Development of a new biotic index to assess freshwater pollution

We developed a new biotic index of species pollution value (SPV) and community pollution value (CPV) based on the correlation of protozoan communities with chemical water quality to assess freshwater pollution. Five hundred and twenty-three species of protozoa SPV were established based on the data of River Hangjiang and Lake Donghu. The present research was conducted in order to further consummate the biotic index. Protozoa of the water system in Changde City were collected from 16 stations using the PFU method and the water chemical parameters of the stations were analyzed. The results showed that CPV calculated from SPV had a close correlation with the degree of water pollution (p < 0.00001), which indicated that the method is reliable. By combining the data of River Hangjiang, Lake Donghu and Changde City, the final form of SPV was accomplished and the SPV list increased to 757. The ultimate water standard evaluated by CPV calculated from SPV was proposed.     

Air pollution forecast in cities by an air pollution index highly correlated with meteorological variables

There are many different air pollution indexes which represent the global urban air pollution situation. The daily index studied here is also highly correlated with meteorological variables and this index is capable of identifying those variables that significantly affect the air pollution. The index is connected with attention levels of NO₂, CO and O₃ concentrations. The attention levels are fixed by a law proposed by the Italian Ministries of Health and Environment. The relation of that index with some meteorological variables is analysed by the linear multiple partial correlation statistical method. Florence, Milan and Vicence were selected to show the correlation among the air pollution index and the daily thermic excursion, the previous day's air pollution index and the wind speed. During the January–March period the correlation coefficient reaches 0.85 at Milan. The deterministic methods of forecasting air pollution concentrations show very high evaluation errors and are applied on limited areas around the observation stations, as opposed to the whole urban areas. The global air pollution, instead of the concentrations at specific observation stations, allows the evaluation of the level of the sanitary risk regarding the whole urban population.     

上海市霾污染判别指标体系初步研究

随着全球变暖及工业污染排放的蔓延式发展,由空气污染带来的区域能见度恶化越来越成为中国东部经济发达地区所面临的严重区域性问题,常出现大范围的雾霾天气,造成区域空气质量的恶化及能见度水平的下降.基于对能见度及相对湿度、颗粒物浓度和组分的观测分析及影响能见度的相对湿度和空气污染因子的理论分析,提出了环保范畴内的霾污染概念,并制定了包括能见度、颗粒物浓度及其组分的上海市霾污染判别指标体系,基于颗粒物组分观测数据,对相对湿度因子进行了参数化.根据所提出的指标体系,通过上海市某点位一年观测数据的跟踪分析发现,根据霾污染判别指标体系计算得到的霾污染天数与气象学意义上的霾天数具有很好的对应关系,且新的判别指标体系能很好地区分霾污染和浮尘过程.     

Epiphytic lichen diversity on dead and dying conifers under different levels of atmospheric pollution

Based on literature data, epiphytic lichen abundance was comparably studied in montane woodlands on healthy versus dead or dying conifers of Europe and North America in areas with different levels of atmospheric pollution. Study sites comprised Picea abies forests in the Harz Mountains and in the northern Alps, Germany, Picea rubens-Abies balsamea forests on Whiteface Mountain, Adirondacks, New York, U.S.A. and Picea engelmannii-Abies lasiocarpa forests in the Salish Mountains, Montana, U.S.A. Detrended correspondence analysis showed that epiphytic lichen vegetation differed more between healthy and dead or dying trees at high- versus low-polluted sites. This is attributed to greater differences in chemical habitat conditions between trees of different vitality in highly polluted areas. Based on these results, a hypothetical model of relative importance of site factors for small-scale variation of epiphytic lichen abundance versus atmospheric pollutant load is discussed.     

Seasonal variation of air pollution index: Hong Kong case study

Air pollution is an important and popular topic in Hong Kong as concerns have been raised about the health impacts caused by vehicle exhausts in recent years. In Hong Kong, sulphur dioxide SO2, nitrogen dioxide (NO2), nitric oxide (NO), carbon monoxide (CO), and respirable suspended particulates (RSP) are major air pollutants caused by the dominant usage of diesel fuel by goods vehicles and buses. These major pollutants and the related secondary pollutant, e.g., ozone (O3), become and impose harmful impact on human health in Hong Kong area after the northern shifting of major industries to Mainland China. The air pollution index (API), a referential parameter describing air pollution levels, provides information to enhance the public awareness of air pollutions in time series since 1995. In this study, the varying trends of API and the levels of related air pollutants are analyzed based on the database monitored at a selected roadside air quality monitoring station, i.e., Causeway Bay, during 1999-2003. Firstly, the original measured pollutant data and the resultant APIs are analyzed statistically in different time series including daily, monthly, seasonal patterns. It is found that the daily mean APIs in seasonal period can be regarded as stationary time series. Secondly, the auto-regressive moving average (ARMA) method, implemented by Box-Jenkins model, is used to forecast the API time series in different seasonal specifications. The performance evaluations of the adopted models are also carried out and discussed according to Bayesian information criteria (BIC) and root mean square error (RMSE). The results indicate that the ARMA model can provide reliable, satisfactory predictions for the problem interested and is expecting to be an alternative tool for practical assessment and justification.     

