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Characterizing molecular fingerprint of dissolved organic matter shaped by natural and anthropogenic factors in a large macrophytic shallow lake wetland

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Frontiers of environmental Science & Engineering 2025年第3期19卷 73-86页

作者： Shengwu Yuan Yiping Xu Pengfei Wang Ruili Xie Jiangbo Hu Zhenghui Fu Kun Wang Shuhang Wang Xia Jiang Kaifeng Rao Mei Ma Zijian Wang State Key Laboratory of Environmental Criteria and Risk Assessment National Engineering Laboratory for Lake Pollution Control and Ecological RestorationState Environment Protection Key Laboratory for Lake Pollution ControlState Environmental Protection Key Laboratory of Drinking Water Source ProtectionChinese Research Academy of Environmental ScienceBeijing 100012China Research Center for Eco-Environmental Sciences Chinese Academy of SciencesBeijing 100085China College of Resources and Environment University of Chinese Academy of SciencesBeijing 100049China

Dissolved organic matter(DOM)participates in and affects many biological processes in aquatic ecosystems,altering nutrient cycling,bioavailability and toxicity of *** water contains thousands of DOM molecules,which largely originate from natural sources and are greatly influenced by external inputs through anthropogenic *** elucidate the shaping mechanism of the DOM composition under complex habitat conditions and anthropogenic disturbances,the characteristic molecular fingerprint of DOM was determined based on chemodiversity information obtained via ultrahigh-resolution mass spectrometry for the Baiyangdian wetland(a typical large macrophytic shallow lake in North China).In the Baiyangdian wetland,the DOM composition exhibited high complexity and spatial heterogeneity,and more recalcitrant DOM molecules were identified in summer than in *** multiple natural factors(nutrient concentration,depth of water and quality of submerged macrophytes)and anthropogenic factors(different land uses and discharge of pollutants)were associated with the relative abundance of various DOM molecules,only the influences from the anthropogenic factors were statistically *** alpha diversity index of DOM could reflect variations in both the natural factors(total dissolved phosphorus)and the anthropogenic factors(discharge of nitrogen and phosphorus).Furthermore,the DOM composition in each group were clustered according to the ecological functions(natural reserves,tourism,breeding,and domestic supply functions),and exhibited molecular fingerprint *** study provides an effective approach to characterize the molecular fingerprint features of DOM and critical information to better understand the shaping mechanism of the DOM composition under complex habitat conditions and anthropogenic disturbances.

关键词： DOM FT-ICR-MS Chemodiversity Habitat Ecological function Baiyangdian wetland

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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

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monitoring fossil fuel CO_(2) emissions from co-emitted NO_(2) observed from space:progress,challenges,and future perspectives

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Frontiers of environmental Science & Engineering 2025年第1期19卷 23-45页

作者： Hui Li Jiaxin Qiu Kexin Zhang Bo Zheng Shenzhen Key Laboratory of Ecological Remediation and Carbon Sequestration Institute of Environment and EcologyTsinghua Shenzhen International Graduate SchoolTsinghua UniversityShenzhen 518055China State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084China

Developing an anthropogenic carbon dioxides(CO_(2))emissions monitoring and verification support(MVS)capacity is essential to support the Global Stocktake(GST)and ratchet up Nationally Determined Contributions(NDCs).The 2019 IPCC refinement proposes top-down inversed CO_(2)emissions,primarily from fossil fuel(FFCO_(2)),as a viable emission *** substantial progress in directly inferring FFCO_(2)emissions from CO_(2)observations,substantial challenges remain,particularly in distinguishing local CO_(2)enhancements from the high background due to the long atmospheric ***,using short-lived and co-emitted nitrogen dioxide(NO_(2))as a proxy in FFCO_(2)emission inversion has gained *** methodology is broadly categorized into plume-based and emission ratios(ERs)-based inversion *** the plume-based methods,NO_(2)observations act as locators,constraints,and validators for deciphering CO_(2)plumes downwind of sources,typically at point source and city *** ERs-based inversion approach typically consists of two steps:inferring NO_(2)-based nitrogen oxides(NO_(x))emissions and converting NO_(x)to CO_(2)emissions using CO_(2)-to-NO_(x)*** integrating NO_(2)observations into FFCO_(2)emission inversion offers advantages over the direct CO_(2)-based methods,uncertainties persist,including both structural and data-related *** these uncertainties is a primary focus for future research,which includes deploying nextgeneration satellites and developing advanced inversion ***,data caveats are necessary when releasing data to users to prevent potential *** NO_(2)-based CO_(2)emission inversion requires interdisciplinary collaboration across multiple communities of remote sensing,emission inventory,transport model improvement,and atmospheric inversion algorithm development.

