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学者姓名：郎建垒
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摘要 ：
大气污染领域本体的半自动构建及语义推理
关键词 ：
语义推理 语义推理 注意力机制 注意力机制 大气污染 大气污染 自然语言处理 自然语言处理 实体关系抽取 实体关系抽取 本体 本体
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| GB/T 7714 | 刘博 , 张佳慧 , 李建强 et al. 大气污染领域本体的半自动构建及语义推理 [J]. | 刘博 , 2021 , 47 (3) : 246-259 . |
| MLA | 刘博 et al. "大气污染领域本体的半自动构建及语义推理" . | 刘博 47 . 3 (2021) : 246-259 . |
| APA | 刘博 , 张佳慧 , 李建强 , 李永 , 郎建垒 , 北京工业大学学报 . 大气污染领域本体的半自动构建及语义推理 . | 刘博 , 2021 , 47 (3) , 246-259 . |
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摘要 ：
典型优化目标函数下源参数反演性能对比研究
关键词 ：
目标函数 目标函数 启发式算法 启发式算法 源参数反演 源参数反演 突发大气污染事故 突发大气污染事故 高斯烟羽模型 高斯烟羽模型
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| GB/T 7714 | 胡峰 , 郎建垒 , 毛书帅 et al. 典型优化目标函数下源参数反演性能对比研究 [J]. | 胡峰 , 2021 , 41 (5) : 2081-2089 . |
| MLA | 胡峰 et al. "典型优化目标函数下源参数反演性能对比研究" . | 胡峰 41 . 5 (2021) : 2081-2089 . |
| APA | 胡峰 , 郎建垒 , 毛书帅 , 玄博元 , 中国环境科学 . 典型优化目标函数下源参数反演性能对比研究 . | 胡峰 , 2021 , 41 (5) , 2081-2089 . |
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摘要 ：
基于外场实验数据,从反演高估率、准确性、稳定性角度系统评估了8种典型目标函数在不同未知源参数反演情形下的反演性能差异.研究发现,不同目标函数反演性能差异显著.仅反演单参数源强时(Q),对数变换目标函数高估率最大(79.4%),偏差平方和目标函数准确性最高(P_(ARD<50%)=82.3%,ARD=(35.3±9.1)%),目标函数稳定性无明显差异(CV<0.01).三参数反演(Q,x,y)时,标准化均方根误差目标函数源强高估率最大(98.5%),对数变换目标函数准确性、稳定性最高(P_(ARD<50%)=91.1%,ARD=(48.4±9.8)%;CV=0.01);位置方面,偏差平方和目标函...
关键词 ：
启发式算法 启发式算法 源参数反演 源参数反演 目标函数 目标函数 突发大气污染事故 突发大气污染事故 高斯烟羽模型 高斯烟羽模型
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| GB/T 7714 | 胡峰 , 郎建垒 , 毛书帅 et al. 典型优化目标函数下源参数反演性能对比研究 [J]. | 中国环境科学 , 2021 , 41 (05) : 2081-2089 . |
| MLA | 胡峰 et al. "典型优化目标函数下源参数反演性能对比研究" . | 中国环境科学 41 . 05 (2021) : 2081-2089 . |
| APA | 胡峰 , 郎建垒 , 毛书帅 , 玄博元 . 典型优化目标函数下源参数反演性能对比研究 . | 中国环境科学 , 2021 , 41 (05) , 2081-2089 . |
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摘要 ：
本发明公开了一种实现秸秆露天焚烧大气污染物排放清单快速更新的方法，利用ArcGIS识别逐月秸秆露天焚烧火点；根据地理位置划分为六个区域；基于识别的月‑省秸秆露天焚烧火点的热辐射功率和区域划分，计算月‑省秸秆露天焚烧火点热辐射能；获取基于实际调研的年‑省秸秆露天焚烧比例数据，针对六个区域分别构建基于卫星遥感热辐射能的秸秆露天焚烧比例估算模型；建立包含全部目标年份与省份的年‑省秸秆露天焚烧比例估算数据库；收集自下而上方法估算秸秆露天焚烧大气污染物排放所需其他活动水平数据以及排放因子；估算年‑省秸秆露天焚烧大气污染物排放清单。本发明可实现年‑省秸秆露天焚烧大气污染物排放清单的快速更新。
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| GB/T 7714 | 周颖 , 张钰蓥 , 郎建垒 . 一种秸秆露天焚烧大气污染物排放清单快速更新的方法 : CN202110092935.7[P]. | 2021-01-25 . |
| MLA | 周颖 et al. "一种秸秆露天焚烧大气污染物排放清单快速更新的方法" : CN202110092935.7. | 2021-01-25 . |
| APA | 周颖 , 张钰蓥 , 郎建垒 . 一种秸秆露天焚烧大气污染物排放清单快速更新的方法 : CN202110092935.7. | 2021-01-25 . |
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摘要 ：
Estimating accurately airborne pollutant emissions source information (source strength and location) is important for achieving effective air pollution management or adequate emergency responses to accidents. Inversion method is one of the useful tools to identify the source parameters. The atmospheric dispersion scheme has been proven to be the key to determining the source inversion performance by influencing the accuracy of the dispersion models. Modifying the atmospheric dispersion scheme is an important potential method to improve the inversion performance, but this has not been studied previously. To fill this gap, a novel approach for parameter sensitivity analysis combined with an optimization method was proposed to improve the source inversion performance by optimizing empirical scheme. The dispersion coefficients σy and σz of the typical BRIGGS scheme under different atmospheric dispersion conditions were optimized and used for air