降尘规律研究--兰州城区降尘变化规律及多元自身回归预测

探讨了降尘的年变化、月变化.运用逐步回归筛选变量.采用主分量分析法,得出不同季节的主分量,揭示了降尘的规律,建立了多元自身回归预测方程.对城区降尘进了预测,并与实测作了验证和对比,取得较好效果.     

红外光谱法监测水中矿物油含量

本文提出了一种快速和准确监测水中矿物油环境污染指标的新方法。用多元线性回归和逐步回归法建立测油数学模型,对标准样液在３０００～２８００ｃｍ－１间的四个强吸收峰的光谱数据进行回归处理。对６３件用６种矿物油配制而成的浓度为０～４００ｍｇ／Ｌ的四氯化碳标准液进行了测定。实验结果表明,本文建立的红外光谱法测油模型对宽量程内的矿物油污染量有较好适应性。     

洪泽湖藻类与环境因子逐步回归统计和蓝藻水华初步预测

以洪泽湖2008—2010年的连续监测资料为基础,运用多元逐步回归统计方法,选择水温等12项环境因子与藻类叶绿素a等5项生物因子进行逐步回归分析,找出与生物因子显著相关的因子,建立多元逐步回归方程,预测洪泽湖藻类密度的变化情况,初步进行了洪泽湖蓝藻水华的预测预报。结果显示,总磷、总氮、氨氮和水深为洪泽湖藻类密度的显著相关因子,透明度、水温、水深为蓝藻密度的显著相关因子。     

基于数字图像比色法测定水中氟化物

在酸性溶液中,氟能破坏二甲酚橙〖CD*2〗锆显色体系,使溶液颜色从紫红色变为橙色或黄色,通过采集图像颜色信息构建3个特征颜色分量的多元线性回归模型,用以定量测定水中氟。该方法在0 mg/L~1.5 mg/L范围内线性良好,方法检出限为0.03 mg/L。两个质量浓度水平的氟化物标准溶液6次测定结果的RSD分别为1.4%和1.3%,相对误差分别为0.3%和1.7%。     

基于KZ滤波法的江淮地区PM2.5浓度变化影响分析

基于2018—2020年合肥、芜湖和马鞍山3个城市国控站点的PM_2.5逐日监测数据和同期地面气象观测资料,利用Kolmogorov-Zurbenko(KZ)滤波对PM_2.5日浓度的原始时间序列进行分解,获取短期分量、季节分量和长期分量,并进行多元线性逐步回归构建各分量与气象因子的模型,最后依据短期分量和基线分量的回归模型和残差分析,对序列进行重建,获取消除气象条件影响的PM_2.5长期分量。KZ滤波分析结果表明：2018—2020年气象条件对江淮区域PM_2.5污染改善影响存在波动,在2018—2019年为负贡献,而在2020年秋冬季则变为正贡献;江淮地区3个城市2018年和2020年PM_2.5修正后的长期分量均值表明气象条件对各市PM_2.5改善影响存在差异较大,气象条件对合肥PM_2.5改善的贡献仅为1.0%,芜湖为7.8%,马鞍山为21.0%;NAQPMS数值模式情景分析结果显示,减排措施对江淮之间PM_2.5浓度改...     

改进主成分分析与多元回归融合的汉丰湖水质评估及预测

基于2015—2017年汉丰湖水质监测数据,采用改进主成分分析和多元回归相融合的评价方法对水环境质量状况进行评价。先对水质主要影响因素采用改进主成分分析作降维处理并计算主成分得分值,再对选定的主成分作多元线性回归处理得到水质预测回归模型,并用于研究区水质的评估预测。结果表明:选出的4个主成分因子其累积方差贡献率达到87.3%,实现了数据结构的简化;同时,改进主成分回归预测值总体上更趋近于实测值,其预测结果的相对误差最大值4%,而常规方法预测结果的相对误差最大值接近10%,体现出该方法所建模型具有较高的预测精度。     

