不同区域城市空气O3和PM2.5累积速率时序模型

利用2017—2018年全国7个区域10个典型城市环境空气O₃和PM_2.5浓度数据，统计污染物累积速率，进而采用回归方法拟合污染物浓度及其累积速率的时间序列模型，分析不同区域污染物时序变化特征差异。结果表明：不同区域O₃浓度时序曲线拟合程度总体高于PM_2.5，石家庄O₃拟合程度最高，西安PM_2.5拟合程度最高。以07：00、14：00分别作为O₃、PM_2.5模拟起点是24 h中的最优模型。不同城市夏季O₃小时浓度时序变化曲线均为单峰形态，O₃浓度及累积速率峰值出现时间可能由城市所处经度决定，太原O₃累积最快，西安O₃消解最快。各城市间冬季PM_2.5小时浓度及其累积速率时序变化曲线形态差异较大，沈阳PM_2.5累积和消解均最快。与浓度相比，城市环境空气O₃和PM_2.5累积速率与光照、扩散条件等有更好的时间相关性。     

Desert Water Harvesting to Benefit Wildlife: A Simple, Cheap, And Durable Sub-Surface Water Harvester for Remote Locations

A sub-surface desert water harvester was constructed in the sagebrush steppe habitat of south-central Idaho, U.S.A. The desert water harvester utilizes a buried micro-catchment and three buried storage tanks to augment water for wildlife during the dry season. In this region, mean annual precipitation (MAP) ranges between about 150–250 mm (6″–10″), 70% of which falls during the cold season, November to May. Mid-summer through early autumn, June through October, is the dry portion of the year. During this period, the sub-surface water harvester provides supplemental water for wildlife for 30–90 days, depending upon the precipitation that year. The desert water harvester is constructed with commonly available, “over the counter” materials. The micro-catchment is made of a square-shaped, 20 mL. “PERMALON” polyethylene pond liner (approximately 22.9 m × 22.9 m = 523 m²) buried at a depth of about 60 cm. A PVC pipe connects the harvester with two storage tanks and a drinking trough. The total capacity of the water harvester is about 4777 L (1262 U.S. gallons) which includes three underground storage tanks, a trough and pipes. The drinking trough is refined with an access ramp for birds and small animals. The technology is simple, cheap, and durable and can be adapted to other uses, e.g. drip irrigation, short-term water for small livestock, poultry farming etc. The desert water harvester can be used to concentrate and collect water from precipitation and run-off in semi-arid and arid regions. Water harvested in such a relatively small area will not impact the ground water table but it should help to grow small areas of crops or vegetables to aid villagers in self-sufficiency.     

新疆河流洪水水污染分析

洪水期间的水污染主要来源于面源。其特点是污染物的浓度高、负荷量大。给工业、渔业和人民生活都会带来不同程度的危害。悬浮物是洪水过程中的主要面污染产物，同时还作为载体吸附和携带大量的有机物、无机盐、重金属类和微生物进入水体，致使河流水质下降。在研究洪水灾害的同时。不应忽略洪水期间的水污染问题。     

CTL-12型化学需氧量速测仪与标准法测定CODCr之比较

使用CTL—12型化学需氧量速测仪分析废水化学需氧量，简便、快速，且方法可行，完全能满足环境监测技术要求。     

TSP-PM10-PM2.5-2型中流量大气颗粒物采集系统的开发和应用

自行开发并研制了TSP-PM10-PM2.5-2型中流量TSP、PM10、PM2.5大气颗粒物采集系统,是目前中国唯一可以采集TSP、PM10、PM2.5样品并提供足够的样品量进行大气颗粒物化学成分分析的中流量大气颗粒物采集器.该系统精心设计和加工的限流孔可以保持完全固定的流量,保证切割粒径的稳定,减小采样的误差并方便操作.该系统已经成功地应用于20多个城市和地区大气颗粒物的监测和研究中,为研究大气颗粒物的污染状况和来源提供了有效的技术手段和支持.     

膜、柱串联固相萃取-高效液相色谱法测定水中多环芳烃(PAHs)的研究

建立了用膜-柱串联固相萃取(SPE)技术,甲醇和水作为流动相进行梯度洗脱,紫外和荧光检测器串联的高效液相色谱法(HPLC)分析水中EPA优先监控的16种PAHs的方法.     