pollutant dispersion and source inversion. The results showed that the prediction performance of the air pollutant concentrations was greatly improved with statistical indices |FB| and NMSE decreased by 0.22 and 2.07, respectively; FAC2 and R increased by 0.10, and 0.08, respectively. For source inversion, the results of the significance analysis suggested that the accuracy in the source strength and location parameter (x0) were both significantly improved by ∼271% (relative deviation reduced from 60.0% to 16.2%) and ∼121% (absolute deviation reduced from 27.6 to 12.5 m). The improvement of source strength inversion accuracy was more significant under unstable atmospheric conditions (stability class A, B, and C); the mean absolute relative deviation was reduced by 97.5%. These results can help to obtain more accurate source information and to provide reliable reference for air pollution managements or emergency response to accidents. This study provides a novel and versatile approach to improve estimation performance of pollutant emission sources and enhances our understanding of source inversion.
关键词 ：
Air pollutant emissions Air pollutant emissions Atmospheric dispersion coefficients Atmospheric dispersion coefficients Dispersion scheme optimization Dispersion scheme optimization Sensitivity analysis Sensitivity analysis Source inversion Source inversion
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| GB/T 7714 | Mao Shushuai , Lang Jianlei , Chen Tian et al. Improving source inversion performance of airborne pollutant emissions by modifying atmospheric dispersion scheme through sensitivity analysis combined with optimization model. [J]. | Environmental pollution , 2021 , 284 : 117186 . |
| MLA | Mao Shushuai et al. "Improving source inversion performance of airborne pollutant emissions by modifying atmospheric dispersion scheme through sensitivity analysis combined with optimization model." . | Environmental pollution 284 (2021) : 117186 . |
| APA | Mao Shushuai , Lang Jianlei , Chen Tian , Cheng Shuiyuan . Improving source inversion performance of airborne pollutant emissions by modifying atmospheric dispersion scheme through sensitivity analysis combined with optimization model. . | Environmental pollution , 2021 , 284 , 117186 . |
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摘要 ：
Rural residential coal combustion (RRCC) has detrimental effects on air quality, climate, and human health. There are large uncertainties regarding emissions from RRCC owing to the lack of consideration of several key factors (e.g. combination modes of coal and stoves, combustion modes, and high temporal resolution). In this study, we provided a new estimation framework for RRCC emissions through a case study in the Beijing-Tianjin-Hebei (BTH) region, China. The emission estimations were improved according to four aspects, namely (1) coal-specific and stove-specific coal consumption was calculated based on face-to-face field interviews of 6700 valid volunteers/households covering 288 villages in 50 counties; (2) the influences of combustion modes (flaming and smoldering modes) on emissions were considered; (3) emissions of different fuel-stove combinations were estimated based on coal, stove, and combustion mode-specific RRCC consumption and localised emission factors; and (4) a method for emission estimation with high temporal resolution (1 h) was developed. The results indicated that RRCC emitted 413.6 kt SO2, 