GIS-based optimization for the locations of sewage treatment

Clean water is essential for human survival. However, with the continuous development of human society and industrial modernization, the quality of the water environment is getting worse and the shortage of water resources is becoming more and more serious (Owa et al., 2013). Water pollution will endanger the ecological environment and sustainable development of cities (Dowd et al., 2008). Therefore, it is necessary to strengthen the protection of water resources. 

Industrial wastewater and domestic sewage are the main pollution sources of urban water resources (Fiorentino et al., 2019). The construction of sewage treatment plants plays an important role in mitigating water pollution. Reasonable site selection is essential for the construction of sewage plants with efficient purification efficiency (Lu et al., 2016). Before confirming a location for the construction of sewage treatment plants, it is necessary to consider factors such as population distribution, water distribution and transportation distance. In order to find an optimal site selection, Geographic Information System (GIS) is a suitable technology (Zhao et al., 2009).

With population of 2.3 million, Brisbane is the third largest city in Australia. Reasonable site selection for the construction of sewage treatment plants is important for the sustainable development of Brisbane.

1. Based on GIS and Analytic Hierarchy Process (AHP), to identify the optimal location for the construction of sewage treatment plants in Brisbane.

2.Calculating the service area of the sewage treatment plants in Brisbane.

Figure 1 shows the landcover characteristics of Brisbane and the its location in Australia. Land cover of Brisbane has distinctive characteristics, the whole city is built around the river, especially the distribution pattern of the industrial area along the river.

With popalation more than 2.1 million, Brisbane is the capital of Queensland, Australia, and the third largest city in Australia, second only to Sydney and Melbourne.

This research mainly uses shapefile data released by open street map (OSM). OSM data is hierarchically detailed and includes many facets. For example, the types of landcover in the city not only have points data, but also the area covered, which is convenient for statistics of various distances. The OSM dataset is free with free release license. OSM data provides four download methods, including the free download service provided by Geofabrik ( Geofabrik Download Server ) which is used in this study. This study selected road, landcover, and population data from the dataset.

Filter out the required feature classes from land use data, including residential areas, industrial areas, roads, and water bodies. The roads consist of four levels, which are trunk, primary, secondary and residential. In addition, considering that rivers are possible missing, so comparing the distribution information of the water body based on the Normalized Difference Water Index (NDWI) to check whether the was missing.

The methods includes three parts, which are listed as follow.

1. Data pre-processing for OSM dataset.

2. Spatial analysis for post-processing dataset.

3. Analysis of the results.

Each step is showed in Figure 3.

Figure 4 is the code that includes batch clip control flow and exception handling.

Figure 5 is the code that modified the error message, can print the successful result.

According to previous studies (Attwa, 2020; Hama, 2019), distance of resident districts, business districts, parks and roads are the popular influencing factors to be integrated for the selection for the locations of sewage treatment plants. Hence, this study defines resident districts, business districts, parks and roads as the influencing factors. Specific parameters and weights for the factors can be checked in the code in Figure 9.

Setting field value, and using for-in control every row to update.

In the previous part, the study got a combined feature class including all index. Calculate final score to find the best zone.

The best suitable area for building sewage treatment plants is showed in Figure 17. In the final score distribution map, the best area is smaller than the previous possible distribution map. Because this study considers more factors to avoid some non-allowed zone, suck like park, residential<100m. This not only help us finding the best zone, but also meet 7more stringent requirements.

Checking the topo error in ArcGIS Pro after importing road feature class to file geodatabase. Then, creating a network based on road. And the main cost is the time to travel through roads. The study estimated the service scope of sewage treatment plants based on the sewage pipe network along the road.

Selecting some facilities in the best area above and analyze the service area as shown in Figure 19.

