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.     

Modelling mixing height from routinely measured surface and upper air meteorological data

A one‐dimensional model is developed for the estimation of hourly mixing height values from routinely measured upper air and surface meteorological data. A diagnostic technique is used in the model to calculate the convective and mechanically induced mixing height values under different atmospheric, and day and night‐time conditions. In the scheme, for the day‐time hours, the mixing height is determined as the larger of the convective and mechanically induced mixing height values. For the night‐time hours, only the mechanically induced mixing height values are considered. Three‐hourly mixing height values are modelled using once‐a‐day upper air temperature profile data (from radio‐sonde) and three‐hourly surface meteorological data. The spatial and temporal variation of mixing height are modelled in the Brisbane airshed and their relationship with the atmospheric stability, solar radiation and transport wind speed is developed.     

唐山市钢铁行业大气污染物排放清单建立

以唐山市钢铁企业为研究对象,在收集已有排放源数据的基础上,基于排放因子和活动水平数据采用排放因子法估算了唐山市钢铁企业多个大气污染物的排放量,得到了符合空气质量模型要求的污染源输入数据,建立钢铁行业和主要防控因子污染源数据库。结合唐山市各县区的环境空气质量状况,利用GIS技术,将不同污染因子的排放进行空间分布,最终形成准确完善的多尺度、高时空分辨率大气污染源排放清单。研究介绍了符合中国特色的区域高分辨率大气排放源清单建立的方法体系,为京津冀地区区域大气污染联防联控及2020年大气污染物区域削减计划工作提供数据支撑。     

Support Vector Machine Modeling Using Particle Swarm Optimization Approach for the Retrieval of Atmospheric Ammonia Concentrations

This study was performed in order to improve the estimation accuracy of atmospheric ammonia (NH₃) concentration levels in the Greater Houston area during extended sampling periods. The approach is based on selecting the appropriate penalty coefficient C and kernel parameter σ ². These parameters directly influence the regression accuracy of the support vector machine (SVM) model. In this paper, two artificial intelligence techniques, particle swarm optimization (PSO) and a genetic algorithm (GA), were used to optimize the SVM model parameters. Data regarding meteorological variables (e.g., ambient temperature and wind direction) and the NH₃ concentration levels were employed to develop our two models. The simulation results indicate that both PSO-SVM and GA-SVM methods are effective tools to model the NH₃ concentration levels and can yield good prediction performance based on statistical evaluation criteria. PSO-SVM provides higher retrieval accuracy and faster running speed than GA-SVM. In addition, we used the PSO-SVM technique to estimate 17 drop-off NH₃ concentration values. We obtained forecasting results with good fitting characteristics to a measured curve. This proved that PSO-SVM is an effective method for estimating unavailable NH₃ concentration data at 3, 4, 5, and 6 parts per billion (ppb), respectively. A 4-ppb NH₃ concentration had the optimum prediction performance of the simulation results. These results showed that the selection of the set-point values is a significant factor in compensating for the atmospheric NH₃ dropout data with the PSO-SVM method. This modeling approach will be useful in the continuous assessment of NH₃ sensor discrete data sources.     

Forecasting of daily total atmospheric ozone in Isfahan

A neural network combined to an artificial neural network model is used to forecast daily total atmospheric ozone over Isfahan city in Iran. In this work, in order to forecast the total column ozone over Isfahan, we have examined several neural networks algorithms with different meteorological predictors based on the ozone-meteorological relationships with previous day's ozone value. The meteorological predictors consist of temperatures (dry and dew point) and geopotential heights at standard levels of 100, 50, 30, 20 and 10 hPa with their wind speed and direction. These data together with previous day total ozone forms the input matrix of the neural model that is based on the back propagation algorithm (BPA) structure. The output matrix is the daily total atmospheric ozone. The model was build based on daily data from 1997 to 2004 obtained from Isfahan ozonometric station data. After modeling these data we used 3 year (from 2001 to 2003) of daily total ozone for testing the accuracy of model. In this experiment, with the final neural network, the total ozone are fairly well predicted, with an Agreement Index 76%.     

