Environmental Impact Assessment: A Multi-level, Multi-Parametric Framework For Coastal Waters

Environmental Impact Assessment: A Multi-level, Multi-Parametric Framework For Coastal Waters

M. Lega M. Casazza R. Teta C.J. Zappa 

Department of Engineering, University of Naples “Parthenope”, Naples, Italy

Department of Pharmacy, University of Naples “Federico II”, Naples, Italy

Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York, USA

Page: 
1041-1049
|
DOI: 
https://doi.org/10.2495/SDP-V13-N8-1041-1049
Received: 
N/A
| |
Accepted: 
N/A
| | Citation

OPEN ACCESS

Abstract: 

In any process of Environmental Impact Assessment (EIA) a key role is played by the action of monitoring. Indeed, the acquisition of real field data provides the evidence of the environmental status and identifies hazards and sources of pollution. When environmental pollution is revealed, it is important to identify the source following the source-path-target model. However, when monitoring operations are planned, often the three-dimensional (3D) nature of monitored hotspots is neglected. Instead, information can be gathered through a multi-parametric, multi-level framework, which combines multiple disciplines and generates correlations between several data sets acquired in the analysed scenario. This novel new framework is named MuM3, meaning that the proposed Monitoring (M) is Multi- Disciplinary, Multi-level and Multi-parametric (i.e. Mu) and it is developed in all the three dimensions of physical space (the superscript ‘3’). This paper outlines the implementation of this framework. In particular, monitoring polluted coastal waters refers to one of the critical areas identified by EIA regu- lations. The framework incorporates different spatial scales of observation (Levels) and the potential sensors that can be used at each Level. A three-step work-flow model describes the raw data acquisition and the transformation and integration of different indicators into useful information for EIA. A schematic flow chart describes the approach to developing multi-level, multi-parameter connections. Extension of this framework can be applied to any EIA, especially in the case of critical areas that are identified by the regulations as: (i) Wetlands, riparian areas, river mouths; (ii) Mountain and forest areas; (iii) Nature reserves and parks; (iv) Densely populated areas; (v) Landscapes and sites of histori- cal, cultural or archaeological significance.

Keywords: 

coastal waters, EIA, environmental impact assessment, environmental monitoring, environ- mental pollution, framework, multi-level, multi-parameter, MuM3

  References

[1] Morris, P. & Therivel, R. (eds), Methods of Environmental Impact Assessment. Spon Press: London and New York, 2001.

[2] Directive 2014/52/EU of The European Parliament and of the Council of 16 April 2014 amending Directive 2011/92/EU on the assessment of the effects of certain public and private projects on the environment. Online. eur-lex.europa.eu/legal-content/EN/TXT/ PDF/?uri=CELEX:32014L0052&from=EN (accessed on 10 June, 2018).

[3] Directive 2011/92/EU of The European Parliament and of the Council of 13 December 2011 on the assessment of the effects of certain public and private projects on the environment. Online. eur-lex.europa.eu/legal-content/EN/TXT/PDF/?uri=CELEX:32011L 0092&from=EN (accessed on 10 June, 2018).

[4] Jabeen, S., Shah, M.T., Ahmed, I., Khan, S. & Hayat, M.Q., Physico-chemical parameters of surface and ground water and their environmental impact assessment in the Haripur Basin, Pakistan. Journal of Geochemical Exploration, 138, pp. 1–7, 2014. https://doi.org/10.1016/j.gexplo.2013.12.004

[5] Petrie, J., Diplas, P., Gutierrez, M. & Nam, S., Characterizing the mean flow field in rivers for resource and environmental impact assessments of hydrokinetic energy generation sites. Renewable Energy, 69, pp. 393–401, 2014. https://doi.org/10.1016/j.renene.2014.03.064

[6] Casazza, M., Maraga, F., Liu, G., Lega, M., Turconi, L. & Ulgiati, S., River water quality and its relation with air quality: a long-term case study in a remote and pristine NW Italian headwater catchment. Journal of Environmental Accounting and Management, 5(1), pp. 35–47, 2017. https://doi.org/10.5890/jeam.2017.03.004

[7] Jones, F.C., Cumulative effects assessment: theoretical underpinnings and big problems. Environmental Reviews, 24(2), pp. 187–204, 2016. https://doi.org/10.1139/er-2015-0073

[8] Li, H., Yang, Z., Liu, G., Casazza, M. & Yin, X., Analyzing virtual water pollution transfer embodied in economic activities based on Gray Water Footprint: a case study. Journal of Cleaner Production, 161, pp. 1064–1073, 2017. https://doi.org/10.1016/j.jclepro.2017.05.155

[9] Semary, E. & Adel, N., Diatoms as bioindicators of littoral zone: a case study. Bangladesh Journal of Botany, 45(5), pp. 1113–1121, 2016.

