The application of a high-density street-level air temperature observation network (HiSAN): The relationship between air temperature, urban development, and geographic features

Influence of microclimate control on the growth of asparagus under greenhouse in tropical climates

High temperatures have become common in cities in Taiwan, and this phenomenon has spread to surrounding agricultural areas. Tainan, a city located in a tropical climate zone with agriculture as its primary development industry, is one of the cities considerably affected by the high temperature. High temperatures can reduce crop yields and even cause plant death, especially for vulnerable high-value crops, which are severely to microclimate conditions. Asparagus is a high-value crop that has long been cultivated in the Jiangjun District of Tainan. Recently, asparagus has been planted in greenhouses to protect against pests and natural disasters. However, the greenhouses can overheat. To identify the optimal growth environment for asparagus, this study applies vertical monitoring to record the temperature in the greenhouse and the soil moisture content of a control (canal irrigation) and an experimental (drip irrigation) group. When the surface layer of the soil exceeds 33°C, the tender stems of asparagus bloom readily, reducing its commercial value. Therefore, drip irrigation was conducted with cool water (26°C) to reduce soil temperature in summer and warm water (28°C) to increase soil temperature in winter. The study also recorded the growth of asparagus using daily yields measured by farmers during weighing and packing to understand the benefits of controlling the greenhouse microclimate. This study reports a correlation of 0.85 between asparagus yield and temperature and a correlation of 0.86 between asparagus yield and soil moisture content. The use of a drip irrigation system with a water temperature adjustment function not only saves up to 50% of water but also resulted in an average yield increase of 10% through maintaining stable soil moisture content and temperature. Therefore, the findings of this study can be applied to asparagus yields affected by high temperature and can solve the problems of poor quality in summer and low yield in winter.

Supporting Design to Develop Rural Revitalization through Investigating Village Microclimate Environments: A Case Study of Typical Villages in Northwest China

China has the largest number of villages in the world, and research on rural microclimate will contribute to global climate knowledge. A three-by-three grid method was developed to explore village microclimates through field measurement and ENVI-met simulation. A regression model was used to explore the mechanistic relationship between microclimate and spatial morphology, and predicted mean vote (PMV) was selected to evaluate outdoor thermal comfort. The results showed that ENVI-met was able to evaluate village microclimate, as Pearson’s correlation coefficient was greater than 0.8 and mean absolute percentage error (MAPE) was from 2.16% to 3.79%. Moreover, the air temperature of west–east road was slightly higher than that of south–north, especially in the morning. The height-to-width ratio (H/W) was the most significant factor to affect air temperature compared to percentage of building coverage (PBC) and wind speed. In addition, H/W and air temperature had a relatively strong negative correlation when H/W was between 0.52 and 0.93. PMV indicated that the downwind edge area of prevailing wind in villages was relatively comfortable. This study provides data support and a reference for optimizing village land use, mediating the living environment, and promoting rural revitalization.

A citizen centred urban network for weather and air quality in Australian schools

High-quality, standardized urban canopy layer observations are a worldwide necessity for urban climate and air quality research and monitoring. The Schools Weather and Air Quality (SWAQ) network was developed and distributed across the Greater Sydney region with a view to establish a citizen-centred network for investigation of the intra-urban heterogeneity and inter-parameter dependency of all major urban climate and air quality metrics. The network comprises a matrix of eleven automatic weather stations, nested with a web of six automatic air quality stations, stretched across 2779 km², with average spacing of 10.2 km. Six meteorological parameters and six air pollutants are recorded. The network has a focus on Sydney’s western suburbs of rapid urbanization, but also extends to many eastern coastal sites where there are gaps in existing regulatory networks. Observations and metadata are available from September 2019 and undergo routine quality control, quality assurance and publication. Metadata, original datasets and quality-controlled datasets are open-source and available for extended academic and non-academic use.

Measurement(s)	temperature of air • relative humidity • pressure of air • atmospheric wind speed • atmospheric wind direction • rainfall rate • sulphur dioxide • carbon monoxide • nitrogen dioxide • tropospheric ozone • pm10 • pm2.5
Technology Type(s)	weather station • air quality station
Sample Characteristic - Environment	urban weather and pollution
Sample Characteristic - Location	Sydney, Australia

Fixed and Mobile Low-Cost Sensing Approaches for Microclimate Monitoring in Urban Areas: A Preliminary Study in the City of Bolzano (Italy)

In the current scenario of massive urbanization and global climate change, an intelligent monitoring of the environmental variables is becoming fundamental to ensure good living conditions in cities. Indeed, the acquisition of data with high spatiotemporal resolution can enable the assessment of environmental vulnerabilities in urban areas towards the definition of responsive adaptation and mitigation strategies. In this context, the current work presents a two-fold approach based on low-cost cloud-connected sensors for (i) fixed and (ii) mobile monitoring of several environmental parameters. This paper, which focuses on the measurement aspects of the urban micro-climate, describes in detail the hardware and software components of both approaches, and how to exploit them for setting up a field campaign. The methods were tested in the city of Bolzano (Italy), demonstrating their suitability for identifying the spatial variability of the microclimate in relation to the urban morphology, and for highlighting the presence of the urban heat island and estimating its intensity.

