Evaluation of Three Air Dispersion Models: ISCST2, ISCLT2, and Screen2 for Mercury Emissions in an Urban Area

The goal of this paper is to compare and evaluate the performance of three air quality regulatory models for mercury releases. The models include Industrial Source Complex Short Term model (ISCST2), Industrial Source Complex Long Term model (ISCLT2), and SCREEN2. The evaluation is conducted in multiple point source urban environment using meteorological data, emission inventory and monitoring data for eight stations for the year 1990 to 1992. The performance of the models is evaluated using eight statistical parameters. The comparison of models results for both quarterly and annual averaging periods shows that ISCST2 predictions qualitatively match the observed concentrations; whereas SCREEN2 predicts highest concentrations and ISCLT2 the lowest concentrations. The summary of statistical analysis obtained by using three different methods of observed concentration (Co) and predicted concentrations (Cp) comparison show that the ISCST2 has a better overall performance than ISCLT2 and SCREEN2 models. However, none of the models met the criteria for a reasonable model. Summaries of 95% confidence limits on normalize mean square error (NMSE), geometric mean variance (VG) and geometric mean bias (MG) for each and among model indicate that of the three models, ISCST2 has the best overall performance indicators. Improved model performance may be achieved by incorporating different types of mercury forms into emission rate and air dispersion calculations.     

Particulate matter concentration levels during intense haze event in an urban environment

This study assessed concentration levels of particulate matter (PM) in the ambient environment of Ilorin metropolis, Nigeria, during haze episodes. Meteorological data (wind speed and direction, rainfall data, sunshine data, relative humidity and temperature) were obtained. Aerocet 531S particle counter (MetOne Instruments, USA) was used to measure four mass concentration ranges of PM (PM_1.0, PM_2.5, PM₁₀ and the total suspended particles (TSP)) in 10 locations taking into consideration land use patterns. Surfer® version 8 (Golden Software LLC, USA) was used to model the spatial variation of particulate matter concentration levels using kriging interpolation griding method. Human exposure assessment was done using the total respiratory deposition dose (TRDD) estimates and statutory limit breach (SLB) approaches. The appearance of dominating weak southern atmospheric wind flow was observed as wind speed ranged from 0 to 6.811 m/s while solar radiation periods ranged from 0.3 to 3.5 h/day. The relative humidity of the metropolis ranged between 28 and 57%, while daily temperature was 15 to 36 °C. Highest concentration levels of PM measured were 73.4, 562.7, 7066.3 and 9907.8 μg/m³ for PM_1.0, PM_2.5, PM₁₀ and TSP, respectively. Very strong negative correlations existed between the PM concentration levels and microclimatic parameters. Spatial variation of the concentration level as modelled using Surfer® version 8 indicated that particulate concentration level increases from south to north. Concentration levels of PM for the 24-h averaging period were generally above the 24-h threshold limit value set by the regulatory agencies for all the locations.     

Aerosols concentration in the Candiota area applying different gravimetric methods of sampling and numeric modelling

The main purpose of the present work is to study the concentration of atmospheric particles in the Candiota region, in the state of Rio Grande do Sul, where the Presidente Médici coal power plant is located. Aerosol samples were collected at the studied locations between December 2000 and December 2001 during 24 h periods at 15 day intervals using HV PM10 and dichotomous samplers. Then, the values obtained with the ISCST (Industrial Source Complex Term) model, with the HV PM10 sampler at all studied stations, and with the dichotomous sampler at the 8 de Agosto station were compared with each other. The results show that the values for the model had been underestimated in relation to the HV PM10 data for the studied stations, but agreed with the values obtained with the dichotomous sampler.     

The Impact of Road Traffic in Madrid Community by Using a Nested Gaussian-Mesoscale Air Quality Model (GAMA)

In this contribution we show the integration of a mesoscale air quality model OPANA with the ISCST3 Gaussian model (EPA) in order to analyze the impact of different emission sources and particularly the traffic emission into the different gridboxes which define the OPANA Eulerian structure. The application is done over the Madrid (Spain) regional area with 80 × 100 km and gridboxes of about 5 km. Thousands of Gaussian runs over interested gridboxes are executed in order to simulate the traffic emissions from each gridbox. Each mobile unit is represented by a Gaussian point emitter. Input meteorological variables for the ISCST3 are taken from the OPANA mesoscale air quality model. Results shows that it is possible to model the impact of traffic emissions over each gridbox. A short comparison with air quality monitoring in each gridbox is also shown.     