[10] Teta, R., Della Sala, G., Mangoni, A., Lega, M. & Costantino, V., Tracing cyanobacterial blooms to assess the impact of wastewaters discharges on coastal areas and lakes. International Journal of Sustainable Development and Planning, 11(5), pp. 804–811, 2016. https://doi.org/10.2495/sdp-v11-n5-804-811

[11] Beyer, J., Green, N.W., Brooks, S., Allan, I.J., Ruus, A., Gomes, T., Bråte, I.L.N. & Schøyen, M., Blue mussels (Mytilus edulis spp.) as sentinel organisms in coastal pollution monitoring: a review. Marine Environmental Research, 130, pp. 338–365, 2017. https://doi.org/10.1016/j.marenvres.2017.07.024

[12] Narale, D.D. & Anil, A.C., Spatial distribution of dinoflagellates from the tropical coastal waters of the South Andaman, India: Implications for coastal pollution monitoring. Marine Pollution Bulletin, 115(1–2), pp. 498–506, 2017. https://doi.org/10.1016/j.marpolbul.2016.11.035

[13] Teta, R., Romano, V., Della Sala, G., Picchio, S., De Sterlich, C., Mangoni, A., Di Tullio, G., Costantino, V. & Lega, M., Cyanobacteria as indicators of water quality in Campania coasts, Italy: a monitoring strategy combining remote/proximal sensing and in situ data. Environmental Research Letters, 12(2), p. 024001, 2017. https://doi.org/10.1088/1748-9326/aa5649

[14] Teta, R., Esposito, G., Casazza, M., Zappa, C.J., Endreny, T., Mangoni, A., Costantino, V. & Lega, M., Bioindicators as a tool in environmental impact assessment: cyanobacteria as a sentinel of pollution. International Journal of Sustainable Development and Planning, In press.

[15] Teta, R., Della Sala, G., Glukhov, E., Gerwick, L., Gerwick, W.H., Mangoni, A. & Costantino, V., Combined LC-MS/MS and molecular networking approach reveals new Cyanotoxins from the 2014 Cyanobacterial bloom in Green Lake, Seattle. Environmental Science and Technology, 49(24), pp. 14301–14310, 2015. https://doi.org/10.1021/acs.est.5b04415

[16] Al-Azri, A.R., Piontkovski, S.A., Al-Hashmi, K.A., Goes, J.I., Gomes, H.D.R. & Glibert, P.M., Mesoscale and nutrient conditions associated with the massive 2008 Cochlodinium polykrikoides bloom in the Sea of Oman/Arabian Gulf. Estuaries and Coasts, 37(2), pp. 325–338, 2014. https://doi.org/10.1007/s12237-013-9693-1

[17] Pati, S., Dash, M.K., Mukherjee, C.K., Dash, B. & Pokhrel, S., Assessment of water quality using multivariate statistical techniques in the coastal region of Visakhapatnam, India. Environmental Monitoring and Assessment, 186(10), pp. 6385–6402, 2014. https://doi.org/10.1007/s10661-014-3862-y

[18] Saab, M.A.A. & Hassoun, A.E.R., Effects of organic pollution on environmental conditions and the phytoplankton community in the central Lebanese coastal waters with special attention to toxic algae. Regional Studies in Marine Science, 10, pp. 38–51, 2017. https://doi.org/10.1016/j.rsma.2017.01.003

[19] Shaik, A.U.R., Biswas, H., Babu, N.S., Reddy, N.P.C. & Ansari, Z.A., Investigating the impacts of treated effluent discharge on coastal water health (Visakhapatnam, SW coast of Bay of Bengal, India). Environmental Monitoring and Assessment, 189(12), pp. 643–658, 2017. https://doi.org/10.1007/s10661-017-6344-1