Using a neural network approach and time series data from an international monitoring station in the Yellow Sea for modeling marine ecosystems

The international marine ecological safety monitoring demonstration station in the Yellow Sea was developed as a collaborative project between China and Russia. It is a nonprofit technical workstation designed as a facility for marine scientific research for public welfare. By undertaking long-term monitoring of the marine environment and automatic data collection, this station will provide valuable information for marine ecological protection and disaster prevention and reduction. The results of some initial research by scientists at the research station into predictive modeling of marine ecological environments and early warning are described in this paper. Marine ecological processes are influenced by many factors including hydrological and meteorological conditions, biological factors, and human activities. Consequently, it is very difficult to incorporate all these influences and their interactions in a deterministic or analysis model. A prediction model integrating a time series prediction approach with neural network nonlinear modeling is proposed for marine ecological parameters. The model explores the natural fluctuations in marine ecological parameters by learning from the latest observed data automatically, and then predicting future values of the parameter. The model is updated in a “rolling” fashion with new observed data from the monitoring station. Prediction experiments results showed that the neural network prediction model based on time series data is effective for marine ecological prediction and can be used for the development of early warning systems.     

Emission database for global atmospheric research (Edgar)

Atmospheric chemistry and climate modellers require gridded global emissions data as input into their models. To meet this urgent need a global emissions source database called EDGAR is being developed by TNO and RIVM to estimate for 1990, on a regional and on a grid basis, annual emissions of greenhouse gases (CO₂, CH₄, N₂O, CO, NO_x, non-methane VOC, SO_x), of NH₃, and of ozone depleting compounds (halocarbons) from all known sources. The aim is to establish at due levels of spatial, temporal and source aggregation the emissions from both anthropogenic and biogenic sources: a complete set of data required to estimate the total source strength of the various gases with a 1×1 ° resolution (altitude resolution of 1 km) and a temporal resolution of a month, supplemented by diurnal variation, as agreed upon in the Global Emissions Inventory Activity (GEIA) of the International Atmospheric Chemistry Programme (IGAC). In this way EDGAR will meet the requirements of present and future developments in the field of atmospheric modelling. The data comprise demographic data, social and economic factors, land use distributions and emission factors (with due emphasis on the uncertainty). As understanding in this field is still changing, due attention is paid to flexibility regarding the disaggregation of sources, spatial and temporal resolution and species. The objective and methodology chosen for the construction of the database and the structural design of the database system are presented, as well as the type and sources of data and the approach used for data collection. As an example, the construction of the N₂O inventory is discussed.     

Estimating overall persistence and long-range transport potential of persistent organic pollutants: a comparison of seven multimedia mass balance models and atmospheric transport models

Two different approaches to modeling the environmental fate of organic chemicals have been developed in recent years. The first approach is applied in multimedia box models, calculating average concentrations in homogeneous boxes which represent the different environmental media, based on intermedia partitioning, transport, and degradation processes. In the second approach, used in atmospheric transport models, the spatially and temporally variable atmospheric dynamics form the basis for calculating the environmental distribution of chemicals, from which also exchange processes to other environmental media are modeled. The main goal of the present study was to investigate if the multimedia mass balance models CliMoChem, SimpleBox, EVn-BETR, G-CIEMS, OECD Tool and the atmospheric transport models MSCE-POP and ADEPT predict the same rankings of the overall persistence (P(ov)) and long-range transport potential (LRTP) of POPs, and to explain differences and similarities between the rankings by the mass distributions and inter-compartment mass flows. The study was performed for a group of 14 reference chemicals. For P(ov), the models yield consistent results, owing to the large influence of phase partitioning parameters and degradation rate constants, which are used similarly by all models. Concerning LRTP, there are larger differences between the models than for P(ov), due to different LRTP calculation methods and spatial model resolutions. Between atmospheric transport models and multimedia fate models, no large differences in mass distributions and inter-compartment flows can be recognized. Deviations in mass flows are mainly caused by the geometrical design of the models.     