55.7 kt NOx, 5717.3 kt CO, 149.4 kt VOCs, 167.1 kt PM2.5, 18.2 kt EC, 32.5 kt OC, and 8.2 kt NH3 in 2016. The combination of bituminous coal and an advanced coal stove was the most significant contributor (20.7-71.8%) to various pollutant emissions. Coal combusted under the flaming mode contributed to most (81.9%) of the total coal consumption, and thus emitted the majority (50.8-99.8%) of pollutants, except for VOCs. Meanwhile, that under the smoldering mode only accounted for 18.1% of the total consumption, but contributed 49.2% and 74.7% of the CO and VOCs emissions, respectively. Two clear emission peaks occurred at approximately 7:00-9:00 and 18:00-20:00. The detailed coal consumption and emissions with high temporal and spatial resolution can provide sound data for further research on rural environmental issues and scientific support to pollution control strategies.
关键词 ：
Advanced coal stove Advanced coal stove Coal-stove combination Coal-stove combination Combustion modes Combustion modes Emission factors Emission factors Rural coal emissions Rural coal emissions
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| GB/T 7714 | Zhou Ying , Huang Dawei , Lang Jianlei et al. Improved estimation of rural residential coal emissions considering coal-stove combinations and combustion modes. [J]. | Environmental pollution , 2021 , 272 : 115558 . |
| MLA | Zhou Ying et al. "Improved estimation of rural residential coal emissions considering coal-stove combinations and combustion modes." . | Environmental pollution 272 (2021) : 115558 . |
| APA | Zhou Ying , Huang Dawei , Lang Jianlei , Zi Teng , Chen Dongsheng , Zhang Yuying et al. Improved estimation of rural residential coal emissions considering coal-stove combinations and combustion modes. . | Environmental pollution , 2021 , 272 , 115558 . |
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摘要 ：
Ship-exhausted air pollutants could cause negative impacts on air quality, climate change, and human health. Increasing attention has been paid to investigate the impact of ship emissions on air quality. However, the conclusions are often based on a specific year, the extent to which the inter-annual variation in meteorological conditions affects the contribution is not yet fully addressed. Therefore, in this study, the Weather Research and Forecast model and the Community Multiscale Air Quality model(WRF/CMAQ) were employed to investigate the inter-annual variations in ship-contributed PM2.5 from 2010 to 2019. The Yangtze River Delta (YRD) region in China was selected as the target study area. To highlight the impact of inter-annual meteorological variations, the emission inventory and model configurations were kept the same for the 10-year simulation. We found that: (1) inter-annual meteorological variation had an evident impact on the ship-contributed PM2.5 in most coastal cities around YRD. Taking Shanghai as an example, the contribution varied between 3.05 and 5.74 mu g/m(3), with the fluctuation rate of similar to 65%; (2) the inter-annual changes in ship's contribution showed a trend of almost simultaneous increase and decrease for most cities, which indicates that the impact of inter-annual meteorological variation was more regional than local; (3) the inter-annual changes in the northern part of YRD were significantly higher than those in the south; (4) the most significant inter-annual changes were found in summer, followed by spring, fall and winter.
