Abstract Wildfires are a growing threat to roadway infrastructure. While causing minimal direct damage, wildfires cause substantial indirect damage, including through post-fire debris flows (PFDFs), which occur when moderate intensity rainfall occurs over recently burned areas where soil has been destabilized. PFDFs damage pavement systems, block drains and culverts, and disrupt roadway services. Wildfire frequency and intensity and rainfall intensity are evolving with climate change, which may lead to increased PFDF threats. Public agencies managing roadways need tools to support decision-making that accounts for limited resources and appropriately mitigates fire and debris flow risk. While there have been targeted assessments of fire and PFDF threat in specific areas of Arizona, there is no comprehensive assessment of statewide threat. In this work, we create a statewide assessment of roadway vulnerability to wildfire and PFDFs in Arizona in current conditions and future climate scenarios. We use a state-of-the-art regression-based model to estimate debris flow likelihood on each roadway segment in the state. Our model adapts novel geological methods to assess PFDF threats and engineered infrastructure data to characterize roadway threats. PFDF threat is affected by terrain ruggedness, burn intensity, soil characteristics, and rainfall intensity. Vulnerability of roadways is assessed by overlaying PFDF threat, roadway criticality (using betweenness centrality), and traffic data. We identify roadways most vulnerable in current conditions and estimate how threats change in future climate scenarios. Our results indicate roadways facing the highest PFDF threat are concentrated in the rugged mountains of Southeastern Arizona, the White Mountains of Eastern Arizona, and the Mogollon Rim in Central Arizona. We find that projected changes in precipitation patterns lead to an increase in PFDF threat in much of the state. The framework and results will provide guidance for agencies and decision-makers on where to focus their resources to mitigate the evolving threats of PFDFs.

Abstract The range of potential environmental hazards to which public transit riders and operators are exposed include vibration, noise, and air pollution. Understanding these conditions - and their interactions - at a granular level across geographically expansive networks is critical to assessing, designing, and evaluating options for infrastructure improvements. However, the majority of studies to date have focused on a single or few parameters at varied spatial and temporal scales. This work presents a cost-effective and semi-automated approach to simultaneously measuring vibration, noise, and air pollution (particulate matter) at a resolution sufficient to resolve differences in rider experience on a segment-by-segment basis (≥1 sample·min −1 ). The system was validated through a study of the MBTA urban rapid transit system (Boston, MA). Vibration, noise, and particulate matter hotspots were identified. Vibration data grouped by line, perhaps pointing to differences in train or track characteristics within the transit network, while noise hotspots were not predicted by studied system characteristics and therefore are likely driven by track-specific issues (e.g., aging, curves). Air pollution levels were systematically higher (1) during peak ridership hours and (2) when trains traveled through underground sections of the system, with an abrupt change at the segment where trains transitioned between below and above ground. Correlations are observed between hazards, especially between air pollution and noise (R=0.54), implying that targeted interventions (e.g. related to braking or rail systems) could be especially effective in improving the experience of the rider, especially in underground sections where there is
less ventilation and more reverberation. This study presents the utility of a multihazard approach for improving understanding of the rider experience in public transit systems and as a method for informing transition system management, suggesting an important next step of assessment by infrastructure management experts to develop processes for converting data-driven insights into candidate system improvements.

Abstract The quantification of residential water end uses is an important component of improving the sustainability of urban water infrastructure. Disaggregation and classification methods based on statistical learning are used in research and practice to extract meaningful insights from smart water meter data. These insights can also reflect individual behaviors within the built environment, enabling end-user activity detection from water consumption patterns. In this study, we present an initial framework for classifying residential water end uses and assisting with discerning between perceived typical and atypical water-use behavior in a permanent supportive housing context. Classification schemes, based on fine-resolution temporal flow data, incorporated baseline activity to inform what typical water use was for individuals while also considering general trends in specific end uses such as showers, toilet flushes, and leaks. We found that while atypical activity based on end-use duration and frequency might fall outside the normally-distributed expected value for a period of interest, it need not be the case for all atypical activity. Defining atypical activity based on prescriptive guidelines might not align with normative behavior for an occupant transitioning into housing. Additionally, exogenous variables can affect occupant behavior regarding water end uses and this impact should be accounted for in analytical frameworks. Our findings can specifically inform supportive services provided by stakeholders responsible for the well-being of individuals in their care via non-intrusive, privacy-respecting insights on occupant behavior.

Abstract This perspective uses five recently published data points to explore why local action on urban nature in Africa is important for the world, and what we can all do about it.

Abstract Climate change is expected to increase the severity of hurricanes and tropical storms, posing significant risks to the electricity grid. These include downed power lines, damaged solar panels, and impaired wind turbines from high winds. New York (NY) and New Jersey (NJ) are not spared from these vulnerabilities and must strengthen their infrastructure and mitigate social and technical impacts. Clean energy mandates, such as NJ’s Executive Orders No. 315 and 307 (100% clean energy by 2035 and 11 GW of offshore wind by 2040), and NY’s Executive Order No. 166 (40% emissions reduction by 2030), add urgency to ensuring grid resilience under extreme weather. This study demonstrates the power system cyclone impact model (PCIM), used alongside the GenX electricity system planning tool, to assess grid resilience under hurricane-induced high wind speeds in the NY and NJ region. Results reveal that onshore and offshore wind could contribute additional power during storms, provided transmission and storage systems remain operational. This output helps offset outages elsewhere in the grid across all storm categories. In contrast, solar emerges as a vulnerability due to combined impacts from wind stress and cloud cover, significantly reducing generation during and after storms. Thermal generators show the lowest failure rates, though this may partly reflect current model limitations, as only wind stress and cloud cover are considered, excluding hazards like flooding. Non-served energy costs vary with electricity demand and fluctuations in wind and solar output. July stands out as the most vulnerable month, due to high demand and limited wind generation, leading to higher non-served energy. This research provides a first step toward understanding storm-related grid resilience in NJ and NY. The PCIM is designed to be generalizable, with future work focused on expanding its scope to include additional hazards like storm surge and flooding, and more storm-prone regions.

Abstract Despite major progress in electricity access and renewable energy deployment over recent decades, Senegal continues to face challenges in achieving universal rural electrification, a country where 55% of the rural population lack access. A core issue is the absence of a coherent regulatory framework for rural electrification. Initiatives are fragmented, often pursue competing priorities, and involve a complexity of different actors and institutions. Within this context, mini grids have been promoted by donors and international organisations as a promising solution, and Senegal was once considered a regional leader in this sector. Today, most planned projects are solar or solar–diesel hybrid, as compared to the original diesel-generated systems. However, not only are a significant portion of the previously installed projects presumed to no longer be operational, but their actual and projected contribution to Senegal’s rural electrification rate is relatively small. In the case of mini grids more specifically, there have been inconsistencies with regards to licensing, tariff-setting and arrangements for the arrival of the main grid. Processes and standards for their installation, operation, maintenance and ownership have been carried out in a somewhat haphazard way. Despite the introduction of regulation intended to support an increased role for the private sector in the electricity sector more generally, most mini grids that have been developed to date have been government-owned and donor-funded. Private sector involvement has been largely confined to engineering, procurement, and construction, and operation and maintenance. With this in mind, this paper critically examines the political, institutional, and regulatory barriers to rural electrification in Senegal. It highlights the tension between grid extension and the introduction of decentralised/off-grid systems, finding a significant mismatch between donor ideals and expectations on the one hand and the preferences of the state utility, as well as national and local governments on the other.