关键词： Fossil fuel CO_(2) emissions CO_(2) satellites NO_(2) satellites Emission inversion methods Uncertainty management Future perspectives

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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

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Analysis of the Influencing Factors of Heavy Metal Pollution in the Surrounding Soil of Typical Industrial Enterprises in Sichuan based on Geographical Detectors

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Huanjing Kexue/environmental Science 2025年第5期46卷 3229-3236页

作者： Sun, Dong-Yue Jiang, Xiao-Xu Zhou, Xiao-Bai Yang, Nan Feng, Xue China National Environmental Monitoring Centre Beijing100012 China State Environmental Protection Key Laboratory of Quality Control in Environmental Monitoring Beijing100012 China

The surrounding soils of nine typical industrial enterprises in Sichuan Province were selected as the research objects. The contents of heavy metals Cd，Hg，As，Pb，Cr，Cu，Ni，and Zn in 25 surface soil samples around industrial enterprises were collected and analyzed. The potential ecological risk assessment method was used to evaluate the potential ecological risks of heavy metals in the soil. Based on the geographic detector，ten influencing factors，such as the distance from the enterprise，the relative elevation difference with the location of the enterprise，and the type of industry were selected as independent variables，and the analysis was conducted with the content of heavy metals as the dependent variable，and further verification was conducted using correlation analysis. The results showed that the soils around industrial enterprises in the study area were mainly characterized by "moderate"potential risks，accounting for 40%，whereas the other four risk types each accounted for 8% to 20%. Cd and Hg were the main contributing indicators to the potential ecological risk in the study area. The results of the geographic detector showed that"distance from the enterprise"had the strongest explanatory power for Cd，Pb，and Zn;"industry type"had the strongest explanatory power for As，Cr，Cu，and Ni;and"precipitation"had the strongest explanatory power for Hg. The results of interaction detection showed that after the interaction of each factor，the explanatory power was increased，indicating that the content of heavy metals in soil was affected by a compound of multiple influencing factors. The results of correlation verification showed that there were significant correlations between Cd，As，Pb，and Zn，indicating a certain homogeneity among the four heavy metals，which was basically consistent with the results of the geographic detector and could further validate the results of the geographic detector，making the results more accurate and reliable. © 2025 Science Press. All rights reser

关键词： Heavy metals

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Promotion of symbiotic interaction between phagotrophic protists and beneficial bacteria, mediated via balancing of soil nutrients, reduces the incidence of watermelon Fusarium wilt

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Pedosphere 2025年第2期35卷 352-363页

作者： Yaoyao TONG Li WANG Chuanfa WU Haoqing ZHANG Yangwu DENG Ming CHEN Xianqing ZHENG Weiguang Lü Jianping CHEN Tida GE State Key Laboratory for Quality and Safety of Agro-Products International Science and Technology Cooperation Base for the Regulation of Soil Biological Functions and One Health of Zhejiang Province Ningbo University Key Laboratory of Biotechnology in Plant Protection of MARA and Zhejiang Province Institute of Plant Virology Ningbo University Jiangxi Provincial Key Laboratory of Environmental Pollution Prevention and Control in Mining and Metallurgy Jiangxi University of Science and Technology Eco-environmental Protection Institute of Shanghai Academy of Agricultural Sciences