Integrated assessment of brick kiln emission impacts on air quality

This paper presents monitoring results of daily brick kiln stack emission and the derived emission factors. Emission of individual air pollutant varied significantly during a firing batch (7 days) and between kilns. Average emission factors per 1,000 bricks were 6.35–12.3 kg of CO, 0.52–5.9 kg of SO₂ and 0.64–1.4 kg of particulate matter (PM). PM emission size distribution in the stack plume was determined using a modified cascade impactor. Obtained emission factors and PM size distribution data were used in simulation study using the Industrial Source Complex Short-Term (ISCST3) dispersion model. The model performance was successfully evaluated for the local conditions using the simultaneous ambient monitoring data in 2006 and 2007. SO₂ was the most critical pollutant, exceeding the hourly National Ambient Air Quality Standards over 63 km² out of the 100-km² modelled domain in the base case. Impacts of different emission scenarios on the ambient air quality (SO₂, PM, CO, PM dry deposition flux) were assessed.     

Comparison of averaging techniques for the calculation of the 'European average exposure indicator' for particulate matter

A comparison of various averaging techniques to calculate the Average Exposure Indicator (AEI) specified in European Directive 2008/50/EC for particulate matter in ambient air has been performed. This was done for data from seventeen sites around the UK for which PM(10) mass concentration data is available for the years 1998-2000 and 2008-2010 inclusive. The results have shown that use of the geometric mean produces significantly lower AEI values within the required three year averaging periods and slightly lower changes in the AEI value between the three year averaging periods than the use of the arithmetic mean. The use of weighted means in the calculation, using the data capture at each site as the weighting parameter, has also been tested and this is proposed as a useful way of taking account of the confidence of each data set.     

Predicting saltwater intrusion into aquifers in vicinity of deserts using spatio-temporal kriging

The primary objective of this study was to provide a detailed framework to use the spatio-temporal kriging to model the spatio-temporal variations of salinity data and predict saltwater intrusion into freshwater aquifers in the vicinity of deserts. EC data, measured in extraction wells in the Mahvelat plain located in the Northeastern part of Iran, available from 2007 to 2013, were used to demonstrate the developed framework. The source of data was not a well-designed measurement network. Therefore, to homogenize the data, spatial analysis was used to find EC distribution in the area in each year of study. To conduct the spatial analysis, a guideline and a systematic process were developed to select an appropriate kriging method and optimize its parameters. This process can be applied to different variables. After spatial analysis of EC data for all the years of the analysis period using empirical Bayesian kriging (EBK) method with manually optimized parameters, spatio-temporal and corresponding variogram analysis was conducted using R software. This process was based on a separable product-sum model applied to the data from 2007 to 2012. The data of 2013 and the data available for the years 1999 and 2006 were used for evaluating the performance of the spatio-temporal model. The EC distribution maps, developed for different years until 2021, show a high level of EC in the north, south, and west of the study area and growing saltwater intrusion into the central freshwater aquifer. This result can be attributed to the over-exploitation of the aquifer and hydraulic head and gradient distribution in the area. The framework provided in this study for spatio-temporal analysis of unstructured EC data is useful for groundwater managers in making proper decisions.     

Multi-model ensemble simulation and projection in the climate change in the Mekong River Basin. Part I: temperature

This paper evaluates the performance of the Coupled Model Intercomparison Project phase 5 (CMIP5) in simulating annual and decadal temperature in the Mekong River Basin from 1950 to 2005. By use of Bayesian multi-model averaging method, the future projection of temperature variation under different scenarios are also analyzed. The results show, the performances of climate model are more accurate in space than time, the model can catch the warming characteristics in the Mekong river Basin, but the accuracy of simulation is not good enough. Bayesian multi-model averaging method can improve the annual and decadal temperature simulation when compared to a single result. The projected temperature in Mekong River will increase by 0.88 °C/100 year, 2.15 °C/100 year and 4.96 °C/100 year for the RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively, over the twenty-first century. The findings will be beneficial for local people and policy-maker to formulate regional strategies against the potential menaces of warming scenarios.     

