DataSheet_1_Spatiotemporal distribution, sources, and ecological risk of soil polycyclic aromatic hydrocarbons in Chinese urban agglomerations from 2000 to 2020.pdf

To investigate the spatial and temporal distribution, sources, and ecological risk of soil polycyclic aromatic hydrocarbons (PAHs) in China’s urban agglomerations from 2000 to 2020, a comprehensive search strategy was employed using the keywords “soil”, “PAHs”, and “city”. A total of 122 relevant studies that provided information on individual PAH content during the specified time period were collected. These studies encompassed 20 urban agglomerations in China, which were further categorized into two distinct periods: 2000 to 2010 and 2011 to 2020. The diagnostic ratio method and principal component analysis were employed to identify the sources of PAHs, and a risk quotient model was used to evaluate the soil ecological risk. The results revealed the average PAH content in the 20 urban agglomerations in China from 2011 to 2020 was 2,439 ± 4,633 ng·g-1, which exceeded the severe pollution level cut-off (> 1,000 ng·g-1). The soil PAH content in the period from 2011 to 2020 decreased by 28% compared to the soil PAH content in the period from 2000 to 2010. Soil PAH pollution was more severe in the northern urban agglomerations than in the southern urban agglomerations. Diagnostic ratios and principal component analysis demonstrated that the principal sources in most urban agglomerations in China were traffic and coal combustion. GeoDetector found that coal and fuel oil consumption were the main factors affecting the spatial differentiation of PAHs. The ecological risk quotient showed that approximately 80% of the urban agglomerations were at a medium-high ecological risk from 2000 to 2010, compared with 72% from 2011 to 2020. Thus, it is necessary to deepen energy structure reform to alleviate the threat of serious pollution caused by coal and fuel oil in urban agglomerations.</p

Improved Response of Upconversion Luminescence Color to Pump Power through the Coupling of Er³⁺ and Tm³⁺

Upconversion materials activated with Er3+ have been demonstrated to be potential candidates for multi-color displays, dynamic fluorescence anti-counterfeiting, and so forth. However, due to the similar upconversion pathways for green and red emissions, it remains a challenge to realize highly sensitive multi-color upconversion luminescence by adjusting the laser power. Here, based on Er2Mo4O15 without concentration quenching of luminescence, we demonstrated that the incorporation of heterogeneous Tm3+ impurities could significantly improve the response of luminescence color to laser power. Through solving a set of rate equations, it was clarified that the power-dependent depletion of the 4I11/2 level was responsible for the modulation of luminescence color. Also, it was proved that the enhancement of luminescence was in relation to the blocked energy migration and enhanced radiative transition probability. Our work provides an effective approach to improve the power response of luminescence colors based on the photon upconversion of Er3+

Example of Kp parameter decoding rule representation.

Example of Kp parameter decoding rule representation.</p

Photochemical Formation of C₁–C₅ Alkyl Nitrates in Suburban Hong Kong and over the South China Sea

Alkyl nitrates (RONO₂) are important reservoirs of atmospheric nitrogen, regulating nitrogen cycling and ozone (O₃) formation. In this study, we found that propane and <i>n</i>-butane were significantly lower at the offshore site (WSI) in Hong Kong (<i>p</i> < 0.05), whereas C₃–C₄ RONO₂ were comparable to the suburban site (TC) (<i>p</i> > 0.05). Stronger oxidative capacity at WSI led to more efficient RONO₂ formation. Relative incremental reactivity (RIR) was for the first time used to evaluate RONO₂–precursor relationships. In contrast to a consistently volatile organic compounds (VOC)-limited regime at TC, RONO₂ formation at WSI switched from VOC-limited regime during O₃ episodes to VOC and nitrogen oxides (NO_<i>x</i>) colimited regime during nonepisodes. Furthermore, unlike the predominant contributions of parent hydrocarbons to C₄–C₅ RONO₂, the production of C₁–C₃ RONO₂ was more sensitive to other VOCs like aromatics and carbonyls, which accounted for ∼40–90% of the productions of C₁–C₃ alkylperoxy (RO₂) and alkoxy radicals (RO) at both sites. This resulted from the decomposition of larger RO₂/RO and the change of OH abundance under the photochemistry of other VOCs. This study advanced our understanding of the photochemical formation of RONO₂, particularly the relationships between RONO₂ and their precursors, which were not confined to the parent hydrocarbons

Fuzzy PID control simulation waveform diagram.

As is well known, the metal annealing process has the characteristics of heat concentration and rapid heating. Traditional vacuum annealing furnaces use PID control method, which has problems such as high temperature fluctuation, large overshoot, and long response time during the heating and heating process. Based on this situation, some domestic scholars have adopted fuzzy PID control algorithm in the temperature control of vacuum annealing furnaces. Due to the fact that fuzzy rules are formulated through a large amount of on-site temperature data and experience summary, there is a certain degree of subjectivity, which cannot ensure that each rule is optimal. In response to this drawback, the author combined the technical parameters of vacuum annealing furnace equipment, The fuzzy PID temperature control of the vacuum annealing furnace is optimized using genetic algorithm. Through simulation and comparative analysis, it is concluded that the design of the fuzzy PID vacuum annealing furnace temperature control system based on GA optimization is superior to fuzzy PID and traditional PID control in terms of temperature accuracy, rise time, and overshoot control. Finally, it was verified through offline experiments that the fuzzy PID temperature control system based on GA optimization meets the annealing temperature requirements of metal workpieces and can be applied to the temperature control system of vacuum annealing furnaces.</div

Vacuum annealing furnace heating system control schematic diagram.

