Assessing concentrations and health impacts of air quality management strategies: Framework for Rapid Emissions Scenario and Health impact ESTimation (FRESH-EST)

Synergistic effect of combating air pollutants and carbon emissions in the Yangtze River Delta of China: spatial and temporal divergence analysis and key influencing factors

Synergizing the reduction of air pollutants and carbon emissions (APCE) has become a critical tactic alternative to address the issue of climate change. Taking the Yangtze River Delta (YRD) region of China as a case study, this paper explores the spatial and temporal distribution pattern of the coupling coordination degree (CCD) of combating APCE from 2011 to 2022, analyzes the dynamic change in CCD using the convergence test, and investigates the key factors affecting CCD via the Tobit regression model. The results show that (1) from 2011 to 2022, the air pollutants (AP) and CO2 emission (CE) in the YRD region decrease at the annual rate of 10.32% and 0.85%, respectively; (2) the CCD of reducing APCE in the YRD presents a W-shaped fluctuation before 2016 and then steps into a steady increase status after 2016; (3) the order of CCD in four provincial-level units by 2022 is Shanghai > Zhejiang > Jiangsu > Anhui. The proportion of cities where the CCD of reducing APCE enters the high-coordination period has reached 87.8%; and (4) the Tobit regression results affirm that economic growth, industrial structure, and green technological innovation exacerbate the CCD of combating APCE, while opening-up level mitigates it. The findings offer policymakers valuable insights into the importance of pursuing collaborative governance over APCE and ensuring sustainable development.

Air quality monitoring and measurement in an urban airshed: Contextualizing datasets from the Detroit Michigan area from 1952 to 2020

With urban air quality being a pressing public health concern, community members are becoming increasingly engaged in determining the links between air quality and human health. Although new measurement tools such as low-cost sensors make local data more accessible, a better understanding of gaps in regional datasets is needed to develop effective metropolitan-scale solutions. Using scoping review methodology, we compiled 214 published journal articles and grey literature reports of air quality data from the Detroit, Michigan area from 1952 through 2020. This critical scoping review focuses on air quality datasets, but related topics such as health studies and community-based participatory science studies were examined from the included articles. Most of these publications were peer-reviewed journal articles published after 2001. Particulate matter, nitrous oxides, and sulfur dioxide were the most commonly studied air pollutants, and asthma was the most frequently associated health outcome paired with air pollution datasets. Few publications reported methods for community-based participatory science. This critical scoping review establishes a foundation of historical air quality data for the Detroit metropolitan area and a set of evaluation criteria that can be replicated in other urban centers. This foundation enables future detailed analysis of air quality datasets and showcases strategies for implementing effective community science programs and monitoring efforts.

A review on the role of dispersion and receptor models in asthma research

There is substantial evidence that air pollution exposure is associated with asthma prevalence that affects millions of people worldwide. Air pollutant exposure can be determined using dispersion models and refined with receptor models. Dispersion models offer the advantage of giving spatially distributed outdoor pollutants concentration while the receptor models offer the source apportionment of specific chemical species. However, the use of dispersion and/or receptor models in asthma research requires a multidisciplinary approach, involving experts on air quality and respiratory diseases. Here, we provide a literature review on the role of dispersion and receptor models in air pollution and asthma research, their limitations, gaps and the way forward. We found that the methodologies used to incorporate atmospheric dispersion and receptor models in human health studies may vary considerably, and several of the studies overlook features such as indoor air pollution, model validation and subject pathway between indoor spaces. Studies also show contrasting results of relative risk or odds ratio for a health outcome, even using similar methodologies. Dispersion models are mostly used to estimate air pollution levels outside the subject's home, school or workplace; however, very few studies addressed the subject's routines or indoor/outdoor relationships. Conversely, receptor models are employed in regions where asthma incidence/prevalence is high or where a dispersion model has been previously used for this assessment. Road traffic (vehicle exhaust) and NOx are found to be the most targeted source and pollutant, respectively. Other key findings were the absence of a standard indicator, shortage of studies addressing VOC and UFP, and the shift toward chemical speciation of exposure.

