运用灰色系统理论对城市环境噪声分析与预测

利用灰色系统理论的灰色关联度分析法,对影响佛山市禅城区区域环境噪声的影响因子进行定量分析,结果表明,影响佛山市禅城区区域环境噪声的第一位因素是机动车辆密度;同时建立了城市区域环境噪声的灰色GM（1,1）预测模型,短期预测精度很高,未来5年禅城区区域环境噪声呈平稳下降趋势.为规划防治城市区域环境噪声提供科学依据.     

火电厂大气污染物排放预测模型

根据大气污染物排放浓度变化特点,将无偏GM(1,1)模型与神经网络模型组合,并以矩阵型输入方式替代传统的数列型数据输入方式,得到改进型灰色神经网络模型,称为UGMN模型。接着,采用烟囱入口烟气自动监控系统(CEMS)数据,将模型运用于贵州省某电厂白天及夜间两段时间段内大气污染物排放浓度的模拟与预测。研究结果表明UGMN模型预测精度较好,可以应用于火电厂大气污染物排放浓度预测。     

山东省空气质量预报平台设计及其预报效果评估

基于排放源处理(SMOKE)模型、综合空气质量(CMAQ)模型与气象预报(WRF)模型初步搭建山东省空气质量预报平台,利用污染物在线监测数据和气象站观测数据检验预报平台的预报效果。结果表明,预报平台气象模块的预测效果与文献研究结果较一致;由CMAQ模型对2014年济南、淄博、烟台、威海的SO2、NO2、PM2.5质量浓度进行预测,SO2、NO2、PM2.5预报平均值分别在17.65~48.97、18.69~45.43、34.97~79.15μg/m3;SO2、NO2、PM2.5预报值与监测值的相关系数在0.52~0.74,标准化平均偏差、标准化平均误差、平均相对偏差、平均相对误差分别在-34.00%~-5.73%、11%~47%、-25.00%~-10.21%、20%~42%,预报平台具有良好的预报性能。最后,对未来空气质量数值预报平台的发展提出建议。     

等维灰数递补动态模型在生活垃圾产生量预测中的应用

本文探讨了等经今天为数递补GM（1,1） 模型在垃圾产生量长期预测中的具体应用,比较了该模型与普通二次拟合灰色要 预测精度及预测值的灰区间方面的判别,说明了等维灰数递补GM（1,1）模型在作长期预测时具有一定的优越性。     

稳健GM(1,1)模型及其预测应用

本文提出了稳健GM(1,1)灰色模型,并用该模型建立了山东省工业固体废物产生量数学模型,结果表明,稳健GM(1,1)模型比通常的GM(1,1)模型更具预测应用价值.     

稳健GM(1,1)模型及其预测应用

本文提出了稳健GM(1,1)灰色模型,并用该模型建立了山东省工业固体废物产生量数学模型,结果表明,稳健GM(1,1)模型比通常的GM(1,1)模型更具预测应用价值.     

运用灰色系统理论对城市环境噪声分析与预测

利用灰色系统理论的灰色关联度分析法,对影响佛山市禅城区区域环境噪声的影响因子进行定量分析,结果表明,影响佛山市禅城区区域环境噪声的第一位因素是机动车辆密度;同时建立了城市区域环境噪声的灰色GM(1,1)预测模型,短期预测精度很高,未来5年禅城区区域环境噪声呈平稳下降趋势.为规划防治城市区域环境噪声提供科学依据.     

黄河入海口水质评价与预测

对黄河入海口2004-2011年的水质进行评价与预测,采用灰色聚类法分析水体DO、CODMn、NH3-N 3项指标,总结水质年均变化情况.建立水质GM(1,1)灰色预测模型,用实际水质指标值检验其精度,并用此模型预测未来4年水质变化趋势.结果表明,2004-2015年期间,黄河入海口水质在2004-2007年波动较大,但将越来越好,CODMn、NH3-N呈下降趋势,DO、达标率呈上升趋势,并通过灰色关联分析方法分析水质变化原因以期对黄河入海口水质分析预测与水体保护工作提供参考.     

天津市空气质量变化趋势及主要影响因子分析

空气质量作为重要的环境问题,影响着城市人口健康和经济发展.利用天津市环境质量各种年报资料,综合运用灰色聚类、关联模型对天津市空气质量现状、影响因素做出分析.结果表明,天津市整体空气质量呈上升趋势,2004年后灰色聚类结果均为二类,且一类聚类系数在逐渐增大;空气质量的变化是多种因素共同作用的结果,天津市工业废气排放及能源...     