大气污染预报技术及有关防治对策的研究

依据历史监测数据,研究了大气污染预测预报的方法及污染防治对策。利用主分量分析、多元逐步回归、拟合与逼近等方法,进行了天气预报参数与主要污染物的关系研究,建立了大气污染预测预报模型,制定了大气污染警戒值及警戒措施。模型可做４８小时内大气污染预报及１０日内大气污染趋势分析。     

新疆喀什地区农村水厂供水污染影响因素分析

为查寻影响喀什地区农村水厂水持污染的主要原因,以采取相应的防治对策,对可能影响该地区农村水厂供水污染的２２个因素进行相关和多元逐步回归分析。结果表明,在入选的２２个因素中,水厂制水人员的供水卫生法规和基本卫生知识水平,改水与改善农村环境卫生及健康教育同步实施,水厂建立并执行卫生制度和对水厂供水进行定期卫生监督监测等４个因素对农村水厂的供水卫生状况影响最大。     

便携式气相色谱-氢火焰离子化检测器法测定低浓度总烃的氧气干扰特征研究

研究了采用便携式气相色谱-氢火焰离子化检测器法测定气体中的总烃浓度时,氧气浓度对测定结果的影响,并采用多元线性回归模型对总烃的测定结果进行了校准。当总烃的实际浓度不低于9 mg/m³时,氧气的干扰较小(相对标准偏差≤2%)。在试验的所有总烃实际浓度下(1~200 mg/m³),当氧气浓度为1%~7%时,也无需考虑氧气干扰的影响(相对标准偏差≤2%)。当总烃的实际浓度低于9 mg/m³且氧气浓度为9%~21%时,需依据多元线性回归分析原理并采用模型对测得的总烃浓度进行校准。线性回归方程分析结果显示,模型拟合情况良好(R²=0.999),可以用来修正测试结果,从而得到更加准确的总烃实际浓度。     

兰州市城区冬季大气污染模式确定及预测预报的研究

利用１０年的监测数据，采用主成分分析和多元逐步回归分析方法，推导出兰州市城区冬季大气主要污染物的预测预报模型，同时，结合兰州城区冬季大气污染现状及气象特征，制定了首要污染的警戒值和应急措施，通过验证，模型的精度和警戒值指标符合实际情况并可满足预测预报的要求。     

Biomarkers of internal dose for the assessment of environmental exposure to benzene

The urinary excretion of t,t-muconic acid (t,t-MA), S-phenylmercapturic acid (SPMA) and urinary benzene and the influence of a smoking habit and of exposure to urban traffic on the urinary excretion of these biomarkers were investigated in 137 male adults from the general population. All subjects were not occupationally exposed to benzene and resident in two cities in Puglia (Southern-Italy). Environmental exposure to benzene was measured using passive personal samplers. The biomarkers t,t-MA, SPMA and urinary benzene were determined in urine samples collected from each subject at the end of the environmental sampling. The percentage of cases above the limit of detection was higher for SPMA and urinary benzene in smokers than in non-smokers, and for airborne benzene and urinary benzene in subjects exposed to urban traffic. Airborne benzene was correlated with the time spent in urban traffic during the environmental sampling. Among the biomarkers, urinary benzene was found to be correlated with airborne benzene only in non-smokers, and with the time spent in urban traffic, both in smokers and non-smokers considered together, and in non-smokers only. Finally, multiple regression analysis showed that the urinary excretion of all the biomarkers was dependent on the number of cigarettes smoked per day and, for urinary benzene, also on the time spent in urban traffic. In conclusion, urinary benzene seems to be a more valid biomarker than t,t-MA and SPMA to assess environmental exposure to extremely low concentrations of benzene. Cigarette smoking prevailed over traffic exhaust fumes in determining the internal dose of benzene.     

