[1] Zhou, S., Song, M., Shan, K.,Razaqpur, A.G. & Huang, J.J. (2024b). Parametric and optimization analysesof a dynamic trombe wall incorporating PCM to save heating energy under coldclimate zones. Renewable Energy, 237. https://doi.org/10.1016/j.renene.2024.121537.(IF = 9.0)

[2] Zhou, S., Song, M., Shan, K.,Ghani Razaqpur, A., Huang, J.J., Zhu, X. et al. (2024a). Passive application ofPCMs for the Trombe wall: a review. Energy Storage and Saving. https://doi.org/10.1016/j.enss.2024.06.001.

[3] Wei, Y., Chen, H. & Huang,J.J. (2024). Dynamics of urban latent heat in response to climate change andurbanization: What would be a global threshold? Journal of Hydrology, 643. https://doi.org/10.1016/j.jhydrol.2024.132002.(IF = 5.9)

[4] Wang, J., Chen, C., JeanneHuang, J., Xiao, N. & Li, S. (2025). Synchronous or selective removal ofnitrate and Cr(VI) by montmorillonite supported sulfidized nanoscale zerovalentiron: Role of Fe(II) and S0. Separation and Purification Technology, 354. https://doi.org/10.1016/j.seppur.2024.129006.(IF = 8.1)

[5] Chen, H., Wei, Y. & Huang,J.J. (2024). Divergent Drivers of Declining Urban Vegetation Productivity andTranspiration During Heatwaves. Journal of Geophysical Research: Atmospheres. https://doi.org/10.1029/2023JD040390 (IF = 3.8)

[6] Guo, H. W., Huang, J. J., Zhu,X. T., Tian, S., & Wang, B. L. (2024). Spatiotemporal variationreconstruction of total phosphorus in the Great Lakes since 2002 using remotesensing and deep neural network. Water Research, 255, 15, Article 121493. https://doi.org/10.1016/j.watres.2024.121493(IF = 11.4)

[7] Li, H., Chen, H., & Huang,J. J. (2024). Partitioning urban forest evapotranspiration based on integratingeddy covariance of water vapor and carbon dioxide fluxes. Science of The TotalEnvironment, 935, 18, Article 173201. https://doi.org/10.1016/j.scitotenv.2024.173201(IF = 8.2)

[8] Li, H., Lan, Z. Q., Chen, H.,& Huang, J. J. (2024). How do non-halophyte locust trees thrive intemperate coastal regions: A study of salinity and multiple environmentalfactors on water uptake patterns. Hydrological Processes, 38(3), 14, Article e15122.https://doi.org/10.1002/hyp.15122(IF = 2.8)

[9] Nourani, V., Najafi, H.,Maleki, S., Paknezad, N. J., Huang, J. J., Zhang, P. W., & Mohammadisepasi,S. (2024). Z-number based assessment of groundwater vulnerability to seawaterintrusion. Journal of Hydrology, 632, 16, Article 130859. https://doi.org/10.1016/j.jhydrol.2024.130859(IF = 5.9)

[10] Wei, Y. Z., Chen, H., &Huang, J. J. (2024). Response of surface energy components to urban heatwavesand its impact on human comfort in coastal city. Urban Climate, 54, 15, Article101836. https://doi.org/10.1016/j.uclim.2024.101836(IF = 6.0)

[11] Zhang, Z. J., Zhang, H., Jin,Y. F., Guo, H. W., Tian, S., Huang, J. J., & Zhu, X. T. (2024). Analysingthe spatiotemporal variation and influencing factors of Lake Chaohu's CDOM overthe past 40 years using machine learning. Ecohydrology, 17(3), 12. https://doi.org/10.1002/eco.2639(IF = 2.5)

[12] Zhu, X. T., Guo, H. W., &Huang, J. J. (2024). Urban flood susceptibility mapping using remote sensing,social sensing and an ensemble machine learning model. Sustainable Cities andSociety, 108, 16, Article 105508. https://doi.org/10.1016/j.scs.2024.105508(IF = 10.5)

[13] Chen, H., Li, H., Wei, Y. Z.,McBean, E., Liang, H., Wang, W. M., & Huang, J. J. (2024). Partitioningeddy covariance CO2 fluxes into ecosystem respiration and gross primaryproductivity through a new hybrid four sub-deep neural network. Agriculture Ecosystems& Environment, 361, 16, Article 108810. https://doi.org/10.1016/j.agee.2023.108810(IF = 6.0)