深圳市区空气污染的人工神经网络预测

利用深圳市2006至2013年的大气污染物监测浓度数据和气象资料,分析深圳市空气质量的逐月分布变化特征。采用Pearson相关分析,选择显著相关因子,分别以BP神经网络和RBF神经网络构建空气质量预测模型,对该市2013年SO2、NO2、PM103种空气污染物的月均值进行预测。实验结果表明,通过Pearson相关分析建立的预测模型有更高的预报精度。BP和RBF 2种网络预测效果都比较理想,对不同污染物的预测精度各有高低。但BP网络的构建和参数优化过程较为复杂且网络训练结果不稳定,而RBF网络构建和训练简单,时间短而结果稳定。在综合性能上,RBF网络用于环境空气污染物浓度的预测具有更强的适用性。     

Defining the photochemical contribution to particulate matter in urban areas using time-series analysis

The objective of this project is to demonstrate how the ambient air measurement record can be used to define the relationship between O3 (as a surrogate for photochemistry) and secondary particulate matter (PM) in urban air. The approach used is to develop a time-series transfer-function model describing the daily PM10 (PM with less than 10 microm aerodynamic diameter) concentration as a function of lagged PM and current and lagged O3, NO or NO2, CO, and SO2. Approximately 3 years of daily average PM10, daily maximum 8-hr average O3 and CO, daily 24-hr average SO2 and NO2, and daily 6:00 a.m.-9:00 a.m. average NO from the Aerometric Information Retrieval System (AIRS) air quality subsystem are used for this analysis. Urban areas modeled are Chicago, IL; Los Angeles, CA; Phoenix, AZ; Philadelphia, PA; Sacramento, CA; and Detroit, MI. Time-series analysis identified significant autocorrelation in the O3, PM10, NO, NO2, CO, and SO2 series. Cross correlations between PM10 (dependent variable) and gaseous pollutants (independent variables) show that all of the gases are significantly correlated with PM10 and that O3 is also significantly correlated lagged up to two previous days. Once a transfer-function model of current PM10 is defined for an urban location, the effect of an O3-control strategy on PM concentrations is estimated by calculating daily PM10 concentrations with reduced O3 concentrations. Forecasted summertime PM10 reductions resulting from a 5 percent decrease in ambient O3 range from 1.2 microg/m3 (3.03%) in Chicago to 3.9 microg/m3 (7.65%) in Phoenix.     

天津市灰霾评价等级指标体系研究

根据天津市2003—2007年灰霾日的污染物浓度和气象资料,应用主成分分析方法得出影响灰霾的5个主要因子(SO2、相对湿度、总云量、PM10和风速)。对相对湿度、总云量和风速3个气象因子的历史资料进行频数统计分析,并建立了各气象因子的等级划分标准。利用灰色聚类法构建了天津市灰霾评价的等级指标体系,灰霾等级划分结果表明,天津市轻度灰霾和重度灰霾出现天数相对较少,均以中度灰霾为主;轻度灰霾大多出现在春季和夏季;重度灰霾主要出现在冬季,春季出现的比例最小;综合评价分析,冬季灰霾污染程度最为严重。     

Evaluation of air quality by passive and active sampling in an urban city in Turkey: current status and spatial analysis of air pollution exposure

Concentrations of air pollutants, nitrogen dioxide (NO(2)), sulfur dioxide (SO(2)), ozone (O(3)), particulate matter (PM(2.5) and PM(10)), trace metals, and polycyclic aromatic hydrocarbons (PAHs) were measured in 2008 and 2009 in the city of Eski?ehir, central Turkey. Spatial distributions of NO(2), SO(2), and ozone were determined by passive sampling campaigns carried out during two different seasons with fairly large spatial coverage. A basic population exposure assessment was carried out employing Geographical Information System techniques by combining population density maps with pollutant distribution maps of NO(2) and SO(2). It was found that 95 % of the population is exposed to NO(2) levels close to the World Health Organization guideline value. Regarding SO(2), a large proportion of the population (83 %) is exposed to levels above the WHO second interim target value. Concentrations of all the pollutants showed a seasonal pattern increasing in winter period, except for ozone having higher concentrations in summer season. Daily PM(10) and PM(2.5) concentrations exceeded European Union limit values almost every sampling day. Toxic fractions of the measured PAHs were calculated and approximately fourfold increase was observed in winter period. Copper, Pb, Sn, As, Cd, Zn, Sb, and Se were found to be moderately to highly enriched in PM(10) fraction, indicating anthropogenic input to those elements measured. Exposure assessment results indicate the need for action to reduce pollutant emissions especially in the city center. Passive sampling turns out to be a practical and economical tool for air quality assessment with large spatial coverage.     

