瓦里关山大气CO本底变化

利用1992 01～2002 12期间的实测资料,分析了瓦里关全球基准站(36°17′N,100°54′E,海拔3816m)大气CO本底特征,并探讨了与源汇过程的关系.结果表明,瓦里关山大气CO体积分数本底范围与北半球平均水平基本相符,但增长趋势及年增长率波动与北半球平均状况并不完全一致,多年平均季节变化与同纬度海洋边界层(MBL)参比值以及北半球平均值也有较大差异,是所在地区多种CO源汇和大气输送共同作用的结果.瓦里关山大气CO本底观测资料既能体现亚洲内陆地域特点又具有全球代表性,辅以其它相关资料,还可进一步揭示中国内陆高原大气CO本底特征的成因.     

宁波市大气污染概况及影响因素分析

为研究宁波市大气污染状况及其影响因素，利用2013—2018年宁波市国控站点实时监测污染物数据以及气象数据，探讨分析了宁波市大气污染特征以及所受气象因素的影响概况。结果表明：宁波市颗粒物污染和O₃污染呈现典型的季节性特征，颗粒物浓度冬季最高，O₃最大滑动8 h平均质量浓度春、秋季最高。宁波市O₃污染问题越来越突出，且呈现出春、秋季O₃超标天数最多的季节变化特征。O₃小时质量浓度与气温和太阳辐射成正相关关系，NO₂和颗粒物浓度与气温成负相关关系。NO₂与O₃浓度成负相关关系，与颗粒物浓度成正相关关系。     

退化生态系统的诊断特征及其评价指标体系

退化生态系统具有结构和功能简化、生产力和抗逆力低、生物多样性下降等基本诊断特征,由于区域背景和人类活动干扰的差异,生态系统退化具有不同的类型、过程和阶段。为了定量地研究退化生态系统,文中分别建立了土壤、植被、大气、水体,以及农业生态系统、区域自然－人类复合生态系统退化的评价指标体系,其中,土壤退化指标包括物理退化、化学退化、生物退化和土壤污染四类指标;植被退化则从数量、结构、功能与生产力、品质四个     

台山电厂5号机组烟气脱硝液氨储存区设计

介绍了广东省台山电厂5号机组烟气脱硝工程选用液氨作为反应剂的工艺设计以及针对危险化学品液氨采取的一些具体措施,并对液氨储存区的设计提出了一些看法.     

Major air pollutants in Bursa,Turkey: their levels,temporal changes,interactions, and sources

Bursa is one of the largest cities of Turkey and it hosts 17 organized industrial zones. Parallel to the increase in population, rapidly growing energy consumption, and increased numbers of transport vehicles have impacts on the air quality of the city. In this study, regularly calibrated automatic samplers were employed to get the levels of air pollution in Bursa. The concentrations of CH₄ and N-CH₄ as well as the major air pollutants including PM₁₀, PM_2.5, NO, NO₂, NO_x, SO₂, CO, and O₃, were determined for 2016 and 2017 calendar years. Their levels were 1641.62?±?718.25, 33.11?±?5.45, 42.10?±?10.09, 26.41?±?9.01, 19.47?±?16.51, 46.73?±?16.56, 66.23?±?32.265, 7.60?±?3.43, 659.397?±?192.73, and 51.92?±?25.63 µg/m³ for 2016, respectively. Except for O₃, seasonal concentrations were higher in winter and autumn for both years. O₃, CO, and SO₂ had never exceeded the limit values specified in the regulations yet PM₁₀, PM_2.5, and NO₂ had violated the limits in some days. The ratios of CO/NO_x, SO₂/NO_x, and PM_2.5/PM₁₀ were examined to characterize the emission sources. Generally, domestic and industrial emissions were dominated in the fall and winter seasons, yet traffic emissions were effective in spring and summer seasons. As a result of the correlation process between O_x and NO_x, it was concluded that the most important source of O_x concentrations in winter was NO_x and O₃ was in summer.     

成都市夏季近地面臭氧污染气象特征

利用2016年7月成都市8个环境监测站点的臭氧、NO_2的监测资料以及成都市国家基准气象站和基本气象站的观测资料,对成都市夏季臭氧、NO_2浓度和气象要素的日变化特征和臭氧污染过程进行了分析。研究结果表明:成都市臭氧污染受综合气象条件和NO_2浓度的影响,高温、低湿、强辐射有利于臭氧大量生成,NO_2浓度高低决定了臭氧浓度的峰值大小;在污染期间,大气边界层高度远高于本地平均水平,数值约为平均水平的2～3倍;成都市臭氧污染的主要影响因子存在地区差异,成都市区的臭氧主要来自于自身的光化学反应,而灵岩寺地区的臭氧来自于VOCs和大气水平输送。     

