Local Decision Research Articles

Multi-Objective Cooperative Adaptive Cruise Control Platooning of Intelligent Connected Commercial Vehicles in Event-Triggered Conditions

With the rapid increase in vehicle ownership and increasingly stringent emission regulations, addressing the energy consumption of and emissions from commercial vehicles have become critical challenges. This study introduces a multi-objective cooperative adaptive cruise control (CACC) strategy, designed for intelligent connected commercial vehicle platoons, operating in event-triggered conditions. A hierarchical control framework is utilized: the upper layer handles reference speed planning based on vehicle dynamics and constraints, while the lower layer uses distributed model predictive control (DMPC) to manage vehicle following. DMPC is chosen for its ability to manage distributed platoons by enabling vehicles to make local decisions, while maintaining system-wide coordination. Additionally, adaptive particle swarm optimization (APSO) is employed during the optimization process to solve the optimal problem efficiently. APSO is employed for its computational efficiency and adaptability, ensuring quick convergence to optimal solutions with reduced overheads. An event-triggering mechanism is integrated to further reduce the computational demands. The simulation results show that the proposed approach reduces fuel consumption by 8.05% and NOx emissions by 10.15%, while ensuring stable platoon operation during dynamic driving conditions. The effectiveness of the control strategy is validated through extensive simulations, highlighting superior performance compared to conventional methods.

Gridded global dataset of industrial water use predicted using the Random Forest

Spatially distributed industrial water use (IWU) data are essential for effective region-specific water resource management. Such data are often scarce in underdeveloped and developing countries. We propose a random forest regression model to predict IWU at a spatial resolution of 0.5° by combining socioeconomic, climatic, and geographical datasets. These datasets included nighttime light (NL), global power plants, country-wise IWU, elevation data (DEM), gross domestic product (GDP), road density (RD), cropland (CRP), wetland (WLND), population (POP), precipitation (PCP), temperature (TEMP), wet days (WET) per year, and potential evapotranspiration (PET). The results show that RD, CRP, POP, GDP, DEM, and TEMP were the most influential variables. We assessed the accuracy of the global IWU map using published and observed datasets from various sources for the major industrialized countries such as the USA and China from 2000 to 2015. The predicted global map shows a reasonable distribution of grid-wise values for highly industrialized countries and data-scarce regions. Thus, fine-resolution maps can support local planning and decision-making for large basins worldwide.

Examining the Priorities of Local Health Departments in California: A Mixed Methods Study.

Prioritization is an essential task of local health departments (LHDs). We examined the alignment of priorities reported in Community Health Needs Assessments (CHNA) priorities align with priorities in the Community Health Improvement Plans (CHIP). We report factors that influence the choice of priorities and the alignment of the priorities. A mixed method study design. California. CHNA and CHIP documents were sought for all 58 counties. Interviews were conducted with 19 state and local public health officials. The priorities in the CHNA and the CHIP were coded as i) only in the CHNA, ii) only in the CHIP, or iii) in both the CHNA and the CHIP. The interviewees were asked to share their experiences related to issue prioritization and decision-making in public health agencies. The interviews were coded and thematically analyzed to identify barriers and facilitators of the prioritization process. The alignment between the needs prioritized in CHNA and the priorities targeted in CHIP was 35%. The interviews identify reasons for the misalignment, including a need to include priorities in the CHNA even though LHDs are not able to address them, political factors that influence the selection of priorities, and a lack of discretionary funding or capacity/expertise within the agency or its community partners to respond to the needs identified. The lack of discretionary funding was particularly acute for smaller (rural) LHDs (CMSP) and resulted in their often having to focus on priorities where there was state or federal funding. LHDs face numerous challenges in aligning the priorities reported in the CHNA and the priorities they focus upon in the CHIP. LHDs should consider using a formal, transparent, and evidence-based approach to setting aligning. Future research should focus on developing a formal decision-making process that is appropriate for local public health decision-making.

