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1.
Using within-weather-group air pollution prediction models developed in Part I of this research, this study estimates future air pollution levels for a variety of pollutants (specifically, carbon monoxide – CO, nitrogen dioxide – NO2, ozone – O3, sulphur dioxide – SO2, and suspended particles – SP) under future climate scenarios for four cities in south-central Canada. A statistical downscaling method was used to downscale five general circulation model (GCM) scenarios to selected weather stations. Downscaled GCM scenarios were used to compare respective characteristics of the weather groups developed in Part I; discriminant function analysis was used to allocate future days from two windows of time (2040–2059 and 2070–2089) into one of four weather groups. In Part I, the four weather groups were characterised as hot, cold, air pollution-related, and other (defined as relatively good air quality and comfortable weather conditions). In estimating future daily air pollution concentrations, three future pollutant emission scenarios were considered: Scenario I – emissions decreasing 20% by 2050, Scenario II – future emissions remaining at the same level as at the end of the twentieth century, and Scenario III – emissions increasing 20% by 2050. The results showed that, due to increased temperatures, the average annual number of days with high O3 levels in the four selected cities could increase by more than 40–100% by the 2050s and 70–200% by the 2080s (from the current areal average of 8 days) under the three pollutant emission scenarios. The corresponding number of low O3 days could decrease by 4–10% and 5–15% (from the current areal average of 312 days). For the rest of the pollutants, future air pollution levels will depend on future pollutant emission levels. Under emission Scenarios II and III, the average annual number of high pollution days could increase 20–40% and 80–180%, respectively, by the middle and late part of this century. In contrast, under Scenario I, the average annual number of high pollution days could decrease by 10–65%.  相似文献   

2.
以长三角3省1市为研究区,旨在构建长三角地区土壤水分长时间序列,为农业生产和遥感算法提供数据支撑。研究基于空间匹配的站点土壤水分数据和气象数据,利用主成分分析得到4个有效主成分作为线性回归和BP神经网络模型的输入因子,建立土壤水分与气象因子间的定量关系,并评估所构建模型的精度。结果表明,基于全部站点数据建立的单一BP神经网络模型优于单一线性回归模型。单一线性回归模型的R 2=0.34,RMSE=0.046 m3/m3,MAE=3.67%;而单一BP神经网络模型的训练、验证和测试3个数据集的R 2均在0.64以上,RMSE<0.043 m3/m3,MAE低于3.4%。根据逐个站点分别构建分站点的BP神经网络模型,其总体精度高于基于全部站点数据构建的单一BP神经网络模型。分站点构建的BP神经网络模型的总体精度方面,3个数据集的R 2均值在0.75以上,RMSE<0.039 m3/m3,MAE低于3%。通过对逐个站点分别构建BP神经网络模型,获得了精度较高、较稳定的土壤水分拟合结果。  相似文献   

3.

Purpose

Soil-plant transfer models are needed to predict levels of mercury (Hg) in vegetables when evaluating food chain risks of Hg contamination in agricultural soils.

Materials and methods

A total of 21 soils covering a wide range of soil properties were spiked with HgCl2 to investigate the transfer characteristics of Hg from soil to carrot in a greenhouse experiment. The major controlling factors and prediction models were identified and developed using path analysis and stepwise multiple linear regression analysis.

Results and discussion

Carrot Hg concentration was positively correlated with soil total Hg concentration (R 2?=?0.54, P?<?0.001), and the log-transformation greatly improved the correlation (R 2?=?0.76, P?<?0.001). Acidic soil exhibited the highest bioconcentration factor (BCF) (ratio of Hg concentration in carrot to that in soil), while calcareous soil showed the lowest BCF among the 21 soil types. The significant direct effects of soil total Hg (Hgsoil), pH, and free Al oxide (AlOX) on the carrot Hg concentration (Hgcarrot) as revealed by path analysis were consistent with the result from stepwise multiple linear regression that yielded a three-term regression model: log [Hgcarrot]?=?0.52log [Hgsoil]???0.06pH???0.64log [AlOX]???1.05 (R 2?=?0.81, P?<?0.001).

