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This study aims to address accuracy challenges in assessing air pollution health impacts using Environmental Benefits Mapping and Analysis Program (BenMap), caused by limited meteorological factor data and missing pollutant data. By employing data increment strategies and multiple machine learning models, this research explores the effects of data volume, time steps, and meteorological factors on model prediction performance using several years of data from Tianjin City as an example. The findings indicate that increasing training data volume enhances the performance of Random Forest Regressor (RF) and Decision Tree Regressor (DT) models, especially for predicting CO, NO2, and PM2.5. The optimal prediction time step varies by pollutant, with the DT model achieving the highest R2 value (0.99) for CO and O3. Combining multiple meteorological factors, such as atmospheric pressure, relative humidity, and dew point temperature, significantly improves model accuracy. When using three meteorological factors, the model achieves an R2 of 0.99 for predicting CO, NO2, PM10, PM2.5, and SO2. Health impact assessments using BenMap demonstrated that the predicted all-cause mortality and specific disease mortalities were highly consistent with actual values, confirming the model\'s accuracy in assessing health impacts from air pollution. For instance, the predicted and actual all-cause mortality for PM2.5 were both 3120; for cardiovascular disease, both were 1560; and for respiratory disease, both were 780. To validate its generalizability, this method was applied to Chengdu, China, using several years of data for training and prediction of PM2.5, CO, NO2, O3, PM10, and SO2, incorporating atmospheric pressure, relative humidity, and dew point temperature. The model maintained excellent performance, confirming its broad applicability. Overall, we conclude that the machine learning and BenMap-based methods show high accuracy and reliability in predicting air pollutant concentrations and health impacts, providing a valuable reference for air pollution assessment.

This study introduces a novel integrated quantitative modeling framework to assess the multiple environmental, health, and economic benefits from implementing carbon capture technology in the power sector of Basra province, Iraq. This province is struggling with significant environmental challenges and air pollution caused by extensive oil extraction operations. First, the developed modeling framework quantifies the captured CO2 emissions and the equivalent avoided PM2.5 emissions resulting from the use of carbon capture units in existing power plants. This is achieved through a detailed simulation of the monoethanolamine (MEA) capture process using Aspen. Second, the impact of avoided PM2.5 exposure on public health is evaluated by developing and applying a dynamic dispersion model across the districts where the power plants are located. Third, it quantifies the expected health benefits, using the health impact assessment method. This method is based on a comprehensive meta-analysis of concentration-response functions, and it utilizes a Recurrent Neural Network prediction framework based on the Long-Short Term Memory (LSTM) method to predict the relative risk value of six health outcomes. Finally, the economic value of avoided health burdens is estimated by employing the Value of Statistical Life (VSL) and the Cost of Illness (COI) approaches. According to the findings, implementing new carbon capture units in the selected power plants in the area will lead to a reduction of 7.697 million tons of carbon dioxide per year in the total emission of pollutants from the current power generation units in the region. The integrated assessment results demonstrate a significant reduction in PM2.5 emissions, amounting to 2299 tons per year, leading to the avoidance of 1328 premature deaths and 217 hospital admissions, resulting in annual savings of $1846 million from the avoided mortalities and morbidities cases and creation of 29,607 green jobs in Basra Province.

UNASSIGNED: Understanding and addressing how an individual\'s social, political, economic, and cultural context affects their ability to achieve optimal health is essential to designing and implementing interventions. Before evaluating two parenting programs, in partnership with four children\'s mental health organizations, we used the Health Equity Impact Assessment tool (HEIA) to identify groups that may experience unintended health impacts, as well as generated mitigation strategies to address these impacts.
UNASSIGNED: HEIA activities included a review of the published literature, a review of organizational documents, key informant interviews with staff (n = 12) and other related community service providers (n = 7), and a geographic information systems analysis. All sources of evidence were considered and analyzed using reflective thematic analysis. Summary reports were shared with all partners.
UNASSIGNED: A range of groups were identified as at risk of experiencing unintended health impacts, including caregivers who are racialized, immigrants, Indigenous, living with mental health issues or addictions, dealing with intellectual challenges and/or low literacy levels, survivors of childhood trauma, single parent families, or families experiencing financial difficulties. Unintended health impacts were sorted into 6 main themes which fell under the overarching themes of accessibility of the programs and cultural appropriateness. Mitigation strategies as well as innovative strategies already being applied by participating organizations are discussed.
UNASSIGNED: Although this HEIA focused on parenting programs, the findings address equity issues applicable to the provision of a wide spectrum of children\'s mental health services.