General plume dispersion model (GPDM) for point source emission

Gaussian-based dispersion models are widely used to estimate local pollution levels. The accuracy of such models depends on stability classification schemes as well as plume rise equations. A general plume dispersion model (GPDM) for a point source emission, based on Gaussian plume dispersion equation, was developed. The program complex was developed using Java and Visual basic tools. It has the flexibility of using five kinds of stability classification schemes, i.e., Lapse Rate, Pasquill–Gifford (PG), Turner, σ–θ and Richardson number. It also has the option of using two types of plume rise formulations – Briggs and Holland’s. The model, applicable for both rural and urban roughness conditions, uses meteorological and emission data as its input parameters, and calculates concentrations of pollutant at the center of each cell in a predefined grid area with respect to the given source location. Its performance was tested by comparing with 4-h average field data of continuous releases of SO₂ from Dadri thermal power plant (Uttar Pradesh, India). Results showed that the Turner scheme used with Holland’s equation gives the best outcome having a degree of agreement (d) of 0.522.     

Variations in Environmental Tritium Dose Estimates Due to Meteorological Data Averaging and Uncertainties in Pathway Model Parameters

The large amounts of tritium produced at the Savannah River Site (SRS) coupled with the current dose reconstruction study at the facility emphasize the importance of ensuring accurate and efficient prediction of tritium doses to the public. Presently, dose estimates to the general population in the site vicinity are calculated annually using a five year meteorological database. Determining whether detailed monthly dose estimates are necessary or whether annual averaged data is sufficient offers the potential for more efficient dose prediction. In this study, off site collective committed doses and maximum individual doses due to atmospheric tritium releases were calculated according to the methods outlined in the U.S. Nuclear Regulatory Commission's Regulatory Guide 1.109 and compared using monthly versus five-year meteorological data and source terms. Site-specific variables not currently utilized at SRS for annual dose estimates also have been included. In addition, the range of predicted doses, based on the distribution in model parameters given in the literature, were estimated. Finally, a sensitivity analysis was performed in order to determine the influence of model inputs on dose estimates. Results corroborate previous studies by indicating that the primary contributor to infant tritium dose is the ingestion of milk, while for all other age groups, the most important pathway is the ingestion of vegetation. These relative pathway contributions remain constant throughout the year for infants; for children, teenagers, and adults, however, inhalation and absorption of tritium through the skin increases in relative importance in the months of June to September. It was found that the model utilized was most sensitive to dose factors, the ratio of the specific activity of tritium oxide in vegetation to the specific activity of atmospheric tritium oxide, and breathing rates. Most importantly, it was found that over a five-year period, the use of averaged meteorological data results in total individual doses that are only 2 to 6% higher than doses determined monthly, depending on the pathway of interest.     

Statistical analysis of PM10 concentrations at different locations in Malaysia

Malaysia has experienced several haze events since the 1980s as a consequence of the transboundary movement of air pollutants emitted from forest fires and open burning activities. Hazy episodes can result from local activities and be categorized as "localized haze". General probability distributions (i.e., gamma and log-normal) were chosen to analyze the PM(10) concentrations data at two different types of locations in Malaysia: industrial (Johor Bahru and Nilai) and residential (Kota Kinabalu and Kuantan). These areas were chosen based on their frequently high PM(10) concentration readings. The best models representing the areas were chosen based on their performance indicator values. The best distributions provided the probability of exceedances and the return period between the actual and predicted concentrations based on the threshold limit given by the Malaysian Ambient Air Quality Guidelines (24-h average of 150 μg/m(3)) for PM(10) concentrations. The short-term prediction for PM(10) exceedances in 14 days was obtained using the autoregressive model.     

Statistical trends in ground-water monitoring data at a landfill Superfund site: A case study

