APPLICATION OF A MULTI-METHOD COLLABORATIVE SUBMERGED OUTFALL IDENTIFICATION TECHNOLOGY SYSTEM
-
摘要: 入河(海)排污口等点源污染与地表水环境质量密切相关,也是造成生态环境污染的主要原因。科学、有效地监测、评价和整治水环境污染,系统全面地掌握入河(海)排污口的各项数据并纳入水生态环境智能监测监管系统数据库中至关重要。针对水下排污口难以排查取证的技术痛点,基于声学、光学等多种探测手段建立了1套多源数据融合的水下排污口二级排查技术体系。以上海市某镇河道水下排污口排查为例,通过多方法协同,在累计35.5 km的岸线排查区域内共识别定位了51处疑似水下排污口,其中49处复核确认存在,排查成功率为96.1%。具体的,新发现水下排污口18处,已有登记的水下排污口31处也均被识别发现,即对已知水下排污口的排查成功率达到100%,进一步验证了二级排查技术体系的可靠性,为透明规范排污、科学精准治污及生态环境保护提供了翔实的基础数据。Abstract: Point source pollution, such as rivers (seas) sewage outfalls, is closely related to the environmental quality of surface water. It is also the main cause of ecological environment pollution. Scientific and effective monitoring, evaluation and remediation of water environment pollution, systematically mastering the data of the rivers (seas) outfalls, and combining it into the water ecological environment intelligent monitoring and supervision system database is essential. In this paper, for the objective problem that submerged outfalls are difficult to investigate and review, a set of multi-source data fusion technology systems for secondary investigation of submerged outfalls was established based on acoustic, optical and other detection means. Taking a survey in a Town in Shanghai as an example, 51 suspected submerged outfalls were identified and located within a total of 35.5 km of shoreline survey area by multi-method collaboration. Among them, 49 locations were reviewed and confirmed to be present, with a success rate of 96.1%. After the review, 18 new submerged outfalls were found, and 31 registered submerged outfalls were also identified, i.e. the success rate of the known underwater outfalls reached 100%, which further verified the reliability of the secondary survey technology system. This also provides detailed basic data for the transparent regulation of emissions, scientific and precise pollution control and ecological environmental protection.
-
[1] DOS SANTOS D M, BURUAEM L, GONCALVES R M, et al. Multiresidue determination and predicted risk assessment of contaminants of emerging concern in marine sediments from the vicinities of submarine sewage outfalls[J]. Marine pollution bulletin, 2018, 129(1): 299-307. [2] MENENDEZ A N, LOPOLITO M F, BADANO N D, et al. Influence of projected outfalls in the Plata River on limited water use zones[C]//International Symposium on Outfall Systems, Mar del Plata, Argentina, Mayo. 2011. [3] STALTER D, MAGDEBURG A, QUEDNOW K, et al. Do contaminants originating from state-of-the-art treated wastewater impact the ecological quality of surface waters?[J]. PLoS One, 2013, 8(4): e60616. [4] 洪运富,杨海军,李营,等.水源地污染源无人机遥感监测[J].中国环境监测,2015,31(5):163-166. [5] ROHMANA Q A, FISCHER A, GEMMILL J, et al. Preliminary river outfalls assessment[R]. Victoria: Clean Ocean Foundation,2021, 4-8. [6] ROHMANA Q A Y, FISCHER A M, CEMMILL J, et al. Increased transparency and resource prioritization for the management of pollutants from wastewater treatment plants: a national perspective from Australia[J]. Frontiers in Marine Science, 2020, 7: 564598. [7] 马礼.高精度实景三维与水质反演在排污口排查中的应用[J].测绘通报,2021(7):107-110. [8] 张元敏.无人机航测技术在入海排污口排查中的应用[J].测绘通报,2020(1):146-149153. [9] 王军霞,敬红,邱立莉,等.长江经济带入河排污口监测体系构建研究[J].环境工程,2019,37(10):44-48. [10] 车刘生.东江源重点入河排污口监测评价与整治探讨[J].人民珠江,2018,39(6):16-20. [11] 冯磊,崔胜涛.