Optimizing China's economic performance through government hierarchy restructuring

AbstractChina's Province‐managing‐county (PMC) reform policy aims to mitigate fiscal stress and augment expenditure autonomy for county governments by eradicating the subordinate fiscal relationship between prefectures and counties. The research augments the time‐varying difference‐in‐difference technique, utilizing a longitudinal dataset at the county level to examine the incentive effects of this reform on economic performance and local government policy decision. The empirical investigation divulges that the local fiscal incentives introduced by this reform through alleviating fiscal stress and providing expenditure autonomy significantly impact economic performance and influence local government policy decisions. The outcomes become more noticeable in economically developed regions and those characterized by enhanced institutional quality and reduced migration barriers. Additionally, the reform encourages counties to adopt a more proactive fiscal policy characterized by increased capital expenditure to promote economic performance while reducing expenditures on social security subsidies and administrative costs affirming that economic performance targets of county governments garbles the composition of public expenditure. These results support positing that a flattened hierarchical structure ameliorates delays in fiscal matters and facilitates communication across various levels of government in the context of improved institutional quality and reduced migration barriers however at the expense of reduced public expenditures. The findings additionally signifies that PMC policy reform become more incentivized in the political competition for economic performance.

Local knowledge, formal evidence, and policy decisions

Local knowledge, formal evidence, and policy decisions

Enhancing flood forecasting accuracy in Data-Scarce regions through advanced modeling approaches

Flood forecasting in data-scarce regions poses significant challenges due to irregular rainfall patterns and limited hydrological monitoring networks, particularly in semi-arid regions in Africa, South America, and Asia. However, despite significant efforts and advancements, there remains a substantial gap in the accurate prediction of flood events necessary for effective risk management and mitigation, evidenced by the recurrence of devastating floods in middle to low-income countries in recent years. Here, we address this problem by testing advanced modeling techniques in a local African case, using a combination of statistical methods for extreme event prediction, hydrodynamic modeling, and remote sensing data, to recommend the most adapted and accurate approach under a variety of settings. Our case study is an emerging urban area in Northern Morocco, situated in a triangular plain interposed between adverse geomorphological and precipitation settings, and unregulated expansion flow, creating an exceptionally overwhelming context for disastrous floods. In the absence of previous studies, we integrate frequency distribution analysis to predict extreme rainfall events and flood flow modeling to simulate floodplain inundation. Data sources included high-resolution remote sensing, local hydrological measurements, fine topographical data, and interviews with stakeholders. We found the Pearson Type 3 distribution to be the most suitable for modeling extreme precipitation in coastal areas, whereas the Generalized Extreme Value (GEV) distribution better fits inland areas. For flood flow assessment, the Gradex method proved to be the most accurate, while other empirical methods outlined critical limitations. Findings reveal that advanced hydrodynamic models significantly enhance flood hazard assessments, even in regions with limited data, showing outstanding correlations with previous flood records and stakeholder feedback. The outcomes carry critical implications for highlighting the importance of selecting appropriate models based on geographical and climatic conditions to inform more resilient urban planning and disaster management practices. We anticipate that these insights will support local decision-makers and urban planners in developing strategies that enhance community resilience and reduce the adverse impacts of flooding. Our work contributes to the broader field of flood risk management, providing a foundation for future developments and practical applications in similar regions worldwide.

Using random forests to explore the feasibility of groundwater knowledge transfer between the contiguous US and Denmark

Abstract Groundwater is our largest freshwater reservoir, playing an important role in the global hydrologic cycle. Lack of reliable groundwater data restricts the development of global groundwater monitoring systems linking observations with modeling at spatial scales relevant for local decision making. Despite the growing interests in machine learning (ML) for groundwater resource modeling, taking ML models to the global scale is still outstanding due to sparse groundwater data. The contiguous US (CONUS) has extensive groundwater information covering a wide range of hydrogeologic settings. We hypothesize that a ML model trained on the CONUS is transferable to other regions, and thus can be used to produce a global water table depth (WTD) map within the bounds of transferability. To test this hypothesis, we conduct a study on transferring groundwater knowledge between the CONUS and Denmark, using several random forest models trained against ∼30 m resolution long-term mean WTD data. The joint model trained on data from the CONUS and Denmark outperforms the individual models trained separately, implying similarities within global groundwater systems. The largest improvement occurs in Denmark, where the testing Nash-Sutcliffe efficiency rises from 0.68 to 0.95. SHapley Additive exPlanations (SHAP) values are utilized to express the importance of input variables. While annual mean precipitation plays a key role in the joint model and the model for the CONUS, it is the second least important input variable in the model for Denmark where local processes dominate. Moreover, Köppen-Geiger climate classification shows a significant impact on the model testing performance and the importance ranking of input variables, which might be a missing input variable in the applied random forest models. This study provides unique insights into future ML model developments towards global groundwater monitoring and improves our confidence in producing a hyper-resolution global WTD map for sustainable freshwater management.

