Modelling individual preferences for environmental policy drivers: Empirical evidence of Italian lifestyle changes using a latent class approach

Degraded air quality severely affects the health of citizens worldwide. The design of effective policies requires exploring public preferences for environmental and air quality policy instruments. Within the EC-FP7 SEFIRA project, using a choice experiment that stresses the trade-offs between attributes, this study investigates public preferences for environmental policy drivers in Italy. The main objective is to investigate the role played by selected policy drivers in determining policy preferences, complemented by elasticity and willingness to pay estimations. Preference heterogeneity and the role of socio-economic and attitudinal variables are explored with a latent class model over 2400 respondents sampled across Italy. The results allow identifying the different role played by the policy drivers across the classes. It emerged that most of the respondents (43%) are particularly sensitive to the cost components (cost sensitive respondents). The remaining respondents instead show an important sensitivity towards personal engagement in term of changes in the mobility and eating habits (lifestyle-change sensitive respondents). However, while 29% of them perceive these habits’ changes as negatively impacting on the personal utility, the other 28% of respondents translate the potential changes in the habitual behaviour of driving and eating as environmental and health benefits. Based on the modelling results, potential policies are simulated reporting respondents’ reaction to selected scenarios. It shows the crucial role played by reduction of premature deaths due to atmospheric pollution and measure cost.     

Nitrogen cycling of atmosphere-plant-soft system in the typical Calamagrostis angustifolia wetland in the Sanjiang Plain,Northeast China

The nitrogen(N)distribution and cycling of atmosphere-plant-soil system in the typical meadow Calamagrostis angustifolia wetland (TMCW)and marsh meadow Calamagrostis angustifolia wetland(MMCW)in the Sanjiang plain were studied by a compartment model.The results showed that the N wet deposition amount was 0.757 gN/(m~2.a),and total inorganic N(TIN)was the main body (0.640 gN/(m~2.a)).The ammonia volatilization amounts of TMCW and MMCW soils in growing season were 0.635 and 0.687 gN/m~2, and the denitrification gaseous lost amounts were 0.617 and 0.405 gN/m~2,respectively.In plant subsystem,the N was mainly stored in root and litter.Soil organic N was the main N storage of the two plant-soil systems and the proportions of it were 93.98% and 92.16%, respectively.The calculation results of N turnovers among compartments of TMCW and MMCW showed that the uptake amounts of root were 23.02 and 28.18 gN/(m~2.a)and the values of aboveground were 11.31 and 6.08 gN/(m~2.a),the re-translocation amounts from aboveground to root were 5.96 and 2.70 gN/(m~2.a),the translocation amounts from aboveground living body to litter were 5.35 and 3.38 gN/(m~2.a),the translocation amounts from litter to soil were larger than 1.55 and 3.01 gN/(m~2.a),the translocation amounts from root to soil were 14.90 and 13.17 gN/(m~2.a),and the soil(0-15 cm)N net mineralization amounts were 1.94 and 0.55 gN/(m~2.a), respectively.The study of N balance indicated that the two plant-soil systems might be situated in the status of lacking N,and the status might induce the degradation of C.angustifolia wetland.     

Residual dynamics of thiacloprid in medical herbs marjoram, thyme, and camomile and in soil

Thiacloprid is a new insecticide of the chloronicotinyl family. To assess its risk after application, residual characteristics of thiacloprid in marjoram, thyme, and camomile and in soil were studied under field conditions. The active ingredient was extracted from the plant material using a mixture of acetone-water. After filtration, the extract was concentrated to the aqueous phase, diluted with water, and portioned against ethyl acetate on a matrix solid phase dispersion column. Thiacloprid was extracted from soil using a mixture of methanol-water, filtered, and re.extracted （clean up） with dichloromethane. The residues were quantified using HPLC-MS-MS. The methods were validated by recovery experiments. Thiacloprid residues in marjoram, thyme, and camomile and in soil persisted beyond 10, 14, 14, and 21 d but no residues were detected after 14, 21, 21, and 28 d, respectively. The data obtained in this study indicated that the biexponential model is more suitable than the first-order function to describe the decline of thiacloprid in fresh marjoram, fresh thyme, and dried camomile flowers with half-life （t1/2） of 1.1, 0.7, and 1.2 d, respectively. However, both the first-order function and biexponential model were found to be applicable for dissipation of thiacloprid in soil with almost the same t1/2 values of 3.5 and 3.6 d. The results indicated that thiacloprid dissipates rapidly and does not accumulate in the tested herbs and in soil.     

Artificial neural network model for identifying taxi gross emitter from remote sensing data of vehicle emission

Vehicle emission has been the major source of air pollution in urban areas in the past two decades. This article proposes an artificial neural network model for identifying the taxi gross emitters based on the remote sensing data. After carrying out the field test in Guangzhou and analyzing various factors from the emission data, the artificial neural network modeling was proved to be an advisable method of identifying the gross emitters. On the basis of the principal component analysis and the selection of algorithm and architecture, the Back-Propagation neural network model with 8-17-1 architecture was established as the optimal approach for this purpose. It gave a percentage of hits of 93%. Our previous research result and the result from aggression analysis were compared, and they provided respectively the percentage of hits of 81.63% and 75%. This comparison demonstrates the potentiality and validity of the proposed method in the identification of taxi gross emitters.     

