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学者姓名：郎建垒
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摘要 ：
comparison with the IMSC. Results showed that the IMSC more accurately, robustly, and reasonably estimated NOx emissions. Compared to the IMWSC, the IMSC improved the mean correlation between NOx x emissions and NO2 2 observational concentrations by 25.0%, enhancing the correlation between NOx emissions and NO2 column concentrations by 111.3%. The similar NOx emission change ratios (6 average = 5.1%) between the typical months in 2021 and 2020 among the different counties indicated a more robust performance of the IMSC than the IMWSC (6 average = 55.2%). In addition, the NOx x emissions inversed by the IMSC also showed better consistency with social activity levels (i.e., electricity consumption). The typical monthly Linyi's county-level NOx x emission characterization was also studied. NOx emissions were lower in April, July, and October 2021 than the same period in 2020 due to COVID-19 and pollution controls. This study provides strategies for swiftly and accurately estimating pollutant emissions.
关键词 ：
Optimization inversion Optimization inversion Correction matrix Correction matrix Emission inventories Emission inventories WRF-CAMx-PSAT WRF-CAMx-PSAT Source-receptor Source-receptor
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| GB/T 7714 | Li, Mengzhen , Lang, Jianlei , Zhou, Ying et al. Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China [J]. | ATMOSPHERIC ENVIRONMENT , 2024 , 338 . |
| MLA | Li, Mengzhen et al. "Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China" . | ATMOSPHERIC ENVIRONMENT 338 (2024) . |
| APA | Li, Mengzhen , Lang, Jianlei , Zhou, Ying , Shen, Zeya , Chen, Dongsheng , Li, Jia et al. Inversing NOx emissions based on an optimization model that combines a source-receptor relationship correction matrix and monitoring data: A case study in Linyi, China . | ATMOSPHERIC ENVIRONMENT , 2024 , 338 . |
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摘要 ：
Rural residential coal combustion (RRCC) is an important CO 2 emission source in rural areas and these emissions vary greatly by coal and stove types. The lack of CO 2 emission estimations for different coal-stove combinations restricts refinement and region-differentiated synergistic reduction of pollution and CO 2 emissions from RRCC. This study established the coal and stove specific CO 2 emissions inventory of RRCC based on investigated coal combustion of various coal and stove types, taking the region of Hebei in China as an example. The synergistic reduction effect of CO 2 and pollutants was evaluated using 4 emission reduction scenarios. CO 2 emission was 67.4% higher than estimates of RRCC based on the Statistical Yearbook and IPCC emission factors. The combination of bituminous coal and an advanced coal stove was the greatest contributor (62.1%) to CO 2 emissions. Bituminous coal burnt in home Arcola has the highest per capita hourly and per unit calorific value CO 2 emission, 2.3 and 2.4 times higher than the combination of anthracite briquette coal and pit coal stove and the combination of bituminous coal and an advanced coal stove, respectively. Except for NOx and CO, the coal-stove combination with the lowest per capita hourly and per unit calorific value emissions is anthracite briquette coal and an advanced coal stove. Scenario analysis shows that updated coal stoves feature the highest pollutant emission reduction, but greater CO 2 and NO X emissions. For individual cities, synergistic pollution and carbon reduction is seen in Tangshan and Qinhuangdao, and organic carbon, CO, and SO 2 have better synergistic positive reduction with CO 2 , while NO X has shown a negative effect with CO 2 in other cities with the best coal and stove update scenario. Compared with the coal and stove update scenario, emission reduction effects with clean fuel substitution varies across cities. This study is helpful to understand the possible non-synergy of pollutant and CO 2 reduction in the process of RRCC management. Attention should be given to issues related to the synergistic control plan of pollution and CO 2 reduction for RRCC with local adaptation.