Conclusions

Soil Hg concentration, pH, and AlOX content were the three most important variables associated with carrot Hg concentration. The extended Freundlich-type function could well describe Hg transfer from soil to carrot.  相似文献   

4.
Purpose

Soil pollution indices are an effective tool in the computation of metal contamination in soil. They monitor soil quality and ensure future sustainability in agricultural systems. However, calculating a soil pollution index requires laboratory measurements of multiple soil heavy metals, which increases the cost and complexity of evaluating soil heavy metal pollution. Visible and near-infrared spectroscopy (VNIR, 350–2500 nm) has been widely used in predicting soil properties due to its advantages of a rapid analysis, non-destructiveness, and a low cost.

Methods

In this study, we evaluated the ability of the VNIR to predict soil heavy metals (As, Cu, Pb, Zn, and Cr) and two commonly used soil pollution indices (Nemerow integrated pollution index, NIPI; potential ecological risk index, RI). Three nonlinear machine learning techniques, including cubist regression tree (Cubist), Gaussian process regression (GPR), and support vector machine (SVM), were compared with partial least squares regression (PLSR) to determine the most suitable model for predicting the soil heavy metals and pollution indices.

Results

The results showed that the nonlinear machine learning models performed significantly better than the PLSR model in most cases. Overall, the SVM model showed a higher prediction accuracy and a stronger generalization for Zn (R2V?=?0.95, RMSEV?=?6.75 mg kg?1), Cu (R2V?=?0.95, RMSEV?=?8.04 mg kg?1), Cr (R2V?=?0.90, RMSEV?=?6.57 mg kg?1), Pb (R2V?=?0.86, RMSEV?=?4.14 mg kg?1), NIPI (R2V?=?0.93, RMSEV?=?0.31), and RI (R2V?=?0.90, RMSEV 3.88). In addition, the research results proved that the high prediction accuracy of the three heavy metal elements Cu, Pb, and Zn and their significant positive correlations with the soil pollution indices were the reason for the accurate prediction of NIPI and RI.

Conclusion

Using VNIR to obtain soil pollution indices quickly and accurately is of great significance for the comprehensive evaluation, prevention, and control of soil heavy metal pollution.

  相似文献   

5.
ABSTRACT

Soil hydraulic parameters like moisture content at field capacity and permanent wilting point constitute significant input parameters of various biophysical models and agricultural practices (irrigation timing and amount of irrigation to be applied). In this study, the performance of three different methods (Multiple linear regression – MLR, Artificial Neural Network – ANN and Adaptive Neuro-Fuzzy Inference System – ANFIS) with different input parameters in prediction of field capacity and permanent wilting point from easily obtained soil characteristics were compared. Correlation analysis indicated that clay content, sand content, cation exchange capacity, CaCO3, and organic matter had significant correlations with FC and PWP (p < .01). Validation results revealed that the ANN model with the greatest R2 and the lowest MAE and RMSE value exhibited better performance for prediction of FC and PWP than the MLR and ANFIS models. ANN model had R2 = 0.83, MAE = 2.36% and RMSE = 3.30% for FC and R2 = 0.81, MAE = 2.15%, RMSE = 2.89% for PWP in training dataset; R2 = 0.80, MAE = 2.27%, RMSE = 3.12% for FC and R2 = 0.83, MAE = 1.84%, RMSE = 2.40% for PWP in testing dataset. Also, Bayesian Regularization (BR) algorithm exhibited better performance for both FC and PWP than the other training algorithms.  相似文献   