Elucidating the microbial mechanisms that trigger Fusarium wilt represents a key step in addressing the barriers to sustainable cropping. However,from the perspective of the complete microbiome, the integrated role of soil nutrients and microbial community in the fields with different rates of wilt disease remains unclear. In this study, we examined the potential interrelationships among the nutrients, bacteria, fungi, and protists in rhizospheric soils collected from the fields with watermelon cropping for 7 years at the Zhuanghang Experimental Station of Shanghai Academy of Agricultural Sciences,China. The soils collected were characterized by a high(HW, 81.25%) or low(LW, 6.25%) wilting rate. The HW soil was found to contain a higher abundance of Fusarium oxysporum(1.30-fold higher) than the LW soil, along with higher contents of available phosphorus(1.31-fold higher) and available potassium(2.39-fold higher). In addition, the interkingdom correlation between protists and bacteria in the HW soil was 2.08-fold higher than that in the LW ***, structural equation modeling revealed that an excess of soil available potassium enhanced the predation by potentially detrimental phagotrophic protists on potentially beneficial bacteria. In summary, our findings indicated that a balanced nutrient input and the interactions between protists(Cercomonas and Colpoda) and beneficial bacteria(Bacillus) played important roles in controlling the incidence of watermelon Fusarium wilt.

关键词： co-occurrence network continuous cropping Fusarium oxysporum microbial community nitrogen limitation soil-borne pathogen

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On using an aerosol thermodynamic model to calculate aerosol acidity of coarse particles

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Journal of environmental Sciences 2025年第2期148卷 46-56页

作者： Zhengyang Fang Shuwei Dong Chengpeng Huang Shiguo Jia Fu Wang Haoming Liu He Meng Lan Luo Yizhu Chen Huanhuan Zhang Rui Li Yujiao Zhu Mingjin Tang State Key Laboratory of Organic Geochemistry Guangdong Key Laboratory of Environmental Protection and Resources UtilizationGuangzhou Institute of GeochemistryChinese Academy of SciencesGuangzhou 510640China College of Earth and Planetary Sciences University of Chinese Academy of SciencesBeijing 101408China Longhua Center for Disease Control and Prevention of Shenzhen Shenzhen 518109China School of Atmospheric Sciences and Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai) Sun Yat-sen UniversityZhuhai 519082China Institute for Environmental and Climate Research Jinan UniversityGuangzhou 510632China Qingdao Eco-environment Monitoring Center of Shandong Province Qingdao 266003China Environment Research Institute Shandong UniversityQingdao 266237China

Thermodynamic modeling is still themostwidely usedmethod to characterize aerosol acidity,a critical physicochemical property of atmospheric ***,it remains unclear whether gas-aerosol partitioning should be incorporated when thermodynamicmodels are employed to estimate the acidity of coarse *** this work,field measurements were conducted at a coastal city in northern China across three seasons,and covered wide ranges of temperature,relative humidity and NH_(3) *** examined the performance of different modes of ISORROPIA-II(a widely used aerosol thermodynamic model)in estimating aerosol acidity of coarse and fine *** M0 mode,which incorporates gas-phase data and runs the model in the forward mode,provided reasonable estimation of aerosol acidity for coarse and fine *** to M0,the M1 mode,which runs the model in the forward mode but does not include gas-phase data,may capture the general trend of aerosol acidity but underestimates pH for both coarse and fine particles;M2,which runs the model in the reverse mode,results in large errors in estimated aerosol pH for both coarse and fine particles and should not be used for aerosol acidity ***,M1 significantly underestimates liquid water contents for both fine and coarse particles,while M2 provides reliable estimation of liquid water *** summary,our work highlights the importance of incorporating gas-aerosol partitioning when estimating coarse particle acidity,and thus may help improve our understanding of acidity of coarse particles.

关键词： Aerosol acidity Coarse particles Thermodynamic model Aerosol liquid water ISORROPIA-II Gas-aerosol partitioning

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Method for Simultaneous Quantifying Five Types of Microplastics by Tubular Furnace Pyrolysis-thermal Desorption-gas Chromatography-mass Spectrometry

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Huanjing Kexue/environmental Science 2025年第5期46卷 3200-3208页

