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COVID-19 has had an impact on the entire humankind and has been proved to spread in deadly waves. As a result, preparedness and planning are required to better deal with the epidemic’s upcoming waves. Effective planning, on the other hand, necessitates detailed vulnerability assessments at all levels, from the national to the state or regional. There are several issues at the regional level, and each region has its own features. As a result, each region needs its own COVID-19 vulnerability assessment. In terms of climate, terrain and demographics, the state of Uttarakhand differs significantly from the rest of India. As a result, a vulnerability assessment of the next COVID-19 variation (Omicron BA.2) is required for district-level planning to meet regional concerns. A total of 17 variables were chosen for this study, including demographic, socio-economic, infrastructure, epidemiological and tourism-related factors. AHP was used to compute their weights. After applying min–max normalisation to the data, a district-level quantitative SWOT is created to compare the performance of 13 Uttarakhand districts. A COVID-19 vulnerability index (normalised R i ) ranging between 0 and 1 was produced, and district-level vulnerabilities were mapped. Quantitative SWOT results depict that Dehradun is a best performing district followed by Haridwar, while Bageshwar, Rudra Prayag, Champawat and Pithoragarh are on the weaker side and the normalised Ri proves Dehradun, Nainital, Champawat, Bageshwar and Chamoli to be least vulnerable to COVID-19 (normalised R i ≤ 0.25) and Pithoragarh to be the most vulnerable district (normalised R i > 0.90). Pauri Garwal and Uttarkashi are moderately vulnerable (normalised R i 0.50 to 0.75). Electronic supplementary material The online version of this article (10.1007/s10668-022-02727-3) contains supplementary material, which is available to authorised users.

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Disposal of collected waste is the least preferable way of sustainable solid waste management. But most of the cities in developing nations prefer to use open dumping in an inappropriate and non-scientific way, causing negative impacts on the environment as well as human health. This study offers a novel approach for scientific landfill site selection and sustainable waste management in Aligarh city, India. This could be possible through relevant data collection, selection of suitable models for criterion weighting, and model validation. In order to prepare a suitable landfill site selection map, a GIS-based ensemble FAHP-SVM and FAHP-RF model was implemented. Considering the previous studies and the characteristics and the study area, a total of eighteen thematic layers (decision criteria) were selected. The result reveals that land value, nearness to residential roads, nearness to hospitals and clinics, distance from waste bins, and NDBI having a fuzzy weight of > 0.10, indicates significant factors; whereas land elevation, land slope, surface temperature, soil moisture index, NDVI and urban classification having a fuzzy weight of 0, indicates these criteria have no importance for the present study. The result further reveals that FAHP-RF with an AUC value of 0.9182 is the more accurate model in comparison to FAHP-SVM. According to the final result of weight-based overlay, a total of seven potential landfill sites were identified, out of which three sites were determined as most suitable by considering current land cover, environmental and economic concerns, and public opinions. This study proposed a zonal division model based on the location of suitable landfill sites for sustainable waste management in the study area. The findings of this study may provide a guideline to the decision-makers and planners for optimal landfill site selection in other cities of developing countries.