6.
Color sensor technologies offer opportunities for affordable and rapid assessment of soil organic carbon (SOC) and total nitrogen (TN) in the field, but the applicability of these technologies may vary by soil type. The objective of this study was to use an inexpensive color sensor to develop SOC and TN prediction models for the Russian Chernozem (Haplic Chernozem) in the Kursk region of Russia. Twenty-one dried soil samples were analyzed using a Nix Pro? color sensor that is controlled through a mobile application and Bluetooth to collect CIEL*a*b* (darkness to lightness, green to red, and blue to yellow) color data. Eleven samples were randomly selected to be used to construct prediction models and the remaining ten samples were set aside for cross validation. The root mean squared error (RMSE) was calculated to determine each model’s prediction error. The data from the eleven soil samples were used to develop the natural log of SOC (lnSOC) and TN (lnTN) prediction models using depth, L*, a*, and b* for each sample as predictor variables in regression analyses. Resulting residual plots, root mean square errors (RMSE), mean squared prediction error (MSPE) and coefficients of determination (R2, adjusted R2) were used to assess model fit for each of the SOC and total N prediction models. Final models were fit using all soil samples, which included depth and color variables, for lnSOC (R2 = 0.987, Adj. R2 = 0.981, RMSE = 0.003, p-value < 0.001, MSPE = 0.182) and lnTN (R2 = 0.980 Adj. R2 = 0.972, RMSE = 0.004, p-value < 0.001, MSPE = 0.001). Additionally, final models were fit for all soil samples, which included only color variables, for lnSOC (R2 = 0.959 Adj. R2 = 0.949, RMSE = 0.007, p-value < 0.001, MSPE = 0.536) and lnTN (R2 = 0.912 Adj. R2 = 0.890, RMSE = 0.015, p-value < 0.001, MSPE = 0.001). The results suggest that soil color may be used for rapid assessment of SOC and TN in these agriculturally important soils.  相似文献   

7.
This study aimed at examining effective sample treatments and spectral processing for an alternate method of soil nitrate determination using the attenuated total reflectance (ATR) of Fourier transform infrared (FTIR) spectroscopy. Prior to FTIR measurements, soil samples were prepared as paste to enhance adhesion between the ATR crystal and sample. The similar nitrate peak heights of soil pastes and their supernatants indicated that the nitrate in the liquid portion of the soil paste mainly responded to the FTIR signal. Using a 0.01-M CaSO4 solution for the soil paste, which has no interference bands in the characteristic spectra of the analyte, increased the concentration of the nitrates to be measured. Second-order derivatives were used in the prediction model to minimize the interference effects and enhance the performance. The second-order derivative spectra contained a unique nitrate peak in a range of 1,400–1,200 cm?1 without interference of carbonate. A partial least square regression model using second-order derivative spectra performed well (R 2?=?0.995, root mean square error (RMSE)?=?23.5, ratio of prediction to deviation (RPD)?=?13.8) on laboratory samples. Prediction results were also good for a test set of agricultural field soils with a CaCO3 concentration of 6% to 8% (R 2?=?0.97, RMSE?=?18.6, RPD?=?3.5). Application of the prediction model based on soil paste samples to nitrate stock solution resulted in an increased RMSE (62.3); however, validation measures were still satisfactory (R 2?=?0.99, RPD?=?3.0).  相似文献   

8.
Measurements were made of concentrations and vertical flux densities (using the eddy correlation technique) for SO2 over a deciduous forest during March and April 1990. These were compared with estimates of dry deposition velocities, obtained from resistance analogue models that employ measured meteorological data as input. The models include: (1) the dry deposition module that forms part of a larger Eulerian air quality model, known as ADOM (Acid Deposition and Oxidant Model), (2) a modified version of ADOM and (3) a new parameterization for R c . The observations yielded V d values of about 0.5 cm s?1 for the daytime and 0.1 cm s?1 at night. ADOM's estimates were much larger, averaging about 3.0 cm s?1 during the daytime and about 2.0 cm s?1 at night. When the ADOM canopy resistance was increased by modifying its formulation, the modified dry deposition estimates were slightly larger during the day than at night, averaging about 0.5 cm s?1. The new parameterization for R c yielded smaller V d estimates with no diurnal variation. An attempt is made to relate the observed diurnal cycle of the dry deposition velocity to meteorological parameters.  相似文献   