[14] Chen, H., Wei, Y. Z., &Huang, J. J. (2024). Widespread increase in plant transpiration driven byglobal greening. Global and Planetary Change, 235, 17, Article 104395. https://doi.org/10.1016/j.gloplacha.2024.104395(IF = 4.0)

[15] Feng, S. T., Zhang, B. C.,Wang, J. S., & Huang, J. J. (2024). Zero-valent iron in phosphate removal:Unraveling the role of particle size and dissolved oxygen. Journal of WaterProcess Engineering, 60, 10, Article 105180. https://doi.org/10.1016/j.jwpe.2024.105180(IF = 6.3)

[16] Chang, C., Chen, Y., &Huang, J. J. (2023). A comparison study on the role of urbanization in alteringthe short-duration and long-duration intense rainfall. Science of The TotalEnvironment, 857, 159290 (IF = 9.8)

[17] Chang, C. C., Chen, Y. H.,& Huang, J. J. (2023). Variability of Rainfall Areal Reduction Factors fora Coastal City: A Case Study of Shenzhen, China. Journal of HydrologicEngineering, 28(6), 12, Article 05023008. https://doi.org/10.1061/jhyeff.Heeng-5813(IF = 2.4)

[18] Chen, H., Huang, J. J., Dash,S. S., McBean, E., Singh, V. P., Li, H., Wei, Y. Z., Zhang, P. W., & Zhou,Z. Q. (2023). A non-linear theoretical dry/wet boundary-based two-sourcetrapezoid model for estimation of land surface evapotranspiration. HydrologicalSciences Journal, 68(11), 1591-1609. https://doi.org/10.1080/02626667.2023.2224921(IF = 3.5)

[19] Chen, H., Huang, J. J., Li, H.,Wei, Y. Z., & Zhu, X. T. (2023). Revealing the response of urban heatisland effect to water body evaporation from main urban and suburb areas.Journal of Hydrology, 623, 21, Article 129687. https://doi.org/10.1016/j.jhydrol.2023.129687(IF = 6.4)

[20] Chen, H., Huang, J. J., Liang,H., Wang, W., Li, H., Wei, Y., Jiang, A. Z., & Zhang, P. (2023). Canevaporation from urban impervious surfaces be ignored? Journal of Hydrology,616, 128582 (IF = 6.4)

[21] Chen, H., Huang, J. J., Liang,H., Wang, W. M., Li, H., Wei, Y. Z., Jiang, A. Z., & Zhang, P. W. (2023).Integration of flux footprint and physical mechanism into convolutional neuralnetwork model for enhanced simulation of urban evapotranspiration. Journal ofHydrology, 619, 18, Article 129016. https://doi.org/10.1016/j.jhydrol.2022.129016(IF = 6.4)

[22] Chen, H., Razaqpur, A. G., Wei,Y. Z., Huang, J. J., Li, H., & McBean, E. (2023). Estimation of global landsurface evapotranspiration and its trend using a surface energy balanceconstrained deep learning model. Journal of Hydrology, 627, 18, Article 130224.https://doi.org/10.1016/j.jhydrol.2023.130224(IF = 6.4)

[23] Chen, H., Wei, Y. Z., &Huang, J. J. (2023). Altered landscape pattern dominates the declined urbanevapotranspiration trend. Journal of Hydrology, 627, 11, Article 130296. https://doi.org/10.1016/j.jhydrol.2023.130296(IF = 6.4)

[24] Chen, H., Zhou, Z. Q., Li, H.,Wei, Y. Z., Huang, J. H., Liang, H., & Wang, W. M. (2023). Evaluation thePerformance of Three Types of Two-Source Evapotranspiration Models in UrbanWoodland Areas. Sustainability, 15(12), 18, Article 9826. https://doi.org/10.3390/su15129826(IF = 3.9)

[25] Fan, L., Wu, R., Huang, J. J.,& Selvaganapathy, P. R. (2023). One-step fabrication of the novelelectrochemical sensing platform for the ultrasensitive determination ofMicrocystin-LR. Sensors and Actuators B-Chemical, 390, 9, Article 133961. https://doi.org/10.1016/j.snb.2023.133961(IF = 8.4)

[26] Fan, L., Wu, R., Patel, V.,Huang, J. J., & Selvaganapathy, P. R. (2023). Solid-state, reagent-free andone-step laser-induced synthesis of graphene-supported metal nanocompositesfrom metal leaves and application to glucose sensing. Analytica Chimica Acta,1264, 10, Article 341248. https://doi.org/10.1016/j.aca.2023.341248(IF = 6.2)

