Strategic environmental assessment of mining activities: A methodology for quantification of cumulative impacts on the air quality

Environmental impact assessments in Brazil have usually focused solely on project-related issues without considering the regional context. Although required by current environmental legislation, cumulative impact assessments have not been included in the overall environmental assessment of projects. However, in recent Strategic Environmental Assessment (SEA) studies of policies, plans, and programs undertaken on a voluntary basis in support of the decision-making process, this kind of assessment has been performed especially with respect to air quality. This paper presents the application of a methodology for the quantification of cumulative impacts on air quality under high uncertainty caused by various mining activities in a single region that is recommended for SEA studies. In this way, the methodology presented here is suitable for areas lacking detailed modeling information. The developed approach uses a relatively simplified mathematical model, lowering information gathering costs and requiring little processing time. The application of the methodology is illustrated in the case of a SEA of the Corumbá Mining and Industrial Complex Development Program. Despite the lack of data needed for a minimum characterization of conditions of the area surrounding the region modeled, the quantification of impact cumulativeness on air quality has played an important role in the context of the SEA.     

Forecasts using neural network versus Box-Jenkins methodology for ambient air quality monitoring data

This study explores ambient air quality forecasts using the conventional time-series approach and a neural network. Sulfur dioxide and ozone monitoring data collected from two background stations and an industrial station are used. Various learning methods and varied numbers of hidden layer processing units of the neural network model are tested. Results obtained from the time-series and neural network models are discussed and compared on the basis of their performance for 1-step-ahead and 24-step-ahead forecasts. Although both models perform well for 1-step-ahead prediction, some neural network results reveal a slightly better forecast without manually adjusting model parameters, according to the results. For a 24-step-ahead forecast, most neural network results are as good as or superior to those of the time-series model. With the advantages of self-learning, self-adaptation, and parallel processing, the neural network approach is a promising technique for developing an automated short-term ambient air quality forecast system.     

Influence of African dust on the levels of atmospheric particulates in the Canary Islands air quality network

Time series of levels of atmospheric particulate matter (TSP and PM10) were studied at 19 air quality monitoring stations in the islands of Tenerife and Gran Canaria (Canary Islands) during the period 1998–2000. After analysing seasonal variations, attention was focused on the detection of high TSP and PM10 events and on the identification of their natural or anthropogenic origins. Back-trajectory analysis and TOMS-NASA aerosol index as well as satellite imagery (SeaWIFS-NASA) were used to identify three types of African dust outbreaks differing in seasonal occurrence, source origin and impact on TSP/PM10 levels. Mean annual and daily TSP and PM10 levels were compared with the forthcoming limit values of the EU Air Quality Directive EC/30/1999, and the results showed that the annual and daily limit values established for 2010 would only be met at rural stations. PM levels at urban background, urban and industrial sites would exceed the 2010 objectives. Only the levels at the urban-background stations would meet the requirements for 2005 despite the fact that the trade winds result in lower levels of atmospheric pollutants in the Canary Islands than in continental environments. The results highlight the role of African dust contributions when implementing the limit values of the EU directive.     

Design methodology for optimum dosage air monitoring site selection

An air monitoring site selection procedure has been developed that ranks potential air monitoring sites according to their ability to represent the ambient dosage (i.e. the product of the concentration by exposure time) pattern in a monitoring network. The Dosage Monitoring Survey Design (DMSS) analyzes the dosage impact at grid receptors by dispersion modeling. High-dosage grids become potential monitoring sites. The uniqueness of DMSS is the introduction of a cluster of contiguous grid receptors that exceed a threshold value. One station is assigned to the cluster, thus eliminating redundancies among adjacent high dosage grids. The site selection procedure specifies locations for high-dosage monitoring stations along with the cluster area capabilities of each station. An efficiency term based on the ratio of a station's dosage measuring capabilities to the total dosage in the network provides a method of ranking stations. An analysis of the design procedure shows that as the threshold concentration decreased the distance from the source where the maximum dosage was found increased and there was a slight increase in station efficiency. Also, as averaging time increased, higher efficiencies were achieved for individual stations.     