9.
基于连续小波变换和随机森林的芦苇叶片汞含量反演   总被引：3，自引：0，他引：3  
植物重金属污染是当今世界面临的重大生态环境问题之一,高光谱技术为快速、大面积监测植被重金属含量提供了可能性。本研究以重金属汞(Hg)和湿地植物芦苇为研究对象,采用连续小波变换(CWT)和随机森林(RF)算法相结合的方法建立芦苇叶片总汞含量反演模型,以期寻求一种较为精准的植物汞污染反演模型,未来可通过高光谱技术建立模型来无损、快速估测湿地植物重金属汞污染情况,为湿地生态系统的监测提供方法支持。结果表明:芦苇叶片总汞含量敏感波段主要分布在可见光波段419~522 nm、664~695 nm和724~876nm以及近红外波段1 450~1 558 nm和1 972~2 500 nm;经CWT变换后,小波系数与叶片总汞含量的相关系数绝对值提高0.04~0.18,所构建的预测反演模型拟合效果R~2提高0.107~0.177,模型精度RMSE提高0.008~0.013,其中利用经小波变换的去包络线光谱(CR-CWT)数据建立的RF模型对芦苇叶片总汞含量的反演精度和拟合效果最优(R~2=0.713,RMSE=0.127);同时在土壤总汞含量约为20 mg?kg~(-1)时,采用CR-CWT数据构建RF模型的方法来反演芦苇叶片总汞含量更为准确和可靠(R~2=0.825,RMSE=0.051)。因此,利用RF算法进行植被重金属含量的反演具有一定的现实可行性,而结合CWT后所构建的反演模型对指导植被重金属含量监测更具参考价值,应用前景广阔。  相似文献   

10.
为更好地研究利用光谱反映的土壤重金属信息，实现具有多重金属复合污染问题的铅锌矿区土壤重金属含量高光谱快速估测，该研究以河北省某铅锌矿区为例，首先对研究区土壤的Cu、Cr、Ni、Zn、Cd、Pb污染状况进行了评价分析，其次基于实验室高光谱数据，组合变换光谱、特征变量和反演算法形成不同反演策略，通过各反演策略下的重金属反演精度比较，定量分析不同光谱预处理、特征选择和建模算法的优劣与适应性，构建最优反演模型。研究结果表明：1）研究区土壤Cr、Ni清洁程度较好，其余Cu、Zn、Cd、Pb均有不同程度污染；参比当地土壤背景值，区域内梅罗综合污染指数均值29.7，为重度污染，潜在生态风险因子均值1330.3，处于高生态风险状态；2）光谱预处理可以增强土壤重金属信息表达。其中，光谱微分效果较好，但易受噪声影响，而多元散射校正、标准正态变量、倒数对数变换可以进行光谱去噪，提升处理效果；3）特征选择方法中，相关系数法选择特征波段数目多，不同重金属反演R2 差异较大；Boruta法选择特征波段数目少，不同重金属反演R2 差异较小；4）BPNN、XGBoost可以较好描述重金属含量与光谱的非线性关系，相较于其他算法具有更好表现，分别实现了Cr、Ni、Zn和Pb、Cd的最优反演，SVMR实现了Cu的最优反演。研究表明，不同的光谱预处理、特征选择与建模算法对于土壤重金属含量的反演均具有较大影响，选择合适的处理、建模算法可以有效提升反演精度。该研究为进一步实现高效、准确、大范围遥感监测铅锌矿区土壤重金属污染状况提供参考依据。  相似文献   

11.

Purpose

The main objective of this study was to examine the potential of using hyperspectral image analysis for prediction of total carbon (TC), total nitrogen (TN) and their isotope composition (δ13C and δ15N) in forest leaf litterfall samples.

Materials and methods

Hyperspectral images were captured from ground litterfall samples of a natural forest in the spectral range of 400–1700 nm. A partial least-square regression model (PLSR) was used to correlate the relative reflectance spectra with TC, TN, δ13C and δ15N in the litterfall samples. The most important wavelengths were selected using β coefficient, and the final models were developed using the most important wavelengths. The models were, then, tested using an external validation set.

