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1.
Harmful algal blooms (HABs) diminish the utility of reservoirs for drinking water supply, irrigation, recreation, and ecosystem service provision. HABs decrease water quality and are a significant health concern in surface water bodies. Near real-time monitoring of HABs in reservoirs and small water bodies is essential to understand the dynamics of turbidity and HAB formation. This study uses satellite imagery to remotely sense chlorophyll-a concentrations (chl-a), phycocyanin concentrations, and turbidity in two reservoirs, the Grand Lake O′ the Cherokees and Hudson Reservoir, OK, USA, to develop a tool for near real-time monitoring of HABs. Landsat-8 and Sentinel-2 imagery from 2013 to 2017 and from 2015 to 2020 were used to train and test three different models that include multiple regression, support vector regression (SVR), and random forest regression (RFR). Performance was assessed by comparing the three models to estimate chl-a, phycocyanin, and turbidity. The results showed that RFR achieved the best performance, with R2 values of 0.75, 0.82, and 0.79 for chl-a, turbidity, and phycocyanin, while multiple regression had R2 values of 0.29, 0.51, and 0.46 and SVR had R2 values of 0.58, 0.62, and 0.61 on the testing datasets, respectively. This paper examines the potential of the developed open-source satellite remote sensing tool for monitoring reservoirs in Oklahoma to assess spatial and temporal variations in surface water quality.  相似文献   
2.
Deep learning (DL) models are increasingly used to make accurate hindcasts of management-relevant variables, but they are less commonly used in forecasting applications. Data assimilation (DA) can be used for forecasts to leverage real-time observations, where the difference between model predictions and observations today is used to adjust the model to make better predictions tomorrow. In this use case, we developed a process-guided DL and DA approach to make 7-day probabilistic forecasts of daily maximum water temperature in the Delaware River Basin in support of water management decisions. Our modeling system produced forecasts of daily maximum water temperature with an average root mean squared error (RMSE) from 1.1 to 1.4°C for 1-day-ahead and 1.4 to 1.9°C for 7-day-ahead forecasts across all sites. The DA algorithm marginally improved forecast performance when compared with forecasts produced using the process-guided DL model alone (0%–14% lower RMSE with the DA algorithm). Across all sites and lead times, 65%–82% of observations were within 90% forecast confidence intervals, which allowed managers to anticipate probability of exceedances of ecologically relevant thresholds and aid in decisions about releasing reservoir water downstream. The flexibility of DL models shows promise for forecasting other important environmental variables and aid in decision-making.  相似文献   
3.
浮游植物是水生态系统中最重要的组成部分,因其对水环境变化敏感而常被用于指示水环境状态,但其对环境的响应受分类方法的影响.为了解洞庭湖浮游植物种群(门、属)和功能群(FG)两种分类法对环境的驱动响应特征和适用性,于2019年3~12月分水期对该湖共进行了4次采样,比较分析了浮游植物种群和功能群的分布特征及其与环境因子的响应关系,并对比了TLI指数、 Shannon-Wiener指数、Q指数等评价方法在洞庭湖的适用性.结果表明,洞庭湖共检出浮游植物6门61属,可划分为23个功能群和9个优势功能群,功能群演替趋势为P/MP/D(3月)■月)■月)■(12月).层次分割结果表明,洞庭湖浮游植物的种群分布与变化受环境因子的驱动大于空间驱动;影响浮游植物种群和功能群的主要环境因子为水温(WT)、高锰酸盐指数、溶解氧(DO)、电导率(Cond)、水位(WL)和总磷(TP),环境因子对两者的独立解释性排序相差不大.RDA分析表明,浮游植物功能群对环境因子的响应要优于浮游植物种群.综合对比分析发现,利用Q指数进行水质评价在洞庭湖水体有较好的适用性.  相似文献   
4.