The application of analytical hierarchy process to assess adaptation strategies for flood and landslides risks: A case study of a multi-risk area community

The increasing frequency of flood-related emergencies has heightened discussions on climate adaptation and risk management. Despite investments in infrastructure, early warning systems, urban planning improvements, and awareness initiatives, sustained attention and action are essential. The efficacy of preventive measures relies heavily on local community decisions and their cooperation with authorities, impacting the entire risk management cycle. This study investigates the factors influencing flood and landslide adaptation strategies and their perception by citizens in vulnerable areas, aiming to identify key variables that shape citizen behaviour for effective planning and management of adaptation actions. Focusing on Ischia Island, Italy, a multi-risk area, the study applied the Analytic Hierarchy Process (AHP) in focus groups to achieve three objectives: i) Highlight the critical role of citizen perceptions in forming adaptation strategies, emphasizing the need for cohesive efforts among policymakers, experts, and communities to enhance resilience. ii) Examine residents' views on flood and landslide risks to understand local concerns and vulnerabilities. iii) Use multi-criteria analysis to evaluate the importance of factors shaping adaptation strategies, providing a systematic framework for decision-making in disaster risk management. Findings reveal an optimism bias in respondents' perceptions of their home security despite recognizing the area's vulnerability and mistrust in authorities. Environmental and social factors are deemed most important, with environmental strategies focused on harm reduction and local environment enhancement, and social strategies emphasizing information dissemination and mortality reduction. This research offers valuable insights into community perceptions and factors influencing adaptation strategies for flood and landslide risks.

Integrating Water Quality Index (WQI) and Multivariate Statistics for Regional Surface Water Quality Evaluation: Key Parameter Identification and Human Health Risk Assessment

Assessing regional water quality and evaluating the associated risks to human health posed by aquatic contaminants are paramount for conserving and managing surface water resources and formulating effective local policy decisions. This study utilizes 17 water quality parameters collected monthly from nine national monitoring stations in Nanning City, China, from January 2021 to March 2023, i.e., water temperature (WT), pH, dissolved oxygen (DO), permanganate index (CODMn), chemical oxygen demand (COD), five-day biochemical oxygen demand (BOD5), ammonia nitrogen (NH3-N), total phosphorus (TP), copper (Cu), zinc (Zn), fluoride (F-), selenium (Se), arsenic (As), mercury (Hg), cadmium (Cd), chromium (Cr), and lead (Pb). Analyses were performed utilizing the Water Quality Index (WQI) and multiple stepwise regression to ascertain seasonal and spatial variations in water quality and to identify key water quality parameters. Human health risks were evaluated, focusing on eight heavy metals. The results indicated that the average WQI for the designated area was 94.1, with individual monitoring stations displaying WQIs ranging from 93.22 to 96.44, categorizing the water quality as “excellent”. The WQI exhibited seasonal fluctuations, peaking in spring and winter while decreasing in summer and autumn. All measured parameters met national standards for Class I and II surface waters. The stepwise regression analysis facilitated the construction of minimized WQI models (WQImin) derived from three different training and testing datasets, with a WQImin model incorporating six key parameters, i.e., DO, CODMn, NH3-N, Hg, WT, and Se. The concentration of heavy metals in the water exhibited a sequence of Zn (3.35 µg/L) > Cr (2.00 µg/L) > Cu (1.36 µg/L) > As (1.29 µg/L) > Se (0.32 µg/L) > Pb (0.17 µg/L) > Cd (0.03 µg/L) > Hg (0.01 µg/L), with all metals adhering to the Class I standard. Children are more susceptible to the adverse effects of heavy metal contamination than adults, exhibiting a total environmental health risk of 1.28 times greater. Cr was identified as the predominant contributor to the aggregate health risk, accounting for over 80% of the risk in adults and children, followed by As (19.1%). Future protection efforts are recommended to prioritize the control of Cr and As concentrations in Nanning City.