Results and discussion

The results showed that the data of TC and δ13C could not be fitted to the PLSR model, possibly due to small variations observed in the TC and δ13C data. The model, however, was fitted well to TN and δ15N. The cross-validation R2 cv of the models for TN and δ15N were 0.74 and 0.67 with the RMSEcv of 0.53% and 1.07‰, respectively. The external validation R2 ex of the prediction was 0.64 and 0.67, and the RMSEex was 0.53% and 1.19 ‰, for TN and δ15N, respectively. The ratio of performance to deviation (RPD) of the predictions was 1.48 and 1.53, respectively, for TN and δ15N, showing that the models were reliable for the prediction of TN and δ15N in new forest leaf litterfall samples.

Conclusions

The PLSR model was not successful in predicting TC and δ13C in forest leaf litterfall samples using hyperspectral data. The predictions of TN and δ15N values in the external litterfall samples were reliable, and PLSR can be used for future prediction.
  相似文献   

12.
ABSTRACT

The objective of this study was to develop a Linear Regression Model for the prediction of soil bulk density based on organic matter content (OM) and textural fractions (% sand, silt and clay) as well as the soil exchangeable sodium percentage (ESP) based on soil sodium adsorption ratio (SAR) in some salt affected soils of Sahl El-Hossinia, El-Sharkia Governorate, Egypt. For this purpose, 160 samples were randomly taken from top of the surface soil (0–30 cm) from different locations and samples were subjected to various analyzes. XLSTAT Version 2016.02.27444 software was used to build and test conceptual and empirical models. The statistical results of the study indicated that to predict soil bulk density (BD) based on organic matter content and textural fractions the Multiple linear regression model BD = 1.817–0.730 × OM – 0.002 × Clay – 0.001 × Silt with R2 = 0.794. On the other hand, to predict soil ESP based on SAR the linear regression model ESP = 5.577 + 0.851 × SAR with R2 = 0.773. A Linear Regression Model for prediction of BD and ESP of Sahl El-Hossinia, El-Sharkia Governorate, Egypt, can be used with high prediction.  相似文献   

13.
Gluten aggregation properties were investigated by means of the GlutoPeak device, a viscometer recently proposed as a rapid and sensitive test for measurement of wheat flour technological performance. In this study, 62 soft wheat flour samples of different quality and end use were utilized to evaluate if the GlutoPeak parameters could adequately predict chemical and rheological characteristics of soft wheat flour dough, that is, protein content measured by the Kjeldahl method, dough strength measured by a Chopin alveograph, and dough stability and water absorption measured by a Brabender farinograph. Linear correlation analysis showed that most GlutoPeak curve parameters were strongly correlated with protein content, dough strength, and water absorption. The statistical models, obtained by a stepwise multiple regression method, showed the GlutoPeak device to be a promising tool to characterize wheat flour (Radj2 = 0.84 for protein content, Radj2 = 0.71 for dough strength, and Radj2 = 0.67 for water absorption). The rather high accuracy of the prediction models for the three mentioned parameters confirmed that GlutoPeak parameters are well correlated with other frequently used flour quality parameters and are able to describe flour technological performance.  相似文献   

14.
[目的]研究黑龙江省西部地区"三北"工程区不同类型土壤的水分动态特征及其与气象因子的相关性,为该地区土壤墒情预测提供科学参考。[方法]通过建立小型基准气象观测站定点观测土壤水分含量及气象因子,并利用回归分析建立了无降雨条件下土壤水分的预测模型。[结果](1)生长季土壤水分变化均呈现消退期的现象,其中以黑土和黑钙土表现最为显著。3种土壤类型水分含量的变异系数都随土壤深度增大呈递减趋势。(2)相关分析结果表明,土壤水分含量与光照强度和大气温度均表现为负相关,与空气湿度表现为正相关,与降雨量和风速相关系数较小。(3)黑土和黑钙土的土壤水分日消耗量可由光照强度(X1)、湿度(X2)、风速(X3)和大气温度(X4)的变化来解释。[结论]土壤水分受气象因子综合调控,根据气象因子建立的模型可以用来预测无降雨条件下土壤水分的变化。  相似文献   