This paper describes the use of statistical regression models to characterize temporal trends in groundwater monitoring data collected between 1980 and 1990 on 15 wells and 13 parameters (195 cases in all) at the KL Avenue landfill site in Kalamazoo County, Michigan. This site was used as a municipal landfill prior to 1980, then was placed on the Superfund site list in 1982 after ground-water contamination was found.Six temporal regression trend models were defined using linear and quadratic regression models. These trends were used to classify each of the 195 cases as: improving, deteriorating, or stable over the 1980–1990 time period. Using these classifications it was determined that there were more than twice as many improving cases as deteriorating conditions at the KL site during this time period. These models provide a method for visualizing and interpreting trends in ground-water quality at individual well locations within the contaminant plume and for assessing the chemical trend behavior of the overall plume. The improving, deteriorating, and stable trend categories were developed for two purposes. The first purpose is to facilitate comprehension of information contained in large amounts of water quality data. The second is to assist communication among the many different groups of people who recommend actions, including remediation responsibilities at Superfund sites, like the KL site.A normal probability model was used in the trend classifications. This model contained provisions to accommodate nondetect data and other abnormal laboratory determinations which can influence the trend selection process. The robustness of this classification procedure was examined using a lognormal probability model. The overall conclusions about the KL site using the lognormal model were similar to those obtained using the normal model. However, some individual trend indications were different using the lognormal model. The Shapiro-Wilk test was used to check the adequacy of both the normal and lognormal models. The lognormal model was found to be a somewhat more adequate model for fitting the KL site data, but was not found to be superior to the normal model for each case.The normal and lognormal models were both found to be suitable for determining overall trend conditions at this site. Both models are recommended for these purposes assuming an understanding of the statistical constraints and hydrochemical context. However, it is recommended that the search for more adequate trend models continues.     

基于人工神经网络的夜光藻密度预测模型

利用人工神经网络 BP算法 ,对各种理化因子与赤潮中夜光藻密度建立了人工神经网络预报模型 ,并利用该模型对各种理化因子与夜光藻密度的非线性对应规律进行了研究。结果表明 ,模型较好地反映了存在的对应规律     

Comparing the Number of Ozone Exceedances in Different Seasons of the Year in Mexico City

Ozone air pollution is a serious problem in several cities of the world. Hence, to analyse the behaviour of this pollutant is a very important issue. One problem of interest is to study the behaviour of the inter-occurrences times between two ozone exceedances, i.e. between two days in which the pollutant’s measurement surpasses a given threshold. Another interest resides in comparing the behaviour of ozone measurements in different seasons of the year. In this paper we use some Poisson models to analyse this problem. The time interval at which the ozone measurements were taken is split into subintervals corresponding roughly to the seasons of the year. We consider three parametric forms for the mean of the Poisson model, and consequently for the mean of the inter-occurrences times. In each model, the parameters describing its mean are estimated using Bayesian inference via Markov chain Monte Carlo methods. The models are applied to the ozone measurements provided by the Mexico City monitoring network. Theoretical results suggest that an increase has occurred in the mean inter-exceedances times and this is corroborated by the observed data. Differences between the behaviour of the pollutant during different seasons of the year are also detected as well as similarities in the same season in different years. Besides estimating the mean of the Poisson models, inference for the possible presence and location of change-points indicating change of parameters of the model is also performed.     

A model for species diversity monitoring at community level and its applications

A simple, handly and powerful tool, called the DIMO model based on the Shannon-Wiener index, has been developed for analysis and evaluation of species diversity at plant community (alpha) level. Three axes, i.e. species richness, species evenness and the Shannon-Wiener index, were plotted in a two-dimensional space. Any change in these parameters and their relationship can be easily analysed and demonstrated by the model. A modified Shannon-Wiener index,Q, based on the DIMO model, was constructed to integrate species richness and evenness. The model and theQ index were tested with the real-life data collected from vegetation monitoring by the Swedish National Environmental Monitoring Program (PMK) during a 12-year period. Because of its dynamic features, the DIMO model is especially suitable for biodiversity monitoring at community level and can be applied to other levels as well if one replaces species taxa by other categories.     

Development of geomorphologic instantaneous unit hydrograph for a large watershed

Hill torrents cause a lot of environmental and property damage in Pakistan every year. Proper assessment of direct runoff in the form of hill torrents is essential for protection of environment, property, and human life. In this paper, direct surface runoff hydrograph (DSRH) was derived for a large catchment using the geomorphologic instantaneous unit hydrograph concept. The catchment with hill torrent flows in semi-arid region of Pakistan was selected for this study. It was divided into series of linear cascades and hydrologic parameters required for Nash's conceptual model, and were estimated using geomorphology of the basin. Geomorphologic parameters were derived from satellite images of the basin and ERDAS and ArcGIS were used for data processing. Computer program was developed to systematically estimate the dynamic velocity, its related parameters by optimization and thereby to simulate the DSRH. The data regarding rainfall-runoff and satellite images were collected from Punjab Irrigation and Power Department, Pakistan. Model calibration and validation was made for 15 rainfall-runoff events. Ten events were used for calibration and five for validation. Model efficiency was found to be more than 90% and root mean square error to be about 5%. Impact of variation in model parameters (shape parameter and storage coefficient) on DSRH was investigated. For shape parameter, the number of linear cascades varied from 1 to 3 and it was found that the shaper parameter value of 3 produced the best DSRH. Various values of storage coefficient were used and it was observed that the value determined from geomorphology and the dynamic velocity produced the best results.     

