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As the environmental awareness of urban citizens increases, traditional air quality monitoring stations cannot satisfy the need for air quality data at high temporal and spatial resolution due to their high construction and maintenance costs. Low-cost air quality monitors are being increasingly used for this purpose because of their portability and affordable price. However, low-cost monitors are usually beset by data quality issues, and the number of mounted air pollutant sensors is limited by the restriction of the cost and size of monitors. Therefore, we propose to extend the use of air quality monitor data via a deep learning technique called long short-term memory (LSTM). The extension is embodied in two aspects: first, calibration of air pollutant concentration data; and second, provision of indicative information about air pollutants where no corresponding sensors are available. A low-cost air quality monitor called KOALA (Knowing Our Ambient Local Air Quality), which was deployed in Sydney (Australia), was used as an example to prove this method's feasibility. Data from a 90-day period were used for model training, and data from a 30-day period were used for model validation. Random forest models were used for selecting the most useful LSTM model input variables. Historical 24-h information was incorporated to improve the performance of the LSTM models. The results showed that: first, LSTM models can be used to calibrate KOALA carbon monoxide (CO) data with the optimum input being raw CO measurements and the corresponding standard deviation information; and second, LSTM models can be used to estimate ozone concentration with the optimum input being CO concentration and three meteorological parameters [i.e. top soil layer temperature, 10 m U wind (earth-relative), and net shortwave radiation flux at the ground] generated through a deterministic model known as WRF (Weather Research and Forecasting). In addition, the LSTM ozone estimation model showed good performance at both the training location and a location 11 km away, indicating that the proposed method can be used to provide indicative information about air pollutants around the training location.

There are currently more than 400 cities that are subject to the urban heat island (UHI) effect, whose summer temperature can be over 15 °C above the human thermal comfort zone. As the scope of urbanization expands, more people will feel the influence of the UHI effect. Since cool coatings can serve as a mitigation measure against the UHI effect, this research proposes a method that can estimate its mitigation effect in any given region. The main idea of this method is to simulate the function of cool coatings via increasing the albedo values in the Weather Research and Forecasting (WRF) model. The main novelty of this method is that it incorporates detailed land categorization data to simulate realistic urban morphology for the purpose of improving model performance. To demonstrate the feasibility of the proposed method, the UHI mitigation effect of cool coatings was estimated in the city of Sydney during two consecutive sweltering days (7–8 January 2018) via the WRF model. The results showed that the proposed method fulfilled its purpose. To be specific, as the consequence of a 0.35 albedo increase in urban Sydney, the whole urban area will be subject to an average temperature decrease of 0.76 °C, while some regions will experience a temperature decrease as great as 5.71 °C during the hottest hour. However, this value for a given zone, such as the downtown coast area, was closely related to the local wind directions. The results also showed that the values of different urban canopy parameters could be treated as auxiliary information of WRF modeling results and used to identify the locations that suffered the most from the UHI effect. Therefore, the proposed method can help decision-makers and stakeholders to better analyze the UHI mitigation potential of cool coatings. Additionally, it indicates incorporating detailed land categorization data is an effective way of improving UHI numerical simulations.