Achieving a climate-resilient future requires rapid, sustained and far-reaching transformations in energy, land-use, infrastructure and industrial systems. Large-scale expansion of renewable energy can play a critical role in meeting the world’s growing energy demands and in the fight against climate change. However, even ‘clean’ energy sources can have significant unintended impacts on the environment. The guidelines aim to provide practical support for solar and wind energy developments by effectively managing risks and improving overall outcomes related to biodiversity and ecosystem services. They are industry-focused and can be applied across the whole project development life cycle, from early planning through to decommissioning and repowering, using the mitigation hierarchy as a clear framework for planning and implementation. The mitigation hierarchy is applied to direct, indirect and cumulative impacts.

Until the end of 2001, commercial air travel had experienced a substantial growth, increasing fourfold in three decades. However, concerns for the health and safety of air quality in the aircraft cabin environment raised by ight crews and passengers also increased greatly. Consequently, the US Congress twice directed the Federal Aviation Administration to commission the National Research Council to study this problem. The concerns included low ventilation rate (less than 5 l/s per person); reduced partial pressure of oxygen and its effect on susceptible people; very low relative humidities (10-20 per cent); ozone (sometimes over 100 ppb); cosmic radiation, which increases with altitude and latitude; fumes from breakdown products of leaks of lubricants and hydraulic uids; and the airborne spread of infectious diseases. The outbreak of severe acute respiratory syndrome (SARS) and its apparent transmission by and within aircraft in 2003 highlighted the importance of ventilation, ltration and disinfection systems on aircraft, which are reviewed in this paper. This paper is based, in part, upon the NRC 1986 and NRC 2002 reports, in which coauthor John Spengler participated as cochair (1986) and member (2002).

Urban Internet of Things (IoT) is in an early speculative phase. Often linked to the smart city movement, it provides a way of sensing and collecting data—environmental, societal, and transitional—both automatically, remotely, and with increasing levels of spatial and temporal detail. From city-wide data collection down to the scale of individual buildings and rooms, this chapter details the technology behind the rise of IoT in urban areas and explores the challenges (societal and technical) behind city-wide deployments. Drawing from a series of deployments at the Queen Elizabeth Olympic Park, London, it details the challenges and opportunities for mass data collection. Widening out the view, it looks at what is becoming known as “the humble lamp post” in Urban IoT fields to detail the potential of Urban IoT with the objects that already form part of the urban fabric. Finally, it examines the potential of Urban IoT for input into urban modeling and how we are on the edge of a shift in the collection, analysis, and communication of urban data.

Biodiversity surveys are often required for development projects in cities that could affect protected species such as bats. Bats are important biodiversity indicators of the wider health of the environment and activity surveys of bat species are used to report on the performance of mitigation actions. Typically, sensors are used in the field to listen to the ultrasonic echolocation calls of bats or the audio data is recorded for post‐processing to calculate the activity levels. Current methods rely on significant human input and therefore present an opportunity for continuous monitoring and in situ machine learning detection of bat calls in the field. Here, we show the results from a longitudinal study of 15 novel Internet connected bat sensors—Echo Boxes—in a large urban park. The study provided empirical evidence of how edge processing can reduce network traffic and storage demands by several orders of magnitude, making it possible to run continuous monitoring activities for many months including periods which traditionally would not be monitored. Our results demonstrate how the combination of artificial intelligence techniques and low‐cost sensor networks can be used to create novel insights for ecologists and conservation decision‐makers.