浅谈GAM与GM（1,1）模型

对ＧＡＭ水环境预测模型提出了四点不同看法，与有关作者商榷，指出ＧＭ（１，１）模型与ＧＡＭ模型二者并无优劣之差，精度亦相当。     

也谈GAM与GM（1,1）模型

通过对环境预测中灰色建模方法的回顾，说明了ＧＡＭ和ＧＭ（１，１）模型各有所长，最重要的是建模手段的丰富。     

纺织厂车间噪声的灰色GIM（1）预测模型

利用灰色ＧＩＭ（１）模型的非时序直接建模法原理,对纺织厂织造车间的环境噪声分别建立了等间隔和不等间隔的非时序预测模型。实测研究表明ＧＩＭ（１）模型优于常规ＧＭ（１,１）模型,且所需建模数据大为减少,预测精度更高（达９９．９９％）,从而拓宽了灰色预测的适用范围。     

GEMS／Air介绍（1）1993年监测报告（I）

ＧＥＭＳ／Ａｉｒ介绍（１）１９９３年监测报告（Ｉ）朱玉栋（中国环境监测总站，北京１０００１２）全球环境大气监测（简称为ＧＥＭＳ／Ａｉｒ）１９９３年在中国的工作情况如下；１．ＧＥＭＳ／Ａｉｒ监测站位情况目前在中国参加ＧＥＭＳ／Ａｉｒ工作的城市共有五个，...     

地表水COD浓度灰色预测的GPPM（1）模型

根据地表水中ＣＯＤ浓度的时序数据，建立了ＧＰＰＭ（１）预测模型，结果表明ＧＰＰＭ（１）模型的预测精度优于常规灰色ＧＭ（１，１）模型，它为环境系统的拟合，预测和决策提供了新的方法途径。     

燃煤（油）固定污染源二氧化硫排放总量监测（四）烟气采样器及其应用

燃煤（油）固定污染源二氧化硫排放总量监测（四）烟气采样器及其应用易江（中国环境监测总站１０００１２）接１９９６，１Ｏ（１）：４５～４８７ＧＭ—１００型、ＳＧＳ一９４型烟气采样器及其应用ＧＭ——１００型、ＳＧＳ—９４型烟气采样器是吸收、消化国外先进技术...     

带有阶跃函数的GIM（1）预测模型及其应用

结合某水域地面水ＲＯＤ５的预测实践,研究提出了带有阶跃函数的ＧＩＭ（１）预测模型,并给出了估计这种模型参数的简便方法,从而扩大了ＧＩＭ（１）模型在环境系统中的应用范围,实例表明,运用该方法得到的模型比常规ＧＩＭ（１）具有更高的精度。     

水质GM（1，1）和Ae~（a（k－1））模型预测

水质ＧＭ（１，１）和Ａｅ~（ａ（ｋ－１））模型预测易江（中国环境监测总站，北京１０００１２）水环境质量变化，归纳起来受自然因素和人为因素的影响。自然因素发生着缓慢的变化，对水环境质量变化的作用是渐进、平稳和相对微弱的。人为因素则不然，当主要污染源得到?..     

GM（1,1）模型简化算法及其在噪声影响预测中的应用

对ＧＭ（１，１）模型经差分形式化及令定写成直线方程形式。据最小二乘法原理，原方程的参数辨识可借助线性回归方法来实现。从而使ＧＭ（１，１）模型的应用更显普及和实用化。     

用计算器灰色预测环境污染

建立了环境污染预测的ＧＭ（１．１）模型，并在ＣＡＳＩＯｆｘ－３８００Ｐ计算器上进行程序设计。实例应用结果表明，用计算器来实现环境污染灰色预测，不仅简便、快速、结果准确可靠，而且较之计算机更为方便实用。     

The Geocybernetic Assessment Matrix (GAM) — A new assessment tool for evaluating the level and nature of sustainability or unsustainability

Evaluating sustainability from EIA-based assessments has been problematic at best. This is due to the use of reductionist and qualitative approaches which is dependent upon the perspective of the assessor(s). Therefore, a more rigorous and holistic approach is required to evaluate sustainability in a more consistent way. In this paper, a matrix-based methodology in order to assess the indicated level and nature of sustainability for any project, policy, indicators, legislation, regulation, or other framework is described. The Geocybernetic Assessment Matrix (GAM) is designed to evaluate the level and nature of sustainability or unsustainability occurring in respect the fundamental and complex geocybernetic paradigms. The GAM method is described in detail in respect to the theory behind it and the methodology. The GAM is then demonstrated using an appropriate case study — Part 1 of the UK Climate Change Act (2008) concerning carbon budgets and targets. The results indicate that the Part 1 of Act may not achieve the desired goals in contributing towards sustainable development through the stated mechanisms for carbon budgets and targets. The paper then discusses the broader context of the GAM with respect to the core themes evident in the development and application of the GAM of: sustainability science; sustainability assessment; application value of the GAM; and future research and development.     