作者： Wu, Zhi-Xin Liu, Lin Yu, Ruo-Zhen Deng, Zi-Yi Yang, Gang Wu, Yu-Xuan Liu, Cheng-You Liu, Fan-Chen Zhang, Bing Yang, Ying Zheng, Han-Yun Zhang, Zi-Ye Li, Jia-Nan Huang, Lin-Yan Yang, Yu-Jue Zhao, Ya-Xian Zhao, Gao-Feng Zhang, Li-Fei Liu, Guo-Rui Dai, Ran Liu, Ya-Qing Pei, Shu-Wei Tang, Han-Yu Wang, Hong-Wei Gao, Jun-Min Abdul, Qadeer An, Li-Hui Zhao, Xing-Ru State Key Laboratory of Environmental Criteria and Risk Assessment National Engineering Laboratory for Lake Pollution Control and Ecological Restoration Environmental Standards Institute Chinese Research Academy of Environmental Sciences Beijing100012 China School of Life Sciences Hebei University Baoding071002 China Institute of Environment and Sustainable Development in Agriculture Chinese Academy of Agricultural Sciences Beijing100081 China State Environmental Protection Key Laboratory of Environmental Pollutant Metrology and Reference Materials Institute of Environmental Reference Materials Environmental Development Center The Ministry of Ecology and Environment Beijing100029 China State Environmental Protection Key Laboratory of Dioxin Pollution Control National Research Center for Environmental Analysis and Measurement Beijing100029 China State Key Laboratory of Environmental Chemistry and Ecotoxicology Research Center for Eco-Environmental Sciences Chinese Academy of Sciences Beijing100085 China Markes International Limited / SepSolve Analytical Ltd. Shanghai200233 China Key Laboratory of the Three Gorges Reservoir Region's Eco-Environment Ministry of Education Chongqing University Chongqing400045 China

A susceptible method has been established to simultaneously quantify five types of microplastics greater than 0.22 µm across various environmental matrices，namely，polyethylene（PE），polypropylene（PP），polystyrene（PS），polyvinyl chloride（PVC），and polyethylene terephthalate（PET）. In detail，five types of microplastics were completely pyrolyzed within a tubular furnace. Pyrolyzates were captured using a Tenax TA absorbent. Subsequently，target compounds were rereleased in a thermal desorption instrument and transferred into gas chromatography/mass spectrometry（GC/MS）. The indicative compounds were filtered and selected to identify and quantify target microplastics. The instrument detection limits for the five types of microplastics ranged from 0.03 µg to 1.91 µg，whereas the method detection limits of target microplastics were 0.07-2.87 µg·L-1in water，0.31-16.52 µg·g-1 in soil/sediment，and 0.11-7.41 µg·g-1 in the organism，respectively. The relative standard deviations of 3.31%-22.37%，recoveries of 74.21%-119.63%，and quantitative ranges of 3.7-75 µg for PS;15-300 µg for PP，PVC，and PET;and 30-600 µg for PE were also implemented. Importantly，this method had simple requirements for sample pretreatment，avoided the interference of complex matrix，and improved the repeatability and reliability of results. Subsequently，the technique quantified target microplastics in water，soil，sediments，and biological tissue. The results showed that the total mass concentrations of five microplastics in water samples were 4.48-37.34 µg·L-1 and 10.55-218.98 µg·g-1 in soil and sediments，respectively，and 8.82-19.81 µg·g-1 in biological samples. This present study provided a reliable technical guarantee for future investigation and monitoring of environmental microplastic pollution. © 2025 Science Press. All rights reserved.

关键词： Polyvinyl chlorides

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Acriflavine-based ionic covalent organic frameworks for adsorption-assisted photocatalytic reduction of hexavalent chromium

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Chemical Engineering Journal 2025年 512卷

作者： Zhou, Yun-Jie Liu, Jing-Qi Cao, Dong-Xiao Feng, Xue-Nan Tang, An-Na Jiang, Hong-Xin Kong, De-Ming State Key Laboratory of Medicinal Chemical Biology Tianjin Key Laboratory of Biosensing and Molecular Recognition Research Centre for Analytical Sciences College of Chemistry Nankai University Tianjin300071 China Agro-Environmental Protection Institute Key Laboratory for Environmental Factors Control of Agro-product Quality Safety Laboratory of Environmental Factors Risk Assessment of Agro-Product Quality Safety Ministry of Agriculture Tianjin300191 China Key Laboratory of Odor Pollution Control Ministry of Ecology and Environment Tianjin Academy of Environmental Sciences Tianjin300191 China