无人机遥感技术在海域监测陆源排污口中的应用[J].测绘与空间地理信息,2019,42(5):107-109. [12] 陈超帅,王世勇.大疆无人机目标红外辐射特性测量及温度反演[J].光电工程,2017,44(4):427-434, 464. [13] JENSEN A M, NEILSON B T, MCKEE M, et al. Thermal remote sensing with an autonomous unmanned aerial remote sensing platform for surface stream temperatures[C]//2012 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2012: 5049-5052. [14] BURGUERA A, OLIVER G. High-resolution underwater mapping using side-scan sonar[J]. PLoS One, 2016, 11(1): e0146396. [15] PASQUALINI V, PERGENT-MARTINI C, CLABAUTl P, et al. Mapping ofposidonia oceanicausing aerial photographs and side scan sonar: Application off the Island of Corsica (France)[J]. Estuarine, Coastal and Shelf Science, 1998, 47(3): 359-367. [16] REED S, WOOD J, VAZQUEZ J, et al. A smart ROV solution for ship hull and harbor inspection[C]//Sensors, and Command, Control, Communications, and Intelligence (C3I) Technologies for Homeland Security and Homeland Defense IX, SPIE, 2010, 7666: 535-546. [17] GUERNEVE T, PETILLOT Y. Underwater 3d reconstruction using blueview imaging sonar[C]//OCEANS 2015-Genova, IEEE, 2015: 1-7. [18] BORISOVA T, RACEVSKIS L, KIPP J. Stakeholder analysis of a collaborative watershed management process: a florida case study 1[J]. JAWRA Journal of the American Water Resources Association, 2012, 48(2): 277-296. [19] DURAM L A, LOFTUS T, ADAMS J, et al. Assessing the US watershed management movement: national trends and an Illinois case study[J]. Water International, 2008, 33(2): 231-242. [20] PRAKASH A. Thermal remote sensing: concepts, issues and applications[J]. International Archives of Photogrammetry and Remote Sensing, 2000, 33(PART 1): 239-243. [21] SHENG H, CHAO H, Coopmans C, et al. Low-cost UAV-based thermal infrared remote sensing: platform, calibration and applications[C]//Proceedings of 2010 IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications, IEEE, 2010: 38-43. [22] WATTS A C, AMBROSIA V G, HINKLEY E A. Unmanned aircraft systems in remote sensing and scientific research: Classification and considerations of use[J]. Remote Sensing, 2012, 4(6): 1671-1692. [23] YAO H, QIN R, CHEN X. Unmanned aerial vehicle for remote sensing applications: a review[J]. Remote Sensing, 2019, 11(12): 1443. [24] 李明辉,潘国秀,冯健,等. 打点定位的方法、装置、系统及计算机存储介质:CN111356937A[P].2020. [25] KUM B C, SHIN D H, LEE J H, et al. Monitoring applications for multifunctional unmanned surface vehicles in marine coastal environments[J]. Journal of Coastal Research, 2018,85: 1381-1385. [26] 陈锋. 无人艇应用于海洋环境监测及海洋管理的前景与展望[J]. 机电设备, 2015, 32(6):21-24. [27] 熊亚洲, 张晓杰, 冯海涛, 等. 一种面向多任务应用的无人水面艇[J].船舶工程, 2012, 34(1):16-19. [28] 李勇航,单晨晨,苏明,等.声学水面无人艇在浅水海底地貌调查中的应用[J].海洋地质与第四纪地质,2020,40(6):219-226. [29] 严俊. 多波束与侧扫声呐高质量测量信息获取与叠加[D].武汉: 武汉大学,2017. [30] CAPOCCI R, DOOLY G, OMERDIC E, et al. Inspection-class remotely operated vehicles: a review[J]. Journal of Marine Science and Engineering, 2017, 5(1): 13. [31] 桑金.观察型水下机器人ROV系统配置研究[J].海洋测绘,2012,32(4):81-84. [32] AYKIN M D, NEGAHDARIPOUR S. Forward-look 2-D sonar image formation and 3-D reconstruction[C]//2013 OCEANS-San Diego, IEEE, 2013: 1-10. [33] CHEN B, YANG Y, ZHOU J, et al. Damage detection of underwater foundation of a Chinese ancient stone arch bridge via sonar-based techniques[J]. Measurement, 2021, 169: 108283.
点击查看大图
计量
- 文章访问数: 54
- HTML全文浏览量: 5
- PDF下载量: 5
- 被引次数: 0