[20] Tedd, K.M., Coxon, C.E., Misstear, B.D.R., Daly, D., Craig, M., Mannix, A. & Williams, N.H., An integrated pressure and pathway approach to the spatial analysis of groundwater nitrate: A case study from the southeast of Ireland. Science of the Total Environment, 476, pp. 460–476, 2014. https://doi.org/10.1016/j.scitotenv.2013.12.085

[21] Amin, M.N., Kroeze, C. & Strokal, M., Human waste: An underestimated source of nutrient pollution in coastal seas of Bangladesh, India and Pakistan. Marine Pollution Bulletin, 118(1–2), pp. 131–140, 2017. https://doi.org/10.1016/j.marpolbul.2017.02.045

[22] Lega, M. & Persechino, G., GIS and infrared aerial view: Advanced tools for the early detection of environmental violations. WIT Transactions on Ecology and the Environment, 180, pp. 225–235, 2014. https://doi.org/10.2495/wm140191

[23] Gargiulo, F., Persechino, G., Lega, M. & Errico, A., IDES project: a new effective tool for safety and security in the environment. International Conference on Algorithms and Architectures for Parallel Processing, Springer, Cham, pp. 201–208, 2013.

[24] Errico, A., Angelino, C.V., Cicala, L., Persechino, G., Ferrara, C., Lega, M., Vallario, A., Parente, C., Masi, G., Gaetano, R., Scarpa, G., Amitrano, D., Giuseppe Ruello, G., Verdoliva, L. & Poggi, G., Detection of environmental hazards through the feature-based fusion of optical and SAR data: a case study in southern Italy. International Journal of Remote Sensing, 36(13), pp. 3345–3367, 2015. https://doi.org/10.1080/01431161.2015.1054960

[25] Errico, A., Angelino, C.V., Cicala, L., Podobinski, D.P., Persechino, G., Ferrara, C., Lega, M., Vallario, A., Parente, C., Masi, G., Gaetano, R., Scarpa, G., Amitrano, D., Ruello, G., Verdoliva, L. & Poggi, G., SAR/multispectral image fusion for the detection of environmental hazards with a GIS. In Proceedings of SPIE. The International Society for Optical Engineering, 2014. https://doi.org/10.1117/12.2066476

[26] Liu, G., Yin, X., Pengue, W., Benetto, E., Huisingh, D., Schnitzer, H., Wang, Y. & Casazza, M., Environmental accounting: In between raw data and information use for management practices. Journal of Cleaner Production, 197(1), pp. 1056–1068, 2018.

[27] Lega, M. & Endreny, T., Quantifying the environmental impact of pollutant plumes from coastal rivers with remote sensing and river basin modelling. International Journal of Sustainable Development and Planning, 11(5), pp. 651–662, 2016. https://doi.org/10.2495/sdp-v11-n5-651-662

[28] Ferrara, C., Lega, M., Fusco, G., Bishop, P. & Endreny, T., Characterization of terrestrial discharges into coastal waters with thermal imagery from a hierarchical monitoring program. Water, 9(7), p. 500, 2017. https://doi.org/10.3390/w9070500

[29] Casazza, M., Lega, M., Liu, G., Ulgiati, S. & Endreny, T.A., Aerosol pollution, including eroded soils, intensifies cloud growth, precipitation, and soil erosion: a review. Journal of Cleaner Production, 189, pp. 135–144, 2018. https://doi.org/10.1016/j.jclepro.2018.04.004

[30] Yang, Q., Liu, G., Hao, Y., Coscieme, L., Zhang, J., Jiang, N., Casazza, M. & Giannetti, B.F., Quantitative analysis of the dynamic changes of ecological security in the provinces of China through emergy-ecological footprint hybrid indicators. Journal of Cleaner Production, 184, pp. 678–695, 2018. https://doi.org/10.1016/j.jclepro.2018.02.271

[31] Liu, G., Brown, M.T. & Casazza, M., Enhancing the sustainability narrative through a deeper understanding of sustainable development indicators. Sustainability, 9(6), p. 1078, 2017. https://doi.org/10.3390/su9061078