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Development of an automated photolysis rates prediction system based on machine learning

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Journal of environmental Sciences 2025年第5期151卷 211-224页

作者： Weijun Pan Sunling Gong Huabing Ke Xin Li Duohong Chen Cheng Huang Danlin Song State Key laboratory of Severe Weather&Key laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological SciencesBeijing 100081China College of environmental Sciences and Engineering Peking UniversityBeijing 100871China State environmental Key laboratory of Reginal Air Quality monitoring Guangdong Ecological Environmental Monitoring CenterGuangzhou 510308China State environmental Protection Key laboratory of Formation and Prevention of the Urban Air Complex Shanghai Academy of Environmental SciencesShanghai 200233China Chengdu Academy of environmental Sciences Chengdu 610072China National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research InstituteMacao University of Science and TechnologyMacao 999078China

Based on observed meteorological elements,photolysis rates(J-values)and pollutant concentrations,an automated J-values predicting system by machine learning(J-ML)has been developed to reproduce and predict the J-values of O^(1)D,NO_(2),HONO,H_(2)O_(2),HCHO,and NO_(3),which are the crucial values for the prediction of the atmospheric oxidation capacity(AOC)and secondary pollutant concentrations such as ozone(O_(3)),secondary organic aerosols(SOA).The J-ML can self-select the optimal“Model+Hyperparameters”without human *** evaluated results showed that the J-ML had a good performance to reproduce the J-values wheremost of the correlation(R)coefficients exceed 0.93 and the accuracy(P)values are in the range of 0.68-0.83,comparing with the J-values from observations and from the tropospheric ultraviolet and visible(TUV)radiation model in Beijing,Chengdu,Guangzhou and Shanghai,*** hourly prediction was also well performed with R from 0.78 to 0.81 for next 3-days and from 0.69 to 0.71 for next 7-days,*** with O_(3)concentrations by using J-values from the TUV model,an emission-driven observation-based model(e-OBM)by using the J-values from the J-ML showed a 4%-12%increase in R and 4%-30%decrease in ME,indicating that the J-ML could be used as an excellent supplement to traditional numerical *** feature importance analysis concluded that the key influential parameter was the surface solar downwards radiation for all J-values,and the other dominant factors for all J-values were 2-m mean temperature,O_(3),total cloud cover,boundary layer height,relative humidity and surface pressure.

关键词： J-values Automated prediction system Machine learning Short-term prediction O_(3) simulated improvement

A comparative study on the formation of nitrogen-containing organic compounds in cloud droplets and aerosol particles

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Journal of environmental Sciences 2025年第3期149卷 456-464页

作者： Guohua Zhang TaoWang Qinhao Lin Kun Liu Wei Sun Duohong Chen Lei Li Xinming Wang Xinhui Bi State Key laboratory of Organic Geochemistry and Guangdong Provincial Key laboratory of environmental Protection and Resources Utilization Guangzhou Institute of GeochemistryChinese Academy of Sciences(CAS)Guangzhou 510640China Guangdong-Hong Kong-Macao Joint laboratory for environmental Pollution and Control Guangzhou Institute of GeochemistryCASGuangzhou 510640China University of Chinese Academy of Sciences Beijing 100049China School of environmental Science and Engineering Institute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China State environmental Protection Key laboratory of Regional Air Quality monitoring Guangdong Environmental Monitoring CenterGuangzhou 510308China Institute of Mass Spectrometer and Atmospheric Environment Jinan UniversityGuangzhou 510632China

Nitrogen-containing organic compounds(NOCs)may potentially contribute to aqueous secondary organic aerosols,yet the different formation of NOCs in aerosol particles and cloud droplets remains *** the in-situ measurements performed at a mountain site(1690 m a.s.l.)in southern China,we investigated the formation of NOCs in the cloud droplets and the cloud-free particles,based on their mixing state information of NOCscontaining particles by single particle mass *** relative abundance of NOCs in the cloud-free particles was significantly higher than those in cloud residual(cloud RES)*** were highly correlated with carbonyl compounds(including glyoxalate and methylglyoxal)in the cloud-free particles,however,limited correlation was observed for cloud RES *** of their mixing state and temporal variations highlights that NOCs was mainly formed from the carbonyl compounds and ammonium in the cloud-free particles,rather than in the cloud RES *** results support that the formation of NOCs from carbonyl compounds is facilitated in concentrated solutions in wet aerosols,rather than cloud *** addition,we have identified the transport of biomass burning particles that facilitate the formation of NOCs,and that the observed NOCs is most likely contributed to the light *** findings have implications for the evaluation of NOCs formation and their contribution to light absorption.