15.
This study assessed the effect of ambient air pollution on leaf characteristics of white willow, northern red oak, and Scots pine. Willow, oak, and pine saplings were planted at sixteen locations in Belgium, where nitrogen dioxide (NO2), ozone (O3), sulfur dioxide (SO2), and particulate matter (PM10) concentrations were continuously measured. The trees were exposed to ambient air during 6 months (April–September 2010), and, thereafter, specific leaf area (SLA), stomatal resistance (R s), leaf fluctuating asymmetry (FA), drop contact angle (CA), relative chlorophyll content, and chlorophyll fluorescence (F v/F m) were measured. Leaf characteristics of willow, oak, and pine were differently related to the ambient air pollution, indicating a species-dependent response. Willow and pine had a higher SLA at measuring stations with higher NO2 and lower O3 concentrations. Willow had a higher R s and pine had a higher F v/F m at measuring stations with a higher NO2 and lower O3 concentrations, while oak had a higher F v/F m and a lower FA at measuring stations with a higher NO2 and lower O3 concentrations. FA and R s of willow, oak, and pine, SLA of oak, and CA of willow were rather an indicator for local adaptation to the micro-environment than an indicator for the ambient air pollution.  相似文献   

16.
How the mixture of tree species modifies short-term decomposition has been well documented using litterbag studies. However, how litter of different tree species interact in the long-term is obscured by our inability to visually recognize the species identity of residual decomposition products in the two most decomposed layers of the forest floor (i.e. the Oe and Oa layers respectively). To overcome this problem, we used Near Infrared Reflectance Spectroscopy (NIRS) to determine indirectly the species composition of forest floor layers. For this purpose, controlled mixtures of increasing complexity comprising beech and spruce foliage materials at various stages of decomposition from sites differing in soil acid-base status were created. In addition to the controlled mixtures, natural mixtures of litterfall from mixed stands were used to develop prediction models. Following a calibration/validation procedure, the best regression models to predict the actual species proportion from spectral properties were selected for each tree species based on the highest coefficient of determination (R2) and the lowest root mean square error of prediction (RMSEP). For the validation, the R2 (predictions versus true proportions) were 0.95 and 0.94 for both beech and spruce components in mixtures of materials at all stages of decomposition from the gradient of sites. The R2 decreased only marginally by 0.04 when models were tested on independent samples of similar composition. The best models were used to predict the beech-spruce proportion in Oe and Oa layers of unknown composition. They provided in most cases plausible predictions when compared to the composition of the canopy above the sampling points. Thus, tedious and potentially erroneous hand sorting of forest floor layers may be replaced by the use of NIRS models to determine species composition, even at late stages of decomposition.  相似文献   

17.
准确地预测区域蒸散有助于区域水资源的合理利用,减少水资源浪费。为从多项气象因子中筛选出核心因子,构建少因子蒸散预测模型,高效精确预测蒸散,该研究在九大农业区选取23个典型站点,搜集降水量、日照时数等8个气象因子数据,使用分类回归树（classification and regression tree,CART）对气象因子进行重要度排序。基于排序结果,选取排序前3～5项气象因子,基于极限学习机（extreme learning machine,ELM）模型对蒸散进行预测。同时,使用遗传算法（genetic algorithm,GA）,粒子群算法（particle swarm optimization,PSO）,麻雀搜索算法（sparrow search algorithm,SSA）对ELM模型进行优化,并使用这3种优化算法（GA-ELM、PSO-ELM、SSA-ELM）构建少因子混合优化蒸散预测模型。结果表明：1）基于CART算法重要度排序结果,蒸散的主要影响因子依次是降水量、日照时数、平均本站气压、日最高气温、平均相对湿度。2）3种优化算法预测模型中,PSO-ELM模型的预测精度最高,23个站点的蒸散预测的均方根误差为6.608～22.077 mm/d,纳什效率系数为0.824～0.998,R2为0.908～0.995,平均绝对误差为5.075～16.677 mm/d。3）ELM模型在云贵高原区和四川盆地及周边地区有较好的适用性,3种优化算法在华南区和云贵高原区有较好的适用性,其中PSO-ELM模型的适用性最高。研究结果为中国九大农业区域的作物需水量计算提供参考。  相似文献   

