Reliability of differing densities of sample grids used for the monitoring of forest condition in Europe

Concern about the possible deterioration of forest health led to the establishment in the 1980s of inventories of forest condition throughout Europe. International standardisation of the programmes was sought and a number of recommendations were made concerning sampling and assessment procedures. One of the most important rulings was that the assessment should be made on a systematic grid, the minimum density of which was 16×16 km. However, many countries adopted denser sampling grids, with 4×4 km being used in several countries and 1×1 km being used in the Netherlands. With five or more years of monitoring completed, there is a growing belief that a rapid and irreversible decline in forest health is not occurring. Consequently, some countries/regions are seeking to reduce their annual investment in forest health monitoring.The precision of national/regional estimates of forest health can be directly related to the sample size. As the sample size decreases, so also does the precision of the estimates. This is illustrated using data collected in Switzerland in 1992 and using grid densities of 4×4 km, 8×8 km, 12×12 km and 16×16 km. The value of the data is dependent on the sample size and the degree to which it is broken down (by region or species). The loss of precision associated with most subdivisions at the 8×8 km grid level remains acceptable, but a sharp deterioration in the precision occurs at the 12×12 km and 16×16 km grid levels. This has considerable implications for the interpretation of the inventories from those countries using a 16×16 km grid. In Switzerland, a reduction from the current 4×4 km grid to an 8×8 km grid (i.e. 75% reduction in sample size) would have relatively little impact on the overall results from the annual inventories of forest health.     

Development and Testing of 2D Finite Difference Model in Open Channels

This study develops a depth-averaged two-dimensional (2D) numerical model using a finite difference method (FDM) on a staggered grid. The governing equations were solved using the Marker and Cell method that was developed at the Los Alamos laboratories by Harlow and Welch in 1965. In the paper, an explicit FDM was used to solve the governing equations. A first-order approximation was used for the temporal derivative. Second-order central difference approximations were used for space discretization. The time step is limited by the Courant–Friedrichs–Lewy (CFL) condition. The time step used in this study depends on the grid spacing and velocity components in the x- and y-directions. The study is divided into two steps: the first step is to develop a depth-averaged 2D numerical model to simulate the flow process. The second constructs a module to calculate the bed load transport and simulate the river morphology in the areas that have steep slopes, torrents, and mountain rivers. Developed model was applied to the artificial channel and a flood event in the Asungjun River section of the mountainous Yangyang Namdae River (South Korea). General simulation results showed that the developed model was in good agreement with the observed data.     

Treatment of Urban Areas within a Regional Transport Model of Sulphur and Nitrogen Oxides

The air pollution transport model UGEM (The University of Greenwich Evaluation Model) has been developed to evaluate medium-range transport and deposition of sulphur and oxidised nitrogen from all types of sources of emissions in the UK and to estimate their average annual deposition and concentrations across the UK. The model has been tested for its predictions against the available measurements.This study was focused on a possibility of applying the UGEM model to the assessment of air quality on a local scale. One parameter in the model is crucial, the local deposition fraction. The effect of this parameter on quality of the model predictions has been studied for different scales of UGEM output, such as the whole territory of the UK, a rural region and an urban area.The results of the study show that the magnitude of the local deposition fraction should be different for each grid square to reach the best agreement of predictions of concentrations with measurements. Applying a local value of the parameter to each grid square will improve the model predictions of the concentrations in urban areas in particular and will not affect the quality of model predictions of the wet deposition.     