关键词： Nitrogen-containing organic compounds Individual particles Cloud Carbonyl compounds Mixing state

High summer background O_(3)levels in the desert of northwest China

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Journal of environmental Sciences 2025年第5期151卷 516-528页

作者： Xinbing Ren Fang Wang Bayi Wu Shaoting Zhang Lei Zhang Xingjun Zhou Yuanzhe Ren Yongjing Ma Feng Hao Yongli Tian Jinyuan Xin Key laboratory of Atmospheric Environment and Extreme Meteorology Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China State Key laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry(LAPC) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijing 100029China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 100049China Inner Mongolia environmental monitoring Center Alashan Substation 750300China Inner Mongolia environmental monitoring Center Hohhot 010011China laboratory for Supervision and Evaluation of Pollution Reduction and Carbon Reduction in Arid and Semi-Arid Regions Inner Mongolia Environmental Monitoring CenterHohhot 010011China

Generally speaking,the precursors of ozone(O_(3)),nitrogen oxides and volatile organic compounds are very low in desert areas due to the lack of anthropogenic emissions and natural emissions,and thus O_(3)concentrations are relatively ***,high summer background concentrations of about 100μg/m^(3)or 60 ppb were found in the Alxa Desert in the highland of northwest China based on continuous summer observations from 2019 to 2021,which was higher than the most of natural background areas or clean areas in world for summer O_(3)background *** high O_(3)background concentrations were related to surface features and ***-intensity anthropogenic activity areas in desert areas can cause increased O_(3)concentrations or pollution,but also generated O_(3)depleting substances such as nitrous oxide,which eventually reduced the regional O_(3)baseline *** dioxide(NO2)also had a dual effect on O_(3)generation,showing promotion at low concentrations and inhibition at high *** addition,sand-dust weather reduced O_(3)clearly,but O_(3)eventually stabilized around the background concentration values and did not vary with sand-dust particulate matter.

关键词： Alxa Desert O_(3)background concentration Anthropogenic effect Sand-dust weather Northwest China

Factors influencing the effectiveness of SM-VCE method in solving 3D surface deformation

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Geodesy and Geodynamics 2025年第1期16卷 55-66页

作者： Xupeng Liu Guangyu Xu Mingkai Chen Tengxu Zhang School of Surveying and Geoinformation Engineering East China University of Technology Key laboratory of Mine environmental monitoring and Improving Around Poyang Lake of Ministry of Natural Resources East China University of Technology College of Resources and environmental Science and Engineering Hubei University of Science and Technology

The recovery of coseismic 3-dimensional(3D) surface deformation field plays a crucial role in studying seismic source characteristics and earthquake hazards. As of now, there are two main types of methods for recovering coseismic 3D surface deformations: the first type is to solve the problem directly by using the least squares method based on observations from three or more viewpoints, and the second type solves the problem by combining ascending and descending In SAR line-of-sight(LOS) observations with constraint models. The former type is mainly applicable to surface rupture earthquakes, because when an earthquake ruptures to the surface, we can usually obtain surface deformation observations from three or more views. The latter type applies to earthquakes that do not cause surface ruptures and have extensive blind faults. Currently, most research focuses on improving the above types of ***, some key factors in the coseismic 3D surface deformation inversion are rarely mentioned, such as the influence of window size on the inversion results in the strain model and variance component estimation method(SM-VCE), and whether the outliers in the observational data are considered. So, we developed a new chain of integrating In SAR observation and SM-VCE model to systematically assess the impacts of window size and outliers on coseismic 3D surface deformation inversions. Through simulation experiments, we observed that the selection of window size significantly impacts the accuracy of the results. Specifically, larger window sizes lead to wider residuals ranges in the fault region, resulting in the loss of extreme solution values when using a window of 15 × 15 pixels(i.e., 7.5 km × 7.5 km). Hence, we recommend utilizing windows with 7 × 7 pixels or 9 × 9 pixels for optimal accuracy, as larger window sizes diminish the significance of the outcomes. The elimination of points displaying different deformation directions helps reduce residuals and preserves near-

关键词： InSAR 3D surface deformation Window size Robust estimation

Effect of gradual increase of salt on performance and microbial community during granulation process

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Journal of environmental Sciences 2025年第1期147卷 404-413页

作者： Mengfei Wang Junguo He Xiangke Dong Ruimiao Zhang School of Environment Harbin Institute of TechnologyHarbin 150090China State Key laboratory of Urban Water Resources and Environment Harbin Institute of TechnologyHarbin 150090China School of Civil Engineering Guangzhou UniversityGuangzhou 510006China National Marine environmental monitoring Center Dalian 116023China