Predicting dissolved oxygen concentration using kernel regression modeling approaches with nonlinear hydro-chemical data

Kernel function-based regression models were constructed and applied to a nonlinear hydro-chemical dataset pertaining to surface water for predicting the dissolved oxygen levels. Initial features were selected using nonlinear approach. Nonlinearity in the data was tested using BDS statistics, which revealed the data with nonlinear structure. Kernel ridge regression, kernel principal component regression, kernel partial least squares regression, and support vector regression models were developed using the Gaussian kernel function and their generalization and predictive abilities were compared in terms of several statistical parameters. Model parameters were optimized using the cross-validation procedure. The proposed kernel regression methods successfully captured the nonlinear features of the original data by transforming it to a high dimensional feature space using the kernel function. Performance of all the kernel-based modeling methods used here were comparable both in terms of predictive and generalization abilities. Values of the performance criteria parameters suggested for the adequacy of the constructed models to fit the nonlinear data and their good predictive capabilities.     

Using GA-Ridge regression to select hydro-geological parameters influencing groundwater pollution vulnerability

For groundwater conservation and management, it is important to accurately assess groundwater pollution vulnerability. This study proposed an integrated model using ridge regression and a genetic algorithm (GA) to effectively select the major hydro-geological parameters influencing groundwater pollution vulnerability in an aquifer. The GA-Ridge regression method determined that depth to water, net recharge, topography, and the impact of vadose zone media were the hydro-geological parameters that influenced trichloroethene pollution vulnerability in a Korean aquifer. When using these selected hydro-geological parameters, the accuracy was improved for various statistical nonlinear and artificial intelligence (AI) techniques, such as multinomial logistic regression, decision trees, artificial neural networks, and case-based reasoning. These results provide a proof of concept that the GA-Ridge regression is effective at determining influential hydro-geological parameters for the pollution vulnerability of an aquifer, and in turn, improves the AI performance in assessing groundwater pollution vulnerability.     

Selecting optimal hyperspectral bands to discriminate nitrogen status in durum wheat: a comparison of statistical approaches

Hyperspectral data can provide prediction of physical and chemical vegetation properties, but data handling, analysis, and interpretation still limit their use. In this study, different methods for selecting variables were compared for the analysis of on-the-ground hyperspectral signatures of wheat grown under a wide range of nitrogen supplies. Spectral signatures were recorded at the end of stem elongation, booting, and heading stages in 100 georeferenced locations, using a 512-channel portable spectroradiometer operating in the 325–1075-nm range. The following procedures were compared: (i) a heuristic combined approach including lambda-lambda R² (LL R²) model, principal component analysis (PCA), and stepwise discriminant analysis (SDA); (ii) variable importance for projection (VIP) statistics derived from partial least square (PLS) regression (PLS-VIP); and (iii) multiple linear regression (MLR) analysis through maximum R-square improvement (MAXR) and stepwise algorithms. The discriminating capability of selected wavelengths was evaluated by canonical discriminant analysis. Leaf-nitrogen concentration was quantified on samples collected at the same locations and dates and used as response variable in regressive methods. The different methods resulted in differences in the number and position of the selected wavebands. Bands extracted through regressive methods were mostly related to response variable, as shown by the importance of the visible region for PLS and stepwise. Band selection techniques can be extremely useful not only to improve the power of predictive models but also for data interpretation or sensor design.     

Assessment of dermal exposure to benzene and toluene in shoe manufacturing by activated carbon cloth patches