Thermo-Hygrometric Variability on Waterfronts in Negative Radiation Balance: A Case Study of Balneário Camboriú/SC, Brazil

Extensive urbanization around the world has resulted in the consumption of massive vegetated areas and natural resources. To this end, one strategy for urban development is to consolidate urban areas. In Balneário Camboriú/SC, Brazil, this trend has transformed the city into a vertical built-up area on its coastal strip, accommodating a large amount of buildings both in terms of quantity and number of floors. This research aims to quantify the thermo-hygrometric fluctuation on the waterfront of Balneário Camboriú, in negative radiation balance. To acquire the data on air temperature (Ta) and relative humidity (RH), two mobile transects and measuring at two fixed points were made in a situation of negative radiation balance on 26 August 2019, in the winter period of the Southern Hemisphere. The collection work began at 06:00:00 a.m. (before sunrise, the peak of the negative radiation balance), on Atlântica Avenue (waterfront) and Brasil Avenue (parallel to the waterfront). It was verified that the Ta varied from 16.0 °C to 19.0 °C, and the RH remained over 80% during the entire route. At the meteorological shelters, the temperature presented a variation from 14.4 °C to 17.7 °C, and the RH ranged from 79.6% to 91.3% between the two points. The spatial variability in the Ta and RH along the paths travelled and at the fixed points is directly related to the land cover, represented especially by the buildings’ verticalization and data collection time.

Influence of Weather Factors on Thermal Comfort in Subtropical Urban Environments

Urbanization has influenced the distribution of heat in urban environments. The mutual influence between weather factors and urban forms created by dense buildings intensify human perception of the deteriorating thermal environment in subtropics. Past studies have used real-world measurements and theoretical simulations to understand the relationship between climate factors and the urban heat island effect. However, few studies have examined how weather factors and urban forms are connected to the thermal environment. To understand the influence of various weather factors on urban thermal environments in various urban forms, this study applied structural equation modeling to assumptions of linear relationships and used quantitative statistical analysis of weather data as well as structural conversion of this data to establish the structural relationships between variables. Our objective was to examine the relationships among urban forms, weather factors, and thermal comfort. Our results indicate that weather factors do indeed exert influence on thermal comfort in urban environments. In addition, the thermal comfort of urban thermal environments varies with location and building density. In hot and humid environments in the subtropics, humidity and wind speed have an even more profound impact on the thermal environment. Apparent temperature can be used to examine differences in thermal comfort and urban forms. This study also proved that an urban wind field can effectively mitigate the urban heat island effect. Ventilation driven by wind and thermal buoyancy can dissipate heat islands and take the heat away from urban areas.

A Weather Forecast Model Accuracy Analysis and ECMWF Enhancement Proposal by Neural Network

This paper presents a neural network approach for weather forecast improvement. Predicted parameters, such as air temperature or precipitation, play a crucial role not only in the transportation sector but they also influence people’s everyday activities. Numerical weather models require real measured data for the correct forecast run. This data is obtained from automatic weather stations by intelligent sensors. Sensor data collection and its processing is a necessity for finding the optimal weather conditions estimation. The European Centre for Medium-Range Weather Forecasts (ECMWF) model serves as the main base for medium-range predictions among the European countries. This model is capable of providing forecast up to 10 days with horizontal resolution of 9 km. Although ECMWF is currently the global weather system with the highest horizontal resolution, this resolution is still two times worse than the one offered by limited area (regional) numeric models (e.g., ALADIN that is used in many European and north African countries). They use global forecasting model and sensor-based weather monitoring network as the input parameters (global atmospheric situation at regional model geographic boundaries, description of atmospheric condition in numerical form), and because the analysed area is much smaller (typically one country), computing power allows them to use even higher resolution for key meteorological parameters prediction. However, the forecast data obtained from regional models are available only for a specific country, and end-users cannot find them all in one place. Furthermore, not all members provide open access to these data. Since the ECMWF model is commercial, several web services offer it free of charge. Additionally, because this model delivers forecast prediction for the whole of Europe (and for the whole world, too), this attitude is more user-friendly and attractive for potential customers. Therefore, the proposed novel hybrid method based on machine learning is capable of increasing ECMWF forecast outputs accuracy to the same level as limited area models provide, and it can deliver a more accurate forecast in real-time.