Modelling and mapping of copper runoff for Europe

A predictive runoff rate model for copper has been refined and used to generate copper runoff maps for Europe. The new model is based on laboratory and field runoff data and expresses the runoff rate R (g m(-2) yr(-1)) through two contributions, both with a physical meaning: R = (0.37SO(0.5)(2) = 0.96 rain10(-0.62 pH) (cos(theta)/cos(45 degree)). Input parameters are the SO(2) concentration (microg m(-3)), pH, amount of rain (mm yr(-1)), and surface angle of inclination (theta). The first contribution originates from dry periods between rain events (the first-flush effect) and the second from the rain events. The dry term has been refined in comparison to the original model by assuming a mass balance between measured corrosion mass loss, calculated copper retention in the patina and predicted copper runoff. The refined model predicts 76% of all reported runoff rates, worldwide, within 35% from their measured value. This includes sites with low SO(2) concentration, where the original model erroneously predicted higher runoff rates than corrosion rates. Based on environmental data from the EMEP programme for the years 1980-2000, the new model has been used to derive runoff rate maps for Europe with 50 x 50 km grid resolution. The runoff mapping shows a substantial reduction in runoff rate over the investigated time period, and with copper runoff rates now generally less than 2 g m(-2) yr(-1).     

A Spatially Explicit Model for Estimating Annual Average Loads of Nonpoint Source Nutrient at the Watershed Scale

The overloaded nonpoint source (NPS) nutrients in upper streams always result in the nutrient enrichment at lakes and estuaries downstream. As NPS pollution has become a serious environmental concern in watershed management, the information about nutrient output distribution across a watershed has been critical in the designing of regional development policies. But existing watershed evaluation models often encounter difficulties in application because of their complicated structures and strict requirements for the input data. In this paper, a spatially explicit and process-based model, Integrated Grid’s Exporting and Delivery model, was introduced to estimate annual in-stream nutrient levels. Each grid cell in this model was regarded as having potentials of both exporting new nutrients and trapping nutrients passing by. The combined nutrient dynamics of a grid is mainly determined by the grid’s features in land use/land cover, soil drainage, and geomorphology. This simple-concept model was tested at some basins in north Georgia in the USA. Stations in one basin were used to calibrate the model. Then an external validation was employed by applying the calibrated model to stations in the other neighbor basins. Model evaluation statistics implied the model’s validity and good performance in estimating the annual NPS nutrients’ fluxes at the watershed scale. This study also provides a promising prospect that in-stream annual nutrient loads can be accurately estimated from a few public available datasets.     

The use of sensory perception indicators for improving the characterization and modelling of total petroleum hydrocarbon (TPH) grade in soils

This paper proposes a multistep approach for creating a 3D stochastic model of total petroleum hydrocarbon (TPH) grade in potentially polluted soils of a deactivated oil storage site by using chemical analysis results as primary or hard data and classes of sensory perception variables as secondary or soft data. First, the statistical relationship between the sensory perception variables (e.g. colour, odour and oil–water reaction) and TPH grade is analysed, after which the sensory perception variable exhibiting the highest correlation is selected (oil–water reaction in this case study). The probabilities of cells belonging to classes of oil–water reaction are then estimated for the entire soil volume using indicator kriging. Next, local histograms of TPH grade for each grid cell are computed, combining the probabilities of belonging to a specific sensory perception indicator class and conditional to the simulated values of TPH grade. Finally, simulated images of TPH grade are generated by using the P-field simulation algorithm, utilising the local histograms of TPH grade for each grid cell. The set of simulated TPH values allows several calculations to be performed, such as average values, local uncertainties and the probability of the TPH grade of the soil exceeding a specific threshold value.     

An evaluation of grid size uncertainty in empirical soil loss modeling with digital elevation models

This paper presents a study on the effect of topographic variability on grid-based empirical estimation of soil erosion and sediment transport with raster geographic information systems (GIS). An original digital elevation model (DEM) of 10 m resolution for a case watershed is resampled to six realizations of greater grid sizes for a comparative examination. The Universal Soil Loss Equation (USLE) and a distance-based sediment delivery equation are applied to the watershed to calculate soil loss from each cell and total sediment transport to streams, respectively. The results suggest that the selection of the DEM gird size has considerable influence on the soil loss estimation with the empirical models. The estimate of total soil loss from the watershed decreases significantly with the increasing DEM cell size as the spatial variability is reduced by the cell aggregation. The empirical modeling approach is a useful tool for qualitative assessment of soil erosion, provided that spatial variability can be adequately represented by applied DEMs. However, discretion is suggested for its applications to quantitative estimation of soil loss concerning the sensitivity to the grid size selection.     