Progress in developing an ANN model for air pollution index forecast

An air pollution index (API) reporting system is introduced to selected cities of China for public communication on air quality data. Shanghai is the first city in China providing daily average API reports and forecasts. This paper describes the development of an artificial neural network (ANN) model for the API forecasting in Shanghai. It is a multiple layer perceptron (MLP) network, with meteorological forecasting data as the main input, to output the next day average API values. However, the initial version of the MLP model did not work well. To improve the model, a series of tests were conducted with respect to the training method and structure optimization. Based on the test results, the training algorithm was modified and a new model was built. The new model is now being used in Shanghai for API forecasting. Its performance is shown reasonably well in comparison with observation. The application of the old model was only weakly correlated with observation. In 1-year application, the correlation coefficients were 0.2314, 0.1022 and 0.1710 for TSP, SO₂ and NO_x, respectively. But for the new model, for over 8 months application, the correlation coefficients are raised to 0.6056, 0.6993 and 0.6300 for PM₁₀, SO₂, and NO₂. Further, the new algorithm does not rely on manpower intervention so that it is now being applied in several other Chinese cities with quite different meteorological conditions. The structure of the model and the application results are presented in this paper and also the problems to be further studied.     

Metabolic diversity of the heterotrophic microorganisms and potential link to pollution of the Rouge River

The heterotrophic microbial communities of the Rouge River were tracked using Biolog Ecoplates to understand the metabolic diversity at different temporal and spatial scales, and potential link to river pollution. Site less impacted by anthrophogenic sources (site 1), showed markedly lower metabolic diversity. The only substrates that were utilized in the water samples were carbohydrates. Sites more impacted by anthrophogenic sources (sites 8 and 9) showed higher metabolic diversity. Higher functional diversity was linked to the physico-chemical and biological properties of the water samples (i.e. higher concentrations of DO, DOC, chlorophyll, and bacterial density). Biolog analysis was found to be useful in differentiating metabolic diversity between microbial communities; in determining factors that most influence the separation of communities; and in identifying which substrates were most utilized by the communities. It can also be used as an effective ecological indicator of changes in river function attributable to urbanization and pollution.     

Variability of water quality and metal pollution index in the Ganges River,Bangladesh

Environmental Science and Pollution Research - The Ganges River is one of the biggest transboundary streams in the Indian sub-continent. The significant part of this waterway channel drains one of...     

Temporal and spatial assessment of groundwater contamination with nitrate using nitrate pollution index (NPI), groundwater pollution index (GPI), and GIS (case study: Essaouira basin,Morocco)

Environmental Science and Pollution Research - Groundwater aquifers in Morocco’s coastal regions are under serious threat as a result of climate change. This study was conducted to evaluate...     

应用地质累积指数评价抚顺市PM10中元素的污染

2006-2007年采暖季、风沙季和非采暖季分别在抚顺市的6个采样点采集PM10样品,用等离子体原子发射光谱(ICP-AES)法测定样品中Ti、Al、Mn、Mg、Ca、Na、K、Cu、Zn、As、Pb、Cr、Ni、Co、Cd、Fe、V等17种元素的含量,并用地质累积指数对其污染状况进行初步评价。结果表明:(1)从PM10中元素在不同采样点的含量看,抚顺市PM10中Ti、Mn、Mg、Cu、Zn、Pb、Cr、Ni、Co这9种元素在各采样点间的差别较大;Al、Ca、Na、K、As、Cd、Fe、V这8种元素差别较小。(2)从PM10中元素在不同采样季的含量看,抚顺市PM10中Mn、Mg含量的季间差别较大,其余15种元素季间差别较小。(3)Zn、Cd污染较重;Ti、Al、Mg、Ca、Na、K、As、Fe和V污染较轻;其他6种元素在6个采样点和3个采样季污染程度差别较大。(4)水库采样点各元素污染级别均不是最高;新华采样点PM10中Cu、Zn、Pb、Cr、Ni、Co、Cd污染级别均较高。(5)3个采样季PM10中Cd、Zn污染均较重,属于重度或严重污染;在采暖季PM10中Cu、Pb、Cr的地质累积指数较风沙季、非采暖季大;在非采暖季PM10中Mn、Co受到的污染比采暖季和风沙季稍严重。     

