南京市重点工业源对城市空气质量影响的数值模拟

运用南京大学空气质量数值预报系统,对2005年1月6─7日南京典型天气条件形成的污染过程进行数值模拟,计算分析了重污染发生时的城市污染气象环境和影响因子,同时针对该城市重点工业源区对城市主要空气污染物浓度分布的贡献做了分析.结果表明:重污染状况发生在长时间逆温条件下,尤以6日23:00—7日04:00时逆温最强(强度可达1.25　℃/hm,逆温层厚200　m),此时风速较小,同时在市区出现较强的气流辐合,在这种气象条件下, NO₂,SO₂和PM₁₀的质量浓度最高.南京市相对封闭的宁镇丘陵地形以及受东郊紫金山的影响,也是造成主城区重污染的重要影响因子之一.南京市城北工业区与主城区毗邻而且污染物排放量较大,在冬季主导风向为东北风时对主城区污染物的浓度具有显著贡献.      

应用不同结构参数预测取代苯甲醛类化合物的分配性质

测定了28种取代苯甲醛类化合物的正辛醇/水分配系数（Kow）和水溶解度（Sw）,采用量化参数和分子连接性指数来构建定量结构－性质相关（QSPR）模型．所得模型能较好地预测该类化合物的Kow和Sw．Kow和Sw平均估算误差分别为0.15和0.20个对数单位．模型的统计检验表明,模型中自变量不存在相关关系;残差呈正态分布．     

库尔勒市大气颗粒物污染特征与影响因素分析

针对库尔勒市PM 10、PM 2.5年均浓度超标现象,基于市区3个环境监测站2013—2017年的逐时观测数据,分析PM 10、PM 2.5污染特征、成因及其主要影响因素。结果表明:①2013—2017年库尔勒市PM 10年均浓度变化较大且无明显趋势,PM 2.5年均浓度整体呈下降趋势;②季节尺度上,库尔勒市PM 10在每年2—5月呈现高浓度,PM 2.5高浓度期则为10月至翌年5月;③城郊的开发区站PM 10浓度最高,老城区的州政府站PM 2.5浓度最高,在PM 10和PM 2.5的高浓度期空间差异尤其显著;④PM 10与风速显著正相关,来自塔克拉玛干沙漠的风蚀沙尘颗粒物是库尔勒地区颗粒污染物的主要来源;⑤库尔勒市PM 10主要为外源输入,PM 2.5则以城市内源为主,相对湿度、风速、风向、温度等气象条件是影响大气颗粒物浓度及分布的重要因素。     

基于光谱技术的土壤多参数快速检测进展与展望

传统的土壤采样和实验室分析方法往往工作量大、前处理复杂、测定周期长、时效差、费用高,因此,迫切需要寻找快速、准确的土壤多参数检测方法,以实现多尺度土壤属性数据的快速获取与更新。系统梳理了便携式X射线荧光光谱(PXRF)、可见光-近红外反射光谱(Vis-NIR)、激光诱导击穿光谱(LIBS)和电弧发射光谱(AES)等光谱技术的原理、特点以及在土壤多参数快速检测中的应用及其研究进展。提出,未来在提高现有土壤多参数光谱检测精度和适用性的基础上,应结合ICP-MS等传统实验室方法,综合应用多种光谱技术以及化学计量学、现代信息技术和空间分析技术等,实现区域多种土壤属性的快速获取与实时监测,以满足我国当前土壤多参数、高精度快速检测及土壤质量精细化管理的需要。     

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.     

Impacts of Meteorological Factors on PM10: Artificial Neural Networks (ANN) and Multiple Linear Regression (MLR) Approaches

In this study, prediction capacities of multi-linear regression (MLR) and artificial neural networks (ANN) onto coarse particulate matter (PM₁₀) concentrations were investigated. Different meteorological factors on particulate pollution were chosen for operating variables in the model analyses. Two different regions (urban and industrial) were identified in the region of Kocaeli, Turkey. All data sets were obtained from air quality monitoring network of the Ministry of Environment and Urban Planning, and 120 data sets were used in the MLR and ANN models. Regression equations explained the effects of the meteorological factors in MLR analyses. In the ANN model, backpropagation network with two hidden layers has achieved the best prediction efficiency. Determination coefficients and error values were examined for each model. ANN models displayed more accurate results compared to MLR.     

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.     

Production of chicken fat ethyl esters via optomized protocols with dry washing by silica gel

The chicken fat ethyl ester (CFEE) was developed through alkali-catalyzed transesterification with ethanol using potassium hydroxide (KOH) as a catalyst. Parameters affecting the process of transesterification such as the catalyst concentration, ethanol to oil molar ratio, reaction temperature, and reaction duration are investigated. As a result, maximum CFEEs of 90% (~96.21 ester content% w/w) was obtained under optimal conditions of 1.0 wt.% KOH, 8:1 ethanol to fat molar ratio, a reaction temperature of 70°C, and a duration of 90 min. Dry washing method using (3.0% w/w) of silica gel was used to purify the crude ethyl ester from the residual catalyst, glycerol, and other impurities. Properties of the produced ethyl esters were determined and found in accordance with specifications prescribed by the ASTM standards. Moreover, blends of CFEEs and petrodiesel were prepared and evaluated according to ASTM test methods.     

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。     

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.