Environmental computing: Concept, evolution, and challenges
WEI Zeyang1, LIU Yi1, WANG Chunyan1, ZHANG Jia2, BIAN Jiang2, YAO Linjie1, LIN Sijie1,3, EWE Kaijie1
1. School of Environment, Tsinghua University, Beijing 100084, China; 2. Microsoft Research Asia, Beijing 100080, China; 3. School of Environmental Science & Engineering, Southern University of Science & Technology, Shenzhen 518055, China
Abstract：As an emerging interdisciplinary concept, environmental computing is a term used for the quantitative research process of environmental process numerical analysis and (or) environmental data analysis based on computing. Under this conceptual framework, various kinds of environment and computational science integrations are discussed together for ensuring development in this field as well as summarizing advanced research models and methods. This paper introduces the definition and basic characteristics of environmental computing and explains the methodological characteristics of various types of environmental computing based on typical cases. Environmental computing has transitioned from theory-driven to data-driven and then to hybrid computing. The comprehensive computing framework shows considerable advantages compared to conventional approaches or single methods. To achieve significant breakthroughs, researchers need to constantly explore basic theories, including environmental and computational theories, and promote the transformation of environmental thinking to adapt to the frontier content of computational science. Additionally, challenges such as big data theory, technical application scenarios, and computing power also need to be overcome.
[1] CHIRAS D D. Environmental science[M]. Sudbury: Jones and Bartlett Publishers, Inc., 2001. [2] FELLOWS M R, PARBERRY I. SIGACT trying to get children excited about CS[J]. Computing Research News, 1993, 5(1): 7. [3] LI Q J, PRIGIOBBE V. Numerical simulations of the migration of fine particles through porous media[J]. Transport in Porous Media, 2018, 122(3): 745-759. [4] ZHAO Y, WANG L, LUO J M, et al. Deep learning prediction of polycyclic aromatic hydrocarbons in the high arctic[J]. Environmental Science & Technology, 2019, 53(22): 13238-13245. [5] ODUYEMI K O K, DAVIDSON B. The impacts of road traffic management on urban air quality[J]. Science of the Total Environment, 1998, 218(1): 59-66. [6] HUNTER J S. 1 Environmetrics: An emerging science[J]. Handbook of Statistics, 1994, 12: 1-7. [7] FREW J E, DOZIER J. Environmental informatics[J]. Annual Review of Environment and Resources, 2012, 37: 449-472. [8] HEIKKURINEN M, SCHIFFERS M, KRANZLMVLLER D. Environmental computing 1.0: The dawn of a concept[C]//Proceedings of International Symposium on Grids and Clouds 2015. Taipei, China: Academia Sinica, 2015. [9] REFSGAARD J C, VAN DER SLUIJS J P, BROWN J, et al. A framework for dealing with uncertainty due to model structure error[J]. Advances in Water Resources, 2006, 29(11): 1586-1597. [10] ZHANG Z M. Study on SWAP water quality model based on uncertainty analysis[D]. Beijing: Capital Normal University, 2013. (in Chinese) 张质明. 基于不确定性分析的WASP水质模型研究[D]. 北京: 首都师范大学, 2013. [11] JANSSEN H. Monte-Carlo based uncertainty analysis: Sampling efficiency and sampling convergence[J]. Reliability Engineering & System Safety, 2013, 109: 123-132. [12] DONG X, DU P F, LI Z Y, et al. Parameter identification and validation of SWMM in simulation of impervious urban land surface runoff[J]. Environmental Science, 2008, 29(6): 1495-1501. (in Chinese) 董欣, 杜鹏飞, 李志一, 等. SWMM模型在城市不透水区地表径流模拟中的参数识别与验证[J]. 