浅谈海水溶解氧污染指数计算中的有关问题

分别采用《环境影响评价技术导则地面水环境》(HJ/T 2.3-1993)和《海洋调查规范》(GB 12763.4-2007)中两种不同的公式计算海水饱和溶解氧,探讨了海水溶解氧污染指数计算中存在的问题.结果表明,饱和溶解氧随盐度的增加而逐渐增大,在正常海水盐度范围(25～35)内,两种公式计算所得的饱和溶解氧的相对偏差平均值为10.1％,而由此引起的溶解氧污染指数相对偏差的平均值则高达49.2％.因此,饱和溶解氧的求算公式对溶解氧污染指数的计算结果具有显著影响,海水饱和溶解氧的计算应采用GB 12763.4-2007中的公式,而不能简单套用HJ/T 2.3-1993中的公式.     

Evaluation of air pollution tolerance index and anticipated performance index of plants and their application in development of green space along the urban areas

Air pollution due to vehicular emissions has become one of the most serious problems in the whole world and has resulted in huge threat to both the environment and the health of living organisms (plants, humans, animals, microorganisms). Plants growing along the roadsides get affected at the maximum as they are the primary recipients to different air pollutants and show varied levels of tolerance and sensitivity. Taking this into account, the present work was based on assessment of seasonal variation in air pollution tolerance index (APTI) and anticipated performance index (API) of four roadside plants, namely, Alstonia scholaris, Nerium oleander, Tabernaemontana coronaria, and Thevetia peruviana belonging to family Apocynaceae. APTI was calculated by the determination of four important biochemical parameters, viz., pH, relative water content (RWC), total chlorophyll (TChl), and ascorbic acid (AsA) content of leaves. The leaf samples were collected from plants growing at seven different sites of Amritsar (Punjab), India, for pre-monsoon and post-monsoon seasons. Highest APTI (82.14) was reported in N. oleander during the pre-monsoon season while the lowest was recorded in T. coronaria (18.59) in the post-monsoon season. On the basis of API score, A. scholaris was anticipated to be an excellent performer during the pre-monsoon and post-monsoon seasons followed by N. oleander, T. coronaria, and T. peruviana. Linear regression analysis and Pearson’s correlation coefficient depicted significant positive correlation between APTI and ascorbic acid content during the pre-monsoon and post-monsoon seasons.     

Assessment of anticipated performance index of some deciduous plant species under dust air pollution

Environmental Science and Pollution Research - Green vegetation improvement is an economical strategy to mitigate dust air pollution. The anticipated performance index (API) is considered a main...     

Evaluation of environmental impact produced by different economic activities with the global pollution index

Introduction

The paper analyses the environment pollution state in different case studies of economic activities (i.e. co-generation electric and thermal power production, iron profile manufacturing, cement processing, waste landfilling, and wood furniture manufacturing), evaluating mainly the environmental cumulative impacts (e.g. cumulative impact against the health of the environment and different life forms).

Materials and methods

The status of the environment (air, water resources, soil, and noise) is analysed with respect to discharges such as gaseous discharges in the air, final effluents discharged in natural receiving basins or sewerage system, and discharges onto the soil together with the principal pollutants expressed by different environmental indicators corresponding to each specific productive activity. The alternative methodology of global pollution index (I _GP ^* ) for quantification of environmental impacts is applied.

Results and discussion

Environmental data analysis permits the identification of potential impact, prediction of significant impact, and evaluation of cumulative impact on a commensurate scale by evaluation scores (ES_i) for discharge quality, and global effect to the environment pollution state by calculation of the global pollution index (I _GP ^* ).

Conclusions

The I _GP ^* values for each productive unit (i.e. 1.664?C2.414) correspond to an ??environment modified by industrial/economic activity within admissible limits, having potential of generating discomfort effects??. The evaluation results are significant in view of future development of each productive unit and sustain the economic production in terms of environment protection with respect to a preventive environment protection scheme and continuous measures of pollution control.