关键词 ：
Synergy of pollutants and carbon emission Synergy of pollutants and carbon emission Rural residential coal combustion Rural residential coal combustion reduction reduction Coal-stove combination Coal-stove combination CO2 emissions CO2 emissions
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| GB/T 7714 | Zhou, Ying , Liu, Jing , Lang, Jianlei et al. Necessary CO 2 emissions improvement based on rural residential coal/ stove-specific coal combustion and prefectural-differentiation synergistic effect of pollutants and carbon emission reduction [J]. | JOURNAL OF CLEANER PRODUCTION , 2024 , 449 . |
| MLA | Zhou, Ying et al. "Necessary CO 2 emissions improvement based on rural residential coal/ stove-specific coal combustion and prefectural-differentiation synergistic effect of pollutants and carbon emission reduction" . | JOURNAL OF CLEANER PRODUCTION 449 (2024) . |
| APA | Zhou, Ying , Liu, Jing , Lang, Jianlei , Zhao, Beibei , Guo, Xiurui , Chen, Dongsheng et al. Necessary CO 2 emissions improvement based on rural residential coal/ stove-specific coal combustion and prefectural-differentiation synergistic effect of pollutants and carbon emission reduction . | JOURNAL OF CLEANER PRODUCTION , 2024 , 449 . |
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摘要 ：
Inter-provincial trade leads to changes in CO2 and air pollutant emissions. However, there is a research gap regarding the coordinated effects (co-effects) between embodied CO2 and air pollutant emissions in trade. Understanding co-effects in inter-provincial trade is a prerequisite for driving the green transformation of trade and achieving coordination between pollution and carbon reduction. Here, we calculated provincial-level CO2 and air pollutant emission leakage in 2012 and 2017 based on a modified input-output model and, for the first time, investigated the co-effects between CO2 and air pollutant emission leakage caused by emissions transfers in China. Three types of co-effects, categorized as co-benefits, trade-offs, and co-damage, were discovered and defined to reveal the provincial differences. Furthermore, combined with structural decomposition analysis (SDA), we calculated the interannual variation in trade-induced emissions and identified the key driving factors of provincial-level co-effects from 2012 to 2017. Optimizing the energy structure has led to the greatest co-benefits, while changes in the industrial structure and emission coefficients have led to limited co-benefits in specific provinces. Variations in trade volume have led to co-damages across all provinces, and changes in emission coefficients have led to trade-offs in the majority of provinces. The case analysis confirmed that identifying and adjusting the key driving factors of co-effects can promote the transformation from co-damage and trade-offs to co-benefits. The findings implied a new approach for the reduction in pollution and carbon through inter-provincial trade.