环境科学, 2008, 29(6): 1495-1501. [13] ESTRADA V, DIAZ M S. Global sensitivity analysis in the development of first principle-based eutrophication models[J]. Environmental Modelling & Software, 2010, 25(12): 1539-1551. [14] GUO J S, LI S H, LONG T R. Study and progress of water quality model and its application[J]. Journal of Chongqing Jianzhu University, 2002, 24(2): 109-115. (in Chinese) 郭劲松, 李胜海, 龙腾锐. 水质模型及其应用研究进展[J]. 重庆建筑大学学报, 2002, 24(2): 109-115. [15] JIA H F, CHENG S T, DU W T. Integration of GIS with the surface water quality model WASP5[J]. Journal of Tsinghua University (Science and Technology), 2001, 41(8): 125-128. (in Chinese) 贾海峰, 程声通, 杜文涛. GIS与地表水水质模型WASP5的集成[J]. 清华大学学报(自然科学版), 2001, 41(8): 125-128. [16] SCHROEDER F. Water quality in the Elbe estuary: Significance of different processes for the oxygen deficit at Hamburg[J]. Environmental Modeling & Assessment, 1997, 2(1): 73-82. [17] SOHMA A, SATO T, NAKATA K. New numerical model study on a tidal flat system-seasonal, daily and tidal variations[J]. Spill Science & Technology Bulletin, 2000, 6(2): 173-185. [18] HUA F, WEST J R, BARKER R A, et al. Modelling of chlorine decay in municipal water supplies[J]. Water Research, 1999, 33(12): 2735-2746. [19] JONKERGOUW P M R, KHU S T, SAVIC D A, et al. A variable rate coefficient chlorine decay model[J]. Environmental Science & Technology, 2009, 43(2): 408-414. [20] LU W, ZHANG X J. Dynamic model of bacteria growth in water distribution system[J]. China Water & Wastewater, 2006, 22(18): 8-10. (in Chinese) 鲁巍, 张晓健. 给水管网细菌生长的动力学模型[J]. 中国给水排水, 2006, 22(18): 8-10. [21] YARWOOD G, JUNG J, WHITTEN G Z, et al. Updates to the carbon bond mechanism for version 6 (CB6)[C]//Proceedings of the 9th Annual CMAS Conference. Chapel Hill, USA: CMAS, 2010: 11-13. [22] CAO L, LI S M, YI Z W, et al. Simplification of carbon bond mechanism IV (CBM-IV) under different Initial conditions by using concentration sensitivity analysis[J]. Molecules, 2019, 24(13): 2463. [23] LU J Z, CHEN X L, LI H, et al. Soil erosion changes based on GIS/RS and USLE in Poyang Lake Basin[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011, 27(2): 337-344. (in Chinese) 陆建忠, 陈晓玲, 李辉, 等. 基于GIS/RS和USLE鄱阳湖流域土壤侵蚀变化[J]. 农业工程学报, 2011, 27(2): 337-344. [24] FU B J, YU L, LV Y H, et al. Assessing the soil erosion control service of ecosystems change in the Loess Plateau of China[J]. Ecological Complexity, 2011, 8(4): 284-293. [25] ARNOLD J G, SRINIVASAN R, MUTTIAH R S, et al. Large area hydrologic modeling and assessment Part Ⅰ: Model development[J]. JAWRA Journal of the American Water Resources Association, 1998, 34(1): 73-89. [26] XUE Q, LIANG B, LIU X L. Dynamic predicting model of transportation of organic contaminants and analysis of model parameters[J]. Geotechnical Investigation & Surveying, 2003(6): 17-20. (in Chinese) 薛强, 梁冰, 刘晓丽. 有机污染物运移的动力学预测模型及模型参数分析[J]. 工程勘察, 2003(6): 17-20. [27] HOU G G, CHEN J J, WU S Z, et al. Modeling research on landfill gas production[J]. Research of Environmental Sciences, 2009, 22(10): 1181-1186. (in Chinese) 侯贵光, 陈家军, 吴舜泽, 等. 填埋场产气规律的模型预测[J]. 环境科学研究, 2009, 22(10): 1181-1186. [28] DURMUSOGLU E, CORAPCIOGLU M Y, TUNCAY K. Landfill settlement with decomposition and gas generation[J]. Journal of Environmental Engineering, 2005, 131(9): 1311-1321. [29] HAN J H, LI Z F. Methodological challenges faced by computational social science[J]. Studies in Dialectics of Nature, 2018, 34(4): 14-19. (in Chinese) 韩军徽, 李正风. 计算社会科学的方法论挑战[J]. 