长江中游城市群太阳辐射长期变化特征及其与气象要素的关系研究

基于长江中游5个气象辐射站点1961年以来的每日太阳辐射成分及其他气象观测数据,应用线性回归、相关分析、Pettitt突变检验等方法,探讨了近半世纪以来长江中游城市群太阳辐射的变化特征,指明地面接收太阳辐射的突变年份,并分析了太阳辐射与相关气象要素的关系。结果表明:近53年来长江中游地面接收太阳总辐射呈现先下降("变暗")、后上升("变亮")的趋势,该区域的辐射变化与全球范围内的"变暗"及"变亮"的变化趋势是一致的。日照时数与太阳辐射呈现较为显著的正相关性,相关系数r为0.63;而降水量则与太阳辐射呈现负相关性,相关系数r为–0.41。太阳辐射的这种突变变化也直接导致了气温的变化,在"变暗"阶段,太阳辐射与最高气温、气温日较差呈现出显著的正相关性,相关系数分别达到0.61和0.82,但其与最低气温、平均气温的相关性则不显著;在"变亮"阶段,太阳辐射与最高气温、气温日较差、平均气温呈现出正相关性,相关系数分别为0.56、0.62以及0.46,但其与最低气温的相关性则不显著。     

2013—2018年海口市区臭氧浓度变化特征

利用海口市区4个站点2013—2018年臭氧（O₃）逐时观测资料,分析了O₃不同时间尺度变化特征、超标情况及其与前体物NO₂和气象因子的关系。结果表明：海口市区O₃浓度总体偏低,但2013—2018年各站点均有明显的上升趋势,海大站、海师站、龙华站和秀英站的气候倾向率分别为1.26、3.84、3.02、2.93 μg/（m³·a）,其中海师站、龙华站和秀英站分别通过了95%、98%、90%的信度检验。秋、冬季海口市O₃浓度最高,其中10月O₃浓度月平均最大值出现的概率最大,最小值主要出现在8月。O₃浓度日变化表现为单峰型,早晨08：00前后O₃浓度最低,午后15：00最高。海大站超标率最高（2.82%）,海师站和龙华站次之,秀英站最小。4个站点的超标日O₃浓度均逐年上升,表明海口市O₃超标现象在恶化。O₃浓度与日照时间成正相关关系,与相对湿度成负相关关系。当相对湿度为60%~85%、日最高气温为20~30℃时,海口市发生O₃浓度超标的概率较高。     

Estimation of daily sunshine duration using support vector machines

Although sunshine duration (SD) is one of the most frequently measured meteorological parameters, there is a lack of measurements in some parts of the world. Hence, it should be estimated accurately for areas where no reliable measurement is possible. The main objective of this study is to evaluate the potential of support vector machine (SVM) approach for estimating daily SD. For this purpose, three different kernels of SVM, such as linear, polynomial, and radial basis function (RBF), were used. Different combinations of five related meteorological parameters, namely cloud cover, maximum temperature (T_max), minimum temperature (T_min), relative humidity (RH), and wind speed (WS), and one astronomic parameter, day length, were considered as the inputs of the models, and the output was obtained as daily SD. Simulated values of the models were compared with ground measured values, and concluded that the usage of the SVM-RBF estimator with combination of all input attributes produced the best results. The coefficient of determination, root mean square error, and mean absolute error were found to be 0.8435, 1.5105 h, and 1.0771 h, respectively, for the pooled four-year daily data set of 14 stations in Turkey. It was also deduced that accuracy increased as the number of attributes increased and the major contribution to this came from RH as compared with T_max, T_min, and WS. This study has shown that the SVM methodology can be a good alternative for conventional and artificial neural network methods for estimating daily SD.     

Evaluation of various global solar radiation models for Nigeria

This study assessed the performance of six solar radiation models. The objective was to determine the most accurate model for estimating global solar radiation on a horizontal surface in Nigeria. Twenty-two years meteorological data sets collected from the Nigerian Meteorological agency and the National Aeronautics and Space Administration for the three regions, covering the entire climatic zones in Nigeria were utilized for calibrating and validating the selected models for Nigeria. The accuracy and applicability of various models were determined for three locations (Abuja, Benin City, and Sokoto), which spread across Nigeria using seven viable statistical indices. This study found that the estimation results of considered models are statistically significant at the 95% confidence level, but their accuracy varies from one location to another. However, the multivariable regression relationship deduced in terms of sunshine ratio, air temperature ratio, maximum air temperature, and cloudiness performs better than other relationships. The multivariable relationship has the least root mean square error and mean absolute bias error, not exceeding 1.0854 and 0.8160 MJ m^?2 day^?1, respectively, and monthly relative percentage error in the range of ± 12% for the study areas.