[27] Foroumandi, E., Nourani, V.,Huang, J. J., & Moradkhani, H. (2023). Drought monitoring by downscalingGRACE-derived terrestrial water storage anomalies: A deep learning approach.Journal of Hydrology, 616, 16, Article 128838. https://doi.org/10.1016/j.jhydrol.2022.128838(IF = 6.4)

[28] Guo, H. W., Zhu, X. T., Huang,J. J., Zhang, Z. J., Tian, S., & Chen, Y. H. (2023). An enhanced deeplearning approach to assessing inland lake water quality and its response toclimate and anthropogenic factors. Journal of Hydrology, 620, 19, Article129466. https://doi.org/10.1016/j.jhydrol.2023.129466(IF = 6.4)

[29] Nourani, V., Tapeh, A. H. G.,Khodkar, K., & Huang, J. J. (2023). Assessing long-term climate changeimpact on spatiotemporal changes of groundwater level usingautoregressive-based and ensemble machine learning models. Journal ofEnvironmental Management, 336, 14, Article 117653. https://doi.org/10.1016/j.jenvman.2023.117653(IF = 8.7)

[30] Pan, L., He, X. P., Chen, J.H., Huang, J. J., Wang, Y. N., Liang, S. K., & Wang, B. D. (2023).Detection, occurrence, influencing factors and environmental risks of paralyticshellfish toxins in seawater in a typical mariculture bay. Chemosphere, 313,11, Article 137372. https://doi.org/10.1016/j.chemosphere.2022.137372(IF = 8.8)

[31] Tian, S., Guo, H. W., Xu, W.,Zhu, X. T., Wang, B., Zeng, Q. H., Mai, Y. Q., & Huang, J. H. J. (2023).Remote sensing retrieval of inland water quality parameters using Sentinel-2and multiple machine learning algorithms. Environmental Science and PollutionResearch, 30(7), 18617-18630. https://doi.org/10.1007/s11356-022-23431-9(IF = 5.8)

[32] Yan, R., & Huang, J. J.(2023). Confident learning-based Gaussian mixture model for leakage detectionin water distribution networks. Water Research, 247, 15, Article 120773. https://doi.org/10.1016/j.watres.2023.120773(IF = 12.8)

[33] Zhang, B. C., Wang, J. S.,Feng, S. T., Huang, J. J., & Han, X. Y. (2023). The roles of differentFe-based materials in alleviating the stress of Cr(VI) on anammox activity:Performance and mechanism. Chemical Engineering Journal, 475, 13, Article 145739.https://doi.org/10.1016/j.cej.2023.145739(IF = 15.1)

[34] Zhu, X. T., Guo, H. W., Huang,J. J., Tian, S., & Zhang, Z. J. (2023). A hybrid decomposition and Machinelearning model for forecasting Chlorophyll-a and total nitrogen concentrationin coastal waters. Journal of Hydrology, 619, 19, Article 129207. https://doi.org/10.1016/j.jhydrol.2023.129207(IF = 6.4)

[35] Pan, L., Huang, J. J., Chen, J.H., He, X. P., Wang, Y. N., Wang, J. M., & Wang, B. D. (2022). Tracedetermination of multiple hydrophilic cyanotoxins in freshwater by off- andon-line solid phase extraction coupled to liquid chromatography-tandem massspectrometry. Science of The Total Environment, 853, 11, Article 158545.https://doi.org/10.1016/j.scitotenv.2022.158545

[36] Wang, J. S., Huang, J. J.,Zhou, Y., Liao, Y., Li, S., Zhang, B. C., & Feng, S. T. (2022). SynchronousN and P Removal in Carbon-Coated Nanoscale Zerovalent Iron AutotrophicDenitrification-The Synergy of the Carbon Shell and P Removal [; Early Access].Environmental Science & Technology, 13.https://doi.org/10.1021/acs.est.2c02376

[37] Yang, C., Xiao, N., Yang, S.S., & Huang, J. J. (2022). Micro response mechanism of mini MFC sensorperformance to temperature and its applicability to actual wastewater. ChemicalEngineering Science, 263, 9, Article 118124. https://doi.org/10.1016/j.ces.2022.118124

[38] Zhu, X. T., Guo, H. W., Huang,J. J., Tian, S., Xu, W., & Mai, Y. Q. (2022). An ensemble machine learningmodel for water quality estimation in coastal area based on remote sensingimagery. Journal of Environmental Management, 323, 12, Article 116187.https://doi.org/10.1016/j.jenvman.2022.116187

[39] Wang, J., Zhang, B.,Huang J.* ,Liao, Y., Xiao, N. (2022).Elucidating the Role of Carbon Shell in AutotrophicDenitrification Driven by Carbon-coated Nanoscale Zerovalent Iron,ChemicalEngineering Journal,434(10). https://doi.org/10.1016/j.cej.2022.134656.