Previous studies on the relationship between urban form and air quality: (1) report mixed results among specific aspects of urban spatial structure (e.g., urban expansion, form, or shape) and (2) use primarily cross-sectional approaches with a single year of data. This study takes advantage of a multi-decade, longitudinal approach to investigate the impact of urban spatial structure on population-weighted concentrations of PM2.5 and NO2. Based on fixed-effect regression models for 481 urban areas in the United States spanning from 1990 to 2015, we found significant associations between various aspects of urban spatial structure and air quality after controlling for meteorological and socio-economic factors. Our results show that population density, compact urban form, circularity, and green space are associated with lower concentrations. Conversely, higher rates of urban expansion, industrial area, and polycentricity are associated with higher concentrations. For large cities (total population: 180,262,404), we found that increasing key factors from each urban spatial structure category (i.e., greenness, population density, compactness, circularity) by a modest 10% results in 10,387 (12,376) fewer deaths for PM2.5 (NO2). We recommend that policymakers adopt comprehensive strategies to increase population density, compactness, and green spaces while slowing urban expansion to reduce the health burden of air quality in US cities.

The combustion of coal in power plants releases significant amounts of polycyclic aromatic hydrocarbons (PAHs), which are highly toxic and carcinogenic. This study assesses the ecological and human health impacts of PAHs contamination from a coal-fired power plant over 8 years. The monitoring site selection considered the distance from the power plant and the prevailing wind direction in the investigated area. The results reveal that, during the monitoring period, PAH levels increased on average by 43%, 61%, and 37% in the zone of the prevailing wind direction, in the area proximate to the power plant, and the zone distant from it, respectively. The site, which has a radius of 4.5 km in the prevailing wind direction, exhibited the highest ecological and human health impacts. Additionally, a strong correlation was observed between environmental and human health impacts, depending on the distance from the power plant, particularly in areas with the prevailing wind direction. These insights contribute to a comprehensive understanding of the intricate dynamics linking power plant emissions, PAHs contamination, and their far-reaching consequences on the environment and human health.

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OBJECTIVE: Health Impact Assessment (HIA) is an evidence-based approach to assess the likely public health impacts of a policy or plan in any sector. Several HIA frameworks are available to guide practitioners doing a HIA. This systematic review sought to determine whether these support practitioners to meet best practice principles defined by the International Association for Impact Assessment.
METHODS: This was a systematic review.
METHODS: Three complementary search strategies were used to identify frameworks in June 2022. We used three databases to find completed HIAs published in the last five years and hand-searched their reference lists for frameworks. We also searched 23 HIA repositories using Google\'s Advanced function and contacted HIA practitioners via two international mailing lists. We used a bespoke quality appraisal tool to assess frameworks against the principles.
RESULTS: The search identified 24 HIA frameworks. None of the frameworks achieved a \'good\' rating for all best practice principles. Many identified the principles but did not provide guidance on how to meet them at all HIA steps. The highest number of frameworks were rated \'good\' for ethical use of evidence and comprehensive approach to health (n = 15). Eight frameworks were rated as \'good\' for participation, and two for equity. The highest number of frameworks rated \'poor\' for sustainability (n = 11).
CONCLUSIONS: There is marked variation in the degree to which HIA frameworks support the best practice principles. HIA practitioners could select elements from different frameworks for practical guidance to meet all the best practice principles.

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This study examines the allocation of COVID-19 funding for Indigenous Peoples in Canada, Australia, New Zealand, and the United States during the pandemic\'s first wave. Indigenous communities, already facing health disparities, systemic discrimination, and historical forces of colonisation, found themselves further vulnerable to the virus. Analysing the funding policies of these countries, we employed a Health Equity Impact Assessment (HEIA) tool and an Indigenous Lens Tool supplement to evaluate potential impacts. Our results identify three major funding equity issues: unique health and service needs, socioeconomic disparities, and limited access to community and culturally safe health services. Despite efforts for equitable funding, a lack of meaningful consultation led to shortcomings, as seen in Canada\'s state of emergency declaration and legal disputes in the United States. New Zealand stood out for integrating Māori perspectives, showcasing the importance of consultation. The study calls for a reconciliation-minded path, aligning with Truth and Reconciliation principles, the UN Declaration on the Rights of Indigenous Peoples, and evolving government support. The paper concludes that co-creating equitable funding policies grounded in Indigenous knowledge requires partnership, meaningful consultation, and organisational cultural humility. Even in emergencies, these measures ensure responsiveness and respect for Indigenous self-determination.