关键词 ：
inter-annual meteorological variation inter-annual meteorological variation PM2.5 PM2.5 ship emission ship emission WRF/CMAQ WRF/CMAQ Yangtze River Delta Yangtze River Delta
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| GB/T 7714 | Chen, Dongsheng , Liang, Dingyue , Li, Lei et al. The Temporal and Spatial Changes of Ship-Contributed PM2.5 Due to the Inter-Annual Meteorological Variation in Yangtze River Delta, China [J]. | ATMOSPHERE , 2021 , 12 (6) . |
| MLA | Chen, Dongsheng et al. "The Temporal and Spatial Changes of Ship-Contributed PM2.5 Due to the Inter-Annual Meteorological Variation in Yangtze River Delta, China" . | ATMOSPHERE 12 . 6 (2021) . |
| APA | Chen, Dongsheng , Liang, Dingyue , Li, Lei , Guo, Xiurui , Lang, Jianlei , Zhou, Ying . The Temporal and Spatial Changes of Ship-Contributed PM2.5 Due to the Inter-Annual Meteorological Variation in Yangtze River Delta, China . | ATMOSPHERE , 2021 , 12 (6) . |
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摘要 ：
Atmospheric visibility is an indicator of atmospheric transparency and its range directly reflects the quality of the atmospheric environment. With the acceleration of industrialization and urbanization, the natural environment has suffered some damages. In recent decades, the level of atmospheric visibility shows an overall downward trend. A decrease in atmospheric visibility will lead to a higher frequency of haze, which will seriously affect people's normal life, and also have a significant negative economic impact. The causal relationship mining of atmospheric visibility can reveal the potential relation between visibility and other influencing factors, which is very important in environmental management, air pollution control and haze control. However, causality mining based on statistical methods and traditional machine learning techniques usually achieve qualitative results that are hard to measure the degree of causality accurately. This article proposed the seq2seq-LSTM Granger causality analysis method for mining the causality relationship between atmospheric visibility and its influencing factors. In the experimental part, by comparing with methods such as linear regression, random forest, gradient boosting decision tree, light gradient boosting machine, and extreme gradient boosting, it turns out that the visibility prediction accuracy based on the seq2seq-LSTM model is about 10% higher than traditional machine learning methods. Therefore, the causal relationship mining based on this method can deeply reveal the implicit relationship between them and provide theoretical support for air pollution control.
关键词 ：
Atmospheric visibility Atmospheric visibility deep learning deep learning granger causality granger causality multidimensional time series multidimensional time series
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| GB/T 7714 | Liu, Bo , He, Xi , Song, Mingdong et al. A Method for Mining Granger Causality Relationship on Atmospheric Visibility [J]. | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (5) . |
| MLA | Liu, Bo et al. "A Method for Mining Granger Causality Relationship on Atmospheric Visibility" . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA 15 . 5 (2021) . |
| APA | Liu, Bo , He, Xi , Song, Mingdong , Li, Jiangqiang , Qu, Guangzhi , Lang, Jianlei et al. A Method for Mining Granger Causality Relationship on Atmospheric Visibility . | ACM TRANSACTIONS ON KNOWLEDGE DISCOVERY FROM DATA , 2021 , 15 (5) . |
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摘要 ：
Although weather conditions significantly affect air pollutant concentrations, few quantitative studies have been conducted on the effects of long-term and seasonal changes in meteorology on air quality. Hence, in this study, the trends in Shandong Province, China, for six criteria pollutants (viz., sulfur dioxide [SO2], carbon monoxide [CO], particulate matter [PM] with an aerodynamic diameter of < 10 mu m [PM10], PM with an aerodynamic diameter of < 2.5 mu m [PM2.5], nitrogen dioxide [NO2], and ozone [O-3]) were analyzed for the period of 2013-2019, when overall emissions of air pollutants decreased, and the Weather Research and Forecasting model coupled with Chemistry (WRF/Chem) was applied to evaluate the role of inter-annual and seasonal meteorological changes. Five of the six criteria pollutants exhibited a sharp drop in concentration until 2017 and a gradual decline afterward, with the maximum and minimum annual values occurring during winter and summer, respectively. In contrast, the level of O-3 rose between 2013 and 2019 and displayed the opposite seasonal trend. Also, the diurnal concentrations of the first five criteria pollutants showed a typical bimodal distribution, whereas those of the O-3 showed a typical unimodal distribution. Furthermore, a trimodal distribution was observed for the ratios between the diurnal PM2.5 and PM10 concentrations. Using 2013 as the baseline, the inter-annual meteorological changes accounted for only 3.4-18.6% of the decrease in the five criteria pollutants-with little effect on the O-3-between 2015 and 2019, indicating that emission control measures drove the long-term improvement in air quality during these years. However, seasonal meteorological factors, which favored diffusion during summer and winter but accumulation during spring and autumn, played a larger role in the short term for all six species, especially during winter, when they reduced concentrations (excluding those of SO2 in 2019 and O-3 altogether) by 6.5-31.0%.