水质是岩溶区可持续发展中最重要的环境问题之一.为探讨铅锌矿周边岩溶流域重金属污染及健康风险,采集了思的河岩溶流域河水和地下水样品18组,测定了9种重金属的浓度(Cu、Pb、Zn、Cd、Mn、Fe、As、Cr和Sr),采用了主成分分析、相关分析、水质指数、内梅罗综合污染指数、危害商和危害指数进行分析.结果表明,思的河水呈微碱性,河水中Cu、Pb、Zn、Cd、Mn、Fe、As和Sr浓度距离尾矿库越远其浓度越低.主成分分析和相关分析表明,思的河岩溶流域的重金属主要来自矿山废水的排放(55.42%)、碳酸岩风化溶解(21.41%)和人类活动(14.72%).河水中82%的样品为优质水,地下水样均为优质水,河水中内梅罗综合污染指数为4.12,属于强污染级,所有危害指数均在1以下.Pb、Zn、As、Cd和Cr是思的河岩溶流域内有潜在威胁的金属.重金属进入岩溶管道后浓度发生了明显变化,表明岩溶含水层特有性质影响着重金属浓度的空间变化.研究结果可为思的河流域和类似岩溶水流域的水资源污染防治和人类健康保护提供数据参考.  相似文献   
5.
This study investigated the water quality variation spanning 30 years (1986–2017) in 16 catchments of Hong Kong against different urbanization indices, namely, built area fraction; population; and product of population and built area fraction. Pearson correlations of three different periods of time (1988–1990, 1998–2000, and 2015–2017) indicated that water quality trends were dependent on the urbanization index. Total solids, nitrite-nitrogen, total phosphorus, electrical conductivity, dissolved oxygen, and flow rate had significant deteriorative trends (Pearson r > 0.5 and p < 0.05) with population and product of built area and population. Results also interpreted that built area fraction and product of built area and population were the worst and best indices that represented urbanization and/or its impacts, respectively. Mann-Kendall test for the entire 30 year period showed that water quality had improved with time with respect to certain water quality parameters (e.g., dissolved oxygen, ammoniacal nitrogen and total suspended solids). The results portrayed that although the urbanization of catchments had increased with time, the river water quality with respect to many parameters showed signs of improvement and the legislative measures implemented seemed to be effective in controlling pollution.  相似文献   
6.
为定量评估生物炭对主粮作物产量的影响,收集了公开发表的116篇相关文献,共866对数据,采用Meta分析法定量分析了生物炭对我国主粮作物产量的影响及其影响因子,同时构建结构方程模型(SEM)进一步解释了因子间的交互关系.结果表明,与不施用生物炭相比,生物炭施用后可改善主粮田土壤理化性质,提高主粮作物产量,平均增产率为8.77%.其中,当生物炭pH为7~8时,平均增产率最大,可达26.49%;其C/N<60时,平均增产率为13.73%,显著高于C/N≥60的平均增产率.将生物炭施入酸性或中性土壤中,更能发挥其增产效应.当施炭量为10~20 t·hm-2时,小麦和玉米的平均增产率最大;施炭量为15~25 t·hm-2时,水稻平均增产率最大.但是,不同施炭水平的水稻增产率相近,可考虑损失部分产量,适当减施以兼顾经济效益.此外,生物炭增产效应会随施用年限增加而不断减弱,一般3 a后增产不显著.SEM表明生物炭施用量不仅直接影响主粮作物产量,还通过改善土壤肥力间接影响主粮作物产量,而生物炭C/N和pH仅通过改善土壤肥力影响主粮作物产量.因此,今后...  相似文献   
7.
