三种环境动力学模型建模方式的比较

在农药和化学品的风险评估工作中,环境动力学模型正得到越来越多的应用。采用Stella软件、Matlab M文件和Matlab Simulink 组件3种建模方式分别构建了微宇宙、湖泊和海湾系统的多介质环境动力学模型,并对3种方式的建模难度、计算耗时及模型直观性进行了比较。研究结果表明：Stella模型最直观,但提供的计算方法有限,更适用于微分方程数量不多且计算周期短的模型;M文件模型建模速度最快,但在直观性和计算速度上没有优势,不建议在环境动力学模型中使用;Simulink模型兼具直观性和计算速度快的优点,无论微分方程数量多少,在需要进行长周期计算的模型中都最具优势。     

广州市2002～2003年空气污染指数分析

根据广州市2002年1月1日至2003年12月31日的空气污染指数资料,对广州市每日的空气污染指数和可吸入颗粒物、NO2和SO2的污染指数、空气污染指数的月平均值和年平均值、空气质量级别、首要污染物等指标进行了统计分析.结果表明,广州市的空气污染状况与大尺度的天气现象如季风和降雨量等密切相关.     

北京市常见树种叶片吸滞颗粒物能力时间动态研究

目前,以显微镜观察叶片微观结构已被证明是研究叶片吸滞颗粒物机理的有效方法.本文利用颗粒物再悬浮法和原子力显微镜,观察了北京市主要园林树种吸滞颗粒物的能力和叶片的表面特征,并探讨了不同树种吸滞颗粒物能力随时间变化的规律及叶片微观结构对滞尘能力的影响.结果表明:1针叶树种吸滞总悬浮颗粒物(TSP)能力大于阔叶树种,排序为:油松((27.13±0.44)μg·cm~(-2))白皮松((10.74±0.23)μg·cm~(-2))五角枫((8.24±0.18)μg·cm~(-2))柳树((7.71±0.18)μg·cm~(-2))银杏((6.43±0.17)μg·cm~(-2))杨树((6.17±0.19)μg·cm~(-2)),不同时间段树种滞尘能力不一致;2观测期间,针叶树种吸滞TSP和粗颗粒物(PM10)能力随月份呈U型趋势,在8、9和10月最低,随后又逐渐上升,而阔叶树种吸滞颗粒物能力则呈倒U型趋势,在7、8月最高,但不同树种吸滞细颗粒物(PM2.5)能力随时间变化均无明显规律性;3通过对叶片表面原子力显微镜(AFM)结构观测发现,叶片表面粗糙度越大,其吸滞颗粒物能力越强.     

南宁市大气颗粒物TSP、PM10、PM2.5污染水平研究

2002年在南宁市的5个典型城市功能区内,共采集了125个大气样品(按季节分别采集),初步调查了大气中颗粒物TSP、PM10、PM2.5的污染状况。结果表明,南宁市TSP、PM10、PM2.5的污染很严重,超标率分别为67．5％、82．5％、92．5％,对人体健康危害更大的PM2．5占到了PM10的63．5％左右。重污染区PM2.5的浓度超过轻污染区近一倍。     

Levels and major sources of PM2.5 and PM10 in Bangkok Metropolitan Region

This research was the first long-term attempt to concurrently measure and identify major sources of both PM₁₀ and PM_2.5 in Bangkok Metropolitan Region (BMR). Ambient PM₁₀ and PM_2.5 were evaluated at four monitoring stations and analyzed for elemental compositions, water-soluble ions, and total carbon during February 2002–January 2003. Fifteen chemical elements, four water-soluble ions, and total carbon were analyzed to assist major source identification by a receptor model approach, known as chemical mass balance. PM₁₀ and PM_2.5 were significantly different (p < 0.05) at all sites and 24 h averages were high at traffic location while two separated residential sites were similar. Seasonal difference of PM₁₀ and PM_2.5 concentrations was distinct between dry and wet seasons. Major source of PM₁₀ at the traffic site indicated that automobile emissions and biomass burning-related sources contributed approximately 33% each. Automobiles contributed approximately 39 and 22% of PM₁₀ mass at two residential sites while biomass burning contributed about 36 and 28%. PM₁₀ from re-suspended soil and cooking sources accounted for 10 to 15% at a residential site. Major sources of PM_2.5 at traffic site were automobile and biomass burning, contributing approximately 32 and 26%, respectively. Biomass burning was the major source of PM_2.5 mass concentrations at residential sites. Meat cooking also accounted for 31% of PM_2.5 mass at a low impact site. Automobile, biomass burning, and road dust were less significant, contributed 10, 6, and 5%, respectively. Major sources identification at some location had difficulty to achieve performance criteria due to limited source profiles. Improved in characterize other sources profiles will help local authority to better air quality.     