自然辩证法研究, 2018, 34(4): 14-19. [30] MILLIE D F, WECKMAN G R, YOUNG Ⅱ W A, et al. Modeling microalgal abundance with artificial neural networks: Demonstration of a heuristic 'Grey-Box' to deconvolve and quantify environmental influences[J]. Environmental Modelling & Software, 2012, 38: 27-39. [31] HUANG Y Y, CIAIS P, LUO Y Q, et al. Tradeoff of CO2 and CH4 emissions from global peatlands under water-table drawdown[J]. Nature Climate Change, 2021, 11(7): 618-622. [32] ISSAKA S, ASHRAF M A. Impact of soil erosion and degradation on water quality: A review[J]. Geology, Ecology, and Landscapes, 2017, 1(1): 1-11. [33] BELL G, HEY T, SZALAY A. Beyond the data deluge[J]. Science, 2009, 323(5919): 1297-1298. [34] DHAR V. Data science and prediction[J]. Communications of the ACM, 2013, 56(12): 64-73. [35] HEY A J G, TANSLEY S, TOLLE K M. The fourth paradigm: Data-intensive scientific discovery[M]. Redmond: Microsoft Research, 2009. [36] LOHR S. Data-ISM: The revolution transforming decision making, consumer behavior, and almost everything else[M]. New York: Harper Business, 2015. [37] HARARI Y N. Dataism is our new god[J]. New Perspectives Quarterly, 2017, 34(2): 36-43. [38] JIANG H Q, LU Y L, ZHOU S, et al. Progress in research and application of ecological environment big data[J]. Chinese Journal of Environmental Management, 2019, 11(6): 11-15. (in Chinese) 蒋洪强, 卢亚灵, 周思, 等. 生态环境大数据研究与应用进展[J]. 中国环境管理, 2019, 11(6): 11-15. [39] SUGON. Ecological environment cloud platform of Ministry of Ecological Environment[EB/OL]. [2021-12-10]. https://www.sugon.com/case?id=113#. (in Chinese) 中科曙光. 生态环境部生态环境云平台[EB/OL]. [2021-12-10]. https://www.sugon.com/case?id=113#. [40] BAIDU AI CLOUD. Smart ecological environment solutions[EB/OL]. [2021-12-10]. https://cloud.baidu.com/solution/city/ecologyandenvironment.html. (in Chinese) 百度智慧云. 智慧生态环境解决方案[EB/OL]. [2021-12-10]. https://cloud.baidu.com/solution/city/eco-logyandenvironment.html. [41] SHANHE. National ecological environment big data supercomputing cloud center[EB/OL]. [2021-12-10]. https://shanhe.com/casedetails/86. (in Chinese) 山河. 国家生态环境大数据超算云中心[EB/OL]. [2021-12-10]. https://shanhe.com/casedetails/86. [42] ONC strategic plan[EB/OL]. (2015-01-01)[2021-12-13]. https://www.oceannetworks.ca. [43] WANG Z S, ZHAO J, LIN S J, et al. Identification of industrial land parcels and its implications for environmental risk management in the Beijing-Tianjin-Hebei urban agglomeration[J] Sustainability, 2019, 12(1): 174. [44] CUI Y L, XIE X, LIU Y. Social media and mobility landscape: Uncovering spatial patterns of urban human mobility with multi source data[J]. Frontiers of Environmental Science & Engineering, 2018, 12(5): 1-14. [45] CUI Y L. Data-driven urban resident travel demand simulation and carbon footprint accounting[D]. Beijing: Tsinghua University, 2019. (in Chinese) 崔一澜. 数据驱动的城市居民出行模拟与碳排放评估[D]. 北京: 清华大学, 2019. [46] PENG F. Methods and case study of mapping urban noise via big data analysis[D]. Beijing: Tsinghua University, 2016. (in Chinese) 彭帆. 基于大数据建模的城市噪声地图研制方法与案例研究[D]. 北京: 清华大学, 2016. [47] SONG G C. Methods and case study of dynamic simulation of urban noise based on big-data analysis[D]. Beijing: Tsinghua University, 2020. (in Chinese) 宋广超. 基于大数据分析的城市环境噪声动态模拟方法与案例研究[D]. 北京: 清华大学, 2020. [48] REICHSTEIN M, CAMPS-VALLS G, STEVENS B, et al. Deep learning and process understanding for data-driven earth system science[J]. Nature, 2019, 566(7743): 195-204. [49] YU X P. Research on intelligent evaluation software of major water pollution sources based on big data of environmental protection[D]. Daqing: Northeast Petroleum University, 2019. (in Chinese) 于欣平. 基于环保大数据的重大水污染源智能评价软件研究[D]. 