利用卡尔费休法可直接测定PM2.5水分含量,方法精密度及准确度均较好.将该方法应用于北京市城区站点2020年全年的PM2.5分析,结果显示PM2.5水分浓度年均值为(5.0±4.1)µg/m3,在PM2.5占比为(12.5±4.8)%,与PM2.5质量浓度呈显著相关.水分质量浓度与PM2.5的质量浓度月度及季节变化趋势基本一致.研究发现,随着空气污染加重,水分质量浓度及其在PM2.5占比均呈上升趋势,二者相关性明显增强.可见污染发生时,水分增加有利于颗粒物吸湿增长从而推高污染水平,对PM2.5的贡献同步增强.当沙尘污染发生时湿度处于同期较低水平,不利于细颗粒物的吸湿增长,水分质量浓度及其占比均处于较低水平. PM2.5水分与二次离子及有机物均有很好的相关性,说明水分为气态污染物提供非均相转化载体,促进硝酸盐、硫酸盐、有机物的进一步生成.PM2.5水分与地壳物质无相关性,证实地壳元素为一次源,不受水分影响.  相似文献   
8.
研究基于郑州与福州两地区GNIP(1985—1992年)大气降水同位素资料,对其大气降水同位素的季节变化以及环境因子进行比较分析。结果表明,郑州地区较福州地区季节变化明显,且两地区与温度和降水量均呈现负相关关系;根据两地区大气降水线方程得出,福州地区大气降水线方程斜率和截距大于郑州地区;两地区的d-excess值夏季高,冬季低;福州地区受台风影响,两地区降水量差别较大导致降水量在决定两地区月加权平均d-excess值时,福州地区整体比郑州地区偏大;采用MeteoInfo软件,并利用由美国国家大气研究中心所提供的气象资料,对两地区气团轨迹进行后向模拟,比较分析得出:郑州地区在夏季大部分水汽来自南海,春季、秋季和冬季的水汽均来自北方大陆;福州地区在夏季的水汽全部水汽来自低纬度的海洋,而春季、秋季和冬季的水汽仅有少部份来自北方大陆。  相似文献   
9.
Sectorial approach for monitoring heavy metal pollution in rivers has failed to report realistic pollution status and associated ecological and human health risks. The increasing spread of heavy metals from different sources and emerging risks to human and environmental health call for reexamining heavy metal pollution monitoring frameworks. Also, the sources, spread, and load of heavy metals in the environment have changed significantly over time, requiring consequent modification in the monitoring frameworks. Therefore, studies on heavy metal monitoring in rivers conducted in the last decade were evaluated for experimental designs, research frameworks, and data presentations. Most studies (∼99%) (i) lacked inclusiveness of all environmental compartments; (ii) focused on “one pollutant – one/two compartment” or sometimes “one pollutant – one compartment – one effect” approach; and (iii) remained “data-rich but information poor.” An ecological approach with integrative system thinking is proposed to develop a holistic approach for monitoring river pollution. It is visualized that heavy metal monitoring, risk analyses, and water management must incorporate tracking pollutants in different environmental compartments of a river (water, sediment, and floodplain/bank soil) and consider correlating it with riverbank land use. The systems-based pollution monitoring and assessment studies will reveal the critical factors that drive heavy metals pollutant movement in ecosystems and associated potential risks to the environment, wildlife, and humans. Also, water quality and pollution indexing tools would help better communicate complex pollution data and associated risks among all stakeholders. Therefore, integrating systems approaches in scientific- and policy-based tools would help sustainably manage the health of rivers, wildlife, and humans.  相似文献   
10.
In the United States, thermal power plant electrical generators (EGs) are large water diverters and consumptive users who need water for cooling. Retrofitting existing cooling systems to dry cooling and building new facilities with dry cooling can save water and reduce EG's vulnerability to drought. However, this can be an expensive source of water. We estimate that the cost of water saved by retrofitting cooling in existing EGs ranges from $0.04/m3 to $18/m3 depending on facility characteristics. Also water savings from building new EGs with dry cooling ranges in cost per unit water from $1.29/m3 to $2.24/m3. We compare costs with that for water development projects identified in the Texas State Water Plan. We find the water cost from converting to dry cooling is lower than many of the water development possibilities. We then estimate the impact of climate change on the cost of water saved, finding climate change can increase EG water use by up to 9.3% and lower the costs of water saved. Generally, it appears that water planners might consider cooling alterations as a cost competitive water development alternative whose cost would be further decreased by climate change.  相似文献   
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