Evaluation of levels, sources and distribution of toxic elements in PM10 in a suburban industrial region, Rio de Janeiro, Brazil

The Oswaldo Cruz Foundation Campus (FIOCRUZ), in a suburban region of the city of Rio de Janeiro, was selected as a case study to assess the pollution released from vehicle and industrial facilities in Basin III, the most polluted area of the city. Concentrations of particulate matter (PM10) and trace metals in airborne particles were determined in an intensive field campaign. The samplings were performed every six days for 24 h periods, using a PM10 high volume sampler, from September 2004 to August 2005. PM10 mass concentrations were determined gravimetrically and the metals by ICP-OES. For PM10, the arithmetic mean for the period is 169 ± 42 μg m⁻³ which is 3.4 times the national recommended standard of 50 μg m⁻³. Additionally, 51% of the samplings exceeded the recommended 24 h limit of 150 μg m⁻³. Ca, Mg, Fe, Zn and Al were the metals that presented the higher concentrations. The correlation matrix gave two main clusters and three significant principal components (PC). Both PC1 and PC2 are associated to crustal, vehicular and industrial emissions while PC3 is mainly associated to geological material. Enrichment factors for Zn, Cu, Cd and Pb indicate that for these elements, anthropic sources prevail over natural inputs. PM10 levels showed a good correlation with hospital admissions for respiratory diseases in children and elderly people.     

Monitoring of ambient particles and heavy metals in a residential area of Seoul, Korea

In this study, ambient TSP, PM10, and PM2.5 in a residential area located in the northern part of Seoul were monitored every other month for 1 year from April 2005 to February 2006. The monthly average levels of TSP, PM10, and PM2.5 had ranges of 71∼158, 40∼106, and 28∼43 μg/m³, respectively. TSP and PM10 showed highest concentration in April; this seems to be due to Asian dust from China and/or Mongolia. However, the fine particle of PM2.5 showed a relatively constant level during the monitoring period. Heavy metals in PM 10 and PM2.5, such as Cr, As, Cd, Mn, Zn and Pb, were also analysed during the same period. The monthly average concentrations of heavy metal in PM2.5 were Cr:1.9∼22.7 ng/m³; As:0.9∼2.5 ng/m³; Cd: 0.6∼7 ng/m³; Mn:6.1∼22.6 ng/m³; Zn: 38.9∼204.8 ng/m³, and Pb: 21.6∼201.1 ng/m³. For the health risk assessment of heavy metals in ambient particles, excess cancer risks were calculated using IRIS unit risk. As a result, the excess cancer risks of chromium, cadmium, and arsenic were shown to be more than one per million based on the annual concentration of heavy metals, and chromium showed the highest excess cancer risk in ambient particles in Seoul.     

PM<Subscript>10</Subscript> and Ultrafine Particles Counts In-Vehicle and On-Road in the Athens Area

This work presents the first results of a study concerning on-road and in-vehicle exposure to particulate matter in the area of Athens. PM₁₀ concentration measurements were conducted by TSI DustTrak, while driving along routes with different characteristics of traffic density, during September 2003–March 2004. Concurrent measurements of the ultrafine particles (UFPs) number concentration were also conducted, by condensation particle counter during part of the days. Pedestrian exposure to PM₁₀ and UFPs was also studied through stationary measurements on the kerbside of selected roads on November 2003 and February 2004. A major avenue, a heavy-trafficked road across a children hospital and two central roads, one in a residential and one in a commercial area were selected for measurement. The results indicate that every day commuters are exposed to significant concentration levels. Higher exposures were observed in heavy-trafficked areas and during rush hours. Mean PM₁₀ in-vehicle and on-road concentrations ranged from 30–320 μg/m³ and 70–285 μg/m³, respectively. The ultrafine particles number concentrations were in the range of 5.0 × 10⁴–17.3 × 10⁴ particles/cm³ in-vehicle and 3.1 × 10⁴–7.3 × 10⁴ particles/cm³ on the kerbside of a central residential road. Both PM₁₀ and UFPs concentrations presented repeated short-term peak exposures. The results clearly point out the importance of the road microenvironment (in-vehicle and on kerbside) for population exposure in urban areas.     

The sustainability of China's major mining cities

The aim of this paper is to identify principal factors controlling the Degree of Sustainable Development of Mineral Resources (DSDMR) of mining cities and then to measure their DSDMR and reveal their developing trends. To do this, 78 Chinese mining cities are used as an example. These cities are classified on a hierarchical level of the DSDMR in order to support decision-making of sustainable development. Six principal factors controlling the DSDMR are recognized using factor analysis. They are used to measure the DSDMR of the Chinese mining cities and their developing trends. The results show that in terms of the type of mineral resources, the DSDMR decreases from petroleum to multi-resources to non-metal to coal to metal cities. It decreases also from middle to old to young aged cities and from eastern to central to western cities in geographical location. In addition, large and very large cities have higher DSDMR values than middle- and small-sized cities. These Chinese mining cities are classified into six clusters by cluster analysis, which forms the basis for policy making.