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Land is a pricy and scarce resource, particularly in urban areas. If it is utilized effectively, suitable sites can be allocated for urban wastewater safe disposal through effluents treatment and reuse. This study used multicriteria decision-making approach known as weighted linear combination to map the suitability of an on-site natural wastewater treatment system in the urban and industrial environments—selecting Faisalabad City in Pakistan as the study area. Location of the study region in a water-scarce zone increases the scope of the untreated wastewater disposal into land and water bodies as well as its reuse for irrigation. Data on climate parameters (i.e., temperature, rainfall, and evapotranspiration), geophysical parameters, (i.e., including soil, topography, depth to groundwater table, and groundwater electrical conductivity), and socioeconomic parameters (i.e., land cover maps and proximity from roads/railways and rivers/canals) are used for the development of weighted linear combination based suitability index maps. Analytical hierarchical process-based heuristic approach is adapted to estimate the relative importance of all the parameters. Weights with less than 0.10 consistency ratio are used in the preparation of the final suitability map. The land area required for the proposed on-site wastewater treatment system is estimated as a function of climatic factors and wastewater characteristics. Adoption of the proposed multicriteria framework helped identifying four potentially environment-friendly and economically viable sites. The results show that geospatial multicriteria framework could efficiently help urban planners in sustainable wastewater management, particularly in developing countries where climate-change-induced water scarcity and associated health issues are critical. • Better wastewater management in cities is critical for socio-ecological well-being. • A spatial framework for siting an on-site wastewater treatment plant is proposed. • The framework identifies potential ergonomic sites in a decentralized manner. • The framework acts as a decision-support-system for urban planning/design. • The application of the spatial framework is equally useful for other urban regions. [ABSTRACT FROM AUTHOR]

Achieving Sustainable Development Goal (SDG)-4 prerequisites quality education provisioning. In this context, we present important insights and references for educational interventions/investments to be tailored to local necessities in Pakistan. Several spatial statistical models such as the Global Moran's I-based spatial autocorrelation, multivariate clustering, and the Cluster and Outlier model are used to explore geographic heterogeneities and patterns. Additionally, significant determinants among several socio-economic, spatio-environmental, and infrastructural variables are identified for education status (EdS) using regression. As a result, a large geographic inequality regarding EdS is found in Pakistan. While a strong spatial association is evident, the districts in northern Punjab are identified as significant hotspots—higher EdS clusters (∼22% of total districts, 95% confidence). Majority of the 44% poorly performing districts belong to Balochistan province (95% confidence). Overall, the educational status in Punjab is higher as compared with other provinces. We find that four out of seven potential factors (i.e., poverty, urbanization, electricity accessibility, and school infrastructure) are statistically significant determinants of EdS. Among these, poverty is the most strongly associated (mean coefficient value −18.848) factor to control EdS. The results have important implications to decision-making for immediate or gradual actions in the context of spatially equitable provisioning of quality education through an informed prioritization (i.e., low performing districts). Based on the findings, while rigorous measures are needed for low performing regions and the identified determinants to improve education status, this study sheds light on the mechanisms to achieve SDG4, consequently promoting human well-being through educating communities. • Pakistan's EdS is analyzed at the district-level. • Spatial-statistical techniques are coupled for EdS profiling. • While 44% districts show very poor EdS, poverty is the most significantly associated. • Lowest EdS is observed for districts in Balochistan province. • Results provide important references for policy, decisions, and actions to achieve SDG4. [ABSTRACT FROM AUTHOR]