网格监测统计积分模式的推导与应用

把大气污染物的空间分布看成是连续变化的超曲面,采用积分法推导网格监测点位均值的积分模式,并由此推导监测范围内污染物总量模式。利用丹东市环境空气SO2被动扩散网格监测数据,采用此模式计算其趋势值和均值。     

Playing Pollution Games with Thermal Electricity Generators

This paper discusses the economic and environmental implications of a stylised electricity market with transmission grid constraints and shared temporal pollution standards that restrict the joint strategy space of the agents. These are problematic to enforce if individual monitoring is impossible or very expensive. For such situations, we propose a time-dependent (or “open-loop dynamic”), game-theoretic model capable of analysing coupled constraints equilibria, also known as generalised Nash. We compute these equilibria for thermal generators subjected to annual pollution limits and instantaneous grid restrictions for a three-node dc model with a two-period load duration curve. The model illustrates the possibility that well-meaning environmental regulation might harm consumer surplus. It also highlights the cost to efficiency of regulatory attempts to ease the burden of compliance.     

A Method for Locating Influential Pollution Sources and Estimating Their Contributions

This work develops a source-oriented approach to locate the influential pollution sources and estimate their contributions to pollutant concentrations observed at a receptor site. The domain containing possible influential pollution sources is divided into systematic grid cells, and the influential grid sources are determined based on the locations of the segment endpoints of air trajectories arriving at the receptors. The contribution of each grid source is initially calculated using a formula derived from a Lagrangian box model and including the effects of source emissions, atmospheric dilution, and chemical transformation and deposition. The formula is described in detail in this study. Finally, the average contribution of each grid source is determined based on numerous analyzed events. The proposed approach was used to locate influential pollution sources and determine their contributions to a rural monitoring station during periods of high SO₂ pollution in southern Taiwan. The contributions of sources in various 2 km by 2 km grid cells, five districts, three source categories, 8 industrial areas, and a power plant were evaluated. The results show that the major influential sources were in the northwestern region of south Taiwan, and belonged to three district and point sources. Furthermore, two industrial areas close to the evaluated stations were also very significant, and contributed about 30% of the total pollution.     

Nonpoint Source Pollution Assessment of Wujiang RiverWatershed in Guizhou Province,SW China

The amount of pollution from nonpoint sources flowing in the streams of the Wujiang River watershed in Guizhou Province, SW China, is estimated by a geographic information system (GIS)-based method using rainfall, surface runoff and land use data. A grid of cells of 100 m in size is laid over the landscape. For each cell, mean annual surface runoff is estimated from rainfall and percent land use, and expected pollutant concentration is estimated from land use. The product of surface runoff and concentration gives expected pollutant loading from that cell. These loadings are accumulated going downstream to give the expected annual pollutant loadings in streams and rivers. By dividing these accumulated loadings by the similarly accumulated mean annual surface runoff, the expected pollutant concentration from nonpoint sources is determined for each location in a stream or river. Observed pollutant concentrations in the watershed are averaged at each sample point and compared to the expected concentrations at the same locations determined from the grid cell model. In general, annual nonpoint source nutrient loadings in the Wujiang River watershed are seen to be predominantly from the agricultural and meadow areas. The total annual loadings through the outlet of the watershed are 40,309 and 2,607 tons for total nitrogen (TN) and total phosphorus (TP), respectively.     

Application of artificial neural network approach and remotely sensed imagery for regional eco-environmental quality evaluation