Interpretation of roadside PM10 monitoring data from Sunderland,United Kingdom

Roadside PM₁₀ has been monitored by Partisol® at three sitesin Sunderland between August 1997 and February 1998. The sites chosen were an inner city kerbside site; a roadside site adjacentto a dual carriageway on the outskirts of Sunderland with an openaspect; and a rural site.The results indicate that there is a seasonal variation in the relationship between the sites in terms of monitored PM₁₀.In the winter there is a poor correlation between the sites whereas in the summer significant correlations are obtained. Of the sites monitored PM₁₀ is consistently highest at the inner city roadside site. During the summer, exceedances of theU.K. 50 g m^-3 standard (DETR, 2000) are associated with conditions suitable for the build-up of photochemical pollutionhowever during the winter period exceedances are recorded duringa variety of weather conditions.At the dual carriageway site PM_2.5 has also been recorded and contributions to measured PM₁₀ are 77% in summer and68% in winter. The results illustrate a number of inconsistencies between this study utilising the Partisol® andothers reporting results where PM₁₀ has been monitored by TEOM®.     

Using improved neural network model to analyze RSP,NOx and NO2 levels in urban air in Mong Kok,Hong Kong

As the health impact of air pollutants existing in ambient addresses much attention in recent years, forecasting of airpollutant parameters becomes an important and popular topic inenvironmental science. Airborne pollution is a serious, and willbe a major problem in Hong Kong within the next few years. InHong Kong, Respirable Suspended Particulate (RSP) and NitrogenOxides NO_x and NO₂ are major air pollutants due to thedominant diesel fuel usage by public transportation and heavyvehicles. Hence, the investigation and prediction of the influence and the tendency of these pollutants are ofsignificance to public and the city image. The multi-layerperceptron (MLP) neural network is regarded as a reliable andcost-effective method to achieve such tasks. The works presentedhere involve developing an improved neural network model, whichcombines the principal component analysis (PCA) technique and theradial basis function (RBF) network, and forecasting thepollutant levels and tendencies based in the recorded data. Inthe study, the PCA is firstly used to reduce and orthogonalizethe original input variables (data), these treated variables arethen used as new input vectors in RBF neural network modelestablished for forecasting the pollutant tendencies. Comparingwith the general neural network models, the proposed modelpossesses simpler network architecture, faster training speed,and more satisfactory predicting performance. This improvedmodel is evaluated by using hourly time series of RSP, NO_x and NO₂ concentrations collected at Mong Kok Roadside Gaseous Monitory Station in Hong Kong during the year 2000. By comparing the predicted RSP, NO_x and NO₂ concentrationswith the actual data of these pollutants recorded at the monitorystation, the effectiveness of the proposed model has been proven.Therefore, in authors' opinion, the model presented in the paper is a potential tool in forecasting air quality parameters and hasadvantages over the traditional neural network methods.     

Trace Metal Concentration in Roadside Surface Soil and Tree Back: A Measurement of Local Atmospheric Pollution in Abuja, Nigeria

Cadmium, copper, lead, nickel and zinc concentrations were analysed by atomic absorption spectrophotometry in surface soil and tree bark from different districts of Abuja, Nigeria, in order to determine the atmospheric trace metal input in the area.Elevated concentrations of some of the studied metals were observed in the soil and tree bark samples from the commercial/high traffic areas of the city compared to backgroundvalues. In soil samples, the average concentration of the metals were 0.6±0.4, 18.0±4.0, 281±39, 16±4 and66±23 g g^-1 dry weight for Cd, Cu, Pb, Ni and Zn, respectively, whilst the average concentrations in tree bark were 0.3±0.2, 12±4, 133±32, 13±3 and 61±10 g g^-1 dry weight for Cd, Cu, Pb, Ni and Zn, respectively. The trend in trace metal levels suggested that automobile emissions are a major source of these metals as the highest concentrations of Pb and Zn were recorded in the commercial areas of the city known for their high traffic densities. The levels of metal in the study area were relativelylow compared to levels found in some larger and older cities in various countries worldwide.     

地表水样品自然沉降时间对总磷测定结果的影响分析

地表水中总磷含量高低是评定水质优劣的一项重要指标,分析数据表明丰水期中总磷主要来源于泥沙中吸附的有机磷和无机磷,而溶解态磷含量较少;水样中总磷含量随着自然沉降时间的延长而逐渐降低.因此样品采集回来后样品的自然沉降时间是影响地表水中总磷测定结果的一个主要原因之一.