大庆: 东北石油大学, 2019. [50] LYU B, HU Y T, ZHANG W X, et al. Fusion method combining ground-level observations with chemical transport model predictions using an ensemble deep learning framework: Application in China to estimate spatiotemporally-resolved PM2.5 exposure fields in 2014—2017[J]. Environmental Science & Technology, 2019, 53(13): 7306-7315. [51] JUNG M, REICHSTEIN M, CIAIS P, et al. Recent decline in the global land evapotranspiration trend due to limited moisture supply[J]. Nature, 2010, 467(7318): 951-954. [52] JUNG M, REICHSTEIN M, MARGOLIS H A, et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations[J]. Journal of Geophysical Research: Biogeosciences, 2011, 116(G3): G00J07. [53] LI W, WU G D, ZHANG F, et al. Hyperspectral image classification using deep pixel-pair features[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55(2): 844-853. [54] LI J C, DAI W, METZE F, et al. A comparison of deep learning methods for environmental sound detection[C]//Proceedings of 2017 IEEE International Conference on Acoustics, Speech and Signal Processing. New Orleans, USA: IEEE, 2017: 126-130. [55] CHEN J L, HUANG G R, CHEN W J. Towards better flood risk management: Assessing flood risk and investigating the potential mechanism based on machine learning models[J]. Journal of Environmental Management, 2021, 293: 112810. [56] VANDAL T, KODRA E, GANGULY S, et al. Generating high resolution climate change projections through single image super-resolution: An abridged version[C]//Proceedings of the 27th International Joint Conference on Artificial Intelligence. Stockholm, Sweden: IJCAI, 2018: 5389-5393. [57] BARZEGAR R, AALAMI M T, ADAMOWSKI J. Coupling a hybrid CNN-LSTM deep learning model with a boundary corrected maximal overlap discrete wavelet transform for multiscale lake water level forecasting[J]. Journal of Hydrology, 2021, 598: 126196. [58] AHMED A A M, DEO R C, FENG Q, et al. Deep learning hybrid model with Boruta-Random forest optimiser algorithm for streamflow forecasting with climate mode indices, rainfall, and periodicity[J]. Journal of Hydrology, 2021, 599: 126350. [59] MUÑOZ D F, MUÑOZ P, MOFTAKHARI H, et al. From local to regional compound flood mapping with deep learning and data fusion techniques[J]. Science of the Total Environment, 2021, 782: 146927. [60] SINGHA S, PASUPULETI S, SINGHA S S, et al. Prediction of groundwater quality using efficient machine learning technique[J]. Chemosphere, 2021, 276: 130265. [61] LANDSCHVTZER P, GRUBER N, BAKKER D C E, et al. A neural network-based estimate of the seasonal to inter-annual variability of the Atlantic Ocean carbon sink[J]. Biogeosciences, 2013, 10(11): 7793-7815. [62] CALDWELL P M, BRETHERTON C S, ZELINKA M D, et al. Statistical significance of climate sensitivity predictors obtained by data mining[J]. Geophysical Research Letters, 2014, 41(5): 1803-1808. [63] JEONG N, CHUNG T H, TONG T Z. Predicting micropollutant removal by reverse osmosis and nanofiltration membranes: Is machine learning viable?[J]. Environmental Science & Technology, 2021, 55(16): 11348-11359. [64] KARNIADAKIS G E, KEVREKIDIS I G, LU L, et al. Physics-informed machine learning[J]. Nature Reviews Physics, 2021, 3(6): 422-440. [65] LANSER D, VERWER J G. Analysis of operator splitting for advection- diffusion- reaction problems from air pollution modelling[J]. Journal of Computational and Applied Mathematics, 1999, 111(1-2): 201-216. [66] DE BÉZENAC E, PAJOT A, GALLINARI P. Deep learning for physical processes: Incorporating prior scientific knowledge[J]. Journal of Statistical Mechanics: Theory and Experiment, 2019, 2019: 124009. [67] HANSON P C, STILLMAN A B, JIA X W, et al. Predicting lake surface water