The Use of the Internet of Things for Estimating Personal Pollution Exposure

This paper proposes a framework for an Air Quality Decision Support System (AQDSS), and as a proof of concept, develops an Internet of Things (IoT) application based on this framework. This application was assessed by means of a case study in the City of Madrid. We employed different sensors and combined outdoor and indoor data with spatiotemporal activity patterns to estimate the Personal Air Pollution Exposure (PAPE) of an individual. This pilot case study presents evidence that PAPE can be estimated by employing indoor air quality monitors and e-beacon technology that have not previously been used in similar studies and have the advantages of being low-cost and unobtrusive to the individual. In future work, our IoT application can be extended to include prediction models, enabling dynamic feedback about PAPE risks. Furthermore, PAPE data from this type of application could be useful for air quality policy development as well as in epidemiological studies that explore the effects of air pollution on certain diseases.

Air pollutant strategies to reduce adverse health impacts and health inequalities: a quantitative assessment for Detroit, Michigan

The development of air quality management (AQM) strategies provides opportunities to improve public health and reduce health inequalities. This study evaluates health and inequality impacts of alternate SO2 control strategies in Detroit, MI, a designated non-attainment area. Control alternatives include uniform reductions across sources, ranking approaches based on total emissions and health impacts per ton of pollutant emitted, and optimizations that meet concentration and health goals. Using dispersion modeling and quantitative health impact assessment (HIA), these strategies are evaluated in terms of ambient concentrations, health impacts, and the inequality in health risks. The health burden attributable to SO2 emissions in Detroit falls primarily among children and includes 70 hospitalizations and 6,000 asthma-related respiratory symptom-days annually, equivalent to 7 disability-adjusted life years (DALYs). The health burden disproportionately falls on Hispanic/Latino residents, residents with less than a high school diploma, and foreign-born residents. Control strategies that target smaller facilities near exposed populations provide the greatest benefit in terms of the overall health burden reductions and the inequality of attributable health risk; conventional strategies that target the largest emission sources can increase inequality and provide only modest health benefits. The assessment is novel in using spatial analyses that account for urban scale gradients in exposure, demographics, vulnerability, and population health. We show that quantitative HIA methods can be used to develop AQM strategies that simultaneously meet environmental, public health, and environmental justice goals, advancing AQM beyond its current compliance-oriented focus.

Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan

The environmental burden of disease is the mortality and morbidity attributable to exposures of air pollution and other stressors. The inequality metrics used in cumulative impact and environmental justice studies can be incorporated into environmental burden studies to better understand the health disparities of ambient air pollutant exposures. This study examines the diseases and health disparities attributable to air pollutants for the Detroit urban area. We apportion this burden to various groups of emission sources and pollutants, and show how the burden is distributed among demographic and socioeconomic subgroups. The analysis uses spatially-resolved estimates of exposures, baseline health rates, age-stratified populations, and demographic characteristics that serve as proxies for increased vulnerability, e.g., race/ethnicity and income. Based on current levels, exposures to fine particulate matter (PM_2.5), ozone (O₃), sulfur dioxide (SO₂), and nitrogen dioxide (NO₂) are responsible for more than 10,000 disability-adjusted life years (DALYs) per year, causing an annual monetized health impact of $6.5 billion. This burden is mainly driven by PM_2.5 and O₃ exposures, which cause 660 premature deaths each year among the 945,000 individuals in the study area. NO₂ exposures, largely from traffic, are important for respiratory outcomes among older adults and children with asthma, e.g., 46% of air-pollution related asthma hospitalizations are due to NO₂ exposures. Based on quantitative inequality metrics, the greatest inequality of health burdens results from industrial and traffic emissions. These metrics also show disproportionate burdens among Hispanic/Latino populations due to industrial emissions, and among low income populations due to traffic emissions. Attributable health burdens are a function of exposures, susceptibility and vulnerability (e.g., baseline incidence rates), and population density. Because of these dependencies, inequality metrics should be calculated using the attributable health burden when feasible to avoid potentially underestimating inequality. Quantitative health impact and inequality analyses can inform health and environmental justice evaluations, providing important information to decision makers for prioritizing strategies to address exposures at the local level.