大气颗粒物采样器的设计与应用

自行开发设计大气颗粒物双通道采样器,通过更换粒径切割器实现对大气总悬浮颗粒物(TSP)、可吸入颗粒物(PM10)或细颗粒物(PM2.5)样品的采集。该仪器通过流量传感器、比例阀和流量控制板精确控制每个通道的采样流量,控制系统还可实时采集大气环境温度、压力、湿度、风速和风向5个气象参数,并根据环境温度、压力的变化对采样的体积流量进行补偿计算,以确保进样口体积流量恒定,保证粒径切割准确。在2011年深圳大学生运动会期间(8月12—23日)和后期(8月24日至9月4日),利用该仪器进行了大气环境野外观测实验,并对该仪器和Thermo2300采样器的结果进行了平行性分析和对比分析,其平行结果和对比结果相关系数均大于0.99。     

Forecasts using Box–Jenkins models for the ambient air quality data of Delhi City

The monthly maximum of the 24-h average time-series data of ambient air quality-sulphur dioxide (SO(2)), nitrogen dioxide (NO(2)) and suspended particulate matter (SPM) concentration monitored at the six National Ambient Air Quality Monitoring (NAAQM) stations in Delhi, was analysed using Box-Jenkins modelling approach (Box et al. 1994). Univariate linear stochastic models were developed to examine the degree of prediction possible for situations where only the past record of pollutant data are available. In all, 18 models were developed, three for each station for each of the respective pollutant. The model evaluation statistics suggest that considerably satisfactory real-time forecasts of pollution concentrations can be generated using the Box-Jenkins approach. The developed models can be used to provide short-term, real-time forecasts of extreme air pollution concentrations for the Air Quality Control Region (AQCR) of Delhi City, India.     

Mann–Kendall trend of pollutants,temperature and humidity over an urban station of India with forecast verification using different ARIMA models

The purpose of the present research is to identify the trends in the concentrations of few atmospheric pollutants and meteorological parameters over an urban station Kolkata (22° 32′ N; 88° 20′ E), India, during the period from 2002 to 2011 and subsequently develop models for precise forecast of the concentration of the pollutants and the meteorological parameters over the station Kolkata. The pollutants considered in this study are sulphur dioxide (SO₂), nitrogen dioxide (NO₂), particulates of size 10-μm diameters (PM₁₀), carbon monoxide (CO) and tropospheric ozone (O3). The meteorological parameters considered are the surface temperature and relative humidity. The Mann–Kendall, non-parametric statistical analysis is implemented to observe the trends in the data series of the selected parameters. A time series approach with autoregressive integrated moving average (ARIMA) modelling is used to provide daily forecast of the parameters with precision. ARIMA models of different categories; ARIMA (1, 1, 1), ARIMA (0, 2, 2) and ARIMA (2, 1, 2) are considered and the skill of each model is estimated and compared in forecasting the concentration of the atmospheric pollutants and meteorological parameters. The results of the study reveal that the ARIMA (0, 2, 2) is the best statistical model for forecasting the daily concentration of pollutants as well as the meteorological parameters over Kolkata. The result is validated with the observation of 2012.     

Use of Neural Net Models to Forecast Atmospheric Pollution

The forecast of the CO and NO₂ concentration levels has been obtained by the 3-layer Perceptron Neural Network with Error Backpropagation learning rule.This study shows the 3-layer Perceptron performances in relationship with the choice of the activation functions parameters (depending on the statistical values of the input/output variables).A first simulation set using data at 1 hour before has been utilized to forecast CO levels. A second simulation set with data at 12–24 hours before has been used to forecast NO₂ levels.The Neural Net's performance appears to be very good both for the parameters activation function optimisation and the variables choice.