酸雨pH值分级短期预报方法研究初探

酸雨的预报方法种类繁多,主要包括统计预报﹑数值预报及主观综合预报等.但这些预测研究大都是年度季度等较长时段的预测,感情用事为纛预测方法主要以线性统计方法为主.     

海洋性气候对天津市滨海地区空气质量的影响及预报

对天津市滨海地区污染气象条件与空气质量进行了相关分析研究,并在此基础上建立该地区空气质量统计预报方法,建立的统计方程经过试预报验证,取得良好的预报效果。     

空气污染预报污染物排放清单数据库的研究与建立

介绍了天津市空气污染预报污染源数据库建立的目的和方法，该数据库不同于常规的污染源数据库，要按模型需要对污染源信息进行了模式化处理，研究结果表明：该数据库应用于空气污染数值预报系统，预报结果达到课题设计的精度要求。     

黄河三角洲人工草场群落土壤盐分动态集成预报模型及其应用

提出了以ＧＭ（１，１）模型拟合发展的趋势、傅里叶变换撮周期分量ＡＲ（ｐ）模型模拟随机过程的集成预报模型，并用于黄河三角洲人工草场群落土壤盐分的定量研究中，经理论和应用检验证明：该模型能以较高的精度模拟或预报土壤盐分在较长时期内的动态变化过程，方法简单，计算工作量小，并优于传统的单一预报模型。     

应用于水文预报的优化BP神经网络研究

利用广东省滨江流域的水文观测资料，建立了以前期降水量为预报因子、以水位为输出的BP人工神经网络水文预报模型。首先采用了合理的方法进行样本组织，进而利用最优子集回归技术进行输入因子的确定，然后进行了不同隐层节点数、不同转移函数、不同训练算法的组合试验，确定了应用于水文预报中的优化BP神经网络：网络结构为8-9-1；转移函数的组合方式为tansig-线性函数；训练算法为采用evenberg-Marquardt(Lm)算法。为便于精度分析，还采用了最优子集回归模型作了研究。结果表明，优化BP网络模型无论在拟合精度还是在预测精度上都高于最优子集模型。总的来说BP网络是一种精度较高的水文预测模型。     

基于关键气象因子和天气类型的广州空气污染预报方法应用

以广州市环境监测站2007年1月—2009年12月监测的空气污染指数(API)以及SO2、NO2、PM10等污染物为研究对象,探讨了广州市空气污染的变化规律;并分析它们与常规观测的地面气象资料之间的关系;建立了API指数以及SO2、NO2、PM10等污染物的预报方程;为了弥补单纯基于数学模型预报的不足,分析和确定了造成广州严重空气污染的典型天气类型和相关指标,以便在具体的业务预报中可以根据天气形势和相关指标做进一步的订正;该预报方法于2010年10月起应用于广州市环境监测中心站,作为2010年16届亚运会广州空气质量预报预警服务的重要工具之一,运行服务效果良好.     

BP模型的改进及其在大气污染预报中的应用

针对传统BP模型存在着训练速度较慢、局部极值以及最佳网络结构无法准确确定的不足，进行了改进，应用于城市空气污染预报，建立大气污染浓度的神经网络预测模型。计算结果表明，应用改进的BP模型进行大气污染预报能够得到更好的预测结果，具有很强的实用性。     

城区大气污染物—NOx浓度预测预报模式研究

利用道路边大气污染的常年监测数据，对风向与无限线源成任意角的高斯内插大气扩散模式进行了系统测试，分析了影响预报精度的主要因素，在分析武汉市气象因素和天气状况的基础上，对模式进行了恰当地修正，经实测数据验证，预报准确度有了明显地提高。     

动态系统物元模型在综合水预报中的研究和应用

在研究物元分析理论的基础上,以不同时段的水体综合水质建立动态系统物元,动用综合评判模型对其进行综合水质评 定,并通过建立回归预警模型进行综合水质预报。在实例应用中取得了较满意的结果,论证了该方法的合理性和可行性。为环境质量预测提供了一种全新的思路和方法。     