OBJECTIVES: The aim of this investigation was to use activated carbon cloth (ACC) patches to study the probability and extent of dermal exposure to benzene and toluene in a shoe factory. METHODS: Inhalation and dermal exposure loading were measured simultaneously in 70 subjects on multiple days resulting in 113 observations. Dermal exposure loading was assessed by ACC patches attached to likely exposed skin areas (e.g. the palm of the hand and abdomen). A control patch at the chest and an organic vapor monitor (OVM) were used to adjust the hand and abdomen patches for the contribution from the air through passive absorption of benzene and toluene on the ACC patches. Systemic exposure was assessed by quantification of unmetabolized benzene (UBz) and toluene (UTol) in urine. RESULTS: Mean air concentrations for the study population were 1.5 and 7.5 ppm for benzene and toluene, respectively. Iterative regression analyses between the control patch, OVM and the dermal patches showed that only a small proportion of the ACC patches at the hand had likely benzene (n = 4; mean 133 microg cm(-2) h(-1)) or toluene (n = 5; mean 256 microg cm(-2) h(-1)) contamination. Positive patches were exclusively observed among subjects performing the task of gluing. Significant dermal exposure loading to the abdomen was detected only for toluene (n = 2; mean 235 microg cm(-2) h(-1)). No relation was found between having a positive hand or abdomen ACC patch and UBz or UTol levels. In contrast a strong association was found between air levels of benzene (p = 0.0016) and toluene (p < 0.0001) and their respective urinary levels. CONCLUSIONS: ACC patches are shown to be a useful technique for quantifying the probability of dermal exposure to organic solvents and to provide estimates of the potential contribution of the dermal pathway to systemic exposure. Using ACC patches we show that dermal exposure to benzene and toluene in a shoe manufacturing factory is probably rare, and when it occurred exposures were relatively low and did not significantly contribute to systemic exposure.     

The impact of domestic wood burning on personal, indoor and outdoor levels of 1,3-butadiene, benzene, formaldehyde and acetaldehyde

The aim of this study was to quantify personal exposure and indoor levels of the suspected or known carcinogenic compounds 1,3-butadiene, benzene, formaldehyde and acetaldehyde in a small Swedish town where wood burning for space heating is common. Subjects (wood burners, n = 14), living in homes with daily use of wood-burning appliances were compared with referents (n = 10) living in the same residential area. Personal exposure and stationary measurements indoors and at an ambient site were performed with diffusive samplers for 24 h. In addition, 7 day measurements of 1,3-butadiene and benzene were performed inside and outside the homes. Wood burners had significantly higher median personal exposure to 1,3-butadiene (0.18 microg m(-3)) compared with referents (0.12 microg m(-3)), which was also reflected in the indoor levels. Significantly higher indoor levels of benzene were found in the wood-burning homes (3.0 microg m(-3)) compared with the reference homes (1.5 microg m(-3)). With regard to aldehydes, median levels obtained from personal and indoor measurements were similar although the four most extreme acetaldehyde levels were all found in wood burners. High correlations were found between personal and indoor levels for all substances (r(s) > 0.8). In a linear regression model, type of wood-burning appliance, burning time and number of wood replenishments were significant factors for indoor levels of 1,3-butadiene. Domestic wood burning seems to increase personal exposure to 1,3-butadiene as well as indoor levels of 1,3-butadiene and benzene and possibly also acetaldehyde. The cancer risk from these compounds at exposure to wood smoke is, however, estimated to be low in developed countries.     

Predictions of soil organic carbon using laboratory-based hyperspectral data in the northern Tianshan mountains, China

This paper presents a study dealing with soil organic carbon (SOC) estimation of soil through the combination of soil spectroscopy and multivariate stepwise linear regression. Soil samples were collected in the three sub-regions, dominated by brown calcic soil, in the northern Tianshan Mountains, China. Spectral measurements for all soil samples were performed in a controlled laboratory environment by a portable ASD FieldSpec FR spectrometer (350–2,500 nm). Twelve types of transformations were applied to the soil reflectance to remove the noise and to linearize the correlation between reflectance and SOC content. Based on the spectral reflectance and its derivatives, hyperspectral models can be built using correlation analysis and multivariable statistical methods. The results show that the main response range of soil organic carbon is between 400 and 750 nm. Correlation analysis indicated that SOC has stronger correlation with the second derivative than with the original reflectance and other transformations data. The two models developed with laboratory spectra gave good predictions of SOC, with root mean square error (RMSE) <5.0. The use of the full visible near-infrared spectral range gave better SOC predictions than using visible separately. The multivariate stepwise linear regression of second derivate model (model A) is optimal for estimating SOC content, with a determination coefficient of 0.894 and RMSE of 0.322. The results of this research study indicated that, for the grassland regions, combining soil spectroscopy and mathematical statistical methods does favor accurate prediction of SOC.     