[40] Guo,H., Huang, J*.,Tian,S.,Zhu, X., Wang, B., Zhang,Z. (2022).Performance of deep learning in mappingwater quality of Lake Simcoe with long-term Landsat archive,ISPRS Journal ofPhotogrammetry and Remote Sensing,183,451-469. https://doi.org/10.1016/j.isprsjprs.2021.11.023.

[41] Chen, H., Huang, J*., McBean,E., Singh, V. (2022).Assessing the effects of end-members determination onregional latent heat flux simulation in trapezoidal framework basedmodel.Agricultural and Forest Meteorology,312,108734.https://doi.org/10.1016/j.agrformet.2021.108734.

[42] Chen, H., Huang, J*., McBean,E.,Sandeep,S.D., Lan, Z.Z.,Jiawei Zhang.,Gao, J,J. McBean, E., Singh, V.(2022). Development of a Three-Source Remote Sensing Model for Estimation ofUrban Evapotranspiration, Advances in Water Resources 2022, 161,104126.https://doi.org/10.1016/j.advwatres.161.104126.

[43] Chen, H., Huang, J*.,Jiang,A.Z.,Han Li.,McBean, E., Singh, V. Jiawei Zhang., Lan, Z.Z.,Gao, J,J.(2022).An Enhanced Shuttleworth-Wallace Model for Simulation ofEvapotranspiration and its Components, Agricultural and ForestMeteorology,313,108769.https://doi.org/10.1016/j.agrformet.2021.108769.

[44] Chen, H., Huang,J*.,Sandeep,S.D., McBean, E., Singh, V,Wei,Y., Han Li. (2022).Assessing theimpact of urbanization on urban evapotranspiration and its components using anovel four-source energy balance model, Agricultural and Forest Meteorology,316,108853.https://doi.org/10.1016/j.agrformet.2022.108853.

[45] Chen, H., Huang,J*.,Sandeep,S.D., Wei,Y., Han Li. (2022).A hybrid deep learning framework withphysical process description for simulation of evapotranspiration, Journal ofHydrology,606,127422. https://doi.org/10.1016/j.jhydrol.2021.127422.

[46] Xiao, N.,Wang,B.,Huang J.*,Huang,Z.,Shi,L.(2022). Aeration strategy based on numerical modelling and theresponse mechanism of microbial communities under various operating conditions, Journal of Environmental Management,310,114752. https://doi.org/10.1016/j.jenvman.2022.114752.

[47] Huang,J*.,Xiao,M.,Li,Y.,Ran,Y.,Qian,Z. (2022).The optimization of Low ImpactDevelopment placement considering life cycle cost using GeneticAlgorithm,Journal of EnvironmentalManagement,309,114700.https://doi.org/10.1016/j.jenvman.2022.114700.

[48] Mei, X., Huang, J*.,Jue,H.(2022).Impacts of hydrophobic, hydrophilic, superhydrophobic andsuperhydrophilic nanofibrous substrates on the thin film composite forwardosmosis membranes, Journal of Environmental Chemical Engineering,10(7).https://doi.org/10.1016/j.jece.2021.106958.

[49] Liang Fan, Jinhui Jeanne Huang*, Ching Y. Lo, Bin Zhou and Xujin Fu. (2022).UltrasensitivePhotoelectrochemical Microcystin-LR Immunosensor Using Carboxyl-FunctionalizedGraphene Oxide Enhanced Gold Nanoclusters for Signal Amplification, Analytica ChimicaActa,1185, https://doi.org/10.1016/j.aca.2021.339078.

[50] Xiao, N., Huang J.*,Catherine,N,M. (2022).The dynamics of soil microbial community structure andnitrogen metabolism influenced by agriculture practices and rainfall,AppliedSoil Ecology,172,104351.https://doi.org/10.1016/j.apsoil.2021.104351.

[51] Liang Fan, Jinhui Jeanne Huang*, Ching Y. Lo, Bin Zhou and Xujin Fu. (2022).Simplified validation of theELISA kit determination of Microcystins in surface water,Water Science &Technology,85(3):900-913.https://doi.org/10.2166/wst.2021.640.

