# Comparing Monitoring Networks to Assess Urban Heat Islands in Smart Cities
## 1 Introduction
Consequently, using auxiliary sources, such as weather stations, as evidenced by a substantial study conducted in China, becomes imperative to substantiate remotely sensed data [31]. Urban sensor networks have emerged as vital tools for weather data collection and analysis, supported by initiatives like the Smart Cities Marketplace [32] and the Smart Cities Information System [33]. The EUMETNET community, comprising European National Meteorological and Hydrological Services (NMHSs), has promoted citizen cooperation in weather monitoring [34]. This community obtains data mainly from four sources: Netamo, a manufacturer of smart home devices; Sencrop and WEENAT, which are used for agricultural purposes; and the Weather Observations Website (WOW), which enables users to register CWS from different providers to automatically upload weather observations [35]. Numerous studies have utilized CWS data for UHI research. For instance, WU and HetWeerActueel were used to investigate urban effects on temperature and precipitation in the Netherlands [36]. In London, Netatmo data revealed temperature differences of 1 to 6 \({}^{\circ}\)C [37], while a Berlin study using 1500 stations highlighted the importance of data quality and representativeness [38]. In Vienna and Amsterdam, comparisons were made with reference stations [39,40]. These findings underscore the potential of CWS networks in UHI research, while also emphasizing the need for rigorous quality control due to their variability and lack of standardization compared to NMHS stations. A range of quality control procedures have been the subject of study; notably, [PERSON] et al. [41] developed a statistically based quality control system for crowdsourced air temperature data. Other experiences are based on deploying their own networks to provide CWS availability in a particular area or density. Several notable instances of success with commercial devices have been documented. In Taiwan, mobile transect measurements were combined with fixed stations using low-cost sensors [42]. A similar approach was developed in Chile, identifying UHI ranging from 0.5 \({}^{\circ}\)C to 13 \({}^{\circ}\)C [43]. In Rome, a study utilized monthly maximum and minimum climate data from urban fixed stations and reference airport stations to dynamically simulate the impact on building energy performance, with maximum UHI intensities ranging from 3.1 \({}^{\circ}\)C during the day to 3.5 \({}^{\circ}\)C at night [44]. Regarding the use of proprietary networks and custom-built stations, a study conducted in Taipei investigated the impact of vehicular traffic on UHI intensity. This was achieved through the deployment of high-precision sensors shielded from solar radiation, Arduino-based devices, and installations positioned between 3 and 4.5 m above ground level [45]. Similarly, in London, Arduino technology was employed to assess whether low-cost sensors could detect small-scale temperature variations [46]. The Birmingham Urban Climate Laboratory provides an overview of the logistical aspects of implementing an urban weather network, including selecting suitable locations, equipment testing and calibration, quality control mechanisms, and using existing Wi-Fi networks for data transmission [47]. Furthermore, other issues have received less attention. These include selecting IoT communication protocols that optimize range, data transmission speed, energy efficiency, and cost-effectiveness [48]. The utilization of RTD, which is pivotal for smart city networks [49], and the implementation of scalable information system architecture and open-source hardware and software should be incorporated into any current network. Moreover, the quality of RTD network records based on low-cost stations for UHI identification has not been systematically validated [38]. While CWSs are fundamental in collecting urban climate data, sensor precision variability, station location differences, and lack of standardization in data collection methods can affect the reliability of results [50].