Shallow geothermal energy (SGE) is a part of geothermal resources and is mainly used through ground source heat pumps (GSHP). However, the potential of SGE varies from region to region due to different geological conditions. There is a lack of regulations and codes for assessing SGE, which makes the design and planning of GSHP restricted. In this study, an evaluation system of the suitability of GSHP in a region of Qingdao by using Analytic Hierarchy Process (AHP) is proposed, and the test area is divided into three suitability levels based on suitability scores. The evaluation system contains property indicators, elemental indicators, and their weights. The result shows that the highly suitable area for the application of GSHP in the test area is 110.04 km2, accounting for 41.8% of the whole test area. The area of moderately suitable area is 65.02 km2, accounting for 24.7%, and GSHP should be developed and utilized on the basis of full demonstration in this level. The unsuitable area for GSHP is 88.19 km2, accounting for 33.5%. The indicator weights in this article may only be applicable to the Qingdao area and cities with similar geological conditions to Qingdao. However, the indicators within this evaluation system can be applied to the vast majority of locations where GSHP are to be developed, as it provides a method of assessment in terms of geological conditions, groundwater conditions, construction conditions, and ecological aspects.

Background: Indoor air quality (IAQ) in schools can affect the performance and health of occupants, especially young children. Increased public attention on IAQ during the COVID-19 pandemic and bushfires have boosted the development and application of data-driven models, such as artificial neural networks (ANNs) that can be used to predict levels of pollutants and indoor exposures. Methods: This review summarises the types and sources of indoor air pollutants (IAP) and the indicators of IAQ. This is followed by a systematic evaluation of ANNs as predictive models of IAQ in schools, including predictive neural network algorithms and modelling processes. The methods for article selection and inclusion followed a systematic, four-step process: identification, screening, eligibility, and inclusion. Results: After screening and selection, nine predictive papers were included in this review. Traditional ANNs were used most frequently, while recurrent neural networks (RNNs) models analysed time-series issues such as IAQ better. Meanwhile, current prediction research mainly focused on using indoor PM2.5 and CO2 concentrations as output variables in schools and did not cover common air pollutants. Although studies have highlighted the impact of school building parameters and occupancy parameters on IAQ, it is difficult to incorporate them in predictive models. Conclusions: This review presents the current state of IAQ predictive models and identifies the limitations and future research directions for schools.

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