Quality matters: A comprehensive comparative study of edge computing simulators

Edge computing, by pushing resources closer to the network’s edge, is revolutionizing data processing, enabling real-time analysis and localized decision-making on resource-constrained devices. However, designing and validating innovative solutions within this dynamic environment presents significant challenges.While numerous edge simulators exist, their shortcomings often restrict researchers’ ability to address their research questions. Many of them serve specific use cases (e.g., small scale scenarios, static application placement, etc.), lacking the breadth and flexibility needed for diverse research requirements. Furthermore, the lack of benchmarking studies complicates the process of selecting the right simulators for a specific need.This study addresses these limitations by presenting a comprehensive evaluation of 24 edge computing simulators, filling a critical gap in the literature. We propose a robust methodology that assesses the simulators based on functionality, maintenance, scalability, reliability, efficiency, and usability. Our study provides clear classifications and comparisons of the simulators to assist researchers in selecting the most suitable one for their edge computing scenarios. Additionally, we identify and discuss opportunities and challenges for advancing the current state of the art in edge simulation, highlighting potential areas for future research.

Exploring the impact of locking down citizen’s voice on citizen participation in local government budgeting during COVID-19 public restrictions in Zimbabwe

The COVID-19 pandemic presented unmatched challenges for citizen participation worldwide in local government budgeting processes. Zimbabwe in particular was grappling with the pandemic’s effects and pre-existing governance issues. The pandemic ignited the central government to put COVID-19 public restrictions militating on citizen engagement in the budgeting process. The implemented measures were aimed at curbing the spread of the virus. While these measures were necessary for public health, they unintentionally constrained citizens’ ability to be unreservedly involved in local government decision-making. The paper employed a qualitative case study research design, employing interviews, Google Forms, and document analysis, to gather data exploring the experiences and perspectives of citizens, civil society organizations, and local government officials regarding the repercussions of constrained citizen participation in local government budgeting during the pandemic. The argument was hinged on the participatory theory based on two broad views: the normative and the instrumentalist perspectives. The paper notes that COVID-19 restrictions severely constrained citizen participation, limiting public input, deliberation, and accountability opportunities. The inability to convene public meetings, consultations, and workshops weakened the citizen-government engagement process, hindering transparency and the ability of citizens to influence resource allocation and stewardship. Furthermore, the restricted participation by marginalized communities exacerbates existing inequalities due to the technological divide hindering their ability to voice their concerns and interests in the budgeting processes. The paper calls for the exigent need for inventive methodologies to guarantee comprehensive and evocative citizen participation, by leveraging digital technologies and promoting alternative channels for engagement to augment citizen participation in local government budgeting.

Spatio-temporal variability and trends of air pollutants in the Metropolitan Area of Curitiba

Monitoring air pollutants over time is essential for identifying and addressing trends, which may help improve air quality management and safeguard public health. This study investigates the spatio-temporal variability of air quality in the Metropolitan Area of Curitiba (MAC), Brazil, focusing on six pollutants (SO2, NO2, NOx, O3, CO, and PM10) measured at eight monitoring stations from 2003 to 2017. We conducted statistical analyses, including diurnal cycles, seasonal variability, spatio-temporal correlations, conditional bivariate probability functions, Theil-Sen trend analysis, and comparison with national quality standards (NAQS) and World Health Organization (WHO) guidelines. The analyses revealed large variations in pollutant concentrations across the study area. For instance, stations strongly impacted by industrial emissions presented the highest mean annual SO2 (20–28 μg/m3) and PM10 (32–34 μg/m3) concentrations, while those mostly impacted by traffic showed elevated NO2 (31–39 μg/m3), NOx (63–86 μg/m3) and CO (0.6–0.8 mg/m3) concentrations. The two residential stations recorded the highest O3 concentrations (annual mean of 30–32 μg/m3). Seasonal and diurnal patterns varied by pollutant, with winter experiencing higher concentrations and O3 peaking in spring. SO2 concentrations presented no clear seasonal or diurnal cycle patterns, and showed the highest spatial variability. Significant decreasing annual trends were observed for SO2 (−5.9%), NO2 (−2.6%), NOx (−2.6%), CO (−5.4%), and PM10 (−3.7%), which suggests the success of emission reduction programs implemented in the road transportation and industrial sectors. However, O3 concentrations increased at most stations (+3.3%/yr), likely due to reduced NOx emissions, increased emissions of volatile organic compounds from on-road transport biofuels, and regional O3 transport. Although exceedances of NAQS decreased over time, concentrations of most pollutants remained above WHO guidelines, except for CO. These results highlight the importance of targeted emission control strategies for both industrial and vehicular sources to improve local air quality and inform future policy decisions.

