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.     

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.     

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。     

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。     

A set of suggested air quality indices for Canada

An approach to producing air quality indices over a wide area is discussed. The proposed indices cover aspects of pollution in cities, around cities, and in rural areas. Data are shown on a local and regional basis, and the production of national indices is attempted. Lack of comprehensive data makes detailed evaluation difficult.Views expressed are not necessarily those of the Department.     

A methodology to urban air quality assessment during large time periods of winter using computational fluid dynamic models

The representativeness of point measurements in urban areas is limited due to the strong heterogeneity of the atmospheric flows in cities. To get information on air quality in the gaps between measurement points, and have a 3D field of pollutant concentration, Computational Fluid Dynamic (CFD) models can be used. However, unsteady simulations during time periods of the order of months, often required for regulatory purposes, are not possible for computational reasons. The main objective of this study is to develop a methodology to evaluate the air quality in a real urban area during large time periods by means of steady CFD simulations. One steady simulation for each inlet wind direction was performed and factors like the number of cars inside each street, the length of streets and the wind speed and direction were taken into account to compute the pollutant concentration. This approach is only valid in winter time when the pollutant concentrations are less affected by atmospheric chemistry. A model based on the steady-state Reynolds-Averaged Navier–Stokes equations (RANS) and standard k-? turbulence model was used to simulate a set of 16 different inlet wind directions over a real urban area (downtown Pamplona, Spain). The temporal series of NO_x and PM₁₀ and the spatial differences in pollutant concentration of NO₂ and BTEX obtained were in agreement with experimental data. Inside urban canopy, an important influence of urban boundary layer dynamics on the pollutant concentration patterns was observed. Large concentration differences between different zones of the same square were found. This showed that concentration levels measured by an automatic monitoring station depend on its location in the street or square, and a modelling methodology like this is useful to complement the experimental information. On the other hand, this methodology can also be applied to evaluate abatement strategies by redistributing traffic emissions.     

A directional passive air sampler for monitoring polycyclic aromatic hydrocarbons (PAHs) in air mass

A passive air sampler was developed for collecting polycyclic aromatic hydrocarbons (PAHs) in air mass from various directions. The airflow velocity within the sampler was assessed for its responses to ambient wind speed and direction. The sampler was examined for trapped particles, evaluated quantitatively for influence of airflow velocity and temperature on PAH uptake, examined for PAH uptake kinetics, calibrated against active sampling, and finally tested in the field. The airflow volume passing the sampler was linearly proportional to ambient wind speed and sensitive to wind direction. The uptake rate for an individual PAH was a function of airflow velocity, temperature and the octanol-air partitioning coefficient of the PAH. For all PAHs with more than two rings, the passive sampler operated in a linear uptake phase for three weeks. Different PAH concentrations were obtained in air masses from different directions in the field test.     

Pragmatic estimation of a spatio-temporal air quality model with irregular monitoring data

Statistical analyses of health effects of air pollution have increasingly used GIS-based covariates for prediction of ambient air quality in “land use” regression models. More recently these spatial regression models have accounted for spatial correlation structure in combining monitoring data with land use covariates. We present a flexible spatio-temporal modeling framework and pragmatic, multi-step estimation procedure that accommodates essentially arbitrary patterns of missing data with respect to an ideally complete space by time matrix of observations on a network of monitoring sites. The methodology incorporates a model for smooth temporal trends with coefficients varying in space according to Partial Least Squares regressions on a large set of geographic covariates and nonstationary modeling of spatio-temporal residuals from these regressions. This work was developed to provide spatial point predictions of PM_2.5 concentrations for the Multi-Ethnic Study of Atherosclerosis and Air Pollution (MESA Air) using irregular monitoring data derived from the AQS regulatory monitoring network and supplemental short-time scale monitoring campaigns conducted to better predict intra-urban variation in air quality. We demonstrate the interpretation and accuracy of this methodology in modeling data from 2000 through 2006 in six U.S. metropolitan areas and establish a basis for likelihood-based estimation.     

