Research and innovation paving the way for climate neutrality in urban transport: Analysis of 362 cities on their journey to zero emissions.

The EU Mission on Climate Neutral and Smart Cities is an ambitious initiative aiming to involve a wide range of stakeholders and deliver 100 climate-neutral and smart cities by 2030. We analysed the information submitted in the expressions of interest by 362 candidate cities. The majority of the cities' strategies for climate neutrality include urban transport as a main sector and combine the introduction of new technologies with the promotion of public transport and active mobility. We combined the information from the EU Mission candidate cities with data from the CORDIS and TRIMIS databases, and applied a clustering algorithm to measure proximity to foci of H2020 funding. Our results suggest that preparedness for the EU Mission is correlated with research and innovation activities on transport and mobility. Horizon 2020 activities specific to transport and mobility significantly increased the likelihood of a city to be a candidate. Among the various transport technology research pathways, smart mobility appears to have a major role in the development of solutions for climate neutrality.

Localized synergies between heat waves and urban heat islands: Implications on human thermal comfort and urban heat management.

Heat waves (HWs) and urban heat islands (UHIs) can potentially interact. The mechanisms behind their synergy are not fully disclosed. Starting from the localized UHI phenomenon, this study aims i) to reveal their associated impacts on human thermal comfort through three different definitions of HW events, based on air temperature (airT), wet-bulb globe temperature (WBGT) and human-perceived temperature (AppT) respectively, and ii) to understand the role of air moisture and wind. The analysis was conducted in four districts (NH, JD, MH and XJH) with different urban development patterns and geographic conditions, in the megacity of Shanghai with a subtropical humid climate. Results evidenced the localized interplay between HWs and UHIs. The results indicate that less urbanized districts were generally more sensitive to the synergies. JD district recorded the highest urban heat island intensity (UHII) amplification, regardless of the specific HW definition. Notably, during AppT-HWs, the increment was observed in terms of maximum (1.3 °C), daily average (0.8 °C), diurnal (0.4 °C) and nocturnal UHII (1.0 °C). Nevertheless, localized synergies between HWs and UHIs at different stations also exhibited some commonalities. Under airT-HW, the UHII was amplified throughout the day at all stations. Under WBGT-HW, diurnal UHII (especially at 11:00-17:00 LST) was consistently amplified at all stations. Under AppT-HW conditions, the nocturnal UHII was slightly amplified at all stations. Air moisture and wind alleviated the synergistic heat exacerbation to the benefit of thermal comfort. The extent depended on geographic condition, diurnal and nocturnal scenarios, temperature type and HW/normal conditions. Stronger HW-UHI synergies indicate the necessity to develop specific urban heat emergency response plans, able to capture and intervene on the underlying mechanisms. This study paves to way to their identification.

Meteorological influence on forecasting urban pollutants: Long-term predictability versus extreme events in a spatially heterogeneous urban ecosystem.

With the escalation of heat- and pollution-related threats in cities across the globe, timely counteractions and emergency procedures are vital, which calls for accurate co-prediction of urban heat and air quality under both standard conditions and under extreme events. In this study, we used historical hourly data recorded at 9 sites across the Sydney metropolitan area to test the performance of long short-term memory (LSTM) forecasting architectures in predicting 5 urban pollutants based on different combinations of meteorological inputs and considering standard, bushfire, and pandemic lockdown conditions. We demonstrate that, in most cases and even in a fast-growing city, there is no significant benefit achieved by including extra predictors to temperature and humidity, when adequate forecasting techniques capable of learning long-term dependencies are used. Further, in agreement with previous studies, we provide evidence of ozone's higher responsiveness to all weather parameters and thus enhanced predictability and PM10's lower predictability as compared to all other considered urban pollutants. The prediction accuracy tends to be comparable between standard conditions and bushfire events. However, the predictability significantly declines under anomalies in anthropogenic patterns and urban metabolic rates as those recorded during the pandemic. The inclusion of local emission sources and anthropogenic factors in the input dataset is considered necessary for NO and PM10 to properly predict urban air quality, especially under human-related extreme conditions.

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.

On the linkage between urban heat island and urban pollution island: Three-decade literature review towards a conceptual framework.

With the doubling of urban population within the next two decades and the disproportionate growth of megacities, it is critical to explore the synergism between urban heat and pollution. In this paper, a systematic review is conducted on the existing knowledge, collected since 1990, on the link between urban heat island (UHI) and urban pollution island (UPI). Results from 16 countries and 11 Köppen-Geiger climatic zones are perused and compared to delineate methodological and experimental trends, geographical dependencies and research gaps. Detailed content analysis is conducted according to five prominent topics: i) the role of UHI on temperature-dependent chemistry, ii) the daytime/nighttime variability in the UHI-UPI interaction, iii) the role of urban geomorphic types, forms and growth schemes, iv) future trends and v) primary and secondary effects of UHI mitigation on urban air quality. Different approaches and observations are eventually harmonized to outline opportunities and challenges towards the disentanglement and/or the two-way mitigation of both phenomena. This will help governments and urban planners to deliver coping strategies and precautions towards a more salutogenic urban design.