The Spatial-Scale Effect of an Atmospheric Environmental Impact Assessment in Regional Strategic Environmental Assessment (R-SEA)

When evaluating the atmospheric environment in regional strategic environment assessment (R-SEA), the variation and choice of the spatial scale have a substantial influence on the conclusions of the assessment. In this study, we used numerical simulation to investigate the spatial-scale effect. Two varying spatial extents and two varying spatial details of pollutant emission data (emission inventories in this case) were provided for numerical modeling, and output distributions of atmospheric pollutants at different air pollution levels were compared. The results show that the resolution and spatial range of data collection do indeed influence the atmospheric prediction and assessment results in R-SEA. The spatial-scale effect is more significant under the air pollution condition than under excellent and good air quality conditions. A comparison of varying spatial extents of emission inventory shows that narrowing the prediction area to a local scale is more conducive to identifying the impact of local pollution sources. A comparison of varying spatial details of emission inventory indicates that a higher resolution is favorable for identifying local high concentrations of pollutants and their locations.     

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.     

The use of lichen growth abnormalities as an early warning indicator of forest dieback

Combinations of pollutants including acidic fog and ozone occur at high levels at a number of sites in eastern North America and Europe. Mountainous regions such as the Laurentians (Quebec), Appalachians (N.Y.) and the Green Mountains (Vermont) are especially vulnerable, with both conifers and hardwoods being affected. Ongoing measurements of atmospheric chemistry (e.g. The Chemistry of High Elevation Fog-CHEF project of the Canadian Atmospheric Environment Service) reveal that extreme cloudwater events of less than pH 3.0 and ozone episodes in excess of 100 ppb are common occurrences. The purpose of this study was to gather information about the response of epiphytic lichens to deteriorating air quality at selected locations for which atmospheric chemical data are readily available. A multidisciplinary approach is being used to analyse the lichens.Morphological and cellular aberrations previously documented by the authors to occur in terricolous lichens exposed to simulated acidic rain events will be evaluated for their usefulness are early warning indicators of forest decline. In addition, tissue chemistry of species such as Hypogymnia physodes will be correlated with parameters such as altitude and decline index and compared with published elemental values for lichens from similarly polluted sites in Europe and Scandinavia.     

Sensitivity of a Land Change Model to Pixel Resolution and Precision of the Independent Variable

It is important to know how the results from a land change model vary based on both the pixel resolution of the maps and the precision of the independent variables because subjective decisions or default values frequently determine these two factors. This paper presents an approach to measure the variation in model accuracy that is triggered by alteration of the pixel resolution and the precision of the independent variable, which are bins of distance to previously built area for our case study. We illustrate the principles with an application of the Geomod land change model contained in the Idrisi GIS, applied to simulate the gain of built land in central Massachusetts, USA. Results reveal four general principles: (1) change in pixel resolution using the majority-takes-all rule can influence quantity error, (2) change in bin width of an independent variable does not influence the quantity error, (3) resolution and bin width interact so that bin width does not have an effect on error when bin widths are smaller than the pixel resolution, and (4) researchers are wise to examine the implications of their subjective decisions by plotting clearly how the resolution and bin size influence the mathematical relationships that the model uses. We have found no universal, hard, and fast rules that dictate how to decide on an appropriate pixel resolution and bin width, but our method demonstrates how these decisions can be influential. These insights can offer scientists guidance in how to prepare data in an appropriate manner.     

Multi-scale Atmospheric Dispersion Modelling by Use of Adaptive Gridding Techniques

An accurate prediction of the transport-reaction behaviour of atmospheric chemical species is required to fully understand the impact on the environment of pollution emissions. Elevated levels of secondary pollutants such as ozone in the lower atmosphere can be harmful to the health of both plants and animals, and can cause damage to property present in the urban environment. Detailed models of pollution mechanisms must therefore be developed through comparisons with field measurements to aid the selection of effective abatement policies. Such models must satisfy accuracy requirements both in terms of the number of species represented, and the spatial resolution of species profiles. Computational expense often compels current models to sacrifice detail in one of these areas. This paper attempts to address the latter point by presenting an atmospheric transport-reaction modelling strategy based upon a finite volume discretisation of the atmospheric dispersion equation. The source terms within this equation are provided by an appropriate reduced chemical scheme modelling the major species in the boundary layer. Reaction and transport discretisations are solved efficiently via a splitting technique applied at the level of the non-linear equations. The solution grid is generated using time dependant adaptive techniques, which provide a finer grid around regions of high spatial error in order to adequately resolve species concentration profiles. The techniques discussed are applied in two dimensions employing emissions from both point and area sources. Preliminary results show that the application of adaptive gridding techniques to atmospheric dynamics modelling can provide more accurately resolved species concentration profiles, accompanied by a reduced CPU time invested in solution. Such a model will provide the basis for high resolution studies of the multiple scale interactions between spatially inhomogeneous source patterns in urban and regional environments.     