Salinity was considered to have effects on the characteristics,performance microbial communities of aerobic granular *** study investigated granulation process with gradual increase of salt under different *** identical sequencing batch reactors were operated,while the influent of Ra and Rb was subjected to stepwise increments of NaCl concentrations(0-4 g/L and 0-10 g/L).The presence of filamentous bacteria may contribute to granules formed under lower salinity conditions,potentially leading to granules *** removal efficiency achieved in both reactors although therewas a small accumulation of nitrite in Rb at later *** removal efficiencies of chemical oxygen demand(COD),total nitrogen(TN),and total phosphorus(TP)in Ra were 95.31%,93.70%and 88.66%,while the corresponding removal efficiencies in Rb were 94.19%,89.79%and 80.74%.Salinity stimulated extracellular polymeric substances(EPS)secretion and enriched EPS producing bacteria to help maintain the integrity and stability of the aerobic *** nitrifying bacteria were responsible for NH_(4)^(+)-N and NO_(2)^(-)-N oxidation of salinity systems and large number of denitrifying bacteria were detected,which ensure the high removal efficiency of TN in the systems.

关键词： Aerobic granular sludge(AGS)Salinity Nutrient removal Extracellular polymeric substances(EPS) Microbial community

Emission control status and future perspectives of diesel trucks in China

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Journal of environmental Sciences 2025年第2期148卷 702-713页

作者： Shihai Zhang Mingliang Fu Hefeng Zhang Hang Yin Yan Ding State environmental Protection Key laboratory of Vehicle Emission Control and Simulation Chinese Research Academy of Environmental SciencesBeijing 100012China Vehicle Emission Control Center Chinese Research Academy of Environmental SciencesBeijing 100012China

Chinese diesel trucks are the main contributors to NOx and particulate matter(PM)vehicle *** increase in diesel trucks could aggravate air pollution and damage human *** Chinese government has recently implemented a series of emission control technologies andmeasures for air quality *** paper summarizes recent control technologies and measures for diesel truck emissions in China and introduces the comprehensive application of control technologies and measures in Beijing-Tianjin-Hebei and surrounding *** onlinemonitoring technology has been adopted according to the China VI standard for heavy-duty diesel trucks,and control measures such as transportation structure adjustment and heavy pollution enterprise classification control continue to support the battle action plan for pollution *** and suggestions are provided for promoting pollution control and supervision of diesel truck emissions:adhere to the concept of overall management and control,vigorously promote the application of systematic and technological means in emission monitoring,continuously facilitate cargo transportation structure adjustment and promote new energy freight *** paper aims to accelerate the implementation of control technologies and measures throughout *** is endeavouring to control diesel truck exhaust *** is willing to cooperate with the world to protect the global ecological environment.

关键词： Air quality Diesel truck emissions Emission control technology Control measures

Machine learning-assisted fluorescence visualization for sequential quantitative detection of aluminum and fluoride ions

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Journal of environmental Sciences 2025年第3期149卷 68-78页

作者： Qiang Zhang Xin Li Long Yu Lingxiao Wang Zhiqing Wen Pengchen Su Zhenli Sun Suhua Wang School of environmental Science and Engineering Guangdong University of Petrochemical TechnologyMaoming 525000China MOE Key laboratory of Resources and environmental System Optimization College of Environmental Science and EngineeringNorth China Electric Power UniversityBeijing 102206China

The presence of aluminum(Al^(3+))and fluoride(F^(−))ions in the environment can be harmful to ecosystems and human health,highlighting the need for accurate and efficient *** this paper,an innovative approach is presented that leverages the power of machine learning to enhance the accuracy and efficiency of fluorescence-based detection for sequential quantitative analysis of aluminum(Al^(3+))and fluoride(F^(−))ions in aqueous *** proposed method involves the synthesis of sulfur-functionalized carbon dots(C-dots)as fluorescence probes,with fluorescence enhancement upon interaction with Al^(3+)ions,achieving a detection limit of 4.2 nmol/***,in the presence of F^(−)ions,fluorescence is quenched,with a detection limit of 47.6 nmol/*** fingerprints of fluorescence images are extracted using a cross-platform computer vision library in Python,followed by data ***,the fingerprint data is subjected to cluster analysis using the K-means model from machine learning,and the average Silhouette Coefficient indicates excellent model ***,a regression analysis based on the principal component analysis method is employed to achieve more precise quantitative analysis of aluminum and fluoride *** results demonstrate that the developed model excels in terms of accuracy and *** groundbreaking model not only showcases exceptional performance but also addresses the urgent need for effective environmental monitoring and risk assessment,making it a valuable tool for safeguarding our ecosystems and public health.