Daily mortality and air pollution in The Netherlands

We studied the association of daily mortality with short-term variations in the ambient concentrations of major gaseous pollutants and PM in the Netherlands. The magnitude of the association in the four major urban areas was compared with that in the remainder of the country. Daily cause-specific mortality counts, air quality, temperature, relative humidity, and influenza data were obtained from 1986 to 1994. The relationship between daily mortality and air pollution was modeled using Poisson regression analysis. We adjusted for potential confounding due to long-term and seasonal trends, influenza epidemics, ambient temperature and relative humidity, day of the week, and holidays, using generalized additive models. Influenza episodes were associated with increased mortality up to 3 weeks later. Daily mortality was significantly associated with the concentration of all air pollutants. An increase in the PM10 concentration by 100 micrograms/m3 was associated with a relative risk (RR) of 1.02 for total mortality. The largest RRs were found for pneumonia deaths. Ozone had the most consistent, independent association with mortality. Particulate air pollution (e.g., PM10, black smoke [BS]) was not more consistently associated with mortality than were the gaseous pollutants SO2 and NO2. Aerosol SO4(-2), NO3-, and BS were more consistently associated with total mortality than was PM10. The RRs for all pollutants were substantially larger in the summer months than in the winter months. The RR of total mortality for PM10 was 1.10 for the summer and 1.03 for the winter. There was no consistent difference between RRs in the four major urban areas and the more rural areas.     

Carbon monoxide concentration forecasting in Santiago, Chile

In the city of Santiago, Chile, air quality is defined in terms of particulate matter with an aerodynamic diameter < or = 10 microm (PM10) concentrations. An air quality forecasting model based on past concentrations of PM10 and meteorological conditions currently is used by the metropolitan agency for the environment, which allows restrictions to emissions to be imposed in advance. This model, however, fails to forecast between 40 and 50% of the days considered to be harmful for the inhabitants every year. Given that a high correlation between particulate matter and carbon monoxide (CO) concentrations is observed at monitoring stations in the city, a model for CO concentration forecasting would be a useful tool to complement information about expected air quality in the city. Here, the results of a neural network-based model aimed to forecast maximum values of the 8-hr moving average of CO concentrations for the next day are presented. Forecasts from the neural network model are compared with those produced with linear regressions. The neural network model seems to leave more room to adjust free parameters with 1-yr data to predict the following year's values. We have worked with 3 yr of data measured at the monitoring station located in the zone with the worst air quality in the city of Santiago, Chile.     

空气中PM_(10)浓度的BP神经网络预报研究

建立了某市PM10浓度预报的分段BP神经网络模型,经验证,所建立的BP预报模型,预测精度比较高,PM10日平均浓度误差大多在-0．010～0．010mg／m^3范围内,相对误差在-20％～20％,表明BP神经网络对PM10的浓度预报是一种有效的工具。     

Spatiotemporal Features of Severe Air Pollution in Northern Taiwan (8 pp)

Background, Aims and Scope This research attempted to identify the dominant factors simultaneously affecting the airborne concentrations of five air pollutants with principal component analysis and to determine the meteorologically related parameters that cause severe air-pollution events. According to the definition of subPSI and PSI values through the U.S. EPA, the historical raw data of five criteria air pollutants, SO2, CO, O3, PM10 and NO2, were calculated as daily subPSI values. In addition to the airborne concentrations, this study simultaneous collected the surface meteorological parameters of the Taipei meteorological station, established by the Central Weather Bureau. Methods Principal component analysis was conducted to screen severe air pollution scenarios for five air pollutants: SO2, CO, O3, PM10 and NO2. The concentrations of various air pollutants measured at 17 air-quality stations in northern Taiwan from 1995 to 2001 were transformed into daily subPSI values. The correlation analysis of the five air pollutants and four meteorological parameters (wind speed, temperature, mixing height and ventilation rate) were included in this research. After screening severe air pollution scenarios, this study recognized the synoptic patterns easily causing the severe air-pollution events. Results and Discussion Analytical results showed that the eigenvalues of the first two principal components for SO2, CO, O3, PM10 and NO2 were greater than 1. The first component of five air pollutants explained 64, 64, 67, 76 and 63% of subPSI variance for SO2, CO, O3, PM10 and NO2, respectively. Only the correlation coefficient of NO2 and CO had statistically significant positive values (0.82); other pollutant pairs presented medium (0.4 to 0.7) or low (0 to 0.4) positive values. The correlation coefficients for air pollutants and three meteorological parameters (wind speed, mixing height and ventilation index) were medium or low negative values. In northern Taiwan, spring was most likely induced high concentrations and the component scores of the first component for SO2, CO, PM10 and NO2; summer was the worst season that caused high O3 episodes. Consequently, the analytical results of factor loadings for the first principal component and emission inventory of various sources revealed that mobile sources were dominant factors affecting ambient air quality in northern Taiwan. Conclusion According to the results of principal component analysis for the five air pollutants, the first two of 17 components were cited as major factors and explained 71% of subPSI variance. Based on the inventory of NOx emissions and the isopleth diagram of factor loading for the first component, mobile sources in the southwest Taipei City accounted for the highest factor loading values and emission inventory values. Synoptic analysis and principal component analysis demonstrated that three types of weather patterns (high-pressure recirculation, prefrontal warm sector and the southwesterly wind system) easily caused the severe air-pollution scenarios. In summary, if severe air-pollution days occurred, the average meteorological parameters experienced adverse conditions for diffusing air pollutants; that is, the average values of wind speed, mixing height and ventilation index were lower than 2.1 ms-1, 360 m and 800 m2s-1, respectively. If one of the three synoptic patterns were to occur in combination with adverse meteorological conditions, severe air-pollution events would be developed. Recommendation and Outlook By utilizing synoptic patterns, this work found three weather systems easily caused severe air-pollution events over northern Taiwan. Analytical results showed, respectively, the wind speed and mixing height were less than 2.1 m/s and 360 m during severe air-pollution events.     