A Simple Model of Pollutant Concentrations in a Street Canyon

A single compartment model has been constructed for predicting hourly concentrations of pollutant concentrations arising from vehicular emissions within a typical street canyon. The model takes account of traffic densities and composition to estimate pollutant emissions within the model compartment. Meteorological data on wind speed and direction are used to define the exchanges of pollutants between the compartment and the surrounding air. A parameter is also included to describe the exchange in calm conditions. The pollutant concentrations are then estimated from a steady state mass balance equation for the compartment, assuming conservation of pollutants. The model was applied to the prediction of carbon monoxide concentrations in Hope Street, Glasgow. Model parameters were fitted using field measurements, together with concurrent meteorological data and traffic flows estimated from traffic census data for Hope Street. The model accounted well for the observed variations in carbon monoxide. It was found that the model parameters varied seasonally, perhaps due to differences in atmospheric stability which have not so far been included in the model formulation.     

大气污染预报技术及有关防治对策的研究

依据历史监测数据，研究了大气污染预测预报的方法及污染防治对策。利用主分量分析、多元逐步回归、拟合与逼近等方法，进行了天气预报参数与主要污染物的关系研究，建立了大气污染预测预报模型，制定了大气污染警戒值及警戒措施。模型可做４８小时内大气污染预报及１０日内大气污染趋势分析。     

Using multi-criteria decision analysis to assess the vulnerability of drinking water utilities

Outbreaks of microbiological waterborne disease have increased governmental concern regarding the importance of drinking water safety. Considering the multi-barrier approach to safe drinking water may improve management decisions to reduce contamination risks. However, the application of this approach must consider numerous and diverse kinds of information simultaneously. This makes it difficult for authorities to apply the approach to decision making. For this reason, multi-criteria decision analysis can be helpful in applying the multi-barrier approach to vulnerability assessment. The goal of this study is to propose an approach based on a multi-criteria analysis method in order to rank drinking water systems (DWUs) based on their vulnerability to microbiological contamination. This approach is illustrated with an application carried out on 28 DWUs supplied by groundwater in the Province of Québec, Canada. The multi-criteria analysis method chosen is measuring attractiveness by a categorical based evaluation technique methodology allowing the assessment of a microbiological vulnerability indicator (MVI) for each DWU. Results are presented on a scale ranking DWUs from less vulnerable to most vulnerable to contamination. MVI results are tested using a sensitivity analysis on barrier weights and they are also compared with historical data on contamination at the utilities. The investigation demonstrates that MVI provides a good representation of the vulnerability of DWUs to microbiological contamination.     

New Unstructured Mesh Water Quality Model for Cooling Water Biocide Discharges

A new unstructured mesh coastal water and air quality model has been developed that includes species transport, nonlinear decay, by-product formation, and mass-exchange between sea and atmosphere. The model has been programmed with a graphical user interface and is applicable to coastal seawater, lakes, and rivers. Focused on species conversion and interaction with the atmosphere, the water and air quality model follows a modular approach. It is a compatible module which simulates distributions based on fluid dynamic field data of underlying existing hydrodynamic and atmospheric simulations. Nonlinear and spline approximations of decay and growth kinetics, by-product formation, and joint sea–atmosphere simulation have been embedded. The Windows application software includes functions allowing error analysis concerning mesh and finite volume approximation. In this work, a submerged residual chlorine cooling water discharge and halogenated matter by-product formation has been simulated. An error analysis has been carried out by varying vertical meshing, time-steps and comparing results based on explicit and implicit finite volume approximation. The new model has been named 3D Simulation for Marine and Atmospheric Reactive Transport, in short 3D SMART.     