[52] Vahid Nourani, Nardin JabbarianPaknezhad, Jinhui Jeanne Huang *. (2022).Application of PPIE method to assessthe uncertainty and accuracy of multi-climate model-based temperature andprecipitation downscaling,Theoretical and Applied Climatology,147(2).https://doi.org/10.1007/s00704-021-03884-7.

[53] Wang, J., Huang J.*,Catherine,N,M. (2022).Seasonal source identification and source-specifichealth risk assessment of pollutants in road dust,Environmental Science andPollution, Research,29(37). https://doi.org/10.1007/s11356-021-16326-8.

[54] Chen, H., Huang, J*., McBean,E.,Sandeep,S.D.,Han Li., Jiawei Zhang., Lan, Z.Z.,Gao, J,J. (2022).Evapotranspiration partitioning based on field-stable oxygen isotopeobservations for an urban locust forest land. Submit to Ecohydrology.

[55] Guo,H., Huang, J*., Zhu, X.,Wang, B., Tian,S.,Wang,X., Mai.Y. (2021). A generalized machine learningapproach for dissolved oxygen estimation at multiple spatiotemporal scalesusing remote sensing, Environmental Pollution,288,117734.https://doi.org/10.1016/j.envpol.2021.117734.

[56] Chen, H., Huang, J*., McBean,E., Singh, V. (2021). Evaluation of alternative two-source remote sensingmodels in partitioning of land evapotranspiration. Journal of Hydrology.https://doi.org/10.1016/j.jhydrol.2021.126029.

[57] Chenchao Chang, Yu Li, YihengChen, Jinhui Jeanne Huang* , Ya Zhang. (2021). Advanced statistical analyses ofurbanization impacts on heavy rainfall in the Beijing metropolitan area. UrbanClimate. https://doi.org/10.1016/j.uclim.2021.100987.

[58] Yang, C., Xiao, N., Chang, Z.,Huang,J.*, Zeng, W.(2021). Biodegradation of TOC by Nano-Fe2O3 modified SMFCand its potential environmental effects, ChemistrySelect.,6(22).https://doi.org/10.1002/slct. 202101125.

[59] Xiao, N., Wang,B.,Huang J.*(2021).Hydrodynamic optimization for design and operating parameters of aninnovative continuous-flow miniaturized MFC biosensor,Chemical EngineeringScience,235:116505.https://doi.org/10.1016/j.ces.2021.116505.

[60] Huang, J., Chen, S., Liao, Y.,Chen, Y., You X., Wang. R. (2021). Performance, fouling and Cleaning of a thinfilm composite hollow fiber membrane during fertiliser-drawn forward osmosisprocess for micro-polluted water, Environmental Science Water Research andTechnology.2021,7:1279-1291.https://doi.org/10.1016/j.jece.2021.106958.

[61] Wang, J., Huang J.*,Iseult, L.(2021).Seasonal and short-term variations of bacteria and pathogenic bacteriaon road deposited sediments,Environmental Research 204(8):111903.https://doi.org/10.1016/j.envres.2021.111903.

[62] Zeng, X., Huang, J.*, Hua, B.(2021). Efficient phosphorus removal by a novel halotolerant fungusAureobasidium sp. MSP8 and the application potential in saline industrialwastewater treatment. Bioresource Technology,334,125237.https://doi.org/10.1016/j.biortech.2021.125237.

[63] Xiao, N., Wang, B., Huang J.*(2020). Optimization of a continuous-flow miniaturized MFC biosensor byhydrodynamic computational modelling and experimental investigation, ChemicalEngineering Journal (SCI 1区，IF=8.355), submitted on March 21, 2020.

[64] Chen, H., Huang, J*., McBean,E. (2020).Development of a Trapezoidal Framework-Based Model (PCALEP) forPartition of Land Evapotranspiration, Journal of Hydrology, 124994, 0022-1694.https://doi.org/10.1016/j.jhydrol.2020.124994.

[65] Zhao, W., Huang, J.*, Hua, B.,Droste, R. (2020). Photosynthetic bioaugmentation strategy for releasingrecovering volatile fatty acid inhibition of the anaerobic digestion system,Bioresources Technology, 2020, 311:123501https://doi.org/10.1016/j.biortech.2020.123501.

[66] Chen, L., Huang, J.*, Hua, B.,Droste, R. Salman,M., Zhao, W.; (2020). Effect of steel slag on the anaerobicgranulation in recycling waste activated sludge to produce anaerobic granularsludge, Chemosphere,2020, 257, 127291.https://doi.org/10.1016/j.chemosphere.2020.127291.