Local synergies and antagonisms between meteorological factors and air pollution: A 15-year comprehensive study in the Sydney region.

Associated with rapid urbanization and escalation of bushfire events, Sydney has experienced significant air quality degradation in the XXI century. In this study, we present a 15-year retrospective analysis on the influence of individual meteorological factors on major air pollutants (NO2, O3, PM10 and PM2.5) at 14 different sites in Greater Sydney and Illawarra. By applying a newly developed "zooming in" approach to long-term ground-based data, we disclose general, seasonal, daily and hourly patterns while increasing the level of spatial associativity. We provide evidence on the pivotal role played by urbanization, sprawling dynamics, global warming and bushfires on local meteorology and air pollution. We strike associations between temperature and O3, both as average trends and extremes, on account of increasing heat island effects. The role of wind in a coastal-basin environment, influenced by a vast desert biome inland, is investigated. A steady trend towards stagnation is outlined, boosted by enhanced urban roughness and intensified heat island circulation. Relative humidity is also crucial in the modulation between NO2 and O3. With a sharp tendency towards drier and hotter microclimates, NO2 levels dropped by approximately 50% over the years at all locations, while O3's median levels almost doubled in the last 10 years. Further, O3 and PMs shifted towards more frequent extreme events, strongly associated with the exacerbation of bushfire events. Such results suggest an urgent need to prioritize emission control, building air tightness improvement and urban heat mitigation, towards a future-proof governance in Sydney and similar regions in the world.

Holistic approach to assess co-benefits of local climate mitigation in a hot humid region of Australia.

Overheated outdoor environments adversely impact urban sustainability and livability. Urban areas are particularly affected by heat waves and global climate change, which is a serious threat due to increasing heat stress and thermal risk for residents. The tropical city of Darwin, Australia, for example, is especially susceptible to urban overheating that can kill inhabitants. Here, using a modeling platform supported by detailed measurements of meteorological data, we report the first quantified analysis of the urban microclimate and evaluate the impacts of heat mitigation technologies to decrease the ambient temperature in the city of Darwin. We present a holistic study that quantifies the benefits of city-scale heat mitigation to human health, energy consumption, and peak electricity demand. The best-performing mitigation scenario, which combines cool materials, shading, and greenery, reduces the peak ambient temperature by 2.7 °C and consequently decreases the peak electricity demand and the total annual cooling load by 2% and 7.2%, respectively. Further, the proposed heat mitigation approach can save 9.66 excess deaths per year per 100,000 people within the Darwin urban health district. Our results confirm the technological possibilities for urban heat mitigation, which serves as a strategy for mitigating the severity of cumulative threats to urban sustainability.

Predicting the magnitude and the characteristics of the urban heat island in coastal cities in the proximity of desert landforms. The case of Sydney.

The urban heat island is a vastly documented climatological phenomenon, but when it comes to coastal cities, close to desert areas, its analysis becomes extremely challenging, given the high temporal variability and spatial heterogeneity. The strong dependency on the synoptic weather conditions, rather than on city-specific, constant features, hinders the identification of recurrent patterns, leading conventional predicting algorithms to fail. In this paper, an advanced artificial intelligence technique based on long short-term memory (LSTM) model is applied to gain insight and predict the highly fluctuating heat island intensity (UHII) in the city of Sydney, Australia, governed by the dualistic system of cool sea breeze from the ocean and hot western winds from the vast desert biome inlands. Hourly measurements of temperature, collected for a period of 18 years (1999-2017) from 8 different sites in a 50 km radius from the coastline, were used to train (80%) and test (20%) the model. Other inputs included date, time, and previously computed UHII, feedbacked to the model with an optimized time step of six hours. A second set of models integrated wind speed at the reference station to account for the sea breeze effect. The R2 ranged between 0.770 and 0.932 for the training dataset and between 0.841 and 0.924 for the testing dataset, with the best performance attained right in correspondence of the city hot spots. Unexpectedly, very little benefit (0.06-0.43%) was achieved by including the sea breeze among the input variables. Overall, this study is insightful of a rather rare climatological case at the watershed between maritime and desertic typicality. We proved that accurate UHII predictions can be achieved by learning from long-term air temperature records, provided that an appropriate predicting architecture is utilized.