Modeling of NO x Emissions from Oil Refinery Furnaces

The current paper investigates the possibility of establishing an empirically based model for predicting the emission rate of nitrogen oxides (NO _x ) from oil refinery furnaces, in order to continually track emissions with respect to environmental licence limits. Model input data were collected by direct stack monitoring using an electrochemical cell NO _x analyser, as well as a range of telemetry sensors to obtain refinery process parameters. Principal Component Analysis (PCA), in conjunction with Partial Least Squares (PLS) regression was then used to build a series of models able to predict NO _x emissions from the furnaces. The models produced were proven to be robust, with a relatively high accuracy, and are able to predict NO _x levels over the range of operating conditions which were sampled. It was found that due to structural/operational variations a separate model is usually required for each furnace. The models can be integrated with the refinery operating system to predict NO _x emission rates on a continuous basis. Two models representing structurally different furnaces are considered in this paper. This revised version was published online in July 2006 with corrections to the Cover Date.     

Fuzzy Logic Model to Estimate Seasonal Pseudo Steady State Chlorophyll-A Concentrations in Reservoirs

A fuzzy logic model is developed to estimate pseudo steady state chlorophyll-a concentrations in a very large and deep dam reservoir, namely Keban Dam Reservoir, which is also highly spatial and temporal variable. The estimation power of the developed fuzzy logic model was tested by comparing its performance with that from the classical multiple regression model. The data include chlorophyll-a concentrations in Keban lake as a response variable, as well as several water quality variables such as PO₄ phosphorus, NO₃ nitrogen, alkalinity, suspended solids concentration, pH, water temperature, electrical conductivity, dissolved oxygen concentration and Secchi depth as independent environmental variables. Because of the complex nature of the studied water body, as well as non-significant functional relationships among the water quality variables to the chlorophyll-a concentration, an initial analysis is conducted to select the most important variables that can be used in estimating the chlorophyll-a concentrations within the studied water body. Following the outcomes from this initial analysis, the fuzzy logic model is developed to estimate the chlorophyll-a concentrations and the advantages of this new model is demonstrated in model fitting over the traditional multiple regression method.     

Assessment of Wet Sulfate Concentration Changes at CAPMoN Sites, Canada

This paper describes a statistical analysis of wet sulfate deposition data sampled by the Canadian Air and Precipitation Monitoring Network (CAPMoN) since 1988 till 1997. The goal of the investigation is to detect presence of prevailing significant changes in the probability distribution of annual samples collected by the network at each site. The considerations are based on a first order autoregression model with second order polynomial trend and methods used for analysis of variance and multiple comparison. Unlike studies suggesting existence of long term trends in the data, methods applied here indicate absence of any systematic changes in the observed annual concentration patterns at most of the sites.     

城市生活垃圾理化特性研究

本文对北京昌平冬季城市生活垃圾的理化特性进行了研究。测定了垃圾的物相组成，分析了垃圾中Ｃ、Ｈ、Ｏ、Ｎ、Ｓ的含量，进行了原生垃圾浸出毒性试验，测定了垃圾的发热量及其它化学性能，为探索城市垃圾的减量化、无害化、资源化的技术途径提供了技术支持。     

Nitrate leaching in agriculture to upper groundwater in the sandy regions of the Netherlands during the 1992–1995 period

The Dutch National Monitoring Programme for Effectiveness of the Minerals Policy (LMM) was initiated to allow detection of a statutory reduction in nitrate leaching caused by a decreasing N load. The starting point, or baseline, was taken as the nitrate concentration of the upper metre of groundwater sampled on 99 farms in the 1992–1995 period in the sandy areas of the Netherlands, where predominantly grass and maize grow. We found here that a reduction in nitrate leaching of more than 20% in future would almost certainly be detected with the LMM. Detecting downward trends due to decreasing N load will require nitrate concentrations to also be related to soil drainage, precipitation excess leading to groundwater recharge and to location. Furthermore, we found that about 16% of the N load in the Dutch sandy regions was being leached to the upper metre of groundwater in the 1992–1995 period. The critical N load in approximately 1990 for exceeding the EC limit value for nitrate, NO₃, (50 mg L^–1) in the upper metre of groundwater for the mean situation for grassland, maize and arable land in the sandy area was found to be 210 kg ha^–1 a^–1. Because manure management has been altered, the critical load found will be lower than the current critical load .     