[67] Huang, J*.,Chen, H., Li, T.,McBean, E., Singh V. (2020), A Modified Trapezoidal Framework Model forPartitioning Regional Evapotranspiration, Hydrological Processes,https://doi.org/10.22541/au.158602500.05780501.

[68] Guo, H.,Huang, J.*,Chen,B.,McBean, E. (2020).Machine learning based strategy to retrieve water qualityfor small scale urban waterbody by Sentinel-2, Remote Sensinghttps://doi.org/10.1080/01431161.2020.1846222.

[69] Xiao, N., Wu, R., Huang J.*andSelvaganapathy, P. (2020). Influence of wastewater microbial community on theperformance of miniaturized microbial fuel cell biosensor, BioresourceTechnology，https://doi.org/10.1016/j.biortech.2020.122777.

[70] Chen, H., Huang, J*., McBean,E. (2020).Quantitative Assessment of Agriculture Practices on FarmlandEvapotranspiration Using Eddy Covariance Method and Numeric Modelling, WaterResources Management, https://doi.org/10.1007/s11269-019-02448-9.

[71] Chen, H., Huang, J*., McBean,E. (2020). Partitioning of Daily Evapotranspiration using a ModifiedShuttleworth-Wallace Model and Artificial Intelligence Model for a CabbageFarmland, Agricultural Water Management , https://doi.org/10.1016/j.agwat.2019.105923.

[72] Zeng, X., Huang, J.*, Hua, B.,Champagne P. (2020). Nitrogen removal bacterial strains, MSNA-1 and MSD4, withwide ranges of salinity and pH resistances, Bioresources Technology,https://doi.org/10.1016/j.biortech.2020.123309.

[73] Kuang, D. Weichao Kong; YuxiangWen; Mengxian Zhao; Jinhui Huang*, Chen Yang (2020) Solution classificationwith portable smartphone-based spectrometer system under variant shootingconditions by using convolutional neural network, IEEE Sensors Journal, https://doi.org/10.1109/JSEN.2020.2983733.

[74] Wang, J., Huang, J.J.* and Li,J. (2020); Characterization of the Pollutant Build-up Processes andConcentration/Mass Load in Road Deposited Sediments over a Long Dry Period,Science of The Total Environment,https://doi.org/10.1016/j.scitotenv.2020.137282.

[75] Xiao, N., Wu, R., Huang J.*andSelvaganapathy, P. (2020). Anode surface modification regulates biofilmcommunity population and the performance of micro-MFC based biochemical oxygendemand sensor, Chemical Engineering Science , https://doi.org/10.1016/j.ces.2020.115691.

[76] Liao, Y., Zheng, G., Huang,J.*, Tian, M., Wang, R., (2020). Development of robust and superhydrophobicmembranes to mitigate membrane scaling and fouling in membrane distillation,Journal of Membrane Science, https://doi.org/10.1016/j.memsci.2020.117962.

[77] Li, Y.; Huang, J.*.; Hu, M.;Hong, Y.; Tanaka, K.; (2019) Design of low impact development in the urbancontext considering hydrological performance and life-cycle cost, Journal ofFlood Risk Management.

[78] Wang, J., Huang, J.*, Li,J.(2019). The Study of Road Sediment Build-up Processes in a Long Dry Period inSemi-Arid Area of China, Science of the Total Environment,696.https://doi.org/10.1016/j.scitotenv.2019.133788.

[79] Liu, B., Huang, J*., andMcBean, E. A. (2019). Risk Assessment of Hybrid Rain Harvesting System andOther Small Drinking Water Supply Systems by Game Theory and Fuzzy LogicModelling, Science of Total Environment, https://doi.org/10.1016/j.scitotenv.2019.134436.

[80] Xiao, N., Wu, R., Huang J.*andSelvaganapathy, P. (2019). Development of a xurographically fabricatedminiaturized low-cost, high-performance microbial fuel cell and its applicationfor sensing biological oxygen demand, Sensors & Actuators: B. Chemical ,https://doi.org/10.1016/j.snb.2019.127432.

[81] Ali, S., Hua, B., Huang, J.*,Droste., R, Zhou, Q., Zhao, W. (2019). Effect of different initial low pHconditions on biogas production, composition, and shift in the aceticlasticmethanogenic population, Bioresources Technology, https://doi.org/10.1016/j.biortech.2019.121579.

