A synthesized from bauxite tailings

Copper ions were first adsorbed by zeolite 4A synthesized from bauxite tailings, the desorption of Cu(II) using Na₂EDTA solutions was performed, and the recycling of zeolite 4A in adsorption and desorption was systematically investigated. It was observed that the Cu(II) removal efficiency was directly dependent on the initial pH value. The maximum removal efficiency of Cu(II) was 96.2% with zeolite 4A when the initial pH value was 5.0. Cu(II) was completely absorbed in the first 30 min. It was also observed that the desorption efficiency and zeolite recovery were highly dependent on the initial pH and concentration of Na₂EDTA in the solution. The desorption efficiency and percent of zeolite recovered were 73.6 and 85.9%, respectively, when the Na₂EDTA solution concentration was 0.05 mol L^?1 and the pH value was 8. The recovered zeolites were pure single phase and highly crystalline. After 3 cycles, the removal efficiency of Cu(II) was as high as 78.9%, and the zeolite recovery was 46.9%, indicating that the recovered zeolites have good adsorption capacity and can repeatedly absorb Cu(II).

     

济南市大气污染防治三区划定研究

根据《山东省区域性大气污染物综合排放标准》(DB37/2376-2013)实施要求,开展济南市市域范围大气污染防治三区划定。划定过程以地理信息系统空间分析技术为手段,根据各区域大气环境承载力、人口密度分布和生态环境敏感程度三个因素的空间分布差异,实现三区划分,形成划定初步方案。同时,结合相关规划和重点企业/工业园区空间布局状况,对初步划定方案进行调整,形成三区划定最终方案,并在此基础上提出各分区大气污染控制措施。     

Impact of gasoline engine deposits on light duty vehicle emissions: in-use case study in Beijing, China

Tailpipe emissions from light-duty gasoline vehicles usually deteriorate over time. The accumulation of engine deposits due to inadequate gasoline detergency is considered to be one of the major causes of such emission deterioration. Six in-use light-duty gasoline vehicles in Beijing were tested to investigate the impact of engine deposits on emissions of hydrocarbons (HC), carbon monoxide (CO) and nitrogen oxides (NO _x ). Emissions under cold start and hot running test conditions from the six light duty vehicles were measured before and after engine deposits were removed. Results show that although individual vehicles reacted differently for each of the pollutants, elimination of engine deposits on average reduced HC emissions under hot running conditions by 29.4%, CO emissions under cold start conditions by 23.0% and CO emissions under hot running conditions by 35.5% (t < 0.05 in all cases). No pollutant emissions increased with statistical significance (t < 0.05) after the removal of engine deposits. Variations of emission changes upon removal of engine deposits were observed. Such variations are in line with previous studies, implying that the impact patterns of engine deposits on vehicle emissions may be subject to many influencing factors that are not fully understood and difficult to control under all conditions. A statistical view of the impact of engine deposits on vehicle emissions may be appropriate for evaluation of emissions reductions across a city or a country. It is necessary to maintain sufficient and effective gasoline fuel detergency in practice to keep the engines clean and in turn reduce vehicle emissions.     

大气例行点位常规监测污染物的相关性分析及防控措施研究

根据济南市历下区5个大气例行监测点位2015年上半年PM2.5、PM10、SO2、CO、NO2、O3逐小时浓度的监测数据,通过SPSS软件对各项污染物的相关性进行分析得出:CO与PM2.5、PM10、SO2、NO2强相关性出现次数最多,表明CO排放源是引起颗粒物污染的主要原因之一.对监测点位周边2 km范围内机动车尾气和餐饮燃煤两项污染源进行排放量估算得出:机动车尾气CO、NOx、PM2.5和PM10年排放量分别为388.18吨、111.18吨、4.35吨和4.72吨;餐饮燃煤CO、SO2、NOx年排放量分别为36.0吨、24.0吨和9.6吨.因此,控制CO排放源对改善济南市大气环境质量至关重要.     

济南市冬季重污染过程重点工业源对空气质量影响模拟研究

利用CALPUFF空气质量模式,结合济南市环境空气质量例行监测数据和气象观测资料,模拟分析冬季重污染过程中济南市重点工业高架点源对近地面环境空气质量的影响.结果表明:冬季地面受热后产生的热辐射减弱,大气受热辐射影响变小,易产生逆温天气,大气层结相对稳定,大气垂直运动减弱,使得近地面污染物和水汽不断积聚,形成重污染天气.该重污染过程中,济南市全部源贡献率最大为NO_2,SO_2次之,一次PM_(10)最小,依次为30.64%、17.75%和3.60%;占标率最大为NO_2,SO_2次之,一次PM_(10)最小,依次为8.01%、5.01%、0.72%.同时,上述3项污染物的占标率和贡献率均与监测值相关性较差,说明在不利气象条件下重点工业高架点源对近地面监测点位环境空气质量影响有限.