Design of water quality monitoring networks with two information scenarios in tropical Andean basins

Design and redesign of water quality monitoring networks were evaluated for two similarly sized watersheds in the tropical Andes via optimization techniques using geographic information system technology (GIS) and a matter-element analysis of 5-day biological oxygen demand (BOD₅) and total suspended solids (TSS). This resulted in a flexible, objectively based design for a 1128-km² watershed without prior water quality data (La Miel River), and a network redesign of a 1052-km² watershed with historical water quality monitoring (Chinchiná River). Monitoring design for the undocumented basin incorporated mathematical expressions for physical, anthropological, and historical factors—and was based on clear objectives for diagnosis and intervention of water pollution. Network redesign identified network redundancy, which resulted in a 64% reduction in the number of water quality monitoring stations along the channel, and a 78% reduction of stations throughout the basin. Most tropical drainage basins throughout the world have little to no prior water quality data. But even in well-studied drainage basins like the Chinchiná River, which is among the most thoroughly studied basins in Colombia, redesign of historical and existing monitoring networks will become a standard tool to advance the restoration of polluted surface waters, not only in Colombia, but also throughout the world.     

A regional air quality model for the Kwinana industrial area of Western Australia

The development and application of a three dimensional regional air dispersion model to the Kwinana industrial area is described. Spatial variation in the flow and variations in surface roughness are incorporated and the model results are shown to be in close agreement with visual observations.     

New analytical approach to monitoring air quality in historical monuments through the isotopic ratio of CO2

Environmental Science and Pollution Research - In this study, we evaluated indoor air quality to highlight the effects of environmental pollution in the field of cultural heritage. In particular,...     

Statistical and diagnostic evaluation of the ADMS-Urban model compared with an urban air quality monitoring network

The present study examines the behaviour of the ADMS-Urban air quality forecasting model in predicting dispersion of traffic-related pollutants in urban areas. The study has been carried out in Ravenna (NE Italy), a medium-sized town where pollution produced by vehicle traffic accounts for most of the emissions. ADMS-Urban performances have been assessed through statistical analysis, by comparing carbon monoxide concentrations (vehicle traffic tracing pollutant) estimated by the model with concentrations measured by stations of the air quality monitoring network. Although the correspondence of values estimated by ADMS-Urban with measured values turns out to be satisfactory, the study shows that the model tends to produce an underestimated value compared with the actual situation, and identifies a corrective method that makes it possible to improve the relevant performances. Furthermore, the diagnostic analysis highlights that the model performances depend upon some meteorological parameters.     

A theoretical framework for the episodic-urban air quality management plan (e-UAQMP)

The present research proposes the local urban air quality management plan which combines two different modelling approaches (hybrid model) and possesses an improved predictive ability including the ‘probabilistic exceedances over norms’ and their ‘frequency of occurrences’ and so termed, herein, as episodic-urban air quality management plan (e-UAQMP). The e-UAQMP deals with the consequences of ‘extreme’ concentrations of pollutant, mainly occurring at urban ‘hotspots’ e.g. traffic junctions, intersections and signalized roadways and are also influenced by complexities of traffic generated ‘wake’ effects. The e-UAQMP (based on probabilistic approach), also acts as an efficient preventive measure to predict the ‘probability of exceedances’ so as to prepare a successful policy responses in relation to the protection of urban environment as well as disseminating information to its sensitive ‘receptors’. The e-UAQMP may be tailored to the requirements of the local area for the policy implementation programmes. The importance of such policy-making framework in the context of current air pollution ‘episodes’ in urban environments is discussed. The hybrid model that is based on both deterministic and stochastic based approaches predicting the ‘average’ as well as ‘extreme’ concentration distribution of air pollutants together in form of probability has been used at two air quality control regions (AQCRs) in the Delhi city, India, in formulating and executing the e-UAQMP—first, the income tax office (ITO), one of the busiest signalized traffic intersection and second, the Sirifort, one of the busiest signalized roadways.     

interpretation of air quality data with respect to the national ambient air quality standards

San Diego Gas &; Electric has developed a quality assurance program for continuous emission monitors (CEM). Extractive, rather than in situ, monitors were selected as a result of an in-house evaluation program. Two extractive systems have been certified and a good operating and maintenance record has been established on these systems. A successful program requires the involvement and support of all affected personnel. It is desirable to have one or two key personnel coordinate the development of the program. It is also highly desirable to have good in-house source testing capabilities.     

A comparison of air quality indices for a port and harbour region in India

The present paper discusses and compares four different air quality indices, viz. arithmetic, multiplicative, unweighted arithmetic, and unweighted multiplicative, which were considered for characterising the coastal air quality at the Jawaharlal Nehru Port, Mumbai, India. Suspended particulate matter, oxides of nitrogen, and sulphur dioxide were used as the parameters of air quality indices. Value function graphs for above-mentioned variables were developed using Indian ambient air quality standards for different areas. Product moment correlation coefficients for various air quality indices were determined using the SPSS software package to evaluate correlation among various indices.