linking various aspects of atmospheric change through a systems analysis of food

Although interdisciplinary collaboration to address a singleenvironmental problem is more common than in the past, all toooften the significant atmospheric problems of our day such asstratospheric ozone depletion, acidic deposition or climaticchange are addressed on a single issue basis. Systems analysis isa way of looking at a problem in a holistic, integrated fashionthrough including as many as practicable of the importantcomponents, and the linkages among them. Systems analysisoften begins with a conceptual model which, even if lackingquantification, is a useful means of changing ones thinking to amulti-issue approach. If possible, conceptual models areoperationalized by quantification (using the best availablescientific knowledge) of the stocks and flows of the relevantcomponents of the problem, and the processes that are involved.In this paper, a systems approach to food production is used tolink various atmospheric issues such as regional acidification andclimatic change. A spreadsheet model of food demand andproduction in various world regions examined the possible effectof atmospheric change on how much food we can grow, andwhether or not we may be able to meet the increased demand inthe year 2025. Using relatively modest changes in factors ofagricultural production, the spreadsheet model calculated globalshortfalls by the year 2025 of the order of 10 to 20% in someimportant agricultural crops, despite the improvements in cropproduction factors that are envisaged by the Food andAgricultural Organization from now until the year 2010, and thatwere extrapolated in this paper to 2025. The model alsocalculated that climatic change in combination with eithertropospheric ozone or increased UV-B radiation caused bydepletion of the stratospheric ozone layer may in general makethe situation worse than in the case of climatic change alone.Given the large uncertainties in the input data, the results in thispaper should not be viewed as predictions but rather as anexample of taking a relatively simple systems approach to foodproduction using a spreadsheet model, and calculating the effectsthat various aspects of atmospheric change might have upon it.Therefore, it is extremely important to know the effects uponcrop production factors of climatic change, tropospheric ozoneand increased UV-B radiation not only as individual issues, butalso of their combined effect since it is probable that in manyregions they will occur in combination.     

A new multiscale approach for monitoring vegetation using remote sensing-based indicators in laboratory, field, and landscape

Remote sensing is an important tool for studying patterns in surface processes on different spatiotemporal scales. However, differences in the spatiospectral and temporal resolution of remote sensing data as well as sensor-specific surveying characteristics very often hinder comparative analyses and effective up- and downscaling analyses. This paper presents a new methodical framework for combining hyperspectral remote sensing data on different spatial and temporal scales. We demonstrate the potential of using the “One Sensor at Different Scales” (OSADIS) approach for the laboratory (plot), field (local), and landscape (regional) scales. By implementing the OSADIS approach, we are able (1) to develop suitable stress-controlled vegetation indices for selected variables such as the Leaf Area Index (LAI), chlorophyll, photosynthesis, water content, nutrient content, etc. over a whole vegetation period. Focused laboratory monitoring can help to document additive and counteractive factors and processes of the vegetation and to correctly interpret their spectral response; (2) to transfer the models obtained to the landscape level; (3) to record imaging hyperspectral information on different spatial scales, achieving a true comparison of the structure and process results; (4) to minimize existing errors from geometrical, spectral, and temporal effects due to sensor- and time-specific differences; and (5) to carry out a realistic top- and downscaling by determining scale-dependent correction factors and transfer functions. The first results of OSADIS experiments are provided by controlled whole vegetation experiments on barley under water stress on the plot scale to model LAI using the vegetation indices Normalized Difference Vegetation Index (NDVI) and green NDVI (GNDVI). The regression model ascertained from imaging hyperspectral AISA-EAGLE/HAWK (DUAL) data was used to model LAI. This was done by using the vegetation index GNDVI with an R ² of 0.83, which was transferred to airborne hyperspectral data on the local and regional scales. For this purpose, hyperspectral imagery was collected at three altitudes over a land cover gradient of 25 km within a timeframe of a few minutes, yielding a spatial resolution from 1 to 3 m. For all recorded spatial scales, both the LAI and the NDVI were determined. The spatial properties of LAI and NDVI of all recorded hyperspectral images were compared using semivariance metrics derived from the variogram. The first results show spatial differences in the heterogeneity of LAI and NDVI from 1 to 3 m with the recorded hyperspectral data. That means that differently recorded data on different scales might not sufficiently maintain the spatial properties of high spatial resolution hyperspectral images.