[82] Huang, J.*,Tian,Y., Wang, R.,Tian, M., Liao, Y (2019). Fabrication of bead-on-string polyacrylonitrilenanofibrous air filters with superior filtration efficiency and ultralowpressure drop, SEPARATION AND PURIFICATION TECHNOLOGY, https://doi.org/10.1016/j.seppur.2019.116377.

[83] McBean, E., Salsali, H.,Bhatti, M., and Huang, J. * (2019). Source Characterization of Disinfectants inMunicipal Wastewaters, Acta Chimica and Pharmceutica Indica.

[84] Huang, J. , Zhang, N., Choi,G., McBean, E. A., and Zhang, Q.(2018). Spatiotemporal Patterns and Trends ofPrecipitation and Their Correlations with Related Meteorological Factors by TwoSets of Reanalysis Data in China, Hydrol. Earth Syst. Sci. Discuss.,https://doi.org/10.5194/hess-2017-756.

[85] E.,McBean, Hamid Salsali, MunirBhatti and J Huang * (2018). Funeral Homes and Slaughterhouses: Contributionsof Emerging Contaminants to Municipal Wastewaters, Acta Chimica andPharmaceutica India, 2018,8(2).

[86] Wen, Y., Kuang, D., Huang J.and Zhang, Yi. (2017). Microaxicave Colour Analysis System for FluorideConcentration using a Smart Phone. RSC Advances, 2017,7,42339 (DOI:10.1039/c7Ra07727k).

[87] McBean, E., and Huang, J;(2017). Sustainability Risks of Coastal Cities from Climate Change, The GlobalEnvironmental Engineers, 2017, 4, 1-9.

[88] Huang, J*., Li, Y.; Yin, J.;McBean, E.; (2016). Precipitation Regional Extreme Mapping as a Tool forUngauged Areas and the Assessment of Climate Changes, Hydrological Processes,30, 1940–1954  (DOI: 10.1002/hyp.10743).

[89] Choulnard, A., Anderson, B.,Wotton, B. and Huang, J. (2015). Comparative study of cold-climate constructedwetland technology in Canada and Northern China for water resource protection,Environmental Reviews.

[90] Huang, J*., Lin, X.; Wang, H.;Wang, J.; (2015). The precipitation driven correlation based mapping method(PCM) for identifying the critical source areas of non-point source pollution,Journal of Hydrology, 524:100–110，doi:10.1016/j.jhydrol.2015.02.011.

[91] Zhang, Y., Huang, J*., Chen,L., Qi, L., (2015). Eutrophication Forecasting and Management by ArtificialNeural Network: a Case Study at Yuqiao Reservoir in North China, Journal ofHydroinformatics, 17（4），Pages 679–695，doi:10.2166/hydro.2015.115.

[92] Huang, J*., and Xiang, W.;(2015). Investigation of Point Source and Non-Point Source Pollution forPanjiakou Reservoir in North China by Modelling Approach, Water QualityResearch Journal of Canada, V50 (2), pp 167–181,doi: 10.2166/wqrjc.2014.019.

[93] Huang, J*., Du, M. ;McBean, E.;Wang, H.; Wang, J-H. (2014). A coupled Bayesian and fault tree methodology toassess future groundwater conditions in light of climate change, Hydrology andEarth System Sciences (Discussion) 11, 9361–9397, 2014 , doi:10.5194/hessd-11-9361-2014.

[94] Huang, J*., Gao, X.; Balch, G.;Wootton, B.; Jorgensen, S.; Anderson, B. (2014). Modelling of Vertical FlowConstructed Wetlands for Treatment of Domestic Sewage and Stormwater Runoff bySubWet 2.0, Ecological Engineering, 74, 8-12;http://dx.doi.org/10.1016/j.ecoleng.2014.10.027.

[95] Huang, J*., Li, Y.; Niu, S.;Zhou, S. (2014). Assessing the Performances of LID Alternatives by Long TermSimulation for Semi-Arid Area in Tianjin, Northern China, Water Science andTechnology. 2014;69(3):566-572. doi:10.2166/wst.2014.228.

[96] Zhang, C, McBean, E., andHuang, J. (2014). “A Virtual Water Assessment Methodology For Cropping PatternInvestigation”, Water Resources Management, June 2014, Volume 28, Issue 8, pp2331-2349 (SCI, IF2.463), DOI 10.1007/s11269-014-0618-y.

[97] Qi, L., Zhang, Y., Peng, J.,Qi, C., Huang, J. and Liu, D. (2014). Water requirement of vegetation andinfiltration method to determine ecological water requirement of dried up,Water Science and Technology 2014;69(3):566-572. doi: 10.2166/wst.2013.743.