动态系统物元模型在综合水质预报中的研究和应用

在研究物元分析理论的基础上,以不同时段的水体综合水质建立动态系统物元,运用综合评判模型对其进行综合水质评定,并通过建立回归预警模型进行综合水质预报。在实例应用中取得了较满意的结果,论证了该方法的合理性和可靠性,为环境质量预测提供了一种全新的思路和方法。     

MODIS气溶胶光学厚度对城市空气质量的指示

空气污染指数API(Air Pollution Index)是关系大众生产生活的一个重要指标,论证了一种由MODIS(中分辨率成像光谱仪)反演的气溶胶光学厚度产品AOT(Aerosol Optics Thickness)预报空气质量的方法,即依据API的分级标准,将API用特定的步长分组,再把各组的AOT和API均值建立相关模型,并用该模型来预测空气质量指数,经验证,预测值和实际值之间的精度为77.59%,基本达到业务运行的要求。     

Scaling up of biomass simulation for Eucalyptus plantations based on landsenses ecology

ABSTRACT

Sustainable forest management on a regional scale requires accurate biomass estimation. At present, technologically comprehensive forecasting estimates are generated using process-based ecological models. However, isolation of the ecological factors that cause uncertainty in model behavior is difficult. To solve this problem, this study aimed to construct a meliorization model evaluation framework to explain uncertainty in model behavior with respect to both the mechanisms and algorithms involved in ecological forecasting based on the principle of landsenses ecology. We introduce a complicated ecological driving mechanism to the process-based ecological model using analytical software and algorithms. Subsequently, as a case study, we apply the meliorization model evaluation framework to detect Eucalyptus biomass forest patches at a regional scale (196,158 ha) using the 3PG2 (Physiological Principles in Predicting Growth) model. Our results show that this technique improves the accuracy of ecological simulation for ecological forecasting and prevents new uncertainties from being produced by adding a new driving mechanism to the original model structure. This result was supported by our Eucalyptus biomass simulation using the 3PG2 model, in which ecological factors caused 21.83% and 9.05% uncertainty in model behavior temporal and spatial forecasting, respectively. In conclusion, the systematic meliorization model evaluation framework reported here provides a new method that could be applied to research requiring comprehensive ecological forecasting. Sustainable forest management on regional scales contributes to accurate forest biomass simulation through the principle of landsenses ecology, in which mix-marching data and a meliorization model are combined.     

Determination of the principal factors of river water quality through cluster analysis method and its prediction

In this paper, an artificial neural network model was built to predict the Chemical Oxygen Demand (COD_Mn) measured by permanganate index in Songhua River. To enhance the prediction accuracy, principal factors were determined through the analysis of the weight relation between influencing factors and forecasting object using cluster analysis method, which optimized the topological structure of the prediction model input items of the artificial neural network. It was shown that application of the principal factors in water quality prediction model can improve its forecasting skill significantly through the comparison between results of prediction by artificial neural network and the measurements of the COD_Mn. This methodology is also applicable to various water quality prediction targets of other water bodies and it is valuable for theoretical study and practical application.     

区域大气环境质量变化趋势研究

本文研究了城市及其开发区域大气环境质量变化趋势的计算方法，并对一城市开发区进行了实例研究，应用本文的方法可以对城市及其开发区进行多点源和的影响下大气环境质量模拟。预测，评价与规划。     

A fast Bayesian method for updating and forecasting hourly ozone levels

A Bayesian hierarchical space-time model is proposed by combining information from real-time ambient AIRNow air monitoring data, and output from a computer simulation model known as the Community Multi-scale Air Quality (Eta-CMAQ) forecast model. A model validation analysis shows that the model predicted maps are more accurate than the maps based solely on the Eta-CMAQ forecast data for a 2 week test period. These out-of sample spatial predictions and temporal forecasts also outperform those from regression models with independent Gaussian errors. The method is fully Bayesian and is able to instantly update the map for the current hour (upon receiving monitor data for the current hour) and forecast the map for several hours ahead. In particular, the 8 h average map which is the average of the past 4 h, current hour and 3 h ahead is instantly obtained at the current hour. Based on our validation, the exact Bayesian method is preferable to more complex models in a real-time updating and forecasting environment.     

