A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China

The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R, +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.     

Modified nanoscale zero-valent iron in persulfate activation for organic pollution remediation: a review

Environmental Science and Pollution Research - Under the action of different activators, persulfate can produce sulfate radicals (SO4·–) with strong oxidizing ability, which can destruct...     

Dispersion of carbon dioxide released from buried high-pressure pipeline over complex terrain

Environmental Science and Pollution Research - To quantitatively assess the risks associated with Carbon Capture and Storage (CCS) technology, a better understanding of the dispersion...     

A sulfur-resistant CuS-modified active coke for mercury removal from municipal solid waste incineration flue gas

Environmental Science and Pollution Research - Adsorption is a typical method for air pollutant removal from flue gas. A CuS-modified active coke (CuS/AC) sorbent was developed to improve the...     

Critical Risk Indicators (CRIs) for the electric power grid: a survey and discussion of interconnected effects

The electric power grid is a critical societal resource connecting multiple infrastructural domains such as agriculture, transportation, and manufacturing. The electrical grid as an infrastructure is shaped by human activity and public policy in terms of demand and supply requirements. Further, the grid is subject to changes and stresses due to diverse factors including solar weather, climate, hydrology, and ecology. The emerging interconnected and complex network dependencies make such interactions increasingly dynamic, posing novel risks, and presenting new challenges to manage the coupled human–natural system. This paper provides a survey of models and methods that seek to explore the significant interconnected impact of the electric power grid and interdependent domains. We also provide relevant critical risk indicators (CRIs) across diverse domains that may be used to assess risks to electric grid reliability, including climate, ecology, hydrology, finance, space weather, and agriculture. We discuss the convergence of indicators from individual domains to explore possible systemic risk, i.e., holistic risk arising from cross-domain interconnections. Further, we propose a compositional approach to risk assessment that incorporates diverse domain expertise and information, data science, and computer science to identify domain-specific CRIs and their union in systemic risk indicators. Our study provides an important first step towards data-driven analysis and predictive modeling of risks in interconnected human–natural systems.

     

Coupling loss characteristics of N-P-C through runoff and sediment in the hilly region of SE China under simulated rainfall

Environmental Science and Pollution Research - Soil total carbon (TC), phosphorus (P), and nitrogen (N) exports from the weathered granite slopes are greatly influenced by the complex hydrological...     

垃圾焚烧炉燃烧过程的优化控制策略

焚烧炉控制旨在使城市垃圾经处理后达到无害化、减量化和资源化的目标,既有利于环境保护,也有良好的社会经济效益。针对垃圾焚烧过程中由于燃烧对象复杂性导致的控制难题,提出了一种对垃圾焚烧系统实施优化控制的仿人智能控制策略。研究出稳定燃烧评判指标、控制模型、工程控制算法,并进行仿真试验。系统仿真试验结果表明:仿人智能控制策略鲁棒性强、控制品种好,是一种有效的控制策略。     

液柱塔喷浆管道设计与优化

为了研究喷淋液柱吸收塔喷浆管道的喷嘴喷射流量以及液柱高度,对喷浆管道的流场进行了研究。采用基于计算流体力学的Fluent软件平台,对某电厂2×360MW机组液柱脱硫塔喷浆管建立数学物理模型。采用基于有限体积法的SIMPLE算法进行计算,计算对比了不同结构喷浆管各喷嘴对应的液柱高度分布以及流量分布,得到了最优化的液柱塔喷浆管道结构,并根据计算结果指导设计。     

稳健统计在快速生物降解性试验质量分析中的应用

快速生物降解性试验在新化学物质的生态毒理测试中占据重要地位,其测试数据的可靠性直接影响后续的测试策略和物质的申报登记、以及新化学物质进入环境后的风险管理。为研究OECD 301系列试验(301B、301D和301F)的质量,选用本机构近5年来积累的标准化物质苯甲酸钠14 d快速生物降解率数据,首次采用稳健统计技术,以稳健总计统计量的Z比分数作为评价指标,对各试验数据和试验系统的质量开展评估。结果显示,当301B、301D和301F试验的苯甲酸钠降解率分别在63.8%~88.0%、63.4%~89.2%和66.0%~99.8%范围内时,表明测试结果满意,该批次试验数据质量良好;若苯甲酸钠降解率不在此范围,则表明测试结果可能存在问题,甚至得到的可能是错误数据,需要进一步验证。据此提出了对实际测试工作具有指导意义的质量分析对照表,以期为测试机构开展新化学物质快速生物降解性试验时的质量分析提供参考。