Eco-environment quality evaluation is an important research theme in environment management. In the present study, Fuzhou city in China was selected as a study area and a limited number of 222 sampling field sites were first investigated in situ with the help of a GPS device. Every sampling site was assessed by ecological experts and given an Eco-environment Background Value (EBV) based on a scoring and ranking system. The higher the EBV, the better the ecological environmental quality. Then, three types of eco-environmental attributes that are physically-based and easily-quantifiable at a grid level were extracted: (1) remote sensing derived attributes (vegetation index, wetness index, soil brightness index, surface land temperature index), (2) meteorological attributes (annual temperature and annual precipitation), and (3) terrain attribute (elevation). A Back Propagation (BP) Artificial Neural Network (ANN) model was proposed for the EBV validation and prediction. A three-layer BP ANN model was designed to automatically learn the internal relationship using a training set of known EBV and eco-environmental attributes, followed by the application of the model for predicting EBV values across the whole study area. It was found that the performance of the BP ANN model was satisfactory and capable of an overall prediction accuracy of 82.4%, with a Kappa coefficient of 0.801 in the validation. The evaluation results showed that the eco-environmental quality of Fuzhou city is considered as satisfactory. Through analyzing the spatial correlation between the eco-environmental quality and land uses, it was found that the best eco-environmental areas were related to forest lands, whereas the urban area had the relatively worst eco-environmental quality. Human activities are still considered as a major impact on the eco-environmental quality in this area.     

Heat wave hazard classification and risk assessment using artificial intelligence fuzzy logic

The average summer temperatures as well as the frequency and intensity of hot days and heat waves are expected to increase due to climate change. Motivated by this consequence, we propose a methodology to evaluate the monthly heat wave hazard and risk and its spatial distribution within large cities. A simple urban climate model with assimilated satellite-derived land surface temperature images was used to generate a historic database of urban air temperature fields. Heat wave hazard was then estimated from the analysis of these hourly air temperatures distributed at a 1-km grid over Athens, Greece, by identifying the areas that are more likely to suffer higher temperatures in the case of a heat wave event. Innovation lies in the artificial intelligence fuzzy logic model that was used to classify the heat waves from mild to extreme by taking into consideration their duration, intensity and time of occurrence. The monthly hazard was subsequently estimated as the cumulative effect from the individual heat waves that occurred at each grid cell during a month. Finally, monthly heat wave risk maps were produced integrating geospatial information on the population vulnerability to heat waves calculated from socio-economic variables.     

An air quality balance index estimating the total amount of air pollutants at ground level

A new index named Air Quality Balance Index (AQBI), which is able to characterise the amount of pollution level in a selected area, is proposed. This index is a function of the ratios between pollutant concentration values and their standards; it aims at identifying all situations in which there is a possible environmental risk even when several pollutants are below their limit values but air quality is reduced. AQBI is evaluated by using a high-resolution three-dimensional dispersion model: the air concentration for each substance is computed starting from detailed emissions sources: point, line and area emissions hourly modulated. This model is driven with accurate meteorological data from ground stations and remote sensing systems providing vertical profiles of temperature and wind; these data are integrated with wind and temperature profiles at higher altitudes obtained by a Local Area Model. The outputs of the dispersion model are compared with pollutant concentrations provided by measuring stations, in order to recalibrate emission data. A three-dimensional high resolution grid of AQBI data is evaluated for an industrial area close to Alessandria (Northern Italy), assessing air quality and environmental conditions. Performance of AQBI is compared with the Air Quality Index (AQI) developed by the U.S. Environmental Protection Agency. AQBI, computed taking into account all pollutants, is able to point out situations not evidenced by AQI, based on a preset limited number of substances; therefore, AQBI is a good tool for evaluating the air quality either in urban and in industrial areas. The AQBI values at ground level, in selected points, are in agreement with in situ observations.     

Grid search: an innovative method for the estimation of the rates of lead exchange between body compartments

This paper describes a new metabolic model for lead in humans and a numerical method to solve the differential equations governing the transfer of lead between body compartments. The model includes 3 compartments-cortical bone, trabecular bone and blood-and accounts for absorption from external sources and release through excreta. Estimation of the lead kinetics parameters was performed using the grid search method. Grid search is a simple procedure that allows the fit of an arbitrary function to data. When applied to data from occupationally exposed populations, the method demonstrated the exposure dependence of the rate of lead uptake and release by the compartments in the model. The results confirm and refine previous observations of the significant decrease of the transfer rate of lead from cortical bone to blood with increasing exposure, as expressed by half-lives of (in years): 6.5 +/- 0.7, 13.6 +/- 1.0 and 47.5 +/- 2.3, in subgroups of low, intermediate and high long-term lead exposure. A similar trend was observed for the transfer rate from trabecular bone, which could be statistically supported for the first time. Reduction by a factor of 7 to 10 in the default values assigned to the fractional removal of lead from cortical bone to plasma in existing metabolic models was also predicted. These results can be used in the review of current metabolic models for lead, which are still based on the assumption of a constant rate of lead removal from bone, independently of the level of exposure.     