A method to determine the effect of mineral dust aerosols on air quality

Natural mineral dust storms (DS) from the Arabo-African region blow over the Mediterranean, reach Israel, and add to the anthropogenic particulate pollution. The effects of mineral dust on air quality in Israel were investigated using only PM10 and PM2.5 automatic measurements. The method does not require any other inputs such as satellite observations, model back-trajectories, dust forecast models, or mineralogical analyses. The method employs an automatic algorithm with three thresholds: the half-hour PM10 average must be above 100, this level is maintained for at least 3 h, and the maximum concentration recorded is above 180 μg m^?3. The algorithm was designed for Israel, but can be adapted for other locations.The contribution of DS caused PM10 values to exceed the Israeli annual standard of 60 μg m^?3 year^?1 in 6 of the 12 years examined. The DS contribution to PM10 annual average ranged from 9.4% to 29.5%. The level recommended by WHO, 20 μg m^?3 year^?1, was exceeded every year even without the DS contribution. The number of days in which the daily Israeli standard (150 μg m^?3) was exceeded during the 12 years was 6–20 days per year. The number of days in which the daily standard was exceeded shows an increasing trend of 7 days per decade.PM2.5 in Israel is in the range 40–56% of PM10. PM2.5 values were over the recommended standard with and without DS. The contribution of DS to annual average of PM2.5 ranged from 3.6% to 19.1%.The automatic algorithm was calibrated with a list of Dust Storms identified by visual means supported by mineralogical analysis. Mineralogical analyses of single particles were performed using Environmental Scanning Electron Microscope (ESEM). Two representative samples are given. The main difference is that the particles of the Saudi-Arabian storm had much more palygorskite, while the North-African storm had more sea-salt and organic particles. The mineral composition differences indicate that analysis can differentiate between sources.     

A holistic approach for optimal design of air quality monitoring network expansion in an urban area

This paper presents an objective methodology for determining the optimum number of ambient air quality stations in a monitoring network. The methodology integrates the multiple-criteria method with the spatial correlation technique. The pollutant concentration and population exposure data are used in this methodology in different ways. In the first stage, the Fuzzy Analytic Hierarchy Process (FAHP) with triangular fuzzy numbers (TFNs) is used to identify the most desirable monitoring locations. The network configuration is then determined on the basis of the concept of sphere of influences (SOIs). The SOIs are dictated by a predetermined cutoff value (r_c) in the spatial correlation coefficients (r) between the pollutant concentrations at the monitoring stations identified from first step and the corresponding concentrations at neighboring locations in the region. Finally, the optimal station locations are ranked by using combined utility scores gained from the first and second steps. The expansion of air quality monitoring network of Riyadh city in Saudi Arabia is used as a case study to demonstrate the proposed methodology.     

Interpretation of air quality in relation to monitoring station's surroundings

This study explores the appropriateness of the locality of air monitoring stations which are meant to indicate air quality in the area. Daily variations in NO₂ and PM₁₀ concentrations at 14 monitoring stations in Hong Kong are examined. The daily variations in NO₂ at a number of background monitoring stations exhibit patterns similar to variations in traffic volume while variations in PM₁₀ concentration exhibit less discernible pattern. Principal component analysis (PCA) and cluster analysis (CA) are applied to analyse NO₂ and PM₁₀ measurements between January 2001 and December 2005. The results show that NO₂ concentrations at background stations within the urban area are highly influenced by vehicle emissions. The effect vehicle emission has on NO₂ at stations within new towns is smaller. CA results also show that variations in PM₁₀ concentrations are distinguished by the area the station is located in. PCA results show that there are two principal components (PC's) associated with variations in roadside concentration of PM₁₀. The strong influence of roadside emissions towards concentrations of NO₂ and PM₁₀ at a number of urban background stations may be due to their close proximity to busy roadways and the high density of surrounding tall buildings, which creates an enclosure that hinders dispersion of roadside emissions and results in air pollution behaviour that reflects variation in traffic.     

Studies of air quality predictors based on neural networks

In recent years, urban air pollution has emerged as an acute problem because of its negative effect on health and living conditions. Regional air quality problems, in general, are linked to violations of specified air quality standards. The current study aims to find neural network based air quality predictors, which can work with a limited number of datasets and are robust enough to handle data with noise and errors. A number of available variations of neural network models, such as the Recurrent Network Model (RNM), the Change Point Detection Model with RNM (CPDM), the Sequential Network Construction Model (SNCM), the Self Organising Feature Model (SOFM), and the Moving Window Model (MWM), were implemented using MATLAB software for predicting air quality. Developed models were run to simulate and forecast based on the annual average data for 15 years from 1985 to 1999 for seven parameters, viz. VOC, NO_x, CO, SO₂, PM₁₀, PM_2.5 and NH₃ for one county of California, USA. The models were fitted with first nine years of data to predict data for remaining six years. The models, in general, could predict air quality patterns with modest accuracy. However, the SOFM model performed extremely well in comparison with the other models for predicting long-term (annual) data.     