Reflective Tool on Advanced Access to Support Primary Healthcare Teams: Development and Validation of an Online Questionnaire.

Awareness of their standing relative to best practices motivates primary healthcare (PHC) teams to improve their practices. However, gathering the data necessary to create such a portrait is a challenge. An effective way to support the improvement of the practices of PHC teams is to simplify the availability of data portraying aspects of their practices that might need improvement. Timely access is one of the foremost challenges of PHC. Yet, very few tools supporting reflections on the implementation of best practices to improve access are available to PHC teams. To develop an online reflective tool that evaluates the state of a PHC team member's advanced access practice and formulates customized recommendations for improvement. This sequential multimethod study was informed by a literature review and an expert panel composed of researchers, patients, provincial and local decision-makers, and PHC clinical and administrative staff in the province of Quebec, Canada. Consensus was reached on the content of the questionnaire and the prioritization of the recommendations. No reflective tool on advanced access practices was found in the literature review. Grey literature was used to create an initial version of the questionnaire. This version was revised and enriched through consultation phases with the expert panel. Then, five iterations of the tool were tested with 169 PHC team members, which led to the conception of two distinct versions: one for clinical staff and one for administrative agents responsible for appointment booking. The final versions of the reflective tool are available online in both English and French. This reflective tool provides a portrait of PHC team members' advanced access practices as well as an automated report that contains personalized and prioritized recommendations for improvement. Further developments are necessary for its optimal use among PHC professionals other than physicians and nurse practitioners.

Predicting corrosion current density in magnesium alloy battery anodes: machine learning approach using rapid miner

ABSTRACT Magnesium (Mg) alloys are increasingly recognised for their potential as anode materials in batteries due to their high specific capacity and cost-effectiveness. However, their susceptibility to corrosion poses a significant challenge to practical applications. This study explores the effectiveness of various machine learning models – local polynomial regression, Decision Trees, Random Forest, Gradient Boost, and Extreme Gradient Boost – in predicting the corrosion behaviour of Mg alloy battery anodes in a 3.5 wt.% NaCl electrolyte. Experimental data on the corrosion current density of Mg alloys are collected and analysed using the RapidMiner tool for model training and evaluation. The findings highlight that the Extreme Gradient Boost (XGBoost) model excels among other machine learning techniques in accurately forecasting the corrosion rates of Mg alloys. XGBoost achieves an exceptional R2 value of 0.9931 in Tafel plots, indicating a robust fit to the data as evidenced by scatter plots. Furthermore, the XGBoost model demonstrates strong positive monotonic and ordinal relationships between actual and predicted corrosion current densities, as indicated by a Spearman correlation coefficient of 1 and a Kendall tau coefficient of 0.99. These outcomes underscore XGBoost's potential as a powerful tool for enhancing the understanding and management of corrosion in Mg alloy battery applications.

Using the R Package, nsink, to assess landscape N removal in coastal catchments.

Excess nitrogen (N) loading to coastal ecosystems impairs estuarine water quality. Land management decisions made within estuarine watersheds have a direct impact on downstream N delivery. Natural features within watersheds can act as landscape sinks for N, such as wetlands, streams and ponds that transform dissolved N into gaseous N, effectively removing it from the aquatic system. Identifying and evaluating these landscape sinks and their spatial relationship to N sources can help managers understand the effects of alternative decisions on downstream resources. The N-Sink approach uses widely available GIS data to identify landscape sinks within HUC-12 (or larger) catchments, estimate their N removal potential and map the effect of those sinks on N movement through the catchment. Static maps are produced to visualize N removal efficiency, transport and delivery, the latter in the form of an index. The R package nsink was developed to facilitate data acquisition, processing and visualization. nsink creates static maps for a specific HUC-12, or users can visit the University of Connecticut website to explore previously mapped areas. Users can investigate specific flowpaths interactively by clicking on any location within the catchment. A flowpath is generated with a table describing N removal along each segment. We describe the motivation behind developing nsink, discuss implementation in R, and present two use case examples. nsink is available from https://github.com/USEPA/nsink. N-Sink is a decision support tool created for local decision-makers and NGOs to facilitate better understanding of the relationship between land use and downstream N delivery. Local decision-makers that have prioritized N mitigation in their long-term planning can use nsink to better understand the potential impact of proposed development projects, zoning variances, and land acquisition or restoration. nsink also allows resource economists to investigate the tradeoffs among different, often costly, N reduction strategies.