[98] Huang, J* .(2014). TheDevelopment of a Design and Modelling Framework for Grey Water Reuse inTianjin, China, ASCE International Conference on Sustainable Infrastructure2014, Long Beach, USA, November 6-8, 2014. (Keynotes Speech).http://dx.doi.org/10.1061/9780784478745.050 EI.

[99] Li, T. and Huang, J*. (2014).Separation of Soil Evaporation And Vegetation Transpiration By MODIS Data ForCentral And Northern China. CUNY Academic Works.http://academicworks.cuny.edu/cc_conf_hic/172.

[100] Li, H., Huang, J*, Zhou, X.,Lin, C., Luo, Y. (2012).Isolation and Identification of Two PsychrotrophsStrains and Preliminary Research on Their Application. Meteorological andEnvironment Research.

[101] heng, H., Huang, J. and McBean,E. (2011).Reply to comment on “Using Bayesian Statistics to Estimate the Coefficients Of aTwo-Component Second-order Chlorine Bulk Decay Model for a Water DistributionSystem”. WaterResearch, 2011,45(6), 2355-2357.

[102] Huang, J., and McBean, E.(2009).Data Mining to Identify Contaminant Intrusion Events into Water DistributionSystems. ASCE Journal of Water Resources Planning and Management, 2009,135(6),466-474.

[103] Lee, M., McBean, E., Schuster,C., and Huang, J., (2009). A Fault Tree and Fuzzy Logic Methodology for RiskAssessment of Drinking Water Supply Failures, ASCE Journal of Water ResourcesPlanning and Management, 135(6), 547-552.

[104] Huang, J. and McBean, E. A.(2008). Using Bayesian Statistics to Estimate the Chlorine Wall DecayCoefficients for a Water Supply System, ASCE Journal of Water ResourcesPlanning and Management, 134(2), 129-137.

[105] Huang, J., McBean, E. A. andShen, H.(2008).  Data Mining as a Tool toIdentify Contaminant Sources in Water Distribution Systems, 10th InternationalWater Distribution System Analysis conference, Kruger National Park, SouthAfrica. August 17-20, 2008, www.uj.ac.za/wdsa2008 EI,  http://dx.doi.org/10.1061/41024(340)97 EI

[106] Huang, J. and McBean, E. A.(2007). Using Bayesian Statistics to Estimate the Coefficients of a Two-Component Second-Order Chlorine Decay Model for a Water Distribution System,Water Research, 41(2), 287-294.

[107] Ostfeld et al; (2008). TheBattle of Water Sensor Networks (BWSN): A Design Challenge for Engineers andAlgorithms, ASCE Journal of Water Resources Planning and Management, 134(6),556-558.

[108] Huang, J. and McBean E A.;(2007). Chapter 19, Water Quality Modeling using Fault Tree Method.Contemporary Modeling of Urban Water Systems, Monograph15. Published by CHI,Guelph, ISBN: 139780973671636.

[109] Huang, J. and McBean, E. A.(2006). Chapter 11, Comparison of Analytical and Simulation Approaches forAssessing Robustness of Reliability for Water Distribution Systems. IntelligentModeling of Urban Water Systems, Monograph 14, Published by CHI, Guelph, ISBN:0973671629.

[110] Huang, J. and McBean, E. A.(2006). Use of Bayesian Statistics to Study Chlorine Decay for a WaterDistribution System, Water Distribution System Analysis 2006, Cincinnati, OhioUSA. August 27-29, 2006, www.eng.uc.edu/wdsa2006/ EI. http://dx.doi.org/10.1061/40941(247)148EI.

[111] Huang, J., McBean, E. A., andJames, W. (2006). Multi-objective Optimization Approach for Monitoring SensorPlacement in Water Distribution Systems, Water Distribution System Analysis2006, Cincinnati, Ohio USA. August 27-29, 2006, www.eng.uc.edu/wdsa2006/ EI,http://dx.doi.org/10.1061/40941(247)113.

[112] Huang, J., McBean, E., andJames, W. (2005). Chapter 7, A Review of Reliability Analysis for Water Qualityin Water Distribution Systems, Urban Water Systems, James, et al. (editors),Monograph 13, Published by CHI, Guelph, ISBN: 0973671602.

[113] Huang, J.; James, W. and James,W. R. C. (2005). Chapter 3, A Lifecycle Cost Based Design Optimization Modelfor Stormwater Management System. Effective Modelling of Urban Water Systems,Monograph 13, Published by CHI, Guelph, ISBN: 0973671602.