Neural networks and periodic components used in air quality forecasting

Forecasting of air quality parameters is one topic of air quality research today due to the health effects caused by airborne pollutants in urban areas. The work presented here aims at comparing two principally different neural network methods that have been considered as potential tools in that area and assessing them in relation to regression with periodic components. Self-organizing maps (SOM) represent a form of competitive learning in which a neural network learns the structure of the data. Multi-layer perceptrons (MLPs) have been shown to be able to learn complex relationships between input and output variables. In addition, the effect of removing periodic components is evaluated with respect to neural networks. The methods were evaluated using hourly time series of NO₂ and basic meteorological variables collected in the city of Stockholm in 1994–1998. The estimated values for forecasting were calculated in three ways: using the periodic components alone, applying neural network methods to the residual values after removing the periodic components, and applying only neural networks to the original data. The results showed that the best forecast estimates can be achieved by directly applying a MLP network to the original data, and thus, that a combination of the periodic regression method and neural algorithms does not give any advantage over a direct application of neural algorithms.     

Interactive design of urban level air quality monitoring network

An interactive optimization method for designing an air quality monitoring network in an urban area is proposed. The main purpose is to determine representative areas of monitoring stations rather than their precise locations. Two topologies are introduced to define similarities among pre-divided uniform meshes. The first is derived from the differences of long-term averages of pollutant concentrations between every two meshes and used in the Ward method clustering. The second is obtained by the cross-impacts between pairs of meshes and used as a constraint in the clustering process. Participation of specialists in the optimization process is allowed in such a way that they can modify the second topology by taking account of economic and physical conditions as well as inaccuracy of simulation models. This technique is applied to the NO_x monitoring network of Kyoto, Japan.     

微环境新风量的检测原理及方法研究

新风量是评价室内微环境空气卫生质量的主要卫生指标之一,也是计算室内某种气体单位时间排放量的重要参数。以CO2作为示踪气体,利用于冰升华和人体呼吸产生CO2示踪气体两种测量方法对室内和车内微环境进行了检测,并考虑室内人呼出CO2量的影响,运用箱子模式的各种推导公式（稳态法、解析解法和差分法）对新风量进行了计算,并对结果进行了讨论。结果表明,没有人存在下,用箱子模式的解析解法和差分法计算的新风量值没有明显的统计差异;微环境内有人时必须考虑人释放的影响,这样箱子模式的各种推导公式都可以计算新风量值,且结果准确,准确度高。利用人体呼吸产生CO2示踪气体法,用差分法计算结果不理想,偏差很大;用稳态法计算重现性高,结果可靠。     

A methodology for the estimation of road transport air emissions in urban areas of Italy

This paper focuses on the top–down approach for estimating road transport emissions at a local level. A bottom–up approach is preferable where the data and information required by estimation methodologies are available at regional territorial level. In the absence of this regional data, emissions are adapted from national to smaller levels by means of proxy variables. This study highlights the importance of an improvement of the top–down methodology and identifies a corrective index to better characterise road transport emissions at a local level. A set of indicators related to transport activities is selected in order to identify homogeneous areas in the Italian territory. For each area, COPERT (Computer Programme to estimate Emissions from Road Traffic) methodology is applied to estimate the atmospheric emissions of different pollutants; the same methodology is used to calculate road transport emissions at a national level. The results, calculated according to vehicle category and driving mode, are compared with those deriving from a spatial disaggregation of national data by means of simple surrogate variables.     

环境空气质量自动监测站的管理与维护

随着工业化、城镇化的深入推进,二氧化硫、氮氧化物、烟粉尘和挥发性有机物等各类污染物排放到环境中,致使中国大气受到严重污染,给人体的健康、动植物的生长、发育和繁殖等带来负面的影响。为实时监测环境空气质量,建立环境空气质量自动监测站逐渐成为大气污染防治的主要手段。文中以环境空气质量自动监测站为研究对象,提出环境空气质量自动监测站管理与维护面临的问题,探讨相应的解决措施,以期为环境空气质量自动监测站的管理与维护提供参考依据。