A statistical assessment of saturation and mobile sampling strategies to estimate long-term average concentrations across urban areas

The objectives of this study were: (1) to quantify the errors associated with saturation air quality monitoring in estimating the long-term (i.e., annual and 5 yr) mean at a given site from four 2-week measurements, once per season; and (2) to develop a sampling strategy to guide the deployment of mobile air quality facilities for characterizing intraurban gradients of air pollutants, that is, to determine how often a given location should be visited to obtain relatively accurate estimates of the mean air pollutant concentrations. Computer simulations were conducted by randomly sampling ambient monitoring data collected in six Canadian cities at a variety of settings (e.g., population-based sites, near-roadway sites). The 5-yr (1998-2002) dataset consisted of hourly measurements of nitric oxide (NO), nitrogen dioxide (NO2), oxides of nitrogen (NOx), sulfur dioxide (SO2), coarse particulate matter (PM10), fine particulate matter (PM2.5), and CO. The strategy of randomly selecting one 2-week measurement per season to determine the annual or long-term average concentration yields estimates within 30% of the true value 95% of the time for NO2, PM10 and NOx. Larger errors, up to 50%, are expected for NO, SO2, PM2.5, and CO. Combining concentrations from 85 random 1-hr visits per season provides annual and 5-yr average estimates within 30% of the true value with good confidence. Overall, the magnitude of error in the estimates was strongly correlated with the variability of the pollutant. A better estimation can be expected for pollutants known to be less temporally variable and/or over geographic areas where concentrations are less variable. By using multiple sites located in different settings, the relationships determined for estimation error versus number of measurement periods used to determine long-term average are expected to realistically portray the true distribution. Thus, the results should be a good indication of the potential errors one could expect in a variety of different cities, particularly in more northern latitudes.     

Air pollution and health studies in China--policy implications

During the rapid economic development in China, ambient air pollutants in major cities, including PM10 (particulate matter with aerodynamic diameter < or =10 microm) and SO2 have been reduced due to various measures taken to reduce or control sources of emissions, whereas NO2 is stable or slightly increased. However, air pollution levels in China are still at the higher end of the world level. Less information is available regarding changes in national levels of other pollutants such as PM2.5 and ozone. The Chinese Ministry of Environmental Protection (MOEP) set an index for "controlling/reducing total SO2 emissions" to evaluate the efficacy of air pollution control strategy in the country. Total SO2 emissions declined for the first time in 2007. Chinese epidemiologic studies evidenced adverse health effects of ambient air pollution similar to those reported from developed countries, though risk estimates on mortality/morbidity per unit increase of air pollutant are somewhat smaller than those reported in developed countries. Disease burden on health attributable to air pollution is relatively greater in China because of higher pollution levels. Improving ambient air quality has substantial and measurable public health benefits in China. It is recommended that the current Chinese air quality standards be updated/revised and the target for "controlling/reducing total SO2 emissions" be maintained and another target for "reducing total NO2 emissions" be added in view of rapid increase in motor vehicles. Continuous and persistent efforts should be taken to improve ambient air quality.     

Air quality impacts of power plant emissions in Beijing

The CALMET/CALPUFF modeling system was applied to estimate the air quality impacts of power plants in 2000 and 2008 in Beijing, and the intake fractions (IF) were calculated to see the public health risks posed. Results show that in 2000 the high emission contribution induced a relatively small contribution to average ambient concentration and a significant impact on the urban area (9.52 microg/m(3) of SO(2) and 5.29 microg/m(3) of NO(x)). The IF of SO(2), NO(x) and PM(10) are 7.4 x 10(-6), 7.4 x 10(-6) and 8.7 x 10(-5), respectively. Control measures such as fuel substitution, flue gas desulfurization, dust control improvement and flue gas denitration planned before 2008 will greatly mitigate the SO(2) and PM(10) pollution, especially alleviating the pressure on the urban area to reach the National Ambient Air Quality Standard (NAAQS). NO(x) pollution will be mitigated with 34% decrease in concentration but further controls are still needed.