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1.
Background, Aim and Scope Air quality is an field of major concern in large cities. This problem has led administrations to introduce plans and regulations
to reduce pollutant emissions. The analysis of variations in the concentration of pollutants is useful when evaluating the
effectiveness of these plans. However, such an analysis cannot be undertaken using standard statistical techniques, due to
the fact that concentrations of atmospheric pollutants often exhibit a lack of normality and are autocorrelated. On the other
hand, if long-term trends of any pollutant’s emissions are to be detected, meteorological effects must be removed from the
time series analysed, due to their strong masking effects.
Materials and Methods The application of statistical methods to analyse temporal variations is illustrated using monthly carbon monoxide (CO) concentrations
observed at an urban site. The sampling site is located at a street intersection in central Valencia (Spain) with a high traffic
density. Valencia is the third largest city in Spain. It is a typical Mediterranean city in terms of its urban structure and
climatology. The sampling site started operation in January 1994 and monitored CO ground level concentrations until February
2002. Its geographic coordinates are W0°22′52″ N39°28′05″ and its altitude is 11 m. Two nonparametric trend tests are applied.
One of these is robust against serial correlation with regards to the false rejection rate, when observations have a strong
persistence or when the sample size per month is small. A nonparametric analysis of the homogeneity of trends between seasons
is also discussed. A multiple linear regression model is used with the transformed data, including the effect of meteorological
variables. The method of generalized least squares is applied to estimate the model parameters to take into account the serial
dependence of the residuals of this model. This study also assesses temporal changes using the Kolmogorov-Zurbenko (KZ) filter.
The KZ filter has been shown to be an effective way to remove the influence of meteorological conditions on O3 and PM to examine underlying trends.
Results The nonparametric tests indicate a decreasing, significant trend in the sampled site. The application of the linear model
yields a significant decrease every twelve months of 15.8% for the average monthly CO concentration. The 95% confidence interval
for the trend ranges from 13.9% to 17.7%. The seasonal cycle also provides significant results. There are no differences in
trends throughout the months. The percentage of CO variance explained by the linear model is 90.3%. The KZ filter separates
out long, short-term and seasonal variations in the CO series. The estimated, significant, long-term trend every year results
in 10.3% with this method. The 95% confidence interval ranges from 8.8% to 11.9%. This approach explains 89.9% of the CO temporal
variations.
Discussion The differences between the linear model and KZ filter trend estimations are due to the fact that the KZ filter performs the
analysis on the smoothed data rather than the original data. In the KZ filter trend estimation, the effect of meteorological
conditions has been removed. The CO short-term componentis attributable to weather and short-term fluctuations in emissions.
There is a significant seasonal cycle. This component is a result of changes in the traffic, the yearly meteorological cycle
and the interactions between these two factors. There are peaks during the autumn and winter months, which have more traffic
density in the sampled site. There is a minimum during the month of August, reflecting the very low level of vehicle emissions
which is a direct consequence of the holiday period.
Conclusions The significant, decreasing trend implies to a certain extent that the urban environment in the area is improving. This trend
results from changes in overall emissions, pollutant transport, climate, policy and economics. It is also due to the effect
of introducing reformulated gasoline. The additives enable vehicles to burn fuel with a higher air/fuel ratio, thereby lowering
the emission of CO. The KZ filter has been the most effective method to separate the CO series components and to obtain an
estimate of the long-term trend due to changes in emissions, removing the effect of meteorological conditions.
Recommendations and Perspectives Air quality managers and policy-makers must understand the link between climate and pollutants to select optimal pollutant
reduction strategies and avoid exceeding emission directives. This paper analyses eight years of ambient CO data at a site
with a high traffic density, and provides results that are useful for decision-making. The assessment of long-term changes
in air pollutants to evaluate reduction strategies has to be done while taking into account meteorological variability 相似文献
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采用遥感尾气测试系统实测了柴油车在实际道路工况下的CO、HC和NO排放特征,修正了排放因子的计算方法,并与车载排放测试系统(PEMS)实测结果进行了验证,获得了实测车辆的CO、HC和NO排放因子.测试结果显示,在各种遥感监测的工况下柴油车尾气中均含有较高浓度的氧气,未考虑氧气影响的燃烧方程反演获得的各污染物体积浓度计算值与PEMS实测值的偏差较大,且氧气浓度越大,偏差越大.经过氧气修正的燃烧方程反演计算的尾气浓度与PEMS实测值吻合度大幅提升,适用于实际工况下遥感检测车辆尾气的反演计算.修正算法得到CO、HC和NO的排放因子离散性较小,精确度较高,可以为量化柴油车尾气排放贡献提供科学依据. 相似文献
4.
基于RF-LSTM的鸡舍恶臭气体预测研究 总被引:1,自引:0,他引:1
以鸡舍氨气为研究对象,对鸡舍氨气预测模型进行了研究.首先,利用随机森林算法(RF)对影响鸡舍氨气浓度的环境变量进行重要性排序,选取温度、湿度、光照、气象温度、降雨量作为模型的输入变量;在此基础上,构建了基于长短时记忆神经网络(LSTM)的鸡舍氨气浓度预测模型,并将提出的预测模型应用于江苏省宜兴市某养鸡场的氨气浓度预测中,并与LSTM模型、RF-Elman模型和RF-BP模型进行了对比实验,结果表明,基于RF-LSTM模型的预测效果最好,其平均绝对误差(MAE)、平均绝对百分误差(MAPE)和均方根误差(RMSE)分别为0.9183、4.9637%和1.4262;同时,为了验证该模型的性能,本文还实现了不同时间尺度的鸡舍氨气浓度预测,提前2h、3h、4h、5h氨气预测的平均绝对误差(MAE)分别为1.6218、2.1991、2.8553和3.0677.本文提出的预测模型提高了鸡舍氨气浓度的预测精度,可为减少鸡舍恶臭气体排放提供科学依据. 相似文献
5.