Observational study of ground-level ozone and climatic factors in Craiova, Romania, based on one-year high-resolution data.

Air pollution is a multifaceted issue affecting people's health, environment, and biodiversity. Gaining comprehension of the interactions between natural and anthropocentric pollutant concentrations and local climate is challenging. This study aims to address the following two questions: (1) What is the influential mechanism of climatic and anthropogenic factors on the ground-level ozone (O3) concentrations in an urban environment during different seasons? (2) Can the ozone weekend effect be observed in a medium-sized city like Craiova, and under which conditions? In order to answer these questions, ozone interactions with meteorological parameters (temperature, pressure, relative humidity) and pollutant concentrations (particulate matter, carbon dioxide, volatile organic compounds, formaldehyde, nitrogen dioxide, nitric oxide and carbon monoxide) is evaluated based on a one-year dataset given by a low-cost sensor and one-year dataset provided by the National Environment Agency. Using two statistical analysis programs, Python and SPSS, a good understanding of the correlations between these variables and ozone concentration is obtained. The SPSS analysis underscores the significant impact of three meteorological factors and nine other pollutants on the ozone level. A positive correlation is noticed in the summer when sunlight is intense and photochemical reactions are elevated. The relationship between temperature and ozone concentration is strong and positive, as confirmed by Spearman's rho correlation coefficient (r = 0.880). A significant negative correlation is found between relative humidity and ozone (r = -0.590). Moreover, the analysis shows that particulate matter concentrations exhibit a significant negative correlation with ozone (r ≈ -0.542), indicating that higher particulate matter concentrations reduce ozone levels. Volatile organic compounds show a significant negative correlation with ozone (r = -0.156). A negative relationship between ozone and carbon dioxide (r = -0.343), indicates that elevated carbon dioxide levels might also suppress ozone concentrations. A significant positive correlation between nitrogen dioxide and ozone (r = 0.060), highlights the role of nitrogen dioxide in the production of ozone through photochemical reactions. However, nitric oxide shows a negative correlation with ozone (r = -0.055) due to its role in ozone formation. Carbon monoxide has no statistically significant effect on ozone concentration. To observe the differences between weekdays and weekends, T-Test was used. Even though significant differences were observed in temperature, humidity, carbon dioxide, volatile organic compounds, nitrogen dioxide, nitric oxide and carbon monoxide levels between weekdays and weekends, the T-Test did not highlight a significant weekend ozone effect in a mid-sized city as Craiova. Using Python, the daily values were calculated and compared with the limit values recommended by the World Health Organization (WHO) and European Environment Agency (EEA). The WHO O3 recommended levels were exceeded for 13 times in one year. This study offers a comprehensive understanding of ozone pollution in a mid-sized city as Craiova, serving as a valuable reference for local decision-makers. It provides critical insights into the seasonal dynamics of ozone levels, emphasizing the significant role of temperature in ozone formation and the complex interactions between various pollutants and meteorological factors.

Toowoomba Healthy Towns: A Citizen Science Initiative on Active Transport in Regional South East Queensland, Australia.

Engaging in active transport will enable individuals across the life course to increase their habitual levels of physical activity. The aim of this study was to engage citizen scientists (CS) to identify factors that influence active transport in their community. The Our Voice citizen science methodology developed at Stanford University was employed. CS completed a "discovery walk," taking photos of things that help or hinder active transport. Three groups were recruited: children aged 10-12years (n = 11), adults (n = 12), and older adults >65years (n = 10). CS rated each photo and provided a narrative explaining their reason for taking the photo. Once all CS in a specific age group had completed the walk, they met for a group discussion. The CS worked together to analyze the data, arranging them into themes and identifying priority actions. Two CS representatives from each age group presented their findings to the local council decision makers. Children highlighted the need for safer crossings, especially from their school to the local park, and suggested zebra crossings as a solution. The adults highlighted that road signs prioritized vehicles, not pedestrians, and requested that this be reversed. Older adults noted that the poor condition of the pavements made it harder for people in wheelchairs to navigate. The CS recommendations have informed the council's walking and cycling network program. Engaging with citizen science provided the community with an opportunity to advocate for meaningful yet realistic improvements in the built environment that could promote neighborhood active transport and physical activity across the lifespan.