Air quality improvement in Los Angeles—perspectives for developing cities

Air quality improvement in Los Angeles can inform air quality policies in developing cities. Emission control efforts, their results, costs and health benefits are briefly summarized. Today's developing cities face new challenges including regional pollution. Air quality issues in Beijing are briefly compared and contrasted with Los Angeles. Opportunities for co-benefits for climate and air quality improvement are identified. Air quality improvement in Los Angeles, California is reviewed with an emphasis on aspects that may inform air quality policy formulation in developing cities. In the mid-twentieth century the air quality in Los Angeles was degraded to an extent comparable to the worst found in developing cities today; ozone exceeded 600 ppb and annual average particulate matter <10 mm reached ~150 mg·m⁻³. Today's air quality is much better due to very effective emission controls; e.g., modern automobiles emit about 1% of the hydrocarbons and carbon monoxide emitted by vehicles of 50 years ago. An overview is given of the emission control efforts in Los Angeles and their impact on ambient concentrations of primary and secondary pollutants; the costs and health benefits of these controls are briefly summarized. Today's developing cities have new challenges that are discussed: the effects of regional pollution transport are much greater in countries with very high population densities; often very large current populations must be supplied with goods and services even while economic development and air quality concerns are addressed; and many of currently developing cities are located in or close to the tropics where photochemical processing of pollution is expected to be more rapid than at higher latitudes. The air quality issues of Beijing are briefly compared and contrasted with those of Los Angeles, and the opportunities for co-benefits for climate and air quality improvement are pointed out.     

城市给水管网余氯的预测模型

选择余氯为研究对象,以南方某市给水管网水质监测的数据为基础,使用线性回归和非线性神经网络(ANN)方法建立模型,找到了一种利用在线监测数据和人工监测数据实时预测管网余氯的方法。通过建立给水管网水质模型,可以由监测系统动态回传的数据来实时的预测下一天人工点的水质。模拟的结果显示ANN模型比线性回归模型有更好的预测能力,预测的平均相对误差:ANN模型为14.9%,线性回归模型为25.8%。使用ANN模型可以实现实时预测。     

The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continental United States

A sensitivity study is performed to examine the impact of lateral boundary conditions (LBCs) on the NOAA-EPA operational Air Quality Forecast Guidance over continental USA. We examined six LBCS: the fixed profile LBC, three global LBCs, and two ozonesonde LBCs for summer 2006. The simulated results from these six runs are compared to IONS ozonesonde and surface ozone measurements from August 1 to 5, 2006. The choice of LBCs can affect the ozone prediction throughout the domain, and mainly influence the predictions in upper altitude or near inflow boundaries, such as the US west coast and the northern border. Statistical results shows that the use of global model predictions for LBCs could improve the correlation coefficients of surface ozone prediction over the US west coast, but could also increase the ozone mean bias in most regions of the domain depending on global models. In this study, the use of the MOZART (Model for Ozone And Related chemical Tracers) prediction for CMAQ (Community Multiscale Air Quality) LBC shows a better surface ozone prediction than that with fixed LBC, especially over the US west coast. The LBCs derived from ozonesonde measurements yielded better O₃ correlations in the upper troposphere.     

Short-term prediction of the influent quantity time series of wastewater treatment plant based on a chaos neural network model

By predicting influent quantity, a wastewater treatment plant (WWTP) can be well controlled. The nonlinear dynamic characteristic of WWTP influent quantity time series was analyzed, with the assumption that the series was predictable. Based on this, a short-term forecasting chaos neural network model of WWTP influent quantity was built by phase space reconstruction. Reasonable forecasting results were achieved using this method.