A method to combine non-probability sample data with probability sample data in estimating spatial means of environmental variables

In estimating spatial means of environmental variables of a region from datacollected by convenience or purposive sampling, validity of the results canbe ensured by collecting additional data through probability sampling. Theprecision of the estimator that uses the probability sample can beincreased by interpolating the values at the nonprobability sample points tothe probability sample points, and using these interpolated values as anauxiliary variable in the difference or regression estimator. Theseestimators are (approximately) unbiased, even when the nonprobability sampleis severely biased such as in preferential samples. The gain in precisioncompared to the estimator in combination with Simple Random Samplingis controlled by the correlation between the target variable andinterpolated variable. This correlation is determined by the size (density)and spatial coverage of the nonprobability sample, and the spatialcontinuity of the target variable. In a case study the average ratio of thevariances of the simple regression estimator and estimator was 0.68for preferential samples of size 150 with moderate spatial clustering, and0.80 for preferential samples of similar size with strong spatialclustering. In the latter case the simple regression estimator wassubstantially more precise than the simple difference estimator.     

典型重工业城市空气重污染过程特征与来源解析

为揭示邯郸市空气污染过程及形成原因,以邯郸市环境监测中心为采样点,对采样滤膜进行离子和碳质组分测试,探讨PM_2.5组分浓度变化特征,并利用WRF-CAMx空气质量模型模拟分析2017~2018年秋冬季3次重污染前后邯郸市各个地区各类污染源大气污染排放对PM_2.5质量浓度的贡献.结果显示,重污染期间邯郸市水溶性粒子占PM_2.5质量浓度的62.4%,二次离子中呈现NO₃^- > SO₄^2- > NH₄⁺变化趋势.受地面均压场和高压底部控制及500hPa高空纬向环流影响,污染物水平方向和垂直方向传输受到抑制,同时边界层高度的降低进一步加剧PM_2.5污染浓度的升高,随着西伯利亚东部高压和欧亚大陆高压南下以及边界层高度的上升,3次重污染过程得以彻底清除.PSAT示踪模块结果表明复兴区,丛台区和永年区是邯郸市PM_2.5浓度贡献的主要区县,3个区县重污染贡献总和为66.8%~72.2%,重污染时段冶金,交通源和居民散煤燃烧是3大主要污染源.     

江西省“三线冲突”空间特征及其强度影响因素

科学划定“三线”（城镇开发边界、永久基本农田、生态保护红线）有利于合理安排国土空间资源,识别“三线冲突”强度影响因素,可加深对国土空间规划的认识。以省级空间规划试点区——江西省为例,通过“三线冲突强度指数”识别不同区域“三线冲突”差异,利用空间自相关分析探究空间特征,并应用地理加权回归模型分析不同冲突类型的影响因素。结果表明：研究区内,城镇—生态、城镇—农业、生态—农业、城镇—生态—农业冲突面积分别为4305.791 km²、2892.894 km²、1845.341 km²、159.444 km²,各类型冲突存在明显的空间聚集性;固定资产投资强度、建设用地适宜区面积占比和地形位指数分别为城镇—生态、城镇—农业和生态—农业冲突强度的主要影响因素,城镇化率和距河流距离对城镇—生态—农业冲突强度具有影响作用。研究结果可为今后合理布局“三线”、实现国土空间结构优化提供参考依据。     

皖西地区贫困空间分异的影响机制

区域性贫困是当前中国社会经济和区域发展不平衡的核心表现。以大别山连片特困区的皖西地区为例,以乡镇贫困发生率为贫困空间分异指标,以地理环境、经济环境、社会环境的20个因素为自变量,综合运用相关分析、空间回归、地理加权回归等方法,探究各因素对贫困空间分异的影响。结果表明：平均坡度、人均规模以上工业总产值、二三产业收入占纯收入比例、医疗卫生机构床位数是导致皖西地区贫困空间分异的核心因素,且其影响具有较强的空间异质性;地理机制、经济机制、社会机制是皖西地区贫困空间分异的主要机制,其中,地理环境是基础影响,经济环境的影响较强,社会环境影响广且大多与地理环境相关。研究结果对贫困干预的地理瞄准具有较强的借鉴意义。     

Stream Temperature Modeling Using Functional Regression Models

Stream temperature is one of the most important environmental variables in lotic habitats as it has important and direct impacts on the ecosystem. Given the continuous nature of this variable, the aim of this paper was to introduce functional regression for the air‐stream temperature relation, being capable to model an entire seasonal or annual curve of temperatures as one entity, rather than multiple daily or weekly values in classical models. Three types of functional models were explored in the study and compared to two classical models (Generalized Additive Model and Logistic Model) for six rivers from the United States The results show the functional models have the best performance for all the considered rivers. When comparing functional models between them, one variant of the historical functional model performs better than the two other models and is the most parsimonious. Functional regression leads to encouraging results to model the complete annual stream temperature curve as one entity compared to other classical approaches.