Healthier, cleaner, faster: a case study of climate action for public health in Oxfordshire

BackgroundAt a local level, climate action informs each of our current public health priorities in Oxfordshire and should enable us to deliver better health for local people faster, and more fairly. MethodsWe present Oxfordshire as a case study of climate action for public health. Delivery of climate action for public health in Oxfordshire is being informed by evidence set out in this year's Director of Public Health Annual Report which identifies the local impacts of climate change on health, across five key domains (temperature, air, water, food, nature) and provides a roadmap for how local politicians and decision-makers can act to improve health, look after our environment, and reduce emissions. FindingsIn Oxfordshire, climate action for health spans five cross-cutting themes. First, energy efficient healthy homes and buildings (initiatives include Better Housing Better Health service and Schools Energy Efficiency Loans). Second, sustainable travel and clean air (initiatives include School Streets to restrict motor traffic at pick-up/drop-off, school air quality sensors and roll-out of 159 zero-emissions buses). Third, green health and social care (initiatives include Greener Practice Oxfordshire and expansion of Council supply chain reporting of social care services). Fourth, healthy and sustainable diets (initiatives include the Oxfordshire Food Strategy in conjunction with Good Food Oxfordshire). Fifth, accessible green spaces, clean water and nature (initiatives include the Oxfordshire Local Nature Partnership and District Council programmes). InterpretationWe present clear examples of climate action for public health, at the local authority level, which have been or are currently being evaluated. In the context of a new central government, which has committed to shift the focus of the NHS into the community, to establish publicly-owned renewable energy, to reform planning and expand devolution, there are clear opportunities for local authorities to be able to deliver more and faster on climate and health. FundingLC discloses funding from the NIHR for her academic clinical fellowship, which has allowed for time to develop the abstract. The funder had no role in designing the question, the analysis or the interpretation of the results.

Improving Snowplowing Operations in Utah Through Optimization and Visualization

This paper optimizes snowplowing routes across 12 regions in northern Utah, achieving reductions in total travel time, turnaround time, and deadhead miles by averages of 5.04%, 15.01%, and 14.84% respectively. It assesses tradeoffs between operational policies such as echelon vs. non-echelon routing and centralized vs. decentralized optimization, aiding local management decisions. Furthermore, the paper utilizes data visualization to showcase the effectiveness of new routes compared with current practice, providing valuable insights to the Utah Department of Transportation.

Study of the Future Evolution of the Urban Climate of Paris by Statistical–Dynamical Downscaling of the EURO-CORDEX Ensemble

Abstract High-resolution urban climate projections are needed for local decision-making on climate change adaptation. Regional climate models have resolutions that are too coarse to simulate the urban climate at such resolutions. A novel statistical–dynamical downscaling (SDD) approach is used here to downscale the EURO-CORDEX ensemble to a resolution of 1 km while adding the effect of the city of Paris (France) on air temperature. The downscaled atmospheric fields are then used to drive the Town Energy Balance urban canopy model to produce high-resolution temperature maps over the period 1970–2099, while maintaining the city’s land cover in its present state. The different steps of the SDD are evaluated for the summer season. The regional climate models simulate minimum (maximum) temperatures (TN/TX) that are too high (low). After correction and downscaling, the urban simulations inherit some of these biases but give satisfactory results for summer urban heat islands (UHIs), with average biases of −0.6 K at night and +0.3 K during the day. Changes in future summer temperatures are then studied for two greenhouse gas emission scenarios, RCP4.5 and RCP8.5. Outside the city, the simulations project average increases of 4.1 and 4.8 K for TN and TX for RCP8.5, respectively. In the city, warming is lower, resulting in a decrease in UHIs of −0.19 K at night (from 2.1 to 1.9 K) and −0.16 K during the day. The changes in UHIs are explained by higher rates of warming in rural temperatures due to lower summer precipitation and soil water content and are partially offset by increased ground heat storage in the city.