The impacts of population change on carbon emissions in China during 1978–2008

This study examines the impacts of population size, population structure, and consumption level on carbon emissions in China from 1978 to 2008. To this end, we expanded the stochastic impacts by regression on population, affluence, and technology model and used the ridge regression method, which overcomes the negative influences of multicollinearity among independent variables under acceptable bias. Results reveal that changes in consumption level and population structure were the major impact factors, not changes in population size. Consumption level and carbon emissions were highly correlated. In terms of population structure, urbanization, population age, and household size had distinct effects on carbon emissions. Urbanization increased carbon emissions, while the effect of age acted primarily through the expansion of the labor force and consequent overall economic growth. Shrinking household size increased residential consumption, resulting in higher carbon emissions. Households, rather than individuals, are a more reasonable explanation for the demographic impact on carbon emissions. Potential social policies for low carbon development are also discussed.     

Spatial analysis and land use regression of VOCs and NO2 in Dallas, Texas during two seasons

Passive air sampling for nitrogen dioxide (NO(2)) and select volatile organic compounds (VOCs) was conducted at 24 fire stations and a compliance monitoring site in Dallas, Texas, USA during summer 2006 and winter 2008. This ambient air monitoring network was established to assess intra-urban gradients of air pollutants to evaluate the impact of traffic and urban emissions on air quality. Ambient air monitoring and GIS data from spatially representative fire station sites were collected to assess spatial variability. Pairwise comparisons were conducted on the ambient data from the selected sites based on city section. These weeklong samples yielded NO(2) and benzene levels that were generally higher during the winter than the summer. With respect to the location within the city, the central section of Dallas was generally higher for NO(2) and benzene than north and south. Land use regression (LUR) results revealed spatial gradients in NO(2) and selected VOCs in the central and some northern areas. The process used to select spatially representative sites for air sampling and the results of analyses of coarse- and fine-scale spatial variability of air pollutants on a seasonal basis provide insights to guide future ambient air exposure studies in assessing intra-urban gradients and traffic impacts.     

Environmental and biological monitoring of benzene in traffic policemen, police drivers and rural outdoor male workers

To evaluate exposure to benzene in urban and rural areas, an investigation into personal exposure to benzene in traffic policemen, police drivers and rural (roadmen) male outdoor workers was carried out. Personal samples and data acquired using fixed monitoring stations located in different areas of the city were used to measure personal exposure to benzene in 62 non-smoker traffic policemen, 22 police drivers and 57 roadmen. Blood benzene, urinary trans-trans muconic acid (t,t-MA) and S-phenyl-mercapturic acid (S-PMA) were measured at the end of work shift in 62 non-smoker traffic policemen, 22 police drivers and 57 roadmen and 34 smoker traffic policemen, 21 police drivers and 53 roadmen. Exposure to benzene was similar among non-smoker traffic policemen and police drivers and higher among non-smoker urban workers compared to rural workers. Blood benzene, t,t-MA and S-PMA were similar among non-smoker traffic policemen and police drivers; blood benzene and t,t-MA were significantly higher in non-smoker urban workers compared to rural workers. Significant increases in t,t-MA were found in smokers vs. non-smokers. In non-smoker urban workers airborne benzene and blood benzene, and t,t-MA and S-PMA were significantly correlated. This study gives an evaluation of the exposure to benzene in an urban area, comparing people working in the street or in cars, to people working in a rural area. Benzene is a certain carcinogen for humans. The results we showed should lead to more in-depth studies about the effects on health of these categories of workers.