18.
浙南春茶开采前后气象条件分析及开采期预报   总被引：2，自引：0，他引：2  
利用2001-2014年浙南遂昌县5个春茶主栽品种的开采期资料和同期气温、降水量、日照时数和空气相对湿度等气象资料,应用数理统计方法,分析各气象要素对浙南春茶开采期的影响,探讨不同品种春茶开采期的临界温度与积温范围,建立基于气象要素的春茶开采期预报模型。结果表明：浙南5个主栽品种茶叶的开采期以乌牛早最早,为2月24日,其余依次为：龙井43为3月9日,安吉白茶3月11日,福鼎白茶3月16日,鸠坑3月27日;春茶开采期与1-2月的月平均气温呈显著负相关关系（P＜0.05）,1-2月气温越高,春茶开采期提前越明显。将开采期日序与气象要素进行逐步回归分析,分别建立长期（1月底）、中期（2月底）的春茶开采期预报模型;2013年和2014年的预报结果检验表明,模型预报准确性随预报日离开采时间的缩短而提高;通过分析各品种春茶萌动温度和所需积温后提出,实际应用中可以在预报模型的基础上,根据天气预报的温度计算积温,调整各品种的开采期预报结果。  相似文献   

19.
Methylcyclopentadienyl manganese tricarbonyl (MMT) is an organic derivative of manganese used as an additive in unleaded gasoline in Canada since 1977. Moreover, Canada is the only country in the world to have authorized the replacement of lead alkyls by MMT in gasoline. The purpose of the present study is to assess the importance of air contamination by Mn in relation to other air pollutants (gaseous and particulates), meteorological variables and traffic density. The concentration of both the gaseous (O3, CO, NO, NO2, SO2) and the particulate pollutants (Mn, Pb, NO? 3, SO?? 4, TSP) had been measured by the Montreal Urban Community in 1990 at seven sampling stations located in high traffic and low traffic density areas. Data on the meteorological conditions during that same period were also used. Non-parametric correlation, ANOVA and discriminant analyses were used to compare gaseous and particulate pollutants found between both levels of traffic density. In almost 50% of the daily air samples measured in 1990, the Mn concentrations are higher than the urban background level estimated at 0.04 μg m?3 and the variations of Mn concentrations are significantly correlated in time with traffic density. Moreover, Mn and TSP discriminate the best high and low traffic density areas. No significant differences have been observed between Pb, O3 and SO2 concentrations in both areas. These results should not be interpreted in terms of potential health effects since it is presently impossible to determine the fate of the Mn in the environment and its importance in terms of human exposure.  相似文献   

20.
基于局部加权回归的土壤全氮含量可见-近红外光谱反演   总被引：6，自引：0，他引：6  
全氮是土壤肥力的重要指标,对作物产量具有决定性作用,采用土壤可见-近红外(Vis-NIR)光谱预测技术及时获取土壤全氮含量信息具有重要意义。采用来自5省的450个土壤样本来验证局部加权回归方法(LWR)结合Vis-NIR光谱技术预测大面积土壤全氮含量的适用性。结果表明,LWR模型的预测效果优于偏最小二乘回归(PLSR)、人工神经网络(ANN)和支持向量机(SVM),选取主成分数为5,相似样本为40时,模型验证的决定系数(RP2)为0.83,均方根误差(RMSEP)为0.25 g kg-1,测定值标准偏差与标准预测误差的比值(RPD)达到2.41。LWR从建模集中选取与验证样本相似的土样作为局部建模样本,降低了差别大的样本对模型的干扰,从而提高了模型的预测能力。因此,LWR建模方法通过大范围、大样本土壤光谱数据进行大尺度区域的全氮等土壤属性预测时能够发挥更好的作用。  相似文献   

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