phosphorus dynamics using process-guided machine learning[J]. Ecological Modelling, 2020, 430: 109136. [68] READ J S, JIA X W, WILLARD J, et al. Process‐guided deep learning predictions of lake water temperature[J]. Water Resources Research, 2019, 55(11): 9173-9190. [69] TAHMASEBI P, KAMRAVA S, BAI T, et al. Machine learning in geo-and environmental sciences: From small to large scale[J]. Advances in Water Resources, 2020, 142: 103619. [70] WILLARD J D, READ J S, APPLING A P, et al. Predicting water temperature dynamics of unmonitored lakes with meta-transfer learning[J]. Water Resources Research, 2021, 57(7): e2021WR029579. [71] BECK H E, VAN DIJK A I M, DE ROO A, et al. Global-scale regionalization of hydrologic model parameters[J]. Water Resources Research, 2016, 52(5): 3599-3622. [72] XING J, ZHENG S X, DING D, et al. Deep learning for prediction of the air quality response to emission changes[J]. Environmental Science & Technology, 2020, 54(14): 8589-8600. [73] JIANG S J, ZHENG Y, SOLOMATINE D. Improving AI system awareness of geoscience knowledge: Symbiotic integration of physical approaches and deep learning[J]. Geophysical Research Letters, 2020, 47(13): e2020GL088229. [74] CASTRUCCIO S, MCINERNEY D J, STEIN M L, et al. Statistical emulation of climate model projections based on precomputed GCM runs[J]. Journal of Climate, 2014, 27(5): 1829-1844. [75] DONG Y. Study on coupling modelling and control measures of urban and rural nonpoint source pollution in Dianchi Lake basin[D]. Beijing: Tsinghua University, 2016. (in Chinese) 东阳. 滇池流域城市和农村非点源污染耦合模拟与控制策略研究[D]. 北京: 清华大学, 2016. [76] CHEN B, WANG X D, WANG R X, et al. The Grey-box based modeling approach research integrating fusion mechanism and data[J]. Journal of System Simulation, 2019, 31(12): 2575-2583. (in Chinese) 陈彬, 王小东, 王戎骁, 等. 融合机理与数据的灰箱系统建模方法研究[J]. 系统仿真学报, 2019, 31(12): 2575-2583. [77] SONG G J, GUO X D, YANG X, et al. ARIMA-SVM combination prediction of PM2.5 concentration in Shenyang[J]. China Environmental Science, 2018, 38(11): 4031-4039. (in Chinese) 宋国君, 国潇丹, 杨啸, 等. 沈阳市PM2.5浓度ARIMA-SVM组合预测研究[J]. 中国环境科学, 2018, 38(11): 4031-4039. [78] SENENT-APARICIO J, JIMENO-SÁEZ P, BUENO-CRESPO A, et al. Coupling machine-learning techniques with SWAT model for instantaneous peak flow prediction[J]. Biosystems Engineering, 2019, 177: 67-77. [79] CHANG W, CHEN X. Monthly rainfall-runoff modeling at watershed scale: A comparative study of data-driven and theory-driven approaches[J]. Water, 2018, 10(9): 1116. [80] VULOVA S, MEIER F, ROCHA A D, et al. Modeling urban evapotranspiration using remote sensing, flux footprints, and artificial intelligence[J]. Science of the Total Environment, 2021, 786: 147293. [81] TAO F, LIU W R, LIU J H, et al. Digital twin and its potential application exploration[J]. Computer Integrated Manufacturing Systems, 2018, 24(1): 1-18. (in Chinese) 陶飞, 刘蔚然, 刘检华, 等. 数字孪生及其应用探索[J]. 计算机集成制造系统, 2018, 24(1): 1-18. [82] WANG A J. Big data of water system-"Integration" is big[R/OL]. (2021-11-05)[2021-12-01]. https://huanbao.bjx.com.cn/news/20211105/1186039.shtml. (in Chinese) 王爱杰. 水系统大数据——有"融"乃大[R/OL]. (2021-11-05) [2021-12-01]. https://huanbao.bjx.com.cn/news/20211105/1186039.shtml. [83] Cary Institute. Hudson River ecosystem study[EB/OL]. [2021-12-01]. https://www.caryinstitute.org/science/research-projects/hudson-river-ecosystem-study. [84] JIN R. From technology to science, where does Chinese AI go?[R/OL]. (2021-08-24)[2021-10-31]. 金榕. 从技术到科学, 中国AI向何处去?[R/OL]. (2021-08-24) [2021-10-31]. https://t.cj.sina.com.cn/articles/view/2357213493/8c803935020013o2u.
2022, Vol. 62 