关键词： Machine learning Aluminum ion detection Fluorine ion detection Fluorescence probe K-means model

MAX-DOAS observations of pollutant distribution and transboundary transport in typical regions of China

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Journal of environmental Sciences 2025年第5期151卷 652-666页

作者： Hongmei Ren Ang Li Zhaokun Hu Hairong Zhang Jiangman Xu Xinyan Yang Jinji Ma Shuai Wang School of Physics and Electronic Information Anhui Normal UniversityWuhu 241000China Key laboratory of environmental Optics and Technology Anhui Institute of Optics and Fine MechanicsHefei Institutes of Physical ScienceChinese Academy of SciencesHefei 230031China University of Science and Technology of China Hefei 230026China School of Geography and Tourism Anhui Normal UniversityWuhu 241000China China National environmental monitoring Centre Beijing 100012China

Studying the spatiotemporal distribution and transboundary transport of aerosols,NO_(2),SO_(2),and HCHO in typical regions is crucial for understanding regional pollution *** a 2-year study using multi-axis differential optical absorption spectroscopy in Qingdao,Shanghai,Xi’an,and Kunming,we investigated pollutant distribution and transport across Eastern China-Ocean,Tibetan Plateau-Central and Eastern China,and China-Southeast Asia ***,pollutant distributionwas ***,frequently clouded and misty,exhibited consistently high aerosol optical depth throughout the *** Qingdao and Shanghai,NO_(2)and SO_(2),as well as SO_(2)in Xi’an,increased in *** HCHO in summer in Shanghai and Xi’an,especially Xi’an,suggests potential ozone pollution ***,pollutant transportation across interfaces was *** the Eastern China-Ocean interface,the gas transport flux was the largest among other interfaces,with the outflux exceeding the influx,especially in winter and *** input of pollutants from the Tibetan Plateau to central-eastern Chinawas larger than the output in winter and spring,with SO_(2)having the highest transport flux in *** pollution input from Southeast Asia to China significantly exceeded the output,with spring and winter inputs being 3.22 and 3.03 times the output,***,the transportation characteristics of a pollution event at Kunming were *** this period,pollutants were transported from west to east,with themaximum SO_(2)transport flux at an altitude of 2.87 km equaling 27.74μg/(m^(2)·s).It is speculated that this pollution was caused by the transport from Southeast Asian countries to Kunming.

关键词： Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) Pollutants Spatiotemporal distribution Transboundary transport

Insights into the seasonal characteristics of single particle aerosols in Chengdu based on SPAMS

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Journal of environmental Sciences 2025年第3期149卷 431-443页

作者： Junke Zhang Yunfei Su Chunying Chen Xinyi Fu Yuhan Long Xiaoxue Peng Xiaojuan Huang Gehui Wang Wei Zhang Faculty of Geosciences and environmental Engineering Southwest Jiaotong UniversityChengdu 611756China Department of environmental Science&Engineering Shanghai Key Laboratory of Atmospheric Particle Pollution and Prevention(LAP3)Fudan UniversityShanghai 200438China Key Lab of Geographic Information Science of the Ministry of Education School of Geographic SciencesEast China Normal UniversityShanghai 200241China Sichuan Ecological Environment monitoring Station Chengdu 610091China

To investigate the seasonal characteristics in air pollution in Chengdu,a single particle aerosol mass spectrometry was used to continuously observe atmospheric fine particulate matter during one-month periods in summer and winter,*** results showed that,apart from O_(3),the concentrations of other pollutants(CO,NO_(2),SO_(2),PM_(2.5)and PM_(10))were significantly higher in winter than in *** single particle aerosols were divided into seven categories:biomass burning(BB),coal combustion(CC),Dust,vehicle emission(VE),K mixedwith nitrate(K-NO_(3)),Kmixed with sulfate and nitrate(K-SN),and K mixedwith sulfate(K-SO_(4))*** highest contributions in both seasons were VE particles(24%).The higher contributions of K-SO_(4)(16%)and K-NO_(3)(10%)particles occurred in summer and winter,respectively,as a result of their different formation mechanisms.S-containing(KSO_(4)and K-SN),VE,and BB particles caused the evolution of pollution in both seasons,and they can be considered as targets for future pollution *** mixing of primary sources particles(VE,Dust,CC,and BB)with secondary components was stronger in winter than in *** summer,as pollution worsens,the mixing of primary sources particles with 62[NO_(3)]−weakened,but themixing with 97[HSO_(4)]−***,in winter,the mixing state of particles did not exhibit an obvious evolution *** potential source areas in summer were mainly distributed in the southern region of Sichuan,while in winter,besides the southern region,the contribution of the western region cannot be ignored.

关键词： Single particle aerosols Chemical composition Mixing state Regional transport Seasonal characteristics