Vacuum annealing furnace heating system control schematic diagram.</p

S1 Data -

As is well known, the metal annealing process has the characteristics of heat concentration and rapid heating. Traditional vacuum annealing furnaces use PID control method, which has problems such as high temperature fluctuation, large overshoot, and long response time during the heating and heating process. Based on this situation, some domestic scholars have adopted fuzzy PID control algorithm in the temperature control of vacuum annealing furnaces. Due to the fact that fuzzy rules are formulated through a large amount of on-site temperature data and experience summary, there is a certain degree of subjectivity, which cannot ensure that each rule is optimal. In response to this drawback, the author combined the technical parameters of vacuum annealing furnace equipment, The fuzzy PID temperature control of the vacuum annealing furnace is optimized using genetic algorithm. Through simulation and comparative analysis, it is concluded that the design of the fuzzy PID vacuum annealing furnace temperature control system based on GA optimization is superior to fuzzy PID and traditional PID control in terms of temperature accuracy, rise time, and overshoot control. Finally, it was verified through offline experiments that the fuzzy PID temperature control system based on GA optimization meets the annealing temperature requirements of metal workpieces and can be applied to the temperature control system of vacuum annealing furnaces.</div

Ultrafine Particles in Indoor Air of a School: Possible Role of Secondary Organic Aerosols

The aim of this work was to investigate ultrafine particles (5 particle cm−3. The indoor particle concentrations exceeded outdoor concentrations by approximately 1 order of magnitude, with a count median diameter ranging from 20 to 50 nm. Significant increases also occurred during cleaning activities, when detergents were used. GC-MS analysis conducted on 4 samples randomly selected from about 30 different paints and glues, as well as the detergent used in the school, showed that d-limonene was one of the main organic compounds of the detergent, however, it was not detected in the samples of the paints and the glue. Controlled experiments showed that this monoterpene, emitted from the detergent, reacted with O3 (at outdoor ambient concentrations ranging from 0.06 to 0.08 ppm) and formed secondary organic aerosols. Further investigations to identify other liquids that may be potential sources of the precursors of secondary organic aerosols were outside the scope of this project, however, it is expected that the problem identified by this study could be more widely spread, since most primary schools use liquid materials for art classes, and all schools use detergents for cleaning. Further studies are therefore recommended to better understand this phenomenon and also to minimize exposure of school children to ultrafine particles from these indoor sources

Step response curve of vacuum annealing furnace.

As is well known, the metal annealing process has the characteristics of heat concentration and rapid heating. Traditional vacuum annealing furnaces use PID control method, which has problems such as high temperature fluctuation, large overshoot, and long response time during the heating and heating process. Based on this situation, some domestic scholars have adopted fuzzy PID control algorithm in the temperature control of vacuum annealing furnaces. Due to the fact that fuzzy rules are formulated through a large amount of on-site temperature data and experience summary, there is a certain degree of subjectivity, which cannot ensure that each rule is optimal. In response to this drawback, the author combined the technical parameters of vacuum annealing furnace equipment, The fuzzy PID temperature control of the vacuum annealing furnace is optimized using genetic algorithm. Through simulation and comparative analysis, it is concluded that the design of the fuzzy PID vacuum annealing furnace temperature control system based on GA optimization is superior to fuzzy PID and traditional PID control in terms of temperature accuracy, rise time, and overshoot control. Finally, it was verified through offline experiments that the fuzzy PID temperature control system based on GA optimization meets the annealing temperature requirements of metal workpieces and can be applied to the temperature control system of vacuum annealing furnaces.</div

Step response curve of controlled object.

As is well known, the metal annealing process has the characteristics of heat concentration and rapid heating. Traditional vacuum annealing furnaces use PID control method, which has problems such as high temperature fluctuation, large overshoot, and long response time during the heating and heating process. Based on this situation, some domestic scholars have adopted fuzzy PID control algorithm in the temperature control of vacuum annealing furnaces. Due to the fact that fuzzy rules are formulated through a large amount of on-site temperature data and experience summary, there is a certain degree of subjectivity, which cannot ensure that each rule is optimal. In response to this drawback, the author combined the technical parameters of vacuum annealing furnace equipment, The fuzzy PID temperature control of the vacuum annealing furnace is optimized using genetic algorithm. Through simulation and comparative analysis, it is concluded that the design of the fuzzy PID vacuum annealing furnace temperature control system based on GA optimization is superior to fuzzy PID and traditional PID control in terms of temperature accuracy, rise time, and overshoot control. Finally, it was verified through offline experiments that the fuzzy PID temperature control system based on GA optimization meets the annealing temperature requirements of metal workpieces and can be applied to the temperature control system of vacuum annealing furnaces.</div