Ionic covalent organic frameworks (iCOFs) have drawn considerable attention because of their high surface charge, excellent self-dispersion, and facile stripping properties, making them versatile materials in various applications. However, the variety of reported iCOFs remains limited, with many exhibiting significant steric hindrances at ionic sites, underscoring the importance of developing new iCOFs to expand this family. In this study, we demonstrate that the inexpensive dye acriflavine (AF) is a promising candidate monomer for iCOF synthesis. By combining AF with different aldehyde monomers, we synthesized three novel AF-based iCOFs, which readily self-strip into nanosheets, thereby fully exposing the active sites and reducing mass transfer pathways. Among these, AF-Tp, characterized by its abundant C=O groups, exhibits the best performance in dealing with hexavalent chromium (Cr(VI)) pollution, attributed to its broader light absorption range, narrower bandgap, and enhanced Cr(VI) adsorption capacity. The complete reduction of highly toxic Cr(VI) to Cr(III), an essential micronutrient for humans, is realized within a remarkably short duration of just 15 min through an adsorption-assisted photocatalytic reduction mechanism. Through experiments and theoretical calculations, the corresponding working mechanisms and structure–function relationships were elucidated. This study not only introduces a new monomer for iCOFs synthesis but also advances their applications in environmental remediation and industrial wastewater treatment. © 2025 Elsevier B.V.

关键词： Acriflavine Hexavalent chromium Ionic covalent organic frameworks Nanosheets Photocatalysis

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Cost-Effective Mapping of Hyperlocal Air Pollution Using Large-Scale Mobile monitoring and Land-Use Machine Learning

ACS ES&T Air

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ACS ES&T Air 2025年第2期2卷 151-161页

作者： Tie Zheng Yifan Wen Sheng Xiang Pan Yang Xuan Zheng Yan You Shaojun Zhang Ye Wu School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing 100084 China State Key Joint Laboratory of Environmental Simulation and Pollution Control School of Environment Beijing Normal University Beijing 100875 China State Key Laboratory of Pollution Control and Resource Reuse Tongji University Shanghai 200092 China；College of Environmental Science and Engineering Tongji University Shanghai 200092 China College of Chemistry and Environmental Engineering Shenzhen University Shenzhen 518060 China Macao Environmental Research Institute Macau University of Science and Technology Macau 999078 China；National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Macau University of Science and Technology Macao 999078 China School of Environment State Key Joint Laboratory of Environment Simulation and Pollution Control Tsinghua University Beijing 100084 China；State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex Beijing 100084 China；Beijing Laboratory of Environmental Frontier Technologies Beijing 100084 China

The advent of large-scale mobile monitoring using fast-response instruments has enabled hyperlocal mapping (≤100 m) of traffic-related air pollution (TRAP), with important implications for air quality management. However, most related studies have been confined within small areas due to the high cost and labor intensity. This study pioneers a cost-effective TRAP mapping method by incorporating large-scale mobile monitoring and land-use machine learning (LUML). Here, over 4.6 million 1 Hz high-frequency measurements (∼1300 h) were collected on a part of major roadways in the Chinese megacity of Shenzhen. Unmeasured locations were estimated by LUML models to reduce measurement costs and labor intensity. Various ML algorithms and varying spatial aggregation segment lengths were incorporated to optimize the model performance. Hyperlocal maps of NO, NO2, and PM2.5were predicted across the entire road network covering over 1700 km2. Based on our results, LU-RF (random forest) for mapping NO and NO2and LU-GBM (Gradient Boosting Machine) for mapping PM2.5, demonstrated superior performance. Deep learning models, in contrast, did not yield comparable results. Finer partitioning of road segments (≤100 m) improved NO prediction performance, but worsened that for NO2and PM2.5. By deployment of optimal ML algorithms and segment lengths, the TRAP mapping accuracy increased by 20–80% compared to conventional land-use regression models. This study provides a promising and cost-effective approach to hyperlocal air pollution mapping and management in cities worldwide.

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