崇明东滩湿地沉积物重金属污染的磁诊断

对取自崇明东滩的高潮滩、中潮滩和低潮滩的沉积物柱样进行了磁学、粒度、重金属含量等指标的分析、比较与综合研究. 结果表明:在以粉砂为主的崇明东滩,通过污染负荷指数法进行评价,除低潮滩无污染外,中潮滩和高潮滩均有中度的重金属污染. 选取中度重金属污染的高潮滩沉积物柱样进行粒度、重金属含量以及磁性参数的相关性分析显示,χ_fd,χ_ARM/SIRM与重金属含量和粘土(粒径<4 μm)之间有较高的相关性,并以此建立了磁诊断线性回归模型;χ_fd和χ_ARM/SIRM可作为重金属含量的替代指标.      

春季人工湿地常用植物对总氮去除效果的数学模型研究

选取芦苇、菖蒲、美人蕉和香蒲等4种南京地区常见的人工湿地常用植物作为供试植物,对其在春季对生活污水总氮(TN)去除效果的数学模型进行研究.通过监测引种植物后污水中TN浓度,利用回归分析建立湿地植物去除污染物的数学模型.结果表明:在毒季种植植物后污水中的TN浓度按线性关系减少,其中芦苇、菖蒲和香蒲对污染物的去除效果明显,其直线的相关系数分别达到0.927,0.929和0.979;美人蕉在春季对TN的去除能力较差,其线性仅为0.561.     

Evaluation of accuracy of linear regression models in predicting urban stormwater discharge characteristics

Stormwater runoff has been identified as a source of pollution for the environment, especially for receiving waters. In order to quantify and manage the impacts of stormwater runoff on the environment, predictive models and mathematical models have been developed. Predictive tools such as regression models have been widely used to predict stormwater discharge characteristics. Storm event characteristics, such as antecedent dry days （ADD）, have been related to response variables, such as pollutant loads and concentrations. However it has been a controversial issue among many studies to consider ADD as an important variable in predicting stormwater discharge characteristics. In this study, we examined the accuracy of general linear regression models in predicting discharge characteristics of roadway runoff. A total of 17 storm events were monitored in two highway segments, located in Gwangju, Korea. Data from the monitoring were used to calibrate United States Environmental Protection Agency＇s Storm Water Management Model （SWMM）. The calibrated SWMM was simulated for 55 storm events, and the results of total suspended solid （TSS） discharge loads and event mean concentrations （EMC） were extracted. From these data, linear regression models were developed. R2 and p-values of the regression of ADD for both TSS loads and EMCs were investigated. Results showed that pollutant loads were better predicted than pollutant EMC in the multiple regression models. Regression may not provide the true effect of site-specific characteristics, due to uncertainty in the data.     

合肥城郊典型源头溪流不同渠道形态的氮磷滞留特征

为揭示源头溪流中深潭和曲折沟渠两种典型渠道形态的氮磷养分滞留特征,在合肥城郊二十埠河的某一级支流上,以NH4Cl和KH2PO4为添加营养盐,以NaCl为保守型示踪剂,开展现场示踪实验.在此基础上,利用OTIS模型软件、暂态存储参数和养分螺旋原理,解析深潭和曲折沟渠氮磷滞留特征.结果表明:1深潭的As值较曲折沟渠大,但其α值则较弯曲沟渠小1个数量级,而且As和α值随水文条件变化均不显著;2深潭中主渠道流动水体的NH+4-λ较其暂态存储区的NH+4-λs高2~3个数量级,曲折沟渠的NH+4-λ与NH+4-λs数值较为接近;3深潭中NH+4-Vf较SRP-Vf高1~2个数量级,而在曲折沟渠中,不仅NH+4-Vf与SRP-Vf数值较为相近,NH+4-Sw与SRP-Sw也基本相当;4深潭的NH+4-U较SRP-U高出2~3个数量级,曲折沟渠NH+4-U则较SRP-U高出1~2个数量级;5总的来说,在对NH+4和SRP滞留影响方面,深潭和曲折沟渠存在较大的差异性,且在深潭中NH+4的滞留效应显著超过SRP.     

杭州市灰霾与非灰霾日不同粒径大气颗粒物来源解析

在2011年典型灰霾和非灰霾天气下,采集了杭州市不同粒径的大气颗粒物样品,测定并分析各粒径段颗粒物的质量浓度及其化学成分;同时采集并分析了主要污染源排放的颗粒物样品,通过CMB(化学质量平衡)模型进行源解析. 结果表明:灰霾天气下,二次粒子是杭州市各粒径段颗粒物的首要贡献源,其对≤1.1、>1.1~3.3、>3.3~5.8和>5.8~10μm粒径段的颗粒物贡献率分别为60.4%、62.2%、54.8%和46.5%. 在一次排放源中,机动车尾气是≤1.1和>1.1~3.3μm粒径段颗粒物的重要来源,贡献率分别为13.8%和12.2%;城市扬尘是>3.3~5.8μm粒径段颗粒物的重要来源,贡献率达到16.0%;而建筑施工尘是>5.8~10μm粒径段颗粒物的重要来源,贡献率为14.2%. 非灰霾天气下,随着颗粒物粒径的增加,二次粒子的贡献率显著下降,对≤1.1μm粒径段颗粒物的贡献率为42.7%,而对>5.8~10μm粒径段颗粒物的贡献率仅为15.5%;机动车是各粒径段颗粒物的重要贡献源,贡献率均在20%以上;煤烟尘是≤3.3μm细粒径段颗粒物的重要贡献源类,贡献率为22.0%;城市扬尘是>3.3~5.8μm粒径段颗粒物的重要来源,贡献率为18.3%;建筑施工尘依然是>5.8~10μm粒径段颗粒物的重要来源,贡献率为21.4%.      

基于Tripod Beta事故模型的触电事故原因分析

应用Tripod Beta事故模型对某厂触电事故进行了深入分析,得到了事故原因,并提出预防类似事故的建议措施。