Compared with this study, most coastal cities sewage treatment plants locate in the lower reaches of rivers or at the estuary of the sea. For example, Zhao et al. (2009) reported that Guangzhou sewage treatment plant should be close to the sea outlet. Wang et al. (2021) generated some site selections of the location for sewage treatment plant near sea in Xiamen. Apart from the influencing factors of resident districts, business districts, parks and roads, many studies also used DEM or slope as the influencing factors (Agarwal, 2019; Deepa, 2012). In this study, the spatial heterogeneity of the urban slope of Brisbane is relatively small. Thus, this study did not define slope as the influencing factor. To validate the result in this study, this study tries to compare the previous studies focusing on selection for the locations of sewage treatment plants in Brisbane. Because there is almost no study about optimization for the locations of sewage treatment plants in Brisbane in Google Scholar, this study compares the result in this study with the sewage treatment plants locations in Google map. There are 6 sewage treatment plants around the central city area of Brisbane, including Aqua-Nova, Filtek Australia PTY Ltd., Ecofarmer Australia, Luggage Point Sewage Treatment Plant and so on. Except Luggage Point Sewage Treatment Plant, other 5 sewage treatment plants are in the ‘Best area’ in Figure 19, which indicates the result in this study is reasonable.

According to Figure 17, Figure 19 and Figure 20, the ‘best location’ generated in this study and the sewage treatment plants locations in Brisbane in Google map are around industrial area, which is conducive to the intensification of sewage treatment.

Based on GIS, it can be effective to find out the location of the sewage treatment plants, which is conducive to the intensification of sewage treatment and the reducing cost of sewage treatment. Therefore, GIS has obvious advantages and wide application prospects in the site selection of sewage treatment plants.

1. The best location for the sewage treatment plant is mainly distributed along the river. The advantage is that it is convenient to purify the water body and save the cost.

2. The best location is distributed around the industrial area to reduce the cost of industrial sewage treatment and discharge, and reduce the impact of sewage on the environment.

3. At present, the distribution of factories is concentrated in the downstream river area, which has a great impact on the natural water body, and more sewage treatment plants need to be built.

4. The upstream of the river is not suitable for building sewage treatment plants.

Agarwal, R., Srivastava, K. A., & Nigam, K. A. (2019). GIS and AHP based site suitability for sewage treatment plant in sultanpur district, India. Int J Innov Techno Expl Engine (IJITEE), 8(6S4), 961-964.

Attwa, M., & Zamzam, S. (2020). An integrated approach of GIS and geoelectrical techniques for wastewater leakage investigations: Active constraint balancing and genetic algorithms application. Journal of Applied Geophysics, 175, 103992.

Deepa, K., & Krishnaveni, M. (2012). Suitable site selection of decentralised treatment plants using multicriteria approach in GIS. Journal of Geographic Information System, 4 (3), 7.

Dowd, B. M., Press, D., & Los Huertos, M. (2008). Agricultural nonpoint source water pollution policy: The case of California's Central Coast. Agriculture, ecosystems & environment, 128(3), 151-161.

Fiorentino, A., Di Cesare, A., Eckert, E. M., Rizzo, L., Fontaneto, D., Yang, Y., & Corno, G. (2019). Impact of industrial wastewater on the dynamics of antibiotic resistance genes in a full-scale urban wastewater treatment plant. Science of the Total Environment, 646, 1204-1210.

Hama, A. R., Al-Suhili, R. H., & Ghafour, Z. J. (2019). A multi-criteria GIS model for suitability analysis of locations of decentralized wastewater treatment units: case study in Sulaimania, Iraq. Heliyon, 5(3), e01355.

Lu, S., Zhang, X., Wang, J., & Pei, L. (2016). Impacts of different media on constructed wetlands for rural household sewage treatment. Journal of Cleaner Production, 127, 325-330.

Owa, F. D. (2013). Water pollution: sources, effects, control and management. Mediterranean journal of social sciences, 4(8), 65-65.

Wang, C., Guo, Z., Li, Q., & Fang, J. (2021). Study on layout optimization of sewage outfalls: a case study of wastewater treatment plants in Xiamen. Scientific Reports, 11(1), 1-12.

Zhao, Y. W., Qin, Y., Chen, B., Zhao, X., Li, Y., Yin, X. A., & Chen, G. Q. (2009). GIS-based optimization for the locations of sewage treatment plants and sewage outfalls–A case study of Nansha District in Guangzhou City, China. Communications in Nonlinear Science and Numerical Simulation, 14(4), 1746-1757.