Search for: All records

Abstract Increasing the albedo of urban surfaces, through strategies like white roof installations, has emerged as a promising approach for urban climate adaptation. Yet, modeling these strategies on a large scale is limited by the use of static urban surface albedo representations in the Earth system models. In this study, we developed a new transient urban surface albedo scheme in the Community Earth System Model and evaluated evolving adaptation strategies under varying urban surface albedo configurations. Our simulations model a gradual increase in the urban surface albedo of roofs, impervious roads, and walls from 2015 to 2099 under the SSP3‐7.0 scenario. Results highlight the cooling effects of roof albedo modifications, which reduce the annual‐mean canopy urban heat island intensity from 0.8°C in 2015 to 0.2°C by 2099. Compared to high‐density and medium‐density urban areas, higher albedo configurations are more effective in cooling environments within tall building districts. Additionally, urban surface albedo changes lead to changes in building energy consumption, where high albedo results in more indoor heating usage in urban areas located beyond 30°N and 25°S. This scheme offers potential applications like simulating natural albedo variations across urban surfaces and enables the inclusion of other urban parameters, such as surface emissivity. 

Abstract Cities are concentrators of complex, multi‐sectoral interactions. As keystones in the interconnected human‐Earth system, cities have an outsized impact on the Earth system. We describe a multi‐lens framework for organizing our understanding of the complexity of urban systems and scientific research on urban systems, which may be useful for natural system scientists exploring the ways their work can be made more actionable. We then describe four critical dimensions along which improvements are needed to advance the urban research that addresses urgent climate challenges: (a) solutions‐oriented research, (b) equity‐centered assessments which rely on fine‐scale human and ecological data, (c) co‐production of knowledge, and (d) better integration of human and natural systems occurring through theory, observation, and modeling. 

Abstract India is largely devoid of high‐quality and reliable on‐the‐ground measurements of fine particulate matter (PM_2.5). Ground‐level PM_2.5concentrations are estimated from publicly available satellite Aerosol Optical Depth (AOD) products combined with other information. Prior research has largely overlooked the possibility of gaining additional accuracy and insights into the sources of PM using satellite retrievals of tropospheric trace gas columns. We evaluate the information content of tropospheric trace gas columns for PM_2.5estimates over India within a modeling testbed using an Automated Machine Learning (AutoML) approach, which selects from a menu of different machine learning tools based on the data set. We then quantify the relative information content of tropospheric trace gas columns, AOD, meteorological fields, and emissions for estimating PM_2.5over four Indian sub‐regions on daily and monthly time scales. Our findings suggest that, regardless of the specific machine learning model assumptions, incorporating trace gas modeled columns improves PM_2.5estimates. We use the ranking scores produced from the AutoML algorithm and Spearman’s rank correlation to infer or link the possible relative importance of primary versus secondary sources of PM_2.5as a first step toward estimating particle composition. Our comparison of AutoML‐derived models to selected baseline machine learning models demonstrates that AutoML is at least as good as user‐chosen models. The idealized pseudo‐observations (chemical‐transport model simulations) used in this work lay the groundwork for applying satellite retrievals of tropospheric trace gases to estimate fine particle concentrations in India and serve to illustrate the promise of AutoML applications in atmospheric and environmental research. 

Abstract Silk nanofibers (SNFs) from abundant sources are low‐cost and environmentally friendly. Combined with other functional materials, SNFs can help create bioelectronics with excellent biocompatibility without environmental concerns. However, it is still challenging to construct an SNF‐based composite with high conductivity, flexibility, and mechanical strength for all SNF‐based electronics. Herein, this work reports the design and fabrication of Ti₃C₂Tx‐silver@silk nanofibers (Ti3C2Tx‐Ag@SNF) composites with multi‐dimensional heterogeneous conductive networks using combined in situ growth and vacuum filtration methods. The ultrahigh electrical conductivity of Ti₃C₂T_x‐Ag@SNF composites (142959 S m⁻¹) provides the kirigami‐patterned soft heaters with a rapid heating rate of 87 °C s⁻¹. The multi‐dimensional heterogeneous network further allows the creation of electromagnetic interference shielding devices with an exceptionally high specific shielding effectiveness of 10,088 dB cm⁻¹. Besides working as a triboelectric layer to harvest the mechanical energy and recognize the hand gesture, the Ti₃C₂T_x‐Ag@SNF composites can also be combined with an ionic layer to result in a capacitive pressure sensor with a high sensitivity of 410 kPa⁻¹in a large range due to electronic‐double layer effect. The applications of the Ti₃C₂T_x‐Ag@SNF composites in recognizing human gestures and human‐machine interfaces to wirelessly control a trolley demonstrate the future development of all SNF‐based electronics.