Exposure Modeling(曝光建模)研究综述
Exposure Modeling 曝光建模 - , environmental fate, exposure modeling, and mitigation measures) as well as direct and indirect effects of fungicides on microorganisms, macrophytes, invertebrates, and vertebrates. [1] Recent FindingsIn general, the studies included in this review employed rigorous methodology with large sample sizes, appropriate study designs to maximize available cohort study or administrative data sources, and exposure modeling that accounted for spatial patterns in air pollution levels. [2] Refined assessments should ultimately depend on best‐available data for exposure modeling. [3] When estimating long-term pollutant concentrations via exposure modeling, facility-level annual average emission rates are readily available as model inputs for most air pollutants. [4] With the data from HQMS, many companies and research institutes demonstrate an accurate air pollution map on the Internet, which is valuable for many issues related to air quality, including exposure modeling and urban planning. [5]、环境归宿、暴露模型和缓解措施）以及杀菌剂对微生物、大型植物、无脊椎动物和脊椎动物的直接和间接影响。 [1] 最新发现一般而言，本综述中的研究采用了具有大样本量的严格方法、适当的研究设计以最大限度地利用可用的队列研究或行政数据源，以及考虑空气污染水平空间模式的暴露模型。 [2] 完善的评估最终应依赖于暴露建模的最佳可用数据。 [3] 在通过暴露建模估算长期污染物浓度时，设施级年平均排放率很容易作为大多数空气污染物的模型输入。 [4] 借助 HQMS 的数据，许多公司和研究机构在互联网上展示了准确的空气污染地图，这对于与空气质量相关的许多问题（包括暴露建模和城市规划）都很有价值。 [5]
Quality Exposure Modeling 质量曝光建模
BACKGROUND AND AIM: High-resolution, high-quality exposure modeling is critical for assessing the health effects of PM2. [1] High-resolution, high-quality exposure modeling is critical for assessing the health effects of ambient PM2. [2]背景和目的：高分辨率、高质量的暴露建模对于评估 PM2.5 对健康的影响至关重要。 [1] 高分辨率、高质量的暴露建模对于评估环境 PM2.5 对健康的影响至关重要。 [2]
exposure modeling approach 曝光建模方法
5 components estimated with two exposure modeling approaches, namely, supervised linear regression (SLR) and random forest (RF) algorithms. [1] This exposure modeling approach has been used by regulatory agencies in other countries/regions such as the United States, Canada and the European Union. [2] BACKGROUND Moderate correlations were previously observed between individual estimates of traffic-related air pollution (TRAP) produced by different exposure modeling approaches. [3]使用两种曝光建模方法估计的 5 个分量，即监督线性回归 (SLR) 和随机森林 (RF) 算法。 [1] 这种暴露建模方法已被美国、加拿大和欧盟等其他国家/地区的监管机构使用。 [2] 背景 先前在不同暴露建模方法产生的交通相关空气污染 (TRAP) 的个体估计之间观察到中等相关性。 [3]
exposure modeling showed
Exposure modeling showed that adults are exposed to FRs released from upholstered furniture mostly by dermal contact and children are exposed via dermal and ingestion exposure. [1] Results from exposure modeling showed that pedestrian and bicyclist volume was characterized by transportation network, population, traffic generators, and land use variables. [2]暴露模型显示，成人主要通过皮肤接触从软垫家具释放的阻燃剂暴露，儿童通过皮肤和食入暴露暴露。 [1] 暴露模型的结果表明，行人和骑自行车的人的数量由交通网络、人口、交通产生者和土地利用变量来表征。 [2]
exposure modeling framework
To improve the scientific community's modeling capabilities specific to this issue, we propose a noise exposure modeling framework that uses agent-based activity, multi-agent travel simulation and a European standardized noise emission and propagation model. [1] A goal of this study was to develop an exposure modeling framework that integrates agent-based activity and travel simulation with air pollution modeling for Tampa, Florida. [2]为了提高科学界针对该问题的建模能力，我们提出了一个噪声暴露建模框架，该框架使用基于代理的活动、多代理旅行模拟和欧洲标准化的噪声发射和传播模型。 [1] 本研究的一个目标是开发一个暴露建模框架，该框架将基于代理的活动和旅行模拟与佛罗里达州坦帕市的空气污染建模相结合。 [2]