关键词 ：
multi-regional input-output model multi-regional input-output model CO2 CO2 air pollutants air pollutants inter-provincial trade inter-provincial trade coordinated effects coordinated effects
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| GB/T 7714 | Qi, Peng , Lang, Jianlei , Wang, Xiaoqi et al. The Coordinated Effects of CO2 and Air Pollutant Emission Changes Induced by Inter-Provincial Trade in China [J]. | SUSTAINABILITY , 2024 , 16 (4) . |
| MLA | Qi, Peng et al. "The Coordinated Effects of CO2 and Air Pollutant Emission Changes Induced by Inter-Provincial Trade in China" . | SUSTAINABILITY 16 . 4 (2024) . |
| APA | Qi, Peng , Lang, Jianlei , Wang, Xiaoqi , Zhou, Ying , Qi, Haoyun , Cheng, Shuiyuan . The Coordinated Effects of CO2 and Air Pollutant Emission Changes Induced by Inter-Provincial Trade in China . | SUSTAINABILITY , 2024 , 16 (4) . |
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摘要 ：
本发明公开了一种大气污染物排放清单的联合优化反演方法及系统，属于大气环境技术领域，方法包括：获取气象场数据，结合气象模型和空气质量模型得到气象场数值模型，并代入先验排放清单数据，模拟得到模拟贡献浓度和浓度贡献系数；根据空气质量监测数据和浓度贡献系数，以计算浓度与监测浓度误差最小为优化目标、构建优化方程；根据气象变化对污染物浓度变化的影响量化值及不同时段的波动，构建气象约束条件；构建污染物排放清单联合反演模型，求解得到污染物排放优化反演清单。通过本发明的技术方案，在用浓度进行排放量优化时，减少了气象条件对污染物浓度的影响，降低了反演优化清单的不确定性，更准确快速地实现排放清单反演。
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| GB/T 7714 | 周颖 , 李佳 , 郎建垒 et al. 一种大气污染物排放清单的联合优化反演方法及系统 : CN202310746146.X[P]. | 2023-06-25 . |
| MLA | 周颖 et al. "一种大气污染物排放清单的联合优化反演方法及系统" : CN202310746146.X. | 2023-06-25 . |
| APA | 周颖 , 李佳 , 郎建垒 , 张晔华 . 一种大气污染物排放清单的联合优化反演方法及系统 : CN202310746146.X. | 2023-06-25 . |
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摘要 ：
本发明公开一种复杂下垫面下局地尺度大气污染源强快速反算方法，属于大气污染防治与源强反算技术领域；该方法包括：对待研究区域构建CFD模型；在CFD模型的计算域范围内选取若干采样点；基于CFD模型进行仿真计算，采集不同源强情景下各个采样点的污染物浓度数据；构建源强反算模型并获取传递系数；构建传递系数响应面模型；获取监测点的污染物浓度监测数据；将监测点的污染物浓度监测数据输入到源强反算模型和传递系数响应面模型中进行源强反算。本发明充分考虑局地尺度复杂下垫面物理意义的同时对复杂的仿真模型进行统计学简化，能够更快速、有效的为污染事件的响应以及局地尺度精细化管控提供科学支撑。
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| GB/T 7714 | 郎建垒 , 王奕璇 , 周颖 . 复杂下垫面下局地尺度大气污染源强快速反算方法及系统 : CN202310903823.4[P]. | 2023-07-24 . |
| MLA | 郎建垒 et al. "复杂下垫面下局地尺度大气污染源强快速反算方法及系统" : CN202310903823.4. | 2023-07-24 . |
| APA | 郎建垒 , 王奕璇 , 周颖 . 复杂下垫面下局地尺度大气污染源强快速反算方法及系统 : CN202310903823.4. | 2023-07-24 . |
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摘要 ：
本发明提供一种大气污染协同控制区域、时间识别方法，涉及大气污染防控技术领域，包括：模拟研究区域的气象条件、获取大气污染物多尺度排放数据，构建研究区域各污染源间的高时间分辨率大气污染物传输矩阵；计算各污染源对目标区域重污染过程每个时刻的贡献度；应用模糊C均值聚类得到每个时刻需要协同控制的污染源；计算各污染源对目标区域重污染过程的总贡献度和控制频率；应用系统聚类识别出重污染过程需协同控制的污染源；分别计算需协同控制的污染源每个时刻的大气污染物排放对重污染过程的总贡献浓度；应用K均值聚类得到重污染过程中各污染源启动和解除大气污染协同控制的时间。本发明将污染物排放控制的时间尺度精细化到小时。
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| GB/T 7714 | 郎建垒 , 李凯 , 周颖 et al. 一种大气污染协同控制区域、时间识别方法 : CN202310186701.8[P]. | 2023-03-02 . |
| MLA | 郎建垒 et al. "一种大气污染协同控制区域、时间识别方法" : CN202310186701.8. | 2023-03-02 . |