Biomonitoring of testate amoebae (protozoa) as toxic metals absorbed in aquatic bryophytes from the Hg-Tl mineralized area (China)

A new method has been developed for the determination of chemical oxygen demand (COD) in pulping effluent using chemometrics-assisted spectrophotometry. Two calibration models were established by inducing UV-visible spectroscopy (model 1) and derivative spectroscopy (model 2), combined with the chemometrics software Smica-P. Correlation coefficients of the two models are 0.9954 (model 1) and 0.9963 (model 2) when COD of samples is in the range of 0 to 405 mg/L. Sensitivities of the two models are 0.0061 (model 1) and 0.0056 (model 2) and method detection limits are 2.02?C2.45 mg/L (model 1) and 2.13?C2.51 mg/L (model 2). Validation experiment showed that the average standard deviation of model 2 was 1.11 and that of model 1 was 1.54. Similarly, average relative error of model 2 (4.25%) was lower than model 1 (5.00%), which indicated that the predictability of model 2 was better than that of model 1. Chemometrics-assisted spectrophotometry method did not need chemical reagents and digestion which were required in the conventional methods, and the testing time of the new method was significantly shorter than the conventional ones. The proposed method can be used to measure COD in pulping effluent as an environmentally friendly approach with satisfactory results.     

A Long-Term Electricity Dispatch Model with the TIMES Framework

A new Swiss TIMES (The Integrated MARKAL–EFOM System) electricity model with an hourly representation of inter-temporal detail and a century-long model horizon has been developed to explore the TIMES framework’s suitability as a long-term electricity dispatch model. To understand the incremental insights from this hourly model, it is compared to an aggregated model with only two diurnal timeslices like in most MARKAL/TIMES models. Two scenarios have been analysed with both models to answer the following questions: Are there differences in model solutions? What are the benefits of having a high number of timeslices? Are there any computational limitations? The primary objective of this paper is to understand the differences between the solutions of the two models, rather than Swiss policy implication or potential uncertainties in input parameters and assumptions. The analysis reveals that the hourly model offers powerful insights into the electricity generation schedule. Nevertheless, the TIMES framework cannot substitute for a dispatch model because some features cannot be represented; however, the long model time horizon and integrated system approaches of TIMES provide features not available in conventional dispatch models. The methodology of the model development and insights from the model comparison are described.     

Decision Analysis Methodology to Evaluate Integrated Solid Waste Management Alternatives

Value-focused thinking, a decision analysis technique, is used to produce a multiple-objective model that captures a site's municipal solid waste (MSW) management goals, objectives, and concerns in order to facilitate the evaluation of competing strategies. The model ranks competing MSW alternatives based on how well they meet the decision maker's strategic objective, a 20-year compliant MSW system. Sensitivity analysis is incorporated in the model to assess and illustrate the effects of changes in model objective weights and changes in model parameters. Overall, the model provides decision-makers with a decision tool to make a better decision when choosing a new MSW management strategy. The entire process is applied to a case study using Earekson Air Station, a remote U.S. Air Force installation.     

北京市公交车噪声测试与分析

公交车已成为当前北京市道路交通噪声的主要束源之一,针对公交车声源模型缺乏而沿用大型车声源模型所致的噪声预测误差问题,在北京市选取了两类常见公交车进行了537辆车的单车通过噪声测试,在无效数据剔除和背景噪声修正后,利用回归分析法获得了北京市公交车声源模型,通过与现有《公路建设项目环境影响评价规范》中大型车声源模型的比较,显示出建立北京市公交车噪声声源模型的必要性。基于《公路建设项目环境影响评价规范》中的道路交通噪声预测方法,提出了符合北京市实际情况的道路交通噪声预测模型。     

Ecological study of river Suswa: modeling DO and BOD

In this paper limnological status of river Suswa was observed for a period of two years. A water quality Beck modified Khanna Bhutiani model (BMKB model) was developed to calculate DO (dissolved Oxygen) and BOD (biochemical oxygen demand). The model was developed to calculate DO and BOD by using DO/BOD of same place and upstream in previous season which results in Single output. This model gives the seasonal value on the basis of previously taken upstream and downstream observations/concentrations of DO and BOD. The model was calibrated and verified for the water quality data (Physico-chemical data) of samples collected from river Suswa in different seasons. The model gave good agreement between data observed by it and the data observed manually, thus substantiating the validity of the model. Only minor differences were observed in physical, chemical and heavy metals of all the four sampling stations during the course of study.     

基于LSTM-GCN的PM2.5浓度预测模型

应用机器学习算法开展空气质量预测已成为当前研究热点之一,空气质量监测数据具有显著的时空特征,即具有时间维度时序特征和空间维度传输演化特征。面向空气质量监测数据,联合LSTM提取的时间特征和GCN提取的空间特征,提出预测PM_2.5浓度的LSTM-GCN组合模型。以北京市35个空气质量监测站2018—2020年监测数据进行仿真实验,并将LSTM-GCN模型与LSTM模型、GCN模型以及时空地理加权回归模型(GTWR)进行对比,结果显示:LSTM-GCN模型相较于LSTM模型均方根误差(RMSE)、平均绝对误差(MAE)分别降低了11.68%、7.34%;相较于GCN模型RMSE、MAE分别降低了40.22%、36.37%;相较于GTWR模型RMSE、MAE分别降低了17.52%、23.69%,表明所提出LSTM-GCN模型在准确率上有所提升。用LSTM-GCN模型预测2021年1—7月PM_2.5浓度,结果显示预测效果较好。