关键词 ：
Air pollution Air pollution Diurnal variation Diurnal variation Meteorological condition Meteorological condition Seasonal variation Seasonal variation WRF/Chem WRF/Chem
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| GB/T 7714 | Zhao, Na , Wang, Gang , Li, Guohao et al. Trends in Air Pollutant Concentrations and the Impact of Meteorology in Shandong Province, Coastal China, during 2013-2019 [J]. | AEROSOL AND AIR QUALITY RESEARCH , 2021 , 21 (6) . |
| MLA | Zhao, Na et al. "Trends in Air Pollutant Concentrations and the Impact of Meteorology in Shandong Province, Coastal China, during 2013-2019" . | AEROSOL AND AIR QUALITY RESEARCH 21 . 6 (2021) . |
| APA | Zhao, Na , Wang, Gang , Li, Guohao , Lang, Jianlei . Trends in Air Pollutant Concentrations and the Impact of Meteorology in Shandong Province, Coastal China, during 2013-2019 . | AEROSOL AND AIR QUALITY RESEARCH , 2021 , 21 (6) . |
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摘要 ：
为了明确大气污染物、污染源、影响因素、评价指标、危害等之间的关系,分析大气污染传播路径,建立了一个较为清晰、完善的大气污染领域本体.首先,基于机器学习和自然语言处理等技术,提出一种基于注意力机制的序列标注联合抽取实体关系的方法,在双向长短时记忆(long short-term memory,LSTM)网络模型中加入注意力机制,并将实体和关系联合标注,从而进行实体关系抽取.其次,结合词频-逆文档频率(term frequency-inverse document frequency,TF-IDF)核心概念挖掘方法进行知识抽取,并将概念、属性、关系和实例组织起来,从而实现大气污染本体模型的半自动构建.最后,在本体和实例的基础上通过Protégé的SPARQL Query模块和HermiT推理机分别进行条件推理和可视化推理.结果表明,基于注意力机制的序列标注实体关系联合抽取方法所构建的大气污染领域本体包含核心实体68个,实例数360个,相较于现有的本领域本体,在全面性、有效性、准确性和可重用性方面都有较好表现,同时推理出了Ca2+和K+等污染离子的传播路径.因此,基于注意力机制的序列标注联合抽取实体关系的方法能够有效地半自动构建大气污染领域本体,推理出清晰的大气污染传播路径.
关键词 ：
大气污染 大气污染 实体关系抽取 实体关系抽取 本体 本体 注意力机制 注意力机制 自然语言处理 自然语言处理 语义推理 语义推理
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复制并粘贴一种已设定好的引用格式，或利用其中一个链接导入到文献管理软件中。
| GB/T 7714 | 刘博 , 张佳慧 , 李建强 et al. 大气污染领域本体的半自动构建及语义推理 [J]. | 北京工业大学学报 , 2021 , 47 (3) : 246-259 . |
| MLA | 刘博 et al. "大气污染领域本体的半自动构建及语义推理" . | 北京工业大学学报 47 . 3 (2021) : 246-259 . |
| APA | 刘博 , 张佳慧 , 李建强 , 李永 , 郎建垒 . 大气污染领域本体的半自动构建及语义推理 . | 北京工业大学学报 , 2021 , 47 (3) , 246-259 . |
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