| APA | 郎建垒 , 李凯 , 周颖 , 梁晓玉 , 玄博元 . 一种大气污染协同控制区域、时间识别方法 : CN202310186701.8. | 2023-03-02 . |
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摘要 ：
本发明公开一种监测站点风速和风向协同时序预测方法，属于大气污染气象要素预测技术领域；该方法包括：获取目标站点的气象数据，对风速数据和风向数据进行分解；通过风向数据将气压梯度力分解；利用压强正弦分量Pu、压强余弦分量Pv和气象数据与经向风分量u和纬向风分量v的关联程度筛选关键影响因子；分别构建u、v分量与关键影响因子之间的关系模型；根据关系模型进行预测；对u、v分量的预测结果进行合成。本发明还提供一种监测站点风速和风向协同时序预测系统。本发明通过分别构建基于关键影响因子的u、v分量时序预测模型，实现了对风速、风向准确预测，可为面向监测站点的大气污染气象条件分析与精细化防控奠定基础。
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| GB/T 7714 | 郎建垒 , 严允序 , 玄博元 et al. 一种监测站点风速和风向协同时序预测方法及系统 : CN202310935610.X[P]. | 2023-07-28 . |
| MLA | 郎建垒 et al. "一种监测站点风速和风向协同时序预测方法及系统" : CN202310935610.X. | 2023-07-28 . |
| APA | 郎建垒 , 严允序 , 玄博元 , 周颖 . 一种监测站点风速和风向协同时序预测方法及系统 : CN202310935610.X. | 2023-07-28 . |
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摘要 ：
本发明公开了一种监测站点大气污染物本地排放贡献的量化识别方法，属于大气污染防治领域；包括：基于空气质量模型模拟得到各站点区域贡献浓度，逐时刻计算各站点区域贡献与全部站点区域贡献平均浓度的比值，即为区域贡献不均匀系数；引入区域平均和本地贡献优化变量，将区域贡献平均优化浓度与区域贡献不均匀系数相乘，得到区域贡献优化浓度表达式；以本地和区域贡献优化浓度之和与监测浓度误差最小化为目标，构建目标方程；确定本地贡献优化浓度的限制范围，联合目标方程构建优化模型；根据监测浓度与区域贡献不均匀系数，通过求解优化模型，得到站点的本地贡献优化浓度。本发明实现了站点本地贡献浓度的量化识别，为精细化污染管控提供支撑。
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| GB/T 7714 | 郎建垒 , 李孟珍 , 周颖 . 一种监测站点大气污染物本地排放贡献的量化识别方法 : CN202310935719.3[P]. | 2023-07-28 . |
| MLA | 郎建垒 et al. "一种监测站点大气污染物本地排放贡献的量化识别方法" : CN202310935719.3. | 2023-07-28 . |
| APA | 郎建垒 , 李孟珍 , 周颖 . 一种监测站点大气污染物本地排放贡献的量化识别方法 : CN202310935719.3. | 2023-07-28 . |
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摘要 ：
本发明公开了一种基于站点浓度差异的本外地排放贡献估算方法，属于大气污染防治技术领域；包括：基于城市监测站点各污染物浓度数据，逐时刻分别计算不同污染物的站点间浓度差异(变异系数CVc)和站点浓度平均值；将逐时刻浓度平均值进行区间划分；基于气象和空气质量模型，模拟得到逐时刻的外地排放贡献率；计算不同浓度区间对应的CVc和外地排放贡献率平均值；对外地排放贡献率和CVc均值进行数学拟合，得到外地排放贡献估算模型；基于该模型，快速估算本/外地排放对大气污染物的浓度贡献。本发明基于站点间浓度差异建立外地排放贡献估算模型，可基于环境监测浓度数据快速估算城市本/外地排放贡献，为城市污染管控提供支撑。
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| GB/T 7714 | 郎建垒 , 杨欣欣 , 周颖 . 一种基于站点浓度差异的本外地排放贡献估算方法 : CN202310658278.7[P]. | 2023-06-06 . |
| MLA | 郎建垒 et al. "一种基于站点浓度差异的本外地排放贡献估算方法" : CN202310658278.7. | 2023-06-06 . |
| APA | 郎建垒 , 杨欣欣 , 周颖 . 一种基于站点浓度差异的本外地排放贡献估算方法 : CN202310658278.7. | 2023-06-06 . |
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摘要 ：
本发明公开了一种基于大气污染浓度变化率的突出值站点识别方法，属于大气污染防治技术领域；包括：基于各站点长时间序列历史浓度数据，计算逐时刻的站点浓度平均值；基于逐时刻平均值，依据各污染物浓度标准，划分逐时刻污染等级；逐站点计算研究时刻相较上一时刻的浓度变化率；将逐时刻的浓度变化率与划分的逐时刻污染等级相匹配，得到不同污染等级的时刻变化率数组，分别绘制概率分布图；依据概率分布图，分别确定变化率上限值，作为不同污染等级的突出值识别标准，即：标准变化率限值；基于标准限值，实时动态识别突出值站点与时刻。本发明基于浓度变化率实时动态识别需要重点管控的站点和管控时刻，可为面向监测站点的污染精细化管控提供技术支撑。
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| GB/T 7714 | 郎建垒 , 杨欣欣 , 周颖 . 一种基于大气污染浓度变化率的突出值站点识别方法 : CN202310506028.1[P]. | 2023-05-08 . |
| MLA | 郎建垒 et al. "一种基于大气污染浓度变化率的突出值站点识别方法" : CN202310506028.1. | 2023-05-08 . |
| APA | 郎建垒 , 杨欣欣 , 周颖 . 一种基于大气污染浓度变化率的突出值站点识别方法 : CN202310506028.1. | 2023-05-08 . |
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