Peng Du Jianguo Liu Huaqiao Gui Jiaoshi Zhang Tongzhu Yu Jie Wang Yin Cheng Yihuai Lu Yawei Yao Qiang Fu ChihChieh Chen 《环境科学学报(英文版)》2020,32(1):238-249
PM_(2.5) separator directly affects the accuracy of PM_(2.5) sampling.The specification testing and evaluation for PM_(2.5) separator is particularly important,especially under China's wide variation of terrain and climate.In this study,first a static test apparatus based on polydisperse aerosol was established and calibrated to evaluate the performance of the PM_(2.5) separators.A uniform mixing chamber was developed to make particles mix completely.The aerosol concentration relative standard deviations of three test points at the same horizontal chamber position were less than 0.57%,and the particle size distribution obeyed logarithmic normal distribution with an R~2 of 0.996.The flow rate deviation between the measurement and the set point flow rate agreed to within ± 1.0% in the range of -40 to 50℃.Secondly,the separation,flow and loading characteristics of three cyclone separators(VSCC-A,SCC-A and SCC112) were evaluated using this system.The results showed that the 50% cutoff sizes(D_(50)) of the three cyclones were 2.48,2.47 and 2.44 μm when worked at the manufacturer's recommended flow rates,respectively.The geometric standard deviation(GSD) of the capture efficiency of VSCCA was 1.23,showed a slightly sharper than SCC-A(GSD = 1.27),while the SCC112 did not meet the relevant indicator(GSD = 1.2 ± 0.1) with a GSD = 1.44.The flow rate and loading test had a great effect on D_(50),while the GSD remained almost the same as before.In addition,the maintenance frequency under different air pollution conditions of the cyclones was summarized according to the loading test. 相似文献
6.
反硝化作用是地下水硝酸盐污染去除最重要的过程.由于水文地质条件和水文地球化学环境的复杂性和不确定性,精准测定含水层反硝化速率是反硝化过程的研究难点.选取潮白河冲洪积扇中部中国环境科学研究院地下水创新野外基地作为研究区,基于野外原位试验和15N同位素示踪法提出一种含水层反硝化速率的测定方法.该方法综合体现了研究区实际水文地质条件和水文地球化学环境对反硝化作用的影响,并充分考虑了硝酸盐在含水层中稀释弥散作用对计算结果的影响.结果表明:①潮白河冲洪积扇中部某地地下26~28 m处于还原环境,含水介质以粉细砂为主,ρ(NO3-N)平均值为2.77 mg/L.②地下26~28 m反硝化速率在349.52~562.99 μg/(kg·d)(以N计,下同)之间,平均值为450.31 μg/(kg·d).通过与研究区含水介质、采样深度和硝酸盐背景值相似的国内外案例对比研究,初步评估结果处于合理区间.③测试结果具有一定不确定性,主要来自忽略中间产物NO2-和NO的计算方法、扰动采样方法、N2O的操作规范程度及采样频率等方面.研究方法为测定含水层硝酸盐速率研究提供了新的思路,研究结果可为地下水中硝酸盐转化过程机理研究、地下水硝酸盐污染修复及风险管控提供关键的理论支撑数据. 相似文献
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目的形成布置压电阵列元件薄壁结构的建模方法,通过时域仿真验证控制效果。方法通过有限元软件建立了布置压电阵列元件薄壁结构的动力学模型,采用ANSYS APDL参数化语言编写程序,自动生成带有压电阵列分流电路的薄壁结构有限元模型,并开展了动力学仿真研究。结果实现了连接分流电路的周期压电结构时域仿真,验证了压电阵列分流阻尼技术在薄壁结构振动控制中的有效性。结论所形成的布置压电阵列元件薄壁结构的建模和仿真方法,可为航空薄壁结构的振动控制提供技术支持。 相似文献
10.
目的分析两相流引起的冲击力与管道固有频率的流固耦合特性。方法针对海洋油田水下集输-立管系统内严重段塞流这一不稳定流动,通过构建气液两相流固耦合预测模型,分析管内段塞频率、管外涡街频率等流动特性与管道固有频率的关系,获取严重段塞流不同阶段对管道的冲击力特性。结果在水平方向上,管道所受冲击力最大部位为水平管与下倾管连接处,约为703.3 N;在垂直方向上,管道所受冲击力最大部位为下倾管底部与立管连接处,约为-993.5 N。管道所受冲击力与管内两相流引发的压力波动存在明显的一致性,在液塞喷发阶段,会引起整个管道冲击力的剧增。结论严重段塞流同一周期内,不同流动阶段所引发的管道振动特性不同,且不可忽视,在实际海管设计及使用过程中,需要结合管道运行工况对海管振动特性进行综合评估。 相似文献