Assessment of gas dispersion near an operating landfill treated by different intermediate covers with soil alone, low-density polyethylene (LLDPE), or ethylene vinyl alcohol (EVOH) geomembrane.

This study evaluated the impact area of odiferous gas (i.e., H2S) dispersion near an operating landfill installed with three different intermediate covers, including soil alone, linear low-density polyethylene (LLDPE), or ethylene vinyl alcohol (EVOH) geomembrane (GM). By using the finite element method employing Reynolds-averaged Navier-Stokes and Fick's Law coupled equations, the performance of the different cover cases for reducing odor dispersion was comparatively evaluated considering environmental factors, including topographic, meteorology, and gas emission. The odor dispersion patterns and the size of affected residents were analyzed for the twelve different scenarios varied with the cover type and seasonal variation. According to the results, it was found that the wind speed affected the time of odor dispersions more with the relatively flat terrain conditions around the landfill but barely affected the size of the dispersion area. Moreover, it was found that the higher concentration (100 ppb) of odor gas is mainly located within a 5.0-km distance from the landfill. Among four seasons, the odor covers the largest area in summer, which is mainly due to the landfill producing more odor gas and giving a higher source concentration in summer. The gas dispersion simulation for different covers showed that the type of covering layer significantly affects the impact area boundary of gas odor. The results showed that the odor area of the LLDPE GM cover case is 1.3% of soil alone case, and the case of EVOH GM is 14.5% of LLDPE GM case. At the same time, the number of residents that may be affected by the odor of the LLDPE GM case and EVOH GM case is 4.81% and 0.63% of soil alone case, respectively.

Machine Learning Approach for Automated Detection of Irregular Walking Surfaces for Walkability Assessment with Wearable Sensor.

The walkability of a neighborhood impacts public health and leads to economic and environmental benefits. The condition of sidewalks is a significant indicator of a walkable neighborhood as it supports and encourages pedestrian travel and physical activity. However, common sidewalk assessment practices are subjective, inefficient, and ineffective. Current alternate methods for objective and automated assessment of sidewalk surfaces do not consider pedestrians' physiological responses. We developed a novel classification framework for the detection of irregular walking surfaces that uses a machine learning approach to analyze gait parameters extracted from a single wearable accelerometer. We also identified the most suitable location for sensor placement. Experiments were conducted on 12 subjects walking on good and irregular walking surfaces with sensors attached at three different locations: right ankle, lower back, and back of the head. The most suitable location for sensor placement was at the ankle. Among the five classifiers trained with gait features from the ankle sensor, Support Vector Machine (SVM) was found to be the most effective model since it was the most robust to subject differences. The model's performance was improved with post-processing. This demonstrates that the SVM model trained with accelerometer-based gait features can be used as an objective tool for the assessment of sidewalk walking surface conditions.

Community-Engaged Public Health Research to Inform Hospital Campus Planning in a Low Socioeconomic Status Urban Neighborhood.

OBJECTIVE: To compare sociodemographic and motivational factors for healthcare use and identify desirable health-promoting resources among groups in a low socioeconomic status (SES) community in Chicago, IL. BACKGROUND: Disparities in health services and outcomes are well established in low SES urban neighborhoods in the United States and many factors beyond service availability and quality impact community health. Yet there is no clear process for engaging communities in building resources to improve population-level health in such locales. METHODS: A hospital building project led to a partnership of public health researchers, architects, and planners who conducted community-engaged research. We collected resident data and compared factors for healthcare use and choice and likelihood of engaging new health-promoting services. RESULTS: Neighborhood areas were strongly associated with ethnic groupings, and there were differences between groups in healthcare choice and service needs, such as, proximity to home was more important to Latinos than African Americans in choice of healthcare facility ( p adj = .001). Latinos expressed higher likelihood to use a fitness facility ( p adj = .001). Despite differences in vehicle ownership, >75% of all respondents indicated that nearby public transportation was important in choosing healthcare. CONCLUSION: Knowledge of community needs and heterogeneity is essential to decision makers of facility and community development plans. Partnerships between public health, urban planning, architecture, and local constituents should be cultivated toward focus on reducing health disparities. Further work to integrate community perspectives through the planning and design process and to evaluate the long-term impact of such efforts is needed.