A Model for Landscape Planning Under Complex Spatial Conditions

We present a new mathematical programming framework that is adaptable to a variety of spatially explicit landscape problems in environmental investment, conservation, and land-use planning, transport planning, and agriculture. As part of capturing spatial interdependencies, the framework considers decision variables at two levels, finely spaced grid cells and landholdings. We applied the framework to an environmental investment problem using objective functions representing biodiversity and carbon sequestration. We also tested the model to optimize the path of a road through part of the landscape. Using the Nambucca case study in eastern Australia, we applied a hybrid greedy randomised adaptive search procedure (GRASP) to find solutions to the model.     

基于GIS的唐山地区大气污染物时空分布研究

应用数据统计和ArcGIS对北方重工业城市唐山地区2014年14个县(区)18个空气自动监测站的数据进行时空分布特征分析,监测的污染物为PM_(10)、SO_2、NO_2、PM_(2.5)、O_3、CO共6项。利用ArcGIS对各个自动监测站污染物数据建立网格模型,采用反距离权重法分别对年均、采暖期、非采暖期的环境空气质量综合指数和6项污染因子浓度的空间分布进行估算,直观比较了污染物在不同时期内的空间分布状况。结果表明,空气质量时间分布较为明显,非采暖期明显好于采暖期。同时,计算出每个网格单元污染指数的标准偏差,结合气象气候、地形地势、工业发展等情况,分析得出北部山区、市中心区附近区域空气质量波动较大。为区域大气污染有针对性的综合防治、联防联控及污染物区域削减计划打下数据基础。     

Simulation of Wind Circulation and Pollutant Diffusion Over the Pearl River Delta Region

Changes in urban surface areas and population growth have significantly affected the weather and environment. Emissions of nitrogen oxides are increasing in the Pearl River Delta region. Nitrogen compounds emitted by factories and motor vehicles are the major sources of nitric pollution. To study the impacts of urbanization and the relationship between pollutant diffusion and the atmospheric environment, the nonhydrostatic mesoscale forecast model MM5 (v3.73), which was developed by Penn State University and the National Center of Atmospheric Research, and a mass continuity equation for air pollutants, were used in this study. Two experiments were designed. One experiment (BE) applied horizontal grid resolutions of 27, 9, 3, and 1?km in four nested domains. The other experiment adopted new land-use data (in domain 4) directly retrieved from Landsat Thematic Mapper imagery to replace the 1980s data of the United States Geological Survey in BE. A 48-h simulation (from 0000?UTC on 21 October to 0000?UTC on 23 October 2008) was conducted, with the first 12?h being the spin-up time and the remaining 36?h being the effective simulation, so as to capture the diurnal features of the thermally induced winds associated with the land–sea breeze and urban heat island circulations. The different results obtained from the two tests for wind circulation and air pollution dispersion and transportation in the Pearl River Delta region were analyzed. The simulated results show that the both experiments can well simulate land–sea breeze circulation and remarkable land–sea breeze evolution, comparing with observation data. The height of the PBL had a significant diurnal cycle. The structure of the wind field can obviously impact the dispersion of the NO _x in three dimensions. Nitrogen oxides mainly diffused along the dominant wind direction (east or southeast wind), therefore the majority of the pollutants accumulated in the northwest region of the fine domain in both simulation experiments. However, it induced the pollutants concentration in an irregular pattern due to the fine-resolution grid spaces and complicated inland wind field in the northwest area of the inner domain. Moreover, increasing the proportion of urban surface caused sensible heat flux increase, latent heat flux decrease and humility reducing relatively in the region of urban surface characteristics apparently. Urbanization will cause pollution accumulated severely over the urban surface.